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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e231e1eaa7ce384f59537a65dbeac3fb63856ca5 | 253 | py | Python | python_scripts_experiment/textprocess/line_to_dn.py | erc-dharma/tfd-sanskrit-philology | f4970fddc4583a9b950c6ba7331f4f7e0267b7fb | [
"CC-BY-4.0"
] | 1 | 2022-03-27T15:45:47.000Z | 2022-03-27T15:45:47.000Z | python_scripts_experiment/textprocess/line_to_dn.py | erc-dharma/tfd-sanskrit-philology | f4970fddc4583a9b950c6ba7331f4f7e0267b7fb | [
"CC-BY-4.0"
] | null | null | null | python_scripts_experiment/textprocess/line_to_dn.py | erc-dharma/tfd-sanskrit-philology | f4970fddc4583a9b950c6ba7331f4f7e0267b7fb | [
"CC-BY-4.0"
] | null | null | null | from textprocess import simpletxtdn_line
def line_to_dn(line):
"""
Just print out one line of Skt in Unicode converted to Devanagari
"""
print(simpletxtdn_line.simpletxtdn_line(line))
return simpletxtdn_line.simpletxtdn_line(line)
| 25.3 | 69 | 0.754941 | 34 | 253 | 5.411765 | 0.558824 | 0.407609 | 0.282609 | 0.326087 | 0.369565 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.177866 | 253 | 9 | 70 | 28.111111 | 0.884615 | 0.256917 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.25 | 0 | 0.75 | 0.25 | 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 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 |
e23a039671ae5f2db4d5f3c3aa1c3b08c7d90d41 | 14,683 | py | Python | src/fiesta/apps/base/migrations/0001_initial.py | lerooze/django-fiesta | d521f50bcdd3d40e91f0474ec2fa7e256758e0a5 | [
"BSD-3-Clause"
] | null | null | null | src/fiesta/apps/base/migrations/0001_initial.py | lerooze/django-fiesta | d521f50bcdd3d40e91f0474ec2fa7e256758e0a5 | [
"BSD-3-Clause"
] | 3 | 2019-10-29T23:31:01.000Z | 2020-03-31T03:08:28.000Z | src/fiesta/apps/base/migrations/0001_initial.py | lerooze/django-fiesta | d521f50bcdd3d40e91f0474ec2fa7e256758e0a5 | [
"BSD-3-Clause"
] | null | null | null | # Generated by Django 3.0b1 on 2019-10-29 18:12
import django.contrib.auth.models
import django.contrib.auth.validators
import django.core.validators
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
import re
import versionfield
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='User',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('password', models.CharField(max_length=128, verbose_name='password')),
('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')),
('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')),
('username', models.CharField(error_messages={'unique': 'A user with that username already exists.'}, help_text='Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.', max_length=150, unique=True, validators=[django.contrib.auth.validators.UnicodeUsernameValidator()], verbose_name='username')),
('first_name', models.CharField(blank=True, max_length=30, verbose_name='first name')),
('last_name', models.CharField(blank=True, max_length=150, verbose_name='last name')),
('email', models.EmailField(blank=True, max_length=254, verbose_name='email address')),
('is_staff', models.BooleanField(default=False, help_text='Designates whether the user can log into this admin site.', verbose_name='staff status')),
('is_active', models.BooleanField(default=True, help_text='Designates whether this user should be treated as active. Unselect this instead of deleting accounts.', verbose_name='active')),
('date_joined', models.DateTimeField(default=django.utils.timezone.now, verbose_name='date joined')),
],
options={
'verbose_name': 'User',
'verbose_name_plural': 'Users',
'abstract': False,
},
managers=[
('objects', django.contrib.auth.models.UserManager()),
],
),
migrations.CreateModel(
name='Agency',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('object_id', models.CharField(blank=True, db_index=True, max_length=31, validators=[django.core.validators.RegexValidator(re.compile('[A-Za-z][A-Za-z0-9_\\-]*(\\.[A-Za-z][A-Za-z0-9_\\-]*)*'), 'Enter a value of type NestedNCNameIDType that has pattern "([A-Za-z][A-Za-z0-9_\\-]*(\\.[A-Za-z][A-Za-z0-9_\\-]*)*)"', 'invalid_pattern')], verbose_name='ID')),
('name', models.CharField(blank=True, max_length=127, verbose_name='Name')),
('name_en', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('name_el', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('description', models.CharField(blank=True, max_length=511, verbose_name='Description')),
('description_en', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('description_el', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
],
options={
'verbose_name': 'Agency',
'verbose_name_plural': 'Agencies',
'abstract': False,
},
),
migrations.CreateModel(
name='Annotation',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('object_id', models.CharField(blank=True, max_length=31, verbose_name='ID')),
('annotation_title', models.CharField(blank=True, max_length=127, verbose_name='title')),
('annotation_type', models.CharField(blank=True, max_length=31, verbose_name='type')),
('annotation_url', models.URLField(blank=True, verbose_name='URL')),
('text', models.TextField(blank=True, verbose_name='Text')),
('text_en', models.TextField(blank=True, null=True, verbose_name='Text')),
('text_el', models.TextField(blank=True, null=True, verbose_name='Text')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='DataConsumer',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(blank=True, max_length=127, verbose_name='Name')),
('name_en', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('name_el', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('description', models.CharField(blank=True, max_length=511, verbose_name='Description')),
('description_en', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('description_el', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('object_id', models.CharField(db_index=True, max_length=31, validators=[django.core.validators.RegexValidator(re.compile('^[A-Za-z0-9_@$\\-]+$'), 'Enter a value of type IDType that has pattern "(^[A-Za-z0-9_@$\\-]+$)"', 'invalid_pattern')], verbose_name='ID')),
],
options={
'verbose_name': 'Data consumer',
'verbose_name_plural': 'Data consumers',
'ordering': ['container', 'object_id'],
'abstract': False,
},
),
migrations.CreateModel(
name='DataConsumerScheme',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(blank=True, max_length=127, verbose_name='Name')),
('name_en', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('name_el', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('description', models.CharField(blank=True, max_length=511, verbose_name='Description')),
('description_en', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('description_el', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('version', versionfield.VersionField(default='1.0')),
('valid_from', models.DateTimeField(blank=True, null=True, verbose_name='Valid from')),
('valid_to', models.DateTimeField(blank=True, null=True, verbose_name='Valid to')),
('object_id', models.CharField(db_index=True, max_length=31, validators=[django.core.validators.RegexValidator(re.compile('^[A-Za-z0-9_@$\\-]+$'), 'Enter a value of type IDType that has pattern "(^[A-Za-z0-9_@$\\-]+$)"', 'invalid_pattern')], verbose_name='ID')),
('is_final', models.BooleanField(default=False, verbose_name='Is final')),
],
options={
'verbose_name': 'Data consumer scheme',
'verbose_name_plural': 'Data consumer schemes',
'ordering': ['agency', 'object_id', '-version'],
'abstract': False,
},
),
migrations.CreateModel(
name='DataProvider',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(blank=True, max_length=127, verbose_name='Name')),
('name_en', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('name_el', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('description', models.CharField(blank=True, max_length=511, verbose_name='Description')),
('description_en', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('description_el', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('object_id', models.CharField(db_index=True, max_length=31, validators=[django.core.validators.RegexValidator(re.compile('^[A-Za-z0-9_@$\\-]+$'), 'Enter a value of type IDType that has pattern "(^[A-Za-z0-9_@$\\-]+$)"', 'invalid_pattern')], verbose_name='ID')),
],
options={
'verbose_name': 'Data provider',
'verbose_name_plural': 'Data providers',
'ordering': ['container', 'object_id'],
'abstract': False,
},
),
migrations.CreateModel(
name='DataProviderScheme',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(blank=True, max_length=127, verbose_name='Name')),
('name_en', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('name_el', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('description', models.CharField(blank=True, max_length=511, verbose_name='Description')),
('description_en', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('description_el', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('version', versionfield.VersionField(default='1.0')),
('valid_from', models.DateTimeField(blank=True, null=True, verbose_name='Valid from')),
('valid_to', models.DateTimeField(blank=True, null=True, verbose_name='Valid to')),
('object_id', models.CharField(db_index=True, max_length=31, validators=[django.core.validators.RegexValidator(re.compile('^[A-Za-z0-9_@$\\-]+$'), 'Enter a value of type IDType that has pattern "(^[A-Za-z0-9_@$\\-]+$)"', 'invalid_pattern')], verbose_name='ID')),
('is_final', models.BooleanField(default=False, verbose_name='Is final')),
],
options={
'verbose_name': 'Data provider scheme',
'verbose_name_plural': 'Data provider schemes',
'ordering': ['agency', 'object_id', '-version'],
'abstract': False,
},
),
migrations.CreateModel(
name='OrganisationUnit',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('path', models.CharField(max_length=255, unique=True)),
('depth', models.PositiveIntegerField()),
('numchild', models.PositiveIntegerField(default=0)),
('name', models.CharField(blank=True, max_length=127, verbose_name='Name')),
('name_en', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('name_el', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('description', models.CharField(blank=True, max_length=511, verbose_name='Description')),
('description_en', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('description_el', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('object_id', models.CharField(db_index=True, max_length=31, validators=[django.core.validators.RegexValidator(re.compile('^[A-Za-z0-9_@$\\-]+$'), 'Enter a value of type IDType that has pattern "(^[A-Za-z0-9_@$\\-]+$)"', 'invalid_pattern')], verbose_name='ID')),
],
options={
'verbose_name': 'Organisation unit',
'verbose_name_plural': 'Organisation units',
'ordering': ['container', 'object_id'],
'abstract': False,
},
),
migrations.CreateModel(
name='OrganisationUnitScheme',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(blank=True, max_length=127, verbose_name='Name')),
('name_en', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('name_el', models.CharField(blank=True, max_length=127, null=True, verbose_name='Name')),
('description', models.CharField(blank=True, max_length=511, verbose_name='Description')),
('description_en', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('description_el', models.CharField(blank=True, max_length=511, null=True, verbose_name='Description')),
('version', versionfield.VersionField(default='1.0')),
('valid_from', models.DateTimeField(blank=True, null=True, verbose_name='Valid from')),
('valid_to', models.DateTimeField(blank=True, null=True, verbose_name='Valid to')),
('object_id', models.CharField(db_index=True, max_length=31, validators=[django.core.validators.RegexValidator(re.compile('^[A-Za-z0-9_@$\\-]+$'), 'Enter a value of type IDType that has pattern "(^[A-Za-z0-9_@$\\-]+$)"', 'invalid_pattern')], verbose_name='ID')),
('is_final', models.BooleanField(default=False, verbose_name='Is final')),
('agency', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='base.Agency', verbose_name='Agency')),
],
options={
'verbose_name': 'Organisation unit scheme',
'verbose_name_plural': 'Organisation unit schemes',
'ordering': ['agency', 'object_id', '-version'],
'abstract': False,
},
),
]
| 69.919048 | 370 | 0.60696 | 1,607 | 14,683 | 5.368388 | 0.114499 | 0.128782 | 0.082879 | 0.133534 | 0.776168 | 0.746841 | 0.746841 | 0.727136 | 0.716008 | 0.66628 | 0 | 0.019646 | 0.233876 | 14,683 | 209 | 371 | 70.253589 | 0.747266 | 0.003065 | 0 | 0.643564 | 1 | 0.009901 | 0.230459 | 0.018994 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.004951 | 0.039604 | 0 | 0.059406 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
e245722f9563a56a0e406609674278f6d30f4d1a | 39 | py | Python | login_register/forms.py | imranparuk/whether-the-weather | 347f3fe97975a6980a37cc60f32e65fdb04a89c6 | [
"MIT"
] | null | null | null | login_register/forms.py | imranparuk/whether-the-weather | 347f3fe97975a6980a37cc60f32e65fdb04a89c6 | [
"MIT"
] | 1 | 2020-06-05T18:25:45.000Z | 2020-06-05T18:25:45.000Z | login_register/forms.py | imranparuk/whether-the-weather | 347f3fe97975a6980a37cc60f32e65fdb04a89c6 | [
"MIT"
] | null | null | null | import django
from django import forms
| 13 | 24 | 0.846154 | 6 | 39 | 5.5 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.153846 | 39 | 2 | 25 | 19.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
e25bb55cc2f1c6184c56669c6989c8b8fc135b40 | 32 | py | Python | src/Enigma/__init__.py | vtarale/Chat | faa6b2168eb014d399a41b5c86a67edf57476819 | [
"MIT"
] | 1 | 2022-03-03T04:13:56.000Z | 2022-03-03T04:13:56.000Z | src/Enigma/__init__.py | vtarale/Chat | faa6b2168eb014d399a41b5c86a67edf57476819 | [
"MIT"
] | null | null | null | src/Enigma/__init__.py | vtarale/Chat | faa6b2168eb014d399a41b5c86a67edf57476819 | [
"MIT"
] | null | null | null | print("Thanks for using Enigma") | 32 | 32 | 0.78125 | 5 | 32 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.09375 | 32 | 1 | 32 | 32 | 0.862069 | 0 | 0 | 0 | 0 | 0 | 0.69697 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
e26cacb26840b6098fc1a9e81bdf92631c668fbe | 96 | py | Python | venv/lib/python3.8/site-packages/pip/_vendor/html5lib/treebuilders/__init__.py | GiulianaPola/select_repeats | 17a0d053d4f874e42cf654dd142168c2ec8fbd11 | [
"MIT"
] | 2 | 2022-03-13T01:58:52.000Z | 2022-03-31T06:07:54.000Z | venv/lib/python3.8/site-packages/pip/_vendor/html5lib/treebuilders/__init__.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | 19 | 2021-11-20T04:09:18.000Z | 2022-03-23T15:05:55.000Z | venv/lib/python3.8/site-packages/pip/_vendor/html5lib/treebuilders/__init__.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | null | null | null | /home/runner/.cache/pip/pool/03/2b/12/272bcf7e290230cb1356f6b1c2480389e10b0f975f47c149200baaee16 | 96 | 96 | 0.895833 | 9 | 96 | 9.555556 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.458333 | 0 | 96 | 1 | 96 | 96 | 0.4375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
e28b779dcc50cf0e44cf7eac42348d0171a1cacc | 109 | py | Python | src/domain/errors/invalid_image_path_failure.py | OzielFilho/ProjetoFinalPdi | c9e6fe415f1a985d6eeac204580d3ab623026665 | [
"MIT"
] | null | null | null | src/domain/errors/invalid_image_path_failure.py | OzielFilho/ProjetoFinalPdi | c9e6fe415f1a985d6eeac204580d3ab623026665 | [
"MIT"
] | null | null | null | src/domain/errors/invalid_image_path_failure.py | OzielFilho/ProjetoFinalPdi | c9e6fe415f1a985d6eeac204580d3ab623026665 | [
"MIT"
] | null | null | null | from domain.errors.image_failure import ImageFailure
class InvalidImagePathFailure(ImageFailure):
pass
| 18.166667 | 52 | 0.834862 | 11 | 109 | 8.181818 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.119266 | 109 | 5 | 53 | 21.8 | 0.9375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
e2a894eaa3be5c8fb07e9ef313a5733d0802bd30 | 7,901 | py | Python | tests/datetime/test_start_end_of.py | Sn3akyP3t3/pendulum | 7ce170bdc64199d74e09e347402983f1bb015f63 | [
"MIT"
] | 2 | 2021-11-08T02:45:29.000Z | 2021-11-08T09:41:03.000Z | tests/datetime/test_start_end_of.py | Sn3akyP3t3/pendulum | 7ce170bdc64199d74e09e347402983f1bb015f63 | [
"MIT"
] | null | null | null | tests/datetime/test_start_end_of.py | Sn3akyP3t3/pendulum | 7ce170bdc64199d74e09e347402983f1bb015f63 | [
"MIT"
] | 1 | 2019-01-26T17:42:15.000Z | 2019-01-26T17:42:15.000Z | import pendulum
import pytest
from ..conftest import assert_datetime
def test_start_of_second():
d = pendulum.now()
new = d.start_of('second')
assert isinstance(new, pendulum.DateTime)
assert_datetime(new, d.year, d.month, d.day, d.hour, d.minute, d.second, 0)
def test_end_of_second():
d = pendulum.now()
new = d.end_of('second')
assert isinstance(new, pendulum.DateTime)
assert_datetime(new, d.year, d.month, d.day, d.hour, d.minute, d.second, 999999)
def test_start_of_minute():
d = pendulum.now()
new = d.start_of('minute')
assert isinstance(new, pendulum.DateTime)
assert_datetime(new, d.year, d.month, d.day, d.hour, d.minute, 0, 0)
def test_end_of_minute():
d = pendulum.now()
new = d.end_of('minute')
assert isinstance(new, pendulum.DateTime)
assert_datetime(new, d.year, d.month, d.day, d.hour, d.minute, 59, 999999)
def test_start_of_hour():
d = pendulum.now()
new = d.start_of('hour')
assert isinstance(new, pendulum.DateTime)
assert_datetime(new, d.year, d.month, d.day, d.hour, 0, 0, 0)
def test_end_of_hour():
d = pendulum.now()
new = d.end_of('hour')
assert isinstance(new, pendulum.DateTime)
assert_datetime(new, d.year, d.month, d.day, d.hour, 59, 59, 999999)
def test_start_of_day():
d = pendulum.now()
new = d.start_of('day')
assert isinstance(new, pendulum.DateTime)
assert_datetime(new, d.year, d.month, d.day, 0, 0, 0, 0)
def test_end_of_day():
d = pendulum.now()
new = d.end_of('day')
assert isinstance(new, pendulum.DateTime)
assert_datetime(new, d.year, d.month, d.day, 23, 59, 59, 999999)
def test_start_of_month_is_fluid():
d = pendulum.now()
assert isinstance(d.start_of('month'), pendulum.DateTime)
def test_start_of_month_from_now():
d = pendulum.now()
new = d.start_of('month')
assert_datetime(new, d.year, d.month, 1, 0, 0, 0, 0)
def test_start_of_month_from_last_day():
d = pendulum.datetime(2000, 1, 31, 2, 3, 4)
new = d.start_of('month')
assert_datetime(new, 2000, 1, 1, 0, 0, 0, 0)
def test_start_of_year_is_fluid():
d = pendulum.now()
new = d.start_of('year')
assert isinstance(new, pendulum.DateTime)
def test_start_of_year_from_now():
d = pendulum.now()
new = d.start_of('year')
assert_datetime(new, d.year, 1, 1, 0, 0, 0, 0)
def test_start_of_year_from_first_day():
d = pendulum.datetime(2000, 1, 1, 1, 1, 1)
new = d.start_of('year')
assert_datetime(new, 2000, 1, 1, 0, 0, 0, 0)
def test_start_of_year_from_last_day():
d = pendulum.datetime(2000, 12, 31, 23, 59, 59)
new = d.start_of('year')
assert_datetime(new, 2000, 1, 1, 0, 0, 0, 0)
def test_end_of_month_is_fluid():
d = pendulum.now()
assert isinstance(d.end_of('month'), pendulum.DateTime)
def test_end_of_month():
d = pendulum.datetime(2000, 1, 1, 2, 3, 4).end_of('month')
new = d.end_of('month')
assert_datetime(new, 2000, 1, 31, 23, 59, 59)
def test_end_of_month_from_last_day():
d = pendulum.datetime(2000, 1, 31, 2, 3, 4)
new = d.end_of('month')
assert_datetime(new, 2000, 1, 31, 23, 59, 59)
def test_end_of_year_is_fluid():
d = pendulum.now()
assert isinstance(d.end_of('year'), pendulum.DateTime)
def test_end_of_year_from_now():
d = pendulum.now().end_of('year')
new = d.end_of('year')
assert_datetime(new, d.year, 12, 31, 23, 59, 59, 999999)
def test_end_of_year_from_first_day():
d = pendulum.datetime(2000, 1, 1, 1, 1, 1)
new = d.end_of('year')
assert_datetime(new, 2000, 12, 31, 23, 59, 59, 999999)
def test_end_of_year_from_last_day():
d = pendulum.datetime(2000, 12, 31, 23, 59, 59, 999999)
new = d.end_of('year')
assert_datetime(new, 2000, 12, 31, 23, 59, 59, 999999)
def test_start_of_decade_is_fluid():
d = pendulum.now()
assert isinstance(d.start_of('decade'), pendulum.DateTime)
def test_start_of_decade_from_now():
d = pendulum.now()
new = d.start_of('decade')
assert_datetime(new, d.year - d.year % 10, 1, 1, 0, 0, 0, 0)
def test_start_of_decade_from_first_day():
d = pendulum.datetime(2000, 1, 1, 1, 1, 1)
new = d.start_of('decade')
assert_datetime(new, 2000, 1, 1, 0, 0, 0, 0)
def test_start_of_decade_from_last_day():
d = pendulum.datetime(2009, 12, 31, 23, 59, 59)
new = d.start_of('decade')
assert_datetime(new, 2000, 1, 1, 0, 0, 0, 0)
def test_end_of_decade_is_fluid():
d = pendulum.now()
assert isinstance(d.end_of('decade'), pendulum.DateTime)
def test_end_of_decade_from_now():
d = pendulum.now()
new = d.end_of('decade')
assert_datetime(new, d.year - d.year % 10 + 9, 12, 31, 23, 59, 59, 999999)
def test_end_of_decade_from_first_day():
d = pendulum.datetime(2000, 1, 1, 1, 1, 1)
new = d.end_of('decade')
assert_datetime(new, 2009, 12, 31, 23, 59, 59, 999999)
def test_end_of_decade_from_last_day():
d = pendulum.datetime(2009, 12, 31, 23, 59, 59, 999999)
new = d.end_of('decade')
assert_datetime(new, 2009, 12, 31, 23, 59, 59, 999999)
def test_start_of_century_is_fluid():
d = pendulum.now()
assert isinstance(d.start_of('century'), pendulum.DateTime)
def test_start_of_century_from_now():
d = pendulum.now()
new = d.start_of('century')
assert_datetime(new, d.year - d.year % 100 + 1, 1, 1, 0, 0, 0, 0)
def test_start_of_century_from_first_day():
d = pendulum.datetime(2001, 1, 1, 1, 1, 1)
new = d.start_of('century')
assert_datetime(new, 2001, 1, 1, 0, 0, 0, 0)
def test_start_of_century_from_last_day():
d = pendulum.datetime(2100, 12, 31, 23, 59, 59)
new = d.start_of('century')
assert_datetime(new, 2001, 1, 1, 0, 0, 0, 0)
def test_end_of_century_is_fluid():
d = pendulum.now()
assert isinstance(d.end_of('century'), pendulum.DateTime)
def test_end_of_century_from_now():
now = pendulum.now()
d = now.end_of('century')
assert_datetime(d, now.year - now.year % 100 + 100, 12, 31, 23, 59, 59, 999999)
def test_end_of_century_from_first_day():
d = pendulum.datetime(2001, 1, 1, 1, 1, 1)
new = d.end_of('century')
assert_datetime(new, 2100, 12, 31, 23, 59, 59, 999999)
def test_end_of_century_from_last_day():
d = pendulum.datetime(2100, 12, 31, 23, 59, 59, 999999)
new = d.end_of('century')
assert_datetime(new, 2100, 12, 31, 23, 59, 59, 999999)
def test_average_is_fluid():
d = pendulum.now().average()
assert isinstance(d, pendulum.DateTime)
def test_average_from_same():
d1 = pendulum.datetime(2000, 1, 31, 2, 3, 4)
d2 = pendulum.datetime(2000, 1, 31, 2, 3, 4).average(d1)
assert_datetime(d2, 2000, 1, 31, 2, 3, 4)
def test_average_from_greater():
d1 = pendulum.datetime(2000, 1, 1, 1, 1, 1, tz='local')
d2 = pendulum.datetime(2009, 12, 31, 23, 59, 59, tz='local').average(d1)
assert_datetime(d2, 2004, 12, 31, 12, 30, 30)
def test_average_from_lower():
d1 = pendulum.datetime(2009, 12, 31, 23, 59, 59, tz='local')
d2 = pendulum.datetime(2000, 1, 1, 1, 1, 1, tz='local').average(d1)
assert_datetime(d2, 2004, 12, 31, 12, 30, 30)
def start_of_with_invalid_unit():
with pytest.raises(ValueError):
pendulum.now().start_of('invalid')
def end_of_with_invalid_unit():
with pytest.raises(ValueError):
pendulum.now().end_of('invalid')
def test_start_of_with_transition():
d = pendulum.datetime(2013, 10, 27, 23, 59, 59, tz='Europe/Paris')
assert d.offset == 3600
assert d.start_of('month').offset == 7200
assert d.start_of('day').offset == 7200
assert d.start_of('year').offset == 3600
def test_end_of_with_transition():
d = pendulum.datetime(2013, 3, 31, tz='Europe/Paris')
assert d.offset == 3600
assert d.end_of('month').offset == 7200
assert d.end_of('day').offset == 7200
assert d.end_of('year').offset == 3600
| 27.918728 | 84 | 0.658904 | 1,334 | 7,901 | 3.68066 | 0.050975 | 0.046843 | 0.100407 | 0.057026 | 0.929328 | 0.899389 | 0.805703 | 0.725458 | 0.685947 | 0.62668 | 0 | 0.109431 | 0.188077 | 7,901 | 282 | 85 | 28.017731 | 0.655963 | 0 | 0 | 0.428571 | 0 | 0 | 0.037337 | 0 | 0 | 0 | 0 | 0 | 0.312169 | 1 | 0.243386 | false | 0 | 0.015873 | 0 | 0.259259 | 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 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2c8ad69e07c20d0ebb68cce253756251749e7edf | 31,164 | py | Python | hostel.py | RisinPhoenix12/Hostel-Management-Database | 9c38323ede65f80a036525e488659256bef2052c | [
"MIT"
] | 1 | 2021-07-24T14:12:45.000Z | 2021-07-24T14:12:45.000Z | hostel.py | RisinPhoenix12/Hostel-Management-Database | 9c38323ede65f80a036525e488659256bef2052c | [
"MIT"
] | null | null | null | hostel.py | RisinPhoenix12/Hostel-Management-Database | 9c38323ede65f80a036525e488659256bef2052c | [
"MIT"
] | null | null | null | import tkinter as tk
import mysql.connector
from tkinter import *
from tkinter import ttk
class hostels:
def __init__(self,root):
self.root=root
self.root.geometry('500x500')
self.root.title('Hostel Management')
b1 = Button(self.root, text="Student",width=20,height=5,bg='#abcfd9', command=self.istud)
b2 = Button(self.root, text="Employee",width=20,height=5,bg='#abcfd9', command=self.iemp)
b3 = Button(self.root, text="Complaint",width=20,height=5,bg='#abcfd9', command=self.icomp)
b4 = Button(self.root, text="Gate Record",width=20,height=5,bg='#abcfd9', command=self.igr)
b1.place(anchor=CENTER,relx=0.3,rely=0.3)
b2.place(anchor=CENTER,relx=0.7,rely=0.3)
b3.place(anchor=CENTER,relx=0.3,rely=0.6)
b4.place(anchor=CENTER,relx=0.7,rely=0.6)
config = {
'user': 'root',
'password': 'qwerty1234',
'host': 'localhost',
'database': 'hosteldb',
'raise_on_warnings': True,
}
self.cnx = mysql.connector.connect(**config)
self.root.mainloop()
#################### STUDENT ####################
def istud(self):
def add_stud():
name=f1.get()
rn=f2.get()
dob=f3.get()
g=f4.get()
add=f5.get()
mbno=f6.get()
yr=f7.get()
br=f8.get()
hid=f9.get()
ft=f10.get()
rm=f11.get()
c=self.cnx.cursor()
c.execute('INSERT INTO `student` VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)',
(name,rn,dob,g,add,mbno,yr,br,hid,ft,rm))
c.close()
self.cnx.commit()
def search_stud():
if (len(f1.get())!=0):
nm=f1.get()
# print(nm)
c=self.cnx.cursor()
q="SELECT * FROM STUDENT WHERE name='"+nm+"';"
c.execute(q)
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
nw.mainloop()
elif(len(f2.get())!=0):
nm=f2.get()
# print(nm)
c=self.cnx.cursor()
q="SELECT * FROM STUDENT WHERE roll_no="+nm+";"
c.execute(q)
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
nw.mainloop()
elif(len(f9.get())!=0):
nm=f9.get()
# print(nm)
c=self.cnx.cursor()
q="SELECT * FROM STUDENT WHERE hostel_id="+nm+";"
c.execute(q)
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
nw.mainloop()
def show_stud():
if (len(f12.get())==0):
c=self.cnx.cursor()
c.execute("SELECT * FROM student")
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
nw.mainloop()
elif(f12.get()=='Yes'):
c=self.cnx.cursor()
c.execute("SELECT * FROM failed_student")
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
nw.mainloop()
def update_stud():
if(f12.get()=='Yes'):
rn=f2.get()
c=self.cnx.cursor()
c.callproc("fail",(rn,))
c.close()
self.cnx.commit()
elif(f12.get()=="No"):
c=self.cnx.cursor()
c.callproc("forward",(1,))
c.close()
self.cnx.commit()
def clear_stud():
for i in tree.get_children():
tree.delete(i)
l=[f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11]
for i in l:
i.delete(0,END)
def delete_stud():
rn=f2.get()
c=self.cnx.cursor()
q="DELETE FROM STUDENT WHERE roll_no="+rn+";"
c.execute(q)
c.close()
self.cnx.commit()
def getvalue(event):
l=[f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11]
for i in l:
i.delete(0,END)
s=tree.selection()
#print(s)
# l1=[]
a=tree.item(s)
#print(a)
l1=a['values']
#print(l1)
for i in range(len(l)):
l[i].insert(0,l1[i])
#New window
nw=tk.Tk()
nw.geometry('750x750')
nw.title("Student")
upperframe=Frame(nw,width=500,bd=5,height=80)
lowerframe=Frame(nw,width=200,bd=5,height=445)
upperframe.pack(side=TOP,fill=BOTH,expand=TRUE)
lowerframe.pack(side=TOP,fill=BOTH,expand=TRUE)
#Tree
tree=ttk.Treeview(lowerframe)
tree.pack(side=TOP)
tree['columns']=("name","roll_no","dob","gender","address","contact_no","year","branch","hostel_id","flat","room")
tree.column("#0",width=0)
tree.column("name",anchor=CENTER,width="120")
tree.column("roll_no",anchor=CENTER,width="100")
tree.column("dob",anchor=CENTER,width="100")
tree.column("gender",anchor=CENTER,width="100")
tree.column("address",anchor=CENTER,width="120")
tree.column("contact_no",anchor=CENTER,width="100")
tree.column("year",anchor=CENTER,width="100")
tree.column("branch",anchor=CENTER,width="100")
tree.column("hostel_id",anchor=CENTER,width="100")
tree.column("flat",anchor=CENTER,width="80")
tree.column("room",anchor=CENTER,width="120")
tree.heading("#0",text='')
tree.heading("name",text="Name",anchor=CENTER)
tree.heading("roll_no",text="Roll No",anchor=CENTER)
tree.heading("dob",text="DOB",anchor=CENTER)
tree.heading("gender",text="Gender",anchor=CENTER)
tree.heading("address",text="Address",anchor=CENTER)
tree.heading("contact_no",text="Contact No",anchor=CENTER)
tree.heading("year",text="Year",anchor=CENTER)
tree.heading("branch",text="Branch",anchor=CENTER)
tree.heading("hostel_id",text="Hostel ID",anchor=CENTER)
tree.heading("flat",text="Flat",anchor=CENTER)
tree.heading("room",text="Room",anchor=CENTER)
#tree.insert(parent='',index='end',iid=0,text='',values=('a',1,'1990-01-01','f','a',1,'1','cse',1,101,'a'))
tree.bind('<Double-Button-1>',getvalue)
#labels
label1 = tk.Label(upperframe, text="Name: ")
label2 = tk.Label(upperframe, text="Roll no: ")
label3 = tk.Label(upperframe, text="DOB: ")
label4 = tk.Label(upperframe,text="Gender:")
label5 = tk.Label(upperframe,text="Address:")
label6 = tk.Label(upperframe,text="Contact number:")
label7 = tk.Label(upperframe,text="Year:")
label8 = tk.Label(upperframe, text="Branch:")
label9 = tk.Label(upperframe,text="Hostel ID:")
label10 = tk.Label(upperframe,text="Flat:")
label11 = tk.Label(upperframe,text="Room:")
label12 = tk.Label(upperframe,text="Fail*:")
#entries
f1 = tk.Entry(upperframe)
f2 = tk.Entry(upperframe)
f3 = tk.Entry(upperframe)
f4 = tk.Entry(upperframe)
f5 = tk.Entry(upperframe)
f6 = tk.Entry(upperframe)
f7 = tk.Entry(upperframe)
f8 = tk.Entry(upperframe)
f9 = tk.Entry(upperframe)
f10 = tk.Entry(upperframe)
f11 = tk.Entry(upperframe)
f12 = tk.Entry(upperframe)
#Buttons
b1 = Button(upperframe, text="Add",width=6, command=add_stud)
b2 = Button(upperframe, text="Search",width=6, command=search_stud)
b3 = Button(upperframe, text="Show",width=6, command=show_stud)
b4 = Button(upperframe, text="Update",width=6, command=update_stud)
b5 = Button(upperframe, text="Clear",width=6, command=clear_stud)
b6 = Button(upperframe, text="Delete",width=6, command=delete_stud)
#Grid
label1.place(anchor=CENTER,relx=0.2,rely=0.125)
label2.place(anchor=CENTER,relx=0.6,rely=0.125)
label3.place(anchor=CENTER,relx=0.2,rely=0.25)
label4.place(anchor=CENTER,relx=0.6,rely=0.25)
label5.place(anchor=CENTER,relx=0.2,rely=0.375)
label6.place(anchor=CENTER,relx=0.6,rely=0.375)
label7.place(anchor=CENTER,relx=0.2,rely=0.5)
label8.place(anchor=CENTER,relx=0.6,rely=0.5)
label9.place(anchor=CENTER,relx=0.2,rely=0.625)
label10.place(anchor=CENTER,relx=0.6,rely=0.625)
label11.place(anchor=CENTER,relx=0.2,rely=0.75)
label12.place(anchor=CENTER,relx=0.6,rely=0.75)
f1.place(anchor=CENTER,relx=0.4,rely=0.125)
f2.place(anchor=CENTER,relx=0.8,rely=0.125)
f3.place(anchor=CENTER,relx=0.4,rely=0.25)
f4.place(anchor=CENTER,relx=0.8,rely=0.25)
f5.place(anchor=CENTER,relx=0.4,rely=0.375)
f6.place(anchor=CENTER,relx=0.8,rely=0.375)
f7.place(anchor=CENTER,relx=0.4,rely=0.5)
f8.place(anchor=CENTER,relx=0.8,rely=0.5)
f9.place(anchor=CENTER,relx=0.4,rely=0.625)
f10.place(anchor=CENTER,relx=0.8,rely=0.625)
f11.place(anchor=CENTER,relx=0.4,rely=0.75)
f12.place(anchor=CENTER,relx=0.8,rely=0.75)
b1.place(anchor=CENTER,relx=0.25,rely=0.875)
b2.place(anchor=CENTER,relx=0.5,rely=0.875)
b3.place(anchor=CENTER,relx=0.75,rely=0.875)
b4.place(anchor=CENTER,relx=0.25,rely=0.97)
b5.place(anchor=CENTER,relx=0.5,rely=0.97)
b6.place(anchor=CENTER,relx=0.75,rely=0.97)
nw.mainloop()
#################### EMPLOYEE ####################
def iemp(self):
def add_emp():
name=f1.get()
eid=f2.get()
dob=f3.get()
g=f4.get()
add=f5.get()
mbno=f6.get()
dsg=f7.get()
hid=f8.get()
c=self.cnx.cursor()
c.execute("""INSERT INTO employee (`name`, `employee_id`, `dob`, `gender`, `address`,`contact_no`, `designation`, `hostel_id`)
VALUES (""" + "%s,"*7 +"%s);",
(name,eid,dob,g,add,mbno,dsg,hid))
c.close()
self.cnx.commit()
def search_emp():
if (len(f1.get())!=0):
nm=f1.get()
# print(nm)
c=self.cnx.cursor()
q="SELECT * FROM EMPLOYEE WHERE name='"+nm+"';"
c.execute(q)
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
nw.mainloop()
elif(len(f2.get())!=0):
eid=f2.get()
# print(nm)
c=self.cnx.cursor()
q="SELECT * FROM EMPLOYEE WHERE employee_id="+eid+";"
c.execute(q)
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
nw.mainloop()
elif(len(f8.get())!=0):
hid=f8.get()
# print(nm)
c=self.cnx.cursor()
q="SELECT * FROM EMPLOYEE WHERE hostel_id="+hid+";"
c.execute(q)
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
nw.mainloop()
def show_emp():
c=self.cnx.cursor()
c.execute("SELECT * FROM employee")
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
c.close()
nw.mainloop()
def update_emp():
if(len(f9.get())!=0):
eid=f2.get()
dt=f9.get()
c=self.cnx.cursor()
q="UPDATE EMPLOYEE SET termination_date='"+dt+"' WHERE employee_id="+eid+";"
c.execute(q)
self.cnx.commit()
else:
name=f1.get()
eid=f2.get()
dob=f3.get()
g=f4.get()
add=f5.get()
mbno=f6.get()
dsg=f7.get()
hid=f8.get()
c=self.cnx.cursor()
c.execute("UPDATE EMPLOYEE SET name=%s,dob=%s,gender=%s,address=%s,contact_no=%s,designation=%s,hostel_id=%s WHERE employee_id=%s",
(name,dob,g,add,mbno,dsg,hid,eid))
self.cnx.commit()
def clear_emp():
for i in tree.get_children():
tree.delete(i)
l=[f1,f2,f3,f4,f5,f6,f7,f8,f9]
for i in l:
i.delete(0,END)
nw.mainloop()
def delete_emp():
eid=f2.get()
c=self.cnx.cursor()
q="DELETE FROM EMPLOYEE WHERE employee_id="+eid+";"
c.execute(q)
c.close()
self.cnx.commit()
def getvalue(event):
l=[f1,f2,f3,f4,f5,f6,f7,f8,f9]
for i in l:
i.delete(0,END)
s=tree.selection()
#print(s)
# l1=[]
a=tree.item(s)
#print(a)
l1=a['values']
#print(l1)
l[0].insert(0,l1[0])
l[1].insert(0,l1[1])
l[2].insert(0,l1[2])
l[3].insert(0,l1[3])
l[4].insert(0,l1[4])
l[5].insert(0,l1[5])
l[6].insert(0,l1[8])
l[7].insert(0,l1[9])
l[8].insert(0,l1[7])
#New window
nw=tk.Tk()
nw.geometry('900x900')
nw.title("Employee")
upperframe=Frame(nw,width=500,bd=5,height=80)
lowerframe=Frame(nw,width=200,bd=5,height=445)
upperframe.pack(side=TOP,fill=BOTH,expand=TRUE)
lowerframe.pack(side=TOP,fill=BOTH,expand=TRUE)
#Tree
tree=ttk.Treeview(lowerframe)
tree.pack(side=TOP)
tree['columns']=("name","eid","dob","gender","address","contact_no","doj","td","designation","hostel_id","salary")
tree.column("#0",width=0)
tree.column("name",anchor=CENTER,width="120")
tree.column("eid",anchor=CENTER,width="100")
tree.column("dob",anchor=CENTER,width="100")
tree.column("gender",anchor=CENTER,width="100")
tree.column("address",anchor=CENTER,width="120")
tree.column("contact_no",anchor=CENTER,width="100")
tree.column("doj",anchor=CENTER,width="100")
tree.column("td",anchor=CENTER,width="100")
tree.column("designation",anchor=CENTER,width="100")
tree.column("hostel_id",anchor=CENTER,width="100")
tree.column("salary",anchor=CENTER,width="100")
tree.heading("#0",text='')
tree.heading("name",text="Name",anchor=CENTER)
tree.heading("eid",text="Employee ID",anchor=CENTER)
tree.heading("dob",text="DOB",anchor=CENTER)
tree.heading("gender",text="Gender",anchor=CENTER)
tree.heading("address",text="Address",anchor=CENTER)
tree.heading("contact_no",text="Contact No",anchor=CENTER)
tree.heading("doj",text="Date of joining",anchor=CENTER)
tree.heading("td",text="Termination date",anchor=CENTER)
tree.heading("designation",text="designation",anchor=CENTER)
tree.heading("hostel_id",text="Hostel ID",anchor=CENTER)
tree.heading("salary",text="Salary",anchor=CENTER)
tree.bind('<Double-Button-1>',getvalue)
#tree.insert(parent='',index='end',iid=0,text='',values=('a',1,'1990-01-01','f','a',1,'1990-02-03','2020-12-06','warden',1,1500))
#labels
label1 = tk.Label(upperframe, text="Name: ")
label2 = tk.Label(upperframe, text="Employee ID: ")
label3 = tk.Label(upperframe, text="DOB: ")
label4 = tk.Label(upperframe,text="Gender:")
label5 = tk.Label(upperframe,text="Address:")
label6 = tk.Label(upperframe,text="Contact number:")
label7 = tk.Label(upperframe,text="Designation:")
label8 = tk.Label(upperframe,text="Hostel ID:")
label9 = tk.Label(upperframe,text="Termination Date*:")
#entries
f1 = tk.Entry(upperframe)
f2 = tk.Entry(upperframe)
f3 = tk.Entry(upperframe)
f4 = tk.Entry(upperframe)
f5 = tk.Entry(upperframe)
f6 = tk.Entry(upperframe)
f7 = tk.Entry(upperframe)
f8 = tk.Entry(upperframe)
f9 = tk.Entry(upperframe)
#Buttons
b1 = Button(upperframe, text="Add",width=6,command=add_emp)
b2 = Button(upperframe, text="Search",width=6,command=search_emp)
b3 = Button(upperframe, text="Show",width=6,command=show_emp)
b4 = Button(upperframe, text="Update",width=6,command=update_emp)
b5 = Button(upperframe, text="Clear",width=6,command=clear_emp)
b6 = Button(upperframe, text="Delete",width=6,command=delete_emp)
#Grid
label1.place(anchor=CENTER,relx=0.2,rely=0.125)
label2.place(anchor=CENTER,relx=0.6,rely=0.125)
label3.place(anchor=CENTER,relx=0.2,rely=0.25)
label4.place(anchor=CENTER,relx=0.6,rely=0.25)
label5.place(anchor=CENTER,relx=0.2,rely=0.375)
label6.place(anchor=CENTER,relx=0.6,rely=0.375)
label7.place(anchor=CENTER,relx=0.2,rely=0.5)
label8.place(anchor=CENTER,relx=0.6,rely=0.5)
label9.place(anchor=CENTER,relx=0.2,rely=0.625)
f1.place(anchor=CENTER,relx=0.4,rely=0.125)
f2.place(anchor=CENTER,relx=0.8,rely=0.125)
f3.place(anchor=CENTER,relx=0.4,rely=0.25)
f4.place(anchor=CENTER,relx=0.8,rely=0.25)
f5.place(anchor=CENTER,relx=0.4,rely=0.375)
f6.place(anchor=CENTER,relx=0.8,rely=0.375)
f7.place(anchor=CENTER,relx=0.4,rely=0.5)
f8.place(anchor=CENTER,relx=0.8,rely=0.5)
f9.place(anchor=CENTER,relx=0.4,rely=0.625)
b1.place(anchor=CENTER,relx=0.25,rely=0.75)
b2.place(anchor=CENTER,relx=0.5,rely=0.75)
b3.place(anchor=CENTER,relx=0.75,rely=0.75)
b4.place(anchor=CENTER,relx=0.25,rely=0.875)
b5.place(anchor=CENTER,relx=0.5,rely=0.875)
b6.place(anchor=CENTER,relx=0.75,rely=0.875)
nw.mainloop()
#################### COMPLAINT ####################
def icomp(self):
def add_comp():
rn=f2.get()
dsc=f3.get()
c=self.cnx.cursor()
c.execute('INSERT INTO complaint(roll_no,description) VALUES(%s,%s)',
(rn,dsc))
c.close()
self.cnx.commit()
#print('Value Inserted')
def search_comp():
rn=f2.get()
# print(nm)
c=self.cnx.cursor()
q="SELECT * FROM COMPLAINT WHERE roll_no='"+rn+"';"
c.execute(q)
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
nw.mainloop()
def show_comp():
c=self.cnx.cursor()
c.execute("SELECT * FROM complaint")
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
c.close()
nw.mainloop()
def update_comp():
if(len(f5.get())!=0):
cid=f1.get()
rn=f2.get()
dt=f5.get()
c=self.cnx.cursor()
q="UPDATE COMPLAINT SET resolved_date='"+dt+"' WHERE complaint_id="+cid+" AND roll_no="+rn+";"
c.execute(q)
self.cnx.commit()
def clear_comp():
for i in tree.get_children():
tree.delete(i)
l=[f1,f2,f3,f4,f5]
for i in l:
i.delete(0,END)
nw.mainloop()
def delete_comp():
cid=f1.get()
rn=f2.get()
c=self.cnx.cursor()
q="DELETE FROM COMPLAINT WHERE complaint_id="+cid+" AND roll_no="+rn+";"
#print(q)
c.execute(q)
c.close()
self.cnx.commit()
def getvalue(event):
l=[f1,f2,f3,f4,f5]
for i in l:
i.delete(0,END)
s=tree.selection()
#print(s)
# l1=[]
a=tree.item(s)
#print(a)
l1=a['values']
#print(l1)
for i in range(len(l)):
l[i].insert(0,l1[i])
#New window
nw=tk.Tk()
nw.geometry('900x900')
nw.title("Complaint")
upperframe=Frame(nw,width=500,bd=5,height=80)
lowerframe=Frame(nw,width=200,bd=5,height=445)
upperframe.pack(side=TOP,fill=BOTH,expand=TRUE)
lowerframe.pack(side=TOP,fill=BOTH,expand=TRUE)
#Tree
tree=ttk.Treeview(lowerframe)
tree.pack(side=TOP)
tree['columns']=("cid","roll_no","description","registered_date","resolved_date")
tree.column("#0",width=0)
tree.column("cid",anchor=CENTER,width="120")
tree.column("roll_no",anchor=CENTER,width="100")
tree.column("description",anchor=CENTER,width="100")
tree.column("registered_date",anchor=CENTER,width="100")
tree.column("resolved_date",anchor=CENTER,width="120")
tree.heading("#0",text='')
tree.heading("cid",text="Complaint ID",anchor=CENTER)
tree.heading("roll_no",text="Roll No",anchor=CENTER)
tree.heading("description",text="Description",anchor=CENTER)
tree.heading("registered_date",text="Registered Date",anchor=CENTER)
tree.heading("resolved_date",text="Resolved Date",anchor=CENTER)
tree.bind('<Double-Button-1>',getvalue)
#tree.insert(parent='',index='end',iid=0,text='',values=(1,5,'Housekeeping','2017-11-21',None))
#labels
label1 = tk.Label(upperframe, text="Complaint ID*: ")
label2 = tk.Label(upperframe, text="Roll No: ")
label3 = tk.Label(upperframe, text="Description: ")
label4 = tk.Label(upperframe,text="Registered Date*:")
label5 = tk.Label(upperframe,text="Resolved Date*:")
#entries
f1 = tk.Entry(upperframe)
f2 = tk.Entry(upperframe)
f3 = tk.Entry(upperframe)
f4 = tk.Entry(upperframe)
f5 = tk.Entry(upperframe)
#Buttons
b1 = Button(upperframe, text="Add",width=6,command=add_comp)
b2 = Button(upperframe, text="Search",width=6,command=search_comp)
b3 = Button(upperframe, text="Show",width=6,command=show_comp)
b4 = Button(upperframe, text="Update",width=6,command=update_comp)
b5 = Button(upperframe, text="Clear",width=6,command=clear_comp)
b6 = Button(upperframe, text="Delete",width=6,command=delete_comp)
#Grid
label1.place(anchor=CENTER,relx=0.2,rely=0.125)
label2.place(anchor=CENTER,relx=0.6,rely=0.125)
label3.place(anchor=CENTER,relx=0.2,rely=0.25)
label4.place(anchor=CENTER,relx=0.6,rely=0.25)
label5.place(anchor=CENTER,relx=0.2,rely=0.375)
f1.place(anchor=CENTER,relx=0.4,rely=0.125)
f2.place(anchor=CENTER,relx=0.8,rely=0.125)
f3.place(anchor=CENTER,relx=0.4,rely=0.25)
f4.place(anchor=CENTER,relx=0.8,rely=0.25)
f5.place(anchor=CENTER,relx=0.4,rely=0.375)
b1.place(anchor=CENTER,relx=0.25,rely=0.625)
b2.place(anchor=CENTER,relx=0.5,rely=0.625)
b3.place(anchor=CENTER,relx=0.75,rely=0.625)
b4.place(anchor=CENTER,relx=0.25,rely=0.75)
b5.place(anchor=CENTER,relx=0.5,rely=0.75)
b6.place(anchor=CENTER,relx=0.75,rely=0.75)
nw.mainloop()
#################### GATE RECORD ####################
def igr(self):
def add_gr():
rn=f1.get()
prp=f2.get()
c=self.cnx.cursor()
c.execute('INSERT INTO gate_record(roll_no,purpose) VALUES(%s,%s)',
(rn,prp))
c.close()
self.cnx.commit()
#print('Value Inserted')
def search_gr():
rn=f1.get()
# print(nm)
c=self.cnx.cursor()
q="SELECT * FROM GATE_RECORD WHERE roll_no='"+rn+"';"
c.execute(q)
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
nw.mainloop()
def show_gr():
c=self.cnx.cursor()
c.execute("SELECT * FROM gate_record;")
f=c.fetchall()
for i in tree.get_children():
tree.delete(i)
for i in range(len(f)):
tree.insert(parent='',index='end',iid=i,text='',values=f[i])
c.close()
nw.mainloop()
def update_gr():
rn=f1.get()
c=self.cnx.cursor()
c.callproc("gate_record_in",(rn,))
self.cnx.commit()
def clear_gr():
for i in tree.get_children():
tree.delete(i)
l=[f1,f2,f3,f4]
for i in l:
i.delete(0,END)
nw.mainloop()
def delete_gr():
rn=f1.get()
et=f4.get()
c=self.cnx.cursor()
q="DELETE FROM GATE_RECORD WHERE roll_no="+rn+" AND exit_time='"+et+"';"
#print(q)
c.execute(q)
c.close()
self.cnx.commit()
def getvalue(event):
l=[f1,f2,f3,f4]
for i in l:
i.delete(0,END)
s=tree.selection()
#print(s)
# l1=[]
a=tree.item(s)
#print(a)
l1=a['values']
#print(l1)
for i in range(len(l)):
l[i].insert(0,l1[i])
#New window
nw=tk.Tk()
nw.geometry('900x900')
nw.title("Gate Record")
upperframe=Frame(nw,width=500,bd=5,height=80)
lowerframe=Frame(nw,width=200,bd=5,height=445)
upperframe.pack(side=TOP,fill=BOTH,expand=TRUE)
lowerframe.pack(side=TOP,fill=BOTH,expand=TRUE)
#Tree
tree=ttk.Treeview(lowerframe)
tree.pack(side=TOP)
tree['columns']=("roll_no","purpose","entry_time","exit_time")
tree.column("#0",width=0)
tree.column("roll_no",anchor=CENTER,width="100")
tree.column("purpose",anchor=CENTER,width="120")
tree.column("entry_time",anchor=CENTER,width="200")
tree.column("exit_time",anchor=CENTER,width="200")
tree.heading("#0",text='')
tree.heading("roll_no",text="Roll No",anchor=CENTER)
tree.heading("purpose",text="Purpose",anchor=CENTER)
tree.heading("entry_time",text="Entry Time",anchor=CENTER)
tree.heading("exit_time",text="Leaving Time",anchor=CENTER)
tree.bind('<Double-Button-1>',getvalue)
#tree.insert(parent='',index='end',iid=0,text='',values=(3,'Fun','2018-11-01 12:45:10','2017-11-21 15:48:10'))
#labels
label1 = tk.Label(upperframe, text="Roll No: ")
label2 = tk.Label(upperframe, text="Purpose: ")
label3 = tk.Label(upperframe, text="Entry Time*: ")
label4 = tk.Label(upperframe,text="Leaving Time*:")
#entries
f1 = tk.Entry(upperframe)
f2 = tk.Entry(upperframe)
f3 = tk.Entry(upperframe)
f4 = tk.Entry(upperframe)
#Buttons
b1 = Button(upperframe, text="Add",width=6,command=add_gr)
b2 = Button(upperframe, text="Search",width=6,command=search_gr)
b3 = Button(upperframe, text="Show",width=6,command=show_gr)
b4 = Button(upperframe, text="Update",width=6,command=update_gr)
b5 = Button(upperframe, text="Clear",width=6,command=clear_gr)
b6 = Button(upperframe, text="Delete",width=6,command=delete_gr)
#Grid
label1.place(anchor=CENTER,relx=0.2,rely=0.25)
label2.place(anchor=CENTER,relx=0.6,rely=0.25)
label3.place(anchor=CENTER,relx=0.2,rely=0.375)
label4.place(anchor=CENTER,relx=0.6,rely=0.375)
f1.place(anchor=CENTER,relx=0.4,rely=0.25)
f2.place(anchor=CENTER,relx=0.8,rely=0.25)
f3.place(anchor=CENTER,relx=0.4,rely=0.375)
f4.place(anchor=CENTER,relx=0.8,rely=0.375)
b1.place(anchor=CENTER,relx=0.25,rely=0.625)
b2.place(anchor=CENTER,relx=0.5,rely=0.625)
b3.place(anchor=CENTER,relx=0.75,rely=0.625)
b4.place(anchor=CENTER,relx=0.25,rely=0.75)
b5.place(anchor=CENTER,relx=0.5,rely=0.75)
b6.place(anchor=CENTER,relx=0.75,rely=0.75)
nw.mainloop() | 38.569307 | 148 | 0.511423 | 4,038 | 31,164 | 3.913323 | 0.063893 | 0.11391 | 0.094672 | 0.116947 | 0.834135 | 0.804835 | 0.769903 | 0.752247 | 0.71048 | 0.603658 | 0 | 0.056973 | 0.321878 | 31,164 | 808 | 149 | 38.569307 | 0.690768 | 0.029072 | 0 | 0.644615 | 0 | 0.004615 | 0.103031 | 0.006773 | 0 | 0 | 0 | 0 | 0 | 1 | 0.050769 | false | 0.001538 | 0.006154 | 0 | 0.058462 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2c9ebdc19c36c3a993f8f2dd3f94ad541c92e0f4 | 68 | py | Python | Image-Editor/src/Functions/readImage.py | TheCodingJungle/Python-Projects | eaec5b363b190fb013bcacbed48410c4b338fcc5 | [
"MIT"
] | 5 | 2021-02-08T13:53:16.000Z | 2021-09-20T05:14:19.000Z | Image-Editor/src/Functions/readImage.py | TheCodingJungle/Python-Projects | eaec5b363b190fb013bcacbed48410c4b338fcc5 | [
"MIT"
] | 1 | 2021-07-29T20:00:34.000Z | 2021-07-29T20:00:34.000Z | Image-Editor/src/Functions/readImage.py | TheCodingJungle/Python-Projects | eaec5b363b190fb013bcacbed48410c4b338fcc5 | [
"MIT"
] | 1 | 2021-08-31T04:22:17.000Z | 2021-08-31T04:22:17.000Z | from cv2 import imread
def readImage(path):
return imread(path) | 17 | 23 | 0.75 | 10 | 68 | 5.1 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.017857 | 0.176471 | 68 | 4 | 23 | 17 | 0.892857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 6 |
2ca25864fb937c6166adcad576c4626aabeb50b3 | 2,536 | py | Python | website_handling/collect_websites.py | Dr3xler/CookieConsentChecker | 816cdfb9d9dc741c57dbcd5e9c9ef59837196631 | [
"MIT"
] | null | null | null | website_handling/collect_websites.py | Dr3xler/CookieConsentChecker | 816cdfb9d9dc741c57dbcd5e9c9ef59837196631 | [
"MIT"
] | 3 | 2021-04-29T22:57:09.000Z | 2021-05-03T15:32:39.000Z | website_handling/collect_websites.py | Dr3xler/CookieConsentChecker | 816cdfb9d9dc741c57dbcd5e9c9ef59837196631 | [
"MIT"
] | 1 | 2021-08-29T09:53:09.000Z | 2021-08-29T09:53:09.000Z | import os
from sys import platform
from pathlib import Path
def generate_website_list(driver, websites):
"""This method will load websites from a file and adds them to a new list if user input is y"""
cookie_exists_list = []
list_count = 0
for website in websites:
name = website.split('www.')[1]
try:
driver.get(website)
except:
continue
driver.execute_script("return document.readyState")
choice = input('save website?')
if choice == "y":
cookie_exists_list.append(website)
list_count += 1
print(list_count)
if choice == "n":
continue
if choice == "stop":
break
if choice == "exit":
break
else:
continue
cookie_websites = open("data/cookie_websites.txt", "w")
for line in cookie_exists_list:
cookie_websites.write(line + "\n")
cookie_websites.close()
def generate_success_list(driver, websites):
"""This method will load websites with addon and adds them to a new list if user input is y"""
cookie_exists_list = []
list_count = 0
print(platform)
# the extension directory needs to be the one of your local machine
#linux
if platform == "linux":
extension_dir = os.getenv("HOME") + "/.mozilla/firefox/7ppp44j6.default-release/extensions/"
driver.install_addon(extension_dir + 'jid1-KKzOGWgsW3Ao4Q@jetpack.xpi', temporary=True)
#windows
if platform == "win32":
extension_dir = str(Path.home()) + "/AppData/Roaming/Mozilla/Firefox/Profiles/shdzeteb.default-release/extensions/"
print(extension_dir)
driver.install_addon(extension_dir + 'jid1-KKzOGWgsW3Ao4Q@jetpack.xpi', temporary=True)
for website in websites:
name = website.split('www.')[1]
try:
driver.get(website)
except:
continue
driver.execute_script("return document.readyState")
choice = input('save website?')
if choice == "y":
cookie_exists_list.append(website)
list_count += 1
print(list_count)
if choice == "n":
continue
if choice == "stop":
break
if choice == "exit":
break
else:
continue
cookie_websites = open("data/cookie_addon_success.txt", "w")
for line in cookie_exists_list:
cookie_websites.write(line + "\n")
cookie_websites.close()
| 27.868132 | 123 | 0.602918 | 296 | 2,536 | 5.030405 | 0.341216 | 0.042982 | 0.064473 | 0.045668 | 0.723976 | 0.723976 | 0.723976 | 0.723976 | 0.664876 | 0.664876 | 0 | 0.01009 | 0.29653 | 2,536 | 90 | 124 | 28.177778 | 0.824552 | 0.101341 | 0 | 0.8 | 1 | 0 | 0.164607 | 0.109003 | 0 | 0 | 0 | 0 | 0 | 1 | 0.030769 | false | 0 | 0.046154 | 0 | 0.076923 | 0.061538 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
e2c8c466d2a40b78436550ed0b32ea3c1fa123af | 1,350 | py | Python | awwardsapp/migrations/0008_auto_20201025_1829.py | budds300/Awwwards | 220d3188adafb688114363daed8ad97aa8f8f948 | [
"MIT"
] | null | null | null | awwardsapp/migrations/0008_auto_20201025_1829.py | budds300/Awwwards | 220d3188adafb688114363daed8ad97aa8f8f948 | [
"MIT"
] | null | null | null | awwardsapp/migrations/0008_auto_20201025_1829.py | budds300/Awwwards | 220d3188adafb688114363daed8ad97aa8f8f948 | [
"MIT"
] | null | null | null | # Generated by Django 3.1.2 on 2020-10-25 15:29
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('awwardsapp', '0007_rating'),
]
operations = [
migrations.AlterField(
model_name='rating',
name='rate_content',
field=models.PositiveSmallIntegerField(choices=[(10, '10-Outstanding'), (9, '9-Exceeds Expectations'), (8, '8-Excellent'), (7, '7-Good'), (6, '6-Barely Above Average'), (5, '5-Average'), (4, '4-Poor'), (3, '3-Awful'), (2, '2-Dreadful'), (1, '1-Troll')]),
),
migrations.AlterField(
model_name='rating',
name='rate_design',
field=models.PositiveSmallIntegerField(choices=[(10, '10-Outstanding'), (9, '9-Exceeds Expectations'), (8, '8-Excellent'), (7, '7-Good'), (6, '6-Barely Above Average'), (5, '5-Average'), (4, '4-Poor'), (3, '3-Awful'), (2, '2-Dreadful'), (1, '1-Troll')]),
),
migrations.AlterField(
model_name='rating',
name='rate_usability',
field=models.PositiveSmallIntegerField(choices=[(10, '10-Outstanding'), (9, '9-Exceeds Expectations'), (8, '8-Excellent'), (7, '7-Good'), (6, '6-Barely Above Average'), (5, '5-Average'), (4, '4-Poor'), (3, '3-Awful'), (2, '2-Dreadful'), (1, '1-Troll')]),
),
]
| 46.551724 | 266 | 0.567407 | 162 | 1,350 | 4.685185 | 0.320988 | 0.079051 | 0.098814 | 0.114625 | 0.782609 | 0.782609 | 0.782609 | 0.725955 | 0.725955 | 0.725955 | 0 | 0.080264 | 0.215556 | 1,350 | 28 | 267 | 48.214286 | 0.636449 | 0.033333 | 0 | 0.545455 | 1 | 0 | 0.320798 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.045455 | 0 | 0.181818 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
394091a9e3495315385c0d2ab74e3ee861025a44 | 607 | py | Python | link_energy.py | PloFz/Molecular_Formulations | d759270f9675368185d808a42fc808554d65cdee | [
"MIT"
] | 1 | 2020-01-07T17:32:53.000Z | 2020-01-07T17:32:53.000Z | link_energy.py | PloFz/Molecular_Formulations | d759270f9675368185d808a42fc808554d65cdee | [
"MIT"
] | null | null | null | link_energy.py | PloFz/Molecular_Formulations | d759270f9675368185d808a42fc808554d65cdee | [
"MIT"
] | null | null | null | import PBL, PBL_stern
dens = np.array([ 2. ])#, 4., 8., 16., 32. ])
link_energy = []
for dd in dens:
energy_p = PBL.solvation_energy('peptide2', dd, info=True)
energy_r = PBL.solvation_energy('rna', dd, info=True)
energy_c = PBL.solvation_energy('complex', dd, info=True)
link_energy.append(energy_c - (energy_p + energy_r))
exit()
for dd in dens:
energy_p = PBL_stern.solvation_energy('peptide2', dd, info=True)
energy_r = PBL_stern.solvation_energy('rna', dd, info=True)
energy_c = PBL_stern.solvation_energy('complex', dd, info=True)
link_energy.append(energy_c - (energy_p + energy_r))
| 25.291667 | 65 | 0.710049 | 98 | 607 | 4.142857 | 0.27551 | 0.221675 | 0.147783 | 0.157635 | 0.842365 | 0.842365 | 0.842365 | 0.738916 | 0.738916 | 0.339901 | 0 | 0.017143 | 0.135091 | 607 | 23 | 66 | 26.391304 | 0.75619 | 0.034596 | 0 | 0.285714 | 0 | 0 | 0.061538 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.071429 | 0 | 0.071429 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
1a4eab006673f8b7cb2570e8dd76142314019fa5 | 37 | py | Python | odbctools/odbcmanager/__init__.py | N-C-C/odbctools | c1fb4760b6c595537f2fb373469c9922c7bd7af8 | [
"MIT"
] | 2 | 2020-08-31T18:28:45.000Z | 2022-02-10T23:58:16.000Z | odbctools/odbcmanager/__init__.py | N-C-C/odbctools | c1fb4760b6c595537f2fb373469c9922c7bd7af8 | [
"MIT"
] | null | null | null | odbctools/odbcmanager/__init__.py | N-C-C/odbctools | c1fb4760b6c595537f2fb373469c9922c7bd7af8 | [
"MIT"
] | null | null | null | from .odbcmanager import OdbcManager
| 18.5 | 36 | 0.864865 | 4 | 37 | 8 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.108108 | 37 | 1 | 37 | 37 | 0.969697 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
1a65f542894d9a8ecf82ea1438b204215ff5d6c5 | 87 | py | Python | fsdviz/stocking/views/__init__.py | AdamCottrill/fsdivz | 98dd1f35a08dba26424e2951a40715e01399478c | [
"MIT"
] | null | null | null | fsdviz/stocking/views/__init__.py | AdamCottrill/fsdivz | 98dd1f35a08dba26424e2951a40715e01399478c | [
"MIT"
] | 6 | 2020-02-12T00:03:40.000Z | 2020-11-30T01:20:56.000Z | fsdviz/stocking/views/__init__.py | AdamCottrill/fsdviz | 98dd1f35a08dba26424e2951a40715e01399478c | [
"MIT"
] | null | null | null | from .CWTEventViews import *
from .EventUploadViews import *
from .EventViews import *
| 21.75 | 31 | 0.793103 | 9 | 87 | 7.666667 | 0.555556 | 0.289855 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 87 | 3 | 32 | 29 | 0.92 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
1a70921227ea5c429f3eab59b1cdd08e98714637 | 32 | py | Python | shadowrun_prototype/defs/bitm.py | holy-crust/reclaimer | 0aa693da3866ce7999c68d5f71f31a9c932cdb2c | [
"MIT"
] | null | null | null | shadowrun_prototype/defs/bitm.py | holy-crust/reclaimer | 0aa693da3866ce7999c68d5f71f31a9c932cdb2c | [
"MIT"
] | null | null | null | shadowrun_prototype/defs/bitm.py | holy-crust/reclaimer | 0aa693da3866ce7999c68d5f71f31a9c932cdb2c | [
"MIT"
] | null | null | null | from ...hek.defs.bitm import *
| 16 | 31 | 0.65625 | 5 | 32 | 4.2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15625 | 32 | 1 | 32 | 32 | 0.777778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
1a7278089532f2844441d6b70060598f1ab124cf | 43 | py | Python | src/crazy_joe/__init__.py | dpasse/crazy_joe | bb77b2f8ee2cfb26cd8f2c45e0f0da2c2db3f805 | [
"MIT"
] | null | null | null | src/crazy_joe/__init__.py | dpasse/crazy_joe | bb77b2f8ee2cfb26cd8f2c45e0f0da2c2db3f805 | [
"MIT"
] | null | null | null | src/crazy_joe/__init__.py | dpasse/crazy_joe | bb77b2f8ee2cfb26cd8f2c45e0f0da2c2db3f805 | [
"MIT"
] | null | null | null | from .table_generator import TableGenerator | 43 | 43 | 0.906977 | 5 | 43 | 7.6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.069767 | 43 | 1 | 43 | 43 | 0.95 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
1a8022cff4a05d519194be639cf440990eec9b9e | 50,995 | py | Python | pybind/slxos/v16r_1_00b/tm_state/tmdevicestataggr/__init__.py | shivharis/pybind | 4e1c6d54b9fd722ccec25546ba2413d79ce337e6 | [
"Apache-2.0"
] | null | null | null | pybind/slxos/v16r_1_00b/tm_state/tmdevicestataggr/__init__.py | shivharis/pybind | 4e1c6d54b9fd722ccec25546ba2413d79ce337e6 | [
"Apache-2.0"
] | null | null | null | pybind/slxos/v16r_1_00b/tm_state/tmdevicestataggr/__init__.py | shivharis/pybind | 4e1c6d54b9fd722ccec25546ba2413d79ce337e6 | [
"Apache-2.0"
] | 1 | 2021-11-05T22:15:42.000Z | 2021-11-05T22:15:42.000Z |
from operator import attrgetter
import pyangbind.lib.xpathhelper as xpathhelper
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType
from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType
from pyangbind.lib.base import PybindBase
from decimal import Decimal
from bitarray import bitarray
import __builtin__
class tmdevicestataggr(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module brocade-sysdiag-operational - based on the path /tm-state/tmdevicestataggr. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: Get TM device stats from all towers and all slots
"""
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__slot','__tower','__ingress_total_pkt_cnt_aggr','__enque_pkt_cnt_aggr','__deque_pkt_cnt_aggr','__total_discard_pkt_cnt_aggr','__deleted_pkt_cnt_aggr','__invalid_queue_pkt_aggr','__cpu_pkt_cnt_aggr','__uni_pkt_cnt_aggr','__mcast_pkt_cnt_aggr','__uni_discard_pkt_cnt_aggr','__mcast_discard_pkt_cnt_aggr','__fqp_pkt_cnt_aggr','__ehp_discard_cnt_aggr',)
_yang_name = 'tmdevicestataggr'
_rest_name = 'tmdevicestataggr'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
path_helper_ = kwargs.pop("path_helper", None)
if path_helper_ is False:
self._path_helper = False
elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper):
self._path_helper = path_helper_
elif hasattr(self, "_parent"):
path_helper_ = getattr(self._parent, "_path_helper", False)
self._path_helper = path_helper_
else:
self._path_helper = False
extmethods = kwargs.pop("extmethods", None)
if extmethods is False:
self._extmethods = False
elif extmethods is not None and isinstance(extmethods, dict):
self._extmethods = extmethods
elif hasattr(self, "_parent"):
extmethods = getattr(self._parent, "_extmethods", None)
self._extmethods = extmethods
else:
self._extmethods = False
self.__slot = YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..65535']},int_size=16), is_leaf=True, yang_name="slot", rest_name="slot", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint16', is_config=False)
self.__cpu_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="cpu-pkt-cnt-aggr", rest_name="cpu-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
self.__uni_discard_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="uni-discard-pkt-cnt-aggr", rest_name="uni-discard-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
self.__uni_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="uni-pkt-cnt-aggr", rest_name="uni-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
self.__mcast_discard_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="mcast-discard-pkt-cnt-aggr", rest_name="mcast-discard-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
self.__deleted_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="deleted-pkt-cnt-aggr", rest_name="deleted-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
self.__deque_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="deque-pkt-cnt-aggr", rest_name="deque-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
self.__ehp_discard_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="ehp-discard-cnt-aggr", rest_name="ehp-discard-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
self.__enque_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="enque-pkt-cnt-aggr", rest_name="enque-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
self.__invalid_queue_pkt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="invalid-queue-pkt-aggr", rest_name="invalid-queue-pkt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
self.__total_discard_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="total-discard-pkt-cnt-aggr", rest_name="total-discard-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
self.__tower = YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..65535']},int_size=16), is_leaf=True, yang_name="tower", rest_name="tower", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint16', is_config=False)
self.__fqp_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="fqp-pkt-cnt-aggr", rest_name="fqp-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
self.__ingress_total_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="ingress-total-pkt-cnt-aggr", rest_name="ingress-total-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
self.__mcast_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="mcast-pkt-cnt-aggr", rest_name="mcast-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'tm-state', u'tmdevicestataggr']
def _rest_path(self):
if hasattr(self, "_parent"):
if self._rest_name:
return self._parent._rest_path()+[self._rest_name]
else:
return self._parent._rest_path()
else:
return [u'tm-state', u'tmdevicestataggr']
def _get_slot(self):
"""
Getter method for slot, mapped from YANG variable /tm_state/tmdevicestataggr/slot (uint16)
YANG Description: slot id to get tm devices stats
"""
return self.__slot
def _set_slot(self, v, load=False):
"""
Setter method for slot, mapped from YANG variable /tm_state/tmdevicestataggr/slot (uint16)
If this variable is read-only (config: false) in the
source YANG file, then _set_slot is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_slot() directly.
YANG Description: slot id to get tm devices stats
"""
parent = getattr(self, "_parent", None)
if parent is not None and load is False:
raise AttributeError("Cannot set keys directly when" +
" within an instantiated list")
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..65535']},int_size=16), is_leaf=True, yang_name="slot", rest_name="slot", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint16', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """slot must be of a type compatible with uint16""",
'defined-type': "uint16",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..65535']},int_size=16), is_leaf=True, yang_name="slot", rest_name="slot", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint16', is_config=False)""",
})
self.__slot = t
if hasattr(self, '_set'):
self._set()
def _unset_slot(self):
self.__slot = YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..65535']},int_size=16), is_leaf=True, yang_name="slot", rest_name="slot", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint16', is_config=False)
def _get_tower(self):
"""
Getter method for tower, mapped from YANG variable /tm_state/tmdevicestataggr/tower (uint16)
YANG Description: tower id to get tm device stats
"""
return self.__tower
def _set_tower(self, v, load=False):
"""
Setter method for tower, mapped from YANG variable /tm_state/tmdevicestataggr/tower (uint16)
If this variable is read-only (config: false) in the
source YANG file, then _set_tower is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_tower() directly.
YANG Description: tower id to get tm device stats
"""
parent = getattr(self, "_parent", None)
if parent is not None and load is False:
raise AttributeError("Cannot set keys directly when" +
" within an instantiated list")
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..65535']},int_size=16), is_leaf=True, yang_name="tower", rest_name="tower", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint16', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """tower must be of a type compatible with uint16""",
'defined-type': "uint16",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..65535']},int_size=16), is_leaf=True, yang_name="tower", rest_name="tower", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint16', is_config=False)""",
})
self.__tower = t
if hasattr(self, '_set'):
self._set()
def _unset_tower(self):
self.__tower = YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['0..65535']},int_size=16), is_leaf=True, yang_name="tower", rest_name="tower", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint16', is_config=False)
def _get_ingress_total_pkt_cnt_aggr(self):
"""
Getter method for ingress_total_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/ingress_total_pkt_cnt_aggr (uint64)
YANG Description: ingress_total_pkt_cnt
"""
return self.__ingress_total_pkt_cnt_aggr
def _set_ingress_total_pkt_cnt_aggr(self, v, load=False):
"""
Setter method for ingress_total_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/ingress_total_pkt_cnt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_ingress_total_pkt_cnt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_ingress_total_pkt_cnt_aggr() directly.
YANG Description: ingress_total_pkt_cnt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="ingress-total-pkt-cnt-aggr", rest_name="ingress-total-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """ingress_total_pkt_cnt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="ingress-total-pkt-cnt-aggr", rest_name="ingress-total-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__ingress_total_pkt_cnt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_ingress_total_pkt_cnt_aggr(self):
self.__ingress_total_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="ingress-total-pkt-cnt-aggr", rest_name="ingress-total-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
def _get_enque_pkt_cnt_aggr(self):
"""
Getter method for enque_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/enque_pkt_cnt_aggr (uint64)
YANG Description: enque_pkt_cnt
"""
return self.__enque_pkt_cnt_aggr
def _set_enque_pkt_cnt_aggr(self, v, load=False):
"""
Setter method for enque_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/enque_pkt_cnt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_enque_pkt_cnt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_enque_pkt_cnt_aggr() directly.
YANG Description: enque_pkt_cnt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="enque-pkt-cnt-aggr", rest_name="enque-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """enque_pkt_cnt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="enque-pkt-cnt-aggr", rest_name="enque-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__enque_pkt_cnt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_enque_pkt_cnt_aggr(self):
self.__enque_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="enque-pkt-cnt-aggr", rest_name="enque-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
def _get_deque_pkt_cnt_aggr(self):
"""
Getter method for deque_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/deque_pkt_cnt_aggr (uint64)
YANG Description: deque_pkt_cnt
"""
return self.__deque_pkt_cnt_aggr
def _set_deque_pkt_cnt_aggr(self, v, load=False):
"""
Setter method for deque_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/deque_pkt_cnt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_deque_pkt_cnt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_deque_pkt_cnt_aggr() directly.
YANG Description: deque_pkt_cnt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="deque-pkt-cnt-aggr", rest_name="deque-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """deque_pkt_cnt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="deque-pkt-cnt-aggr", rest_name="deque-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__deque_pkt_cnt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_deque_pkt_cnt_aggr(self):
self.__deque_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="deque-pkt-cnt-aggr", rest_name="deque-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
def _get_total_discard_pkt_cnt_aggr(self):
"""
Getter method for total_discard_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/total_discard_pkt_cnt_aggr (uint64)
YANG Description: total_discard_pkt_cnt
"""
return self.__total_discard_pkt_cnt_aggr
def _set_total_discard_pkt_cnt_aggr(self, v, load=False):
"""
Setter method for total_discard_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/total_discard_pkt_cnt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_total_discard_pkt_cnt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_total_discard_pkt_cnt_aggr() directly.
YANG Description: total_discard_pkt_cnt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="total-discard-pkt-cnt-aggr", rest_name="total-discard-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """total_discard_pkt_cnt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="total-discard-pkt-cnt-aggr", rest_name="total-discard-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__total_discard_pkt_cnt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_total_discard_pkt_cnt_aggr(self):
self.__total_discard_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="total-discard-pkt-cnt-aggr", rest_name="total-discard-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
def _get_deleted_pkt_cnt_aggr(self):
"""
Getter method for deleted_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/deleted_pkt_cnt_aggr (uint64)
YANG Description: deleted_pkt_cnt
"""
return self.__deleted_pkt_cnt_aggr
def _set_deleted_pkt_cnt_aggr(self, v, load=False):
"""
Setter method for deleted_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/deleted_pkt_cnt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_deleted_pkt_cnt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_deleted_pkt_cnt_aggr() directly.
YANG Description: deleted_pkt_cnt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="deleted-pkt-cnt-aggr", rest_name="deleted-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """deleted_pkt_cnt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="deleted-pkt-cnt-aggr", rest_name="deleted-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__deleted_pkt_cnt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_deleted_pkt_cnt_aggr(self):
self.__deleted_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="deleted-pkt-cnt-aggr", rest_name="deleted-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
def _get_invalid_queue_pkt_aggr(self):
"""
Getter method for invalid_queue_pkt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/invalid_queue_pkt_aggr (uint64)
YANG Description: invalid_queue_pkt
"""
return self.__invalid_queue_pkt_aggr
def _set_invalid_queue_pkt_aggr(self, v, load=False):
"""
Setter method for invalid_queue_pkt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/invalid_queue_pkt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_invalid_queue_pkt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_invalid_queue_pkt_aggr() directly.
YANG Description: invalid_queue_pkt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="invalid-queue-pkt-aggr", rest_name="invalid-queue-pkt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """invalid_queue_pkt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="invalid-queue-pkt-aggr", rest_name="invalid-queue-pkt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__invalid_queue_pkt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_invalid_queue_pkt_aggr(self):
self.__invalid_queue_pkt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="invalid-queue-pkt-aggr", rest_name="invalid-queue-pkt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
def _get_cpu_pkt_cnt_aggr(self):
"""
Getter method for cpu_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/cpu_pkt_cnt_aggr (uint64)
YANG Description: cpu_pkt_cnt
"""
return self.__cpu_pkt_cnt_aggr
def _set_cpu_pkt_cnt_aggr(self, v, load=False):
"""
Setter method for cpu_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/cpu_pkt_cnt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_cpu_pkt_cnt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_cpu_pkt_cnt_aggr() directly.
YANG Description: cpu_pkt_cnt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="cpu-pkt-cnt-aggr", rest_name="cpu-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """cpu_pkt_cnt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="cpu-pkt-cnt-aggr", rest_name="cpu-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__cpu_pkt_cnt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_cpu_pkt_cnt_aggr(self):
self.__cpu_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="cpu-pkt-cnt-aggr", rest_name="cpu-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
def _get_uni_pkt_cnt_aggr(self):
"""
Getter method for uni_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/uni_pkt_cnt_aggr (uint64)
YANG Description: uni_pkt_cnt
"""
return self.__uni_pkt_cnt_aggr
def _set_uni_pkt_cnt_aggr(self, v, load=False):
"""
Setter method for uni_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/uni_pkt_cnt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_uni_pkt_cnt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_uni_pkt_cnt_aggr() directly.
YANG Description: uni_pkt_cnt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="uni-pkt-cnt-aggr", rest_name="uni-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """uni_pkt_cnt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="uni-pkt-cnt-aggr", rest_name="uni-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__uni_pkt_cnt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_uni_pkt_cnt_aggr(self):
self.__uni_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="uni-pkt-cnt-aggr", rest_name="uni-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
def _get_mcast_pkt_cnt_aggr(self):
"""
Getter method for mcast_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/mcast_pkt_cnt_aggr (uint64)
YANG Description: mcast_pkt_cnt
"""
return self.__mcast_pkt_cnt_aggr
def _set_mcast_pkt_cnt_aggr(self, v, load=False):
"""
Setter method for mcast_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/mcast_pkt_cnt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_mcast_pkt_cnt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_mcast_pkt_cnt_aggr() directly.
YANG Description: mcast_pkt_cnt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="mcast-pkt-cnt-aggr", rest_name="mcast-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """mcast_pkt_cnt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="mcast-pkt-cnt-aggr", rest_name="mcast-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__mcast_pkt_cnt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_mcast_pkt_cnt_aggr(self):
self.__mcast_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="mcast-pkt-cnt-aggr", rest_name="mcast-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
def _get_uni_discard_pkt_cnt_aggr(self):
"""
Getter method for uni_discard_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/uni_discard_pkt_cnt_aggr (uint64)
YANG Description: uni_discard_pkt_cnt
"""
return self.__uni_discard_pkt_cnt_aggr
def _set_uni_discard_pkt_cnt_aggr(self, v, load=False):
"""
Setter method for uni_discard_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/uni_discard_pkt_cnt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_uni_discard_pkt_cnt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_uni_discard_pkt_cnt_aggr() directly.
YANG Description: uni_discard_pkt_cnt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="uni-discard-pkt-cnt-aggr", rest_name="uni-discard-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """uni_discard_pkt_cnt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="uni-discard-pkt-cnt-aggr", rest_name="uni-discard-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__uni_discard_pkt_cnt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_uni_discard_pkt_cnt_aggr(self):
self.__uni_discard_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="uni-discard-pkt-cnt-aggr", rest_name="uni-discard-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
def _get_mcast_discard_pkt_cnt_aggr(self):
"""
Getter method for mcast_discard_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/mcast_discard_pkt_cnt_aggr (uint64)
YANG Description: mcast_discard_pkt_cnt
"""
return self.__mcast_discard_pkt_cnt_aggr
def _set_mcast_discard_pkt_cnt_aggr(self, v, load=False):
"""
Setter method for mcast_discard_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/mcast_discard_pkt_cnt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_mcast_discard_pkt_cnt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_mcast_discard_pkt_cnt_aggr() directly.
YANG Description: mcast_discard_pkt_cnt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="mcast-discard-pkt-cnt-aggr", rest_name="mcast-discard-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """mcast_discard_pkt_cnt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="mcast-discard-pkt-cnt-aggr", rest_name="mcast-discard-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__mcast_discard_pkt_cnt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_mcast_discard_pkt_cnt_aggr(self):
self.__mcast_discard_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="mcast-discard-pkt-cnt-aggr", rest_name="mcast-discard-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
def _get_fqp_pkt_cnt_aggr(self):
"""
Getter method for fqp_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/fqp_pkt_cnt_aggr (uint64)
YANG Description: fqp_pkt_cnt
"""
return self.__fqp_pkt_cnt_aggr
def _set_fqp_pkt_cnt_aggr(self, v, load=False):
"""
Setter method for fqp_pkt_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/fqp_pkt_cnt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_fqp_pkt_cnt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_fqp_pkt_cnt_aggr() directly.
YANG Description: fqp_pkt_cnt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="fqp-pkt-cnt-aggr", rest_name="fqp-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """fqp_pkt_cnt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="fqp-pkt-cnt-aggr", rest_name="fqp-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__fqp_pkt_cnt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_fqp_pkt_cnt_aggr(self):
self.__fqp_pkt_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="fqp-pkt-cnt-aggr", rest_name="fqp-pkt-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
def _get_ehp_discard_cnt_aggr(self):
"""
Getter method for ehp_discard_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/ehp_discard_cnt_aggr (uint64)
YANG Description: ehp_discard_cnt
"""
return self.__ehp_discard_cnt_aggr
def _set_ehp_discard_cnt_aggr(self, v, load=False):
"""
Setter method for ehp_discard_cnt_aggr, mapped from YANG variable /tm_state/tmdevicestataggr/ehp_discard_cnt_aggr (uint64)
If this variable is read-only (config: false) in the
source YANG file, then _set_ehp_discard_cnt_aggr is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_ehp_discard_cnt_aggr() directly.
YANG Description: ehp_discard_cnt
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="ehp-discard-cnt-aggr", rest_name="ehp-discard-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """ehp_discard_cnt_aggr must be of a type compatible with uint64""",
'defined-type': "uint64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="ehp-discard-cnt-aggr", rest_name="ehp-discard-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)""",
})
self.__ehp_discard_cnt_aggr = t
if hasattr(self, '_set'):
self._set()
def _unset_ehp_discard_cnt_aggr(self):
self.__ehp_discard_cnt_aggr = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="ehp-discard-cnt-aggr", rest_name="ehp-discard-cnt-aggr", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-sysdiag-operational', defining_module='brocade-sysdiag-operational', yang_type='uint64', is_config=False)
slot = __builtin__.property(_get_slot)
tower = __builtin__.property(_get_tower)
ingress_total_pkt_cnt_aggr = __builtin__.property(_get_ingress_total_pkt_cnt_aggr)
enque_pkt_cnt_aggr = __builtin__.property(_get_enque_pkt_cnt_aggr)
deque_pkt_cnt_aggr = __builtin__.property(_get_deque_pkt_cnt_aggr)
total_discard_pkt_cnt_aggr = __builtin__.property(_get_total_discard_pkt_cnt_aggr)
deleted_pkt_cnt_aggr = __builtin__.property(_get_deleted_pkt_cnt_aggr)
invalid_queue_pkt_aggr = __builtin__.property(_get_invalid_queue_pkt_aggr)
cpu_pkt_cnt_aggr = __builtin__.property(_get_cpu_pkt_cnt_aggr)
uni_pkt_cnt_aggr = __builtin__.property(_get_uni_pkt_cnt_aggr)
mcast_pkt_cnt_aggr = __builtin__.property(_get_mcast_pkt_cnt_aggr)
uni_discard_pkt_cnt_aggr = __builtin__.property(_get_uni_discard_pkt_cnt_aggr)
mcast_discard_pkt_cnt_aggr = __builtin__.property(_get_mcast_discard_pkt_cnt_aggr)
fqp_pkt_cnt_aggr = __builtin__.property(_get_fqp_pkt_cnt_aggr)
ehp_discard_cnt_aggr = __builtin__.property(_get_ehp_discard_cnt_aggr)
_pyangbind_elements = {'slot': slot, 'tower': tower, 'ingress_total_pkt_cnt_aggr': ingress_total_pkt_cnt_aggr, 'enque_pkt_cnt_aggr': enque_pkt_cnt_aggr, 'deque_pkt_cnt_aggr': deque_pkt_cnt_aggr, 'total_discard_pkt_cnt_aggr': total_discard_pkt_cnt_aggr, 'deleted_pkt_cnt_aggr': deleted_pkt_cnt_aggr, 'invalid_queue_pkt_aggr': invalid_queue_pkt_aggr, 'cpu_pkt_cnt_aggr': cpu_pkt_cnt_aggr, 'uni_pkt_cnt_aggr': uni_pkt_cnt_aggr, 'mcast_pkt_cnt_aggr': mcast_pkt_cnt_aggr, 'uni_discard_pkt_cnt_aggr': uni_discard_pkt_cnt_aggr, 'mcast_discard_pkt_cnt_aggr': mcast_discard_pkt_cnt_aggr, 'fqp_pkt_cnt_aggr': fqp_pkt_cnt_aggr, 'ehp_discard_cnt_aggr': ehp_discard_cnt_aggr, }
| 74.445255 | 666 | 0.759212 | 7,090 | 50,995 | 5.136107 | 0.028068 | 0.062282 | 0.08156 | 0.037814 | 0.948126 | 0.918166 | 0.889688 | 0.874777 | 0.863545 | 0.84943 | 0 | 0.033443 | 0.116933 | 50,995 | 684 | 667 | 74.554094 | 0.775204 | 0.171762 | 0 | 0.518617 | 0 | 0.039894 | 0.382992 | 0.251214 | 0 | 0 | 0 | 0 | 0 | 1 | 0.12766 | false | 0 | 0.021277 | 0 | 0.257979 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
1a88d14a1c0ecde2fa05b14b38f25644b23f5525 | 67 | py | Python | tests/core/test_import.py | gsalgado/asyncio-run-in-process | 60ea6da60c8e245587b1d5e375fbf736c5a096fa | [
"MIT"
] | 12 | 2019-11-06T00:46:52.000Z | 2021-11-04T14:10:13.000Z | tests/core/test_import.py | gsalgado/asyncio-run-in-process | 60ea6da60c8e245587b1d5e375fbf736c5a096fa | [
"MIT"
] | 20 | 2019-11-05T20:19:44.000Z | 2020-12-17T15:57:41.000Z | tests/core/test_import.py | gsalgado/asyncio-run-in-process | 60ea6da60c8e245587b1d5e375fbf736c5a096fa | [
"MIT"
] | 6 | 2019-11-05T19:36:53.000Z | 2021-11-04T14:10:02.000Z | def test_import():
import asyncio_run_in_process # noqa: F401
| 22.333333 | 47 | 0.746269 | 10 | 67 | 4.6 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.054545 | 0.179104 | 67 | 2 | 48 | 33.5 | 0.781818 | 0.149254 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 1 | 0 | 1.5 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
1aad9f12ddb6ac8c72660e284119f89039a4aebe | 46 | py | Python | src/menten_gcn/playground/__init__.py | MentenAI/menten_gcn | bcc7642cb32ab4e60a97687de17c1aa2dc4b5421 | [
"MIT"
] | 11 | 2020-12-15T15:36:47.000Z | 2022-03-10T19:23:36.000Z | src/menten_gcn/playground/__init__.py | MentenAI/menten_gcn | bcc7642cb32ab4e60a97687de17c1aa2dc4b5421 | [
"MIT"
] | 2 | 2021-01-14T15:04:59.000Z | 2021-01-14T19:24:00.000Z | src/menten_gcn/playground/__init__.py | MentenAI/menten_gcn | bcc7642cb32ab4e60a97687de17c1aa2dc4b5421 | [
"MIT"
] | null | null | null | from .nbody_convs import * # noqa: F401,F403
| 23 | 45 | 0.717391 | 7 | 46 | 4.571429 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.157895 | 0.173913 | 46 | 1 | 46 | 46 | 0.684211 | 0.326087 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
46d2b17e273baeb619c402f6a1fb2c6d36a59b05 | 96 | py | Python | venv/lib/python3.8/site-packages/pip/_vendor/html5lib/treebuilders/etree_lxml.py | Retraces/UkraineBot | 3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71 | [
"MIT"
] | 2 | 2022-03-13T01:58:52.000Z | 2022-03-31T06:07:54.000Z | venv/lib/python3.8/site-packages/pip/_vendor/html5lib/treebuilders/etree_lxml.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | 19 | 2021-11-20T04:09:18.000Z | 2022-03-23T15:05:55.000Z | venv/lib/python3.8/site-packages/pip/_vendor/html5lib/treebuilders/etree_lxml.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | null | null | null | /home/runner/.cache/pip/pool/f6/0a/83/8ecf88c6c3e10586b9729befd85675299946f371a2baccb69459af2241 | 96 | 96 | 0.895833 | 9 | 96 | 9.555556 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.4375 | 0 | 96 | 1 | 96 | 96 | 0.458333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
46d978486ad6997dea3c2fa68d9377592d3fe89b | 101 | py | Python | stixcore/tmtc/__init__.py | nicHoch/STIXCore | 16822bbb37046f8e6c03be51909cfc91e9822cf7 | [
"BSD-3-Clause"
] | 1 | 2022-03-31T13:42:43.000Z | 2022-03-31T13:42:43.000Z | stixcore/tmtc/__init__.py | nicHoch/STIXCore | 16822bbb37046f8e6c03be51909cfc91e9822cf7 | [
"BSD-3-Clause"
] | 192 | 2020-11-03T22:40:19.000Z | 2022-03-31T15:17:13.000Z | stixcore/tmtc/__init__.py | nicHoch/STIXCore | 16822bbb37046f8e6c03be51909cfc91e9822cf7 | [
"BSD-3-Clause"
] | 3 | 2020-11-09T15:05:18.000Z | 2022-01-21T07:52:51.000Z | from stixcore.tmtc.packet_factory import Packet
from stixcore.tmtc.tm import *
__all__ = ['Packet']
| 20.2 | 47 | 0.782178 | 14 | 101 | 5.285714 | 0.571429 | 0.324324 | 0.432432 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.118812 | 101 | 4 | 48 | 25.25 | 0.831461 | 0 | 0 | 0 | 0 | 0 | 0.059406 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
20068fb9f19301820864ea58094f4c61da651883 | 2,291 | py | Python | src/snowflakes/tests/test_fixtures.py | Lattice-Data/snovault | 384f4a3bf3afcc4b825f98c80d18a5852d46ef5c | [
"MIT"
] | 14 | 2016-04-21T08:27:41.000Z | 2021-06-01T11:35:28.000Z | src/snowflakes/tests/test_fixtures.py | Lattice-Data/snovault | 384f4a3bf3afcc4b825f98c80d18a5852d46ef5c | [
"MIT"
] | 122 | 2016-07-01T23:38:41.000Z | 2022-03-11T23:11:47.000Z | src/snowflakes/tests/test_fixtures.py | Lattice-Data/snovault | 384f4a3bf3afcc4b825f98c80d18a5852d46ef5c | [
"MIT"
] | 24 | 2016-04-21T08:27:46.000Z | 2022-01-11T19:00:28.000Z | import pytest
@pytest.yield_fixture(scope='session')
def minitestdata(app, conn):
tx = conn.begin_nested()
from webtest import TestApp
environ = {
'HTTP_ACCEPT': 'application/json',
'REMOTE_USER': 'TEST',
}
testapp = TestApp(app, environ)
item = {
'name': 'NIS39344',
'title': 'Grant to make snow',
'project': 'ENCODE',
'rfa': 'ENCODE3',
}
testapp.post_json('/awards', item, status=201)
yield
tx.rollback()
@pytest.yield_fixture(scope='session')
def minitestdata2(app, conn):
tx = conn.begin_nested()
from webtest import TestApp
environ = {
'HTTP_ACCEPT': 'application/json',
'REMOTE_USER': 'TEST',
}
testapp = TestApp(app, environ)
item = {
'name': 'NIS39344',
'title': 'Grant to make snow',
'project': 'ENCODE',
'rfa': 'ENCODE3',
}
testapp.post_json('/awards', item, status=201)
yield
tx.rollback()
@pytest.mark.usefixtures('minitestdata')
def test_fixtures1(testapp):
""" This test is not really exhaustive.
Still need to inspect the sql log to verify fixture correctness.
"""
res = testapp.get('/awards').maybe_follow()
items = res.json['@graph']
assert len(items) == 1
# Trigger an error
item = {'foo': 'bar'}
res = testapp.post_json('/awards', item, status=422)
assert res.json['errors']
res = testapp.get('/awards').maybe_follow()
items = res.json['@graph']
assert len(items) == 1
item = {
'name': 'NIS39339',
'title': 'Grant to make snow',
'project': 'ENCODE',
'rfa': 'ENCODE3',
}
testapp.post_json('/awards', item, status=201)
res = testapp.get('/awards').maybe_follow()
items = res.json['@graph']
assert len(items) == 2
# Trigger an error
item = {'foo': 'bar'}
res = testapp.post_json('/awards', item, status=422)
assert res.json['errors']
res = testapp.get('/awards').maybe_follow()
items = res.json['@graph']
assert len(items) == 2
def test_fixtures2(minitestdata2, testapp):
# http://stackoverflow.com/questions/15775601/mutually-exclusive-fixtures
res = testapp.get('/awards/')
items = res.json['@graph']
assert len(items) == 1
| 23.864583 | 77 | 0.5945 | 265 | 2,291 | 5.067925 | 0.316981 | 0.052122 | 0.055845 | 0.078183 | 0.77513 | 0.77513 | 0.730454 | 0.730454 | 0.706627 | 0.706627 | 0 | 0.028918 | 0.245308 | 2,291 | 95 | 78 | 24.115789 | 0.747831 | 0.09079 | 0 | 0.808824 | 0 | 0 | 0.198063 | 0 | 0 | 0 | 0 | 0 | 0.102941 | 1 | 0.058824 | false | 0 | 0.044118 | 0 | 0.102941 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
644e80249b519b4e4d48e50e99bc0314532424ed | 226 | py | Python | kwat/grid/__init__.py | KwatME/ccal | d96dfa811482eee067f346386a2181ec514625f4 | [
"MIT"
] | 5 | 2017-05-05T17:50:28.000Z | 2019-01-30T19:23:02.000Z | kwat/grid/__init__.py | KwatME/ccal | d96dfa811482eee067f346386a2181ec514625f4 | [
"MIT"
] | 5 | 2017-05-05T01:52:31.000Z | 2019-04-20T21:06:05.000Z | kwat/grid/__init__.py | KwatME/ccal | d96dfa811482eee067f346386a2181ec514625f4 | [
"MIT"
] | 5 | 2017-07-17T18:55:54.000Z | 2019-02-02T04:46:19.000Z | from .get_1d_grid import get_1d_grid
from .get_1d_grid_resolution import get_1d_grid_resolution
from .make_1d_grid import make_1d_grid
from .make_nd_grid import make_nd_grid
from .plot import plot
from .reflect import reflect
| 32.285714 | 58 | 0.867257 | 42 | 226 | 4.238095 | 0.238095 | 0.202247 | 0.202247 | 0.146067 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.029703 | 0.106195 | 226 | 6 | 59 | 37.666667 | 0.851485 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
64655ad3ef883d87e49adabe3ea0e869033fc1b6 | 25 | py | Python | bigsimr/__init__.py | SchisslerGroup/python-bigsimr | 5133356938a1a4e197b92618c034876e0c937760 | [
"MIT"
] | 10 | 2019-02-13T16:53:24.000Z | 2021-11-13T08:34:19.000Z | bigsimr/__init__.py | SchisslerGroup/python-bigsimr | 5133356938a1a4e197b92618c034876e0c937760 | [
"MIT"
] | 2 | 2019-08-31T04:37:46.000Z | 2019-11-11T03:02:37.000Z | bigsimr/__init__.py | SchisslerGroup/python-bigsimr | 5133356938a1a4e197b92618c034876e0c937760 | [
"MIT"
] | 2 | 2019-08-18T09:48:03.000Z | 2021-11-13T08:34:38.000Z | from .setup import setup
| 12.5 | 24 | 0.8 | 4 | 25 | 5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16 | 25 | 1 | 25 | 25 | 0.952381 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
64b0611ee32522917a8305a77c5023f903630ea4 | 43 | py | Python | env/lib/python3.6/site-packages/sqlDataframe/__init__.py | sehan10/SQL_Pandas | bb14b25445e659701488e9c5bb3ce6896b4e3862 | [
"MIT"
] | null | null | null | env/lib/python3.6/site-packages/sqlDataframe/__init__.py | sehan10/SQL_Pandas | bb14b25445e659701488e9c5bb3ce6896b4e3862 | [
"MIT"
] | null | null | null | env/lib/python3.6/site-packages/sqlDataframe/__init__.py | sehan10/SQL_Pandas | bb14b25445e659701488e9c5bb3ce6896b4e3862 | [
"MIT"
] | 1 | 2020-11-04T06:37:19.000Z | 2020-11-04T06:37:19.000Z | from .main import sqlCred, read_sql, to_sql | 43 | 43 | 0.813953 | 8 | 43 | 4.125 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.116279 | 43 | 1 | 43 | 43 | 0.868421 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
64ce9d3e4dc64983eb3ff7074772b744d6f9bb55 | 2,618 | py | Python | generate_yarnmodels/generate_inplane.py | kamleshbhalui/MADYPG | b91c7af0b7a1382fed0a5fb3147f13594ddec71e | [
"MIT"
] | null | null | null | generate_yarnmodels/generate_inplane.py | kamleshbhalui/MADYPG | b91c7af0b7a1382fed0a5fb3147f13594ddec71e | [
"MIT"
] | null | null | null | generate_yarnmodels/generate_inplane.py | kamleshbhalui/MADYPG | b91c7af0b7a1382fed0a5fb3147f13594ddec71e | [
"MIT"
] | null | null | null | import generate
import numpy as np
import time
rge5xy = [-0.2,0.0,0.33,0.67,1.0]
rge5a = [-0.5,-0.25,0.0,0.25,0.5]
rge9xy = np.concatenate([np.linspace(-0.2,0.0,9 - 9*4//6), np.linspace(0.0,1.0,9*4//6 + 1)[1:]])
rge9a = np.linspace(-0.7,0.7,9)
rge15xy = np.concatenate([np.linspace(-0.2,0.0,15 - 15*4//6), np.linspace(0.0,1.0,15*4//6 + 1)[1:]])
rge15a = np.linspace(-0.7,0.7,15)
rge31xy = np.concatenate([np.linspace(-0.2,0.0,31 - 31*4//6), np.linspace(0.0,1.0,31*4//6 + 1)[1:]])
rge31a = np.linspace(-0.7,0.7,31)
timings = []
timingsfile = 'timings_%s.txt' % time.strftime("%Y%m%d-%H%M%S")
for name, pypfile in [
# ['stocktiniest', "pyp/stocktiniest.pyp"],
['stock', "pyp/hylc2020/stockinette.pyp"],
['rib', "pyp/hylc2020/cartridge_belt_rib.pyp"],
['honey', "pyp/hylc2020/slip_stitch_honeycomb.pyp"],
['basket', "pyp/hylc2020/basket.pyp"],
['satin', "pyp/hylc2020/satin.pyp"],
]:
for tstname,rgesxsy,rgesa in [
# ['15',rge15xy,rge15a],
['9',rge9xy,rge9a],
]:
print("Pattern:",name)
print("Samples: %d x %d x %d (Total: %d)" % (len(rgesxsy),len(rgesa),len(rgesxsy), len(rgesxsy)*len(rgesxsy)*len(rgesa)))
t0 = time.perf_counter()
generate.generate_data("model_%s_%s" % (name,tstname), pypfile, rgesxsy,
rgesa, rgesxsy)
dt = time.perf_counter() - t0
timings.append(["model_%s_%s" % (name,tstname), dt])
with open(timingsfile, 'a') as f1:
f1.write("%s %s\n" % ("model_%s_%s" % (name,tstname), dt))
# generate tiny stock 9x9x9 for comparison against 15x15x15 if it matters at extreme scales.
# for name, pypfile in [
# ['stocktiniest', "pyp/stocktiniest.pyp"],
# # ['stock', "pyp/hylc2020/stockinette.pyp"],
# # ['rib', "pyp/hylc2020/cartridge_belt_rib.pyp"],
# # ['honey', "pyp/hylc2020/slip_stitch_honeycomb.pyp"],
# # ['basket', "pyp/hylc2020/basket.pyp"],
# # ['satin', "pyp/hylc2020/satin.pyp"],
# ]:
# for tstname,rgesxsy,rgesa in [
# ['9',rge9xy,rge9a],
# ]:
# print("Pattern:",name)
# print("Samples: %d x %d x %d (Total: %d)" % (len(rgesxsy),len(rgesa),len(rgesxsy), len(rgesxsy)*len(rgesxsy)*len(rgesa)))
# t0 = time.perf_counter()
# generate.generate_data("model_%s_%s" % (name,tstname), pypfile, rgesxsy,
# rgesa, rgesxsy)
# dt = time.perf_counter() - t0
# timings.append(["model_%s_%s" % (name,tstname), dt])
# with open(timingsfile, 'a') as f1:
# f1.write("%s %s\n" % ("model_%s_%s" % (name,tstname), dt))
# with open(timingsfile, 'a') as f1:
# for name, dt in timings:
# f1.write("%s %s\n" % (name, dt)) | 39.074627 | 127 | 0.598549 | 404 | 2,618 | 3.811881 | 0.220297 | 0.012987 | 0.064286 | 0.042857 | 0.801948 | 0.795455 | 0.768182 | 0.768182 | 0.684416 | 0.684416 | 0 | 0.083564 | 0.172651 | 2,618 | 67 | 128 | 39.074627 | 0.627424 | 0.431245 | 0 | 0.0625 | 1 | 0 | 0.192044 | 0.100137 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.09375 | 0 | 0.09375 | 0.0625 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
b37dde5abfeb34f059d3eec87675987642185cc4 | 3,713 | py | Python | tests/asyncio/test_result_proxy.py | grignards/sqlalchemy_aio | cbcc9e4503cd0183fa6d12db9ea812544867e1c3 | [
"MIT"
] | 321 | 2016-10-04T12:58:42.000Z | 2022-01-19T13:47:53.000Z | tests/asyncio/test_result_proxy.py | grignards/sqlalchemy_aio | cbcc9e4503cd0183fa6d12db9ea812544867e1c3 | [
"MIT"
] | 42 | 2017-07-10T17:41:51.000Z | 2022-03-16T08:59:56.000Z | tests/asyncio/test_result_proxy.py | grignards/sqlalchemy_aio | cbcc9e4503cd0183fa6d12db9ea812544867e1c3 | [
"MIT"
] | 25 | 2016-10-10T08:45:31.000Z | 2021-08-04T05:44:37.000Z | import pytest
from sqlalchemy import func, select
from sqlalchemy.schema import CreateTable
from sqlalchemy_aio.base import AsyncResultProxy
pytestmark = pytest.mark.noextras
@pytest.mark.asyncio
async def test_result_proxy(asyncio_engine):
result = await asyncio_engine.execute(select([1]))
assert isinstance(result, AsyncResultProxy)
await result.close()
@pytest.mark.asyncio
async def test_fetchone(asyncio_engine):
result = await asyncio_engine.execute(select([1]))
assert await result.fetchone() == (1,)
await result.close()
@pytest.mark.asyncio
async def test_fetchmany_value(asyncio_engine):
result = await asyncio_engine.execute(select([1]))
assert await result.fetchmany() == [(1,)]
await result.close()
@pytest.mark.asyncio
async def test_fetchmany_quantity(asyncio_engine, mytable):
await asyncio_engine.execute(CreateTable(mytable))
await asyncio_engine.execute(mytable.insert())
await asyncio_engine.execute(mytable.insert())
result = await asyncio_engine.execute(select([mytable]))
rows = await result.fetchmany(1)
assert len(rows) == 1
await result.close()
await asyncio_engine.execute(mytable.delete())
@pytest.mark.asyncio
async def test_fetchmany_all(asyncio_engine, mytable):
await asyncio_engine.execute(CreateTable(mytable))
await asyncio_engine.execute(mytable.insert())
await asyncio_engine.execute(mytable.insert())
await asyncio_engine.execute(mytable.insert())
result = await asyncio_engine.execute(select([mytable]))
rows = await result.fetchmany(100)
assert len(rows) == 3
await result.close()
await asyncio_engine.execute(mytable.delete())
@pytest.mark.asyncio
async def test_fetchall(asyncio_engine):
result = await asyncio_engine.execute(select([1]))
assert await result.fetchall() == [(1,)]
@pytest.mark.asyncio
async def test_scalar(asyncio_engine):
result = await asyncio_engine.execute(select([1]))
assert await result.scalar() == 1
@pytest.mark.asyncio
async def test_first(asyncio_engine):
result = await asyncio_engine.execute(select([1]))
assert await result.first() == (1,)
await result.close()
@pytest.mark.asyncio
async def test_keys(asyncio_engine):
result = await asyncio_engine.execute(select([func.now().label('time')]))
assert await result.keys() == ['time']
await result.close()
@pytest.mark.asyncio
async def test_returns_rows(asyncio_engine, mytable):
result = await asyncio_engine.execute(select([1]))
assert result.returns_rows
await result.close()
result = await asyncio_engine.execute(CreateTable(mytable))
assert not result.returns_rows
await result.close()
@pytest.mark.asyncio
async def test_rowcount(asyncio_engine, mytable):
await asyncio_engine.execute(CreateTable(mytable))
await asyncio_engine.execute(mytable.insert())
await asyncio_engine.execute(mytable.insert())
result = await asyncio_engine.execute(mytable.delete())
assert result.rowcount == 2
@pytest.mark.asyncio
async def test_inserted_primary_key(asyncio_engine, mytable):
await asyncio_engine.execute(CreateTable(mytable))
result = await asyncio_engine.execute(mytable.insert())
assert result.inserted_primary_key == [1]
@pytest.mark.asyncio
async def test_aiter(asyncio_engine, mytable):
await asyncio_engine.execute(CreateTable(mytable))
await asyncio_engine.execute(mytable.insert())
await asyncio_engine.execute(mytable.insert())
result = await asyncio_engine.execute(select([mytable]))
fetched = []
async for row in result:
fetched.append(row)
await asyncio_engine.execute(mytable.delete())
assert len(fetched) == 2
| 31.201681 | 77 | 0.74495 | 466 | 3,713 | 5.787554 | 0.126609 | 0.212088 | 0.206897 | 0.287356 | 0.81053 | 0.808306 | 0.733407 | 0.673341 | 0.638487 | 0.565072 | 0 | 0.006591 | 0.141934 | 3,713 | 118 | 78 | 31.466102 | 0.839925 | 0 | 0 | 0.544444 | 0 | 0 | 0.002155 | 0 | 0 | 0 | 0 | 0 | 0.155556 | 1 | 0 | false | 0 | 0.044444 | 0 | 0.044444 | 0 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
b3cd28077e5e0b2cbae068af7a79a44b71f8a699 | 20 | py | Python | client/__init__.py | ShizuApp/TwitchBot | 9df8de08ae77604ad24d4a4b5664ef27838e9dab | [
"MIT"
] | 20 | 2017-06-16T22:39:22.000Z | 2022-03-13T16:52:20.000Z | bodhion/__init__.py | webclinic017/bodhion | f6d388abc5305afcec279d2e7c9ee1635c5c7031 | [
"MIT"
] | 4 | 2017-09-06T12:42:32.000Z | 2021-06-01T09:29:34.000Z | bodhion/__init__.py | webclinic017/bodhion | f6d388abc5305afcec279d2e7c9ee1635c5c7031 | [
"MIT"
] | 3 | 2017-09-12T19:33:33.000Z | 2020-08-01T22:01:56.000Z | from .bot import Bot | 20 | 20 | 0.8 | 4 | 20 | 4 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15 | 20 | 1 | 20 | 20 | 0.941176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
b3d9683682c75ca1f83a24f37dbe24b5c2cbfffb | 96 | py | Python | venv/lib/python3.8/site-packages/poetry/json/__init__.py | GiulianaPola/select_repeats | 17a0d053d4f874e42cf654dd142168c2ec8fbd11 | [
"MIT"
] | 2 | 2022-03-13T01:58:52.000Z | 2022-03-31T06:07:54.000Z | venv/lib/python3.8/site-packages/poetry/json/__init__.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | 19 | 2021-11-20T04:09:18.000Z | 2022-03-23T15:05:55.000Z | venv/lib/python3.8/site-packages/poetry/json/__init__.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | null | null | null | /home/runner/.cache/pip/pool/31/1c/d2/198df0e598f0017770f50ace3e2f7f2452d048ede3449bdf1b3b9256ac | 96 | 96 | 0.895833 | 9 | 96 | 9.555556 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.416667 | 0 | 96 | 1 | 96 | 96 | 0.479167 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
b60a690cb1a6c84bbbbfb24c39366adb3ee69ec4 | 127 | py | Python | video_records/__init__.py | selva604/DA_TBN | 240b3561eb5957e2827a5b8ef8fbd991d51489f4 | [
"MIT"
] | 1 | 2021-06-17T12:32:21.000Z | 2021-06-17T12:32:21.000Z | video_records/__init__.py | selva604/DA_TBN | 240b3561eb5957e2827a5b8ef8fbd991d51489f4 | [
"MIT"
] | null | null | null | video_records/__init__.py | selva604/DA_TBN | 240b3561eb5957e2827a5b8ef8fbd991d51489f4 | [
"MIT"
] | null | null | null | from .epickitchens55_record import EpicKitchens55_VideoRecord
from .epickitchens100_record import EpicKitchens100_VideoRecord
| 31.75 | 63 | 0.913386 | 12 | 127 | 9.333333 | 0.5 | 0.214286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.084746 | 0.070866 | 127 | 3 | 64 | 42.333333 | 0.864407 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
37475109aa605c8e45bb09aed0994d0d27be1372 | 177 | py | Python | src/Scripts/generate-queries-for-threads.py | jasonfeihe/BitFunnel | 859f3038e1978d066426ebaef17354c8ec408402 | [
"MIT"
] | 364 | 2016-08-08T21:45:24.000Z | 2022-03-27T13:46:51.000Z | src/Scripts/generate-queries-for-threads.py | jasonfeihe/BitFunnel | 859f3038e1978d066426ebaef17354c8ec408402 | [
"MIT"
] | 321 | 2016-08-08T22:00:28.000Z | 2020-11-30T23:03:16.000Z | src/Scripts/generate-queries-for-threads.py | jasonfeihe/BitFunnel | 859f3038e1978d066426ebaef17354c8ec408402 | [
"MIT"
] | 44 | 2016-08-14T16:20:29.000Z | 2021-10-03T07:46:26.000Z | for i in range(1, 17):
print("cd /tmp/threads/{}".format(i))
print("threads {}".format(i))
print("query log /home/danluu/dev/wikipedia.100.150/config/QueryLog.txt")
| 35.4 | 77 | 0.649718 | 28 | 177 | 4.107143 | 0.785714 | 0.226087 | 0.243478 | 0.330435 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.058824 | 0.135593 | 177 | 4 | 78 | 44.25 | 0.69281 | 0 | 0 | 0 | 0 | 0 | 0.519774 | 0.305085 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.75 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
374a21e284ab15ec69ccfc0ed3d60a7cd905f5b7 | 45 | py | Python | peleffybenchmarktools/solvent/__init__.py | martimunicoy/offpele-benchmarks | 20af939ce60252c05e0c1e44b85cf89a4f8a2245 | [
"MIT"
] | null | null | null | peleffybenchmarktools/solvent/__init__.py | martimunicoy/offpele-benchmarks | 20af939ce60252c05e0c1e44b85cf89a4f8a2245 | [
"MIT"
] | 7 | 2020-08-07T14:51:02.000Z | 2020-10-30T20:18:38.000Z | peleffybenchmarktools/solvent/__init__.py | martimunicoy/offpele-benchmarks | 20af939ce60252c05e0c1e44b85cf89a4f8a2245 | [
"MIT"
] | null | null | null | from .solventhandler import SolventBenchmark
| 22.5 | 44 | 0.888889 | 4 | 45 | 10 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088889 | 45 | 1 | 45 | 45 | 0.97561 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
8061b1b6f91f83316cba16d1e3acdfb407b74722 | 1,401 | py | Python | temboo/core/Library/NYTimes/CampaignFinance/Committees/__init__.py | jordanemedlock/psychtruths | 52e09033ade9608bd5143129f8a1bfac22d634dd | [
"Apache-2.0"
] | 7 | 2016-03-07T02:07:21.000Z | 2022-01-21T02:22:41.000Z | temboo/core/Library/NYTimes/CampaignFinance/Committees/__init__.py | jordanemedlock/psychtruths | 52e09033ade9608bd5143129f8a1bfac22d634dd | [
"Apache-2.0"
] | null | null | null | temboo/core/Library/NYTimes/CampaignFinance/Committees/__init__.py | jordanemedlock/psychtruths | 52e09033ade9608bd5143129f8a1bfac22d634dd | [
"Apache-2.0"
] | 8 | 2016-06-14T06:01:11.000Z | 2020-04-22T09:21:44.000Z | from temboo.Library.NYTimes.CampaignFinance.Committees.CommitteeContributions import CommitteeContributions, CommitteeContributionsInputSet, CommitteeContributionsResultSet, CommitteeContributionsChoreographyExecution
from temboo.Library.NYTimes.CampaignFinance.Committees.CommitteeContributionsToCandidate import CommitteeContributionsToCandidate, CommitteeContributionsToCandidateInputSet, CommitteeContributionsToCandidateResultSet, CommitteeContributionsToCandidateChoreographyExecution
from temboo.Library.NYTimes.CampaignFinance.Committees.CommitteeDetails import CommitteeDetails, CommitteeDetailsInputSet, CommitteeDetailsResultSet, CommitteeDetailsChoreographyExecution
from temboo.Library.NYTimes.CampaignFinance.Committees.CommitteeFilings import CommitteeFilings, CommitteeFilingsInputSet, CommitteeFilingsResultSet, CommitteeFilingsChoreographyExecution
from temboo.Library.NYTimes.CampaignFinance.Committees.CommitteeSearch import CommitteeSearch, CommitteeSearchInputSet, CommitteeSearchResultSet, CommitteeSearchChoreographyExecution
from temboo.Library.NYTimes.CampaignFinance.Committees.LeadershipPACs import LeadershipPACs, LeadershipPACsInputSet, LeadershipPACsResultSet, LeadershipPACsChoreographyExecution
from temboo.Library.NYTimes.CampaignFinance.Committees.NewCommittees import NewCommittees, NewCommitteesInputSet, NewCommitteesResultSet, NewCommitteesChoreographyExecution
| 175.125 | 272 | 0.925054 | 84 | 1,401 | 15.428571 | 0.416667 | 0.054012 | 0.091821 | 0.12963 | 0.26466 | 0.26466 | 0 | 0 | 0 | 0 | 0 | 0 | 0.034975 | 1,401 | 7 | 273 | 200.142857 | 0.95858 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 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 | 6 |
80a8468fdb4ed59a8cf27a3f1eaada4a853a42bf | 44 | py | Python | practice/cToF.py | tntptntp/CMPTGCS-20 | f9fbb32199c2b415614e914a84560a2080949965 | [
"MIT"
] | null | null | null | practice/cToF.py | tntptntp/CMPTGCS-20 | f9fbb32199c2b415614e914a84560a2080949965 | [
"MIT"
] | null | null | null | practice/cToF.py | tntptntp/CMPTGCS-20 | f9fbb32199c2b415614e914a84560a2080949965 | [
"MIT"
] | null | null | null | def cToF(cTemp):
return (cTemp*9./5)+32
| 14.666667 | 26 | 0.613636 | 8 | 44 | 3.375 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 0.181818 | 44 | 2 | 27 | 22 | 0.638889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 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 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
03b7ae7c3e0d90df745c2ef701ec7315711a0c51 | 5,379 | py | Python | xview/models/vgg16.py | ethz-asl/modular_semantic_segmentation | 7c950f24df11540a7ddae4ff806d5b31934a3210 | [
"BSD-3-Clause"
] | 20 | 2018-08-01T15:02:59.000Z | 2021-04-19T07:22:17.000Z | xview/models/vgg16.py | davesean/modular_semantic_segmentation | 5f9e34243915b862e8fef5e6195f1e29f4cebf50 | [
"BSD-3-Clause"
] | null | null | null | xview/models/vgg16.py | davesean/modular_semantic_segmentation | 5f9e34243915b862e8fef5e6195f1e29f4cebf50 | [
"BSD-3-Clause"
] | 9 | 2018-08-01T15:03:03.000Z | 2019-12-17T05:12:48.000Z | from tensorflow.python.layers.layers import max_pooling2d
from .custom_layers import conv2d, adap_conv
from copy import deepcopy
def vgg16(inputs, prefix, params):
"""VGG16 image encoder.
Args:
inputs: input tensor, of dimensions [batchsize, width, height, #channels]
prefix: name prefix for produced weights
params: extra parameters for convolutional layers
Returns:
dict of all layer names and their (intermediate) output
"""
conv1_1 = conv2d(inputs, 64, [3, 3], name='{}_conv1_1'.format(prefix), **params)
conv1_2 = conv2d(conv1_1, 64, [3, 3], name='{}_conv1_2'.format(prefix), **params)
pool1 = max_pooling2d(conv1_2, [2, 2], [2, 2], name='{}_pool1'.format(prefix))
conv2_1 = conv2d(pool1, 128, [3, 3], name='{}_conv2_1'.format(prefix), **params)
conv2_2 = conv2d(conv2_1, 128, [3, 3], name='{}_conv2_2'.format(prefix), **params)
pool2 = max_pooling2d(conv2_2, [2, 2], [2, 2], name='{}_pool2'.format(prefix))
conv3_1 = conv2d(pool2, 256, [3, 3], name='{}_conv3_1'.format(prefix), **params)
conv3_2 = conv2d(conv3_1, 256, [3, 3], name='{}_conv3_2'.format(prefix), **params)
conv3_3 = conv2d(conv3_2, 256, [3, 3], name='{}_conv3_3'.format(prefix), **params)
pool3 = max_pooling2d(conv3_3, [2, 2], [2, 2], name='{}_pool3'.format(prefix))
conv4_1 = conv2d(pool3, 512, [3, 3], name='{}_conv4_1'.format(prefix), **params)
conv4_2 = conv2d(conv4_1, 512, [3, 3], name='{}_conv4_2'.format(prefix), **params)
conv4_3 = conv2d(conv4_2, 512, [3, 3], name='{}_conv4_3'.format(prefix), **params)
pool4 = max_pooling2d(conv4_3, [2, 2], [2, 2], name='{}_pool4'.format(prefix))
conv5_1 = conv2d(pool4, 512, [3, 3], name='{}_conv5_1'.format(prefix), **params)
conv5_2 = conv2d(conv5_1, 512, [3, 3], name='{}_conv5_2'.format(prefix), **params)
conv5_3 = conv2d(conv5_2, 512, [3, 3], name='{}_conv5_3'.format(prefix), **params)
return {
'conv1_1': conv1_1,
'conv1_2': conv1_2,
'pool1': pool1,
'conv2_1': conv2_1,
'conv2_2': conv2_2,
'pool2': pool2,
'conv3_1': conv3_1,
'conv3_2': conv3_2,
'conv3_3': conv3_3,
'pool3': pool3,
'conv4_1': conv4_1,
'conv4_2': conv4_2,
'conv4_3': conv4_3,
'pool4': pool4,
'conv5_1': conv5_1,
'conv5_2': conv5_2,
'conv5_3': conv5_3}
def progressive_vgg16(inputs, columns, prefix, params, adapter_params):
"""VGG16 image encoder, defined as progressive network.
Args:
inputs: input tensor, of dimensions [batchsize, width, height, #channels]
columns: previously trained vgg16 encoders, given as dict:
{<layer name>: <list of outputs from columns>}
prefix: name prefix for produced weights
params: extra parameters for convolutional layers
adapter_params: parameters for adapter-blocks additional to params
Returns:
dict of all layer names and their (intermediate) output
"""
all_adapter_params = deepcopy(params)
all_adapter_params.update(adapter_params)
# The first layer is always independent
conv1_1 = conv2d(inputs, 64, [3, 3], name='{}_conv1_1'.format(prefix), **params)
conv1_2 = adap_conv(conv1_1, columns['conv1_1'], 64, [3, 3],
name='{}_conv1_2'.format(prefix), **all_adapter_params)
pool1 = max_pooling2d(conv1_2, [2, 2], [2, 2], name='{}_pool1'.format(prefix))
conv2_1 = conv2d(pool1, 128, [3, 3], name='{}_conv2_1'.format(prefix), **params)
conv2_2 = adap_conv(conv2_1, columns['conv2_1'], 128, [3, 3],
name='{}_conv2_2'.format(prefix), **all_adapter_params)
pool2 = max_pooling2d(conv2_2, [2, 2], [2, 2], name='{}_pool2'.format(prefix))
conv3_1 = conv2d(pool2, 256, [3, 3], name='{}_conv3_1'.format(prefix), **params)
conv3_2 = conv2d(conv3_1, 256, [3, 3], name='{}_conv3_2'.format(prefix), **params)
conv3_3 = adap_conv(conv3_2, columns['conv3_2'], 256, [3, 3],
name='{}_conv3_3'.format(prefix), **all_adapter_params)
pool3 = max_pooling2d(conv3_3, [2, 2], [2, 2], name='{}_pool3'.format(prefix))
conv4_1 = conv2d(pool3, 512, [3, 3], name='{}_conv4_1'.format(prefix), **params)
conv4_2 = conv2d(conv4_1, 512, [3, 3], name='{}_conv4_2'.format(prefix), **params)
conv4_3 = adap_conv(conv4_2, columns['conv4_2'], 512, [3, 3],
name='{}_conv4_3'.format(prefix), **all_adapter_params)
pool4 = max_pooling2d(conv4_3, [2, 2], [2, 2], name='{}_pool4'.format(prefix))
conv5_1 = conv2d(pool4, 512, [3, 3], name='{}_conv5_1'.format(prefix), **params)
conv5_2 = conv2d(conv5_1, 512, [3, 3], name='{}_conv5_2'.format(prefix), **params)
conv5_3 = adap_conv(conv5_2, columns['conv5_2'], 512, [3, 3],
name='{}_conv5_3'.format(prefix), **all_adapter_params)
return {
'conv1_1': conv1_1,
'conv1_2': conv1_2,
'pool1': pool1,
'conv2_1': conv2_1,
'conv2_2': conv2_2,
'pool2': pool2,
'conv3_1': conv3_1,
'conv3_2': conv3_2,
'conv3_3': conv3_3,
'pool3': pool3,
'conv4_1': conv4_1,
'conv4_2': conv4_2,
'conv4_3': conv4_3,
'pool4': pool4,
'conv5_1': conv5_1,
'conv5_2': conv5_2,
'conv5_3': conv5_3}
| 48.026786 | 86 | 0.612567 | 758 | 5,379 | 4.081794 | 0.110818 | 0.131868 | 0.05042 | 0.015514 | 0.798966 | 0.798966 | 0.782805 | 0.782805 | 0.782805 | 0.782805 | 0 | 0.119753 | 0.216025 | 5,379 | 111 | 87 | 48.459459 | 0.613944 | 0.150028 | 0 | 0.731707 | 0 | 0 | 0.12937 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.02439 | false | 0 | 0.036585 | 0 | 0.085366 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
03ca95cd71df67d4999f01ee7d57df2852640be5 | 5,619 | py | Python | tests/unit/models/geometry/ConduitLengthsTest.py | halotudio/openPNM-copy2 | d400ec65e9421256a531f6d22a38255b002d5dcb | [
"MIT"
] | 1 | 2021-05-01T11:10:43.000Z | 2021-05-01T11:10:43.000Z | tests/unit/models/geometry/ConduitLengthsTest.py | halotudio/openPNM-copy2 | d400ec65e9421256a531f6d22a38255b002d5dcb | [
"MIT"
] | 2 | 2020-06-26T19:58:23.000Z | 2021-12-14T07:16:41.000Z | tests/unit/models/geometry/ConduitLengthsTest.py | halotudio/openPNM-copy2 | d400ec65e9421256a531f6d22a38255b002d5dcb | [
"MIT"
] | null | null | null | import pytest
import numpy as np
from numpy.testing import assert_allclose
import openpnm as op
import openpnm.models.geometry.conduit_lengths as mods
class ConduitLengthsTest:
def setup_class(self):
self.net = op.network.Cubic(shape=[3, 1, 1], spacing=1.0)
self.geo = op.geometry.GenericGeometry(
network=self.net, pores=self.net.Ps, throats=self.net.Ts
)
self.air = op.phases.Air(network=self.net)
self.phys = op.physics.GenericPhysics(
network=self.net, phase=self.air, geometry=self.geo
)
self.geo["throat.diameter"] = 0.4
self.geo["pore.diameter"] = [0.5, 0.9, 0.7]
def test_spheres_and_cylinders(self):
L_actual = mods.spheres_and_cylinders(self.geo)
L_desired = np.array([[0.15 , 0.44688711, 0.40311289],
[0.40311289, 0.30965898, 0.28722813]])
assert_allclose(L_actual, L_desired)
# Incompatible data with model assumptions
self.geo["pore.diameter"][1] = 0.3
with pytest.raises(Exception):
L_actual = mods.spheres_and_cylinders(self.geo)
self.geo["pore.diameter"][1] = 0.9
# Overlapping pores
self.geo["pore.diameter"][0] = 1.2
L_actual = mods.spheres_and_cylinders(self.geo)
L_desired = np.array([[5.78750000e-01, 1.00000000e-15, 4.21250000e-01],
[4.03112887e-01, 3.09658980e-01, 2.87228132e-01]])
assert_allclose(L_actual, L_desired)
self.geo["pore.diameter"][0] = 0.5
def test_circles_and_rectangles(self):
L_actual = mods.circles_and_rectangles(self.geo)
L_desired = np.array([[0.15 , 0.44688711, 0.40311289],
[0.40311289, 0.30965898, 0.28722813]])
assert_allclose(L_actual, L_desired)
# Incompatible data with model assumptions
self.geo["pore.diameter"][1] = 0.3
with pytest.raises(Exception):
L_actual = mods.circles_and_squares(self.geo)
self.geo["pore.diameter"][1] = 0.9
# Overlapping pores
self.geo["pore.diameter"][0] = 1.2
L_actual = mods.circles_and_rectangles(self.geo)
L_desired = np.array([[5.78750000e-01, 1.00000000e-15, 4.21250000e-01],
[4.03112887e-01, 3.09658980e-01, 2.87228132e-01]])
assert_allclose(L_actual, L_desired)
self.geo["pore.diameter"][0] = 0.5
def test_cones_and_cylinders(self):
L_actual = mods.cones_and_cylinders(self.geo)
L_desired = np.array([[0.25, 0.3, 0.45],
[0.45, 0.2, 0.35]])
assert_allclose(L_actual, L_desired)
# Overlapping pores
self.geo["pore.diameter"][0] = 1.2
L_actual = mods.cones_and_cylinders(self.geo)
L_desired = np.array([[5.7875e-01, 1.0000e-15, 4.2125e-01],
[4.5000e-01, 2.0000e-01, 3.5000e-01]])
assert_allclose(L_actual, L_desired)
self.geo["pore.diameter"][0] = 0.5
def test_trapezoids_and_rectangles(self):
L_actual = mods.trapezoids_and_rectangles(self.geo)
L_desired = np.array([[0.25, 0.3, 0.45],
[0.45, 0.2, 0.35]])
assert_allclose(L_actual, L_desired)
# Overlapping pores
self.geo["pore.diameter"][0] = 1.2
L_actual = mods.trapezoids_and_rectangles(self.geo)
L_desired = np.array([[5.7875e-01, 1.0000e-15, 4.2125e-01],
[4.5000e-01, 2.0000e-01, 3.5000e-01]])
assert_allclose(L_actual, L_desired)
self.geo["pore.diameter"][0] = 0.5
def test_pyramids_and_cuboids(self):
L_actual = mods.pyramids_and_cuboids(self.geo)
L_desired = np.array([[0.25, 0.3, 0.45],
[0.45, 0.2, 0.35]])
assert_allclose(L_actual, L_desired)
# Overlapping pores
self.geo["pore.diameter"][0] = 1.2
L_actual = mods.pyramids_and_cuboids(self.geo)
L_desired = np.array([[5.7875e-01, 1.0000e-15, 4.2125e-01],
[4.5000e-01, 2.0000e-01, 3.5000e-01]])
assert_allclose(L_actual, L_desired)
self.geo["pore.diameter"][0] = 0.5
def test_cubes_and_cuboids(self):
L_actual = mods.cubes_and_cuboids(self.geo)
L_desired = np.array([[0.25, 0.3, 0.45],
[0.45, 0.2, 0.35]])
assert_allclose(L_actual, L_desired)
# Overlapping pores
self.geo["pore.diameter"][0] = 1.2
L_actual = mods.cubes_and_cuboids(self.geo)
L_desired = np.array([[6.0e-01, 1.0e-15, 4.0e-01],
[4.5e-01, 2.0e-01, 3.5e-01]])
assert_allclose(L_actual, L_desired)
self.geo["pore.diameter"][0] = 0.5
def test_squares_and_rectangles(self):
L_actual = mods.squares_and_rectangles(self.geo)
L_desired = np.array([[0.25, 0.3, 0.45],
[0.45, 0.2, 0.35]])
assert_allclose(L_actual, L_desired)
# Overlapping pores
self.geo["pore.diameter"][0] = 1.2
L_actual = mods.cubes_and_cuboids(self.geo)
L_desired = np.array([[6.0e-01, 1.0e-15, 4.0e-01],
[4.5e-01, 2.0e-01, 3.5e-01]])
assert_allclose(L_actual, L_desired)
self.geo["pore.diameter"][0] = 0.5
if __name__ == '__main__':
t = ConduitLengthsTest()
self = t
t.setup_class()
for item in t.__dir__():
if item.startswith('test'):
print(f'Running test: {item}')
t.__getattribute__(item)()
| 42.568182 | 80 | 0.579463 | 801 | 5,619 | 3.883895 | 0.134831 | 0.085503 | 0.067181 | 0.11604 | 0.807457 | 0.797814 | 0.758599 | 0.758599 | 0.752491 | 0.752491 | 0 | 0.135623 | 0.279587 | 5,619 | 131 | 81 | 42.89313 | 0.632905 | 0.036839 | 0 | 0.690909 | 0 | 0 | 0.054424 | 0 | 0 | 0 | 0 | 0 | 0.136364 | 1 | 0.072727 | false | 0 | 0.045455 | 0 | 0.127273 | 0.009091 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
45aadb4bfe2d39cde5839f163e88fdf99e04b050 | 37,794 | py | Python | settlement/settle_stock.py | jiafeiyan/xops | fba70cc5282a040ae5f2f1266b86e12dd54edd65 | [
"Apache-2.0"
] | 1 | 2018-01-22T06:38:06.000Z | 2018-01-22T06:38:06.000Z | settlement/settle_stock.py | jiafeiyan/my_python_util | fba70cc5282a040ae5f2f1266b86e12dd54edd65 | [
"Apache-2.0"
] | null | null | null | settlement/settle_stock.py | jiafeiyan/my_python_util | fba70cc5282a040ae5f2f1266b86e12dd54edd65 | [
"Apache-2.0"
] | null | null | null | # -*- coding: UTF-8 -*-
import json
from utils import Configuration, mysql, log, parse_conf_args, process_assert
def settle_stock(context, conf):
result_code = 0
logger = log.get_logger(category="SettleStock")
settlement_group_id = conf.get("settlementGroupId")
settlement_id = conf.get("settlementId")
logger.info("[settle stock %s] begin" % (json.dumps(conf, encoding="UTF-8", ensure_ascii=False)))
mysql_pool = mysql(configs=context.get("mysql").get(conf.get("mysqlId")))
mysql_conn = mysql_pool.get_cnx()
mysql_conn.set_charset_collation('utf8')
try:
mysql_conn.start_transaction()
cursor = mysql_conn.cursor()
logger.info("[get current trading day]......")
sql = """SELECT t1.tradingday FROM siminfo.t_tradesystemtradingday t1, siminfo.t_tradesystemsettlementgroup t2 WHERE t1.tradesystemid = t2.tradesystemid and t2.settlementgroupid = %s"""
cursor.execute(sql, (settlement_group_id,))
row = cursor.fetchone()
current_trading_day = str(row[0])
logger.info("[get current trading day] current_trading_day = %s" % (current_trading_day))
logger.info("[get next trading day]......")
# 判断是否跳过节假日
holiday = conf.get("holiday")
if holiday is True or holiday is None:
sql = """SELECT DAY FROM siminfo.t_TradingCalendar t WHERE t.day > %s AND t.tra = '1' ORDER BY DAY LIMIT 1"""
else:
sql = """SELECT DAY FROM siminfo.t_TradingCalendar t WHERE t.day > %s ORDER BY DAY LIMIT 1"""
cursor.execute(sql, (current_trading_day,))
row = cursor.fetchone()
next_trading_day = str(row[0])
logger.info("[get next trading day] next_trading_day = %s" % (next_trading_day))
# 检查结算状态
logger.info("[check settlement status]......")
sql = """SELECT
t1.tradingday, t1.settlementgroupid, t1.settlementid, t1.settlementstatus
FROM
dbclear.t_settlement t1
WHERE t1.tradingday = %s
AND t1.settlementgroupid = %s
AND t1.settlementid = %s for update"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
row = cursor.fetchone()
if row is None:
logger.error("[settle stock] Error: There is no data for %s-%s." % (settlement_group_id, settlement_id))
result_code = -1
elif row[3] != '0':
logger.error("[settle stock] Error: Settlement for %s-%s has done." % (settlement_group_id, settlement_id))
result_code = -1
else:
#计算结算价 股票中结算价设置为收盘价，如有除权除息，设置为除权价
logger.info("[calculate settlement price]......")
sql = """UPDATE dbclear.t_marketdata t, (SELECT t.tradingday, t.settlementgroupid, t.settlementid, t.instrumentid,
ROUND(((CASE WHEN t.closeprice = 0 and t.lastprice = 0 THEN t.precloseprice when t.closeprice = 0 and t.lastprice != 0 THEN t.lastprice ELSE t.closeprice END) - IFNULL(t1.beforerate, 0) + IFNULL(t3.beforerate, 0) * IFNULL(t3.price, 0))/(1 + IFNULL(t2.beforerate, 0) + IFNULL(t3.beforerate, 0)), CASE WHEN t.InstrumentID like '51%' or t.InstrumentID like '15%' or t.InstrumentID like '50%' or t.InstrumentID like '16%' or t.InstrumentID like '18%' THEN 3 ELSE 2 END ) AS settlementprice
FROM dbclear.t_marketdata t
LEFT JOIN (SELECT settlementgroupid, securityid, beforerate, afterrate, price FROM siminfo.t_securityprofit WHERE securitytype = 'GP' AND profittype = 'HL' AND cqdate = %s) t1
ON (t.settlementgroupid = t1.settlementgroupid AND t.instrumentid = t1.securityid)
LEFT JOIN (SELECT settlementgroupid, securityid, beforerate, afterrate, price FROM siminfo.t_securityprofit WHERE securitytype = 'GP' AND profittype = 'S' AND cqdate = %s) t2
ON (t.settlementgroupid = t2.settlementgroupid AND t.instrumentid = t2.securityid)
LEFT JOIN (SELECT settlementgroupid, securityid, beforerate, afterrate, price FROM siminfo.t_securityprofit WHERE securitytype = 'GP' AND profittype = 'P' AND cqdate = %s) t3
ON (t.settlementgroupid = t3.settlementgroupid AND t.instrumentid = t3.securityid)
WHERE t.tradingday = %s AND t.settlementgroupid = %s AND t.settlementid = %s) t1
SET t.settlementprice = t1.settlementprice, t.closeprice = t1.settlementprice WHERE t .tradingday = %s AND t.settlementgroupid = %s AND t.settlementid = %s and t.tradingday = t1.tradingday
AND t.settlementgroupid = t1.settlementgroupid AND t.settlementid = t1.settlementid AND t.instrumentid = t1.instrumentid"""
cursor.execute(sql, (next_trading_day, next_trading_day, next_trading_day, current_trading_day, settlement_group_id, settlement_id, current_trading_day, settlement_group_id, settlement_id))
#计算交易手续费
logger.info("[calculate trade trans fee]......")
sql = """DELETE FROM dbclear.t_clienttransfee WHERE tradingday = %s AND settlementgroupid = %s AND settlementid = %s"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
sql = """INSERT INTO dbclear.t_clienttransfee(TradingDay, SettlementGroupID, SettlementID, ParticipantID, ClientID, AccountID, ProductGroupID, ProductID, UnderlyingInstrID, InstrumentID, TradeID, OrderSysID, Direction, TradingRole, HedgeFlag, OffsetFlag, Volume, Price, TransFeeRatio, ValueMode, MinFee, MaxFee, TransFee, Tax)
SELECT t1.tradingday, t1.settlementgroupid, t1.settlementid, t1.participantid, t1.clientid, t1.accountid, t3.productgroupid, t3.productid, t3.underlyinginstrid, t1.instrumentid, t1.tradeid, t1.ordersysid, t1.direction, t1.tradingrole, t1.hedgeflag, t1.offsetflag, t1.volume, t1.price,
CASE
WHEN t1.direction = '0' THEN
t2.openfeeratio
WHEN t1.direction = '1' THEN
t2.closeyesterdayfeeratio
END AS transfeeratio,
t2.valuemode,
CASE
WHEN t1.direction = '0' THEN
t2.minopenfee
WHEN t1.direction = '1' THEN
t2.minclosefee
END AS minfee,
CASE
WHEN t1.direction = '0' THEN
t2.maxopenfee
WHEN t1.direction = '1' THEN
t2.maxclosefee
END AS maxfee,
IF(t2.valuemode='2',
ROUND((CASE
WHEN t1.direction = '0' THEN
t2.openfeeratio
WHEN t1.direction = '1' THEN
t2.closeyesterdayfeeratio
END) * t1.volume * t3.volumemultiple,
2),
ROUND((CASE
WHEN t1.direction = '0' THEN
t2.openfeeratio
WHEN t1.direction = '1' THEN
t2.closeyesterdayfeeratio
END) * t1.price * t1.volume * t3.volumemultiple,
2)) AS transfee,
(CASE
WHEN t1.direction = '0' THEN
0
WHEN t1.direction = '1' THEN
ROUND(t1.price * t1.volume * 0.001, 2)
END) + (CASE
WHEN SUBSTR(t1.instrumentid, 1, 1) = '6' THEN
ROUND(t1.price * t1.volume * 0.00002, 2)
ELSE 0
END) AS tax
FROM dbclear.t_trade t1, dbclear.t_clienttransfeeratio t2, siminfo.t_instrument t3
WHERE t1.tradingday = t2.tradingday AND t1.settlementgroupid = t2.settlementgroupid AND t1.settlementid = t2.settlementid
AND t2.participantid = '00000000' AND t2.clientid = '00000000' AND t1.instrumentid = t2.instrumentid
AND t1.tradingrole = t2.tradingrole AND t1.hedgeflag = t2.hedgeflag AND t1.settlementgroupid = t3.settlementgroupid
AND t1.instrumentid = t3.instrumentid AND t1.tradingday = %s AND t1.settlementgroupid = %s
AND t1.settlementid = %s"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
sql = """INSERT INTO dbclear.t_clienttransfee(TradingDay, SettlementGroupID, SettlementID, ParticipantID, ClientID, AccountID, ProductGroupID, ProductID, UnderlyingInstrID, InstrumentID, TradeID, OrderSysID, Direction, TradingRole, HedgeFlag, OffsetFlag, Volume, Price, TransFeeRatio, ValueMode, MinFee, MaxFee, TransFee)
SELECT t1.tradingday, t1.settlementgroupid, t1.settlementid, t1.participantid, t1.clientid, t1.accountid, t3.productgroupid, t3.productid, t3.underlyinginstrid, t1.instrumentid, t1.tradeid, t1.ordersysid, t1.direction, t1.tradingrole, t1.hedgeflag, t1.offsetflag, t1.volume, t1.price,
CASE
WHEN t1.direction = '0' THEN
t2.openfeeratio
WHEN t1.direction = '1' THEN
t2.closeyesterdayfeeratio
END AS transfeeratio,
t2.valuemode,
CASE
WHEN t1.direction = '0' THEN
t2.minopenfee
WHEN t1.direction = '1' THEN
t2.minclosefee
END AS minfee,
CASE
WHEN t1.direction = '0' THEN
t2.maxopenfee
WHEN t1.direction = '1' THEN
t2.maxclosefee
END AS maxfee,
IF(t2.valuemode='2',
ROUND((CASE
WHEN t1.direction = '0' THEN
t2.openfeeratio
WHEN t1.direction = '1' THEN
t2.closeyesterdayfeeratio
END) * t1.volume * t3.volumemultiple,
2),
ROUND((CASE
WHEN t1.direction = '0' THEN
t2.openfeeratio
WHEN t1.direction = '1' THEN
t2.closeyesterdayfeeratio
END) * t1.price * t1.volume * t3.volumemultiple,
2)) AS transfee
FROM dbclear.t_trade t1, dbclear.t_clienttransfeeratio t2, siminfo.t_instrument t3
WHERE t1.tradingday = t2.tradingday AND t1.settlementgroupid = t2.settlementgroupid AND t1.settlementid = t2.settlementid
AND t2.participantid = t1.participantid AND t2.clientid = '00000000' AND t1.instrumentid = t2.instrumentid
AND t1.tradingrole = t2.tradingrole AND t1.hedgeflag = t2.hedgeflag AND t1.settlementgroupid = t3.settlementgroupid
AND t1.instrumentid = t3.instrumentid AND t1.tradingday = %s AND t1.settlementgroupid = %s
AND t1.settlementid = %s
ON DUPLICATE KEY UPDATE transfeeratio = VALUES(transfeeratio), valuemode = VALUES(valuemode),
minfee = VALUES(minfee), maxfee = VALUES(maxfee), transfee = VALUES(transfee)"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
sql = """INSERT INTO dbclear.t_clienttransfee(TradingDay, SettlementGroupID, SettlementID, ParticipantID, ClientID, AccountID, ProductGroupID, ProductID, UnderlyingInstrID, InstrumentID, TradeID, OrderSysID, Direction, TradingRole, HedgeFlag, OffsetFlag, Volume, Price, TransFeeRatio, ValueMode, MinFee, MaxFee, TransFee)
SELECT t1.tradingday, t1.settlementgroupid, t1.settlementid, t1.participantid, t1.clientid, t1.accountid, t3.productgroupid, t3.productid, t3.underlyinginstrid, t1.instrumentid, t1.tradeid, t1.ordersysid, t1.direction, t1.tradingrole, t1.hedgeflag, t1.offsetflag, t1.volume, t1.price,
CASE
WHEN t1.direction = '0' THEN
t2.openfeeratio
WHEN t1.direction = '1' THEN
t2.closeyesterdayfeeratio
END AS transfeeratio,
t2.valuemode,
CASE
WHEN t1.direction = '0' THEN
t2.minopenfee
WHEN t1.direction = '1' THEN
t2.minclosefee
END AS minfee,
CASE
WHEN t1.direction = '0' THEN
t2.maxopenfee
WHEN t1.direction = '1' THEN
t2.maxclosefee
END AS maxfee,
IF(t2.valuemode='2',
ROUND((CASE
WHEN t1.direction = '0' THEN
t2.openfeeratio
WHEN t1.direction = '1' THEN
t2.closeyesterdayfeeratio
END) * t1.volume * t3.volumemultiple,
2),
ROUND((CASE
WHEN t1.direction = '0' THEN
t2.openfeeratio
WHEN t1.direction = '1' THEN
t2.closeyesterdayfeeratio
END) * t1.price * t1.volume * t3.volumemultiple,
2)) AS transfee
FROM dbclear.t_trade t1, dbclear.t_clienttransfeeratio t2, siminfo.t_instrument t3
WHERE t1.tradingday = t2.tradingday AND t1.settlementgroupid = t2.settlementgroupid AND t1.settlementid = t2.settlementid
AND t2.participantid = t1.participantid AND t2.clientid = t1.clientid AND t1.instrumentid = t2.instrumentid
AND t1.tradingrole = t2.tradingrole AND t1.hedgeflag = t2.hedgeflag AND t1.settlementgroupid = t3.settlementgroupid
AND t1.instrumentid = t3.instrumentid AND t1.tradingday = %s AND t1.settlementgroupid = %s
AND t1.settlementid = %s
ON DUPLICATE KEY UPDATE transfeeratio = VALUES(transfeeratio), valuemode = VALUES(valuemode),
minfee = VALUES(minfee), maxfee = VALUES(maxfee), transfee = VALUES(transfee)"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
# 合并客户手续费
logger.info("[calculate client trans fee]......")
sql = """INSERT INTO dbclear.t_clientfund(tradingday, settlementgroupid, settlementid, participantid, clientid, accountid, available, transfee, delivfee, positionmargin, profit, stockvalue)
SELECT t.tradingday, t.settlementgroupid, t.settlementid, t.participantid, t.clientid, t.accountid, 0,
ROUND(SUM(CASE WHEN t.calctransfee < t.minfee THEN t.minfee WHEN t.calctransfee > t.maxfee THEN t.minfee ELSE t.calctransfee END), 2) + ROUND(SUM(t.tax), 2) AS transfee, 0, 0, 0, 0
FROM(SELECT tt.tradingday, tt.settlementgroupid, tt.settlementid, tt.participantid, tt.clientid, tt.accountid, tt.ordersysid, tt.minfee, tt.maxfee,
SUM(tt.transfee) AS calctransfee, SUM(tt.tax) AS tax
FROM dbclear.t_clienttransfee tt
WHERE tt.tradingday = %s AND tt.settlementgroupid = %s AND tt.settlementid = %s
GROUP BY tt.tradingday, tt.settlementgroupid,tt.settlementid, tt.participantid, tt.clientid, tt.accountid, tt.ordersysid, tt.minfee, tt.maxfee ) t
GROUP BY t.tradingday, t.settlementgroupid, t.settlementid, t.participantid, t.clientid, t.accountid
ON DUPLICATE KEY UPDATE dbclear.t_clientfund.transfee = VALUES(transfee)"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
# 更新客户可用资金变化
logger.info("[calculate client available change]......")
sql = """INSERT INTO dbclear.t_clientfund(tradingday, settlementgroupid, settlementid, participantid, clientid, accountid, available, transfee, delivfee, positionmargin, profit, stockvalue)
SELECT t1.tradingday, t1.settlementgroupid, t1.settlementid, t1.participantid, t1.clientid, t1.accountid, SUM(IF(t1.direction = '0', -1 * t1.volume * t1.price, t1.volume * t1.price) * t2.volumemultiple) AS available, 0, 0, 0, 0, 0
FROM dbclear.t_trade t1, siminfo.t_instrument t2
WHERE t1.settlementgroupid = t2.settlementgroupid AND t1.instrumentid = t2.instrumentid
AND t1.tradingday = %s AND t1.settlementgroupid = %s AND t1.settlementid = %s
GROUP BY t1.tradingday, t1.settlementgroupid, t1.settlementid, t1.participantid, t1.clientid, t1.accountid
ON DUPLICATE KEY UPDATE dbclear.t_clientfund.available = VALUES(available)"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
# 更新客户股票盈利
logger.info("[calculate client stock profit]......")
sql = """INSERT INTO dbclear.t_clientfund(tradingday, settlementgroupid, settlementid, participantid, clientid, accountid, available, transfee, delivfee, positionmargin, profit, stockvalue)
SELECT %s AS tradingday, t1.settlementgroupid, t1.settlementid, t1.participantid, t1.clientid, t3.accountid, 0, 0, 0, 0, SUM(t1.position * t2.afterrate) AS profit, 0
FROM dbclear.t_ClientPositionForSecurityProfit t1, (SELECT settlementgroupid, securityid, djdate, afterrate FROM siminfo.t_securityprofit WHERE securitytype = 'GP' AND profittype = 'HL' AND dzdate = %s) t2, siminfo.t_account t3
WHERE t1.djdate = t2.djdate AND t1.settlementgroupid = t2.settlementgroupid
AND t1.instrumentid = t2.securityid AND t1.settlementgroupid = t3.settlementgroupid AND t1.participantid = t3.participantid
AND t1.settlementgroupid = %s AND t1.settlementid = %s
GROUP BY t1.settlementgroupid, t1.settlementid, t1.participantid, t1.clientid, t3.accountid
ON DUPLICATE KEY UPDATE dbclear.t_clientfund.profit = VALUES(profit)"""
cursor.execute(sql, (current_trading_day, next_trading_day, settlement_group_id, settlement_id))
# 更新客户股票配股占用资金
logger.info("[calculate client stock profit]......")
sql = """INSERT INTO dbclear.t_clientfund(tradingday, settlementgroupid, settlementid, participantid, clientid, accountid, available, transfee, delivfee, positionmargin, profit, stockvalue)
SELECT %s, t1.settlementgroupid, t1.settlementid, t1.participantid, t1.clientid, t3.accountid, 0, 0, 0, SUM(t1.position * t2.beforerate * t2.price) AS positionmargin, 0, 0
FROM dbclear.t_ClientPositionForSecurityProfit t1, (SELECT settlementgroupid, securityid, djdate, beforerate, price FROM siminfo.t_securityprofit WHERE securitytype = 'GP' AND profittype = 'P' AND enddate = %s) t2, siminfo.t_account t3
WHERE t1.djdate = t2.djdate AND t1.settlementgroupid = t2.settlementgroupid
AND t1.instrumentid = t2.securityid AND t1.settlementgroupid = t3.settlementgroupid AND t1.participantid = t3.participantid
AND t1.settlementgroupid = %s AND t1.settlementid = %s
GROUP BY t1.settlementgroupid, t1.settlementid, t1.participantid, t1.clientid, t3.accountid
ON DUPLICATE KEY UPDATE dbclear.t_clientfund.positionmargin = VALUES(positionmargin)"""
cursor.execute(sql, (current_trading_day, current_trading_day, settlement_group_id, settlement_id))
# 更新客户持仓
logger.info("[update client position]......")
sql = """INSERT INTO dbclear.t_clientposition(TradingDay,SettlementGroupID,SettlementID,HedgeFlag,PosiDirection,YdPosition,Position,LongFrozen,ShortFrozen,YdLongFrozen,YdShortFrozen,BuyTradeVolume,SellTradeVolume,PositionCost,YdPositionCost,UseMargin,FrozenMargin,LongFrozenMargin,ShortFrozenMargin,FrozenPremium,InstrumentID,ParticipantID,ClientID)
SELECT tradingday,SettlementGroupID,SettlementID,HedgeFlag,'9',0,SUM(Position),SUM(LongFrozen),SUM(ShortFrozen),0,0,SUM(BuyTradeVolume),SUM(SellTradeVolume),SUM(PositionCost),0,SUM(UseMargin),0,0,0,0,InstrumentID,ParticipantID,ClientID
FROM dbclear.t_clientposition t WHERE t .tradingday = %s AND t.settlementgroupid = %s AND t.settlementid = %s
GROUP BY t.tradingday, t.settlementgroupid, t.settlementid, t.hedgeflag, t.InstrumentID, t.ParticipantID, t.ClientID"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
sql = """DELETE FROM dbclear.t_ClientPosition WHERE tradingday = %s AND SettlementGroupID = %s AND SettlementID = %s AND PosiDirection != '9'"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
sql = """DELETE FROM dbclear.t_ClientPosition WHERE tradingday = %s AND SettlementGroupID = %s AND SettlementID = %s AND Position = 0"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
sql = """UPDATE dbclear.t_clientposition t,(SELECT t.tradingday, t.settlementgroupid, t.settlementid, t.participantid, t.clientid, t.instrumentid,
ROUND(SUM(CASE WHEN t.calctransfee < t.minfee THEN t.minfee WHEN t.calctransfee > t.maxfee THEN t.minfee ELSE t.calctransfee END), 2) AS transfee,
ROUND(SUM(t.tax), 2) as tax
FROM(SELECT tt.tradingday, tt.settlementgroupid, tt.settlementid, tt.participantid, tt.clientid, tt.instrumentid, tt.ordersysid, tt.minfee, tt.maxfee,
ROUND(SUM(tt.transfee), 2) AS calctransfee, ROUND(sum(tt.tax), 2) AS tax
FROM dbclear.t_clienttransfee tt
WHERE tt.tradingday = %s AND tt.settlementgroupid = %s AND tt.settlementid = %s
GROUP BY tt.tradingday, tt.settlementgroupid,tt.settlementid, tt.participantid, tt.clientid, tt.instrumentid, tt.ordersysid, tt.minfee, tt.maxfee ) t
GROUP BY t.tradingday, t.settlementgroupid, t.settlementid, t.participantid, t.clientid, t.instrumentid) t1
SET t.usemargin = t.usemargin + t1.transfee + t1.tax
WHERE t.tradingday = %s AND t.settlementgroupid = %s AND t.settlementid = %s
AND t.tradingday = t1.tradingday AND t.settlementgroupid = t1.settlementgroupid AND t.settlementid = t1.settlementid
AND t.participantid = t1.participantid AND t.clientid = t1.clientid AND t.instrumentid = t1.instrumentid"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id, current_trading_day, settlement_group_id, settlement_id))
sql = """UPDATE dbclear.t_clientposition t,(
SELECT t.settlementgroupid, t.securityid, SUM(t.addposrate) AS addposrate, SUM(t.profit) AS profit, SUM(t.peirate) AS peirate, SUM(t.peiprice) AS peiprice, SUM(t.songrate) AS songrate
FROM(
SELECT settlementgroupid, securityid, SUM(IFNULL(beforerate, 0)) AS addposrate, 0 AS profit, 0 AS peirate, 0 AS peiprice, 0 AS songrate
FROM siminfo.t_securityprofit
WHERE securitytype = 'GP' AND (profittype = 'S' OR profittype = 'P') AND cqdate = %s
GROUP BY settlementgroupid, securityid
UNION
SELECT settlementgroupid, securityid, 0 AS addposrate, afterrate AS profit, 0 AS peirate, 0 AS peiprice, 0 AS songrate
FROM siminfo.t_securityprofit
WHERE securitytype = 'GP' AND profittype = 'HL' AND dzdate = %s
UNION
SELECT settlementgroupid, securityid, 0 AS addposrate, 0 AS profit, beforerate AS peirate, price AS peiprice, 0 AS songrate
FROM siminfo.t_securityprofit
WHERE securitytype = 'GP' AND profittype = 'P' AND cqdate = %s
UNION
SELECT settlementgroupid, securityid, 0 AS addposrate, 0 AS profit, 0 AS peirate, 0 AS peiprice, beforerate AS songrate
FROM siminfo.t_securityprofit
WHERE securitytype = 'GP' AND profittype = 'S' AND cqdate = %s) t
GROUP BY t.settlementgroupid, t.securityid) t1
SET t.position = t.position * (1 + t1.addposrate), t.usemargin = t.usemargin + t.position * t1.peirate * t1.peiprice - t.position * t1.profit
WHERE t.tradingday = %s AND t.settlementgroupid = %s AND t.settlementid = %s AND t.settlementgroupid = t1.settlementgroupid AND t.instrumentid = t1.securityid"""
cursor.execute(sql, (next_trading_day, next_trading_day, next_trading_day, next_trading_day, current_trading_day, settlement_group_id, settlement_id))
sql = """UPDATE dbclear.t_clientposition t
SET t.positioncost = t.usemargin
WHERE t.tradingday = %s AND t.settlementgroupid = %s AND t.settlementid = %s"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
# 更新会员持仓
# 更新客户股票市值
logger.info("[calculate client stock value]......")
sql = """INSERT INTO dbclear.t_clientfund(tradingday, settlementgroupid, settlementid, participantid, clientid, accountid, available, transfee, delivfee, positionmargin, profit, stockvalue)
SELECT t1.tradingday, t1.settlementgroupid, t1.settlementid, t1.participantid, t1.clientid, t3.accountid, 0, 0, 0, 0, 0, SUM(t1.position * t2.settlementprice) AS stockvalue
FROM dbclear.t_clientposition t1, dbclear.t_marketdata t2, siminfo.t_account t3
WHERE t1.tradingday = t2.tradingday AND t1.settlementgroupid = t2.settlementgroupid AND t1.settlementid = t2.settlementid
AND t1.instrumentid = t2.instrumentid AND t1.settlementgroupid = t3.settlementgroupid AND t1.participantid = t3.participantid
AND t1.tradingday = %s AND t1.settlementgroupid = %s AND t1.settlementid = %s
GROUP BY t1.tradingday, t1.settlementgroupid, t1.settlementid, t1.participantid, t1.clientid, t3.accountid
ON DUPLICATE KEY UPDATE dbclear.t_clientfund.stockvalue = VALUES(stockvalue)"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
# 更新客户分红持仓表数据
logger.info("[update ClientPositionForSecurityProfit]......")
sql = """DELETE FROM dbclear.t_clientpositionforsecurityprofit
WHERE NOT EXISTS
(SELECT
settlementgroupid,
securityid,
djdate
FROM
(SELECT
settlementgroupid,
securityid,
djdate
FROM
siminfo.t_securityprofit
WHERE cqdate > %s
AND securitytype = 'GP'
AND (profittype = 'S'
OR profittype = 'P')
UNION
SELECT
settlementgroupid,
securityid,
djdate
FROM
siminfo.t_securityprofit
WHERE dzdate > %s
AND securitytype = 'GP'
AND profittype = 'HL') t
WHERE dbclear.t_clientpositionforsecurityprofit.settlementgroupid = %s
AND dbclear.t_clientpositionforsecurityprofit.settlementid = %s
AND dbclear.t_clientpositionforsecurityprofit.settlementgroupid = t.settlementgroupid
AND dbclear.t_clientpositionforsecurityprofit.instrumentid = t.securityid
AND dbclear.t_clientpositionforsecurityprofit.djdate = t.djdate)"""
cursor.execute(sql, (next_trading_day, next_trading_day, settlement_group_id, settlement_id))
# 更新结算状态
logger.info("[update settlement status]......")
sql = """UPDATE dbclear.t_settlement SET settlementstatus = '1' WHERE tradingday = %s AND settlementgroupid = %s AND settlementid = %s AND settlementstatus = '0'"""
cursor.execute(sql, (current_trading_day, settlement_group_id, settlement_id))
mysql_conn.commit()
except Exception as e:
logger.error("[settle stock] Error: %s" % (e))
result_code = -1
finally:
mysql_conn.close()
logger.info("[settle stock] end")
return result_code
def main():
base_dir, config_names, config_files, add_ons = parse_conf_args(__file__, config_names=["mysql"])
context, conf = Configuration.load(base_dir=base_dir, config_names=config_names, config_files=config_files)
process_assert(settle_stock(context, conf))
if __name__ == "__main__":
main()
| 93.08867 | 570 | 0.480605 | 3,124 | 37,794 | 5.729193 | 0.081306 | 0.015644 | 0.026819 | 0.034697 | 0.772042 | 0.746787 | 0.7274 | 0.718404 | 0.692312 | 0.668902 | 0 | 0.027125 | 0.450812 | 37,794 | 405 | 571 | 93.318519 | 0.83518 | 0.004233 | 0 | 0.544444 | 0 | 0.225 | 0.868786 | 0.080427 | 0 | 0 | 0 | 0 | 0.005556 | 1 | 0.005556 | false | 0 | 0.005556 | 0 | 0.013889 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
45e5454ef4fe532d78294527c1253e4739f762c9 | 511 | py | Python | pygalgen/generator/common/source_file_parsing/parsing_exceptions.py | Kulivox/PyGalGen | 816004bce50703737384e2fbdcfe43b61ce2f4dd | [
"MIT"
] | null | null | null | pygalgen/generator/common/source_file_parsing/parsing_exceptions.py | Kulivox/PyGalGen | 816004bce50703737384e2fbdcfe43b61ce2f4dd | [
"MIT"
] | null | null | null | pygalgen/generator/common/source_file_parsing/parsing_exceptions.py | Kulivox/PyGalGen | 816004bce50703737384e2fbdcfe43b61ce2f4dd | [
"MIT"
] | null | null | null | """
Module containing Exceptions that can be raised during param discovery
"""
class ArgumentParsingDiscoveryError(Exception):
"""
Exception for general error encountered during param extraction
"""
pass
class ArgParseImportNotFound(ArgumentParsingDiscoveryError):
"""
Exception raised in case ArgParser was not imported
"""
pass
class ArgParserNotUsed(ArgumentParsingDiscoveryError):
"""
Exception raised in case no uses of argument parser were found
"""
pass | 24.333333 | 70 | 0.731898 | 49 | 511 | 7.632653 | 0.693878 | 0.304813 | 0.235294 | 0.245989 | 0.26738 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.203523 | 511 | 21 | 71 | 24.333333 | 0.918919 | 0.48728 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0.166667 | 0 | 0.666667 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 6 |
b3061ddff013db444dae6c0a0d9d3b6ed10bb351 | 44 | py | Python | gdcdatamodel/models/qcreport.py | NCI-GDC/gdcdatamodel | 924fc8ab695b1cbb0131636ffcb6d3881db2e200 | [
"Apache-2.0"
] | 27 | 2016-06-24T20:32:44.000Z | 2022-01-17T07:53:48.000Z | gdcdatamodel/models/qcreport.py | NCI-GDC/gdcdatamodel | 924fc8ab695b1cbb0131636ffcb6d3881db2e200 | [
"Apache-2.0"
] | 63 | 2016-07-20T21:40:11.000Z | 2021-08-12T18:39:21.000Z | gdcdatamodel/models/qcreport.py | NCI-GDC/gdcdatamodel | 924fc8ab695b1cbb0131636ffcb6d3881db2e200 | [
"Apache-2.0"
] | 5 | 2016-10-20T20:00:09.000Z | 2020-08-14T08:55:40.000Z | from gdc_ng_models.models.qcreport import *
| 22 | 43 | 0.840909 | 7 | 44 | 5 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.090909 | 44 | 1 | 44 | 44 | 0.875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
b34368aa16bd80010a6d853496223d21462b2e40 | 28 | py | Python | Back/ecoreleve_server/modules/users/__init__.py | NaturalSolutions/ecoReleve-Data | 535a6165984544902563eca7cb10d07f1686c963 | [
"MIT"
] | 15 | 2015-02-15T18:02:54.000Z | 2021-10-31T00:08:41.000Z | Back/ecoreleve_server/modules/users/__init__.py | NaturalSolutions/ecoReleve-Data | 535a6165984544902563eca7cb10d07f1686c963 | [
"MIT"
] | 505 | 2015-03-24T15:16:55.000Z | 2022-03-21T22:17:11.000Z | Back/ecoreleve_server/modules/users/__init__.py | NaturalSolutions/ecoReleve-Data | 535a6165984544902563eca7cb10d07f1686c963 | [
"MIT"
] | 31 | 2015-04-09T10:48:31.000Z | 2020-12-08T16:32:30.000Z | from .user_model import *
| 9.333333 | 26 | 0.714286 | 4 | 28 | 4.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.214286 | 28 | 2 | 27 | 14 | 0.863636 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
b36d24b74a4da71352bae5fc3b759891a0187b93 | 147 | py | Python | pwngef/functions/__init__.py | int-0x03/pwngef | 3d362ac56b3149ffa90a511935d5f68559dc1d8f | [
"MIT"
] | null | null | null | pwngef/functions/__init__.py | int-0x03/pwngef | 3d362ac56b3149ffa90a511935d5f68559dc1d8f | [
"MIT"
] | null | null | null | pwngef/functions/__init__.py | int-0x03/pwngef | 3d362ac56b3149ffa90a511935d5f68559dc1d8f | [
"MIT"
] | null | null | null | #!/usr/bin/python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
__functions__ = []
| 21 | 38 | 0.829932 | 18 | 147 | 5.777778 | 0.611111 | 0.288462 | 0.461538 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115646 | 147 | 6 | 39 | 24.5 | 0.8 | 0.108844 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.75 | 0 | 0.75 | 0.25 | 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 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ff28244de2968a4dc68e88b7d44ae0253b5a0b09 | 29 | py | Python | database/__init__.py | airlovelq/flask-frame | 86a97522a6eff4e34f0cd5c131ebf68f7c78390a | [
"Apache-2.0"
] | null | null | null | database/__init__.py | airlovelq/flask-frame | 86a97522a6eff4e34f0cd5c131ebf68f7c78390a | [
"Apache-2.0"
] | null | null | null | database/__init__.py | airlovelq/flask-frame | 86a97522a6eff4e34f0cd5c131ebf68f7c78390a | [
"Apache-2.0"
] | null | null | null | from .db_store import DbStore | 29 | 29 | 0.862069 | 5 | 29 | 4.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.103448 | 29 | 1 | 29 | 29 | 0.923077 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ff487ba408728041a390854e5f9680a26f002e5f | 23 | py | Python | __init__.py | mgsosna/py-utils | 311639de53c50022b0ea001e999719139b1166b3 | [
"MIT"
] | 3 | 2021-04-23T20:41:42.000Z | 2022-03-30T11:06:26.000Z | __init__.py | mgsosna/py-utils | 311639de53c50022b0ea001e999719139b1166b3 | [
"MIT"
] | null | null | null | __init__.py | mgsosna/py-utils | 311639de53c50022b0ea001e999719139b1166b3 | [
"MIT"
] | null | null | null | from .jewelry import *
| 11.5 | 22 | 0.73913 | 3 | 23 | 5.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.173913 | 23 | 1 | 23 | 23 | 0.894737 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ffa846f7628c112910e00c6ef4f8c43fc5337017 | 3,398 | py | Python | source/figures/paraglider/demonstration/plot_polars.py | pfheatwole/thesis | b8686e6bf1e32ef579ffa45068b430ddc42a3b06 | [
"CC-BY-4.0"
] | null | null | null | source/figures/paraglider/demonstration/plot_polars.py | pfheatwole/thesis | b8686e6bf1e32ef579ffa45068b430ddc42a3b06 | [
"CC-BY-4.0"
] | null | null | null | source/figures/paraglider/demonstration/plot_polars.py | pfheatwole/thesis | b8686e6bf1e32ef579ffa45068b430ddc42a3b06 | [
"CC-BY-4.0"
] | null | null | null | """Plot polar curves for Niviuk Hook 3 and annotate them with flight data."""
import matplotlib.pyplot as plt
import numpy as np
import pfh.glidersim as gsim
import util
gliders = util.build_paragliders()
savefig = False
# savefig = True
###############################################################################
# Wing test: Parapente Mag (French), size=25 with a pod harness
# (`Hook 3 Parapente Mag 148.pdf`)
accelerating, braking = gsim.extras.compute_polars.compute_polar_data(gliders["6a_25"])
deltas_a = accelerating["delta"]
deltas_b = braking["delta"]
thetas_a = accelerating["theta_b"]
thetas_b = braking["theta_b"]
v_RM2e_a = accelerating["v_RM2e"]
v_RM2e_b = braking["v_RM2e"]
# Calculate the best glide ratio, assuming that happens at zero brakes
BG = braking["v_RM2e"][0]
BGR = BG[0] / BG[2]
print(f"Size 25, best glide (zero brakes): {BG} (ratio: {BGR})")
# Polar curve: vertical versus horizontal airspeed
fig, ax = plt.subplots(figsize=(10, 5))
# Theoretical data
ax.plot(v_RM2e_a.T[0], v_RM2e_a.T[2], "g")
ax.plot(v_RM2e_b.T[0], v_RM2e_b.T[2], "r")
ax.plot([0, 17.5], [0, 17.5 / BGR], "b--", linewidth=0.75) # Theoretical best glide
# Experimental data
speeds = np.array([24, 38, 52]) # min, trim, max [km/h]
ax.vlines(speeds / 3.6, 0.5, 3, colors="k", linestyles="dashed", linewidth=0.75)
ax.plot([9.22], [1.05], "k.") # Min sink
ax.plot([37.5 / 3.6], [(37.5 / 3.6) / 9.3], "k.") # Best glide
# ax.plot([0, 17.5], [0, 17.5 / 9.3], "k-", linewidth=1.00) # Experimental best glide
ax.set_aspect("equal")
ax.set_xlim(0, 20)
ax.set_ylim(-0.05, 3.5)
ax.invert_yaxis()
ax.set_xlabel("Horizontal airspeed [m/s]")
ax.set_ylabel("Sink rate [m/s]")
ax.set_title("Niviuk Hook 3, size 25")
ax.grid(which="both")
# ax.minorticks_on()
if savefig:
fig.savefig("equilibrium_airspeed.svg")
###############################################################################
# Wing test: Parapente (Spanish), size=27 with a pod harness
# (`Hook 3 perfils.pdf`)
print()
accelerating, braking = gsim.extras.compute_polars.compute_polar_data(gliders["6a_27"])
deltas_a = accelerating["delta"]
deltas_b = braking["delta"]
thetas_a = accelerating["theta_b"]
thetas_b = braking["theta_b"]
v_RM2e_a = accelerating["v_RM2e"]
v_RM2e_b = braking["v_RM2e"]
# Calculate the best glide ratio, assuming that happens at zero brakes
BG = braking["v_RM2e"][0]
BGR = BG[0] / BG[2]
print(f"Size 27, best glide (zero brakes): {BG} (ratio: {BGR})")
# Polar curve: vertical versus horizontal airspeed
fig, ax = plt.subplots(figsize=(10, 5))
# Theoretical data
ax.plot(v_RM2e_a.T[0], v_RM2e_a.T[2], "g")
ax.plot(v_RM2e_b.T[0], v_RM2e_b.T[2], "r")
ax.plot([0, 17.5], [0, 17.5 / BGR], "b--", linewidth=0.75) # Theoretical best glide
# Experimental data
speeds = np.array([24, 40, 54]) # min, trim, max [km/h]
ax.vlines(speeds / 3.6, 0.5, 3, colors="k", linestyles="dashed", linewidth=0.75)
ax.plot([9.72], [1.15], "k.") # Min sink
ax.plot([40 / 3.6], [(40 / 3.6) / 9.5], "k.") # Best glide
# ax.plot([0, 17.5], [0, 17.5 / 9.5], "k-", linewidth=1.00) # Experimental best glide
ax.set_aspect("equal")
ax.set_xlim(0, 20)
ax.set_ylim(-0.05, 3.5)
ax.invert_yaxis()
ax.set_xlabel("Horizontal airspeed [m/s]")
ax.set_ylabel("Sink rate [m/s]")
ax.set_title("Niviuk Hook 3, size 27")
ax.grid(which="both")
# ax.minorticks_on()
if savefig:
fig.savefig("equilibrium_airspeed_27.svg")
plt.show()
| 32.673077 | 87 | 0.648028 | 580 | 3,398 | 3.675862 | 0.248276 | 0.042214 | 0.015009 | 0.020638 | 0.836773 | 0.82364 | 0.804878 | 0.804878 | 0.804878 | 0.804878 | 0 | 0.064625 | 0.125662 | 3,398 | 103 | 88 | 32.990291 | 0.652979 | 0.266627 | 0 | 0.65625 | 0 | 0 | 0.185491 | 0.022155 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.0625 | 0 | 0.0625 | 0.046875 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
4485b7238b99f456818990ca6e418971d5d766d4 | 4,546 | py | Python | architecture_flexible.py | Frank-W-B/kaggle-carvana | af09a9b324af8a6e246b0469f4ae1b95c9c5b77d | [
"MIT"
] | null | null | null | architecture_flexible.py | Frank-W-B/kaggle-carvana | af09a9b324af8a6e246b0469f4ae1b95c9c5b77d | [
"MIT"
] | null | null | null | architecture_flexible.py | Frank-W-B/kaggle-carvana | af09a9b324af8a6e246b0469f4ae1b95c9c5b77d | [
"MIT"
] | null | null | null |
from keras.models import Model, Sequential
from keras.layers import Input
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D, UpSampling2D
from keras.layers.normalization import BatchNormalization
from keras.layers.core import Layer, Activation, Reshape, Permute
def set_architecture(n_classes, img_shape, conv_layers_in_block=1):
model = Sequential()
conv_kernel = (3, 3) # changing will affect padding and stride
init = 'he_normal'
# Encoding
# Block 1
model.add(Conv2D(64, conv_kernel, padding='same', input_shape=img_shape, kernel_initializer=init, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
model.add(Activation('relu'))
for l in range(conv_layers_in_block-1):
model.add(Conv2D(64, conv_kernel, padding='same', kernel_initializer=init, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), data_format='channels_first'))
# Block 2
for l in range(conv_layers_in_block):
model.add(Conv2D(128, conv_kernel, padding='same', kernel_initializer=init, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), data_format='channels_first'))
# Block 3
for l in range(conv_layers_in_block):
model.add(Conv2D(256, conv_kernel, padding='same', kernel_initializer=init, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), data_format='channels_first'))
# Block 4
for l in range(conv_layers_in_block):
model.add(Conv2D(512, conv_kernel, padding='same', kernel_initializer=init, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
model.add(Activation('relu'))
# Decoding
# Block 4
model.add(ZeroPadding2D((1,1), data_format='channels_first' ))
model.add(Conv2D(512, conv_kernel, padding='valid', kernel_initializer=init, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
for l in range(conv_layers_in_block-1):
model.add(Conv2D(512, conv_kernel, padding='same', kernel_initializer=init, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
# Block 3
model.add(UpSampling2D((2,2), data_format='channels_first'))
model.add(ZeroPadding2D((1,1), data_format='channels_first'))
model.add(Conv2D(256, conv_kernel, padding='valid', kernel_initializer=init, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
for l in range(conv_layers_in_block-1):
model.add(Conv2D(256, conv_kernel, padding='same', kernel_initializer=init, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
# Block 2
model.add(UpSampling2D((2,2), data_format='channels_first'))
model.add(ZeroPadding2D((1,1), data_format='channels_first'))
model.add(Conv2D(128, conv_kernel, padding='valid', kernel_initializer=init, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
for l in range(conv_layers_in_block-1):
model.add(Conv2D(128, conv_kernel, padding='same', kernel_initializer=init, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
# Block 1
model.add(UpSampling2D((2,2), data_format='channels_first'))
model.add(ZeroPadding2D((1,1), data_format='channels_first'))
model.add(Conv2D(64, conv_kernel, padding='valid', data_format='channels_first'))
model.add(BatchNormalization(axis=1))
for l in range(conv_layers_in_block-1):
model.add(Conv2D(64, conv_kernel, padding='same', kernel_initializer=init, data_format='channels_first'))
model.add(BatchNormalization(axis=1))
model.add(Conv2D(n_classes, conv_kernel, padding='same', data_format='channels_first' ))
output_shape = model.output_shape
outputHeight = output_shape[2]
outputWidth = output_shape[3]
model.add(Reshape((-1, outputHeight*outputWidth)))
model.add(Permute((2, 1)))
model.add(Activation('softmax'))
return model
if __name__ == '__main__':
n_classes = 2
img_channels = 3
img_h = 256
img_w = 256
conv_layers_in_block = 1
input_shape = (img_channels, img_h, img_w)
model = set_architecture(n_classes, input_shape, conv_layers_in_block)
| 41.706422 | 132 | 0.712055 | 611 | 4,546 | 5.072013 | 0.121113 | 0.116167 | 0.1394 | 0.178122 | 0.766376 | 0.751855 | 0.744434 | 0.735399 | 0.735399 | 0.735399 | 0 | 0.034456 | 0.157281 | 4,546 | 108 | 133 | 42.092593 | 0.774471 | 0.026837 | 0 | 0.594595 | 0 | 0 | 0.099819 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.013514 | false | 0 | 0.067568 | 0 | 0.094595 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
448ccc9101b47ec237706b0b90fe8acc003dfb39 | 390 | py | Python | tkmvc2/controllers/__init__.py | unodan/tkmvc2 | 3bfe31a7c7b47c16032a46ad7e2e423ce4577922 | [
"MIT"
] | null | null | null | tkmvc2/controllers/__init__.py | unodan/tkmvc2 | 3bfe31a7c7b47c16032a46ad7e2e423ce4577922 | [
"MIT"
] | 1 | 2021-11-15T17:49:24.000Z | 2021-11-15T17:49:24.000Z | tkmvc2/controllers/__init__.py | unodan/tkmvc2 | 3bfe31a7c7b47c16032a46ad7e2e423ce4577922 | [
"MIT"
] | 1 | 2021-01-11T09:04:41.000Z | 2021-01-11T09:04:41.000Z | ########################################################################################################################
# File: __init__.py
# Author: Dan Huckson, https://github.com/unodan
# Date: 2018-09-20
########################################################################################################################
from tkmvc.controllers.controller import Controller
| 48.75 | 120 | 0.276923 | 20 | 390 | 5.2 | 0.95 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.021918 | 0.064103 | 390 | 7 | 121 | 55.714286 | 0.263014 | 0.217949 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
925befcf8d0d54868cccec58237e4d799fa154b6 | 39 | py | Python | widgets/htmltemplate/__init__.py | cheak1974/remi-app-template | 6c724dda0294b41b906134b9062eadce0f6c0617 | [
"Apache-2.0"
] | 12 | 2020-02-24T08:44:29.000Z | 2022-02-21T07:18:31.000Z | widgets/htmltemplate/__init__.py | cheak1974/remi-app-template | 6c724dda0294b41b906134b9062eadce0f6c0617 | [
"Apache-2.0"
] | null | null | null | widgets/htmltemplate/__init__.py | cheak1974/remi-app-template | 6c724dda0294b41b906134b9062eadce0f6c0617 | [
"Apache-2.0"
] | 3 | 2020-05-02T15:47:09.000Z | 2021-06-12T23:56:52.000Z | from .htmltemplate import Htmltemplate
| 19.5 | 38 | 0.871795 | 4 | 39 | 8.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.102564 | 39 | 1 | 39 | 39 | 0.971429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
92688ee227e3098a08e1cd32d4b2480dccc95637 | 1,389 | py | Python | crafters/audio/SlidingWindowAudioSlicer/tests/test_audiosegmenter.py | YueLiu-jina/jina-hub | e0a7dc95dbd69a55468acbf4194ddaf11fd5aa6c | [
"Apache-2.0"
] | null | null | null | crafters/audio/SlidingWindowAudioSlicer/tests/test_audiosegmenter.py | YueLiu-jina/jina-hub | e0a7dc95dbd69a55468acbf4194ddaf11fd5aa6c | [
"Apache-2.0"
] | null | null | null | crafters/audio/SlidingWindowAudioSlicer/tests/test_audiosegmenter.py | YueLiu-jina/jina-hub | e0a7dc95dbd69a55468acbf4194ddaf11fd5aa6c | [
"Apache-2.0"
] | null | null | null | import numpy as np
from .. import AudioSlicer, SlidingWindowAudioSlicer
class TestClass:
def test_segment_mono(self):
n_frames = 100
frame_length = 2048
signal_orig = np.random.randn(frame_length * n_frames)
crafter = AudioSlicer(frame_length)
crafted_chunks = crafter.craft(signal_orig, 0)
assert len(crafted_chunks) == n_frames
def test_segment_stereo(self):
n_frames = 100
frame_length = 2048
signal_orig = np.random.randn(2, frame_length * n_frames)
crafter = AudioSlicer(frame_length)
crafted_chunks = crafter.craft(signal_orig, 0)
assert len(crafted_chunks) == n_frames * 2
def test_sliding_window_mono(self):
n_frames = 100
frame_length = 2048
signal_orig = np.random.randn(frame_length * n_frames)
crafter = SlidingWindowAudioSlicer(frame_length, frame_length // 2)
crafted_chunks = crafter.craft(signal_orig, 0)
assert len(crafted_chunks) == n_frames * 2 - 1
def test_sliding_window_stereo(self):
n_frames = 100
frame_length = 2048
signal_orig = np.random.randn(2, frame_length * n_frames)
crafter = SlidingWindowAudioSlicer(frame_length, frame_length // 2)
crafted_chunks = crafter.craft(signal_orig, 0)
assert len(crafted_chunks) == (n_frames * 2 - 1) * 2
| 30.195652 | 75 | 0.668107 | 174 | 1,389 | 5.034483 | 0.206897 | 0.175799 | 0.050228 | 0.063927 | 0.836758 | 0.836758 | 0.836758 | 0.836758 | 0.836758 | 0.836758 | 0 | 0.040385 | 0.25126 | 1,389 | 45 | 76 | 30.866667 | 0.801923 | 0 | 0 | 0.645161 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.129032 | 1 | 0.129032 | false | 0 | 0.064516 | 0 | 0.225806 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
92b4a7270d9100fed4ce1b5ab8433753c44208c7 | 23 | py | Python | src/pywmapi/experimental/__init__.py | leonardodalinky/pywmapi | b9e2650761895eb9a88170497c4d209b5dd6870c | [
"MIT"
] | 4 | 2022-01-27T14:31:38.000Z | 2022-03-25T08:52:01.000Z | src/pywmapi/experimental/__init__.py | leonardodalinky/pywmapi | b9e2650761895eb9a88170497c4d209b5dd6870c | [
"MIT"
] | null | null | null | src/pywmapi/experimental/__init__.py | leonardodalinky/pywmapi | b9e2650761895eb9a88170497c4d209b5dd6870c | [
"MIT"
] | null | null | null | from . import auctions
| 11.5 | 22 | 0.782609 | 3 | 23 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.173913 | 23 | 1 | 23 | 23 | 0.947368 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
92b8b9b3ab8eb01553859ea46fccdd7aaf8811a0 | 98 | py | Python | openprocurement/auctions/core/plugins/contracting/v3/interfaces.py | EBRD-ProzorroSale/openprocurement.auctions.core | 52bd59f193f25e4997612fca0f87291decf06966 | [
"Apache-2.0"
] | 2 | 2016-09-15T20:17:43.000Z | 2017-01-08T03:32:43.000Z | openprocurement/auctions/core/plugins/contracting/v3/interfaces.py | EBRD-ProzorroSale/openprocurement.auctions.core | 52bd59f193f25e4997612fca0f87291decf06966 | [
"Apache-2.0"
] | 183 | 2017-12-21T11:04:37.000Z | 2019-03-27T08:14:34.000Z | openprocurement/auctions/core/plugins/contracting/v3/interfaces.py | EBRD-ProzorroSale/openprocurement.auctions.core | 52bd59f193f25e4997612fca0f87291decf06966 | [
"Apache-2.0"
] | 12 | 2016-09-05T12:07:48.000Z | 2019-02-26T09:24:17.000Z | # -*- coding: utf-8
from zope.interface import Interface
class IContractV3(Interface):
pass
| 14 | 36 | 0.72449 | 12 | 98 | 5.916667 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.024691 | 0.173469 | 98 | 6 | 37 | 16.333333 | 0.851852 | 0.173469 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 6 |
2b803c133f79f2e54eee2f91d39791aba1d6b6ea | 7,311 | py | Python | tests/test_apply.py | Madoshakalaka/hfstol | e37318f4d46c5552722f7639e87181aabeb49788 | [
"MIT"
] | 2 | 2019-07-24T02:28:54.000Z | 2019-08-21T15:54:09.000Z | tests/test_apply.py | Madoshakalaka/hfstol | e37318f4d46c5552722f7639e87181aabeb49788 | [
"MIT"
] | 9 | 2019-07-23T22:10:35.000Z | 2021-06-02T01:28:14.000Z | tests/test_apply.py | Madoshakalaka/hfstol | e37318f4d46c5552722f7639e87181aabeb49788 | [
"MIT"
] | null | null | null | import os
import subprocess
import sys
import unittest
from os import path
import pytest
from hfstol import main
@pytest.mark.parametrize(
"surface_form,result",
[
("nîskâw", (("nîskâw", "+V", "+II", "+Ind", "+Prs", "+3Sg"),)),
("nipâw", (("nipâw", "+V", "+AI", "+Ind", "+Prs", "+3Sg"),)),
],
)
def test_single_analysis_single_deep_form(cree_analyzer, surface_form, result):
assert cree_analyzer.feed(surface_form) == result
@pytest.mark.parametrize(
"surface_form,result",
[
(
"nîskâw",
(("n", "î", "s", "k", "â", "w", "+V", "+II", "+Ind", "+Prs", "+3Sg"),),
),
("nipâw", (("n", "i", "p", "â", "w", "+V", "+AI", "+Ind", "+Prs", "+3Sg"),)),
],
)
def test_single_analysis_single_deep_form_split(cree_analyzer, surface_form, result):
assert cree_analyzer.feed(surface_form, concat=False) == result
@pytest.mark.parametrize(
"surface_form,result",
[("niska", (("niska", "+N", "+A", "+Sg"), ("niska", "+N", "+A", "+Obv")))],
)
def test_singled_analysis_multiple_deep_form(cree_analyzer, surface_form, result):
assert cree_analyzer.feed(surface_form) == result
@pytest.mark.parametrize(
"surface_form,result",
[
(
"niska",
(
("n", "i", "s", "k", "a", "+N", "+A", "+Sg"),
("n", "i", "s", "k", "a", "+N", "+A", "+Obv"),
),
)
],
)
def test_singled_analysis_multiple_deep_form_split(cree_analyzer, surface_form, result):
assert cree_analyzer.feed(surface_form, concat=False) == result
@pytest.mark.parametrize("surface_form,result", [("abcdefg", ()), ("", ())])
def test_singled_analysis_out_side_alphabet(cree_analyzer, surface_form, result):
assert cree_analyzer.feed(surface_form) == result
assert cree_analyzer.feed(surface_form, concat=False) == result
@pytest.mark.parametrize(
"surface_forms,result",
[
(
("niskak", "nîskâw", "nipâw", "abcdefg"),
{
"niskak": {
("niska", "+N", "+A", "+Pl"),
("nîskâw", "+V", "+II", "+Cnj", "+Prs", "+3Sg"),
},
"nîskâw": {("nîskâw", "+V", "+II", "+Ind", "+Prs", "+3Sg")},
"nipâw": {("nipâw", "+V", "+AI", "+Ind", "+Prs", "+3Sg")},
"abcdefg": set(),
},
)
],
)
def test_multiple_analyses(cree_analyzer, surface_forms, result):
assert cree_analyzer.feed_in_bulk(surface_forms) == result
@pytest.mark.parametrize(
"surface_forms,result",
[
(
("niskak", "nîskâw", "nipâw", "abcdefg"),
{
"niskak": {
("n", "i", "s", "k", "a", "+N", "+A", "+Pl"),
("n", "î", "s", "k", "â", "w", "+V", "+II", "+Cnj", "+Prs", "+3Sg"),
},
"nîskâw": {
("n", "î", "s", "k", "â", "w", "+V", "+II", "+Ind", "+Prs", "+3Sg")
},
"nipâw": {
("n", "i", "p", "â", "w", "+V", "+AI", "+Ind", "+Prs", "+3Sg")
},
"abcdefg": set(),
},
)
],
)
def test_multiple_analyses_split(cree_analyzer, surface_forms, result):
assert cree_analyzer.feed_in_bulk(surface_forms, concat=False) == result
@pytest.mark.parametrize(
"surface_forms,result",
[
(
("niskak", "nîskâw", "nipâw", "abcdefg"),
{
"niskak": {"niska+N+A+Pl", "nîskâw+V+II+Cnj+Prs+3Sg"},
"nîskâw": {"nîskâw+V+II+Ind+Prs+3Sg"},
"nipâw": {"nipâw+V+AI+Ind+Prs+3Sg"},
"abcdefg": set(),
},
)
],
)
def test_multiple_analyses_fast(cree_analyzer, surface_forms, result):
assert cree_analyzer.feed_in_bulk_fast(surface_forms) == result
assert cree_analyzer.feed_in_bulk_fast(surface_forms, multi_process=2) == result
@pytest.mark.parametrize("surface_form,result", [("niska+N+A+Pl", (("niskak",),))])
def test_single_generation_single_deep_form(cree_generator, surface_form, result):
assert cree_generator.feed(surface_form) == result
@pytest.mark.parametrize(
"surface_form,result", [("niska+N+A+Pl", (("n", "i", "s", "k", "a", "k"),))]
)
def test_single_generation_single_deep_form_split(cree_generator, surface_form, result):
assert cree_generator.feed(surface_form, concat=False) == result
@pytest.mark.parametrize(
"surface_form,result",
[("nipâw+V+AI+Ind+Prs+12Pl", (("kinipânaw",), ("kinipânânaw",)))],
)
def test_single_generation_multiple_deep_form(cree_generator, surface_form, result):
assert cree_generator.feed(surface_form) == result
@pytest.mark.parametrize(
"surface_form,result",
[
(
"nipâw+V+AI+Ind+Prs+12Pl",
(
("k", "i", "n", "i", "p", "â", "n", "a", "w"),
("k", "i", "n", "i", "p", "â", "n", "â", "n", "a", "w"),
),
)
],
)
def test_single_generation_multiple_deep_form_split(
cree_generator, surface_form, result
):
assert cree_generator.feed(surface_form, concat=False) == result
@pytest.mark.parametrize("surface_form,result", [("abcdefg", ()), ("", ())])
def test_single_generation_outside_of_alphabet(cree_generator, surface_form, result):
# fixme: 'I+lov` hangs, 'i+lov' doesn't
assert cree_generator.feed(surface_form) == result
assert cree_generator.feed(surface_form, concat=False) == result
@pytest.mark.parametrize(
"surface_forms,result",
[
(
["niska+N+A+Pl", "nipâw+V+AI+Ind+Prs+12Pl", "abcdefg", ""],
{
"niska+N+A+Pl": {("niskak",)},
"nipâw+V+AI+Ind+Prs+12Pl": {("kinipânaw",), ("kinipânânaw",)},
"abcdefg": set(),
"": set(),
},
)
],
)
def test_multiple_generation(cree_generator, surface_forms, result):
assert cree_generator.feed_in_bulk(surface_forms) == result
@pytest.mark.parametrize(
"surface_forms,result",
[
(
["niska+N+A+Pl", "nipâw+V+AI+Ind+Prs+12Pl", "abcdefg", ""],
{
"niska+N+A+Pl": {("n", "i", "s", "k", "a", "k")},
"nipâw+V+AI+Ind+Prs+12Pl": {
("k", "i", "n", "i", "p", "â", "n", "a", "w"),
("k", "i", "n", "i", "p", "â", "n", "â", "n", "a", "w"),
},
"abcdefg": set(),
"": set(),
},
)
],
)
def test_multiple_generation_split(cree_generator, surface_forms, result):
assert cree_generator.feed_in_bulk(surface_forms, concat=False) == result
@pytest.mark.parametrize(
"surface_forms,result",
[
(
["niska+N+A+Pl", "nipâw+V+AI+Ind+Prs+12Pl", "abcdefg", ""],
{
"niska+N+A+Pl": {"niskak"},
"nipâw+V+AI+Ind+Prs+12Pl": {"kinipânaw", "kinipânânaw"},
"abcdefg": set(),
"": set(),
},
)
],
)
def test_multiple_generation_fast(cree_generator, surface_forms, result):
assert cree_generator.feed_in_bulk_fast(surface_forms) == result
assert cree_generator.feed_in_bulk_fast(surface_forms, multi_process=2) == result
| 31.377682 | 88 | 0.522911 | 815 | 7,311 | 4.469939 | 0.094479 | 0.096624 | 0.121329 | 0.122976 | 0.940159 | 0.930277 | 0.927807 | 0.86275 | 0.853418 | 0.853418 | 0 | 0.00581 | 0.270141 | 7,311 | 232 | 89 | 31.512931 | 0.676912 | 0.005061 | 0 | 0.370558 | 0 | 0 | 0.183168 | 0.034653 | 0 | 0 | 0 | 0.00431 | 0.101523 | 1 | 0.081218 | false | 0 | 0.035533 | 0 | 0.116751 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
2ba1ffad78a439072434a2b6569f6901a2c24801 | 35 | py | Python | hyper/__init__.py | sehoonha/pydart_green_stair | a644355c6ef8068aea5cd0c807b74454f2f9ce4d | [
"MIT"
] | null | null | null | hyper/__init__.py | sehoonha/pydart_green_stair | a644355c6ef8068aea5cd0c807b74454f2f9ce4d | [
"MIT"
] | null | null | null | hyper/__init__.py | sehoonha/pydart_green_stair | a644355c6ef8068aea5cd0c807b74454f2f9ce4d | [
"MIT"
] | null | null | null | from stair_latch import StairLatch
| 17.5 | 34 | 0.885714 | 5 | 35 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114286 | 35 | 1 | 35 | 35 | 0.967742 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
2bdad824eb7c5c886053f64eb1394f4eeb1fba5b | 128 | py | Python | gadget/__init__.py | martincss/iccpy | d3b5f71136835502b04ba26f9656252b735895f4 | [
"MIT"
] | null | null | null | gadget/__init__.py | martincss/iccpy | d3b5f71136835502b04ba26f9656252b735895f4 | [
"MIT"
] | null | null | null | gadget/__init__.py | martincss/iccpy | d3b5f71136835502b04ba26f9656252b735895f4 | [
"MIT"
] | null | null | null |
from .snapshot import save_snapshot, load_snapshot_files, load_snapshot, load_ICsnapshot
from .subfind import SubfindCatalogue
| 32 | 88 | 0.867188 | 16 | 128 | 6.625 | 0.5625 | 0.226415 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.09375 | 128 | 3 | 89 | 42.666667 | 0.913793 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
9214ee602078b3694add41bd9eb6c58bf2949fbe | 34 | py | Python | quicklock3/__init__.py | untixx/quicklock | 3e7ad13619e7bf98a5ce5ec135feba58eef19cb5 | [
"MIT"
] | null | null | null | quicklock3/__init__.py | untixx/quicklock | 3e7ad13619e7bf98a5ce5ec135feba58eef19cb5 | [
"MIT"
] | null | null | null | quicklock3/__init__.py | untixx/quicklock | 3e7ad13619e7bf98a5ce5ec135feba58eef19cb5 | [
"MIT"
] | null | null | null | from .quicklock3 import singleton
| 17 | 33 | 0.852941 | 4 | 34 | 7.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.033333 | 0.117647 | 34 | 1 | 34 | 34 | 0.933333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ecf579517fd9ce9389d7534c4d943828cfaf70dc | 163 | py | Python | crypto/babyrsa/babyrsa.py | vidner/codepwnda-ctf | 7e086044b753fe555b44395b79827d2f5b89da1d | [
"Unlicense"
] | 6 | 2021-02-18T15:07:55.000Z | 2022-02-04T01:38:10.000Z | crypto/babyrsa/release/babyrsa.py | vidner/codepwnda-ctf | 7e086044b753fe555b44395b79827d2f5b89da1d | [
"Unlicense"
] | null | null | null | crypto/babyrsa/release/babyrsa.py | vidner/codepwnda-ctf | 7e086044b753fe555b44395b79827d2f5b89da1d | [
"Unlicense"
] | null | null | null | flag = open("flag").read()
m = int(flag.encode("hex"), 0x16)
e = 0x10001
n = 0x54012066b18843995165c3c0d783aa9e31e796f6928ea4bfe0728b1d1bad6271
print pow(m, e, n)
| 27.166667 | 70 | 0.754601 | 19 | 163 | 6.473684 | 0.736842 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.376712 | 0.104294 | 163 | 5 | 71 | 32.6 | 0.465753 | 0 | 0 | 0 | 0 | 0 | 0.042945 | 0 | 0 | 0 | 0.472393 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0.2 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a63bea7a639509d764cec91bb33bc6cd5db9959c | 152 | py | Python | trello/main/controller.py | maong0927/trello-clone-server | efaeb90057769b45dd73cc91463431da341dc798 | [
"MIT"
] | 2 | 2021-08-17T01:08:35.000Z | 2021-08-25T14:51:11.000Z | trello/main/controller.py | maong0927/trello-clone-server | efaeb90057769b45dd73cc91463431da341dc798 | [
"MIT"
] | null | null | null | trello/main/controller.py | maong0927/trello-clone-server | efaeb90057769b45dd73cc91463431da341dc798 | [
"MIT"
] | null | null | null | from flask_restful import Resource
class MainController(Resource):
def __init__(self):
pass
def get(self):
return "hi trello"
| 16.888889 | 34 | 0.664474 | 18 | 152 | 5.333333 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.263158 | 152 | 8 | 35 | 19 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0.059211 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0.166667 | 0.166667 | 0.166667 | 0.833333 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 6 |
a64ab3d01136c7628b41493592aad2cef451c36d | 38 | py | Python | contact_book/src/contact_book/service/__init__.py | marianne-manaog/ContactBookPythonApplication | 7adcdeb1f7664c1867e8b9939792ed0325ca20c8 | [
"MIT"
] | null | null | null | contact_book/src/contact_book/service/__init__.py | marianne-manaog/ContactBookPythonApplication | 7adcdeb1f7664c1867e8b9939792ed0325ca20c8 | [
"MIT"
] | null | null | null | contact_book/src/contact_book/service/__init__.py | marianne-manaog/ContactBookPythonApplication | 7adcdeb1f7664c1867e8b9939792ed0325ca20c8 | [
"MIT"
] | null | null | null | from . import contacts_service, utils
| 19 | 37 | 0.815789 | 5 | 38 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.131579 | 38 | 1 | 38 | 38 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a67966bf73c287aa4dad3a4d322e51f5b2ec11e3 | 351 | py | Python | packageinstaller.py | HariCane193/Brilliant-Pro | 5149a8205b24a7c488340869bc927fb1912a8f4d | [
"MIT"
] | 1 | 2020-07-27T06:44:53.000Z | 2020-07-27T06:44:53.000Z | packageinstaller.py | HariCane193/Brilliant-Pro | 5149a8205b24a7c488340869bc927fb1912a8f4d | [
"MIT"
] | null | null | null | packageinstaller.py | HariCane193/Brilliant-Pro | 5149a8205b24a7c488340869bc927fb1912a8f4d | [
"MIT"
] | null | null | null | import sys
import subprocess
#pip as subprocess
subprocess.check_call([sys.executable,'-m','pip','install','instabot'])
subprocess.check_call([sys.executable,'-m','pip','install','mysql-connector-python'])
subprocess.check_call([sys.executable,'-m','pip','install','Pillow'])
subprocess.check_call([sys.executable,'-m','pip','install','matplotlib'])
| 39 | 85 | 0.740741 | 45 | 351 | 5.688889 | 0.355556 | 0.234375 | 0.296875 | 0.34375 | 0.671875 | 0.671875 | 0.671875 | 0.671875 | 0 | 0 | 0 | 0 | 0.034188 | 351 | 8 | 86 | 43.875 | 0.755162 | 0.048433 | 0 | 0 | 0 | 0 | 0.282282 | 0.066066 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 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 | 0 | 0 | 0 | 6 |
a6a1175306e37e24be16b44f39ad874b5edc1137 | 109 | py | Python | base/src/shallowflow/base/help/__init__.py | waikato-datamining/shallow-flow | 3f1d99921e5138598eb164edeb1d23e6f199501c | [
"MIT"
] | null | null | null | base/src/shallowflow/base/help/__init__.py | waikato-datamining/shallow-flow | 3f1d99921e5138598eb164edeb1d23e6f199501c | [
"MIT"
] | 2 | 2021-08-18T22:00:08.000Z | 2021-08-18T22:00:47.000Z | base/src/shallowflow/base/help/__init__.py | waikato-datamining/shallowflow | 3f1d99921e5138598eb164edeb1d23e6f199501c | [
"MIT"
] | null | null | null | from ._Markdown import Markdown
from ._PlainText import PlainText
from ._class_doc import markdown_class_doc
| 27.25 | 42 | 0.862385 | 15 | 109 | 5.866667 | 0.4 | 0.318182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.110092 | 109 | 3 | 43 | 36.333333 | 0.907216 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
a6b389bfc3a191363e3ea420e6dcc476687d7de1 | 15,884 | py | Python | src/userservice/db.py | Budget-Web-App/budget-web-app | 6cd1f70bea2e5c7bd674cd383177cca3c8e8ff11 | [
"MIT"
] | null | null | null | src/userservice/db.py | Budget-Web-App/budget-web-app | 6cd1f70bea2e5c7bd674cd383177cca3c8e8ff11 | [
"MIT"
] | null | null | null | src/userservice/db.py | Budget-Web-App/budget-web-app | 6cd1f70bea2e5c7bd674cd383177cca3c8e8ff11 | [
"MIT"
] | null | null | null | import logging
from sqlalchemy import create_engine, MetaData, Table, Column, String, Date, LargeBinary, and_
import random
from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor
class UserDb:
"""
UserDb provides a set of helper functions over SQLAlchemy
to handle db operations for userservice
"""
def __init__(self, uri, logger=logging):
self.engine = create_engine(uri)
self.logger = logger
self.users_table = Table(
'users',
MetaData(self.engine),
Column('userid', String, primary_key=True),
Column('email', String, unique=True, nullable=False),
Column('passhash', LargeBinary, nullable=False),
Column('timezone', String, nullable=False),
Column('registereddate', Date, nullable=True),
)
# Set up tracing autoinstrumentation for sqlalchemy
SQLAlchemyInstrumentor().instrument(
engine=self.engine,
service='users',
)
def add_user(self, user):
"""Add a user to the database.
Params: user - a key/value dict of attributes describing a new user
{'email': email, 'password': password, ...}
Raises: SQLAlchemyError if there was an issue with the database
"""
statement = self.users_table.insert().values(user)
self.logger.debug('QUERY: %s', str(statement))
with self.engine.connect() as conn:
conn.execute(statement)
def generate_userid(self):
"""Generates a globally unique alphanumerical userid."""
self.logger.debug('Generating an account ID')
userid = None
with self.engine.connect() as conn:
while userid is None:
userid = str(random.randint(1e9, (1e10 - 1)))
statement = self.users_table.select().where(
self.users_table.c.userid == userid
)
self.logger.debug('QUERY: %s', str(statement))
result = conn.execute(statement).first()
# If there already exists an account, try again.
if result is not None:
userid = None
self.logger.debug(
'RESULT: account ID already exists. Trying again')
self.logger.debug('RESULT: account ID generated.')
return userid
def get_user(self, email):
"""Get user data for the specified email.
Params: email - the email of the user
Return: a key/value dict of user attributes,
{'email': email, 'userid': userid, ...}
or None if that user does not exist
Raises: SQLAlchemyError if there was an issue with the database
"""
statement = self.users_table.select().where(
self.users_table.c.email == email)
self.logger.debug('QUERY: %s', str(statement))
with self.engine.connect() as conn:
result = conn.execute(statement).first()
self.logger.debug('RESULT: fetched user data for %s', email)
return dict(result) if result is not None else None
class BudgetDb:
"""
BudgetDb provides a set of helper functions over SQLAlchemy
to handle db operations for userservice
"""
def __init__(self, uri, logger=logging):
self.engine = create_engine(uri)
self.logger = logger
self.budgets_table = Table(
'budgets',
MetaData(self.engine),
Column('budgetid', String, primary_key=True),
Column('displayname', String, nullable=False),
Column('budgetnotes', String, nullable=True),
Column('accessdate', Date, nullable=False),
Column('userid', String, nullable=False),
)
# Set up tracing autoinstrumentation for sqlalchemy
SQLAlchemyInstrumentor().instrument(
engine=self.engine,
service='budgets',
)
def add_budget(self, budget):
"""Add a budget to the database.
Params: budget - a key/value dict of attributes describing a new budget
{'email': email, 'password': password, ...}
Raises: SQLAlchemyError if there was an issue with the database
"""
statement = self.budgets_table.insert().values(budget)
self.logger.debug('QUERY: %s', str(statement))
with self.engine.connect() as conn:
conn.execute(statement)
def generate_budgetid(self):
"""Generates a globally unique alphanumerical budgetid."""
self.logger.debug('Generating an account ID')
budgetid = None
with self.engine.connect() as conn:
while budgetid is None:
budgetid = str(random.randint(1e9, (1e10 - 1)))
statement = self.budgets_table.select().where(
self.budgets_table.c.budgetid == budgetid
)
self.logger.debug('QUERY: %s', str(statement))
result = conn.execute(statement).first()
# If there already exists an account, try again.
if result is not None:
budgetid = None
self.logger.debug(
'RESULT: account ID already exists. Trying again')
self.logger.debug('RESULT: account ID generated.')
return budgetid
def get_budget(self, budgetid):
"""Get user data for the specified email.
Params: email - the email of the user
Return: a key/value dict of user attributes,
{'email': email, 'userid': userid, ...}
or None if that user does not exist
Raises: SQLAlchemyError if there was an issue with the database
"""
statement = self.budgets_table.select().where(
self.budgets_table.c.budgetid == budgetid)
self.logger.debug('QUERY: %s', str(statement))
with self.engine.connect() as conn:
result = conn.execute(statement).first()
self.logger.debug('RESULT: fetched budget data for %s', budgetid)
return dict(result) if result is not None else None
def get_budgets(self, userid):
statement = self.budgets_table.select().where(
self.budgets_table.c.userid == userid)
self.logger.debug('QUERY: %s', str(statement))
with self.engine.connect() as conn:
results = conn.execute(statement)
self.logger.debug('RESULT: fetched budgets data for %s', userid)
return [dict(result) for result in results] if results is not None else None
def delete_budget(self, budgetid):
"""_summary_
Args:
budgetid (_type_): _description_
"""
statement = self.budgets_table.delete().where(
self.budgets_table.c.budgetid == budgetid)
with self.engine.connect() as conn:
results = conn.execute(statement)
self.logger.debug('QUERY: %s', str(statement))
return {}
def update_budget(self, budget: dict):
statement = self.budgets_table.update().values(budget).where(
self.budgets_table.c.budgetid == budget["budgetid"])
with self.engine.connect() as conn:
results = conn.execute(statement)
class CategoryDb:
"""
CategoryDb provides a set of helper functions over SQLAlchemy
to handle db operations for userservice
"""
def __init__(self, uri, logger=logging):
self.engine = create_engine(uri)
self.logger = logger
self.categories_table = Table(
'categories',
MetaData(self.engine),
Column('categoryid', String, primary_key=True),
Column('displayname', String, nullable=False),
Column('parentid', String, nullable=True),
Column('budgetid', String, nullable=False),
)
# Set up tracing autoinstrumentation for sqlalchemy
SQLAlchemyInstrumentor().instrument(
engine=self.engine,
service='categories',
)
def add_category(self, category):
"""Add a budget to the database.
Params: budget - a key/value dict of attributes describing a new budget
{'email': email, 'password': password, ...}
Raises: SQLAlchemyError if there was an issue with the database
"""
statement = self.categories_table.insert().values(category)
self.logger.debug('QUERY: %s', str(statement))
with self.engine.connect() as conn:
conn.execute(statement)
def generate_categoryid(self):
"""Generates a globally unique alphanumerical budgetid."""
self.logger.debug('Generating an account ID')
categoryid = None
with self.engine.connect() as conn:
while categoryid is None:
categoryid = str(random.randint(1e9, (1e10 - 1)))
statement = self.categories_table.select().where(
self.categories_table.c.categoryid == categoryid
)
self.logger.debug('QUERY: %s', str(statement))
result = conn.execute(statement).first()
# If there already exists an account, try again.
if result is not None:
categoryid = None
self.logger.debug(
'RESULT: account ID already exists. Trying again')
self.logger.debug('RESULT: account ID generated.')
return categoryid
def get_category(self, categoryid):
"""Get user data for the specified email.
Params: email - the email of the user
Return: a key/value dict of user attributes,
{'email': email, 'userid': userid, ...}
or None if that user does not exist
Raises: SQLAlchemyError if there was an issue with the database
"""
statement = self.categories_table.select().where(
self.categories_table.c.categoryid == categoryid)
self.logger.debug('QUERY: %s', str(statement))
with self.engine.connect() as conn:
result = conn.execute(statement).first()
self.logger.debug('RESULT: fetched budget data for %s', categoryid)
return dict(result) if result is not None else None
def get_categories(self, budgetid):
statement = self.categories_table.select().where(and_(self.categories_table.c.budgetid == budgetid,self.categories_table.c.parentid == None))
self.logger.debug('QUERY: %s', str(statement))
with self.engine.connect() as conn:
results = conn.execute(statement)
self.logger.debug('RESULT: fetched categories for budgetid %s', budgetid)
return [dict(result) for result in results] if results is not None else None
def delete_category(self, categoryid):
"""_summary_
Args:
budgetid (_type_): _description_
"""
statement = self.budgets_table.delete().where(
self.categories_table.c.categoryid == categoryid)
with self.engine.connect() as conn:
results = conn.execute(statement)
self.logger.debug('QUERY: %s', str(statement))
return {}
def update_category(self, category: dict):
statement = self.budgets_table.update().values(category).where(
self.categories_table.c.budgetid == category["budgetid"])
with self.engine.connect() as conn:
results = conn.execute(statement)
class MonthDb:
"""
MonthDb provides a set of helper functions over SQLAlchemy
to handle db operations for userservice
"""
def __init__(self, uri, logger=logging):
self.engine = create_engine(uri)
self.logger = logger
self.months_table = Table(
'months',
MetaData(self.engine),
Column('monthid', String, primary_key=True),
Column('budgetid', String, nullable=False),
Column('month', String, nullable=False),
Column('note', String, nullable=True),
Column('income', String, nullable=True),
Column('budgeted', String, nullable=True),
Column('activity', String, nullable=True),
Column('to_be_budgeted', String, nullable=True),
Column('age_of_money', String, nullable=True),
)
# Set up tracing autoinstrumentation for sqlalchemy
SQLAlchemyInstrumentor().instrument(
engine=self.engine,
service='months',
)
def add_month(self, month):
"""Add a month to the database.
Params: budget - a key/value dict of attributes describing a new month
Raises: SQLAlchemyError if there was an issue with the database
"""
statement = self.months_table.insert().values(month)
self.logger.debug('QUERY: %s', str(statement))
with self.engine.connect() as conn:
conn.execute(statement)
def generate_monthid(self):
"""Generates a globally unique alphanumerical budgetid."""
self.logger.debug('Generating an account ID')
monthid = None
with self.engine.connect() as conn:
while monthid is None:
monthid = str(random.randint(1e9, (1e10 - 1)))
statement = self.months_table.select().where(
self.months_table.c.monthid == monthid
)
self.logger.debug('QUERY: %s', str(statement))
result = conn.execute(statement).first()
# If there already exists an account, try again.
if result is not None:
monthid = None
self.logger.debug(
'RESULT: account ID already exists. Trying again')
self.logger.debug('RESULT: account ID generated.')
return monthid
def get_month(self, monthid):
"""Get month data for the specified email.
Params: email - the email of the user
Return: a key/value dict of user attributes,
{'email': email, 'userid': userid, ...}
or None if that user does not exist
Raises: SQLAlchemyError if there was an issue with the database
"""
statement = self.months_table.select().where(
self.months_table.c.monthid == monthid)
self.logger.debug('QUERY: %s', str(statement))
with self.engine.connect() as conn:
result = conn.execute(statement).first()
self.logger.debug('RESULT: fetched budget data for %s', monthid)
return dict(result) if result is not None else None
def get_months(self, budgetid):
statement = self.months_table.select().where(and_(self.months_table.c.budgetid == budgetid,self.months_table.c.parentid == None))
self.logger.debug('QUERY: %s', str(statement))
with self.engine.connect() as conn:
results = conn.execute(statement)
self.logger.debug('RESULT: fetched budgets data for %s', budgetid)
return [dict(result) for result in results] if results is not None else None
def delete_month(self, monthid):
"""_summary_
Args:
budgetid (_type_): _description_
"""
statement = self.budgets_table.delete().where(
self.months_table.c.monthid == monthid)
with self.engine.connect() as conn:
results = conn.execute(statement)
self.logger.debug('QUERY: %s', str(statement))
return {}
def update_month(self, month: dict):
statement = self.budgets_table.update().values(month).where(
self.months_table.c.budgetid == month["budgetid"])
with self.engine.connect() as conn:
results = conn.execute(statement) | 40.938144 | 149 | 0.599156 | 1,764 | 15,884 | 5.329932 | 0.085601 | 0.043608 | 0.05903 | 0.046905 | 0.821846 | 0.795256 | 0.773878 | 0.747288 | 0.721761 | 0.717613 | 0 | 0.002152 | 0.297784 | 15,884 | 388 | 150 | 40.938144 | 0.840775 | 0.194913 | 0 | 0.56917 | 0 | 0 | 0.088598 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.098814 | false | 0.003953 | 0.01581 | 0 | 0.185771 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
a6d0dcff744a59e3cc48d1bb338feaeb5cc111ac | 107 | py | Python | CursoIntensivoPython/curso-intensivo-python-master/capitulo_02/exercicios/citando_einstein.py | SweydAbdul/estudos-python | b052708d0566a0afb9a1c04d035467d45f820879 | [
"MIT"
] | null | null | null | CursoIntensivoPython/curso-intensivo-python-master/capitulo_02/exercicios/citando_einstein.py | SweydAbdul/estudos-python | b052708d0566a0afb9a1c04d035467d45f820879 | [
"MIT"
] | null | null | null | CursoIntensivoPython/curso-intensivo-python-master/capitulo_02/exercicios/citando_einstein.py | SweydAbdul/estudos-python | b052708d0566a0afb9a1c04d035467d45f820879 | [
"MIT"
] | null | null | null | print(f'Albert Einstein certa vez disse: "Uma pessoa que nunca cometeu um erro jamais tentou nada novo."')
| 53.5 | 106 | 0.775701 | 18 | 107 | 4.611111 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.149533 | 107 | 1 | 107 | 107 | 0.912088 | 0 | 0 | 0 | 0 | 0 | 0.897196 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
470dd30df9e9059c8f3cb7fcbd10e4c5b4172e2d | 41,082 | py | Python | utils/fileFilter.py | badri/django-profile | ab5d60a4f2a78181de24f4b4a5abbcdd977f8ed9 | [
"BSD-2-Clause"
] | 1 | 2016-05-08T21:14:07.000Z | 2016-05-08T21:14:07.000Z | utils/fileFilter.py | badri/django-profile | ab5d60a4f2a78181de24f4b4a5abbcdd977f8ed9 | [
"BSD-2-Clause"
] | null | null | null | utils/fileFilter.py | badri/django-profile | ab5d60a4f2a78181de24f4b4a5abbcdd977f8ed9 | [
"BSD-2-Clause"
] | null | null | null | import os
from pyPdf import PdfFileReader
'''
If something screws up, we return empty string.
TODO:
# add more doc formats like html, rtf, openoffice etc
# handle all exceptions
# make sure we have doc or pdf headers to prevent storing of malformed input
# warning: all file handlers are django file objects, not python file objects.
badri.dilbert@gmail.com
'''
class InvalidFileExtension(Exception):
def __init__(self, fileName, extension):
self.fileName = fileName
self.extension = extension
def extractText(f):
'generic function which will call appropriate extractor depending on file ext.'
fileName, ext = os.path.splitext(f.name)
if ext == '.pdf':
return extractTextFromPdf(f)
elif ext == '.doc':
return extractTextFromDoc(f)
elif ext == '.txt':
return extractTextFromTxt(f)
else:
raise InvalidFileExtension(f.name, ext)
def extractTextFromPdf(f):
'uses pyPdf and extracts text from pdf file.'
# must handle exceptions
inp = PdfFileReader(file(f.name,'rb'))
str = ''
for each_page in range(inp.getNumPages()):
str += inp.getPage(each_page).extractText()
return str
def extractTextFromDoc(f):
'''
MS word files are converted to txt by using antiword program. works for most cases.
'''
str = os.popen("antiword "+ f).read()
# process p
return str
def extractTextFromTxt(f):
'plain txt files.'
#inputf = open(f)
#str = inputf.read()
#inputf.close()
#return str
from tempfile import NamedTemporaryFile
destination = NamedTemporaryFile()
destination.write(f.read())
import codecs
str = codecs.open(destination.name, 'r', 'utf-8', 'ignore')
return str.read()
tests = r"""
>>> from utils.fileFilter import *
>>> from django.core.files import File
>>> f=File(open('foo.pdf', 'rb'))
>>> f
<File: foo.pdf>
>>> extractTextFromPdf(f)
u'DSC2001Proceedingsofthe2ndInternationalWorkshoponDistributedStatisticalComputingMarch15-17,Vienna,Austriahttp://www.ci.tuwien.ac.at/Conferences/DSC-2001K.Hornik&F.Leisch(eds.)ISSN1609-395XOctave:Past,Present,andFutureJohnW.EatonDepartmentofChemicalEngineeringUniversityofWisconsin-MadisonMadison,WIUSA53706AbstractThispaperoutlinesthehistoryanddevelopmentofGNUOctave,aninter-preterforahigh-levelmatrix-basedlanguagefornumericalcomputations.Anumberofundesirablefeaturesofthecurrentimplementationareexamined,andproposalsforfuturedevelopmentarepresented.1IntroductionGNUOctaveisahigh-levelmatrix-basedlanguage,primarilyintendedfornumericalcomputations.Itprovidesaconvenientcommandlineinterfaceforsolvinglinearandnonlinearproblemsnumerically,andforperformingothernumericalexperimentsusingalanguagethatismostlycompatiblewithMatlab.1Itmayalsobeusedasabatch-orientedlanguage.Octaveincludesacollectionoftoolsforsolvingcommonnumericallinearalgebraproblems,therootsofnonlinearequations,integratingordinaryfunctions,manipulatingpolynomials,andintegratingordinarytialandtial-algebraicequations.Itiseasilyextensibleandcustomizableviafunc-tionswritteninOctave\'sownlanguage,orusingdynamically-loadedmoduleswrit-teninC++,C,Fortran,orotherlanguages.OctaveisfreesoftwaredistributedunderthetermsoftheGNUGeneralPub-licLicense(GPL)aspublishedbytheFreeSoftwareFoundation(FSF).Sources,jwe@bevo.che.wisc.edu1MatlabisaregisteredtrademarkoftheMathWorks,Inc.ProceedingsofDSC20012mailinglistarchives,andotherinformationareavailableonthewebathttp://www.octave.org.JohnW.EatonistheprimaryauthorofOctave,thoughmanyothershavecon-tributedtamountsofcode,documentation,ideas,and(ofcourse)bugreports.AmongthelargercontributionsarethestatisticsfunctionswrittenbyKurtHornikandhisstudentsatTechnischeUnivatWienandthelinearcon-trolsystemsfunctionswrittenbyScottedwardHodelandhisstudentsatAuburnUniversity.Muchofthebasicnumericalcapabilitiesarebasedonwell-knownFor-tranlibrariessuchasODEPACK[6],DASSL[11],MINPACK[9],LAPACK[1],andtheBLAS[7,3,2].OctavealsoreliesonBisonandFlex(theGNUparsergeneratortools),theGNUReadlinelibrary(forcommand-lineeditingandhistory),andtheKpathsearchlibrary(forquicklyinlargedirectorytrees).Octavewasoriginallyconceived(inabout1988)tobecompanionsoftwareforanundergraduate-leveltextbookonchemicalreactordesignbeingwrittenbyJamesB.RawlingsoftheUniversityofWisconsin-MadisonandJohnG.EkerdtoftheUni-versityofTexas.Weoriginallyenvisionedsomespecializedtoolsforthesolutionofchemicalreactordesignproblems,probablyintheformofasubroutinelibraryinFortran.However,wehadoftenseenstudentsstrugglewithFortranprogrammingassignmentsandspendfartoomuchtimetryingtounderstandwhytheirFortrancodefailedandnotenoughtimelearningaboutchemicalengineering.Theseexpe-riencesmotivatedustoattempttobuildamuchmoretool.WebelievedthatwithaninteractiveenvironmentlikeOctave,moststudentswouldbeabletopickupthebasicsquickly,andbeginusingittlyinjustafewhours.WeweresomewhatfamiliarwithMatlab,havingseentheoriginalFortranversionaroundthesametimethatwewerebeginningtothinkaboutsoftwaretoolsforchemicalreactordesign.Additionally,agrowingnumberofourcolleagueswerebeginningtousethenewcommercialversionofMatlabastheirprimarycomputationaltool.WeeventuallydecidedtoimplementsomethingthatwouldbemostlyMatlab-compatiblesothatourcolleaguescouldswitchtousingOctavewithouthavingtolearnacompletelynewlanguage.AlthoughwechosetoimplementanearlyMatlab-compatiblelanguage,wedidnotwanttolimitourselvesstrictlytothatlanguage.Fromthebeginningoftheproject,weplannedtoaddvariousadditionalfeaturestothelanguageandhopedtobeabletothosethingsthatwebelievedweremisfeaturesofthelanguageorjustsimplybugs.Nowitseemsthatthegoalof\\compatibilityatareasonablecost"hascausedmanyproblemsfortheproject,aswewillseeindetailinSection4.Full-timedevelopmentbeganintheSpringof1992.ThealphareleasewasJanuary4,1993,andversion1.0wasreleasedFebruary17,1994.Sincethen,Octavehasbeenthroughseveralmajorrevisions,isincludedwithDebian,SuSE,andRedHatLinuxdistributions.IthasalsobeenreviewedintheLinuxJournal[10]andtheJournalofAppliedEconometrics[4].ThenextsectionincludesamorecompletehistoryofOctave\'sdevelopment.Clearly,Octaveisnowmuchmorethanjustanother\\courseware"packagewithlimitedutilitybeyondtheclassroom.Althoughourinitialgoalsweresomewhatvague,weknewthatwewantedtocreatesomethingthatwouldenablestudentstosolverealisticproblems,andthattheycoulduseformanythingsotherthanProceedingsofDSC20013chemicalreactordesignproblems.Today,thousandsofpeopleworldwideareusingOctaveinteaching,research,andcommercialapplications.BecauseOctavemaybefreelydistributed,itistoestimatethetotalnumberofpeoplewhouseit.ThereareatleastfourmirrorsoftheOctaveFTPsite,andtheOctavesourcesarealsoavailablefromtheGNUFTPsiteandallofitsmirrors(morethan75advertisedsitesatlastcount).TheFTPlogsfromoursystemaloneshowedmorethan19,000transfersoftheOctavesourceinthe950daysthatOctave2.0.xwasavailable.ManyLinuxusersreceiveOctaveinbinaryformaswell.NoonereallyknowshowmanypeopleusingLinuxarealsousingOctave,butsomerecentestimatesplacethenumberofLinuxusersatmorethan20million.JustabouteveryonethinksthatthenameOctavehassomethingtodowithmusic,butitisactuallythenameofoneoftheauthor\'sformerprofessorswhowroteafamoustextbook[8]onchemicalreactionengineering,andwhowasalsowellknownforhisabilitytodoquick\\backoftheenvelope"calculations.2DevelopmentHistoryIbeganworkingontheinterpreterforOctaveonFebruary20,1992whileIwasstillmyPh.D.thesisattheUniversityofTexas.IhadbeenexperimentingwithsomeC++classesfornumericalproblemsforaboutayearbeforethat,butOctavewouldbemyrealprojectinC++.TheearlyentriesintheChangeLogshowthatprogresswasrapid,andbyearlyJuly,Octavewasabletosolvesomesimpletialequations.BySeptember,severalothermembersofmyresearchgroupbegantryingtouseit(andlotsofbugs).OnJanuary4,1993,ImadeOctaveavailableforanonymousFTPandpostedanannouncementtoseveralUsenetgroups.Bythistime,Octaveincludedabout100built-infunctionsand30functionandcouldhandlemostoftheoperatorsavailableinthethencurrentversionofMatlab.Duringthisyear,Iwastheonlypersonwhohaddoneanyprogrammingontheproject.Today,itishardtoimagineafreesoftwareprojectintendedforwidespreadusethatwouldbedevelopedinsuchisolation.Aftertherelease,Ibegantoreceivefeedbackalmostimmediately,andIwasencouragedbythelevelofinterest.IcontinuedtoputnewtestreleasesontheFTPsiteforaboutanotheryear,butmadenomoreannouncementsoutsideoftheOctavemailinglistsuntilversion1.0wasreleasedonFebruary17,1994.Duringthenextyear,manypeoplereportedbugsandrequestedfeatures,andsomesentpatchesornewfunctions.Iperiodicallymadesnapshotsavailable,butthenextpublicrelease(version1.1.0)wasnotmadeuntilJanuary12,1995.Developmentwasinterruptedinmid-1995asourresearchgroupmovedfromTexastoWisconsin.MuchofmyworkonOctaveoverthenextyearfocusedoncleaninguptheinternals(pluggingmemoryleaks,tryingtouseC++moreely,etc.),addingnewfunctions,andmakingOctavereadyforanewrelease.Octavehadgainedtcapabilitybythetimeversion2.0wasreleasedonDecember10,1996.ProceedingsofDSC20014Forthenextseveralmonths,Iworkedmostlytobugs,releasingversions2.0.1through2.0.5infairlyquicksuccession.Ihadhopedtoquicklyreachamostlybug-freestatesothatIcouldbeginconcentratingonversion2.1andaddingtnewfeatures,butthatneverseemedtohappen.Eventually,IdecidedtofollowtheleadoftheLinuxkerneldeveloperswhohadoptedforstableandunstabledevelopmentbranches,andIsplitthe2.1.xsourcessometimeinmid-1997andbeganmakingperiodicreleasesfromboththedevelopmentandstablebranches.Theideawastospeeduptheincorporationofnewideasintheunstablebranchwhilemaintainingtheotherbranchforbugonly,butIfoundittodenyusefulnewfeaturestotheusersofthestableversion,soIoftenspentextratimepatchingthestablesourcestoincludenewfeatures.Toavoidwastingtimelikethisinthefuture,Iwouldonlyattempttohavetwoactivelymaintaineddevelopmenttreesifanotherindividual(orgroup)wereavailabletomaintainthestablebranchwhileIworkwiththedevelopmentsources(I\'vetriedtovolunteersforthistask,butsofar,noonehassteppedforward).InMayof1997,RichardStallmanandIagreedtomakeOctaveanpartoftheGNUproject(theonlyrealchangewasthatIbegancallingit\\GNUOctave"ratherthanjust\\Octave").Byearly1999,manyfreesoftwareprojectsbegantobeavailablebyanonymousCVS,andinresponsetosomediscussionontheOctavemailinglists,Imadearead-onlycopyoftheOctaveCVSarchiveavailable.TheideawasthatthiswouldhelptospeedupOctavedevelopmentbyallowingpeopletoseetheirchangesinthesourcetreequickly,withouthavingtowaitforanewsnapshot.IbelievethatEricRaymond\'spaper,TheCathedralandtheBazaar[12]convincedmanypeoplethatanopendevelopmentmodelwasthekeytorapiddevelopment,butIdon\'tbelieveitisthatsimple.Wide-openavailabilityofsourcecodedoesn\'tmeanmuchwithoutadedicatedgroupofhackerswhoaregenerallyalignedtowardacommongoal.Atthetimeofthiswriting,Octaveisausefulandwidelyusedtoolfornumericalcomputing.Aswithanysizeablepieceofsoftware,however,therearemanyareasforimprovement.Ibelievethereareproblemswiththelanguagethatshouldbe(anumberoftheseareexaminedinthenextsection).Octavealsolacksafewimportantdatatypessuchassparsematricesandmultidimensionalmatrices.SomeuserswouldbfromaversionofOctavethatcouldeasilysupportparallelprocessing.Otherswouldliketoseemoreadvancedgraphicsandgraphicaluserinterface(GUI)features.And,ofcourse,manyusersrequestimprovedcompatibilitywithMatlab(wewillexaminethistopicinmoredetailinSection4).OnDecember7,2000,IannouncedtotheOctavemailinglistthatIamplanningtotakeabreakfromOctave,reviewtheprogresswehavemade,anddecidewhatisthebestcourseforthefuture.ThatmayinvolvesomederivativeofOctave,oritmaybesomethingthatisonlylooselyrelated.IfitisasystembasedonOctave,thenIwouldlikeforittoavoidmanyoftheproblemsdescribedinthenextsection.ProceedingsofDSC200153DesignandImplementationInthefollowingsections,variousfeaturesofOctaveareexamined.SomeareworthnotingbecausetheyarevaluablefeaturesthatshouldprobablybekeptinanyfutureOctavederivatives,andothersarementionedbecausetheyarethetroublespotsthatshouldberemoved.Insomecases,thetroublesomefeaturesarequitepopularwithformerMatlabusers,soIhavetriedtopresentclearargumentsforwhythesefeaturesarereallynotsogoodafterall.AlthoughIwouldliketobeabletosaythatallofthebadfeaturesofOctavewereinheritedfromMatlab,Imustadmitthatquiteafewofthemaremyowninvention.3.1FreesoftwareOneofOctave\'smostimportantfeaturesisthatitisfreesoftware.Allofthesourcecodeisavailable,sousersareabletoexamineexactlyhoweachcomputationisperformed.Thereareno\\black-boxes"thatcannotbeopened.UsersarealsofreetomodifyanypartofOctaveanddistributetheirchanges(providedthattheyfollowthetermsoftheGPL).Ibelievethatitisimportantforeveryone,especiallyresearchersandacademics,tohaveaccesstothedetailsofallcomputationstheyperform,sothatitispossiblefortheresultstobereviewedandreproduced.3.2C++asimplementationlanguageOctaveiswritteninamixtureofC++,C,Fortran,andOctave\'sownextensionlanguage.OnlyafewnewfunctionshavebeenimplementedinCorFortran.MostoftheinterpreteriswritteninC++,andIamstillnotsurethatthiswasthebestchoice.WhenIbeganworkonOctaveinearly1992,adraftANSI/ISOstandardforC++wasintheworks(theAnnotatedC++ReferenceManual[5]hadbeenpublishedin1990),butthelanguagewasstillevolving.BecausevendorC++compilersvariedwidelyintheircapabilities,Idecidedthatitwouldbetootowritecodethatcouldbecompiledbyverymanyofthem.Instead,IchosetotrytomakeOctaveworkonlyonthosesystemsthathadworkingportsoftheGNUC++compiler.Thischoicemadeportabilitysomewhateasier,butatamountofwasstillrequiredtokeepuptodateasnewlanguagefeatureswereaddedandsomeoldonesremoved,invalidatingpreviouslyworkingcode.EachnewreleaseoftheGNUC++compilerseemedbothablessingandacurse.IbelievethatusingCinsteadwouldnothavebeenmuchbetter,however,asImuchpreferthefeaturesofC++classesoverwhatisavailableinC,andtheyhavemadememorymanagementeasier.Thestringclassofthestandardlibraryhasbeenquitehelpful,andtemplateshavealsobeenusefulinOctave\'slow-levelarrayclasses.3.3ApplicationlayersOctavehasafairlycleanseparationbetweentheinterpreterandthelibrariesthatimplementthebasicnumericalfunctions.Therearethreemainlayers:ProceedingsofDSC20016Parserandinterpreter,mostlywritteninC++.Thisisbuiltintheformofalibrary(liboctinterp)plusonesmall(about500lines)containingthemainOctaveprogram.Theliboctavelibrary,aC++interfacetonumericalcodeandtoUnixlibraryfunctionsandsystemcalls.ThislibraryprovidesthematrixandarrayclassesforOctaveandpresentsaniceinterfacethathidesdetailssuchasworkar-raysandmanyothermemorymanagementandportabilityproblemsfromtheinterpreter.Thelibcruftlibrary,whichisacollectionofmostlyFortrancodewrittenbyothersthatprovidesmostofthebasenumericalcapability.Ibelievethatthisdivisionisreasonable,butsometwouldnothurt.Forexample,theliboctavelibraryshouldprobablybedividedintoatleasttwoparts,onefornumericaltasksandonefortheoperatingsysteminterface,sothatuserswhoonlywantthenumericalcapabilitieswouldnotalsohavetolinkcodeforcommandlineediting,searching,andPosixfunctionality.Theliboctavelibraryshouldalsobeexaminedandrevisedwhereneededtopresentaconsis-tentinterfaceforprogrammerswishingtoaccessOctave\'sfunctionalityatthislevel.Itmightalsobeusefultoapublicinterfacetotheinterpreterlibraryliboctinterp,sothattheOctaveinterpretercouldmoreeasilybeembeddedinotherapplications.3.4InterpreterimplementationThedesignoftheinterpreterwasoriginallypatternedaftertheGNUshellBash,primarilybecauseIhadthesourcecodeforBashandfounditreasonablyeasytoun-derstand.Theparserconvertssourcecodetoaninternaltreestructure.Functionsarestoredaslistsofstatementsalongwithinformationaboutinputandoutputparametersandlocalsymbols.Theinterpreterevaluatescodebyloopingovertheelementsinalistofstatements,andbyrecursivelyevaluatingnodesinexpressiontrees.ThelexerandparserarewrittenusingFlexandBison,whichmakethemrela-tivelyeasytomodifyandextend.ThereisalsoaC++classinterfacetotheparsetreethatprovidesacleaninterfaceforwalkingthetree.Thisiscurrentlyusedforpretty-printingfunctionsandforsettingbreakpointsforOctave\'sinteractivedebugger.2Usingasimpletree-basedinterpreterseemedeasytoimplementandagoodwayformetomakeitreliable,thoughIsuspectthatitisonereasonthatOctave\'sinterpreterisslow.PaulKienzle,aOctaveuserandcontributor,recentlycomparedOctave\'sper-formanceonaloopoftheformx=[1:100000];total=0;2Thisfeatureiscurrentlyunderdevelopment.ProceedingsofDSC20017SystemCPUTimeMethod(seconds)Octave24.scriptOctave6.9C++usingoperationsonoctave_valueOctave0.032C++usingoperationsonMatrixGuile1.3scriptclisp3.3scriptclisp0.70byte-compiledonloadPython0.73scriptOCAML0.17scriptOCAML0.034compiledscriptGCC0.20unoptimizedGCC0.0060optimizedTable1:PerformanceofOctavecomparedtootherinterpretersandcompilers.fori=1:100000total=total+x(i);endandobtainedtheresultsshowninTable1.Clearly,thereisalotofroomforimprovementintheimplementationofloopsandarrayindexinginOctave.Atthispoint,IbelievethatitmaymakemoresensetomodifyOctave\'sparsertoemitSchemeorsomeothergeneral-purposelanguageforwhichafastinterpreterexists,andthenexecutethecodewithinthatcontextinsteadoftryingtoimprovetheperformanceofOctave\'sinterpreter.ThemotivationwouldbetoimprovethespeedandreliabilityofOctave\'sinterpreterwhileatthesametimereducingtherequiredtomaintainit.Presumably,themaintainersofthegeneral-purposescriptinglanguagewouldbeexpertsininterpreterdesign,whichwouldleaveusmoretimetocontributenumericalfunctionalityinstead.3.5Dynamically-linkedfunctionsAlthoughOctaveisextensibleusingtheOctavelanguage,italsoallowsextensionstobewritteninC++(orothercompiledlanguagesthatcanbecalledfromC++).Usersaregenerallydiscouragedfromwritingextensionsasdynamically-linkedfunctionsunlessacceptableperformancecannotbeachievedusingOctave\'sownextensionlanguage,orthereisaneedtolinktoexistingcompiledcode.Nevertheless,thisisanimportantfeatureforcaseswhenperformanceiscritical,orwhencodealreadyexistsinsomeotherlanguage.3.6ExtensibledatatypesOctavealsoallowsuserstonewdatatypesandloadthematruntimeusingadynamically-linkedfunction.Currently,thenewdatatypemustbebyaC++class,butitwouldbeusefultoprovideawayofdoingthiswithinOctave\'sProceedingsofDSC20018language.TheMatlablanguagenowallowsthecreationofnewdatatypes,butthemethodfordoingsodoesnotseemtobeparticularlywelldesigned,andprobablyshouldnotbecopied.3.7Mixed-typeoperationsBinaryoperationsinOctaveincludemixed-typeoperationsformanydatatypes.Forexample,therearespecialfunctionsforoperationsoncomplexandrealmatrices.Savingmemoryandavoidingunnecessarycopyingoflargeamountsofdataarethemotivationsforprovidingtheseextrafunctionsratherthansimplypromotingtheoperandstoacommontype.Inascalar-basedlanguage,onecantooperationsonsimilartypesonly,becausethecostofconvertinganoperandtothewidertypeissmall.Butwhenworkingwithlargearraysthecostofconvertinganoperandcanbequitelarge.Oneproblemwiththisapproachisthatasnewdatatypesareadded,thereisanexplosioninthenumberofnewfunctionsformixed-typeoperationsthatneedtobeItmaybepossibletogetaroundthisbysomecleveruseoftemplates,butIstillseethisproblemasunsolved.3.8DatatypeconversionOctavecanautomaticallyconvertobjectsofonedatatypetoanotherwhenitseemsreasonabletodoso.OneexampleofthetypeofconversionthatOctavecanperformisanarrowingconversionthatmayhappenatobjectcreation.Forexample,giventhecomplexnumbersx=1+iandy=1-i,thetypeoftheresultoftheexpressionx+ywillbereal,notcomplex.Theconversionhappenswhenthenewobjectthatholdstheresultiscreated.Theresultisoriginallycomplex,buttheconstructorforthecomplexscalarobjectcheckstoseeiftheimaginarypartiszero,andifso,convertstheresulttoarealvalue.Similarconversionscanhappenforcomplexmatrices.Thereissomeoverheadforthesenarrowingconversions,butthereisalsoapotentialbifsubsequentoperationscanbeperformedusinglessexpensiveoperationsonthenarrowertype.Octavealsoperformsallarithmeticindoubleprecision(therearenointegervalueswiththeintegerarithmeticrulesusedinlanguageslikeCorFortran)sointegervaluesareconvertedtodoubleprecisionvalueswhentheyareparsed.Asaconsequence,theexpression1/2producesthevalue0.5,not0.Thistypeofconversionmakessomesense,becausethevalueof1/2isonehalf,notzero.Itwouldbeequallyvalidforthetypeoftheresulttoberationalratherthanadoubleprecision.Itisnotclearwhetherthelackofintegerarithmeticisaseriouslimitation.ItdoesnotseemtobemuchofapracticalproblemforthetypesofprogramsthataretypicallywrittenusingOctave.CurrentlytherearenointegermatricesinOctave,butiftheyareadded,somedesigndecisionsmustbecarefullyconsidered.Forexample,shouldresultsforallbinaryoperationsbeconvertedtodoubles?Iftheresultsareallintegervaluesandnoovwoccurs,itwouldbemorememoryttoreturnanotherintegermatrix.ProceedingsofDSC20019Ifresultoftheoperationsonsomeoftheelementscouldproducenon-integervaluesorovw,thenshouldtheresultsfollowtherulesofintegerarithmeticusedbyotherlanguages(thiswouldbeinconsistentwiththebehaviorforscalars,unlessthoserulesarealsochanged)orshouldtheresultsbedoubleprecisionvalues?Perhapsthesimplestapproachwouldbetodisableallautomaticconversions,andimplementintegerarithmetic.Unfortunately,thismaynotbethemostusefuldesignforthetypicaluserofasystemlikeOctave.3.9ParserproblemsThereareanumberofinparsingtheMatlablanguage.BecauseOc-tavetriestobecompatibleinmanyways,Octave\'sparseristlymorecomplexthatitreallyoughttobe.Bydroppingsomeofthemoreinconsistentlanguagefeatures,Octave\'sparsercouldbemuchsimplerandeasiertounderstandandmaintain.Transposeoperatorandcharacterstrings.Thesinglequotecharacter(\')isusedasthetransposeoperatorandtodelimitstrings,sotheparsermustdoalotofextraworktokeeptrackofwhenthesinglequoteshouldberecognizedasatransposeoperatororwhenitshouldberecognizedasastringdelimiter.Thelanguagewouldbemoreconsistentifthesinglequotecharacterwereusedasatransposeoperatoronly.ThiswouldallowOctave\'sparsertobetlysimplerandalsomakeparsingeasierforhumans.Whitespaceinmatrixlists.Anotherexampleofunnecessaryparsercomplexityisthefeaturethatallowsmatricestobewritten[123]whichisinterpretedasavectorofthreeelements.Forsimplecasesliketheoneabove,thisseemsbutformorecomplexcases,itisambiguous.Forexample,itisnotimmediatelyclearwhether[f(x)-3]shouldbeequivalentto[f,(x),-3](threedistinctelements)or[f(x),-3](twoelements|afunctioncallorindexedarrayfollowedbyascalar)or[f,(x)-3](twoelements|avariable(orfunctioncall,sinceMatlabdoesnotrequirefunctioncallswithnoargumentstobewrittenasf())followedbyabinaryexpression)orProceedingsofDSC200110[f(x)-3](oneelement,theresultofsubtractingascalarfromtheresultofthefunctioncallorindexedarray).Ialsoseenoreasonforthemeaningoftheexpressionf(x)-3tochangewhenitissurroundedbysquarebrackets.Foranyfutureproject,Iwouldhavetheparserignorespacesinsidematrixlistsandrequireseparatorsbetweenelements.Command-stylefunctioncallsyntax.Matlaballowsfunctionstobecalledusingacommand-stylesyntax.Forexample,thefunctionsavecanbecalledusingthesyntaxsave-asciivarorsave("-ascii","var")Bothformsareequivalent.UsersmayalsofunctionsintheMatlablanguagethatmaybecalledthisway.Nospecialfunctiondeclarationisneeded,theonlyrequirementisthatthefunctionbeabletoacceptallargumentspassedasstrings.Althoughthisseemslikeanicefeature,itleadstoaninconsistencythatwouldbebetteravoided.Theproblemisthat,givenafunctioncalledfcn,Matlabwillparsebothofthefollowingstatementsfcn-1fcn-1asasubtractionof1fromthevaluereturnedfromthefunctionfcn,butwillparsefcn-1asacalltothefunctionfcnwiththestringargument"-1",anditwillparsefcn-1asacalltothefunctionfcnwiththestringarguments"-"and"1".Evenworse,Matlabwon\'tallowyoutobypasstheconfusionbywritingf()-1becauseitdoesn\'tallowthisnotationforcallingafunctionwithnoarguments(theMatlabparseremitsanerrorfortheemptypairofparentheses).Ibelievethatthiskindofinconsistencyisunacceptableinaprogramminglan-guage.Forthisreason,Octavedoesnotcurrentlyallowuserstocalltheirownfunctionsusingacommand-stylesyntax,thoughitdoeshavesomespecialbuilt-infunctionsthatmaybecalledthisway(e.g.,load,save,ls,etc.).Thecommand-stylesyntaxisuseful,however,andmanyusersitawkwardtohavetotypethingslikeProceedingsofDSC200111ls("-l""/foo/bar")whentheyareusedtotypingjustls-l/foo/baratashellprompt.Becausethelanguagehassomecommandsthatcanbecalledwithacommand-stylesyntax,itseemsinconsistenttoforbidusersfromtheirownfunctionsthatcanbecalledusingasimilarsyntax.ForanyfutureOctave-likeproject,Iproposerequiringthatfunctioncallsyntax(i.e.,foo("-arg"))beusedinscriptsorfunctionandthenrequiringaofsomesortthatcanbeusedonthecommandlinetointroducecommand-likesyntax.Forexample,somethinglikeoctave:13>M-xls-l/foo/barsimilartothewayEmacsusestheM-x(orESCx)forcallingfunctionsinter-actively.Thissolutionwouldcompletelyavoidtheandwouldnotbemuchhardertotype.Idon\'tbelievethatforcingtheuseoffunctioncallsyntax(i.e.,ls("-l","/foo/bar"))inscriptsandfunctionwouldposemuchofaproblem(itdoesnotseemtobeaproblemforusersofEmacsandEmacsLisp).3.10Built-invariablestocontrolsemanticsTheOctavelanguagehasanumberofbuilt-invariablesthatmaybeusedtocontrolvariousfeaturesoftheinterpreterandeventhelanguage.Insomecasesthesevariablesarerelativelyharmless,butinothers,theycancausettrouble.Amongtheleastharmfulvariablesarethoselikehistory_size,whichspthenumberofcommandlinestosaveinthecommand-linehistoryorPAGER,whichsptheprogramtouseasaforoutputthatisgoingtothescreenininteractivesessions.Variablessuchastheseonlyuserinteractionandcanbesafelyignoredwhenwritingnewfunctionsorscripts.Anothersetofvariablesthebehavioroftheparser.Forexample,thevariablewhitespace_in_literal_matrix,isusedtocontrolthewaythatOctaveparsesmatrixlists.Continuingtheexamplefromtheprevioussection,considerthematrixlist[f(x)-3]Ifthevalueofwhitespace_in_literal_matrixis"ignore",thencommasorsemi-colonsarerequiredtoseparateelementsinthelist,andallwhitespaceisignored.Ifthevalueis"traditional",thenOctaveparsesthecommandasMatlabwould,whichwouldproduce[f,(x),-3](threedistinctelements).Otherwise,Octavewillnottreatspacesbetweenaniden-andanopenparenthesischaracterasanelementseparator(thisisthewayOctaveoriginallybehaved,beforesomeonenoticedthatitwasnotcompatiblewithMatlab).ProceedingsofDSC200112Thevariablewhitespace_in_literal_matrixwasaddedsothatuserscouldchoosewhethertheywantedOctavetoworkasitalwayshad,ortobehaveinaMatlab-compatibleway,ortorequireuserstoexplicitlyseparateelementsinmatrixlists.Unfortunately,thismeansthattowritescriptsthatcanbeinterpretedcorrectlyregardlessofthevalueofthisvariable,onemustavoidspacesbetweenidenandopenparenthesischaracters(orsurroundtheexpressioninanothersetofparenthesis,likethis:(f(x)))andusecommasandsemicolonstoexplicitlyseparateelements.Themostproblematicsetofvariablesrun-timebehavior.MostofthesewereintroducedtoallowcompatibilitywithwhatIbelievedtobemisfeaturesofMatlabwhilealsoallowingOctavetobehaveinamoreconsistentway.Herearetwoexamplesofthistypeofvariable.Thefunctionzerosmaybeusedtocreateamatrixwithzeros.Itcanbecalledinseveraltways.Thesimplestiszeros(n)whichwillcreateasquarematrixwithdimensionnthatiswithzeros.Iftheargumentisarealnumberbutnotaninteger,bothMatlabandOctavesilentlyroundtothenearestinteger.Ifthevalueoftheargumentiscomplex,Matlabsilentlyignorestheimaginarypart,andifitisnegative,Matlabtreatsitaszero.AfterIhadalreadyimplementedthezerosfunctioninOctavewithratherstrictrequirementsfortheargumenttobeaninteger,Inoticedtheseotheroddbehaviorsanddecidedtoallowthem.Inthecaseofnon-integervalues,Iassumed(incor-rectly?)thatitmightbefairlycommonforthedimensionstobecomputedbysomemethodthatwouldproduceanon-integervalue,soIsimplyallowedOc-tavetobehaveasMatlabdoes.Butforthecasesofcomplexandnegativeargu-mentvalues,Idecidedtomakecompatiblebehavioroptionalthroughthevariables.ok_to_lose_imaginary_partandtreat_neg_dim_as_zero.Inowbelievethatthisisanexampleofcompatibilitygonetoofar.Itseemstomethatsimplyrejectinganynon-integerargumentisreasonable.Anyonethatneedstopassacomplex(oranynon-integer)valuetozeros(orsimilarfunctions)coulddosobyexplicitlyconvertingtheargumenttoaninteger.Myguessisthatthiswouldnotcausetroubleformuchexistingcode.3.11Overloadingof()InMatlabandOctave,parenthesesareusedtodelimitargumentlistsforfunctioncallsandindexlistsforarrayindexing.Intheexpressionx(y,z)itisnotimmediatelyclearwhetherthesymbolxisafunctionthatiscalledwithtwoarguments,oranarraythatisindexedbytwovalues.AlthoughthisisvirtuallyimpossibletochangewithoutbreakingnearlyallthecodewrittenforOctaveupuntilnow,itwouldbenicetobeabletodistinguishfunctioncallsfromarrayindexingbysyntaxalone.ProceedingsofDSC2001133.12SyntaxproblemsformatrixlistsThesquarebracketsusedtointroducematrixlistsarelimitedtotwodimensionalmatrices.Originally,Matlabonlyhadtwo-dimensionalmatrices,andarraysofthreeormoredimensionswerenotallowed.EveninthecurrentversionofMatlabthereisapparentlynolist-likesyntaxforintroducingamatrixofmorethantwodimensions.SimilarlimitationsappeartoexistforMatlab\'s\\Cell"datatypeaswell.BecausethematrixlistnotationoccursinmuchofthecodewrittenforOctave,itwouldbequitetoremovethisrestrictionwithoutbreakingmanyexistingprograms.3.13FlatnamespaceOctavehasnogoodwaytocontrolnamespaces.Allfunctionssharethesamenamespace,andarelikely.Matlabdoesallowprivatefunctionswhichareonlyvisibleassubfunctionsinasinglefunctionbutthatseemstoolimited.Ibelievethatpropersupportofnamespaceswouldbeuseful,particularlyasthenumberoffunctionsincreases,thoughitmaynotbeabsolutelynecessaryifsomecareisexercisedbypeoplewritingfunctionsforotherstouse.Emacs,whichhasmanythousandsoffunctionswrittenforitthataresharedbymanypeople,hasnoprovisionfornamespacesotherthansomesuggestednamingconventionsforfunctionsandglobalvariables.3.14GnuplotplotcommandsyntaxOctavehasatightlycoupledinterfacetognuplotthatisimplementedbyduplicatingmuchofthegnuplotcommandsyntaxdirectlyinOctave\'sparser.AtIwasnotsureitwouldevenbepossibletoimplementthisfeatureatallbecauseitseemedtocreatetoomanyintheparser,butIwaseventuallyabletomakeitwork.IbelievethatOctavewouldbebettertodayifIhadfailedandgivenup,butaftersomediscussionswithothers,Iwasconvincedthattheproblemscouldbesolvedwithenoughcode.Unfortunately,Ilackedtheexperiencetoseehowmuchtroubleitwouldreallycause.Obviously,thegnuplot-stylecommandsthatOctaveunderstandsarefairlysim-pletoconvertintocommandsthatcanbepassedtognuplot.Itwouldbemuchmoretoconvertthemintocommandsorsubroutinecallstoanotherplottingpackage,however,soreplacinggnuplotbyanotherplottingpackingisproblematic,becauseitimpliesalargeorbreakingbackwardcompatibility.Probablyitwouldbebesttodropthisfunctionalityinanyfutureproject.4MATLABCompatibilityIssuesIoriginallybelievedthatcompatibilitywithMatlabwasanimportantandrea-sonablegoal(butcertainlynotthemostimportantone).IbelievedthatitwouldhelppeoplebeginusingOctavebymakingiteasiertolearnandbymakingitpos-sibleforthemtousecodetheyhaddevelopedforMatlabwithouthavingtomakeProceedingsofDSC200114substantialchanges.Unfortunately,attemptingtoprovidegoodMatlabcompati-bilityhasalsocausedmanyproblemsanddivertedresourcesawayfromimprovingthecapabilitiesofOctaveinothermoreimportantareas.BecauseofitsclosecompatibilitywithMatlab,OctaveattractedmanyMat-labusers.Somefoundfeaturesthatwerenotsupported,orbehaviorsthatwereinconsistent,andreportedthemasbugs.Theseproblemreportswereoftengivenarelativelyhighpriority,particularlywhentheproblemswereeitherminororseemedgratuitous.AlthoughIdidnotintendto,IbelievethatbytheseproblemsandmakingOctaveevenmorecompatiblewithMatlab,IencouragedpeopletothinkthataprimarygoalwastocreateanexactreplicaofMatlab.Inolongerbelievethatcompatibilityisareasonablegoal.Itinnovationandplacestheprojectinthepositionofneverbeingaleader,alwaysbeingafollower,andquitefarbehindalmostallthetime.Ifcompatibilityisagoal,almostnoinnovationisallowed,becausethereisthepotentialforanynewfeaturetobeincompatiblewithfutureversionsofMatlab.(Thisisnotjustanimaginedproblem|ithashappenedinthepast,andIexpectitwillhappenagain.)Whenincompatibilitieslikethisarise,thereareseveralchoices.OneistoaddthenewMatlab-compatiblefeatureandstillsupporttheoldwaytoo.Evenifthisispossible(itmaybethatthefeaturessimplycannotcoexist),itaddsadditionalmaintenancecostsbecauseonemustnowsupporttwotwaysofdoingessen-tiallythesamething.Anotherchoiceistoadoptthenewwayanddroptheold,butbreakingbackwardcompatibilityisusuallyseenasundesirable.Ignoringthenewfeatureisalsopossible,buttendstoresultinbugreportsaskingwhythereisanincompatibility(typicallytheassumptionseemstobethatwhoeverimplementedtheOctavefeaturesomehowgotitwrong).Decidingthatitwouldbebesttoavoidinnovationandfocusoncompletecom-patibilityisalsoapathtochoose.Becausethereisnostandardforit,theoftheMatlablanguageiswhateverbehaviorthecurrentversionofMatlabhappenstoexhibit.Attemptingcompatibilityrequiresfollowingamovingtarget,andbecauseTheMathWorksalmostneveropenlydiscusstheirdevelopmentplansinadvanceofnewreleases,thecompatibilitywillbeyearsbehindateachnewreleaseofthetargetsystem.Therearealsosomenon-technicalproblemsassociatedwithstrivingforcompat-ibilitywithasystemlikeMatlab.IbelievethatmanypeoplehavestartedusingOctavebecausetheyseeitasa\\Matlabclone"availablefreeofchargeratherthanafreelyavailablesystemfordoingusefulwork.Often,theseusershaveassumedtheroleof\\customers"whowouldaskforhelp,abugoranewfeature,thenwaitforasolutioninsteadofcontributing.Idon\'tbelievethatthistypeofbehavioristhenormamongsuccessfulfreesoftwareprojects.Indeed,Ibelievethatforafreesoftwareprojecttobesuccessful,itmusthavemanycompetentuserswhoalsofunctionintheroleofdevelopers.Althoughtherehavebeensometcon-tributions,Octavehasneverdevelopedastrongcoreofdedicatedandcompetentdevelopers.ProceedingsofDSC2001155FutureImmediateplansforOctaveincludethereleaseofanew\\stable"versionthatIwillmaintaintoseriousbugsonly.IfpeopleareinterestedinhavingsomeformofOctavecontinuealongitspreviousdevelopmentpath,theywillneedtoorganizethatyfocusisnowonfuturedirections.AlthoughIwillnotbeworkingmuchwithOctaveasitiscurrentlyimplemented,thefuturemayinvolveaderivativeofOctave.IftheproblemsdescribedinSection3areinthewaysthatIhaveproposed,IbelieveOctavewillprovideamuch-improvedlanguage.ItwillremainsimilartoMatlab,butincompatible.Itwillbeeasyforusersofonelanguagetounderstandtheother,but,inmostcases,tosharecodebetweenthetwosystems.Ifthesechangesaremade,Icanseetadvantagesanddisadvantages.Onthepositiveside,wewillhaveamoreconsistentlanguageandaninterpreterthatiseasiertomaintain,andtherewillbelesswastedontheimplementationofpoorlydesignedfeaturesjusttohavecompatibility.Somepossibledisadvantagesarethatpeoplewillgenerallyhavetochooseonesystemortheother,welosesomeuserswhoseecompatibilityasanabsolutemust,andweinventyetanotherscriptinglanguage.AnotherpossibilityforthefutureistoleavetheOctavelanguagealoneandfocusingonadditionalfeatures(sparsematrices,parallelism,etc.)whilefendingtherequestsforadditionalMatlabcompatibility.ButaslongasOctaveisgenerallycompatible,usersarelikelytocontinuetowantmore,andIwouldpreferacleanbreakfromthisproblem.Ifitisundesirabletoinventyetanotherscriptinglanguage,thenperhapsitwouldbebesttosimplychooseanothergeneralpurposescriptinglanguagethatalreadyexistsandaddthenumericalfeaturesfromOctavetoit.IwasaskedaboutamergerofthistypeforOctaveandPython,butasIexaminedPythonmoreclosely,Idecidedthatitdidnotseemtobewell-suitedfornumericalcomputing(thoughmanyseemtobeusingitforthatpurposeanyway).Otherpopularscriptinglanguageshavesimilarproblems.Atthispoint,I\'mleaningmoretowardastreamlinedOctavebuiltontopofsomefastergeneral-purposescriptinglanguagesuchasGuile,theGNUproject\'sembeddableSchemeinterpreter.ThiswouldpreservethesyntaxofOctave(whichseemstobereasonablygoodforexpressingnumericalalgorithms)andallowaccesstothecapabilitiesofthemoregeneralpurposelanguage.ReasonsforchoosingGuilearethatitisintendedtobeusedasanembeddedinterpreter,anditshouldnotbetoototranslateOctavetoScheme.6FinalCommentsSomecommentsinthispaper(particularlythoserelatedtotheproblemswithMat-labcompatibility)mayseemquitepessimistic,butIdon\'tthinkthatthesituationishopeless.ThoseofuswhocareaboutfreesoftwaretoolslikeOctaveshouldseektoavoidtheseproblemsbynotmakingcompatibilitywithproprietarysoftwareaprimarygoal,particularlyifdoingsocomesataverylargecost.Wemustbe-ProceedingsofDSC200116comeleadersratherthanfollowersanddevelopavisionbeyondreimplementationofexistingproprietarytools.Tobesuccessful,aprojectlikeOctaveneedstohavemanycontributorsalignedwithacommongoal.Iwouldliketoseethatgoalbeafreelyavailable,highqualitysystemfornumerical(andpossiblysymbolic)computationsthatinspirespeopletocontributeinnovativeideasandcode.References[1]E.Anderson,Z.Bai,C.Bischof,J.Demmel,J.Dongarra,J.DuCroz,A.Green-baum,S.Hammarling,A.McKenny,S.Ostrouchov,andD.Sorensen.LAPACKUsers\'Guide.SIAM,Philidelphia,1992.[2]JackJ.Dongarra,JeremeyDuCroz,SvenHammarling,andIainAsetoflevel3basiclinearalgebrasubprograms.ACMTransactionsonMathematicalSoftware,16(1):1{17,1990.[3]JackJ.Dongarra,JeremyDuCroz,SvenHammarling,andRichardJ.Han-son.AnextendedsetofFORTRANbasiclinearalgebrasubprograms.ACMTransactionsonMathematicalSoftware,14(1):1{17,March1988.[4]DirkEddelbuettel.EconometricswithOctave.JournalofAppliedEconomet-rics,15:531{542,2000.[5]MargaretA.EllisandBjarneStroustrup.TheAnnotatedC++ReferenceMan-ual.Addison-Wesley,Reading,MA,1990.[6]AlanC.Hindmarsh.ODEPACK,asystematizedcollectionofODEsolvers.InR.S.Stepleman,editor,Computing,pages55{64,Amsterdam,1983.North-Holland.[7]C.L.Lawson,R.J.Hanson,D.R.Kincaid,andF.T.Krogh.BasiclinearalgebrasubprogramsforFortranusage.ACMTransactionsonMathematicalSoftware,5(3):308{323,September1979.[8]OctaveLevenspiel.ChemicalReactionEngineering.JohnWileyandSons,NewYork,2ndedition,1972.[9]JorgeJ.More,BurtonS.Garbow,andKennethE.Hillstrom.Userguideforminpack-1.TechnicalReportANL{80{74,ArgonneNationalLaboratory,March1980.[10]MalcolmMurphy.Octave:Afree,high-levellangaugeformathematics.LinuxJournal,July1997.[11]L.R.Petzold.AdescriptionofDASSL:Atial-algebraicsystemsolver.InR.S.Stepleman,editor,Computing,pages65{68,Amsterdam,1983.North-Holland.ProceedingsofDSC200117[12]EricS.Raymond.Thecathedralandthebazaar.Webdocument,availablefromhttp://www.tuxedo.org/~esr/writings/cathedral-bazaar,1997.'
>>> f=File(open('foo.txt'))
>>> extractText(f)
' P Lakshmi Narasimhan\n\n\nCareer Profile\n\n\n Seeking a challenging position that leverages my experience and also provides opportunities for professional and personal growth.\n\n\nAreas of interest\n\n\n *\n\n usability focus of various programming languages\n *\n\n C++ standardization\n *\n\n Software engineering practices and dynamics of open source projects\n\n\nTechnical skills\n\n\n *\n\n I feel at home in a linux environment.working experience in UNIX variants like AIX, HP-UX and Windows based systems. Strong exposure to the GNU toolchain, UNIX-ish tools like awk, sed and shell scripts.\n *\n\n good knowledge of C and C++. This involves:\n o finding out a bug and patching it in considerable time for a medium to large sized software system.\n o knowledge of STL.\n\n\n * proficiency in python. This involves:\n o scripting all odd tasks which cannot be done using C/C++ or which do not have any performance constraints\n o quick prototyping\n o experience in the Django web framework\n\n\n * TTCN-3\n * javascript, jquery\n\n * sparing knowledge of verilog and lisp.\n * software tools : subversion, Rational Clearcase, Microsoft VSS, GNU Make, Trac.\n\n\nEducational Qualification\n\n\n *\n\n B.Tech in Electronics and Communication engineering, SASTRA, Tanjavur, India, year of passing June 2006. GPA - 7.5/10.0.\n\n\nProfessional Experience Summary\n\n\nEmployer: Huawei\n\nDesignation: software engineer\n\nPeriod: July 2006 till date\n\nwe are designing a distributed platform which provides system level services for telecom applications. We mostly work on C++ and implement storage facility, high availability, adapters, CORBA based request brokers and telecom service life cycle management.\nOccasionally shell and python are used for odd scripting jobs.\nTechnologies used: Sqlite, distributed programming APIs in C++ similar to ACE, python, CORBA like proprietary system.\n\n\n 1. I work as a developer and tester of a telecom product dubbed ENIP.\n 2. It is a layer on top of the operating system which serves as a playground for various telecom services to run.\n 3. I work on the development of core services of ENIP. It involves various concepts of distributed programming like data persistence and synchronization(using sqlite), multi-threading etc.\n 4. I got a product consciousness award in 2007 for automating the build system(using python) and writing a program which automatically inserts debug logs in a C++ file(using C++).\n\n\nOrganization: SASTRA Linux User Group\n\nDesignation: Coordinator\n\nPeriod: June 2003 to May 2006\n\n 1. I was the Linux User Group coordinator of my college and my main activity was to advocate the use of Linux in the campus.\n 2. It involved giving a series of presentations and lectures during weekends on various technical and non technical aspects of Linux and open source.\n\n\n contribution to various open source projects like django(www.djangoproject.com) and openc++ MOP(opencxx.sf.net).\n\n\nPersonal information\n\n\n *\n\n Email: badri DOT dilbert AT gmail DOT com\n *\n\n Snail mail: 82/1, Annayappa Garden, New Tippsandra, Bangalore-75.\n *\n\n Phone : 9986040652.\n\n'
>>> a=File(open('get_profile_alt.py'))
>>> extractText(a)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/git-repos/django-profile/utils/fileFilter.py", line 29, in extractText
raise InvalidFileExtension(f.name, ext)
utils.fileFilter.InvalidFileExtension
"""
| 501 | 35,488 | 0.928436 | 2,031 | 41,082 | 18.766617 | 0.679468 | 0.002466 | 0.001732 | 0.00084 | 0.004565 | 0 | 0 | 0 | 0 | 0 | 0 | 0.013219 | 0.025899 | 41,082 | 81 | 35,489 | 507.185185 | 0.939227 | 0.002215 | 0 | 0.074074 | 0 | 0.055556 | 0.972986 | 0.013088 | 0 | 0 | 0 | 0.012346 | 0 | 0 | null | null | 0.037037 | 0.12963 | null | null | 0.018519 | 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 | 0 | null | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
4720b7c2b976585f48a4a07772dc5f89e28ff37e | 37 | py | Python | orchestra/contrib/saas/services/__init__.py | RubenPX/django-orchestra | 5ab4779e1ae12ec99569d682601b7810587ed381 | [
"Unlicense"
] | 68 | 2015-02-09T10:28:44.000Z | 2022-03-12T11:08:36.000Z | orchestra/contrib/saas/services/__init__.py | RubenPX/django-orchestra | 5ab4779e1ae12ec99569d682601b7810587ed381 | [
"Unlicense"
] | 17 | 2015-05-01T18:10:03.000Z | 2021-03-19T21:52:55.000Z | orchestra/contrib/saas/services/__init__.py | RubenPX/django-orchestra | 5ab4779e1ae12ec99569d682601b7810587ed381 | [
"Unlicense"
] | 29 | 2015-03-31T04:51:03.000Z | 2022-02-17T02:58:50.000Z | from .options import SoftwareService
| 18.5 | 36 | 0.864865 | 4 | 37 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.108108 | 37 | 1 | 37 | 37 | 0.969697 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
47252f8137e76c256e44ffbc0d20d03d6239a6fa | 118 | py | Python | tests/utils/__init__.py | LucaCappelletti94/setup_python_package | 61b5f3cff1ed3181f932293c63c4fcb71cbe0062 | [
"MIT"
] | 5 | 2019-09-17T14:46:35.000Z | 2020-06-06T08:17:02.000Z | tests/utils/__init__.py | LucaCappelletti94/setup_python_package | 61b5f3cff1ed3181f932293c63c4fcb71cbe0062 | [
"MIT"
] | 2 | 2020-12-18T01:47:55.000Z | 2020-12-25T10:08:30.000Z | tests/utils/__init__.py | LucaCappelletti94/setup_python_package | 61b5f3cff1ed3181f932293c63c4fcb71cbe0062 | [
"MIT"
] | null | null | null | from .clone_repo import clone_test_repo, delete_test_repo
__all__ = [
"clone_test_repo",
"delete_test_repo"
] | 19.666667 | 57 | 0.754237 | 17 | 118 | 4.470588 | 0.411765 | 0.421053 | 0.342105 | 0.5 | 0.710526 | 0.710526 | 0 | 0 | 0 | 0 | 0 | 0 | 0.161017 | 118 | 6 | 58 | 19.666667 | 0.767677 | 0 | 0 | 0 | 0 | 0 | 0.260504 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.2 | 0 | 0.2 | 0 | 1 | 0 | 0 | null | 1 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5b59b0f55510abc6bf70ad4a786d1682a19dc213 | 2,476 | py | Python | efficient_rl/environment/oo_mdp/TaxiRelationClass.py | rlagywjd802/efficient_rl | 6a82bfc10d814f5d36a7c211d645aa35ea380acf | [
"MIT"
] | 8 | 2020-06-25T10:16:48.000Z | 2022-02-15T09:12:04.000Z | efficient_rl/environment/oo_mdp/TaxiRelationClass.py | rlagywjd802/efficient_rl | 6a82bfc10d814f5d36a7c211d645aa35ea380acf | [
"MIT"
] | null | null | null | efficient_rl/environment/oo_mdp/TaxiRelationClass.py | rlagywjd802/efficient_rl | 6a82bfc10d814f5d36a7c211d645aa35ea380acf | [
"MIT"
] | 2 | 2020-12-30T07:39:38.000Z | 2021-04-12T14:57:13.000Z | import numpy as np
class TaxiRelations:
@staticmethod
def touch_south(o1, o2):
# north wall indices that hold the same x position as object 1
north_walls_idx = np.logical_and(o2['position'] == 'above', o2['x'] == o1['x'])
# check for collision on north wall
collide_n = np.any(o2['y'][north_walls_idx] == o1['y'])
# south wall indices that hold the same x position as object 1
south_walls_idx = np.logical_and(o2['position'] == 'below', o2['x'] == o1['x'])
# check for collision on south wall
collide_s = np.any(o2['y'][south_walls_idx] == o1['y'] - 1)
return collide_s or collide_n
@staticmethod
def touch_north(o1, o2):
# north wall indices that hold the same x position as object 1
north_walls_idx = np.logical_and(o2['position'] == 'above', o2['x'] == o1['x'])
# check for collision on north wall
collide_n = np.any(o2['y'][north_walls_idx] == o1['y'] + 1)
# south wall indices that hold the same x position as object 1
south_walls_idx = np.logical_and(o2['position'] == 'below', o2['x'] == o1['x'])
collide_s = np.any(o2['y'][south_walls_idx] == o1['y'])
return collide_s or collide_n
@staticmethod
def touch_east(o1, o2):
# east wall indices that hold the same y position as object 1
east_wall_idx = np.logical_and(o2['position'] == 'right', o2['y'] == o1['y'])
# check for collision on east wall
collide_e = np.any(o2['x'][east_wall_idx] == o1['x'])
# west wall indices that hold the same y position as object 1
west_wall_idx = np.logical_and(o2['position'] == 'left', o2['y'] == o1['y'])
collide_w = np.any(o2['x'][west_wall_idx] == o1['x'] + 1)
return collide_e or collide_w
@staticmethod
def touch_west(o1, o2):
# east wall indices that hold the same y position as object 1
east_wall_idx = np.logical_and(o2['position'] == 'right', o2['y'] == o1['y'])
# check for collision on east wall
collide_e = np.any(o2['x'][east_wall_idx] == o1['x'] - 1)
# west wall indices that hold the same y position as object 1
west_wall_idx = np.logical_and(o2['position'] == 'left', o2['y'] == o1['y'])
collide_w = np.any(o2['x'][west_wall_idx] == o1['x'])
return collide_e or collide_w
@staticmethod
def on(o1, o2):
return o1['x'] == o2['x'] and o1['y'] == o2['y']
| 45.851852 | 87 | 0.598142 | 391 | 2,476 | 3.634271 | 0.117647 | 0.019001 | 0.084448 | 0.106967 | 0.921182 | 0.918367 | 0.918367 | 0.918367 | 0.850106 | 0.788177 | 0 | 0.03556 | 0.250404 | 2,476 | 53 | 88 | 46.716981 | 0.730065 | 0.262924 | 0 | 0.515152 | 0 | 0 | 0.076201 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.151515 | false | 0 | 0.030303 | 0.030303 | 0.363636 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5b655c8d4bfc48c86a5a580f5c97c6e2d654b3dc | 68 | py | Python | scripts/evidence/sdk/__init__.py | Oneledger/protocol | 1008fd12d384c9821be2a2ea34b3061cf24ae6bf | [
"Apache-2.0"
] | 38 | 2018-06-30T15:22:06.000Z | 2022-03-20T22:23:07.000Z | scripts/evidence/sdk/__init__.py | Oneledger/protocol | 1008fd12d384c9821be2a2ea34b3061cf24ae6bf | [
"Apache-2.0"
] | 27 | 2018-09-02T09:57:19.000Z | 2021-09-17T18:06:35.000Z | scripts/evidence/sdk/__init__.py | Oneledger/protocol | 1008fd12d384c9821be2a2ea34b3061cf24ae6bf | [
"Apache-2.0"
] | 13 | 2018-06-30T15:22:08.000Z | 2020-07-28T15:00:40.000Z | from actions import *
from cmd_call import *
from rpc_call import *
| 17 | 22 | 0.779412 | 11 | 68 | 4.636364 | 0.545455 | 0.392157 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.176471 | 68 | 3 | 23 | 22.666667 | 0.910714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5b77149d421545dfae58f3c11c83eb36980262f1 | 349,781 | py | Python | test/faceMatch.py | ngd-b/python-demo | 0341c1620bcde1c1d886cb9e75dc6db3722273c8 | [
"MIT"
] | 1 | 2019-10-09T13:40:13.000Z | 2019-10-09T13:40:13.000Z | test/faceMatch.py | ngd-b/python-demo | 0341c1620bcde1c1d886cb9e75dc6db3722273c8 | [
"MIT"
] | null | null | null | test/faceMatch.py | ngd-b/python-demo | 0341c1620bcde1c1d886cb9e75dc6db3722273c8 | [
"MIT"
] | null | null | null | # encoding:utf-8
import requests
import json
'''
百度人脸识别
'''
# 获取access_token
CLIENT_ID = '0s0zCsqh2MKwlSwh48PSeNZY'
CLIENT_SECRET = 'AZIUzKsRUY5kEXUl9glBYmuipW8lTKmj'
# 图片比对数据
match_url = 'https://aip.baidubce.com/rest/2.0/face/v3/match'
def matchFace(token):
#
img1 = 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"
img2 = ""
img3 = ""
#图像数据
data = [
{
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",
"image_type":"BASE64",
"face_type":"LIVE",
"quality_control":"LOW"
},
{
"image":img1,
"image_type":"BASE64",
"face_type":"LIVE",
"quality_control":"LOW"
}
]
# 请求头设置
headers = {
'content-type':'application/json'
}
response = requests.post(match_url+"?access_token="+token,data=json.dumps(data),headers=headers)
if response:
print(response.json());
# 获取Access_token
AuthUrl = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id='+CLIENT_ID+'&client_secret='+CLIENT_SECRET
response = requests.get(AuthUrl)
if response:
res = response.json()
AccessToken = res['access_token']
# 调用测试人脸比对
matchFace(AccessToken);
| 6,995.62 | 221,521 | 0.964026 | 11,393 | 349,781 | 29.59519 | 0.946722 | 0.000071 | 0.000095 | 0.000113 | 0.000261 | 0.000261 | 0.000261 | 0.000261 | 0.000261 | 0 | 0 | 0.159586 | 0.000863 | 349,781 | 49 | 221,522 | 7,138.387755 | 0.805216 | 0.000212 | 0 | 0.228571 | 0 | 0.085714 | 0.997932 | 0.997072 | 0 | 1 | 0 | 0 | 0 | 1 | 0.028571 | false | 0.028571 | 0.057143 | 0 | 0.085714 | 0.028571 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5b8994b5cff3a215d3cd68c9a257d065a212bca0 | 13,772 | py | Python | ondewo/nlu/context_pb2_grpc.py | foldvaridominic/ondewo-nlu-client-python | a4e766252fc2fdd2372860755082480b4234609a | [
"Apache-2.0"
] | null | null | null | ondewo/nlu/context_pb2_grpc.py | foldvaridominic/ondewo-nlu-client-python | a4e766252fc2fdd2372860755082480b4234609a | [
"Apache-2.0"
] | 5 | 2021-11-23T09:43:28.000Z | 2021-12-17T15:09:06.000Z | ondewo/nlu/context_pb2_grpc.py | ondewo/ondewo-vtsi-client-python | 8339dbe355d42ef7d02d441c6604200bbaae491a | [
"Apache-2.0"
] | 1 | 2022-02-22T08:54:57.000Z | 2022-02-22T08:54:57.000Z | # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
"""Client and server classes corresponding to protobuf-defined services."""
import grpc
from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2
from ondewo.nlu import context_pb2 as ondewo_dot_nlu_dot_context__pb2
class ContextsStub(object):
"""A context represents additional information included with user input or with
an intent returned by the Dialogflow API. Contexts are helpful for
differentiating user input which may be vague or have a different meaning
depending on additional details from your application such as user setting
and preferences, previous user input, where the user is in your application,
geographic location, and so on.
You can include contexts as input parameters of a
[DetectIntent][google.cloud.dialogflow.v2.Sessions.DetectIntent] (or
[StreamingDetectIntent][google.cloud.dialogflow.v2.Sessions.StreamingDetectIntent]) request,
or as output contexts included in the returned intent.
Contexts expire when an intent is matched, after the number of `DetectIntent`
requests specified by the `lifespan_count` parameter, or after 10 minutes
if no intents are matched for a `DetectIntent` request.
For more information about contexts, see the
[Dialogflow documentation](https://dialogflow.com/docs/contexts).
"""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.ListContexts = channel.unary_unary(
'/ondewo.nlu.Contexts/ListContexts',
request_serializer=ondewo_dot_nlu_dot_context__pb2.ListContextsRequest.SerializeToString,
response_deserializer=ondewo_dot_nlu_dot_context__pb2.ListContextsResponse.FromString,
)
self.GetContext = channel.unary_unary(
'/ondewo.nlu.Contexts/GetContext',
request_serializer=ondewo_dot_nlu_dot_context__pb2.GetContextRequest.SerializeToString,
response_deserializer=ondewo_dot_nlu_dot_context__pb2.Context.FromString,
)
self.CreateContext = channel.unary_unary(
'/ondewo.nlu.Contexts/CreateContext',
request_serializer=ondewo_dot_nlu_dot_context__pb2.CreateContextRequest.SerializeToString,
response_deserializer=ondewo_dot_nlu_dot_context__pb2.Context.FromString,
)
self.UpdateContext = channel.unary_unary(
'/ondewo.nlu.Contexts/UpdateContext',
request_serializer=ondewo_dot_nlu_dot_context__pb2.UpdateContextRequest.SerializeToString,
response_deserializer=ondewo_dot_nlu_dot_context__pb2.Context.FromString,
)
self.DeleteContext = channel.unary_unary(
'/ondewo.nlu.Contexts/DeleteContext',
request_serializer=ondewo_dot_nlu_dot_context__pb2.DeleteContextRequest.SerializeToString,
response_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString,
)
self.DeleteAllContexts = channel.unary_unary(
'/ondewo.nlu.Contexts/DeleteAllContexts',
request_serializer=ondewo_dot_nlu_dot_context__pb2.DeleteAllContextsRequest.SerializeToString,
response_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString,
)
class ContextsServicer(object):
"""A context represents additional information included with user input or with
an intent returned by the Dialogflow API. Contexts are helpful for
differentiating user input which may be vague or have a different meaning
depending on additional details from your application such as user setting
and preferences, previous user input, where the user is in your application,
geographic location, and so on.
You can include contexts as input parameters of a
[DetectIntent][google.cloud.dialogflow.v2.Sessions.DetectIntent] (or
[StreamingDetectIntent][google.cloud.dialogflow.v2.Sessions.StreamingDetectIntent]) request,
or as output contexts included in the returned intent.
Contexts expire when an intent is matched, after the number of `DetectIntent`
requests specified by the `lifespan_count` parameter, or after 10 minutes
if no intents are matched for a `DetectIntent` request.
For more information about contexts, see the
[Dialogflow documentation](https://dialogflow.com/docs/contexts).
"""
def ListContexts(self, request, context):
"""Returns the list of all contexts in the specified session.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def GetContext(self, request, context):
"""Retrieves the specified context.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def CreateContext(self, request, context):
"""Creates a context.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def UpdateContext(self, request, context):
"""Updates the specified context.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def DeleteContext(self, request, context):
"""Deletes the specified context.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def DeleteAllContexts(self, request, context):
"""Deletes all active contexts in the specified session.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_ContextsServicer_to_server(servicer, server):
rpc_method_handlers = {
'ListContexts': grpc.unary_unary_rpc_method_handler(
servicer.ListContexts,
request_deserializer=ondewo_dot_nlu_dot_context__pb2.ListContextsRequest.FromString,
response_serializer=ondewo_dot_nlu_dot_context__pb2.ListContextsResponse.SerializeToString,
),
'GetContext': grpc.unary_unary_rpc_method_handler(
servicer.GetContext,
request_deserializer=ondewo_dot_nlu_dot_context__pb2.GetContextRequest.FromString,
response_serializer=ondewo_dot_nlu_dot_context__pb2.Context.SerializeToString,
),
'CreateContext': grpc.unary_unary_rpc_method_handler(
servicer.CreateContext,
request_deserializer=ondewo_dot_nlu_dot_context__pb2.CreateContextRequest.FromString,
response_serializer=ondewo_dot_nlu_dot_context__pb2.Context.SerializeToString,
),
'UpdateContext': grpc.unary_unary_rpc_method_handler(
servicer.UpdateContext,
request_deserializer=ondewo_dot_nlu_dot_context__pb2.UpdateContextRequest.FromString,
response_serializer=ondewo_dot_nlu_dot_context__pb2.Context.SerializeToString,
),
'DeleteContext': grpc.unary_unary_rpc_method_handler(
servicer.DeleteContext,
request_deserializer=ondewo_dot_nlu_dot_context__pb2.DeleteContextRequest.FromString,
response_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString,
),
'DeleteAllContexts': grpc.unary_unary_rpc_method_handler(
servicer.DeleteAllContexts,
request_deserializer=ondewo_dot_nlu_dot_context__pb2.DeleteAllContextsRequest.FromString,
response_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'ondewo.nlu.Contexts', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
# This class is part of an EXPERIMENTAL API.
class Contexts(object):
"""A context represents additional information included with user input or with
an intent returned by the Dialogflow API. Contexts are helpful for
differentiating user input which may be vague or have a different meaning
depending on additional details from your application such as user setting
and preferences, previous user input, where the user is in your application,
geographic location, and so on.
You can include contexts as input parameters of a
[DetectIntent][google.cloud.dialogflow.v2.Sessions.DetectIntent] (or
[StreamingDetectIntent][google.cloud.dialogflow.v2.Sessions.StreamingDetectIntent]) request,
or as output contexts included in the returned intent.
Contexts expire when an intent is matched, after the number of `DetectIntent`
requests specified by the `lifespan_count` parameter, or after 10 minutes
if no intents are matched for a `DetectIntent` request.
For more information about contexts, see the
[Dialogflow documentation](https://dialogflow.com/docs/contexts).
"""
@staticmethod
def ListContexts(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/ondewo.nlu.Contexts/ListContexts',
ondewo_dot_nlu_dot_context__pb2.ListContextsRequest.SerializeToString,
ondewo_dot_nlu_dot_context__pb2.ListContextsResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def GetContext(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/ondewo.nlu.Contexts/GetContext',
ondewo_dot_nlu_dot_context__pb2.GetContextRequest.SerializeToString,
ondewo_dot_nlu_dot_context__pb2.Context.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def CreateContext(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/ondewo.nlu.Contexts/CreateContext',
ondewo_dot_nlu_dot_context__pb2.CreateContextRequest.SerializeToString,
ondewo_dot_nlu_dot_context__pb2.Context.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def UpdateContext(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/ondewo.nlu.Contexts/UpdateContext',
ondewo_dot_nlu_dot_context__pb2.UpdateContextRequest.SerializeToString,
ondewo_dot_nlu_dot_context__pb2.Context.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def DeleteContext(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/ondewo.nlu.Contexts/DeleteContext',
ondewo_dot_nlu_dot_context__pb2.DeleteContextRequest.SerializeToString,
google_dot_protobuf_dot_empty__pb2.Empty.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def DeleteAllContexts(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/ondewo.nlu.Contexts/DeleteAllContexts',
ondewo_dot_nlu_dot_context__pb2.DeleteAllContextsRequest.SerializeToString,
google_dot_protobuf_dot_empty__pb2.Empty.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
| 47.489655 | 111 | 0.693363 | 1,422 | 13,772 | 6.453587 | 0.122363 | 0.03487 | 0.040536 | 0.05067 | 0.861937 | 0.861937 | 0.833933 | 0.793832 | 0.685082 | 0.672551 | 0 | 0.004972 | 0.240561 | 13,772 | 289 | 112 | 47.653979 | 0.872454 | 0.252759 | 0 | 0.57513 | 1 | 0 | 0.077897 | 0.040694 | 0 | 0 | 0 | 0 | 0 | 1 | 0.072539 | false | 0 | 0.015544 | 0.031088 | 0.134715 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
5b9acab96167646b28b3f42d085260cad956982d | 209 | py | Python | hs_app_netCDF/admin.py | ResearchSoftwareInstitute/MyHPOM | 2d48fe5ac8d21173b1685eb33059bb391fe24414 | [
"BSD-3-Clause"
] | 1 | 2018-09-17T13:07:29.000Z | 2018-09-17T13:07:29.000Z | hs_app_netCDF/admin.py | ResearchSoftwareInstitute/MyHPOM | 2d48fe5ac8d21173b1685eb33059bb391fe24414 | [
"BSD-3-Clause"
] | 100 | 2017-08-01T23:48:04.000Z | 2018-04-03T13:17:27.000Z | hs_app_netCDF/admin.py | ResearchSoftwareInstitute/MyHPOM | 2d48fe5ac8d21173b1685eb33059bb391fe24414 | [
"BSD-3-Clause"
] | 2 | 2017-07-27T20:41:33.000Z | 2017-07-27T22:40:57.000Z | from mezzanine.pages.admin import PageAdmin
from django.contrib.gis import admin
from .models import NetcdfResource
# admin.site.register(MyResource, PageAdmin)
admin.site.register(NetcdfResource, PageAdmin)
| 29.857143 | 46 | 0.837321 | 26 | 209 | 6.730769 | 0.538462 | 0.102857 | 0.194286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.086124 | 209 | 6 | 47 | 34.833333 | 0.91623 | 0.200957 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.75 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
5bad650851f7b0cbf50d0e6ebffea0953ad01118 | 74 | py | Python | Imaobong Tom/Phase 1/Python Basic 1/Day 6/No. 7.py | CodedLadiesInnovateTech/-python-challenge-solutions | 430cd3eb84a2905a286819eef384ee484d8eb9e7 | [
"MIT"
] | 6 | 2020-05-23T19:53:25.000Z | 2021-05-08T20:21:30.000Z | Imaobong Tom/Phase 1/Python Basic 1/Day 6/No. 7.py | CodedLadiesInnovateTech/-python-challenge-solutions | 430cd3eb84a2905a286819eef384ee484d8eb9e7 | [
"MIT"
] | 8 | 2020-05-14T18:53:12.000Z | 2020-07-03T00:06:20.000Z | Imaobong Tom/Phase 1/Python Basic 1/Day 6/No. 7.py | CodedLadiesInnovateTech/-python-challenge-solutions | 430cd3eb84a2905a286819eef384ee484d8eb9e7 | [
"MIT"
] | 39 | 2020-05-10T20:55:02.000Z | 2020-09-12T17:40:59.000Z | import multiprocessing
print("cpu count is", multiprocessing.cpu_count())
| 24.666667 | 50 | 0.810811 | 9 | 74 | 6.555556 | 0.666667 | 0.271186 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.081081 | 74 | 2 | 51 | 37 | 0.867647 | 0 | 0 | 0 | 0 | 0 | 0.162162 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.5 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 6 |
751849753aeed99eb4d654a97adcbfce7de16778 | 43 | py | Python | common/filters/__init__.py | Jenks18/mfl_api | ecbb8954053be06bbcac7e1132811d73534c78d9 | [
"MIT"
] | 19 | 2015-04-16T09:37:08.000Z | 2022-02-10T11:50:30.000Z | common/filters/__init__.py | Jenks18/mfl_api | ecbb8954053be06bbcac7e1132811d73534c78d9 | [
"MIT"
] | 125 | 2015-03-26T14:05:49.000Z | 2020-05-14T08:16:50.000Z | common/filters/__init__.py | Jenks18/mfl_api | ecbb8954053be06bbcac7e1132811d73534c78d9 | [
"MIT"
] | 39 | 2015-04-15T09:17:33.000Z | 2022-03-28T18:08:16.000Z | from .filter_declarations import * # NOQA
| 21.5 | 42 | 0.767442 | 5 | 43 | 6.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.162791 | 43 | 1 | 43 | 43 | 0.888889 | 0.093023 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
7520070a9b3093fa64f91561981fec22386223f1 | 185 | py | Python | pkgs/werkzeug-0.11.4-py27_0/info/recipe/run_test.py | wangyum/anaconda | 6e5a0dbead3327661d73a61e85414cf92aa52be6 | [
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | pkgs/werkzeug-0.11.4-py27_0/info/recipe/run_test.py | wangyum/anaconda | 6e5a0dbead3327661d73a61e85414cf92aa52be6 | [
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | pkgs/werkzeug-0.11.4-py27_0/info/recipe/run_test.py | wangyum/anaconda | 6e5a0dbead3327661d73a61e85414cf92aa52be6 | [
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | from werkzeug.security import safe_str_cmp
assert safe_str_cmp('bar', 'bar')
assert safe_str_cmp(u'baz', u'baz')
assert safe_str_cmp(u'zas', 'zas')
assert safe_str_cmp('asd', u'asd')
| 23.125 | 42 | 0.745946 | 35 | 185 | 3.657143 | 0.371429 | 0.273438 | 0.390625 | 0.5 | 0.265625 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.097297 | 185 | 7 | 43 | 26.428571 | 0.766467 | 0 | 0 | 0 | 0 | 0 | 0.12973 | 0 | 0 | 0 | 0 | 0 | 0.8 | 1 | 0 | true | 0 | 0.2 | 0 | 0.2 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
75541295f95f2a350bf059ae7df29cef8fe22848 | 5,312 | py | Python | notebooks/source/kernels.py | jafluri/cosmo_estimators | 1b676f14e58ad67fa2644591cf14ea9fd1077272 | [
"MIT"
] | 3 | 2021-07-26T10:17:00.000Z | 2021-12-14T11:09:47.000Z | notebooks/source/kernels.py | jafluri/cosmo_estimators | 1b676f14e58ad67fa2644591cf14ea9fd1077272 | [
"MIT"
] | null | null | null | notebooks/source/kernels.py | jafluri/cosmo_estimators | 1b676f14e58ad67fa2644591cf14ea9fd1077272 | [
"MIT"
] | null | null | null | import mpmath as mp
import numpy as np
"""
This file contains function with the mpmath library featuring arbitrary precise floats
"""
# Note: for float32 this is pretty close to the min logable val
min_val = 1e-42
@mp.workdps(100)
def gaussian_kernel(d, scale=0.05, use_mp=False, return_mp=False):
"""
Gaussian kernel with scale
:param d: the input of the kernel, might be numpy array but not mp matrix
:param scale: the scale
:param use_mp: if True use mpmath
:param return_mp: if True return mpf object
:return: the kernel evaluated at d and scale
"""
if not use_mp:
if return_mp:
print("To return an mpf object, set use_mp=True, returning numpy object...")
norm = np.sqrt(2 * np.pi) * scale
chi = -0.5 * (d / scale) ** 2
return np.maximum(np.exp(chi) / (norm), min_val)
else:
if isinstance(d, np.ndarray):
is_array = True
shape = d.shape
d = d.ravel().astype(np.float64)
else:
is_array = False
d = np.array([d]).astype(np.float64)
res = []
for dd in d:
norm = mp.sqrt(2 * mp.pi) * scale
chi = -0.5 * (mp.mpf(dd) / scale) ** 2
res.append(mp.exp(chi) / norm)
if is_array:
if return_mp:
return np.array(res).reshape(shape)
else:
return np.array(res, dtype=np.float64).reshape(shape)
else:
if return_mp:
return res[0]
else:
return np.float64(res[0])
@mp.workdps(100)
def logistic_kernel(d, scale=0.05, use_mp=False, return_mp=False):
"""
logistic kernel with scale
:param d: the input of the kernel, might be numpy array but not mp matrix
:param scale: the scale
:param use_mp: if True use mpmath
:param return_mp: if True return mpf object
:return: the kernel evaluated at d and scale
"""
if not use_mp:
if return_mp:
print("To return an mpf object, set use_mp=True, returning numpy object...")
pos_exp = np.exp(d / scale)
neg_exp = np.exp(-d / scale)
return np.maximum(1.0 / (scale * (pos_exp + neg_exp + 2)), min_val)
else:
if isinstance(d, np.ndarray):
is_array = True
shape = d.shape
d = d.ravel().astype(np.float64)
else:
is_array = False
d = np.array([d]).astype(np.float64)
res = []
for dd in d:
pos_exp = mp.exp(mp.mpf(dd) / scale)
neg_exp = mp.exp(-mp.mpf(dd) / scale)
res.append(1.0 / (scale * (pos_exp + neg_exp + 2.0)))
if is_array:
if return_mp:
return np.array(res).reshape(shape)
else:
return np.array(res, dtype=np.float64).reshape(shape)
else:
if return_mp:
return res[0]
else:
return np.float64(res[0])
@mp.workdps(100)
def sigmoid_kernel(d, scale=0.05, use_mp=False, return_mp=False):
"""
Sigmoid kernel with scale
:param d: the input of the kernel, might be numpy array but not mp matrix
:param scale: the scale
:param use_mp: if True use mpmath
:param return_mp: if True return mpf object
:return: the kernel evaluated at d and scale
"""
if not use_mp:
if return_mp:
print("To return an mpf object, set use_mp=True, returning numpy object...")
pos_exp = np.exp(d/scale)
neg_exp = np.exp(-d/scale)
return np.maximum(2.0/(np.pi*scale*(pos_exp + neg_exp)), min_val)
else:
if isinstance(d, np.ndarray):
is_array = True
shape = d.shape
d = d.ravel().astype(np.float64)
else:
is_array = False
d = np.array([d]).astype(np.float64)
res = []
for dd in d:
pos_exp = mp.exp(mp.mpf(dd) / scale)
neg_exp = mp.exp(-mp.mpf(dd) / scale)
res.append(2.0 / (mp.pi * scale * (pos_exp + neg_exp)))
if is_array:
if return_mp:
return np.array(res).reshape(shape)
else:
return np.array(res, dtype=np.float64).reshape(shape)
else:
if return_mp:
return res[0]
else:
return np.float64(res[0])
# mpmath functions
@mp.workdps(100)
def mp_mean(arr):
"""
Calculates the mean of the array of mpf values
:param arr: array of mp.mpf floats
:return: the mean as mpf
"""
arr = arr.ravel()
N = arr.size
res = mp.mpf(0.0)
for a in arr:
res = res + a
return res/N
@mp.workdps(100)
def mp_std(arr, ddof=0):
"""
Calculates the standard deviation of the array of mpf values
:param arr: array of mp.mpf floats
:param ddof: Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
where N represents the number of elements. By default ddof is zero. (np.std convention)
:return: the mean as mpf
"""
arr = arr.ravel()
N = arr.size
# get the mean
mean = mp_mean(arr)
res = mp.mpf(0.0)
for a in arr:
res = res + (a - mean)**2
return mp.sqrt(res / (N - ddof))
| 29.027322 | 94 | 0.556099 | 782 | 5,312 | 3.69821 | 0.147059 | 0.04426 | 0.03112 | 0.033195 | 0.790456 | 0.770401 | 0.757261 | 0.757261 | 0.74343 | 0.74343 | 0 | 0.023789 | 0.335279 | 5,312 | 182 | 95 | 29.186813 | 0.795242 | 0.23381 | 0 | 0.79646 | 0 | 0 | 0.052701 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.044248 | false | 0 | 0.017699 | 0 | 0.212389 | 0.026549 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7556d8101d8d4a46efe5ab806dea3b6faffe3185 | 159 | py | Python | melody/admin.py | juancopi81/komposair | ee5d86573ebbc3520f92dd6f7f5ae1c27038599b | [
"MIT"
] | 18 | 2020-12-23T18:37:24.000Z | 2021-11-20T15:50:11.000Z | melody/admin.py | juancopi81/komposair | ee5d86573ebbc3520f92dd6f7f5ae1c27038599b | [
"MIT"
] | null | null | null | melody/admin.py | juancopi81/komposair | ee5d86573ebbc3520f92dd6f7f5ae1c27038599b | [
"MIT"
] | 3 | 2021-01-02T15:26:45.000Z | 2021-04-02T20:42:45.000Z | from django.contrib import admin
from .models import Melody, Vote, Comment
admin.site.register(Melody)
admin.site.register(Vote)
admin.site.register(Comment)
| 22.714286 | 41 | 0.811321 | 23 | 159 | 5.608696 | 0.478261 | 0.209302 | 0.395349 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.08805 | 159 | 6 | 42 | 26.5 | 0.889655 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.4 | 0 | 0.4 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
f3308b7a14101e753630d39c7b8d2f2da93fa343 | 35 | py | Python | datastock/tests/__init__.py | ToFuProject/datastock | a1179b169485042d3c14c668fa7781eb162e38aa | [
"MIT"
] | 4 | 2022-02-24T14:36:25.000Z | 2022-03-08T11:49:35.000Z | datastock/tests/__init__.py | ToFuProject/datastock | a1179b169485042d3c14c668fa7781eb162e38aa | [
"MIT"
] | 12 | 2022-02-24T15:08:14.000Z | 2022-03-23T16:51:54.000Z | datastock/tests/__init__.py | ToFuProject/datastock | a1179b169485042d3c14c668fa7781eb162e38aa | [
"MIT"
] | null | null | null |
from . import test_01_DataStock
| 7 | 31 | 0.771429 | 5 | 35 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.071429 | 0.2 | 35 | 4 | 32 | 8.75 | 0.821429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
f337b67ce098a0e7329bd11fed25eca1384e5c77 | 187 | py | Python | Python/Ejercicios resueltos/Herencia/3.2. Usuario/Persona.py | judrodriguezgo/DesarrolloWeb | a020b1eb734e243114982cde9edfc3c25d60047a | [
"MIT"
] | 1 | 2021-10-30T16:54:25.000Z | 2021-10-30T16:54:25.000Z | Python/Ejercicios resueltos/Herencia/3.2. Usuario/Persona.py | judrodriguezgo/DesarrolloWeb | a020b1eb734e243114982cde9edfc3c25d60047a | [
"MIT"
] | null | null | null | Python/Ejercicios resueltos/Herencia/3.2. Usuario/Persona.py | judrodriguezgo/DesarrolloWeb | a020b1eb734e243114982cde9edfc3c25d60047a | [
"MIT"
] | 3 | 2021-11-23T22:24:15.000Z | 2021-12-31T23:51:47.000Z | class Persona:
def __init__(self, nombre, edad):
self.nombre = nombre
self.edad = edad
def descripcion(self):
return self.nombre + " tiene " + str(self.edad) + " años." | 26.714286 | 62 | 0.636364 | 24 | 187 | 4.791667 | 0.5 | 0.26087 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.229947 | 187 | 7 | 62 | 26.714286 | 0.798611 | 0 | 0 | 0 | 0 | 0 | 0.069149 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0.166667 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
f374262b5dbffce38b230d30d194c81a15a140de | 47 | py | Python | test.py | redwankarimsony/Kaggle-Cassava-Leaf-Disease-Classification | e3aefc21f39fc4ad3a8dcb64ca8d0e4b444c76b9 | [
"MIT"
] | null | null | null | test.py | redwankarimsony/Kaggle-Cassava-Leaf-Disease-Classification | e3aefc21f39fc4ad3a8dcb64ca8d0e4b444c76b9 | [
"MIT"
] | null | null | null | test.py | redwankarimsony/Kaggle-Cassava-Leaf-Disease-Classification | e3aefc21f39fc4ad3a8dcb64ca8d0e4b444c76b9 | [
"MIT"
] | null | null | null | import tensorflow as tf
print(tf.__version__) | 15.666667 | 24 | 0.808511 | 7 | 47 | 4.857143 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.12766 | 47 | 3 | 25 | 15.666667 | 0.829268 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.5 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 6 |
f37939aa55cc20028d3c4297b97f0ac248544edc | 41 | py | Python | src/neural_networks/architectures/RestrictedBoltzmannMachine.py | jmetzz/ml-fundamentals | 36ab416f4ca0737c76f76b24dec3b51c4c7a0071 | [
"MIT"
] | 1 | 2018-05-10T11:00:34.000Z | 2018-05-10T11:00:34.000Z | src/neural_networks/architectures/RestrictedBoltzmannMachine.py | jmetzz/ml-fundamentals | 36ab416f4ca0737c76f76b24dec3b51c4c7a0071 | [
"MIT"
] | null | null | null | src/neural_networks/architectures/RestrictedBoltzmannMachine.py | jmetzz/ml-fundamentals | 36ab416f4ca0737c76f76b24dec3b51c4c7a0071 | [
"MIT"
] | null | null | null | class RestrictedBoltzmannMachine:
pass
| 13.666667 | 33 | 0.853659 | 3 | 41 | 11.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121951 | 41 | 2 | 34 | 20.5 | 0.972222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
3473a2a0e0402b830262373c97258e464d546996 | 110 | py | Python | app/blueprints/__init__.py | mkorcha/CoyoteLab | 8932d9cc35fb840e468368c2e1249ca4811b59d0 | [
"MIT"
] | 2 | 2016-12-01T00:10:46.000Z | 2016-12-31T19:18:35.000Z | app/blueprints/__init__.py | mkorcha/CoyoteLab | 8932d9cc35fb840e468368c2e1249ca4811b59d0 | [
"MIT"
] | null | null | null | app/blueprints/__init__.py | mkorcha/CoyoteLab | 8932d9cc35fb840e468368c2e1249ca4811b59d0 | [
"MIT"
] | null | null | null | from . import auth
from . import instructor
from . import main
from . import student
from . import workspace
| 15.714286 | 24 | 0.763636 | 15 | 110 | 5.6 | 0.466667 | 0.595238 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.190909 | 110 | 6 | 25 | 18.333333 | 0.94382 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
cab1b52d6da264b7fb2dab989d7bcdd2f68ae9d5 | 189 | py | Python | l5kit/l5kit/random/__init__.py | xiaoxiaoheimei/l5kit | 1ea2f8cecfe7ad974419bf5c8519ab3a21300119 | [
"Apache-2.0"
] | 5 | 2021-02-27T07:40:41.000Z | 2022-03-24T07:03:35.000Z | l5kit/l5kit/random/__init__.py | phalanx-hk/l5kit | 92c88c4050b946b6828a47ddb3d13da9d87f9f7d | [
"Apache-2.0"
] | 1 | 2021-02-23T18:55:05.000Z | 2021-02-23T18:55:05.000Z | l5kit/l5kit/random/__init__.py | phalanx-hk/l5kit | 92c88c4050b946b6828a47ddb3d13da9d87f9f7d | [
"Apache-2.0"
] | 2 | 2021-12-07T06:59:06.000Z | 2022-01-22T07:19:18.000Z | from .random_generator import GaussianRandomGenerator, LambdaRandomGenerator, ReplayRandomGenerator
__all__ = ["GaussianRandomGenerator", "LambdaRandomGenerator", "ReplayRandomGenerator"]
| 47.25 | 99 | 0.862434 | 11 | 189 | 14.363636 | 0.727273 | 0.556962 | 0.822785 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.063492 | 189 | 3 | 100 | 63 | 0.892655 | 0 | 0 | 0 | 0 | 0 | 0.343915 | 0.343915 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 1 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
cadecd10f89faf20e614ebd8fefe1f46ba9b3800 | 35,195 | py | Python | VESIcal/batchmodel.py | kaylai/VESIcal | 3ea18b0ce30b30fb55786346c37ef8f428ee5034 | [
"MIT"
] | 16 | 2020-06-22T09:07:32.000Z | 2022-01-12T13:42:12.000Z | VESIcal/batchmodel.py | kaylai/VESIcal | 3ea18b0ce30b30fb55786346c37ef8f428ee5034 | [
"MIT"
] | 136 | 2020-05-22T21:43:23.000Z | 2022-03-07T22:06:33.000Z | build/lib/VESIcal/batchmodel.py | kaylai/VESIcal | 3ea18b0ce30b30fb55786346c37ef8f428ee5034 | [
"MIT"
] | 3 | 2021-05-18T08:21:02.000Z | 2022-03-25T01:08:10.000Z | from VESIcal import core
from VESIcal import models
from VESIcal import calculate_classes
from VESIcal import batchfile
import numpy as np
import warnings as w
import sys
from thermoengine import equilibrate
w.filterwarnings("ignore", message="rubicon.objc.ctypes_patch has only been "
"tested ")
# -------------- MELTS preamble --------------- #
# instantiate thermoengine equilibrate MELTS instance
melts = equilibrate.MELTSmodel('1.2.0')
# Suppress phases not required in the melts simulation
phases = melts.get_phase_names()
for phase in phases:
melts.set_phase_inclusion_status({phase: False})
melts.set_phase_inclusion_status({'Fluid': True, 'Liquid': True})
# --------------------------------------------- #
# -------------- BATCH PROCESSING ----------- #
class BatchFile(batchfile.BatchFile):
"""Performs model functions on a batchfile.BatchFile object
"""
pass
def get_XH2O_fluid(self, sample, temperature, pressure, H2O, CO2):
"""An internally used function to calculate fluid composition.
Parameters
----------
sample: dictionary
Sample composition in wt% oxides
temperature: float
Temperature in degrees C.
pressure: float
Pressure in bars
H2O: float
wt% H2O in the system
CO2: float
wt% CO2 in the system
Returns
-------
float
Mole fraction of H2O in the H2O-CO2 fluid
"""
pressureMPa = pressure / 10.0
bulk_comp = {oxide: sample[oxide] for oxide in core.oxides}
bulk_comp["H2O"] = H2O
bulk_comp["CO2"] = CO2
melts.set_bulk_composition(bulk_comp)
output = melts.equilibrate_tp(temperature, pressureMPa,
initialize=True)
(status, temperature, pressureMPa, xmlout) = output[0]
fluid_comp = melts.get_composition_of_phase(xmlout, phase_name='Fluid',
mode='component')
# NOTE mode='component' returns endmember component keys with values
# in mol fraction.
if "Water" in fluid_comp:
H2O_fl = fluid_comp["Water"]
else:
H2O_fl = 0.0
return H2O_fl
def calculate_dissolved_volatiles(self, temperature, pressure, X_fluid=1,
print_status=True, model='MagmaSat',
record_errors=False, **kwargs):
"""
Calculates the amount of H2O and CO2 dissolved in a magma at the given
P/T conditions and fluid composition. Fluid composition will be
matched to within 0.0001 mole fraction.
Parameters
----------
temperature: float, int, or str
Temperature, in degrees C. Can be passed as float, in which case
the passed value is used as the temperature for all samples.
Alternatively, temperature information for each individual
sample may already be present in the BatchFile object. If so, pass
the str value corresponding to the column title in the BatchFile
object.
pressure: float, int, or str
Pressure, in bars. Can be passed as float or int, in which case the
passed value is used as the pressure for all samples.
Alternatively, pressure information for each individual sample may
already be present in the BatchFile object. If so, pass the str
value corresponding to the column title in the BatchFile object.
X_fluid: float, int, or str
OPTIONAL: Default value is 1. The mole fraction of H2O in the
H2O-CO2 fluid. X_fluid=1 is a pure H2O fluid. X_fluid=0 is a pure
CO2 fluid. Can be passed as a float or int, in which case the
passed value is used as the X_fluid for all samples.
Alternatively, X_fluid information for each individual sample may
already be present in the BatchFile object. If so, pass the str
value corresponding to the column title in the BatchFile object.
print_status: bool
OPTIONAL: The default value is True, in which case the progress of
the calculation will be printed to the terminal. If set to False,
nothing will be printed. MagmaSat calculations tend to be slow,
and so a value of True is recommended for most use cases.
model: string
OPTIONAL: Default is 'MagmaSat'. Any other model name can be
passed here.
record_errors: bool
OPTIONAL: If True, any errors arising during the calculation will
be recorded as a column.
Returns
-------
pandas DataFrame
Original data passed plus newly calculated values are returned.
"""
dissolved_data = self.get_data().copy()
if isinstance(temperature, str):
file_has_temp = True
temp_name = temperature
elif isinstance(temperature, float) or isinstance(temperature, int):
file_has_temp = False
else:
raise core.InputError("temp must be type str or float or int")
if isinstance(pressure, str):
file_has_press = True
press_name = pressure
elif isinstance(pressure, float) or isinstance(pressure, int):
file_has_press = False
else:
raise core.InputError("pressure must be type str or float or int")
if isinstance(X_fluid, str):
file_has_X = True
X_name = X_fluid
elif isinstance(X_fluid, float) or isinstance(X_fluid, int):
file_has_X = False
if X_fluid != 0 and X_fluid != 1:
if X_fluid < 0.001 or X_fluid > 0.999:
raise core.InputError("X_fluid is calculated to a "
"precision of 0.0001 mole fraction. "
"Value for X_fluid must be between "
"0.0001 and 0.9999.")
else:
raise core.InputError("X_fluid must be type str or float or int")
# Check if the model passed as the attribute "model_type"
# Currently only implemented for MagmaSat type models
if hasattr(model, 'model_type') is True:
model = model.model_type
H2Ovals = []
CO2vals = []
warnings = []
errors = []
if model in models.get_model_names(model='mixed'):
for index, row in dissolved_data.iterrows():
try:
if file_has_temp:
temperature = row[temp_name]
if file_has_press:
pressure = row[press_name]
if file_has_X:
X_fluid = row[X_name]
# Get sample comp as Sample class with defaults
bulk_comp = self.get_sample_composition(
index,
normalization=self.default_normalization,
units='wtpt_oxides', asSampleClass=True)
bulk_comp.set_default_units(self.default_units)
bulk_comp.set_default_normalization(
self.default_normalization)
calc = calculate_classes.calculate_dissolved_volatiles(
sample=bulk_comp, pressure=pressure,
temperature=temperature,
X_fluid=(X_fluid, 1-X_fluid),
model=model, silence_warnings=True,
**kwargs)
H2Ovals.append(calc.result['H2O_liq'])
CO2vals.append(calc.result['CO2_liq'])
warnings.append(calc.calib_check)
errors.append('')
except Exception:
H2Ovals.append(np.nan)
CO2vals.append(np.nan)
warnings.append('Calculation Failed.')
errors.append(sys.exc_info()[0])
dissolved_data["H2O_liq_VESIcal"] = H2Ovals
dissolved_data["CO2_liq_VESIcal"] = CO2vals
if file_has_temp is False:
dissolved_data["Temperature_C_VESIcal"] = temperature
if file_has_press is False:
dissolved_data["Pressure_bars_VESIcal"] = pressure
if file_has_X is False:
dissolved_data["X_fluid_input_VESIcal"] = X_fluid
dissolved_data["Model"] = model
dissolved_data["Warnings"] = warnings
if record_errors:
dissolved_data["Errors"] = errors
return dissolved_data
elif model == 'MagmaSat':
XH2Ovals = []
XCO2vals = []
FluidProportionvals = []
iterno = 0
for index, row in dissolved_data.iterrows():
iterno += 1
if print_status:
percent = iterno/len(dissolved_data.index)
batchfile.status_bar.status_bar(percent, index)
if file_has_temp:
temperature = row[temp_name]
if temperature <= 0:
H2Ovals.append(np.nan)
CO2vals.append(np.nan)
XH2Ovals.append(np.nan)
XCO2vals.append(np.nan)
FluidProportionvals.append(np.nan)
warnings.append("Sample skipped. Bad temperature.")
errors.append(sys.exc_info()[0])
w.warn("Temperature for sample " + str(index) +
" is <=0. Skipping sample.", stacklevel=2)
if file_has_press:
pressure = row[press_name]
if temperature > 0 and pressure <= 0:
H2Ovals.append(np.nan)
CO2vals.append(np.nan)
XH2Ovals.append(np.nan)
XCO2vals.append(np.nan)
FluidProportionvals.append(np.nan)
warnings.append("Sample skipped. Bad pressure.")
errors.append(sys.exc_info()[0])
w.warn("Pressure for sample " + str(index) +
" is <=0. Skipping sample.", stacklevel=2)
if file_has_X:
X_fluid = row[X_name]
if temperature > 0 and pressure > 0 and X_fluid < 0:
H2Ovals.append(np.nan)
CO2vals.append(np.nan)
XH2Ovals.append(np.nan)
XCO2vals.append(np.nan)
FluidProportionvals.append(np.nan)
warnings.append("Sample skipped. Bad X_fluid.")
errors.append(sys.exc_info()[0])
w.warn("X_fluid for sample " + str(index) +
" is <0. Skipping sample.", stacklevel=2)
if temperature > 0 and pressure > 0 and X_fluid > 1:
H2Ovals.append(np.nan)
CO2vals.append(np.nan)
XH2Ovals.append(np.nan)
XCO2vals.append(np.nan)
FluidProportionvals.append(np.nan)
warnings.append("Sample skipped. Bad X_fluid.")
errors.append(sys.exc_info()[0])
w.warn("X_fluid for sample " + str(index) +
" is >1. Skipping sample.", stacklevel=2)
if (temperature > 0 and pressure > 0 and
X_fluid >= 0 and X_fluid <= 1):
try:
# Get sample comp as Sample class with defaults
bulk_comp = self.get_sample_composition(
index,
normalization=self.default_normalization,
units='wtpt_oxides',
asSampleClass=True)
bulk_comp.set_default_units(self.default_units)
bulk_comp.set_default_normalization(
self.default_normalization)
calc = calculate_classes.calculate_dissolved_volatiles(
sample=bulk_comp, pressure=pressure,
temperature=temperature,
X_fluid=X_fluid, model=model,
silence_warnings=True, verbose=True)
H2Ovals.append(calc.result['H2O_liq'])
CO2vals.append(calc.result['CO2_liq'])
XH2Ovals.append(calc.result['XH2O_fl'])
XCO2vals.append(calc.result['XCO2_fl'])
FluidProportionvals.append(
calc.result['FluidProportion_wt'])
warnings.append(calc.calib_check)
errors.append('')
except Exception:
H2Ovals.append(np.nan)
CO2vals.append(np.nan)
XH2Ovals.append(np.nan)
XCO2vals.append(np.nan)
FluidProportionvals.append(np.nan)
warnings.append('Calculation Failed.')
errors.append(sys.exc_info()[0])
dissolved_data["H2O_liq_VESIcal"] = H2Ovals
dissolved_data["CO2_liq_VESIcal"] = CO2vals
if file_has_temp is False:
dissolved_data["Temperature_C_VESIcal"] = temperature
if file_has_press is False:
dissolved_data["Pressure_bars_VESIcal"] = pressure
if file_has_X is False:
dissolved_data["X_fluid_input_VESIcal"] = X_fluid
dissolved_data["Model"] = model
dissolved_data["Warnings"] = warnings
if record_errors:
dissolved_data["Errors"] = errors
return dissolved_data
else:
XH2Ovals = []
XCO2vals = []
FluidProportionvals = []
for index, row in dissolved_data.iterrows():
if file_has_temp:
temperature = row[temp_name]
if file_has_press:
pressure = row[press_name]
if file_has_X:
X_fluid = row[X_name]
# Get sample comp as Sample class with defaults
bulk_comp = self.get_sample_composition(
index,
normalization=self.default_normalization,
units='wtpt_oxides',
asSampleClass=True)
bulk_comp.set_default_units(self.default_units)
bulk_comp.set_default_normalization(self.default_normalization)
if 'Water' in model:
try:
calc = calculate_classes.calculate_dissolved_volatiles(
sample=bulk_comp, pressure=pressure,
temperature=temperature,
X_fluid=X_fluid, model=model,
silence_warnings=True)
H2Ovals.append(calc.result)
warnings.append(calc.calib_check)
except Exception:
H2Ovals.append(0)
warnings.append('Calculation Failed #001')
if 'Carbon' in model:
try:
calc = calculate_classes.calculate_dissolved_volatiles(
sample=bulk_comp, pressure=pressure,
temperature=temperature,
X_fluid=X_fluid, model=model,
silence_warnings=True)
CO2vals.append(calc.result)
warnings.append(calc.calib_check)
except Exception:
CO2vals.append(0)
warnings.append('Calculation Failed #002')
if 'Water' in model:
dissolved_data["H2O_liq_VESIcal"] = H2Ovals
if 'Carbon' in model:
dissolved_data["CO2_liq_VESIcal"] = CO2vals
if file_has_temp is False:
dissolved_data["Temperature_C_VESIcal"] = temperature
if file_has_press is False:
dissolved_data["Pressure_bars_VESIcal"] = pressure
if file_has_X is False:
dissolved_data["X_fluid_input_VESIcal"] = X_fluid
dissolved_data["Model"] = model
dissolved_data["Warnings"] = warnings
return dissolved_data
def calculate_equilibrium_fluid_comp(self, temperature, pressure=None,
print_status=False, model='MagmaSat',
**kwargs):
"""
Returns H2O and CO2 concentrations in wt% or mole fraction in a fluid
in equilibrium with the given sample(s) at the given P/T condition.
Parameters
----------
sample: BatchFile object
Compositional information on samples in oxides.
temperature: float, int, or str
Temperature, in degrees C. Can be passed as float, in which case
the passed value is used as the temperature for all samples.
Alternatively, temperature information for each individual
sample may already be present in the BatchFile object. If so, pass
the str value corresponding to the column title in the BatchFile
object.
presure: float, int, or str
Pressure, in bars. Can be passed as float or int, in which case
the passed value is used as the pressure for all samples.
Alternatively, pressure information for each individual sample may
already be present in the BatchFile object. If so, pass the str
value corresponding to the column title in the BatchFile object.
model: string
OPTIONAL: Default is 'MagmaSat'. Any other model name can be
passed here.
Returns
-------
pandas DataFrame
Original data passed plus newly calculated values are returned.
"""
fluid_data = self.get_data().copy()
# Check if the model passed as the attribute "model_type"
# Currently only implemented for MagmaSat type models
if hasattr(model, 'model_type') is True:
model = model.model_type
if isinstance(temperature, str):
file_has_temp = True
temp_name = temperature
elif isinstance(temperature, float) or isinstance(temperature, int):
file_has_temp = False
else:
raise core.InputError("temp must be type str or float or int")
if isinstance(pressure, str):
file_has_press = True
press_name = pressure
elif (isinstance(pressure, float) or isinstance(pressure, int) or
pressure is None):
file_has_press = False
else:
raise core.InputError("pressure must be type str or float or int")
H2Ovals = []
CO2vals = []
warnings = []
if (model in models.get_model_names(model='mixed') or
model == "MooreWater"):
for index, row in fluid_data.iterrows():
try:
if file_has_temp:
temperature = row[temp_name]
if file_has_press:
pressure = row[press_name]
# Get sample comp as Sample class with defaults
bulk_comp = self.get_sample_composition(
index,
normalization=self.default_normalization,
units='wtpt_oxides', asSampleClass=True)
bulk_comp.set_default_units(self.default_units)
bulk_comp.set_default_normalization(
self.default_normalization)
calc = calculate_classes.calculate_equilibrium_fluid_comp(
sample=bulk_comp, pressure=pressure,
temperature=temperature,
model=model, silence_warnings=True,
**kwargs)
H2Ovals.append(calc.result['H2O'])
CO2vals.append(calc.result['CO2'])
if calc.result['H2O'] == 0 and calc.result['CO2'] == 0:
warnings.append(calc.calib_check + "Sample not " +
"saturated at these conditions")
else:
warnings.append(calc.calib_check)
except Exception:
H2Ovals.append(np.nan)
CO2vals.append(np.nan)
warnings.append("Calculation Failed.")
fluid_data["XH2O_fl_VESIcal"] = H2Ovals
fluid_data["XCO2_fl_VESIcal"] = CO2vals
if file_has_temp is False:
fluid_data["Temperature_C_VESIcal"] = temperature
if file_has_press is False:
fluid_data["Pressure_bars_VESIcal"] = pressure
fluid_data["Model"] = model
fluid_data["Warnings"] = warnings
return fluid_data
elif model == 'MagmaSat':
iterno = 0
for index, row in fluid_data.iterrows():
iterno += 1
if print_status:
percent = iterno/len(fluid_data.index)
batchfile.status_bar.status_bar(percent, index)
if file_has_temp:
temperature = row[temp_name]
if temperature <= 0:
H2Ovals.append(np.nan)
CO2vals.append(np.nan)
warnings.append("Calculation skipped. Bad temperature.")
w.warn("Temperature for sample " + str(index) +
" is <=0. Skipping sample.",
stacklevel=2)
if file_has_press:
pressure = row[press_name]
if temperature > 0 and pressure <= 0:
H2Ovals.append(np.nan)
CO2vals.append(np.nan)
warnings.append("Calculation skipped. Bad pressure.")
w.warn("Pressure for sample " + str(index) +
" is <=0. Skipping sample.", stacklevel=2)
if temperature > 0 and pressure > 0:
try:
# Get sample comp as Sample class with defaults
bulk_comp = self.get_sample_composition(
index,
normalization=self.default_normalization,
units='wtpt_oxides', asSampleClass=True)
bulk_comp.set_default_units(self.default_units)
bulk_comp.set_default_normalization(
self.default_normalization)
calc = (
calculate_classes.calculate_equilibrium_fluid_comp(
sample=bulk_comp, pressure=pressure,
temperature=temperature,
model=model, silence_warnings=True))
H2Ovals.append(calc.result['H2O'])
CO2vals.append(calc.result['CO2'])
if calc.result['H2O'] == 0 and calc.result['CO2'] == 0:
warnings.append(calc.calib_check + "Sample not " +
"saturated at these conditions")
else:
warnings.append(calc.calib_check)
except Exception:
H2Ovals.append(np.nan)
CO2vals.append(np.nan)
warnings.append("Calculation Failed.")
fluid_data["XH2O_fl_VESIcal"] = H2Ovals
fluid_data["XCO2_fl_VESIcal"] = CO2vals
if file_has_temp is False:
fluid_data["Temperature_C_VESIcal"] = temperature
if file_has_press is False:
fluid_data["Pressure_bars_VESIcal"] = pressure
fluid_data["Model"] = model
fluid_data["Warnings"] = warnings
return fluid_data
else:
saturated = []
for index, row in fluid_data.iterrows():
try:
if file_has_temp:
temperature = row[temp_name]
if file_has_press:
pressure = row[press_name]
# Get sample comp as Sample class with defaults
bulk_comp = self.get_sample_composition(
index,
normalization=self.default_normalization,
units='wtpt_oxides', asSampleClass=True)
bulk_comp.set_default_units(self.default_units)
bulk_comp.set_default_normalization(
self.default_normalization)
calc = calculate_classes.calculate_equilibrium_fluid_comp(
sample=bulk_comp, pressure=pressure,
temperature=temperature,
model=model, silence_warnings=True)
saturated.append(calc.result)
warnings.append(calc.calib_check)
except Exception:
saturated.append(np.nan)
warnings.append("Calculation Failed.")
fluid_data["Saturated_VESIcal"] = saturated
if file_has_temp is False:
fluid_data["Temperature_C_VESIcal"] = temperature
if file_has_press is False:
fluid_data["Pressure_bars_VESIcal"] = pressure
fluid_data["Model"] = model
fluid_data["Warnings"] = warnings
return fluid_data
def calculate_saturation_pressure(self, temperature, print_status=None,
model='MagmaSat', **kwargs):
"""
Calculates the saturation pressure of multiple sample compositions in
the BatchFile.
Parameters
----------
temperature: float, int, or str
Temperature at which to calculate saturation pressures, in
degrees C. Can be passed as float or int, in which case the
passed value is used as the temperature for all samples.
Alternatively, temperature information for each individual sample
may already be present in the passed BatchFile object. If so, pass
the str value corresponding to the column title in the passed
BatchFile object.
print_status: bool
OPTIONAL: The default value for MagmaSat is True and the default
for all other models is False. If set to True, the progress of the
calculation will be printed to the terminal. If set to False,
nothing will be printed. MagmaSat calculations tend to be slow,
and so a value of True is recommended more most use cases.
model: string
OPTIONAL: Default is 'MagmaSat'. Any other model name can be
passed here.
Returns
-------
pandas DataFrame object
Values returned are saturation pressure in bars, the mass of
fluid present, and the composition of the fluid present.
"""
satp_data = self.get_data().copy()
# Check if the model passed has the attribute "model_type"
# Currently only implemented for MagmaSat type models
if hasattr(model, 'model_type') is True:
model = model.model_type
# set default print_status to True for MagmaSat, False for other
# models if user doesn't pass any option
if print_status is None:
if model == 'MagmaSat':
print_status = True
else:
print_status = False
if isinstance(temperature, str):
file_has_temp = True
temp_name = temperature
elif isinstance(temperature, float) or isinstance(temperature, int):
file_has_temp = False
else:
raise core.InputError("temperature must be type str or float or "
"int")
if model != 'MagmaSat':
satP = []
warnings = []
piStar = []
iterno = 0
for index, row in satp_data.iterrows():
iterno += 1
if print_status:
percent = iterno/len(satp_data.index)
batchfile.status_bar.status_bar(percent, index)
if file_has_temp:
temperature = row[temp_name]
# Get sample comp as Sample class with defaults
bulk_comp = self.get_sample_composition(
index,
normalization=self.default_normalization,
units='wtpt_oxides', asSampleClass=True)
bulk_comp.set_default_units(self.default_units)
bulk_comp.set_default_normalization(self.default_normalization)
calc = calculate_classes.calculate_saturation_pressure(
sample=bulk_comp, temperature=temperature,
model=model, silence_warnings=True,
**kwargs)
satP.append(calc.result)
warnings.append(calc.calib_check)
if model == 'ShishkinaIdealMixing':
piStar.append(
models.default_models['ShishkinaIdealMixing'
].models[1].PiStar(bulk_comp))
satp_data["SaturationP_bars_VESIcal"] = satP
if file_has_temp is False:
satp_data["Temperature_C_VESIcal"] = temperature
satp_data["Model"] = model
satp_data["Warnings"] = warnings
if len(piStar) > 0:
satp_data['PiStar_VESIcal'] = piStar
return satp_data
elif model == 'MagmaSat':
satP = []
flmass = []
flH2O = []
flCO2 = []
flsystem_wtper = []
warnings = []
iterno = 0
for index, row in satp_data.iterrows():
iterno += 1
if print_status:
percent = iterno/len(satp_data.index)
batchfile.status_bar.status_bar(percent, index)
if file_has_temp:
temperature = row[temp_name]
if temperature <= 0:
satP.append(np.nan)
flmass.append(np.nan)
flsystem_wtper.append(np.nan)
flH2O.append(np.nan)
flCO2.append(np.nan)
warnings.append("Calculation skipped. Bad temperature.")
w.warn("Temperature for sample " + str(index) +
" is <=0. Skipping sample.", stacklevel=2)
if temperature > 0:
try:
# Get sample comp as Sample class with defaults
bulk_comp = self.get_sample_composition(
index,
normalization=self.default_normalization,
units='wtpt_oxides', asSampleClass=True)
bulk_comp.set_default_units(self.default_units)
bulk_comp.set_default_normalization(
self.default_normalization)
calc = calculate_classes.calculate_saturation_pressure(
sample=bulk_comp, temperature=temperature,
model=model, verbose=True,
silence_warnings=True)
satP.append(calc.result["SaturationP_bars"])
flmass.append(calc.result["FluidMass_grams"])
flsystem_wtper.append(
calc.result["FluidProportion_wt"])
flH2O.append(calc.result["XH2O_fl"])
flCO2.append(calc.result["XCO2_fl"])
warnings.append(calc.calib_check)
except Exception:
satP.append(np.nan)
flmass.append(np.nan)
flsystem_wtper.append(np.nan)
flH2O.append(np.nan)
flCO2.append(np.nan)
warnings.append("Calculation Failed")
satp_data["SaturationP_bars_VESIcal"] = satP
if file_has_temp is False:
satp_data["Temperature_C_VESIcal"] = temperature
satp_data["XH2O_fl_VESIcal"] = flH2O
satp_data["XCO2_fl_VESIcal"] = flCO2
satp_data["FluidMass_grams_VESIcal"] = flmass
satp_data["FluidSystem_wt_VESIcal"] = flsystem_wtper
satp_data["Model"] = model
satp_data["Warnings"] = warnings
return satp_data
def BatchFile_from_DataFrame(dataframe, units='wtpt_oxides', label=None):
"""
Transforms any pandas DataFrame object into a VESIcal BatchFile object.
Parameters
----------
Same as batchfile.BatchFile_from_DataFrame()
Returns
-------
VESIcal.BatchFile object
"""
return BatchFile(filename=None, dataframe=dataframe, units=units,
label=label)
| 44.720457 | 79 | 0.514278 | 3,417 | 35,195 | 5.129646 | 0.083992 | 0.018371 | 0.028868 | 0.011867 | 0.77516 | 0.757131 | 0.750228 | 0.736935 | 0.724042 | 0.704302 | 0 | 0.011296 | 0.416451 | 35,195 | 786 | 80 | 44.777354 | 0.842146 | 0.174911 | 0 | 0.758303 | 0 | 0 | 0.086885 | 0.016942 | 0 | 0 | 0 | 0 | 0 | 1 | 0.009225 | false | 0.001845 | 0.01476 | 0 | 0.04428 | 0.01845 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
1b2ce76ea6dc0e5948e33e5f4ee7ee3be85a72ae | 20 | py | Python | website/plugins/latex/__init__.py | brandonjflannery/data-analysis-template | 178a088c15956331b59b16b4b05b9b3ba58d41b5 | [
"MIT"
] | 1 | 2020-08-14T16:13:47.000Z | 2020-08-14T16:13:47.000Z | website/plugins/latex/__init__.py | johnjdailey/data-analysis-template | 178a088c15956331b59b16b4b05b9b3ba58d41b5 | [
"MIT"
] | null | null | null | website/plugins/latex/__init__.py | johnjdailey/data-analysis-template | 178a088c15956331b59b16b4b05b9b3ba58d41b5 | [
"MIT"
] | 1 | 2020-07-23T05:09:07.000Z | 2020-07-23T05:09:07.000Z | from .latex import * | 20 | 20 | 0.75 | 3 | 20 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15 | 20 | 1 | 20 | 20 | 0.882353 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
1b9c42c52c5f3facd5ed06798417992f008f1d5f | 139 | py | Python | jobs/admin.py | abdur-rakib/django-job-portal-site | a676635f08e36cf4fef47d595e634f1aa9b8a940 | [
"MIT"
] | 3 | 2020-02-10T19:03:26.000Z | 2020-02-20T08:36:14.000Z | jobs/admin.py | RakibWebDeveloper/django-job-portal-site | a676635f08e36cf4fef47d595e634f1aa9b8a940 | [
"MIT"
] | null | null | null | jobs/admin.py | RakibWebDeveloper/django-job-portal-site | a676635f08e36cf4fef47d595e634f1aa9b8a940 | [
"MIT"
] | 1 | 2021-09-24T21:51:07.000Z | 2021-09-24T21:51:07.000Z | from django.contrib import admin
from .models import *
admin.site.register(Job)
admin.site.register(Applicant)
admin.site.register(About) | 19.857143 | 32 | 0.805755 | 20 | 139 | 5.6 | 0.55 | 0.241071 | 0.455357 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.086331 | 139 | 7 | 33 | 19.857143 | 0.88189 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.4 | 0 | 0.4 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
1baf1e5e0293c7f594b39a9747c13814e95dafc1 | 653 | py | Python | min/sys_info.py | Richienb/ros-code | fbca60d965ff1792b380dd457d4af881b4f4b889 | [
"Apache-2.0"
] | 10 | 2017-10-21T06:02:42.000Z | 2021-04-27T08:53:00.000Z | min/sys_info.py | Richienb/ros-code | fbca60d965ff1792b380dd457d4af881b4f4b889 | [
"Apache-2.0"
] | 4 | 2018-08-07T14:27:58.000Z | 2019-07-06T18:01:51.000Z | min/sys_info.py | Richienb/ros-code | fbca60d965ff1792b380dd457d4af881b4f4b889 | [
"Apache-2.0"
] | 2 | 2018-03-24T18:48:57.000Z | 2018-06-26T23:43:11.000Z | import platform
import sys
from pprint import pprint
VER="ROS Code 2.0 | Running on {} {} | Python Version {}.{}.{}".format(platform.system(),platform.release(),list(sys.version_info)[0],list(sys.version_info)[1],list(sys.version_info)[2])
VAR=[str(platform.system()),str(platform.release()),str(sys.version_info),str(list(sys.version_info)[0]),str(list(sys.version_info)[1]),str(list(sys.version_info)[2])]
def print_ver():
print(VER)
def get_ver():
return VER
def print_var():
print(VAR)
def get_var():
return VAR
def pprint_var():
pprint(VAR)
if __name__=="__main__":
print_ver()
# Created by pyminifier (https://github.com/liftoff/pyminifier)
| 34.368421 | 186 | 0.728943 | 104 | 653 | 4.375 | 0.346154 | 0.153846 | 0.215385 | 0.237363 | 0.27033 | 0 | 0 | 0 | 0 | 0 | 0 | 0.013311 | 0.079632 | 653 | 18 | 187 | 36.277778 | 0.74376 | 0.093415 | 0 | 0 | 0 | 0 | 0.110169 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.294118 | false | 0 | 0.176471 | 0.117647 | 0.588235 | 0.470588 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 6 |
1bcf240a26054e743591ac615941bd1510365403 | 12,690 | py | Python | Tests/ML/utils/test_model_util.py | mkoivi-ms/InnerEye-DeepLearning | 5879efe407ed411eb8393d6bd30757d0d775b863 | [
"MIT"
] | 1 | 2021-01-20T10:11:51.000Z | 2021-01-20T10:11:51.000Z | Tests/ML/utils/test_model_util.py | mkoivi-ms/InnerEye-DeepLearning | 5879efe407ed411eb8393d6bd30757d0d775b863 | [
"MIT"
] | null | null | null | Tests/ML/utils/test_model_util.py | mkoivi-ms/InnerEye-DeepLearning | 5879efe407ed411eb8393d6bd30757d0d775b863 | [
"MIT"
] | null | null | null | # ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ------------------------------------------------------------------------------------------
import pytest
import torch
import os
from torch.optim import Optimizer
from torch import nn
from typing import Callable
from InnerEye.ML.utils.model_util import ModelAndInfo
from InnerEye.ML.utils.device_aware_module import DeviceAwareModule
from InnerEye.Common.common_util import ModelExecutionMode
from InnerEye.ML.models.architectures.base_model import BaseModel
from InnerEye.ML.model_config_base import ModelConfigBase
from InnerEye.ML.config import SegmentationModelBase
from InnerEye.ML.models.parallel.data_parallel import DataParallelModel
from Tests.ML.configs.ClassificationModelForTesting import ClassificationModelForTesting
from Tests.ML.configs.DummyModel import DummyModel
from Tests.ML.util import no_gpu_available
from InnerEye.Common.fixed_paths_for_tests import full_ml_test_data_path
@pytest.mark.parametrize("config, checkpoint_path",
[(DummyModel(), "checkpoints/1_checkpoint.pth.tar"),
(ClassificationModelForTesting(),
"classification_data_generated_random/checkpoints/1_checkpoint.pth.tar")])
def test_try_create_model_and_load_from_checkpoint(config: ModelConfigBase, checkpoint_path: str) -> None:
# no checkpoint path provided
model_and_info = ModelAndInfo(config,
model_execution_mode=ModelExecutionMode.TEST,
checkpoint_path=None)
with pytest.raises(ValueError):
model_and_info.model
model_loaded = model_and_info.try_create_model_and_load_from_checkpoint()
assert model_loaded
if isinstance(config, SegmentationModelBase):
assert isinstance(model_and_info.model, BaseModel)
else:
assert isinstance(model_and_info.model, DeviceAwareModule)
# Invalid checkpoint path provided
model_and_info = ModelAndInfo(config,
model_execution_mode=ModelExecutionMode.TEST,
checkpoint_path=full_ml_test_data_path("non_exist.pth.tar"))
model_loaded = model_and_info.try_create_model_and_load_from_checkpoint()
assert not model_loaded
# Current code assumes that even if this function returns False, the model itself was created, only the checkpoint
# loading failed.
if isinstance(config, SegmentationModelBase):
assert isinstance(model_and_info.model, BaseModel)
else:
assert isinstance(model_and_info.model, DeviceAwareModule)
# Valid checkpoint path provided
model_and_info = ModelAndInfo(config,
model_execution_mode=ModelExecutionMode.TEST,
checkpoint_path=full_ml_test_data_path(checkpoint_path))
model_loaded = model_and_info.try_create_model_and_load_from_checkpoint()
assert model_loaded
if isinstance(config, SegmentationModelBase):
assert isinstance(model_and_info.model, BaseModel)
else:
assert isinstance(model_and_info.model, DeviceAwareModule)
assert model_and_info.checkpoint_epoch == 1
@pytest.mark.gpu
@pytest.mark.skipif(no_gpu_available, reason="Testing shift to DataParallelModel requires a GPU")
@pytest.mark.parametrize("model_execution_mode", [ModelExecutionMode.TRAIN, ModelExecutionMode.TEST])
@pytest.mark.parametrize("config, checkpoint_path",
[(DummyModel(), "checkpoints/1_checkpoint.pth.tar"),
(ClassificationModelForTesting(),
"classification_data_generated_random/checkpoints/1_checkpoint.pth.tar")])
def test_try_create_model_load_from_checkpoint_and_adjust(config: ModelConfigBase, checkpoint_path: str,
model_execution_mode: ModelExecutionMode) -> None:
config.use_gpu = True
# no checkpoint path provided
model_and_info = ModelAndInfo(config,
model_execution_mode=model_execution_mode,
checkpoint_path=None)
with pytest.raises(ValueError):
model_and_info.model
model_loaded = model_and_info.try_create_model_load_from_checkpoint_and_adjust()
assert model_loaded
assert isinstance(model_and_info.model, DataParallelModel)
# Invalid checkpoint path provided
model_and_info = ModelAndInfo(config,
model_execution_mode=model_execution_mode,
checkpoint_path=full_ml_test_data_path("non_exist.pth.tar"))
model_loaded = model_and_info.try_create_model_load_from_checkpoint_and_adjust()
assert not model_loaded
# Current code assumes that even if this function returns False, the model itself was created, only the checkpoint
# loading failed.
assert isinstance(model_and_info.model, DataParallelModel)
# Valid checkpoint path provided
model_and_info = ModelAndInfo(config,
model_execution_mode=model_execution_mode,
checkpoint_path=full_ml_test_data_path(checkpoint_path))
model_loaded = model_and_info.try_create_model_load_from_checkpoint_and_adjust()
assert model_loaded
assert isinstance(model_and_info.model, DataParallelModel)
assert model_and_info.checkpoint_epoch == 1
@pytest.mark.parametrize("config, checkpoint_path",
[(DummyModel(), "checkpoints/1_checkpoint.pth.tar"),
(ClassificationModelForTesting(),
"classification_data_generated_random/checkpoints/1_checkpoint.pth.tar")])
def test_try_create_optimizer_and_load_from_checkpoint(config: ModelConfigBase, checkpoint_path: str) -> None:
# no checkpoint path provided
model_and_info = ModelAndInfo(config,
model_execution_mode=ModelExecutionMode.TEST,
checkpoint_path=None)
with pytest.raises(ValueError):
model_and_info.optimizer
model_loaded = model_and_info.try_create_model_and_load_from_checkpoint()
assert model_loaded
optimizer_loaded = model_and_info.try_create_optimizer_and_load_from_checkpoint()
assert optimizer_loaded
assert isinstance(model_and_info.optimizer, Optimizer)
# Invalid checkpoint path provided
model_and_info = ModelAndInfo(config,
model_execution_mode=ModelExecutionMode.TEST,
checkpoint_path=full_ml_test_data_path("non_exist.pth.tar"))
model_loaded = model_and_info.try_create_model_and_load_from_checkpoint()
assert not model_loaded
# Current code assumes that even if this function returns False, the model itself was created, only the checkpoint
# loading failed.
optimizer_loaded = model_and_info.try_create_optimizer_and_load_from_checkpoint()
assert not optimizer_loaded
# Current code assumes that even if this function returns False,
# the optimizer itself was created, only the checkpoint loading failed.
assert isinstance(model_and_info.optimizer, Optimizer)
# Valid checkpoint path provided
model_and_info = ModelAndInfo(config,
model_execution_mode=ModelExecutionMode.TEST,
checkpoint_path=full_ml_test_data_path(checkpoint_path))
model_loaded = model_and_info.try_create_model_and_load_from_checkpoint()
assert model_loaded
assert model_and_info.checkpoint_epoch == 1
optimizer_loaded = model_and_info.try_create_optimizer_and_load_from_checkpoint()
assert optimizer_loaded
assert isinstance(model_and_info.optimizer, Optimizer)
assert model_and_info.checkpoint_epoch == 1
@pytest.mark.parametrize("config, checkpoint_path",
[(ClassificationModelForTesting(),
"classification_data_generated_random/checkpoints/1_checkpoint.pth.tar")])
def test_try_create_mean_teacher_model_and_load_from_checkpoint(config: ModelConfigBase, checkpoint_path: str) -> None:
config.mean_teacher_alpha = 0.999
# no checkpoint path provided
model_and_info = ModelAndInfo(config,
model_execution_mode=ModelExecutionMode.TEST,
checkpoint_path=None)
with pytest.raises(ValueError):
model_and_info.mean_teacher_model
model_loaded = model_and_info.try_create_mean_teacher_model_and_load_from_checkpoint()
assert model_loaded
if isinstance(config, SegmentationModelBase):
assert isinstance(model_and_info.mean_teacher_model, BaseModel)
else:
assert isinstance(model_and_info.mean_teacher_model, DeviceAwareModule)
# Invalid checkpoint path provided
model_and_info = ModelAndInfo(config,
model_execution_mode=ModelExecutionMode.TEST,
checkpoint_path=full_ml_test_data_path("non_exist.pth.tar"))
model_loaded = model_and_info.try_create_mean_teacher_model_and_load_from_checkpoint()
assert not model_loaded
# Current code assumes that even if this function returns False, the model itself was created, only the checkpoint
# loading failed.
if isinstance(config, SegmentationModelBase):
assert isinstance(model_and_info.mean_teacher_model, BaseModel)
else:
assert isinstance(model_and_info.mean_teacher_model, DeviceAwareModule)
# Valid checkpoint path provided
model_and_info = ModelAndInfo(config,
model_execution_mode=ModelExecutionMode.TEST,
checkpoint_path=full_ml_test_data_path(checkpoint_path))
model_loaded = model_and_info.try_create_mean_teacher_model_and_load_from_checkpoint()
assert model_loaded
if isinstance(config, SegmentationModelBase):
assert isinstance(model_and_info.mean_teacher_model, BaseModel)
else:
assert isinstance(model_and_info.mean_teacher_model, DeviceAwareModule)
assert model_and_info.checkpoint_epoch == 1
@pytest.mark.parametrize("config",
[DummyModel(), ClassificationModelForTesting()])
def test_save_checkpoint(config: ModelConfigBase) -> None:
"""
Test that checkpoints are saved correctly
"""
config.mean_teacher_alpha = 0.999
model_and_info = ModelAndInfo(config,
model_execution_mode=ModelExecutionMode.TEST,
checkpoint_path=None)
model_and_info.try_create_model_and_load_from_checkpoint()
model_and_info.try_create_mean_teacher_model_and_load_from_checkpoint()
model_and_info.try_create_optimizer_and_load_from_checkpoint()
def get_constant_init_function(constant: float) -> Callable:
def init(layer: nn.Module) -> None:
if type(layer) == nn.Conv3d:
layer.weight.data.fill_(constant) # type: ignore
return init
assert model_and_info.mean_teacher_model is not None # for mypy
model_and_info.model.apply(get_constant_init_function(1.0))
model_and_info.mean_teacher_model.apply(get_constant_init_function(2.0))
epoch = 3
checkpoint_path = config.get_path_to_checkpoint(epoch=epoch)
checkpoint_dir = checkpoint_path.parent
if not os.path.isdir(checkpoint_dir):
os.makedirs(checkpoint_dir)
model_and_info.save_checkpoint(epoch=epoch)
model_and_info_restored = ModelAndInfo(config,
model_execution_mode=ModelExecutionMode.TEST,
checkpoint_path=config.get_path_to_checkpoint(epoch=epoch))
model_and_info_restored.try_create_model_load_from_checkpoint_and_adjust()
model_and_info_restored.try_create_mean_teacher_model_load_from_checkpoint_and_adjust()
assert model_and_info_restored.mean_teacher_model is not None # for mypy
for module in model_and_info_restored.model.modules():
if type(module) == nn.Conv3d:
assert torch.equal(module.weight.detach(), torch.full_like(module.weight.detach(), 1.0)) # type: ignore
for module in model_and_info_restored.mean_teacher_model.modules():
if type(module) == nn.Conv3d:
assert torch.equal(module.weight.detach(), torch.full_like(module.weight.detach(), 2.0)) # type: ignore
| 48.996139 | 119 | 0.706383 | 1,446 | 12,690 | 5.829876 | 0.108575 | 0.076868 | 0.096797 | 0.04484 | 0.827046 | 0.819098 | 0.795848 | 0.768446 | 0.747094 | 0.727284 | 0 | 0.003226 | 0.218282 | 12,690 | 258 | 120 | 49.186047 | 0.846573 | 0.114421 | 0 | 0.684492 | 0 | 0 | 0.054235 | 0.033238 | 0 | 0 | 0 | 0 | 0.224599 | 1 | 0.037433 | false | 0 | 0.090909 | 0 | 0.13369 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
946fc7e570a42dbf8aeefa867d02131449d3c0f5 | 229 | py | Python | baselines/metaLearners_745103/src/learner/pretrained_encoders/__init__.py | ebadrian/ss-metadl | e79f74731c15d431d240e9ec0692d8d7ef1cb23e | [
"Apache-2.0"
] | null | null | null | baselines/metaLearners_745103/src/learner/pretrained_encoders/__init__.py | ebadrian/ss-metadl | e79f74731c15d431d240e9ec0692d8d7ef1cb23e | [
"Apache-2.0"
] | null | null | null | baselines/metaLearners_745103/src/learner/pretrained_encoders/__init__.py | ebadrian/ss-metadl | e79f74731c15d431d240e9ec0692d8d7ef1cb23e | [
"Apache-2.0"
] | null | null | null | from .mobilenet_pretrained import mobilenet_v2
from .resnet_pretrained import resnet18, resnet34, resnet50, resnet101, resnet152, resnext50_32x4d, resnext101_32x8d
from .squeezenet_pretrained import squeezenet1_0, squeezenet1_1 | 76.333333 | 117 | 0.868996 | 27 | 229 | 7.074074 | 0.703704 | 0.251309 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.133971 | 0.087336 | 229 | 3 | 118 | 76.333333 | 0.779904 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
9480b0885b3844d40eaaaab324b46250a1441ab0 | 5,365 | py | Python | tests/happy/test_04_happy_link_module.py | mingchik/happy | 5d998f4aa01d375770fa57a23f819dcf9f434625 | [
"Apache-2.0"
] | null | null | null | tests/happy/test_04_happy_link_module.py | mingchik/happy | 5d998f4aa01d375770fa57a23f819dcf9f434625 | [
"Apache-2.0"
] | null | null | null | tests/happy/test_04_happy_link_module.py | mingchik/happy | 5d998f4aa01d375770fa57a23f819dcf9f434625 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
#
# Copyright (c) 2016-2017 Nest Labs, Inc.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
##
# @file
# Tests happy for creating, deleting, and listing nodes.
#
import pexpect
import os
import unittest
import happy.HappyLinkAdd
import happy.HappyLinkDelete
import happy.HappyLinkList
class test_happy_node_module(unittest.TestCase):
def setUp(self):
pass
def test_node(self):
options = happy.HappyLinkAdd.option()
options["type"] = "thread"
options["quiet"] = True
addLink = happy.HappyLinkAdd.HappyLinkAdd(options)
addLink.run()
options = happy.HappyLinkAdd.option()
options["type"] = "wifi"
options["quiet"] = True
addLink = happy.HappyLinkAdd.HappyLinkAdd(options)
addLink.run()
options = happy.HappyLinkAdd.option()
options["type"] = "wan"
options["quiet"] = True
addLink = happy.HappyLinkAdd.HappyLinkAdd(options)
addLink.run()
options = happy.HappyLinkAdd.option()
options["type"] = "cellular"
options["quiet"] = True
addLink = happy.HappyLinkAdd.HappyLinkAdd(options)
addLink.run()
options = happy.HappyLinkList.option()
options["quiet"] = True
listLink = happy.HappyLinkList.HappyLinkList(options)
listLink.run()
options = happy.HappyLinkDelete.option()
options["link_id"] = "thread0"
options["quiet"] = True
deleteLink = happy.HappyLinkDelete.HappyLinkDelete(options)
deleteLink.run()
options = happy.HappyLinkDelete.option()
options["link_id"] = "wifi0"
options["quiet"] = True
deleteLink = happy.HappyLinkDelete.HappyLinkDelete(options)
deleteLink.run()
options = happy.HappyLinkDelete.option()
options["link_id"] = "wan0"
options["quiet"] = True
deleteLink = happy.HappyLinkDelete.HappyLinkDelete(options)
deleteLink.run()
options = happy.HappyLinkDelete.option()
options["link_id"] = "cellular0"
options["quiet"] = True
deleteLink = happy.HappyLinkDelete.HappyLinkDelete(options)
deleteLink.run()
options = happy.HappyLinkList.option()
options["quiet"] = True
listLink = happy.HappyLinkList.HappyLinkList(options)
listLink.run()
# TAP
options = happy.HappyLinkAdd.option()
options["type"] = "thread"
options["tap"] = True
options["quiet"] = True
addLink = happy.HappyLinkAdd.HappyLinkAdd(options)
addLink.run()
options = happy.HappyLinkAdd.option()
options["type"] = "wifi"
options["tap"] = True
options["quiet"] = True
addLink = happy.HappyLinkAdd.HappyLinkAdd(options)
addLink.run()
options = happy.HappyLinkAdd.option()
options["type"] = "wan"
options["tap"] = True
options["quiet"] = True
addLink = happy.HappyLinkAdd.HappyLinkAdd(options)
addLink.run()
options = happy.HappyLinkAdd.option()
options["type"] = "cellular"
options["tap"] = True
options["quiet"] = True
addLink = happy.HappyLinkAdd.HappyLinkAdd(options)
addLink.run()
options = happy.HappyLinkList.option()
options["quiet"] = True
listLink = happy.HappyLinkList.HappyLinkList(options)
listLink.run()
options = happy.HappyLinkDelete.option()
options["link_id"] = "thread0"
options["quiet"] = True
deleteLink = happy.HappyLinkDelete.HappyLinkDelete(options)
deleteLink.run()
options = happy.HappyLinkDelete.option()
options["link_id"] = "wifi0"
options["quiet"] = True
deleteLink = happy.HappyLinkDelete.HappyLinkDelete(options)
deleteLink.run()
options = happy.HappyLinkDelete.option()
options["link_id"] = "wan0"
options["quiet"] = True
deleteLink = happy.HappyLinkDelete.HappyLinkDelete(options)
deleteLink.run()
options = happy.HappyLinkDelete.option()
options["link_id"] = "cellular0"
options["quiet"] = True
deleteLink = happy.HappyLinkDelete.HappyLinkDelete(options)
deleteLink.run()
options = happy.HappyLinkList.option()
options["quiet"] = True
listLink = happy.HappyLinkList.HappyLinkList(options)
listLink.run()
child = pexpect.spawn("happy-state")
child.expect(' Prefixes\r\n\r\nNODES Name Interface Type IPs\r\n\r\n')
child.close(force=True)
def tearDown(self):
os.system("happy-state-delete")
if __name__ == "__main__":
unittest.main()
| 31.558824 | 132 | 0.623672 | 525 | 5,365 | 6.335238 | 0.253333 | 0.072159 | 0.096212 | 0.072159 | 0.739627 | 0.739627 | 0.739627 | 0.739627 | 0.711365 | 0.711365 | 0 | 0.005058 | 0.263001 | 5,365 | 169 | 133 | 31.745562 | 0.836115 | 0.132339 | 0 | 0.854701 | 0 | 0.008547 | 0.094344 | 0.004534 | 0 | 0 | 0 | 0 | 0 | 1 | 0.025641 | false | 0.008547 | 0.051282 | 0 | 0.08547 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
94813b37c101f1fa236a8dc1c0ff19136b7f684f | 2,519 | py | Python | tests/test_upload_triggers.py | Robpol86/FlashAirMusic | acc5db1c4a1590623168eed05e48a8975f340d9f | [
"MIT"
] | null | null | null | tests/test_upload_triggers.py | Robpol86/FlashAirMusic | acc5db1c4a1590623168eed05e48a8975f340d9f | [
"MIT"
] | 5 | 2016-01-03T08:39:06.000Z | 2016-02-16T03:30:50.000Z | tests/test_upload_triggers.py | Robpol86/FlashAirMusic | acc5db1c4a1590623168eed05e48a8975f340d9f | [
"MIT"
] | null | null | null | """Test functions in module."""
import asyncio
import socket
from flash_air_music.upload import triggers
def test_skip(monkeypatch, caplog, shutdown_future):
"""Test with watch_for_flashair() disabled/skipped.
:param monkeypatch: pytest fixture.
:param caplog: pytest extension fixture.
:param shutdown_future: conftest fixture.
"""
loop = asyncio.get_event_loop()
shutdown_future.set_result(True)
monkeypatch.setattr(triggers, 'GLOBAL_MUTABLE_CONFIG', {'--ip-addr': None})
loop.run_until_complete(triggers.watch_for_flashair())
messages = [r.message for r in caplog.records if r.name.startswith('flash_air_music')]
assert '127.0.0.1 is reachable. calling run().' not in messages
assert 'watch_for_flashair() saw shutdown signal.' in messages
def test_fail(monkeypatch, caplog, shutdown_future):
"""Test socket error.
:param monkeypatch: pytest fixture.
:param caplog: pytest extension fixture.
:param shutdown_future: conftest fixture.
"""
def func(*_):
"""Raise exception."""
raise socket.error('Error')
loop = asyncio.get_event_loop()
shutdown_future.set_result(True)
monkeypatch.setattr(triggers, 'GLOBAL_MUTABLE_CONFIG', {'--ip-addr': '127.0.0.1'})
monkeypatch.setattr(triggers.socket, 'connect', func)
loop.run_until_complete(triggers.watch_for_flashair())
messages = [r.message for r in caplog.records if r.name.startswith('flash_air_music')]
assert '127.0.0.1 is reachable. calling run().' not in messages
assert 'watch_for_flashair() saw shutdown signal.' in messages
def test_success(monkeypatch, caplog, shutdown_future):
"""Test two loop iterations.
:param monkeypatch: pytest fixture.
:param caplog: pytest extension fixture.
:param shutdown_future: conftest fixture.
"""
loop = asyncio.get_event_loop()
tries = list(range(3))
monkeypatch.setattr(triggers, 'GLOBAL_MUTABLE_CONFIG', {'--ip-addr': '127.0.0.1'})
monkeypatch.setattr(triggers, 'run', asyncio.coroutine(lambda *_: tries.pop() or shutdown_future.set_result(True)))
monkeypatch.setattr(triggers, 'SUCCESS_SLEEP', 1)
monkeypatch.setattr(triggers.socket, 'connect', lambda *_: None)
loop.run_until_complete(triggers.watch_for_flashair())
messages = [r.message for r in caplog.records if r.name.startswith('flash_air_music')]
assert '127.0.0.1 is reachable. calling run().' in messages
assert 'watch_for_flashair() saw shutdown signal.' in messages
| 35.478873 | 119 | 0.718936 | 327 | 2,519 | 5.357798 | 0.247706 | 0.071918 | 0.063927 | 0.017123 | 0.839041 | 0.77911 | 0.748858 | 0.748858 | 0.718607 | 0.718607 | 0 | 0.01513 | 0.160381 | 2,519 | 70 | 120 | 35.985714 | 0.813239 | 0.196904 | 0 | 0.545455 | 0 | 0 | 0.218397 | 0.032374 | 0 | 0 | 0 | 0 | 0.181818 | 1 | 0.121212 | false | 0 | 0.090909 | 0 | 0.212121 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
846d0cda378744f39bb2dadd0befa385e0da701e | 37 | py | Python | flaskcbv/forms/__init__.py | procool/flaskcbv | 18c254c10ef03145073e1264a06a0313e811ad29 | [
"BSD-2-Clause"
] | 1 | 2020-02-24T13:08:16.000Z | 2020-02-24T13:08:16.000Z | flaskcbv/forms/__init__.py | procool/flaskcbv | 18c254c10ef03145073e1264a06a0313e811ad29 | [
"BSD-2-Clause"
] | null | null | null | flaskcbv/forms/__init__.py | procool/flaskcbv | 18c254c10ef03145073e1264a06a0313e811ad29 | [
"BSD-2-Clause"
] | null | null | null | from flaskcbv.forms.form import Form
| 18.5 | 36 | 0.837838 | 6 | 37 | 5.166667 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.108108 | 37 | 1 | 37 | 37 | 0.939394 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
848098d8aa19cc5785d1e2be4020cf99a755c8dc | 160 | py | Python | csv_cti/blueprints/web_api/__init__.py | Osmond1689/csv-cti | 84be8241e9ba50f495b23775eb153e4129845474 | [
"MIT"
] | null | null | null | csv_cti/blueprints/web_api/__init__.py | Osmond1689/csv-cti | 84be8241e9ba50f495b23775eb153e4129845474 | [
"MIT"
] | null | null | null | csv_cti/blueprints/web_api/__init__.py | Osmond1689/csv-cti | 84be8241e9ba50f495b23775eb153e4129845474 | [
"MIT"
] | null | null | null | from flask import Blueprint
web_api=Blueprint('web_api',__name__,template_folder='templates')
from .views import agents,call,dids,exts,queues,tiers,cdr,groups | 32 | 65 | 0.825 | 24 | 160 | 5.208333 | 0.791667 | 0.192 | 0.24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0625 | 160 | 5 | 66 | 32 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0.099379 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0.666667 | 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 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 6 |
84a63f71b77093b37b09a39ea1e6260935d29936 | 81 | py | Python | jacdac/verified_telemetry/__init__.py | microsoft/jacdac-python | 712ad5559e29065f5eccb5dbfe029c039132df5a | [
"MIT"
] | 1 | 2022-02-15T21:30:36.000Z | 2022-02-15T21:30:36.000Z | jacdac/verified_telemetry/__init__.py | microsoft/jacdac-python | 712ad5559e29065f5eccb5dbfe029c039132df5a | [
"MIT"
] | null | null | null | jacdac/verified_telemetry/__init__.py | microsoft/jacdac-python | 712ad5559e29065f5eccb5dbfe029c039132df5a | [
"MIT"
] | 1 | 2022-02-08T19:32:45.000Z | 2022-02-08T19:32:45.000Z | # Autogenerated file.
from .client import VerifiedTelemetryClient # type: ignore
| 27 | 58 | 0.814815 | 8 | 81 | 8.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.123457 | 81 | 2 | 59 | 40.5 | 0.929577 | 0.395062 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
84b8a01b58f99fa834de70fe520184a160d4d21d | 46 | py | Python | pepdna/utils/apps/httperfpy/httperfpy/__init__.py | kr1stj0n/pep-dna | 17136ca8c16238423bf1ca1ab4302d51d2fc8e57 | [
"RSA-MD",
"Linux-OpenIB",
"FTL",
"TCP-wrappers",
"CECILL-B"
] | 3 | 2021-11-12T13:01:51.000Z | 2022-02-19T09:06:23.000Z | pepdna/utils/apps/httperfpy/httperfpy/__init__.py | kr1stj0n/pep-dna | 17136ca8c16238423bf1ca1ab4302d51d2fc8e57 | [
"RSA-MD",
"Linux-OpenIB",
"FTL",
"TCP-wrappers",
"CECILL-B"
] | null | null | null | pepdna/utils/apps/httperfpy/httperfpy/__init__.py | kr1stj0n/pep-dna | 17136ca8c16238423bf1ca1ab4302d51d2fc8e57 | [
"RSA-MD",
"Linux-OpenIB",
"FTL",
"TCP-wrappers",
"CECILL-B"
] | null | null | null | from httperfpy import Httperf, HttperfParser
| 15.333333 | 44 | 0.847826 | 5 | 46 | 7.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.130435 | 46 | 2 | 45 | 23 | 0.975 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
84c3d1e48f1a6f59854f2423007d52e0fe16de4d | 82 | py | Python | aggan_pytorch/__init__.py | TheSignPainter/AGGAN | d75144f81df3f5a0a761d48c6285c38e74002be3 | [
"Apache-2.0"
] | null | null | null | aggan_pytorch/__init__.py | TheSignPainter/AGGAN | d75144f81df3f5a0a761d48c6285c38e74002be3 | [
"Apache-2.0"
] | null | null | null | aggan_pytorch/__init__.py | TheSignPainter/AGGAN | d75144f81df3f5a0a761d48c6285c38e74002be3 | [
"Apache-2.0"
] | null | null | null | from aggan_pytorch.aggan_pytorch import Trainer, AGGAN, NanException, ModelLoader
| 41 | 81 | 0.865854 | 10 | 82 | 6.9 | 0.7 | 0.347826 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.085366 | 82 | 1 | 82 | 82 | 0.92 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
ca6137ddc851237e68402de893ca8d1e2e1d90c4 | 30,306 | py | Python | manila/tests/api/v2/test_consistency_groups.py | nidhimittalhada/access_group_repo | 62f3365bc5fb728fcca692a9b3977690fabcd78f | [
"Apache-2.0"
] | 1 | 2019-05-06T10:33:38.000Z | 2019-05-06T10:33:38.000Z | manila/tests/api/v2/test_consistency_groups.py | nidhimittalhada/access_group_repo | 62f3365bc5fb728fcca692a9b3977690fabcd78f | [
"Apache-2.0"
] | 5 | 2015-08-13T15:17:28.000Z | 2016-08-02T02:55:01.000Z | manila/tests/api/v2/test_consistency_groups.py | nidhimittalhada/access_group_repo | 62f3365bc5fb728fcca692a9b3977690fabcd78f | [
"Apache-2.0"
] | 3 | 2019-05-03T12:32:47.000Z | 2021-01-30T20:26:19.000Z | # Copyright 2015 Alex Meade
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import copy
import datetime
import uuid
import ddt
import mock
from oslo_config import cfg
from oslo_serialization import jsonutils
import six
import webob
from manila.api.openstack import wsgi
import manila.api.v2.consistency_groups as cgs
from manila.common import constants
import manila.consistency_group.api as cg_api
from manila import context
from manila import db
from manila import exception
from manila import policy
from manila.share import share_types
from manila import test
from manila.tests.api import fakes
from manila.tests import db_utils
CONF = cfg.CONF
@ddt.ddt
class CGApiTest(test.TestCase):
"""Consistency Groups API Test suite."""
def setUp(self):
super(self.__class__, self).setUp()
self.controller = cgs.CGController()
self.resource_name = self.controller.resource_name
self.fake_share_type = {'id': six.text_type(uuid.uuid4())}
self.api_version = '2.4'
self.request = fakes.HTTPRequest.blank('/consistency-groups',
version=self.api_version,
experimental=True)
self.flags(rpc_backend='manila.openstack.common.rpc.impl_fake')
self.admin_context = context.RequestContext('admin', 'fake', True)
self.member_context = context.RequestContext('fake', 'fake')
self.mock_policy_check = self.mock_object(
policy, 'check_policy', mock.Mock(return_value=True))
self.context = self.request.environ['manila.context']
def _get_context(self, role):
return getattr(self, '%s_context' % role)
def _setup_cg_data(self, cg=None, version='2.7'):
if cg is None:
cg = db_utils.create_consistency_group(
status=constants.STATUS_AVAILABLE)
req = fakes.HTTPRequest.blank('/v2/fake/consistency-groups/%s/action' %
cg['id'], version=version)
req.headers[wsgi.API_VERSION_REQUEST_HEADER] = version
req.headers[wsgi.EXPERIMENTAL_API_REQUEST_HEADER] = 'True'
return cg, req
def _get_fake_cg(self, **values):
cg = {
'id': 'fake_id',
'user_id': 'fakeuser',
'project_id': 'fakeproject',
'status': constants.STATUS_CREATING,
'name': None,
'description': None,
'host': None,
'source_cgsnapshot_id': None,
'share_network_id': None,
'share_server_id': None,
'share_types': [],
'created_at': datetime.datetime(1, 1, 1, 1, 1, 1),
}
cg.update(**values)
expected_cg = copy.deepcopy(cg)
del expected_cg['user_id']
del expected_cg['share_server_id']
expected_cg['links'] = mock.ANY
expected_cg['share_types'] = [st['share_type_id']
for st in cg.get('share_types')]
return cg, expected_cg
def _get_fake_simple_cg(self, **values):
cg = {
'id': 'fake_id',
'name': None,
}
cg.update(**values)
expected_cg = copy.deepcopy(cg)
expected_cg['links'] = mock.ANY
return cg, expected_cg
def test_cg_create(self):
fake_cg, expected_cg = self._get_fake_cg()
self.mock_object(share_types, 'get_default_share_type',
mock.Mock(return_value=self.fake_share_type))
self.mock_object(self.controller.cg_api, 'create',
mock.Mock(return_value=fake_cg))
body = {"consistency_group": {}}
context = self.request.environ['manila.context']
res_dict = self.controller.create(self.request, body)
self.controller.cg_api.create.assert_called_once_with(
context, share_type_ids=[self.fake_share_type['id']])
self.assertEqual(expected_cg, res_dict['consistency_group'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_invalid_cgsnapshot_state(self):
fake_snap_id = six.text_type(uuid.uuid4())
self.mock_object(self.controller.cg_api, 'create',
mock.Mock(side_effect=exception.InvalidCGSnapshot(
reason='bad status'
)))
body = {"consistency_group": {"source_cgsnapshot_id": fake_snap_id}}
self.assertRaises(webob.exc.HTTPConflict,
self.controller.create, self.request, body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_no_default_share_type(self):
fake_cg, expected_cg = self._get_fake_cg()
self.mock_object(share_types, 'get_default_share_type',
mock.Mock(return_value=None))
self.mock_object(self.controller.cg_api, 'create',
mock.Mock(return_value=fake_cg))
body = {"consistency_group": {}}
self.assertRaises(webob.exc.HTTPBadRequest, self.controller.create,
self.request, body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_with_name(self):
fake_name = 'fake_name'
fake_cg, expected_cg = self._get_fake_cg(name=fake_name)
self.mock_object(share_types, 'get_default_share_type',
mock.Mock(return_value=self.fake_share_type))
self.mock_object(self.controller.cg_api, 'create',
mock.Mock(return_value=fake_cg))
body = {"consistency_group": {"name": fake_name}}
res_dict = self.controller.create(self.request, body)
self.controller.cg_api.create.assert_called_once_with(
self.context, name=fake_name,
share_type_ids=[self.fake_share_type['id']])
self.assertEqual(expected_cg, res_dict['consistency_group'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_with_description(self):
fake_description = 'fake_description'
fake_cg, expected_cg = self._get_fake_cg(description=fake_description)
self.mock_object(share_types, 'get_default_share_type',
mock.Mock(return_value=self.fake_share_type))
self.mock_object(self.controller.cg_api, 'create',
mock.Mock(return_value=fake_cg))
body = {"consistency_group": {"description": fake_description}}
res_dict = self.controller.create(self.request, body)
self.controller.cg_api.create.assert_called_once_with(
self.context, description=fake_description,
share_type_ids=[self.fake_share_type['id']])
self.assertEqual(expected_cg, res_dict['consistency_group'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_with_share_types(self):
fake_share_types = [{"share_type_id": self.fake_share_type['id']}]
fake_cg, expected_cg = self._get_fake_cg(share_types=fake_share_types)
self.mock_object(self.controller.cg_api, 'create',
mock.Mock(return_value=fake_cg))
body = {"consistency_group": {
"share_types": [self.fake_share_type['id']]}}
res_dict = self.controller.create(self.request, body)
self.controller.cg_api.create.assert_called_once_with(
self.context, share_type_ids=[self.fake_share_type['id']])
self.assertEqual(expected_cg, res_dict['consistency_group'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_with_source_cgsnapshot_id(self):
fake_snap_id = six.text_type(uuid.uuid4())
fake_cg, expected_cg = self._get_fake_cg(
source_cgsnapshot_id=fake_snap_id)
self.mock_object(share_types, 'get_default_share_type',
mock.Mock(return_value=self.fake_share_type))
self.mock_object(self.controller.cg_api, 'create',
mock.Mock(return_value=fake_cg))
body = {"consistency_group": {
"source_cgsnapshot_id": fake_snap_id}}
res_dict = self.controller.create(self.request, body)
self.controller.cg_api.create.assert_called_once_with(
self.context, source_cgsnapshot_id=fake_snap_id)
self.assertEqual(expected_cg, res_dict['consistency_group'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_with_share_network_id(self):
fake_net_id = six.text_type(uuid.uuid4())
fake_cg, expected_cg = self._get_fake_cg(
share_network_id=fake_net_id)
self.mock_object(share_types, 'get_default_share_type',
mock.Mock(return_value=self.fake_share_type))
self.mock_object(self.controller.cg_api, 'create',
mock.Mock(return_value=fake_cg))
body = {"consistency_group": {
"share_network_id": fake_net_id}}
res_dict = self.controller.create(self.request, body)
self.controller.cg_api.create.assert_called_once_with(
self.context, share_network_id=fake_net_id,
share_type_ids=mock.ANY)
self.assertEqual(expected_cg, res_dict['consistency_group'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_no_default_share_type_with_cgsnapshot(self):
fake_snap_id = six.text_type(uuid.uuid4())
fake_cg, expected_cg = self._get_fake_cg()
self.mock_object(share_types, 'get_default_share_type',
mock.Mock(return_value=None))
self.mock_object(self.controller.cg_api, 'create',
mock.Mock(return_value=fake_cg))
body = {"consistency_group": {
"source_cgsnapshot_id": fake_snap_id}}
res_dict = self.controller.create(self.request, body)
self.controller.cg_api.create.assert_called_once_with(
self.context, source_cgsnapshot_id=fake_snap_id)
self.assertEqual(expected_cg, res_dict['consistency_group'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_with_name_and_description(self):
fake_name = 'fake_name'
fake_description = 'fake_description'
fake_cg, expected_cg = self._get_fake_cg(name=fake_name,
description=fake_description)
self.mock_object(share_types, 'get_default_share_type',
mock.Mock(return_value=self.fake_share_type))
self.mock_object(self.controller.cg_api, 'create',
mock.Mock(return_value=fake_cg))
body = {"consistency_group": {"name": fake_name,
"description": fake_description}}
res_dict = self.controller.create(self.request, body)
self.controller.cg_api.create.assert_called_once_with(
self.context, name=fake_name, description=fake_description,
share_type_ids=[self.fake_share_type['id']])
self.assertEqual(expected_cg, res_dict['consistency_group'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_invalid_body(self):
body = {"not_consistency_group": {}}
self.assertRaises(webob.exc.HTTPBadRequest, self.controller.create,
self.request, body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_invalid_body_share_types_and_source_cgsnapshot(self):
body = {"consistency_group": {"share_types": [],
"source_cgsnapshot_id": ""}}
self.assertRaises(webob.exc.HTTPBadRequest, self.controller.create,
self.request, body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_source_cgsnapshot_not_in_available(self):
fake_snap_id = six.text_type(uuid.uuid4())
body = {"consistency_group": {"source_cgsnapshot_id": fake_snap_id}}
self.mock_object(self.controller.cg_api, 'create', mock.Mock(
side_effect=exception.InvalidCGSnapshot(reason='blah')))
self.assertRaises(webob.exc.HTTPConflict, self.controller.create,
self.request, body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_source_cgsnapshot_does_not_exist(self):
fake_snap_id = six.text_type(uuid.uuid4())
body = {"consistency_group": {"source_cgsnapshot_id": fake_snap_id}}
self.mock_object(self.controller.cg_api, 'create', mock.Mock(
side_effect=exception.CGSnapshotNotFound(
cgsnapshot_id=fake_snap_id)))
self.assertRaises(webob.exc.HTTPBadRequest, self.controller.create,
self.request, body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_source_cgsnapshot_not_a_uuid(self):
fake_snap_id = "Not a uuid"
body = {"consistency_group": {"source_cgsnapshot_id": fake_snap_id}}
self.assertRaises(webob.exc.HTTPBadRequest, self.controller.create,
self.request, body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_share_network_id_not_a_uuid(self):
fake_net_id = "Not a uuid"
body = {"consistency_group": {"share_network_id": fake_net_id}}
self.assertRaises(webob.exc.HTTPBadRequest, self.controller.create,
self.request, body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_invalid_body_share_types_not_a_list(self):
body = {"consistency_group": {"share_types": ""}}
self.assertRaises(webob.exc.HTTPBadRequest, self.controller.create,
self.request, body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_invalid_body_invalid_field(self):
body = {"consistency_group": {"unknown_field": ""}}
exc = self.assertRaises(webob.exc.HTTPBadRequest,
self.controller.create,
self.request, body)
self.assertTrue('unknown_field' in six.text_type(exc))
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_with_invalid_share_types_field(self):
body = {"consistency_group": {"share_types": 'iamastring'}}
self.assertRaises(webob.exc.HTTPBadRequest, self.controller.create,
self.request, body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_create_with_invalid_share_types_field_not_uuids(self):
body = {"consistency_group": {"share_types": ['iamastring']}}
self.assertRaises(webob.exc.HTTPBadRequest, self.controller.create,
self.request, body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'create')
def test_cg_update_with_name_and_description(self):
fake_name = 'fake_name'
fake_description = 'fake_description'
fake_cg, expected_cg = self._get_fake_cg(name=fake_name,
description=fake_description)
self.mock_object(self.controller.cg_api, 'get',
mock.Mock(return_value=fake_cg))
self.mock_object(self.controller.cg_api, 'update',
mock.Mock(return_value=fake_cg))
body = {"consistency_group": {"name": fake_name,
"description": fake_description}}
context = self.request.environ['manila.context']
res_dict = self.controller.update(self.request, fake_cg['id'], body)
self.controller.cg_api.update.assert_called_once_with(
context, fake_cg,
{"name": fake_name, "description": fake_description})
self.assertEqual(expected_cg, res_dict['consistency_group'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'update')
def test_cg_update_cg_not_found(self):
body = {"consistency_group": {}}
self.mock_object(self.controller.cg_api, 'get',
mock.Mock(side_effect=exception.NotFound))
self.assertRaises(webob.exc.HTTPNotFound,
self.controller.update,
self.request, 'fake_id', body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'update')
def test_cg_update_invalid_body(self):
body = {"not_consistency_group": {}}
self.assertRaises(webob.exc.HTTPBadRequest,
self.controller.update,
self.request, 'fake_id', body)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'update')
def test_cg_update_invalid_body_invalid_field(self):
body = {"consistency_group": {"unknown_field": ""}}
exc = self.assertRaises(webob.exc.HTTPBadRequest,
self.controller.update,
self.request, 'fake_id', body)
self.assertTrue('unknown_field' in six.text_type(exc))
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'update')
def test_cg_update_invalid_body_readonly_field(self):
body = {"consistency_group": {"share_types": []}}
exc = self.assertRaises(webob.exc.HTTPBadRequest,
self.controller.update,
self.request, 'fake_id', body)
self.assertTrue('share_types' in six.text_type(exc))
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'update')
def test_cg_list_index(self):
fake_cg, expected_cg = self._get_fake_simple_cg()
self.mock_object(cg_api.API, 'get_all',
mock.Mock(return_value=[fake_cg]))
res_dict = self.controller.index(self.request)
self.assertEqual([expected_cg], res_dict['consistency_groups'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'get_all')
def test_cg_list_index_no_cgs(self):
self.mock_object(cg_api.API, 'get_all',
mock.Mock(return_value=[]))
res_dict = self.controller.index(self.request)
self.assertEqual([], res_dict['consistency_groups'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'get_all')
def test_cg_list_index_with_limit(self):
fake_cg, expected_cg = self._get_fake_simple_cg()
fake_cg2, expected_cg2 = self._get_fake_simple_cg(id="fake_id2")
self.mock_object(cg_api.API, 'get_all',
mock.Mock(return_value=[fake_cg, fake_cg2]))
req = fakes.HTTPRequest.blank('/consistency_groups?limit=1',
version=self.api_version,
experimental=True)
req_context = req.environ['manila.context']
res_dict = self.controller.index(req)
self.assertEqual(1, len(res_dict['consistency_groups']))
self.assertEqual([expected_cg], res_dict['consistency_groups'])
self.mock_policy_check.assert_called_once_with(
req_context, self.resource_name, 'get_all')
def test_cg_list_index_with_limit_and_offset(self):
fake_cg, expected_cg = self._get_fake_simple_cg()
fake_cg2, expected_cg2 = self._get_fake_simple_cg(id="fake_id2")
self.mock_object(cg_api.API, 'get_all',
mock.Mock(return_value=[fake_cg, fake_cg2]))
req = fakes.HTTPRequest.blank('/consistency_groups?limit=1&offset=1',
version=self.api_version,
experimental=True)
req_context = req.environ['manila.context']
res_dict = self.controller.index(req)
self.assertEqual(1, len(res_dict['consistency_groups']))
self.assertEqual([expected_cg2], res_dict['consistency_groups'])
self.mock_policy_check.assert_called_once_with(
req_context, self.resource_name, 'get_all')
def test_cg_list_detail(self):
fake_cg, expected_cg = self._get_fake_cg()
self.mock_object(cg_api.API, 'get_all',
mock.Mock(return_value=[fake_cg]))
res_dict = self.controller.detail(self.request)
self.assertEqual([expected_cg], res_dict['consistency_groups'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'get_all')
def test_cg_list_detail_no_cgs(self):
self.mock_object(cg_api.API, 'get_all',
mock.Mock(return_value=[]))
res_dict = self.controller.detail(self.request)
self.assertEqual([], res_dict['consistency_groups'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'get_all')
def test_cg_list_detail_with_limit(self):
fake_cg, expected_cg = self._get_fake_cg()
fake_cg2, expected_cg2 = self._get_fake_cg(id="fake_id2")
self.mock_object(cg_api.API, 'get_all',
mock.Mock(return_value=[fake_cg, fake_cg2]))
req = fakes.HTTPRequest.blank('/consistency_groups?limit=1',
version=self.api_version,
experimental=True)
req_context = req.environ['manila.context']
res_dict = self.controller.detail(req)
self.assertEqual(1, len(res_dict['consistency_groups']))
self.assertEqual([expected_cg], res_dict['consistency_groups'])
self.mock_policy_check.assert_called_once_with(
req_context, self.resource_name, 'get_all')
def test_cg_list_detail_with_limit_and_offset(self):
fake_cg, expected_cg = self._get_fake_cg()
fake_cg2, expected_cg2 = self._get_fake_cg(id="fake_id2")
self.mock_object(cg_api.API, 'get_all',
mock.Mock(return_value=[fake_cg, fake_cg2]))
req = fakes.HTTPRequest.blank('/consistency_groups?limit=1&offset=1',
version=self.api_version,
experimental=True)
req_context = req.environ['manila.context']
res_dict = self.controller.detail(req)
self.assertEqual(1, len(res_dict['consistency_groups']))
self.assertEqual([expected_cg2], res_dict['consistency_groups'])
self.mock_policy_check.assert_called_once_with(
req_context, self.resource_name, 'get_all')
def test_cg_delete(self):
fake_cg, expected_cg = self._get_fake_cg()
self.mock_object(cg_api.API, 'get',
mock.Mock(return_value=fake_cg))
self.mock_object(cg_api.API, 'delete')
res = self.controller.delete(self.request, fake_cg['id'])
self.assertEqual(202, res.status_code)
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'delete')
def test_cg_delete_cg_not_found(self):
fake_cg, expected_cg = self._get_fake_cg()
self.mock_object(cg_api.API, 'get',
mock.Mock(side_effect=exception.NotFound))
self.assertRaises(webob.exc.HTTPNotFound, self.controller.delete,
self.request, fake_cg['id'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'delete')
def test_cg_delete_in_conflicting_status(self):
fake_cg, expected_cg = self._get_fake_cg()
self.mock_object(cg_api.API, 'get',
mock.Mock(return_value=fake_cg))
self.mock_object(cg_api.API, 'delete', mock.Mock(
side_effect=exception.InvalidConsistencyGroup(reason='blah')))
self.assertRaises(webob.exc.HTTPConflict, self.controller.delete,
self.request, fake_cg['id'])
self.mock_policy_check.assert_called_once_with(
self.context, self.resource_name, 'delete')
def test_cg_show(self):
fake_cg, expected_cg = self._get_fake_cg()
self.mock_object(cg_api.API, 'get',
mock.Mock(return_value=fake_cg))
req = fakes.HTTPRequest.blank(
'/consistency_groups/%s' % fake_cg['id'],
version=self.api_version, experimental=True)
req_context = req.environ['manila.context']
res_dict = self.controller.show(req, fake_cg['id'])
self.assertEqual(expected_cg, res_dict['consistency_group'])
self.mock_policy_check.assert_called_once_with(
req_context, self.resource_name, 'get')
def test_cg_show_as_admin(self):
fake_cg, expected_cg = self._get_fake_cg()
expected_cg['share_server_id'] = None
self.mock_object(cg_api.API, 'get',
mock.Mock(return_value=fake_cg))
req = fakes.HTTPRequest.blank(
'/consistency_groups/%s' % fake_cg['id'],
version=self.api_version, experimental=True)
admin_context = req.environ['manila.context'].elevated()
req.environ['manila.context'] = admin_context
res_dict = self.controller.show(req, fake_cg['id'])
self.assertEqual(expected_cg, res_dict['consistency_group'])
self.mock_policy_check.assert_called_once_with(
admin_context, self.resource_name, 'get')
def test_cg_show_cg_not_found(self):
fake_cg, expected_cg = self._get_fake_cg()
self.mock_object(cg_api.API, 'get',
mock.Mock(side_effect=exception.NotFound))
req = fakes.HTTPRequest.blank(
'/consistency_groups/%s' % fake_cg['id'],
version=self.api_version, experimental=True)
req_context = req.environ['manila.context']
self.assertRaises(webob.exc.HTTPNotFound, self.controller.show,
req, fake_cg['id'])
self.mock_policy_check.assert_called_once_with(
req_context, self.resource_name, 'get')
@ddt.data(*fakes.fixture_reset_status_with_different_roles)
@ddt.unpack
def test_consistency_groups_reset_status_with_different_roles(
self, role, valid_code, valid_status, version):
ctxt = self._get_context(role)
cg, req = self._setup_cg_data(version=version)
if float(version) > 2.6:
action_name = 'reset_status'
else:
action_name = 'os-reset_status'
body = {action_name: {'status': constants.STATUS_ERROR}}
req.method = 'POST'
req.headers['content-type'] = 'application/json'
req.body = six.b(jsonutils.dumps(body))
req.headers['X-Openstack-Manila-Api-Version'] = version
req.environ['manila.context'] = ctxt
with mock.patch.object(
policy, 'check_policy', fakes.mock_fake_admin_check):
resp = req.get_response(fakes.app())
# validate response code and model status
self.assertEqual(valid_code, resp.status_int)
if valid_code == 404:
self.assertRaises(exception.NotFound,
db.consistency_group_get,
ctxt,
cg['id'])
else:
actual_model = db.consistency_group_get(ctxt, cg['id'])
self.assertEqual(valid_status, actual_model['status'])
@ddt.data(*fakes.fixture_force_delete_with_different_roles)
@ddt.unpack
def test_consistency_group_force_delete_with_different_roles(self, role,
resp_code,
version):
ctxt = self._get_context(role)
cg, req = self._setup_cg_data(version=version)
req.method = 'POST'
req.headers['content-type'] = 'application/json'
if float(version) > 2.6:
action_name = 'force_delete'
else:
action_name = 'os-force_delete'
body = {action_name: {}}
req.body = six.b(jsonutils.dumps(body))
req.environ['manila.context'] = ctxt
with mock.patch.object(
policy, 'check_policy', fakes.mock_fake_admin_check):
resp = req.get_response(fakes.app())
# validate response
self.assertEqual(resp_code, resp.status_int)
| 44.177843 | 79 | 0.637366 | 3,662 | 30,306 | 4.925451 | 0.069361 | 0.035483 | 0.042579 | 0.053224 | 0.841714 | 0.812663 | 0.800965 | 0.783778 | 0.770028 | 0.742418 | 0 | 0.002897 | 0.259586 | 30,306 | 685 | 80 | 44.242336 | 0.800927 | 0.022768 | 0 | 0.661202 | 0 | 0 | 0.100051 | 0.017368 | 0 | 0 | 0 | 0 | 0.178506 | 1 | 0.083789 | false | 0 | 0.038251 | 0.001821 | 0.131148 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
ca8f02b023708bb8f90b91f71354aba543f42a81 | 8,978 | py | Python | tests/test20140716.py | yaukwankiu/armor | 6c57df82fe3e7761f43f9fbfe4f3b21882c91436 | [
"CC0-1.0"
] | 1 | 2015-11-06T06:41:33.000Z | 2015-11-06T06:41:33.000Z | tests/test20140716.py | yaukwankiu/armor | 6c57df82fe3e7761f43f9fbfe4f3b21882c91436 | [
"CC0-1.0"
] | null | null | null | tests/test20140716.py | yaukwankiu/armor | 6c57df82fe3e7761f43f9fbfe4f3b21882c91436 | [
"CC0-1.0"
] | null | null | null | # test20140716.py
# constructing the objects for the report
"""
COMPREF: march 20140311.0000
-1. COMPREF Intensity plot:Scale = 2
0. COMPREF Intensity plot:Scale = 4
1. COMPREF Max Intensity:Scale plot (Scale 1=0.05deg)
20140311.0000
2. COMPREF Max Intensity:Intensity plot (corresp. to scale)
3. COMPREF: Maximal Spectrum:(2-day average)
4. COMPREF: Total Spectrum:(2-day average)
WRF01-WRF20
5. WRF01- 20140311.0300 - plot
6. WRF01 intensity plot scale 2
7. WRF01 intensity plot scale 4
8. WRF01 Max Intensity Scale plot
9. WRF01 Max intensity intensity plot
10 WRF01-14 max spec (2-day average)
11 WRF01-14 total spec(2-day average)
12. COMPREF v WRF: Max spec
13. COMPREF v WRF: Total spec
WRF14
14. WRF14
15. WRF14 intensity plot scale2
16 ... scale 4
17. ... max intensity scale plot
18. ... max intensity intensity plot
19. ... max spec (2-day average)
20. ... total spec(2-day average)
21. COMPREF v WRF14 max spec
22. COMPREF v WRF14 total spec
"""
# imports
from armor.initialise import *
#from armor import objects4 as ob
#from armor import pattern
#from armor import defaultParameters as dp
#import time, os, shutil, re
#import numpy as np
#import matplotlib.pyplot as plt
from armor.graphics import specContour
#import pickle, os
# setups
inputFolderCOMPREF = 'C:/yau/1404716726.06COMPREF_Rainband_March_2014/'
inputFolderWRF= 'C:/yau/1404716726.08WRF_Rainband_March_2014/'
outputFolder = 'c:/yau/july2014report/'
#inputFolderCOMPREF = '/media/TOSHIBA EXT/ARMOR/labLogs2/july2014report/1404716726.06COMPREF_Rainband_March_2014/'
#inputFolderWRF= '/media/TOSHIBA EXT/ARMOR/labLogs2/july2014report/1404716726.08WRF_Rainband_March_2014/'
#outputFolder = dp.root + 'labLogs2/july2014report/'
WRFwindow = (200,200,600,560)
sigmas = [1, 2, 4, 5, 8, 10, 16, 20, 32, 40, 64, 80, 128]
plt.close()
##########################################################
# 1. COMPREF
"""
COMPREF: march 20140311.0000
-1. COMPREF Intensity plot:Scale = 2
0. COMPREF Intensity plot:Scale = 4
1. COMPREF Max Intensity:Scale plot (Scale 1=0.05deg)
20140311.0000
2. COMPREF Max Intensity:Intensity plot (corresp. to scale)
3. COMPREF: Maximal Spectrum:(2-day average)
4. COMPREF: Total Spectrum:(2-day average)
"""
m = ob.march2014('20140311.0000')[0]
m.load()
m.show(block=False)
m.saveImage(outputFolder+ '1.png')
m1 = m.getWindow(*WRFwindow)
m1.show(block=False)
m1.saveImage(outputFolder+'2.png')
m2 = m1.coarser().coarser()
m2.show(block=False)
m2.saveImage(outputFolder+'3.png')
x2 = m2.powerSpec(outputFolder=outputFolder, toPlotContours=True, toPlot3d=True,
vmin=-2.0, vmax=3.0)
resp = x2['responseImages']
resp.shape
for i in range(13):
plt.close()
plt.imshow(resp[:,:,i], origin='lower')
plt.colorbar()
plt.title('Laplacian-of-Gaussian filter; sigma=' + str(sigmas[i]))
plt.savefig(outputFolder+m.name+ "_sigma" + str(sigmas[i])+ ".png")
plt.show(block=False)
plt.close()
###
# 3. COMPREF: Maximal Spectrum(2-day average)
# 4. COMPREF: Total Spectrum(2-day average)
L = os.listdir(inputFolderCOMPREF)
Ltotal = [v for v in L if 'XYZ.pydump' in v]
Lmax = [v for v in L if 'XYZmax.pydump' in v]
LtotalCOMPREF = Ltotal
LmaxCOMPREF = Lmax
inputFolder = inputFolderCOMPREF
testName = 'Contour_Spec_COMPREF_Rainband_March_2014'
# ----> contourPlotTest.py lines 19-56
#XYZoutsCOMPREF = XYZouts
##################
"""
WRF01-WRF14
5. WRF01- 20140311.0300 - plot
6. WRF01 intensity plot scale 2
7. WRF01 intensity plot scale 4
8. WRF01 Max Intensity Scale plot
9. WRF01 Max intensity intensity plot
10 WRF01-14 max spec (2-day average)
11 WRF01-14 total spec(2-day average)
12. COMPREF v WRF: Max spec
13. COMPREF v WRF: Total spec
"""
w = marchwrf('20140311.0300')[0]
x2 = w.powerSpec(outputFolder=outputFolder, toPlotContours=True, toPlot3d=True, vmin=-2., vmax=3.)
resp = x2['responseImages']
resp.shape
for i in range(13):
plt.close()
plt.imshow(resp[:,:,i], origin='lower')
plt.colorbar()
plt.title('Laplacian-of-Gaussian filter; sigma=' + str(sigmas[i]))
plt.savefig(outputFolder+w.name+ "_sigma" + str(sigmas[i])+ ".png")
plt.show(block=False)
plt.close()
###
# 3. COMPREF: Maximal Spectrum(2-day average)
# 4. COMPREF: Total Spectrum(2-day average)
# imports
from armor.initialise import *
#from armor import pattern
#from armor import defaultParameters as dp
#import time, os, shutil, re
#import numpy as np
#import matplotlib.pyplot as plt
from armor.graphics import specContour
#import pickle, os
# setups
#inputFolderCOMPREF = 'C:/yau/1404716726.06COMPREF_Rainband_March_2014/'
#inputFolderWRF= 'C:/yau/1404716726.08WRF_Rainband_March_2014/'
inputFolderCOMPREF = '/media/TOSHIBA EXT/ARMOR/labLogs2/july2014report/1404716726.06COMPREF_Rainband_March_2014/'
inputFolderWRF = '/media/TOSHIBA EXT/ARMOR/labLogs2/july2014report/1404716726.08WRF_Rainband_March_2014/'
outputFolder = dp.root + 'labLogs2/july2014report/'
WRFwindow = (200,200,600,560)
sigmas = [1, 2, 4, 5, 8, 10, 16, 20, 32, 40, 64, 80, 128]
######
# setups
inputFolderCOMPREF = 'C:/yau/1404716726.06COMPREF_Rainband_March_2014/'
inputFolderWRF= 'C:/yau/1404716726.08WRF_Rainband_March_2014/'
outputFolder = 'c:/yau/july2014report/'
plt.close()
########
inputFolder = inputFolderWRF
testName = 'Contour_Spec_WRF_Rainband_March_2014'
L = os.listdir(inputFolder)
L = [v for v in L if 'WRF15' not in v]
L = [v for v in L if '0313.0300' not in v]
L = [v for v in L if '0313.0600' not in v]
L = [v for v in L if '0313.0900' not in v]
Ltotal = [v for v in L if 'XYZ.pydump' in v]
Lmax = [v for v in L if 'XYZmax.pydump' in v]
Ltotal[:20]
Lmax[:20]
LtotalWRF= Ltotal
LmaxWRF = Lmax
# ----> contourPlotTest.py lines 19-56
#XYZoutsRADAR = XYZouts
###########
# dual plots
# 2014-07-16
# single plots
def specContourWithXYZout(L, inputFolder=inputFolderWRF, label="", vmin="", vmax="", title=""):
if label=="":
label = str(time.time())
plt.close()
Z = np.zeros((13,8))
for frameCount, fileName in enumerate(L):
XYZ = pickle.load(open(inputFolder+fileName,'r'))
#X = XYZ['X']
#Y = XYZ['Y']
Z1 = XYZ['Z']
Z += Z1
XYZ['Z'] = Z/ (frameCount+1)
#vmins = (np.log10(XYZ["Z"])* (Z>0)).min()
#vmaxs = (np.log10(XYZ["Z"])* (Z>0)).max()
X = XYZ['X']
Y = XYZ['Y']
XYZout = specContour.specContour(XYZ, display=True, outputFolder=outputFolder,
#vmin=-1.0, vmax=3.6,
vmin=vmin, vmax=vmax,
fileName = testName+ label + "_average_of_" + str(frameCount+1) +'images.png',
title=title,
)
plt.close()
print testName, "number of frames", frameCount+1
return XYZout
XYZ1 = specContourWithXYZout(L=LmaxCOMPREF, inputFolder=inputFolderCOMPREF, label="COMPREF_max", vmin=-1, vmax=5,
title = "Mean Max Spectrum for WRF")
XYZ2 = specContourWithXYZout(L=LmaxWRF, label="WRF_max", vmin=-1, vmax=5, title="Mean Max Spectrum for COMPREF")
# dual plots
def crossContourWithXYZout(L, inputFolder, L2, inputFolder2, label="", vmin=-1, vmax=5, title=""):
if label=="":
label = str(time.time())
plt.close()
Z = np.zeros((13,8))
for frameCount, fileName in enumerate(L):
XYZ = pickle.load(open(inputFolder+fileName,'r'))
#X = XYZ['X']
#Y = XYZ['Y']
Z1 = XYZ['Z']
Z += Z1
XYZ['Z'] = Z/ (frameCount+1)
#vmins = (np.log10(XYZ["Z"])* (Z>0)).min()
#vmaxs = (np.log10(XYZ["Z"])* (Z>0)).max()
X = XYZ['X']
Y = XYZ['Y']
###
Z2 = np.zeros((13,8))
for frameCount, fileName in enumerate(L2):
XYZ = pickle.load(open(inputFolder2+fileName,'r'))
#X = XYZ['X']
#Y = XYZ['Y']
Z3 = XYZ['Z']
Z2 += Z3
XYZ2={'X':X,
'Y':Y,
'Z': Z2/ (frameCount+1),
}
XYZout = specContour.specContour(XYZ, XYZ2, display=True, outputFolder=outputFolder,
#vmin=-1.0, vmax=3.6,
vmin=vmin, vmax=vmax,
fileName = testName+ label + "_average_of_" + str(frameCount+1) +'images.png',
title=title,
)
plt.close()
print testName, "number of frames", frameCount+1
return XYZout
crossContourWithXYZout(LmaxWRF, inputFolderWRF, LmaxCOMPREF, inputFolderCOMPREF,title="Mean Max Spectra: COMPREF(red)-WRF", vmin=-1, vmax=5)
crossContourWithXYZout(LtotalWRF, inputFolderWRF, LtotalCOMPREF, inputFolderCOMPREF,title="Total Max Spectra: COMPREF(red)-WRF", vmin=-1, vmax=5)
| 29.926667 | 145 | 0.633549 | 1,200 | 8,978 | 4.698333 | 0.174167 | 0.032281 | 0.027315 | 0.02696 | 0.752749 | 0.735012 | 0.735012 | 0.733061 | 0.715679 | 0.672402 | 0 | 0.095469 | 0.22065 | 8,978 | 299 | 146 | 30.026756 | 0.710304 | 0.168857 | 0 | 0.566929 | 0 | 0 | 0.173737 | 0.090829 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.031496 | null | null | 0.015748 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
04a7737dea9012f68efb43a932a67ce314b515de | 8,723 | py | Python | tests/ti_deps/deps/trigger_rule_dep.py | rubeshdcube/incubator-airflow | 5419fbb78a2ea2388456c356d2f899ea1991b2de | [
"Apache-2.0"
] | 4 | 2017-06-25T14:09:31.000Z | 2020-11-20T09:51:24.000Z | tests/ti_deps/deps/trigger_rule_dep.py | rubeshdcube/incubator-airflow | 5419fbb78a2ea2388456c356d2f899ea1991b2de | [
"Apache-2.0"
] | 1 | 2017-07-04T07:31:15.000Z | 2017-07-06T06:01:42.000Z | tests/ti_deps/deps/trigger_rule_dep.py | rubeshdcube/incubator-airflow | 5419fbb78a2ea2388456c356d2f899ea1991b2de | [
"Apache-2.0"
] | 2 | 2018-04-09T15:13:50.000Z | 2019-06-14T07:19:46.000Z | # -*- coding: utf-8 -*-
#
# 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 unittest
from airflow.utils.trigger_rule import TriggerRule
from airflow.ti_deps.deps.trigger_rule_dep import TriggerRuleDep
from airflow.utils.state import State
from fake_models import FakeTask, FakeTI
class TriggerRuleDepTest(unittest.TestCase):
def test_no_upstream_tasks(self):
"""
If the TI has no upstream TIs then there is nothing to check and the dep is passed
"""
task = FakeTask(
trigger_rule=TriggerRule.ALL_DONE,
upstream_list=[])
ti = FakeTI(
task=task,
state=State.UP_FOR_RETRY)
self.assertTrue(TriggerRuleDep().is_met(ti=ti, dep_context=None))
def test_dummy_tr(self):
"""
The dummy trigger rule should always pass this dep
"""
task = FakeTask(
trigger_rule=TriggerRule.DUMMY,
upstream_list=[])
ti = FakeTI(
task=task,
state=State.UP_FOR_RETRY)
self.assertTrue(TriggerRuleDep().is_met(ti=ti, dep_context=None))
def test_one_success_tr_success(self):
"""
One-success trigger rule success
"""
task = FakeTask(
trigger_rule=TriggerRule.ONE_SUCCESS,
upstream_task_ids=[])
ti = FakeTI(task=task)
dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
ti=ti,
successes=1,
skipped=2,
failed=2,
upstream_failed=2,
done=2,
flag_upstream_failed=False,
session="Fake Session"))
self.assertEqual(len(dep_statuses), 0)
def test_one_success_tr_failure(self):
"""
One-success trigger rule failure
"""
task = FakeTask(
trigger_rule=TriggerRule.ONE_SUCCESS,
upstream_task_ids=[])
ti = FakeTI(task=task)
dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
ti=ti,
successes=0,
skipped=2,
failed=2,
upstream_failed=2,
done=2,
flag_upstream_failed=False,
session="Fake Session"))
self.assertEqual(len(dep_statuses), 1)
self.assertFalse(dep_statuses[0].passed)
def test_one_failure_tr_failure(self):
"""
One-failure trigger rule failure
"""
task = FakeTask(
trigger_rule=TriggerRule.ONE_FAILED,
upstream_task_ids=[])
ti = FakeTI(task=task)
dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
ti=ti,
successes=2,
skipped=0,
failed=0,
upstream_failed=0,
done=2,
flag_upstream_failed=False,
session="Fake Session"))
def test_one_failure_tr_success(self):
"""
One-failure trigger rule success
"""
task = FakeTask(
trigger_rule=TriggerRule.ONE_FAILED,
upstream_task_ids=[])
ti = FakeTI(task=task)
dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
ti=ti,
successes=0,
skipped=2,
failed=2,
upstream_failed=0,
done=2,
flag_upstream_failed=False,
session="Fake Session"))
self.assertEqual(len(dep_statuses), 0)
dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
ti=ti,
successes=0,
skipped=2,
failed=0,
upstream_failed=2,
done=2,
flag_upstream_failed=False,
session="Fake Session"))
self.assertEqual(len(dep_statuses), 0)
def test_all_success_tr_success(self):
"""
All-success trigger rule success
"""
task = FakeTask(
trigger_rule=TriggerRule.ALL_SUCCESS,
upstream_task_ids=["FakeTaskID"])
ti = FakeTI(task=task)
dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
ti=ti,
successes=1,
skipped=0,
failed=0,
upstream_failed=0,
done=1,
flag_upstream_failed=False,
session="Fake Session"))
self.assertEqual(len(dep_statuses), 0)
def test_all_success_tr_failure(self):
"""
All-success trigger rule failure
"""
task = FakeTask(
trigger_rule=TriggerRule.ALL_SUCCESS,
upstream_task_ids=["FakeTaskID", "OtherFakeTaskID"])
ti = FakeTI(task=task)
dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
ti=ti,
successes=1,
skipped=0,
failed=1,
upstream_failed=0,
done=2,
flag_upstream_failed=False,
session="Fake Session"))
self.assertEqual(len(dep_statuses), 1)
self.assertFalse(dep_statuses[0].passed)
def test_all_failed_tr_success(self):
"""
All-failed trigger rule success
"""
task = FakeTask(
trigger_rule=TriggerRule.ALL_FAILED,
upstream_task_ids=["FakeTaskID", "OtherFakeTaskID"])
ti = FakeTI(task=task)
dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
ti=ti,
successes=0,
skipped=0,
failed=2,
upstream_failed=0,
done=2,
flag_upstream_failed=False,
session="Fake Session"))
self.assertEqual(len(dep_statuses), 0)
def test_all_failed_tr_failure(self):
"""
All-failed trigger rule failure
"""
task = FakeTask(
trigger_rule=TriggerRule.ALL_FAILED,
upstream_task_ids=["FakeTaskID", "OtherFakeTaskID"])
ti = FakeTI(task=task)
dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
ti=ti,
successes=2,
skipped=0,
failed=0,
upstream_failed=0,
done=2,
flag_upstream_failed=False,
session="Fake Session"))
self.assertEqual(len(dep_statuses), 1)
self.assertFalse(dep_statuses[0].passed)
def test_all_done_tr_success(self):
"""
All-done trigger rule success
"""
task = FakeTask(
trigger_rule=TriggerRule.ALL_DONE,
upstream_task_ids=["FakeTaskID", "OtherFakeTaskID"])
ti = FakeTI(task=task)
dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
ti=ti,
successes=2,
skipped=0,
failed=0,
upstream_failed=0,
done=2,
flag_upstream_failed=False,
session="Fake Session"))
self.assertEqual(len(dep_statuses), 0)
def test_all_done_tr_failure(self):
"""
All-done trigger rule failure
"""
task = FakeTask(
trigger_rule=TriggerRule.ALL_DONE,
upstream_task_ids=["FakeTaskID", "OtherFakeTaskID"])
ti = FakeTI(task=task)
dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
ti=ti,
successes=1,
skipped=0,
failed=0,
upstream_failed=0,
done=1,
flag_upstream_failed=False,
session="Fake Session"))
self.assertEqual(len(dep_statuses), 1)
self.assertFalse(dep_statuses[0].passed)
def test_unknown_tr(self):
"""
Unknown trigger rules should cause this dep to fail
"""
task = FakeTask(
trigger_rule="Unknown Trigger Rule",
upstream_task_ids=[])
ti = FakeTI(task=task)
dep_statuses = tuple(TriggerRuleDep()._evaluate_trigger_rule(
ti=ti,
successes=1,
skipped=0,
failed=0,
upstream_failed=0,
done=1,
flag_upstream_failed=False,
session="Fake Session"))
self.assertEqual(len(dep_statuses), 1)
self.assertFalse(dep_statuses[0].passed)
| 29.469595 | 90 | 0.575261 | 936 | 8,723 | 5.138889 | 0.143162 | 0.089189 | 0.051351 | 0.062162 | 0.799792 | 0.753846 | 0.753846 | 0.753846 | 0.745322 | 0.702079 | 0 | 0.01391 | 0.332454 | 8,723 | 295 | 91 | 29.569492 | 0.812124 | 0.120142 | 0 | 0.885572 | 0 | 0 | 0.040608 | 0 | 0 | 0 | 0 | 0 | 0.089552 | 1 | 0.064677 | false | 0.024876 | 0.024876 | 0 | 0.094527 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
04b94805aaee0415d0e9c0d74ddb62ec91719945 | 68 | py | Python | geeksw/optimize/__init__.py | guitargeek/geeksw | d6e774824eae713e68cf23ae242933be9df52fcf | [
"MIT"
] | 2 | 2019-04-11T22:26:29.000Z | 2021-06-23T19:59:36.000Z | geeksw/optimize/__init__.py | guitargeek/geeksw | d6e774824eae713e68cf23ae242933be9df52fcf | [
"MIT"
] | null | null | null | geeksw/optimize/__init__.py | guitargeek/geeksw | d6e774824eae713e68cf23ae242933be9df52fcf | [
"MIT"
] | null | null | null | from .nnls import nnls
from .minuit_minimize import minuit_minimize
| 22.666667 | 44 | 0.852941 | 10 | 68 | 5.6 | 0.5 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.117647 | 68 | 2 | 45 | 34 | 0.933333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
04cea7e7297cfecbd6d3107e54bd0a2064f02bd8 | 1,138 | py | Python | opti_models/models/backbones/torchvision_models/mnasnet.py | IlyaDobrynin/opti_models | 342b87e782650e8803196c52eee2b0d21f1d9576 | [
"MIT"
] | 62 | 2020-09-07T10:30:09.000Z | 2021-12-21T21:27:54.000Z | opti_models/models/backbones/torchvision_models/mnasnet.py | IlyaDobrynin/opti_models | 342b87e782650e8803196c52eee2b0d21f1d9576 | [
"MIT"
] | null | null | null | opti_models/models/backbones/torchvision_models/mnasnet.py | IlyaDobrynin/opti_models | 342b87e782650e8803196c52eee2b0d21f1d9576 | [
"MIT"
] | 2 | 2020-11-06T14:31:30.000Z | 2021-09-26T12:16:10.000Z | from torchvision import models
__all__ = ['mnasnet0_5', 'mnasnet0_75', 'mnasnet1_0', 'mnasnet1_3']
def mnasnet0_5(pretrained: bool, progress: bool = True, requires_grad: bool = True):
model = models.mnasnet0_5(pretrained=pretrained, progress=progress)
for params in model.parameters():
params.requires_grad = requires_grad
return model
def mnasnet0_75(pretrained: bool, progress: bool = True, requires_grad: bool = True):
model = models.mnasnet0_75(pretrained=pretrained, progress=progress)
for params in model.parameters():
params.requires_grad = requires_grad
return model
def mnasnet1_0(pretrained: bool, progress: bool = True, requires_grad: bool = True):
model = models.mnasnet1_0(pretrained=pretrained, progress=progress)
for params in model.parameters():
params.requires_grad = requires_grad
return model
def mnasnet1_3(pretrained: bool, progress: bool = True, requires_grad: bool = True):
model = models.mnasnet1_3(pretrained=pretrained, progress=progress)
for params in model.parameters():
params.requires_grad = requires_grad
return model
| 35.5625 | 85 | 0.735501 | 141 | 1,138 | 5.737589 | 0.170213 | 0.177998 | 0.108776 | 0.128554 | 0.881335 | 0.881335 | 0.881335 | 0.881335 | 0.881335 | 0.881335 | 0 | 0.028632 | 0.171353 | 1,138 | 31 | 86 | 36.709677 | 0.829268 | 0 | 0 | 0.545455 | 0 | 0 | 0.036028 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.181818 | false | 0 | 0.045455 | 0 | 0.409091 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
04d1232e1e1c06ad5e17ede3a1bbe5ebf260b8d2 | 119 | py | Python | app/api/v2/black_list.py | jomasim/heroku-devops | baaa639f6288673cc902d8fe0df211a7eeac08e2 | [
"MIT"
] | null | null | null | app/api/v2/black_list.py | jomasim/heroku-devops | baaa639f6288673cc902d8fe0df211a7eeac08e2 | [
"MIT"
] | 14 | 2018-10-27T20:55:42.000Z | 2018-11-19T17:19:47.000Z | app/api/v2/black_list.py | jomasim/store-api-v2 | ee569a17911b0cfbf67b644bd454fd4b895af95f | [
"MIT"
] | null | null | null | blacklist=set()
def get_black_list():
return blacklist
def add_to_black_list(jti):
return blacklist.add(jti)
| 14.875 | 29 | 0.739496 | 18 | 119 | 4.611111 | 0.555556 | 0.216867 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.159664 | 119 | 7 | 30 | 17 | 0.83 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0 | 0 | 0.4 | 0.8 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
04d30635bb736fbc2d38babdb47e014c60165c27 | 40 | py | Python | wagtail/core/whitelist.py | stevedya/wagtail | 52e5abfe62547cdfd90ea7dfeb8bf5a52f16324c | [
"BSD-3-Clause"
] | 1 | 2022-02-09T05:25:30.000Z | 2022-02-09T05:25:30.000Z | wagtail/core/whitelist.py | stevedya/wagtail | 52e5abfe62547cdfd90ea7dfeb8bf5a52f16324c | [
"BSD-3-Clause"
] | null | null | null | wagtail/core/whitelist.py | stevedya/wagtail | 52e5abfe62547cdfd90ea7dfeb8bf5a52f16324c | [
"BSD-3-Clause"
] | null | null | null | from wagtail.whitelist import * # noqa
| 20 | 39 | 0.75 | 5 | 40 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.175 | 40 | 1 | 40 | 40 | 0.909091 | 0.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
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