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 |
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32723dd3f0bead7721e030113132efbb0b4d6887 | 13,702 | py | Python | botbot_plugins/tests/test_vote.py | metabrainz/brainzbot-plugins | 28b253fd65ea10c84e1f841cc3d2576af263be87 | [
"BSD-3-Clause"
] | 4 | 2018-01-01T02:46:55.000Z | 2018-02-15T09:07:51.000Z | botbot_plugins/tests/test_vote.py | metabrainz/brainzbot-plugins | 28b253fd65ea10c84e1f841cc3d2576af263be87 | [
"BSD-3-Clause"
] | 2 | 2018-01-14T02:43:40.000Z | 2018-01-21T13:13:32.000Z | botbot_plugins/tests/test_vote.py | metabrainz/brainzbot-plugins | 28b253fd65ea10c84e1f841cc3d2576af263be87 | [
"BSD-3-Clause"
] | 2 | 2018-01-01T02:47:01.000Z | 2018-01-06T06:58:24.000Z | # -*- coding: utf-8 -*-
import pytest
from botbot_plugins.base import DummyApp
from botbot_plugins.plugins import vote
@pytest.fixture
def app():
return DummyApp(test_plugin=vote.Plugin(), command_prefix="!")
def test_no_concurrent_voting(app):
assert app.respond("!startvote") == ["Voting has started."]
assert app.respond("!startvote") == [u"repl_user: There’s already a vote going on. Use the “endvote” command to end it before starting a new one."]
app.respond("!endvote")
def test_no_vote_running(app):
assert app.respond("+1") == []
assert app.respond("+something") == []
assert app.respond("!vote +something") == [u"No vote has been started. Use the “startvote” command to do so."]
assert app.respond("!countvotes") == [u"No vote has been started. Use the “startvote” command to do so."]
assert app.respond("!abstain") == [u"No vote has been started. Use the “startvote” command to do so."]
assert app.respond("!cancelvotes") == [u"No vote has been started. Use the “startvote” command to do so."]
assert app.respond("!endvote") == [u"No vote has been started. Use the “startvote” command to do so."]
def test_boolean_voting(app):
assert app.respond("!startvote") == ["Voting has started."]
app.respond("+1")
assert app.respond("!countvotes") == ["[+1: repl_user] [-0: ] [\\0: ]"]
app.respond("\\1")
assert app.respond("!countvotes") == ["[+0: ] [-0: ] [\\1: repl_user]"]
app.respond("-1")
app.respond("\\o")
assert app.respond("!countvotes") == ["[+0: ] [-1: repl_user] [\\0: ]"]
# Double-voting
app.respond("+1")
app.respond("+1")
assert app.respond("!countvotes") == ["[+1: repl_user] [-0: ] [\\0: ]"]
# Explicit voting
app.respond("!vote -1")
assert app.respond("!countvotes") == ["[+0: ] [-1: repl_user] [\\0: ]"]
app.respond("!vote \\1")
assert app.respond("!countvotes") == ["[+0: ] [-0: ] [\\1: repl_user]"]
app.respond("!vote +1")
assert app.respond("!vote \o") == ["The only valid way to abstain is using \\1."]
assert app.respond("!countvotes") == ["[+1: repl_user] [-0: ] [\\0: ]"]
# Additional words after
app.respond("-1 foo bar")
assert app.respond("!countvotes") == ["[+0: ] [-1: repl_user] [\\0: ]"]
app.respond("+1 coolio")
assert app.respond("!countvotes") == ["[+1: repl_user] [-0: ] [\\0: ]"]
app.respond("!vote -1 foo bar")
assert app.respond("!countvotes") == ["[+0: ] [-1: repl_user] [\\0: ]"]
app.respond("!vote +1 coolio")
assert app.respond("!countvotes") == ["[+1: repl_user] [-0: ] [\\0: ]"]
# Multiple users
app.respond("-1", User="jesus")
assert app.respond("!countvotes") == ["[+1: repl_user] [-1: jesus] [\\0: ]"]
app.respond("-1")
assert app.respond("!countvotes") == ["[+0: ] [-2: jesus, repl_user] [\\0: ]"]
# Invalid option
assert app.respond("+invalid") == []
assert app.respond("!vote +invalid") == [u"“invalid” is not a valid option."]
assert app.respond("!endvote") == ["Voting has ended."]
# With name
assert app.respond("!startvote is jesus real?") == [u"Voting has started for proposal “is jesus real?”."]
assert app.respond("!endvote") == [u"Voting has ended for proposal “is jesus real?”."]
def test_non_boolean_voting(app):
assert app.respond("!startvote [port, firewall, app]") == ["Voting has started."]
app.respond("+port")
assert app.respond("!countvotes") == ["[app(+0, -0): ] [firewall(+0, -0): ] [port(+1, -0): repl_user] [\\0: ]"]
app.respond("-firewall")
assert app.respond("!countvotes") == ["[app(+0, -0): ] [firewall(+0, -1): -repl_user] [port(+1, -0): repl_user] [\\0: ]"]
app.respond("\\1")
assert app.respond("!countvotes") == ["[app(+0, -0): ] [firewall(+0, -0): ] [port(+0, -0): ] [\\1: repl_user]"]
# Double-voting
app.respond("+app")
app.respond("+app")
assert app.respond("!countvotes") == ["[app(+1, -0): repl_user] [firewall(+0, -0): ] [port(+0, -0): ] [\\0: ]"]
# Explicit voting
app.respond("!vote -app")
assert app.respond("!countvotes") == ["[app(+0, -1): -repl_user] [firewall(+0, -0): ] [port(+0, -0): ] [\\0: ]"]
app.respond("!vote app")
assert app.respond("!countvotes") == ["[app(+1, -0): repl_user] [firewall(+0, -0): ] [port(+0, -0): ] [\\0: ]"]
# Closest match
app.respond("-app is not cool")
assert app.respond("!countvotes") == ["[app(+0, -1): -repl_user] [firewall(+0, -0): ] [port(+0, -0): ] [\\0: ]"]
app.respond("+port is a good one")
assert app.respond("!countvotes") == ["[app(+0, -1): -repl_user] [firewall(+0, -0): ] [port(+1, -0): repl_user] [\\0: ]"]
app.respond("+app is kinda cool")
assert app.respond("!countvotes") == ["[app(+1, -0): repl_user] [firewall(+0, -0): ] [port(+1, -0): repl_user] [\\0: ]"]
# Multiple users
app.respond("-app", User="jesus")
assert app.respond("!countvotes") == ["[app(+1, -1): repl_user; -jesus] [firewall(+0, -0): ] [port(+1, -0): repl_user] [\\0: ]"]
app.respond("+port", User="eladio")
assert app.respond("!countvotes") == ["[app(+1, -1): repl_user; -jesus] [firewall(+0, -0): ] [port(+2, -0): repl_user, eladio] [\\0: ]"]
app.respond("+app", User="eladio")
assert app.respond("!countvotes") == ["[app(+2, -1): repl_user, eladio; -jesus] [firewall(+0, -0): ] [port(+2, -0): repl_user, eladio] [\\0: ]"]
app.respond("\\1", User="jesus")
assert app.respond("!countvotes") == ["[app(+2, -0): repl_user, eladio] [firewall(+0, -0): ] [port(+2, -0): repl_user, eladio] [\\1: jesus]"]
# Invalid option
assert app.respond("+invalid") == []
assert app.respond("!vote +invalid") == [u"“invalid” is not a valid option."]
assert app.respond("!endvote") == ["Voting has ended."]
# With name
assert app.respond("!startvote is jesus real? [port, firewall, app]") == [u"Voting has started for proposal “is jesus real?”."]
assert app.respond("!endvote") == [u"Voting has ended for proposal “is jesus real?”."]
# Closest match needs to be greedy
app.respond("!startvote [hello, hello world]")
app.respond("+hello")
assert app.respond("!countvotes") == ["[hello(+1, -0): repl_user] [hello world(+0, -0): ] [\\0: ]"]
app.respond("-hello world")
assert app.respond("!countvotes") == ["[hello(+1, -0): repl_user] [hello world(+0, -1): -repl_user] [\\0: ]"]
app.respond("+hello\tworld")
assert app.respond("!countvotes") == ["[hello(+1, -0): repl_user] [hello world(+1, -0): repl_user] [\\0: ]"]
app.respond("!endvote")
# Duplicate options
app.respond("!startvote [hello, hello, world, world]")
app.respond("+hello")
app.respond("-world")
assert app.respond("!countvotes") == ["[hello(+1, -0): repl_user] [world(+0, -1): -repl_user] [\\0: ]"]
app.respond("!endvote")
# With explicitly empty options
assert app.respond("!startvote []") == ["Voting has started."]
app.respond("+1")
assert app.respond("!countvotes") == ["[+1: repl_user] [-0: ] [\\0: ]"]
app.respond("!endvote")
assert app.respond("!startvote [ , ]") == ["Voting has started."]
app.respond("+1")
assert app.respond("!countvotes") == ["[+1: repl_user] [-0: ] [\\0: ]"]
app.respond("!endvote")
assert app.respond("!startvote [,]") == ["Voting has started."]
app.respond("+1")
assert app.respond("!countvotes") == ["[+1: repl_user] [-0: ] [\\0: ]"]
app.respond("!endvote")
def test_cancelvotes(app):
assert app.respond("!startvote") == ["Voting has started."]
app.respond("+1")
app.respond("+1", User="jesus")
assert app.respond("!countvotes") == ["[+2: repl_user, jesus] [-0: ] [\\0: ]"]
app.respond("!cancelvotes")
assert app.respond("!countvotes") == ["[+1: jesus] [-0: ] [\\0: ]"]
app.respond("!endvote")
assert app.respond("!startvote [port, firewall, app]")
app.respond("+port")
app.respond("-firewall")
assert app.respond("!countvotes") == ["[app(+0, -0): ] [firewall(+0, -1): -repl_user] [port(+1, -0): repl_user] [\\0: ]"]
app.respond("!endvote")
def test_explicit_abstain(app):
assert app.respond("!startvote") == ["Voting has started."]
app.respond("!abstain")
assert app.respond("!countvotes") == ["[+0: ] [-0: ] [\\1: repl_user]"]
app.respond("!endvote")
def test_endvote_prints_countvote(app):
assert app.respond("!startvote") == ["Voting has started."]
app.respond("+1")
assert app.respond("!endvote")[0].split('\n') == ["Voting has ended.", "[+1: repl_user] [-0: ] [\\0: ]"]
assert app.respond("!startvote") == ["Voting has started."]
app.respond("+1")
assert app.respond("!countvotes") == ["[+1: repl_user] [-0: ] [\\0: ]"]
app.respond("+1")
assert app.respond("!endvote") == ["Voting has ended."]
assert app.respond("!startvote") == ["Voting has started."]
app.respond("+1")
assert app.respond("!countvotes") == ["[+1: repl_user] [-0: ] [\\0: ]"]
app.respond("\\1")
assert app.respond("!endvote")[0].split('\n') == ["Voting has ended.", "[+0: ] [-0: ] [\\1: repl_user]"]
def test_cancelvote_when_novotes(app):
assert app.respond("!startvote") == ["Voting has started."]
assert app.respond("!cancelvotes") == []
app.respond("!endvote")
def test_unicode(app):
assert app.respond(u"!startvote Должен ли Владимир Путин стать следующим президентом России? [да, нет, Может быть]") == \
[u"Voting has started for proposal “Должен ли Владимир Путин стать следующим президентом России?”."]
app.respond(u"+Может быть")
assert app.respond("!countvotes") == [u"[Может быть(+1, -0): repl_user] [да(+0, -0): ] [нет(+0, -0): ] [\\0: ]"]
app.respond(u"-нет")
assert app.respond("!countvotes") == [u"[Может быть(+1, -0): repl_user] [да(+0, -0): ] [нет(+0, -1): -repl_user] [\\0: ]"]
app.respond(u"+да", User="Российский патриот")
assert app.respond("!countvotes") == [u"[Может быть(+1, -0): repl_user] [да(+1, -0): Российский патриот] [нет(+0, -1): -repl_user] [\\0: ]"]
assert app.respond("!endvote") == [u"Voting has ended for proposal “Должен ли Владимир Путин стать следующим президентом России?”."]
def test_emoji_voting(app):
app.respond("!startvote")
app.respond(u"👍", User="thumbs up")
assert app.respond("!countvotes") == ["[+1: thumbs up] [-0: ] [\\0: ]"]
app.respond(u"👍🏻", User="light thumbs up")
assert app.respond("!countvotes") == ["[+2: thumbs up, light thumbs up] [-0: ] [\\0: ]"]
app.respond(u"👍🏼", User="medium-light thumbs up")
assert app.respond("!countvotes") == ["[+3: thumbs up, light thumbs up, medium-light thumbs up] [-0: ] [\\0: ]"]
app.respond(u"👍🏽", User="medium thumbs up")
assert app.respond("!countvotes") == ["[+4: thumbs up, light thumbs up, medium-light thumbs up, medium thumbs up] [-0: ] [\\0: ]"]
app.respond(u"👍🏾", User="medium-dark thumbs up")
assert app.respond("!countvotes") == ["[+5: thumbs up, light thumbs up, medium-light thumbs up, medium thumbs up, medium-dark thumbs up] [-0: ] [\\0: ]"]
app.respond(u"👍🏿", User="dark thumbs up")
assert app.respond("!countvotes") == ["[+6: thumbs up, light thumbs up, medium-light thumbs up, medium thumbs up, medium-dark thumbs up, dark thumbs up] [-0: ] [\\0: ]"]
app.respond(u"😍", User="smiling heart face")
assert app.respond("!countvotes") == ["[+7: thumbs up, light thumbs up, medium-light thumbs up, medium thumbs up, medium-dark thumbs up, dark thumbs up, smiling heart face] [-0: ] [\\0: ]"]
app.respond(u"😻", User="smiling cat heart face")
assert app.respond("!countvotes") == ["[+8: thumbs up, light thumbs up, medium-light thumbs up, medium thumbs up, medium-dark thumbs up, dark thumbs up, smiling heart face, smiling cat heart face] [-0: ] [\\0: ]"]
app.respond("!endvote")
app.respond("!startvote")
app.respond(u"👎", User="thumbs down")
assert app.respond("!countvotes") == ["[+0: ] [-1: thumbs down] [\\0: ]"]
app.respond(u"👎🏻", User="light thumbs down")
assert app.respond("!countvotes") == ["[+0: ] [-2: thumbs down, light thumbs down] [\\0: ]"]
app.respond(u"👎🏼", User="medium-light thumbs down")
assert app.respond("!countvotes") == ["[+0: ] [-3: thumbs down, light thumbs down, medium-light thumbs down] [\\0: ]"]
app.respond(u"👎🏽", User="medium thumbs down")
assert app.respond("!countvotes") == ["[+0: ] [-4: thumbs down, light thumbs down, medium-light thumbs down, medium thumbs down] [\\0: ]"]
app.respond(u"👎🏾", User="medium-dark thumbs down")
assert app.respond("!countvotes") == ["[+0: ] [-5: thumbs down, light thumbs down, medium-light thumbs down, medium thumbs down, medium-dark thumbs down] [\\0: ]"]
app.respond(u"👎🏿", User="dark thumbs down")
assert app.respond("!countvotes") == ["[+0: ] [-6: thumbs down, light thumbs down, medium-light thumbs down, medium thumbs down, medium-dark thumbs down, dark thumbs down] [\\0: ]"]
app.respond(u"–1", User="en dash")
assert app.respond("!countvotes") == ["[+0: ] [-7: thumbs down, light thumbs down, medium-light thumbs down, medium thumbs down, medium-dark thumbs down, dark thumbs down, en dash] [\\0: ]"]
app.respond(u"—1", User="em dash")
assert app.respond("!countvotes") == ["[+0: ] [-8: thumbs down, light thumbs down, medium-light thumbs down, medium thumbs down, medium-dark thumbs down, dark thumbs down, en dash, em dash] [\\0: ]"]
app.respond(u"―1", User="horizontal bar")
assert app.respond("!countvotes") == ["[+0: ] [-9: thumbs down, light thumbs down, medium-light thumbs down, medium thumbs down, medium-dark thumbs down, dark thumbs down, en dash, em dash, horizontal bar] [\\0: ]"]
app.respond("!endvote")
| 55.25 | 219 | 0.59765 | 1,858 | 13,702 | 4.376749 | 0.087729 | 0.222577 | 0.190851 | 0.191835 | 0.850836 | 0.810379 | 0.745204 | 0.676832 | 0.631948 | 0.581284 | 0 | 0.025745 | 0.169391 | 13,702 | 247 | 220 | 55.473684 | 0.686407 | 0.020362 | 0 | 0.40404 | 0 | 0.176768 | 0.554528 | 0 | 0 | 0 | 0 | 0 | 0.489899 | 1 | 0.055556 | false | 0 | 0.015152 | 0.005051 | 0.075758 | 0.005051 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
32849fd6aa1b6ff945f18f5d705692361245efe1 | 477 | py | Python | Draft.py | TerryCh1995/CondenseNet-chy | 9ec67fea0b8dc2e472896e37d7f71139b841581b | [
"MIT"
] | 1 | 2018-01-16T01:28:02.000Z | 2018-01-16T01:28:02.000Z | Draft.py | TerryCh1995/CondenseNet-chy | 9ec67fea0b8dc2e472896e37d7f71139b841581b | [
"MIT"
] | null | null | null | Draft.py | TerryCh1995/CondenseNet-chy | 9ec67fea0b8dc2e472896e37d7f71139b841581b | [
"MIT"
] | 1 | 2018-03-07T03:23:14.000Z | 2018-03-07T03:23:14.000Z | import tensorflow as tf
A = tf.get_variable('A', [2, 2], initializer=tf.constant_initializer(0.0))
B = tf.get_variable('B', [2, 2], initializer=tf.constant_initializer(0.0))
tf.add_to_collection('H', A)
tf.add_to_collection('H', B)
a = tf.get_variable('a', [2, 2], initializer=tf.constant_initializer(0.0))
b = tf.get_variable('b', [2, 2], initializer=tf.constant_initializer(0.0))
tf.add_to_collection('H', a)
tf.add_to_collection('H', b)
print(tf.get_collection('H')) | 34.071429 | 74 | 0.708595 | 85 | 477 | 3.776471 | 0.2 | 0.077882 | 0.161994 | 0.186916 | 0.872274 | 0.872274 | 0.872274 | 0.872274 | 0.872274 | 0.872274 | 0 | 0.036952 | 0.092243 | 477 | 14 | 75 | 34.071429 | 0.704388 | 0 | 0 | 0 | 0 | 0 | 0.018828 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.1 | 0 | 0.1 | 0.1 | 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 |
0892f729b77850f8d0addf0bade80ede3e00255b | 9,077 | py | Python | tests/test_electricanalysis.py | pyansys/pydpf-post | 1b9242af515b33ce35cdc3448d5c0b8b4aec06ee | [
"MIT"
] | 19 | 2021-10-15T14:15:52.000Z | 2022-03-13T12:15:58.000Z | tests/test_electricanalysis.py | lynch1972/pydpf-post | 8fea9103259786067d3451dc12e7c0ae5a38ea33 | [
"MIT"
] | 21 | 2021-10-12T16:28:23.000Z | 2022-03-30T14:22:29.000Z | tests/test_electricanalysis.py | pyansys/DPF-Post | 8fea9103259786067d3451dc12e7c0ae5a38ea33 | [
"MIT"
] | 2 | 2022-02-09T13:39:08.000Z | 2022-03-14T09:16:41.000Z | import numpy as np
import pytest
from ansys.dpf import post
from ansys.dpf.post import errors as dpf_errors
from ansys.dpf.post.common import _PhysicsType
from ansys.dpf.post.electric_results import ElectricField, ElectricPotential
def test_electricfield(rth_electric):
solution = post.load_solution(rth_electric)
assert solution._model.metadata.result_info.physics_type == _PhysicsType.thermal
ef = solution.electric_field()
assert isinstance(ef, ElectricField)
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.nodal
assert len(s[0].data[20]) == 3
assert np.isclose(s[0].data[23][1], 19.562952041625977)
# with dpf.core operator
from ansys.dpf import core
op = core.Operator("EF")
op.inputs.requested_location.connect(core.locations.nodal)
op.inputs.data_sources.connect(core.DataSources(rth_electric))
fc = op.outputs.fields_container()
assert len(fc) == s.num_fields
assert fc[0].location == s[0].location
assert len(fc[0].data[20]) == len(s[0].data[20])
assert np.allclose(s[0].data.tolist(), fc[0].data.tolist())
def test_electricfield_nodscoping(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_field(node_scoping=[2])
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.nodal
assert len(s[0].data) == 1
assert len(s[0].data[0]) == 3
assert np.allclose(
s[0].data[0].tolist(), [5.25223311e-14, 1.95629520e01, 2.82945325e-14]
)
ef = solution.electric_field(location=post.locations.elemental, node_scoping=[2])
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.elemental
assert len(s[0].data) == 8
assert len(s[0].data[0]) == 3
assert np.allclose(
s[0].data[0].tolist(), [-3.41948692e-14, 1.95629520e01, 7.77156117e-15]
)
ef = solution.electric_field(
location=post.locations.elemental_nodal, node_scoping=[2]
)
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.elemental_nodal
assert len(s[0].data) == 8
assert len(s[0].data[0]) == 3
assert np.allclose(
s[0].data.tolist(), [2.63128894e-11, 1.95629520e01, 2.62733394e-11]
)
@pytest.mark.skipif(
True, reason="element scoping not available with electrical results."
)
def test_electricfield_elemscoping(rth_electric):
raise Exception("Element scoping on electric_field does not work.")
solution = post.load_solution(rth_electric)
ef = solution.electric_field(element_scoping=[2])
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.nodal
assert len(s[0].data) == 20
assert len(s[0].data[0]) == 3
# assert np.isclose(s[0].data[0].tolist(), [2.63128894e-11, 1.95629520e+01, 2.62733394e-11])
ef = solution.electric_field(location=post.locations.elemental, element_scoping=[2])
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.elemental
assert len(s[0].data) == 3
# assert np.isclose(s[0].data.tolist(), [-3.41948692e-14, 1.95629520e+01, 7.77156117e-15])
ef = solution.electric_field(
location=post.locations.elemental_nodal, element_scoping=[2]
)
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.elemental_nodal
assert len(s[0].data) == 8
assert len(s[0].data[0]) == 3
# assert np.isclose(s[0].data.tolist(), [-3.41948692e-14, 1.95629520e+01, 7.77156117e-15])
def test_electricfield_nodlocation(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_field()
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.nodal
def test_electricfield_elemlocation(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_field(location=post.locations.elemental)
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.elemental
def test_electricfield_elemnodlocation(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_field(location=post.locations.elemental_nodal)
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.elemental_nodal
def test_electricfield_timescoping(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_field(time_scoping=1)
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.nodal
assert len(s[0].data[20]) == 3
assert np.isclose(s[0].data[23][1], 19.562952041625977)
def test_electricfield_time(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_field(time=1.0)
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.nodal
assert len(s[0].data[20]) == 3
assert np.isclose(s[0].data[23][1], 19.562952041625977)
def test_electricfield_set(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_field(set=1)
s = ef.vector
assert s.num_fields == 1
assert s[0].location == post.locations.nodal
assert len(s[0].data[20]) == 3
assert np.isclose(s[0].data[23][1], 19.562952041625977)
def test_electricpotential(rth_electric):
solution = post.load_solution(rth_electric)
assert solution._model.metadata.result_info.physics_type == _PhysicsType.thermal
ef = solution.electric_potential()
assert isinstance(ef, ElectricPotential)
s = ef.scalar
assert s.num_fields == 1
assert s[0].location == post.locations.nodal
assert len(s[0].data) == 4125
assert np.isclose(s[0].data[23], 0.09781476007338061)
# with dpf.core operator
from ansys.dpf import core
op = core.Operator("VOLT")
# op.inputs.requested_location.connect(core.locations.nodal)
op.inputs.data_sources.connect(core.DataSources(rth_electric))
fc = op.outputs.fields_container()
assert len(fc) == s.num_fields
assert fc[0].location == s[0].location
assert len(fc[0].data) == len(s[0].data)
assert np.allclose(s[0].data.tolist(), fc[0].data.tolist())
comp = core.operators.logic.identical_fc()
comp.inputs.fields_containerA.connect(fc)
comp.inputs.fields_containerB.connect(s.result_fields_container)
out = comp.outputs.boolean()
assert out == True
to_return = "node scoping and element scoping returns the same"
def test_electricpotential_nodscoping(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_potential(node_scoping=[2])
s = ef.scalar
assert s.num_fields == 1
assert s[0].location == post.locations.nodal
assert len(s[0].data) == 1
assert np.isclose(s[0].data[0], 0.048907380036668786)
@pytest.mark.skipif(
True, reason="element scoping not available with electrical results."
)
def test_electricpotential_elemscoping(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_potential(node_scoping=[2])
s = ef.scalar
assert s.num_fields == 1
assert s[0].location == post.locations.nodal
assert len(s[0].data) == 1
# assert np.isclose(s[0].data[0], 0.02445369)
raise Exception(to_return)
def test_electricpotential_nodlocation(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_potential(location=post.locations.nodal)
s = ef.scalar
assert s.num_fields == 1
assert s[0].location == post.locations.nodal
def test_electricpotential_elemlocation(rth_electric):
solution = post.load_solution(rth_electric)
with pytest.raises(dpf_errors.NodalLocationError):
solution.electric_potential(location=post.locations.elemental)
def test_electricpotential_elemnodallocation(rth_electric):
solution = post.load_solution(rth_electric)
with pytest.raises(dpf_errors.NodalLocationError):
solution.electric_potential(location=post.locations.elemental_nodal)
def test_electricpotential_timescoping(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_potential(time_scoping=[1])
s = ef.scalar
assert s.num_fields == 1
assert len(s[0].data) == 4125
assert s[0].location == post.locations.nodal
assert np.isclose(s[0].data[0], 0.07336107005500624)
def test_electricpotential_time(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_potential(set=1)
s = ef.scalar
assert s.num_fields == 1
assert len(s[0].data) == 4125
assert s[0].location == post.locations.nodal
assert np.isclose(s[0].data[0], 0.07336107005500624)
def test_electricpotential_set(rth_electric):
solution = post.load_solution(rth_electric)
ef = solution.electric_potential(time=1.0)
s = ef.scalar
assert s.num_fields == 1
assert len(s[0].data) == 4125
assert s[0].location == post.locations.nodal
assert np.isclose(s[0].data[0], 0.07336107005500624)
| 35.73622 | 96 | 0.707723 | 1,298 | 9,077 | 4.812018 | 0.098613 | 0.020173 | 0.039385 | 0.033141 | 0.834774 | 0.824688 | 0.817323 | 0.808838 | 0.791867 | 0.787224 | 0 | 0.071739 | 0.164592 | 9,077 | 253 | 97 | 35.87747 | 0.751945 | 0.046381 | 0 | 0.651961 | 0 | 0 | 0.024399 | 0 | 0 | 0 | 0 | 0 | 0.421569 | 1 | 0.088235 | false | 0 | 0.039216 | 0 | 0.127451 | 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 |
089e7ff3cb4a8a41ff7948a1a96beac05c89fc89 | 9,018 | py | Python | RED_Dataset.py | OPTML-Group/RED-ICLR22 | 15be8bc24cea7ba41e764dcb869708e2d66ee57f | [
"MIT"
] | 3 | 2022-03-10T02:19:03.000Z | 2022-03-31T01:54:19.000Z | RED_Dataset.py | Yifanfanfanfan/Reverse-Engineering-of-Imperceptible-Adversarial-Image-Perturbations | 660b41e01465dd0c3a21829f6bc34e4796e96f94 | [
"MIT"
] | null | null | null | RED_Dataset.py | Yifanfanfanfan/Reverse-Engineering-of-Imperceptible-Adversarial-Image-Perturbations | 660b41e01465dd0c3a21829f6bc34e4796e96f94 | [
"MIT"
] | null | null | null | # coding: utf-8
import cv2
from torch.utils.data import Dataset
import Transform_Model as TM
import random
# import dlib
import numpy as np
from PIL import Image
import torchvision.transforms.functional as tf
from torchvision import transforms
import torch
class FaceDataset(Dataset):
def __init__(self, txt_path, transform = None):
fh= open(txt_path, 'r')
clean_imgs = []
adv_imgs = []
for line in fh:
line = line.rstrip()
words = line.split()
clean_imgs.append(words[0])
adv_imgs.append(words[1])
self.clean_imgs = clean_imgs # 最主要就是要生成这个list， 然后DataLoader中给index，通过getitem读取图片数据
self.adv_imgs = adv_imgs
self.transform = transform
def rotation(self, image1, image2):
# get a random angle range from (-180, 180)
angle = transforms.RandomRotation.get_params([-180, 180])
# same angle rotation for image1 and image2
image1 = image1.rotate(angle)
image2 = image2.rotate(angle)
image1 = tf.to_tensor(image1)
image2 = tf.to_tensor(image2)
return image1, image2
def flip(self, image1, image2):
# 50% prob to horizontal flip and vertical flip
if random.random() > 0.5:
image1 = tf.hflip(image1)
image2 = tf.hflip(image2)
if random.random() > 0.5:
image1 = tf.vflip(image1)
image2 = tf.vflip(image2)
image1 = tf.to_tensor(image1)
image2 = tf.to_tensor(image2)
return image1, image2
def __getitem__(self, index):
clean_address = self.clean_imgs[index]
adv_address = self.adv_imgs[index]
clean_img = TM.preprocess_image(cv2.imread(clean_address))
adv_img = TM.preprocess_image(cv2.imread(adv_address))
# if self.transform is not None:
# clean_img = self.transform(clean_img)
# adv_img = self.transform(adv_img)
if self.transform == 'rotation':
clean_img, adv_img = self.rotation(clean_img, adv_img)
elif self.transform == 'flip':
clean_img, adv_img = self.flip(clean_img, adv_img)
else:
clean_img = tf.to_tensor(clean_img)
adv_img = tf.to_tensor(adv_img)
return clean_img, adv_img
def __len__(self):
return len(self.clean_imgs)
class FaceDatasetTransformTest(Dataset):
def __init__(self, txt_path, transform = None):
fh= open(txt_path, 'r')
clean_imgs = []
adv_imgs = []
for line in fh:
line = line.rstrip()
words = line.split()
clean_imgs.append(words[0])
adv_imgs.append(words[1])
self.clean_imgs = clean_imgs # 最主要就是要生成这个list， 然后DataLoader中给index，通过getitem读取图片数据
self.adv_imgs = adv_imgs
self.transform = transform
def rotation(self, image1, image2):
# get a random angle range from (-180, 180)
angle = transforms.RandomRotation.get_params([-180, 180])
# same angle rotation for image1 and image2
image1 = image1.rotate(angle)
image2 = image2.rotate(angle)
image1 = tf.to_tensor(image1)
image2 = tf.to_tensor(image2)
return image1, image2
def flip(self, image1, image2):
# 50% prob to horizontal flip and vertical flip
if random.random() > 0.5:
image1 = tf.hflip(image1)
image2 = tf.hflip(image2)
if random.random() > 0.5:
image1 = tf.vflip(image1)
image2 = tf.vflip(image2)
image1 = tf.to_tensor(image1)
image2 = tf.to_tensor(image2)
return image1, image2
def hflip(self, image1, image2):
image1 = tf.hflip(image1)
image2 = tf.hflip(image2)
image1 = tf.to_tensor(image1)
image2 = tf.to_tensor(image2)
return image1, image2
def vflip(self, image1, image2):
image1 = tf.vflip(image1)
image2 = tf.vflip(image2)
image1 = tf.to_tensor(image1)
image2 = tf.to_tensor(image2)
return image1, image2
def rotation_new(self, image1, image2):
if random.random() > 0.5:
angle = transforms.RandomRotation.get_params([40, 50])
else:
angle = transforms.RandomRotation.get_params([-50, -40])
image1 = image1.rotate(angle)
image2 = image2.rotate(angle)
image1 = tf.to_tensor(image1)
image2 = tf.to_tensor(image2)
return image1, image2
def __getitem__(self, index):
clean_address = self.clean_imgs[index]
adv_address = self.adv_imgs[index]
clean_img = TM.preprocess_image(cv2.imread(clean_address))
adv_img = TM.preprocess_image(cv2.imread(adv_address))
# if self.transform is not None:
# clean_img = self.transform(clean_img)
# adv_img = self.transform(adv_img)
if self.transform == 'rotation':
clean_img_transform, adv_img_transform = self.rotation(clean_img, adv_img)
elif self.transform == 'flip':
clean_img_transform, adv_img_transform = self.flip(clean_img, adv_img)
elif self.transform == 'hflip':
clean_img_transform, adv_img_transform = self.hflip(clean_img, adv_img)
elif self.transform == 'vflip':
clean_img_transform, adv_img_transform = self.vflip(clean_img, adv_img)
elif self.transform == 'rotation_new':
clean_img_transform, adv_img_transform = self.rotation_new(clean_img, adv_img)
clean_img = tf.to_tensor(clean_img)
adv_img = tf.to_tensor(adv_img)
return clean_img, adv_img, clean_img_transform, adv_img_transform
def __len__(self):
return len(self.clean_imgs)
class Labeled_FaceDataset(Dataset):
def __init__(self, txt_path, label):
fh = open(txt_path, 'r')
clean_imgs = []
adv_imgs = []
# labels = []
for line in fh:
line = line.rstrip()
words = line.split()
clean_imgs.append(words[0])
adv_imgs.append(words[1])
# labels.append(label)
self.clean_imgs = clean_imgs # 最主要就是要生成这个list， 然后DataLoader中给index，通过getitem读取图片数据
self.adv_imgs = adv_imgs
self.label = label
def __getitem__(self, index):
clean_address = self.clean_imgs[index]
adv_address = self.adv_imgs[index]
clean_img = TM.preprocess_image(cv2.imread(clean_address))
adv_img = TM.preprocess_image(cv2.imread(adv_address))
# print(clean_img.type)
clean_img = tf.to_tensor(clean_img)
adv_img = tf.to_tensor(adv_img)
return torch.cat((adv_img-clean_img, clean_img),0), self.label
def __len__(self):
return len(self.clean_imgs)
class Labeled_FaceDataset_new(Dataset):
def __init__(self, txt_path, label):
fh = open(txt_path, 'r')
clean_imgs = []
adv_imgs = []
# labels = []
for line in fh:
line = line.rstrip()
words = line.split()
clean_imgs.append(words[0])
adv_imgs.append(words[1])
# labels.append(label)
self.clean_imgs = clean_imgs # 最主要就是要生成这个list， 然后DataLoader中给index，通过getitem读取图片数据
self.adv_imgs = adv_imgs
self.label = label
def __getitem__(self, index):
clean_address = self.clean_imgs[index]
adv_address = self.adv_imgs[index]
clean_img = TM.preprocess_image(cv2.imread(clean_address))
adv_img = TM.preprocess_image(cv2.imread(adv_address))
# print(clean_img.type)
clean_img = tf.to_tensor(clean_img)
adv_img = tf.to_tensor(adv_img)
return (adv_img - clean_img), self.label
def __len__(self):
return len(self.clean_imgs)
class Labeled_FaceDataset_incremental(Dataset):
def __init__(self, txt_path, label, known):
fh = open(txt_path, 'r')
clean_imgs = []
adv_imgs = []
# labels = []
for line in fh:
line = line.rstrip()
words = line.split()
clean_imgs.append(words[0])
adv_imgs.append(words[1])
# labels.append(label)
self.clean_imgs = clean_imgs # 最主要就是要生成这个list， 然后DataLoader中给index，通过getitem读取图片数据
self.adv_imgs = adv_imgs
self.label = label
self.known = known
def __getitem__(self, index):
clean_address = self.clean_imgs[index]
adv_address = self.adv_imgs[index]
clean_img = TM.preprocess_image(cv2.imread(clean_address))
adv_img = TM.preprocess_image(cv2.imread(adv_address))
# print(clean_img.type)
clean_img = tf.to_tensor(clean_img)
adv_img = tf.to_tensor(adv_img)
return (adv_img - clean_img), self.label, self.known
def __len__(self):
return len(self.clean_imgs) | 34.419847 | 97 | 0.611998 | 1,115 | 9,018 | 4.693274 | 0.086996 | 0.065737 | 0.045863 | 0.048156 | 0.924517 | 0.901586 | 0.883623 | 0.838907 | 0.813682 | 0.806612 | 0 | 0.025789 | 0.29053 | 9,018 | 262 | 98 | 34.419847 | 0.792123 | 0.103349 | 0 | 0.816327 | 0 | 0 | 0.006329 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.112245 | false | 0 | 0.045918 | 0.02551 | 0.270408 | 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 |
08d8da5354b86fed5a1ac6f8316892815e9a1585 | 26,492 | py | Python | compiler/tests/sample_dicts.py | UoW-CPC/adt-generator-api | 0adabc00348cafea7b6fe085e3efac908c99a70c | [
"Apache-2.0"
] | null | null | null | compiler/tests/sample_dicts.py | UoW-CPC/adt-generator-api | 0adabc00348cafea7b6fe085e3efac908c99a70c | [
"Apache-2.0"
] | null | null | null | compiler/tests/sample_dicts.py | UoW-CPC/adt-generator-api | 0adabc00348cafea7b6fe085e3efac908c99a70c | [
"Apache-2.0"
] | null | null | null | # algodt_old = {'description':
# {'id': 'algorithm_10824912410291',
# 'name': 'Object Detection Algorithm for detection of faulty weld seams',
# 'description': 'This algorithm can be used to solve a specifc problem, and applies some fancy technologies.',
# 'classificationSchema': 'ML',
# 'type':
# ['neural network',
# 'deep learning'
# ],
# 'author': 'DFKI',
# 'date': '06/04/2021',
# 'version': '1.0'
# },
# 'algorithm':
# {'listOfMicroservices':
# ['microserviceA',
# 'microserviceB',
# 'microserviceC'],
# 'abstractHostDefinition':
# [{'microserviceA': 'host1'},
# {'microserviceB': 'host2'},
# {'microserviceC': 'host3'}
# ]
# }
# }
#
# idt_old = {'description':
# {'id': 'algorithm_10824912410291',
# 'name': 'Object Detection Algorithm for detection of faulty weld seams',
# 'description': 'This algorithm can be used to solve a specifc problem, and applies some fancy technologies.',
# 'classificationSchema': 'ML',
# 'type':
# ['neural network',
# 'deep learning'
# ],
# 'author': 'DFKI',
# 'date': '06/04/2021',
# 'version': '1.0'
# }
# }
#
#
# mdt_old = {'description':
# {'id': 'microservice_12312124',
# 'name': 'Object Detection for faulty parts',
# 'author': 'DFKI',
# 'date': '06/04/2021',
# 'version': '1.0',
# 'description': 'This microservices solves a certain problem using very specific methods. It supports some special input types, and allows results to be presented in chosen format.',
# 'classificationSchema': 'other',
# 'type':
# ['neural network',
# 'deep learning'
# ],
# 'software':
# ['Apache Kafka',
# 'TensorFlow'
# ],
# 'softwareVersion':
# ['Kafka 2.7.0',
# 'TensorFlow 2.4.1'
# ]},
# 'service':
# {'containerFormat': 'Docker',
# 'image': 'dockerhub://dfki/object_detection/stuff',
# 'deploymentFormat': 'docker run',
# 'deploymentData': 'docker run image-name -p 8080:8080',
# 'dependencyRequirements': 'MicroserviceAsset_ID_123',
# 'storageRequirements': 'needs to share volume with MicroserviceAsset_ID_123',
# 'limitations': 'can only work with jpg files'},
# 'containerConfiguration':
# {'name': 'stuff_detection',
# 'command': '["sudo ./start.sh"]',
# 'args': '["-h", "test_argument"]',
# 'labels': '["label1", "label2"]',
# 'env': '["env1", "env2"]',
# 'optional1': '["optional1"]',
# 'opitonal2': '["optional1"]'},
# 'hardwareRequirements':
# {'recommendedNumberOfGPUCores': 2,
# 'minimumNumberOfGPUCores': 1,
# 'recommendedGPURAM': 6,
# 'minimumGPURAM': 1,
# 'gpuType': 'NVidia (compute capability >= 7.0)',
# 'hpcRequired': '"True"',
# 'hpcType': 'hpcType',
# 'edgeType': 'NVIDIA Jetson AGX',
# 'recommendedRAM': 16,
# 'minimumRAM': 2,
# 'recommendedCPUs': 4,
# 'minimumCPUs': 2,
# 'requiredDiskSpace': 42},
# 'OSRequirements':
# {'osArch': 'x86_64',
# 'osType': 'linux',
# 'osDistribution': 'ubuntu',
# 'osVersion': '20.04'},
# 'inputData':
# {'inputData':
# {'DATA_KIND': 'FILE, STREAM',
# 'DATA_DIRECTION': 'SOURCE'},
# 'dataObjects':
# {'DATA_KIND':
# ['FILE',
# 'STREAM'
# ],
# 'DATA_DIRECTION':
# ['SINK',
# 'BIDIRECTIONAL'
# ],
# 'DATA_FORMAT':
# ['application/zip',
# 'image/jpg'
# ],
# 'DATA_SOURCE_TYPE':
# ['MYSQL', 'KAFKA'
# ],
# 'DATA_PROTOCOL':
# ['HTTP',
# 'HTTPS'
# ],
# 'DATA_AUTH_TYPE':
# ['tls_mutual',
# 'userpass'
# ],
# 'DATA_MYSQL_DIALECT':
# ['mariadbdialect',
# 'sampledialect'
# ],
# 'DATA_MQTT_PROTOCOL_VERSION':
# ['2.3.1',
# '2.3.2'],
# 'DATA_KAFKA_BROKER_VERSION':
# ['2.7.1',
# '2.5'
# ],
# 'DATA_S3_REGION':
# ['eu-central-1',
# 'eu-central-2'],
# 'DATA_SCHEMA':
# ['jpg',
# 'png'
# ]}},
# 'outputData':
# {'outputData':
# {'DATA_KIND': 'FILE, STREAM',
# 'DATA_DIRECTION': 'SINK'},
# 'dataObjects':
# {'DATA_KIND':
# ['FILE',
# 'STREAM'
# ],
# 'DATA_DIRECTION':
# ['SINK',
# 'BIDIRECTIONAL'
# ],
# 'DATA_FORMAT':
# ['application/zip',
# 'image/jpg'
# ],
# 'DATA_SOURCE_TYPE':
# ['MYSQL',
# 'KAFKA'
# ],
# 'DATA_PROTOCOL':
# ['HTTP',
# 'HTTPS'
# ],
# 'DATA_AUTH_TYPE':
# ['tls_mutual',
# 'userpass'],
# 'DATA_MYSQL_DIALECT':
# ['mariadbdialect',
# 'sampledialect'
# ],
# 'DATA_MQTT_PROTOCOL_VERSION':
# ['2.3.1',
# '2.3.2'
# ],
# 'DATA_KAFKA_BROKER_VERSION':
# ['2.7.1',
# '2.5'
# ],
# 'DATA_S3_REGION':
# ['eu-central-1',
# 'eu-central-2'],
# 'DATA_SCHEMA':
# ['jpg',
# 'png'
# ]}},
# 'model':
# {'modelTypes':
# ['SavedModel1 (Tensorflo1)',
# 'SavedModel2 (Tensorflo2)'],
# 'modelRecommendedAuthTools':
# ['SavedModel1 (Tensorflo3)',
# 'SavedModel2 (Tensorflo4)']}}
#
#
# mdt_temp = {'description':
# {'id': 'microservice_12312124',
# 'name': 'Object Detection for faulty parts',
# 'author': 'DFKI',
# 'date': '06/04/2021',
# 'version': '1.0',
# 'description': 'This microservices solves a certain problem using very specific methods. It supports some special input types, and allows results to be presented in chosen format.',
# 'classificationSchema': 'other',
# 'type':
# ['neural network',
# 'deep learning'
# ],
# 'software':
# ['Apache Kafka',
# 'TensorFlow'
# ],
# 'softwareVersion':
# ['Kafka 2.7.0',
# 'TensorFlow 2.4.1'
# ]},
# 'service':
# {'containerFormat': 'Docker',
# 'image': 'dockerhub://dfki/object_detection/stuff',
# 'deploymentFormat': 'docker run',
# 'deploymentData': 'docker run image-name -p 8080:8080',
# 'dependencyRequirements': 'MicroserviceAsset_ID_123',
# 'storageRequirements': 'needs to share volume with MicroserviceAsset_ID_123',
# 'limitations': 'can only work with jpg files'},
# 'containerConfiguration':
# {'name': 'stuff_detection',
# 'command': '["sudo ./start.sh"]',
# 'args': '["-h", "test_argument"]',
# 'labels': '["label1", "label2"]',
# 'env': '["env1", "env2"]',
# 'optional1': '["optional1"]',
# 'opitonal2': '["optional1"]'},
# 'hardwareRequirements':
# {'recommendedNumberOfGPUCores': 2,
# 'minimumNumberOfGPUCores': 1,
# 'recommendedGPURAM': 6,
# 'minimumGPURAM': 1,
# 'gpuType': 'NVidia (compute capability >= 7.0)',
# 'hpcRequired': '"True"',
# 'hpcType': 'hpcType',
# 'edgeType': 'NVIDIA Jetson AGX',
# 'recommendedRAM': 16,
# 'minimumRAM': 2,
# 'recommendedCPUs': 4,
# 'minimumCPUs': 2,
# 'requiredDiskSpace': 42},
# 'OSRequirements':
# {'osArch': 'x86_64',
# 'osType': 'linux',
# 'osDistribution': 'ubuntu',
# 'osVersion': '20.04'},
# 'inputData':
# {'inputData':
# {'DATA_KIND': 'FILE, STREAM',
# 'DATA_DIRECTION': 'SOURCE'},
# 'dataObjects':
# {'DATA_KIND':
# ['FILE',
# 'STREAM'
# ],
# 'DATA_DIRECTION':
# ['SINK',
# 'BIDIRECTIONAL'
# ],
# 'DATA_FORMAT':
# ['application/zip',
# 'image/jpg'
# ],
# 'DATA_SOURCE_TYPE':
# ['MYSQL', 'KAFKA'
# ],
# 'DATA_PROTOCOL':
# ['HTTP',
# 'HTTPS'
# ],
# 'DATA_AUTH_TYPE':
# ['tls_mutual',
# 'userpass'
# ],
# 'DATA_MYSQL_DIALECT':
# ['mariadbdialect',
# 'sampledialect'
# ],
# 'DATA_MQTT_PROTOCOL_VERSION':
# ['2.3.1',
# '2.3.2'],
# 'DATA_KAFKA_BROKER_VERSION':
# ['2.7.1',
# '2.5'
# ],
# 'DATA_S3_REGION':
# ['eu-central-1',
# 'eu-central-2'],
# 'DATA_SCHEMA':
# ['jpg',
# 'png'
# ]}},
# 'outputData':
# {'outputData':
# {'DATA_KIND': 'FILE, STREAM',
# 'DATA_DIRECTION': 'SINK'},
# 'dataObjects':
# {'DATA_KIND':
# ['FILE',
# 'STREAM'
# ],
# 'DATA_DIRECTION':
# ['SINK',
# 'BIDIRECTIONAL'
# ],
# 'DATA_FORMAT':
# ['application/zip',
# 'image/jpg'
# ],
# 'DATA_SOURCE_TYPE':
# ['MYSQL',
# 'KAFKA'
# ],
# 'DATA_PROTOCOL':
# ['HTTP',
# 'HTTPS'
# ],
# 'DATA_AUTH_TYPE':
# ['tls_mutual',
# 'userpass'],
# 'DATA_MYSQL_DIALECT':
# ['mariadbdialect',
# 'sampledialect'
# ],
# 'DATA_MQTT_PROTOCOL_VERSION':
# ['2.3.1',
# '2.3.2'
# ],
# 'DATA_KAFKA_BROKER_VERSION':
# ['2.7.1',
# '2.5'
# ],
# 'DATA_S3_REGION':
# ['eu-central-1',
# 'eu-central-2'],
# 'DATA_SCHEMA':
# ['jpg',
# 'png'
# ]}},
# 'model':
# {'modelTypes':
# ['SavedModel1 (Tensorflo1)',
# 'SavedModel2 (Tensorflo2)'],
# 'modelRecommendedAuthTools':
# ['SavedModel1 (Tensorflo3)',
# 'SavedModel2 (Tensorflo4)']},
# 'manifest': {'apiVersion': 'v1', 'kind': 'Pod', 'metadata': {'name': 'busybox-sleep'}, 'spec': {'containers': [{'name': 'busybox', 'image': 'busybox', 'args': ['sleep', '1000000']}]}}
# }
algodt = {
'id': 'algorithm_10824912410291',
'name': 'Object Detection Algorithm for detection of faulty weld seams',
'description': 'This algorithm can be used to solve a specifc problem, and applies some fancy technologies.',
'classificationSchema': 'ML',
'type':
['neural network','deep learning'],
'author': 'DFKI',
'date': '06/04/2021',
'version': '1.0',
'listOfMicroservices':
['microserviceA','microserviceB','microserviceC'],
'abstractHostDefinition':
{
'microserviceA': 'microserviceA',
'microserviceB': 'microserviceA',
'microserviceC': 'microserviceC'
}
}
ddt = {
'id': 'deployment_10824912410291',
'name': 'Object Detection Algorithm for detection of faulty weld seams',
'author': 'DFKI',
'created_at': '06/04/2021',
'version': '1.0',
'licensor': 'Licensor 1',
'scope': 'This algorithm can be used to solve a specifc problem, and applies some fancy technologies.',
'host_name': 'host1',
'deployment_id': 'depl1',
'instance_type_id': 'ins1',
'key_pair_id': 'key1',
'opened_port': 8080,
'endpoint': 'end1'
}
mdt_kube = {
'id': 'microservice_12312124',
'name': 'Object Detection for faulty parts',
'author': 'DFKI',
'date': '06/04/2021',
'version': '1.0',
'description': 'This microservices solves a certain problem using very specific methods. It supports some special input types, and allows results to be presented in chosen format.',
'classificationSchema': 'other',
'type':
['neural network','deep learning'],
'software':
['Apache Kafka','TensorFlow'],
'softwareVersion':
['Kafka 2.7.0','TensorFlow 2.4.1'],
'containerFormat': 'Docker',
'image': 'dockerhub://dfki/object_detection/stuff',
'deploymentFormat': 'kubernetes-manifest',
'deploymentData':
{
'apiVersion': 'v1', 'kind': 'Pod',
'metadata':
{'name': 'busybox-sleep'},
'spec':
{'containers':
[
{'name': 'busybox', 'image': 'busybox', 'args': ['sleep', '1000000']}
]
}
},
'dependencyRequirements': 'MicroserviceAsset_ID_123',
'storageRequirements': 'needs to share volume with MicroserviceAsset_ID_123',
'limitations': 'can only work with jpg files',
'recommendedNumberOfGPUCores': 2,
'minimumNumberOfGPUCores': 1,
'recommendedGPURAM': 6,
'minimumGPURAM': 1,
'gpuType': 'NVidia (compute capability >= 7.0)',
'hpcRequired': '"True"',
'hpcType': 'hpcType',
'edgeType': 'NVIDIA Jetson AGX',
'recommendedRAM': 16,
'minimumRAM': 2,
'recommendedCPUs': 4,
'minimumCPUs': 2,
'requiredDiskSpace': 42,
'osArch': 'x86_64',
'osType': 'linux',
'osDistribution': 'ubuntu',
'osVersion': '20.04',
'inputData':
[
{
'INPUT_ID': 'SAMPLE_ID',
'DATA_KIND':
['FILE','STREAM'],
'DATA_DIRECTION':
['SINK','BIDIRECTIONAL'],
'DATA_FORMAT':
['application/zip','image/jpg'],
'DATA_SOURCE_TYPE':
['MYSQL', 'KAFKA'],
'DATA_PROTOCOL':
['HTTP','HTTPS'],
'DATA_AUTH_TYPE':
['tls_mutual','userpass'],
'DATA_MYSQL_DIALECT':
['mariadbdialect','sampledialect'],
'DATA_MQTT_PROTOCOL_VERSION':
['2.3.1','2.3.2'],
'DATA_KAFKA_BROKER_VERSION':
['2.7.1','2.5'],
'DATA_S3_REGION':
['eu-central-1','eu-central-2'],
'DATA_SCHEMA':
['jpg','png' ]
}
],
'outputData':
[
{
'OUTPUT_ID': 'SAMPLEO_ID',
'DATA_KIND':
['FILE','STREAM'],
'DATA_DIRECTION':
['SINK','BIDIRECTIONAL'],
'DATA_FORMAT':
['application/zip','image/jpg'],
'DATA_SOURCE_TYPE':
['MYSQL','KAFKA'],
'DATA_PROTOCOL':
['HTTP','HTTPS'],
'DATA_AUTH_TYPE':
['tls_mutual','userpass'],
'DATA_MYSQL_DIALECT':
['mariadbdialect','sampledialect'],
'DATA_MQTT_PROTOCOL_VERSION':
['2.3.1','2.3.2'],
'DATA_KAFKA_BROKER_VERSION':
['2.7.1','2.5'],
'DATA_S3_REGION':
['eu-central-1','eu-central-2'],
'DATA_SCHEMA':
['jpg','png']
}
],
'modelTypes':
['SavedModel1 (Tensorflo1)','SavedModel2 (Tensorflo2)'],
'modelRecommendedAuthTools':
['SavedModel1 (Tensorflo3)','SavedModel2 (Tensorflo4)'],
'parameters':
[
{
'name':'n1', 'type':'int','mandatory': True,
'defaultValue':'45','description':'sample n1'
},
{
'name':'n2', 'type':'bool','mandatory': False,
'defaultValue':'45','description':'sample n2'
}
],
'metric': []
}
mdt_dock = {
'id': 'microservice_12312124',
'name': 'Object Detection for faulty parts',
'author': 'DFKI',
'date': '06/04/2021',
'version': '1.0',
'description': 'This microservices solves a certain problem using very specific methods. It supports some special input types, and allows results to be presented in chosen format.',
'classificationSchema': 'other',
'type':
['neural network','deep learning'],
'software':
['Apache Kafka','TensorFlow'],
'softwareVersion':
['Kafka 2.7.0','TensorFlow 2.4.1'],
'containerFormat': 'Docker',
'image': 'dbs-container-repo.emgora.eu/db-ristra-cli-cpu:1.0.0',
'deploymentFormat': 'docker-compose',
'deploymentData':
{
'version': '3.9',
'services':
{
'web':
{
'build': '.',
'ports':
['5000:5000'],
'volumes':
['.:/code', 'logvolume01:/var/log'],
'links': ['redis']
},
'redis': {'image': 'redis'}
},
'volumes':
{
'logvolume01': {}
}
},
'dependencyRequirements': 'MicroserviceAsset_ID_123',
'storageRequirements': 'needs to share volume with MicroserviceAsset_ID_123',
'limitations': 'can only work with jpg files',
'recommendedNumberOfGPUCores': 2,
'minimumNumberOfGPUCores': 1,
'recommendedGPURAM': 6,
'minimumGPURAM': 1,
'gpuType': 'NVidia (compute capability >= 7.0)',
'hpcRequired': '"True"',
'hpcType': 'hpcType',
'edgeType': 'NVIDIA Jetson AGX',
'recommendedRAM': 16,
'minimumRAM': 2,
'recommendedCPUs': 4,
'minimumCPUs': 2,
'requiredDiskSpace': 42,
'osArch': 'x86_64',
'osType': 'linux',
'osDistribution': 'ubuntu',
'osVersion': '20.04',
'inputData':
[
{
'INPUT_ID': 'SAMPLE_ID',
'DATA_KIND':
['FILE','STREAM'],
'DATA_DIRECTION':
['SINK','BIDIRECTIONAL'],
'DATA_FORMAT':
['application/zip','image/jpg'],
'DATA_SOURCE_TYPE':
['MYSQL', 'KAFKA'],
'DATA_PROTOCOL':
['HTTP','HTTPS'],
'DATA_AUTH_TYPE':
['tls_mutual','userpass'],
'DATA_MYSQL_DIALECT':
['mariadbdialect','sampledialect'],
'DATA_MQTT_PROTOCOL_VERSION':
['2.3.1','2.3.2'],
'DATA_KAFKA_BROKER_VERSION':
['2.7.1','2.5'],
'DATA_S3_REGION':
['eu-central-1','eu-central-2'],
'DATA_SCHEMA':
['jpg','png' ]
}
],
'outputData':
[
{
'OUTPUT_ID': 'SAMPLEO_ID',
'DATA_KIND':
['FILE','STREAM'],
'DATA_DIRECTION':
['SINK','BIDIRECTIONAL'],
'DATA_FORMAT':
['application/zip','image/jpg'],
'DATA_SOURCE_TYPE':
['MYSQL','KAFKA'],
'DATA_PROTOCOL':
['HTTP','HTTPS'],
'DATA_AUTH_TYPE':
['tls_mutual','userpass'],
'DATA_MYSQL_DIALECT':
['mariadbdialect','sampledialect'],
'DATA_MQTT_PROTOCOL_VERSION':
['2.3.1','2.3.2'],
'DATA_KAFKA_BROKER_VERSION':
['2.7.1','2.5'],
'DATA_S3_REGION':
['eu-central-1','eu-central-2'],
'DATA_SCHEMA':
['jpg','png']
}
],
'modelTypes':
['SavedModel1 (Tensorflo1)','SavedModel2 (Tensorflo2)'],
'modelRecommendedAuthTools':
['SavedModel1 (Tensorflo3)','SavedModel2 (Tensorflo4)'],
'parameters':
[
{
'name':'n1', 'type':'int','mandatory': True,
'defaultValue':'45','description':'sample n1'
},
{
'name':'n2', 'type':'bool','mandatory': False,
'defaultValue':'45','description':'sample n2'
}
],
'metric': []
}
mdt_ristra = {
'id': 'microservice_12312124',
'name': 'Object Detection for faulty parts',
'author': 'DFKI',
'date': '06/04/2021',
'version': '1.0',
'description': 'This microservices solves a certain problem using very specific methods. It supports some special input types, and allows results to be presented in chosen format.',
'classificationSchema': 'other',
'type':
['neural network','deep learning'],
'software':
['Apache Kafka','TensorFlow'],
'softwareVersion':
['Kafka 2.7.0','TensorFlow 2.4.1'],
'containerFormat': 'Docker',
'image': 'dbs-container-repo.emgora.eu/db-ristra-cli-cpu:1.0.0',
'deploymentFormat': 'docker-compose',
'deploymentData':
{
"version": "'3'",
"services":
{
"ristra":
{
"image": "dbs-container-repo.emgora.eu/db-ristra-cli-cpu:1.0.0",
"command": "python3 start.py ${Model_URI}",
"depends_on":
{
"rclone":
{
"condition": "service_healthy"
}
},
"volumes":
[
{
"type": "bind",
"source": "./data",
"target": "/data",
"bind":
{
"propagation": "rshared"
}
}
],
"privileged": "true"
}
}
},
'dependencyRequirements': 'MicroserviceAsset_ID_123',
'storageRequirements': 'needs to share volume with MicroserviceAsset_ID_123',
'limitations': 'can only work with jpg files',
'recommendedNumberOfGPUCores': 2,
'minimumNumberOfGPUCores': 1,
'recommendedGPURAM': 6,
'minimumGPURAM': 1,
'gpuType': 'NVidia (compute capability >= 7.0)',
'hpcRequired': '"True"',
'hpcType': 'hpcType',
'edgeType': 'NVIDIA Jetson AGX',
'recommendedRAM': 16,
'minimumRAM': 2,
'recommendedCPUs': 4,
'minimumCPUs': 2,
'requiredDiskSpace': 42,
'osArch': 'x86_64',
'osType': 'linux',
'osDistribution': 'ubuntu',
'osVersion': '20.04',
'inputData':
[
{
'INPUT_ID': 'SAMPLE_ID',
'DATA_KIND':
['FILE','STREAM'],
'DATA_DIRECTION':
['SINK','BIDIRECTIONAL'],
'DATA_FORMAT':
['application/zip','image/jpg'],
'DATA_SOURCE_TYPE':
['MYSQL', 'KAFKA'],
'DATA_PROTOCOL':
['HTTP','HTTPS'],
'DATA_AUTH_TYPE':
['tls_mutual','userpass'],
'DATA_MYSQL_DIALECT':
['mariadbdialect','sampledialect'],
'DATA_MQTT_PROTOCOL_VERSION':
['2.3.1','2.3.2'],
'DATA_KAFKA_BROKER_VERSION':
['2.7.1','2.5'],
'DATA_S3_REGION':
['eu-central-1','eu-central-2'],
'DATA_SCHEMA':
['jpg','png' ]
}
],
'outputData':
[
{
'OUTPUT_ID': 'SAMPLEO_ID',
'DATA_KIND':
['FILE','STREAM'],
'DATA_DIRECTION':
['SINK','BIDIRECTIONAL'],
'DATA_FORMAT':
['application/zip','image/jpg'],
'DATA_SOURCE_TYPE':
['MYSQL','KAFKA'],
'DATA_PROTOCOL':
['HTTP','HTTPS'],
'DATA_AUTH_TYPE':
['tls_mutual','userpass'],
'DATA_MYSQL_DIALECT':
['mariadbdialect','sampledialect'],
'DATA_MQTT_PROTOCOL_VERSION':
['2.3.1','2.3.2'],
'DATA_KAFKA_BROKER_VERSION':
['2.7.1','2.5'],
'DATA_S3_REGION':
['eu-central-1','eu-central-2'],
'DATA_SCHEMA':
['jpg','png']
}
],
'modelTypes':
['SavedModel1 (Tensorflo1)','SavedModel2 (Tensorflo2)'],
'modelRecommendedAuthTools':
['SavedModel1 (Tensorflo3)','SavedModel2 (Tensorflo4)'],
'parameters':
[
{
'name':'n1', 'type':'int','mandatory': True,
'defaultValue':'45','description':'sample n1'
},
{
'name':'n2', 'type':'bool','mandatory': False,
'defaultValue':'45','description':'sample n2'
}
],
'metric': []
} | 33.365239 | 196 | 0.437151 | 1,958 | 26,492 | 5.772217 | 0.143003 | 0.014157 | 0.014865 | 0.022297 | 0.947266 | 0.947266 | 0.929305 | 0.929305 | 0.929305 | 0.924969 | 0 | 0.040409 | 0.401215 | 26,492 | 794 | 197 | 33.365239 | 0.672067 | 0.472105 | 0 | 0.708625 | 0 | 0.013986 | 0.519701 | 0.078509 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.013986 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
3eae78136a5d36855657645c393f671c2e6c24c2 | 12,270 | py | Python | src/abaqus/Odb/HistoryPoint.py | Haiiliin/PyAbaqus | f20db6ebea19b73059fe875a53be370253381078 | [
"MIT"
] | 7 | 2022-01-21T09:15:45.000Z | 2022-02-15T09:31:58.000Z | src/abaqus/Odb/HistoryPoint.py | Haiiliin/PyAbaqus | f20db6ebea19b73059fe875a53be370253381078 | [
"MIT"
] | null | null | null | src/abaqus/Odb/HistoryPoint.py | Haiiliin/PyAbaqus | f20db6ebea19b73059fe875a53be370253381078 | [
"MIT"
] | null | null | null | import typing
from abaqusConstants import *
from .OdbAssembly import OdbAssembly
from .OdbInstance import OdbInstance
from .OdbMeshElement import OdbMeshElement
from .OdbMeshNode import OdbMeshNode
from .OdbPart import OdbPart
from .OdbSet import OdbSet
from .SectionPoint import SectionPoint
class HistoryPoint:
"""The HistoryPoint object specifies the point at which history data will be collected. The
HistoryPoint object is a temporary object used as an argument to the HistoryRegion
method.
Attributes
----------
ipNumber: int
An Int specifying the integration point. This argument is used to define a history
output position of INTEGRATION_POINT or ELEMENT_FACE_INTEGRATION_POINT. The default
value is 0.
face: SymbolicConstant
A SymbolicConstant specifying the element face. This argument is used to define a
history output position of ELEMENT_FACE or ELEMENT_FACE_INTEGRATION_POINT. Possible
values are:
- FACE_UNKOWN, specifying this value indicates that no value has been specified.
- FACE1, specifying this value indicates that element face 1 has been specified.
- FACE2, specifying this value indicates that element face 2 has been specified.
- FACE3, specifying this value indicates that element face 3 has been specified.
- FACE4, specifying this value indicates that element face 4 has been specified.
- FACE5, specifying this value indicates that element face 5 has been specified.
- FACE6, specifying this value indicates that element face 6 has been specified.
- SIDE1, specifying this value indicates that element side 1 has been specified.
- SIDE2, specifying this value indicates element side 2 has been specified.
- END1, specifying this value indicates that element end 1 has been specified.
- END2, specifying this value indicates that element end 2 has been specified.
- END3, specifying this value indicates that element end 3 has been specified.
The default value is FACE_UNKNOWN.
position: SymbolicConstant
A SymbolicConstant specifying the result position of the history point. Possible values
are:
- NODAL, specifying the values calculated at the nodes.
- ELEMENT_NODAL, specifying the values obtained by extrapolating results calculated at
the integration points.
- INTEGRATION_POINT, specifying the values calculated at the integration points.
- ELEMENT_FACE, specifying the results obtained for surface variables such as cavity
radiation that are defined for the surface facets of an element.
- ELEMENT_FACE_INTEGRATION_POINT, specifying the results obtained for surface variables
such as cavity radiation that are defined for the surface facets of an element when the
surface facets have integration points.
- WHOLE_ELEMENT, specifying the results obtained for whole element variables.
- WHOLE_REGION, specifying the results for an entire region of the model.
- WHOLE_PART_INSTANCE, specifying the results for an entire part instance of the model.
- WHOLE_MODEL, specifying the results for the entire model.
element: OdbMeshElement
An :py:class:`~abaqus.Odb.OdbMeshElement.OdbMeshElement` object specifying the element for which the data are to be collected.
sectionPoint: SectionPoint
A :py:class:`~abaqus.Odb.SectionPoint.SectionPoint` object.
region: OdbSet
An :py:class:`~abaqus.Odb.OdbSet.OdbSet` object specifying the region for which the data are to be collected.
assembly: OdbAssembly
An :py:class:`~abaqus.Odb.OdbAssembly.OdbAssembly` object specifying the assembly for which the data are to be collected.
instance: OdbInstance
An :py:class:`~abaqus.Odb.OdbInstance.OdbInstance` object specifying the instance for which the data are to be collected.
Notes
-----
This object can be accessed by:
.. code-block:: python
import odbAccess
session.odbs[name].steps[name].historyRegions[name].point
"""
# An Int specifying the integration point. This argument is used to define a history
# output position of INTEGRATION_POINT or ELEMENT_FACE_INTEGRATION_POINT. The default
# value is 0.
ipNumber: int = 0
# A SymbolicConstant specifying the element face. This argument is used to define a
# history output position of ELEMENT_FACE or ELEMENT_FACE_INTEGRATION_POINT. Possible
# values are:
# - FACE_UNKOWN, specifying this value indicates that no value has been specified.
# - FACE1, specifying this value indicates that element face 1 has been specified.
# - FACE2, specifying this value indicates that element face 2 has been specified.
# - FACE3, specifying this value indicates that element face 3 has been specified.
# - FACE4, specifying this value indicates that element face 4 has been specified.
# - FACE5, specifying this value indicates that element face 5 has been specified.
# - FACE6, specifying this value indicates that element face 6 has been specified.
# - SIDE1, specifying this value indicates that element side 1 has been specified.
# - SIDE2, specifying this value indicates element side 2 has been specified.
# - END1, specifying this value indicates that element end 1 has been specified.
# - END2, specifying this value indicates that element end 2 has been specified.
# - END3, specifying this value indicates that element end 3 has been specified.
# The default value is FACE_UNKNOWN.
face: SymbolicConstant = FACE_UNKNOWN
# A SymbolicConstant specifying the result position of the history point. Possible values
# are:
# - NODAL, specifying the values calculated at the nodes.
# - ELEMENT_NODAL, specifying the values obtained by extrapolating results calculated at
# the integration points.
# - INTEGRATION_POINT, specifying the values calculated at the integration points.
# - ELEMENT_FACE, specifying the results obtained for surface variables such as cavity
# radiation that are defined for the surface facets of an element.
# - ELEMENT_FACE_INTEGRATION_POINT, specifying the results obtained for surface variables
# such as cavity radiation that are defined for the surface facets of an element when the
# surface facets have integration points.
# - WHOLE_ELEMENT, specifying the results obtained for whole element variables.
# - WHOLE_REGION, specifying the results for an entire region of the model.
# - WHOLE_PART_INSTANCE, specifying the results for an entire part instance of the model.
# - WHOLE_MODEL, specifying the results for the entire model.
position: SymbolicConstant = None
# An OdbMeshElement object specifying the element for which the data are to be collected.
element: OdbMeshElement = OdbMeshElement()
# A SectionPoint object.
sectionPoint: SectionPoint = None
# An OdbSet object specifying the region for which the data are to be collected.
region: OdbSet = OdbSet('set', tuple[OdbMeshNode]())
# An OdbAssembly object specifying the assembly for which the data are to be collected.
assembly: OdbAssembly = OdbAssembly()
# An OdbInstance object specifying the instance for which the data are to be collected.
instance: OdbInstance = OdbInstance('instance', OdbPart('part', THREE_D, DEFORMABLE_BODY))
@typing.overload
def __init__(self, node: OdbMeshNode):
"""This method creates a HistoryPoint object for a node.
Notes
-----
This function can be accessed by:
.. code-block:: python
odbAccess.HistoryPoint
Parameters
----------
node
An OdbMeshNode object specifying the node for which the data are to be collected.
Returns
-------
A HistoryPoint object.
"""
pass
@typing.overload
def __init__(self, element: OdbMeshElement, ipNumber: int = 0, sectionPoint: SectionPoint = None,
face: SymbolicConstant = FACE_UNKNOWN, node: OdbMeshNode = OdbMeshNode()):
"""This method creates a HistoryPoint object for an element.
Notes
-----
This function can be accessed by:
.. code-block:: python
odbAccess.HistoryPoint
Parameters
----------
element
An OdbMeshElement object specifying the element for which the data are to be collected.
ipNumber
An Int specifying the integration point. This argument is used to define a history
output position of INTEGRATION_POINT or ELEMENT_FACE_INTEGRATION_POINT. The default
value is 0.
sectionPoint
A SectionPoint object.
face
A SymbolicConstant specifying the element face. This argument is used to define a
history output position of ELEMENT_FACE or ELEMENT_FACE_INTEGRATION_POINT. Possible
values are:
- FACE_UNKOWN, specifying this value indicates that no value has been specified.
- FACE1, specifying this value indicates that element face 1 has been specified.
- FACE2, specifying this value indicates that element face 2 has been specified.
- FACE3, specifying this value indicates that element face 3 has been specified.
- FACE4, specifying this value indicates that element face 4 has been specified.
- FACE5, specifying this value indicates that element face 5 has been specified.
- FACE6, specifying this value indicates that element face 6 has been specified.
- SIDE1, specifying this value indicates that element side 1 has been specified.
- SIDE2, specifying this value indicates element side 2 has been specified.
- END1, specifying this value indicates that element end 1 has been specified.
- END2, specifying this value indicates that element end 2 has been specified.
- END3, specifying this value indicates that element end 3 has been specified.
The default value is FACE_UNKNOWN.
node
An OdbMeshNode object specifying the node for which the data are to be collected.
Returns
-------
A HistoryPoint object.
"""
pass
@typing.overload
def __init__(self, region: OdbSet):
"""This method creates a HistoryPoint object for a region.
Notes
-----
This function can be accessed by:
.. code-block:: python
odbAccess.HistoryPoint
Parameters
----------
region
An OdbSet object specifying the region for which the data are to be collected.
Returns
-------
A HistoryPoint object.
"""
pass
@typing.overload
def __init__(self, assembly: OdbAssembly):
"""This method creates a HistoryPoint object for the OdbAssembly object.
Notes
-----
This function can be accessed by:
.. code-block:: python
odbAccess.HistoryPoint
Parameters
----------
assembly
An OdbAssembly object specifying the assembly for which the data are to be collected.
Returns
-------
A HistoryPoint object.
"""
pass
@typing.overload
def __init__(self, instance: OdbInstance):
"""This method creates a HistoryPoint object for the OdbInstance object.
Notes
-----
This function can be accessed by:
.. code-block:: python
odbAccess.HistoryPoint
Parameters
----------
instance
An OdbInstance object specifying the instance for which the data are to be collected.
Returns
-------
A HistoryPoint object.
"""
pass
def __init__(self, *args, **kwargs):
pass
| 44.296029 | 134 | 0.677588 | 1,470 | 12,270 | 5.601361 | 0.095918 | 0.063153 | 0.08307 | 0.122419 | 0.851348 | 0.835681 | 0.835681 | 0.832038 | 0.804712 | 0.804712 | 0 | 0.007942 | 0.271394 | 12,270 | 276 | 135 | 44.456522 | 0.913087 | 0.786471 | 0 | 0.305556 | 0 | 0 | 0.009671 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | false | 0.166667 | 0.25 | 0 | 0.666667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 8 |
3efe310864c958514f798614f11c2e17ab789c7a | 113,620 | py | Python | qwe/navigation/tests/nav_tests.py | IEEERobotics/high-level | a50f2170ca81a16bd50b50f970f9e3fe9c656bfa | [
"BSD-2-Clause"
] | 1 | 2017-08-07T06:03:53.000Z | 2017-08-07T06:03:53.000Z | qwe/navigation/tests/nav_tests.py | IEEERobotics/high-level | a50f2170ca81a16bd50b50f970f9e3fe9c656bfa | [
"BSD-2-Clause"
] | null | null | null | qwe/navigation/tests/nav_tests.py | IEEERobotics/high-level | a50f2170ca81a16bd50b50f970f9e3fe9c656bfa | [
"BSD-2-Clause"
] | null | null | null | #!/usr/bin/env python
# Standard library imports
import unittest
import sys
import logging
import logging.config
from multiprocessing import Process, Manager, Queue
import os
import pprint as pp
from datetime import datetime
from time import sleep
from math import pi, radians, degrees, sqrt
from random import randint
# Dict of error codes and their human-readable names
errors = {100 : "ERROR_BAD_CWD"}
errors.update(dict((v,k) for k,v in errors.iteritems())) # Converts errors to a two-way dict
config = { "si_timeout" : .1 }
# Find path to ./qwe directory. Allows for flexibility in the location tests are fired from.
if os.getcwd().endswith("qwe"):
path_to_qwe = "./"
elif os.getcwd().endswith("qwe/navigation"):
path_to_qwe = "../"
elif os.getcwd().endswith("qwe/navigation/tests"):
path_to_qwe = "../../"
else:
print "Error: Bad CWD"
sys.exit(errors["ERROR_BAD_CWD"])
sys.path.append(path_to_qwe) # Makes local module imports work as if in qwe
sys.path.append(path_to_qwe + "mapping") # Makes map unpickle work
# Local module imports
import mapping.pickler as mapper
import navigation.nav as nav
import localizer.localizer as localizer
import comm.serial_interface as comm
# Paths to various files from qwe
path_to_env = path_to_qwe + "navigation/envs/env.cfg"
path_to_sbpl = path_to_qwe + "navigation/sbpl/cmake_build/bin/test_sbpl"
path_to_sol = path_to_qwe + "navigation/sols/sol.txt"
def fakeLoc(testQueue, bot_loc, logger):
while True:
logger.info("testQueue is waiting on data")
ideal_loc = testQueue.get()
logger.info("testQueue received {}".format(str(ideal_loc)))
if type(ideal_loc) == str and ideal_loc == "die":
logger.info("fakeLoc is exiting")
sys.exit(0)
bot_loc["x"] = ideal_loc["x"]
bot_loc["y"] = ideal_loc["y"]
bot_loc["theta"] = ideal_loc["theta"]
logger.debug("fakeLoc set bot_loc to {} {} {}".format(bot_loc["x"], bot_loc["y"], bot_loc["theta"]))
bot_loc["dirty"] = False
logger.info("fakeLoc set bot_loc to clean")
class TestFileGeneration(unittest.TestCase):
def setUp(self):
"""Create nav object and feed it appropriate data"""
# Create file and stream handlers
self.file_handler = logging.handlers.RotatingFileHandler(path_to_qwe + "logs/unittests.log", maxBytes=512000, backupCount=50)
self.file_handler.setLevel(logging.DEBUG)
self.stream_handler = logging.StreamHandler()
self.stream_handler.setLevel(logging.WARN)
# Create formatter and add to handlers
formatter = logging.Formatter('%(asctime)s | %(levelname)s | %(filename)s | %(funcName)s | %(lineno)d | %(message)s')
self.file_handler.setFormatter(formatter)
self.stream_handler.setFormatter(formatter)
# Create logger and add handlers
self.logger = logging.getLogger("unittest")
self.logger.setLevel(logging.DEBUG)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(self.stream_handler)
self.logger.debug("Logger is set up")
# Start serial communication to low-level board
self.si = comm.SerialInterface(timeout=config["si_timeout"])
self.si.start() # Displays an error if port not found (not running on Pandaboard)
self.logger.info("Serial interface set up")
# Build Queue objects for IPC. Name shows producer_consumer.
self.qNav_loc = Queue()
self.qMove_nav = Queue()
self.logger.debug("Queue objects created")
# Get map, waypoints
self.course_map = mapper.unpickle_map(path_to_qwe + "mapping/map.pkl")
self.logger.info("Map unpickled")
self.waypoints = mapper.unpickle_waypoints(path_to_qwe + "mapping/waypoints.pkl")
self.logger.info("Waypoints unpickled")
# Build shared data structures
self.manager = Manager()
self.start_x = self.waypoints["start"][1][0]
self.start_y = self.waypoints["start"][1][1]
self.start_theta = self.waypoints["start"][2]
self.logger.debug("Start waypoint is {}, {}, {}".format(self.start_x, self.start_y, self.start_theta))
self.bot_loc = self.manager.dict(x=self.start_x, y=self.start_y, theta=self.start_theta, dirty=False)
self.bot_state = self.manager.dict(nav_type=None, action_type=None)
self.logger.debug("Shared data structures created")
# Build nav object
self.scNav = comm.SerialCommand(self.si.commands, self.si.responses)
self.scNav.compassReset()
self.Nav = nav.Nav(self.bot_loc, self.qNav_loc, self.scNav, self.bot_state, self.qMove_nav, \
self.logger)
self.logger.info("Nav object instantiated")
def tearDown(self):
"""Close serial interface threads"""
self.logger.removeHandler(self.file_handler)
self.logger.removeHandler(self.stream_handler)
self.scNav.quit()
self.si.join()
def test_env_and_sol_file_generation(self):
"""Delete environment file and then generate it, to confirm that it's created"""
# Check if env file already exits and if it does delete it
if os.path.isfile(path_to_env):
os.remove(path_to_env)
self.logger.info("Environment file existed and was removed")
else:
self.logger.info("No environment file existed before test")
# Check if sol file already exits and if it does delete it
if os.path.isfile(path_to_sol):
os.remove(path_to_sol)
self.logger.info("Solution file existed and was removed")
else:
self.logger.info("No solution file existed before test")
# Call Nav.start to setup Nav, but don't enter queue blocking loop
start_rv = self.Nav.start(doLoop=False)
# Check return value of call to Nav.start
if start_rv is not None:
self.logger.error("Return value of Nav.start was: " + nav.errors[start_rv])
self.assertTrue(start_rv is None, "Nav.start returned " + str(start_rv))
# Generate env file
end_x = self.waypoints["grnd2ramp"][0][0]* float(nav.env_config["cellsize"])
end_y = self.waypoints["grnd2ramp"][0][1]* float(nav.env_config["cellsize"])
genSol_rv = self.Nav.genSol(end_x, end_y, 0)
# Check return value of call to Nav.genSol
if type(genSol_rv) is not list and genSol_rv in nav.errors:
self.logger.error("Return value of Nav.genSol was: " + nav.errors[genSol_rv])
self.assertTrue(genSol_rv not in nav.errors, "Nav.genSol failed with " + nav.errors[genSol_rv])
# Confirm that env file was generated
self.assertTrue(os.path.isfile(path_to_env), "Env file not found at " + path_to_env)
class TestDirs(unittest.TestCase):
def setUp(self):
"""Create nav object and feed it appropriate data"""
# Create file and stream handlers
self.file_handler = logging.handlers.RotatingFileHandler(path_to_qwe + "logs/unittests.log", maxBytes=512000, backupCount=50)
self.file_handler.setLevel(logging.DEBUG)
self.stream_handler = logging.StreamHandler()
self.stream_handler.setLevel(logging.WARN)
# Create formatter and add to handlers
formatter = logging.Formatter('%(asctime)s | %(levelname)s | %(filename)s | %(funcName)s | %(lineno)d | %(message)s')
self.file_handler.setFormatter(formatter)
self.stream_handler.setFormatter(formatter)
# Create logger and add handlers
self.logger = logging.getLogger("unittest")
self.logger.setLevel(logging.DEBUG)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(self.stream_handler)
self.logger.debug("Logger is set up")
# Start serial communication to low-level board
self.si = comm.SerialInterface(timeout=config["si_timeout"])
self.si.start() # Displays an error if port not found (not running on Pandaboard)
self.logger.info("Serial interface set up")
# Build Queue objects for IPC. Name shows producer_consumer.
self.qNav_loc = Queue()
self.qMove_nav = Queue()
self.logger.debug("Queue objects created")
# Get map, waypoints and map properties
self.course_map = mapper.unpickle_map(path_to_qwe + "mapping/map.pkl")
self.logger.info("Map unpickled")
self.waypoints = mapper.unpickle_waypoints(path_to_qwe + "mapping/waypoints.pkl")
self.logger.info("Waypoints unpickled")
self.map_properties = mapper.unpickle_map_prop_vars(path_to_qwe + "mapping/map_prop_vars.pkl")
self.logger.debug("Map properties unpickled")
# Find start location
self.start_x = self.waypoints["start"][1][0]
self.start_y = self.waypoints["start"][1][1]
self.start_theta = self.waypoints["start"][2]
self.logger.debug("Start waypoint is {}, {}, {}".format(self.start_x, self.start_y, self.start_theta))
# Build shared data structures
self.manager = Manager()
self.bot_loc = self.manager.dict(x=self.start_x, y=self.start_y, theta=self.start_theta, dirty=False)
self.bot_state = self.manager.dict(nav_type=None, action_type=None, naving=False) #nav_type is "micro" or "macro"
self.zones = self.manager.dict()
self.logger.debug("Shared data structures created")
self.bot_state["zone_change"] = 1
# Build nav object
self.scNav = comm.SerialCommand(self.si.commands, self.si.responses)
self.scNav.compassReset()
self.Nav = nav.Nav(self.bot_loc, self.qNav_loc, self.scNav, self.bot_state, self.qMove_nav, self.logger)
self.logger.info("Nav object instantiated")
self.Nav.start(doLoop=False)
self.logger.info("Started nav object")
def tearDown(self):
"""Close serial interface threads"""
# Join serial interface process
self.scNav.quit()
self.si.join()
self.logger.info("Joined serial interface process")
# Remove loggers. Not doing this results in the same log entry being written many times.
self.logger.removeHandler(self.file_handler)
self.logger.removeHandler(self.stream_handler)
def test_getDir_0(self):
degs_in = 0
dir_out = "east"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_45(self):
degs_in = 45
dir_out = "east"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_315(self):
degs_in = 315
dir_out = "east"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_30(self):
degs_in = 30
dir_out = "east"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_340(self):
degs_in = 340
dir_out = "east"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_270(self):
degs_in = 270
dir_out = "north"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_225(self):
degs_in = 225
dir_out = "north"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_314dot9(self):
degs_in = 314.9
dir_out = "north"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_300(self):
degs_in = 300
dir_out = "north"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_250(self):
degs_in = 250
dir_out = "north"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_180(self):
degs_in = 180
dir_out = "west"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_135(self):
degs_in = 180
dir_out = "west"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_224dot9(self):
degs_in = 224.9
dir_out = "west"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_160(self):
degs_in = 160
dir_out = "west"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_200(self):
degs_in = 200
dir_out = "west"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_90(self):
degs_in = 90
dir_out = "south"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_45dot1(self):
degs_in = 45.1
dir_out = "south"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_134dot9(self):
degs_in = 134.9
dir_out = "south"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_60(self):
degs_in = 60
dir_out = "south"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_130(self):
degs_in = 130
dir_out = "south"
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_neg(self):
degs_in = -90
dir_out = nav.errors["BAD_INPUT"]
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_getDir_large(self):
degs_in = 400
dir_out = nav.errors["BAD_INPUT"]
result = self.Nav.getDir(radians(degs_in))
self.assertEqual(dir_out, result, "Failed to convert {} radians to {}, got {}".format(degs_in, dir_out, result))
def test_addDirsToSensors_heading0(self):
heading = radians(0)
front_dir = "east"
left_dir = "north"
back_dir = "west"
right_dir = "south"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading45(self):
heading = radians(45)
front_dir = "east"
left_dir = "north"
back_dir = "west"
right_dir = "south"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading315(self):
heading = radians(315)
front_dir = "east"
left_dir = "north"
back_dir = "west"
right_dir = "south"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading30(self):
heading = radians(30)
front_dir = "east"
left_dir = "north"
back_dir = "west"
right_dir = "south"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading340(self):
heading = radians(340)
front_dir = "east"
left_dir = "north"
back_dir = "west"
right_dir = "south"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading270(self):
heading = radians(270)
front_dir = "north"
left_dir = "west"
back_dir = "south"
right_dir = "east"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading225(self):
heading = radians(225)
front_dir = "north"
left_dir = "west"
back_dir = "south"
right_dir = "east"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_headingi314dot9(self):
heading = radians(314.9)
front_dir = "north"
left_dir = "west"
back_dir = "south"
right_dir = "east"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading300(self):
heading = radians(300)
front_dir = "north"
left_dir = "west"
back_dir = "south"
right_dir = "east"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading250(self):
heading = radians(250)
front_dir = "north"
left_dir = "west"
back_dir = "south"
right_dir = "east"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading180(self):
heading = radians(180)
front_dir = "west"
left_dir = "south"
back_dir = "east"
right_dir = "north"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading135(self):
heading = radians(135)
front_dir = "west"
left_dir = "south"
back_dir = "east"
right_dir = "north"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading224dot9(self):
heading = radians(224.9)
front_dir = "west"
left_dir = "south"
back_dir = "east"
right_dir = "north"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading170(self):
heading = radians(170)
front_dir = "west"
left_dir = "south"
back_dir = "east"
right_dir = "north"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading200(self):
heading = radians(200)
front_dir = "west"
left_dir = "south"
back_dir = "east"
right_dir = "north"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading90(self):
heading = radians(90)
front_dir = "south"
left_dir = "east"
back_dir = "north"
right_dir = "west"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading45dot1(self):
heading = radians(45.1)
front_dir = "south"
left_dir = "east"
back_dir = "north"
right_dir = "west"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading134dot9(self):
heading = radians(134.9)
front_dir = "south"
left_dir = "east"
back_dir = "north"
right_dir = "west"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading87(self):
heading = radians(87)
front_dir = "south"
left_dir = "east"
back_dir = "north"
right_dir = "west"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
def test_addDirsToSensors_heading120(self):
heading = radians(120)
front_dir = "south"
left_dir = "east"
back_dir = "north"
right_dir = "west"
left_us = randint(0, 100)
right_us = randint(0, 100)
front_us = randint(0, 100)
back_us = randint(0, 100)
sensor_data = { "result" : True, "msg" : "This is fake", "id" : 0, "heading" : heading,
"accel" : {"x" : nav.config["default_accel_x"], "y" : nav.config["default_accel_y"],
"z" : nav.config["default_accel_z"]},
"ultrasonic" : {"left" : left_us, "right" : right_us,
"front" : front_us, "back" : back_us}}
self.logger.info("Input sensor data: {}".format(str(sensor_data)))
result = self.Nav.addDirsToSensors(sensor_data)
self.logger.info("Result sensor data: {}".format(str(result)))
self.assertTrue("us_dir" in result, "Ultrasonic direction dict wasn't in result dict")
self.assertEqual(result["us_dir"][front_dir], front_us, "Front dir wrong, expected {} to be {} but was {}".format( \
front_dir, front_us, result["us_dir"][front_dir]))
self.assertEqual(result["us_dir"][left_dir], left_us, "Left dir wrong, expected {} to be {} but was {}".format( \
left_dir, left_us, result["us_dir"][left_dir]))
self.assertEqual(result["us_dir"][back_dir], back_us, "Back dir wrong, expected {} to be {} but was {}".format( \
back_dir, back_us, result["us_dir"][back_dir]))
self.assertEqual(result["us_dir"][right_dir], right_us, "Right dir wrong, expected {} to be {} but was {}".format( \
right_dir, right_us, result["us_dir"][right_dir]))
class TestSBPL(unittest.TestCase):
def setUp(self):
"""Create nav object and feed it appropriate data"""
# Create file and stream handlers
self.file_handler = logging.handlers.RotatingFileHandler(path_to_qwe + "logs/unittests.log", maxBytes=512000, backupCount=50)
self.file_handler.setLevel(logging.DEBUG)
self.stream_handler = logging.StreamHandler()
self.stream_handler.setLevel(logging.WARN)
# Create formatter and add to handlers
formatter = logging.Formatter('%(asctime)s | %(levelname)s | %(filename)s | %(funcName)s | %(lineno)d | %(message)s')
self.file_handler.setFormatter(formatter)
self.stream_handler.setFormatter(formatter)
# Create logger and add handlers
self.logger = logging.getLogger("unittest")
self.logger.setLevel(logging.DEBUG)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(self.stream_handler)
self.logger.debug("Logger is set up")
# Start serial communication to low-level board
self.si = comm.SerialInterface(timeout=config["si_timeout"])
self.si.start() # Displays an error if port not found (not running on Pandaboard)
self.logger.info("Serial interface set up")
# Build Queue objects for IPC. Name shows producer_consumer.
self.qNav_loc = Queue()
self.qMove_nav = Queue()
self.logger.debug("Queue objects created")
# Get map, waypoints and map properties
self.course_map = mapper.unpickle_map(path_to_qwe + "mapping/map.pkl")
self.logger.info("Map unpickled")
self.waypoints = mapper.unpickle_waypoints(path_to_qwe + "mapping/waypoints.pkl")
self.logger.info("Waypoints unpickled")
self.map_properties = mapper.unpickle_map_prop_vars(path_to_qwe + "mapping/map_prop_vars.pkl")
self.logger.debug("Map properties unpickled")
# Find start location
self.start_x = self.waypoints["start"][1][0]
self.start_y = self.waypoints["start"][1][1]
self.start_theta = self.waypoints["start"][2]
self.logger.debug("Start waypoint is {}, {}, {}".format(self.start_x, self.start_y, self.start_theta))
# Build shared data structures
self.manager = Manager()
self.bot_loc = self.manager.dict(x=self.start_x, y=self.start_y, theta=self.start_theta, dirty=False)
self.bot_state = self.manager.dict(nav_type=None, action_type=None, naving=False) #nav_type is "micro" or "macro"
self.zones = self.manager.dict()
self.logger.debug("Shared data structures created")
self.bot_state["zone_change"] = 1
# Build nav object
self.scNav = comm.SerialCommand(self.si.commands, self.si.responses)
self.scNav.compassReset()
self.Nav = nav.Nav(self.bot_loc, self.qNav_loc, self.scNav, self.bot_state, self.qMove_nav, self.logger)
self.logger.info("Nav object instantiated")
self.Nav.start(doLoop=False)
self.logger.info("Started nav object")
def tearDown(self):
"""Close serial interface threads"""
# Join serial interface process
self.scNav.quit()
self.si.join()
self.logger.info("Joined serial interface process")
# Remove loggers. Not doing this results in the same log entry being written many times.
self.logger.removeHandler(self.file_handler)
self.logger.removeHandler(self.stream_handler)
#@unittest.expectedFailure
def test_debug0(self):
# Set start location
cur_x = 0.684678374009
cur_y = 0.31290085154
cur_theta = 6.26825975093
self.logger.debug("Current pose in meters is {}, {}, {}".format(cur_x, cur_y, cur_theta))
# Convert current location to inches and set it in bot_loc
self.bot_loc["x"] = self.Nav.XYTobot_locUC(cur_x)
self.bot_loc["y"] = self.Nav.XYTobot_locUC(cur_y)
self.bot_loc["theta"] = self.Nav.thetaTobot_locUC(cur_theta)
self.logger.debug("Current pose in inches is {} {} {}".format(self.bot_loc["x"], self.bot_loc["y"], self.bot_loc["theta"]))
# Set goal pose
goal_x = 1.20015
goal_y = 0.28575
goal_theta = 0.0
# Convert current location to inches and set it in bot_loc
self.bot_loc["x"] = self.Nav.XYTobot_locUC(cur_x)
self.bot_loc["y"] = self.Nav.XYTobot_locUC(cur_y)
self.bot_loc["theta"] = self.Nav.thetaTobot_locUC(cur_theta)
self.logger.debug("Current pose in inches is {} {} {}".format(self.bot_loc["x"], self.bot_loc["y"], self.bot_loc["theta"]))
# Generate solution
sol = self.Nav.genSol(goal_x, goal_y, goal_theta)
self.assertNotEqual(sol, nav.errors["NO_SOL"], "SBPL offset fix failed to find a solution")
#@unittest.expectedFailure
def test_debug1(self):
# Set current location
cur_x = 0.652150850914
cur_y = 0.30267696651
cur_theta = 0.401430134
self.logger.debug("Current pose in meters is {}, {}, {}".format(cur_x, cur_y, cur_theta))
# Set goal pose
goal_x = 1.20015
goal_y = 0.28575
goal_theta = 0.0
# Convert current location to inches and set it in bot_loc
self.bot_loc["x"] = self.Nav.XYTobot_locUC(cur_x)
self.bot_loc["y"] = self.Nav.XYTobot_locUC(cur_y)
self.bot_loc["theta"] = self.Nav.thetaTobot_locUC(cur_theta)
self.logger.debug("Current pose in inches is {} {} {}".format(self.bot_loc["x"], self.bot_loc["y"], self.bot_loc["theta"]))
# Generate solution
sol = self.Nav.genSol(goal_x, goal_y, goal_theta)
self.assertNotEqual(sol, nav.errors["NO_SOL"], "SBPL offset fix failed to find a solution")
#@unittest.expectedFailure
def test_debug2(self):
# Set current location
cur_x = 0.652150850914
cur_y = 0.30267696651
cur_theta = 0.401430134
self.logger.debug("Current pose in meters is {}, {}, {}".format(cur_x, cur_y, cur_theta))
# Set goal pose
goal_x = 1.20015
goal_y = 0.28575
goal_theta = 0.0
# Convert current location to inches and set it in bot_loc
self.bot_loc["x"] = self.Nav.XYTobot_locUC(cur_x)
self.bot_loc["y"] = self.Nav.XYTobot_locUC(cur_y)
self.bot_loc["theta"] = self.Nav.thetaTobot_locUC(cur_theta)
self.logger.debug("Current pose in inches is {} {} {}".format(self.bot_loc["x"], self.bot_loc["y"], self.bot_loc["theta"]))
# Generate solution
sol = self.Nav.genSol(goal_x, goal_y, goal_theta)
self.assertNotEqual(sol, nav.errors["NO_SOL"], "SBPL offset fix failed to find a solution")
def test_debug0_offset(self):
offset = .03
# Set start location
cur_x = 0.684678374009
cur_y = 0.31290085154
cur_theta = 6.26825975093
self.logger.debug("Current pose in meters is {}, {}, {}".format(cur_x, cur_y, cur_theta))
# Convert current location to inches and set it in bot_loc
self.bot_loc["x"] = self.Nav.XYTobot_locUC(cur_x)
self.bot_loc["y"] = self.Nav.XYTobot_locUC(cur_y)
self.bot_loc["theta"] = self.Nav.thetaTobot_locUC(cur_theta)
self.logger.debug("Current pose in inches is {} {} {}".format(self.bot_loc["x"], self.bot_loc["y"], self.bot_loc["theta"]))
# Set goal pose
goal_x = 1.20015 + offset
goal_y = 0.28575 + offset
goal_theta = 0.0
# Convert current location to inches and set it in bot_loc
self.bot_loc["x"] = self.Nav.XYTobot_locUC(cur_x)
self.bot_loc["y"] = self.Nav.XYTobot_locUC(cur_y)
self.bot_loc["theta"] = self.Nav.thetaTobot_locUC(cur_theta)
self.logger.debug("Current pose in inches is {} {} {}".format(self.bot_loc["x"], self.bot_loc["y"], self.bot_loc["theta"]))
# Generate solution
sol = self.Nav.genSol(goal_x, goal_y, goal_theta)
self.assertNotEqual(sol, nav.errors["NO_SOL"], "Offset of {} didn't fix SBPL fail".format(offset))
def test_debug1_offset(self):
offset = .03
# Set current location
cur_x = 0.652150850914
cur_y = 0.30267696651
cur_theta = 0.401430134
self.logger.debug("Current pose in meters is {}, {}, {}".format(cur_x, cur_y, cur_theta))
# Set goal pose
goal_x = 1.20015 + offset
goal_y = 0.28575 + offset
goal_theta = 0.0
# Convert current location to inches and set it in bot_loc
self.bot_loc["x"] = self.Nav.XYTobot_locUC(cur_x)
self.bot_loc["y"] = self.Nav.XYTobot_locUC(cur_y)
self.bot_loc["theta"] = self.Nav.thetaTobot_locUC(cur_theta)
self.logger.debug("Current pose in inches is {} {} {}".format(self.bot_loc["x"], self.bot_loc["y"], self.bot_loc["theta"]))
# Generate solution
sol = self.Nav.genSol(goal_x, goal_y, goal_theta)
self.assertNotEqual(sol, nav.errors["NO_SOL"], "Offset of {} didn't fix SBPL fail".format(offset))
def test_debug2_offset(self):
offset = .03
# Set current location
cur_x = 0.652150850914
cur_y = 0.30267696651
cur_theta = 0.401430134
self.logger.debug("Current pose in meters is {}, {}, {}".format(cur_x, cur_y, cur_theta))
# Set goal pose
goal_x = 1.20015 + offset
goal_y = 0.28575 + offset
goal_theta = 0.0
# Convert current location to inches and set it in bot_loc
self.bot_loc["x"] = self.Nav.XYTobot_locUC(cur_x)
self.bot_loc["y"] = self.Nav.XYTobot_locUC(cur_y)
self.bot_loc["theta"] = self.Nav.thetaTobot_locUC(cur_theta)
self.logger.debug("Current pose in inches is {} {} {}".format(self.bot_loc["x"], self.bot_loc["y"], self.bot_loc["theta"]))
# Generate solution
sol = self.Nav.genSol(goal_x, goal_y, goal_theta)
self.assertNotEqual(sol, nav.errors["NO_SOL"], "Offset of {} didn't fix SBPL fail".format(offset))
class TestFullInteraction(unittest.TestCase):
def setUp(self):
"""Create nav object and feed it appropriate data"""
# Create file and stream handlers
self.file_handler = logging.handlers.RotatingFileHandler(path_to_qwe + "logs/unittests.log", maxBytes=512000, backupCount=50)
self.file_handler.setLevel(logging.DEBUG)
self.stream_handler = logging.StreamHandler()
self.stream_handler.setLevel(logging.WARN)
# Create formatter and add to handlers
formatter = logging.Formatter('%(asctime)s | %(levelname)s | %(filename)s | %(funcName)s | %(lineno)d | %(message)s')
self.file_handler.setFormatter(formatter)
self.stream_handler.setFormatter(formatter)
# Create logger and add handlers
self.logger = logging.getLogger("unittest")
self.logger.setLevel(logging.DEBUG)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(self.stream_handler)
self.logger.debug("Logger is set up")
# Start serial communication to low-level board
self.si = comm.SerialInterface(timeout=config["si_timeout"])
self.si.start() # Displays an error if port not found (not running on Pandaboard)
self.logger.info("Serial interface set up")
# Build Queue objects for IPC. Name shows producer_consumer.
self.qNav_loc = Queue()
self.qMove_nav = Queue()
self.testQueue = Queue()
self.logger.debug("Queue objects created")
# Get map, waypoints and map properties
self.course_map = mapper.unpickle_map(path_to_qwe + "mapping/map.pkl")
self.logger.info("Map unpickled")
self.waypoints = mapper.unpickle_waypoints(path_to_qwe + "mapping/waypoints.pkl")
self.logger.info("Waypoints unpickled")
self.map_properties = mapper.unpickle_map_prop_vars(path_to_qwe + "mapping/map_prop_vars.pkl")
self.logger.debug("Map properties unpickled")
# Find start location
self.start_x = self.waypoints["start"][1][0]
self.start_y = self.waypoints["start"][1][1]
self.start_theta = self.waypoints["start"][2]
self.logger.debug("Start waypoint is {}, {}, {}".format(self.start_x, self.start_y, self.start_theta))
# Build shared data structures
self.manager = Manager()
self.bot_loc = self.manager.dict(x=self.start_x, y=self.start_y, theta=self.start_theta, dirty=False)
self.bot_state = self.manager.dict(nav_type=None, action_type=None, naving=False) #nav_type is "micro" or "macro"
self.zones = self.manager.dict()
self.logger.debug("Shared data structures created")
self.bot_state["zone_change"] = 1
# Start fakeLoc process
#self.pfakeLoc = Process(target=fakeLoc, args=(self.testQueue, self.bot_loc, self.logger))
#self.pfakeLoc.start()
#self.logger.info("fakeLoc process started")
# Start nav process
self.scNav = comm.SerialCommand(self.si.commands, self.si.responses)
self.scNav.compassReset()
self.logger.debug("First possible sensor read: {}".format(str(self.scNav.getAllSensorData())))
self.pNav = Process(target=nav.run, args=(self.bot_loc, self.qNav_loc, self.scNav, \
self.bot_state, self.qMove_nav, self.logger))
self.logger.debug("First possible sensor read: {}".format(str(self.scNav.getAllSensorData())))
#self.pNav = Process(target=nav.run, args=(self.bot_loc, self.qNav_loc, self.scNav, \
# self.bot_state, self.qMove_nav, self.logger, self.testQueue))
self.pNav.start()
self.logger.info("Navigator process started")
# Start localizer process, pass it shared data, waypoints, map_properties course_map and queue for talking to nav
self.pLocalizer = Process(target=localizer.run, args=(self.bot_loc, self.zones, self.map_properties, self.course_map, \
self.waypoints, self.qNav_loc, self.bot_state, self.logger))
self.pLocalizer.start()
self.logger.info("Localizer process started")
def tearDown(self):
"""Close serial interface threads"""
# Pass a die command to nav
self.logger.info("Telling nav to die")
self.qMove_nav.put("die")
# Join nav process
self.pNav.join()
self.logger.info("Joined navigation process")
# Pass a die command to loc
self.logger.info("Telling loc to die")
self.qNav_loc.put("die")
self.pLocalizer.join()
self.logger.info("Joined localizer process")
# Pass a die command to loc
#self.pfakeLoc.join()
#self.logger.info("Joined fakeLoc process")
# Join serial interface process
self.scNav.quit()
self.si.join()
self.logger.info("Joined serial interface process")
# Remove loggers. Not doing this results in the same log entry being written many times.
self.logger.removeHandler(self.file_handler)
self.logger.removeHandler(self.stream_handler)
def test_start_at_goal(self):
"""Pass in a goal pose that's the same as the start pose"""
# Build goal pose that's the same as the start pose
self.logger.debug("Building goal pose")
# Build goal pose
goal_x = float(self.bot_loc["x"])
goal_y = float(self.bot_loc["y"])
goal_theta = float(self.bot_loc["theta"])
goal_pose = nav.macro_move(goal_x, goal_y, goal_theta, datetime.now())
self.logger.debug("Created goal pose {}".format(str(goal_pose)))
# Send goal pose via queue
self.logger.debug("About to send goal pose to queue with ID {}".format(str(self.qMove_nav)))
self.qMove_nav.put(goal_pose)
self.logger.debug("Put goal pose into queue")
def test_start_nearly_at_goal(self):
"""Pass in a goal pose that's nearly the same as the start pose"""
# Build goal pose that's the same as the start pose
self.logger.debug("Building goal pose")
# Build goal pose
goal_x = float(self.bot_loc["x"]) + (float(nav.env_config["cellsize"]) / 2 / 0.0254)
goal_y = float(self.bot_loc["y"]) + (float(nav.env_config["cellsize"]) / 2 / 0.0254)
goal_theta = float(self.bot_loc["theta"]) + (nav.config["thetaErr"] / 2)
goal_pose = nav.macro_move(goal_x, goal_y, goal_theta, datetime.now())
self.logger.debug("Created goal pose {}".format(str(goal_pose)))
# Send goal pose via queue
self.logger.debug("About to send goal pose to queue with ID {}".format(str(self.qMove_nav)))
self.qMove_nav.put(goal_pose)
self.logger.debug("Put goal pose into queue")
@unittest.skip("Not very useful, and breaks when error changes")
def test_simple_XY_move(self):
"""Pass in a goal pose that only differs on the XY plane from the start pose"""
self.logger.debug("Building goal pose")
# Build goal pose
goal_x = float(self.bot_loc["x"]) + (float(nav.env_config["cellsize"]) * 20 / 0.0254)
goal_y = float(self.bot_loc["y"]) + (float(nav.env_config["cellsize"]) * 25 / 0.0254)
#goal_x = float(self.bot_loc["x"]) + (nav.config["XYerr"] * 20 / 0.0254)
#goal_y = float(self.bot_loc["y"]) + (nav.config["XYErr"] * 25 / 0.0254)
goal_theta = float(self.bot_loc["theta"])
goal_pose = nav.macro_move(goal_x, goal_y, goal_theta, datetime.now())
self.logger.debug("Created goal pose {}".format(str(goal_pose)))
# Send goal pose via queue
self.logger.debug("About to send goal pose to queue with ID {}".format(str(self.qMove_nav)))
self.qMove_nav.put(goal_pose)
self.logger.debug("Put goal pose into queue")
@unittest.skip("Not very useful, and breaks when error changes")
def test_simple_theta_move(self):
"""Pass in a goal pose that's different from the goal pose in the theta dimension only"""
self.logger.debug("Building goal pose")
# Build goal pose
goal_x = float(self.bot_loc["x"])
goal_y = float(self.bot_loc["y"])
goal_theta = float(self.bot_loc["theta"]) + (nav.config["thetaErr"] * 3)
goal_pose = nav.macro_move(goal_x, goal_y, goal_theta, datetime.now())
self.logger.debug("Created goal pose {}".format(str(goal_pose)))
# Send goal pose via queue
self.logger.debug("About to send goal pose to queue with ID {}".format(str(self.qMove_nav)))
self.qMove_nav.put(goal_pose)
self.logger.debug("Put goal pose into queue")
@unittest.skip("Not very useful, and breaks when error changes")
def test_simple_XYTheta_move(self):
"""Pass in a goal pose that differes in X, Y and theta from the start pose"""
self.logger.debug("Building goal pose")
# Build goal pose
goal_x = float(self.bot_loc["x"]) + (float(nav.env_config["cellsize"]) * 20 / 0.0254)
goal_y = float(self.bot_loc["y"]) + (float(nav.env_config["cellsize"]) * 25 / 0.0254)
goal_theta = float(self.bot_loc["theta"]) + (nav.config["thetaErr"] * 3)
goal_pose = nav.macro_move(goal_x, goal_y, goal_theta, datetime.now())
self.logger.debug("Created goal pose {}".format(str(goal_pose)))
# Send goal pose via queue
self.logger.debug("About to send goal pose to queue with ID {}".format(str(self.qMove_nav)))
self.qMove_nav.put(goal_pose)
self.logger.debug("Put goal pose into queue")
@unittest.skip("Not very useful, and breaks when error changes")
def test_two_moves(self):
"""Pass two moves to nav before telling it to die"""
self.logger.debug("Building goal pose")
# Build goal pose
goal_x0 = float(self.bot_loc["x"]) + (float(nav.env_config["cellsize"]) * 20 / 0.0254)
goal_y0 = float(self.bot_loc["y"]) + (float(nav.env_config["cellsize"]) * 25 / 0.0254)
goal_theta0 = float(self.bot_loc["theta"]) + (nav.config["thetaErr"] * 3)
goal_pose0 = nav.macro_move(goal_x0, goal_y0, goal_theta0, datetime.now())
self.logger.debug("Created goal pose {}".format(str(goal_pose0)))
# Send goal pose via queue
self.logger.debug("About to send goal pose to queue with ID {}".format(str(self.qMove_nav)))
self.qMove_nav.put(goal_pose0)
self.logger.debug("Put goal pose into queue")
# Build goal pose
goal_x1 = float(self.bot_loc["x"]) - (float(nav.env_config["cellsize"]) * 10 / 0.0254)
goal_y1 = float(self.bot_loc["y"]) - (float(nav.env_config["cellsize"]) * 10 / 0.0254)
goal_theta1 = float(self.bot_loc["theta"]) + (nav.config["thetaErr"] * 6)
goal_pose1 = nav.macro_move(goal_x1, goal_y1, goal_theta1, datetime.now())
self.logger.debug("Created goal pose {}".format(str(goal_pose1)))
# Send goal pose via queue
self.logger.debug("About to send goal pose to queue with ID {}".format(str(self.qMove_nav)))
self.qMove_nav.put(goal_pose1)
self.logger.debug("Put goal pose into queue")
def test_move_to_loading(self):
"""Pass in a goal pose that differes in X, Y and theta from the start pose"""
self.logger.debug("Building goal pose")
# Build goal pose
goal_x = self.waypoints["St01"][1][0]
goal_y = self.waypoints["St01"][1][1]
goal_theta = self.waypoints["St01"][2]
goal_pose = nav.macro_move(goal_x, goal_y, goal_theta, datetime.now())
self.logger.debug("Created goal pose {}".format(str(goal_pose)))
# Send goal pose via queue
self.logger.debug("About to send goal pose to queue with ID {}".format(str(self.qMove_nav)))
self.qMove_nav.put(goal_pose)
self.logger.debug("Put goal pose into queue")
def test_move_to_loading_then_land(self):
"""Pass in a goal pose that differes in X, Y and theta from the start pose"""
self.logger.debug("Building goal pose")
# Build goal pose
goal_x0 = self.waypoints["St01"][1][0]
goal_y0 = self.waypoints["St01"][1][1]
goal_theta0 = self.waypoints["St01"][2]
goal_pose0 = nav.macro_move(goal_x0, goal_y0, goal_theta0, datetime.now())
self.logger.debug("Created goal pose {}".format(str(goal_pose0)))
# Send goal pose via queue
self.logger.debug("About to send goal pose to queue with ID {}".format(str(self.qMove_nav)))
self.qMove_nav.put(goal_pose0)
self.logger.debug("Put goal pose into queue")
goal_x1 = self.waypoints["L06"][1][0]
goal_y1 = self.waypoints["L06"][1][1]
goal_theta1 = self.waypoints["L06"][2]
goal_pose1 = nav.macro_move(goal_x1, goal_y1, goal_theta1, datetime.now())
self.logger.debug("Created goal pose {}".format(str(goal_pose1)))
# Send goal pose via queue
self.logger.debug("About to send goal pose to queue with ID {}".format(str(self.qMove_nav)))
self.qMove_nav.put(goal_pose1)
self.logger.debug("Put goal pose into queue")
def test_start_to_L01(self):
"""Pass in a goal pose that differes in X, Y and theta from the start pose"""
self.logger.debug("Building goal pose")
# Build goal pose
goal_x = self.waypoints["L01"][1][0]
goal_y = self.waypoints["L01"][1][1]
goal_theta = self.waypoints["L01"][2]
goal_pose = nav.macro_move(goal_x, goal_y, goal_theta, datetime.now())
self.logger.debug("Created goal pose {}".format(str(goal_pose)))
# Send goal pose via queue
self.logger.debug("About to send goal pose to queue with ID {}".format(str(self.qMove_nav)))
self.qMove_nav.put(goal_pose)
self.logger.debug("Put goal pose into queue")
def test_turn_90(self):
goal_x = self.waypoints["start"][0][0] * float(nav.env_config["cellsize"]) * 39.3701
goal_y = self.waypoints["start"][0][1] * float(nav.env_config["cellsize"]) * 39.3701
goal_theta = pi/2
goal_pose = nav.macro_move(goal_x, goal_y, goal_theta, datetime.now())
self.logger.debug("Created goal pose {}".format(str(goal_pose)))
# Send goal pose via queue
self.logger.debug("About to send goal pose to queue with ID {}".format(str(self.qMove_nav)))
self.qMove_nav.put(goal_pose)
self.logger.debug("Put goal pose into queue")
class TestCleanSol(unittest.TestCase):
def setUp(self):
"""Create nav object and feed it appropriate data"""
# Create file and stream handlers
self.file_handler = logging.handlers.RotatingFileHandler(path_to_qwe + "logs/unittests.log", maxBytes=512000, backupCount=50)
self.file_handler.setLevel(logging.DEBUG)
self.stream_handler = logging.StreamHandler()
self.stream_handler.setLevel(logging.WARN)
# Create formatter and add to handlers
formatter = logging.Formatter('%(asctime)s | %(levelname)s | %(filename)s | %(funcName)s | %(lineno)d | %(message)s')
self.file_handler.setFormatter(formatter)
self.stream_handler.setFormatter(formatter)
# Create logger and add handlers
self.logger = logging.getLogger("unittest")
self.logger.setLevel(logging.DEBUG)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(self.stream_handler)
self.logger.debug("Logger is set up")
# Start serial communication to low-level board
self.si = comm.SerialInterface(timeout=config["si_timeout"])
self.si.start() # Displays an error if port not found (not running on Pandaboard)
self.logger.info("Serial interface set up")
# Build Queue objects for IPC. Name shows producer_consumer.
self.qNav_loc = Queue()
self.qMove_nav = Queue()
self.logger.debug("Queue objects created")
# Get map, waypoints
self.waypoints = mapper.unpickle_waypoints(path_to_qwe + "mapping/waypoints.pkl")
self.logger.info("Waypoints unpickled")
# Build shared data structures
self.manager = Manager()
self.start_x = self.waypoints["start"][1][0]
self.start_y = self.waypoints["start"][1][1]
self.start_theta = self.waypoints["start"][2]
self.logger.debug("Start waypoint is {}, {}, {}".format(self.start_x, self.start_y, self.start_theta))
self.bot_loc = self.manager.dict(x=self.start_x, y=self.start_y, theta=self.start_theta, dirty=False)
self.bot_state = self.manager.dict(nav_type=None, action_type=None)
self.logger.debug("Shared data structures created")
# Build nav object
self.scNav = comm.SerialCommand(self.si.commands, self.si.responses)
self.scNav.compassReset()
self.Nav = nav.Nav(self.bot_loc, self.qNav_loc, self.scNav, self.bot_state, self.qMove_nav, \
self.logger)
self.logger.info("Nav object instantiated")
self.Nav.start(doLoop=False)
self.logger.info("Started nav object")
def tearDown(self):
"""Close serial interface threads"""
self.logger.removeHandler(self.file_handler)
self.logger.removeHandler(self.stream_handler)
self.scNav.quit()
self.si.join()
@unittest.expectedFailure
def test_start_to_L01(self):
"""Create a solution from start to L01 and clean its XY moves to be of a given size."""
# Build goal pose
goal_x = self.Nav.XYFromMoveQUC(self.waypoints["L01"][1][0])
goal_y = self.Nav.XYFromMoveQUC(self.waypoints["L01"][1][1])
goal_theta = self.Nav.thetaFromMoveQUC(self.waypoints["L01"][2])
self.logger.info("Goal pose is {} {} {}".format(goal_x, goal_y, goal_theta))
# Generate solution using SBPL
#self.logger.debug("Need solution from {} {} {} to {} {} {}
sol = self.Nav.genSol(goal_x, goal_y, goal_theta)
self.logger.info("Built solution: {}".format(str(sol)))
# Convert XY translations to be of desired length
clean_sol = self.Nav.cleanSol(sol)
self.logger.info("Cleaned solution: {}".format(str(clean_sol)))
# Setup some initial vars
total_dx, total_dy, total_dTheta, total_disp, disp, last_disp, last_dyn_dem = 0, 0, 0, 0, 0, None, None
for i in range(1, len(clean_sol)):
# Find which dimension changed between these steps
dyn_dem = self.Nav.whichXYTheta(clean_sol[i-1], clean_sol[i])
self.logger.debug("Dynamic dimension was {}".format(dyn_dem))
# Confirm that change was in XY or theta, not both
self.assertNotEqual(dyn_dem, nav.errors["ERROR_ARCS_DISALLOWED"], "Arc encountered!")
if dyn_dem == "xy":
# Find dx and dy between last step and this step
dx = clean_sol[i]["cont_x"] - clean_sol[i-1]["cont_x"]
dy = clean_sol[i]["cont_y"] - clean_sol[i-1]["cont_y"]
self.logger.debug("(dx, dy) was ({}, {})".format(dx, dy))
# Find XY plane displacement between last step and this step
disp = sqrt(dx**2 + dy**2)
self.logger.debug("XY displacement was {}".format(disp))
# Confirm that the displacement was less than or equal to the user-defined ideal displacement
self.assertLessEqual(disp, nav.config["XY_mv_len"], "Change XY ({}) is larger than expected ({})".format(disp, \
nav.config["XY_mv_len"]))
# If this is a series of XY moves, check that the previous one was of correct len
if last_dyn_dem == "xy":
self.assertEqual(last_disp, nav.config["XY_mv_len"], "Non-last in XY move series ({}) wasn't full len ({})".format( \
last_disp, nav.config["XY_mv_len"]))
# Update dx and dy sums
total_dx += dx
total_dy += dy
total_disp += disp
self.logger.debug("(total_dx, total_dy, total_disp) is ({}, {}, {})".format(total_dx, total_dy, total_disp))
elif dyn_dem == "theta":
# Calculate dTheta
dTheta = clean_sol[i]["cont_theta"] - clean_sol[i-1]["cont_theta"]
self.logger.debug("dTheta is {}".format(dTheta))
# Update dTheta sum
total_dTheta += dTheta
self.logger.debug("total_dTheta is {}".format(total_dTheta))
else:
# This would indicate an error in whichXYTheta
self.fail("Unknown dynamic dimension {}, check whichXYTheta".format(dyn_dem))
# Update past-state vars for displacement and dynamic dimension
last_disp = disp
last_dyn_dem = dyn_dem
exptd_total_disp = sqrt((sol[-1]["cont_x"] - sol[0]["cont_x"])**2 + (sol[-1]["cont_y"] - sol[0]["cont_y"])**2)
exptd_total_dx = sol[-1]["cont_x"] - sol[0]["cont_x"]
exptd_total_dy = sol[-1]["cont_y"] - sol[0]["cont_y"]
self.logger.info("Expected totals (disp, dx, dy) are ({}, {}, {})".format(exptd_total_disp, exptd_total_dx, \
exptd_total_dy))
self.assertAlmostEqual(total_disp, exptd_total_disp, places=4, msg="Total disp {} not close enough to expected {}".format( \
total_disp, exptd_total_disp))
self.assertAlmostEqual(total_dx, exptd_total_dx, places=4, msg="Total dx {} not close enough to expected {}".format( \
total_dx, exptd_total_dx))
self.assertAlmostEqual(total_dy, exptd_total_dy, places=4, msg="Total dt {} not close enough to expected {}".format( \
total_dy, exptd_total_dy))
self.assertAlmostEqual(sol[0]["cont_x"], clean_sol[0]["cont_x"], "Start X of sol ({}) != clean sol ({})".format( \
sol[0]["cont_x"], clean_sol[0]["cont_x"]))
self.assertAlmostEqual(sol[0]["cont_y"], clean_sol[0]["cont_y"], "Start Y of sol ({}) != clean sol ({})".format( \
sol[0]["cont_y"], clean_sol[0]["cont_y"]))
self.assertAlmostEqual(sol[0]["cont_theta"], clean_sol[0]["cont_theta"], "Start theta of sol ({}) != clean sol({}) ".format( \
sol[0]["cont_theta"], clean_sol[0]["cont_theta"]))
class TestUC(unittest.TestCase):
def setUp(self):
"""Create nav object and feed it appropriate data"""
# Create file and stream handlers
self.file_handler = logging.handlers.RotatingFileHandler(path_to_qwe + "logs/unittests.log", maxBytes=512000, backupCount=50)
self.file_handler.setLevel(logging.DEBUG)
self.stream_handler = logging.StreamHandler()
self.stream_handler.setLevel(logging.WARN)
# Create formatter and add to handlers
formatter = logging.Formatter('%(asctime)s | %(levelname)s | %(filename)s | %(funcName)s | %(lineno)d | %(message)s')
self.file_handler.setFormatter(formatter)
self.stream_handler.setFormatter(formatter)
# Create logger and add handlers
self.logger = logging.getLogger("unittest")
self.logger.setLevel(logging.DEBUG)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(self.stream_handler)
self.logger.debug("Logger is set up")
# Start serial communication to low-level board
self.si = comm.SerialInterface(timeout=config["si_timeout"])
self.si.start() # Displays an error if port not found (not running on Pandaboard)
self.logger.info("Serial interface set up")
# Build Queue objects for IPC. Name shows producer_consumer.
self.qNav_loc = Queue()
self.qMove_nav = Queue()
self.logger.debug("Queue objects created")
# Get map, waypoints
self.waypoints = mapper.unpickle_waypoints(path_to_qwe + "mapping/waypoints.pkl")
self.logger.info("Waypoints unpickled")
# Build shared data structures
self.manager = Manager()
self.start_x = self.waypoints["start"][1][0]
self.start_y = self.waypoints["start"][1][1]
self.start_theta = self.waypoints["start"][2]
self.logger.debug("Start waypoint is {}, {}, {}".format(self.start_x, self.start_y, self.start_theta))
self.bot_loc = self.manager.dict(x=self.start_x, y=self.start_y, theta=self.start_theta, dirty=False)
self.bot_state = self.manager.dict(nav_type=None, action_type=None)
self.logger.debug("Shared data structures created")
# Build nav object
self.scNav = comm.SerialCommand(self.si.commands, self.si.responses)
self.scNav.compassReset()
self.Nav = nav.Nav(self.bot_loc, self.qNav_loc, self.scNav, self.bot_state, self.qMove_nav, \
self.logger)
self.logger.info("Nav object instantiated")
def tearDown(self):
"""Close serial interface threads"""
self.logger.removeHandler(self.file_handler)
self.logger.removeHandler(self.stream_handler)
self.scNav.quit()
self.si.join()
def test_debug0(self):
"""Testing translation from comm units to radians for debuging"""
commResult = -113.48034456
actual_result = 6.08512474229
desired_result = -0.1980605648869636
result0 = self.Nav.angleFromCommUC(commResult)
self.assertEqual(result0, desired_result, "Failed to convert {} signed tenths of degrees to {} rads, result was {}".format( \
commResult, desired_result, actual_result))
def test_XY_bot_loc_UC(self):
testVal_in = 7.125
testVal_m = 0.180975
valNavUnits = self.Nav.XYFrombot_locUC(testVal_in)
self.assertEqual(valNavUnits, testVal_m)
valExUnits = self.Nav.XYTobot_locUC(valNavUnits)
self.assertEqual(valExUnits, testVal_in)
def test_distToCommUC(self):
"""Test converting meters to encoder units"""
testVal_m0 = .5
testVal_enc0 = int(round(testVal_m0 * 39.3701 * (1633/9.89)))
result0 = self.Nav.distToCommUC(testVal_m0)
self.assertEqual(testVal_enc0, result0, "Failed to convert {} meters to {} ECs, result was {} ECs".format( \
testVal_m0, testVal_enc0, result0))
testVal_m1 = 10
testVal_enc1 = int(round(testVal_m1 * 39.3701 * (1633/9.89)))
result1 = self.Nav.distToCommUC(testVal_m1)
self.assertEqual(testVal_enc1, result1, "Failed to convert {} meters to {} ECs, result was {} ECs".format( \
testVal_m1, testVal_enc1, result1))
testVal_m2 = .001
testVal_enc2 = int(round(testVal_m2 * 39.3701 * (1633/9.89)))
result2 = self.Nav.distToCommUC(testVal_m2)
self.assertEqual(testVal_enc2, result2, "Failed to convert {} meters to {} ECs, result was {} ECs".format( \
testVal_m2, testVal_enc2, result2))
def test_distFromCommUC(self):
"""Test converting encoder units to meters"""
testVal_m0 = .5
testVal_enc0 = testVal_m0 * 39.3701 * (1633/9.89)
result0 = self.Nav.distFromCommUC(testVal_enc0)
self.assertEqual(testVal_m0, result0, "Failed to convert {} ECs to {} meters, result was {} meters".format( \
testVal_enc0, testVal_m0, result0))
testVal_m1 = 10
testVal_enc1 = testVal_m1 * 39.3701 * (1633/9.89)
result1 = self.Nav.distFromCommUC(testVal_enc1)
self.assertEqual(testVal_m1, result1, "Failed to convert {} ECs to {} meters, result was {} meters".format( \
testVal_enc1, testVal_m1, result1))
testVal_m2 = .001
testVal_enc2 = testVal_m2 * 39.3701 * (1633/9.89)
result2 = self.Nav.distFromCommUC(testVal_enc2)
self.assertEqual(testVal_m2, result2, "Failed to convert {} ECs to {} meters, result was {} meters".format( \
testVal_enc2, testVal_m2, result2))
def test_angleToCommUC(self):
testVal_rad0 = pi/2
testVal_comm0 = 900
result0 = self.Nav.angleToCommUC(testVal_rad0)
self.assertEqual(testVal_comm0, result0, "Failed to convert {} rads to {} signed tenths of degs, result was {}".format( \
testVal_rad0, testVal_comm0, result0))
testVal_rad0 = pi
testVal_comm0 = 1800
result0 = self.Nav.angleToCommUC(testVal_rad0)
self.assertEqual(testVal_comm0, result0, "Failed to convert {} rads to {} signed tenths of degs, result was {}".format( \
testVal_rad0, testVal_comm0, result0))
testVal_rad0 = 3*pi/2
testVal_comm0 = -900
result0 = self.Nav.angleToCommUC(testVal_rad0)
self.assertEqual(testVal_comm0, result0, "Failed to convert {} rads to {} signed tenths of degs, result was {}".format( \
testVal_rad0, testVal_comm0, result0))
testVal_rad0 = 2*pi
testVal_comm0 = 0
result0 = self.Nav.angleToCommUC(testVal_rad0)
self.assertEqual(testVal_comm0, result0, "Failed to convert {} rads to {} signed tenths of degs, result was {}".format( \
testVal_rad0, testVal_comm0, result0))
testVal_rad0 = 3*pi
testVal_comm0 = 1800
result0 = self.Nav.angleToCommUC(testVal_rad0)
self.assertEqual(testVal_comm0, result0, "Failed to convert {} rads to {} signed tenths of degs, result was {}".format( \
testVal_rad0, testVal_comm0, result0))
testVal_rad0 = 100*pi
testVal_comm0 = 0
result0 = self.Nav.angleToCommUC(testVal_rad0)
self.assertEqual(testVal_comm0, result0, "Failed to convert {} rads to {} signed tenths of degs, result was {}".format( \
testVal_rad0, testVal_comm0, result0))
testVal_rad0 = -pi/2
testVal_comm0 = -900
result0 = self.Nav.angleToCommUC(testVal_rad0)
self.assertEqual(testVal_comm0, result0, "Failed to convert {} rads to {} signed tenths of degs, result was {}".format( \
testVal_rad0, testVal_comm0, result0))
testVal_rad0 = -3*pi/2
testVal_comm0 = 900
result0 = self.Nav.angleToCommUC(testVal_rad0)
self.assertEqual(testVal_comm0, result0, "Failed to convert {} rads to {} signed tenths of degs, result was {}".format( \
testVal_rad0, testVal_comm0, result0))
testVal_rad0 = .001*pi
testVal_comm0 = int(round(1.8000000000000002))
result0 = self.Nav.angleToCommUC(testVal_rad0)
self.assertEqual(testVal_comm0, result0, "Failed to convert {} rads to {} signed tenths of degs, result was {}".format( \
testVal_rad0, testVal_comm0, result0))
def test_angleFromCommUC(self):
testVal_rad0 = pi/2
testVal_comm0 = 900
result0 = self.Nav.angleFromCommUC(testVal_comm0)
self.assertEqual(testVal_rad0, result0, "Failed to convert {} signed tenths of degrees to {} rads, result was {}".format( \
testVal_comm0, testVal_rad0, result0))
testVal_rad0 = pi
testVal_comm0 = 1800
result0 = self.Nav.angleFromCommUC(testVal_comm0)
self.assertEqual(testVal_rad0, result0, "Failed to convert {} signed tenths of degrees to {} rads, result was {}".format( \
testVal_comm0, testVal_rad0, result0))
testVal_rad0 = round(-1.570796326793, 5)
testVal_comm0 = -900
result0 = round(self.Nav.angleFromCommUC(testVal_comm0), 5)
self.assertEqual(testVal_rad0, result0, "Failed to convert {} signed tenths of deg to {} rads, rst was {}".format( \
testVal_comm0, testVal_rad0, result0))
testVal_rad0 = 0
testVal_comm0 = 0
result0 = self.Nav.angleFromCommUC(testVal_comm0)
self.assertEqual(testVal_rad0, result0, "Failed to convert {} signed tenths of degrees to {} rads, result was {}".format( \
testVal_comm0, testVal_rad0, result0))
testVal_rad0 = pi
testVal_comm0 = 1800
result0 = self.Nav.angleFromCommUC(testVal_comm0)
self.assertEqual(testVal_rad0, result0, "Failed to convert {} signed tenths of degrees to {} rads, result was {}".format( \
testVal_comm0, testVal_rad0, result0))
testVal_rad0 = 0
testVal_comm0 = 0
result0 = self.Nav.angleFromCommUC(testVal_comm0)
self.assertEqual(testVal_rad0, result0, "Failed to convert {} signed tenths of degrees to {} rads, result was {}".format( \
testVal_comm0, testVal_rad0, result0))
class TestlocsEqual(unittest.TestCase):
def setUp(self):
"""Create nav object and feed it appropriate data"""
# Create file and stream handlers
self.file_handler = logging.handlers.RotatingFileHandler(path_to_qwe + "logs/unittests.log", maxBytes=512000, backupCount=50)
self.file_handler.setLevel(logging.DEBUG)
self.stream_handler = logging.StreamHandler()
self.stream_handler.setLevel(logging.WARN)
# Create formatter and add to handlers
formatter = logging.Formatter('%(asctime)s | %(levelname)s | %(filename)s | %(funcName)s | %(lineno)d | %(message)s')
self.file_handler.setFormatter(formatter)
self.stream_handler.setFormatter(formatter)
# Create logger and add handlers
self.logger = logging.getLogger("unittest")
self.logger.setLevel(logging.DEBUG)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(self.stream_handler)
self.logger.debug("Logger is set up")
# Start serial communication to low-level board
self.si = comm.SerialInterface(timeout=config["si_timeout"])
self.si.start() # Displays an error if port not found (not running on Pandaboard)
self.logger.info("Serial interface set up")
# Build Queue objects for IPC. Name shows producer_consumer.
self.qNav_loc = Queue()
self.qMove_nav = Queue()
self.logger.debug("Queue objects created")
# Get map, waypoints
self.course_map = mapper.unpickle_map(path_to_qwe + "mapping/map.pkl")
self.logger.info("Map unpickled")
self.waypoints = mapper.unpickle_waypoints(path_to_qwe + "mapping/waypoints.pkl")
self.logger.info("Waypoints unpickled")
# Build shared data structures
self.manager = Manager()
self.start_x = self.waypoints["start"][1][0]
self.start_y = self.waypoints["start"][1][1]
self.start_theta = self.waypoints["start"][2]
self.logger.debug("Start waypoint is {}, {}, {}".format(self.start_x, self.start_y, self.start_theta))
self.bot_loc = self.manager.dict(x=self.start_x, y=self.start_y, theta=self.start_theta, dirty=False)
self.bot_state = self.manager.dict(nav_type=None, action_type=None)
self.logger.debug("Shared data structures created")
# Build nav object
self.scNav = comm.SerialCommand(self.si.commands, self.si.responses)
self.scNav.compassReset()
self.Nav = nav.Nav(self.bot_loc, self.qNav_loc, self.scNav, self.bot_state, self.qMove_nav, \
self.logger)
self.logger.info("Nav object instantiated")
def tearDown(self):
"""Close serial interface threads"""
self.logger.removeHandler(self.file_handler)
self.logger.removeHandler(self.stream_handler)
self.scNav.quit()
self.si.join()
def test_locsEqual_default_config_mixed_sign_twice_error(self):
"""Test function that's to check if two poses are equal to within some error. This test uses twice the acceptable error and
mixed negative and positive values"""
# Translate bot_loc data into internal units
x0 = nav.config["XYErr"]
y0 = nav.config["XYErr"]
theta0 = nav.config["thetaErr"]
# Create a second pose that's off by twice of the acceptable error
x1 = nav.config["XYErr"] * -1
y1 = nav.config["XYErr"] * -1
theta1 = nav.config["thetaErr"] * -1
self.logger.info("Testing locsEqual with {} {} {} and {} {} {}".format(x0, y0, theta0, x1, y1, theta1))
result = self.Nav.locsEqual(x0, y0, theta0, x1, y1, theta1)
self.logger.debug("locsEqual returned {}".format(result))
self.assertFalse(result, "locsEqual returned True with mixed sign values and diff of twice the acceptable error")
def test_locsEqual_default_config_mixed_sign_half_error(self):
"""Test function that's to check if two poses are equal to within some error. This test uses half the acceptable error and
mixed negative and positive values"""
# Translate bot_loc data into internal units
x0 = nav.config["XYErr"] / 4
y0 = nav.config["XYErr"] / 4
theta0 = nav.config["thetaErr"] / 4
# Create a second pose that's off by half of the acceptable error
x1 = nav.config["XYErr"] / -4
y1 = nav.config["XYErr"] / -4
theta1 = nav.config["thetaErr"] / -4
self.logger.info("Testing locsEqual with {} {} {} and {} {} {}".format(x0, y0, theta0, x1, y1, theta1))
result = self.Nav.locsEqual(x0, y0, theta0, x1, y1, theta1)
self.logger.debug("locsEqual returned {}".format(result))
self.assertTrue(result, "locsEqual returned False with mixed sign values and diff of half the acceptable error")
def test_locsEqual_default_config_neg_vals_half_error(self):
"""Test function that's to check if two poses are equal to within some error. This test uses half the acceptable error and
negative values"""
# Translate bot_loc data into internal units
x0 = self.Nav.XYFrombot_locUC(self.bot_loc["x"]) * -1
y0 = self.Nav.XYFrombot_locUC(self.bot_loc["y"]) * -1
theta0 = self.Nav.thetaFrombot_locUC(self.bot_loc["theta"]) * -1
# Create a second pose that's off by half of the acceptable error
x1 = x0 + nav.config["XYErr"] / -2
y1 = y0 + nav.config["XYErr"] / -2
theta1 = theta0 + nav.config["thetaErr"] / -2
self.logger.info("Testing locsEqual with {} {} {} and {} {} {}".format(x0, y0, theta0, x1, y1, theta1))
result = self.Nav.locsEqual(x0, y0, theta0, x1, y1, theta1)
self.logger.debug("locsEqual returned {}".format(result))
self.assertTrue(result, "locsEqual returned False with negative values and diff of half the acceptable error")
def test_locsEqual_default_config_neg_vals_twice_error(self):
"""Test function that's to check if two poses are equal to within some error. This test uses twice the acceptable error and
negative values"""
# Translate bot_loc data into internal units
x0 = self.Nav.XYFrombot_locUC(self.bot_loc["x"]) * -1
y0 = self.Nav.XYFrombot_locUC(self.bot_loc["y"]) * -1
theta0 = self.Nav.thetaFrombot_locUC(self.bot_loc["theta"]) * -1
# Create a second pose that's off by twice the acceptable error
x1 = x0 + nav.config["XYErr"] * -2
y1 = y0 + nav.config["XYErr"] * -2
theta1 = theta0 + nav.config["thetaErr"] * -2
self.logger.info("Testing locsEqual with {} {} {} and {} {} {}".format(x0, y0, theta0, x1, y1, theta1))
result = self.Nav.locsEqual(x0, y0, theta0, x1, y1, theta1)
self.logger.debug("locsEqual returned {}".format(result))
self.assertFalse(result, "locsEqual returned True with negative values and with diff twice of acceptable error")
def test_locsEqual_default_config_neg_vals_0_error(self):
"""Test function that's to check if two poses are equal to within some error. This test uses zero error and negative values."""
self.logger.info("Testing locsEqual with {} {} {} and {} {} {}".format(-1, -.5, -.25, -1, -.5, -.25))
result = self.Nav.locsEqual(-1, -.5, -.25, -1, -.5, -.25)
self.logger.debug("locsEqual returned {}".format(result))
self.assertTrue(result, "locsEqual returned False with negative values and zero error")
def test_locsEqual_default_config_0_vals(self):
"""Test function that's to check if two poses are equal to within some error. This test uses zero for all values."""
self.logger.info("Testing locsEqual with {} {} {} and {} {} {}".format(0, 0, 0, 0, 0, 0))
result = self.Nav.locsEqual(0, 0, 0, 0, 0, 0)
self.logger.debug("locsEqual returned {}".format(result))
self.assertTrue(result, "locsEqual returned False with all-zero inputs")
def test_locsEqual_default_config_0_error(self):
"""Test function that's to check if two poses are equal to within some error. This test uses zero error."""
# Translate bot_loc data into internal units
x0 = self.Nav.XYFrombot_locUC(self.bot_loc["x"])
y0 = self.Nav.XYFrombot_locUC(self.bot_loc["y"])
theta0 = self.Nav.thetaFrombot_locUC(self.bot_loc["theta"])
self.logger.info("Testing locsEqual with {} {} {} and {} {} {}".format(x0, y0, theta0, x0, y0, theta0))
result = self.Nav.locsEqual(x0, y0, theta0, x0, y0, theta0)
self.logger.debug("locsEqual returned {}".format(result))
self.assertTrue(result, "locsEqual returned False when diff 0")
def test_locsEqual_default_config_twice_error(self):
"""Test function that's to check if two poses are equal to within some error. This test uses twice the acceptable error."""
# Translate bot_loc data into internal units
x0 = self.Nav.XYFrombot_locUC(self.bot_loc["x"])
y0 = self.Nav.XYFrombot_locUC(self.bot_loc["y"])
theta0 = self.Nav.thetaFrombot_locUC(self.bot_loc["theta"])
# Create a second pose that's off by half of the acceptable error
x1 = x0 + nav.config["XYErr"] * 2
y1 = y0 + nav.config["XYErr"] * 2
theta1 = theta0 + nav.config["thetaErr"] * 2
self.logger.info("Testing locsEqual with {} {} {} and {} {} {}".format(x0, y0, theta0, x1, y1, theta1))
result = self.Nav.locsEqual(x0, y0, theta0, x1, y1, theta1)
self.logger.debug("locsEqual returned {}".format(result))
self.assertFalse(result, "locsEqual returned True when diff was twice of acceptable error")
def test_locsEqual_default_config_half_error(self):
"""Test function that's to check if two poses are equal to within some error. This test uses half the acceptable error."""
# Translate bot_loc data into internal units
x0 = self.Nav.XYFrombot_locUC(self.bot_loc["x"])
y0 = self.Nav.XYFrombot_locUC(self.bot_loc["y"])
theta0 = self.Nav.thetaFrombot_locUC(self.bot_loc["theta"])
# Create a second pose that's off by half of the acceptable error
x1 = x0 + nav.config["XYErr"]/2
y1 = y0 + nav.config["XYErr"]/2
theta1 = theta0 + nav.config["thetaErr"]/2
self.logger.info("Testing locsEqual with {} {} {} and {} {} {}".format(x0, y0, theta0, x1, y1, theta1))
result = self.Nav.locsEqual(x0, y0, theta0, x1, y1, theta1)
self.logger.debug("locsEqual returned {}".format(result))
self.assertTrue(result, "locsEqual returned False when diff was half of acceptable error")
class TestwhichXYTheta(unittest.TestCase):
def setUp(self):
"""Create nav object and feed it appropriate data"""
# Create file and stream handlers
self.file_handler = logging.handlers.RotatingFileHandler(path_to_qwe + "logs/unittests.log", maxBytes=512000, backupCount=50)
self.file_handler.setLevel(logging.DEBUG)
self.stream_handler = logging.StreamHandler()
self.stream_handler.setLevel(logging.WARN)
# Create formatter and add to handlers
formatter = logging.Formatter('%(asctime)s | %(levelname)s | %(filename)s | %(funcName)s | %(lineno)d | %(message)s')
self.file_handler.setFormatter(formatter)
self.stream_handler.setFormatter(formatter)
# Create logger and add handlers
self.logger = logging.getLogger("unittest")
self.logger.setLevel(logging.DEBUG)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(self.stream_handler)
self.logger.debug("Logger is set up")
# Start serial communication to low-level board
self.si = comm.SerialInterface(timeout=config["si_timeout"])
self.si.start() # Displays an error if port not found (not running on Pandaboard)
self.logger.info("Serial interface set up")
# Build shared data structures
self.manager = Manager()
self.bot_loc = self.manager.dict(x=1, y=1, theta=0, dirty=False) # Same params used in the env1.txt example file
self.bot_state = self.manager.dict(nav_type=None, action_type=None)
self.logger.debug("Shared data structures created")
# Build Queue objects for IPC. Name shows producer_consumer.
self.qNav_loc = Queue()
self.qMove_nav = Queue()
self.logger.debug("Queue objects created")
# Get map, waypoints
self.course_map = mapper.unpickle_map(path_to_qwe + "mapping/map.pkl")
self.logger.info("Map unpickled")
self.waypoints = mapper.unpickle_waypoints(path_to_qwe + "mapping/waypoints.pkl")
self.logger.info("Waypoints unpickled")
# Build nav object
self.scNav = comm.SerialCommand(self.si.commands, self.si.responses)
self.scNav.compassReset()
self.Nav = nav.Nav(self.bot_loc, self.qNav_loc, self.scNav, self.bot_state, self.qMove_nav, \
self.logger)
self.logger.info("Nav object instantiated")
def tearDown(self):
"""Close serial interface threads"""
self.logger.removeHandler(self.file_handler)
self.logger.removeHandler(self.stream_handler)
self.scNav.quit()
self.si.join()
def test_whichXYTheta_x_change(self):
"""Test helper function that finds if movement is to be in the XY plane or the theta dimension. This test checks steps with
a difference in x value."""
sol = [{'cont_theta': 0.000,
'cont_x': 3.350,
'cont_y': 3.250,
'theta': 0,
'x': 33,
'y': 32},
{'cont_theta': 0.000,
'cont_x': 3.250,
'cont_y': 3.250,
'theta': 0,
'x': 32,
'y': 32}]
self.logger.debug("Testing whichXYTheta with sol: " + str(sol))
result = self.Nav.whichXYTheta(sol[0], sol[1])
self.logger.info("whichXYTheta returned: {}".format(result))
self.assertEqual(result, "xy", "Expected \"xy\" but received {}".format(result))
def test_whichXYTheta_y_change(self):
"""Test helper function that finds if movement is to be in the XY plane or the theta dimension. This test checks steps a
difference in y value."""
sol = [{'cont_theta': 0.000,
'cont_x': 3.250,
'cont_y': 3.250,
'theta': 0,
'x': 32,
'y': 32},
{'cont_theta': 0.000,
'cont_x': 3.250,
'cont_y': 4.250,
'theta': 0,
'x': 32,
'y': 40}]
self.logger.debug("Testing whichXYTheta with sol: " + str(sol))
result = self.Nav.whichXYTheta(sol[0], sol[1])
self.logger.info("whichXYTheta returned: {}".format(result))
self.assertEqual(result, "xy", "Expected \"xy\" but received {}".format(result))
def test_whichXYTheta_theta_change(self):
"""Test helper function that finds if movement is to be in the XY plane or the theta dimension. This test checks steps with a
change in the theta value."""
sol = [{'cont_theta': 0.393,
'cont_x': 2.450,
'cont_y': 1.750,
'theta': 1,
'x': 24,
'y': 17},
{'cont_theta': 0.785,
'cont_x': 2.450,
'cont_y': 1.750,
'theta': 2,
'x': 24,
'y': 17}]
self.logger.debug("Testing whichXYTheta with sol: " + str(sol))
result = self.Nav.whichXYTheta(sol[0], sol[1])
self.logger.info("whichXYTheta returned: {}".format(result))
self.assertEqual(result, "theta", "Expected \"theta\" but received {}".format(result))
def test_whichXYTheta_x_y_change(self):
"""Test helper function that finds if movement is to be in the XY plane or the theta dimension. This test checks changes in
x, y and theta."""
sol = [{'cont_theta': '0.3926991',
'cont_x': '0.2762250',
'cont_y': '0.2254250',
'theta': '1',
'x': '43',
'y': '35'},
{'cont_theta': '0.3926991',
'cont_x': '0.3143250',
'cont_y': '0.2444750',
'theta': '1',
'x': '49',
'y': '38'}]
self.logger.debug("Testing whichXYTheta with sol: " + str(sol))
result = self.Nav.whichXYTheta(sol[0], sol[1])
self.logger.info("whichXYTheta returned: {}".format(result))
self.assertEqual(result, "xy", "Expected \"xy\" but received {}".format(result))
def test_whichXYTheta_x_theta_change(self):
"""Test helper function that finds if movement is to be in the XY plane or the theta dimension. This test checks changes in x
and theta."""
sol = [{'cont_theta': '0.0000000',
'cont_x': '0.2762250',
'cont_y': '0.2762250',
'theta': '0',
'x': '43',
'y': '43'},
{'cont_theta': '0.3926991',
'cont_x': '0.3143250',
'cont_y': '0.2762250',
'theta': '1',
'x': '49',
'y': '43'}]
self.logger.debug("Testing whichXYTheta with sol: " + str(sol))
result = self.Nav.whichXYTheta(sol[0], sol[1])
self.logger.info("whichXYTheta returned: {}".format(result))
self.assertEqual(result, nav.errors["ERROR_ARCS_DISALLOWED"],
"Expected {} but received {}".format(nav.errors["ERROR_ARCS_DISALLOWED"], result))
def test_whichXYTheta_y_theta_change(self):
"""Test helper function that finds if movement is to be in the XY plane or the theta dimension. This test checks changes in y
and theta."""
sol = [{'cont_theta': '0.0000000',
'cont_x': '0.2762250',
'cont_y': '0.2762250',
'theta': '0',
'x': '43',
'y': '43'},
{'cont_theta': '0.3926991',
'cont_x': '0.2762250',
'cont_y': '0.2063750',
'theta': '1',
'x': '43',
'y': '32'}]
self.logger.debug("Testing whichXYTheta with sol: " + str(sol))
result = self.Nav.whichXYTheta(sol[0], sol[1])
self.logger.info("whichXYTheta returned: {}".format(result))
self.assertEqual(result, nav.errors["ERROR_ARCS_DISALLOWED"],
"Expected {} but received {}".format(nav.errors["ERROR_ARCS_DISALLOWED"], result))
def test_whichXYTheta_x_y_theta_change(self):
"""Test helper function that finds if movement is to be in the XY plane or the theta dimension. This test checks changes in
x, y and theta."""
sol = [{'cont_theta': '0.0000000',
'cont_x': '0.2762250',
'cont_y': '0.2762250',
'theta': '0',
'x': '43',
'y': '43'},
{'cont_theta': '0.3926991',
'cont_x': '0.3143250',
'cont_y': '0.2063750',
'theta': '1',
'x': '49',
'y': '32'}]
self.logger.debug("Testing whichXYTheta with sol: " + str(sol))
result = self.Nav.whichXYTheta(sol[0], sol[1])
self.logger.info("whichXYTheta returned: {}".format(result))
self.assertEqual(result, nav.errors["ERROR_ARCS_DISALLOWED"],
"Expected {} but received {}".format(nav.errors["ERROR_ARCS_DISALLOWED"], result))
def test_whichXYTheta_no_change(self):
"""Test helper function that finds if movement is to be in the XY plane or the theta dimension. This test checks steps with
no previous move and no difference in any attribute."""
sol = [{'cont_theta': 0.000,
'cont_x': 3.350,
'cont_y': 3.250,
'theta': 0,
'x': 33,
'y': 32},
{'cont_theta': 0.000,
'cont_x': 3.350,
'cont_y': 3.250,
'theta': 0,
'x': 33,
'y': 32}]
self.logger.debug("Testing whichXYTheta with sol: " + str(sol))
result = self.Nav.whichXYTheta(sol[0], sol[1])
self.logger.info("whichXYTheta returned: {}".format(result))
self.assertEqual(result, nav.errors["ERROR_NO_CHANGE"], "Expected {} but received \
{}".format(nav.errors["ERROR_NO_CHANGE"], result))
if __name__ == "__main__":
unittest.main() # Execute all tests
| 44.107143 | 131 | 0.624978 | 15,255 | 113,620 | 4.489282 | 0.04215 | 0.042054 | 0.025699 | 0.015186 | 0.889551 | 0.878702 | 0.868991 | 0.860318 | 0.8516 | 0.84335 | 0 | 0.025966 | 0.239051 | 113,620 | 2,575 | 132 | 44.124272 | 0.766132 | 0.065913 | 0 | 0.77 | 0 | 0.004706 | 0.214641 | 0.004685 | 0 | 0 | 0 | 0 | 0.106471 | 0 | null | null | 0.004706 | 0.008824 | null | null | 0.001176 | 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 |
f5ac9ad2939a09371a31d2a0489f42491a6edba0 | 128 | py | Python | omoide/domain/__init__.py | IgorZyktin/Omoide | 42eeafce05e0efcfeb62a12bf508971680e6b17d | [
"MIT"
] | null | null | null | omoide/domain/__init__.py | IgorZyktin/Omoide | 42eeafce05e0efcfeb62a12bf508971680e6b17d | [
"MIT"
] | 32 | 2021-09-02T06:38:59.000Z | 2021-10-17T07:44:10.000Z | omoide/domain/__init__.py | IgorZyktin/Omoide | 42eeafce05e0efcfeb62a12bf508971680e6b17d | [
"MIT"
] | 1 | 2021-08-28T11:17:55.000Z | 2021-08-28T11:17:55.000Z | # -*- coding: utf-8 -*-
from omoide.domain.auth import *
from omoide.domain.preview import *
from omoide.domain.search import *
| 25.6 | 35 | 0.726563 | 18 | 128 | 5.166667 | 0.555556 | 0.322581 | 0.516129 | 0.473118 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.009009 | 0.132813 | 128 | 4 | 36 | 32 | 0.828829 | 0.164063 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
f5e6e83816a9f223cac6190c23bd4dc81f3e56c5 | 79 | py | Python | solaris/utils/cli.py | rbavery/solaris | 0d7bd1439a96c243d7810fcddf776b7e635a05ea | [
"Apache-2.0"
] | 367 | 2019-05-05T22:09:39.000Z | 2022-03-27T10:05:16.000Z | 3-SatShipAI/solaris/utils/cli.py | Z-Zheng/SpaceNet_SAR_Buildings_Solutions | 6a9c3962d987d985384d0d41a187f5fbfadac82c | [
"Apache-2.0"
] | 396 | 2019-04-30T21:51:12.000Z | 2022-03-31T09:21:09.000Z | 3-SatShipAI/solaris/utils/cli.py | Z-Zheng/SpaceNet_SAR_Buildings_Solutions | 6a9c3962d987d985384d0d41a187f5fbfadac82c | [
"Apache-2.0"
] | 120 | 2019-06-29T20:20:08.000Z | 2022-03-10T07:37:57.000Z | def _func_wrapper(func_to_call, arg_dict):
return func_to_call(**arg_dict)
| 26.333333 | 42 | 0.78481 | 14 | 79 | 3.857143 | 0.571429 | 0.222222 | 0.37037 | 0.481481 | 0.62963 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.113924 | 79 | 2 | 43 | 39.5 | 0.771429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 8 |
a0d9f4a7f4d821813e3e0fd5f318743bf31dff57 | 114 | py | Python | relocate_venv/__init__.py | firedrakeproject/relocate-venv | 87167b71b9b485f3077f9026f730416b55f332a7 | [
"Apache-2.0"
] | 9 | 2017-05-26T09:04:57.000Z | 2020-09-28T07:12:04.000Z | relocate_venv/__init__.py | firedrakeproject/relocate-venv | 87167b71b9b485f3077f9026f730416b55f332a7 | [
"Apache-2.0"
] | 4 | 2017-02-20T16:07:16.000Z | 2017-02-21T17:29:31.000Z | relocate_venv/__init__.py | firedrakeproject/relocate-venv | 87167b71b9b485f3077f9026f730416b55f332a7 | [
"Apache-2.0"
] | null | null | null | from .core import handle_args
def entrypt():
"""Entry point for the application script"""
handle_args()
| 16.285714 | 48 | 0.692982 | 15 | 114 | 5.133333 | 0.866667 | 0.25974 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.201754 | 114 | 6 | 49 | 19 | 0.846154 | 0.333333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
9d3e4e0ffe71263d0eec0547f6a1990d97af0475 | 1,522 | py | Python | crypto_compare/apis/top.py | konsh/crypto_compare | 48511ca22c217a30a7aa3945550853bd3e91a0c7 | [
"MIT"
] | 28 | 2017-08-30T18:05:20.000Z | 2022-03-31T10:28:38.000Z | crypto_compare/apis/top.py | konsh/crypto_compare | 48511ca22c217a30a7aa3945550853bd3e91a0c7 | [
"MIT"
] | 2 | 2017-12-25T22:11:07.000Z | 2018-11-24T08:19:07.000Z | crypto_compare/apis/top.py | konsh/crypto_compare | 48511ca22c217a30a7aa3945550853bd3e91a0c7 | [
"MIT"
] | 9 | 2017-11-15T19:01:54.000Z | 2021-06-19T11:04:27.000Z |
def top_exchanges(self, **kwargs):
"""
https://min-api.cryptocompare.com/
Keyword arguments:
inside kwargs
fsym - From Symbol
tsym - To Symbol
extraParams - Name of your application
sign - If set to true, the server will sign the requests.
limit - default 5, max 50, min 1
"""
fsym, tsym, querystring = self._get_querystring(kwargs)
self._is_params_valid(fsym=fsym, tsym=tsym)
return self._fetch_data(self.TOP_EXCHANGES_URL+querystring)
def top_volumes(self, **kwargs):
"""
https://min-api.cryptocompare.com/
Keyword arguments:
inside kwargs
tsym - To Symbol
extraParams - Name of your application
sign - If set to true, the server will sign the requests.
limit - default 20, max 1000, min 1
"""
fsym, tsym, querystring = self._get_querystring(kwargs)
self._is_params_valid(tsym=tsym)
return self._fetch_data(self.TOP_VOLUMES_URL+querystring)
def top_pairs(self, **kwargs):
"""
https://min-api.cryptocompare.com/
Keyword arguments:
inside kwargs
fsym - From Symbol
extraParams - Name of your application
sign - If set to true, the server will sign the requests.
limit - default 5, max 50, min 1
"""
fsym, tsym, querystring = self._get_querystring(kwargs)
self._is_params_valid(fsym=fsym)
return self._fetch_data(self.TOP_PAIRS_URL+querystring) | 21.742857 | 69 | 0.63272 | 191 | 1,522 | 4.884817 | 0.26178 | 0.034298 | 0.048232 | 0.057878 | 0.881029 | 0.881029 | 0.853162 | 0.853162 | 0.780279 | 0.780279 | 0 | 0.013699 | 0.280552 | 1,522 | 70 | 70 | 21.742857 | 0.838356 | 0.497372 | 0 | 0.25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0 | 0 | 0.5 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
c21ee7ffe34cd377707686f9a9b93249cc6ed099 | 242 | py | Python | __init__.py | annejan/badge-md-reader | c0dc204ca5602a8c18a94848311f5da28eb025ba | [
"Unlicense"
] | null | null | null | __init__.py | annejan/badge-md-reader | c0dc204ca5602a8c18a94848311f5da28eb025ba | [
"Unlicense"
] | null | null | null | __init__.py | annejan/badge-md-reader | c0dc204ca5602a8c18a94848311f5da28eb025ba | [
"Unlicense"
] | null | null | null | import easydraw
easydraw.messageCentered("Usage:\n\nfrom md_reader import reader\nreader.read('/lib/md_reader/readme.md')", True, "/media/alert.png")
print("Usage:\n\nfrom md_reader import reader\nreader.read('/lib/md_reader/readme.md')\n") | 80.666667 | 134 | 0.768595 | 38 | 242 | 4.789474 | 0.447368 | 0.175824 | 0.120879 | 0.142857 | 0.67033 | 0.67033 | 0.67033 | 0.67033 | 0.67033 | 0.67033 | 0 | 0 | 0.053719 | 242 | 3 | 135 | 80.666667 | 0.79476 | 0 | 0 | 0 | 0 | 0.666667 | 0.73029 | 0.39834 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0.333333 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
dfc42004cd3e3d9d1a7991e30f7ca6b58ddd9eb9 | 235 | py | Python | anuvaad-api/anuvaad-annotation/sentence-annotation/src/resources/__init__.py | ManavTriesStuff/anuvaad | 6993e3ac78818c171c173ccf8acf962ff57856a4 | [
"MIT"
] | 15 | 2021-01-08T08:42:30.000Z | 2022-03-12T17:52:15.000Z | anuvaad-api/anuvaad-annotation/sentence-annotation/src/resources/__init__.py | ManavTriesStuff/anuvaad | 6993e3ac78818c171c173ccf8acf962ff57856a4 | [
"MIT"
] | 16 | 2021-01-21T01:38:51.000Z | 2022-01-20T08:59:52.000Z | anuvaad-api/anuvaad-annotation/sentence-annotation/src/resources/__init__.py | ManavTriesStuff/anuvaad | 6993e3ac78818c171c173ccf8acf962ff57856a4 | [
"MIT"
] | 25 | 2020-08-26T11:25:38.000Z | 2022-03-29T04:40:21.000Z | from .annotation_task import AnnotationTaskUserTaskSearchResource, AnnotationTaskTaskIdSearchResource, AnnotationTaskTaskTypeSearchResource
from .annotation_task import AnnotationTaskCreateResource, AnnotationTaskSaveAnnotationResource | 117.5 | 139 | 0.93617 | 13 | 235 | 16.769231 | 0.692308 | 0.12844 | 0.165138 | 0.220183 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.042553 | 235 | 2 | 140 | 117.5 | 0.968889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
a05148967c0252b0984251b00b5b28ef13f13d9b | 219 | py | Python | src/models/utils/abstract_model_wrapper.py | Jud1cator/training-pipeline | d129c54985c67844b391701228f35dc014203aaa | [
"MIT"
] | null | null | null | src/models/utils/abstract_model_wrapper.py | Jud1cator/training-pipeline | d129c54985c67844b391701228f35dc014203aaa | [
"MIT"
] | null | null | null | src/models/utils/abstract_model_wrapper.py | Jud1cator/training-pipeline | d129c54985c67844b391701228f35dc014203aaa | [
"MIT"
] | null | null | null | from torch.nn import Module
class AbstractModelWrapper(Module):
def forward(self, *args, **kwargs):
self.get_model().forward(*args, **kwargs)
def get_model(self, *args, **kwargs):
return self
| 21.9 | 49 | 0.657534 | 27 | 219 | 5.259259 | 0.555556 | 0.211268 | 0.197183 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.205479 | 219 | 9 | 50 | 24.333333 | 0.816092 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.166667 | 0.166667 | 0.833333 | 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 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 7 |
a06e75fe92a6bcc2e0d35d0055090ca4afce6bfd | 13,122 | py | Python | Functions_Plotting/PlotClass2.py | hombregula/imcafe_cv | 5f6fb6b776efb50e74fe94cc2d53ebddb83ef550 | [
"Apache-2.0"
] | 1 | 2015-07-04T12:54:53.000Z | 2015-07-04T12:54:53.000Z | Functions_Plotting/PlotClass2.py | hombregula/imcafe_cv | 5f6fb6b776efb50e74fe94cc2d53ebddb83ef550 | [
"Apache-2.0"
] | null | null | null | Functions_Plotting/PlotClass2.py | hombregula/imcafe_cv | 5f6fb6b776efb50e74fe94cc2d53ebddb83ef550 | [
"Apache-2.0"
] | null | null | null | '''
Created on 15/11/2013
@author: hombregula
'''
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.art3d as art3d
from Funciones import minComparison , MAXComparison
import wx
class Pintar2:
def __init__(self,Nodos,Bars,Skins,Figura):
self.Nodes=Nodos
self.Skin=Skins
self.Bars=Bars
self.ax=Figura
self.LimX=(9999999999,-9999999999)
self.LimY=(9999999999,-9999999999)
self.LimZ=(9999999999,-9999999999)
self.FactorLim=1.0
def Plot000(self):
self.ax.scatter (0,0,0,c='g')
def PlotNodes(self):
Indice=self.Nodes.keys()
for i in Indice:
self.ax.scatter (self.Nodes[i][0],self.Nodes[i][1],self.Nodes[i][2],c='g')
self.LimX=(minComparison(self.LimX[0],self.Nodes[i][0]),MAXComparison(self.LimX[1],self.Nodes[i][0]))
self.LimY=(minComparison(self.LimY[0],self.Nodes[i][1]),MAXComparison(self.LimY[1],self.Nodes[i][1]))
self.LimZ=(minComparison(self.LimZ[0],self.Nodes[i][2]),MAXComparison(self.LimZ[1],self.Nodes[i][2]))
def PlotBars(self):
try:
Indice=self.Bars.keys()
self.ArrayBars=[]
for i in Indice:
x = np.linspace(self.Nodes[self.Bars[i][0]][0], self.Nodes[self.Bars[i][1]][0], 100)
y = np.linspace(self.Nodes[self.Bars[i][0]][1], self.Nodes[self.Bars[i][1]][1], 100)
z = np.linspace(self.Nodes[self.Bars[i][0]][2], self.Nodes[self.Bars[i][1]][2], 100)
mylabel=str(i)+'_b'
self.ArrayBars=self.ArrayBars + [self.ax.plot(x, y, z,'y',linewidth=2,picker=5,label=mylabel)]
#self.ax.plot(x, y, z,'y',linewidth=1)
self.PlotBarsLimits()
except:
print i
print self.Nodes[self.Bars[i][3]][0]
print self.Nodes[self.Bars[i][3]][1]
print self.Nodes[self.Bars[i][3]][2]
raise
def PlotBars_Clips(self, parent,axes):
try:
MisBarras= parent.Analysis.Analysis.keys()
Indice=self.Bars.keys()
self.axes=axes
self.ArrayBars=[]
self.ArrayBars0=[]
self.ArrayBars1=[]
self.ArrayBars2=[]
for i in Indice:
if (i in MisBarras)==False:
x = np.linspace(self.Nodes[self.Bars[i][0]][0], self.Nodes[self.Bars[i][1]][0], 100)
y = np.linspace(self.Nodes[self.Bars[i][0]][1], self.Nodes[self.Bars[i][1]][1], 100)
z = np.linspace(self.Nodes[self.Bars[i][0]][2], self.Nodes[self.Bars[i][1]][2], 100)
mylabel=str(i)+'_b'
self.ArrayBars=self.ArrayBars + [self.ax.plot(x, y, z,'y',linewidth=0.5,picker=5,label=mylabel)]
#self.ax.plot(x, y, z,'y',linewidth=1)
else:
x = np.linspace(self.Nodes[self.Bars[i][0]][0], self.Nodes[self.Bars[i][1]][0], 100)
y = np.linspace(self.Nodes[self.Bars[i][0]][1], self.Nodes[self.Bars[i][1]][1], 100)
z = np.linspace(self.Nodes[self.Bars[i][0]][2], self.Nodes[self.Bars[i][1]][2], 100)
if float(parent.Analysis.Analysis[i].minimorum[0][1]) < 1.00001:
mylabel=str(i)+'_b_r'
self.ArrayBars0=self.ArrayBars0 + [self.ax.plot(x, y, z, color=(1,0,0),linewidth=2,picker=5,label=mylabel)]
elif float(parent.Analysis.Analysis[i].minimorum[0][1]) > 1.10:
mylabel=str(i)+'_b_a'
self.ArrayBars2=self.ArrayBars2 + [self.ax.plot(x, y, z,color=(0,0.69,0.94),linewidth=2,picker=5,label=mylabel)]
else:
mylabel=str(i)+'_b_n'
self.ArrayBars1=self.ArrayBars1 + [self.ax.plot(x, y, z,color=(1,0.4,0),linewidth=2,picker=5,label=mylabel)]
self.PlotBarsLimits()
'''
yellow_proxy = plt.Rectangle((0, 0), 1, 1, fc="y")
red_proxy = plt.Rectangle((0, 0), 1, 1, fc="r")
blue_proxy = plt.Rectangle((0, 0), 1, 1, fc="b")
orange_proxy = plt.Rectangle((0, 0), 1, 1, fc="g")
'''
yellow_proxy = plt.Rectangle((0, 0), 1, 1, fc="y")
red_proxy = plt.Rectangle((0, 0), 1, 1, fc="r")
orange_proxy = plt.Rectangle((0, 0), 1, 1, fc=(1,0.4,0))
blue_proxy = plt.Rectangle((0, 0), 1, 1, fc=(0,0.69,0.94))
#ax.legend([blue_proxy,red_proxy],['cars','bikes'])
#self.axes.legend([yellow_proxy,red_proxy,blue_proxy,green_proxy],['Not analized','RF < 1.10','RF < 3.00','RF > 3.00'],framealpha=0.5,frameon=None)
self.axes.legend([yellow_proxy,red_proxy,orange_proxy,blue_proxy],['Not analized','RF < 1.00','RF < 1.10','RF > 1.10'],framealpha=0.5)
except:
raise
def PlotBars_Clips_Inputs(self, parent,axes):
try:
MisBarras= parent.Analysis.Analysis.keys()
Indice=self.Bars.keys()
self.axes=axes
self.ArrayBars=[]
self.ArrayBars0=[]
self.ArrayBars1=[]
self.ArrayBars2=[]
for i in Indice:
if (i in MisBarras)==False:
x = np.linspace(self.Nodes[self.Bars[i][0]][0], self.Nodes[self.Bars[i][1]][0], 100)
y = np.linspace(self.Nodes[self.Bars[i][0]][1], self.Nodes[self.Bars[i][1]][1], 100)
z = np.linspace(self.Nodes[self.Bars[i][0]][2], self.Nodes[self.Bars[i][1]][2], 100)
mylabel=str(i)+'_b'
self.ArrayBars=self.ArrayBars + [self.ax.plot(x, y, z,'y',linewidth=0.5,picker=5,label=mylabel)]
#self.ax.plot(x, y, z,'y',linewidth=1)
else:
x = np.linspace(self.Nodes[self.Bars[i][0]][0], self.Nodes[self.Bars[i][1]][0], 100)
y = np.linspace(self.Nodes[self.Bars[i][0]][1], self.Nodes[self.Bars[i][1]][1], 100)
z = np.linspace(self.Nodes[self.Bars[i][0]][2], self.Nodes[self.Bars[i][1]][2], 100)
if (parent.Analysis.elementDict[i])=='TYPICAL':
mylabel=str(i)+'_b_r'
self.ArrayBars0=self.ArrayBars0 + [self.ax.plot(x, y, z, color=(1,0,0),linewidth=2,picker=5,label=mylabel)]
elif (parent.Analysis.elementDict[i])=='CONTINUOUS':
mylabel=str(i)+'_b_a'
self.ArrayBars2=self.ArrayBars2 + [self.ax.plot(x, y, z,color=(0,0.69,0.94),linewidth=2,picker=5,label=mylabel)]
elif (parent.Analysis.elementDict[i])=='INTEGRAL':
mylabel=str(i)+'_b_n'
self.ArrayBars1=self.ArrayBars1 + [self.ax.plot(x, y, z,color=(1,0.4,0),linewidth=2,picker=5,label=mylabel)]
self.PlotBarsLimits()
'''
yellow_proxy = plt.Rectangle((0, 0), 1, 1, fc="y")
red_proxy = plt.Rectangle((0, 0), 1, 1, fc="r")
blue_proxy = plt.Rectangle((0, 0), 1, 1, fc="b")
orange_proxy = plt.Rectangle((0, 0), 1, 1, fc="g")
'''
yellow_proxy = plt.Rectangle((0, 0), 1, 1, fc="y")
red_proxy = plt.Rectangle((0, 0), 1, 1, fc="r")
blue_proxy = plt.Rectangle((0, 0), 1, 1, fc=(0,0.69,0.94))
orange_proxy = plt.Rectangle((0, 0), 1, 1, fc=(1,0.4,0))
#ax.legend([blue_proxy,red_proxy],['cars','bikes'])
#self.axes.legend([yellow_proxy,red_proxy,blue_proxy,green_proxy],['Not analized','RF < 1.10','RF < 3.00','RF > 3.00'],framealpha=0.5,frameon=None)
self.axes.legend([yellow_proxy,red_proxy,orange_proxy,blue_proxy],['Not analized','Typical','Integral','Continuous'],framealpha=0.5)
except:
raise
def PlotBarsLimits(self):
try:
Indice=self.Bars.keys()
self.ArrayBars=[]
for i in Indice:
for t in (0,1):
self.LimX=(minComparison(self.LimX[0],self.Nodes[self.Bars[i][t]][0]),MAXComparison(self.LimX[1],self.Nodes[self.Bars[i][t]][0]))
self.LimY=(minComparison(self.LimY[0],self.Nodes[self.Bars[i][t]][1]),MAXComparison(self.LimY[1],self.Nodes[self.Bars[i][t]][1]))
self.LimZ=(minComparison(self.LimZ[0],self.Nodes[self.Bars[i][t]][2]),MAXComparison(self.LimZ[1],self.Nodes[self.Bars[i][t]][2]))
except:
dial = wx.MessageDialog(None, 'Error loading file. Nodes of ' + i + ' bar has not been located', 'Error', wx.OK | wx.ICON_ERROR)
dial.ShowModal()
print self.Nodes[self.Bars[i][3]][0]
print self.Nodes[self.Bars[i][3]][1]
print self.Nodes[self.Bars[i][3]][2]
raise
def PlotShells(self):
try:
Indice=self.Skin.keys()
self.ArrayShells=[]
for i in Indice:
N1 = (self.Nodes[self.Skin[i][0]][0], self.Nodes[self.Skin[i][0]][1], self.Nodes[self.Skin[i][0]][2])
N2 = (self.Nodes[self.Skin[i][1]][0], self.Nodes[self.Skin[i][1]][1], self.Nodes[self.Skin[i][1]][2])
N3 = (self.Nodes[self.Skin[i][2]][0], self.Nodes[self.Skin[i][2]][1], self.Nodes[self.Skin[i][2]][2])
try:
N4 = (self.Nodes[self.Skin[i][3]][0], self.Nodes[self.Skin[i][3]][1], self.Nodes[self.Skin[i][3]][2])
verts=[[N1,N2,N3,N4]]
except:
verts=[[N1,N2,N3]]
codes=(1,2,2,2,79)
mylabel=str(i)+'_s'
poligono=art3d.Poly3DCollection(verts,facecolor='b',alpha=0.4,picker=5,label=mylabel)
self.ArrayShells=self.ArrayShells+[self.ax.add_collection3d(poligono, zdir='y')]
#self.ax.add_collection3d(poligono, zdir='y')
self.PlotShellsLimits()
except:
dial = wx.MessageDialog(None, 'Error loading file. Nodes of ' + i + ' shell has not been located', 'Error', wx.OK | wx.ICON_ERROR)
dial.ShowModal()
raise
def PlotShellsLimits(self):
try:
Indice=self.Skin.keys()
self.ArrayShells=[]
for i in Indice:
for t in (0,3):
self.LimX=(minComparison(self.LimX[0],self.Nodes[self.Skin[i][t]][0]),MAXComparison(self.LimX[1],self.Nodes[self.Skin[i][t]][0]))
self.LimY=(minComparison(self.LimY[0],self.Nodes[self.Skin[i][t]][1]),MAXComparison(self.LimY[1],self.Nodes[self.Skin[i][t]][1]))
self.LimZ=(minComparison(self.LimZ[0],self.Nodes[self.Skin[i][t]][2]),MAXComparison(self.LimZ[1],self.Nodes[self.Skin[i][t]][2]))
except:
print i
def __Redifine_Nodes__(self,Xn,Yn,Zn):
for i in self.Nodes.keys():
self.Nodes[i]=[self.Nodes[i][0]-Xn,self.Nodes[i][1]-Yn,self.Nodes[i][2]-Zn]
def Redifine_2_Zero(self):
if self.LimX[1]<=0:
Xn=-self.LimX[0]
if self.LimX[0]>=0:
Xn=-self.LimX[0]
if self.LimX[0]<=0:
if self.LimX[1]>=0:
if self.LimX[1]*0.85 > abs(self.LimX[0]):
Xn=-self.LimX[0]
else:
Xn=0
if self.LimY[1]<=0:
Yn=-self.LimY[0]
if self.LimY[0]>=0:
Yn=-self.LimY[0]
if self.LimY[0]<=0:
if self.LimY[1]>=0:
if self.LimY[1]*0.85 > abs(self.LimY[0]):
Yn=-self.LimY[0]
else:
Yn=0
if self.LimZ[1]<=0:
Zn=-self.LimZ[0]
if self.LimZ[0]>=0:
Zn=-self.LimZ[0]
if self.LimZ[0]<=0:
if self.LimZ[1]>=0:
if self.LimZ[1]*0.95 > abs(self.LimZ[0]):
Zn=-self.LimZ[0]
else:
Zn=0
for i in self.Nodes.keys():
self.Nodes[i]=[self.Nodes[i][0]+Xn,self.Nodes[i][1]+Yn,self.Nodes[i][2]+Zn]
def LimitesX(self,min,Max):
self.ax.set_xlim3d(min,Max)
def LimitesY(self,min,Max):
self.ax.set_ylim3d(min,Max)
def LimitesZ(self,min,Max):
self.ax.set_zlim3d(min,Max)
| 49.704545 | 160 | 0.494056 | 1,783 | 13,122 | 3.593943 | 0.095906 | 0.113764 | 0.121723 | 0.111423 | 0.837859 | 0.817728 | 0.748908 | 0.725343 | 0.705836 | 0.667447 | 0 | 0.063155 | 0.331504 | 13,122 | 263 | 161 | 49.893536 | 0.667351 | 0.043515 | 0 | 0.57971 | 0 | 0 | 0.022087 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.028986 | null | null | 0.038647 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
269aaef343a072ab87465e596cd7fb01a14f0b43 | 4,452 | py | Python | netcal/Decorator.py | by-liu/calibration-framework | f1995512fea511572171974913fe5b569cac0cd0 | [
"Apache-2.0"
] | 148 | 2019-10-29T03:23:04.000Z | 2022-03-30T12:36:20.000Z | netcal/Decorator.py | EFS-OpenSource/calibration-framework | 7b306e4bbe6361d411b209759b7ba3d016bd0d17 | [
"Apache-2.0"
] | 19 | 2020-02-05T06:00:07.000Z | 2022-03-17T06:37:19.000Z | netcal/Decorator.py | EFS-OpenSource/calibration-framework | 7b306e4bbe6361d411b209759b7ba3d016bd0d17 | [
"Apache-2.0"
] | 25 | 2019-11-30T23:03:01.000Z | 2022-02-16T20:40:29.000Z | # Copyright (C) 2019-2021 Ruhr West University of Applied Sciences, Bottrop, Germany
# AND Elektronische Fahrwerkssysteme, Gaimersheim, Germany
#
# This Source Code Form is subject to the terms of the Apache License 2.0
# If a copy of the APL2 was not distributed with this
# file, You can obtain one at https://www.apache.org/licenses/LICENSE-2.0.txt.
import numpy as np
import torch
from functools import wraps
def accepts(*types):
"""
Decorator for function arg check
"""
def check_accepts(f):
assert len(types)+1 == f.__code__.co_argcount, "Unequal amount of defined parameter types and existing parameters."
@wraps(f)
def new_f(*args, **kwds):
for i, (a, t) in enumerate(zip(args[1:], types), start=1):
if t is None:
continue
if type(t) == tuple:
for st in t:
if type(a) == st:
break
else:
raise AssertionError("arg \'%s\' does not match one of types %s" % (f.__code__.co_varnames[i], str(t)))
else:
assert isinstance(a, t), "arg \'%s\' does not match %s" % (f.__code__.co_varnames[i],t)
return f(*args, **kwds)
new_f.__name__ = f.__name__
return new_f
return check_accepts
def dimensions(*dim):
"""
Decorator for numpy array dimension check
"""
def check_dim(f):
assert len(dim)+1 == f.__code__.co_argcount, "Unequal amount of defined dimensions and existing parameters."
@wraps(f)
def new_f(*args, **kwds):
for i, (a, d) in enumerate(zip(args[1:], dim), start=1):
if d is None:
continue
assert isinstance(a, (np.ndarray, torch.Tensor, )), "arg \'%s\' does not match %s or %s" % (f.__code__.co_varnames[i], np.ndarray, torch.Tensor)
if type(d) == tuple:
assert len(a.shape) in d, "dimension of arg \'%s\' must match %s but is %d" % (f.__code__.co_varnames[i], str(d), len(a.shape))
elif type(d) == int:
assert len(a.shape) == d, "dimension of arg \'%s\' must match %s but is %d" % (f.__code__.co_varnames[i], str(d), len(a.shape))
return f(*args, **kwds)
new_f.__name__ = f.__name__
return new_f
return check_dim
def global_accepts(*types):
"""
Decorator for global function's arg check
"""
def check_accepts(f):
assert len(types) == f.__code__.co_argcount, "Unequal amount of defined parameter types and existing parameters."
@wraps(f)
def new_f(*args, **kwds):
for i, (a, t) in enumerate(zip(args, types)):
if t is None:
continue
if type(t) == tuple:
for st in t:
if type(a) == st:
break
else:
raise AssertionError("arg \'%s\' does not match one of types %s" % (f.__code__.co_varnames[i], str(t)))
else:
assert isinstance(a, t), "arg \'%s\' does not match %s" % (f.__code__.co_varnames[i],t)
return f(*args, **kwds)
new_f.__name__ = f.__name__
return new_f
return check_accepts
def global_dimensions(*dim):
"""
Decorator for global function's numpy array dimension check
"""
def check_dim(f):
assert len(dim) == f.__code__.co_argcount, "Unequal amount of defined dimensions and existing parameters."
@wraps(f)
def new_f(*args, **kwds):
for i, (a, d) in enumerate(zip(args, dim)):
if d is None:
continue
assert isinstance(a, (np.ndarray, torch.Tensor)), "arg \'%s\' does not match %s or %s" % (f.__code__.co_varnames[i], np.ndarray, torch.Tensor)
if type(d) == tuple:
assert len(a.shape) in d, "dimension of arg \'%s\' must match %s but is %d" % (f.__code__.co_varnames[i], str(d), len(a.shape))
elif type(d) == int:
assert len(a.shape) == d, "dimension of arg \'%s\' must match %s but is %d" % (f.__code__.co_varnames[i], str(d), len(a.shape))
return f(*args, **kwds)
new_f.__name__ = f.__name__
return new_f
return check_dim
| 36.491803 | 160 | 0.542902 | 603 | 4,452 | 3.802653 | 0.192371 | 0.030528 | 0.042739 | 0.065416 | 0.803314 | 0.778892 | 0.778892 | 0.778892 | 0.778892 | 0.744876 | 0 | 0.006406 | 0.333783 | 4,452 | 121 | 161 | 36.793388 | 0.766689 | 0.116352 | 0 | 0.779221 | 0 | 0 | 0.167528 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 1 | 0.155844 | false | 0 | 0.038961 | 0 | 0.350649 | 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 |
f85abb7001b0bfa7d6cfb23e3bb3e3ad19ef03b9 | 40 | py | Python | learnml/io/__init__.py | spyridon97/Learn-Machine-Learning | 4678430b40a45f25fe9d9dc4400450b974d0b6fb | [
"MIT"
] | null | null | null | learnml/io/__init__.py | spyridon97/Learn-Machine-Learning | 4678430b40a45f25fe9d9dc4400450b974d0b6fb | [
"MIT"
] | null | null | null | learnml/io/__init__.py | spyridon97/Learn-Machine-Learning | 4678430b40a45f25fe9d9dc4400450b974d0b6fb | [
"MIT"
] | null | null | null | from .read_dataset import read_dataset
| 20 | 39 | 0.85 | 6 | 40 | 5.333333 | 0.666667 | 0.6875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 40 | 1 | 40 | 40 | 0.914286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 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 |
f8aa719022bfda075f885919c529a2ab5ee22362 | 149 | py | Python | B-CNA-410-LYN-4-1-groundhog/src/helpme.py | Neotoxic-off/Epitech2024 | 8b3dd04fa9ac2b7019c0b5b1651975a7252d929b | [
"Apache-2.0"
] | 2 | 2022-02-07T12:44:51.000Z | 2022-02-08T12:04:08.000Z | B-CNA-410-LYN-4-1-groundhog/src/helpme.py | Neotoxic-off/Epitech2024 | 8b3dd04fa9ac2b7019c0b5b1651975a7252d929b | [
"Apache-2.0"
] | null | null | null | B-CNA-410-LYN-4-1-groundhog/src/helpme.py | Neotoxic-off/Epitech2024 | 8b3dd04fa9ac2b7019c0b5b1651975a7252d929b | [
"Apache-2.0"
] | 1 | 2022-01-23T21:26:06.000Z | 2022-01-23T21:26:06.000Z | #!/usr/bin/env python3
def helpme():
print("SYNOPSIS\n\t./groundhog period\n\nDESCRIPTION\n\tperiod\tthe number of days defining a period")
| 29.8 | 107 | 0.718121 | 23 | 149 | 4.652174 | 0.869565 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007813 | 0.14094 | 149 | 4 | 108 | 37.25 | 0.828125 | 0.14094 | 0 | 0 | 0 | 0.5 | 0.756098 | 0.479675 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0 | 0.5 | 0.5 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 8 |
3e8c222719a7d63dbc6a1ac4f07b20e459011f81 | 11,577 | py | Python | google/ads/google_ads/v5/proto/services/batch_job_service_pb2_grpc.py | arammaliachi/google-ads-python | a4fe89567bd43eb784410523a6306b5d1dd9ee67 | [
"Apache-2.0"
] | 1 | 2021-04-09T04:28:47.000Z | 2021-04-09T04:28:47.000Z | google/ads/google_ads/v5/proto/services/batch_job_service_pb2_grpc.py | arammaliachi/google-ads-python | a4fe89567bd43eb784410523a6306b5d1dd9ee67 | [
"Apache-2.0"
] | null | null | null | google/ads/google_ads/v5/proto/services/batch_job_service_pb2_grpc.py | arammaliachi/google-ads-python | a4fe89567bd43eb784410523a6306b5d1dd9ee67 | [
"Apache-2.0"
] | null | null | null | # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
"""Client and server classes corresponding to protobuf-defined services."""
import grpc
from google.ads.google_ads.v5.proto.resources import batch_job_pb2 as google_dot_ads_dot_googleads__v5_dot_proto_dot_resources_dot_batch__job__pb2
from google.ads.google_ads.v5.proto.services import batch_job_service_pb2 as google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2
from google.longrunning import operations_pb2 as google_dot_longrunning_dot_operations__pb2
class BatchJobServiceStub(object):
"""Proto file describing the BatchJobService.
Service to manage batch jobs.
"""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.MutateBatchJob = channel.unary_unary(
'/google.ads.googleads.v5.services.BatchJobService/MutateBatchJob',
request_serializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.MutateBatchJobRequest.SerializeToString,
response_deserializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.MutateBatchJobResponse.FromString,
)
self.GetBatchJob = channel.unary_unary(
'/google.ads.googleads.v5.services.BatchJobService/GetBatchJob',
request_serializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.GetBatchJobRequest.SerializeToString,
response_deserializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_resources_dot_batch__job__pb2.BatchJob.FromString,
)
self.ListBatchJobResults = channel.unary_unary(
'/google.ads.googleads.v5.services.BatchJobService/ListBatchJobResults',
request_serializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.ListBatchJobResultsRequest.SerializeToString,
response_deserializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.ListBatchJobResultsResponse.FromString,
)
self.RunBatchJob = channel.unary_unary(
'/google.ads.googleads.v5.services.BatchJobService/RunBatchJob',
request_serializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.RunBatchJobRequest.SerializeToString,
response_deserializer=google_dot_longrunning_dot_operations__pb2.Operation.FromString,
)
self.AddBatchJobOperations = channel.unary_unary(
'/google.ads.googleads.v5.services.BatchJobService/AddBatchJobOperations',
request_serializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.AddBatchJobOperationsRequest.SerializeToString,
response_deserializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.AddBatchJobOperationsResponse.FromString,
)
class BatchJobServiceServicer(object):
"""Proto file describing the BatchJobService.
Service to manage batch jobs.
"""
def MutateBatchJob(self, request, context):
"""Mutates a batch job.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def GetBatchJob(self, request, context):
"""Returns the batch job.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def ListBatchJobResults(self, request, context):
"""Returns the results of the batch job. The job must be done.
Supports standard list paging.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def RunBatchJob(self, request, context):
"""Runs the batch job.
The Operation.metadata field type is BatchJobMetadata. When finished, the
long running operation will not contain errors or a response. Instead, use
ListBatchJobResults to get the results of the job.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def AddBatchJobOperations(self, request, context):
"""Add operations to the batch job.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_BatchJobServiceServicer_to_server(servicer, server):
rpc_method_handlers = {
'MutateBatchJob': grpc.unary_unary_rpc_method_handler(
servicer.MutateBatchJob,
request_deserializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.MutateBatchJobRequest.FromString,
response_serializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.MutateBatchJobResponse.SerializeToString,
),
'GetBatchJob': grpc.unary_unary_rpc_method_handler(
servicer.GetBatchJob,
request_deserializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.GetBatchJobRequest.FromString,
response_serializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_resources_dot_batch__job__pb2.BatchJob.SerializeToString,
),
'ListBatchJobResults': grpc.unary_unary_rpc_method_handler(
servicer.ListBatchJobResults,
request_deserializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.ListBatchJobResultsRequest.FromString,
response_serializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.ListBatchJobResultsResponse.SerializeToString,
),
'RunBatchJob': grpc.unary_unary_rpc_method_handler(
servicer.RunBatchJob,
request_deserializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.RunBatchJobRequest.FromString,
response_serializer=google_dot_longrunning_dot_operations__pb2.Operation.SerializeToString,
),
'AddBatchJobOperations': grpc.unary_unary_rpc_method_handler(
servicer.AddBatchJobOperations,
request_deserializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.AddBatchJobOperationsRequest.FromString,
response_serializer=google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.AddBatchJobOperationsResponse.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'google.ads.googleads.v5.services.BatchJobService', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
# This class is part of an EXPERIMENTAL API.
class BatchJobService(object):
"""Proto file describing the BatchJobService.
Service to manage batch jobs.
"""
@staticmethod
def MutateBatchJob(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/google.ads.googleads.v5.services.BatchJobService/MutateBatchJob',
google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.MutateBatchJobRequest.SerializeToString,
google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.MutateBatchJobResponse.FromString,
options, channel_credentials,
call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def GetBatchJob(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/google.ads.googleads.v5.services.BatchJobService/GetBatchJob',
google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.GetBatchJobRequest.SerializeToString,
google_dot_ads_dot_googleads__v5_dot_proto_dot_resources_dot_batch__job__pb2.BatchJob.FromString,
options, channel_credentials,
call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def ListBatchJobResults(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/google.ads.googleads.v5.services.BatchJobService/ListBatchJobResults',
google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.ListBatchJobResultsRequest.SerializeToString,
google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.ListBatchJobResultsResponse.FromString,
options, channel_credentials,
call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def RunBatchJob(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/google.ads.googleads.v5.services.BatchJobService/RunBatchJob',
google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.RunBatchJobRequest.SerializeToString,
google_dot_longrunning_dot_operations__pb2.Operation.FromString,
options, channel_credentials,
call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def AddBatchJobOperations(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/google.ads.googleads.v5.services.BatchJobService/AddBatchJobOperations',
google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.AddBatchJobOperationsRequest.SerializeToString,
google_dot_ads_dot_googleads__v5_dot_proto_dot_services_dot_batch__job__service__pb2.AddBatchJobOperationsResponse.FromString,
options, channel_credentials,
call_credentials, compression, wait_for_ready, timeout, metadata)
| 53.846512 | 173 | 0.734387 | 1,215 | 11,577 | 6.450206 | 0.111111 | 0.056144 | 0.044405 | 0.055506 | 0.826592 | 0.813704 | 0.803624 | 0.746842 | 0.734082 | 0.695802 | 0 | 0.008486 | 0.206012 | 11,577 | 214 | 174 | 54.098131 | 0.844104 | 0.074976 | 0 | 0.471698 | 1 | 0 | 0.09531 | 0.068309 | 0 | 0 | 0 | 0 | 0 | 1 | 0.075472 | false | 0 | 0.025157 | 0.031447 | 0.150943 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
e464dd00dd16c79d80147b66fa314f6e3a096c24 | 15,070 | py | Python | consultantform/migrations/0002_auto_20170915_1756.py | rajeshgupta14/pathscriptfinal | 1a0b933d00b902588dfe30b9bea62c3e0c7ec4a2 | [
"Apache-2.0"
] | null | null | null | consultantform/migrations/0002_auto_20170915_1756.py | rajeshgupta14/pathscriptfinal | 1a0b933d00b902588dfe30b9bea62c3e0c7ec4a2 | [
"Apache-2.0"
] | null | null | null | consultantform/migrations/0002_auto_20170915_1756.py | rajeshgupta14/pathscriptfinal | 1a0b933d00b902588dfe30b9bea62c3e0c7ec4a2 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
# Generated by Django 1.11.5 on 2017-09-15 12:26
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('consultantform', '0001_initial'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('myapp', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='subsidiary',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='strategyp',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='strategyp',
name='project',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Project'),
),
migrations.AddField(
model_name='strategy',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='strategy',
name='project',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Project'),
),
migrations.AddField(
model_name='scriptp',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='scriptp',
name='project',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Project'),
),
migrations.AddField(
model_name='script',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='script',
name='project',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Project'),
),
migrations.AddField(
model_name='relatedcompany',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='problemsolvingp',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='problemsolvingp',
name='project',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Project'),
),
migrations.AddField(
model_name='problemsolving',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='problemsolving',
name='project',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Project'),
),
migrations.AddField(
model_name='miomp',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='miomp',
name='project',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Project'),
),
migrations.AddField(
model_name='miom',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='miom',
name='project',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Project'),
),
migrations.AddField(
model_name='duediligencep',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='duediligencep',
name='project',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Project'),
),
migrations.AddField(
model_name='duediligence',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='duediligence',
name='project',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Project'),
),
migrations.AddField(
model_name='digitalizationp',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='digitalizationp',
name='project',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Project'),
),
migrations.AddField(
model_name='digitalization',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='digitalization',
name='project',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Project'),
),
migrations.AddField(
model_name='branch',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c1',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='ca', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c10',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='cj', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c11',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='ck1', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c12',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='cl2', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c13',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='cm3', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c2',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='cb', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c3',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='cc', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c4',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='cd', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c5',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='ce', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c6',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='cf', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c7',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='cg', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c8',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='ch', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='c9',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='ci', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheckb',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c1',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c1', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c10',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c10', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c11',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c11', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c12',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c12', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c13',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c13', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c2',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c2', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c3',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c3', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c4',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c4', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c5',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c5', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c6',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c6', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c7',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c7', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c8',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c8', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='c9',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='c9', to=settings.AUTH_USER_MODEL, verbose_name='Currently With'),
),
migrations.AddField(
model_name='backgroundcheck',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='article',
name='company_name',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='myapp.Client'),
),
migrations.AddField(
model_name='article',
name='services_opted',
field=models.ManyToManyField(blank=True, null=True, to='myapp.Product'),
),
]
| 49.087948 | 184 | 0.633245 | 1,647 | 15,070 | 5.644809 | 0.063145 | 0.049909 | 0.085834 | 0.134882 | 0.945251 | 0.945251 | 0.920835 | 0.920835 | 0.920835 | 0.920835 | 0 | 0.006876 | 0.237624 | 15,070 | 306 | 185 | 49.248366 | 0.802333 | 0.004512 | 0 | 0.852349 | 1 | 0 | 0.129809 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.013423 | 0 | 0.026846 | 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 |
e495dc7214b90412e5635dfb758280832e57af50 | 203 | py | Python | web/views.py | dariomtz/pmAPI | aa80f0019d1e6c9771b5d865381def371cc96d09 | [
"MIT"
] | null | null | null | web/views.py | dariomtz/pmAPI | aa80f0019d1e6c9771b5d865381def371cc96d09 | [
"MIT"
] | null | null | null | web/views.py | dariomtz/pmAPI | aa80f0019d1e6c9771b5d865381def371cc96d09 | [
"MIT"
] | null | null | null | from django.shortcuts import render
from django.http import HttpResponse
def home(request):
return render(request, 'web/home.html')
def login(request):
return render(request, 'web/login.html')
| 22.555556 | 44 | 0.753695 | 28 | 203 | 5.464286 | 0.5 | 0.130719 | 0.248366 | 0.339869 | 0.379085 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 203 | 8 | 45 | 25.375 | 0.874286 | 0 | 0 | 0 | 0 | 0 | 0.133005 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 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 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 8 |
e4b6c320442d9cd556927e297ce96f41b979fdab | 13,272 | py | Python | sdk/python/pulumi_vault/aws/auth_backend_identity_whitelist.py | pulumi/pulumi-vault | 1682875f4a5d7d508f36e166529ad2b8aec34090 | [
"ECL-2.0",
"Apache-2.0"
] | 10 | 2019-10-07T17:44:18.000Z | 2022-03-30T20:46:33.000Z | sdk/python/pulumi_vault/aws/auth_backend_identity_whitelist.py | pulumi/pulumi-vault | 1682875f4a5d7d508f36e166529ad2b8aec34090 | [
"ECL-2.0",
"Apache-2.0"
] | 79 | 2019-10-11T18:13:07.000Z | 2022-03-31T21:09:41.000Z | sdk/python/pulumi_vault/aws/auth_backend_identity_whitelist.py | pulumi/pulumi-vault | 1682875f4a5d7d508f36e166529ad2b8aec34090 | [
"ECL-2.0",
"Apache-2.0"
] | 2 | 2019-10-28T10:08:40.000Z | 2020-03-17T14:20:55.000Z | # 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__ = ['AuthBackendIdentityWhitelistArgs', 'AuthBackendIdentityWhitelist']
@pulumi.input_type
class AuthBackendIdentityWhitelistArgs:
def __init__(__self__, *,
backend: Optional[pulumi.Input[str]] = None,
disable_periodic_tidy: Optional[pulumi.Input[bool]] = None,
safety_buffer: Optional[pulumi.Input[int]] = None):
"""
The set of arguments for constructing a AuthBackendIdentityWhitelist resource.
:param pulumi.Input[str] backend: The path of the AWS backend being configured.
:param pulumi.Input[bool] disable_periodic_tidy: If set to true, disables the periodic
tidying of the identity-whitelist entries.
:param pulumi.Input[int] safety_buffer: The amount of extra time, in minutes, that must
have passed beyond the roletag expiration, before it is removed from the
backend storage.
"""
if backend is not None:
pulumi.set(__self__, "backend", backend)
if disable_periodic_tidy is not None:
pulumi.set(__self__, "disable_periodic_tidy", disable_periodic_tidy)
if safety_buffer is not None:
pulumi.set(__self__, "safety_buffer", safety_buffer)
@property
@pulumi.getter
def backend(self) -> Optional[pulumi.Input[str]]:
"""
The path of the AWS backend being configured.
"""
return pulumi.get(self, "backend")
@backend.setter
def backend(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "backend", value)
@property
@pulumi.getter(name="disablePeriodicTidy")
def disable_periodic_tidy(self) -> Optional[pulumi.Input[bool]]:
"""
If set to true, disables the periodic
tidying of the identity-whitelist entries.
"""
return pulumi.get(self, "disable_periodic_tidy")
@disable_periodic_tidy.setter
def disable_periodic_tidy(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "disable_periodic_tidy", value)
@property
@pulumi.getter(name="safetyBuffer")
def safety_buffer(self) -> Optional[pulumi.Input[int]]:
"""
The amount of extra time, in minutes, that must
have passed beyond the roletag expiration, before it is removed from the
backend storage.
"""
return pulumi.get(self, "safety_buffer")
@safety_buffer.setter
def safety_buffer(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "safety_buffer", value)
@pulumi.input_type
class _AuthBackendIdentityWhitelistState:
def __init__(__self__, *,
backend: Optional[pulumi.Input[str]] = None,
disable_periodic_tidy: Optional[pulumi.Input[bool]] = None,
safety_buffer: Optional[pulumi.Input[int]] = None):
"""
Input properties used for looking up and filtering AuthBackendIdentityWhitelist resources.
:param pulumi.Input[str] backend: The path of the AWS backend being configured.
:param pulumi.Input[bool] disable_periodic_tidy: If set to true, disables the periodic
tidying of the identity-whitelist entries.
:param pulumi.Input[int] safety_buffer: The amount of extra time, in minutes, that must
have passed beyond the roletag expiration, before it is removed from the
backend storage.
"""
if backend is not None:
pulumi.set(__self__, "backend", backend)
if disable_periodic_tidy is not None:
pulumi.set(__self__, "disable_periodic_tidy", disable_periodic_tidy)
if safety_buffer is not None:
pulumi.set(__self__, "safety_buffer", safety_buffer)
@property
@pulumi.getter
def backend(self) -> Optional[pulumi.Input[str]]:
"""
The path of the AWS backend being configured.
"""
return pulumi.get(self, "backend")
@backend.setter
def backend(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "backend", value)
@property
@pulumi.getter(name="disablePeriodicTidy")
def disable_periodic_tidy(self) -> Optional[pulumi.Input[bool]]:
"""
If set to true, disables the periodic
tidying of the identity-whitelist entries.
"""
return pulumi.get(self, "disable_periodic_tidy")
@disable_periodic_tidy.setter
def disable_periodic_tidy(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "disable_periodic_tidy", value)
@property
@pulumi.getter(name="safetyBuffer")
def safety_buffer(self) -> Optional[pulumi.Input[int]]:
"""
The amount of extra time, in minutes, that must
have passed beyond the roletag expiration, before it is removed from the
backend storage.
"""
return pulumi.get(self, "safety_buffer")
@safety_buffer.setter
def safety_buffer(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "safety_buffer", value)
class AuthBackendIdentityWhitelist(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
backend: Optional[pulumi.Input[str]] = None,
disable_periodic_tidy: Optional[pulumi.Input[bool]] = None,
safety_buffer: Optional[pulumi.Input[int]] = None,
__props__=None):
"""
Configures the periodic tidying operation of the whitelisted identity entries.
For more information, see the
[Vault docs](https://www.vaultproject.io/api-docs/auth/aws#configure-identity-whitelist-tidy-operation).
## Example Usage
```python
import pulumi
import pulumi_vault as vault
example_auth_backend = vault.AuthBackend("exampleAuthBackend", type="aws")
example_auth_backend_identity_whitelist = vault.aws.AuthBackendIdentityWhitelist("exampleAuthBackendIdentityWhitelist",
backend=example_auth_backend.path,
safety_buffer=3600)
```
## Import
AWS auth backend identity whitelists can be imported using `auth/`, the `backend` path, and `/config/tidy/identity-whitelist` e.g.
```sh
$ pulumi import vault:aws/authBackendIdentityWhitelist:AuthBackendIdentityWhitelist example auth/aws/config/tidy/identity-whitelist
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] backend: The path of the AWS backend being configured.
:param pulumi.Input[bool] disable_periodic_tidy: If set to true, disables the periodic
tidying of the identity-whitelist entries.
:param pulumi.Input[int] safety_buffer: The amount of extra time, in minutes, that must
have passed beyond the roletag expiration, before it is removed from the
backend storage.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: Optional[AuthBackendIdentityWhitelistArgs] = None,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Configures the periodic tidying operation of the whitelisted identity entries.
For more information, see the
[Vault docs](https://www.vaultproject.io/api-docs/auth/aws#configure-identity-whitelist-tidy-operation).
## Example Usage
```python
import pulumi
import pulumi_vault as vault
example_auth_backend = vault.AuthBackend("exampleAuthBackend", type="aws")
example_auth_backend_identity_whitelist = vault.aws.AuthBackendIdentityWhitelist("exampleAuthBackendIdentityWhitelist",
backend=example_auth_backend.path,
safety_buffer=3600)
```
## Import
AWS auth backend identity whitelists can be imported using `auth/`, the `backend` path, and `/config/tidy/identity-whitelist` e.g.
```sh
$ pulumi import vault:aws/authBackendIdentityWhitelist:AuthBackendIdentityWhitelist example auth/aws/config/tidy/identity-whitelist
```
:param str resource_name: The name of the resource.
:param AuthBackendIdentityWhitelistArgs 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(AuthBackendIdentityWhitelistArgs, 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,
backend: Optional[pulumi.Input[str]] = None,
disable_periodic_tidy: Optional[pulumi.Input[bool]] = None,
safety_buffer: Optional[pulumi.Input[int]] = 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__ = AuthBackendIdentityWhitelistArgs.__new__(AuthBackendIdentityWhitelistArgs)
__props__.__dict__["backend"] = backend
__props__.__dict__["disable_periodic_tidy"] = disable_periodic_tidy
__props__.__dict__["safety_buffer"] = safety_buffer
super(AuthBackendIdentityWhitelist, __self__).__init__(
'vault:aws/authBackendIdentityWhitelist:AuthBackendIdentityWhitelist',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
backend: Optional[pulumi.Input[str]] = None,
disable_periodic_tidy: Optional[pulumi.Input[bool]] = None,
safety_buffer: Optional[pulumi.Input[int]] = None) -> 'AuthBackendIdentityWhitelist':
"""
Get an existing AuthBackendIdentityWhitelist 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] backend: The path of the AWS backend being configured.
:param pulumi.Input[bool] disable_periodic_tidy: If set to true, disables the periodic
tidying of the identity-whitelist entries.
:param pulumi.Input[int] safety_buffer: The amount of extra time, in minutes, that must
have passed beyond the roletag expiration, before it is removed from the
backend storage.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _AuthBackendIdentityWhitelistState.__new__(_AuthBackendIdentityWhitelistState)
__props__.__dict__["backend"] = backend
__props__.__dict__["disable_periodic_tidy"] = disable_periodic_tidy
__props__.__dict__["safety_buffer"] = safety_buffer
return AuthBackendIdentityWhitelist(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def backend(self) -> pulumi.Output[Optional[str]]:
"""
The path of the AWS backend being configured.
"""
return pulumi.get(self, "backend")
@property
@pulumi.getter(name="disablePeriodicTidy")
def disable_periodic_tidy(self) -> pulumi.Output[Optional[bool]]:
"""
If set to true, disables the periodic
tidying of the identity-whitelist entries.
"""
return pulumi.get(self, "disable_periodic_tidy")
@property
@pulumi.getter(name="safetyBuffer")
def safety_buffer(self) -> pulumi.Output[Optional[int]]:
"""
The amount of extra time, in minutes, that must
have passed beyond the roletag expiration, before it is removed from the
backend storage.
"""
return pulumi.get(self, "safety_buffer")
| 42.951456 | 140 | 0.663201 | 1,468 | 13,272 | 5.771117 | 0.124659 | 0.055831 | 0.069523 | 0.023371 | 0.770066 | 0.753659 | 0.749646 | 0.740321 | 0.734301 | 0.727809 | 0 | 0.000904 | 0.249548 | 13,272 | 308 | 141 | 43.090909 | 0.849699 | 0.387734 | 0 | 0.701389 | 1 | 0 | 0.107381 | 0.047725 | 0 | 0 | 0 | 0 | 0 | 1 | 0.152778 | false | 0.006944 | 0.034722 | 0 | 0.277778 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
90174ab540782dcabed8c634fab1901debace077 | 4,529 | py | Python | scripts/setup_devices.py | adityadangeska/lmc-base-classes | a3dada19b27fcc889546d754ef94986c55da5acc | [
"BSD-3-Clause"
] | 1 | 2019-05-31T09:47:31.000Z | 2019-05-31T09:47:31.000Z | scripts/setup_devices.py | adityadangeska/lmc-base-classes | a3dada19b27fcc889546d754ef94986c55da5acc | [
"BSD-3-Clause"
] | null | null | null | scripts/setup_devices.py | adityadangeska/lmc-base-classes | a3dada19b27fcc889546d754ef94986c55da5acc | [
"BSD-3-Clause"
] | null | null | null | from PyTango import Database, DbDevInfo
import os, signal
# A reference on the Database
db = Database()
# Kill running servers
# Get device info of Tile
try:
dev_info = db.get_device_info('test/tile/1')
if dev_info.pid != 0:
os.kill(dev_info.pid, signal.SIGTERM) #o r signal.SIGKILL
print "Killed PID: %s" % dev_info.pid
except Exception as ex:
print "No process to kill for test/tile/1"
#get device info of ObsConf
try:
dev_info = db.get_device_info('test/obsconf/1')
if dev_info.pid != 0:
os.kill(dev_info.pid, signal.SIGTERM) #o r signal.SIGKILL
print "Killed PID: %s" % dev_info.pid
except Exception as ex:
print "No process to kill for test/obsconf/1"
#get device info of TPM
try:
dev_info = db.get_device_info('test/tpm_board/1')
if dev_info.pid != 0:
os.kill(dev_info.pid, signal.SIGTERM)
print "Killed PID: %s" % dev_info.pid
except Exception as ex:
print "No process to kill for test/tpm_board/1"
#get device info of Tile
def tile_device_info(device_id):
device_name = 'test/tile/%s' % device_id
try:
dev_info = db.get_device_info(device_name)
except Exception as ex:
dev_info = None
if not dev_info is None:
print "Device <<%s>> found:" % device_name
print "Name: %s" % (dev_info.name)
print "Class Name: %s" % (dev_info.class_name)
print "Full Name: %s" % (dev_info.ds_full_name)
print "Exported: %s" % (dev_info.exported)
print "IOR: %s" % (dev_info.ior)
print "Version: %s" % (dev_info.version)
print "PID: %s" % (dev_info.pid)
print "Started Date: %s" % (dev_info.started_date)
print "Stopped Date: %s" % (dev_info.stopped_date)
else:
# Define Tile device name
new_device_name = device_name
# Define the Tango Class served by this DServer
dev_info = DbDevInfo()
dev_info._class = "Tile_DS"
dev_info.server = "Tile_DS/test"
# add the device
dev_info.name = new_device_name
print("Creating device: %s" % new_device_name)
db.add_device(dev_info)
#get device info of TPM
def tpm_device_info(device_id):
device_name = 'test/tpm_board/%s' % device_id
try:
dev_info = db.get_device_info(device_name)
except Exception as ex:
dev_info = None
if not dev_info is None:
print "Device <<%s>> found:" % device_name
print "Name: %s" % (dev_info.name)
print "Class Name: %s" % (dev_info.class_name)
print "Full Name: %s" % (dev_info.ds_full_name)
print "Exported: %s" % (dev_info.exported)
print "IOR: %s" % (dev_info.ior)
print "Version: %s" % (dev_info.version)
print "PID: %s" % (dev_info.pid)
print "Started Date: %s" % (dev_info.started_date)
print "Stopped Date: %s" % (dev_info.stopped_date)
else:
# Define device name
new_device_name = device_name
# Define the Tango Class served by this DServer
dev_info = DbDevInfo()
dev_info._class = "TPM_DS"
dev_info.server = "TPM_DS/test"
# add the device
dev_info.name = new_device_name
print("Creating device: %s" % new_device_name)
db.add_device(dev_info)
#get device info of ObsConf
def obsconf_device_info(device_id):
device_name = 'test/obsconf/%s' % device_id
try:
dev_info = db.get_device_info(device_name)
except Exception as ex:
dev_info = None
if not dev_info is None:
print "Device <<%s>> found:" % device_name
print "Name: %s" % (dev_info.name)
print "Class Name: %s" % (dev_info.class_name)
print "Full Name: %s" % (dev_info.ds_full_name)
print "Exported: %s" % (dev_info.exported)
print "IOR: %s" % (dev_info.ior)
print "Version: %s" % (dev_info.version)
print "PID: %s" % (dev_info.pid)
print "Started Date: %s" % (dev_info.started_date)
print "Stopped Date: %s" % (dev_info.stopped_date)
else:
# Define device name
new_device_name = device_name
# Define the Tango Class served by this DServer
dev_info = DbDevInfo()
dev_info._class = "ObservationConfiguration"
dev_info.server = "ObservationConfiguration/test"
# add the device
dev_info.name = new_device_name
print("Creating device: %s" % new_device_name)
db.add_device(dev_info)
obsconf_device_info('main')
tpm_device_info(1)
tile_device_info(1) | 34.838462 | 65 | 0.633032 | 664 | 4,529 | 4.090361 | 0.111446 | 0.162371 | 0.088365 | 0.039764 | 0.875552 | 0.842047 | 0.842047 | 0.806701 | 0.774669 | 0.774669 | 0 | 0.003268 | 0.25679 | 4,529 | 130 | 66 | 34.838462 | 0.803624 | 0.104217 | 0 | 0.764706 | 0 | 0 | 0.187964 | 0.013125 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.019608 | null | null | 0.382353 | 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 |
5f791a97e8d6339016206aff343c023336ebea79 | 5,176 | py | Python | pyaz/batch/node/file/__init__.py | py-az-cli/py-az-cli | 9a7dc44e360c096a5a2f15595353e9dad88a9792 | [
"MIT"
] | null | null | null | pyaz/batch/node/file/__init__.py | py-az-cli/py-az-cli | 9a7dc44e360c096a5a2f15595353e9dad88a9792 | [
"MIT"
] | null | null | null | pyaz/batch/node/file/__init__.py | py-az-cli/py-az-cli | 9a7dc44e360c096a5a2f15595353e9dad88a9792 | [
"MIT"
] | 1 | 2022-02-03T09:12:01.000Z | 2022-02-03T09:12:01.000Z | '''
Manage Batch compute node files.
'''
from .... pyaz_utils import _call_az
def delete(file_path, node_id, pool_id, account_endpoint=None, account_key=None, account_name=None, recursive=None, yes=None):
'''
Required Parameters:
- file_path -- The path to the file or directory that you want to delete.
- node_id -- The ID of the Compute Node from which you want to delete the file.
- pool_id -- The ID of the Pool that contains the Compute Node.
Optional Parameters:
- account_endpoint -- Batch service endpoint. Alternatively, set by environment variable: AZURE_BATCH_ENDPOINT
- account_key -- Batch account key. Alternatively, set by environment variable: AZURE_BATCH_ACCESS_KEY
- account_name -- Batch account name. Alternatively, set by environment variable: AZURE_BATCH_ACCOUNT
- recursive -- Whether to delete children of a directory. If the filePath parameter represents a directory instead of a file, you can set recursive to true to delete the directory and all of the files and subdirectories in it. If recursive is false then the directory must be empty or deletion will fail.
- yes -- Do not prompt for confirmation.
'''
return _call_az("az batch node file delete", locals())
def download(destination, file_path, node_id, pool_id, account_endpoint=None, account_key=None, account_name=None, end_range=None, if_modified_since=None, if_unmodified_since=None, start_range=None):
'''
Download the content of the a node file.
Required Parameters:
- destination -- The path to the destination file or directory.
- file_path -- The path to the Compute Node file that you want to get the content of.
- node_id -- The ID of the Compute Node that contains the file.
- pool_id -- The ID of the Pool that contains the Compute Node.
Optional Parameters:
- account_endpoint -- Batch service endpoint. Alternatively, set by environment variable: AZURE_BATCH_ENDPOINT
- account_key -- Batch account key. Alternatively, set by environment variable: AZURE_BATCH_ACCESS_KEY
- account_name -- Batch account name. Alternatively, set by environment variable: AZURE_BATCH_ACCOUNT
- end_range -- The byte range to be retrieved. If not set the file will be retrieved to the end.
- if_modified_since -- A timestamp indicating the last modified time of the resource known to the client. The operation will be performed only if the resource on the service has been modified since the specified time.
- if_unmodified_since -- A timestamp indicating the last modified time of the resource known to the client. The operation will be performed only if the resource on the service has not been modified since the specified time.
- start_range -- The byte range to be retrieved. If not set the file will be retrieved from the beginning.
'''
return _call_az("az batch node file download", locals())
def show(file_path, node_id, pool_id, account_endpoint=None, account_key=None, account_name=None, if_modified_since=None, if_unmodified_since=None):
'''
Required Parameters:
- file_path -- The path to the Compute Node file that you want to get the properties of.
- node_id -- The ID of the Compute Node that contains the file.
- pool_id -- The ID of the Pool that contains the Compute Node.
Optional Parameters:
- account_endpoint -- Batch service endpoint. Alternatively, set by environment variable: AZURE_BATCH_ENDPOINT
- account_key -- Batch account key. Alternatively, set by environment variable: AZURE_BATCH_ACCESS_KEY
- account_name -- Batch account name. Alternatively, set by environment variable: AZURE_BATCH_ACCOUNT
- if_modified_since -- A timestamp indicating the last modified time of the resource known to the client. The operation will be performed only if the resource on the service has been modified since the specified time.
- if_unmodified_since -- A timestamp indicating the last modified time of the resource known to the client. The operation will be performed only if the resource on the service has not been modified since the specified time.
'''
return _call_az("az batch node file show", locals())
def list(node_id, pool_id, account_endpoint=None, account_key=None, account_name=None, filter=None, recursive=None):
'''
Required Parameters:
- node_id -- The ID of the Compute Node whose files you want to list.
- pool_id -- The ID of the Pool that contains the Compute Node.
Optional Parameters:
- account_endpoint -- Batch service endpoint. Alternatively, set by environment variable: AZURE_BATCH_ENDPOINT
- account_key -- Batch account key. Alternatively, set by environment variable: AZURE_BATCH_ACCESS_KEY
- account_name -- Batch account name. Alternatively, set by environment variable: AZURE_BATCH_ACCOUNT
- filter -- An OData $filter clause. For more information on constructing this filter, see https://docs.microsoft.com/en-us/rest/api/batchservice/odata-filters-in-batch#list-compute-node-files.
- recursive -- Whether to list children of a directory.
'''
return _call_az("az batch node file list", locals())
| 62.361446 | 308 | 0.753284 | 767 | 5,176 | 4.946545 | 0.164276 | 0.01845 | 0.056932 | 0.091724 | 0.757775 | 0.757775 | 0.757775 | 0.729309 | 0.715076 | 0.677122 | 0 | 0 | 0.183733 | 5,176 | 82 | 309 | 63.121951 | 0.897988 | 0.780719 | 0 | 0 | 0 | 0 | 0.109865 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.444444 | false | 0 | 0.111111 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 |
5fa38f2396831f9ddbff7ec47fa6e293434f5f33 | 429 | py | Python | python/testData/highlighting/unpackingStar.py | jnthn/intellij-community | 8fa7c8a3ace62400c838e0d5926a7be106aa8557 | [
"Apache-2.0"
] | 1 | 2018-10-13T19:43:36.000Z | 2018-10-13T19:43:36.000Z | python/testData/highlighting/unpackingStar.py | jnthn/intellij-community | 8fa7c8a3ace62400c838e0d5926a7be106aa8557 | [
"Apache-2.0"
] | 173 | 2018-07-05T13:59:39.000Z | 2018-08-09T01:12:03.000Z | python/testData/highlighting/unpackingStar.py | jnthn/intellij-community | 8fa7c8a3ace62400c838e0d5926a7be106aa8557 | [
"Apache-2.0"
] | 1 | 2018-10-03T12:35:06.000Z | 2018-10-03T12:35:06.000Z | 1, *x
(1, *x)
[1, *x]
{1, *x}
if <error descr="Can't use starred expression here">*x</error>:
pass
1 + (<error descr="Can't use starred expression here">*x</error>)
1 + (*x,)
def f(x):
return x, <error descr="Can't use starred expression here">*x</error>
def g(x):
yield from x, <error descr="Can't use starred expression here">*x</error>
yield x, <error descr="Can't use starred expression here">*x</error> | 22.578947 | 77 | 0.629371 | 76 | 429 | 3.552632 | 0.236842 | 0.177778 | 0.240741 | 0.259259 | 0.855556 | 0.855556 | 0.825926 | 0.825926 | 0.825926 | 0.825926 | 0 | 0.017045 | 0.179487 | 429 | 19 | 78 | 22.578947 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0.383721 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0.076923 | 0 | null | null | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 10 |
39e395fa7240ddc4814479657e47318ed25c571d | 123 | py | Python | maid_manga_id/maid_client.py | rushkii/maid_manga_id | b80b6a79206dd1894a8df612ff4eff6e44bd4d50 | [
"MIT"
] | 5 | 2020-12-02T12:43:28.000Z | 2022-02-22T14:31:37.000Z | maid_manga_id/maid_client.py | rushkii/maid_manga_id | b80b6a79206dd1894a8df612ff4eff6e44bd4d50 | [
"MIT"
] | null | null | null | maid_manga_id/maid_client.py | rushkii/maid_manga_id | b80b6a79206dd1894a8df612ff4eff6e44bd4d50 | [
"MIT"
] | 2 | 2021-07-21T16:25:03.000Z | 2021-09-23T13:43:47.000Z | from maid_manga_id.scaffold import Maid
from maid_manga_id.methods import Methods
class MaidManga(Methods, Maid):
pass | 24.6 | 41 | 0.821138 | 19 | 123 | 5.105263 | 0.526316 | 0.164948 | 0.268041 | 0.309278 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.130081 | 123 | 5 | 42 | 24.6 | 0.906542 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.25 | 0.5 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 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 | 1 | 1 | 0 | 1 | 0 | 0 | 8 |
f2d2355f8b5bc4c16841393601da59b546e807a7 | 202 | py | Python | ulmo/cuahsi/his_central/__init__.py | timcera/ulmo | f927a33e1366851aa0656656d0cb0d2068d29c78 | [
"BSD-3-Clause"
] | 3 | 2017-09-17T21:27:48.000Z | 2022-03-15T12:58:53.000Z | ulmo/cuahsi/his_central/__init__.py | timcera/ulmo | f927a33e1366851aa0656656d0cb0d2068d29c78 | [
"BSD-3-Clause"
] | null | null | null | ulmo/cuahsi/his_central/__init__.py | timcera/ulmo | f927a33e1366851aa0656656d0cb0d2068d29c78 | [
"BSD-3-Clause"
] | 3 | 2021-02-23T06:26:00.000Z | 2021-02-23T06:26:18.000Z | """
ulmo.cuahsi.his_central
~~~~~~~~~~~~~~~~~~~~~~~
`CUAHSI HIS Central`_ web services
.. _CUAHSI HIS Central: http://his.cuahsi.org/hiscentral.html
"""
from .core import get_services
| 20.2 | 65 | 0.60396 | 23 | 202 | 5.130435 | 0.608696 | 0.228814 | 0.40678 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.178218 | 202 | 9 | 66 | 22.444444 | 0.710843 | 0.722772 | 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 |
f2d3fc44a0ab51a32b7239c346af9acd670852f1 | 8,830 | py | Python | tests/testLocalbook.py | JackBenny39/mmabm | e79d91232016167bff914495ee63e18063a1697b | [
"BSD-3-Clause"
] | 2 | 2019-04-23T17:16:54.000Z | 2019-05-30T17:15:48.000Z | tests/testLocalbook.py | JackBenny39/mmabm | e79d91232016167bff914495ee63e18063a1697b | [
"BSD-3-Clause"
] | null | null | null | tests/testLocalbook.py | JackBenny39/mmabm | e79d91232016167bff914495ee63e18063a1697b | [
"BSD-3-Clause"
] | null | null | null | from mmabm.localbook import Localbook
from mmabm.shared import Side, OType
import unittest
class TestLocalbook(unittest.TestCase):
def setUp(self):
'''
setUp creates the Localbook instance and a set of orders
'''
self.local = Localbook()
self.q1_buy = {'order_id': 1, 'trader_id': 1001,'timestamp': 2, 'type': OType.ADD,
'quantity': 1, 'side': Side.BID, 'price': 50}
self.q2_buy = {'order_id': 2, 'trader_id': 1001, 'timestamp': 3, 'type': OType.ADD,
'quantity': 1, 'side': Side.BID, 'price': 50}
self.q3_buy = {'order_id': 1, 'trader_id': 1010, 'timestamp': 4, 'type': OType.ADD,
'quantity': 3, 'side': Side.BID, 'price': 49}
self.q4_buy = {'order_id': 1, 'trader_id': 1011, 'timestamp': 5, 'type': OType.ADD,
'quantity': 3, 'side': Side.BID, 'price': 47}
self.q1_sell = {'order_id': 3, 'trader_id': 1001, 'timestamp': 2, 'type': OType.ADD,
'quantity': 1, 'side': Side.ASK, 'price': 52}
self.q2_sell = {'order_id': 4, 'trader_id': 1001, 'timestamp': 3, 'type': OType.ADD,
'quantity': 1, 'side': Side.ASK, 'price': 52}
self.q3_sell = {'order_id': 2, 'trader_id': 1010, 'timestamp': 4, 'type': OType.ADD,
'quantity': 3, 'side': Side.ASK, 'price': 53}
self.q4_sell = {'order_id': 2, 'trader_id': 1011, 'timestamp': 5, 'type': OType.ADD,
'quantity': 3, 'side': Side.ASK, 'price': 55}
def test_add_order(self):
# 2 buy orders
self.assertFalse(self.local.bid_book_prices)
self.assertFalse(self.local.bid_book)
self.local.add_order(self.q1_buy)
self.assertTrue(50 in self.local.bid_book_prices)
self.assertTrue(50 in self.local.bid_book.keys())
self.assertEqual(self.local.bid_book[50]['num_orders'], 1)
self.assertEqual(self.local.bid_book[50]['size'], 1)
self.assertEqual(self.local.bid_book[50]['order_ids'][0], 1)
del self.q1_buy['type']
del self.q1_buy['trader_id']
self.assertDictEqual(self.local.bid_book[50]['orders'][1], self.q1_buy)
self.local.add_order(self.q2_buy)
self.assertEqual(self.local.bid_book[50]['num_orders'], 2)
self.assertEqual(self.local.bid_book[50]['size'], 2)
self.assertEqual(self.local.bid_book[50]['order_ids'][1], 2)
del self.q2_buy['type']
del self.q2_buy['trader_id']
self.assertDictEqual(self.local.bid_book[50]['orders'][2], self.q2_buy)
# 2 sell orders
self.assertFalse(self.local.ask_book_prices)
self.assertFalse(self.local.ask_book)
self.local.add_order(self.q1_sell)
self.assertTrue(52 in self.local.ask_book_prices)
self.assertTrue(52 in self.local.ask_book.keys())
self.assertEqual(self.local.ask_book[52]['num_orders'], 1)
self.assertEqual(self.local.ask_book[52]['size'], 1)
self.assertEqual(self.local.ask_book[52]['order_ids'][0], 3)
del self.q1_sell['type']
del self.q1_sell['trader_id']
self.assertDictEqual(self.local.ask_book[52]['orders'][3], self.q1_sell)
self.local.add_order(self.q2_sell)
self.assertEqual(self.local.ask_book[52]['num_orders'], 2)
self.assertEqual(self.local.ask_book[52]['size'], 2)
self.assertEqual(self.local.ask_book[52]['order_ids'][1], 4)
del self.q2_sell['type']
del self.q2_sell['trader_id']
self.assertDictEqual(self.local.ask_book[52]['orders'][4], self.q2_sell)
def test_remove_order(self):
# buy orders
self.local.add_order(self.q1_buy)
self.local.add_order(self.q2_buy)
self.assertTrue(50 in self.local.bid_book_prices)
self.assertTrue(50 in self.local.bid_book.keys())
self.assertEqual(self.local.bid_book[50]['num_orders'], 2)
self.assertEqual(self.local.bid_book[50]['size'], 2)
self.assertEqual(len(self.local.bid_book[50]['order_ids']), 2)
# remove first order
self.local.remove_order(Side.BID, 50, 1)
self.assertEqual(self.local.bid_book[50]['num_orders'], 1)
self.assertEqual(self.local.bid_book[50]['size'], 1)
self.assertEqual(len(self.local.bid_book[50]['order_ids']), 1)
self.assertFalse(1 in self.local.bid_book[50]['orders'].keys())
self.assertTrue(50 in self.local.bid_book_prices)
# remove second order
self.local.remove_order(Side.BID, 50, 2)
self.assertFalse(self.local.bid_book_prices)
self.assertEqual(self.local.bid_book[50]['num_orders'], 0)
self.assertEqual(self.local.bid_book[50]['size'], 0)
self.assertEqual(len(self.local.bid_book[50]['order_ids']), 0)
self.assertFalse(2 in self.local.bid_book[50]['orders'].keys())
self.assertFalse(50 in self.local.bid_book_prices)
# remove second order again
self.local.remove_order(Side.BID, 50, 2)
self.assertFalse(self.local.bid_book_prices)
self.assertEqual(self.local.bid_book[50]['num_orders'], 0)
self.assertEqual(self.local.bid_book[50]['size'], 0)
self.assertEqual(len(self.local.bid_book[50]['order_ids']), 0)
self.assertFalse(2 in self.local.bid_book[50]['orders'].keys())
# sell orders
self.local.add_order(self.q1_sell)
self.local.add_order(self.q2_sell)
self.assertTrue(52 in self.local.ask_book_prices)
self.assertTrue(52 in self.local.ask_book.keys())
self.assertEqual(self.local.ask_book[52]['num_orders'], 2)
self.assertEqual(self.local.ask_book[52]['size'], 2)
self.assertEqual(len(self.local.ask_book[52]['order_ids']), 2)
# remove first order
self.local.remove_order(Side.ASK, 52, 3)
self.assertEqual(self.local.ask_book[52]['num_orders'], 1)
self.assertEqual(self.local.ask_book[52]['size'], 1)
self.assertEqual(len(self.local.ask_book[52]['order_ids']), 1)
self.assertFalse(3 in self.local.ask_book[52]['orders'].keys())
self.assertTrue(52 in self.local.ask_book_prices)
# remove second order
self.local.remove_order(Side.ASK, 52, 4)
self.assertFalse(self.local.ask_book_prices)
self.assertEqual(self.local.ask_book[52]['num_orders'], 0)
self.assertEqual(self.local.ask_book[52]['size'], 0)
self.assertEqual(len(self.local.ask_book[52]['order_ids']), 0)
self.assertFalse(4 in self.local.ask_book[52]['orders'].keys())
self.assertFalse(52 in self.local.ask_book_prices)
# remove second order again
self.local.remove_order(Side.ASK, 52, 4)
self.assertFalse(self.local.ask_book_prices)
self.assertEqual(self.local.ask_book[52]['num_orders'], 0)
self.assertEqual(self.local.ask_book[52]['size'], 0)
self.assertEqual(len(self.local.ask_book[52]['order_ids']), 0)
self.assertFalse(4 in self.local.ask_book[52]['orders'].keys())
def test_modify_order(self):
# Buy order
q1 = {'order_id': 1, 'trader_id': 1001, 'timestamp': 5, 'type': OType.ADD,
'quantity': 2, 'side': Side.BID, 'price': 50}
self.local.add_order(q1)
self.assertEqual(self.local.bid_book[50]['size'], 2)
# remove 1
self.local.modify_order(Side.BID, 1, 1, 50)
self.assertEqual(self.local.bid_book[50]['size'], 1)
self.assertEqual(self.local.bid_book[50]['orders'][1]['quantity'], 1)
self.assertTrue(self.local.bid_book_prices)
# remove remainder
self.local.modify_order(Side.BID, 1, 1, 50)
self.assertFalse(self.local.bid_book_prices)
self.assertEqual(self.local.bid_book[50]['num_orders'], 0)
self.assertEqual(self.local.bid_book[50]['size'], 0)
self.assertFalse(1 in self.local.bid_book[50]['orders'].keys())
# Sell order
q2 = {'order_id': 2, 'trader_id': 1001, 'timestamp': 5, 'type': OType.ADD,
'quantity': 2, 'side': Side.ASK, 'price': 50}
self.local.add_order(q2)
self.assertEqual(self.local.ask_book[50]['size'], 2)
# remove 1
self.local.modify_order(Side.ASK, 1, 2, 50)
self.assertEqual(self.local.ask_book[50]['size'], 1)
self.assertEqual(self.local.ask_book[50]['orders'][2]['quantity'], 1)
self.assertTrue(self.local.ask_book_prices)
# remove remainder
self.local.modify_order(Side.ASK, 1, 2, 50)
self.assertFalse(self.local.ask_book_prices)
self.assertEqual(self.local.ask_book[50]['num_orders'], 0)
self.assertEqual(self.local.ask_book[50]['size'], 0)
self.assertFalse(2 in self.local.ask_book[50]['orders'].keys())
| 53.515152 | 93 | 0.627407 | 1,272 | 8,830 | 4.194969 | 0.058176 | 0.173726 | 0.092204 | 0.122939 | 0.904235 | 0.899175 | 0.85476 | 0.837894 | 0.785982 | 0.727511 | 0 | 0.052246 | 0.210985 | 8,830 | 165 | 94 | 53.515152 | 0.71365 | 0.035108 | 0 | 0.546763 | 0 | 0 | 0.111059 | 0 | 0 | 0 | 0 | 0 | 0.589928 | 1 | 0.028777 | false | 0 | 0.021583 | 0 | 0.057554 | 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 |
f2fe6ee6434ea609e1c1b6133003532594204c11 | 2,409 | py | Python | tests/node/Additive.py | Gawaboumga/PyMatex | 3ccc0aa23211a064aa31a9b509b108cd606a4992 | [
"MIT"
] | 1 | 2019-03-05T09:45:04.000Z | 2019-03-05T09:45:04.000Z | tests/node/Additive.py | Gawaboumga/PyMatex | 3ccc0aa23211a064aa31a9b509b108cd606a4992 | [
"MIT"
] | null | null | null | tests/node/Additive.py | Gawaboumga/PyMatex | 3ccc0aa23211a064aa31a9b509b108cd606a4992 | [
"MIT"
] | null | null | null | from tests import BaseTest
from pymatex.node import Addition, Constant, Negate, Subtraction, Variable
class AdditiveTests(BaseTest.BaseTest):
def test_read_addition_of_constants(self):
ast = self.parse('3 + 2')
self.assertEqual(ast, Addition(Constant('3'), Constant('2')))
def test_write_addition_of_constants(self):
ast = self.parse('3 + 2')
self.assertEqual(str(ast), '(3 + 2)')
def test_read_addition_of_multiple_constants(self):
ast = self.parse('3 + 2 + 5')
self.assertEqual(ast, Addition(Addition(Constant('3'), Constant('2')), Constant('5')))
ast = self.parse('3+2+5')
self.assertEqual(ast, Addition(Addition(Constant('3'), Constant('2')), Constant('5')))
def test_read_addition_of_variables(self):
ast = self.parse('x + y')
self.assertEqual(ast, Addition(Variable('x'), Variable('y')))
ast = self.parse('x+y')
self.assertEqual(ast, Addition(Variable('x'), Variable('y')))
def test_write_addition_of_variables(self):
ast = self.parse('x+y')
self.assertEqual(str(ast), '(x + y)')
def test_read_addition_of_constant_and_variable(self):
ast = self.parse('n+1')
self.assertEqual(ast, Addition(Variable('n'), Constant('1')))
def test_read_subtraction_of_constants(self):
ast = self.parse('3 - 2')
self.assertEqual(ast, Subtraction(Constant('3'), Constant('2')))
def test_write_subtraction_of_constants(self):
ast = self.parse('3 - 2')
self.assertEqual(str(ast), '(3 - 2)')
def test_read_subtraction_of_multiple_constants(self):
ast = self.parse('3 - 2 - 5')
self.assertEqual(ast, Subtraction(Subtraction(Constant('3'), Constant('2')), Constant('5')))
def test_read_subtraction_of_variables(self):
ast = self.parse('x - y')
self.assertEqual(ast, Subtraction(Variable('x'), Variable('y')))
def test_write_subtraction_of_variables(self):
ast = self.parse('x-y')
self.assertEqual(str(ast), '(x - y)')
def test_read_subtraction_of_negate_variable(self):
ast = self.parse('x - -y')
self.assertEqual(ast, Subtraction(Variable('x'), Negate(Variable('y'))))
def test_read_substraction_of_constant_and_variable(self):
ast = self.parse('n-1')
self.assertEqual(ast, Subtraction(Variable('n'), Constant('1')))
| 37.061538 | 100 | 0.643836 | 315 | 2,409 | 4.736508 | 0.111111 | 0.070375 | 0.120643 | 0.13941 | 0.869973 | 0.779491 | 0.779491 | 0.717158 | 0.717158 | 0.684316 | 0 | 0.019588 | 0.194687 | 2,409 | 64 | 101 | 37.640625 | 0.749485 | 0 | 0 | 0.326087 | 0 | 0 | 0.052719 | 0 | 0 | 0 | 0 | 0 | 0.326087 | 1 | 0.282609 | false | 0 | 0.043478 | 0 | 0.347826 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
8423e4fc51bb5b70f7a71a065e0b06dd08a2dce7 | 10,296 | py | Python | pizza_cutter/des_pizza_cutter/tests/test_des_info.py | beckermr/pizza-cutter | 04eefd2d4b2a63975fe809c60b5c8e7e3fcf26c6 | [
"BSD-3-Clause"
] | null | null | null | pizza_cutter/des_pizza_cutter/tests/test_des_info.py | beckermr/pizza-cutter | 04eefd2d4b2a63975fe809c60b5c8e7e3fcf26c6 | [
"BSD-3-Clause"
] | 194 | 2018-10-24T23:40:47.000Z | 2021-11-17T16:02:35.000Z | pizza_cutter/des_pizza_cutter/tests/test_des_info.py | beckermr/pizza-cutter | 04eefd2d4b2a63975fe809c60b5c8e7e3fcf26c6 | [
"BSD-3-Clause"
] | null | null | null | import copy
import yaml
import pytest
from .._des_info import check_info, flag_data_in_info
INFO_YAML = """\
band: z
bmask_ext: msk
bmask_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/multiepoch/Y6A1/r4575/DES2005-5123/p01/coadd/DES2005-5123_r4575p01_z.fits.fz
cat_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/multiepoch/Y6A1/r4575/DES2005-5123/p01/cat/DES2005-5123_r4575p01_z_cat.fits
compression: .fz
filename: DES2005-5123_r4575p01_z.fits
image_ext: sci
image_flags: 0
image_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/multiepoch/Y6A1/r4575/DES2005-5123/p01/coadd/DES2005-5123_r4575p01_z.fits.fz
image_shape:
- 10000
- 10000
magzp: 30.0
path: OPS/multiepoch/Y6A1/r4575/DES2005-5123/p01/coadd
pfw_attempt_id: 2730721
position_offset: 1
psf_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/multiepoch/Y6A1/r4575/DES2005-5123/p01/psf/DES2005-5123_r4575p01_z_psfcat.psf
scale: 1.0
seg_ext: sci
seg_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/multiepoch/Y6A1/r4575/DES2005-5123/p01/seg/DES2005-5123_r4575p01_z_segmap.fits
gaia_stars_file: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/cal/cat_tile_gaia/v1/DES2005-5123_GAIA_DR2_v1.fits
src_info:
- band: z
bkg_ext: sci
bkg_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y6A1/r4433/20150911/D00473830/p01/red/bkg/D00473830_z_c05_r4433p01_bkg.fits.fz
bmask_ext: msk
bmask_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y6A1/r4433/20150911/D00473830/p01/red/immask/D00473830_z_c05_r4433p01_immasked.fits.fz
ccdnum: 5
compression: .fz
expnum: 473830
filename: D00473830_z_c05_r4433p01_immasked.fits
head_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/multiepoch/Y6A1/r4575/DES2005-5123/p01/aux/DES2005-5123_r4575p01_D00473830_z_c05_scamp.ohead
image_ext: sci
image_flags: 0
image_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y6A1/r4433/20150911/D00473830/p01/red/immask/D00473830_z_c05_r4433p01_immasked.fits.fz
image_shape:
- 4096
- 2048
magzp: 31.292797088623047
path: OPS/finalcut/Y6A1/r4433/20150911/D00473830/p01/red/immask
pfw_attempt_id: 2730721
piff_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y6A1_PIFF/20150911-r5018/D00473830/p01/psf/D00473830_z_c05_r5018p01_piff-model.fits
position_offset: 1
psf_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y6A1/r4433/20150911/D00473830/p01/psf/D00473830_z_c05_r4433p01_psfexcat.psf
psfex_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y6A1/r4433/20150911/D00473830/p01/psf/D00473830_z_c05_r4433p01_psfexcat.psf
scale: 0.30400530659860264
seg_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y6A1/r4433/20150911/D00473830/p01/seg/D00473830_z_c05_r4433p01_segmap.fits.fz
tilename: DES2005-5123
weight_ext: wgt
weight_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y6A1/r4433/20150911/D00473830/p01/red/immask/D00473830_z_c05_r4433p01_immasked.fits.fz
piff_info:
desdm_flags: 0
fwhm_cen: 2.0
star_t_std: 0.03
star_t_mean: 0.5
nstar: 55
exp_star_t_mean: 0.55
exp_star_t_std: 0.02
- band: z
bkg_ext: sci
bkg_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y5A1/r3515/20170906/D00675122/p01/red/bkg/D00675122_z_c56_r3515p01_bkg.fits.fz
bmask_ext: msk
bmask_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y5A1/r3515/20170906/D00675122/p01/red/immask/D00675122_z_c56_r3515p01_immasked.fits.fz
ccdnum: 56
compression: .fz
expnum: 675122
filename: D00675122_z_c56_r3515p01_immasked.fits
head_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/multiepoch/Y6A1/r4575/DES2005-5123/p01/aux/DES2005-5123_r4575p01_D00675122_z_c56_scamp.ohead
image_ext: sci
image_flags: 0
image_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y5A1/r3515/20170906/D00675122/p01/red/immask/D00675122_z_c56_r3515p01_immasked.fits.fz
image_shape:
- 4096
- 2048
magzp: 31.4688777923584
path: OPS/finalcut/Y5A1/r3515/20170906/D00675122/p01/red/immask
pfw_attempt_id: 2730721
piff_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y6A1_PIFF/20170906-r5022/D00675122/p01/psf/D00675122_z_c56_r5022p01_piff-model.fits
position_offset: 1
psf_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y5A1/r3515/20170906/D00675122/p01/psf/D00675122_z_c56_r3515p01_psfexcat.psf
psfex_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y5A1/r3515/20170906/D00675122/p01/psf/D00675122_z_c56_r3515p01_psfexcat.psf
scale: 0.2584930572454005
seg_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y5A1/r3515/20170906/D00675122/p01/seg/D00675122_z_c56_r3515p01_segmap.fits.fz
tilename: DES2005-5123
weight_ext: wgt
weight_path: /Users/beckermr/MEDS_DIR/des-pizza-slices-y6-v6/DES2005-5123/sources-z/OPS/finalcut/Y5A1/r3515/20170906/D00675122/p01/red/immask/D00675122_z_c56_r3515p01_immasked.fits.fz
piff_info:
desdm_flags: 10
fwhm_cen: 2.0
star_t_std: 0.03
star_t_mean: 0.5
nstar: 55
exp_star_t_mean: 0.55
exp_star_t_std: 0.02
""" # noqa
def test_flag_data_in_info():
info = copy.deepcopy(yaml.safe_load(INFO_YAML))
flag_data_in_info(
info=info,
config={
"single_epoch": {
"piff_cuts": dict(
max_fwhm_cen=3,
min_nstar=25,
max_exp_T_mean_fac=4,
max_ccd_T_std_fac=0.3,
),
},
},
)
assert info["src_info"][0]["image_flags"] == 0
assert info["src_info"][1]["image_flags"] == 2**0
def test_check_info_smoke():
info = copy.deepcopy(yaml.safe_load(INFO_YAML))
check_info(info=info)
def test_check_info_coadd_paths():
info = copy.deepcopy(yaml.safe_load(INFO_YAML))
coadd_keys = [
"image_path", "seg_path", "bmask_path",
"gaia_stars_file", "psf_path",
]
for key in coadd_keys:
_info = copy.deepcopy(info)
_info[key] = _info[key].replace("DES2005-5123", "DES2005-5823")
with pytest.raises(RuntimeError) as e:
check_info(info=_info)
assert _info[key] in str(e.value)
def test_check_info_coadd_scale():
info = copy.deepcopy(yaml.safe_load(INFO_YAML))
info["scale"] = 2.0
with pytest.raises(RuntimeError) as e:
check_info(info=info)
assert "coadd image scale" in str(e.value)
def test_check_info_band_entries():
info = copy.deepcopy(yaml.safe_load(INFO_YAML))
_info = copy.deepcopy(info)
_info["band"] = "a"
with pytest.raises(RuntimeError) as e:
check_info(info=_info)
assert "band entries do not all match" in str(e.value)
_info = copy.deepcopy(info)
_info["src_info"][0]["band"] = "a"
with pytest.raises(RuntimeError) as e:
check_info(info=_info)
assert "band entries do not all match" in str(e.value)
def test_check_info_coadd_band():
info = copy.deepcopy(yaml.safe_load(INFO_YAML))
band = info["band"]
ends = dict(
bmask_path=f"_{band}.fits.fz",
cat_path=f"_{band}_cat.fits",
image_path=f"_{band}.fits.fz",
psf_path=f"_{band}_psfcat.psf",
seg_path=f"_{band}_segmap.fits",
)
for key, end in ends.items():
_info = copy.deepcopy(info)
_info[key] = _info[key].replace(end, end.replace(f"_{band}", "_a"))
with pytest.raises(RuntimeError) as e:
check_info(info=_info)
assert f"doesn't end with {end}" in str(e.value)
def test_check_info_se_files():
info = copy.deepcopy(yaml.safe_load(INFO_YAML))
se_keys = [
"bkg_path",
"bmask_path",
"image_path",
"piff_path",
"psfex_path",
"psf_path",
"seg_path",
"weight_path",
]
band = info["band"]
for i in range(len(info["src_info"])):
ii = info["src_info"][i]
ccd_slug = "D%08d_%s_c%02d_" % (ii["expnum"], band, ii["ccdnum"])
for key in se_keys:
_info = copy.deepcopy(info)
_info["src_info"][i][key] = _info["src_info"][i][key].replace(
ccd_slug, ccd_slug.replace(f"_{band}", "_a")
)
with pytest.raises(RuntimeError) as e:
check_info(info=_info)
assert f"doesn't start with {ccd_slug}" in str(e.value)
def test_check_info_se_tilename():
info = copy.deepcopy(yaml.safe_load(INFO_YAML))
for i in range(len(info["src_info"])):
_info = copy.deepcopy(info)
_info["src_info"][i]["tilename"] = "blah"
with pytest.raises(RuntimeError) as e:
check_info(info=_info)
assert "has the wrong tilename" in str(e.value)
def test_check_info_se_scamp_header():
info = copy.deepcopy(yaml.safe_load(INFO_YAML))
key = "head_path"
for i in range(len(info["src_info"])):
_info = copy.deepcopy(info)
_info["src_info"][i][key] = _info["src_info"][i][key].replace(
_info["src_info"][i]["tilename"], "blah"
)
with pytest.raises(RuntimeError) as e:
check_info(info=_info)
assert f"doesn't start with {_info['src_info'][i]['tilename']}" in str(e.value)
_info = copy.deepcopy(info)
scamp_slug = "_%s_c%02d_scamp.ohead" % (
_info['src_info'][i]['band'],
_info['src_info'][i]["ccdnum"],
)
_info["src_info"][i][key] = _info["src_info"][i][key].replace(
scamp_slug, "blah"
)
with pytest.raises(RuntimeError) as e:
check_info(info=_info)
assert f"doesn't end with {scamp_slug}" in str(e.value)
| 41.184 | 185 | 0.71183 | 1,608 | 10,296 | 4.311567 | 0.116915 | 0.069811 | 0.058849 | 0.069234 | 0.80124 | 0.773547 | 0.758258 | 0.751478 | 0.69436 | 0.632915 | 0 | 0.167395 | 0.155206 | 10,296 | 249 | 186 | 41.349398 | 0.629685 | 0.000389 | 0 | 0.431818 | 0 | 0.109091 | 0.62206 | 0.405539 | 0 | 0 | 0 | 0 | 0.05 | 1 | 0.040909 | false | 0 | 0.018182 | 0 | 0.059091 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
8429fc21ebc0ef70dc078bf33f32de3ee5a35888 | 18,471 | py | Python | services/users/project/tests/test_auth.py | eventprotocol/event-protocol-webapp | 38ccdc63bc744576ebb3631b7e17cfd4a09216b6 | [
"MIT"
] | null | null | null | services/users/project/tests/test_auth.py | eventprotocol/event-protocol-webapp | 38ccdc63bc744576ebb3631b7e17cfd4a09216b6 | [
"MIT"
] | 11 | 2020-09-05T14:16:23.000Z | 2022-03-03T22:33:14.000Z | services/users/project/tests/test_auth.py | eventprotocol/event-protocol-webapp | 38ccdc63bc744576ebb3631b7e17cfd4a09216b6 | [
"MIT"
] | null | null | null | import json
import unittest
from flask import current_app
from project import db
from project.api.models import User
from project.tests.base import BaseTestCase
from project.tests.utils import add_user
signature = '0xca55365c9c00cd84edeaf6e716f6b37672d' \
+ 'f2872e48f5b7d5977551742c8c9de3f71d5c28f016a0' \
+ '752d54d53e0bb0a8b995ab4478aff3bcfcb24324248396e461c'
class TestAuthBlueprint(BaseTestCase):
def test_registration_normal(self):
"""
Checks if we can properly register a user
"""
with self.client:
response = self.client.post(
'/users/auth/register',
data=json.dumps({
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7',
'signed_message': signature
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(data['status'] == 'success')
self.assertIn('Registration Success', data['message'])
self.assertTrue(data['auth_token'])
self.assertTrue(response.content_type == "application/json")
self.assertEqual(response.status_code, 201)
def test_registration_duplicate_registration(self):
"""
Checks if failure is thrown if a duplicate user is added
"""
add_user("0x0d604c28a2a7c199c7705859c3f88a71cce2acb7")
with self.client:
response = self.client.post(
'/users/auth/register',
data=json.dumps({
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7',
'signed_message': signature
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(response.status_code == 400)
self.assertRaises(KeyError, lambda: data['auth_token'])
self.assertIn('User already exists', data['message'])
self.assertIn('fail', data['status'])
def test_registration_invalid_json_empty(self):
"""
Checks if failure is thrown if a invalid json is given
"""
with self.client:
response = self.client.post(
'/users/auth/register',
data=json.dumps({}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(response.status_code == 400)
self.assertRaises(KeyError, lambda: data['auth_token'])
self.assertIn('Invalid payload', data['message'])
self.assertIn('fail', data['status'])
def test_registration_invalid_json_no_eth_address(self):
"""
Checks if failure is thrown if eth_address is not given
"""
with self.client:
response = self.client.post(
'/users/auth/register',
data=json.dumps({
'signed_message': signature
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(response.status_code == 400)
self.assertRaises(KeyError, lambda: data['auth_token'])
self.assertIn('Eth address error', data['message'])
self.assertIn('fail', data['status'])
def test_registration_invalid_json_no_signature(self):
"""
Checks if failure is thrown if no signature is provided
"""
with self.client:
response = self.client.post(
'/users/auth/register',
data=json.dumps({
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7',
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(response.status_code == 400)
self.assertRaises(KeyError, lambda: data['auth_token'])
self.assertIn('Signed message error', data['message'])
self.assertIn('fail', data['status'])
def test_login_normal(self):
"""
Test if we can login normally
"""
with self.client:
add_user("0x0d604c28a2a7c199c7705859c3f88a71cce2acb7")
response = self.client.post(
'/users/auth/login',
data=json.dumps({
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7',
'signed_message': signature
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(response.status_code == 200)
self.assertIn('success', data['status'])
self.assertIn('Successfully logged in', data['message'])
self.assertTrue(data['auth_token'])
self.assertTrue(response.content_type == 'application/json')
def test_login_not_registered(self):
"""
Test if error message is thrown if user is not registered
"""
with self.client:
response = self.client.post(
'/users/auth/login',
data=json.dumps({
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7',
'signed_message': signature
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(response.status_code == 404)
self.assertIn('fail', data['status'])
self.assertIn('User does not exist', data['message'])
self.assertRaises(KeyError, lambda: data['auth_token'])
self.assertTrue(response.content_type == 'application/json')
def test_login_empty_json(self):
"""
Test if error message is thrown if user is not registered
"""
with self.client:
response = self.client.post(
'/users/auth/login',
data=json.dumps({}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(response.status_code == 400)
self.assertIn('fail', data['status'])
self.assertIn('Invalid payload', data['message'])
self.assertRaises(KeyError, lambda: data['auth_token'])
def test_login_invalid_json_no_eth_address(self):
"""
Checks if failure is thrown if eth_address is not given
"""
with self.client:
response = self.client.post(
'/users/auth/login',
data=json.dumps({
'signed_message': signature
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(response.status_code == 400)
self.assertIn('Eth address error', data['message'])
self.assertRaises(KeyError, lambda: data['auth_token'])
self.assertIn('fail', data['status'])
def test_login_invalid_json_no_signature(self):
"""
Checks if failure is thrown if no signature is provided
"""
with self.client:
response = self.client.post(
'/users/auth/login',
data=json.dumps({
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7',
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(response.status_code == 400)
self.assertIn('Signed message error', data['message'])
self.assertRaises(KeyError, lambda: data['auth_token'])
self.assertIn('fail', data['status'])
def test_logout_normal(self):
"""
Checks if we can logout normally after login
"""
current_app.config['TOKEN_EXPIRATION_SECONDS'] = 3
with self.client:
add_user('0x0d604c28a2a7c199c7705859c3f88a71cce2acb7')
# user login
test_login = self.client.post(
'/users/auth/login',
data=json.dumps({
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7',
'signed_message': signature
}),
content_type='application/json'
)
# user logout
token = json.loads(test_login.data.decode())['auth_token']
response = self.client.post(
'/users/auth/logout',
data=json.dumps({
'auth_token': f'{token}',
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7'
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(data['status'] == 'success')
self.assertTrue(data['message'] == 'Successfully logged out')
self.assertEqual(response.status_code, 200)
def test_logout_expired_token(self):
"""
Checks for failure if the token has already expired
"""
# remove delay in expiration of token
current_app.config['TOKEN_EXPIRATION_SECONDS'] = -1
with self.client:
add_user('0x0d604c28a2a7c199c7705859c3f88a71cce2acb7')
# user login
test_login = self.client.post(
'/users/auth/login',
data=json.dumps({
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7',
'signed_message': signature
}),
content_type='application/json'
)
token = json.loads(test_login.data.decode())['auth_token']
response = self.client.post(
'/users/auth/logout',
data=json.dumps({
'auth_token': f'{token}',
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7'
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(data['status'] == 'fail')
self.assertTrue(
data['message'] == 'Signature expired please reauthenticate')
self.assertEqual(response.status_code, 401)
def test_logout_invalid_token(self):
"""
Checks for failure if we try to logout with invalid token
"""
with self.client:
response = self.client.post(
'/users/auth/logout',
data=json.dumps({
'auth_token': 'invalid',
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7'
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(data['status'] == 'fail')
self.assertTrue(
data['message'] == 'Invalid token please reauthenticate')
self.assertEqual(response.status_code, 401)
def test_logout_invalid_inactive(self):
"""
Checks if logout fails due to inactive
"""
with self.client:
add_user('0x0d604c28a2a7c199c7705859c3f88a71cce2acb7')
# set user activity to false
user = User.query.filter_by(
eth_address='0x0d604c28a2a7c199c7705859c3f88a71cce2acb7'
).first()
user.active = False
db.session.commit()
test_login = self.client.post(
'/users/auth/login',
data=json.dumps({
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7',
'signed_message': signature
}),
content_type='application/json'
)
token = json.loads(test_login.data.decode())['auth_token']
response = self.client.post(
'/users/auth/logout',
data=json.dumps({
'auth_token': f'{token}',
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7'
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(data['status'] == 'fail')
self.assertTrue(
data['message'] == 'Please provide a valid auth token')
self.assertEqual(response.status_code, 401)
def test_status_normal(self):
"""
Checks if we can see status normally
"""
current_app.config['TOKEN_EXPIRATION_SECONDS'] = 3
with self.client:
add_user('0x0d604c28a2a7c199c7705859c3f88a71cce2acb7')
# user login
test_login = self.client.post(
'/users/auth/login',
data=json.dumps({
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7',
'signed_message': signature
}),
content_type='application/json'
)
token = json.loads(test_login.data.decode())['auth_token']
response = self.client.post(
'/users/auth/status',
data=json.dumps({
'auth_token': f'{token}',
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7'
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(data['status'] == 'success')
self.assertTrue(data['data'] is not None)
self.assertTrue(data['data']['eth_address'] ==
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7')
self.assertTrue(data['data']['active'] is True)
self.assertEqual(response.status_code, 200)
def test_status_expired_token(self):
"""
Checks for failure if the token has already expired
"""
# remove delay in expiration of token
current_app.config['TOKEN_EXPIRATION_SECONDS'] = -1
with self.client:
add_user('0x0d604c28a2a7c199c7705859c3f88a71cce2acb7')
# user login
test_login = self.client.post(
'/users/auth/login',
data=json.dumps({
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7',
'signed_message': signature
}),
content_type='application/json'
)
token = json.loads(test_login.data.decode())['auth_token']
response = self.client.post(
'/users/auth/status',
data=json.dumps({
'auth_token': f'{token}',
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7'
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(data['status'] == 'fail')
self.assertTrue(
data['message'] == 'Signature expired please reauthenticate')
self.assertEqual(response.status_code, 401)
def test_status_invalid_token(self):
"""
Checks for failure if token is invalid
"""
with self.client:
response = self.client.post(
'/users/auth/status',
data=json.dumps({
'auth_token': 'invalid',
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7'
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(data['status'] == 'fail')
self.assertTrue(
data['message'] == 'Invalid token please reauthenticate')
self.assertEqual(response.status_code, 401)
def test_status_invalid_inactive(self):
"""
Checks for failure if we check for status when the user
is inactive
"""
with self.client:
add_user('0x0d604c28a2a7c199c7705859c3f88a71cce2acb7')
user = User.query.filter_by(
eth_address='0x0d604c28a2a7c199c7705859c3f88a71cce2acb7'
).first()
user.active = False
db.session.commit()
test_login = self.client.post(
'/users/auth/login',
data=json.dumps({
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7',
'signed_message': signature
}),
content_type='application/json'
)
test_login_data = json.loads(test_login.data.decode())
self.assertTrue(test_login_data['status'] == 'success')
self.assertTrue(test_login_data['message'] ==
'Successfully logged in')
self.assertEqual(test_login.status_code, 200)
response = self.client.post(
'/users/auth/status',
data=json.dumps({
'auth_token': 'invalid',
'eth_address':
'0x0d604c28a2a7c199c7705859c3f88a71cce2acb7'
}),
content_type='application/json'
)
data = json.loads(response.data.decode())
self.assertTrue(data['status'] == 'fail')
# self.assertTrue(
# data['message'] == 'Please provide a valid auth token')
self.assertEqual(response.status_code, 401)
if __name__ == '__main__':
unittest.main()
| 36.576238 | 77 | 0.533377 | 1,578 | 18,471 | 6.105196 | 0.084918 | 0.035707 | 0.061657 | 0.072867 | 0.880735 | 0.870355 | 0.849803 | 0.819909 | 0.798941 | 0.78908 | 0 | 0.080755 | 0.36311 | 18,471 | 504 | 78 | 36.64881 | 0.738184 | 0.062043 | 0 | 0.8 | 0 | 0 | 0.227195 | 0.09057 | 0 | 0 | 0.079264 | 0 | 0.189333 | 1 | 0.048 | false | 0 | 0.018667 | 0 | 0.069333 | 0.002667 | 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 |
080588bfdca8bc8baee2de09db74bbce8b2f71b7 | 2,295 | py | Python | test/test_generate_asv.py | linyc74/qiime2_pipeline | 30d903a70b3e3b363995d9cb820443ed60244895 | [
"MIT"
] | null | null | null | test/test_generate_asv.py | linyc74/qiime2_pipeline | 30d903a70b3e3b363995d9cb820443ed60244895 | [
"MIT"
] | null | null | null | test/test_generate_asv.py | linyc74/qiime2_pipeline | 30d903a70b3e3b363995d9cb820443ed60244895 | [
"MIT"
] | null | null | null | from .setup import TestCase
from qiime2_pipeline.generate_asv import GenerateASVConcatPairedEnd, GenerateASVPoolPairedEnd, GenerateASVSingleEnd
class TestGenerateASVConcatPairedEnd(TestCase):
def setUp(self):
self.set_up(py_path=__file__)
def tearDown(self):
self.tear_down()
def test_main(self):
feature_table_qza, feature_sequence_qza = GenerateASVConcatPairedEnd(self.settings).main(
fq_dir=f'{self.indir}/fq_dir',
fq1_suffix='_L001_R1_001.fastq.gz',
fq2_suffix='_L001_R2_001.fastq.gz',
clip_r1_5_prime=17,
clip_r2_5_prime=0
)
for expected, actual in [
(f'{self.workdir}/dada2-feature-table.qza', feature_table_qza),
(f'{self.workdir}/dada2-feature-sequence.qza', feature_sequence_qza),
]:
self.assertFileExists(expected, actual)
class TestGenerateASVPoolPairedEnd(TestCase):
def setUp(self):
self.set_up(py_path=__file__)
def tearDown(self):
self.tear_down()
def test_main(self):
feature_table_qza, feature_sequence_qza = GenerateASVPoolPairedEnd(self.settings).main(
fq_dir=f'{self.indir}/fq_dir',
fq1_suffix='_L001_R1_001.fastq.gz',
fq2_suffix='_L001_R2_001.fastq.gz',
clip_r1_5_prime=17,
clip_r2_5_prime=0
)
for expected, actual in [
(f'{self.workdir}/dada2-feature-table.qza', feature_table_qza),
(f'{self.workdir}/dada2-feature-sequence.qza', feature_sequence_qza),
]:
self.assertFileExists(expected, actual)
class TestGenerateASVSingleEnd(TestCase):
def setUp(self):
self.set_up(py_path=__file__)
def tearDown(self):
self.tear_down()
def test_main(self):
feature_table_qza, feature_sequence_qza = GenerateASVSingleEnd(self.settings).main(
fq_dir=f'{self.indir}/fq_dir',
fq_suffix='_L001_R1_001.fastq.gz',
clip_5_prime=17
)
for expected, actual in [
(f'{self.workdir}/dada2-feature-table.qza', feature_table_qza),
(f'{self.workdir}/dada2-feature-sequence.qza', feature_sequence_qza),
]:
self.assertFileExists(expected, actual)
| 32.785714 | 115 | 0.646623 | 273 | 2,295 | 5.106227 | 0.208791 | 0.077475 | 0.096844 | 0.094692 | 0.785509 | 0.785509 | 0.769727 | 0.769727 | 0.769727 | 0.769727 | 0 | 0.036374 | 0.245316 | 2,295 | 69 | 116 | 33.26087 | 0.768476 | 0 | 0 | 0.759259 | 1 | 0 | 0.173856 | 0.14902 | 0 | 0 | 0 | 0 | 0.055556 | 1 | 0.166667 | false | 0 | 0.037037 | 0 | 0.259259 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
081427e953320c9f50e907f546632587fb599934 | 124 | py | Python | diffusions/datasets.py | JTT94/example_diffusion | f6f588c40a741611e9296dacad71c3782541c25c | [
"MIT"
] | null | null | null | diffusions/datasets.py | JTT94/example_diffusion | f6f588c40a741611e9296dacad71c3782541c25c | [
"MIT"
] | 1 | 2022-03-28T15:37:14.000Z | 2022-03-28T15:37:14.000Z | diffusions/datasets.py | JTT94/example_diffusion | f6f588c40a741611e9296dacad71c3782541c25c | [
"MIT"
] | null | null | null | def central_scalar(x):
return lambda x: x * 2. - 1.
def inverse_central_scalar(x):
return lambda x: (x + 1.) / 2.
| 17.714286 | 34 | 0.612903 | 21 | 124 | 3.47619 | 0.428571 | 0.356164 | 0.383562 | 0.547945 | 0.767123 | 0.767123 | 0.767123 | 0 | 0 | 0 | 0 | 0.042553 | 0.241935 | 124 | 6 | 35 | 20.666667 | 0.734043 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 10 |
082125ec7fa346aa836ed29fff81062513188411 | 90 | py | Python | calendartools/periods/__init__.py | chrischambers/django-calendartools | 7fb2cb88a5913df1c01f4f92bcbd0d4a2d2f98fe | [
"BSD-3-Clause"
] | 1 | 2015-12-15T19:12:14.000Z | 2015-12-15T19:12:14.000Z | calendartools/periods/__init__.py | chrischambers/django-calendartools | 7fb2cb88a5913df1c01f4f92bcbd0d4a2d2f98fe | [
"BSD-3-Clause"
] | null | null | null | calendartools/periods/__init__.py | chrischambers/django-calendartools | 7fb2cb88a5913df1c01f4f92bcbd0d4a2d2f98fe | [
"BSD-3-Clause"
] | null | null | null | from calendartools.periods.proxybase import *
from calendartools.periods.periods import *
| 30 | 45 | 0.844444 | 10 | 90 | 7.6 | 0.5 | 0.447368 | 0.631579 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088889 | 90 | 2 | 46 | 45 | 0.926829 | 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 |
083e47d1b263cbbef812a2c26d07de24f7059ee7 | 251 | py | Python | manager/downloads/downloads/application/use_cases/file_retry_all.py | G4brym/download-manager | 8795d09d8f63511c980d3f10e6b2b762d41bff0c | [
"MIT"
] | 3 | 2021-04-28T14:29:06.000Z | 2022-03-27T21:02:32.000Z | manager/downloads/downloads/application/use_cases/file_retry_all.py | G4brym/docker-download-manager | 8795d09d8f63511c980d3f10e6b2b762d41bff0c | [
"MIT"
] | 5 | 2021-08-04T21:37:00.000Z | 2021-08-04T21:37:02.000Z | manager/downloads/downloads/application/use_cases/file_retry_all.py | G4brym/docker-download-manager | 8795d09d8f63511c980d3f10e6b2b762d41bff0c | [
"MIT"
] | 1 | 2021-09-06T15:45:37.000Z | 2021-09-06T15:45:37.000Z | from downloads.application.repositories import FilesRepository
class FileRetryAll:
def __init__(self, files_repo: FilesRepository) -> None:
self.files_repo = files_repo
def execute(self) -> None:
self.files_repo.retry_all()
| 25.1 | 62 | 0.729084 | 29 | 251 | 6 | 0.586207 | 0.206897 | 0.224138 | 0.195402 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.187251 | 251 | 9 | 63 | 27.888889 | 0.852941 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.166667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 |
084d90ed5fcb6e23521867fad9a46daf691aa179 | 6,022 | py | Python | tests/test_detrending.py | lgbouma/cdips | 187e15e620cd44160372dbfa9da989d38722c3e5 | [
"MIT"
] | 1 | 2019-10-04T02:03:25.000Z | 2019-10-04T02:03:25.000Z | tests/test_detrending.py | lgbouma/cdips | 187e15e620cd44160372dbfa9da989d38722c3e5 | [
"MIT"
] | 3 | 2019-08-17T20:33:23.000Z | 2021-08-18T17:55:10.000Z | tests/test_detrending.py | lgbouma/cdips | 187e15e620cd44160372dbfa9da989d38722c3e5 | [
"MIT"
] | null | null | null | from cdips.lcproc import detrend as dtr
from glob import glob
import os, textwrap, re
import numpy as np, pandas as pd, matplotlib.pyplot as plt
from numpy import array as nparr
from datetime import datetime
def plot_detrending_from_tfa(time, tfatime, rawflux, tfaflux, flat_flux,
trend_flux, ap_index=2, obsd_midtimes=None,
returnfig=False, savpath=None):
plt.close('all')
nrows = 3
fig, axs = plt.subplots(nrows=nrows, ncols=1, sharex=True, figsize=(18,8))
axs = axs.flatten()
apstr = 'AP{:d}'.format(ap_index)
stagestrs = ( ['RM{:d}'.format(ap_index),
'TF{:d}'.format(ap_index),
'DTR{:d}'.format(ap_index)] )
yvals = [rawflux,tfaflux,flat_flux]
nums = list(range(len(yvals)))
for ax, yval, txt, num in zip(axs, yvals, stagestrs, nums):
if 'TF' in txt or 'DTR' in txt:
ax.scatter(tfatime, yval, c='black', alpha=0.9, zorder=2, s=10,
rasterized=True, linewidths=0)
elif 'BKGD' in txt or 'RM' in txt:
ax.scatter(time, yval, c='black', alpha=0.9, zorder=2, s=10,
rasterized=True, linewidths=0)
if 'TF' in txt and len(stagestrs)==3:
ax.scatter(tfatime, trend_flux, c='red', alpha=0.9, zorder=1, s=5,
rasterized=True, linewidths=0)
ax.get_yaxis().set_tick_params(which='both', direction='in',
labelsize='large')
ax.get_xaxis().set_tick_params(which='both', direction='in',
labelsize='large')
if not isinstance(obsd_midtimes, np.ndarray):
for ax in axs:
ylim = ax.get_ylim()
ax.set_ylim((min(ylim), max(ylim)))
axs[-1].set_xlabel('BJDTDB', fontsize='large')
axs[-1].xaxis.get_offset_text().set_fontsize('large')
# make the y label
ax_hidden = fig.add_subplot(111, frameon=False)
ax_hidden.tick_params(labelcolor='none', top=False, bottom=False,
left=False, right=False)
axs[0].set_ylabel('raw flux IRM2', fontsize='large', labelpad=27)
axs[1].set_ylabel('tfa flux TFA2', fontsize='large', labelpad=27)
axs[2].set_ylabel('detrended flux DTR2', fontsize='large', labelpad=27)
if not savpath:
savpath = 'temp_{:s}.png'.format(apstr)
fig.tight_layout(h_pad=0.)
if returnfig:
return fig
else:
fig.savefig(savpath, dpi=250, bbox_inches='tight')
print('%sZ: made plot: %s' % (datetime.utcnow().isoformat(), savpath))
def plot_detrending_from_raw(time, tfatime, rawflux, tfaflux, flat_flux,
trend_flux, ap_index=2, obsd_midtimes=None,
returnfig=False, savpath=None):
plt.close('all')
nrows = 2
fig, axs = plt.subplots(nrows=nrows, ncols=1, sharex=True, figsize=(18,8))
axs = axs.flatten()
apstr = 'AP{:d}'.format(ap_index)
stagestrs = ( ['RM{:d}'.format(ap_index),
'DTR{:d}'.format(ap_index)] )
yvals = [rawflux,flat_flux]
nums = list(range(len(yvals)))
for ax, yval, txt, num in zip(axs, yvals, stagestrs, nums):
if 'TF' in txt or 'DTR' in txt:
ax.scatter(tfatime, yval, c='black', alpha=0.9, zorder=2, s=10,
rasterized=True, linewidths=0)
elif 'BKGD' in txt or 'RM' in txt:
ax.scatter(time, yval, c='black', alpha=0.9, zorder=2, s=10,
rasterized=True, linewidths=0)
if 'RM' in txt and len(stagestrs)==2:
ax.scatter(tfatime, trend_flux, c='red', alpha=0.9, zorder=1, s=5,
rasterized=True, linewidths=0)
ax.get_yaxis().set_tick_params(which='both', direction='in',
labelsize='large')
ax.get_xaxis().set_tick_params(which='both', direction='in',
labelsize='large')
if not isinstance(obsd_midtimes, np.ndarray):
for ax in axs:
ylim = ax.get_ylim()
ax.set_ylim((min(ylim), max(ylim)))
axs[-1].set_xlabel('BJDTDB', fontsize='large')
axs[-1].xaxis.get_offset_text().set_fontsize('large')
# make the y label
ax_hidden = fig.add_subplot(111, frameon=False)
ax_hidden.tick_params(labelcolor='none', top=False, bottom=False,
left=False, right=False)
axs[0].set_ylabel('raw flux IRM2', fontsize='large', labelpad=27)
axs[1].set_ylabel('detrended flux DTR2', fontsize='large', labelpad=27)
if not savpath:
savpath = 'temp_{:s}.png'.format(apstr)
fig.tight_layout(h_pad=0.)
if returnfig:
return fig
else:
fig.savefig(savpath, dpi=250, bbox_inches='tight')
print('%sZ: made plot: %s' % (datetime.utcnow().isoformat(), savpath))
def test_detrending(source_id=None):
df = pd.read_csv('data/example_data_{}.csv'.format(source_id))
outpng = '{}_detrend_test_from_tfa.png'.format(source_id)
flat_flux, trend_flux = dtr.detrend_flux(
nparr(df.tfatime), nparr(df.tfaflux), break_tolerance=0.5
)
plot_detrending_from_tfa(nparr(df.time), nparr(df.tfatime),
nparr(df.rawflux), nparr(df.tfaflux),
flat_flux, trend_flux, ap_index=2,
returnfig=False, savpath=outpng)
outpng = '{}_detrend_test_from_raw.png'.format(source_id)
flat_flux, trend_flux = dtr.detrend_flux(
nparr(df.time), nparr(df.rawflux), break_tolerance=0.5
)
plot_detrending_from_raw(nparr(df.time), nparr(df.tfatime),
nparr(df.rawflux), nparr(df.tfaflux),
flat_flux, trend_flux, ap_index=2,
returnfig=False, savpath=outpng)
if __name__ == "__main__":
test_detrending(source_id='5326491313765089792')
test_detrending(source_id='5334408965769940608')
| 37.17284 | 78 | 0.585686 | 797 | 6,022 | 4.27478 | 0.212045 | 0.024655 | 0.018491 | 0.028764 | 0.861755 | 0.835045 | 0.835045 | 0.815087 | 0.815087 | 0.815087 | 0 | 0.02983 | 0.27632 | 6,022 | 161 | 79 | 37.403727 | 0.75195 | 0.00548 | 0 | 0.739496 | 0 | 0 | 0.081189 | 0.013365 | 0 | 0 | 0 | 0 | 0 | 1 | 0.02521 | false | 0 | 0.05042 | 0 | 0.092437 | 0.016807 | 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 |
f2446369b316119a28ee8fd648cd17d03ca6ca3c | 123 | py | Python | JSonExample.py | ksbhatkana/ksbhat-for-python | 2165e25347092274f1a08c9d9d0645c7f709e9c5 | [
"MIT"
] | null | null | null | JSonExample.py | ksbhatkana/ksbhat-for-python | 2165e25347092274f1a08c9d9d0645c7f709e9c5 | [
"MIT"
] | null | null | null | JSonExample.py | ksbhatkana/ksbhat-for-python | 2165e25347092274f1a08c9d9d0645c7f709e9c5 | [
"MIT"
] | null | null | null | import json
print(json.dumps({"c":0,"b":0,"a":0},sort_keys=True))
print(json.dumps({'4':5,'6':7},sort_keys=True,indent=4))
| 30.75 | 56 | 0.658537 | 26 | 123 | 3.038462 | 0.615385 | 0.227848 | 0.35443 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.067227 | 0.03252 | 123 | 3 | 57 | 41 | 0.596639 | 0 | 0 | 0 | 0 | 0 | 0.04065 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 0.666667 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 7 |
f28b88aaf71e791c2956f925bd398548105c3602 | 4,545 | py | Python | Settings/independent_experiments/basic_models_cooking.py | previtus/MGR-Project-Code | 1126215059eb3f731dcf78ec24d9a480e73abce6 | [
"MIT"
] | null | null | null | Settings/independent_experiments/basic_models_cooking.py | previtus/MGR-Project-Code | 1126215059eb3f731dcf78ec24d9a480e73abce6 | [
"MIT"
] | null | null | null | Settings/independent_experiments/basic_models_cooking.py | previtus/MGR-Project-Code | 1126215059eb3f731dcf78ec24d9a480e73abce6 | [
"MIT"
] | null | null | null | def Setup(Settings,DefaultModel):
# basic_models_cooking.py
Settings["experiment_name"] = "BasicModelCookingShow"
Settings["graph_histories"] = ['together']
# it's not about the results, but about the journey!
# we are interested in ResNet50
# and these datasets
# "1200x_markable_299x299", "5556x_mark_res_299x299", "5556x_markable_640x640"
# will cook them into this:
''' with current seed
shared/features_train_1200x_markable_299x299299-full-seed13_resnet50.npy
shared/features_train_5556x_mark_res_299x299299-full-seed13_resnet50.npy
shared/features_train_5556x_markable_640x640640-full-seed13_resnet50.npy
shared/features_validation_1200x_markable_299x299299-full-seed13_resnet50.npy
shared/features_validation_5556x_mark_res_299x299299-full-seed13_resnet50.npy
shared/features_validation_5556x_markable_640x640640-full-seed13_resnet50.npy
'''
n=0
Settings["models"][n]["model_type"] = 'simple_cnn_with_top'
Settings["models"][n]["dataset_name"] = "5556x_reslen30_299px"
Settings["models"][n]["pixels"] = 299
Settings["models"][n]["cnn_model"] = 'resnet50'
Settings["models"][n]["unique_id"] = 'resnet50_5556x_reslen30_299px'
Settings["models"][n]["cooking_method"] = 'generators' # 'direct' or 'generators'
Settings["models"][n]["epochs"] = 5
Settings["models"].append(DefaultModel.copy())
n=1
Settings["models"][n]["dataset_pointer"] = -1 # 0 - reuse the first dataset
Settings["models"][n]["model_type"] = 'simple_cnn_with_top'
Settings["models"][n]["dataset_name"] = "5556x_reslen20_299px"
Settings["models"][n]["pixels"] = 299
Settings["models"][n]["cnn_model"] = 'resnet50'
Settings["models"][n]["unique_id"] = 'resnet50_5556x_reslen20_299px'
Settings["models"][n]["cooking_method"] = 'generators' # 'direct' or 'generators'
Settings["models"][n]["epochs"] = 5
Settings["models"].append(DefaultModel.copy())
n=2
Settings["models"][n]["dataset_pointer"] = -1 # 0 - reuse the first dataset
Settings["models"][n]["model_type"] = 'simple_cnn_with_top'
Settings["models"][n]["dataset_name"] = "5556x_minlen30_640px"
Settings["models"][n]["pixels"] = 640
Settings["models"][n]["cnn_model"] = 'resnet50'
Settings["models"][n]["unique_id"] = 'resnet50_5556x_minlen30_640px'
Settings["models"][n]["cooking_method"] = 'generators' # 'direct' or 'generators'
Settings["models"][n]["epochs"] = 5
Settings["models"].append(DefaultModel.copy())
n=3
Settings["models"][n]["dataset_pointer"] = -1 # 0 - reuse the first dataset
Settings["models"][n]["model_type"] = 'simple_cnn_with_top'
Settings["models"][n]["dataset_name"] = "5556x_minlen20_640px"
Settings["models"][n]["pixels"] = 640
Settings["models"][n]["cnn_model"] = 'resnet50'
Settings["models"][n]["unique_id"] = 'resnet50_5556x_minlen20_640px'
Settings["models"][n]["cooking_method"] = 'generators' # 'direct' or 'generators'
Settings["models"][n]["epochs"] = 5
'''
n=0
Settings["models"][n]["model_type"] = 'img_osm_mix'
Settings["models"][n]["dataset_name"] = "1200x_markable_299x299"
Settings["models"][n]["pixels"] = 299
Settings["models"][n]["cnn_model"] = 'resnet50'
Settings["models"][n]["unique_id"] = 'resnet50_1200x_markable_299x299'
Settings["models"][n]["cooking_method"] = 'generators' # 'direct' or 'generators'
Settings["models"][n]["epochs"] = 5
Settings["models"].append(DefaultModel.copy())
n=1
Settings["models"][n]["dataset_pointer"] = -1 # 0 - reuse the first dataset
Settings["models"][n]["dataset_name"] = "5556x_mark_res_299x299"
Settings["models"][n]["pixels"] = 299
Settings["models"][n]["cnn_model"] = 'resnet50'
Settings["models"][n]["unique_id"] = 'resnet50_5556x_mark_res_299x299'
Settings["models"][n]["cooking_method"] = 'generators' # 'direct' or 'generators'
Settings["models"][n]["epochs"] = 5
Settings["models"].append(DefaultModel.copy())
n=2
Settings["models"][n]["dataset_pointer"] = -1 # 0 - reuse the first dataset
Settings["models"][n]["dataset_name"] = "5556x_markable_640x640"
Settings["models"][n]["pixels"] = 640
Settings["models"][n]["cnn_model"] = 'resnet50'
Settings["models"][n]["unique_id"] = 'resnet50_5556x_markable_640x640'
Settings["models"][n]["cooking_method"] = 'generators' # 'direct' or 'generators'
Settings["models"][n]["epochs"] = 5
'''
return Settings
| 46.377551 | 85 | 0.674587 | 550 | 4,545 | 5.334545 | 0.165455 | 0.271984 | 0.265849 | 0.08998 | 0.884799 | 0.875937 | 0.835037 | 0.785958 | 0.785958 | 0.732788 | 0 | 0.087236 | 0.144995 | 4,545 | 97 | 86 | 46.85567 | 0.667782 | 0.090209 | 0 | 0.619048 | 0 | 0 | 0.427239 | 0.063899 | 0 | 0 | 0 | 0 | 0 | 1 | 0.02381 | false | 0 | 0 | 0 | 0.047619 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
f29f0c2eba294ee711c291ee40fe0f18b0cb526d | 1,884 | py | Python | internetdefense/apps/analytics/queries.py | gnubrasil/idl-members | 25f0c048c9cae047cedc4aa0eb58548fac760849 | [
"MIT"
] | 175 | 2015-01-01T06:23:10.000Z | 2021-12-07T09:08:51.000Z | internetdefense/apps/analytics/queries.py | jwyterlin/idl-members | 76c467ff38de1c46666d911837f934a76aa6fc7b | [
"MIT"
] | 5 | 2015-03-21T03:34:32.000Z | 2017-03-03T00:19:49.000Z | internetdefense/apps/analytics/queries.py | jwyterlin/idl-members | 76c467ff38de1c46666d911837f934a76aa6fc7b | [
"MIT"
] | 38 | 2015-02-03T23:49:05.000Z | 2020-07-30T16:26:56.000Z | from django.conf import settings
engine = settings.DATABASES['default']['ENGINE']
if engine == 'django.db.backends.postgresql_psycopg2':
reach = """
SELECT AVG(daily_reach) FROM (
SELECT COUNT(DISTINCT ip) AS daily_reach,
date_part('day', time) AS day,
date_part('month', time) AS month,
date_part('year', time) AS year
FROM analytics_impression
WHERE time > (now() - interval '168 hour')
GROUP BY year, month, day
) AS reach;
"""
sites = """
SELECT AVG(daily_reach) FROM (
SELECT COUNT(DISTINCT embedding_url) AS daily_reach,
date_part('day', time) AS day,
date_part('month', time) AS month,
date_part('year', time) AS year
FROM analytics_impression
WHERE time > (now() - interval '168 hour')
GROUP BY year, month, day
) AS reach;
"""
else:
sites = """
SELECT AVG(daily_reach) FROM (
SELECT COUNT(DISTINCT embedding_url) as daily_reach,
strftime('%%d', time) AS day,
strftime('%%m', time) AS month,
strftime('%%Y', time) AS year
FROM analytics_impression
WHERE time > datetime('now', '-7 days')
GROUP BY year, month, day
);
"""
reach = """
SELECT AVG(daily_reach) FROM (
SELECT COUNT(DISTINCT ip) as daily_reach,
strftime('%%d', time) AS day,
strftime('%%m', time) AS month,
strftime('%%Y', time) AS year
FROM analytics_impression
WHERE time > datetime('now', '-7 days')
GROUP BY year, month, day
);
"""
| 36.230769 | 66 | 0.492038 | 198 | 1,884 | 4.575758 | 0.237374 | 0.07947 | 0.06181 | 0.083885 | 0.878587 | 0.878587 | 0.878587 | 0.878587 | 0.878587 | 0.878587 | 0 | 0.007972 | 0.400743 | 1,884 | 51 | 67 | 36.941176 | 0.794508 | 0 | 0 | 0.833333 | 0 | 0 | 0.909236 | 0.02017 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.020833 | 0 | 0.020833 | 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 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
f2a4c821cf883c873387b23c0951fd1198b31634 | 17,283 | py | Python | tests/ci/unit_tests/pipeline_config/pipeline/test_frames_puff_map.py | Food-X-Technologies/foodx_devops_tools | 57d1bf1304d9c9a386eaffa427f9eb36c410c350 | [
"MIT"
] | 3 | 2021-06-23T20:53:43.000Z | 2022-01-26T14:19:43.000Z | tests/ci/unit_tests/pipeline_config/pipeline/test_frames_puff_map.py | Food-X-Technologies/foodx_devops_tools | 57d1bf1304d9c9a386eaffa427f9eb36c410c350 | [
"MIT"
] | 33 | 2021-08-09T15:44:51.000Z | 2022-03-03T18:28:02.000Z | tests/ci/unit_tests/pipeline_config/pipeline/test_frames_puff_map.py | Food-X-Technologies/foodx_devops_tools | 57d1bf1304d9c9a386eaffa427f9eb36c410c350 | [
"MIT"
] | 1 | 2021-06-23T20:53:52.000Z | 2021-06-23T20:53:52.000Z | # Copyright (c) 2021 Food-X Technologies
#
# This file is part of foodx_devops_tools.
#
# You should have received a copy of the MIT License along with
# foodx_devops_tools. If not, see <https://opensource.org/licenses/MIT>.
import logging
import pytest
from foodx_devops_tools.pipeline_config import (
FramesDefinition,
PipelineConfiguration,
PuffMapGeneratedFiles,
SubscriptionsDefinition,
)
from foodx_devops_tools.pipeline_config.exceptions import (
PipelineConfigurationError,
)
from tests.ci.support.pipeline_config import MOCK_PATHS, MOCK_SECRET
log = logging.getLogger(__name__)
def test_mismatched_frames_raises1(mock_loads, mock_results):
mock_results.frames = FramesDefinition.parse_obj(
{
"frames": {
"frames": {
"f1": {
"applications": {
"a1": {
"steps": [
{
"resource_group": "a1_group",
"name": "a1stp1",
"mode": "Incremental",
},
],
},
},
"folder": "some/path",
},
},
},
}
).frames
mock_results.puff_map = PuffMapGeneratedFiles.parse_obj(
{
"puff_map": {
"frames": {
"f1": {
"applications": {
"a1": {
"arm_parameters_files": {
"r1": {
"sys1_c1_r1a": {
"a1stp1": "some/path/puff1.json"
}
}
}
}
}
},
"f2": {
"applications": {
"a2": {
"arm_parameters_files": {
"r1": {
"sys1_c1_r1a": {
"a2stp1": "some/path/puff1.json"
}
}
}
}
}
},
},
},
}
).puff_map
mock_loads(mock_results)
with pytest.raises(
PipelineConfigurationError,
match=r"Frame definitions mismatch between frames and puff map",
):
PipelineConfiguration.from_files(MOCK_PATHS, MOCK_SECRET)
def test_mismatched_frames_raises2(mock_loads, mock_results):
mock_results.frames = FramesDefinition.parse_obj(
{
"frames": {
"frames": {
"f1": {
"applications": {
"a1": {
"steps": [
{
"resource_group": "a1_group",
"name": "a1stp1",
"mode": "Incremental",
},
],
},
},
"folder": "some/path",
},
"f2": {
"applications": {
"a2": {
"steps": [
{
"resource_group": "a2_group",
"name": "a2stp1",
"mode": "Incremental",
},
],
},
},
"folder": "some/path",
},
},
},
}
).frames
mock_results.puff_map = PuffMapGeneratedFiles.parse_obj(
{
"puff_map": {
"frames": {
"f1": {
"applications": {
"a1": {
"arm_parameters_files": {
"r1": {
"sys1_c1_r1a": {
"a1stp1": "some/path/puff1.json"
}
}
}
}
}
},
},
},
}
).puff_map
mock_loads(mock_results)
with pytest.raises(
PipelineConfigurationError,
match=r"Frame definitions mismatch between frames and puff map",
):
PipelineConfiguration.from_files(MOCK_PATHS, MOCK_SECRET)
def test_mismatched_applications_raises1(mock_loads, mock_results):
mock_results.frames = FramesDefinition.parse_obj(
{
"frames": {
"frames": {
"f1": {
"applications": {
"a1": {
"steps": [
{
"resource_group": "a1_group",
"name": "a1stp1",
"mode": "Incremental",
},
],
},
},
"folder": "some/path",
},
},
},
}
).frames
mock_results.puff_map = PuffMapGeneratedFiles.parse_obj(
{
"puff_map": {
"frames": {
"f1": {
"applications": {
"a1": {
"arm_parameters_files": {
"r1": {
"sys1_c1_r1a": {
"a1stp1": "some/path/puff1.json"
}
}
}
},
"a2": {
"arm_parameters_files": {
"r1": {
"sys1_c1_r1a": {
"a2stp1": "some/path/puff1.json"
}
}
}
},
}
},
},
},
}
).puff_map
mock_loads(mock_results)
with pytest.raises(
PipelineConfigurationError,
match=r"Application definitions mismatch between frames and puff "
r"map",
):
PipelineConfiguration.from_files(MOCK_PATHS, MOCK_SECRET)
def test_mismatched_applications_raises2(mock_loads, mock_results):
mock_results.frames = FramesDefinition.parse_obj(
{
"frames": {
"frames": {
"f1": {
"applications": {
"a1": {
"steps": [
{
"resource_group": "a1_group",
"name": "a1stp1",
"mode": "Incremental",
},
],
},
"a2": {
"steps": [
{
"resource_group": "a2_group",
"name": "a2stp1",
"mode": "Incremental",
},
],
},
},
"folder": "some/path",
},
},
},
}
).frames
mock_results.puff_map = PuffMapGeneratedFiles.parse_obj(
{
"puff_map": {
"frames": {
"f1": {
"applications": {
"a1": {
"arm_parameters_files": {
"r1": {
"sys1_c1_r1a": {
"a1stp1": "some/path/puff1.json"
}
}
}
},
}
},
},
},
}
).puff_map
mock_loads(mock_results)
with pytest.raises(
PipelineConfigurationError,
match=r"Application definitions mismatch between frames and puff "
r"map",
):
PipelineConfiguration.from_files(MOCK_PATHS, MOCK_SECRET)
def test_bad_puff_map_release_state_raises(mock_loads, mock_results):
mock_results.frames = FramesDefinition.parse_obj(
{
"frames": {
"frames": {
"f1": {
"applications": {
"a1": {
"steps": [
{
"resource_group": "a1_group",
"name": "a1stp1",
"mode": "Incremental",
},
],
},
},
"folder": "some/path",
},
},
},
}
).frames
mock_results.puff_map = PuffMapGeneratedFiles.parse_obj(
{
"puff_map": {
"frames": {
"f1": {
"applications": {
"a1": {
"arm_parameters_files": {
"bad_state": {
"sys1_c1_r1a": {
"a1stp1": "some/path/puff1.json"
}
}
}
},
}
},
},
},
}
).puff_map
mock_loads(mock_results)
with pytest.raises(
PipelineConfigurationError, match=r"Bad release state in puff map"
):
PipelineConfiguration.from_files(MOCK_PATHS, MOCK_SECRET)
def test_bad_puff_map_subscription_raises(mock_loads, mock_results):
mock_results.frames = FramesDefinition.parse_obj(
{
"frames": {
"frames": {
"f1": {
"applications": {
"a1": {
"steps": [
{
"resource_group": "a1_group",
"name": "a1stp1",
"mode": "Incremental",
},
],
},
},
"folder": "some/path",
},
},
},
}
).frames
mock_results.puff_map = PuffMapGeneratedFiles.parse_obj(
{
"puff_map": {
"frames": {
"f1": {
"applications": {
"a1": {
"arm_parameters_files": {
"r1": {
"bad_sub": {
"a1stp1": "some/path/puff1.json"
},
"sys1_c1_r1a": {
"a1stp1": "some/path/puff1.json"
},
},
},
},
},
},
},
},
}
).puff_map
mock_loads(mock_results)
with pytest.raises(
PipelineConfigurationError, match=r"Bad subscription in puff map"
):
PipelineConfiguration.from_files(MOCK_PATHS, MOCK_SECRET)
def test_missing_application_step_raises1(mock_loads, mock_results):
mock_results.frames = FramesDefinition.parse_obj(
{
"frames": {
"frames": {
"f1": {
"applications": {
"a1": {
"steps": [
{
"resource_group": "a1_group",
"name": "a1stp1",
"mode": "Incremental",
},
{
"resource_group": "a1_group",
"name": "a1stp2",
"mode": "Incremental",
},
],
},
},
"folder": "some/path",
},
},
},
}
).frames
mock_results.puff_map = PuffMapGeneratedFiles.parse_obj(
{
"puff_map": {
"frames": {
"f1": {
"applications": {
"a1": {
"arm_parameters_files": {
"r1": {
"sys1_c1_r1a": {
"a1stp2": "some/path/puff1.json"
}
}
}
}
}
}
}
},
}
).puff_map
mock_loads(mock_results)
with pytest.raises(
PipelineConfigurationError,
match=r"Application step name mismatch between frames and puff map",
):
PipelineConfiguration.from_files(MOCK_PATHS, MOCK_SECRET)
def test_missing_application_step_raises2(mock_loads, mock_results):
mock_results.frames = FramesDefinition.parse_obj(
{
"frames": {
"frames": {
"f1": {
"applications": {
"a1": {
"steps": [
{
"resource_group": "a1_group",
"name": "a1stp2",
"mode": "Incremental",
},
],
},
},
"folder": "some/path",
},
},
},
}
).frames
mock_results.puff_map = PuffMapGeneratedFiles.parse_obj(
{
"puff_map": {
"frames": {
"f1": {
"applications": {
"a1": {
"arm_parameters_files": {
"r1": {
"sys1_c1_r1a": {
"a1stp1": "some/path/puff1.json",
"a1stp2": "some/path/puff2.json",
}
}
}
}
}
}
}
},
}
).puff_map
mock_loads(mock_results)
with pytest.raises(
PipelineConfigurationError,
match=r"Application step name mismatch between frames and puff map",
):
PipelineConfiguration.from_files(MOCK_PATHS, MOCK_SECRET)
| 34.428287 | 77 | 0.287045 | 836 | 17,283 | 5.666268 | 0.129187 | 0.074309 | 0.04391 | 0.067553 | 0.885159 | 0.880304 | 0.865949 | 0.865949 | 0.865949 | 0.865949 | 0 | 0.024352 | 0.629347 | 17,283 | 501 | 78 | 34.497006 | 0.715111 | 0.01244 | 0 | 0.571734 | 0 | 0 | 0.132415 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.017131 | false | 0 | 0.010707 | 0 | 0.027837 | 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 |
f2c811c9b778fda51df81c17bd8d8db438992bf7 | 8,989 | py | Python | parser/PiListener.py | bordaigorl/lemma9 | a60254908858c4d3d358f96b87ea2b896c24ed4d | [
"CC-BY-4.0"
] | null | null | null | parser/PiListener.py | bordaigorl/lemma9 | a60254908858c4d3d358f96b87ea2b896c24ed4d | [
"CC-BY-4.0"
] | null | null | null | parser/PiListener.py | bordaigorl/lemma9 | a60254908858c4d3d358f96b87ea2b896c24ed4d | [
"CC-BY-4.0"
] | 1 | 2020-07-18T08:45:41.000Z | 2020-07-18T08:45:41.000Z | # Generated from /home/fms/Daten/B-Praktika Jobs/ImperialCollege/ideal-completions-security-code/parser/Pi.g4 by ANTLR 4.7
from antlr4 import *
# This class defines a complete listener for a parse tree produced by PiParser.
class PiListener(ParseTreeListener):
# Enter a parse tree produced by PiParser#program.
def enterProgram(self, ctx):
pass
# Exit a parse tree produced by PiParser#program.
def exitProgram(self, ctx):
pass
# Enter a parse tree produced by PiParser#helpers.
def enterHelpers(self, ctx):
pass
# Exit a parse tree produced by PiParser#helpers.
def exitHelpers(self, ctx):
pass
# Enter a parse tree produced by PiParser#helper.
def enterHelper(self, ctx):
pass
# Exit a parse tree produced by PiParser#helper.
def exitHelper(self, ctx):
pass
# Enter a parse tree produced by PiParser#globalnames.
def enterGlobalnames(self, ctx):
pass
# Exit a parse tree produced by PiParser#globalnames.
def exitGlobalnames(self, ctx):
pass
# Enter a parse tree produced by PiParser#globalname.
def enterGlobalname(self, ctx):
pass
# Exit a parse tree produced by PiParser#globalname.
def exitGlobalname(self, ctx):
pass
# Enter a parse tree produced by PiParser#definitions.
def enterDefinitions(self, ctx):
pass
# Exit a parse tree produced by PiParser#definitions.
def exitDefinitions(self, ctx):
pass
# Enter a parse tree produced by PiParser#definition.
def enterDefinition(self, ctx):
pass
# Exit a parse tree produced by PiParser#definition.
def exitDefinition(self, ctx):
pass
# Enter a parse tree produced by PiParser#limit.
def enterLimit(self, ctx):
pass
# Exit a parse tree produced by PiParser#limit.
def exitLimit(self, ctx):
pass
# Enter a parse tree produced by PiParser#nullprocess.
def enterNullprocess(self, ctx):
pass
# Exit a parse tree produced by PiParser#nullprocess.
def exitNullprocess(self, ctx):
pass
# Enter a parse tree produced by PiParser#newnames.
def enterNewnames(self, ctx):
pass
# Exit a parse tree produced by PiParser#newnames.
def exitNewnames(self, ctx):
pass
# Enter a parse tree produced by PiParser#parallels.
def enterParallels(self, ctx):
pass
# Exit a parse tree produced by PiParser#parallels.
def exitParallels(self, ctx):
pass
# Enter a parse tree produced by PiParser#comp.
def enterComp(self, ctx):
pass
# Exit a parse tree produced by PiParser#comp.
def exitComp(self, ctx):
pass
# Enter a parse tree produced by PiParser#sublimit.
def enterSublimit(self, ctx):
pass
# Exit a parse tree produced by PiParser#sublimit.
def exitSublimit(self, ctx):
pass
# Enter a parse tree produced by PiParser#iterproccall.
def enterIterproccall(self, ctx):
pass
# Exit a parse tree produced by PiParser#iterproccall.
def exitIterproccall(self, ctx):
pass
# Enter a parse tree produced by PiParser#proccalldef.
def enterProccalldef(self, ctx):
pass
# Exit a parse tree produced by PiParser#proccalldef.
def exitProccalldef(self, ctx):
pass
# Enter a parse tree produced by PiParser#processcall.
def enterProcesscall(self, ctx):
pass
# Exit a parse tree produced by PiParser#processcall.
def exitProcesscall(self, ctx):
pass
# Enter a parse tree produced by PiParser#procid.
def enterProcid(self, ctx):
pass
# Exit a parse tree produced by PiParser#procid.
def exitProcid(self, ctx):
pass
# Enter a parse tree produced by PiParser#actions.
def enterActions(self, ctx):
pass
# Exit a parse tree produced by PiParser#actions.
def exitActions(self, ctx):
pass
# Enter a parse tree produced by PiParser#action.
def enterAction(self, ctx):
pass
# Exit a parse tree produced by PiParser#action.
def exitAction(self, ctx):
pass
# Enter a parse tree produced by PiParser#inputpattern.
def enterInputpattern(self, ctx):
pass
# Exit a parse tree produced by PiParser#inputpattern.
def exitInputpattern(self, ctx):
pass
# Enter a parse tree produced by PiParser#names.
def enterNames(self, ctx):
pass
# Exit a parse tree produced by PiParser#names.
def exitNames(self, ctx):
pass
# Enter a parse tree produced by PiParser#newname.
def enterNewname(self, ctx):
pass
# Exit a parse tree produced by PiParser#newname.
def exitNewname(self, ctx):
pass
# Enter a parse tree produced by PiParser#listofargs.
def enterListofargs(self, ctx):
pass
# Exit a parse tree produced by PiParser#listofargs.
def exitListofargs(self, ctx):
pass
# Enter a parse tree produced by PiParser#arguments.
def enterArguments(self, ctx):
pass
# Exit a parse tree produced by PiParser#arguments.
def exitArguments(self, ctx):
pass
# Enter a parse tree produced by PiParser#argument.
def enterArgument(self, ctx):
pass
# Exit a parse tree produced by PiParser#argument.
def exitArgument(self, ctx):
pass
# Enter a parse tree produced by PiParser#listofvars.
def enterListofvars(self, ctx):
pass
# Exit a parse tree produced by PiParser#listofvars.
def exitListofvars(self, ctx):
pass
# Enter a parse tree produced by PiParser#variables.
def enterVariables(self, ctx):
pass
# Exit a parse tree produced by PiParser#variables.
def exitVariables(self, ctx):
pass
# Enter a parse tree produced by PiParser#variable.
def enterVariable(self, ctx):
pass
# Exit a parse tree produced by PiParser#variable.
def exitVariable(self, ctx):
pass
# Enter a parse tree produced by PiParser#sizedvar.
def enterSizedvar(self, ctx):
pass
# Exit a parse tree produced by PiParser#sizedvar.
def exitSizedvar(self, ctx):
pass
# Enter a parse tree produced by PiParser#size.
def enterSize(self, ctx):
pass
# Exit a parse tree produced by PiParser#size.
def exitSize(self, ctx):
pass
# Enter a parse tree produced by PiParser#msgout.
def enterMsgout(self, ctx):
pass
# Exit a parse tree produced by PiParser#msgout.
def exitMsgout(self, ctx):
pass
# Enter a parse tree produced by PiParser#msg.
def enterMsg(self, ctx):
pass
# Exit a parse tree produced by PiParser#msg.
def exitMsg(self, ctx):
pass
# Enter a parse tree produced by PiParser#basicmsg.
def enterBasicmsg(self, ctx):
pass
# Exit a parse tree produced by PiParser#basicmsg.
def exitBasicmsg(self, ctx):
pass
# Enter a parse tree produced by PiParser#encrymsg.
def enterEncrymsg(self, ctx):
pass
# Exit a parse tree produced by PiParser#encrymsg.
def exitEncrymsg(self, ctx):
pass
# Enter a parse tree produced by PiParser#aencrymsg.
def enterAencrymsg(self, ctx):
pass
# Exit a parse tree produced by PiParser#aencrymsg.
def exitAencrymsg(self, ctx):
pass
# Enter a parse tree produced by PiParser#signmsg.
def enterSignmsg(self, ctx):
pass
# Exit a parse tree produced by PiParser#signmsg.
def exitSignmsg(self, ctx):
pass
# Enter a parse tree produced by PiParser#pubkeymsg.
def enterPubkeymsg(self, ctx):
pass
# Exit a parse tree produced by PiParser#pubkeymsg.
def exitPubkeymsg(self, ctx):
pass
# Enter a parse tree produced by PiParser#pairmsg.
def enterPairmsg(self, ctx):
pass
# Exit a parse tree produced by PiParser#pairmsg.
def exitPairmsg(self, ctx):
pass
# Enter a parse tree produced by PiParser#pattern.
def enterPattern(self, ctx):
pass
# Exit a parse tree produced by PiParser#pattern.
def exitPattern(self, ctx):
pass
# Enter a parse tree produced by PiParser#adecpattern.
def enterAdecpattern(self, ctx):
pass
# Exit a parse tree produced by PiParser#adecpattern.
def exitAdecpattern(self, ctx):
pass
# Enter a parse tree produced by PiParser#veripattern.
def enterVeripattern(self, ctx):
pass
# Exit a parse tree produced by PiParser#veripattern.
def exitVeripattern(self, ctx):
pass
# Enter a parse tree produced by PiParser#newlines.
def enterNewlines(self, ctx):
pass
# Exit a parse tree produced by PiParser#newlines.
def exitNewlines(self, ctx):
pass
| 23.348052 | 122 | 0.653243 | 1,131 | 8,989 | 5.191866 | 0.147657 | 0.086853 | 0.144755 | 0.260559 | 0.785252 | 0.785252 | 0.780484 | 0.779632 | 0.614782 | 0.614782 | 0 | 0.000617 | 0.279008 | 8,989 | 384 | 123 | 23.408854 | 0.905416 | 0.477027 | 0 | 0.494118 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.494118 | false | 0.494118 | 0.005882 | 0 | 0.505882 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 9 |
4b4b36b183814cee8a65f5c31d093083d5ab7166 | 23,567 | py | Python | tests/test_mde_speed.py | liuyxpp/chebpy | 05a9492d0d78591a39923e4a85a0f24bcc79ae4f | [
"BSD-3-Clause"
] | null | null | null | tests/test_mde_speed.py | liuyxpp/chebpy | 05a9492d0d78591a39923e4a85a0f24bcc79ae4f | [
"BSD-3-Clause"
] | null | null | null | tests/test_mde_speed.py | liuyxpp/chebpy | 05a9492d0d78591a39923e4a85a0f24bcc79ae4f | [
"BSD-3-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
#/usr/bin/env python
"""
test_mde_speed
==============
Speed of modiffied diffusion equation (MDE) solvers.
"""
from time import time, clock
import numpy as np
from numpy.fft import fft, ifft
from scipy.io import savemat, loadmat
from scipy.linalg import eigvals
from scipy.integrate import simps, romb
import matplotlib.pyplot as plt
from timer import Timer
import mpltex.acs # ACS configured matplotlib
from chebpy import cheb_mde_oss, cheb_mde_osc, OSS, OSC
from chebpy import OSCHEB
from chebpy import BC, ETDRK4
from chebpy import clencurt_weights_fft, cheb_quadrature_clencurt
from chebpy import cheb_D1_mat, cheb_D2_mat_dirichlet_robin
from chebpy import cheb_D2_mat_dirichlet_dirichlet
from chebpy import cheb_D2_mat
from chebpy import cheb_interpolation_1d
from chebpy import oss_integral_weights
from chebpy import etdrk4_coeff_nondiag
def init_fourier(N, L, show=False):
'''
For equispaced grid.
'''
ii = np.arange(N+1)
x = 1. * ii * L / N
sech = 1. / np.cosh(.75 * (2.*x - L))
W = 1. - 2. * sech * sech
u0 = np.ones_like(x)
if show:
plt.figure()
plt.plot(x, W, 'b')
plt.axis([0, 10, -1.1, 1.1,])
plt.xlabel('$z$')
plt.ylabel('$w(z)$')
plt.savefig('benchmark/w(z)', bbox_inches='tight')
plt.show()
return W, u0, x
def init_chebyshev_fredrikson(N, L, show=False):
'''
For Chebyshev grid.
'''
ii = np.arange(N+1)
x = np.cos(np.pi * ii / N)
x = .5 * (x + 1) * L
sech = 1. / np.cosh(.75 * (2.*x - L))
W = 1. - 2. * sech * sech
u0 = np.ones_like(x)
u0[0] = 0.; u0[-1] = 0.
if show:
plt.figure()
plt.plot(x, W, 'b')
plt.axis([0, 10, -1.1, 1.1,])
plt.xlabel('$z$')
plt.ylabel('$w(z)$')
plt.savefig('benchmark/w(z)', bbox_inches='tight')
plt.show()
return W, u0, x
def init_chebyshev(N, L, show=True):
'''
For Chebyshev grid.
'''
ii = np.arange(N+1)
x = np.cos(np.pi * ii / N)
x = .5 * (x + 1) * L
W = -.1 * (np.pi * x / 4)**2
u0 = np.zeros(N+1)
w = clencurt_weights_fft(N)
ix = 40
u0[ix] = (2.0/L) / w[ix]
if show:
plt.figure()
plt.plot(x, W, 'b')
#plt.axis([0, 10, -1.1, 1.1,])
plt.xlabel('$z$')
plt.ylabel('$w(z)$')
#plt.savefig('benchmark/w(z)', bbox_inches='tight')
plt.show()
plt.plot(x, u0, 'r')
#plt.axis([0, 10, -1.1, 1.1,])
plt.xlabel('$z$')
plt.ylabel('$u0(z)$')
plt.show()
return W, u0, x
def test_exact_dirichlet(oss=0,oscheb=0,etdrk4=0):
L = 10.0
if oss:
N = 1024 #4096
Ns = 1000 + 1 #100000 + 1
W, u0, x = init_fourier(N, L)
u0[0] = 0.; u0[N] = 0.;
print 'OSS N = ', N, ' Ns = ', Ns-1
#q1, x1 = cheb_mde_oss(W, u0, L, Ns)
oss_solver = OSS(L, N, Ns)
q1, x1 = oss_solver.solve(W, u0)
Q1 = L * oss_integral_weights(q1)
#data_name = 'benchmark/exact/OSS_N' + str(N) + '_Ns' + str(Ns-1)
data_name = 'OSS_N' + str(N) + '_Ns' + str(Ns-1)
savemat(data_name,{
'N':N, 'Ns':Ns-1, 'W':W, 'u0':u0, 'Lz':L,
'x':x, 'q':q1, 'Q':Q1})
plt.plot(x1, q1, 'b')
plt.axis([0, 10, 0, 1.15])
#plt.show()
if oscheb:
N = 128 #16384
Ns = 200 + 1 #1000000 + 1
W, u0, x = init_chebyshev_fredrikson(N, L)
u0[0] = 0; u0[N] = 0;
print 'OSCHEB N = ', N, ' Ns = ', Ns-1
#q2 = cheb_mde_dirichlet_oscheb(W, u0, L, Ns)
oscheb_sovler = OSCHEB(L, N, Ns)
q2, x2 = oscheb_sovler.solve(W, u0)
Q2 = 0.5 * L * cheb_quadrature_clencurt(q2)
#data_name = 'benchmark/exact/OSCHEB_N' + str(N) + '_Ns' + str(Ns-1)
data_name = 'OSCHEB_N' + str(N) + '_Ns' + str(Ns-1)
savemat(data_name,{
'N':N, 'Ns':Ns-1, 'W':W, 'u0':u0, 'Lz':L,
'x':x2, 'q':q2, 'Q':Q2})
plt.plot(x2, q2, 'g')
plt.axis([0, 10, 0, 1.15])
plt.xlabel('$z$')
plt.ylabel('$q(z)$')
plt.savefig(data_name, bbox_inches='tight')
#plt.show()
if etdrk4:
N = 128
Ns = 200 + 1 #20000 + 1
algo = 1
scheme = 1
W, u0, x = init_chebyshev_fredrikson(N, L)
u0[0] = 0; u0[N] = 0;
print 'ETDRK4 N = ', N, ' Ns = ', Ns-1
#q3, x3 = cheb_mde_dirichlet_etdrk4(W, u0, L, Ns, algo, scheme)
etdrk4_solver = ETDRK4(L, N, Ns)
q3, x3 = etdrk4_solver.solve(W, u0)
Q3 = 0.5 * L * cheb_quadrature_clencurt(q3)
#data_name = 'benchmark/exact/ETDRK4_N' + str(N) + '_Ns' + str(Ns-1)
data_name = 'ETDRK4_N' + str(N) + '_Ns' + str(Ns-1)
savemat(data_name,{
'N':N, 'Ns':Ns-1, 'W':W, 'u0':u0, 'Lz':L,
'x':x, 'q':q3, 'Q':Q3})
plt.plot(x3, q3, 'r')
plt.axis([0, 10, 0, 1.15])
plt.xlabel('$z$')
plt.ylabel('$q(z)$')
plt.savefig(data_name, bbox_inches='tight')
plt.show()
def test_exact_neumann(osc=0,oscheb=0,etdrk4=0):
L = 10.0
if osc:
N = 128
Ns = 1000 + 1 #20000 + 1
W, u0, x = init_fourier(N, L)
print 'OSC N = ', N, ' Ns = ', Ns-1
#q1, x1 = cheb_mde_osc(W, u0, L, Ns)
osc_solver = OSC(L, N, Ns)
q1, x1 = osc_solver.solve(W, u0)
Q1 = L * simps(q1, dx=1./N)
#data_name = 'benchmark/NBC-NBC/exact/OSS_N'
data_name = 'OSS_N'
data_name = data_name + str(N) + '_Ns' + str(Ns-1)
savemat(data_name,{
'N':N, 'Ns':Ns-1, 'W':W, 'u0':u0, 'Lz':L,
'x':x, 'q':q1, 'Q':Q1})
plt.plot(x1, q1, 'b')
plt.axis([0, 10, 0, 1.15])
plt.xlabel('$z$')
plt.ylabel('$q(z)$')
plt.savefig(data_name, bbox_inches='tight')
#plt.show()
if oscheb:
N = 128
Ns = 200 + 1 #20000 + 1
W, u0, x = init_chebyshev_fredrikson(N, L)
print 'OSCHEB N = ', N, ' Ns = ', Ns-1
#q2 = cheb_mde_neumann_oscheb(W, u0, L, Ns)
oscheb_sovler = OSCHEB(L, N, Ns, bc=BC('Neumann'))
q2, x2 = oscheb_sovler.solve(W, u0)
Q2 = 0.5 * L * cheb_quadrature_clencurt(q2)
#data_name = 'benchmark/NBC-NBC/exact/OSCHEB_N'
data_name = 'OSCHEB_N'
data_name = data_name + str(N) + '_Ns' + str(Ns-1)
savemat(data_name,{
'N':N, 'Ns':Ns-1, 'W':W, 'u0':u0, 'Lz':L,
'x':x2, 'q':q2, 'Q':Q2})
plt.plot(x2, q2, 'g')
plt.axis([0, 10, 0, 1.15])
plt.xlabel('$z$')
plt.ylabel('$q(z)$')
plt.savefig(data_name, bbox_inches='tight')
#plt.show()
if etdrk4:
N = 128
Ns = 200 + 1
algo = 1
scheme = 1
W, u0, x = init_chebyshev_fredrikson(N, L)
print 'ETDRK4 N = ', N, ' Ns = ', Ns-1
#q3, x3 = cheb_mde_neumann_etdrk4(W, u0, L, Ns, None, algo, scheme)
lbc = BC('Neumann')
rbc = BC('Neumann')
etdrk4_solver = ETDRK4(L, N, Ns, h=None, lbc=lbc, rbc=rbc)
q3, x3 = etdrk4_solver.solve(W, u0)
Q3 = 0.5 * L * cheb_quadrature_clencurt(q3)
#if scheme == 0:
# data_name = 'benchmark/NBC-NBC/exact/ETDRK4_Cox_N'
# data_name = data_name + str(N) + '_Ns' + str(Ns-1)
#else:
# data_name = 'benchmark/NBC-NBC/exact/ETDRK4_Krogstad_N'
# data_name = data_name + str(N) + '_Ns' + str(Ns-1)
#savemat(data_name,{
# 'N':N, 'Ns':Ns-1, 'W':W, 'u0':u0, 'Lz':L,
# 'x':x, 'q':q3, 'Q':Q3})
plt.plot(x3, q3, 'r')
plt.axis([0, 10, 0, 1.15])
plt.xlabel('$z$')
plt.ylabel('$q(z)$')
#plt.savefig(data_name, bbox_inches='tight')
plt.show()
def test_exact_neumann_dirichlet():
L = 10
N = 128
Ns = 200 + 1 #20000 + 1
algo = 1
scheme = 1
W, u0, x = init_chebyshev_fredrikson(N, L)
u0[0] = 0.
print 'ETDRK4 N = ', N, ' Ns = ', Ns-1
#q3, x3 = cheb_mde_neumann_dirichlet_etdrk4(W, u0, L, Ns, algo, scheme)
lbc = BC('Neumann')
rbc = BC('Dirichlet')
etdrk4_solver = ETDRK4(L, N, Ns, h=None, lbc=lbc, rbc=rbc)
q3, x3 = etdrk4_solver.solve(W, u0)
Q3 = 0.5 * L * cheb_quadrature_clencurt(q3)
if scheme == 0:
#data_name = 'benchmark/NBC-DBC/exact/ETDRK4_Cox_N'
data_name = 'ETDRK4_Cox_N'
data_name = data_name + str(N) + '_Ns' + str(Ns-1)
else:
#data_name = 'benchmark/NBC-DBC/exact/ETDRK4_Krogstad_N'
data_name = 'ETDRK4_Krogstad_N'
data_name = data_name + str(N) + '_Ns' + str(Ns-1)
savemat(data_name,{
'N':N, 'Ns':Ns-1, 'W':W, 'u0':u0, 'Lz':L,
'x':x, 'q':q3, 'Q':Q3})
plt.plot(x3, q3, 'r')
plt.axis([0, 10, 0, 1.15])
plt.xlabel('$z$')
plt.ylabel('$q(z)$')
plt.savefig(data_name, bbox_inches='tight')
plt.show()
def test_exact_robin_dirichlet():
L = 10.0
N = 128
Ns = 200 + 1 # 20000 + 1
ka = 1.0
algo = 1
scheme = 1
W, u0, x = init_chebyshev_fredrikson(N, L)
u0[0] = 0.
print 'ETDRK4 N = ', N, ' Ns = ', Ns-1
#q3, x3 = cheb_mde_robin_dirichlet_etdrk4(W, u0, L, Ns, ka, algo, scheme)
lbc = BC('Robin', (1.0, ka, 0.0))
rbc = BC('Dirichlet')
etdrk4_solver = ETDRK4(L, N, Ns, h=None, lbc=lbc, rbc=rbc)
q3, x3 = etdrk4_solver.solve(W, u0)
Q3 = 0.5 * L * cheb_quadrature_clencurt(q3)
if scheme == 0:
#data_name = 'benchmark/RBC-DBC/exact/ETDRK4_Cox_N'
data_name = 'ETDRK4_Cox_N'
data_name = data_name + str(N) + '_Ns' + str(Ns-1)
else:
#data_name = 'benchmark/RBC-DBC/exact/ETDRK4_Krogstad_N'
data_name = 'ETDRK4_Krogstad_N'
data_name = data_name + str(N) + '_Ns' + str(Ns-1)
savemat(data_name,{
'N':N, 'Ns':Ns-1, 'W':W, 'u0':u0, 'Lz':L,
'x':x, 'q':q3, 'Q':Q3})
plt.plot(x3, q3, 'r')
plt.axis([0, 10, 0, 1.15])
plt.xlabel('$z$')
plt.ylabel('$q(z)$')
plt.savefig(data_name, bbox_inches='tight')
plt.show()
def test_exact_robin():
L = 10
N = 128
Ns = 200 + 1 #20000 + 1
ka = -1. * L
kb = 0.5 * L
algo = 1
scheme = 1
W, u0, x = init_chebyshev_fredrikson(N, L)
print 'ETDRK4 N = ', N, ' Ns = ', Ns-1
#q3, x3 = cheb_mde_robin_etdrk4(W, u0, L, Ns, ka, kb, algo, scheme)
lbc = BC('Robin', (1.0, ka, 0.0))
rbc = BC('Robin', (1.0, kb, 0.0))
etdrk4_solver = ETDRK4(L, N, Ns, h=None, lbc=lbc, rbc=rbc)
q3, x3 = etdrk4_solver.solve(W, u0)
Q3 = 0.5 * L * cheb_quadrature_clencurt(q3)
if scheme == 0:
#data_name = 'benchmark/RBC-RBC/exact/ETDRK4_Cox_N'
data_name = 'ETDRK4_Cox_N'
data_name = data_name + str(N) + '_Ns' + str(Ns-1)
else:
#data_name = 'benchmark/RBC-RBC/exact/ETDRK4_Krogstad_N'
data_name = 'ETDRK4_Krogstad_N'
data_name = data_name + str(N) + '_Ns' + str(Ns-1)
savemat(data_name,{
'N':N, 'Ns':Ns-1, 'W':W, 'u0':u0, 'Lz':L,
'x':x, 'q':q3, 'Q':Q3})
plt.plot(x3, q3, 'r')
plt.axis([0, 10, 0, 1.15])
plt.xlabel('$z$')
plt.ylabel('$q(z)$')
plt.savefig(data_name, bbox_inches='tight')
plt.show()
def test_speed_space_oss():
'''
Confirm the complexity O(NlnN) of OSS.
'''
# Construct reference solution
oscheb_ref = '../benchmark/exact/OSCHEB_N'
oscheb_ref = oscheb_ref + '8192_Ns200000.mat'
mat = loadmat(oscheb_ref)
q_ref = mat['q']
Q_ref = mat['Q'][0,0]
N_ref = mat['N']
Ns_ref = mat['Ns']
L = 10
n = 18 # Nmax = 2^n
Ns = 200+1 # highest accuracy for reference. h = 1e-4
M_array = np.ones(n-1) # number of same run
M_array[:11] = 5000
M_array[11:14] = 1000 #8192, 16384, 32768
M_array[14] = 500 # 65536
M_array[15] = 200 # 131072
M_array[16] = 100 # 262144
is_save = 1
N_array = []
t_full_array = [] # include initialization
t_array = [] # do not include initialization
err_array = []
i = 0
for N in 2**np.arange(2, n+1):
M = int(M_array[i])
W, u0, x = init_fourier(N, L)
u0[0] = 0.; u0[N] = 0.;
with Timer() as t:
for m in xrange(M):
solver = OSS(L, N, Ns)
q, x = solver.solve(W, u0)
t_full = t.secs / M
t_full_array.append(t_full)
solver = OSS(L, N, Ns)
with Timer() as t:
for m in xrange(M):
q, x = solver.solve(W, u0)
t = t.secs / M
t_array.append(t)
N_array.append(N)
q.shape = (q.size,)
Q = L * oss_integral_weights(q)
err = np.abs(Q - Q_ref) / np.abs(Q_ref)
err_array.append(err)
print N, '\t', t_full_array[-1], '\t',
print t_array[-1], '\t', err_array[-1]
i += 1
if is_save:
savemat('speed_OSS_N',{
'N':N_array, 'Ns':Ns-1, 'N_ref':N_ref, 'Ns_ref':Ns_ref,
't_full':t_full_array, 't':t_array, 'err':err_array})
plt.figure()
ax = plt.subplot(111)
ax.plot(N_array, t_full_array, '.-', label='Full')
ax.plot(N_array, t_array, '.-', label='Core')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('$N$')
plt.ylabel('Computer time')
plt.grid('on')
ax.legend(loc='upper left')
if is_save:
plt.savefig('speed_OSS_N', bbox_inches='tight')
plt.show()
plt.figure()
ax = plt.subplot(111)
ax.plot(err_array, t_array, 'o-')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('Relative error in $Q$')
plt.ylabel('Computer time')
plt.grid('on')
if is_save:
plt.savefig('speed_error_OSS_N', bbox_inches='tight')
plt.show()
def test_speed_accuracy_oss():
'''
Computation time vs. error.
'''
# Construct reference solution
oscheb_ref = '../benchmark/exact/OSCHEB_N'
oscheb_ref = oscheb_ref + '8192_Ns200000.mat'
mat = loadmat(oscheb_ref)
q_ref = mat['q']
Q_ref = mat['Q'][0,0]
N_ref = mat['N']
Ns_ref = mat['Ns']
L = 10
n = 17 # Nmax = 2^n
Ns = 20000+1 # highest accuracy for reference. h = 1e-4
M_array = np.ones(n-1) # number of same run
M_array[:7] = 600
M_array[7:10] = 300 # 512, 1024, 2048
M_array[10] = 160 # 4096
M_array[11] = 80 # 8192
M_array[12] = 40 #16384, 32768
M_array[13] = 20 #16384, 32768
M_array[14] = 10 # 65536
M_array[15] = 3 # 131072
is_save = 1
N_array = []
t_array = [] # do not include initialization
err_array = []
i = 0
for N in 2**np.arange(2, n+1):
M = int(M_array[i])
W, u0, x = init_fourier(N, L)
u0[0] = 0.; u0[N] = 0.;
solver = OSS(L, N, Ns)
with Timer() as t:
for m in xrange(M):
q, x = solver.solve(W, u0)
t = t.secs / M
t_array.append(t)
N_array.append(N)
q.shape = (q.size,)
Q = L * oss_integral_weights(q)
err = np.abs(Q - Q_ref) / np.abs(Q_ref)
err_array.append(err)
print N, '\t', t_array[-1], '\t', err_array[-1]
i += 1
if is_save:
savemat('speed_OSS_accuracy',{
'N':N_array, 'Ns':Ns-1, 'N_ref':N_ref, 'Ns_ref':Ns_ref,
't':t_array, 'err':err_array})
plt.figure()
ax = plt.subplot(111)
ax.plot(N_array, t_array, 'o-')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('$N$')
plt.ylabel('Computer time')
plt.grid('on')
if is_save:
plt.savefig('speed_OSS_accuracy', bbox_inches='tight')
plt.show()
plt.figure()
ax = plt.subplot(111)
ax.plot(t_array, err_array, 'o-')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('Computer time')
plt.ylabel('Relative error in $Q$')
plt.grid('on')
if is_save:
plt.savefig('speed_error_OSS_accuracy', bbox_inches='tight')
plt.show()
def test_speed_space_oscheb():
'''
Confirm the complexity O(NlnN) of OSCHEB.
'''
# Construct reference solution
oscheb_ref = '../benchmark/exact/OSCHEB_N'
oscheb_ref = oscheb_ref + '8192_Ns200000.mat'
mat = loadmat(oscheb_ref)
q_ref = mat['q']
Q_ref = mat['Q'][0,0]
N_ref = mat['N']
Ns_ref = mat['Ns']
L = 10
n = 10 # Nmax = 2^n
Ns = 200+1 # highest accuracy for reference. h = 1e-4
M_array = np.ones(n-1) # number of same run
M_array[:5] = 1000 # 4, 8, 16, 32, 64
M_array[5] = 500 # 128
M_array[6] = 200 # 256
M_array[7] = 100 # 512
M_array[8] = 50 # 1024
is_save = 1
N_array = []
t_full_array = [] # include initialization
t_array = [] # do not include initialization
err_array = []
i = 0
for N in 2**np.arange(2, n+1):
M = int(M_array[i])
W, u0, x = init_chebyshev_fredrikson(N, L)
u0[0] = 0.; u0[N] = 0.;
solver = OSCHEB(L, N, Ns)
t = clock()
for m in xrange(M):
q, x = solver.solve(W, u0)
t = (clock() - t) / M
t_array.append(t)
t_full = clock()
for m in xrange(M):
solver = OSCHEB(L, N, Ns)
q, x = solver.solve(W, u0)
t_full = (clock() - t_full) / M
t_full_array.append(t_full)
N_array.append(N)
q.shape = (q.size,)
Q = 0.5 * L * cheb_quadrature_clencurt(q)
err = np.abs(Q - Q_ref) / np.abs(Q_ref)
err_array.append(err)
print N, '\t', t_full_array[-1], '\t',
print t_array[-1], '\t', err_array[-1]
i += 1
if is_save:
savemat('speed_OSCHEB_N',{
'N':N_array, 'Ns':Ns-1, 'N_ref':N_ref, 'Ns_ref':Ns_ref,
't_full':t_full_array, 't':t_array, 'err':err_array})
plt.figure()
ax = plt.subplot(111)
ax.plot(N_array, t_full_array, '.-', label='Full')
ax.plot(N_array, t_array, '.-', label='Core')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('$N$')
plt.ylabel('Computer time')
plt.grid('on')
ax.legend(loc='upper left')
if is_save:
plt.savefig('speed_OSCHEB_N', bbox_inches='tight')
plt.show()
plt.figure()
ax = plt.subplot(111)
ax.plot(err_array, t_array, 'o-')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('Relative error in $Q$')
plt.ylabel('Computer time')
plt.grid('on')
if is_save:
plt.savefig('speed_error_OSCHEB_N', bbox_inches='tight')
plt.show()
def test_speed_accuracy_oscheb():
'''
Computation time vs. error.
'''
# Construct reference solution
oscheb_ref = '../benchmark/exact/OSCHEB_N'
oscheb_ref = oscheb_ref + '8192_Ns200000.mat'
mat = loadmat(oscheb_ref)
q_ref = mat['q']
Q_ref = mat['Q'][0,0]
N_ref = mat['N']
Ns_ref = mat['Ns']
L = 10
n = 10 # Nmax = 2^n
Ns = 20000+1
M_array = np.ones(n-1) # number of same run
#M_array[:5] = 1000 # 4, 8, 16, 32, 64
#M_array[5] = 500 # 128
#M_array[6] = 200 # 256
#M_array[7] = 100 # 512
#M_array[8] = 50 # 1024
is_save = 1
N_array = []
t_array = [] # do not include initialization
err_array = []
i = 0
for N in 2**np.arange(2, n+1):
M = int(M_array[i])
W, u0, x = init_chebyshev_fredrikson(N, L)
u0[0] = 0.; u0[N] = 0.;
solver = OSCHEB(L, N, Ns)
t = clock()
for m in xrange(M):
q, x = solver.solve(W, u0)
t = (clock() - t) / M
t_array.append(t)
N_array.append(N)
q.shape = (q.size,)
Q = 0.5 * L * cheb_quadrature_clencurt(q)
err = np.abs(Q - Q_ref) / np.abs(Q_ref)
err_array.append(err)
print N, '\t', t_array[-1], '\t', err_array[-1]
i += 1
if is_save:
savemat('speed_OSCHEB_accuracy',{
'N':N_array, 'Ns':Ns-1, 'N_ref':N_ref, 'Ns_ref':Ns_ref,
't':t_array, 'err':err_array})
plt.figure()
ax = plt.subplot(111)
ax.plot(N_array, t_array, 'o-')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('$N$')
plt.ylabel('Computer time')
plt.grid('on')
if is_save:
plt.savefig('speed_OSCHEB_accuracy', bbox_inches='tight')
plt.show()
plt.figure()
ax = plt.subplot(111)
ax.plot(t_array, err_array, 'o-')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('Computer time')
plt.ylabel('Relative error in $Q$')
plt.grid('on')
if is_save:
plt.savefig('speed_error_OSCHEB_accuracy', bbox_inches='tight')
plt.show()
def test_speed_space_etdrk4():
'''
The expect complexity for ETDRK4 is O(N^2).
However, due to the calculation of matrix exponential,
it exceeds O(N^2) for large N.
'''
# Construct reference solution
oscheb_ref = '../benchmark/exact/OSCHEB_N'
oscheb_ref = oscheb_ref + '8192_Ns200000.mat'
mat = loadmat(oscheb_ref)
q_ref = mat['q']
Q_ref = mat['Q'][0,0]
N_ref = mat['N']
Ns_ref = mat['Ns']
L = 10.0
n = 10 # Nmax = 2^n
Ns = 200+1 # highest accuracy for reference. h = 1e-4
M_array = np.ones(n-1) # number of same run
M_array[0:5] = 1000 # 4, 8, 16, 32, 64
M_array[5] = 500 # 128
M_array[6] = 100 # 256
M_array[7] = 20 # 512
M_array[8] = 5 # 1024
N_array = []
t_full_array = []
t_array = []
err_array = []
i = 0
for N in 2**np.arange(2, n+1):
M = int(M_array[i])
W, u0, x = init_chebyshev_fredrikson(N, L)
solver = ETDRK4(L, N, Ns)
t = clock()
for m in xrange(M):
q, x = solver.solve(W, u0)
t = (clock() - t) / M
t_array.append(t)
t_full = clock()
for m in xrange(M):
solver = ETDRK4(L, N, Ns)
q, x = solver.solve(W, u0)
t_full = (clock() - t_full) / M
t_full_array.append(t_full)
N_array.append(N)
q.shape = (q.size,)
Q = 0.5 * L * cheb_quadrature_clencurt(q)
err = np.abs(Q - Q_ref) / np.abs(Q_ref)
err_array.append(err)
print N, '\t', t_full_array[-1], '\t',
print t_array[-1], '\t', err_array[-1]
i += 1
is_save = 1
is_display = 1
if is_save:
savemat('speed_ETDRK4_N',{
'N':N_array, 'Ns':Ns-1, 'N_ref':N_ref, 'Ns_ref':Ns_ref,
't_full':t_full_array, 't':t_array, 'err':err_array})
if is_display:
plt.figure()
ax = plt.subplot(111)
ax.plot(N_array, t_full_array, '.-', label='Full')
ax.plot(N_array, t_array, '.-', label='Core')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('$N$')
plt.ylabel('Computer time')
plt.grid('on')
ax.legend(loc='upper left')
if is_save:
plt.savefig('speed_ETDRK4_N', bbox_inches='tight')
plt.show()
plt.figure()
ax = plt.subplot(111)
ax.plot(err_array, t_array, 'o-')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('Relative error in $Q$')
plt.ylabel('Computer time')
plt.grid('on')
if is_save:
plt.savefig('speed_error_ETDRK4_N', bbox_inches='tight')
plt.show()
if __name__ == '__main__':
#test_exact_dirichlet(1,1,1)
#test_exact_neumann(1,1,1)
#test_exact_neumann_dirichlet()
#test_exact_robin_dirichlet()
#test_exact_robin()
#test_speed_space_oss()
#test_speed_accuracy_oss()
#test_speed_space_oscheb()
test_speed_accuracy_oscheb()
#test_speed_space_etdrk4()
| 28.916564 | 77 | 0.529766 | 3,708 | 23,567 | 3.19849 | 0.067422 | 0.01543 | 0.009696 | 0.031872 | 0.862732 | 0.849916 | 0.820658 | 0.8043 | 0.794941 | 0.77597 | 0 | 0.066037 | 0.298977 | 23,567 | 814 | 78 | 28.952088 | 0.651837 | 0.124072 | 0 | 0.827751 | 0 | 0 | 0.087897 | 0.011413 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.030303 | null | null | 0.027113 | 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 |
4bb86148ccbcfb7cf662a457034da6e56d28a96f | 2,534 | py | Python | tests/algos/torch/test_bc_impl.py | meokz/d3rlpy | 40504e2d8b424547558ab82786c523e8f4626a82 | [
"MIT"
] | 2 | 2021-04-21T08:19:29.000Z | 2021-05-17T09:08:06.000Z | tests/algos/torch/test_bc_impl.py | meokz/d3rlpy | 40504e2d8b424547558ab82786c523e8f4626a82 | [
"MIT"
] | null | null | null | tests/algos/torch/test_bc_impl.py | meokz/d3rlpy | 40504e2d8b424547558ab82786c523e8f4626a82 | [
"MIT"
] | null | null | null | import pytest
from d3rlpy.algos.torch.bc_impl import BCImpl, DiscreteBCImpl
from d3rlpy.augmentation import AugmentationPipeline
from tests.algos.algo_test import torch_impl_tester, DummyScaler
@pytest.mark.parametrize('observation_shape', [(100, ), (4, 84, 84)])
@pytest.mark.parametrize('action_size', [2])
@pytest.mark.parametrize('learning_rate', [1e-3])
@pytest.mark.parametrize('eps', [1e-8])
@pytest.mark.parametrize('use_batch_norm', [True, False])
@pytest.mark.parametrize('scaler', [None, DummyScaler()])
@pytest.mark.parametrize('augmentation', [AugmentationPipeline()])
@pytest.mark.parametrize('n_augmentations', [1])
@pytest.mark.parametrize('encoder_params', [{}])
def test_bc_impl(observation_shape, action_size, learning_rate, eps,
use_batch_norm, scaler, augmentation, n_augmentations,
encoder_params):
impl = BCImpl(observation_shape,
action_size,
learning_rate,
eps,
use_batch_norm,
use_gpu=False,
scaler=scaler,
augmentation=augmentation,
n_augmentations=n_augmentations,
encoder_params=encoder_params)
torch_impl_tester(impl, discrete=False, imitator=True)
@pytest.mark.parametrize('observation_shape', [(100, ), (4, 84, 84)])
@pytest.mark.parametrize('action_size', [2])
@pytest.mark.parametrize('learning_rate', [1e-3])
@pytest.mark.parametrize('eps', [1e-8])
@pytest.mark.parametrize('beta', [0.5])
@pytest.mark.parametrize('use_batch_norm', [True, False])
@pytest.mark.parametrize('scaler', [None, DummyScaler()])
@pytest.mark.parametrize('augmentation', [AugmentationPipeline()])
@pytest.mark.parametrize('n_augmentations', [1])
@pytest.mark.parametrize('encoder_params', [{}])
def test_bc_impl(observation_shape, action_size, learning_rate, eps, beta,
use_batch_norm, scaler, augmentation, n_augmentations,
encoder_params):
impl = DiscreteBCImpl(observation_shape,
action_size,
learning_rate,
eps,
beta,
use_batch_norm,
use_gpu=False,
scaler=scaler,
augmentation=augmentation,
n_augmentations=n_augmentations,
encoder_params=encoder_params)
torch_impl_tester(impl, discrete=True, imitator=True)
| 43.689655 | 74 | 0.631413 | 260 | 2,534 | 5.923077 | 0.2 | 0.123377 | 0.259091 | 0.067532 | 0.853896 | 0.853896 | 0.853896 | 0.853896 | 0.853896 | 0.853896 | 0 | 0.016798 | 0.248224 | 2,534 | 57 | 75 | 44.45614 | 0.791601 | 0 | 0 | 0.769231 | 0 | 0 | 0.084451 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.038462 | false | 0 | 0.076923 | 0 | 0.115385 | 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 |
29b558a14ba0ec2a0123bcffc8f6b03693cb1e0c | 1,005 | py | Python | spring/fitsprep/zip_folders.py | RobertJaro/SpringProject | c1ca42650e5dfc6918b7e239fd52b02402ccb1c0 | [
"Apache-2.0"
] | null | null | null | spring/fitsprep/zip_folders.py | RobertJaro/SpringProject | c1ca42650e5dfc6918b7e239fd52b02402ccb1c0 | [
"Apache-2.0"
] | null | null | null | spring/fitsprep/zip_folders.py | RobertJaro/SpringProject | c1ca42650e5dfc6918b7e239fd52b02402ccb1c0 | [
"Apache-2.0"
] | null | null | null | import shutil
shutil.make_archive('/observations/solarnet-campaign/level1_5/halph_2019-07-17', 'zip', '/observations/solarnet-campaign/level1_5/halph/17')
shutil.make_archive('/observations/solarnet-campaign/level1_5/caiik_2019-07-17', 'zip', '/observations/solarnet-campaign/level1_5/caiik/17')
shutil.make_archive('/observations/solarnet-campaign/level1_5/caiik_2019-07-18', 'zip', '/observations/solarnet-campaign/level1_5/caiik/18')
shutil.make_archive('/observations/solarnet-campaign/level1_5/caiik_2019-07-19', 'zip', '/observations/solarnet-campaign/level1_5/caiik/19')
shutil.make_archive('/observations/solarnet-campaign/level1_5/bband_2019-07-17', 'zip', '/observations/solarnet-campaign/level1_5/bband/17')
shutil.make_archive('/observations/solarnet-campaign/level1_5/bband_2019-07-23', 'zip', '/observations/solarnet-campaign/level1_5/bband/23')
shutil.make_archive('/observations/solarnet-campaign/level1_5/halph_2019-07-18', 'zip', '/observations/solarnet-campaign/level1_5/halph/18')
| 77.307692 | 140 | 0.80597 | 142 | 1,005 | 5.507042 | 0.133803 | 0.358056 | 0.501279 | 0.608696 | 0.969309 | 0.969309 | 0.969309 | 0.827366 | 0.827366 | 0.569054 | 0 | 0.100205 | 0.026866 | 1,005 | 12 | 141 | 83.75 | 0.699387 | 0 | 0 | 0 | 0 | 0 | 0.759204 | 0.738308 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.125 | 0 | 0.125 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
d9a9776a5b52f25f5783c94dbfec41fc14d82981 | 251 | py | Python | python/testData/inspections/PyArgumentListInspection/dictFromKeys.py | Sajaki/intellij-community | 6748af2c40567839d11fd652ec77ba263c074aad | [
"Apache-2.0"
] | 1 | 2020-05-14T18:47:46.000Z | 2020-05-14T18:47:46.000Z | python/testData/inspections/PyArgumentListInspection/dictFromKeys.py | Sajaki/intellij-community | 6748af2c40567839d11fd652ec77ba263c074aad | [
"Apache-2.0"
] | null | null | null | python/testData/inspections/PyArgumentListInspection/dictFromKeys.py | Sajaki/intellij-community | 6748af2c40567839d11fd652ec77ba263c074aad | [
"Apache-2.0"
] | null | null | null | print(dict.fromkeys(<warning descr="Parameter(s) unfilledPossible callees:dict.fromkeys(cls: Type[dict], __iterable: Iterable[_T])dict.fromkeys(cls: Type[dict], __iterable: Iterable[_T], __value: _S)">)</warning>)
print(dict.fromkeys(['foo', 'bar']))
| 83.666667 | 213 | 0.74502 | 33 | 251 | 5.393939 | 0.484848 | 0.269663 | 0.191011 | 0.213483 | 0.449438 | 0.449438 | 0.449438 | 0.449438 | 0 | 0 | 0 | 0 | 0.055777 | 251 | 2 | 214 | 125.5 | 0.751055 | 0 | 0 | 0 | 0 | 0.5 | 0.673307 | 0.227092 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 1 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 9 |
d9ae1ecf9659c41b22e178e7e360bb09e84f62fb | 93 | py | Python | tests/conftest.py | WxBDM/metar_to_xml | 29a3025d756fece0543f4ae53cb31b4175e89bff | [
"MIT"
] | null | null | null | tests/conftest.py | WxBDM/metar_to_xml | 29a3025d756fece0543f4ae53cb31b4175e89bff | [
"MIT"
] | null | null | null | tests/conftest.py | WxBDM/metar_to_xml | 29a3025d756fece0543f4ae53cb31b4175e89bff | [
"MIT"
] | null | null | null | import pytest
from tests.fixtures.metars import *
from tests.fixtures.parsed_metars import *
| 23.25 | 42 | 0.827957 | 13 | 93 | 5.846154 | 0.538462 | 0.236842 | 0.447368 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.107527 | 93 | 3 | 43 | 31 | 0.915663 | 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 |
d9c744b8041c398f568eda8563fecc2e82ba6001 | 135 | py | Python | src/field_schnet/utils/__init__.py | atomistic-machine-learning/field_schnet | 0dcc72a91eaa6eb9d65183a8b6fb98a4330d1e5b | [
"MIT"
] | 4 | 2021-06-19T01:21:41.000Z | 2021-08-21T01:47:29.000Z | src/field_schnet/utils/__init__.py | atomistic-machine-learning/field_schnet | 0dcc72a91eaa6eb9d65183a8b6fb98a4330d1e5b | [
"MIT"
] | null | null | null | src/field_schnet/utils/__init__.py | atomistic-machine-learning/field_schnet | 0dcc72a91eaa6eb9d65183a8b6fb98a4330d1e5b | [
"MIT"
] | null | null | null | from field_schnet.utils.basic_utils import *
from field_schnet.utils.script_utils import *
from field_schnet.utils.qmmm_utils import *
| 33.75 | 45 | 0.844444 | 21 | 135 | 5.142857 | 0.380952 | 0.25 | 0.416667 | 0.555556 | 0.574074 | 0.574074 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088889 | 135 | 3 | 46 | 45 | 0.878049 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
d9e672745398b1bcc1c4c7daa44b9f92c24c3876 | 307 | py | Python | moderngl_window/geometry/__init__.py | minuJeong/moderngl-window | 6386478f1e6b07cefda8f4d9324d972ab88b34ec | [
"MIT"
] | 142 | 2019-11-11T23:14:28.000Z | 2022-03-29T08:37:03.000Z | moderngl_window/geometry/__init__.py | minuJeong/moderngl-window | 6386478f1e6b07cefda8f4d9324d972ab88b34ec | [
"MIT"
] | 107 | 2019-10-31T20:31:45.000Z | 2022-03-23T15:01:41.000Z | moderngl_window/geometry/__init__.py | minuJeong/moderngl-window | 6386478f1e6b07cefda8f4d9324d972ab88b34ec | [
"MIT"
] | 36 | 2019-12-12T16:14:10.000Z | 2022-01-18T22:58:21.000Z | from moderngl_window.geometry.attributes import AttributeNames # noqa
from moderngl_window.geometry.cube import cube # noqa
from moderngl_window.geometry.bbox import bbox # noqa
from moderngl_window.geometry.sphere import sphere # noqa
from moderngl_window.geometry.quad import quad_2d, quad_fs # noqa
| 51.166667 | 70 | 0.830619 | 43 | 307 | 5.767442 | 0.325581 | 0.241935 | 0.362903 | 0.524194 | 0.483871 | 0 | 0 | 0 | 0 | 0 | 0 | 0.00369 | 0.117264 | 307 | 5 | 71 | 61.4 | 0.911439 | 0.078176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
d9e749ef4748345b7d9a8340bd4b1e1f7fdd9179 | 40,066 | py | Python | sdk/communication/azure-communication-phonenumbers/azure/communication/phonenumbers/_generated/aio/operations/_phone_numbers_operations.py | vincenttran-msft/azure-sdk-for-python | 348b56f9f03eeb3f7b502eed51daf494ffff874d | [
"MIT"
] | 1 | 2021-09-07T18:39:05.000Z | 2021-09-07T18:39:05.000Z | sdk/communication/azure-communication-phonenumbers/azure/communication/phonenumbers/_generated/aio/operations/_phone_numbers_operations.py | vincenttran-msft/azure-sdk-for-python | 348b56f9f03eeb3f7b502eed51daf494ffff874d | [
"MIT"
] | null | null | null | sdk/communication/azure-communication-phonenumbers/azure/communication/phonenumbers/_generated/aio/operations/_phone_numbers_operations.py | vincenttran-msft/azure-sdk-for-python | 348b56f9f03eeb3f7b502eed51daf494ffff874d | [
"MIT"
] | 1 | 2022-03-04T06:21:56.000Z | 2022-03-04T06:21:56.000Z | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union
import warnings
from azure.core.async_paging import AsyncItemPaged, AsyncList
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
from azure.core.polling.async_base_polling import AsyncLROBasePolling
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._phone_numbers_operations import build_cancel_operation_request, build_get_by_number_request, build_get_operation_request, build_get_search_result_request, build_list_phone_numbers_request, build_purchase_phone_numbers_request_initial, build_release_phone_number_request_initial, build_search_available_phone_numbers_request_initial, build_update_capabilities_request_initial
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class PhoneNumbersOperations:
"""PhoneNumbersOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.communication.phonenumbers.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
async def _search_available_phone_numbers_initial(
self,
country_code: str,
body: "_models.PhoneNumberSearchRequest",
**kwargs: Any
) -> "_models.PhoneNumberSearchResult":
cls = kwargs.pop('cls', None) # type: ClsType["_models.PhoneNumberSearchResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
_json = self._serialize.body(body, 'PhoneNumberSearchRequest')
request = build_search_available_phone_numbers_request_initial(
country_code=country_code,
api_version=api_version,
content_type=content_type,
json=_json,
template_url=self._search_available_phone_numbers_initial.metadata['url'],
)
request = _convert_request(request)
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
response_headers = {}
response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
response_headers['Operation-Location']=self._deserialize('str', response.headers.get('Operation-Location'))
response_headers['operation-id']=self._deserialize('str', response.headers.get('operation-id'))
response_headers['search-id']=self._deserialize('str', response.headers.get('search-id'))
deserialized = self._deserialize('PhoneNumberSearchResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, response_headers)
return deserialized
_search_available_phone_numbers_initial.metadata = {'url': '/availablePhoneNumbers/countries/{countryCode}/:search'} # type: ignore
@distributed_trace_async
async def begin_search_available_phone_numbers(
self,
country_code: str,
body: "_models.PhoneNumberSearchRequest",
**kwargs: Any
) -> AsyncLROPoller["_models.PhoneNumberSearchResult"]:
"""Search for available phone numbers to purchase.
Search for available phone numbers to purchase.
:param country_code: The ISO 3166-2 country code, e.g. US.
:type country_code: str
:param body: The phone number search request.
:type body: ~azure.communication.phonenumbers.models.PhoneNumberSearchRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncLROBasePolling. Pass in False
for this operation to not poll, or pass in your own initialized polling object for a personal
polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either PhoneNumberSearchResult or the
result of cls(response)
:rtype:
~azure.core.polling.AsyncLROPoller[~azure.communication.phonenumbers.models.PhoneNumberSearchResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType["_models.PhoneNumberSearchResult"]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = await self._search_available_phone_numbers_initial(
country_code=country_code,
body=body,
api_version=api_version,
content_type=content_type,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
response_headers = {}
response = pipeline_response.http_response
response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
response_headers['Operation-Location']=self._deserialize('str', response.headers.get('Operation-Location'))
response_headers['operation-id']=self._deserialize('str', response.headers.get('operation-id'))
response_headers['search-id']=self._deserialize('str', response.headers.get('search-id'))
deserialized = self._deserialize('PhoneNumberSearchResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, response_headers)
return deserialized
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
if polling is True: polling_method = AsyncLROBasePolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs)
elif polling is False: polling_method = AsyncNoPolling()
else: polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_search_available_phone_numbers.metadata = {'url': '/availablePhoneNumbers/countries/{countryCode}/:search'} # type: ignore
@distributed_trace_async
async def get_search_result(
self,
search_id: str,
**kwargs: Any
) -> "_models.PhoneNumberSearchResult":
"""Gets a phone number search result by search id.
Gets a phone number search result by search id.
:param search_id: The search Id.
:type search_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: PhoneNumberSearchResult, or the result of cls(response)
:rtype: ~azure.communication.phonenumbers.models.PhoneNumberSearchResult
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.PhoneNumberSearchResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
request = build_get_search_result_request(
search_id=search_id,
api_version=api_version,
template_url=self.get_search_result.metadata['url'],
)
request = _convert_request(request)
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.CommunicationErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('PhoneNumberSearchResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_search_result.metadata = {'url': '/availablePhoneNumbers/searchResults/{searchId}'} # type: ignore
async def _purchase_phone_numbers_initial(
self,
search_id: Optional[str] = None,
**kwargs: Any
) -> None:
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
_body = _models.PhoneNumberPurchaseRequest(search_id=search_id)
_json = self._serialize.body(_body, 'PhoneNumberPurchaseRequest')
request = build_purchase_phone_numbers_request_initial(
api_version=api_version,
content_type=content_type,
json=_json,
template_url=self._purchase_phone_numbers_initial.metadata['url'],
)
request = _convert_request(request)
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
response_headers = {}
response_headers['Operation-Location']=self._deserialize('str', response.headers.get('Operation-Location'))
response_headers['operation-id']=self._deserialize('str', response.headers.get('operation-id'))
response_headers['purchase-id']=self._deserialize('str', response.headers.get('purchase-id'))
if cls:
return cls(pipeline_response, None, response_headers)
_purchase_phone_numbers_initial.metadata = {'url': '/availablePhoneNumbers/:purchase'} # type: ignore
@distributed_trace_async
async def begin_purchase_phone_numbers(
self,
search_id: Optional[str] = None,
**kwargs: Any
) -> AsyncLROPoller[None]:
"""Purchases phone numbers.
Purchases phone numbers.
:param search_id: The search id.
:type search_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncLROBasePolling. Pass in False
for this operation to not poll, or pass in your own initialized polling object for a personal
polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[None]
:raises: ~azure.core.exceptions.HttpResponseError
"""
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType[None]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = await self._purchase_phone_numbers_initial(
search_id=search_id,
api_version=api_version,
content_type=content_type,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
if polling is True: polling_method = AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif polling is False: polling_method = AsyncNoPolling()
else: polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_purchase_phone_numbers.metadata = {'url': '/availablePhoneNumbers/:purchase'} # type: ignore
@distributed_trace_async
async def get_operation(
self,
operation_id: str,
**kwargs: Any
) -> "_models.PhoneNumberOperation":
"""Gets an operation by its id.
Gets an operation by its id.
:param operation_id: The id of the operation.
:type operation_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: PhoneNumberOperation, or the result of cls(response)
:rtype: ~azure.communication.phonenumbers.models.PhoneNumberOperation
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.PhoneNumberOperation"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
request = build_get_operation_request(
operation_id=operation_id,
api_version=api_version,
template_url=self.get_operation.metadata['url'],
)
request = _convert_request(request)
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.CommunicationErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
response_headers = {}
response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
deserialized = self._deserialize('PhoneNumberOperation', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, response_headers)
return deserialized
get_operation.metadata = {'url': '/phoneNumbers/operations/{operationId}'} # type: ignore
@distributed_trace_async
async def cancel_operation(
self,
operation_id: str,
**kwargs: Any
) -> None:
"""Cancels an operation by its id.
Cancels an operation by its id.
:param operation_id: The id of the operation.
:type operation_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
request = build_cancel_operation_request(
operation_id=operation_id,
api_version=api_version,
template_url=self.cancel_operation.metadata['url'],
)
request = _convert_request(request)
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.CommunicationErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
if cls:
return cls(pipeline_response, None, {})
cancel_operation.metadata = {'url': '/phoneNumbers/operations/{operationId}'} # type: ignore
async def _update_capabilities_initial(
self,
phone_number: str,
calling: Optional[Union[str, "_models.PhoneNumberCapabilityType"]] = None,
sms: Optional[Union[str, "_models.PhoneNumberCapabilityType"]] = None,
**kwargs: Any
) -> "_models.PurchasedPhoneNumber":
cls = kwargs.pop('cls', None) # type: ClsType["_models.PurchasedPhoneNumber"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
content_type = kwargs.pop('content_type', "application/merge-patch+json") # type: Optional[str]
_body = _models.PhoneNumberCapabilitiesRequest(calling=calling, sms=sms)
if _body is not None:
_json = self._serialize.body(_body, 'PhoneNumberCapabilitiesRequest')
else:
_json = None
request = build_update_capabilities_request_initial(
phone_number=phone_number,
api_version=api_version,
content_type=content_type,
json=_json,
template_url=self._update_capabilities_initial.metadata['url'],
)
request = _convert_request(request)
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
response_headers = {}
response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
response_headers['Operation-Location']=self._deserialize('str', response.headers.get('Operation-Location'))
response_headers['operation-id']=self._deserialize('str', response.headers.get('operation-id'))
response_headers['capabilities-id']=self._deserialize('str', response.headers.get('capabilities-id'))
deserialized = self._deserialize('PurchasedPhoneNumber', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, response_headers)
return deserialized
_update_capabilities_initial.metadata = {'url': '/phoneNumbers/{phoneNumber}/capabilities'} # type: ignore
@distributed_trace_async
async def begin_update_capabilities(
self,
phone_number: str,
calling: Optional[Union[str, "_models.PhoneNumberCapabilityType"]] = None,
sms: Optional[Union[str, "_models.PhoneNumberCapabilityType"]] = None,
**kwargs: Any
) -> AsyncLROPoller["_models.PurchasedPhoneNumber"]:
"""Updates the capabilities of a phone number.
Updates the capabilities of a phone number.
:param phone_number: The phone number id in E.164 format. The leading plus can be either + or
encoded as %2B, e.g. +11234567890.
:type phone_number: str
:param calling: Capability value for calling.
:type calling: str or ~azure.communication.phonenumbers.models.PhoneNumberCapabilityType
:param sms: Capability value for SMS.
:type sms: str or ~azure.communication.phonenumbers.models.PhoneNumberCapabilityType
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncLROBasePolling. Pass in False
for this operation to not poll, or pass in your own initialized polling object for a personal
polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either PurchasedPhoneNumber or the result
of cls(response)
:rtype:
~azure.core.polling.AsyncLROPoller[~azure.communication.phonenumbers.models.PurchasedPhoneNumber]
:raises: ~azure.core.exceptions.HttpResponseError
"""
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
content_type = kwargs.pop('content_type', "application/merge-patch+json") # type: Optional[str]
polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType["_models.PurchasedPhoneNumber"]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = await self._update_capabilities_initial(
phone_number=phone_number,
calling=calling,
sms=sms,
api_version=api_version,
content_type=content_type,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
response_headers = {}
response = pipeline_response.http_response
response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
response_headers['Operation-Location']=self._deserialize('str', response.headers.get('Operation-Location'))
response_headers['operation-id']=self._deserialize('str', response.headers.get('operation-id'))
response_headers['capabilities-id']=self._deserialize('str', response.headers.get('capabilities-id'))
deserialized = self._deserialize('PurchasedPhoneNumber', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, response_headers)
return deserialized
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
if polling is True: polling_method = AsyncLROBasePolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs)
elif polling is False: polling_method = AsyncNoPolling()
else: polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_update_capabilities.metadata = {'url': '/phoneNumbers/{phoneNumber}/capabilities'} # type: ignore
@distributed_trace_async
async def get_by_number(
self,
phone_number: str,
**kwargs: Any
) -> "_models.PurchasedPhoneNumber":
"""Gets the details of the given purchased phone number.
Gets the details of the given purchased phone number.
:param phone_number: The purchased phone number whose details are to be fetched in E.164
format, e.g. +11234567890.
:type phone_number: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: PurchasedPhoneNumber, or the result of cls(response)
:rtype: ~azure.communication.phonenumbers.models.PurchasedPhoneNumber
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.PurchasedPhoneNumber"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
request = build_get_by_number_request(
phone_number=phone_number,
api_version=api_version,
template_url=self.get_by_number.metadata['url'],
)
request = _convert_request(request)
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.CommunicationErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('PurchasedPhoneNumber', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_by_number.metadata = {'url': '/phoneNumbers/{phoneNumber}'} # type: ignore
async def _release_phone_number_initial(
self,
phone_number: str,
**kwargs: Any
) -> None:
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
request = build_release_phone_number_request_initial(
phone_number=phone_number,
api_version=api_version,
template_url=self._release_phone_number_initial.metadata['url'],
)
request = _convert_request(request)
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
response_headers = {}
response_headers['Operation-Location']=self._deserialize('str', response.headers.get('Operation-Location'))
response_headers['operation-id']=self._deserialize('str', response.headers.get('operation-id'))
response_headers['release-id']=self._deserialize('str', response.headers.get('release-id'))
if cls:
return cls(pipeline_response, None, response_headers)
_release_phone_number_initial.metadata = {'url': '/phoneNumbers/{phoneNumber}'} # type: ignore
@distributed_trace_async
async def begin_release_phone_number(
self,
phone_number: str,
**kwargs: Any
) -> AsyncLROPoller[None]:
"""Releases a purchased phone number.
Releases a purchased phone number.
:param phone_number: Phone number to be released, e.g. +11234567890.
:type phone_number: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncLROBasePolling. Pass in False
for this operation to not poll, or pass in your own initialized polling object for a personal
polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[None]
:raises: ~azure.core.exceptions.HttpResponseError
"""
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType[None]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = await self._release_phone_number_initial(
phone_number=phone_number,
api_version=api_version,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
if polling is True: polling_method = AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif polling is False: polling_method = AsyncNoPolling()
else: polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_release_phone_number.metadata = {'url': '/phoneNumbers/{phoneNumber}'} # type: ignore
@distributed_trace
def list_phone_numbers(
self,
skip: Optional[int] = 0,
top: Optional[int] = 100,
**kwargs: Any
) -> AsyncIterable["_models.PurchasedPhoneNumbers"]:
"""Gets the list of all purchased phone numbers.
Gets the list of all purchased phone numbers.
:param skip: An optional parameter for how many entries to skip, for pagination purposes. The
default value is 0.
:type skip: int
:param top: An optional parameter for how many entries to return, for pagination purposes. The
default value is 100.
:type top: int
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either PurchasedPhoneNumbers or the result of
cls(response)
:rtype:
~azure.core.async_paging.AsyncItemPaged[~azure.communication.phonenumbers.models.PurchasedPhoneNumbers]
:raises: ~azure.core.exceptions.HttpResponseError
"""
api_version = kwargs.pop('api_version', "2022-01-11-preview2") # type: str
cls = kwargs.pop('cls', None) # type: ClsType["_models.PurchasedPhoneNumbers"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
def prepare_request(next_link=None):
if not next_link:
request = build_list_phone_numbers_request(
api_version=api_version,
skip=skip,
top=top,
template_url=self.list_phone_numbers.metadata['url'],
)
request = _convert_request(request)
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
else:
request = build_list_phone_numbers_request(
api_version=api_version,
skip=skip,
top=top,
template_url=next_link,
)
request = _convert_request(request)
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.url = self._client.format_url(request.url, **path_format_arguments)
path_format_arguments = {
"endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
}
request.method = "GET"
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize("PurchasedPhoneNumbers", pipeline_response)
list_of_elem = deserialized.phone_numbers
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, AsyncList(list_of_elem)
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.CommunicationErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
return pipeline_response
return AsyncItemPaged(
get_next, extract_data
)
list_phone_numbers.metadata = {'url': '/phoneNumbers'} # type: ignore
| 46.319075 | 394 | 0.668297 | 4,326 | 40,066 | 5.960703 | 0.07374 | 0.034903 | 0.024316 | 0.023191 | 0.855154 | 0.830451 | 0.812611 | 0.775498 | 0.752114 | 0.730978 | 0 | 0.009128 | 0.237134 | 40,066 | 864 | 395 | 46.372685 | 0.834517 | 0.071807 | 0 | 0.726631 | 0 | 0 | 0.109592 | 0.047476 | 0 | 0 | 0 | 0 | 0 | 1 | 0.012346 | false | 0 | 0.026455 | 0 | 0.097002 | 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 |
8a4b05c4fb4ba41009adc3bce5935c1ff5666053 | 6,859 | py | Python | empower/unittest/projects.py | ericbrinckhaus/empower-runtime-modified | ecd7c1e9f1c19a629abdcb5c55257377313246ea | [
"Apache-2.0"
] | null | null | null | empower/unittest/projects.py | ericbrinckhaus/empower-runtime-modified | ecd7c1e9f1c19a629abdcb5c55257377313246ea | [
"Apache-2.0"
] | null | null | null | empower/unittest/projects.py | ericbrinckhaus/empower-runtime-modified | ecd7c1e9f1c19a629abdcb5c55257377313246ea | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python3
#
# Copyright (c) 2019 Roberto Riggio
#
# 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.
"""Projects unit tests."""
import unittest
from .common import BaseTest
class TestProjects(BaseTest):
"""Projects unit tests."""
def test_simple_gets(self):
"""test_simple_gets"""
self.get(("root", "root", "/projects"), 200)
def test_create_new_project(self):
"""test_create_new_project"""
data = {
"version": "1.0",
"desc": "Test project",
"owner": "foo",
"wifi_props": {"invalid_field": 1}
}
params = \
("root", "root", "/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26")
self.post(params, data, 400)
data = {
"version": "1.0",
"desc": "Test project",
"owner": "foo",
"lte_props": {"invalid_field": 1}
}
params = \
("root", "root", "/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26")
self.post(params, data, 400)
data = {
"version": "1.0",
"desc": "Test project",
"owner": "foo"
}
params = \
("root", "root", "/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26")
self.post(params, data, 201)
self.get(("root", "root",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.get(("foo", "foo",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.get(("bar", "bar",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.delete(("foo", "foo",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 204)
def test_create_wifi_project(self):
"""test_create_wifi_project"""
data = {
"version": "1.0",
"owner": "foo",
"desc": "Test project",
"wifi_props": {
"ssid": "EmPOWER",
"bssid_type": "unique"
}
}
params = \
("root", "root", "/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26")
self.post(params, data, 201)
self.get(("root", "root",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.get(("foo", "foo",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.get(("bar", "bar",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.delete(("foo", "foo",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 204)
def test_create_wifi_project_default_bssid_type(self):
"""test_create_wifi_project_default_bssid_type."""
data = {
"version": "1.0",
"owner": "foo",
"desc": "Test project",
"wifi_props": {
"ssid": "EmPOWER"
}
}
params = \
("root", "root", "/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26")
self.post(params, data, 201)
self.get(("root", "root",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.get(("foo", "foo",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.get(("bar", "bar",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.delete(("foo", "foo",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 204)
def test_create_wifi_project_wrong_bssid_type(self):
"""test_create_wifi_project_wrong_bssid_type."""
data = {
"version": "1.0",
"owner": "foo",
"desc": "Test project",
"wifi_props": {
"ssid": "EmPOWER",
"bssid_type": "wrong"
}
}
params = \
("root", "root", "/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26")
self.post(params, data, 400)
def test_create_lte_project(self):
"""test_create_lte_project."""
data = {
"version": "1.0",
"owner": "foo",
"desc": "Test project",
"lte_props": {
"plmnid": {
"mcc": "001",
"mnc": "01"
}
}
}
params = \
("root", "root", "/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26")
self.post(params, data, 201)
self.get(("root", "root",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.get(("foo", "foo",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.get(("bar", "bar",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.delete(("foo", "foo",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 204)
def test_create_lte_project_wrong_plmnid(self):
"""test_create_lte_project_wrong_plmnid."""
data = {
"version": "1.0",
"owner": "foo",
"desc": "Test project",
"lte_props": {
"plmnid": {
"mcc": "wrong mcc",
"mnc": "01"
}
}
}
params = \
("root", "root", "/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26")
self.post(params, data, 400)
def test_create_lora_project(self):
"""test_create_lora_project"""
data = {
"version": "1.0",
"owner": "foo",
"desc": "Test LoRA project",
"lora_props": {
"netid": 0x24
}
}
params = \
("root", "root", "/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26")
self.post(params, data, 201)
self.get(("root", "root",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.get(("foo", "foo",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.get(("bar", "bar",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 200)
self.delete(("foo", "foo",
"/projects/52313ecb-9d00-4b7d-b873-b55d3d9ada26"), 204)
if __name__ == '__main__':
unittest.main()
| 29.187234 | 78 | 0.509258 | 690 | 6,859 | 4.943478 | 0.186957 | 0.13603 | 0.170038 | 0.204046 | 0.782175 | 0.759015 | 0.74377 | 0.716799 | 0.716799 | 0.69569 | 0 | 0.148862 | 0.334014 | 6,859 | 234 | 79 | 29.311966 | 0.597855 | 0.125383 | 0 | 0.727273 | 0 | 0 | 0.351256 | 0.224844 | 0 | 0 | 0.000674 | 0 | 0 | 1 | 0.051948 | false | 0 | 0.012987 | 0 | 0.071429 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
8a7e8cce8897775c738e7f8c6fe5fb35f70a4d8f | 5,820 | py | Python | rqalpha/utilzld/LinkList/LList.py | zoulida/sdufeQuant | dc3715a62f620c0a437daacfe9a113d5a6ecb62d | [
"Apache-2.0"
] | null | null | null | rqalpha/utilzld/LinkList/LList.py | zoulida/sdufeQuant | dc3715a62f620c0a437daacfe9a113d5a6ecb62d | [
"Apache-2.0"
] | null | null | null | rqalpha/utilzld/LinkList/LList.py | zoulida/sdufeQuant | dc3715a62f620c0a437daacfe9a113d5a6ecb62d | [
"Apache-2.0"
] | 1 | 2019-09-19T07:37:36.000Z | 2019-09-19T07:37:36.000Z | __author__ = 'zoulida'
class Node:
def __init__(self, init_data):
self.data = init_data
self.next = None
def get_data(self):
return self.data
def get_next(self):
return self.next
def set_data(self, new_data):
self.data = new_data
def set_next(self, new_next):
self.next = new_next
class orderedList:#有序
def __init__(self):
self.head = None
def __str__(self):
print_list = []
current = self.head
while current is not None:
print_list.append(current.get_data())
current = current.get_next()
return str(print_list)
def is_empty(self):
return self.head is None
def size(self):
current = self.head
count = 0
while current is not None:
count += 1
current = current.get_next()
return count
def add(self, item):
current = self.head
previous = None
while current is not None:
if current.get_data() > item:
break
previous = current
current = current.get_next()
temp = Node(item)
if previous is None:
temp.set_next(self.head)
self.head = temp
else:
temp.set_next(current)
previous.set_next(temp)
def remove(self, item):
current = self.head
previous = None
found = False
while not found:
if current.get_data() == item:
found = True
else:
previous = current
current = current.get_next()
if previous is None:
self.head = current.get_next()
else:
previous.set_next(current.get_next())
def search(self, item):
current = self.head
while current is not None:
if current.get_data() == item:
return True
if current.get_data() > item:
return False
else:
current = current.get_next()
return False
def insert(self, pos, item):
node = Node(item)
if pos == 0:
node.set_next(self.head)
self.head = node
else:
current = self.head
previous = None
while self.index(current.get_data()) != pos:
previous = current
current = current.get_next()
if current is None:
break
previous.set_next(node)
node.set_next(current)
def pop(self, index=None):
if index is None:
index = self.size() - 1
if index < 0:
index = self.size() - abs(index)
if index < 0 or (index >= self.size()):
raise IndexError
current = self.head
previous = None
while self.index(current.get_data()) != index:
previous = current
current = current.get_next()
item = current.get_data()
if previous is None:
self.head = current.get_next()
else:
previous.set_next(current.get_next())
return item
class UnorderedList:#无序
def __init__(self):
self.head = None
def __str__(self):
print_list = []
current = self.head
while current is not None:
print_list.append(current.get_data())
current = current.get_next()
return str(print_list)
def is_empty(self):
return self.head is None
def size(self):
current = self.head
count = 0
while current is not None:
count += 1
current = current.get_next()
return count
def add(self, item): #在头部插入
temp = Node(item)
temp.set_next(self.head)
self.head = temp
def remove(self, item):
current = self.head
previous = None
found = False
while not found:
if current.get_data() == item:
found = True
else:
previous = current
current = current.get_next()
if previous is None:
self.head = current.get_next()
else:
previous.set_next(current.get_next())
def search(self, item):
current = self.head
while current is not None:
if current.get_data() == item:
return True
current = current.get_next()
return False
def insert(self, pos, item):
node = Node(item)
if pos == 0:
node.set_next(self.head)
self.head = node
else:
current = self.head
previous = None
while self.index(current.get_data()) != pos:
previous = current
current = current.get_next()
if current is None:
break
previous.set_next(node)
node.set_next(current)
def pop(self, index=None):
if index is None:
index = self.size() - 1
if index < 0:
index = self.size() - abs(index)
if index < 0 or (index >= self.size()):
raise IndexError
current = self.head
previous = None
while self.index(current.get_data()) != index:
previous = current
current = current.get_next()
item = current.get_data()
if previous is None:
self.head = current.get_next()
else:
previous.set_next(current.get_next())
return item
if __name__ == '__main__':
orderlist = orderedList()
orderlist.add(9)
orderlist.add(5)
orderlist.add(10)
orderlist.add(3)
orderlist.add(1)
print(orderlist) | 27.196262 | 56 | 0.517869 | 658 | 5,820 | 4.430091 | 0.091185 | 0.120069 | 0.100858 | 0.093654 | 0.848714 | 0.848714 | 0.827444 | 0.818182 | 0.798285 | 0.798285 | 0 | 0.005099 | 0.393471 | 5,820 | 214 | 57 | 27.196262 | 0.82068 | 0.001546 | 0 | 0.867725 | 0 | 0 | 0.002582 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.121693 | false | 0 | 0 | 0.021164 | 0.216931 | 0.037037 | 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 |
8a803697329844c26c6bc8de11be92d7a5aac5cc | 155 | py | Python | textmining_pnud/__init__.py | dlegor/textmining_pnud | a9f37e439f6b02940743d2e361f817cb49da6e04 | [
"MIT"
] | null | null | null | textmining_pnud/__init__.py | dlegor/textmining_pnud | a9f37e439f6b02940743d2e361f817cb49da6e04 | [
"MIT"
] | null | null | null | textmining_pnud/__init__.py | dlegor/textmining_pnud | a9f37e439f6b02940743d2e361f817cb49da6e04 | [
"MIT"
] | null | null | null | from .basic import load_data,report_leght_string,report_missinvalues
__all__=['load_data',
'report_leght_string',
'report_missinvalues'] | 31 | 68 | 0.748387 | 18 | 155 | 5.777778 | 0.555556 | 0.153846 | 0.269231 | 0.365385 | 0.826923 | 0.826923 | 0.826923 | 0 | 0 | 0 | 0 | 0 | 0.16129 | 155 | 5 | 69 | 31 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0.301282 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.25 | 0 | 0.25 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
8a80b59d0dfe66092b4f102cedf47c398a098a71 | 12,003 | py | Python | nailgun/nailgun/test/unit/test_attributes_validator.py | dnikishov/fuel-web | 152c2072cf585fc61d7e157ccf9a7ea1d0377daa | [
"Apache-2.0"
] | null | null | null | nailgun/nailgun/test/unit/test_attributes_validator.py | dnikishov/fuel-web | 152c2072cf585fc61d7e157ccf9a7ea1d0377daa | [
"Apache-2.0"
] | null | null | null | nailgun/nailgun/test/unit/test_attributes_validator.py | dnikishov/fuel-web | 152c2072cf585fc61d7e157ccf9a7ea1d0377daa | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
# Copyright 2013 Mirantis, Inc.
#
# 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 mock import Mock
from mock import patch
import json
import yaml
from nailgun.api.v1.validators.cluster import AttributesValidator
from nailgun.errors import errors
from nailgun.test.base import BaseTestCase
class TestAttributesValidator(BaseTestCase):
def test_generated_attributes_validation(self):
self.assertRaises(errors.InvalidData,
AttributesValidator.validate,
'{"generated": {"name": "test"}}')
def test_editable_attributes_validation(self):
self.assertRaises(errors.InvalidData,
AttributesValidator.validate,
'{"editable": "name"}')
def test_missing_type(self):
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
value: 'x'
weight: 80
'''
self.assertRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
def test_missing_value(self):
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
type: checkbox
weight: 80
'''
self.assertRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
def test_invalid_regexp(self):
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
type: text
value: '212a'
regex:
error: Invalid
source: ^\d+$
weight: 80
'''
self.assertRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
def test_checkbox_value(self):
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
type: checkbox
value: true
weight: 80
'''
self.assertNotRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
type: checkbox
value: 'x'
weight: 80
'''
self.assertRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
def test_custom_repo_configuration_value(self):
attrs = '''
editable:
storage:
repos:
description: desc
type: custom_repo_configuration
value:
- name: ubuntu
priority: null
section: main universe multiverse
suite: trusty
type: deb
uri: http://archive.ubuntu.com/ubuntu/
- name: ubuntu-updates
priority: null
section: main universe multiverse
suite: trusty-updates
type: deb
uri: http://archive.ubuntu.com/ubuntu/
'''
self.assertNotRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
def test_password_value(self):
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
type: password
value: '2'
weight: 80
'''
self.assertNotRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
type: password
value: 2
weight: 80
'''
self.assertRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
def test_radio_value(self):
attrs = '''
editable:
storage:
syslog_transport:
label: Syslog transport protocol
type: radio
value: tcp
values:
- data: udp
description: ''
label: UDP
- data: tcp
description: ''
label: TCP
- data: missing-description
label: Missing Description
weight: 3
'''
self.assertNotRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
def test_select_value(self):
attrs = '''
editable:
common:
libvirt_type:
label: Hypervisor type
type: select
value: qemu
values:
- data: kvm
label: KVM
description: KVM description
- data: qemu
label: QEMU
description: QEMU description
'''
self.assertNotRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
def test_text_value(self):
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
type: text
value: '2'
weight: 80
'''
self.assertNotRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
type: text
value: 2
weight: 80
'''
self.assertRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
def test_textarea_value(self):
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
type: textarea
value: '2'
weight: 80
'''
self.assertNotRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
type: textarea
value: 2
weight: 80
'''
self.assertRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
def test_text_list_value(self):
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
type: text_list
value: ['2']
weight: 80
'''
# check that text_list value is a list
self.assertNotRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
attrs = '''
editable:
storage:
osd_pool_size:
description: desc
label: OSD Pool Size
type: text_list
value: 2
weight: 80
'''
self.assertRaises(errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attrs))
@patch('nailgun.objects.Cluster.get_updated_editable_attributes')
def test_invalid_provisioning_method(self, mock_cluster_attrs):
attrs = {'editable': {'provision': {'method':
{'value': 'not_image', 'type': 'text'}}}}
mock_cluster_attrs.return_value = attrs
cluster_mock = Mock(release=Mock(environment_version='7.0'))
self.assertRaises(errors.InvalidData,
AttributesValidator.validate,
json.dumps(attrs), cluster_mock)
@patch('nailgun.objects.Cluster.get_updated_editable_attributes')
def test_provision_method_missing(self, mock_cluster_attrs):
attrs = {'editable': {'method':
{'value': 'not_image', 'type': 'text'}}}
mock_cluster_attrs.return_value = attrs
cluster_mock = Mock(release=Mock(environment_version='7.0'))
self.assertRaises(errors.InvalidData,
AttributesValidator.validate,
json.dumps(attrs), cluster_mock)
@patch('nailgun.objects.Cluster.get_updated_editable_attributes')
def test_provision_method_passed(self, mock_cluster_attrs):
attrs = {'editable': {'provision': {'method':
{'value': 'image', 'type': 'text'}}}}
mock_cluster_attrs.return_value = attrs
cluster_mock = Mock(
is_locked=False, release=Mock(environment_version='7.0')
)
self.assertNotRaises(errors.InvalidData,
AttributesValidator.validate,
json.dumps(attrs), cluster_mock)
@patch('nailgun.objects.Cluster.get_updated_editable_attributes')
def test_provision_method_passed_old(self, mock_cluster_attrs):
attrs = {'editable': {'provision': {'method':
{'value': 'image', 'type': 'text'}}}}
mock_cluster_attrs.return_value = attrs
cluster_mock = Mock(
is_locked=False, release=Mock(environment_version='6.0')
)
self.assertNotRaises(errors.InvalidData,
AttributesValidator.validate,
json.dumps(attrs), cluster_mock)
def test_valid_attributes(self):
valid_attibutes = [
'{"editable": {"name": "test"}}',
'{"name": "test"}',
]
for attributes in valid_attibutes:
self.assertNotRaises(errors.InvalidData,
AttributesValidator.validate,
attributes)
self.assertNotRaises(
errors.InvalidData,
AttributesValidator.validate_editable_attributes,
yaml.load(attributes))
| 32.884932 | 78 | 0.512955 | 990 | 12,003 | 6.054545 | 0.178788 | 0.030364 | 0.047714 | 0.176176 | 0.757257 | 0.747581 | 0.731565 | 0.730063 | 0.700701 | 0.665666 | 0 | 0.007923 | 0.411147 | 12,003 | 364 | 79 | 32.975275 | 0.840125 | 0.053403 | 0 | 0.746795 | 0 | 0 | 0.432047 | 0.021594 | 0 | 0 | 0 | 0 | 0.076923 | 1 | 0.057692 | false | 0.016026 | 0.022436 | 0 | 0.083333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
8acd977da70017fb723d93d15e3054a79546d838 | 2,397 | py | Python | tests/unitary/LiquidityGaugeV3/test_deposit_withdraw.py | AqualisDAO/curve-dao-contracts | beec73a068da8ed01c0f710939dc5adb776d565b | [
"MIT"
] | 217 | 2020-06-24T14:01:21.000Z | 2022-03-29T08:35:24.000Z | tests/unitary/LiquidityGaugeV3/test_deposit_withdraw.py | AqualisDAO/curve-dao-contracts | beec73a068da8ed01c0f710939dc5adb776d565b | [
"MIT"
] | 25 | 2020-06-24T09:39:02.000Z | 2022-03-22T17:03:00.000Z | tests/unitary/LiquidityGaugeV3/test_deposit_withdraw.py | AqualisDAO/curve-dao-contracts | beec73a068da8ed01c0f710939dc5adb776d565b | [
"MIT"
] | 110 | 2020-07-10T22:45:49.000Z | 2022-03-29T02:51:08.000Z | import brownie
import pytest
@pytest.fixture(scope="module", autouse=True)
def deposit_setup(accounts, gauge_v3, mock_lp_token):
mock_lp_token.approve(gauge_v3, 2 ** 256 - 1, {"from": accounts[0]})
def test_deposit(accounts, gauge_v3, mock_lp_token):
balance = mock_lp_token.balanceOf(accounts[0])
gauge_v3.deposit(100000, {"from": accounts[0]})
assert mock_lp_token.balanceOf(gauge_v3) == 100000
assert mock_lp_token.balanceOf(accounts[0]) == balance - 100000
assert gauge_v3.totalSupply() == 100000
assert gauge_v3.balanceOf(accounts[0]) == 100000
def test_deposit_zero(accounts, gauge_v3, mock_lp_token):
balance = mock_lp_token.balanceOf(accounts[0])
gauge_v3.deposit(0, {"from": accounts[0]})
assert mock_lp_token.balanceOf(gauge_v3) == 0
assert mock_lp_token.balanceOf(accounts[0]) == balance
assert gauge_v3.totalSupply() == 0
assert gauge_v3.balanceOf(accounts[0]) == 0
def test_deposit_insufficient_balance(accounts, gauge_v3, mock_lp_token):
with brownie.reverts():
gauge_v3.deposit(100000, {"from": accounts[1]})
def test_withdraw(accounts, gauge_v3, mock_lp_token):
balance = mock_lp_token.balanceOf(accounts[0])
gauge_v3.deposit(100000, {"from": accounts[0]})
gauge_v3.withdraw(100000, {"from": accounts[0]})
assert mock_lp_token.balanceOf(gauge_v3) == 0
assert mock_lp_token.balanceOf(accounts[0]) == balance
assert gauge_v3.totalSupply() == 0
assert gauge_v3.balanceOf(accounts[0]) == 0
def test_withdraw_zero(accounts, gauge_v3, mock_lp_token):
balance = mock_lp_token.balanceOf(accounts[0])
gauge_v3.deposit(100000, {"from": accounts[0]})
gauge_v3.withdraw(0, {"from": accounts[0]})
assert mock_lp_token.balanceOf(gauge_v3) == 100000
assert mock_lp_token.balanceOf(accounts[0]) == balance - 100000
assert gauge_v3.totalSupply() == 100000
assert gauge_v3.balanceOf(accounts[0]) == 100000
def test_withdraw_new_epoch(accounts, chain, gauge_v3, mock_lp_token):
balance = mock_lp_token.balanceOf(accounts[0])
gauge_v3.deposit(100000, {"from": accounts[0]})
chain.sleep(86400 * 400)
gauge_v3.withdraw(100000, {"from": accounts[0]})
assert mock_lp_token.balanceOf(gauge_v3) == 0
assert mock_lp_token.balanceOf(accounts[0]) == balance
assert gauge_v3.totalSupply() == 0
assert gauge_v3.balanceOf(accounts[0]) == 0
| 34.73913 | 73 | 0.722153 | 339 | 2,397 | 4.837758 | 0.117994 | 0.136585 | 0.154268 | 0.182927 | 0.845122 | 0.845122 | 0.793902 | 0.793902 | 0.793902 | 0.793902 | 0 | 0.083415 | 0.144764 | 2,397 | 68 | 74 | 35.25 | 0.716585 | 0 | 0 | 0.659574 | 0 | 0 | 0.019191 | 0 | 0 | 0 | 0 | 0 | 0.425532 | 1 | 0.148936 | false | 0 | 0.042553 | 0 | 0.191489 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
76da333ce802cfe936a2483ad6f3b8eff505debd | 350,881 | py | Python | aise/scenes/scenes_plane.py | rmaugusto/aise | 3152642d6de887588972956913429ef22b2ce597 | [
"MIT"
] | null | null | null | aise/scenes/scenes_plane.py | rmaugusto/aise | 3152642d6de887588972956913429ef22b2ce597 | [
"MIT"
] | null | null | null | aise/scenes/scenes_plane.py | rmaugusto/aise | 3152642d6de887588972956913429ef22b2ce597 | [
"MIT"
] | null | null | null | from ursina import *
from time import perf_counter
scene_parent = Entity()
if __name__ == '__main__':
app = Ursina()
t = perf_counter()
# unique meshes
meshes = {
'Cube_001' : Mesh(
vertices=[(-1.0, 1.0, -1.0), (-1.0, -1.0, 1.0), (-1.0, -1.0, -1.0), (-1.0, 1.0, 1.0), (1.0, -1.0, 1.0), (-1.0, -1.0, 1.0), (1.0, 1.0, 1.0), (1.0, -1.0, -1.0), (1.0, -1.0, 1.0), (1.0, 1.0, -1.0), (-1.0, -1.0, -1.0), (1.0, -1.0, -1.0), (1.0, -1.0, 1.0), (-1.0, -1.0, -1.0), (-1.0, -1.0, 1.0), (0.0911, 1.0, 0.1263), (0.0887, 1.0, 0.1438), (-1.0, 1.0, 1.0), (1.0, 1.0, 1.0), (0.9073, 1.0, 0.3488), (0.9122, 1.0, 0.3359), (0.3266, -0.7181, 0.7187), (0.3352, 1.0, 0.7321), (0.3266, 1.0, 0.7187), (0.7004, -0.7181, 0.0789), (0.7083, -0.7181, 0.0848), (0.7366, -0.7181, 0.1164), (0.7156, -0.7181, 0.4717), (0.7059, 1.0, 0.4879), (0.7059, -0.7181, 0.4879), (0.1185, -0.7181, -0.1677), (0.113, 1.0, -0.1538), (0.1185, 1.0, -0.1677), (0.7198, -0.7181, -0.0507), (0.707, 1.0, -0.0352), (0.707, -0.7181, -0.0352), (0.3352, -0.7181, 0.7321), (0.3449, 1.0, 0.7449), (0.3352, 1.0, 0.7321), (0.7156, -0.7181, 0.4717), (0.7305, 1.0, 0.4575), (0.7156, 1.0, 0.4717), (0.113, -0.7181, -0.1538), (0.1084, 1.0, -0.1392), (0.113, 1.0, -0.1538), (0.7334, -0.7181, -0.0649), (0.7198, 1.0, -0.0507), (0.7198, -0.7181, -0.0507), (0.3449, -0.7181, 0.7449), (0.3559, 1.0, 0.7567), (0.3449, 1.0, 0.7449), (0.7305, -0.7181, 0.4575), (0.7491, 1.0, 0.4448), (0.7305, 1.0, 0.4575), (0.1084, -0.7181, -0.1392), (0.1046, 1.0, -0.1231), (0.1084, 1.0, -0.1392), (0.7334, -0.7181, -0.0649), (0.7469, 1.0, -0.0781), (0.7334, 1.0, -0.0649), (0.3559, -0.7181, 0.7567), (0.3686, 1.0, 0.767), (0.3559, 1.0, 0.7567), (0.7491, -0.7181, 0.4448), (0.7693, 1.0, 0.4331), (0.7491, 1.0, 0.4448), (0.1046, -0.7181, -0.1231), (0.1019, 1.0, -0.1047), (0.1046, 1.0, -0.1231), (0.7469, -0.7181, -0.0781), (0.7595, 1.0, -0.0906), (0.7469, 1.0, -0.0781), (0.7693, -0.7181, 0.4331), (0.7902, 1.0, 0.4223), (0.7693, 1.0, 0.4331), (0.1019, -0.7181, -0.1047), (0.1003, 1.0, -0.0832), (0.1019, 1.0, -0.1047), (0.7705, -0.7181, -0.1027), (0.7595, 1.0, -0.0906), (0.7595, -0.7181, -0.0906), (0.7902, -0.7181, 0.4223), (0.8114, 1.0, 0.4119), (0.7902, 1.0, 0.4223), (0.1003, -0.7181, -0.0832), (0.1, 1.0, -0.0579), (0.1003, 1.0, -0.0832), (0.779, -0.7181, -0.1149), (0.7705, 1.0, -0.1027), (0.7705, -0.7181, -0.1027), (0.8114, -0.7181, 0.4119), (0.8321, 1.0, 0.4019), (0.8114, 1.0, 0.4119), (0.1, -0.7181, -0.0579), (0.101, 1.0, -0.0281), (0.1, 1.0, -0.0579), (0.7843, -0.7181, -0.1275), (0.779, 1.0, -0.1149), (0.779, -0.7181, -0.1149), (0.8321, -0.7181, 0.4019), (0.8518, 1.0, 0.392), (0.8321, 1.0, 0.4019), (0.101, -0.7181, -0.0281), (0.102, 1.0, 0.0022), (0.101, 1.0, -0.0281), (0.7855, -0.7181, -0.1408), (0.7843, 1.0, -0.1275), (0.7843, -0.7181, -0.1275), (0.8518, -0.7181, 0.392), (0.8698, 1.0, 0.382), (0.8518, 1.0, 0.392), (0.102, -0.7181, 0.0022), (0.1018, 1.0, 0.0285), (0.102, 1.0, 0.0022), (0.7819, -0.7181, -0.1551), (0.7855, 1.0, -0.1408), (0.7855, -0.7181, -0.1408), (0.8698, -0.7181, 0.382), (0.8855, 1.0, 0.3716), (0.8698, 1.0, 0.382), (0.1018, -0.7181, 0.0285), (0.1006, 1.0, 0.0517), (0.1018, 1.0, 0.0285), (0.7726, -0.7181, -0.1709), (0.7819, 1.0, -0.1551), (0.7819, -0.7181, -0.1551), (0.8855, -0.7181, 0.3716), (0.8982, 1.0, 0.3606), (0.8855, 1.0, 0.3716), (0.1006, -0.7181, 0.0517), (0.0987, 1.0, 0.0723), (0.1006, 1.0, 0.0517), (0.7613, -0.7181, -0.1869), (0.7726, 1.0, -0.1709), (0.7726, -0.7181, -0.1709), (0.9073, -0.7181, 0.3488), (0.8982, 1.0, 0.3606), (0.8982, -0.7181, 0.3606), (0.0987, -0.7181, 0.0723), (0.0963, 1.0, 0.0912), (0.0987, 1.0, 0.0723), (0.7518, -0.7181, -0.2019), (0.7613, 1.0, -0.1869), (0.7613, -0.7181, -0.1869), (0.9122, -0.7181, 0.3359), (0.9073, 1.0, 0.3488), (0.9073, -0.7181, 0.3488), (0.0963, -0.7181, 0.0912), (0.0937, 1.0, 0.1089), (0.0963, 1.0, 0.0912), (0.7435, -0.7181, -0.2159), (0.7518, 1.0, -0.2019), (0.7518, -0.7181, -0.2019), (0.9122, -0.7181, 0.3217), (0.9122, 1.0, 0.3359), (0.9122, -0.7181, 0.3359), (0.0937, -0.7181, 0.1089), (0.0911, 1.0, 0.1263), (0.0937, 1.0, 0.1089), (0.736, -0.7181, -0.2292), (0.7435, 1.0, -0.2159), (0.7435, -0.7181, -0.2159), (0.9106, -0.7181, 0.3067), (0.9122, 1.0, 0.3217), (0.9122, -0.7181, 0.3217), (0.0911, -0.7181, 0.1263), (0.0887, 1.0, 0.1438), (0.0911, 1.0, 0.1263), (0.7286, -0.7181, -0.2417), (0.736, 1.0, -0.2292), (0.736, -0.7181, -0.2292), (0.9107, -0.7181, 0.2913), (0.9106, 1.0, 0.3067), (0.9106, -0.7181, 0.3067), (0.0887, -0.7181, 0.1438), (0.0868, 1.0, 0.1623), (0.0887, 1.0, 0.1438), (0.7208, -0.7181, -0.2537), (0.7286, 1.0, -0.2417), (0.7286, -0.7181, -0.2417), (0.9119, -0.7181, 0.2758), (0.9107, 1.0, 0.2913), (0.9107, -0.7181, 0.2913), (0.0868, -0.7181, 0.1623), (0.0857, 1.0, 0.1825), (0.0868, 1.0, 0.1623), (0.7121, -0.7181, -0.2651), (0.7208, 1.0, -0.2537), (0.7208, -0.7181, -0.2537), (0.9137, -0.7181, 0.2605), (0.9119, 1.0, 0.2758), (0.9119, -0.7181, 0.2758), (0.0857, -0.7181, 0.1825), (0.0855, 1.0, 0.205), (0.0857, 1.0, 0.1825), (0.3686, -0.7181, 0.767), (0.3817, 1.0, 0.7758), (0.3686, 1.0, 0.767), (0.7018, -0.7181, -0.2761), (0.7121, 1.0, -0.2651), (0.7121, -0.7181, -0.2651), (0.9155, -0.7181, 0.2455), (0.9137, 1.0, 0.2605), (0.9137, -0.7181, 0.2605), (0.0855, -0.7181, 0.205), (0.0866, 1.0, 0.2305), (0.0855, 1.0, 0.205), (0.3817, -0.7181, 0.7758), (0.3939, 1.0, 0.7835), (0.3817, 1.0, 0.7758), (0.6896, -0.7181, -0.2869), (0.7018, 1.0, -0.2761), (0.7018, -0.7181, -0.2761), (0.9169, -0.7181, 0.2311), (0.9155, 1.0, 0.2455), (0.9155, -0.7181, 0.2455), (0.0866, -0.7181, 0.2305), (0.0881, 1.0, 0.2571), (0.0866, 1.0, 0.2305), (0.3939, -0.7181, 0.7835), (0.4057, 1.0, 0.7899), (0.3939, 1.0, 0.7835), (0.6747, -0.7181, -0.2974), (0.6896, 1.0, -0.2869), (0.6896, -0.7181, -0.2869), (0.9173, -0.7181, 0.2175), (0.9169, 1.0, 0.2311), (0.9169, -0.7181, 0.2311), (0.0881, -0.7181, 0.2571), (0.0892, 1.0, 0.2826), (0.0881, 1.0, 0.2571), (0.4057, -0.7181, 0.7899), (0.4173, 1.0, 0.7949), (0.4057, 1.0, 0.7899), (0.6568, -0.7181, -0.3079), (0.6747, 1.0, -0.2974), (0.6747, -0.7181, -0.2974), (0.9162, -0.7181, 0.205), (0.9173, 1.0, 0.2175), (0.9173, -0.7181, 0.2175), (0.0892, -0.7181, 0.2826), (0.0901, 1.0, 0.3068), (0.0892, 1.0, 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(-0.35114, -0.2579, 0.57675), (-0.35114, -0.2579, 0.57675), (-0.35114, -0.2579, 0.57675), (0.68874, -0.23428, -0.16615), (0.68874, -0.23428, -0.16615), (0.68874, -0.23428, -0.16615), (-0.60084, -0.22096, -0.39177), (-0.60084, -0.22096, -0.39177), (-0.60084, -0.22096, -0.39177), (0.6062, -0.25261, -0.31597), (0.6062, -0.25261, -0.31597), (0.6062, -0.25261, -0.31597), (-0.48391, -0.25129, 0.48756), (-0.48391, -0.25129, 0.48756), (-0.48391, -0.25129, 0.48756), (0.68663, -0.23633, -0.16622), (0.68663, -0.23633, -0.16622), (0.68663, -0.23633, -0.16622), (-0.65062, -0.22626, -0.29622), (-0.65062, -0.22626, -0.29622), (-0.65062, -0.22626, -0.29622), (0.66231, -0.254, -0.16136), (0.66231, -0.254, -0.16136), (0.66231, -0.254, -0.16136), (-0.55922, -0.2452, 0.41423), (-0.55922, -0.2452, 0.41423), (-0.55922, -0.2452, 0.41423), (0.6887, -0.2386, -0.14588), (0.6887, -0.2386, -0.14588), (0.6887, -0.2386, -0.14588), (-0.65861, -0.23477, -0.25965), (-0.65861, -0.23477, -0.25965), (-0.65861, -0.23477, -0.25965), (0.6854, -0.25202, -0.00803), (0.6854, -0.25202, -0.00803), (0.6854, -0.25202, -0.00803), (-0.59768, -0.24078, 0.36732), (-0.59768, -0.24078, 0.36732), (-0.59768, -0.24078, 0.36732), (0.69332, -0.24133, -0.10133), (0.69332, -0.24133, -0.10133), (0.69332, -0.24133, -0.10133), (-0.65029, -0.2383, -0.27068), (-0.65029, -0.2383, -0.27068), (-0.65029, -0.2383, -0.27068), (0.68104, -0.24834, 0.11869), (0.68104, -0.24834, 0.11869), (0.68104, -0.24834, 0.11869), (-0.61482, -0.23764, 0.34521), (-0.61482, -0.23764, 0.34521), (-0.61482, -0.23764, 0.34521), (0.69559, -0.24471, -0.02673), (0.69559, -0.24471, -0.02673), (0.69559, -0.24471, -0.02673), (-0.64431, -0.23722, -0.28741), (-0.64431, -0.23722, -0.28741), (-0.64431, -0.23722, -0.28741), (0.65893, -0.24608, 0.21913), (0.65893, -0.24608, 0.21913), (0.65893, -0.24608, 0.21913), (-0.61834, -0.23518, 0.34404), (-0.61834, -0.23518, 0.34404), (-0.61834, -0.23518, 0.34404), (0.68496, -0.24872, 0.08302), (0.68496, -0.24872, 0.08302), (0.68496, -0.24872, 0.08302), (-0.64418, -0.23559, -0.29185), (-0.64418, -0.23559, -0.29185), (-0.64418, -0.23559, -0.29185), (0.62172, -0.24615, 0.30892), (0.62172, -0.24615, 0.30892), (0.62172, -0.24615, 0.30892), (-0.6109, -0.23302, 0.36102), (-0.6109, -0.23302, 0.36102), (-0.6109, -0.23302, 0.36102), (0.64556, -0.25269, 0.22425), (0.64556, -0.25269, 0.22425), (0.64556, -0.25269, 0.22425), (-0.65151, -0.23353, -0.27998), (-0.65151, -0.23353, -0.27998), (-0.65151, -0.23353, -0.27998), (0.57686, -0.24528, 0.38889), (0.57686, -0.24528, 0.38889), (0.57686, -0.24528, 0.38889)],
colors=[],
uvs=[],
),
}
print('loaded models:', perf_counter() - t)
t = perf_counter()
scene_parent.ground = Entity(
name='ground',
parent=scene_parent,
position=Vec3(0.0, 0.0, 0.0),
rotation=(-0.0, -0.0, -0.0),
scale=Vec3(25.0, 1.0, 25.0),
model=copy(meshes['Cube_001']),
ignore=True,
)
scene_parent.water = Entity(
name='water',
parent=scene_parent,
position=Vec3(9.4229, 0.16684, 5.07759),
rotation=(-0.0, -0.0, -0.0),
scale=Vec3(1.0, 0.42939, 1.0),
model=copy(meshes['Mesh']),
ignore=True,
)
print('created entities:', perf_counter() - t)
if __name__ == '__main__':
EditorCamera()
app.run()
| 6,620.396226 | 96,268 | 0.546456 | 78,901 | 350,881 | 2.429792 | 0.066286 | 0.107901 | 0.134701 | 0.167542 | 0.997235 | 0.976809 | 0.960196 | 0.857344 | 0.783531 | 0.697772 | 0 | 0.614308 | 0.112833 | 350,881 | 52 | 96,269 | 6,747.711538 | 0.001558 | 0.000037 | 0 | 0.318182 | 0 | 0 | 0.000234 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.045455 | 0 | 0.045455 | 0.045455 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
76e83393d09845ba29f40f19d24e09bd10c5d118 | 205 | py | Python | h1st/model/ensemble/ensemble_modeler.py | TheVinhLuong102/H1st | 0c6f56d3a078817c36b208ae4f4c519cb35d5c18 | [
"Apache-2.0"
] | null | null | null | h1st/model/ensemble/ensemble_modeler.py | TheVinhLuong102/H1st | 0c6f56d3a078817c36b208ae4f4c519cb35d5c18 | [
"Apache-2.0"
] | null | null | null | h1st/model/ensemble/ensemble_modeler.py | TheVinhLuong102/H1st | 0c6f56d3a078817c36b208ae4f4c519cb35d5c18 | [
"Apache-2.0"
] | null | null | null | from typing import List
from h1st.model.predictive_model import PredictiveModel
from h1st.model.modeler import Modeler
from h1st.model.ml_modeler import MLModeler
class EnsembleModeler(Modeler):
pass
| 25.625 | 55 | 0.839024 | 28 | 205 | 6.071429 | 0.5 | 0.141176 | 0.229412 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.016575 | 0.117073 | 205 | 7 | 56 | 29.285714 | 0.922652 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.166667 | 0.666667 | 0 | 0.833333 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 7 |
76f0f407f70fe4183f83b7fac5841e389bd66685 | 3,847 | py | Python | test/unit/test_tyre_deg_curve_quadratic.py | JamHil12/Formula1_Strategy_Model | 22eb34ceee63c6d20d6da3c65d96c7cb78a4f642 | [
"MIT"
] | 1 | 2021-11-14T23:40:01.000Z | 2021-11-14T23:40:01.000Z | test/unit/test_tyre_deg_curve_quadratic.py | JamHil12/Formula1_Strategy_Model | 22eb34ceee63c6d20d6da3c65d96c7cb78a4f642 | [
"MIT"
] | null | null | null | test/unit/test_tyre_deg_curve_quadratic.py | JamHil12/Formula1_Strategy_Model | 22eb34ceee63c6d20d6da3c65d96c7cb78a4f642 | [
"MIT"
] | null | null | null | import unittest
import modelling_utilities as mu
import numpy as np
class TyreDegCurveQuadratic_ReturnsCorrectArray(unittest.TestCase):
# tyre_deg_curve_quadratic should return a numpy array of floats
def test_soft_tyre(self):
tyre_age = np.arange(3, 18)
tyre_description = np.array(['Soft'] * 15)
soft_tyre_deg_quadratic = 0.02
soft_tyre_deg_linear = 0.002
medium_tyre_pace_deficit = 0.7
medium_tyre_deg_quadratic = 0.015
medium_tyre_deg_linear = 0.002
hard_tyre_pace_deficit = 1.2
hard_tyre_deg_quadratic = 0.01
hard_tyre_deg_linear = 0.002
# The expected entries in the resulting array are at^2 + bt, where t is tyre age in laps, and a, b are the soft tyre quadratic, linear parameters
expected_result = np.array([0.186, 0.328, 0.51, 0.732, 0.994, 1.296, 1.638, 2.02, 2.442, 2.904, 3.406, 3.948, 4.53, 5.152, 5.814])
np.testing.assert_allclose(mu.tyre_deg_curve_quadratic(tyre_age, tyre_description, soft_tyre_deg_quadratic, soft_tyre_deg_linear,
medium_tyre_pace_deficit, medium_tyre_deg_quadratic, medium_tyre_deg_linear,
hard_tyre_pace_deficit, hard_tyre_deg_quadratic, hard_tyre_deg_linear),
expected_result)
def test_medium_tyre(self):
tyre_age = np.arange(3, 18)
tyre_description = np.array(['Medium'] * 15)
soft_tyre_deg_quadratic = 0.02
soft_tyre_deg_linear = 0.002
medium_tyre_pace_deficit = 0.7
medium_tyre_deg_quadratic = 0.015
medium_tyre_deg_linear = 0.002
hard_tyre_pace_deficit = 1.2
hard_tyre_deg_quadratic = 0.01
hard_tyre_deg_linear = 0.002
# The expected entries in the resulting array are at^2 + bt + c, where t is tyre age in laps, and a, b, c are the medium tyre quadratic, linear, deficit parameters
expected_result = np.array([0.841, 0.948, 1.085, 1.252, 1.449, 1.676, 1.933, 2.22, 2.537, 2.884, 3.261, 3.668, 4.105, 4.572, 5.069])
np.testing.assert_allclose(mu.tyre_deg_curve_quadratic(tyre_age, tyre_description, soft_tyre_deg_quadratic, soft_tyre_deg_linear,
medium_tyre_pace_deficit, medium_tyre_deg_quadratic, medium_tyre_deg_linear,
hard_tyre_pace_deficit, hard_tyre_deg_quadratic, hard_tyre_deg_linear),
expected_result)
def test_hard_tyre(self):
tyre_age = np.arange(3, 18)
tyre_description = np.array(['Hard'] * 15)
soft_tyre_deg_quadratic = 0.02
soft_tyre_deg_linear = 0.002
medium_tyre_pace_deficit = 0.7
medium_tyre_deg_quadratic = 0.015
medium_tyre_deg_linear = 0.002
hard_tyre_pace_deficit = 1.2
hard_tyre_deg_quadratic = 0.01
hard_tyre_deg_linear = 0.002
# The expected entries in the resulting array are at^2 + bt + c, where t is tyre age in laps, and a, b, c are the hard tyre quadratic, linear, deficit parameters
expected_result = np.array([1.296, 1.368, 1.46, 1.572, 1.704, 1.856, 2.028, 2.22, 2.432, 2.664, 2.916, 3.188, 3.48, 3.792, 4.124])
np.testing.assert_allclose(mu.tyre_deg_curve_quadratic(tyre_age, tyre_description, soft_tyre_deg_quadratic, soft_tyre_deg_linear,
medium_tyre_pace_deficit, medium_tyre_deg_quadratic, medium_tyre_deg_linear,
hard_tyre_pace_deficit, hard_tyre_deg_quadratic, hard_tyre_deg_linear),
expected_result)
if __name__ == '__main__':
unittest.main() | 62.048387 | 171 | 0.630101 | 560 | 3,847 | 3.983929 | 0.196429 | 0.125504 | 0.12909 | 0.068579 | 0.804124 | 0.804124 | 0.789332 | 0.789332 | 0.789332 | 0.738234 | 0 | 0.098933 | 0.293215 | 3,847 | 62 | 172 | 62.048387 | 0.721589 | 0.13725 | 0 | 0.722222 | 0 | 0 | 0.006637 | 0 | 0 | 0 | 0 | 0 | 0.055556 | 1 | 0.055556 | false | 0 | 0.055556 | 0 | 0.12963 | 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 |
0a0f0534f7a678d671d1e03b1a61a22dc1a250b6 | 26,427 | py | Python | conjugateRegularVerbs.py | BrianH2/conjugate-spanish-verbs | 6bb16fe5de7b9b68930099005bc1b758935709ae | [
"MIT"
] | null | null | null | conjugateRegularVerbs.py | BrianH2/conjugate-spanish-verbs | 6bb16fe5de7b9b68930099005bc1b758935709ae | [
"MIT"
] | null | null | null | conjugateRegularVerbs.py | BrianH2/conjugate-spanish-verbs | 6bb16fe5de7b9b68930099005bc1b758935709ae | [
"MIT"
] | 1 | 2022-02-11T14:03:40.000Z | 2022-02-11T14:03:40.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import json, sys
verbs = [x.strip() for x in sys.argv[1].split(',')]
print(verbs)
count = 0
conjugatedVerbs = []
print("\nStarting to process " + str(len(verbs)) + " \n")
for verb in verbs:
count = count + 1
infinitive = verb
pastparticiple = ""
if infinitive[-2:] == "ir":
verbType = "IR"
elif infinitive[-2:] == "er":
verbType = "ER"
elif infinitive[-2:] == "ar":
verbType = "AR"
else:
verbType = "Unknown"
continue
infinitiveNoEnding = infinitive[:len(infinitive)-2]
haber = {"gerund": "habiendo","pastparticiple": "habido","fulltenses": {"indicative conditional": {"mood": "indicative","tense": "conditional","1s": "habría","2s": "habrías","3s": "habría","1p": "habríamos","2p": "habríais","3p": "habrían"},"indicative future": {"mood": "indicative","tense": "future","1s": "habré","2s": "habrás","3s": "habrá","1p": "habremos","2p": "habréis","3p": "habrán"},"indicative imperfect": {"mood": "indicative","tense": "imperfect","1s": "había","2s": "habías","3s": "había","1p": "habíamos","2p": "habíais","3p": "habían"},"indicative present": {"mood": "indicative","tense": "present","1s": "he","2s": "has","3s": "ha","1p": "hemos","2p": "habéis","3p": "han"},"indicative preterite": {"mood": "indicative","tense": "preterite","1s": "hube","2s": "hubiste","3s": "hubo","1p": "hubimos","2p": "hubisteis","3p": "hubieron"},"subjunctive future": {"mood": "subjunctive","tense": "future","1s": "hubiere","2s": "hubieres","3s": "hubiere","1p": "hubiéremos","2p": "hubiereis","3p": "hubieren"},"subjunctive imperfect": {"mood": "subjunctive","tense": "imperfect","1s": "hubiera","2s": "hubieras","3s": "hubiera","1p": "hubiéramos","2p": "hubierais","3p": "hubieran"},"subjunctive present": {"mood": "subjunctive","tense": "present","1s": "haya","2s": "hayas","3s": "haya","1p": "hayamos","2p": "hayáis","3p": "hayan"}}}
if verbType == "IR":
pastparticiple = infinitiveNoEnding + "ido"
conjugation = {
"gerund": infinitiveNoEnding + "iendo",
"pastparticiple": pastparticiple,
"fulltenses": {
"imperative affirmative present": {
"mood": "imperative",
"tense": "affirmative present",
"1s": "-",
"2s": infinitiveNoEnding + "e",
"3s": infinitiveNoEnding + "a",
"1p": "-",
"2p": infinitiveNoEnding + "id",
"3p": infinitiveNoEnding + "an"
},
"imperative negative present": {
"mood": "imperative",
"tense": "negative present",
"1s": "-",
"2s": infinitiveNoEnding + "as",
"3s": infinitiveNoEnding + "a",
"1p": "-",
"2p": infinitiveNoEnding + "áis",
"3p": infinitiveNoEnding + "an"
},
"indicative conditional": {
"mood": "indicative",
"tense": "conditional",
"1s": infinitiveNoEnding + "iría",
"2s": infinitiveNoEnding + "irías",
"3s": infinitiveNoEnding + "iría",
"1p": infinitiveNoEnding + "iríamos",
"2p": infinitiveNoEnding + "iríais",
"3p": infinitiveNoEnding + "irían"
},
"indicative conditional perfect": {
"mood": "indicative",
"tense": "conditional perfect",
"1s": haber["fulltenses"]["indicative conditional"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative conditional"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative conditional"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative conditional"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative conditional"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative conditional"]["3p"] + " " + pastparticiple
},
"indicative future": {
"mood": "indicative",
"tense": "future",
"1s": infinitiveNoEnding + "iré",
"2s": infinitiveNoEnding + "irás",
"3s": infinitiveNoEnding + "irá",
"1p": infinitiveNoEnding + "iremos",
"2p": infinitiveNoEnding + "iréis",
"3p": infinitiveNoEnding + "irán"
},
"indicative future perfect": {
"mood": "indicative",
"tense": "future perfect",
"1s": haber["fulltenses"]["indicative future"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative future"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative future"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative future"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative future"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative future"]["3p"] + " " + pastparticiple
},
"indicative imperfect": {
"mood": "indicative",
"tense": "imperfect",
"1s": infinitiveNoEnding + "ía",
"2s": infinitiveNoEnding + "ías",
"3s": infinitiveNoEnding + "ía",
"1p": infinitiveNoEnding + "íamos",
"2p": infinitiveNoEnding + "íais",
"3p": infinitiveNoEnding + "ían"
},
"indicative past perfect": {
"mood": "indicative",
"tense": "past perfect",
"1s": haber["fulltenses"]["indicative imperfect"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative imperfect"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative imperfect"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative imperfect"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative imperfect"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative imperfect"]["3p"] + " " + pastparticiple
},
"indicative present": {
"mood": "indicative",
"tense": "present",
"1s": infinitiveNoEnding + "o",
"2s": infinitiveNoEnding + "es",
"3s": infinitiveNoEnding + "e",
"1p": infinitiveNoEnding + "imos",
"2p": infinitiveNoEnding + "ís",
"3p": infinitiveNoEnding + "en"
},
"indicative present perfect": {
"mood": "indicative",
"tense": "present perfect",
"1s": haber["fulltenses"]["indicative present"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative present"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative present"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative present"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative present"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative present"]["3p"] + " " + pastparticiple
},
"indicative preterite": {
"mood": "indicative",
"tense": "preterite",
"1s": infinitiveNoEnding + "í",
"2s": infinitiveNoEnding + "iste",
"3s": infinitiveNoEnding + "ió",
"1p": infinitiveNoEnding + "imos",
"2p": infinitiveNoEnding + "isteis",
"3p": infinitiveNoEnding + "ieron"
},
"indicative preterite archaic": {
"mood": "indicative",
"tense": "preterite archaic",
"1s": haber["fulltenses"]["indicative preterite"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative preterite"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative preterite"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative preterite"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative preterite"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative preterite"]["3p"] + " " + pastparticiple
},
"subjunctive future": {
"mood": "subjunctive",
"tense": "future",
"1s": infinitiveNoEnding + "iere",
"2s": infinitiveNoEnding + "ieres",
"3s": infinitiveNoEnding + "iere",
"1p": infinitiveNoEnding + "iéremos",
"2p": infinitiveNoEnding + "iereis",
"3p": infinitiveNoEnding + "ieren"
},
"subjunctive future perfect": {
"mood": "subjunctive",
"tense": "future perfect",
"1s": haber["fulltenses"]["subjunctive future"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["subjunctive future"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["subjunctive future"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["subjunctive future"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["subjunctive future"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["subjunctive future"]["3p"] + " " + pastparticiple
},
"subjunctive imperfect": {
"mood": "subjunctive",
"tense": "imperfect",
"1s": infinitiveNoEnding + "iera",
"2s": infinitiveNoEnding + "ieras",
"3s": infinitiveNoEnding + "iera",
"1p": infinitiveNoEnding + "iéramos",
"2p": infinitiveNoEnding + "ierais",
"3p": infinitiveNoEnding + "ieran"
},
"subjunctive past perfect": {
"mood": "subjunctive",
"tense": "past perfect",
"1s": haber["fulltenses"]["subjunctive imperfect"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["subjunctive imperfect"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["subjunctive imperfect"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["subjunctive imperfect"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["subjunctive imperfect"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["subjunctive imperfect"]["3p"] + " " + pastparticiple
},
"subjunctive present": {
"mood": "subjunctive",
"tense": "present",
"1s": infinitiveNoEnding + "a",
"2s": infinitiveNoEnding + "as",
"3s": infinitiveNoEnding + "a",
"1p": infinitiveNoEnding + "amos",
"2p": infinitiveNoEnding + "áis",
"3p": infinitiveNoEnding + "an"
},
"subjunctive present perfect": {
"mood": "subjunctive",
"tense": "present perfect",
"1s": haber["fulltenses"]["subjunctive present"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["subjunctive present"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["subjunctive present"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["subjunctive present"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["subjunctive present"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["subjunctive present"]["3p"] + " " + pastparticiple
}
}
}
elif verbType == "ER":
pastparticiple = infinitiveNoEnding + "ido"
conjugation = {
"gerund": infinitiveNoEnding + "iendo",
"pastparticiple": pastparticiple,
"fulltenses": {
"imperative affirmative present": {
"mood": "imperative",
"tense": "affirmative present",
"1s": "-",
"2s": infinitiveNoEnding + "e",
"3s": infinitiveNoEnding + "a",
"1p": "-",
"2p": infinitiveNoEnding + "ed",
"3p": infinitiveNoEnding + "an"
},
"imperative negative present": {
"mood": "imperative",
"tense": "negative present",
"1s": "-",
"2s": infinitiveNoEnding + "as",
"3s": infinitiveNoEnding + "a",
"1p": "-",
"2p": infinitiveNoEnding + "áis",
"3p": infinitiveNoEnding + "an"
},
"indicative conditional": {
"mood": "indicative",
"tense": "conditional",
"1s": infinitiveNoEnding + "ería",
"2s": infinitiveNoEnding + "erías",
"3s": infinitiveNoEnding + "ería",
"1p": infinitiveNoEnding + "eríamos",
"2p": infinitiveNoEnding + "eríais",
"3p": infinitiveNoEnding + "erían"
},
"indicative conditional perfect": {
"mood": "indicative",
"tense": "conditional perfect",
"1s": haber["fulltenses"]["indicative conditional"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative conditional"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative conditional"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative conditional"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative conditional"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative conditional"]["3p"] + " " + pastparticiple
},
"indicative future": {
"mood": "indicative",
"tense": "future",
"1s": infinitiveNoEnding + "eré",
"2s": infinitiveNoEnding + "erás",
"3s": infinitiveNoEnding + "erá",
"1p": infinitiveNoEnding + "eremos",
"2p": infinitiveNoEnding + "eréis",
"3p": infinitiveNoEnding + "erán"
},
"indicative future perfect": {
"mood": "indicative",
"tense": "future perfect",
"1s": haber["fulltenses"]["indicative future"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative future"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative future"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative future"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative future"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative future"]["3p"] + " " + pastparticiple
},
"indicative imperfect": {
"mood": "indicative",
"tense": "imperfect",
"1s": infinitiveNoEnding + "ía",
"2s": infinitiveNoEnding + "ías",
"3s": infinitiveNoEnding + "ía",
"1p": infinitiveNoEnding + "íamos",
"2p": infinitiveNoEnding + "íais",
"3p": infinitiveNoEnding + "ían"
},
"indicative past perfect": {
"mood": "indicative",
"tense": "past perfect",
"1s": haber["fulltenses"]["indicative imperfect"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative imperfect"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative imperfect"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative imperfect"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative imperfect"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative imperfect"]["3p"] + " " + pastparticiple
},
"indicative present": {
"mood": "indicative",
"tense": "present",
"1s": infinitiveNoEnding + "o",
"2s": infinitiveNoEnding + "es",
"3s": infinitiveNoEnding + "e",
"1p": infinitiveNoEnding + "emos",
"2p": infinitiveNoEnding + "éis",
"3p": infinitiveNoEnding + "en"
},
"indicative present perfect": {
"mood": "indicative",
"tense": "present perfect",
"1s": haber["fulltenses"]["indicative present"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative present"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative present"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative present"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative present"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative present"]["3p"] + " " + pastparticiple
},
"indicative preterite": {
"mood": "indicative",
"tense": "preterite",
"1s": infinitiveNoEnding + "í",
"2s": infinitiveNoEnding + "iste",
"3s": infinitiveNoEnding + "ió",
"1p": infinitiveNoEnding + "imos",
"2p": infinitiveNoEnding + "isteis",
"3p": infinitiveNoEnding + "ieron"
},
"indicative preterite archaic": {
"mood": "indicative",
"tense": "preterite archaic",
"1s": haber["fulltenses"]["indicative preterite"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative preterite"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative preterite"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative preterite"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative preterite"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative preterite"]["3p"] + " " + pastparticiple
},
"subjunctive future": {
"mood": "subjunctive",
"tense": "future",
"1s": infinitiveNoEnding + "iere",
"2s": infinitiveNoEnding + "ieres",
"3s": infinitiveNoEnding + "iere",
"1p": infinitiveNoEnding + "iéremos",
"2p": infinitiveNoEnding + "iereis",
"3p": infinitiveNoEnding + "ieren"
},
"subjunctive future perfect": {
"mood": "subjunctive",
"tense": "future perfect",
"1s": haber["fulltenses"]["subjunctive future"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["subjunctive future"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["subjunctive future"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["subjunctive future"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["subjunctive future"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["subjunctive future"]["3p"] + " " + pastparticiple
},
"subjunctive imperfect": {
"mood": "subjunctive",
"tense": "imperfect",
"1s": infinitiveNoEnding + "iera",
"2s": infinitiveNoEnding + "ieras",
"3s": infinitiveNoEnding + "iera",
"1p": infinitiveNoEnding + "iéramos",
"2p": infinitiveNoEnding + "ierais",
"3p": infinitiveNoEnding + "ieran"
},
"subjunctive past perfect": {
"mood": "subjunctive",
"tense": "past perfect",
"1s": haber["fulltenses"]["subjunctive imperfect"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["subjunctive imperfect"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["subjunctive imperfect"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["subjunctive imperfect"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["subjunctive imperfect"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["subjunctive imperfect"]["3p"] + " " + pastparticiple
},
"subjunctive present": {
"mood": "subjunctive",
"tense": "present",
"1s": infinitiveNoEnding + "a",
"2s": infinitiveNoEnding + "as",
"3s": infinitiveNoEnding + "a",
"1p": infinitiveNoEnding + "amos",
"2p": infinitiveNoEnding + "áis",
"3p": infinitiveNoEnding + "an"
},
"subjunctive present perfect": {
"mood": "subjunctive",
"tense": "present perfect",
"1s": haber["fulltenses"]["subjunctive present"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["subjunctive present"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["subjunctive present"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["subjunctive present"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["subjunctive present"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["subjunctive present"]["3p"] + " " + pastparticiple
}
}
}
elif verbType == "AR":
pastparticiple = infinitiveNoEnding + "ado"
conjugation = {
"gerund": infinitiveNoEnding + "ando",
"pastparticiple": pastparticiple,
"fulltenses": {
"imperative affirmative present": {
"mood": "imperative",
"tense": "affirmative present",
"1s": "-",
"2s": infinitiveNoEnding + "a",
"3s": infinitiveNoEnding + "e",
"1p": "-",
"2p": infinitiveNoEnding + "ad",
"3p": infinitiveNoEnding + "en"
},
"imperative negative present": {
"mood": "imperative",
"tense": "negative present",
"1s": "-",
"2s": infinitiveNoEnding + "es",
"3s": infinitiveNoEnding + "e",
"1p": "-",
"2p": infinitiveNoEnding + "éis",
"3p": infinitiveNoEnding + "en"
},
"indicative conditional": {
"mood": "indicative",
"tense": "conditional",
"1s": infinitiveNoEnding + "aría",
"2s": infinitiveNoEnding + "arías",
"3s": infinitiveNoEnding + "aría",
"1p": infinitiveNoEnding + "aríamos",
"2p": infinitiveNoEnding + "aríais",
"3p": infinitiveNoEnding + "arían"
},
"indicative conditional perfect": {
"mood": "indicative",
"tense": "conditional perfect",
"1s": haber["fulltenses"]["indicative conditional"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative conditional"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative conditional"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative conditional"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative conditional"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative conditional"]["3p"] + " " + pastparticiple
},
"indicative future": {
"mood": "indicative",
"tense": "future",
"1s": infinitiveNoEnding + "aré",
"2s": infinitiveNoEnding + "arás",
"3s": infinitiveNoEnding + "ará",
"1p": infinitiveNoEnding + "aremos",
"2p": infinitiveNoEnding + "aréis",
"3p": infinitiveNoEnding + "arán"
},
"indicative future perfect": {
"mood": "indicative",
"tense": "future perfect",
"1s": haber["fulltenses"]["indicative future"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative future"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative future"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative future"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative future"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative future"]["3p"] + " " + pastparticiple
},
"indicative imperfect": {
"mood": "indicative",
"tense": "imperfect",
"1s": infinitiveNoEnding + "aba",
"2s": infinitiveNoEnding + "abas",
"3s": infinitiveNoEnding + "aba",
"1p": infinitiveNoEnding + "ábamos",
"2p": infinitiveNoEnding + "abais",
"3p": infinitiveNoEnding + "aban"
},
"indicative past perfect": {
"mood": "indicative",
"tense": "past perfect",
"1s": haber["fulltenses"]["indicative imperfect"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative imperfect"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative imperfect"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative imperfect"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative imperfect"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative imperfect"]["3p"] + " " + pastparticiple
},
"indicative present": {
"mood": "indicative",
"tense": "present",
"1s": infinitiveNoEnding + "o",
"2s": infinitiveNoEnding + "as",
"3s": infinitiveNoEnding + "a",
"1p": infinitiveNoEnding + "amos",
"2p": infinitiveNoEnding + "áis",
"3p": infinitiveNoEnding + "an"
},
"indicative present perfect": {
"mood": "indicative",
"tense": "present perfect",
"1s": haber["fulltenses"]["indicative present"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative present"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative present"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative present"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative present"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative present"]["3p"] + " " + pastparticiple
},
"indicative preterite": {
"mood": "indicative",
"tense": "preterite",
"1s": infinitiveNoEnding + "é",
"2s": infinitiveNoEnding + "aste",
"3s": infinitiveNoEnding + "ó",
"1p": infinitiveNoEnding + "amos",
"2p": infinitiveNoEnding + "asteis",
"3p": infinitiveNoEnding + "aron"
},
"indicative preterite archaic": {
"mood": "indicative",
"tense": "preterite archaic",
"1s": haber["fulltenses"]["indicative preterite"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["indicative preterite"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["indicative preterite"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["indicative preterite"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["indicative preterite"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["indicative preterite"]["3p"] + " " + pastparticiple
},
"subjunctive future": {
"mood": "subjunctive",
"tense": "future",
"1s": infinitiveNoEnding + "are",
"2s": infinitiveNoEnding + "ares",
"3s": infinitiveNoEnding + "are",
"1p": infinitiveNoEnding + "áremos",
"2p": infinitiveNoEnding + "areis",
"3p": infinitiveNoEnding + "aren"
},
"subjunctive future perfect": {
"mood": "subjunctive",
"tense": "future perfect",
"1s": haber["fulltenses"]["subjunctive future"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["subjunctive future"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["subjunctive future"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["subjunctive future"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["subjunctive future"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["subjunctive future"]["3p"] + " " + pastparticiple
},
"subjunctive imperfect": {
"mood": "subjunctive",
"tense": "imperfect",
"1s": infinitiveNoEnding + "ara",
"2s": infinitiveNoEnding + "aras",
"3s": infinitiveNoEnding + "ara",
"1p": infinitiveNoEnding + "áramos",
"2p": infinitiveNoEnding + "ais",
"3p": infinitiveNoEnding + "aran"
},
"subjunctive past perfect": {
"mood": "subjunctive",
"tense": "past perfect",
"1s": haber["fulltenses"]["subjunctive imperfect"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["subjunctive imperfect"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["subjunctive imperfect"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["subjunctive imperfect"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["subjunctive imperfect"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["subjunctive imperfect"]["3p"] + " " + pastparticiple
},
"subjunctive present": {
"mood": "subjunctive",
"tense": "present",
"1s": infinitiveNoEnding + "e",
"2s": infinitiveNoEnding + "es",
"3s": infinitiveNoEnding + "e",
"1p": infinitiveNoEnding + "emos",
"2p": infinitiveNoEnding + "éis",
"3p": infinitiveNoEnding + "en"
},
"subjunctive present perfect": {
"mood": "subjunctive",
"tense": "present perfect",
"1s": haber["fulltenses"]["subjunctive present"]["1s"] + " " + pastparticiple,
"2s": haber["fulltenses"]["subjunctive present"]["2s"] + " " + pastparticiple,
"3s": haber["fulltenses"]["subjunctive present"]["3s"] + " " + pastparticiple,
"1p": haber["fulltenses"]["subjunctive present"]["1p"] + " " + pastparticiple,
"2p": haber["fulltenses"]["subjunctive present"]["2p"] + " " + pastparticiple,
"3p": haber["fulltenses"]["subjunctive present"]["3p"] + " " + pastparticiple
}
}
}
conjugation["infinitive"] = infinitive
conjugation["verbType"] = verbType
conjugatedVerbs.append(conjugation)
print(str(count) + ": conjugated " + infinitive + " (as " + verbType + " form)")
# SAVE AS JSON
jsonFile = open('conjugatedVerbs.json', 'w')
json.dump(conjugatedVerbs, jsonFile)
| 43.753311 | 1,346 | 0.603625 | 2,196 | 26,427 | 7.264117 | 0.096995 | 0.135406 | 0.141048 | 0.034604 | 0.854626 | 0.850364 | 0.847668 | 0.819333 | 0.814882 | 0.814882 | 0 | 0.024166 | 0.179513 | 26,427 | 603 | 1,347 | 43.825871 | 0.711525 | 0.002081 | 0 | 0.734349 | 0 | 0 | 0.375934 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.001692 | 0 | 0.001692 | 0.005076 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
0a242e270ebe8b4523c1852e419e518f16150462 | 49,232 | py | Python | objectModel/Python/tests/cdm/projection/test_projection_rename.py | dhoffland/CDM | e2c7718a4755e2c0b112b579bde009fd3a5bf37a | [
"CC-BY-4.0",
"MIT"
] | 1 | 2020-10-17T14:07:55.000Z | 2020-10-17T14:07:55.000Z | objectModel/Python/tests/cdm/projection/test_projection_rename.py | dhoffland/CDM | e2c7718a4755e2c0b112b579bde009fd3a5bf37a | [
"CC-BY-4.0",
"MIT"
] | 5 | 2021-07-05T15:32:15.000Z | 2022-01-04T16:51:11.000Z | objectModel/Python/tests/cdm/projection/test_projection_rename.py | dhoffland/CDM | e2c7718a4755e2c0b112b579bde009fd3a5bf37a | [
"CC-BY-4.0",
"MIT"
] | null | null | null | # Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
import os
import unittest
from cdm.enums import CdmObjectType
from cdm.objectmodel import CdmCorpusDefinition, CdmFolderDefinition, CdmEntityDefinition
from cdm.storage import LocalAdapter
from cdm.utilities import ResolveOptions, AttributeResolutionDirectiveSet
from tests.common import async_test, TestHelper
from tests.utilities.projection_test_utils import ProjectionTestUtils
class ProjectionRenameTest(unittest.TestCase):
"""A test class for testing the RenameAttributes operation in a projection as well as SelectsSomeAvoidNames in a resolution guidance"""
# All possible combinations of the different resolution directives
res_opts_combinations = [
[],
['referenceOnly'],
['normalized'],
['structured'],
['referenceOnly', 'normalized'],
['referenceOnly', 'structured'],
['normalized', 'structured'],
['referenceOnly', 'normalized', 'structured']
]
# The path between TestDataPath and test_name.
tests_subpath = os.path.join('Cdm', 'Projection', 'TestProjectionRename')
@async_test
async def test_entity_attribute_proj_using_object_model(self):
"""Test for creating a projection with an RenameAttributes operation on an entity attribute using the object model """
corpus = TestHelper.get_local_corpus(self.tests_subpath, 'TestEntityAttributeProjUsingObjectModel') # type: CdmCorpusDefinition
corpus.storage.mount('local', LocalAdapter(TestHelper.get_actual_output_folder_path(self.tests_subpath, 'TestEntityAttributeProjUsingObjectModel')))
local_root = corpus.storage.fetch_root_folder('local') # type: CdmFolderDefinition
# Create an entity
entity = ProjectionTestUtils.create_entity(corpus, local_root) # type: CdmEntityDefinition
# Create a projection
projection = ProjectionTestUtils.create_projection(corpus, local_root) # type: CdmProjection
# Create an RenameAttributes operation
rename_attrs_op = corpus.make_object(CdmObjectType.OPERATION_RENAME_ATTRIBUTES_DEF) # type: CdmOperationRenameAttributes
rename_attrs_op.rename_format = '{a}-{o}-{m}'
projection.operations.append(rename_attrs_op)
# Create an entity reference to hold this projection
projection_entity_ref = corpus.make_object(CdmObjectType.ENTITY_REF, None) # type: CdmEntityReference
projection_entity_ref.explicit_reference = projection
# Create an entity attribute that contains this projection and add this to the entity
entity_attribute = corpus.make_object(CdmObjectType.ENTITY_ATTRIBUTE_DEF, 'TestEntityAttribute') # type: CdmEntityAttributeDefinition
entity_attribute.entity = projection_entity_ref
entity.attributes.append(entity_attribute)
# Resolve the entity.
resolved_entity = await entity.create_resolved_entity_async('Resolved_{}.cdm.json'.format(entity.entity_name), None, local_root) # type: CdmEntityDefinition
# Verify correctness of the resolved attributes after running the RenameAttributes operation
# Original set of attributes: ['id', 'name', 'value', 'date']
# Rename all attributes with format '{a}-{o}-{m}'
self.assertEqual(4, len(resolved_entity.attributes))
self.assertEqual('TestEntityAttribute--id', resolved_entity.attributes[0].name)
self.assertEqual('TestEntityAttribute--name', resolved_entity.attributes[1].name)
self.assertEqual('TestEntityAttribute--value', resolved_entity.attributes[2].name)
self.assertEqual('TestEntityAttribute--date', resolved_entity.attributes[3].name)
@async_test
async def test_entity_proj_using_object_model(self):
"""Test for creating a projection with an RenameAttributes operation on an entity definition using the object model. """
corpus = TestHelper.get_local_corpus(self.tests_subpath, 'TestEntityProjUsingObjectModel') # type: CdmCorpusDefinition
corpus.storage.mount('local', LocalAdapter(TestHelper.get_actual_output_folder_path(self.tests_subpath, 'TestEntityProjUsingObjectModel')))
local_root = corpus.storage.fetch_root_folder('local') # type: CdmFolderDefinition
# Create an entity
entity = ProjectionTestUtils.create_entity(corpus, local_root) # type: CdmEntityDefinition
# Create a projection
projection = ProjectionTestUtils.create_projection(corpus, local_root) # type: CdmProjection
# Create an RenameAttributes operation
rename_attrs_op = corpus.make_object(CdmObjectType.OPERATION_RENAME_ATTRIBUTES_DEF) # type: CdmOperationRenameAttributes
rename_attrs_op.rename_format = '{A}.{o}.{M}'
projection.operations.append(rename_attrs_op)
# Create an entity reference to hold this projection
projection_entity_ref = corpus.make_object(CdmObjectType.ENTITY_REF, None) # type: CdmEntityReference
projection_entity_ref.explicit_reference = projection
# Set the entity's extends_entity to be the projection
entity.extends_entity = projection_entity_ref
# Resolve the entity
resolved_entity = await entity.create_resolved_entity_async('Resolved_{}.cdm.json'.format(entity.entity_name), None, local_root) # type: CdmEntityDefinition
# Verify correctness of the resolved attributes after running the RenameAttributes operation
# Original set of attributes: ['id', 'name', 'value', 'date']
# Rename all attributes with format {A}.{o}.{M}
self.assertEqual(4, len(resolved_entity.attributes))
self.assertEqual('..Id', resolved_entity.attributes[0].name)
self.assertEqual('..Name', resolved_entity.attributes[1].name)
self.assertEqual('..Value', resolved_entity.attributes[2].name)
self.assertEqual('..Date', resolved_entity.attributes[3].name)
@async_test
async def test_nested_proj_using_object_model(self):
"""Test for creating nested projections with RenameAttributes operations using the object model"""
corpus = TestHelper.get_local_corpus(self.tests_subpath, 'TestNestedProjUsingObjectModel') # type: CdmCorpusDefinition
corpus.storage.mount('local', LocalAdapter(TestHelper.get_actual_output_folder_path(self.tests_subpath, 'TestNestedProjUsingObjectModel')))
local_root = corpus.storage.fetch_root_folder('local') # type: CdmFolderDefinition
# Create an entity
entity = ProjectionTestUtils.create_entity(corpus, local_root) # type: CdmEntityDefinition
# Create a projection
projection = ProjectionTestUtils.create_projection(corpus, local_root) # type: CdmProjection
# Create an RenameAttributes operation
rename_attrs_op = corpus.make_object(CdmObjectType.OPERATION_RENAME_ATTRIBUTES_DEF) # type: CdmOperationRenameAttributes
rename_attrs_op.rename_format = '{A}.{o}.{M}'
projection.operations.append(rename_attrs_op)
# Create an entity reference to hold this projection
projection_entity_ref = corpus.make_object(CdmObjectType.ENTITY_REF, None) # type: CdmEntityReference
projection_entity_ref.explicit_reference = projection
# Create another projection that uses the previous projection as its source
projection2 = corpus.make_object(CdmObjectType.PROJECTION_DEF) # type: CdmProjection
projection2.source = projection_entity_ref
# Create an RenameAttributes operation
rename_attrs_op2 = corpus.make_object(CdmObjectType.OPERATION_RENAME_ATTRIBUTES_DEF) # type: CdmOperationRenameAttributes
rename_attrs_op2.rename_format = '{a}-{o}-{m}'
rename_attrs_op2.apply_to = [ 'name' ]
projection2.operations.append(rename_attrs_op2)
# Create an entity reference to hold this projection
projectionEntityRef2 = corpus.make_object(CdmObjectType.ENTITY_REF, None) # type: CdmEntityReference
projectionEntityRef2.explicit_reference = projection2
# Create an entity attribute that contains this projection and add this to the entity
entity_attribute = corpus.make_object(CdmObjectType.ENTITY_ATTRIBUTE_DEF, 'TestEntityAttribute') # type: CdmEntityAttributeDefinition
entity_attribute.entity = projectionEntityRef2
entity.attributes.append(entity_attribute)
# Resolve the entity
resolved_entity = await entity.create_resolved_entity_async('Resolved_{}.cdm.json'.format(entity.entity_name), None, local_root) # type: CdmEntityDefinition
# Verify correctness of the resolved attributes after running the RenameAttributes operations
# Original set of attributes: ['id', 'name', 'value', 'date']
# Rename all attributes attributes with format {A}.{o}.{M}, then rename 'name' with format '{a}-{o}-{m}'
self.assertEqual(4, len(resolved_entity.attributes))
self.assertEqual('TestEntityAttribute..Id', resolved_entity.attributes[0].name)
self.assertEqual('TestEntityAttribute--TestEntityAttribute..Name', resolved_entity.attributes[1].name)
self.assertEqual('TestEntityAttribute..Value', resolved_entity.attributes[2].name)
self.assertEqual('TestEntityAttribute..Date', resolved_entity.attributes[3].name)
@async_test
async def test_repeated_pattern_proj(self):
"""Test correctness when renameFormat has repeated pattern"""
corpus = TestHelper.get_local_corpus(self.tests_subpath, 'TestEntityAttributeProjUsingObjectModel') # type: CdmCorpusDefinition
corpus.storage.mount('local', LocalAdapter(TestHelper.get_actual_output_folder_path(self.tests_subpath, 'TestEntityAttributeProjUsingObjectModel')))
local_root = corpus.storage.fetch_root_folder('local') # type: CdmFolderDefinition
# Create an entity
entity = ProjectionTestUtils.create_entity(corpus, local_root) # type: CdmEntityDefinition
# Create a projection
projection = ProjectionTestUtils.create_projection(corpus, local_root) # type: CdmProjection
# Create an RenameAttributes operation
rename_attrs_op = corpus.make_object(CdmObjectType.OPERATION_RENAME_ATTRIBUTES_DEF) # type: CdmOperationRenameAttributes
rename_attrs_op.rename_format = '{a}-{M}-{o}-{A}-{m}-{O}'
projection.operations.append(rename_attrs_op)
# Create an entity reference to hold this projection
projection_entity_ref = corpus.make_object(CdmObjectType.ENTITY_REF, None) # type: CdmEntityReference
projection_entity_ref.explicit_reference = projection
# Create an entity attribute that contains this projection and add this to the entity
entity_attribute = corpus.make_object(CdmObjectType.ENTITY_ATTRIBUTE_DEF, 'TestEntityAttribute') # type: CdmEntityAttributeDefinition
entity_attribute.entity = projection_entity_ref
entity.attributes.append(entity_attribute)
# Resolve the entity.
resolved_entity = await entity.create_resolved_entity_async('Resolved_{}.cdm.json'.format(entity.entity_name), None, local_root) # type: CdmEntityDefinition
# Verify correctness of the resolved attributes after running the RenameAttributes operation
# Original set of attributes: ['id', 'name', 'value', 'date']
# Rename all attributes with format '{a}-{M}-{o}-{A}-{m}-{O}'
self.assertEqual(4, len(resolved_entity.attributes))
self.assertEqual('TestEntityAttribute-Id--TestEntityAttribute-id-', resolved_entity.attributes[0].name)
self.assertEqual('TestEntityAttribute-Name--TestEntityAttribute-name-', resolved_entity.attributes[1].name)
self.assertEqual('TestEntityAttribute-Value--TestEntityAttribute-value-', resolved_entity.attributes[2].name)
self.assertEqual('TestEntityAttribute-Date--TestEntityAttribute-date-', resolved_entity.attributes[3].name)
@async_test
async def test_conditional_proj_using_object_model(self):
"""Test for creating a projection with an RenameAttributes operation and a condition using the object model"""
corpus = TestHelper.get_local_corpus(self.tests_subpath, 'TestConditionalProjUsingObjectModel') # type: CdmCorpusDefinition
corpus.storage.mount('local', LocalAdapter(TestHelper.get_actual_output_folder_path(self.tests_subpath, 'TestConditionalProjUsingObjectModel')))
local_root = corpus.storage.fetch_root_folder('local') # type: CdmFolderDefinition
# Create an entity.
entity = ProjectionTestUtils.create_entity(corpus, local_root) # type: CdmEntityDefinition
# Create a projection with a condition that states the operation should only execute when the resolution directive is 'referenceOnly'.
projection = ProjectionTestUtils.create_projection(corpus, local_root) # type: CdmProjection
projection.condition = 'referenceOnly==true'
# Create an RenameAttributes operation
rename_attrs_op = corpus.make_object(CdmObjectType.OPERATION_RENAME_ATTRIBUTES_DEF) # type: CdmOperationRenameAttributes
rename_attrs_op.rename_format = '{A}.{o}.{M}'
projection.operations.append(rename_attrs_op)
# Create an entity reference to hold this projection.
projection_entity_ref = corpus.make_object(CdmObjectType.ENTITY_REF, None) # type: CdmEntityReference
projection_entity_ref.explicit_reference = projection
# Create an entity attribute that contains this projection and add this to the entity.
entity_attribute = corpus.make_object(CdmObjectType.ENTITY_ATTRIBUTE_DEF, 'TestEntityAttribute') # type: CdmEntityAttributeDefinition
entity_attribute.entity = projection_entity_ref
entity.attributes.append(entity_attribute)
# Create resolution options with the 'referenceOnly' directive.
res_opt = ResolveOptions(entity.in_document)
res_opt.directives = AttributeResolutionDirectiveSet({'referenceOnly'})
# Resolve the entity with 'referenceOnly'
resolved_entity_with_reference_only = await entity.create_resolved_entity_async('Resolved_{}.cdm.json'.format(entity.entity_name), res_opt, local_root) # type: CdmEntityDefinition
# Verify correctness of the resolved attributes after running the RenameAttributes operation
# Original set of attributes: ['id', 'name', 'value', 'date']
# Rename all attributes with format '{A}.{o}.{M}'
self.assertEqual(4, len(resolved_entity_with_reference_only.attributes))
self.assertEqual('TestEntityAttribute..Id', resolved_entity_with_reference_only.attributes[0].name)
self.assertEqual('TestEntityAttribute..Name', resolved_entity_with_reference_only.attributes[1].name)
self.assertEqual('TestEntityAttribute..Value', resolved_entity_with_reference_only.attributes[2].name)
self.assertEqual('TestEntityAttribute..Date', resolved_entity_with_reference_only.attributes[3].name)
# Now resolve the entity with the 'structured' directive
res_opt.directives = AttributeResolutionDirectiveSet({ 'structured' })
resolved_entity_with_structured = await entity.create_resolved_entity_async('Resolved_{}.cdm.json'.format(entity.entity_name), res_opt, local_root) # type: CdmEntityDefinition
# Verify correctness of the resolved attributes after running the RenameAttributes operation
# Original set of attributes: ['id', 'name', 'value', 'date']
# Renamed attributes: none, condition was false
self.assertEqual(4, len(resolved_entity_with_structured.attributes))
self.assertEqual('id', resolved_entity_with_structured.attributes[0].name)
self.assertEqual('name', resolved_entity_with_structured.attributes[1].name)
self.assertEqual('value', resolved_entity_with_structured.attributes[2].name)
self.assertEqual('date', resolved_entity_with_structured.attributes[3].name)
@async_test
async def test_rename_format_as_string_proj(self):
"""RenameAttributes with a plain string as rename format."""
test_name = 'test_rename_format_as_string_proj'
entity_name = 'NewPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['name', 'age', 'address', 'phoneNumber', 'email'].
# Renamed attribute 'address' with format 'whereYouLive'.
self.assertEqual(5, len(resolved_entity.attributes))
self.assertEqual('name', resolved_entity.attributes[0].name)
self.assertEqual('age', resolved_entity.attributes[1].name)
self.assertEqual('whereYouLive', resolved_entity.attributes[2].name)
self.assertEqual('phoneNumber', resolved_entity.attributes[3].name)
self.assertEqual('email', resolved_entity.attributes[4].name)
@async_test
async def test_rename_format(self):
"""RenameFormat on an entity attribute."""
test_name = 'test_rename_format'
entity_name = 'NewPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['PersonInfoName', 'PersonInfoAge', 'PersonInfoAddress', 'PersonInfoPhoneNumber', 'PersonInfoEmail'].
# Renamed all attributes with format {a}.{o}.{M}
self.assertEqual(5, len(resolved_entity.attributes))
self.assertEqual('PersonInfo..Name', resolved_entity.attributes[0].name)
self.assertEqual('PersonInfo..Age', resolved_entity.attributes[1].name)
self.assertEqual('PersonInfo..Address', resolved_entity.attributes[2].name)
self.assertEqual('PersonInfo..PhoneNumber', resolved_entity.attributes[3].name)
self.assertEqual('PersonInfo..Email', resolved_entity.attributes[4].name)
@async_test
async def test_rename_format_proj(self):
"""RenameFormat on an entity attribute."""
test_name = 'test_rename_format_proj'
entity_name = 'NewPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['PersonInfoName', 'PersonInfoAge', 'PersonInfoAddress', 'PersonInfoPhoneNumber', 'PersonInfoEmail'].
# Renamed all attributes with format {a}.{o}.{M}
self.assertEqual(5, len(resolved_entity.attributes))
self.assertEqual('PersonInfo..Name', resolved_entity.attributes[0].name)
self.assertEqual('PersonInfo..Age', resolved_entity.attributes[1].name)
self.assertEqual('PersonInfo..Address', resolved_entity.attributes[2].name)
self.assertEqual('PersonInfo..PhoneNumber', resolved_entity.attributes[3].name)
self.assertEqual('PersonInfo..Email', resolved_entity.attributes[4].name)
@async_test
async def test_single_nested_proj(self):
"""A nested RenameAttributes operation in a single projection."""
test_name = 'test_single_nested_proj'
entity_name = 'NewPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['name', 'age', 'address', 'phoneNumber', 'email'].
# Renamed all attributes with format 'New{M}'.
self.assertEqual(5, len(resolved_entity.attributes))
self.assertEqual('NewName', resolved_entity.attributes[0].name)
self.assertEqual('NewAge', resolved_entity.attributes[1].name)
self.assertEqual('NewAddress', resolved_entity.attributes[2].name)
self.assertEqual('NewPhoneNumber', resolved_entity.attributes[3].name)
self.assertEqual('NewEmail', resolved_entity.attributes[4].name)
@async_test
async def test_nested_proj(self):
"""Nested projections with RenameAttributes"""
test_name = 'test_nested_proj'
entity_name = 'NewPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['name', 'age', 'address', 'phoneNumber', 'email']
# Rename all attributes attributes with format {A}.{o}.{M}, then rename 'age' with format '{a}-{o}-{m}'
self.assertEqual(5, len(resolved_entity.attributes))
self.assertEqual('PersonInfo..Name', resolved_entity.attributes[0].name)
self.assertEqual('PersonInfo--PersonInfo..Age', resolved_entity.attributes[1].name)
self.assertEqual('PersonInfo..Address', resolved_entity.attributes[2].name)
self.assertEqual('PersonInfo..PhoneNumber', resolved_entity.attributes[3].name)
self.assertEqual('PersonInfo..Email', resolved_entity.attributes[4].name)
@async_test
async def test_multiple_rename(self):
"""Multiple RenameAttributes in a single projection."""
test_name = 'test_multiple_rename'
entity_name = 'NewPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['name', 'age', 'address', 'phoneNumber', 'email']
# Rename attributes 'age' to 'yearsOld' then 'address' to 'homePlace'
self.assertEqual(7, len(resolved_entity.attributes))
self.assertEqual('name', resolved_entity.attributes[0].name)
self.assertEqual('yearsOld', resolved_entity.attributes[1].name)
self.assertEqual('address', resolved_entity.attributes[2].name)
self.assertEqual('phoneNumber', resolved_entity.attributes[3].name)
self.assertEqual('email', resolved_entity.attributes[4].name)
self.assertEqual('age', resolved_entity.attributes[5].name)
self.assertEqual('homePlace', resolved_entity.attributes[6].name)
@async_test
async def test_extends_entity_proj(self):
"""RenameFormat on an entity definition."""
test_name = 'test_extends_entity_proj'
entity_name = 'Child'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['name', 'age', 'address', 'phoneNumber', 'email'].
# All attributes renamed with format '{a}.{o}.{M}'.
self.assertEqual(5, len(resolved_entity.attributes))
self.assertEqual('..name', resolved_entity.attributes[0].name)
self.assertEqual('..age', resolved_entity.attributes[1].name)
self.assertEqual('..address', resolved_entity.attributes[2].name)
self.assertEqual('..phoneNumber', resolved_entity.attributes[3].name)
self.assertEqual('..email', resolved_entity.attributes[4].name)
@async_test
async def test_extends_entity(self):
"""RenameFormat on an entity definition.
NOTE: this is not supported with resolution guidance."""
test_name = 'test_extends_entity'
entity_name = 'Child'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['name', 'age', 'address', 'phoneNumber', 'email'].
# Renamed attributes: [] with format '{a}.{o}.{M}'.
self.assertEqual(5, len(resolved_entity.attributes))
self.assertEqual('name', resolved_entity.attributes[0].name)
self.assertEqual('age', resolved_entity.attributes[1].name)
self.assertEqual('address', resolved_entity.attributes[2].name)
self.assertEqual('phoneNumber', resolved_entity.attributes[3].name)
self.assertEqual('email', resolved_entity.attributes[4].name)
@async_test
async def test_polymorphic_proj(self):
"""RenameAttributes on a polymorphic source"""
test_name = 'test_polymorphic_proj'
entity_name = 'BusinessPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ["emailId", "address", "isPrimary", "phoneId", "number", "socialId", "account"]
# Renamed all attributes with format {A}.{o}.{M}.
self.assertEqual(7, len(resolved_entity.attributes))
self.assertEqual('ContactAt..EmailId', resolved_entity.attributes[0].name)
self.assertEqual('ContactAt..Address', resolved_entity.attributes[1].name)
self.assertEqual('ContactAt..IsPrimary', resolved_entity.attributes[2].name)
self.assertEqual('ContactAt..PhoneId', resolved_entity.attributes[3].name)
self.assertEqual('ContactAt..Number', resolved_entity.attributes[4].name)
self.assertEqual('ContactAt..SocialId', resolved_entity.attributes[5].name)
self.assertEqual('ContactAt..Account', resolved_entity.attributes[6].name)
@async_test
async def test_polymorphic_apply_to_proj(self):
"""RenameAttributes on a polymorphic source"""
test_name = 'test_polymorphic_apply_to_proj'
entity_name = 'BusinessPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ["emailId", "address", "isPrimary", "phoneId", "number", "socialId", "account"]
# Renamed attributes: [address, number] with format {A}.{o}.{M}
self.assertEqual(7, len(resolved_entity.attributes))
self.assertEqual('emailId', resolved_entity.attributes[0].name)
self.assertEqual('ContactAt..Address', resolved_entity.attributes[1].name)
self.assertEqual('isPrimary', resolved_entity.attributes[2].name)
self.assertEqual('phoneId', resolved_entity.attributes[3].name)
self.assertEqual('ContactAt..Number', resolved_entity.attributes[4].name)
self.assertEqual('socialId', resolved_entity.attributes[5].name)
self.assertEqual('account', resolved_entity.attributes[6].name)
@async_test
async def test_polymorphic(self):
"""SelectsSomeAvoidNames on a polymorphic source"""
test_name = 'test_polymorphic'
entity_name = 'BusinessPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ["emailId", "address", "isPrimary", "phoneId", "number", "socialId", "account"]
# Renamed all attributes with format '{A}.{o}.{M}'
self.assertEqual(7, len(resolved_entity.attributes))
self.assertEqual('ContactAt..EmailId', resolved_entity.attributes[0].name)
self.assertEqual('ContactAt..Address', resolved_entity.attributes[1].name)
self.assertEqual('ContactAt..IsPrimary', resolved_entity.attributes[2].name)
self.assertEqual('ContactAt..PhoneId', resolved_entity.attributes[3].name)
self.assertEqual('ContactAt..Number', resolved_entity.attributes[4].name)
self.assertEqual('ContactAt..SocialId', resolved_entity.attributes[5].name)
self.assertEqual('ContactAt..Account', resolved_entity.attributes[6].name)
@async_test
async def test_array_source_proj(self):
"""RenameAttributes on an array source"""
test_name = 'test_array_source_proj'
entity_name = 'FriendGroup'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributeslen(: ['perso)', 'name1', 'age1', 'address1', 'phoneNumber1', 'email1', ..., 'email3'] (16 total)
# Attributes renamed from format {a}{M} to {a}.{o}.{M}
# NOTE: This behavior is different in the rename projection. The ordinal is this case is leaked by the resolution guidance
self.assertEqual(16, len(resolved_entity.attributes))
self.assertEqual('GroupOfPeople..PersonCount', resolved_entity.attributes[0].name)
self.assertEqual('GroupOfPeople..Name1', resolved_entity.attributes[1].name)
self.assertEqual('GroupOfPeople..Age1', resolved_entity.attributes[2].name)
self.assertEqual('GroupOfPeople..Address1', resolved_entity.attributes[3].name)
self.assertEqual('GroupOfPeople..PhoneNumber1', resolved_entity.attributes[4].name)
self.assertEqual('GroupOfPeople..Email1', resolved_entity.attributes[5].name)
self.assertEqual('GroupOfPeople..Name2', resolved_entity.attributes[6].name)
self.assertEqual('GroupOfPeople..Age2', resolved_entity.attributes[7].name)
self.assertEqual('GroupOfPeople..Address2', resolved_entity.attributes[8].name)
self.assertEqual('GroupOfPeople..PhoneNumber2', resolved_entity.attributes[9].name)
self.assertEqual('GroupOfPeople..Email2', resolved_entity.attributes[10].name)
self.assertEqual('GroupOfPeople..Name3', resolved_entity.attributes[11].name)
self.assertEqual('GroupOfPeople..Age3', resolved_entity.attributes[12].name)
self.assertEqual('GroupOfPeople..Address3', resolved_entity.attributes[13].name)
self.assertEqual('GroupOfPeople..PhoneNumber3', resolved_entity.attributes[14].name)
self.assertEqual('GroupOfPeople..Email3', resolved_entity.attributes[15].name)
@async_test
async def test_array_source(self):
"""RenameFormat on an array source"""
test_name = 'test_array_source'
entity_name = 'FriendGroup'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributeslen(: ['GroupOfPeoplePerso)', 'GroupOfPeopleName1', 'GroupOfPeopleAge1', 'GroupOfPeopleAddress1',
# 'GroupOfPeoplePhoneNumber1', 'GroupOfPeopleEmail1', ..., 'GroupOfPeopleEmail3'] (16 total)
# Attributes renamed from format {a}{M} to {a}.{o}.{M}
# NOTE: This behavior is different in the rename projection. The ordinal is this case is leaked by the resolution guidance
self.assertEqual(16, len(resolved_entity.attributes))
self.assertEqual('GroupOfPeople..PersonCount', resolved_entity.attributes[0].name)
self.assertEqual('GroupOfPeople.1.Name1', resolved_entity.attributes[1].name)
self.assertEqual('GroupOfPeople.1.Age1', resolved_entity.attributes[2].name)
self.assertEqual('GroupOfPeople.1.Address1', resolved_entity.attributes[3].name)
self.assertEqual('GroupOfPeople.1.PhoneNumber1', resolved_entity.attributes[4].name)
self.assertEqual('GroupOfPeople.1.Email1', resolved_entity.attributes[5].name)
self.assertEqual('GroupOfPeople.2.Name2', resolved_entity.attributes[6].name)
self.assertEqual('GroupOfPeople.2.Age2', resolved_entity.attributes[7].name)
self.assertEqual('GroupOfPeople.2.Address2', resolved_entity.attributes[8].name)
self.assertEqual('GroupOfPeople.2.PhoneNumber2', resolved_entity.attributes[9].name)
self.assertEqual('GroupOfPeople.2.Email2', resolved_entity.attributes[10].name)
self.assertEqual('GroupOfPeople.3.Name3', resolved_entity.attributes[11].name)
self.assertEqual('GroupOfPeople.3.Age3', resolved_entity.attributes[12].name)
self.assertEqual('GroupOfPeople.3.Address3', resolved_entity.attributes[13].name)
self.assertEqual('GroupOfPeople.3.PhoneNumber3', resolved_entity.attributes[14].name)
self.assertEqual('GroupOfPeople.3.Email3', resolved_entity.attributes[15].name)
@async_test
async def test_array_source_rename_apply_to_proj(self):
"""RenameFormat on an array source using apply to."""
test_name = 'test_array_source_rename_apply_to_proj'
entity_name = 'FriendGroup'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributeslen(: ['perso)', 'name1', 'age1', 'address1', 'phoneNumber1', 'email1', ..., 'email3'] (16 total).
# Renamed attributes: ['age1', 'age2', 'age3'] with the format '{a}.{o}.{M}'.
self.assertEqual(16, len(resolved_entity.attributes))
self.assertEqual('personCount', resolved_entity.attributes[0].name)
self.assertEqual('name1', resolved_entity.attributes[1].name)
self.assertEqual('GroupOfPeople..Age1', resolved_entity.attributes[2].name)
self.assertEqual('address1', resolved_entity.attributes[3].name)
self.assertEqual('phoneNumber1', resolved_entity.attributes[4].name)
self.assertEqual('email1', resolved_entity.attributes[5].name)
self.assertEqual('name2', resolved_entity.attributes[6].name)
self.assertEqual('GroupOfPeople..Age2', resolved_entity.attributes[7].name)
self.assertEqual('address2', resolved_entity.attributes[8].name)
self.assertEqual('phoneNumber2', resolved_entity.attributes[9].name)
self.assertEqual('email2', resolved_entity.attributes[10].name)
self.assertEqual('name3', resolved_entity.attributes[11].name)
self.assertEqual('GroupOfPeople..Age3', resolved_entity.attributes[12].name)
self.assertEqual('address3', resolved_entity.attributes[13].name)
self.assertEqual('phoneNumber3', resolved_entity.attributes[14].name)
self.assertEqual('email3', resolved_entity.attributes[15].name)
@async_test
async def test_conditional_proj(self):
"""RenameAttributes with a condition."""
test_name = 'test_conditional_proj'
entity_name = 'NewPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, [ 'referenceOnly' ]) # type: CdmEntityDefinition
# Original set of attributes: ['name', 'age', 'address', 'phoneNumber', 'email'].
# Renamed attributes with format '{M}.{o}.{a}'
self.assertEqual(5, len(resolved_entity.attributes))
self.assertEqual('Name..personInfo', resolved_entity.attributes[0].name)
self.assertEqual('Age..personInfo', resolved_entity.attributes[1].name)
self.assertEqual('Address..personInfo', resolved_entity.attributes[2].name)
self.assertEqual('PhoneNumber..personInfo', resolved_entity.attributes[3].name)
self.assertEqual('Email..personInfo', resolved_entity.attributes[4].name)
resolved_entity2 = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['name', 'age', 'address', 'phoneNumber', 'email'].
# Renamed attributes: none, condition was false.
self.assertEqual(5, len(resolved_entity2.attributes))
self.assertEqual('name', resolved_entity2.attributes[0].name)
self.assertEqual('age', resolved_entity2.attributes[1].name)
self.assertEqual('address', resolved_entity2.attributes[2].name)
self.assertEqual('phoneNumber', resolved_entity2.attributes[3].name)
self.assertEqual('email', resolved_entity2.attributes[4].name)
@async_test
async def test_empty_apply_to(self):
"""RenameAttributes with an empty apply to list."""
test_name = 'test_empty_apply_to'
entity_name = 'NewPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['name', 'age', 'address', 'phoneNumber', 'email'].
# Renamed attributes: [].
self.assertEqual(5, len(resolved_entity.attributes))
self.assertEqual('name', resolved_entity.attributes[0].name)
self.assertEqual('age', resolved_entity.attributes[1].name)
self.assertEqual('address', resolved_entity.attributes[2].name)
self.assertEqual('phoneNumber', resolved_entity.attributes[3].name)
self.assertEqual('email', resolved_entity.attributes[4].name)
@async_test
async def test_group_proj(self):
"""RenameFormat on an entity with an attribute group."""
test_name = 'test_group_proj'
entity_name = 'NewPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['name', 'age', 'address', 'phoneNumber', 'email'].
# Rename all attributes with format {a}-{o}-{M}
self.assertEqual(5, len(resolved_entity.attributes))
self.assertEqual('PersonInfo--Name', resolved_entity.attributes[0].name)
self.assertEqual('PersonInfo--Age', resolved_entity.attributes[1].name)
self.assertEqual('PersonInfo--Address', resolved_entity.attributes[2].name)
self.assertEqual('PersonInfo--PhoneNumber', resolved_entity.attributes[3].name)
self.assertEqual('PersonInfo--Email', resolved_entity.attributes[4].name)
@async_test
async def test_group_rename(self):
"""RenameFormat on an entity with an attribute group."""
test_name = 'test_group_rename'
entity_name = 'NewPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['PersonInfoName', 'PersonInfoAge', 'PersonInfoAddress', 'PersonInfoPhoneNumber', 'PersonInfoEmail'].
# Rename all attributes with format {a}-{o}-{M}
self.assertEqual(5, len(resolved_entity.attributes))
self.assertEqual('PersonInfo--Name', resolved_entity.attributes[0].name)
self.assertEqual('PersonInfo--Age', resolved_entity.attributes[1].name)
self.assertEqual('PersonInfo--Address', resolved_entity.attributes[2].name)
self.assertEqual('PersonInfo--PhoneNumber', resolved_entity.attributes[3].name)
self.assertEqual('PersonInfo--Email', resolved_entity.attributes[4].name)
@async_test
async def test_rename_and_exclude_proj(self):
"""Test RenameFormat applying a rename nested in a exclude operation"""
test_name = 'test_rename_and_exclude_proj'
entity_name = 'NewPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, []) # type: CdmEntityDefinition
# Original set of attributes: ['name', 'age', 'address', 'phoneNumber', 'email']
# Rename all attributes with format {a}-{o}-{M} and remove ['age', 'PersonInfo--PhoneNumber']
self.assertEqual(3, len(resolved_entity.attributes))
self.assertEqual('PersonInfo--Name', resolved_entity.attributes[0].name)
self.assertEqual('PersonInfo--Address', resolved_entity.attributes[1].name)
self.assertEqual('PersonInfo--Email', resolved_entity.attributes[2].name)
@unittest.skip
@async_test
async def test_EA_name_proj(self):
"""RenameAttributes with an entity attribute name on an inline entity reference that contains entity attributes.
This is testing that, for the case of the structured directive, we can filter using the name of an entity attribute.
the inline entity reference to rename the entire entity attribute group"""
test_name = 'test_EA_name_proj'
entity_name = 'NewPerson'
corpus = ProjectionTestUtils.get_corpus(test_name, self.tests_subpath) # type: CdmCorpusDefinition
for res_opt in self.res_opts_combinations:
await ProjectionTestUtils.load_entity_for_resolution_option_and_save(self, corpus, test_name, self.tests_subpath, entity_name, res_opt)
entity = await corpus.fetch_object_async('local:/{0}.cdm.json/{0}'.format(entity_name)) # type: CdmEntityDefinition
resolved_entity = await ProjectionTestUtils.get_resolved_entity(corpus, entity, [ 'structured' ]) # type: CdmEntityDefinition
# Original set of attributes: ['name', 'age', 'address', 'phoneNumber', 'email', 'title', 'company', 'tenure'].
# Rename with format '{a}-{o}-{M}' attributes ['PersonInfoAge', 'OccupationInfo']
# 'OccupationInfo' is an entity attribute
self.assertEqual(2, len(resolved_entity.attributes)) # attribute group created because of structured directive.
att_group = resolved_entity.attributes[0].explicit_reference # type: CdmAttributeGroupDefinition
self.assertEqual('PersonInfo', att_group.get_name())
self.assertEqual(5, len(att_group.members))
self.assertEqual('name', att_group.members[0].name)
self.assertEqual('age', att_group.members[1].name)
self.assertEqual('address', att_group.members[2].name)
self.assertEqual('phoneNumber', att_group.members[3].name)
self.assertEqual('email', att_group.members[4].name)
att_group2 = resolved_entity.attributes[1].explicit_reference
self.assertEqual('PersonInfo--OccupationInfo', att_group.get_name())
self.assertEqual(3, len(att_group2.members))
self.assertEqual('title', att_group2.members[0].name)
self.assertEqual('company', att_group2.members[1].name)
self.assertEqual('tenure', att_group2.members[2].name)
| 64.187744 | 188 | 0.729566 | 5,502 | 49,232 | 6.317521 | 0.05289 | 0.096263 | 0.121522 | 0.020714 | 0.884692 | 0.866538 | 0.853736 | 0.835611 | 0.791191 | 0.73463 | 0 | 0.009056 | 0.163349 | 49,232 | 766 | 189 | 64.27154 | 0.834814 | 0.19664 | 0 | 0.581489 | 0 | 0 | 0.125703 | 0.062745 | 0 | 0 | 0 | 0 | 0.406439 | 1 | 0 | false | 0 | 0.016097 | 0 | 0.022133 | 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 |
6a5074586959f9ad9df1a5883299e5641d7e506c | 2,509 | py | Python | scripts/scales.py | LouisJustinTALLOT/cheatsheets | 78d949bb5ddf8b53fdf34df1393462db8d0b3ee2 | [
"BSD-2-Clause"
] | 1 | 2021-03-20T18:33:02.000Z | 2021-03-20T18:33:02.000Z | scripts/scales.py | LouisJustinTALLOT/cheatsheets | 78d949bb5ddf8b53fdf34df1393462db8d0b3ee2 | [
"BSD-2-Clause"
] | null | null | null | scripts/scales.py | LouisJustinTALLOT/cheatsheets | 78d949bb5ddf8b53fdf34df1393462db8d0b3ee2 | [
"BSD-2-Clause"
] | 1 | 2021-07-17T09:10:03.000Z | 2021-07-17T09:10:03.000Z | import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(0.4,2/3*0.4))
ax = fig.add_axes([0,0,1,1], frameon=False)
ax.tick_params(axis='both', which='both', length=0)
ax.set_xlim(-2,2)
X = np.linspace(-2,+2,1001)
Y = np.sin(X*2.5*2*np.pi)
# Linear scale
# -----------------------------------------------------------------------------
ax.set_xlim(X.min(), X.max())
ax.set_xscale("linear")
ax.plot(X, Y, color="C1", linewidth=0.75)
ax.set_ylim(-2.5,1.5)
ax.text(0, 0.12, "-∞", ha="left", va="bottom", size=3, transform=ax.transAxes)
ax.text(0, 0.15, "⇤", ha="left", va="top", size=4, transform=ax.transAxes)
ax.text(1, 0.12, "+∞", ha="right", va="bottom", size=3, transform=ax.transAxes)
ax.text(1, 0.15, "⇥", ha="right", va="top", size=4, transform=ax.transAxes)
plt.savefig("../figures/scale-linear.pdf")
ax.clear()
# Log scale
# -----------------------------------------------------------------------------
ax.set_xscale("log", base=10)
ax.plot(X, Y, color="C1", linewidth=0.75)
ax.set_ylim(-2.5,1.5)
ax.text(0, 0.12, "0", ha="left", va="bottom", size=3, transform=ax.transAxes)
ax.text(0, 0.15, "⇤", ha="left", va="top", size=4, transform=ax.transAxes)
ax.text(1, 0.12, "+∞", ha="right", va="bottom", size=3, transform=ax.transAxes)
ax.text(1, 0.15, "⇥", ha="right", va="top", size=4, transform=ax.transAxes)
plt.savefig("../figures/scale-log.pdf")
ax.clear()
# Symlog scale
# -----------------------------------------------------------------------------
ax.set_xscale("symlog", base=10, linthresh=1)
ax.plot(X, Y, color="C1", linewidth=0.75)
ax.set_ylim(-2.5,1.5)
ax.text(0, 0.12, "-∞", ha="left", va="bottom", size=3, transform=ax.transAxes)
ax.text(0, 0.15, "⇤", ha="left", va="top", size=4, transform=ax.transAxes)
ax.text(1, 0.12, "+∞", ha="right", va="bottom", size=3, transform=ax.transAxes)
ax.text(1, 0.15, "⇥", ha="right", va="top", size=4, transform=ax.transAxes)
plt.savefig("../figures/scale-symlog.pdf")
ax.clear()
# Symlog scale
# -----------------------------------------------------------------------------
ax.set_xscale("logit")
ax.plot(X, Y, color="C1", linewidth=0.75)
ax.set_ylim(-2.5,1.5)
ax.text(0, 0.12, "0", ha="left", va="bottom", size=3, transform=ax.transAxes)
ax.text(0, 0.15, "⇤", ha="left", va="top", size=4, transform=ax.transAxes)
ax.text(1, 0.12, "1", ha="right", va="bottom", size=3, transform=ax.transAxes)
ax.text(1, 0.15, "⇥", ha="right", va="top", size=4, transform=ax.transAxes)
plt.savefig("../figures/scale-logit.pdf")
ax.clear()
| 41.131148 | 79 | 0.56118 | 431 | 2,509 | 3.269142 | 0.164733 | 0.068133 | 0.227111 | 0.187367 | 0.777147 | 0.777147 | 0.777147 | 0.777147 | 0.731725 | 0.731725 | 0 | 0.061739 | 0.0833 | 2,509 | 60 | 80 | 41.816667 | 0.545217 | 0.143483 | 0 | 0.6 | 0 | 0 | 0.14259 | 0.048621 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.044444 | 0 | 0.044444 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
6a61aa1f90848992a8e2d5fdf0d084ecc5d88a1d | 34,568 | py | Python | ot/unbalanced.py | tbng/POT | 0cb2b2efe901ed74c614046d250518769f870313 | [
"MIT"
] | null | null | null | ot/unbalanced.py | tbng/POT | 0cb2b2efe901ed74c614046d250518769f870313 | [
"MIT"
] | null | null | null | ot/unbalanced.py | tbng/POT | 0cb2b2efe901ed74c614046d250518769f870313 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Regularized Unbalanced OT solvers
"""
# Author: Hicham Janati <hicham.janati@inria.fr>
# License: MIT License
from __future__ import division
import warnings
import numpy as np
from scipy.special import logsumexp
# from .utils import unif, dist
def sinkhorn_unbalanced(a, b, M, reg, reg_m, method='sinkhorn', numItermax=1000,
stopThr=1e-6, verbose=False, log=False, **kwargs):
r"""
Solve the unbalanced entropic regularization optimal transport problem
and return the OT plan
The function solves the following optimization problem:
.. math::
W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + reg_m KL(\gamma 1, a) + reg_m KL(\gamma^T 1, b)
s.t.
\gamma\geq 0
where :
- M is the (dim_a, dim_b) metric cost matrix
- :math:`\Omega` is the entropic regularization
term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
- a and b are source and target unbalanced distributions
- KL is the Kullback-Leibler divergence
The algorithm used for solving the problem is the generalized
Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_
Parameters
----------
a : np.ndarray (dim_a,)
Unnormalized histogram of dimension dim_a
b : np.ndarray (dim_b,) or np.ndarray (dim_b, n_hists)
One or multiple unnormalized histograms of dimension dim_b
If many, compute all the OT distances (a, b_i)
M : np.ndarray (dim_a, dim_b)
loss matrix
reg : float
Entropy regularization term > 0
reg_m: float
Marginal relaxation term > 0
method : str
method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or
'sinkhorn_reg_scaling', see those function for specific parameters
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
if n_hists == 1:
gamma : (dim_a x dim_b) ndarray
Optimal transportation matrix for the given parameters
log : dict
log dictionary returned only if `log` is `True`
else:
ot_distance : (n_hists,) ndarray
the OT distance between `a` and each of the histograms `b_i`
log : dict
log dictionary returned only if `log` is `True`
Examples
--------
>>> import ot
>>> a=[.5, .5]
>>> b=[.5, .5]
>>> M=[[0., 1.], [1., 0.]]
>>> ot.sinkhorn_unbalanced(a, b, M, 1, 1)
array([[0.51122823, 0.18807035],
[0.18807035, 0.51122823]])
References
----------
.. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal
Transport, Advances in Neural Information Processing Systems
(NIPS) 26, 2013
.. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for
Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519.
.. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016).
Scaling algorithms for unbalanced transport problems. arXiv preprint
arXiv:1607.05816.
.. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. :
Learning with a Wasserstein Loss, Advances in Neural Information
Processing Systems (NIPS) 2015
See Also
--------
ot.unbalanced.sinkhorn_knopp_unbalanced : Unbalanced Classic Sinkhorn [10]
ot.unbalanced.sinkhorn_stabilized_unbalanced:
Unbalanced Stabilized sinkhorn [9][10]
ot.unbalanced.sinkhorn_reg_scaling_unbalanced:
Unbalanced Sinkhorn with epslilon scaling [9][10]
"""
if method.lower() == 'sinkhorn':
return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m,
numItermax=numItermax,
stopThr=stopThr, verbose=verbose,
log=log, **kwargs)
elif method.lower() == 'sinkhorn_stabilized':
return sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m,
numItermax=numItermax,
stopThr=stopThr,
verbose=verbose,
log=log, **kwargs)
elif method.lower() in ['sinkhorn_reg_scaling']:
warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp')
return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m,
numItermax=numItermax,
stopThr=stopThr, verbose=verbose,
log=log, **kwargs)
else:
raise ValueError("Unknown method '%s'." % method)
def sinkhorn_unbalanced2(a, b, M, reg, reg_m, method='sinkhorn',
numItermax=1000, stopThr=1e-6, verbose=False,
log=False, **kwargs):
r"""
Solve the entropic regularization unbalanced optimal transport problem and
return the loss
The function solves the following optimization problem:
.. math::
W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + reg_m KL(\gamma 1, a) + reg_m KL(\gamma^T 1, b)
s.t.
\gamma\geq 0
where :
- M is the (dim_a, dim_b) metric cost matrix
- :math:`\Omega` is the entropic regularization term
:math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
- a and b are source and target unbalanced distributions
- KL is the Kullback-Leibler divergence
The algorithm used for solving the problem is the generalized
Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_
Parameters
----------
a : np.ndarray (dim_a,)
Unnormalized histogram of dimension dim_a
b : np.ndarray (dim_b,) or np.ndarray (dim_b, n_hists)
One or multiple unnormalized histograms of dimension dim_b
If many, compute all the OT distances (a, b_i)
M : np.ndarray (dim_a, dim_b)
loss matrix
reg : float
Entropy regularization term > 0
reg_m: float
Marginal relaxation term > 0
method : str
method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or
'sinkhorn_reg_scaling', see those function for specific parameters
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
ot_distance : (n_hists,) ndarray
the OT distance between `a` and each of the histograms `b_i`
log : dict
log dictionary returned only if `log` is `True`
Examples
--------
>>> import ot
>>> a=[.5, .10]
>>> b=[.5, .5]
>>> M=[[0., 1.],[1., 0.]]
>>> ot.unbalanced.sinkhorn_unbalanced2(a, b, M, 1., 1.)
array([0.31912866])
References
----------
.. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal
Transport, Advances in Neural Information Processing Systems
(NIPS) 26, 2013
.. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for
Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519.
.. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016).
Scaling algorithms for unbalanced transport problems. arXiv preprint
arXiv:1607.05816.
.. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. :
Learning with a Wasserstein Loss, Advances in Neural Information
Processing Systems (NIPS) 2015
See Also
--------
ot.unbalanced.sinkhorn_knopp : Unbalanced Classic Sinkhorn [10]
ot.unbalanced.sinkhorn_stabilized: Unbalanced Stabilized sinkhorn [9][10]
ot.unbalanced.sinkhorn_reg_scaling: Unbalanced Sinkhorn with epslilon scaling [9][10]
"""
b = np.asarray(b, dtype=np.float64)
if len(b.shape) < 2:
b = b[:, None]
if method.lower() == 'sinkhorn':
return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m,
numItermax=numItermax,
stopThr=stopThr, verbose=verbose,
log=log, **kwargs)
elif method.lower() == 'sinkhorn_stabilized':
return sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m,
numItermax=numItermax,
stopThr=stopThr,
verbose=verbose,
log=log, **kwargs)
elif method.lower() in ['sinkhorn_reg_scaling']:
warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp')
return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m,
numItermax=numItermax,
stopThr=stopThr, verbose=verbose,
log=log, **kwargs)
else:
raise ValueError('Unknown method %s.' % method)
def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000,
stopThr=1e-6, verbose=False, log=False, **kwargs):
r"""
Solve the entropic regularization unbalanced optimal transport problem and return the loss
The function solves the following optimization problem:
.. math::
W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \reg_m KL(\gamma 1, a) + \reg_m KL(\gamma^T 1, b)
s.t.
\gamma\geq 0
where :
- M is the (dim_a, dim_b) metric cost matrix
- :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
- a and b are source and target unbalanced distributions
- KL is the Kullback-Leibler divergence
The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_
Parameters
----------
a : np.ndarray (dim_a,)
Unnormalized histogram of dimension dim_a
b : np.ndarray (dim_b,) or np.ndarray (dim_b, n_hists)
One or multiple unnormalized histograms of dimension dim_b
If many, compute all the OT distances (a, b_i)
M : np.ndarray (dim_a, dim_b)
loss matrix
reg : float
Entropy regularization term > 0
reg_m: float
Marginal relaxation term > 0
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshold on error (> 0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
if n_hists == 1:
gamma : (dim_a x dim_b) ndarray
Optimal transportation matrix for the given parameters
log : dict
log dictionary returned only if `log` is `True`
else:
ot_distance : (n_hists,) ndarray
the OT distance between `a` and each of the histograms `b_i`
log : dict
log dictionary returned only if `log` is `True`
Examples
--------
>>> import ot
>>> a=[.5, .5]
>>> b=[.5, .5]
>>> M=[[0., 1.],[1., 0.]]
>>> ot.unbalanced.sinkhorn_knopp_unbalanced(a, b, M, 1., 1.)
array([[0.51122823, 0.18807035],
[0.18807035, 0.51122823]])
References
----------
.. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016).
Scaling algorithms for unbalanced transport problems. arXiv preprint
arXiv:1607.05816.
.. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. :
Learning with a Wasserstein Loss, Advances in Neural Information
Processing Systems (NIPS) 2015
See Also
--------
ot.lp.emd : Unregularized OT
ot.optim.cg : General regularized OT
"""
a = np.asarray(a, dtype=np.float64)
b = np.asarray(b, dtype=np.float64)
M = np.asarray(M, dtype=np.float64)
dim_a, dim_b = M.shape
if len(a) == 0:
a = np.ones(dim_a, dtype=np.float64) / dim_a
if len(b) == 0:
b = np.ones(dim_b, dtype=np.float64) / dim_b
if len(b.shape) > 1:
n_hists = b.shape[1]
else:
n_hists = 0
if log:
log = {'err': []}
# we assume that no distances are null except those of the diagonal of
# distances
if n_hists:
u = np.ones((dim_a, 1)) / dim_a
v = np.ones((dim_b, n_hists)) / dim_b
a = a.reshape(dim_a, 1)
else:
u = np.ones(dim_a) / dim_a
v = np.ones(dim_b) / dim_b
# Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute
K = np.empty(M.shape, dtype=M.dtype)
np.divide(M, -reg, out=K)
np.exp(K, out=K)
fi = reg_m / (reg_m + reg)
err = 1.
for i in range(numItermax):
uprev = u
vprev = v
Kv = K.dot(v)
u = (a / Kv) ** fi
Ktu = K.T.dot(u)
v = (b / Ktu) ** fi
if (np.any(Ktu == 0.)
or np.any(np.isnan(u)) or np.any(np.isnan(v))
or np.any(np.isinf(u)) or np.any(np.isinf(v))):
# we have reached the machine precision
# come back to previous solution and quit loop
warnings.warn('Numerical errors at iteration %s' % i)
u = uprev
v = vprev
break
err_u = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), 1.)
err_v = abs(v - vprev).max() / max(abs(v).max(), abs(vprev).max(), 1.)
err = 0.5 * (err_u + err_v)
if log:
log['err'].append(err)
if verbose:
if i % 50 == 0:
print(
'{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19)
print('{:5d}|{:8e}|'.format(i, err))
if err < stopThr:
break
if log:
log['logu'] = np.log(u + 1e-300)
log['logv'] = np.log(v + 1e-300)
if n_hists: # return only loss
res = np.einsum('ik,ij,jk,ij->k', u, K, v, M)
if log:
return res, log
else:
return res
else: # return OT matrix
if log:
return u[:, None] * K * v[None, :], log
else:
return u[:, None] * K * v[None, :]
def sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, tau=1e5, numItermax=1000,
stopThr=1e-6, verbose=False, log=False,
**kwargs):
r"""
Solve the entropic regularization unbalanced optimal transport
problem and return the loss
The function solves the following optimization problem using log-domain
stabilization as proposed in [10]:
.. math::
W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + reg_m KL(\gamma 1, a) + reg_m KL(\gamma^T 1, b)
s.t.
\gamma\geq 0
where :
- M is the (dim_a, dim_b) metric cost matrix
- :math:`\Omega` is the entropic regularization
term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})`
- a and b are source and target unbalanced distributions
- KL is the Kullback-Leibler divergence
The algorithm used for solving the problem is the generalized
Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_
Parameters
----------
a : np.ndarray (dim_a,)
Unnormalized histogram of dimension dim_a
b : np.ndarray (dim_b,) or np.ndarray (dim_b, n_hists)
One or multiple unnormalized histograms of dimension dim_b
If many, compute all the OT distances (a, b_i)
M : np.ndarray (dim_a, dim_b)
loss matrix
reg : float
Entropy regularization term > 0
reg_m: float
Marginal relaxation term > 0
tau : float
thershold for max value in u or v for log scaling
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
if n_hists == 1:
gamma : (dim_a x dim_b) ndarray
Optimal transportation matrix for the given parameters
log : dict
log dictionary returned only if `log` is `True`
else:
ot_distance : (n_hists,) ndarray
the OT distance between `a` and each of the histograms `b_i`
log : dict
log dictionary returned only if `log` is `True`
Examples
--------
>>> import ot
>>> a=[.5, .5]
>>> b=[.5, .5]
>>> M=[[0., 1.],[1., 0.]]
>>> ot.unbalanced.sinkhorn_stabilized_unbalanced(a, b, M, 1., 1.)
array([[0.51122823, 0.18807035],
[0.18807035, 0.51122823]])
References
----------
.. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016).
Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.
.. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. :
Learning with a Wasserstein Loss, Advances in Neural Information
Processing Systems (NIPS) 2015
See Also
--------
ot.lp.emd : Unregularized OT
ot.optim.cg : General regularized OT
"""
a = np.asarray(a, dtype=np.float64)
b = np.asarray(b, dtype=np.float64)
M = np.asarray(M, dtype=np.float64)
dim_a, dim_b = M.shape
if len(a) == 0:
a = np.ones(dim_a, dtype=np.float64) / dim_a
if len(b) == 0:
b = np.ones(dim_b, dtype=np.float64) / dim_b
if len(b.shape) > 1:
n_hists = b.shape[1]
else:
n_hists = 0
if log:
log = {'err': []}
# we assume that no distances are null except those of the diagonal of
# distances
if n_hists:
u = np.ones((dim_a, n_hists)) / dim_a
v = np.ones((dim_b, n_hists)) / dim_b
a = a.reshape(dim_a, 1)
else:
u = np.ones(dim_a) / dim_a
v = np.ones(dim_b) / dim_b
# print(reg)
# Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute
K = np.empty(M.shape, dtype=M.dtype)
np.divide(M, -reg, out=K)
np.exp(K, out=K)
fi = reg_m / (reg_m + reg)
cpt = 0
err = 1.
alpha = np.zeros(dim_a)
beta = np.zeros(dim_b)
while (err > stopThr and cpt < numItermax):
uprev = u
vprev = v
Kv = K.dot(v)
f_alpha = np.exp(- alpha / (reg + reg_m))
f_beta = np.exp(- beta / (reg + reg_m))
if n_hists:
f_alpha = f_alpha[:, None]
f_beta = f_beta[:, None]
u = ((a / (Kv + 1e-16)) ** fi) * f_alpha
Ktu = K.T.dot(u)
v = ((b / (Ktu + 1e-16)) ** fi) * f_beta
absorbing = False
if (u > tau).any() or (v > tau).any():
absorbing = True
if n_hists:
alpha = alpha + reg * np.log(np.max(u, 1))
beta = beta + reg * np.log(np.max(v, 1))
else:
alpha = alpha + reg * np.log(np.max(u))
beta = beta + reg * np.log(np.max(v))
K = np.exp((alpha[:, None] + beta[None, :] -
M) / reg)
v = np.ones_like(v)
Kv = K.dot(v)
if (np.any(Ktu == 0.)
or np.any(np.isnan(u)) or np.any(np.isnan(v))
or np.any(np.isinf(u)) or np.any(np.isinf(v))):
# we have reached the machine precision
# come back to previous solution and quit loop
warnings.warn('Numerical errors at iteration %s' % cpt)
u = uprev
v = vprev
break
if (cpt % 10 == 0 and not absorbing) or cpt == 0:
# we can speed up the process by checking for the error only all
# the 10th iterations
err = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(),
1.)
if log:
log['err'].append(err)
if verbose:
if cpt % 200 == 0:
print(
'{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19)
print('{:5d}|{:8e}|'.format(cpt, err))
cpt = cpt + 1
if err > stopThr:
warnings.warn("Stabilized Unbalanced Sinkhorn did not converge." +
"Try a larger entropy `reg` or a lower mass `reg_m`." +
"Or a larger absorption threshold `tau`.")
if n_hists:
logu = alpha[:, None] / reg + np.log(u)
logv = beta[:, None] / reg + np.log(v)
else:
logu = alpha / reg + np.log(u)
logv = beta / reg + np.log(v)
if log:
log['logu'] = logu
log['logv'] = logv
if n_hists: # return only loss
res = logsumexp(np.log(M + 1e-100)[:, :, None] + logu[:, None, :] +
logv[None, :, :] - M[:, :, None] / reg, axis=(0, 1))
res = np.exp(res)
if log:
return res, log
else:
return res
else: # return OT matrix
ot_matrix = np.exp(logu[:, None] + logv[None, :] - M / reg)
if log:
return ot_matrix, log
else:
return ot_matrix
def barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3,
numItermax=1000, stopThr=1e-6,
verbose=False, log=False):
r"""Compute the entropic unbalanced wasserstein barycenter of A with stabilization.
The function solves the following optimization problem:
.. math::
\mathbf{a} = arg\min_\mathbf{a} \sum_i Wu_{reg}(\mathbf{a},\mathbf{a}_i)
where :
- :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized
Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced)
- :math:`\mathbf{a}_i` are training distributions in the columns of
matrix :math:`\mathbf{A}`
- reg and :math:`\mathbf{M}` are respectively the regularization term and
the cost matrix for OT
- reg_mis the marginal relaxation hyperparameter
The algorithm used for solving the problem is the generalized
Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_
Parameters
----------
A : np.ndarray (dim, n_hists)
`n_hists` training distributions a_i of dimension dim
M : np.ndarray (dim, dim)
ground metric matrix for OT.
reg : float
Entropy regularization term > 0
reg_m : float
Marginal relaxation term > 0
tau : float
Stabilization threshold for log domain absorption.
weights : np.ndarray (n_hists,) optional
Weight of each distribution (barycentric coodinates)
If None, uniform weights are used.
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshold on error (> 0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
a : (dim,) ndarray
Unbalanced Wasserstein barycenter
log : dict
log dictionary return only if log==True in parameters
References
----------
.. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré,
G. (2015). Iterative Bregman projections for regularized transportation
problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
.. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016).
Scaling algorithms for unbalanced transport problems. arXiv preprint
arXiv:1607.05816.
"""
dim, n_hists = A.shape
if weights is None:
weights = np.ones(n_hists) / n_hists
else:
assert(len(weights) == A.shape[1])
if log:
log = {'err': []}
fi = reg_m / (reg_m + reg)
u = np.ones((dim, n_hists)) / dim
v = np.ones((dim, n_hists)) / dim
# print(reg)
# Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute
K = np.empty(M.shape, dtype=M.dtype)
np.divide(M, -reg, out=K)
np.exp(K, out=K)
fi = reg_m / (reg_m + reg)
cpt = 0
err = 1.
alpha = np.zeros(dim)
beta = np.zeros(dim)
q = np.ones(dim) / dim
for i in range(numItermax):
qprev = q.copy()
Kv = K.dot(v)
f_alpha = np.exp(- alpha / (reg + reg_m))
f_beta = np.exp(- beta / (reg + reg_m))
f_alpha = f_alpha[:, None]
f_beta = f_beta[:, None]
u = ((A / (Kv + 1e-16)) ** fi) * f_alpha
Ktu = K.T.dot(u)
q = (Ktu ** (1 - fi)) * f_beta
q = q.dot(weights) ** (1 / (1 - fi))
Q = q[:, None]
v = ((Q / (Ktu + 1e-16)) ** fi) * f_beta
absorbing = False
if (u > tau).any() or (v > tau).any():
absorbing = True
alpha = alpha + reg * np.log(np.max(u, 1))
beta = beta + reg * np.log(np.max(v, 1))
K = np.exp((alpha[:, None] + beta[None, :] -
M) / reg)
v = np.ones_like(v)
Kv = K.dot(v)
if (np.any(Ktu == 0.)
or np.any(np.isnan(u)) or np.any(np.isnan(v))
or np.any(np.isinf(u)) or np.any(np.isinf(v))):
# we have reached the machine precision
# come back to previous solution and quit loop
warnings.warn('Numerical errors at iteration %s' % cpt)
q = qprev
break
if (i % 10 == 0 and not absorbing) or i == 0:
# we can speed up the process by checking for the error only all
# the 10th iterations
err = abs(q - qprev).max() / max(abs(q).max(),
abs(qprev).max(), 1.)
if log:
log['err'].append(err)
if verbose:
if i % 50 == 0:
print(
'{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19)
print('{:5d}|{:8e}|'.format(i, err))
if err < stopThr:
break
if err > stopThr:
warnings.warn("Stabilized Unbalanced Sinkhorn did not converge." +
"Try a larger entropy `reg` or a lower mass `reg_m`." +
"Or a larger absorption threshold `tau`.")
if log:
log['niter'] = i
log['logu'] = np.log(u + 1e-300)
log['logv'] = np.log(v + 1e-300)
return q, log
else:
return q
def barycenter_unbalanced_sinkhorn(A, M, reg, reg_m, weights=None,
numItermax=1000, stopThr=1e-6,
verbose=False, log=False):
r"""Compute the entropic unbalanced wasserstein barycenter of A.
The function solves the following optimization problem with a
.. math::
\mathbf{a} = arg\min_\mathbf{a} \sum_i Wu_{reg}(\mathbf{a},\mathbf{a}_i)
where :
- :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized
Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced)
- :math:`\mathbf{a}_i` are training distributions in the columns of matrix
:math:`\mathbf{A}`
- reg and :math:`\mathbf{M}` are respectively the regularization term and
the cost matrix for OT
- reg_mis the marginal relaxation hyperparameter
The algorithm used for solving the problem is the generalized
Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_
Parameters
----------
A : np.ndarray (dim, n_hists)
`n_hists` training distributions a_i of dimension dim
M : np.ndarray (dim, dim)
ground metric matrix for OT.
reg : float
Entropy regularization term > 0
reg_m: float
Marginal relaxation term > 0
weights : np.ndarray (n_hists,) optional
Weight of each distribution (barycentric coodinates)
If None, uniform weights are used.
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshold on error (> 0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
a : (dim,) ndarray
Unbalanced Wasserstein barycenter
log : dict
log dictionary return only if log==True in parameters
References
----------
.. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G.
(2015). Iterative Bregman projections for regularized transportation
problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
.. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016).
Scaling algorithms for unbalanced transport problems. arXiv preprin
arXiv:1607.05816.
"""
dim, n_hists = A.shape
if weights is None:
weights = np.ones(n_hists) / n_hists
else:
assert(len(weights) == A.shape[1])
if log:
log = {'err': []}
K = np.exp(- M / reg)
fi = reg_m / (reg_m + reg)
v = np.ones((dim, n_hists))
u = np.ones((dim, 1))
q = np.ones(dim)
err = 1.
for i in range(numItermax):
uprev = u.copy()
vprev = v.copy()
qprev = q.copy()
Kv = K.dot(v)
u = (A / Kv) ** fi
Ktu = K.T.dot(u)
q = ((Ktu ** (1 - fi)).dot(weights))
q = q ** (1 / (1 - fi))
Q = q[:, None]
v = (Q / Ktu) ** fi
if (np.any(Ktu == 0.)
or np.any(np.isnan(u)) or np.any(np.isnan(v))
or np.any(np.isinf(u)) or np.any(np.isinf(v))):
# we have reached the machine precision
# come back to previous solution and quit loop
warnings.warn('Numerical errors at iteration %s' % i)
u = uprev
v = vprev
q = qprev
break
# compute change in barycenter
err = abs(q - qprev).max()
err /= max(abs(q).max(), abs(qprev).max(), 1.)
if log:
log['err'].append(err)
# if barycenter did not change + at least 10 iterations - stop
if err < stopThr and i > 10:
break
if verbose:
if i % 10 == 0:
print(
'{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19)
print('{:5d}|{:8e}|'.format(i, err))
if log:
log['niter'] = i
log['logu'] = np.log(u + 1e-300)
log['logv'] = np.log(v + 1e-300)
return q, log
else:
return q
def barycenter_unbalanced(A, M, reg, reg_m, method="sinkhorn", weights=None,
numItermax=1000, stopThr=1e-6,
verbose=False, log=False, **kwargs):
r"""Compute the entropic unbalanced wasserstein barycenter of A.
The function solves the following optimization problem with a
.. math::
\mathbf{a} = arg\min_\mathbf{a} \sum_i Wu_{reg}(\mathbf{a},\mathbf{a}_i)
where :
- :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized
Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced)
- :math:`\mathbf{a}_i` are training distributions in the columns of matrix
:math:`\mathbf{A}`
- reg and :math:`\mathbf{M}` are respectively the regularization term and
the cost matrix for OT
- reg_mis the marginal relaxation hyperparameter
The algorithm used for solving the problem is the generalized
Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_
Parameters
----------
A : np.ndarray (dim, n_hists)
`n_hists` training distributions a_i of dimension dim
M : np.ndarray (dim, dim)
ground metric matrix for OT.
reg : float
Entropy regularization term > 0
reg_m: float
Marginal relaxation term > 0
weights : np.ndarray (n_hists,) optional
Weight of each distribution (barycentric coodinates)
If None, uniform weights are used.
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshold on error (> 0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
a : (dim,) ndarray
Unbalanced Wasserstein barycenter
log : dict
log dictionary return only if log==True in parameters
References
----------
.. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G.
(2015). Iterative Bregman projections for regularized transportation
problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
.. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016).
Scaling algorithms for unbalanced transport problems. arXiv preprin
arXiv:1607.05816.
"""
if method.lower() == 'sinkhorn':
return barycenter_unbalanced_sinkhorn(A, M, reg, reg_m,
weights=weights,
numItermax=numItermax,
stopThr=stopThr, verbose=verbose,
log=log, **kwargs)
elif method.lower() == 'sinkhorn_stabilized':
return barycenter_unbalanced_stabilized(A, M, reg, reg_m,
weights=weights,
numItermax=numItermax,
stopThr=stopThr,
verbose=verbose,
log=log, **kwargs)
elif method.lower() in ['sinkhorn_reg_scaling']:
warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp')
return barycenter_unbalanced(A, M, reg, reg_m,
weights=weights,
numItermax=numItermax,
stopThr=stopThr, verbose=verbose,
log=log, **kwargs)
else:
raise ValueError("Unknown method '%s'." % method)
| 33.757813 | 130 | 0.550972 | 4,443 | 34,568 | 4.21472 | 0.082377 | 0.01004 | 0.014098 | 0.006835 | 0.945851 | 0.936452 | 0.922514 | 0.910819 | 0.905586 | 0.884172 | 0 | 0.029069 | 0.332244 | 34,568 | 1,023 | 131 | 33.790811 | 0.782177 | 0.510154 | 0 | 0.761905 | 0 | 0 | 0.066795 | 0 | 0 | 0 | 0 | 0 | 0.005291 | 1 | 0.018519 | false | 0 | 0.010582 | 0 | 0.084656 | 0.021164 | 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 |
6a658bb94d2b25fbc3e2580e2f6019dcae4e0c79 | 3,828 | py | Python | qa327_test/frontend/registration/test_24.py | marywhetham11/3xCoolTech | 582e00a4c16016e545fedcbb14a745d125db94e0 | [
"MIT"
] | null | null | null | qa327_test/frontend/registration/test_24.py | marywhetham11/3xCoolTech | 582e00a4c16016e545fedcbb14a745d125db94e0 | [
"MIT"
] | null | null | null | qa327_test/frontend/registration/test_24.py | marywhetham11/3xCoolTech | 582e00a4c16016e545fedcbb14a745d125db94e0 | [
"MIT"
] | 1 | 2021-07-08T20:27:50.000Z | 2021-07-08T20:27:50.000Z | import pytest
from seleniumbase import BaseCase
from qa327_test.conftest import base_url
from unittest.mock import patch
from qa327.models import db, User
from werkzeug.security import generate_password_hash, check_password_hash
"""
This file defines all requirement tests for R2.4.
R2.4 - The registration form can be submitted as a POST request to the current URL (/register)
"""
# Mock a sample user
test_user = User(
email='test_frontend@test.com',
name='test_frontend',
password=generate_password_hash('test_frontend')
)
class FrontEndRegistrationR4(BaseCase):
@patch('qa327.backend.register_user', return_value=test_user)
def test_submitSuccessful(self, *_):
"""
This function tests that the /register POST request works properly
when the registration form is submitted successfully (with valid
information)
"""
# open logout page
self.open(base_url + '/logout')
# open register page
self.open(base_url + '/register')
# fill email, user name and password
self.type("#email", test_user.email)
self.type("#name", test_user.name)
self.type("#password", test_user.password)
self.type("#password2", test_user.password)
# click enter button
self.click('input[type="submit"]')
# test if the login page loads correctly
# test if the login title loads correctly
self.assert_element("h1")
self.assert_text("Log In", "h1")
# test if the login form loads correctly
self.assert_element("form")
# test if the email element loads correctly
self.assert_element('form div label[for="email"]')
self.assert_text("Email", 'form div label[for="email"]')
self.assert_element("form div #email")
# test if the password element loads correctly
self.assert_element('form div label[for="password"]')
self.assert_text("Password", 'form div label[for="password"]')
self.assert_element("form div #password")
# test if the login button loads correctly
self.assert_element('form div input[type="submit"]')
def test_submitUnsuccessful(self, *_):
"""
This function tests that the /register POST request works properly
when the registration form is submitted unsuccessfully (with invalid
information)
"""
# open logout page
self.open(base_url + '/logout')
# open register page
self.open(base_url + '/register')
# fill email, user name and password
self.type("#email", "")
self.type("#name", test_user.name)
self.type("#password", test_user.password)
self.type("#password2", test_user.password)
# click enter button
self.click('input[type="submit"]')
# test if the login page loads correctly
# test if the login title loads correctly
self.assert_element("h1")
self.assert_text("Log In", "h1")
# test if the error message loads correctly
self.assert_element("#message")
self.assert_text("Email format is incorrect", "#message")
# test if the login form loads correctly
self.assert_element("form")
# test if the email element loads correctly
self.assert_element('form div label[for="email"]')
self.assert_text("Email", 'form div label[for="email"]')
self.assert_element("form div #email")
# test if the password element loads correctly
self.assert_element('form div label[for="password"]')
self.assert_text("Password", 'form div label[for="password"]')
self.assert_element("form div #password")
# test if the login button loads correctly
self.assert_element('form div input[type="submit"]') | 40.294737 | 94 | 0.654911 | 485 | 3,828 | 5.061856 | 0.204124 | 0.089613 | 0.10387 | 0.102648 | 0.731161 | 0.718534 | 0.718534 | 0.718534 | 0.718534 | 0.718534 | 0 | 0.006878 | 0.240334 | 3,828 | 95 | 95 | 40.294737 | 0.837345 | 0.268809 | 0 | 0.627451 | 1 | 0 | 0.256672 | 0.052198 | 0 | 0 | 0 | 0 | 0.431373 | 1 | 0.039216 | false | 0.235294 | 0.117647 | 0 | 0.176471 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
6a6aba23a0f9db6af0e8988907075b3f308d21e4 | 344,914 | py | Python | cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_mpls_ldp_cfg.py | Maikor/ydk-py | b86c4a7c570ae3b2c5557d098420446df5de4929 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_mpls_ldp_cfg.py | Maikor/ydk-py | b86c4a7c570ae3b2c5557d098420446df5de4929 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_mpls_ldp_cfg.py | Maikor/ydk-py | b86c4a7c570ae3b2c5557d098420446df5de4929 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | """ Cisco_IOS_XR_mpls_ldp_cfg
This module contains a collection of YANG definitions
for Cisco IOS\-XR mpls\-ldp package configuration.
This module contains definitions
for the following management objects\:
mpls\-ldp\: MPLS LDP configuration
This YANG module augments the
Cisco\-IOS\-XR\-snmp\-agent\-cfg
module with configuration data.
Copyright (c) 2013\-2018 by Cisco Systems, Inc.
All rights reserved.
"""
from collections import OrderedDict
from ydk.types import Entity, EntityPath, Identity, Enum, YType, YLeaf, YLeafList, YList, LeafDataList, Bits, Empty, Decimal64
from ydk.filters import YFilter
from ydk.errors import YError, YModelError
from ydk.errors.error_handler import handle_type_error as _handle_type_error
class MldpPolicyMode(Enum):
"""
MldpPolicyMode (Enum Class)
Mldp policy mode
.. data:: inbound = 1
Inbound route policy
.. data:: outbound = 2
Outbound route policy
"""
inbound = Enum.YLeaf(1, "inbound")
outbound = Enum.YLeaf(2, "outbound")
class MplsLdpAdvertiseBgpAcl(Enum):
"""
MplsLdpAdvertiseBgpAcl (Enum Class)
Mpls ldp advertise bgp acl
.. data:: peer_acl = 1
BGP prefixes advertised to peers permitted by
ACL
"""
peer_acl = Enum.YLeaf(1, "peer-acl")
class MplsLdpDownstreamOnDemand(Enum):
"""
MplsLdpDownstreamOnDemand (Enum Class)
Mpls ldp downstream on demand
.. data:: peer_acl = 1
Downstream on Demand peers permitted by ACL
"""
peer_acl = Enum.YLeaf(1, "peer-acl")
class MplsLdpExpNull(Enum):
"""
MplsLdpExpNull (Enum Class)
Mpls ldp exp null
.. data:: all = 1
Advertise explicit-null for all connected
prefixes to all peers
.. data:: for_ = 2
Advertise explicit-null for prefix(es)
permitted by prefix ACL
.. data:: to = 3
Advertise explicit-null for all connected
prefixes to peer(s) permitted by peer ACL
.. data:: for_to = 4
Advertise explicit-null for prefix(es)
permitted by prefix ACL to peer(s) permitted by
peer ACL
"""
all = Enum.YLeaf(1, "all")
for_ = Enum.YLeaf(2, "for")
to = Enum.YLeaf(3, "to")
for_to = Enum.YLeaf(4, "for-to")
class MplsLdpLabelAdvertise(Enum):
"""
MplsLdpLabelAdvertise (Enum Class)
Mpls ldp label advertise
.. data:: for_ = 1
Advertise label for prefix(es) permitted by
prefix ACL
.. data:: for_to = 2
Advertise label for prefix(es) permitted by
prefix ACL to peer(s) permitted by peer ACL
"""
for_ = Enum.YLeaf(1, "for")
for_to = Enum.YLeaf(2, "for-to")
class MplsLdpLabelAllocation(Enum):
"""
MplsLdpLabelAllocation (Enum Class)
Mpls ldp label allocation
.. data:: acl = 1
Allocate label for prefixes permitted by ACL
.. data:: host = 2
Allocate label for host routes only
"""
acl = Enum.YLeaf(1, "acl")
host = Enum.YLeaf(2, "host")
class MplsLdpNbrPassword(Enum):
"""
MplsLdpNbrPassword (Enum Class)
Mpls ldp nbr password
.. data:: disable = 1
Disable the global default password for this
neighbor
.. data:: specified = 2
Specify a password for this neighbor
"""
disable = Enum.YLeaf(1, "disable")
specified = Enum.YLeaf(2, "specified")
class MplsLdpSessionProtection(Enum):
"""
MplsLdpSessionProtection (Enum Class)
Mpls ldp session protection
.. data:: all = 1
Protect all peer sessions
.. data:: for_ = 2
Protect peer session(s) permitted by peer ACL
.. data:: all_with_duration = 3
Protect all peer sessions and holdup protection
for given duration
.. data:: for_with_duration = 4
Protect peer session(s) permitted by peer ACL
and holdup protection for given duration
.. data:: all_with_forever = 5
Protect all peer sessions and holdup protection
forever
.. data:: for_with_forever = 6
Protect peer session(s) permitted by peer ACL
and holdup protection forever
"""
all = Enum.YLeaf(1, "all")
for_ = Enum.YLeaf(2, "for")
all_with_duration = Enum.YLeaf(3, "all-with-duration")
for_with_duration = Enum.YLeaf(4, "for-with-duration")
all_with_forever = Enum.YLeaf(5, "all-with-forever")
for_with_forever = Enum.YLeaf(6, "for-with-forever")
class MplsLdpTargetedAccept(Enum):
"""
MplsLdpTargetedAccept (Enum Class)
Mpls ldp targeted accept
.. data:: all = 1
Accept targeted hello from all
.. data:: from_ = 2
Accept targeted hello from peer ACL
"""
all = Enum.YLeaf(1, "all")
from_ = Enum.YLeaf(2, "from")
class MplsLdpTransportAddress(Enum):
"""
MplsLdpTransportAddress (Enum Class)
Mpls ldp transport address
.. data:: interface = 1
Use interface IP address
.. data:: address = 2
Use given IP address
"""
interface = Enum.YLeaf(1, "interface")
address = Enum.YLeaf(2, "address")
class MplsLdpafName(Enum):
"""
MplsLdpafName (Enum Class)
Mpls ldpaf name
.. data:: ipv4 = 4
IPv4
.. data:: ipv6 = 6
IPv6
"""
ipv4 = Enum.YLeaf(4, "ipv4")
ipv6 = Enum.YLeaf(6, "ipv6")
class MplsLdp(Entity):
"""
MPLS LDP configuration
.. attribute:: default_vrf
Global VRF attribute configuration for MPLS LDP
**type**\: :py:class:`DefaultVrf <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf>`
.. attribute:: vrfs
VRF Table attribute configuration for MPLS LDP
**type**\: :py:class:`Vrfs <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs>`
.. attribute:: global_
Global configuration for MPLS LDP
**type**\: :py:class:`Global <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global>`
.. attribute:: enable
Enable Label Distribution Protocol (LDP) globally.Without creating this object the LDP feature will not be enabled. Deleting this object will stop the LDP feature
**type**\: :py:class:`Empty<ydk.types.Empty>`
**mandatory**\: True
This class is a :ref:`presence class<presence-class>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp, self).__init__()
self._top_entity = None
self.yang_name = "mpls-ldp"
self.yang_parent_name = "Cisco-IOS-XR-mpls-ldp-cfg"
self.is_top_level_class = True
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("default-vrf", ("default_vrf", MplsLdp.DefaultVrf)), ("vrfs", ("vrfs", MplsLdp.Vrfs)), ("global", ("global_", MplsLdp.Global))])
self.is_presence_container = True
self._leafs = OrderedDict([
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.enable = None
self.default_vrf = MplsLdp.DefaultVrf()
self.default_vrf.parent = self
self._children_name_map["default_vrf"] = "default-vrf"
self.vrfs = MplsLdp.Vrfs()
self.vrfs.parent = self
self._children_name_map["vrfs"] = "vrfs"
self.global_ = MplsLdp.Global()
self.global_.parent = self
self._children_name_map["global_"] = "global"
self._segment_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp, ['enable'], name, value)
class DefaultVrf(Entity):
"""
Global VRF attribute configuration for MPLS LDP
.. attribute:: afs
Address Family specific configuration for MPLS LDP
**type**\: :py:class:`Afs <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs>`
.. attribute:: global_
Default VRF Global configuration for MPLS LDP
**type**\: :py:class:`Global <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global>`
.. attribute:: interfaces
MPLS LDP configuration pertaining to interfaces
**type**\: :py:class:`Interfaces <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf, self).__init__()
self.yang_name = "default-vrf"
self.yang_parent_name = "mpls-ldp"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("afs", ("afs", MplsLdp.DefaultVrf.Afs)), ("global", ("global_", MplsLdp.DefaultVrf.Global)), ("interfaces", ("interfaces", MplsLdp.DefaultVrf.Interfaces))])
self._leafs = OrderedDict()
self.afs = MplsLdp.DefaultVrf.Afs()
self.afs.parent = self
self._children_name_map["afs"] = "afs"
self.global_ = MplsLdp.DefaultVrf.Global()
self.global_.parent = self
self._children_name_map["global_"] = "global"
self.interfaces = MplsLdp.DefaultVrf.Interfaces()
self.interfaces.parent = self
self._children_name_map["interfaces"] = "interfaces"
self._segment_path = lambda: "default-vrf"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf, [], name, value)
class Afs(Entity):
"""
Address Family specific configuration for MPLS
LDP
.. attribute:: af
Configure data for given Address Family
**type**\: list of :py:class:`Af <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs, self).__init__()
self.yang_name = "afs"
self.yang_parent_name = "default-vrf"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("af", ("af", MplsLdp.DefaultVrf.Afs.Af))])
self._leafs = OrderedDict()
self.af = YList(self)
self._segment_path = lambda: "afs"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs, [], name, value)
class Af(Entity):
"""
Configure data for given Address Family
.. attribute:: af_name (key)
Address Family type
**type**\: :py:class:`MplsLdpafName <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpafName>`
.. attribute:: label
Configure Label policies and control
**type**\: :py:class:`Label <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label>`
.. attribute:: discovery
Configure Discovery parameters
**type**\: :py:class:`Discovery <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Discovery>`
.. attribute:: traffic_engineering
MPLS Traffic Engingeering parameters for LDP
**type**\: :py:class:`TrafficEngineering <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering>`
.. attribute:: neighbor
Configuration related to Neighbors
**type**\: :py:class:`Neighbor <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Neighbor>`
.. attribute:: redistribution_protocol
MPLS LDP configuration for protocol redistribution
**type**\: :py:class:`RedistributionProtocol <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol>`
.. attribute:: enable
Enable Address Family
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af, self).__init__()
self.yang_name = "af"
self.yang_parent_name = "afs"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = ['af_name']
self._child_classes = OrderedDict([("label", ("label", MplsLdp.DefaultVrf.Afs.Af.Label)), ("discovery", ("discovery", MplsLdp.DefaultVrf.Afs.Af.Discovery)), ("traffic-engineering", ("traffic_engineering", MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering)), ("neighbor", ("neighbor", MplsLdp.DefaultVrf.Afs.Af.Neighbor)), ("redistribution-protocol", ("redistribution_protocol", MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol))])
self._leafs = OrderedDict([
('af_name', (YLeaf(YType.enumeration, 'af-name'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpafName', '')])),
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.af_name = None
self.enable = None
self.label = MplsLdp.DefaultVrf.Afs.Af.Label()
self.label.parent = self
self._children_name_map["label"] = "label"
self.discovery = MplsLdp.DefaultVrf.Afs.Af.Discovery()
self.discovery.parent = self
self._children_name_map["discovery"] = "discovery"
self.traffic_engineering = MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering()
self.traffic_engineering.parent = self
self._children_name_map["traffic_engineering"] = "traffic-engineering"
self.neighbor = MplsLdp.DefaultVrf.Afs.Af.Neighbor()
self.neighbor.parent = self
self._children_name_map["neighbor"] = "neighbor"
self.redistribution_protocol = MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol()
self.redistribution_protocol.parent = self
self._children_name_map["redistribution_protocol"] = "redistribution-protocol"
self._segment_path = lambda: "af" + "[af-name='" + str(self.af_name) + "']"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/afs/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af, ['af_name', 'enable'], name, value)
class Label(Entity):
"""
Configure Label policies and control
.. attribute:: remote
Configure remote/peer label policies and control
**type**\: :py:class:`Remote <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Remote>`
.. attribute:: local
Configure local label policies and control
**type**\: :py:class:`Local <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Local>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label, self).__init__()
self.yang_name = "label"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("remote", ("remote", MplsLdp.DefaultVrf.Afs.Af.Label.Remote)), ("local", ("local", MplsLdp.DefaultVrf.Afs.Af.Label.Local))])
self._leafs = OrderedDict()
self.remote = MplsLdp.DefaultVrf.Afs.Af.Label.Remote()
self.remote.parent = self
self._children_name_map["remote"] = "remote"
self.local = MplsLdp.DefaultVrf.Afs.Af.Label.Local()
self.local.parent = self
self._children_name_map["local"] = "local"
self._segment_path = lambda: "label"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label, [], name, value)
class Remote(Entity):
"""
Configure remote/peer label policies and
control
.. attribute:: accept
Configure inbound label acceptance
**type**\: :py:class:`Accept <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Remote, self).__init__()
self.yang_name = "remote"
self.yang_parent_name = "label"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("accept", ("accept", MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept))])
self._leafs = OrderedDict()
self.accept = MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept()
self.accept.parent = self
self._children_name_map["accept"] = "accept"
self._segment_path = lambda: "remote"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Remote, [], name, value)
class Accept(Entity):
"""
Configure inbound label acceptance
.. attribute:: peer_accept_policies
Configuration related to neighbors for inbound label acceptance
**type**\: :py:class:`PeerAcceptPolicies <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept, self).__init__()
self.yang_name = "accept"
self.yang_parent_name = "remote"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("peer-accept-policies", ("peer_accept_policies", MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies))])
self._leafs = OrderedDict()
self.peer_accept_policies = MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies()
self.peer_accept_policies.parent = self
self._children_name_map["peer_accept_policies"] = "peer-accept-policies"
self._segment_path = lambda: "accept"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept, [], name, value)
class PeerAcceptPolicies(Entity):
"""
Configuration related to neighbors for
inbound label acceptance
.. attribute:: peer_accept_policy
Control acceptance of labels from a neighbor for prefix(es) using ACL
**type**\: list of :py:class:`PeerAcceptPolicy <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies, self).__init__()
self.yang_name = "peer-accept-policies"
self.yang_parent_name = "accept"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("peer-accept-policy", ("peer_accept_policy", MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy))])
self._leafs = OrderedDict()
self.peer_accept_policy = YList(self)
self._segment_path = lambda: "peer-accept-policies"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies, [], name, value)
class PeerAcceptPolicy(Entity):
"""
Control acceptance of labels from a
neighbor for prefix(es) using ACL
.. attribute:: lsr_id (key)
LSR ID of neighbor
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: label_space_id (key)
Label space ID of neighbor
**type**\: int
**range:** 0..4294967295
.. attribute:: prefix_acl_name
Name of prefix ACL
**type**\: str
**mandatory**\: True
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy, self).__init__()
self.yang_name = "peer-accept-policy"
self.yang_parent_name = "peer-accept-policies"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['lsr_id','label_space_id']
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('lsr_id', (YLeaf(YType.str, 'lsr-id'), ['str'])),
('label_space_id', (YLeaf(YType.uint32, 'label-space-id'), ['int'])),
('prefix_acl_name', (YLeaf(YType.str, 'prefix-acl-name'), ['str'])),
])
self.lsr_id = None
self.label_space_id = None
self.prefix_acl_name = None
self._segment_path = lambda: "peer-accept-policy" + "[lsr-id='" + str(self.lsr_id) + "']" + "[label-space-id='" + str(self.label_space_id) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy, ['lsr_id', 'label_space_id', 'prefix_acl_name'], name, value)
class Local(Entity):
"""
Configure local label policies and control
.. attribute:: advertise
Configure outbound label advertisement
**type**\: :py:class:`Advertise <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise>`
.. attribute:: allocate
Control local label allocation for prefix(es)
**type**\: :py:class:`Allocate <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Local.Allocate>`
.. attribute:: implicit_null_override
Control use of implicit\-null label for set of prefix(es)
**type**\: str
.. attribute:: default_route
Enable MPLS forwarding for default route
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Local, self).__init__()
self.yang_name = "local"
self.yang_parent_name = "label"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("advertise", ("advertise", MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise)), ("allocate", ("allocate", MplsLdp.DefaultVrf.Afs.Af.Label.Local.Allocate))])
self._leafs = OrderedDict([
('implicit_null_override', (YLeaf(YType.str, 'implicit-null-override'), ['str'])),
('default_route', (YLeaf(YType.empty, 'default-route'), ['Empty'])),
])
self.implicit_null_override = None
self.default_route = None
self.advertise = MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise()
self.advertise.parent = self
self._children_name_map["advertise"] = "advertise"
self.allocate = MplsLdp.DefaultVrf.Afs.Af.Label.Local.Allocate()
self.allocate.parent = self
self._children_name_map["allocate"] = "allocate"
self._segment_path = lambda: "local"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Local, ['implicit_null_override', 'default_route'], name, value)
class Advertise(Entity):
"""
Configure outbound label advertisement
.. attribute:: peer_advertise_policies
Configure peer centric outbound label advertisement using ACL
**type**\: :py:class:`PeerAdvertisePolicies <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies>`
.. attribute:: prefix_advertise_policies
Configure prefix centric outbound label advertisement using ACL
**type**\: :py:class:`PrefixAdvertisePolicies <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PrefixAdvertisePolicies>`
.. attribute:: explicit_null
Configure advertisment of explicit\-null for connected prefixes
**type**\: :py:class:`ExplicitNull <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.ExplicitNull>`
.. attribute:: interfaces
Configure outbound label advertisement for an interface
**type**\: :py:class:`Interfaces <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.Interfaces>`
.. attribute:: disable
Disable label advertisement
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise, self).__init__()
self.yang_name = "advertise"
self.yang_parent_name = "local"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("peer-advertise-policies", ("peer_advertise_policies", MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies)), ("prefix-advertise-policies", ("prefix_advertise_policies", MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PrefixAdvertisePolicies)), ("explicit-null", ("explicit_null", MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.ExplicitNull)), ("interfaces", ("interfaces", MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.Interfaces))])
self._leafs = OrderedDict([
('disable', (YLeaf(YType.empty, 'disable'), ['Empty'])),
])
self.disable = None
self.peer_advertise_policies = MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies()
self.peer_advertise_policies.parent = self
self._children_name_map["peer_advertise_policies"] = "peer-advertise-policies"
self.prefix_advertise_policies = MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PrefixAdvertisePolicies()
self.prefix_advertise_policies.parent = self
self._children_name_map["prefix_advertise_policies"] = "prefix-advertise-policies"
self.explicit_null = MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.ExplicitNull()
self.explicit_null.parent = self
self._children_name_map["explicit_null"] = "explicit-null"
self.interfaces = MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.Interfaces()
self.interfaces.parent = self
self._children_name_map["interfaces"] = "interfaces"
self._segment_path = lambda: "advertise"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise, ['disable'], name, value)
class PeerAdvertisePolicies(Entity):
"""
Configure peer centric outbound label
advertisement using ACL
.. attribute:: peer_advertise_policy
Control advertisement of prefix(es) using ACL
**type**\: list of :py:class:`PeerAdvertisePolicy <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies, self).__init__()
self.yang_name = "peer-advertise-policies"
self.yang_parent_name = "advertise"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("peer-advertise-policy", ("peer_advertise_policy", MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy))])
self._leafs = OrderedDict()
self.peer_advertise_policy = YList(self)
self._segment_path = lambda: "peer-advertise-policies"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies, [], name, value)
class PeerAdvertisePolicy(Entity):
"""
Control advertisement of prefix(es) using
ACL
.. attribute:: lsr_id (key)
LSR ID of neighbor
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: label_space_id (key)
Label space ID of neighbor
**type**\: int
**range:** 0..4294967295
.. attribute:: prefix_acl_name
Name of prefix ACL
**type**\: str
**mandatory**\: True
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy, self).__init__()
self.yang_name = "peer-advertise-policy"
self.yang_parent_name = "peer-advertise-policies"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['lsr_id','label_space_id']
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('lsr_id', (YLeaf(YType.str, 'lsr-id'), ['str'])),
('label_space_id', (YLeaf(YType.uint32, 'label-space-id'), ['int'])),
('prefix_acl_name', (YLeaf(YType.str, 'prefix-acl-name'), ['str'])),
])
self.lsr_id = None
self.label_space_id = None
self.prefix_acl_name = None
self._segment_path = lambda: "peer-advertise-policy" + "[lsr-id='" + str(self.lsr_id) + "']" + "[label-space-id='" + str(self.label_space_id) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy, ['lsr_id', 'label_space_id', 'prefix_acl_name'], name, value)
class PrefixAdvertisePolicies(Entity):
"""
Configure prefix centric outbound label
advertisement using ACL
.. attribute:: prefix_advertise_policy
Control advertisement of prefix(es) using ACL
**type**\: list of :py:class:`PrefixAdvertisePolicy <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PrefixAdvertisePolicies.PrefixAdvertisePolicy>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PrefixAdvertisePolicies, self).__init__()
self.yang_name = "prefix-advertise-policies"
self.yang_parent_name = "advertise"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("prefix-advertise-policy", ("prefix_advertise_policy", MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PrefixAdvertisePolicies.PrefixAdvertisePolicy))])
self._leafs = OrderedDict()
self.prefix_advertise_policy = YList(self)
self._segment_path = lambda: "prefix-advertise-policies"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PrefixAdvertisePolicies, [], name, value)
class PrefixAdvertisePolicy(Entity):
"""
Control advertisement of prefix(es) using
ACL
.. attribute:: prefix_acl_name (key)
Name of prefix ACL
**type**\: str
**pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+
.. attribute:: advertise_type
Label advertise type
**type**\: :py:class:`MplsLdpLabelAdvertise <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpLabelAdvertise>`
.. attribute:: peer_acl_name
Name of peer ACL
**type**\: str
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PrefixAdvertisePolicies.PrefixAdvertisePolicy, self).__init__()
self.yang_name = "prefix-advertise-policy"
self.yang_parent_name = "prefix-advertise-policies"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['prefix_acl_name']
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('prefix_acl_name', (YLeaf(YType.str, 'prefix-acl-name'), ['str'])),
('advertise_type', (YLeaf(YType.enumeration, 'advertise-type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpLabelAdvertise', '')])),
('peer_acl_name', (YLeaf(YType.str, 'peer-acl-name'), ['str'])),
])
self.prefix_acl_name = None
self.advertise_type = None
self.peer_acl_name = None
self._segment_path = lambda: "prefix-advertise-policy" + "[prefix-acl-name='" + str(self.prefix_acl_name) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.PrefixAdvertisePolicies.PrefixAdvertisePolicy, ['prefix_acl_name', 'advertise_type', 'peer_acl_name'], name, value)
class ExplicitNull(Entity):
"""
Configure advertisment of explicit\-null
for connected prefixes.
.. attribute:: explicit_null_type
Explicit Null command variant
**type**\: :py:class:`MplsLdpExpNull <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpExpNull>`
.. attribute:: prefix_acl_name
Name of prefix ACL
**type**\: str
.. attribute:: peer_acl_name
Name of peer ACL
**type**\: str
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.ExplicitNull, self).__init__()
self.yang_name = "explicit-null"
self.yang_parent_name = "advertise"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('explicit_null_type', (YLeaf(YType.enumeration, 'explicit-null-type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpExpNull', '')])),
('prefix_acl_name', (YLeaf(YType.str, 'prefix-acl-name'), ['str'])),
('peer_acl_name', (YLeaf(YType.str, 'peer-acl-name'), ['str'])),
])
self.explicit_null_type = None
self.prefix_acl_name = None
self.peer_acl_name = None
self._segment_path = lambda: "explicit-null"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.ExplicitNull, ['explicit_null_type', 'prefix_acl_name', 'peer_acl_name'], name, value)
class Interfaces(Entity):
"""
Configure outbound label advertisement for
an interface
.. attribute:: interface
Control advertisement of interface's host IP address
**type**\: list of :py:class:`Interface <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.Interfaces.Interface>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.Interfaces, self).__init__()
self.yang_name = "interfaces"
self.yang_parent_name = "advertise"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("interface", ("interface", MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.Interfaces.Interface))])
self._leafs = OrderedDict()
self.interface = YList(self)
self._segment_path = lambda: "interfaces"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.Interfaces, [], name, value)
class Interface(Entity):
"""
Control advertisement of interface's host
IP address
.. attribute:: interface_name (key)
Name of interface
**type**\: str
**pattern:** [a\-zA\-Z0\-9.\_/\-]+
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.Interfaces.Interface, self).__init__()
self.yang_name = "interface"
self.yang_parent_name = "interfaces"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['interface_name']
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('interface_name', (YLeaf(YType.str, 'interface-name'), ['str'])),
])
self.interface_name = None
self._segment_path = lambda: "interface" + "[interface-name='" + str(self.interface_name) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Advertise.Interfaces.Interface, ['interface_name'], name, value)
class Allocate(Entity):
"""
Control local label allocation for
prefix(es)
.. attribute:: allocation_type
Label allocation type
**type**\: :py:class:`MplsLdpLabelAllocation <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpLabelAllocation>`
.. attribute:: prefix_acl_name
Name of prefix ACL
**type**\: str
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Allocate, self).__init__()
self.yang_name = "allocate"
self.yang_parent_name = "local"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('allocation_type', (YLeaf(YType.enumeration, 'allocation-type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpLabelAllocation', '')])),
('prefix_acl_name', (YLeaf(YType.str, 'prefix-acl-name'), ['str'])),
])
self.allocation_type = None
self.prefix_acl_name = None
self._segment_path = lambda: "allocate"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Label.Local.Allocate, ['allocation_type', 'prefix_acl_name'], name, value)
class Discovery(Entity):
"""
Configure Discovery parameters
.. attribute:: targeted_hello_accept
Configure acceptance from and responding to targeted hellos
**type**\: :py:class:`TargetedHelloAccept <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Discovery.TargetedHelloAccept>`
.. attribute:: transport_address
Global discovery transport address for address family
**type**\: union of the below types:
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
**type**\: str
**pattern:** ((\:\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\|[0\-9a\-fA\-F]{0,4}))\|(((25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)?
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Discovery, self).__init__()
self.yang_name = "discovery"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("targeted-hello-accept", ("targeted_hello_accept", MplsLdp.DefaultVrf.Afs.Af.Discovery.TargetedHelloAccept))])
self._leafs = OrderedDict([
('transport_address', (YLeaf(YType.str, 'transport-address'), ['str','str'])),
])
self.transport_address = None
self.targeted_hello_accept = MplsLdp.DefaultVrf.Afs.Af.Discovery.TargetedHelloAccept()
self.targeted_hello_accept.parent = self
self._children_name_map["targeted_hello_accept"] = "targeted-hello-accept"
self._segment_path = lambda: "discovery"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Discovery, ['transport_address'], name, value)
class TargetedHelloAccept(Entity):
"""
Configure acceptance from and responding to
targeted hellos.
.. attribute:: accept_type
Type of acceptance
**type**\: :py:class:`MplsLdpTargetedAccept <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpTargetedAccept>`
.. attribute:: peer_acl_name
Name of peer ACL
**type**\: str
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Discovery.TargetedHelloAccept, self).__init__()
self.yang_name = "targeted-hello-accept"
self.yang_parent_name = "discovery"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('accept_type', (YLeaf(YType.enumeration, 'accept-type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpTargetedAccept', '')])),
('peer_acl_name', (YLeaf(YType.str, 'peer-acl-name'), ['str'])),
])
self.accept_type = None
self.peer_acl_name = None
self._segment_path = lambda: "targeted-hello-accept"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Discovery.TargetedHelloAccept, ['accept_type', 'peer_acl_name'], name, value)
class TrafficEngineering(Entity):
"""
MPLS Traffic Engingeering parameters for LDP
.. attribute:: auto_tunnel_mesh
MPLS Traffic Engineering auto\-tunnel mesh parameters for LDP
**type**\: :py:class:`AutoTunnelMesh <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering, self).__init__()
self.yang_name = "traffic-engineering"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("auto-tunnel-mesh", ("auto_tunnel_mesh", MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh))])
self._leafs = OrderedDict()
self.auto_tunnel_mesh = MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh()
self.auto_tunnel_mesh.parent = self
self._children_name_map["auto_tunnel_mesh"] = "auto-tunnel-mesh"
self._segment_path = lambda: "traffic-engineering"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering, [], name, value)
class AutoTunnelMesh(Entity):
"""
MPLS Traffic Engineering auto\-tunnel mesh
parameters for LDP
.. attribute:: group_ids
Enable interfaces in specific MPLS TE auto\-tunnel mesh\-groups
**type**\: :py:class:`GroupIds <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh.GroupIds>`
.. attribute:: group_all
Enable all MPLS TE auto\-tunnel mesh\-group interfaces
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh, self).__init__()
self.yang_name = "auto-tunnel-mesh"
self.yang_parent_name = "traffic-engineering"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("group-ids", ("group_ids", MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh.GroupIds))])
self._leafs = OrderedDict([
('group_all', (YLeaf(YType.empty, 'group-all'), ['Empty'])),
])
self.group_all = None
self.group_ids = MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh.GroupIds()
self.group_ids.parent = self
self._children_name_map["group_ids"] = "group-ids"
self._segment_path = lambda: "auto-tunnel-mesh"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh, ['group_all'], name, value)
class GroupIds(Entity):
"""
Enable interfaces in specific MPLS TE
auto\-tunnel mesh\-groups
.. attribute:: group_id
Auto\-mesh group identifier to enable
**type**\: list of :py:class:`GroupId <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh.GroupIds.GroupId>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh.GroupIds, self).__init__()
self.yang_name = "group-ids"
self.yang_parent_name = "auto-tunnel-mesh"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("group-id", ("group_id", MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh.GroupIds.GroupId))])
self._leafs = OrderedDict()
self.group_id = YList(self)
self._segment_path = lambda: "group-ids"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh.GroupIds, [], name, value)
class GroupId(Entity):
"""
Auto\-mesh group identifier to enable
.. attribute:: mesh_group_id (key)
Mesh group ID
**type**\: int
**range:** 0..4294967295
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh.GroupIds.GroupId, self).__init__()
self.yang_name = "group-id"
self.yang_parent_name = "group-ids"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['mesh_group_id']
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('mesh_group_id', (YLeaf(YType.uint32, 'mesh-group-id'), ['int'])),
])
self.mesh_group_id = None
self._segment_path = lambda: "group-id" + "[mesh-group-id='" + str(self.mesh_group_id) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.TrafficEngineering.AutoTunnelMesh.GroupIds.GroupId, ['mesh_group_id'], name, value)
class Neighbor(Entity):
"""
Configuration related to Neighbors
.. attribute:: addresses
Configuration related to neighbors using neighbor address
**type**\: :py:class:`Addresses <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Neighbor.Addresses>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Neighbor, self).__init__()
self.yang_name = "neighbor"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("addresses", ("addresses", MplsLdp.DefaultVrf.Afs.Af.Neighbor.Addresses))])
self._leafs = OrderedDict()
self.addresses = MplsLdp.DefaultVrf.Afs.Af.Neighbor.Addresses()
self.addresses.parent = self
self._children_name_map["addresses"] = "addresses"
self._segment_path = lambda: "neighbor"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Neighbor, [], name, value)
class Addresses(Entity):
"""
Configuration related to neighbors using
neighbor address
.. attribute:: address
IP address based configuration related to a neighbor
**type**\: list of :py:class:`Address <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.Neighbor.Addresses.Address>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Neighbor.Addresses, self).__init__()
self.yang_name = "addresses"
self.yang_parent_name = "neighbor"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("address", ("address", MplsLdp.DefaultVrf.Afs.Af.Neighbor.Addresses.Address))])
self._leafs = OrderedDict()
self.address = YList(self)
self._segment_path = lambda: "addresses"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Neighbor.Addresses, [], name, value)
class Address(Entity):
"""
IP address based configuration related to a
neighbor
.. attribute:: ip_address (key)
The IP address
**type**\: union of the below types:
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
**type**\: str
**pattern:** ((\:\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\|[0\-9a\-fA\-F]{0,4}))\|(((25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)?
.. attribute:: targeted
Establish targeted session with given address
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.Neighbor.Addresses.Address, self).__init__()
self.yang_name = "address"
self.yang_parent_name = "addresses"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['ip_address']
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('ip_address', (YLeaf(YType.str, 'ip-address'), ['str','str'])),
('targeted', (YLeaf(YType.empty, 'targeted'), ['Empty'])),
])
self.ip_address = None
self.targeted = None
self._segment_path = lambda: "address" + "[ip-address='" + str(self.ip_address) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.Neighbor.Addresses.Address, ['ip_address', 'targeted'], name, value)
class RedistributionProtocol(Entity):
"""
MPLS LDP configuration for protocol
redistribution
.. attribute:: bgp
MPLS LDP configuration for protocol redistribution
**type**\: :py:class:`Bgp <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol, self).__init__()
self.yang_name = "redistribution-protocol"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("bgp", ("bgp", MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp))])
self._leafs = OrderedDict()
self.bgp = MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp()
self.bgp.parent = self
self._children_name_map["bgp"] = "bgp"
self._segment_path = lambda: "redistribution-protocol"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol, [], name, value)
class Bgp(Entity):
"""
MPLS LDP configuration for protocol
redistribution
.. attribute:: as_
MPLS LDP configuration for protocol redistribution
**type**\: :py:class:`As <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp.As>`
.. attribute:: advertise_to
ACL containing list of neighbors for BGP route redistribution
**type**\: :py:class:`AdvertiseTo <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp.AdvertiseTo>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp, self).__init__()
self.yang_name = "bgp"
self.yang_parent_name = "redistribution-protocol"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("as", ("as_", MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp.As)), ("advertise-to", ("advertise_to", MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp.AdvertiseTo))])
self._leafs = OrderedDict()
self.as_ = MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp.As()
self.as_.parent = self
self._children_name_map["as_"] = "as"
self.advertise_to = MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp.AdvertiseTo()
self.advertise_to.parent = self
self._children_name_map["advertise_to"] = "advertise-to"
self._segment_path = lambda: "bgp"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp, [], name, value)
class As(Entity):
"""
MPLS LDP configuration for protocol
redistribution
.. attribute:: as_xx
First half of BGP AS number in XX.YY format. Mandatory Must be a non\-zero value if second half is zero
**type**\: int
**range:** 0..65535
.. attribute:: as_yy
Second half of BGP AS number in XX.YY format. Mandatory Must be a non\-zero value if first half is zero
**type**\: int
**range:** 0..4294967295
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp.As, self).__init__()
self.yang_name = "as"
self.yang_parent_name = "bgp"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('as_xx', (YLeaf(YType.uint32, 'as-xx'), ['int'])),
('as_yy', (YLeaf(YType.uint32, 'as-yy'), ['int'])),
])
self.as_xx = None
self.as_yy = None
self._segment_path = lambda: "as"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp.As, ['as_xx', 'as_yy'], name, value)
class AdvertiseTo(Entity):
"""
ACL containing list of neighbors for BGP
route redistribution
.. attribute:: type
advertise to peer acl type
**type**\: :py:class:`MplsLdpAdvertiseBgpAcl <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpAdvertiseBgpAcl>`
.. attribute:: peer_acl_name
Name of peer ACL
**type**\: str
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp.AdvertiseTo, self).__init__()
self.yang_name = "advertise-to"
self.yang_parent_name = "bgp"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('type', (YLeaf(YType.enumeration, 'type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpAdvertiseBgpAcl', '')])),
('peer_acl_name', (YLeaf(YType.str, 'peer-acl-name'), ['str'])),
])
self.type = None
self.peer_acl_name = None
self._segment_path = lambda: "advertise-to"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Afs.Af.RedistributionProtocol.Bgp.AdvertiseTo, ['type', 'peer_acl_name'], name, value)
class Global(Entity):
"""
Default VRF Global configuration for MPLS LDP
.. attribute:: session
LDP Session parameters
**type**\: :py:class:`Session <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global.Session>`
.. attribute:: neighbor
Configuration related to Neighbors
**type**\: :py:class:`Neighbor <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global.Neighbor>`
.. attribute:: graceful_restart
Configuration for per\-VRF LDP Graceful Restart parameters
**type**\: :py:class:`GracefulRestart <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global.GracefulRestart>`
.. attribute:: router_id
Configuration for LDP Router ID (LDP ID)
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global, self).__init__()
self.yang_name = "global"
self.yang_parent_name = "default-vrf"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("session", ("session", MplsLdp.DefaultVrf.Global.Session)), ("neighbor", ("neighbor", MplsLdp.DefaultVrf.Global.Neighbor)), ("graceful-restart", ("graceful_restart", MplsLdp.DefaultVrf.Global.GracefulRestart))])
self._leafs = OrderedDict([
('router_id', (YLeaf(YType.str, 'router-id'), ['str'])),
])
self.router_id = None
self.session = MplsLdp.DefaultVrf.Global.Session()
self.session.parent = self
self._children_name_map["session"] = "session"
self.neighbor = MplsLdp.DefaultVrf.Global.Neighbor()
self.neighbor.parent = self
self._children_name_map["neighbor"] = "neighbor"
self.graceful_restart = MplsLdp.DefaultVrf.Global.GracefulRestart()
self.graceful_restart.parent = self
self._children_name_map["graceful_restart"] = "graceful-restart"
self._segment_path = lambda: "global"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global, ['router_id'], name, value)
class Session(Entity):
"""
LDP Session parameters
.. attribute:: protection
Configure Session Protection parameters
**type**\: :py:class:`Protection <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global.Session.Protection>`
.. attribute:: downstream_on_demand
ACL with the list of neighbors configured for Downstream on Demand
**type**\: :py:class:`DownstreamOnDemand <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global.Session.DownstreamOnDemand>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global.Session, self).__init__()
self.yang_name = "session"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("protection", ("protection", MplsLdp.DefaultVrf.Global.Session.Protection)), ("downstream-on-demand", ("downstream_on_demand", MplsLdp.DefaultVrf.Global.Session.DownstreamOnDemand))])
self._leafs = OrderedDict()
self.protection = MplsLdp.DefaultVrf.Global.Session.Protection()
self.protection.parent = self
self._children_name_map["protection"] = "protection"
self.downstream_on_demand = MplsLdp.DefaultVrf.Global.Session.DownstreamOnDemand()
self.downstream_on_demand.parent = self
self._children_name_map["downstream_on_demand"] = "downstream-on-demand"
self._segment_path = lambda: "session"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global.Session, [], name, value)
class Protection(Entity):
"""
Configure Session Protection parameters
.. attribute:: protection_type
Session protection type
**type**\: :py:class:`MplsLdpSessionProtection <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpSessionProtection>`
.. attribute:: peer_acl_name
Name of peer ACL
**type**\: str
.. attribute:: duration
Holdup duration
**type**\: int
**range:** 30..2147483
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global.Session.Protection, self).__init__()
self.yang_name = "protection"
self.yang_parent_name = "session"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('protection_type', (YLeaf(YType.enumeration, 'protection-type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpSessionProtection', '')])),
('peer_acl_name', (YLeaf(YType.str, 'peer-acl-name'), ['str'])),
('duration', (YLeaf(YType.uint32, 'duration'), ['int'])),
])
self.protection_type = None
self.peer_acl_name = None
self.duration = None
self._segment_path = lambda: "protection"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/global/session/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global.Session.Protection, ['protection_type', 'peer_acl_name', 'duration'], name, value)
class DownstreamOnDemand(Entity):
"""
ACL with the list of neighbors configured for
Downstream on Demand
.. attribute:: type
Downstream on demand type
**type**\: :py:class:`MplsLdpDownstreamOnDemand <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpDownstreamOnDemand>`
.. attribute:: peer_acl_name
Name of peer ACL
**type**\: str
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global.Session.DownstreamOnDemand, self).__init__()
self.yang_name = "downstream-on-demand"
self.yang_parent_name = "session"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('type', (YLeaf(YType.enumeration, 'type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpDownstreamOnDemand', '')])),
('peer_acl_name', (YLeaf(YType.str, 'peer-acl-name'), ['str'])),
])
self.type = None
self.peer_acl_name = None
self._segment_path = lambda: "downstream-on-demand"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/global/session/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global.Session.DownstreamOnDemand, ['type', 'peer_acl_name'], name, value)
class Neighbor(Entity):
"""
Configuration related to Neighbors
.. attribute:: ldp_ids
Configuration related to Neighbors using LDP Id
**type**\: :py:class:`LdpIds <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global.Neighbor.LdpIds>`
.. attribute:: dual_stack
Configuration related to neighbor transport
**type**\: :py:class:`DualStack <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global.Neighbor.DualStack>`
.. attribute:: password
Default password for all neigbors
**type**\: str
**pattern:** (!.+)\|([^!].+)
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global.Neighbor, self).__init__()
self.yang_name = "neighbor"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("ldp-ids", ("ldp_ids", MplsLdp.DefaultVrf.Global.Neighbor.LdpIds)), ("dual-stack", ("dual_stack", MplsLdp.DefaultVrf.Global.Neighbor.DualStack))])
self._leafs = OrderedDict([
('password', (YLeaf(YType.str, 'password'), ['str'])),
])
self.password = None
self.ldp_ids = MplsLdp.DefaultVrf.Global.Neighbor.LdpIds()
self.ldp_ids.parent = self
self._children_name_map["ldp_ids"] = "ldp-ids"
self.dual_stack = MplsLdp.DefaultVrf.Global.Neighbor.DualStack()
self.dual_stack.parent = self
self._children_name_map["dual_stack"] = "dual-stack"
self._segment_path = lambda: "neighbor"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global.Neighbor, ['password'], name, value)
class LdpIds(Entity):
"""
Configuration related to Neighbors using LDP
Id
.. attribute:: ldp_id
LDP ID based configuration related to a neigbor
**type**\: list of :py:class:`LdpId <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global.Neighbor.LdpIds.LdpId>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global.Neighbor.LdpIds, self).__init__()
self.yang_name = "ldp-ids"
self.yang_parent_name = "neighbor"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("ldp-id", ("ldp_id", MplsLdp.DefaultVrf.Global.Neighbor.LdpIds.LdpId))])
self._leafs = OrderedDict()
self.ldp_id = YList(self)
self._segment_path = lambda: "ldp-ids"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/global/neighbor/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global.Neighbor.LdpIds, [], name, value)
class LdpId(Entity):
"""
LDP ID based configuration related to a
neigbor
.. attribute:: lsr_id (key)
LSR ID of neighbor
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: label_space_id (key)
Label space ID of neighbor
**type**\: int
**range:** 0..4294967295
.. attribute:: password
Password for MD5 authentication for this neighbor
**type**\: :py:class:`Password <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global.Neighbor.LdpIds.LdpId.Password>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global.Neighbor.LdpIds.LdpId, self).__init__()
self.yang_name = "ldp-id"
self.yang_parent_name = "ldp-ids"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = ['lsr_id','label_space_id']
self._child_classes = OrderedDict([("password", ("password", MplsLdp.DefaultVrf.Global.Neighbor.LdpIds.LdpId.Password))])
self._leafs = OrderedDict([
('lsr_id', (YLeaf(YType.str, 'lsr-id'), ['str'])),
('label_space_id', (YLeaf(YType.uint32, 'label-space-id'), ['int'])),
])
self.lsr_id = None
self.label_space_id = None
self.password = MplsLdp.DefaultVrf.Global.Neighbor.LdpIds.LdpId.Password()
self.password.parent = self
self._children_name_map["password"] = "password"
self._segment_path = lambda: "ldp-id" + "[lsr-id='" + str(self.lsr_id) + "']" + "[label-space-id='" + str(self.label_space_id) + "']"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/global/neighbor/ldp-ids/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global.Neighbor.LdpIds.LdpId, ['lsr_id', 'label_space_id'], name, value)
class Password(Entity):
"""
Password for MD5 authentication for this
neighbor
.. attribute:: command_type
Command type for password configuration
**type**\: :py:class:`MplsLdpNbrPassword <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpNbrPassword>`
.. attribute:: password
The neighbor password
**type**\: str
**pattern:** (!.+)\|([^!].+)
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global.Neighbor.LdpIds.LdpId.Password, self).__init__()
self.yang_name = "password"
self.yang_parent_name = "ldp-id"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('command_type', (YLeaf(YType.enumeration, 'command-type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpNbrPassword', '')])),
('password', (YLeaf(YType.str, 'password'), ['str'])),
])
self.command_type = None
self.password = None
self._segment_path = lambda: "password"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global.Neighbor.LdpIds.LdpId.Password, ['command_type', 'password'], name, value)
class DualStack(Entity):
"""
Configuration related to neighbor transport
.. attribute:: transport_connection
Configuration related to neighbor transport
**type**\: :py:class:`TransportConnection <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global.Neighbor.DualStack.TransportConnection>`
.. attribute:: tlv_compliance
Configuration to enable neighbor dual\-stack tlv\-compliance
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global.Neighbor.DualStack, self).__init__()
self.yang_name = "dual-stack"
self.yang_parent_name = "neighbor"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("transport-connection", ("transport_connection", MplsLdp.DefaultVrf.Global.Neighbor.DualStack.TransportConnection))])
self._leafs = OrderedDict([
('tlv_compliance', (YLeaf(YType.empty, 'tlv-compliance'), ['Empty'])),
])
self.tlv_compliance = None
self.transport_connection = MplsLdp.DefaultVrf.Global.Neighbor.DualStack.TransportConnection()
self.transport_connection.parent = self
self._children_name_map["transport_connection"] = "transport-connection"
self._segment_path = lambda: "dual-stack"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/global/neighbor/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global.Neighbor.DualStack, ['tlv_compliance'], name, value)
class TransportConnection(Entity):
"""
Configuration related to neighbor transport
.. attribute:: prefer
Configuration related to neighbor dual\-stack xport\-connection preference
**type**\: :py:class:`Prefer <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global.Neighbor.DualStack.TransportConnection.Prefer>`
.. attribute:: max_wait
Configuration related to neighbor dual\-stack xport\-connection max\-wait
**type**\: int
**range:** 0..60
**units**\: second
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global.Neighbor.DualStack.TransportConnection, self).__init__()
self.yang_name = "transport-connection"
self.yang_parent_name = "dual-stack"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("prefer", ("prefer", MplsLdp.DefaultVrf.Global.Neighbor.DualStack.TransportConnection.Prefer))])
self._leafs = OrderedDict([
('max_wait', (YLeaf(YType.uint32, 'max-wait'), ['int'])),
])
self.max_wait = None
self.prefer = MplsLdp.DefaultVrf.Global.Neighbor.DualStack.TransportConnection.Prefer()
self.prefer.parent = self
self._children_name_map["prefer"] = "prefer"
self._segment_path = lambda: "transport-connection"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/global/neighbor/dual-stack/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global.Neighbor.DualStack.TransportConnection, ['max_wait'], name, value)
class Prefer(Entity):
"""
Configuration related to neighbor
dual\-stack xport\-connection preference
.. attribute:: ipv4
Configuration related to neighbor dual\-stack xport\-connection preference ipv4
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global.Neighbor.DualStack.TransportConnection.Prefer, self).__init__()
self.yang_name = "prefer"
self.yang_parent_name = "transport-connection"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('ipv4', (YLeaf(YType.empty, 'ipv4'), ['Empty'])),
])
self.ipv4 = None
self._segment_path = lambda: "prefer"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/global/neighbor/dual-stack/transport-connection/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global.Neighbor.DualStack.TransportConnection.Prefer, ['ipv4'], name, value)
class GracefulRestart(Entity):
"""
Configuration for per\-VRF LDP Graceful Restart
parameters
.. attribute:: helper_peer
Configure parameters related to GR peer(s) opearating in helper mode
**type**\: :py:class:`HelperPeer <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Global.GracefulRestart.HelperPeer>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global.GracefulRestart, self).__init__()
self.yang_name = "graceful-restart"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("helper-peer", ("helper_peer", MplsLdp.DefaultVrf.Global.GracefulRestart.HelperPeer))])
self._leafs = OrderedDict()
self.helper_peer = MplsLdp.DefaultVrf.Global.GracefulRestart.HelperPeer()
self.helper_peer.parent = self
self._children_name_map["helper_peer"] = "helper-peer"
self._segment_path = lambda: "graceful-restart"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global.GracefulRestart, [], name, value)
class HelperPeer(Entity):
"""
Configure parameters related to GR peer(s)
opearating in helper mode
.. attribute:: maintain_on_local_reset
Maintain the state of a GR peer upon a local reset
**type**\: str
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Global.GracefulRestart.HelperPeer, self).__init__()
self.yang_name = "helper-peer"
self.yang_parent_name = "graceful-restart"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('maintain_on_local_reset', (YLeaf(YType.str, 'maintain-on-local-reset'), ['str'])),
])
self.maintain_on_local_reset = None
self._segment_path = lambda: "helper-peer"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/global/graceful-restart/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Global.GracefulRestart.HelperPeer, ['maintain_on_local_reset'], name, value)
class Interfaces(Entity):
"""
MPLS LDP configuration pertaining to interfaces
.. attribute:: interface
MPLS LDP configuration for a particular interface
**type**\: list of :py:class:`Interface <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces, self).__init__()
self.yang_name = "interfaces"
self.yang_parent_name = "default-vrf"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("interface", ("interface", MplsLdp.DefaultVrf.Interfaces.Interface))])
self._leafs = OrderedDict()
self.interface = YList(self)
self._segment_path = lambda: "interfaces"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces, [], name, value)
class Interface(Entity):
"""
MPLS LDP configuration for a particular
interface
.. attribute:: interface_name (key)
Name of interface
**type**\: str
**pattern:** [a\-zA\-Z0\-9.\_/\-]+
.. attribute:: afs
Address Family specific configuration for MPLS LDP intf
**type**\: :py:class:`Afs <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Afs>`
.. attribute:: global_
Per VRF interface Global configuration for MPLS LDP
**type**\: :py:class:`Global <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Global>`
.. attribute:: enable
Enable Label Distribution Protocol (LDP) on thisinterface
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface, self).__init__()
self.yang_name = "interface"
self.yang_parent_name = "interfaces"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = ['interface_name']
self._child_classes = OrderedDict([("afs", ("afs", MplsLdp.DefaultVrf.Interfaces.Interface.Afs)), ("global", ("global_", MplsLdp.DefaultVrf.Interfaces.Interface.Global))])
self._leafs = OrderedDict([
('interface_name', (YLeaf(YType.str, 'interface-name'), ['str'])),
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.interface_name = None
self.enable = None
self.afs = MplsLdp.DefaultVrf.Interfaces.Interface.Afs()
self.afs.parent = self
self._children_name_map["afs"] = "afs"
self.global_ = MplsLdp.DefaultVrf.Interfaces.Interface.Global()
self.global_.parent = self
self._children_name_map["global_"] = "global"
self._segment_path = lambda: "interface" + "[interface-name='" + str(self.interface_name) + "']"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/default-vrf/interfaces/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface, ['interface_name', 'enable'], name, value)
class Afs(Entity):
"""
Address Family specific configuration for
MPLS LDP intf
.. attribute:: af
Configure data for given Address Family
**type**\: list of :py:class:`Af <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Afs, self).__init__()
self.yang_name = "afs"
self.yang_parent_name = "interface"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("af", ("af", MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af))])
self._leafs = OrderedDict()
self.af = YList(self)
self._segment_path = lambda: "afs"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Afs, [], name, value)
class Af(Entity):
"""
Configure data for given Address Family
.. attribute:: af_name (key)
Address Family name
**type**\: :py:class:`MplsLdpafName <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpafName>`
.. attribute:: discovery
Configure interface discovery parameters
**type**\: :py:class:`Discovery <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Discovery>`
.. attribute:: igp
LDP interface IGP configuration
**type**\: :py:class:`Igp <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Igp>`
.. attribute:: mldp
Interface configuration parameters for mLDP
**type**\: :py:class:`Mldp <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Mldp>`
.. attribute:: enable
Enable Address Family
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af, self).__init__()
self.yang_name = "af"
self.yang_parent_name = "afs"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['af_name']
self._child_classes = OrderedDict([("discovery", ("discovery", MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Discovery)), ("igp", ("igp", MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Igp)), ("mldp", ("mldp", MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Mldp))])
self._leafs = OrderedDict([
('af_name', (YLeaf(YType.enumeration, 'af-name'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpafName', '')])),
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.af_name = None
self.enable = None
self.discovery = MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Discovery()
self.discovery.parent = self
self._children_name_map["discovery"] = "discovery"
self.igp = MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Igp()
self.igp.parent = self
self._children_name_map["igp"] = "igp"
self.mldp = MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Mldp()
self.mldp.parent = self
self._children_name_map["mldp"] = "mldp"
self._segment_path = lambda: "af" + "[af-name='" + str(self.af_name) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af, ['af_name', 'enable'], name, value)
class Discovery(Entity):
"""
Configure interface discovery parameters
.. attribute:: transport_address
MPLS LDP configuration for interface discovery transportaddress
**type**\: :py:class:`TransportAddress <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Discovery.TransportAddress>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Discovery, self).__init__()
self.yang_name = "discovery"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("transport-address", ("transport_address", MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Discovery.TransportAddress))])
self._leafs = OrderedDict()
self.transport_address = MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Discovery.TransportAddress()
self.transport_address.parent = self
self._children_name_map["transport_address"] = "transport-address"
self._segment_path = lambda: "discovery"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Discovery, [], name, value)
class TransportAddress(Entity):
"""
MPLS LDP configuration for interface
discovery transportaddress.
.. attribute:: address_type
Transport address option
**type**\: :py:class:`MplsLdpTransportAddress <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpTransportAddress>`
.. attribute:: address
IP address
**type**\: union of the below types:
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
**type**\: str
**pattern:** ((\:\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\|[0\-9a\-fA\-F]{0,4}))\|(((25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)?
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Discovery.TransportAddress, self).__init__()
self.yang_name = "transport-address"
self.yang_parent_name = "discovery"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('address_type', (YLeaf(YType.enumeration, 'address-type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpTransportAddress', '')])),
('address', (YLeaf(YType.str, 'address'), ['str','str'])),
])
self.address_type = None
self.address = None
self._segment_path = lambda: "transport-address"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Discovery.TransportAddress, ['address_type', 'address'], name, value)
class Igp(Entity):
"""
LDP interface IGP configuration
.. attribute:: disable_auto_config
Disable IGP Auto\-config on this interface
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Igp, self).__init__()
self.yang_name = "igp"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('disable_auto_config', (YLeaf(YType.empty, 'disable-auto-config'), ['Empty'])),
])
self.disable_auto_config = None
self._segment_path = lambda: "igp"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Igp, ['disable_auto_config'], name, value)
class Mldp(Entity):
"""
Interface configuration parameters for mLDP
.. attribute:: disable
Disable mLDP on LDP enabled interface
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Mldp, self).__init__()
self.yang_name = "mldp"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('disable', (YLeaf(YType.empty, 'disable'), ['Empty'])),
])
self.disable = None
self._segment_path = lambda: "mldp"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Afs.Af.Mldp, ['disable'], name, value)
class Global(Entity):
"""
Per VRF interface Global configuration for
MPLS LDP
.. attribute:: discovery
Configure interface discovery parameters
**type**\: :py:class:`Discovery <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Global.Discovery>`
.. attribute:: igp
LDP IGP configuration
**type**\: :py:class:`Igp <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Global, self).__init__()
self.yang_name = "global"
self.yang_parent_name = "interface"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("discovery", ("discovery", MplsLdp.DefaultVrf.Interfaces.Interface.Global.Discovery)), ("igp", ("igp", MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp))])
self._leafs = OrderedDict()
self.discovery = MplsLdp.DefaultVrf.Interfaces.Interface.Global.Discovery()
self.discovery.parent = self
self._children_name_map["discovery"] = "discovery"
self.igp = MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp()
self.igp.parent = self
self._children_name_map["igp"] = "igp"
self._segment_path = lambda: "global"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Global, [], name, value)
class Discovery(Entity):
"""
Configure interface discovery parameters
.. attribute:: link_hello
LDP Link Hellos
**type**\: :py:class:`LinkHello <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Global.Discovery.LinkHello>`
.. attribute:: disable_quick_start
Disable discovery's quick start mode
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Global.Discovery, self).__init__()
self.yang_name = "discovery"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("link-hello", ("link_hello", MplsLdp.DefaultVrf.Interfaces.Interface.Global.Discovery.LinkHello))])
self._leafs = OrderedDict([
('disable_quick_start', (YLeaf(YType.empty, 'disable-quick-start'), ['Empty'])),
])
self.disable_quick_start = None
self.link_hello = MplsLdp.DefaultVrf.Interfaces.Interface.Global.Discovery.LinkHello()
self.link_hello.parent = self
self._children_name_map["link_hello"] = "link-hello"
self._segment_path = lambda: "discovery"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Global.Discovery, ['disable_quick_start'], name, value)
class LinkHello(Entity):
"""
LDP Link Hellos
.. attribute:: interval
Link Hello interval
**type**\: int
**range:** 1..65535
**units**\: second
**default value**\: 5
.. attribute:: dual_stack
Dual Stack Address Family Preference
**type**\: :py:class:`MplsLdpafName <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpafName>`
**default value**\: ipv4
.. attribute:: hold_time
Time (seconds) \- 65535 implies infinite
**type**\: int
**range:** 1..65535
**units**\: second
**default value**\: 15
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Global.Discovery.LinkHello, self).__init__()
self.yang_name = "link-hello"
self.yang_parent_name = "discovery"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('interval', (YLeaf(YType.uint32, 'interval'), ['int'])),
('dual_stack', (YLeaf(YType.enumeration, 'dual-stack'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpafName', '')])),
('hold_time', (YLeaf(YType.uint32, 'hold-time'), ['int'])),
])
self.interval = None
self.dual_stack = None
self.hold_time = None
self._segment_path = lambda: "link-hello"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Global.Discovery.LinkHello, ['interval', 'dual_stack', 'hold_time'], name, value)
class Igp(Entity):
"""
LDP IGP configuration
.. attribute:: sync
LDP IGP synchronization
**type**\: :py:class:`Sync <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp, self).__init__()
self.yang_name = "igp"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("sync", ("sync", MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync))])
self._leafs = OrderedDict()
self.sync = MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync()
self.sync.parent = self
self._children_name_map["sync"] = "sync"
self._segment_path = lambda: "igp"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp, [], name, value)
class Sync(Entity):
"""
LDP IGP synchronization
.. attribute:: delay
LDP IGP synchronization delay time
**type**\: :py:class:`Delay <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync.Delay>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync, self).__init__()
self.yang_name = "sync"
self.yang_parent_name = "igp"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("delay", ("delay", MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync.Delay))])
self._leafs = OrderedDict()
self.delay = MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync.Delay()
self.delay.parent = self
self._children_name_map["delay"] = "delay"
self._segment_path = lambda: "sync"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync, [], name, value)
class Delay(Entity):
"""
LDP IGP synchronization delay time
.. attribute:: on_session_up
Interface sync up delay after session up
**type**\: :py:class:`OnSessionUp <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync.Delay.OnSessionUp>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync.Delay, self).__init__()
self.yang_name = "delay"
self.yang_parent_name = "sync"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("on-session-up", ("on_session_up", MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync.Delay.OnSessionUp))])
self._leafs = OrderedDict()
self.on_session_up = MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync.Delay.OnSessionUp()
self.on_session_up.parent = self
self._children_name_map["on_session_up"] = "on-session-up"
self._segment_path = lambda: "delay"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync.Delay, [], name, value)
class OnSessionUp(Entity):
"""
Interface sync up delay after session up
.. attribute:: disable
Disable delay after session up
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: timeout
Time (seconds)
**type**\: int
**range:** 5..300
**units**\: second
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync.Delay.OnSessionUp, self).__init__()
self.yang_name = "on-session-up"
self.yang_parent_name = "delay"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('disable', (YLeaf(YType.empty, 'disable'), ['Empty'])),
('timeout', (YLeaf(YType.uint32, 'timeout'), ['int'])),
])
self.disable = None
self.timeout = None
self._segment_path = lambda: "on-session-up"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.DefaultVrf.Interfaces.Interface.Global.Igp.Sync.Delay.OnSessionUp, ['disable', 'timeout'], name, value)
class Vrfs(Entity):
"""
VRF Table attribute configuration for MPLS LDP
.. attribute:: vrf
VRF attribute configuration for MPLS LDP
**type**\: list of :py:class:`Vrf <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs, self).__init__()
self.yang_name = "vrfs"
self.yang_parent_name = "mpls-ldp"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("vrf", ("vrf", MplsLdp.Vrfs.Vrf))])
self._leafs = OrderedDict()
self.vrf = YList(self)
self._segment_path = lambda: "vrfs"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs, [], name, value)
class Vrf(Entity):
"""
VRF attribute configuration for MPLS LDP
.. attribute:: vrf_name (key)
VRF Name
**type**\: str
**pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+
.. attribute:: global_
Per VRF Global configuration for MPLS LDP
**type**\: :py:class:`Global <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Global>`
.. attribute:: afs
Address Family specific configuration for MPLS LDP vrf
**type**\: :py:class:`Afs <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs>`
.. attribute:: interfaces
MPLS LDP configuration pertaining to interfaces
**type**\: :py:class:`Interfaces <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Interfaces>`
.. attribute:: enable
Enable VRF
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf, self).__init__()
self.yang_name = "vrf"
self.yang_parent_name = "vrfs"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = ['vrf_name']
self._child_classes = OrderedDict([("global", ("global_", MplsLdp.Vrfs.Vrf.Global)), ("afs", ("afs", MplsLdp.Vrfs.Vrf.Afs)), ("interfaces", ("interfaces", MplsLdp.Vrfs.Vrf.Interfaces))])
self._leafs = OrderedDict([
('vrf_name', (YLeaf(YType.str, 'vrf-name'), ['str'])),
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.vrf_name = None
self.enable = None
self.global_ = MplsLdp.Vrfs.Vrf.Global()
self.global_.parent = self
self._children_name_map["global_"] = "global"
self.afs = MplsLdp.Vrfs.Vrf.Afs()
self.afs.parent = self
self._children_name_map["afs"] = "afs"
self.interfaces = MplsLdp.Vrfs.Vrf.Interfaces()
self.interfaces.parent = self
self._children_name_map["interfaces"] = "interfaces"
self._segment_path = lambda: "vrf" + "[vrf-name='" + str(self.vrf_name) + "']"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/vrfs/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf, ['vrf_name', 'enable'], name, value)
class Global(Entity):
"""
Per VRF Global configuration for MPLS LDP
.. attribute:: session
LDP Session parameters
**type**\: :py:class:`Session <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Global.Session>`
.. attribute:: neighbor
Configuration related to Neighbors
**type**\: :py:class:`Neighbor <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Global.Neighbor>`
.. attribute:: graceful_restart
Configuration for per\-VRF LDP Graceful Restart parameters
**type**\: :py:class:`GracefulRestart <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Global.GracefulRestart>`
.. attribute:: router_id
Configuration for LDP Router ID (LDP ID)
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Global, self).__init__()
self.yang_name = "global"
self.yang_parent_name = "vrf"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("session", ("session", MplsLdp.Vrfs.Vrf.Global.Session)), ("neighbor", ("neighbor", MplsLdp.Vrfs.Vrf.Global.Neighbor)), ("graceful-restart", ("graceful_restart", MplsLdp.Vrfs.Vrf.Global.GracefulRestart))])
self._leafs = OrderedDict([
('router_id', (YLeaf(YType.str, 'router-id'), ['str'])),
])
self.router_id = None
self.session = MplsLdp.Vrfs.Vrf.Global.Session()
self.session.parent = self
self._children_name_map["session"] = "session"
self.neighbor = MplsLdp.Vrfs.Vrf.Global.Neighbor()
self.neighbor.parent = self
self._children_name_map["neighbor"] = "neighbor"
self.graceful_restart = MplsLdp.Vrfs.Vrf.Global.GracefulRestart()
self.graceful_restart.parent = self
self._children_name_map["graceful_restart"] = "graceful-restart"
self._segment_path = lambda: "global"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Global, ['router_id'], name, value)
class Session(Entity):
"""
LDP Session parameters
.. attribute:: downstream_on_demand
ACL with the list of neighbors configured for Downstream on Demand
**type**\: :py:class:`DownstreamOnDemand <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Global.Session.DownstreamOnDemand>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Global.Session, self).__init__()
self.yang_name = "session"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("downstream-on-demand", ("downstream_on_demand", MplsLdp.Vrfs.Vrf.Global.Session.DownstreamOnDemand))])
self._leafs = OrderedDict()
self.downstream_on_demand = MplsLdp.Vrfs.Vrf.Global.Session.DownstreamOnDemand()
self.downstream_on_demand.parent = self
self._children_name_map["downstream_on_demand"] = "downstream-on-demand"
self._segment_path = lambda: "session"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Global.Session, [], name, value)
class DownstreamOnDemand(Entity):
"""
ACL with the list of neighbors configured
for Downstream on Demand
.. attribute:: type
Downstream on demand type
**type**\: :py:class:`MplsLdpDownstreamOnDemand <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpDownstreamOnDemand>`
.. attribute:: peer_acl_name
Name of peer ACL
**type**\: str
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Global.Session.DownstreamOnDemand, self).__init__()
self.yang_name = "downstream-on-demand"
self.yang_parent_name = "session"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('type', (YLeaf(YType.enumeration, 'type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpDownstreamOnDemand', '')])),
('peer_acl_name', (YLeaf(YType.str, 'peer-acl-name'), ['str'])),
])
self.type = None
self.peer_acl_name = None
self._segment_path = lambda: "downstream-on-demand"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Global.Session.DownstreamOnDemand, ['type', 'peer_acl_name'], name, value)
class Neighbor(Entity):
"""
Configuration related to Neighbors
.. attribute:: dual_stack
Configuration related to neighbor transport
**type**\: :py:class:`DualStack <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack>`
.. attribute:: ldp_ids
Configuration related to Neighbors using LDP Id
**type**\: :py:class:`LdpIds <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds>`
.. attribute:: password
Default password for all neigbors
**type**\: str
**pattern:** (!.+)\|([^!].+)
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Global.Neighbor, self).__init__()
self.yang_name = "neighbor"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("dual-stack", ("dual_stack", MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack)), ("ldp-ids", ("ldp_ids", MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds))])
self._leafs = OrderedDict([
('password', (YLeaf(YType.str, 'password'), ['str'])),
])
self.password = None
self.dual_stack = MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack()
self.dual_stack.parent = self
self._children_name_map["dual_stack"] = "dual-stack"
self.ldp_ids = MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds()
self.ldp_ids.parent = self
self._children_name_map["ldp_ids"] = "ldp-ids"
self._segment_path = lambda: "neighbor"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Global.Neighbor, ['password'], name, value)
class DualStack(Entity):
"""
Configuration related to neighbor transport
.. attribute:: transport_connection
Configuration related to neighbor transport
**type**\: :py:class:`TransportConnection <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack.TransportConnection>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack, self).__init__()
self.yang_name = "dual-stack"
self.yang_parent_name = "neighbor"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("transport-connection", ("transport_connection", MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack.TransportConnection))])
self._leafs = OrderedDict()
self.transport_connection = MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack.TransportConnection()
self.transport_connection.parent = self
self._children_name_map["transport_connection"] = "transport-connection"
self._segment_path = lambda: "dual-stack"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack, [], name, value)
class TransportConnection(Entity):
"""
Configuration related to neighbor transport
.. attribute:: prefer
Configuration related to neighbor dual\-stack xport\-connection preference
**type**\: :py:class:`Prefer <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack.TransportConnection.Prefer>`
.. attribute:: max_wait
Configuration related to neighbor dual\-stack xport\-connection max\-wait
**type**\: int
**range:** 0..60
**units**\: second
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack.TransportConnection, self).__init__()
self.yang_name = "transport-connection"
self.yang_parent_name = "dual-stack"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("prefer", ("prefer", MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack.TransportConnection.Prefer))])
self._leafs = OrderedDict([
('max_wait', (YLeaf(YType.uint32, 'max-wait'), ['int'])),
])
self.max_wait = None
self.prefer = MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack.TransportConnection.Prefer()
self.prefer.parent = self
self._children_name_map["prefer"] = "prefer"
self._segment_path = lambda: "transport-connection"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack.TransportConnection, ['max_wait'], name, value)
class Prefer(Entity):
"""
Configuration related to neighbor
dual\-stack xport\-connection preference
.. attribute:: ipv4
Configuration related to neighbor dual\-stack xport\-connection preference ipv4
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack.TransportConnection.Prefer, self).__init__()
self.yang_name = "prefer"
self.yang_parent_name = "transport-connection"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('ipv4', (YLeaf(YType.empty, 'ipv4'), ['Empty'])),
])
self.ipv4 = None
self._segment_path = lambda: "prefer"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Global.Neighbor.DualStack.TransportConnection.Prefer, ['ipv4'], name, value)
class LdpIds(Entity):
"""
Configuration related to Neighbors using LDP
Id
.. attribute:: ldp_id
LDP ID based configuration related to a neigbor
**type**\: list of :py:class:`LdpId <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds.LdpId>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds, self).__init__()
self.yang_name = "ldp-ids"
self.yang_parent_name = "neighbor"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("ldp-id", ("ldp_id", MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds.LdpId))])
self._leafs = OrderedDict()
self.ldp_id = YList(self)
self._segment_path = lambda: "ldp-ids"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds, [], name, value)
class LdpId(Entity):
"""
LDP ID based configuration related to a
neigbor
.. attribute:: lsr_id (key)
LSR ID of neighbor
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: label_space_id (key)
Label space ID of neighbor
**type**\: int
**range:** 0..4294967295
.. attribute:: password
Password for MD5 authentication for this neighbor
**type**\: :py:class:`Password <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds.LdpId.Password>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds.LdpId, self).__init__()
self.yang_name = "ldp-id"
self.yang_parent_name = "ldp-ids"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['lsr_id','label_space_id']
self._child_classes = OrderedDict([("password", ("password", MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds.LdpId.Password))])
self._leafs = OrderedDict([
('lsr_id', (YLeaf(YType.str, 'lsr-id'), ['str'])),
('label_space_id', (YLeaf(YType.uint32, 'label-space-id'), ['int'])),
])
self.lsr_id = None
self.label_space_id = None
self.password = MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds.LdpId.Password()
self.password.parent = self
self._children_name_map["password"] = "password"
self._segment_path = lambda: "ldp-id" + "[lsr-id='" + str(self.lsr_id) + "']" + "[label-space-id='" + str(self.label_space_id) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds.LdpId, ['lsr_id', 'label_space_id'], name, value)
class Password(Entity):
"""
Password for MD5 authentication for this
neighbor
.. attribute:: command_type
Command type for password configuration
**type**\: :py:class:`MplsLdpNbrPassword <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpNbrPassword>`
.. attribute:: password
The neighbor password
**type**\: str
**pattern:** (!.+)\|([^!].+)
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds.LdpId.Password, self).__init__()
self.yang_name = "password"
self.yang_parent_name = "ldp-id"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('command_type', (YLeaf(YType.enumeration, 'command-type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpNbrPassword', '')])),
('password', (YLeaf(YType.str, 'password'), ['str'])),
])
self.command_type = None
self.password = None
self._segment_path = lambda: "password"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Global.Neighbor.LdpIds.LdpId.Password, ['command_type', 'password'], name, value)
class GracefulRestart(Entity):
"""
Configuration for per\-VRF LDP Graceful
Restart parameters
.. attribute:: helper_peer
Configure parameters related to GR peer(s) opearating in helper mode
**type**\: :py:class:`HelperPeer <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Global.GracefulRestart.HelperPeer>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Global.GracefulRestart, self).__init__()
self.yang_name = "graceful-restart"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("helper-peer", ("helper_peer", MplsLdp.Vrfs.Vrf.Global.GracefulRestart.HelperPeer))])
self._leafs = OrderedDict()
self.helper_peer = MplsLdp.Vrfs.Vrf.Global.GracefulRestart.HelperPeer()
self.helper_peer.parent = self
self._children_name_map["helper_peer"] = "helper-peer"
self._segment_path = lambda: "graceful-restart"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Global.GracefulRestart, [], name, value)
class HelperPeer(Entity):
"""
Configure parameters related to GR peer(s)
opearating in helper mode
.. attribute:: maintain_on_local_reset
Maintain the state of a GR peer upon a local reset
**type**\: str
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Global.GracefulRestart.HelperPeer, self).__init__()
self.yang_name = "helper-peer"
self.yang_parent_name = "graceful-restart"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('maintain_on_local_reset', (YLeaf(YType.str, 'maintain-on-local-reset'), ['str'])),
])
self.maintain_on_local_reset = None
self._segment_path = lambda: "helper-peer"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Global.GracefulRestart.HelperPeer, ['maintain_on_local_reset'], name, value)
class Afs(Entity):
"""
Address Family specific configuration for MPLS
LDP vrf
.. attribute:: af
Configure data for given Address Family
**type**\: list of :py:class:`Af <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs, self).__init__()
self.yang_name = "afs"
self.yang_parent_name = "vrf"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("af", ("af", MplsLdp.Vrfs.Vrf.Afs.Af))])
self._leafs = OrderedDict()
self.af = YList(self)
self._segment_path = lambda: "afs"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs, [], name, value)
class Af(Entity):
"""
Configure data for given Address Family
.. attribute:: af_name (key)
Address Family name
**type**\: :py:class:`MplsLdpafName <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpafName>`
.. attribute:: discovery
Configure Discovery parameters
**type**\: :py:class:`Discovery <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Discovery>`
.. attribute:: label
Configure Label policies and control
**type**\: :py:class:`Label <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label>`
.. attribute:: enable
Enable Address Family
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af, self).__init__()
self.yang_name = "af"
self.yang_parent_name = "afs"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['af_name']
self._child_classes = OrderedDict([("discovery", ("discovery", MplsLdp.Vrfs.Vrf.Afs.Af.Discovery)), ("label", ("label", MplsLdp.Vrfs.Vrf.Afs.Af.Label))])
self._leafs = OrderedDict([
('af_name', (YLeaf(YType.enumeration, 'af-name'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpafName', '')])),
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.af_name = None
self.enable = None
self.discovery = MplsLdp.Vrfs.Vrf.Afs.Af.Discovery()
self.discovery.parent = self
self._children_name_map["discovery"] = "discovery"
self.label = MplsLdp.Vrfs.Vrf.Afs.Af.Label()
self.label.parent = self
self._children_name_map["label"] = "label"
self._segment_path = lambda: "af" + "[af-name='" + str(self.af_name) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af, ['af_name', 'enable'], name, value)
class Discovery(Entity):
"""
Configure Discovery parameters
.. attribute:: transport_address
Global discovery transport address for address family
**type**\: union of the below types:
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
**type**\: str
**pattern:** ((\:\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\|[0\-9a\-fA\-F]{0,4}))\|(((25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)?
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Discovery, self).__init__()
self.yang_name = "discovery"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('transport_address', (YLeaf(YType.str, 'transport-address'), ['str','str'])),
])
self.transport_address = None
self._segment_path = lambda: "discovery"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Discovery, ['transport_address'], name, value)
class Label(Entity):
"""
Configure Label policies and control
.. attribute:: remote
Configure remote/peer label policies and control
**type**\: :py:class:`Remote <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote>`
.. attribute:: local
Configure local label policies and control
**type**\: :py:class:`Local <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label, self).__init__()
self.yang_name = "label"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("remote", ("remote", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote)), ("local", ("local", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local))])
self._leafs = OrderedDict()
self.remote = MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote()
self.remote.parent = self
self._children_name_map["remote"] = "remote"
self.local = MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local()
self.local.parent = self
self._children_name_map["local"] = "local"
self._segment_path = lambda: "label"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label, [], name, value)
class Remote(Entity):
"""
Configure remote/peer label policies and
control
.. attribute:: accept
Configure inbound label acceptance
**type**\: :py:class:`Accept <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote, self).__init__()
self.yang_name = "remote"
self.yang_parent_name = "label"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("accept", ("accept", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept))])
self._leafs = OrderedDict()
self.accept = MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept()
self.accept.parent = self
self._children_name_map["accept"] = "accept"
self._segment_path = lambda: "remote"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote, [], name, value)
class Accept(Entity):
"""
Configure inbound label acceptance
.. attribute:: peer_accept_policies
Configuration related to Neighbors for inbound label acceptance
**type**\: :py:class:`PeerAcceptPolicies <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept, self).__init__()
self.yang_name = "accept"
self.yang_parent_name = "remote"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("peer-accept-policies", ("peer_accept_policies", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies))])
self._leafs = OrderedDict()
self.peer_accept_policies = MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies()
self.peer_accept_policies.parent = self
self._children_name_map["peer_accept_policies"] = "peer-accept-policies"
self._segment_path = lambda: "accept"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept, [], name, value)
class PeerAcceptPolicies(Entity):
"""
Configuration related to Neighbors for
inbound label acceptance
.. attribute:: peer_accept_policy
Control acceptasnce of labels from a neighbor for prefix(es) using ACL
**type**\: list of :py:class:`PeerAcceptPolicy <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies, self).__init__()
self.yang_name = "peer-accept-policies"
self.yang_parent_name = "accept"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("peer-accept-policy", ("peer_accept_policy", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy))])
self._leafs = OrderedDict()
self.peer_accept_policy = YList(self)
self._segment_path = lambda: "peer-accept-policies"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies, [], name, value)
class PeerAcceptPolicy(Entity):
"""
Control acceptasnce of labels from a
neighbor for prefix(es) using ACL
.. attribute:: label_space_id (key)
Label space ID of neighbor
**type**\: int
**range:** 0..4294967295
.. attribute:: peer_accept_policy_data
Data container
**type**\: :py:class:`PeerAcceptPolicyData <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy.PeerAcceptPolicyData>`
.. attribute:: lsr_id
keys\: lsr\-id
**type**\: list of :py:class:`LsrId <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy.LsrId>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy, self).__init__()
self.yang_name = "peer-accept-policy"
self.yang_parent_name = "peer-accept-policies"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['label_space_id']
self._child_classes = OrderedDict([("peer-accept-policy-data", ("peer_accept_policy_data", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy.PeerAcceptPolicyData)), ("lsr-id", ("lsr_id", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy.LsrId))])
self._leafs = OrderedDict([
('label_space_id', (YLeaf(YType.uint32, 'label-space-id'), ['int'])),
])
self.label_space_id = None
self.peer_accept_policy_data = MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy.PeerAcceptPolicyData()
self.peer_accept_policy_data.parent = self
self._children_name_map["peer_accept_policy_data"] = "peer-accept-policy-data"
self.lsr_id = YList(self)
self._segment_path = lambda: "peer-accept-policy" + "[label-space-id='" + str(self.label_space_id) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy, ['label_space_id'], name, value)
class PeerAcceptPolicyData(Entity):
"""
Data container.
.. attribute:: prefix_acl_name
Name of prefix ACL
**type**\: str
**mandatory**\: True
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy.PeerAcceptPolicyData, self).__init__()
self.yang_name = "peer-accept-policy-data"
self.yang_parent_name = "peer-accept-policy"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('prefix_acl_name', (YLeaf(YType.str, 'prefix-acl-name'), ['str'])),
])
self.prefix_acl_name = None
self._segment_path = lambda: "peer-accept-policy-data"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy.PeerAcceptPolicyData, ['prefix_acl_name'], name, value)
class LsrId(Entity):
"""
keys\: lsr\-id
.. attribute:: lsr_id (key)
LSR ID of neighbor
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: prefix_acl_name
Name of prefix ACL
**type**\: str
**mandatory**\: True
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy.LsrId, self).__init__()
self.yang_name = "lsr-id"
self.yang_parent_name = "peer-accept-policy"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['lsr_id']
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('lsr_id', (YLeaf(YType.str, 'lsr-id'), ['str'])),
('prefix_acl_name', (YLeaf(YType.str, 'prefix-acl-name'), ['str'])),
])
self.lsr_id = None
self.prefix_acl_name = None
self._segment_path = lambda: "lsr-id" + "[lsr-id='" + str(self.lsr_id) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Remote.Accept.PeerAcceptPolicies.PeerAcceptPolicy.LsrId, ['lsr_id', 'prefix_acl_name'], name, value)
class Local(Entity):
"""
Configure local label policies and control
.. attribute:: advertise
Configure outbound label advertisement
**type**\: :py:class:`Advertise <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise>`
.. attribute:: allocate
Control local label allocation for prefix(es)
**type**\: :py:class:`Allocate <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Allocate>`
.. attribute:: implicit_null_override
Control use of implicit\-null label for set of prefix(es)
**type**\: str
.. attribute:: default_route
Enable MPLS forwarding for default route
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local, self).__init__()
self.yang_name = "local"
self.yang_parent_name = "label"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("advertise", ("advertise", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise)), ("allocate", ("allocate", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Allocate))])
self._leafs = OrderedDict([
('implicit_null_override', (YLeaf(YType.str, 'implicit-null-override'), ['str'])),
('default_route', (YLeaf(YType.empty, 'default-route'), ['Empty'])),
])
self.implicit_null_override = None
self.default_route = None
self.advertise = MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise()
self.advertise.parent = self
self._children_name_map["advertise"] = "advertise"
self.allocate = MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Allocate()
self.allocate.parent = self
self._children_name_map["allocate"] = "allocate"
self._segment_path = lambda: "local"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local, ['implicit_null_override', 'default_route'], name, value)
class Advertise(Entity):
"""
Configure outbound label advertisement
.. attribute:: peer_advertise_policies
Configure peer centric outbound label advertisement using ACL
**type**\: :py:class:`PeerAdvertisePolicies <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies>`
.. attribute:: interfaces
Configure outbound label advertisement for an interface
**type**\: :py:class:`Interfaces <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.Interfaces>`
.. attribute:: explicit_null
Configure advertisment of explicit\-null for connected prefixes
**type**\: :py:class:`ExplicitNull <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.ExplicitNull>`
.. attribute:: disable
Disable label advertisement
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise, self).__init__()
self.yang_name = "advertise"
self.yang_parent_name = "local"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("peer-advertise-policies", ("peer_advertise_policies", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies)), ("interfaces", ("interfaces", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.Interfaces)), ("explicit-null", ("explicit_null", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.ExplicitNull))])
self._leafs = OrderedDict([
('disable', (YLeaf(YType.empty, 'disable'), ['Empty'])),
])
self.disable = None
self.peer_advertise_policies = MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies()
self.peer_advertise_policies.parent = self
self._children_name_map["peer_advertise_policies"] = "peer-advertise-policies"
self.interfaces = MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.Interfaces()
self.interfaces.parent = self
self._children_name_map["interfaces"] = "interfaces"
self.explicit_null = MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.ExplicitNull()
self.explicit_null.parent = self
self._children_name_map["explicit_null"] = "explicit-null"
self._segment_path = lambda: "advertise"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise, ['disable'], name, value)
class PeerAdvertisePolicies(Entity):
"""
Configure peer centric outbound label
advertisement using ACL
.. attribute:: peer_advertise_policy
Control advertisement of prefix(es) using ACL
**type**\: list of :py:class:`PeerAdvertisePolicy <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies, self).__init__()
self.yang_name = "peer-advertise-policies"
self.yang_parent_name = "advertise"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("peer-advertise-policy", ("peer_advertise_policy", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy))])
self._leafs = OrderedDict()
self.peer_advertise_policy = YList(self)
self._segment_path = lambda: "peer-advertise-policies"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies, [], name, value)
class PeerAdvertisePolicy(Entity):
"""
Control advertisement of prefix(es)
using ACL
.. attribute:: label_space_id (key)
Label space ID of neighbor
**type**\: int
**range:** 0..4294967295
.. attribute:: peer_advertise_policy_data
Data container
**type**\: :py:class:`PeerAdvertisePolicyData <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy.PeerAdvertisePolicyData>`
.. attribute:: lsr_id
keys\: lsr\-id
**type**\: list of :py:class:`LsrId <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy.LsrId>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy, self).__init__()
self.yang_name = "peer-advertise-policy"
self.yang_parent_name = "peer-advertise-policies"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['label_space_id']
self._child_classes = OrderedDict([("peer-advertise-policy-data", ("peer_advertise_policy_data", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy.PeerAdvertisePolicyData)), ("lsr-id", ("lsr_id", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy.LsrId))])
self._leafs = OrderedDict([
('label_space_id', (YLeaf(YType.uint32, 'label-space-id'), ['int'])),
])
self.label_space_id = None
self.peer_advertise_policy_data = MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy.PeerAdvertisePolicyData()
self.peer_advertise_policy_data.parent = self
self._children_name_map["peer_advertise_policy_data"] = "peer-advertise-policy-data"
self.lsr_id = YList(self)
self._segment_path = lambda: "peer-advertise-policy" + "[label-space-id='" + str(self.label_space_id) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy, ['label_space_id'], name, value)
class PeerAdvertisePolicyData(Entity):
"""
Data container.
.. attribute:: prefix_acl_name
Name of prefix ACL
**type**\: str
**mandatory**\: True
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy.PeerAdvertisePolicyData, self).__init__()
self.yang_name = "peer-advertise-policy-data"
self.yang_parent_name = "peer-advertise-policy"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('prefix_acl_name', (YLeaf(YType.str, 'prefix-acl-name'), ['str'])),
])
self.prefix_acl_name = None
self._segment_path = lambda: "peer-advertise-policy-data"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy.PeerAdvertisePolicyData, ['prefix_acl_name'], name, value)
class LsrId(Entity):
"""
keys\: lsr\-id
.. attribute:: lsr_id (key)
LSR ID of neighbor
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: prefix_acl_name
Name of prefix ACL
**type**\: str
**mandatory**\: True
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy.LsrId, self).__init__()
self.yang_name = "lsr-id"
self.yang_parent_name = "peer-advertise-policy"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['lsr_id']
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('lsr_id', (YLeaf(YType.str, 'lsr-id'), ['str'])),
('prefix_acl_name', (YLeaf(YType.str, 'prefix-acl-name'), ['str'])),
])
self.lsr_id = None
self.prefix_acl_name = None
self._segment_path = lambda: "lsr-id" + "[lsr-id='" + str(self.lsr_id) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.PeerAdvertisePolicies.PeerAdvertisePolicy.LsrId, ['lsr_id', 'prefix_acl_name'], name, value)
class Interfaces(Entity):
"""
Configure outbound label advertisement
for an interface
.. attribute:: interface
Control advertisement of interface's host IP address
**type**\: list of :py:class:`Interface <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.Interfaces.Interface>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.Interfaces, self).__init__()
self.yang_name = "interfaces"
self.yang_parent_name = "advertise"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("interface", ("interface", MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.Interfaces.Interface))])
self._leafs = OrderedDict()
self.interface = YList(self)
self._segment_path = lambda: "interfaces"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.Interfaces, [], name, value)
class Interface(Entity):
"""
Control advertisement of interface's
host IP address
.. attribute:: interface_name (key)
Name of interface
**type**\: str
**pattern:** [a\-zA\-Z0\-9.\_/\-]+
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.Interfaces.Interface, self).__init__()
self.yang_name = "interface"
self.yang_parent_name = "interfaces"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['interface_name']
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('interface_name', (YLeaf(YType.str, 'interface-name'), ['str'])),
])
self.interface_name = None
self._segment_path = lambda: "interface" + "[interface-name='" + str(self.interface_name) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.Interfaces.Interface, ['interface_name'], name, value)
class ExplicitNull(Entity):
"""
Configure advertisment of explicit\-null
for connected prefixes.
.. attribute:: explicit_null_type
Explicit Null command variant
**type**\: :py:class:`MplsLdpExpNull <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpExpNull>`
.. attribute:: prefix_acl_name
Name of prefix ACL
**type**\: str
.. attribute:: peer_acl_name
Name of peer ACL
**type**\: str
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.ExplicitNull, self).__init__()
self.yang_name = "explicit-null"
self.yang_parent_name = "advertise"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('explicit_null_type', (YLeaf(YType.enumeration, 'explicit-null-type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpExpNull', '')])),
('prefix_acl_name', (YLeaf(YType.str, 'prefix-acl-name'), ['str'])),
('peer_acl_name', (YLeaf(YType.str, 'peer-acl-name'), ['str'])),
])
self.explicit_null_type = None
self.prefix_acl_name = None
self.peer_acl_name = None
self._segment_path = lambda: "explicit-null"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Advertise.ExplicitNull, ['explicit_null_type', 'prefix_acl_name', 'peer_acl_name'], name, value)
class Allocate(Entity):
"""
Control local label allocation for
prefix(es)
.. attribute:: allocation_type
Label allocation type
**type**\: :py:class:`MplsLdpLabelAllocation <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpLabelAllocation>`
.. attribute:: prefix_acl_name
Name of prefix ACL
**type**\: str
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Allocate, self).__init__()
self.yang_name = "allocate"
self.yang_parent_name = "local"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('allocation_type', (YLeaf(YType.enumeration, 'allocation-type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpLabelAllocation', '')])),
('prefix_acl_name', (YLeaf(YType.str, 'prefix-acl-name'), ['str'])),
])
self.allocation_type = None
self.prefix_acl_name = None
self._segment_path = lambda: "allocate"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Afs.Af.Label.Local.Allocate, ['allocation_type', 'prefix_acl_name'], name, value)
class Interfaces(Entity):
"""
MPLS LDP configuration pertaining to
interfaces
.. attribute:: interface
MPLS LDP configuration for a particular interface
**type**\: list of :py:class:`Interface <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Interfaces.Interface>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Interfaces, self).__init__()
self.yang_name = "interfaces"
self.yang_parent_name = "vrf"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("interface", ("interface", MplsLdp.Vrfs.Vrf.Interfaces.Interface))])
self._leafs = OrderedDict()
self.interface = YList(self)
self._segment_path = lambda: "interfaces"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Interfaces, [], name, value)
class Interface(Entity):
"""
MPLS LDP configuration for a particular
interface
.. attribute:: interface_name (key)
Name of interface
**type**\: str
**pattern:** [a\-zA\-Z0\-9.\_/\-]+
.. attribute:: afs
Address Family specific configuration for MPLS LDP vrf intf
**type**\: :py:class:`Afs <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs>`
.. attribute:: enable
Enable Label Distribution Protocol (LDP) on thisinterface
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Interfaces.Interface, self).__init__()
self.yang_name = "interface"
self.yang_parent_name = "interfaces"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['interface_name']
self._child_classes = OrderedDict([("afs", ("afs", MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs))])
self._leafs = OrderedDict([
('interface_name', (YLeaf(YType.str, 'interface-name'), ['str'])),
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.interface_name = None
self.enable = None
self.afs = MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs()
self.afs.parent = self
self._children_name_map["afs"] = "afs"
self._segment_path = lambda: "interface" + "[interface-name='" + str(self.interface_name) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Interfaces.Interface, ['interface_name', 'enable'], name, value)
class Afs(Entity):
"""
Address Family specific configuration for
MPLS LDP vrf intf
.. attribute:: af
Configure data for given Address Family
**type**\: list of :py:class:`Af <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs, self).__init__()
self.yang_name = "afs"
self.yang_parent_name = "interface"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("af", ("af", MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af))])
self._leafs = OrderedDict()
self.af = YList(self)
self._segment_path = lambda: "afs"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs, [], name, value)
class Af(Entity):
"""
Configure data for given Address Family
.. attribute:: af_name (key)
Address Family name
**type**\: :py:class:`MplsLdpafName <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpafName>`
.. attribute:: discovery
Configure interface discovery parameters
**type**\: :py:class:`Discovery <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af.Discovery>`
.. attribute:: enable
Enable Address Family
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af, self).__init__()
self.yang_name = "af"
self.yang_parent_name = "afs"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['af_name']
self._child_classes = OrderedDict([("discovery", ("discovery", MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af.Discovery))])
self._leafs = OrderedDict([
('af_name', (YLeaf(YType.enumeration, 'af-name'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpafName', '')])),
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.af_name = None
self.enable = None
self.discovery = MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af.Discovery()
self.discovery.parent = self
self._children_name_map["discovery"] = "discovery"
self._segment_path = lambda: "af" + "[af-name='" + str(self.af_name) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af, ['af_name', 'enable'], name, value)
class Discovery(Entity):
"""
Configure interface discovery parameters
.. attribute:: transport_address
MPLS LDP configuration for interface discovery transportaddress
**type**\: :py:class:`TransportAddress <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af.Discovery.TransportAddress>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af.Discovery, self).__init__()
self.yang_name = "discovery"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("transport-address", ("transport_address", MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af.Discovery.TransportAddress))])
self._leafs = OrderedDict()
self.transport_address = MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af.Discovery.TransportAddress()
self.transport_address.parent = self
self._children_name_map["transport_address"] = "transport-address"
self._segment_path = lambda: "discovery"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af.Discovery, [], name, value)
class TransportAddress(Entity):
"""
MPLS LDP configuration for interface
discovery transportaddress.
.. attribute:: address_type
Transport address option
**type**\: :py:class:`MplsLdpTransportAddress <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpTransportAddress>`
.. attribute:: address
IP address
**type**\: union of the below types:
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
**type**\: str
**pattern:** ((\:\|[0\-9a\-fA\-F]{0,4})\:)([0\-9a\-fA\-F]{0,4}\:){0,5}((([0\-9a\-fA\-F]{0,4}\:)?(\:\|[0\-9a\-fA\-F]{0,4}))\|(((25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])\\.){3}(25[0\-5]\|2[0\-4][0\-9]\|[01]?[0\-9]?[0\-9])))(%[\\p{N}\\p{L}]+)?
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af.Discovery.TransportAddress, self).__init__()
self.yang_name = "transport-address"
self.yang_parent_name = "discovery"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('address_type', (YLeaf(YType.enumeration, 'address-type'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpTransportAddress', '')])),
('address', (YLeaf(YType.str, 'address'), ['str','str'])),
])
self.address_type = None
self.address = None
self._segment_path = lambda: "transport-address"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Vrfs.Vrf.Interfaces.Interface.Afs.Af.Discovery.TransportAddress, ['address_type', 'address'], name, value)
class Global(Entity):
"""
Global configuration for MPLS LDP
.. attribute:: entropy_label
Configure for LDP Entropy\-Label
**type**\: :py:class:`EntropyLabel <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.EntropyLabel>`
.. attribute:: session
LDP Session parameters
**type**\: :py:class:`Session <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Session>`
.. attribute:: igp
LDP IGP configuration
**type**\: :py:class:`Igp <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Igp>`
.. attribute:: enable_logging
Enable logging of events
**type**\: :py:class:`EnableLogging <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.EnableLogging>`
.. attribute:: signalling
Configure LDP signalling parameters
**type**\: :py:class:`Signalling <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Signalling>`
.. attribute:: nsr
Configure LDP Non\-Stop Routing
**type**\: :py:class:`Nsr <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Nsr>`
.. attribute:: graceful_restart
Configuration for LDP Graceful Restart parameters
**type**\: :py:class:`GracefulRestart <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.GracefulRestart>`
.. attribute:: discovery
Configure Discovery parameters
**type**\: :py:class:`Discovery <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Discovery>`
.. attribute:: mldp
MPLS mLDP configuration
**type**\: :py:class:`Mldp <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp>`
.. attribute:: disable_implicit_ipv4
Disable the implicit enabling for IPv4 address family
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: ltrace_buf_multiplier
Configure Ltrace Buffer Multiplier
**type**\: int
**range:** 1..5
**default value**\: 1
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global, self).__init__()
self.yang_name = "global"
self.yang_parent_name = "mpls-ldp"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("entropy-label", ("entropy_label", MplsLdp.Global.EntropyLabel)), ("session", ("session", MplsLdp.Global.Session)), ("igp", ("igp", MplsLdp.Global.Igp)), ("enable-logging", ("enable_logging", MplsLdp.Global.EnableLogging)), ("signalling", ("signalling", MplsLdp.Global.Signalling)), ("nsr", ("nsr", MplsLdp.Global.Nsr)), ("graceful-restart", ("graceful_restart", MplsLdp.Global.GracefulRestart)), ("discovery", ("discovery", MplsLdp.Global.Discovery)), ("mldp", ("mldp", MplsLdp.Global.Mldp))])
self._leafs = OrderedDict([
('disable_implicit_ipv4', (YLeaf(YType.empty, 'disable-implicit-ipv4'), ['Empty'])),
('ltrace_buf_multiplier', (YLeaf(YType.uint32, 'ltrace-buf-multiplier'), ['int'])),
])
self.disable_implicit_ipv4 = None
self.ltrace_buf_multiplier = None
self.entropy_label = MplsLdp.Global.EntropyLabel()
self.entropy_label.parent = self
self._children_name_map["entropy_label"] = "entropy-label"
self.session = MplsLdp.Global.Session()
self.session.parent = self
self._children_name_map["session"] = "session"
self.igp = MplsLdp.Global.Igp()
self.igp.parent = self
self._children_name_map["igp"] = "igp"
self.enable_logging = MplsLdp.Global.EnableLogging()
self.enable_logging.parent = self
self._children_name_map["enable_logging"] = "enable-logging"
self.signalling = MplsLdp.Global.Signalling()
self.signalling.parent = self
self._children_name_map["signalling"] = "signalling"
self.nsr = MplsLdp.Global.Nsr()
self.nsr.parent = self
self._children_name_map["nsr"] = "nsr"
self.graceful_restart = MplsLdp.Global.GracefulRestart()
self.graceful_restart.parent = self
self._children_name_map["graceful_restart"] = "graceful-restart"
self.discovery = MplsLdp.Global.Discovery()
self.discovery.parent = self
self._children_name_map["discovery"] = "discovery"
self.mldp = MplsLdp.Global.Mldp()
self.mldp.parent = self
self._children_name_map["mldp"] = "mldp"
self._segment_path = lambda: "global"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global, ['disable_implicit_ipv4', 'ltrace_buf_multiplier'], name, value)
class EntropyLabel(Entity):
"""
Configure for LDP Entropy\-Label
.. attribute:: enable
none
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.EntropyLabel, self).__init__()
self.yang_name = "entropy-label"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.enable = None
self._segment_path = lambda: "entropy-label"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.EntropyLabel, ['enable'], name, value)
class Session(Entity):
"""
LDP Session parameters
.. attribute:: backoff_time
Configure Session Backoff parameters
**type**\: :py:class:`BackoffTime <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Session.BackoffTime>`
.. attribute:: hold_time
LDP Session holdtime
**type**\: int
**range:** 15..65535
**units**\: second
**default value**\: 180
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Session, self).__init__()
self.yang_name = "session"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("backoff-time", ("backoff_time", MplsLdp.Global.Session.BackoffTime))])
self._leafs = OrderedDict([
('hold_time', (YLeaf(YType.uint32, 'hold-time'), ['int'])),
])
self.hold_time = None
self.backoff_time = MplsLdp.Global.Session.BackoffTime()
self.backoff_time.parent = self
self._children_name_map["backoff_time"] = "backoff-time"
self._segment_path = lambda: "session"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Session, ['hold_time'], name, value)
class BackoffTime(Entity):
"""
Configure Session Backoff parameters
.. attribute:: initial_backoff_time
Initial session backoff time (seconds)
**type**\: int
**range:** 5..2147483
**units**\: second
**default value**\: 15
.. attribute:: max_backoff_time
Maximum session backoff time (seconds)
**type**\: int
**range:** 5..2147483
**units**\: second
**default value**\: 120
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Session.BackoffTime, self).__init__()
self.yang_name = "backoff-time"
self.yang_parent_name = "session"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('initial_backoff_time', (YLeaf(YType.uint32, 'initial-backoff-time'), ['int'])),
('max_backoff_time', (YLeaf(YType.uint32, 'max-backoff-time'), ['int'])),
])
self.initial_backoff_time = None
self.max_backoff_time = None
self._segment_path = lambda: "backoff-time"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/session/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Session.BackoffTime, ['initial_backoff_time', 'max_backoff_time'], name, value)
class Igp(Entity):
"""
LDP IGP configuration
.. attribute:: sync
LDP IGP synchronization
**type**\: :py:class:`Sync <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Igp.Sync>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Igp, self).__init__()
self.yang_name = "igp"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("sync", ("sync", MplsLdp.Global.Igp.Sync))])
self._leafs = OrderedDict()
self.sync = MplsLdp.Global.Igp.Sync()
self.sync.parent = self
self._children_name_map["sync"] = "sync"
self._segment_path = lambda: "igp"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Igp, [], name, value)
class Sync(Entity):
"""
LDP IGP synchronization
.. attribute:: delay
LDP IGP synchronization delay time
**type**\: :py:class:`Delay <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Igp.Sync.Delay>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Igp.Sync, self).__init__()
self.yang_name = "sync"
self.yang_parent_name = "igp"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("delay", ("delay", MplsLdp.Global.Igp.Sync.Delay))])
self._leafs = OrderedDict()
self.delay = MplsLdp.Global.Igp.Sync.Delay()
self.delay.parent = self
self._children_name_map["delay"] = "delay"
self._segment_path = lambda: "sync"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/igp/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Igp.Sync, [], name, value)
class Delay(Entity):
"""
LDP IGP synchronization delay time
.. attribute:: on_session_up
Interface sync up delay after session up
**type**\: int
**range:** 5..300
**units**\: second
.. attribute:: on_proc_restart
Global sync up delay to be used after process restart
**type**\: int
**range:** 60..600
**units**\: second
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Igp.Sync.Delay, self).__init__()
self.yang_name = "delay"
self.yang_parent_name = "sync"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('on_session_up', (YLeaf(YType.uint32, 'on-session-up'), ['int'])),
('on_proc_restart', (YLeaf(YType.uint32, 'on-proc-restart'), ['int'])),
])
self.on_session_up = None
self.on_proc_restart = None
self._segment_path = lambda: "delay"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/igp/sync/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Igp.Sync.Delay, ['on_session_up', 'on_proc_restart'], name, value)
class EnableLogging(Entity):
"""
Enable logging of events
.. attribute:: nsr
Enable logging of NSR events
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: neighbor_changes
Enable logging of neighbor events
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: adjacency
Enable logging of adjacency events
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: session_protection
Enable logging of session protection events
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: gr_session_changes
Enable logging of Graceful Restart (GR) events
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.EnableLogging, self).__init__()
self.yang_name = "enable-logging"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('nsr', (YLeaf(YType.empty, 'nsr'), ['Empty'])),
('neighbor_changes', (YLeaf(YType.empty, 'neighbor-changes'), ['Empty'])),
('adjacency', (YLeaf(YType.empty, 'adjacency'), ['Empty'])),
('session_protection', (YLeaf(YType.empty, 'session-protection'), ['Empty'])),
('gr_session_changes', (YLeaf(YType.empty, 'gr-session-changes'), ['Empty'])),
])
self.nsr = None
self.neighbor_changes = None
self.adjacency = None
self.session_protection = None
self.gr_session_changes = None
self._segment_path = lambda: "enable-logging"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.EnableLogging, ['nsr', 'neighbor_changes', 'adjacency', 'session_protection', 'gr_session_changes'], name, value)
class Signalling(Entity):
"""
Configure LDP signalling parameters
.. attribute:: dscp
DSCP for control packets
**type**\: int
**range:** 0..63
**default value**\: 48
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Signalling, self).__init__()
self.yang_name = "signalling"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('dscp', (YLeaf(YType.uint32, 'dscp'), ['int'])),
])
self.dscp = None
self._segment_path = lambda: "signalling"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Signalling, ['dscp'], name, value)
class Nsr(Entity):
"""
Configure LDP Non\-Stop Routing
.. attribute:: enable
none
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Nsr, self).__init__()
self.yang_name = "nsr"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.enable = None
self._segment_path = lambda: "nsr"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Nsr, ['enable'], name, value)
class GracefulRestart(Entity):
"""
Configuration for LDP Graceful Restart
parameters
.. attribute:: reconnect_timeout
Configure Graceful Restart Reconnect Timeout value
**type**\: int
**range:** 60..1800
**units**\: second
**default value**\: 120
.. attribute:: enable
none
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: forwarding_hold_time
Configure Graceful Restart Session holdtime
**type**\: int
**range:** 60..1800
**units**\: second
**default value**\: 180
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.GracefulRestart, self).__init__()
self.yang_name = "graceful-restart"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('reconnect_timeout', (YLeaf(YType.uint32, 'reconnect-timeout'), ['int'])),
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
('forwarding_hold_time', (YLeaf(YType.uint32, 'forwarding-hold-time'), ['int'])),
])
self.reconnect_timeout = None
self.enable = None
self.forwarding_hold_time = None
self._segment_path = lambda: "graceful-restart"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.GracefulRestart, ['reconnect_timeout', 'enable', 'forwarding_hold_time'], name, value)
class Discovery(Entity):
"""
Configure Discovery parameters
.. attribute:: link_hello
LDP Link Hellos
**type**\: :py:class:`LinkHello <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Discovery.LinkHello>`
.. attribute:: targeted_hello
LDP Targeted Hellos
**type**\: :py:class:`TargetedHello <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Discovery.TargetedHello>`
.. attribute:: disable_instance_tlv
Disable transmit and receive processing for private Instance TLV in LDP discovery hello messages
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: disable_quick_start
Disable discovery's quick start mode
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Discovery, self).__init__()
self.yang_name = "discovery"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("link-hello", ("link_hello", MplsLdp.Global.Discovery.LinkHello)), ("targeted-hello", ("targeted_hello", MplsLdp.Global.Discovery.TargetedHello))])
self._leafs = OrderedDict([
('disable_instance_tlv', (YLeaf(YType.empty, 'disable-instance-tlv'), ['Empty'])),
('disable_quick_start', (YLeaf(YType.empty, 'disable-quick-start'), ['Empty'])),
])
self.disable_instance_tlv = None
self.disable_quick_start = None
self.link_hello = MplsLdp.Global.Discovery.LinkHello()
self.link_hello.parent = self
self._children_name_map["link_hello"] = "link-hello"
self.targeted_hello = MplsLdp.Global.Discovery.TargetedHello()
self.targeted_hello.parent = self
self._children_name_map["targeted_hello"] = "targeted-hello"
self._segment_path = lambda: "discovery"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Discovery, ['disable_instance_tlv', 'disable_quick_start'], name, value)
class LinkHello(Entity):
"""
LDP Link Hellos
.. attribute:: interval
Link Hello interval
**type**\: int
**range:** 1..65535
**units**\: second
**default value**\: 5
.. attribute:: hold_time
Time (seconds) \- 65535 implies infinite
**type**\: int
**range:** 1..65535
**units**\: second
**default value**\: 15
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Discovery.LinkHello, self).__init__()
self.yang_name = "link-hello"
self.yang_parent_name = "discovery"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('interval', (YLeaf(YType.uint32, 'interval'), ['int'])),
('hold_time', (YLeaf(YType.uint32, 'hold-time'), ['int'])),
])
self.interval = None
self.hold_time = None
self._segment_path = lambda: "link-hello"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/discovery/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Discovery.LinkHello, ['interval', 'hold_time'], name, value)
class TargetedHello(Entity):
"""
LDP Targeted Hellos
.. attribute:: interval
Targeted Hello interval
**type**\: int
**range:** 1..65535
**units**\: second
**default value**\: 10
.. attribute:: hold_time
Time (seconds) \- 65535 implies infinite
**type**\: int
**range:** 1..65535
**units**\: second
**default value**\: 90
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Discovery.TargetedHello, self).__init__()
self.yang_name = "targeted-hello"
self.yang_parent_name = "discovery"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('interval', (YLeaf(YType.uint32, 'interval'), ['int'])),
('hold_time', (YLeaf(YType.uint32, 'hold-time'), ['int'])),
])
self.interval = None
self.hold_time = None
self._segment_path = lambda: "targeted-hello"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/discovery/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Discovery.TargetedHello, ['interval', 'hold_time'], name, value)
class Mldp(Entity):
"""
MPLS mLDP configuration
.. attribute:: vrfs
VRF Table attribute configuration for MPLS LDP
**type**\: :py:class:`Vrfs <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.Vrfs>`
.. attribute:: default_vrf
Default VRF attribute configuration for mLDP
**type**\: :py:class:`DefaultVrf <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.DefaultVrf>`
.. attribute:: mldp_global
Global configuration for mLDP
**type**\: :py:class:`MldpGlobal <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.MldpGlobal>`
.. attribute:: enable
Enable Multicast Label Distribution Protocol (mLDP)
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp, self).__init__()
self.yang_name = "mldp"
self.yang_parent_name = "global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("vrfs", ("vrfs", MplsLdp.Global.Mldp.Vrfs)), ("default-vrf", ("default_vrf", MplsLdp.Global.Mldp.DefaultVrf)), ("mldp-global", ("mldp_global", MplsLdp.Global.Mldp.MldpGlobal))])
self._leafs = OrderedDict([
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.enable = None
self.vrfs = MplsLdp.Global.Mldp.Vrfs()
self.vrfs.parent = self
self._children_name_map["vrfs"] = "vrfs"
self.default_vrf = MplsLdp.Global.Mldp.DefaultVrf()
self.default_vrf.parent = self
self._children_name_map["default_vrf"] = "default-vrf"
self.mldp_global = MplsLdp.Global.Mldp.MldpGlobal()
self.mldp_global.parent = self
self._children_name_map["mldp_global"] = "mldp-global"
self._segment_path = lambda: "mldp"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp, ['enable'], name, value)
class Vrfs(Entity):
"""
VRF Table attribute configuration for MPLS LDP
.. attribute:: vrf
VRF attribute configuration for MPLS LDP
**type**\: list of :py:class:`Vrf <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.Vrfs.Vrf>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.Vrfs, self).__init__()
self.yang_name = "vrfs"
self.yang_parent_name = "mldp"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("vrf", ("vrf", MplsLdp.Global.Mldp.Vrfs.Vrf))])
self._leafs = OrderedDict()
self.vrf = YList(self)
self._segment_path = lambda: "vrfs"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/mldp/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.Vrfs, [], name, value)
class Vrf(Entity):
"""
VRF attribute configuration for MPLS LDP
.. attribute:: vrf_name (key)
VRF Name
**type**\: str
**length:** 1..32
.. attribute:: enable
Enable Multicast Label Distribution Protocol (mLDP)
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: afs
Address Family specific operational data
**type**\: :py:class:`Afs <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.Vrfs.Vrf.Afs>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.Vrfs.Vrf, self).__init__()
self.yang_name = "vrf"
self.yang_parent_name = "vrfs"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = ['vrf_name']
self._child_classes = OrderedDict([("afs", ("afs", MplsLdp.Global.Mldp.Vrfs.Vrf.Afs))])
self._leafs = OrderedDict([
('vrf_name', (YLeaf(YType.str, 'vrf-name'), ['str'])),
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.vrf_name = None
self.enable = None
self.afs = MplsLdp.Global.Mldp.Vrfs.Vrf.Afs()
self.afs.parent = self
self._children_name_map["afs"] = "afs"
self._segment_path = lambda: "vrf" + "[vrf-name='" + str(self.vrf_name) + "']"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/mldp/vrfs/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.Vrfs.Vrf, ['vrf_name', 'enable'], name, value)
class Afs(Entity):
"""
Address Family specific operational data
.. attribute:: af
Operational data for given Address Family
**type**\: list of :py:class:`Af <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs, self).__init__()
self.yang_name = "afs"
self.yang_parent_name = "vrf"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("af", ("af", MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af))])
self._leafs = OrderedDict()
self.af = YList(self)
self._segment_path = lambda: "afs"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs, [], name, value)
class Af(Entity):
"""
Operational data for given Address Family
.. attribute:: af_name (key)
Address Family name
**type**\: :py:class:`MplsLdpafName <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpafName>`
.. attribute:: recursive_forwarding
Enable recursive forwarding
**type**\: :py:class:`RecursiveForwarding <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.RecursiveForwarding>`
.. attribute:: mldp_recursive_fec
MPLS mLDP Recursive FEC
**type**\: :py:class:`MldpRecursiveFec <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MldpRecursiveFec>`
.. attribute:: neighbor_policies
MLDP neighbor policies
**type**\: :py:class:`NeighborPolicies <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.NeighborPolicies>`
.. attribute:: mo_frr
MPLS mLDP MoFRR
**type**\: :py:class:`MoFrr <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MoFrr>`
.. attribute:: make_before_break
MPLS mLDP Make\-Before\-Break configuration
**type**\: :py:class:`MakeBeforeBreak <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MakeBeforeBreak>`
.. attribute:: csc
MPLS mLDP CSC
**type**\: :py:class:`Csc <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.Csc>`
.. attribute:: enable
Enable Multicast Label Distribution Protocol (mLDP) under AF
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: mldp_rib_unicast_always
Enable MPLS MLDP RIB unicast\-always configuration
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af, self).__init__()
self.yang_name = "af"
self.yang_parent_name = "afs"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['af_name']
self._child_classes = OrderedDict([("recursive-forwarding", ("recursive_forwarding", MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.RecursiveForwarding)), ("mldp-recursive-fec", ("mldp_recursive_fec", MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MldpRecursiveFec)), ("neighbor-policies", ("neighbor_policies", MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.NeighborPolicies)), ("mo-frr", ("mo_frr", MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MoFrr)), ("make-before-break", ("make_before_break", MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MakeBeforeBreak)), ("csc", ("csc", MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.Csc))])
self._leafs = OrderedDict([
('af_name', (YLeaf(YType.enumeration, 'af-name'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpafName', '')])),
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
('mldp_rib_unicast_always', (YLeaf(YType.empty, 'mldp-rib-unicast-always'), ['Empty'])),
])
self.af_name = None
self.enable = None
self.mldp_rib_unicast_always = None
self.recursive_forwarding = MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.RecursiveForwarding()
self.recursive_forwarding.parent = self
self._children_name_map["recursive_forwarding"] = "recursive-forwarding"
self.mldp_recursive_fec = MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MldpRecursiveFec()
self.mldp_recursive_fec.parent = self
self._children_name_map["mldp_recursive_fec"] = "mldp-recursive-fec"
self.neighbor_policies = MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.NeighborPolicies()
self.neighbor_policies.parent = self
self._children_name_map["neighbor_policies"] = "neighbor-policies"
self.mo_frr = MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MoFrr()
self.mo_frr.parent = self
self._children_name_map["mo_frr"] = "mo-frr"
self.make_before_break = MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MakeBeforeBreak()
self.make_before_break.parent = self
self._children_name_map["make_before_break"] = "make-before-break"
self.csc = MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.Csc()
self.csc.parent = self
self._children_name_map["csc"] = "csc"
self._segment_path = lambda: "af" + "[af-name='" + str(self.af_name) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af, ['af_name', 'enable', 'mldp_rib_unicast_always'], name, value)
class RecursiveForwarding(Entity):
"""
Enable recursive forwarding
.. attribute:: enable
Enable recursive forwarding
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: policy
Recursive forwarding policy name
**type**\: str
**length:** 1..64
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.RecursiveForwarding, self).__init__()
self.yang_name = "recursive-forwarding"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
('policy', (YLeaf(YType.str, 'policy'), ['str'])),
])
self.enable = None
self.policy = None
self._segment_path = lambda: "recursive-forwarding"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.RecursiveForwarding, ['enable', 'policy'], name, value)
class MldpRecursiveFec(Entity):
"""
MPLS mLDP Recursive FEC
.. attribute:: enable
Enable MPLS mLDP Recursive FEC
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: policy
Route policy name
**type**\: str
**length:** 1..64
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MldpRecursiveFec, self).__init__()
self.yang_name = "mldp-recursive-fec"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
('policy', (YLeaf(YType.str, 'policy'), ['str'])),
])
self.enable = None
self.policy = None
self._segment_path = lambda: "mldp-recursive-fec"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MldpRecursiveFec, ['enable', 'policy'], name, value)
class NeighborPolicies(Entity):
"""
MLDP neighbor policies
.. attribute:: neighbor_policy
Route Policy
**type**\: list of :py:class:`NeighborPolicy <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.NeighborPolicies.NeighborPolicy>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.NeighborPolicies, self).__init__()
self.yang_name = "neighbor-policies"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("neighbor-policy", ("neighbor_policy", MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.NeighborPolicies.NeighborPolicy))])
self._leafs = OrderedDict()
self.neighbor_policy = YList(self)
self._segment_path = lambda: "neighbor-policies"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.NeighborPolicies, [], name, value)
class NeighborPolicy(Entity):
"""
Route Policy
.. attribute:: root_address (key)
Neighbor Address
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: policy_mode (key)
Inbound/Outbound Policy
**type**\: :py:class:`MldpPolicyMode <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MldpPolicyMode>`
.. attribute:: route_policy
Route policy name
**type**\: str
**length:** 1..64
**mandatory**\: True
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.NeighborPolicies.NeighborPolicy, self).__init__()
self.yang_name = "neighbor-policy"
self.yang_parent_name = "neighbor-policies"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['root_address','policy_mode']
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('root_address', (YLeaf(YType.str, 'root-address'), ['str'])),
('policy_mode', (YLeaf(YType.enumeration, 'policy-mode'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MldpPolicyMode', '')])),
('route_policy', (YLeaf(YType.str, 'route-policy'), ['str'])),
])
self.root_address = None
self.policy_mode = None
self.route_policy = None
self._segment_path = lambda: "neighbor-policy" + "[root-address='" + str(self.root_address) + "']" + "[policy-mode='" + str(self.policy_mode) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.NeighborPolicies.NeighborPolicy, ['root_address', 'policy_mode', 'route_policy'], name, value)
class MoFrr(Entity):
"""
MPLS mLDP MoFRR
.. attribute:: enable
Enable MPLS mLDP MoFRR
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: policy
Route policy name
**type**\: str
**length:** 1..64
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MoFrr, self).__init__()
self.yang_name = "mo-frr"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
('policy', (YLeaf(YType.str, 'policy'), ['str'])),
])
self.enable = None
self.policy = None
self._segment_path = lambda: "mo-frr"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MoFrr, ['enable', 'policy'], name, value)
class MakeBeforeBreak(Entity):
"""
MPLS mLDP Make\-Before\-Break configuration
.. attribute:: signaling
Enable MPLS mLDP MBB signaling
**type**\: :py:class:`Signaling <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MakeBeforeBreak.Signaling>`
.. attribute:: policy
Route policy name
**type**\: str
**length:** 1..64
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MakeBeforeBreak, self).__init__()
self.yang_name = "make-before-break"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("signaling", ("signaling", MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MakeBeforeBreak.Signaling))])
self._leafs = OrderedDict([
('policy', (YLeaf(YType.str, 'policy'), ['str'])),
])
self.policy = None
self.signaling = MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MakeBeforeBreak.Signaling()
self.signaling.parent = self
self._children_name_map["signaling"] = "signaling"
self._segment_path = lambda: "make-before-break"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MakeBeforeBreak, ['policy'], name, value)
class Signaling(Entity):
"""
Enable MPLS mLDP MBB signaling
.. attribute:: forward_delay
Forwarding Delay in Seconds
**type**\: int
**range:** 0..600
**units**\: second
.. attribute:: delete_delay
Delete Delay in seconds
**type**\: int
**range:** 0..60
**units**\: second
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MakeBeforeBreak.Signaling, self).__init__()
self.yang_name = "signaling"
self.yang_parent_name = "make-before-break"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('forward_delay', (YLeaf(YType.uint32, 'forward-delay'), ['int'])),
('delete_delay', (YLeaf(YType.uint32, 'delete-delay'), ['int'])),
])
self.forward_delay = None
self.delete_delay = None
self._segment_path = lambda: "signaling"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.MakeBeforeBreak.Signaling, ['forward_delay', 'delete_delay'], name, value)
class Csc(Entity):
"""
MPLS mLDP CSC
.. attribute:: enable
Enable MPLS mLDP CSC
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.Csc, self).__init__()
self.yang_name = "csc"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.enable = None
self._segment_path = lambda: "csc"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.Vrfs.Vrf.Afs.Af.Csc, ['enable'], name, value)
class DefaultVrf(Entity):
"""
Default VRF attribute configuration for mLDP
.. attribute:: afs
Address Family specific operational data
**type**\: :py:class:`Afs <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.DefaultVrf.Afs>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.DefaultVrf, self).__init__()
self.yang_name = "default-vrf"
self.yang_parent_name = "mldp"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("afs", ("afs", MplsLdp.Global.Mldp.DefaultVrf.Afs))])
self._leafs = OrderedDict()
self.afs = MplsLdp.Global.Mldp.DefaultVrf.Afs()
self.afs.parent = self
self._children_name_map["afs"] = "afs"
self._segment_path = lambda: "default-vrf"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/mldp/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.DefaultVrf, [], name, value)
class Afs(Entity):
"""
Address Family specific operational data
.. attribute:: af
Operational data for given Address Family
**type**\: list of :py:class:`Af <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.DefaultVrf.Afs.Af>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.DefaultVrf.Afs, self).__init__()
self.yang_name = "afs"
self.yang_parent_name = "default-vrf"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("af", ("af", MplsLdp.Global.Mldp.DefaultVrf.Afs.Af))])
self._leafs = OrderedDict()
self.af = YList(self)
self._segment_path = lambda: "afs"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/mldp/default-vrf/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.DefaultVrf.Afs, [], name, value)
class Af(Entity):
"""
Operational data for given Address Family
.. attribute:: af_name (key)
Address Family name
**type**\: :py:class:`MplsLdpafName <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdpafName>`
.. attribute:: recursive_forwarding
Enable recursive forwarding
**type**\: :py:class:`RecursiveForwarding <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.RecursiveForwarding>`
.. attribute:: mldp_recursive_fec
MPLS mLDP Recursive FEC
**type**\: :py:class:`MldpRecursiveFec <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MldpRecursiveFec>`
.. attribute:: neighbor_policies
MLDP neighbor policies
**type**\: :py:class:`NeighborPolicies <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.NeighborPolicies>`
.. attribute:: mo_frr
MPLS mLDP MoFRR
**type**\: :py:class:`MoFrr <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MoFrr>`
.. attribute:: make_before_break
MPLS mLDP Make\-Before\-Break configuration
**type**\: :py:class:`MakeBeforeBreak <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MakeBeforeBreak>`
.. attribute:: csc
MPLS mLDP CSC
**type**\: :py:class:`Csc <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.Csc>`
.. attribute:: enable
Enable Multicast Label Distribution Protocol (mLDP) under AF
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: mldp_rib_unicast_always
Enable MPLS MLDP RIB unicast\-always configuration
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af, self).__init__()
self.yang_name = "af"
self.yang_parent_name = "afs"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = ['af_name']
self._child_classes = OrderedDict([("recursive-forwarding", ("recursive_forwarding", MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.RecursiveForwarding)), ("mldp-recursive-fec", ("mldp_recursive_fec", MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MldpRecursiveFec)), ("neighbor-policies", ("neighbor_policies", MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.NeighborPolicies)), ("mo-frr", ("mo_frr", MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MoFrr)), ("make-before-break", ("make_before_break", MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MakeBeforeBreak)), ("csc", ("csc", MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.Csc))])
self._leafs = OrderedDict([
('af_name', (YLeaf(YType.enumeration, 'af-name'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MplsLdpafName', '')])),
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
('mldp_rib_unicast_always', (YLeaf(YType.empty, 'mldp-rib-unicast-always'), ['Empty'])),
])
self.af_name = None
self.enable = None
self.mldp_rib_unicast_always = None
self.recursive_forwarding = MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.RecursiveForwarding()
self.recursive_forwarding.parent = self
self._children_name_map["recursive_forwarding"] = "recursive-forwarding"
self.mldp_recursive_fec = MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MldpRecursiveFec()
self.mldp_recursive_fec.parent = self
self._children_name_map["mldp_recursive_fec"] = "mldp-recursive-fec"
self.neighbor_policies = MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.NeighborPolicies()
self.neighbor_policies.parent = self
self._children_name_map["neighbor_policies"] = "neighbor-policies"
self.mo_frr = MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MoFrr()
self.mo_frr.parent = self
self._children_name_map["mo_frr"] = "mo-frr"
self.make_before_break = MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MakeBeforeBreak()
self.make_before_break.parent = self
self._children_name_map["make_before_break"] = "make-before-break"
self.csc = MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.Csc()
self.csc.parent = self
self._children_name_map["csc"] = "csc"
self._segment_path = lambda: "af" + "[af-name='" + str(self.af_name) + "']"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/mldp/default-vrf/afs/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af, ['af_name', 'enable', 'mldp_rib_unicast_always'], name, value)
class RecursiveForwarding(Entity):
"""
Enable recursive forwarding
.. attribute:: enable
Enable recursive forwarding
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: policy
Recursive forwarding policy name
**type**\: str
**length:** 1..64
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.RecursiveForwarding, self).__init__()
self.yang_name = "recursive-forwarding"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
('policy', (YLeaf(YType.str, 'policy'), ['str'])),
])
self.enable = None
self.policy = None
self._segment_path = lambda: "recursive-forwarding"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.RecursiveForwarding, ['enable', 'policy'], name, value)
class MldpRecursiveFec(Entity):
"""
MPLS mLDP Recursive FEC
.. attribute:: enable
Enable MPLS mLDP Recursive FEC
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: policy
Route policy name
**type**\: str
**length:** 1..64
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MldpRecursiveFec, self).__init__()
self.yang_name = "mldp-recursive-fec"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
('policy', (YLeaf(YType.str, 'policy'), ['str'])),
])
self.enable = None
self.policy = None
self._segment_path = lambda: "mldp-recursive-fec"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MldpRecursiveFec, ['enable', 'policy'], name, value)
class NeighborPolicies(Entity):
"""
MLDP neighbor policies
.. attribute:: neighbor_policy
Route Policy
**type**\: list of :py:class:`NeighborPolicy <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.NeighborPolicies.NeighborPolicy>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.NeighborPolicies, self).__init__()
self.yang_name = "neighbor-policies"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("neighbor-policy", ("neighbor_policy", MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.NeighborPolicies.NeighborPolicy))])
self._leafs = OrderedDict()
self.neighbor_policy = YList(self)
self._segment_path = lambda: "neighbor-policies"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.NeighborPolicies, [], name, value)
class NeighborPolicy(Entity):
"""
Route Policy
.. attribute:: root_address (key)
Neighbor Address
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: policy_mode (key)
Inbound/Outbound Policy
**type**\: :py:class:`MldpPolicyMode <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MldpPolicyMode>`
.. attribute:: route_policy
Route policy name
**type**\: str
**length:** 1..64
**mandatory**\: True
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.NeighborPolicies.NeighborPolicy, self).__init__()
self.yang_name = "neighbor-policy"
self.yang_parent_name = "neighbor-policies"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = ['root_address','policy_mode']
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('root_address', (YLeaf(YType.str, 'root-address'), ['str'])),
('policy_mode', (YLeaf(YType.enumeration, 'policy-mode'), [('ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg', 'MldpPolicyMode', '')])),
('route_policy', (YLeaf(YType.str, 'route-policy'), ['str'])),
])
self.root_address = None
self.policy_mode = None
self.route_policy = None
self._segment_path = lambda: "neighbor-policy" + "[root-address='" + str(self.root_address) + "']" + "[policy-mode='" + str(self.policy_mode) + "']"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.NeighborPolicies.NeighborPolicy, ['root_address', 'policy_mode', 'route_policy'], name, value)
class MoFrr(Entity):
"""
MPLS mLDP MoFRR
.. attribute:: enable
Enable MPLS mLDP MoFRR
**type**\: :py:class:`Empty<ydk.types.Empty>`
.. attribute:: policy
Route policy name
**type**\: str
**length:** 1..64
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MoFrr, self).__init__()
self.yang_name = "mo-frr"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
('policy', (YLeaf(YType.str, 'policy'), ['str'])),
])
self.enable = None
self.policy = None
self._segment_path = lambda: "mo-frr"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MoFrr, ['enable', 'policy'], name, value)
class MakeBeforeBreak(Entity):
"""
MPLS mLDP Make\-Before\-Break configuration
.. attribute:: signaling
Enable MPLS mLDP MBB signaling
**type**\: :py:class:`Signaling <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MakeBeforeBreak.Signaling>`
.. attribute:: policy
Route policy name
**type**\: str
**length:** 1..64
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MakeBeforeBreak, self).__init__()
self.yang_name = "make-before-break"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([("signaling", ("signaling", MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MakeBeforeBreak.Signaling))])
self._leafs = OrderedDict([
('policy', (YLeaf(YType.str, 'policy'), ['str'])),
])
self.policy = None
self.signaling = MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MakeBeforeBreak.Signaling()
self.signaling.parent = self
self._children_name_map["signaling"] = "signaling"
self._segment_path = lambda: "make-before-break"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MakeBeforeBreak, ['policy'], name, value)
class Signaling(Entity):
"""
Enable MPLS mLDP MBB signaling
.. attribute:: forward_delay
Forwarding Delay in Seconds
**type**\: int
**range:** 0..600
**units**\: second
.. attribute:: delete_delay
Delete Delay in seconds
**type**\: int
**range:** 0..60
**units**\: second
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MakeBeforeBreak.Signaling, self).__init__()
self.yang_name = "signaling"
self.yang_parent_name = "make-before-break"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('forward_delay', (YLeaf(YType.uint32, 'forward-delay'), ['int'])),
('delete_delay', (YLeaf(YType.uint32, 'delete-delay'), ['int'])),
])
self.forward_delay = None
self.delete_delay = None
self._segment_path = lambda: "signaling"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.MakeBeforeBreak.Signaling, ['forward_delay', 'delete_delay'], name, value)
class Csc(Entity):
"""
MPLS mLDP CSC
.. attribute:: enable
Enable MPLS mLDP CSC
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.Csc, self).__init__()
self.yang_name = "csc"
self.yang_parent_name = "af"
self.is_top_level_class = False
self.has_list_ancestor = True
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('enable', (YLeaf(YType.empty, 'enable'), ['Empty'])),
])
self.enable = None
self._segment_path = lambda: "csc"
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.DefaultVrf.Afs.Af.Csc, ['enable'], name, value)
class MldpGlobal(Entity):
"""
Global configuration for mLDP
.. attribute:: logging
MPLS mLDP logging
**type**\: :py:class:`Logging <ydk.models.cisco_ios_xr.Cisco_IOS_XR_mpls_ldp_cfg.MplsLdp.Global.Mldp.MldpGlobal.Logging>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.MldpGlobal, self).__init__()
self.yang_name = "mldp-global"
self.yang_parent_name = "mldp"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([("logging", ("logging", MplsLdp.Global.Mldp.MldpGlobal.Logging))])
self._leafs = OrderedDict()
self.logging = MplsLdp.Global.Mldp.MldpGlobal.Logging()
self.logging.parent = self
self._children_name_map["logging"] = "logging"
self._segment_path = lambda: "mldp-global"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/mldp/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.MldpGlobal, [], name, value)
class Logging(Entity):
"""
MPLS mLDP logging
.. attribute:: notifications
MPLS mLDP logging notifications
**type**\: :py:class:`Empty<ydk.types.Empty>`
"""
_prefix = 'mpls-ldp-cfg'
_revision = '2017-09-30'
def __init__(self):
super(MplsLdp.Global.Mldp.MldpGlobal.Logging, self).__init__()
self.yang_name = "logging"
self.yang_parent_name = "mldp-global"
self.is_top_level_class = False
self.has_list_ancestor = False
self.ylist_key_names = []
self._child_classes = OrderedDict([])
self._leafs = OrderedDict([
('notifications', (YLeaf(YType.empty, 'notifications'), ['Empty'])),
])
self.notifications = None
self._segment_path = lambda: "logging"
self._absolute_path = lambda: "Cisco-IOS-XR-mpls-ldp-cfg:mpls-ldp/global/mldp/mldp-global/%s" % self._segment_path()
self._is_frozen = True
def __setattr__(self, name, value):
self._perform_setattr(MplsLdp.Global.Mldp.MldpGlobal.Logging, ['notifications'], name, value)
def clone_ptr(self):
self._top_entity = MplsLdp()
return self._top_entity
| 49.308649 | 611 | 0.429664 | 27,709 | 344,914 | 5.074452 | 0.015879 | 0.023498 | 0.029586 | 0.022901 | 0.924065 | 0.908213 | 0.892794 | 0.875868 | 0.858664 | 0.841567 | 0 | 0.013449 | 0.475707 | 344,914 | 6,994 | 612 | 49.315699 | 0.764096 | 0.187374 | 0 | 0.763361 | 0 | 0.009015 | 0.100578 | 0.024586 | 0.001932 | 0 | 0 | 0 | 0 | 1 | 0.09047 | false | 0.009981 | 0.00161 | 0 | 0.150032 | 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 |
6a9e4b7c027b8edc3c65bdbcf048d5144188e4a0 | 17,056 | py | Python | keras/keras_parameterized_test.py | Halo9Pan/dive-keras | 7d4c5572fa3a9fc2542a1314d06c555f67575cb0 | [
"Apache-2.0"
] | 37,222 | 2017-12-13T00:52:55.000Z | 2022-03-31T22:34:35.000Z | keras/keras_parameterized_test.py | amirsadafi/keras | f1e9c76675981ee6683f54a3ce569212d551d12d | [
"Apache-2.0"
] | 7,624 | 2017-12-13T01:03:40.000Z | 2022-03-31T23:57:24.000Z | keras/keras_parameterized_test.py | amirsadafi/keras | f1e9c76675981ee6683f54a3ce569212d551d12d | [
"Apache-2.0"
] | 14,914 | 2017-12-13T02:30:46.000Z | 2022-03-30T14:49:16.000Z | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for Keras testing_utils."""
import tensorflow.compat.v2 as tf
import unittest
from absl.testing import parameterized
import keras
from keras import keras_parameterized
from keras import testing_utils
class KerasParameterizedTest(keras_parameterized.TestCase):
def test_run_with_all_model_types(self):
model_types = []
models = []
class ExampleTest(keras_parameterized.TestCase):
def runTest(self):
pass
@keras_parameterized.run_with_all_model_types
def testBody(self):
model_types.append(testing_utils.get_model_type())
models.append(testing_utils.get_small_mlp(1, 4, input_dim=3))
e = ExampleTest()
e.testBody_functional()
e.testBody_subclass()
e.testBody_sequential()
self.assertLen(model_types, 3)
self.assertAllEqual(model_types, [
"functional",
"subclass",
"sequential"
])
# Validate that the models are what they should be
self.assertTrue(models[0]._is_graph_network)
self.assertFalse(models[1]._is_graph_network)
self.assertNotIsInstance(models[0], keras.models.Sequential)
self.assertNotIsInstance(models[1], keras.models.Sequential)
self.assertIsInstance(models[2], keras.models.Sequential)
ts = unittest.makeSuite(ExampleTest)
res = unittest.TestResult()
ts.run(res)
self.assertLen(model_types, 6)
def test_run_with_all_model_types_and_extra_params(self):
model_types = []
models = []
class ExampleTest(keras_parameterized.TestCase):
def runTest(self):
pass
@keras_parameterized.run_with_all_model_types
@parameterized.named_parameters(
[dict(testcase_name="_0", with_brackets=True),
dict(testcase_name="_1", with_brackets=False)])
def testBody(self, with_brackets):
with_brackets = "with_brackets" if with_brackets else "without_brackets"
model_types.append((with_brackets, testing_utils.get_model_type()))
models.append(testing_utils.get_small_mlp(1, 4, input_dim=3))
e = ExampleTest()
e.testBody_0_functional()
e.testBody_0_subclass()
e.testBody_0_sequential()
e.testBody_1_functional()
e.testBody_1_subclass()
e.testBody_1_sequential()
self.assertLen(model_types, 6)
self.assertAllEqual(model_types, [
("with_brackets", "functional"),
("with_brackets", "subclass"),
("with_brackets", "sequential"),
("without_brackets", "functional"),
("without_brackets", "subclass"),
("without_brackets", "sequential"),
])
# Validate that the models are what they should be
self.assertTrue(models[0]._is_graph_network)
self.assertFalse(models[1]._is_graph_network)
self.assertNotIsInstance(models[0], keras.models.Sequential)
self.assertNotIsInstance(models[1], keras.models.Sequential)
self.assertIsInstance(models[2], keras.models.Sequential)
ts = unittest.makeSuite(ExampleTest)
res = unittest.TestResult()
ts.run(res)
self.assertLen(model_types, 12)
def test_run_with_all_model_types_exclude_one(self):
model_types = []
models = []
class ExampleTest(keras_parameterized.TestCase):
def runTest(self):
pass
@keras_parameterized.run_with_all_model_types(exclude_models="sequential")
def testBody(self):
model_types.append(testing_utils.get_model_type())
models.append(testing_utils.get_small_mlp(1, 4, input_dim=3))
e = ExampleTest()
if hasattr(e, "testBody_functional"):
e.testBody_functional()
if hasattr(e, "testBody_subclass"):
e.testBody_subclass()
if hasattr(e, "testBody_sequential"):
e.testBody_sequential()
self.assertLen(model_types, 2)
self.assertAllEqual(model_types, [
"functional",
"subclass"
])
# Validate that the models are what they should be
self.assertTrue(models[0]._is_graph_network)
self.assertFalse(models[1]._is_graph_network)
self.assertNotIsInstance(models[0], keras.models.Sequential)
self.assertNotIsInstance(models[1], keras.models.Sequential)
ts = unittest.makeSuite(ExampleTest)
res = unittest.TestResult()
ts.run(res)
self.assertLen(model_types, 4)
def test_run_with_all_model_types_exclude_multiple(self):
model_types = []
models = []
class ExampleTest(keras_parameterized.TestCase):
def runTest(self):
pass
@keras_parameterized.run_with_all_model_types(
exclude_models=["sequential", "functional"])
def testBody(self):
model_types.append(testing_utils.get_model_type())
models.append(testing_utils.get_small_mlp(1, 4, input_dim=3))
e = ExampleTest()
if hasattr(e, "testBody_functional"):
e.testBody_functional()
if hasattr(e, "testBody_subclass"):
e.testBody_subclass()
if hasattr(e, "testBody_sequential"):
e.testBody_sequential()
self.assertLen(model_types, 1)
self.assertAllEqual(model_types, [
"subclass"
])
# Validate that the models are what they should be
self.assertFalse(models[0]._is_graph_network)
self.assertNotIsInstance(models[0], keras.models.Sequential)
ts = unittest.makeSuite(ExampleTest)
res = unittest.TestResult()
ts.run(res)
self.assertLen(model_types, 2)
def test_run_all_keras_modes(self):
l = []
class ExampleTest(keras_parameterized.TestCase):
def runTest(self):
pass
@keras_parameterized.run_all_keras_modes()
def testBody(self):
mode = "eager" if tf.executing_eagerly() else "graph"
should_run_eagerly = testing_utils.should_run_eagerly()
l.append((mode, should_run_eagerly))
e = ExampleTest()
if not tf.__internal__.tf2.enabled():
e.testBody_v1_session()
e.testBody_v2_eager()
e.testBody_v2_function()
if not tf.__internal__.tf2.enabled():
self.assertLen(l, 3)
self.assertAllEqual(l, [
("graph", False),
("eager", True),
("eager", False),
])
ts = unittest.makeSuite(ExampleTest)
res = unittest.TestResult()
ts.run(res)
self.assertLen(l, 6)
else:
self.assertLen(l, 2)
self.assertAllEqual(l, [
("eager", True),
("eager", False),
])
ts = unittest.makeSuite(ExampleTest)
res = unittest.TestResult()
ts.run(res)
self.assertLen(l, 4)
def test_run_all_keras_modes_extra_params(self):
l = []
class ExampleTest(keras_parameterized.TestCase):
def runTest(self):
pass
@keras_parameterized.run_all_keras_modes()
@parameterized.named_parameters(
[dict(testcase_name="_0", with_brackets=True),
dict(testcase_name="_1", with_brackets=False)])
def testBody(self, with_brackets):
mode = "eager" if tf.executing_eagerly() else "graph"
with_brackets = "with_brackets" if with_brackets else "without_brackets"
should_run_eagerly = testing_utils.should_run_eagerly()
l.append((with_brackets, mode, should_run_eagerly))
e = ExampleTest()
if not tf.__internal__.tf2.enabled():
e.testBody_0_v1_session()
e.testBody_1_v1_session()
e.testBody_0_v2_eager()
e.testBody_0_v2_function()
e.testBody_1_v2_eager()
e.testBody_1_v2_function()
expected_combinations = {
("with_brackets", "eager", True),
("with_brackets", "eager", False),
("without_brackets", "eager", True),
("without_brackets", "eager", False),
}
if not tf.__internal__.tf2.enabled():
expected_combinations = expected_combinations.union({
("with_brackets", "graph", False),
("without_brackets", "graph", False),
})
self.assertLen(l, len(expected_combinations))
self.assertEqual(set(l), expected_combinations)
ts = unittest.makeSuite(ExampleTest)
res = unittest.TestResult()
ts.run(res)
self.assertLen(l, len(expected_combinations) * 2)
def test_run_all_keras_modes_always_skip_v1(self):
l = []
class ExampleTest(keras_parameterized.TestCase):
def runTest(self):
pass
@keras_parameterized.run_all_keras_modes(always_skip_v1=True)
def testBody(self):
mode = "eager" if tf.executing_eagerly() else "graph"
should_run_eagerly = testing_utils.should_run_eagerly()
l.append((mode, should_run_eagerly))
e = ExampleTest()
if hasattr(e, "testBody_v1_session"):
e.testBody_v1_session()
if hasattr(e, "testBody_v2_eager"):
e.testBody_v2_eager()
if hasattr(e, "testBody_v2_function"):
e.testBody_v2_function()
self.assertLen(l, 2)
self.assertEqual(
set(l), {
("eager", True),
("eager", False),
})
def test_run_all_keras_modes_with_all_model_types(self):
l = []
class ExampleTest(keras_parameterized.TestCase):
def runTest(self):
pass
@keras_parameterized.run_with_all_model_types
@keras_parameterized.run_all_keras_modes
def testBody(self):
mode = "eager" if tf.executing_eagerly() else "graph"
should_run_eagerly = testing_utils.should_run_eagerly()
l.append((mode, should_run_eagerly, testing_utils.get_model_type()))
e = ExampleTest()
e.testBody_v2_eager_functional()
e.testBody_v2_function_functional()
e.testBody_v2_eager_sequential()
e.testBody_v2_function_sequential()
e.testBody_v2_eager_subclass()
e.testBody_v2_function_subclass()
if not tf.__internal__.tf2.enabled():
e.testBody_v1_session_functional()
e.testBody_v1_session_sequential()
e.testBody_v1_session_subclass()
expected_combinations = {
("eager", True, "functional"),
("eager", False, "functional"),
("eager", True, "sequential"),
("eager", False, "sequential"),
("eager", True, "subclass"),
("eager", False, "subclass"),
}
if not tf.__internal__.tf2.enabled():
expected_combinations = expected_combinations.union({
("graph", False, "functional"),
("graph", False, "sequential"),
("graph", False, "subclass"),
})
self.assertLen(l, len(expected_combinations))
self.assertEqual(set(l), expected_combinations)
ts = unittest.makeSuite(ExampleTest)
res = unittest.TestResult()
ts.run(res)
self.assertLen(l, len(expected_combinations) * 2)
def test_run_all_model_types_with_all_keras_modes(self):
l = []
class ExampleTest(keras_parameterized.TestCase):
def runTest(self):
pass
@keras_parameterized.run_all_keras_modes
@keras_parameterized.run_with_all_model_types
def testBody(self):
mode = "eager" if tf.executing_eagerly() else "graph"
should_run_eagerly = testing_utils.should_run_eagerly()
l.append((mode, should_run_eagerly, testing_utils.get_model_type()))
e = ExampleTest()
e.testBody_functional_v2_eager()
e.testBody_functional_v2_function()
e.testBody_sequential_v2_eager()
e.testBody_sequential_v2_function()
e.testBody_subclass_v2_eager()
e.testBody_subclass_v2_function()
if not tf.__internal__.tf2.enabled():
e.testBody_functional_v1_session()
e.testBody_sequential_v1_session()
e.testBody_subclass_v1_session()
expected_combinations = {
("eager", True, "functional"),
("eager", False, "functional"),
("eager", True, "sequential"),
("eager", False, "sequential"),
("eager", True, "subclass"),
("eager", False, "subclass"),
}
if not tf.__internal__.tf2.enabled():
expected_combinations = expected_combinations.union({
("graph", False, "functional"),
("graph", False, "sequential"),
("graph", False, "subclass"),
})
self.assertLen(l, len(expected_combinations))
self.assertEqual(set(l), expected_combinations)
ts = unittest.makeSuite(ExampleTest)
res = unittest.TestResult()
ts.run(res)
self.assertLen(l, len(expected_combinations) * 2)
def test_run_all_keras_modes_with_all_model_types_annotate_class(self):
l = []
@keras_parameterized.run_with_all_model_types
@keras_parameterized.run_all_keras_modes
class ExampleTest(keras_parameterized.TestCase):
def runTest(self):
pass
@parameterized.named_parameters(dict(testcase_name="_arg",
arg=True))
def testBody(self, arg):
mode = "eager" if tf.executing_eagerly() else "graph"
should_run_eagerly = testing_utils.should_run_eagerly()
l.append((mode, should_run_eagerly, testing_utils.get_model_type()))
e = ExampleTest()
e.testBody_arg_v2_eager_functional()
e.testBody_arg_v2_function_functional()
e.testBody_arg_v2_eager_sequential()
e.testBody_arg_v2_function_sequential()
e.testBody_arg_v2_eager_subclass()
e.testBody_arg_v2_function_subclass()
if not tf.__internal__.tf2.enabled():
e.testBody_arg_v1_session_functional()
e.testBody_arg_v1_session_sequential()
e.testBody_arg_v1_session_subclass()
expected_combinations = {
("eager", True, "functional"),
("eager", False, "functional"),
("eager", True, "sequential"),
("eager", False, "sequential"),
("eager", True, "subclass"),
("eager", False, "subclass"),
}
if not tf.__internal__.tf2.enabled():
expected_combinations = expected_combinations.union({
("graph", False, "functional"),
("graph", False, "sequential"),
("graph", False, "subclass"),
})
self.assertLen(l, len(expected_combinations))
self.assertEqual(set(l), expected_combinations)
ts = unittest.makeSuite(ExampleTest)
res = unittest.TestResult()
ts.run(res)
self.assertLen(l, len(expected_combinations) * 2)
def test_run_all_keras_modes_with_all_model_types_annotate_class_2(self):
l = []
@keras_parameterized.run_with_all_model_types
class ExampleTest(keras_parameterized.TestCase):
def runTest(self):
pass
@keras_parameterized.run_all_keras_modes
@parameterized.named_parameters(dict(testcase_name="_arg",
arg=True))
def testBody(self, arg):
mode = "eager" if tf.executing_eagerly() else "graph"
should_run_eagerly = testing_utils.should_run_eagerly()
l.append((mode, should_run_eagerly, testing_utils.get_model_type()))
e = ExampleTest()
e.testBody_arg_v2_eager_functional()
e.testBody_arg_v2_function_functional()
e.testBody_arg_v2_eager_sequential()
e.testBody_arg_v2_function_sequential()
e.testBody_arg_v2_eager_subclass()
e.testBody_arg_v2_function_subclass()
if not tf.__internal__.tf2.enabled():
e.testBody_arg_v1_session_functional()
e.testBody_arg_v1_session_sequential()
e.testBody_arg_v1_session_subclass()
expected_combinations = {
("eager", True, "functional"),
("eager", False, "functional"),
("eager", True, "sequential"),
("eager", False, "sequential"),
("eager", True, "subclass"),
("eager", False, "subclass"),
}
if not tf.__internal__.tf2.enabled():
expected_combinations = expected_combinations.union({
("graph", False, "functional"),
("graph", False, "sequential"),
("graph", False, "subclass"),
})
self.assertLen(l, len(expected_combinations))
self.assertEqual(set(l), expected_combinations)
ts = unittest.makeSuite(ExampleTest)
res = unittest.TestResult()
ts.run(res)
self.assertLen(l, len(expected_combinations) * 2)
@keras_parameterized.run_all_keras_modes
@parameterized.named_parameters(dict(testcase_name="argument",
arg=True))
def test_run_all_keras_modes_extra_params_2(self, arg):
self.assertEqual(arg, True)
@keras_parameterized.run_with_all_model_types
@parameterized.named_parameters(dict(testcase_name="argument",
arg=True))
def test_run_with_all_model_types_extra_params_2(self, arg):
self.assertEqual(arg, True)
if __name__ == "__main__":
tf.test.main()
| 31.067395 | 80 | 0.67208 | 1,997 | 17,056 | 5.41362 | 0.083125 | 0.059939 | 0.031079 | 0.026732 | 0.843955 | 0.812321 | 0.800758 | 0.782259 | 0.760799 | 0.743224 | 0 | 0.010388 | 0.209838 | 17,056 | 548 | 81 | 31.124088 | 0.791793 | 0.052064 | 0 | 0.778313 | 0 | 0 | 0.076123 | 0 | 0 | 0 | 0 | 0 | 0.127711 | 1 | 0.084337 | false | 0.026506 | 0.014458 | 0 | 0.127711 | 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 |
0a75299b847d160d03e1e8502577b674b7ef2754 | 16,502 | py | Python | tests/fixtures/sami.py | vpaul-dev/pycaption-github-release-notes | d265389807f76330196093e5231d2bf5c699619d | [
"Apache-2.0"
] | 183 | 2015-01-26T00:28:48.000Z | 2022-03-29T19:51:55.000Z | tests/fixtures/sami.py | vpaul-dev/pycaption-github-release-notes | d265389807f76330196093e5231d2bf5c699619d | [
"Apache-2.0"
] | 132 | 2015-01-06T08:11:11.000Z | 2022-03-31T19:30:57.000Z | tests/fixtures/sami.py | vpaul-dev/pycaption-github-release-notes | d265389807f76330196093e5231d2bf5c699619d | [
"Apache-2.0"
] | 150 | 2015-01-16T18:03:59.000Z | 2022-03-16T01:11:10.000Z | import pytest
@pytest.fixture(scope="session")
def sample_sami():
return """
<SAMI><HEAD><TITLE>NOVA3213</TITLE><STYLE TYPE="text/css">
<!--
P { margin-left: 1pt;
margin-right: 1pt;
margin-bottom: 2pt;
margin-top: 2pt;
text-align: center;
font-size: 10pt;
font-family: Arial;
font-weight: normal;
font-style: normal;
color: #ffeedd; }
.ENCC {Name: English; lang: en-US; SAMI_Type: CC;}
--></STYLE></HEAD><BODY>
<SYNC start="9209"><P class="ENCC">
( clock ticking )
</P></SYNC>
<SYNC start="12312"><P class="ENCC"> </P></SYNC>
<SYNC start="14848"><P class="ENCC">
MAN:<br/>
When we think<br/>
\u266a ...say bow, wow, \u266a
</P></SYNC>
<SYNC start="17000"><P class="ENCC">
<SPAN Style="text-align:right;">we have this vision of Einstein</SPAN>
</P></SYNC>
<SYNC start="18752"><P class="ENCC">
<br/>
as an old, wrinkly man<br/>
with white hair.
</P></SYNC>
<SYNC start="20887"><P class="ENCC">
MAN 2:<br/>
E equals m c-squared is<br/>
not about an old Einstein.
</P></SYNC>
<SYNC start="26760"><P class="ENCC">
MAN 2:<br/>
It's all about an eternal Einstein.
</P></SYNC>
<SYNC start="32200"><P class="ENCC">
<LAUGHING & WHOOPS!>
</P></SYNC>
</BODY></SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_with_style_tags():
return """
<SAMI><HEAD><TITLE>NOVA3213</TITLE><STYLE TYPE="text/css">
<!--
P { margin-left: 1pt;
margin-right: 1pt;
margin-bottom: 2pt;
margin-top: 2pt;
text-align: center;
font-size: 10pt;
font-family: Arial;
font-weight: normal;
font-style: normal;
color: #ffeedd; }
.ENCC {Name: English; lang: en-US; SAMI_Type: CC;}
--></STYLE></HEAD><BODY>
<SYNC start="9209"><P class="ENCC">
I <b>do</b> <i>not</i> want to go <u>home</u>.<br />
I don't like it <i><u><b>there</b></u></i>.
</P></SYNC>
<SYNC start="12312"><P class="ENCC"> </P></SYNC>
</BODY></SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_with_css_inline_style():
return """
<SAMI><HEAD><TITLE>NOVA3213</TITLE><STYLE TYPE="text/css">
<!--
P { margin-left: 1pt;
margin-right: 1pt;
margin-bottom: 2pt;
margin-top: 2pt;
text-align: center;
font-size: 10pt;
font-family: Arial;
font-weight: normal;
font-style: normal;
color: #ffeedd; }
.ENCC {Name: English; lang: en-US; SAMI_Type: CC;}
--></STYLE></HEAD><BODY>
<SYNC start="9209"><P class="ENCC">
I <span style="font-weight: bold">do</span> <span style="font-style: italic">not</span> want to go <span style="text-decoration: underline">home</span>.<br />
I don't like it <span style="font-weight:bold;font-style:italic;text-decoration:underline">there</span>.
</P></SYNC>
<SYNC start="12312"><P class="ENCC"> </P></SYNC>
</BODY></SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_with_css_id_style():
return """
<SAMI><HEAD><TITLE>NOVA3213</TITLE><STYLE TYPE="text/css">
<!--
P { margin-left: 1pt;
margin-right: 1pt;
margin-bottom: 2pt;
margin-top: 2pt;
text-align: center;
font-size: 10pt;
font-family: Arial;
font-weight: normal;
font-style: normal;
color: #ffeedd; }
#StyleItalic { font-style: italic; }
#StyleBold { font-weight: bold; }
#StyleUnderline { text-decoration: underline; }
#StyleItalicBoldUnderline { font-style: italic; font-weight: bold; text-decoration: underline; }
.ENCC {Name: English; lang: en-US; SAMI_Type: CC;}
--></STYLE></HEAD><BODY>
<SYNC start="9209"><P class="ENCC" id="StyleItalic">
This is in italics.
</P></SYNC>
<SYNC start="12312"><P class="ENCC"> </P></SYNC>
<SYNC start="14848"><P class="ENCC" id="StyleUnderline">
This is underlined.
</P></SYNC>
<SYNC start="17000"><P class="ENCC" id="StyleBold">
This is bold.
</P></SYNC>
<SYNC start="18752"><P class="ENCC"> </P></SYNC>
<SYNC start="20887"><P class="ENCC" id="StyleItalicBoldUnderline">
This is everything together.
</P></SYNC>
<SYNC start="26760"><P class="ENCC"> </P></SYNC>
</BODY></SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_empty():
return """
<SAMI><HEAD><TITLE>NOVA3213</TITLE><STYLE TYPE="text/css">
<!--
P { margin-left: 1pt;
margin-right: 1pt;
margin-bottom: 2pt;
margin-top: 2pt;
text-align: center;
font-size: 10pt;
font-family: Arial;
font-weight: normal;
font-style: normal;
color: #ffeedd; }
.ENCC {Name: English; lang: en-US; SAMI_Type: CC;}
--></STYLE></HEAD><BODY>
</BODY></SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_syntax_error():
return """
<SAMI>
<Head>
<title>ir2014_111</title>
<STYLE TYPE="text/css">
<!--
P { margin-left: 1pt;
margin-right: 1pt;
margin-bottom: 2pt;
margin-top: 2pt;
text-align: center;
font-size: 10pt;
font-family: Arial;
font-weight: normal;
font-style: normal;
color: #ffffff; }
#Small {Name:SmallTxt; font-family:Arial;font-weight:normal;font-size:10pt;color:#ffffff;}
#Big {Name:BigTxt; font-family:Arial;font-weight:bold;font-size:12pt;color:#ffffff;}
.ENCC {Name:English; lang: en-US; SAMI_Type: CC;}
-->
</Style>
</Head>
<BODY>
<Sync Start=0><P Class=ENCC>
<Sync Start=5905><P Class=ENCC>>>> PRESENTATION OF "IDAHO<br>REPORTS" ON IDAHO PUBLIC
<Sync Start=7073><P Class=ENCC>TELEVISION IS MADE POSSIBLE<br>THROUGH THE GENEROUS SUPPORT OF
</Body>
</SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_double_br():
return """
<SAMI><HEAD><TITLE>NOVA3213</TITLE>
</HEAD><BODY>
<SYNC start="14848"><P class="ENCC">
MAN:<br/><br/>
When we think<br/>
of "E equals m c-squared",
</BODY></SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_partial_margins():
return """
<SAMI>
<HEAD>
<STYLE TYPE="Text/css">
<!--
P {margin-left: 29pt; margin-right: 29pt; font-size: 24pt; text-align: center; font-family: Tahoma; font-weight: bold; color: #FFFFFF; background-color: #000000;}
.SUBTTL {Name: 'Subtitles'; Lang: en-US; SAMIType: CC;}
-->
</STYLE>
</HEAD>
<BODY>
<SYNC START=133>
<P CLASS=SUBTTL>>> COMING UP NEXT, IT IS<br>APPLAUSE AMERICA.
</BODY>
</SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_partial_margins_relativized():
return """<sami>
<head>
<style type="text/css">
<!--
p {
background-color: #000;
color: #ffffff;
font-family: Tahoma;
font-size: 24pt;
font-weight: bold;
margin-bottom: 0%;
margin-left: 6.04%;
margin-right: 6.04%;
margin-top: 0%;
text-align: center;
}
.subttl {
lang: en-US;
margin-bottom: 0%;
margin-left: 6.04%;
margin-right: 6.04%;
margin-top: 0%;
name: "Subtitles";
samitype: CC;
}
-->
</style>
</head>
<body>
<sync start="133">
<p class="subttl" p_style="class:subttl;">
>> COMING UP NEXT, IT IS<br/>
APPLAUSE AMERICA.
</p>
</sync>
</body>
</sami>"""
@pytest.fixture(scope="session")
def sample_sami_lang_margin():
return """
<SAMI>
<HEAD>
<STYLE TYPE="Text/css">
<!--
P {font-size: 24pt; text-align: center; font-family: Tahoma; font-weight: bold; color: #FFFFFF; background-color: #000000;}
.SUBTTL {Name: 'Subtitles'; Lang: en-US; SAMIType: CC; margin-top: 20px; margin-right: 20px; margin-bottom: 20px; margin-left: 20px;}
-->
</STYLE>
</HEAD>
<BODY>
<SYNC START=133>
<P CLASS=SUBTTL>>> COMING UP NEXT, IT IS<br>APPLAUSE AMERICA.
</BODY>
</SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_with_span():
return """
<SAMI>
<HEAD>
<STYLE TYPE="Text/css">
<!--
P {font-size: 24pt; text-align: center; font-family: Tahoma; font-weight: bold; color: #FFFFFF; background-color: #000000;}
.ENCC {Name: 'Subtitles'; Lang: en-US; SAMIType: CC; margin-top: 20px; margin-right: 20px; margin-bottom: 20px; margin-left: 20px;}
-->
</STYLE>
</HEAD>
<BODY>
<SYNC start="133">
<P class="ENCC">
<SPAN Style="font-size:36pt;">we have this vision of Einstein</SPAN>
</P>
</SYNC>
</BODY>
</SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_with_bad_span_align():
return """
<SAMI>
<HEAD>
<STYLE TYPE="Text/css">
<!--
P {font-size: 24pt; text-align: center; font-family: Tahoma; font-weight: bold; color: #FFFFFF; background-color: #000000;}
.ENCC {Name: 'Subtitles'; Lang: en-US; SAMIType: CC; margin-top: 20px; margin-right: 20px; margin-bottom: 20px; margin-left: 20px;}
-->
</STYLE>
</HEAD>
<BODY>
<SYNC start="133">
<P class="ENCC">
Some say <SPAN Style="text-align:right;">we have this vision of Einstein</SPAN> as an old, wrinkly man
</P>
</SYNC>
</BODY>
</SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_with_bad_div_align():
return """
<SAMI>
<HEAD>
<STYLE TYPE="Text/css">
<!--
P {font-size: 24pt; text-align: center; font-family: Tahoma; font-weight: bold; color: #FFFFFF; background-color: #000000;}
.ENCC {Name: 'Subtitles'; Lang: en-US; SAMIType: CC; margin-top: 20px; margin-right: 20px; margin-bottom: 20px; margin-left: 20px;}
-->
</STYLE>
</HEAD>
<BODY>
<SYNC start="133">
<P class="ENCC">
Some say <DIV Style="text-align:right;">we have this vision of Einstein</DIV> as an old, wrinkly man
</P>
</SYNC>
</BODY>
</SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_with_p_align():
return """
<SAMI>
<HEAD>
<STYLE TYPE="Text/css">
<!--
P {font-size: 24pt; text-align: center; font-family: Tahoma; font-weight: bold; color: #FFFFFF; background-color: #000000;}
.ENCC {Name: 'Subtitles'; Lang: en-US; SAMIType: CC; margin-top: 20px; margin-right: 20px; margin-bottom: 20px; margin-left: 20px;}
-->
</STYLE>
</HEAD>
<BODY>
<SYNC start="133">
<P class="ENCC" Style="text-align:right;">
Some say we have this vision of Einstein as an old, wrinkly man
</P>
</SYNC>
</BODY>
</SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_with_p_and_span_align():
return """
<SAMI>
<HEAD>
<STYLE TYPE="Text/css">
<!--
P {font-size: 24pt; text-align: center; font-family: Tahoma; font-weight: bold; color: #FFFFFF; background-color: #000000;}
.ENCC {Name: 'Subtitles'; Lang: en-US; SAMIType: CC; margin-top: 20px; margin-right: 20px; margin-bottom: 20px; margin-left: 20px;}
-->
</STYLE>
</HEAD>
<BODY>
<SYNC start="133">
<P class="ENCC" Style="text-align:right;">
<SPAN Style="text-align:left;">Some say we have this vision of Einstein as an old, wrinkly man</SPAN>
</P>
</SYNC>
</BODY>
</SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_with_multiple_span_aligns():
return """
<SAMI>
<HEAD>
<STYLE TYPE="Text/css">
<!--
P {font-size: 24pt; text-align: center; font-family: Tahoma; font-weight: bold; color: #FFFFFF; background-color: #000000;}
.ENCC {Name: 'Subtitles'; Lang: en-US; SAMIType: CC; margin-top: 20px; margin-right: 20px; margin-bottom: 20px; margin-left: 20px;}
-->
</STYLE>
</HEAD>
<BODY>
<SYNC start="133">
<P class="ENCC">
<SPAN Style="text-align:right">Some say </SPAN>
<SPAN Style="text-align:left;">we have this vision of Einstein </SPAN>
as an old, wrinkly man
</P>
</SYNC>
</BODY>
</SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_no_lang():
return """
<SAMI>
<Head><STYLE TYPE="text/css"></Style></Head>
<BODY>
<Sync Start=0><P Class=ENCC></p></sync>
<Sync Start=1301><P Class=ENCC>>> FUNDING FOR OVERHEARD</p></sync>
</Body>
</SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_with_lang():
return """
<sami>
<head>
<style type="text/css"><!--.en-US {lang: en-US;}--></style>
</head>
<body>
<sync start="1301"><p class="en-US">>> FUNDING FOR OVERHEARD</p></sync>
</body>
</sami>
"""
@pytest.fixture(scope="session")
def sample_sami_with_multi_lang():
return """
<sami>
<head>
<style type="text/css"><!--.en-US {lang: en-US;} .de-DE {lang: de-DE;}--></style>
</head>
<body>
<sync start="14848">
<p class="en-US">Butterfly.</p>
<p class="de-DE">Schmetterling.</p>
</sync>
</body>
</sami>
"""
@pytest.fixture(scope="session")
def sample_sami_with_multiple_p():
return """
<SAMI>
<HEAD>
<STYLE TYPE="Text/css">
<!--
P {font-size: 24pt; text-align: center; font-family: Tahoma; font-weight: bold; color: #FFFFFF; background-color: #000000;}
.ENCC {Name: 'Subtitles'; Lang: en-US; SAMIType: CC; margin-top: 20px; margin-right: 20px; margin-bottom: 20px; margin-left: 20px;}
-->
</STYLE>
</HEAD>
<BODY>
<SYNC start="133">
<P class="ENCC" Style="text-align:right;">
1st paragraph.
</P>
<P class="ENCC" Style="text-align:left;">
2nd paragraph.
</P>
</SYNC>
<SYNC start="1337">
<P class="ENCC" Style="text-align:right;">
3rd paragraph.
</P>
</SYNC>
</BODY>
</SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_empty_cue_output():
return """
<sami>
<head>
<style type="text/css">
<!--
.en-US {
lang: en-US;
}
-->
</style>
</head>
<body>
<sync start="1209">
<p class="en-US">
abc
</p>
</sync>
</body>
</sami>
"""
@pytest.fixture(scope="session")
def sample_sami_with_invalid_inline_style():
return """
<SAMI><HEAD>
<STYLE TYPE="text/css">
<!--
.ENCC {Name: 'Subtitles'; Lang: en-US; SAMIType: CC; margin-top: 20px; margin-right: 20px; margin-bottom: 20px; margin-left: 20px;}
-->
</STYLE></HEAD>
<BODY>
<SYNC start="133">
<P class="ENCC" Style="text-align:right:font-style:italic">
Some say we have this vision of Einstein as an old, wrinkly man
</P>
</SYNC>
</BODY></SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_including_hexadecimal_charref():
return """
<SAMI><HEAD><STYLE TYPE="text/css">
<!--
.ENCC {Name: English; lang: en-US; SAMI_Type: CC;}
--></STYLE></HEAD><BODY>
<SYNC start="101"><P class="ENCC">> ></P></SYNC>
</BODY></SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_including_decimal_charref():
return """
<SAMI><HEAD><STYLE TYPE="text/css">
<!--
.ENCC {Name: English; lang: en-US; SAMI_Type: CC;}
--></STYLE></HEAD><BODY>
<SYNC start="101"><P class="ENCC">> ></P></SYNC>
</BODY></SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_including_html5_entityref():
return """
<SAMI><HEAD><STYLE TYPE="text/css">
<!--
.ENCC {Name: English; lang: en-US; SAMI_Type: CC;}
--></STYLE></HEAD><BODY>
<SYNC start="1301"><P class="ENCC">★_★</P></SYNC>
</BODY></SAMI>
"""
@pytest.fixture(scope="session")
def sample_sami_with_unclosed_tag():
return """
<SAMI><HEAD><STYLE TYPE="text/css">
<!--
.ENCC {Name: English; lang: en-US; SAMI_Type: CC;}
--></STYLE></HEAD><BODY>
<SYNC start="1101"><P class="ENCC">.</P></SYNC>
</BODY>
"""
@pytest.fixture(scope="session")
def sample_sami_with_inline_lang():
return """
<SAMI><HEAD></HEAD><BODY>
<SYNC start="1201"><P lang="en-US">Inlined.</P></SYNC>
</BODY></SAMI>
"""
# we do not seem to support nested spans, update this if fixed.
@pytest.fixture(scope="session")
def sample_sami_from_dfxp_with_nested_spans():
return """<sami>
<head>
<style type="text/css">
<!--
.s1 {
font-style: italic;
}
.s2 {
font-weight: bold;
}
.s3 {
text-decoration: underline;
}
.en-US {
lang: en-US;
}
-->
</style>
</head>
<body>
<sync start="3209">
<p class="en-US">
That is <span class="s3" style="classes:['s3'];class:s3;"></span> <span class="s2" style="classes:['s2'];class:s2;"></span> <span class="s1" style="classes:['s1'];class:s1;">nested</span> .
</p>
</sync>
</body>
</sami>"""
@pytest.fixture(scope="session")
def sample_sami_with_separate_multi_lang():
return """<sami>
<head>
<style type="text/css">
<!--
.en-UK {
lang: en-UK;
}
.en-US {
lang: en-US;
}
-->
</style>
</head>
<body>
<sync start="1209">
<p class="en-UK">
British text.
</p>
</sync>
<sync start="3209">
<p class="en-US">
English text.
</p>
</sync>
<sync start="7209">
<p class="en-UK">
OTHER British text.
</p>
</sync>
</body>
</sami>
"""
| 23.70977 | 194 | 0.60423 | 2,268 | 16,502 | 4.339947 | 0.102734 | 0.030479 | 0.039622 | 0.073656 | 0.826476 | 0.796505 | 0.783501 | 0.757797 | 0.711064 | 0.691049 | 0 | 0.034748 | 0.187311 | 16,502 | 695 | 195 | 23.743885 | 0.699202 | 0.003697 | 0 | 0.764228 | 0 | 0.055285 | 0.853154 | 0.112659 | 0 | 0 | 0 | 0 | 0 | 1 | 0.047154 | true | 0 | 0.001626 | 0.047154 | 0.095935 | 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 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
0ae082b2d14f901adc6880f13972ae791c962e87 | 5,534 | py | Python | imperative/python/megengine/functional/quantized.py | yang-shuohao/MegEngine | 2e8742086563ea442c357b14560245c54e0aa0a3 | [
"Apache-2.0"
] | 1 | 2020-12-11T04:08:25.000Z | 2020-12-11T04:08:25.000Z | imperative/python/megengine/functional/quantized.py | yang-shuohao/MegEngine | 2e8742086563ea442c357b14560245c54e0aa0a3 | [
"Apache-2.0"
] | null | null | null | imperative/python/megengine/functional/quantized.py | yang-shuohao/MegEngine | 2e8742086563ea442c357b14560245c54e0aa0a3 | [
"Apache-2.0"
] | null | null | null | # MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# pylint: disable=too-many-lines
from typing import Tuple, Union
from ..core.ops import builtin
from ..core.tensor.core import apply
from ..tensor import Tensor
from .debug_param import get_conv_execution_strategy
from .types import _pair, _pair_nonzero
def conv_bias_activation(
inp: Tensor,
weight: Tensor,
bias: Tensor,
dtype=None,
stride: Union[int, Tuple[int, int]] = 1,
padding: Union[int, Tuple[int, int]] = 0,
dilation: Union[int, Tuple[int, int]] = 1,
groups: int = 1,
nonlinear_mode="IDENTITY",
conv_mode="CROSS_CORRELATION",
compute_mode="DEFAULT",
) -> Tensor:
"""
Convolution bias with activation operation, only for inference.
:param inp: feature map of the convolution operation.
:param weight: convolution kernel.
:param bias: bias added to the result of convolution
:param stride: stride of the 2D convolution operation. Default: 1
:param padding: size of the paddings added to the input on both sides of its spatial dimensions. Only zero-padding is supported. Default: 0
:param dilation: dilation of the 2D convolution operation. Default: 1
:param groups: number of groups into which the input and output channels are divided, so as to perform a "grouped convolution". When ``groups`` is not 1,
``in_channels`` and ``out_channels`` must be divisible by ``groups``,
and the shape of weight should be `(groups, out_channel // groups,
in_channels // groups, height, width)`.
:type conv_mode: string or :class:`P.Convolution.Mode`.
:param conv_mode: supports 'CROSS_CORRELATION' or 'CONVOLUTION'. Default:
'CROSS_CORRELATION'
:param dtype: support for ``np.dtype``, Default: np.int8
:type compute_mode: string or
:class:`P.Convolution.ComputeMode`.
:param compute_mode: when set to "DEFAULT", no special requirements will be
placed on the precision of intermediate results. When set to "FLOAT32",
"Float32" would be used for accumulator and intermediate result, but only effective when input and output are of Float16 dtype.
"""
ph, pw = _pair(padding)
sh, sw = _pair_nonzero(stride)
dh, dw = _pair_nonzero(dilation)
sparse_type = "DENSE" if groups == 1 else "GROUP"
op = builtin.ConvBiasForward(
stride_h=sh,
stride_w=sw,
pad_h=ph,
pad_w=pw,
dilate_h=dh,
dilate_w=dw,
dtype=dtype,
format="NCHW",
strategy=get_conv_execution_strategy(),
nonlineMode=nonlinear_mode,
mode=conv_mode,
compute_mode=compute_mode,
sparse=sparse_type,
)
(outputs,) = apply(op, inp, weight, bias)
return outputs
def batch_conv_bias_activation(
inp: Tensor,
weight: Tensor,
bias: Tensor,
dtype=None,
stride: Union[int, Tuple[int, int]] = 1,
padding: Union[int, Tuple[int, int]] = 0,
dilation: Union[int, Tuple[int, int]] = 1,
groups: int = 1,
nonlinear_mode="IDENTITY",
conv_mode="CROSS_CORRELATION",
compute_mode="DEFAULT",
) -> Tensor:
"""
Batch convolution bias with activation operation, only for inference.
:param inp: feature map of the convolution operation.
:param weight: convolution kernel in batched way.
:param bias: bias added to the result of convolution
:param stride: stride of the 2D convolution operation. Default: 1
:param padding: size of the paddings added to the input on both sides of its spatial dimensions. Only zero-padding is supported. Default: 0
:param dilation: dilation of the 2D convolution operation. Default: 1
:param groups: number of groups into which the input and output channels are divided, so as to perform a "grouped convolution". When ``groups`` is not 1,
``in_channels`` and ``out_channels`` must be divisible by ``groups``,
and the shape of weight should be `(groups, out_channel // groups,
in_channels // groups, height, width)`.
:type conv_mode: string or :class:`P.Convolution.Mode`.
:param conv_mode: supports 'CROSS_CORRELATION' or 'CONVOLUTION'. Default:
'CROSS_CORRELATION'
:param dtype: support for ``np.dtype``, Default: np.int8
:type compute_mode: string or
:class:`P.Convolution.ComputeMode`.
:param compute_mode: when set to "DEFAULT", no special requirements will be
placed on the precision of intermediate results. When set to "FLOAT32",
"Float32" would be used for accumulator and intermediate result, but only effective when input and output are of Float16 dtype.
"""
ph, pw = _pair(padding)
sh, sw = _pair_nonzero(stride)
dh, dw = _pair_nonzero(dilation)
sparse_type = "DENSE" if groups == 1 else "GROUP"
op = builtin.BatchConvBiasForward(
stride_h=sh,
stride_w=sw,
pad_h=ph,
pad_w=pw,
dilate_h=dh,
dilate_w=dw,
dtype=dtype,
format="NCHW",
strategy=get_conv_execution_strategy(),
nonlineMode=nonlinear_mode,
mode=conv_mode,
compute_mode=compute_mode,
sparse=sparse_type,
)
(outputs,) = apply(op, inp, weight, bias)
return outputs
| 40.691176 | 157 | 0.683231 | 750 | 5,534 | 4.934667 | 0.245333 | 0.029722 | 0.021075 | 0.025939 | 0.855985 | 0.855985 | 0.855985 | 0.855985 | 0.855985 | 0.855985 | 0 | 0.010715 | 0.22425 | 5,534 | 135 | 158 | 40.992593 | 0.851386 | 0.573365 | 0 | 0.837838 | 0 | 0 | 0.042416 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.027027 | false | 0 | 0.081081 | 0 | 0.135135 | 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 |
7c2057844a5555b2f01bf3d4b4b32866167f0bde | 35 | py | Python | dags/save_to_bigquery.py | chilldenaya/letsgobali | 145eedcdd3e658eb929b75f96e174d8d7c5e0384 | [
"Apache-2.0"
] | null | null | null | dags/save_to_bigquery.py | chilldenaya/letsgobali | 145eedcdd3e658eb929b75f96e174d8d7c5e0384 | [
"Apache-2.0"
] | null | null | null | dags/save_to_bigquery.py | chilldenaya/letsgobali | 145eedcdd3e658eb929b75f96e174d8d7c5e0384 | [
"Apache-2.0"
] | null | null | null | def save_to_bigquery():
return
| 11.666667 | 23 | 0.714286 | 5 | 35 | 4.6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 35 | 2 | 24 | 17.5 | 0.821429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0.5 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 7 |
7c55598e5862d21666581317b1f9b27c30db2fc2 | 159,260 | py | Python | argo/workflows/client/api/workflow_service_api.py | argentumcode/argo-client-python | 31c1519056379d3f046d4b522f37af87243fdbb4 | [
"Apache-2.0"
] | null | null | null | argo/workflows/client/api/workflow_service_api.py | argentumcode/argo-client-python | 31c1519056379d3f046d4b522f37af87243fdbb4 | [
"Apache-2.0"
] | null | null | null | argo/workflows/client/api/workflow_service_api.py | argentumcode/argo-client-python | 31c1519056379d3f046d4b522f37af87243fdbb4 | [
"Apache-2.0"
] | null | null | null | # coding: utf-8
"""
Argo Server API
You can get examples of requests and responses by using the CLI with `--gloglevel=9`, e.g. `argo list --gloglevel=9` # noqa: E501
The version of the OpenAPI document: v3.0.4
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from argo.workflows.client.api_client import ApiClient
from argo.workflows.client.exceptions import ( # noqa: F401
ApiTypeError,
ApiValueError
)
class WorkflowServiceApi(object):
"""NOTE: This class is auto generated by OpenAPI Generator
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def create_workflow(self, namespace, body, **kwargs): # noqa: E501
"""create_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.create_workflow(namespace, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param V1alpha1WorkflowCreateRequest body: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1alpha1Workflow
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.create_workflow_with_http_info(namespace, body, **kwargs) # noqa: E501
def create_workflow_with_http_info(self, namespace, body, **kwargs): # noqa: E501
"""create_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.create_workflow_with_http_info(namespace, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param V1alpha1WorkflowCreateRequest body: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1alpha1Workflow, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'body'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method create_workflow" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `create_workflow`") # noqa: E501
# verify the required parameter 'body' is set
if self.api_client.client_side_validation and ('body' not in local_var_params or # noqa: E501
local_var_params['body'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `body` when calling `create_workflow`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1alpha1Workflow', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def delete_workflow(self, namespace, name, **kwargs): # noqa: E501
"""delete_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.delete_workflow(namespace, name, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param str delete_options_grace_period_seconds: The duration in seconds before the object should be deleted. Value must be non-negative integer. The value zero indicates delete immediately. If this value is nil, the default grace period for the specified type will be used. Defaults to a per object value if not specified. zero means delete immediately. +optional.
:param str delete_options_preconditions_uid: Specifies the target UID. +optional.
:param str delete_options_preconditions_resource_version: Specifies the target ResourceVersion +optional.
:param bool delete_options_orphan_dependents: Deprecated: please use the PropagationPolicy, this field will be deprecated in 1.7. Should the dependent objects be orphaned. If true/false, the \"orphan\" finalizer will be added to/removed from the object's finalizers list. Either this field or PropagationPolicy may be set, but not both. +optional.
:param str delete_options_propagation_policy: Whether and how garbage collection will be performed. Either this field or OrphanDependents may be set, but not both. The default policy is decided by the existing finalizer set in the metadata.finalizers and the resource-specific default policy. Acceptable values are: 'Orphan' - orphan the dependents; 'Background' - allow the garbage collector to delete the dependents in the background; 'Foreground' - a cascading policy that deletes all dependents in the foreground. +optional.
:param list[str] delete_options_dry_run: When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed +optional.
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: object
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.delete_workflow_with_http_info(namespace, name, **kwargs) # noqa: E501
def delete_workflow_with_http_info(self, namespace, name, **kwargs): # noqa: E501
"""delete_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.delete_workflow_with_http_info(namespace, name, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param str delete_options_grace_period_seconds: The duration in seconds before the object should be deleted. Value must be non-negative integer. The value zero indicates delete immediately. If this value is nil, the default grace period for the specified type will be used. Defaults to a per object value if not specified. zero means delete immediately. +optional.
:param str delete_options_preconditions_uid: Specifies the target UID. +optional.
:param str delete_options_preconditions_resource_version: Specifies the target ResourceVersion +optional.
:param bool delete_options_orphan_dependents: Deprecated: please use the PropagationPolicy, this field will be deprecated in 1.7. Should the dependent objects be orphaned. If true/false, the \"orphan\" finalizer will be added to/removed from the object's finalizers list. Either this field or PropagationPolicy may be set, but not both. +optional.
:param str delete_options_propagation_policy: Whether and how garbage collection will be performed. Either this field or OrphanDependents may be set, but not both. The default policy is decided by the existing finalizer set in the metadata.finalizers and the resource-specific default policy. Acceptable values are: 'Orphan' - orphan the dependents; 'Background' - allow the garbage collector to delete the dependents in the background; 'Foreground' - a cascading policy that deletes all dependents in the foreground. +optional.
:param list[str] delete_options_dry_run: When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed +optional.
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(object, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'name',
'delete_options_grace_period_seconds',
'delete_options_preconditions_uid',
'delete_options_preconditions_resource_version',
'delete_options_orphan_dependents',
'delete_options_propagation_policy',
'delete_options_dry_run'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_workflow" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `delete_workflow`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `delete_workflow`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
query_params = []
if 'delete_options_grace_period_seconds' in local_var_params and local_var_params['delete_options_grace_period_seconds'] is not None: # noqa: E501
query_params.append(('deleteOptions.gracePeriodSeconds', local_var_params['delete_options_grace_period_seconds'])) # noqa: E501
if 'delete_options_preconditions_uid' in local_var_params and local_var_params['delete_options_preconditions_uid'] is not None: # noqa: E501
query_params.append(('deleteOptions.preconditions.uid', local_var_params['delete_options_preconditions_uid'])) # noqa: E501
if 'delete_options_preconditions_resource_version' in local_var_params and local_var_params['delete_options_preconditions_resource_version'] is not None: # noqa: E501
query_params.append(('deleteOptions.preconditions.resourceVersion', local_var_params['delete_options_preconditions_resource_version'])) # noqa: E501
if 'delete_options_orphan_dependents' in local_var_params and local_var_params['delete_options_orphan_dependents'] is not None: # noqa: E501
query_params.append(('deleteOptions.orphanDependents', local_var_params['delete_options_orphan_dependents'])) # noqa: E501
if 'delete_options_propagation_policy' in local_var_params and local_var_params['delete_options_propagation_policy'] is not None: # noqa: E501
query_params.append(('deleteOptions.propagationPolicy', local_var_params['delete_options_propagation_policy'])) # noqa: E501
if 'delete_options_dry_run' in local_var_params and local_var_params['delete_options_dry_run'] is not None: # noqa: E501
query_params.append(('deleteOptions.dryRun', local_var_params['delete_options_dry_run'])) # noqa: E501
collection_formats['deleteOptions.dryRun'] = 'multi' # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/{name}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='object', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def get_workflow(self, namespace, name, **kwargs): # noqa: E501
"""get_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.get_workflow(namespace, name, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param str get_options_resource_version: resourceVersion sets a constraint on what resource versions a request may be served from. See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str fields: Fields to be included or excluded in the response. e.g. \"spec,status.phase\", \"-status.nodes\".
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1alpha1Workflow
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.get_workflow_with_http_info(namespace, name, **kwargs) # noqa: E501
def get_workflow_with_http_info(self, namespace, name, **kwargs): # noqa: E501
"""get_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.get_workflow_with_http_info(namespace, name, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param str get_options_resource_version: resourceVersion sets a constraint on what resource versions a request may be served from. See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str fields: Fields to be included or excluded in the response. e.g. \"spec,status.phase\", \"-status.nodes\".
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1alpha1Workflow, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'name',
'get_options_resource_version',
'fields'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method get_workflow" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `get_workflow`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `get_workflow`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
query_params = []
if 'get_options_resource_version' in local_var_params and local_var_params['get_options_resource_version'] is not None: # noqa: E501
query_params.append(('getOptions.resourceVersion', local_var_params['get_options_resource_version'])) # noqa: E501
if 'fields' in local_var_params and local_var_params['fields'] is not None: # noqa: E501
query_params.append(('fields', local_var_params['fields'])) # 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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/{name}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1alpha1Workflow', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def lint_workflow(self, namespace, body, **kwargs): # noqa: E501
"""lint_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.lint_workflow(namespace, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param V1alpha1WorkflowLintRequest body: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1alpha1Workflow
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.lint_workflow_with_http_info(namespace, body, **kwargs) # noqa: E501
def lint_workflow_with_http_info(self, namespace, body, **kwargs): # noqa: E501
"""lint_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.lint_workflow_with_http_info(namespace, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param V1alpha1WorkflowLintRequest body: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1alpha1Workflow, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'body'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method lint_workflow" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `lint_workflow`") # noqa: E501
# verify the required parameter 'body' is set
if self.api_client.client_side_validation and ('body' not in local_var_params or # noqa: E501
local_var_params['body'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `body` when calling `lint_workflow`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/lint', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1alpha1Workflow', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def list_workflows(self, namespace, **kwargs): # noqa: E501
"""list_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.list_workflows(namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str list_options_label_selector: A selector to restrict the list of returned objects by their labels. Defaults to everything. +optional.
:param str list_options_field_selector: A selector to restrict the list of returned objects by their fields. Defaults to everything. +optional.
:param bool list_options_watch: Watch for changes to the described resources and return them as a stream of add, update, and remove notifications. Specify resourceVersion. +optional.
:param bool list_options_allow_watch_bookmarks: allowWatchBookmarks requests watch events with type \"BOOKMARK\". Servers that do not implement bookmarks may ignore this flag and bookmarks are sent at the server's discretion. Clients should not assume bookmarks are returned at any specific interval, nor may they assume the server will send any BOOKMARK event during a session. If this is not a watch, this field is ignored. If the feature gate WatchBookmarks is not enabled in apiserver, this field is ignored. +optional.
:param str list_options_resource_version: resourceVersion sets a constraint on what resource versions a request may be served from. See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str list_options_resource_version_match: resourceVersionMatch determines how resourceVersion is applied to list calls. It is highly recommended that resourceVersionMatch be set for list calls where resourceVersion is set See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str list_options_timeout_seconds: Timeout for the list/watch call. This limits the duration of the call, regardless of any activity or inactivity. +optional.
:param str list_options_limit: limit is a maximum number of responses to return for a list call. If more items exist, the server will set the `continue` field on the list metadata to a value that can be used with the same initial query to retrieve the next set of results. Setting a limit may return fewer than the requested amount of items (up to zero items) in the event all requested objects are filtered out and clients should only use the presence of the continue field to determine whether more results are available. Servers may choose not to support the limit argument and will return all of the available results. If limit is specified and the continue field is empty, clients may assume that no more results are available. This field is not supported if watch is true. The server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects. If objects are updated during a chunked list the version of the object that was present at the time the first list result was calculated is returned.
:param str list_options_continue: The continue option should be set when retrieving more results from the server. Since this value is server defined, clients may only use the continue value from a previous query result with identical query parameters (except for the value of continue) and the server may reject a continue value it does not recognize. If the specified continue value is no longer valid whether due to expiration (generally five to fifteen minutes) or a configuration change on the server, the server will respond with a 410 ResourceExpired error together with a continue token. If the client needs a consistent list, it must restart their list without the continue field. Otherwise, the client may send another list request with the token received with the 410 error, the server will respond with a list starting from the next key, but from the latest snapshot, which is inconsistent from the previous list results - objects that are created, modified, or deleted after the first list request will be included in the response, as long as their keys are after the \"next key\". This field is not supported when watch is true. Clients may start a watch from the last resourceVersion value returned by the server and not miss any modifications.
:param str fields: Fields to be included or excluded in the response. e.g. \"items.spec,items.status.phase\", \"-items.status.nodes\".
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1alpha1WorkflowList
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.list_workflows_with_http_info(namespace, **kwargs) # noqa: E501
def list_workflows_with_http_info(self, namespace, **kwargs): # noqa: E501
"""list_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.list_workflows_with_http_info(namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str list_options_label_selector: A selector to restrict the list of returned objects by their labels. Defaults to everything. +optional.
:param str list_options_field_selector: A selector to restrict the list of returned objects by their fields. Defaults to everything. +optional.
:param bool list_options_watch: Watch for changes to the described resources and return them as a stream of add, update, and remove notifications. Specify resourceVersion. +optional.
:param bool list_options_allow_watch_bookmarks: allowWatchBookmarks requests watch events with type \"BOOKMARK\". Servers that do not implement bookmarks may ignore this flag and bookmarks are sent at the server's discretion. Clients should not assume bookmarks are returned at any specific interval, nor may they assume the server will send any BOOKMARK event during a session. If this is not a watch, this field is ignored. If the feature gate WatchBookmarks is not enabled in apiserver, this field is ignored. +optional.
:param str list_options_resource_version: resourceVersion sets a constraint on what resource versions a request may be served from. See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str list_options_resource_version_match: resourceVersionMatch determines how resourceVersion is applied to list calls. It is highly recommended that resourceVersionMatch be set for list calls where resourceVersion is set See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str list_options_timeout_seconds: Timeout for the list/watch call. This limits the duration of the call, regardless of any activity or inactivity. +optional.
:param str list_options_limit: limit is a maximum number of responses to return for a list call. If more items exist, the server will set the `continue` field on the list metadata to a value that can be used with the same initial query to retrieve the next set of results. Setting a limit may return fewer than the requested amount of items (up to zero items) in the event all requested objects are filtered out and clients should only use the presence of the continue field to determine whether more results are available. Servers may choose not to support the limit argument and will return all of the available results. If limit is specified and the continue field is empty, clients may assume that no more results are available. This field is not supported if watch is true. The server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects. If objects are updated during a chunked list the version of the object that was present at the time the first list result was calculated is returned.
:param str list_options_continue: The continue option should be set when retrieving more results from the server. Since this value is server defined, clients may only use the continue value from a previous query result with identical query parameters (except for the value of continue) and the server may reject a continue value it does not recognize. If the specified continue value is no longer valid whether due to expiration (generally five to fifteen minutes) or a configuration change on the server, the server will respond with a 410 ResourceExpired error together with a continue token. If the client needs a consistent list, it must restart their list without the continue field. Otherwise, the client may send another list request with the token received with the 410 error, the server will respond with a list starting from the next key, but from the latest snapshot, which is inconsistent from the previous list results - objects that are created, modified, or deleted after the first list request will be included in the response, as long as their keys are after the \"next key\". This field is not supported when watch is true. Clients may start a watch from the last resourceVersion value returned by the server and not miss any modifications.
:param str fields: Fields to be included or excluded in the response. e.g. \"items.spec,items.status.phase\", \"-items.status.nodes\".
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1alpha1WorkflowList, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'list_options_label_selector',
'list_options_field_selector',
'list_options_watch',
'list_options_allow_watch_bookmarks',
'list_options_resource_version',
'list_options_resource_version_match',
'list_options_timeout_seconds',
'list_options_limit',
'list_options_continue',
'fields'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method list_workflows" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `list_workflows`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
query_params = []
if 'list_options_label_selector' in local_var_params and local_var_params['list_options_label_selector'] is not None: # noqa: E501
query_params.append(('listOptions.labelSelector', local_var_params['list_options_label_selector'])) # noqa: E501
if 'list_options_field_selector' in local_var_params and local_var_params['list_options_field_selector'] is not None: # noqa: E501
query_params.append(('listOptions.fieldSelector', local_var_params['list_options_field_selector'])) # noqa: E501
if 'list_options_watch' in local_var_params and local_var_params['list_options_watch'] is not None: # noqa: E501
query_params.append(('listOptions.watch', local_var_params['list_options_watch'])) # noqa: E501
if 'list_options_allow_watch_bookmarks' in local_var_params and local_var_params['list_options_allow_watch_bookmarks'] is not None: # noqa: E501
query_params.append(('listOptions.allowWatchBookmarks', local_var_params['list_options_allow_watch_bookmarks'])) # noqa: E501
if 'list_options_resource_version' in local_var_params and local_var_params['list_options_resource_version'] is not None: # noqa: E501
query_params.append(('listOptions.resourceVersion', local_var_params['list_options_resource_version'])) # noqa: E501
if 'list_options_resource_version_match' in local_var_params and local_var_params['list_options_resource_version_match'] is not None: # noqa: E501
query_params.append(('listOptions.resourceVersionMatch', local_var_params['list_options_resource_version_match'])) # noqa: E501
if 'list_options_timeout_seconds' in local_var_params and local_var_params['list_options_timeout_seconds'] is not None: # noqa: E501
query_params.append(('listOptions.timeoutSeconds', local_var_params['list_options_timeout_seconds'])) # noqa: E501
if 'list_options_limit' in local_var_params and local_var_params['list_options_limit'] is not None: # noqa: E501
query_params.append(('listOptions.limit', local_var_params['list_options_limit'])) # noqa: E501
if 'list_options_continue' in local_var_params and local_var_params['list_options_continue'] is not None: # noqa: E501
query_params.append(('listOptions.continue', local_var_params['list_options_continue'])) # noqa: E501
if 'fields' in local_var_params and local_var_params['fields'] is not None: # noqa: E501
query_params.append(('fields', local_var_params['fields'])) # 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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1alpha1WorkflowList', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def pod_logs(self, namespace, name, pod_name, **kwargs): # noqa: E501
"""DEPRECATED: Cannot work via HTTP if podName is an empty string. Use WorkflowLogs. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.pod_logs(namespace, name, pod_name, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param str pod_name: (required)
:param str log_options_container: The container for which to stream logs. Defaults to only container if there is one container in the pod. +optional.
:param bool log_options_follow: Follow the log stream of the pod. Defaults to false. +optional.
:param bool log_options_previous: Return previous terminated container logs. Defaults to false. +optional.
:param str log_options_since_seconds: A relative time in seconds before the current time from which to show logs. If this value precedes the time a pod was started, only logs since the pod start will be returned. If this value is in the future, no logs will be returned. Only one of sinceSeconds or sinceTime may be specified. +optional.
:param str log_options_since_time_seconds: Represents seconds of UTC time since Unix epoch 1970-01-01T00:00:00Z. Must be from 0001-01-01T00:00:00Z to 9999-12-31T23:59:59Z inclusive.
:param int log_options_since_time_nanos: Non-negative fractions of a second at nanosecond resolution. Negative second values with fractions must still have non-negative nanos values that count forward in time. Must be from 0 to 999,999,999 inclusive. This field may be limited in precision depending on context.
:param bool log_options_timestamps: If true, add an RFC3339 or RFC3339Nano timestamp at the beginning of every line of log output. Defaults to false. +optional.
:param str log_options_tail_lines: If set, the number of lines from the end of the logs to show. If not specified, logs are shown from the creation of the container or sinceSeconds or sinceTime +optional.
:param str log_options_limit_bytes: If set, the number of bytes to read from the server before terminating the log output. This may not display a complete final line of logging, and may return slightly more or slightly less than the specified limit. +optional.
:param bool log_options_insecure_skip_tls_verify_backend: insecureSkipTLSVerifyBackend indicates that the apiserver should not confirm the validity of the serving certificate of the backend it is connecting to. This will make the HTTPS connection between the apiserver and the backend insecure. This means the apiserver cannot verify the log data it is receiving came from the real kubelet. If the kubelet is configured to verify the apiserver's TLS credentials, it does not mean the connection to the real kubelet is vulnerable to a man in the middle attack (e.g. an attacker could not intercept the actual log data coming from the real kubelet). +optional.
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: StreamResultOfIoArgoprojWorkflowV1alpha1LogEntry
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.pod_logs_with_http_info(namespace, name, pod_name, **kwargs) # noqa: E501
def pod_logs_with_http_info(self, namespace, name, pod_name, **kwargs): # noqa: E501
"""DEPRECATED: Cannot work via HTTP if podName is an empty string. Use WorkflowLogs. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.pod_logs_with_http_info(namespace, name, pod_name, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param str pod_name: (required)
:param str log_options_container: The container for which to stream logs. Defaults to only container if there is one container in the pod. +optional.
:param bool log_options_follow: Follow the log stream of the pod. Defaults to false. +optional.
:param bool log_options_previous: Return previous terminated container logs. Defaults to false. +optional.
:param str log_options_since_seconds: A relative time in seconds before the current time from which to show logs. If this value precedes the time a pod was started, only logs since the pod start will be returned. If this value is in the future, no logs will be returned. Only one of sinceSeconds or sinceTime may be specified. +optional.
:param str log_options_since_time_seconds: Represents seconds of UTC time since Unix epoch 1970-01-01T00:00:00Z. Must be from 0001-01-01T00:00:00Z to 9999-12-31T23:59:59Z inclusive.
:param int log_options_since_time_nanos: Non-negative fractions of a second at nanosecond resolution. Negative second values with fractions must still have non-negative nanos values that count forward in time. Must be from 0 to 999,999,999 inclusive. This field may be limited in precision depending on context.
:param bool log_options_timestamps: If true, add an RFC3339 or RFC3339Nano timestamp at the beginning of every line of log output. Defaults to false. +optional.
:param str log_options_tail_lines: If set, the number of lines from the end of the logs to show. If not specified, logs are shown from the creation of the container or sinceSeconds or sinceTime +optional.
:param str log_options_limit_bytes: If set, the number of bytes to read from the server before terminating the log output. This may not display a complete final line of logging, and may return slightly more or slightly less than the specified limit. +optional.
:param bool log_options_insecure_skip_tls_verify_backend: insecureSkipTLSVerifyBackend indicates that the apiserver should not confirm the validity of the serving certificate of the backend it is connecting to. This will make the HTTPS connection between the apiserver and the backend insecure. This means the apiserver cannot verify the log data it is receiving came from the real kubelet. If the kubelet is configured to verify the apiserver's TLS credentials, it does not mean the connection to the real kubelet is vulnerable to a man in the middle attack (e.g. an attacker could not intercept the actual log data coming from the real kubelet). +optional.
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(StreamResultOfIoArgoprojWorkflowV1alpha1LogEntry, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'name',
'pod_name',
'log_options_container',
'log_options_follow',
'log_options_previous',
'log_options_since_seconds',
'log_options_since_time_seconds',
'log_options_since_time_nanos',
'log_options_timestamps',
'log_options_tail_lines',
'log_options_limit_bytes',
'log_options_insecure_skip_tls_verify_backend'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method pod_logs" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `pod_logs`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `pod_logs`") # noqa: E501
# verify the required parameter 'pod_name' is set
if self.api_client.client_side_validation and ('pod_name' not in local_var_params or # noqa: E501
local_var_params['pod_name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `pod_name` when calling `pod_logs`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
if 'pod_name' in local_var_params:
path_params['podName'] = local_var_params['pod_name'] # noqa: E501
query_params = []
if 'log_options_container' in local_var_params and local_var_params['log_options_container'] is not None: # noqa: E501
query_params.append(('logOptions.container', local_var_params['log_options_container'])) # noqa: E501
if 'log_options_follow' in local_var_params and local_var_params['log_options_follow'] is not None: # noqa: E501
query_params.append(('logOptions.follow', local_var_params['log_options_follow'])) # noqa: E501
if 'log_options_previous' in local_var_params and local_var_params['log_options_previous'] is not None: # noqa: E501
query_params.append(('logOptions.previous', local_var_params['log_options_previous'])) # noqa: E501
if 'log_options_since_seconds' in local_var_params and local_var_params['log_options_since_seconds'] is not None: # noqa: E501
query_params.append(('logOptions.sinceSeconds', local_var_params['log_options_since_seconds'])) # noqa: E501
if 'log_options_since_time_seconds' in local_var_params and local_var_params['log_options_since_time_seconds'] is not None: # noqa: E501
query_params.append(('logOptions.sinceTime.seconds', local_var_params['log_options_since_time_seconds'])) # noqa: E501
if 'log_options_since_time_nanos' in local_var_params and local_var_params['log_options_since_time_nanos'] is not None: # noqa: E501
query_params.append(('logOptions.sinceTime.nanos', local_var_params['log_options_since_time_nanos'])) # noqa: E501
if 'log_options_timestamps' in local_var_params and local_var_params['log_options_timestamps'] is not None: # noqa: E501
query_params.append(('logOptions.timestamps', local_var_params['log_options_timestamps'])) # noqa: E501
if 'log_options_tail_lines' in local_var_params and local_var_params['log_options_tail_lines'] is not None: # noqa: E501
query_params.append(('logOptions.tailLines', local_var_params['log_options_tail_lines'])) # noqa: E501
if 'log_options_limit_bytes' in local_var_params and local_var_params['log_options_limit_bytes'] is not None: # noqa: E501
query_params.append(('logOptions.limitBytes', local_var_params['log_options_limit_bytes'])) # noqa: E501
if 'log_options_insecure_skip_tls_verify_backend' in local_var_params and local_var_params['log_options_insecure_skip_tls_verify_backend'] is not None: # noqa: E501
query_params.append(('logOptions.insecureSkipTLSVerifyBackend', local_var_params['log_options_insecure_skip_tls_verify_backend'])) # 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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/{name}/{podName}/log', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='StreamResultOfIoArgoprojWorkflowV1alpha1LogEntry', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def resubmit_workflow(self, namespace, name, body, **kwargs): # noqa: E501
"""resubmit_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.resubmit_workflow(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowResubmitRequest body: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1alpha1Workflow
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.resubmit_workflow_with_http_info(namespace, name, body, **kwargs) # noqa: E501
def resubmit_workflow_with_http_info(self, namespace, name, body, **kwargs): # noqa: E501
"""resubmit_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.resubmit_workflow_with_http_info(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowResubmitRequest body: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1alpha1Workflow, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'name',
'body'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method resubmit_workflow" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `resubmit_workflow`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `resubmit_workflow`") # noqa: E501
# verify the required parameter 'body' is set
if self.api_client.client_side_validation and ('body' not in local_var_params or # noqa: E501
local_var_params['body'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `body` when calling `resubmit_workflow`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/{name}/resubmit', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1alpha1Workflow', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def resume_workflow(self, namespace, name, body, **kwargs): # noqa: E501
"""resume_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.resume_workflow(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowResumeRequest body: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1alpha1Workflow
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.resume_workflow_with_http_info(namespace, name, body, **kwargs) # noqa: E501
def resume_workflow_with_http_info(self, namespace, name, body, **kwargs): # noqa: E501
"""resume_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.resume_workflow_with_http_info(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowResumeRequest body: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1alpha1Workflow, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'name',
'body'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method resume_workflow" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `resume_workflow`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `resume_workflow`") # noqa: E501
# verify the required parameter 'body' is set
if self.api_client.client_side_validation and ('body' not in local_var_params or # noqa: E501
local_var_params['body'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `body` when calling `resume_workflow`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/{name}/resume', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1alpha1Workflow', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def retry_workflow(self, namespace, name, body, **kwargs): # noqa: E501
"""retry_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.retry_workflow(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowRetryRequest body: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1alpha1Workflow
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.retry_workflow_with_http_info(namespace, name, body, **kwargs) # noqa: E501
def retry_workflow_with_http_info(self, namespace, name, body, **kwargs): # noqa: E501
"""retry_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.retry_workflow_with_http_info(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowRetryRequest body: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1alpha1Workflow, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'name',
'body'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method retry_workflow" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `retry_workflow`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `retry_workflow`") # noqa: E501
# verify the required parameter 'body' is set
if self.api_client.client_side_validation and ('body' not in local_var_params or # noqa: E501
local_var_params['body'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `body` when calling `retry_workflow`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/{name}/retry', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1alpha1Workflow', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def set_workflow(self, namespace, name, body, **kwargs): # noqa: E501
"""set_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.set_workflow(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowSetRequest body: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1alpha1Workflow
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.set_workflow_with_http_info(namespace, name, body, **kwargs) # noqa: E501
def set_workflow_with_http_info(self, namespace, name, body, **kwargs): # noqa: E501
"""set_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.set_workflow_with_http_info(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowSetRequest body: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1alpha1Workflow, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'name',
'body'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method set_workflow" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `set_workflow`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `set_workflow`") # noqa: E501
# verify the required parameter 'body' is set
if self.api_client.client_side_validation and ('body' not in local_var_params or # noqa: E501
local_var_params['body'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `body` when calling `set_workflow`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/{name}/set', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1alpha1Workflow', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def stop_workflow(self, namespace, name, body, **kwargs): # noqa: E501
"""stop_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.stop_workflow(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowStopRequest body: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1alpha1Workflow
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.stop_workflow_with_http_info(namespace, name, body, **kwargs) # noqa: E501
def stop_workflow_with_http_info(self, namespace, name, body, **kwargs): # noqa: E501
"""stop_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.stop_workflow_with_http_info(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowStopRequest body: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1alpha1Workflow, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'name',
'body'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method stop_workflow" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `stop_workflow`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `stop_workflow`") # noqa: E501
# verify the required parameter 'body' is set
if self.api_client.client_side_validation and ('body' not in local_var_params or # noqa: E501
local_var_params['body'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `body` when calling `stop_workflow`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/{name}/stop', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1alpha1Workflow', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def submit_workflow(self, namespace, body, **kwargs): # noqa: E501
"""submit_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.submit_workflow(namespace, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param V1alpha1WorkflowSubmitRequest body: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1alpha1Workflow
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.submit_workflow_with_http_info(namespace, body, **kwargs) # noqa: E501
def submit_workflow_with_http_info(self, namespace, body, **kwargs): # noqa: E501
"""submit_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.submit_workflow_with_http_info(namespace, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param V1alpha1WorkflowSubmitRequest body: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1alpha1Workflow, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'body'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method submit_workflow" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `submit_workflow`") # noqa: E501
# verify the required parameter 'body' is set
if self.api_client.client_side_validation and ('body' not in local_var_params or # noqa: E501
local_var_params['body'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `body` when calling `submit_workflow`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/submit', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1alpha1Workflow', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def suspend_workflow(self, namespace, name, body, **kwargs): # noqa: E501
"""suspend_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.suspend_workflow(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowSuspendRequest body: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1alpha1Workflow
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.suspend_workflow_with_http_info(namespace, name, body, **kwargs) # noqa: E501
def suspend_workflow_with_http_info(self, namespace, name, body, **kwargs): # noqa: E501
"""suspend_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.suspend_workflow_with_http_info(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowSuspendRequest body: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1alpha1Workflow, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'name',
'body'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method suspend_workflow" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `suspend_workflow`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `suspend_workflow`") # noqa: E501
# verify the required parameter 'body' is set
if self.api_client.client_side_validation and ('body' not in local_var_params or # noqa: E501
local_var_params['body'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `body` when calling `suspend_workflow`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/{name}/suspend', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1alpha1Workflow', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def terminate_workflow(self, namespace, name, body, **kwargs): # noqa: E501
"""terminate_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.terminate_workflow(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowTerminateRequest body: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: V1alpha1Workflow
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.terminate_workflow_with_http_info(namespace, name, body, **kwargs) # noqa: E501
def terminate_workflow_with_http_info(self, namespace, name, body, **kwargs): # noqa: E501
"""terminate_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.terminate_workflow_with_http_info(namespace, name, body, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param V1alpha1WorkflowTerminateRequest body: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(V1alpha1Workflow, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'name',
'body'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method terminate_workflow" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `terminate_workflow`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `terminate_workflow`") # noqa: E501
# verify the required parameter 'body' is set
if self.api_client.client_side_validation and ('body' not in local_var_params or # noqa: E501
local_var_params['body'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `body` when calling `terminate_workflow`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/{name}/terminate', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1alpha1Workflow', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def watch_events(self, namespace, **kwargs): # noqa: E501
"""watch_events # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.watch_events(namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str list_options_label_selector: A selector to restrict the list of returned objects by their labels. Defaults to everything. +optional.
:param str list_options_field_selector: A selector to restrict the list of returned objects by their fields. Defaults to everything. +optional.
:param bool list_options_watch: Watch for changes to the described resources and return them as a stream of add, update, and remove notifications. Specify resourceVersion. +optional.
:param bool list_options_allow_watch_bookmarks: allowWatchBookmarks requests watch events with type \"BOOKMARK\". Servers that do not implement bookmarks may ignore this flag and bookmarks are sent at the server's discretion. Clients should not assume bookmarks are returned at any specific interval, nor may they assume the server will send any BOOKMARK event during a session. If this is not a watch, this field is ignored. If the feature gate WatchBookmarks is not enabled in apiserver, this field is ignored. +optional.
:param str list_options_resource_version: resourceVersion sets a constraint on what resource versions a request may be served from. See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str list_options_resource_version_match: resourceVersionMatch determines how resourceVersion is applied to list calls. It is highly recommended that resourceVersionMatch be set for list calls where resourceVersion is set See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str list_options_timeout_seconds: Timeout for the list/watch call. This limits the duration of the call, regardless of any activity or inactivity. +optional.
:param str list_options_limit: limit is a maximum number of responses to return for a list call. If more items exist, the server will set the `continue` field on the list metadata to a value that can be used with the same initial query to retrieve the next set of results. Setting a limit may return fewer than the requested amount of items (up to zero items) in the event all requested objects are filtered out and clients should only use the presence of the continue field to determine whether more results are available. Servers may choose not to support the limit argument and will return all of the available results. If limit is specified and the continue field is empty, clients may assume that no more results are available. This field is not supported if watch is true. The server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects. If objects are updated during a chunked list the version of the object that was present at the time the first list result was calculated is returned.
:param str list_options_continue: The continue option should be set when retrieving more results from the server. Since this value is server defined, clients may only use the continue value from a previous query result with identical query parameters (except for the value of continue) and the server may reject a continue value it does not recognize. If the specified continue value is no longer valid whether due to expiration (generally five to fifteen minutes) or a configuration change on the server, the server will respond with a 410 ResourceExpired error together with a continue token. If the client needs a consistent list, it must restart their list without the continue field. Otherwise, the client may send another list request with the token received with the 410 error, the server will respond with a list starting from the next key, but from the latest snapshot, which is inconsistent from the previous list results - objects that are created, modified, or deleted after the first list request will be included in the response, as long as their keys are after the \"next key\". This field is not supported when watch is true. Clients may start a watch from the last resourceVersion value returned by the server and not miss any modifications.
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: StreamResultOfV1Event
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.watch_events_with_http_info(namespace, **kwargs) # noqa: E501
def watch_events_with_http_info(self, namespace, **kwargs): # noqa: E501
"""watch_events # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.watch_events_with_http_info(namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str list_options_label_selector: A selector to restrict the list of returned objects by their labels. Defaults to everything. +optional.
:param str list_options_field_selector: A selector to restrict the list of returned objects by their fields. Defaults to everything. +optional.
:param bool list_options_watch: Watch for changes to the described resources and return them as a stream of add, update, and remove notifications. Specify resourceVersion. +optional.
:param bool list_options_allow_watch_bookmarks: allowWatchBookmarks requests watch events with type \"BOOKMARK\". Servers that do not implement bookmarks may ignore this flag and bookmarks are sent at the server's discretion. Clients should not assume bookmarks are returned at any specific interval, nor may they assume the server will send any BOOKMARK event during a session. If this is not a watch, this field is ignored. If the feature gate WatchBookmarks is not enabled in apiserver, this field is ignored. +optional.
:param str list_options_resource_version: resourceVersion sets a constraint on what resource versions a request may be served from. See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str list_options_resource_version_match: resourceVersionMatch determines how resourceVersion is applied to list calls. It is highly recommended that resourceVersionMatch be set for list calls where resourceVersion is set See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str list_options_timeout_seconds: Timeout for the list/watch call. This limits the duration of the call, regardless of any activity or inactivity. +optional.
:param str list_options_limit: limit is a maximum number of responses to return for a list call. If more items exist, the server will set the `continue` field on the list metadata to a value that can be used with the same initial query to retrieve the next set of results. Setting a limit may return fewer than the requested amount of items (up to zero items) in the event all requested objects are filtered out and clients should only use the presence of the continue field to determine whether more results are available. Servers may choose not to support the limit argument and will return all of the available results. If limit is specified and the continue field is empty, clients may assume that no more results are available. This field is not supported if watch is true. The server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects. If objects are updated during a chunked list the version of the object that was present at the time the first list result was calculated is returned.
:param str list_options_continue: The continue option should be set when retrieving more results from the server. Since this value is server defined, clients may only use the continue value from a previous query result with identical query parameters (except for the value of continue) and the server may reject a continue value it does not recognize. If the specified continue value is no longer valid whether due to expiration (generally five to fifteen minutes) or a configuration change on the server, the server will respond with a 410 ResourceExpired error together with a continue token. If the client needs a consistent list, it must restart their list without the continue field. Otherwise, the client may send another list request with the token received with the 410 error, the server will respond with a list starting from the next key, but from the latest snapshot, which is inconsistent from the previous list results - objects that are created, modified, or deleted after the first list request will be included in the response, as long as their keys are after the \"next key\". This field is not supported when watch is true. Clients may start a watch from the last resourceVersion value returned by the server and not miss any modifications.
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(StreamResultOfV1Event, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'list_options_label_selector',
'list_options_field_selector',
'list_options_watch',
'list_options_allow_watch_bookmarks',
'list_options_resource_version',
'list_options_resource_version_match',
'list_options_timeout_seconds',
'list_options_limit',
'list_options_continue'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method watch_events" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `watch_events`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
query_params = []
if 'list_options_label_selector' in local_var_params and local_var_params['list_options_label_selector'] is not None: # noqa: E501
query_params.append(('listOptions.labelSelector', local_var_params['list_options_label_selector'])) # noqa: E501
if 'list_options_field_selector' in local_var_params and local_var_params['list_options_field_selector'] is not None: # noqa: E501
query_params.append(('listOptions.fieldSelector', local_var_params['list_options_field_selector'])) # noqa: E501
if 'list_options_watch' in local_var_params and local_var_params['list_options_watch'] is not None: # noqa: E501
query_params.append(('listOptions.watch', local_var_params['list_options_watch'])) # noqa: E501
if 'list_options_allow_watch_bookmarks' in local_var_params and local_var_params['list_options_allow_watch_bookmarks'] is not None: # noqa: E501
query_params.append(('listOptions.allowWatchBookmarks', local_var_params['list_options_allow_watch_bookmarks'])) # noqa: E501
if 'list_options_resource_version' in local_var_params and local_var_params['list_options_resource_version'] is not None: # noqa: E501
query_params.append(('listOptions.resourceVersion', local_var_params['list_options_resource_version'])) # noqa: E501
if 'list_options_resource_version_match' in local_var_params and local_var_params['list_options_resource_version_match'] is not None: # noqa: E501
query_params.append(('listOptions.resourceVersionMatch', local_var_params['list_options_resource_version_match'])) # noqa: E501
if 'list_options_timeout_seconds' in local_var_params and local_var_params['list_options_timeout_seconds'] is not None: # noqa: E501
query_params.append(('listOptions.timeoutSeconds', local_var_params['list_options_timeout_seconds'])) # noqa: E501
if 'list_options_limit' in local_var_params and local_var_params['list_options_limit'] is not None: # noqa: E501
query_params.append(('listOptions.limit', local_var_params['list_options_limit'])) # noqa: E501
if 'list_options_continue' in local_var_params and local_var_params['list_options_continue'] is not None: # noqa: E501
query_params.append(('listOptions.continue', local_var_params['list_options_continue'])) # 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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/stream/events/{namespace}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='StreamResultOfV1Event', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def watch_workflows(self, namespace, **kwargs): # noqa: E501
"""watch_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.watch_workflows(namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str list_options_label_selector: A selector to restrict the list of returned objects by their labels. Defaults to everything. +optional.
:param str list_options_field_selector: A selector to restrict the list of returned objects by their fields. Defaults to everything. +optional.
:param bool list_options_watch: Watch for changes to the described resources and return them as a stream of add, update, and remove notifications. Specify resourceVersion. +optional.
:param bool list_options_allow_watch_bookmarks: allowWatchBookmarks requests watch events with type \"BOOKMARK\". Servers that do not implement bookmarks may ignore this flag and bookmarks are sent at the server's discretion. Clients should not assume bookmarks are returned at any specific interval, nor may they assume the server will send any BOOKMARK event during a session. If this is not a watch, this field is ignored. If the feature gate WatchBookmarks is not enabled in apiserver, this field is ignored. +optional.
:param str list_options_resource_version: resourceVersion sets a constraint on what resource versions a request may be served from. See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str list_options_resource_version_match: resourceVersionMatch determines how resourceVersion is applied to list calls. It is highly recommended that resourceVersionMatch be set for list calls where resourceVersion is set See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str list_options_timeout_seconds: Timeout for the list/watch call. This limits the duration of the call, regardless of any activity or inactivity. +optional.
:param str list_options_limit: limit is a maximum number of responses to return for a list call. If more items exist, the server will set the `continue` field on the list metadata to a value that can be used with the same initial query to retrieve the next set of results. Setting a limit may return fewer than the requested amount of items (up to zero items) in the event all requested objects are filtered out and clients should only use the presence of the continue field to determine whether more results are available. Servers may choose not to support the limit argument and will return all of the available results. If limit is specified and the continue field is empty, clients may assume that no more results are available. This field is not supported if watch is true. The server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects. If objects are updated during a chunked list the version of the object that was present at the time the first list result was calculated is returned.
:param str list_options_continue: The continue option should be set when retrieving more results from the server. Since this value is server defined, clients may only use the continue value from a previous query result with identical query parameters (except for the value of continue) and the server may reject a continue value it does not recognize. If the specified continue value is no longer valid whether due to expiration (generally five to fifteen minutes) or a configuration change on the server, the server will respond with a 410 ResourceExpired error together with a continue token. If the client needs a consistent list, it must restart their list without the continue field. Otherwise, the client may send another list request with the token received with the 410 error, the server will respond with a list starting from the next key, but from the latest snapshot, which is inconsistent from the previous list results - objects that are created, modified, or deleted after the first list request will be included in the response, as long as their keys are after the \"next key\". This field is not supported when watch is true. Clients may start a watch from the last resourceVersion value returned by the server and not miss any modifications.
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: StreamResultOfIoArgoprojWorkflowV1alpha1WorkflowWatchEvent
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.watch_workflows_with_http_info(namespace, **kwargs) # noqa: E501
def watch_workflows_with_http_info(self, namespace, **kwargs): # noqa: E501
"""watch_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.watch_workflows_with_http_info(namespace, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str list_options_label_selector: A selector to restrict the list of returned objects by their labels. Defaults to everything. +optional.
:param str list_options_field_selector: A selector to restrict the list of returned objects by their fields. Defaults to everything. +optional.
:param bool list_options_watch: Watch for changes to the described resources and return them as a stream of add, update, and remove notifications. Specify resourceVersion. +optional.
:param bool list_options_allow_watch_bookmarks: allowWatchBookmarks requests watch events with type \"BOOKMARK\". Servers that do not implement bookmarks may ignore this flag and bookmarks are sent at the server's discretion. Clients should not assume bookmarks are returned at any specific interval, nor may they assume the server will send any BOOKMARK event during a session. If this is not a watch, this field is ignored. If the feature gate WatchBookmarks is not enabled in apiserver, this field is ignored. +optional.
:param str list_options_resource_version: resourceVersion sets a constraint on what resource versions a request may be served from. See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str list_options_resource_version_match: resourceVersionMatch determines how resourceVersion is applied to list calls. It is highly recommended that resourceVersionMatch be set for list calls where resourceVersion is set See https://kubernetes.io/docs/reference/using-api/api-concepts/#resource-versions for details. Defaults to unset +optional
:param str list_options_timeout_seconds: Timeout for the list/watch call. This limits the duration of the call, regardless of any activity or inactivity. +optional.
:param str list_options_limit: limit is a maximum number of responses to return for a list call. If more items exist, the server will set the `continue` field on the list metadata to a value that can be used with the same initial query to retrieve the next set of results. Setting a limit may return fewer than the requested amount of items (up to zero items) in the event all requested objects are filtered out and clients should only use the presence of the continue field to determine whether more results are available. Servers may choose not to support the limit argument and will return all of the available results. If limit is specified and the continue field is empty, clients may assume that no more results are available. This field is not supported if watch is true. The server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects. If objects are updated during a chunked list the version of the object that was present at the time the first list result was calculated is returned.
:param str list_options_continue: The continue option should be set when retrieving more results from the server. Since this value is server defined, clients may only use the continue value from a previous query result with identical query parameters (except for the value of continue) and the server may reject a continue value it does not recognize. If the specified continue value is no longer valid whether due to expiration (generally five to fifteen minutes) or a configuration change on the server, the server will respond with a 410 ResourceExpired error together with a continue token. If the client needs a consistent list, it must restart their list without the continue field. Otherwise, the client may send another list request with the token received with the 410 error, the server will respond with a list starting from the next key, but from the latest snapshot, which is inconsistent from the previous list results - objects that are created, modified, or deleted after the first list request will be included in the response, as long as their keys are after the \"next key\". This field is not supported when watch is true. Clients may start a watch from the last resourceVersion value returned by the server and not miss any modifications.
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(StreamResultOfIoArgoprojWorkflowV1alpha1WorkflowWatchEvent, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'list_options_label_selector',
'list_options_field_selector',
'list_options_watch',
'list_options_allow_watch_bookmarks',
'list_options_resource_version',
'list_options_resource_version_match',
'list_options_timeout_seconds',
'list_options_limit',
'list_options_continue'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method watch_workflows" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `watch_workflows`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
query_params = []
if 'list_options_label_selector' in local_var_params and local_var_params['list_options_label_selector'] is not None: # noqa: E501
query_params.append(('listOptions.labelSelector', local_var_params['list_options_label_selector'])) # noqa: E501
if 'list_options_field_selector' in local_var_params and local_var_params['list_options_field_selector'] is not None: # noqa: E501
query_params.append(('listOptions.fieldSelector', local_var_params['list_options_field_selector'])) # noqa: E501
if 'list_options_watch' in local_var_params and local_var_params['list_options_watch'] is not None: # noqa: E501
query_params.append(('listOptions.watch', local_var_params['list_options_watch'])) # noqa: E501
if 'list_options_allow_watch_bookmarks' in local_var_params and local_var_params['list_options_allow_watch_bookmarks'] is not None: # noqa: E501
query_params.append(('listOptions.allowWatchBookmarks', local_var_params['list_options_allow_watch_bookmarks'])) # noqa: E501
if 'list_options_resource_version' in local_var_params and local_var_params['list_options_resource_version'] is not None: # noqa: E501
query_params.append(('listOptions.resourceVersion', local_var_params['list_options_resource_version'])) # noqa: E501
if 'list_options_resource_version_match' in local_var_params and local_var_params['list_options_resource_version_match'] is not None: # noqa: E501
query_params.append(('listOptions.resourceVersionMatch', local_var_params['list_options_resource_version_match'])) # noqa: E501
if 'list_options_timeout_seconds' in local_var_params and local_var_params['list_options_timeout_seconds'] is not None: # noqa: E501
query_params.append(('listOptions.timeoutSeconds', local_var_params['list_options_timeout_seconds'])) # noqa: E501
if 'list_options_limit' in local_var_params and local_var_params['list_options_limit'] is not None: # noqa: E501
query_params.append(('listOptions.limit', local_var_params['list_options_limit'])) # noqa: E501
if 'list_options_continue' in local_var_params and local_var_params['list_options_continue'] is not None: # noqa: E501
query_params.append(('listOptions.continue', local_var_params['list_options_continue'])) # 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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflow-events/{namespace}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='StreamResultOfIoArgoprojWorkflowV1alpha1WorkflowWatchEvent', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def workflow_logs(self, namespace, name, **kwargs): # noqa: E501
"""workflow_logs # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.workflow_logs(namespace, name, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param str pod_name:
:param str log_options_container: The container for which to stream logs. Defaults to only container if there is one container in the pod. +optional.
:param bool log_options_follow: Follow the log stream of the pod. Defaults to false. +optional.
:param bool log_options_previous: Return previous terminated container logs. Defaults to false. +optional.
:param str log_options_since_seconds: A relative time in seconds before the current time from which to show logs. If this value precedes the time a pod was started, only logs since the pod start will be returned. If this value is in the future, no logs will be returned. Only one of sinceSeconds or sinceTime may be specified. +optional.
:param str log_options_since_time_seconds: Represents seconds of UTC time since Unix epoch 1970-01-01T00:00:00Z. Must be from 0001-01-01T00:00:00Z to 9999-12-31T23:59:59Z inclusive.
:param int log_options_since_time_nanos: Non-negative fractions of a second at nanosecond resolution. Negative second values with fractions must still have non-negative nanos values that count forward in time. Must be from 0 to 999,999,999 inclusive. This field may be limited in precision depending on context.
:param bool log_options_timestamps: If true, add an RFC3339 or RFC3339Nano timestamp at the beginning of every line of log output. Defaults to false. +optional.
:param str log_options_tail_lines: If set, the number of lines from the end of the logs to show. If not specified, logs are shown from the creation of the container or sinceSeconds or sinceTime +optional.
:param str log_options_limit_bytes: If set, the number of bytes to read from the server before terminating the log output. This may not display a complete final line of logging, and may return slightly more or slightly less than the specified limit. +optional.
:param bool log_options_insecure_skip_tls_verify_backend: insecureSkipTLSVerifyBackend indicates that the apiserver should not confirm the validity of the serving certificate of the backend it is connecting to. This will make the HTTPS connection between the apiserver and the backend insecure. This means the apiserver cannot verify the log data it is receiving came from the real kubelet. If the kubelet is configured to verify the apiserver's TLS credentials, it does not mean the connection to the real kubelet is vulnerable to a man in the middle attack (e.g. an attacker could not intercept the actual log data coming from the real kubelet). +optional.
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: StreamResultOfIoArgoprojWorkflowV1alpha1LogEntry
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.workflow_logs_with_http_info(namespace, name, **kwargs) # noqa: E501
def workflow_logs_with_http_info(self, namespace, name, **kwargs): # noqa: E501
"""workflow_logs # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.workflow_logs_with_http_info(namespace, name, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str namespace: (required)
:param str name: (required)
:param str pod_name:
:param str log_options_container: The container for which to stream logs. Defaults to only container if there is one container in the pod. +optional.
:param bool log_options_follow: Follow the log stream of the pod. Defaults to false. +optional.
:param bool log_options_previous: Return previous terminated container logs. Defaults to false. +optional.
:param str log_options_since_seconds: A relative time in seconds before the current time from which to show logs. If this value precedes the time a pod was started, only logs since the pod start will be returned. If this value is in the future, no logs will be returned. Only one of sinceSeconds or sinceTime may be specified. +optional.
:param str log_options_since_time_seconds: Represents seconds of UTC time since Unix epoch 1970-01-01T00:00:00Z. Must be from 0001-01-01T00:00:00Z to 9999-12-31T23:59:59Z inclusive.
:param int log_options_since_time_nanos: Non-negative fractions of a second at nanosecond resolution. Negative second values with fractions must still have non-negative nanos values that count forward in time. Must be from 0 to 999,999,999 inclusive. This field may be limited in precision depending on context.
:param bool log_options_timestamps: If true, add an RFC3339 or RFC3339Nano timestamp at the beginning of every line of log output. Defaults to false. +optional.
:param str log_options_tail_lines: If set, the number of lines from the end of the logs to show. If not specified, logs are shown from the creation of the container or sinceSeconds or sinceTime +optional.
:param str log_options_limit_bytes: If set, the number of bytes to read from the server before terminating the log output. This may not display a complete final line of logging, and may return slightly more or slightly less than the specified limit. +optional.
:param bool log_options_insecure_skip_tls_verify_backend: insecureSkipTLSVerifyBackend indicates that the apiserver should not confirm the validity of the serving certificate of the backend it is connecting to. This will make the HTTPS connection between the apiserver and the backend insecure. This means the apiserver cannot verify the log data it is receiving came from the real kubelet. If the kubelet is configured to verify the apiserver's TLS credentials, it does not mean the connection to the real kubelet is vulnerable to a man in the middle attack (e.g. an attacker could not intercept the actual log data coming from the real kubelet). +optional.
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(StreamResultOfIoArgoprojWorkflowV1alpha1LogEntry, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'namespace',
'name',
'pod_name',
'log_options_container',
'log_options_follow',
'log_options_previous',
'log_options_since_seconds',
'log_options_since_time_seconds',
'log_options_since_time_nanos',
'log_options_timestamps',
'log_options_tail_lines',
'log_options_limit_bytes',
'log_options_insecure_skip_tls_verify_backend'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method workflow_logs" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'namespace' is set
if self.api_client.client_side_validation and ('namespace' not in local_var_params or # noqa: E501
local_var_params['namespace'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `namespace` when calling `workflow_logs`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `workflow_logs`") # noqa: E501
collection_formats = {}
path_params = {}
if 'namespace' in local_var_params:
path_params['namespace'] = local_var_params['namespace'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
query_params = []
if 'pod_name' in local_var_params and local_var_params['pod_name'] is not None: # noqa: E501
query_params.append(('podName', local_var_params['pod_name'])) # noqa: E501
if 'log_options_container' in local_var_params and local_var_params['log_options_container'] is not None: # noqa: E501
query_params.append(('logOptions.container', local_var_params['log_options_container'])) # noqa: E501
if 'log_options_follow' in local_var_params and local_var_params['log_options_follow'] is not None: # noqa: E501
query_params.append(('logOptions.follow', local_var_params['log_options_follow'])) # noqa: E501
if 'log_options_previous' in local_var_params and local_var_params['log_options_previous'] is not None: # noqa: E501
query_params.append(('logOptions.previous', local_var_params['log_options_previous'])) # noqa: E501
if 'log_options_since_seconds' in local_var_params and local_var_params['log_options_since_seconds'] is not None: # noqa: E501
query_params.append(('logOptions.sinceSeconds', local_var_params['log_options_since_seconds'])) # noqa: E501
if 'log_options_since_time_seconds' in local_var_params and local_var_params['log_options_since_time_seconds'] is not None: # noqa: E501
query_params.append(('logOptions.sinceTime.seconds', local_var_params['log_options_since_time_seconds'])) # noqa: E501
if 'log_options_since_time_nanos' in local_var_params and local_var_params['log_options_since_time_nanos'] is not None: # noqa: E501
query_params.append(('logOptions.sinceTime.nanos', local_var_params['log_options_since_time_nanos'])) # noqa: E501
if 'log_options_timestamps' in local_var_params and local_var_params['log_options_timestamps'] is not None: # noqa: E501
query_params.append(('logOptions.timestamps', local_var_params['log_options_timestamps'])) # noqa: E501
if 'log_options_tail_lines' in local_var_params and local_var_params['log_options_tail_lines'] is not None: # noqa: E501
query_params.append(('logOptions.tailLines', local_var_params['log_options_tail_lines'])) # noqa: E501
if 'log_options_limit_bytes' in local_var_params and local_var_params['log_options_limit_bytes'] is not None: # noqa: E501
query_params.append(('logOptions.limitBytes', local_var_params['log_options_limit_bytes'])) # noqa: E501
if 'log_options_insecure_skip_tls_verify_backend' in local_var_params and local_var_params['log_options_insecure_skip_tls_verify_backend'] is not None: # noqa: E501
query_params.append(('logOptions.insecureSkipTLSVerifyBackend', local_var_params['log_options_insecure_skip_tls_verify_backend'])) # 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 = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/workflows/{namespace}/{name}/log', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='StreamResultOfIoArgoprojWorkflowV1alpha1LogEntry', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
| 64.58232 | 1,403 | 0.658257 | 19,598 | 159,260 | 5.149913 | 0.029136 | 0.039394 | 0.064224 | 0.021401 | 0.98487 | 0.982274 | 0.980006 | 0.975864 | 0.972416 | 0.967432 | 0 | 0.015453 | 0.275505 | 159,260 | 2,465 | 1,404 | 64.608519 | 0.859269 | 0.527339 | 0 | 0.796813 | 0 | 0 | 0.247673 | 0.117106 | 0 | 0 | 0 | 0 | 0 | 1 | 0.027888 | false | 0 | 0.003984 | 0 | 0.059761 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
7c5e91544e75b751cbc7c9a08536a6b02f7b3639 | 43,229 | py | Python | src/v5.1/resources/swagger_client/api/learning_standards_api.py | xmarcosx/edfi-notebook | 0564ebdf1d0f45a9d25056e7e61369f0a837534d | [
"Apache-2.0"
] | 2 | 2021-04-27T17:18:17.000Z | 2021-04-27T19:14:39.000Z | src/v5.1/resources/swagger_client/api/learning_standards_api.py | xmarcosx/edfi-notebook | 0564ebdf1d0f45a9d25056e7e61369f0a837534d | [
"Apache-2.0"
] | null | null | null | src/v5.1/resources/swagger_client/api/learning_standards_api.py | xmarcosx/edfi-notebook | 0564ebdf1d0f45a9d25056e7e61369f0a837534d | [
"Apache-2.0"
] | 1 | 2022-01-06T09:43:11.000Z | 2022-01-06T09:43:11.000Z | # coding: utf-8
"""
Ed-Fi Operational Data Store API
The Ed-Fi ODS / API enables applications to read and write education data stored in an Ed-Fi ODS through a secure REST interface. *** > *Note: Consumers of ODS / API information should sanitize all data for display and storage. The ODS / API provides reasonable safeguards against cross-site scripting attacks and other malicious content, but the platform does not and cannot guarantee that the data it contains is free of all potentially harmful content.* *** # noqa: E501
OpenAPI spec version: 3
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 swagger_client.api_client import ApiClient
class LearningStandardsApi(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_learning_standard_by_id(self, id, **kwargs): # noqa: E501
"""Deletes an existing resource using the resource identifier. # noqa: E501
The DELETE operation is used to delete an existing resource by identifier. If the resource doesn't exist, an error will result (the resource will not be found). # 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_learning_standard_by_id(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: A resource identifier that uniquely identifies the resource. (required)
:param str if_match: The ETag header value used to prevent the DELETE from removing a resource modified by another consumer.
: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_learning_standard_by_id_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.delete_learning_standard_by_id_with_http_info(id, **kwargs) # noqa: E501
return data
def delete_learning_standard_by_id_with_http_info(self, id, **kwargs): # noqa: E501
"""Deletes an existing resource using the resource identifier. # noqa: E501
The DELETE operation is used to delete an existing resource by identifier. If the resource doesn't exist, an error will result (the resource will not be found). # 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_learning_standard_by_id_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: A resource identifier that uniquely identifies the resource. (required)
:param str if_match: The ETag header value used to prevent the DELETE from removing a resource modified by another consumer.
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'if_match'] # 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_learning_standard_by_id" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if self.api_client.client_side_validation and ('id' not in params or
params['id'] is None): # noqa: E501
raise ValueError("Missing the required parameter `id` when calling `delete_learning_standard_by_id`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
if 'if_match' in params:
header_params['If-Match'] = params['if_match'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2_client_credentials'] # noqa: E501
return self.api_client.call_api(
'/ed-fi/learningStandards/{id}', '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 deletes_learning_standards(self, **kwargs): # noqa: E501
"""Retrieves deleted resources based on change version. # noqa: E501
The DELETES operation is used to retrieve deleted resources. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.deletes_learning_standards(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int offset: Indicates how many items should be skipped before returning results.
:param int limit: Indicates the maximum number of items that should be returned in the results.
:param int min_change_version: Used in synchronization to set sequence minimum ChangeVersion
:param int max_change_version: Used in synchronization to set sequence maximum ChangeVersion
:return: list[EdFiLearningStandard]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.deletes_learning_standards_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.deletes_learning_standards_with_http_info(**kwargs) # noqa: E501
return data
def deletes_learning_standards_with_http_info(self, **kwargs): # noqa: E501
"""Retrieves deleted resources based on change version. # noqa: E501
The DELETES operation is used to retrieve deleted resources. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.deletes_learning_standards_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int offset: Indicates how many items should be skipped before returning results.
:param int limit: Indicates the maximum number of items that should be returned in the results.
:param int min_change_version: Used in synchronization to set sequence minimum ChangeVersion
:param int max_change_version: Used in synchronization to set sequence maximum ChangeVersion
:return: list[EdFiLearningStandard]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['offset', 'limit', 'min_change_version', 'max_change_version'] # 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 deletes_learning_standards" % key
)
params[key] = val
del params['kwargs']
if self.api_client.client_side_validation and ('limit' in params and params['limit'] > 500): # noqa: E501
raise ValueError("Invalid value for parameter `limit` when calling `deletes_learning_standards`, must be a value less than or equal to `500`") # noqa: E501
if self.api_client.client_side_validation and ('limit' in params and params['limit'] < 0): # noqa: E501
raise ValueError("Invalid value for parameter `limit` when calling `deletes_learning_standards`, must be a value greater than or equal to `0`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'offset' in params:
query_params.append(('offset', params['offset'])) # noqa: E501
if 'limit' in params:
query_params.append(('limit', params['limit'])) # noqa: E501
if 'min_change_version' in params:
query_params.append(('minChangeVersion', params['min_change_version'])) # noqa: E501
if 'max_change_version' in params:
query_params.append(('maxChangeVersion', params['max_change_version'])) # 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
# 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 = ['oauth2_client_credentials'] # noqa: E501
return self.api_client.call_api(
'/ed-fi/learningStandards/deletes', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[EdFiLearningStandard]', # 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_learning_standards(self, **kwargs): # noqa: E501
"""Retrieves specific resources using the resource's property values (using the \"Get\" pattern). # noqa: E501
This GET operation provides access to resources using the \"Get\" search pattern. The values of any properties of the resource that are specified will be used to return all matching results (if it exists). # 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_learning_standards(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int offset: Indicates how many items should be skipped before returning results.
:param int limit: Indicates the maximum number of items that should be returned in the results.
:param int min_change_version: Used in synchronization to set sequence minimum ChangeVersion
:param int max_change_version: Used in synchronization to set sequence maximum ChangeVersion
:param bool total_count: Indicates if the total number of items available should be returned in the 'Total-Count' header of the response. If set to false, 'Total-Count' header will not be provided.
:param str learning_standard_id: The identifier for the specific learning standard (e.g., 111.15.3.1.A).
:param str parent_learning_standard_id: The identifier for the specific learning standard (e.g., 111.15.3.1.A).
:param str learning_standard_category_descriptor: An additional classification of the type of a specific learning standard.
:param str learning_standard_scope_descriptor: Signals the scope of usage the standard. Does not necessarily relate the standard to the governing body.
:param str course_title: The official Course Title with which this learning standard is associated.
:param str description: The text of the statement. The textual content that either describes a specific competency such as \"Apply the Pythagorean Theorem to determine unknown side lengths in right triangles in real-world and mathematical problems in two and three dimensions.\" or describes a less granular group of competencies within the taxonomy of the standards document, e.g. \"Understand and apply the Pythagorean Theorem,\" or \"Geometry\".
:param str id:
:param str learning_standard_item_code: A code designated by the promulgating body to identify the statement, e.g. 1.N.3 (usually not globally unique).
:param str namespace: The namespace of the organization or entity who governs the standard. It is recommended the namespaces observe a URI format and begin with a domain name under the governing organization or entity control.
:param str success_criteria: One or more statements that describes the criteria used by teachers and students to check for attainment of a learning standard. This criteria gives clear indications as to the degree to which learning is moving through the Zone or Proximal Development toward independent achievement of the LearningStandard.
:param str uri: An unambiguous reference to the statement using a network-resolvable URI.
:return: list[EdFiLearningStandard]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_learning_standards_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_learning_standards_with_http_info(**kwargs) # noqa: E501
return data
def get_learning_standards_with_http_info(self, **kwargs): # noqa: E501
"""Retrieves specific resources using the resource's property values (using the \"Get\" pattern). # noqa: E501
This GET operation provides access to resources using the \"Get\" search pattern. The values of any properties of the resource that are specified will be used to return all matching results (if it exists). # 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_learning_standards_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int offset: Indicates how many items should be skipped before returning results.
:param int limit: Indicates the maximum number of items that should be returned in the results.
:param int min_change_version: Used in synchronization to set sequence minimum ChangeVersion
:param int max_change_version: Used in synchronization to set sequence maximum ChangeVersion
:param bool total_count: Indicates if the total number of items available should be returned in the 'Total-Count' header of the response. If set to false, 'Total-Count' header will not be provided.
:param str learning_standard_id: The identifier for the specific learning standard (e.g., 111.15.3.1.A).
:param str parent_learning_standard_id: The identifier for the specific learning standard (e.g., 111.15.3.1.A).
:param str learning_standard_category_descriptor: An additional classification of the type of a specific learning standard.
:param str learning_standard_scope_descriptor: Signals the scope of usage the standard. Does not necessarily relate the standard to the governing body.
:param str course_title: The official Course Title with which this learning standard is associated.
:param str description: The text of the statement. The textual content that either describes a specific competency such as \"Apply the Pythagorean Theorem to determine unknown side lengths in right triangles in real-world and mathematical problems in two and three dimensions.\" or describes a less granular group of competencies within the taxonomy of the standards document, e.g. \"Understand and apply the Pythagorean Theorem,\" or \"Geometry\".
:param str id:
:param str learning_standard_item_code: A code designated by the promulgating body to identify the statement, e.g. 1.N.3 (usually not globally unique).
:param str namespace: The namespace of the organization or entity who governs the standard. It is recommended the namespaces observe a URI format and begin with a domain name under the governing organization or entity control.
:param str success_criteria: One or more statements that describes the criteria used by teachers and students to check for attainment of a learning standard. This criteria gives clear indications as to the degree to which learning is moving through the Zone or Proximal Development toward independent achievement of the LearningStandard.
:param str uri: An unambiguous reference to the statement using a network-resolvable URI.
:return: list[EdFiLearningStandard]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['offset', 'limit', 'min_change_version', 'max_change_version', 'total_count', 'learning_standard_id', 'parent_learning_standard_id', 'learning_standard_category_descriptor', 'learning_standard_scope_descriptor', 'course_title', 'description', 'id', 'learning_standard_item_code', 'namespace', 'success_criteria', 'uri'] # 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_learning_standards" % key
)
params[key] = val
del params['kwargs']
if self.api_client.client_side_validation and ('limit' in params and params['limit'] > 500): # noqa: E501
raise ValueError("Invalid value for parameter `limit` when calling `get_learning_standards`, must be a value less than or equal to `500`") # noqa: E501
if self.api_client.client_side_validation and ('limit' in params and params['limit'] < 0): # noqa: E501
raise ValueError("Invalid value for parameter `limit` when calling `get_learning_standards`, must be a value greater than or equal to `0`") # noqa: E501
if self.api_client.client_side_validation and ('learning_standard_id' in params and
len(params['learning_standard_id']) > 60):
raise ValueError("Invalid value for parameter `learning_standard_id` when calling `get_learning_standards`, length must be less than or equal to `60`") # noqa: E501
if self.api_client.client_side_validation and ('parent_learning_standard_id' in params and
len(params['parent_learning_standard_id']) > 60):
raise ValueError("Invalid value for parameter `parent_learning_standard_id` when calling `get_learning_standards`, length must be less than or equal to `60`") # noqa: E501
if self.api_client.client_side_validation and ('learning_standard_category_descriptor' in params and
len(params['learning_standard_category_descriptor']) > 306):
raise ValueError("Invalid value for parameter `learning_standard_category_descriptor` when calling `get_learning_standards`, length must be less than or equal to `306`") # noqa: E501
if self.api_client.client_side_validation and ('learning_standard_scope_descriptor' in params and
len(params['learning_standard_scope_descriptor']) > 306):
raise ValueError("Invalid value for parameter `learning_standard_scope_descriptor` when calling `get_learning_standards`, length must be less than or equal to `306`") # noqa: E501
if self.api_client.client_side_validation and ('course_title' in params and
len(params['course_title']) > 60):
raise ValueError("Invalid value for parameter `course_title` when calling `get_learning_standards`, length must be less than or equal to `60`") # noqa: E501
if self.api_client.client_side_validation and ('description' in params and
len(params['description']) > 1024):
raise ValueError("Invalid value for parameter `description` when calling `get_learning_standards`, length must be less than or equal to `1024`") # noqa: E501
if self.api_client.client_side_validation and ('learning_standard_item_code' in params and
len(params['learning_standard_item_code']) > 60):
raise ValueError("Invalid value for parameter `learning_standard_item_code` when calling `get_learning_standards`, length must be less than or equal to `60`") # noqa: E501
if self.api_client.client_side_validation and ('namespace' in params and
len(params['namespace']) > 255):
raise ValueError("Invalid value for parameter `namespace` when calling `get_learning_standards`, length must be less than or equal to `255`") # noqa: E501
if self.api_client.client_side_validation and ('success_criteria' in params and
len(params['success_criteria']) > 150):
raise ValueError("Invalid value for parameter `success_criteria` when calling `get_learning_standards`, length must be less than or equal to `150`") # noqa: E501
if self.api_client.client_side_validation and ('uri' in params and
len(params['uri']) > 255):
raise ValueError("Invalid value for parameter `uri` when calling `get_learning_standards`, length must be less than or equal to `255`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'offset' in params:
query_params.append(('offset', params['offset'])) # noqa: E501
if 'limit' in params:
query_params.append(('limit', params['limit'])) # noqa: E501
if 'min_change_version' in params:
query_params.append(('minChangeVersion', params['min_change_version'])) # noqa: E501
if 'max_change_version' in params:
query_params.append(('maxChangeVersion', params['max_change_version'])) # noqa: E501
if 'total_count' in params:
query_params.append(('totalCount', params['total_count'])) # noqa: E501
if 'learning_standard_id' in params:
query_params.append(('learningStandardId', params['learning_standard_id'])) # noqa: E501
if 'parent_learning_standard_id' in params:
query_params.append(('parentLearningStandardId', params['parent_learning_standard_id'])) # noqa: E501
if 'learning_standard_category_descriptor' in params:
query_params.append(('learningStandardCategoryDescriptor', params['learning_standard_category_descriptor'])) # noqa: E501
if 'learning_standard_scope_descriptor' in params:
query_params.append(('learningStandardScopeDescriptor', params['learning_standard_scope_descriptor'])) # noqa: E501
if 'course_title' in params:
query_params.append(('courseTitle', params['course_title'])) # noqa: E501
if 'description' in params:
query_params.append(('description', params['description'])) # noqa: E501
if 'id' in params:
query_params.append(('id', params['id'])) # noqa: E501
if 'learning_standard_item_code' in params:
query_params.append(('learningStandardItemCode', params['learning_standard_item_code'])) # noqa: E501
if 'namespace' in params:
query_params.append(('namespace', params['namespace'])) # noqa: E501
if 'success_criteria' in params:
query_params.append(('successCriteria', params['success_criteria'])) # noqa: E501
if 'uri' in params:
query_params.append(('uri', params['uri'])) # 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
# 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 = ['oauth2_client_credentials'] # noqa: E501
return self.api_client.call_api(
'/ed-fi/learningStandards', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[EdFiLearningStandard]', # 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_learning_standards_by_id(self, id, **kwargs): # noqa: E501
"""Retrieves a specific resource using the resource's identifier (using the \"Get By Id\" pattern). # noqa: E501
This GET operation retrieves a resource by the specified resource identifier. # 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_learning_standards_by_id(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: A resource identifier that uniquely identifies the resource. (required)
:param str if_none_match: The previously returned ETag header value, used here to prevent the unnecessary data transfer of an unchanged resource.
:return: EdFiLearningStandard
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_learning_standards_by_id_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.get_learning_standards_by_id_with_http_info(id, **kwargs) # noqa: E501
return data
def get_learning_standards_by_id_with_http_info(self, id, **kwargs): # noqa: E501
"""Retrieves a specific resource using the resource's identifier (using the \"Get By Id\" pattern). # noqa: E501
This GET operation retrieves a resource by the specified resource identifier. # 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_learning_standards_by_id_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: A resource identifier that uniquely identifies the resource. (required)
:param str if_none_match: The previously returned ETag header value, used here to prevent the unnecessary data transfer of an unchanged resource.
:return: EdFiLearningStandard
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'if_none_match'] # 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_learning_standards_by_id" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if self.api_client.client_side_validation and ('id' not in params or
params['id'] is None): # noqa: E501
raise ValueError("Missing the required parameter `id` when calling `get_learning_standards_by_id`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
if 'if_none_match' in params:
header_params['If-None-Match'] = params['if_none_match'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2_client_credentials'] # noqa: E501
return self.api_client.call_api(
'/ed-fi/learningStandards/{id}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='EdFiLearningStandard', # 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 post_learning_standard(self, learning_standard, **kwargs): # noqa: E501
"""Creates or updates resources based on the natural key values of the supplied resource. # noqa: E501
The POST operation can be used to create or update resources. In database terms, this is often referred to as an \"upsert\" operation (insert + update). Clients should NOT include the resource \"id\" in the JSON body because it will result in an error (you must use a PUT operation to update a resource by \"id\"). The web service will identify whether the resource already exists based on the natural key values provided, and update or create the resource appropriately. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.post_learning_standard(learning_standard, async_req=True)
>>> result = thread.get()
:param async_req bool
:param EdFiLearningStandard learning_standard: The JSON representation of the \"learningStandard\" resource to be created or updated. (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.post_learning_standard_with_http_info(learning_standard, **kwargs) # noqa: E501
else:
(data) = self.post_learning_standard_with_http_info(learning_standard, **kwargs) # noqa: E501
return data
def post_learning_standard_with_http_info(self, learning_standard, **kwargs): # noqa: E501
"""Creates or updates resources based on the natural key values of the supplied resource. # noqa: E501
The POST operation can be used to create or update resources. In database terms, this is often referred to as an \"upsert\" operation (insert + update). Clients should NOT include the resource \"id\" in the JSON body because it will result in an error (you must use a PUT operation to update a resource by \"id\"). The web service will identify whether the resource already exists based on the natural key values provided, and update or create the resource appropriately. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.post_learning_standard_with_http_info(learning_standard, async_req=True)
>>> result = thread.get()
:param async_req bool
:param EdFiLearningStandard learning_standard: The JSON representation of the \"learningStandard\" resource to be created or updated. (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['learning_standard'] # 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 post_learning_standard" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'learning_standard' is set
if self.api_client.client_side_validation and ('learning_standard' not in params or
params['learning_standard'] is None): # noqa: E501
raise ValueError("Missing the required parameter `learning_standard` when calling `post_learning_standard`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'learning_standard' in params:
body_params = params['learning_standard']
# 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 = ['oauth2_client_credentials'] # noqa: E501
return self.api_client.call_api(
'/ed-fi/learningStandards', 'POST',
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 put_learning_standard(self, id, learning_standard, **kwargs): # noqa: E501
"""Updates or creates a resource based on the resource identifier. # noqa: E501
The PUT operation is used to update or create a resource by identifier. If the resource doesn't exist, the resource will be created using that identifier. Additionally, natural key values cannot be changed using this operation, and will not be modified in the database. If the resource \"id\" is provided in the JSON body, it will be ignored as well. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.put_learning_standard(id, learning_standard, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: A resource identifier that uniquely identifies the resource. (required)
:param EdFiLearningStandard learning_standard: The JSON representation of the \"learningStandard\" resource to be created or updated. (required)
:param str if_match: The ETag header value used to prevent the PUT from updating a resource modified by another consumer.
: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.put_learning_standard_with_http_info(id, learning_standard, **kwargs) # noqa: E501
else:
(data) = self.put_learning_standard_with_http_info(id, learning_standard, **kwargs) # noqa: E501
return data
def put_learning_standard_with_http_info(self, id, learning_standard, **kwargs): # noqa: E501
"""Updates or creates a resource based on the resource identifier. # noqa: E501
The PUT operation is used to update or create a resource by identifier. If the resource doesn't exist, the resource will be created using that identifier. Additionally, natural key values cannot be changed using this operation, and will not be modified in the database. If the resource \"id\" is provided in the JSON body, it will be ignored as well. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.put_learning_standard_with_http_info(id, learning_standard, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: A resource identifier that uniquely identifies the resource. (required)
:param EdFiLearningStandard learning_standard: The JSON representation of the \"learningStandard\" resource to be created or updated. (required)
:param str if_match: The ETag header value used to prevent the PUT from updating a resource modified by another consumer.
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'learning_standard', 'if_match'] # 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 put_learning_standard" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if self.api_client.client_side_validation and ('id' not in params or
params['id'] is None): # noqa: E501
raise ValueError("Missing the required parameter `id` when calling `put_learning_standard`") # noqa: E501
# verify the required parameter 'learning_standard' is set
if self.api_client.client_side_validation and ('learning_standard' not in params or
params['learning_standard'] is None): # noqa: E501
raise ValueError("Missing the required parameter `learning_standard` when calling `put_learning_standard`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
if 'if_match' in params:
header_params['If-Match'] = params['if_match'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'learning_standard' in params:
body_params = params['learning_standard']
# 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 = ['oauth2_client_credentials'] # noqa: E501
return self.api_client.call_api(
'/ed-fi/learningStandards/{id}', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 57.561917 | 493 | 0.662264 | 5,348 | 43,229 | 5.161743 | 0.078908 | 0.040283 | 0.018366 | 0.015649 | 0.939612 | 0.920377 | 0.902445 | 0.891867 | 0.877667 | 0.874334 | 0 | 0.016622 | 0.259617 | 43,229 | 750 | 494 | 57.638667 | 0.845873 | 0.425409 | 0 | 0.703884 | 0 | 0.033981 | 0.273156 | 0.099325 | 0 | 0 | 0 | 0 | 0 | 1 | 0.031553 | false | 0 | 0.009709 | 0 | 0.087379 | 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 |
7c824e699bce43232d27a58d4d1fbf1a6bcbbd83 | 56,315 | py | Python | sdks/python/appcenter_sdk/api/push_api.py | Brantone/appcenter-sdks | eeb063ecf79908b6e341fb00196d2cd9dc8f3262 | [
"MIT"
] | null | null | null | sdks/python/appcenter_sdk/api/push_api.py | Brantone/appcenter-sdks | eeb063ecf79908b6e341fb00196d2cd9dc8f3262 | [
"MIT"
] | 6 | 2019-10-23T06:38:53.000Z | 2022-01-22T07:57:58.000Z | sdks/python/appcenter_sdk/api/push_api.py | Brantone/appcenter-sdks | eeb063ecf79908b6e341fb00196d2cd9dc8f3262 | [
"MIT"
] | 2 | 2019-10-23T06:31:05.000Z | 2021-08-21T17:32:47.000Z | # coding: utf-8
"""
App Center Client
Microsoft Visual Studio App Center API # noqa: E501
OpenAPI spec version: preview
Contact: benedetto.abbenanti@gmail.com
Project Repository: https://github.com/b3nab/appcenter-sdks
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from appcenter_sdk.api_client import ApiClient
class pushApi(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 Push_ConfigExists(self, owner_name, app_name, **kwargs): # noqa: E501
"""Push_ConfigExists # noqa: E501
Returns whether a push configuration exists for the selected app. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_ConfigExists(owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.Push_ConfigExists_with_http_info(owner_name, app_name, **kwargs) # noqa: E501
else:
(data) = self.Push_ConfigExists_with_http_info(owner_name, app_name, **kwargs) # noqa: E501
return data
def Push_ConfigExists_with_http_info(self, owner_name, app_name, **kwargs): # noqa: E501
"""Push_ConfigExists # noqa: E501
Returns whether a push configuration exists for the selected app. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_ConfigExists_with_http_info(owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['owner_name', 'app_name'] # noqa: E501
all_params.append('async')
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 Push_ConfigExists" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'owner_name' is set
if ('owner_name' not in params or
params['owner_name'] is None):
raise ValueError("Missing the required parameter `owner_name` when calling `Push_ConfigExists`") # noqa: E501
# verify the required parameter 'app_name' is set
if ('app_name' not in params or
params['app_name'] is None):
raise ValueError("Missing the required parameter `app_name` when calling `Push_ConfigExists`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner_name' in params:
path_params['owner_name'] = params['owner_name'] # noqa: E501
if 'app_name' in params:
path_params['app_name'] = params['app_name'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'multipart/form-data', 'application/json-patch+json']) # noqa: E501
# Authentication setting
auth_settings = ['APIToken'] # noqa: E501
return self.api_client.call_api(
'/v0.1/apps/{owner_name}/{app_name}/push/notifications_config', 'HEAD',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ErrorResponse', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_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 Push_GetConfig(self, owner_name, app_name, **kwargs): # noqa: E501
"""Push_GetConfig # noqa: E501
Get the push configuration for the selected app. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_GetConfig(owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.Push_GetConfig_with_http_info(owner_name, app_name, **kwargs) # noqa: E501
else:
(data) = self.Push_GetConfig_with_http_info(owner_name, app_name, **kwargs) # noqa: E501
return data
def Push_GetConfig_with_http_info(self, owner_name, app_name, **kwargs): # noqa: E501
"""Push_GetConfig # noqa: E501
Get the push configuration for the selected app. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_GetConfig_with_http_info(owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['owner_name', 'app_name'] # noqa: E501
all_params.append('async')
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 Push_GetConfig" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'owner_name' is set
if ('owner_name' not in params or
params['owner_name'] is None):
raise ValueError("Missing the required parameter `owner_name` when calling `Push_GetConfig`") # noqa: E501
# verify the required parameter 'app_name' is set
if ('app_name' not in params or
params['app_name'] is None):
raise ValueError("Missing the required parameter `app_name` when calling `Push_GetConfig`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner_name' in params:
path_params['owner_name'] = params['owner_name'] # noqa: E501
if 'app_name' in params:
path_params['app_name'] = params['app_name'] # 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', 'application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'multipart/form-data', 'application/json-patch+json']) # noqa: E501
# Authentication setting
auth_settings = ['APIToken'] # noqa: E501
return self.api_client.call_api(
'/v0.1/apps/{owner_name}/{app_name}/push/notifications_config', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ErrorResponse', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_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 Push_SetConfig(self, owner_name, app_name, body, **kwargs): # noqa: E501
"""Push_SetConfig # noqa: E501
Set the push configuration for the selected app. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_SetConfig(owner_name, app_name, body, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:param object body: Notification configurations. (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.Push_SetConfig_with_http_info(owner_name, app_name, body, **kwargs) # noqa: E501
else:
(data) = self.Push_SetConfig_with_http_info(owner_name, app_name, body, **kwargs) # noqa: E501
return data
def Push_SetConfig_with_http_info(self, owner_name, app_name, body, **kwargs): # noqa: E501
"""Push_SetConfig # noqa: E501
Set the push configuration for the selected app. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_SetConfig_with_http_info(owner_name, app_name, body, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:param object body: Notification configurations. (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['owner_name', 'app_name', 'body'] # noqa: E501
all_params.append('async')
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 Push_SetConfig" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'owner_name' is set
if ('owner_name' not in params or
params['owner_name'] is None):
raise ValueError("Missing the required parameter `owner_name` when calling `Push_SetConfig`") # noqa: E501
# verify the required parameter 'app_name' is set
if ('app_name' not in params or
params['app_name'] is None):
raise ValueError("Missing the required parameter `app_name` when calling `Push_SetConfig`") # noqa: E501
# 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 `Push_SetConfig`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner_name' in params:
path_params['owner_name'] = params['owner_name'] # noqa: E501
if 'app_name' in params:
path_params['app_name'] = params['app_name'] # noqa: E501
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', '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 = ['APIToken'] # noqa: E501
return self.api_client.call_api(
'/v0.1/apps/{owner_name}/{app_name}/push/notifications_config', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ErrorResponse', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_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 Push_DeleteConfig(self, owner_name, app_name, **kwargs): # noqa: E501
"""Push_DeleteConfig # noqa: E501
Delete the push configuration for the selected app. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_DeleteConfig(owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.Push_DeleteConfig_with_http_info(owner_name, app_name, **kwargs) # noqa: E501
else:
(data) = self.Push_DeleteConfig_with_http_info(owner_name, app_name, **kwargs) # noqa: E501
return data
def Push_DeleteConfig_with_http_info(self, owner_name, app_name, **kwargs): # noqa: E501
"""Push_DeleteConfig # noqa: E501
Delete the push configuration for the selected app. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_DeleteConfig_with_http_info(owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['owner_name', 'app_name'] # noqa: E501
all_params.append('async')
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 Push_DeleteConfig" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'owner_name' is set
if ('owner_name' not in params or
params['owner_name'] is None):
raise ValueError("Missing the required parameter `owner_name` when calling `Push_DeleteConfig`") # noqa: E501
# verify the required parameter 'app_name' is set
if ('app_name' not in params or
params['app_name'] is None):
raise ValueError("Missing the required parameter `app_name` when calling `Push_DeleteConfig`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner_name' in params:
path_params['owner_name'] = params['owner_name'] # noqa: E501
if 'app_name' in params:
path_params['app_name'] = params['app_name'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'multipart/form-data', 'application/json-patch+json']) # noqa: E501
# Authentication setting
auth_settings = ['APIToken'] # noqa: E501
return self.api_client.call_api(
'/v0.1/apps/{owner_name}/{app_name}/push/notifications_config', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ErrorResponse', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_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 Push_Get(self, notification_id, owner_name, app_name, **kwargs): # noqa: E501
"""Push_Get # noqa: E501
Get details about a specific notification. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_Get(notification_id, owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string notification_id: The id of the notification. (required)
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.Push_Get_with_http_info(notification_id, owner_name, app_name, **kwargs) # noqa: E501
else:
(data) = self.Push_Get_with_http_info(notification_id, owner_name, app_name, **kwargs) # noqa: E501
return data
def Push_Get_with_http_info(self, notification_id, owner_name, app_name, **kwargs): # noqa: E501
"""Push_Get # noqa: E501
Get details about a specific notification. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_Get_with_http_info(notification_id, owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string notification_id: The id of the notification. (required)
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['notification_id', 'owner_name', 'app_name'] # noqa: E501
all_params.append('async')
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 Push_Get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'notification_id' is set
if ('notification_id' not in params or
params['notification_id'] is None):
raise ValueError("Missing the required parameter `notification_id` when calling `Push_Get`") # noqa: E501
# verify the required parameter 'owner_name' is set
if ('owner_name' not in params or
params['owner_name'] is None):
raise ValueError("Missing the required parameter `owner_name` when calling `Push_Get`") # noqa: E501
# verify the required parameter 'app_name' is set
if ('app_name' not in params or
params['app_name'] is None):
raise ValueError("Missing the required parameter `app_name` when calling `Push_Get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'notification_id' in params:
path_params['notification_id'] = params['notification_id'] # noqa: E501
if 'owner_name' in params:
path_params['owner_name'] = params['owner_name'] # noqa: E501
if 'app_name' in params:
path_params['app_name'] = params['app_name'] # 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', 'application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'multipart/form-data', 'application/json-patch+json']) # noqa: E501
# Authentication setting
auth_settings = ['APIToken'] # noqa: E501
return self.api_client.call_api(
'/v0.1/apps/{owner_name}/{app_name}/push/notifications/{notification_id}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ErrorResponse', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_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 Push_List(self, owner_name, app_name, **kwargs): # noqa: E501
"""Push_List # noqa: E501
Get a list of notifications from the service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_List(owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:param integer $top: The maximum number of results to return. (0 will fetch all results)(optional)
:param string $skiptoken: The value identifies a starting point in the collection of entities. This parameter along with limit is used to perform pagination.(optional)
:param string $orderby: controls the sorting order and sorting based on which column(optional)
:param string $inlinecount: Controls whether or not to include a count of all the items across all pages.(optional)
:param boolean include_archived: Include arhived push notifications(optional)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.Push_List_with_http_info(owner_name, app_name, **kwargs) # noqa: E501
else:
(data) = self.Push_List_with_http_info(owner_name, app_name, **kwargs) # noqa: E501
return data
def Push_List_with_http_info(self, owner_name, app_name, **kwargs): # noqa: E501
"""Push_List # noqa: E501
Get a list of notifications from the service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_List_with_http_info(owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:param integer $top: The maximum number of results to return. (0 will fetch all results)(optional, default to )
:param string $skiptoken: The value identifies a starting point in the collection of entities. This parameter along with limit is used to perform pagination.(optional)
:param string $orderby: controls the sorting order and sorting based on which column(optional, default to )
:param string $inlinecount: Controls whether or not to include a count of all the items across all pages.(optional, default to )
:param boolean include_archived: Include arhived push notifications(optional)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['owner_name', 'app_name', '$top', '$skiptoken', '$orderby', '$inlinecount', 'include_archived'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method Push_List" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'owner_name' is set
if ('owner_name' not in params or
params['owner_name'] is None):
raise ValueError("Missing the required parameter `owner_name` when calling `Push_List`") # noqa: E501
# verify the required parameter 'app_name' is set
if ('app_name' not in params or
params['app_name'] is None):
raise ValueError("Missing the required parameter `app_name` when calling `Push_List`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner_name' in params:
path_params['owner_name'] = params['owner_name'] # noqa: E501
if 'app_name' in params:
path_params['app_name'] = params['app_name'] # noqa: E501
query_params = []
if '$top' in params:
query_params.append(('$top', params['$top'])) # noqa: E501
if '$skiptoken' in params:
query_params.append(('$skiptoken', params['$skiptoken'])) # noqa: E501
if '$orderby' in params:
query_params.append(('$orderby', params['$orderby'])) # noqa: E501
if '$inlinecount' in params:
query_params.append(('$inlinecount', params['$inlinecount'])) # noqa: E501
if 'include_archived' in params:
query_params.append(('include_archived', params['include_archived'])) # 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', 'application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'multipart/form-data', 'application/json-patch+json']) # noqa: E501
# Authentication setting
auth_settings = ['APIToken'] # noqa: E501
return self.api_client.call_api(
'/v0.1/apps/{owner_name}/{app_name}/push/notifications', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ErrorResponse', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_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 Push_Send(self, owner_name, app_name, body, **kwargs): # noqa: E501
"""Push_Send # noqa: E501
Send a notification to one or more devices. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_Send(owner_name, app_name, body, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:param object body: Notification specifications. (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.Push_Send_with_http_info(owner_name, app_name, body, **kwargs) # noqa: E501
else:
(data) = self.Push_Send_with_http_info(owner_name, app_name, body, **kwargs) # noqa: E501
return data
def Push_Send_with_http_info(self, owner_name, app_name, body, **kwargs): # noqa: E501
"""Push_Send # noqa: E501
Send a notification to one or more devices. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_Send_with_http_info(owner_name, app_name, body, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:param object body: Notification specifications. (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['owner_name', 'app_name', 'body'] # noqa: E501
all_params.append('async')
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 Push_Send" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'owner_name' is set
if ('owner_name' not in params or
params['owner_name'] is None):
raise ValueError("Missing the required parameter `owner_name` when calling `Push_Send`") # noqa: E501
# verify the required parameter 'app_name' is set
if ('app_name' not in params or
params['app_name'] is None):
raise ValueError("Missing the required parameter `app_name` when calling `Push_Send`") # noqa: E501
# 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 `Push_Send`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner_name' in params:
path_params['owner_name'] = params['owner_name'] # noqa: E501
if 'app_name' in params:
path_params['app_name'] = params['app_name'] # noqa: E501
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', '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 = ['APIToken'] # noqa: E501
return self.api_client.call_api(
'/v0.1/apps/{owner_name}/{app_name}/push/notifications', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ErrorResponse', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_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 Push_Delete(self, owner_name, app_name, body, **kwargs): # noqa: E501
"""Push_Delete # noqa: E501
Delete a notification. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_Delete(owner_name, app_name, body, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:param object body: List of notification ids (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.Push_Delete_with_http_info(owner_name, app_name, body, **kwargs) # noqa: E501
else:
(data) = self.Push_Delete_with_http_info(owner_name, app_name, body, **kwargs) # noqa: E501
return data
def Push_Delete_with_http_info(self, owner_name, app_name, body, **kwargs): # noqa: E501
"""Push_Delete # noqa: E501
Delete a notification. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_Delete_with_http_info(owner_name, app_name, body, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:param object body: List of notification ids (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['owner_name', 'app_name', 'body'] # noqa: E501
all_params.append('async')
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 Push_Delete" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'owner_name' is set
if ('owner_name' not in params or
params['owner_name'] is None):
raise ValueError("Missing the required parameter `owner_name` when calling `Push_Delete`") # noqa: E501
# verify the required parameter 'app_name' is set
if ('app_name' not in params or
params['app_name'] is None):
raise ValueError("Missing the required parameter `app_name` when calling `Push_Delete`") # noqa: E501
# 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 `Push_Delete`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner_name' in params:
path_params['owner_name'] = params['owner_name'] # noqa: E501
if 'app_name' in params:
path_params['app_name'] = params['app_name'] # noqa: E501
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 = ['APIToken'] # noqa: E501
return self.api_client.call_api(
'/v0.1/apps/{owner_name}/{app_name}/push/notifications', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ErrorResponse', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_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 Push_DeleteInstallId(self, install_id, owner_name, app_name, **kwargs): # noqa: E501
"""Push_DeleteInstallId # noqa: E501
Delete a device with the selected installId. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_DeleteInstallId(install_id, owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string install_id: device install id (required)
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.Push_DeleteInstallId_with_http_info(install_id, owner_name, app_name, **kwargs) # noqa: E501
else:
(data) = self.Push_DeleteInstallId_with_http_info(install_id, owner_name, app_name, **kwargs) # noqa: E501
return data
def Push_DeleteInstallId_with_http_info(self, install_id, owner_name, app_name, **kwargs): # noqa: E501
"""Push_DeleteInstallId # noqa: E501
Delete a device with the selected installId. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_DeleteInstallId_with_http_info(install_id, owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string install_id: device install id (required)
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['install_id', 'owner_name', 'app_name'] # noqa: E501
all_params.append('async')
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 Push_DeleteInstallId" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'install_id' is set
if ('install_id' not in params or
params['install_id'] is None):
raise ValueError("Missing the required parameter `install_id` when calling `Push_DeleteInstallId`") # noqa: E501
# verify the required parameter 'owner_name' is set
if ('owner_name' not in params or
params['owner_name'] is None):
raise ValueError("Missing the required parameter `owner_name` when calling `Push_DeleteInstallId`") # noqa: E501
# verify the required parameter 'app_name' is set
if ('app_name' not in params or
params['app_name'] is None):
raise ValueError("Missing the required parameter `app_name` when calling `Push_DeleteInstallId`") # noqa: E501
collection_formats = {}
path_params = {}
if 'install_id' in params:
path_params['install_id'] = params['install_id'] # noqa: E501
if 'owner_name' in params:
path_params['owner_name'] = params['owner_name'] # noqa: E501
if 'app_name' in params:
path_params['app_name'] = params['app_name'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'multipart/form-data', 'application/json-patch+json']) # noqa: E501
# Authentication setting
auth_settings = ['APIToken'] # noqa: E501
return self.api_client.call_api(
'/v0.1/apps/{owner_name}/{app_name}/push/devices/{install_id}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ErrorResponse', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_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 Push_ExportDevicesStatus(self, export_id, owner_name, app_name, **kwargs): # noqa: E501
"""Push_ExportDevicesStatus # noqa: E501
Get the status of an export operation. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_ExportDevicesStatus(export_id, owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string export_id: The id of the export. (required)
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.Push_ExportDevicesStatus_with_http_info(export_id, owner_name, app_name, **kwargs) # noqa: E501
else:
(data) = self.Push_ExportDevicesStatus_with_http_info(export_id, owner_name, app_name, **kwargs) # noqa: E501
return data
def Push_ExportDevicesStatus_with_http_info(self, export_id, owner_name, app_name, **kwargs): # noqa: E501
"""Push_ExportDevicesStatus # noqa: E501
Get the status of an export operation. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_ExportDevicesStatus_with_http_info(export_id, owner_name, app_name, async=True)
>>> result = thread.get()
:param async bool
:param string export_id: The id of the export. (required)
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['export_id', 'owner_name', 'app_name'] # noqa: E501
all_params.append('async')
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 Push_ExportDevicesStatus" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'export_id' is set
if ('export_id' not in params or
params['export_id'] is None):
raise ValueError("Missing the required parameter `export_id` when calling `Push_ExportDevicesStatus`") # noqa: E501
# verify the required parameter 'owner_name' is set
if ('owner_name' not in params or
params['owner_name'] is None):
raise ValueError("Missing the required parameter `owner_name` when calling `Push_ExportDevicesStatus`") # noqa: E501
# verify the required parameter 'app_name' is set
if ('app_name' not in params or
params['app_name'] is None):
raise ValueError("Missing the required parameter `app_name` when calling `Push_ExportDevicesStatus`") # noqa: E501
collection_formats = {}
path_params = {}
if 'export_id' in params:
path_params['export_id'] = params['export_id'] # noqa: E501
if 'owner_name' in params:
path_params['owner_name'] = params['owner_name'] # noqa: E501
if 'app_name' in params:
path_params['app_name'] = params['app_name'] # 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', 'application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'multipart/form-data', 'application/json-patch+json']) # noqa: E501
# Authentication setting
auth_settings = ['APIToken'] # noqa: E501
return self.api_client.call_api(
'/v0.1/apps/{owner_name}/{app_name}/push/device_exports/{export_id}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ErrorResponse', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_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 Push_ExportDevices(self, owner_name, app_name, body, **kwargs): # noqa: E501
"""Push_ExportDevices # noqa: E501
Exports information for all devices using Push to Azure Blob Storage # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_ExportDevices(owner_name, app_name, body, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:param object body: Export configurations. (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.Push_ExportDevices_with_http_info(owner_name, app_name, body, **kwargs) # noqa: E501
else:
(data) = self.Push_ExportDevices_with_http_info(owner_name, app_name, body, **kwargs) # noqa: E501
return data
def Push_ExportDevices_with_http_info(self, owner_name, app_name, body, **kwargs): # noqa: E501
"""Push_ExportDevices # noqa: E501
Exports information for all devices using Push to Azure Blob Storage # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.Push_ExportDevices_with_http_info(owner_name, app_name, body, async=True)
>>> result = thread.get()
:param async bool
:param string owner_name: The name of the owner (required)
:param string app_name: The name of the application (required)
:param object body: Export configurations. (required)
:return: ErrorResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['owner_name', 'app_name', 'body'] # noqa: E501
all_params.append('async')
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 Push_ExportDevices" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'owner_name' is set
if ('owner_name' not in params or
params['owner_name'] is None):
raise ValueError("Missing the required parameter `owner_name` when calling `Push_ExportDevices`") # noqa: E501
# verify the required parameter 'app_name' is set
if ('app_name' not in params or
params['app_name'] is None):
raise ValueError("Missing the required parameter `app_name` when calling `Push_ExportDevices`") # noqa: E501
# 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 `Push_ExportDevices`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner_name' in params:
path_params['owner_name'] = params['owner_name'] # noqa: E501
if 'app_name' in params:
path_params['app_name'] = params['app_name'] # noqa: E501
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', '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 = ['APIToken'] # noqa: E501
return self.api_client.call_api(
'/v0.1/apps/{owner_name}/{app_name}/push/device_exports', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ErrorResponse', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_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)
| 43.756799 | 175 | 0.61987 | 6,650 | 56,315 | 5.026165 | 0.03609 | 0.051221 | 0.031594 | 0.042125 | 0.96323 | 0.951053 | 0.947463 | 0.940821 | 0.940821 | 0.936333 | 0 | 0.016772 | 0.287472 | 56,315 | 1,286 | 176 | 43.790824 | 0.816204 | 0.073089 | 0 | 0.799431 | 0 | 0 | 0.233956 | 0.049963 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.00569 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
7ca9ee068ca914e1215236d391d9084130fd9509 | 2,624 | py | Python | contrib/attic/pydatavec/pydatavec/conditions.py | eric-erki/deeplearning4j | b9d462f66879e9315767b70190bd2ab31b9a3275 | [
"Apache-2.0"
] | null | null | null | contrib/attic/pydatavec/pydatavec/conditions.py | eric-erki/deeplearning4j | b9d462f66879e9315767b70190bd2ab31b9a3275 | [
"Apache-2.0"
] | null | null | null | contrib/attic/pydatavec/pydatavec/conditions.py | eric-erki/deeplearning4j | b9d462f66879e9315767b70190bd2ab31b9a3275 | [
"Apache-2.0"
] | null | null | null | # /* ******************************************************************************
# * Copyright (c) 2021 Deeplearning4j Contributors
# *
# * This program and the accompanying materials are made available under the
# * terms of the Apache License, Version 2.0 which is available at
# * https://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.
# *
# * SPDX-License-Identifier: Apache-2.0
# ******************************************************************************/
################################################################################
#
# This program and the accompanying materials are made available under the
# terms of the Apache License, Version 2.0 which is available at
# https://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.
#
# SPDX-License-Identifier: Apache-2.0
################################################################################
class Condition(object):
@property
def name(self):
return self.__class__.__name__
class InSet(Condition):
def __init__(self, column, set):
self.column = column
self.set = set
class NotInSet(Condition):
def __init__(self, column, set):
self.column = column
self.set = set
class Equals(Condition):
def __init__(self, column, value):
self.column = column
self.value = value
class NotEquals(Condition):
def __init__(self, column, value):
self.column = column
self.value = value
class LessThan(Condition):
def __init__(self, column, value):
self.column = column
self.value = value
class LessThanOrEqual(Condition):
def __init__(self, column, value):
self.column = column
self.value = value
class GreaterThan(Condition):
def __init__(self, column, value):
self.column = column
self.value = value
class GreaterThanOrEqual(Condition):
def __init__(self, column, value):
self.column = column
self.value = value
| 30.511628 | 84 | 0.605183 | 296 | 2,624 | 5.22973 | 0.277027 | 0.103359 | 0.082687 | 0.103359 | 0.881783 | 0.881783 | 0.881783 | 0.881783 | 0.881783 | 0.881783 | 0 | 0.008088 | 0.198933 | 2,624 | 85 | 85 | 30.870588 | 0.728354 | 0.498476 | 0 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0 | 0.027778 | 0.527778 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 8 |
7cc29b29430ef0e7a8a92bae5ac2c132c85c5b6d | 83,066 | py | Python | cogs/mod.py | TechnoFrost27/MainesianUtilities | 23c4b2f289cce144242dd6f9c6f76f51bd0f1682 | [
"MIT"
] | null | null | null | cogs/mod.py | TechnoFrost27/MainesianUtilities | 23c4b2f289cce144242dd6f9c6f76f51bd0f1682 | [
"MIT"
] | 1 | 2022-01-25T03:01:07.000Z | 2022-01-25T03:01:07.000Z | cogs/mod.py | TechnoFrost27/MainesianUtilities | 23c4b2f289cce144242dd6f9c6f76f51bd0f1682 | [
"MIT"
] | 1 | 2022-01-25T02:54:23.000Z | 2022-01-25T02:54:23.000Z | import nextcord
import asyncio
from nextcord.ext import commands
import random
from tinydb import TinyDB, Query
import re
from discord_slash import cog_ext, SlashContext
from discord_slash.utils.manage_commands import create_option, create_choice
import typing
import time
time_regex = re.compile(r"(\d{1,5}(?:[.,]?\d{1,5})?)([smhd])")
time_dict = {"h": 3600, "s": 1, "m": 60, "d": 86400}
class TimeConverter(commands.Converter):
async def convert(self, ctx, argument):
matches = time_regex.findall(argument.lower())
time = 0
for v, k in matches:
try:
time += time_dict[k] * float(v)
except KeyError:
raise commands.BadArgument(
"{} is an invalid time-key! h/m/s/d are valid!".format(k))
except ValueError:
raise commands.BadArgument("{} is not a number!".format(v))
return time
async def convert(argument):
matches = time_regex.findall(argument.lower())
time = 0
for v, k in matches:
try:
time += time_dict[k] * float(v)
except KeyError:
raise commands.BadArgument(
"{} is an invalid time-key! h/m/s/d are valid!".format(k))
except ValueError:
raise commands.BadArgument("{} is not a number!".format(v))
return time
class Moderation(commands.Cog):
def __init__(self, bot):
self.bot = bot
@commands.cooldown(1, 5, commands.BucketType.user)
@cog_ext.cog_slash(name="ping",
description="This allows you to check my ping.")
async def _ping(self, ctx: SlashContext):
start_time = time.time()
message = await ctx.send(embed=nextcord.Embed(title="Testing Ping...", color=nextcord.Color.random()))
end_time = time.time()
await message.edit(embed=nextcord.Embed(
title=f"Latency: {round(self.bot.latency * 1000)}ms\nAPI: {round((end_time - start_time) * 1000)}ms",
color=nextcord.Color.random()))
@commands.command(aliases=['p'])
async def ping(self, ctx):
"""Get the bot's current websocket and API latency."""
start_time = time.time()
message = await ctx.send(embed=nextcord.Embed(title="Testing Ping...", color=nextcord.Color.random()))
end_time = time.time()
await message.edit(embed=nextcord.Embed(
title=f"Latency: {round(self.bot.latency * 1000)}ms\nAPI: {round((end_time - start_time) * 1000)}ms",
color=nextcord.Color.random()))
@cog_ext.cog_slash(name="slowmode", description="Allows you to put or remove a slowmode in your channel.",
options=[
create_option(name="duration",
description="The time you want the slowmode to be (e.g: 2h or 5m)",
option_type=3,
required=False)])
async def _slowmode(self, ctx: SlashContext, duration='0'):
try:
if duration.isdigit():
duration = int(duration)
else:
duration = await convert(duration)
except:
await ctx.send(embed=nextcord.Embed(title="Please give a proper duration", color=nextcord.Color.random()))
return
if ctx.author.guild_permissions.ban_members:
if duration == 0:
if ctx.channel.slowmode_delay == 0:
await ctx.send(embed=nextcord.Embed(title="Slowmode disabled already dumbass",
color=nextcord.Color.random()))
await ctx.channel.edit(slowmode_delay=duration)
embed = nextcord.Embed(
title="Slowmode Disabled!",
description=f"Y'all can talk your heart out now.",
color=nextcord.Color.random()
)
elif duration <= 21600:
await ctx.channel.edit(slowmode_delay=duration)
embed = nextcord.Embed(
title="Slowmode Enabled!",
description=f"There is a {int(duration)} seconds slowmode on this channel now.",
color=nextcord.Color.random()
)
else:
embed = nextcord.Embed(title="Did you know?",
description=f"Discord allows slowmodes up to 21600 seconds on its channels, which is equal to 360m, which is 6h!",
color=nextcord.Color.random())
embed.set_footer(text="Point being, you can't set a slowmode above that")
await ctx.send(embed=embed)
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!",
description="You require the server's mod permission.",
color=nextcord.Color.red()))
@commands.command(aliases=['slomo', 'slowmo', 'sm', 'slo', 'smode'])
async def slowmode(self, ctx, duration):
try:
if duration.isdigit():
duration = int(duration)
else:
duration = await convert(duration)
except:
await ctx.send(embed=nextcord.Embed(title="Please give a proper duration", color=nextcord.Color.random()))
return
if ctx.author.guild_permissions.ban_members:
if duration == 0:
await ctx.channel.edit(slowmode_delay=duration)
embed = nextcord.Embed(
title="Slowmode Disabled!",
description=f"Y'all can talk your heart out now.",
color=nextcord.Color.random()
)
elif duration <= 21600:
await ctx.channel.edit(slowmode_delay=duration)
embed = nextcord.Embed(
title="Slowmode Enabled!",
description=f"There is a {int(duration)} seconds slowmode on this channel now.",
color=nextcord.Color.random()
)
else:
embed = nextcord.Embed(title="Did you know?",
description=f"Discord allows slowmodes up to 21600 seconds on its channels, which is equal to 360m, which is 6h!",
color=nextcord.Color.random())
embed.set_footer(text="Point being, you can't set a slowmode above that")
await ctx.send(embed=embed)
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!",
description="You require the server's mod permission.",
color=nextcord.Color.red()))
@cog_ext.cog_slash(name="blacklist", description="Blacklists a user.",
options=[
create_option(name="member",
description="The member you want to blacklist",
option_type=6,
required=True)])
async def _blacklist(self, ctx: SlashContext, member: nextcord.Member):
db = TinyDB('databases/blacklist.json')
guild_id_var = ctx.guild.id
if ctx.author.id == 815555652780294175 or ctx.author.id == 723032217504186389:
db.insert({'guild_id': guild_id_var, 'blacklisted': str(member.id)})
await ctx.send(embed=nextcord.Embed(title=f"I'm Sorry, but my boss wants you blacklisted"))
return
elif ctx.author.guild_permissions.ban_members:
if not member:
await ctx.send(embed=nextcord.Embed(title="Please provide a member to blacklist smh"))
return
if member == ctx.author:
embed = nextcord.Embed(title="Bruh why are you trying to blacklist yourself.",
description="I refuse to let your stupidity get the better of you.",
color=nextcord.Color.random())
embed.set_footer(text="Users these days...")
await ctx.send(embed=embed)
return
elif member.id == 815555652780294175 or member.id == 723032217504186389:
await ctx.send(
embed=nextcord.Embed(title="Buddy you can't blacklist the boss <a:ZO_BlobCool:866263738545078302>"))
return
elif member.guild_permissions.ban_members:
await ctx.send(
embed=nextcord.Embed(title="Halt! (lmao)",
description="You cannot just go ahead and stop your fellow admins from using me!",
color=nextcord.Color.red()))
return
elif {"guild_id": guild_id_var, "blacklisted": str(member.id)} in db.all():
await ctx.send(embed=nextcord.Embed(title=f"{member.display_name} is already blacklisted...",
description="Jeez why do you hate him so much",
color=nextcord.Color.teal()))
return
else:
db.insert({'guild_id': guild_id_var, 'blacklisted': str(member.id)})
await ctx.send(embed=nextcord.Embed(title=f"{member.display_name} is blacklisted",
description="He can no longer use me :cry:",
color=nextcord.Color.teal()))
return
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require the administrator permission.",
color=nextcord.Color.red()))
@commands.command(aliases=['blist', 'blackl', 'bl'])
async def blacklist(self, ctx, member: nextcord.Member):
db = TinyDB('databases/blacklist.json')
guild_id_var = ctx.guild.id
print(ctx.author.guild_permissions.ban_members)
if ctx.author.id == 815555652780294175 or ctx.author.id == 723032217504186389:
db.insert({'guild_id': guild_id_var, 'blacklisted': str(member.id)})
await ctx.send(embed=nextcord.Embed(color=nextcord.Color.random(),
title=f"I'm Sorry, but my boss wants you blacklisted"))
return
elif ctx.author.guild_permissions.ban_members == False:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require the administrator permission.",
color=nextcord.Color.red()))
return
elif ctx.author.guild_permissions.ban_members == True:
if not member:
await ctx.send(embed=nextcord.Embed(title="Please provide a member to blacklist smh"))
return
if member == ctx.author:
embed = nextcord.Embed(title="Bruh why are you trying to blacklist yourself.",
description="I refuse to let your stupidity get the better of you.",
color=nextcord.Color.random())
embed.set_footer(text="Users these days...")
await ctx.send(embed=embed)
return
elif member.id == 815555652780294175 or member.id == 723032217504186389:
await ctx.send(
embed=nextcord.Embed(title="Buddy you can't blacklist the boss <a:ZO_BlobCool:866263738545078302>"))
return
elif {"guild_id": guild_id_var, "blacklisted": str(member.id)} in db.all():
await ctx.send(embed=nextcord.Embed(title=f"{member.display_name} is already blacklisted...",
description="Jeez why do you hate him so much",
color=nextcord.Color.teal()))
return
elif member.guild_permissions.ban_members:
await ctx.send(
embed=nextcord.Embed(title="Halt! (lmao)",
description="You cannot just go ahead and stop your fellow admins from using me!",
color=nextcord.Color.red()))
return
elif {"guild_id": guild_id_var, "blacklisted": str(member.id)} in db.all():
await ctx.send(embed=nextcord.Embed(title=f"{member.display_name} is already blacklisted...",
description="Jeez why do you hate him so much",
color=nextcord.Color.teal()))
return
else:
db.insert({'guild_id': guild_id_var, 'blacklisted': str(member.id)})
await ctx.send(embed=nextcord.Embed(title=f"{member.display_name} is blacklisted",
description="He can no longer use me :cry:",
color=nextcord.Color.teal()))
return
@cog_ext.cog_slash(name="unblacklist", description="Unblacklists a user.",
options=[
create_option(name="member",
description="The member you want to unblacklist",
option_type=6,
required=True)])
async def _unblacklist(self, ctx: SlashContext, member: nextcord.Member):
db = TinyDB('databases/blacklist.json')
if ctx.author.guild_permissions.ban_members or ctx.author.id == 815555652780294175 or \
ctx.author.id == 723032217504186389:
if not member:
embed = nextcord.Embed(title="Please provide-", description="a member to unblacklist!",
color=nextcord.Color.random())
embed.set_footer(text="I mean, seriously... isn't this obvious?")
await ctx.send(embed=embed)
return
query = Query()
try:
db.remove(query.blacklisted == str(member.id))
if ctx.author.id == 815555652780294175 or ctx.author.id == 723032217504186389:
await ctx.send(embed=nextcord.Embed(title=f"{member.display_name} is unblacklisted",
description="My boss asked me to do so... :joy:",
color=nextcord.Color.random()))
else:
await ctx.send(embed=nextcord.Embed(title=f"{member.display_name} is unblacklisted",
description="He can now use me! :joy:",
color=nextcord.Color.random()))
except:
await ctx.send(embed=nextcord.Embed(title="Nope!",
description=f"{member.display_name} is not blacklisted in this server."))
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require the administrator permission.",
color=nextcord.Color.red()))
@commands.command(aliases=['unbl', 'ubl', 'unblackl', 'unblist', 'ublackl', 'ublist'])
async def unblacklist(self, ctx, member: nextcord.Member):
db = TinyDB('databases/blacklist.json')
if ctx.author.guild_permissions.ban_members or ctx.author.id == 815555652780294175 or \
ctx.author.id == 723032217504186389:
if not member:
embed = nextcord.Embed(title="Please provide-", description="a member to unblacklist!",
color=nextcord.Color.random())
embed.set_footer(text="I mean, seriously... isn't this obvious?")
await ctx.send(embed=embed)
return
query = Query()
validity = str(db.search(query.blacklisted == str(member.id)))
if validity == '[]':
await ctx.send(embed=nextcord.Embed(title="Nope!",
description=f"{member.display_name} is not blacklisted in this server.",
color=nextcord.Color.random()))
return
db.remove(query.blacklisted == str(member.id))
if ctx.author.id == 815555652780294175 or ctx.author.id == 723032217504186389:
await ctx.send(embed=nextcord.Embed(title=f"{member.display_name} is unblacklisted",
description="My boss asked me to do so... :joy:",
color=nextcord.Color.random()))
else:
await ctx.send(embed=nextcord.Embed(title=f"{member.display_name} is unblacklisted",
description="He can now use me! :joy:",
color=nextcord.Color.random()))
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require the administrator permission.",
color=nextcord.Color.red()))
@cog_ext.cog_slash(name="clear", description="Clears messages in a channel.",
options=[
create_option(name="number",
description="The number of messages you want to clear",
option_type=4,
required=True),
create_option(name="member",
description="The member whose messages you want to clear",
option_type=6,
required=False)])
async def _clear(self, ctx: SlashContext, number: int, member: nextcord.Member = None):
if ctx.author.guild_permissions.manage_messages:
async def goodie(ctx, number, member):
times = number+1
while times > 0:
deleted = await ctx.channel.purge(limit=times, check=lambda message: message.author == member)
times -= len(deleted)
embed = nextcord.Embed(title=f"Done clearing {times} messages of specified user.",
description=f"I hope you're proud, it was a lot of work.",
color=nextcord.Color.random())
embed.set_footer(text=f"Imagine being an ungrateful swine")
await ctx.send(embed=embed, delete_after=7)
if member is None:
await ctx.channel.purge(limit=number)
embed_message = await ctx.send(
embed=nextcord.Embed(title=f"{number} messages deleted", color=nextcord.Color.random()))
await embed_message.delete(delay=3)
return
else:
try:
await asyncio.wait_for(timeout=300, fut=goodie(ctx, number, member))
except asyncio.TimeoutError:
await ctx.send(embed=nextcord.Embed(title="I searched for 5 whole minutes",
description=f"But I couldn't find {number} messages, which is very SUS\nAlthough I deleted {number - goodie(ctx, number, member)} messages",
color=nextcord.Color.random()))
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!",
description="You require the Manage Messages permission.",
color=nextcord.Color.green()))
@commands.command(aliases=['cl', 'purge', 'delete'])
async def clear(self, ctx, times: int, member: nextcord.Member = None):
if ctx.author.guild_permissions.manage_messages:
async def goodie(ctx, times, member):
number = times+1
while number > 0:
deleted = await ctx.channel.purge(limit=number, check=lambda message: message.author == member)
number -= len(deleted)
embed = nextcord.Embed(title=f"Done clearing {times} messages of specified user.",
description=f"I hope you're proud, it was a lot of work.",
color=nextcord.Color.random())
embed.set_footer(text=f"Imagine being an ungrateful swine")
await ctx.send(embed=embed, delete_after=7)
return number
if member is None:
await ctx.channel.purge(limit=times)
embed_message = await ctx.send(
embed=nextcord.Embed(title=f"{times} messages deleted", color=nextcord.Color.random()))
await embed_message.delete(delay=3)
else:
try:
await asyncio.wait_for(timeout=300, fut=goodie(ctx, times, member))
except asyncio.TimeoutError:
await ctx.send(embed=nextcord.Embed(title="I searched for 5 whole minutes",
description=f"But I couldn't find {times} messages, which is very SUS\nAlthough I deleted {times - goodie(ctx, times, member)} messages",
color=nextcord.Color.random()))
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!",
description="You require the Manage Messages permission.",
color=nextcord.Color.green()))
@cog_ext.cog_slash(name="warn", description="Gives a warning to a user.",
options=[
create_option(name="member",
description="The member who you want to warn",
option_type=6,
required=True),
create_option(name="reason",
description="The reason for the warn",
option_type=3,
required=False)])
async def _warn(self, ctx: SlashContext, member: nextcord.Member, *, reason: str):
db = TinyDB('databases/warnings.json')
guild_id_var = ctx.guild.id
if ctx.author != member:
if ctx.author.guild_permissions.ban_members:
if member.guild_permissions.ban_members:
await ctx.send(embed=nextcord.Embed(title="ALERT! ALERT! :dizzy_face:",
description="Warning fellow admins is a no-no, kids!",
color=nextcord.Color.random()))
return
elif not reason:
await ctx.send(embed=nextcord.Embed(title="Please provide a reason",
color=nextcord.Color.random()))
return
elif len(reason) > 150:
await ctx.send(
embed=nextcord.Embed(title=f"The reason for warning cannot be more then 150 characters long!",
description=f"You are {len(reason) - 150} characters over the limit!",
color=nextcord.Color.random()))
return
else:
await ctx.send(
embed=nextcord.Embed(title=f"{member.display_name} has been warned", description=reason,
color=nextcord.Color.random()))
db.insert({'guild_id': guild_id_var, 'member': str(member), 'reason': reason})
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!",
description="You require the administrator permission.",
color=nextcord.Color.red()))
else:
await ctx.send(embed=nextcord.Embed(title="Stop trying to warn yourself.",
description="IT. IS. A. BAD. THING.",
color=nextcord.Color.random()))
@commands.command()
async def warn(self, ctx, member: nextcord.Member, *, reason: str):
db = TinyDB('databases/warnings.json')
guild_id_var = ctx.guild.id
if ctx.author != member:
if ctx.author.guild_permissions.ban_members:
if member.guild_permissions.ban_members:
await ctx.send(embed=nextcord.Embed(title="ALERT! ALERT! :dizzy_face:",
description="Warning fellow admins is a no-no, kids!",
color=nextcord.Color.random()))
return
elif not reason:
await ctx.send(embed=nextcord.Embed(title="Please provide a reason",
color=nextcord.Color.random()))
return
elif len(reason) > 150:
await ctx.send(
embed=nextcord.Embed(title=f"The reason for warning cannot be more then 150 characters long!",
description=f"You are {len(reason) - 150} characters over the limit!",
color=nextcord.Color.random()))
return
else:
await ctx.send(
embed=nextcord.Embed(title=f"{member.display_name} has been warned", description=reason,
color=nextcord.Color.random()))
db.insert({'guild_id': guild_id_var, 'member': str(member), 'reason': reason})
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!",
description="You require the administrator permission.",
color=nextcord.Color.red()))
else:
await ctx.send(embed=nextcord.Embed(title="Stop trying to warn yourself.",
description="IT. IS. A. BAD. THING.",
color=nextcord.Color.random()))
@cog_ext.cog_slash(
name="userwarn",
description="Displays the history of warnings given to a user.",
options=[
create_option(name="member",
description="The member whose criminal record you want to access",
option_type=6,
required=True)])
async def _userwarn(self, ctx: SlashContext, member: nextcord.Member):
db = TinyDB('databases/warnings.json')
guild_id_var = ctx.guild.id
query = Query()
a = db.search((query['guild_id'] == guild_id_var) & (query['member'] == str(member)))
embed = nextcord.Embed(title=f"Here are the warnings for {member.display_name}:", description="Warnings",
color=nextcord.Color.dark_red())
if len(a) == 0:
embed = nextcord.Embed(title="This user has a MIND BLOWING number of warnings!!",
description="0, to be exact",
color=nextcord.Color.green())
embed.set_footer(text="Clean record for now, eh?")
else:
i = 0
for a in a:
i += 1
b = a.get('reason')
embed.add_field(name=f"{i}. ", value=b, inline=False)
embed.set_footer(text="Someone's been a naughty boi. Unless you're a girl.")
await ctx.send(embed=embed)
@commands.command(aliases=['warnings', 'warning', 'userw', 'uwarn', 'uw'])
async def userwarn(self, ctx, member: nextcord.Member):
db = TinyDB('databases/warnings.json')
guild_id_var = ctx.guild.id
query = Query()
a = db.search((query['guild_id'] == guild_id_var) & (query['member'] == str(member)))
embed = nextcord.Embed(title=f"Here are the warnings for {member.display_name}:", description="Warnings",
color=nextcord.Color.dark_red())
if member.guild_permissions.ban_members:
await ctx.send(embed=nextcord.Embed(title="This man/woman is an administrator",
description="So he has no warnings at all!",
color=nextcord.Color.random()))
return
elif len(a) == 0:
embed = nextcord.Embed(title="This user has a MIND BLOWING number of warnings!!",
description="0, to be exact",
color=0xa6ff00)
embed.set_footer(text="Clean record for now, eh?")
else:
i = 0
for a in a:
i += 1
b = a.get('reason')
embed.add_field(name=f"{i}. ", value=b, inline=False)
embed.set_footer(text="Someone's been a naughty boi. Unless you're a girl.")
await ctx.send(embed=embed)
@cog_ext.cog_slash(
name="removewarn",
description="Removes a user's warnings.",
options=[
create_option(name="member",
description="The member whose criminal record you want to access",
option_type=6,
required=True),
create_option(name="reason",
description="The warn reason (from userwarn)",
option_type=3,
required=True)])
async def _removewarn(self, ctx: SlashContext, member: nextcord.Member, *, reason=None):
db = TinyDB('databases/warnings.json')
guild_id_var = ctx.guild.id
query = Query()
if ctx.author.guild_permissions.ban_members:
if member.guild_permissions.ban_members:
await ctx.send(embed=nextcord.Embed(title="This man/woman is an administrator",
description="They doesn't have warnings.\nLet alone REMOVABLE WARNINGS",
color=nextcord.Color.random()))
return
elif reason is None:
await ctx.send(
embed=nextcord.Embed(title=f"Can you mention WHICH warning I should remove",
description=f"I must make sure I remove the warn fr the correct reason",
color=nextcord.Color.random()))
elif reason == "all":
await ctx.send(
embed=nextcord.Embed(title=f"This user has been forgiven completely",
description=f"Their record was cleaned with ONE COMMAND",
color=nextcord.Color.random()))
elif len(reason) > 150:
await ctx.send(
embed=nextcord.Embed(title=f"The reason for warning cannot be more then 150 characters long!",
description=f"You are {len(reason) - 150} characters over the limit!\nI assure you, this user doesn't have such a long warn reason.",
color=nextcord.Color.random()))
return
else:
if str(db.search(query['guild_id'] == guild_id_var and query['member'] == str(member) and query[
'reason'] == reason)) == "[]":
await ctx.send(
embed=nextcord.Embed(
title=f"{member.display_name}'s isn't warned for {reason} in this server",
description=f"You may want to check their userwarns again.",
color=nextcord.Color.random()))
return
db.remove(query['guild_id'] == guild_id_var and query['member'] == str(member) and query[
'reason'] == reason)
await ctx.send(
embed=nextcord.Embed(title=f"{member.display_name}'s warning has been removed",
description=f"They had been previously warned for {reason}",
color=nextcord.Color.random()))
@commands.command(aliases=["rwarn", "remwarn", "rw"])
async def removewarn(self, ctx, member: nextcord.Member, *, reason=None):
db = TinyDB('databases/warnings.json')
guild_id_var = ctx.guild.id
query = Query()
if ctx.author.guild_permissions.ban_members:
if member.guild_permissions.ban_members:
await ctx.send(embed=nextcord.Embed(title="This man/woman is an administrator",
description="They doesn't have warnings.\nLet alone REMOVABLE WARNINGS",
color=nextcord.Color.random()))
return
elif reason is None:
await ctx.send(
embed=nextcord.Embed(title=f"Can you mention WHICH warning I should remove",
description=f"I must make sure I remove the warn fr the correct reason",
color=nextcord.Color.random()))
elif reason == "all":
await ctx.send(
embed=nextcord.Embed(title=f"This user has been forgiven completely",
description=f"Their record was cleaned with ONE COMMAND",
color=nextcord.Color.random()))
elif len(reason) > 150:
await ctx.send(
embed=nextcord.Embed(title=f"The reason for warning cannot be more then 150 characters long!",
description=f"You are {len(reason) - 150} characters over the limit!\nI assure you, this user doesn't have such a long warn reason.",
color=nextcord.Color.random()))
return
else:
if str(db.search(query['guild_id'] == guild_id_var and query['member'] == str(member) and query['reason'] == reason)) == "[]":
await ctx.send(
embed=nextcord.Embed(
title=f"{member.display_name}'s isn't warned for {reason} in this server",
description=f"You may want to check their userwarns again.",
color=nextcord.Color.random()))
return
db.remove(query['guild_id'] == guild_id_var and query['member'] == str(member) and query['reason'] == reason)
await ctx.send(
embed=nextcord.Embed(title=f"{member.display_name}'s warning has been removed",
description=f"They had been previously warned for {reason}",
color=nextcord.Color.random()))
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!",
description="You require the administrator permission.",
color=nextcord.Color.red()))
@cog_ext.cog_slash(name="unmute", description="Allows user from typing in the server.",
options=[
create_option(name="member",
description="Member who you want to unmute.",
option_type=6,
required=True)])
async def _unmute(self, ctx: SlashContext, member: nextcord.Member):
if ctx.author.guild_permissions.manage_messages:
guild = ctx.guild
muted_role = nextcord.utils.get(guild.roles, name="Is Muted")
if muted_role in member.roles:
embed = nextcord.Embed(title=f"{member.display_name} has now been unmuted!!",
color=nextcord.Color.blurple())
embed.set_footer(text="Rejoice son, don't make this mistake again")
await member.remove_roles(muted_role)
await ctx.send(embed=embed)
else:
embed = nextcord.Embed(title="This user isn't even muted.",
description="Forgiveness maybe a good thing.\nBut you're still WASTING MY TIME.",
color=nextcord.Color.random())
embed.set_footer(text="If only the world had a bit of common sense...")
await ctx.send(embed=embed)
return
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require to be an admin!",
color=nextcord.Color.red()))
@commands.command(aliases=['unm', 'um'])
async def unmute(self, ctx, member: nextcord.Member):
if ctx.author.guild_permissions.manage_messages:
guild = ctx.guild
muted_role = nextcord.utils.get(guild.roles, name="Is Muted")
if muted_role in member.roles:
embed = nextcord.Embed(title=f"{member.display_name} has now been unmuted!!",
color=nextcord.Color.blurple())
embed.set_footer(text="Rejoice son, don't make this mistake again")
await member.remove_roles(muted_role)
await ctx.send(embed=embed)
else:
embed = nextcord.Embed(title="This user isn't even muted.",
description="Forgiveness maybe a good thing.\nBut you're still WASTING MY TIME.",
color=nextcord.Color.random())
embed.set_footer(text="If only the world had a bit of common sense...")
await ctx.send(embed=embed)
return
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require to be an admin!",
color=nextcord.Color.red()))
@cog_ext.cog_slash(name="mute", description="Stop user from typing in the server.",
options=[
create_option(name="member",
description="Member who you want to mute.",
option_type=6,
required=True),
create_option(name="reason",
description="Reason for muting the member.",
option_type=3,
required=False)
])
async def _mute(self, ctx: SlashContext, member: nextcord.Member, *, reason="No reason given"):
if ctx.author.guild_permissions.manage_messages:
if member == ctx.author:
embed = nextcord.Embed(title="Why would you even DO that?",
description=f"Did you really just try to mute yourself? :person_facepalming:",
color=nextcord.Color.random())
embed.set_footer(text="Sometimes I just wonder...")
await ctx.send(embed=embed)
return
if member.guild_permissions.ban_members:
embed = nextcord.Embed(title="Nuh uh not happening",
description="You can't just mute your fellow admins.",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
guild = ctx.guild
muted_role = nextcord.utils.get(guild.roles, name="Is Muted")
if muted_role in member.roles:
embed = nextcord.Embed(title="Already muted idiot",
description="How many times do you wish to mute this dude?",
color=nextcord.Color.random())
embed.set_footer(text="I feel sorry for my bro")
await ctx.send(embed=embed)
return
if muted_role is None:
perms = nextcord.Permissions(speak=False, send_messages=False, read_message_history=True,
read_messages=True)
await guild.create_role(name="Is Muted", color=nextcord.Color.dark_gray(), permissions=perms)
muted_role = nextcord.utils.get(guild.roles, name=" Is Muted")
membervar = member.display_name
embed = nextcord.Embed(title="Muted", description=f"{membervar} was muted.",
color=nextcord.Color.random())
embed.add_field(name="Reason:", value=reason, inline=True)
await ctx.send(embed=embed)
if muted_role is not None:
await member.add_roles(muted_role, reason=reason)
else:
await ctx.send("Couldn't mute user")
return
for channel in guild.channels:
await channel.set_permissions(muted_role, send_messages=False, speak=False)
try:
await member.send(f" You have been muted in: {guild.name} reason: {reason}")
except:
print("Oops Could not dm user")
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require to be an admin!",
color=nextcord.Color.red()))
@commands.command(aliases=['m'])
async def mute(self, ctx, member: nextcord.Member, *, reason="No reason given"):
if ctx.author.guild_permissions.manage_messages:
if member == ctx.author:
embed = nextcord.Embed(title="Why would you even DO that?",
description=f"Did you really just try to mute yourself? :person_facepalming:",
color=nextcord.Color.random())
embed.set_footer(text="Sometimes I just wonder...")
await ctx.send(embed=embed)
return
if member.guild_permissions.ban_members:
embed = nextcord.Embed(title="Nuh uh not happening",
description="You can't just mute your fellow admins.",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
guild = ctx.guild
muted_role = nextcord.utils.get(guild.roles, name="Is Muted")
if muted_role in member.roles:
embed = nextcord.Embed(title="Already muted idiot",
description="How many times do you wish to mute this dude?",
color=nextcord.Color.random())
embed.set_footer(text="I feel sorry for my bro")
await ctx.send(embed=embed)
return
if muted_role is None:
perms = nextcord.Permissions(speak=False, send_messages=False, read_message_history=True,
read_messages=True)
await guild.create_role(name="Is Muted", color=nextcord.Color.dark_gray(), permissions=perms)
muted_role = nextcord.utils.get(guild.roles, name=" Is Muted")
membervar = member.display_name
embed = nextcord.Embed(title="Muted", description=f"{membervar} was muted.",
color=nextcord.Color.random())
embed.add_field(name="Reason:", value=reason, inline=True)
await ctx.send(embed=embed)
if muted_role is not None:
await member.add_roles(muted_role, reason=reason)
else:
await ctx.send("Couldn't mute user")
return
for channel in guild.channels:
await channel.set_permissions(muted_role, send_messages=False, speak=False)
try:
await member.send(f" You have been muted in: {guild.name} Reason: {reason}")
except:
print("Oops Could not dm user")
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require to be an admin!",
color=nextcord.Color.red()))
@cog_ext.cog_slash(name="kick", description="Kicks the specified user from the server.",
options=[
create_option(name="member",
description="Member who you want to kick.",
option_type=6,
required=True),
create_option(name="reason",
description="Reason for kicking the member.",
option_type=3,
required=False)
])
async def _kick(self, ctx: SlashContext, member: nextcord.Member, reason: str = "None specified"):
if ctx.author.guild_permissions.kick_members:
if member == ctx.author:
embed = nextcord.Embed(title="Why would you even DO that?",
description=f"Did you really just try to kick yourself? :person_facepalming:",
color=nextcord.Color.random())
embed.set_footer(text="Sometimes I just wonder...")
await ctx.send(embed=embed)
return
if member.guild_permissions.ban_members:
embed = nextcord.Embed(title="Nuh uh not happening",
description="You can't just kick your fellow admins.",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
message = f"You have been kicked from {ctx.guild.name} for Reason: {reason}"
try:
await member.send(message)
except:
pass
await ctx.guild.kick(user=member, reason=reason)
await ctx.channel.send(embed=nextcord.Embed(title=f"{member} has been kicked!\nReason: {reason}",
color=nextcord.Color.random()))
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require the Kick Member permission.",
color=nextcord.Color.green()))
@commands.command(aliases=['k'])
async def kick(self, ctx, member: nextcord.Member, *,reason: str = "None specified"):
if ctx.author.guild_permissions.kick_members:
if member == ctx.author:
embed = nextcord.Embed(title="Why would you even DO that?",
description=f"Did you really just try to kick yourself? :person_facepalming:",
color=nextcord.Color.random())
embed.set_footer(text="Sometimes I just wonder...")
await ctx.send(embed=embed)
return
if member.guild_permissions.ban_members:
embed = nextcord.Embed(title="Nuh uh not happening",
description="You can't just kick your fellow admins.",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
message = f"You have been kicked from {ctx.guild.name} for Reason: {reason}"
try:
await member.send(message)
except:
pass
await ctx.guild.kick(user=member, reason=reason)
await ctx.channel.send(embed=nextcord.Embed(title=f"{member} has been kicked!\nReason: {reason}",
color=nextcord.Color.random()))
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require the Kick Member permission.",
color=nextcord.Color.green()))
@cog_ext.cog_slash(name="unban", description="Unbans users, obviously.",
options=[
create_option(name="member",
description="Member who you want to unban.",
option_type=6,
required=True)])
async def _unban(self, ctx: SlashContext, member: nextcord.User = None):
if ctx.author.guild_permissions.ban_members:
if member is None or member == ctx.message.author:
await ctx.channel.send("You cannot unban yourself")
return
await ctx.guild.unban(member)
await ctx.channel.send(embed=nextcord.Embed(title=f"{member} is unbanned!", color=nextcord.Color.random()))
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require the Ban Member permission.",
color=nextcord.Color.green()))
@commands.command(aliases=['unb', 'ub'])
async def unban(self, ctx, member: nextcord.User = None):
if ctx.author.guild_permissions.ban_members:
if member is None or member == ctx.message.author:
await ctx.channel.send("You cannot unban yourself")
return
await ctx.guild.unban(member)
await ctx.channel.send(embed=nextcord.Embed(title=f"{member} is unbanned!", color=nextcord.Color.random()))
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require the Ban Member permission.",
color=nextcord.Color.green()))
@cog_ext.cog_slash(name="ban", description="Bans users, like, DUH.",
options=[
create_option(name="member",
description="Member who you want to ban.",
option_type=6,
required=True),
create_option(name="reason",
description="Reason for banning the member.",
option_type=3,
required=False)
])
async def _ban(self, ctx: SlashContext, member: nextcord.Member, *, reason=None):
if ctx.author.guild_permissions.ban_members:
banned_gifs = ["https://media.tenor.com/images/d41f93e7538f0afb56ad1450fed9c02e/tenor.gif",
"https://media.tenor.com/images/048b3da98bfc09b882d3801cb8eb0c1f/tenor.gif",
"https://media.tenor.com/images/fe829734d0d3b1d5faf7bb92c1a951aa/tenor.gif",
"https://media.tenor.com/images/fe829734d0d3b1d5faf7bb92c1a951aa/tenor.gif",
"https://media.tenor.com/images/1a84c478d1073757cf8929a89e47bbfc/tenor.gif"]
if member == ctx.message.author:
if member == ctx.author:
embed = nextcord.Embed(title="Why would you even DO that?",
description=f"Did you really just try to ban yourself? :person_facepalming: ",
color=nextcord.Color.random())
embed.set_footer(text="Sometimes I just wonder...")
await ctx.send(embed=embed)
return
if member.guild_permissions.ban_members:
embed = nextcord.Embed(title="Nuh uh not happening",
description="You can't just ban your fellow admins.",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
if reason is None:
reason = "No reason specified"
await ctx.guild.ban(user=member, reason=reason)
message = nextcord.Embed(title=f"You have been banned from {ctx.guild.name} for {reason}",
color=nextcord.Color.random())
try:
await member.send(embed=message)
except:
pass
embed1 = nextcord.Embed(
title=f"{member.display_name} has been banned for {reason}",
description=f"Their mouth has been perma-shut",
color=nextcord.Color.random()
)
embed1.set_image(url=random.choice(banned_gifs))
await ctx.send(embed=embed1)
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require the Ban Member permission.",
color=nextcord.Color.green()))
@commands.command(aliases=['b'])
async def ban(self, ctx, member: nextcord.Member, *, reason=None):
if ctx.author.guild_permissions.ban_members:
banned_gifs = ["https://media.tenor.com/images/d41f93e7538f0afb56ad1450fed9c02e/tenor.gif",
"https://media.tenor.com/images/048b3da98bfc09b882d3801cb8eb0c1f/tenor.gif",
"https://media.tenor.com/images/fe829734d0d3b1d5faf7bb92c1a951aa/tenor.gif",
"https://media.tenor.com/images/fe829734d0d3b1d5faf7bb92c1a951aa/tenor.gif",
"https://media.tenor.com/images/1a84c478d1073757cf8929a89e47bbfc/tenor.gif"]
if member == ctx.message.author:
if member == ctx.author:
embed = nextcord.Embed(title="Why would you even DO that?",
description=f"Did you really just try to ban yourself? :person_facepalming: ",
color=nextcord.Color.random())
embed.set_footer(text="Sometimes I just wonder...")
await ctx.send(embed=embed)
return
if member.guild_permissions.ban_members:
embed = nextcord.Embed(title="Nuh uh not happening",
description="You can't just ban your fellow admins.",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
if reason is None:
reason = "No reason specified"
await ctx.guild.ban(user=member, reason=reason)
message = nextcord.Embed(title=f"You have been banned from {ctx.guild.name} for {reason}",
color=nextcord.Color.random())
try:
await member.send(embed=message)
except:
pass
embed1 = nextcord.Embed(
title=f"{member.display_name} has been banned for {reason}",
description=f"Their mouth has been perma-shut",
color=nextcord.Color.random()
)
embed1.set_image(url=random.choice(banned_gifs))
await ctx.send(embed=embed1)
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!", description="You require the Ban Member permission.",
color=nextcord.Color.green()))
@cog_ext.cog_slash(name="nick",
description="Change nicknames in the server by using this feature",
options=[create_option(name="member",
description="The person whose nick you wanna change",
required=False,
option_type=6),
create_option(name="nick",
description="The nick you want to change it to",
required=False,
option_type=3)])
async def _nick(self, ctx: SlashContext, member=None, *, nick=None):
if member is None and nick is None:
if ctx.author.guild_permissions.change_nickname:
await ctx.author.edit(nick=None)
embed = nextcord.Embed(title=f"Your nickname has been removed",
description=f"Your name is now displayed as {ctx.author.display_name}",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
else:
await ctx.send(
embed=nextcord.Embed(title="I refuse", description="You require the change nickname permission.",
color=nextcord.Color.random()))
return
if member is not None:
try:
member = await commands.MemberConverter().convert(ctx, member)
except:
pass
if isinstance(member, str) or (isinstance(member, nextcord.Member) and ctx.author == member):
if ctx.author.guild_permissions.change_nickname:
if type(member) is not nextcord.Member:
if nick is None:
nick = str(member)
else:
nick = str(member) + " " + nick
if len(nick) > 48:
embed = nextcord.Embed(title=f"That nickname is TOO LONG",
description=f" I'd probably get bored changing it.\nTry a nickname that has less then 32 characters.",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
await ctx.author.edit(nick=nick)
embed = nextcord.Embed(title=f"Your nickname has been changed",
description=f"Your name is now displayed as {nick}",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
else:
await ctx.send(
embed=nextcord.Embed(title="I refuse", description="You require the change nickname permission.",
color=nextcord.Color.random()))
return
elif isinstance(member, nextcord.Member):
if ctx.author.guild_permissions.manage_nicknames:
if nick is None:
await member.edit(nick=member.name)
embed = nextcord.Embed(title=f"Nickname removed for {member.name}",
description=f"Their name is now displayed as {member.name}",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
elif len(nick) > 48:
embed = nextcord.Embed(title=f"That nickname is TOO LONG",
description=f" I'd probably get bored changing it.\nTry a nickname that has less then 48 characters.",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
await member.edit(nick=nick)
embed = nextcord.Embed(title=f"Nickname changed for {member.name}",
description=f"Their name is now displayed as {nick}",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
else:
await ctx.send(
embed=nextcord.Embed(title="I refuse", description="You require the manage nicknames permission.",
color=nextcord.Color.random()))
@commands.command(aliases=['n'])
async def nick(self, ctx, member=None, *, nick=None):
if member is None and nick is None:
if ctx.author.guild_permissions.change_nickname:
await ctx.author.edit(nick=None)
embed = nextcord.Embed(title=f"Your nickname has been removed",
description=f"Your name is now displayed as {ctx.author.display_name}",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
else:
await ctx.send(
embed=nextcord.Embed(title="I refuse", description="You require the change nickname permission.",
color=nextcord.Color.random()))
return
if member is not None:
try:
member = await commands.MemberConverter().convert(ctx, member)
except:
pass
if isinstance(member, str) or (isinstance(member, nextcord.Member) and ctx.author == member):
if ctx.author.guild_permissions.change_nickname:
if type(member) is not nextcord.Member:
if nick is None:
nick = str(member)
else:
nick = str(member) + " " + nick
if len(nick) > 48:
embed = nextcord.Embed(title=f"That nickname is TOO LONG",
description=f" I'd probably get bored changing it.\nTry a nickname that has less then 32 characters.",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
await ctx.author.edit(nick=nick)
embed = nextcord.Embed(title=f"Your nickname has been changed",
description=f"Your name is now displayed as {nick}",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
else:
await ctx.send(
embed=nextcord.Embed(title="I refuse", description="You require the change nickname permission.",
color=nextcord.Color.random()))
return
elif isinstance(member, nextcord.Member):
if ctx.author.guild_permissions.manage_nicknames:
if nick is None:
await member.edit(nick=member.name)
embed = nextcord.Embed(title=f"Nickname removed for {member.name}",
description=f"Their name is now displayed as {member.name}",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
elif len(nick) > 48:
embed = nextcord.Embed(title=f"That nickname is TOO LONG",
description=f" I'd probably get bored changing it.\nTry a nickname that has less then 48 characters.",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
await member.edit(nick=nick)
embed = nextcord.Embed(title=f"Nickname changed for {member.name}",
description=f"Their name is now displayed as {nick}",
color=nextcord.Color.random())
await ctx.send(embed=embed)
return
else:
await ctx.send(
embed=nextcord.Embed(title="I refuse", description="You require the manage nicknames permission.",
color=nextcord.Color.random()))
@cog_ext.cog_slash(name="afk",
description="Shows your friends that you are afk for some reason.",
options=[
create_option(name="reason",
description="The reason which people see when they @you",
required=True,
option_type=3)
])
async def _afk(self, ctx: SlashContext, *, reason=None):
if reason is None:
embed = nextcord.Embed(title="Give ME A REASON",
description="You can't be afk for no reason!",
color=nextcord.Color.random())
embed.set_footer(text="That's Louis' job")
await ctx.send(embed=embed)
return
if len(reason) > 50:
embed = nextcord.Embed(title="I'm sorry.",
description="I got bored reading your LONG reason.\nSo I ignored it.",
color=nextcord.Color.random())
embed.set_footer(text="Nothing more than 50 characters please")
await ctx.send(embed=embed)
return
db = TinyDB('databases/afk.json')
db.insert({'afk_user': ctx.author.id, 'reason': reason})
await ctx.send(embed=nextcord.Embed(title=f"Ok {ctx.author.display_name}.",
description=f"I have set your status as afk for {reason}.",
color=nextcord.Color.random()))
@commands.command()
async def afk(self, ctx, *, reason=None):
if reason is None:
embed = nextcord.Embed(title="Give ME A REASON",
description="You can't be afk for no reason",
color=nextcord.Color.random())
embed.set_footer(text="That's Louis' job")
await ctx.send(embed=embed)
return
if len(reason) > 50:
embed = nextcord.Embed(title="I'm sorry.",
description="I got bored reading your LONG reason.\nSo I ignored it.",
color=nextcord.Color.random())
embed.set_footer(text="Nothing more than 50 characters please")
await ctx.send(embed=embed)
return
db = TinyDB('databases/afk.json')
db.insert({'afk_user': ctx.author.id, 'reason': reason})
await ctx.send(embed=nextcord.Embed(title=f"Ok {ctx.author.display_name}.",
description=f"I have set your status as afk for {reason}.",
color=nextcord.Color.random()))
@nick.error
async def nick_error(self, ctx, error):
if isinstance(error, commands.errors.CommandInvokeError):
embed = nextcord.Embed(title=f"Nope, the member is more powerful than me",
description=f"Maybe put my role above him :pleading_face:",
color=nextcord.Color.random())
embed.set_footer(text="I feel weak")
await ctx.send(embed=embed)
raise error
@slowmode.error
async def slowmode_error(self, ctx, error):
if isinstance(error, commands.MissingRequiredArgument):
if ctx.channel.slowmode_delay == 0:
await ctx.send(embed=nextcord.Embed(title="Slowmode disabled already dumbass",
color=nextcord.Color.random()))
elif ctx.author.guild_permissions.ban_members:
await ctx.channel.edit(slowmode_delay=0)
await ctx.send(embed=nextcord.Embed(title="Slowmode disabled!", color=nextcord.Color.dark_magenta(),
description="Now y'all can talk your heart out"))
else:
await ctx.send(
embed=nextcord.Embed(title="Stop right there!",
description="You require the server's mod permission.",
color=nextcord.Color.green()))
if isinstance(error, commands.BadArgument):
await ctx.send(
embed=nextcord.Embed(title="How hard is it to set a slowmode :rolling_eyes: ",
color=nextcord.Color.magenta(),
description=f"Do {ctx.prefix}slowmode to disable it and {ctx.prefix}slowmode 10 to set slowmode of 10 secs"))
@blacklist.error
async def blacklist_error(self, ctx, error):
if isinstance(error, commands.MissingRequiredArgument):
embed = nextcord.Embed(title=f"C'mon dude",
description=f"I don't really want to stop people from using me\nBut if you really want me too, then at least tell me who to stop?",
color=nextcord.Color.random())
embed.set_footer(text="The least you can do")
await ctx.send(embed=embed)
elif isinstance(error, commands.MemberNotFound):
embed = nextcord.Embed(title=f"Please stop making this hard for me...",
description=f"Just mention who I must stop.\nRandom names won't really do",
color=nextcord.Color.random())
embed.set_footer(text="Is this necessary")
await ctx.send(embed=embed)
else:
raise error
@unblacklist.error
async def unblacklist_error(self, ctx, error):
if isinstance(error, commands.MissingRequiredArgument):
embed = nextcord.Embed(title=f"Are u serious?",
description=f"Reminding you the blacklisting thin air is NOT possible",
color=nextcord.Color.random())
embed.set_footer(text="I mean, isn't it obvious?")
await ctx.send(embed=embed)
elif isinstance(error, commands.MemberNotFound):
embed = nextcord.Embed(title=f"Stop memeing. Just stop.",
description=f"This user is not in this server.",
color=nextcord.Color.random())
embed.set_footer(text="Have some mercy...")
await ctx.send(embed=embed)
else:
raise error
@clear.error
async def clear_error(self, ctx, error):
if isinstance(error, commands.MissingRequiredArgument):
embed = nextcord.Embed(title=f"That's pretty vague",
description=f"You tell me to clear message but don't tell me how many.\nSo do I clear them all?",
color=nextcord.Color.random())
embed.set_footer(text="Maybe NOT a good idea...")
await ctx.send(embed=embed)
elif isinstance(error, commands.errors.BadArgument):
embed = nextcord.Embed(title=f"Numbers. -_-",
description=f"I can only clear a number of messages. What else did you expect?",
color=nextcord.Color.random())
embed.set_footer(text="You be being sus")
await ctx.send(embed=embed)
elif isinstance(error, commands.errors.MemberNotFound):
embed = nextcord.Embed(title=f"Couldn't find this member",
description=f"Why can't you just gve me\nA PROPER MEMBER",
color=nextcord.Color.random())
embed.set_footer(text="Im really so bored of this")
await ctx.send(embed=embed)
else:
raise error
@warn.error
async def warn_error(self, ctx, error):
if isinstance(error, commands.MemberNotFound):
embed = nextcord.Embed(title=f"I couldn't find this dude.",
description=f"So instead I warned my friend Louis here...",
color=nextcord.Color.random())
embed.set_footer(text="Wait... what have you done to Louis?")
await ctx.send(embed=embed)
elif isinstance(error, commands.MissingRequiredArgument):
if "reason" in str(error.param):
embed = nextcord.Embed(title=f"Alright I'll bite",
description=f"What should I warn the user for?",
color=nextcord.Color.random())
embed.set_footer(text="Can't just warn him cause you said so can I?")
await ctx.send(embed=embed)
else:
embed = nextcord.Embed(title=f"Alright I'll bite",
description=f"Who am I supposed to warn?",
color=nextcord.Color.random())
embed.set_footer(text="Mentioning that wud be gr8")
await ctx.send(embed=embed)
else:
raise error
@userwarn.error
async def userwarn_error(self, ctx, error):
if isinstance(error, commands.MissingRequiredArgument):
embed = nextcord.Embed(title=f"I refuse",
description=f"I simply refuse to give you the warnings of *NOTHING*",
color=nextcord.Color.random())
embed.set_footer(text="That would be a crime")
await ctx.send(embed=embed)
elif isinstance(error, commands.MemberNotFound):
embed = nextcord.Embed(title=f"Ok no",
description=f"Reminding you that seeing the warnings of an invalid user is not allowed!",
color=nextcord.Color.random())
embed.set_footer(text="Kids these days...")
await ctx.send(embed=embed)
else:
raise error
@removewarn.error
async def removewarn_error(self, ctx, error):
if isinstance(error, commands.MemberNotFound):
embed = nextcord.Embed(title=f"I couldn't find this dude.",
description=f"So instead I removed the warn of my friend Louis here...",
color=nextcord.Color.random())
embed.set_footer(text="Wait... what have you done to Louis?")
await ctx.send(embed=embed)
elif isinstance(error, commands.MissingRequiredArgument):
embed = nextcord.Embed(title=f"I refuse",
description=f"I simply refuse to remove the warnings of *NOTHING*",
color=nextcord.Color.random())
embed.set_footer(text="That would be a stupid thing to do")
await ctx.send(embed=embed)
else:
raise error
@unmute.error
async def unmute_error(self, ctx, error):
if isinstance(error, commands.MissingRequiredArgument):
embed = nextcord.Embed(title=f"Mention the user please",
description=f"I cannot unmute the void obviously",
color=nextcord.Color.random())
embed.set_footer(text="Mentioning someone helps tho")
await ctx.send(embed=embed)
elif isinstance(error, commands.MemberNotFound):
embed = nextcord.Embed(title=f"I really don't like this",
description=f"Pretty sure that Mr. Nothing couldn't talk in the first place.",
color=nextcord.Color.random())
embed.set_footer(text="unmuting nothing is a horrific idea")
await ctx.send(embed=embed)
elif isinstance(error, commands.errors.CommandInvokeError):
embed = nextcord.Embed(title=f"Nope, the member is more powerful than me",
description=f"Maybe put my role above him :pleading_face:",
color=nextcord.Color.random())
embed.set_footer(text="I feel weak")
await ctx.send(embed=embed)
else:
raise error
@kick.error
async def kick_error(self, ctx, error):
if isinstance(error, commands.MissingRequiredArgument):
embed = nextcord.Embed(title=f"Mention the user please",
description=f"I cannot kick the void obviously",
color=nextcord.Color.random())
embed.set_footer(text="Mentioning someone helps tho")
await ctx.send(embed=embed)
elif isinstance(error, commands.MemberNotFound):
embed = nextcord.Embed(title=f"I really don't like this",
description=f"Pretty sure that this person didn't exist in the first place.",
color=nextcord.Color.random())
embed.set_footer(text="Kicking the air... *shudder")
await ctx.send(embed=embed)
else:
raise error
@mute.error
async def mute_error(self, ctx, error):
if isinstance(error, commands.MissingRequiredArgument):
embed = nextcord.Embed(title=f"Muting is not nice...",
description=f"But if you insist on it, mention *WHO* you want to mute.",
color=nextcord.Color.random())
embed.set_footer(text="Because respect")
await ctx.send(embed=embed)
elif isinstance(error, commands.MemberNotFound):
embed = nextcord.Embed(title=f"Muting random people is acceptable...",
description=f"...when the people actually exist",
color=nextcord.Color.random())
embed.set_footer(text="So make sure they do")
await ctx.send(embed=embed)
else:
raise error
@ban.error
async def ban_error(self, ctx, error):
if isinstance(error, commands.MissingRequiredArgument):
embed = nextcord.Embed(title=f"If only you were competent",
description=f"You would know the banning no one is a waste of time.",
color=nextcord.Color.random())
embed.set_footer(text="Unpoggers indeed")
await ctx.send(embed=embed)
elif isinstance(error, commands.MemberNotFound):
embed = nextcord.Embed(title=f"Banning is a sad thing",
description=f"It becomes 10 times worse when you can't even properly tell me who to ban!",
color=nextcord.Color.random())
embed.set_footer(text="I may not have a life but still")
await ctx.send(embed=embed)
elif isinstance(error, commands.errors.CommandInvokeError):
embed = nextcord.Embed(title=f"Nope, the member is more powerful than me",
description=f"Maybe put my role above him :pleading_face:",
color=nextcord.Color.random())
embed.set_footer(text="I feel weak")
await ctx.send(embed=embed)
else:
raise error
@unban.error
async def unban_error(self, ctx, error):
if isinstance(error, commands.MissingRequiredArgument):
embed = nextcord.Embed(title=f"Unbanning is a sign of mercy",
description=f"But it would make you look better in front of your friends if you mention someone to ban.",
color=nextcord.Color.random())
embed.set_footer(text="IOn the bright side, you can now unban someone")
await ctx.send(embed=embed)
elif isinstance(error, commands.UserNotFound):
embed = nextcord.Embed(title=f"Ahh the difficulty..",
description=f"It must be so hard for you to be able to mention a valid user.",
color=nextcord.Color.random())
embed.set_footer(text="This is sarcasm")
await ctx.send(embed=embed)
elif isinstance(error, commands.errors.CommandInvokeError):
embed = nextcord.Embed(title=f"Hold up!",
description=f"What do you think I am? The server owner?\nI can't do that, I don't got the permission!",
color=nextcord.Color.random())
embed.set_footer(text="Stop trying to take my rights")
await ctx.send(embed=embed)
else:
raise error
def setup(bot):
bot.add_cog(Moderation(bot))
| 52.606713 | 196 | 0.514555 | 8,552 | 83,066 | 4.945978 | 0.073784 | 0.035368 | 0.073621 | 0.091896 | 0.900492 | 0.884557 | 0.879001 | 0.862405 | 0.834768 | 0.820937 | 0 | 0.013879 | 0.392832 | 83,066 | 1,578 | 197 | 52.640051 | 0.824784 | 0 | 0 | 0.81196 | 0 | 0.008646 | 0.20903 | 0.011769 | 0 | 0 | 0.000096 | 0 | 0 | 1 | 0.001441 | false | 0.004323 | 0.007205 | 0 | 0.066282 | 0.002161 | 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 |
7cf61360ffdf4400f3ede268dbe80af79130f725 | 64 | py | Python | test/examples/99_dart_example/dart_example.py | personalrobotics/chimera | 089e8360da01c04777c904e3106d822aa49e00de | [
"BSD-3-Clause"
] | 11 | 2017-05-05T14:01:21.000Z | 2020-07-09T14:05:54.000Z | test/examples/99_dart_example/dart_example.py | personalrobotics/chimera | 089e8360da01c04777c904e3106d822aa49e00de | [
"BSD-3-Clause"
] | 162 | 2017-03-11T04:32:32.000Z | 2020-12-20T06:45:56.000Z | test/examples/99_dart_example/dart_example.py | personalrobotics/chimera | 089e8360da01c04777c904e3106d822aa49e00de | [
"BSD-3-Clause"
] | 3 | 2019-01-13T18:38:21.000Z | 2019-12-26T22:08:45.000Z | # import dart_example_pybind11
import dart_example_boost_python
| 21.333333 | 32 | 0.90625 | 9 | 64 | 5.888889 | 0.666667 | 0.377358 | 0.641509 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.033898 | 0.078125 | 64 | 2 | 33 | 32 | 0.864407 | 0.4375 | 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 |
7cf85689d4e05281686171e992ca592a9915b03c | 245 | py | Python | scripts/git.py | yipinliu/intellij-rust | d9e1f446f0b373094f3ef35de7677f0dba137b29 | [
"MIT"
] | null | null | null | scripts/git.py | yipinliu/intellij-rust | d9e1f446f0b373094f3ef35de7677f0dba137b29 | [
"MIT"
] | null | null | null | scripts/git.py | yipinliu/intellij-rust | d9e1f446f0b373094f3ef35de7677f0dba137b29 | [
"MIT"
] | null | null | null | from typing import Optional
from common import execute_command
def git_command(*args, print_stdout=True, check=True, cwd: Optional[str] = None) -> str:
return execute_command("git", *args, print_stdout=print_stdout, check=check, cwd=cwd)
| 30.625 | 89 | 0.763265 | 36 | 245 | 5.027778 | 0.5 | 0.18232 | 0.165746 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.126531 | 245 | 7 | 90 | 35 | 0.845794 | 0 | 0 | 0 | 0 | 0 | 0.012245 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.5 | 0.25 | 1 | 0.5 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 7 |
6b19d1cbc40ae6b1f29b64a5609e2b3b5be020f9 | 8,434 | py | Python | code_files/__init__registeration.py | myndtrust/CMU-Thesis-master | 44e882b65c2925707c4728a9be84abfd95c07532 | [
"MIT"
] | 1 | 2021-04-09T14:41:05.000Z | 2021-04-09T14:41:05.000Z | code_files/__init__registeration.py | myndtrust/CMU-Thesis-master | 44e882b65c2925707c4728a9be84abfd95c07532 | [
"MIT"
] | null | null | null | code_files/__init__registeration.py | myndtrust/CMU-Thesis-master | 44e882b65c2925707c4728a9be84abfd95c07532 | [
"MIT"
] | null | null | null | from gym.envs.registration import register
import os
import fileinput
FD = os.path.dirname(os.path.realpath(__file__));
register(
id='Eplus-demo-v1',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-8-6-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/pittsburgh_TMY3.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/demo_5z/learning/cfg/variables_v0.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/demo_5z/learning/idf/5ZoneAutoDXVAV_v0.idf', # The idf file
'env_name': 'Eplus-demo-v1',
})
register(
id='Eplus-dc_golden-v0',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-8-6-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/USA_CO_Golden-NREL.724666_TMY3.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/datacenters/cfg/variables_v1.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/datacenters/idf/1ZD_CRAC_wPumpedDXCoolingCoil_golden.idf', # The idf file
'env_name': 'Eplus-dc_golden-v0',
})
register(
id='Eplus-dc_Singapore-v0',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-8-6-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/SGP_Singapore_486980_IWEC.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/datacenters/cfg/variables_v2.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/datacenters/idf/2ZoneDataCenterHVAC_wEconomizer_RL_git.idf', # The idf file
'env_name': 'Eplus-dc_Singapore-v0',
})
register(
id='Eplus-dc_Singapore-v1',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-8-9-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/SGP_Singapore_486980_IWEC.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/datacenters/cfg/variables_v2.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/datacenters/idf/eplus89/2ZoneDataCenterHVAC_wEconomizer_RL_git_86to89.idf', # The idf file
'env_name': 'Eplus-dc_Singapore-v1',
})
register(
id='Eplus-dc_Ashburn-v0',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-8-6-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/USA_VA_Arlington-Ronald.Reagan.Washington.Natl.AP.724050_TMY3.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/datacenters/cfg/variables_v2.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/datacenters/idf/2ZoneDataCenterHVAC_wEconomizer_RL_git.idf', # The idf file
'env_name': 'Eplus-dc_Ashburn-v0',
})
register(
id='Eplus-dc_Ashburn-v1',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-8-9-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/USA_VA_Arlington-Ronald.Reagan.Washington.Natl.AP.724050_TMY3.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/datacenters/cfg/variables_v2.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/datacenters/idf/eplus89/2ZoneDataCenterHVAC_wEconomizer_RL_git_86to89.idf', # The idf file
'env_name': 'Eplus-dc_Ashburn-v1',
})
register(
id='Eplus-dc_SanFrancisco-v0',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-8-6-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/USA_CA_San.Francisco.724940_TMY2.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/datacenters/cfg/variables_v2.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/datacenters/idf/2ZoneDataCenterHVAC_wEconomizer_RL_git.idf', # The idf file
'env_name': 'Eplus-dc_SanFrancisco-v0',
})
register(
id='Eplus-dc_SanFrancisco-v1',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-8-9-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/USA_CA_San.Francisco.724940_TMY2.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/datacenters/cfg/variables_v2.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/datacenters/idf/eplus89/2ZoneDataCenterHVAC_wEconomizer_RL_git_86to89.idf', # The idf file
'env_name': 'Eplus-dc_SanFrancisco-v1',
})
register(
id='Eplus-dc_Haarlem-v0',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-8-6-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/NLD_Amsterdam.062400_IWEC.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/datacenters/cfg/variables_v2.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/datacenters/idf/2ZoneDataCenterHVAC_wEconomizer_RL_git.idf', # The idf file
'env_name': 'Eplus-dc_Haarlem-v0',
})
register(
id='Eplus-dc_Haarlem-v1',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-8-9-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/NLD_Amsterdam.062400_IWEC.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/datacenters/cfg/variables_v2.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/datacenters/idf/eplus89/2ZoneDataCenterHVAC_wEconomizer_RL_git_86to89.idf', # The idf file
'env_name': 'Eplus-dc_Haarlem-v1',
})
register(
id='Eplus-dc_Carrollton-v0',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-8-6-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/USA_TX_Dallas-Addison.AP.722598_TMY3.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/datacenters/cfg/variables_v2.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/datacenters/idf/2ZoneDataCenterHVAC_wEconomizer_RL.idf', # The idf file
'env_name': 'Eplus-dc_Carrolton-v0',
})
register(
id='Eplus-dc_Carrollton-v1',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-8-9-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/USA_TX_Dallas-Addison.AP.722598_TMY3.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/datacenters/cfg/variables_v2.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/datacenters/idf/eplus89/2ZoneDataCenterHVAC_wEconomizer_RL_git_86to89.idf', # The idf file
'env_name': 'Eplus-dc_Carrolton-v1',
})
register(
id='Eplus-demo-v92',
entry_point='eplus_env.envs:EplusEnv',
kwargs={'eplus_path':FD + '/envs/EnergyPlus-9-2-0/', # The EnergyPlus software path
'weather_path':FD + '/envs/weather/USA_CO_Golden-NREL.724666_TMY3.epw', # The epw weather file
'bcvtb_path':FD + '/envs/bcvtb/', # The BCVTB path
'variable_path':FD + '/envs/eplus_models/datacenters/cfg/variables_v1.cfg', # The cfg file
'idf_path':FD + '/envs/eplus_models/demo_5z/learning/idf/eplus92_5ZoneAutoDXVAV.idf', # The idf file
'env_name': 'Eplus-demo-v92',
});
| 56.226667 | 138 | 0.656154 | 1,128 | 8,434 | 4.687943 | 0.082447 | 0.073752 | 0.12292 | 0.073752 | 0.95329 | 0.907337 | 0.896747 | 0.896747 | 0.878026 | 0.856278 | 0 | 0.031097 | 0.199312 | 8,434 | 149 | 139 | 56.604027 | 0.751962 | 0.140147 | 0 | 0.716418 | 0 | 0 | 0.61698 | 0.451218 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.022388 | 0 | 0.022388 | 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 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
8634874ca50a21906f3659719fd821d5e93a86af | 12,538 | py | Python | blockfrost/client.py | reis-p/python-blockfrost | f92547ac6f520760bdaa344132270e1570bec987 | [
"MIT"
] | null | null | null | blockfrost/client.py | reis-p/python-blockfrost | f92547ac6f520760bdaa344132270e1570bec987 | [
"MIT"
] | null | null | null | blockfrost/client.py | reis-p/python-blockfrost | f92547ac6f520760bdaa344132270e1570bec987 | [
"MIT"
] | 1 | 2021-07-13T15:36:16.000Z | 2021-07-13T15:36:16.000Z | """
Blockfrost API DataHandler for the dadascience project
"""
import requests
from .exceptions import BlockfrostAPIException
class Client:
API_URL_MAINNET = 'https://cardano-mainnet.blockfrost.io/api'
API_URL_TESTNET = 'https://cardano-testnet.blockfrost.io/api'
API_VERSION = 'v0'
URL_ADDRESS = 'addresses/{}'
def __init__(self, api_key, testnet=False):
self.api_key = api_key
self.api_url_mainnet = self.API_URL_MAINNET
self.api_url_testnet = self.API_URL_TESTNET
self.response = None
self.testnet = testnet
self.api_version = self.API_VERSION
self.session = self._init_session()
def _init_session(self):
header = self._get_headers()
session = requests.session()
session.headers.update(header)
return session
def _get_headers(self):
headers = {
'Accept': 'application/json',
}
if self.api_key:
headers['project_id'] = self.api_key
else:
raise ValueError('No API Key defined')
return headers
def _request(self, method, uri, **kwargs):
self.response = getattr(self.session, method)(uri, **kwargs)
return self._handle_response(self.response)
@staticmethod
def _handle_response(response):
if not response.status_code == 200:
raise BlockfrostAPIException(response, response.status_code, response.text)
try:
return response.json()
except ValueError:
raise ValueError
def _get(self, path, **kwargs):
return self._request_api('get', path, **kwargs)
def _request_api(self, method, path, **kwargs):
uri = self._create_uri(path)
return self._request(method, uri, **kwargs)
def _create_uri(self, path):
url = self.api_url_mainnet
if self.testnet:
url = self.api_url_testnet
v = self.api_version
return url + '/' + v + '/' + path
@staticmethod
def _get_payload_from_params(params):
payload = ''
for item in params.items():
if item[0] != 'details':
payload = item[1]
if params['details']:
payload = payload + '/' + params['details']
return payload
# User Side
def get_address(self, address, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Addresses/paths/~1addresses~1{address}/get
:param address: required
:type address: str
:return: Blockfrost API response
"""
path = 'addresses/' + address
return self._get(path, params=kwargs)
def get_address_details(self, address, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Addresses/paths/~1addresses~1{address}~1total/get
:param address: required
:type address: str
:return: Blockfrost API response
"""
path = 'addresses/' + address + '/total'
return self._get(path, params=kwargs)
def get_address_utxos(self, address, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Addresses/paths/~1addresses~1{address}~1utxos/get
:param address: required
:type address: str
:return: Blockfrost API response
"""
path = 'addresses/' + address + '/utxos'
return self._get(path, params=kwargs)
def get_address_transactions(self, address, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Addresses/paths/~1addresses~1{address}~1transactions/get
:param address: required
:type address: str
:return: Blockfrost API response
"""
path = 'addresses/' + address + '/transactions'
return self._get(path, params=kwargs)
def get_latest_block(self, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Blocks
:return: Blockfrost API response
"""
path = 'blocks/latest'
return self._get(path, params=kwargs)
def get_latest_block_txs(self, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Blocks
:return: Blockfrost API response
"""
path = 'blocks/latest/txs'
return self._get(path, params=kwargs)
def get_specific_block(self, hash_or_number, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Blocks/paths/~1blocks~1{hash_or_number}/get
:param hash_or_number: Block hash or number
:type hash_or_number: str
:return: Blockfrost API response
"""
path = 'blocks/' + str(hash_or_number)
return self._get(path, params=kwargs)
def get_specific_block_in_slot(self, slot_number, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Blocks/paths/~1blocks~1slot~1{slot_number}/get
:param slot_number: slot number
:type slot_number: int
:return: Blockfrost API response
"""
path = '/blocks/slot/' + str(slot_number)
return self._get(path, params=kwargs)
def get_specific_block_in_epoch_slot(self, epoch_number, slot_number_epoch, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Blocks/paths/~1blocks~1epoch~1{epoch_number}~1slot~1{slot_number}/get
:param epoch_number: epoch number
:type epoch_number: int
:param slot_number_epoch: slot number
:type slot_number_epoch: int
:return: Blockfrost API response
"""
path = '/blocks/epoch/' + str(epoch_number) + '/slot/' + str(slot_number_epoch)
return self._get(path, params=kwargs)
def get_next_blocks(self, hash_or_number, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Blocks/paths/~1blocks~1{hash_or_number}~1next/get
:param hash_or_number:
:type hash_or_number: str
:return: Blockfrost API response
"""
path = '/blocks/' + hash_or_number + '/next'
return self._get(path, params=kwargs)
def get_previous_blocks(self, hash_or_number, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Blocks/paths/~1blocks~1{hash_or_number}~1previous/get
:param hash_or_number: Block hash or number
:type hash_or_number: str
:return: Blockfrost API response
"""
path = '/blocks/' + hash_or_number + '/previous'
return self._get(path, params=kwargs)
def get_block_txs(self, hash_or_number, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Blocks/paths/~1blocks~1{hash_or_number}~1txs/get
:param hash_or_number: Block hash or number
:type hash_or_number: str
:return: Blockfrost API response
"""
path = '/blocks/' + hash_or_number + '/txs'
return self._get(path, params=kwargs)
def get_latest_epoch(self, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Epochs
:return: Blockfrost API response
"""
path = '/epochs/latest'
return self._get(path, params=kwargs)
def get_latest_epoch_protocol_params(self, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Epochs/paths/~1epochs~1latest~1parameters/get
:return: Blockfrost API response
"""
path = '/epochs/latest/parameters'
return self._get(path, params=kwargs)
def get_specific_epoch(self, epoch_number, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Epochs/paths/~1epochs~1{number}/get
:param epoch_number: epoch number
:type epoch_number: int
:return: Blockfrost API response
"""
path = '/epochs/' + str(epoch_number)
return self._get(path, params=kwargs)
def get_next_epochs(self, epoch_number, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Epochs/paths/~1epochs~1{number}~1next/get
:param epoch_number: starting epoch number
:type epoch_number: int
:return: Blockfrost API response
"""
path = '/epochs/' + str(epoch_number) + '/next'
return self._get(path, params=kwargs)
def get_previous_epochs(self, epoch_number, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Epochs/paths/~1epochs~1{number}~1previous/get
:param epoch_number: starting epoch number
:type epoch_number: int
:return: Blockfrost API response
"""
path = '/epochs/' + str(epoch_number) + '/previous'
return self._get(path, params=kwargs)
def get_active_stake_distribution(self, epoch_number, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Epochs/paths/~1epochs~1{number}~1stakes/get
:param epoch_number: Epoch number for stake distribution
:return: Blockfrost API response
"""
path = '/epochs/' + str(epoch_number) + '/stakes'
return self._get(path, params=kwargs)
def get_stake_distribution_by_pool(self, epoch_number, pool_id, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Epochs/paths/~1epochs~1{number}~1stakes~1{pool_id}/get
:param epoch_number: Epoch number for stake distribution
:type epoch_number: int
:param pool_id: pool id BECH32
:type pool_id; str
:return: Blockfrost API response
"""
path = '/epochs/' + str(epoch_number) + '/stakes/' + pool_id
return self._get(path, params=kwargs)
def get_block_distribution(self, epoch_number, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Epochs/paths/~1epochs~1{number}~1blocks/get
:param epoch_number: Epoch number for block distribution
:type epoch_number: int
:return: Blockfrost API response
"""
path = '/epochs/' + str(epoch_number) + '/blocks'
return self._get(path, params=kwargs)
def get_block_distribution_by_pool(self, epoch_number, pool_id, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Epochs/paths/~1epochs~1{number}~1blocks~1{pool_id}/get
:param epoch_number: Epoch number for block distribution
:type epoch_number: int
:param pool_id: pool id BECH32
:type pool_id; str
:return: Blockfrost API response
"""
path = '/epochs/' + str(epoch_number) + '/blocks/' + pool_id
return self._get(path, params=kwargs)
def get_protocol_params_for_epoch(self, epoch_number, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Epochs/paths/~1epochs~1{number}~1blocks/get
:param epoch_number: Epoch number for block distribution
:type epoch_number: int
:return: Blockfrost API response
"""
path = '/epochs/' + str(epoch_number) + '/parameters'
return self._get(path, params=kwargs)
def get_blockchain_genesis(self, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Ledger/paths/~1genesis/get
:return: Blockfrost API response
"""
path = '/genesis'
return self._get(path, params=kwargs)
def get_transaction_metadata_labels(self, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Metadata
:return: Blockfrost API response
"""
path = '/metadata/txs/labels'
return self._get(path, params=kwargs)
def get_transaction_metadata_json(self, label, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Metadata/paths/~1metadata~1txs~1labels~1{label}/get
:param label: Metadata label
:type label: str
:return: Blockfrost API response
"""
path = '/metadata/txs/labels/' + label
return self._get(path, params=kwargs)
def get_transaction_metadata_cbor(self, label, **kwargs):
"""
see:
https://docs.blockfrost.io/#tag/Cardano-Metadata/paths/~1metadata~1txs~1labels~1{label}/get
:param label: Metadata label
:type label: str
:return: Blockfrost API response
"""
path = '/metadata/txs/labels/' + label + '/cbor'
return self._get(path, params=kwargs)
| 32.066496 | 117 | 0.611262 | 1,453 | 12,538 | 5.099105 | 0.092223 | 0.066811 | 0.049129 | 0.063166 | 0.771494 | 0.760292 | 0.746794 | 0.718451 | 0.707248 | 0.644621 | 0 | 0.008378 | 0.266948 | 12,538 | 390 | 118 | 32.148718 | 0.797737 | 0.365848 | 0 | 0.202899 | 0 | 0 | 0.08875 | 0.010199 | 0 | 0 | 0 | 0 | 0 | 1 | 0.253623 | false | 0 | 0.014493 | 0.007246 | 0.550725 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 |
863643c6a7654d66635743388be1c4d1f1e3908f | 3,107 | py | Python | ccdb/misc/models.py | thermokarst/ccdb-api | 01d76d75ffaaa9949991cdc3ac43b9ae388ad2a6 | [
"MIT"
] | null | null | null | ccdb/misc/models.py | thermokarst/ccdb-api | 01d76d75ffaaa9949991cdc3ac43b9ae388ad2a6 | [
"MIT"
] | 24 | 2017-01-09T12:51:13.000Z | 2018-04-30T17:40:27.000Z | ccdb/misc/models.py | thermokarst/ccdb-api | 01d76d75ffaaa9949991cdc3ac43b9ae388ad2a6 | [
"MIT"
] | null | null | null | from django.db import models
class MeasurementUnit(models.Model):
name = models.CharField(max_length=100)
code = models.CharField(max_length=25)
unit_class = models.CharField(max_length=50, blank=True)
description = models.CharField(max_length=255, blank=True)
sort_order = models.IntegerField(blank=True, null=True)
def __str__(self):
return self.code
class Meta:
unique_together = ('name', 'code')
ordering = ['sort_order']
class MeasurementType(models.Model):
name = models.CharField(max_length=100)
code = models.CharField(max_length=10, blank=True)
measurement_type_class = models.CharField(max_length=50, blank=True)
description = models.CharField(max_length=255, blank=True)
default_measurement_unit = models.ForeignKey(
'MeasurementUnit',
blank=True,
null=True,
related_name='measurement_types',
on_delete=models.CASCADE,
)
sort_order = models.IntegerField(blank=True, null=True)
def __str__(self):
return self.name
class Meta:
unique_together = ('name', 'code', 'measurement_type_class')
ordering = ['sort_order']
class Material(models.Model):
name = models.CharField(max_length=100)
code = models.CharField(max_length=10, blank=True)
material_class = models.CharField(max_length=50, blank=True)
description = models.CharField(max_length=255, blank=True)
sort_order = models.IntegerField(blank=True, null=True)
def __str__(self):
return self.name
class Meta:
unique_together = ('name', 'code')
ordering = ['sort_order']
class Color(models.Model):
name = models.CharField(max_length=50)
code = models.CharField(max_length=10, blank=True)
color_number = models.FloatField(blank=True, null=True)
sort_order = models.IntegerField(blank=True, null=True)
def __str__(self):
return self.name
class Meta:
unique_together = ('name', 'code', 'color_number')
ordering = ['sort_order']
class Container(models.Model):
name = models.CharField(max_length=100)
code = models.CharField(max_length=10, blank=True)
application = models.CharField(max_length=50, blank=True)
color = models.ForeignKey(Color, blank=True, null=True,
related_name='containers',
on_delete=models.CASCADE)
material = models.ForeignKey(Material, blank=True, null=True,
related_name='containers',
on_delete=models.CASCADE)
volume = models.FloatField(blank=True, null=True)
measurement_unit = models.ForeignKey(MeasurementUnit, blank=True,
null=True, related_name='containers',
on_delete=models.CASCADE)
sort_order = models.IntegerField(blank=True, null=True)
def __str__(self):
return self.name
class Meta:
unique_together = ('name', 'code', 'color', 'material', 'volume')
ordering = ['sort_order']
| 33.771739 | 78 | 0.648214 | 355 | 3,107 | 5.470423 | 0.146479 | 0.101957 | 0.15757 | 0.210093 | 0.833162 | 0.832132 | 0.800206 | 0.762101 | 0.742019 | 0.742019 | 0 | 0.01738 | 0.240747 | 3,107 | 91 | 79 | 34.142857 | 0.80585 | 0 | 0 | 0.591549 | 0 | 0 | 0.06598 | 0.007081 | 0 | 0 | 0 | 0 | 0 | 1 | 0.070423 | false | 0 | 0.014085 | 0.070423 | 0.690141 | 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 |
8645ee8b47cce9b7936a04f43062ab09ed5a720e | 173 | py | Python | calabiyau/ui/views/__init__.py | TachyonicProject/calabiyau | 415a8ada4a93ee84c4776e89c9442af328dcfdd6 | [
"BSD-3-Clause"
] | null | null | null | calabiyau/ui/views/__init__.py | TachyonicProject/calabiyau | 415a8ada4a93ee84c4776e89c9442af328dcfdd6 | [
"BSD-3-Clause"
] | 8 | 2019-06-06T11:01:48.000Z | 2019-06-06T12:18:03.000Z | calabiyau/ui/views/__init__.py | TachyonicProject/calabiyau | 415a8ada4a93ee84c4776e89c9442af328dcfdd6 | [
"BSD-3-Clause"
] | 3 | 2019-03-28T07:36:22.000Z | 2019-12-27T12:10:14.000Z | import calabiyau.ui.views.virtual
import calabiyau.ui.views.pool
import calabiyau.ui.views.packages
import calabiyau.ui.views.subscribers
import calabiyau.ui.views.sessions
| 28.833333 | 37 | 0.855491 | 25 | 173 | 5.92 | 0.36 | 0.506757 | 0.574324 | 0.743243 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.057803 | 173 | 5 | 38 | 34.6 | 0.907975 | 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 |
864a8c2ff18744a58b8c9574c6dddf0222e45285 | 31,421 | py | Python | main1.0.py | GEEKYH/syphu_getcoursetable_demo | 9dcb84b6321dfeba83566238dedf514e9b52314a | [
"Apache-2.0"
] | null | null | null | main1.0.py | GEEKYH/syphu_getcoursetable_demo | 9dcb84b6321dfeba83566238dedf514e9b52314a | [
"Apache-2.0"
] | null | null | null | main1.0.py | GEEKYH/syphu_getcoursetable_demo | 9dcb84b6321dfeba83566238dedf514e9b52314a | [
"Apache-2.0"
] | null | null | null | import re
import datetime
import time
from bs4 import BeautifulSoup
#import ics
from ics import Calendar,Event
def printInfo():
print('使用须知:')
print('使用本脚本之前，你需要先将HTML文件名修改为\'课程表.html\'，且放置在脚本所在目录，并且使用编辑器编辑本脚本以修改学期开始日期。')
print('导入时，请务必确认时间正确，以避免不必要的麻烦。')
print('\n')
def is_number(s):
try:
float(s)
return True
except :
pass
try:
import unicodedata
unicodedata.numeric(s)
return True
except (TypeError, ValueError):
pass
return False
class generator:
soup = None
c = Calendar()
info = []
map = [1,2,3,4,5,6,7]
start_h = (8,10,13,15,18,19,21,22,15,16,17,18,19,20)
end_h = (10,12,15,17,19,21,22,23,16,17,18,19,20,21)
start_m = (30,20,30,20,00,40,20,50,25,20,15,30,25,20)
end_m = (10,00,10,00,30,10,10,40,50,5,0,15,10,5)
## revise the date here
term_start_time = datetime.datetime.strptime('2021-03-07 00:00:00+0800',
'%Y-%m-%d %H:%M:%S%z')
def __init__(self):
with open("课程表.html", "rb") as f:
html = f.read().decode("gbk")
f.close()
self.soup = BeautifulSoup(html, "html.parser")
#表格处理
def parser(self):
position=[
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(4) > td:nth-of-type(2)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(4) > td:nth-child(3)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(4) > td:nth-child(4)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(4) > td:nth-child(5)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(4) > td:nth-child(6)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(4) > td:nth-child(7)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(4) > td:nth-child(8)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(5) > td:nth-child(2)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(5) > td:nth-child(3)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(5) > td:nth-child(4)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(5) > td:nth-child(5)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(5) > td:nth-child(6)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(5) > td:nth-child(7)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(5) > td:nth-child(8)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(6) > td:nth-child(2)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(6) > td:nth-child(3)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(6) > td:nth-child(4)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(6) > td:nth-child(5)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(6) > td:nth-child(6)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(6) > td:nth-child(7)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(6) > td:nth-child(8)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(7) > td:nth-child(2)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(7) > td:nth-child(3)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(7) > td:nth-child(4)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(7) > td:nth-child(5)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(7) > td:nth-child(6)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(7) > td:nth-child(7)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(7) > td:nth-child(8)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(8) > td:nth-child(2)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(8) > td:nth-child(3)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(8) > td:nth-child(4)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(8) > td:nth-child(5)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(8) > td:nth-child(6)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(8) > td:nth-child(7)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(8) > td:nth-child(8)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(9) > td:nth-child(2)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(9) > td:nth-child(3)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(9) > td:nth-child(4)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(9) > td:nth-child(5)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(9) > td:nth-child(6)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(9) > td:nth-child(7)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(9) > td:nth-child(8)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(10) > td:nth-child(2)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(10) > td:nth-child(3)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(10) > td:nth-child(4)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(10) > td:nth-child(5)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(10) > td:nth-child(6)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(10) > td:nth-child(7)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(10) > td:nth-child(8)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(11) > td:nth-child(2)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(11) > td:nth-child(3)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(11) > td:nth-child(4)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(11) > td:nth-child(5)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(11) > td:nth-child(6)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(11) > td:nth-child(7)',
'body > table > tbody > tr:nth-child(2) > td > table > tbody > tr:nth-child(2) > td > table > tbody > tr > td > div > table > tbody > tr:nth-child(11) > td:nth-child(8)',
]
weekday = 0
count = 0
row = 0
#下面的大循环一次处理一个“格子”，一个格子里有好几门课
for posit in position:
count = count + 1
#变量row指明当前在哪一行
#定位
spt = str(self.soup.select(posit))
#各种替换
pattern = re.compile(r'<[^>]+>',re.S)
pattern1 = re.compile(r'cutcut',re.S)
pattern2 = re.compile(r'2节',re.S)
result = pattern.sub('cut', spt)
result = pattern1.sub('cut', result)
result = pattern2.sub('off', result)
result = result[4:-7]
result = result.replace(" ","")
resultlist = result.split("offcut")
#self.info.append({count:count})
for course in resultlist:
#course = course.replace(" ","")
courseinfo = course.split("cut")
course_dict = {}
if len(courseinfo) == 4:
course_dict['course_name'] = courseinfo[0]
course_dict['teacher_name'] = courseinfo[1]
course_dict['place'] = courseinfo[2]
course_dict['time'] = courseinfo[3]
course_dict['clip'] = count
self.info.append(course_dict)
elif len(courseinfo) == 3:
course_dict['course_name'] = courseinfo[0]
course_dict['place'] = courseinfo[1]
course_dict['time'] = courseinfo[2]
course_dict['clip'] = count
self.info.append(course_dict)
else:
self.info.append({'没有课':"None","clip":"0"})
#continue
#由于是横向扫描，当处理完7个格子就需要换行
'''
if count > 6:
row = row + 1
weekday = 0
count = 0
course_time[weekday][row] = info
weekday = weekday + 1
'''
#print(self.info)
'''
for row in rows:
columns = row.findAll('tr')
for column in columns:
courses = column.findAll('td')
for course in courses:
course = str(course)
self.info.append(course)
#course = course.lstrip(str(re.search(r'<td>|<td rowspan=\"\d\">',course))).rstrip('</td>')
#print(course)
'''
#写入日历文件
def write_into_ics(self):
count = 0
for course in self.info:
#print(self.info)
if course.get("没有课") == "None":
continue
else:
weeks = course["time"][0:-1]
weekday = course.get("clip") % 7
time_cur = int(course.get("clip") / 7)#第几节
if course.get("clip") % 7 == 0:
weekday = 7
time_cur = int(course.get("clip") /7) - 1
time_end = time_cur
#第一节
temp = sorted(self.info,key = lambda x:x['clip']!=course["clip"])
#print(temp)
for next_course in temp:
if next_course["clip"] == course["clip"]:
if next_course["course_name"] == course.get('course_name') and next_course["time"] == course.get('time'):
next_course["没有课"]="None"
if next_course.get("teacher_name") != None and next_course.get("teacher_name") not in course.get('teacher_name'):
course["teacher_name"] = course.get("teacher_name") + "、" + next_course.get("teacher_name")
if next_course.get("place") not in course.get('place'):
course["place"] = course.get("place") + "、" + next_course.get("place")
else:
break
temp = sorted(self.info,key = lambda x:x["clip"]!=int(course["clip"]) + 7)
samecourse_pass = 0
for next_course in temp:
if next_course["clip"] == int(course["clip"]) + 7:
if next_course["course_name"] == course.get('course_name') and next_course["time"] == course.get('time'):
if next_course["course_name"] == course.get('course_name') and samecourse_pass == 1:
next_course["没有课"]="None"
continue
else:
next_course["没有课"]="None"
time_end = time_cur + 1
self.add_course(course,weekday,time_cur,time_end,weeks)
samecourse_pass = 1
else:
continue
else:
break
if time_end == time_cur:
self.add_course(course,weekday,time_cur,time_end,weeks)
'''
#第二节
temp = sorted(self.info,key = lambda x:x["clip"]!=int(course["clip"]) + 7)
for next_course in temp:
if next_course["clip"] == int(course["clip"]) + 7 and next_course["course_name"] == course.get('course_name') and next_course["time"] == course.get('time'):
next_course["没有课"]="None"
#第三节
temp2 = sorted(self.info,key = lambda x:x["clip"]!=int(course["clip"]) + 14)
for next_course in temp2:
if next_course["clip"] == int(course["clip"]) + 14 and next_course["course_name"] == course.get('course_name') and next_course["time"] == course.get('time'):
#第四节
next_course["没有课"]="None"
temp3 = sorted(self.info,key = lambda x:x["clip"]!=int(course["clip"]) + 21)
for next_course in temp3:
if next_course["clip"] == int(course["clip"]) + 21 and next_course["course_name"] == course.get('course_name') and next_course["time"] == course.get('time'):
time_end = time_cur + 3
next_course["没有课"]="None"
print(next_course)
self.add_course(course,weekday,time_cur,time_end,weeks)
break
else:
time_end = time_cur + 2
self.add_course(course,weekday,time_cur,time_end,weeks)
break
else:
time_end = time_cur + 1
self.add_course(course,weekday,time_cur,time_end,weeks)
break
break
if time_end == time_cur:
self.add_course(course,weekday,time_cur,time_end,weeks)
'''
'''
#以下代码确认是不是同一个课程不同老师
try:
#course_index_cur = self.info.index(2)
#course_index_end = self.info.index(3)
#ls_course = self.info[course_index_cur:course_index_end]
for last_course in self.info[course_index_cur+1:course_index_end-1]:
for key in last_course:
#print(last_course,key)
pass
for last_course in self.info[course_index_cur+1:course_index_end-1]:
print(last_course)
if last_course["course_name"] == course.get('course_name') and last_course["place"] == course.get('place') and last_course["time"] == course.get('time') and last_course["teacher_name"] != course.get('teacher_name'):
course["teacher_name"] = course.get("teacher_name") + "、" + last_course.get("teacher_name")
#time_end = time_cur + 1
#print("tttttttt")
#print(a)
#print(str(self.info[a]))
#print("doon")
#self.info.pop(a)
#self.info.insert(a,"没有课")
#print("non")
# self.add_course(course,weekday,time_cur,time_end,weeks)
#a = int(self.info.index(last_course))
#self.info[a] = "没有课"
except Exception as e:
#time_end = time_cur
#self.add_course(course,weekday,time_cur,time_end,weeks)
print(e)
#以下代码是确认下面一节大课是否相同
course_index = (int(time_cur) + 1) * 7 + weekday
try:
course_index_cur = self.info.index(course_index)
course_index_end = self.info.index(course_index + 1)
for last_course in self.info[course_index_cur+1:course_index_end-1]:
if last_course["course_name"] == course.get('course_name') and last_course["place"] == course.get('place') and last_course["time"] == course.get('time') and last_course["teacher_name"] != course.get('teacher_name'):
course["teacher_name"] = course.get("teacher_name") + "、" + last_course.get("teacher_name")
time_end = time_cur + 1
self.info[self.info.index(last_course)] = "没有课"
self.add_course(course,weekday,time_cur,time_end,weeks)
except:
time_end = time_cur
self.add_course(course,weekday,time_cur,time_end,weeks)
'''
#创建日历文件
with open('syphu.ics', 'w', encoding='utf-8') as my_file:
my_file.writelines(self.c)
#写入日历文件
def add_course(self,course,weekday,time_cur,time_end,weeks):
#print(course)
local = course['place']
#print(weeks)
#for key in course:
e = Event()
#if("." in weeks):
if True:
weeks = weeks.split('.')
for week in weeks:
if("-" in week):
week = week.split('-')
week_cur = int(week[0])
week_end = int(week[1])
while week_cur <= week_end:
e = Event()
e.name = course.get('course_name')
e.location = local
if str(course.get("teacher_name")) != "None":
e.description = str(course.get('teacher_name'))
offset = datetime.timedelta(days=(week_cur-1)*7+weekday,hours=self.start_h[int(time_cur)],minutes=self.start_m[int(time_cur)])
e.begin = self.term_start_time + offset
offset = datetime.timedelta(days=(week_cur-1)*7+weekday,hours=self.end_h[int(time_end)],minutes=self.end_m[int(time_end)])
e.end = self.term_start_time + offset
week_cur+=1
self.c.events.add(e)
else:
e = Event()
e.name = course.get('course_name')
e.location = local
if str(course.get("teacher_name")) != "None":
e.description = str(course.get('teacher_name'))
offset = datetime.timedelta(days=(int(week)-1)*7+weekday,hours=self.start_h[int(time_cur)],minutes=self.start_m[int(time_cur)])
e.begin = self.term_start_time + offset
offset = datetime.timedelta(days=(int(week)-1)*7+weekday,hours=self.end_h[int(time_end)],minutes=self.end_m[int(time_end)])
e.end = self.term_start_time + offset
self.c.events.add(e)
#week = self.info[start+2].lstrip('</td>第').rstrip('周</td>')
#remark = self.info[start+3].lstrip('</td>').rstrip('</td>')
'''
e = Event()
e.name = course.get(course_name)
e.location = local
e.description = str(course.get(teacher_name) + course.get(place))
offset = datetime.timedelta(days=2*7+weekday,hours=8,minutes=30)
e.begin = self.term_start_time + offset
offset = datetime.timedelta(days=2*7+weekday,hours=8,minutes=30)
e.end = self.term_start_time + offset
#week_cur+=1
self.c.events.add(e)
'''
'''
e = Event()
if('-' in week):
week = week.split('-')
#print(week)
week_cur = int(week[0])
week_end = int(week[1])
while week_cur <= week_end:
e = Event()
e.name = course_name
e.location = local
e.description = remark
offset = datetime.timedelta(days=(week_cur-1)*7+weekday,hours=self.start_h[int(time[0])],minutes=self.start_m[int(time[0])])
e.begin = self.term_start_time + offset
offset = datetime.timedelta(days=(week_cur-1)*7+weekday,hours=self.end_h[int(time[1])],minutes=self.end_m[int(time[1])])
e.end = self.term_start_time + offset
week_cur+=1
self.c.events.add(e)
else:
week = week.split(',')
#print(week)
for we in week:
e = Event()
e.name = course_name
e.location = local
e.description = remark
offset = datetime.timedelta(days=(int(we)-1)*7+weekday,hours=self.start_h[int(time[0])],minutes=self.start_m[int(time[0])])
e.begin = self.term_start_time + offset
offset = datetime.timedelta(days=(int(we)-1)*7+weekday,hours=self.end_h[int(time[1])],minutes=self.end_m[int(time[1])])
e.end = self.term_start_time + offset
self.c.events.add(e)
'''
def main():
printInfo()
#a = input('确认后输入1以继续...')
a = "1"
if a=='1':
g = generator()
g.parser()
g.write_into_ics()
if __name__ == '__main__':
main()
| 69.824444 | 260 | 0.4152 | 3,433 | 31,421 | 3.718031 | 0.068745 | 0.175494 | 0.210592 | 0.19743 | 0.785569 | 0.765512 | 0.757678 | 0.745691 | 0.732059 | 0.719994 | 0 | 0.028823 | 0.456733 | 31,421 | 449 | 261 | 69.979955 | 0.718922 | 0.014544 | 0 | 0.238532 | 0 | 0.256881 | 0.483873 | 0.003989 | 0 | 0 | 0 | 0 | 0 | 1 | 0.03211 | false | 0.022936 | 0.027523 | 0 | 0.119266 | 0.027523 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
86574c4933939491aaebd019164a0d6d67bad6fc | 56,992 | py | Python | ApproxSrc/functional/approx_conv2d.py | sirius0000/SR-Mongoose | 068068c7fbc6d1b1bb33ffa31529dda55580b7f2 | [
"MIT"
] | 5 | 2021-07-08T12:28:27.000Z | 2022-03-10T16:44:25.000Z | ApproxSrc/functional/approx_conv2d.py | sirius0000/SR-Mongoose | 068068c7fbc6d1b1bb33ffa31529dda55580b7f2 | [
"MIT"
] | null | null | null | ApproxSrc/functional/approx_conv2d.py | sirius0000/SR-Mongoose | 068068c7fbc6d1b1bb33ffa31529dda55580b7f2 | [
"MIT"
] | 2 | 2021-10-20T04:29:11.000Z | 2022-03-10T16:44:29.000Z | import torch
#import approx_conv2d
from ..modules.utils import topk_indices
''' shape - shape of grad_input. expected: (batch, in_channels,h,w)
weight - weight tensor, shape (out_channels, in_channels, h,w)
grad_output - grad output tensor, shape (batch, out_channels, h,w)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of out_channels to sample
minimal_k - Minimal number of out_channels to keep in the sampling
'''
def conv2d_bwd_topk(shape, weight, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k):
#print("Sanity check - conv2d_bwd_topk is used with sample_ratio = " + str(sample_ratio) + " and minimal_k = " + str(minimal_k))
#print("shape: {}".format(shape))
#print("weight size: {}".format(weight.size()))
#print("grad_output size: {}".format(grad_output.size()))
#print("sample_ratio: {}".format(sample_ratio))
#print("minimal_k: {}".format(minimal_k))
out_channels = weight.size()[0]
# calculate the number of input channels to sample
k_candidate = int(float(out_channels)*sample_ratio)
# make k at least minimal_k
k = min(max(k_candidate,minimal_k),out_channels)
# if because of minimal_k or sample_ratio k equals the number of features, perform full conv2d_bwd instead of approximating
if k == out_channels:
return approx_conv2d.backward_input(shape, weight, grad_output, stride, padding, dilation, groups, False, False, True)
# calculate norms of output channels
weight_out_channels_norms = torch.norm(weight.view(out_channels,-1),dim=1, p=2)
grad_output_out_channels_norms = torch.norm(grad_output.view(grad_output.size()[0],out_channels,-1), dim=2, p=2)
grad_output_out_channels_norms = torch.norm(grad_output_out_channels_norms, dim=0, p=2)
grad_output_out_channels_norms = torch.squeeze(grad_output_out_channels_norms)
# multiply both norms element-wise to and pick the indices of the top K channels
norm_mult = torch.mul(weight_out_channels_norms, grad_output_out_channels_norms)
# top_k_indices = torch.topk(norm_mult,k)[1]
top_k_indices = topk_indices(norm_mult,k)
# pick top-k channels to form new smaller tensors
weight_top_k_channels = torch.index_select(weight,dim = 0, index = top_k_indices)
grad_output_top_k_channels = torch.index_select(grad_output,dim = 1, index = top_k_indices)
# compute sampled tensors
grad_input_approx = approx_conv2d.backward_input(shape, weight_top_k_channels, grad_output_top_k_channels, stride, padding, dilation, groups, False, False, True)
return grad_input_approx
''' shape - shape of grad_input. expected: (batch, in_channels,h,w)
weight - weight tensor, shape (out_channels, in_channels, h,w)
grad_output - grad output tensor, shape (batch, out_channels, h,w)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of out_channels to sample
minimal_k - Minimal number of out_channels to keep in the sampling
'''
def conv2d_bwd_random_sampling(shape, weight, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k, with_replacement, optimal_prob, scale):
#print("Sanity check - conv2d_bwd_random_sampling is used with sample_ratio = " + str(sample_ratio) + "and minimal_k = " + str(minimal_k))
#print("shape: {}".format(shape))
#print("weight size: {}".format(weight.size()))
#print("grad_output size: {}".format(grad_output.size()))
#print("sample_ratio: {}".format(sample_ratio))
#print("minimal_k: {}".format(minimal_k))
#print("with_replacement: {}".format(with_replacement))
#print("optimal_prob: {}".format(optimal_prob))
#print("scale: {}".format(scale))
out_channels = weight.size()[0]
device = weight.device
# calculate the number of input channels to sample
k_candidate = int(float(out_channels)*sample_ratio)
# make k at least minimal_k
k = min(max(k_candidate,minimal_k),out_channels)
# if because of minimal_k or sample_ratio k equals the number of features, perform full conv2d_bwd instead of approximating
if k == out_channels:
return approx_conv2d.backward_input(shape, weight, grad_output, stride, padding, dilation, groups, False, False, True)
if optimal_prob == True:
# calculate norms of output channels
weight_out_channels_norms = torch.norm(weight.view(out_channels,-1),dim=1, p=2)
grad_output_out_channels_norms = torch.norm(grad_output.view(grad_output.size()[0],out_channels,-1),p=2, dim=2)
grad_output_out_channels_norms = torch.norm(grad_output_out_channels_norms, dim=0, p=2)
grad_output_out_channels_norms = torch.squeeze(grad_output_out_channels_norms)
# multiply both norms element-wise
norm_mult = torch.mul(weight_out_channels_norms, grad_output_out_channels_norms)
# use epsilon-optimal sampling to allow learning random weights and to bound the scaling factor
epsilon = 0.1
if epsilon > 0:
sum_norm_mult = torch.sum(norm_mult)
norm_mult = torch.div(norm_mult, sum_norm_mult)
uniform = torch.ones_like(norm_mult)/out_channels
norm_mult = (1-epsilon)*norm_mult + epsilon*uniform
# no need to normalize, it is already done by torch.multinomial
# calculate number of nonzero elements in norm_mult. this serves
# two purposes:
# 1. Possibly reduce number of sampled pairs, as zero elements in norm_mult will not contribute to the result
# 2. Prevents scaling of zero values
nnz = (norm_mult!=0).sum()
if nnz == 0:
#print("zero multiply detected! scenario not optimzied (todo)")
return approx_conv2d.backward_input(shape, weight, grad_output, stride, padding, dilation, groups, False, False, True)
k = min(k,nnz)
indices = torch.multinomial(norm_mult,k,replacement=with_replacement)
# pick top-k channels to form new smaller tensors
weight_top_k_channels = torch.index_select(weight,dim = 0, index = indices)
grad_output_top_k_channels = torch.index_select(grad_output,dim = 1, index = indices)
if scale == True:
# when sampling without replacement a more complicated scaling factor is required (see Horvitz and Thompson, 1952)
assert(with_replacement == True)
# scale out_channels by 1/(k*p_i) to get unbiased estimation
sum_norm_mult = torch.sum(norm_mult)
scale_factors = torch.div(sum_norm_mult,torch.mul(norm_mult,k))
weight_top_k_channels = torch.mul(weight_top_k_channels, scale_factors[indices].view(-1,1,1,1))
else:
# uniform sampling
if with_replacement == True:
indices = torch.randint(low=0,high=out_channels,size=(k,),device=device)
else:
uniform_dist = torch.ones(out_channels,device=device)
indices = torch.multinomial(uniform_dist,k,replacement=False)
# pick k channels to form new smaller tensors
weight_top_k_channels = torch.index_select(weight,dim = 0, index = indices)
grad_output_top_k_channels = torch.index_select(grad_output,dim = 1, index = indices)
if scale == True:
# scale column-row pairs by 1/(k*p_i) to get unbiased estimation
# in case of uniform distribution, p_i = 1/in_features when sampling with replacement
# when sampling without replacement a different scaling factor is required (see Horvitz and Thompson, 1952), but
# for uniform sampling it turns to be in_features/k as well
scale_factor = out_channels/k
weight_top_k_channels = torch.mul(weight_top_k_channels, scale_factor)
# compute sampled tensors
grad_input_approx = approx_conv2d.backward_input(shape, weight_top_k_channels, grad_output_top_k_channels, stride, padding, dilation, groups, False, False, True)
return grad_input_approx
''' shape - shape of grad_input. expected: (batch, in_channels,h,w)
weight - weight tensor, shape (out_channels, in_channels, h,w)
grad_output - grad output tensor, shape (batch, out_channels, h,w)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of out_channels to sample
minimal_k - Minimal number of out_channels to keep in the sampling
'''
def conv2d_bwd_bernoulli_sampling(shape, weight, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k, scale):
#print("Sanity check - conv2d_bwd_bernoulli_sampling is used with sample_ratio = " + str(sample_ratio) + "and minimal_k = " + str(minimal_k))
#print("shape: {}".format(shape))
#print("weight size: {}".format(weight.size()))
#print("grad_output size: {}".format(grad_output.size()))
#print("sample_ratio: {}".format(sample_ratio))
#print("minimal_k: {}".format(minimal_k))
#print("scale: {}".format(scale))
out_channels = weight.size()[0]
device = weight.device
# calculate the number of input channels to sample
k_candidate = int(float(out_channels)*sample_ratio)
# make k at least minimal_k
k = min(max(k_candidate,minimal_k),out_channels)
# if because of minimal_k or sample_ratio k equals the number of features, perform full conv2d_bwd instead of approximating
if k == out_channels:
return approx_conv2d.backward_input(shape, weight, grad_output, stride, padding, dilation, groups, False, False, True)
# calculate norms of output channels
weight_out_channels_norms = torch.norm(weight.view(out_channels,-1),dim=1, p=2)
grad_output_out_channels_norms = torch.norm(grad_output.view(grad_output.size()[0],out_channels,-1),p=2, dim=2)
grad_output_out_channels_norms = torch.norm(grad_output_out_channels_norms, dim=0, p=2)
grad_output_out_channels_norms = torch.squeeze(grad_output_out_channels_norms)
# multiply both norms element-wise
norm_mult = torch.mul(weight_out_channels_norms, grad_output_out_channels_norms)
sum_norm_mult = norm_mult.sum()
# calculate number of nonzero elements in norm_mult. this serves
# two purposes:
# 1. Possibly reduce number of sampled pairs, as zero elements in norm_mult will not contribute to the result
# 2. Prevents scaling of zero values
nnz = (norm_mult!=0).sum()
if nnz == 0:
#print("zero multiply detected! scenario not optimzied (todo)")
return approx_conv2d.backward_input(shape, weight, grad_output, stride, padding, dilation, groups, False, False, True)
k = min(k,nnz)
prob_dist = k * torch.div(norm_mult,sum_norm_mult)
prob_dist = prob_dist.clamp(min=0, max=1)
# use epsilon-optimal sampling to allow learning random weights and to bound the scaling factor
epsilon = 0.1
if epsilon > 0:
uniform = torch.ones_like(prob_dist)/out_channels
prob_dist = (1-epsilon)*prob_dist + epsilon*uniform
indices = torch.bernoulli(prob_dist).nonzero(as_tuple=True)[0]
if len(indices) == 0:
print("no elements selected - hmm")
indices = torch.arange(k, device=device)
# pick top-k channels to form new smaller tensors
weight_top_k_channels = torch.index_select(weight,dim = 0, index = indices)
grad_output_top_k_channels = torch.index_select(grad_output,dim = 1, index = indices)
if scale == True:
# scale out_channels by 1/(p_i) to get unbiased estimation
scale_factors = torch.div(1,prob_dist)
weight_top_k_channels = torch.mul(weight_top_k_channels, scale_factors[indices].view(-1,1,1,1))
# compute sampled tensors
grad_input_approx = approx_conv2d.backward_input(shape, weight_top_k_channels, grad_output_top_k_channels, stride, padding, dilation, groups, False, False,True)
return grad_input_approx
''' shape - shape of grad_input. expected: (batch, in_channels,h,w)
weight - weight tensor, shape (out_channels, in_channels, h,w)
grad_output - grad output tensor, shape (batch, out_channels, h,w)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of out_channels to sample
minimal_k - Minimal number of out_channels to keep in the sampling
'''
def approx_conv2d_func_bwd(shape, weight, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k):
#return approx_conv2d.backward_input(shape, weight, grad_output, stride, padding, dilation, groups, False, False, True)
#return conv2d_bwd_topk(shape, weight, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k)
#return conv2d_bwd_random_sampling(shape, weight, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k, with_replacement=True, optimal_prob=True, scale=True)
return conv2d_bwd_bernoulli_sampling(shape, weight, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k, scale=True)
''' input - input tensor, shape (batch, in_channels, h, w)
weight_shape - shape of grad_weight. expected: (out_channels, in_channels,h,w)
grad_output - grad output tensor, shape (batch, out_channels, h,w)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of out_channels to sample
minimal_k - Minimal number of out_channels to keep in the sampling
'''
def conv2d_wu_topk(input, weight_shape, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k):
#print("Sanity check - conv2d_bwd_wu is used with sample_ratio = " + str(sample_ratio) + "and minimal_k = " + str(minimal_k))
#print("input: {}".format(input))
#print("weight_shape: {}".format(weight_shape))
#print("input size: {}".format(input.size()))
#print("grad_output size: {}".format(grad_output.size()))
#print("sample_ratio: {}".format(sample_ratio))
#print("minimal_k: {}".format(minimal_k))
batch = input.size()[0]
# calculate the number of minibatch examples to sample
k_candidate = int(float(batch)*sample_ratio)
# make k at least minimal_k
k = min(max(k_candidate,minimal_k),batch)
# if because of minimal_k or sample_ratio k equals the minibatch size, perform full conv2d_wu instead of approximating
if k == batch:
return approx_conv2d.backward_weight(input, weight_shape, grad_output, stride, padding, dilation, groups, False, False, True)
# calculate norms of minibatch examples
input_batch_norms = torch.norm(input.view(batch,-1),dim=1, p=2)
grad_output_batch_norms = torch.norm(grad_output.view(batch,-1),dim=1, p=2)
# multiply both norms element-wise to and pick the indices of the top K minibatch examples
norm_mult = torch.mul(input_batch_norms, grad_output_batch_norms)
# top_k_indices = torch.topk(norm_mult,k)[1]
top_k_indices = topk_indices(norm_mult,k)
# pick top-k batch examples to form new smaller tensors
input_top_k_batch = torch.index_select(input,dim = 0, index = top_k_indices)
grad_output_top_k_batch = torch.index_select(grad_output,dim = 0, index = top_k_indices)
# compute sampled tensors
grad_weight_approx = approx_conv2d.backward_weight(input_top_k_batch, weight_shape, grad_output_top_k_batch, stride, padding, dilation, groups, False, False, True)
return grad_weight_approx
''' input - input tensor, shape (batch, in_channels, h, w)
weight_shape - shape of grad_weight. expected: (out_channels, in_channels,h,w)
grad_output - grad output tensor, shape (batch, out_channels, h,w)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of out_channels to sample
minimal_k - Minimal number of out_channels to keep in the sampling
'''
def conv2d_wu_random_sampling(input, weight_shape, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k, with_replacement, optimal_prob, scale):
#print("Sanity check - conv2d_bwd_wu is used with sample_ratio = " + str(sample_ratio) + "and minimal_k = " + str(minimal_k))
#print("input: {}".format(input))
#print("weight_shape: {}".format(weight_shape))
#print("input size: {}".format(input.size()))
#print("grad_output size: {}".format(grad_output.size()))
#print("sample_ratio: {}".format(sample_ratio))
#print("minimal_k: {}".format(minimal_k))
#print("with_replacement: {}".format(with_replacement))
#print("optimal_prob: {}".format(optimal_prob))
#print("scale: {}".format(scale))
batch = input.size()[0]
device = input.device
# calculate the number of minibatch examples to sample
k_candidate = int(float(batch)*sample_ratio)
# make k at least minimal_k
k = min(max(k_candidate,minimal_k),batch)
# if because of minimal_k or sample_ratio k equals the minibatch size, perform full conv2d_wu instead of approximating
if k == batch:
return approx_conv2d.backward_weight(input, weight_shape, grad_output, stride, padding, dilation, groups, False, False, True)
if optimal_prob == True:
# calculate norms of output channels
input_batch_norms = torch.norm(input.view(batch, -1),dim=1, p=2)
grad_output_batch_norms = torch.norm(grad_output.view(batch, -1) ,dim=1, p=2)
# multiply both norms element-wise
norm_mult = torch.mul(input_batch_norms, grad_output_batch_norms)
# use epsilon-optimal sampling to allow learning random weights and to bound the scaling factor
epsilon = 0.1
if epsilon > 0:
sum_norm_mult = torch.sum(norm_mult)
norm_mult = torch.div(norm_mult, sum_norm_mult)
uniform = torch.ones_like(norm_mult)/batch
norm_mult = (1-epsilon)*norm_mult + epsilon*uniform
# no need to normalize, it is already done by torch.multinomial
# calculate number of nonzero elements in norm_mult. this serves
# two purposes:
# 1. Possibly reduce number of sampled pairs, as zero elements in norm_mult will not contribute to the result
# 2. Prevents scaling of zero values
nnz = (norm_mult!=0).sum()
if nnz == 0:
#print("zero multiply detected! scenario not optimzied (todo)")
return approx_conv2d.backward_weight(input, weight_shape, grad_output, stride, padding, dilation, groups, False, False, True)
k = min(k,nnz)
indices = torch.multinomial(norm_mult,k,replacement=with_replacement)
# pick top-k minibatch examples to form new smaller tensors
input_top_k_batch = torch.index_select(input,dim = 0, index = indices)
grad_output_top_k_batch = torch.index_select(grad_output,dim = 0, index = indices)
if scale == True:
# when sampling without replacement a more complicated scaling factor is required (see Horvitz and Thompson, 1952)
assert(with_replacement == True)
# scale out_channels by 1/(k*p_i) to get unbiased estimation
sum_norm_mult = torch.sum(norm_mult)
scale_factors = torch.div(sum_norm_mult,torch.mul(norm_mult,k))
input_top_k_batch = torch.mul(input_top_k_batch, scale_factors[indices].view(-1,1,1,1))
else:
# uniform sampling
if with_replacement == True:
indices = torch.randint(low=0,high=batch,size=(k,),device=device)
else:
uniform_dist = torch.ones(batch,device=device)
indices = torch.multinomial(uniform_dist,k,replacement=False)
# pick top-k minibatch examples to form new smaller tensors
input_top_k_batch = torch.index_select(input,dim = 0, index = indices)
grad_output_top_k_batch = torch.index_select(grad_output,dim = 0, index = indices)
if scale == True:
# scale sampled batch examples by 1/(k*p_i) to get unbiased estimation
# in case of uniform distribution, p_i = 1/in_features when sampling with replacement
# when sampling without replacement a different scaling factor is required (see Horvitz and Thompson, 1952), but
# for uniform sampling it turns to be in_features/k as well
scale_factor = batch/k
input_top_k_batch = torch.mul(input_top_k_batch, scale_factor)
# compute sampled tensors
grad_weight_approx = approx_conv2d.backward_weight(input_top_k_batch, weight_shape, grad_output_top_k_batch, stride, padding, dilation, groups, False, False, True)
return grad_weight_approx
''' input - input tensor, shape (batch, in_channels, h, w)
weight_shape - shape of grad_weight. expected: (out_channels, in_channels,h,w)
grad_output - grad output tensor, shape (batch, out_channels, h,w)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of out_channels to sample
minimal_k - Minimal number of out_channels to keep in the sampling
'''
def conv2d_wu_bernoulli_sampling(input, weight_shape, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k, scale):
#print("Sanity check - conv2d_wu_bernoulli is used with sample_ratio = " + str(sample_ratio) + "and minimal_k = " + str(minimal_k))
#print("input: {}".format(input))
#print("weight_shape: {}".format(weight_shape))
#print("input size: {}".format(input.size()))
#print("grad_output size: {}".format(grad_output.size()))
#print("sample_ratio: {}".format(sample_ratio))
#print("minimal_k: {}".format(minimal_k))
#print("scale: {}".format(scale))
batch = input.size()[0]
device = input.device
# calculate the number of minibatch examples to sample
k_candidate = int(float(batch)*sample_ratio)
# make k at least minimal_k
k = min(max(k_candidate,minimal_k),batch)
# if because of minimal_k or sample_ratio k equals the minibatch size, perform full conv2d_wu instead of approximating
if k == batch:
return approx_conv2d.backward_weight(input, weight_shape, grad_output, stride, padding, dilation, groups, False, False, True)
# calculate norms of output channels
input_batch_norms = torch.norm(input.view(batch, -1),dim=1, p=2)
grad_output_batch_norms = torch.norm(grad_output.view(batch, -1) ,dim=1, p=2)
# multiply both norms element-wise
norm_mult = torch.mul(input_batch_norms, grad_output_batch_norms)
sum_norm_mult = norm_mult.sum()
# calculate number of nonzero elements in norm_mult. this serves
# two purposes:
# 1. Possibly reduce number of sampled pairs, as zero elements in norm_mult will not contribute to the result
# 2. Prevents scaling of zero values
nnz = (norm_mult!=0).sum()
if nnz == 0:
#print("zero multiply detected! scenario not optimzied (todo)")
return approx_conv2d.backward_weight(input, weight_shape, grad_output, stride, padding, dilation, groups, False, False, True)
k = min(k,nnz)
prob_dist = k * torch.div(norm_mult,sum_norm_mult)
prob_dist = prob_dist.clamp(min=0, max=1)
# use epsilon-optimal sampling to allow learning random weights and to bound the scaling factor
epsilon = 0.1
if epsilon > 0:
uniform = torch.ones_like(prob_dist)/batch
prob_dist = (1-epsilon)*prob_dist + epsilon*uniform
indices = torch.bernoulli(prob_dist).nonzero(as_tuple=True)[0]
if len(indices) == 0:
print("no elements selected - hmm")
indices = torch.arange(k, device=device)
# pick top-k minibatch examples to form new smaller tensors
input_top_k_batch = torch.index_select(input,dim = 0, index = indices)
grad_output_top_k_batch = torch.index_select(grad_output,dim = 0, index = indices)
if scale == True:
# scale out_channels by 1/(p_i) to get unbiased estimation
scale_factors = torch.div(1,prob_dist)
input_top_k_batch = torch.mul(input_top_k_batch, scale_factors[indices].view(-1,1,1,1))
# compute sampled tensors
grad_weight_approx = approx_conv2d.backward_weight(input_top_k_batch, weight_shape, grad_output_top_k_batch, stride, padding, dilation, groups, False, False, True)
return grad_weight_approx
''' input - input tensor, shape (batch, in_channels, h, w)
weight_shape - shape of grad_weight. expected: (out_channels, in_channels,h,w)
grad_output - grad output tensor, shape (batch, out_channels, h,w)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of out_channels to sample
minimal_k - Minimal number of out_channels to keep in the sampling
'''
def approx_conv2d_func_wu(input, weight_shape, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k):
#return approx_conv2d.backward_weight(input, weight_shape, grad_output, stride, padding, dilation, groups, False, False, True)
#return conv2d_wu_topk(input, weight_shape, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k)
#return conv2d_wu_random_sampling(input, weight_shape, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k, with_replacement=True, optimal_prob=True, scale=True)
return conv2d_wu_bernoulli_sampling(input, weight_shape, grad_output, stride, padding, dilation, groups, sample_ratio,minimal_k, scale=True)
def approx_conv2d_func_forward(A,B,bias, stride, padding, dilation, groups, sample_ratio,minimal_k):
#return torch.nn.functional.conv2d(input=A, weight=B, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
return conv2d_top_k(A=A.float(),B=B.float(),bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups, sample_ratio=sample_ratio, minimal_k=minimal_k)
# return conv2d_top_k_weights(A=A.float(),B=B.float(),bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups, sample_ratio=sample_ratio, minimal_k=minimal_k)
#return conv2d_top_k_approx(A=A.float(),B=B.float(),bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups, sample_ratio=sample_ratio, minimal_k=minimal_k)
#return conv2d_top_k_adaptive(A=A.float(),B=B.float(),bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups, sample_ratio=sample_ratio, minimal_k=minimal_k)
#return approx_conv2d.forward(A, B, bias, stride, padding, dilation, groups, sample_ratio, minimal_k, False, False, True)
#return conv2d_narrow(A=A.float(),B=B.float(),bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups, sample_ratio=sample_ratio, minimal_k=minimal_k)
#return conv2d_uniform_sampling(A=A.float(),B=B.float(),bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups, sample_ratio=sample_ratio, minimal_k=minimal_k)
#return conv2d_random_sampling(A=A.float(),B=B.float(),bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups, sample_ratio=sample_ratio, minimal_k=minimal_k, with_replacement=True, optimal_prob=True, scale=True)
#return conv2d_random_sampling_adaptive(A=A.float(),B=B.float(),bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups, sample_ratio=sample_ratio, minimal_k=minimal_k, with_replacement=False, optimal_prob=True, scale=False)
#return conv2d_bernoulli_sampling(A=A.float(),B=B.float(),bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups, sample_ratio=sample_ratio, minimal_k=minimal_k, scale=True)
''' Approximates 2d convolution with channel sampling
A - input tensor, shape (batch, in_channels, h, w)
B - input matrices, shape (out_channels, in_channels, kw, kw)
bias - bias vector, shape (out_channels)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of in_channels to sample
minimal_k - Minimal number of in_channels to keep in the sampling
with_replacement - True means sampling is done with replacement, False means sampling without replacement
optimal_prob - True means sampling probability is proportional to |Ai|*|Bi|. False means uniform distribution.
scale - True means each input channel is scaled by 1/sqrt(K*pi) to ensure bias 0
'''
def conv2d_random_sampling(A,B,bias, stride, padding, dilation, groups, sample_ratio, minimal_k, with_replacement, optimal_prob, scale):
#print("Sanity check - conv2d_random_sampling is used with sample_ratio = " + str(sample_ratio) + "and minimal_k = " + str(minimal_k))
#print("A size: {}".format(A.size()))
#print("B size: {}".format(B.size()))
#print("bias size: {}".format(bias.size()))
#print("sample_ratio: {}".format(sample_ratio))
#print("minimal_k: {}".format(minimal_k))
#print("with_replacement: {}".format(with_replacement))
#print("optimal_prob: {}".format(optimal_prob))
#print("scale: {}".format(scale))
#print("A mean: {}".format(A.mean()))
#print("A std: {}".format(A.std()))
#print("B mean: {}".format(B.mean()))
#print("B std: {}".format(B.std()))
in_channels = A.size()[1]
device = A.device
# calculate the number of input channels to sample
k_candidate = int(float(in_channels)*sample_ratio)
# make k at least minimal_k
k = min(max(k_candidate,minimal_k),in_channels)
# if because of minimal_k or sample_ratio k equals the number of features, perform full conv2d instead of approximating
if k == in_channels:
return torch.nn.functional.conv2d(input=A, weight=B, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
if optimal_prob == True:
with torch.no_grad():
# calculate norms of the input channels of A and B
a_channel_norms = torch.norm(A.view(A.size()[0],A.size()[1],-1),p=2, dim=2)
a_channel_norms = torch.norm(a_channel_norms, dim=0, p=2)
a_channel_norms = torch.squeeze(a_channel_norms)
b_channel_norms = torch.norm(B.view(B.size()[0],B.size()[1],-1),p=2, dim=2)
b_channel_norms = torch.norm(b_channel_norms, dim=0, p=2)
b_channel_norms = torch.squeeze(b_channel_norms)
# multiply both norms element-wise
norm_mult = torch.mul(a_channel_norms,b_channel_norms)
# use epsilon-optimal sampling to allow learning random weights and to bound the scaling factor
epsilon = 0.1
if epsilon > 0:
sum_norm_mult = torch.sum(norm_mult)
norm_mult = torch.div(norm_mult, sum_norm_mult)
uniform = torch.ones_like(norm_mult)/in_channels
norm_mult = (1-epsilon)*norm_mult + epsilon*uniform
# no need to normalize, it is already done by torch.multinomial
# calculate number of nonzero elements in norm_mult. this serves
# two purposes:
# 1. Possibly reduce number of sampled pairs, as zero elements in norm_mult will not contribute to the result
# 2. Prevents scaling of zero values
nnz = (norm_mult!=0).sum()
if nnz == 0:
#print("zero multiply detected! scenario not optimzied (todo)")
return torch.nn.functional.conv2d(input=A, weight=B, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
k = min(k,nnz)
indices = torch.multinomial(norm_mult,k,replacement=with_replacement)
# pick k channels to form new smaller tensors
A_top_k_channels = torch.index_select(A,dim = 1, index = indices)
B_top_k_channels = torch.index_select(B,dim = 1, index = indices)
if scale == True:
# when sampling without replacement a more complicated scaling factor is required (see Horvitz and Thompson, 1952)
assert(with_replacement == True)
# scale column-row pairs by 1/(k*p_i) to get unbiased estimation
with torch.no_grad():
sum_norm_mult = torch.sum(norm_mult)
scale_factors = torch.div(sum_norm_mult,torch.mul(norm_mult,k))
A_top_k_channels = torch.mul(A_top_k_channels, scale_factors[indices].view(1,-1,1,1))
else:
# uniform sampling
if with_replacement == True:
indices = torch.randint(low=0,high=in_channels,size=(k,),device=device)
else:
uniform_dist = torch.ones(in_channels,device=device)
indices = torch.multinomial(uniform_dist,k,replacement=False)
# pick k column-row pairs to form new smaller matrices
A_top_k_channels = torch.index_select(A, dim=1, index=indices)
B_top_k_channels = torch.index_select(B, dim=1, index=indices)
if scale == True:
# scale column-row pairs by 1/(k*p_i) to get unbiased estimation
# in case of uniform distribution, p_i = 1/in_features when sampling with replacement
# when sampling without replacement a different scaling factor is required (see Horvitz and Thompson, 1952), but
# for uniform sampling it turns to be in_features/k as well
scale_factor = in_channels/k
A_top_k_channels = torch.mul(A_top_k_channels, scale_factor)
# convolve smaller tensors
C_approx = torch.nn.functional.conv2d(input=A_top_k_channels, weight=B_top_k_channels, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
return C_approx
''' Approximates 2d convolution with channel sampling
the number of channels sampled will vary according to norm concentration
A - input tensor, shape (batch, in_channels, h, w)
B - input matrices, shape (out_channels, in_channels, kw, kw)
bias - bias vector, shape (out_channels)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of in_channels to sample
minimal_k - Minimal number of in_channels to keep in the sampling
with_replacement - True means sampling is done with replacement, False means sampling without replacement
optimal_prob - True means sampling probability is proportional to |Ai|*|Bi|. False means uniform distribution.
scale - True means each input channel is scaled by 1/sqrt(K*pi) to ensure bias 0
'''
def conv2d_random_sampling_adaptive(A,B,bias, stride, padding, dilation, groups, sample_ratio, minimal_k, with_replacement, optimal_prob, scale):
#print("Sanity check - conv2d_random_sampling adaptive is used with sample_ratio = " + str(sample_ratio) + "and minimal_k = " + str(minimal_k))
#print("A size: {}".format(A.size()))
#print("B size: {}".format(B.size()))
#print("bias size: {}".format(bias.size()))
#print("sample_ratio: {}".format(sample_ratio))
#print("minimal_k: {}".format(minimal_k))
#print("with_replacement: {}".format(with_replacement))
#print("optimal_prob: {}".format(optimal_prob))
#print("scale: {}".format(scale))
#print("A mean: {}".format(A.mean()))
#print("A std: {}".format(A.std()))
#print("B mean: {}".format(B.mean()))
#print("B std: {}".format(B.std()))
in_channels = A.size()[1]
device = A.device
# if because of minimal_k or sample_ratio k equals the number of features, perform full conv2d instead of approximating
if minimal_k >= in_channels or sample_ratio == 1.0:
return torch.nn.functional.conv2d(input=A, weight=B, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
if optimal_prob == True:
with torch.no_grad():
# calculate norms of the input channels of A and B
a_channel_norms = torch.norm(A.view(A.size()[0],A.size()[1],-1),p=2, dim=2)
a_channel_norms = torch.norm(a_channel_norms, dim=0, p=2)
a_channel_norms = torch.squeeze(a_channel_norms)
b_channel_norms = torch.norm(B.view(B.size()[0],B.size()[1],-1),p=2, dim=2)
b_channel_norms = torch.norm(b_channel_norms, dim=0, p=2)
b_channel_norms = torch.squeeze(b_channel_norms)
# multiply both norms element-wise
norm_mult = torch.mul(a_channel_norms,b_channel_norms)
# use epsilon-optimal sampling to allow learning random weights and to bound the scaling factor
epsilon = 0.1
if epsilon > 0:
sum_norm_mult = torch.sum(norm_mult)
norm_mult = torch.div(norm_mult, sum_norm_mult)
uniform = torch.ones_like(norm_mult)/in_channels
norm_mult = (1-epsilon)*norm_mult + epsilon*uniform
# no need to normalize, it is already done by torch.multinomial
# calculate number of nonzero elements in norm_mult. this serves
# two purposes:
# 1. Possibly reduce number of sampled pairs, as zero elements in norm_mult will not contribute to the result
# 2. Prevents scaling of zero values
nnz = (norm_mult!=0).sum()
if nnz == 0:
#print("zero multiply detected! scenario not optimzied (todo)")
return torch.nn.functional.conv2d(input=A, weight=B, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
sum_norm_mult = torch.sum(norm_mult)
sorted_indices = topk_indices(norm_mult,in_channels)
for k in range(minimal_k, in_channels):
if norm_mult[sorted_indices[:k]].sum() >= sum_norm_mult*sample_ratio:
break
indices = torch.multinomial(norm_mult,k,replacement=with_replacement)
# pick k channels to form new smaller tensors
A_top_k_channels = torch.index_select(A,dim = 1, index = indices)
B_top_k_channels = torch.index_select(B,dim = 1, index = indices)
if scale == True:
# when sampling without replacement a more complicated scaling factor is required (see Horvitz and Thompson, 1952)
assert(with_replacement == True)
# scale column-row pairs by 1/(k*p_i) to get unbiased estimation
with torch.no_grad():
sum_norm_mult = torch.sum(norm_mult)
scale_factors = torch.div(sum_norm_mult,torch.mul(norm_mult,k))
A_top_k_channels = torch.mul(A_top_k_channels, scale_factors[indices].view(1,-1,1,1))
else:
# uniform sampling
print('adaptive sampling not implemented yet for uniform sampling')
exit()
# convolve smaller tensors
C_approx = torch.nn.functional.conv2d(input=A_top_k_channels, weight=B_top_k_channels, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
return C_approx
''' Approximates 2d convolution with channel sampling according to largest norm
A - input tensor, shape (batch, in_channels, h, w)
B - input matrices, shape (out_channels, in_channels, kw, kw)
bias - bias vector, shape (out_channels)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of in_channels to sample
minimal_k - Minimal number of in_channels to keep in the sampling
'''
def conv2d_top_k(A,B,bias, stride, padding, dilation, groups, sample_ratio, minimal_k):
#print("Sanity check - conv2d_top_k is used with sample_ratio = " + str(sample_ratio) + " and minimal_k = " + str(minimal_k))
in_channels = A.size()[1]
# calculate the number of channels to sample for the forward propagation phase
k_candidate = int(float(in_channels)*sample_ratio)
# make k at least min_clrows (similar to meProp)
k = min(max(k_candidate,minimal_k),in_channels)
# if because of minimal_k or sample_ratio k equals the number of features, perform full conv2d instead of approximating
if k == in_channels:
return torch.nn.functional.conv2d(input=A, weight=B, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
# calculate norms of the columns of A and rows of B
with torch.no_grad():
a_channel_norms = torch.norm(A.view(A.size()[0],A.size()[1],-1),p=2, dim=2)
a_channel_norms = torch.norm(a_channel_norms, dim=0, p=2)
a_channel_norms = torch.squeeze(a_channel_norms)
b_channel_norms = torch.norm(B.view(B.size()[0],B.size()[1],-1),p=2, dim=2)
b_channel_norms = torch.norm(b_channel_norms, dim=0, p=2)
b_channel_norms = torch.squeeze(b_channel_norms)
# multiply both norms element-wise to and pick the indices of the top K column-row pairs
norm_mult = torch.mul(a_channel_norms,b_channel_norms)
#top_k_indices = torch.topk(norm_mult,k)[1]
top_k_indices = topk_indices(norm_mult,k)
# pick top-k column-row pairs to form new smaller matrices
A_top_k_cols = torch.index_select(A,dim = 1, index = top_k_indices)
B_top_k_rows = torch.index_select(B,dim = 1, index = top_k_indices)
# multiply smaller matrices
C_approx = torch.nn.functional.conv2d(input=A_top_k_cols, weight=B_top_k_rows, bias=bias, stride=stride, padding=padding,
dilation=dilation, groups=groups)
return C_approx
''' Approximates 2d convolution with channel sampling according to largest norm
A - input tensor, shape (batch, in_channels, h, w)
B - input matrices, shape (out_channels, in_channels, kw, kw)
bias - bias vector, shape (out_channels)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of in_channels to sample
minimal_k - Minimal number of in_channels to keep in the sampling
'''
def conv2d_top_k_weights(A,B,bias, stride, padding, dilation, groups, sample_ratio, minimal_k):
#print("Sanity check - conv2d_top_k_weights is used with sample_ratio = " + str(sample_ratio) + " and minimal_k = " + str(minimal_k))
in_channels = A.size()[1]
# calculate the number of channels to sample for the forward propagation phase
k_candidate = int(float(in_channels)*sample_ratio)
# make k at least min_clrows (similar to meProp)
k = min(max(k_candidate,minimal_k),in_channels)
# if because of minimal_k or sample_ratio k equals the number of features, perform full conv2d instead of approximating
if k == in_channels:
return torch.nn.functional.conv2d(input=A, weight=B, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
# calculate norms of rows of B
with torch.no_grad():
b_channel_norms = torch.norm(B.view(B.size()[0],B.size()[1],-1),p=2, dim=2)
b_channel_norms = torch.norm(b_channel_norms, dim=0, p=2)
b_channel_norms = torch.squeeze(b_channel_norms)
#top_k_indices = torch.topk(norm_mult,k)[1]
top_k_indices = topk_indices(b_channel_norms,k)
# pick top-k column-row pairs to form new smaller matrices
A_top_k_cols = torch.index_select(A,dim = 1, index = top_k_indices)
B_top_k_rows = torch.index_select(B,dim = 1, index = top_k_indices)
# multiply smaller matrices
C_approx = torch.nn.functional.conv2d(input=A_top_k_cols, weight=B_top_k_rows, bias=bias, stride=stride, padding=padding,
dilation=dilation, groups=groups)
return C_approx
''' Approximates 2d convolution with channel sampling according to largest norm
A - input tensor, shape (batch, in_channels, h, w)
B - input matrices, shape (out_channels, in_channels, kw, kw)
bias - bias vector, shape (out_channels)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of in_channels to sample
minimal_k - Minimal number of in_channels to keep in the sampling
returns conv result and selected indices
'''
def conv2d_top_k_weights_dist(A,B,bias, stride, padding, dilation, groups, sample_ratio, minimal_k):
#print("Sanity check - conv2d_top_k_weights is used with sample_ratio = " + str(sample_ratio) + " and minimal_k = " + str(minimal_k))
in_channels = A.size()[1]
# calculate the number of channels to sample for the forward propagation phase
k_candidate = int(float(in_channels)*sample_ratio)
# make k at least min_clrows (similar to meProp)
k = min(max(k_candidate,minimal_k),in_channels)
# if because of minimal_k or sample_ratio k equals the number of features, perform full conv2d instead of approximating
if k == in_channels:
return torch.nn.functional.conv2d(input=A, weight=B, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups), torch.arange(in_channels, device=A.device)
# calculate norms of rows of B
with torch.no_grad():
b_channel_norms = torch.norm(B.view(B.size()[0],B.size()[1],-1),p=2, dim=2)
b_channel_norms = torch.norm(b_channel_norms, dim=0, p=2)
b_channel_norms = torch.squeeze(b_channel_norms)
# add explicit sorting because of strange indeterministic behavior across multiple GPUs
top_k_indices = torch.topk(b_channel_norms,k)[1].sort()[0]
#top_k_indices = topk_indices(b_channel_norms,k).sort()[0]
# pick top-k column-row pairs to form new smaller matrices
A_top_k_cols = torch.index_select(A,dim = 1, index = top_k_indices)
B_top_k_rows = torch.index_select(B,dim = 1, index = top_k_indices)
# multiply smaller matrices
C_approx = torch.nn.functional.conv2d(input=A_top_k_cols, weight=B_top_k_rows, bias=bias, stride=stride, padding=padding,
dilation=dilation, groups=groups)
return C_approx, top_k_indices
''' Approximates 2d convolution with channel sampling according to largest norm
the norm is sampled from a subset of the reduction dimension
A - input tensor, shape (batch, in_channels, h, w)
B - input matrices, shape (out_channels, in_channels, kw, kw)
bias - bias vector, shape (out_channels)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of in_channels to sample
minimal_k - Minimal number of in_channels to keep in the sampling
'''
def conv2d_top_k_approx(A,B,bias, stride, padding, dilation, groups, sample_ratio, minimal_k):
#print("Sanity check - conv2d_top_k_approx is used with sample_ratio = " + str(sample_ratio) + "and minimal_k = " + str(minimal_k))
in_channels = A.size()[1]
# calculate the number of channels to sample for the forward propagation phase
k_candidate = int(float(in_channels)*sample_ratio)
# make k at least min_clrows (similar to meProp)
k = min(max(k_candidate,minimal_k),in_channels)
# if because of minimal_k or sample_ratio k equals the number of features, perform full conv2d instead of approximating
if k == in_channels:
return torch.nn.functional.conv2d(input=A, weight=B, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
# calculate norms of the columns of A and rows of B
with torch.no_grad():
a_channel_norms = torch.norm(A[:,:,torch.randint(A.size()[2],size=[1],dtype=torch.long), torch.randint(A.size()[3],size=[1],dtype=torch.long)], dim=0, p=2)
a_channel_norms = torch.squeeze(a_channel_norms)
b_channel_norms = torch.norm(B[:,:,torch.randint(B.size()[2],size=[1],dtype=torch.long), torch.randint(B.size()[3],size=[1],dtype=torch.long)], dim=0, p=2)
b_channel_norms = torch.squeeze(b_channel_norms)
# multiply both norms element-wise to and pick the indices of the top K column-row pairs
norm_mult = torch.mul(a_channel_norms,b_channel_norms)
#top_k_indices = torch.topk(norm_mult,k)[1]
top_k_indices = topk_indices(norm_mult,k)
# pick top-k column-row pairs to form new smaller matrices
A_top_k_cols = torch.index_select(A,dim = 1, index = top_k_indices)
B_top_k_rows = torch.index_select(B,dim = 1, index = top_k_indices)
# multiply smaller matrices
C_approx = torch.nn.functional.conv2d(input=A_top_k_cols, weight=B_top_k_rows, bias=bias, stride=stride, padding=padding,
dilation=dilation, groups=groups)
return C_approx
''' Approximates 2d convolution with channel sampling according to largest norm
the number of channels sampled will vary according to norm concentration
A - input tensor, shape (batch, in_channels, h, w)
B - input matrices, shape (out_channels, in_channels, kw, kw)
bias - bias vector, shape (out_channels)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of in_channels to sample
minimal_k - Minimal number of in_channels to keep in the sampling
'''
def conv2d_top_k_adaptive(A,B,bias, stride, padding, dilation, groups, sample_ratio, minimal_k):
#print("Sanity check - conv2d_top_k_adaptive is used with sample_ratio = " + str(sample_ratio) + "and minimal_k = " + str(minimal_k))
in_channels = A.size()[1]
# if because of minimal_k k equals the number of features, perform full conv2d instead of approximating
if sample_ratio == 1.0 or minimal_k >= in_channels:
return torch.nn.functional.conv2d(input=A, weight=B, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
# calculate norms of the columns of A and rows of B
with torch.no_grad():
a_channel_norms = torch.norm(A.view(A.size()[0],A.size()[1],-1),p=2, dim=2)
a_channel_norms = torch.norm(a_channel_norms, dim=0, p=2)
a_channel_norms = torch.squeeze(a_channel_norms)
b_channel_norms = torch.norm(B.view(B.size()[0],B.size()[1],-1),p=2, dim=2)
b_channel_norms = torch.norm(b_channel_norms, dim=0, p=2)
b_channel_norms = torch.squeeze(b_channel_norms)
# multiply both norms element-wise to and pick the indices of the top K column-row pairs
norm_mult = torch.mul(a_channel_norms,b_channel_norms)
sum_norm_mult = torch.sum(norm_mult)
#top_k_indices = torch.topk(norm_mult,k)[1]
sorted_indices = topk_indices(norm_mult,in_channels)
k = minimal_k
for k in range(minimal_k, in_channels):
if norm_mult[sorted_indices[:k]].sum() >= sum_norm_mult*sample_ratio:
top_k_indices = sorted_indices[:k]
break
# pick top-k column-row pairs to form new smaller matrices
A_top_k_cols = torch.index_select(A,dim = 1, index = top_k_indices)
B_top_k_rows = torch.index_select(B,dim = 1, index = top_k_indices)
# multiply smaller matrices
C_approx = torch.nn.functional.conv2d(input=A_top_k_cols, weight=B_top_k_rows, bias=bias, stride=stride, padding=padding,
dilation=dilation, groups=groups)
return C_approx
def conv2d_narrow(A,B,bias, stride, padding, dilation, groups, sample_ratio, minimal_k):
#print("Sanity check - conv2d_narrow is used with sample_ratio = " + str(sample_ratio) + "and minimal_k = " + str(minimal_k))
# calculate the number of channels to sample for the forward propagation phase
k_candidate = int(float(B.size()[1])*sample_ratio)
# make k at least min_clrows (similar to meProp)
k = min(max(k_candidate,minimal_k),B.size()[1])
A_top_k_cols = torch.narrow(A,dim = 1, start=0, length=k)
B_top_k_rows = torch.narrow(B,dim = 1, start=0, length=k)
# multiply smaller matrices
C_approx = torch.nn.functional.conv2d(input=A_top_k_cols, weight=B_top_k_rows, bias=bias, stride=stride, padding=padding,
dilation=dilation, groups=groups)
return C_approx
def conv2d_uniform_sampling(A,B,bias, stride, padding, dilation, groups, sample_ratio, minimal_k):
# print("Sanity check - conv2d_uniform_sampling is used, sample_ratio = " + str(sample_ratio) + " minimal_k = " + str(minimal_k))
# calculate the number of input channels to sample for the forward propagation phase
k_candidate = int(float(B.size()[1])*sample_ratio)
# make k at least min_clrows (similar to meProp)
k = min(max(k_candidate,minimal_k),B.size()[1])
indices = torch.randperm(B.size()[1])[:k].cuda()
# pick top-k column-row pairs to form new smaller matrices
A_top_k_cols = torch.index_select(A, dim=1, index=indices)
B_top_k_rows = torch.index_select(B,dim = 1, index = indices)
# convolve smaller tensors
C_approx = torch.nn.functional.conv2d(input=A_top_k_cols, weight=B_top_k_rows, bias=bias, stride=stride, padding=padding,
dilation=dilation, groups=groups)
return C_approx
''' Approximates 2d convolution with bernoulli channel sampling
A - input tensor, shape (batch, in_channels, h, w)
B - input matrices, shape (out_channels, in_channels, kw, kw)
bias - bias vector, shape (out_channels)
stride, padding, dilation, groups as in regular conv2d
sample_ratio - Ratio of in_channels to sample
minimal_k - Minimal number of in_channels to keep in the sampling
scale - True means each input channel is scaled by 1/sqrt(K*pi) to ensure bias 0
'''
def conv2d_bernoulli_sampling(A,B,bias, stride, padding, dilation, groups, sample_ratio, minimal_k, scale):
#print("Sanity check - conv2d_bernoulli_sampling is used with sample_ratio = " + str(sample_ratio) + "and minimal_k = " + str(minimal_k))
#print("A size: {}".format(A.size()))
#print("B size: {}".format(B.size()))
#print("bias size: {}".format(bias.size()))
#print("sample_ratio: {}".format(sample_ratio))
#print("minimal_k: {}".format(minimal_k))
#print("scale: {}".format(scale))
#print("A mean: {}".format(A.mean()))
#print("A std: {}".format(A.std()))
#print("B mean: {}".format(B.mean()))
#print("B std: {}".format(B.std()))
in_channels = A.size()[1]
device = A.device
# calculate the number of input channels to sample
k_candidate = int(float(in_channels)*sample_ratio)
# make k at least minimal_k
k = min(max(k_candidate,minimal_k),in_channels)
# if because of minimal_k or sample_ratio k equals the number of features, perform full conv2d instead of approximating
if k == in_channels:
return torch.nn.functional.conv2d(input=A, weight=B, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
with torch.no_grad():
# calculate norms of the input channels of A and B
a_channel_norms = torch.norm(A.view(A.size()[0],A.size()[1],-1),p=2, dim=2)
a_channel_norms = torch.norm(a_channel_norms, dim=0, p=2)
a_channel_norms = torch.squeeze(a_channel_norms)
b_channel_norms = torch.norm(B.view(B.size()[0],B.size()[1],-1),p=2, dim=2)
b_channel_norms = torch.norm(b_channel_norms, dim=0, p=2)
b_channel_norms = torch.squeeze(b_channel_norms)
# multiply both norms element-wise
norm_mult = torch.mul(a_channel_norms,b_channel_norms)
sum_norm_mult = norm_mult.sum()
# calculate number of nonzero elements in norm_mult. this serves
# two purposes:
# 1. Possibly reduce number of sampled pairs, as zero elements in norm_mult will not contribute to the result
# 2. Prevents scaling of zero values
nnz = (norm_mult!=0).sum()
if nnz == 0:
#print("zero multiply detected! scenario not optimzied (todo)")
return torch.nn.functional.conv2d(input=A, weight=B, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
k = min(k,nnz)
prob_dist = k * torch.div(norm_mult,sum_norm_mult)
prob_dist = prob_dist.clamp(min=0, max=1)
# use epsilon-optimal sampling to allow learning random weights and to bound the scaling factor
epsilon = 0.1
if epsilon > 0:
uniform = torch.ones_like(prob_dist)/in_channels
prob_dist = (1-epsilon)*prob_dist + epsilon*uniform
indices = torch.bernoulli(prob_dist).nonzero(as_tuple=True)[0]
if len(indices) == 0:
print("no elements selected - hmm")
indices = torch.arange(k, device=device)
# pick k channels to form new smaller tensors
A_top_k_channels = torch.index_select(A,dim = 1, index = indices)
B_top_k_channels = torch.index_select(B,dim = 1, index = indices)
if scale == True:
# scale column-row pairs by 1/(p_i) to get unbiased estimation
with torch.no_grad():
scale_factors = torch.div(1,prob_dist)
A_top_k_channels = torch.mul(A_top_k_channels, scale_factors[indices].view(1,-1,1,1))
# convolve smaller tensors
C_approx = torch.nn.functional.conv2d(input=A_top_k_channels, weight=B_top_k_channels, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
return C_approx
| 53.463415 | 248 | 0.693185 | 8,309 | 56,992 | 4.543387 | 0.03069 | 0.031999 | 0.033377 | 0.042913 | 0.977166 | 0.971524 | 0.969193 | 0.967286 | 0.96416 | 0.958041 | 0 | 0.010869 | 0.205713 | 56,992 | 1,065 | 249 | 53.513615 | 0.823076 | 0.326362 | 0 | 0.853147 | 0 | 0 | 0.004441 | 0 | 0 | 0 | 0 | 0.006573 | 0.009324 | 1 | 0.044289 | false | 0 | 0.004662 | 0.006993 | 0.142191 | 0.009324 | 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 |
86ae933a23ba052ef22055c740f3a174a2c73d9d | 14,575 | py | Python | src/syft/lib/python/bool.py | manisoftwartist/PySyft | 19cf2cbc11efaae16932f4a5aa9a225060675bd0 | [
"MIT"
] | null | null | null | src/syft/lib/python/bool.py | manisoftwartist/PySyft | 19cf2cbc11efaae16932f4a5aa9a225060675bd0 | [
"MIT"
] | null | null | null | src/syft/lib/python/bool.py | manisoftwartist/PySyft | 19cf2cbc11efaae16932f4a5aa9a225060675bd0 | [
"MIT"
] | null | null | null | # stdlib
from typing import Any
from typing import List
from typing import Optional
# third party
from google.protobuf.reflection import GeneratedProtocolMessageType
# syft relative
from ... import deserialize
from ... import serialize
from ...core.common import UID
from ...core.store.storeable_object import StorableObject
from ...decorators import syft_decorator
from ...proto.lib.python.bool_pb2 import Bool as Bool_PB
from ...util import aggressive_set_attr
from .primitive_factory import PrimitiveFactory
from .primitive_interface import PyPrimitive
from .util import SyPrimitiveRet
def dispatch_other(obj: Any) -> bool:
if isinstance(obj, Bool):
return obj.value
return obj
class Bool(int, PyPrimitive):
@syft_decorator(typechecking=True, prohibit_args=False)
def __new__(cls, value: Any = None, id: Optional[UID] = None) -> "Bool":
value = bool(value)
obj = int.__new__(cls, value) # type: ignore
return obj
@syft_decorator(typechecking=True, prohibit_args=False)
def __init__(self, value: Any = None, id: Optional[UID] = None):
self.value: bool = bool(value)
self._id: UID = id if id else UID()
@property
def id(self) -> UID:
"""We reveal PyPrimitive.id as a property to discourage users and
developers of Syft from modifying .id attributes after an object
has been initialized.
:return: returns the unique id of the object
:rtype: UID
"""
return self._id
@syft_decorator(typechecking=True, prohibit_args=True)
def upcast(self) -> bool:
return bool(self)
@syft_decorator(typechecking=True, prohibit_args=False)
def __abs__(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.__abs__())
@syft_decorator(typechecking=True, prohibit_args=False)
def __add__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__add__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __and__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__and__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __bool__(self) -> bool:
return bool(self.value)
@syft_decorator(typechecking=True, prohibit_args=False)
def __ceil__(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.__ceil__())
@syft_decorator(typechecking=True, prohibit_args=False)
def __divmod__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
tpl = self.value.__divmod__(other)
return PrimitiveFactory.generate_primitive(value=tpl)
@syft_decorator(typechecking=True, prohibit_args=False)
def __eq__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__eq__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __float__(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.__float__())
@syft_decorator(typechecking=True, prohibit_args=False)
def __floor__(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.__floor__())
@syft_decorator(typechecking=True, prohibit_args=False)
def __floordiv__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__floordiv__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __ge__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__ge__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __gt__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__gt__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __hash__(self) -> int:
return PrimitiveFactory.generate_primitive(value=self.value.__hash__())
@syft_decorator(typechecking=True, prohibit_args=False)
def __invert__(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.__invert__())
def __int__(self) -> int:
return int(self.value)
@syft_decorator(typechecking=True, prohibit_args=False)
def __le__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__le__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __lshift__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__lshift__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __lt__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__lt__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __mod__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__mod__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __mul__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__mul__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __ne__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__ne__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __neg__(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.__neg__())
@syft_decorator(typechecking=True, prohibit_args=False)
def __or__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__or__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __pos__(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.__pos__())
@syft_decorator(typechecking=True, prohibit_args=False)
def __pow__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__pow__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __radd__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__radd__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __rand__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__rand__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __repr__(self) -> str:
return bool(self.value).__repr__()
@syft_decorator(typechecking=True, prohibit_args=False)
def __rdivmod__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
tpl = self.value.__rdivmod__(other)
return PrimitiveFactory.generate_primitive(value=tpl)
@syft_decorator(typechecking=True, prohibit_args=False)
def __rfloordiv__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(
value=self.value.__rfloordiv__(other)
)
@syft_decorator(typechecking=True, prohibit_args=False)
def __rlshift__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__rlshift__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __rmod__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__rmod__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __rmul__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__rmul__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __ror__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__ror__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __round__(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.__round__())
@syft_decorator(typechecking=True, prohibit_args=False)
def __rpow__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__rpow__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __rrshift__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__rrshift__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __rshift__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__rshift__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __rsub__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__rsub__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __rtruediv__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__rtruediv__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __rxor__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__rxor__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __str__(self) -> str:
return bool(self.value).__str__()
@syft_decorator(typechecking=True, prohibit_args=False)
def __sub__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__sub__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __truediv__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__truediv__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def __trunc__(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.__trunc__())
@syft_decorator(typechecking=True, prohibit_args=False)
def __xor__(self, other: Any) -> SyPrimitiveRet:
other = dispatch_other(other)
return PrimitiveFactory.generate_primitive(value=self.value.__xor__(other))
@syft_decorator(typechecking=True, prohibit_args=False)
def as_integer_ratio(self) -> SyPrimitiveRet:
res = self.value.as_integer_ratio()
return PrimitiveFactory.generate_primitive(value=res)
@syft_decorator(typechecking=True, prohibit_args=False)
def bit_length(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.bit_length())
@syft_decorator(typechecking=True, prohibit_args=False)
def conjugate(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.conjugate())
@syft_decorator(typechecking=True, prohibit_args=False)
def denominator(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.denominator)
# TODO: add support for properties on these 4 functions
@syft_decorator(typechecking=True, prohibit_args=False)
def imag(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.imag)
@syft_decorator(typechecking=True, prohibit_args=False)
def numerator(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.numerator)
@syft_decorator(typechecking=True, prohibit_args=False)
def real(self) -> SyPrimitiveRet:
return PrimitiveFactory.generate_primitive(value=self.value.real)
@syft_decorator(typechecking=True)
def _object2proto(self) -> Bool_PB:
return Bool_PB(id=serialize(obj=self.id), data=self)
@staticmethod
@syft_decorator(typechecking=True)
def _proto2object(proto: Bool_PB) -> "Bool":
return Bool(id=deserialize(blob=proto.id), value=proto.data)
@staticmethod
def get_protobuf_schema() -> GeneratedProtocolMessageType:
return Bool_PB
class BoolWrapper(StorableObject):
def __init__(self, value: object):
super().__init__(
data=value,
id=getattr(value, "id", UID()),
tags=getattr(value, "tags", []),
description=getattr(value, "description", ""),
)
self.value = value
def _data_object2proto(self) -> Bool_PB:
_object2proto = getattr(self.data, "_object2proto", None)
if _object2proto:
return _object2proto()
@staticmethod
def _data_proto2object(proto: Bool_PB) -> "BoolWrapper":
return Bool._proto2object(proto=proto)
@staticmethod
def get_data_protobuf_schema() -> GeneratedProtocolMessageType:
return Bool_PB
@staticmethod
def get_wrapped_type() -> type:
return Bool
@staticmethod
def construct_new_object(
id: UID,
data: StorableObject,
description: Optional[str],
tags: Optional[List[str]],
) -> StorableObject:
setattr(data, "_id", id)
data.tags = tags
data.description = description
return data
aggressive_set_attr(obj=Bool, name="serializable_wrapper_type", attr=BoolWrapper)
| 40.598886 | 88 | 0.724803 | 1,607 | 14,575 | 6.186683 | 0.105787 | 0.07584 | 0.143331 | 0.166264 | 0.766948 | 0.752062 | 0.731845 | 0.72068 | 0.622108 | 0.352042 | 0 | 0.000915 | 0.175094 | 14,575 | 358 | 89 | 40.712291 | 0.826 | 0.021063 | 0 | 0.3663 | 1 | 0 | 0.005416 | 0.001758 | 0 | 0 | 0 | 0.002793 | 0 | 1 | 0.245421 | false | 0 | 0.051282 | 0.098901 | 0.545788 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 |
86b224039c5f5865c5a066849e9e44b0e7991b25 | 6,369 | py | Python | commercialoperator/components/compliances/serializers.py | sharpeez/ledger | 0ea05669f488336a63e2bdd6390725d00d619e9a | [
"Apache-2.0"
] | null | null | null | commercialoperator/components/compliances/serializers.py | sharpeez/ledger | 0ea05669f488336a63e2bdd6390725d00d619e9a | [
"Apache-2.0"
] | null | null | null | commercialoperator/components/compliances/serializers.py | sharpeez/ledger | 0ea05669f488336a63e2bdd6390725d00d619e9a | [
"Apache-2.0"
] | null | null | null | from django.conf import settings
from ledger.accounts.models import EmailUser,Address
from commercialoperator.components.compliances.models import (
Compliance, ComplianceUserAction, ComplianceLogEntry, ComplianceAmendmentRequest, ComplianceAmendmentReason
)
from rest_framework import serializers
class EmailUserSerializer(serializers.ModelSerializer):
class Meta:
model = EmailUser
fields = ('id','email','first_name','last_name','title','organisation')
class ComplianceSerializer(serializers.ModelSerializer):
regions = serializers.CharField(source='proposal.region')
activity = serializers.CharField(source='proposal.activity')
title = serializers.CharField(source='proposal.title')
holder = serializers.CharField(source='proposal.applicant')
processing_status = serializers.CharField(source='get_processing_status_display')
customer_status = serializers.CharField(source='get_customer_status_display')
submitter = serializers.SerializerMethodField(read_only=True)
documents = serializers.SerializerMethodField()
#submitter = serializers.CharField(source='submitter.get_full_name')
submitter = serializers.SerializerMethodField(read_only=True)
allowed_assessors = EmailUserSerializer(many=True)
#assigned_to = serializers.CharField(source='assigned_to.get_full_name')
assigned_to = serializers.SerializerMethodField(read_only=True)
requirement = serializers.CharField(source='requirement.requirement', required=False, allow_null=True)
approval_lodgement_number = serializers.SerializerMethodField()
class Meta:
model = Compliance
fields = (
'id',
'proposal',
'due_date',
'processing_status',
'customer_status',
'regions',
'activity',
'title',
'text',
'holder',
'assigned_to',
'approval',
'documents',
'requirement',
'can_user_view',
'can_process',
'reference',
'lodgement_number',
'lodgement_date',
'submitter',
'allowed_assessors',
'lodgement_date',
'approval_lodgement_number'
)
def get_documents(self,obj):
return [[d.name,d._file.url,d.can_delete,d.id] for d in obj.documents.all()]
def get_approval_lodgement_number(self,obj):
return obj.approval.lodgement_number
def get_assigned_to(self,obj):
if obj.assigned_to:
return obj.assigned_to.get_full_name()
return None
def get_submitter(self,obj):
if obj.submitter:
return obj.submitter.get_full_name()
return None
class InternalComplianceSerializer(serializers.ModelSerializer):
regions = serializers.CharField(source='proposal.region')
activity = serializers.CharField(source='proposal.activity')
title = serializers.CharField(source='proposal.title')
holder = serializers.CharField(source='proposal.applicant')
processing_status = serializers.CharField(source='get_processing_status_display')
customer_status = serializers.CharField(source='get_customer_status_display')
submitter = serializers.SerializerMethodField(read_only=True)
documents = serializers.SerializerMethodField()
#submitter = serializers.CharField(source='submitter.get_full_name')
submitter = serializers.SerializerMethodField(read_only=True)
allowed_assessors = EmailUserSerializer(many=True)
#assigned_to = serializers.CharField(source='assigned_to.get_full_name')
#assigned_to = serializers.SerializerMethodField(read_only=True)
requirement = serializers.CharField(source='requirement.requirement', required=False, allow_null=True)
approval_lodgement_number = serializers.SerializerMethodField()
class Meta:
model = Compliance
fields = (
'id',
'proposal',
'due_date',
'processing_status',
'customer_status',
'regions',
'activity',
'title',
'text',
'holder',
'assigned_to',
'approval',
'documents',
'requirement',
'can_user_view',
'can_process',
'reference',
'lodgement_number',
'lodgement_date',
'submitter',
'allowed_assessors',
'lodgement_date',
'approval_lodgement_number'
)
def get_documents(self,obj):
return [[d.name,d._file.url,d.can_delete,d.id] for d in obj.documents.all()]
def get_approval_lodgement_number(self,obj):
return obj.approval.lodgement_number
# def get_assigned_to(self,obj):
# if obj.assigned_to:
# return obj.assigned_to.get_full_name()
# return None
def get_submitter(self,obj):
if obj.submitter:
return obj.submitter.get_full_name()
return None
class SaveComplianceSerializer(serializers.ModelSerializer):
class Meta:
model = Compliance
fields = (
'id',
'title',
'text',
)
class ComplianceActionSerializer(serializers.ModelSerializer):
who = serializers.CharField(source='who.get_full_name')
class Meta:
model = ComplianceUserAction
fields = '__all__'
class ComplianceCommsSerializer(serializers.ModelSerializer):
documents = serializers.SerializerMethodField()
class Meta:
model = ComplianceLogEntry
fields = '__all__'
def get_documents(self,obj):
return [[d.name,d._file.url] for d in obj.documents.all()]
class ComplianceAmendmentRequestSerializer(serializers.ModelSerializer):
#reason = serializers.SerializerMethodField()
class Meta:
model = ComplianceAmendmentRequest
fields = '__all__'
# def get_reason (self,obj):
# return obj.get_reason_display()
class CompAmendmentRequestDisplaySerializer(serializers.ModelSerializer):
reason = serializers.SerializerMethodField()
class Meta:
model = ComplianceAmendmentRequest
fields = '__all__'
def get_reason (self,obj):
#return obj.get_reason_display()
return obj.reason.reason if obj.reason else None
| 34.427027 | 111 | 0.66808 | 594 | 6,369 | 6.942761 | 0.164983 | 0.092144 | 0.119787 | 0.065955 | 0.809408 | 0.78225 | 0.769399 | 0.769399 | 0.769399 | 0.769399 | 0 | 0 | 0.236615 | 6,369 | 184 | 112 | 34.61413 | 0.848211 | 0.093421 | 0 | 0.788732 | 0 | 0 | 0.152604 | 0.036111 | 0 | 0 | 0 | 0 | 0 | 1 | 0.06338 | false | 0 | 0.028169 | 0.042254 | 0.485915 | 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 |
86cf97ab9a0001766ebf27fc980c94a9e4922923 | 101,347 | py | Python | dgmvae/models/sent_models.py | wenxianxian/demvae | 0ca6a869c28c245beac7a0e5f68639e07d8d8841 | [
"Apache-2.0"
] | 17 | 2020-09-19T16:09:32.000Z | 2022-03-09T07:22:59.000Z | dgmvae/models/sent_models.py | wenxianxian/dgmvae | 0ca6a869c28c245beac7a0e5f68639e07d8d8841 | [
"Apache-2.0"
] | 1 | 2021-03-11T13:06:57.000Z | 2021-03-12T08:25:16.000Z | dgmvae/models/sent_models.py | wenxianxian/dgmvae | 0ca6a869c28c245beac7a0e5f68639e07d8d8841 | [
"Apache-2.0"
] | 2 | 2020-11-18T09:18:18.000Z | 2021-05-02T09:18:26.000Z | import torch
import torch.nn as nn
import torch.nn.functional as F
from dgmvae.dataset.corpora import PAD, BOS, EOS, UNK
from torch.autograd import Variable
from dgmvae import criterions
from dgmvae.enc2dec.decoders import DecoderRNN
from dgmvae.enc2dec.encoders import EncoderRNN
from dgmvae.utils import INT, FLOAT, LONG, cast_type
from dgmvae import nn_lib
import numpy as np
from dgmvae.models.model_bases import BaseModel
from dgmvae.enc2dec.decoders import GEN, TEACH_FORCE
from dgmvae.utils import Pack, kl_anneal_function, interpolate, idx2onehot
import itertools
import math
class SVAE(BaseModel):
def __init__(self, corpus, config):
super(SVAE, self).__init__(config)
self.vocab = corpus.vocab
self.rev_vocab = corpus.rev_vocab
self.vocab_size = len(self.vocab)
self.embed_size = config.embed_size
self.max_utt_len = config.max_utt_len
self.go_id = self.rev_vocab[BOS]
self.eos_id = self.rev_vocab[EOS]
self.unk_id = self.rev_vocab[UNK]
self.num_layer_enc = config.num_layer_enc
self.num_layer_dec = config.num_layer_dec
self.dropout = config.dropout
self.enc_cell_size = config.enc_cell_size
self.dec_cell_size = config.dec_cell_size
self.rnn_cell = config.rnn_cell
self.max_dec_len = config.max_dec_len
self.use_attn = config.use_attn
self.beam_size = config.beam_size
self.utt_type = config.utt_type
self.bi_enc_cell = config.bi_enc_cell
self.attn_type = config.attn_type
self.enc_out_size = self.enc_cell_size * 2 if self.bi_enc_cell else self.enc_cell_size
self.concat_decoder_input = config.concat_decoder_input if "concat_decoder_input" in config else False
self.posterior_sample_n = config.post_sample_num if "post_sample_num" in config else 1
# build model here
self.embedding = nn.Embedding(self.vocab_size, self.embed_size,
padding_idx=self.rev_vocab[PAD])
self.x_encoder = EncoderRNN(self.embed_size, self.enc_cell_size,
dropout_p=self.dropout,
rnn_cell=self.rnn_cell,
variable_lengths=self.config.fix_batch,
bidirection=self.bi_enc_cell,
n_layers=self.num_layer_enc)
self.q_y_mean = nn.Linear(self.enc_out_size, config.latent_size)
self.q_y_logvar = nn.Linear(self.enc_out_size, config.latent_size)
self.q_c = nn.Linear(self.enc_out_size, config.k * config.mult_k)
self.cat_connector = nn_lib.GumbelConnector()
self.dec_init_connector = nn_lib.LinearConnector(
config.latent_size + config.k * config.mult_k,
self.dec_cell_size,
self.rnn_cell == 'lstm',
has_bias=False)
self.decoder = DecoderRNN(self.vocab_size, self.max_dec_len,
self.embed_size + self.config.latent_size if self.concat_decoder_input else self.embed_size,
self.dec_cell_size,
self.go_id, self.eos_id, self.unk_id,
n_layers=self.num_layer_dec, rnn_cell=self.rnn_cell,
input_dropout_p=self.dropout,
dropout_p=self.dropout,
use_attention=self.use_attn,
attn_size=self.enc_cell_size,
attn_mode=self.attn_type,
use_gpu=self.use_gpu,
tie_output_embed=config.tie_output_embed if "tie_output_embed" in config else False,
embedding=self.embedding)
self.nll_loss = criterions.NLLEntropy(self.rev_vocab[PAD], self.config)
self.ppl = criterions.Perplexity(self.rev_vocab[PAD], self.config)
self.cat_kl_loss = criterions.CatKLLoss()
self.cross_ent_loss = criterions.CrossEntropyoss()
self.entropy_loss = criterions.Entropy()
self.log_py = nn.Parameter(torch.log(torch.ones(self.config.latent_size,
self.config.k) / config.k),
requires_grad=True)
self.register_parameter('log_py', self.log_py)
self.log_uniform_y = Variable(torch.log(torch.ones(1) / config.k))
if self.use_gpu:
self.log_uniform_y = self.log_uniform_y.cuda()
self.kl_w = 0.0
self.return_latent_key = ('log_qy', 'dec_init_state', 'y_ids')
@staticmethod
def add_args(parser):
from dgmvae.utils import str2bool
# Latent variable:
parser.add_argument('--latent_size', type=int, default=40, help="The latent size of continuous latent variable.")
parser.add_argument('--mult_k', type=int, default=20, help="The number of discrete latent variables.")
parser.add_argument('--k', type=int, default=5, help="The dimension of discrete latent variable.")
# Network setting:
parser.add_argument('--rnn_cell', type=str, default='gru')
parser.add_argument('--embed_size', type=int, default=512)
parser.add_argument('--utt_type', type=str, default='rnn')
parser.add_argument('--enc_cell_size', type=int, default=512)
parser.add_argument('--dec_cell_size', type=int, default=512)
parser.add_argument('--bi_enc_cell', type=str2bool, default=True)
parser.add_argument('--num_layer_enc', type=int, default=1)
parser.add_argument('--num_layer_dec', type=int, default=1)
parser.add_argument('--use_attn', type=str2bool, default=False)
parser.add_argument('--attn_type', type=str, default='cat')
parser.add_argument('--tie_output_embed', type=str2bool, default=True)
parser.add_argument('--max_utt_len', type=int, default=40)
parser.add_argument('--max_dec_len', type=int, default=40)
parser.add_argument('--max_vocab_cnt', type=int, default=10000)
# Other settings
parser.add_argument('--use_mutual', type=str2bool, default=False)
parser.add_argument('--concat_decoder_input', type=str2bool, default=True)
parser.add_argument('--gmm', type=str2bool, default=False)
return parser
def reparameterization(self, mu, logvar, sample=True):
if self.training or sample:
std = torch.exp(0.5 * logvar)
z = self.torch2var(torch.randn(mu.size()))
z = z * std + mu
return z
else:
return mu
def model_sel_loss(self, loss, batch_cnt):
return loss.elbo
def valid_loss(self, loss, batch_cnt=None, step=None):
if batch_cnt is not None:
step = batch_cnt
if step is not None and 'anneal_function' in self.config:
vae_kl_weight = kl_anneal_function(self.config.anneal_function, step,
self.config.anneal_k, self.config.anneal_x0)
else:
vae_kl_weight = 1.0
if not self.config.anneal:
vae_kl_weight = 1.0
mi_weight = 0.0 if self.config.use_mutual else 1.0
total_loss = loss.nll + vae_kl_weight * (
loss.agg_ckl + mi_weight * loss.mi + loss.zkl)
return total_loss
def zkl_loss(self, qy_mean, qy_logvar):
KL_loss = -0.5 * torch.mean(torch.sum((1 + qy_logvar - qy_mean.pow(2) - qy_logvar.exp()), dim=1))
return KL_loss
def forward(self, data_feed, mode, gen_type='greedy', sample_n=1, return_latent=False):
posterior_sample_n = self.posterior_sample_n if self.training else 1
if isinstance(data_feed, tuple):
data_feed = data_feed[0]
batch_size = len(data_feed['output_lens'])
out_utts = self.np2var(data_feed['outputs'], LONG)
# output encoder
output_embedding = self.embedding(out_utts)
x_outs, x_last = self.x_encoder(output_embedding)
if type(x_last) is tuple:
x_last = x_last[0].view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1, self.enc_out_size)
else:
x_last = x_last.view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1,
self.enc_out_size)
# posterior network
qy_mean = self.q_y_mean(x_last) # batch x (latent_size*mult_k)
qy_logvar = self.q_y_logvar(x_last)
q_z = self.reparameterization(qy_mean.repeat(posterior_sample_n, 1),
qy_logvar.repeat(posterior_sample_n, 1),
sample=gen_type != "greedy" or mode != GEN) # batch x latent_size
qc_logits = self.q_c(x_last).view(-1, self.config.k) # batch*mult_k x k
log_qc = F.log_softmax(qc_logits, qc_logits.dim() - 1)
# switch that controls the sampling
sample_y, y_ids = self.cat_connector(qc_logits.repeat(posterior_sample_n, 1),
1.0, self.use_gpu,
hard=not self.training, return_max_id=True)
# sample_y: [batch*mult_k, k], y_ids: [batch*mult_k, 1]
sample_y = sample_y.view(-1, self.config.mult_k * self.config.k)
y_ids = y_ids.view(-1, self.config.mult_k)
# map sample to initial state of decoder
dec_init_state = self.dec_init_connector(torch.cat((sample_y, q_z), dim=1))
# get decoder inputs
labels = out_utts[:, 1:].contiguous()
dec_inputs = out_utts[:, 0:-1]
# decode
dec_outs, dec_last, dec_ctx = self.decoder(batch_size * posterior_sample_n,
dec_inputs.repeat(posterior_sample_n, 1),
dec_init_state,
mode=mode, gen_type=gen_type,
beam_size=self.beam_size,
latent_variable=q_z)
dec_ctx[DecoderRNN.KEY_LATENT] = y_ids
# compute loss or return results
if mode == GEN:
return dec_ctx, labels
else:
# RNN reconstruction
nll = self.nll_loss(dec_outs, labels.repeat(posterior_sample_n, 1))
ppl = self.ppl(dec_outs, labels.repeat(posterior_sample_n, 1))
# Regularization terms
avg_log_qc = torch.exp(log_qc.view(-1, self.config.mult_k, self.config.k))
avg_log_qc = torch.log(torch.mean(avg_log_qc, dim=0) + 1e-15)
agg_ckl = self.cat_kl_loss(avg_log_qc, self.log_uniform_y, batch_size, unit_average=True, average=False)
agg_ckl = torch.sum(agg_ckl)
ckl_real = self.cat_kl_loss(log_qc, self.log_uniform_y, batch_size, unit_average=True, average=False)
ckl_real = torch.sum(torch.mean(ckl_real.view(-1, self.config.mult_k), dim=0))
zkl = self.zkl_loss(qy_mean, qy_logvar) # [batch_size x mult_k]
mi = - torch.sum(torch.exp(avg_log_qc) * avg_log_qc) + torch.sum(torch.exp(log_qc) * log_qc) / batch_size
results = Pack(nll=nll, agg_ckl=agg_ckl, mi=mi, real_ckl=ckl_real, elbo=nll+ckl_real+zkl, zkl=zkl, PPL=ppl)
if return_latent:
results['log_qy'] = log_qc
results['dec_init_state'] = dec_init_state
results['y_ids'] = y_ids
return results
def sampling(self, batch_size):
sample_y = torch.randint(0, self.config.k, [batch_size, self.config.mult_k], dtype=torch.long).cuda()
zs = self.torch2var(torch.randn(batch_size, self.config.latent_size))
cs = self.torch2var(idx2onehot(sample_y.view(-1), self.config.k)).view(-1, self.config.mult_k * self.config.k)
dec_init_state = self.dec_init_connector(torch.cat((cs, zs), dim=1))
_, _, outputs = self.decoder(cs.size(0),
None, dec_init_state,
mode=GEN, gen_type=self.config.gen_type,
beam_size=self.config.beam_size,
latent_variable=zs
)
return outputs
def sampling_for_likelihood(self, batch_size, data_feed, sample_num, sample_type="LL"):
# Importance sampling...
assert sample_type in ("LL", "logLL")
# just for calculating log-likelihood
if type(data_feed) is tuple:
data_feed = data_feed[0]
batch_size = len(data_feed['output_lens'])
out_utts = self.np2var(data_feed['outputs'], LONG) # batch_size * seq_len
out_utts = out_utts.repeat(sample_num, 1)
labels = out_utts[:, 1:].contiguous()
dec_inputs = out_utts[:, 0:-1]
output_embedding = self.embedding(out_utts)
x_outs, x_last = self.x_encoder(output_embedding)
if type(x_last) is tuple:
x_last = x_last[0].view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1, self.enc_out_size)
else:
x_last = x_last.view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1,
self.enc_out_size)
qy_mean = self.q_y_mean(x_last) # [batch_size * sample_num, latent_size]
qy_logvar = self.q_y_logvar(x_last)
q_z = self.reparameterization(qy_mean, qy_logvar, sample=True)
# [batch_size * sample_num, latent_size]
log_qzx = torch.sum(
- (q_z - qy_mean) * (q_z - qy_mean) / (2 * torch.exp(qy_logvar)) - 0.5 * qy_logvar - 0.5 * math.log(
math.pi * 2),
dim=-1)
log_pz = torch.sum(
- (q_z) * (q_z) / 2 - 0.5 * math.log(math.pi * 2),
dim=-1)
qc_logits = self.q_c(x_last).view(-1, self.config.k) # batch*mult_k x k
log_qcx = F.log_softmax(qc_logits, qc_logits.dim() - 1)
sample_c = torch.multinomial(torch.exp(log_qcx), 1) # .view(-1, self.config.mult_k) # [batch_size, mult_k]
log_qcx = torch.sum(torch.gather(log_qcx, 1, sample_c).view(-1, self.config.mult_k), dim=-1)
sample_c = self.torch2var(idx2onehot(sample_c.view(-1), self.config.k)).view(-1, self.config.mult_k * self.config.k)
log_pc = math.log(1.0 / self.config.k) * self.config.mult_k
# Calculate p(x|z)
dec_init_state = self.dec_init_connector(torch.cat((sample_c, q_z), dim=1))
dec_outs, dec_last, outputs = self.decoder(sample_c.size(0),
dec_inputs,
dec_init_state,
mode=TEACH_FORCE,
gen_type=self.config.gen_type,
beam_size=self.beam_size,
latent_variable=q_z)
nll = F.nll_loss(dec_outs.view(-1, dec_outs.size(-1)), labels.view(-1), reduction="none").view(out_utts.size(0),
-1)
nll = torch.sum(nll, dim=-1)
ll = torch.exp(-nll.double() + log_pz.double() + log_pc - log_qzx.double() - log_qcx.double())
if sample_type == "logLL":
return (-nll.double() + log_pz.double() + log_pc - log_qzx.double() - log_qcx.double()).view(-1, sample_num)
else:
ll = ll.view(-1, sample_num)
return ll
class DiVAE(BaseModel):
def __init__(self, corpus, config):
super(DiVAE, self).__init__(config)
self.vocab = corpus.vocab
self.rev_vocab = corpus.rev_vocab
self.vocab_size = len(self.vocab)
self.embed_size = config.embed_size
self.max_utt_len = config.max_utt_len
self.go_id = self.rev_vocab[BOS]
self.eos_id = self.rev_vocab[EOS]
self.unk_id = self.rev_vocab[UNK]
self.pad_id = self.rev_vocab[PAD]
self.num_layer_enc = config.num_layer_enc
self.num_layer_dec = config.num_layer_dec
self.dropout = config.dropout
self.enc_cell_size = config.enc_cell_size
self.dec_cell_size = config.dec_cell_size
self.rnn_cell = config.rnn_cell
self.max_dec_len = config.max_dec_len
self.use_attn = config.use_attn
self.beam_size = config.beam_size
self.utt_type = config.utt_type
self.bi_enc_cell = config.bi_enc_cell
self.attn_type = config.attn_type
self.enc_out_size = self.enc_cell_size * 2 if self.bi_enc_cell else self.enc_cell_size
self.posterior_sample_n = config.post_sample_num if "post_sample_num" in config else 1
self.concat_decoder_input = config.concat_decoder_input if "concat_decoder_input" in config else False
self.use_kl = getattr(config, "use_kl", True)
# build model here
self.embedding = nn.Embedding(self.vocab_size, self.embed_size,
padding_idx=self.rev_vocab[PAD])
self.x_encoder = EncoderRNN(self.embed_size, self.enc_cell_size,
dropout_p=self.dropout,
rnn_cell=self.rnn_cell,
variable_lengths=self.config.fix_batch,
bidirection=self.bi_enc_cell,
n_layers=self.num_layer_enc)
self.q_y = nn.Linear(self.enc_out_size, config.mult_k * config.k)
self.cat_connector = nn_lib.GumbelConnector()
self.dec_init_connector = nn_lib.LinearConnector(config.mult_k * config.k,
self.dec_cell_size,
self.rnn_cell == 'lstm',
has_bias=False)
self.decoder = DecoderRNN(self.vocab_size,
self.max_dec_len,
self.embed_size + self.config.mult_k * self.config.k if self.concat_decoder_input else self.embed_size,
self.dec_cell_size,
self.go_id, self.eos_id, self.unk_id,
n_layers=self.num_layer_dec, rnn_cell=self.rnn_cell,
input_dropout_p=self.dropout,
dropout_p=self.dropout,
use_attention=self.use_attn,
attn_size=self.enc_cell_size,
attn_mode=self.attn_type,
use_gpu=self.use_gpu,
tie_output_embed=config.tie_output_embed,
embedding=self.embedding)
self.nll_loss = criterions.NLLEntropy(self.rev_vocab[PAD], self.config)
self.ppl = criterions.Perplexity(self.rev_vocab[PAD], self.config)
self.cat_kl_loss = criterions.CatKLLoss()
self.cross_ent_loss = criterions.CrossEntropyoss()
self.entropy_loss = criterions.Entropy()
self.log_py = nn.Parameter(torch.log(torch.ones(self.config.mult_k,
self.config.k)/config.k),
requires_grad=True)
self.register_parameter('log_py', self.log_py)
self.log_uniform_y = Variable(torch.log(torch.ones(1) / config.k))
if self.use_gpu:
self.log_uniform_y = self.log_uniform_y.cuda()
self.kl_w = 0.0
self.return_latent_key = ("dec_init_state", "log_qy", "y_ids")
@staticmethod
def add_args(parser):
from dgmvae.utils import str2bool
# Latent variable:
parser.add_argument('--k', type=int, default=5, help="Latent size of discrete latent variable")
parser.add_argument('--mult_k', type=int, default=20, help="The number of discrete latent variables.")
# Network setting:
parser.add_argument('--rnn_cell', type=str, default='gru')
parser.add_argument('--embed_size', type=int, default=512)
parser.add_argument('--utt_type', type=str, default='rnn')
parser.add_argument('--enc_cell_size', type=int, default=512)
parser.add_argument('--dec_cell_size', type=int, default=512)
parser.add_argument('--bi_enc_cell', type=str2bool, default=True)
parser.add_argument('--num_layer_enc', type=int, default=1)
parser.add_argument('--num_layer_dec', type=int, default=1)
parser.add_argument('--use_attn', type=str2bool, default=False)
parser.add_argument('--attn_type', type=str, default='cat')
parser.add_argument('--tie_output_embed', type=str2bool, default=True)
parser.add_argument('--max_utt_len', type=int, default=40)
parser.add_argument('--max_dec_len', type=int, default=40)
parser.add_argument('--max_vocab_cnt', type=int, default=10000)
# Other settings:
parser.add_argument('--use_mutual', type=str2bool, default=False)
parser.add_argument('--use_kl', type=str2bool, default=True)
parser.add_argument('--concat_decoder_input', type=str2bool, default=True)
parser.add_argument('--gmm', type=str2bool, default=False)
return parser
def valid_loss(self, loss, batch_cnt=None, step=None):
if batch_cnt is not None:
step = batch_cnt
if step is not None and 'anneal_function' in self.config:
vae_kl_weight = kl_anneal_function(self.config.anneal_function, step,
self.config.anneal_k, self.config.anneal_x0)
else:
vae_kl_weight = 1.0
if self.config.use_mutual or self.config.anneal is not True:
vae_kl_weight = 1.0
total_loss = loss.nll
if not self.use_kl:
return total_loss
if self.config.use_mutual:
total_loss += (vae_kl_weight * loss.agg_ckl)
else:
total_loss += (vae_kl_weight * loss.ckl_real)
return total_loss
def model_sel_loss(self, loss, batch_cnt):
if not self.use_kl: # DAE
return loss.nll
else:
if "sel_metric" in self.config and self.config.sel_metric == "elbo":
return loss.elbo
return self.valid_loss(loss)
# return loss.elbo
def forward(self, data_feed, mode, gen_type='greedy', sample_n=1, return_latent=False):
posterior_sample_n = self.posterior_sample_n if self.training else 1
if isinstance(data_feed, tuple):
data_feed = data_feed[0]
batch_size = len(data_feed['output_lens'])
out_utts = self.np2var(data_feed['outputs'], LONG)
# output encoder
output_embedding = self.embedding(out_utts)
x_outs, x_last = self.x_encoder(output_embedding)
if type(x_last) is tuple:
x_last = x_last[0].view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1, self.enc_out_size)
else:
x_last = x_last.view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1,
self.enc_out_size)
# posterior network
qy_logits = self.q_y(x_last).view(-1, self.config.k)
log_qy = F.log_softmax(qy_logits, qy_logits.dim()-1)
# switch that controls the sampling
sample_y, y_ids = self.cat_connector(qy_logits.repeat(posterior_sample_n, 1),
1.0, self.use_gpu, hard=not self.training, return_max_id=True)
sample_y = sample_y.view(-1, self.config.k * self.config.mult_k)
y_ids = y_ids.view(-1, self.config.mult_k)
# map sample to initial state of decoder
dec_init_state = self.dec_init_connector(sample_y)
# get decoder inputs
labels = out_utts[:, 1:].contiguous()
dec_inputs = out_utts[:, 0:-1]
# decode
dec_outs, dec_last, dec_ctx = self.decoder(batch_size * posterior_sample_n,
dec_inputs.repeat(posterior_sample_n, 1),
dec_init_state,
mode=mode, gen_type=gen_type,
beam_size=self.beam_size,
latent_variable=sample_y)
# compute loss or return results
if mode == GEN:
return dec_ctx, labels
else:
# RNN reconstruction
nll = self.nll_loss(dec_outs, labels.repeat(posterior_sample_n, 1))
if self.config.avg_type == "seq":
ppl = self.ppl(dec_outs, labels.repeat(posterior_sample_n, 1))
# regularization
log_qy = log_qy.view(-1, self.config.mult_k, self.config.k)
avg_log_qc = torch.log(torch.mean(torch.exp(log_qy), dim=0) + 1e-15)
agg_ckl = self.cat_kl_loss(avg_log_qc, self.log_uniform_y, batch_size, unit_average=True, average=False)
agg_ckl = torch.sum(agg_ckl)
ckl_real = self.cat_kl_loss(log_qy, self.log_uniform_y, batch_size, unit_average=True, average=False)
ckl_real = torch.sum(torch.mean(ckl_real.view(-1, self.config.k), dim=0))
# H(C) - H(C|X)
mi = - torch.sum(torch.exp(avg_log_qc) * avg_log_qc) + torch.sum(torch.exp(log_qy) * log_qy) / batch_size
results = Pack(nll=nll, mi=mi, ckl_real=ckl_real,
elbo=nll+ckl_real, agg_ckl=agg_ckl)
if self.config.avg_type == "seq":
results['PPL'] = ppl
if return_latent:
results['log_qy'] = log_qy
results['dec_init_state'] = dec_init_state
results['y_ids'] = y_ids
return results
def sampling(self, batch_size):
sample_y = torch.randint(0, self.config.k, [batch_size, self.config.mult_k], dtype=torch.long).cuda()
cs = self.torch2var(idx2onehot(sample_y.view(-1), self.config.k)).view(-1, self.config.mult_k * self.config.k)
dec_init_state = self.dec_init_connector(cs)
_, _, outputs = self.decoder(cs.size(0),
None, dec_init_state,
mode=GEN, gen_type=self.config.gen_type,
beam_size=self.config.beam_size,
latent_variable=cs)
return outputs
def sampling_for_likelihood(self, batch_size, data_feed, sample_num, sample_type="LL"):
# Importance sampling...
# just for calculating log-likelihood
if type(data_feed) is tuple:
data_feed = data_feed[0]
batch_size = len(data_feed['output_lens'])
out_utts = self.np2var(data_feed['outputs'], LONG) # batch_size * seq_len
out_utts = out_utts.repeat(sample_num, 1)
labels = out_utts[:, 1:].contiguous()
dec_inputs = out_utts[:, 0:-1]
output_embedding = self.embedding(out_utts)
x_outs, x_last = self.x_encoder(output_embedding)
if type(x_last) is tuple:
x_last = x_last[0].view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1, self.enc_out_size)
else:
x_last = x_last.view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1,
self.enc_out_size)
qy_logits = self.q_y(x_last).view(-1, self.config.k)
log_qy = F.log_softmax(qy_logits, -1)
sampling_c = torch.multinomial(torch.exp(log_qy), 1) # .view(-1, self.config.mult_k) # [batch_size * mult_k, 1]
log_qcx = torch.sum(torch.gather(log_qy, 1, sampling_c).view(-1, self.config.mult_k), dim=-1)
sampling_c = self.torch2var(idx2onehot(sampling_c.view(-1), self.config.k)).view(-1, self.config.mult_k * self.config.k)
# print(log_qcx.size())
log_pc = math.log(1.0 / self.config.k) * self.config.mult_k
# Calculate p(x|z)
dec_init_state = self.dec_init_connector(sampling_c)
dec_outs, dec_last, outputs = self.decoder(sampling_c.size(0),
dec_inputs, dec_init_state,
mode=TEACH_FORCE,
gen_type=self.config.gen_type,
beam_size=self.config.beam_size,
latent_variable=sampling_c if self.concat_decoder_input else None)
# nll = self.nll_loss(dec_outs, labels)
nll = F.nll_loss(dec_outs.view(-1, dec_outs.size(-1)), labels.view(-1), reduction="none").view(out_utts.size(0),
-1)
nll = torch.sum(nll, dim=-1)
ll = torch.exp(-nll.double() + log_pc - log_qcx.double()) # log (p(z)p(x|z) / q(z|x))
ll = ll.view(-1, sample_num)
# nll_per = torch.log(torch.mean(ll, dim=-1)) #
# batch_size = nll_per.size(0)
# nll_per = torch.sum(nll_per)
return ll
class GMVAE(BaseModel):
def __init__(self, corpus, config):
super(GMVAE, self).__init__(config)
self.vocab = corpus.vocab
self.rev_vocab = corpus.rev_vocab
self.vocab_size = len(self.vocab)
self.embed_size = config.embed_size
self.max_utt_len = config.max_utt_len
self.go_id = self.rev_vocab[BOS]
self.eos_id = self.rev_vocab[EOS]
self.unk_id = self.rev_vocab[UNK]
self.pad_id = self.rev_vocab[PAD]
self.num_layer_enc = config.num_layer_enc
self.num_layer_dec = config.num_layer_dec
self.dropout = config.dropout
self.enc_cell_size = config.enc_cell_size
self.dec_cell_size = config.dec_cell_size
self.rnn_cell = config.rnn_cell
self.max_dec_len = config.max_dec_len
self.use_attn = config.use_attn
self.beam_size = config.beam_size
self.utt_type = config.utt_type
self.bi_enc_cell = config.bi_enc_cell
self.attn_type = config.attn_type
self.enc_out_size = self.enc_cell_size * 2 if self.bi_enc_cell else self.enc_cell_size
self.concat_decoder_input = config.concat_decoder_input if "concat_decoder_input" in config else False
self.posterior_sample_n = config.post_sample_num if "post_sample_num" in config else 1
# build model here
self.embedding = nn.Embedding(self.vocab_size, self.embed_size,
padding_idx=self.rev_vocab[PAD])
self.dec_embedding = nn.Embedding(self.vocab_size, self.embed_size,
padding_idx=self.rev_vocab[PAD])
self.x_encoder = EncoderRNN(self.embed_size, self.enc_cell_size,
dropout_p=self.dropout,
rnn_cell=self.rnn_cell,
variable_lengths=self.config.fix_batch,
bidirection=self.bi_enc_cell,
n_layers=self.num_layer_enc)
self.decoder = DecoderRNN(self.vocab_size, self.max_dec_len,
self.embed_size + self.config.mult_k * self.config.latent_size if self.concat_decoder_input else self.embed_size,
self.dec_cell_size,
self.go_id, self.eos_id, self.unk_id,
n_layers=self.num_layer_dec, rnn_cell=self.rnn_cell,
input_dropout_p=self.dropout,
dropout_p=self.dropout,
use_attention=self.use_attn,
attn_size=self.enc_cell_size,
attn_mode=self.attn_type,
use_gpu=self.use_gpu,
tie_output_embed=config.tie_output_embed,
embedding=self.dec_embedding)
self.q_y_mean = nn.Linear(self.enc_out_size, config.latent_size * config.mult_k)
self.q_y_logvar = nn.Linear(self.enc_out_size, config.latent_size * config.mult_k)
self.post_c = nn.Sequential(
nn.Linear(self.enc_out_size, self.enc_out_size),
nn.ReLU(),
nn.Linear(self.enc_out_size, self.config.mult_k * self.config.k),
)
self.dec_init_connector = nn_lib.LinearConnector(
config.latent_size * config.mult_k,
self.dec_cell_size,
self.rnn_cell == 'lstm',
has_bias=False)
self.cat_connector = nn_lib.GumbelConnector()
self.nll_loss = criterions.NLLEntropy(self.rev_vocab[PAD], self.config)
self.cat_kl_loss = criterions.CatKLLoss()
self.ppl = criterions.Perplexity(self.rev_vocab[PAD], self.config)
self.init_gaussian()
self.return_latent_key = ('log_qy', 'dec_init_state', 'y_ids', 'z')
self.kl_w = 0.0
@staticmethod
def add_args(parser):
from dgmvae.utils import str2bool
# Latent variable:
parser.add_argument('--latent_size', type=int, default=2, help="The latent size of continuous latent variable.")
parser.add_argument('--mult_k', type=int, default=20, help="The number of discrete latent variables.")
parser.add_argument('--k', type=int, default=5, help="The dimension of discrete latent variable.")
# Network setting:
parser.add_argument('--rnn_cell', type=str, default='gru')
parser.add_argument('--embed_size', type=int, default=512)
parser.add_argument('--utt_type', type=str, default='rnn')
parser.add_argument('--enc_cell_size', type=int, default=512)
parser.add_argument('--dec_cell_size', type=int, default=512)
parser.add_argument('--bi_enc_cell', type=str2bool, default=True)
parser.add_argument('--num_layer_enc', type=int, default=1)
parser.add_argument('--num_layer_dec', type=int, default=1)
parser.add_argument('--use_attn', type=str2bool, default=False)
parser.add_argument('--attn_type', type=str, default='cat')
parser.add_argument('--tie_output_embed', type=str2bool, default=True)
parser.add_argument('--max_utt_len', type=int, default=40)
parser.add_argument('--max_dec_len', type=int, default=40)
parser.add_argument('--max_vocab_cnt', type=int, default=10000)
# Dispersed GMVAE settings:
parser.add_argument('--use_mutual', type=str2bool, default=False)
parser.add_argument('--beta', type=float, default=0.2)
parser.add_argument('--concat_decoder_input', type=str2bool, default=True)
parser.add_argument('--gmm', type=str2bool, default=True)
parser.add_argument('--klw_for_ckl', type=float, default=1.0)
parser.add_argument('--klw_for_zkl', type=float, default=1.0)
parser.add_argument('--pretrain_ae_step', type=int, default=0)
return parser
def init_gaussian(self):
self._log_uniform_y = Variable(torch.log(torch.ones(1) / self.config.k))
if self.use_gpu:
self._log_uniform_y = self.log_uniform_y.cuda()
mus = torch.randn(self.config.mult_k, self.config.k, self.config.latent_size)
logvar = torch.randn(self.config.mult_k, self.config.k, self.config.latent_size)
if torch.cuda.is_available():
mus = mus.cuda()
logvar = logvar.cuda()
self._gaussian_mus = torch.nn.Parameter(mus, requires_grad=True) # change: False
self._gaussian_logvar = torch.nn.Parameter(logvar, requires_grad=True) # change: False
@property
def gaussian_mus(self):
return self._gaussian_mus
@property
def gaussian_logvar(self):
return self._gaussian_logvar
@property
def log_uniform_y(self):
return self._log_uniform_y
def model_sel_loss(self, loss, batch_cnt):
if batch_cnt is not None and batch_cnt < self.config.pretrain_ae_step:
return loss.nll
if "sel_metric" in self.config and self.config.sel_metric == "elbo":
return loss.elbo
return self.valid_loss(loss)
def freeze_recognition_net(self):
for param in self.embedding.parameters():
param.requires_grad = False
for param in self.x_encoder.parameters():
param.requires_grad = False
for param in self.q_y_mean.parameters():
param.requires_grad = False
for param in self.q_y_logvar.parameters():
param.requires_grad = False
for param in self.post_c.parameters():
param.requires_grad = False
for param in self.dec_init_connector.parameters():
param.requires_grad = False
def freeze_generation_net(self):
for param in self.decoder.parameters():
param.requires_grad = False
self.gaussian_mus.requires_grad = False
self.gaussian_logvar.requires_grad = False
def unfreeze_all(self):
for param in self.parameters():
param.requires_grad = True
def valid_loss(self, loss, batch_cnt=None, step=None):
if batch_cnt is not None:
step = batch_cnt
if batch_cnt is not None and batch_cnt < self.config.pretrain_ae_step:
return loss.nll
if step == self.config.pretrain_ae_step:
self.flush_valid = True
if step is not None and 'anneal_function' in self.config:
vae_kl_weight = kl_anneal_function(self.config.anneal_function, step - self.config.pretrain_ae_step,
self.config.anneal_k, self.config.anneal_x0,
self.config.anneal_warm_up_step if "anneal_warm_up_step" in self.config else 0,
self.config.anneal_warm_up_step if "anneal_warm_up_value" in self.config else 0)
else:
vae_kl_weight = 1.0
if not self.config.anneal:
vae_kl_weight = 1.0
mi_weight = 0.0 if self.config.use_mutual else 1.0
total_loss = loss.nll + vae_kl_weight * (self.config.klw_for_ckl * (loss.agg_ckl + mi_weight * loss.mi) +
self.config.klw_for_zkl * (loss.zkl + self.config.beta * loss.dispersion)
)
return total_loss
def reparameterization(self, mu, logvar, sample=True):
if self.training or sample:
std = torch.exp(0.5 * logvar)
z = self.torch2var(torch.randn(mu.size()))
z = z * std + mu
return z
else:
return mu
def zkl_loss(self, tgt_probs, mean, log_var, mean_prior=True):
mean = mean.view(-1, self.config.mult_k, self.config.latent_size)
log_var = log_var.view(-1, self.config.mult_k, self.config.latent_size)
if mean_prior:
tgt_probs_ = tgt_probs.unsqueeze(-1).expand(-1, -1, -1, self.config.latent_size)
eta1 = self.gaussian_mus / torch.exp(self.gaussian_logvar) # eta1 = \Sigma^-1 * mu
eta2 = -0.5 * torch.pow(torch.exp(self.gaussian_logvar), -1)
Eeta1 = torch.sum(tgt_probs_ * eta1, dim=-2) # [batch_size, mult_k, latent_size]
Eeta2 = torch.sum(tgt_probs_ * eta2, dim=-2)
Emu = -0.5 * Eeta1 / Eeta2
Evar = -0.5 * torch.pow(Eeta2, -1)
# [batch_size, mult_k, latent_size]
kl = 0.5 * (
torch.sum(log_var.exp().div(Evar), dim=-1)
+ torch.sum((Emu - mean).pow(2) / Evar, dim=-1)
- mean.size(-1)
+ torch.sum(Evar.log() - log_var, dim=-1)
)
# [batch_size, mult_k]
return kl
mu_repeat = mean.unsqueeze(-2).expand(-1, -1, self.config.k, -1) # batch_size x k x z_dim
logvar_repeat = log_var.unsqueeze(-2).expand(-1, -1, self.config.k, -1)
gaussian_logvars = self.gaussian_logvar
kl = 0.5 * (
torch.sum(logvar_repeat.exp().div(gaussian_logvars.exp()), dim=-1)
+ torch.sum((self.gaussian_mus - mu_repeat).pow(2) / gaussian_logvars.exp(), dim=-1)
- mean.size(-1)
+ torch.sum((gaussian_logvars - logvar_repeat), dim=-1)
) # batch_size x mult_k x k
return torch.sum(kl * tgt_probs, dim=-1) # batch_size*mult_k
def dispersion(self, tgt_probs):
# tgt_probs: batch_size x mult_k x k
tgt_probs_ = tgt_probs.unsqueeze(-1).expand(-1, -1, -1, self.config.latent_size)
eta1 = self.gaussian_mus / torch.exp(self.gaussian_logvar) # eta1 = \Sigma^-1 * mu
eta2 = -0.5 * torch.pow(torch.exp(self.gaussian_logvar), -1)
Eeta1 = torch.sum(tgt_probs_ * eta1, dim=-2) # [batch_size, mult_k, latent_size]
Eeta2 = torch.sum(tgt_probs_ * eta2, dim=-2)
AE = -0.25 * Eeta1 * Eeta1 / Eeta2 - 0.5 * torch.log(-2 * Eeta2) # [batch_size, mult_k, latent_size]
AE = torch.mean(torch.sum(AE, dim=(-1, -2)))
EA = torch.sum(-0.25 * eta1 * eta1 / eta2 - 0.5 * torch.log(-2 * eta2), dim=-1) # [mult_k, k]
EA = torch.mean(torch.sum(tgt_probs * EA, dim=(-1,-2)))
return EA-AE
def param_var(self, tgt_probs):
# Weighted variance of natural parameters
# tgt_probs: batch_size x mult_k x k
tgt_probs_ = tgt_probs.unsqueeze(-1).expand(-1, -1, -1, self.config.latent_size)
eta1 = self.gaussian_mus / torch.exp(self.gaussian_logvar) # eta1 = \Sigma^-1 * mu
eta2 = -0.5 * torch.pow(torch.exp(self.gaussian_logvar), -1)
var_eta1 = torch.sum(tgt_probs_ * (eta1 * eta1), dim=-2) - torch.sum(tgt_probs_ * eta1, dim=-2).pow(2)
var_eta2 = torch.sum(tgt_probs_ * (eta2 * eta2), dim=-2) - torch.sum(tgt_probs_ * eta2, dim=-2).pow(2)
return torch.sum(var_eta1 + var_eta2) / tgt_probs.size(0)
def forward(self, data_feed, mode, gen_type='greedy', sample_n=1, return_latent=False):
posterior_sample_n = self.posterior_sample_n if self.training else 1
if type(data_feed) is tuple:
data_feed = data_feed[0]
batch_size = len(data_feed['output_lens'])
out_utts = self.np2var(data_feed['outputs'], LONG)
# output encoder
output_embedding = self.embedding(out_utts)
x_outs, x_last = self.x_encoder(output_embedding)
if type(x_last) is tuple:
x_last = x_last[0].view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1, self.enc_out_size)
else:
x_last = x_last.view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1,
self.enc_out_size)
# q(z|x)
qz_mean = self.q_y_mean(x_last) # batch x (latent_size*mult_k)
qz_logvar = self.q_y_logvar(x_last)
sample_z = self.reparameterization(qz_mean.repeat(posterior_sample_n, 1),
qz_logvar.repeat(posterior_sample_n, 1),
sample=gen_type != "greedy" or mode != GEN) # batch x (latent_size*mult_k)
# q(c|x)
qc_logits = self.post_c(x_last).view(-1, self.config.k)
log_qc = F.log_softmax(qc_logits, -1) # [batch*mult_k, k]
sample_c, c_ids = self.cat_connector(qc_logits, 1.0, self.use_gpu, hard=not self.training, return_max_id=True)
# sample_c: [batch*mult_k, k], c_ids: [batch*mult_k, 1]
# sample_c = sample_c.view(-1, self.config.mult_k * self.config.k)
c_ids = c_ids.view(-1, self.config.mult_k)
# Prepare for decoding
dec_init_state = self.dec_init_connector(sample_z)
labels = out_utts[:, 1:].contiguous()
dec_inputs = out_utts[:, 0:-1]
if self.config.word_dropout_rate > 0:
# randomly replace decoder input with <unk>
prob = torch.rand(dec_inputs.size())
prob[(dec_inputs.data - self.go_id) * (dec_inputs.data - self.pad_id) * (dec_inputs.data - self.eos_id) == 0] = 1
dec_inputs_copy = dec_inputs.clone()
dec_inputs_copy[prob < self.config.word_dropout_rate] = self.unk_id
dec_inputs = dec_inputs_copy
# decode
dec_outs, dec_last, dec_ctx = self.decoder(batch_size * posterior_sample_n,
dec_inputs.repeat(posterior_sample_n, 1),
dec_init_state,
mode=mode, gen_type=gen_type,
beam_size=self.beam_size,
latent_variable=sample_z if self.concat_decoder_input else None)
# compute loss or return results
if mode == GEN:
return dec_ctx, labels
else:
# RNN reconstruction
nll = self.nll_loss(dec_outs, labels.repeat(posterior_sample_n, 1))
ppl = self.ppl(dec_outs, labels.repeat(posterior_sample_n, 1))
# Regularization terms
qc = torch.exp(log_qc.view(-1, self.config.mult_k, self.config.k))
# ZKL & dispersion term
zkl = self.zkl_loss(qc, qz_mean, qz_logvar, mean_prior=True) # [batch_size x mult_k]
zkl_real = self.zkl_loss(qc, qz_mean, qz_logvar, mean_prior=False) # [batch_size x mult_k]
zkl = torch.sum(torch.mean(zkl, dim=0))
zkl_real = torch.sum(torch.mean(zkl_real, dim=0))
dispersion = self.dispersion(qc)
# CKL & MI term
avg_log_qc = torch.log(torch.mean(qc, dim=0) + 1e-15)
agg_ckl = self.cat_kl_loss(avg_log_qc, self.log_uniform_y, batch_size, unit_average=True, average=False)
agg_ckl = torch.sum(agg_ckl)
ckl_real = self.cat_kl_loss(log_qc, self.log_uniform_y, batch_size, unit_average=True, average=False)
ckl_real = torch.sum(torch.mean(ckl_real.view(-1, self.config.mult_k), dim=0))
# H(C) - H(C|X)
mi = - torch.sum(torch.exp(avg_log_qc) * avg_log_qc) + torch.sum(torch.exp(log_qc) * log_qc) / batch_size
results = Pack(nll=nll, agg_ckl=agg_ckl, mi=mi, zkl=zkl, dispersion=dispersion, PPL=ppl,
real_zkl=zkl_real, real_ckl=ckl_real, elbo=nll + ckl_real + zkl_real,
param_var=self.param_var(tgt_probs=qc))
if return_latent:
results['log_qy'] = log_qc
results['dec_init_state'] = dec_init_state
results['y_ids'] = c_ids
results['z'] = sample_z
return results
def sampling_for_likelihood(self, batch_size, data_feed, sample_num, sample_type="LL",
):
# Importance sampling for estimating the log-likelihood
assert sample_type in ("LL", "logLL")
if type(data_feed) is tuple:
data_feed = data_feed[0]
batch_size = len(data_feed['output_lens'])
out_utts = self.np2var(data_feed['outputs'], LONG) # batch_size * seq_len
out_utts = out_utts.repeat(sample_num, 1)
labels = out_utts[:, 1:].contiguous()
dec_inputs = out_utts[:, 0:-1]
output_embedding = self.embedding(out_utts)
x_outs, x_last = self.x_encoder(output_embedding)
if type(x_last) is tuple:
x_last = x_last[0].view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1, self.enc_out_size)
else:
x_last = x_last.view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1,
self.enc_out_size)
# q(z|x)
qz_mean = self.q_y_mean(x_last) # batch x (latent_size*mult_k)
qz_logvar = self.q_y_logvar(x_last)
sample_z = self.reparameterization(qz_mean, qz_logvar, sample=True)
log_qzx = torch.sum(
- (sample_z - qz_mean) * (sample_z - qz_mean) / (2 * torch.exp(qz_logvar)) - 0.5 * qz_logvar - 0.5 * math.log(
math.pi * 2),
dim=-1)
sample_z_repeat = sample_z.view(-1, self.config.mult_k, 1, self.config.latent_size).repeat(1, 1, self.config.k, 1)
log_pzc = torch.sum(
- (sample_z_repeat - self.gaussian_mus) * (sample_z_repeat - self.gaussian_mus) / (2 * torch.exp(self.gaussian_logvar))
- 0.5 * self.gaussian_logvar - 0.5 * math.log(math.pi * 2),
dim=-1) # [batch_size, mult_k, k]
log_pz = torch.log(torch.mean(torch.exp(log_pzc.double()), dim=-1)) #
log_pz = torch.sum(log_pz, dim=-1)
# Calculate p(x|z)
dec_init_state = self.dec_init_connector(sample_z)
dec_outs, dec_last, outputs = self.decoder(sample_z.size(0),
dec_inputs, dec_init_state,
mode=TEACH_FORCE,
gen_type=self.config.gen_type,
beam_size=self.config.beam_size,
latent_variable=sample_z if self.concat_decoder_input else None)
nll = F.nll_loss(dec_outs.view(-1, dec_outs.size(-1)), labels.view(-1), reduction="none").view(out_utts.size(0),
-1)
nll = torch.sum(nll, dim=-1)
if sample_type == "logLL":
return (-nll.double() + log_pz - log_qzx.double()).view(-1, sample_num)
else:
ll = torch.exp(-nll.double() + log_pz - log_qzx.double()) # exp ( log (p(z)p(x|z) / q(z|x)) )
ll = ll.view(-1, sample_num)
return ll
def sampling(self, batch_size):
sample_c = torch.randint(0, self.config.k, [batch_size, self.config.mult_k], dtype=torch.long).cuda()
index = (self.torch2var(torch.arange(self.config.mult_k) * self.config.k) + sample_c).view(-1)
mean = self.gaussian_mus.view(-1, self.config.latent_size)[index].squeeze()
sigma = torch.exp(self.gaussian_logvar * 0.5).view(-1, self.config.latent_size)[index].squeeze()
zs = self.reparameterization(mean, 2 * torch.log(torch.abs(sigma) + 1e-15), sample=True)
zs = zs.view(-1, self.config.mult_k * self.config.latent_size)
dec_init_state = self.dec_init_connector(zs)
_, _, outputs = self.decoder(zs.size(0),
None, dec_init_state,
mode=GEN, gen_type="greedy",
beam_size=self.config.beam_size,
latent_variable=zs if self.concat_decoder_input else None)
return outputs
class GMVAE_fb(GMVAE):
@staticmethod
def add_args(parser):
from dgmvae.utils import str2bool
# Latent variable:
parser.add_argument('--latent_size', type=int, default=2, help="The latent size of continuous latent variable.")
parser.add_argument('--mult_k', type=int, default=20, help="The number of discrete latent variables.")
parser.add_argument('--k', type=int, default=5, help="The dimension of discrete latent variable.")
# Network setting:
parser.add_argument('--rnn_cell', type=str, default='gru')
parser.add_argument('--embed_size', type=int, default=512)
parser.add_argument('--utt_type', type=str, default='rnn')
parser.add_argument('--enc_cell_size', type=int, default=512)
parser.add_argument('--dec_cell_size', type=int, default=512)
parser.add_argument('--bi_enc_cell', type=str2bool, default=True)
parser.add_argument('--num_layer_enc', type=int, default=1)
parser.add_argument('--num_layer_dec', type=int, default=1)
parser.add_argument('--use_attn', type=str2bool, default=False)
parser.add_argument('--attn_type', type=str, default='cat')
parser.add_argument('--tie_output_embed', type=str2bool, default=True)
parser.add_argument('--max_utt_len', type=int, default=40)
parser.add_argument('--max_dec_len', type=int, default=40)
parser.add_argument('--max_vocab_cnt', type=int, default=10000)
# Dispersed GMVAE settings:
parser.add_argument('--use_mutual', type=str2bool, default=False)
parser.add_argument('--beta', type=float, default=0.2)
parser.add_argument('--concat_decoder_input', type=str2bool, default=True)
parser.add_argument('--gmm', type=str2bool, default=True)
parser.add_argument('--pretrain_ae_step', type=int, default=0)
# Free bits setting:
parser.add_argument('--max_fb_c', type=float, default=5.0)
parser.add_argument('--max_fb_z', type=float, default=10.0)
return parser
def model_sel_loss(self, loss, batch_cnt): # return albo
if batch_cnt is not None and batch_cnt < self.config.pretrain_ae_step:
return loss.nll
if "sel_metric" in self.config and self.config.sel_metric == "elbo":
return loss.elbo
return self.valid_loss(loss)
def valid_loss(self, loss, batch_cnt=None, step=None):
if batch_cnt is not None:
step = batch_cnt
if step < self.config.pretrain_ae_step:
return loss.nll # AE
if step == self.config.pretrain_ae_step:
self.flush_valid = True
if step is not None and 'anneal_function' in self.config:
vae_kl_weight = kl_anneal_function(self.config.anneal_function, step - self.config.pretrain_ae_step,
self.config.anneal_k, self.config.anneal_x0,
self.config.anneal_warm_up_step if "anneal_warm_up_step" in self.config else 0,
self.config.anneal_warm_up_value if "anneal_warm_up_value" in self.config else 0)
else:
vae_kl_weight = 1.0
if not self.config.anneal:
vae_kl_weight = 1.0
total_loss = loss.nll + vae_kl_weight * (loss.agg_ckl + loss.zkl)
return total_loss
def forward(self, data_feed, mode, gen_type='greedy', sample_n=1, return_latent=False):
posterior_sample_n = self.posterior_sample_n if self.training else 1
if type(data_feed) is tuple:
data_feed = data_feed[0]
batch_size = len(data_feed['output_lens'])
out_utts = self.np2var(data_feed['outputs'], LONG)
# output encoder
output_embedding = self.embedding(out_utts)
x_outs, x_last = self.x_encoder(output_embedding)
if type(x_last) is tuple:
x_last = x_last[0].view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1, self.enc_out_size)
else:
x_last = x_last.view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1,
self.enc_out_size)
# q(z|x)
qz_mean = self.q_y_mean(x_last) # batch x (latent_size*mult_k)
qz_logvar = self.q_y_logvar(x_last)
sample_z = self.reparameterization(qz_mean.repeat(posterior_sample_n, 1),
qz_logvar.repeat(posterior_sample_n, 1),
sample=gen_type != "greedy" or mode != GEN) # batch x (latent_size*mult_k)
# q(c|x)
qc_logits = self.post_c(x_last).view(-1, self.config.k)
log_qc = F.log_softmax(qc_logits, -1) # [batch*mult_k, k]
sample_c, c_ids = self.cat_connector(qc_logits, 1.0, self.use_gpu, hard=not self.training, return_max_id=True)
# sample_c: [batch*mult_k, k], c_ids: [batch*mult_k, 1]
# sample_c = sample_c.view(-1, self.config.mult_k * self.config.k)
c_ids = c_ids.view(-1, self.config.mult_k)
# Prepare for decoding
dec_init_state = self.dec_init_connector(sample_z)
labels = out_utts[:, 1:].contiguous()
dec_inputs = out_utts[:, 0:-1]
if self.config.word_dropout_rate > 0:
# randomly replace decoder input with <unk>
prob = torch.rand(dec_inputs.size())
prob[(dec_inputs.data - self.go_id) * (dec_inputs.data - self.pad_id) * (
dec_inputs.data - self.eos_id) == 0] = 1
dec_inputs_copy = dec_inputs.clone()
dec_inputs_copy[prob < self.config.word_dropout_rate] = self.unk_id
dec_inputs = dec_inputs_copy
# decode
dec_outs, dec_last, dec_ctx = self.decoder(batch_size * posterior_sample_n,
dec_inputs.repeat(posterior_sample_n, 1),
dec_init_state,
mode=mode, gen_type=gen_type,
beam_size=self.beam_size,
latent_variable=sample_z if self.concat_decoder_input else None)
# compute loss or return results
if mode == GEN:
return dec_ctx, labels
else:
# RNN reconstruction
nll = self.nll_loss(dec_outs, labels.repeat(posterior_sample_n, 1))
ppl = self.ppl(dec_outs, labels.repeat(posterior_sample_n, 1))
# Regularization terms
qc = torch.exp(log_qc.view(-1, self.config.mult_k, self.config.k))
# ZKL & dispersion term
# zkl = self.zkl_loss(qc, qz_mean, qz_logvar, mean_prior=True) # [batch_size x mult_k]
zkl_real = self.zkl_loss(qc, qz_mean, qz_logvar, mean_prior=False) # [batch_size x mult_k]
zkl = torch.gt(zkl_real, self.config.max_fb_z / self.config.mult_k).float() * zkl_real
zkl = torch.sum(torch.mean(zkl, dim=0))
zkl_real = torch.sum(torch.mean(zkl_real, dim=0))
dispersion = self.dispersion(qc)
# CKL & MI term
avg_log_qc = torch.log(torch.mean(qc, dim=0) + 1e-15)
# agg_ckl = self.cat_kl_loss(avg_log_qc, self.log_uniform_y, batch_size, unit_average=True, average=False)
# agg_ckl = torch.sum(agg_ckl)
ckl_real = self.cat_kl_loss(log_qc, self.log_uniform_y, batch_size, unit_average=True, average=False)
agg_ckl = torch.gt(ckl_real, self.config.max_fb_c / self.config.mult_k).float() * ckl_real
ckl_real = torch.sum(torch.mean(ckl_real.view(-1, self.config.mult_k), dim=0))
agg_ckl = torch.sum(torch.mean(agg_ckl.view(-1, self.config.mult_k), dim=0))
# H(C) - H(C|X)
mi = - torch.sum(torch.exp(avg_log_qc) * avg_log_qc) + torch.sum(torch.exp(log_qc) * log_qc) / batch_size
results = Pack(nll=nll, agg_ckl=agg_ckl, mi=mi, zkl=zkl, dispersion=dispersion, PPL=ppl,
real_zkl=zkl_real, real_ckl=ckl_real, elbo=nll + ckl_real + zkl_real,
param_var=self.param_var(tgt_probs=qc))
if return_latent:
results['log_qy'] = log_qc
results['dec_init_state'] = dec_init_state
results['y_ids'] = c_ids
results['z'] = sample_z
return results
class GMVAE_MoP(BaseModel):
def __init__(self, corpus, config):
super(GMVAE_MoP, self).__init__(config)
self.vocab = corpus.vocab
self.rev_vocab = corpus.rev_vocab
self.vocab_size = len(self.vocab)
self.embed_size = config.embed_size
self.max_utt_len = config.max_utt_len
self.go_id = self.rev_vocab[BOS]
self.eos_id = self.rev_vocab[EOS]
self.unk_id = self.rev_vocab[corpus.unk]
self.pad_id = self.rev_vocab[PAD]
self.num_layer_enc = config.num_layer_enc
self.num_layer_dec = config.num_layer_dec
self.dropout = config.dropout
self.enc_cell_size = config.enc_cell_size
self.dec_cell_size = config.dec_cell_size
self.rnn_cell = config.rnn_cell
self.max_dec_len = config.max_dec_len
self.use_attn = config.use_attn
self.beam_size = config.beam_size
self.utt_type = config.utt_type
self.bi_enc_cell = config.bi_enc_cell
self.attn_type = config.attn_type
self.enc_out_size = self.enc_cell_size * 2 if self.bi_enc_cell else self.enc_cell_size
self.concat_decoder_input = config.concat_decoder_input if "concat_decoder_input" in config else False
self.posterior_sample_n = config.post_sample_num if "post_sample_num" in config else 1
# build model here
self.embedding = nn.Embedding(self.vocab_size, self.embed_size,
padding_idx=self.rev_vocab[PAD])
self.x_encoder = EncoderRNN(self.embed_size, self.enc_cell_size,
dropout_p=self.dropout,
rnn_cell=self.rnn_cell,
variable_lengths=self.config.fix_batch,
bidirection=self.bi_enc_cell,
n_layers=self.num_layer_enc)
self.decoder = DecoderRNN(self.vocab_size, self.max_dec_len,
self.embed_size + self.config.mult_k * self.config.latent_size if self.concat_decoder_input else self.embed_size,
self.dec_cell_size,
self.go_id, self.eos_id, self.unk_id,
n_layers=self.num_layer_dec, rnn_cell=self.rnn_cell,
input_dropout_p=self.dropout,
dropout_p=self.dropout,
use_attention=self.use_attn,
attn_size=self.enc_cell_size,
attn_mode=self.attn_type,
use_gpu=self.use_gpu,
tie_output_embed=config.tie_output_embed,
embedding=self.embedding)
self.q_y_mean = nn.Linear(self.enc_out_size, config.latent_size * config.mult_k)
self.q_y_logvar = nn.Linear(self.enc_out_size, config.latent_size * config.mult_k)
self.post_c = nn.Sequential(
nn.Linear(self.enc_out_size, self.enc_out_size),
nn.ReLU(),
nn.Linear(self.enc_out_size, self.config.mult_k * self.config.k),
)
self.dec_init_connector = nn_lib.LinearConnector(
config.latent_size * config.mult_k,
self.dec_cell_size,
self.rnn_cell == 'lstm',
has_bias=False)
self.cat_connector = nn_lib.GumbelConnector()
self.nll_loss = criterions.NLLEntropy(self.rev_vocab[PAD], self.config)
self.cat_kl_loss = criterions.CatKLLoss()
self.ppl = criterions.Perplexity(self.rev_vocab[PAD], self.config)
self.init_gaussian()
self.return_latent_key = ('log_qy', 'dec_init_state', 'y_ids', 'z')
self.kl_w = 0.0
@staticmethod
def add_args(parser):
from dgmvae.utils import str2bool
# Latent variable:
parser.add_argument('--latent_size', type=int, default=2, help="The latent size of continuous latent variable.")
parser.add_argument('--mult_k', type=int, default=20, help="The number of discrete latent variables.")
parser.add_argument('--k', type=int, default=5, help="The dimension of discrete latent variable.")
# Network setting:
parser.add_argument('--rnn_cell', type=str, default='gru')
parser.add_argument('--embed_size', type=int, default=512)
parser.add_argument('--utt_type', type=str, default='rnn')
parser.add_argument('--enc_cell_size', type=int, default=512)
parser.add_argument('--dec_cell_size', type=int, default=512)
parser.add_argument('--bi_enc_cell', type=str2bool, default=True)
parser.add_argument('--num_layer_enc', type=int, default=1)
parser.add_argument('--num_layer_dec', type=int, default=1)
parser.add_argument('--use_attn', type=str2bool, default=False)
parser.add_argument('--attn_type', type=str, default='cat')
parser.add_argument('--tie_output_embed', type=str2bool, default=True)
parser.add_argument('--max_utt_len', type=int, default=40)
parser.add_argument('--max_dec_len', type=int, default=40)
parser.add_argument('--max_vocab_cnt', type=int, default=10000)
# Dispersed GMVAE settings:
parser.add_argument('--use_mutual', type=str2bool, default=False)
parser.add_argument('--beta', type=float, default=0.2)
parser.add_argument('--concat_decoder_input', type=str2bool, default=True)
parser.add_argument('--gmm', type=str2bool, default=True)
return parser
def init_gaussian(self):
self._log_uniform_y = Variable(torch.log(torch.ones(1) / self.config.k))
if self.use_gpu:
self._log_uniform_y = self.log_uniform_y.cuda()
mus = torch.randn(self.config.mult_k, self.config.k, self.config.latent_size)
logvar = torch.randn(self.config.mult_k, self.config.k, self.config.latent_size)
if torch.cuda.is_available():
mus = mus.cuda()
logvar = logvar.cuda()
self._gaussian_mus = torch.nn.Parameter(mus, requires_grad=True) # change: False
self._gaussian_logvar = torch.nn.Parameter(logvar, requires_grad=True) # change: False
@property
def gaussian_mus(self):
return self._gaussian_mus
@property
def gaussian_logvar(self):
return self._gaussian_logvar
@property
def log_uniform_y(self):
return self._log_uniform_y
def model_sel_loss(self, loss, batch_cnt): # return albo
if "sel_metric" in self.config and self.config.sel_metric == "elbo":
return loss.elbo
return self.valid_loss(loss)
def valid_loss(self, loss, batch_cnt=None, step=None):
# loss = Pack(nll=nll, agg_ckl=agg_ckl, mi=mi, zkl=zkl, mean_var=mean_var, PPL=ppl,
# real_zkl=zkl_real, real_ckl=ckl_real, elbo=nll + ckl_real + zkl_real,
# param_var=self.param_var(tgt_probs=qc))
if batch_cnt is not None:
step = batch_cnt
if step is not None and 'anneal_function' in self.config:
vae_kl_weight = kl_anneal_function(self.config.anneal_function, step,
self.config.anneal_k, self.config.anneal_x0,
self.config.anneal_warm_up_step if "anneal_warm_up_step" in self.config else 0,
self.config.anneal_warm_up_step if "anneal_warm_up_value" in self.config else 0)
else:
vae_kl_weight = 1.0
if not self.config.anneal:
vae_kl_weight = 1.0
mi_weight = 0.0 if self.config.use_mutual else 1.0
total_loss = loss.nll + vae_kl_weight * loss.zkl
return total_loss
def reparameterization(self, mu, logvar, sample=True):
if self.training or sample:
std = torch.exp(0.5 * logvar)
z = self.torch2var(torch.randn(mu.size()))
z = z * std + mu
return z
else:
return mu
def zkl_loss(self, tgt_probs, mean, log_var, mean_prior=True):
mean = mean.view(-1, self.config.mult_k, self.config.latent_size)
log_var = log_var.view(-1, self.config.mult_k, self.config.latent_size)
if mean_prior:
tgt_probs_ = tgt_probs.unsqueeze(-1).expand(-1, -1, -1, self.config.latent_size)
eta1 = self.gaussian_mus / torch.exp(self.gaussian_logvar) # eta1 = \Sigma^-1 * mu
eta2 = -0.5 * torch.pow(torch.exp(self.gaussian_logvar), -1)
Eeta1 = torch.sum(tgt_probs_ * eta1, dim=-2) # [batch_size, mult_k, latent_size]
Eeta2 = torch.sum(tgt_probs_ * eta2, dim=-2)
Emu = -0.5 * Eeta1 / Eeta2
Evar = -0.5 * torch.pow(Eeta2, -1)
# [batch_size, mult_k, latent_size]
kl = 0.5 * (
torch.sum(log_var.exp().div(Evar), dim=-1)
+ torch.sum((Emu - mean).pow(2) / Evar, dim=-1)
- mean.size(-1)
+ torch.sum(Evar.log() - log_var, dim=-1)
)
# [batch_size, mult_k]
return kl
mu_repeat = mean.unsqueeze(-2).expand(-1, -1, self.config.k, -1) # batch_size x k x z_dim
logvar_repeat = log_var.unsqueeze(-2).expand(-1, -1, self.config.k, -1)
gaussian_logvars = self.gaussian_logvar
kl = 0.5 * (
torch.sum(logvar_repeat.exp().div(gaussian_logvars.exp()), dim=-1)
+ torch.sum((self.gaussian_mus - mu_repeat).pow(2) / gaussian_logvars.exp(), dim=-1)
- mean.size(-1)
+ torch.sum((gaussian_logvars - logvar_repeat), dim=-1)
) # batch_size x mult_k x k
return torch.sum(kl * tgt_probs, dim=-1) # batch_size*mult_k
def dispersion(self, tgt_probs):
# tgt_probs: batch_size x mult_k x k
tgt_probs_ = tgt_probs.unsqueeze(-1).expand(-1, -1, -1, self.config.latent_size)
eta1 = self.gaussian_mus / torch.exp(self.gaussian_logvar) # eta1 = \Sigma^-1 * mu
eta2 = -0.5 * torch.pow(torch.exp(self.gaussian_logvar), -1)
Eeta1 = torch.sum(tgt_probs_ * eta1, dim=-2) # [batch_size, mult_k, latent_size]
Eeta2 = torch.sum(tgt_probs_ * eta2, dim=-2)
AE = -0.25 * Eeta1 * Eeta1 / Eeta2 - 0.5 * torch.log(-2 * Eeta2) # [batch_size, mult_k, latent_size]
AE = torch.mean(torch.sum(AE, dim=(-1, -2)))
EA = torch.sum(-0.25 * eta1 * eta1 / eta2 - 0.5 * torch.log(-2 * eta2), dim=-1) # [mult_k, k]
EA = torch.mean(torch.sum(tgt_probs * EA, dim=(-1, -2)))
return EA - AE
def param_var(self, tgt_probs):
# Weighted variance of natural parameters
# tgt_probs: batch_size x mult_k x k
tgt_probs_ = tgt_probs.unsqueeze(-1).expand(-1, -1, -1, self.config.latent_size)
eta1 = self.gaussian_mus / torch.exp(self.gaussian_logvar) # eta1 = \Sigma^-1 * mu
eta2 = -0.5 * torch.pow(torch.exp(self.gaussian_logvar), -1)
var_eta1 = torch.sum(tgt_probs_ * (eta1 * eta1), dim=-2) - torch.sum(tgt_probs_ * eta1, dim=-2).pow(2)
var_eta2 = torch.sum(tgt_probs_ * (eta2 * eta2), dim=-2) - torch.sum(tgt_probs_ * eta2, dim=-2).pow(2)
return torch.sum(var_eta1 + var_eta2) / tgt_probs.size(0)
def _get_pzc(self, sample_z):
# sample_z: [batch_size, latent_size * multi_k]
# Prior: [multi_k, k, latent_size]
bsz = sample_z.size(0)
multi_k, k, ls = self.gaussian_mus.size()
gaussian_mus = self.gaussian_mus.unsqueeze(0).expand(bsz, multi_k, k, ls)
gaussian_logvar = self.gaussian_logvar.unsqueeze(0).expand(bsz, multi_k, k, ls)
sample_z = sample_z.view(-1, multi_k, 1, ls).expand(bsz, multi_k, k, ls)
log_pz = - 0.5 * (sample_z - gaussian_mus) * (sample_z - gaussian_mus) / \
torch.exp(gaussian_logvar) - 0.5 * math.log(math.pi * 2) - 0.5 * gaussian_logvar
return torch.sum(log_pz, dim=-1)
def forward(self, data_feed, mode, gen_type='greedy', sample_n=1, return_latent=False):
posterior_sample_n = self.posterior_sample_n if self.training else 1
if type(data_feed) is tuple:
data_feed = data_feed[0]
batch_size = len(data_feed['output_lens'])
out_utts = self.np2var(data_feed['outputs'], LONG)
# output encoder
output_embedding = self.embedding(out_utts)
x_outs, x_last = self.x_encoder(output_embedding)
if type(x_last) is tuple:
x_last = x_last[0].view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1, self.enc_out_size)
else:
x_last = x_last.view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1,
self.enc_out_size)
# q(z|x)
qz_mean = self.q_y_mean(x_last) # batch x (latent_size*mult_k)
qz_logvar = self.q_y_logvar(x_last)
sample_z = self.reparameterization(qz_mean.repeat(posterior_sample_n, 1),
qz_logvar.repeat(posterior_sample_n, 1),
sample=gen_type != "greedy" or mode != GEN) # batch x (latent_size*mult_k)
# q(c|x)
qc_logits = self.post_c(x_last).view(-1, self.config.k)
log_qc = F.log_softmax(qc_logits, -1) # [batch*mult_k, k]
sample_c, c_ids = self.cat_connector(qc_logits, 1.0, self.use_gpu, hard=not self.training, return_max_id=True)
# sample_c: [batch*mult_k, k], c_ids: [batch*mult_k, 1]
# sample_c = sample_c.view(-1, self.config.mult_k * self.config.k)
c_ids = c_ids.view(-1, self.config.mult_k)
# Prepare for decoding
dec_init_state = self.dec_init_connector(sample_z)
labels = out_utts[:, 1:].contiguous()
dec_inputs = out_utts[:, 0:-1]
if self.config.word_dropout_rate > 0:
# randomly replace decoder input with <unk>
prob = torch.rand(dec_inputs.size())
prob[(dec_inputs.data - self.go_id) * (dec_inputs.data - self.pad_id) * (
dec_inputs.data - self.eos_id) == 0] = 1
dec_inputs_copy = dec_inputs.clone()
dec_inputs_copy[prob < self.config.word_dropout_rate] = self.unk_id
dec_inputs = dec_inputs_copy
# decode
dec_outs, dec_last, dec_ctx = self.decoder(batch_size * posterior_sample_n,
dec_inputs.repeat(posterior_sample_n, 1),
dec_init_state,
mode=mode, gen_type=gen_type,
beam_size=self.beam_size,
latent_variable=sample_z if self.concat_decoder_input else None)
# compute loss or return results
if mode == GEN:
return dec_ctx, labels
else:
# RNN reconstruction
nll = self.nll_loss(dec_outs, labels.repeat(posterior_sample_n, 1))
ppl = self.ppl(dec_outs, labels.repeat(posterior_sample_n, 1))
# Regularization terms
# ZKL:
log_qz = - 0.5 * (sample_z - qz_mean.repeat(posterior_sample_n, 1)) \
* (sample_z - qz_mean.repeat(posterior_sample_n, 1)) / torch.exp(qz_logvar.repeat(posterior_sample_n, 1)) \
- 0.5 * qz_logvar.repeat(posterior_sample_n, 1) - 0.5 * math.log(math.pi * 2)
log_qz = torch.sum(log_qz, dim=-1)
log_pzc = self._get_pzc(sample_z) # [batch_size x multi_k x k]
log_pz = torch.sum(torch.log(torch.mean(torch.exp(log_pzc), dim=-1) + 1e-15), dim=-1)
zkl = torch.mean(log_qz - log_pz)
# qc = q(z|x) * p(c|z)
log_qc = F.log_softmax(log_pzc, dim=-1)
qc = torch.exp(log_qc.view(-1, self.config.mult_k, self.config.k))
dispersion = self.dispersion(qc)
# MI term
avg_log_qc = torch.log(torch.mean(qc, dim=0) + 1e-15)
mi = - torch.sum(torch.exp(avg_log_qc) * avg_log_qc) + torch.sum(torch.exp(log_qc) * log_qc) / log_qc.size(0)
results = Pack(nll=nll, mi=mi, zkl=zkl, dispersion=dispersion, PPL=ppl,
elbo=nll + zkl,
param_var=self.param_var(tgt_probs=qc))
if return_latent:
results['log_qy'] = log_qc
results['dec_init_state'] = dec_init_state
results['y_ids'] = c_ids
results['z'] = sample_z
return results
def sampling_for_likelihood(self, batch_size, data_feed, sample_num, sample_type="LL",
):
# Importance sampling for estimating the log-likelihood
assert sample_type in ("LL", "logLL")
if type(data_feed) is tuple:
data_feed = data_feed[0]
batch_size = len(data_feed['output_lens'])
out_utts = self.np2var(data_feed['outputs'], LONG) # batch_size * seq_len
out_utts = out_utts.repeat(sample_num, 1)
labels = out_utts[:, 1:].contiguous()
dec_inputs = out_utts[:, 0:-1]
output_embedding = self.embedding(out_utts)
x_outs, x_last = self.x_encoder(output_embedding)
if type(x_last) is tuple:
x_last = x_last[0].view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1, self.enc_out_size)
else:
x_last = x_last.view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1,
self.enc_out_size)
# q(z|x)
qz_mean = self.q_y_mean(x_last) # batch x (latent_size*mult_k)
qz_logvar = self.q_y_logvar(x_last)
sample_z = self.reparameterization(qz_mean, qz_logvar, sample=True)
log_qzx = torch.sum(
- (sample_z - qz_mean) * (sample_z - qz_mean) / (
2 * torch.exp(qz_logvar)) - 0.5 * qz_logvar - 0.5 * math.log(
math.pi * 2),
dim=-1)
sample_z_repeat = sample_z.view(-1, self.config.mult_k, 1, self.config.latent_size).repeat(1, 1, self.config.k,
1)
log_pzc = torch.sum(
- (sample_z_repeat - self.gaussian_mus) * (sample_z_repeat - self.gaussian_mus) / (
2 * torch.exp(self.gaussian_logvar))
- 0.5 * self.gaussian_logvar - 0.5 * math.log(math.pi * 2),
dim=-1) # [batch_size, mult_k, k]
log_pz = torch.log(torch.mean(torch.exp(log_pzc.double()), dim=-1)) #
log_pz = torch.sum(log_pz, dim=-1)
# Calculate p(x|z)
dec_init_state = self.dec_init_connector(sample_z)
dec_outs, dec_last, outputs = self.decoder(sample_z.size(0),
dec_inputs, dec_init_state,
mode=TEACH_FORCE,
gen_type=self.config.gen_type,
beam_size=self.config.beam_size,
latent_variable=sample_z if self.concat_decoder_input else None)
nll = F.nll_loss(dec_outs.view(-1, dec_outs.size(-1)), labels.view(-1), reduction="none").view(out_utts.size(0),
-1)
nll = torch.sum(nll, dim=-1)
if sample_type == "logLL":
return (-nll.double() + log_pz - log_qzx.double()).view(-1, sample_num)
else:
ll = torch.exp(-nll.double() + log_pz - log_qzx.double()) # exp ( log (p(z)p(x|z) / q(z|x)) )
ll = ll.view(-1, sample_num)
return ll
def sampling(self, batch_size):
sample_c = torch.randint(0, self.config.k, [batch_size, self.config.mult_k], dtype=torch.long).cuda()
index = (self.torch2var(torch.arange(self.config.mult_k) * self.config.k) + sample_c).view(-1)
mean = self.gaussian_mus.view(-1, self.config.latent_size)[index].squeeze()
sigma = torch.exp(self.gaussian_logvar * 0.5).view(-1, self.config.latent_size)[index].squeeze()
zs = self.reparameterization(mean, 2 * torch.log(torch.abs(sigma) + 1e-15), sample=True)
zs = zs.view(-1, self.config.mult_k * self.config.latent_size)
dec_init_state = self.dec_init_connector(zs)
_, _, outputs = self.decoder(zs.size(0),
None, dec_init_state,
mode=GEN, gen_type="greedy",
beam_size=self.config.beam_size,
latent_variable=zs if self.concat_decoder_input else None)
return outputs
class VAE(BaseModel):
def __init__(self, corpus, config):
super(VAE, self).__init__(config)
self.vocab = corpus.vocab
self.rev_vocab = corpus.rev_vocab
self.vocab_size = len(self.vocab)
self.embed_size = config.embed_size
self.max_utt_len = config.max_utt_len
self.go_id = self.rev_vocab[BOS]
self.eos_id = self.rev_vocab[EOS]
self.unk_id = self.rev_vocab[UNK]
self.pad_id = self.rev_vocab[PAD]
self.num_layer_enc = config.num_layer_enc
self.num_layer_dec = config.num_layer_dec
self.dropout = config.dropout
self.enc_cell_size = config.enc_cell_size
self.dec_cell_size = config.dec_cell_size
self.rnn_cell = config.rnn_cell
self.max_dec_len = config.max_dec_len
self.use_attn = config.use_attn
self.beam_size = config.beam_size
self.utt_type = config.utt_type
self.bi_enc_cell = config.bi_enc_cell
self.attn_type = config.attn_type
self.enc_out_size = self.enc_cell_size * 2 if self.bi_enc_cell else self.enc_cell_size
self.posterior_sample_n = config.post_sample_num if "post_sample_num" in config else 1
self.concat_decoder_input = config.concat_decoder_input if "concat_decoder_input" in config else False
self.use_kl = getattr(config, "use_kl", True)
# build model here
self.embedding = nn.Embedding(self.vocab_size, self.embed_size,
padding_idx=self.rev_vocab[PAD])
self.x_encoder = EncoderRNN(self.embed_size, self.enc_cell_size,
dropout_p=self.dropout,
rnn_cell=self.rnn_cell,
variable_lengths=self.config.fix_batch,
bidirection=self.bi_enc_cell,
n_layers=self.num_layer_enc
)
self.q_z_mean = nn.Linear(self.enc_out_size, config.latent_size)
self.q_z_logvar = nn.Linear(self.enc_out_size, config.latent_size)
self.cat_connector = nn_lib.GumbelConnector()
self.dec_init_connector = nn_lib.LinearConnector(config.latent_size,
self.dec_cell_size,
self.rnn_cell == 'lstm',
has_bias=False)
self.decoder = DecoderRNN(self.vocab_size, self.max_dec_len,
self.embed_size + self.config.latent_size if self.concat_decoder_input else self.embed_size,
self.dec_cell_size,
self.go_id, self.eos_id, self.unk_id,
n_layers=self.num_layer_dec, rnn_cell=self.rnn_cell,
input_dropout_p=self.dropout,
dropout_p=self.dropout,
use_attention=self.use_attn,
attn_size=self.enc_cell_size,
attn_mode=self.attn_type,
use_gpu=self.use_gpu,
embedding=self.embedding,
softmax_temperature=self.config.softmax_temperature if "softmax_temperature" in self.config else 1.0)
self.nll_loss = criterions.NLLEntropy(self.rev_vocab[PAD], self.config)
self.ppl = criterions.Perplexity(self.rev_vocab[PAD], self.config)
self.cat_kl_loss = criterions.CatKLLoss()
self.cross_ent_loss = criterions.CrossEntropyoss()
self.entropy_loss = criterions.Entropy()
if 'bow_loss' in self.config and self.config.bow_loss:
self.bow_mlp = nn.Linear(config.latent_size, self.vocab_size)
self.bow_loss = True
self.bow_entropy = criterions.BowEntropy(self.rev_vocab[PAD], self.config)
else:
self.bow_loss = False
self.kl_w = 0.0
self.return_latent_key = ("dec_init_state", "qz_mean", "qz_logvar", "q_z")
@staticmethod
def add_args(parser):
from dgmvae.utils import str2bool
# Latent variable:
parser.add_argument('--latent_size', type=int, default=40, help="The latent size of continuous latent variable.")
# Network setting:
parser.add_argument('--rnn_cell', type=str, default='gru')
parser.add_argument('--embed_size', type=int, default=512)
parser.add_argument('--utt_type', type=str, default='rnn')
parser.add_argument('--enc_cell_size', type=int, default=512)
parser.add_argument('--dec_cell_size', type=int, default=512)
parser.add_argument('--bi_enc_cell', type=str2bool, default=True)
parser.add_argument('--num_layer_enc', type=int, default=1)
parser.add_argument('--num_layer_dec', type=int, default=1)
parser.add_argument('--use_attn', type=str2bool, default=False)
parser.add_argument('--attn_type', type=str, default='cat')
parser.add_argument('--tie_output_embed', type=str2bool, default=True)
parser.add_argument('--max_utt_len', type=int, default=40)
parser.add_argument('--max_dec_len', type=int, default=40)
parser.add_argument('--max_vocab_cnt', type=int, default=10000)
parser.add_argument('--use_kl', type=str2bool, default=True, help="use_kl=False: AE; use_kl=True, VAE.")
parser.add_argument('--bow_loss', type=str2bool, default=False, help="adding bow loss to objective.")
parser.add_argument('--concat_decoder_input', type=str2bool, default=True)
parser.add_argument('--gmm', type=str2bool, default=False)
return parser
def valid_loss(self, loss, batch_cnt=None, step = None):
if batch_cnt is not None:
step = batch_cnt
if step is not None and 'anneal_function' in self.config:
vae_kl_weight = kl_anneal_function(self.config.anneal_function, step,
self.config.anneal_k, self.config.anneal_x0)
else:
vae_kl_weight = 1.0
if not self.use_kl:
loss.KL_loss = 0.0
total_loss = loss.nll + vae_kl_weight * loss.KL_loss
if self.bow_loss and self.training:
total_loss += loss.bow_loss
return total_loss
def model_sel_loss(self, loss, batch_cnt): # return albo
if not self.use_kl:
return loss.nll
return loss.ELBO
def reparameterization(self, mu, logvar, batch=False, sample=True):
if not self.use_kl:
sample = False
if self.training or sample:
std = torch.exp(0.5 * logvar)
z = self.torch2var(torch.randn(mu.size()))
z = z * std + mu
return z
else:
return mu
def forward(self, data_feed, mode, gen_type='greedy', sample_n=1, return_latent=False):
posterior_sample_n = self.posterior_sample_n if self.training else 1
if type(data_feed) is tuple:
data_feed = data_feed[0]
batch_size = len(data_feed['output_lens'])
out_utts = self.np2var(data_feed['outputs'], LONG)
# output encoder
output_embedding = self.embedding(out_utts)
x_outs, x_last = self.x_encoder(output_embedding)
if type(x_last) is tuple:
x_last = x_last[0].view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1, self.enc_out_size)
else:
x_last = x_last.view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1,
self.enc_out_size)
# posterior network
qz_mean = self.q_z_mean(x_last)
qz_logvar = self.q_z_logvar(x_last)
q_z = self.reparameterization(qz_mean.repeat(posterior_sample_n, 1),
qz_logvar.repeat(posterior_sample_n, 1), batch=True,
sample=gen_type != "greedy" or mode != GEN) # batch x (latent_size*mult_k)
# map sample to initial state of decoder
dec_init_state = self.dec_init_connector(q_z)
# get decoder inputs
labels = out_utts[:, 1:].contiguous()
dec_inputs = out_utts[:, 0:-1]
if self.config.word_dropout_rate > 0:
# randomly replace decoder input with <unk>
prob = torch.rand(dec_inputs.size())
prob[(dec_inputs.data - self.go_id) * (dec_inputs.data - self.pad_id) == 0] = 1
decoder_input_sequence = dec_inputs.clone()
decoder_input_sequence[prob < self.config.word_dropout_rate] = self.unk_id
# input_embedding = self.embedding(decoder_input_sequence)
dec_inputs = decoder_input_sequence
# decode
dec_outs, dec_last, dec_ctx = self.decoder(batch_size * posterior_sample_n,
dec_inputs.repeat(posterior_sample_n, 1),
dec_init_state,
mode=mode, gen_type=gen_type,
beam_size=self.beam_size,
latent_variable=q_z if self.concat_decoder_input else None)
# compute loss or return results
if mode == GEN:
return dec_ctx, labels
else:
# RNN reconstruction
nll = self.nll_loss(dec_outs, labels.repeat(posterior_sample_n, 1))
ppl = self.ppl(dec_outs, labels.repeat(posterior_sample_n, 1))
KL_loss = -0.5 * torch.mean(torch.sum((1 + qz_logvar - qz_mean.pow(2) - qz_logvar.exp()), dim=1))
if not self.use_kl:
KL_loss = torch.zeros([]).cuda()
if self.bow_loss:
bow_logits = self.bow_mlp(q_z)
bow_loss = self.bow_entropy(F.log_softmax(bow_logits), labels)
else:
bow_loss = torch.zeros([]).cuda()
results = Pack(nll=nll, KL_loss=KL_loss, ELBO=nll+KL_loss, PPL=ppl, bow_loss=bow_loss)
if return_latent:
for key in self.return_latent_key:
results[key] = eval(key)
return results
def sampling_for_likelihood(self, batch_size, data_feed, sample_num, sample_type="LL"):
# Importance sampling...
assert sample_type in ("LL", "logLL")
if type(data_feed) is tuple:
data_feed = data_feed[0]
batch_size = len(data_feed['output_lens'])
out_utts = self.np2var(data_feed['outputs'], LONG) # batch_size * seq_len
out_utts = out_utts.repeat(sample_num, 1)
labels = out_utts[:, 1:].contiguous()
dec_inputs = out_utts[:, 0:-1]
output_embedding = self.embedding(out_utts)
x_outs, x_last = self.x_encoder(output_embedding)
if type(x_last) is tuple:
x_last = x_last[0].view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1, self.enc_out_size)
else:
x_last = x_last.view(self.num_layer_enc, 1 + int(self.bi_enc_cell), -1, self.enc_cell_size)[-1]
x_last = x_last.transpose(0, 1).contiguous().view(-1,
self.enc_out_size)
qz_mean = self.q_z_mean(x_last) # [batch_size * sample_num, latent_size]
qz_logvar = self.q_z_logvar(x_last)
q_z = self.reparameterization(qz_mean, qz_logvar, batch=True, sample=True)
log_qzx = torch.sum(
- (q_z - qz_mean) * (q_z - qz_mean) / (2 * torch.exp(qz_logvar)) -0.5 * qz_logvar - 0.5 * math.log(math.pi * 2),
dim=-1)
dec_init_state = self.dec_init_connector(q_z)
dec_outs, dec_last, outputs = self.decoder(q_z.size(0),
dec_inputs, dec_init_state,
mode=TEACH_FORCE,
gen_type=self.config.gen_type,
beam_size=self.config.beam_size,
latent_variable=q_z if self.concat_decoder_input else None)
nll = F.nll_loss(dec_outs.view(-1, dec_outs.size(-1)), labels.view(-1), reduction="none").view(out_utts.size(0), -1)
nll = torch.sum(nll, dim=-1)
log_pz = torch.sum(- 0.5 * q_z * q_z - 0.5 * math.log(math.pi * 2), dim=-1) # [batch_size * sample_num, ]
ll = torch.exp(-nll.double() + log_pz.double() - log_qzx.double()) # log (p(z)p(x|z) / q(z|x))
if sample_type == "logLL":
return (-nll.double() + log_pz.double() - log_qzx.double()).view(-1, sample_num)
else:
ll = ll.view(-1, sample_num)
return ll
def sampling(self, batch_size):
zs = self.torch2var(torch.randn(batch_size, self.config.latent_size))
dec_init_state = self.dec_init_connector(zs)
dec_outs, dec_last, outputs = self.decoder(zs.size(0),
None, dec_init_state,
mode=GEN, gen_type="greedy",
beam_size=self.config.beam_size,
latent_variable=zs)
return outputs
class RNNLM(BaseModel):
def __init__(self, corpus, config):
super(RNNLM, self).__init__(config)
self.vocab = corpus.vocab
self.rev_vocab = corpus.rev_vocab
self.vocab_size = len(self.vocab)
self.embed_size = config.embed_size
self.max_utt_len = config.max_utt_len
self.go_id = self.rev_vocab[BOS]
self.eos_id = self.rev_vocab[EOS]
self.unk_id = self.rev_vocab[UNK]
self.num_layer = config.num_layer
self.dropout = config.dropout
self.dec_cell_size = config.dec_cell_size
self.rnn_cell = config.rnn_cell
self.max_dec_len = config.max_dec_len
self.beam_size = config.beam_size
self.utt_type = config.utt_type
# build model here
self.embedding = nn.Embedding(self.vocab_size, self.embed_size,
padding_idx=self.rev_vocab[PAD])
self.decoder = DecoderRNN(self.vocab_size, self.max_dec_len,
self.embed_size, self.dec_cell_size,
self.go_id, self.eos_id, self.unk_id,
n_layers=config.num_layer, rnn_cell=self.rnn_cell,
input_dropout_p=self.dropout,
dropout_p=self.dropout,
use_attention=False,
# attn_size=self.enc_cell_size,
# attn_mode='cat',
use_gpu=self.use_gpu,
embedding=self.embedding)
self.nll_loss = criterions.NLLEntropy(self.rev_vocab[PAD], self.config)
self.ppl = criterions.Perplexity(self.rev_vocab[PAD], self.config)
self.cat_kl_loss = criterions.CatKLLoss()
self.cross_ent_loss = criterions.CrossEntropyoss()
self.entropy_loss = criterions.Entropy()
# self.kl_w = 0.0
for para in self.parameters():
nn.init.uniform_(para.data, -0.1, 0.1)
# self.return_latent_key = ("dec_init_state", "qy_mean", "qy_logvar", "q_z")
@staticmethod
def add_args(parser):
from dgmvae.utils import str2bool
# Network setting:
parser.add_argument('--rnn_cell', type=str, default='gru')
parser.add_argument('--embed_size', type=int, default=512)
parser.add_argument('--utt_type', type=str, default='rnn')
parser.add_argument('--dec_cell_size', type=int, default=512)
parser.add_argument('--num_layer', type=int, default=1)
parser.add_argument('--tie_output_embed', type=str2bool, default=True)
parser.add_argument('--max_dec_len', type=int, default=40)
parser.add_argument('--max_utt_len', type=int, default=40)
parser.add_argument('--max_vocab_cnt', type=int, default=10000)
return parser
def valid_loss(self, loss, batch_cnt=None, step = None):
return loss.nll
def model_sel_loss(self, loss, batch_cnt):
return loss.nll
def reparameterization(self, mu, logvar, batch=False, sample=False):
if 'use_KL' in self.config and not self.config.use_KL:
sample = False
if self.training or sample:
std = torch.exp(0.5 * logvar)
z = self.torch2var(torch.randn(mu.size()))
z = z * std + mu
return z
else:
return mu
def forward(self, data_feed, mode, gen_type='greedy', sample_n=1, return_latent=False):
if type(data_feed) is tuple:
data_feed = data_feed[0]
batch_size = len(data_feed['output_lens'])
out_utts = self.np2var(data_feed['outputs'], LONG)
# map sample to initial state of decoder
# dec_init_state = self.dec_init_connector(q_z)
# get decoder inputs
labels = out_utts[:, 1:].contiguous()
dec_inputs = out_utts[:, 0:-1]
# decode
dec_outs, dec_last, dec_ctx = self.decoder(batch_size,
dec_inputs, None, # dec_init_state
mode=mode, gen_type=gen_type,
beam_size=self.beam_size)
# compute loss or return results
if mode == GEN:
return dec_ctx, labels
else:
# RNN reconstruction
nll = self.nll_loss(dec_outs, labels)
ppl = self.ppl(dec_outs, labels)
results = Pack(nll=nll, PPL=ppl)
if return_latent:
for key in self.return_latent_key:
results[key] = eval(key)
return results
def sampling(self, batch_size):
_, _, outputs = self.decoder(batch_size,
None, None, # dec_init_state
mode=GEN, gen_type="sample",
beam_size=self.beam_size)
return outputs
| 50.521934 | 147 | 0.581922 | 13,655 | 101,347 | 4.040791 | 0.02673 | 0.045671 | 0.04221 | 0.014952 | 0.945267 | 0.935553 | 0.9237 | 0.914783 | 0.902351 | 0.885351 | 0 | 0.016021 | 0.308968 | 101,347 | 2,005 | 148 | 50.547132 | 0.771839 | 0.054378 | 0 | 0.84981 | 0 | 0 | 0.038163 | 0.001381 | 0 | 0 | 0 | 0 | 0.002535 | 1 | 0.043726 | false | 0 | 0.014575 | 0.005703 | 0.117871 | 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 |
8126bbc3d69a3f8db8b403c354b25cd048c32481 | 305 | py | Python | tests/parser/aggregates.count.13.test.py | veltri/DLV2 | 944aaef803aa75e7ec51d7e0c2b0d964687fdd0e | [
"Apache-2.0"
] | null | null | null | tests/parser/aggregates.count.13.test.py | veltri/DLV2 | 944aaef803aa75e7ec51d7e0c2b0d964687fdd0e | [
"Apache-2.0"
] | null | null | null | tests/parser/aggregates.count.13.test.py | veltri/DLV2 | 944aaef803aa75e7ec51d7e0c2b0d964687fdd0e | [
"Apache-2.0"
] | null | null | null | input = """
a(a).
b(1).
b(2).
cap(1).
all(X,Y) | nall(X,Y) :- a(X), b(Y).
:- #count{Y:all(a,Y)} > C, cap(C).
:- #count{Y:all(a,Y)} < C, cap(C).
"""
output = """
a(a).
b(1).
b(2).
cap(1).
all(X,Y) | nall(X,Y) :- a(X), b(Y).
:- #count{Y:all(a,Y)} > C, cap(C).
:- #count{Y:all(a,Y)} < C, cap(C).
"""
| 11.296296 | 35 | 0.409836 | 70 | 305 | 1.785714 | 0.185714 | 0.064 | 0.288 | 0.32 | 0.912 | 0.912 | 0.912 | 0.912 | 0.912 | 0.912 | 0 | 0.024096 | 0.183607 | 305 | 26 | 36 | 11.730769 | 0.477912 | 0 | 0 | 0.888889 | 0 | 0.111111 | 0.898361 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
8131491bbe0ab0fac8fe214e3bc2fbadfd3fb5f7 | 5,848 | py | Python | Scripts/model.py | tasnim7ahmed/Extended-Cyberbullying-Detection | 8be85735f7fbc299f838d5cb67eccaa668feeff0 | [
"MIT"
] | null | null | null | Scripts/model.py | tasnim7ahmed/Extended-Cyberbullying-Detection | 8be85735f7fbc299f838d5cb67eccaa668feeff0 | [
"MIT"
] | null | null | null | Scripts/model.py | tasnim7ahmed/Extended-Cyberbullying-Detection | 8be85735f7fbc299f838d5cb67eccaa668feeff0 | [
"MIT"
] | null | null | null | import torch
import torch.nn as nn
import numpy as np
from transformers import BertModel, RobertaModel, XLNetModel, DistilBertModel, GPT2Model
from common import get_parser
parser = get_parser()
args = parser.parse_args()
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
class BertFGBC(nn.Module):
def __init__(self, pretrained_model = args.pretrained_model):
super().__init__()
self.Bert = BertModel.from_pretrained(pretrained_model)
self.drop1 = nn.Dropout(args.dropout)
self.linear = nn.Linear(args.bert_hidden, 64)
self.batch_norm = nn.LayerNorm(64)
self.drop2 = nn.Dropout(args.dropout)
self.out = nn.Linear(64, args.classes)
def forward(self, input_ids, attention_mask, token_type_ids):
_,last_hidden_state = self.Bert(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
return_dict=False
)
#print(f'Last Hidden State - {last_hidden_state.shape}')
bo = self.drop1(last_hidden_state)
#print(f'Dropout1 - {bo.shape}')
bo = self.linear(bo)
#print(f'Linear1 - {bo.shape}')
bo = self.batch_norm(bo)
#print(f'BatchNorm - {bo.shape}')
bo = nn.Tanh()(bo)
bo = self.drop2(bo)
#print(f'Dropout2 - {bo.shape}')
output = self.out(bo)
#print(f'Output - {output.shape}')
return output
class GPT2FGBC(nn.Module):
def __init__(self, pretrained_model = args.pretrained_model):
super().__init__()
self.GPT2 = GPT2Model.from_pretrained(pretrained_model)
self.drop1 = nn.Dropout(args.dropout)
self.linear = nn.Linear(args.gpt2_hidden, 64)
self.batch_norm = nn.LayerNorm(64)
self.drop2 = nn.Dropout(args.dropout)
self.out = nn.Linear(64, args.classes)
def forward(self, input_ids, attention_mask):
last_hidden_state = self.GPT2(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=False
)
mean_last_hidden_state = self.pool_hidden_state(last_hidden_state)
bo = self.drop1(mean_last_hidden_state)
bo = self.linear(bo)
bo = self.batch_norm(bo)
bo = nn.Tanh()(bo)
bo = self.drop2(bo)
output = self.out(bo)
return output
def pool_hidden_state(self, last_hidden_state):
last_hidden_state = last_hidden_state[0]
mean_last_hidden_state = torch.mean(last_hidden_state, 1)
return mean_last_hidden_state
class RobertaFGBC(nn.Module):
def __init__(self, pretrained_model = args.pretrained_model):
super().__init__()
self.Roberta = RobertaModel.from_pretrained(pretrained_model)
self.drop1 = nn.Dropout(args.dropout)
self.linear = nn.Linear(args.roberta_hidden, 64)
self.batch_norm = nn.LayerNorm(64)
self.drop2 = nn.Dropout(args.dropout)
self.out = nn.Linear(64, args.classes)
def forward(self, input_ids, attention_mask):
_,last_hidden_state = self.Roberta(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=False
)
bo = self.drop1(last_hidden_state)
bo = self.linear(bo)
bo = self.batch_norm(bo)
bo = nn.Tanh()(bo)
bo = self.drop2(bo)
output = self.out(bo)
return output
class DistilBertFGBC(nn.Module):
def __init__(self, pretrained_model = args.pretrained_model):
super().__init__()
self.DistilBert = DistilBertModel.from_pretrained(pretrained_model)
self.drop1 = nn.Dropout(args.dropout)
self.linear = nn.Linear(args.distilbert_hidden, 64)
self.batch_norm = nn.LayerNorm(64)
self.drop2 = nn.Dropout(args.dropout)
self.out = nn.Linear(64, args.classes)
def forward(self, input_ids, attention_mask):
last_hidden_state = self.DistilBert(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=False
)
mean_last_hidden_state = self.pool_hidden_state(last_hidden_state)
bo = self.drop1(mean_last_hidden_state)
bo = self.linear(bo)
bo = self.batch_norm(bo)
bo = nn.Tanh()(bo)
bo = self.drop2(bo)
output = self.out(bo)
return output
def pool_hidden_state(self, last_hidden_state):
last_hidden_state = last_hidden_state[0]
mean_last_hidden_state = torch.mean(last_hidden_state, 1)
return mean_last_hidden_state
class XLNetFGBC(nn.Module):
def __init__(self, pretrained_model = args.pretrained_model):
super().__init__()
self.XLNet = XLNetModel.from_pretrained(pretrained_model)
self.drop1 = nn.Dropout(args.dropout)
self.linear = nn.Linear(args.xlnet_hidden, 64)
self.batch_norm = nn.LayerNorm(64)
self.drop2 = nn.Dropout(args.dropout)
self.out = nn.Linear(64, args.classes)
def forward(self, input_ids, attention_mask, token_type_ids):
last_hidden_state = self.XLNet(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
return_dict=False
)
mean_last_hidden_state = self.pool_hidden_state(last_hidden_state)
bo = self.drop1(mean_last_hidden_state)
bo = self.linear(bo)
bo = self.batch_norm(bo)
bo = nn.Tanh()(bo)
bo = self.drop2(bo)
output = self.out(bo)
return output
def pool_hidden_state(self, last_hidden_state):
last_hidden_state = last_hidden_state[0]
mean_last_hidden_state = torch.mean(last_hidden_state, 1)
return mean_last_hidden_state | 33.803468 | 88 | 0.644494 | 755 | 5,848 | 4.696689 | 0.101987 | 0.130288 | 0.152284 | 0.080372 | 0.825719 | 0.820925 | 0.80203 | 0.80203 | 0.795544 | 0.795544 | 0 | 0.014908 | 0.254446 | 5,848 | 173 | 89 | 33.803468 | 0.798395 | 0.036252 | 0 | 0.744526 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.094891 | false | 0 | 0.036496 | 0 | 0.226277 | 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 |
d4e725d1f845aa48e3f81bd9400f993ec01d32ac | 4,378 | py | Python | pyrcn/preprocessingData.py | TUD-STKS/PyRCN | 26fb7f0d55e8c8925f692191c56db2ea32e3f630 | [
"BSD-3-Clause"
] | 35 | 2020-07-21T18:11:01.000Z | 2022-03-28T01:31:11.000Z | pyrcn/preprocessingData.py | TUD-STKS/PyRCN | 26fb7f0d55e8c8925f692191c56db2ea32e3f630 | [
"BSD-3-Clause"
] | 21 | 2020-12-30T14:25:26.000Z | 2021-12-02T10:34:43.000Z | pyrcn/preprocessingData.py | TUD-STKS/PyRCN | 26fb7f0d55e8c8925f692191c56db2ea32e3f630 | [
"BSD-3-Clause"
] | 10 | 2020-07-15T11:22:21.000Z | 2022-03-18T10:27:47.000Z | import pandas as pd
import numpy as np
from scipy.io.wavfile import read as read_wav
import librosa
import os
from sklearn.preprocessing import MinMaxScaler
def preprocessing_audio(data_info_path, audio_path):
sampleRate = 16000 # Ziel Samplefrequence in Hz
cutAudio = 0.3 # je am Anfang/Ende abgeschnittener Audioanteil (um Pause zu entfernen)
lengthAudio = 1 - 2 * cutAudio # gesamtlänge der Vokaldatei in Prozent/100
audio = [] # Liste für Audiodateien
vocalInfo = [] # Liste für Vokalinfo
_, _, filenames = next(os.walk(data_info_path)) # filenames aus ordner mit .csv entnehmen
# Audio und zugehörigen Vokal in Listen speichern
for i in range(len(filenames)):
name = filenames[i]
data_info = pd.read_csv(data_info_path + "/" + name) # .csv für Audio einlesen
timemarkBeginn = data_info['Beginn'] # Inhalt von .csv aufteilen
timemarkEnde = data_info['Ende']
vokal = data_info['Vokal']
nameAudio = name.replace("csv", "wav") # Name des Audiofiles erstellen
pathAudio = audio_path + "/" + nameAudio # Pfad des Ausiofiles
Fs, _ = read_wav(pathAudio) # SampleRate des Origianl-Audios
for i in range(len(timemarkBeginn)):
timemark1 = timemarkBeginn[i]
timemark2 = timemarkEnde[i]
vocalLength = (timemark2 - timemark1) / Fs # Vokallänge mit Pause in Sekunden
offset1 = (timemark1 / Fs + cutAudio * vocalLength) # in Sekunden, start des Vokals in Sekunden in wav-file
dauer = vocalLength * lengthAudio # in Sekunden, % vorne und hinten abschneiden um Pause abzutrennen
y, _ = librosa.load(path=pathAudio, sr=sampleRate, mono=True, offset=offset1,
duration=dauer) # , dtype=<class 'numpy.float32'>, res_type='kaiser_best')
y = librosa.util.normalize(y)
audio.append(y)
vocalInfo.append(vokal[i])
return audio, vocalInfo, sampleRate
def preprocessing_audio_fb(data_info_path, audio_path): # unterschied: Normierung des Audiosignals, |y|<1 um tanh im esn zu verwenden
sampleRate = 16000 # Ziel Samplefrequence in Hz
cutAudio = 0.3 # je am Anfang/Ende abgeschnittener Audioanteil (um Pause zu entfernen)
lengthAudio = 1 - 2 * cutAudio # gesamtlänge der Vokaldatei in Prozent/100
audio = [] # Liste für Audiodateien
vocalInfo = [] # Liste für Vokalinfo
_, _, filenames = next(os.walk(data_info_path)) # filenames aus ordner mit .csv entnehmen
# Audio und zugehörigen Vokal in Listen speichern
for i in range(len(filenames)):
scaler = MinMaxScaler(feature_range=(0,0.999))
name = filenames[i]
data_info = pd.read_csv(data_info_path + "/" + name) # .csv für Audio einlesen
timemarkBeginn = data_info['Beginn'] # Inhalt von .csv aufteilen
timemarkEnde = data_info['Ende']
vokal = data_info['Vokal']
nameAudio = name.replace("csv", "wav") # Name des Audiofiles erstellen
pathAudio = audio_path + "/" + nameAudio # Pfad des Ausiofiles
Fs, _ = read_wav(pathAudio) # SampleRate des Origianl-Audios
for i in range(len(timemarkBeginn)):
timemark1 = timemarkBeginn[i]
timemark2 = timemarkEnde[i]
vocalLength = (timemark2 - timemark1) / Fs # Vokallänge mit Pause in Sekunden
offset1 = (timemark1 / Fs + cutAudio * vocalLength) # in Sekunden, start des Vokals in Sekunden in wav-file
dauer = vocalLength * lengthAudio # in Sekunden, % vorne und hinten abschneiden um Pause abzutrennen
y, _ = librosa.load(path=pathAudio, sr=sampleRate, mono=True, offset=offset1,
duration=dauer) # , dtype=<class 'numpy.float32'>, res_type='kaiser_best')
y = scaler.fit_transform(y.reshape(-1, 1))
audio.append(y)
vocalInfo.append(vokal[i])
audioVocalOne = []
vocalInfoOne = []
for i in range(len(audio)):
if vocalInfo[i]=='a' or vocalInfo[i]=='u':
audioVocalOne.append(audio[i])
vocalInfoOne.append(vocalInfo[i])
#return audio, vocalInfo, sampleRate
return audioVocalOne, vocalInfoOne, sampleRate | 49.75 | 137 | 0.636135 | 501 | 4,378 | 5.467066 | 0.283433 | 0.040891 | 0.026287 | 0.02008 | 0.829135 | 0.786418 | 0.786418 | 0.762322 | 0.762322 | 0.762322 | 0 | 0.015728 | 0.273869 | 4,378 | 88 | 138 | 49.75 | 0.845863 | 0.304249 | 0 | 0.724638 | 0 | 0 | 0.015936 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.028986 | false | 0 | 0.086957 | 0 | 0.144928 | 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 |
d4eb332e6e67119e40b5a1f17df920f44efd52c7 | 8,267 | py | Python | tests/snapshots/snap_test_holidata/test_holidata_produces_holidays_for_locale_and_year[en_CA-2020] 1.py | gour/holidata | 89c7323f9c5345a3ecbf5cd5a835b0e08cfebc13 | [
"MIT"
] | 32 | 2019-04-12T08:01:34.000Z | 2022-02-28T04:41:50.000Z | tests/snapshots/snap_test_holidata/test_holidata_produces_holidays_for_locale_and_year[en_CA-2020] 1.py | gour/holidata | 89c7323f9c5345a3ecbf5cd5a835b0e08cfebc13 | [
"MIT"
] | 74 | 2019-07-09T16:35:20.000Z | 2022-03-09T16:41:34.000Z | tests/snapshots/snap_test_holidata/test_holidata_produces_holidays_for_locale_and_year[en_CA-2020] 1.py | gour/holidata | 89c7323f9c5345a3ecbf5cd5a835b0e08cfebc13 | [
"MIT"
] | 20 | 2019-01-28T07:41:02.000Z | 2022-02-16T02:38:57.000Z | [
{
'date': '2020-01-01',
'description': "New Year's Day",
'locale': 'en-CA',
'notes': '',
'region': '',
'type': 'NF'
},
{
'date': '2020-02-17',
'description': 'Family Day',
'locale': 'en-CA',
'notes': '',
'region': 'AB',
'type': 'V'
},
{
'date': '2020-02-17',
'description': 'Family Day',
'locale': 'en-CA',
'notes': '',
'region': 'ON',
'type': 'V'
},
{
'date': '2020-02-17',
'description': 'Family Day',
'locale': 'en-CA',
'notes': '',
'region': 'SK',
'type': 'V'
},
{
'date': '2020-02-17',
'description': 'Family Day',
'locale': 'en-CA',
'notes': '',
'region': 'NB',
'type': 'V'
},
{
'date': '2020-02-17',
'description': 'Louis Riel Day',
'locale': 'en-CA',
'notes': '',
'region': 'MB',
'type': 'V'
},
{
'date': '2020-02-17',
'description': 'Islander Day',
'locale': 'en-CA',
'notes': '',
'region': 'PE',
'type': 'V'
},
{
'date': '2020-04-10',
'description': 'Good Friday',
'locale': 'en-CA',
'notes': '',
'region': '',
'type': 'NRV'
},
{
'date': '2020-04-13',
'description': 'Easter Monday',
'locale': 'en-CA',
'notes': '',
'region': 'AB',
'type': 'RV'
},
{
'date': '2020-04-13',
'description': 'Easter Monday',
'locale': 'en-CA',
'notes': '',
'region': 'PE',
'type': 'RV'
},
{
'date': '2020-04-13',
'description': 'Easter Monday',
'locale': 'en-CA',
'notes': '',
'region': 'QC',
'type': 'RV'
},
{
'date': '2020-05-18',
'description': "National Patriots' Day",
'locale': 'en-CA',
'notes': '',
'region': 'QC',
'type': 'V'
},
{
'date': '2020-05-18',
'description': 'Victoria Day',
'locale': 'en-CA',
'notes': '',
'region': 'AB',
'type': 'V'
},
{
'date': '2020-05-18',
'description': 'Victoria Day',
'locale': 'en-CA',
'notes': '',
'region': 'BC',
'type': 'V'
},
{
'date': '2020-05-18',
'description': 'Victoria Day',
'locale': 'en-CA',
'notes': '',
'region': 'MB',
'type': 'V'
},
{
'date': '2020-05-18',
'description': 'Victoria Day',
'locale': 'en-CA',
'notes': '',
'region': 'NS',
'type': 'V'
},
{
'date': '2020-05-18',
'description': 'Victoria Day',
'locale': 'en-CA',
'notes': '',
'region': 'ON',
'type': 'V'
},
{
'date': '2020-05-18',
'description': 'Victoria Day',
'locale': 'en-CA',
'notes': '',
'region': 'SK',
'type': 'V'
},
{
'date': '2020-05-18',
'description': 'Victoria Day',
'locale': 'en-CA',
'notes': '',
'region': 'NT',
'type': 'V'
},
{
'date': '2020-05-18',
'description': 'Victoria Day',
'locale': 'en-CA',
'notes': '',
'region': 'NU',
'type': 'V'
},
{
'date': '2020-05-18',
'description': 'Victoria Day',
'locale': 'en-CA',
'notes': '',
'region': 'YT',
'type': 'V'
},
{
'date': '2020-06-24',
'description': 'National Holiday',
'locale': 'en-CA',
'notes': '',
'region': 'QC',
'type': 'F'
},
{
'date': '2020-07-01',
'description': 'Canada Day',
'locale': 'en-CA',
'notes': '',
'region': '',
'type': 'NF'
},
{
'date': '2020-08-03',
'description': 'August Civic Holiday',
'locale': 'en-CA',
'notes': '',
'region': 'NT',
'type': 'V'
},
{
'date': '2020-08-03',
'description': 'August Civic Holiday',
'locale': 'en-CA',
'notes': '',
'region': 'NU',
'type': 'V'
},
{
'date': '2020-08-03',
'description': 'Saskatchewan Day',
'locale': 'en-CA',
'notes': '',
'region': 'SK',
'type': 'V'
},
{
'date': '2020-08-03',
'description': 'Heritage Day',
'locale': 'en-CA',
'notes': '',
'region': 'AB',
'type': 'V'
},
{
'date': '2020-08-03',
'description': 'Heritage Day',
'locale': 'en-CA',
'notes': '',
'region': 'NS',
'type': 'V'
},
{
'date': '2020-08-03',
'description': 'New Brunswick Day',
'locale': 'en-CA',
'notes': '',
'region': 'NB',
'type': 'V'
},
{
'date': '2020-09-07',
'description': 'Labour Day',
'locale': 'en-CA',
'notes': '',
'region': '',
'type': 'NV'
},
{
'date': '2020-10-12',
'description': 'Thanksgiving Day',
'locale': 'en-CA',
'notes': '',
'region': 'AB',
'type': 'V'
},
{
'date': '2020-10-12',
'description': 'Thanksgiving Day',
'locale': 'en-CA',
'notes': '',
'region': 'BC',
'type': 'V'
},
{
'date': '2020-10-12',
'description': 'Thanksgiving Day',
'locale': 'en-CA',
'notes': '',
'region': 'MB',
'type': 'V'
},
{
'date': '2020-10-12',
'description': 'Thanksgiving Day',
'locale': 'en-CA',
'notes': '',
'region': 'NL',
'type': 'V'
},
{
'date': '2020-10-12',
'description': 'Thanksgiving Day',
'locale': 'en-CA',
'notes': '',
'region': 'ON',
'type': 'V'
},
{
'date': '2020-10-12',
'description': 'Thanksgiving Day',
'locale': 'en-CA',
'notes': '',
'region': 'QC',
'type': 'V'
},
{
'date': '2020-10-12',
'description': 'Thanksgiving Day',
'locale': 'en-CA',
'notes': '',
'region': 'SK',
'type': 'V'
},
{
'date': '2020-10-12',
'description': 'Thanksgiving Day',
'locale': 'en-CA',
'notes': '',
'region': 'NT',
'type': 'V'
},
{
'date': '2020-10-12',
'description': 'Thanksgiving Day',
'locale': 'en-CA',
'notes': '',
'region': 'NU',
'type': 'V'
},
{
'date': '2020-10-12',
'description': 'Thanksgiving Day',
'locale': 'en-CA',
'notes': '',
'region': 'YT',
'type': 'V'
},
{
'date': '2020-11-11',
'description': 'Remembrance Day',
'locale': 'en-CA',
'notes': '',
'region': 'AB',
'type': 'F'
},
{
'date': '2020-11-11',
'description': 'Remembrance Day',
'locale': 'en-CA',
'notes': '',
'region': 'BC',
'type': 'F'
},
{
'date': '2020-11-11',
'description': 'Remembrance Day',
'locale': 'en-CA',
'notes': '',
'region': 'NB',
'type': 'F'
},
{
'date': '2020-11-11',
'description': 'Remembrance Day',
'locale': 'en-CA',
'notes': '',
'region': 'NL',
'type': 'F'
},
{
'date': '2020-11-11',
'description': 'Remembrance Day',
'locale': 'en-CA',
'notes': '',
'region': 'NT',
'type': 'F'
},
{
'date': '2020-12-25',
'description': 'Christmas Day',
'locale': 'en-CA',
'notes': '',
'region': '',
'type': 'NRF'
},
{
'date': '2020-12-26',
'description': 'Boxing Day',
'locale': 'en-CA',
'notes': '',
'region': '',
'type': 'NRF'
}
] | 21.87037 | 48 | 0.362042 | 706 | 8,267 | 4.239377 | 0.104816 | 0.125626 | 0.157033 | 0.23555 | 0.91146 | 0.903775 | 0.892082 | 0.841296 | 0.815236 | 0.815236 | 0 | 0.076376 | 0.4045 | 8,267 | 378 | 49 | 21.87037 | 0.531586 | 0 | 0 | 0.690476 | 0 | 0 | 0.385704 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
d4f849095ca70679b0c4eb02b8ecbbba79bd59b3 | 8,729 | py | Python | tests/integration/events/v1/test_subscription.py | Atharva2011/twilio-python | 5397b41e0a93fd85d5a39b584289910785e19cd1 | [
"MIT"
] | null | null | null | tests/integration/events/v1/test_subscription.py | Atharva2011/twilio-python | 5397b41e0a93fd85d5a39b584289910785e19cd1 | [
"MIT"
] | null | null | null | tests/integration/events/v1/test_subscription.py | Atharva2011/twilio-python | 5397b41e0a93fd85d5a39b584289910785e19cd1 | [
"MIT"
] | null | null | null | # coding=utf-8
r"""
This code was generated by
\ / _ _ _| _ _
| (_)\/(_)(_|\/| |(/_ v1.0.0
/ /
"""
from tests import IntegrationTestCase
from tests.holodeck import Request
from twilio.base import serialize
from twilio.base.exceptions import TwilioException
from twilio.http.response import Response
class SubscriptionTestCase(IntegrationTestCase):
def test_list_request(self):
self.holodeck.mock(Response(500, ''))
with self.assertRaises(TwilioException):
self.client.events.v1.subscriptions.list()
self.holodeck.assert_has_request(Request(
'get',
'https://events.twilio.com/v1/Subscriptions',
))
def test_read_empty_response(self):
self.holodeck.mock(Response(
200,
'''
{
"subscriptions": [],
"meta": {
"page": 0,
"page_size": 10,
"first_page_url": "https://events.twilio.com/v1/Subscriptions?PageSize=10&Page=0",
"previous_page_url": null,
"url": "https://events.twilio.com/v1/Subscriptions?PageSize=10&Page=0",
"next_page_url": null,
"key": "subscriptions"
}
}
'''
))
actual = self.client.events.v1.subscriptions.list()
self.assertIsNotNone(actual)
def test_read_results_response(self):
self.holodeck.mock(Response(
200,
'''
{
"subscriptions": [
{
"account_sid": "ACaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"date_created": "2015-07-30T20:00:00Z",
"date_updated": "2015-07-30T20:01:33Z",
"sid": "DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"sink_sid": "DGaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"description": "A subscription",
"url": "https://events.twilio.com/v1/Subscriptions/DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"links": {
"subscribed_events": "https://events.twilio.com/v1/Subscriptions/DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/SubscribedEvents"
}
},
{
"account_sid": "ACaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"date_created": "2015-07-30T20:00:00Z",
"date_updated": "2015-07-30T20:01:33Z",
"sid": "DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab",
"sink_sid": "DGaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"description": "Another subscription",
"url": "https://events.twilio.com/v1/Subscriptions/DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab",
"links": {
"subscribed_events": "https://events.twilio.com/v1/Subscriptions/DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab/SubscribedEvents"
}
}
],
"meta": {
"page": 0,
"page_size": 20,
"first_page_url": "https://events.twilio.com/v1/Subscriptions?PageSize=20&Page=0",
"previous_page_url": null,
"url": "https://events.twilio.com/v1/Subscriptions?PageSize=20&Page=0",
"next_page_url": null,
"key": "subscriptions"
}
}
'''
))
actual = self.client.events.v1.subscriptions.list()
self.assertIsNotNone(actual)
def test_fetch_request(self):
self.holodeck.mock(Response(500, ''))
with self.assertRaises(TwilioException):
self.client.events.v1.subscriptions("DFXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX").fetch()
self.holodeck.assert_has_request(Request(
'get',
'https://events.twilio.com/v1/Subscriptions/DFXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX',
))
def test_fetch_response(self):
self.holodeck.mock(Response(
200,
'''
{
"account_sid": "ACaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"date_created": "2015-07-30T20:00:00Z",
"date_updated": "2015-07-30T20:01:33Z",
"sid": "DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"sink_sid": "DGaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"description": "A subscription",
"url": "https://events.twilio.com/v1/Subscriptions/DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"links": {
"subscribed_events": "https://events.twilio.com/v1/Subscriptions/DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/SubscribedEvents"
}
}
'''
))
actual = self.client.events.v1.subscriptions("DFXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX").fetch()
self.assertIsNotNone(actual)
def test_create_request(self):
self.holodeck.mock(Response(500, ''))
with self.assertRaises(TwilioException):
self.client.events.v1.subscriptions.create(description="description", sink_sid="DGXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX", types=[{}])
values = {
'Description': "description",
'SinkSid': "DGXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
'Types': serialize.map([{}], lambda e: serialize.object(e)),
}
self.holodeck.assert_has_request(Request(
'post',
'https://events.twilio.com/v1/Subscriptions',
data=values,
))
def test_create_response(self):
self.holodeck.mock(Response(
201,
'''
{
"account_sid": "ACaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"date_created": "2015-07-30T20:00:00Z",
"date_updated": "2015-07-30T20:01:33Z",
"sid": "DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"sink_sid": "DGaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"description": "A subscription",
"url": "https://events.twilio.com/v1/Subscriptions/DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"links": {
"subscribed_events": "https://events.twilio.com/v1/Subscriptions/DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/SubscribedEvents"
}
}
'''
))
actual = self.client.events.v1.subscriptions.create(description="description", sink_sid="DGXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX", types=[{}])
self.assertIsNotNone(actual)
def test_update_request(self):
self.holodeck.mock(Response(500, ''))
with self.assertRaises(TwilioException):
self.client.events.v1.subscriptions("DFXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX").update()
self.holodeck.assert_has_request(Request(
'post',
'https://events.twilio.com/v1/Subscriptions/DFXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX',
))
def test_update_response(self):
self.holodeck.mock(Response(
200,
'''
{
"account_sid": "ACaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"date_created": "2015-07-30T20:00:00Z",
"date_updated": "2020-07-30T20:01:33Z",
"sid": "DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"sink_sid": "DGaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab",
"description": "Updated description",
"url": "https://events.twilio.com/v1/Subscriptions/DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"links": {
"subscribed_events": "https://events.twilio.com/v1/Subscriptions/DFaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/SubscribedEvents"
}
}
'''
))
actual = self.client.events.v1.subscriptions("DFXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX").update()
self.assertIsNotNone(actual)
def test_delete_request(self):
self.holodeck.mock(Response(500, ''))
with self.assertRaises(TwilioException):
self.client.events.v1.subscriptions("DFXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX").delete()
self.holodeck.assert_has_request(Request(
'delete',
'https://events.twilio.com/v1/Subscriptions/DFXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX',
))
def test_delete_response(self):
self.holodeck.mock(Response(
204,
None,
))
actual = self.client.events.v1.subscriptions("DFXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX").delete()
self.assertTrue(actual)
| 38.117904 | 145 | 0.560774 | 667 | 8,729 | 7.212894 | 0.157421 | 0.093536 | 0.067138 | 0.078986 | 0.859281 | 0.833922 | 0.812513 | 0.7963 | 0.728747 | 0.684265 | 0 | 0.038007 | 0.321801 | 8,729 | 228 | 146 | 38.285088 | 0.774662 | 0.012487 | 0 | 0.577778 | 1 | 0 | 0.177534 | 0.077942 | 0 | 0 | 0 | 0 | 0.177778 | 1 | 0.122222 | false | 0 | 0.055556 | 0 | 0.188889 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
be2254b676e33a9032c5ffcb06caa044007b30ba | 364,208 | py | Python | public/images/Prdikt_Alg_V2-2.py | sunilramawat/prdikt | 24b1db498bfbd29bc735bf40a36f79a26cf4078e | [
"MIT"
] | null | null | null | public/images/Prdikt_Alg_V2-2.py | sunilramawat/prdikt | 24b1db498bfbd29bc735bf40a36f79a26cf4078e | [
"MIT"
] | null | null | null | public/images/Prdikt_Alg_V2-2.py | sunilramawat/prdikt | 24b1db498bfbd29bc735bf40a36f79a26cf4078e | [
"MIT"
] | null | null | null | {
"cells": [
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"#Importing relevant libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import datetime\n",
"from datetime import datetime,timedelta, date, time\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from statistics import mode\n",
"from itertools import chain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Webhook Send"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"def webhook_send(data_to_send):\n",
" \n",
" import json \n",
" import requests \n",
" \n",
" answer = str(input('Send Data? (Yes/No): '))\n",
" \n",
" if answer == 'Yes':\n",
" destination_url = str(input('Enter URL to send data to: '))\n",
" \n",
" data = data_to_send.to_json(orient='split')\n",
" r = requests.post(destination_url,data=json.dumps(data), \n",
" headers = {'Content-Type':'application/json'})\n",
" \n",
" print('Data Succesfully Sent to {}'.format(destination_url))\n",
" else:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"custom_syle = {'axes.grid': False,'xtick.bottom': True,\n",
" 'ytick.left': True, 'patch.edgecolor': 'black',\n",
" 'patch.force_edgecolor': False}\n",
"\n",
"sns.set_style('darkgrid', rc= custom_syle)\n",
"plt.style.use('dark_background')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"sleep_csv = 'SLEEP.csv'\n",
"ex_csv = 'HEARTRATE_AUTO.csv'\n",
"age_csv = 'USER.csv'\n",
"# num_of_days_to_show = 7"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"def choose_num_days_to_show(auto=bool):\n",
" \"\"\"\n",
" Select how many days to show for graphs and data throughout the report.\n",
" \n",
" Auto mode is for autoamted report generation\n",
" \n",
" \"\"\"\n",
" if auto == True:\n",
" \n",
" if len(sleep_data_adj_len) < 7:\n",
"\n",
" num_days_shown = len(sleep_data_adj_len)\n",
"\n",
" else:\n",
" num_days_shown = 7\n",
" \n",
" return num_days_shown\n",
" \n",
" else:\n",
" num_days_shown = int(input('Enter Number of Days to show: '))\n",
" return num_days_shown"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing Data"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"sleep_data_all = pd.read_csv(sleep_csv)\n",
"\n",
"#Change this so we can test for different number of days \n",
"sleep_data_adj_len = sleep_data_all[:4].copy()\n",
"\n",
"#number of days to show\n",
"num_days_shown = choose_num_days_to_show(auto=True)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"sleep_data_adj_len.rename(columns= {'start':'BT', 'stop' :'WT', 'date':'Date'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>deepSleepTime</th>\n",
" <th>shallowSleepTime</th>\n",
" <th>wakeTime</th>\n",
" <th>BT</th>\n",
" <th>WT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-11-14</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2021-11-12 21:00:00+0000</td>\n",
" <td>2021-11-12 21:00:00+0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2021-11-15</td>\n",
" <td>109</td>\n",
" <td>343</td>\n",
" <td>1</td>\n",
" <td>2021-11-14 20:24:00+0000</td>\n",
" <td>2021-11-15 03:57:00+0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2021-11-16</td>\n",
" <td>80</td>\n",
" <td>238</td>\n",
" <td>2</td>\n",
" <td>2021-11-15 18:54:00+0000</td>\n",
" <td>2021-11-16 01:54:00+0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2021-11-17</td>\n",
" <td>114</td>\n",
" <td>316</td>\n",
" <td>9</td>\n",
" <td>2021-11-16 19:12:00+0000</td>\n",
" <td>2021-11-17 03:28:00+0000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date deepSleepTime shallowSleepTime wakeTime \\\n",
"0 2021-11-14 0 0 0 \n",
"1 2021-11-15 109 343 1 \n",
"2 2021-11-16 80 238 2 \n",
"3 2021-11-17 114 316 9 \n",
"\n",
" BT WT \n",
"0 2021-11-12 21:00:00+0000 2021-11-12 21:00:00+0000 \n",
"1 2021-11-14 20:24:00+0000 2021-11-15 03:57:00+0000 \n",
"2 2021-11-15 18:54:00+0000 2021-11-16 01:54:00+0000 \n",
"3 2021-11-16 19:12:00+0000 2021-11-17 03:28:00+0000 "
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sleep_data_adj_len"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sleep Data Manipulation + Calculating Sleep Duration"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"def avg_time(datetimes):\n",
" total = sum(dt.hour * 3600 + dt.minute * 60 + dt.second for dt in datetimes)\n",
" avg = total / len(datetimes)\n",
" minutes, seconds = divmod(int(avg), 60)\n",
" hours, minutes = divmod(minutes, 60)\n",
" return datetime.combine(date(1900, 1, 1), time(hours, minutes, seconds))\n",
"\n",
"def find_empty_rows(df,reset_index=False):\n",
" rows_to_change = []\n",
" \n",
" if reset_index == True:\n",
" df.reset_index(inplace=True, drop=True)\n",
" \n",
" for i in range(len(df)):\n",
" if df['deepSleepTime'][i]==0 and df['shallowSleepTime'][i]== 0 and df['wakeTime'][i] == 0:\n",
" rows_to_change.append(i)\n",
" return rows_to_change\n",
"\n",
"def find_and_replace_empty_times(df, wake_times_dt_format, rows_to_change):\n",
"\n",
" wake_times_dt_format = [i for j, i in enumerate(wake_times_dt_format) if j not in rows_to_change]\n",
" \n",
" for i in range(len(rows_to_change)):\n",
" new_dt = str(wake_times_dt_format[rows_to_change[i]-1].date() + timedelta(days=1)) + ' ' + str(avg_time(wake_times_dt_format).time()) \n",
" wake_times_dt_format.insert(rows_to_change[i],datetime.strptime(new_dt[0:16], '%Y-%m-%d %H:%M'))\n",
"\n",
" return wake_times_dt_format\n",
"\n",
"def find_and_replace_sleep_scores(sleep_duration_data, rows_to_change):\n",
"\n",
" sleep_duration_data = [i for j, i in enumerate(sleep_duration_data) if j not in rows_to_change]\n",
" \n",
" for i in range(len(rows_to_change)): \n",
" sleep_duration_data.insert(rows_to_change[i],np.mean(sleep_duration_data))\n",
"\n",
" return sleep_duration_data\n",
"\n",
"def find_and_replace_sleep_debt(sleep_debt_data, rows_to_change):\n",
" \n",
" sleep_debt_data = [i for j, i in enumerate(sleep_debt_data) if j not in rows_to_change]\n",
" \n",
" for i in range(len(rows_to_change)): \n",
" sleep_debt_data.insert(rows_to_change[i],0)\n",
"\n",
" return sleep_debt_data\n",
"\n",
"def find_and_replace_SC(bt_sleep_cons,wt_sleep_cons):\n",
" \n",
" rows_to_change = [] \n",
" \n",
" for i in range(len(bt_sleep_cons)):\n",
" if bt_sleep_cons[i]==1440 and wt_sleep_cons[i]== 1440:\n",
" rows_to_change.append(i)\n",
" rows_to_change\n",
" \n",
" if len(rows_to_change) == 0:\n",
" return bt_sleep_cons, wt_sleep_cons\n",
" \n",
" else:\n",
" \n",
" bt_sleep_cons_data = [i for j, i in enumerate(bt_sleep_cons) if j not in rows_to_change]\n",
" wt_sleep_cons_data = [i for j, i in enumerate(wt_sleep_cons) if j not in rows_to_change]\n",
" \n",
" new_bt = round(np.mean(bt_sleep_cons_data))\n",
" new_wt = round(np.mean(wt_sleep_cons_data))\n",
" \n",
" new_bt_sleep_cons_data = [new_bt if x==1440 else x for x in bt_sleep_cons]\n",
" new_wt_sleep_cons_data = [new_wt if x==1440 else x for x in wt_sleep_cons]\n",
"\n",
" \n",
" return new_bt_sleep_cons_data, new_wt_sleep_cons_data"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"rows_to_change = find_empty_rows(sleep_data_adj_len)\n",
"\n",
"#For graphs later on\n",
"labels_to_change = find_empty_rows(sleep_data_adj_len[-num_days_shown:], reset_index=True)\n",
"\n",
"#Converting Start and Stop times to datetime.datetime objects \n",
"## Use as Master List of Datetime.datetime for prediction later on, change col values for date to strings \n",
"wt_dt_form = [datetime.strptime(sleep_data_adj_len['WT'][i][0:19], '%Y-%m-%d %H:%M:%S') for i in range(len(sleep_data_adj_len))]\n",
"bt_dt_form = [datetime.strptime(sleep_data_adj_len['BT'][i][0:19], '%Y-%m-%d %H:%M:%S') for i in range(len(sleep_data_adj_len))]\n",
"\n",
"wt_dt_form = find_and_replace_empty_times(sleep_data_adj_len,wt_dt_form, rows_to_change=rows_to_change)\n",
"bt_dt_form = find_and_replace_empty_times(sleep_data_adj_len,bt_dt_form, rows_to_change=rows_to_change)\n",
"\n",
"#Converting from datetime.datetime objects --> Datetime format for table \n",
"\n",
"## S1: Convert datetime.datetime objects to unix timestamps\n",
"sleep_data_adj_len['BT'] = [bt_dt_form[i].timestamp() for i in range(len(sleep_data_adj_len))]\n",
"sleep_data_adj_len['WT'] = [wt_dt_form[i].timestamp() for i in range(len(sleep_data_adj_len))]\n",
"\n",
"sleep_data_adj_len['Date'] = [datetime.strftime(i, '%d/%m/%Y') for i in wt_dt_form]\n",
"\n",
"### Calculate Sleep duration while WT,BT while WT/BT data is float type\n",
"# print(sleep_data_adj_len['WT'][4:7].values)\n",
"# # print(sleep_data_adj_len['BT'][4:7].values)\n",
"\n",
"sleep_dur_mins_temp = [int(i) for i in (sleep_data_adj_len['WT'].values - sleep_data_adj_len['BT'].values)/60]\n",
"sleep_dur_hrs_temp = [round(i,2) for i in (sleep_data_adj_len['WT'].values - sleep_data_adj_len['BT'].values)/3600]\n",
"\n",
"sleep_data_adj_len['Sleep Duration Mins'] = [round(i) for i in find_and_replace_sleep_scores(sleep_dur_mins_temp, rows_to_change)]\n",
"sleep_data_adj_len['Sleep Duration Hrs'] = [round(i,2) for i in find_and_replace_sleep_scores(sleep_dur_hrs_temp, rows_to_change)]\n",
"sleep_debt_data = sleep_data_adj_len.apply(lambda row : round((row['Sleep Duration Hrs'] - 8),2),axis=1)\n",
"sleep_data_adj_len['Daily Sleep Debt'] = find_and_replace_sleep_debt(sleep_debt_data, rows_to_change)\n",
"\n",
"#Converting from Unixtimestamps to appropriate Datetime format for table \n",
"\n",
"## S2: Converting Unixtimestamps to Timestrings\n",
"sleep_data_adj_len['BT'] = [datetime.fromtimestamp(i).strftime('%H:%M') for i in sleep_data_adj_len['BT']]\n",
"sleep_data_adj_len['WT'] = [datetime.fromtimestamp(i).strftime('%H:%M') for i in sleep_data_adj_len['WT']]\n",
"\n",
"## S3: Converting Timestrings to Datetime format \n",
"sleep_data_adj_len['BT'] = [datetime.strptime(i,'%H:%M').time() for i in sleep_data_adj_len['BT']]\n",
"sleep_data_adj_len['WT'] = [datetime.strptime(i,'%H:%M').time() for i in sleep_data_adj_len['WT']]\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>deepSleepTime</th>\n",
" <th>shallowSleepTime</th>\n",
" <th>wakeTime</th>\n",
" <th>BT</th>\n",
" <th>WT</th>\n",
" <th>Sleep Duration Mins</th>\n",
" <th>Sleep Duration Hrs</th>\n",
" <th>Daily Sleep Debt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18/11/2021</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19:30:00</td>\n",
" <td>03:06:00</td>\n",
" <td>456</td>\n",
" <td>7.61</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15/11/2021</td>\n",
" <td>109</td>\n",
" <td>343</td>\n",
" <td>1</td>\n",
" <td>20:24:00</td>\n",
" <td>03:57:00</td>\n",
" <td>453</td>\n",
" <td>7.55</td>\n",
" <td>-0.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16/11/2021</td>\n",
" <td>80</td>\n",
" <td>238</td>\n",
" <td>2</td>\n",
" <td>18:54:00</td>\n",
" <td>01:54:00</td>\n",
" <td>420</td>\n",
" <td>7.00</td>\n",
" <td>-1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17/11/2021</td>\n",
" <td>114</td>\n",
" <td>316</td>\n",
" <td>9</td>\n",
" <td>19:12:00</td>\n",
" <td>03:28:00</td>\n",
" <td>496</td>\n",
" <td>8.27</td>\n",
" <td>0.27</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date deepSleepTime shallowSleepTime wakeTime BT WT \\\n",
"0 18/11/2021 0 0 0 19:30:00 03:06:00 \n",
"1 15/11/2021 109 343 1 20:24:00 03:57:00 \n",
"2 16/11/2021 80 238 2 18:54:00 01:54:00 \n",
"3 17/11/2021 114 316 9 19:12:00 03:28:00 \n",
"\n",
" Sleep Duration Mins Sleep Duration Hrs Daily Sleep Debt \n",
"0 456 7.61 0.00 \n",
"1 453 7.55 -0.45 \n",
"2 420 7.00 -1.00 \n",
"3 496 8.27 0.27 "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sleep_data_adj_len"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculating Sleep Consistency + SDD + Daily Sleep Score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <u>Formulas</u>"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"def convert_time(time_lst:list):\n",
" \"\"\"\n",
" Converts datetimes to integers so they are appropriately spaced based on the the 24hr clock.\n",
" \n",
" Any time between 0 - 12 converted to 24 hour clock:\n",
" e.g. 7:27 --> (24 + 7x60) + 27 ---> 1887\n",
" \n",
" Any other time (13 --> 23):\n",
" e.g. 21:35 --> (23*60) + 35 ---> 1295 \n",
" \n",
" \n",
" \"\"\"\n",
" converted_times = [] \n",
" \n",
" for i in range(len(time_lst)):\n",
"\n",
" if time_lst[i].hour == 0:\n",
" converted_time = 24*60 + time_lst[i].minute\n",
" converted_times.append(converted_time)\n",
" \n",
"\n",
" elif 0 < time_lst[i].hour < 12:\n",
" converted_time = (24 + time_lst[i].hour)*60 + time_lst[i].minute\n",
" converted_times.append(converted_time)\n",
" \n",
"\n",
" else:\n",
" converted_time = time_lst[i].hour*60 + time_lst[i].minute\n",
" converted_times.append(converted_time)\n",
"\n",
" return converted_times\n",
"\n",
"def daily_sleep_consistency(bed_time_list:list,wake_time_list:list):\n",
" \"\"\"\n",
" Calculates the Sleep Consistency based on the average BT/WT variability from the mean.\n",
" \n",
" \"\"\"\n",
" import numpy as np \n",
" \n",
" penalisation_factor = 5 \n",
" bt_mean = np.mean(bed_time_list)\n",
" wt_mean = np.mean(wake_time_list)\n",
" \n",
" bt_sub_mean = [] \n",
" wt_sub_mean = []\n",
" \n",
" for i in range(len(bed_time_list)):\n",
" bt_sub_mean.append(abs(bed_time_list[i] - bt_mean))\n",
" wt_sub_mean.append(abs(wake_time_list[i] - wt_mean))\n",
" \n",
" avg_bt_variability = np.mean(bt_sub_mean)/bt_mean\n",
" avg_wt_variability = np.mean(wt_sub_mean)/wt_mean\n",
" \n",
" daily_sleep_consistency = 100 - (((avg_bt_variability+avg_wt_variability)*100)*penalisation_factor)\n",
" \n",
" if daily_sleep_consistency < -100:\n",
" daily_sleep_consistency = -100\n",
" return round(daily_sleep_consistency,1)\n",
" \n",
" else:\n",
" return round(daily_sleep_consistency,1)\n",
" \n",
"def apply_sleep_consistency(wt_list:list, bt_list:list, print_text = True):\n",
" \"\"\"\n",
" Applies sleep consistency using BTs and WTs over the last 4 days. \n",
" \n",
" \"\"\"\n",
" daily_sleep_cons_lst_1 = []\n",
"\n",
" #1440 represents a complete 0 which will usually only occur in when theres missing sleep\n",
" # Replace this with \n",
" \n",
" [None if x==1440 else x for x in bt_list]\n",
" [None if x==1440 else x for x in wt_list]\n",
"\n",
"\n",
" \n",
" for i in range(len(wt_list)):\n",
"\n",
" if i == 0:\n",
"\n",
"# w_times = [wt_list[i]]\n",
"# b_times = [bt_list[i]]\n",
" daily_sleep_cons_lst_1.insert(i,daily_sleep_consistency(bed_time_list=[bt_list[i]],wake_time_list=[wt_list[i]]))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i))\n",
" print('Wake Time Range = {}'.format(max([wt_list[i]])-min([wt_list[i]])))\n",
" print('Bed Time Range = {}'.format(max([bt_list[i]])-min([bt_list[i]])))\n",
" print('Wake Times', [wt_list[i]])\n",
" print('Bed Times', [bt_list[i]])\n",
" print(\"Daily Sleep Consistency = {}\".format(daily_sleep_consistency(bed_time_list=[bt_list[i]],wake_time_list=[wt_list[i]])))\n",
" print('\\n')\n",
"\n",
" elif i == 1:\n",
"\n",
"# w_times_1 = [wt_list[i],wt_list[i-1]]\n",
"# b_times_1 = [bt_list[i],bt_list[i-1]]\n",
" \n",
" daily_sleep_cons_lst_1.insert(i,daily_sleep_consistency(bed_time_list=[bt_list[i],bt_list[i-1]],wake_time_list=[wt_list[i],wt_list[i-1]]))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i))\n",
" print('Wake Times', [wt_list[i],wt_list[i-1]])\n",
" print('Bed Times', [bt_list[i],bt_list[i-1]])\n",
" print('Wake Time Range = {}'.format(max([wt_list[i],wt_list[i-1]])-min([wt_list[i],wt_list[i-1]])))\n",
" print('Bed Time Range = {}'.format(max([bt_list[i],bt_list[i-1]])-min([bt_list[i],bt_list[i-1]])))\n",
" print(\"Daily Sleep Consistency = {}\".format(daily_sleep_consistency(bed_time_list=[bt_list[i],bt_list[i-1]],wake_time_list=[wt_list[i],wt_list[i-1]])))\n",
" print('\\n')\n",
"\n",
" elif i == 2 :\n",
"# w_times = [wt_list[i],wt_list[i-1], wt_list[i-2]]\n",
"# b_times = [bt_list[i],bt_list[i-1], bt_list[i-2]]\n",
" daily_sleep_cons_lst_1.insert(i,daily_sleep_consistency(bed_time_list=[bt_list[i],bt_list[i-1], bt_list[i-2]],wake_time_list=[wt_list[i],wt_list[i-1], wt_list[i-2]]))\n",
" \n",
" if print_text == True: \n",
" print('Day ' + str(i))\n",
" print('Wake Times', [wt_list[i],wt_list[i-1], wt_list[i-2]])\n",
" print('Bed Times', [bt_list[i],bt_list[i-1], bt_list[i-2]])\n",
" print('Wake Time Range = {}'.format(max([wt_list[i],wt_list[i-1], wt_list[i-2]])-min([wt_list[i],wt_list[i-1], wt_list[i-2]])))\n",
" print('Bed Time Range = {}'.format(max([bt_list[i],bt_list[i-1], bt_list[i-2]])-min([bt_list[i],bt_list[i-1], bt_list[i-2]])))\n",
" print(\"Daily Sleep Consistency = {}\".format(daily_sleep_consistency(bed_time_list=[bt_list[i],bt_list[i-1], bt_list[i-2]],wake_time_list=[wt_list[i],wt_list[i-1], wt_list[i-2]])))\n",
" print('\\n')\n",
"\n",
" else:\n",
" \n",
"# w_times = [wt_list[i],wt_list[i-1], wt_list[i-2], wt_list[i-3]] \n",
"# b_times = [bt_list[i],bt_list[i-1], bt_list[i-2], bt_list[i-3]] \n",
" daily_sleep_cons_lst_1.insert(i,daily_sleep_consistency(bed_time_list=[bt_list[i],bt_list[i-1], bt_list[i-2], bt_list[i-3]] ,wake_time_list=[wt_list[i],wt_list[i-1], wt_list[i-2], wt_list[i-3]]))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i))\n",
" print('Wake Times', [wt_list[i],wt_list[i-1], wt_list[i-2], wt_list[i-3]])\n",
" print('Bed Times', [bt_list[i],bt_list[i-1], bt_list[i-2], bt_list[i-3]] )\n",
" print('Wake Time Range = {}'.format(max([wt_list[i],wt_list[i-1], wt_list[i-2], wt_list[i-3]])-min([wt_list[i],wt_list[i-1], wt_list[i-2], wt_list[i-3]])))\n",
" print('Bed Time Range = {}'.format(max([bt_list[i],bt_list[i-1], bt_list[i-2], bt_list[i-3]] )-min([bt_list[i],bt_list[i-1], bt_list[i-2], bt_list[i-3]] )))\n",
" print(\"Daily Sleep Consistency = {}\".format(daily_sleep_consistency(bed_time_list=[bt_list[i],bt_list[i-1], bt_list[i-2], bt_list[i-3]] ,wake_time_list=[wt_list[i],wt_list[i-1], wt_list[i-2], wt_list[i-3]])))\n",
" print('\\n')\n",
" \n",
" return daily_sleep_cons_lst_1 \n",
"\n",
"def daily_SDD(sleep_duration:int,recommended_sleep:int,previous_3_days_sleep:list):\n",
" \"\"\"\n",
" Calculates the Daily SDD using recommended sleep duration, requires last 3 days of sleep to calculate.\n",
" \n",
" \"\"\"\n",
" w1 = 0.13833333 \n",
" \n",
" sleep_duration_mins = sleep_duration*60\n",
" recommended_sleep_duration = recommended_sleep*60\n",
" \n",
" recommended_sleep_4_days= recommended_sleep*(len(previous_3_days_sleep))\n",
" \n",
" total_previous_3_days_sleep_hours = sum(previous_3_days_sleep)\n",
" \n",
" sleep_debt_penalisation = (total_previous_3_days_sleep_hours-recommended_sleep_4_days)*2\n",
" \n",
" if 0 <=sleep_duration_mins<=480:\n",
" \n",
" if len(previous_3_days_sleep) == 0:\n",
" \n",
" sleep_duration_score = ((sleep_duration_mins/recommended_sleep_duration)*100)\n",
" return round(sleep_duration_score,1)\n",
" \n",
" else:\n",
" \n",
" if sleep_debt_penalisation > 0: \n",
" sleep_duration_score = ((sleep_duration_mins/recommended_sleep_duration)*100)\n",
" return round(sleep_duration_score,1)\n",
" \n",
" else:\n",
" sleep_duration_score = ((sleep_duration_mins/recommended_sleep_duration)*100) + sleep_debt_penalisation\n",
" return round(sleep_duration_score,1)\n",
"\n",
" elif 480 < sleep_duration_mins <= 540:\n",
" \n",
" if len(previous_3_days_sleep) == 0:\n",
" \n",
" sleep_duration_score = 200 - ((sleep_duration_mins/recommended_sleep_duration)*100)\n",
" return round(sleep_duration_score,1)\n",
" \n",
" else:\n",
" \n",
" if sleep_debt_penalisation > 0:\n",
" sleep_duration_score = 200 - ((sleep_duration_mins/recommended_sleep_duration)*100)\n",
" return round(sleep_duration_score, 1)\n",
"\n",
" else:\n",
" sleep_duration_score = 200 - ((sleep_duration_mins/recommended_sleep_duration)*100) + sleep_debt_penalisation\n",
" return round(sleep_duration_score, 1)\n",
"\n",
" elif 540 < sleep_duration_mins <= 1160:\n",
" \n",
" if len(previous_3_days_sleep) == 0:\n",
" s_debt_penalty = ((sleep_duration_mins/60) - recommended_sleep)*2\n",
" sleep_duration_score = (75 - ((sleep_duration_mins-600)*w1)) + s_debt_penalty\n",
" return round(sleep_duration_score,1)\n",
" \n",
" else:\n",
" \n",
" if sleep_debt_penalisation > 0:\n",
" sleep_duration_score = ((75 - (sleep_duration_mins-600)*w1)) + sleep_debt_penalisation\n",
" return round(sleep_duration_score, 1)\n",
" \n",
" else:\n",
" sleep_duration_score = ((75 - (sleep_duration_mins-600)*w1)) + sleep_debt_penalisation\n",
" return round(sleep_duration_score, 1)\n",
" \n",
" else:\n",
" return round(0,1)\n",
" \n",
"\n",
"def apply_SDD(rec_sleep_dur:int, sleep_duration_list:list, print_text=True):\n",
" \"\"\"\n",
" Applies the Daily SDD using sleep duration ussing previous 3 days sleep for each day.\n",
" \n",
" \"\"\"\n",
"\n",
" daily_SDD_scores = [] \n",
" \n",
" for i in range(len(sleep_duration_list)):\n",
" \n",
" if i == 0:\n",
"\n",
" sleep_duration = sleep_duration_list[i] \n",
" sleep_duration_times = []\n",
" daily_SDD_scores.append(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i+1))\n",
" print('Sleep Duration Times', sleep_duration_times)\n",
" print('Day ' + str(i+1), 'Sleep Duration = {}'.format(sleep_duration) )\n",
" print('Daily SDD = {}'.format(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times)))\n",
" print('\\n')\n",
" \n",
"\n",
" elif i == 1:\n",
" \n",
" sleep_duration_times = [sleep_duration_list[i-1]]\n",
" sleep_duration = sleep_duration_list[i]\n",
" daily_SDD_scores.append(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i+1))\n",
" print('Sleep Duration Times', sleep_duration_times)\n",
" print('Day ' + str(i+1), 'Sleep Duration = {}'.format(sleep_duration) )\n",
" print('Daily SDD = {}'.format(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times)))\n",
" print('\\n')\n",
" \n",
"\n",
" elif i == 2 :\n",
"\n",
" sleep_duration_times =[sleep_duration_list[i-1], sleep_duration_list[i-2]]\n",
" sleep_duration = sleep_duration_list[i] \n",
" daily_SDD_scores.append(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i+1))\n",
" print('Sleep Duration Times', sleep_duration_times)\n",
" print('Day ' + str(i+1), 'Sleep Duration = {}'.format(sleep_duration) )\n",
" print('Daily SDD = {}'.format(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times)))\n",
" print('\\n')\n",
"\n",
" else:\n",
"\n",
" sleep_duration_times =[sleep_duration_list[i-1], sleep_duration_list[i-2], sleep_duration_list[i-3]]\n",
" sleep_duration = sleep_duration_list[i]\n",
" daily_SDD_scores.append(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i+1))\n",
" print('Sleep Duration Times', sleep_duration_times)\n",
" print('Day ' + str(i+1), 'Sleep Duration = {}'.format(sleep_duration) )\n",
" print('Daily SDD = {}'.format(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times)))\n",
" print('\\n')\n",
" \n",
" return daily_SDD_scores"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Date', 'deepSleepTime', 'shallowSleepTime', 'wakeTime', 'BT', 'WT',\n",
" 'Sleep Duration Mins', 'Sleep Duration Hrs', 'Daily Sleep Debt'],\n",
" dtype='object')"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sleep_data_adj_len.columns"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"#Converting Times to format for Sleep Consistency, Applying SC formula, Inserting results\n",
"\n",
"##S1: Convert BT + WT to apporpiate nubmers\n",
"bt_sleep_cons = convert_time(sleep_data_adj_len['BT'].values)\n",
"wt_sleep_cons = convert_time(sleep_data_adj_len['WT'].values)\n",
"\n",
"##S2: Replace any missing data with mean of converted sleep conssistency values for BT and WT data \n",
"bt_consitency_data, wt_consistency_data = find_and_replace_SC(bt_sleep_cons=bt_sleep_cons, \n",
" wt_sleep_cons=wt_sleep_cons)\n",
"##S3:Apply SC formula to data \n",
"daily_SC_list = apply_sleep_consistency(wt_list=wt_consistency_data, \n",
" bt_list= bt_consitency_data, print_text=False)\n",
"##S3:Insert resultss to Dataframe \n",
"sleep_data_adj_len.insert(len(sleep_data_adj_len.columns), 'Daily Sleep Consistency',daily_SC_list)\n",
"\n",
"#Applying SDD formula, Inserting results\n",
"sleep_dur_hrs = sleep_data_adj_len['Sleep Duration Hrs'].values.flatten()\n",
"daily_SDD_list = apply_SDD(rec_sleep_dur=8,sleep_duration_list=sleep_dur_hrs, print_text=False)\n",
"sleep_data_adj_len.insert(len(sleep_data_adj_len.columns), 'Daily SDD',daily_SDD_list)\n",
"\n",
"#Calculating Overall Sleep Score, Applying sleep score formula across every row in the Table\n",
"sleep_data_adj_len['Daily Sleep Score'] = sleep_data_adj_len.apply(\n",
" lambda row : (row['Daily Sleep Consistency']*0.3) + (row['Daily SDD']*0.7), axis=1)\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"sleep_data_adj_len.drop(columns = ['deepSleepTime', 'shallowSleepTime', 'wakeTime'], inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Final Sleep Data Table"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>BT</th>\n",
" <th>WT</th>\n",
" <th>Sleep Duration Mins</th>\n",
" <th>Sleep Duration Hrs</th>\n",
" <th>Daily Sleep Debt</th>\n",
" <th>Daily Sleep Consistency</th>\n",
" <th>Daily SDD</th>\n",
" <th>Daily Sleep Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18/11/2021</td>\n",
" <td>19:30:00</td>\n",
" <td>03:06:00</td>\n",
" <td>456</td>\n",
" <td>7.61</td>\n",
" <td>0.00</td>\n",
" <td>100.0</td>\n",
" <td>95.1</td>\n",
" <td>96.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15/11/2021</td>\n",
" <td>20:24:00</td>\n",
" <td>03:57:00</td>\n",
" <td>453</td>\n",
" <td>7.55</td>\n",
" <td>-0.45</td>\n",
" <td>81.0</td>\n",
" <td>93.6</td>\n",
" <td>89.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16/11/2021</td>\n",
" <td>18:54:00</td>\n",
" <td>01:54:00</td>\n",
" <td>420</td>\n",
" <td>7.00</td>\n",
" <td>-1.00</td>\n",
" <td>73.0</td>\n",
" <td>85.8</td>\n",
" <td>81.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17/11/2021</td>\n",
" <td>19:12:00</td>\n",
" <td>03:28:00</td>\n",
" <td>496</td>\n",
" <td>8.27</td>\n",
" <td>0.27</td>\n",
" <td>77.3</td>\n",
" <td>92.9</td>\n",
" <td>88.22</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date BT WT Sleep Duration Mins Sleep Duration Hrs \\\n",
"0 18/11/2021 19:30:00 03:06:00 456 7.61 \n",
"1 15/11/2021 20:24:00 03:57:00 453 7.55 \n",
"2 16/11/2021 18:54:00 01:54:00 420 7.00 \n",
"3 17/11/2021 19:12:00 03:28:00 496 8.27 \n",
"\n",
" Daily Sleep Debt Daily Sleep Consistency Daily SDD Daily Sleep Score \n",
"0 0.00 100.0 95.1 96.57 \n",
"1 -0.45 81.0 93.6 89.82 \n",
"2 -1.00 73.0 85.8 81.96 \n",
"3 0.27 77.3 92.9 88.22 "
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sleep_data_adj_len"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prepping Data for Graphs "
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"def bt_conversion_graph(bed_times):\n",
" \"\"\"\n",
" Converts bed times into number that reprsents that times adjusted position on the graph.\n",
" datetime.time --> int \n",
" \n",
" \n",
" e.g. 22:24PM ---> -1.76\n",
" \n",
" \"\"\"\n",
" \n",
" time_lst = []\n",
" \n",
" for i in range(len(bed_times)):\n",
" if int(bed_times[i].strftime('%H%M'))/100 >=12:\n",
" time_lst.append((int(bed_times[i].strftime('%H%M'))/100)-24)\n",
" else:\n",
" time_lst.append((int(bed_times[i].strftime('%H%M'))/100))\n",
" \n",
" return time_lst"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"#MASTER VARIABLE TO CHANGE WHICH DECIDES HOW MANY OF THE PAST DAYS TO SHOW IN SLEEP CONSISTENCY GRAPH \n",
"\n",
"final_w_times = [(int(i.strftime('%H%M'))/100) for i in sleep_data_adj_len['WT'][-num_days_shown:].values]\n",
"final_b_times = bt_conversion_graph(sleep_data_adj_len['BT'][-num_days_shown:].values)\n",
"sleep_dur_times = [str(round(i,1)) + ' Hours' for i in sleep_data_adj_len['Sleep Duration Hrs'].values]\n",
" \n",
"bed_time_labels = [str(i)[0:5] for i in sleep_data_adj_len['BT'][-num_days_shown:].values]\n",
"wake_time_labels = [str(i)[0:5] for i in sleep_data_adj_len['WT'][-num_days_shown:].values]\n",
"\n",
"#Creating axis labels and annotation labels\n",
"sleep_dur_labels = [str(i).split('.')[0] + ' Hours' + '\\n' + str(int(int(str(round(i,1)).split('.')[1])/10*60)) + ' Mins' for i in sleep_data_adj_len['Sleep Duration Hrs'].values[-num_days_shown:]]\n",
"last7daylbls = [i.strftime('%A') for i in wt_dt_form[-num_days_shown:]]\n",
"date_labels = [i.strftime('%d/%m') for i in wt_dt_form[-num_days_shown:]]\n",
"final_date_labels = [last7daylbls[i] + '\\n(' + date_labels[i] + ')' for i in range(len(last7daylbls))]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"final_w_times = [i for j, i in enumerate(final_w_times) if j not in labels_to_change]\n",
"final_b_times = [i for j, i in enumerate(final_b_times) if j not in labels_to_change] \n",
"sleep_dur_labels = [i for j, i in enumerate(sleep_dur_labels) if j not in labels_to_change]\n",
"bed_time_labels = [i for j, i in enumerate(bed_time_labels) if j not in labels_to_change] \n",
"wake_time_labels = [i for j, i in enumerate(wake_time_labels) if j not in labels_to_change] \n",
"\n",
"for i in range(len(labels_to_change)): \n",
" final_b_times.insert(labels_to_change[i],0)\n",
" final_w_times.insert(labels_to_change[i],0)\n",
" bed_time_labels.insert(labels_to_change[i],'')\n",
" wake_time_labels.insert(labels_to_change[i],'')\n",
" sleep_dur_labels.insert(labels_to_change[i],'No\\nSleep\\nDetected')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting Sleep Consistency "
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"def plot_sleep_cons(w_times, b_times, bt_labels, wt_labels, sd_labels, fd_labels):\n",
" bt_labels_h = [0,0.7,0.7,0.8,0.75,0.75,0.75,0.75]\n",
" wt_labels_h = [0,0.4,0.4,0.4,0.4,0.4,0.4,0.4] \n",
" sd_labels_h = [0,3.3,4,3.3,3.4,3.5,3.5,3.5]\n",
"\n",
" bar_width_adj = [0,0.1,0.4,0.27,0.45,0.55,0.63,0.75]\n",
" bt_labels_adj = [0,0.03,0.14,.08,0.13,0.17,0.17,0.21]\n",
" wt_labels_adj = [0,0.03,0.14,0.08,0.13,0.17,0.17,0.21] \n",
" sd_labels_adj = [0,0.04,0.19,0.11,0.16,0.22,0.26,0.32]\n",
" \n",
" y_labels = ['16:00','18:00','20:00','22:00','00:00','02:00', '04:00', '06:00', '08:00', '10:00','12:00','14:00','16:00']\n",
" ytickss= [-8,-6,-4,-2,0,2,4,6,8,10,12,14,16]\n",
"\n",
" fig = plt.figure(figsize=(10,7.5))\n",
" ax = plt.subplot(111)\n",
"\n",
" ax.spines['top'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)\n",
"\n",
" plt.ylim(-6,16)\n",
" plt.xticks(rotation=0, fontsize=14)\n",
" plt.yticks(ticks = ytickss,labels=y_labels,fontsize=14)\n",
"# plt.title('Sleep Consistency from {} to {}'.format(date_labels[0],date_labels[-1]), pad=30,fontsize=18)\n",
" # plt.xlabel('Date', labelpad=20, fontsize=15, loc='center')\n",
" # plt.ylabel('Time', labelpad=40, fontsize=15, loc='center', rotation=0)\n",
" \n",
" if len(w_times) == 1:\n",
" ax.bar(0,0)\n",
" \n",
" if len(w_times) == 2: \n",
" ax.bar(0,0)\n",
" plt.gcf().subplots_adjust(left=0.01, right=0.35)\n",
"\n",
" upper = w_times\n",
" lower = b_times\n",
" height = [upper[i] - lower[i] for i in range(len(upper))]\n",
"\n",
" ax.bar(fd_labels, height, bottom=lower,color='mediumslateblue', width=bar_width_adj[num_days_shown],align='center')\n",
"\n",
" # print(num_of_days_to_show)\n",
" for i in range(num_days_shown):\n",
" ax.annotate(bt_labels[i],xy=(i-bt_labels_adj[num_days_shown],lower[i] - bt_labels_h[num_days_shown]),fontsize=14)\n",
" ax.annotate(wt_labels[i], xy= (i-wt_labels_adj[num_days_shown], upper[i] + wt_labels_h[num_days_shown]),fontsize=14)\n",
" ax.annotate(sd_labels[-(num_days_shown - i)], xy = (i-sd_labels_adj[num_days_shown], (upper[i]- height[i])+ sd_labels_h[num_days_shown]), fontsize=14)\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x540 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_sleep_cons(w_times=final_w_times, b_times=final_b_times, \n",
" wt_labels=wake_time_labels, bt_labels=bed_time_labels, \n",
" sd_labels=sleep_dur_labels, fd_labels=final_date_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <u>Calculating Weekly Consistency + SDD <u>"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def weekly_sleep_consistency(bed_time_list:list,wake_time_list:list):\n",
" import numpy as np \n",
" \n",
" bt_mean = np.mean(bed_time_list)\n",
" wt_mean = np.mean(wake_time_list)\n",
" \n",
" bt_sub_mean = [] \n",
" wt_sub_mean = []\n",
" \n",
" assert len(bed_time_list) == len(wake_time_list), f\" length of bed time list {len(bed_time_list)} not the same as wake time list {len(wake_time_list)}\"\n",
" \n",
" for i in range(len(bed_time_list)):\n",
" bt_sub_mean.append(abs(bed_time_list[i] - bt_mean))\n",
" wt_sub_mean.append(abs(wake_time_list[i] - wt_mean))\n",
" \n",
" avg_bt_variability = np.mean(bt_sub_mean)/bt_mean\n",
" avg_wt_variability = np.mean(wt_sub_mean)/wt_mean\n",
" \n",
" weekly_sleep_consistency = 100 - ((avg_bt_variability+avg_wt_variability)*100)*5\n",
" \n",
" return round(weekly_sleep_consistency,1)\n",
"\n",
"def weekly_SDD(recommended_sleep_duration:int,weeks_sleep:list):\n",
" \n",
" total_weeks_sleep_hours = sum(weeks_sleep)\n",
" total_weeks_sleep_mins = total_weeks_sleep_hours*60\n",
" penalisation_factor = 1.5\n",
" \n",
" recommended_sleep_duration_mins = recommended_sleep_duration*60 \n",
" recommended_sleep_duration_hours = recommended_sleep_duration\n",
" \n",
" sleep_debt_mins = total_weeks_sleep_mins-recommended_sleep_duration_mins\n",
" sleep_debt_hours = total_weeks_sleep_hours - recommended_sleep_duration_hours\n",
" \n",
" \n",
" assert len(weeks_sleep) == 7, f\"Not calculating the last 7 days inclusive but{len(weeks_sleep)}\"\n",
" \n",
" if 0 <=total_weeks_sleep_mins<=3360:\n",
" \n",
" weekly_SDD_score = ((total_weeks_sleep_mins/3360)*100) + (sleep_debt_hours*penalisation_factor)\n",
" \n",
" return round(weekly_SDD_score,1)\n",
" \n",
" elif 3360 < total_weeks_sleep_mins <= 5726:\n",
" \n",
" weekly_SDD_score = 200 - ((total_weeks_sleep_mins/3360)*100) - (sleep_debt_hours*penalisation_factor)\n",
" return round(weekly_SDD_score,2)\n",
" \n",
" else:\n",
" return round(0,1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def weekly_sleep_table(sleep_data):\n",
" \n",
" if len(sleep_data_adj_len) < 7:\n",
" print('Not Enough Data')\n",
" \n",
" return None\n",
" \n",
" else:\n",
" past7_days_Sleep_cons = weekly_sleep_consistency(bed_time_list=convert_time(sleep_data['BT'].values[-7:]), \n",
" wake_time_list=convert_time(sleep_data['WT'].values[-7:]))\n",
"\n",
"\n",
" past7_days_SDD = weekly_SDD(recommended_sleep_duration=56, \n",
" weeks_sleep=sleep_data['Sleep Duration Hrs'][-7:].values)\n",
"\n",
" print('Past 7 Days Sleep Consistency = {}'.format(past7_days_Sleep_cons), '\\n')\n",
" print('Past 7 Days SDD = {}'.format(past7_days_SDD), '\\n')\n",
" print('Past 7 Days Sleep Score = {}'.format(round(past7_days_Sleep_cons*0.3 + past7_days_SDD*0.7),1), '\\n')\n",
"\n",
" weekly_sleep_data = pd.DataFrame()\n",
" weekly_sleep_data['Week Dates'] = [i for i in range(len(sleep_data)//7)]\n",
" weekly_sleep_data['Weekly Sleep Consistency'] = [i for i in range(len(sleep_data)//7)]\n",
" weekly_sleep_data['Weekly SDD'] = [i for i in range(len(sleep_data)//7)]\n",
" weekly_sleep_data['Weekly Sleep Debt(Hrs)'] = [i for i in range(len(sleep_data)//7)]\n",
" weekly_sleep_data['Weekly Sleep Score'] = [i for i in range(len(sleep_data)//7)]\n",
"\n",
" if len(sleep_data)%7 ==0:\n",
" print('Full Week')\n",
"\n",
" for i in range(len(sleep_data)//7):\n",
" weekly_sleep_data['Week Dates'].iloc[i] = str(sleep_data['Date'].values[0+(i*7)]) + ' to '+ str(sleep_data['Date'].values[6+(i*7)])\n",
" weekly_sleep_data['Weekly Sleep Consistency'].iloc[i] = weekly_sleep_consistency(bed_time_list=convert_time(sleep_data['BT'][0+(i*7):7+(i*7)].values), \n",
" wake_time_list=convert_time(sleep_data['WT'][0+(i*7):7+(i*7)].values))\n",
" weekly_sleep_data['Weekly SDD'].iloc[i] = weekly_SDD(recommended_sleep_duration=56, weeks_sleep=sleep_data['Sleep Duration Hrs'][0+(i*7):7+(i*7)].values)\n",
" weekly_sleep_data['Weekly Sleep Debt(Hrs)'].iloc[i] = sum(sleep_data['Sleep Duration Hrs'][0+(i*7):7+(i*7)].values) - 56\n",
" weekly_sleep_data['Weekly Sleep Score'].iloc[i] = round(weekly_sleep_data['Weekly Sleep Consistency'][i]*0.3 + weekly_sleep_data['Weekly SDD'][i]*0.7,1)\n",
" return weekly_sleep_data \n",
"\n",
" else:\n",
" print('Do not have full {} weeks data, can only display {} weeks data'.format(int(len(sleep_data)/7+1),len(sleep_data)//7))\n",
"\n",
" for i in range(len(sleep_data)//7):\n",
" weekly_sleep_data['Week Dates'] = [str(sleep_data['Date'].values[0+(i*7)]) + ' to '+ str(sleep_data['Date'].values[6+(i*7)])]\n",
" weekly_sleep_data['Weekly Sleep Consistency'] = weekly_sleep_consistency(bed_time_list=convert_time(sleep_data['BT'][0+(i*7):7+(i*7)].values), wake_time_list=convert_time(sleep_data['WT'][0+(i*7):7+(i*7)].values))\n",
" weekly_sleep_data['Weekly SDD'] = weekly_SDD(recommended_sleep_duration=56, weeks_sleep=sleep_data['Sleep Duration Hrs'][0+(i*7):7+(i*7)].values)\n",
" weekly_sleep_data['Weekly Sleep Debt(Hrs)'] = sum(sleep_data['Sleep Duration Hrs'][0+(i*7):7+(i*7)].values) - 56\n",
" weekly_sleep_data['Weekly Sleep Score'] = round(weekly_sleep_data['Weekly Sleep Consistency'][i]*0.3 + weekly_sleep_data['Weekly SDD'][i]*0.7,1)\n",
" \n",
" return weekly_sleep_data "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"weekly_sleep_table(sleep_data_adj_len)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise data"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"def count_consec_ex_mins(listrand:list, consec_mins:int, print_txt= False):\n",
" \n",
" count=1\n",
" consec_list=[]\n",
" \n",
" #Count consecutives\n",
" for i in range(len(listrand[:-1])):\n",
" if listrand[i]+1 == listrand[i+1]:\n",
" count+=1\n",
" else:\n",
" consec_list.append(count)\n",
" count=1\n",
"\n",
" # Account for the last iteration\n",
" consec_list.append(count) \n",
" \n",
" final_lst = []\n",
" \n",
" for i in range(len(consec_list)):\n",
" if consec_list[i] > consec_mins:\n",
" final_lst.append(consec_list[i])\n",
" else:\n",
" continue\n",
" \n",
" if print_txt == True:\n",
" print(final_lst)\n",
" \n",
" return sum(final_lst)\n",
"\n",
"def daily_ex_score(vig_mins:int, mod_mins:int):\n",
" w1 = 3.72093023\n",
" w2 = 0.93023256\n",
" ex_mins = (vig_mins*2) + mod_mins\n",
" \n",
" if 0<=ex_mins<=21.5:\n",
" ex_score = ex_mins*w1\n",
" return round(ex_score,1)\n",
"\n",
" elif 21.5<ex_mins<=43:\n",
" ex_score = 80 + (ex_mins-21.5)*w2\n",
" return round(ex_score, 1)\n",
"\n",
" else:\n",
" return 100 \n",
"\n",
"def weekly_ex_score(vig_mins:int, mod_mins:int):\n",
" \n",
" ex_mins =(vig_mins*2)+mod_mins\n",
" w1 = 0.53333333\n",
" w2 = 0.13333333\n",
" \n",
" if 0<=ex_mins<=150:\n",
" ex_score = ex_mins*w1\n",
" return round(ex_score,1)\n",
"\n",
" elif 150<ex_mins<=300:\n",
" ex_score = 80 + (ex_mins-150)*w2\n",
" return round(ex_score, 1)\n",
"\n",
" else:\n",
" return 100"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"#Calculate Age\n",
"age_data = pd.read_csv(age_csv)\n",
"bday = datetime.strptime(age_data['birthday'][0], '%Y-%m')\n",
"today = date.today()\n",
"age = int(str(today)[0:4]) - int(str(bday)[0:4])\n",
"\n",
"#Calculate maximal HR and Vig and Mod HR thresholds\n",
"#Mod = 64% and Vig = 77% based on https://www.cdc.gov/physicalactivity/basics/measuring/heartrate.htm\n",
"\n",
"maximal_hr = 220-age\n",
"mod_thresh = int(maximal_hr*0.6)\n",
"vig_thresh = int(maximal_hr*0.75)\n",
"\n",
"#Load in Exercise data \n",
"ex_data_all = pd.read_csv(ex_csv)\n",
"dates = sorted(list(set(ex_data_all['date'])))[:]\n",
"# wake_time_int = [int(i.strftime('%H%M')) for i in ex_df['Wake Time'].values]\n",
"# w_times_bhr = [(int(str(i)[0:2])*60) + (int(str(i)[3:5])) for i in ex_df['Wake Time'].values]\n",
"\n",
"ex_df = pd.DataFrame()\n",
"ex_df['Date'] = sleep_data_adj_len['Date'].values\n",
"ex_df['Bed Time'] = sleep_data_adj_len['BT'].values\n",
"ex_df['Wake Time'] = sleep_data_adj_len['WT'].values\n",
"\n",
"total_ex_mins = [] \n",
"vig_ex_mins = [] \n",
"mod_ex_mins = [] \n",
"\n",
"#Filtering ex data and calculating consecutive mins above certain thresholds\n",
"consec_mins = 5\n",
"print_txt = False\n",
"for i in range(len(ex_df)):\n",
"# total_ex_mins.append(count_consec_ex_mins([i for i in ex_data_all[(ex_data_all['date'] == dates[i]) & (ex_data_all['heartRate'] >= mod_thresh)].index],consec_mins))\n",
" mod_ex_mins.append(count_consec_ex_mins([i for i in ex_data_all[(ex_data_all['date'] == dates[i]) & (ex_data_all['heartRate'] >= mod_thresh) & (vig_thresh >= ex_data_all['heartRate'])].index],consec_mins))\n",
" vig_ex_mins.append(count_consec_ex_mins([i for i in ex_data_all[(ex_data_all['date'] == dates[i]) & (ex_data_all['heartRate'] >= vig_thresh)].index],consec_mins))\n",
" total_ex_mins.append(count_consec_ex_mins([i for i in ex_data_all[(ex_data_all['date'] == dates[i]) & (ex_data_all['heartRate'] >= mod_thresh) & (vig_thresh >= ex_data_all['heartRate'])].index],consec_mins) + count_consec_ex_mins([i for i in ex_data_all[(ex_data_all['date'] == dates[i]) & (ex_data_all['heartRate'] >= vig_thresh)].index],consec_mins))\n",
"##Can use the following code to double check number of consecutive minutes being found per date \n",
"# print(dates[2])\n",
"# count_consec_ex_mins([i for i in ex_data_all[(ex_data_all['date'] == dates[2]) & (ex_data_all['heartRate'] >= mod_thresh)].index],consec_mins, print_txt=True)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/tomaszkostuch/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" iloc._setitem_with_indexer(indexer, value)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Bed Time</th>\n",
" <th>Wake Time</th>\n",
" <th>Exercise Mins</th>\n",
" <th>Moderate Ex Mins</th>\n",
" <th>Vig Ex Mins</th>\n",
" <th>Daily Ex Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>14/11/2021</td>\n",
" <td>07:27:00</td>\n",
" <td>14:04:00</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>29.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15/11/2021</td>\n",
" <td>21:35:00</td>\n",
" <td>07:05:00</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16/11/2021</td>\n",
" <td>22:40:00</td>\n",
" <td>06:36:00</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>22.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17/11/2021</td>\n",
" <td>22:12:00</td>\n",
" <td>06:31:00</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>33.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Bed Time Wake Time Exercise Mins Moderate Ex Mins \\\n",
"0 14/11/2021 07:27:00 14:04:00 8 8 \n",
"1 15/11/2021 21:35:00 07:05:00 7 7 \n",
"2 16/11/2021 22:40:00 06:36:00 6 6 \n",
"3 17/11/2021 22:12:00 06:31:00 9 9 \n",
"\n",
" Vig Ex Mins Daily Ex Score \n",
"0 0 29.8 \n",
"1 0 26 \n",
"2 0 22.3 \n",
"3 0 33.5 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Creating Ex Table\n",
"ex_df['Exercise Mins'] = total_ex_mins \n",
"ex_df['Moderate Ex Mins'] = mod_ex_mins\n",
"ex_df['Vig Ex Mins'] = vig_ex_mins\n",
"ex_df['Daily Ex Score'] = ''\n",
"\n",
"for i in range(len(ex_df)):\n",
" ex_df['Daily Ex Score'].iloc[i] = daily_ex_score(vig_mins=ex_df['Vig Ex Mins'].values[i], mod_mins=ex_df['Moderate Ex Mins'].values[i])\n",
"\n",
"ex_df"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"def weekly_ex_table(ex_df):\n",
" \n",
" if len(ex_df) < 7:\n",
" print('Not Enough Data')\n",
" return None\n",
" \n",
" else:\n",
" \n",
" weekly_ex_data = pd.DataFrame()\n",
" weekly_ex_data['Week'] = [i for i in range(len(ex_df)//7)]\n",
" weekly_ex_data['Total Ex Mins'] = [i for i in range(len(ex_df)//7)]\n",
" weekly_ex_data['Total Vig Mins'] = [i for i in range(len(ex_df)//7)]\n",
" weekly_ex_data['Total Mod Mins'] = [i for i in range(len(ex_df)//7)]\n",
" weekly_ex_data['Weekly Exercise Score'] = [i for i in range(len(ex_df)//7)]\n",
" weekly_ex_data['Average Exercise Mins per Day'] = [i for i in range(len(ex_df)//7)]\n",
"\n",
" if len(ex_df)%7 == 0:\n",
" print('Full {} Weeks Data'.format(int(len(ex_df)/7)))\n",
" for i in range(len(ex_df)//7):\n",
" weekly_ex_data['Week'].iloc[i]= str(ex_df['Date'].values[0+(i*7)]) + ' to '+ str(ex_df['Date'].values[0+(i*7)])\n",
" weekly_ex_data['Total Ex Mins'].iloc[i] = sum(ex_df['Exercise Mins'][0+(i*7):7+(i*7)])\n",
" weekly_ex_data['Total Vig Mins'].iloc[i] = sum(ex_df['Vig Ex Mins'][0+(i*7):7+(i*7)])\n",
" weekly_ex_data['Total Mod Mins'].iloc[i] = sum(ex_df['Moderate Ex Mins'][0+(i*7):7+(i*7)])\n",
" weekly_ex_data['Weekly Exercise Score'].iloc[i] = weekly_ex_score(vig_mins = weekly_ex_data['Total Vig Mins'].values[i], mod_mins=weekly_ex_data['Total Mod Mins'].values[i])\n",
" weekly_ex_data['Average Exercise Mins per Day'].iloc[i] = round(weekly_ex_data['Total Ex Mins'][i]/7,1)\n",
"\n",
" return weekly_ex_data \n",
"\n",
" else:\n",
" print('Do not have full {} weeks data, can only display {} weeks data'.format(int(len(ex_df)/7+1),len(ex_df)//7))\n",
"\n",
" for i in range(len(ex_df)//7):\n",
" weekly_ex_data['Week'].iloc[i] = str(ex_df['Date'].values[0+(i*7)]) + ' to '+ str(ex_df['Date'].values[6+(i*7)])\n",
" weekly_ex_data['Total Ex Mins'].iloc[i] = sum(ex_df['Exercise Mins'][0+(i*7):7+(i*7)])\n",
" weekly_ex_data['Total Vig Mins'].iloc[i] = sum(ex_df['Vig Ex Mins'][0+(i*7):7+(i*7)])\n",
" weekly_ex_data['Total Mod Mins'].iloc[i] = sum(ex_df['Moderate Ex Mins'][0+(i*7):7+(i*7)])\n",
" weekly_ex_data['Weekly Exercise Score'].iloc[i] = weekly_ex_score(vig_mins = weekly_ex_data['Total Vig Mins'].values[i], mod_mins=weekly_ex_data['Total Mod Mins'].values[i])\n",
" weekly_ex_data['Average Exercise Mins per Day'].iloc[i] = round(weekly_ex_data['Total Ex Mins'][i]/7,1)\n",
"\n",
"\n",
" return weekly_ex_data "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"weekly_ex_table(ex_df)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>BT</th>\n",
" <th>WT</th>\n",
" <th>Sleep Duration Mins</th>\n",
" <th>Sleep Duration Hrs</th>\n",
" <th>Daily Sleep Debt</th>\n",
" <th>Daily Sleep Consistency</th>\n",
" <th>Daily SDD</th>\n",
" <th>Daily Sleep Score</th>\n",
" <th>Exercise Mins</th>\n",
" <th>Moderate Ex Mins</th>\n",
" <th>Vig Ex Mins</th>\n",
" <th>Daily Ex Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18/11/2021</td>\n",
" <td>19:30:00</td>\n",
" <td>03:06:00</td>\n",
" <td>456</td>\n",
" <td>7.61</td>\n",
" <td>0.00</td>\n",
" <td>100.0</td>\n",
" <td>95.1</td>\n",
" <td>96.57</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15/11/2021</td>\n",
" <td>20:24:00</td>\n",
" <td>03:57:00</td>\n",
" <td>453</td>\n",
" <td>7.55</td>\n",
" <td>-0.45</td>\n",
" <td>81.0</td>\n",
" <td>93.6</td>\n",
" <td>89.82</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16/11/2021</td>\n",
" <td>18:54:00</td>\n",
" <td>01:54:00</td>\n",
" <td>420</td>\n",
" <td>7.00</td>\n",
" <td>-1.00</td>\n",
" <td>73.0</td>\n",
" <td>85.8</td>\n",
" <td>81.96</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17/11/2021</td>\n",
" <td>19:12:00</td>\n",
" <td>03:28:00</td>\n",
" <td>496</td>\n",
" <td>8.27</td>\n",
" <td>0.27</td>\n",
" <td>77.3</td>\n",
" <td>92.9</td>\n",
" <td>88.22</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>22.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date BT WT Sleep Duration Mins Sleep Duration Hrs \\\n",
"0 18/11/2021 19:30:00 03:06:00 456 7.61 \n",
"1 15/11/2021 20:24:00 03:57:00 453 7.55 \n",
"2 16/11/2021 18:54:00 01:54:00 420 7.00 \n",
"3 17/11/2021 19:12:00 03:28:00 496 8.27 \n",
"\n",
" Daily Sleep Debt Daily Sleep Consistency Daily SDD Daily Sleep Score \\\n",
"0 0.00 100.0 95.1 96.57 \n",
"1 -0.45 81.0 93.6 89.82 \n",
"2 -1.00 73.0 85.8 81.96 \n",
"3 0.27 77.3 92.9 88.22 \n",
"\n",
" Exercise Mins Moderate Ex Mins Vig Ex Mins Daily Ex Score \n",
"0 0 0 0 0.0 \n",
"1 0 0 0 0.0 \n",
"2 0 0 0 0.0 \n",
"3 6 6 0 22.3 "
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_daily_data_table = pd.merge(sleep_data_adj_len,\n",
" ex_df[['Exercise Mins', 'Moderate Ex Mins', 'Vig Ex Mins', 'Daily Ex Score']],\n",
" left_index=True, right_index=True)\n",
"\n",
"final_daily_data_table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sleep Debt Positive + Negative "
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"def add_accumulated_sleep_debt(df):\n",
"\n",
" asd = [] \n",
"\n",
" df.insert(6,'Sleep Debt(Neg)', '')\n",
" df.insert(7,'Sleep Debt(Pos)', '')\n",
"\n",
" for i in range(len(df)):\n",
" asd.append(sum(df['Daily Sleep Debt'][:i+1]))\n",
"\n",
" if df['Daily Sleep Debt'][i] > 0:\n",
" df['Sleep Debt(Pos)'][i] = df['Daily Sleep Debt'][i]\n",
" df['Sleep Debt(Neg)'][i] = 0 \n",
"\n",
" else:\n",
" df['Sleep Debt(Pos)'][i] = 0 \n",
" df['Sleep Debt(Neg)'][i] = df['Daily Sleep Debt'][i]\n",
"\n",
" df.insert(8,'ASD',asd)\n",
" \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "cannot insert Sleep Debt(Neg), already exists",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [89]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m final_daily_data_table \u001b[38;5;241m=\u001b[39m \u001b[43madd_accumulated_sleep_debt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfinal_daily_data_table\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m final_daily_data_table\n",
"Input \u001b[0;32mIn [62]\u001b[0m, in \u001b[0;36madd_accumulated_sleep_debt\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_accumulated_sleep_debt\u001b[39m(df):\n\u001b[1;32m 3\u001b[0m asd \u001b[38;5;241m=\u001b[39m [] \n\u001b[0;32m----> 5\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSleep Debt(Neg)\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m df\u001b[38;5;241m.\u001b[39minsert(\u001b[38;5;241m7\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSleep Debt(Pos)\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(df)):\n",
"File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/frame.py:4442\u001b[0m, in \u001b[0;36mDataFrame.insert\u001b[0;34m(self, loc, column, value, allow_duplicates)\u001b[0m\n\u001b[1;32m 4436\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 4437\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot specify \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mallow_duplicates=True\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m when \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4438\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mself.flags.allows_duplicate_labels\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is False.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4439\u001b[0m )\n\u001b[1;32m 4440\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_duplicates \u001b[38;5;129;01mand\u001b[39;00m column \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[1;32m 4441\u001b[0m \u001b[38;5;66;03m# Should this be a different kind of error??\u001b[39;00m\n\u001b[0;32m-> 4442\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot insert \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcolumn\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, already exists\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4443\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m 4444\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloc must be int\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[0;31mValueError\u001b[0m: cannot insert Sleep Debt(Neg), already exists"
]
}
],
"source": [
"final_daily_data_table = add_accumulated_sleep_debt(final_daily_data_table)\n",
"final_daily_data_table"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Send Data? (Yes/No): yes\n"
]
}
],
"source": [
"webhook_send(final_daily_data_table)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot Exercise vs time "
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"def plot_ex(df, num_of_days_to_show:int):\n",
" \n",
" #Adjustments for graphs\n",
" bar_adj_2 = [0,0.07,0.17,0.27,0.32,0.45,0.55,0.75]\n",
" ant_adj = [0,0.0225,0.059,0.095,0.12,0.15,0.17,0.21]\n",
" optimal_adj_2 = [0,0.02,0.05,0.1,0.12,0.13,0.13,0.2] \n",
" sufficient_adj_2 = [0,0.022,0.052,0.112,0.14,0.14,0.14,0.24] \n",
" low_adj_2 = [0,0.013,0.032,0.07,0.08,0.08,0.08,0.13] \n",
" \n",
" df = df[-num_of_days_to_show:]\n",
" df.reset_index(drop=True, inplace=True)\n",
" df.insert(0,'Date Labels', final_date_labels)\n",
" \n",
" \n",
" fig, axes = plt.subplots(2,1, sharex=True,figsize=(10,10))\n",
" c_map_1 = {'Moderate Ex Mins':'seagreen', 'Vig Ex Mins':'salmon'}\n",
" \n",
" \n",
" if len(df) < 2:\n",
" df[-num_of_days_to_show:].plot(kind='line', x='Date Labels', y= 'Daily Ex Score', ax=axes[0], \n",
" marker='o', markersize=5)\n",
" else:\n",
" df[-num_of_days_to_show:-1].plot(kind='line', x='Date Labels', y= 'Daily Ex Score', ax=axes[0], \n",
" marker='o', markersize=5)\n",
"\n",
" sns.despine()\n",
"\n",
" axes[1].set_ylim(0,140)\n",
" axes[0].set_ylim(-1.5,101)\n",
" axes[1].tick_params(axis='x', labelsize=15)\n",
" axes[0].tick_params(axis='y', labelsize=15)\n",
" axes[1].tick_params(axis='y', labelsize=15)\n",
" \n",
" x = [-1]+[i for i in range(num_of_days_to_show)]+[num_of_days_to_show+1]\n",
" red_zone = [50]*(num_of_days_to_show+2)\n",
" yellow_zone= [75]*(num_of_days_to_show+2)\n",
"\n",
"\n",
" #Shading Areas behind the graph \n",
" axes[0].fill_between(x, red_zone, -1.5,\n",
" facecolor=\"orange\", # The fill color\n",
" color='red', # The outline color\n",
" alpha=0.2)\n",
"\n",
" axes[0].fill_between(x, red_zone, 75,\n",
" facecolor=\"orange\", # The fill color\n",
" color='yellow', # The outline color\n",
" alpha=0.2)\n",
"\n",
" axes[0].fill_between(x, yellow_zone, 100,\n",
" facecolor=\"orange\", # The fill color\n",
" color='green', # The outline color\n",
" alpha=0.2)\n",
"\n",
" colors = ['acquamarine', 'lime']\n",
"\n",
" le = df[-num_of_days_to_show:].plot(kind='bar', stacked='True', \n",
" x='Date Labels', y = ['Moderate Ex Mins','Vig Ex Mins'],\n",
" ax=axes[1],width=bar_adj_2[num_of_days_to_show], rot=0, color=c_map_1, xlabel='')\n",
"\n",
"\n",
" for i in range(num_of_days_to_show):\n",
" axes[1].annotate(str(df['Exercise Mins'][-num_of_days_to_show:][i]) + ' Mins',\n",
" xy=(i-ant_adj[num_of_days_to_show],df['Exercise Mins'][-num_of_days_to_show:][i]+2),fontsize=12)\n",
"\n",
" axes[0].annotate('Optimal', xy=(((axes[0].get_xlim()[0] + axes[0].get_xlim()[1])/2)-optimal_adj_2[num_of_days_to_show],95), size=12)\n",
" axes[0].annotate('Sufficient', xy=(((axes[0].get_xlim()[0] + axes[0].get_xlim()[1])/2)-sufficient_adj_2[num_of_days_to_show],62.5), size=12)\n",
" axes[0].annotate('Low', xy=(((axes[0].get_xlim()[0] + axes[0].get_xlim()[1])/2)-low_adj_2[num_of_days_to_show], 30), size=12)\n",
" axes[1].grid(axis='y', linewidth=0.07)\n",
" \n",
" return fig, axes"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(<Figure size 720x720 with 2 Axes>,\n",
" array([<AxesSubplot:xlabel='Date Labels'>, <AxesSubplot:>], dtype=object))"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_ex(final_daily_data_table, num_days_shown)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot Sleep vs time "
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"def plot_sleep_time(df, num_of_days_to_show):\n",
" \n",
" #Graph adjustments\n",
" optimal_adj_3 = [0,-0.12,-0.09,-0.07,-0.07,0,0,0] \n",
" sufficient_adj_3 = [0,-0.119,-0.085,-0.06,-0.04,0.04,0.04,0.04] \n",
" low_adj_3 = [0,-0.125,-0.11,-0.1,-0.1,-0.08,-0.08,-0.07] \n",
" bar_adj_3 = [0,0.05,0.125,0.175,0.2,0.25,0.35,0.45]\n",
" \n",
" c_map = {'Sleep Debt(Pos)':'green', 'Sleep Debt(Neg)':'red'}\n",
"\n",
" fig, axes = plt.subplots(2,1, sharex=True,figsize=(10,10))\n",
"\n",
" df = df[-num_of_days_to_show:]\n",
" df.reset_index(drop=True, inplace=True)\n",
" df.insert(0,'Date Labels', final_date_labels)\n",
" \n",
" df[-num_of_days_to_show:].plot(kind='line', x = 'Date Labels', y = 'Daily Sleep Score', ax=axes[0],\n",
" marker='o', markersize=5, )\n",
"\n",
" df.plot.area(x='Date', y='ASD', ax=axes[1], style='-o', alpha=0.3, stacked=False)\n",
"\n",
" df[-num_of_days_to_show:].plot(kind='bar', x='Date Labels', y = ['Sleep Debt(Pos)','Sleep Debt(Neg)'], \n",
" width=bar_adj_3[num_of_days_to_show],align='center', ax=axes[1], color=c_map, stacked=True, \n",
" rot=0)\n",
"\n",
"\n",
" sns.despine()\n",
" axes[0].set_ylim(0,101)\n",
" axes[1].set_ylim(-5,5)\n",
" # axes[0].set_ylabel('Daily \\n Sleep \\n Performance', rotation=0, labelpad=30, size=13)\n",
" # axes[1].set_ylabel('Hours', rotation=0, \n",
" # labelpad=25, size=13)\n",
" # axes[1].set_xlabel('Date',labelpad=15, size=15)\n",
" axes[1].tick_params(axis='x', labelsize=14)\n",
" axes[0].tick_params(axis='y', labelsize=14)\n",
" axes[1].tick_params(axis='y', labelsize=14)\n",
" axes[1].grid(axis='y', linewidth=0.07)\n",
"\n",
" x = [-1]+[i for i in range(num_of_days_to_show)]+[num_of_days_to_show+1]\n",
" red_zone = [50]*(num_of_days_to_show+2)\n",
" yellow_zone= [75]*(num_of_days_to_show+2)\n",
"\n",
"\n",
" #Shading Areas behind the graph \n",
" axes[0].fill_between(x, red_zone, 0,\n",
" facecolor=\"orange\", # The fill color\n",
" color='red', # The outline color\n",
" alpha=0.2)\n",
"\n",
" axes[0].fill_between(x, red_zone, 75,\n",
" facecolor=\"orange\", # The fill color\n",
" color='yellow', # The outline color\n",
" alpha=0.2)\n",
"\n",
" axes[0].fill_between(x, yellow_zone, 100,\n",
" facecolor=\"orange\", # The fill color\n",
" color='green', # The outline color\n",
" alpha=0.2)\n",
"\n",
" axes[0].annotate('Optimal', xy=((axes[0].get_xlim()[0] + axes[0].get_xlim()[1]/2 -optimal_adj_3[num_of_days_to_show],97)), size=13)\n",
" axes[0].annotate('Sufficient', xy=((axes[0].get_xlim()[0] + axes[0].get_xlim()[1]/2 - sufficient_adj_3[num_of_days_to_show],62.5)), size=13)\n",
" axes[0].annotate('Low', xy=((axes[0].get_xlim()[0] + axes[0].get_xlim()[1]/2 - low_adj_3[num_of_days_to_show],30)), size=13)\n",
" \n",
" return fig, axes"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(<Figure size 720x720 with 2 Axes>,\n",
" array([<AxesSubplot:xlabel='Date Labels'>,\n",
" <AxesSubplot:xlabel='Date Labels'>], dtype=object))"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_sleep_time(final_daily_data_table, num_days_shown)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Final Weekly Dataset"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"weekly_sleep = weekly_sleep_table(sleep_data_adj_len)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"weekly_ex = weekly_ex_table(ex_df)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"weekly_ex"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"def final_weekly_table(weekly_ex, weekly_sleep):\n",
" \n",
" if ((weekly_ex is None) and (weekly_sleep is None)):\n",
" \n",
" print('Not Enough Data')\n",
" \n",
" return None\n",
" else:\n",
" \n",
" if len(weekly_ex) > 0:\n",
"\n",
" weekly_ex_data_final = weekly_ex.drop('Week', axis=1)\n",
" weekly_data_all = pd.merge(weekly_sleep,weekly_ex, left_index=True, right_index=True)\n",
" weekly_data_all.columns\n",
"\n",
" else:\n",
" weekly_data_all = pd.merge(weekly_sleep_data,weekly_ex_data, left_index=True, right_index=True)\n",
"\n",
" weekly_data_all.drop('Week', axis=1, inplace=True) \n",
" \n",
" return weekly_data_all\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"final_weekly_table(weekly_ex, weekly_sleep)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predicting Performance "
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"def roundTime(dt=None, roundTo=60):\n",
" \"\"\"Round a datetime object to any time lapse in seconds\n",
" dt : datetime.datetime object, default now.\n",
" roundTo : Closest number of seconds to round to, default 1 minute.\n",
" Author: Thierry Husson 2012 - Use it as you want but don't blame me.\n",
" \"\"\"\n",
" if dt == None : dt = datetime.datetime.now()\n",
" seconds = (dt.replace(tzinfo=None) - dt.min).seconds\n",
" rounding = (seconds+roundTo/2) // roundTo * roundTo\n",
" return dt + timedelta(0,rounding-seconds,-dt.microsecond)\n",
"\n",
"def create_sleep_cycle_df(df, bt_dt_form:list, wt_dt_form:list, days_to_calculate:int, avg_cycle_start_print=False):\n",
" \"\"\"\n",
" Calculates Sleep Midpoints, Cycle Starts and Sleep Chronotype. \n",
" \n",
" Returns a pandas dataframe with relvant data and the average cycle start time. \n",
" \n",
" \"\"\"\n",
" from statistics import mode\n",
" \n",
" sleep_duration_halved = [i for i in round(df['Sleep Duration Hrs']/2,1)]\n",
"\n",
" sleep_cycle = pd.DataFrame()\n",
" sleep_midpoints = [] \n",
"\n",
" #Calculating sleep midpoints by adding half of the sleep duration to the bedtime\n",
" for i in range(len(df)):\n",
" sleep_midpoints.append(bt_dt_form[i]+ timedelta(hours=int(str(sleep_duration_halved[i]).split('.')[0]), minutes=int(int(str(sleep_duration_halved[i]).split('.')[1])*0.1*60))) \n",
"\n",
" sleep_cycle['Date'] = [i.date() for i in wt_dt_form]\n",
" sleep_cycle['Day'] = [i.strftime('%A') for i in sleep_cycle['Date']]\n",
" sleep_cycle['Sleep Midpoint'] = [i.time() for i in sleep_midpoints]\n",
" cycle_starts = [roundTime(i,roundTo=3600) for i in wt_dt_form]\n",
" sleep_cycle['Cycle Starts'] = [i.time() for i in cycle_starts]\n",
" act_cycle_starts = [i for i in wt_dt_form]\n",
" sleep_cycle['Actual Cycle Starts'] = act_cycle_starts\n",
" \n",
" #Calculating Avg Cycle Start Time \n",
" avg_cycle_start = roundTime(avg_time(sleep_cycle['Cycle Starts']),roundTo=1800)\n",
" \n",
" if avg_cycle_start_print == True: \n",
" print('Average Cycle Start Time = {}'.format(avg_cycle_start.time()),'\\n')\n",
"\n",
" #Calculating Sleep Chronotype\n",
" sleep_cycle['Sleeping Chronotype'] = ''\n",
"\n",
" three_am = datetime(6,1,2,3,0).time()\n",
" six_am = datetime(6,1,2,6,0).time()\n",
" midnight = datetime(6,1,2,0,0).time()\n",
"\n",
" sleep_chrono_type = []\n",
" \n",
" for i in range(len(sleep_midpoints)):\n",
"\n",
" if midnight > sleep_midpoints[i].time() > six_am:\n",
" sleep_chrono_type.append('Night Owl')\n",
"\n",
" elif three_am > sleep_midpoints[i].time()> midnight:\n",
" sleep_chrono_type.append('Lark')\n",
"\n",
" else:\n",
" sleep_chrono_type.append('Third Bird')\n",
"\n",
"\n",
" sleep_cycle['Sleeping Chronotype'] = sleep_chrono_type\n",
" \n",
" if len(sleep_cycle) < days_to_calculate:\n",
" print('Your Sleep Chronotype is currently being determined, ready in {} days'.format(days_to_calculate - len(sleep_cycle)))\n",
" \n",
" else:\n",
" print('Your Sleeping Type is {}'.format(mode(list(sleep_cycle['Sleeping Chronotype'].values))))\n",
" \n",
" \n",
" return sleep_cycle, avg_cycle_start\n",
"\n",
"def calculate_avg_cs_per_day(df):\n",
" \"\"\"\n",
" Calculates the average cycle starts per each day of the week.\n",
" \n",
" Returns Datagrame with results and the dictionary with the values \n",
" \"\"\"\n",
" \n",
" days_of_week = list(set(sleep_cycle_df['Day'].values))\n",
" avg_cycle_starts_per_day = {} \n",
" dict_list_cycle_starts = {} \n",
"\n",
" #Calculating the average and rounding then storeing in Dictionary\n",
" for i in range(len(days_of_week)):\n",
"\n",
" #Dict with the averages for each day \n",
" avg_cycle_starts_per_day[days_of_week[i]]= roundTime(avg_time(df[sleep_cycle_df['Day'] == days_of_week[i]]['Actual Cycle Starts']), roundTo=1800)\n",
"\n",
" #Dict with list of cycle starts for each day \n",
" dict_list_cycle_starts[days_of_week[i]] = df[sleep_cycle_df['Day'] == days_of_week[i]]['Actual Cycle Starts']\n",
"\n",
" #Creating DF\n",
" avg_cycle_per_day= pd.DataFrame.from_dict(data = {'Avg Cycle Start Time':[i.time() for i in avg_cycle_starts_per_day.values()], \n",
" 'Day':avg_cycle_starts_per_day.keys()})\n",
" return avg_cycle_per_day,avg_cycle_starts_per_day\n",
" \n",
"def calculating_hrly_perf_capacity(sleep_df, final_daily_df, sleep_cycle_df):\n",
" \"\"\"\n",
" Calculates the performance capacity values for every half hour of the day using sleep and exercise data.\n",
" \n",
" \"\"\"\n",
" \n",
" ## ThirdBird Graph preset built of someone who's sleep midpoint is 3 and sleeps total of 8 hours so cycle starts at 7 \n",
" ### Will have to shift numbers accordingly\n",
" ### e.g. if someones cycles starts at 5 and are 3rdbird then has to shift by -2\n",
" #### Amount to shift array = cycle start - 7 \n",
" ##### From 7AM to 6AM(Next Day)\n",
" lark_third_bird_hardcoded = [50,57.5,65,75,90,100,90,75,50,60,70,80,85,80,70,60,50,45,30,25,25,25,30,40]\n",
"\n",
"\n",
" ## NightOwl Graph preset built of someone who's sleep midpoint is 6 and sleeps total of 8 hours so cycle starts at 10 \n",
" ### Will have to shift numbers accordingly\n",
" ### e.g. if someones cycles starts at 7 and are nightowl then has to shift by +1\n",
" #### Amount to shift array = cycle start - 6 \n",
" #####These start from 5AM to 4AM(Next Day)\n",
" night_owl_harcoded = [25.0,25.0,30.0,45.0,50.0,60.0,70.0,80.0,85.0,80.0,70.0,60.0,50.0,75.0,90.0,100.0,90.0,75.0,65.0,57.5,50.0,40.0,30.0,25.0]\n",
"\n",
" last_7_days = sleep_df[-6:]\n",
" prdikt_perf_capacity = round((last_7_days['Daily Sleep Score'].values[-1]*0.7)+ (weekly_ex_score(mod_mins = sum(final_daily_df['Moderate Ex Mins']), vig_mins = sum(final_daily_df['Vig Ex Mins']))*0.3),1)\n",
"\n",
" print('Sleep Performance = ', last_7_days['Daily Sleep Score'].values[-1])\n",
" print('Ex Performance = ', weekly_ex_score(mod_mins = sum(final_daily_df['Moderate Ex Mins']), vig_mins = sum(final_daily_df['Vig Ex Mins'])))\n",
" print('Prdikt Perforamnce Capacity = ', prdikt_perf_capacity)\n",
"\n",
" # hrly_perf_capacity = [round((prdikt_perf_capacity*0.01*i),1) for i in lark_third_bird_hardcoded]\n",
" # hrly_perf_capacity\n",
"\n",
" lisst = list(sleep_cycle_df['Sleeping Chronotype'].values)\n",
" print('Mode =',mode(lisst))\n",
"\n",
" #Deciding which hourly performance capacity values to use based on most occuring \n",
" ##This step may need to change to when graphs are produced \n",
" ###My logic thinks if you are more than often sleeping like a lark then your circadian ryhytm will adjust to this \n",
"\n",
" if mode(lisst) == 'Lark':\n",
" hrly_perf_capacity = [round((prdikt_perf_capacity*0.01*i),1) for i in lark_third_bird_hardcoded]\n",
"\n",
" elif mode(lisst) == 'Third Bird':\n",
" hrly_perf_capacity = [round((prdikt_perf_capacity*0.01*i),1) for i in lark_third_bird_hardcoded]\n",
" else:\n",
" hrly_perf_capacity = [round((prdikt_perf_capacity*0.01*i),1) for i in night_owl_harcoded]\n",
"\n",
"\n",
" return hrly_perf_capacity\n",
"\n",
"def plot_todays_perf_curve(y, sleep_cycle_df, rows_to_change):\n",
" \n",
" \"\"\"\n",
" \n",
" Plots todays predicted performance capacity. \n",
" \n",
" \"\"\"\n",
" #More data entries we are missing the creater the error shading(purple)will become \n",
" deteoriation_factor = [0.01,0.025,0.05, 0.075,0.1,0.125,0.15, 0.175, 0.2, 0.225]\n",
" upper_bound = [round((deteoriation_factor[len(rows_to_change)]*i)+i,1) for i in hrly_perf_capacity]\n",
" lower_bound = [round(i-(deteoriation_factor[len(rows_to_change)]*i),1) for i in hrly_perf_capacity]\n",
" \n",
" x = [str((avg_time(sleep_cycle_df['Cycle Starts']) + timedelta(hours=j)).time())[:5] for j in range(len(y))]\n",
" \n",
" plt.figure(figsize=(25,15))\n",
" plt.ylim(0,100)\n",
" plt.scatter(x,y, s=150, c=y,cmap ='RdYlGn',alpha=1)\n",
" plt.xticks(size=20, rotation=45)\n",
" plt.yticks(ticks = [i for i in range(0,110,10)],labels =[i for i in range(0,110,10)], size=20)\n",
" plt.tick_params(axis='y', labelsize=25)\n",
" plt.tick_params(axis='x', labelsize=25)\n",
" plt.fill_between(x, lower_bound, upper_bound, alpha=0.3, color='Purple')\n",
" plt.title('Today\\'s Predicted Performance',size=30)\n",
" sns.despine()\n",
" plt.grid(axis='x', linewidth=0.07)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Calculate all Sleep Midpoints and Cycle Starts and Print Sleeping Chronotype"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your Sleep Chronotype is currently being determined, ready in 10 days\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Day</th>\n",
" <th>Sleep Midpoint</th>\n",
" <th>Cycle Starts</th>\n",
" <th>Actual Cycle Starts</th>\n",
" <th>Sleeping Chronotype</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-11-18</td>\n",
" <td>Thursday</td>\n",
" <td>23:18:00</td>\n",
" <td>03:00:00</td>\n",
" <td>2021-11-18 03:06:00</td>\n",
" <td>Third Bird</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2021-11-15</td>\n",
" <td>Monday</td>\n",
" <td>00:12:00</td>\n",
" <td>04:00:00</td>\n",
" <td>2021-11-15 03:57:00</td>\n",
" <td>Lark</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2021-11-16</td>\n",
" <td>Tuesday</td>\n",
" <td>22:24:00</td>\n",
" <td>02:00:00</td>\n",
" <td>2021-11-16 01:54:00</td>\n",
" <td>Third Bird</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2021-11-17</td>\n",
" <td>Wednesday</td>\n",
" <td>23:18:00</td>\n",
" <td>03:00:00</td>\n",
" <td>2021-11-17 03:28:00</td>\n",
" <td>Third Bird</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Day Sleep Midpoint Cycle Starts Actual Cycle Starts \\\n",
"0 2021-11-18 Thursday 23:18:00 03:00:00 2021-11-18 03:06:00 \n",
"1 2021-11-15 Monday 00:12:00 04:00:00 2021-11-15 03:57:00 \n",
"2 2021-11-16 Tuesday 22:24:00 02:00:00 2021-11-16 01:54:00 \n",
"3 2021-11-17 Wednesday 23:18:00 03:00:00 2021-11-17 03:28:00 \n",
"\n",
" Sleeping Chronotype \n",
"0 Third Bird \n",
"1 Lark \n",
"2 Third Bird \n",
"3 Third Bird "
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sleep_cycle_df, avg_cycle_start = create_sleep_cycle_df(df=sleep_data_adj_len, bt_dt_form=bt_dt_form, wt_dt_form=wt_dt_form, days_to_calculate=14)\n",
"\n",
"#If sleep dates and days are the same this implies the sleeping pattern is very inconsistent likely due to shift work \n",
"sleep_cycle_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1b. Calculate Avg Cycle Starts based on Day of the week "
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Avg Cycle Start Time</th>\n",
" <th>Day</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>04:00:00</td>\n",
" <td>Monday</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>03:00:00</td>\n",
" <td>Thursday</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>02:00:00</td>\n",
" <td>Tuesday</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>03:30:00</td>\n",
" <td>Wednesday</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Avg Cycle Start Time Day\n",
"0 04:00:00 Monday\n",
"1 03:00:00 Thursday\n",
"2 02:00:00 Tuesday\n",
"3 03:30:00 Wednesday"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avg_cycle_per_day, avg_cycle_starts_per_day = calculate_avg_cs_per_day(sleep_cycle_df)\n",
"avg_cycle_per_day"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Combine Hardcoded values to the Sleep + Exercise Index to get values for each our "
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>BT</th>\n",
" <th>WT</th>\n",
" <th>Sleep Duration Mins</th>\n",
" <th>Sleep Duration Hrs</th>\n",
" <th>Daily Sleep Debt</th>\n",
" <th>Sleep Debt(Neg)</th>\n",
" <th>Sleep Debt(Pos)</th>\n",
" <th>ASD</th>\n",
" <th>Daily Sleep Consistency</th>\n",
" <th>Daily SDD</th>\n",
" <th>Daily Sleep Score</th>\n",
" <th>Exercise Mins</th>\n",
" <th>Moderate Ex Mins</th>\n",
" <th>Vig Ex Mins</th>\n",
" <th>Daily Ex Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18/11/2021</td>\n",
" <td>19:30:00</td>\n",
" <td>03:06:00</td>\n",
" <td>456</td>\n",
" <td>7.61</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" <td>100.0</td>\n",
" <td>95.1</td>\n",
" <td>96.57</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15/11/2021</td>\n",
" <td>20:24:00</td>\n",
" <td>03:57:00</td>\n",
" <td>453</td>\n",
" <td>7.55</td>\n",
" <td>-0.45</td>\n",
" <td>-0.45</td>\n",
" <td>0</td>\n",
" <td>-0.45</td>\n",
" <td>81.0</td>\n",
" <td>93.6</td>\n",
" <td>89.82</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16/11/2021</td>\n",
" <td>18:54:00</td>\n",
" <td>01:54:00</td>\n",
" <td>420</td>\n",
" <td>7.00</td>\n",
" <td>-1.00</td>\n",
" <td>-1.0</td>\n",
" <td>0</td>\n",
" <td>-1.45</td>\n",
" <td>73.0</td>\n",
" <td>85.8</td>\n",
" <td>81.96</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17/11/2021</td>\n",
" <td>19:12:00</td>\n",
" <td>03:28:00</td>\n",
" <td>496</td>\n",
" <td>8.27</td>\n",
" <td>0.27</td>\n",
" <td>0</td>\n",
" <td>0.27</td>\n",
" <td>-1.18</td>\n",
" <td>77.3</td>\n",
" <td>92.9</td>\n",
" <td>88.22</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>22.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date BT WT Sleep Duration Mins Sleep Duration Hrs \\\n",
"0 18/11/2021 19:30:00 03:06:00 456 7.61 \n",
"1 15/11/2021 20:24:00 03:57:00 453 7.55 \n",
"2 16/11/2021 18:54:00 01:54:00 420 7.00 \n",
"3 17/11/2021 19:12:00 03:28:00 496 8.27 \n",
"\n",
" Daily Sleep Debt Sleep Debt(Neg) Sleep Debt(Pos) ASD \\\n",
"0 0.00 0.0 0 0.00 \n",
"1 -0.45 -0.45 0 -0.45 \n",
"2 -1.00 -1.0 0 -1.45 \n",
"3 0.27 0 0.27 -1.18 \n",
"\n",
" Daily Sleep Consistency Daily SDD Daily Sleep Score Exercise Mins \\\n",
"0 100.0 95.1 96.57 0 \n",
"1 81.0 93.6 89.82 0 \n",
"2 73.0 85.8 81.96 0 \n",
"3 77.3 92.9 88.22 6 \n",
"\n",
" Moderate Ex Mins Vig Ex Mins Daily Ex Score \n",
"0 0 0 0.0 \n",
"1 0 0 0.0 \n",
"2 0 0 0.0 \n",
"3 6 0 22.3 "
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_daily_data_table"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sleep Performance = 88.22\n",
"Ex Performance = 3.2\n",
"Prdikt Perforamnce Capacity = 62.7\n",
"Mode = Third Bird\n"
]
}
],
"source": [
"hrly_perf_capacity = calculating_hrly_perf_capacity(sleep_data_adj_len, final_daily_data_table, sleep_cycle_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Plotting Graph "
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1800x1080 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_todays_perf_curve(hrly_perf_capacity, sleep_cycle_df, rows_to_change)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating Calendar Heatmap "
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"def prep_data_for_heatmap(sleep_cycle_df,avg_cycle_starts_per_day:dict,\n",
" hrly_perf_capacity:list, calculation_time:int):\n",
" \n",
" if len(sleep_cycle_df) < calculation_time:\n",
" print('Not Enough Data')\n",
" \n",
" return None, None\n",
" else:\n",
" \n",
" #Creating Labels for Graph \n",
" next7dates = [sleep_cycle_df['Date'].iloc[-1] + timedelta(hours=24*(i+1)) for i in range(0,7)]\n",
" next7_fut_dates = [datetime.strftime(i, '%d/%m') for i in next7dates]\n",
" next7days = [i.strftime('%A') for i in next7dates]\n",
" next7_df = pd.DataFrame(data={'Days':next7days, 'Dates':next7dates})\n",
" next7_df['Cycle Start'] = ''\n",
" x_labels = [next7days[i] + '\\n' + '('+ next7_fut_dates[i]+ ')' for i in range(len(next7days))]\n",
"\n",
" #Creating new Dataframe with predicted cycle starts for the next 7 days based on the average times for previous days \n",
" for i in range(len(next7_df['Days'])):\n",
"\n",
" #If we don't have the seperate date then use the avg \n",
" if next7_df['Days'][i] in avg_cycle_starts_per_day.keys():\n",
" next7_df['Cycle Start'].iloc[i] = avg_cycle_starts_per_day[next7_df['Days'][i]]\n",
"\n",
" else:\n",
" print('{} not in data so will use avg = {}'.format(next7_df['Days'][i], avg_cycle_start))\n",
" next7_df['Cycle Start'].iloc[i] = avg_cycle_start \n",
"\n",
"\n",
" #Creating New Calendar dataframe which contains performance capcity for every half an hour\n",
" daypredict_fix = pd.DataFrame()\n",
" daypredict_fix['Hrly Capacity'] = hrly_perf_capacity\n",
" time_hours= [str((avg_cycle_start + timedelta(hours=j)).time())[:5] for j in range(len(hrly_perf_capacity))]\n",
" daypredict_fix['Hours'] = time_hours\n",
" daypredict_fix['New Hours'] = ''\n",
"\n",
" new_hours_lst = [] \n",
" for i in range(len(hrly_perf_capacity)):\n",
" new_hours_lst.append(daypredict_fix['Hours'][i][:2] + ':30')\n",
"\n",
" daypredict_fix['New Hours'] = new_hours_lst\n",
"\n",
" #Calculating performance capacity values for half hourly points between hours \n",
" perf_capac_vals= [round((hrly_perf_capacity[i] + hrly_perf_capacity[i+1])/2,1) for i in range(23)] + [round((hrly_perf_capacity[0] + hrly_perf_capacity[-1])/2,1)]\n",
" daypredict_fix['New Hours PC']= perf_capac_vals\n",
"\n",
" #Appending hourly and half hourly into one list of performance capacity values\n",
" final_pc = [] \n",
" for i in range(len(daypredict_fix)):\n",
" final_pc.append(daypredict_fix['Hrly Capacity'][i])\n",
" final_pc.append(daypredict_fix['New Hours PC'][i])\n",
"\n",
"\n",
" #Creating long list of of times for each day 48 time points for each day - 48*7 \n",
" z_list = [] \n",
" for i in range(0,7):\n",
" z_list.append([str((next7_df['Cycle Start'].values[i] + timedelta(hours=j/2)).time())[:5] for j in range(len(final_pc))])\n",
"\n",
"\n",
" cycle_starts = [roundTime(i,roundTo=3600) for i in wt_dt_form]\n",
" x = [str((cycle_starts[0] + timedelta(hours=j)).time())[:5] for j in range(len(final_pc))]\n",
" y = final_pc\n",
"\n",
" calendar_df = pd.DataFrame({'Time': x, 'Performance Capacity':y})\n",
"\n",
" #Flattening long list \n",
" x_new = list(chain.from_iterable(z_list))\n",
" y_new = final_pc * 7\n",
"\n",
" #Creating final table that graph wil lbe generated from \n",
" calendar_df = pd.DataFrame({'Time': x_new, 'Performance Capacity':y_new})\n",
" calendar_df['Date'] = list(chain.from_iterable([[next7_fut_dates[i]]*len(final_pc) for i in range(7)]))\n",
" calendar_final = calendar_df.pivot(columns='Date', index='Time', values='Performance Capacity')\n",
"\n",
" #Find average wake time and natch the cycle start on the graph to that\n",
" new_axis = [str((avg_cycle_start + timedelta(hours=j/2)).time())[:5] for j in range(len(final_pc))]\n",
" calendar_final= calendar_final.reindex(new_axis)\n",
"\n",
" return calendar_final, x_labels \n",
"\n",
"\n",
"def plot_weekly_predicted_performance(heatmap_df, next): \n",
" \n",
" #Plotting Heatmap \n",
" x_labels = [next7days[i] + '\\n' + '('+ next7_fut_dates[i]+ ')' for i in range(len(next7days))]\n",
" plt.figure(figsize=(22.5,17.5))\n",
" ax_1 = sns.heatmap(heatmap_df, cmap = 'RdYlGn', linewidths=0.01, linecolor='black', annot=False,\n",
" cbar_kws={'ticks': [0,10,20,30,40,50,60,70,80,90,100],\n",
" 'extend':'both'})\n",
"\n",
"\n",
" plt.yticks(rotation=0, size=20)\n",
"\n",
" plt.xticks(ticks = [0.5,1.5,2.5,3.5,4.5,5.5,6.5],labels = x_labels , rotation = 0, size=20)\n",
" plt.tick_params(axis='y', labelsize=22)\n",
" plt.tick_params(axis='x', labelsize=22)\n",
" plt.title('Predicted Performance Capacity Over the Next 7 Days', size=30)\n",
"\n",
" ax_1.figure.axes[-1].set_ylabel('Performance\\nCapacity', size=20, labelpad=50, rotation = 0)\n",
" cax = plt.gcf().axes[-1]\n",
" cax.tick_params(labelsize=22)\n",
" ax_1.set(xlabel=None)\n",
" ax_1.set(ylabel=None)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"heatmap_df, x_labels = prep_data_for_heatmap(sleep_cycle_df,avg_cycle_starts_per_day,\n",
" hrly_perf_capacity, calculation_time=7)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"def plot_weekly_predicted_performance(heatmap_df, x_labels, calculation_time:int): \n",
" \n",
" if len(sleep_cycle_df) < calculation_time:\n",
" \n",
" print('Not Enough Data')\n",
" \n",
" else:\n",
" \n",
" #Plotting Heatmap \n",
" plt.figure(figsize=(22.5,17.5))\n",
"\n",
" ax_1 = sns.heatmap(heatmap_df, cmap = 'RdYlGn', linewidths=0.01, linecolor='black', annot=False,\n",
" cbar_kws={'ticks': [0,10,20,30,40,50,60,70,80,90,100],\n",
" 'extend':'both'})\n",
"\n",
"\n",
" plt.yticks(rotation=0, size=20)\n",
"\n",
" plt.xticks(ticks = [0.5,1.5,2.5,3.5,4.5,5.5,6.5],labels = x_labels , rotation = 0, size=20)\n",
" plt.tick_params(axis='y', labelsize=22)\n",
" plt.tick_params(axis='x', labelsize=22)\n",
" plt.title('Predicted Performance Capacity Over the Next 7 Days', size=30)\n",
"\n",
" ax_1.figure.axes[-1].set_ylabel('Performance\\nCapacity', size=20, labelpad=50, rotation = 0)\n",
" cax = plt.gcf().axes[-1]\n",
" cax.tick_params(labelsize=22)\n",
" ax_1.set(xlabel=None)\n",
" ax_1.set(ylabel=None)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"plot_weekly_predicted_performance(heatmap_df, x_labels, calculation_time=7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Key Report Information"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"def generate_report_key_info(sleep_cycle_df, final_daily_data_table, hrly_perf_capacity, \n",
" num_days_shown:int, days_to_calculate:int, rows_to_change:list):\n",
"\n",
" print('Sleep Data')\n",
" print('=========================================================================================================', '\\n')\n",
"\n",
" print('Sleep Chronotype')\n",
" print('-----------------', '\\n')\n",
" \n",
" if len(sleep_cycle_df) < days_to_calculate:\n",
" print('Your Sleep Chronotype is currently being determined, ready in {} days'.format(days_to_calculate - len(sleep_cycle_df)), '\\n'*2)\n",
"\n",
" else:\n",
" print('Your Sleeping Type is {}'.format(mode(list(sleep_cycle_df['Sleeping Chronotype'].values))), '\\n'*2)\n",
"\n",
" print('Sleep Performance')\n",
" print('-----------------', '\\n')\n",
" avg_sleep_perf = np.mean(sleep_data_adj_len['Daily Sleep Score'].values)\n",
" lower_bound_sp = round(avg_sleep_perf - np.std(sleep_data_adj_len['Daily Sleep Score'].values))\n",
" upper_bound_sp = round(avg_sleep_perf + np.std(sleep_data_adj_len['Daily Sleep Score'].values))\n",
" typical_range_sp = str(lower_bound_sp) + ' - ' + str(upper_bound_sp)\n",
" \n",
" \n",
" print('Todays Daily Sleep Performance = {}'.format(round(final_daily_data_table['Daily Sleep Score'].values[-1])))\n",
" print('Sleep Performance Typical Range = {}'.format(typical_range_sp), '\\n'*2)\n",
"\n",
" \n",
"\n",
" print('Sleep Consistency')\n",
" print('-----------------', '\\n')\n",
"\n",
" print('Last 7 Days Sleep Consistency = {}'.format(weekly_sleep_consistency(bed_time_list=convert_time(final_daily_data_table['BT'].values[-7:]), \n",
" wake_time_list=convert_time(final_daily_data_table['WT'].values[-7:]))), '\\n'*2)\n",
"\n",
" print('Sleep Duration')\n",
" print('--------------', '\\n')\n",
" \n",
" avg_sleep_dur = round(np.mean(sleep_data_adj_len['Sleep Duration Hrs']),1)\n",
" avg_sleep_string_form = str(avg_sleep_dur)[0] + ' Hours ' + str(int(float(str(avg_sleep_dur)[1:])*60)) + ' Mins'\n",
" \n",
" avg_sleep_dur_past7 = round(np.mean(sleep_data_adj_len['Sleep Duration Hrs'][-7:]),1)\n",
" avg_sleep_string_form_past7 = str(avg_sleep_dur_past7)[0] + ' Hours ' + str(int(float(str(avg_sleep_dur_past7)[1:])*60)) + ' Mins'\n",
" \n",
" lower_bound = avg_sleep_dur - np.std(sleep_data_adj_len['Sleep Duration Hrs'].values)\n",
" upper_bound = avg_sleep_dur + np.std(sleep_data_adj_len['Sleep Duration Hrs'].values)\n",
" lower_bound_str = str(lower_bound)[0] + ' Hours ' + str(int(float(str(lower_bound)[1:])*60)) + ' mins'\n",
" upper_bound_str = str(upper_bound)[0] + ' Hours ' + str(int(float(str(upper_bound)[1:])*60)) + ' mins'\n",
" typical_range = lower_bound_str + ' - ' + upper_bound_str\n",
"\n",
"\n",
" \n",
" print('Avg Sleep Duration (All Sleep Data) = {}'.format(avg_sleep_string_form))\n",
" print('Avg Sleep Duration(Past {} Days) = {}'.format(len(sleep_data_adj_len['Sleep Duration Hrs'][-num_days_shown:]), avg_sleep_string_form_past7), '\\n')\n",
" \n",
" print('Over Last {} Days: '.format(num_days_shown))\n",
" print('Avg Bed Time: {}'.format(avg_time(final_daily_data_table['BT'][-num_days_shown:]).time()))\n",
" print('Avg Wake Time: {}'.format(avg_time(final_daily_data_table['WT'][-num_days_shown:]).time()), '\\n'*2)\n",
" \n",
" print('All Time: '.format(num_days_shown))\n",
" print('Avg Bed Time: {}'.format(avg_time(final_daily_data_table['BT']).time()))\n",
" print('Avg Wake Time: {}'.format(avg_time(final_daily_data_table['WT']).time()), '\\n')\n",
" \n",
" print('Last Nights Sleep Duration = {}'.format(str(final_daily_data_table['Sleep Duration Hrs'].values[-1])[0] + ' Hours ' + str(int(float(str(final_daily_data_table['Sleep Duration Hrs'].values[-1])[1:])*60)) + ' mins'))\n",
" print('Typical Sleep Duration Range = {}'.format(typical_range), '\\n')\n",
" \n",
" print('Sleep Debt')\n",
" print('------------', '\\n')\n",
" accum_SD = sum(final_daily_data_table['Daily Sleep Debt'])\n",
" asd_hrs = int(str(accum_SD).split('.')[0])\n",
" asd_mins = int(float('0.'+str(sum(final_daily_data_table['Daily Sleep Debt'])).split('.')[1])*60)\n",
" sleep_need_tonight = float(8 - accum_SD)\n",
"\n",
" print('Accumulated Sleep Debt in last {} days = {} hours {} mins'.format(len(final_daily_data_table),asd_hrs,asd_mins ), '\\n')\n",
" print('Tomorrows sleep need = {}'.format(str((int(str(sleep_need_tonight).split('.')[0])))) + ' Hours ' + str(int(float('0.' + str(sleep_need_tonight).split('.')[1])*60)) + ' Mins')\n",
" print('Bed Time to eradicate all Sleep Debt Tonight = {}'.format((avg_time(final_daily_data_table['WT']) - timedelta(hours=8-asd_hrs, minutes=-asd_mins)).time()),'\\n')\n",
" \n",
" \n",
" acc_sd_week = ((56+(accum_SD))/7)- 8\n",
" sleep_need_week = float(8-acc_sd_week)\n",
" acc_sd_week_hrs = (int(str(acc_sd_week ).split('.')[0]))\n",
" acc_sd_week_mins = int(float('0.' + str(acc_sd_week ).split('.')[1])*60)\n",
"\n",
" print('Sleep Need over the Next 7 days = {}'.format(str(int(str(sleep_need_week).split('.')[0]))) + ' Hours ' + str(round(float('0.' + str(sleep_need_week).split('.')[1])*60)) + ' Mins')\n",
" print('Bed Time across next 7 Days to eradicated Sleep Debt= {} '.format((avg_time(final_daily_data_table['WT']) - timedelta(hours=8-acc_sd_week_hrs, minutes=-acc_sd_week_mins)).time()), '\\n'*2)\n",
"\n",
" print('Exercise Data')\n",
" print('========================================================================================================', '\\n')\n",
" print('Total Moderate Ex Mins over last {} days = {} '.format(num_days_shown, sum(final_daily_data_table['Moderate Ex Mins'][-num_days_shown:])))\n",
" print('Total Vigorous Ex Mins over last {} days = {}'.format(num_days_shown, sum(final_daily_data_table['Vig Ex Mins'][-num_days_shown:])), '\\n'*2)\n",
"\n",
" print('Predicted Data')\n",
" print('========================================================================================================', '\\n')\n",
" print('Peak Predicted Perforamnce = {}'.format(round(max(hrly_perf_capacity))), '\\n'*2)\n",
" \n",
" print('Missing Data')\n",
" print('========================================================================================================', '\\n')\n",
" print('Number of data entries missing from user = {}'.format(len(rows_to_change)))\n",
" \n",
" print('Dates Missing = {} '.format([i for i in final_daily_data_table['Date'].values[rows_to_change]]))\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sleep Data\n",
"========================================================================================================= \n",
"\n",
"Sleep Chronotype\n",
"----------------- \n",
"\n",
"Your Sleep Chronotype is currently being determined, ready in 10 days \n",
"\n",
"\n",
"Sleep Performance\n",
"----------------- \n",
"\n",
"Todays Daily Sleep Performance = 88\n",
"Sleep Performance Typical Range = 84 - 94 \n",
"\n",
"\n",
"Sleep Consistency\n",
"----------------- \n",
"\n",
"Last 7 Days Sleep Consistency = 77.3 \n",
"\n",
"\n",
"Sleep Duration\n",
"-------------- \n",
"\n",
"Avg Sleep Duration (All Sleep Data) = 7 Hours 36 Mins\n",
"Avg Sleep Duration(Past 4 Days) = 7 Hours 36 Mins \n",
"\n",
"Over Last 4 Days: \n",
"Avg Bed Time: 19:30:00\n",
"Avg Wake Time: 03:06:15 \n",
"\n",
"\n",
"All Time: \n",
"Avg Bed Time: 19:30:00\n",
"Avg Wake Time: 03:06:15 \n",
"\n",
"Last Nights Sleep Duration = 8 Hours 16 mins\n",
"Typical Sleep Duration Range = 7 Hours 8 mins - 8 Hours 3 mins \n",
"\n",
"Sleep Debt\n",
"------------ \n",
"\n",
"Accumulated Sleep Debt in last 4 days = -1 hours 10 mins \n",
"\n",
"Tomorrows sleep need = 9 Hours 10 Mins\n",
"Bed Time to eradicate all Sleep Debt Tonight = 18:16:15 \n",
"\n",
"Sleep Need over the Next 7 days = 8 Hours 10 Mins\n",
"Bed Time across next 7 Days to eradicated Sleep Debt= 19:16:15 \n",
"\n",
"\n",
"Exercise Data\n",
"======================================================================================================== \n",
"\n",
"Total Moderate Ex Mins over last 4 days = 6 \n",
"Total Vigorous Ex Mins over last 4 days = 0 \n",
"\n",
"\n",
"Predicted Data\n",
"======================================================================================================== \n",
"\n",
"Peak Predicted Perforamnce = 63 \n",
"\n",
"\n",
"Missing Data\n",
"======================================================================================================== \n",
"\n",
"Number of data entries missing from user = 1\n",
"Dates Missing = ['18/11/2021'] \n"
]
}
],
"source": [
"generate_report_key_info(sleep_cycle_df, final_daily_data_table, hrly_perf_capacity, \n",
" num_days_shown, days_to_calculate=14, rows_to_change=rows_to_change)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| 115.255696 | 115,884 | 0.801119 | 24,383 | 364,208 | 11.840258 | 0.306812 | 0.003679 | 0.004157 | 0.003741 | 0.146065 | 0.128895 | 0.11646 | 0.105351 | 0.097675 | 0.090976 | 0 | 0.130076 | 0.103696 | 364,208 | 3,159 | 115,885 | 115.292181 | 0.754314 | 0 | 0 | 0.577398 | 0 | 0.066793 | 0.919508 | 0.741417 | 0 | 1 | 0 | 0 | 0.000633 | 1 | 0 | true | 0.000317 | 0.004748 | 0 | 0.004748 | 0.042735 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
0772d7dd68dc118086e88e7a8442b64867b55417 | 8,360 | py | Python | tests/test_MI_and_generators.py | EI-research-group/deep-ei | c8f6f203f429deca73c08dd0d25aafa93a2ff749 | [
"MIT"
] | 8 | 2020-11-26T01:41:37.000Z | 2022-01-24T13:15:12.000Z | tests/test_MI_and_generators.py | EI-research-group/deep-ei | c8f6f203f429deca73c08dd0d25aafa93a2ff749 | [
"MIT"
] | null | null | null | tests/test_MI_and_generators.py | EI-research-group/deep-ei | c8f6f203f429deca73c08dd0d25aafa93a2ff749 | [
"MIT"
] | 2 | 2021-08-25T11:49:06.000Z | 2022-01-09T09:19:50.000Z | import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
from deep_ei import MI, _chunk_sizes, _indices_and_batch_sizes
#######################################
# MI Tests #
#######################################
def test_MI_0():
x = torch.tensor([0.3, 0.2, 0.4, 0.7])
y = torch.tensor([0.6, 0.7, 0.2, 0.3])
correct_MI = 0.31127812445913294
measured_MI = MI(x, y, bins=2)
error = 1e-6
assert correct_MI - error <= measured_MI <= correct_MI + error
def test_MI_1():
x = torch.tensor([0.0, 0.111, 0.45, 0.9])
y = torch.tensor([0.6, 1.0, 0.2, 0.3])
correct_MI = 0.31127812445913294
measured_MI = MI(x, y, bins=2)
error = 1e-6
assert correct_MI - error <= measured_MI <= correct_MI + error
def test_MI_2():
x = torch.tensor([0.0, 0.0, 1.0, 1.0])
y = torch.tensor([1.0, 1.0, 0.0, 0.0])
correct_MI = 1.0
measured_MI = MI(x, y, bins=2)
error = 1e-6
assert correct_MI - error <= measured_MI <= correct_MI + error
def test_MI_3():
x = torch.tensor([0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0])
y = torch.tensor([1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0])
correct_MI = 1.0
measured_MI = MI(x, y, bins=2)
error = 1e-6
assert correct_MI - error <= measured_MI <= correct_MI + error
def test_MI_4():
x = torch.tensor([0.0, 0.3, 0.6, 1.0])
y = torch.tensor([0.3, 0.6, 1.0, 0.0])
correct_MI = 2.0
measured_MI = MI(x, y, bins=4)
error = 1e-6
assert correct_MI - error <= measured_MI <= correct_MI + error
#######################################
# _chunk_sizes tests #
#######################################
def test_chunk_sizes_0():
samples = 20
num_inputs = 4
limit = 10
correct_sequence = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
generated_sequence = list(_chunk_sizes(samples, num_inputs, 1, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_1():
samples = 10
num_inputs = 3
limit = 10
correct_sequence = [3, 3, 3, 1]
generated_sequence = list(_chunk_sizes(samples, num_inputs, 1, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_2():
samples = 11
num_inputs = 5
limit = 14
correct_sequence = [2, 2, 2, 2, 2, 1]
generated_sequence = list(_chunk_sizes(samples, num_inputs, 1, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_3():
samples = 20
num_inputs = 5
limit = 23
correct_sequence = [4, 4, 4, 4, 4]
generated_sequence = list(_chunk_sizes(samples, num_inputs, 1, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_4():
samples = 20
num_inputs = 5
limit = 100
correct_sequence = [20]
generated_sequence = list(_chunk_sizes(samples, num_inputs, 1, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_5():
samples = 20
num_inputs = 5
limit = 101
correct_sequence = [20]
generated_sequence = list(_chunk_sizes(samples, num_inputs, 1, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_6():
samples = 20
num_inputs = 5
limit = 99
correct_sequence = [19, 1]
generated_sequence = list(_chunk_sizes(samples, num_inputs, 1, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_7():
samples = 50
num_inputs = 5
limit = 99
correct_sequence = [19, 19, 12]
generated_sequence = list(_chunk_sizes(samples, num_inputs, 1, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_8():
samples = 10
num_inputs = 5
limit = 100
correct_sequence = [10]
generated_sequence = list(_chunk_sizes(samples, num_inputs, 1, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_9():
samples = 10
num_inputs = 5
num_outputs = 10
limit = 100
correct_sequence = [10]
generated_sequence = list(_chunk_sizes(samples, num_inputs, num_outputs, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_10():
samples = 10
num_inputs = 5
num_outputs = 20
limit = 100
correct_sequence = [5, 5]
generated_sequence = list(_chunk_sizes(samples, num_inputs, num_outputs, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_11():
samples = 10
num_inputs = 5
num_outputs = 25
limit = 100
correct_sequence = [4, 4, 2]
generated_sequence = list(_chunk_sizes(samples, num_inputs, num_outputs, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_12():
samples = 25
num_inputs = 5
num_outputs = 15
limit = 100
correct_sequence = [6, 6, 6, 6, 1]
generated_sequence = list(_chunk_sizes(samples, num_inputs, num_outputs, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
def test_chunk_sizes_13():
samples = 25
num_inputs = 15
num_outputs = 5
limit = 100
correct_sequence = [6, 6, 6, 6, 1]
generated_sequence = list(_chunk_sizes(samples, num_inputs, num_outputs, limit))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
assert correct_sequence[i] == generated_sequence[i]
#######################################
# _indices_and_batch_sizes tests #
#######################################
def test_indices_and_batch_sizes_0():
samples = 10
batch_size = 3
correct_sequence = [((0, 3), 3), ((3, 6), 3), ((6, 9), 3), ((9, 10), 1)]
generated_sequence = list(_indices_and_batch_sizes(samples, batch_size))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
(ci0, ci1), csize = correct_sequence[i]
(gi0, gi1), gsize = generated_sequence[i]
assert ci0 == gi0
assert ci1 == gi1
assert csize == gsize
def test_indices_and_batch_sizes_1():
samples = 10
batch_size = 5
correct_sequence = [((0, 5), 5), ((5, 10), 5)]
generated_sequence = list(_indices_and_batch_sizes(samples, batch_size))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
(ci0, ci1), csize = correct_sequence[i]
(gi0, gi1), gsize = generated_sequence[i]
assert ci0 == gi0
assert ci1 == gi1
assert csize == gsize
def test_indices_and_batch_sizes_2():
samples = 10
batch_size = 15
correct_sequence = [((0, 10), 10)]
generated_sequence = list(_indices_and_batch_sizes(samples, batch_size))
assert len(correct_sequence) == len(generated_sequence)
for i in range(len(correct_sequence)):
(ci0, ci1), csize = correct_sequence[i]
(gi0, gi1), gsize = generated_sequence[i]
assert ci0 == gi0
assert ci1 == gi1
assert csize == gsize
| 33.98374 | 84 | 0.645215 | 1,194 | 8,360 | 4.259631 | 0.062814 | 0.200551 | 0.12033 | 0.08022 | 0.883995 | 0.864923 | 0.838576 | 0.800629 | 0.78313 | 0.780967 | 0 | 0.060058 | 0.213278 | 8,360 | 245 | 85 | 34.122449 | 0.713243 | 0.010885 | 0 | 0.681159 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.217391 | 1 | 0.10628 | false | 0 | 0.024155 | 0 | 0.130435 | 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 |
07b2aa7a23dc352de151495c35f1f1c96791c3d2 | 7,071 | py | Python | examples/nonogram/tests/test_trim.py | notechats/notegame | 3d9538b98cb6b0b240956b1271e028b22458fc54 | [
"Apache-2.0"
] | 17 | 2018-08-07T21:38:53.000Z | 2022-01-15T15:15:58.000Z | examples/nonogram/tests/test_trim.py | notechats/notegame | 3d9538b98cb6b0b240956b1271e028b22458fc54 | [
"Apache-2.0"
] | null | null | null | examples/nonogram/tests/test_trim.py | notechats/notegame | 3d9538b98cb6b0b240956b1271e028b22458fc54 | [
"Apache-2.0"
] | 5 | 2018-10-16T10:47:03.000Z | 2021-04-10T21:13:32.000Z | # -*- coding: utf-8 -*-
from __future__ import unicode_literals, print_function
import pytest
from pynogram.core.color import ColorBlock
from pynogram.core.common import (
SPACE_COLORED,
NonogramError,
BlottedBlock,
)
from pynogram.core.line.base import TrimmedSolver
space = SPACE_COLORED
BB = BlottedBlock
def f(*args, **kwargs):
return TrimmedSolver.starting_solved(*args, **kwargs)
class TestTrimming(object):
def test_empty_line(self):
assert f([], [space] * 3) == (3, 0)
def test_solved_fully_one_block(self):
assert f(
[ColorBlock(2, 2)],
[2, 2]
) == (2, 1)
def test_solved_fully_one_block_leading_spaces(self):
assert f(
[ColorBlock(2, 2)],
[space] * 2 + [2, 2]
) == (4, 1)
def test_solved_fully_one_block_surrounded_spaces(self):
assert f(
[ColorBlock(2, 2)],
[space] * 2 + [2, 2] + [space]
) == (5, 1)
def test_solved_fully_same_colors(self):
assert f(
[ColorBlock(2, 2), ColorBlock(1, 2)],
[space, 2, 2, space, 2, space]
) == (6, 2)
def test_solved_fully_different_colors_with_space(self):
assert f(
[ColorBlock(2, 2), ColorBlock(2, 4)],
[space, 2, 2, space, 4, 4]
) == (6, 2)
def test_solved_fully_different_colors_without_space(self):
assert f(
[ColorBlock(2, 2), ColorBlock(2, 4)],
[space, 2, 2, 4, 4]
) == (5, 2)
def test_solved_fully_three_colors(self):
assert f(
[ColorBlock(2, 2), ColorBlock(1, 4), ColorBlock(2, 8)],
[space, 2, 2, 4, space, space, 8, 8] + [space] * 4
) == (12, 3)
def test_solved_partial_one_block(self):
assert f(
[ColorBlock(2, 2), ColorBlock(1, 2)],
[space] * 3 + [space | 2] * 3
) == (3, 0)
def test_solved_partial_same_colors(self):
assert f(
[ColorBlock(2, 2), ColorBlock(1, 2)],
[space, 2, 2, space, space | 2]
) == (4, 1)
def test_solved_partial_same_colors_second_block_not_full(self):
assert f(
[ColorBlock(2, 2), ColorBlock(2, 2)],
[2, 2, space, 2, space | 2]
) == (3, 1)
def test_solved_partial_different_colors_with_space(self):
assert f(
[ColorBlock(2, 2), ColorBlock(1, 4)],
[space, 2, 2, space, space | 4]
) == (4, 1)
def test_solved_partial_different_colors_without_space(self):
assert f(
[ColorBlock(2, 2), ColorBlock(1, 4)],
[2, 2, space | 4]
) == (2, 1)
def test_solved_partial_three_blocks(self):
assert f(
[ColorBlock(2, 2), ColorBlock(1, 4), ColorBlock(2, 4), ColorBlock(2, 2)],
[2, 2, 4, space, 4, 4, 2, space | 2]
) == (6, 3)
def test_bad_no_description_but_has_colors(self):
with pytest.raises(NonogramError, match='^Bad block index 0'):
f(
[],
[space, 2, space],
)
def test_bad_not_enough_line_for_block(self):
with pytest.raises(NonogramError, match='^The 0-th block .+ cannot be allocated'):
f(
[ColorBlock(2, 2)],
[space, space, 2],
)
def test_bad_two_blocks(self):
with pytest.raises(NonogramError, match='^The next .+ cannot be allocated'):
f(
[ColorBlock(2, 2), ColorBlock(1, 2)],
[2, 2],
)
class TestTrimmingBlotted(object):
def test_solved_fully_one_block(self):
assert f(
[ColorBlock(BB, 2)],
[2, 2]
) == (2, 1)
def test_solved_fully_one_block_leading_spaces(self):
assert f(
[ColorBlock(BB, 2)],
[space] * 2 + [2, 2]
) == (4, 1)
def test_solved_fully_one_block_surrounded_spaces(self):
assert f(
[ColorBlock(BB, 2)],
[space] * 2 + [2, 2] + [space]
) == (5, 1)
def test_solved_fully_same_colors(self):
assert f(
[ColorBlock(BB, 2), ColorBlock(BB, 2)],
[space, 2, 2, space, 2, space]
) == (6, 2)
def test_solved_fully_different_colors_with_space_first_blot(self):
assert f(
[ColorBlock(BB, 2), ColorBlock(2, 4)],
[space, 2, 2, space, 4, 4]
) == (6, 2)
def test_solved_fully_different_colors_with_space_second_blot(self):
assert f(
[ColorBlock(2, 2), ColorBlock(BB, 4)],
[space, 2, 2, space, 4, 4]
) == (6, 2)
def test_solved_fully_different_colors_with_space_both_blots(self):
assert f(
[ColorBlock(BB, 2), ColorBlock(BB, 4)],
[space, 2, 2, space, 4, 4]
) == (6, 2)
def test_solved_fully_different_colors_without_space(self):
assert f(
[ColorBlock(2, 2), ColorBlock(BB, 4)],
[space, 2, 2, 4, 4]
) == (5, 2)
def test_solved_fully_three_colors(self):
assert f(
[ColorBlock(BB, 2), ColorBlock(1, 4), ColorBlock(BB, 8)],
[space, 2, 2, 4, space, space, 8, 8] + [space] * 4
) == (12, 3)
def test_solved_partial_one_block(self):
assert f(
[ColorBlock(BB, 2), ColorBlock(1, 2)],
[space] * 3 + [space | 2] * 3
) == (3, 0)
def test_solved_partial_same_colors_second_blot(self):
assert f(
[ColorBlock(2, 2), ColorBlock(BB, 2)],
[space, 2, 2, space, space | 2]
) == (4, 1)
def test_solved_partial_same_colors_both_blots(self):
assert f(
[ColorBlock(BB, 2), ColorBlock(BB, 2)],
[space, 2, 2, space, space | 2]
) == (4, 1)
def test_solved_partial_same_colors_second_block_not_full(self):
assert f(
[ColorBlock(2, 2), ColorBlock(BB, 2)],
[2, 2, space, 2, space | 2]
) == (3, 1)
def test_solved_partial_different_colors_with_space(self):
assert f(
[ColorBlock(BB, 2), ColorBlock(BB, 4)],
[space, 2, 2, space, space | 4]
) == (4, 1)
def test_solved_partial_different_colors_without_space_first_blot(self):
assert f(
[ColorBlock(BB, 2), ColorBlock(1, 4)],
[2, 2, space | 4]
) == (2, 1)
def test_solved_partial_different_colors_without_space_second_blot(self):
assert f(
[ColorBlock(2, 2), ColorBlock(BB, 4)],
[2, 2, space | 4]
) == (2, 1)
def test_solved_partial_three_blocks(self):
assert f(
[ColorBlock(2, 2), ColorBlock(BB, 4),
ColorBlock(2, 4), ColorBlock(BB, 2)],
[2, 2, 4, space, 4, 4, 2, space | 2]
) == (6, 3)
def test_solved_partial_remove_prefix(self):
assert f(
[ColorBlock(BB, 2), ColorBlock(BB, 4)],
[space, 2, 2, 3, space, 4, 4, space | 5]
) == (2, 0)
| 29.961864 | 90 | 0.52878 | 899 | 7,071 | 3.934372 | 0.096774 | 0.03732 | 0.099519 | 0.184054 | 0.84959 | 0.829799 | 0.818208 | 0.774668 | 0.772971 | 0.761097 | 0 | 0.062381 | 0.331212 | 7,071 | 235 | 91 | 30.089362 | 0.685557 | 0.00297 | 0 | 0.727749 | 0 | 0 | 0.012486 | 0 | 0 | 0 | 0 | 0 | 0.167539 | 1 | 0.188482 | false | 0 | 0.026178 | 0.005236 | 0.230366 | 0.005236 | 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 | 0 | 0 | 0 | 0 | 8 |
07c0621df25e98ca27f7b8f2705504dbea55ab41 | 2,236 | py | Python | 06-1. mini batch.py | Adrian123K/dl | b3b0cc500afa4b31112ca3d0bb75fbea331f9c94 | [
"MIT"
] | null | null | null | 06-1. mini batch.py | Adrian123K/dl | b3b0cc500afa4b31112ca3d0bb75fbea331f9c94 | [
"MIT"
] | null | null | null | 06-1. mini batch.py | Adrian123K/dl | b3b0cc500afa4b31112ca3d0bb75fbea331f9c94 | [
"MIT"
] | null | null | null | # # import sys, os
# # import pickle
# # sys.path.append(os.pardir)
# # from dataset.mnist import load_mnist
# # from common.functions import sigmoid, softmax, np
# #
# # def get_data():
# # (x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=False )
# # return x_test, t_test
# #
# # def init_network():
# # with open('d:/dl/sample_weight.pkl','rb') as f:
# # network=pickle.load(f)
# #
# # return network
# #
# # def predict(network, x):
# # W1, W2, W3 = network['W1'], network['W2'], network['W3']
# # b1, b2, b3 = network['b1'], network['b2'], network['b3']
# #
# # a1 = np.dot(x, W1) + b1
# # z1 = sigmoid(a1)
# # a2 = np.dot(z1, W2) + b2
# # z2 = sigmoid(a2)
# # a3 = np.dot(z2, W3) + b3
# # y = softmax(a3)
# #
# # return y
# #
# # x, t = get_data()
# # network = init_network()
# #
# # batch_size = 100
# #
# # for i in range(0,len(x),batch_size):
# # batch_mask = np.random.choice(len(x),batch_size)
# # x_batch = x[batch_mask]
# # y = predict(network, x_batch)
# # print(y)
#
# import sys, os
# import pickle
# sys.path.append(os.pardir)
# from dataset.mnist import load_mnist
# from common.functions import sigmoid, softmax, np
#
# def get_data():
# (x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=False )
# return x_test, t_test
#
# def init_network():
# with open('d:/dl/sample_weight.pkl','rb') as f:
# network=pickle.load(f)
#
# return network
#
# def predict(network, x):
# W1, W2, W3 = network['W1'], network['W2'], network['W3']
# b1, b2, b3 = network['b1'], network['b2'], network['b3']
#
# a1 = np.dot(x, W1) + b1
# z1 = sigmoid(a1)
# a2 = np.dot(z1, W2) + b2
# z2 = sigmoid(a2)
# a3 = np.dot(z2, W3) + b3
# y = softmax(a3)
#
# return y
#
# x, t = get_data()
# network = init_network()
#
# batch_size = 100
#
# for i in range(0,len(x),batch_size):
# cnt = 0
# batch_mask = np.random.choice(len(x),batch_size)
# x_batch = x[batch_mask]
# t_batch = t[batch_mask]
# y_batch = predict(network, x_batch)
# cnt += sum(np.argmax(y_batch, axis=1) == t_batch)
# print(cnt)
| 26.939759 | 109 | 0.578265 | 336 | 2,236 | 3.702381 | 0.223214 | 0.048232 | 0.019293 | 0.032154 | 0.909968 | 0.909968 | 0.909968 | 0.909968 | 0.909968 | 0.909968 | 0 | 0.038394 | 0.231216 | 2,236 | 82 | 110 | 27.268293 | 0.685282 | 0.908318 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
ed0d02aa45f306b9a8d3352092dde35ee5c497f1 | 19,229 | py | Python | handlers/part.py | jam0929/hanasee-server-python | 87d68a1ea86b2ca65b704c73ac52f74db5739cce | [
"MIT"
] | null | null | null | handlers/part.py | jam0929/hanasee-server-python | 87d68a1ea86b2ca65b704c73ac52f74db5739cce | [
"MIT"
] | null | null | null | handlers/part.py | jam0929/hanasee-server-python | 87d68a1ea86b2ca65b704c73ac52f74db5739cce | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import datetime
from init import InitHandler
from model.parts import Parts
from model.hanasies import Hanasies
from model.users import Users
from model.likes import Likes
from model.hanasy_bookmarks import HanasyBookmarks
from google.appengine.ext import ndb
from model.notifications import Logs
import re
import logging
import urllib2
from model.notifications import Messages
from google.appengine.api import urlfetch
class PartHandler(InitHandler):
def __init__(self, request, response):
InitHandler.__init__(self, request, response)
def getlist(self, **kwargs):
result = {
'code': 400,
'message': 'bad request'
}
options = {}
for item in self.arguments:
options[item] = self.arguments.get(item)
parts = Parts.getlist(options)
result['code'] = 200
result['message'] = 'OK'
result['Parts'] = self.listToObject(parts)
return self.createRes(200, result)
def post(self, **kwargs):
result = {
'code': 400,
'message': 'bad request'
}
try:
uid = self.get_user().get('uid') if kwargs.get('uid') == 'me' else int(kwargs.get('uid', 0))
hid = int(kwargs.get('hid'))
pid = int(kwargs.get('pid', 0))
except ValueError, e:
result['code'] = 400
result['message'] = 'bad request'
return self.createRes(400, result)
if not self.get_user():
result['code'] = 401
result['message'] = 'not allowed'
return self.createRes(401, result)
if uid and (uid != self.get_user().get('uid')):
result['code'] = 401
result['message'] = 'not allowed'
return self.createRes(401, result)
author = Users.get(id=uid)
hanasy = Hanasies.get(id=hid, parent=author.key)
if type(hanasy) == ndb.key.Key:
result['code'] = 404
result['message'] = 'not found'
return self.createRes(404, result)
if not pid:
# post new part
arguments = self.arguments
args_require = ['content', 'image']
# check parameter validation
if len(set(arguments) & set(args_require)) == 0:
result['code'] = 400
result['message'] = 'bad request'
return self.createRes(400, result)
part = Parts(auto_id=True, parent=hanasy.key)
url_regex = re.compile(
r'^(?:http|ftp)s?://' # http:// or https://
r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|' #domain...
r'localhost|' #localhost...
r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' # ...or ip
r'(?::\d+)?' # optional port
r'(?:/?|[/?]\S+)$', re.IGNORECASE)
video_regex = re.compile('^(?:https?://)?(?:www.)?(?:youtu.be/|youtube.com/(?:embed/|v/|watch\?v=|watch\?.+&v=))((\w|-){11})')
if self.arguments.get('content'):
if bool(video_regex.search(self.arguments.get('content'))):
self.arguments['videoUrl'] = video_regex.findall(self.arguments.get('content'))[0]
del self.arguments['content']
elif bool(url_regex.search(self.arguments.get('content'))):
try:
response = urllib2.urlopen(self.arguments.get('content'))
if bool(re.search('image',response.info().getheader('Content-Type'))):
self.arguments['imageUrl'] = self.arguments.get('content')
del self.arguments['content']
except Exception:
logging.error("image upload error")
part.set(self.convertRequsetParameter(self.arguments))
url = '/part/%s/%s/%s' % (author.key.id(), hanasy.key.id(), part.key.id())
#prerender - Hwan Oh 1406290646
prerenderUrl = "http://api.seo4ajax.com/c674edfc1fb2b6541c18aff2bb3e8264"+url
prerenderRpc = urlfetch.create_rpc()
urlfetch.make_fetch_call(prerenderRpc, prerenderUrl);
#/prerender
if hanasy.status != 'onair':
message = u'\'%s\' 하나시의 상태가 변했습니다' % hanasy.title
url = '/hanasee/%s/%s' % (author.key.id(), hanasy.key.id())
Messages(user=Hanasies.get_actioned_user(hanasy.key),
action_user=author.key,
action='hanasy_status_change',
settings='favoriteHanaseeNews',
app_name='hanasee',
hanasy=hanasy.key,
author=author.key,
visible=True,
message=message,
url=url).send(['APP','MAIL'])
hanasy.updated = datetime.datetime.now()
hanasy.partCount = int(hanasy.partCount if hanasy.partCount else 0) + 1
hanasy.status = 'onair'
hanasy.put()
if part:
result['code'] = 201
result['message'] = 'OK'
result['Part'] = part.to_obj()
return self.createRes(201, result)
else:
result['code'] = 400
result['message'] = 'already exists'
return self.createRes(400, result)
def migrate(self, **kwargs):
result = {
'code': 400,
'message': 'bad request'
}
options = {}
author_email = self.request.get('author')
hanasy_created = self.request.get('screated')
pid = 0
author_info = Users.find(author_email)
author = author_info.key.get()
options['author'] = author_info.key
options['created'] = hanasy_created
[hanasies, _, _], _ = Hanasies.find(options)
hanasy = hanasies[0]
if type(hanasy) == ndb.key.Key:
result['code'] = 404
result['message'] = 'not found'
return self.createRes(404, result)
if not pid:
# post new part
arguments = self.arguments
args_require = ['content', 'imageUrl', 'image', 'videoUrl']
# check parameter validation
if len(set(arguments) & set(args_require)) == 0:
result['code'] = 400
result['message'] = 'bad request'
return self.createRes(400, result)
part = Parts(auto_id=True, parent=hanasy.key)
part.set(self.convertRequsetParameter(self.arguments, ['author','screated']))
hanasy.updated = datetime.datetime.now()
hanasy.partCount = int(hanasy.partCount if hanasy.partCount else 0) + 1
hanasy.status = 'onair'
hanasy.put()
if part:
result['code'] = 201
result['message'] = 'OK'
result['Part'] = part.to_obj()
return self.createRes(201, result)
else:
result['code'] = 400
result['message'] = 'already exists'
return self.createRes(400, result)
def get(self, **kwargs):
result = {
'code': 400,
'message': 'bad request'
}
try:
uid = self.get_user().get('uid') if kwargs.get('uid') == 'me' else int(kwargs.get('uid', 0))
hid = int(kwargs.get('hid'))
pid = int(kwargs.get('pid', 0))
except ValueError, e:
result['code'] = 400
result['message'] = 'bad request'
return self.createRes(401, result)
if kwargs.get('uid') == 'me' and not self.get_user():
result['code'] = 401
result['message'] = 'not logged in'
return self.createRes(401, result)
author = Users.get(id=uid)
if type(author) == ndb.key.Key:
result['code'] = 401
result['message'] = 'not allowed'
return self.createRes(401, result)
hanasy = Hanasies.get(id=hid, parent=author.key)
if type(hanasy) == ndb.key.Key:
result['code'] = 404
result['message'] = 'not found'
return self.createRes(404, result)
if not pid:
# get all parts in a hanasy
options = {}
for item in self.arguments:
options[item] = self.arguments.get(item)
bFound = None
if self.get_user():
mark, bFound = HanasyBookmarks.find(ndb.Key(Users, self.get_user().get('uid')), hanasy.key)
if bFound:
options['mark'] = mark.position.get()
parts = Parts.find(hanasy.key, options)
like_items = []
if self.get_user():
likes = Likes.find(ndb.Key(Users, self.get_user().get('uid')), [part.key for part in parts])
like_items = [item.target.id() for item in likes]
result['code'] = 200
result['message'] = 'OK'
result['Parts'] = self.listToObject(parts)
result['Liked'] = like_items
if bFound:
result['Marked'] = mark.position.id()
return self.createRes(200, result)
else:
# part detail
part = Parts.get(id=pid, parent=hanasy.key)
if type(part) == ndb.key.Key:
result['code'] = 404
result['message'] = 'not found'
return self.createRes(404, result)
else:
like_items = []
bFound = False
if self.get_user():
likes = Likes.find(ndb.Key(Users, self.get_user().get('uid')), [part.key])
like_items = [item.target.id() for item in likes]
mark, bFound = HanasyBookmarks.find(ndb.Key(Users, self.get_user().get('uid')), hanasy.key)
result['code'] = 200
result['message'] = 'OK'
result['Part'] = part.to_obj()
result['Liked'] = like_items
if bFound:
result['Marked'] = mark.position.id()
return self.createRes(200, result)
def delete(self, **kwargs):
result = {
'code': 400,
'message': 'bad request'
}
try:
uid = self.get_user().get('uid') if kwargs.get('uid') == 'me' else int(kwargs.get('uid', 0))
hid = int(kwargs.get('hid'))
pid = int(kwargs.get('pid'))
except ValueError, e:
result['code'] = 400
result['message'] = 'bad request'
return self.createRes(401, result)
if not self.get_user():
result['code'] = 401
result['message'] = 'not allowed'
return self.createRes(401, result)
if uid and (uid != self.get_user().get('uid')):
result['code'] = 401
result['message'] = 'not allowed'
return self.createRes(401, result)
author = Users.get(id=uid)
hanasy = Hanasies.get(id=hid, parent=author.key)
if type(hanasy) == ndb.key.Key:
result['code'] = 404
result['message'] = 'not found'
return self.createRes(404, result)
hanasy.updated = datetime.datetime.now()
hanasy.partCount = int(hanasy.partCount if hanasy.partCount else 0) - 1
hanasy.status = 'onair'
hanasy.put()
part = Parts.get(id=pid, parent=hanasy.key)
if type(part) is ndb.key.Key:
result['code'] = 404
result['message'] = 'not found'
return self.createRes(404, result)
else:
part.key.delete()
result['code'] = 200
result['message'] = 'OK'
result['Hanasee'] = hanasy.to_obj()
return self.createRes(200, result)
def action(self, **kwargs):
result = {
'code': 400,
'message': 'bad request'
}
try:
uid = self.get_user().get('uid') if kwargs.get('uid') == 'me' else int(kwargs.get('uid', 0))
hid = int(kwargs.get('hid'))
pid = int(kwargs.get('pid'))
action = kwargs.get('action')
except ValueError, e:
result['code'] = 400
result['message'] = 'bad request'
return self.createRes(401, result)
if not self.get_user():
result['code'] = 401
result['message'] = 'not allowed'
return self.createRes(401, result)
user = Users.get(id=self.get_user().get('uid'))
author = Users.get(id=uid)
hanasy = Hanasies.get(id=hid, parent=author.key)
if type(hanasy) == ndb.key.Key:
result['code'] = 404
result['message'] = 'not found'
return self.createRes(404, result)
part = Parts.get(id=pid, parent=hanasy.key)
if action in ['like', 'unlike']:
like = Likes.find(ndb.Key(Users, self.get_user().get('uid')), [part.key])
if len(like) > 0 and action == 'unlike':
like[0].key.delete()
elif len(like) == 0 and action == 'like':
like = Likes(auto_id=True)
like.user = user.key
like.target = part.key
like.put()
message = self.arguments.get(
'message',
u'%s 님이 당신의 파트를 좋아합니다' % (getattr(user, 'nickname') if user else u'익명'))
url = self.arguments.get('url', '/part/%s/%s/%s' % (author.key.id(), hanasy.key.id(), part.key.id()))
if author.key.id() != self.get_user().get('uid'):
Messages(user=author.key,
action_user=ndb.Key(Users, self.get_user().get('uid')),
action='part_like',
app_name='hanasee',
settings='myHanaseeReact',
hanasy=hanasy.key,
author=author.key,
part=part.key,
visible=True,
message=message,
url=url).send(['APP','MAIL'])
if hasattr(part, 'content'):
message = u'%s 님이 \'%s\' 파트를 좋아합니다' % (getattr(user, 'nickname') if user else u'익명', part.content)
url = self.arguments.get('url', '/part/%s/%s/%s' % (author.key.id(), hanasy.key.id(), part.key.id()))
Messages(user=ndb.Key(Users, self.get_user().get('uid')),
action_user=ndb.Key(Users, self.get_user().get('uid')),
action='like',
app_name='hanasee',
settings='myReact',
hanasy=hanasy.key,
author=author.key,
part=part.key,
visible=True,
message=message,
url=url).send(['SNS'])
else:
result['code'] = 500
result['message'] = 'internal error'
return self.createRes(500, result)
part.likeCount = int(part.likeCount if part.likeCount else 0) + (1 if action == 'like' else -1)
part.put()
result['code'] = 200
result['message'] = 'OK'
result['Part'] = part.to_obj()
return self.createRes(200, result)
elif action == 'share':
part.shareCount = int(part.shareCount if part.shareCount else 0) + 1
part.put()
message = self.arguments.get(
'message',
u'%s 님이 당신의 파트를 공유했습니다' % (getattr(user, 'nickname') if user else u'익명'))
url = self.arguments.get('url', '/part/%s/%s/%s' % (author.key.id(), hanasy.key.id(), part.key.id()))
if self.get_user() and author.key.id() != self.get_user().get('uid'):
Messages(user=author.key,
action_user=ndb.Key(Users, self.get_user().get('uid')) if self.get_user() else {},
action='part_share',
app_name='hanasee',
settings='myHanaseeReact',
hanasy=hanasy.key,
author=author.key,
part=part.key,
visible=True,
message=message,
url=url).send(['APP','MAIL'])
result['code'] = 200
result['message'] = 'OK'
result['Part'] = part.to_obj()
return self.createRes(200, result)
elif action in ['mark', 'unmark']:
mark, bFound = HanasyBookmarks.find(user.key, hanasy.key)
if bFound and action == 'unmark':
mark.key.delete()
elif action == 'mark':
mark.position = Parts.get(id=pid, parent=hanasy.key).key
mark.put()
message = self.arguments.get(
'message',
u'%s 님이 당신의 하나시에 북마크를 꽂았습니다' % (getattr(user, 'nickname') if user else u'익명'))
url = self.arguments.get('url', '/hanasee/%s/%s' % (author.key.id(), hanasy.key.id()))
if author.key.id() != self.get_user().get('uid'):
Messages(user=author.key,
action_user=ndb.Key(Users, self.get_user().get('uid')),
action='part_mark',
app_name='hanasee',
settings='myHanaseeReact',
hanasy=hanasy.key,
author=author.key,
part=part.key,
visible=True,
message=message,
url=url).send(['APP','MAIL'])
else:
result['code'] = 400
result['message'] = 'bad request'
return self.createRes(400, result)
result['code'] = 200
result['message'] = 'OK'
result['Part'] = part.to_obj()
return self.createRes(200, result)
elif action in ['addcomment', 'delcomment']:
if action == 'addcomment':
message = self.arguments.get(
'message',
u'%s 님이 당신의 파트에 댓글을 달았습니다' % (getattr(user, 'nickname') if user else u'익명'))
url = self.arguments.get('url', '/part/%s/%s/%s' % (author.key.id(), hanasy.key.id(), part.key.id()))
if self.get_user() and author.key.id() != self.get_user().get('uid'):
Messages(user=author.key,
action_user=ndb.Key(Users, self.get_user().get('uid')) if self.get_user() else {},
action='part_addcomment',
app_name='hanasee',
settings='comment',
hanasy=hanasy.key,
author=author.key,
part=part.key,
visible=True,
message=message,
url=url).send(['APP','MAIL'])
part.commentCount = int(part.commentCount if part.commentCount else 0) + (1 if action == 'addcomment' else -1)
part.put()
hanasy.commentCount = int(hanasy.commentCount if hanasy.commentCount else 0) + (1 if action == 'addcomment' else -1)
hanasy.put()
result['code'] = 200
result['message'] = 'OK'
result['Part'] = part.to_obj()
return self.createRes(200, result)
else:
# invalid action
result['code'] = 404
result['message'] = 'not found'
return self.createRes(404, result)
def delete_all(self, **kwargs):
Parts.delete_all()
self.createRes(200, {'message': 'OK'})
def like_migrate(self, **kwargs):
result = {
'code': 200,
'message': 'ok'
}
author = Users.find(self.request.get('author'))
user = Users.find(self.request.get('user'))
options = {}
options['author'] = author.key
options['created'] = self.request.get('created')
[hanasies, _, _], _ = Hanasies.find(options)
hanasy = hanasies[0] if len(hanasies) > 0 else None
if hanasy:
options['created'] = self.request.get('tcreated')
parts = Parts.find(hanasy.key, options)
part = parts[0] if len(parts) > 0 else None
like = Likes(auto_id=True)
like.user = user.key
like.target = part.key
like.put()
return self.createRes(200, result)
def mark_migrate(self, **kwargs):
result = {
'code': 200,
'message': 'ok'
}
author = Users.find(self.request.get('author'))
user = Users.find(self.request.get('user'))
options = {}
options['author'] = author.key
options['created'] = self.request.get('created')
[hanasies, _, _], _ = Hanasies.find(options)
hanasy = hanasies[0] if len(hanasies) > 0 else None
if hanasy:
options['created'] = self.request.get('tcreated')
parts = Parts.find(hanasy.key, options)
part = parts[0] if len(parts) > 0 else None
mark, bFound = HanasyBookmarks.find(user.key, hanasy.key)
mark.position = part.key
mark.put()
return self.createRes(200, result) | 33.735088 | 133 | 0.561184 | 2,322 | 19,229 | 4.599053 | 0.099483 | 0.040266 | 0.06583 | 0.028842 | 0.769829 | 0.759715 | 0.721603 | 0.712707 | 0.704279 | 0.669538 | 0 | 0.024301 | 0.278798 | 19,229 | 570 | 134 | 33.735088 | 0.745746 | 0.013521 | 0 | 0.725532 | 0 | 0.006383 | 0.125374 | 0.01164 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.029787 | null | null | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
ed2a5d90bcf0c6ffcc1ce28d9f9d0dfeb953eaac | 32,896 | py | Python | fhir/resources/tests/test_task.py | mmabey/fhir.resources | cc73718e9762c04726cd7de240c8f2dd5313cbe1 | [
"BSD-3-Clause"
] | null | null | null | fhir/resources/tests/test_task.py | mmabey/fhir.resources | cc73718e9762c04726cd7de240c8f2dd5313cbe1 | [
"BSD-3-Clause"
] | null | null | null | fhir/resources/tests/test_task.py | mmabey/fhir.resources | cc73718e9762c04726cd7de240c8f2dd5313cbe1 | [
"BSD-3-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Profile: http://hl7.org/fhir/StructureDefinition/Task
Release: R4
Version: 4.0.1
Build ID: 9346c8cc45
Last updated: 2019-11-01T09:29:23.356+11:00
"""
import io
import json
import os
import unittest
import pytest
from .. import task
from ..fhirdate import FHIRDate
from .fixtures import force_bytes
@pytest.mark.usefixtures("base_settings")
class TaskTests(unittest.TestCase):
def instantiate_from(self, filename):
datadir = os.environ.get("FHIR_UNITTEST_DATADIR") or ""
with io.open(os.path.join(datadir, filename), "r", encoding="utf-8") as handle:
js = json.load(handle)
self.assertEqual("Task", js["resourceType"])
return task.Task(js)
def testTask1(self):
inst = self.instantiate_from("task-example6.json")
self.assertIsNotNone(inst, "Must have instantiated a Task instance")
self.implTask1(inst)
js = inst.as_json()
self.assertEqual("Task", js["resourceType"])
inst2 = task.Task(js)
self.implTask1(inst2)
def implTask1(self, inst):
self.assertEqual(
inst.authoredOn.date, FHIRDate("2016-10-31T08:25:05+10:00").date
)
self.assertEqual(inst.authoredOn.as_json(), "2016-10-31T08:25:05+10:00")
self.assertEqual(
force_bytes(inst.businessStatus.text),
force_bytes("test completed and posted"),
)
self.assertEqual(force_bytes(inst.code.text), force_bytes("Lipid Panel"))
self.assertEqual(
force_bytes(inst.description),
force_bytes(
"Create order for getting specimen, Set up inhouse testing, generate order for any sendouts and submit with specimen"
),
)
self.assertEqual(
inst.executionPeriod.end.date, FHIRDate("2016-10-31T18:45:05+10:00").date
)
self.assertEqual(
inst.executionPeriod.end.as_json(), "2016-10-31T18:45:05+10:00"
)
self.assertEqual(
inst.executionPeriod.start.date, FHIRDate("2016-10-31T08:25:05+10:00").date
)
self.assertEqual(
inst.executionPeriod.start.as_json(), "2016-10-31T08:25:05+10:00"
)
self.assertEqual(
force_bytes(inst.groupIdentifier.system),
force_bytes("http:/goodhealth.org/accession/identifiers"),
)
self.assertEqual(force_bytes(inst.groupIdentifier.use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.groupIdentifier.value), force_bytes("G20170201-001")
)
self.assertEqual(force_bytes(inst.id), force_bytes("example6"))
self.assertEqual(
force_bytes(inst.identifier[0].system),
force_bytes("http:/goodhealth.org/identifiers"),
)
self.assertEqual(force_bytes(inst.identifier[0].use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.identifier[0].value), force_bytes("20170201-001")
)
self.assertEqual(force_bytes(inst.intent), force_bytes("order"))
self.assertEqual(
inst.lastModified.date, FHIRDate("2016-10-31T18:45:05+10:00").date
)
self.assertEqual(inst.lastModified.as_json(), "2016-10-31T18:45:05+10:00")
self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST"))
self.assertEqual(
force_bytes(inst.meta.tag[0].display), force_bytes("test health data")
)
self.assertEqual(
force_bytes(inst.meta.tag[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"),
)
self.assertEqual(
force_bytes(inst.output[0].type.text),
force_bytes("DiagnosticReport generated"),
)
self.assertEqual(
force_bytes(inst.output[1].type.text), force_bytes("collected specimen")
)
self.assertEqual(
force_bytes(inst.performerType[0].coding[0].code), force_bytes("performer")
)
self.assertEqual(
force_bytes(inst.performerType[0].coding[0].display),
force_bytes("Performer"),
)
self.assertEqual(
force_bytes(inst.performerType[0].coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/task-performer-type"),
)
self.assertEqual(
force_bytes(inst.performerType[0].text), force_bytes("Performer")
)
self.assertEqual(force_bytes(inst.priority), force_bytes("routine"))
self.assertEqual(
force_bytes(inst.reasonCode.text),
force_bytes(
"The Task.reason should only be included if there is no Task.focus or if it differs from the reason indicated on the focus"
),
)
self.assertEqual(
inst.restriction.period.end.date, FHIRDate("2016-11-02T09:45:05+10:00").date
)
self.assertEqual(
inst.restriction.period.end.as_json(), "2016-11-02T09:45:05+10:00"
)
self.assertEqual(inst.restriction.repetitions, 1)
self.assertEqual(force_bytes(inst.status), force_bytes("completed"))
self.assertEqual(force_bytes(inst.text.status), force_bytes("generated"))
def testTask2(self):
inst = self.instantiate_from("task-example-fm-poll.json")
self.assertIsNotNone(inst, "Must have instantiated a Task instance")
self.implTask2(inst)
js = inst.as_json()
self.assertEqual("Task", js["resourceType"])
inst2 = task.Task(js)
self.implTask2(inst2)
def implTask2(self, inst):
self.assertEqual(
inst.authoredOn.date, FHIRDate("2018-10-12T08:25:05+10:00").date
)
self.assertEqual(inst.authoredOn.as_json(), "2018-10-12T08:25:05+10:00")
self.assertEqual(force_bytes(inst.code.coding[0].code), force_bytes("poll"))
self.assertEqual(
force_bytes(inst.code.coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/financialtaskcode"),
)
self.assertEqual(force_bytes(inst.id), force_bytes("fm-example2"))
self.assertEqual(
force_bytes(inst.identifier[0].system),
force_bytes("http:/happyvalley.com/task"),
)
self.assertEqual(force_bytes(inst.identifier[0].use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.identifier[0].value), force_bytes("20181012-005")
)
self.assertEqual(
force_bytes(inst.input[0].type.coding[0].code), force_bytes("include")
)
self.assertEqual(
force_bytes(inst.input[0].type.coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/financialtaskinputtype"),
)
self.assertEqual(
force_bytes(inst.input[0].valueCode), force_bytes("ClaimResponse")
)
self.assertEqual(
force_bytes(inst.input[1].type.coding[0].code), force_bytes("period")
)
self.assertEqual(
force_bytes(inst.input[1].type.coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/financialtaskinputtype"),
)
self.assertEqual(
inst.input[1].valuePeriod.end.date, FHIRDate("2018-10-12").date
)
self.assertEqual(inst.input[1].valuePeriod.end.as_json(), "2018-10-12")
self.assertEqual(
inst.input[1].valuePeriod.start.date, FHIRDate("2018-10-01").date
)
self.assertEqual(inst.input[1].valuePeriod.start.as_json(), "2018-10-01")
self.assertEqual(force_bytes(inst.intent), force_bytes("order"))
self.assertEqual(
inst.lastModified.date, FHIRDate("2018-10-12T08:25:05+10:00").date
)
self.assertEqual(inst.lastModified.as_json(), "2018-10-12T08:25:05+10:00")
self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST"))
self.assertEqual(
force_bytes(inst.meta.tag[0].display), force_bytes("test health data")
)
self.assertEqual(
force_bytes(inst.meta.tag[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"),
)
self.assertEqual(force_bytes(inst.priority), force_bytes("stat"))
self.assertEqual(force_bytes(inst.status), force_bytes("requested"))
self.assertEqual(force_bytes(inst.text.status), force_bytes("generated"))
def testTask3(self):
inst = self.instantiate_from("task-example1.json")
self.assertIsNotNone(inst, "Must have instantiated a Task instance")
self.implTask3(inst)
js = inst.as_json()
self.assertEqual("Task", js["resourceType"])
inst2 = task.Task(js)
self.implTask3(inst2)
def implTask3(self, inst):
self.assertEqual(
inst.authoredOn.date, FHIRDate("2016-10-31T08:25:05+10:00").date
)
self.assertEqual(inst.authoredOn.as_json(), "2016-10-31T08:25:05+10:00")
self.assertEqual(
force_bytes(inst.businessStatus.text), force_bytes("waiting for specimen")
)
self.assertEqual(force_bytes(inst.code.text), force_bytes("Lipid Panel"))
self.assertEqual(force_bytes(inst.contained[0].id), force_bytes("signature"))
self.assertEqual(
force_bytes(inst.description),
force_bytes(
"Create order for getting specimen, Set up inhouse testing, generate order for any sendouts and submit with specimen"
),
)
self.assertEqual(
inst.executionPeriod.start.date, FHIRDate("2016-10-31T08:25:05+10:00").date
)
self.assertEqual(
inst.executionPeriod.start.as_json(), "2016-10-31T08:25:05+10:00"
)
self.assertEqual(
force_bytes(inst.groupIdentifier.system),
force_bytes("http:/goodhealth.org/accession/identifiers"),
)
self.assertEqual(force_bytes(inst.groupIdentifier.use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.groupIdentifier.value), force_bytes("G20170201-001")
)
self.assertEqual(force_bytes(inst.id), force_bytes("example1"))
self.assertEqual(
force_bytes(inst.identifier[0].system),
force_bytes("http:/goodhealth.org/identifiers"),
)
self.assertEqual(force_bytes(inst.identifier[0].use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.identifier[0].value), force_bytes("20170201-001")
)
self.assertEqual(force_bytes(inst.intent), force_bytes("order"))
self.assertEqual(
inst.lastModified.date, FHIRDate("2016-10-31T09:45:05+10:00").date
)
self.assertEqual(inst.lastModified.as_json(), "2016-10-31T09:45:05+10:00")
self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST"))
self.assertEqual(
force_bytes(inst.meta.tag[0].display), force_bytes("test health data")
)
self.assertEqual(
force_bytes(inst.meta.tag[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"),
)
self.assertEqual(
force_bytes(inst.performerType[0].coding[0].code), force_bytes("performer")
)
self.assertEqual(
force_bytes(inst.performerType[0].coding[0].display),
force_bytes("Performer"),
)
self.assertEqual(
force_bytes(inst.performerType[0].coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/task-performer-type"),
)
self.assertEqual(
force_bytes(inst.performerType[0].text), force_bytes("Performer")
)
self.assertEqual(force_bytes(inst.priority), force_bytes("routine"))
self.assertEqual(
force_bytes(inst.reasonCode.text),
force_bytes(
"The Task.reason should only be included if there is no Task.focus or if it differs from the reason indicated on the focus"
),
)
self.assertEqual(
inst.restriction.period.end.date, FHIRDate("2016-11-02T09:45:05+10:00").date
)
self.assertEqual(
inst.restriction.period.end.as_json(), "2016-11-02T09:45:05+10:00"
)
self.assertEqual(inst.restriction.repetitions, 1)
self.assertEqual(force_bytes(inst.status), force_bytes("in-progress"))
self.assertEqual(force_bytes(inst.text.status), force_bytes("generated"))
def testTask4(self):
inst = self.instantiate_from("task-example-fm-reprocess.json")
self.assertIsNotNone(inst, "Must have instantiated a Task instance")
self.implTask4(inst)
js = inst.as_json()
self.assertEqual("Task", js["resourceType"])
inst2 = task.Task(js)
self.implTask4(inst2)
def implTask4(self, inst):
self.assertEqual(
inst.authoredOn.date, FHIRDate("2018-10-04T08:25:05+10:00").date
)
self.assertEqual(inst.authoredOn.as_json(), "2018-10-04T08:25:05+10:00")
self.assertEqual(
force_bytes(inst.code.coding[0].code), force_bytes("reprocess")
)
self.assertEqual(
force_bytes(inst.code.coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/financialtaskcode"),
)
self.assertEqual(force_bytes(inst.id), force_bytes("fm-example4"))
self.assertEqual(
force_bytes(inst.identifier[0].system),
force_bytes("http:/happyvalley.com/task"),
)
self.assertEqual(force_bytes(inst.identifier[0].use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.identifier[0].value), force_bytes("20181012-006")
)
self.assertEqual(
force_bytes(inst.input[0].type.coding[0].code), force_bytes("origresponse")
)
self.assertEqual(
force_bytes(inst.input[0].type.coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/financialtaskinputtype"),
)
self.assertEqual(
force_bytes(inst.input[1].type.coding[0].code), force_bytes("reference")
)
self.assertEqual(
force_bytes(inst.input[1].type.coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/financialtaskinputtype"),
)
self.assertEqual(force_bytes(inst.input[1].valueString), force_bytes("BR12345"))
self.assertEqual(
force_bytes(inst.input[2].type.coding[0].code), force_bytes("item")
)
self.assertEqual(
force_bytes(inst.input[2].type.coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/financialtaskinputtype"),
)
self.assertEqual(inst.input[2].valuePositiveInt, 2)
self.assertEqual(
force_bytes(inst.input[3].type.coding[0].code), force_bytes("item")
)
self.assertEqual(
force_bytes(inst.input[3].type.coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/financialtaskinputtype"),
)
self.assertEqual(inst.input[3].valuePositiveInt, 3)
self.assertEqual(force_bytes(inst.intent), force_bytes("order"))
self.assertEqual(
inst.lastModified.date, FHIRDate("2018-10-04T08:25:05+10:00").date
)
self.assertEqual(inst.lastModified.as_json(), "2018-10-04T08:25:05+10:00")
self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST"))
self.assertEqual(
force_bytes(inst.meta.tag[0].display), force_bytes("test health data")
)
self.assertEqual(
force_bytes(inst.meta.tag[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"),
)
self.assertEqual(force_bytes(inst.priority), force_bytes("stat"))
self.assertEqual(force_bytes(inst.status), force_bytes("requested"))
self.assertEqual(force_bytes(inst.text.status), force_bytes("generated"))
def testTask5(self):
inst = self.instantiate_from("task-example3.json")
self.assertIsNotNone(inst, "Must have instantiated a Task instance")
self.implTask5(inst)
js = inst.as_json()
self.assertEqual("Task", js["resourceType"])
inst2 = task.Task(js)
self.implTask5(inst2)
def implTask5(self, inst):
self.assertEqual(
inst.authoredOn.date, FHIRDate("2016-03-10T22:39:32-04:00").date
)
self.assertEqual(inst.authoredOn.as_json(), "2016-03-10T22:39:32-04:00")
self.assertEqual(force_bytes(inst.code.text), force_bytes("Refill Request"))
self.assertEqual(force_bytes(inst.id), force_bytes("example3"))
self.assertEqual(force_bytes(inst.intent), force_bytes("order"))
self.assertEqual(
inst.lastModified.date, FHIRDate("2016-03-10T22:39:32-04:00").date
)
self.assertEqual(inst.lastModified.as_json(), "2016-03-10T22:39:32-04:00")
self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST"))
self.assertEqual(
force_bytes(inst.meta.tag[0].display), force_bytes("test health data")
)
self.assertEqual(
force_bytes(inst.meta.tag[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"),
)
self.assertEqual(force_bytes(inst.status), force_bytes("draft"))
self.assertEqual(force_bytes(inst.text.status), force_bytes("generated"))
def testTask6(self):
inst = self.instantiate_from("task-example-fm-status-resp.json")
self.assertIsNotNone(inst, "Must have instantiated a Task instance")
self.implTask6(inst)
js = inst.as_json()
self.assertEqual("Task", js["resourceType"])
inst2 = task.Task(js)
self.implTask6(inst2)
def implTask6(self, inst):
self.assertEqual(
inst.authoredOn.date, FHIRDate("2018-10-04T08:25:05+10:00").date
)
self.assertEqual(inst.authoredOn.as_json(), "2018-10-04T08:25:05+10:00")
self.assertEqual(force_bytes(inst.code.coding[0].code), force_bytes("status"))
self.assertEqual(
force_bytes(inst.code.coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/financialtaskcode"),
)
self.assertEqual(force_bytes(inst.id), force_bytes("fm-example6"))
self.assertEqual(
force_bytes(inst.identifier[0].system),
force_bytes("http:/happyvalley.com/task"),
)
self.assertEqual(force_bytes(inst.identifier[0].use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.identifier[0].value), force_bytes("20181012-001")
)
self.assertEqual(
force_bytes(inst.identifier[1].system),
force_bytes("http://nationalinsurers.com/identifiers/12345"),
)
self.assertEqual(force_bytes(inst.identifier[1].use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.identifier[1].value), force_bytes("123GB5674")
)
self.assertEqual(force_bytes(inst.intent), force_bytes("order"))
self.assertEqual(
inst.lastModified.date, FHIRDate("2018-10-04T08:25:05+10:00").date
)
self.assertEqual(inst.lastModified.as_json(), "2018-10-04T08:25:05+10:00")
self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST"))
self.assertEqual(
force_bytes(inst.meta.tag[0].display), force_bytes("test health data")
)
self.assertEqual(
force_bytes(inst.meta.tag[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"),
)
self.assertEqual(
force_bytes(inst.output[0].type.coding[0].code), force_bytes("status")
)
self.assertEqual(
force_bytes(inst.output[0].type.coding[0].system),
force_bytes("http://hl7.org/financial-taskoutputtype"),
)
self.assertEqual(force_bytes(inst.output[0].valueCode), force_bytes("complete"))
self.assertEqual(force_bytes(inst.priority), force_bytes("stat"))
self.assertEqual(force_bytes(inst.status), force_bytes("completed"))
self.assertEqual(force_bytes(inst.text.status), force_bytes("generated"))
def testTask7(self):
inst = self.instantiate_from("task-example2.json")
self.assertIsNotNone(inst, "Must have instantiated a Task instance")
self.implTask7(inst)
js = inst.as_json()
self.assertEqual("Task", js["resourceType"])
inst2 = task.Task(js)
self.implTask7(inst2)
def implTask7(self, inst):
self.assertEqual(
inst.authoredOn.date, FHIRDate("2016-10-31T08:45:05+10:00").date
)
self.assertEqual(inst.authoredOn.as_json(), "2016-10-31T08:45:05+10:00")
self.assertEqual(
force_bytes(inst.businessStatus.text), force_bytes("waiting for patient")
)
self.assertEqual(
force_bytes(inst.code.text), force_bytes("Specimen Collection")
)
self.assertEqual(
inst.executionPeriod.start.date, FHIRDate("2016-10-31T08:45:05+10:00").date
)
self.assertEqual(
inst.executionPeriod.start.as_json(), "2016-10-31T08:45:05+10:00"
)
self.assertEqual(
force_bytes(inst.groupIdentifier.system),
force_bytes("http:/goodhealth.org/accession/identifiers"),
)
self.assertEqual(force_bytes(inst.groupIdentifier.use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.groupIdentifier.value), force_bytes("G20170201-001")
)
self.assertEqual(force_bytes(inst.id), force_bytes("example2"))
self.assertEqual(
force_bytes(inst.identifier[0].system),
force_bytes("http:/goodhealth.org/identifiers"),
)
self.assertEqual(force_bytes(inst.identifier[0].use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.identifier[0].value), force_bytes("20170201-002")
)
self.assertEqual(force_bytes(inst.intent), force_bytes("filler-order"))
self.assertEqual(
inst.lastModified.date, FHIRDate("2016-10-31T09:45:05+10:00").date
)
self.assertEqual(inst.lastModified.as_json(), "2016-10-31T09:45:05+10:00")
self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST"))
self.assertEqual(
force_bytes(inst.meta.tag[0].display), force_bytes("test health data")
)
self.assertEqual(
force_bytes(inst.meta.tag[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"),
)
self.assertEqual(
force_bytes(inst.performerType[0].coding[0].code), force_bytes("performer")
)
self.assertEqual(
force_bytes(inst.performerType[0].coding[0].display),
force_bytes("Performer"),
)
self.assertEqual(
force_bytes(inst.performerType[0].coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/task-performer-type"),
)
self.assertEqual(
force_bytes(inst.performerType[0].text), force_bytes("Performer")
)
self.assertEqual(force_bytes(inst.priority), force_bytes("routine"))
self.assertEqual(
inst.restriction.period.end.date, FHIRDate("2016-11-01T09:45:05+10:00").date
)
self.assertEqual(
inst.restriction.period.end.as_json(), "2016-11-01T09:45:05+10:00"
)
self.assertEqual(inst.restriction.repetitions, 1)
self.assertEqual(force_bytes(inst.status), force_bytes("accepted"))
self.assertEqual(force_bytes(inst.text.status), force_bytes("generated"))
def testTask8(self):
inst = self.instantiate_from("task-example-fm-release.json")
self.assertIsNotNone(inst, "Must have instantiated a Task instance")
self.implTask8(inst)
js = inst.as_json()
self.assertEqual("Task", js["resourceType"])
inst2 = task.Task(js)
self.implTask8(inst2)
def implTask8(self, inst):
self.assertEqual(
inst.authoredOn.date, FHIRDate("2018-10-04T08:25:05+10:00").date
)
self.assertEqual(inst.authoredOn.as_json(), "2018-10-04T08:25:05+10:00")
self.assertEqual(force_bytes(inst.code.coding[0].code), force_bytes("release"))
self.assertEqual(
force_bytes(inst.code.coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/financialtaskcode"),
)
self.assertEqual(force_bytes(inst.id), force_bytes("fm-example3"))
self.assertEqual(
force_bytes(inst.identifier[0].system),
force_bytes("http:/happyvalley.com/task"),
)
self.assertEqual(force_bytes(inst.identifier[0].use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.identifier[0].value), force_bytes("20181012-001")
)
self.assertEqual(
force_bytes(inst.input[0].type.coding[0].code), force_bytes("origresponse")
)
self.assertEqual(
force_bytes(inst.input[0].type.coding[0].system),
force_bytes("http://hl7.org/financial-taskinputtype"),
)
self.assertEqual(force_bytes(inst.intent), force_bytes("order"))
self.assertEqual(
inst.lastModified.date, FHIRDate("2018-10-04T08:25:05+10:00").date
)
self.assertEqual(inst.lastModified.as_json(), "2018-10-04T08:25:05+10:00")
self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST"))
self.assertEqual(
force_bytes(inst.meta.tag[0].display), force_bytes("test health data")
)
self.assertEqual(
force_bytes(inst.meta.tag[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"),
)
self.assertEqual(force_bytes(inst.priority), force_bytes("stat"))
self.assertEqual(force_bytes(inst.status), force_bytes("requested"))
self.assertEqual(force_bytes(inst.text.status), force_bytes("generated"))
def testTask9(self):
inst = self.instantiate_from("task-example-fm-cancel.json")
self.assertIsNotNone(inst, "Must have instantiated a Task instance")
self.implTask9(inst)
js = inst.as_json()
self.assertEqual("Task", js["resourceType"])
inst2 = task.Task(js)
self.implTask9(inst2)
def implTask9(self, inst):
self.assertEqual(
inst.authoredOn.date, FHIRDate("2018-10-04T08:25:05+10:00").date
)
self.assertEqual(inst.authoredOn.as_json(), "2018-10-04T08:25:05+10:00")
self.assertEqual(force_bytes(inst.code.coding[0].code), force_bytes("cancel"))
self.assertEqual(
force_bytes(inst.code.coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/financialtaskcode"),
)
self.assertEqual(force_bytes(inst.id), force_bytes("fm-example1"))
self.assertEqual(
force_bytes(inst.identifier[0].system),
force_bytes("http:/happyvalley.com/task"),
)
self.assertEqual(force_bytes(inst.identifier[0].use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.identifier[0].value), force_bytes("20181012-001")
)
self.assertEqual(force_bytes(inst.intent), force_bytes("order"))
self.assertEqual(
inst.lastModified.date, FHIRDate("2018-10-04T08:25:05+10:00").date
)
self.assertEqual(inst.lastModified.as_json(), "2018-10-04T08:25:05+10:00")
self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST"))
self.assertEqual(
force_bytes(inst.meta.tag[0].display), force_bytes("test health data")
)
self.assertEqual(
force_bytes(inst.meta.tag[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"),
)
self.assertEqual(force_bytes(inst.priority), force_bytes("stat"))
self.assertEqual(force_bytes(inst.status), force_bytes("requested"))
self.assertEqual(force_bytes(inst.text.status), force_bytes("generated"))
def testTask10(self):
inst = self.instantiate_from("task-example5.json")
self.assertIsNotNone(inst, "Must have instantiated a Task instance")
self.implTask10(inst)
js = inst.as_json()
self.assertEqual("Task", js["resourceType"])
inst2 = task.Task(js)
self.implTask10(inst2)
def implTask10(self, inst):
self.assertEqual(
inst.authoredOn.date, FHIRDate("2016-10-31T08:25:05+10:00").date
)
self.assertEqual(inst.authoredOn.as_json(), "2016-10-31T08:25:05+10:00")
self.assertEqual(
force_bytes(inst.businessStatus.text),
force_bytes("specimen received, test in progress"),
)
self.assertEqual(force_bytes(inst.code.text), force_bytes("Lipid Panel"))
self.assertEqual(
force_bytes(inst.description),
force_bytes(
"Create order for getting specimen, Set up inhouse testing, generate order for any sendouts and submit with specimen"
),
)
self.assertEqual(
inst.executionPeriod.start.date, FHIRDate("2016-10-31T08:25:05+10:00").date
)
self.assertEqual(
inst.executionPeriod.start.as_json(), "2016-10-31T08:25:05+10:00"
)
self.assertEqual(
force_bytes(inst.groupIdentifier.system),
force_bytes("http:/goodhealth.org/accession/identifiers"),
)
self.assertEqual(force_bytes(inst.groupIdentifier.use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.groupIdentifier.value), force_bytes("G20170201-001")
)
self.assertEqual(force_bytes(inst.id), force_bytes("example5"))
self.assertEqual(
force_bytes(inst.identifier[0].system),
force_bytes("http:/goodhealth.org/identifiers"),
)
self.assertEqual(force_bytes(inst.identifier[0].use), force_bytes("official"))
self.assertEqual(
force_bytes(inst.identifier[0].value), force_bytes("20170201-001")
)
self.assertEqual(force_bytes(inst.intent), force_bytes("order"))
self.assertEqual(
inst.lastModified.date, FHIRDate("2016-10-31T16:45:05+10:00").date
)
self.assertEqual(inst.lastModified.as_json(), "2016-10-31T16:45:05+10:00")
self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST"))
self.assertEqual(
force_bytes(inst.meta.tag[0].display), force_bytes("test health data")
)
self.assertEqual(
force_bytes(inst.meta.tag[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"),
)
self.assertEqual(
force_bytes(inst.output[0].type.text), force_bytes("collected specimen")
)
self.assertEqual(
force_bytes(inst.performerType[0].coding[0].code), force_bytes("performer")
)
self.assertEqual(
force_bytes(inst.performerType[0].coding[0].display),
force_bytes("Performer"),
)
self.assertEqual(
force_bytes(inst.performerType[0].coding[0].system),
force_bytes("http://terminology.hl7.org/CodeSystem/task-performer-type"),
)
self.assertEqual(
force_bytes(inst.performerType[0].text), force_bytes("Performer")
)
self.assertEqual(force_bytes(inst.priority), force_bytes("routine"))
self.assertEqual(
force_bytes(inst.reasonCode.text),
force_bytes(
"The Task.reason should only be included if there is no Task.focus or if it differs from the reason indicated on the focus"
),
)
self.assertEqual(
inst.restriction.period.end.date, FHIRDate("2016-11-02T09:45:05+10:00").date
)
self.assertEqual(
inst.restriction.period.end.as_json(), "2016-11-02T09:45:05+10:00"
)
self.assertEqual(inst.restriction.repetitions, 1)
self.assertEqual(force_bytes(inst.status), force_bytes("in-progress"))
self.assertEqual(force_bytes(inst.text.status), force_bytes("generated"))
| 43.802929 | 139 | 0.629073 | 3,838 | 32,896 | 5.280875 | 0.065399 | 0.183047 | 0.182554 | 0.228192 | 0.92535 | 0.923426 | 0.912868 | 0.899891 | 0.884991 | 0.878133 | 0 | 0.060863 | 0.232825 | 32,896 | 750 | 140 | 43.861333 | 0.742244 | 0.005107 | 0 | 0.601132 | 0 | 0.004243 | 0.191748 | 0.062317 | 0 | 0 | 0 | 0 | 0.387553 | 1 | 0.029703 | false | 0 | 0.011315 | 0 | 0.043847 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
ed3b603336572a4b9f6188bd85fbf5613faf554f | 88 | py | Python | visflux/__init__.py | ikspike/d3flux | 787ce3fea39651a70aee69e7a109c446a9568e1b | [
"MIT"
] | null | null | null | visflux/__init__.py | ikspike/d3flux | 787ce3fea39651a70aee69e7a109c446a9568e1b | [
"MIT"
] | null | null | null | visflux/__init__.py | ikspike/d3flux | 787ce3fea39651a70aee69e7a109c446a9568e1b | [
"MIT"
] | null | null | null | from visflux.core.flux_layouts import flux_map
from visflux.core.display_tools import *
| 29.333333 | 46 | 0.852273 | 14 | 88 | 5.142857 | 0.642857 | 0.305556 | 0.416667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.090909 | 88 | 2 | 47 | 44 | 0.9 | 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 |
ed412948330e32849caad5fb4a2157640f520c37 | 5,094 | py | Python | stockprophet/db/manager/sync_api/stock_metadata.py | chihyi-liao/stockprophet | 891c91b2a446e3bd30bb56b88be3874d7dda1b8d | [
"BSD-3-Clause"
] | 1 | 2021-11-15T13:07:19.000Z | 2021-11-15T13:07:19.000Z | stockprophet/db/manager/sync_api/stock_metadata.py | chihyi-liao/stockprophet | 891c91b2a446e3bd30bb56b88be3874d7dda1b8d | [
"BSD-3-Clause"
] | null | null | null | stockprophet/db/manager/sync_api/stock_metadata.py | chihyi-liao/stockprophet | 891c91b2a446e3bd30bb56b88be3874d7dda1b8d | [
"BSD-3-Clause"
] | 1 | 2021-09-15T09:25:39.000Z | 2021-09-15T09:25:39.000Z | from sqlalchemy.orm.session import Session
from sqlalchemy import exc, insert, select, update, delete, asc
from stockprophet.db.model.stock import stock_table
from stockprophet.db.model.stock_type import stock_type_table
from stockprophet.db.model.stock_metadata import stock_metadata_table
from stockprophet.db.log import get_logger
logger = get_logger(__name__)
def create_api(s: Session, data_list: list = None) -> bool:
"""依據資料清單建立metadata資料"""
result = False
if data_list is None:
data_list = []
if len(data_list) == 0:
return result
query = insert(stock_metadata_table).values(data_list)
try:
s.execute(query)
s.commit()
result = True
except exc.SQLAlchemyError as e:
s.rollback()
s.close()
logger.error(str(e))
finally:
return result
def read_api(s: Session, code: str) -> list:
"""依據股票代號查詢metadata"""
result = []
query = select([
stock_metadata_table.c.id, stock_table.c.code, stock_table.c.name,
stock_metadata_table.c.daily_history_create_date, stock_metadata_table.c.daily_history_update_date,
stock_metadata_table.c.weekly_history_create_date, stock_metadata_table.c.weekly_history_update_date,
stock_metadata_table.c.monthly_history_create_date, stock_metadata_table.c.monthly_history_update_date,
stock_metadata_table.c.income_create_date, stock_metadata_table.c.income_update_date,
stock_metadata_table.c.balance_create_date, stock_metadata_table.c.balance_update_date]
).select_from(
stock_metadata_table.join(
stock_table, stock_table.c.id == stock_metadata_table.c.stock_id
)
).where(stock_table.c.code == code).limit(1)
try:
for r in s.execute(query):
result.append({
'id': r[0], 'code': r[1], 'name': r[2],
'daily_history_create_date': r[3], 'daily_history_update_date': r[4],
'weekly_history_create_date': r[5], 'weekly_history_update_date': r[6],
'monthly_history_create_date': r[7], 'monthly_history_update_date': r[8],
'income_create_date': r[9], 'income_update_date': r[10],
'balance_create_date': r[11], 'balance_update_date': r[12]})
except exc.SQLAlchemyError as e:
s.rollback()
s.close()
logger.error(str(e))
finally:
return result
def readall_api(s: Session, type_s: str) -> list:
"""查詢所有個股的metadata資料"""
result = []
subquery = select([stock_type_table.c.id]).where(stock_type_table.c.name == type_s).limit(1)
query = select([
stock_metadata_table.c.id, stock_table.c.code, stock_table.c.name,
stock_metadata_table.c.daily_history_create_date, stock_metadata_table.c.daily_history_update_date,
stock_metadata_table.c.weekly_history_create_date, stock_metadata_table.c.weekly_history_update_date,
stock_metadata_table.c.monthly_history_create_date, stock_metadata_table.c.monthly_history_update_date,
stock_metadata_table.c.income_create_date, stock_metadata_table.c.income_update_date,
stock_metadata_table.c.balance_create_date, stock_metadata_table.c.balance_update_date]
).select_from(
stock_metadata_table.join(
stock_table, stock_table.c.id == stock_metadata_table.c.stock_id
)
).where(
stock_table.c.stock_type_id == subquery
).order_by(asc(stock_table.c.code))
try:
for r in s.execute(query):
result.append({
'id': r[0], 'code': r[1], 'name': r[2],
'daily_history_create_date': r[3], 'daily_history_update_date': r[4],
'weekly_history_create_date': r[5], 'weekly_history_update_date': r[6],
'monthly_history_create_date': r[7], 'monthly_history_update_date': r[8],
'income_create_date': r[9], 'income_update_date': r[10],
'balance_create_date': r[11], 'balance_update_date': r[12]})
except exc.SQLAlchemyError as e:
s.rollback()
s.close()
logger.error(str(e))
finally:
return result
def update_api(s: Session, oid: int, update_data: dict = None) -> bool:
"""依據id更新metadata資料"""
result = False
if update_data is None:
update_data = {}
if not update_data:
return result
query = update(stock_metadata_table).where(stock_metadata_table.c.id == oid).values(update_data)
try:
s.execute(query)
s.commit()
result = True
except exc.SQLAlchemyError as e:
s.rollback()
s.close()
logger.error(str(e))
finally:
return result
def delete_api(s: Session, oid: int) -> bool:
"""依據id刪除metadata資料"""
result = False
query = delete(stock_metadata_table).where(stock_metadata_table.c.id == oid)
try:
s.execute(query)
s.commit()
result = True
except exc.SQLAlchemyError as e:
s.rollback()
s.close()
logger.error(str(e))
finally:
return result
| 35.873239 | 111 | 0.660974 | 686 | 5,094 | 4.590379 | 0.138484 | 0.070499 | 0.182915 | 0.156875 | 0.753573 | 0.733884 | 0.712925 | 0.712925 | 0.712925 | 0.712925 | 0 | 0.008895 | 0.227523 | 5,094 | 141 | 112 | 36.12766 | 0.79136 | 0.017079 | 0 | 0.756522 | 0 | 0 | 0.096366 | 0.062638 | 0 | 0 | 0 | 0 | 0 | 1 | 0.043478 | false | 0 | 0.052174 | 0 | 0.156522 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
ed612d64d0b03896abdce351726d3ee25b9057e6 | 47,773 | py | Python | external/openglcts/scripts/build_mustpass.py | akihikodaki/VK-GL-CTS | 2d1377ec02b5b46a1cd946c5a27fa4a8f9e1e1f5 | [
"Apache-2.0"
] | null | null | null | external/openglcts/scripts/build_mustpass.py | akihikodaki/VK-GL-CTS | 2d1377ec02b5b46a1cd946c5a27fa4a8f9e1e1f5 | [
"Apache-2.0"
] | null | null | null | external/openglcts/scripts/build_mustpass.py | akihikodaki/VK-GL-CTS | 2d1377ec02b5b46a1cd946c5a27fa4a8f9e1e1f5 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
#-------------------------------------------------------------------------
#
# Copyright 2015 The Android Open Source Project
# Copyright (C) 2016 The Khronos Group Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#-------------------------------------------------------------------------
import os
import sys
from collections import OrderedDict
from build_caselists import Module, getModuleByName, DEFAULT_BUILD_DIR, DEFAULT_TARGET
from mustpass import Project, Package, Mustpass, Configuration, include, exclude, genMustpassLists
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "..", "scripts"))
from build.common import DEQP_DIR
from build.config import ANY_GENERATOR, BuildConfig
COPYRIGHT_DECLARATION = """\
/* Copyright (C) 2016-2017 The Khronos Group Inc
*
* 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.
*/"""
buildPath = DEFAULT_BUILD_DIR.format(targetName = DEFAULT_TARGET, buildType = "Release")
#-------------------------------------------------- ES MUSTPASS----------------------------------------------------------------------
CTS_AOSP_MP_DATA_DIR = os.path.join(DEQP_DIR, "external", "openglcts", "data", "mustpass", "gles", "aosp_mustpass")
CTS_AOSP_MP_DEVICE_DIR = "gl_cts/data/mustpass/gles/aosp_mustpass"
CTS_MP_INC_DIR = os.path.join(DEQP_DIR, "external", "openglcts", "modules", "runner")
CTS_AOSP_MP_ES_PROJECT = Project(name = "AOSP Mustpass ES", path = CTS_AOSP_MP_DATA_DIR, incpath = CTS_MP_INC_DIR, devicepath = CTS_AOSP_MP_DEVICE_DIR, copyright = COPYRIGHT_DECLARATION)
CTS_KHR_MP_DATA_DIR = os.path.join(DEQP_DIR, "external", "openglcts", "data", "mustpass", "gles", "khronos_mustpass")
CTS_KHR_MP_DEVICE_DIR = "gl_cts/data/mustpass/gles/khronos_mustpass"
CTS_KHR_MP_ES_PROJECT = Project(name = "Khronos Mustpass ES", path = CTS_KHR_MP_DATA_DIR, incpath = CTS_MP_INC_DIR, devicepath = CTS_KHR_MP_DEVICE_DIR, copyright = COPYRIGHT_DECLARATION)
CTS_AOSP_MP_EGL_DEVICE_DIR = "gl_cts/data/mustpass/egl/aosp_mustpass"
CTS_AOSP_MP_EGL_DATA_DIR = os.path.join(DEQP_DIR, "external", "openglcts", "data", "mustpass", "egl", "aosp_mustpass")
CTS_AOSP_MP_EGL_PROJECT = Project(name = "AOSP Mustpass EGL", path = CTS_AOSP_MP_EGL_DATA_DIR, incpath = CTS_MP_INC_DIR, devicepath = CTS_AOSP_MP_EGL_DEVICE_DIR, copyright = COPYRIGHT_DECLARATION)
CTS_KHR_MP_NOCTX_DATA_DIR = os.path.join(DEQP_DIR, "external", "openglcts", "data", "mustpass", "gles", "khronos_mustpass_noctx")
CTS_KHR_MP_NOCTX_DEVICE_DIR = "gl_cts/data/mustpass/gles/khronos_mustpass_noctx"
CTS_KHR_MP_NOCTX_ES_PROJECT = Project(name = "Khronos Mustpass ES NoContext", path = CTS_KHR_MP_NOCTX_DATA_DIR, incpath = CTS_MP_INC_DIR, devicepath = CTS_KHR_MP_NOCTX_DEVICE_DIR, copyright = COPYRIGHT_DECLARATION)
CTS_KHR_MP_SINGLE_DATA_DIR = os.path.join(DEQP_DIR, "external", "openglcts", "data", "mustpass", "gles", "khronos_mustpass_single")
CTS_KHR_MP_SINGLE_DEVICE_DIR = "gl_cts/data/mustpass/gles/khronos_mustpass_single"
CTS_KHR_MP_SINGLE_ES_PROJECT = Project(name = "Khronos Mustpass ES Single Config", path = CTS_KHR_MP_SINGLE_DATA_DIR, incpath = CTS_MP_INC_DIR, devicepath = CTS_KHR_MP_SINGLE_DEVICE_DIR, copyright = COPYRIGHT_DECLARATION)
EGL_MODULE = getModuleByName("dEQP-EGL")
ES2CTS_MODULE = getModuleByName("dEQP-GLES2")
ES3CTS_MODULE = getModuleByName("dEQP-GLES3")
ES31CTS_MODULE = getModuleByName("dEQP-GLES31")
GL45ES3_MODULE = getModuleByName("dEQP-GL45-ES3")
GL45ES31_MODULE = getModuleByName("dEQP-GL45-ES31")
ES2KHR_MODULE = getModuleByName("KHR-GLES2")
ES3KHR_MODULE = getModuleByName("KHR-GLES3")
ES31KHR_MODULE = getModuleByName("KHR-GLES31")
ES32KHR_MODULE = getModuleByName("KHR-GLES32")
NOCTX_ES2_KHR_MODULE = getModuleByName("KHR-NOCTX-ES2")
NOCTX_ES32_KHR_MODULE = getModuleByName("KHR-NOCTX-ES32")
SINGLE_ES32_KHR_MODULE = getModuleByName("KHR-Single-GLES32")
ES2GTF_MODULE = getModuleByName("GTF-GLES2")
ES3GTF_MODULE = getModuleByName("GTF-GLES3")
ES31GTF_MODULE = getModuleByName("GTF-GLES31")
GLCTS_GLES2_PKG = Package(module = ES2CTS_MODULE, configurations = [
# Master
Configuration(name = "master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = [include("gles2-master.txt")]),
])
GLCTS_3_2_2_GLES3_PKG = Package(module = ES3CTS_MODULE, configurations = [
# Master
Configuration(name = "master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = [include("gles3-master.txt")]),
# Rotations
Configuration(name = "rotate-portrait",
glconfig = "rgba8888d24s8ms0",
rotation = "0",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = [include("gles3-master.txt"), include("gles3-rotation.txt")]),
Configuration(name = "rotate-landscape",
glconfig = "rgba8888d24s8ms0",
rotation = "90",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = [include("gles3-master.txt"), include("gles3-rotation.txt")]),
Configuration(name = "rotate-reverse-portrait",
glconfig = "rgba8888d24s8ms0",
rotation = "180",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = [include("gles3-master.txt"), include("gles3-rotation.txt")]),
Configuration(name = "rotate-reverse-landscape",
glconfig = "rgba8888d24s8ms0",
rotation = "270",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = [include("gles3-master.txt"), include("gles3-rotation.txt")]),
# MSAA
Configuration(name = "multisample",
glconfig = "rgba8888d24s8ms4",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = [include("gles3-master.txt"),
include("gles3-multisample.txt"),
exclude("gles3-multisample-issues.txt")]),
# Pixel format
Configuration(name = "565-no-depth-no-stencil",
glconfig = "rgb565d0s0ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = [include("gles3-master.txt"),
include("gles3-pixelformat.txt"),
exclude("gles3-pixelformat-issues.txt")]),
])
GLCTS_3_2_2_GLES31_PKG = Package(module = ES31CTS_MODULE, configurations = [
# Master
Configuration(name = "master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = [include("gles31-master.txt")]),
# Rotations
Configuration(name = "rotate-portrait",
glconfig = "rgba8888d24s8ms0",
rotation = "0",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = [include("gles31-master.txt"), include("gles31-rotation.txt")]),
Configuration(name = "rotate-landscape",
glconfig = "rgba8888d24s8ms0",
rotation = "90",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = [include("gles31-master.txt"), include("gles31-rotation.txt")]),
Configuration(name = "rotate-reverse-portrait",
glconfig = "rgba8888d24s8ms0",
rotation = "180",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = [include("gles31-master.txt"), include("gles31-rotation.txt")]),
Configuration(name = "rotate-reverse-landscape",
glconfig = "rgba8888d24s8ms0",
rotation = "270",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = [include("gles31-master.txt"), include("gles31-rotation.txt")]),
# MSAA
Configuration(name = "multisample",
glconfig = "rgba8888d24s8ms4",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = [include("gles31-master.txt"), include("gles31-multisample.txt")]),
# Pixel format
Configuration(name = "565-no-depth-no-stencil",
glconfig = "rgb565d0s0ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = [include("gles31-master.txt"), include("gles31-pixelformat.txt")]),
])
# 3.2.3.x
GLCTS_3_2_3_EGL_COMMON_FILTERS = [include("egl-master.txt"),
exclude("egl-test-issues.txt"),
exclude("egl-internal-api-tests.txt"),
exclude("egl-driver-issues.txt")
]
GLCTS_3_2_3_EGL_PKG = Package(module = EGL_MODULE, configurations = [
# Master
Configuration(name = "master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = GLCTS_3_2_3_EGL_COMMON_FILTERS),
])
GLCTS_3_2_3_GLES2_COMMON_FILTERS = [
include("gles2-master.txt"),
exclude("gles2-test-issues.txt"),
exclude("gles2-spec-issues.txt"),
exclude("gles2-driver-issues.txt"),
exclude("gles2-hw-issues.txt")
]
GLCTS_3_2_3_GLES2_PKG = Package(module = ES2CTS_MODULE, configurations = [
# Master
Configuration(name = "master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = GLCTS_3_2_3_GLES2_COMMON_FILTERS),
])
GLCTS_3_2_3_GLES3_COMMON_FILTERS = [
include("gles3-master.txt"),
exclude("gles3-test-issues.txt"),
exclude("gles3-spec-issues.txt"),
exclude("gles3-driver-issues.txt"),
]
GLCTS_3_2_3_GLES3_PKG = Package(module = ES3CTS_MODULE, configurations = [
# Master
Configuration(name = "master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = GLCTS_3_2_3_GLES3_COMMON_FILTERS + [exclude("gles3-hw-issues.txt")]),
# Rotations
Configuration(name = "rotate-portrait",
glconfig = "rgba8888d24s8ms0",
rotation = "0",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = GLCTS_3_2_3_GLES3_COMMON_FILTERS + [include("gles3-rotation.txt")]),
Configuration(name = "rotate-landscape",
glconfig = "rgba8888d24s8ms0",
rotation = "90",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = GLCTS_3_2_3_GLES3_COMMON_FILTERS + [include("gles3-rotation.txt")]),
Configuration(name = "rotate-reverse-portrait",
glconfig = "rgba8888d24s8ms0",
rotation = "180",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = GLCTS_3_2_3_GLES3_COMMON_FILTERS + [include("gles3-rotation.txt")]),
Configuration(name = "rotate-reverse-landscape",
glconfig = "rgba8888d24s8ms0",
rotation = "270",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = GLCTS_3_2_3_GLES3_COMMON_FILTERS + [include("gles3-rotation.txt")]),
# MSAA
Configuration(name = "multisample",
glconfig = "rgba8888d24s8ms4",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = GLCTS_3_2_3_GLES3_COMMON_FILTERS + [include("gles3-multisample.txt"), exclude("gles3-multisample-hw-issues.txt")]),
# Pixel format
Configuration(name = "565-no-depth-no-stencil",
glconfig = "rgb565d0s0ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = GLCTS_3_2_3_GLES3_COMMON_FILTERS + [include("gles3-pixelformat.txt")]),
])
GLCTS_3_2_3_GLES31_COMMON_FILTERS = [
include("gles31-master.txt"),
exclude("gles31-test-issues.txt"),
exclude("gles31-spec-issues.txt"),
exclude("gles31-driver-issues.txt"),
exclude("gles31-hw-issues.txt")
]
GLCTS_3_2_3_GLES31_PKG = Package(module = ES31CTS_MODULE, configurations = [
# Master
Configuration(name = "master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = GLCTS_3_2_3_GLES31_COMMON_FILTERS),
# Rotations
Configuration(name = "rotate-portrait",
glconfig = "rgba8888d24s8ms0",
rotation = "0",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = GLCTS_3_2_3_GLES31_COMMON_FILTERS + [include("gles31-rotation.txt")]),
Configuration(name = "rotate-landscape",
glconfig = "rgba8888d24s8ms0",
rotation = "90",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = GLCTS_3_2_3_GLES31_COMMON_FILTERS + [include("gles31-rotation.txt")]),
Configuration(name = "rotate-reverse-portrait",
glconfig = "rgba8888d24s8ms0",
rotation = "180",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = GLCTS_3_2_3_GLES31_COMMON_FILTERS + [include("gles31-rotation.txt")]),
Configuration(name = "rotate-reverse-landscape",
glconfig = "rgba8888d24s8ms0",
rotation = "270",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = GLCTS_3_2_3_GLES31_COMMON_FILTERS + [include("gles31-rotation.txt")]),
# MSAA
Configuration(name = "multisample",
glconfig = "rgba8888d24s8ms4",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = [include("gles31-master.txt"),
include("gles31-multisample.txt"),
exclude("gles31-multisample-test-issues.txt")]),
# Pixel format
Configuration(name = "565-no-depth-no-stencil",
glconfig = "rgb565d0s0ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = GLCTS_3_2_3_GLES31_COMMON_FILTERS + [include("gles31-pixelformat.txt")]),
])
GLCTS_3_2_3_GLES32_KHR_COMMON_FILTERS = [
include("gles32-khr-master.txt"),
exclude("gles32-khr-test-issues.txt"),
exclude("gles32-khr-spec-issues.txt")
]
GLCTS_3_2_3_GLES32_KHR_PKG_1CFG = Package(module = ES32KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = GLCTS_3_2_3_GLES32_KHR_COMMON_FILTERS),
Configuration(name = "khr-master",
surfacewidth = "113",
surfaceheight = "47",
baseseed = "2",
filters = GLCTS_3_2_3_GLES32_KHR_COMMON_FILTERS),
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "-1",
baseseed = "3",
fboconfig = "rgba8888d24s8",
filters = GLCTS_3_2_3_GLES32_KHR_COMMON_FILTERS),
Configuration(name = "khr-master",
surfacewidth = "-1",
surfaceheight = "64",
baseseed = "3",
fboconfig = "rgba8888d24s8",
filters = GLCTS_3_2_3_GLES32_KHR_COMMON_FILTERS),
])
GLCTS_3_2_3_GLES32_KHR_PKG_N1CFG = Package(module = ES32KHR_MODULE, useforfirsteglconfig = False, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = GLCTS_3_2_3_GLES32_KHR_COMMON_FILTERS),
Configuration(name = "khr-master",
surfacewidth = "113",
surfaceheight = "47",
baseseed = "2",
filters = GLCTS_3_2_3_GLES32_KHR_COMMON_FILTERS),
])
# master
MAIN_EGL_COMMON_FILTERS = [include("egl-master.txt"),
exclude("egl-test-issues.txt"),
exclude("egl-internal-api-tests.txt")]
MAIN_EGL_PKG = Package(module = EGL_MODULE, configurations = [
# Master
Configuration(name = "master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = MAIN_EGL_COMMON_FILTERS),
])
MAIN_GLES2_COMMON_FILTERS = [
include("gles2-master.txt"),
exclude("gles2-test-issues.txt"),
exclude("gles2-spec-issues.txt")
]
MAIN_GLES2_PKG = Package(module = ES2CTS_MODULE, configurations = [
# Master
Configuration(name = "master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = MAIN_GLES2_COMMON_FILTERS),
])
MAIN_GLES3_COMMON_FILTERS = [
include("gles3-master.txt"),
exclude("gles3-test-issues.txt"),
exclude("gles3-spec-issues.txt")
]
MAIN_GLES3_PKG = Package(module = ES3CTS_MODULE, configurations = [
# Master
Configuration(name = "master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = MAIN_GLES3_COMMON_FILTERS),
# Rotations
Configuration(name = "rotate-portrait",
glconfig = "rgba8888d24s8ms0",
rotation = "0",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = MAIN_GLES3_COMMON_FILTERS + [include("gles3-rotation.txt")]),
Configuration(name = "rotate-landscape",
glconfig = "rgba8888d24s8ms0",
rotation = "90",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = MAIN_GLES3_COMMON_FILTERS + [include("gles3-rotation.txt")]),
Configuration(name = "rotate-reverse-portrait",
glconfig = "rgba8888d24s8ms0",
rotation = "180",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = MAIN_GLES3_COMMON_FILTERS + [include("gles3-rotation.txt")]),
Configuration(name = "rotate-reverse-landscape",
glconfig = "rgba8888d24s8ms0",
rotation = "270",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = MAIN_GLES3_COMMON_FILTERS + [include("gles3-rotation.txt")]),
# MSAA
Configuration(name = "multisample",
glconfig = "rgba8888d24s8ms4",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = MAIN_GLES3_COMMON_FILTERS + [include("gles3-multisample.txt")]),
# Pixel format
Configuration(name = "565-no-depth-no-stencil",
glconfig = "rgb565d0s0ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = MAIN_GLES3_COMMON_FILTERS + [include("gles3-pixelformat.txt")]),
])
MAIN_GLES31_COMMON_FILTERS = [
include("gles31-master.txt"),
exclude("gles31-test-issues.txt"),
exclude("gles31-spec-issues.txt")
]
MAIN_GLES31_PKG = Package(module = ES31CTS_MODULE, configurations = [
# Master
Configuration(name = "master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = MAIN_GLES31_COMMON_FILTERS),
# Rotations
Configuration(name = "rotate-portrait",
glconfig = "rgba8888d24s8ms0",
rotation = "0",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = MAIN_GLES31_COMMON_FILTERS + [include("gles31-rotation.txt")]),
Configuration(name = "rotate-landscape",
glconfig = "rgba8888d24s8ms0",
rotation = "90",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = MAIN_GLES31_COMMON_FILTERS + [include("gles31-rotation.txt")]),
Configuration(name = "rotate-reverse-portrait",
glconfig = "rgba8888d24s8ms0",
rotation = "180",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = MAIN_GLES31_COMMON_FILTERS + [include("gles31-rotation.txt")]),
Configuration(name = "rotate-reverse-landscape",
glconfig = "rgba8888d24s8ms0",
rotation = "270",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = MAIN_GLES31_COMMON_FILTERS + [include("gles31-rotation.txt")]),
# MSAA
Configuration(name = "multisample",
glconfig = "rgba8888d24s8ms4",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = MAIN_GLES31_COMMON_FILTERS + [include("gles31-multisample.txt")]),
# Pixel format
Configuration(name = "565-no-depth-no-stencil",
glconfig = "rgb565d0s0ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
os = "android",
filters = MAIN_GLES31_COMMON_FILTERS + [include("gles31-pixelformat.txt")]),
])
GLCTS_GLES2_KHR_PKG_1CFG = Package(module = ES2KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles2-khr-master.txt")]),
])
GLCTS_GLES2_DEQP_PKG_1CFG = Package(module = ES2CTS_MODULE, configurations = [
# Master
Configuration(name = "deqp-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles2-deqp-master.txt")]),
])
GLCTS_GLES2_GTF_PKG_1CFG = Package(module = ES2GTF_MODULE, configurations = [
# Master
Configuration(name = "gtf-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles2-gtf-master.txt")]),
Configuration(name = "gtf-master",
surfacewidth = "113",
surfaceheight = "47",
baseseed = "2",
filters = [include("gles2-gtf-master.txt")]),
Configuration(name = "gtf-master",
surfacewidth = "64",
surfaceheight = "-1",
baseseed = "3",
fboconfig = "rgba8888d24s8",
filters = [include("gles2-gtf-master.txt")]),
Configuration(name = "gtf-master",
surfacewidth = "-1",
surfaceheight = "64",
baseseed = "3",
fboconfig = "rgba8888d24s8",
filters = [include("gles2-gtf-master.txt")]),
Configuration(name = "gtf-egl",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles2-gtf-egl.txt")]),
Configuration(name = "gtf-egl",
surfacewidth = "113",
surfaceheight = "47",
baseseed = "2",
filters = [include("gles2-gtf-egl.txt")]),
])
GLCTS_GLES2_KHR_PKG_N1CFG = Package(module = ES2KHR_MODULE, useforfirsteglconfig = False, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles2-khr-master.txt")]),
])
GLCTS_GLES2_DEQP_PKG_N1CFG = Package(module = ES2CTS_MODULE, useforfirsteglconfig = False, configurations = [
# Master
Configuration(name = "deqp-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles2-deqp-master.txt")]),
])
GLCTS_GLES2_GTF_PKG_N1CFG = Package(module = ES2GTF_MODULE, useforfirsteglconfig = False, configurations = [
# Master
Configuration(name = "gtf-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles2-gtf-master.txt")]),
Configuration(name = "gtf-master",
surfacewidth = "113",
surfaceheight = "47",
baseseed = "2",
filters = [include("gles2-gtf-master.txt")]),
])
GLCTS_GLES3_DEQP_PKG_1CFG = Package(module = ES3CTS_MODULE, configurations = [
# Master
Configuration(name = "deqp-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles3-deqp-master.txt")]),
])
GLCTS_GLES3_KHR_PKG_1CFG = Package(module = ES3KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles3-khr-master.txt")]),
])
GLCTS_GLES3_GTF_PKG_1CFG = Package(module = ES3GTF_MODULE, configurations = [
# Master
Configuration(name = "gtf-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles3-gtf-master.txt")]),
Configuration(name = "gtf-master",
surfacewidth = "113",
surfaceheight = "47",
baseseed = "2",
filters = [include("gles3-gtf-master.txt")]),
Configuration(name = "gtf-master",
surfacewidth = "64",
surfaceheight = "-1",
baseseed = "3",
fboconfig = "rgba8888d24s8",
filters = [include("gles3-gtf-master.txt")]),
Configuration(name = "gtf-master",
surfacewidth = "-1",
surfaceheight = "64",
baseseed = "3",
fboconfig = "rgba8888d24s8",
filters = [include("gles3-gtf-master.txt")]),
])
GLCTS_GLES3_DEQP_PKG_N1CFG = Package(module = ES3CTS_MODULE, useforfirsteglconfig = False, configurations = [
# Master
Configuration(name = "deqp-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles3-deqp-master.txt")]),
])
GLCTS_GLES3_KHR_PKG_N1CFG = Package(module = ES3KHR_MODULE, useforfirsteglconfig = False, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles3-khr-master.txt")]),
])
GLCTS_GLES3_GTF_PKG_N1CFG = Package(module = ES3GTF_MODULE, useforfirsteglconfig = False, configurations = [
# Master
Configuration(name = "gtf-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles3-gtf-master.txt")]),
Configuration(name = "gtf-master",
surfacewidth = "113",
surfaceheight = "47",
baseseed = "2",
filters = [include("gles3-gtf-master.txt")]),
])
GLCTS_GLES31_DEQP_PKG_1CFG = Package(module = ES31CTS_MODULE, configurations = [
# Master
Configuration(name = "deqp-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles31-deqp-master.txt")]),
])
GLCTS_GLES31_KHR_PKG_1CFG = Package(module = ES31KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles31-khr-master.txt")]),
])
GLCTS_GLES31_GTF_PKG_1CFG = Package(module = ES31GTF_MODULE, configurations = [
# Master
Configuration(name = "gtf-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles31-gtf-master.txt")]),
Configuration(name = "gtf-master",
surfacewidth = "113",
surfaceheight = "47",
baseseed = "2",
filters = [include("gles31-gtf-master.txt")]),
Configuration(name = "gtf-master",
surfacewidth = "64",
surfaceheight = "-1",
baseseed = "3",
fboconfig = "rgba8888d24s8",
filters = [include("gles31-gtf-master.txt")]),
Configuration(name = "gtf-master",
surfacewidth = "-1",
surfaceheight = "64",
baseseed = "3",
fboconfig = "rgba8888d24s8",
filters = [include("gles31-gtf-master.txt")]),
])
GLCTS_GLES31_KHR_PKG_N1CFG = Package(module = ES31KHR_MODULE, useforfirsteglconfig = False, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles31-khr-master.txt")]),
])
GLCTS_GLES31_DEQP_PKG_N1CFG = Package(module = ES31CTS_MODULE, useforfirsteglconfig = False, configurations = [
# Master
Configuration(name = "deqp-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles31-deqp-master.txt")]),
])
GLCTS_GLES31_GTF_PKG_N1CFG = Package(module = ES31GTF_MODULE, useforfirsteglconfig = False, configurations = [
# Master
Configuration(name = "gtf-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles31-gtf-master.txt")]),
Configuration(name = "gtf-master",
surfacewidth = "113",
surfaceheight = "47",
baseseed = "2",
filters = [include("gles31-gtf-master.txt")]),
])
MAIN_GLES32_COMMON_FILTERS = [
include("gles32-khr-master.txt"),
exclude("gles32-khr-test-issues.txt"),
exclude("gles32-khr-spec-issues.txt")
]
GLCTS_GLES32_KHR_PKG_1CFG = Package(module = ES32KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = MAIN_GLES32_COMMON_FILTERS),
Configuration(name = "khr-master",
surfacewidth = "113",
surfaceheight = "47",
baseseed = "2",
filters = MAIN_GLES32_COMMON_FILTERS),
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "-1",
baseseed = "3",
fboconfig = "rgba8888d24s8",
filters = MAIN_GLES32_COMMON_FILTERS),
Configuration(name = "khr-master",
surfacewidth = "-1",
surfaceheight = "64",
baseseed = "3",
fboconfig = "rgba8888d24s8",
filters = MAIN_GLES32_COMMON_FILTERS),
])
GLCTS_GLES32_KHR_PKG_N1CFG = Package(module = ES32KHR_MODULE, useforfirsteglconfig = False, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = MAIN_GLES32_COMMON_FILTERS),
Configuration(name = "khr-master",
surfacewidth = "113",
surfaceheight = "47",
baseseed = "2",
filters = MAIN_GLES32_COMMON_FILTERS),
])
GLCTS_NOCTX_ES2_KHR_PKG = Package(module = NOCTX_ES2_KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-noctx-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles2-khr-master.txt")]),
])
GLCTS_NOCTX_ES32_KHR_PKG = Package(module = NOCTX_ES32_KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-noctx-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = MAIN_GLES32_COMMON_FILTERS),
])
GLCTS_SINGLE_ES32_KHR_PKG = Package(module = SINGLE_ES32_KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-single",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gles32-khr-single.txt")]),
])
ES_MUSTPASS_LISTS = [
# 3.2.2.x
Mustpass(project = CTS_KHR_MP_ES_PROJECT, version = "3.2.2.x", isCurrent=False,
packages = [GLCTS_GLES2_KHR_PKG_1CFG,
GLCTS_GLES2_DEQP_PKG_1CFG,
GLCTS_GLES2_GTF_PKG_1CFG,
GLCTS_GLES2_KHR_PKG_N1CFG,
GLCTS_GLES2_DEQP_PKG_N1CFG,
GLCTS_GLES2_GTF_PKG_N1CFG,
GLCTS_GLES3_KHR_PKG_1CFG,
GLCTS_GLES3_DEQP_PKG_1CFG,
GLCTS_GLES3_GTF_PKG_1CFG,
GLCTS_GLES3_KHR_PKG_N1CFG,
GLCTS_GLES3_DEQP_PKG_N1CFG,
GLCTS_GLES3_GTF_PKG_N1CFG,
GLCTS_GLES31_KHR_PKG_1CFG,
GLCTS_GLES31_DEQP_PKG_1CFG,
GLCTS_GLES31_GTF_PKG_1CFG,
GLCTS_GLES31_KHR_PKG_N1CFG,
GLCTS_GLES31_DEQP_PKG_N1CFG,
GLCTS_GLES31_GTF_PKG_N1CFG,
GLCTS_GLES32_KHR_PKG_1CFG,
GLCTS_GLES32_KHR_PKG_N1CFG,
]),
Mustpass(project = CTS_AOSP_MP_ES_PROJECT, version = "3.2.2.x", isCurrent=False,
packages = [GLCTS_GLES2_PKG, GLCTS_3_2_2_GLES3_PKG, GLCTS_3_2_2_GLES31_PKG]),
# 3.2.3.x
Mustpass(project = CTS_KHR_MP_ES_PROJECT, version = "3.2.3.x", isCurrent=False,
packages = [GLCTS_GLES2_KHR_PKG_1CFG,
GLCTS_GLES2_GTF_PKG_1CFG,
GLCTS_GLES2_KHR_PKG_N1CFG,
GLCTS_GLES2_GTF_PKG_N1CFG,
GLCTS_GLES3_KHR_PKG_1CFG,
GLCTS_GLES3_GTF_PKG_1CFG,
GLCTS_GLES3_KHR_PKG_N1CFG,
GLCTS_GLES3_GTF_PKG_N1CFG,
GLCTS_GLES31_KHR_PKG_1CFG,
GLCTS_GLES31_GTF_PKG_1CFG,
GLCTS_GLES31_KHR_PKG_N1CFG,
GLCTS_GLES31_GTF_PKG_N1CFG,
GLCTS_3_2_3_GLES32_KHR_PKG_1CFG,
GLCTS_3_2_3_GLES32_KHR_PKG_N1CFG,
]),
Mustpass(project = CTS_AOSP_MP_ES_PROJECT, version = "3.2.3.x", isCurrent=False,
packages = [GLCTS_3_2_3_GLES2_PKG, GLCTS_3_2_3_GLES3_PKG, GLCTS_3_2_3_GLES31_PKG]),
Mustpass(project = CTS_AOSP_MP_EGL_PROJECT, version = "3.2.3.x", isCurrent=False,
packages = [GLCTS_3_2_3_EGL_PKG]),
# 3.2.4.x
Mustpass(project = CTS_KHR_MP_ES_PROJECT, version = "3.2.4.x", isCurrent=False,
packages = [GLCTS_GLES2_KHR_PKG_1CFG,
GLCTS_GLES2_KHR_PKG_N1CFG,
GLCTS_GLES3_KHR_PKG_1CFG,
GLCTS_GLES3_KHR_PKG_N1CFG,
GLCTS_GLES31_KHR_PKG_1CFG,
GLCTS_GLES31_KHR_PKG_N1CFG,
GLCTS_3_2_3_GLES32_KHR_PKG_1CFG,
GLCTS_3_2_3_GLES32_KHR_PKG_N1CFG,
]),
Mustpass(project = CTS_KHR_MP_NOCTX_ES_PROJECT, version = "3.2.4.x", isCurrent=False,
packages = [GLCTS_NOCTX_ES2_KHR_PKG, GLCTS_NOCTX_ES32_KHR_PKG]),
Mustpass(project = CTS_AOSP_MP_ES_PROJECT, version = "3.2.4.x", isCurrent=False,
packages = [GLCTS_3_2_3_GLES2_PKG, GLCTS_3_2_3_GLES3_PKG, GLCTS_3_2_3_GLES31_PKG]),
Mustpass(project = CTS_AOSP_MP_EGL_PROJECT, version = "3.2.4.x", isCurrent=False,
packages = [GLCTS_3_2_3_EGL_PKG]),
# 3.2.5.x
Mustpass(project = CTS_KHR_MP_ES_PROJECT, version = "3.2.5.x", isCurrent=False,
packages = [GLCTS_GLES2_KHR_PKG_1CFG,
GLCTS_GLES2_KHR_PKG_N1CFG,
GLCTS_GLES3_KHR_PKG_1CFG,
GLCTS_GLES3_KHR_PKG_N1CFG,
GLCTS_GLES31_KHR_PKG_1CFG,
GLCTS_GLES31_KHR_PKG_N1CFG,
GLCTS_GLES32_KHR_PKG_1CFG,
GLCTS_GLES32_KHR_PKG_N1CFG,
]),
Mustpass(project = CTS_KHR_MP_NOCTX_ES_PROJECT, version = "3.2.5.x", isCurrent=False,
packages = [GLCTS_NOCTX_ES2_KHR_PKG, GLCTS_NOCTX_ES32_KHR_PKG]),
Mustpass(project = CTS_AOSP_MP_ES_PROJECT, version = "3.2.5.x", isCurrent=False,
packages = [GLCTS_3_2_3_GLES2_PKG, GLCTS_3_2_3_GLES3_PKG, GLCTS_3_2_3_GLES31_PKG]),
Mustpass(project = CTS_AOSP_MP_EGL_PROJECT, version = "3.2.5.x", isCurrent=False,
packages = [GLCTS_3_2_3_EGL_PKG]),
# 3.2.6.x
Mustpass(project = CTS_KHR_MP_ES_PROJECT, version = "3.2.6.x", isCurrent=False,
packages = [GLCTS_GLES2_KHR_PKG_1CFG,
GLCTS_GLES2_KHR_PKG_N1CFG,
GLCTS_GLES3_KHR_PKG_1CFG,
GLCTS_GLES3_KHR_PKG_N1CFG,
GLCTS_GLES31_KHR_PKG_1CFG,
GLCTS_GLES31_KHR_PKG_N1CFG,
GLCTS_GLES32_KHR_PKG_1CFG,
GLCTS_GLES32_KHR_PKG_N1CFG,
]),
Mustpass(project = CTS_KHR_MP_NOCTX_ES_PROJECT, version = "3.2.6.x", isCurrent=False,
packages = [GLCTS_NOCTX_ES2_KHR_PKG, GLCTS_NOCTX_ES32_KHR_PKG]),
Mustpass(project = CTS_KHR_MP_SINGLE_ES_PROJECT, version = "3.2.6.x", isCurrent=False,
packages = [GLCTS_SINGLE_ES32_KHR_PKG]),
Mustpass(project = CTS_AOSP_MP_ES_PROJECT, version = "3.2.6.x", isCurrent=False,
packages = [GLCTS_3_2_3_GLES2_PKG, GLCTS_3_2_3_GLES3_PKG, GLCTS_3_2_3_GLES31_PKG]),
Mustpass(project = CTS_AOSP_MP_EGL_PROJECT, version = "3.2.6.x", isCurrent=False,
packages = [GLCTS_3_2_3_EGL_PKG]),
# main
Mustpass(project = CTS_KHR_MP_ES_PROJECT, version = "main", isCurrent=True,
packages = [GLCTS_GLES2_KHR_PKG_1CFG,
GLCTS_GLES2_KHR_PKG_N1CFG,
GLCTS_GLES3_KHR_PKG_1CFG,
GLCTS_GLES3_KHR_PKG_N1CFG,
GLCTS_GLES31_KHR_PKG_1CFG,
GLCTS_GLES31_KHR_PKG_N1CFG,
GLCTS_GLES32_KHR_PKG_1CFG,
GLCTS_GLES32_KHR_PKG_N1CFG,
]),
Mustpass(project = CTS_KHR_MP_NOCTX_ES_PROJECT, version = "main", isCurrent=True,
packages = [GLCTS_NOCTX_ES2_KHR_PKG, GLCTS_NOCTX_ES32_KHR_PKG]),
Mustpass(project = CTS_KHR_MP_SINGLE_ES_PROJECT, version = "main", isCurrent=True,
packages = [GLCTS_SINGLE_ES32_KHR_PKG]),
Mustpass(project = CTS_AOSP_MP_ES_PROJECT, version = "main", isCurrent=True,
packages = [GLCTS_3_2_3_GLES2_PKG, GLCTS_3_2_3_GLES3_PKG, GLCTS_3_2_3_GLES31_PKG]),
Mustpass(project = CTS_AOSP_MP_EGL_PROJECT, version = "main", isCurrent=True,
packages = [GLCTS_3_2_3_EGL_PKG])
]
ES_BUILD_CONFIG = BuildConfig(buildPath, "Debug", ["-DDEQP_TARGET=%s" % DEFAULT_TARGET, "-DGLCTS_GTF_TARGET=gles32"])
#-------------------------------------------------- GL MUSTPASS----------------------------------------------------------------------
GL_CTS_MP_INC_DIR = os.path.join(DEQP_DIR, "external", "openglcts", "modules", "runner")
GL_CTS_KHR_MP_DATA_DIR = os.path.join(DEQP_DIR, "external", "openglcts", "data", "mustpass", "gl", "khronos_mustpass")
GL_CTS_KHR_MP_DEVICE_DIR = "gl_cts/data/mustpass/gl/khronos_mustpass"
GL_CTS_KHR_MP_PROJECT = Project(name = "Khronos Mustpass GL", path = GL_CTS_KHR_MP_DATA_DIR, incpath = GL_CTS_MP_INC_DIR, devicepath = GL_CTS_KHR_MP_DEVICE_DIR, copyright = COPYRIGHT_DECLARATION)
GL_CTS_KHR_MP_NOCTX_DATA_DIR = os.path.join(DEQP_DIR, "external", "openglcts", "data", "mustpass", "gl", "khronos_mustpass_noctx")
GL_CTS_KHR_MP_NOCTX_DEVICE_DIR = "gl_cts/data/mustpass/gl/khronos_mustpass_noctx"
GL_CTS_NOCTX_PROJECT = Project(name = "Khronos Mustpass GL NoContext", path = GL_CTS_KHR_MP_NOCTX_DATA_DIR, incpath = GL_CTS_MP_INC_DIR, devicepath = GL_CTS_KHR_MP_NOCTX_DEVICE_DIR, copyright = COPYRIGHT_DECLARATION)
GL_CTS_KHR_MP_SINGLE_DATA_DIR = os.path.join(DEQP_DIR, "external", "openglcts", "data", "mustpass", "gl", "khronos_mustpass_single")
GL_CTS_KHR_MP_SINGLE_DEVICE_DIR = "gl_cts/data/mustpass/gl/khronos_mustpass_single"
GL_CTS_KHR_SINGLE_PROJECT = Project(name = "Khronos Mustpass GL Single Config", path = GL_CTS_KHR_MP_SINGLE_DATA_DIR, incpath = GL_CTS_MP_INC_DIR, devicepath = GL_CTS_KHR_MP_SINGLE_DEVICE_DIR, copyright = COPYRIGHT_DECLARATION)
GL_CTS_KHR_MP_GLES_DATA_DIR = os.path.join(DEQP_DIR, "external", "openglcts", "data", "mustpass", "gl", "aosp_mustpass")
GL_CTS_KHR_MP_GLES_DEVICE_DIR = "gl_cts/data/mustpass/gl/aosp_mustpass"
GL_CTS_GLES_PROJECT = Project(name = "Khronos Mustpass AOSP for GL", path = GL_CTS_KHR_MP_GLES_DATA_DIR, incpath = GL_CTS_MP_INC_DIR, devicepath = GL_CTS_KHR_MP_GLES_DEVICE_DIR, copyright = COPYRIGHT_DECLARATION)
GL_MODULES = OrderedDict([
('KHR-GL46', ['master', [include('gl46-master.txt'), exclude('gl46-test-issues.txt')]]),
('KHR-GL45', ['master', [include('gl45-master.txt'), exclude('gl45-test-issues.txt')]]),
('KHR-GL44', ['master', [include('gl44-master.txt'), exclude('gl44-test-issues.txt')]]),
('KHR-GL43', ['master', [include('gl43-master.txt'), exclude('gl43-test-issues.txt')]]),
('KHR-GL42', ['master', [include('gl42-master.txt'), exclude('gl42-test-issues.txt')]]),
('KHR-GL42-COMPAT', ['master', [include('gl42-compat-master.txt')]]),
('KHR-GL41', ['master', [include('gl41-master.txt'), exclude('gl41-test-issues.txt')]]),
('KHR-GL40', ['master', [include('gl40-master.txt'), exclude('gl40-test-issues.txt')]]),
('KHR-GL33', ['master', [include('gl33-master.txt'), exclude('gl33-test-issues.txt')]]),
('KHR-GL32', ['master', [include('gl32-master.txt'), exclude('gl32-test-issues.txt')]]),
('KHR-GL31', ['master', [include('gl31-master.txt'), exclude('gl31-test-issues.txt')]]),
('KHR-GL30', ['master', [include('gl30-master.txt'), exclude('gl30-test-issues.txt')]]),
('GTF-GL46', ['gtf-master', [include('gl46-gtf-master.txt')]]),
('GTF-GL45', ['gtf-master', [include('gl45-gtf-master.txt')]]),
('GTF-GL44', ['gtf-master', [include('gl44-gtf-master.txt')]]),
('GTF-GL43', ['gtf-master', [include('gl43-gtf-master.txt')]]),
('GTF-GL42', ['gtf-master', [include('gl42-gtf-master.txt')]]),
('GTF-GL41', ['gtf-master', [include('gl41-gtf-master.txt')]]),
('GTF-GL40', ['gtf-master', [include('gl40-gtf-master.txt')]]),
('GTF-GL33', ['gtf-master', [include('gl33-gtf-master.txt')]]),
('GTF-GL32', ['gtf-master', [include('gl32-gtf-master.txt')]]),
('GTF-GL31', ['gtf-master', [include('gl31-gtf-master.txt')]]),
('GTF-GL30', ['gtf-master', [include('gl30-gtf-master.txt')]])
])
NOCTX_GL30_KHR_MODULE = getModuleByName("KHR-NOCTX-GL30")
NOCTX_GL40_KHR_MODULE = getModuleByName("KHR-NOCTX-GL40")
NOCTX_GL43_KHR_MODULE = getModuleByName("KHR-NOCTX-GL43")
NOCTX_GL45_KHR_MODULE = getModuleByName("KHR-NOCTX-GL45")
SINGLE_GL43_KHR_MODULE = getModuleByName("KHR-Single-GL43")
SINGLE_GL44_KHR_MODULE = getModuleByName("KHR-Single-GL44")
SINGLE_GL45_KHR_MODULE = getModuleByName("KHR-Single-GL45")
SINGLE_GL46_KHR_MODULE = getModuleByName("KHR-Single-GL46")
GLCTS_NOCTX_GL30_KHR_PKG = Package(module = NOCTX_GL30_KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gl30-khr-master.txt")]),
])
GLCTS_NOCTX_GL40_KHR_PKG = Package(module = NOCTX_GL40_KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gl40-khr-master.txt")]),
])
GLCTS_NOCTX_GL43_KHR_PKG = Package(module = NOCTX_GL43_KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gl43-khr-master.txt")]),
])
GLCTS_NOCTX_GL45_KHR_PKG = Package(module = NOCTX_GL45_KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-master",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gl45-khr-master.txt")]),
])
GLCTS_SINGLE_GL43_KHR_PKG = Package(module = SINGLE_GL43_KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-single",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gl43-khr-single.txt")]),
])
GLCTS_SINGLE_GL44_KHR_PKG = Package(module = SINGLE_GL44_KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-single",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gl44-khr-single.txt")]),
])
GLCTS_SINGLE_GL45_KHR_PKG = Package(module = SINGLE_GL45_KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-single",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gl45-khr-single.txt")]),
])
GLCTS_SINGLE_GL46_KHR_PKG = Package(module = SINGLE_GL46_KHR_MODULE, configurations = [
# Master
Configuration(name = "khr-single",
surfacewidth = "64",
surfaceheight = "64",
baseseed = "1",
filters = [include("gl46-khr-single.txt")]),
])
MAIN_GL_ES3_PKG = Package(module = GL45ES3_MODULE, configurations = [
# Master
Configuration(name = "es3-master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = [include("gl45es3-master.txt"), exclude("gl45es3-test-issues.txt"), exclude("gl45es3-spec-issues.txt")])
])
MAIN_GL_ES31_PKG = Package(module = GL45ES31_MODULE, configurations = [
# Master
Configuration(name = "es31-master",
glconfig = "rgba8888d24s8ms0",
rotation = "unspecified",
surfacewidth = "256",
surfaceheight = "256",
filters = [include("gl45es31-master.txt"), exclude("gl45es31-test-issues.txt"), exclude("gl45es31-spec-issues.txt")])
])
def generateGLMustpass():
gl_packages = []
for packageName in GL_MODULES:
cfgName = GL_MODULES[packageName][0]
cfgFilter = GL_MODULES[packageName][1]
config_w64xh64 = Configuration(name = cfgName, surfacewidth = "64", surfaceheight = "64", baseseed = "1", filters = cfgFilter)
config_w113xh47 = Configuration(name = cfgName, surfacewidth = "113", surfaceheight = "47", baseseed = "2", filters = cfgFilter)
config_w64 = Configuration(name = cfgName, surfacewidth = "64", surfaceheight = "-1", baseseed = "3", fboconfig = "rgba8888d24s8", filters = cfgFilter)
config_h64 = Configuration(name = cfgName, surfacewidth = "-1", surfaceheight = "64", baseseed = "3", fboconfig = "rgba8888d24s8", filters = cfgFilter)
pkgModule = getModuleByName(packageName)
pkg0 = Package(module = pkgModule,
useforfirsteglconfig = True,
configurations = [
config_w64xh64, config_w113xh47, config_w64, config_h64
]
)
pkg1 = Package(module = pkgModule,
useforfirsteglconfig = False,
configurations = [
config_w64xh64, config_w113xh47,
]
)
gl_packages.append(pkg0)
gl_packages.append(pkg1)
mustpass = [Mustpass(project = GL_CTS_KHR_MP_PROJECT, version = "4.6.0.x", isCurrent=False, packages = gl_packages),
Mustpass(project = GL_CTS_NOCTX_PROJECT, version = "4.6.0.x", isCurrent=False, packages = [GLCTS_NOCTX_GL30_KHR_PKG, GLCTS_NOCTX_GL40_KHR_PKG, GLCTS_NOCTX_GL43_KHR_PKG, GLCTS_NOCTX_GL45_KHR_PKG]),
Mustpass(project = GL_CTS_KHR_MP_PROJECT, version = "4.6.1.x", isCurrent=False, packages = gl_packages),
Mustpass(project = GL_CTS_NOCTX_PROJECT, version = "4.6.1.x", isCurrent=False, packages = [GLCTS_NOCTX_GL30_KHR_PKG, GLCTS_NOCTX_GL40_KHR_PKG, GLCTS_NOCTX_GL43_KHR_PKG, GLCTS_NOCTX_GL45_KHR_PKG]),
Mustpass(project = GL_CTS_KHR_SINGLE_PROJECT, version = "4.6.1.x", isCurrent=False, packages = [GLCTS_SINGLE_GL43_KHR_PKG, GLCTS_SINGLE_GL44_KHR_PKG, GLCTS_SINGLE_GL45_KHR_PKG, GLCTS_SINGLE_GL46_KHR_PKG]),
Mustpass(project = GL_CTS_GLES_PROJECT, version = "4.6.1.x", isCurrent=False, packages = [MAIN_GL_ES3_PKG, MAIN_GL_ES31_PKG]),
Mustpass(project = GL_CTS_KHR_MP_PROJECT, version = "main", isCurrent=True, packages = gl_packages),
Mustpass(project = GL_CTS_NOCTX_PROJECT, version = "main", isCurrent=True, packages = [GLCTS_NOCTX_GL30_KHR_PKG, GLCTS_NOCTX_GL40_KHR_PKG, GLCTS_NOCTX_GL43_KHR_PKG, GLCTS_NOCTX_GL45_KHR_PKG]),
Mustpass(project = GL_CTS_KHR_SINGLE_PROJECT, version = "main", isCurrent=True, packages = [GLCTS_SINGLE_GL43_KHR_PKG, GLCTS_SINGLE_GL44_KHR_PKG, GLCTS_SINGLE_GL45_KHR_PKG, GLCTS_SINGLE_GL46_KHR_PKG]),
Mustpass(project = GL_CTS_GLES_PROJECT, version = "main", isCurrent=True, packages = [MAIN_GL_ES3_PKG, MAIN_GL_ES31_PKG]),
]
return mustpass
GL_BUILD_CONFIG = BuildConfig(buildPath, "Debug", ["-DDEQP_TARGET=%s" % DEFAULT_TARGET, "-DGLCTS_GTF_TARGET=gl"])
if __name__ == "__main__":
gtfCMakeLists = os.path.join(DEQP_DIR, "external", "kc-cts", "src", "GTF_ES", "CMakeLists.txt")
if os.path.isfile(gtfCMakeLists) == False:
raise Exception("GTF sources not found. GTF module is required to build the mustpass files. 'cd external && python fetch_kc_cts.py'")
genMustpassLists(ES_MUSTPASS_LISTS, ANY_GENERATOR, ES_BUILD_CONFIG)
gl_mustpass_lists = generateGLMustpass()
genMustpassLists(gl_mustpass_lists, ANY_GENERATOR, GL_BUILD_CONFIG)
| 37.854992 | 229 | 0.665125 | 5,504 | 47,773 | 5.485465 | 0.046148 | 0.060811 | 0.006061 | 0.014838 | 0.876656 | 0.824424 | 0.807366 | 0.785904 | 0.770866 | 0.744767 | 0 | 0.061237 | 0.192263 | 47,773 | 1,261 | 230 | 37.885012 | 0.721183 | 0.033324 | 0 | 0.710088 | 0 | 0.000979 | 0.211188 | 0.049884 | 0 | 0 | 0 | 0 | 0 | 1 | 0.000979 | false | 0.066601 | 0.006856 | 0 | 0.008815 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
ed87132c1b11ab635b863d5151b5564cb977f818 | 10,163 | py | Python | makahiki/apps/widgets/raffle/tests/view_raffle_tests.py | justinslee/Wai-Not-Makahiki | 4b7dd685012ec64758affe0ecee3103596d16aa7 | [
"MIT"
] | 1 | 2015-07-22T11:31:20.000Z | 2015-07-22T11:31:20.000Z | makahiki/apps/widgets/raffle/tests/view_raffle_tests.py | justinslee/Wai-Not-Makahiki | 4b7dd685012ec64758affe0ecee3103596d16aa7 | [
"MIT"
] | null | null | null | makahiki/apps/widgets/raffle/tests/view_raffle_tests.py | justinslee/Wai-Not-Makahiki | 4b7dd685012ec64758affe0ecee3103596d16aa7 | [
"MIT"
] | null | null | null | """Raffle View Test"""
import datetime
import re
from django.test import TransactionTestCase
from django.core.urlresolvers import reverse
from apps.managers.challenge_mgr import challenge_mgr
from apps.managers.challenge_mgr.models import RoundSetting
from apps.utils import test_utils
from apps.widgets.raffle.models import RafflePrize
class RafflePrizesTestCase(TransactionTestCase):
"""Raffle Test"""
fixtures = ["demo_teams.json"]
def setUp(self):
"""Set up rounds, team, and a user."""
# Set up rounds.
test_utils.set_two_rounds()
# Set up user
self.user = test_utils.setup_user(username="user", password="changeme")
challenge_mgr.register_page_widget("win", "raffle")
self.client.login(username="user", password="changeme")
def testIndex(self):
"""Check that we can load the index page."""
raffle_prize = RafflePrize(
title="Test raffle prize",
description="A raffle prize for testing",
round=RoundSetting.objects.get(name="Round 3"),
value=5,
)
raffle_prize.save()
response = self.client.get(reverse("win_index"))
self.failUnlessEqual(response.status_code, 200)
self.assertContains(response, "Round 2 Raffle",
msg_prefix="We should be in round 2 of the raffle.")
print response
self.assertContains(response,
"Your Total Raffle Tickets: <em class=\"raffle-ticket-num\">0</em>,",
msg_prefix="User should not have any raffle tickets.")
self.assertContains(response,
"Allocated: <em class=\"raffle-ticket-num\">0</em>,",
msg_prefix="User should not have any raffle tickets.")
self.assertContains(response,
"Available: <em class=\"raffle-ticket-num\">0</em>",
msg_prefix="User should not have any raffle tickets.")
deadline = challenge_mgr.get_round_info()["end"]
date_string = deadline.strftime("%b. %d, %Y, %I:%M ")
date_string = re.sub(r" \b0", " ", date_string)
#self.assertContains(response, "Deadline for Round 2 submissions: " + date_string,
# msg_prefix="Raffle should have the correct deadline.")
# Give the user some points and see if their tickets update.
profile = self.user.get_profile()
profile.add_points(25, datetime.datetime.today(), "test")
profile.save()
response = self.client.get(reverse("win_index"))
self.assertContains(response,
"Your Total Raffle Tickets: <em class=\"raffle-ticket-num\">1</em>",
msg_prefix="User should have 1 raffle ticket.")
self.assertContains(response,
"Allocated: <em class=\"raffle-ticket-num\">0</em>,",
msg_prefix="User should have 1 raffle ticket.")
self.assertContains(response,
"Available: <em class=\"raffle-ticket-num\">1</em>",
msg_prefix="User should have 1 raffle ticket.")
def testAddRemoveTicket(self):
"""Test that we can add and remove a ticket for a prize."""
raffle_prize = RafflePrize(
title="Test raffle prize",
description="A raffle prize for testing",
round=RoundSetting.objects.get(name="Round 2"),
value=5,
)
raffle_prize.save()
profile = self.user.get_profile()
profile.add_points(25, datetime.datetime.today(), "test")
profile.save()
# Test that we can add a ticket.
response = self.client.get(reverse("win_index"))
self.assertContains(response, reverse("raffle_add_ticket", args=(raffle_prize.id,)),
msg_prefix="There should be a url to add a ticket.")
# Test adding a ticket to a prize.
response = self.client.post(reverse("raffle_add_ticket", args=(raffle_prize.id,)),
follow=True)
self.failUnlessEqual(response.status_code, 200)
self.assertContains(response,
"Your Total Raffle Tickets: <em class=\"raffle-ticket-num\">1</em>",
msg_prefix="User should have one allocated ticket.")
self.assertContains(response,
"Allocated: <em class=\"raffle-ticket-num\">1</em>,",
msg_prefix="User should have one allocated ticket.")
self.assertContains(response,
"Available: <em class=\"raffle-ticket-num\">0</em>",
msg_prefix="User should have one allocated ticket.")
self.assertContains(response, reverse("raffle_remove_ticket", args=(raffle_prize.id,)),
msg_prefix="There should be an url to remove a ticket.")
self.assertNotContains(response, reverse("raffle_add_ticket", args=(raffle_prize.id,)),
msg_prefix="There should not be an url to add a ticket.")
# Test adding another ticket to the prize.
profile.add_points(25, datetime.datetime.today(), "test")
profile.save()
response = self.client.post(reverse("raffle_add_ticket", args=(raffle_prize.id,)),
follow=True)
self.assertContains(response,
"Your Total Raffle Tickets: <em class=\"raffle-ticket-num\">2</em>",
msg_prefix="User should have two allocated tickets.")
self.assertContains(response,
"Allocated: <em class=\"raffle-ticket-num\">2</em>,",
msg_prefix="User should have two allocated tickets.")
self.assertContains(response,
"Available: <em class=\"raffle-ticket-num\">0</em>",
msg_prefix="User should have two allocated tickets.")
# Test removing a ticket.
response = self.client.post(reverse("raffle_remove_ticket", args=(raffle_prize.id,)),
follow=True)
self.assertContains(response,
"Your Total Raffle Tickets: <em class=\"raffle-ticket-num\">2</em>",
msg_prefix="User should have one allocated ticket and one available.")
self.assertContains(response,
"Allocated: <em class=\"raffle-ticket-num\">1</em>,",
msg_prefix="User should have one allocated ticket and one available.")
self.assertContains(response,
"Available: <em class=\"raffle-ticket-num\">1</em>",
msg_prefix="User should have one allocated ticket and one available.")
self.assertContains(response, reverse("raffle_add_ticket", args=(raffle_prize.id,)),
msg_prefix="There should be a url to add a ticket.")
self.assertContains(response, reverse("raffle_remove_ticket", args=(raffle_prize.id,)),
msg_prefix="There should be an url to remove a ticket.")
def testAddRemoveWithoutTicket(self):
"""Test that the user cannot remove a ticket from a prize they did not
allocate tickets in."""
raffle_prize = RafflePrize(
title="Test raffle prize",
description="A raffle prize for testing",
round=RoundSetting.objects.get(name="Round 1"),
value=5,
)
raffle_prize.save()
# Test removing a ticket.
response = self.client.post(reverse("raffle_remove_ticket", args=(raffle_prize.id,)),
follow=True)
self.failUnlessEqual(response.status_code, 200)
self.assertContains(response,
"Your Total Raffle Tickets: <em class=\"raffle-ticket-num\">0</em>",
msg_prefix="User should have no tickets available")
self.assertContains(response,
"Allocated: <em class=\"raffle-ticket-num\">0</em>,",
msg_prefix="User should have no tickets available")
self.assertContains(response,
"Available: <em class=\"raffle-ticket-num\">0</em>",
msg_prefix="User should have no tickets available")
self.assertNotContains(response, reverse("raffle_add_ticket", args=(raffle_prize.id,)),
msg_prefix="There should not be a url to add a ticket.")
self.assertNotContains(response, reverse("raffle_remove_ticket", args=(raffle_prize.id,)),
msg_prefix="There should not be a url to remove a ticket.")
def testAddWithoutTicket(self):
"""
Test that the user cannot add a ticket to a raffle if they don't have any tickets.
"""
raffle_prize = RafflePrize(
title="Test raffle prize",
description="A raffle prize for testing",
round=RoundSetting.objects.get(name="Round 1"),
value=5,
)
raffle_prize.save()
# Test adding a ticket.
response = self.client.post(reverse("raffle_add_ticket", args=(raffle_prize.id,)),
follow=True)
self.failUnlessEqual(response.status_code, 200)
self.assertContains(response,
"Your Total Raffle Tickets: <em class=\"raffle-ticket-num\">0</em>",
msg_prefix="User should have no tickets available")
self.assertContains(response,
"Allocated: <em class=\"raffle-ticket-num\">0</em>,",
msg_prefix="User should have no tickets available")
self.assertContains(response,
"Available: <em class=\"raffle-ticket-num\">0</em>",
msg_prefix="User should have no tickets available")
self.assertNotContains(response, reverse("raffle_add_ticket", args=(raffle_prize.id,)),
msg_prefix="There should not be a url to add a ticket.")
self.assertNotContains(response, reverse("raffle_remove_ticket", args=(raffle_prize.id,)),
msg_prefix="There should not be a url to remove a ticket.")
def testPrizeOutsideOfRound(self):
"""
Test that a raffle prize outside of the round does not appear in the list.
"""
raffle_prize = RafflePrize(
title="Test raffle prize",
description="A raffle prize for testing",
round=RoundSetting.objects.get(name="Round 1"),
value=5,
)
raffle_prize.save()
response = self.client.get(reverse("win_index"))
self.failUnlessEqual(response.status_code, 200)
self.assertNotContains(response, "Test raffle prize")
| 46.406393 | 98 | 0.631408 | 1,225 | 10,163 | 5.142041 | 0.124082 | 0.062867 | 0.111446 | 0.063343 | 0.803778 | 0.785363 | 0.776949 | 0.771392 | 0.770281 | 0.763772 | 0 | 0.007733 | 0.249237 | 10,163 | 218 | 99 | 46.619266 | 0.817824 | 0.03926 | 0 | 0.782353 | 0 | 0 | 0.285992 | 0 | 0 | 0 | 0 | 0 | 0.188235 | 0 | null | null | 0.011765 | 0.047059 | null | null | 0.005882 | 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 |
9c09a4d6bcdd9d180121ea535eef66daf2c17128 | 378,251 | pyt | Python | eran/NNet/nnet/ACASXU_run2a_5_8_batch_2000_16bit.pyt | pauls658/ReluDiff-ICSE2020-Artifact | 212854fe04f482183c239e5dfec70106a9a83df8 | [
"Apache-2.0"
] | 7 | 2020-01-27T21:25:49.000Z | 2022-01-07T04:37:37.000Z | eran/NNet/nnet/ACASXU_run2a_5_8_batch_2000_16bit.pyt | yqtianust/ReluDiff-ICSE2020-Artifact | 149f6efe4799602db749faa576980c36921a07c7 | [
"Apache-2.0"
] | 1 | 2022-01-25T17:41:54.000Z | 2022-01-26T02:27:51.000Z | eran/NNet/nnet/ACASXU_run2a_5_8_batch_2000_16bit.pyt | yqtianust/ReluDiff-ICSE2020-Artifact | 149f6efe4799602db749faa576980c36921a07c7 | [
"Apache-2.0"
] | 3 | 2020-03-14T17:12:17.000Z | 2022-03-16T09:50:46.000Z | ReLU
[[-1.22364, -0.00944126, -0.810391, 0.0674626, 0.0331024], [-0.324506, 2.51008, -3.26326, -0.0167542, 0.557671], [-0.175872, 1.61603, -1.68477, -0.0200658, -0.632399], [-0.110428, 1.07836, 1.22588, 0.503452, -0.184421], [-0.0433206, 0.548392, 1.48964, -0.150276, 0.146735], [0.0403693, 0.566056, -0.535388, 0.0209682, -1.17675], [-0.166577, 0.309364, 0.119931, -0.343652, 0.413484], [-0.000778406, 1.28686, -1.5959, -0.0375963, 0.479811], [0.094843, -1.14365, 0.0593118, 0.309324, -0.483113], [0.0679282, -0.0567188, 0.0297961, -0.324139, -0.186163], [-6.25029e-05, 0.000151498, 0.0253881, -0.0143051, 0.00214161], [1.08379, 0.0125729, -0.00811056, 0.080149, 0.0550935], [-0.0660049, 1.08944, 0.098722, 0.174017, 0.243226], [0.0214981, -1.34225, -1.2069, 0.576033, -0.223012], [-0.11648, -0.613922, 0.682569, -0.00292964, -0.205024], [-0.00434708, -0.558844, -0.0721779, 0.204131, -0.012846], [-0.137321, -1.60403, -0.313702, -0.227908, 0.473758], [-0.0836317, -2.4342, 1.74218, 0.0508148, -0.370485], [0.0401923, -0.475003, -0.872888, 0.363054, -0.391918], [-0.0282808, 0.0506169, 0.256596, -0.82379, 0.82544], [-1.13584, -0.210861, 0.0908107, -0.293971, 0.0586987], [0.12293, -1.24712, 1.0498, 0.165529, -0.175538], [0.0409494, 0.732379, -1.32052, -0.38384, 0.219592], [0.121014, 1.54491, -0.137242, 0.349345, -0.508539], [0.314288, 0.426539, 0.368413, -0.0184869, 0.0652885], [-0.0426176, -1.42517, 1.67703, -0.0328494, 0.517838], [0.00348179, 0.568789, -1.18383, 0.418485, -0.40431], [0.0260755, 2.12133, -0.159878, 0.483853, -0.62792], [-0.0474103, -0.942987, 1.09201, -0.048571, 0.208633], [-0.010326, -0.0426827, 0.0414574, -0.0116507, -1.69004], [-0.0632964, -1.76113, -0.0352928, 0.342101, -0.532949], [-0.0571681, -0.566805, -0.503282, -0.311096, 0.19237], [-0.0802596, 1.33464, -1.71486, -0.05862, 0.480875], [-0.0223692, -0.463606, -1.37601, -0.113315, 0.134162], [0.0186341, 0.614076, -0.789113, -0.153881, 0.299923], [-0.0907316, -0.226533, -0.0137597, 0.438472, -0.513207], [0.168957, 1.18998, 0.204045, 0.510663, -0.0425877], [0.0919504, 0.784212, -1.24023, -0.000309889, -0.0953461], [-2.27536, 0.12375, -0.130336, 0.0569241, -0.000754443], [-0.0073761, 1.24143, -0.861916, -0.00813758, 0.140988], [-0.0508644, 1.82126, 0.122728, -0.00704721, -0.323328], [-0.827136, 0.185072, 0.0413587, 0.0921635, -0.188473], [0.0151528, 0.424028, -0.38103, 1.07357, -1.24138], [-0.143119, -1.31388, 1.55046, -0.0491276, 0.510088], [0.0129448, -0.302139, 0.0305339, 0.423848, 0.151605], [-0.111765, 0.829996, 0.234447, 0.787289, -0.831681], [-2.07231, -0.119973, 0.132682, 0.0599825, 9.62287e-05], [-0.0367581, 0.0440479, -1.26881, 0.0758394, 0.132676], [0.412411, -0.261863, 0.856361, 0.958962, -1.16471], [0.088841, 0.209498, 0.306201, 0.23334, 0.620798], [-1.224, -0.00944, -0.8105, 0.06744, 0.0331], [-0.3245, 2.51, -3.264, -0.01675, 0.5576], [-0.1759, 1.616, -1.685, -0.02007, -0.6323], [-0.1104, 1.078, 1.226, 0.5034, -0.1844], [-0.04333, 0.5483, 1.489, -0.1503, 0.1467], [0.04037, 0.566, -0.535, 0.02097, -1.177], [-0.1666, 0.3093, 0.11993, -0.3438, 0.4136], [-0.000778, 1.287, -1.596, -0.0376, 0.4797], [0.09485, -1.144, 0.05933, 0.3093, -0.4832], [0.06793, -0.05673, 0.0298, -0.3242, -0.1862], [-6.25e-05, 0.0001515, 0.02539, -0.014305, 0.002142], [1.084, 0.01257, -0.00811, 0.08014, 0.05508], [-0.066, 1.09, 0.0987, 0.1741, 0.2433], [0.0215, -1.342, -1.207, 0.576, -0.223], [-0.11646, -0.614, 0.6826, -0.00293, -0.2051], [-0.00435, -0.559, -0.0722, 0.2041, -0.01285], [-0.1373, -1.6045, -0.3137, -0.2279, 0.4739], [-0.0836, -2.434, 1.742, 0.0508, -0.3706], [0.0402, -0.475, -0.873, 0.363, -0.3918], [-0.02827, 0.05063, 0.2566, -0.8237, 0.8257], [-1.136, -0.2108, 0.0908, -0.294, 0.0587], [0.1229, -1.247, 1.05, 0.1655, -0.1755], [0.04095, 0.7324, -1.32, -0.3838, 0.2196], [0.12103, 1.545, -0.1372, 0.3494, -0.5083], [0.3142, 0.4265, 0.3684, -0.0185, 0.0653], [-0.0426, -1.425, 1.677, -0.03284, 0.518], [0.00348, 0.569, -1.184, 0.4185, -0.4043], [0.02608, 2.121, -0.1599, 0.484, -0.628], [-0.04742, -0.943, 1.092, -0.04858, 0.2086], [-0.01032, -0.0427, 0.04144, -0.01165, -1.69], [-0.0633, -1.761, -0.03528, 0.342, -0.5327], [-0.05716, -0.567, -0.5034, -0.311, 0.1924], [-0.08026, 1.335, -1.715, -0.05862, 0.481], [-0.02237, -0.4636, -1.376, -0.11334, 0.1342], [0.01863, 0.6143, -0.789, -0.1539, 0.2998], [-0.09076, -0.2266, -0.01376, 0.4385, -0.513], [0.169, 1.19, 0.2041, 0.5107, -0.0426], [0.092, 0.784, -1.24, -0.00031, -0.09534], [-2.275, 0.1238, -0.1304, 0.05692, -0.0007544], [-0.007378, 1.241, -0.862, -0.00814, 0.141], [-0.05087, 1.821, 0.12274, -0.007046, -0.3232], [-0.827, 0.185, 0.04135, 0.09216, -0.1885], [0.01515, 0.424, -0.381, 1.073, -1.241], [-0.1431, -1.313, 1.551, -0.04913, 0.5103], [0.01295, -0.3022, 0.03053, 0.4238, 0.1516], [-0.11176, 0.83, 0.2345, 0.787, -0.8315], [-2.072, -0.12, 0.1327, 0.06, 9.62e-05], [-0.03674, 0.04404, -1.269, 0.07587, 0.1327], [0.4124, -0.262, 0.8564, 0.959, -1.165], [0.08887, 0.2095, 0.3062, 0.2334, 0.6206]]
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ReLU
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ReLU
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ReLU
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ReLU
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ReLU
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[-0.0200873, 0.0181348, -0.0185215, 0.0184039, -0.0183086, -0.02008, 0.01813, -0.01852, 0.0184, -0.01831]
| 17,193.227273 | 72,907 | 0.547298 | 104,307 | 378,251 | 1.98468 | 0.175693 | 0.491257 | 0.729523 | 0.962959 | 0.249546 | 0.249449 | 0.249449 | 0.249449 | 0.249449 | 0.249449 | 0 | 0.63442 | 0.137784 | 378,251 | 21 | 72,908 | 18,011.952381 | 0.000337 | 0 | 0 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
9c1b8633d472f16e5708d5265e49a89f3a3f110b | 155 | py | Python | src/GridCal/Engine/Simulations/StateEstimation/__init__.py | mzy2240/GridCal | 0352f0e9ce09a9c037722bf2f2afc0a31ccd2880 | [
"BSD-3-Clause"
] | 284 | 2016-01-31T03:20:44.000Z | 2022-03-17T21:16:52.000Z | src/GridCal/Engine/Simulations/StateEstimation/__init__.py | mzy2240/GridCal | 0352f0e9ce09a9c037722bf2f2afc0a31ccd2880 | [
"BSD-3-Clause"
] | 94 | 2016-01-14T13:37:40.000Z | 2022-03-28T03:13:56.000Z | src/GridCal/Engine/Simulations/StateEstimation/__init__.py | mzy2240/GridCal | 0352f0e9ce09a9c037722bf2f2afc0a31ccd2880 | [
"BSD-3-Clause"
] | 84 | 2016-03-29T10:43:04.000Z | 2022-02-22T16:26:55.000Z |
from GridCal.Engine.Simulations.StateEstimation.state_stimation_driver import *
from GridCal.Engine.Simulations.StateEstimation.state_estimation import *
| 38.75 | 79 | 0.877419 | 17 | 155 | 7.823529 | 0.588235 | 0.165414 | 0.255639 | 0.421053 | 0.721805 | 0.721805 | 0 | 0 | 0 | 0 | 0 | 0 | 0.058065 | 155 | 3 | 80 | 51.666667 | 0.910959 | 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 | 1 | 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 |
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