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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e46b23e379d1cc66ba883b178518ba8867ac1710 | 29 | py | Python | Edabit/Buggy Code (Part 1)/Sol.py | Pandz18/C-Programs | 9d9b47516d3f65d348f9f72b9c0edda8246e9fab | [
"MIT"
] | null | null | null | Edabit/Buggy Code (Part 1)/Sol.py | Pandz18/C-Programs | 9d9b47516d3f65d348f9f72b9c0edda8246e9fab | [
"MIT"
] | null | null | null | Edabit/Buggy Code (Part 1)/Sol.py | Pandz18/C-Programs | 9d9b47516d3f65d348f9f72b9c0edda8246e9fab | [
"MIT"
] | null | null | null | def cubes(a):
return a ** 3
| 9.666667 | 14 | 0.586207 | 6 | 29 | 2.833333 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.045455 | 0.241379 | 29 | 2 | 15 | 14.5 | 0.727273 | 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 |
e4b38225d70e8d388165d5ba00fa5e9fe7999b7d | 228 | py | Python | tests/utils.py | RomainDuclos/sage-engine | 333997c658ea44e643bed636c5297e5e998ef97c | [
"MIT"
] | null | null | null | tests/utils.py | RomainDuclos/sage-engine | 333997c658ea44e643bed636c5297e5e998ef97c | [
"MIT"
] | null | null | null | tests/utils.py | RomainDuclos/sage-engine | 333997c658ea44e643bed636c5297e5e998ef97c | [
"MIT"
] | 3 | 2019-01-03T12:49:54.000Z | 2019-01-18T16:32:53.000Z | class DummyDataset:
def __init__(self, doc, name):
self._name = name
self._doc = doc
def get_graph(self, name):
return self._doc
def has_graph(self, name):
return self._name == name
| 20.727273 | 34 | 0.596491 | 30 | 228 | 4.2 | 0.366667 | 0.253968 | 0.190476 | 0.301587 | 0.365079 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.307018 | 228 | 10 | 35 | 22.8 | 0.797468 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.375 | false | 0 | 0 | 0.25 | 0.75 | 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 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
90366390bd29d2e0a37591f301da8e9d85d06440 | 42 | py | Python | gpsearch/examples/oscillator/__init__.py | Fluid-Dynamics-Group/gpsearch | 8c5758c9fb2b623ef79952c3e9c113cb157d79bc | [
"MIT"
] | 6 | 2020-07-13T00:02:17.000Z | 2022-03-11T08:49:27.000Z | gpsearch/examples/oscillator/__init__.py | Fluid-Dynamics-Group/gpsearch | 8c5758c9fb2b623ef79952c3e9c113cb157d79bc | [
"MIT"
] | null | null | null | gpsearch/examples/oscillator/__init__.py | Fluid-Dynamics-Group/gpsearch | 8c5758c9fb2b623ef79952c3e9c113cb157d79bc | [
"MIT"
] | 9 | 2020-07-18T13:29:46.000Z | 2022-03-22T15:14:14.000Z | from .oscillator import Noise, Oscillator
| 21 | 41 | 0.833333 | 5 | 42 | 7 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.119048 | 42 | 1 | 42 | 42 | 0.945946 | 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 |
5f5cc118cbd144e24b3ec71c7b88fdfb30df7e00 | 13,971 | py | Python | tests/test_session.py | ciprianmiclaus/clevertim-api-py | 42117044b34a83eaf0e2b05645a1bf42a8bbd440 | [
"BSD-3-Clause"
] | 1 | 2017-05-03T17:58:27.000Z | 2017-05-03T17:58:27.000Z | tests/test_session.py | ciprianmiclaus/clevertim-api-py | 42117044b34a83eaf0e2b05645a1bf42a8bbd440 | [
"BSD-3-Clause"
] | null | null | null | tests/test_session.py | ciprianmiclaus/clevertim-api-py | 42117044b34a83eaf0e2b05645a1bf42a8bbd440 | [
"BSD-3-Clause"
] | null | null | null | import json
from clevertimapi.session import Session, SessionError
try:
import unittest.mock as mock
except ImportError:
import mock
import sys
if sys.version_info[:2] < (2, 7):
import unittest2 as unittest
else:
import unittest
class FakeEndpoint(object):
def __init__(self, session, key=None, lazy_load=False):
pass
class TestSession(unittest.TestCase):
def setUp(self):
self.payload = {
'id': 3434,
'key1': 1,
'key2': '2',
'key3': [1, '2', [3]]
}
self.response = {
'status': 'OK',
'content': [
self.payload
]
}
def test_get_without_register_fails(self):
session = Session(api_key='APIKEY')
with self.assertRaises(KeyError):
session.get('FakeEndpoint', key=1, lazy_load=True)
def test_enpoint_accepted_types(self):
Session.register_endpoint(FakeEndpoint)
accepted_types = Session.enpoint_accepted_types('FakeEndpoint')
self.assertEqual(len(accepted_types), 1)
self.assertTrue(accepted_types[0] is FakeEndpoint)
Session.deregister_endpoint(FakeEndpoint)
def test_is_registered_endpoint(self):
self.assertFalse(Session.is_registered_endpoint(FakeEndpoint))
self.assertFalse(Session.is_registered_endpoint(SessionError))
self.assertFalse(Session.is_registered_endpoint('FakeEndpoint'))
self.assertFalse(Session.is_registered_endpoint('SessionError'))
Session.register_endpoint(SessionError)
Session.register_endpoint(FakeEndpoint)
self.assertTrue(Session.is_registered_endpoint(FakeEndpoint))
self.assertTrue(Session.is_registered_endpoint(SessionError))
self.assertTrue(Session.is_registered_endpoint('FakeEndpoint'))
self.assertTrue(Session.is_registered_endpoint('SessionError'))
Session.deregister_endpoint(SessionError)
Session.deregister_endpoint(FakeEndpoint)
self.assertFalse(Session.is_registered_endpoint(FakeEndpoint))
self.assertFalse(Session.is_registered_endpoint(SessionError))
self.assertFalse(Session.is_registered_endpoint('FakeEndpoint'))
self.assertFalse(Session.is_registered_endpoint('SessionError'))
self.assertFalse(Session.is_registered_endpoint(dict))
def test_get_after_deregister_fails(self):
session = Session(api_key='APIKEY')
Session.register_endpoint(FakeEndpoint)
Session.deregister_endpoint(FakeEndpoint)
with self.assertRaises(KeyError):
session.get('FakeEndpoint', key=1, lazy_load=True)
def test_register_get(self):
session = Session(api_key='APIKEY')
Session.register_endpoint(FakeEndpoint)
ret = session.get('FakeEndpoint', key=1, lazy_load=True)
self.assertIsInstance(ret, FakeEndpoint)
# second request hit the cache
ret2 = session.get('FakeEndpoint', key=1, lazy_load=True)
self.assertIsInstance(ret2, FakeEndpoint)
self.assertTrue(ret is ret2)
Session.deregister_endpoint(FakeEndpoint)
def test_invalid_method_raises(self):
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake')
with self.assertRaises(AssertionError):
session.make_request(endpoint='/endpoint', resource_id=3434, method='INVALID')
@mock.patch('requests.get')
def test_make_request_get(self, mockRequestsGET):
response = mock.Mock()
response.status_code = 200
response.text = json.dumps(self.response)
mockRequestsGET.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake')
ret = session.make_request(endpoint='/endpoint', resource_id=3434, method='GET')
self.assertEqual(ret, self.payload)
mockRequestsGET.assert_called_once_with('http://localhost:8000/fake/endpoint/3434', headers=mock.ANY)
@mock.patch('requests.get')
def test_make_request_get_invalid_http_code_raises(self, mockRequestsGET):
response = mock.Mock()
response.status_code = 500
response.text = json.dumps(self.response)
mockRequestsGET.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake')
with self.assertRaises(SessionError):
session.make_request(endpoint='/endpoint', resource_id=3434, method='GET')
@mock.patch('requests.post')
def test_make_request_post(self, mockRequestsPOST):
response = mock.Mock()
response.status_code = 200
response.text = json.dumps(self.response)
mockRequestsPOST.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake/')
ret = session.make_request(endpoint='endpoint', method='POST', payload=self.payload)
self.assertEqual(ret, self.payload)
mockRequestsPOST.assert_called_once_with('http://localhost:8000/fake/endpoint', headers=mock.ANY, data=json.dumps(self.payload, separators=(',', ':')))
@mock.patch('requests.post')
def test_make_request_post_invalid_http_code_raises(self, mockRequestsPOST):
response = mock.Mock()
response.status_code = 401
response.text = json.dumps(self.response)
mockRequestsPOST.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake/')
with self.assertRaises(SessionError):
session.make_request(endpoint='endpoint', method='POST', payload=self.payload)
@mock.patch('requests.put')
def test_make_request_put(self, mockRequestsPUT):
response = mock.Mock()
response.status_code = 200
response.text = json.dumps(self.response)
mockRequestsPUT.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake/')
ret = session.make_request(endpoint='endpoint', resource_id=3434, method='PUT', payload=self.payload)
self.assertEqual(ret, self.payload)
mockRequestsPUT.assert_called_once_with('http://localhost:8000/fake/endpoint/3434', headers=mock.ANY, data=json.dumps(self.payload, separators=(',', ':')))
@mock.patch('requests.put')
def test_make_request_put_invalid_http_code_raises(self, mockRequestsPUT):
response = mock.Mock()
response.status_code = 404
response.text = json.dumps(self.response)
mockRequestsPUT.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake/')
with self.assertRaises(SessionError):
session.make_request(endpoint='endpoint', resource_id=3434, method='PUT', payload=self.payload)
@mock.patch('requests.delete')
def test_make_request_delete(self, mockRequestsDELETE):
response = mock.Mock()
response.status_code = 200
response.text = json.dumps({'status': 'OK'})
mockRequestsDELETE.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake/')
ret = session.make_request(endpoint='endpoint', resource_id='3434', method='DELETE', payload=None)
self.assertEqual(ret, {'status': 'OK'})
mockRequestsDELETE.assert_called_once_with('http://localhost:8000/fake/endpoint/3434', headers=mock.ANY)
@mock.patch('requests.delete')
def test_make_request_delete_invalid_http_code_raises(self, mockRequestsDELETE):
response = mock.Mock()
response.status_code = 470
response.text = json.dumps({'status': 'OK'})
mockRequestsDELETE.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake/')
with self.assertRaises(SessionError):
session.make_request(endpoint='endpoint', resource_id='3434', method='DELETE', payload=None)
@mock.patch('requests.get')
def test_caching_enabled_2nd_get_hits_cache(self, mockRequestsGET):
response = mock.Mock()
response.status_code = 200
response.text = json.dumps(self.response)
mockRequestsGET.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake', enable_caching=True)
ret = session.make_request(endpoint='/endpoint', resource_id=3434, method='GET')
self.assertIsNotNone(ret)
# 2nd request should hit the cache
ret2 = session.make_request(endpoint='/endpoint', resource_id=3434, method='GET')
mockRequestsGET.assert_called_once_with('http://localhost:8000/fake/endpoint/3434', headers=mock.ANY)
self.assertTrue(ret is ret2)
@mock.patch('requests.get')
def test_caching_enabled_2nd_get_with_reload_hits_server(self, mockRequestsGET):
response = mock.Mock()
response.status_code = 200
response.text = json.dumps(self.response)
mockRequestsGET.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake', enable_caching=True)
ret = session.make_request(endpoint='/endpoint', resource_id=3434, method='GET')
self.assertIsNotNone(ret)
# 2nd request should hit the cache
ret2 = session.make_request(endpoint='/endpoint', resource_id=3434, method='GET', reload=True)
self.assertEqual(mockRequestsGET.call_count, 2)
self.assertTrue(ret is not ret2)
self.assertEqual(ret, ret2)
@mock.patch('requests.get')
def test_caching_disabled_2nd_get_hits_server(self, mockRequestsGET):
response = mock.Mock()
response.status_code = 200
response.text = json.dumps(self.response)
mockRequestsGET.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake', enable_caching=False)
ret = session.make_request(endpoint='/endpoint', resource_id=3434, method='GET')
self.assertIsNotNone(ret)
# 2nd request should hit the cache
ret2 = session.make_request(endpoint='/endpoint', resource_id=3434, method='GET')
self.assertEqual(mockRequestsGET.call_count, 2)
self.assertTrue(ret is not ret2)
self.assertEqual(ret, ret2)
@mock.patch('requests.post')
def test_cache_enabled_post_updates_the_cache(self, mockRequestsPOST):
response = mock.Mock()
response.status_code = 200
response.text = json.dumps(self.response)
mockRequestsPOST.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake/')
self.assertIsNone(session._get_cached_value(endpoint='endpoint', resource_id=3434))
ret = session.make_request(endpoint='endpoint', method='POST', payload=self.payload)
self.assertEqual(ret, self.payload)
self.assertEqual(session._get_cached_value(endpoint='endpoint', resource_id=3434), self.payload)
mockRequestsPOST.assert_called_once_with('http://localhost:8000/fake/endpoint', headers=mock.ANY, data=json.dumps(self.payload, separators=(',', ':')))
# now a get without reload, should return from the cache
ret = session.make_request(endpoint='endpoint', resource_id=self.payload['id'], method='GET')
self.assertEqual(ret, self.payload)
@mock.patch('requests.put')
def test_cache_enabled_put_updates_the_cache(self, mockRequestsPUT):
response = mock.Mock()
response.status_code = 200
response.text = json.dumps(self.response)
mockRequestsPUT.return_value = response
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake/')
self.assertIsNone(session._get_cached_value(endpoint='endpoint', resource_id=3434))
ret = session.make_request(endpoint='endpoint', resource_id=3434, method='PUT', payload=self.payload)
self.assertEqual(ret, self.payload)
self.assertEqual(session._get_cached_value(endpoint='endpoint', resource_id=3434), self.payload)
mockRequestsPUT.assert_called_once_with('http://localhost:8000/fake/endpoint/3434', headers=mock.ANY, data=json.dumps(self.payload, separators=(',', ':')))
# now a get without reload, should return from the cache
ret = session.make_request(endpoint='endpoint', resource_id=3434, method='GET')
self.assertEqual(ret, self.payload)
@mock.patch('requests.delete')
@mock.patch('requests.put')
def test_cache_enabled_delete_clears_the_cache(self, mockRequestsPUT, mockRequestsDELETE):
response = mock.Mock()
response.status_code = 200
response.text = json.dumps(self.response)
mockRequestsPUT.return_value = response
response2 = mock.Mock()
response2.status_code = 200
response2.text = json.dumps({'status': 'OK'})
mockRequestsDELETE.return_value = response2
session = Session(api_key='APIKEY', endpoint_url='http://localhost:8000/fake/')
self.assertIsNone(session._get_cached_value(endpoint='endpoint', resource_id=3434))
ret = session.make_request(endpoint='endpoint', resource_id=3434, method='PUT', payload=self.payload)
self.assertEqual(ret, self.payload)
self.assertEqual(session._get_cached_value(endpoint='endpoint', resource_id=3434), self.payload)
mockRequestsPUT.assert_called_once_with('http://localhost:8000/fake/endpoint/3434', headers=mock.ANY, data=json.dumps(self.payload, separators=(',', ':')))
# now a get without reload, should return from the cache
ret = session.make_request(endpoint='endpoint', resource_id=3434, method='GET')
self.assertEqual(ret, self.payload)
# now a delete should clear the cache
ret = session.make_request(endpoint='endpoint', resource_id=3434, method='DELETE')
self.assertIsNone(session._get_cached_value(endpoint='endpoint', resource_id=3434))
| 50.803636 | 163 | 0.698232 | 1,611 | 13,971 | 5.863439 | 0.085661 | 0.034935 | 0.06606 | 0.071565 | 0.894559 | 0.864916 | 0.851895 | 0.84766 | 0.806161 | 0.771226 | 0 | 0.02679 | 0.182449 | 13,971 | 274 | 164 | 50.989051 | 0.80021 | 0.023477 | 0 | 0.651261 | 0 | 0 | 0.113466 | 0 | 0 | 0 | 0 | 0 | 0.252101 | 1 | 0.092437 | false | 0.004202 | 0.033613 | 0 | 0.134454 | 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 |
f2cd72685c462c05e6adc4df8812a561196f07aa | 871 | py | Python | gym_learning_to_learn/__init__.py | bstriner/gym-learning-to-learn | 4cd93bf7a306255771a32e0d97b3d705b2666656 | [
"MIT"
] | 1 | 2021-06-14T15:37:32.000Z | 2021-06-14T15:37:32.000Z | gym_learning_to_learn/__init__.py | bstriner/gym-learning-to-learn | 4cd93bf7a306255771a32e0d97b3d705b2666656 | [
"MIT"
] | null | null | null | gym_learning_to_learn/__init__.py | bstriner/gym-learning-to-learn | 4cd93bf7a306255771a32e0d97b3d705b2666656 | [
"MIT"
] | 1 | 2017-01-27T05:49:59.000Z | 2017-01-27T05:49:59.000Z | from gym.envs.registration import register
register(
id='SGD-MNIST-Discrete-v0',
entry_point='gym_learning_to_learn.envs:MnistSgdDiscreteEnv',
tags={'wrapper_config.TimeLimit.max_episode_steps': 1000},
nondeterministic=True
)
register(
id='SGD-MNIST-Continuous-v0',
entry_point='gym_learning_to_learn.envs:MnistSgdContinuousEnv',
tags={'wrapper_config.TimeLimit.max_episode_steps': 1000},
nondeterministic=True
)
register(
id='SGD-Polynomial-Discrete-v0',
entry_point='gym_learning_to_learn.envs:PolynomialSgdDiscreteEnv',
tags={'wrapper_config.TimeLimit.max_episode_steps': 1000},
nondeterministic=True
)
register(
id='SGD-Polynomial-Continuous-v0',
entry_point='gym_learning_to_learn.envs:PolynomialSgdContinuousEnv',
tags={'wrapper_config.TimeLimit.max_episode_steps': 1000},
nondeterministic=True
)
| 29.033333 | 72 | 0.768083 | 102 | 871 | 6.284314 | 0.313725 | 0.062403 | 0.081123 | 0.093604 | 0.765991 | 0.765991 | 0.765991 | 0.765991 | 0.765991 | 0.49766 | 0 | 0.02594 | 0.114811 | 871 | 29 | 73 | 30.034483 | 0.805447 | 0 | 0 | 0.48 | 0 | 0 | 0.532721 | 0.532721 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.04 | 0 | 0.04 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
f2d83cbddbb23d3d71838104084ffec81ad07af3 | 2,866 | py | Python | pyedgeconnect/orch/_ospf.py | SPOpenSource/edgeconnect-python | 158aad220f8cacfa029df41b0ac2a37f7dac943f | [
"MIT"
] | 15 | 2021-07-02T17:09:13.000Z | 2022-02-08T17:06:51.000Z | pyedgeconnect/orch/_ospf.py | SPOpenSource/edgeconnect-python | 158aad220f8cacfa029df41b0ac2a37f7dac943f | [
"MIT"
] | null | null | null | pyedgeconnect/orch/_ospf.py | SPOpenSource/edgeconnect-python | 158aad220f8cacfa029df41b0ac2a37f7dac943f | [
"MIT"
] | 4 | 2021-07-16T00:05:24.000Z | 2022-03-26T02:04:17.000Z | # MIT License
# (C) Copyright 2021 Hewlett Packard Enterprise Development LP.
#
# ospf : apis for get config and state of ospf
def get_appliance_ospf_config(
self,
ne_id: str,
) -> dict:
"""Get appliance OSPF configuration
.. list-table::
:header-rows: 1
* - Swagger Section
- Method
- Endpoint
* - ospf
- GET
- /ospf/config/system/{neId}
:param ne_id: Appliance id in the format of integer.NE e.g. ``3.NE``
:type ne_id: str
:return: Returns dictionary of OSPF configuration info
:rtype: dict
"""
return self._get("/ospf/config/system/{}".format(ne_id))
def get_appliance_ospf_interfaces_config(
self,
ne_id: str,
) -> dict:
"""
Get appliance OSPF interfaces configuration
.. list-table::
:header-rows: 1
* - Swagger Section
- Method
- Endpoint
* - ospf
- GET
- /ospf/config/interfaces/{neId}
:param ne_id: Appliance id in the format of integer.NE e.g. ``3.NE``
:type ne_id: str
:return: Returns dictionary of OSPF interfaces configuration info
:rtype: dict
"""
return self._get("/ospf/config/interfaces/{}".format(ne_id))
def get_appliance_ospf_state(
self,
ne_id: str,
) -> dict:
"""Get appliance OSPF state
.. list-table::
:header-rows: 1
* - Swagger Section
- Method
- Endpoint
* - ospf
- GET
- /ospf/state/system/{neId}
:param ne_id: Appliance id in the format of integer.NE e.g. ``3.NE``
:type ne_id: str
:return: Returns dictionary of OSPF system state
:rtype: dict
"""
return self._get("/ospf/state/system/{}".format(ne_id))
def get_appliance_ospf_interfaces_state(
self,
ne_id: str,
) -> dict:
"""Get appliance OSPF interfaces state
.. list-table::
:header-rows: 1
* - Swagger Section
- Method
- Endpoint
* - ospf
- GET
- /ospf/state/interfaces/{neId}
:param ne_id: Appliance id in the format of integer.NE e.g. ``3.NE``
:type ne_id: str
:return: Returns dictionary of OSPF interfaces state
:rtype: dict
"""
return self._get("/ospf/state/interfaces/{}".format(ne_id))
def get_appliance_ospf_neighbors_state(
self,
ne_id: str,
) -> dict:
"""Get appliance OSPF neighbors state
.. list-table::
:header-rows: 1
* - Swagger Section
- Method
- Endpoint
* - ospf
- GET
- /ospf/state/neighbors/{neId}
:param ne_id: Appliance id in the format of integer.NE e.g. ``3.NE``
:type ne_id: str
:return: Returns dictionary of OSPF neighbors state
:rtype: dict
"""
return self._get("/ospf/state/interfaces/{}".format(ne_id))
| 22.746032 | 72 | 0.580949 | 356 | 2,866 | 4.55618 | 0.148876 | 0.049322 | 0.098644 | 0.05857 | 0.91492 | 0.903822 | 0.903822 | 0.903822 | 0.845869 | 0.605425 | 0 | 0.00699 | 0.301117 | 2,866 | 125 | 73 | 22.928 | 0.802796 | 0.643754 | 0 | 0.68 | 0 | 0 | 0.16482 | 0.16482 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0 | 0 | 0.4 | 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 |
8402abc42660336d281e4b5f3e7958afbbbb1a60 | 40 | py | Python | sandbox/jydontgiveitup.py | writecrow/crow_training | 17324ce93608acf997c2880b587dd9483729b895 | [
"MIT"
] | 7 | 2018-02-27T15:24:10.000Z | 2018-02-27T22:20:58.000Z | sandbox/jydontgiveitup.py | writecrow/crow_training | 17324ce93608acf997c2880b587dd9483729b895 | [
"MIT"
] | 11 | 2018-02-21T03:07:44.000Z | 2018-02-27T22:33:29.000Z | sandbox/jydontgiveitup.py | writecrow/crow_training | 17324ce93608acf997c2880b587dd9483729b895 | [
"MIT"
] | null | null | null | print("Hello, Mark! This is Ji-young!")
| 20 | 39 | 0.675 | 7 | 40 | 3.857143 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 40 | 1 | 40 | 40 | 0.771429 | 0 | 0 | 0 | 0 | 0 | 0.75 | 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 |
ffe05c249b21208f541207699fa863e5676ae7bc | 1,351 | py | Python | hrsalespipes/dashboard/migrations/0003_auto_20200403_1410.py | hanztura/hrsalespipes | 77accf3132726ced05d84fa2a41891b841f310b8 | [
"Apache-2.0"
] | 3 | 2020-03-26T12:43:43.000Z | 2021-05-10T14:35:51.000Z | hrsalespipes/dashboard/migrations/0003_auto_20200403_1410.py | hanztura/hrsalespipes | 77accf3132726ced05d84fa2a41891b841f310b8 | [
"Apache-2.0"
] | 5 | 2021-04-08T21:15:15.000Z | 2022-02-10T11:03:12.000Z | hrsalespipes/dashboard/migrations/0003_auto_20200403_1410.py | hanztura/hrsalespipes | 77accf3132726ced05d84fa2a41891b841f310b8 | [
"Apache-2.0"
] | 1 | 2022-01-30T19:24:48.000Z | 2022-01-30T19:24:48.000Z | # Generated by Django 2.2.10 on 2020-04-03 14:10
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('dashboard', '0002_auto_20200319_0743'),
]
operations = [
migrations.AddField(
model_name='dashboard',
name='consultant_leaderboard_dashboard_last_12_months_label',
field=models.CharField(blank=True, default='', max_length=100),
),
migrations.AddField(
model_name='dashboard',
name='consultant_leaderboard_dashboard_this_month_label',
field=models.CharField(blank=True, default='', max_length=100),
),
migrations.AddField(
model_name='dashboard',
name='sjpc_this_month_label',
field=models.CharField(blank=True, default='Successful job placements per consultant this month', max_length=100),
),
migrations.AddField(
model_name='dashboard',
name='sjpi_label',
field=models.CharField(blank=True, default='Successful job placements per industry', max_length=100),
),
migrations.AddField(
model_name='dashboard',
name='ytd_client_performance_label',
field=models.CharField(blank=True, default='', max_length=100),
),
]
| 34.641026 | 126 | 0.626203 | 139 | 1,351 | 5.863309 | 0.381295 | 0.110429 | 0.141104 | 0.165644 | 0.730061 | 0.730061 | 0.730061 | 0.730061 | 0.730061 | 0.457669 | 0 | 0.049395 | 0.265729 | 1,351 | 38 | 127 | 35.552632 | 0.772177 | 0.034049 | 0 | 0.5625 | 1 | 0 | 0.250959 | 0.133538 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.03125 | 0 | 0.125 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
082ad01aaaf1dc495dd13adf13beaf27bc35d4d8 | 199 | py | Python | visualizer/visualizer/__init__.py | zdobroff1/CSE505 | d9f74dddc24ce6570abfd87b5e5cfaeca71c4c0c | [
"MIT"
] | null | null | null | visualizer/visualizer/__init__.py | zdobroff1/CSE505 | d9f74dddc24ce6570abfd87b5e5cfaeca71c4c0c | [
"MIT"
] | null | null | null | visualizer/visualizer/__init__.py | zdobroff1/CSE505 | d9f74dddc24ce6570abfd87b5e5cfaeca71c4c0c | [
"MIT"
] | null | null | null | from configuration import *
from gui import *
from model import *
from modelView import *
from network import *
from parser import *
from visualizerItem import *
from visualizerGraphicItem import *
| 19.9 | 35 | 0.79397 | 24 | 199 | 6.583333 | 0.416667 | 0.443038 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.165829 | 199 | 9 | 36 | 22.111111 | 0.951807 | 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 |
0834c6910cabf4561c917807d8050f33291826dd | 5,993 | py | Python | src/evolvepy/generator/mutation/mutation.py | EltonCN/evolvepy | 4489264d6c03ea4f3c23ea665fdf12fe4ead1ccc | [
"MIT"
] | 1 | 2022-01-13T21:11:53.000Z | 2022-01-13T21:11:53.000Z | src/evolvepy/generator/mutation/mutation.py | EltonCN/evolvepy | 4489264d6c03ea4f3c23ea665fdf12fe4ead1ccc | [
"MIT"
] | null | null | null | src/evolvepy/generator/mutation/mutation.py | EltonCN/evolvepy | 4489264d6c03ea4f3c23ea665fdf12fe4ead1ccc | [
"MIT"
] | null | null | null | from typing import Tuple, Union, List, Callable, Optional
from numpy.typing import ArrayLike
import numpy as np
import numba
from evolvepy.generator.context import Context
from .numeric_mutation import sum_mutation
from .binary_mutation import bit_mutation
from evolvepy.generator import ChromosomeOperator
def default_mutation(type):
if (np.dtype(type).char in np.typecodes["AllFloat"] or
np.dtype(type).char in np.typecodes["AllInteger"]):
return sum_mutation
else:
return bit_mutation
class NumericMutationLayer(ChromosomeOperator):
'''
Layer destinated to apply the Numeric chromosome operations.
'''
def __init__(self, mutation_function:Callable, existence_rate:float, gene_rate:float, mutation_range:Tuple[float, float], name: str = None, chromosome_names: Union[str, List[str], None] = None):
'''
Generic caller to a mutation function passed as parameters.
Args:
mutation_function (class Callable): Define the function which will be used
existence_rate (float): Probability of first mutation
gene_rate (float): Probability of another gene mutation
name (string): Name for the layer
chromosome_names (Union[str, List[str], None]): Array of chromosomes names (optional)
'''
parameters = {"existence_rate":existence_rate, "gene_rate":gene_rate, "mutation_range_min":mutation_range[0], "mutation_range_max":mutation_range[1]}
dynamic_parameters = dict.fromkeys(list(parameters.keys()), True)
parameters["mutation_function_name"] = mutation_function.__name__
super().__init__(name=name, dynamic_parameters=dynamic_parameters, parameters=parameters, chromosome_names=chromosome_names)
self._mutation_function = mutation_function
def call_chromosomes(self, chromosomes: np.ndarray, fitness:np.ndarray, context:Context, name:Optional[str]) -> np.ndarray:
existence_rate = self.parameters["existence_rate"]
gene_rate = self.parameters["gene_rate"]
mutation_range = (self.parameters["mutation_range_min"], self.parameters["mutation_range_max"])
return NumericMutationLayer.mutate(chromosomes, self._mutation_function, existence_rate, gene_rate, mutation_range)
@staticmethod
@numba.njit()#parallel=True)
def mutate(chromosomes:np.ndarray, mutation_function:Callable, existence_rate:float, gene_rate:float, mutation_range:Tuple[float, float]):
'''
Generic caller to a mutation function passed as parameters.
Args:
chromosomes (np.ArrayLike): Array of chromosomes
existence_rate (float): Probability of first mutation
gene_rate (float): Probability of another gene mutation
mutation_function (class Callable): Define the function which will be used
Returns:
result (np.ArrayLike): return a new mutated population
'''
result = np.empty_like(chromosomes)
n = chromosomes.shape[0]
for i in numba.prange(n):
result[i] = mutation_function(chromosomes[i], existence_rate, gene_rate, mutation_range)
return result
class BinaryMutationLayer(ChromosomeOperator):
'''
Layer destinated to apply the Binary chromosome operations.
'''
def __init__(self, mutation_function:Callable, existence_rate:float, gene_rate:float, name: str = None, chromosome_names: Union[str, List[str], None] = None):
'''
Generic caller to a mutation function passed as parameters.
Args:
mutation_function (class Callable): Define the function which will be used
existence_rate (float): Probability of first mutation
gene_rate (float): Probability of another gene mutation
name (string): Name for the layer
chromosome_names (Union[str, List[str], None]): Array of chromosomes names (optional)
'''
parameters = {"existence_rate":existence_rate, "gene_rate":gene_rate}
dynamic_parameters = dict.fromkeys(list(parameters.keys()), True)
parameters["mutation_function_name"] = mutation_function.__name__
super().__init__(name=name, dynamic_parameters=dynamic_parameters, parameters=parameters, chromosome_names=chromosome_names)
self._mutation_function = mutation_function
def call_chromosomes(self, chromosomes: np.ndarray, fitness:np.ndarray, context:Context, name:Optional[str]) -> np.ndarray:
'''
Apply the mutation on the chromosomes
Args:
chromosomes (np.ArrayLike): Array of chromosomes
fitness (np.array): Probability of first mutation
context (class Context): Probability of another gene mutation
name (string): Define the function which will be
Returns:
BinaryMutationLayer.mutate: mutation function
'''
existence_rate = self.parameters["existence_rate"]
gene_rate = self.parameters["gene_rate"]
return BinaryMutationLayer.mutate(chromosomes, self._mutation_function, existence_rate, gene_rate)
@staticmethod
@numba.njit()
def mutate(chromosomes:np.ndarray, mutation_function:Callable, existence_rate:float, gene_rate:float):
'''
Generic caller to a mutation function passed as parameters.
Args:
chromosomes (np.ArrayLike): array of chromosomes
existence_rate (float): probability of first mutation
gene_rate (float): probability of another gene mutation
mutation_function (class Callable): Define the function which will be used
Returns:
result (np.ArrayLike): return a new mutated population
'''
result = np.empty_like(chromosomes)
n = chromosomes.shape[0]
for i in numba.prange(n):
result[i] = mutation_function(chromosomes[i], existence_rate, gene_rate)
return result
| 42.807143 | 198 | 0.693476 | 684 | 5,993 | 5.885965 | 0.153509 | 0.099354 | 0.029806 | 0.043716 | 0.803527 | 0.800298 | 0.765524 | 0.730253 | 0.730253 | 0.701441 | 0 | 0.000858 | 0.222426 | 5,993 | 139 | 199 | 43.115108 | 0.86309 | 0.338228 | 0 | 0.490566 | 0 | 0 | 0.063698 | 0.012401 | 0 | 0 | 0 | 0 | 0 | 1 | 0.132075 | false | 0 | 0.150943 | 0 | 0.433962 | 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 |
f242d764fce23e7782a81b471140b8697191ffb7 | 141 | py | Python | tests/test_extract.py | kwatsen/xiax | 4cad5c36a87968bc00d18756c3f707020b204845 | [
"0BSD"
] | null | null | null | tests/test_extract.py | kwatsen/xiax | 4cad5c36a87968bc00d18756c3f707020b204845 | [
"0BSD"
] | null | null | null | tests/test_extract.py | kwatsen/xiax | 4cad5c36a87968bc00d18756c3f707020b204845 | [
"0BSD"
] | null | null | null | import xiax
# Extraction tests
## Positive tests
def test_pos1():
assert 1 == 1
## Negative tests
#def test_neg1():
# assert 1 == 2
| 9.4 | 18 | 0.64539 | 20 | 141 | 4.45 | 0.65 | 0.179775 | 0.269663 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.055556 | 0.234043 | 141 | 14 | 19 | 10.071429 | 0.768519 | 0.546099 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.333333 | 1 | 0.333333 | true | 0 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 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 |
f27ba049708819daac9cc4804b09a91398fd121a | 334 | py | Python | reinforcement_learning/rl_deepracer_robomaker_coach_gazebo/src/markov/tests/test_constant.py | jpmarques19/tensorflwo-test | 0ff8b06e0415075c7269820d080284a42595bb2e | [
"Apache-2.0"
] | 5 | 2019-01-19T23:53:35.000Z | 2022-01-29T14:04:31.000Z | reinforcement_learning/rl_deepracer_robomaker_coach_gazebo/src/markov/tests/test_constant.py | jpmarques19/tensorflwo-test | 0ff8b06e0415075c7269820d080284a42595bb2e | [
"Apache-2.0"
] | 4 | 2020-09-26T01:30:01.000Z | 2022-02-10T02:20:35.000Z | reinforcement_learning/rl_deepracer_robomaker_coach_gazebo/src/markov/tests/test_constant.py | jpmarques19/tensorflwo-test | 0ff8b06e0415075c7269820d080284a42595bb2e | [
"Apache-2.0"
] | 7 | 2020-03-04T22:23:51.000Z | 2021-07-13T14:05:46.000Z | AWS_REGION = "us-east-1"
MODEL_METADATA_S3_KEY = "s3://simapp-testcases-687392285187/simapp-testcases-prefix/model/model_metadata.json"
REWARD_FUNCTION_S3_SOURCE = "s3://simapp-testcases-687392285187/simapp-testcases-prefix/customer_reward_function.py"
S3_BUCKET = "simapp-testcases-687392285187"
S3_PREFIX = "simapp-testcases-prefix" | 66.8 | 116 | 0.832335 | 45 | 334 | 5.911111 | 0.444444 | 0.338346 | 0.304511 | 0.218045 | 0.37594 | 0.37594 | 0.37594 | 0 | 0 | 0 | 0 | 0.134375 | 0.041916 | 334 | 5 | 117 | 66.8 | 0.696875 | 0 | 0 | 0 | 0 | 0 | 0.689552 | 0.662687 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 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 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
f28a7a0301712a0365d0ae0ec7bbe444ab2ad874 | 169 | py | Python | src/clickgen/packer/__init__.py | KaizIqbal/clickgen | cab0d0c005c7714cb0271809745a2dae321aa7eb | [
"MIT"
] | 2 | 2020-06-06T03:34:29.000Z | 2020-07-29T06:47:23.000Z | src/clickgen/packer/__init__.py | KaizIqbal/clickgen | cab0d0c005c7714cb0271809745a2dae321aa7eb | [
"MIT"
] | null | null | null | src/clickgen/packer/__init__.py | KaizIqbal/clickgen | cab0d0c005c7714cb0271809745a2dae321aa7eb | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from clickgen.packer.windows import pack_win
from clickgen.packer.x11 import pack_x11
__all__ = ["pack_win", "pack_x11"]
| 21.125 | 44 | 0.721893 | 26 | 169 | 4.384615 | 0.615385 | 0.210526 | 0.315789 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.047297 | 0.12426 | 169 | 7 | 45 | 24.142857 | 0.722973 | 0.248521 | 0 | 0 | 0 | 0 | 0.128 | 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 |
4b4d02bb51566f31154bc806c44f465638420a45 | 5,947 | py | Python | src/ggrc/migrations/versions/20140103201245_b58e88da095_remove_risk_models.py | Smotko/ggrc-core | b3abb58b24e7559960d71a94ba79c75539e7fe29 | [
"Apache-2.0"
] | null | null | null | src/ggrc/migrations/versions/20140103201245_b58e88da095_remove_risk_models.py | Smotko/ggrc-core | b3abb58b24e7559960d71a94ba79c75539e7fe29 | [
"Apache-2.0"
] | 12 | 2015-01-08T14:50:19.000Z | 2017-11-29T19:37:53.000Z | src/ggrc/migrations/versions/20140103201245_b58e88da095_remove_risk_models.py | mikecb/ggrc-core | 1cda560cb0920021416e07740c6cca1acba56268 | [
"ECL-2.0",
"Apache-2.0"
] | 1 | 2015-01-08T13:25:09.000Z | 2015-01-08T13:25:09.000Z | # Copyright (C) 2015 Google Inc., authors, and contributors <see AUTHORS file>
# Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file>
# Created By: anze@reciprocitylabs.com
# Maintained By: anze@reciprocitylabs.com
"""Remove Risk models
Revision ID: b58e88da095
Revises: 4db2d8962a62
Create Date: 2014-01-03 20:12:45.253372
"""
# revision identifiers, used by Alembic.
revision = 'b58e88da095'
down_revision = '4db2d8962a62'
from alembic import op
import sqlalchemy as sa
def upgrade():
op.drop_table(u'risk_risky_attributes')
op.drop_table(u'control_risks')
op.drop_table(u'risks')
op.drop_table(u'risky_attributes')
def downgrade():
op.create_table(u'risky_attributes',
sa.Column(u'id', sa.Integer(), nullable=False),
sa.Column(u'modified_by_id', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'created_at', sa.DateTime(), nullable=True),
sa.Column(u'updated_at', sa.DateTime(), nullable=True),
sa.Column(u'description', sa.Text(), nullable=True),
sa.Column(u'url', sa.String(length=250), nullable=True),
sa.Column(u'start_date', sa.DATE(), nullable=True),
sa.Column(u'end_date', sa.DATE(), nullable=True),
sa.Column(u'slug', sa.String(length=250), nullable=False),
sa.Column(u'title', sa.String(length=250), nullable=False),
sa.Column(u'type_string', sa.String(length=250), nullable=True),
sa.Column(u'context_id', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'contact_id', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'notes', sa.Text(), nullable=True),
sa.Column(u'status', sa.String(length=250), nullable=True),
sa.Column(u'reference_url', sa.String(length=250), nullable=True),
sa.ForeignKeyConstraint(['context_id'], [u'contexts.id'], name=u'fk_risky_attributes_contexts'),
sa.PrimaryKeyConstraint(u'id'),
)
op.create_unique_constraint('uq_risky_attributes', 'risky_attributes', ['slug',])
op.create_table(u'risks',
sa.Column(u'id', sa.Integer(), nullable=False),
sa.Column(u'modified_by_id', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'created_at', sa.DateTime(), nullable=True),
sa.Column(u'updated_at', sa.DateTime(), nullable=True),
sa.Column(u'description', sa.Text(), nullable=True),
sa.Column(u'url', sa.String(length=250), nullable=True),
sa.Column(u'start_date', sa.DATE(), nullable=True),
sa.Column(u'end_date', sa.DATE(), nullable=True),
sa.Column(u'slug', sa.String(length=250), nullable=False),
sa.Column(u'title', sa.String(length=250), nullable=False),
sa.Column(u'kind', sa.String(length=250), nullable=True),
sa.Column(u'likelihood', sa.Text(), nullable=True),
sa.Column(u'threat_vector', sa.Text(), nullable=True),
sa.Column(u'trigger', sa.Text(), nullable=True),
sa.Column(u'preconditions', sa.Text(), nullable=True),
sa.Column(u'likelihood_rating', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'financial_impact_rating', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'reputational_impact_rating', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'operational_impact_rating', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'inherent_risk', sa.Text(), nullable=True),
sa.Column(u'risk_mitigation', sa.Text(), nullable=True),
sa.Column(u'residual_risk', sa.Text(), nullable=True),
sa.Column(u'impact', sa.Text(), nullable=True),
sa.Column(u'context_id', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'contact_id', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'notes', sa.Text(), nullable=True),
sa.Column(u'status', sa.String(length=250), nullable=True),
sa.Column(u'reference_url', sa.String(length=250), nullable=True),
sa.ForeignKeyConstraint(['context_id'], [u'contexts.id'], name=u'fk_risks_contexts'),
sa.PrimaryKeyConstraint(u'id'),
)
op.create_unique_constraint('uq_risks', 'risks', ['slug',])
op.create_table(u'control_risks',
sa.Column(u'id', sa.Integer(), nullable=False),
sa.Column(u'modified_by_id', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'created_at', sa.DateTime(), nullable=True),
sa.Column(u'updated_at', sa.DateTime(), nullable=True),
sa.Column(u'control_id', sa.Integer(), autoincrement=False, nullable=False),
sa.Column(u'risk_id', sa.Integer(), autoincrement=False, nullable=False),
sa.Column(u'context_id', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'status', sa.String(length=250), nullable=True),
sa.ForeignKeyConstraint(['context_id'], [u'contexts.id'], name=u'fk_control_risks_contexts'),
sa.ForeignKeyConstraint(['control_id'], [u'controls.id'], name=u'control_risks_ibfk_1'),
sa.ForeignKeyConstraint(['risk_id'], [u'risks.id'], name=u'control_risks_ibfk_2'),
sa.PrimaryKeyConstraint(u'id'),
)
op.create_table(u'risk_risky_attributes',
sa.Column(u'id', sa.Integer(), nullable=False),
sa.Column(u'modified_by_id', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'created_at', sa.DateTime(), nullable=True),
sa.Column(u'updated_at', sa.DateTime(), nullable=True),
sa.Column(u'risk_id', sa.Integer(), autoincrement=False, nullable=False),
sa.Column(u'risky_attribute_id', sa.Integer(), autoincrement=False, nullable=False),
sa.Column(u'context_id', sa.Integer(), autoincrement=False, nullable=True),
sa.Column(u'status', sa.String(length=250), nullable=True),
sa.ForeignKeyConstraint(['context_id'], [u'contexts.id'], name=u'fk_risk_risky_attributes_contexts'),
sa.ForeignKeyConstraint(['risk_id'], [u'risks.id'], name=u'risk_risky_attributes_ibfk_1'),
sa.ForeignKeyConstraint(['risky_attribute_id'], [u'risky_attributes.id'], name=u'risk_risky_attributes_ibfk_2'),
sa.PrimaryKeyConstraint(u'id'),
)
| 51.713043 | 116 | 0.708929 | 852 | 5,947 | 4.823944 | 0.134977 | 0.116788 | 0.131387 | 0.214112 | 0.805109 | 0.779562 | 0.749635 | 0.702433 | 0.687348 | 0.654015 | 0 | 0.019307 | 0.111653 | 5,947 | 114 | 117 | 52.166667 | 0.75866 | 0.063393 | 0 | 0.521739 | 0 | 0 | 0.207269 | 0.04642 | 0 | 0 | 0 | 0 | 0 | 1 | 0.021739 | false | 0 | 0.021739 | 0 | 0.043478 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
4b67a11cee21edd06d7651732c1b612bce3b52d0 | 5,482 | py | Python | pensa/clusters/wss.py | NeilJ-Thomson/pensa | f2cc586ad8c4b60177051fc9a5d2da087ac1b6fb | [
"MIT"
] | 55 | 2020-11-18T07:03:46.000Z | 2022-03-29T02:47:10.000Z | pensa/clusters/wss.py | NeilJ-Thomson/pensa | f2cc586ad8c4b60177051fc9a5d2da087ac1b6fb | [
"MIT"
] | 11 | 2020-11-18T16:43:43.000Z | 2022-02-22T20:02:22.000Z | pensa/clusters/wss.py | NeilJ-Thomson/pensa | f2cc586ad8c4b60177051fc9a5d2da087ac1b6fb | [
"MIT"
] | 11 | 2020-11-19T04:34:36.000Z | 2022-03-01T23:48:57.000Z | import numpy as np
import scipy as sp
import scipy.stats
import mdshare
import pyemma
from pyemma.util.contexts import settings
import MDAnalysis as mda
import matplotlib.pyplot as plt
from pensa.clusters import obtain_clusters, obtain_combined_clusters
def wss_over_number_of_clusters(data, algorithm='kmeans',
max_iter=100, num_repeats = 5, max_num_clusters = 12,
plot_file = None):
"""
Calculates the within-sum-of-squares (WSS) for different numbers of clusters,
averaged over several iterations.
Parameters
----------
data : float array
Trajectory data [frames,frame_data]
algorithm : string
The algorithm to use for the clustering.
Options: kmeans, rspace.
Default: kmeans
max_iter : int, optional
Maximum number of iterations.
Default: 100.
num_repeats : int, optional
Number of times to run the clustering for each number of clusters.
Default: 5.
max_num_clusters : int, optional
Maximum number of clusters for k-means clustering.
Default: 12.
plot_file : str, optional
Name of the file to save the plot.
Returns
-------
all_wss : float array
WSS values for each number of clusters (starting at 2).
std_wss : float array
Standard deviations of the WSS.
"""
# Initialize lists
all_wss = []
std_wss = []
# Loop over the number of clusters
for nc in range(1,max_num_clusters):
rep_wss = []
# Run each clustering several times.
for repeat in range(num_repeats):
# Get clusters and WSS for this repetition.
cc = obtain_clusters(data, algorithm=algorithm, max_iter=max_iter,
num_clusters=nc, plot=False)
cidx, wss, centroids = cc
rep_wss.append(wss)
# Calculate mean and standard deviation for this number of clusters.
all_wss.append(np.mean(rep_wss))
std_wss.append(np.std(rep_wss))
# Plot the WSS over the number of clusters
fig, ax = plt.subplots(1,1, figsize=[4,3], dpi=300)
ax.errorbar(np.arange(len(all_wss))+2,np.array(all_wss),yerr=np.array(std_wss)/np.sqrt(num_repeats))
ax.set_xlabel('number of clusters')
ax.set_ylabel('total WSS')
fig.tight_layout()
# Save the plot to file.
if plot_file: fig.savefig(plot_file)
return all_wss, std_wss
def wss_over_number_of_combined_clusters(data_a, data_b, label_a = 'Sim A', label_b = 'Sim B', start_frame = 0,
algorithm='kmeans', max_iter=100, num_repeats = 5, max_num_clusters = 12,
plot_file = None):
"""
Calculates the Within-Sum-of-Squares for different numbers of clusters,
averaged over several iterations.
Parameters
----------
data_a : float array
Trajectory data [frames,frame_data]
data_b : float array
Trajectory data [frames,frame_data]
label_a : str, optional
Label for the plot.
label_b : str, optional
Label for the plot.
start_frame : int, optional
Frame from which the clustering data starts.
algorithm : string
The algorithm to use for the clustering.
Options: kmeans, rspace.
Default: kmeans
max_iter : int, optional
Maximum number of iterations.
Default: 100.
num_repeats : int, optional
Number of times to run the clustering for each number of clusters.
Default: 5.
max_num_clusters : int, optional
Maximum number of clusters for k-means clustering.
Default: 12.
plot_file : str, optional
Name of the file to save the plot.
Returns
-------
all_wss : float array
WSS values for each number of clusters (starting at 2).
std_wss : float array
Standard deviations of the WSS.
"""
# Initialize lists
all_wss = []
std_wss = []
# Loop over the number of clusters
for nc in range(1,max_num_clusters):
rep_wss = []
# Run each clustering several times.
for repeat in range(num_repeats):
# Get clusters and WSS for this repetition.
cc = obtain_combined_clusters(data_a, data_b, label_a, label_b, start_frame,
algorithm=algorithm, max_iter=max_iter, num_clusters=nc,
plot=False)
cidx, cond, oidx, wss, centroids = cc
rep_wss.append(wss)
# Calculate mean and standard deviation for this number of clusters.
all_wss.append(np.mean(rep_wss))
std_wss.append(np.std(rep_wss))
# Plot the WSS over the number of clusters
fig, ax = plt.subplots(1,1, figsize=[4,3], dpi=300)
ax.errorbar(np.arange(len(all_wss))+2,np.array(all_wss),yerr=np.array(std_wss)/np.sqrt(num_repeats))
ax.set_xlabel('number of clusters')
ax.set_ylabel('total WSS')
fig.tight_layout()
# Save the plot to file.
if plot_file: fig.savefig(plot_file)
return all_wss, std_wss
| 36.065789 | 115 | 0.591025 | 698 | 5,482 | 4.488539 | 0.196275 | 0.051069 | 0.076604 | 0.019151 | 0.883179 | 0.871688 | 0.855091 | 0.817747 | 0.797319 | 0.797319 | 0 | 0.012322 | 0.33382 | 5,482 | 151 | 116 | 36.304636 | 0.845564 | 0.477381 | 0 | 0.615385 | 0 | 0 | 0.029734 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.038462 | false | 0 | 0.173077 | 0 | 0.25 | 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 |
4b9aa0e3a9d19ce7c8400b077196bc9f112664de | 16,713 | py | Python | cla_backend/libs/eligibility_calculator/tests/test_case_data.py | uk-gov-mirror/ministryofjustice.cla_backend | 4d524c10e7bd31f085d9c5f7bf6e08a6bb39c0a6 | [
"MIT"
] | 3 | 2019-10-02T15:31:03.000Z | 2022-01-13T10:15:53.000Z | cla_backend/libs/eligibility_calculator/tests/test_case_data.py | uk-gov-mirror/ministryofjustice.cla_backend | 4d524c10e7bd31f085d9c5f7bf6e08a6bb39c0a6 | [
"MIT"
] | 206 | 2015-01-02T16:50:11.000Z | 2022-02-16T20:16:05.000Z | cla_backend/libs/eligibility_calculator/tests/test_case_data.py | uk-gov-mirror/ministryofjustice.cla_backend | 4d524c10e7bd31f085d9c5f7bf6e08a6bb39c0a6 | [
"MIT"
] | 6 | 2015-03-23T23:08:42.000Z | 2022-02-15T17:04:44.000Z | import random
import unittest
from ..models import CaseData
from ..exceptions import PropertyExpectedException
from .fixtures import get_default_case_data
class TestCaseData(unittest.TestCase):
def test_total_income_calculation(self):
default_data = get_default_case_data(
you__income__earnings=0,
you__income__self_employment_drawings=0,
you__income__benefits=0,
you__income__tax_credits=0,
you__income__child_benefits=0,
you__income__maintenance_received=0,
you__income__pension=60,
you__income__other_income=0,
)
cd = CaseData(**default_data)
ti = cd.total_income
income = cd.you.income
gross_income_orig = 0
for prop in income.PROPERTY_META.keys():
part = getattr(income, prop, 0)
gross_income_orig += part
self.assertEqual(gross_income_orig, ti)
def test_total_income_calculation_with_partner(self):
combined_income = 31710
default_data = get_default_case_data(
you__income__earnings=10000,
you__income__self_employment_drawings=10,
you__income__benefits=20,
you__income__tax_credits=30,
you__income__child_benefits=40,
you__income__maintenance_received=50,
you__income__pension=60,
you__income__other_income=4000,
partner__income__earnings=10000,
partner__income__self_employment_drawings=100,
partner__income__benefits=200,
partner__income__tax_credits=300,
partner__income__child_benefits=0,
partner__income__maintenance_received=400,
partner__income__pension=500,
partner__income__other_income=6000,
facts__has_partner=True,
)
cd = CaseData(**default_data)
ti = cd.total_income
income = cd.you.income
gross_income_orig = (
income.earnings
+ income.self_employment_drawings
+ income.benefits
+ income.tax_credits
+ income.child_benefits
+ income.maintenance_received
+ income.pension
+ income.other_income
)
gross_income_orig += (
cd.partner.income.earnings
+ cd.partner.income.self_employment_drawings
+ cd.partner.income.benefits
+ cd.partner.income.tax_credits
+ cd.partner.income.child_benefits
+ cd.partner.income.maintenance_received
+ cd.partner.income.pension
+ cd.partner.income.other_income
)
self.assertEqual(gross_income_orig, ti)
self.assertEqual(combined_income, ti)
def test_bad_property_set_exception(self):
cdd = get_default_case_data(foo="bar", bar__baz=24)
with self.assertRaises(PropertyExpectedException):
CaseData(**cdd)
def test_getattr_raises_if_accessing_invalid_prop(self):
with self.assertRaises(AttributeError):
cd = CaseData()
cd.foo
def test_get_total_income_no_partner(self):
cdd = get_default_case_data(
you__income__earnings=265700,
you__income__self_employment_drawings=10,
you__income__benefits=20,
you__income__tax_credits=30,
you__income__child_benefits=40,
you__income__maintenance_received=50,
you__income__pension=60,
you__income__other_income=0,
)
cd = CaseData(**cdd)
self.assertFalse(cd.facts.has_partner)
self.assertEqual(265910, cd.total_income)
# TODO: fix this to check nested properties
# def test_provide_partner_earnings_required_partner_other_income(self):
# with self.assertRaises(PropertyExpectedException):
# cdd = get_default_case_data(
# you__income__earnings=1,
# you__income__other_income=1,
# partner__income__earnings=1,
# facts__has_partner=True
# )
# cd = CaseData(**cdd)
# cd.total_income
def test_get_total_income_with_partner(self):
cdd = get_default_case_data(
you__income__earnings=265700,
you__income__self_employment_drawings=10,
you__income__benefits=20,
you__income__tax_credits=30,
you__income__child_benefits=40,
you__income__maintenance_received=50,
you__income__pension=60,
you__income__other_income=0,
partner__income__earnings=100,
partner__income__self_employment_drawings=100,
partner__income__benefits=200,
partner__income__tax_credits=300,
partner__income__child_benefits=0,
partner__income__maintenance_received=400,
partner__income__pension=500,
partner__income__other_income=2,
facts__has_partner=True,
)
cd = CaseData(**cdd)
self.assertEqual(267512, cd.total_income)
def test_is_partner_disputed_true(self):
cdd = get_default_case_data(facts__has_partner=True, facts__is_partner_opponent=True)
cd = CaseData(**cdd)
self.assertTrue(cd.facts.has_disputed_partner)
def test_is_partner_disputed_false(self):
cdd = get_default_case_data(facts__has_partner=False, facts__is_partner_opponent=True)
cd = CaseData(**cdd)
self.assertFalse(cd.facts.has_disputed_partner)
def test_is_partner_disputed_not_opponent(self):
cdd = get_default_case_data(facts__has_partner=True, facts__is_partner_opponent=False)
cd = CaseData(**cdd)
self.assertFalse(cd.facts.has_disputed_partner)
def test_is_partner_disputed_no_partner_not_opponent(self):
cdd = get_default_case_data(facts__has_partner=False, facts__is_partner_opponent=False)
cd = CaseData(**cdd)
self.assertFalse(cd.facts.has_disputed_partner)
def test_get_non_disputed_liquid_capital(self):
cdd = get_default_case_data(
you__savings__bank_balance=0,
you__savings__credit_balance=0,
you__savings__asset_balance=0,
you__savings__investment_balance=0,
partner__savings__bank_balance=0,
partner__savings__credit_balance=0,
partner__savings__asset_balance=0,
partner__savings__investment_balance=0,
)
cd = CaseData(**cdd)
self.assertEqual(0, cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_only(self):
cdd = get_default_case_data(
you__savings__bank_balance=10000,
you__savings__credit_balance=0,
you__savings__asset_balance=0,
you__savings__investment_balance=0,
partner__savings__bank_balance=0,
partner__savings__credit_balance=0,
partner__savings__asset_balance=0,
partner__savings__investment_balance=0,
)
cd = CaseData(**cdd)
self.assertEqual(10000, cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_credit_balance(self):
cdd = get_default_case_data(
you__savings__bank_balance=10000,
you__savings__credit_balance=10,
you__savings__asset_balance=0,
you__savings__investment_balance=0,
partner__savings__bank_balance=0,
partner__savings__credit_balance=0,
partner__savings__asset_balance=0,
partner__savings__investment_balance=0,
)
cd = CaseData(**cdd)
self.assertEqual(10010, cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_valuable(self):
cdd = get_default_case_data(
you__savings__bank_balance=10000,
you__savings__credit_balance=0,
you__savings__asset_balance=1000,
you__savings__investment_balance=0,
partner__savings__bank_balance=0,
partner__savings__credit_balance=0,
partner__savings__asset_balance=0,
partner__savings__investment_balance=0,
)
cd = CaseData(**cdd)
self.assertEqual(11000, cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_investment_balance(self):
cdd = get_default_case_data(
you__savings__bank_balance=10000,
you__savings__credit_balance=0,
you__savings__asset_balance=0,
you__savings__investment_balance=5000,
partner__savings__bank_balance=0,
partner__savings__credit_balance=0,
partner__savings__asset_balance=0,
partner__savings__investment_balance=0,
)
cd = CaseData(**cdd)
self.assertEqual(15000, cd.non_disputed_liquid_capital)
# TODO: Fix invalid state check
# def test_inconsistent_state(self):
# cdd = get_default_case_data(
# you__savings__bank_balance=10000,
# you__savings__credit_balance=0,
# you__savings__asset_balance=0,
# you__savings__investment_balance=0,
# partner__savings__bank_balance=10000,
# partner__savings__credit_balance=0,
# partner__savings__asset_balance=0,
# partner__savings__investment_balance=0,
# facts__has_partner=False,
# )
# with self.assertRaises(InvalidStateException):
# cd = CaseData(**cdd)
def test_get_non_disputed_liquid_capital_savings_with_partner(self):
cdd = get_default_case_data(
you__savings__bank_balance=10000,
you__savings__credit_balance=0,
you__savings__asset_balance=0,
you__savings__investment_balance=0,
partner__savings__bank_balance=1,
partner__savings__credit_balance=0,
partner__savings__asset_balance=0,
partner__savings__investment_balance=0,
facts__has_partner=True,
)
cd = CaseData(**cdd)
self.assertEqual(10001, cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_with_partner_credit_balance(self):
cdd = get_default_case_data(
you__savings__bank_balance=10000,
you__savings__credit_balance=00,
you__savings__asset_balance=0,
you__savings__investment_balance=0,
partner__savings__bank_balance=0,
partner__savings__credit_balance=20,
partner__savings__asset_balance=0,
partner__savings__investment_balance=0,
facts__has_partner=True,
)
cd = CaseData(**cdd)
self.assertEqual(10020, cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_with_partner_savings(self):
cdd = get_default_case_data(
you__savings__bank_balance=10000,
you__savings__credit_balance=00,
you__savings__asset_balance=0,
you__savings__investment_balance=0,
partner__savings__bank_balance=10,
partner__savings__credit_balance=0,
partner__savings__asset_balance=0,
partner__savings__investment_balance=0,
facts__has_partner=True,
)
cd = CaseData(**cdd)
self.assertEqual(10010, cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_with_partner_valuables(self):
cdd = get_default_case_data(
you__savings__bank_balance=10000,
you__savings__credit_balance=00,
you__savings__asset_balance=5000,
you__savings__investment_balance=0,
partner__savings__bank_balance=0,
partner__savings__credit_balance=0,
partner__savings__asset_balance=0,
partner__savings__investment_balance=0,
facts__has_partner=True,
)
cd = CaseData(**cdd)
self.assertEqual(15000, cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_with_partner_investment_balance(self):
cdd = get_default_case_data(
you__savings__bank_balance=10000,
you__savings__credit_balance=00,
you__savings__asset_balance=0,
you__savings__investment_balance=0,
partner__savings__bank_balance=0,
partner__savings__credit_balance=0,
partner__savings__asset_balance=0,
partner__savings__investment_balance=100,
facts__has_partner=True,
)
cd = CaseData(**cdd)
self.assertEqual(10100, cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_only_partner_savings(self):
cdd = get_default_case_data(
you__savings__bank_balance=0,
you__savings__credit_balance=0,
you__savings__asset_balance=0,
you__savings__investment_balance=0,
partner__savings__bank_balance=0,
partner__savings__credit_balance=0,
partner__savings__asset_balance=0,
partner__savings__investment_balance=100,
facts__has_partner=True,
)
cd = CaseData(**cdd)
self.assertEqual(100, cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_only_partner_credit_balance(self):
cdd = get_default_case_data(
you__savings__bank_balance=0,
you__savings__credit_balance=200,
you__savings__asset_balance=0,
you__savings__investment_balance=0,
partner__savings__bank_balance=0,
partner__savings__credit_balance=0,
partner__savings__asset_balance=0,
partner__savings__investment_balance=0,
facts__has_partner=True,
)
cd = CaseData(**cdd)
self.assertEqual(200, cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_random_values_no_partner(self):
for i in range(0, 500):
# ghetto quick-check
steps = [random.randint(0, 50000)]
for n in range(3):
step = random.randint(0, steps[-1])
steps.append(step)
cdd = get_default_case_data(
you__savings__bank_balance=steps[0],
you__savings__credit_balance=steps[1],
you__savings__asset_balance=steps[2],
you__savings__investment_balance=steps[3],
)
cd = CaseData(**cdd)
self.assertEqual(sum(steps), cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_random_values_with_partner(self):
for i in range(0, 500):
# ghetto quick-check
steps = [random.randint(0, 50000)]
for n in range(7):
step = random.randint(0, steps[-1])
steps.append(step)
cdd = get_default_case_data(
you__savings__bank_balance=steps[0],
you__savings__credit_balance=steps[1],
you__savings__asset_balance=steps[2],
you__savings__investment_balance=steps[3],
partner__savings__bank_balance=steps[4],
partner__savings__credit_balance=steps[5],
partner__savings__asset_balance=steps[6],
partner__savings__investment_balance=steps[7],
facts__has_partner=True,
)
cd = CaseData(**cdd)
self.assertEqual(sum(steps), cd.non_disputed_liquid_capital)
def test_get_non_disputed_liquid_capital_savings_random_values_only_partner(self):
for i in range(0, 500):
# ghetto quick-check
steps = [random.randint(0, 50000)]
for n in range(3):
step = random.randint(0, steps[-1])
steps.append(step)
cdd = get_default_case_data(
you__savings__bank_balance=0,
you__savings__credit_balance=0,
you__savings__asset_balance=0,
you__savings__investment_balance=0,
partner__savings__bank_balance=steps[0],
partner__savings__credit_balance=steps[1],
partner__savings__asset_balance=steps[2],
partner__savings__investment_balance=steps[3],
facts__has_partner=True,
)
cd = CaseData(**cdd)
self.assertEqual(sum(steps), cd.non_disputed_liquid_capital)
| 40.763415 | 95 | 0.659846 | 1,882 | 16,713 | 5.175345 | 0.077577 | 0.068172 | 0.075462 | 0.108419 | 0.850308 | 0.803799 | 0.79271 | 0.786653 | 0.779877 | 0.762628 | 0 | 0.034591 | 0.27697 | 16,713 | 409 | 96 | 40.863081 | 0.771433 | 0.062407 | 0 | 0.632948 | 0 | 0 | 0.000192 | 0 | 0 | 0 | 0 | 0.002445 | 0.078035 | 1 | 0.072254 | false | 0 | 0.014451 | 0 | 0.089595 | 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 |
4bb17b67b3bdb6bd2cb7059e89e2883b31e3fef4 | 5,396 | py | Python | tests/src/common/test_decorator.py | Thiqah-Lab/aws-serverless-skeleton | d34adddb6613f2eb40e92ed483bdfbfe72332257 | [
"MIT"
] | 46 | 2019-04-08T19:09:51.000Z | 2021-12-09T23:54:35.000Z | tests/src/common/test_decorator.py | Thiqah-Lab/aws-serverless-skeleton | d34adddb6613f2eb40e92ed483bdfbfe72332257 | [
"MIT"
] | 5 | 2019-04-08T17:14:37.000Z | 2019-04-08T17:15:27.000Z | tests/src/common/test_decorator.py | Thiqah-Lab/aws-serverless-skeleton | d34adddb6613f2eb40e92ed483bdfbfe72332257 | [
"MIT"
] | 1 | 2021-07-26T08:19:12.000Z | 2021-07-26T08:19:12.000Z | import unittest
from unittest.mock import call, MagicMock, patch
import src.common.context
from src.common.decorator import api_response
from src.common.decorator import gateway_request_interceptor
from src.common.encoder import PynamoDbEncoder
from src.common.http_response import HTTPStatus
class TestDecorator(unittest.TestCase):
@patch('src.common.decorator.get_logger')
@patch('os.environ')
def test_gateway_request_interceptor_request_id_present(self, mock_environ, mock_logger):
# given
context = {}
authorizer = {}
event = {'requestContext': {'requestId': '12345', 'authorizer': authorizer}}
mock_environ.get = MagicMock(return_value="dev")
mock_method = MagicMock()
# when
decorated = gateway_request_interceptor(mock_method)
result = decorated(event=event, context=context)
# then
self.assertEqual(src.common.context.REQUEST_ID, '12345')
self.assertEqual(result, mock_method.return_value)
mock_method.assert_called_with(event, context)
self.assertEqual(mock_logger.return_value.debug.mock_calls, [
call("Lambda event: %s", {})])
@patch('src.common.decorator.get_logger')
@patch('os.environ')
def test_gateway_request_interceptor_request_id_present_prod(self, mock_environ, mock_logger):
# given
context = {}
authorizer = {}
event = {'requestContext': {'requestId': '12345', 'authorizer': authorizer}}
mock_environ.get = MagicMock(return_value="prod")
mock_method = MagicMock()
# when
decorated = gateway_request_interceptor(mock_method)
result = decorated(event=event, context=context)
# then
self.assertEqual(src.common.context.REQUEST_ID, '12345')
self.assertEqual(result, mock_method.return_value)
mock_method.assert_called_with(event, context)
mock_logger.return_value.debug.assert_not_called()
@patch('src.common.decorator.get_logger')
@patch('os.environ')
def test_gateway_request_interceptor_request_id_not_present(self, mock_environ, mock_logger):
# given
event = {'application': 'my app'}
context = {'dummy': 1}
mock_environ.get = MagicMock(return_value="dev")
src.common.context.REQUEST_ID = None
mock_method = MagicMock()
# when
decorated = gateway_request_interceptor(mock_method)
result = decorated(event=event, context=context)
# then
self.assertEqual(src.common.context.REQUEST_ID, None)
self.assertEqual(result, mock_method.return_value)
mock_method.assert_called_with(event, context)
mock_logger.return_value.debug.assert_not_called()
@patch('src.common.decorator.get_logger')
@patch('os.environ')
def test_gateway_request_interceptor_lambda_exception(self, mock_environ, mock_logger):
# given
event = {'application': 'my app'}
context = {'dummy': 1}
mock_environ.get = MagicMock(return_value="dev")
src.common.context.REQUEST_ID = None
mock_exception = Exception()
mock_method = MagicMock(side_effect=[mock_exception])
# when
decorated = gateway_request_interceptor(mock_method)
# then
try:
decorated(event=event, context=context)
except Exception as ex:
self.assertEqual(ex, mock_exception)
self.assertEqual(src.common.context.REQUEST_ID, None)
mock_method.assert_called_with(event, context)
mock_logger.return_value.debug.assert_not_called()
self.assertEqual(mock_logger.return_value.error.mock_calls, [
call("Error within lambda function.", exc_info=1)])
@patch('src.common.decorator.HTTPResponse')
def test_api_response_with_status_only(self, mock_http_response):
# given
mock_method = MagicMock(return_value=HTTPStatus.BAD_REQUEST)
# when
decorator = api_response()
decorated = decorator(mock_method)
decorated("dummy")
# then
self.assertEqual(mock_http_response.to_json_response.call_args,
call(HTTPStatus.BAD_REQUEST))
self.assertEqual(mock_method.call_args, call("dummy"))
@patch('src.common.decorator.HTTPResponse')
def test_api_response_without_status(self, mock_http_response):
# given
mock_method = MagicMock(return_value="response")
# when
decorator = api_response()
decorated = decorator(mock_method)
decorated("dummy")
# then
self.assertEqual(mock_http_response.to_ok_json.call_args,
call(body="response", encoder=PynamoDbEncoder))
self.assertEqual(mock_method.call_args, call("dummy"))
@patch('src.common.decorator.HTTPResponse')
def test_api_response_with_both_response_and_status(self, mock_http_response):
# given
mock_method = MagicMock(return_value=(HTTPStatus.BAD_REQUEST, "response"))
# when
decorator = api_response()
decorated = decorator(mock_method)
decorated("dummy")
# then
self.assertEqual(mock_http_response.to_json_response.call_args,
call(HTTPStatus.BAD_REQUEST, "response"))
self.assertEqual(mock_method.call_args, call("dummy"))
| 36.459459 | 98 | 0.676427 | 603 | 5,396 | 5.769486 | 0.14262 | 0.068985 | 0.046565 | 0.046278 | 0.845358 | 0.818339 | 0.797643 | 0.780972 | 0.755102 | 0.755102 | 0 | 0.005485 | 0.222943 | 5,396 | 147 | 99 | 36.707483 | 0.824231 | 0.020571 | 0 | 0.659794 | 0 | 0 | 0.097473 | 0.042371 | 0 | 0 | 0 | 0 | 0.237113 | 1 | 0.072165 | false | 0 | 0.072165 | 0 | 0.154639 | 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 |
4bb726319ff83cfc16a64ce8235eb27dc967b62a | 48 | py | Python | commands_async/adminx.py | alessandrohc/django-cmd-async | da644b865eb0ba111c9e1539a3890322e4335d8d | [
"MIT"
] | 1 | 2021-03-25T06:56:44.000Z | 2021-03-25T06:56:44.000Z | commands_async/adminx.py | alessandrohc/django-cmd-async | da644b865eb0ba111c9e1539a3890322e4335d8d | [
"MIT"
] | null | null | null | commands_async/adminx.py | alessandrohc/django-cmd-async | da644b865eb0ba111c9e1539a3890322e4335d8d | [
"MIT"
] | 1 | 2021-09-17T10:55:14.000Z | 2021-09-17T10:55:14.000Z | from xadmin.sites import site
# site.register() | 16 | 29 | 0.770833 | 7 | 48 | 5.285714 | 0.857143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 48 | 3 | 30 | 16 | 0.880952 | 0.3125 | 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 |
29bcfcb73f2ec441c96911dafb8c20ab8c7a89e4 | 130,317 | py | Python | interaction_sqlova.py | shloksah/MISP | c7482b0c1ecceafa261d4aab0da7d6af9141f37d | [
"MIT"
] | 54 | 2019-10-07T03:36:25.000Z | 2021-12-27T02:11:11.000Z | interaction_sqlova.py | shloksah/MISP | c7482b0c1ecceafa261d4aab0da7d6af9141f37d | [
"MIT"
] | 1 | 2021-08-13T07:48:15.000Z | 2021-08-31T01:30:12.000Z | interaction_sqlova.py | shloksah/MISP | c7482b0c1ecceafa261d4aab0da7d6af9141f37d | [
"MIT"
] | 4 | 2020-01-29T17:38:28.000Z | 2021-12-10T19:09:37.000Z | # Adapted from SQLova script for interaction.
# @author: Ziyu Yao
# Oct 7th, 2020
#
import os, sys, argparse, re, json, pickle, math
from copy import deepcopy
from matplotlib.pylab import *
import torch.nn as nn
import torch
import torch.nn.functional as F
import random as python_random
# import torchvision.datasets as dsets
import numpy as np
import time, datetime, pytimeparse
import SQLova_model.bert.tokenization as tokenization
from SQLova_model.bert.modeling import BertConfig, BertModel
from SQLova_model.sqlova.utils.utils_wikisql import *
from SQLova_model.sqlova.model.nl2sql.wikisql_models import *
from SQLova_model.sqlnet.dbengine import DBEngine
from SQLova_model.agent import Agent
from SQLova_model.world_model import WorldModel
from SQLova_model.error_detector import *
from MISP_SQL.question_gen import QuestionGenerator
from SQLova_model.environment import UserSim, RealUser, ErrorEvaluator, GoldUserSim
from user_study_utils import *
np.set_printoptions(precision=3)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
EARLY_STOP_EPOCH_STAGE1=10
EARLY_STOP_EPOCH_STAGE2=5
EARLY_THRESHOLD=30000
def construct_hyper_param(parser):
parser.add_argument("--bS", default=1, type=int, help="Batch size")
parser.add_argument("--model_type", default='Seq2SQL_v1', type=str,
help="Type of model.")
parser.add_argument('--seed', type=int, default=0, help='Random seed.')
parser.add_argument('--model_dir', type=str, required=True, help='Which folder to save the model checkpoints.')
# 1.2 BERT Parameters
parser.add_argument("--vocab_file",
default='vocab.txt', type=str,
help="The vocabulary file that the BERT model was trained on.")
parser.add_argument("--max_seq_length",
default=222, type=int, # Set based on maximum length of input tokens.
help="The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded.")
parser.add_argument("--num_target_layers",
default=2, type=int,
help="The Number of final layers of BERT to be used in downstream task.")
parser.add_argument('--lr_bert', default=1e-5, type=float, help='BERT model learning rate.')
parser.add_argument('--no_pretraining', action='store_true', help='Use BERT pretrained model')
parser.add_argument("--bert_type_abb", default='uS', type=str,
help="Type of BERT model to load. e.g.) uS, uL, cS, cL, and mcS")
# 1.3 Seq-to-SQL module parameters
parser.add_argument('--lS', default=2, type=int, help="The number of LSTM layers.")
parser.add_argument('--dr', default=0.3, type=float, help="Dropout rate.")
parser.add_argument('--lr', default=1e-3, type=float, help="Learning rate.")
parser.add_argument("--hS", default=100, type=int, help="The dimension of hidden vector in the seq-to-SQL module.")
# 1.4 Execution-guided decoding beam-size. It is used only in test.py
# parser.add_argument('--EG',
# default=False,
# action='store_true',
# help="If present, Execution guided decoding is used in test.")
# parser.add_argument('--beam_size', # used for non-interactive decoding only
# type=int,
# default=4,
# help="The size of beam for smart decoding")
# Job setting
parser.add_argument('--job', default='test_w_interaction', choices=['test_w_interaction', 'online_learning'],
help='Set the job. For parser pretraining, see other scripts.')
# Data setting
parser.add_argument('--data', default='dev', choices=['dev', 'test', 'user_study', 'online'],
help='which dataset to test.')
parser.add_argument('--data_seed', type=int, default=0, choices=[0, 10, 100],
help='Seed for simulated online data order.')
# Model (initialization/testing) setting
parser.add_argument('--setting', default='full_train',
choices=['full_train', 'online_pretrain_1p', 'online_pretrain_5p', 'online_pretrain_10p'],
help='Model setting; checkpoints will be loaded accordingly.')
# for interaction
parser.add_argument('--num_options', type=str, default='3',
help='[INTERACTION] Number of options (inf or an int number).')
parser.add_argument('--user', type=str, default='sim', choices=['sim', 'gold_sim', 'real'],
help='[INTERACTION] The user setting.')
parser.add_argument('--err_detector', type=str, default='any',
help='[INTERACTION] The error detector: '
'(1) prob=x for using policy probability threshold;'
'(2) stddev=x for using Bayesian dropout threshold (need to set --dropout and --passes);'
'(3) any for querying about every policy action;'
'(4) perfect for using a simulated perfect detector.')
parser.add_argument('--friendly_agent', type=int, default=0, choices=[0, 1],
help='[INTERACTION] If 1, the agent will not trigger further interactions '
'if any wrong decision is not resolved during parsing.')
parser.add_argument('--output_path', type=str, default='temp', help='[INTERACTION] Where to save outputs.')
parser.add_argument('--dropout', type=float, default=0.0,
help='[INTERACTION] Dropout rate for Bayesian dropout-based uncertainty analysis.')
parser.add_argument('--passes', type=int, default=1,
help='[INTERACTION] Number of decoding passes for Bayesian dropout-based uncertainty analysis.')
parser.add_argument('--ask_structure', type=int, default=0, choices=[0, 1],
help='[INTERACTION] Set to True to allow questions about query structure (WHERE clause).')
# for online learning
parser.add_argument('--update_iter', default=1000, type=int,
help="[LEARNING] Number of iterations per update.")
parser.add_argument('--supervision', default='misp_neil',
choices=['full_expert', 'misp_neil', 'misp_neil_pos', 'misp_neil_perfect',
'bin_feedback', 'bin_feedback_expert',
'self_train', 'self_train_0.5'],
help='[LEARNING] Online learning supervision based on different algorithms.')
parser.add_argument('--start_iter', default=0, type=int, help='[LEARNING] Iteration to start.')
parser.add_argument('--end_iter', default=-1, type=int, help='[LEARNING] Iteration to end.')
parser.add_argument('--auto_iter', default=0, type=int, choices=[0, 1],
help='[LEARNING] If 1, unless args.start_iter > 0 is specified, the system will automatically '
'search for `start_iter` given the aggregated training data. '
'Only applies to args.supervision = misp_neil/bin_feedback(_expert).')
args = parser.parse_args()
map_bert_type_abb = {'uS': 'uncased_L-12_H-768_A-12',
'uL': 'uncased_L-24_H-1024_A-16',
'cS': 'cased_L-12_H-768_A-12',
'cL': 'cased_L-24_H-1024_A-16',
'mcS': 'multi_cased_L-12_H-768_A-12'}
args.bert_type = map_bert_type_abb[args.bert_type_abb]
print(f"BERT-type: {args.bert_type}")
# Decide whether to use lower_case.
if args.bert_type_abb == 'cS' or args.bert_type_abb == 'cL' or args.bert_type_abb == 'mcS':
args.do_lower_case = False
else:
args.do_lower_case = True
# Seeds for random number generation
if args.data == "online":
print("## online data seed: %d" % args.data_seed)
print("## random seed: %d" % args.seed)
python_random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
# args.toy_model = not torch.cuda.is_available()
args.toy_model = False
args.toy_size = 12
print("Testing data: {}".format(args.data))
return args
def get_bert(BERT_PT_PATH, bert_type, do_lower_case, no_pretraining):
bert_config_file = os.path.join(BERT_PT_PATH, f'bert_config_{bert_type}.json')
vocab_file = os.path.join(BERT_PT_PATH, f'vocab_{bert_type}.txt')
init_checkpoint = os.path.join(BERT_PT_PATH, f'pytorch_model_{bert_type}.bin')
bert_config = BertConfig.from_json_file(bert_config_file)
tokenizer = tokenization.FullTokenizer(
vocab_file=vocab_file, do_lower_case=do_lower_case)
bert_config.print_status()
model_bert = BertModel(bert_config)
if no_pretraining:
pass
else:
model_bert.load_state_dict(torch.load(init_checkpoint, map_location='cpu'))
print("Load pre-trained parameters.")
model_bert.to(device)
return model_bert, tokenizer, bert_config
def get_models(args, BERT_PT_PATH, trained=False, path_model_bert=None, path_model=None):
# some constants
agg_ops = ['', 'MAX', 'MIN', 'COUNT', 'SUM', 'AVG']
cond_ops = ['=', '>', '<', 'OP'] # do not know why 'OP' required. Hence,
print(f"Batch_size = {args.bS}")
# print(f"Batch_size = {args.bS * args.accumulate_gradients}")
print(f"BERT parameters:")
print(f"learning rate: {args.lr_bert}")
# print(f"Fine-tune BERT: {args.fine_tune}")
# Get BERT
model_bert, tokenizer, bert_config = get_bert(BERT_PT_PATH, args.bert_type, args.do_lower_case,
args.no_pretraining)
args.iS = bert_config.hidden_size * args.num_target_layers # Seq-to-SQL input vector dimenstion
# Get Seq-to-SQL
n_cond_ops = len(cond_ops)
n_agg_ops = len(agg_ops)
print(f"Seq-to-SQL: the number of final BERT layers to be used: {args.num_target_layers}")
print(f"Seq-to-SQL: the size of hidden dimension = {args.hS}")
print(f"Seq-to-SQL: LSTM encoding layer size = {args.lS}")
print(f"Seq-to-SQL: dropout rate = {args.dr}")
print(f"Seq-to-SQL: learning rate = {args.lr}")
model = Seq2SQL_v1(args.iS, args.hS, args.lS, args.dr, n_cond_ops, n_agg_ops)
model = model.to(device)
if trained:
assert path_model_bert != None
assert path_model != None
if torch.cuda.is_available():
res = torch.load(path_model_bert)
else:
res = torch.load(path_model_bert, map_location='cpu')
model_bert.load_state_dict(res['model_bert'])
model_bert.to(device)
if torch.cuda.is_available():
res = torch.load(path_model)
else:
res = torch.load(path_model, map_location='cpu')
model.load_state_dict(res['model'])
return model, model_bert, tokenizer, bert_config
def get_opt(model, model_bert, fine_tune):
if fine_tune:
opt = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr, weight_decay=0)
opt_bert = torch.optim.Adam(filter(lambda p: p.requires_grad, model_bert.parameters()),
lr=args.lr_bert, weight_decay=0)
else:
opt = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr, weight_decay=0)
opt_bert = None
return opt, opt_bert
def report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_q, pr_ans,
cnt_list, current_cnt):
cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x = current_cnt
print(f'cnt = {cnt} / {cnt_tot} ===============================')
print(f'headers: {hds}')
print(f'nlu: {nlu}')
# print(f's_sc: {s_sc[0]}')
# print(f's_sa: {s_sa[0]}')
# print(f's_wn: {s_wn[0]}')
# print(f's_wc: {s_wc[0]}')
# print(f's_wo: {s_wo[0]}')
# print(f's_wv: {s_wv[0][0]}')
print(f'===============================')
print(f'g_sc : {g_sc}')
print(f'pr_sc: {pr_sc}')
print(f'g_sa : {g_sa}')
print(f'pr_sa: {pr_sa}')
print(f'g_wn : {g_wn}')
print(f'pr_wn: {pr_wn}')
print(f'g_wc : {g_wc}')
print(f'pr_wc: {pr_wc}')
print(f'g_wo : {g_wo}')
print(f'pr_wo: {pr_wo}')
print(f'g_wv : {g_wv}')
# print(f'pr_wvi: {pr_wvi}')
print('g_wv_str:', g_wv_str)
print('p_wv_str:', pr_wv_str)
print(f'g_sql_q: {g_sql_q}')
print(f'pr_sql_q: {pr_sql_q}')
print(f'g_ans: {g_ans}')
print(f'pr_ans: {pr_ans}')
print(f'--------------------------------')
print(cnt_list)
print(f'acc_lx = {cnt_lx/cnt:.3f}, acc_x = {cnt_x/cnt:.3f}\n',
f'acc_sc = {cnt_sc/cnt:.3f}, acc_sa = {cnt_sa/cnt:.3f}, acc_wn = {cnt_wn/cnt:.3f}\n',
f'acc_wc = {cnt_wc/cnt:.3f}, acc_wo = {cnt_wo/cnt:.3f}, acc_wv = {cnt_wv/cnt:.3f}')
print(f'===============================')
def print_result(epoch, acc, dname):
ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x = acc
print(f'{dname} results ------------')
print(
f" Epoch: {epoch}, ave loss: {ave_loss}, acc_sc: {acc_sc:.3f}, acc_sa: {acc_sa:.3f}, acc_wn: {acc_wn:.3f}, \
acc_wc: {acc_wc:.3f}, acc_wo: {acc_wo:.3f}, acc_wvi: {acc_wvi:.3f}, acc_wv: {acc_wv:.3f}, acc_lx: {acc_lx:.3f}, acc_x: {acc_x:.3f}"
)
def real_user_interaction(data_loader, data_table, user, agent, tokenizer,
max_seq_length, num_target_layers, path_db, save_path):
dset_name = "test"
if os.path.isfile(save_path):
saved_results = json.load(open(save_path, "r"))
interaction_records = saved_results['interaction_records']
count_exit = saved_results['count_exit']
time_spent = datetime.timedelta(seconds=pytimeparse.parse(saved_results['time_spent']))
st_pos = saved_results['st']
current_cnt = eval(saved_results['current_cnt'])
[cnt_tot, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x] = current_cnt
else:
cnt_sc = 0
cnt_sa = 0
cnt_wn = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_wvi = 0
cnt_lx = 0
cnt_x = 0
interaction_records = {}
count_exit = 0
time_spent = datetime.timedelta()
st_pos = 0
cnt_tot = 1
cnt = 0
engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
for iB, t in enumerate(data_loader):
assert len(t) == 1
if cnt < st_pos:
cnt += 1
continue
# Get fields
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, data_table, no_hs_t=True, no_sql_t=True)
g_sql_q = generate_sql_q(sql_i, tb)
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv = get_g(sql_i)
g_wvi_corenlp = get_g_wvi_corenlp(t)
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(agent.world_model.bert_config, agent.world_model.model_bert, tokenizer,
nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
g_wvi = get_g_wvi_bert_from_g_wvi_corenlp(t_to_tt_idx, g_wvi_corenlp)
g_wv_str, g_wv_str_wp = convert_pr_wvi_to_string(g_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
os.system('clear') # clear screen
print_header(len(data_loader.dataset) - cnt) # interface header
print(bcolors.BOLD + "Suppose you are given a table with the following " +
bcolors.BLUE + "header" + bcolors.ENDC +
bcolors.BOLD + ":" + bcolors.ENDC)
user.show_table(t[0]['table_id']) # print table
print(bcolors.BOLD + "\nAnd you want to answer the following " +
bcolors.PINK + "question" + bcolors.ENDC +
bcolors.BOLD + " based on this table:" + bcolors.ENDC)
print(bcolors.PINK + bcolors.BOLD + t[0]['question'] + bcolors.ENDC + "\n")
print(bcolors.BOLD + "To help you get the answer automatically,"
" the system has the following yes/no questions for you."
"\n(When no question prompts, please " +
bcolors.GREEN + "continue" + bcolors.ENDC +
bcolors.BOLD + " to the next case)\n" + bcolors.ENDC)
start_signal = input(bcolors.BOLD + "Ready? please press '" +
bcolors.GREEN + "Enter" + bcolors.ENDC + bcolors.BOLD + "' to start!" + bcolors.ENDC)
while start_signal != "":
start_signal = input(bcolors.BOLD + "Ready? please press '" +
bcolors.GREEN + "Enter" + bcolors.ENDC + bcolors.BOLD + "' to start!" + bcolors.ENDC)
start_time = datetime.datetime.now()
# init decode
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
input_item = [tb, nlu_t, nlu, hds]
else:
input_item = [wemb_n, l_n, wemb_h, l_hpu, l_hs, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu]
init_hyp = agent.world_model.decode(input_item, dec_beam_size=1, bool_verbal=False)[0]
# interaction
g_sql = sql_i[0]
g_sql["g_wvi"] = g_wvi[0]
hyp, bool_exit = agent.real_user_interactive_parsing_session(
user, input_item, g_sql, init_hyp, bool_verbal=False)
print("\nPredicted SQL: {}\n".format(hyp.sql))
per_time_spent = datetime.datetime.now() - start_time
time_spent += per_time_spent
print("Your time spent: {}".format(per_time_spent))
if bool_exit:
count_exit += 1
# post survey
print("-" * 50)
print("Post-study Survey: ")
bool_unclear = input("Is the " + bcolors.BOLD + bcolors.PINK + "initial question" +
bcolors.ENDC + " clear?\nPlease enter y/n: ")
while bool_unclear not in {'y', 'n'}:
bool_unclear = input("Is the " + bcolors.BOLD + bcolors.PINK + "initial question" +
bcolors.ENDC + " clear?\nPlease enter y/n: ")
print("-" * 50)
pr_sc = [hyp.sql_i['sel']]
pr_sa = [hyp.sql_i['agg']]
pr_wn = [len(hyp.sql_i['conds'])]
pr_wc = [[col for col, _, _ in hyp.sql_i['conds']]]
pr_wo = [[op for _, op, _ in hyp.sql_i['conds']]]
pr_sql_i = [hyp.sql_i]
pr_sql_q = [hyp.sql]
# Follosing variables are just for the consistency with no-EG case.
pr_wvi = None # not used
pr_wv_str = None
pr_wv_str_wp = None
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_wvi1_list, \
cnt_lx1_list, cnt_x1_list, cnt_list1, g_ans, pr_ans = agent.evaluation(
[pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi, pr_sql_i], [g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi, sql_i], engine, tb)
# save interaction records
record = {'nl': t[0]['question'], 'true_sql': g_sql_q[0], 'true_sql_i': "{}".format(sql_i[0]),
'init_sql': init_hyp.sql, 'init_sql_i': "{}".format(init_hyp.sql_i),
'sql': hyp.sql, 'sql_i': "{}".format(hyp.sql_i),
'dec_seq': "{}".format(hyp.dec_seq), 'tag_seq': "{}".format(hyp.tag_seq),
'logprob': "{}".format(hyp.logprob), #test time without dropout
'lx_correct': int(sum(cnt_lx1_list)), 'x_correct': int(sum(cnt_x1_list)),
'exit': bool_exit, 'q_counter': user.q_counter,
'questioned_indices': user.questioned_pointers,
'questioned_tags': "{}".format(user.questioned_tags),
'per_time_spent': str(per_time_spent), 'bool_unclear':bool_unclear,
'feedback_records': "{}".format(user.feedback_records),
'undo_semantic_units': "{}".format(user.undo_semantic_units),
'idx': iB}
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
record.update({'logprob_list': "{}".format(hyp.logprob_list),
'test_tag_seq': "{}".format(hyp.test_tag_seq)})
# interaction_records.append(record)
interaction_records[cnt] = record
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
current_cnt = [cnt_tot, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x]
cnt += 1
print("Saving records...")
json.dump({'interaction_records': interaction_records,
'current_cnt': "{}".format(current_cnt),
'st': cnt, 'time_spent': str(time_spent),
'count_exit': count_exit},
open(save_path, "w"), indent=4)
end_signal = input(bcolors.GREEN + bcolors.BOLD +
"Next? Press 'Enter' to continue, Ctrl+C to quit." + bcolors.ENDC)
if end_signal != "":
return
print(bcolors.RED + bcolors.BOLD + "Congratulations! You have completed all your task!" + bcolors.ENDC)
print("Your average time spent: {}".format((time_spent / len(interaction_records))))
print("You exited %d times." % count_exit)
def interaction(data_loader, data_table, user, agent, tokenizer,
max_seq_length, num_target_layers, detail=False, st_pos=0, cnt_tot=1,
path_db=None, dset_name='test', wikisql_sample_ids=None, bool_interaction=True):
ave_loss = 0
cnt = 0
cnt_sc = 0
cnt_sa = 0
cnt_wn = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_wvi = 0
cnt_lx = 0
cnt_x = 0
cnt_list = []
results = []
interaction_records = []
count_exit = 0
time_spent = 0.
count_failure = 0
engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
for iB, t in enumerate(data_loader):
if wikisql_sample_ids is not None and iB not in wikisql_sample_ids:
continue
cnt += len(t)
if cnt < st_pos:
continue
# Get fields
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, data_table, no_hs_t=True, no_sql_t=True)
g_sql_q = generate_sql_q(sql_i, tb)
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv = get_g(sql_i)
g_wvi_corenlp = get_g_wvi_corenlp(t)
start_time = time.time()
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(agent.world_model.bert_config, agent.world_model.model_bert, tokenizer,
nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
try:
g_wvi = get_g_wvi_bert_from_g_wvi_corenlp(t_to_tt_idx, g_wvi_corenlp)
g_wv_str, g_wv_str_wp = convert_pr_wvi_to_string(g_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
except:
# Exception happens when where-condition is not found in nlu_tt.
# In this case, that train example is not used.
# During test, that example considered as wrongly answered.
count_failure += 1
results1 = {}
results1["error"] = "Skip happened"
results1["nlu"] = nlu[0]
results1["table_id"] = tb[0]["id"]
results.append(results1)
print("## Failure %d" % count_failure)
interaction_records.append({'nl': t[0]['question'], 'true_sql': g_sql_q[0],
'true_sql_i': "{}".format(sql_i[0]), "q_counter": 0,
"questioned_indices": []})
continue
print("\n" + "#" * 50)
print("NL input: {}\nTrue SQL: {}".format(t[0]['question'], g_sql_q[0]))
# init decode
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
input_item = [tb, nlu_t, nlu, hds]
else:
input_item = [wemb_n, l_n, wemb_h, l_hpu, l_hs, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu]
hyp = agent.world_model.decode(input_item, dec_beam_size=1, bool_verbal=False)[0]
print("## time spent per decode: {:.3f}".format(time.time() - start_time))
print("-" * 50 + "\nBefore interaction: \ninitial SQL: {}".format(hyp.sql))
Hypothesis.print_hypotheses([hyp])
pr_sc = [hyp.sql_i['sel']]
pr_sa = [hyp.sql_i['agg']]
pr_wn = [len(hyp.sql_i['conds'])]
pr_wc = [[col for col, _, _ in hyp.sql_i['conds']]]
pr_wo = [[op for _, op, _ in hyp.sql_i['conds']]]
pr_sql_i = [hyp.sql_i]
pr_wvi = None # not used
print("initial evaluation: ")
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_wvi1_list, \
cnt_lx1_list, cnt_x1_list, cnt_list1, g_ans, pr_ans = agent.evaluation(
[pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi, pr_sql_i],
[g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi, sql_i], engine, tb, bool_verbal=True)
if not bool_interaction:
record = {'nl': t[0]['question'], 'true_sql': g_sql_q[0], 'true_sql_i': "{}".format(sql_i[0]),
'sql': "{}".format(hyp.sql), 'sql_i': "{}".format(hyp.sql_i),
'dec_seq': "{}".format(hyp.dec_seq), 'tag_seq': "{}".format(hyp.tag_seq),
'logprob': "{}".format(hyp.logprob),
'lx_correct': int(sum(cnt_lx1_list)), 'x_correct': int(sum(cnt_x1_list)),
"q_counter": 0, "questioned_indices": []}
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
record.update({'logprob_list': "{}".format(hyp.logprob_list),
'test_tag_seq': "{}".format(hyp.test_tag_seq)})
interaction_records.append(record)
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
# report
if detail:
pr_wv_str = None
current_cnt = [cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x]
report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_i, pr_ans,
cnt_list1, current_cnt)
continue
# interaction
g_sql = sql_i[0]
g_sql["g_wvi"] = g_wvi[0]
hyp, bool_exit = agent.interactive_parsing_session(user, input_item, g_sql, hyp, bool_verbal=False)
print("-" * 50 + "\nAfter interaction:\nfinal SQL: {}".format(hyp.sql))
Hypothesis.print_hypotheses([hyp])
print("final evaluation: ")
# Saving for the official evaluation later.
results1 = {}
results1["query"] = hyp.sql_i
results1["table_id"] = tb[0]["id"]
results1["nlu"] = nlu[0]
results.append(results1)
pr_sc = [hyp.sql_i['sel']]
pr_sa = [hyp.sql_i['agg']]
pr_wn = [len(hyp.sql_i['conds'])]
pr_wc = [[col for col, _, _ in hyp.sql_i['conds']]]
pr_wo = [[op for _, op, _ in hyp.sql_i['conds']]]
pr_sql_i = [hyp.sql_i]
pr_sql_q = [hyp.sql]
# Follosing variables are just for the consistency with no-EG case.
pr_wvi = None # not used
pr_wv_str = None
pr_wv_str_wp = None
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_wvi1_list, \
cnt_lx1_list, cnt_x1_list, cnt_list1, g_ans, pr_ans = agent.evaluation(
[pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi, pr_sql_i],
[g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi, sql_i], engine, tb, bool_verbal=True)
# save interaction records
record = {'nl': t[0]['question'], 'true_sql': g_sql_q[0], 'true_sql_i': "{}".format(sql_i[0]),
'sql': hyp.sql, 'sql_i': "{}".format(hyp.sql_i),
'dec_seq': "{}".format(hyp.dec_seq), 'tag_seq': "{}".format(hyp.tag_seq),
'logprob': "{}".format(hyp.logprob), #test time without dropout
'lx_correct': int(sum(cnt_lx1_list)), 'x_correct': int(sum(cnt_x1_list)),
'exit': bool_exit, 'q_counter': user.q_counter,
'questioned_indices': user.questioned_pointers,
'questioned_tags': "{}".format(user.questioned_tags)}
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
record.update({'logprob_list': "{}".format(hyp.logprob_list),
'test_tag_seq': "{}".format(hyp.test_tag_seq)})
interaction_records.append(record)
time_spent += (time.time() - start_time)
if bool_exit:
count_exit += 1
# stat
ave_loss += 0.
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
current_cnt = [cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x]
cnt_list.append(cnt_list1)
# report
if detail:
report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_q, pr_ans,
cnt_list1, current_cnt)
ave_loss /= cnt
acc_sc = cnt_sc / cnt
acc_sa = cnt_sa / cnt
acc_wn = cnt_wn / cnt
acc_wc = cnt_wc / cnt
acc_wo = cnt_wo / cnt
acc_wvi = cnt_wvi / cnt
acc_wv = cnt_wv / cnt
acc_lx = cnt_lx / cnt
acc_x = cnt_x / cnt
if not bool_interaction:
acc = [ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x]
return acc, results, cnt_list, interaction_records
# stats
q_count = sum([item['q_counter'] for item in interaction_records])
dist_q_count = sum([len(set(item['questioned_indices'])) for item in interaction_records])
print("#questions: {}, #questions per example: {:.3f} (exclude options: {:.3f}).".format(
q_count, q_count * 1.0 / len(interaction_records), dist_q_count * 1.0 / len(interaction_records)))
print("#exit: {}".format(count_exit))
print("Avg time spent: {:.3f}".format((time_spent / len(interaction_records))))
acc = [ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x]
return acc, results, cnt_list, interaction_records
def train_fast(train_loader, train_table, model, model_bert, opt, bert_config, tokenizer,
max_seq_length, num_target_layers, accumulate_gradients=1, check_grad=True,
st_pos=0, opt_bert=None, path_db=None, dset_name='train'):
model.train()
model_bert.train()
ave_loss = 0
cnt = 0 # count the # of examples
cnt_sc = 0 # count the # of correct predictions of select column
cnt_sa = 0 # of selectd aggregation
cnt_wn = 0 # of where number
cnt_wc = 0 # of where column
cnt_wo = 0 # of where operator
cnt_wv = 0 # of where-value
cnt_wvi = 0 # of where-value index (on question tokens)
cnt_lx = 0 # of logical form acc
cnt_x = 0 # of execution acc
# # Engine for SQL querying.
# engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
for iB, t in enumerate(train_loader):
cnt += len(t)
if cnt < st_pos:
continue
if len(t[0]) == 25:
input_ids, input_mask, segment_ids, tokens, tb, sql_i, hds, i_nlu, i_hds, l_n, l_hpu_batch, l_hs, \
nlu, nlu_t, nlu_tt, t_to_tt_idx, tt_to_t_idx, g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wvi, g_wvi_corenlp = \
list(zip(*t))
weight_sa = weight_sc = weight_wn = weight_wc = weight_wo = weight_wvi = None
else:
assert len(t[0]) == 31
input_ids, input_mask, segment_ids, tokens, tb, sql_i, hds, i_nlu, i_hds, l_n, l_hpu_batch, l_hs, \
nlu, nlu_t, nlu_tt, t_to_tt_idx, tt_to_t_idx, g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wvi, g_wvi_corenlp, \
weight_sc, weight_sa, weight_wn, weight_wc, weight_wo, weight_wvi = \
list(zip(*t))
l_hpu = [hpu1 for l_hpu1 in l_hpu_batch for hpu1 in l_hpu1]
# bert encoding
all_input_ids = torch.tensor(input_ids, dtype=torch.long).to(device)
all_input_mask = torch.tensor(input_mask, dtype=torch.long).to(device)
all_segment_ids = torch.tensor(segment_ids, dtype=torch.long).to(device)
wemb_n, wemb_h = get_wemb_bert_fast(bert_config, model_bert, i_hds, l_n, l_hpu, l_hs,
all_input_ids, all_segment_ids, all_input_mask,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
# score
s_sc, s_sa, s_wn, s_wc, s_wo, s_wv = model(wemb_n, l_n, wemb_h, l_hpu, l_hs,
g_sc=g_sc, g_sa=g_sa, g_wn=g_wn, g_wc=g_wc, g_wvi=g_wvi)
# Calculate loss & step
loss = Loss_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi,
weight_sc=weight_sc, weight_sa=weight_sa, weight_wn=weight_wn,
weight_wc=weight_wc, weight_wo=weight_wo, weight_wvi=weight_wvi)
# Calculate gradient
if iB % accumulate_gradients == 0: # mode
# at start, perform zero_grad
if opt:
opt.zero_grad()
if opt_bert:
opt_bert.zero_grad()
loss.backward()
if accumulate_gradients == 1:
if opt:
opt.step()
if opt_bert:
opt_bert.step()
elif iB % accumulate_gradients == (accumulate_gradients - 1):
# at the final, take step with accumulated graident
loss.backward()
if opt:
opt.step()
if opt_bert:
opt_bert.step()
else:
# at intermediate stage, just accumulates the gradients
loss.backward()
# Prediction
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi = pred_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, )
pr_wv_str, pr_wv_str_wp = convert_pr_wvi_to_string(pr_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
# Sort pr_wc:
# Sort pr_wc when training the model as pr_wo and pr_wvi are predicted using ground-truth where-column (g_wc)
# In case of 'dev' or 'test', it is not necessary as the ground-truth is not used during inference.
pr_wc_sorted = sort_pr_wc(pr_wc, g_wc)
pr_sql_i = generate_sql_i(pr_sc, pr_sa, pr_wn, pr_wc_sorted, pr_wo, pr_wv_str, nlu)
# Cacluate accuracy
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, \
cnt_wc1_list, cnt_wo1_list, \
cnt_wvi1_list, cnt_wv1_list = get_cnt_sw_list(g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi,
sql_i, pr_sql_i,
mode='train')
cnt_lx1_list = get_cnt_lx_list(cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list,
cnt_wo1_list, cnt_wv1_list)
# lx stands for logical form accuracy
# # Execution accuracy test.
# cnt_x1_list, g_ans, pr_ans = get_cnt_x_list(engine, tb, g_sc, g_sa, sql_i, pr_sc, pr_sa, pr_sql_i)
# statistics
ave_loss += loss.item()
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_lx += sum(cnt_lx1_list)
# cnt_x += sum(cnt_x1_list)
ave_loss /= cnt
acc_sc = cnt_sc / cnt
acc_sa = cnt_sa / cnt
acc_wn = cnt_wn / cnt
acc_wc = cnt_wc / cnt
acc_wo = cnt_wo / cnt
acc_wvi = cnt_wv / cnt
acc_wv = cnt_wv / cnt
acc_lx = cnt_lx / cnt
acc_x = cnt_x / cnt
acc = [ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x]
aux_out = 1
return acc, aux_out
def test_fast(data_loader, data_table, model, model_bert, bert_config, tokenizer, max_seq_length,
num_target_layers, detail=False, st_pos=0, cnt_tot=1, EG=False, beam_size=4,
path_db=None, dset_name='test', bool_ex=False):
model.eval()
model_bert.eval()
ave_loss = 0
cnt = 0
cnt_sc = 0
cnt_sa = 0
cnt_wn = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_wvi = 0
cnt_lx = 0
cnt_x = 0
cnt_list = []
engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
results = []
for iB, t in enumerate(data_loader):
cnt += len(t)
if cnt < st_pos:
continue
input_ids, input_mask, segment_ids, tokens, tb, sql_i, hds, i_nlu, i_hds, l_n, l_hpu_batch, l_hs, \
nlu, nlu_t, nlu_tt, t_to_tt_idx, tt_to_t_idx, g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wvi, g_wvi_corenlp = \
list(zip(*t))
l_hpu = [hpu1 for l_hpu1 in l_hpu_batch for hpu1 in l_hpu1]
try:
g_wv_str, g_wv_str_wp = convert_pr_wvi_to_string(g_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
except:
# Exception happens when where-condition is not found in nlu_tt.
# In this case, that train example is not used.
# During test, that example considered as wrongly answered.
for b in range(len(nlu)):
results1 = {}
results1["error"] = "Skip happened"
results1["nlu"] = nlu[b]
results1["table_id"] = tb[b]["id"]
results.append(results1)
continue
# bert encoding
all_input_ids = torch.tensor(input_ids, dtype=torch.long).to(device)
all_input_mask = torch.tensor(input_mask, dtype=torch.long).to(device)
all_segment_ids = torch.tensor(segment_ids, dtype=torch.long).to(device)
wemb_n, wemb_h = get_wemb_bert_fast(bert_config, model_bert, i_hds, l_n, l_hpu, l_hs,
all_input_ids, all_segment_ids, all_input_mask,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
# model specific part
# score
if not EG:
# No Execution guided decoding
s_sc, s_sa, s_wn, s_wc, s_wo, s_wv = model(wemb_n, l_n, wemb_h, l_hpu, l_hs)
# get loss & step
loss = Loss_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi)
# prediction
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi = pred_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, )
pr_wv_str, pr_wv_str_wp = convert_pr_wvi_to_string(pr_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
# g_sql_i = generate_sql_i(g_sc, g_sa, g_wn, g_wc, g_wo, g_wv_str, nlu)
pr_sql_i = generate_sql_i(pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, nlu)
else:
# Execution guided decoding
prob_sca, prob_w, prob_wn_w, pr_sc, pr_sa, pr_wn, pr_sql_i = model.beam_forward(wemb_n, l_n, wemb_h, l_hpu,
l_hs, engine, tb,
nlu_t, nlu_tt,
tt_to_t_idx, nlu,
beam_size=beam_size)
# sort and generate
pr_wc, pr_wo, pr_wv, pr_sql_i = sort_and_generate_pr_w(pr_sql_i)
# Follosing variables are just for the consistency with no-EG case.
pr_wvi = None # not used
pr_wv_str = None
pr_wv_str_wp = None
loss = torch.tensor([0])
g_sql_q = generate_sql_q(sql_i, tb)
pr_sql_q = generate_sql_q(pr_sql_i, tb)
# Saving for the official evaluation later.
for b, pr_sql_i1 in enumerate(pr_sql_i):
results1 = {}
results1["query"] = pr_sql_i1
results1["table_id"] = tb[b]["id"]
results1["nlu"] = nlu[b]
results.append(results1)
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, \
cnt_wc1_list, cnt_wo1_list, \
cnt_wvi1_list, cnt_wv1_list = get_cnt_sw_list(g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi,
sql_i, pr_sql_i,
mode='test')
cnt_lx1_list = get_cnt_lx_list(cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list,
cnt_wo1_list, cnt_wv1_list)
# Execution accura y test
cnt_x1_list = []
g_ans = pr_ans = None
# lx stands for logical form accuracy
# Execution accuracy test.
if bool_ex:
cnt_x1_list, g_ans, pr_ans = get_cnt_x_list(engine, tb, g_sc, g_sa, sql_i, pr_sc, pr_sa, pr_sql_i)
# stat
ave_loss += loss.item()
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
current_cnt = [cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x]
cnt_list1 = [cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_lx1_list,
cnt_x1_list]
cnt_list.append(cnt_list1)
# report
if detail:
report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_q, pr_ans,
cnt_list1, current_cnt)
ave_loss /= cnt
acc_sc = cnt_sc / cnt
acc_sa = cnt_sa / cnt
acc_wn = cnt_wn / cnt
acc_wc = cnt_wc / cnt
acc_wo = cnt_wo / cnt
acc_wvi = cnt_wvi / cnt
acc_wv = cnt_wv / cnt
acc_lx = cnt_lx / cnt
acc_x = cnt_x / cnt
acc = [ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x]
return acc, results, cnt_list
def run_epochs(model, model_bert, opt, opt_bert, bert_config, tokenizer, path_wikisql, model_path,
train_loader, train_table, dev_loader, dev_table, test_loader, test_table,
early_stop_ep=None, bool_eval=True, startime_time=None):
# some args
tepoch = 100
accumulate_gradients = 4
assert bool_eval
print("## Actual tepoch %d, accumulate_gradients %d " % (tepoch, accumulate_gradients))
print("## Early stop epoch: {}".format(early_stop_ep))
max_seq_length = 222
num_target_layers = 2
acc_lx_t_best = -1
acc_ex_t_best = -1
epoch_best = -1
patience_counter = 0
for epoch in range(tepoch):
# train
acc_train, aux_out_train = train_fast(train_loader,
train_table,
model,
model_bert,
opt,
bert_config,
tokenizer,
max_seq_length,
num_target_layers,
accumulate_gradients,
opt_bert=opt_bert,
st_pos=0,
path_db=path_wikisql,
dset_name='train')
print_result(epoch, acc_train, 'train')
# check DEV
if bool_eval:
with torch.no_grad():
acc_dev, results_dev, cnt_list = test_fast(dev_loader,
dev_table,
model,
model_bert,
bert_config,
tokenizer,
max_seq_length,
num_target_layers,
detail=False,
path_db=path_wikisql,
st_pos=0,
dset_name='dev', EG=False, bool_ex=False)
print_result(epoch, acc_dev, 'dev')
# save best model
# Based on Dev Set logical accuracy lx
acc_lx_t = acc_dev[-2]
acc_ex_t = acc_dev[-1]
if acc_lx_t > acc_lx_t_best:
acc_lx_t_best = acc_lx_t
acc_ex_t_best = acc_ex_t
epoch_best = epoch
patience_counter = 0
# save best model
state = {'model': model.state_dict()}
torch.save(state, os.path.join(model_path, 'model_best.pt'))
state = {'model_bert': model_bert.state_dict()}
torch.save(state, os.path.join(model_path, 'model_bert_best.pt'))
else:
patience_counter += 1
if early_stop_ep is not None and patience_counter == early_stop_ep:
print(" Early stop!")
break
print(f" Best Dev lx acc: {acc_lx_t_best} at epoch: {epoch_best}")
print(" Time stamp: {}".format(datetime.datetime.now()))
if startime_time is not None:
print(" Time spent: {}".format(datetime.datetime.now() - startime_time))
sys.stdout.flush()
# load back the best model checkpoint
print("Loading back best checkpoints...")
if torch.cuda.is_available():
res = torch.load(os.path.join(model_path, 'model_bert_best.pt'))
else:
res = torch.load(os.path.join(model_path, 'model_bert_best.pt'), map_location='cpu')
model_bert.load_state_dict(res['model_bert'])
model_bert.to(device)
if torch.cuda.is_available():
res = torch.load(os.path.join(model_path, 'model_best.pt'))
else:
res = torch.load(os.path.join(model_path, 'model_best.pt'), map_location='cpu')
model.load_state_dict(res['model'])
# evaluate: dev lx/ex acc, test lx/ex acc
with torch.no_grad():
acc_dev, results_dev, cnt_list = test_fast(dev_loader,
dev_table,
model,
model_bert,
bert_config,
tokenizer,
max_seq_length,
num_target_layers,
detail=False,
path_db=path_wikisql,
st_pos=0,
dset_name='dev', EG=False, bool_ex=True)
print_result(-1, acc_dev, 'dev')
dev_acc_lx_t_best = acc_dev[-2]
dev_acc_ex_t_best = acc_dev[-1]
acc_test, results_test, cnt_list = test_fast(test_loader,
test_table,
model,
model_bert,
bert_config,
tokenizer,
max_seq_length,
num_target_layers,
detail=False,
path_db=path_wikisql,
st_pos=0,
dset_name='test', EG=False, bool_ex=True)
print_result(-1, acc_test, 'test')
test_acc_lx_t_best = acc_test[-2]
test_acc_ex_t_best = acc_test[-1]
return dev_acc_lx_t_best, dev_acc_ex_t_best, test_acc_lx_t_best, test_acc_ex_t_best
def online_learning_full_expert(agent, init_train_data, online_train_data, train_table,
val_data, val_table, test_data, test_table,
path_db, model_save_path, update_iter, model_renew_fn,
start_idx=0, end_idx=-1, batch_size=16):
# online learning with full supervision (complete SQL query annotation)
num_total_examples = len(online_train_data)
print("## data size: %d " % num_total_examples)
print("## update_iter: %d " % update_iter)
print("## start_idx: %d" % start_idx)
print("## end_idx: %d" % end_idx)
learning_start_time = datetime.datetime.now()
print("## Online starting time: {}".format(learning_start_time))
annotation_costs = []
# pre-calculate annotation cost
for item in online_train_data:
query = item[5] # 'query'
cost = 2 + len(query["conds"]) * 3
annotation_costs.append(cost)
for st in np.arange(start_idx, num_total_examples, update_iter):
annotation_buffer = online_train_data[0: st+update_iter]
iter_annotation_buffer = online_train_data[st: st+update_iter]
count_iter = len(annotation_buffer)
print("~~~\nUpdating base semantic parser at iter {}".format(count_iter))
# print information about buffer
for item in iter_annotation_buffer:
print("NL input: {}".format(item[12])) # 'question'
model = agent.world_model.semparser
model_bert = agent.world_model.model_bert
print("Retraining from scratch...")
update_buffer = init_train_data + annotation_buffer
model_renew_fn(model, model_bert)
print("Train data size: %d" % len(update_buffer))
opt, opt_bert = get_opt(model, model_bert, True)
train_loader, dev_loader = get_loader_wikisql(update_buffer, val_data, batch_size, shuffle_train=True)
test_loader = get_loader_wikisql_v2(test_data, batch_size, False)
# train
print("## Starting update at iter {}, anno_cost {}...time spent {}".format(
count_iter, sum(annotation_costs[0:st+update_iter]), datetime.datetime.now() - learning_start_time))
model_dir = os.path.join(model_save_path, '%d/' % count_iter)
if not os.path.isdir(model_dir):
os.mkdir(model_dir)
dev_acc_lx_t_best, dev_acc_ex_t_best, test_acc_lx_t_best, test_acc_ex_t_best = run_epochs(
model, model_bert, opt, opt_bert, bert_config, tokenizer, path_db,
model_dir, train_loader, train_table, dev_loader, val_table, test_loader, test_table,
early_stop_ep=EARLY_STOP_EPOCH_STAGE1 if count_iter <= EARLY_THRESHOLD else EARLY_STOP_EPOCH_STAGE2,
bool_eval=True, startime_time=learning_start_time)
print("## Ending update at iter {}, anno_cost {}, dev acc_lx {}, dev acc_ex {}, test acc_lx {}, "
"test acc_ex {}...time spent {}\n".format(
count_iter, sum(annotation_costs[0:st+update_iter]), dev_acc_lx_t_best, dev_acc_ex_t_best,
test_acc_lx_t_best, test_acc_ex_t_best,
datetime.datetime.now() - learning_start_time))
sys.stdout.flush()
if end_idx != -1 and count_iter == end_idx:
print("## Ending online learning at iter {}\n".format(end_idx))
break
print("## End full training at time {}...time spent {}\n".format(
datetime.datetime.now(), datetime.datetime.now() - learning_start_time))
def extract_weighted_example(source_t, tt_to_t_idx, gen_sql_i, gen_tag_seq, feedback_records=None,
weight_mode='pos,neg,conf', conf_threshold=None):
def check_invalidity(weight_sql):
return weight_sql['sel'] or weight_sql['agg'] or sum([sum(cond) for cond in weight_sql['conds']])
annotated_example = deepcopy(source_t)
annotated_example["sql"] = gen_sql_i
annotated_example["query"] = gen_sql_i
annotated_example["wvi_corenlp"] = []
for _cond in gen_sql_i["conds"]:
for su in gen_tag_seq:
if su[0] == WHERE_VAL and _cond[0] == su[1][0][-1] and _cond[1] == su[2][-1] and \
_cond[2] == su[3][-1]:
annotated_example["wvi_corenlp"].append([tt_to_t_idx[su[3][0]], tt_to_t_idx[su[3][1]]])
assert len(annotated_example["wvi_corenlp"]) == len(gen_sql_i["conds"])
# get weights
if weight_mode == "pos" or weight_mode == "pos,conf":
annotated_example["weight_sql"] = {'sel': 0.0, 'agg': 0.0,
'conds': [[0.0, 0.0, 0.0] for _ in range(len(gen_sql_i["conds"]))]}
# add pos
for su, label in feedback_records:
if label == 'no':
continue
seg_id = su[0]
if seg_id == SELECT_COL and annotated_example["sql"]["sel"] == su[1][-1]:
annotated_example["weight_sql"]["sel"] = 1.0
elif seg_id == SELECT_AGG and annotated_example["sql"]["agg"] == su[2][-1]:
annotated_example["weight_sql"]["agg"] = 1.0
elif seg_id == WHERE_COL:
col_idx = su[1][-1]
for idx in range(len(annotated_example["sql"]["conds"])):
if annotated_example["sql"]["conds"][idx][0] == col_idx:
annotated_example["weight_sql"]["conds"][idx][0] = 1.0
break
elif seg_id == WHERE_OP:
col_idx = su[1][0][-1]
op_idx = su[2][-1]
for idx in range(len(annotated_example["sql"]["conds"])):
if annotated_example["sql"]["conds"][idx][0] == col_idx and \
annotated_example["sql"]["conds"][idx][1] == op_idx:
annotated_example["weight_sql"]["conds"][idx][1] = 1.0
break
elif seg_id == WHERE_VAL:
col_idx = su[1][0][-1]
op_idx = su[2][-1]
val_str = su[3][-1]
for idx in range(len(annotated_example["sql"]["conds"])):
if annotated_example["sql"]["conds"][idx][0] == col_idx and \
annotated_example["sql"]["conds"][idx][1] == op_idx and \
annotated_example["sql"]["conds"][idx][2] == val_str:
annotated_example["weight_sql"]["conds"][idx][2] = 1.0
break
if weight_mode == "pos,conf":
# add confident decisions
for su in gen_tag_seq:
prob = su[-2]
if prob is None or prob < conf_threshold:
continue
seg_id = su[0]
if seg_id == SELECT_COL and annotated_example["sql"]["sel"] == su[1][-1]:
annotated_example["weight_sql"]["sel"] = 1.0
elif seg_id == SELECT_AGG and annotated_example["sql"]["agg"] == su[2][-1]:
annotated_example["weight_sql"]["agg"] = 1.0
elif seg_id == WHERE_COL:
col_idx = su[1][-1]
for idx in range(len(annotated_example["sql"]["conds"])):
if annotated_example["sql"]["conds"][idx][0] == col_idx:
annotated_example["weight_sql"]["conds"][idx][0] = 1.0
break
elif seg_id == WHERE_OP:
col_idx = su[1][0][-1]
op_idx = su[2][-1]
for idx in range(len(annotated_example["sql"]["conds"])):
if annotated_example["sql"]["conds"][idx][0] == col_idx and \
annotated_example["sql"]["conds"][idx][1] == op_idx:
annotated_example["weight_sql"]["conds"][idx][1] = 1.0
break
elif seg_id == WHERE_VAL:
col_idx = su[1][0][-1]
op_idx = su[2][-1]
val_str = su[3][-1]
for idx in range(len(annotated_example["sql"]["conds"])):
if annotated_example["sql"]["conds"][idx][0] == col_idx and \
annotated_example["sql"]["conds"][idx][1] == op_idx and \
annotated_example["sql"]["conds"][idx][2] == val_str:
annotated_example["weight_sql"]["conds"][idx][2] = 1.0
break
if "weight_sql" not in annotated_example or check_invalidity(annotated_example["weight_sql"]):
return annotated_example
else:
return None
def online_learning(supervision, user, agent, init_train_data, online_data_loader, train_table,
val_data, val_table, test_data, test_table, update_iter, model_save_path, record_save_path,
model_renew_fn, max_seq_length=222, num_target_layers=2, detail=False,
st_pos=0, end_pos=-1, cnt_tot=1, path_db=None, batch_size=16):
ave_loss = 0
cnt = 0
cnt_sc = 0
cnt_sa = 0
cnt_wn = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_wvi = 0
cnt_lx = 0
cnt_x = 0
cnt_list = []
results = []
interaction_records_dict = {'records': [], 'start_iter': 0}
interaction_records = interaction_records_dict['records']
count_exit = 0
count_failure = 0
count_iter = 0 # online iteration
num_total_examples = len(online_data_loader.dataset)
annotation_buffer = [] # processed
iter_annotation_buffer = [] # processed
assert supervision.startswith('misp_neil')
weight_mode = "pos,conf" # misp_neil
if supervision == "misp_neil_pos":
weight_mode = "pos"
print("## supervision: %s, weight_mode: %s " % (supervision, weight_mode))
print("## data size: %d " % num_total_examples)
print("## update_iter: %d " % update_iter)
print("## st_pos: %d " % st_pos)
# preprocessing initial training data
init_train_data = data_preprocessing(agent.world_model.tokenizer, init_train_data, train_table,
max_seq_length, bool_remove_none=True,
bool_loss_weight=weight_mode != "pos,neg,conf")
if st_pos > 0:
print("## WARNING: inaccurate interaction performance report...")
print("Loading interaction records from %s..." % record_save_path)
interaction_records_dict = json.load(open(record_save_path, 'r'))
interaction_records = interaction_records_dict['records']
print("Record item size: %d " % len(interaction_records))
learning_start_time = datetime.datetime.now()
print("## Online starting time: {}".format(learning_start_time))
dset_name = 'train'
engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
for iB, t in enumerate(online_data_loader):
cnt += len(t)
assert len(t) == 1
# if cnt <= st_pos:
# count_iter += 1
# continue
# Get fields
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, train_table, no_hs_t=True, no_sql_t=True)
g_sql_q = generate_sql_q(sql_i, tb)
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv = get_g(sql_i)
g_wvi_corenlp = get_g_wvi_corenlp(t)
# if the record has contained this piece
if len(interaction_records) >= cnt:
record = interaction_records[cnt - 1]
if 'sql_i' not in record: # failure case
continue
gen_sql_i = eval(record['sql_i'])
gen_tag_seq = eval(record['tag_seq'])
assert g_sql_q[0] == record['true_sql']
# BERT processing: 2nd tokenization using WordPiece
tt_to_t_idx1 = [] # number indicates where sub-token belongs to in 1st-level-tokens (here, CoreNLP).
for (i, token) in enumerate(nlu_t[0]):
sub_tokens = agent.world_model.tokenizer.tokenize(token)
for sub_token in sub_tokens:
tt_to_t_idx1.append(i)
if 'feedback_records' in record:
feedback_records = eval(record['feedback_records'])
else:
feedback_records = None
assert weight_mode == "pos,neg,conf"
# extract example and add to annotation buffer
annotated_example = extract_weighted_example(t[0], tt_to_t_idx1, gen_sql_i, gen_tag_seq,
feedback_records, weight_mode,
agent.error_detector.prob_threshold)
if annotated_example is not None:
iter_annotation_buffer.append(annotated_example)
count_iter += 1
if count_iter % update_iter == 0:
print(" count_iter %d, nl %s" % (count_iter, record['nl']))
print(" Time stamp: {}".format(datetime.datetime.now()))
else:
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(agent.world_model.bert_config, agent.world_model.model_bert,
agent.world_model.tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
try:
g_wvi = get_g_wvi_bert_from_g_wvi_corenlp(t_to_tt_idx, g_wvi_corenlp)
g_wv_str, g_wv_str_wp = convert_pr_wvi_to_string(g_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
except:
# Exception happens when where-condition is not found in nlu_tt.
# In this case, that train example is not used.
# During test, that example considered as wrongly answered.
count_failure += 1
results1 = {}
results1["error"] = "Skip happened"
results1["nlu"] = nlu[0]
results1["table_id"] = tb[0]["id"]
results.append(results1)
print("## Failure %d" % count_failure)
interaction_records.append({'nl': t[0]['question'], 'true_sql': g_sql_q[0],
'true_sql_i': "{}".format(sql_i[0]),
"questioned_indices": [], 'q_counter': 0})
continue
print("\n" + "#" * 50)
print("NL input: {}\nTrue SQL: {}".format(t[0]['question'], g_sql_q[0]))
# init decode
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
input_item = [tb, nlu_t, nlu, hds]
else:
input_item = [wemb_n, l_n, wemb_h, l_hpu, l_hs, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu]
hyp = agent.world_model.decode(input_item, dec_beam_size=1, bool_verbal=False)[0]
print("-" * 50 + "\nBefore interaction: \ninitial SQL: {}".format(hyp.sql))
Hypothesis.print_hypotheses([hyp])
pr_sc = [hyp.sql_i['sel']]
pr_sa = [hyp.sql_i['agg']]
pr_wn = [len(hyp.sql_i['conds'])]
pr_wc = [[col for col, _, _ in hyp.sql_i['conds']]]
pr_wo = [[op for _, op, _ in hyp.sql_i['conds']]]
pr_sql_i = [hyp.sql_i]
pr_wvi = None # not used
print("initial evaluation: ")
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_wvi1_list, \
cnt_lx1_list, cnt_x1_list, cnt_list1, g_ans, pr_ans = agent.evaluation(
[pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi, pr_sql_i],
[g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi, sql_i], engine, tb, bool_verbal=True)
g_sql = sql_i[0]
g_sql["g_wvi"] = g_wvi[0]
hyp, bool_exit = agent.interactive_parsing_session(user, input_item, g_sql, hyp, bool_verbal=False)
print("-" * 50 + "\nAfter interaction:\nfinal SQL: {}".format(hyp.sql))
Hypothesis.print_hypotheses([hyp])
print("final evaluation: ")
# Saving for the official evaluation later.
results1 = {}
results1["query"] = hyp.sql_i
results1["table_id"] = tb[0]["id"]
results1["nlu"] = nlu[0]
results.append(results1)
pr_sc = [hyp.sql_i['sel']]
pr_sa = [hyp.sql_i['agg']]
pr_wn = [len(hyp.sql_i['conds'])]
pr_wc = [[col for col, _, _ in hyp.sql_i['conds']]]
pr_wo = [[op for _, op, _ in hyp.sql_i['conds']]]
pr_sql_i = [hyp.sql_i]
pr_sql_q = [hyp.sql]
# Follosing variables are just for the consistency with no-EG case.
pr_wvi = None # not used
pr_wv_str = None
pr_wv_str_wp = None
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_wvi1_list, \
cnt_lx1_list, cnt_x1_list, cnt_list1, g_ans, pr_ans = agent.evaluation(
[pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi, pr_sql_i],
[g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi, sql_i], engine, tb, bool_verbal=True)
# save interaction records
record = {'nl': t[0]['question'], 'true_sql': g_sql_q[0], 'true_sql_i': "{}".format(sql_i[0]),
'sql': hyp.sql, 'sql_i': "{}".format(hyp.sql_i),
'dec_seq': "{}".format(hyp.dec_seq), 'tag_seq': "{}".format(hyp.tag_seq),
'logprob': "{}".format(hyp.logprob), # test time without dropout
'lx_correct': int(sum(cnt_lx1_list)), 'x_correct': int(sum(cnt_x1_list)),
'exit': bool_exit, 'q_counter': user.q_counter,
'questioned_indices': user.questioned_pointers,
'questioned_tags': "{}".format(user.questioned_tags),
'feedback_records': "{}".format(user.feedback_records)}
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
record.update({'logprob_list': "{}".format(hyp.logprob_list),
'test_tag_seq': "{}".format(hyp.test_tag_seq)})
interaction_records.append(record)
# extract example and add to annotation buffer
annotated_example = extract_weighted_example(t[0], tt_to_t_idx[0], hyp.sql_i, hyp.tag_seq,
user.feedback_records, weight_mode,
agent.error_detector.prob_threshold)
if annotated_example is not None:
iter_annotation_buffer.append(annotated_example)
if bool_exit:
count_exit += 1
# stat
ave_loss += 0.
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
current_cnt = [cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x]
cnt_list.append(cnt_list1)
# report
if detail:
report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_q, pr_ans,
cnt_list1, current_cnt)
count_iter += 1
del wemb_n, wemb_h # garbage collecting
if count_iter % update_iter == 0 or count_iter == num_total_examples: # update model
if count_iter <= st_pos:
# preprocessing
iter_annotation_buffer = data_preprocessing(agent.world_model.tokenizer,
iter_annotation_buffer, train_table,
max_seq_length, bool_remove_none=True,
bool_loss_weight=weight_mode != "pos,neg,conf")
annotation_buffer.extend(iter_annotation_buffer)
iter_annotation_buffer = []
continue
print("\n~~~\nCurrent interaction performance (iter {}): ".format(count_iter)) # interaction so far
_ave_loss = ave_loss / cnt
_acc_sc = cnt_sc / cnt
_acc_sa = cnt_sa / cnt
_acc_wn = cnt_wn / cnt
_acc_wc = cnt_wc / cnt
_acc_wo = cnt_wo / cnt
_acc_wvi = cnt_wvi / cnt
_acc_wv = cnt_wv / cnt
_acc_lx = cnt_lx / cnt
_acc_x = cnt_x / cnt
_acc = [_ave_loss, _acc_sc, _acc_sa, _acc_wn, _acc_wc, _acc_wo, _acc_wvi, _acc_wv, _acc_lx, _acc_x]
print("Interaction acc: {}".format(_acc))
q_count = sum([item['q_counter'] for item in interaction_records])
dist_q_count = sum([len(set(item['questioned_indices'])) for item in interaction_records])
print("Interaction #questions: {}, #questions per example: {:.3f} (exclude options: {:.3f}).".format(
q_count, q_count * 1.0 / len(interaction_records), dist_q_count * 1.0 / len(interaction_records)))
print("Interaction #exit: {}".format(count_exit))
print("~~~\n")
print("Saving interaction records to %s..." % record_save_path)
json.dump(interaction_records_dict, open(record_save_path, 'w'), indent=4)
# preprocessing
iter_annotation_buffer = data_preprocessing(agent.world_model.tokenizer,
iter_annotation_buffer, train_table,
max_seq_length, bool_remove_none=True,
bool_loss_weight=weight_mode != "pos,neg,conf")
annotation_buffer.extend(iter_annotation_buffer)
# parser update
print("~~~\nUpdating base semantic parser at iter {}".format(count_iter))
model = agent.world_model.semparser
model_bert = agent.world_model.model_bert
print("Retraining from scratch...")
update_buffer = init_train_data + annotation_buffer
# reset parameters
model_renew_fn(model, model_bert)
print("Train data size: %d " % len(update_buffer))
opt, opt_bert = get_opt(model, model_bert, True)
train_loader, dev_loader = get_loader_wikisql(update_buffer, val_data, batch_size, shuffle_train=True)
test_loader = get_loader_wikisql_v2(test_data, batch_size, False)
# train
print("## Starting update at iter {}, anno_cost {}...time spent {}".format(
count_iter, sum([item['q_counter'] for item in interaction_records]),
datetime.datetime.now() - learning_start_time))
model_dir = os.path.join(model_save_path, '%d/' % count_iter)
if not os.path.isdir(model_dir):
os.mkdir(model_dir)
dev_acc_lx_t_best, dev_acc_ex_t_best, test_acc_lx_t_best, test_acc_ex_t_best = run_epochs(
model, model_bert, opt, opt_bert, agent.world_model.bert_config,
agent.world_model.tokenizer, path_db, model_dir, train_loader,
train_table, dev_loader, val_table, test_loader, test_table,
early_stop_ep=EARLY_STOP_EPOCH_STAGE1 if count_iter <= EARLY_THRESHOLD else EARLY_STOP_EPOCH_STAGE2,
bool_eval=True, startime_time=learning_start_time)
print("## Ending update at iter {}, anno_cost {}, dev acc_lx {}, dev acc_ex {}, test acc_lx {},"
"test acc_ex {}...time spent {}\n".format(
count_iter, sum([item['q_counter'] for item in interaction_records]),
dev_acc_lx_t_best, dev_acc_ex_t_best, test_acc_lx_t_best, test_acc_ex_t_best,
datetime.datetime.now() - learning_start_time))
print("Update interaction_records_dict: start_iter = %d." % count_iter)
interaction_records_dict['start_iter'] = count_iter
print("Saving interaction records to %s..." % record_save_path)
json.dump(interaction_records_dict, open(record_save_path, 'w'), indent=4)
# clean
iter_annotation_buffer = []
# check end_pos
if end_pos != -1 and count_iter == end_pos:
print("## Ending online learning at iter {}\n".format(end_pos))
break
ave_loss /= cnt
acc_sc = cnt_sc / cnt
acc_sa = cnt_sa / cnt
acc_wn = cnt_wn / cnt
acc_wc = cnt_wc / cnt
acc_wo = cnt_wo / cnt
acc_wvi = cnt_wvi / cnt
acc_wv = cnt_wv / cnt
acc_lx = cnt_lx / cnt
acc_x = cnt_x / cnt
print("## End online learning at time {}...time spent {}\n".format(
datetime.datetime.now(), datetime.datetime.now() - learning_start_time))
# stats
q_count = sum([item['q_counter'] for item in interaction_records])
dist_q_count = sum([len(set(item['questioned_indices'])) for item in interaction_records])
print("#questions: {}, #questions per example: {:.3f} (exclude options: {:.3f}).".format(
q_count, q_count * 1.0 / len(interaction_records), dist_q_count * 1.0 / len(interaction_records)))
print("#exit: {}".format(count_exit))
acc = [ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x]
return acc, results, cnt_list, interaction_records_dict
def online_learning_self_train(supervision, agent, init_train_data, online_data_loader, train_table,
val_data, val_table, test_data, test_table, update_iter, model_save_path, record_save_path,
model_renew_fn, max_seq_length=222, num_target_layers=2, detail=False,
st_pos=0, end_pos=-1, cnt_tot=1, path_db=None, batch_size=16):
ave_loss = 0
cnt = 0
cnt_sc = 0
cnt_sa = 0
cnt_wn = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_wvi = 0
cnt_lx = 0
cnt_x = 0
cnt_list = []
results = []
interaction_records_dict = {'records': [], 'start_iter': 0}
interaction_records = interaction_records_dict['records']
count_exit = 0
count_failure = 0
count_iter = 0 # online iteration
num_total_examples = len(online_data_loader.dataset)
annotation_buffer = [] # processed
iter_annotation_buffer = [] # processed
print("## supervision:", supervision)
print("## data size: %d " % num_total_examples)
print("## update_iter: %d " % update_iter)
conf_threshold = None
if supervision == 'self_train_0.5':
conf_threshold = 0.5
print("## conf_threshold:", str(conf_threshold))
print("## st_pos: %d " % st_pos)
# preprocessing initial training data
init_train_data = data_preprocessing(agent.world_model.tokenizer, init_train_data, train_table,
max_seq_length, bool_remove_none=True,
bool_loss_weight=False)
if st_pos > 0:
print("## WARNING: inaccurate interaction performance report...")
print("Loading interaction records from %s..." % record_save_path)
interaction_records_dict = json.load(open(record_save_path, 'r'))
interaction_records = interaction_records_dict['records']
print("Record item size: %d " % len(interaction_records))
learning_start_time = datetime.datetime.now()
print("## Online starting time: {}".format(learning_start_time))
dset_name = 'train'
engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
for iB, t in enumerate(online_data_loader):
cnt += len(t)
assert len(t) == 1
# if cnt <= st_pos:
# count_iter += 1
# continue
# Get fields
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, train_table, no_hs_t=True, no_sql_t=True)
g_sql_q = generate_sql_q(sql_i, tb)
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv = get_g(sql_i)
g_wvi_corenlp = get_g_wvi_corenlp(t)
# if the record has contained this piece
if len(interaction_records) >= cnt:
record = interaction_records[cnt - 1]
if 'sql_i' not in record: # failure case
continue
if conf_threshold is None or float(record['logprob']) > np.log(conf_threshold):
gen_sql_i = eval(record['sql_i'])
gen_tag_seq = eval(record['tag_seq'])
assert g_sql_q[0] == record['true_sql']
# BERT processing: 2nd tokenization using WordPiece
tt_to_t_idx1 = [] # number indicates where sub-token belongs to in 1st-level-tokens (here, CoreNLP).
for (i, token) in enumerate(nlu_t[0]):
sub_tokens = agent.world_model.tokenizer.tokenize(token)
for sub_token in sub_tokens:
tt_to_t_idx1.append(i)
# extract example and add to annotation buffer
annotated_example = extract_weighted_example(t[0], tt_to_t_idx1, gen_sql_i, gen_tag_seq)
if annotated_example is not None:
iter_annotation_buffer.append(annotated_example)
count_iter += 1
if count_iter % update_iter == 0:
print(" count_iter %d, nl %s" % (count_iter, record['nl']))
print(" Time stamp: {}".format(datetime.datetime.now()))
else:
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(agent.world_model.bert_config, agent.world_model.model_bert,
agent.world_model.tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
try:
g_wvi = get_g_wvi_bert_from_g_wvi_corenlp(t_to_tt_idx, g_wvi_corenlp)
g_wv_str, g_wv_str_wp = convert_pr_wvi_to_string(g_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
except:
# Exception happens when where-condition is not found in nlu_tt.
# In this case, that train example is not used.
# During test, that example considered as wrongly answered.
count_failure += 1
results1 = {}
results1["error"] = "Skip happened"
results1["nlu"] = nlu[0]
results1["table_id"] = tb[0]["id"]
results.append(results1)
print("## Failure %d" % count_failure)
interaction_records.append({'nl': t[0]['question'], 'true_sql': g_sql_q[0],
'true_sql_i': "{}".format(sql_i[0]),
"questioned_indices": [], 'q_counter': 0})
continue
print("\n" + "#" * 50)
print("NL input: {}\nTrue SQL: {}".format(t[0]['question'], g_sql_q[0]))
# init decode
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
input_item = [tb, nlu_t, nlu, hds]
else:
input_item = [wemb_n, l_n, wemb_h, l_hpu, l_hs, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu]
hyp = agent.world_model.decode(input_item, dec_beam_size=1, bool_verbal=False)[0]
print("-" * 50 + "\nBefore interaction: \ninitial SQL: {}".format(hyp.sql))
Hypothesis.print_hypotheses([hyp])
pr_sc = [hyp.sql_i['sel']]
pr_sa = [hyp.sql_i['agg']]
pr_wn = [len(hyp.sql_i['conds'])]
pr_wc = [[col for col, _, _ in hyp.sql_i['conds']]]
pr_wo = [[op for _, op, _ in hyp.sql_i['conds']]]
pr_sql_i = [hyp.sql_i]
pr_wvi = None # not used
print("initial evaluation: ")
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_wvi1_list, \
cnt_lx1_list, cnt_x1_list, cnt_list1, g_ans, pr_ans = agent.evaluation(
[pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi, pr_sql_i],
[g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi, sql_i], engine, tb, bool_verbal=True)
record = {'nl': t[0]['question'], 'true_sql': g_sql_q[0], 'true_sql_i': "{}".format(sql_i[0]),
'sql': "{}".format(hyp.sql), 'sql_i': "{}".format(hyp.sql_i),
'dec_seq': "{}".format(hyp.dec_seq), 'tag_seq': "{}".format(hyp.tag_seq),
'logprob': "{}".format(hyp.logprob),
'lx_correct': int(sum(cnt_lx1_list)), 'x_correct': int(sum(cnt_x1_list)),
'q_counter': 0, 'questioned_indices': []}
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
record.update({'logprob_list': "{}".format(hyp.logprob_list),
'test_tag_seq': "{}".format(hyp.test_tag_seq)})
interaction_records.append(record)
# extract example and add to annotation buffer
if conf_threshold is None or hyp.logprob > np.log(conf_threshold):
annotated_example = extract_weighted_example(t[0], tt_to_t_idx[0], hyp.sql_i, hyp.tag_seq)
if annotated_example is not None:
iter_annotation_buffer.append(annotated_example)
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
cnt_list.append(cnt_list1)
# report
if detail:
pr_wv_str = None
current_cnt = [cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x]
report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_i, pr_ans,
cnt_list1, current_cnt)
count_iter += 1
del wemb_n, wemb_h # garbage collecting
if count_iter % update_iter == 0 or count_iter == num_total_examples: # update model
if count_iter <= st_pos:
# preprocessing
iter_annotation_buffer = data_preprocessing(agent.world_model.tokenizer,
iter_annotation_buffer, train_table,
max_seq_length, bool_remove_none=True,
bool_loss_weight=False)
annotation_buffer.extend(iter_annotation_buffer)
iter_annotation_buffer = []
continue
print("\n~~~\nCurrent interaction performance (iter {}): ".format(count_iter)) # interaction so far
_ave_loss = ave_loss / cnt
_acc_sc = cnt_sc / cnt
_acc_sa = cnt_sa / cnt
_acc_wn = cnt_wn / cnt
_acc_wc = cnt_wc / cnt
_acc_wo = cnt_wo / cnt
_acc_wvi = cnt_wvi / cnt
_acc_wv = cnt_wv / cnt
_acc_lx = cnt_lx / cnt
_acc_x = cnt_x / cnt
_acc = [_ave_loss, _acc_sc, _acc_sa, _acc_wn, _acc_wc, _acc_wo, _acc_wvi, _acc_wv, _acc_lx, _acc_x]
print("Interaction acc: {}".format(_acc))
q_count = sum([item['q_counter'] for item in interaction_records])
dist_q_count = sum([len(set(item['questioned_indices'])) for item in interaction_records])
print("Interaction #questions: {}, #questions per example: {:.3f} (exclude options: {:.3f}).".format(
q_count, q_count * 1.0 / len(interaction_records), dist_q_count * 1.0 / len(interaction_records)))
print("Interaction #exit: {}".format(count_exit))
print("~~~\n")
print("Saving interaction records to %s..." % record_save_path)
json.dump(interaction_records_dict, open(record_save_path, 'w'), indent=4)
# preprocessing
iter_annotation_buffer = data_preprocessing(agent.world_model.tokenizer,
iter_annotation_buffer, train_table,
max_seq_length, bool_remove_none=True,
bool_loss_weight=False)
annotation_buffer.extend(iter_annotation_buffer)
# parser update
print("~~~\nUpdating base semantic parser at iter {}".format(count_iter))
model = agent.world_model.semparser
model_bert = agent.world_model.model_bert
print("Retraining from scratch...")
update_buffer = init_train_data + annotation_buffer
# reset parameters
model_renew_fn(model, model_bert)
print("Train data size: %d " % len(update_buffer))
opt, opt_bert = get_opt(model, model_bert, True)
train_loader, dev_loader = get_loader_wikisql(update_buffer, val_data, batch_size, shuffle_train=True)
test_loader = get_loader_wikisql_v2(test_data, batch_size, False)
# train
print("## Starting update at iter {}, anno_cost {}...time spent {}".format(
count_iter, sum([item['q_counter'] for item in interaction_records]),
datetime.datetime.now() - learning_start_time))
model_dir = os.path.join(model_save_path, '%d/' % count_iter)
if not os.path.isdir(model_dir):
os.mkdir(model_dir)
dev_acc_lx_t_best, dev_acc_ex_t_best, test_acc_lx_t_best, test_acc_ex_t_best = run_epochs(
model, model_bert, opt, opt_bert, agent.world_model.bert_config,
agent.world_model.tokenizer, path_db, model_dir, train_loader,
train_table, dev_loader, val_table, test_loader, test_table,
early_stop_ep=EARLY_STOP_EPOCH_STAGE1 if count_iter <= EARLY_THRESHOLD else EARLY_STOP_EPOCH_STAGE2,
bool_eval=True, startime_time=learning_start_time)
print("## Ending update at iter {}, anno_cost {}, dev acc_lx {}, dev acc_ex {}, test acc_lx {},"
"test acc_ex {}...time spent {}\n".format(
count_iter, sum([item['q_counter'] for item in interaction_records]),
dev_acc_lx_t_best, dev_acc_ex_t_best, test_acc_lx_t_best, test_acc_ex_t_best,
datetime.datetime.now() - learning_start_time))
print("Update interaction_records_dict: start_iter = %d." % count_iter)
interaction_records_dict['start_iter'] = count_iter
print("Saving interaction records to %s..." % record_save_path)
json.dump(interaction_records_dict, open(record_save_path, 'w'), indent=4)
# clean
iter_annotation_buffer = []
# check end_pos
if end_pos != -1 and count_iter == end_pos:
print("## Ending online learning at iter {}\n".format(end_pos))
break
ave_loss /= cnt
acc_sc = cnt_sc / cnt
acc_sa = cnt_sa / cnt
acc_wn = cnt_wn / cnt
acc_wc = cnt_wc / cnt
acc_wo = cnt_wo / cnt
acc_wvi = cnt_wvi / cnt
acc_wv = cnt_wv / cnt
acc_lx = cnt_lx / cnt
acc_x = cnt_x / cnt
print("## End online learning at time {}...time spent {}\n".format(
datetime.datetime.now(), datetime.datetime.now() - learning_start_time))
# stats
q_count = sum([item['q_counter'] for item in interaction_records])
dist_q_count = sum([len(set(item['questioned_indices'])) for item in interaction_records])
print("#questions: {}, #questions per example: {:.3f} (exclude options: {:.3f}).".format(
q_count, q_count * 1.0 / len(interaction_records), dist_q_count * 1.0 / len(interaction_records)))
print("#exit: {}".format(count_exit))
acc = [ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x]
return acc, results, cnt_list, interaction_records_dict
def online_learning_bin_feedback(supervision, agent, init_train_data, online_data_loader, train_table,
val_data, val_table, test_data, test_table, model_save_path, record_save_path, path_db,
update_iter, model_renew_fn, max_seq_length=222, num_target_layers=2,
detail=False, cnt_tot=1, start_idx=0, end_idx=-1, batch_size=16):
ave_loss = 0
cnt = 0
cnt_sc = 0
cnt_sa = 0
cnt_wn = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_wvi = 0
cnt_lx = 0
cnt_x = 0
cnt_list = []
results = []
interaction_records_dict = {'records': [], 'start_iter': 0}
interaction_records = interaction_records_dict['records']
count_exit = 0
count_failure = 0
count_iter = 0 # online iteration
num_total_examples = len(online_data_loader.dataset)
annotation_buffer = [] # processed
iter_annotation_buffer = [] # processed
print("## data size: %d " % num_total_examples)
print("## update_iter: %d " % update_iter)
print("## start_idx: %d" % start_idx)
print("## end_idx: %d" % end_idx)
# preprocessing initial training data
init_train_data = data_preprocessing(agent.world_model.tokenizer, init_train_data, train_table,
max_seq_length, bool_remove_none=True)
annotation_costs = []
if start_idx > 0:
print("Loading interaction records from %s..." % record_save_path)
interaction_records_dict = json.load(open(record_save_path, 'r'))
interaction_records = interaction_records_dict['records']
print("Record item size: %d " % len(interaction_records))
learning_start_time = datetime.datetime.now()
print("## Online starting time: {}".format(learning_start_time))
dset_name = 'train'
engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
for iB, t in enumerate(online_data_loader):
cnt += len(t)
assert len(t) == 1
# if cnt <= st_pos:
# count_iter += 1
# continue
# Get fields
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, train_table, no_hs_t=True, no_sql_t=True)
g_sql_q = generate_sql_q(sql_i, tb)
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv = get_g(sql_i)
g_wvi_corenlp = get_g_wvi_corenlp(t)
if len(interaction_records) >= cnt:
record = interaction_records[cnt - 1]
if 'sql_i' not in record: # failure case
continue
assert record['nl'] == t[0]['question']
x_correct = record['x_correct']
if x_correct:
gen_sql_i = eval(record['sql_i'])
gen_tag_seq = eval(record['tag_seq'])
# BERT processing: 2nd tokenization using WordPiece
tt_to_t_idx1 = [] # number indicates where sub-token belongs to in 1st-level-tokens (here, CoreNLP).
for (i, token) in enumerate(nlu_t[0]):
sub_tokens = agent.world_model.tokenizer.tokenize(token)
for sub_token in sub_tokens:
tt_to_t_idx1.append(i)
annotated_example = extract_weighted_example(
t[0], tt_to_t_idx1, gen_sql_i, gen_tag_seq)
iter_annotation_buffer.append(annotated_example)
elif supervision == "bin_feedback_expert":
iter_annotation_buffer.append(t[0])
cost = 2 + len(eval(record['true_sql_i'])["conds"]) * 3
annotation_costs.append(cost)
count_iter += 1
if count_iter % update_iter == 0:
print(" count_iter %d, nl %s" % (count_iter, record['nl']))
print(" Time stamp: {}".format(datetime.datetime.now()))
else:
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(agent.world_model.bert_config, agent.world_model.model_bert,
agent.world_model.tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
try:
g_wvi = get_g_wvi_bert_from_g_wvi_corenlp(t_to_tt_idx, g_wvi_corenlp)
g_wv_str, g_wv_str_wp = convert_pr_wvi_to_string(g_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
except:
# Exception happens when where-condition is not found in nlu_tt.
# In this case, that train example is not used.
# During test, that example considered as wrongly answered.
count_failure += 1
results1 = {}
results1["error"] = "Skip happened"
results1["nlu"] = nlu[0]
results1["table_id"] = tb[0]["id"]
results.append(results1)
print("## Failure %d" % count_failure)
interaction_records.append({'nl': t[0]['question'], 'true_sql': g_sql_q[0],
'true_sql_i': "{}".format(sql_i[0]),
"questioned_indices": [], 'q_counter': 0})
continue
print("\n" + "#" * 50)
print("NL input: {}\nTrue SQL: {}".format(t[0]['question'], g_sql_q[0]))
# init decode
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
input_item = [tb, nlu_t, nlu, hds]
else:
input_item = [wemb_n, l_n, wemb_h, l_hpu, l_hs, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu]
hyp = agent.world_model.decode(input_item, dec_beam_size=1, bool_verbal=False)[0]
print("-" * 50 + "\nBefore interaction: \ninitial SQL: {}".format(hyp.sql))
Hypothesis.print_hypotheses([hyp])
pr_sc = [hyp.sql_i['sel']]
pr_sa = [hyp.sql_i['agg']]
pr_wn = [len(hyp.sql_i['conds'])]
pr_wc = [[col for col, _, _ in hyp.sql_i['conds']]]
pr_wo = [[op for _, op, _ in hyp.sql_i['conds']]]
pr_sql_i = [hyp.sql_i]
pr_wvi = None # not used
print("initial evaluation: ")
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_wvi1_list, \
cnt_lx1_list, cnt_x1_list, cnt_list1, g_ans, pr_ans = agent.evaluation(
[pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi, pr_sql_i],
[g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi, sql_i], engine, tb, bool_verbal=True)
record = {'nl': t[0]['question'], 'true_sql': g_sql_q[0], 'true_sql_i': "{}".format(sql_i[0]),
'sql': "{}".format(hyp.sql), 'sql_i': "{}".format(hyp.sql_i),
'dec_seq': "{}".format(hyp.dec_seq), 'tag_seq': "{}".format(hyp.tag_seq),
'logprob': "{}".format(hyp.logprob),
'lx_correct': int(sum(cnt_lx1_list)), 'x_correct': int(sum(cnt_x1_list))}
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
record.update({'logprob_list': "{}".format(hyp.logprob_list),
'test_tag_seq': "{}".format(hyp.test_tag_seq)})
interaction_records.append(record)
if int(sum(cnt_x1_list)) == 1: # execution correct
# iter_annotation_buffer.append(t[0])
annotated_example = extract_weighted_example(
t[0], tt_to_t_idx[0], hyp.sql_i, hyp.tag_seq)
iter_annotation_buffer.append(annotated_example)
elif supervision == "bin_feedback_expert":
iter_annotation_buffer.append(t[0])
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
# report
if detail:
pr_wv_str = None
current_cnt = [cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x]
report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_i, pr_ans,
cnt_list1, current_cnt)
count_iter += 1
del wemb_n, wemb_h # garbage collecting
cost = 2 + len(eval(record['true_sql_i'])["conds"]) * 3
annotation_costs.append(cost)
if count_iter % update_iter == 0 or count_iter == num_total_examples: # update model
if count_iter <= start_idx:
# preprocessing
iter_annotation_buffer = data_preprocessing(agent.world_model.tokenizer,
iter_annotation_buffer, train_table,
max_seq_length, bool_remove_none=True)
annotation_buffer.extend(iter_annotation_buffer)
iter_annotation_buffer = []
continue
print("\n~~~\nCurrent interaction performance (iter {}): ".format(count_iter)) # interaction so far
_ave_loss = ave_loss / cnt
_acc_sc = cnt_sc / cnt
_acc_sa = cnt_sa / cnt
_acc_wn = cnt_wn / cnt
_acc_wc = cnt_wc / cnt
_acc_wo = cnt_wo / cnt
_acc_wvi = cnt_wvi / cnt
_acc_wv = cnt_wv / cnt
_acc_lx = cnt_lx / cnt
_acc_x = cnt_x / cnt
_acc = [_ave_loss, _acc_sc, _acc_sa, _acc_wn, _acc_wc, _acc_wo, _acc_wvi, _acc_wv, _acc_lx, _acc_x]
print("Interaction acc: {}".format(_acc))
print("Saving interaction records to %s..." % record_save_path)
json.dump(interaction_records_dict, open(record_save_path, 'w'), indent=4)
# preprocessing
iter_annotation_buffer = data_preprocessing(agent.world_model.tokenizer,
iter_annotation_buffer, train_table,
max_seq_length, bool_remove_none=True)
annotation_buffer.extend(iter_annotation_buffer)
iter_annotation_buffer = []
print("~~~\nUpdating base semantic parser at iter {}".format(count_iter))
model = agent.world_model.semparser
model_bert = agent.world_model.model_bert
print("Retraining from scratch...")
update_buffer = init_train_data + annotation_buffer
model_renew_fn(model, model_bert)
print("Train data size: %d" % len(update_buffer))
opt, opt_bert = get_opt(model, model_bert, True)
train_loader, dev_loader = get_loader_wikisql(update_buffer, val_data, batch_size, shuffle_train=True)
test_loader = get_loader_wikisql_v2(test_data, batch_size, False)
# train
print("## Starting update at iter {}, anno_cost {}...time spent {}".format(
count_iter, sum(annotation_costs), datetime.datetime.now() - learning_start_time))
model_dir = os.path.join(model_save_path, '%d/' % count_iter)
if not os.path.isdir(model_dir):
os.mkdir(model_dir)
dev_acc_lx_t_best, dev_acc_ex_t_best, test_acc_lx_t_best, test_acc_ex_t_best = run_epochs(
model, model_bert, opt, opt_bert, agent.world_model.bert_config,
agent.world_model.tokenizer, path_db,
model_dir, train_loader, train_table, dev_loader, val_table,
test_loader, test_table,
early_stop_ep=EARLY_STOP_EPOCH_STAGE1 if count_iter <= EARLY_THRESHOLD else EARLY_STOP_EPOCH_STAGE2,
bool_eval=True, startime_time=learning_start_time)
print("## Ending update at iter {}, anno_cost {}, dev acc_lx {}, dev acc_ex {}, test acc_lx {}, "
"test acc_ex {}...time spent {}\n".format(
count_iter, sum(annotation_costs), dev_acc_lx_t_best, dev_acc_ex_t_best,
test_acc_lx_t_best, test_acc_ex_t_best,
datetime.datetime.now() - learning_start_time))
print("Update interaction_records_dict: start_iter = %d." % count_iter)
interaction_records_dict['start_iter'] = count_iter
print("Saving interaction records to %s..." % record_save_path)
json.dump(interaction_records_dict, open(record_save_path, 'w'), indent=4)
sys.stdout.flush()
if end_idx != -1 and count_iter == end_idx:
print("## Ending online learning at iter {}\n".format(end_idx))
break
print("## End full training at time {}...time spent {}\n".format(
datetime.datetime.now(), datetime.datetime.now() - learning_start_time))
def online_learning_misp_perfect(user, agent, online_data_loader, train_table,
update_iter, model_save_path, record_save_path,
max_seq_length=222, num_target_layers=2, st_pos=0, end_pos=-1):
# This function simulates MISP_NEIL^*, i.e., the best version of MISP with a perfect error detector
# and a perfect interaction design (thus can get gold answers and detect redundant/missing components).
# The learned parser will be the same as "full expert" parser.
assert args.ask_structure and args.user == "gold_sim" and args.err_detector == "perfect"
cnt = 0
interaction_records = []
count_exit = 0
count_failure = 0
count_iter = 0 # online iteration
num_total_examples = len(online_data_loader.dataset)
if st_pos > 0:
print("Loading interaction records from %s..." % record_save_path)
interaction_records = json.load(open(record_save_path, 'r'))
print("Record item size: %d " % len(interaction_records))
dset_name = 'train'
for iB, t in enumerate(online_data_loader):
cnt += len(t)
assert len(t) == 1
if len(interaction_records) >= cnt:
record = interaction_records[cnt - 1]
if 'sql_i' not in record: # failure case
continue
count_iter += 1
else:
# Get fields
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, train_table, no_hs_t=True, no_sql_t=True)
g_sql_q = generate_sql_q(sql_i, tb)
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv = get_g(sql_i)
g_wvi_corenlp = get_g_wvi_corenlp(t)
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(agent.world_model.bert_config, agent.world_model.model_bert,
agent.world_model.tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
try:
g_wvi = get_g_wvi_bert_from_g_wvi_corenlp(t_to_tt_idx, g_wvi_corenlp)
g_wv_str, g_wv_str_wp = convert_pr_wvi_to_string(g_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
except:
# Exception happens when where-condition is not found in nlu_tt.
# In this case, that train example is not used.
# During test, that example considered as wrongly answered.
count_failure += 1
print("## Failure %d" % count_failure)
interaction_records.append({'nl': t[0]['question'], 'true_sql': g_sql_q[0],
'true_sql_i': "{}".format(sql_i[0]),
"questioned_indices": [], 'q_counter': 0,
'count_additional_q': 0})
continue
print("\n" + "#" * 50)
print("NL input: {}\nTrue SQL: {}".format(t[0]['question'], g_sql_q[0]))
# init decode
if isinstance(agent.error_detector, ErrorDetectorBayesDropout):
input_item = [tb, nlu_t, nlu, hds]
else:
input_item = [wemb_n, l_n, wemb_h, l_hpu, l_hs, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu]
hyp = agent.world_model.decode(input_item, dec_beam_size=1, bool_verbal=False)[0]
print("-" * 50 + "\nBefore interaction: \ninitial SQL: {}".format(hyp.sql))
# interaction
g_sql = sql_i[0]
g_sql["g_wvi"] = g_wvi[0]
hyp, bool_exit = agent.interactive_parsing_session(user, input_item, g_sql, hyp, bool_verbal=False)
print("-" * 50 + "\nAfter interaction:\nfinal SQL: {}".format(hyp.sql))
Hypothesis.print_hypotheses([hyp])
# check missing/redundant part
assert hyp.sql_i['sel'] == sql_i[0]['sel']
assert hyp.sql_i['agg'] == sql_i[0]['agg']
count_additional_q = 0
if len(hyp.sql_i['conds']) < len(sql_i[0]['conds']): # missing conditions
count_additional_q += (len(sql_i[0]['conds']) - len(hyp.sql_i['conds'])) * 3
elif len(hyp.sql_i['conds']) > len(sql_i[0]['conds']):
for col, op, val in hyp.sql_i['conds']:
if col not in [_col for _col, _op, _val in sql_i[0]['conds']]:
count_additional_q += 3
elif (col, op) not in [(_col, _op) for _col, _op, _val in sql_i[0]['conds']]:
count_additional_q += 2
elif (col, op, val) not in [(_col, _op, _val) for _col, _op, _val in sql_i[0]['conds']]:
count_additional_q += 1
print("count_additional_q: {}".format(count_additional_q))
# save interaction records
record = {'nl': t[0]['question'], 'true_sql': g_sql_q[0], 'true_sql_i': "{}".format(sql_i[0]),
'sql': hyp.sql, 'sql_i': "{}".format(hyp.sql_i),
'dec_seq': "{}".format(hyp.dec_seq), 'tag_seq': "{}".format(hyp.tag_seq),
'logprob': "{}".format(hyp.logprob), # test time without dropout
'exit': bool_exit, 'q_counter': user.q_counter,
'count_additional_q': count_additional_q,
'questioned_indices': user.questioned_pointers,
'questioned_tags': "{}".format(user.questioned_tags),
'feedback_records': "{}".format(user.feedback_records)}
interaction_records.append(record)
if bool_exit:
count_exit += 1
count_iter += 1
del wemb_n, wemb_h # garbage collecting
if count_iter % update_iter == 0 or count_iter == num_total_examples: # update model
if count_iter < st_pos:
continue
if count_iter > st_pos:
# report q counts
q_count = sum([item['q_counter'] + item['count_additional_q'] for item in interaction_records])
print("## End update at iter {}, anno_cost {}\n".format(count_iter, q_count))
print("Saving interaction records to %s..." % record_save_path)
json.dump(interaction_records, open(record_save_path, 'w'), indent=4)
# check end_pos
if end_pos != -1 and count_iter == end_pos:
print("## Ending online learning at iter {}\n".format(end_pos))
print(datetime.datetime.now())
break
# loading models
model_dir = os.path.join(model_save_path, '%d/' % count_iter)
print("Loading model from %s..." % model_dir)
path_model = os.path.join(model_dir, 'model_best.pt')
path_model_bert = os.path.join(model_dir, 'model_bert_best.pt')
if torch.cuda.is_available():
res = torch.load(path_model_bert)
else:
res = torch.load(path_model_bert, map_location='cpu')
agent.world_model.model_bert.load_state_dict(res['model_bert'])
agent.world_model.model_bert.to(device)
if torch.cuda.is_available():
res = torch.load(path_model)
else:
res = torch.load(path_model, map_location='cpu')
agent.world_model.semparser.load_state_dict(res['model'])
print(datetime.datetime.now())
print("Saving interaction records to %s..." % record_save_path)
json.dump(interaction_records, open(record_save_path, 'w'), indent=4)
def load_processed_wikisql_data(path_wikisql, dset_name):
data = pickle.load(open(os.path.join(path_wikisql, '%s_tok_processed.pkl' % dset_name), 'rb'))
path_table = os.path.join(path_wikisql, dset_name + '.tables.jsonl')
table = {}
with open(path_table) as f:
for idx, line in enumerate(f):
t1 = json.loads(line.strip())
table[t1['id']] = t1
return data, table
if __name__ == '__main__':
## 1. Hyper parameters
parser = argparse.ArgumentParser()
args = construct_hyper_param(parser)
## 2. Paths
path_wikisql = 'SQLova_model/download/data/'
BERT_PT_PATH = 'SQLova_model/download/bert/'
model_dir = args.model_dir
print("## job: {}".format(args.job))
print("## setting: {}".format(args.setting))
print("## model_dir: {}".format(args.model_dir))
if args.auto_iter:
print("## auto_iter is on.")
print("\targs.start_iter=%d, args.end_iter=%d." % (args.start_iter, args.end_iter))
path_model_bert = os.path.join(model_dir, "model_bert_best.pt")
path_model = os.path.join(model_dir, "model_best.pt")
## 3. Load data
if args.job == 'online_learning':
dev_data, dev_table = load_processed_wikisql_data(path_wikisql, 'dev')
test_data, test_table = load_processed_wikisql_data(path_wikisql, 'test')
test_data = [item for item in test_data if item is not None]
else:
if args.data == "user_study":
test_data, test_table = load_wikisql_data(path_wikisql, mode="test", toy_model=args.toy_model,
toy_size=args.toy_size, no_hs_tok=True)
sampled_ids = json.load(open("SQLova_model/download/data/user_study_ids.json", "r"))
test_data = [test_data[idx] for idx in sampled_ids]
else:
# args.data in ["dev", "test"]
test_data, test_table = load_wikisql_data(
path_wikisql, mode=args.data,
toy_model=args.toy_model, toy_size=args.toy_size, no_hs_tok=True)
# 4. Build & Load models
model, model_bert, tokenizer, bert_config = get_models(args, BERT_PT_PATH, trained=True,
path_model_bert=path_model_bert,
path_model=path_model)
model.eval()
model_bert.eval()
## 5. Create ISQL agent
print("Creating MISP agent...")
question_generator = QuestionGenerator()
error_evaluator = ErrorEvaluator()
print("## user: {}".format(args.user))
if args.user == "real":
user = RealUser(error_evaluator, test_table)
elif args.user == "gold_sim":
user = GoldUserSim(error_evaluator, bool_structure_question=args.ask_structure)
else:
assert not args.ask_structure, "UserSim with ask_struct=1 is not supported!"
user = UserSim(error_evaluator)
if args.err_detector == 'any':
error_detector = ErrorDetectorProbability(1.1) # ask any SU
elif args.err_detector.startswith('prob='):
prob = float(args.err_detector[5:])
error_detector = ErrorDetectorProbability(prob)
print("Error Detector: probability threshold = %.3f" % prob)
assert args.passes == 1, "Error: For prob-based evaluation, set --passes 1."
elif args.err_detector.startswith('stddev='):
stddev = float(args.err_detector[7:])
error_detector = ErrorDetectorBayesDropout(stddev)
print("Error Detector: Bayesian Dropout Stddev threshold = %.3f" % stddev)
print("num passes: %d, dropout rate: %.3f" % (args.passes, args.dropout))
assert args.passes > 1, "Error: For dropout-based evaluation, set --passes 10."
elif args.err_detector == "perfect":
error_detector = ErrorDetectorSim()
print("Error Detector: using a simulated perfect detector.")
else:
raise Exception("Invalid error detector setup %s!" % args.err_detector)
if args.num_options == 'inf':
print("WARNING: Unlimited options!")
num_options = np.inf
else:
num_options = int(args.num_options)
print("num_options: {}".format(num_options))
print("ask_structure: {}".format(args.ask_structure))
world_model = WorldModel((bert_config, model_bert, tokenizer, args.max_seq_length, args.num_target_layers),
model, num_options, num_passes=args.passes, dropout_rate=args.dropout,
bool_structure_question=args.ask_structure)
print("friendly_agent: {}".format(args.friendly_agent))
agent = Agent(world_model, error_detector, question_generator, bool_mistake_exit=args.friendly_agent,
bool_structure_question=args.ask_structure)
## 6. Test
if not os.path.exists(os.path.dirname(args.output_path)):
os.mkdir(os.path.dirname(args.output_path))
if args.job == 'online_learning':
assert args.data == "online"
print("## supervision: {}".format(args.supervision))
print("## update_iter: {}".format(args.update_iter))
if args.setting == "online_pretrain_1p":
online_setup_indices = json.load(open(path_wikisql + "online_setup_1p.json"))
elif args.setting == "online_pretrain_5p":
online_setup_indices = json.load(open(path_wikisql + "online_setup_5p.json"))
elif args.setting == "online_pretrain_10p":
online_setup_indices = json.load(open(path_wikisql + "online_setup_10p.json"))
else:
raise Exception("Invalid args.setting={}".format(args.setting))
if args.supervision == 'full_expert':
train_data, train_table = load_processed_wikisql_data(path_wikisql, "train") # processed data
else:
train_data, train_table = load_wikisql_data(path_wikisql, mode="train", toy_model=args.toy_model,
toy_size=args.toy_size, no_hs_tok=True) # raw data
init_train_indices = set(online_setup_indices["train"])
init_train_data = [train_data[idx] for idx in init_train_indices if train_data[idx] is not None]
print("## Update init train size %d " % len(init_train_data))
online_train_indices = online_setup_indices["online_seed%d" % args.data_seed]
online_train_data = [train_data[idx] for idx in online_train_indices if train_data[idx] is not None]
print("## Update online train size %d " % len(online_train_data))
online_data_loader = torch.utils.data.DataLoader(
batch_size=1, # must be 1
dataset=online_train_data,
shuffle=False,
num_workers=1, # 4
collate_fn=lambda x: x # now dictionary values are not merged!
)
def create_new_model(model, model_bert):
# parser
def param_reset(m):
if type(m) in {nn.LSTM, nn.Linear}:
m.reset_parameters()
model.apply(param_reset)
model.eval()
# bert
init_checkpoint = os.path.join(BERT_PT_PATH, 'pytorch_model_{}.bin'.format(args.bert_type))
model_bert.load_state_dict(torch.load(init_checkpoint, map_location='cpu'))
print("Reload pre-trained BERT parameters.")
model_bert.to(device)
model_bert.eval()
if args.supervision in ("misp_neil", "misp_neil_pos"):
subdir = "%s_OP%s_ED%s_SETTING%s_ITER%d_DATASEED%d%s%s/" % (
args.supervision, args.num_options, args.err_detector, args.setting,
args.update_iter, args.data_seed,
("_FRIENDLY" if args.friendly_agent else ""),
("_GoldUser" if args.user == "gold_sim" else ""))
if not os.path.isdir(os.path.join(model_dir, subdir)):
os.mkdir(os.path.join(model_dir, subdir))
if args.auto_iter and args.start_iter == 0 and os.path.exists(args.output_path):
record_save_path = args.output_path
print("Loading interaction records from %s..." % record_save_path)
interaction_records_dict = json.load(open(record_save_path, 'r'))
args.start_iter = interaction_records_dict['start_iter']
print("AUTO start_iter = %d." % args.start_iter)
if args.start_iter > 0:
print("Loading previous checkpoints at iter {}...".format(args.start_iter))
start_path_model = os.path.join(model_dir, subdir, '%d' % args.start_iter, 'model_best.pt')
start_path_model_bert = os.path.join(model_dir, subdir, '%d' % args.start_iter, 'model_bert_best.pt')
if torch.cuda.is_available():
res = torch.load(start_path_model_bert)
else:
res = torch.load(start_path_model_bert, map_location='cpu')
agent.world_model.model_bert.load_state_dict(res['model_bert'])
agent.world_model.model_bert.to(device)
if torch.cuda.is_available():
res = torch.load(start_path_model)
else:
res = torch.load(start_path_model, map_location='cpu')
agent.world_model.semparser.load_state_dict(res['model'])
online_learning(args.supervision, user, agent, init_train_data, online_data_loader,
train_table, dev_data, dev_table, test_data, test_table, args.update_iter,
os.path.join(model_dir, subdir), args.output_path, create_new_model,
max_seq_length=222, num_target_layers=2, detail=False,
st_pos=args.start_iter, end_pos=args.end_iter,
cnt_tot=1, path_db=path_wikisql, batch_size=args.bS)
elif args.supervision.startswith('self_train'):
subdir = "%s_SETTING%s_ITER%d_DATASEED%d/" % (
args.supervision, args.setting, args.update_iter,
args.data_seed)
if not os.path.isdir(os.path.join(model_dir, subdir)):
os.mkdir(os.path.join(model_dir, subdir))
if args.auto_iter and args.start_iter == 0 and os.path.exists(args.output_path):
record_save_path = args.output_path
print("Loading interaction records from %s..." % record_save_path)
interaction_records_dict = json.load(open(record_save_path, 'r'))
args.start_iter = interaction_records_dict['start_iter']
print("AUTO start_iter = %d." % args.start_iter)
if args.start_iter > 0:
print("Loading previous checkpoints at iter {}...".format(args.start_iter))
start_path_model = os.path.join(model_dir, subdir, '%d' % args.start_iter, 'model_best.pt')
start_path_model_bert = os.path.join(model_dir, subdir, '%d' % args.start_iter, 'model_bert_best.pt')
if torch.cuda.is_available():
res = torch.load(start_path_model_bert)
else:
res = torch.load(start_path_model_bert, map_location='cpu')
agent.world_model.model_bert.load_state_dict(res['model_bert'])
agent.world_model.model_bert.to(device)
if torch.cuda.is_available():
res = torch.load(start_path_model)
else:
res = torch.load(start_path_model, map_location='cpu')
agent.world_model.semparser.load_state_dict(res['model'])
online_learning_self_train(args.supervision, agent, init_train_data, online_data_loader, train_table,
dev_data, dev_table, test_data, test_table, args.update_iter,
os.path.join(model_dir, subdir), args.output_path,
create_new_model, max_seq_length=222, num_target_layers=2, detail=False,
st_pos=args.start_iter, end_pos=args.end_iter,
cnt_tot=1, path_db=path_wikisql, batch_size=args.bS)
elif args.supervision == "full_expert":
subdir = "full_expert_SETTING%s_ITER%d_DATASEED%d/" % (
args.setting, args.update_iter, args.data_seed)
if not os.path.isdir(os.path.join(model_dir, subdir)):
os.mkdir(os.path.join(model_dir, subdir))
assert not args.auto_iter, "--auto_iter is not allowed for Full Expert experiments!"
online_learning_full_expert(agent, init_train_data, online_train_data, train_table,
dev_data, dev_table, test_data, test_table,
path_wikisql, os.path.join(model_dir, subdir), args.update_iter,
create_new_model, start_idx=args.start_iter, end_idx=args.end_iter,
batch_size=args.bS)
elif args.supervision in {"bin_feedback", "bin_feedback_expert"}:
subdir = "%s_SETTING%s_ITER%d_DATASEED%d/" % (
args.supervision, args.setting, args.update_iter, args.data_seed)
if not os.path.isdir(os.path.join(model_dir, subdir)):
os.mkdir(os.path.join(model_dir, subdir))
if args.auto_iter and args.start_iter == 0 and os.path.exists(args.output_path):
record_save_path = args.output_path
print("Loading interaction records from %s..." % record_save_path)
interaction_records_dict = json.load(open(record_save_path, 'r'))
args.start_iter = interaction_records_dict['start_iter']
print("AUTO start_iter = %d." % args.start_iter)
if args.start_iter > 0:
print("Loading previous checkpoints at iter {}...".format(args.start_iter))
start_path_model = os.path.join(model_dir, subdir, '%d' % args.start_iter, 'model_best.pt')
start_path_model_bert = os.path.join(model_dir, subdir, '%d' % args.start_iter, 'model_bert_best.pt')
if torch.cuda.is_available():
res = torch.load(start_path_model_bert)
else:
res = torch.load(start_path_model_bert, map_location='cpu')
agent.world_model.model_bert.load_state_dict(res['model_bert'])
agent.world_model.model_bert.to(device)
if torch.cuda.is_available():
res = torch.load(start_path_model)
else:
res = torch.load(start_path_model, map_location='cpu')
agent.world_model.semparser.load_state_dict(res['model'])
online_learning_bin_feedback(args.supervision, agent, init_train_data, online_data_loader, train_table,
dev_data, dev_table, test_data, test_table,
os.path.join(model_dir, subdir), args.output_path,
path_wikisql, args.update_iter, create_new_model,
start_idx=args.start_iter, end_idx=args.end_iter, batch_size=args.bS)
else:
assert args.supervision == "misp_neil_perfect"
subdir = "full_expert_SETTING%s_ITER%d_DATASEED%d/" % (
args.setting, args.update_iter, args.data_seed)
if args.start_iter > 0:
print("Loading previous checkpoints at iter {}...".format(args.start_iter))
start_path_model = os.path.join(model_dir, subdir, '%d' % args.start_iter, 'model_best.pt')
start_path_model_bert = os.path.join(model_dir, subdir, '%d' % args.start_iter, 'model_bert_best.pt')
if torch.cuda.is_available():
res = torch.load(start_path_model_bert)
else:
res = torch.load(start_path_model_bert, map_location='cpu')
agent.world_model.model_bert.load_state_dict(res['model_bert'])
agent.world_model.model_bert.to(device)
if torch.cuda.is_available():
res = torch.load(start_path_model)
else:
res = torch.load(start_path_model, map_location='cpu')
agent.world_model.semparser.load_state_dict(res['model'])
online_learning_misp_perfect(user, agent, online_data_loader, train_table,
args.update_iter, os.path.join(model_dir, subdir),
args.output_path, st_pos=args.start_iter, end_pos=args.end_iter)
else:
# test_w_interaction
test_loader = torch.utils.data.DataLoader(
batch_size=1, # must be 1
dataset=test_data,
shuffle=False,
num_workers=1, # 4
collate_fn=lambda x: x # now dictionary values are not merged!
)
if args.user == "real":
with torch.no_grad():
real_user_interaction(test_loader, test_table, user, agent, tokenizer, args.max_seq_length,
args.num_target_layers, path_wikisql, args.output_path)
else:
with torch.no_grad():
acc_test, results_test, cnt_list, interaction_records = interaction(
test_loader, test_table, user, agent, tokenizer, args.max_seq_length, args.num_target_layers,
detail=True, path_db=path_wikisql, st_pos=0,
dset_name="test" if args.data == "user_study" else args.data)
print(acc_test)
# save results for the official evaluation
path_save_for_evaluation = os.path.dirname(args.output_path)
save_for_evaluation(path_save_for_evaluation, results_test, args.output_path[args.output_path.index('records_'):])
json.dump(interaction_records, open(args.output_path, "w"), indent=4)
| 47.805209 | 139 | 0.57397 | 17,351 | 130,317 | 3.970203 | 0.044781 | 0.011091 | 0.013936 | 0.007839 | 0.773397 | 0.74207 | 0.722473 | 0.714344 | 0.703703 | 0.694093 | 0 | 0.01148 | 0.314886 | 130,317 | 2,725 | 140 | 47.822752 | 0.760086 | 0.055281 | 0 | 0.700236 | 0 | 0.00331 | 0.119454 | 0.00635 | 0 | 0 | 0 | 0 | 0.011348 | 1 | 0.009929 | false | 0.003783 | 0.009456 | 0.000473 | 0.02695 | 0.11253 | 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 |
d99db026371cf1c02fa48f10271aa2e49c0ab4ab | 22,137 | py | Python | project-management-env/backend/projects/tests.py | paolo-demagistris-polito/pm-lab-polito-EnvForDigitalProjectDelivery | 07e121a6613398bf3a8fbb9ec6831720bfcf2c33 | [
"MIT"
] | null | null | null | project-management-env/backend/projects/tests.py | paolo-demagistris-polito/pm-lab-polito-EnvForDigitalProjectDelivery | 07e121a6613398bf3a8fbb9ec6831720bfcf2c33 | [
"MIT"
] | null | null | null | project-management-env/backend/projects/tests.py | paolo-demagistris-polito/pm-lab-polito-EnvForDigitalProjectDelivery | 07e121a6613398bf3a8fbb9ec6831720bfcf2c33 | [
"MIT"
] | null | null | null | from django.urls import reverse
from rest_framework import status
from rest_framework.test import APITestCase
from accounts.models import User
from projects.models import Project
from accounts import views as account_views
from projects import views as project_views
from guardian.shortcuts import get_user_perms
class ProjectTest(APITestCase):
def generate_pmo_user_data(self):
return {
"first_name": "Luca",
"last_name": "Verdi",
"email": "pmo@email.com",
"password": "pmo12345",
"confirm_password": "pmo12345"
}
def generate_ps_user_data(self):
return {
"first_name": "Giovanni",
"last_name": "Valdieri",
"email": "ps@email.com",
"password": "ps123456",
"confirm_password": "ps123456"
}
def generate_project_data(self, author):
return {
"project_name": "test project",
"author": author
}
def define_user_role(self, user, role=None):
db_user = User.objects.get(pk=user.data['user']['id'])
db_user.user_role = role
db_user.save()
def post_user_registration(self, user):
url = reverse(account_views.RegisterAPI.name)
response = self.client.post(url, data=user, format='json')
return response
def post_user_login(self, user):
url = reverse(account_views.LoginAPI.name)
response = self.client.post(url, data=user, format='json')
return response
def post_create_project(self, project, token=None):
url = reverse(project_views.ProjectAPI.name)
auth_token = f'Token {token}'
response = self.client.post(url, data=project, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def test_post_create_project(self):
"""
Ensure we can create a project as Project Management Office
"""
user = self.generate_pmo_user_data()
reg_response = self.post_user_registration(user)
self.define_user_role(reg_response, 'PMO')
response = self.post_user_login(user)
assert response.status_code == status.HTTP_200_OK
# Authorized Project Management Office can create a new project
author = response.data['user']['id']
auth_token = response.data['auth_token']
project = self.generate_project_data(author=author)
response = self.post_create_project(project=project)
assert response.status_code == status.HTTP_401_UNAUTHORIZED
response = self.post_create_project(project=project, token=auth_token)
assert response.status_code == status.HTTP_201_CREATED
assert response.data.get('project').get('project_name') == project.get('project_name')
def patch_edit_project_name(self, project_id, new_name, token=None):
url = reverse(project_views.EditProjectAPI.name, None, {project_id})
auth_token = f'Token {token}'
project = {'project_name': new_name}
response = self.client.patch(url, data=project, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def test_patch_edit_project_name(self):
"""
Ensure we can update a project name with permission of 'hasChangeProjectPermission'
"""
user_pmo = self.generate_pmo_user_data()
reg = self.post_user_registration(user_pmo)
self.define_user_role(reg, 'PMO')
response = self.post_user_login(user_pmo)
assert response.status_code == status.HTTP_200_OK
# Authorized Project Management Office can edit a project name
author = response.data['user']['id']
auth_token = response.data['auth_token']
project = self.generate_project_data(author=author)
response = self.post_create_project(project=project, token=auth_token)
assert response.status_code == status.HTTP_201_CREATED
project_id = response.data['project']['id']
response = self.patch_edit_project_name(project_id=project_id, new_name='edited project')
assert response.status_code == status.HTTP_401_UNAUTHORIZED
response = self.patch_edit_project_name(project_id=project_id,
new_name='edited project', token=auth_token)
assert response.status_code == status.HTTP_200_OK
db_project = Project.objects.get(id=project_id)
assert db_project.project_name == 'edited project'
def delete_project(self, project_id, token=None):
url = reverse(project_views.DeleteProjectAPI.name, None, {project_id})
auth_token = f'Token {token}'
response = self.client.delete(url, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def test_delete_project(self):
"""
Ensure we can delete a project with permission of 'hasDeleteProjectPermission'
"""
user = self.generate_pmo_user_data()
reg = self.post_user_registration(user)
self.define_user_role(reg, 'PMO')
response = self.post_user_login(user)
assert response.status_code == status.HTTP_200_OK
# Authorized Project Management Office can delete a project
author = response.data['user']['id']
auth_token = response.data['auth_token']
project = self.generate_project_data(author=author)
response = self.post_create_project(project=project, token=auth_token)
assert response.status_code == status.HTTP_201_CREATED
project_id = response.data['project']['id']
response = self.delete_project(project_id=project_id)
assert response.status_code == status.HTTP_401_UNAUTHORIZED
response = self.delete_project(project_id=project_id, token=auth_token)
assert response.status_code == status.HTTP_204_NO_CONTENT
project_ids = Project.objects.all().values_list('id', flat=True)
assert project_id not in project_ids
# delete non existing project
response = self.delete_project(project_id=project_id, token=auth_token)
assert response.status_code == status.HTTP_404_NOT_FOUND
def get_project_details(self, project_id, token=None):
url = reverse(project_views.ProjectDetailsAPI.name, None, {project_id})
auth_token = f'Token {token}'
response = self.client.get(url, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def test_get_project_details(self):
"""
Ensure we can get details of a single project with permission of 'hasViewProjectPermission'
"""
user = self.generate_pmo_user_data()
reg = self.post_user_registration(user)
self.define_user_role(reg, 'PMO')
response = self.post_user_login(user)
assert response.status_code == status.HTTP_200_OK
author = response.data['user']['id']
auth_token = response.data['auth_token']
project = self.generate_project_data(author=author)
response = self.post_create_project(project=project, token=auth_token)
assert response.status_code == status.HTTP_201_CREATED
project_id = response.data['project']['id']
response = self.get_project_details(project_id=project_id, token=auth_token)
assert response.status_code == status.HTTP_200_OK
assert response.data['id'] == project_id
assert response.data['project_name'] == project['project_name']
# none existing project
project_id = 3
response = self.get_project_details(project_id=project_id, token=auth_token)
assert response.status_code == status.HTTP_404_NOT_FOUND
def patch_add_stakeholder_to_project(self, project_id, stakeholders, token):
url = reverse(project_views.AddStakeholdersToProjectAPI.name, None, {project_id})
auth_token = 'Token '+token
stakeholders = { "stakeholders": stakeholders}
response = self.client.patch(url, data=stakeholders, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def test_patch_add_stakeholder_to_project(self):
"""
Ensure a project author can add stakeholders to a project.
"""
user = self.generate_pmo_user_data()
reg = self.post_user_registration(user)
self.define_user_role(reg, 'PMO')
response = self.post_user_login(user)
assert response.status_code == status.HTTP_200_OK
# create a new project
author = response.data['user']['id']
auth_token = response.data['auth_token']
project = self.generate_project_data(author=author)
response = self.post_create_project(project=project, token=auth_token)
assert response.status_code == status.HTTP_201_CREATED
project_id = response.data['project']['id']
# register a new user
user = self.generate_ps_user_data()
reg = self.post_user_registration(user)
assert reg.status_code == status.HTTP_201_CREATED
self.define_user_role(reg, 'PS')
response = self.post_user_login(user)
assert response.status_code == status.HTTP_200_OK
stakeholders = [response.data.get('user').get('id')]
# add a new stakeholder to project
response = self.patch_add_stakeholder_to_project(project_id=project_id, stakeholders=stakeholders,
token=auth_token)
assert response.status_code == status.HTTP_200_OK
assert response.data.get('detail') == "Stakeholders added successfully"
# count = author + new stakeholder
assert Project.objects.get(id=project_id).stakeholders.count() == 2
def patch_remove_stakeholder_from_project(self, project_id, stakeholders, token):
url = reverse(project_views.RemoveStakeholdersFromProjectAPI.name, None, {project_id})
auth_token = 'Token '+token
stakeholders = { "stakeholders": stakeholders}
response = self.client.patch(url, data=stakeholders, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def test_patch_remove_stakeholder_from_project(self):
"""
Ensure a project author can remove stakeholders from a project.
"""
user = self.generate_pmo_user_data()
reg = self.post_user_registration(user)
self.define_user_role(reg, 'PMO')
response = self.post_user_login(user)
assert response.status_code == status.HTTP_200_OK
# create a new project
author = response.data['user']['id']
auth_token = response.data['auth_token']
project = self.generate_project_data(author=author)
response = self.post_create_project(project=project, token=auth_token)
assert response.status_code == status.HTTP_201_CREATED
project_id = response.data['project']['id']
# register a new user
user = self.generate_ps_user_data()
reg = self.post_user_registration(user)
assert reg.status_code == status.HTTP_201_CREATED
self.define_user_role(reg, 'PS')
response = self.post_user_login(user)
assert response.status_code == status.HTTP_200_OK
stakeholders = [response.data.get('user').get('id')]
# add a new stakeholder to project
response = self.patch_add_stakeholder_to_project(project_id=project_id, stakeholders=stakeholders,
token=auth_token)
assert response.status_code == status.HTTP_200_OK
# count = author + new stakeholder
assert Project.objects.get(id=project_id).stakeholders.count() == 2
# remove a stakeholder from project
response = self.patch_remove_stakeholder_from_project(project_id=project_id, stakeholders=stakeholders,
token=auth_token)
assert response.status_code == status.HTTP_200_OK
assert response.data.get('detail') == "Stakeholders removed successfully"
# count = author + new stakeholder
assert Project.objects.get(id=project_id).stakeholders.count() == 1
def get_stakeholders_of_project(self, project_id, token):
url = reverse(project_views.GetStakeholdersOfProjectAPI.name, None, {project_id})
auth_token = 'Token '+token
response = self.client.get(url, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def test_get_stakeholders_of_project(self):
"""
Ensure we can get stakeholders of a project with permission of 'hasViewProjectPermission'.
"""
user = self.generate_pmo_user_data()
reg = self.post_user_registration(user)
self.define_user_role(reg, 'PMO')
response = self.post_user_login(user)
assert response.status_code == status.HTTP_200_OK
stakeholder_1 = response.data.get('user')
del stakeholder_1['is_active']
# create a new project
author = response.data['user']['id']
auth_token = response.data['auth_token']
project = self.generate_project_data(author=author)
response = self.post_create_project(project=project, token=auth_token)
assert response.status_code == status.HTTP_201_CREATED
project_id = response.data['project']['id']
# register a new user
user = self.generate_ps_user_data()
reg = self.post_user_registration(user)
assert reg.status_code == status.HTTP_201_CREATED
self.define_user_role(reg, 'PS')
response = self.post_user_login(user)
assert response.status_code == status.HTTP_200_OK
stakeholder_2 = response.data.get('user')
del stakeholder_2['is_active']
stakeholders = [response.data.get('user').get('id')]
# add a new stakeholder to project
response = self.patch_add_stakeholder_to_project(project_id=project_id, stakeholders=stakeholders,
token=auth_token)
assert response.status_code == status.HTTP_200_OK
# get stakeholders
response = self.get_stakeholders_of_project(project_id=project_id, token=auth_token)
assert response.status_code == status.HTTP_200_OK
assert len(response.data['stakeholders']) == 2
self.assertEqual([stakeholder_1, stakeholder_2], response.data['stakeholders'])
def get_projects_of_stakeholder(self, token):
url = reverse(project_views.GetProjectsOfStakeholderAPI.name)
auth_token = 'Token '+token
response = self.client.get(url, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def test_get_projects_of_stakeholder(self):
"""
Ensure a stakeholder can get a project list where he is a stakeholder as Account owner.
"""
user = self.generate_pmo_user_data()
reg = self.post_user_registration(user)
self.define_user_role(reg, 'PMO')
response = self.post_user_login(user)
assert response.status_code == status.HTTP_200_OK
# create a new project
author = response.data['user']['id']
auth_token = response.data['auth_token']
project = self.generate_project_data(author=author)
response = self.post_create_project(project=project, token=auth_token)
assert response.status_code == status.HTTP_201_CREATED
project_id = response.data['project']['id']
# register a new user
user = self.generate_ps_user_data()
reg = self.post_user_registration(user)
assert reg.status_code == status.HTTP_201_CREATED
self.define_user_role(reg, 'PS')
login_res = self.post_user_login(user)
assert login_res.status_code == status.HTTP_200_OK
stakeholders = [login_res.data.get('user').get('id')]
# add a new stakeholder to project
response = self.patch_add_stakeholder_to_project(project_id=project_id, stakeholders=stakeholders,
token=auth_token)
assert response.status_code == status.HTTP_200_OK
# get projects of stakeholder
auth_token = login_res.data['auth_token']
response = self.get_projects_of_stakeholder(token=auth_token)
assert response.status_code == status.HTTP_200_OK
assert len(response.data) == 1
def post_assign_project_permissions(self, user_id, project_id, perms, token):
url = reverse(project_views.AddProjectPermissionsOfUserAPI.name)
auth_token = 'Token '+token
permissions = {
"user_id": user_id,
"project_id": project_id,
"permissions": perms
}
response = self.client.post(url, data=permissions, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def get_project_permissions_of_stakeholder(self, user_id, project_id, token):
url = reverse(project_views.GetProjectPermissionsOfUserAPI.name, None, [user_id, project_id])
auth_token = 'Token '+token
response = self.client.get(url, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def post_delete_project_permissions(self, user_id, project_id, perms, token):
url = reverse(project_views.DeleteProjectPermissionsOfUserAPI.name)
auth_token = 'Token '+token
permissions = {
"user_id": user_id,
"project_id": project_id,
"permissions": perms
}
response = self.client.post(url, data=permissions, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def post_assign_all_project_permissions(self, user_id, project_id, token):
url = reverse(project_views.AssignAllProjectPermissionsToStakeholderAPI.name)
auth_token = 'Token '+token
data = {
"user_id": user_id,
"project_id": project_id
}
response = self.client.post(url, data=data, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def test_project_permissions(self):
"""
Ensure a project author can assign and delete main project permissions of stakeholders of the project.
"""
user = self.generate_pmo_user_data()
reg = self.post_user_registration(user)
self.define_user_role(reg, 'PMO')
response = self.post_user_login(user)
assert response.status_code == status.HTTP_200_OK
# create a new project
author = response.data['user']['id']
auth_token = response.data['auth_token']
project = self.generate_project_data(author=author)
response = self.post_create_project(project=project, token=auth_token)
assert response.status_code == status.HTTP_201_CREATED
project = Project.objects.get(id=response.data['project']['id'])
# register a new user
user = self.generate_ps_user_data()
reg = self.post_user_registration(user)
assert reg.status_code == status.HTTP_201_CREATED
self.define_user_role(reg, 'PS')
user = User.objects.get(pk=reg.data.get('user').get('id'))
# add a new stakeholder to project
response = self.patch_add_stakeholder_to_project(project_id=project.id, stakeholders=[user.id],
token=auth_token)
assert response.status_code == status.HTTP_200_OK
# assign main project permissions to a stakeholder
assert not get_user_perms(user, project)
permissions = ["change_project", "add_project", "delete_project", "view_project"]
response = self.post_assign_project_permissions(user_id=user.id, project_id=project.id,
perms=permissions, token=auth_token)
assert response.status_code == status.HTTP_201_CREATED
# get project permissions of a stakeholder
response = self.get_project_permissions_of_stakeholder(user_id=user.id, project_id=project.id,
token=auth_token)
assert response.status_code == status.HTTP_200_OK
perms = response.data.get('permissions')
self.assertEqual(list(perms).sort(), permissions.sort())
# delete main project permissions of a stakehodler
response = self.post_delete_project_permissions(user_id=user.id, project_id=project.id,
perms=permissions, token=auth_token)
assert response.status_code == status.HTTP_204_NO_CONTENT
assert not get_user_perms(user, project)
# assign all permissions
response = self.post_assign_all_project_permissions(user_id=user.id, project_id=project.id,
token=auth_token)
assert response.status_code == status.HTTP_201_CREATED
assert len(get_user_perms(user, project)) == 20
def get_actual_cost_of_project(self, project_id, token):
url = reverse(project_views.GetActualCostOfProjectAPI.name, None, [project_id])
auth_token = 'Token '+token
response = self.client.get(url, HTTP_AUTHORIZATION=auth_token, format='json')
return response
def test_get_actual_cost_of_project(self):
"""
Ensure we can get actual cost of project with permission of 'hasViewProjectPermission'.
"""
user = self.generate_pmo_user_data()
reg = self.post_user_registration(user)
self.define_user_role(reg, 'PMO')
response = self.post_user_login(user)
assert response.status_code == status.HTTP_200_OK
# create a new project
author = response.data['user']['id']
auth_token = response.data['auth_token']
project = self.generate_project_data(author=author)
response = self.post_create_project(project=project, token=auth_token)
assert response.status_code == status.HTTP_201_CREATED
project = response.data['project']['id']
# get actual cost
response = self.get_actual_cost_of_project(project_id=project, token=auth_token)
assert response.status_code == status.HTTP_200_OK
assert response.data.get('actual_cost') == 0.00
assert response.data.get('resource_spendings') == 0.00
assert response.data.get('contract_spendings') == 0.00
| 41.767925 | 114 | 0.676605 | 2,702 | 22,137 | 5.272021 | 0.061806 | 0.053703 | 0.05616 | 0.0702 | 0.821551 | 0.779151 | 0.741594 | 0.722569 | 0.71562 | 0.706845 | 0 | 0.011428 | 0.229164 | 22,137 | 530 | 115 | 41.767925 | 0.823371 | 0.08077 | 0 | 0.632312 | 0 | 0 | 0.057163 | 0 | 0 | 0 | 0 | 0 | 0.194986 | 1 | 0.08078 | false | 0.011142 | 0.022284 | 0.008357 | 0.155989 | 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 |
d9d93f22e689737165938efb3ab608977d448f82 | 77 | py | Python | tests/test_import.py | vaporyproject/pytest-asyncio-network-simulator | 7a7ee136c8e47cde751c1a5af9739b1099810608 | [
"MIT"
] | 4 | 2019-06-05T23:53:04.000Z | 2021-11-04T14:24:21.000Z | tests/test_import.py | vaporyproject/pytest-asyncio-network-simulator | 7a7ee136c8e47cde751c1a5af9739b1099810608 | [
"MIT"
] | 5 | 2018-07-20T20:34:04.000Z | 2019-04-26T23:02:40.000Z | tests/test_import.py | vaporyproject/pytest-asyncio-network-simulator | 7a7ee136c8e47cde751c1a5af9739b1099810608 | [
"MIT"
] | 4 | 2018-08-23T07:43:12.000Z | 2020-10-01T03:00:27.000Z | def test_import():
import pytest_asyncio_network_simulator # noqa: F401
| 25.666667 | 57 | 0.779221 | 10 | 77 | 5.6 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.046154 | 0.155844 | 77 | 2 | 58 | 38.5 | 0.815385 | 0.12987 | 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 |
d9dcb21547d53d92824add9ee2b52662830b6281 | 30 | py | Python | pyoperant/__init__.py | kaichensh/pyoperant | 95e427604cdce07505bd6e3c1592883f45f4fac4 | [
"BSD-3-Clause"
] | 10 | 2015-02-21T22:58:43.000Z | 2021-05-20T22:47:57.000Z | pyoperant/__init__.py | kaichensh/pyoperant | 95e427604cdce07505bd6e3c1592883f45f4fac4 | [
"BSD-3-Clause"
] | 31 | 2015-02-17T16:43:15.000Z | 2020-03-06T23:09:48.000Z | pyoperant/__init__.py | kaichensh/pyoperant | 95e427604cdce07505bd6e3c1592883f45f4fac4 | [
"BSD-3-Clause"
] | 10 | 2015-07-02T18:55:02.000Z | 2021-09-20T22:45:43.000Z | from pyoperant.errors import * | 30 | 30 | 0.833333 | 4 | 30 | 6.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 30 | 1 | 30 | 30 | 0.925926 | 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 |
8a63abd43ab2da5930c4a8b7609bd91e99b5b169 | 6,370 | py | Python | tests/unit/api/test_enrich.py | CiscoSecurity/tr-05-api-module | ce0f8d583b2fce3aadcc5a5c174a5b2b23e14d72 | [
"MIT"
] | 10 | 2019-07-16T15:11:05.000Z | 2022-02-07T19:58:55.000Z | tests/unit/api/test_enrich.py | CiscoSecurity/tr-05-api-module | ce0f8d583b2fce3aadcc5a5c174a5b2b23e14d72 | [
"MIT"
] | 26 | 2019-07-18T09:31:12.000Z | 2021-11-19T09:52:50.000Z | tests/unit/api/test_enrich.py | CiscoSecurity/tr-05-api-module | ce0f8d583b2fce3aadcc5a5c174a5b2b23e14d72 | [
"MIT"
] | 13 | 2019-07-15T12:31:35.000Z | 2021-02-23T16:57:38.000Z | from threatresponse.api import EnrichAPI
from .assertions import *
def test_health_succeeds():
request = invoke(EnrichAPI, lambda api: api.health())
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/health'
)
def test_health_fails():
request = invoke_with_failure(EnrichAPI, lambda api: api.health())
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/health'
)
def test_health_with_id_succeeds():
request = invoke(
EnrichAPI,
lambda api: api.health.module_instance_id('id')
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/health/id'
)
def test_health_with_id_fails():
request = invoke_with_failure(
EnrichAPI,
lambda api: api.health.module_instance_id('id')
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/health/id'
)
def test_deliberate_observables_succeeds():
request = invoke(
EnrichAPI,
lambda api: api.deliberate.observables(payload)
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/deliberate/observables',
json=payload
)
def test_deliberate_observables_fails():
request = invoke_with_failure(
EnrichAPI,
lambda api: api.deliberate.observables(payload)
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/deliberate/observables',
json=payload
)
def test_deliberate_sighting_succeeds():
request = invoke(
EnrichAPI,
lambda api: api.deliberate.sighting(payload)
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/deliberate/sighting',
json=payload
)
def test_deliberate_sighting_fails():
request = invoke_with_failure(
EnrichAPI,
lambda api: api.deliberate.sighting(payload)
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/deliberate/sighting',
json=payload
)
def test_deliberate_sighting_ref_succeeds():
request = invoke(
EnrichAPI,
lambda api: api.deliberate.sighting_ref(payload)
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/deliberate/sighting_ref',
json=payload
)
def test_deliberate_sighting_ref_fails():
request = invoke_with_failure(
EnrichAPI,
lambda api: api.deliberate.sighting_ref(payload)
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/deliberate/sighting_ref',
json=payload
)
def test_observe_observables_succeeds():
request = invoke(EnrichAPI, lambda api: api.observe.observables(payload))
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/observe/observables',
json=payload
)
def test_observe_observables_fails():
request = invoke_with_failure(
EnrichAPI,
lambda api: api.observe.observables(payload)
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/observe/observables',
json=payload
)
def test_observe_sighting_succeeds():
request = invoke(EnrichAPI, lambda api: api.observe.sighting(payload))
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/observe/sighting',
json=payload
)
def test_observe_sighting_fails():
request = invoke_with_failure(
EnrichAPI,
lambda api: api.observe.sighting(payload)
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/observe/sighting',
json=payload
)
def test_observe_sighting_ref_succeeds():
request = invoke(EnrichAPI, lambda api: api.observe.sighting_ref(payload))
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/observe/sighting_ref',
json=payload
)
def test_observe_sighting_ref_fails():
request = invoke_with_failure(
EnrichAPI,
lambda api: api.observe.sighting_ref(payload)
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/observe/sighting_ref',
json=payload
)
def test_refer_observables_succeeds():
request = invoke(EnrichAPI, lambda api: api.refer.observables(payload))
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/refer/observables',
json=payload
)
def test_refer_observables_fails():
request = invoke_with_failure(
EnrichAPI,
lambda api: api.refer.observables(payload)
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/refer/observables',
json=payload
)
def test_refer_sighting_succeeds():
request = invoke(EnrichAPI, lambda api: api.refer.sighting(payload))
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/refer/sighting',
json=payload
)
def test_refer_sighting_fails():
request = invoke_with_failure(
EnrichAPI,
lambda api: api.refer.sighting(payload)
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/refer/sighting',
json=payload
)
def test_refer_sighting_ref_succeeds():
request = invoke(EnrichAPI, lambda api: api.refer.sighting_ref(payload))
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/refer/sighting_ref',
json=payload
)
def test_refer_sighting_ref_fails():
request = invoke_with_failure(
EnrichAPI,
lambda api: api.refer.sighting_ref(payload)
)
request.perform.assert_called_once_with(
'POST',
'/iroh/iroh-enrich/refer/sighting_ref',
json=payload
)
def test_settings_succeeds():
request = invoke(EnrichAPI, lambda api: api.settings.get())
request.perform.assert_called_once_with(
'GET',
'/iroh/iroh-enrich/settings'
)
def test_settings_fails():
request = invoke_with_failure(
EnrichAPI,
lambda api: api.settings.get()
)
request.perform.assert_called_once_with(
'GET',
'/iroh/iroh-enrich/settings'
)
| 24.594595 | 78 | 0.659341 | 702 | 6,370 | 5.7151 | 0.051282 | 0.041874 | 0.107677 | 0.125623 | 0.974576 | 0.97009 | 0.97009 | 0.956381 | 0.916002 | 0.875125 | 0 | 0 | 0.236892 | 6,370 | 258 | 79 | 24.689922 | 0.825345 | 0 | 0 | 0.61244 | 0 | 0 | 0.143171 | 0.127787 | 0 | 0 | 0 | 0 | 0.119617 | 1 | 0.114833 | false | 0 | 0.009569 | 0 | 0.124402 | 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 |
8a820dfceae61e3a358c06a38aac3dbeed42b566 | 62 | py | Python | dask/dataframe/io/parquet/__init__.py | srijan-deepsource/dask | 0673d9084e02f985f3fdf5ba6ede80e8de5ac15c | [
"BSD-3-Clause"
] | 20 | 2015-01-19T14:04:10.000Z | 2020-01-14T03:43:19.000Z | dask/dataframe/io/parquet/__init__.py | srijan-deepsource/dask | 0673d9084e02f985f3fdf5ba6ede80e8de5ac15c | [
"BSD-3-Clause"
] | 30 | 2020-04-15T19:37:40.000Z | 2020-04-22T21:19:35.000Z | dask/dataframe/io/parquet/__init__.py | srijan-deepsource/dask | 0673d9084e02f985f3fdf5ba6ede80e8de5ac15c | [
"BSD-3-Clause"
] | 7 | 2015-01-04T18:50:00.000Z | 2020-07-29T11:00:04.000Z | from .core import read_parquet, to_parquet, read_parquet_part
| 31 | 61 | 0.854839 | 10 | 62 | 4.9 | 0.7 | 0.44898 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.096774 | 62 | 1 | 62 | 62 | 0.875 | 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 |
8ab9f3abd78a5ed9a22b78b4a0cbb941d43a8285 | 165 | py | Python | test.py | Rowan-Montoya/Games-Poll-4 | 6592fad5184ba942287aca3586ac97b36dcb4843 | [
"MIT"
] | null | null | null | test.py | Rowan-Montoya/Games-Poll-4 | 6592fad5184ba942287aca3586ac97b36dcb4843 | [
"MIT"
] | null | null | null | test.py | Rowan-Montoya/Games-Poll-4 | 6592fad5184ba942287aca3586ac97b36dcb4843 | [
"MIT"
] | null | null | null | import subprocess
subprocess.call("wget -O gamepool.sh https://gitlab.com/game.pack-v.2/version.25.04.2021/-/raw/master/chitpoll.sh && bash gamepool.sh", shell=True) | 82.5 | 147 | 0.763636 | 28 | 165 | 4.5 | 0.857143 | 0.15873 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.057692 | 0.054545 | 165 | 2 | 147 | 82.5 | 0.75 | 0 | 0 | 0 | 0 | 0.5 | 0.698795 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 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 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
0a06eae474bf1d6b4cefaac45e03ebbb899498e7 | 193 | py | Python | src/jobs/admin.py | codingforentrepreneurs/matchmaker-2 | c90a20a50d33f2492831426d042c526fb3c574bc | [
"MIT"
] | 80 | 2015-07-23T19:01:46.000Z | 2022-03-27T09:38:29.000Z | src/jobs/admin.py | codingforentrepreneurs/matchmaker-2 | c90a20a50d33f2492831426d042c526fb3c574bc | [
"MIT"
] | 1 | 2018-09-19T19:13:25.000Z | 2018-09-24T20:09:26.000Z | src/jobs/admin.py | codingforentrepreneurs/matchmaker-2 | c90a20a50d33f2492831426d042c526fb3c574bc | [
"MIT"
] | 56 | 2015-07-24T02:59:55.000Z | 2021-08-24T11:53:43.000Z | from django.contrib import admin
# Register your models here.
from .models import Employer, Job, Location
admin.site.register(Job)
admin.site.register(Location)
admin.site.register(Employer) | 21.444444 | 43 | 0.803109 | 27 | 193 | 5.740741 | 0.481481 | 0.174194 | 0.329032 | 0.322581 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.103627 | 193 | 9 | 44 | 21.444444 | 0.895954 | 0.134715 | 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 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
0a4fe2d7c0b8055c319b86bb232bc8ec1a20c923 | 15,994 | py | Python | Rover/install_isolated/lib/python2.7/dist-packages/cartographer_ros_msgs/srv/_SubmapQuery.py | Rose-Hulman-Rover-Team/Rover-2019-2020 | d75a9086fa733f8a8b5240005bee058737ad82c7 | [
"MIT"
] | 1 | 2018-10-04T14:37:00.000Z | 2018-10-04T14:37:00.000Z | TrekBot_WS/install_isolated/lib/python2.7/dist-packages/cartographer_ros_msgs/srv/_SubmapQuery.py | Rafcin/TrekBot | d3dc63e6c16a040b16170f143556ef358018b7da | [
"Unlicense"
] | null | null | null | TrekBot_WS/install_isolated/lib/python2.7/dist-packages/cartographer_ros_msgs/srv/_SubmapQuery.py | Rafcin/TrekBot | d3dc63e6c16a040b16170f143556ef358018b7da | [
"Unlicense"
] | null | null | null | # This Python file uses the following encoding: utf-8
"""autogenerated by genpy from cartographer_ros_msgs/SubmapQueryRequest.msg. Do not edit."""
import sys
python3 = True if sys.hexversion > 0x03000000 else False
import genpy
import struct
class SubmapQueryRequest(genpy.Message):
_md5sum = "5fc429a478a6d73822616720a31a2158"
_type = "cartographer_ros_msgs/SubmapQueryRequest"
_has_header = False #flag to mark the presence of a Header object
_full_text = """
int32 trajectory_id
int32 submap_index
"""
__slots__ = ['trajectory_id','submap_index']
_slot_types = ['int32','int32']
def __init__(self, *args, **kwds):
"""
Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
trajectory_id,submap_index
:param args: complete set of field values, in .msg order
:param kwds: use keyword arguments corresponding to message field names
to set specific fields.
"""
if args or kwds:
super(SubmapQueryRequest, self).__init__(*args, **kwds)
#message fields cannot be None, assign default values for those that are
if self.trajectory_id is None:
self.trajectory_id = 0
if self.submap_index is None:
self.submap_index = 0
else:
self.trajectory_id = 0
self.submap_index = 0
def _get_types(self):
"""
internal API method
"""
return self._slot_types
def serialize(self, buff):
"""
serialize message into buffer
:param buff: buffer, ``StringIO``
"""
try:
_x = self
buff.write(_get_struct_2i().pack(_x.trajectory_id, _x.submap_index))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize(self, str):
"""
unpack serialized message in str into this message instance
:param str: byte array of serialized message, ``str``
"""
try:
end = 0
_x = self
start = end
end += 8
(_x.trajectory_id, _x.submap_index,) = _get_struct_2i().unpack(str[start:end])
return self
except struct.error as e:
raise genpy.DeserializationError(e) #most likely buffer underfill
def serialize_numpy(self, buff, numpy):
"""
serialize message with numpy array types into buffer
:param buff: buffer, ``StringIO``
:param numpy: numpy python module
"""
try:
_x = self
buff.write(_get_struct_2i().pack(_x.trajectory_id, _x.submap_index))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize_numpy(self, str, numpy):
"""
unpack serialized message in str into this message instance using numpy for array types
:param str: byte array of serialized message, ``str``
:param numpy: numpy python module
"""
try:
end = 0
_x = self
start = end
end += 8
(_x.trajectory_id, _x.submap_index,) = _get_struct_2i().unpack(str[start:end])
return self
except struct.error as e:
raise genpy.DeserializationError(e) #most likely buffer underfill
_struct_I = genpy.struct_I
def _get_struct_I():
global _struct_I
return _struct_I
_struct_2i = None
def _get_struct_2i():
global _struct_2i
if _struct_2i is None:
_struct_2i = struct.Struct("<2i")
return _struct_2i
# This Python file uses the following encoding: utf-8
"""autogenerated by genpy from cartographer_ros_msgs/SubmapQueryResponse.msg. Do not edit."""
import sys
python3 = True if sys.hexversion > 0x03000000 else False
import genpy
import struct
import cartographer_ros_msgs.msg
import geometry_msgs.msg
class SubmapQueryResponse(genpy.Message):
_md5sum = "ffc82c14b81fa551bc249c31ba402b2e"
_type = "cartographer_ros_msgs/SubmapQueryResponse"
_has_header = False #flag to mark the presence of a Header object
_full_text = """cartographer_ros_msgs/StatusResponse status
int32 submap_version
cartographer_ros_msgs/SubmapTexture[] textures
================================================================================
MSG: cartographer_ros_msgs/StatusResponse
# Copyright 2018 The Cartographer Authors
#
# 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.
# A common message type to indicate the outcome of a service call.
uint8 code
string message
================================================================================
MSG: cartographer_ros_msgs/SubmapTexture
# Copyright 2017 The Cartographer Authors
#
# 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.
uint8[] cells
int32 width
int32 height
float64 resolution
geometry_msgs/Pose slice_pose
================================================================================
MSG: geometry_msgs/Pose
# A representation of pose in free space, composed of position and orientation.
Point position
Quaternion orientation
================================================================================
MSG: geometry_msgs/Point
# This contains the position of a point in free space
float64 x
float64 y
float64 z
================================================================================
MSG: geometry_msgs/Quaternion
# This represents an orientation in free space in quaternion form.
float64 x
float64 y
float64 z
float64 w
"""
__slots__ = ['status','submap_version','textures']
_slot_types = ['cartographer_ros_msgs/StatusResponse','int32','cartographer_ros_msgs/SubmapTexture[]']
def __init__(self, *args, **kwds):
"""
Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
status,submap_version,textures
:param args: complete set of field values, in .msg order
:param kwds: use keyword arguments corresponding to message field names
to set specific fields.
"""
if args or kwds:
super(SubmapQueryResponse, self).__init__(*args, **kwds)
#message fields cannot be None, assign default values for those that are
if self.status is None:
self.status = cartographer_ros_msgs.msg.StatusResponse()
if self.submap_version is None:
self.submap_version = 0
if self.textures is None:
self.textures = []
else:
self.status = cartographer_ros_msgs.msg.StatusResponse()
self.submap_version = 0
self.textures = []
def _get_types(self):
"""
internal API method
"""
return self._slot_types
def serialize(self, buff):
"""
serialize message into buffer
:param buff: buffer, ``StringIO``
"""
try:
buff.write(_get_struct_B().pack(self.status.code))
_x = self.status.message
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
buff.write(_get_struct_i().pack(self.submap_version))
length = len(self.textures)
buff.write(_struct_I.pack(length))
for val1 in self.textures:
_x = val1.cells
length = len(_x)
# - if encoded as a list instead, serialize as bytes instead of string
if type(_x) in [list, tuple]:
buff.write(struct.pack('<I%sB'%length, length, *_x))
else:
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = val1
buff.write(_get_struct_2id().pack(_x.width, _x.height, _x.resolution))
_v1 = val1.slice_pose
_v2 = _v1.position
_x = _v2
buff.write(_get_struct_3d().pack(_x.x, _x.y, _x.z))
_v3 = _v1.orientation
_x = _v3
buff.write(_get_struct_4d().pack(_x.x, _x.y, _x.z, _x.w))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize(self, str):
"""
unpack serialized message in str into this message instance
:param str: byte array of serialized message, ``str``
"""
try:
if self.status is None:
self.status = cartographer_ros_msgs.msg.StatusResponse()
if self.textures is None:
self.textures = None
end = 0
start = end
end += 1
(self.status.code,) = _get_struct_B().unpack(str[start:end])
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.status.message = str[start:end].decode('utf-8')
else:
self.status.message = str[start:end]
start = end
end += 4
(self.submap_version,) = _get_struct_i().unpack(str[start:end])
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
self.textures = []
for i in range(0, length):
val1 = cartographer_ros_msgs.msg.SubmapTexture()
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
val1.cells = str[start:end]
_x = val1
start = end
end += 16
(_x.width, _x.height, _x.resolution,) = _get_struct_2id().unpack(str[start:end])
_v4 = val1.slice_pose
_v5 = _v4.position
_x = _v5
start = end
end += 24
(_x.x, _x.y, _x.z,) = _get_struct_3d().unpack(str[start:end])
_v6 = _v4.orientation
_x = _v6
start = end
end += 32
(_x.x, _x.y, _x.z, _x.w,) = _get_struct_4d().unpack(str[start:end])
self.textures.append(val1)
return self
except struct.error as e:
raise genpy.DeserializationError(e) #most likely buffer underfill
def serialize_numpy(self, buff, numpy):
"""
serialize message with numpy array types into buffer
:param buff: buffer, ``StringIO``
:param numpy: numpy python module
"""
try:
buff.write(_get_struct_B().pack(self.status.code))
_x = self.status.message
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
buff.write(_get_struct_i().pack(self.submap_version))
length = len(self.textures)
buff.write(_struct_I.pack(length))
for val1 in self.textures:
_x = val1.cells
length = len(_x)
# - if encoded as a list instead, serialize as bytes instead of string
if type(_x) in [list, tuple]:
buff.write(struct.pack('<I%sB'%length, length, *_x))
else:
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = val1
buff.write(_get_struct_2id().pack(_x.width, _x.height, _x.resolution))
_v7 = val1.slice_pose
_v8 = _v7.position
_x = _v8
buff.write(_get_struct_3d().pack(_x.x, _x.y, _x.z))
_v9 = _v7.orientation
_x = _v9
buff.write(_get_struct_4d().pack(_x.x, _x.y, _x.z, _x.w))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize_numpy(self, str, numpy):
"""
unpack serialized message in str into this message instance using numpy for array types
:param str: byte array of serialized message, ``str``
:param numpy: numpy python module
"""
try:
if self.status is None:
self.status = cartographer_ros_msgs.msg.StatusResponse()
if self.textures is None:
self.textures = None
end = 0
start = end
end += 1
(self.status.code,) = _get_struct_B().unpack(str[start:end])
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.status.message = str[start:end].decode('utf-8')
else:
self.status.message = str[start:end]
start = end
end += 4
(self.submap_version,) = _get_struct_i().unpack(str[start:end])
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
self.textures = []
for i in range(0, length):
val1 = cartographer_ros_msgs.msg.SubmapTexture()
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
val1.cells = str[start:end]
_x = val1
start = end
end += 16
(_x.width, _x.height, _x.resolution,) = _get_struct_2id().unpack(str[start:end])
_v10 = val1.slice_pose
_v11 = _v10.position
_x = _v11
start = end
end += 24
(_x.x, _x.y, _x.z,) = _get_struct_3d().unpack(str[start:end])
_v12 = _v10.orientation
_x = _v12
start = end
end += 32
(_x.x, _x.y, _x.z, _x.w,) = _get_struct_4d().unpack(str[start:end])
self.textures.append(val1)
return self
except struct.error as e:
raise genpy.DeserializationError(e) #most likely buffer underfill
_struct_I = genpy.struct_I
def _get_struct_I():
global _struct_I
return _struct_I
_struct_i = None
def _get_struct_i():
global _struct_i
if _struct_i is None:
_struct_i = struct.Struct("<i")
return _struct_i
_struct_2id = None
def _get_struct_2id():
global _struct_2id
if _struct_2id is None:
_struct_2id = struct.Struct("<2id")
return _struct_2id
_struct_B = None
def _get_struct_B():
global _struct_B
if _struct_B is None:
_struct_B = struct.Struct("<B")
return _struct_B
_struct_4d = None
def _get_struct_4d():
global _struct_4d
if _struct_4d is None:
_struct_4d = struct.Struct("<4d")
return _struct_4d
_struct_3d = None
def _get_struct_3d():
global _struct_3d
if _struct_3d is None:
_struct_3d = struct.Struct("<3d")
return _struct_3d
class SubmapQuery(object):
_type = 'cartographer_ros_msgs/SubmapQuery'
_md5sum = 'd39f26c172921775c4ad99dbf7cb0792'
_request_class = SubmapQueryRequest
_response_class = SubmapQueryResponse
| 33.390397 | 145 | 0.639552 | 2,156 | 15,994 | 4.531076 | 0.124304 | 0.03767 | 0.027024 | 0.031324 | 0.772853 | 0.772853 | 0.765278 | 0.754632 | 0.754632 | 0.754632 | 0 | 0.022689 | 0.228398 | 15,994 | 478 | 146 | 33.460251 | 0.7689 | 0.162498 | 0 | 0.691667 | 1 | 0 | 0.234896 | 0.065497 | 0 | 0 | 0.001539 | 0 | 0 | 1 | 0.055556 | false | 0 | 0.022222 | 0 | 0.169444 | 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 |
6a6503b8c286c1bb789c343c7d81f830367008b2 | 1,796 | py | Python | tests/parsers/vsftpd.py | pyllyukko/plaso | 7533db2d1035ca71d264d6281ebd5db2d073c587 | [
"Apache-2.0"
] | 2 | 2019-10-23T03:37:59.000Z | 2020-08-14T17:09:26.000Z | tests/parsers/vsftpd.py | pyllyukko/plaso | 7533db2d1035ca71d264d6281ebd5db2d073c587 | [
"Apache-2.0"
] | null | null | null | tests/parsers/vsftpd.py | pyllyukko/plaso | 7533db2d1035ca71d264d6281ebd5db2d073c587 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Tests for the vsftpd parser."""
import unittest
from plaso.parsers import vsftpd
from tests.parsers import test_lib
class VsftpdLogParserTest(test_lib.ParserTestCase):
"""Tests for the vsftpd parser."""
def testParse(self):
"""Tests the Parse function."""
parser = vsftpd.VsftpdLogParser()
storage_writer = self._ParseFile(['vsftpd.log'], parser)
self.assertEqual(storage_writer.number_of_warnings, 0)
self.assertEqual(storage_writer.number_of_events, 25)
events = list(storage_writer.GetEvents())
expected_event_values = {
'data_type': 'vsftpd:log',
'text': (
'[pid 3] [jean] OK DOWNLOAD: Client "192.168.1.7", '
'"/home/jean/trains/how-thomas-the-tank-engine-works-1.jpg", '
'49283 bytes, 931.38Kbyte/sec'),
'timestamp': '2016-06-10 14:24:19.000000'}
self.CheckEventValues(storage_writer, events[12], expected_event_values)
def testParseWithTimeZone(self):
"""Tests the Parse function with a time zone."""
parser = vsftpd.VsftpdLogParser()
storage_writer = self._ParseFile(['vsftpd.log'], parser, timezone='CET')
self.assertEqual(storage_writer.number_of_warnings, 0)
self.assertEqual(storage_writer.number_of_events, 25)
events = list(storage_writer.GetEvents())
expected_event_values = {
'data_type': 'vsftpd:log',
'text': (
'[pid 3] [jean] OK DOWNLOAD: Client "192.168.1.7", '
'"/home/jean/trains/how-thomas-the-tank-engine-works-1.jpg", '
'49283 bytes, 931.38Kbyte/sec'),
'timestamp': '2016-06-10 12:24:19.000000'}
self.CheckEventValues(storage_writer, events[12], expected_event_values)
if __name__ == '__main__':
unittest.main()
| 30.965517 | 76 | 0.665924 | 224 | 1,796 | 5.160714 | 0.401786 | 0.112457 | 0.076125 | 0.096886 | 0.802768 | 0.719723 | 0.719723 | 0.719723 | 0.719723 | 0.719723 | 0 | 0.063187 | 0.18931 | 1,796 | 57 | 77 | 31.508772 | 0.730769 | 0.094655 | 0 | 0.647059 | 0 | 0.058824 | 0.263716 | 0.073566 | 0 | 0 | 0 | 0 | 0.117647 | 1 | 0.058824 | false | 0 | 0.088235 | 0 | 0.176471 | 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 |
6a673b13e579074b7edb858becb46e1e8af9e13a | 153 | py | Python | PyDictionary/utils.py | b1tninja/PyDictionary | cd097f3504a380c8af8789dd6cff103c32f34309 | [
"MIT"
] | 248 | 2015-01-01T18:00:06.000Z | 2022-03-17T06:03:58.000Z | PyDictionary/utils.py | b1tninja/PyDictionary | cd097f3504a380c8af8789dd6cff103c32f34309 | [
"MIT"
] | 39 | 2015-09-29T21:13:49.000Z | 2022-03-22T18:03:49.000Z | PyDictionary/utils.py | b1tninja/PyDictionary | cd097f3504a380c8af8789dd6cff103c32f34309 | [
"MIT"
] | 82 | 2015-10-03T22:07:49.000Z | 2022-03-27T13:11:34.000Z | import requests
from bs4 import BeautifulSoup
def _get_soup_object(url, parser="html.parser"):
return BeautifulSoup(requests.get(url).text, parser)
| 25.5 | 56 | 0.79085 | 21 | 153 | 5.619048 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007353 | 0.111111 | 153 | 5 | 57 | 30.6 | 0.860294 | 0 | 0 | 0 | 0 | 0 | 0.071895 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.5 | 0.25 | 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 |
6a7496741b153d676d34d35e65f0b9115bd3286a | 1,867 | py | Python | mayan/apps/common/tests/test_classes.py | sophiawa/Mayan-EDMS | 42f20576d0c690b645a60bf53c5169cda4264231 | [
"Apache-2.0"
] | null | null | null | mayan/apps/common/tests/test_classes.py | sophiawa/Mayan-EDMS | 42f20576d0c690b645a60bf53c5169cda4264231 | [
"Apache-2.0"
] | 10 | 2021-03-19T23:48:12.000Z | 2022-03-12T00:41:49.000Z | mayan/apps/common/tests/test_classes.py | sophiawa/Mayan-EDMS | 42f20576d0c690b645a60bf53c5169cda4264231 | [
"Apache-2.0"
] | 1 | 2020-12-17T02:35:09.000Z | 2020-12-17T02:35:09.000Z | from ..http import URL
from .base import BaseTestCase
class URLTestCase(BaseTestCase):
def test_query_to_string(self):
url = URL(query={'a': 1})
self.assertEqual(url.to_string(), '?a=1')
def test_query_list_to_string(self):
url = URL(query={'a': '1'})
url.args.appendlist(key='a', value='2')
self.assertEqual(url.to_string(), '?a=1&a=2')
def test_query_with_question_mark_to_string(self):
url = URL(query={'a': '1?'})
self.assertEqual(url.to_string(), '?a=1%3F')
def test_querystring_with_list_to_string(self):
url = URL(query_string='a=1&a=2')
self.assertEqual(url.args.getlist('a'), ['1', '2'])
def test_querystring_with_question_mark_to_string(self):
url = URL(query_string='a=1?')
self.assertEqual(url.to_string(), '?a=1%3F')
def test_querystring_with_question_mark_encoded_to_string(self):
url = URL(query_string='a=1%3F')
self.assertEqual(url.to_string(), '?a=1%3F')
def test_querystring_to_args(self):
url = URL(query_string='a=1')
self.assertEqual(url.args['a'], '1')
def test_querystring_with_question_mark_encoded_to_args(self):
url = URL(query_string='a=1%3F')
self.assertEqual(url.args['a'], '1?')
def test_querystring_mixed_to_args(self):
url = URL(query_string='a=1&a=2&b=1')
self.assertEqual(url.args.getlist('a'), ['1', '2'])
self.assertEqual(url.args.getlist('b'), ['1'])
def test_path_and_querystring_to_string(self):
url = URL(path='http://example.com', query_string='a=1')
self.assertEqual(url.to_string(), 'http://example.com?a=1')
def test_path_and_query_to_string(self):
url = URL(path='http://example.com', query={'a': 1})
self.assertEqual(url.to_string(), 'http://example.com?a=1')
| 29.634921 | 68 | 0.633101 | 279 | 1,867 | 3.989247 | 0.129032 | 0.039533 | 0.19407 | 0.121294 | 0.867925 | 0.839173 | 0.78796 | 0.755615 | 0.637916 | 0.477987 | 0 | 0.02321 | 0.192287 | 1,867 | 62 | 69 | 30.112903 | 0.714854 | 0 | 0 | 0.236842 | 0 | 0 | 0.093733 | 0 | 0 | 0 | 0 | 0 | 0.315789 | 1 | 0.289474 | false | 0 | 0.052632 | 0 | 0.368421 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 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 | 0 | 0 | 0 | 0 | 6 |
6aa2a9e45b8d1cae451340e1db0697c2901b3aa8 | 18,949 | py | Python | experiments/draw_figures_with_without_adjust.py | sanghack81/SDCIT | 00d060dde733fde9345154a494f81e97fb395ca7 | [
"MIT"
] | 11 | 2017-08-21T15:08:46.000Z | 2021-07-15T10:50:13.000Z | experiments/draw_figures_with_without_adjust.py | ragib06/SDCIT | 74af42d84b4013004049b3715fe5432bd35269f7 | [
"MIT"
] | 4 | 2017-07-22T15:48:18.000Z | 2017-09-08T03:09:02.000Z | experiments/draw_figures_with_without_adjust.py | ragib06/SDCIT | 74af42d84b4013004049b3715fe5432bd35269f7 | [
"MIT"
] | 3 | 2020-02-06T18:45:53.000Z | 2020-08-06T14:50:56.000Z | import collections
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy
import scipy.stats
import seaborn as sns
from experiments.exp_setup import SDCIT_RESULT_DIR, SDCIT_FIGURE_DIR
from sdcit.utils import AUPC
names_chsic_chaotic = ['independent', 'gamma', 'noise', 'trial', 'N', 'runtime', 'statistic', 'pvalue']
names_chsic_postnonlinear = ['independent', 'noise', 'trial', 'N', 'runtime', 'statistic', 'pvalue']
names_kcit_chaotic = ['independent', 'gamma', 'noise', 'trial', 'N', 'runtime', 'statistic', 'boot_p_value', 'appr_p_value']
names_kcit_postnonlinear = ['independent', 'noise', 'trial', 'N', 'runtime', 'statistic', 'boot_p_value', 'appr_p_value']
names_sdcit_chaotic = ['independent', 'gamma', 'trial', 'N', 'statistic', 'pvalue']
names_sdcit_postnonlinear = ['independent', 'noise', 'trial', 'N', 'statistic', 'pvalue']
names_kcipt_chaotic = ['independent', 'gamma', 'trial', 'N', 'statistic', 'pvalue', 'B']
names_kcipt_postnonlinear = ['independent', 'noise', 'trial', 'N', 'statistic', 'pvalue', 'B']
names = {('CHSIC', 'chaotic'): names_chsic_chaotic,
('CHSIC', 'postnonlinear'): names_chsic_postnonlinear,
('KCIT', 'chaotic'): names_kcit_chaotic,
('KCIT', 'postnonlinear'): names_kcit_postnonlinear,
('KCIT2', 'chaotic'): names_kcit_chaotic,
('KCIT2', 'postnonlinear'): names_kcit_postnonlinear,
('SDCIT', 'chaotic'): names_sdcit_chaotic,
('SDCIT', 'postnonlinear'): names_sdcit_postnonlinear,
('SDCIT-wo-adjust', 'chaotic'): names_sdcit_chaotic,
('SDCIT-wo-adjust', 'postnonlinear'): names_sdcit_postnonlinear,
('KCIPT', 'chaotic'): names_kcipt_chaotic,
('KCIPT', 'postnonlinear'): names_kcipt_postnonlinear,
}
pvalue_column = collections.defaultdict(lambda: 'pvalue')
pvalue_column['KCIT'] = 'boot_p_value'
pvalue_column['KCIT2'] = 'boot_p_value'
color_palettes = sns.color_palette('Paired', 10)
method_color_codes = {'KCIT': 3, 'SDCIT': 5, 'KCIPT': 1, 'CHSIC': 9, 'KCIT2': 7, 'SDCIT-wo-adjust': 4}
markers = collections.defaultdict(lambda: 'o')
markers.update({'KCIT': 'o', 'SDCIT': 's', 'KCIPT': '*', 'CHSIC': '^', 'KCIT2': 'o'})
all_algos = ['SDCIT', 'SDCIT-wo-adjust']
def algo_name(org_name):
map = {'SDCIT-wo-adjust': 'SDCIT w/o adjust'}
if org_name in map:
return map[org_name]
else:
return org_name
def draw_aupc_chaotic():
data = 'chaotic'
aupc_data = []
for algo in all_algos:
df = pd.read_csv(SDCIT_RESULT_DIR + '/' + algo.lower() + '_' + data + '.csv', names=names[(algo, data)])
for group_key, group_df in df.groupby(by=['gamma', 'independent', 'N']):
group_key = (int(group_key[0] * 10) / 10, *group_key[1:])
if group_key[1] == 0:
aupc_data.append([algo, *group_key, AUPC(group_df[pvalue_column[algo]])])
print(draw_aupc_chaotic.__name__)
[print(xx) for xx in aupc_data]
aupc_data = np.array(aupc_data)
aupc_df = pd.DataFrame({'algorithm': aupc_data[:, 0],
'gamma': aupc_data[:, 1],
'independent': aupc_data[:, 2],
'N': aupc_data[:, 3],
'AUPC': aupc_data[:, 4]})
aupc_df['gamma'] = aupc_df['gamma'].astype(float)
aupc_df['independent'] = aupc_df['independent'].astype(int)
aupc_df['N'] = aupc_df['N'].map(int)
aupc_df['AUPC'] = aupc_df['AUPC'].astype(float)
aupc_df = aupc_df[aupc_df['independent'] == 0]
aupc_df["algo-N"] = aupc_df["algorithm"].map(str) + aupc_df["N"].map(lambda xxx: ' (' + str(xxx) + ')')
sns_setting()
for k, gdf in aupc_df.groupby(['algorithm', 'N']):
print('chaotic', k, gdf['AUPC'])
if k[1] == 400:
plt.plot(gdf['gamma'], gdf['AUPC'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':', label=algo_name(str(k[0])))
else:
plt.plot(gdf['gamma'], gdf['AUPC'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':', label='_nolegend_')
plt.axes().set_xlabel(r'$\gamma$')
plt.axes().set_ylabel('Area Under Power Curve')
plt.axes().set_ylim([0.45, 1.05])
handles, labels = plt.axes().get_legend_handles_labels()
plt.axes().legend(handles[::-1], labels[::-1])
sns.despine()
plt.savefig(SDCIT_FIGURE_DIR + '/{}_aupc_wo_adjust.pdf'.format(data), transparent=True, bbox_inches='tight', pad_inches=0.02)
plt.close()
def draw_calib_chaotic():
data = 'chaotic'
calib_data = []
for algo in all_algos:
df = pd.read_csv(SDCIT_RESULT_DIR + '/' + algo.lower() + '_' + data + '.csv', names=names[(algo, data)])
for k, gdf in df.groupby(by=['independent', 'gamma', 'N']):
if float(k[0]) == 1:
D, _ = scipy.stats.kstest(gdf[pvalue_column[algo]], 'uniform')
calib_data.append([algo, float(k[1]), int(k[2]), D])
print(draw_calib_chaotic.__name__)
[print(xx) for xx in calib_data]
df = pd.DataFrame(calib_data, columns=['algo', 'gamma', 'N', 'D'])
df['gamma'] = df['gamma'].astype(float)
df['N'] = df['N'].map(int)
df['D'] = df['D'].astype(float)
sns_setting()
for k, gdf in df.groupby(['algo', 'N']):
if k[1] == 400:
plt.plot(gdf['gamma'], gdf['D'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':', label=algo_name(str(k[0])))
else:
plt.plot(gdf['gamma'], gdf['D'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':', label='_nolegend_')
handles, labels = plt.axes().get_legend_handles_labels()
plt.axes().legend(handles[::-1], labels[::-1], ncol=2)
plt.axes().set_xlabel(r'$\gamma$')
plt.axes().set_ylabel('KS test statistic')
plt.axes().invert_yaxis()
plt.axes().set_yticks([0.1, 0.2, 0.3])
handles, labels = plt.axes().get_legend_handles_labels()
plt.axes().legend(handles[::-1], labels[::-1])
sns.despine()
plt.savefig(SDCIT_FIGURE_DIR + '/chaotic_calib_wo_adjust.pdf', transparent=True, bbox_inches='tight', pad_inches=0.02)
plt.close()
def draw_type_I_error_chaotic():
data = 'chaotic'
calib_data = []
for algo in all_algos:
df = pd.read_csv(SDCIT_RESULT_DIR + '/' + algo.lower() + '_' + data + '.csv', names=names[(algo, data)])
for k, gdf in df.groupby(by=['independent', 'gamma', 'N']):
if float(k[0]) == 1:
calib_data.append([algo, float(k[1]), int(k[2]), np.mean(gdf[pvalue_column[algo]] <= 0.05)])
print(draw_type_I_error_chaotic.__name__)
[print(xx) for xx in calib_data]
df = pd.DataFrame(calib_data, columns=['algo', 'gamma', 'N', 'D'])
df['gamma'] = df['gamma'].astype(float)
df['N'] = df['N'].map(int)
df['D'] = df['D'].astype(float)
sns_setting()
for k, gdf in df.groupby(['algo', 'N']):
if k[1] == 400:
plt.plot(gdf['gamma'], gdf['D'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':', label=algo_name(str(k[0])))
else:
plt.plot(gdf['gamma'], gdf['D'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':', label='_nolegend_')
plt.axes().set_xlabel(r'$\gamma$')
plt.axes().set_xticks([0.0, 0.1, 0.2, 0.3, 0.4, 0.5])
plt.axes().set_ylabel('Type I error')
plt.axes().set_ylim([0.0, 0.2])
sns.despine()
plt.savefig(SDCIT_FIGURE_DIR + '/chaotic_type_I_wo_adjust.pdf', transparent=True, bbox_inches='tight', pad_inches=0.02)
plt.close()
def draw_aupc_postnonlinear():
data = 'postnonlinear'
aupc_data = []
for algo in all_algos:
df = pd.read_csv(SDCIT_RESULT_DIR + '/' + algo.lower() + '_' + data + '.csv', names=names[(algo, data)])
for group_key, group_df in df.groupby(by=['noise', 'independent', 'N']):
group_key = (int(group_key[0] * 10) / 10, int(group_key[1]), int(group_key[2]))
aupc_data.append([algo, *group_key, AUPC(group_df[pvalue_column[algo]])])
print(draw_aupc_postnonlinear.__name__)
[print(xx) for xx in aupc_data]
aupc_data = np.array(aupc_data)
aupc_df = pd.DataFrame({'algorithm': [str(v) for v in aupc_data[:, 0]],
'noise': [int(float(v)) for v in aupc_data[:, 1]],
'independent': [int(v) for v in aupc_data[:, 2]],
'N': [int(v) for v in aupc_data[:, 3]],
'AUPC': [float(v) for v in aupc_data[:, 4]]})
aupc_df['dimension'] = (aupc_df['noise'] + 1).astype(int)
aupc_df = aupc_df[aupc_df['independent'] == 0]
aupc_df["algo-N"] = aupc_df["algorithm"].map(str) + aupc_df["N"].map(lambda xxx: ' (' + str(xxx) + ')')
sns_setting()
for k, gdf in aupc_df.groupby(['algorithm', 'N']):
gdf = gdf[gdf['dimension'] <= 5]
if k[1] == 400:
plt.plot(gdf['dimension'], gdf['AUPC'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':', label=algo_name(str(k[0])))
else:
plt.plot(gdf['dimension'], gdf['AUPC'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':', label='_nolegend_')
plt.axes().set_xlabel('dimension')
plt.axes().set_ylabel('Area Under Power Curve')
plt.axes().set_ylim([0.45, 1.05])
sns.despine()
plt.savefig(SDCIT_FIGURE_DIR + '/postnonlinear_aupc_wo_adjust.pdf', transparent=True, bbox_inches='tight', pad_inches=0.02)
plt.close()
def draw_aupc_postnonlinear_highdim():
data = 'postnonlinear'
aupc_data = []
for algo in all_algos:
df = pd.read_csv(SDCIT_RESULT_DIR + '/' + algo.lower() + '_' + data + '.csv', names=names[(algo, data)])
for group_key, group_df in df.groupby(by=['noise', 'independent', 'N']):
group_key = (int(group_key[0] * 10) / 10, int(group_key[1]), int(group_key[2]))
aupc_data.append([algo, *group_key, AUPC(group_df[pvalue_column[algo]])])
print(draw_aupc_postnonlinear_highdim.__name__)
[print(xx) for xx in aupc_data]
aupc_data = np.array(aupc_data)
aupc_df = pd.DataFrame({'algorithm': [str(v) for v in aupc_data[:, 0]],
'noise': [int(float(v)) for v in aupc_data[:, 1]],
'independent': [int(v) for v in aupc_data[:, 2]],
'N': [int(v) for v in aupc_data[:, 3]],
'AUPC': [float(v) for v in aupc_data[:, 4]]})
aupc_df['dimension'] = (aupc_df['noise'] + 1).astype(int)
aupc_df = aupc_df[aupc_df['independent'] == 0]
aupc_df["algo-N"] = aupc_df["algorithm"].map(str) + aupc_df["N"].map(lambda xxx: ' (' + str(xxx) + ')')
sns_setting()
for k, gdf in aupc_df.groupby(['algorithm', 'N']):
if k[1] == 400:
plt.plot([int(v) for v in gdf['dimension']], gdf['AUPC'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':',
label=algo_name(str(k[0])))
plt.axes().set_xlabel('dimension')
plt.axes().set_ylabel('Area Under Power Curve')
plt.axes().set_ylim([0.95, 1.01])
plt.axes().set_xscale('log')
plt.xticks([1, 5, 10, 20, 50], [1, 5, 10, 20, 50])
sns.despine()
plt.savefig(SDCIT_FIGURE_DIR + '/postnonlinear_aupc_highdim_wo_adjust.pdf', transparent=True, bbox_inches='tight', pad_inches=0.02)
plt.close()
def draw_calib_postnonlinear():
data = 'postnonlinear'
calib_data = []
for algo in all_algos:
df = pd.read_csv(SDCIT_RESULT_DIR + '/' + algo.lower() + '_' + data + '.csv', names=names[(algo, data)])
for k, gdf in df.groupby(by=['independent', 'noise', 'N']):
if float(k[0]) == 1:
D, _ = scipy.stats.kstest(gdf[pvalue_column[algo]], 'uniform')
calib_data.append([algo, float(k[1]), int(k[2]), D])
print(draw_calib_postnonlinear.__name__)
[print(xx) for xx in calib_data]
df = pd.DataFrame(calib_data, columns=['algo', 'noise', 'N', 'D'])
df['noise'] = df['noise'].map(int)
df['dimension'] = (df['noise'] + 1).astype(int)
df['N'] = df['N'].map(int)
df['D'] = df['D'].astype(float)
sns_setting()
for k, gdf in df.groupby(['algo', 'N']):
gdf = gdf[gdf['dimension'] <= 5]
if k[1] == 400:
plt.plot([int(v) for v in gdf['dimension']], gdf['D'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':',
label=algo_name(str(k[0])))
else:
plt.plot([int(v) for v in gdf['dimension']], gdf['D'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':',
label='_nolegend_')
plt.axes().set_xlabel('dimension')
plt.axes().set_ylabel('KS test statistic')
plt.axes().invert_yaxis()
plt.axes().set_yticks([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
# plt.title('Postnonlinear')
sns.despine()
plt.savefig(SDCIT_FIGURE_DIR + '/postnonlinear_calib_wo_adjust.pdf', transparent=True, bbox_inches='tight', pad_inches=0.02)
plt.close()
def sns_setting():
paper_rc = {'lines.linewidth': 1, 'lines.markersize': 2}
sns.set_context("paper", rc=paper_rc)
sns.set(style='white', font_scale=1.4)
plt.figure(figsize=[4, 3])
plt.rc('text', usetex=True)
plt.rc('text.latex', preamble=r'\usepackage{cmbright}')
def draw_calib_postnonlinear_highdim():
data = 'postnonlinear'
calib_data = []
for algo in all_algos:
df = pd.read_csv(SDCIT_RESULT_DIR + '/' + algo.lower() + '_' + data + '.csv', names=names[(algo, data)])
for k, gdf in df.groupby(by=['independent', 'noise', 'N']):
if float(k[0]) == 1 and k[2] == 400:
dd, _ = scipy.stats.kstest(gdf[pvalue_column[algo]], 'uniform')
calib_data.append([algo, float(k[1]), int(k[2]), dd])
print(draw_calib_postnonlinear_highdim.__name__)
[print(xx) for xx in calib_data]
df = pd.DataFrame(calib_data, columns=['algo', 'noise', 'N', 'D'])
df['noise'] = df['noise'].map(int)
df['dimension'] = (df['noise'] + 1).astype(int)
df['N'] = df['N'].map(int)
df['D'] = df['D'].astype(float)
sns_setting()
for k, gdf in df.groupby(['algo', 'N']):
print('postnonlinear', k, gdf['D'])
if k[1] == 400:
plt.plot(gdf['dimension'], gdf['D'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':', label=algo_name(str(k[0])))
else:
plt.plot(gdf['dimension'], gdf['D'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':', label='_nolegend_')
plt.axes().set_xlabel('dimension')
plt.axes().set_ylabel('KS test statistic')
plt.axes().set_xscale('log')
plt.axes().invert_yaxis()
plt.xticks([1, 5, 10, 20, 50], [1, 5, 10, 20, 50])
plt.axes().set_yticks([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
sns.despine()
plt.savefig(SDCIT_FIGURE_DIR + '/postnonlinear_calib_highdim_wo_adjust.pdf', transparent=True, bbox_inches='tight', pad_inches=0.02)
plt.close()
def draw_type_I_postnonlinear_highdim():
data = 'postnonlinear'
calib_data = []
for algo in all_algos:
df = pd.read_csv(SDCIT_RESULT_DIR + '/' + algo.lower() + '_' + data + '.csv', names=names[(algo, data)])
for k, gdf in df.groupby(by=['independent', 'noise', 'N']):
if float(k[0]) == 1 and k[2] == 400:
dd = np.mean(gdf[pvalue_column[algo]] <= 0.05)
calib_data.append([algo, float(k[1]), int(k[2]), dd])
print(draw_type_I_postnonlinear_highdim.__name__)
[print(xx) for xx in calib_data]
df = pd.DataFrame(calib_data, columns=['algo', 'noise', 'N', 'D'])
df['noise'] = df['noise'].map(int)
df['dimension'] = (df['noise'] + 1).astype(int)
df['N'] = df['N'].map(int)
df['D'] = df['D'].astype(float)
sns_setting()
for k, gdf in df.groupby(['algo', 'N']):
if k[1] == 400:
plt.plot(gdf['dimension'], gdf['D'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':', label=algo_name(str(k[0])))
else:
plt.plot(gdf['dimension'], gdf['D'], markers[(k[0])], c=color_palettes[method_color_codes[k[0]]] if k[1] == 400 else color_palettes[-0 + method_color_codes[k[0]]],
ls='-' if k[1] == 400 else ':', label='_nolegend_')
plt.axes().set_xlabel('dimension')
plt.axes().set_xscale('log')
plt.xticks([1, 5, 10, 20, 50], [1, 5, 10, 20, 50])
plt.axes().set_ylim([0.0, 0.2])
handles, labels = plt.axes().get_legend_handles_labels()
plt.axes().legend(handles[::-1], labels[::-1])
sns.despine()
plt.savefig(SDCIT_FIGURE_DIR + '/postnonlinear_type_I_highdim_wo_adjust.pdf', transparent=True, bbox_inches='tight', pad_inches=0.02)
plt.close()
if __name__ == '__main__':
# for data in ['chaotic', 'postnonlinear']:
# for algo in all_algos:
# assert exists(SDCIT_RESULT_DIR + '/' + algo.lower() + '_' + data + '.csv'), 'run tests first -- missing {}'.format(algo.lower() + '_' + data + '.csv')
if True:
# chaotic series
draw_aupc_chaotic()
draw_calib_chaotic()
# # postnonlinear-noise
draw_aupc_postnonlinear()
draw_calib_postnonlinear()
draw_aupc_postnonlinear_highdim()
draw_calib_postnonlinear_highdim()
#
# # type I for both
draw_type_I_error_chaotic()
draw_type_I_postnonlinear_highdim()
| 47.491228 | 196 | 0.58763 | 2,730 | 18,949 | 3.869963 | 0.071062 | 0.01098 | 0.014387 | 0.025177 | 0.822338 | 0.804449 | 0.801893 | 0.770185 | 0.749077 | 0.729389 | 0 | 0.031496 | 0.214154 | 18,949 | 398 | 197 | 47.610553 | 0.677993 | 0.016201 | 0 | 0.674699 | 0 | 0 | 0.128012 | 0.015726 | 0 | 0 | 0 | 0 | 0 | 1 | 0.03012 | false | 0 | 0.027108 | 0 | 0.063253 | 0.054217 | 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 |
6aecba57d6767488046ada528c2934076361c22e | 34 | py | Python | app/gui/symmetric/des/__init__.py | vasilypht/Cryptographic-methods | 7a44a46f01d5973d338170cb05272582d34bc1be | [
"MIT"
] | null | null | null | app/gui/symmetric/des/__init__.py | vasilypht/Cryptographic-methods | 7a44a46f01d5973d338170cb05272582d34bc1be | [
"MIT"
] | null | null | null | app/gui/symmetric/des/__init__.py | vasilypht/Cryptographic-methods | 7a44a46f01d5973d338170cb05272582d34bc1be | [
"MIT"
] | null | null | null | from .des_widget import DESWidget
| 17 | 33 | 0.852941 | 5 | 34 | 5.6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 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 |
0a8d3da39da77ba53c53b7670594db00a1b1ffa9 | 46 | py | Python | vendor/django-celery-results-master/t/proj/__init__.py | smudkey/adminset | d19a84706693918ddbfd62f8bf9e837b2e6f6558 | [
"Apache-2.0"
] | 5,079 | 2015-01-01T03:39:46.000Z | 2022-03-31T07:38:22.000Z | desktop/core/ext-py/django_celery_results-1.0.4/t/proj/__init__.py | zks888/hue | 93a8c370713e70b216c428caa2f75185ef809deb | [
"Apache-2.0"
] | 1,623 | 2015-01-01T08:06:24.000Z | 2022-03-30T19:48:52.000Z | desktop/core/ext-py/django_celery_results-1.0.4/t/proj/__init__.py | zks888/hue | 93a8c370713e70b216c428caa2f75185ef809deb | [
"Apache-2.0"
] | 2,033 | 2015-01-04T07:18:02.000Z | 2022-03-28T19:55:47.000Z | from .celery import app as celery_app # noqa
| 23 | 45 | 0.76087 | 8 | 46 | 4.25 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.195652 | 46 | 1 | 46 | 46 | 0.918919 | 0.086957 | 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 |
0ab7358eadc2da21707fbb9863f3fbb41b97d43b | 2,917 | py | Python | tests/test_weasyprint.py | adinhodovic/wagtail-resume | 80dccf1a71b2646d815b0daf44601ae87ff012a2 | [
"MIT"
] | 44 | 2019-12-27T16:49:19.000Z | 2022-03-23T08:09:57.000Z | tests/test_weasyprint.py | adinhodovic/wagtail-resume | 80dccf1a71b2646d815b0daf44601ae87ff012a2 | [
"MIT"
] | 44 | 2020-01-20T21:51:39.000Z | 2022-02-11T21:58:53.000Z | tests/test_weasyprint.py | adinhodovic/wagtail-resume | 80dccf1a71b2646d815b0daf44601ae87ff012a2 | [
"MIT"
] | 12 | 2020-02-03T06:39:07.000Z | 2021-07-02T08:57:18.000Z | import logging
import pytest
from django.urls import reverse
from wagtail.core.models import Site
from .models import CustomResumePage
pytestmark = pytest.mark.django_db
def test_weasyprint(client, mocker):
mocker.patch("wagtail_resume.views.HTML")
site = Site.objects.first()
resume = CustomResumePage(
title="Resume",
full_name="Adin Hodovic",
role="Software engineer",
)
site.root_page.add_child(instance=resume)
# Test random page pdf generation
url = f"{reverse('generate_resume_pdf')}?page_id={resume.id}"
res = client.get(url)
assert "adin-hodovic" in res["content-disposition"]
assert res.status_code == 200
assert res["content-type"] == "application/pdf"
def test_weasyprint_with_font(client, mocker):
mocker.patch("wagtail_resume.views.HTML")
site = Site.objects.first()
resume = CustomResumePage(
title="Resume", full_name="Adin Hodovic", role="Software engineer", font="lato"
)
site.root_page.add_child(instance=resume)
# Test random page pdf generation
url = f"{reverse('generate_resume_pdf')}?page_id={resume.id}"
res = client.get(url)
assert "adin-hodovic" in res["content-disposition"]
assert res.status_code == 200
assert res["content-type"] == "application/pdf"
def test_weasyprint_with_no_page_id(client, mocker):
mocker.patch("wagtail_resume.views.HTML")
site = Site.objects.first()
resume = CustomResumePage(
title="Resume", full_name="Adin Hodovic", role="Software engineer", font="lato"
)
site.root_page.add_child(instance=resume)
# Test random page pdf generation
url = f"{reverse('generate_resume_pdf')}"
res = client.get(url)
assert b"Missing page id for resume generation" in res.content
assert res.status_code == 400
def test_weasyprint_with_no_number(client, mocker):
mocker.patch("wagtail_resume.views.HTML")
site = Site.objects.first()
resume = CustomResumePage(
title="Resume", full_name="Adin Hodovic", role="Software engineer", font="lato"
)
site.root_page.add_child(instance=resume)
# Test random page pdf generation
url = f"{reverse('generate_resume_pdf')}?page_id={resume.id}'"
res = client.get(url)
assert b"Page id is not a number" in res.content
assert res.status_code == 400
def test_weasyprint_no_resume(client, mocker):
mocker.patch("wagtail_resume.views.HTML")
site = Site.objects.first()
resume = CustomResumePage(
title="Resume", full_name="Adin Hodovic", role="Software engineer", font="lato"
)
site.root_page.add_child(instance=resume)
# Test non existent resume
url = f"{reverse('generate_resume_pdf')}?page_id=9999"
res = client.get(url)
assert res.status_code == 404
def test_weasyprint_logger_warnings_disabled():
logger = logging.getLogger("weasyprint")
assert logger.level == 40
| 32.775281 | 87 | 0.701748 | 384 | 2,917 | 5.174479 | 0.205729 | 0.038752 | 0.051334 | 0.057876 | 0.818319 | 0.803724 | 0.792149 | 0.792149 | 0.775038 | 0.775038 | 0 | 0.008739 | 0.176208 | 2,917 | 88 | 88 | 33.147727 | 0.818144 | 0.052108 | 0 | 0.58209 | 0 | 0 | 0.266763 | 0.13012 | 0 | 0 | 0 | 0 | 0.179104 | 1 | 0.089552 | false | 0 | 0.074627 | 0 | 0.164179 | 0.104478 | 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 |
0ac14886a68b2efb0b89a7e6f56fbbb345c633dc | 284 | py | Python | irfUtil/irfUtilLib.py | fermi-lat/irfs | cebe27fc6a974ac4448f15d7944b21e419c585e9 | [
"BSD-3-Clause"
] | null | null | null | irfUtil/irfUtilLib.py | fermi-lat/irfs | cebe27fc6a974ac4448f15d7944b21e419c585e9 | [
"BSD-3-Clause"
] | 4 | 2020-02-21T20:16:38.000Z | 2022-03-22T17:39:03.000Z | irfUtil/irfUtilLib.py | fermi-lat/irfs | cebe27fc6a974ac4448f15d7944b21e419c585e9 | [
"BSD-3-Clause"
] | 1 | 2020-07-07T18:30:05.000Z | 2020-07-07T18:30:05.000Z | #$Id$
def generate(env, **kw):
if not kw.get('depsOnly', 0):
env.Tool('addLibrary', library = ['irfUtil'])
env.Tool('astroLib')
env.Tool('tipLib')
env.Tool('st_facilitiesLib')
env.Tool('addLibrary', library = env['f2cLibs'])
def exists(env):
return 1
| 23.666667 | 53 | 0.605634 | 37 | 284 | 4.621622 | 0.594595 | 0.204678 | 0.19883 | 0.280702 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.013158 | 0.197183 | 284 | 11 | 54 | 25.818182 | 0.736842 | 0.014085 | 0 | 0 | 1 | 0 | 0.258065 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.222222 | false | 0 | 0 | 0.111111 | 0.333333 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
0aef16936cdeac6d7a3b03a6a7446f43e403e562 | 768 | py | Python | octicons16px/shield.py | andrewp-as-is/octicons16px.py | 1272dc9f290619d83bd881e87dbd723b0c48844c | [
"Unlicense"
] | 1 | 2021-01-28T06:47:39.000Z | 2021-01-28T06:47:39.000Z | octicons16px/shield.py | andrewp-as-is/octicons16px.py | 1272dc9f290619d83bd881e87dbd723b0c48844c | [
"Unlicense"
] | null | null | null | octicons16px/shield.py | andrewp-as-is/octicons16px.py | 1272dc9f290619d83bd881e87dbd723b0c48844c | [
"Unlicense"
] | null | null | null |
OCTICON_SHIELD = """
<svg class="octicon octicon-shield" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.467.133a1.75 1.75 0 011.066 0l5.25 1.68A1.75 1.75 0 0115 3.48V7c0 1.566-.32 3.182-1.303 4.682-.983 1.498-2.585 2.813-5.032 3.855a1.7 1.7 0 01-1.33 0c-2.447-1.042-4.049-2.357-5.032-3.855C1.32 10.182 1 8.566 1 7V3.48a1.75 1.75 0 011.217-1.667l5.25-1.68zm.61 1.429a.25.25 0 00-.153 0l-5.25 1.68a.25.25 0 00-.174.238V7c0 1.358.275 2.666 1.057 3.86.784 1.194 2.121 2.34 4.366 3.297a.2.2 0 00.154 0c2.245-.956 3.582-2.104 4.366-3.298C13.225 9.666 13.5 8.36 13.5 7V3.48a.25.25 0 00-.174-.237l-5.25-1.68zM9 10.5a1 1 0 11-2 0 1 1 0 012 0zm-.25-5.75a.75.75 0 10-1.5 0v3a.75.75 0 001.5 0v-3z"></path></svg>
"""
| 153.6 | 741 | 0.666667 | 215 | 768 | 2.376744 | 0.488372 | 0.029354 | 0.029354 | 0.035225 | 0.074364 | 0 | 0 | 0 | 0 | 0 | 0 | 0.545588 | 0.114583 | 768 | 4 | 742 | 192 | 0.205882 | 0 | 0 | 0 | 0 | 0.333333 | 0.968709 | 0.126467 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
0af2af30addd2b5f237e7e3fda9b93b7a0a6bcc5 | 149 | py | Python | audino/backend/init_routes.py | UCSD-E4E/Pyrenote | bede2cfae9cb543a855d5cb01133b8d7c4abaa1c | [
"MIT"
] | 11 | 2021-07-09T21:39:05.000Z | 2022-03-06T23:11:44.000Z | audino/backend/init_routes.py | UCSD-E4E/Pyrenote | bede2cfae9cb543a855d5cb01133b8d7c4abaa1c | [
"MIT"
] | 120 | 2021-07-08T04:15:18.000Z | 2022-02-26T00:21:25.000Z | audino/backend/init_routes.py | UCSD-E4E/Audio_Labeling_System_AID | 00f1084e546f67d98dc5da861997abc256e7133c | [
"MIT"
] | 2 | 2021-02-22T02:07:03.000Z | 2021-05-08T09:20:20.000Z | from .create_app import app, db, migrate, jwt, redis_client
from .routes import auth, api
app.register_blueprint(auth)
app.register_blueprint(api)
| 21.285714 | 59 | 0.798658 | 23 | 149 | 5 | 0.608696 | 0.191304 | 0.347826 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114094 | 149 | 6 | 60 | 24.833333 | 0.871212 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0.5 | 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 | 0 | 1 | 0 | 6 |
0afb278d80c395484cdae052a86f1fd5563ca80b | 7,086 | py | Python | tests/test_window_fns.py | WinVector/data_algebra | 3d6002ddf8231d310e03537a0435df0554b62234 | [
"BSD-3-Clause"
] | 37 | 2019-08-28T08:16:48.000Z | 2022-03-14T21:18:39.000Z | tests/test_window_fns.py | WinVector/data_algebra | 3d6002ddf8231d310e03537a0435df0554b62234 | [
"BSD-3-Clause"
] | 1 | 2019-09-02T23:13:29.000Z | 2019-09-08T01:43:10.000Z | tests/test_window_fns.py | WinVector/data_algebra | 3d6002ddf8231d310e03537a0435df0554b62234 | [
"BSD-3-Clause"
] | 3 | 2019-08-28T12:23:11.000Z | 2020-02-08T19:22:31.000Z | import pytest
import data_algebra
import data_algebra.test_util
from data_algebra.data_ops import * # https://github.com/WinVector/data_algebra
import data_algebra.util
import data_algebra.SQLite
# https://pandas.pydata.org/pandas-docs/stable/reference/groupby.html
def test_window_fns():
d = data_algebra.default_data_model.pd.DataFrame(
{
"g": [1, 2, 2, 3, 3, 3],
"x": [1, 4, 5, 7, 8, 9],
"v": [10, 40, 50, 70, 80, 90],
}
)
table_desciption = describe_table(d)
ops = table_desciption.extend(
{
"row_number": "_row_number()",
# "shift_v": "v.shift()",
},
order_by=["x"],
partition_by=["g"],
).extend(
{
# "ngroup": "_ngroup()",
"size": "_size()",
"size2": "(1).sum()",
"max_v": "v.max()",
"min_v": "v.min()",
"sum_v": "v.sum()",
"mean_v": "v.mean()",
"count_v": "v.count()",
"size_v": "v.size()",
},
partition_by=["g"],
)
expect1 = data_algebra.default_data_model.pd.DataFrame(
{
"g": [1, 2, 2, 3, 3, 3],
"x": [1, 4, 5, 7, 8, 9],
"v": [10, 40, 50, 70, 80, 90],
"row_number": [1, 1, 2, 1, 2, 3],
# "ngroup": [0, 1, 1, 2, 2, 2],
"size": [1, 2, 2, 3, 3, 3],
"size2": [1, 2, 2, 3, 3, 3],
"max_v": [10, 50, 50, 90, 90, 90],
"min_v": [10, 40, 40, 70, 70, 70],
"sum_v": [10, 90, 90, 240, 240, 240],
"mean_v": [10, 45, 45, 80, 80, 80],
# "shift_v": [None, None, 40.0, None, 70.0, 80.0],
"count_v": [1, 2, 2, 3, 3, 3],
"size_v": [1, 2, 2, 3, 3, 3],
}
)
data_algebra.test_util.check_transform(ops=ops, data=d, expect=expect1)
def test_window_fns_pandas_only():
d = data_algebra.default_data_model.pd.DataFrame(
{
"g": [1, 2, 2, 3, 3, 3],
"x": [1, 4, 5, 7, 8, 9],
"v": [10, 40, 50, 70, 80, 90],
}
)
table_desciption = describe_table(d)
ops = table_desciption.extend(
{"row_number": "_row_number()", "shift_v": "v.shift()",},
order_by=["x"],
partition_by=["g"],
).extend(
{
"ngroup": "_ngroup()",
"size": "_size()",
"size2": "(1).sum()",
"max_v": "v.max()",
"min_v": "v.min()",
"sum_v": "v.sum()",
"mean_v": "v.mean()",
"count_v": "v.count()",
"size_v": "v.size()",
},
partition_by=["g"],
)
expect1 = data_algebra.default_data_model.pd.DataFrame(
{
"g": [1, 2, 2, 3, 3, 3],
"x": [1, 4, 5, 7, 8, 9],
"v": [10, 40, 50, 70, 80, 90],
"row_number": [1, 1, 2, 1, 2, 3],
"ngroup": [0, 1, 1, 2, 2, 2],
"size": [1, 2, 2, 3, 3, 3],
"size2": [1, 2, 2, 3, 3, 3],
"max_v": [10, 50, 50, 90, 90, 90],
"min_v": [10, 40, 40, 70, 70, 70],
"sum_v": [10, 90, 90, 240, 240, 240],
"mean_v": [10, 45, 45, 80, 80, 80],
"shift_v": [None, None, 40.0, None, 70.0, 80.0],
"count_v": [1, 2, 2, 3, 3, 3],
"size_v": [1, 2, 2, 3, 3, 3],
}
)
res_pandas = ops.transform(d)
assert data_algebra.test_util.equivalent_frames(res_pandas, expect1)
def test_window_fns_project():
d = data_algebra.default_data_model.pd.DataFrame(
{
"g": [1, 2, 2, 3, 3, 3],
"x": [1, 4, 5, 7, 8, 9],
"v": [10, 40, 50, 70, 80, 90],
}
)
table_desciption = describe_table(d)
ops = table_desciption.extend(
{"row_number": "_row_number()", "shift_v": "v.shift()",},
order_by=["x"],
partition_by=["g"],
).project(
{
# "ngroup": "_ngroup()",
"size": "_size()",
"size2": "(1).sum()",
"max_v": "v.max()",
"min_v": "v.min()",
"sum_v": "v.sum()",
"mean_v": "v.mean()",
"count_v": "v.count()",
"size_v": "v.size()",
},
group_by=["g"],
)
expect1 = data_algebra.default_data_model.pd.DataFrame(
{
"g": [1, 2, 3],
"size": [1, 2, 3],
"size2": [1, 2, 3],
"max_v": [10, 50, 90],
"min_v": [10, 40, 70],
"sum_v": [10, 90, 240],
"mean_v": [10, 45, 80],
"count_v": [1, 2, 3],
"size_v": [1, 2, 3],
}
)
data_algebra.test_util.check_transform(ops=ops, data=d, expect=expect1)
def test_window_fns_project_no_ngroup_project():
d = data_algebra.default_data_model.pd.DataFrame(
{
"g": [1, 2, 2, 3, 3, 3],
"x": [1, 4, 5, 7, 8, 9],
"v": [10, 40, 50, 70, 80, 90],
}
)
table_desciption = describe_table(d)
with pytest.raises(ValueError):
table_desciption.extend(
{"row_number": "_row_number()", "shift_v": "v.shift()",},
order_by=["x"],
partition_by=["g"],
).project(
{
"ngroup": "_ngroup()",
"size": "_size()",
"size2": "(1).sum()",
"max_v": "v.max()",
"min_v": "v.min()",
"sum_v": "v.sum()",
"mean_v": "v.mean()",
"count_v": "v.count()",
"size_v": "v.size()",
},
group_by=["g"],
)
def test_window_fns_project_pandas_only():
d = data_algebra.default_data_model.pd.DataFrame(
{
"g": [1, 2, 2, 3, 3, 3],
"x": [1, 4, 5, 7, 8, 9],
"v": [10, 40, 50, 70, 80, 90],
}
)
table_desciption = describe_table(d)
ops = table_desciption.extend(
{"row_number": "_row_number()", "ngroup": "_ngroup()", "shift_v": "v.shift()",},
order_by=["x"],
partition_by=["g"],
).project(
{
"ng_max": "ngroup.max()",
"ng_min": "ngroup.max()",
"size": "_size()",
"size2": "(1).sum()",
"max_v": "v.max()",
"min_v": "v.min()",
"sum_v": "v.sum()",
"mean_v": "v.mean()",
"count_v": "v.count()",
"size_v": "v.size()",
},
group_by=["g"],
)
res = ops.transform(d)
expect1 = data_algebra.default_data_model.pd.DataFrame(
{
"g": [1, 2, 3],
"ng_max": [0, 1, 2],
"ng_min": [0, 1, 2],
"size": [1, 2, 3],
"size2": [1, 2, 3],
"max_v": [10, 50, 90],
"min_v": [10, 40, 70],
"sum_v": [10, 90, 240],
"mean_v": [10, 45, 80],
"count_v": [1, 2, 3],
"size_v": [1, 2, 3],
}
)
assert data_algebra.test_util.equivalent_frames(res, expect1)
| 28.922449 | 88 | 0.415326 | 912 | 7,086 | 3.017544 | 0.092105 | 0.025436 | 0.018532 | 0.021802 | 0.884811 | 0.856105 | 0.845203 | 0.845203 | 0.813227 | 0.813227 | 0 | 0.106002 | 0.37694 | 7,086 | 244 | 89 | 29.040984 | 0.517327 | 0.03641 | 0 | 0.672986 | 0 | 0 | 0.152493 | 0 | 0 | 0 | 0 | 0 | 0.009479 | 1 | 0.023697 | false | 0 | 0.028436 | 0 | 0.052133 | 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 |
e40c1c906b75ef0835eaf7c50d9eb85e656322c5 | 3,080 | py | Python | dataset.py | mendiguren/neural-network | 626ba9e5324cce18975c98e1d364426a339c8c20 | [
"MIT"
] | 1 | 2015-03-04T14:44:26.000Z | 2015-03-04T14:44:26.000Z | dataset.py | mendiguren/neural-network | 626ba9e5324cce18975c98e1d364426a339c8c20 | [
"MIT"
] | null | null | null | dataset.py | mendiguren/neural-network | 626ba9e5324cce18975c98e1d364426a339c8c20 | [
"MIT"
] | null | null | null | #This class is reponsable of loading diferent datasets
#it's always returns 3 datasets, Train, Validate, Test
import cPickle
import gzip
import os
import urllib
import numpy
def load_mnist():
filepath = './mnist.pkl.gz'
# Download the MNIST dataset if it is not present
if not os.path.isfile(filepath):
origin = 'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz'
print 'Downloading data from %s' % origin
urllib.urlretrieve(origin, filepath)
# Load the dataset
print '... loading data'
#We have to descompress it
f = gzip.open(filepath, 'rb')
train_set, valid_set, test_set = cPickle.load(f)
f.close()
return train_set, valid_set, test_set
def load_dictionary():
dictionary = numpy.zeros(26, dtype='u1, (15,)u1')
dictionary[:] = [(0,[1, 0, 1, 0, 1, 0 , 0, 0, 0 , 0, 1, 0 , 0, 1, 0 ]),
(1,[0, 0, 1, 0, 1, 0 , 0, 0, 1 , 0, 1, 0 , 0, 0, 1 ]),
(2,[0, 0, 0, 0, 1, 1 , 0, 1, 1 , 0, 1, 1 , 0, 0, 0 ]),
(3,[0, 0, 1, 0, 1, 0 , 0, 1, 0 , 0, 1, 0 , 0, 0, 1 ]),
(4,[0, 0, 0, 0, 1, 1 , 0, 0, 1 , 0, 1, 1 , 0, 0, 0 ]),
(5,[0, 0, 0, 0, 1, 1 , 0, 0, 1 , 0, 1, 1 , 0, 1, 1 ]),
(6,[0, 0, 0, 0, 1, 1 , 0, 1, 1 , 0, 1, 0 , 0, 0, 0 ]),
(7,[0, 1, 0, 0, 1, 0 , 0, 0, 0 , 0, 1, 0 , 0, 1, 0 ]),
(8,[0, 0, 0, 1, 0, 1 , 1, 0, 1 , 1, 0, 1 , 0, 0, 0 ]),
(9,[0, 0, 0, 1, 1, 0 , 1, 1, 0 , 0, 1, 0 , 0, 0, 0 ]),
(10,[0, 1, 0, 0, 0, 1 , 0, 1, 1 , 0, 0, 1 , 0, 1, 0 ]),
(11,[0, 1, 1, 0, 1, 1 , 0, 1, 1 , 0, 1, 1 , 0, 0, 0 ]),
(12,[0, 1, 0, 0, 0, 0 , 0, 1, 0 , 0, 1, 0 , 0, 1, 0 ]),
(13,[0, 1, 0, 0, 0, 0 , 0, 0, 0 , 0, 0, 0 , 0, 1, 0 ]),
(14,[1, 0, 1, 0, 1, 0 , 0, 1, 0 , 0, 1, 0 , 1, 0, 1 ]),
(15,[0, 0, 0, 0, 1, 0 , 0, 0, 0 , 0, 1, 1 , 0, 1, 1 ]),
(16,[1, 0, 1, 0, 1, 0 , 0, 1, 0 , 1, 0, 1 , 1, 1, 0 ]),
(17,[0, 0, 1, 0, 1, 0 , 0, 0, 0 , 0, 0, 1 , 0, 1, 0 ]),
(18,[1, 0, 0, 0, 1, 1 , 1, 0, 1 , 1, 1, 0 , 0, 0, 1 ]),
(19,[0, 0, 0, 1, 0, 1 , 1, 0, 1 , 1, 0, 1 , 1, 0, 1 ]),
(20,[0, 1, 0, 0, 1, 0 , 0, 1, 0 , 0, 1, 0 , 0, 0, 0 ]),
(21,[0, 1, 0, 0, 1, 0 , 0, 1, 0 , 0, 1, 0 , 1, 0, 1 ]),
(22,[0, 1, 0, 0, 1, 0 , 0, 1, 0 , 0, 0, 0 , 0, 1, 0 ]),
(23,[0, 1, 0, 0, 1, 0 , 1, 0, 1 , 0, 1, 0 , 0, 1, 0 ]),
(24,[0, 1, 0, 0, 1, 0 , 1, 0, 1 , 1, 0, 1 , 1, 0, 1 ]),
(25,[0, 0, 0, 1, 1, 0 , 1, 0, 1 , 0, 1, 1 , 0, 0, 0 ])]
return dictionary
def load_naive():
dictionary = numpy.zeros(4, dtype='u1, (4,)u1')
dictionary[:] = [(0,[1, 0, 0, 0]),
(1,[0, 1, 0, 0]),
(2,[0, 0, 1, 0]),
(3,[0, 0, 0, 1])]
return dictionary
| 43.380282 | 81 | 0.356494 | 570 | 3,080 | 1.910526 | 0.159649 | 0.225895 | 0.212121 | 0.150597 | 0.433425 | 0.403122 | 0.352617 | 0.342516 | 0.303949 | 0.253444 | 0 | 0.262174 | 0.426623 | 3,080 | 70 | 82 | 44 | 0.354473 | 0.064286 | 0 | 0.038462 | 0 | 0.019231 | 0.048331 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.096154 | null | null | 0.038462 | 0 | 0 | 1 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7c1051455a4e380d746d64bc914755070c4177fd | 528 | py | Python | weasyprint_rest/print/non_closable.py | daKomma/weasyprint-rest | 4473666c71c83aa1b73ee4aecb618e57aac03319 | [
"MIT"
] | 13 | 2021-01-19T09:28:56.000Z | 2022-03-23T17:50:07.000Z | weasyprint_rest/print/non_closable.py | daKomma/weasyprint-rest | 4473666c71c83aa1b73ee4aecb618e57aac03319 | [
"MIT"
] | 47 | 2021-01-19T11:43:23.000Z | 2022-03-31T04:16:18.000Z | weasyprint_rest/print/non_closable.py | daKomma/weasyprint-rest | 4473666c71c83aa1b73ee4aecb618e57aac03319 | [
"MIT"
] | 3 | 2021-10-02T14:16:37.000Z | 2022-03-15T15:02:08.000Z | class NonClosable:
def __init__(self, stream_like):
self.stream_like = stream_like
def close(self):
# Reset file instead of closing it
if hasattr(self.stream_like, "seek"):
self.stream_like.seek(0)
def __bool__(self):
return self.stream_like.__bool__()
def __getattr__(self, name):
return getattr(self.stream_like, name)
def __iter__(self):
return self.stream_like.__iter__()
def __repr__(self):
return self.stream_like.__repr__()
| 25.142857 | 46 | 0.645833 | 66 | 528 | 4.545455 | 0.363636 | 0.3 | 0.373333 | 0.2 | 0.24 | 0 | 0 | 0 | 0 | 0 | 0 | 0.002551 | 0.257576 | 528 | 20 | 47 | 26.4 | 0.762755 | 0.060606 | 0 | 0 | 0 | 0 | 0.008097 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.428571 | false | 0 | 0 | 0.285714 | 0.785714 | 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 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
7c3d4a0b0b81f18f9a732c643ae141b4b31e8468 | 20 | py | Python | wpkit/gitspace/__init__.py | Peiiii/wpkit | 23a07548be766b559b80e3114ecc24e3f2f65ea5 | [
"MIT"
] | null | null | null | wpkit/gitspace/__init__.py | Peiiii/wpkit | 23a07548be766b559b80e3114ecc24e3f2f65ea5 | [
"MIT"
] | null | null | null | wpkit/gitspace/__init__.py | Peiiii/wpkit | 23a07548be766b559b80e3114ecc24e3f2f65ea5 | [
"MIT"
] | null | null | null | from .Store 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 |
7cc32689b00094b523d3cfd0b315cafa9373c8aa | 6,979 | py | Python | spookynet/modules/electron_configurations.py | OUnke/SpookyNet | d57b1fc02c4f1304a9445b2b9aa55a906818dd1b | [
"MIT"
] | 29 | 2021-11-06T17:08:37.000Z | 2022-03-31T17:02:14.000Z | spookynet/modules/electron_configurations.py | OUnke/SpookyNet | d57b1fc02c4f1304a9445b2b9aa55a906818dd1b | [
"MIT"
] | 2 | 2021-12-15T21:58:41.000Z | 2022-03-25T19:41:51.000Z | spookynet/modules/electron_configurations.py | OUnke/SpookyNet | d57b1fc02c4f1304a9445b2b9aa55a906818dd1b | [
"MIT"
] | 3 | 2021-12-16T11:48:18.000Z | 2022-01-16T08:19:39.000Z | #!/usr/bin/env python3
import numpy as np
# fmt: off
electron_config = np.array([
# Z 1s 2s 2p 3s 3p 4s 3d 4p 5s 4d 5p 6s 4f 5d 6p vs vp vd vf
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # n
[ 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], # H
[ 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], # He
[ 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], # Li
[ 4, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], # Be
[ 5, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0], # B
[ 6, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0], # C
[ 7, 2, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0], # N
[ 8, 2, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 4, 0, 0], # O
[ 9, 2, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 0, 0], # F
[ 10, 2, 2, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 6, 0, 0], # Ne
[ 11, 2, 2, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], # Na
[ 12, 2, 2, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], # Mg
[ 13, 2, 2, 6, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0], # Al
[ 14, 2, 2, 6, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0], # Si
[ 15, 2, 2, 6, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0], # P
[ 16, 2, 2, 6, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 4, 0, 0], # S
[ 17, 2, 2, 6, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 0, 0], # Cl
[ 18, 2, 2, 6, 2, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 6, 0, 0], # Ar
[ 19, 2, 2, 6, 2, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], # K
[ 20, 2, 2, 6, 2, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], # Ca
[ 21, 2, 2, 6, 2, 6, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0], # Sc
[ 22, 2, 2, 6, 2, 6, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0], # Ti
[ 23, 2, 2, 6, 2, 6, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 3, 0], # V
[ 24, 2, 2, 6, 2, 6, 1, 5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0], # Cr
[ 25, 2, 2, 6, 2, 6, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 5, 0], # Mn
[ 26, 2, 2, 6, 2, 6, 2, 6, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 6, 0], # Fe
[ 27, 2, 2, 6, 2, 6, 2, 7, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 7, 0], # Co
[ 28, 2, 2, 6, 2, 6, 2, 8, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 8, 0], # Ni
[ 29, 2, 2, 6, 2, 6, 1, 10, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 10, 0], # Cu
[ 30, 2, 2, 6, 2, 6, 2, 10, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 10, 0], # Zn
[ 31, 2, 2, 6, 2, 6, 2, 10, 1, 0, 0, 0, 0, 0, 0, 0, 2, 1, 10, 0], # Ga
[ 32, 2, 2, 6, 2, 6, 2, 10, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 10, 0], # Ge
[ 33, 2, 2, 6, 2, 6, 2, 10, 3, 0, 0, 0, 0, 0, 0, 0, 2, 3, 10, 0], # As
[ 34, 2, 2, 6, 2, 6, 2, 10, 4, 0, 0, 0, 0, 0, 0, 0, 2, 4, 10, 0], # Se
[ 35, 2, 2, 6, 2, 6, 2, 10, 5, 0, 0, 0, 0, 0, 0, 0, 2, 5, 10, 0], # Br
[ 36, 2, 2, 6, 2, 6, 2, 10, 6, 0, 0, 0, 0, 0, 0, 0, 2, 6, 10, 0], # Kr
[ 37, 2, 2, 6, 2, 6, 2, 10, 6, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], # Rb
[ 38, 2, 2, 6, 2, 6, 2, 10, 6, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0], # Sr
[ 39, 2, 2, 6, 2, 6, 2, 10, 6, 2, 1, 0, 0, 0, 0, 0, 2, 0, 1, 0], # Y
[ 40, 2, 2, 6, 2, 6, 2, 10, 6, 2, 2, 0, 0, 0, 0, 0, 2, 0, 2, 0], # Zr
[ 41, 2, 2, 6, 2, 6, 2, 10, 6, 1, 4, 0, 0, 0, 0, 0, 1, 0, 4, 0], # Nb
[ 42, 2, 2, 6, 2, 6, 2, 10, 6, 1, 5, 0, 0, 0, 0, 0, 1, 0, 5, 0], # Mo
[ 43, 2, 2, 6, 2, 6, 2, 10, 6, 2, 5, 0, 0, 0, 0, 0, 2, 0, 5, 0], # Tc
[ 44, 2, 2, 6, 2, 6, 2, 10, 6, 1, 7, 0, 0, 0, 0, 0, 1, 0, 7, 0], # Ru
[ 45, 2, 2, 6, 2, 6, 2, 10, 6, 1, 8, 0, 0, 0, 0, 0, 1, 0, 8, 0], # Rh
[ 46, 2, 2, 6, 2, 6, 2, 10, 6, 0, 10, 0, 0, 0, 0, 0, 0, 0, 10, 0], # Pd
[ 47, 2, 2, 6, 2, 6, 2, 10, 6, 1, 10, 0, 0, 0, 0, 0, 1, 0, 10, 0], # Ag
[ 48, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 0, 0, 0, 0, 0, 2, 0, 10, 0], # Cd
[ 49, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 1, 0, 0, 0, 0, 2, 1, 10, 0], # In
[ 50, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 2, 0, 0, 0, 0, 2, 2, 10, 0], # Sn
[ 51, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 3, 0, 0, 0, 0, 2, 3, 10, 0], # Sb
[ 52, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 4, 0, 0, 0, 0, 2, 4, 10, 0], # Te
[ 53, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 5, 0, 0, 0, 0, 2, 5, 10, 0], # I
[ 54, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 0, 0, 0, 0, 2, 6, 10, 0], # Xe
[ 55, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 1, 0, 0, 0, 1, 0, 0, 0], # Cs
[ 56, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 0, 0, 0, 2, 0, 0, 0], # Ba
[ 57, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 0, 1, 0, 2, 0, 1, 0], # La
[ 58, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 1, 1, 0, 2, 0, 1, 1], # Ce
[ 59, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 3, 0, 0, 2, 0, 0, 3], # Pr
[ 60, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 4, 0, 0, 2, 0, 0, 4], # Nd
[ 61, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 5, 0, 0, 2, 0, 0, 5], # Pm
[ 62, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 6, 0, 0, 2, 0, 0, 6], # Sm
[ 63, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 7, 0, 0, 2, 0, 0, 7], # Eu
[ 64, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 7, 1, 0, 2, 0, 1, 7], # Gd
[ 65, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 9, 0, 0, 2, 0, 0, 9], # Tb
[ 66, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 10, 0, 0, 2, 0, 0, 10], # Dy
[ 67, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 11, 0, 0, 2, 0, 0, 11], # Ho
[ 68, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 12, 0, 0, 2, 0, 0, 12], # Er
[ 69, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 13, 0, 0, 2, 0, 0, 13], # Tm
[ 70, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 0, 0, 2, 0, 0, 14], # Yb
[ 71, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 1, 0, 2, 0, 1, 14], # Lu
[ 72, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 2, 0, 2, 0, 2, 14], # Hf
[ 73, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 3, 0, 2, 0, 3, 14], # Ta
[ 74, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 4, 0, 2, 0, 4, 14], # W
[ 75, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 5, 0, 2, 0, 5, 14], # Re
[ 76, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 6, 0, 2, 0, 6, 14], # Os
[ 77, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 7, 0, 2, 0, 7, 14], # Ir
[ 78, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 1, 14, 9, 0, 1, 0, 9, 14], # Pt
[ 79, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 1, 14, 10, 0, 1, 0, 10, 14], # Au
[ 80, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 10, 0, 2, 0, 10, 14], # Hg
[ 81, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 10, 1, 2, 1, 10, 14], # Tl
[ 82, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 10, 2, 2, 2, 10, 14], # Pb
[ 83, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 10, 3, 2, 3, 10, 14], # Bi
[ 84, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 10, 4, 2, 4, 10, 14], # Po
[ 85, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 10, 5, 2, 5, 10, 14], # At
[ 86, 2, 2, 6, 2, 6, 2, 10, 6, 2, 10, 6, 2, 14, 10, 6, 2, 6, 10, 14] # Rn
], dtype=np.float64)
# fmt: on
# normalize entries (between 0.0 and 1.0)
electron_config = electron_config / np.max(electron_config, axis=0)
| 71.214286 | 76 | 0.337154 | 1,884 | 6,979 | 1.246815 | 0.113588 | 0.395062 | 0.45977 | 0.49553 | 0.708387 | 0.675607 | 0.655598 | 0.633035 | 0.575564 | 0.469987 | 0 | 0.459398 | 0.37183 | 6,979 | 97 | 77 | 71.948454 | 0.076414 | 0.056025 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.010989 | 0 | 0.010989 | 0 | 0 | 0 | 1 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
7ccf9041b19cba597662bba0203174e37a130f79 | 232 | py | Python | terrascript/bitbucket/r.py | vutsalsinghal/python-terrascript | 3b9fb5ad77453d330fb0cd03524154a342c5d5dc | [
"BSD-2-Clause"
] | null | null | null | terrascript/bitbucket/r.py | vutsalsinghal/python-terrascript | 3b9fb5ad77453d330fb0cd03524154a342c5d5dc | [
"BSD-2-Clause"
] | null | null | null | terrascript/bitbucket/r.py | vutsalsinghal/python-terrascript | 3b9fb5ad77453d330fb0cd03524154a342c5d5dc | [
"BSD-2-Clause"
] | null | null | null | # terrascript/bitbucket/r.py
import terrascript
class bitbucket_hook(terrascript.Resource):
pass
class bitbucket_default_reviewers(terrascript.Resource):
pass
class bitbucket_repository(terrascript.Resource):
pass
| 16.571429 | 56 | 0.797414 | 25 | 232 | 7.24 | 0.48 | 0.232044 | 0.381215 | 0.309392 | 0.40884 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.133621 | 232 | 13 | 57 | 17.846154 | 0.900498 | 0.112069 | 0 | 0.428571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.428571 | 0.142857 | 0 | 0.571429 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
7cebbdd9122ccaa3241a27d9ab06d9baf7017b43 | 33 | py | Python | src/flaskdoc/examples/link_example/__init__.py | kulgan/flaskdoc | e61fcbc246bcc3695c0e7dcb474067d47a6d70f0 | [
"Apache-2.0"
] | 4 | 2020-08-17T03:07:26.000Z | 2021-06-24T13:01:56.000Z | src/flaskdoc/examples/link_example/__init__.py | kulgan/flaskdoc | e61fcbc246bcc3695c0e7dcb474067d47a6d70f0 | [
"Apache-2.0"
] | 14 | 2019-10-09T13:50:43.000Z | 2020-08-17T02:35:55.000Z | src/flaskdoc/examples/link_example/__init__.py | kulgan/flaskdoc | e61fcbc246bcc3695c0e7dcb474067d47a6d70f0 | [
"Apache-2.0"
] | 2 | 2020-08-09T06:10:24.000Z | 2022-03-06T11:23:30.000Z | from .v0 import api, info, links
| 16.5 | 32 | 0.727273 | 6 | 33 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.037037 | 0.181818 | 33 | 1 | 33 | 33 | 0.851852 | 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 |
6b0f996cd6ec368fca4de8cd011ff6265fff86b1 | 3,650 | py | Python | src/models/ResGCNv1/modules.py | Liang813/GaitGraph | df8cfd8d1e7a91a738190ba68bc52a67207188e5 | [
"MIT"
] | 57 | 2021-01-14T12:45:04.000Z | 2022-03-22T08:57:26.000Z | src/models/ResGCNv1/modules.py | KennChow/GaitGraph | 749aa32ce079f0afaa39b15a90c8f1664f864436 | [
"MIT"
] | 18 | 2021-02-07T07:37:08.000Z | 2022-03-22T11:17:11.000Z | src/models/ResGCNv1/modules.py | KennChow/GaitGraph | 749aa32ce079f0afaa39b15a90c8f1664f864436 | [
"MIT"
] | 18 | 2021-03-13T11:15:04.000Z | 2022-03-28T05:10:34.000Z | import logging, torch
from torch import nn
def import_class(name):
components = name.split('.')
mod = __import__(components[0])
for comp in components[1:]:
mod = getattr(mod, comp)
return mod
class ResGCN_Module(nn.Module):
def __init__(self, in_channels, out_channels, block, A, initial=False, stride=1, kernel_size=[9,2], **kwargs):
super(ResGCN_Module, self).__init__()
if not len(kernel_size) == 2:
logging.info('')
logging.error('Error: Please check whether len(kernel_size) == 2')
raise ValueError()
if not kernel_size[0] % 2 == 1:
logging.info('')
logging.error('Error: Please check whether kernel_size[0] % 2 == 1')
raise ValueError()
temporal_window_size, max_graph_distance = kernel_size
if initial:
module_res, block_res = False, False
elif block == 'Basic':
module_res, block_res = True, False
else:
module_res, block_res = False, True
if not module_res:
self.residual = lambda x: 0
elif stride == 1 and in_channels == out_channels:
self.residual = lambda x: x
else:
self.residual = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 1, (stride,1)),
nn.BatchNorm2d(out_channels),
)
spatial_block = import_class('models.ResGCNv1.blocks.Spatial_{}_Block'.format(block))
temporal_block = import_class('models.ResGCNv1.blocks.Temporal_{}_Block'.format(block))
self.scn = spatial_block(in_channels, out_channels, max_graph_distance, block_res, **kwargs)
self.tcn = temporal_block(out_channels, temporal_window_size, stride, block_res, **kwargs)
self.edge = nn.Parameter(torch.ones_like(A))
def forward(self, x, A):
return self.tcn(self.scn(x, A*self.edge), self.residual(x))
class AttGCN_Module(nn.Module):
def __init__(self, in_channels, out_channels, block, A, attention, stride=1, kernel_size=[9,2], **kwargs):
super(AttGCN_Module, self).__init__()
if not len(kernel_size) == 2:
logging.info('')
logging.error('Error: Please check whether len(kernel_size) == 2')
raise ValueError()
if not kernel_size[0] % 2 == 1:
logging.info('')
logging.error('Error: Please check whether kernel_size[0] % 2 == 1')
raise ValueError()
temporal_window_size, max_graph_distance = kernel_size
if block == 'Basic':
module_res, block_res = True, False
else:
module_res, block_res = False, True
if not module_res:
self.residual = lambda x: 0
elif stride == 1 and in_channels == out_channels:
self.residual = lambda x: x
else:
self.residual = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 1, (stride,1)),
nn.BatchNorm2d(out_channels),
)
spatial_block = import_class('models.ResGCNv1.blocks.Spatial_{}_Block'.format(block))
temporal_block = import_class('models.ResGCNv1.blocks.Temporal_{}_Block'.format(block))
self.scn = spatial_block(in_channels, out_channels, max_graph_distance, block_res, **kwargs)
self.tcn = temporal_block(out_channels, temporal_window_size, stride, block_res, **kwargs)
self.att = attention(out_channels, **kwargs)
self.edge = nn.Parameter(torch.ones_like(A))
def forward(self, x, A):
return self.att(self.tcn(self.scn(x, A*self.edge), self.residual(x)))
| 39.673913 | 114 | 0.618356 | 460 | 3,650 | 4.663043 | 0.167391 | 0.066667 | 0.048485 | 0.078322 | 0.890443 | 0.880186 | 0.880186 | 0.880186 | 0.852214 | 0.852214 | 0 | 0.014925 | 0.265753 | 3,650 | 91 | 115 | 40.10989 | 0.785448 | 0 | 0 | 0.693333 | 0 | 0 | 0.101096 | 0.043288 | 0 | 0 | 0 | 0 | 0 | 1 | 0.066667 | false | 0 | 0.106667 | 0.026667 | 0.24 | 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 |
6b11df9299581e7855d9781b77f0f03fd6ebbd03 | 29,552 | py | Python | evalml/tests/data_checks_tests/test_invalid_target_data_check.py | Mahesh1822/evalml | aa0ec2379aeba12bbd0dcaaa000f9a2a62064169 | [
"BSD-3-Clause"
] | null | null | null | evalml/tests/data_checks_tests/test_invalid_target_data_check.py | Mahesh1822/evalml | aa0ec2379aeba12bbd0dcaaa000f9a2a62064169 | [
"BSD-3-Clause"
] | null | null | null | evalml/tests/data_checks_tests/test_invalid_target_data_check.py | Mahesh1822/evalml | aa0ec2379aeba12bbd0dcaaa000f9a2a62064169 | [
"BSD-3-Clause"
] | null | null | null | import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from evalml.automl import get_default_primary_search_objective
from evalml.data_checks import (
DataCheckAction,
DataCheckActionCode,
DataCheckError,
DataCheckMessageCode,
DataChecks,
DataCheckWarning,
InvalidTargetDataCheck,
)
from evalml.exceptions import DataCheckInitError
from evalml.objectives import (
MAPE,
MeanSquaredLogError,
RootMeanSquaredLogError,
)
from evalml.problem_types import (
ProblemTypes,
is_binary,
is_multiclass,
is_regression,
)
from evalml.utils.woodwork_utils import numeric_and_boolean_ww
invalid_targets_data_check_name = InvalidTargetDataCheck.name
def test_invalid_target_data_check_invalid_n_unique():
with pytest.raises(
ValueError, match="`n_unique` must be a non-negative integer value."
):
InvalidTargetDataCheck(
"regression",
get_default_primary_search_objective("regression"),
n_unique=-1,
)
def test_invalid_target_data_check_nan_error():
X = pd.DataFrame({"col": [1, 2, 3]})
invalid_targets_check = InvalidTargetDataCheck(
"regression", get_default_primary_search_objective("regression")
)
assert invalid_targets_check.validate(X, y=pd.Series([1, 2, 3])) == {
"warnings": [],
"errors": [],
"actions": [],
}
assert invalid_targets_check.validate(X, y=pd.Series([np.nan, np.nan, np.nan])) == {
"warnings": [],
"errors": [
DataCheckError(
message="Target is either empty or fully null.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_IS_EMPTY_OR_FULLY_NULL,
details={},
).to_dict(),
],
"actions": [],
}
def test_invalid_target_data_check_numeric_binary_classification_valid_float():
y = pd.Series([0.0, 1.0, 0.0, 1.0])
X = pd.DataFrame({"col": range(len(y))})
invalid_targets_check = InvalidTargetDataCheck(
"binary", get_default_primary_search_objective("binary")
)
assert invalid_targets_check.validate(X, y) == {
"warnings": [],
"errors": [],
"actions": [],
}
def test_invalid_target_data_check_multiclass_two_examples_per_class():
y = pd.Series([0] + [1] * 19 + [2] * 80)
X = pd.DataFrame({"col": range(len(y))})
invalid_targets_check = InvalidTargetDataCheck(
"multiclass", get_default_primary_search_objective("binary")
)
expected_message = "Target does not have at least two instances per class which is required for multiclass classification"
# with 1 class not having min 2 instances
assert invalid_targets_check.validate(X, y) == {
"warnings": [],
"errors": [
DataCheckError(
message=expected_message,
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_MULTICLASS_NOT_TWO_EXAMPLES_PER_CLASS,
details={"least_populated_class_labels": [0]},
).to_dict()
],
"actions": [],
}
y = pd.Series([0] + [1] + [2] * 98)
X = pd.DataFrame({"col": range(len(y))})
# with 2 classes not having min 2 instances
assert invalid_targets_check.validate(X, y) == {
"warnings": [],
"errors": [
DataCheckError(
message=expected_message,
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_MULTICLASS_NOT_TWO_EXAMPLES_PER_CLASS,
details={"least_populated_class_labels": [0, 1]},
).to_dict()
],
"actions": [],
}
@pytest.mark.parametrize(
"pd_type", ["int16", "int32", "int64", "float16", "float32", "float64", "bool"]
)
def test_invalid_target_data_check_invalid_pandas_data_types_error(pd_type):
y = pd.Series([0, 1, 0, 0, 1, 0, 1, 0])
y = y.astype(pd_type)
X = pd.DataFrame({"col": range(len(y))})
invalid_targets_check = InvalidTargetDataCheck(
"binary", get_default_primary_search_objective("binary")
)
assert invalid_targets_check.validate(X, y) == {
"warnings": [],
"errors": [],
"actions": [],
}
y = pd.Series(pd.date_range("2000-02-03", periods=5, freq="W"))
X = pd.DataFrame({"col": range(len(y))})
unique_values = y.value_counts().index.tolist()
assert invalid_targets_check.validate(X, y) == {
"warnings": [],
"errors": [
DataCheckError(
message="Target is unsupported {} type. Valid Woodwork logical types include: {}".format(
"Datetime",
", ".join([ltype for ltype in numeric_and_boolean_ww]),
),
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_UNSUPPORTED_TYPE,
details={"unsupported_type": "datetime"},
).to_dict(),
DataCheckError(
message="Binary class targets require exactly two unique values.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_BINARY_NOT_TWO_UNIQUE_VALUES,
details={"target_values": unique_values},
).to_dict(),
],
"actions": [],
}
def test_invalid_target_y_none():
invalid_targets_check = InvalidTargetDataCheck(
"binary", get_default_primary_search_objective("binary")
)
assert invalid_targets_check.validate(pd.DataFrame(), y=None) == {
"warnings": [],
"errors": [
DataCheckError(
message="Target is None",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_IS_NONE,
details={},
).to_dict()
],
"actions": [],
}
def test_invalid_target_data_input_formats():
invalid_targets_check = InvalidTargetDataCheck(
"binary", get_default_primary_search_objective("binary")
)
# test empty pd.Series
X = pd.DataFrame()
messages = invalid_targets_check.validate(X, pd.Series())
assert messages == {
"warnings": [],
"errors": [
DataCheckError(
message="Target is either empty or fully null.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_IS_EMPTY_OR_FULLY_NULL,
details={},
).to_dict()
],
"actions": [],
}
expected = {
"warnings": [],
"errors": [
DataCheckError(
message="3 row(s) (75.0%) of target values are null",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_HAS_NULL,
details={"num_null_rows": 3, "pct_null_rows": 75},
).to_dict(),
DataCheckError(
message="Binary class targets require exactly two unique values.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_BINARY_NOT_TWO_UNIQUE_VALUES,
details={"target_values": [0]},
).to_dict(),
],
"actions": [
DataCheckAction(
DataCheckActionCode.IMPUTE_COL,
data_check_name=invalid_targets_data_check_name,
metadata={
"is_target": True,
"impute_strategy": "most_frequent",
},
).to_dict()
],
}
# test Woodwork
y = pd.Series([None, None, None, 0])
X = pd.DataFrame({"col": range(len(y))})
messages = invalid_targets_check.validate(X, y)
assert messages == expected
# test list
y = [np.nan, np.nan, np.nan, 0]
X = pd.DataFrame({"col": range(len(y))})
messages = invalid_targets_check.validate(X, y)
assert messages == expected
# test np.array
y = np.array([np.nan, np.nan, np.nan, 0])
X = pd.DataFrame({"col": range(len(y))})
messages = invalid_targets_check.validate(X, y)
assert messages == expected
@pytest.mark.parametrize(
"problem_type", [ProblemTypes.BINARY, ProblemTypes.TIME_SERIES_BINARY]
)
def test_invalid_target_data_check_n_unique(problem_type):
y = pd.Series(list(range(100, 200)) + list(range(200)))
unique_values = y.value_counts().index.tolist()[:100] # n_unique defaults to 100
X = pd.DataFrame({"col": range(len(y))})
invalid_targets_check = InvalidTargetDataCheck(
problem_type, get_default_primary_search_objective(problem_type)
)
# Test default value of n_unique
assert invalid_targets_check.validate(X, y) == {
"warnings": [],
"errors": [
DataCheckError(
message="Binary class targets require exactly two unique values.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_BINARY_NOT_TWO_UNIQUE_VALUES,
details={"target_values": unique_values},
).to_dict()
],
"actions": [],
}
# Test number of unique values < n_unique
y = pd.Series(range(20))
X = pd.DataFrame({"col": range(len(y))})
unique_values = y.value_counts().index.tolist()
assert invalid_targets_check.validate(X, y) == {
"warnings": [],
"errors": [
DataCheckError(
message="Binary class targets require exactly two unique values.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_BINARY_NOT_TWO_UNIQUE_VALUES,
details={"target_values": unique_values},
).to_dict()
],
"actions": [],
}
# Test n_unique is None
invalid_targets_check = InvalidTargetDataCheck(
"binary", get_default_primary_search_objective("binary"), n_unique=None
)
y = pd.Series(range(150))
X = pd.DataFrame({"col": range(len(y))})
unique_values = y.value_counts().index.tolist()
assert invalid_targets_check.validate(X, y) == {
"warnings": [],
"errors": [
DataCheckError(
message="Binary class targets require exactly two unique values.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_BINARY_NOT_TWO_UNIQUE_VALUES,
details={"target_values": unique_values},
).to_dict()
],
"actions": [],
}
@pytest.mark.parametrize(
"objective",
[
"Root Mean Squared Log Error",
"Mean Squared Log Error",
"Mean Absolute Percentage Error",
],
)
def test_invalid_target_data_check_invalid_labels_for_nonnegative_objective_names(
objective,
):
X = pd.DataFrame({"column_one": [100, 200, 100, 200, 200, 100, 200, 100] * 25})
y = pd.Series([2, 2, 3, 3, -1, -1, 1, 1] * 25)
data_checks = DataChecks(
[InvalidTargetDataCheck],
{
"InvalidTargetDataCheck": {
"problem_type": "multiclass",
"objective": objective,
}
},
)
assert data_checks.validate(X, y) == {
"warnings": [],
"errors": [
DataCheckError(
message=f"Target has non-positive values which is not supported for {objective}",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_INCOMPATIBLE_OBJECTIVE,
details={
"Count of offending values": sum(
val <= 0 for val in y.values.flatten()
)
},
).to_dict()
],
"actions": [],
}
X = pd.DataFrame({"column_one": [100, 200, 100, 200, 100]})
y = pd.Series([2, 3, 0, 1, 1])
invalid_targets_check = InvalidTargetDataCheck(
problem_type="regression", objective=objective
)
assert invalid_targets_check.validate(X, y) == {
"warnings": [],
"errors": [
DataCheckError(
message=f"Target has non-positive values which is not supported for {objective}",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_INCOMPATIBLE_OBJECTIVE,
details={
"Count of offending values": sum(
val <= 0 for val in y.values.flatten()
)
},
).to_dict()
],
"actions": [],
}
@pytest.mark.parametrize(
"objective", [RootMeanSquaredLogError(), MeanSquaredLogError(), MAPE()]
)
def test_invalid_target_data_check_invalid_labels_for_nonnegative_objective_instances(
objective,
):
X = pd.DataFrame({"column_one": [100, 200, 100, 200, 200, 100, 200, 100] * 25})
y = pd.Series([2, 2, 3, 3, -1, -1, 1, 1] * 25)
data_checks = DataChecks(
[InvalidTargetDataCheck],
{
"InvalidTargetDataCheck": {
"problem_type": "multiclass",
"objective": objective,
}
},
)
assert data_checks.validate(X, y) == {
"warnings": [],
"errors": [
DataCheckError(
message=f"Target has non-positive values which is not supported for {objective.name}",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_INCOMPATIBLE_OBJECTIVE,
details={
"Count of offending values": sum(
val <= 0 for val in y.values.flatten()
)
},
).to_dict()
],
"actions": [],
}
def test_invalid_target_data_check_invalid_labels_for_objectives(
time_series_core_objectives,
):
X = pd.DataFrame({"column_one": [100, 200, 100, 200, 200, 100, 200, 100] * 25})
y = pd.Series([2, 2, 3, 3, -1, -1, 1, 1] * 25)
for objective in time_series_core_objectives:
if not objective.positive_only:
data_checks = DataChecks(
[InvalidTargetDataCheck],
{
"InvalidTargetDataCheck": {
"problem_type": "multiclass",
"objective": objective,
}
},
)
assert data_checks.validate(X, y) == {
"warnings": [],
"errors": [],
"actions": [],
}
X = pd.DataFrame({"column_one": [100, 200, 100, 200, 100]})
y = pd.Series([2, 3, 0, 1, 1])
for objective in time_series_core_objectives:
if not objective.positive_only:
invalid_targets_check = InvalidTargetDataCheck(
problem_type="regression", objective=objective
)
assert invalid_targets_check.validate(X, y) == {
"warnings": [],
"errors": [],
"actions": [],
}
@pytest.mark.parametrize(
"objective",
[
"Root Mean Squared Log Error",
"Mean Squared Log Error",
"Mean Absolute Percentage Error",
],
)
def test_invalid_target_data_check_valid_labels_for_nonnegative_objectives(objective):
X = pd.DataFrame({"column_one": [100, 100, 200, 300, 100, 200, 100] * 25})
y = pd.Series([2, 2, 3, 3, 1, 1, 1] * 25)
data_checks = DataChecks(
[InvalidTargetDataCheck],
{
"InvalidTargetDataCheck": {
"problem_type": "multiclass",
"objective": objective,
}
},
)
assert data_checks.validate(X, y) == {"warnings": [], "errors": [], "actions": []}
def test_invalid_target_data_check_initialize_with_none_objective():
with pytest.raises(DataCheckInitError, match="Encountered the following error"):
DataChecks(
[InvalidTargetDataCheck],
{
"InvalidTargetDataCheck": {
"problem_type": "multiclass",
"objective": None,
}
},
)
def test_invalid_target_data_check_regression_problem_nonnumeric_data():
y_categorical = pd.Series(["Peace", "Is", "A", "Lie"] * 100)
y_mixed_cat_numeric = pd.Series(["Peace", 2, "A", 4] * 100)
y_integer = pd.Series([1, 2, 3, 4])
y_float = pd.Series([1.1, 2.2, 3.3, 4.4])
y_numeric = pd.Series([1, 2.2, 3, 4.4])
data_check_error = DataCheckError(
message=f"Target data type should be numeric for regression type problems.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_UNSUPPORTED_TYPE,
details={},
).to_dict()
invalid_targets_check = InvalidTargetDataCheck(
"regression", get_default_primary_search_objective("regression")
)
assert invalid_targets_check.validate(
X=pd.DataFrame({"col": range(len(y_categorical))}), y=y_categorical
) == {"warnings": [], "errors": [data_check_error], "actions": []}
assert invalid_targets_check.validate(
X=pd.DataFrame({"col": range(len(y_mixed_cat_numeric))}), y=y_mixed_cat_numeric
) == {"warnings": [], "errors": [data_check_error], "actions": []}
assert invalid_targets_check.validate(
X=pd.DataFrame({"col": range(len(y_integer))}), y=y_integer
) == {"warnings": [], "errors": [], "actions": []}
assert invalid_targets_check.validate(
X=pd.DataFrame({"col": range(len(y_float))}), y=y_float
) == {"warnings": [], "errors": [], "actions": []}
assert invalid_targets_check.validate(
X=pd.DataFrame({"col": range(len(y_numeric))}), y=y_numeric
) == {"warnings": [], "errors": [], "actions": []}
def test_invalid_target_data_check_multiclass_problem_binary_data():
y_multiclass = pd.Series([1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3] * 25)
y_binary = pd.Series([0, 1, 1, 1, 0, 0] * 25)
data_check_error = DataCheckError(
message=f"Target has two or less classes, which is too few for multiclass problems. Consider changing to binary.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_MULTICLASS_NOT_ENOUGH_CLASSES,
details={"num_classes": len(set(y_binary))},
).to_dict()
invalid_targets_check = InvalidTargetDataCheck(
"multiclass", get_default_primary_search_objective("multiclass")
)
assert invalid_targets_check.validate(
X=pd.DataFrame({"col": range(len(y_multiclass))}), y=y_multiclass
) == {"warnings": [], "errors": [], "actions": []}
assert invalid_targets_check.validate(
X=pd.DataFrame({"col": range(len(y_binary))}), y=y_binary
) == {"warnings": [], "errors": [data_check_error], "actions": []}
@pytest.mark.parametrize(
"problem_type", [ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS]
)
def test_invalid_target_data_check_multiclass_problem_almost_continuous_data(
problem_type,
):
invalid_targets_check = InvalidTargetDataCheck(
problem_type, get_default_primary_search_objective(problem_type)
)
y_multiclass_high_classes = pd.Series(
list(range(0, 100)) * 3
) # 100 classes, 300 samples, .33 class/sample ratio
X = pd.DataFrame({"col": range(len(y_multiclass_high_classes))})
data_check_warning = DataCheckWarning(
message=f"Target has a large number of unique values, could be regression type problem.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_MULTICLASS_HIGH_UNIQUE_CLASS,
details={"class_to_value_ratio": 1 / 3},
).to_dict()
assert invalid_targets_check.validate(X, y=y_multiclass_high_classes) == {
"warnings": [data_check_warning],
"errors": [],
"actions": [],
}
y_multiclass_med_classes = pd.Series(
list(range(0, 5)) * 20
) # 5 classes, 100 samples, .05 class/sample ratio
X = pd.DataFrame({"col": range(len(y_multiclass_med_classes))})
data_check_warning = DataCheckWarning(
message=f"Target has a large number of unique values, could be regression type problem.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_MULTICLASS_HIGH_UNIQUE_CLASS,
details={"class_to_value_ratio": 0.05},
).to_dict()
assert invalid_targets_check.validate(X, y=y_multiclass_med_classes) == {
"warnings": [data_check_warning],
"errors": [],
"actions": [],
}
y_multiclass_low_classes = pd.Series(
list(range(0, 3)) * 100
) # 2 classes, 300 samples, .01 class/sample ratio
X = pd.DataFrame({"col": range(len(y_multiclass_low_classes))})
assert invalid_targets_check.validate(X, y=y_multiclass_low_classes) == {
"warnings": [],
"errors": [],
"actions": [],
}
def test_invalid_target_data_check_mismatched_indices():
X = pd.DataFrame({"col": [1, 2, 3]})
y_same_index = pd.Series([1, 0, 1])
y_diff_index = pd.Series([0, 1, 0], index=[1, 5, 10])
y_diff_index_order = pd.Series([0, 1, 0], index=[0, 2, 1])
invalid_targets_check = InvalidTargetDataCheck(
"binary", get_default_primary_search_objective("binary")
)
assert invalid_targets_check.validate(X=None, y=y_same_index) == {
"warnings": [],
"errors": [],
"actions": [],
}
assert invalid_targets_check.validate(X, y_same_index) == {
"warnings": [],
"errors": [],
"actions": [],
}
X_index_missing = list(set(y_diff_index.index) - set(X.index))
y_index_missing = list(set(X.index) - set(y_diff_index.index))
assert invalid_targets_check.validate(X, y_diff_index) == {
"warnings": [
DataCheckWarning(
message="Input target and features have mismatched indices. Details will include the first 10 mismatched indices.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.MISMATCHED_INDICES,
details={
"indices_not_in_features": X_index_missing,
"indices_not_in_target": y_index_missing,
},
).to_dict()
],
"errors": [],
"actions": [],
}
assert invalid_targets_check.validate(X, y_diff_index_order) == {
"warnings": [
DataCheckWarning(
message="Input target and features have mismatched indices order.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.MISMATCHED_INDICES_ORDER,
details={},
).to_dict()
],
"errors": [],
"actions": [],
}
# Test that we only store ten mismatches when there are more than 10 differences in indices found
X_large = pd.DataFrame({"col": range(20)})
y_more_than_ten_diff_indices = pd.Series([0, 1] * 10, index=range(20, 40))
X_index_missing = list(set(y_more_than_ten_diff_indices.index) - set(X.index))
y_index_missing = list(set(X_large.index) - set(y_more_than_ten_diff_indices.index))
assert invalid_targets_check.validate(X_large, y_more_than_ten_diff_indices) == {
"warnings": [
DataCheckWarning(
message="Input target and features have mismatched indices. Details will include the first 10 mismatched indices.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.MISMATCHED_INDICES,
details={
"indices_not_in_features": X_index_missing[:10],
"indices_not_in_target": y_index_missing[:10],
},
).to_dict()
],
"errors": [],
"actions": [],
}
def test_invalid_target_data_check_different_lengths():
X = pd.DataFrame({"col": [1, 2, 3]})
y_diff_len = pd.Series([0, 1])
invalid_targets_check = InvalidTargetDataCheck(
"binary", get_default_primary_search_objective("binary")
)
assert invalid_targets_check.validate(X, y_diff_len) == {
"warnings": [
DataCheckWarning(
message="Input target and features have different lengths",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.MISMATCHED_LENGTHS,
details={
"features_length": len(X.index),
"target_length": len(y_diff_len.index),
},
).to_dict(),
DataCheckWarning(
message="Input target and features have mismatched indices. Details will include the first 10 mismatched indices.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.MISMATCHED_INDICES,
details={"indices_not_in_features": [], "indices_not_in_target": [2]},
).to_dict(),
],
"errors": [],
"actions": [],
}
def test_invalid_target_data_check_numeric_binary_does_not_return_warnings():
y = pd.Series([1, 5, 1, 5, 1, 1])
X = pd.DataFrame({"col": range(len(y))})
invalid_targets_check = InvalidTargetDataCheck(
"binary", get_default_primary_search_objective("binary")
)
assert invalid_targets_check.validate(X, y) == {
"warnings": [],
"errors": [],
"actions": [],
}
@pytest.mark.parametrize("use_nullable_types", [True, False])
@pytest.mark.parametrize("problem_type", ProblemTypes.all_problem_types)
def test_invalid_target_data_action_for_data_with_null(
use_nullable_types, problem_type
):
y = pd.Series([None, None, None, 0, 0, 0, 0, 0, 0, 0])
if use_nullable_types:
y = ww.init_series(y, logical_type="IntegerNullable")
X = pd.DataFrame({"col": range(len(y))})
invalid_targets_check = InvalidTargetDataCheck(
problem_type, get_default_primary_search_objective(problem_type)
)
impute_strategy = "mean" if is_regression(problem_type) else "most_frequent"
expected = {
"warnings": [],
"errors": [
DataCheckError(
message="3 row(s) (30.0%) of target values are null",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_HAS_NULL,
details={"num_null_rows": 3, "pct_null_rows": 30.0},
).to_dict()
],
"actions": [
DataCheckAction(
DataCheckActionCode.IMPUTE_COL,
data_check_name=invalid_targets_data_check_name,
metadata={
"is_target": True,
"impute_strategy": impute_strategy,
},
).to_dict()
],
}
if is_binary(problem_type):
expected["errors"].append(
DataCheckError(
message="Binary class targets require exactly two unique values.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_BINARY_NOT_TWO_UNIQUE_VALUES,
details={"target_values": [0]},
).to_dict()
)
elif is_multiclass(problem_type):
expected["errors"].append(
DataCheckError(
message=f"Target has two or less classes, which is too few for multiclass problems. Consider changing to binary.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_MULTICLASS_NOT_ENOUGH_CLASSES,
details={"num_classes": 1},
).to_dict()
)
expected["warnings"].append(
DataCheckWarning(
message=f"Target has a large number of unique values, could be regression type problem.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_MULTICLASS_HIGH_UNIQUE_CLASS,
details={"class_to_value_ratio": 0.1},
).to_dict()
)
messages = invalid_targets_check.validate(X, y)
assert messages == expected
@pytest.mark.parametrize("problem_type", ProblemTypes.all_problem_types)
def test_invalid_target_data_action_for_all_null(problem_type):
invalid_targets_check = InvalidTargetDataCheck(
problem_type, get_default_primary_search_objective(problem_type)
)
y_all_null = pd.Series([None, None, None])
X = pd.DataFrame({"col": range(len(y_all_null))})
expected = {
"warnings": [],
"errors": [
DataCheckError(
message="Target is either empty or fully null.",
data_check_name=invalid_targets_data_check_name,
message_code=DataCheckMessageCode.TARGET_IS_EMPTY_OR_FULLY_NULL,
details={},
).to_dict(),
],
"actions": [],
}
messages = invalid_targets_check.validate(X, y_all_null)
assert messages == expected
| 36.574257 | 131 | 0.607099 | 3,203 | 29,552 | 5.287543 | 0.08492 | 0.047296 | 0.048359 | 0.057393 | 0.824929 | 0.813356 | 0.78035 | 0.742619 | 0.719591 | 0.691249 | 0 | 0.020385 | 0.276259 | 29,552 | 807 | 132 | 36.619579 | 0.771461 | 0.016885 | 0 | 0.616901 | 0 | 0 | 0.148564 | 0.010262 | 0 | 0 | 0 | 0 | 0.056338 | 1 | 0.029577 | false | 0 | 0.014085 | 0 | 0.043662 | 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 |
861ce74103354183de307f9e5ed69bc3f8256a74 | 149 | py | Python | utilities/gpio_dev.py | greencoder/ospi-cli | bf5256e11d14a4a9c3bc4d0eeebfbb51e2269ee3 | [
"MIT"
] | 1 | 2016-05-04T16:43:22.000Z | 2016-05-04T16:43:22.000Z | utilities/gpio_dev.py | greencoder/ospi-cli | bf5256e11d14a4a9c3bc4d0eeebfbb51e2269ee3 | [
"MIT"
] | null | null | null | utilities/gpio_dev.py | greencoder/ospi-cli | bf5256e11d14a4a9c3bc4d0eeebfbb51e2269ee3 | [
"MIT"
] | null | null | null | BCM = 0
OUT = 0
RPI_REVISION = 2
def cleanup():
return
def setmode(mode):
return
def setup(pin, mode):
return
def output(pin, value):
return
| 9.3125 | 23 | 0.677852 | 24 | 149 | 4.166667 | 0.625 | 0.27 | 0.26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.025424 | 0.208054 | 149 | 15 | 24 | 9.933333 | 0.822034 | 0 | 0 | 0.363636 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.363636 | false | 0 | 0 | 0.363636 | 0.727273 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 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 | 0 | 0 | 6 |
8625249c72f01c0257c04601be5c40410fef7dd3 | 2,157 | py | Python | tests/functional/markers/test_skip_unless_on_aix.py | cmcmarrow/pytest-salt-factories | 12515411ea0fa11d7058a9deb61584a56c5f5108 | [
"Apache-2.0"
] | null | null | null | tests/functional/markers/test_skip_unless_on_aix.py | cmcmarrow/pytest-salt-factories | 12515411ea0fa11d7058a9deb61584a56c5f5108 | [
"Apache-2.0"
] | null | null | null | tests/functional/markers/test_skip_unless_on_aix.py | cmcmarrow/pytest-salt-factories | 12515411ea0fa11d7058a9deb61584a56c5f5108 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
"""
tests.functional.markers.test_skip_unless_on_aix
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Test the ``@pytest.mark.skip_unless_on_aix`` marker
"""
import mock
import pytest
def test_skipped(testdir):
testdir.makepyfile(
"""
import pytest
@pytest.mark.skip_unless_on_aix
def test_one():
assert True
"""
)
return_value = False
with mock.patch("saltfactories.utils.platform.is_aix", return_value=return_value):
res = testdir.runpytest_inprocess()
res.assert_outcomes(skipped=1)
try:
res.stdout.no_fnmatch_line("*PytestUnknownMarkWarning*")
except AttributeError: # pragma: no cover
# PyTest 4.6.x
from _pytest.outcomes import Failed
with pytest.raises(Failed):
res.stdout.fnmatch_lines(
["*PytestUnknownMarkWarning*",]
)
def test_not_skipped(testdir):
testdir.makepyfile(
"""
import pytest
@pytest.mark.skip_unless_on_aix
def test_one():
assert True
"""
)
return_value = True
with mock.patch("saltfactories.utils.platform.is_aix", return_value=return_value):
res = testdir.runpytest_inprocess()
res.assert_outcomes(passed=1)
try:
res.stdout.no_fnmatch_line("*PytestUnknownMarkWarning*")
except AttributeError: # pragma: no cover
# PyTest 4.6.x
from _pytest.outcomes import Failed
with pytest.raises(Failed):
res.stdout.fnmatch_lines(
["*PytestUnknownMarkWarning*",]
)
def test_skip_reason(testdir):
testdir.makepyfile(
"""
import pytest
@pytest.mark.skip_unless_on_aix(reason='Because!')
def test_one():
assert True
"""
)
return_value = False
with mock.patch("saltfactories.utils.platform.is_aix", return_value=return_value):
res = testdir.runpytest_inprocess("-ra", "-s", "-vv")
res.assert_outcomes(skipped=1)
res.stdout.fnmatch_lines(["SKIPPED * test_skip_reason.py:*: Because!"])
| 27.303797 | 86 | 0.60408 | 230 | 2,157 | 5.434783 | 0.273913 | 0.0792 | 0.048 | 0.06 | 0.852 | 0.8264 | 0.8064 | 0.8064 | 0.8064 | 0.8064 | 0 | 0.005054 | 0.26611 | 2,157 | 78 | 87 | 27.653846 | 0.784586 | 0.10802 | 0 | 0.65 | 0 | 0 | 0.171657 | 0.153693 | 0 | 0 | 0 | 0 | 0.075 | 1 | 0.075 | false | 0.025 | 0.1 | 0 | 0.175 | 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 |
8626f3a378107943cb7c162faeb58ad2981e0de8 | 93,462 | py | Python | gromacsparser/metainfo/gromacs.py | nomad-coe/nomad-parser-gromacs | b5ea25b92f286ad77b4011051cc6a7a1d494036f | [
"Apache-2.0"
] | null | null | null | gromacsparser/metainfo/gromacs.py | nomad-coe/nomad-parser-gromacs | b5ea25b92f286ad77b4011051cc6a7a1d494036f | [
"Apache-2.0"
] | null | null | null | gromacsparser/metainfo/gromacs.py | nomad-coe/nomad-parser-gromacs | b5ea25b92f286ad77b4011051cc6a7a1d494036f | [
"Apache-2.0"
] | null | null | null | #
# Copyright The NOMAD Authors.
#
# This file is part of NOMAD.
# See https://nomad-lab.eu for further info.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import numpy as np # pylint: disable=unused-import
import typing # pylint: disable=unused-import
from nomad.metainfo import ( # pylint: disable=unused-import
MSection, MCategory, Category, Package, Quantity, Section, SubSection, SectionProxy,
Reference
)
from nomad.metainfo.legacy import LegacyDefinition
from nomad.datamodel.metainfo import common
from nomad.datamodel.metainfo import public
m_package = Package(
name='gromacs_nomadmetainfo_json',
description='None',
a_legacy=LegacyDefinition(name='gromacs.nomadmetainfo.json'))
class x_gromacs_mdin_input_output_files(MCategory):
'''
Parameters of mdin belonging to x_gromacs_section_control_parameters.
'''
m_def = Category(
a_legacy=LegacyDefinition(name='x_gromacs_mdin_input_output_files'))
class x_gromacs_mdin_control_parameters(MCategory):
'''
Parameters of mdin belonging to x_gromacs_section_control_parameters.
'''
m_def = Category(
a_legacy=LegacyDefinition(name='x_gromacs_mdin_control_parameters'))
class x_gromacs_mdin_method(MCategory):
'''
Parameters of mdin belonging to section method.
'''
m_def = Category(
a_legacy=LegacyDefinition(name='x_gromacs_mdin_method'))
class x_gromacs_mdout_single_configuration_calculation(MCategory):
'''
Parameters of mdout belonging to section_single_configuration_calculation.
'''
m_def = Category(
a_legacy=LegacyDefinition(name='x_gromacs_mdout_single_configuration_calculation'))
class x_gromacs_mdout_method(MCategory):
'''
Parameters of mdin belonging to section method.
'''
m_def = Category(
a_legacy=LegacyDefinition(name='x_gromacs_mdout_method'))
class x_gromacs_mdout_run(MCategory):
'''
Parameters of mdin belonging to settings run.
'''
m_def = Category(
categories=[public.settings_run],
a_legacy=LegacyDefinition(name='x_gromacs_mdout_run'))
class x_gromacs_mdin_run(MCategory):
'''
Parameters of mdin belonging to settings run.
'''
m_def = Category(
categories=[public.settings_run],
a_legacy=LegacyDefinition(name='x_gromacs_mdin_run'))
class x_gromacs_section_input_output_files(MSection):
'''
Section to store input and output file names
'''
m_def = Section(validate=False, a_legacy=LegacyDefinition(name='x_gromacs_section_input_output_files'))
x_gromacs_inout_file_topoltpr = Quantity(
type=str,
shape=[],
description='''
Gromacs input topology file.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_file_topoltpr'))
x_gromacs_inout_file_trajtrr = Quantity(
type=str,
shape=[],
description='''
Gromacs input trajectory file.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_file_trajtrr'))
x_gromacs_inout_file_trajcompxtc = Quantity(
type=str,
shape=[],
description='''
Gromacs input compressed trajectory file.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_file_trajcompxtc'))
x_gromacs_inout_file_statecpt = Quantity(
type=str,
shape=[],
description='''
Gromacs input coordinates and state file.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_file_statecpt'))
x_gromacs_inout_file_confoutgro = Quantity(
type=str,
shape=[],
description='''
Gromacs output configuration file.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_file_confoutgro'))
x_gromacs_inout_file_eneredr = Quantity(
type=str,
shape=[],
description='''
Gromacs output energies file.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_file_eneredr'))
class x_gromacs_section_control_parameters(MSection):
'''
Section to store the input and output control parameters
'''
m_def = Section(validate=False, a_legacy=LegacyDefinition(name='x_gromacs_section_control_parameters'))
x_gromacs_inout_control_gromacs_version = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_gromacs_version'))
x_gromacs_inout_control_precision = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_precision'))
x_gromacs_inout_control_memory_model = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_memory_model'))
x_gromacs_inout_control_mpi_library = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_mpi_library'))
x_gromacs_inout_control_openmp_support = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_openmp_support'))
x_gromacs_inout_control_gpu_support = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_gpu_support'))
x_gromacs_inout_control_opencl_support = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_opencl_support'))
x_gromacs_inout_control_invsqrt_routine = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_invsqrt_routine'))
x_gromacs_inout_control_simd_instructions = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_simd_instructions'))
x_gromacs_inout_control_fft_library = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_fft_library'))
x_gromacs_inout_control_rdtscp_usage = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_rdtscp_usage'))
x_gromacs_inout_control_cxx11_compilation = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_cxx11_compilation'))
x_gromacs_inout_control_tng_support = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_tng_support'))
x_gromacs_inout_control_tracing_support = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_tracing_support'))
x_gromacs_inout_control_built_on = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_built_on'))
x_gromacs_inout_control_built_by = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_built_by'))
x_gromacs_inout_control_build_osarch = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_build_osarch'))
x_gromacs_inout_control_build_cpu_vendor = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_build_cpu_vendor'))
x_gromacs_inout_control_build_cpu_brand = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_build_cpu_brand'))
x_gromacs_inout_control_build_cpu_family = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_build_cpu_family'))
x_gromacs_inout_control_build_cpu_features = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_build_cpu_features'))
x_gromacs_inout_control_c_compiler = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_c_compiler'))
x_gromacs_inout_control_c_compiler_flags = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_c_compiler_flags'))
x_gromacs_inout_control_cxx_compiler = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_cxx_compiler'))
x_gromacs_inout_control_cxx_compiler_flags = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_cxx_compiler_flags'))
x_gromacs_inout_control_boost_version = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_boost_version'))
x_gromacs_inout_control_integrator = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_integrator'))
x_gromacs_inout_control_tinit = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_tinit'))
x_gromacs_inout_control_dt = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_dt'))
x_gromacs_inout_control_nsteps = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nsteps'))
x_gromacs_inout_control_initstep = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_initstep'))
x_gromacs_inout_control_simulationpart = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_simulationpart'))
x_gromacs_inout_control_commmode = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_commmode'))
x_gromacs_inout_control_nstcomm = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstcomm'))
x_gromacs_inout_control_bdfric = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_bdfric'))
x_gromacs_inout_control_ldseed = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_ldseed'))
x_gromacs_inout_control_emtol = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_emtol'))
x_gromacs_inout_control_emstep = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_emstep'))
x_gromacs_inout_control_niter = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_niter'))
x_gromacs_inout_control_fcstep = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_fcstep'))
x_gromacs_inout_control_nstcgsteep = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstcgsteep'))
x_gromacs_inout_control_nbfgscorr = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nbfgscorr'))
x_gromacs_inout_control_rtpi = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_rtpi'))
x_gromacs_inout_control_nstxout = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstxout'))
x_gromacs_inout_control_nstvout = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstvout'))
x_gromacs_inout_control_nstfout = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstfout'))
x_gromacs_inout_control_nstlog = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstlog'))
x_gromacs_inout_control_nstcalcenergy = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstcalcenergy'))
x_gromacs_inout_control_nstenergy = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstenergy'))
x_gromacs_inout_control_nstxoutcompressed = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstxoutcompressed'))
x_gromacs_inout_control_compressedxprecision = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_compressedxprecision'))
x_gromacs_inout_control_cutoffscheme = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_cutoffscheme'))
x_gromacs_inout_control_nstlist = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstlist'))
x_gromacs_inout_control_nstype = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstype'))
x_gromacs_inout_control_pbc = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_pbc'))
x_gromacs_inout_control_periodicmolecules = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_periodicmolecules'))
x_gromacs_inout_control_verletbuffertolerance = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_verletbuffertolerance'))
x_gromacs_inout_control_rlist = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_rlist'))
x_gromacs_inout_control_rlistlong = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_rlistlong'))
x_gromacs_inout_control_nstcalclr = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstcalclr'))
x_gromacs_inout_control_coulombtype = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_coulombtype'))
x_gromacs_inout_control_coulombmodifier = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_coulombmodifier'))
x_gromacs_inout_control_rcoulombswitch = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_rcoulombswitch'))
x_gromacs_inout_control_rcoulomb = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_rcoulomb'))
x_gromacs_inout_control_epsilonr = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_epsilonr'))
x_gromacs_inout_control_epsilonrf = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_epsilonrf'))
x_gromacs_inout_control_vdwtype = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_vdwtype'))
x_gromacs_inout_control_vdwmodifier = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_vdwmodifier'))
x_gromacs_inout_control_rvdwswitch = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_rvdwswitch'))
x_gromacs_inout_control_rvdw = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_rvdw'))
x_gromacs_inout_control_dispcorr = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_dispcorr'))
x_gromacs_inout_control_tableextension = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_tableextension'))
x_gromacs_inout_control_fourierspacing = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_fourierspacing'))
x_gromacs_inout_control_fouriernx = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_fouriernx'))
x_gromacs_inout_control_fourierny = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_fourierny'))
x_gromacs_inout_control_fouriernz = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_fouriernz'))
x_gromacs_inout_control_pmeorder = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_pmeorder'))
x_gromacs_inout_control_ewaldrtol = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_ewaldrtol'))
x_gromacs_inout_control_ewaldrtollj = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_ewaldrtollj'))
x_gromacs_inout_control_ljpmecombrule = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_ljpmecombrule'))
x_gromacs_inout_control_ewaldgeometry = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_ewaldgeometry'))
x_gromacs_inout_control_epsilonsurface = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_epsilonsurface'))
x_gromacs_inout_control_implicitsolvent = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_implicitsolvent'))
x_gromacs_inout_control_gbalgorithm = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_gbalgorithm'))
x_gromacs_inout_control_nstgbradii = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstgbradii'))
x_gromacs_inout_control_rgbradii = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_rgbradii'))
x_gromacs_inout_control_gbepsilonsolvent = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_gbepsilonsolvent'))
x_gromacs_inout_control_gbsaltconc = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_gbsaltconc'))
x_gromacs_inout_control_gbobcalpha = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_gbobcalpha'))
x_gromacs_inout_control_gbobcbeta = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_gbobcbeta'))
x_gromacs_inout_control_gbobcgamma = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_gbobcgamma'))
x_gromacs_inout_control_gbdielectricoffset = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_gbdielectricoffset'))
x_gromacs_inout_control_saalgorithm = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_saalgorithm'))
x_gromacs_inout_control_sasurfacetension = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_sasurfacetension'))
x_gromacs_inout_control_tcoupl = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_tcoupl'))
x_gromacs_inout_control_nsttcouple = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nsttcouple'))
x_gromacs_inout_control_nhchainlength = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nhchainlength'))
x_gromacs_inout_control_printnosehooverchainvariables = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_printnosehooverchainvariables'))
x_gromacs_inout_control_pcoupl = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_pcoupl'))
x_gromacs_inout_control_pcoupltype = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_pcoupltype'))
x_gromacs_inout_control_nstpcouple = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstpcouple'))
x_gromacs_inout_control_taup = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_taup'))
x_gromacs_inout_control_compressibility = Quantity(
type=np.dtype(np.float64),
shape=[3, 3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_compressibility'))
x_gromacs_inout_control_compressibility0 = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_compressibility0'))
x_gromacs_inout_control_compressibility1 = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_compressibility1'))
x_gromacs_inout_control_compressibility2 = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_compressibility2'))
x_gromacs_inout_control_refp = Quantity(
type=np.dtype(np.float64),
shape=[3, 3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_refp'))
x_gromacs_inout_control_refp0 = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_refp0'))
x_gromacs_inout_control_refp1 = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_refp1'))
x_gromacs_inout_control_refp2 = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_refp2'))
x_gromacs_inout_control_refcoordscaling = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_refcoordscaling'))
x_gromacs_inout_control_posrescom = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_posrescom'))
x_gromacs_inout_control_posrescom0 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_posrescom0'))
x_gromacs_inout_control_posrescom1 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_posrescom1'))
x_gromacs_inout_control_posrescom2 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_posrescom2'))
x_gromacs_inout_control_posrescomb = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_posrescomb'))
x_gromacs_inout_control_posrescomb0 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_posrescomb0'))
x_gromacs_inout_control_posrescomb1 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_posrescomb1'))
x_gromacs_inout_control_posrescomb2 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_posrescomb2'))
x_gromacs_inout_control_qmmm = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_qmmm'))
x_gromacs_inout_control_qmconstraints = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_qmconstraints'))
x_gromacs_inout_control_qmmmscheme = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_qmmmscheme'))
x_gromacs_inout_control_mmchargescalefactor = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_mmchargescalefactor'))
x_gromacs_inout_control_ngqm = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_ngqm'))
x_gromacs_inout_control_constraintalgorithm = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_constraintalgorithm'))
x_gromacs_inout_control_continuation = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_continuation'))
x_gromacs_inout_control_shakesor = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_shakesor'))
x_gromacs_inout_control_shaketol = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_shaketol'))
x_gromacs_inout_control_lincsorder = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_lincsorder'))
x_gromacs_inout_control_lincsiter = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_lincsiter'))
x_gromacs_inout_control_lincswarnangle = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_lincswarnangle'))
x_gromacs_inout_control_nwall = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nwall'))
x_gromacs_inout_control_walltype = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_walltype'))
x_gromacs_inout_control_wallrlinpot = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_wallrlinpot'))
x_gromacs_inout_control_wallatomtype = Quantity(
type=np.dtype(np.float64),
shape=[2],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_wallatomtype'))
x_gromacs_inout_control_wallatomtype0 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_wallatomtype0'))
x_gromacs_inout_control_wallatomtype1 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_wallatomtype1'))
x_gromacs_inout_control_walldensity = Quantity(
type=np.dtype(np.float64),
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_walldensity'))
x_gromacs_inout_control_walldensity0 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_walldensity0'))
x_gromacs_inout_control_walldensity1 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_walldensity1'))
x_gromacs_inout_control_wallewaldzfac = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_wallewaldzfac'))
x_gromacs_inout_control_pull = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_pull'))
x_gromacs_inout_control_rotation = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_rotation'))
x_gromacs_inout_control_interactivemd = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_interactivemd'))
x_gromacs_inout_control_disre = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_disre'))
x_gromacs_inout_control_disreweighting = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_disreweighting'))
x_gromacs_inout_control_disremixed = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_disremixed'))
x_gromacs_inout_control_drfc = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_drfc'))
x_gromacs_inout_control_drtau = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_drtau'))
x_gromacs_inout_control_nstdisreout = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstdisreout'))
x_gromacs_inout_control_orirefc = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_orirefc'))
x_gromacs_inout_control_oriretau = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_oriretau'))
x_gromacs_inout_control_nstorireout = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nstorireout'))
x_gromacs_inout_control_freeenergy = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_freeenergy'))
x_gromacs_inout_control_cosacceleration = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_cosacceleration'))
x_gromacs_inout_control_deform = Quantity(
type=np.dtype(np.float64),
shape=[3, 3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_deform'))
x_gromacs_inout_control_deform0 = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_deform0'))
x_gromacs_inout_control_deform1 = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_deform1'))
x_gromacs_inout_control_deform2 = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_deform2'))
x_gromacs_inout_control_simulatedtempering = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_simulatedtempering'))
x_gromacs_inout_control_ex = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_ex'))
x_gromacs_inout_control_ext = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_ext'))
x_gromacs_inout_control_ey = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_ey'))
x_gromacs_inout_control_eyt = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_eyt'))
x_gromacs_inout_control_ez = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_ez'))
x_gromacs_inout_control_ezt = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_ezt'))
x_gromacs_inout_control_swapcoords = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_swapcoords'))
x_gromacs_inout_control_adress = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_adress'))
x_gromacs_inout_control_userint1 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_userint1'))
x_gromacs_inout_control_userint2 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_userint2'))
x_gromacs_inout_control_userint3 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_userint3'))
x_gromacs_inout_control_userint4 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_userint4'))
x_gromacs_inout_control_userreal1 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_userreal1'))
x_gromacs_inout_control_userreal2 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_userreal2'))
x_gromacs_inout_control_userreal3 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_userreal3'))
x_gromacs_inout_control_userreal4 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_userreal4'))
x_gromacs_inout_control_nrdf = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nrdf'))
x_gromacs_inout_control_reft = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_reft'))
x_gromacs_inout_control_taut = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_taut'))
x_gromacs_inout_control_annealing = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_annealing'))
x_gromacs_inout_control_annealingnpoints = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_annealingnpoints'))
x_gromacs_inout_control_acc = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_acc'))
x_gromacs_inout_control_nfreeze = Quantity(
type=str,
shape=[3],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_nfreeze'))
x_gromacs_inout_control_energygrpflags = Quantity(
type=np.dtype(np.float64),
shape=[3, 2],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_energygrpflags'))
x_gromacs_inout_control_energygrpflags0 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_energygrpflags0'))
x_gromacs_inout_control_energygrpflags1 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_energygrpflags1'))
x_gromacs_inout_control_energygrpflags2 = Quantity(
type=str,
shape=[],
description='''
Gromacs running environment and control parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_inout_control_energygrpflags2'))
class x_gromacs_section_atom_to_atom_type_ref(MSection):
'''
Section to store atom label to atom type definition list
'''
m_def = Section(validate=False, a_legacy=LegacyDefinition(name='x_gromacs_section_atom_to_atom_type_ref'))
x_gromacs_atom_to_atom_type_ref = Quantity(
type=np.dtype(np.int64),
shape=['number_of_atoms_per_type'],
description='''
Reference to the atoms of each atom type.
''',
a_legacy=LegacyDefinition(name='x_gromacs_atom_to_atom_type_ref'))
class x_gromacs_section_single_configuration_calculation(MSection):
'''
section for gathering values for MD steps
'''
m_def = Section(validate=False, a_legacy=LegacyDefinition(name='x_gromacs_section_single_configuration_calculation'))
class section_system(public.section_system):
m_def = Section(validate=False, extends_base_section=True, a_legacy=LegacyDefinition(name='section_system'))
x_gromacs_atom_positions_image_index = Quantity(
type=np.dtype(np.int32),
shape=['number_of_atoms', 3],
unit='dimensionless',
description='''
PBC image flag index.
''',
a_legacy=LegacyDefinition(name='x_gromacs_atom_positions_image_index'))
x_gromacs_atom_positions_scaled = Quantity(
type=np.dtype(np.float64),
shape=['number_of_atoms', 3],
unit='dimensionless',
description='''
Position of the atoms in a scaled format [0, 1].
''',
a_legacy=LegacyDefinition(name='x_gromacs_atom_positions_scaled'))
x_gromacs_atom_positions_wrapped = Quantity(
type=np.dtype(np.float64),
shape=['number_of_atoms', 3],
unit='meter',
description='''
Position of the atoms wrapped back to the periodic box.
''',
a_legacy=LegacyDefinition(name='x_gromacs_atom_positions_wrapped'))
x_gromacs_lattice_lengths = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Lattice dimensions in a vector. Vector includes [a, b, c] lengths.
''',
categories=[public.configuration_core],
a_legacy=LegacyDefinition(name='x_gromacs_lattice_lengths'))
x_gromacs_lattice_angles = Quantity(
type=np.dtype(np.float64),
shape=[3],
description='''
Angles of lattice vectors. Vector includes [alpha, beta, gamma] in degrees.
''',
categories=[public.configuration_core],
a_legacy=LegacyDefinition(name='x_gromacs_lattice_angles'))
x_gromacs_dummy = Quantity(
type=str,
shape=[],
description='''
dummy
''',
a_legacy=LegacyDefinition(name='x_gromacs_dummy'))
x_gromacs_mdin_finline = Quantity(
type=str,
shape=[],
description='''
finline in mdin
''',
a_legacy=LegacyDefinition(name='x_gromacs_mdin_finline'))
x_gromacs_traj_timestep_store = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_traj_timestep_store'))
x_gromacs_traj_number_of_atoms_store = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_traj_number_of_atoms_store'))
x_gromacs_traj_box_bound_store = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_traj_box_bound_store'))
x_gromacs_traj_box_bounds_store = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_traj_box_bounds_store'))
x_gromacs_traj_variables_store = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_traj_variables_store'))
x_gromacs_traj_atoms_store = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_traj_atoms_store'))
class section_sampling_method(public.section_sampling_method):
m_def = Section(validate=False, extends_base_section=True, a_legacy=LegacyDefinition(name='section_sampling_method'))
x_gromacs_barostat_target_pressure = Quantity(
type=np.dtype(np.float64),
shape=[],
unit='pascal',
description='''
MD barostat target pressure.
''',
categories=[public.settings_sampling, public.settings_molecular_dynamics, public.settings_barostat],
a_legacy=LegacyDefinition(name='x_gromacs_barostat_target_pressure'))
x_gromacs_barostat_tau = Quantity(
type=np.dtype(np.float64),
shape=[],
unit='second',
description='''
MD barostat relaxation time.
''',
categories=[public.settings_sampling, public.settings_molecular_dynamics, public.settings_barostat],
a_legacy=LegacyDefinition(name='x_gromacs_barostat_tau'))
x_gromacs_barostat_type = Quantity(
type=str,
shape=[],
description='''
MD barostat type, valid values are defined in the barostat_type wiki page.
''',
categories=[public.settings_sampling, public.settings_molecular_dynamics, public.settings_barostat],
a_legacy=LegacyDefinition(name='x_gromacs_barostat_type'))
x_gromacs_integrator_dt = Quantity(
type=np.dtype(np.float64),
shape=[],
unit='second',
description='''
MD integration time step.
''',
categories=[public.settings_sampling, public.settings_molecular_dynamics, public.settings_integrator],
a_legacy=LegacyDefinition(name='x_gromacs_integrator_dt'))
x_gromacs_integrator_type = Quantity(
type=str,
shape=[],
description='''
MD integrator type, valid values are defined in the integrator_type wiki page.
''',
categories=[public.settings_sampling, public.settings_molecular_dynamics, public.settings_integrator],
a_legacy=LegacyDefinition(name='x_gromacs_integrator_type'))
x_gromacs_periodicity_type = Quantity(
type=str,
shape=[],
description='''
Periodic boundary condition type in the sampling (non-PBC or PBC).
''',
categories=[public.settings_sampling, public.settings_molecular_dynamics, public.settings_integrator],
a_legacy=LegacyDefinition(name='x_gromacs_periodicity_type'))
x_gromacs_langevin_gamma = Quantity(
type=np.dtype(np.float64),
shape=[],
unit='second',
description='''
Langevin thermostat damping factor.
''',
categories=[public.settings_thermostat, public.settings_sampling, public.settings_molecular_dynamics],
a_legacy=LegacyDefinition(name='x_gromacs_langevin_gamma'))
x_gromacs_number_of_steps_requested = Quantity(
type=np.dtype(np.float64),
shape=[],
description='''
Number of requested MD integration time steps.
''',
categories=[public.settings_sampling, public.settings_molecular_dynamics, public.settings_integrator],
a_legacy=LegacyDefinition(name='x_gromacs_number_of_steps_requested'))
x_gromacs_thermostat_level = Quantity(
type=str,
shape=[],
description='''
MD thermostat level (see wiki: single, multiple, regional).
''',
categories=[public.settings_thermostat, public.settings_sampling, public.settings_molecular_dynamics],
a_legacy=LegacyDefinition(name='x_gromacs_thermostat_level'))
x_gromacs_thermostat_target_temperature = Quantity(
type=np.dtype(np.float64),
shape=[],
unit='kelvin',
description='''
MD thermostat target temperature.
''',
categories=[public.settings_thermostat, public.settings_sampling, public.settings_molecular_dynamics],
a_legacy=LegacyDefinition(name='x_gromacs_thermostat_target_temperature'))
x_gromacs_thermostat_tau = Quantity(
type=np.dtype(np.float64),
shape=[],
unit='second',
description='''
MD thermostat relaxation time.
''',
categories=[public.settings_thermostat, public.settings_sampling, public.settings_molecular_dynamics],
a_legacy=LegacyDefinition(name='x_gromacs_thermostat_tau'))
x_gromacs_thermostat_type = Quantity(
type=str,
shape=[],
description='''
MD thermostat type, valid values are defined in the thermostat_type wiki page.
''',
categories=[public.settings_thermostat, public.settings_sampling, public.settings_molecular_dynamics],
a_legacy=LegacyDefinition(name='x_gromacs_thermostat_type'))
class section_atom_type(common.section_atom_type):
m_def = Section(validate=False, extends_base_section=True, a_legacy=LegacyDefinition(name='section_atom_type'))
x_gromacs_atom_name = Quantity(
type=str,
shape=[],
description='''
Atom name of an atom in topology definition.
''',
a_legacy=LegacyDefinition(name='x_gromacs_atom_name'))
x_gromacs_atom_type = Quantity(
type=str,
shape=[],
description='''
Atom type of an atom in topology definition.
''',
a_legacy=LegacyDefinition(name='x_gromacs_atom_type'))
x_gromacs_atom_element = Quantity(
type=str,
shape=[],
description='''
Atom type of an atom in topology definition.
''',
a_legacy=LegacyDefinition(name='x_gromacs_atom_element'))
x_gromacs_atom_type_element = Quantity(
type=str,
shape=[],
description='''
Element symbol of an atom type.
''',
a_legacy=LegacyDefinition(name='x_gromacs_atom_type_element'))
x_gromacs_atom_type_radius = Quantity(
type=np.dtype(np.float64),
shape=[],
description='''
van der Waals radius of an atom type.
''',
a_legacy=LegacyDefinition(name='x_gromacs_atom_type_radius'))
number_of_atoms_per_type = Quantity(
type=int,
shape=[],
description='''
Number of atoms involved in this type.
''',
a_legacy=LegacyDefinition(name='number_of_atoms_per_type'))
class section_interaction(common.section_interaction):
m_def = Section(validate=False, extends_base_section=True, a_legacy=LegacyDefinition(name='section_interaction'))
x_gromacs_interaction_atom_to_atom_type_ref = Quantity(
type=common.section_atom_type,
shape=['number_of_atoms_per_interaction'],
description='''
Reference to the atom type of each interaction atoms.
''',
a_legacy=LegacyDefinition(name='x_gromacs_interaction_atom_to_atom_type_ref'))
x_gromacs_number_of_defined_pair_interactions = Quantity(
type=np.dtype(np.int32),
shape=[],
description='''
Number of defined pair interactions (L-J pairs).
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_defined_pair_interactions'))
x_gromacs_pair_interaction_atom_type_ref = Quantity(
type=common.section_atom_type,
shape=['x_gromacs_number_of_defined_pair_interactions', 'number_of_atoms_per_interaction'],
description='''
Reference to the atom type for pair interactions.
''',
a_legacy=LegacyDefinition(name='x_gromacs_pair_interaction_atom_type_ref'))
x_gromacs_pair_interaction_parameters = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_defined_pair_interactions', 2],
description='''
Pair interactions parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_pair_interaction_parameters'))
class section_molecule_interaction(common.section_molecule_interaction):
m_def = Section(validate=False, extends_base_section=True, a_legacy=LegacyDefinition(name='section_molecule_interaction'))
x_gromacs_molecule_interaction_atom_to_atom_type_ref = Quantity(
type=common.section_atom_type,
shape=['number_of_atoms_per_interaction'],
description='''
Reference to the atom type of each molecule interaction atoms.
''',
a_legacy=LegacyDefinition(name='x_gromacs_molecule_interaction_atom_to_atom_type_ref'))
x_gromacs_number_of_defined_molecule_pair_interactions = Quantity(
type=np.dtype(np.int32),
shape=[],
description='''
Number of defined pair interactions within a molecule (L-J pairs).
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_defined_molecule_pair_interactions'))
x_gromacs_pair_molecule_interaction_parameters = Quantity(
type=np.dtype(np.float64),
shape=['number_of_defined_molecule_pair_interactions', 2],
description='''
Molecule pair interactions parameters.
''',
a_legacy=LegacyDefinition(name='x_gromacs_pair_molecule_interaction_parameters'))
x_gromacs_pair_molecule_interaction_to_atom_type_ref = Quantity(
type=common.section_atom_type,
shape=['x_gromacs_number_of_defined_pair_interactions', 'number_of_atoms_per_interaction'],
description='''
Reference to the atom type for pair interactions within a molecule.
''',
a_legacy=LegacyDefinition(name='x_gromacs_pair_molecule_interaction_to_atom_type_ref'))
class section_run(public.section_run):
m_def = Section(validate=False, extends_base_section=True, a_legacy=LegacyDefinition(name='section_run'))
x_gromacs_program_version_date = Quantity(
type=str,
shape=[],
description='''
Program version date.
''',
a_legacy=LegacyDefinition(name='x_gromacs_program_version_date'))
x_gromacs_parallel_task_nr = Quantity(
type=np.dtype(np.float64),
shape=[],
description='''
Program task no.
''',
a_legacy=LegacyDefinition(name='x_gromacs_parallel_task_nr'))
x_gromacs_number_of_tasks = Quantity(
type=np.dtype(np.float64),
shape=[],
description='''
Number of tasks in parallel program (MPI).
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_tasks'))
x_gromacs_program_module_version = Quantity(
type=str,
shape=[],
description='''
Gromacs program module (gmx) version.
''',
a_legacy=LegacyDefinition(name='x_gromacs_program_module_version'))
x_gromacs_program_license = Quantity(
type=str,
shape=[],
description='''
Gromacs program license.
''',
a_legacy=LegacyDefinition(name='x_gromacs_program_license'))
x_gromacs_xlo_xhi = Quantity(
type=str,
shape=[],
description='''
test
''',
a_legacy=LegacyDefinition(name='x_gromacs_xlo_xhi'))
x_gromacs_data_file_store = Quantity(
type=str,
shape=[],
description='''
Filename of data file
''',
a_legacy=LegacyDefinition(name='x_gromacs_data_file_store'))
x_gromacs_program_working_path = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_program_working_path'))
x_gromacs_program_execution_host = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_program_execution_host'))
x_gromacs_program_execution_path = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_program_execution_path'))
x_gromacs_program_module = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_program_module'))
x_gromacs_program_execution_date = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_program_execution_date'))
x_gromacs_program_execution_time = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_program_execution_time'))
x_gromacs_mdin_header = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_mdin_header'))
x_gromacs_mdin_wt = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_mdin_wt'))
x_gromacs_section_input_output_files = SubSection(
sub_section=SectionProxy('x_gromacs_section_input_output_files'),
repeats=True,
a_legacy=LegacyDefinition(name='x_gromacs_section_input_output_files'))
x_gromacs_section_control_parameters = SubSection(
sub_section=SectionProxy('x_gromacs_section_control_parameters'),
repeats=True,
a_legacy=LegacyDefinition(name='x_gromacs_section_control_parameters'))
class section_topology(common.section_topology):
m_def = Section(validate=False, extends_base_section=True, a_legacy=LegacyDefinition(name='section_topology'))
x_gromacs_input_units_store = Quantity(
type=str,
shape=[],
description='''
It determines the units of all quantities specified in the input script and data
file, as well as quantities output to the screen, log file, and dump files.
''',
a_legacy=LegacyDefinition(name='x_gromacs_input_units_store'))
x_gromacs_data_bond_types_store = Quantity(
type=np.dtype(np.int32),
shape=[],
description='''
store temporarly
''',
a_legacy=LegacyDefinition(name='x_gromacs_data_bond_types_store'))
x_gromacs_data_bond_count_store = Quantity(
type=np.dtype(np.int32),
shape=[],
description='''
store temporarly
''',
a_legacy=LegacyDefinition(name='x_gromacs_data_bond_count_store'))
x_gromacs_data_angle_count_store = Quantity(
type=np.dtype(np.int32),
shape=[],
description='''
store temporarly
''',
a_legacy=LegacyDefinition(name='x_gromacs_data_angle_count_store'))
x_gromacs_data_atom_types_store = Quantity(
type=np.dtype(np.int32),
shape=[],
description='''
store temporarly
''',
a_legacy=LegacyDefinition(name='x_gromacs_data_atom_types_store'))
x_gromacs_data_dihedral_count_store = Quantity(
type=np.dtype(np.int32),
shape=[],
description='''
store temporarly
''',
a_legacy=LegacyDefinition(name='x_gromacs_data_dihedral_count_store'))
x_gromacs_data_angles_store = Quantity(
type=str,
shape=[],
description='''
store temporarly
''',
a_legacy=LegacyDefinition(name='x_gromacs_data_angles_store'))
x_gromacs_data_angle_list_store = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_data_angle_list_store'))
x_gromacs_data_bond_list_store = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_data_bond_list_store'))
x_gromacs_data_dihedral_list_store = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_data_dihedral_list_store'))
x_gromacs_data_dihedral_coeff_list_store = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_data_dihedral_coeff_list_store'))
x_gromacs_masses_store = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_masses_store'))
x_gromacs_data_topo_list_store = Quantity(
type=str,
shape=[],
description='''
tmp
''',
a_legacy=LegacyDefinition(name='x_gromacs_data_topo_list_store'))
x_gromacs_section_atom_to_atom_type_ref = SubSection(
sub_section=SectionProxy('x_gromacs_section_atom_to_atom_type_ref'),
repeats=True,
a_legacy=LegacyDefinition(name='x_gromacs_section_atom_to_atom_type_ref'))
class section_frame_sequence(public.section_frame_sequence):
m_def = Section(validate=False, extends_base_section=True, a_legacy=LegacyDefinition(name='section_frame_sequence'))
x_gromacs_number_of_volumes_in_sequence = Quantity(
type=int,
shape=[],
description='''
Gives the number of volumes in this sequence of frames, see
x_gromacs_frame_sequence_volume.
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_volumes_in_sequence'))
x_gromacs_number_of_densities_in_sequence = Quantity(
type=int,
shape=[],
description='''
Gives the number of densities in this sequence of frames, see
x_gromacs_frame_sequence_density.
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_densities_in_sequence'))
x_gromacs_number_of_ubond_energies_in_sequence = Quantity(
type=int,
shape=[],
description='''
Gives the number of ubond_energies in this sequence of frames, see
x_gromacs_frame_sequence_ubond_energy.
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_ubond_energies_in_sequence'))
x_gromacs_number_of_bond_energies_in_sequence = Quantity(
type=int,
shape=[],
description='''
Gives the number of bond_energies in this sequence of frames, see
x_gromacs_frame_sequence_bond_energy.
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_bond_energies_in_sequence'))
x_gromacs_number_of_coulomb_sr_energies_in_sequence = Quantity(
type=int,
shape=[],
description='''
Gives the number of coulomb_sr_energies in this sequence of frames, see
x_gromacs_frame_sequence_coulomb_sr_energy.
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_coulomb_sr_energies_in_sequence'))
x_gromacs_number_of_coulomb_14_energies_in_sequence = Quantity(
type=int,
shape=[],
description='''
Gives the number of coulomb_14_energies in this sequence of frames, see
x_gromacs_frame_sequence_coulomb_14_energy.
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_coulomb_14_energies_in_sequence'))
x_gromacs_number_of_lj_sr_energies_in_sequence = Quantity(
type=int,
shape=[],
description='''
Gives the number of lj_sr_energies in this sequence of frames, see
x_gromacs_frame_sequence_lj_sr_energy.
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_lj_sr_energies_in_sequence'))
x_gromacs_number_of_lj_14_energies_in_sequence = Quantity(
type=int,
shape=[],
description='''
Gives the number of lj_14_energies in this sequence of frames, see
x_gromacs_frame_sequence_lj_14_energy.
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_lj_14_energies_in_sequence'))
x_gromacs_number_of_proper_dihedral_energies_in_sequence = Quantity(
type=int,
shape=[],
description='''
Gives the number of proper_dihedral_energies in this sequence of frames, see
x_gromacs_frame_sequence_proper_dihedral_energy.
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_proper_dihedral_energies_in_sequence'))
x_gromacs_number_of_improper_dihedral_energies_in_sequence = Quantity(
type=int,
shape=[],
description='''
Gives the number of improper_dihedral_energies in this sequence of frames, see
x_gromacs_frame_sequence_improper_dihedral_energy.
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_improper_dihedral_energies_in_sequence'))
x_gromacs_number_of_cmap_dihedral_energies_in_sequence = Quantity(
type=int,
shape=[],
description='''
Gives the number of cmap_dihedral_energies in this sequence of frames, see
x_gromacs_frame_sequence_cmap_dihedral_energy.
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_cmap_dihedral_energies_in_sequence'))
x_gromacs_number_of_constrain_rmsd_in_sequence = Quantity(
type=int,
shape=[],
description='''
Gives the number of constrain_rmsd_energies in this sequence of frames, see
x_gromacs_frame_sequence_constrain_rmsd_energy.
''',
a_legacy=LegacyDefinition(name='x_gromacs_number_of_constrain_rmsd_in_sequence'))
x_gromacs_frame_sequence_density_frames = Quantity(
type=np.dtype(np.int32),
shape=['x_gromacs_number_of_densities_in_sequence'],
description='''
Array containing the strictly increasing indices of the frames the
x_gromacs_frame_sequence_densities values refers to. If not given it defaults to
the trivial mapping 0,1,...
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_density_frames'))
x_gromacs_frame_sequence_density = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_densities_in_sequence'],
description='''
Array containing the values of the density along this sequence of frames (i.e., a
trajectory, a frame is one section_single_configuration_calculation). If not all
frames have a value the indices of the frames that have a value are stored in
frame_sequence_density_frames.
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_density'))
x_gromacs_frame_sequence_ubond_energy_frames = Quantity(
type=np.dtype(np.int32),
shape=['x_gromacs_number_of_ubond_energies_in_sequence'],
description='''
Array containing the strictly increasing indices of the frames the
x_gromacs_frame_sequence_ubond_energy values refers to. If not given it defaults
to the trivial mapping 0,1,...
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_ubond_energy_frames'))
x_gromacs_frame_sequence_ubond_energy = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_ubond_energies_in_sequence'],
description='''
Array containing the values of the ubond_energy along this sequence of frames
(i.e., a trajectory, a frame is one section_single_configuration_calculation). If
not all frames have a value the indices of the frames that have a value are stored
in frame_sequence_ubond_energy_frames.
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_ubond_energy'))
x_gromacs_frame_sequence_coulomb_sr_energy_frames = Quantity(
type=np.dtype(np.int32),
shape=['x_gromacs_number_of_coulomb_sr_energy_in_sequence'],
description='''
Array containing the strictly increasing indices of the frames the
x_gromacs_frame_sequence_coulomb_sr_energy values refers to. If not given it
defaults to the trivial mapping 0,1,...
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_coulomb_sr_energy_frames'))
x_gromacs_frame_sequence_coulomb_sr_energy = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_coulomb_sr_energy_in_sequence'],
description='''
Array containing the values of the coulomb_sr_energy along this sequence of frames
(i.e., a trajectory, a frame is one section_single_configuration_calculation). If
not all frames have a value the indices of the frames that have a value are stored
in frame_sequence_coulomb_sr_energy_frames.
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_coulomb_sr_energy'))
x_gromacs_frame_sequence_coulomb_14_energy_frames = Quantity(
type=np.dtype(np.int32),
shape=['x_gromacs_number_of_coulomb_14_energy_in_sequence'],
description='''
Array containing the strictly increasing indices of the frames the
x_gromacs_frame_sequence_coulomb_14_energy values refers to. If not given it
defaults to the trivial mapping 0,1,...
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_coulomb_14_energy_frames'))
x_gromacs_frame_sequence_coulomb_14_energy = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_coulomb_14_energy_in_sequence'],
description='''
Array containing the values of the coulomb_14_energy along this sequence of frames
(i.e., a trajectory, a frame is one section_single_configuration_calculation). If
not all frames have a value the indices of the frames that have a value are stored
in frame_sequence_coulomb_14_energy_frames.
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_coulomb_14_energy'))
x_gromacs_frame_sequence_lj_sr_energy_frames = Quantity(
type=np.dtype(np.int32),
shape=['x_gromacs_number_of_lj_sr_energy_in_sequence'],
description='''
Array containing the strictly increasing indices of the frames the
x_gromacs_frame_sequence_lj_sr_energy values refers to. If not given it defaults
to the trivial mapping 0,1,...
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_lj_sr_energy_frames'))
x_gromacs_frame_sequence_lj_sr_energy = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_lj_sr_energy_in_sequence'],
description='''
Array containing the values of the lj_sr_energy along this sequence of frames
(i.e., a trajectory, a frame is one section_single_configuration_calculation). If
not all frames have a value the indices of the frames that have a value are stored
in frame_sequence_lj_sr_energy_frames.
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_lj_sr_energy'))
x_gromacs_frame_sequence_lj_14_energy_frames = Quantity(
type=np.dtype(np.int32),
shape=['x_gromacs_number_of_lj_14_energy_in_sequence'],
description='''
Array containing the strictly increasing indices of the frames the
x_gromacs_frame_sequence_lj_14_energy values refers to. If not given it defaults
to the trivial mapping 0,1,...
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_lj_14_energy_frames'))
x_gromacs_frame_sequence_lj_14_energy = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_lj_14_energy_in_sequence'],
description='''
Array containing the values of the lj_14_energy along this sequence of frames
(i.e., a trajectory, a frame is one section_single_configuration_calculation). If
not all frames have a value the indices of the frames that have a value are stored
in frame_sequence_lj_14_energy_frames.
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_lj_14_energy'))
x_gromacs_frame_sequence_constrain_rmsd_frames = Quantity(
type=np.dtype(np.int32),
shape=['x_gromacs_number_of_constrain_rmsd_energy_in_sequence'],
description='''
Array containing the strictly increasing indices of the frames the
x_gromacs_frame_sequence_constrain_rmsd_energy values refers to. If not given it
defaults to the trivial mapping 0,1,...
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_constrain_rmsd_frames'))
x_gromacs_frame_sequence_constrain_rmsd = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_constrain_rmsd_in_sequence'],
description='''
Array containing the values of the constrain_rmsd_energy along this sequence of
frames (i.e., a trajectory, a frame is one
section_single_configuration_calculation). If not all frames have a value the
indices of the frames that have a value are stored in
frame_sequence_constrain_rmsd_energy_frames.
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_constrain_rmsd'))
x_gromacs_frame_sequence_cmap_dihedral_energy_frames = Quantity(
type=np.dtype(np.int32),
shape=['x_gromacs_number_of_cmap_dihedral_energy_in_sequence'],
description='''
Array containing the strictly increasing indices of the frames the
x_gromacs_frame_sequence_cmap_dihedral_energy values refers to. If not given it
defaults to the trivial mapping 0,1,...
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_cmap_dihedral_energy_frames'))
x_gromacs_frame_sequence_cmap_dihedral_energy = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_cmap_dihedral_energy_in_sequence'],
description='''
Array containing the values of the cmap_dihedral_energy along this sequence of
frames (i.e., a trajectory, a frame is one
section_single_configuration_calculation). If not all frames have a value the
indices of the frames that have a value are stored in
frame_sequence_cmap_dihedral_energy_frames.
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_cmap_dihedral_energy'))
x_gromacs_frame_sequence_improper_dihedral_energy_frames = Quantity(
type=np.dtype(np.int32),
shape=['x_gromacs_number_of_improper_dihedral_energy_in_sequence'],
description='''
Array containing the strictly increasing indices of the frames the
x_gromacs_frame_sequence_improper_dihedral_energy values refers to. If not given
it defaults to the trivial mapping 0,1,...
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_improper_dihedral_energy_frames'))
x_gromacs_frame_sequence_improper_dihedral_energy = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_improper_dihedral_energy_in_sequence'],
description='''
Array containing the values of the improper_dihedral_energy along this sequence of
frames (i.e., a trajectory, a frame is one
section_single_configuration_calculation). If not all frames have a value the
indices of the frames that have a value are stored in
frame_sequence_improper_dihedral_energy_frames.
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_improper_dihedral_energy'))
x_gromacs_frame_sequence_proper_dihedral_energy_frames = Quantity(
type=np.dtype(np.int32),
shape=['x_gromacs_number_of_proper_dihedral_energy_in_sequence'],
description='''
Array containing the strictly increasing indices of the frames the
x_gromacs_frame_sequence_proper_dihedral_energy values refers to. If not given it
defaults to the trivial mapping 0,1,...
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_proper_dihedral_energy_frames'))
x_gromacs_frame_sequence_proper_dihedral_energy = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_proper_dihedral_energy_in_sequence'],
description='''
Array containing the values of the proper_dihedral_energy along this sequence of
frames (i.e., a trajectory, a frame is one
section_single_configuration_calculation). If not all frames have a value the
indices of the frames that have a value are stored in
frame_sequence_proper_dihedral_energy_frames.
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_proper_dihedral_energy'))
x_gromacs_frame_sequence_bond_energy_frames = Quantity(
type=np.dtype(np.int32),
shape=['x_gromacs_number_of_bond_energies_in_sequence'],
description='''
Array containing the strictly increasing indices of the frames the
x_gromacs_frame_sequence_bond_energy values refers to. If not given it defaults to
the trivial mapping 0,1,...
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_bond_energy_frames'))
x_gromacs_frame_sequence_bond_energy = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_bond_energies_in_sequence'],
description='''
Array containing the values of the bond_energy along this sequence of frames
(i.e., a trajectory, a frame is one section_single_configuration_calculation). If
not all frames have a value the indices of the frames that have a value are stored
in frame_sequence_bond_energy_frames.
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_bond_energy'))
x_gromacs_frame_sequence_volume_frames = Quantity(
type=np.dtype(np.int32),
shape=['x_gromacs_number_of_volumes_in_sequence'],
description='''
Array containing the strictly increasing indices of the frames the
x_gromacs_frame_sequence_volume values refers to. If not given it defaults to the
trivial mapping 0,1,...
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_volume_frames'))
x_gromacs_frame_sequence_volume = Quantity(
type=np.dtype(np.float64),
shape=['x_gromacs_number_of_volumes_in_sequence'],
description='''
Array containing the values of the volume along this sequence of frames (i.e., a
trajectory, a frame is one section_single_configuration_calculation). If not all
frames have a value the indices of the frames that have a value are stored in
frame_sequence_volume_frames.
''',
a_legacy=LegacyDefinition(name='x_gromacs_frame_sequence_volume'))
class section_single_configuration_calculation(public.section_single_configuration_calculation):
m_def = Section(validate=False, extends_base_section=True, a_legacy=LegacyDefinition(name='section_single_configuration_calculation'))
x_gromacs_section_single_configuration_calculation = SubSection(
sub_section=SectionProxy('x_gromacs_section_single_configuration_calculation'),
repeats=True,
a_legacy=LegacyDefinition(name='x_gromacs_section_single_configuration_calculation'))
m_package.__init_metainfo__()
| 34.939065 | 138 | 0.67207 | 10,075 | 93,462 | 5.859653 | 0.051514 | 0.092012 | 0.084999 | 0.126702 | 0.94666 | 0.811454 | 0.774426 | 0.742987 | 0.720154 | 0.697083 | 0 | 0.004342 | 0.238642 | 93,462 | 2,674 | 139 | 34.952132 | 0.825304 | 0.014081 | 0 | 0.654562 | 0 | 0 | 0.406557 | 0.15746 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.002657 | 0 | 0.153676 | 0.000886 | 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 |
862acb67a74e65879574cd6b212b53c0e6493b54 | 4,786 | py | Python | plugins/csv/unit_test/test_json_to_csv_string.py | lukaszlaszuk/insightconnect-plugins | 8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892 | [
"MIT"
] | 46 | 2019-06-05T20:47:58.000Z | 2022-03-29T10:18:01.000Z | plugins/csv/unit_test/test_json_to_csv_string.py | lukaszlaszuk/insightconnect-plugins | 8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892 | [
"MIT"
] | 386 | 2019-06-07T20:20:39.000Z | 2022-03-30T17:35:01.000Z | plugins/csv/unit_test/test_json_to_csv_string.py | lukaszlaszuk/insightconnect-plugins | 8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892 | [
"MIT"
] | 43 | 2019-07-09T14:13:58.000Z | 2022-03-28T12:04:46.000Z | from unittest import TestCase
from komand_csv.actions.json_to_csv_string import JsonToCsvString
from komand_csv.actions.json_to_csv_string.schema import Input, Output
class TestJsonToCsvString(TestCase):
def test_json_to_csv_string(self):
action = JsonToCsvString()
actual = action.run(
{
Input.JSON: [
{"column1": "value1", "column2": "value2", "column3": "value3"},
{"column1": "value4", "column2": "value5", "column3": "value6"},
{"column1": "value7", "column2": "value8", "column3": "value9"},
],
}
)
expected = {
Output.CSV_STRING: "column1,column2,column3\r\nvalue1,value2,value3\r\nvalue4,value5,value6\r\nvalue7,value8,value9\r\n"
}
self.assertEqual(actual, expected)
def test_json_to_csv_string_empty_json(self):
action = JsonToCsvString()
actual = action.run(
{
Input.JSON: [],
}
)
expected = {Output.CSV_STRING: ""}
self.assertEqual(actual, expected)
def test_json_to_csv_string_value_as_array(self):
action = JsonToCsvString()
actual = action.run(
{
Input.JSON: [
{"column1": "value1", "column2": "value2", "column3": "value3"},
{"column1": "value4", "column2": ["value", "value"], "column3": "value6"},
],
}
)
expected = {
Output.CSV_STRING: "column1,column2,column3\r\nvalue1,value2,value3\r\nvalue4,\"['value', 'value']\",value6\r\n"
}
self.assertEqual(actual, expected)
def test_json_to_csv_string_value_as_object(self):
action = JsonToCsvString()
actual = action.run(
{
Input.JSON: [
{"column1": "value1", "column2": "value2", "column3": "value3"},
{"column1": "value4", "column2": {"column2_1": "value", "column": "value"}, "column3": "value6"},
],
}
)
expected = {
Output.CSV_STRING: "column1,column2,column3\r\nvalue1,value2,value3\r\nvalue4,\"{'column2_1': 'value', 'column': 'value'}\",value6\r\n"
}
self.assertEqual(actual, expected)
def test_json_to_csv_string_empty_object(self):
action = JsonToCsvString()
actual = action.run(
{
Input.JSON: [
{"column1": "value1", "column2": "value2", "column3": "value3"},
{"column1": "value4", "column2": "value5", "column3": "value6"},
{},
],
}
)
expected = {
Output.CSV_STRING: "column1,column2,column3\r\nvalue1,value2,value3\r\nvalue4,value5,value6\r\n,,\r\n"
}
self.assertEqual(actual, expected)
def test_json_to_csv_string_empty_fields(self):
action = JsonToCsvString()
actual = action.run(
{
Input.JSON: [
{"column1": "value1", "column2": "", "column3": "value3"},
{"column1": "", "column2": "value5", "column3": "value6"},
{"column1": "value7", "column2": "value8", "column3": ""},
],
}
)
expected = {
Output.CSV_STRING: "column1,column2,column3\r\nvalue1,,value3\r\n,value5,value6\r\nvalue7,value8,\r\n"
}
self.assertEqual(actual, expected)
def test_json_to_csv_string_unicode(self):
action = JsonToCsvString()
actual = action.run(
{
Input.JSON: [
{"column1": "ąaćceę", "column2": "value2", "column3": "value3"},
{"column1": "value4", "column2": "pythöö\u00f6n", "column3": "value6"},
],
}
)
expected = {Output.CSV_STRING: "column1,column2,column3\r\nąaćceę,value2,value3\r\nvalue4,pythööön,value6\r\n"}
self.assertEqual(actual, expected)
def test_json_to_csv_string_unstructured_data(self):
action = JsonToCsvString()
actual = action.run(
{
Input.JSON: [
{"column1": "value1", "column2": "value2", "column3": "value3"},
{"column1": "value4", "column2": "value5"},
{"column1": "value7", "column2": "value8", "column3": "value9", "column4": "value10"},
],
}
)
expected = {
Output.CSV_STRING: "column1,column2,column3,column4\r\nvalue1,value2,value3,\r\nvalue4,value5,,\r\nvalue7,value8,value9,value10\r\n"
}
self.assertEqual(actual, expected)
| 38.910569 | 147 | 0.520476 | 434 | 4,786 | 5.589862 | 0.135945 | 0.066777 | 0.037098 | 0.06183 | 0.87263 | 0.84089 | 0.800495 | 0.749382 | 0.70033 | 0.61789 | 0 | 0.052861 | 0.32804 | 4,786 | 122 | 148 | 39.229508 | 0.701493 | 0 | 0 | 0.451327 | 0 | 0.044248 | 0.271626 | 0.118471 | 0 | 0 | 0 | 0 | 0.070796 | 1 | 0.070796 | false | 0 | 0.026549 | 0 | 0.106195 | 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 |
8639c94a802c121aae83f7ee0080232146a1c473 | 111 | py | Python | tests/activations_test.py | ghost2718/torchlayers | 2f0f44ab64115c0a14ac8a27cf0159c2119d3f8f | [
"MIT"
] | 1 | 2020-04-15T02:17:51.000Z | 2020-04-15T02:17:51.000Z | tests/activations_test.py | devanshuDesai/torchlayers | 585e250c2a03d330841551f3612cfe9588985d13 | [
"MIT"
] | null | null | null | tests/activations_test.py | devanshuDesai/torchlayers | 585e250c2a03d330841551f3612cfe9588985d13 | [
"MIT"
] | null | null | null | import torch
import torchlayers
def test_hardsigmoid():
torchlayers.HardSigmoid()(torch.randn(4, 5, 6))
| 13.875 | 51 | 0.738739 | 14 | 111 | 5.785714 | 0.714286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.031579 | 0.144144 | 111 | 7 | 52 | 15.857143 | 0.821053 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.5 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 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 |
86854f4ae686bf690b66e72c2748923877d1907c | 104 | py | Python | terrascript/openstack/__init__.py | vutsalsinghal/python-terrascript | 3b9fb5ad77453d330fb0cd03524154a342c5d5dc | [
"BSD-2-Clause"
] | null | null | null | terrascript/openstack/__init__.py | vutsalsinghal/python-terrascript | 3b9fb5ad77453d330fb0cd03524154a342c5d5dc | [
"BSD-2-Clause"
] | null | null | null | terrascript/openstack/__init__.py | vutsalsinghal/python-terrascript | 3b9fb5ad77453d330fb0cd03524154a342c5d5dc | [
"BSD-2-Clause"
] | null | null | null | # terrascript/openstack/__init__.py
import terrascript
class openstack(terrascript.Provider):
pass | 17.333333 | 38 | 0.807692 | 11 | 104 | 7.272727 | 0.727273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115385 | 104 | 6 | 39 | 17.333333 | 0.869565 | 0.317308 | 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 |
86bbd68eecdfaff674cffd218e95273044b9e62a | 2,260 | py | Python | tests/gitx/test_co.py | qszhuan/git-x | 1659417a75f78fd47a8672a0411e70bdeb057af3 | [
"MIT"
] | null | null | null | tests/gitx/test_co.py | qszhuan/git-x | 1659417a75f78fd47a8672a0411e70bdeb057af3 | [
"MIT"
] | 1 | 2019-08-17T08:59:43.000Z | 2019-08-17T09:49:54.000Z | tests/gitx/test_co.py | qszhuan/gity | 1659417a75f78fd47a8672a0411e70bdeb057af3 | [
"MIT"
] | null | null | null | import mock
from gitx import Gitx
@mock.patch('gitx.call', return_value=0)
def test_co(mock_call):
Gitx().co("master")
mock_call.assert_called_once_with('git checkout master')
@mock.patch('gitx.call', return_value=0)
def test_co_create_if_not_exist(mock_call):
Gitx().co("master", create_if_not_existed=True)
mock_call.assert_called_once_with('git checkout -b master')
@mock.patch('gitx.call', return_value=0)
def test_co_create_if_not_exist_with_start_point(mock_call):
Gitx().co("master", 'c1ff877', create_if_not_existed=True)
mock_call.assert_called_once_with('git checkout -b master c1ff877')
@mock.patch('gitx.call', return_value=0)
def test_co_ignore_start_point_if_not_create_new(mock_call):
Gitx().co("master", 'c1ff877', create_if_not_existed=False)
mock_call.assert_called_once_with('git checkout master')
@mock.patch('gitx.popen')
@mock.patch('gitx.call', return_value=0)
def test_co_with_partial_branch_name_but_unique(mock_call, mock_popen):
mock_popen.side_effect = lambda x: 'abc\n click\n* master\n' if x == 'git branch' else 0
Gitx().co("ma")
mock_call.assert_called_once_with('git checkout master')
@mock.patch('gitx.popen')
@mock.patch('gitx.call', return_value=0)
def test_co_with_exactly_matched_branch_name_forcely(mock_call, mock_popen):
mock_popen.side_effect = lambda x: 'abc\n click\n* master\n master2\n' if x == 'git branch' else 0
Gitx().co("master", force=True)
mock_call.assert_called_once_with('git checkout master')
@mock.patch('gitx.print_prompt', return_value=0)
@mock.patch('gitx.popen')
@mock.patch('gitx.call', return_value=0)
def test_co_with_partial_branch_name_but_not_unique(mock_call, mock_popen, _):
mock_popen.side_effect = lambda x: 'abc\n click\n* master\n' if x == 'git branch' else 0
Gitx().co("a")
mock_call.assert_called_once_with('git checkout abc')
@mock.patch('gitx.print_prompt', return_value=1)
@mock.patch('gitx.popen')
@mock.patch('gitx.call', return_value=0)
def test_co_with_partial_branch_name_but_not_unique_2(mock_call, mock_popen, _):
mock_popen.side_effect = lambda x: 'abc\n click\n* master\n' if x == 'git branch' else 0
Gitx().co("a")
mock_call.assert_called_once_with('git checkout master')
| 37.666667 | 103 | 0.751327 | 379 | 2,260 | 4.14248 | 0.150396 | 0.081529 | 0.115924 | 0.086624 | 0.910191 | 0.884713 | 0.884713 | 0.848408 | 0.848408 | 0.826752 | 0 | 0.013923 | 0.110177 | 2,260 | 59 | 104 | 38.305085 | 0.766783 | 0 | 0 | 0.5 | 0 | 0 | 0.222566 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 1 | 0.181818 | false | 0 | 0.045455 | 0 | 0.227273 | 0.045455 | 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 |
86e2b3d22a58a8a499db38ccf0e003457eb219f6 | 2,242 | py | Python | tps/problems/migrations/0049_auto_20161130_1357.py | akmohtashami/tps-web | 9dab3ffe97c21f658be30ce2f2711dd93e4ba60f | [
"MIT"
] | 5 | 2019-02-26T06:10:43.000Z | 2021-07-24T17:11:45.000Z | tps/problems/migrations/0049_auto_20161130_1357.py | akmohtashami/tps-web | 9dab3ffe97c21f658be30ce2f2711dd93e4ba60f | [
"MIT"
] | 3 | 2019-08-15T13:56:03.000Z | 2021-06-10T18:43:16.000Z | tps/problems/migrations/0049_auto_20161130_1357.py | jonathanirvings/tps-web | 46519347d4fc8bdced9b5bceb6cdee5ea4e508f2 | [
"MIT"
] | 2 | 2018-12-28T13:12:59.000Z | 2020-12-25T18:42:13.000Z | # -*- coding: utf-8 -*-
# Generated by Django 1.9.7 on 2016-11-30 13:57
from __future__ import unicode_literals
import django.core.validators
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('problems', '0048_exportpackage_exportpackagecreationtask'),
]
operations = [
migrations.AlterField(
model_name='attachment',
name='name',
field=models.CharField(max_length=256, validators=[django.core.validators.RegexValidator(code='invalid_file_name', inverse_match=False, message='Please enter a valid file name.', regex='^[a-zA-Z0-9_\\-](?:\\.|[a-zA-Z0-9_\\-])*$')], verbose_name='name'),
),
migrations.AlterField(
model_name='checker',
name='name',
field=models.CharField(blank=True, max_length=50, validators=[django.core.validators.RegexValidator(code='invalid_file_name', inverse_match=False, message='Please enter a valid file name.', regex='^[a-zA-Z0-9_\\-](?:\\.|[a-zA-Z0-9_\\-])*$')], verbose_name='name'),
),
migrations.AlterField(
model_name='inputgenerator',
name='name',
field=models.CharField(blank=True, max_length=50, validators=[django.core.validators.RegexValidator(code='invalid_file_name', inverse_match=False, message='Please enter a valid file name.', regex='^[a-zA-Z0-9_\\-](?:\\.|[a-zA-Z0-9_\\-])*$')], verbose_name='name'),
),
migrations.AlterField(
model_name='solution',
name='name',
field=models.CharField(blank=True, max_length=255, validators=[django.core.validators.RegexValidator(code='invalid_file_name', inverse_match=False, message='Please enter a valid file name.', regex='^[a-zA-Z0-9_\\-](?:\\.|[a-zA-Z0-9_\\-])*$')], verbose_name='name'),
),
migrations.AlterField(
model_name='validator',
name='name',
field=models.CharField(blank=True, max_length=50, validators=[django.core.validators.RegexValidator(code='invalid_file_name', inverse_match=False, message='Please enter a valid file name.', regex='^[a-zA-Z0-9_\\-](?:\\.|[a-zA-Z0-9_\\-])*$')], verbose_name='name'),
),
]
| 53.380952 | 277 | 0.639161 | 268 | 2,242 | 5.171642 | 0.246269 | 0.05772 | 0.036075 | 0.04329 | 0.760462 | 0.74026 | 0.74026 | 0.74026 | 0.74026 | 0.707071 | 0 | 0.028292 | 0.180196 | 2,242 | 41 | 278 | 54.682927 | 0.725789 | 0.029884 | 0 | 0.529412 | 1 | 0 | 0.269337 | 0.114641 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.088235 | 0 | 0.176471 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
813403e1c424992afe62d4323f0ab7b759abdbe6 | 14,931 | py | Python | aries_cloudagent/holder/tests/test_routes.py | antsab20/aries-cloudagent-python | c8fa5894508d2ecd0d8be933f5ff8a50e5c582e8 | [
"Apache-2.0"
] | 1 | 2021-04-15T09:44:00.000Z | 2021-04-15T09:44:00.000Z | aries_cloudagent/holder/tests/test_routes.py | Mateus-dang/aries-cloudagent-python | d64c3a0102b269fac9b39f30815829a64b74e9ce | [
"Apache-2.0"
] | null | null | null | aries_cloudagent/holder/tests/test_routes.py | Mateus-dang/aries-cloudagent-python | d64c3a0102b269fac9b39f30815829a64b74e9ce | [
"Apache-2.0"
] | null | null | null | import json
from asynctest import mock as async_mock, TestCase as AsyncTestCase
from ...config.injection_context import InjectionContext
from ...ledger.base import BaseLedger
from ...wallet.base import BaseWallet
from ...admin.request_context import AdminRequestContext
from ...indy.holder import IndyHolder
from ...ledger.base import BaseLedger
from ...storage.vc_holder.base import VCHolder
from ...storage.vc_holder.vc_record import VCRecord
from .. import routes as test_module
VC_RECORD = VCRecord(
contexts=[
"https://www.w3.org/2018/credentials/v1",
"https://www.w3.org/2018/credentials/v1/examples",
],
types=[
"VerifiableCredential",
"AlumniCredential",
],
issuer_id="https://example.edu/issuers/565049",
subject_ids=["did:example:ebfeb1f712ebc6f1c276e12ec21"],
schema_ids=["https://example.org/examples/degree.json"],
cred_value={"...": "..."},
given_id="http://example.edu/credentials/3732",
cred_tags={"some": "tag"},
)
class TestHolderRoutes(AsyncTestCase):
def setUp(self):
self.context = AdminRequestContext.test_context()
self.request_dict = {"context": self.context}
self.request = async_mock.MagicMock(
app={},
match_info={},
query={},
__getitem__=lambda _, k: self.request_dict[k],
)
async def test_credentials_get(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
IndyHolder,
async_mock.MagicMock(
get_credential=async_mock.CoroutineMock(
return_value=json.dumps({"hello": "world"})
)
),
)
with async_mock.patch.object(
test_module.web, "json_response", async_mock.Mock()
) as json_response:
result = await test_module.credentials_get(self.request)
json_response.assert_called_once_with({"hello": "world"})
assert result is json_response.return_value
async def test_credentials_get_not_found(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
IndyHolder,
async_mock.MagicMock(
get_credential=async_mock.CoroutineMock(
side_effect=test_module.WalletNotFoundError()
)
),
)
with self.assertRaises(test_module.web.HTTPNotFound):
await test_module.credentials_get(self.request)
async def test_credentials_revoked(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
BaseLedger, async_mock.create_autospec(BaseLedger)
)
self.context.injector.bind_instance(
IndyHolder,
async_mock.MagicMock(
credential_revoked=async_mock.CoroutineMock(return_value=False)
),
)
with async_mock.patch.object(
test_module.web, "json_response", async_mock.Mock()
) as json_response:
result = await test_module.credentials_revoked(self.request)
json_response.assert_called_once_with({"revoked": False})
assert result is json_response.return_value
async def test_credentials_revoked_no_ledger(self):
self.request.match_info = {"credential_id": "dummy"}
with self.assertRaises(test_module.web.HTTPForbidden):
await test_module.credentials_revoked(self.request)
async def test_credentials_not_found(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
BaseLedger, async_mock.create_autospec(BaseLedger)
)
self.context.injector.bind_instance(
IndyHolder,
async_mock.MagicMock(
credential_revoked=async_mock.CoroutineMock(
side_effect=test_module.WalletNotFoundError("no such cred")
)
),
)
with self.assertRaises(test_module.web.HTTPNotFound):
await test_module.credentials_revoked(self.request)
async def test_credentials_x_ledger(self):
self.request.match_info = {"credential_id": "dummy"}
ledger = async_mock.create_autospec(BaseLedger)
self.context.injector.bind_instance(
BaseLedger, async_mock.create_autospec(BaseLedger)
)
self.context.injector.bind_instance(
IndyHolder,
async_mock.MagicMock(
credential_revoked=async_mock.CoroutineMock(
side_effect=test_module.LedgerError("down for maintenance")
)
),
)
with self.assertRaises(test_module.web.HTTPBadRequest):
await test_module.credentials_revoked(self.request)
async def test_attribute_mime_types_get(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
IndyHolder,
async_mock.MagicMock(
get_mime_type=async_mock.CoroutineMock(return_value=None)
),
)
with async_mock.patch.object(test_module.web, "json_response") as mock_response:
await test_module.credentials_attr_mime_types_get(self.request)
mock_response.assert_called_once_with(None)
async def test_credentials_remove(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
IndyHolder,
async_mock.MagicMock(
delete_credential=async_mock.CoroutineMock(return_value=None)
),
)
with async_mock.patch.object(
test_module.web, "json_response", async_mock.Mock()
) as json_response:
result = await test_module.credentials_remove(self.request)
json_response.assert_called_once_with({})
assert result is json_response.return_value
async def test_credentials_remove_not_found(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
IndyHolder,
async_mock.MagicMock(
delete_credential=async_mock.CoroutineMock(
side_effect=test_module.WalletNotFoundError()
)
),
)
with self.assertRaises(test_module.web.HTTPNotFound):
await test_module.credentials_remove(self.request)
async def test_credentials_list(self):
self.request.query = {"start": "0", "count": "10"}
self.context.injector.bind_instance(
IndyHolder,
async_mock.MagicMock(
get_credentials=async_mock.CoroutineMock(
return_value=[{"hello": "world"}]
)
),
)
with async_mock.patch.object(
test_module.web, "json_response", async_mock.Mock()
) as json_response:
result = await test_module.credentials_list(self.request)
json_response.assert_called_once_with({"results": [{"hello": "world"}]})
assert result is json_response.return_value
async def test_credentials_list_x_holder(self):
self.request.query = {"start": "0", "count": "10"}
self.context.injector.bind_instance(
IndyHolder,
async_mock.MagicMock(
get_credentials=async_mock.CoroutineMock(
side_effect=test_module.IndyHolderError()
)
),
)
with self.assertRaises(test_module.web.HTTPBadRequest):
await test_module.credentials_list(self.request)
async def test_w3c_cred_get(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
VCHolder,
async_mock.MagicMock(
retrieve_credential_by_id=async_mock.CoroutineMock(
return_value=VC_RECORD
)
),
)
with async_mock.patch.object(
test_module.web, "json_response", async_mock.Mock()
) as json_response:
result = await test_module.w3c_cred_get(self.request)
json_response.assert_called_once_with(VC_RECORD.serialize())
async def test_w3c_cred_get_not_found_x(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
VCHolder,
async_mock.MagicMock(
retrieve_credential_by_id=async_mock.CoroutineMock(
side_effect=test_module.StorageNotFoundError()
)
),
)
with self.assertRaises(test_module.web.HTTPNotFound):
await test_module.w3c_cred_get(self.request)
async def test_w3c_cred_get_storage_x(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
VCHolder,
async_mock.MagicMock(
retrieve_credential_by_id=async_mock.CoroutineMock(
side_effect=test_module.StorageError()
)
),
)
with self.assertRaises(test_module.web.HTTPBadRequest):
await test_module.w3c_cred_get(self.request)
async def test_w3c_cred_remove(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
VCHolder,
async_mock.MagicMock(
retrieve_credential_by_id=async_mock.CoroutineMock(
return_value=VC_RECORD
),
delete_credential=async_mock.CoroutineMock(return_value=None),
),
)
with async_mock.patch.object(
test_module.web, "json_response", async_mock.Mock()
) as json_response:
result = await test_module.w3c_cred_remove(self.request)
json_response.assert_called_once_with({})
assert result is json_response.return_value
async def test_w3c_cred_remove_not_found_x(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
VCHolder,
async_mock.MagicMock(
retrieve_credential_by_id=async_mock.CoroutineMock(
side_effect=test_module.StorageNotFoundError()
)
),
)
with self.assertRaises(test_module.web.HTTPNotFound):
await test_module.w3c_cred_remove(self.request)
async def test_w3c_cred_remove_storage_x(self):
self.request.match_info = {"credential_id": "dummy"}
self.context.injector.bind_instance(
VCHolder,
async_mock.MagicMock(
retrieve_credential_by_id=async_mock.CoroutineMock(
return_value=VC_RECORD
),
delete_credential=async_mock.CoroutineMock(
side_effect=test_module.StorageError()
),
),
)
with self.assertRaises(test_module.web.HTTPBadRequest):
await test_module.w3c_cred_remove(self.request)
async def test_w3c_creds_list(self):
self.request.json = async_mock.CoroutineMock(
return_value={
"types": [
"VerifiableCredential",
"AlumniCredential",
],
"issuer_id": "https://example.edu/issuers/565049",
"subject_id": "did:example:ebfeb1f712ebc6f1c276e12ec21",
"max_results": "1",
}
)
self.context.injector.bind_instance(
VCHolder,
async_mock.MagicMock(
search_credentials=async_mock.MagicMock(
return_value=async_mock.MagicMock(
fetch=async_mock.CoroutineMock(return_value=[VC_RECORD])
)
)
),
)
with async_mock.patch.object(
test_module.web, "json_response", async_mock.Mock()
) as json_response:
result = await test_module.w3c_creds_list(self.request)
json_response.assert_called_once_with({"results": [VC_RECORD.serialize()]})
async def test_w3c_creds_list_not_found_x(self):
self.request.json = async_mock.CoroutineMock(
return_value={
"types": [
"VerifiableCredential",
"AlumniCredential",
],
"issuer_id": "https://example.edu/issuers/565049",
"subject_id": "did:example:ebfeb1f712ebc6f1c276e12ec21",
"max_results": "1",
}
)
self.context.injector.bind_instance(
VCHolder,
async_mock.MagicMock(
search_credentials=async_mock.MagicMock(
return_value=async_mock.MagicMock(
fetch=async_mock.CoroutineMock(
side_effect=test_module.StorageNotFoundError()
)
)
)
),
)
with self.assertRaises(test_module.web.HTTPNotFound):
await test_module.w3c_creds_list(self.request)
async def test_w3c_creds_list_storage_x(self):
self.request.json = async_mock.CoroutineMock(
return_value={
"types": [
"VerifiableCredential",
"AlumniCredential",
],
"issuer_id": "https://example.edu/issuers/565049",
"subject_id": "did:example:ebfeb1f712ebc6f1c276e12ec21",
"max_results": "1",
}
)
self.context.injector.bind_instance(
VCHolder,
async_mock.MagicMock(
search_credentials=async_mock.MagicMock(
return_value=async_mock.MagicMock(
fetch=async_mock.CoroutineMock(
side_effect=test_module.StorageError()
)
)
)
),
)
with self.assertRaises(test_module.web.HTTPBadRequest):
await test_module.w3c_creds_list(self.request)
async def test_register(self):
mock_app = async_mock.MagicMock()
mock_app.add_routes = async_mock.MagicMock()
await test_module.register(mock_app)
mock_app.add_routes.assert_called_once()
async def test_post_process_routes(self):
mock_app = async_mock.MagicMock(_state={"swagger_dict": {}})
test_module.post_process_routes(mock_app)
assert "tags" in mock_app._state["swagger_dict"]
| 36.957921 | 88 | 0.602706 | 1,486 | 14,931 | 5.741588 | 0.102288 | 0.077004 | 0.061181 | 0.059306 | 0.868847 | 0.853141 | 0.823488 | 0.801688 | 0.786334 | 0.764534 | 0 | 0.011493 | 0.306543 | 14,931 | 403 | 89 | 37.049628 | 0.812536 | 0 | 0 | 0.611898 | 0 | 0 | 0.083384 | 0.010448 | 0 | 0 | 0 | 0 | 0.076487 | 1 | 0.002833 | false | 0 | 0.031161 | 0 | 0.036827 | 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 |
d4b0d02c202a2ad74a538c70435347ed5bb58fc9 | 63 | py | Python | addons14/storage_image_product/tests/__init__.py | odoochain/addons_oca | 55d456d798aebe16e49b4a6070765f206a8885ca | [
"MIT"
] | 1 | 2021-06-10T14:59:13.000Z | 2021-06-10T14:59:13.000Z | addons14/storage_image_product/tests/__init__.py | odoochain/addons_oca | 55d456d798aebe16e49b4a6070765f206a8885ca | [
"MIT"
] | null | null | null | addons14/storage_image_product/tests/__init__.py | odoochain/addons_oca | 55d456d798aebe16e49b4a6070765f206a8885ca | [
"MIT"
] | 1 | 2021-04-09T09:44:44.000Z | 2021-04-09T09:44:44.000Z | from . import test_product_image_relation
from . import common
| 21 | 41 | 0.84127 | 9 | 63 | 5.555556 | 0.777778 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.126984 | 63 | 2 | 42 | 31.5 | 0.909091 | 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 |
d4fe9acc128d855971c6bf5653bf45ce9e2b193b | 19,279 | py | Python | flypylib/fplmodels.py | janelia-flyem/flypylib | b52aa96aca3ae1fcd5e10c22de75e832cf0590c6 | [
"BSD-3-Clause"
] | 10 | 2017-10-27T15:52:29.000Z | 2021-09-02T07:53:18.000Z | flypylib/fplmodels.py | janelia-flyem/flypylib | b52aa96aca3ae1fcd5e10c22de75e832cf0590c6 | [
"BSD-3-Clause"
] | 2 | 2017-10-16T19:39:17.000Z | 2017-10-25T15:39:36.000Z | flypylib/fplmodels.py | janelia-flyem/flypylib | b52aa96aca3ae1fcd5e10c22de75e832cf0590c6 | [
"BSD-3-Clause"
] | 2 | 2017-10-13T21:29:57.000Z | 2022-01-06T01:49:57.000Z | """defines keras models/network architectures to use for object
detection
"""
from flypylib import fplutils
from keras.models import Model
from keras.layers import Dropout, Activation, Conv3D, MaxPooling3D, Cropping3D, UpSampling3D
from keras.layers import BatchNormalization
from keras.layers import Input
from keras.layers import add, concatenate
from tensorflow.python.framework import ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
import tensorflow as tf
import math
import keras.backend as K
import numpy as np
def _to_tensor(x, dtype):
x = ops.convert_to_tensor(x)
if x.dtype != dtype:
x = math_ops.cast(x, dtype)
return x
def masked_weighted_binary_crossentropy(y_true, y_pred):
# Epsilon fuzz factor used throughout the codebase.
_EPSILON = 10e-8
mask = K.cast(K.not_equal(y_true, 2), K.floatx())
y_true = y_true * mask
y_pred = y_pred * mask
epsilon = _to_tensor(_EPSILON, y_pred.dtype.base_dtype)
y_pred = clip_ops.clip_by_value(y_pred, epsilon, 1 - epsilon)
y_pred = math_ops.log(y_pred / (1 - y_pred))
cost = nn.weighted_cross_entropy_with_logits(logits=y_pred, targets=y_true, pos_weight=100)
return K.mean(cost, axis=-1)
def masked_binary_crossentropy(y_true, y_pred):
mask = K.cast(K.not_equal(y_true, 2), K.floatx())
return K.mean(K.binary_crossentropy(y_pred * mask,
y_true * mask), axis=-1)
def masked_focal_loss(y_true, y_pred):
gamma = 2
alpha = 1#0.25
pt = tf.where(tf.equal(y_true, 1), y_pred, 1 - y_pred)
mask = K.cast(K.less(y_true, 2), K.floatx())
return -K.sum(alpha * mask * K.pow(1. - pt, gamma) * K.log(pt+K.epsilon()), axis=-1)
def lb0l1err(y_true, y_pred):
mask = K.cast(K.equal(y_true, 0), K.floatx())
err = y_pred * mask
return K.sum(err) / K.maximum(K.sum(mask), 1)
def lb1l1err(y_true, y_pred):
mask = K.cast(K.equal(y_true, 1), K.floatx())
err = (1-y_pred) * mask
return K.sum(err) / K.maximum(K.sum(mask), 1)
def masked_accuracy(y_true, y_pred):
mask = K.cast(K.not_equal(y_true, 2), K.floatx())
return K.mean(K.equal(y_true * mask,
K.round(y_pred * mask)), axis=-1)
def _bn_relu(input):
"""Helper to build a BN -> relu block
"""
norm = BatchNormalization()(input)
return Activation("relu")(norm)
def baseline_model(in_sz = None):
"""returns simple baseline model
"""
in_sz = fplutils.to3d(in_sz)
in_sz = in_sz + (1,)
inputs = Input(shape=in_sz)
conv1 = Conv3D(32, (3,3,3), use_bias=False)(inputs)
conv1 = _bn_relu(conv1)
pool1 = MaxPooling3D(pool_size=(2,2,2))(conv1)
conv2 = Conv3D(32, (3,3,3), use_bias=False)(pool1)
conv2 = _bn_relu(conv2)
pool2 = MaxPooling3D(pool_size=(2,2,2))(conv2)
conv3 = Conv3D(32, (3,3,3), use_bias=False)(pool2)
conv3 = _bn_relu(conv3)
full1 = Conv3D(64, (1,1,1), use_bias=False)(conv3)
full1 = _bn_relu(full1)
full1 = Dropout(0.5)(full1)
predictions = Conv3D(1, (1,1,1), activation='sigmoid')(full1)
model = Model(inputs=inputs, outputs=predictions)
return model, (18, 7, 4), 102, None
def vgg_like(in_sz = None):
"""returns standard model based on VGG architecture"""
in_sz = fplutils.to3d(in_sz)
in_sz = in_sz + (1,)
inputs = Input(shape=in_sz)
conv1 = Conv3D(48, (3,3,3), use_bias=False)(inputs)
conv1 = _bn_relu(conv1)
conv1 = Conv3D(48, (1,1,1), use_bias=False)(conv1)
conv1 = _bn_relu(conv1)
pool1 = MaxPooling3D(pool_size=(2,2,2))(conv1)
conv2 = Conv3D(48, (3,3,3), use_bias=False)(pool1)
conv2 = _bn_relu(conv2)
conv2 = Conv3D(48, (1,1,1), use_bias=False)(conv2)
conv2 = _bn_relu(conv2)
pool2 = MaxPooling3D(pool_size=(2,2,2))(conv2)
conv3 = Conv3D(48, (3,3,3), use_bias=False)(pool2)
conv3 = _bn_relu(conv3)
full1 = Conv3D(96, (1,1,1), use_bias=False)(conv3)
full1 = _bn_relu(full1)
full1 = Dropout(0.5)(full1)
full2 = Conv3D(96, (1,1,1), use_bias=False)(full1)
full2 = _bn_relu(full2)
full2 = Dropout(0.5)(full2)
predictions = Conv3D(1, (1,1,1), activation='sigmoid')(full2)
model = Model(inputs=inputs, outputs=predictions)
return model, (18, 7, 4), 102, None
def vgg_like2(in_sz = None):
"""returns standard model based on VGG architecture"""
in_sz = fplutils.to3d(in_sz)
in_sz = in_sz + (1,)
inputs = Input(shape=in_sz)
conv1 = Conv3D(48, (3,3,3), use_bias=False)(inputs)
conv1 = _bn_relu(conv1)
conv1 = Conv3D(48, (3,3,3), use_bias=False)(conv1)
conv1 = _bn_relu(conv1)
pool1 = MaxPooling3D(pool_size=(2,2,2))(conv1)
conv2 = Conv3D(48, (3,3,3), use_bias=False)(pool1)
conv2 = _bn_relu(conv2)
conv2 = Conv3D(48, (3,3,3), use_bias=False)(conv2)
conv2 = _bn_relu(conv2)
pool2 = MaxPooling3D(pool_size=(2,2,2))(conv2)
conv3 = Conv3D(48, (3,3,3), use_bias=False)(pool2)
conv3 = _bn_relu(conv3)
full1 = Conv3D(96, (1,1,1), use_bias=False)(conv3)
full1 = _bn_relu(full1)
full1 = Dropout(0.5)(full1)
full2 = Conv3D(96, (1,1,1), use_bias=False)(full1)
full2 = _bn_relu(full2)
full2 = Dropout(0.5)(full2)
predictions = Conv3D(1, (1,1,1), activation='sigmoid')(full2)
model = Model(inputs=inputs, outputs=predictions)
return model, (24, 10, 4), 100, None
def resnet_like(in_sz=None):
""" returns a model that uses residual components
"""
in_sz = fplutils.to3d(in_sz)
in_sz = in_sz + (1,)
inputs = Input(shape=in_sz)
conv1 = Conv3D(32, (3, 3, 3), use_bias=False)(inputs) # 16x16x16
conv1 = _bn_relu(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1) # 8x8x8
conv2 = Conv3D(32, (3, 3, 3), use_bias=False)(pool1) # 6x6x6
conv2 = _bn_relu(conv2)
conv2 = Conv3D(32, (1, 1, 1), use_bias=False)(conv2) # 6x6x6
conv2 = BatchNormalization()(conv2)
crop_pool1 = Cropping3D(cropping=((1, 1), (1, 1), (1, 1)))(pool1)
conv2 = add([crop_pool1, conv2])
conv2 = Activation("relu")(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2) # 3x3x3
conv3 = Conv3D(64, (3, 3, 3), use_bias=False)(pool2) # 1x1x1
conv3 = _bn_relu(conv3)
conv3 = Conv3D(64, (1, 1, 1), use_bias=False)(conv3) # 1x1x1
pool2_shortcut = Conv3D(64, (1, 1, 1), use_bias=False)(pool2)
crop_pool2 = Cropping3D(cropping=((1, 1), (1, 1), (1, 1)))(pool2_shortcut)
conv3 = BatchNormalization()(conv3)
conv3 = add([crop_pool2, conv3])
conv3 = Activation("relu")(conv3)
predictions = Conv3D(1, (1, 1, 1), activation='sigmoid')(conv3)
model = Model(inputs=inputs, outputs=predictions)
return model, (18, 7, 4), 102, None
def unet_like(in_sz=18):
'''
construct a u-net style network
'''
in_sz = fplutils.to3d(in_sz)
in_sz = in_sz + (1,)
inputs = Input(shape=in_sz) # 18x18x18
# down-sample
conv1 = Conv3D(32, (3, 3, 3), use_bias=False)(inputs) # 16x16x16
conv1 = _bn_relu(conv1)
conv1 = Conv3D(32, (1, 1, 1), use_bias=False)(conv1) # 16x16x16
conv1 = _bn_relu(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1) # 8x8x8
conv2 = Conv3D(64, (3, 3, 3), use_bias=False)(pool1) # 6x6x6
conv2 = _bn_relu(conv2)
conv2 = Conv3D(64, (1, 1, 1), use_bias=False)(conv2) # 6x6x6
conv2 = _bn_relu(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2) # 3x3x3
conv3 = Conv3D(128, (1, 1, 1), use_bias=False)(pool2) # 3x3x3
conv3 = _bn_relu(conv3)
# up-sample
up4 = concatenate([UpSampling3D(size=(2, 2, 2))(conv3), conv2]) # 6x6x6
conv4 = Conv3D(64, (3, 3, 3), use_bias=False)(up4) # 4x4x4
conv4 = _bn_relu(conv4)
conv4 = Conv3D(64, (1, 1, 1), use_bias=False)(conv4) # 4x4x4
conv4 = _bn_relu(conv4)
crop_conv1 = Cropping3D(cropping=((4, 4), (4, 4), (4, 4)))(conv1)
up5 = concatenate([UpSampling3D(size=(2, 2, 2))(conv4), crop_conv1]) # 8x8x8
conv5 = Conv3D(32, (3, 3, 3), use_bias=False)(up5) # 6x6x6
conv5 = _bn_relu(conv5)
conv5 = Conv3D(32, (1, 1, 1), use_bias=False)(conv5) # 6x6x6
conv5 = _bn_relu(conv5)
predictions = Conv3D(1, (1, 1, 1), activation='sigmoid', use_bias=False)(conv5) # 6x6x6
model = Model(inputs=inputs, outputs=predictions)
compile_args = {'loss': masked_binary_crossentropy,
'optimizer': 'adam',
'metrics': [masked_accuracy,
lb0l1err, lb1l1err]}
return model, (18, 6, 1), 102, compile_args
def unet_like2(in_sz=24):
'''
construct a u-net style network
'''
in_sz = fplutils.to3d(in_sz)
in_sz = in_sz + (1,)
inputs = Input(shape=in_sz) # 24x24x24
# down-sample
conv1 = Conv3D(32, (3, 3, 3), use_bias=False)(inputs) # 22x22x22
conv1 = _bn_relu(conv1)
conv1 = Conv3D(32, (3, 3, 3), use_bias=False)(conv1) # 20x20x20
conv1 = _bn_relu(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1) # 10x10x10
conv2 = Conv3D(64, (3, 3, 3), use_bias=False)(pool1) # 8x8x8
conv2 = _bn_relu(conv2)
conv2 = Conv3D(64, (3, 3, 3), use_bias=False)(conv2) # 6x6x6
conv2 = _bn_relu(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2) # 3x3x3
conv3 = Conv3D(128, (1, 1, 1), use_bias=False)(pool2) # 3x3x3
conv3 = _bn_relu(conv3)
# up-sample
up4 = concatenate([UpSampling3D(size=(2, 2, 2))(conv3), conv2]) # 6x6x6
conv4 = Conv3D(64, (3, 3, 3), use_bias=False)(up4) # 4x4x4
conv4 = _bn_relu(conv4)
conv4 = Conv3D(64, (1, 1, 1), use_bias=False)(conv4) # 4x4x4
conv4 = _bn_relu(conv4)
crop_conv1 = Cropping3D(cropping=((6, 6), (6, 6), (6, 6)))(conv1)
up5 = concatenate([UpSampling3D(size=(2, 2, 2))(conv4), crop_conv1]) # 8x8x8
conv5 = Conv3D(32, (3, 3, 3), use_bias=False)(up5) # 6x6x6
conv5 = _bn_relu(conv5)
conv5 = Conv3D(32, (1, 1, 1), use_bias=False)(conv5) # 6x6x6
conv5 = _bn_relu(conv5)
predictions = Conv3D(1, (1, 1, 1), activation='sigmoid', use_bias=False)(conv5) # 6x6x6
model = Model(inputs=inputs, outputs=predictions)
compile_args = {'loss': masked_focal_loss, #masked_binary_crossentropy,
'optimizer': 'adam',
'metrics': [masked_accuracy,
lb0l1err, lb1l1err]}
return model, (24, 9, 1), 100, compile_args
def unet_like3(in_sz=32):
'''
construct a u-net style network
'''
in_sz = fplutils.to3d(in_sz)
in_sz = in_sz + (1,)
inputs = Input(shape=in_sz) # 32^2
# down-sample
conv1 = Conv3D(32, (3, 3, 3), use_bias=False)(inputs) # 30
conv1 = _bn_relu(conv1)
conv1 = Conv3D(32, (3, 3, 3), use_bias=False)(conv1) # 28
conv1 = _bn_relu(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1) # 14
conv2 = Conv3D(64, (3, 3, 3), use_bias=False)(pool1) # 12
conv2 = _bn_relu(conv2)
conv2 = Conv3D(64, (3, 3, 3), use_bias=False)(conv2) # 10
conv2 = _bn_relu(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2) # 5
conv3 = Conv3D(128, (3, 3, 3), use_bias=False)(pool2) # 3
conv3 = _bn_relu(conv3)
conv3 = Conv3D(128, (1, 1, 1), use_bias=False)(conv3) # 3
conv3 = _bn_relu(conv3)
# up-sample
crop_conv2 = Cropping3D(cropping=((2, 2), (2, 2), (2, 2)))(conv2)
up4 = concatenate([UpSampling3D(size=(2, 2, 2))(conv3), crop_conv2]) # 6
conv4 = Conv3D(64, (3, 3, 3), use_bias=False)(up4) # 4
conv4 = _bn_relu(conv4)
conv4 = Conv3D(64, (1, 1, 1), use_bias=False)(conv4) # 4
conv4 = _bn_relu(conv4)
crop_conv1 = Cropping3D(cropping=((10, 10), (10, 10), (10, 10)))(conv1)
up5 = concatenate([UpSampling3D(size=(2, 2, 2))(conv4), crop_conv1]) # 8x8x8
conv5 = Conv3D(32, (3, 3, 3), use_bias=False)(up5) # 6
conv5 = _bn_relu(conv5)
conv5 = Conv3D(32, (1, 1, 1), use_bias=False)(conv5) # 6
conv5 = _bn_relu(conv5)
predictions = Conv3D(1, (1, 1, 1), activation='sigmoid', use_bias=False)(conv5) # 6
model = Model(inputs=inputs, outputs=predictions)
compile_args = {'loss': masked_focal_loss, #masked_binary_crossentropy,
'optimizer': 'adam',
'metrics': [masked_accuracy,
lb0l1err, lb1l1err]}
return model, (32, 13, 1), 100, compile_args
def unet_like4(in_sz=40):
'''
construct a u-net style network
'''
in_sz = fplutils.to3d(in_sz)
in_sz = in_sz + (1,)
inputs = Input(shape=in_sz) # 40^2
# down-sample
conv1 = Conv3D(32, (3, 3, 3), use_bias=False)(inputs) # 38
conv1 = _bn_relu(conv1)
conv1 = Conv3D(32, (3, 3, 3), use_bias=False)(conv1) # 36
conv1 = _bn_relu(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1) # 18
conv2 = Conv3D(64, (3, 3, 3), use_bias=False)(pool1) # 16
conv2 = _bn_relu(conv2)
conv2 = Conv3D(64, (3, 3, 3), use_bias=False)(conv2) # 14
conv2 = _bn_relu(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2) # 7
conv3 = Conv3D(128, (3, 3, 3), use_bias=False)(pool2) # 5
conv3 = _bn_relu(conv3)
conv3 = Conv3D(128, (3, 3, 3), use_bias=False)(conv3) # 3
conv3 = _bn_relu(conv3)
# up-sample
crop_conv2 = Cropping3D(cropping=((4, 4), (4, 4), (4, 4)))(conv2)
up4 = concatenate([UpSampling3D(size=(2, 2, 2))(conv3), crop_conv2]) # 6
conv4 = Conv3D(64, (3, 3, 3), use_bias=False)(up4) # 4
conv4 = _bn_relu(conv4)
conv4 = Conv3D(64, (1, 1, 1), use_bias=False)(conv4) # 4
conv4 = _bn_relu(conv4)
crop_conv1 = Cropping3D(cropping=((14, 14), (14, 14), (14, 14)))(conv1)
up5 = concatenate([UpSampling3D(size=(2, 2, 2))(conv4), crop_conv1]) # 8x8x8
conv5 = Conv3D(32, (3, 3, 3), use_bias=False)(up5) # 6
conv5 = _bn_relu(conv5)
conv5 = Conv3D(32, (1, 1, 1), use_bias=False)(conv5) # 6
conv5 = _bn_relu(conv5)
predictions = Conv3D(1, (1, 1, 1), activation='sigmoid', use_bias=False)(conv5) # 6
model = Model(inputs=inputs, outputs=predictions)
compile_args = {'loss': masked_focal_loss, #masked_binary_crossentropy,
'optimizer': 'adam',
'metrics': [masked_accuracy,
lb0l1err, lb1l1err]}
return model, (40, 17, 1), 100, compile_args
def unet_like4b(in_sz=40):
'''
construct a u-net style network
'''
in_sz = fplutils.to3d(in_sz)
in_sz = in_sz + (1,)
inputs = Input(shape=in_sz) # 40^2
# down-sample
conv1 = Conv3D(32, (3, 3, 3), use_bias=False)(inputs) # 38
conv1 = _bn_relu(conv1)
conv1 = Conv3D(32, (3, 3, 3), use_bias=False)(conv1) # 36
conv1 = _bn_relu(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1) # 18
conv2 = Conv3D(64, (3, 3, 3), use_bias=False)(pool1) # 16
conv2 = _bn_relu(conv2)
conv2 = Conv3D(32, (1, 1, 1), use_bias=False)(conv2) # 14
conv2 = _bn_relu(conv2)
conv2 = Conv3D(64, (3, 3, 3), use_bias=False)(conv2) # 14
conv2 = _bn_relu(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2) # 7
conv3 = Conv3D(48, (1, 1, 1), use_bias=False)(pool2) # 7
conv3 = _bn_relu(conv3)
conv3 = Conv3D(128, (3, 3, 3), use_bias=False)(conv3) # 5
conv3 = _bn_relu(conv3)
conv3 = Conv3D(48, (1, 1, 1), use_bias=False)(conv3) # 5
conv3 = _bn_relu(conv3)
conv3 = Conv3D(128, (3, 3, 3), use_bias=False)(conv3) # 3
conv3 = _bn_relu(conv3)
conv3 = Conv3D(48, (1, 1, 1), use_bias=False)(conv3) # 5
conv3 = _bn_relu(conv3)
# up-sample
crop_conv2 = Cropping3D(cropping=((4, 4), (4, 4), (4, 4)))(conv2)
up4 = concatenate([UpSampling3D(size=(2, 2, 2))(conv3), crop_conv2]) # 6
conv4 = Conv3D(64, (3, 3, 3), use_bias=False)(up4) # 4
conv4 = _bn_relu(conv4)
conv4 = Conv3D(64, (1, 1, 1), use_bias=False)(conv4) # 4
conv4 = _bn_relu(conv4)
crop_conv1 = Cropping3D(cropping=((14, 14), (14, 14), (14, 14)))(conv1)
up5 = concatenate([UpSampling3D(size=(2, 2, 2))(conv4), crop_conv1]) # 8x8x8
conv5 = Conv3D(32, (3, 3, 3), use_bias=False)(up5) # 6
conv5 = _bn_relu(conv5)
conv5 = Conv3D(32, (1, 1, 1), use_bias=False)(conv5) # 6
conv5 = _bn_relu(conv5)
predictions = Conv3D(1, (1, 1, 1), activation='sigmoid', use_bias=False)(conv5) # 6
model = Model(inputs=inputs, outputs=predictions)
compile_args = {'loss': masked_focal_loss, #masked_binary_crossentropy,
'optimizer': 'adam',
'metrics': [masked_accuracy,
lb0l1err, lb1l1err]}
return model, (40, 17, 1), 100, compile_args
def unet_like_vol(in_sz=62):
"""construct a u-net style network
"""
in_sz = fplutils.to3d(in_sz)
in_sz = in_sz + (1,)
inputs = Input(shape=in_sz) # 62x62x62
# down-sample
conv1 = Conv3D(16, (3, 3, 3), activation='relu', use_bias=False)(inputs) # 60x60x60
#conv1 = _bn_relu(conv1)
conv1 = Conv3D(16, (1, 1, 1), activation='relu', use_bias=False)(conv1) # 60x60x60
#conv1 = _bn_relu(conv1)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1) # 30x30x30
conv2 = Conv3D(32, (3, 3, 3), activation='relu', use_bias=False)(pool1) # 28x28x28
#conv2 = _bn_relu(conv2)
conv2 = Conv3D(32, (1, 1, 1), activation='relu', use_bias=False)(conv2) # 28x28x28
#conv2 = _bn_relu(conv2)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2) # 14x14x14
conv3 = Conv3D(64, (1, 1, 1), activation='relu', use_bias=False)(pool2) # 14x14x14
#conv3 = _bn_relu(conv3)
conv2_sz = tuple((math.floor((ss-2)/2)-2) for ss in in_sz[0:3])
conv3_sz = tuple(math.floor(ss/2)*2 for ss in conv2_sz) # size after up-sampling conv3
#crop_conv2 = Cropping3D(cropping=(
# (0, conv2_sz[0]-conv3_sz[0]),
# (0, conv2_sz[1]-conv3_sz[1]),
# (0, conv2_sz[2]-conv3_sz[2])))(conv2)
# up-sample
up_conv3 = UpSampling3D(size=(2, 2, 2))(conv3)
up4 = concatenate([up_conv3, conv2]) # 28x28x28
conv4 = Conv3D(64, (3, 3, 3), activation='relu', use_bias=False)(up4) # 26x26x26
#conv4 = _bn_relu(conv4)
conv4 = Conv3D(64, (1, 1, 1), activation='relu', use_bias=False)(conv4) # 26x26x26
#conv4 = _bn_relu(conv4)
conv1_sz = tuple((ss-2) for ss in in_sz[0:3])
conv4_sz = tuple((ss-2)*2 for ss in conv3_sz)
crop_conv1 = Cropping3D(cropping=(
(math.floor((conv1_sz[0]-conv4_sz[0])/2), math.ceil((conv1_sz[0]-conv4_sz[0])/2)),
(math.floor((conv1_sz[1]-conv4_sz[1])/2), math.ceil((conv1_sz[1]-conv4_sz[1])/2)),
(math.floor((conv1_sz[2]-conv4_sz[2])/2), math.ceil((conv1_sz[2]-conv4_sz[2])/2))))(conv1)
up5 = concatenate([UpSampling3D(size=(2, 2, 2))(conv4), crop_conv1]) # 52x52x52
conv5 = Conv3D(32, (3, 3, 3), activation='relu', use_bias=False)(up5) # 50x50x50
#conv5 = _bn_relu(conv5)
conv5 = Conv3D(32, (1, 1, 1), activation='relu', use_bias=False)(conv5) # 50x50x50
#conv5 = _bn_relu(conv5)
predictions = Conv3D(1, (1, 1, 1), activation='sigmoid', use_bias=False)(conv5) # 50x50x50
model = Model(inputs=inputs, output=predictions)
compile_args = {'loss': masked_weighted_binary_crossentropy,
'optimizer': 'adam',
'metrics': ['masked_accuracy']}
return model, (62, 6, 1), 102, compile_args
| 36.582543 | 98 | 0.619119 | 2,933 | 19,279 | 3.894988 | 0.074668 | 0.018908 | 0.095588 | 0.024685 | 0.828431 | 0.804272 | 0.78195 | 0.759541 | 0.724352 | 0.716912 | 0 | 0.118522 | 0.215312 | 19,279 | 526 | 99 | 36.652091 | 0.636634 | 0.087401 | 0 | 0.692935 | 0 | 0 | 0.01597 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.048913 | false | 0 | 0.040761 | 0 | 0.138587 | 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 |
07aef5e9b51de29206b9201fad17f713f20f4280 | 189 | py | Python | req_compile/__main__.py | sputt/qer | ad6a09feddd56059cba5147b0d8341975149e1e6 | [
"MIT"
] | 6 | 2019-02-19T11:54:45.000Z | 2019-07-01T20:17:43.000Z | req_compile/__main__.py | sputt/req-compile | d6a54bb4fecd2de7f380e2c8e3ab602ecaa1cb18 | [
"MIT"
] | 9 | 2019-11-04T19:25:19.000Z | 2021-12-20T21:48:54.000Z | req_compile/__main__.py | sputt/qer | ad6a09feddd56059cba5147b0d8341975149e1e6 | [
"MIT"
] | 1 | 2020-08-20T20:31:28.000Z | 2020-08-20T20:31:28.000Z | """Forward the entrypoint to req_compile.cmdline to allow running via python -m req_compile"""
import req_compile.cmdline
if __name__ == "__main__":
req_compile.cmdline.compile_main()
| 31.5 | 94 | 0.777778 | 27 | 189 | 4.962963 | 0.592593 | 0.298507 | 0.380597 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.126984 | 189 | 5 | 95 | 37.8 | 0.812121 | 0.465608 | 0 | 0 | 0 | 0 | 0.084211 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 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 |
07b42c91256a8ad7ee8dd9e8966b840e8436d636 | 68 | py | Python | models/comment.py | aspa795/api-fastapi-graphql | 208cfd937ef39ea9958698228262a8c8bd45974f | [
"MIT"
] | null | null | null | models/comment.py | aspa795/api-fastapi-graphql | 208cfd937ef39ea9958698228262a8c8bd45974f | [
"MIT"
] | null | null | null | models/comment.py | aspa795/api-fastapi-graphql | 208cfd937ef39ea9958698228262a8c8bd45974f | [
"MIT"
] | null | null | null | from config.settings import Model
class Comment(Model):
pass
| 9.714286 | 33 | 0.735294 | 9 | 68 | 5.555556 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.205882 | 68 | 6 | 34 | 11.333333 | 0.925926 | 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 |
07dd5ae49fb6f55d6dde90c7ad9278e93aa96694 | 90 | py | Python | src/radical/entk/execman/rp/__init__.py | andre-merzky/radical.entk | a63ad9158cf2f58d7bfff017f7da9cd5236429b5 | [
"MIT"
] | 15 | 2018-02-09T10:10:34.000Z | 2021-11-16T07:52:45.000Z | src/radical/entk/execman/rp/__init__.py | andre-merzky/radical.entk | a63ad9158cf2f58d7bfff017f7da9cd5236429b5 | [
"MIT"
] | 418 | 2017-11-21T18:25:19.000Z | 2022-03-31T23:26:35.000Z | src/radical/entk/execman/rp/__init__.py | andre-merzky/radical.entk | a63ad9158cf2f58d7bfff017f7da9cd5236429b5 | [
"MIT"
] | 11 | 2018-12-29T07:18:24.000Z | 2021-02-10T19:43:13.000Z |
from .resource_manager import ResourceManager
from .task_manager import TaskManager
| 18 | 45 | 0.822222 | 10 | 90 | 7.2 | 0.7 | 0.361111 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.155556 | 90 | 4 | 46 | 22.5 | 0.947368 | 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 |
07dff7318bd81273cd0784d7e55ddae3a2633bab | 47 | py | Python | wildebeest/path_funcs/__init__.py | gsganden/wildebeest | 241c58fcea8c5848a276c6100a8f9c283fc2daa1 | [
"BSD-3-Clause"
] | 83 | 2020-07-31T00:33:17.000Z | 2022-02-12T03:06:34.000Z | wildebeest/path_funcs/__init__.py | gsganden/wildebeest | 241c58fcea8c5848a276c6100a8f9c283fc2daa1 | [
"BSD-3-Clause"
] | 30 | 2020-07-28T15:36:29.000Z | 2022-03-23T21:13:32.000Z | wildebeest/path_funcs/__init__.py | gsganden/wildebeest | 241c58fcea8c5848a276c6100a8f9c283fc2daa1 | [
"BSD-3-Clause"
] | 6 | 2020-08-08T14:01:14.000Z | 2022-03-05T00:19:18.000Z | from wildebeest.path_funcs.path_funcs import *
| 23.5 | 46 | 0.851064 | 7 | 47 | 5.428571 | 0.714286 | 0.473684 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.085106 | 47 | 1 | 47 | 47 | 0.883721 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
07e16eb2e92cc164a76a9a130a52cbbb29c800a2 | 28 | py | Python | tests/test_qmsspkg.py | tbrambor/qmsspkg | 54e9cdbf911a7a6ce54b15eba061d1feb6042efd | [
"MIT"
] | null | null | null | tests/test_qmsspkg.py | tbrambor/qmsspkg | 54e9cdbf911a7a6ce54b15eba061d1feb6042efd | [
"MIT"
] | null | null | null | tests/test_qmsspkg.py | tbrambor/qmsspkg | 54e9cdbf911a7a6ce54b15eba061d1feb6042efd | [
"MIT"
] | null | null | null | from qmsspkg import qmsspkg
| 14 | 27 | 0.857143 | 4 | 28 | 6 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 28 | 1 | 28 | 28 | 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 |
07e9cc8c05317982b734e3880592511dec692222 | 70 | py | Python | uc_micro/categories/__init__.py | chrisjsewell/uc.micro-py | dfaa929a7800fb256c835eb626629bc480cbc91a | [
"MIT"
] | null | null | null | uc_micro/categories/__init__.py | chrisjsewell/uc.micro-py | dfaa929a7800fb256c835eb626629bc480cbc91a | [
"MIT"
] | 3 | 2020-10-31T17:15:45.000Z | 2021-01-13T12:21:51.000Z | uc_micro/categories/__init__.py | chrisjsewell/uc.micro-py | dfaa929a7800fb256c835eb626629bc480cbc91a | [
"MIT"
] | 1 | 2020-12-14T21:57:06.000Z | 2020-12-14T21:57:06.000Z | from .Cc import *
from .Cf import *
from .P import *
from .Z import *
| 14 | 17 | 0.657143 | 12 | 70 | 3.833333 | 0.5 | 0.652174 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.228571 | 70 | 4 | 18 | 17.5 | 0.851852 | 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 |
ed13912e1f2f7f6f317ef2f9557587a6ed9809e8 | 113 | py | Python | pyntone/__init__.py | kashi03/python-kintone-sdk | 0306a0c28b02ec53b6338875864ce4a449d1326b | [
"MIT"
] | null | null | null | pyntone/__init__.py | kashi03/python-kintone-sdk | 0306a0c28b02ec53b6338875864ce4a449d1326b | [
"MIT"
] | null | null | null | pyntone/__init__.py | kashi03/python-kintone-sdk | 0306a0c28b02ec53b6338875864ce4a449d1326b | [
"MIT"
] | null | null | null | from pyntone.kintone_rest_api_client import KintoneRestAPIClient
from pyntone.models.base import KintoneBaseModel | 56.5 | 64 | 0.911504 | 14 | 113 | 7.142857 | 0.785714 | 0.22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.061947 | 113 | 2 | 65 | 56.5 | 0.943396 | 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 |
ed2e8d2404647cc92668f139515ba6b79977050b | 4,084 | py | Python | UpWork_Projects/stateSymbolUSA/stateSymbolUSA/spiders/symbols.py | SurendraTamang/Web-Scrapping | 2bb60cce9010b4b68f5c11bf295940832bb5df50 | [
"MIT"
] | null | null | null | UpWork_Projects/stateSymbolUSA/stateSymbolUSA/spiders/symbols.py | SurendraTamang/Web-Scrapping | 2bb60cce9010b4b68f5c11bf295940832bb5df50 | [
"MIT"
] | null | null | null | UpWork_Projects/stateSymbolUSA/stateSymbolUSA/spiders/symbols.py | SurendraTamang/Web-Scrapping | 2bb60cce9010b4b68f5c11bf295940832bb5df50 | [
"MIT"
] | 1 | 2022-01-18T17:15:51.000Z | 2022-01-18T17:15:51.000Z | # -*- coding: utf-8 -*-
import scrapy
class SymbolsSpider(scrapy.Spider):
name = 'symbols'
def start_requests(self):
yield scrapy.Request(
url='http://statesymbolsusa.org',
callback=self.listings
)
def listings(self, response):
links = response.xpath("(//div[@class='item-list'])[1]/ul/li")
for link in links:
state_name = link.xpath("normalize-space(.//a/text())").get()
abs_url = f'''http://statesymbolsusa.org{link.xpath(".//a/@href").get()}'''
yield scrapy.Request(
url=abs_url,
callback=self.parse,
meta={
'state_name': state_name,
}
)
def parse(self, response):
yield {
'State_Name': response.request.meta['state_name'],
'State_Capital': response.xpath("normalize-space(//a[text()='State Capital']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Motto': response.xpath("normalize-space(//a[text()='State Motto']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Flower': response.xpath("normalize-space(//a[text()='State Flower']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Bird': response.xpath("normalize-space(//a[text()='State Bird']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Amphibian': response.xpath("normalize-space(//a[text()='State Amphibian']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Fossil': response.xpath("normalize-space(//a[text()='State Fossil']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Fresh_Water_Fish': response.xpath("normalize-space(//a[text()='State Freshwater Fish']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Fish': response.xpath("normalize-space(//a[text()='State Fish']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Game_Bird': response.xpath("normalize-space(//a[text()='State Game Bird']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Gemstone': response.xpath("normalize-space(//a[text()='State Gemstone']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Insect': response.xpath("normalize-space(//a[text()='State Insect']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Mammal': response.xpath("normalize-space(//a[text()='State Mammal']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Mineral': response.xpath("normalize-space(//a[text()='State Mineral']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Nut': response.xpath("normalize-space(//a[text()='State Nut']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Reptile': response.xpath("normalize-space(//a[text()='State Reptile']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Rock': response.xpath("normalize-space(//a[text()='State Rock']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Soil': response.xpath("normalize-space(//a[text()='State Soil']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Tree': response.xpath("normalize-space(//a[text()='State Tree']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Tree_Fruit': response.xpath("normalize-space(//a[text()='State Tree Fruit']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'State_Wild_Flower': response.xpath("normalize-space(//a[text()='State Wildflower']/parent::div/parent::div/following-sibling::div/span/a/text())").get(),
'URL': response.url
}
| 78.538462 | 176 | 0.628061 | 520 | 4,084 | 4.869231 | 0.138462 | 0.080964 | 0.157583 | 0.165877 | 0.75 | 0.740521 | 0.740521 | 0.565166 | 0.434044 | 0.434044 | 0 | 0.000574 | 0.147405 | 4,084 | 51 | 177 | 80.078431 | 0.726594 | 0.005142 | 0 | 0.044444 | 0 | 0.444444 | 0.624969 | 0.526718 | 0 | 0 | 0 | 0 | 0 | 1 | 0.066667 | false | 0 | 0.022222 | 0 | 0.133333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
ed4e31517f3eeb0df02ee059827eb00aca2b4c35 | 157 | py | Python | lib/python3.9/site-packages/var_dump/__init__.py | mohammadabu/facebook | 0d640ecbfc5c320756eb8634b7eed7353a33d2ca | [
"0BSD"
] | 147 | 2015-01-21T16:41:37.000Z | 2022-03-20T03:04:13.000Z | var_dump/__init__.py | coolman95/python-var-dump | 14613f67992f94424926a2370066d29e757d2a4d | [
"BSD-4-Clause"
] | 10 | 2015-03-05T02:57:59.000Z | 2016-08-21T09:18:48.000Z | var_dump/__init__.py | coolman95/python-var-dump | 14613f67992f94424926a2370066d29e757d2a4d | [
"BSD-4-Clause"
] | 41 | 2015-01-02T13:35:02.000Z | 2022-03-20T03:04:08.000Z | __author__ = 'sha256'
from ._var_dump import var_dump as var_dump
from ._var_dump import var_export as var_export
__all__ = ['var_dump','var_export',]
| 26.166667 | 48 | 0.757962 | 25 | 157 | 4.04 | 0.36 | 0.346535 | 0.217822 | 0.336634 | 0.39604 | 0 | 0 | 0 | 0 | 0 | 0 | 0.022556 | 0.152866 | 157 | 5 | 49 | 31.4 | 0.736842 | 0 | 0 | 0 | 0 | 0 | 0.157895 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
ed6b660bfd0d21fde08d017e8d5e3bd14514c151 | 39 | py | Python | get_filename/__init__.py | we684123/get_filename | b1d59873c5a97a3cd1a3b749d93195fe24d3a79d | [
"MIT"
] | null | null | null | get_filename/__init__.py | we684123/get_filename | b1d59873c5a97a3cd1a3b749d93195fe24d3a79d | [
"MIT"
] | null | null | null | get_filename/__init__.py | we684123/get_filename | b1d59873c5a97a3cd1a3b749d93195fe24d3a79d | [
"MIT"
] | null | null | null | from .get_filename import get_filename
| 19.5 | 38 | 0.871795 | 6 | 39 | 5.333333 | 0.666667 | 0.6875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.102564 | 39 | 1 | 39 | 39 | 0.914286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
9c0639cd3c11314d1c46b73e7861293b6a49947c | 35,419 | py | Python | nova/tests/unit/virt/libvirt/test_fakelibvirt.py | bopopescu/nova-token | ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2 | [
"Apache-2.0"
] | null | null | null | nova/tests/unit/virt/libvirt/test_fakelibvirt.py | bopopescu/nova-token | ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2 | [
"Apache-2.0"
] | null | null | null | nova/tests/unit/virt/libvirt/test_fakelibvirt.py | bopopescu/nova-token | ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2 | [
"Apache-2.0"
] | 2 | 2017-07-20T17:31:34.000Z | 2020-07-24T02:42:19.000Z | begin_unit
comment|'# Copyright 2010 OpenStack Foundation'
nl|'\n'
comment|'#'
nl|'\n'
comment|'# Licensed under the Apache License, Version 2.0 (the "License"); you may'
nl|'\n'
comment|'# not use this file except in compliance with the License. You may obtain'
nl|'\n'
comment|'# a copy of the License at'
nl|'\n'
comment|'#'
nl|'\n'
comment|'# http://www.apache.org/licenses/LICENSE-2.0'
nl|'\n'
comment|'#'
nl|'\n'
comment|'# Unless required by applicable law or agreed to in writing, software'
nl|'\n'
comment|'# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT'
nl|'\n'
comment|'# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the'
nl|'\n'
comment|'# License for the specific language governing permissions and limitations'
nl|'\n'
comment|'# under the License.'
nl|'\n'
nl|'\n'
name|'from'
name|'lxml'
name|'import'
name|'etree'
newline|'\n'
name|'import'
name|'six'
newline|'\n'
nl|'\n'
name|'from'
name|'nova'
op|'.'
name|'compute'
name|'import'
name|'arch'
newline|'\n'
name|'from'
name|'nova'
name|'import'
name|'test'
newline|'\n'
name|'import'
name|'nova'
op|'.'
name|'tests'
op|'.'
name|'unit'
op|'.'
name|'virt'
op|'.'
name|'libvirt'
op|'.'
name|'fakelibvirt'
name|'as'
name|'libvirt'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|function|get_vm_xml
name|'def'
name|'get_vm_xml'
op|'('
name|'name'
op|'='
string|'"testname"'
op|','
name|'uuid'
op|'='
name|'None'
op|','
name|'source_type'
op|'='
string|"'file'"
op|','
nl|'\n'
name|'interface_type'
op|'='
string|"'bridge'"
op|')'
op|':'
newline|'\n'
indent|' '
name|'uuid_tag'
op|'='
string|"''"
newline|'\n'
name|'if'
name|'uuid'
op|':'
newline|'\n'
indent|' '
name|'uuid_tag'
op|'='
string|"'<uuid>%s</uuid>'"
op|'%'
op|'('
name|'uuid'
op|','
op|')'
newline|'\n'
nl|'\n'
dedent|''
name|'return'
string|"'''<domain type='kvm'>\n <name>%(name)s</name>\n%(uuid_tag)s\n <memory>128000</memory>\n <vcpu>1</vcpu>\n <os>\n <type>hvm</type>\n <kernel>/somekernel</kernel>\n <cmdline>root=/dev/sda</cmdline>\n <boot dev='hd'/>\n </os>\n <features>\n <acpi/>\n </features>\n <devices>\n <disk type='file' device='disk'>\n <driver name='qemu' type='qcow2'/>\n <source %(source_type)s='/somefile'/>\n <target dev='vda' bus='virtio'/>\n </disk>\n <interface type='%(interface_type)s'>\n <mac address='05:26:3e:31:28:1f'/>\n <source %(interface_type)s='br100'/>\n </interface>\n <input type='mouse' bus='ps2'/>\n <graphics type='vnc' port='5901' autoport='yes' keymap='en-us'/>\n <graphics type='spice' port='5901' autoport='yes' keymap='en-us'/>\n </devices>\n</domain>'''"
op|'%'
op|'{'
string|"'name'"
op|':'
name|'name'
op|','
nl|'\n'
string|"'uuid_tag'"
op|':'
name|'uuid_tag'
op|','
nl|'\n'
string|"'source_type'"
op|':'
name|'source_type'
op|','
nl|'\n'
string|"'interface_type'"
op|':'
name|'interface_type'
op|'}'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|class|FakeLibvirtTests
dedent|''
name|'class'
name|'FakeLibvirtTests'
op|'('
name|'test'
op|'.'
name|'NoDBTestCase'
op|')'
op|':'
newline|'\n'
DECL|member|tearDown
indent|' '
name|'def'
name|'tearDown'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'super'
op|'('
name|'FakeLibvirtTests'
op|','
name|'self'
op|')'
op|'.'
name|'tearDown'
op|'('
op|')'
newline|'\n'
name|'libvirt'
op|'.'
name|'_reset'
op|'('
op|')'
newline|'\n'
nl|'\n'
DECL|member|get_openAuth_curry_func
dedent|''
name|'def'
name|'get_openAuth_curry_func'
op|'('
name|'self'
op|','
name|'readOnly'
op|'='
name|'False'
op|')'
op|':'
newline|'\n'
DECL|function|fake_cb
indent|' '
name|'def'
name|'fake_cb'
op|'('
name|'credlist'
op|')'
op|':'
newline|'\n'
indent|' '
name|'return'
number|'0'
newline|'\n'
nl|'\n'
dedent|''
name|'creds'
op|'='
op|'['
op|'['
name|'libvirt'
op|'.'
name|'VIR_CRED_AUTHNAME'
op|','
nl|'\n'
name|'libvirt'
op|'.'
name|'VIR_CRED_NOECHOPROMPT'
op|']'
op|','
nl|'\n'
name|'fake_cb'
op|','
nl|'\n'
name|'None'
op|']'
newline|'\n'
name|'flags'
op|'='
number|'0'
newline|'\n'
name|'if'
name|'readOnly'
op|':'
newline|'\n'
indent|' '
name|'flags'
op|'='
name|'libvirt'
op|'.'
name|'VIR_CONNECT_RO'
newline|'\n'
dedent|''
name|'return'
name|'lambda'
name|'uri'
op|':'
name|'libvirt'
op|'.'
name|'openAuth'
op|'('
name|'uri'
op|','
name|'creds'
op|','
name|'flags'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_openAuth_accepts_None_uri_by_default
dedent|''
name|'def'
name|'test_openAuth_accepts_None_uri_by_default'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn_method'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
newline|'\n'
name|'conn'
op|'='
name|'conn_method'
op|'('
name|'None'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertNotEqual'
op|'('
name|'conn'
op|','
name|'None'
op|','
string|'"Connecting to fake libvirt failed"'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_openAuth_can_refuse_None_uri
dedent|''
name|'def'
name|'test_openAuth_can_refuse_None_uri'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn_method'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
newline|'\n'
name|'libvirt'
op|'.'
name|'allow_default_uri_connection'
op|'='
name|'False'
newline|'\n'
name|'self'
op|'.'
name|'addCleanup'
op|'('
name|'libvirt'
op|'.'
name|'_reset'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'ValueError'
op|','
name|'conn_method'
op|','
name|'None'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_openAuth_refuses_invalid_URI
dedent|''
name|'def'
name|'test_openAuth_refuses_invalid_URI'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn_method'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'libvirt'
op|'.'
name|'libvirtError'
op|','
name|'conn_method'
op|','
string|"'blah'"
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_getInfo
dedent|''
name|'def'
name|'test_getInfo'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn_method'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
name|'readOnly'
op|'='
name|'True'
op|')'
newline|'\n'
name|'res'
op|'='
name|'conn_method'
op|'('
name|'None'
op|')'
op|'.'
name|'getInfo'
op|'('
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertIn'
op|'('
name|'res'
op|'['
number|'0'
op|']'
op|','
op|'('
name|'arch'
op|'.'
name|'I686'
op|','
name|'arch'
op|'.'
name|'X86_64'
op|')'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertTrue'
op|'('
number|'1024'
op|'<='
name|'res'
op|'['
number|'1'
op|']'
op|'<='
number|'16384'
op|','
nl|'\n'
string|'"Memory unusually high or low."'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertTrue'
op|'('
number|'1'
op|'<='
name|'res'
op|'['
number|'2'
op|']'
op|'<='
number|'32'
op|','
nl|'\n'
string|'"Active CPU count unusually high or low."'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertTrue'
op|'('
number|'800'
op|'<='
name|'res'
op|'['
number|'3'
op|']'
op|'<='
number|'4500'
op|','
nl|'\n'
string|'"CPU speed unusually high or low."'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertTrue'
op|'('
name|'res'
op|'['
number|'2'
op|']'
op|'<='
op|'('
name|'res'
op|'['
number|'5'
op|']'
op|'*'
name|'res'
op|'['
number|'6'
op|']'
op|')'
op|','
nl|'\n'
string|'"More active CPUs than num_sockets*cores_per_socket"'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_createXML_detects_invalid_xml
dedent|''
name|'def'
name|'test_createXML_detects_invalid_xml'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'_test_XML_func_detects_invalid_xml'
op|'('
string|"'createXML'"
op|','
op|'['
number|'0'
op|']'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_defineXML_detects_invalid_xml
dedent|''
name|'def'
name|'test_defineXML_detects_invalid_xml'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'_test_XML_func_detects_invalid_xml'
op|'('
string|"'defineXML'"
op|','
op|'['
op|']'
op|')'
newline|'\n'
nl|'\n'
DECL|member|_test_XML_func_detects_invalid_xml
dedent|''
name|'def'
name|'_test_XML_func_detects_invalid_xml'
op|'('
name|'self'
op|','
name|'xmlfunc_name'
op|','
name|'args'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'try'
op|':'
newline|'\n'
indent|' '
name|'getattr'
op|'('
name|'conn'
op|','
name|'xmlfunc_name'
op|')'
op|'('
string|'"this is not valid </xml>"'
op|','
op|'*'
name|'args'
op|')'
newline|'\n'
dedent|''
name|'except'
name|'libvirt'
op|'.'
name|'libvirtError'
name|'as'
name|'e'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'e'
op|'.'
name|'get_error_code'
op|'('
op|')'
op|','
name|'libvirt'
op|'.'
name|'VIR_ERR_XML_DETAIL'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'e'
op|'.'
name|'get_error_domain'
op|'('
op|')'
op|','
name|'libvirt'
op|'.'
name|'VIR_FROM_DOMAIN'
op|')'
newline|'\n'
name|'return'
newline|'\n'
dedent|''
name|'raise'
name|'self'
op|'.'
name|'failureException'
op|'('
string|'"Invalid XML didn\'t raise libvirtError"'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_defineXML_defines_domain
dedent|''
name|'def'
name|'test_defineXML_defines_domain'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'conn'
op|'.'
name|'defineXML'
op|'('
name|'get_vm_xml'
op|'('
op|')'
op|')'
newline|'\n'
name|'dom'
op|'='
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
string|"'testname'"
op|','
name|'dom'
op|'.'
name|'name'
op|'('
op|')'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
number|'0'
op|','
name|'dom'
op|'.'
name|'isActive'
op|'('
op|')'
op|')'
newline|'\n'
name|'dom'
op|'.'
name|'undefine'
op|'('
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'libvirt'
op|'.'
name|'libvirtError'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'lookupByName'
op|','
nl|'\n'
string|"'testname'"
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_blockStats
dedent|''
name|'def'
name|'test_blockStats'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'conn'
op|'.'
name|'createXML'
op|'('
name|'get_vm_xml'
op|'('
op|')'
op|','
number|'0'
op|')'
newline|'\n'
name|'dom'
op|'='
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
name|'blockstats'
op|'='
name|'dom'
op|'.'
name|'blockStats'
op|'('
string|"'vda'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'len'
op|'('
name|'blockstats'
op|')'
op|','
number|'5'
op|')'
newline|'\n'
name|'for'
name|'x'
name|'in'
name|'blockstats'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'assertIn'
op|'('
name|'type'
op|'('
name|'x'
op|')'
op|','
name|'six'
op|'.'
name|'integer_types'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_attach_detach
dedent|''
dedent|''
name|'def'
name|'test_attach_detach'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'conn'
op|'.'
name|'createXML'
op|'('
name|'get_vm_xml'
op|'('
op|')'
op|','
number|'0'
op|')'
newline|'\n'
name|'dom'
op|'='
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
name|'xml'
op|'='
string|"'''<disk type='block'>\n <driver name='qemu' type='raw'/>\n <source dev='/dev/nbd0'/>\n <target dev='/dev/vdc' bus='virtio'/>\n </disk>'''"
newline|'\n'
name|'self'
op|'.'
name|'assertTrue'
op|'('
name|'dom'
op|'.'
name|'attachDevice'
op|'('
name|'xml'
op|')'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertTrue'
op|'('
name|'dom'
op|'.'
name|'detachDevice'
op|'('
name|'xml'
op|')'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_info
dedent|''
name|'def'
name|'test_info'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'conn'
op|'.'
name|'createXML'
op|'('
name|'get_vm_xml'
op|'('
op|')'
op|','
number|'0'
op|')'
newline|'\n'
name|'dom'
op|'='
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
name|'info'
op|'='
name|'dom'
op|'.'
name|'info'
op|'('
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'info'
op|'['
number|'0'
op|']'
op|','
name|'libvirt'
op|'.'
name|'VIR_DOMAIN_RUNNING'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'info'
op|'['
number|'1'
op|']'
op|','
number|'128000'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertTrue'
op|'('
name|'info'
op|'['
number|'2'
op|']'
op|'<='
number|'128000'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'info'
op|'['
number|'3'
op|']'
op|','
number|'1'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertIn'
op|'('
name|'type'
op|'('
name|'info'
op|'['
number|'4'
op|']'
op|')'
op|','
name|'six'
op|'.'
name|'integer_types'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_createXML_runs_domain
dedent|''
name|'def'
name|'test_createXML_runs_domain'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'conn'
op|'.'
name|'createXML'
op|'('
name|'get_vm_xml'
op|'('
op|')'
op|','
number|'0'
op|')'
newline|'\n'
name|'dom'
op|'='
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
string|"'testname'"
op|','
name|'dom'
op|'.'
name|'name'
op|'('
op|')'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
number|'1'
op|','
name|'dom'
op|'.'
name|'isActive'
op|'('
op|')'
op|')'
newline|'\n'
name|'dom'
op|'.'
name|'destroy'
op|'('
op|')'
newline|'\n'
name|'try'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
dedent|''
name|'except'
name|'libvirt'
op|'.'
name|'libvirtError'
name|'as'
name|'e'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'e'
op|'.'
name|'get_error_code'
op|'('
op|')'
op|','
name|'libvirt'
op|'.'
name|'VIR_ERR_NO_DOMAIN'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'e'
op|'.'
name|'get_error_domain'
op|'('
op|')'
op|','
name|'libvirt'
op|'.'
name|'VIR_FROM_QEMU'
op|')'
newline|'\n'
name|'return'
newline|'\n'
dedent|''
name|'self'
op|'.'
name|'fail'
op|'('
string|'"lookupByName succeeded for destroyed non-defined VM"'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_defineXML_remembers_uuid
dedent|''
name|'def'
name|'test_defineXML_remembers_uuid'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'uuid'
op|'='
string|"'b21f957d-a72f-4b93-b5a5-45b1161abb02'"
newline|'\n'
name|'conn'
op|'.'
name|'defineXML'
op|'('
name|'get_vm_xml'
op|'('
name|'uuid'
op|'='
name|'uuid'
op|')'
op|')'
newline|'\n'
name|'dom'
op|'='
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'dom'
op|'.'
name|'UUIDString'
op|'('
op|')'
op|','
name|'uuid'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_createWithFlags
dedent|''
name|'def'
name|'test_createWithFlags'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'conn'
op|'.'
name|'defineXML'
op|'('
name|'get_vm_xml'
op|'('
op|')'
op|')'
newline|'\n'
name|'dom'
op|'='
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertFalse'
op|'('
name|'dom'
op|'.'
name|'isActive'
op|'('
op|')'
op|','
string|"'Defined domain was running.'"
op|')'
newline|'\n'
name|'dom'
op|'.'
name|'createWithFlags'
op|'('
number|'0'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertTrue'
op|'('
name|'dom'
op|'.'
name|'isActive'
op|'('
op|')'
op|','
nl|'\n'
string|"'Domain wasn\\'t running after createWithFlags'"
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_managedSave
dedent|''
name|'def'
name|'test_managedSave'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'conn'
op|'.'
name|'defineXML'
op|'('
name|'get_vm_xml'
op|'('
op|')'
op|')'
newline|'\n'
name|'dom'
op|'='
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertFalse'
op|'('
name|'dom'
op|'.'
name|'isActive'
op|'('
op|')'
op|','
string|"'Defined domain was running.'"
op|')'
newline|'\n'
name|'dom'
op|'.'
name|'createWithFlags'
op|'('
number|'0'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'dom'
op|'.'
name|'hasManagedSaveImage'
op|'('
number|'0'
op|')'
op|','
number|'0'
op|')'
newline|'\n'
name|'dom'
op|'.'
name|'managedSave'
op|'('
number|'0'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'dom'
op|'.'
name|'hasManagedSaveImage'
op|'('
number|'0'
op|')'
op|','
number|'1'
op|')'
newline|'\n'
name|'dom'
op|'.'
name|'managedSaveRemove'
op|'('
number|'0'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'dom'
op|'.'
name|'hasManagedSaveImage'
op|'('
number|'0'
op|')'
op|','
number|'0'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_listDomainsId_and_lookupById
dedent|''
name|'def'
name|'test_listDomainsId_and_lookupById'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'conn'
op|'.'
name|'listDomainsID'
op|'('
op|')'
op|','
op|'['
op|']'
op|')'
newline|'\n'
name|'conn'
op|'.'
name|'defineXML'
op|'('
name|'get_vm_xml'
op|'('
op|')'
op|')'
newline|'\n'
name|'dom'
op|'='
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
name|'dom'
op|'.'
name|'createWithFlags'
op|'('
number|'0'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'len'
op|'('
name|'conn'
op|'.'
name|'listDomainsID'
op|'('
op|')'
op|')'
op|','
number|'1'
op|')'
newline|'\n'
nl|'\n'
name|'dom_id'
op|'='
name|'conn'
op|'.'
name|'listDomainsID'
op|'('
op|')'
op|'['
number|'0'
op|']'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'conn'
op|'.'
name|'lookupByID'
op|'('
name|'dom_id'
op|')'
op|','
name|'dom'
op|')'
newline|'\n'
nl|'\n'
name|'dom_id'
op|'='
name|'conn'
op|'.'
name|'listDomainsID'
op|'('
op|')'
op|'['
number|'0'
op|']'
newline|'\n'
name|'try'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'.'
name|'lookupByID'
op|'('
name|'dom_id'
op|'+'
number|'1'
op|')'
newline|'\n'
dedent|''
name|'except'
name|'libvirt'
op|'.'
name|'libvirtError'
name|'as'
name|'e'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'e'
op|'.'
name|'get_error_code'
op|'('
op|')'
op|','
name|'libvirt'
op|'.'
name|'VIR_ERR_NO_DOMAIN'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'e'
op|'.'
name|'get_error_domain'
op|'('
op|')'
op|','
name|'libvirt'
op|'.'
name|'VIR_FROM_QEMU'
op|')'
newline|'\n'
name|'return'
newline|'\n'
dedent|''
name|'raise'
name|'self'
op|'.'
name|'failureException'
op|'('
string|'"Looking up an invalid domain ID didn\'t "'
nl|'\n'
string|'"raise libvirtError"'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_define_and_retrieve
dedent|''
name|'def'
name|'test_define_and_retrieve'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'conn'
op|'.'
name|'listDomainsID'
op|'('
op|')'
op|','
op|'['
op|']'
op|')'
newline|'\n'
name|'conn'
op|'.'
name|'defineXML'
op|'('
name|'get_vm_xml'
op|'('
op|')'
op|')'
newline|'\n'
name|'dom'
op|'='
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
name|'xml'
op|'='
name|'dom'
op|'.'
name|'XMLDesc'
op|'('
number|'0'
op|')'
newline|'\n'
name|'etree'
op|'.'
name|'fromstring'
op|'('
name|'xml'
op|')'
newline|'\n'
nl|'\n'
DECL|member|_test_accepts_source_type
dedent|''
name|'def'
name|'_test_accepts_source_type'
op|'('
name|'self'
op|','
name|'source_type'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'conn'
op|'.'
name|'listDomainsID'
op|'('
op|')'
op|','
op|'['
op|']'
op|')'
newline|'\n'
name|'conn'
op|'.'
name|'defineXML'
op|'('
name|'get_vm_xml'
op|'('
name|'source_type'
op|'='
name|'source_type'
op|')'
op|')'
newline|'\n'
name|'dom'
op|'='
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
name|'xml'
op|'='
name|'dom'
op|'.'
name|'XMLDesc'
op|'('
number|'0'
op|')'
newline|'\n'
name|'tree'
op|'='
name|'etree'
op|'.'
name|'fromstring'
op|'('
name|'xml'
op|')'
newline|'\n'
name|'elem'
op|'='
name|'tree'
op|'.'
name|'find'
op|'('
string|"'./devices/disk/source'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'elem'
op|'.'
name|'get'
op|'('
string|"'file'"
op|')'
op|','
string|"'/somefile'"
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_accepts_source_dev
dedent|''
name|'def'
name|'test_accepts_source_dev'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'_test_accepts_source_type'
op|'('
string|"'dev'"
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_accepts_source_path
dedent|''
name|'def'
name|'test_accepts_source_path'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'_test_accepts_source_type'
op|'('
string|"'path'"
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_network_type_bridge_sticks
dedent|''
name|'def'
name|'test_network_type_bridge_sticks'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'_test_network_type_sticks'
op|'('
string|"'bridge'"
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_network_type_network_sticks
dedent|''
name|'def'
name|'test_network_type_network_sticks'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'_test_network_type_sticks'
op|'('
string|"'network'"
op|')'
newline|'\n'
nl|'\n'
DECL|member|_test_network_type_sticks
dedent|''
name|'def'
name|'_test_network_type_sticks'
op|'('
name|'self'
op|','
name|'network_type'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'conn'
op|'.'
name|'listDomainsID'
op|'('
op|')'
op|','
op|'['
op|']'
op|')'
newline|'\n'
name|'conn'
op|'.'
name|'defineXML'
op|'('
name|'get_vm_xml'
op|'('
name|'interface_type'
op|'='
name|'network_type'
op|')'
op|')'
newline|'\n'
name|'dom'
op|'='
name|'conn'
op|'.'
name|'lookupByName'
op|'('
string|"'testname'"
op|')'
newline|'\n'
name|'xml'
op|'='
name|'dom'
op|'.'
name|'XMLDesc'
op|'('
number|'0'
op|')'
newline|'\n'
name|'tree'
op|'='
name|'etree'
op|'.'
name|'fromstring'
op|'('
name|'xml'
op|')'
newline|'\n'
name|'elem'
op|'='
name|'tree'
op|'.'
name|'find'
op|'('
string|"'./devices/interface'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'elem'
op|'.'
name|'get'
op|'('
string|"'type'"
op|')'
op|','
name|'network_type'
op|')'
newline|'\n'
name|'elem'
op|'='
name|'elem'
op|'.'
name|'find'
op|'('
string|"'./source'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'elem'
op|'.'
name|'get'
op|'('
name|'network_type'
op|')'
op|','
string|"'br100'"
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_getType
dedent|''
name|'def'
name|'test_getType'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'conn'
op|'.'
name|'getType'
op|'('
op|')'
op|','
string|"'QEMU'"
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_getVersion
dedent|''
name|'def'
name|'test_getVersion'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertIsInstance'
op|'('
name|'conn'
op|'.'
name|'getVersion'
op|'('
op|')'
op|','
name|'int'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_getCapabilities
dedent|''
name|'def'
name|'test_getCapabilities'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
name|'etree'
op|'.'
name|'fromstring'
op|'('
name|'conn'
op|'.'
name|'getCapabilities'
op|'('
op|')'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_nwfilter_define_undefine
dedent|''
name|'def'
name|'test_nwfilter_define_undefine'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
comment|"# Will raise an exception if it's not valid XML"
nl|'\n'
name|'xml'
op|'='
string|"'''<filter name='nova-instance-instance-789' chain='root'>\n <uuid>946878c6-3ad3-82b2-87f3-c709f3807f58</uuid>\n </filter>'''"
newline|'\n'
nl|'\n'
name|'conn'
op|'.'
name|'nwfilterDefineXML'
op|'('
name|'xml'
op|')'
newline|'\n'
name|'nwfilter'
op|'='
name|'conn'
op|'.'
name|'nwfilterLookupByName'
op|'('
string|"'nova-instance-instance-789'"
op|')'
newline|'\n'
name|'nwfilter'
op|'.'
name|'undefine'
op|'('
op|')'
newline|'\n'
name|'try'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'.'
name|'nwfilterLookupByName'
op|'('
string|"'nova-instance-instance-789320334'"
op|')'
newline|'\n'
dedent|''
name|'except'
name|'libvirt'
op|'.'
name|'libvirtError'
name|'as'
name|'e'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'e'
op|'.'
name|'get_error_code'
op|'('
op|')'
op|','
name|'libvirt'
op|'.'
name|'VIR_ERR_NO_NWFILTER'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'e'
op|'.'
name|'get_error_domain'
op|'('
op|')'
op|','
name|'libvirt'
op|'.'
name|'VIR_FROM_NWFILTER'
op|')'
newline|'\n'
name|'return'
newline|'\n'
dedent|''
name|'raise'
name|'self'
op|'.'
name|'failureException'
op|'('
string|'"Invalid NWFilter name didn\'t"'
nl|'\n'
string|'" raise libvirtError"'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_compareCPU_compatible
dedent|''
name|'def'
name|'test_compareCPU_compatible'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
nl|'\n'
name|'xml'
op|'='
string|'\'\'\'<cpu>\n <arch>%s</arch>\n <model>%s</model>\n <vendor>%s</vendor>\n <topology sockets="%d" cores="%d" threads="%d"/>\n </cpu>\'\'\''
op|'%'
op|'('
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'arch'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_model'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_vendor'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_sockets'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_cores'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_threads'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'conn'
op|'.'
name|'compareCPU'
op|'('
name|'xml'
op|','
number|'0'
op|')'
op|','
nl|'\n'
name|'libvirt'
op|'.'
name|'VIR_CPU_COMPARE_IDENTICAL'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_compareCPU_incompatible_vendor
dedent|''
name|'def'
name|'test_compareCPU_incompatible_vendor'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
nl|'\n'
name|'xml'
op|'='
string|'\'\'\'<cpu>\n <arch>%s</arch>\n <model>%s</model>\n <vendor>%s</vendor>\n <topology sockets="%d" cores="%d" threads="%d"/>\n </cpu>\'\'\''
op|'%'
op|'('
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'arch'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_model'
op|','
nl|'\n'
string|'"AnotherVendor"'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_sockets'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_cores'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_threads'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'conn'
op|'.'
name|'compareCPU'
op|'('
name|'xml'
op|','
number|'0'
op|')'
op|','
nl|'\n'
name|'libvirt'
op|'.'
name|'VIR_CPU_COMPARE_INCOMPATIBLE'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_compareCPU_incompatible_arch
dedent|''
name|'def'
name|'test_compareCPU_incompatible_arch'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
nl|'\n'
name|'xml'
op|'='
string|'\'\'\'<cpu>\n <arch>%s</arch>\n <model>%s</model>\n <vendor>%s</vendor>\n <topology sockets="%d" cores="%d" threads="%d"/>\n </cpu>\'\'\''
op|'%'
op|'('
string|"'not-a-valid-arch'"
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_model'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_vendor'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_sockets'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_cores'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_threads'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'conn'
op|'.'
name|'compareCPU'
op|'('
name|'xml'
op|','
number|'0'
op|')'
op|','
nl|'\n'
name|'libvirt'
op|'.'
name|'VIR_CPU_COMPARE_INCOMPATIBLE'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_compareCPU_incompatible_model
dedent|''
name|'def'
name|'test_compareCPU_incompatible_model'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
nl|'\n'
name|'xml'
op|'='
string|'\'\'\'<cpu>\n <arch>%s</arch>\n <model>%s</model>\n <vendor>%s</vendor>\n <topology sockets="%d" cores="%d" threads="%d"/>\n </cpu>\'\'\''
op|'%'
op|'('
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'arch'
op|','
nl|'\n'
string|'"AnotherModel"'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_vendor'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_sockets'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_cores'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_threads'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'conn'
op|'.'
name|'compareCPU'
op|'('
name|'xml'
op|','
number|'0'
op|')'
op|','
nl|'\n'
name|'libvirt'
op|'.'
name|'VIR_CPU_COMPARE_INCOMPATIBLE'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_compareCPU_compatible_unspecified_model
dedent|''
name|'def'
name|'test_compareCPU_compatible_unspecified_model'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'conn'
op|'='
name|'self'
op|'.'
name|'get_openAuth_curry_func'
op|'('
op|')'
op|'('
string|"'qemu:///system'"
op|')'
newline|'\n'
nl|'\n'
name|'xml'
op|'='
string|'\'\'\'<cpu>\n <arch>%s</arch>\n <vendor>%s</vendor>\n <topology sockets="%d" cores="%d" threads="%d"/>\n </cpu>\'\'\''
op|'%'
op|'('
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'arch'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_vendor'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_sockets'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_cores'
op|','
nl|'\n'
name|'conn'
op|'.'
name|'host_info'
op|'.'
name|'cpu_threads'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'conn'
op|'.'
name|'compareCPU'
op|'('
name|'xml'
op|','
number|'0'
op|')'
op|','
nl|'\n'
name|'libvirt'
op|'.'
name|'VIR_CPU_COMPARE_IDENTICAL'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_numa_topology_generation
dedent|''
name|'def'
name|'test_numa_topology_generation'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'topology'
op|'='
string|'"""<topology>\n <cells num="2">\n <cell id="0">\n <memory unit="KiB">7870000</memory>\n <pages size="4" unit="KiB">1967500</pages>\n <cpus num="4">\n <cpu id="0" socket_id="0" core_id="0" siblings="0-1"/>\n <cpu id="1" socket_id="0" core_id="0" siblings="0-1"/>\n <cpu id="2" socket_id="0" core_id="1" siblings="2-3"/>\n <cpu id="3" socket_id="0" core_id="1" siblings="2-3"/>\n </cpus>\n </cell>\n <cell id="1">\n <memory unit="KiB">7870000</memory>\n <pages size="4" unit="KiB">1967500</pages>\n <cpus num="4">\n <cpu id="4" socket_id="1" core_id="0" siblings="4-5"/>\n <cpu id="5" socket_id="1" core_id="0" siblings="4-5"/>\n <cpu id="6" socket_id="1" core_id="1" siblings="6-7"/>\n <cpu id="7" socket_id="1" core_id="1" siblings="6-7"/>\n </cpus>\n </cell>\n </cells>\n</topology>\n"""'
newline|'\n'
name|'host_topology'
op|'='
name|'libvirt'
op|'.'
name|'HostInfo'
op|'.'
name|'_gen_numa_topology'
op|'('
nl|'\n'
name|'cpu_nodes'
op|'='
number|'2'
op|','
name|'cpu_sockets'
op|'='
number|'1'
op|','
nl|'\n'
name|'cpu_cores'
op|'='
number|'2'
op|','
name|'cpu_threads'
op|'='
number|'2'
op|','
nl|'\n'
name|'kb_mem'
op|'='
number|'15740000'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'host_topology'
op|'.'
name|'to_xml'
op|'('
op|')'
op|','
nl|'\n'
name|'topology'
op|')'
newline|'\n'
dedent|''
dedent|''
endmarker|''
end_unit
| 13.431551 | 907 | 0.587255 | 5,180 | 35,419 | 3.915058 | 0.07027 | 0.15355 | 0.101578 | 0.074556 | 0.82643 | 0.765187 | 0.731608 | 0.693984 | 0.653353 | 0.633629 | 0 | 0.008691 | 0.116378 | 35,419 | 2,636 | 908 | 13.436646 | 0.639295 | 0 | 0 | 0.929439 | 0 | 0.001517 | 0.378695 | 0.067167 | 0 | 0 | 0 | 0 | 0.020486 | 0 | null | null | 0 | 0.001897 | null | null | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
9c5ce5183f96d1c88553f08d858ec7604b881cb9 | 582 | py | Python | Chapter 07/Chap07_Example7.6.py | bpbpublications/Programming-Techniques-using-Python | 49b785f37e95a3aad1d36cef51e219ac56e5e9f0 | [
"MIT"
] | null | null | null | Chapter 07/Chap07_Example7.6.py | bpbpublications/Programming-Techniques-using-Python | 49b785f37e95a3aad1d36cef51e219ac56e5e9f0 | [
"MIT"
] | null | null | null | Chapter 07/Chap07_Example7.6.py | bpbpublications/Programming-Techniques-using-Python | 49b785f37e95a3aad1d36cef51e219ac56e5e9f0 | [
"MIT"
] | null | null | null | import array as ar
employee_staffnum = ar.array("I", [201, 202, 203, 204, 205] )
myarr_len = len(employee_staffnum) # will return the number of elements.
#before appending an array
for loop in range(myarr_len):
print(f"{loop}th element is:", employee_staffnum[loop], end = ' ')
print()
print("-------------------------")
employee_staffnum.append(206)
myarr_len = len(employee_staffnum)
# will return the number of elements after append
#after appending an array
for loop in range(myarr_len):
print(f"{loop}th element is:", employee_staffnum[loop], end = ' ') | 38.8 | 73 | 0.682131 | 84 | 582 | 4.607143 | 0.428571 | 0.248062 | 0.056848 | 0.098191 | 0.713178 | 0.713178 | 0.713178 | 0.713178 | 0.713178 | 0.713178 | 0 | 0.03681 | 0.159794 | 582 | 15 | 74 | 38.8 | 0.754601 | 0.226804 | 0 | 0.545455 | 0 | 0 | 0.157407 | 0.05787 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.090909 | 0 | 0.090909 | 0.363636 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
92da2ca06fa60efbb87c3557e9eb694d93f29458 | 24 | py | Python | src/futuresales/features/__init__.py | Denchidlo/fs-lib | 7c2b0498483cce499696823218a6d6b07990a4e2 | [
"MIT"
] | null | null | null | src/futuresales/features/__init__.py | Denchidlo/fs-lib | 7c2b0498483cce499696823218a6d6b07990a4e2 | [
"MIT"
] | null | null | null | src/futuresales/features/__init__.py | Denchidlo/fs-lib | 7c2b0498483cce499696823218a6d6b07990a4e2 | [
"MIT"
] | null | null | null | from .extractor import * | 24 | 24 | 0.791667 | 3 | 24 | 6.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 24 | 1 | 24 | 24 | 0.904762 | 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 |
1300511cd6295952ead4e9c321808287bcf13982 | 26 | py | Python | cornac/models/cdr/__init__.py | GuoJingyao/cornac | e7529990ec1dfa586c4af3de98e4b3e00a786578 | [
"Apache-2.0"
] | null | null | null | cornac/models/cdr/__init__.py | GuoJingyao/cornac | e7529990ec1dfa586c4af3de98e4b3e00a786578 | [
"Apache-2.0"
] | null | null | null | cornac/models/cdr/__init__.py | GuoJingyao/cornac | e7529990ec1dfa586c4af3de98e4b3e00a786578 | [
"Apache-2.0"
] | null | null | null | from .recom_cdr import CDR | 26 | 26 | 0.846154 | 5 | 26 | 4.2 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115385 | 26 | 1 | 26 | 26 | 0.913043 | 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 |
1358c233b69eedfea51208c131aafe1e57190c52 | 182 | py | Python | deco/nodes/__init__.py | mfojtak/decor | 203979351635a6794c91200fca4a14296ec9bc37 | [
"MIT"
] | 1 | 2019-09-05T07:23:19.000Z | 2019-09-05T07:23:19.000Z | deco/nodes/__init__.py | mfojtak/decor | 203979351635a6794c91200fca4a14296ec9bc37 | [
"MIT"
] | 2 | 2020-10-25T17:41:08.000Z | 2020-10-26T16:48:19.000Z | deco/nodes/__init__.py | mfojtak/deco | 203979351635a6794c91200fca4a14296ec9bc37 | [
"MIT"
] | null | null | null | from deco.nodes.transform import Transform
from deco.nodes.keras_model import KerasModel
from deco.nodes.sequence import Sequence
from deco.nodes.serving_model import ServingModel | 45.5 | 49 | 0.857143 | 26 | 182 | 5.923077 | 0.423077 | 0.207792 | 0.337662 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.098901 | 182 | 4 | 49 | 45.5 | 0.939024 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
13949ba9367aa74ab706bfb22a9278337c6eb485 | 54,215 | py | Python | tests/test_locales.py | impact27/arrow | 69cc7b0f85d50a7f183991014bec8441373e4157 | [
"Apache-2.0"
] | null | null | null | tests/test_locales.py | impact27/arrow | 69cc7b0f85d50a7f183991014bec8441373e4157 | [
"Apache-2.0"
] | null | null | null | tests/test_locales.py | impact27/arrow | 69cc7b0f85d50a7f183991014bec8441373e4157 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
import pytest
from arrow import arrow, locales
@pytest.mark.usefixtures("lang_locales")
class TestLocaleValidation:
"""Validate locales to ensure that translations are valid and complete"""
def test_locale_validation(self):
for _, locale_cls in self.locales.items():
# 7 days + 1 spacer to allow for 1-indexing of months
assert len(locale_cls.day_names) == 8
assert locale_cls.day_names[0] == ""
# ensure that all string from index 1 onward are valid (not blank or None)
assert all(locale_cls.day_names[1:])
assert len(locale_cls.day_abbreviations) == 8
assert locale_cls.day_abbreviations[0] == ""
assert all(locale_cls.day_abbreviations[1:])
# 12 months + 1 spacer to allow for 1-indexing of months
assert len(locale_cls.month_names) == 13
assert locale_cls.month_names[0] == ""
assert all(locale_cls.month_names[1:])
assert len(locale_cls.month_abbreviations) == 13
assert locale_cls.month_abbreviations[0] == ""
assert all(locale_cls.month_abbreviations[1:])
assert len(locale_cls.names) > 0
assert locale_cls.past is not None
assert locale_cls.future is not None
class TestModule:
def test_get_locale(self, mocker):
mock_locale = mocker.Mock()
mock_locale_cls = mocker.Mock()
mock_locale_cls.return_value = mock_locale
with pytest.raises(ValueError):
arrow.locales.get_locale("locale_name")
cls_dict = arrow.locales._locales
mocker.patch.dict(cls_dict, {"locale_name": mock_locale_cls})
result = arrow.locales.get_locale("locale_name")
assert result == mock_locale
def test_get_locale_by_class_name(self, mocker):
mock_locale_cls = mocker.Mock()
mock_locale_obj = mock_locale_cls.return_value = mocker.Mock()
globals_fn = mocker.Mock()
globals_fn.return_value = {"NonExistentLocale": mock_locale_cls}
with pytest.raises(ValueError):
arrow.locales.get_locale_by_class_name("NonExistentLocale")
mocker.patch.object(locales, "globals", globals_fn)
result = arrow.locales.get_locale_by_class_name("NonExistentLocale")
mock_locale_cls.assert_called_once_with()
assert result == mock_locale_obj
def test_locales(self):
assert len(locales._locales) > 0
@pytest.mark.usefixtures("lang_locale")
class TestEnglishLocale:
def test_describe(self):
assert self.locale.describe("now", only_distance=True) == "instantly"
assert self.locale.describe("now", only_distance=False) == "just now"
def test_format_timeframe(self):
assert self.locale._format_timeframe("hours", 2) == "2 hours"
assert self.locale._format_timeframe("hour", 0) == "an hour"
def test_format_relative_now(self):
result = self.locale._format_relative("just now", "now", 0)
assert result == "just now"
def test_format_relative_past(self):
result = self.locale._format_relative("an hour", "hour", 1)
assert result == "in an hour"
def test_format_relative_future(self):
result = self.locale._format_relative("an hour", "hour", -1)
assert result == "an hour ago"
def test_ordinal_number(self):
assert self.locale.ordinal_number(0) == "0th"
assert self.locale.ordinal_number(1) == "1st"
assert self.locale.ordinal_number(2) == "2nd"
assert self.locale.ordinal_number(3) == "3rd"
assert self.locale.ordinal_number(4) == "4th"
assert self.locale.ordinal_number(10) == "10th"
assert self.locale.ordinal_number(11) == "11th"
assert self.locale.ordinal_number(12) == "12th"
assert self.locale.ordinal_number(13) == "13th"
assert self.locale.ordinal_number(14) == "14th"
assert self.locale.ordinal_number(21) == "21st"
assert self.locale.ordinal_number(22) == "22nd"
assert self.locale.ordinal_number(23) == "23rd"
assert self.locale.ordinal_number(24) == "24th"
assert self.locale.ordinal_number(100) == "100th"
assert self.locale.ordinal_number(101) == "101st"
assert self.locale.ordinal_number(102) == "102nd"
assert self.locale.ordinal_number(103) == "103rd"
assert self.locale.ordinal_number(104) == "104th"
assert self.locale.ordinal_number(110) == "110th"
assert self.locale.ordinal_number(111) == "111th"
assert self.locale.ordinal_number(112) == "112th"
assert self.locale.ordinal_number(113) == "113th"
assert self.locale.ordinal_number(114) == "114th"
assert self.locale.ordinal_number(121) == "121st"
assert self.locale.ordinal_number(122) == "122nd"
assert self.locale.ordinal_number(123) == "123rd"
assert self.locale.ordinal_number(124) == "124th"
def test_meridian_invalid_token(self):
assert self.locale.meridian(7, None) is None
assert self.locale.meridian(7, "B") is None
assert self.locale.meridian(7, "NONSENSE") is None
@pytest.mark.usefixtures("lang_locale")
class TestItalianLocale:
def test_ordinal_number(self):
assert self.locale.ordinal_number(1) == "1º"
@pytest.mark.usefixtures("lang_locale")
class TestSpanishLocale:
def test_ordinal_number(self):
assert self.locale.ordinal_number(1) == "1º"
def test_format_timeframe(self):
assert self.locale._format_timeframe("now", 0) == "ahora"
assert self.locale._format_timeframe("seconds", 1) == "1 segundos"
assert self.locale._format_timeframe("seconds", 3) == "3 segundos"
assert self.locale._format_timeframe("seconds", 30) == "30 segundos"
assert self.locale._format_timeframe("minute", 1) == "un minuto"
assert self.locale._format_timeframe("minutes", 4) == "4 minutos"
assert self.locale._format_timeframe("minutes", 40) == "40 minutos"
assert self.locale._format_timeframe("hour", 1) == "una hora"
assert self.locale._format_timeframe("hours", 5) == "5 horas"
assert self.locale._format_timeframe("hours", 23) == "23 horas"
assert self.locale._format_timeframe("day", 1) == "un día"
assert self.locale._format_timeframe("days", 6) == "6 días"
assert self.locale._format_timeframe("days", 12) == "12 días"
assert self.locale._format_timeframe("week", 1) == "una semana"
assert self.locale._format_timeframe("weeks", 2) == "2 semanas"
assert self.locale._format_timeframe("weeks", 3) == "3 semanas"
assert self.locale._format_timeframe("month", 1) == "un mes"
assert self.locale._format_timeframe("months", 7) == "7 meses"
assert self.locale._format_timeframe("months", 11) == "11 meses"
assert self.locale._format_timeframe("year", 1) == "un año"
assert self.locale._format_timeframe("years", 8) == "8 años"
assert self.locale._format_timeframe("years", 12) == "12 años"
assert self.locale._format_timeframe("now", 0) == "ahora"
assert self.locale._format_timeframe("seconds", -1) == "1 segundos"
assert self.locale._format_timeframe("seconds", -9) == "9 segundos"
assert self.locale._format_timeframe("seconds", -12) == "12 segundos"
assert self.locale._format_timeframe("minute", -1) == "un minuto"
assert self.locale._format_timeframe("minutes", -2) == "2 minutos"
assert self.locale._format_timeframe("minutes", -10) == "10 minutos"
assert self.locale._format_timeframe("hour", -1) == "una hora"
assert self.locale._format_timeframe("hours", -3) == "3 horas"
assert self.locale._format_timeframe("hours", -11) == "11 horas"
assert self.locale._format_timeframe("day", -1) == "un día"
assert self.locale._format_timeframe("days", -2) == "2 días"
assert self.locale._format_timeframe("days", -12) == "12 días"
assert self.locale._format_timeframe("week", -1) == "una semana"
assert self.locale._format_timeframe("weeks", -2) == "2 semanas"
assert self.locale._format_timeframe("weeks", -3) == "3 semanas"
assert self.locale._format_timeframe("month", -1) == "un mes"
assert self.locale._format_timeframe("months", -3) == "3 meses"
assert self.locale._format_timeframe("months", -13) == "13 meses"
assert self.locale._format_timeframe("year", -1) == "un año"
assert self.locale._format_timeframe("years", -4) == "4 años"
assert self.locale._format_timeframe("years", -14) == "14 años"
@pytest.mark.usefixtures("lang_locale")
class TestFrenchLocale:
def test_ordinal_number(self):
assert self.locale.ordinal_number(1) == "1er"
assert self.locale.ordinal_number(2) == "2e"
def test_month_abbreviation(self):
assert "juil" in self.locale.month_abbreviations
@pytest.mark.usefixtures("lang_locale")
class TestFrenchCanadianLocale:
def test_month_abbreviation(self):
assert "juill" in self.locale.month_abbreviations
@pytest.mark.usefixtures("lang_locale")
class TestRussianLocale:
def test_plurals2(self):
assert self.locale._format_timeframe("hours", 0) == "0 часов"
assert self.locale._format_timeframe("hours", 1) == "1 час"
assert self.locale._format_timeframe("hours", 2) == "2 часа"
assert self.locale._format_timeframe("hours", 4) == "4 часа"
assert self.locale._format_timeframe("hours", 5) == "5 часов"
assert self.locale._format_timeframe("hours", 21) == "21 час"
assert self.locale._format_timeframe("hours", 22) == "22 часа"
assert self.locale._format_timeframe("hours", 25) == "25 часов"
# feminine grammatical gender should be tested separately
assert self.locale._format_timeframe("minutes", 0) == "0 минут"
assert self.locale._format_timeframe("minutes", 1) == "1 минуту"
assert self.locale._format_timeframe("minutes", 2) == "2 минуты"
assert self.locale._format_timeframe("minutes", 4) == "4 минуты"
assert self.locale._format_timeframe("minutes", 5) == "5 минут"
assert self.locale._format_timeframe("minutes", 21) == "21 минуту"
assert self.locale._format_timeframe("minutes", 22) == "22 минуты"
assert self.locale._format_timeframe("minutes", 25) == "25 минут"
@pytest.mark.usefixtures("lang_locale")
class TestPolishLocale:
def test_plurals(self):
assert self.locale._format_timeframe("seconds", 0) == "0 sekund"
assert self.locale._format_timeframe("second", 1) == "sekundę"
assert self.locale._format_timeframe("seconds", 2) == "2 sekundy"
assert self.locale._format_timeframe("seconds", 5) == "5 sekund"
assert self.locale._format_timeframe("seconds", 21) == "21 sekund"
assert self.locale._format_timeframe("seconds", 22) == "22 sekundy"
assert self.locale._format_timeframe("seconds", 25) == "25 sekund"
assert self.locale._format_timeframe("minutes", 0) == "0 minut"
assert self.locale._format_timeframe("minute", 1) == "minutę"
assert self.locale._format_timeframe("minutes", 2) == "2 minuty"
assert self.locale._format_timeframe("minutes", 5) == "5 minut"
assert self.locale._format_timeframe("minutes", 21) == "21 minut"
assert self.locale._format_timeframe("minutes", 22) == "22 minuty"
assert self.locale._format_timeframe("minutes", 25) == "25 minut"
assert self.locale._format_timeframe("hours", 0) == "0 godzin"
assert self.locale._format_timeframe("hour", 1) == "godzinę"
assert self.locale._format_timeframe("hours", 2) == "2 godziny"
assert self.locale._format_timeframe("hours", 5) == "5 godzin"
assert self.locale._format_timeframe("hours", 21) == "21 godzin"
assert self.locale._format_timeframe("hours", 22) == "22 godziny"
assert self.locale._format_timeframe("hours", 25) == "25 godzin"
assert self.locale._format_timeframe("weeks", 0) == "0 tygodni"
assert self.locale._format_timeframe("week", 1) == "tydzień"
assert self.locale._format_timeframe("weeks", 2) == "2 tygodnie"
assert self.locale._format_timeframe("weeks", 5) == "5 tygodni"
assert self.locale._format_timeframe("weeks", 21) == "21 tygodni"
assert self.locale._format_timeframe("weeks", 22) == "22 tygodnie"
assert self.locale._format_timeframe("weeks", 25) == "25 tygodni"
assert self.locale._format_timeframe("months", 0) == "0 miesięcy"
assert self.locale._format_timeframe("month", 1) == "miesiąc"
assert self.locale._format_timeframe("months", 2) == "2 miesiące"
assert self.locale._format_timeframe("months", 5) == "5 miesięcy"
assert self.locale._format_timeframe("months", 21) == "21 miesięcy"
assert self.locale._format_timeframe("months", 22) == "22 miesiące"
assert self.locale._format_timeframe("months", 25) == "25 miesięcy"
assert self.locale._format_timeframe("years", 0) == "0 lat"
assert self.locale._format_timeframe("year", 1) == "rok"
assert self.locale._format_timeframe("years", 2) == "2 lata"
assert self.locale._format_timeframe("years", 5) == "5 lat"
assert self.locale._format_timeframe("years", 21) == "21 lat"
assert self.locale._format_timeframe("years", 22) == "22 lata"
assert self.locale._format_timeframe("years", 25) == "25 lat"
@pytest.mark.usefixtures("lang_locale")
class TestIcelandicLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("minute", -1) == "einni mínútu"
assert self.locale._format_timeframe("minute", 1) == "eina mínútu"
assert self.locale._format_timeframe("hours", -2) == "2 tímum"
assert self.locale._format_timeframe("hours", 2) == "2 tíma"
assert self.locale._format_timeframe("now", 0) == "rétt í þessu"
@pytest.mark.usefixtures("lang_locale")
class TestMalayalamLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("hours", 2) == "2 മണിക്കൂർ"
assert self.locale._format_timeframe("hour", 0) == "ഒരു മണിക്കൂർ"
def test_format_relative_now(self):
result = self.locale._format_relative("ഇപ്പോൾ", "now", 0)
assert result == "ഇപ്പോൾ"
def test_format_relative_past(self):
result = self.locale._format_relative("ഒരു മണിക്കൂർ", "hour", 1)
assert result == "ഒരു മണിക്കൂർ ശേഷം"
def test_format_relative_future(self):
result = self.locale._format_relative("ഒരു മണിക്കൂർ", "hour", -1)
assert result == "ഒരു മണിക്കൂർ മുമ്പ്"
@pytest.mark.usefixtures("lang_locale")
class TestHindiLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("hours", 2) == "2 घंटे"
assert self.locale._format_timeframe("hour", 0) == "एक घंटा"
def test_format_relative_now(self):
result = self.locale._format_relative("अभी", "now", 0)
assert result == "अभी"
def test_format_relative_past(self):
result = self.locale._format_relative("एक घंटा", "hour", 1)
assert result == "एक घंटा बाद"
def test_format_relative_future(self):
result = self.locale._format_relative("एक घंटा", "hour", -1)
assert result == "एक घंटा पहले"
@pytest.mark.usefixtures("lang_locale")
class TestCzechLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("hours", 2) == "2 hodiny"
assert self.locale._format_timeframe("hours", 5) == "5 hodin"
assert self.locale._format_timeframe("hour", 0) == "0 hodin"
assert self.locale._format_timeframe("hours", -2) == "2 hodinami"
assert self.locale._format_timeframe("hours", -5) == "5 hodinami"
assert self.locale._format_timeframe("now", 0) == "Teď"
def test_format_relative_now(self):
result = self.locale._format_relative("Teď", "now", 0)
assert result == "Teď"
def test_format_relative_future(self):
result = self.locale._format_relative("hodinu", "hour", 1)
assert result == "Za hodinu"
def test_format_relative_past(self):
result = self.locale._format_relative("hodinou", "hour", -1)
assert result == "Před hodinou"
@pytest.mark.usefixtures("lang_locale")
class TestSlovakLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("seconds", -5) == "5 sekundami"
assert self.locale._format_timeframe("seconds", -2) == "2 sekundami"
assert self.locale._format_timeframe("second", -1) == "sekundou"
assert self.locale._format_timeframe("second", 0) == "0 sekúnd"
assert self.locale._format_timeframe("second", 1) == "sekundu"
assert self.locale._format_timeframe("seconds", 2) == "2 sekundy"
assert self.locale._format_timeframe("seconds", 5) == "5 sekúnd"
assert self.locale._format_timeframe("minutes", -5) == "5 minútami"
assert self.locale._format_timeframe("minutes", -2) == "2 minútami"
assert self.locale._format_timeframe("minute", -1) == "minútou"
assert self.locale._format_timeframe("minute", 0) == "0 minút"
assert self.locale._format_timeframe("minute", 1) == "minútu"
assert self.locale._format_timeframe("minutes", 2) == "2 minúty"
assert self.locale._format_timeframe("minutes", 5) == "5 minút"
assert self.locale._format_timeframe("hours", -5) == "5 hodinami"
assert self.locale._format_timeframe("hours", -2) == "2 hodinami"
assert self.locale._format_timeframe("hour", -1) == "hodinou"
assert self.locale._format_timeframe("hour", 0) == "0 hodín"
assert self.locale._format_timeframe("hour", 1) == "hodinu"
assert self.locale._format_timeframe("hours", 2) == "2 hodiny"
assert self.locale._format_timeframe("hours", 5) == "5 hodín"
assert self.locale._format_timeframe("days", -5) == "5 dňami"
assert self.locale._format_timeframe("days", -2) == "2 dňami"
assert self.locale._format_timeframe("day", -1) == "dňom"
assert self.locale._format_timeframe("day", 0) == "0 dní"
assert self.locale._format_timeframe("day", 1) == "deň"
assert self.locale._format_timeframe("days", 2) == "2 dni"
assert self.locale._format_timeframe("days", 5) == "5 dní"
assert self.locale._format_timeframe("weeks", -5) == "5 týždňami"
assert self.locale._format_timeframe("weeks", -2) == "2 týždňami"
assert self.locale._format_timeframe("week", -1) == "týždňom"
assert self.locale._format_timeframe("week", 0) == "0 týždňov"
assert self.locale._format_timeframe("week", 1) == "týždeň"
assert self.locale._format_timeframe("weeks", 2) == "2 týždne"
assert self.locale._format_timeframe("weeks", 5) == "5 týždňov"
assert self.locale._format_timeframe("months", -5) == "5 mesiacmi"
assert self.locale._format_timeframe("months", -2) == "2 mesiacmi"
assert self.locale._format_timeframe("month", -1) == "mesiacom"
assert self.locale._format_timeframe("month", 0) == "0 mesiacov"
assert self.locale._format_timeframe("month", 1) == "mesiac"
assert self.locale._format_timeframe("months", 2) == "2 mesiace"
assert self.locale._format_timeframe("months", 5) == "5 mesiacov"
assert self.locale._format_timeframe("years", -5) == "5 rokmi"
assert self.locale._format_timeframe("years", -2) == "2 rokmi"
assert self.locale._format_timeframe("year", -1) == "rokom"
assert self.locale._format_timeframe("year", 0) == "0 rokov"
assert self.locale._format_timeframe("year", 1) == "rok"
assert self.locale._format_timeframe("years", 2) == "2 roky"
assert self.locale._format_timeframe("years", 5) == "5 rokov"
assert self.locale._format_timeframe("now", 0) == "Teraz"
def test_format_relative_now(self):
result = self.locale._format_relative("Teraz", "now", 0)
assert result == "Teraz"
def test_format_relative_future(self):
result = self.locale._format_relative("hodinu", "hour", 1)
assert result == "O hodinu"
def test_format_relative_past(self):
result = self.locale._format_relative("hodinou", "hour", -1)
assert result == "Pred hodinou"
@pytest.mark.usefixtures("lang_locale")
class TestBulgarianLocale:
def test_plurals2(self):
assert self.locale._format_timeframe("hours", 0) == "0 часа"
assert self.locale._format_timeframe("hours", 1) == "1 час"
assert self.locale._format_timeframe("hours", 2) == "2 часа"
assert self.locale._format_timeframe("hours", 4) == "4 часа"
assert self.locale._format_timeframe("hours", 5) == "5 часа"
assert self.locale._format_timeframe("hours", 21) == "21 час"
assert self.locale._format_timeframe("hours", 22) == "22 часа"
assert self.locale._format_timeframe("hours", 25) == "25 часа"
# feminine grammatical gender should be tested separately
assert self.locale._format_timeframe("minutes", 0) == "0 минути"
assert self.locale._format_timeframe("minutes", 1) == "1 минута"
assert self.locale._format_timeframe("minutes", 2) == "2 минути"
assert self.locale._format_timeframe("minutes", 4) == "4 минути"
assert self.locale._format_timeframe("minutes", 5) == "5 минути"
assert self.locale._format_timeframe("minutes", 21) == "21 минута"
assert self.locale._format_timeframe("minutes", 22) == "22 минути"
assert self.locale._format_timeframe("minutes", 25) == "25 минути"
@pytest.mark.usefixtures("lang_locale")
class TestMacedonianLocale:
def test_singles_mk(self):
assert self.locale._format_timeframe("second", 1) == "една секунда"
assert self.locale._format_timeframe("minute", 1) == "една минута"
assert self.locale._format_timeframe("hour", 1) == "еден саат"
assert self.locale._format_timeframe("day", 1) == "еден ден"
assert self.locale._format_timeframe("week", 1) == "една недела"
assert self.locale._format_timeframe("month", 1) == "еден месец"
assert self.locale._format_timeframe("year", 1) == "една година"
def test_meridians_mk(self):
assert self.locale.meridian(7, "A") == "претпладне"
assert self.locale.meridian(18, "A") == "попладне"
assert self.locale.meridian(10, "a") == "дп"
assert self.locale.meridian(22, "a") == "пп"
def test_describe_mk(self):
assert self.locale.describe("second", only_distance=True) == "една секунда"
assert self.locale.describe("second", only_distance=False) == "за една секунда"
assert self.locale.describe("minute", only_distance=True) == "една минута"
assert self.locale.describe("minute", only_distance=False) == "за една минута"
assert self.locale.describe("hour", only_distance=True) == "еден саат"
assert self.locale.describe("hour", only_distance=False) == "за еден саат"
assert self.locale.describe("day", only_distance=True) == "еден ден"
assert self.locale.describe("day", only_distance=False) == "за еден ден"
assert self.locale.describe("week", only_distance=True) == "една недела"
assert self.locale.describe("week", only_distance=False) == "за една недела"
assert self.locale.describe("month", only_distance=True) == "еден месец"
assert self.locale.describe("month", only_distance=False) == "за еден месец"
assert self.locale.describe("year", only_distance=True) == "една година"
assert self.locale.describe("year", only_distance=False) == "за една година"
def test_relative_mk(self):
# time
assert self.locale._format_relative("сега", "now", 0) == "сега"
assert self.locale._format_relative("1 секунда", "seconds", 1) == "за 1 секунда"
assert self.locale._format_relative("1 минута", "minutes", 1) == "за 1 минута"
assert self.locale._format_relative("1 саат", "hours", 1) == "за 1 саат"
assert self.locale._format_relative("1 ден", "days", 1) == "за 1 ден"
assert self.locale._format_relative("1 недела", "weeks", 1) == "за 1 недела"
assert self.locale._format_relative("1 месец", "months", 1) == "за 1 месец"
assert self.locale._format_relative("1 година", "years", 1) == "за 1 година"
assert (
self.locale._format_relative("1 секунда", "seconds", -1) == "пред 1 секунда"
)
assert (
self.locale._format_relative("1 минута", "minutes", -1) == "пред 1 минута"
)
assert self.locale._format_relative("1 саат", "hours", -1) == "пред 1 саат"
assert self.locale._format_relative("1 ден", "days", -1) == "пред 1 ден"
assert self.locale._format_relative("1 недела", "weeks", -1) == "пред 1 недела"
assert self.locale._format_relative("1 месец", "months", -1) == "пред 1 месец"
assert self.locale._format_relative("1 година", "years", -1) == "пред 1 година"
def test_plurals_mk(self):
# Seconds
assert self.locale._format_timeframe("seconds", 0) == "0 секунди"
assert self.locale._format_timeframe("seconds", 1) == "1 секунда"
assert self.locale._format_timeframe("seconds", 2) == "2 секунди"
assert self.locale._format_timeframe("seconds", 4) == "4 секунди"
assert self.locale._format_timeframe("seconds", 5) == "5 секунди"
assert self.locale._format_timeframe("seconds", 21) == "21 секунда"
assert self.locale._format_timeframe("seconds", 22) == "22 секунди"
assert self.locale._format_timeframe("seconds", 25) == "25 секунди"
# Minutes
assert self.locale._format_timeframe("minutes", 0) == "0 минути"
assert self.locale._format_timeframe("minutes", 1) == "1 минута"
assert self.locale._format_timeframe("minutes", 2) == "2 минути"
assert self.locale._format_timeframe("minutes", 4) == "4 минути"
assert self.locale._format_timeframe("minutes", 5) == "5 минути"
assert self.locale._format_timeframe("minutes", 21) == "21 минута"
assert self.locale._format_timeframe("minutes", 22) == "22 минути"
assert self.locale._format_timeframe("minutes", 25) == "25 минути"
# Hours
assert self.locale._format_timeframe("hours", 0) == "0 саати"
assert self.locale._format_timeframe("hours", 1) == "1 саат"
assert self.locale._format_timeframe("hours", 2) == "2 саати"
assert self.locale._format_timeframe("hours", 4) == "4 саати"
assert self.locale._format_timeframe("hours", 5) == "5 саати"
assert self.locale._format_timeframe("hours", 21) == "21 саат"
assert self.locale._format_timeframe("hours", 22) == "22 саати"
assert self.locale._format_timeframe("hours", 25) == "25 саати"
# Days
assert self.locale._format_timeframe("days", 0) == "0 дена"
assert self.locale._format_timeframe("days", 1) == "1 ден"
assert self.locale._format_timeframe("days", 2) == "2 дена"
assert self.locale._format_timeframe("days", 3) == "3 дена"
assert self.locale._format_timeframe("days", 21) == "21 ден"
# Weeks
assert self.locale._format_timeframe("weeks", 0) == "0 недели"
assert self.locale._format_timeframe("weeks", 1) == "1 недела"
assert self.locale._format_timeframe("weeks", 2) == "2 недели"
assert self.locale._format_timeframe("weeks", 4) == "4 недели"
assert self.locale._format_timeframe("weeks", 5) == "5 недели"
assert self.locale._format_timeframe("weeks", 21) == "21 недела"
assert self.locale._format_timeframe("weeks", 22) == "22 недели"
assert self.locale._format_timeframe("weeks", 25) == "25 недели"
# Months
assert self.locale._format_timeframe("months", 0) == "0 месеци"
assert self.locale._format_timeframe("months", 1) == "1 месец"
assert self.locale._format_timeframe("months", 2) == "2 месеци"
assert self.locale._format_timeframe("months", 4) == "4 месеци"
assert self.locale._format_timeframe("months", 5) == "5 месеци"
assert self.locale._format_timeframe("months", 21) == "21 месец"
assert self.locale._format_timeframe("months", 22) == "22 месеци"
assert self.locale._format_timeframe("months", 25) == "25 месеци"
# Years
assert self.locale._format_timeframe("years", 1) == "1 година"
assert self.locale._format_timeframe("years", 2) == "2 години"
assert self.locale._format_timeframe("years", 5) == "5 години"
def test_multi_describe_mk(self):
describe = self.locale.describe_multi
fulltest = [("years", 5), ("weeks", 1), ("hours", 1), ("minutes", 6)]
assert describe(fulltest) == "за 5 години 1 недела 1 саат 6 минути"
seconds4000_0days = [("days", 0), ("hours", 1), ("minutes", 6)]
assert describe(seconds4000_0days) == "за 0 дена 1 саат 6 минути"
seconds4000 = [("hours", 1), ("minutes", 6)]
assert describe(seconds4000) == "за 1 саат 6 минути"
assert describe(seconds4000, only_distance=True) == "1 саат 6 минути"
seconds3700 = [("hours", 1), ("minutes", 1)]
assert describe(seconds3700) == "за 1 саат 1 минута"
seconds300_0hours = [("hours", 0), ("minutes", 5)]
assert describe(seconds300_0hours) == "за 0 саати 5 минути"
seconds300 = [("minutes", 5)]
assert describe(seconds300) == "за 5 минути"
seconds60 = [("minutes", 1)]
assert describe(seconds60) == "за 1 минута"
assert describe(seconds60, only_distance=True) == "1 минута"
seconds60 = [("seconds", 1)]
assert describe(seconds60) == "за 1 секунда"
assert describe(seconds60, only_distance=True) == "1 секунда"
@pytest.mark.usefixtures("time_2013_01_01")
@pytest.mark.usefixtures("lang_locale")
class TestHebrewLocale:
def test_couple_of_timeframe(self):
assert self.locale._format_timeframe("days", 1) == "יום"
assert self.locale._format_timeframe("days", 2) == "יומיים"
assert self.locale._format_timeframe("days", 3) == "3 ימים"
assert self.locale._format_timeframe("hours", 1) == "שעה"
assert self.locale._format_timeframe("hours", 2) == "שעתיים"
assert self.locale._format_timeframe("hours", 3) == "3 שעות"
assert self.locale._format_timeframe("week", 1) == "שבוע"
assert self.locale._format_timeframe("weeks", 2) == "שבועיים"
assert self.locale._format_timeframe("weeks", 3) == "3 שבועות"
assert self.locale._format_timeframe("months", 1) == "חודש"
assert self.locale._format_timeframe("months", 2) == "חודשיים"
assert self.locale._format_timeframe("months", 4) == "4 חודשים"
assert self.locale._format_timeframe("years", 1) == "שנה"
assert self.locale._format_timeframe("years", 2) == "שנתיים"
assert self.locale._format_timeframe("years", 5) == "5 שנים"
def test_describe_multi(self):
describe = self.locale.describe_multi
fulltest = [("years", 5), ("weeks", 1), ("hours", 1), ("minutes", 6)]
assert describe(fulltest) == "בעוד 5 שנים, שבוע, שעה ו־6 דקות"
seconds4000_0days = [("days", 0), ("hours", 1), ("minutes", 6)]
assert describe(seconds4000_0days) == "בעוד 0 ימים, שעה ו־6 דקות"
seconds4000 = [("hours", 1), ("minutes", 6)]
assert describe(seconds4000) == "בעוד שעה ו־6 דקות"
assert describe(seconds4000, only_distance=True) == "שעה ו־6 דקות"
seconds3700 = [("hours", 1), ("minutes", 1)]
assert describe(seconds3700) == "בעוד שעה ודקה"
seconds300_0hours = [("hours", 0), ("minutes", 5)]
assert describe(seconds300_0hours) == "בעוד 0 שעות ו־5 דקות"
seconds300 = [("minutes", 5)]
assert describe(seconds300) == "בעוד 5 דקות"
seconds60 = [("minutes", 1)]
assert describe(seconds60) == "בעוד דקה"
assert describe(seconds60, only_distance=True) == "דקה"
@pytest.mark.usefixtures("lang_locale")
class TestMarathiLocale:
def test_dateCoreFunctionality(self):
dt = arrow.Arrow(2015, 4, 11, 17, 30, 00)
assert self.locale.month_name(dt.month) == "एप्रिल"
assert self.locale.month_abbreviation(dt.month) == "एप्रि"
assert self.locale.day_name(dt.isoweekday()) == "शनिवार"
assert self.locale.day_abbreviation(dt.isoweekday()) == "शनि"
def test_format_timeframe(self):
assert self.locale._format_timeframe("hours", 2) == "2 तास"
assert self.locale._format_timeframe("hour", 0) == "एक तास"
def test_format_relative_now(self):
result = self.locale._format_relative("सद्य", "now", 0)
assert result == "सद्य"
def test_format_relative_past(self):
result = self.locale._format_relative("एक तास", "hour", 1)
assert result == "एक तास नंतर"
def test_format_relative_future(self):
result = self.locale._format_relative("एक तास", "hour", -1)
assert result == "एक तास आधी"
# Not currently implemented
def test_ordinal_number(self):
assert self.locale.ordinal_number(1) == "1"
@pytest.mark.usefixtures("lang_locale")
class TestFinnishLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("hours", 2) == ("2 tuntia", "2 tunnin")
assert self.locale._format_timeframe("hour", 0) == ("tunti", "tunnin")
def test_format_relative_now(self):
result = self.locale._format_relative(["juuri nyt", "juuri nyt"], "now", 0)
assert result == "juuri nyt"
def test_format_relative_past(self):
result = self.locale._format_relative(["tunti", "tunnin"], "hour", 1)
assert result == "tunnin kuluttua"
def test_format_relative_future(self):
result = self.locale._format_relative(["tunti", "tunnin"], "hour", -1)
assert result == "tunti sitten"
def test_ordinal_number(self):
assert self.locale.ordinal_number(1) == "1."
@pytest.mark.usefixtures("lang_locale")
class TestGermanLocale:
def test_ordinal_number(self):
assert self.locale.ordinal_number(1) == "1."
def test_define(self):
assert self.locale.describe("minute", only_distance=True) == "eine Minute"
assert self.locale.describe("minute", only_distance=False) == "in einer Minute"
assert self.locale.describe("hour", only_distance=True) == "eine Stunde"
assert self.locale.describe("hour", only_distance=False) == "in einer Stunde"
assert self.locale.describe("day", only_distance=True) == "ein Tag"
assert self.locale.describe("day", only_distance=False) == "in einem Tag"
assert self.locale.describe("week", only_distance=True) == "eine Woche"
assert self.locale.describe("week", only_distance=False) == "in einer Woche"
assert self.locale.describe("month", only_distance=True) == "ein Monat"
assert self.locale.describe("month", only_distance=False) == "in einem Monat"
assert self.locale.describe("year", only_distance=True) == "ein Jahr"
assert self.locale.describe("year", only_distance=False) == "in einem Jahr"
def test_weekday(self):
dt = arrow.Arrow(2015, 4, 11, 17, 30, 00)
assert self.locale.day_name(dt.isoweekday()) == "Samstag"
assert self.locale.day_abbreviation(dt.isoweekday()) == "Sa"
@pytest.mark.usefixtures("lang_locale")
class TestHungarianLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("hours", 2) == "2 óra"
assert self.locale._format_timeframe("hour", 0) == "egy órával"
assert self.locale._format_timeframe("hours", -2) == "2 órával"
assert self.locale._format_timeframe("now", 0) == "éppen most"
@pytest.mark.usefixtures("lang_locale")
class TestEsperantoLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("hours", 2) == "2 horoj"
assert self.locale._format_timeframe("hour", 0) == "un horo"
assert self.locale._format_timeframe("hours", -2) == "2 horoj"
assert self.locale._format_timeframe("now", 0) == "nun"
def test_ordinal_number(self):
assert self.locale.ordinal_number(1) == "1a"
@pytest.mark.usefixtures("lang_locale")
class TestThaiLocale:
def test_year_full(self):
assert self.locale.year_full(2015) == "2558"
def test_year_abbreviation(self):
assert self.locale.year_abbreviation(2015) == "58"
def test_format_relative_now(self):
result = self.locale._format_relative("ขณะนี้", "now", 0)
assert result == "ขณะนี้"
def test_format_relative_past(self):
result = self.locale._format_relative("1 ชั่วโมง", "hour", 1)
assert result == "ในอีก 1 ชั่วโมง"
result = self.locale._format_relative("{0} ชั่วโมง", "hours", 2)
assert result == "ในอีก {0} ชั่วโมง"
result = self.locale._format_relative("ไม่กี่วินาที", "seconds", 42)
assert result == "ในอีกไม่กี่วินาที"
def test_format_relative_future(self):
result = self.locale._format_relative("1 ชั่วโมง", "hour", -1)
assert result == "1 ชั่วโมง ที่ผ่านมา"
@pytest.mark.usefixtures("lang_locale")
class TestBengaliLocale:
def test_ordinal_number(self):
assert self.locale._ordinal_number(0) == "0তম"
assert self.locale._ordinal_number(1) == "1ম"
assert self.locale._ordinal_number(3) == "3য়"
assert self.locale._ordinal_number(4) == "4র্থ"
assert self.locale._ordinal_number(5) == "5ম"
assert self.locale._ordinal_number(6) == "6ষ্ঠ"
assert self.locale._ordinal_number(10) == "10ম"
assert self.locale._ordinal_number(11) == "11তম"
assert self.locale._ordinal_number(42) == "42তম"
assert self.locale._ordinal_number(-1) is None
@pytest.mark.usefixtures("lang_locale")
class TestRomanianLocale:
def test_timeframes(self):
assert self.locale._format_timeframe("hours", 2) == "2 ore"
assert self.locale._format_timeframe("months", 2) == "2 luni"
assert self.locale._format_timeframe("days", 2) == "2 zile"
assert self.locale._format_timeframe("years", 2) == "2 ani"
assert self.locale._format_timeframe("hours", 3) == "3 ore"
assert self.locale._format_timeframe("months", 4) == "4 luni"
assert self.locale._format_timeframe("days", 3) == "3 zile"
assert self.locale._format_timeframe("years", 5) == "5 ani"
def test_relative_timeframes(self):
assert self.locale._format_relative("acum", "now", 0) == "acum"
assert self.locale._format_relative("o oră", "hour", 1) == "peste o oră"
assert self.locale._format_relative("o oră", "hour", -1) == "o oră în urmă"
assert self.locale._format_relative("un minut", "minute", 1) == "peste un minut"
assert (
self.locale._format_relative("un minut", "minute", -1) == "un minut în urmă"
)
assert (
self.locale._format_relative("câteva secunde", "seconds", -1)
== "câteva secunde în urmă"
)
assert (
self.locale._format_relative("câteva secunde", "seconds", 1)
== "peste câteva secunde"
)
assert self.locale._format_relative("o zi", "day", -1) == "o zi în urmă"
assert self.locale._format_relative("o zi", "day", 1) == "peste o zi"
@pytest.mark.usefixtures("lang_locale")
class TestArabicLocale:
def test_timeframes(self):
# single
assert self.locale._format_timeframe("minute", 1) == "دقيقة"
assert self.locale._format_timeframe("hour", 1) == "ساعة"
assert self.locale._format_timeframe("day", 1) == "يوم"
assert self.locale._format_timeframe("month", 1) == "شهر"
assert self.locale._format_timeframe("year", 1) == "سنة"
# double
assert self.locale._format_timeframe("minutes", 2) == "دقيقتين"
assert self.locale._format_timeframe("hours", 2) == "ساعتين"
assert self.locale._format_timeframe("days", 2) == "يومين"
assert self.locale._format_timeframe("months", 2) == "شهرين"
assert self.locale._format_timeframe("years", 2) == "سنتين"
# up to ten
assert self.locale._format_timeframe("minutes", 3) == "3 دقائق"
assert self.locale._format_timeframe("hours", 4) == "4 ساعات"
assert self.locale._format_timeframe("days", 5) == "5 أيام"
assert self.locale._format_timeframe("months", 6) == "6 أشهر"
assert self.locale._format_timeframe("years", 10) == "10 سنوات"
# more than ten
assert self.locale._format_timeframe("minutes", 11) == "11 دقيقة"
assert self.locale._format_timeframe("hours", 19) == "19 ساعة"
assert self.locale._format_timeframe("months", 24) == "24 شهر"
assert self.locale._format_timeframe("days", 50) == "50 يوم"
assert self.locale._format_timeframe("years", 115) == "115 سنة"
@pytest.mark.usefixtures("lang_locale")
class TestNepaliLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("hours", 3) == "3 घण्टा"
assert self.locale._format_timeframe("hour", 0) == "एक घण्टा"
def test_format_relative_now(self):
result = self.locale._format_relative("अहिले", "now", 0)
assert result == "अहिले"
def test_format_relative_future(self):
result = self.locale._format_relative("एक घण्टा", "hour", 1)
assert result == "एक घण्टा पछी"
def test_format_relative_past(self):
result = self.locale._format_relative("एक घण्टा", "hour", -1)
assert result == "एक घण्टा पहिले"
@pytest.mark.usefixtures("lang_locale")
class TestIndonesianLocale:
def test_timeframes(self):
assert self.locale._format_timeframe("hours", 2) == "2 jam"
assert self.locale._format_timeframe("months", 2) == "2 bulan"
assert self.locale._format_timeframe("days", 2) == "2 hari"
assert self.locale._format_timeframe("years", 2) == "2 tahun"
assert self.locale._format_timeframe("hours", 3) == "3 jam"
assert self.locale._format_timeframe("months", 4) == "4 bulan"
assert self.locale._format_timeframe("days", 3) == "3 hari"
assert self.locale._format_timeframe("years", 5) == "5 tahun"
def test_format_relative_now(self):
assert self.locale._format_relative("baru saja", "now", 0) == "baru saja"
def test_format_relative_past(self):
assert self.locale._format_relative("1 jam", "hour", 1) == "dalam 1 jam"
assert self.locale._format_relative("1 detik", "seconds", 1) == "dalam 1 detik"
def test_format_relative_future(self):
assert self.locale._format_relative("1 jam", "hour", -1) == "1 jam yang lalu"
@pytest.mark.usefixtures("lang_locale")
class TestTagalogLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("minute", 1) == "isang minuto"
assert self.locale._format_timeframe("hour", 1) == "isang oras"
assert self.locale._format_timeframe("month", 1) == "isang buwan"
assert self.locale._format_timeframe("year", 1) == "isang taon"
assert self.locale._format_timeframe("seconds", 2) == "2 segundo"
assert self.locale._format_timeframe("minutes", 3) == "3 minuto"
assert self.locale._format_timeframe("hours", 4) == "4 oras"
assert self.locale._format_timeframe("months", 5) == "5 buwan"
assert self.locale._format_timeframe("years", 6) == "6 taon"
def test_format_relative_now(self):
assert self.locale._format_relative("ngayon lang", "now", 0) == "ngayon lang"
def test_format_relative_past(self):
assert self.locale._format_relative("2 oras", "hour", 2) == "2 oras mula ngayon"
def test_format_relative_future(self):
assert self.locale._format_relative("3 oras", "hour", -3) == "nakaraang 3 oras"
def test_ordinal_number(self):
assert self.locale.ordinal_number(0) == "ika-0"
assert self.locale.ordinal_number(1) == "ika-1"
assert self.locale.ordinal_number(2) == "ika-2"
assert self.locale.ordinal_number(3) == "ika-3"
assert self.locale.ordinal_number(10) == "ika-10"
assert self.locale.ordinal_number(23) == "ika-23"
assert self.locale.ordinal_number(100) == "ika-100"
assert self.locale.ordinal_number(103) == "ika-103"
assert self.locale.ordinal_number(114) == "ika-114"
@pytest.mark.usefixtures("lang_locale")
class TestEstonianLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("now", 0) == "just nüüd"
assert self.locale._format_timeframe("second", 1) == "ühe sekundi"
assert self.locale._format_timeframe("seconds", 3) == "3 sekundi"
assert self.locale._format_timeframe("seconds", 30) == "30 sekundi"
assert self.locale._format_timeframe("minute", 1) == "ühe minuti"
assert self.locale._format_timeframe("minutes", 4) == "4 minuti"
assert self.locale._format_timeframe("minutes", 40) == "40 minuti"
assert self.locale._format_timeframe("hour", 1) == "tunni aja"
assert self.locale._format_timeframe("hours", 5) == "5 tunni"
assert self.locale._format_timeframe("hours", 23) == "23 tunni"
assert self.locale._format_timeframe("day", 1) == "ühe päeva"
assert self.locale._format_timeframe("days", 6) == "6 päeva"
assert self.locale._format_timeframe("days", 12) == "12 päeva"
assert self.locale._format_timeframe("month", 1) == "ühe kuu"
assert self.locale._format_timeframe("months", 7) == "7 kuu"
assert self.locale._format_timeframe("months", 11) == "11 kuu"
assert self.locale._format_timeframe("year", 1) == "ühe aasta"
assert self.locale._format_timeframe("years", 8) == "8 aasta"
assert self.locale._format_timeframe("years", 12) == "12 aasta"
assert self.locale._format_timeframe("now", 0) == "just nüüd"
assert self.locale._format_timeframe("second", -1) == "üks sekund"
assert self.locale._format_timeframe("seconds", -9) == "9 sekundit"
assert self.locale._format_timeframe("seconds", -12) == "12 sekundit"
assert self.locale._format_timeframe("minute", -1) == "üks minut"
assert self.locale._format_timeframe("minutes", -2) == "2 minutit"
assert self.locale._format_timeframe("minutes", -10) == "10 minutit"
assert self.locale._format_timeframe("hour", -1) == "tund aega"
assert self.locale._format_timeframe("hours", -3) == "3 tundi"
assert self.locale._format_timeframe("hours", -11) == "11 tundi"
assert self.locale._format_timeframe("day", -1) == "üks päev"
assert self.locale._format_timeframe("days", -2) == "2 päeva"
assert self.locale._format_timeframe("days", -12) == "12 päeva"
assert self.locale._format_timeframe("month", -1) == "üks kuu"
assert self.locale._format_timeframe("months", -3) == "3 kuud"
assert self.locale._format_timeframe("months", -13) == "13 kuud"
assert self.locale._format_timeframe("year", -1) == "üks aasta"
assert self.locale._format_timeframe("years", -4) == "4 aastat"
assert self.locale._format_timeframe("years", -14) == "14 aastat"
@pytest.mark.usefixtures("lang_locale")
class TestPortugueseLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("now", 0) == "agora"
assert self.locale._format_timeframe("second", 1) == "um segundo"
assert self.locale._format_timeframe("seconds", 30) == "30 segundos"
assert self.locale._format_timeframe("minute", 1) == "um minuto"
assert self.locale._format_timeframe("minutes", 40) == "40 minutos"
assert self.locale._format_timeframe("hour", 1) == "uma hora"
assert self.locale._format_timeframe("hours", 23) == "23 horas"
assert self.locale._format_timeframe("day", 1) == "um dia"
assert self.locale._format_timeframe("days", 12) == "12 dias"
assert self.locale._format_timeframe("month", 1) == "um mês"
assert self.locale._format_timeframe("months", 11) == "11 meses"
assert self.locale._format_timeframe("year", 1) == "um ano"
assert self.locale._format_timeframe("years", 12) == "12 anos"
@pytest.mark.usefixtures("lang_locale")
class TestBrazilianPortugueseLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("now", 0) == "agora"
assert self.locale._format_timeframe("second", 1) == "um segundo"
assert self.locale._format_timeframe("seconds", 30) == "30 segundos"
assert self.locale._format_timeframe("minute", 1) == "um minuto"
assert self.locale._format_timeframe("minutes", 40) == "40 minutos"
assert self.locale._format_timeframe("hour", 1) == "uma hora"
assert self.locale._format_timeframe("hours", 23) == "23 horas"
assert self.locale._format_timeframe("day", 1) == "um dia"
assert self.locale._format_timeframe("days", 12) == "12 dias"
assert self.locale._format_timeframe("month", 1) == "um mês"
assert self.locale._format_timeframe("months", 11) == "11 meses"
assert self.locale._format_timeframe("year", 1) == "um ano"
assert self.locale._format_timeframe("years", 12) == "12 anos"
assert self.locale._format_relative("uma hora", "hour", -1) == "faz uma hora"
@pytest.mark.usefixtures("lang_locale")
class TestHongKongLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("now", 0) == "剛才"
assert self.locale._format_timeframe("second", 1) == "1秒"
assert self.locale._format_timeframe("seconds", 30) == "30秒"
assert self.locale._format_timeframe("minute", 1) == "1分鐘"
assert self.locale._format_timeframe("minutes", 40) == "40分鐘"
assert self.locale._format_timeframe("hour", 1) == "1小時"
assert self.locale._format_timeframe("hours", 23) == "23小時"
assert self.locale._format_timeframe("day", 1) == "1天"
assert self.locale._format_timeframe("days", 12) == "12天"
assert self.locale._format_timeframe("week", 1) == "1星期"
assert self.locale._format_timeframe("weeks", 38) == "38星期"
assert self.locale._format_timeframe("month", 1) == "1個月"
assert self.locale._format_timeframe("months", 11) == "11個月"
assert self.locale._format_timeframe("year", 1) == "1年"
assert self.locale._format_timeframe("years", 12) == "12年"
@pytest.mark.usefixtures("lang_locale")
class TestChineseTWLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("now", 0) == "剛才"
assert self.locale._format_timeframe("second", 1) == "1秒"
assert self.locale._format_timeframe("seconds", 30) == "30秒"
assert self.locale._format_timeframe("minute", 1) == "1分鐘"
assert self.locale._format_timeframe("minutes", 40) == "40分鐘"
assert self.locale._format_timeframe("hour", 1) == "1小時"
assert self.locale._format_timeframe("hours", 23) == "23小時"
assert self.locale._format_timeframe("day", 1) == "1天"
assert self.locale._format_timeframe("days", 12) == "12天"
assert self.locale._format_timeframe("week", 1) == "1週"
assert self.locale._format_timeframe("weeks", 38) == "38週"
assert self.locale._format_timeframe("month", 1) == "1個月"
assert self.locale._format_timeframe("months", 11) == "11個月"
assert self.locale._format_timeframe("year", 1) == "1年"
assert self.locale._format_timeframe("years", 12) == "12年"
@pytest.mark.usefixtures("lang_locale")
class TestSwahiliLocale:
def test_format_timeframe(self):
assert self.locale._format_timeframe("now", 0) == "sasa hivi"
assert self.locale._format_timeframe("second", 1) == "sekunde"
assert self.locale._format_timeframe("seconds", 3) == "sekunde 3"
assert self.locale._format_timeframe("seconds", 30) == "sekunde 30"
assert self.locale._format_timeframe("minute", 1) == "dakika moja"
assert self.locale._format_timeframe("minutes", 4) == "dakika 4"
assert self.locale._format_timeframe("minutes", 40) == "dakika 40"
assert self.locale._format_timeframe("hour", 1) == "saa moja"
assert self.locale._format_timeframe("hours", 5) == "saa 5"
assert self.locale._format_timeframe("hours", 23) == "saa 23"
assert self.locale._format_timeframe("day", 1) == "siku moja"
assert self.locale._format_timeframe("days", 6) == "siku 6"
assert self.locale._format_timeframe("days", 12) == "siku 12"
assert self.locale._format_timeframe("month", 1) == "mwezi moja"
assert self.locale._format_timeframe("months", 7) == "miezi 7"
assert self.locale._format_timeframe("week", 1) == "wiki moja"
assert self.locale._format_timeframe("weeks", 2) == "wiki 2"
assert self.locale._format_timeframe("months", 11) == "miezi 11"
assert self.locale._format_timeframe("year", 1) == "mwaka moja"
assert self.locale._format_timeframe("years", 8) == "miaka 8"
assert self.locale._format_timeframe("years", 12) == "miaka 12"
def test_format_relative_now(self):
result = self.locale._format_relative("sasa hivi", "now", 0)
assert result == "sasa hivi"
def test_format_relative_past(self):
result = self.locale._format_relative("saa moja", "hour", 1)
assert result == "muda wa saa moja"
def test_format_relative_future(self):
result = self.locale._format_relative("saa moja", "hour", -1)
assert result == "saa moja iliyopita"
| 48.79838 | 88 | 0.650964 | 6,929 | 54,215 | 4.903594 | 0.086737 | 0.175118 | 0.263237 | 0.298496 | 0.87986 | 0.844925 | 0.743944 | 0.601731 | 0.380169 | 0.370074 | 0 | 0.035213 | 0.201715 | 54,215 | 1,110 | 89 | 48.842342 | 0.74699 | 0.009167 | 0 | 0.268793 | 0 | 0 | 0.160893 | 0 | 0 | 0 | 0 | 0 | 0.719818 | 1 | 0.105923 | false | 0 | 0.003417 | 0 | 0.149203 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
13994f4a80a79c3c3d1049dbfc126c0cf769e058 | 6,022 | py | Python | tests/test_add_datapackage_views.py | frictionlessdata/pilot-ukds | b2834a325d1f31ba0da41aba622b806b7c6947a5 | [
"MIT"
] | 1 | 2017-08-29T12:57:19.000Z | 2017-08-29T12:57:19.000Z | tests/test_add_datapackage_views.py | frictionlessdata/pilot-ukds | b2834a325d1f31ba0da41aba622b806b7c6947a5 | [
"MIT"
] | 4 | 2017-09-04T12:39:09.000Z | 2017-09-15T13:17:34.000Z | tests/test_add_datapackage_views.py | frictionlessdata/pilot-ukds | b2834a325d1f31ba0da41aba622b806b7c6947a5 | [
"MIT"
] | null | null | null | import os
from datapackage_pipelines.utilities.lib_test_helpers import (
mock_processor_test
)
import datapackage_pipelines_ukds.processors
from .test_utils import TestBase
import logging
log = logging.getLogger(__name__)
this_dir, this_filename = os.path.split(__file__)
class TestAddDatapackageViewsProcessor(TestBase):
def test_add_datapackage_views_processor(self):
# input arguments used by our mock `ingest`
datapackage = {
'name': 'my-datapackage',
'resources': []
}
params = {
'views': ['tests/sample_data/sample-view-spec.json']
}
# Path to the processor we want to test
processor_dir = \
os.path.dirname(datapackage_pipelines_ukds.processors.__file__)
processor_path = os.path.join(processor_dir,
'add_datapackage_views.py')
# Trigger the processor with our mock `ingest` and capture what it will
# returned to `spew`.
spew_args, _ = mock_processor_test(processor_path,
(params, datapackage, []))
spew_dp = spew_args[0]
# Asserts for the datapackage
expected_dp = {
'name': 'my-datapackage',
'resources': [],
'views': [
{
'name': 'simple-view-bar',
'resources': ['my-resource'],
'spec': {'group': 'date', 'series': ['my-column'],
'type': 'bar'},
'specType': 'simple',
'title': 'My View Title'
},
{
'name': 'second-view',
'resources': ['my-resource'],
'spec': {'group': 'date', 'series': ['my-column'],
'type': 'bar'},
'specType': 'simple',
'title': 'My Second View Title'
}
]
}
self.assertEqual(spew_dp, expected_dp)
def test_add_datapackage_views_processor_existing_views(self):
'''Adding views to datapackage that has existing views.'''
# input arguments used by our mock `ingest`
datapackage = {
'name': 'my-datapackage',
'resources': [],
'views': [
{
'name': 'existing-view',
'resources': ['my-resource'],
'spec': {'group': 'date', 'series': ['my-column'],
'type': 'bar'},
'specType': 'simple',
'title': 'My Existing View Title'
}]
}
params = {
'views': ['tests/sample_data/sample-view-spec.json']
}
# Path to the processor we want to test
processor_dir = \
os.path.dirname(datapackage_pipelines_ukds.processors.__file__)
processor_path = os.path.join(processor_dir,
'add_datapackage_views.py')
# Trigger the processor with our mock `ingest` and capture what it will
# returned to `spew`.
spew_args, _ = mock_processor_test(processor_path,
(params, datapackage, []))
spew_dp = spew_args[0]
# Asserts for the datapackage
expected_dp = {
'name': 'my-datapackage',
'resources': [],
'views': [
{
'name': 'existing-view',
'resources': ['my-resource'],
'spec': {'group': 'date', 'series': ['my-column'],
'type': 'bar'},
'specType': 'simple',
'title': 'My Existing View Title'
},
{
'name': 'simple-view-bar',
'resources': ['my-resource'],
'spec': {'group': 'date', 'series': ['my-column'],
'type': 'bar'},
'specType': 'simple',
'title': 'My View Title'
},
{
'name': 'second-view',
'resources': ['my-resource'],
'spec': {'group': 'date', 'series': ['my-column'],
'type': 'bar'},
'specType': 'simple',
'title': 'My Second View Title'
}
]
}
self.assertEqual(spew_dp, expected_dp)
def test_add_datapackage_views_processor_dict(self):
# input arguments used by our mock `ingest`
datapackage = {
'name': 'my-datapackage',
'resources': []
}
params = {
'views': ['tests/sample_data/sample-view-spec-dict.json']
}
# Path to the processor we want to test
processor_dir = \
os.path.dirname(datapackage_pipelines_ukds.processors.__file__)
processor_path = os.path.join(processor_dir,
'add_datapackage_views.py')
# Trigger the processor with our mock `ingest` and capture what it will
# returned to `spew`.
spew_args, _ = mock_processor_test(processor_path,
(params, datapackage, []))
spew_dp = spew_args[0]
# Asserts for the datapackage
expected_dp = {
'name': 'my-datapackage',
'resources': [],
'views': [
{
'name': 'simple-view-bar',
'resources': ['my-resource'],
'spec': {'group': 'date', 'series': ['my-column'],
'type': 'bar'},
'specType': 'simple',
'title': 'My View Title'
}
]
}
self.assertEqual(spew_dp, expected_dp)
| 34.609195 | 79 | 0.458818 | 509 | 6,022 | 5.233792 | 0.166994 | 0.015766 | 0.049925 | 0.060435 | 0.876502 | 0.876502 | 0.863363 | 0.863363 | 0.851727 | 0.851727 | 0 | 0.00086 | 0.420458 | 6,022 | 173 | 80 | 34.809249 | 0.762464 | 0.107439 | 0 | 0.699248 | 0 | 0 | 0.215313 | 0.036228 | 0 | 0 | 0 | 0 | 0.022556 | 1 | 0.022556 | false | 0 | 0.037594 | 0 | 0.067669 | 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 |
139dff813b506b12358ae7034bb9babf33870c37 | 36,255 | py | Python | train_style_modules.py | IGLICT/StylizedNeRF | ed8df1f7ac4602a079e514cc7898644ca573cf11 | [
"MIT"
] | null | null | null | train_style_modules.py | IGLICT/StylizedNeRF | ed8df1f7ac4602a079e514cc7898644ca573cf11 | [
"MIT"
] | null | null | null | train_style_modules.py | IGLICT/StylizedNeRF | ed8df1f7ac4602a079e514cc7898644ca573cf11 | [
"MIT"
] | null | null | null | import os
import torch
import shutil
import VGGNet
import argparse
import numpy as np
from tqdm import tqdm
import torch.nn as nn
from models_jt import VAE
from pathlib import Path
from models_jt import Camera
import torch.utils.data as data
from PIL import Image, ImageFile
from torchvision import transforms
import torch.backends.cudnn as cudnn
from tensorboardX import SummaryWriter
from plyfile import PlyElement, PlyData
from Style_function import calc_mean_std
# from pytorch3d.structures import Pointclouds
# from pytorch3d.renderer import compositing
# from pytorch3d.renderer.points import rasterize_points
# cudnn.benchmark = True
# Image.MAX_IMAGE_PIXELS = None # Disable DecompressionBombError
# # Disable OSError: image file is truncated
# ImageFile.LOAD_TRUNCATED_IMAGES = True
def InfiniteSampler(n):
# i = 0
i = n - 1
order = np.random.permutation(n)
while True:
yield order[i]
i += 1
if i >= n:
np.random.seed()
order = np.random.permutation(n)
i = 0
class InfiniteSamplerWrapper(data.sampler.Sampler):
def __init__(self, data_source):
# super(InfiniteSamplerWrapper, self).__init__()
self.num_samples = len(data_source)
def __iter__(self):
return iter(InfiniteSampler(self.num_samples))
def __len__(self):
return 2 ** 31
def train_transform():
transform_list = [
transforms.Resize(size=(512, 512)),
transforms.RandomCrop(256),
transforms.ToTensor()
]
return transforms.Compose(transform_list)
def train_transform2():
transform_list = [
transforms.Resize(size=(512, 512)),
transforms.ToTensor()
]
return transforms.Compose(transform_list)
def default_transform():
transform_list = [
transforms.ToTensor()
]
return transforms.Compose(transform_list)
class FlatFolderDataset(data.Dataset):
def __init__(self, root, transform=None):
super(FlatFolderDataset, self).__init__()
self.root = root
self.paths = list(Path(self.root).glob('*'))
transform = default_transform() if transform is None else transform
self.transform = transform
def __getitem__(self, index):
path = self.paths[index]
img = Image.open(str(path)).convert('RGB')
img = self.transform(img)
return img
def __len__(self):
return len(self.paths)
def name(self):
return 'FlatFolderDataset'
class CoorImageDataset(data.Dataset):
def __init__(self, root):
super(CoorImageDataset, self).__init__()
self.root = root
self.image_paths = sorted(list(Path(self.root).glob('rgb_*.png')))
self.geo_paths = sorted(list(Path(self.root).glob('geometry_*.npz')))
data = np.load(str(self.geo_paths[0]))
self.hwf = data['hwf']
# self.near, self.far = data['near'], data['far']
self.near, self.far = 0., 1.
self.transform = default_transform()
def __getitem__(self, index):
image_path, geo_path = self.image_paths[index], self.geo_paths[index]
img = Image.open(str(image_path)).convert('RGB')
img = self.transform(img)
geo = np.load(str(geo_path))
coor_map, cps = geo['coor_map'], geo['cps']
return img, coor_map, cps
def __len__(self):
return len(self.image_paths)
def name(self):
return 'FlatFolderDataset'
class CoorImageDataset_pl(data.Dataset):
def __init__(self, root, factor=0.01):
super(CoorImageDataset_pl, self).__init__()
self.root = root
self.image_paths = sorted(list(Path(self.root).glob('rgb_*.png')))
self.geo_paths = sorted(list(Path(self.root).glob('geometry_*.npz')))
data = np.load(str(self.geo_paths[0]))
self.hwf = data['hwf']
# self.near, self.far = data['near'], data['far']
self.near, self.far = 0., 1.
self.factor = factor
self.transform = default_transform()
ts = np.zeros([len(self.geo_paths), 3], dtype=np.float32)
for i in range(len(self.geo_paths)):
ts[i] = np.load(str(self.geo_paths[i]))['cps'][:3, 3]
dist = ts[np.newaxis] - ts[:, np.newaxis]
dist = dist ** 2
dist = dist.sum(-1) ** 0.5
self.dist = dist
def get_batch(self, batch_size, index=None):
if index is None:
index = np.random.randint(0, len(self.image_paths))
dists = self.dist[index]
inds = np.argsort(dists)
prange = max(int(self.factor*len(self.image_paths)), batch_size)
inds = inds[:prange]
inds = np.random.choice(inds, [batch_size], replace=(prange <= batch_size))
imgs, coor_maps, cps = [], [], []
for i in range(batch_size):
img, coor_map, cp = self.__getitem__(inds[i])
imgs.append(img)
coor_maps.append(coor_map)
cps.append(cp)
imgs = torch.stack(imgs).float()
coor_maps = torch.from_numpy(np.stack(coor_maps)).float()
cps = torch.from_numpy(np.stack(cps)).float()
return imgs, coor_maps, cps
def __getitem__(self, index):
image_path, geo_path = self.image_paths[index], self.geo_paths[index]
img = Image.open(str(image_path)).convert('RGB')
img = self.transform(img)
geo = np.load(str(geo_path))
coor_map, cps = geo['coor_map'], geo['cps']
return img, coor_map, cps
def __len__(self):
return len(self.image_paths)
def name(self):
return 'FlatFolderDataset'
def adjust_learning_rate(optimizer, iteration_count):
"""Imitating the original implementation"""
lr = args.lr / (1.0 + args.lr_decay * iteration_count)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def finetune_decoder(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
save_dir = Path(args.save_dir)
save_dir.mkdir(exist_ok=True, parents=True)
log_dir = Path(args.log_dir)
log_dir.mkdir(exist_ok=True, parents=True)
writer = SummaryWriter(log_dir=str(log_dir))
decoder = VGGNet.decoder
vgg = VGGNet.vgg
decoder.load_state_dict(torch.load('./models/decoder.pth'))
vgg.load_state_dict(torch.load('./models/vgg_normalised.pth'))
vgg.load_state_dict(torch.load(args.vgg))
vgg = nn.Sequential(*list(vgg.children())[:31])
network = VGGNet.Net(vgg, decoder)
network.train()
network.to(device)
content_tf = train_transform()
style_tf = train_transform()
content_dataset = FlatFolderDataset(args.content_dir, content_tf)
style_dataset = FlatFolderDataset(args.style_dir, style_tf)
content_iter = iter(data.DataLoader(
content_dataset, batch_size=args.batch_size,
sampler=InfiniteSamplerWrapper(content_dataset),
num_workers=args.n_threads))
style_iter = iter(data.DataLoader(
style_dataset, batch_size=args.batch_size,
sampler=InfiniteSamplerWrapper(style_dataset),
num_workers=args.n_threads))
optimizer = torch.optim.Adam(network.decoder.parameters(), lr=args.lr)
for i in tqdm(range(args.max_iter)):
adjust_learning_rate(optimizer, iteration_count=i)
content_images = next(content_iter).to(device)
style_images = next(style_iter).to(device)
loss_c, loss_s = network(content_images, style_images)
loss_c = args.content_weight * loss_c
loss_s = args.style_weight * loss_s
loss = loss_c + loss_s
optimizer.zero_grad()
loss.backward()
optimizer.step()
writer.add_scalar('loss_content', loss_c.item(), i + 1)
writer.add_scalar('loss_style', loss_s.item(), i + 1)
if (i + 1) % args.save_model_interval == 0 or (i + 1) == args.max_iter:
state_dict = network.decoder.state_dict()
for key in state_dict.keys():
state_dict[key] = state_dict[key].to(torch.device('cpu'))
torch.save(state_dict, save_dir /
'decoder_iter_{:d}.pth.tar'.format(i + 1))
writer.close()
def train_vae(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
save_dir = Path(args.save_dir)
save_dir.mkdir(exist_ok=True, parents=True)
log_dir = Path(args.log_dir)
log_dir.mkdir(exist_ok=True, parents=True)
writer = SummaryWriter(log_dir=str(log_dir))
vgg = VGGNet.vgg
vgg.load_state_dict(torch.load('./pretrained/vgg_normalised.pth'))
vgg.load_state_dict(torch.load(args.vgg))
vgg = nn.Sequential(*list(vgg.children())[:31])
vgg.eval()
vgg.to(device)
style_tf = train_transform()
style_dataset = FlatFolderDataset(args.style_dir, style_tf)
style_iter = iter(data.DataLoader(
style_dataset, batch_size=args.batch_size,
sampler=InfiniteSamplerWrapper(style_dataset),
num_workers=args.n_threads))
vae = VAE(data_dim=1024, latent_dim=args.vae_latent, W=args.vae_w, D=args.vae_d, kl_lambda=args.vae_kl_lambda)
vae.train()
vae.to(device)
vae_ckpt = './pretrained/vae.pth'
if os.path.exists(vae_ckpt):
vae.load_state_dict(torch.load(vae_ckpt))
optimizer = torch.optim.Adam(vae.parameters(), lr=args.lr)
for i in tqdm(range(args.max_iter)):
adjust_learning_rate(optimizer, iteration_count=i)
style_images = next(style_iter).to(device)
style_features = vgg(style_images)
style_mean, style_std = calc_mean_std(style_features)
style_features = torch.cat([style_mean.squeeze(), style_std.squeeze()], dim=-1)
recon, _, mu, logvar = vae(style_features)
loss, recon_loss, kl_loss = vae.loss(style_features, recon, mu, logvar, return_losses=True)
optimizer.zero_grad()
loss.backward()
optimizer.step()
writer.add_scalar('Reconstruction Loss', recon_loss.item(), i + 1)
writer.add_scalar('KL Loss', kl_loss.item(), i + 1)
if (i + 1) % 100 == 0:
print("Loss: %.3f | Recon Loss: %.3f| KL Loss: %.3f" % (loss.item(), recon_loss.item(), kl_loss.item()))
if (i + 1) % args.save_model_interval == 0 or (i + 1) == args.max_iter:
state_dict = vae.state_dict()
for key in state_dict.keys():
state_dict[key] = state_dict[key].to(torch.device('cpu'))
torch.save(state_dict, vae_ckpt)
writer.close()
def train_temporal_invoke(save_dir, sv_name, log_dir, is_ndc, nerf_content_dir, style_dir, batch_size, n_threads=8, lr=1e-3, max_iter=1000):
if is_ndc:
print("Using NDC Coordinate System! Check Nerf and dataset to be LLFF !!!!!!!")
temporal_weight, content_weight, style_weight = 50., 1.0, 1.
else:
temporal_weight, content_weight, style_weight = 50., 1.0, 1.
print_interval = 20
save_model_interval = 200
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
save_dir = Path(save_dir)
save_dir.mkdir(exist_ok=True, parents=True)
log_dir = Path(log_dir)
log_dir.mkdir(exist_ok=True, parents=True)
writer = SummaryWriter(log_dir=str(log_dir))
save_dir, log_dir = str(save_dir), str(log_dir)
decoder = VGGNet.decoder
vgg = VGGNet.vgg
ckpts = [os.path.join(save_dir, f) for f in sorted(os.listdir(save_dir)) if sv_name in f]
if len(ckpts) > 0:
ld_dict = torch.load(ckpts[-1])
decoder.load_state_dict(ld_dict['decoder'])
step = ld_dict['step']
else:
print('From original pth file')
decoder.load_state_dict(torch.load('./pretrained/decoder.pth'))
shutil.copy('./pretrained/decoder.pth', save_dir + '/' + sv_name)
step = 0
vgg.load_state_dict(torch.load('./pretrained/vgg_normalised.pth'))
vgg = nn.Sequential(*list(vgg.children())[:31])
network = VGGNet.Net(vgg, decoder)
network.train()
network.to(device)
style_tf = train_transform2()
content_dataset = CoorImageDataset(nerf_content_dir)
style_dataset = FlatFolderDataset(style_dir, style_tf)
# Camera for Rendering
h, w, focal = content_dataset.hwf
h, w = int(h), int(w)
cx, cy = w/2, h/2
near_prj, far_prj = 1e-3, 1e5
projectionMatrix = np.array([[-2*focal/w, 0, 1-2*cx/w, 0],
[0, 2*focal/h, 2*cy/h-1, 0],
[0, 0, -(far_prj+near_prj)/(far_prj-near_prj), -2*far_prj*near_prj/(far_prj-near_prj)],
[0, 0, -1, 0]])
camera = Camera(projectionMatrix=projectionMatrix)
camera.to(device)
content_iter = iter(data.DataLoader(
content_dataset, batch_size=batch_size,
sampler=InfiniteSamplerWrapper(content_dataset),
num_workers=n_threads))
style_iter = iter(data.DataLoader(
style_dataset, batch_size=1,
sampler=InfiniteSamplerWrapper(style_dataset),
num_workers=n_threads))
optimizer = torch.optim.Adam(network.decoder.parameters(), lr=lr)
space_dist_threshold = 5e-2
def adjust_learning_rate_local(optimizer, iteration_count):
"""Imitating the original implementation"""
lr = 1e-4 / (1.0 + 5e-5 * iteration_count)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
for i in tqdm(range(step, max_iter)):
# Sampling Patch
patch_size = 512
if patch_size > 0:
patch_h_min, patch_w_min = np.random.randint(0, h - patch_size), np.random.randint(0, w - patch_size)
patch_h_max, patch_w_max = patch_h_min + patch_size, patch_w_min + patch_size
else:
patch_h_min, patch_w_min = 0, 0
patch_h_max, patch_w_max = h, w
resample_layer = nn.Upsample(size=(int(patch_h_max - patch_h_min), int(patch_w_max - patch_w_min)),
mode='bilinear', align_corners=True)
adjust_learning_rate_local(optimizer, iteration_count=i)
content_images, coor_maps, cps = next(content_iter)
content_images, coor_maps, cps = content_images[..., patch_h_min: patch_h_max, patch_w_min: patch_w_max].to(device),\
coor_maps[:, patch_h_min: patch_h_max, patch_w_min: patch_w_max].to(device),\
cps.to(device)
if is_ndc:
coor_maps = ndc2world(coor_maps, h, w, focal)
# The same style image
style_images = next(style_iter).to(device)
style_images = style_images[:1].expand([batch_size, * style_images.shape[1:]])
loss_c, loss_s, stylized_content = network(content_images, style_images, return_stylized_content=True)
stylized_content = resample_layer(stylized_content)
# Set camera pose
camera.set(cameraPose=cps)
pcl_coor_world0 = coor_maps[0].reshape([-1, 3])
pcl_rgb0 = torch.movedim(stylized_content[0], 0, -1).reshape([-1, 3])
warped_stylized_content0, warped_coor_map0, warped_msks = camera.rasterize(pcl_coor_world0, pcl_rgb0, h=h, w=w)
warped_stylized_content0, warped_coor_map0, warped_msks = warped_stylized_content0[:, patch_h_min: patch_h_max, patch_w_min: patch_w_max],\
warped_coor_map0[:, patch_h_min: patch_h_max, patch_w_min: patch_w_max],\
warped_msks[:, patch_h_min: patch_h_max, patch_w_min: patch_w_max]
coor_dist_msk = (((warped_coor_map0 - coor_maps) ** 2).sum(-1, keepdim=True) < space_dist_threshold ** 2).float()
loss_t = (((torch.movedim(stylized_content, 1, -1) - warped_stylized_content0) ** 2) * warped_msks * coor_dist_msk).mean()
loss_t = temporal_weight * loss_t
loss_c = content_weight * loss_c
loss_s = style_weight * loss_s
loss = loss_c + loss_s + loss_t
optimizer.zero_grad()
loss.backward()
optimizer.step()
writer.add_scalar('loss_content', loss_c.item(), i + 1)
writer.add_scalar('loss_style', loss_s.item(), i + 1)
writer.add_scalar('loss_temporal', loss_t.item(), i + 1)
if (i + 1) % print_interval == 0:
print('Iter %d Content Loss: %.3f Style Loss: %.3f Temporal Loss: %.3f' % (i, loss_c.item(), loss_s.item(), loss_t.item()))
if i == 0 or (i + 1) % save_model_interval == 0 or (i + 1) == max_iter:
state_dict = network.decoder.state_dict()
for key in state_dict.keys():
state_dict[key] = state_dict[key].to(torch.device('cpu'))
sv_dict = {'decoder': state_dict, 'step': (i+1)}
torch.save(sv_dict, save_dir + '/' + sv_name)
warped_stylized_content0 = torch.clamp(warped_stylized_content0, 0, 1).detach().cpu().numpy()
coor_dist_msk = np.broadcast_to(coor_dist_msk.detach().cpu().numpy(), [*coor_dist_msk.shape[:-1], 3])
warped_msks = np.broadcast_to(warped_msks.detach().cpu().numpy(), [*warped_msks.shape[:-1], 3])
stylized_content = torch.movedim(torch.clamp(stylized_content, 0., 1.), 1, -1).detach().cpu().numpy()
for i in range(warped_stylized_content0.shape[0]):
Image.fromarray(np.uint8(255 * warped_stylized_content0[i])).save(log_dir + '/warped_stylized_content_%03d.png' % i)
Image.fromarray(np.uint8(255 * stylized_content[i])).save(log_dir + '/stylized_content_%03d.png' % i)
Image.fromarray(np.uint8(255 * coor_dist_msk[i])).save(log_dir + '/coor_dist_msk_%03d.png' % i)
Image.fromarray(np.uint8(255 * warped_msks[i])).save(log_dir + '/warped_mask_%03d.png' % i)
Image.fromarray(np.uint8(255*torch.movedim(style_images[0], 0, -1).detach().cpu().numpy())).save(log_dir + '/style_image.png')
writer.close()
def train_temporal_invoke_pl(save_dir, sv_name, log_dir, nerf_content_dir, style_dir, batch_size, n_threads=8, lr=1e-3, max_iter=5000):
temporal_weight, content_weight, style_weight = 100., 1.0, 1.
print_interval = 20
save_model_interval = 200
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
save_dir = Path(save_dir)
save_dir.mkdir(exist_ok=True, parents=True)
log_dir = Path(log_dir)
log_dir.mkdir(exist_ok=True, parents=True)
writer = SummaryWriter(log_dir=str(log_dir))
save_dir, log_dir = str(save_dir), str(log_dir)
decoder = VGGNet.decoder
vgg = VGGNet.vgg
ckpts = [os.path.join(save_dir, f) for f in sorted(os.listdir(save_dir)) if sv_name in f]
if len(ckpts) > 0:
ld_dict = torch.load(ckpts[-1])
decoder.load_state_dict(ld_dict['decoder'])
step = ld_dict['step']
else:
print('From original pth file')
decoder.load_state_dict(torch.load('./pretrained/decoder.pth'))
shutil.copy('./pretrained/decoder.pth', save_dir + '/' + sv_name)
step = 0
vgg.load_state_dict(torch.load('./pretrained/vgg_normalised.pth'))
vgg = nn.Sequential(*list(vgg.children())[:31])
network = VGGNet.Net(vgg, decoder)
network.train()
network.to(device)
style_tf = train_transform2()
content_dataset = CoorImageDataset_pl(nerf_content_dir)
style_dataset = FlatFolderDataset(style_dir, style_tf)
# Camera for Rendering
h, w, focal = content_dataset.hwf
h, w = int(h), int(w)
cx, cy = w/2, h/2
near_prj, far_prj = 1e-3, 1e5
projectionMatrix = np.array([[-2*focal/w, 0, 1-2*cx/w, 0],
[0, 2*focal/h, 2*cy/h-1, 0],
[0, 0, -(far_prj+near_prj)/(far_prj-near_prj), -2*far_prj*near_prj/(far_prj-near_prj)],
[0, 0, -1, 0]])
camera = Camera(projectionMatrix=projectionMatrix)
camera.to(device)
style_iter = iter(data.DataLoader(
style_dataset, batch_size=1,
sampler=InfiniteSamplerWrapper(style_dataset),
num_workers=n_threads))
optimizer = torch.optim.Adam(network.decoder.parameters(), lr=lr)
space_dist_threshold = 5e-2
def adjust_learning_rate_local(optimizer, iteration_count):
"""Imitating the original implementation"""
lr = 1e-4 / (1.0 + 5e-5 * iteration_count)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
for i in tqdm(range(step, max_iter)):
# Sampling Patch
patch_size = 512
if patch_size > 0:
patch_h_min, patch_w_min = np.random.randint(0, h - patch_size), np.random.randint(0, w - patch_size)
patch_h_max, patch_w_max = patch_h_min + patch_size, patch_w_min + patch_size
else:
patch_h_min, patch_w_min = 0, 0
patch_h_max, patch_w_max = h, w
resample_layer = nn.Upsample(size=(int(patch_h_max - patch_h_min), int(patch_w_max - patch_w_min)),
mode='bilinear', align_corners=True)
adjust_learning_rate_local(optimizer, iteration_count=i)
content_images, coor_maps, cps = content_dataset.get_batch(batch_size=batch_size)
content_images, coor_maps, cps = content_images[..., patch_h_min: patch_h_max, patch_w_min: patch_w_max].to(device),\
coor_maps[:, patch_h_min: patch_h_max, patch_w_min: patch_w_max].to(device),\
cps.to(device)
# The same style image
style_images = next(style_iter).to(device)
style_images = style_images[:1].expand([batch_size, * style_images.shape[1:]])
loss_c, loss_s, stylized_content = network(content_images, style_images, return_stylized_content=True)
stylized_content = resample_layer(stylized_content)
# Set camera pose
camera.set(cameraPose=cps)
pcl_coor_world0 = coor_maps[0].reshape([-1, 3])
pcl_rgb0 = torch.movedim(stylized_content[0], 0, -1).reshape([-1, 3])
warped_stylized_content0, warped_coor_map0, warped_msks = camera.rasterize(pcl_coor_world0, pcl_rgb0, h=h, w=w)
warped_stylized_content0, warped_coor_map0, warped_msks = warped_stylized_content0[:, patch_h_min: patch_h_max, patch_w_min: patch_w_max],\
warped_coor_map0[:, patch_h_min: patch_h_max, patch_w_min: patch_w_max],\
warped_msks[:, patch_h_min: patch_h_max, patch_w_min: patch_w_max]
coor_dist_msk = (((warped_coor_map0 - coor_maps) ** 2).sum(-1, keepdim=True) < space_dist_threshold ** 2).float()
loss_t = (((torch.movedim(stylized_content, 1, -1) - warped_stylized_content0) ** 2) * warped_msks * coor_dist_msk).mean()
loss_t = temporal_weight * loss_t
loss_c = content_weight * loss_c
loss_s = style_weight * loss_s
loss = loss_c + loss_s + loss_t
optimizer.zero_grad()
loss.backward()
optimizer.step()
writer.add_scalar('loss_content', loss_c.item(), i + 1)
writer.add_scalar('loss_style', loss_s.item(), i + 1)
writer.add_scalar('loss_temporal', loss_t.item(), i + 1)
if (i + 1) % print_interval == 0:
print('Iter %d Content Loss: %.3f Style Loss: %.3f Temporal Loss: %.3f' % (i, loss_c.item(), loss_s.item(), loss_t.item()))
if i == 0 or (i + 1) % save_model_interval == 0 or (i + 1) == max_iter:
state_dict = network.decoder.state_dict()
for key in state_dict.keys():
state_dict[key] = state_dict[key].to(torch.device('cpu'))
sv_dict = {'decoder': state_dict, 'step': (i+1)}
torch.save(sv_dict, save_dir + '/' + sv_name)
warped_stylized_content0 = torch.clamp(warped_stylized_content0, 0, 1).detach().cpu().numpy()
coor_dist_msk = np.broadcast_to(coor_dist_msk.detach().cpu().numpy(), [*coor_dist_msk.shape[:-1], 3])
warped_msks = np.broadcast_to(warped_msks.detach().cpu().numpy(), [*warped_msks.shape[:-1], 3])
stylized_content = torch.movedim(torch.clamp(stylized_content, 0., 1.), 1, -1).detach().cpu().numpy()
for i in range(warped_stylized_content0.shape[0]):
Image.fromarray(np.uint8(255 * warped_stylized_content0[i])).save(log_dir + '/warped_stylized_content_%03d.png' % i)
Image.fromarray(np.uint8(255 * stylized_content[i])).save(log_dir + '/stylized_content_%03d.png' % i)
Image.fromarray(np.uint8(255 * coor_dist_msk[i])).save(log_dir + '/coor_dist_msk_%03d.png' % i)
Image.fromarray(np.uint8(255 * warped_msks[i])).save(log_dir + '/warped_mask_%03d.png' % i)
Image.fromarray(np.uint8(255*torch.movedim(style_images[0], 0, -1).detach().cpu().numpy())).save(log_dir + '/style_image.png')
writer.close()
def write_ply_rgb(points, RGB, filename):
""" Color (N,3) points with labels (N) within range 0 ~ num_classes-1 as PLY file """
N = points.shape[0]
vertex = []
for i in range(N):
vertex.append((points[i, 0], points[i, 1], points[i, 2], RGB[i][0], RGB[i][1], RGB[i][2]))
vertex = np.array(vertex,
dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')])
el = PlyElement.describe(vertex, 'vertex', comments=['vertices'])
PlyData([el], text=True).write(filename)
def ndc2world(coor_ndc, h, w, focal):
z = 2 / (coor_ndc[..., -1] - 1)
x = - w / 2 / focal * z * coor_ndc[..., 0]
y = - h / 2 / focal * z * coor_ndc[..., 1]
coor_world = torch.stack([x, y, z], dim=-1)
return coor_world
def train_temporal_decoder(args):
if not args.no_ndc:
print("Using NDC Coordinate System! Check Nerf and dataset to be LLFF !!!!!!!")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
save_dir = Path(args.save_dir)
save_dir.mkdir(exist_ok=True, parents=True)
log_dir = Path(args.log_dir)
log_dir.mkdir(exist_ok=True, parents=True)
writer = SummaryWriter(log_dir=str(log_dir))
decoder = VGGNet.decoder
vgg = VGGNet.vgg
ckpts = [os.path.join(save_dir, f) for f in sorted(os.listdir(save_dir)) if 'decoder_iter_' in f]
if len(ckpts) > 0 and not args.no_reload:
ld_dict = torch.load(ckpts[-1])
decoder.load_state_dict(ld_dict['decoder'])
step = ld_dict['step']
else:
print('From original pth file')
decoder.load_state_dict(torch.load('./pretrained/decoder.pth'))
step = 0
vgg.load_state_dict(torch.load('./pretrained/vgg_normalised.pth'))
vgg.load_state_dict(torch.load(args.vgg))
vgg = nn.Sequential(*list(vgg.children())[:31])
network = VGGNet.Net(vgg, decoder)
network.train()
network.to(device)
style_tf = train_transform2()
content_dataset = CoorImageDataset(args.nerf_content_dir)
style_dataset = FlatFolderDataset(args.style_dir, style_tf)
# Camera for Rendering
h, w, focal = content_dataset.hwf
h, w = int(h), int(w)
cx, cy = w/2, h/2
near_prj, far_prj = 1e-3, 1e5
projectionMatrix = np.array([[-2*focal/w, 0, 1-2*cx/w, 0],
[0, 2*focal/h, 2*cy/h-1, 0],
[0, 0, -(far_prj+near_prj)/(far_prj-near_prj), -2*far_prj*near_prj/(far_prj-near_prj)],
[0, 0, -1, 0]])
camera = Camera(projectionMatrix=projectionMatrix)
camera.to(device)
content_iter = iter(data.DataLoader(
content_dataset, batch_size=args.batch_size,
sampler=InfiniteSamplerWrapper(content_dataset),
num_workers=args.n_threads))
style_iter = iter(data.DataLoader(
style_dataset, batch_size=1,
sampler=InfiniteSamplerWrapper(style_dataset),
num_workers=args.n_threads))
# Sampling Patch
patch_size = 512
if patch_size > 0:
patch_h_min, patch_w_min = np.random.randint(0, h-patch_size), np.random.randint(0, w-patch_size)
patch_h_max, patch_w_max = patch_h_min + patch_size, patch_w_min + patch_size
else:
patch_h_min, patch_w_min = 0, 0
patch_h_max, patch_w_max = h, w
resample_layer = nn.Upsample(size=(int(patch_h_max - patch_h_min), int(patch_w_max - patch_w_min)), mode='bilinear', align_corners=True)
optimizer = torch.optim.Adam(network.decoder.parameters(), lr=args.lr)
space_dist_threshold = 5e-2
for i in tqdm(range(step, args.max_iter)):
adjust_learning_rate(optimizer, iteration_count=i)
content_images, coor_maps, cps = next(content_iter)
content_images, coor_maps, cps = content_images[..., patch_h_min: patch_h_max, patch_w_min: patch_w_max].to(device),\
coor_maps[:, patch_h_min: patch_h_max, patch_w_min: patch_w_max].to(device),\
cps.to(device)
if not args.no_ndc:
coor_maps = ndc2world(coor_maps, h, w, focal)
# The same style image
style_images = next(style_iter).to(device)
style_images = style_images[:1].expand([args.batch_size, * style_images.shape[1:]])
loss_c, loss_s, stylized_content = network(content_images, style_images, return_stylized_content=True)
stylized_content = resample_layer(stylized_content)
# Set camera pose
camera.set(cameraPose=cps)
pcl_coor_world0 = coor_maps[0].reshape([-1, 3])
pcl_rgb0 = torch.movedim(stylized_content[0], 0, -1).reshape([-1, 3])
warped_stylized_content0, warped_coor_map0, warped_msks = camera.rasterize(pcl_coor_world0, pcl_rgb0, h=h, w=w)
warped_stylized_content0, warped_coor_map0, warped_msks = warped_stylized_content0[:, patch_h_min: patch_h_max, patch_w_min: patch_w_max],\
warped_coor_map0[:, patch_h_min: patch_h_max, patch_w_min: patch_w_max],\
warped_msks[:, patch_h_min: patch_h_max, patch_w_min: patch_w_max]
coor_dist_msk = (((warped_coor_map0 - coor_maps) ** 2).sum(-1, keepdim=True) < space_dist_threshold ** 2).float()
loss_t = (((torch.movedim(stylized_content, 1, -1) - warped_stylized_content0) ** 2) * warped_msks * coor_dist_msk).mean()
loss_t = args.temporal_weight * loss_t
loss_c = args.content_weight * loss_c
loss_s = args.style_weight * loss_s
loss = loss_c + loss_s + loss_t
optimizer.zero_grad()
loss.backward()
optimizer.step()
writer.add_scalar('loss_content', loss_c.item(), i + 1)
writer.add_scalar('loss_style', loss_s.item(), i + 1)
writer.add_scalar('loss_temporal', loss_t.item(), i + 1)
if (i + 1) % args.print_interval == 0:
print('Iter %d Content Loss: %.3f Style Loss: %.3f Temporal Loss: %.3f' % (i, loss_c.item(), loss_s.item(), loss_t.item()))
if i == 0 or (i + 1) % args.save_model_interval == 0 or (i + 1) == args.max_iter:
state_dict = network.decoder.state_dict()
for key in state_dict.keys():
state_dict[key] = state_dict[key].to(torch.device('cpu'))
sv_dict = {'decoder': state_dict, 'step': (i+1)}
torch.save(sv_dict, save_dir /
'decoder_iter_{:d}.pth.tar'.format(i + 1))
# Delete ckpts
ckpts = [os.path.join(save_dir, f) for f in sorted(os.listdir(save_dir)) if 'decoder_iter_' in f]
if len(ckpts) > args.ckp_num:
os.remove(ckpts[0])
warped_stylized_content0 = torch.clamp(warped_stylized_content0, 0, 1).detach().cpu().numpy()
coor_dist_msk = np.broadcast_to(coor_dist_msk.detach().cpu().numpy(), [*coor_dist_msk.shape[:-1], 3])
warped_msks = np.broadcast_to(warped_msks.detach().cpu().numpy(), [*warped_msks.shape[:-1], 3])
stylized_content = torch.movedim(torch.clamp(stylized_content, 0., 1.), 1, -1).detach().cpu().numpy()
for i in range(warped_stylized_content0.shape[0]):
Image.fromarray(np.uint8(255 * warped_stylized_content0[i])).save(args.log_dir + '/warped_stylized_content_%03d.png' % i)
Image.fromarray(np.uint8(255 * stylized_content[i])).save(args.log_dir + '/stylized_content_%03d.png' % i)
Image.fromarray(np.uint8(255 * coor_dist_msk[i])).save(args.log_dir + '/coor_dist_msk_%03d.png' % i)
Image.fromarray(np.uint8(255 * warped_msks[i])).save(args.log_dir + '/warped_mask_%03d.png' % i)
Image.fromarray(np.uint8(255*torch.movedim(style_images[0], 0, -1).detach().cpu().numpy())).save(args.log_dir + '/style_image.png')
writer.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--task', type=str, default='vae',
help='vae or finetune_decoder')
# Basic options
parser.add_argument('--content_dir', type=str, default='./all_contents/',
help='Directory path to a batch of content images')
parser.add_argument('--nerf_content_dir', type=str, default='./nerf_gen_data2/',
help='Directory path to a batch of content images')
parser.add_argument('--style_dir', type=str, default='./all_styles/',
help='Directory path to a batch of style images')
parser.add_argument('--vgg', type=str, default='./pretrained/vgg_normalised.pth')
parser.add_argument('--no_ndc', action='store_true')
parser.add_argument('--no_reload', action='store_true')
# training options
parser.add_argument('--save_dir', default='./pretrained/',
help='Directory to save the model')
parser.add_argument('--ckp_num', type=int, default=3)
parser.add_argument('--log_dir', default='./logs/stylenet/',
help='Directory to save the log')
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--lr_decay', type=float, default=5e-5)
parser.add_argument('--max_iter', type=int, default=160000)
parser.add_argument('--batch_size', type=int, default=8)
parser.add_argument('--style_weight', type=float, default=2.)
parser.add_argument('--content_weight', type=float, default=1.0)
parser.add_argument('--temporal_weight', type=float, default=50.)
parser.add_argument('--n_threads', type=int, default=16)
parser.add_argument('--save_model_interval', type=int, default=200)
parser.add_argument('--print_interval', type=int, default=20)
# train vae options
parser.add_argument('--vae_d', type=int, default=4)
parser.add_argument('--vae_w', type=int, default=512)
parser.add_argument('--vae_latent', type=int, default=32)
parser.add_argument('--vae_kl_lambda', type=float, default=0.1)
args = parser.parse_args()
if args.task == 'finetune_decoder':
finetune_decoder(args)
elif args.task == 'vae':
train_vae(args)
else:
train_temporal_decoder()
| 44.759259 | 148 | 0.614701 | 4,891 | 36,255 | 4.286853 | 0.076876 | 0.014594 | 0.01159 | 0.016025 | 0.796824 | 0.777508 | 0.75471 | 0.744789 | 0.717604 | 0.717032 | 0 | 0.021137 | 0.256185 | 36,255 | 809 | 149 | 44.814586 | 0.756369 | 0.025734 | 0 | 0.700637 | 0 | 0 | 0.066564 | 0.019789 | 0 | 0 | 0 | 0 | 0 | 1 | 0.047771 | false | 0 | 0.028662 | 0.012739 | 0.10828 | 0.023885 | 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 |
13d8e4da8aac2b4967a0443d87e51484bf834652 | 1,248 | py | Python | tests/load/test_load_case.py | CHRUdeLille/scout | 0f70bec32e078d1825ebf20237f4a4979585dffb | [
"BSD-3-Clause"
] | null | null | null | tests/load/test_load_case.py | CHRUdeLille/scout | 0f70bec32e078d1825ebf20237f4a4979585dffb | [
"BSD-3-Clause"
] | null | null | null | tests/load/test_load_case.py | CHRUdeLille/scout | 0f70bec32e078d1825ebf20237f4a4979585dffb | [
"BSD-3-Clause"
] | null | null | null | from scout.load.case import load_case
def test_load_case(case_obj, panel_database):
adapter = panel_database
# GIVEN a database with institute, user, genes, panel but no cases
assert adapter.gene_panels().count() > 0
assert adapter.users().count() > 0
assert adapter.institutes().count() > 0
# WHEN loading a case
adapter._add_case(case_obj)
# THEN assert that the case have been loaded with correct info
assert adapter.cases().count() == 1
loaded_case = adapter.case(case_obj['_id'])
assert loaded_case['_id'] == case_obj['_id']
assert len(loaded_case['panels']) > 0
for panel in loaded_case['panels']:
assert panel['display_name']
def test_load_case_rank_model_version(case_obj, panel_database):
adapter = panel_database
# GIVEN a database with institute, user, genes, panel but no cases
assert adapter.gene_panels().count() > 0
assert adapter.users().count() > 0
assert adapter.institutes().count() > 0
# WHEN loading a case
adapter._add_case(case_obj)
# THEN assert that the case have been loaded with rank_model
loaded_case = adapter.case(case_obj['_id'])
assert loaded_case['rank_model_version'] == case_obj['rank_model_version']
| 31.2 | 78 | 0.702724 | 179 | 1,248 | 4.664804 | 0.268156 | 0.067066 | 0.065868 | 0.091018 | 0.758084 | 0.758084 | 0.706587 | 0.706587 | 0.706587 | 0.706587 | 0 | 0.007929 | 0.191506 | 1,248 | 39 | 79 | 32 | 0.819623 | 0.231571 | 0 | 0.571429 | 0 | 0 | 0.07571 | 0 | 0 | 0 | 0 | 0 | 0.52381 | 1 | 0.095238 | false | 0 | 0.047619 | 0 | 0.142857 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
b9aff5822abd67be7dad00495259fde2db745272 | 31 | py | Python | vnpy/app/rpc_service/ui/__init__.py | funrunskypalace/vnpy | 2d87aede685fa46278d8d3392432cc127b797926 | [
"MIT"
] | 19,529 | 2015-03-02T12:17:35.000Z | 2022-03-31T17:18:27.000Z | vnpy/app/rpc_service/ui/__init__.py | funrunskypalace/vnpy | 2d87aede685fa46278d8d3392432cc127b797926 | [
"MIT"
] | 2,186 | 2015-03-04T23:16:33.000Z | 2022-03-31T03:44:01.000Z | vnpy/app/rpc_service/ui/__init__.py | funrunskypalace/vnpy | 2d87aede685fa46278d8d3392432cc127b797926 | [
"MIT"
] | 8,276 | 2015-03-02T05:21:04.000Z | 2022-03-31T13:13:13.000Z | from .widget import RpcManager
| 15.5 | 30 | 0.83871 | 4 | 31 | 6.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.129032 | 31 | 1 | 31 | 31 | 0.962963 | 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 |
b9f0bcc0cd9fe27673ebd30cffa5b609b32cee9d | 180,200 | py | Python | enaml/core/parse_tab/parsetab.py | pberkes/enaml | cbcbee929e3117dfe56c0b06dc2385acc832b0e8 | [
"BSD-3-Clause-Clear"
] | null | null | null | enaml/core/parse_tab/parsetab.py | pberkes/enaml | cbcbee929e3117dfe56c0b06dc2385acc832b0e8 | [
"BSD-3-Clause-Clear"
] | null | null | null | enaml/core/parse_tab/parsetab.py | pberkes/enaml | cbcbee929e3117dfe56c0b06dc2385acc832b0e8 | [
"BSD-3-Clause-Clear"
] | null | null | null |
# c:\development\enaml\enaml\core\parse_tab\parsetab.py
# This file is automatically generated. Do not edit.
_tabversion = '3.2'
_lr_method = 'LALR'
_lr_signature = '\x03\x98b\xbc2\xd2\xfd\x9cC\x0b\xae\xcc)\xef\xf8\xfb'
_lr_action_items = 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_lr_action = { }
for _k, _v in _lr_action_items.items():
for _x,_y in zip(_v[0],_v[1]):
if not _x in _lr_action: _lr_action[_x] = { }
_lr_action[_x][_k] = _y
del _lr_action_items
_lr_goto_items = {'import_as_name':([335,337,436,438,439,521,527,586,],[433,433,433,525,433,585,433,651,]),'try_stmt':([0,79,364,456,],[6,6,6,6,]),'child_def_suite_items':([782,],[804,]),'small_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[3,3,229,3,342,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,]),'template_inst':([568,638,664,690,714,734,760,782,787,804,],[637,637,713,637,713,637,713,807,713,807,]),'augassign':([16,],[119,]),'import_from':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,]),'small_stmt_list':([0,79,158,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,]),'import_as_names':([335,337,436,439,527,],[437,441,524,528,589,]),'else_stmt':([432,519,],[520,584,]),'enamldef_suite':([531,660,],[595,707,]),'comp_op':([72,198,],[194,319,]),'parameters':([151,],[259,]),'enamldef_impl':([0,76,79,],[9,206,9,]),'factor':([0,1,7,10,14,30,32,34,40,45,51,58,60,64,69,79,80,88,89,93,103,109,110,111,113,115,119,124,136,137,141,147,150,153,158,160,164,166,168,171,172,180,194,210,212,223,225,228,230,231,232,240,241,251,253,255,260,263,267,270,272,277,279,281,284,288,289,298,300,303,306,313,315,319,323,324,326,328,329,330,334,340,346,352,354,357,358,361,364,367,371,376,378,379,385,386,388,410,442,446,455,456,461,463,466,468,473,476,487,489,494,504,505,509,517,535,536,538,539,540,542,545,546,549,554,562,577,583,594,612,616,629,630,633,643,649,654,657,658,659,662,667,669,670,671,673,683,684,691,722,727,735,751,769,791,793,813,],[11,11,11,108,11,11,11,11,154,11,11,11,11,184,11,11,11,11,11,11,11,234,235,236,237,11,11,11,11,11,11,11,11,11,11,11,11,11,11,287,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,382,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,]),'suite':([158,231,306,329,334,346,361,455,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[266,344,402,423,432,445,454,532,537,541,576,606,607,609,625,648,650,697,710,721,723,724,762,]),'globals_list':([182,401,],[304,490,]),'exec_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,41,]),'and_expr_list':([39,],[152,]),'or_test_list':([48,],[159,]),'template_impl':([0,76,79,],[13,205,13,]),'simple_stmt':([0,79,158,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[15,15,265,265,265,265,265,265,265,15,265,15,265,265,265,265,265,265,265,265,265,265,265,265,265,265,265,]),'dotted_as_names_list':([189,],[311,]),'subscriptlist':([172,],[292,]),'testlist':([0,30,79,88,103,119,124,158,228,231,263,306,328,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[16,142,16,214,16,243,245,16,16,16,363,16,422,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,]),'classdef':([0,26,79,364,456,],[17,138,17,17,17,]),'assert_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,18,]),'for_stmt':([0,79,364,456,],[19,19,19,19,]),'template_arglist':([691,],[739,]),'lambdef':([0,1,7,30,34,51,58,60,69,79,80,88,93,103,119,124,150,158,168,172,180,212,225,228,231,232,241,255,263,270,272,277,279,281,288,289,298,300,303,306,315,323,326,328,329,330,334,340,346,352,357,358,361,364,367,371,376,378,379,385,386,388,410,446,455,456,461,463,466,468,473,476,487,489,504,505,509,517,535,536,538,539,540,542,545,546,549,554,562,577,583,612,616,643,649,654,657,658,659,662,667,669,670,671,673,691,722,727,735,751,769,791,793,813,],[20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,]),'expr_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,]),'child_def_suite_item':([782,804,],[805,824,]),'decorator':([0,23,79,364,456,],[23,23,23,23,23,]),'arith_op':([24,134,],[135,246,]),'term':([0,1,7,14,30,32,34,45,51,58,60,69,79,80,88,89,93,103,115,119,124,136,137,141,147,150,153,158,160,164,166,168,172,180,194,210,212,223,225,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]),'template_suite_items':([568,690,],[638,734,]),'import_from_dots':([94,],[220,]),'arglist_list':([168,330,468,],[281,281,546,]),'list_iter':([563,681,],[626,731,]),'dictorsetmaker':([51,],[161,]),'template':([0,79,],[42,42,]),'list_for':([192,563,681,],[314,628,628,]),'subscript':([172,388,476,],[291,477,550,]),'template_inst_suite':([740,801,],[779,822,]),'decorators':([0,23,79,364,456,],[26,133,26,26,26,]),'compound_stmt':([0,79,364,456,],[44,44,44,44,]),'dosm_comma_list':([162,],[271,]),'dotted_name':([67,90,94,220,310,407,],[186,216,219,336,186,186,]),'power':([0,1,7,10,14,30,32,34,40,45,51,58,60,64,69,79,80,88,89,93,103,109,110,111,113,115,119,124,136,137,141,147,150,153,158,160,164,166,168,171,172,180,194,210,212,223,225,228,230,231,232,240,241,251,253,255,260,263,267,270,272,277,279,281,284,288,289,298,300,303,306,313,315,319,323,324,326,328,329,330,334,340,346,352,354,357,358,361,364,367,371,376,378,379,385,386,388,410,442,446,455,456,461,463,466,468,473,476,487,489,494,504,505,509,517,535,536,538,539,540,542,545,546,549,554,562,577,583,594,612,616,629,630,633,643,649,654,657,658,659,662,667,669,670,671,673,683,684,691,722,727,735,751,769,791,793,813,],[38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,]),'xor_expr_list':([12,],[116,]),'stmt':([0,79,364,456,],[46,46,456,456,]),'fplist_list':([294,],[391,]),'xor_expr':([0,1,7,14,30,32,34,45,51,58,60,69,79,80,88,89,93,103,119,124,141,147,150,158,160,168,172,180,194,210,212,223,225,228,230,231,232,241,251,253,255,263,267,270,272,277,279,281,288,289,298,300,303,306,313,315,319,323,324,326,328,329,330,334,340,346,352,354,357,358,361,364,367,371,376,378,379,385,386,388,410,442,446,455,456,461,463,466,468,473,476,487,489,494,504,505,509,517,535,536,538,539,540,542,545,546,549,554,562,577,583,594,612,616,629,630,633,643,649,654,657,658,659,662,667,669,670,671,673,683,684,691,722,727,735,751,769,791,793,813,],[33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,254,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,355,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,33,]),'term_list':([11,],[114,]),'comparison':([0,1,7,30,34,51,58,60,69,79,80,88,89,93,103,119,124,141,150,158,160,168,172,180,210,212,225,228,231,232,241,255,263,267,270,272,277,279,281,288,289,298,300,303,306,315,323,324,326,328,329,330,334,340,346,352,357,358,361,364,367,371,376,378,379,385,386,388,410,442,446,455,456,461,463,466,468,473,476,487,489,494,504,505,509,517,535,536,538,539,540,542,545,546,549,554,562,577,583,594,612,616,629,630,633,643,649,654,657,658,659,662,667,669,670,671,673,683,684,691,722,727,735,751,769,791,793,813,],[50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,]),'pass_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,]),'arith_expr':([0,1,7,14,30,32,34,45,51,58,60,69,79,80,88,89,93,103,115,119,124,141,147,150,153,158,160,164,166,168,172,180,194,210,212,223,225,228,230,231,232,240,241,251,253,255,260,263,267,270,272,277,279,281,288,289,298,300,303,306,313,315,319,323,324,326,328,329,330,334,340,346,352,354,357,358,361,364,367,371,376,378,379,385,386,388,410,442,446,455,456,461,463,466,468,473,476,487,489,494,504,505,509,517,535,536,538,539,540,542,545,546,549,554,562,577,583,594,612,616,629,630,633,643,649,654,657,658,659,662,667,669,670,671,673,683,684,691,722,727,735,751,769,791,793,813,],[53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,274,275,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,]),'enamldef':([0,79,],[54,54,]),'alias_expr':([531,660,664,693,714,760,781,782,787,804,],[602,602,602,747,602,602,747,747,602,747,]),'import_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,49,]),'elif_stmts':([432,516,],[519,581,]),'template_inst_suite_item':([740,794,801,816,],[778,817,821,827,]),'comp_iter':([529,653,],[593,698,]),'print_list_list':([149,452,],[256,256,]),'template_argument':([691,769,793,],[738,792,814,]),'dotted_as_names':([67,],[188,]),'shift_op':([53,167,],[165,276,]),'storage_expr':([531,660,664,693,714,760,781,782,787,804,],[596,596,596,741,596,596,741,741,596,741,]),'template_doc_suite':([412,],[496,]),'return_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,]),'testlist_comp':([1,],[98,]),'old_test':([494,594,629,630,633,683,684,],[564,653,681,682,685,732,733,]),'template_simple_item':([412,568,638,690,734,],[502,635,635,635,635,]),'testlist_safe_list':([564,],[631,]),'template_params':([204,],[321,]),'enaml_module_item':([0,79,],[62,207,]),'child_def_suite':([693,781,],[742,802,]),'continue_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,]),'testlist_list':([83,],[211,]),'dotted_as_name':([67,310,407,],[189,406,493,]),'template_inst_binding':([740,794,801,816,],[775,775,775,775,]),'enamldef_simple_item':([531,660,664,714,760,787,],[601,709,712,712,712,712,]),'equal_list':([16,244,245,],[118,349,350,]),'enamldef_suite_items':([664,760,],[714,787,]),'print_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,]),'binding':([531,660,664,693,714,760,781,782,787,804,],[599,599,599,746,599,599,746,746,599,746,]),'const_expr':([412,568,638,690,734,],[497,497,497,497,497,]),'template_inst_suite_items':([794,],[816,]),'term_op':([11,114,],[112,238,]),'pragma':([0,66,79,568,638,664,690,714,734,760,782,787,804,],[66,66,66,66,66,66,66,66,66,66,66,66,66,]),'atom':([0,1,7,10,14,30,32,34,40,45,51,58,60,64,69,79,80,88,89,93,103,109,110,111,113,115,119,124,136,137,141,147,150,153,158,160,164,166,168,171,172,180,194,210,212,223,225,228,230,231,232,240,241,251,253,255,260,263,267,270,272,277,279,281,284,288,289,298,300,303,306,313,315,319,323,324,326,328,329,330,334,340,346,352,354,357,358,361,364,367,371,376,378,379,385,386,388,410,442,446,455,456,461,463,466,468,473,476,487,489,494,504,505,509,517,535,536,538,539,540,542,545,546,549,554,562,577,583,594,612,616,629,630,633,643,649,654,657,658,659,662,667,669,670,671,673,683,684,691,722,727,735,751,769,791,793,813,],[55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,55,]),'funcdef':([0,26,79,364,456,],[68,139,68,68,68,]),'raise_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,]),'old_lambdef':([494,594,629,630,633,683,684,],[565,565,565,565,565,565,565,]),'exprlist':([32,45,223,313,],[144,157,339,408,]),'expr':([0,1,7,14,30,32,34,45,51,58,60,69,79,80,88,89,93,103,119,124,141,150,158,160,168,172,180,194,210,212,223,225,228,230,231,232,241,251,255,263,267,270,272,277,279,281,288,289,298,300,303,306,313,315,319,323,324,326,328,329,330,334,340,346,352,354,357,358,361,364,367,371,376,378,379,385,386,388,410,442,446,455,456,461,463,466,468,473,476,487,489,494,504,505,509,517,535,536,538,539,540,542,545,546,549,554,562,577,583,594,612,616,629,630,633,643,649,654,657,658,659,662,667,669,670,671,673,683,684,691,722,727,735,751,769,791,793,813,],[72,72,72,117,72,145,72,145,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,317,72,72,145,72,72,343,72,72,72,353,72,72,72,72,72,72,72,72,72,72,72,72,72,72,145,72,411,72,72,72,72,72,72,72,72,72,72,449,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,]),'template_paramlist':([322,],[415,]),'operator_expr':([597,655,656,708,718,743,753,796,],[661,701,703,661,661,661,786,818,]),'except_clause':([266,365,],[365,365,]),'testlist_safe':([494,],[563,]),'template_args':([642,687,718,],[692,692,692,]),'template_id_list':([740,],[776,]),'template_param':([322,508,573,],[417,575,646,]),'and_expr':([0,1,7,14,30,32,34,45,51,58,60,69,79,80,88,89,93,103,115,119,124,141,147,150,158,160,168,172,180,194,210,212,223,225,228,230,231,232,240,241,251,253,255,263,267,270,272,277,279,281,288,289,298,300,303,306,313,315,319,323,324,326,328,329,330,334,340,346,352,354,357,358,361,364,367,371,376,378,379,385,386,388,410,442,446,455,456,461,463,466,468,473,476,487,489,494,504,505,509,517,535,536,538,539,540,542,545,546,549,554,562,577,583,594,612,616,629,630,633,643,649,654,657,658,659,662,667,669,670,671,673,683,684,691,722,727,735,751,769,791,793,813,],[12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,239,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,347,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,]),'yield_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,]),'arith_expr_list':([24,],[134,]),'template_suite_item':([568,638,690,734,],[641,689,641,689,]),'pragmas':([0,66,79,568,638,664,690,714,734,760,782,787,804,],[76,185,76,634,634,634,634,634,634,634,634,634,634,]),'shift_list':([53,],[167,]),'enaml':([0,],[77,]),'subscriptlist_list':([291,],[387,]),'argument':([168,281,330,468,546,],[283,377,283,544,615,]),'enaml_module_body':([0,],[79,]),'pragma_arg':([332,515,],[430,430,]),'fplist':([174,],[295,]),'template_suite':([412,],[499,]),'not_test':([0,1,7,30,34,51,58,60,69,79,80,88,89,93,103,119,124,141,150,158,160,168,172,180,210,212,225,228,231,232,241,255,263,267,270,272,277,279,281,288,289,298,300,303,306,315,323,324,326,328,329,330,334,340,346,352,357,358,361,364,367,371,376,378,379,385,386,388,410,442,446,455,456,461,463,466,468,473,476,487,489,494,504,505,509,517,535,536,538,539,540,542,545,546,549,554,562,577,583,594,612,616,629,630,633,643,649,654,657,658,659,662,667,669,670,671,673,683,684,691,722,727,735,751,769,791,793,813,],[81,81,81,81,81,81,81,81,81,81,81,81,215,81,81,81,81,81,81,81,81,81,81,81,325,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,419,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,]),'print_list':([34,358,],[148,451,]),'template_ids':([740,],[780,]),'break_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,]),'del_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,95,]),'template_inst_impl':([568,634,638,664,690,714,734,760,782,787,804,],[636,686,636,636,636,636,636,636,636,636,636,]),'fpdef':([57,174,258,299,390,394,479,485,556,567,],[177,294,177,397,478,482,551,397,482,177,]),'testlist_comp_list':([99,],[224,]),'small_stmt_list_list':([3,],[101,]),'template_paramlist_list':([417,],[507,]),'list_if':([563,681,],[627,627,]),'pragma_args':([332,515,],[429,580,]),'test':([0,1,7,30,34,51,58,60,69,79,80,88,93,103,119,124,150,158,168,172,180,212,225,228,231,232,241,255,263,270,272,277,279,281,288,289,298,300,303,306,315,323,326,328,329,330,334,340,346,352,357,358,361,364,367,371,376,378,379,385,386,388,410,446,455,456,461,463,466,468,473,476,487,489,504,505,509,517,535,536,538,539,540,542,545,546,549,554,562,577,583,612,616,643,649,654,657,658,659,662,667,669,670,671,673,691,722,727,735,751,769,791,793,813,],[83,99,105,83,149,162,181,183,192,83,208,83,218,83,83,83,257,83,282,290,302,327,341,83,83,105,348,356,83,370,372,373,374,282,383,384,395,399,400,83,409,418,420,83,83,282,83,443,83,448,450,452,83,83,462,464,469,471,472,474,475,290,495,530,83,83,83,83,543,282,548,290,560,561,571,572,83,582,83,83,608,83,610,611,614,282,617,619,83,83,83,672,675,694,83,699,704,705,706,83,83,83,83,725,726,737,83,763,767,784,737,812,737,825,]),'global_stmt':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,]),'import_as_names_list':([433,],[522,]),'with_item':([7,232,],[106,345,]),'import_name':([0,79,103,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,]),'template_arglist_list':([738,],[770,]),'yield_expr':([0,1,79,103,119,124,158,228,231,306,329,334,346,361,364,455,456,461,463,509,535,536,539,562,577,583,649,662,667,669,670,722,],[87,100,87,87,242,244,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,]),'except_clauses':([266,365,],[366,458,]),'comparison_list':([72,],[198,]),'and_test':([0,1,7,30,34,51,58,60,69,79,80,88,93,103,119,124,141,150,158,160,168,172,180,212,225,228,231,232,241,255,263,267,270,272,277,279,281,288,289,298,300,303,306,315,323,326,328,329,330,334,340,346,352,357,358,361,364,367,371,376,378,379,385,386,388,410,442,446,455,456,461,463,466,468,473,476,487,489,494,504,505,509,517,535,536,538,539,540,542,545,546,549,554,562,577,583,594,612,616,629,630,633,643,649,654,657,658,659,662,667,669,670,671,673,683,684,691,722,727,735,751,769,791,793,813,],[48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,268,48,48,48,48,48,48,48,48,48,48,48,369,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48,]),'decorated':([0,79,364,456,],[92,92,92,92,]),'stmt_list':([364,456,],[457,533,]),'elif_stmt':([432,516,],[516,516,]),'dosm_colon_list':([372,],[465,]),'power_list':([55,],[169,]),'while_stmt':([0,79,364,456,],[96,96,96,96,]),'varargslist':([57,258,567,],[178,360,632,]),'dotted_name_list':([187,],[308,]),'listmaker_list':([192,],[316,]),}
_lr_goto = { }
for _k, _v in _lr_goto_items.items():
for _x,_y in zip(_v[0],_v[1]):
if not _x in _lr_goto: _lr_goto[_x] = { }
_lr_goto[_x][_k] = _y
del _lr_goto_items
_lr_productions = [
("S' -> enaml","S'",1,None,None,None),
('enaml -> enaml_module NEWLINE ENDMARKER','enaml',3,'p_enaml1','c:\\development\\enaml\\enaml\\core\\parser.py',261),
('enaml -> enaml_module ENDMARKER','enaml',2,'p_enaml1','c:\\development\\enaml\\enaml\\core\\parser.py',262),
('enaml -> NEWLINE ENDMARKER','enaml',2,'p_enaml2','c:\\development\\enaml\\enaml\\core\\parser.py',267),
('enaml -> ENDMARKER','enaml',1,'p_enaml2','c:\\development\\enaml\\enaml\\core\\parser.py',268),
('enaml_module -> enaml_module_body','enaml_module',1,'p_enaml_module','c:\\development\\enaml\\enaml\\core\\parser.py',273),
('enaml_module_body -> enaml_module_body enaml_module_item','enaml_module_body',2,'p_enaml_module_body1','c:\\development\\enaml\\enaml\\core\\parser.py',294),
('enaml_module_body -> enaml_module_item','enaml_module_body',1,'p_enaml_module_body2','c:\\development\\enaml\\enaml\\core\\parser.py',302),
('enaml_module_item -> stmt','enaml_module_item',1,'p_enaml_module_item','c:\\development\\enaml\\enaml\\core\\parser.py',310),
('enaml_module_item -> enamldef','enaml_module_item',1,'p_enaml_module_item','c:\\development\\enaml\\enaml\\core\\parser.py',311),
('enaml_module_item -> template','enaml_module_item',1,'p_enaml_module_item','c:\\development\\enaml\\enaml\\core\\parser.py',312),
('enamldef -> enamldef_impl','enamldef',1,'p_enamldef1','c:\\development\\enaml\\enaml\\core\\parser.py',354),
('enamldef -> pragmas enamldef_impl','enamldef',2,'p_enamldef2','c:\\development\\enaml\\enaml\\core\\parser.py',359),
('enamldef_impl -> ENAMLDEF NAME LPAR NAME RPAR COLON enamldef_suite','enamldef_impl',7,'p_enamldef_impl1','c:\\development\\enaml\\enaml\\core\\parser.py',366),
('enamldef_impl -> ENAMLDEF NAME LPAR NAME RPAR COLON enamldef_simple_item','enamldef_impl',7,'p_enamldef_impl2','c:\\development\\enaml\\enaml\\core\\parser.py',376),
('enamldef_impl -> ENAMLDEF NAME LPAR NAME RPAR COLON NAME COLON enamldef_suite','enamldef_impl',9,'p_enamldef_impl3','c:\\development\\enaml\\enaml\\core\\parser.py',386),
('enamldef_impl -> ENAMLDEF NAME LPAR NAME RPAR COLON NAME COLON enamldef_simple_item','enamldef_impl',9,'p_enamldef_impl4','c:\\development\\enaml\\enaml\\core\\parser.py',397),
('enamldef_suite -> NEWLINE INDENT enamldef_suite_items DEDENT','enamldef_suite',4,'p_enamldef_suite1','c:\\development\\enaml\\enaml\\core\\parser.py',407),
('enamldef_suite -> NEWLINE INDENT STRING NEWLINE enamldef_suite_items DEDENT','enamldef_suite',6,'p_enamldef_suite2','c:\\development\\enaml\\enaml\\core\\parser.py',414),
('enamldef_suite_items -> enamldef_suite_item','enamldef_suite_items',1,'p_enamldef_suite_items1','c:\\development\\enaml\\enaml\\core\\parser.py',421),
('enamldef_suite_items -> enamldef_suite_items enamldef_suite_item','enamldef_suite_items',2,'p_enamldef_suite_items2','c:\\development\\enaml\\enaml\\core\\parser.py',426),
('enamldef_suite_item -> enamldef_simple_item','enamldef_suite_item',1,'p_enamldef_suite_item','c:\\development\\enaml\\enaml\\core\\parser.py',431),
('enamldef_suite_item -> child_def','enamldef_suite_item',1,'p_enamldef_suite_item','c:\\development\\enaml\\enaml\\core\\parser.py',432),
('enamldef_suite_item -> template_inst','enamldef_suite_item',1,'p_enamldef_suite_item','c:\\development\\enaml\\enaml\\core\\parser.py',433),
('enamldef_simple_item -> binding','enamldef_simple_item',1,'p_enamldef_simple_item1','c:\\development\\enaml\\enaml\\core\\parser.py',438),
('enamldef_simple_item -> ex_binding','enamldef_simple_item',1,'p_enamldef_simple_item1','c:\\development\\enaml\\enaml\\core\\parser.py',439),
('enamldef_simple_item -> alias_expr','enamldef_simple_item',1,'p_enamldef_simple_item1','c:\\development\\enaml\\enaml\\core\\parser.py',440),
('enamldef_simple_item -> storage_expr','enamldef_simple_item',1,'p_enamldef_simple_item1','c:\\development\\enaml\\enaml\\core\\parser.py',441),
('enamldef_simple_item -> PASS NEWLINE','enamldef_simple_item',2,'p_enamldef_simple_item2','c:\\development\\enaml\\enaml\\core\\parser.py',446),
('pragmas -> pragma pragmas','pragmas',2,'p_pragmas1','c:\\development\\enaml\\enaml\\core\\parser.py',454),
('pragmas -> pragma','pragmas',1,'p_pragmas2','c:\\development\\enaml\\enaml\\core\\parser.py',459),
('pragma -> PRAGMA NAME NEWLINE','pragma',3,'p_pragma1','c:\\development\\enaml\\enaml\\core\\parser.py',464),
('pragma -> PRAGMA NAME LPAR RPAR NEWLINE','pragma',5,'p_pragma1','c:\\development\\enaml\\enaml\\core\\parser.py',465),
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('atom -> LBRACE RBRACE','atom',2,'p_atom6','c:\\development\\enaml\\enaml\\core\\parser.py',2828),
('atom -> LBRACE dictorsetmaker RBRACE','atom',3,'p_atom7','c:\\development\\enaml\\enaml\\core\\parser.py',2833),
('atom -> NAME','atom',1,'p_atom8','c:\\development\\enaml\\enaml\\core\\parser.py',2854),
('atom -> NUMBER','atom',1,'p_atom9','c:\\development\\enaml\\enaml\\core\\parser.py',2859),
('atom -> atom_string_list','atom',1,'p_atom10','c:\\development\\enaml\\enaml\\core\\parser.py',2865),
('atom_string_list -> STRING','atom_string_list',1,'p_atom_string_list1','c:\\development\\enaml\\enaml\\core\\parser.py',2871),
('atom_string_list -> atom_string_list STRING','atom_string_list',2,'p_atom_string_list2','c:\\development\\enaml\\enaml\\core\\parser.py',2876),
('listmaker -> test list_for','listmaker',2,'p_listmaker1','c:\\development\\enaml\\enaml\\core\\parser.py',2886),
('listmaker -> test','listmaker',1,'p_listmaker2','c:\\development\\enaml\\enaml\\core\\parser.py',2891),
('listmaker -> test COMMA','listmaker',2,'p_listmaker3','c:\\development\\enaml\\enaml\\core\\parser.py',2896),
('listmaker -> test listmaker_list','listmaker',2,'p_listmaker4','c:\\development\\enaml\\enaml\\core\\parser.py',2901),
('listmaker -> test listmaker_list COMMA','listmaker',3,'p_listmaker5','c:\\development\\enaml\\enaml\\core\\parser.py',2907),
('listmaker_list -> COMMA test','listmaker_list',2,'p_listmaker_list1','c:\\development\\enaml\\enaml\\core\\parser.py',2913),
('listmaker_list -> listmaker_list COMMA test','listmaker_list',3,'p_listmaker_list2','c:\\development\\enaml\\enaml\\core\\parser.py',2918),
('testlist_comp -> test comp_for','testlist_comp',2,'p_testlist_comp1','c:\\development\\enaml\\enaml\\core\\parser.py',2923),
('testlist_comp -> test','testlist_comp',1,'p_testlist_comp2','c:\\development\\enaml\\enaml\\core\\parser.py',2928),
('testlist_comp -> test COMMA','testlist_comp',2,'p_testlist_comp3','c:\\development\\enaml\\enaml\\core\\parser.py',2933),
('testlist_comp -> test testlist_comp_list','testlist_comp',2,'p_testlist_comp4','c:\\development\\enaml\\enaml\\core\\parser.py',2938),
('testlist_comp -> test testlist_comp_list COMMA','testlist_comp',3,'p_testlist_comp5','c:\\development\\enaml\\enaml\\core\\parser.py',2944),
('testlist_comp_list -> COMMA test','testlist_comp_list',2,'p_testlist_comp_list1','c:\\development\\enaml\\enaml\\core\\parser.py',2950),
('testlist_comp_list -> testlist_comp_list COMMA test','testlist_comp_list',3,'p_testlist_comp_list2','c:\\development\\enaml\\enaml\\core\\parser.py',2955),
('trailer -> LPAR RPAR','trailer',2,'p_trailer1','c:\\development\\enaml\\enaml\\core\\parser.py',2960),
('trailer -> LPAR arglist RPAR','trailer',3,'p_trailer2','c:\\development\\enaml\\enaml\\core\\parser.py',2965),
('trailer -> LSQB subscriptlist RSQB','trailer',3,'p_trailer3','c:\\development\\enaml\\enaml\\core\\parser.py',2972),
('trailer -> DOT NAME','trailer',2,'p_trailer4','c:\\development\\enaml\\enaml\\core\\parser.py',2977),
('subscriptlist -> subscript','subscriptlist',1,'p_subscriptlist1','c:\\development\\enaml\\enaml\\core\\parser.py',2982),
('subscriptlist -> subscript COMMA','subscriptlist',2,'p_subscriptlist2','c:\\development\\enaml\\enaml\\core\\parser.py',2987),
('subscriptlist -> subscript subscriptlist_list','subscriptlist',2,'p_subscriptlist3','c:\\development\\enaml\\enaml\\core\\parser.py',2993),
('subscriptlist -> subscript subscriptlist_list COMMA','subscriptlist',3,'p_subscriptlist4','c:\\development\\enaml\\enaml\\core\\parser.py',2999),
('subscriptlist_list -> COMMA subscript','subscriptlist_list',2,'p_subscriptlist_list1','c:\\development\\enaml\\enaml\\core\\parser.py',3005),
('subscriptlist_list -> subscriptlist_list COMMA subscript','subscriptlist_list',3,'p_subscript_list2','c:\\development\\enaml\\enaml\\core\\parser.py',3010),
('subscript -> ELLIPSIS','subscript',1,'p_subscript1','c:\\development\\enaml\\enaml\\core\\parser.py',3015),
('subscript -> test','subscript',1,'p_subcript2','c:\\development\\enaml\\enaml\\core\\parser.py',3020),
('subscript -> COLON','subscript',1,'p_subscript3','c:\\development\\enaml\\enaml\\core\\parser.py',3025),
('subscript -> DOUBLECOLON','subscript',1,'p_subscript4','c:\\development\\enaml\\enaml\\core\\parser.py',3030),
('subscript -> test COLON','subscript',2,'p_subscript5','c:\\development\\enaml\\enaml\\core\\parser.py',3036),
('subscript -> test DOUBLECOLON','subscript',2,'p_subscrip6','c:\\development\\enaml\\enaml\\core\\parser.py',3041),
('subscript -> COLON test','subscript',2,'p_subscript7','c:\\development\\enaml\\enaml\\core\\parser.py',3047),
('subscript -> COLON test COLON','subscript',3,'p_subscript8','c:\\development\\enaml\\enaml\\core\\parser.py',3052),
('subscript -> DOUBLECOLON test','subscript',2,'p_subscript9','c:\\development\\enaml\\enaml\\core\\parser.py',3058),
('subscript -> test COLON test','subscript',3,'p_subscript10','c:\\development\\enaml\\enaml\\core\\parser.py',3063),
('subscript -> test COLON test COLON','subscript',4,'p_subscript11','c:\\development\\enaml\\enaml\\core\\parser.py',3068),
('subscript -> COLON test COLON test','subscript',4,'p_subscript12','c:\\development\\enaml\\enaml\\core\\parser.py',3074),
('subscript -> test COLON test COLON test','subscript',5,'p_subscript13','c:\\development\\enaml\\enaml\\core\\parser.py',3079),
('subscript -> test DOUBLECOLON test','subscript',3,'p_subscript14','c:\\development\\enaml\\enaml\\core\\parser.py',3084),
('exprlist -> expr','exprlist',1,'p_exprlist1','c:\\development\\enaml\\enaml\\core\\parser.py',3089),
('exprlist -> expr COMMA','exprlist',2,'p_exprlist2','c:\\development\\enaml\\enaml\\core\\parser.py',3094),
('exprlist -> expr exprlist_list','exprlist',2,'p_exprlist3','c:\\development\\enaml\\enaml\\core\\parser.py',3101),
('exprlist -> expr exprlist_list COMMA','exprlist',3,'p_exprlist4','c:\\development\\enaml\\enaml\\core\\parser.py',3108),
('exprlist_list -> COMMA expr','exprlist_list',2,'p_exprlist_list1','c:\\development\\enaml\\enaml\\core\\parser.py',3115),
('exprlist_list -> exprlist_list COMMA expr','exprlist_list',3,'p_exprlist_list2','c:\\development\\enaml\\enaml\\core\\parser.py',3120),
('dictorsetmaker -> test COLON test comp_for','dictorsetmaker',4,'p_dictorsetmaker1','c:\\development\\enaml\\enaml\\core\\parser.py',3125),
('dictorsetmaker -> test COLON test','dictorsetmaker',3,'p_dictorsetmaker2','c:\\development\\enaml\\enaml\\core\\parser.py',3130),
('dictorsetmaker -> test COLON test COMMA','dictorsetmaker',4,'p_dictorsetmaker3','c:\\development\\enaml\\enaml\\core\\parser.py',3136),
('dictorsetmaker -> test COLON test dosm_colon_list','dictorsetmaker',4,'p_dictorsetmaker4','c:\\development\\enaml\\enaml\\core\\parser.py',3142),
('dictorsetmaker -> test COLON test dosm_colon_list COMMA','dictorsetmaker',5,'p_dictorsetmaker5','c:\\development\\enaml\\enaml\\core\\parser.py',3148),
('dictorsetmaker -> test comp_for','dictorsetmaker',2,'p_dictorsetmaker6','c:\\development\\enaml\\enaml\\core\\parser.py',3154),
('dictorsetmaker -> test COMMA','dictorsetmaker',2,'p_dictorsetmaker7','c:\\development\\enaml\\enaml\\core\\parser.py',3159),
('dictorsetmaker -> test dosm_comma_list','dictorsetmaker',2,'p_dictorsetmaker8','c:\\development\\enaml\\enaml\\core\\parser.py',3165),
('dictorsetmaker -> test dosm_comma_list COMMA','dictorsetmaker',3,'p_dictorsetmaker9','c:\\development\\enaml\\enaml\\core\\parser.py',3171),
('dosm_colon_list -> COMMA test COLON test','dosm_colon_list',4,'p_dosm_colon_list1','c:\\development\\enaml\\enaml\\core\\parser.py',3177),
('dosm_colon_list -> dosm_colon_list COMMA test COLON test','dosm_colon_list',5,'p_dosm_colon_list2','c:\\development\\enaml\\enaml\\core\\parser.py',3182),
('dosm_comma_list -> COMMA test','dosm_comma_list',2,'p_dosm_comma_list1','c:\\development\\enaml\\enaml\\core\\parser.py',3187),
('dosm_comma_list -> dosm_comma_list COMMA test','dosm_comma_list',3,'p_dosm_comma_list2','c:\\development\\enaml\\enaml\\core\\parser.py',3192),
('arglist -> argument','arglist',1,'p_arglist1','c:\\development\\enaml\\enaml\\core\\parser.py',3197),
('arglist -> argument COMMA','arglist',2,'p_arglist2','c:\\development\\enaml\\enaml\\core\\parser.py',3205),
('arglist -> STAR test','arglist',2,'p_arglist3','c:\\development\\enaml\\enaml\\core\\parser.py',3213),
('arglist -> STAR test COMMA DOUBLESTAR test','arglist',5,'p_arglist4','c:\\development\\enaml\\enaml\\core\\parser.py',3218),
('arglist -> DOUBLESTAR test','arglist',2,'p_arglist5','c:\\development\\enaml\\enaml\\core\\parser.py',3223),
('arglist -> arglist_list argument','arglist',2,'p_arglist6','c:\\development\\enaml\\enaml\\core\\parser.py',3245),
('arglist -> arglist_list argument COMMA','arglist',3,'p_arglist7','c:\\development\\enaml\\enaml\\core\\parser.py',3263),
('arglist -> arglist_list STAR test','arglist',3,'p_arglist8','c:\\development\\enaml\\enaml\\core\\parser.py',3280),
('arglist -> arglist_list STAR test COMMA DOUBLESTAR test','arglist',6,'p_arglist9','c:\\development\\enaml\\enaml\\core\\parser.py',3294),
('arglist -> arglist_list DOUBLESTAR test','arglist',3,'p_arglist10','c:\\development\\enaml\\enaml\\core\\parser.py',3308),
('arglist -> STAR test COMMA argument','arglist',4,'p_arglist11','c:\\development\\enaml\\enaml\\core\\parser.py',3322),
('arglist -> STAR test COMMA argument COMMA DOUBLESTAR test','arglist',7,'p_arglist12','c:\\development\\enaml\\enaml\\core\\parser.py',3333),
('arglist -> STAR test COMMA arglist_list argument','arglist',5,'p_arglist13','c:\\development\\enaml\\enaml\\core\\parser.py',3344),
('arglist -> STAR test COMMA arglist_list argument COMMA DOUBLESTAR test','arglist',8,'p_arglist14','c:\\development\\enaml\\enaml\\core\\parser.py',3361),
('arglist_list -> argument COMMA','arglist_list',2,'p_arglist_list1','c:\\development\\enaml\\enaml\\core\\parser.py',3378),
('arglist_list -> arglist_list argument COMMA','arglist_list',3,'p_arglist_list2','c:\\development\\enaml\\enaml\\core\\parser.py',3385),
('argument -> test','argument',1,'p_argument1','c:\\development\\enaml\\enaml\\core\\parser.py',3392),
('argument -> test comp_for','argument',2,'p_argument2','c:\\development\\enaml\\enaml\\core\\parser.py',3397),
('argument -> test EQUAL test','argument',3,'p_argument3','c:\\development\\enaml\\enaml\\core\\parser.py',3404),
('list_for -> FOR exprlist IN testlist_safe','list_for',4,'p_list_for1','c:\\development\\enaml\\enaml\\core\\parser.py',3415),
('list_for -> FOR exprlist IN testlist_safe list_iter','list_for',5,'p_list_for2','c:\\development\\enaml\\enaml\\core\\parser.py',3422),
('list_iter -> list_for','list_iter',1,'p_list_iter1','c:\\development\\enaml\\enaml\\core\\parser.py',3436),
('list_iter -> list_if','list_iter',1,'p_list_iter2','c:\\development\\enaml\\enaml\\core\\parser.py',3441),
('list_if -> IF old_test','list_if',2,'p_list_if1','c:\\development\\enaml\\enaml\\core\\parser.py',3446),
('list_if -> IF old_test list_iter','list_if',3,'p_list_if2','c:\\development\\enaml\\enaml\\core\\parser.py',3451),
('comp_for -> FOR exprlist IN or_test','comp_for',4,'p_comp_for1','c:\\development\\enaml\\enaml\\core\\parser.py',3456),
('comp_for -> FOR exprlist IN or_test comp_iter','comp_for',5,'p_comp_for2','c:\\development\\enaml\\enaml\\core\\parser.py',3463),
('comp_iter -> comp_for','comp_iter',1,'p_comp_iter1','c:\\development\\enaml\\enaml\\core\\parser.py',3477),
('comp_iter -> comp_if','comp_iter',1,'p_comp_iter2','c:\\development\\enaml\\enaml\\core\\parser.py',3482),
('comp_if -> IF old_test','comp_if',2,'p_comp_if1','c:\\development\\enaml\\enaml\\core\\parser.py',3487),
('comp_if -> IF old_test comp_iter','comp_if',3,'p_comp_if2','c:\\development\\enaml\\enaml\\core\\parser.py',3492),
('testlist_safe -> old_test','testlist_safe',1,'p_testlist_safe1','c:\\development\\enaml\\enaml\\core\\parser.py',3497),
('testlist_safe -> old_test testlist_safe_list','testlist_safe',2,'p_testlist_safe2','c:\\development\\enaml\\enaml\\core\\parser.py',3502),
('testlist_safe -> old_test testlist_safe_list COMMA','testlist_safe',3,'p_testlist_safe3','c:\\development\\enaml\\enaml\\core\\parser.py',3508),
('testlist_safe_list -> COMMA old_test','testlist_safe_list',2,'p_testlist_safe_list1','c:\\development\\enaml\\enaml\\core\\parser.py',3514),
('testlist_safe_list -> testlist_safe_list COMMA old_test','testlist_safe_list',3,'p_testlist_safe_list2','c:\\development\\enaml\\enaml\\core\\parser.py',3519),
('old_test -> or_test','old_test',1,'p_old_test1','c:\\development\\enaml\\enaml\\core\\parser.py',3524),
('old_test -> old_lambdef','old_test',1,'p_old_test2','c:\\development\\enaml\\enaml\\core\\parser.py',3529),
('old_lambdef -> LAMBDA COLON old_test','old_lambdef',3,'p_old_lambdef1','c:\\development\\enaml\\enaml\\core\\parser.py',3534),
('old_lambdef -> LAMBDA varargslist COLON old_test','old_lambdef',4,'p_old_lambdef2','c:\\development\\enaml\\enaml\\core\\parser.py',3541),
('lambdef -> LAMBDA COLON test','lambdef',3,'p_lambdef1','c:\\development\\enaml\\enaml\\core\\parser.py',3548),
('lambdef -> LAMBDA varargslist COLON test','lambdef',4,'p_lambdef2','c:\\development\\enaml\\enaml\\core\\parser.py',3555),
('varargslist -> fpdef COMMA STAR NAME','varargslist',4,'p_varargslist1','c:\\development\\enaml\\enaml\\core\\parser.py',3562),
('varargslist -> fpdef COMMA STAR NAME COMMA DOUBLESTAR NAME','varargslist',7,'p_varargslist2','c:\\development\\enaml\\enaml\\core\\parser.py',3569),
('varargslist -> fpdef COMMA DOUBLESTAR NAME','varargslist',4,'p_varargslist3','c:\\development\\enaml\\enaml\\core\\parser.py',3576),
('varargslist -> fpdef','varargslist',1,'p_varargslist4','c:\\development\\enaml\\enaml\\core\\parser.py',3583),
('varargslist -> fpdef COMMA','varargslist',2,'p_varargslist5','c:\\development\\enaml\\enaml\\core\\parser.py',3590),
('varargslist -> fpdef varargslist_list COMMA STAR NAME','varargslist',5,'p_varargslist6','c:\\development\\enaml\\enaml\\core\\parser.py',3597),
('varargslist -> fpdef varargslist_list COMMA STAR NAME COMMA DOUBLESTAR NAME','varargslist',8,'p_varargslist7','c:\\development\\enaml\\enaml\\core\\parser.py',3606),
('varargslist -> fpdef varargslist_list COMMA DOUBLESTAR NAME','varargslist',5,'p_varargslist8','c:\\development\\enaml\\enaml\\core\\parser.py',3615),
('varargslist -> fpdef varargslist_list','varargslist',2,'p_varargslist9','c:\\development\\enaml\\enaml\\core\\parser.py',3624),
('varargslist -> fpdef varargslist_list COMMA','varargslist',3,'p_varargslist10','c:\\development\\enaml\\enaml\\core\\parser.py',3633),
('varargslist -> fpdef EQUAL test COMMA STAR NAME','varargslist',6,'p_varargslist11','c:\\development\\enaml\\enaml\\core\\parser.py',3642),
('varargslist -> fpdef EQUAL test COMMA STAR NAME COMMA DOUBLESTAR NAME','varargslist',9,'p_varargslist12','c:\\development\\enaml\\enaml\\core\\parser.py',3649),
('varargslist -> fpdef EQUAL test COMMA DOUBLESTAR NAME','varargslist',6,'p_varargslist13','c:\\development\\enaml\\enaml\\core\\parser.py',3656),
('varargslist -> fpdef EQUAL test','varargslist',3,'p_varargslist14','c:\\development\\enaml\\enaml\\core\\parser.py',3663),
('varargslist -> fpdef EQUAL test COMMA','varargslist',4,'p_varargslist15','c:\\development\\enaml\\enaml\\core\\parser.py',3670),
('varargslist -> fpdef EQUAL test varargslist_list COMMA STAR NAME','varargslist',7,'p_varargslist16','c:\\development\\enaml\\enaml\\core\\parser.py',3677),
('varargslist -> fpdef EQUAL test varargslist_list COMMA STAR NAME COMMA DOUBLESTAR NAME','varargslist',10,'p_varargslist17','c:\\development\\enaml\\enaml\\core\\parser.py',3690),
('varargslist -> fpdef EQUAL test varargslist_list COMMA DOUBLESTAR NAME','varargslist',7,'p_varargslist18','c:\\development\\enaml\\enaml\\core\\parser.py',3703),
('varargslist -> fpdef EQUAL test varargslist_list','varargslist',4,'p_varargslist19','c:\\development\\enaml\\enaml\\core\\parser.py',3716),
('varargslist -> fpdef EQUAL test varargslist_list COMMA','varargslist',5,'p_varargslist20','c:\\development\\enaml\\enaml\\core\\parser.py',3729),
('varargslist -> STAR NAME','varargslist',2,'p_varargslist21','c:\\development\\enaml\\enaml\\core\\parser.py',3742),
('varargslist -> STAR NAME COMMA DOUBLESTAR NAME','varargslist',5,'p_varargslist22','c:\\development\\enaml\\enaml\\core\\parser.py',3748),
('varargslist -> DOUBLESTAR NAME','varargslist',2,'p_varargslist23','c:\\development\\enaml\\enaml\\core\\parser.py',3754),
('varargslist_list -> COMMA fpdef','varargslist_list',2,'p_varargslist_list1','c:\\development\\enaml\\enaml\\core\\parser.py',3761),
('varargslist_list -> COMMA fpdef EQUAL test','varargslist_list',4,'p_varargslist_list2','c:\\development\\enaml\\enaml\\core\\parser.py',3766),
('varargslist_list -> varargslist_list COMMA fpdef','varargslist_list',3,'p_varargslist_list3','c:\\development\\enaml\\enaml\\core\\parser.py',3771),
('varargslist_list -> varargslist_list COMMA fpdef EQUAL test','varargslist_list',5,'p_varargslist_list4','c:\\development\\enaml\\enaml\\core\\parser.py',3782),
('fpdef -> NAME','fpdef',1,'p_fpdef1','c:\\development\\enaml\\enaml\\core\\parser.py',3790),
('fpdef -> LPAR fplist RPAR','fpdef',3,'p_fpdef2','c:\\development\\enaml\\enaml\\core\\parser.py',3795),
('fplist -> fpdef','fplist',1,'p_fplist1','c:\\development\\enaml\\enaml\\core\\parser.py',3802),
('fplist -> fpdef COMMA','fplist',2,'p_fplist2','c:\\development\\enaml\\enaml\\core\\parser.py',3807),
('fplist -> fpdef fplist_list','fplist',2,'p_fplist3','c:\\development\\enaml\\enaml\\core\\parser.py',3815),
('fplist -> fpdef fplist_list COMMA','fplist',3,'p_fplist4','c:\\development\\enaml\\enaml\\core\\parser.py',3824),
('fplist_list -> COMMA fpdef','fplist_list',2,'p_fplist_list1','c:\\development\\enaml\\enaml\\core\\parser.py',3833),
('fplist_list -> fplist_list COMMA fpdef','fplist_list',3,'p_fplist_list2','c:\\development\\enaml\\enaml\\core\\parser.py',3838),
]
| 334.32282 | 85,970 | 0.685549 | 37,657 | 180,200 | 3.21008 | 0.045251 | 0.042355 | 0.071723 | 0.092818 | 0.74337 | 0.703777 | 0.668702 | 0.583462 | 0.500538 | 0.456139 | 0 | 0.425405 | 0.019451 | 180,200 | 538 | 85,971 | 334.944238 | 0.258723 | 0.000577 | 0 | 0.003781 | 1 | 0.00189 | 0.318937 | 0.144582 | 0 | 0 | 0 | 0 | 0.009452 | 1 | 0 | false | 0.015123 | 0.047259 | 0 | 0.047259 | 0.024575 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
6a19d6dbd799f4ddab2a574bd7661b80d95bf236 | 263 | py | Python | pyzshcomplete/tests/argparse/conftest.py | marble/pyzshcomplete | c7896c8db3d753fb41fd1de403d9feaf2a3bae1e | [
"MIT"
] | 14 | 2020-05-23T01:52:53.000Z | 2021-09-21T16:41:01.000Z | pyzshcomplete/tests/argparse/conftest.py | marble/pyzshcomplete | c7896c8db3d753fb41fd1de403d9feaf2a3bae1e | [
"MIT"
] | 35 | 2020-03-13T22:46:59.000Z | 2021-09-17T02:48:34.000Z | pyzshcomplete/tests/argparse/conftest.py | marble/pyzshcomplete | c7896c8db3d753fb41fd1de403d9feaf2a3bae1e | [
"MIT"
] | 1 | 2021-09-10T09:25:23.000Z | 2021-09-10T09:25:23.000Z | from pytest import fixture
from argparse import ArgumentParser
@fixture(scope='function')
def default_parser():
return ArgumentParser(prog='program')
@fixture(scope='function')
def empty_parser():
return ArgumentParser(prog='program', add_help=False)
| 20.230769 | 57 | 0.768061 | 31 | 263 | 6.419355 | 0.580645 | 0.120603 | 0.201005 | 0.231156 | 0.371859 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.117871 | 263 | 12 | 58 | 21.916667 | 0.857759 | 0 | 0 | 0.25 | 0 | 0 | 0.114068 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.25 | 0.25 | 0.75 | 0 | 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 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 6 |
dbe71cfc12bf9b658326ce48b254355cde7beb2c | 10,804 | py | Python | sdk/python/pulumi_aws/s3/bucket_notification.py | dixler/pulumi-aws | 88838ed6d412c092717a916b0b5b154f68226c3a | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_aws/s3/bucket_notification.py | dixler/pulumi-aws | 88838ed6d412c092717a916b0b5b154f68226c3a | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_aws/s3/bucket_notification.py | dixler/pulumi-aws | 88838ed6d412c092717a916b0b5b154f68226c3a | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import json
import warnings
import pulumi
import pulumi.runtime
from typing import Union
from .. import utilities, tables
class BucketNotification(pulumi.CustomResource):
bucket: pulumi.Output[str]
"""
The name of the bucket to put notification configuration.
"""
lambda_functions: pulumi.Output[list]
"""
Used to configure notifications to a Lambda Function (documented below).
* `events` (`list`) - Specifies [event](http://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html#notification-how-to-event-types-and-destinations) for which to send notifications.
* `filterPrefix` (`str`) - Specifies object key name prefix.
* `filterSuffix` (`str`) - Specifies object key name suffix.
* `id` (`str`) - Specifies unique identifier for each of the notification configurations.
* `lambda_function_arn` (`str`) - Specifies Amazon Lambda function ARN.
"""
queues: pulumi.Output[list]
"""
The notification configuration to SQS Queue (documented below).
* `events` (`list`) - Specifies [event](http://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html#notification-how-to-event-types-and-destinations) for which to send notifications.
* `filterPrefix` (`str`) - Specifies object key name prefix.
* `filterSuffix` (`str`) - Specifies object key name suffix.
* `id` (`str`) - Specifies unique identifier for each of the notification configurations.
* `queueArn` (`str`) - Specifies Amazon SQS queue ARN.
"""
topics: pulumi.Output[list]
"""
The notification configuration to SNS Topic (documented below).
* `events` (`list`) - Specifies [event](http://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html#notification-how-to-event-types-and-destinations) for which to send notifications.
* `filterPrefix` (`str`) - Specifies object key name prefix.
* `filterSuffix` (`str`) - Specifies object key name suffix.
* `id` (`str`) - Specifies unique identifier for each of the notification configurations.
* `topic_arn` (`str`) - Specifies Amazon SNS topic ARN.
"""
def __init__(__self__, resource_name, opts=None, bucket=None, lambda_functions=None, queues=None, topics=None, __props__=None, __name__=None, __opts__=None):
"""
Manages a S3 Bucket Notification Configuration. For additional information, see the [Configuring S3 Event Notifications section in the Amazon S3 Developer Guide](https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html).
> **NOTE:** S3 Buckets only support a single notification configuration. Declaring multiple `s3.BucketNotification` resources to the same S3 Bucket will cause a perpetual difference in configuration. See the example "Trigger multiple Lambda functions" for an option.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] bucket: The name of the bucket to put notification configuration.
:param pulumi.Input[list] lambda_functions: Used to configure notifications to a Lambda Function (documented below).
:param pulumi.Input[list] queues: The notification configuration to SQS Queue (documented below).
:param pulumi.Input[list] topics: The notification configuration to SNS Topic (documented below).
The **lambda_functions** object supports the following:
* `events` (`pulumi.Input[list]`) - Specifies [event](http://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html#notification-how-to-event-types-and-destinations) for which to send notifications.
* `filterPrefix` (`pulumi.Input[str]`) - Specifies object key name prefix.
* `filterSuffix` (`pulumi.Input[str]`) - Specifies object key name suffix.
* `id` (`pulumi.Input[str]`) - Specifies unique identifier for each of the notification configurations.
* `lambda_function_arn` (`pulumi.Input[str]`) - Specifies Amazon Lambda function ARN.
The **queues** object supports the following:
* `events` (`pulumi.Input[list]`) - Specifies [event](http://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html#notification-how-to-event-types-and-destinations) for which to send notifications.
* `filterPrefix` (`pulumi.Input[str]`) - Specifies object key name prefix.
* `filterSuffix` (`pulumi.Input[str]`) - Specifies object key name suffix.
* `id` (`pulumi.Input[str]`) - Specifies unique identifier for each of the notification configurations.
* `queueArn` (`pulumi.Input[str]`) - Specifies Amazon SQS queue ARN.
The **topics** object supports the following:
* `events` (`pulumi.Input[list]`) - Specifies [event](http://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html#notification-how-to-event-types-and-destinations) for which to send notifications.
* `filterPrefix` (`pulumi.Input[str]`) - Specifies object key name prefix.
* `filterSuffix` (`pulumi.Input[str]`) - Specifies object key name suffix.
* `id` (`pulumi.Input[str]`) - Specifies unique identifier for each of the notification configurations.
* `topic_arn` (`pulumi.Input[str]`) - Specifies Amazon SNS topic ARN.
> This content is derived from https://github.com/terraform-providers/terraform-provider-aws/blob/master/website/docs/r/s3_bucket_notification.html.markdown.
"""
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
if bucket is None:
raise TypeError("Missing required property 'bucket'")
__props__['bucket'] = bucket
__props__['lambda_functions'] = lambda_functions
__props__['queues'] = queues
__props__['topics'] = topics
super(BucketNotification, __self__).__init__(
'aws:s3/bucketNotification:BucketNotification',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name, id, opts=None, bucket=None, lambda_functions=None, queues=None, topics=None):
"""
Get an existing BucketNotification resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param str id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] bucket: The name of the bucket to put notification configuration.
:param pulumi.Input[list] lambda_functions: Used to configure notifications to a Lambda Function (documented below).
:param pulumi.Input[list] queues: The notification configuration to SQS Queue (documented below).
:param pulumi.Input[list] topics: The notification configuration to SNS Topic (documented below).
The **lambda_functions** object supports the following:
* `events` (`pulumi.Input[list]`) - Specifies [event](http://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html#notification-how-to-event-types-and-destinations) for which to send notifications.
* `filterPrefix` (`pulumi.Input[str]`) - Specifies object key name prefix.
* `filterSuffix` (`pulumi.Input[str]`) - Specifies object key name suffix.
* `id` (`pulumi.Input[str]`) - Specifies unique identifier for each of the notification configurations.
* `lambda_function_arn` (`pulumi.Input[str]`) - Specifies Amazon Lambda function ARN.
The **queues** object supports the following:
* `events` (`pulumi.Input[list]`) - Specifies [event](http://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html#notification-how-to-event-types-and-destinations) for which to send notifications.
* `filterPrefix` (`pulumi.Input[str]`) - Specifies object key name prefix.
* `filterSuffix` (`pulumi.Input[str]`) - Specifies object key name suffix.
* `id` (`pulumi.Input[str]`) - Specifies unique identifier for each of the notification configurations.
* `queueArn` (`pulumi.Input[str]`) - Specifies Amazon SQS queue ARN.
The **topics** object supports the following:
* `events` (`pulumi.Input[list]`) - Specifies [event](http://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html#notification-how-to-event-types-and-destinations) for which to send notifications.
* `filterPrefix` (`pulumi.Input[str]`) - Specifies object key name prefix.
* `filterSuffix` (`pulumi.Input[str]`) - Specifies object key name suffix.
* `id` (`pulumi.Input[str]`) - Specifies unique identifier for each of the notification configurations.
* `topic_arn` (`pulumi.Input[str]`) - Specifies Amazon SNS topic ARN.
> This content is derived from https://github.com/terraform-providers/terraform-provider-aws/blob/master/website/docs/r/s3_bucket_notification.html.markdown.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["bucket"] = bucket
__props__["lambda_functions"] = lambda_functions
__props__["queues"] = queues
__props__["topics"] = topics
return BucketNotification(resource_name, opts=opts, __props__=__props__)
def translate_output_property(self, prop):
return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
| 63.928994 | 274 | 0.685579 | 1,270 | 10,804 | 5.707874 | 0.152756 | 0.057663 | 0.050214 | 0.076148 | 0.758036 | 0.743965 | 0.725066 | 0.720651 | 0.700786 | 0.686715 | 0 | 0.002338 | 0.208071 | 10,804 | 168 | 275 | 64.309524 | 0.844904 | 0.540355 | 0 | 0.038462 | 1 | 0 | 0.156471 | 0.017255 | 0 | 0 | 0 | 0 | 0 | 1 | 0.076923 | false | 0.019231 | 0.115385 | 0.038462 | 0.346154 | 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 |
dbfb627f2f7b89e4eddb6f40d976ae05743ec841 | 203 | py | Python | tccli/services/cim/__init__.py | zyh911/tencentcloud-cli | dfc5dbd660d4c60d265921c4edc630091478fc41 | [
"Apache-2.0"
] | null | null | null | tccli/services/cim/__init__.py | zyh911/tencentcloud-cli | dfc5dbd660d4c60d265921c4edc630091478fc41 | [
"Apache-2.0"
] | null | null | null | tccli/services/cim/__init__.py | zyh911/tencentcloud-cli | dfc5dbd660d4c60d265921c4edc630091478fc41 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
from tccli.services.cim.cim_client import register_arg
from tccli.services.cim.cim_client import get_actions_info
from tccli.services.cim.cim_client import AVAILABLE_VERSION_LIST
| 40.6 | 64 | 0.827586 | 32 | 203 | 5 | 0.53125 | 0.16875 | 0.31875 | 0.375 | 0.65625 | 0.65625 | 0.65625 | 0 | 0 | 0 | 0 | 0.005376 | 0.083744 | 203 | 4 | 65 | 50.75 | 0.854839 | 0.103448 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
dbff3ccce4ab4bcf6771280d8a4a3f6334364d36 | 396,500 | py | Python | Mpro_dynamics/Mpro_monomer_w_natural_substrate/Mpro_AVLQS.py | spelmer/covid-moonshot-designs-spe | 02c085fe400c987e8b13a91049f304348cf7a13b | [
"MIT"
] | null | null | null | Mpro_dynamics/Mpro_monomer_w_natural_substrate/Mpro_AVLQS.py | spelmer/covid-moonshot-designs-spe | 02c085fe400c987e8b13a91049f304348cf7a13b | [
"MIT"
] | null | null | null | Mpro_dynamics/Mpro_monomer_w_natural_substrate/Mpro_AVLQS.py | spelmer/covid-moonshot-designs-spe | 02c085fe400c987e8b13a91049f304348cf7a13b | [
"MIT"
] | null | null | null | (1, None, u"binding pocket of SARS-CoV-2 Mpro main protein (aka 3CLpro) with a natural substrate, AVLQS peptide. Subsites colored according to the amino acid side chains recognized:\n\nS1' violet --> SER, P1' residue\nS1 blue --> GLN, P1 residue\nS2 salmon --> LEU, P2 residue\nS4 green --> ALA, P4 residue\n")
import cPickle, base64
try:
from SimpleSession.versions.v65 import beginRestore,\
registerAfterModelsCB, reportRestoreError, checkVersion
except ImportError:
from chimera import UserError
raise UserError('Cannot open session that was saved in a'
' newer version of Chimera; update your version')
checkVersion([1, 14, 42094])
import chimera
from chimera import replyobj
replyobj.status('Restoring session...', \
blankAfter=0)
replyobj.status('Beginning session restore...', \
blankAfter=0, secondary=True)
beginRestore()
def restoreCoreModels():
from SimpleSession.versions.v65 import init, restoreViewer, \
restoreMolecules, restoreColors, restoreSurfaces, \
restoreVRML, restorePseudoBondGroups, restoreModelAssociations
molInfo = cPickle.loads(base64.b64decode('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'))
resInfo = cPickle.loads(base64.b64decode('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'))
atomInfo = cPickle.loads(base64.b64decode('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bondInfo = cPickle.loads(base64.b64decode('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'))
crdInfo = cPickle.loads(base64.b64decode('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'))
surfInfo = {'category': (1, u'main', {}), 'probeRadius': (1, 1.4, {}), 'pointSize': (1, 1, {}), 'name': [u'MSMS main surface of min-out.pdb'], 'density': (1, 2, {}), 'colorMode': (1, 1, {}), 'useLighting': (1, True, {}), 'transparencyBlendMode': (1, 1, {}), 'molecule': [0], 'smoothLines': (1, False, {}), 'lineWidth': (1, 1, {}), 'allComponents': (1, True, {}), 'twoSidedLighting': (1, True, {}), 'customVisibility': ['eJztwTEBAAAMAqA1sH/btfARSAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD2HAAAAFQ89QDCQA=='], 'drawMode': (1, 0, {}), 'display': (1, True, {}), 'customColors': [(0, None, {})]}
vrmlInfo = {'subid': (0, None, {}), 'display': (0, None, {}), 'id': (0, None, {}), 'vrmlString': [], 'name': (0, None, {})}
colors = {u'Ru': ((0.141176, 0.560784, 0.560784), 1, u'default'), u'Re': ((0.14902, 0.490196, 0.670588), 1, u'default'), u'Rf': ((0.8, 0, 0.34902), 1, u'default'), u'Ra': ((0, 0.490196, 0), 1, u'default'), u'Rb': ((0.439216, 0.180392, 0.690196), 1, u'default'), u'Rn': ((0.258824, 0.509804, 0.588235), 1, u'default'), u'Rh': ((0.0392157, 0.490196, 0.54902), 1, u'default'), u'Be': ((0.760784, 1, 0), 1, u'default'), u'Ba': ((0, 0.788235, 0), 1, u'default'), u'Bh': ((0.878431, 0, 0.219608), 1, u'default'), u'Bi': ((0.619608, 0.309804, 0.709804), 1, u'default'), u'Bk': ((0.541176, 0.309804, 0.890196), 1, u'default'), u'Br': ((0.65098, 0.160784, 0.160784), 1, u'default'), u'H': ((1, 1, 1), 1, u'default'), u'P': ((1, 0.501961, 0), 1, u'default'), u'Os': ((0.14902, 0.4, 0.588235), 1, u'default'), u'Es': ((0.701961, 0.121569, 0.831373), 1, u'default'), u'Hg': ((0.721569, 0.721569, 0.815686), 1, u'default'), u'Ge': ((0.4, 0.560784, 0.560784), 1, u'default'), u'Gd': ((0.270588, 1, 0.780392), 1, u'default'), u'Ga': ((0.760784, 0.560784, 0.560784), 1, u'default'), u'Pr': ((0.85098, 1, 0.780392), 1, u'default'),
u'Pt': ((0.815686, 0.815686, 0.878431), 1, u'default'), u'Pu': ((0, 0.419608, 1), 1, u'default'), u'Mg': ((0.541176, 1, 0), 1, u'default'), u'Pb': ((0.341176, 0.34902, 0.380392), 1, u'default'), u'Pa': ((0, 0.631373, 1), 1, u'default'), u'Pd': ((0, 0.411765, 0.521569), 1, u'default'), u'Cd': ((1, 0.85098, 0.560784), 1, u'default'), u'Po': ((0.670588, 0.360784, 0), 1, u'default'), u'Pm': ((0.639216, 1, 0.780392), 1, u'default'), u'Hs': ((0.901961, 0, 0.180392), 1, u'default'), u'Ho': ((0, 1, 0.611765), 1, u'default'), u'Hf': ((0.301961, 0.760784, 1), 1, u'default'), u'K': ((0.560784, 0.25098, 0.831373), 1, u'default'), u'He': ((0.85098, 1, 1), 1, u'default'), u'Md': ((0.701961, 0.0509804, 0.65098), 1, u'default'), u'C': ((0.564706, 0.564706, 0.564706), 1, u'default'), u'Mo': ((0.329412, 0.709804, 0.709804), 1, u'default'), u'Mn': ((0.611765, 0.478431, 0.780392), 1, u'default'), u'O': ((1, 0.0509804, 0.0509804), 1, u'default'), u'Mt': ((0.921569, 0, 0.14902), 1, u'default'), u'S': ((1, 1, 0.188235), 1, u'default'), u'W': ((0.129412, 0.580392, 0.839216), 1, u'default'),
u'Zn': ((0.490196, 0.501961, 0.690196), 1, u'default'), u'Eu': ((0.380392, 1, 0.780392), 1, u'default'), u'Zr': ((0.580392, 0.878431, 0.878431), 1, u'default'), u'Er': ((0, 0.901961, 0.458824), 1, u'default'), u'Ni': ((0.313725, 0.815686, 0.313725), 1, u'default'), u'No': ((0.741176, 0.0509804, 0.529412), 1, u'default'), u'Na': ((0.670588, 0.360784, 0.94902), 1, u'default'), u'Nb': ((0.45098, 0.760784, 0.788235), 1, u'default'), u'Nd': ((0.780392, 1, 0.780392), 1, u'default'), u'Ne': ((0.701961, 0.890196, 0.960784), 1, u'default'), u'Np': ((0, 0.501961, 1), 1, u'default'), u'Fr': ((0.258824, 0, 0.4), 1, u'default'), u'Fe': ((0.878431, 0.4, 0.2), 1, u'default'), u'Fm': ((0.701961, 0.121569, 0.729412), 1, u'default'), u'B': ((1, 0.709804, 0.709804), 1, u'default'), u'F': ((0.564706, 0.878431, 0.313725), 1, u'default'), u'Sr': ((0, 1, 0), 1, u'default'), u'cornflower blue': ((0.392157, 0.584314, 0.929412), 1, u'default'), u'N': ((0.188235, 0.313725, 0.972549), 1, u'default'), u'Kr': ((0.360784, 0.721569, 0.819608), 1, u'default'), u'Si': ((0.941176, 0.784314, 0.627451), 1, u'default'),
u'Sn': ((0.4, 0.501961, 0.501961), 1, u'default'), u'Sm': ((0.560784, 1, 0.780392), 1, u'default'), u'V': ((0.65098, 0.65098, 0.670588), 1, u'default'), u'Sc': ((0.901961, 0.901961, 0.901961), 1, u'default'), u'Sb': ((0.619608, 0.388235, 0.709804), 1, u'default'), u'Sg': ((0.85098, 0, 0.270588), 1, u'default'), u'Se': ((1, 0.631373, 0), 1, u'default'), u'Co': ((0.941176, 0.564706, 0.627451), 1, u'default'), u'Cm': ((0.470588, 0.360784, 0.890196), 1, u'default'), u'Cl': ((0.121569, 0.941176, 0.121569), 1, u'default'), u'Ca': ((0.239216, 1, 0), 1, u'default'), u'Cf': ((0.631373, 0.211765, 0.831373), 1, u'default'), u'Ce': ((1, 1, 0.780392), 1, u'default'), u'Xe': ((0.258824, 0.619608, 0.690196), 1, u'default'), u'Lu': ((0, 0.670588, 0.141176), 1, u'default'), u'light green': ((0.564706, 0.933333, 0.564706), 1, u'default'), u'Cs': ((0.341176, 0.0901961, 0.560784), 1, u'default'), u'Cr': ((0.541176, 0.6, 0.780392), 1, u'default'), u'Cu': ((0.784314, 0.501961, 0.2), 1, u'default'), u'La': ((0.439216, 0.831373, 1), 1, u'default'), u'Li': ((0.8, 0.501961, 1), 1, u'default'),
u'Tl': ((0.65098, 0.329412, 0.301961), 1, u'default'), u'Tm': ((0, 0.831373, 0.321569), 1, u'default'), u'Lr': ((0.780392, 0, 0.4), 1, u'default'), u'Th': ((0, 0.729412, 1), 1, u'default'), u'Ti': ((0.74902, 0.760784, 0.780392), 1, u'default'), u'tan': ((0.823529, 0.705882, 0.54902), 1, u'default'), u'Te': ((0.831373, 0.478431, 0), 1, u'default'), u'Tb': ((0.188235, 1, 0.780392), 1, u'default'), u'Tc': ((0.231373, 0.619608, 0.619608), 1, u'default'), u'Ta': ((0.301961, 0.65098, 1), 1, u'default'), u'Yb': ((0, 0.74902, 0.219608), 1, u'default'), u'Db': ((0.819608, 0, 0.309804), 1, u'default'), u'Dy': ((0.121569, 1, 0.780392), 1, u'default'), u'I': ((0.580392, 0, 0.580392), 1, u'default'), u'salmon': ((0.980392, 0.501961, 0.447059), 1, u'default'), u'U': ((0, 0.560784, 1), 1, u'default'), u'Y': ((0.580392, 1, 1), 1, u'default'), u'Ac': ((0.439216, 0.670588, 0.980392), 1, u'default'), u'Ag': ((0.752941, 0.752941, 0.752941), 1, u'default'), u'Ir': ((0.0901961, 0.329412, 0.529412), 1, u'default'), u'Am': ((0.329412, 0.360784, 0.94902), 1, u'default'), u'Al': ((0.74902, 0.65098, 0.65098), 1, u'default'),
u'As': ((0.741176, 0.501961, 0.890196), 1, u'default'), u'Ar': ((0.501961, 0.819608, 0.890196), 1, u'default'), u'Au': ((1, 0.819608, 0.137255), 1, u'default'), u'At': ((0.458824, 0.309804, 0.270588), 1, u'default'), u'In': ((0.65098, 0.458824, 0.45098), 1, u'default')}
materials = {u'default': ((0.85, 0.85, 0.85), 30)}
pbInfo = {'category': [u'distance monitor', u'hydrogen bonds'], 'bondInfo': [{'color': (0, None, {}), 'atoms': [], 'label': (0, None, {}), 'halfbond': (0, None, {}), 'labelColor': (0, None, {}), 'labelOffset': (0, None, {}), 'drawMode': (0, None, {}), 'display': (0, None, {})}, {'color': (8, 12, {}), 'atoms': [[4997, 3217], [5054, 2493], [2528, 5057], [2546, 5057], [2854, 5021], [5007, 2867], [5023, 3198], [2814, 5052]], 'label': (8, u'', {}), 'halfbond': (8, False, {}), 'labelColor': (8, None, {}), 'labelOffset': (8, chimera.Vector(-1e+99, 0.0, 0.0), {chimera.Vector(-1e+99, 0.0, 0.0): [3], chimera.Vector(-1e+99, 0.0, 0.0): [0], chimera.Vector(-1e+99, 0.0, 0.0): [4], chimera.Vector(-1e+99, 0.0, 0.0): [5], chimera.Vector(-1e+99, 0.0, 0.0): [1], chimera.Vector(-1e+99, 0.0, 0.0): [6], chimera.Vector(-1e+99, 0.0, 0.0): [7]}), 'drawMode': (8, 0, {}), 'display': (8, 2, {})}], 'lineType': (2, 1, {2: [0]}), 'color': (2, 10, {11: [1]}), 'optional': {'fixedLabels': (True, False, (2, 0, {None: [1]}))}, 'display': (2, True, {}), 'showStubBonds': (2, False, {}),
'lineWidth': (2, 1, {}), 'stickScale': (2, 1, {}), 'id': [-2, -1]}
modelAssociations = {}
colorInfo = (14, (u'H', (1, 1, 1, 1)), {(u'S', (1, 1, 0.188235, 1)): [8], (u'N', (0.188235, 0.313725, 0.972549, 1)): [5], (u'', (0, 0.8, 0.9, 1)): [12], (u'green', (0, 1, 0, 1)): [13], (u'O', (1, 0.0509804, 0.0509804, 1)): [7], (u'tan', (0.823529, 0.705882, 0.54902, 1)): [0], (u'yellow', (1, 1, 0, 1)): [10], (u'light green', (0.564706, 0.933333, 0.564706, 1)): [4], (u'salmon', (0.980392, 0.501961, 0.447059, 1)): [2], (u'cornflower blue', (0.392157, 0.584314, 0.929412, 1)): [3], (u'C', (0.564706, 0.564706, 0.564706, 1)): [9], (u'', (0.933333, 0.509804, 0.933333, 1)): [1], (u'gray', (0.745, 0.745, 0.745, 1)): [11]})
viewerInfo = {'cameraAttrs': {'center': (-0.058935182823257, 0.66261393800279, -0.076487559785759), 'fieldOfView': 15.345613904921, 'nearFar': (26.142084994218, -59.816881146529), 'ortho': False, 'eyeSeparation': 50.8, 'focal': 58.219}, 'viewerAttrs': {'silhouetteColor': None, 'clipping': False, 'showSilhouette': False, 'showShadows': False, 'viewSize': 38.006240354004, 'labelsOnTop': True, 'depthCueRange': (0.5, 1), 'silhouetteWidth': 2, 'singleLayerTransparency': True, 'shadowTextureSize': 2048, 'backgroundImage': [None, 1, 2, 1, 0, 0], 'backgroundGradient': [('Chimera default', [(1, 1, 1, 1), (0, 0, 1, 1)], 1), 1, 0, 0], 'depthCue': True, 'highlight': 0, 'scaleFactor': 4.5, 'angleDependentTransparency': True, 'backgroundMethod': 0}, 'viewerHL': 13, 'cameraMode': 'mono', 'detail': 1.5, 'viewerFog': None, 'viewerBG': None}
replyobj.status("Initializing session restore...", blankAfter=0,
secondary=True)
from SimpleSession.versions.v65 import expandSummary
init(dict(enumerate(expandSummary(colorInfo))))
replyobj.status("Restoring colors...", blankAfter=0,
secondary=True)
restoreColors(colors, materials)
replyobj.status("Restoring molecules...", blankAfter=0,
secondary=True)
restoreMolecules(molInfo, resInfo, atomInfo, bondInfo, crdInfo)
replyobj.status("Restoring surfaces...", blankAfter=0,
secondary=True)
restoreSurfaces(surfInfo)
replyobj.status("Restoring VRML models...", blankAfter=0,
secondary=True)
restoreVRML(vrmlInfo)
replyobj.status("Restoring pseudobond groups...", blankAfter=0,
secondary=True)
restorePseudoBondGroups(pbInfo)
replyobj.status("Restoring model associations...", blankAfter=0,
secondary=True)
restoreModelAssociations(modelAssociations)
replyobj.status("Restoring camera...", blankAfter=0,
secondary=True)
restoreViewer(viewerInfo)
try:
restoreCoreModels()
except:
reportRestoreError("Error restoring core models")
replyobj.status("Restoring extension info...", blankAfter=0,
secondary=True)
try:
import StructMeasure
from StructMeasure.DistMonitor import restoreDistances
registerAfterModelsCB(restoreDistances, 1)
except:
reportRestoreError("Error restoring distances in session")
def restoreMidasBase():
formattedPositions = {'binding_site': (4.5, 38.0062403540039, (-0.05893518282325694, 0.6626139380027851, -0.07648755978575927), (26.14208499421779, -59.816881146529305), 58.21900000000001, {(0, 0): ((13.685858625276353, 69.0865196046264, -7.067445962250192), (0.47655019383469965, -0.5368231576952218, 0.696218938350747, 113.88158795255141))}, {(0, 0, 'MSMSModel'): (False, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, False, 5.0), (0, 0, 'Molecule'): (False, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, False, 5.0)}, 0, (-0.05893518282325938, 0.6626139380027922, -0.0764875597857575), False, 15.345613904920642)}
import Midas
Midas.restoreMidasBase(formattedPositions)
try:
restoreMidasBase()
except:
reportRestoreError('Error restoring Midas base state')
def restoreMidasText():
from Midas import midas_text
midas_text.aliases = {'substrate': ':307-311'}
midas_text.userSurfCategories = {}
try:
restoreMidasText()
except:
reportRestoreError('Error restoring Midas text state')
def restore_cap_attributes():
cap_attributes = \
{
'cap_attributes': [
{
'cap_color': None,
'class': 'Model_Capper_State',
'display_style': None,
'surface': ( 0, 0, ),
'version': 1,
},
],
'cap_color': None,
'cap_offset': 0.01,
'class': 'Caps_State',
'default_cap_offset': 0.01,
'mesh_style': False,
'shown': True,
'subdivision_factor': 1.0,
'version': 1,
}
import SurfaceCap.session
SurfaceCap.session.restore_cap_attributes(cap_attributes)
registerAfterModelsCB(restore_cap_attributes)
def restore_volume_data():
volume_data_state = \
{
'class': 'Volume_Manager_State',
'data_and_regions_state': [ ],
'version': 2,
}
from VolumeViewer import session
session.restore_volume_data_state(volume_data_state)
try:
restore_volume_data()
except:
reportRestoreError('Error restoring volume data')
geomData = {'AxisManager': {}, 'CentroidManager': {}, 'PlaneManager': {}}
try:
from StructMeasure.Geometry import geomManager
geomManager._restoreSession(geomData)
except:
reportRestoreError("Error restoring geometry objects in session")
def restoreSession_RibbonStyleEditor():
import SimpleSession
import RibbonStyleEditor
userScalings = []
userXSections = []
userResidueClasses = []
residueData = [(1, 'Chimera default', 'rounded', u'amino acid'), (2, 'Chimera default', 'rounded', u'amino acid'), (3, 'Chimera default', 'rounded', u'amino acid'), (4, 'Chimera default', 'rounded', u'amino acid'), (5, 'Chimera default', 'rounded', u'amino acid'), (6, 'Chimera default', 'rounded', u'amino acid'), (7, 'Chimera default', 'rounded', u'amino acid'), (8, 'Chimera default', 'rounded', u'amino acid'), (9, 'Chimera default', 'rounded', u'amino acid'), (10, 'Chimera default', 'rounded', u'amino acid'), (11, 'Chimera default', 'rounded', u'amino acid'), (12, 'Chimera default', 'rounded', u'amino acid'), (13, 'Chimera default', 'rounded', u'amino acid'), (14, 'Chimera default', 'rounded', u'amino acid'), (15, 'Chimera default', 'rounded', u'amino acid'), (16, 'Chimera default', 'rounded', u'amino acid'), (17, 'Chimera default', 'rounded', u'amino acid'), (18, 'Chimera default', 'rounded', u'amino acid'), (19, 'Chimera default', 'rounded', u'amino acid'), (20, 'Chimera default', 'rounded', u'amino acid'), (21, 'Chimera default', 'rounded', u'amino acid'), (22, 'Chimera default', 'rounded', u'amino acid'),
(23, 'Chimera default', 'rounded', u'amino acid'), (24, 'Chimera default', 'rounded', u'amino acid'), (25, 'Chimera default', 'rounded', u'amino acid'), (26, 'Chimera default', 'rounded', u'amino acid'), (27, 'Chimera default', 'rounded', u'amino acid'), (28, 'Chimera default', 'rounded', u'amino acid'), (29, 'Chimera default', 'rounded', u'amino acid'), (30, 'Chimera default', 'rounded', u'amino acid'), (31, 'Chimera default', 'rounded', u'amino acid'), (32, 'Chimera default', 'rounded', u'amino acid'), (33, 'Chimera default', 'rounded', u'amino acid'), (34, 'Chimera default', 'rounded', u'amino acid'), (35, 'Chimera default', 'rounded', u'amino acid'), (36, 'Chimera default', 'rounded', u'amino acid'), (37, 'Chimera default', 'rounded', u'amino acid'), (38, 'Chimera default', 'rounded', u'amino acid'), (39, 'Chimera default', 'rounded', u'amino acid'), (40, 'Chimera default', 'rounded', u'amino acid'), (41, 'Chimera default', 'rounded', u'amino acid'), (42, 'Chimera default', 'rounded', u'amino acid'), (43, 'Chimera default', 'rounded', u'amino acid'),
(44, 'Chimera default', 'rounded', u'amino acid'), (45, 'Chimera default', 'rounded', u'amino acid'), (46, 'Chimera default', 'rounded', u'amino acid'), (47, 'Chimera default', 'rounded', u'amino acid'), (48, 'Chimera default', 'rounded', u'amino acid'), (49, 'Chimera default', 'rounded', u'amino acid'), (50, 'Chimera default', 'rounded', u'amino acid'), (51, 'Chimera default', 'rounded', u'amino acid'), (52, 'Chimera default', 'rounded', u'amino acid'), (53, 'Chimera default', 'rounded', u'amino acid'), (54, 'Chimera default', 'rounded', u'amino acid'), (55, 'Chimera default', 'rounded', u'amino acid'), (56, 'Chimera default', 'rounded', u'amino acid'), (57, 'Chimera default', 'rounded', u'amino acid'), (58, 'Chimera default', 'rounded', u'amino acid'), (59, 'Chimera default', 'rounded', u'amino acid'), (60, 'Chimera default', 'rounded', u'amino acid'), (61, 'Chimera default', 'rounded', u'amino acid'), (62, 'Chimera default', 'rounded', u'amino acid'), (63, 'Chimera default', 'rounded', u'amino acid'), (64, 'Chimera default', 'rounded', u'amino acid'),
(65, 'Chimera default', 'rounded', u'amino acid'), (66, 'Chimera default', 'rounded', u'amino acid'), (67, 'Chimera default', 'rounded', u'amino acid'), (68, 'Chimera default', 'rounded', u'amino acid'), (69, 'Chimera default', 'rounded', u'amino acid'), (70, 'Chimera default', 'rounded', u'amino acid'), (71, 'Chimera default', 'rounded', u'amino acid'), (72, 'Chimera default', 'rounded', u'amino acid'), (73, 'Chimera default', 'rounded', u'amino acid'), (74, 'Chimera default', 'rounded', u'amino acid'), (75, 'Chimera default', 'rounded', u'amino acid'), (76, 'Chimera default', 'rounded', u'amino acid'), (77, 'Chimera default', 'rounded', u'amino acid'), (78, 'Chimera default', 'rounded', u'amino acid'), (79, 'Chimera default', 'rounded', u'amino acid'), (80, 'Chimera default', 'rounded', u'amino acid'), (81, 'Chimera default', 'rounded', u'amino acid'), (82, 'Chimera default', 'rounded', u'amino acid'), (83, 'Chimera default', 'rounded', u'amino acid'), (84, 'Chimera default', 'rounded', u'amino acid'), (85, 'Chimera default', 'rounded', u'amino acid'),
(86, 'Chimera default', 'rounded', u'amino acid'), (87, 'Chimera default', 'rounded', u'amino acid'), (88, 'Chimera default', 'rounded', u'amino acid'), (89, 'Chimera default', 'rounded', u'amino acid'), (90, 'Chimera default', 'rounded', u'amino acid'), (91, 'Chimera default', 'rounded', u'amino acid'), (92, 'Chimera default', 'rounded', u'amino acid'), (93, 'Chimera default', 'rounded', u'amino acid'), (94, 'Chimera default', 'rounded', u'amino acid'), (95, 'Chimera default', 'rounded', u'amino acid'), (96, 'Chimera default', 'rounded', u'amino acid'), (97, 'Chimera default', 'rounded', u'amino acid'), (98, 'Chimera default', 'rounded', u'amino acid'), (99, 'Chimera default', 'rounded', u'amino acid'), (100, 'Chimera default', 'rounded', u'amino acid'), (101, 'Chimera default', 'rounded', u'amino acid'), (102, 'Chimera default', 'rounded', u'amino acid'), (103, 'Chimera default', 'rounded', u'amino acid'), (104, 'Chimera default', 'rounded', u'amino acid'), (105, 'Chimera default', 'rounded', u'amino acid'), (106, 'Chimera default', 'rounded', u'amino acid'),
(107, 'Chimera default', 'rounded', u'amino acid'), (108, 'Chimera default', 'rounded', u'amino acid'), (109, 'Chimera default', 'rounded', u'amino acid'), (110, 'Chimera default', 'rounded', u'amino acid'), (111, 'Chimera default', 'rounded', u'amino acid'), (112, 'Chimera default', 'rounded', u'amino acid'), (113, 'Chimera default', 'rounded', u'amino acid'), (114, 'Chimera default', 'rounded', u'amino acid'), (115, 'Chimera default', 'rounded', u'amino acid'), (116, 'Chimera default', 'rounded', u'amino acid'), (117, 'Chimera default', 'rounded', u'amino acid'), (118, 'Chimera default', 'rounded', u'amino acid'), (119, 'Chimera default', 'rounded', u'amino acid'), (120, 'Chimera default', 'rounded', u'amino acid'), (121, 'Chimera default', 'rounded', u'amino acid'), (122, 'Chimera default', 'rounded', u'amino acid'), (123, 'Chimera default', 'rounded', u'amino acid'), (124, 'Chimera default', 'rounded', u'amino acid'), (125, 'Chimera default', 'rounded', u'amino acid'), (126, 'Chimera default', 'rounded', u'amino acid'), (127, 'Chimera default', 'rounded', u'amino acid'),
(128, 'Chimera default', 'rounded', u'amino acid'), (129, 'Chimera default', 'rounded', u'amino acid'), (130, 'Chimera default', 'rounded', u'amino acid'), (131, 'Chimera default', 'rounded', u'amino acid'), (132, 'Chimera default', 'rounded', u'amino acid'), (133, 'Chimera default', 'rounded', u'amino acid'), (134, 'Chimera default', 'rounded', u'amino acid'), (135, 'Chimera default', 'rounded', u'amino acid'), (136, 'Chimera default', 'rounded', u'amino acid'), (137, 'Chimera default', 'rounded', u'amino acid'), (138, 'Chimera default', 'rounded', u'amino acid'), (139, 'Chimera default', 'rounded', u'amino acid'), (140, 'Chimera default', 'rounded', u'amino acid'), (141, 'Chimera default', 'rounded', u'amino acid'), (142, 'Chimera default', 'rounded', u'amino acid'), (143, 'Chimera default', 'rounded', u'amino acid'), (144, 'Chimera default', 'rounded', u'amino acid'), (145, 'Chimera default', 'rounded', u'amino acid'), (146, 'Chimera default', 'rounded', u'amino acid'), (147, 'Chimera default', 'rounded', u'amino acid'), (148, 'Chimera default', 'rounded', u'amino acid'),
(149, 'Chimera default', 'rounded', u'amino acid'), (150, 'Chimera default', 'rounded', u'amino acid'), (151, 'Chimera default', 'rounded', u'amino acid'), (152, 'Chimera default', 'rounded', u'amino acid'), (153, 'Chimera default', 'rounded', u'amino acid'), (154, 'Chimera default', 'rounded', u'amino acid'), (155, 'Chimera default', 'rounded', u'amino acid'), (156, 'Chimera default', 'rounded', u'amino acid'), (157, 'Chimera default', 'rounded', u'amino acid'), (158, 'Chimera default', 'rounded', u'amino acid'), (159, 'Chimera default', 'rounded', u'amino acid'), (160, 'Chimera default', 'rounded', u'amino acid'), (161, 'Chimera default', 'rounded', u'amino acid'), (162, 'Chimera default', 'rounded', u'amino acid'), (163, 'Chimera default', 'rounded', u'amino acid'), (164, 'Chimera default', 'rounded', u'amino acid'), (165, 'Chimera default', 'rounded', u'amino acid'), (166, 'Chimera default', 'rounded', u'amino acid'), (167, 'Chimera default', 'rounded', u'amino acid'), (168, 'Chimera default', 'rounded', u'amino acid'), (169, 'Chimera default', 'rounded', u'amino acid'),
(170, 'Chimera default', 'rounded', u'amino acid'), (171, 'Chimera default', 'rounded', u'amino acid'), (172, 'Chimera default', 'rounded', u'amino acid'), (173, 'Chimera default', 'rounded', u'amino acid'), (174, 'Chimera default', 'rounded', u'amino acid'), (175, 'Chimera default', 'rounded', u'amino acid'), (176, 'Chimera default', 'rounded', u'amino acid'), (177, 'Chimera default', 'rounded', u'amino acid'), (178, 'Chimera default', 'rounded', u'amino acid'), (179, 'Chimera default', 'rounded', u'amino acid'), (180, 'Chimera default', 'rounded', u'amino acid'), (181, 'Chimera default', 'rounded', u'amino acid'), (182, 'Chimera default', 'rounded', u'amino acid'), (183, 'Chimera default', 'rounded', u'amino acid'), (184, 'Chimera default', 'rounded', u'amino acid'), (185, 'Chimera default', 'rounded', u'amino acid'), (186, 'Chimera default', 'rounded', u'amino acid'), (187, 'Chimera default', 'rounded', u'amino acid'), (188, 'Chimera default', 'rounded', u'amino acid'), (189, 'Chimera default', 'rounded', u'amino acid'), (190, 'Chimera default', 'rounded', u'amino acid'),
(191, 'Chimera default', 'rounded', u'amino acid'), (192, 'Chimera default', 'rounded', u'amino acid'), (193, 'Chimera default', 'rounded', u'amino acid'), (194, 'Chimera default', 'rounded', u'amino acid'), (195, 'Chimera default', 'rounded', u'amino acid'), (196, 'Chimera default', 'rounded', u'amino acid'), (197, 'Chimera default', 'rounded', u'amino acid'), (198, 'Chimera default', 'rounded', u'amino acid'), (199, 'Chimera default', 'rounded', u'amino acid'), (200, 'Chimera default', 'rounded', u'amino acid'), (201, 'Chimera default', 'rounded', u'amino acid'), (202, 'Chimera default', 'rounded', u'amino acid'), (203, 'Chimera default', 'rounded', u'amino acid'), (204, 'Chimera default', 'rounded', u'amino acid'), (205, 'Chimera default', 'rounded', u'amino acid'), (206, 'Chimera default', 'rounded', u'amino acid'), (207, 'Chimera default', 'rounded', u'amino acid'), (208, 'Chimera default', 'rounded', u'amino acid'), (209, 'Chimera default', 'rounded', u'amino acid'), (210, 'Chimera default', 'rounded', u'amino acid'), (211, 'Chimera default', 'rounded', u'amino acid'),
(212, 'Chimera default', 'rounded', u'amino acid'), (213, 'Chimera default', 'rounded', u'amino acid'), (214, 'Chimera default', 'rounded', u'amino acid'), (215, 'Chimera default', 'rounded', u'amino acid'), (216, 'Chimera default', 'rounded', u'amino acid'), (217, 'Chimera default', 'rounded', u'amino acid'), (218, 'Chimera default', 'rounded', u'amino acid'), (219, 'Chimera default', 'rounded', u'amino acid'), (220, 'Chimera default', 'rounded', u'amino acid'), (221, 'Chimera default', 'rounded', u'amino acid'), (222, 'Chimera default', 'rounded', u'amino acid'), (223, 'Chimera default', 'rounded', u'amino acid'), (224, 'Chimera default', 'rounded', u'amino acid'), (225, 'Chimera default', 'rounded', u'amino acid'), (226, 'Chimera default', 'rounded', u'amino acid'), (227, 'Chimera default', 'rounded', u'amino acid'), (228, 'Chimera default', 'rounded', u'amino acid'), (229, 'Chimera default', 'rounded', u'amino acid'), (230, 'Chimera default', 'rounded', u'amino acid'), (231, 'Chimera default', 'rounded', u'amino acid'), (232, 'Chimera default', 'rounded', u'amino acid'),
(233, 'Chimera default', 'rounded', u'amino acid'), (234, 'Chimera default', 'rounded', u'amino acid'), (235, 'Chimera default', 'rounded', u'amino acid'), (236, 'Chimera default', 'rounded', u'amino acid'), (237, 'Chimera default', 'rounded', u'amino acid'), (238, 'Chimera default', 'rounded', u'amino acid'), (239, 'Chimera default', 'rounded', u'amino acid'), (240, 'Chimera default', 'rounded', u'amino acid'), (241, 'Chimera default', 'rounded', u'amino acid'), (242, 'Chimera default', 'rounded', u'amino acid'), (243, 'Chimera default', 'rounded', u'amino acid'), (244, 'Chimera default', 'rounded', u'amino acid'), (245, 'Chimera default', 'rounded', u'amino acid'), (246, 'Chimera default', 'rounded', u'amino acid'), (247, 'Chimera default', 'rounded', u'amino acid'), (248, 'Chimera default', 'rounded', u'amino acid'), (249, 'Chimera default', 'rounded', u'amino acid'), (250, 'Chimera default', 'rounded', u'amino acid'), (251, 'Chimera default', 'rounded', u'amino acid'), (252, 'Chimera default', 'rounded', u'amino acid'), (253, 'Chimera default', 'rounded', u'amino acid'),
(254, 'Chimera default', 'rounded', u'amino acid'), (255, 'Chimera default', 'rounded', u'amino acid'), (256, 'Chimera default', 'rounded', u'amino acid'), (257, 'Chimera default', 'rounded', u'amino acid'), (258, 'Chimera default', 'rounded', u'amino acid'), (259, 'Chimera default', 'rounded', u'amino acid'), (260, 'Chimera default', 'rounded', u'amino acid'), (261, 'Chimera default', 'rounded', u'amino acid'), (262, 'Chimera default', 'rounded', u'amino acid'), (263, 'Chimera default', 'rounded', u'amino acid'), (264, 'Chimera default', 'rounded', u'amino acid'), (265, 'Chimera default', 'rounded', u'amino acid'), (266, 'Chimera default', 'rounded', u'amino acid'), (267, 'Chimera default', 'rounded', u'amino acid'), (268, 'Chimera default', 'rounded', u'amino acid'), (269, 'Chimera default', 'rounded', u'amino acid'), (270, 'Chimera default', 'rounded', u'amino acid'), (271, 'Chimera default', 'rounded', u'amino acid'), (272, 'Chimera default', 'rounded', u'amino acid'), (273, 'Chimera default', 'rounded', u'amino acid'), (274, 'Chimera default', 'rounded', u'amino acid'),
(275, 'Chimera default', 'rounded', u'amino acid'), (276, 'Chimera default', 'rounded', u'amino acid'), (277, 'Chimera default', 'rounded', u'amino acid'), (278, 'Chimera default', 'rounded', u'amino acid'), (279, 'Chimera default', 'rounded', u'amino acid'), (280, 'Chimera default', 'rounded', u'amino acid'), (281, 'Chimera default', 'rounded', u'amino acid'), (282, 'Chimera default', 'rounded', u'amino acid'), (283, 'Chimera default', 'rounded', u'amino acid'), (284, 'Chimera default', 'rounded', u'amino acid'), (285, 'Chimera default', 'rounded', u'amino acid'), (286, 'Chimera default', 'rounded', u'amino acid'), (287, 'Chimera default', 'rounded', u'amino acid'), (288, 'Chimera default', 'rounded', u'amino acid'), (289, 'Chimera default', 'rounded', u'amino acid'), (290, 'Chimera default', 'rounded', u'amino acid'), (291, 'Chimera default', 'rounded', u'amino acid'), (292, 'Chimera default', 'rounded', u'amino acid'), (293, 'Chimera default', 'rounded', u'amino acid'), (294, 'Chimera default', 'rounded', u'amino acid'), (295, 'Chimera default', 'rounded', u'amino acid'),
(296, 'Chimera default', 'rounded', u'amino acid'), (297, 'Chimera default', 'rounded', u'amino acid'), (298, 'Chimera default', 'rounded', u'amino acid'), (299, 'Chimera default', 'rounded', u'amino acid'), (300, 'Chimera default', 'rounded', u'amino acid'), (301, 'Chimera default', 'rounded', u'amino acid'), (302, 'Chimera default', 'rounded', u'amino acid'), (303, 'Chimera default', 'rounded', u'amino acid'), (304, 'Chimera default', 'rounded', u'amino acid'), (305, 'Chimera default', 'rounded', u'amino acid'), (306, 'Chimera default', 'rounded', u'amino acid'), (307, 'Chimera default', 'rounded', u'amino acid'), (308, 'Chimera default', 'rounded', u'amino acid'), (309, 'Chimera default', 'rounded', u'amino acid'), (310, 'Chimera default', 'rounded', u'amino acid'), (311, 'Chimera default', 'rounded', u'amino acid')]
flags = RibbonStyleEditor.NucleicDefault1
SimpleSession.registerAfterModelsCB(RibbonStyleEditor.restoreState,
(userScalings, userXSections,
userResidueClasses, residueData, flags))
try:
restoreSession_RibbonStyleEditor()
except:
reportRestoreError("Error restoring RibbonStyleEditor state")
trPickle = 'gAJjQW5pbWF0ZS5UcmFuc2l0aW9ucwpUcmFuc2l0aW9ucwpxASmBcQJ9cQMoVQxjdXN0b21fc2NlbmVxBGNBbmltYXRlLlRyYW5zaXRpb24KVHJhbnNpdGlvbgpxBSmBcQZ9cQcoVQZmcmFtZXNxCEsBVQ1kaXNjcmV0ZUZyYW1lcQlLAVUKcHJvcGVydGllc3EKXXELVQNhbGxxDGFVBG5hbWVxDWgEVQRtb2RlcQ5VBmxpbmVhcnEPdWJVCGtleWZyYW1lcRBoBSmBcRF9cRIoaAhLFGgJSwFoCl1xE2gMYWgNaBBoDmgPdWJVBXNjZW5lcRRoBSmBcRV9cRYoaAhLAWgJSwFoCl1xF2gMYWgNaBRoDmgPdWJ1Yi4='
scPickle = 'gAJjQW5pbWF0ZS5TY2VuZXMKU2NlbmVzCnEBKYFxAn1xA1UHbWFwX2lkc3EEfXNiLg=='
kfPickle = 'gAJjQW5pbWF0ZS5LZXlmcmFtZXMKS2V5ZnJhbWVzCnEBKYFxAn1xA1UHZW50cmllc3EEXXEFc2Iu'
def restoreAnimation():
'A method to unpickle and restore animation objects'
# Scenes must be unpickled after restoring transitions, because each
# scene links to a 'scene' transition. Likewise, keyframes must be
# unpickled after restoring scenes, because each keyframe links to a scene.
# The unpickle process is left to the restore* functions, it's
# important that it doesn't happen prior to calling those functions.
import SimpleSession
from Animate.Session import restoreTransitions
from Animate.Session import restoreScenes
from Animate.Session import restoreKeyframes
SimpleSession.registerAfterModelsCB(restoreTransitions, trPickle)
SimpleSession.registerAfterModelsCB(restoreScenes, scPickle)
SimpleSession.registerAfterModelsCB(restoreKeyframes, kfPickle)
try:
restoreAnimation()
except:
reportRestoreError('Error in Animate.Session')
def restoreLightController():
import Lighting
Lighting._setFromParams({'ratio': 1.25, 'brightness': 1.16, 'material': [30.0, (0.85, 0.85, 0.85), 1.0], 'back': [(0.35740674433659325, 0.6604015517481454, -0.6604015517481455), (1.0, 1.0, 1.0), 0.0], 'mode': 'two-point', 'key': [(-0.35740674433659325, 0.6604015517481454, 0.6604015517481455), (1.0, 1.0, 1.0), 1.0], 'contrast': 0.83, 'fill': [(0.25056280708573153, 0.25056280708573153, 0.9351131265310293), (1.0, 1.0, 1.0), 0.0]})
try:
restoreLightController()
except:
reportRestoreError("Error restoring lighting parameters")
def restore_hide_dust():
hide_dust_state = \
{
'class': 'Hide_Dust_State',
'dust_table': {},
'version': 1,
}
try:
import HideDust.session
HideDust.session.restore_hide_dust_state(hide_dust_state)
except:
reportRestoreError('Error restoring hide dust')
registerAfterModelsCB(restore_hide_dust)
def restoreRemainder():
from SimpleSession.versions.v65 import restoreWindowSize, \
restoreOpenStates, restoreSelections, restoreFontInfo, \
restoreOpenModelsAttrs, restoreModelClip, restoreSilhouettes
curSelIds = []
savedSels = []
openModelsAttrs = { 'cofrMethod': 0 }
from chimera import Point
openModelsAttrs['cofr'] = Point(-0.0589352, 0.662614, -0.0764876)
windowSize = (1218, 1266)
xformMap = {0: (((0.4765501938347, -0.53682315769522, 0.69621893835075), 113.88158795255), (13.685858625276, 69.086519604626, -7.0674459622502), True)}
fontInfo = {'face': ('Sans Serif', 'Normal', 16)}
clipPlaneInfo = {}
silhouettes = {0: True, 9880: True, 9890: True, 9889: True}
replyobj.status("Restoring window...", blankAfter=0,
secondary=True)
restoreWindowSize(windowSize)
replyobj.status("Restoring open states...", blankAfter=0,
secondary=True)
restoreOpenStates(xformMap)
replyobj.status("Restoring font info...", blankAfter=0,
secondary=True)
restoreFontInfo(fontInfo)
replyobj.status("Restoring selections...", blankAfter=0,
secondary=True)
restoreSelections(curSelIds, savedSels)
replyobj.status("Restoring openModel attributes...", blankAfter=0,
secondary=True)
restoreOpenModelsAttrs(openModelsAttrs)
replyobj.status("Restoring model clipping...", blankAfter=0,
secondary=True)
restoreModelClip(clipPlaneInfo)
replyobj.status("Restoring per-model silhouettes...", blankAfter=0,
secondary=True)
restoreSilhouettes(silhouettes)
replyobj.status("Restoring remaining extension info...", blankAfter=0,
secondary=True)
try:
restoreRemainder()
except:
reportRestoreError("Error restoring post-model state")
from SimpleSession.versions.v65 import makeAfterModelsCBs
makeAfterModelsCBs()
from SimpleSession.versions.v65 import endRestore
replyobj.status('Finishing restore...', blankAfter=0, secondary=True)
endRestore({'description': u"binding pocket of SARS-CoV-2 Mpro main protein (aka 3CLpro) with a natural substrate, AVLQS peptide. Subsites colored according to the amino acid side chains recognized:\n\nS1' violet --> SER, P1' residue\nS1 blue --> GLN, P1 residue\nS2 salmon --> LEU, P2 residue\nS4 green --> ALA, P4 residue\n"})
replyobj.status('', secondary=True)
replyobj.status('Restore finished.')
| 1,381.533101 | 208,450 | 0.959874 | 8,187 | 396,500 | 46.480029 | 0.501405 | 0.007403 | 0.017163 | 0.01798 | 0.060536 | 0.058655 | 0.027727 | 0.022813 | 0.021932 | 0.021396 | 0 | 0.12792 | 0.010338 | 396,500 | 286 | 208,451 | 1,386.363636 | 0.841833 | 0.000976 | 0 | 0.188755 | 0 | 0.024096 | 0.949063 | 0.91411 | 0 | 1 | 0 | 0 | 0 | 1 | 0.040161 | false | 0 | 0.108434 | 0 | 0.148594 | 0 | 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 |
e065968a41e54b037e4be45008c3fd9d36be5631 | 91 | py | Python | src/elementary_flask/presets/themes/default/_jinja2_env.py | xaled/flaskly | 2ed66d89e42afba830d6c73c9f70f00d1dcac573 | [
"MIT"
] | null | null | null | src/elementary_flask/presets/themes/default/_jinja2_env.py | xaled/flaskly | 2ed66d89e42afba830d6c73c9f70f00d1dcac573 | [
"MIT"
] | null | null | null | src/elementary_flask/presets/themes/default/_jinja2_env.py | xaled/flaskly | 2ed66d89e42afba830d6c73c9f70f00d1dcac573 | [
"MIT"
] | null | null | null | from .._common import make_jinja2_theme_env
jinja2_env = make_jinja2_theme_env('default')
| 22.75 | 45 | 0.835165 | 14 | 91 | 4.857143 | 0.571429 | 0.294118 | 0.441176 | 0.529412 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.036145 | 0.087912 | 91 | 3 | 46 | 30.333333 | 0.783133 | 0 | 0 | 0 | 0 | 0 | 0.076923 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 |
0ec9022df220f0e45f44d3e0a43c6a3a8119458d | 29,480 | py | Python | tests/unit/aiplatform/test_hyperparameter_tuning_job.py | sakagarwal/python-aiplatform | 62b4a1ea589235910c6e87f027899a29bf1bacb1 | [
"Apache-2.0"
] | 1 | 2022-03-30T05:23:29.000Z | 2022-03-30T05:23:29.000Z | tests/unit/aiplatform/test_hyperparameter_tuning_job.py | sakagarwal/python-aiplatform | 62b4a1ea589235910c6e87f027899a29bf1bacb1 | [
"Apache-2.0"
] | null | null | null | tests/unit/aiplatform/test_hyperparameter_tuning_job.py | sakagarwal/python-aiplatform | 62b4a1ea589235910c6e87f027899a29bf1bacb1 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
# Copyright 2021 Google LLC
#
# 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 pytest
import copy
from importlib import reload
from unittest import mock
from unittest.mock import patch
import logging
from google.rpc import status_pb2
from google.cloud import aiplatform
from google.cloud.aiplatform import base
from google.cloud.aiplatform import hyperparameter_tuning as hpt
from google.cloud.aiplatform.compat.types import (
encryption_spec as gca_encryption_spec_compat,
hyperparameter_tuning_job as gca_hyperparameter_tuning_job_compat,
job_state as gca_job_state_compat,
study as gca_study_compat,
)
from google.cloud.aiplatform_v1.services.job_service import client as job_service_client
import test_custom_job
_TEST_PROJECT = "test-project"
_TEST_LOCATION = "us-central1"
_TEST_ID = "1028944691210842416"
_TEST_DISPLAY_NAME = "my_hp_job_1234"
_TEST_PARENT = f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}"
_TEST_STAGING_BUCKET = test_custom_job._TEST_STAGING_BUCKET
_TEST_BASE_OUTPUT_DIR = test_custom_job._TEST_BASE_OUTPUT_DIR
_TEST_HYPERPARAMETERTUNING_JOB_NAME = (
f"{_TEST_PARENT}/hyperparameterTuningJobs/{_TEST_ID}"
)
# CMEK encryption
_TEST_DEFAULT_ENCRYPTION_KEY_NAME = "key_default"
_TEST_DEFAULT_ENCRYPTION_SPEC = gca_encryption_spec_compat.EncryptionSpec(
kms_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME
)
_TEST_SERVICE_ACCOUNT = "vinnys@my-project.iam.gserviceaccount.com"
_TEST_NETWORK = f"projects/{_TEST_PROJECT}/global/networks/{_TEST_ID}"
_TEST_TIMEOUT = 8000
_TEST_RESTART_JOB_ON_WORKER_RESTART = True
_TEST_METRIC_SPEC_KEY = "test-metric"
_TEST_METRIC_SPEC_VALUE = "maximize"
_TEST_PARALLEL_TRIAL_COUNT = 8
_TEST_MAX_TRIAL_COUNT = 64
_TEST_MAX_FAILED_TRIAL_COUNT = 4
_TEST_SEARCH_ALGORITHM = "random"
_TEST_MEASUREMENT_SELECTION = "best"
_TEST_LABELS = {"my_hp_key": "my_hp_value"}
_TEST_BASE_HYPERPARAMETER_TUNING_JOB_PROTO = gca_hyperparameter_tuning_job_compat.HyperparameterTuningJob(
display_name=_TEST_DISPLAY_NAME,
study_spec=gca_study_compat.StudySpec(
metrics=[
gca_study_compat.StudySpec.MetricSpec(
metric_id=_TEST_METRIC_SPEC_KEY, goal=_TEST_METRIC_SPEC_VALUE.upper()
)
],
parameters=[
gca_study_compat.StudySpec.ParameterSpec(
parameter_id="lr",
scale_type=gca_study_compat.StudySpec.ParameterSpec.ScaleType.UNIT_LOG_SCALE,
double_value_spec=gca_study_compat.StudySpec.ParameterSpec.DoubleValueSpec(
min_value=0.001, max_value=0.1
),
),
gca_study_compat.StudySpec.ParameterSpec(
parameter_id="units",
scale_type=gca_study_compat.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
integer_value_spec=gca_study_compat.StudySpec.ParameterSpec.IntegerValueSpec(
min_value=4, max_value=1028
),
),
gca_study_compat.StudySpec.ParameterSpec(
parameter_id="activation",
categorical_value_spec=gca_study_compat.StudySpec.ParameterSpec.CategoricalValueSpec(
values=["relu", "sigmoid", "elu", "selu", "tanh"]
),
),
gca_study_compat.StudySpec.ParameterSpec(
parameter_id="batch_size",
scale_type=gca_study_compat.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
discrete_value_spec=gca_study_compat.StudySpec.ParameterSpec.DiscreteValueSpec(
values=[16, 32]
),
),
],
algorithm=gca_study_compat.StudySpec.Algorithm.RANDOM_SEARCH,
measurement_selection_type=gca_study_compat.StudySpec.MeasurementSelectionType.BEST_MEASUREMENT,
),
parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
max_trial_count=_TEST_MAX_TRIAL_COUNT,
max_failed_trial_count=_TEST_MAX_FAILED_TRIAL_COUNT,
trial_job_spec=test_custom_job._TEST_BASE_CUSTOM_JOB_PROTO.job_spec,
labels=_TEST_LABELS,
encryption_spec=_TEST_DEFAULT_ENCRYPTION_SPEC,
)
_TEST_BASE_TRIAL_PROTO = gca_study_compat.Trial()
def _get_hyperparameter_tuning_job_proto(state=None, name=None, error=None):
hyperparameter_tuning_job_proto = copy.deepcopy(
_TEST_BASE_HYPERPARAMETER_TUNING_JOB_PROTO
)
hyperparameter_tuning_job_proto.name = name
hyperparameter_tuning_job_proto.state = state
hyperparameter_tuning_job_proto.error = error
return hyperparameter_tuning_job_proto
def _get_trial_proto(id=None, state=None):
trial_proto = copy.deepcopy(_TEST_BASE_TRIAL_PROTO)
trial_proto.id = id
trial_proto.state = state
if state == gca_study_compat.Trial.State.ACTIVE:
trial_proto.web_access_uris = test_custom_job._TEST_WEB_ACCESS_URIS
return trial_proto
def _get_hyperparameter_tuning_job_proto_with_enable_web_access(
state=None, name=None, error=None, trials=[]
):
hyperparameter_tuning_job_proto = _get_hyperparameter_tuning_job_proto(
state=state, name=name, error=error,
)
hyperparameter_tuning_job_proto.trial_job_spec.enable_web_access = (
test_custom_job._TEST_ENABLE_WEB_ACCESS
)
if state == gca_job_state_compat.JobState.JOB_STATE_RUNNING:
hyperparameter_tuning_job_proto.trials = trials
return hyperparameter_tuning_job_proto
@pytest.fixture
def get_hyperparameter_tuning_job_mock():
with patch.object(
job_service_client.JobServiceClient, "get_hyperparameter_tuning_job"
) as get_hyperparameter_tuning_job_mock:
get_hyperparameter_tuning_job_mock.side_effect = [
_get_hyperparameter_tuning_job_proto(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_PENDING,
),
_get_hyperparameter_tuning_job_proto(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_RUNNING,
),
_get_hyperparameter_tuning_job_proto(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_SUCCEEDED,
),
_get_hyperparameter_tuning_job_proto(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_SUCCEEDED,
),
]
yield get_hyperparameter_tuning_job_mock
@pytest.fixture
def get_hyperparameter_tuning_job_mock_with_enable_web_access():
with patch.object(
job_service_client.JobServiceClient, "get_hyperparameter_tuning_job"
) as get_hyperparameter_tuning_job_mock:
get_hyperparameter_tuning_job_mock.side_effect = [
_get_hyperparameter_tuning_job_proto_with_enable_web_access(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_PENDING,
),
_get_hyperparameter_tuning_job_proto_with_enable_web_access(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_RUNNING,
trials=[
_get_trial_proto(
id="1", state=gca_study_compat.Trial.State.REQUESTED
),
],
),
_get_hyperparameter_tuning_job_proto_with_enable_web_access(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_RUNNING,
trials=[
_get_trial_proto(id="1", state=gca_study_compat.Trial.State.ACTIVE),
],
),
_get_hyperparameter_tuning_job_proto_with_enable_web_access(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_RUNNING,
trials=[
_get_trial_proto(id="1", state=gca_study_compat.Trial.State.ACTIVE),
],
),
_get_hyperparameter_tuning_job_proto_with_enable_web_access(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_RUNNING,
trials=[
_get_trial_proto(id="1", state=gca_study_compat.Trial.State.ACTIVE),
],
),
_get_hyperparameter_tuning_job_proto_with_enable_web_access(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_RUNNING,
trials=[
_get_trial_proto(
id="1", state=gca_study_compat.Trial.State.SUCCEEDED
),
],
),
_get_hyperparameter_tuning_job_proto_with_enable_web_access(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_SUCCEEDED,
trials=[
_get_trial_proto(
id="1", state=gca_study_compat.Trial.State.SUCCEEDED
),
],
),
_get_hyperparameter_tuning_job_proto_with_enable_web_access(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_SUCCEEDED,
trials=[
_get_trial_proto(
id="1", state=gca_study_compat.Trial.State.SUCCEEDED
),
],
),
_get_hyperparameter_tuning_job_proto_with_enable_web_access(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_SUCCEEDED,
trials=[
_get_trial_proto(
id="1", state=gca_study_compat.Trial.State.SUCCEEDED
),
],
),
]
yield get_hyperparameter_tuning_job_mock
@pytest.fixture
def get_hyperparameter_tuning_job_mock_with_fail():
with patch.object(
job_service_client.JobServiceClient, "get_hyperparameter_tuning_job"
) as get_hyperparameter_tuning_job_mock:
get_hyperparameter_tuning_job_mock.side_effect = [
_get_hyperparameter_tuning_job_proto(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_PENDING,
),
_get_hyperparameter_tuning_job_proto(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_RUNNING,
),
_get_hyperparameter_tuning_job_proto(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_FAILED,
error=status_pb2.Status(message="Test Error"),
),
]
yield get_hyperparameter_tuning_job_mock
@pytest.fixture
def create_hyperparameter_tuning_job_mock():
with mock.patch.object(
job_service_client.JobServiceClient, "create_hyperparameter_tuning_job"
) as create_hyperparameter_tuning_job_mock:
create_hyperparameter_tuning_job_mock.return_value = _get_hyperparameter_tuning_job_proto(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_PENDING,
)
yield create_hyperparameter_tuning_job_mock
@pytest.fixture
def create_hyperparameter_tuning_job_mock_with_enable_web_access():
with mock.patch.object(
job_service_client.JobServiceClient, "create_hyperparameter_tuning_job"
) as create_hyperparameter_tuning_job_mock:
create_hyperparameter_tuning_job_mock.return_value = _get_hyperparameter_tuning_job_proto_with_enable_web_access(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_PENDING,
)
yield create_hyperparameter_tuning_job_mock
@pytest.fixture
def create_hyperparameter_tuning_job_mock_fail():
with mock.patch.object(
job_service_client.JobServiceClient, "create_hyperparameter_tuning_job"
) as create_hyperparameter_tuning_job_mock:
create_hyperparameter_tuning_job_mock.side_effect = RuntimeError("Mock fail")
yield create_hyperparameter_tuning_job_mock
@pytest.fixture
def create_hyperparameter_tuning_job_mock_with_tensorboard():
with mock.patch.object(
job_service_client.JobServiceClient, "create_hyperparameter_tuning_job"
) as create_hyperparameter_tuning_job_mock:
hyperparameter_tuning_job_proto = _get_hyperparameter_tuning_job_proto(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME,
state=gca_job_state_compat.JobState.JOB_STATE_PENDING,
)
hyperparameter_tuning_job_proto.trial_job_spec.tensorboard = (
test_custom_job._TEST_TENSORBOARD_NAME
)
create_hyperparameter_tuning_job_mock.return_value = (
hyperparameter_tuning_job_proto
)
yield create_hyperparameter_tuning_job_mock
class TestHyperparameterTuningJob:
def setup_method(self):
reload(aiplatform.initializer)
reload(aiplatform)
def teardown_method(self):
aiplatform.initializer.global_pool.shutdown(wait=True)
@pytest.mark.parametrize("sync", [True, False])
def test_create_hyperparameter_tuning_job(
self,
create_hyperparameter_tuning_job_mock,
get_hyperparameter_tuning_job_mock,
sync,
):
aiplatform.init(
project=_TEST_PROJECT,
location=_TEST_LOCATION,
staging_bucket=_TEST_STAGING_BUCKET,
encryption_spec_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME,
)
custom_job = aiplatform.CustomJob(
display_name=test_custom_job._TEST_DISPLAY_NAME,
worker_pool_specs=test_custom_job._TEST_WORKER_POOL_SPEC,
base_output_dir=test_custom_job._TEST_BASE_OUTPUT_DIR,
)
job = aiplatform.HyperparameterTuningJob(
display_name=_TEST_DISPLAY_NAME,
custom_job=custom_job,
metric_spec={_TEST_METRIC_SPEC_KEY: _TEST_METRIC_SPEC_VALUE},
parameter_spec={
"lr": hpt.DoubleParameterSpec(min=0.001, max=0.1, scale="log"),
"units": hpt.IntegerParameterSpec(min=4, max=1028, scale="linear"),
"activation": hpt.CategoricalParameterSpec(
values=["relu", "sigmoid", "elu", "selu", "tanh"]
),
"batch_size": hpt.DiscreteParameterSpec(
values=[16, 32], scale="linear"
),
},
parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
max_trial_count=_TEST_MAX_TRIAL_COUNT,
max_failed_trial_count=_TEST_MAX_FAILED_TRIAL_COUNT,
search_algorithm=_TEST_SEARCH_ALGORITHM,
measurement_selection=_TEST_MEASUREMENT_SELECTION,
labels=_TEST_LABELS,
)
job.run(
service_account=_TEST_SERVICE_ACCOUNT,
network=_TEST_NETWORK,
timeout=_TEST_TIMEOUT,
restart_job_on_worker_restart=_TEST_RESTART_JOB_ON_WORKER_RESTART,
sync=sync,
)
job.wait()
expected_hyperparameter_tuning_job = _get_hyperparameter_tuning_job_proto()
create_hyperparameter_tuning_job_mock.assert_called_once_with(
parent=_TEST_PARENT,
hyperparameter_tuning_job=expected_hyperparameter_tuning_job,
)
assert job.state == gca_job_state_compat.JobState.JOB_STATE_SUCCEEDED
assert job.network == _TEST_NETWORK
assert job.trials == []
@pytest.mark.parametrize("sync", [True, False])
def test_run_hyperparameter_tuning_job_with_fail_raises(
self,
create_hyperparameter_tuning_job_mock,
get_hyperparameter_tuning_job_mock_with_fail,
sync,
):
aiplatform.init(
project=_TEST_PROJECT,
location=_TEST_LOCATION,
staging_bucket=_TEST_STAGING_BUCKET,
encryption_spec_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME,
)
custom_job = aiplatform.CustomJob(
display_name=test_custom_job._TEST_DISPLAY_NAME,
worker_pool_specs=test_custom_job._TEST_WORKER_POOL_SPEC,
base_output_dir=test_custom_job._TEST_BASE_OUTPUT_DIR,
)
job = aiplatform.HyperparameterTuningJob(
display_name=_TEST_DISPLAY_NAME,
custom_job=custom_job,
metric_spec={_TEST_METRIC_SPEC_KEY: _TEST_METRIC_SPEC_VALUE},
parameter_spec={
"lr": hpt.DoubleParameterSpec(min=0.001, max=0.1, scale="log"),
"units": hpt.IntegerParameterSpec(min=4, max=1028, scale="linear"),
"activation": hpt.CategoricalParameterSpec(
values=["relu", "sigmoid", "elu", "selu", "tanh"]
),
"batch_size": hpt.DiscreteParameterSpec(
values=[16, 32], scale="linear"
),
},
parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
max_trial_count=_TEST_MAX_TRIAL_COUNT,
max_failed_trial_count=_TEST_MAX_FAILED_TRIAL_COUNT,
search_algorithm=_TEST_SEARCH_ALGORITHM,
measurement_selection=_TEST_MEASUREMENT_SELECTION,
labels=_TEST_LABELS,
)
with pytest.raises(RuntimeError):
job.run(
service_account=_TEST_SERVICE_ACCOUNT,
network=_TEST_NETWORK,
timeout=_TEST_TIMEOUT,
restart_job_on_worker_restart=_TEST_RESTART_JOB_ON_WORKER_RESTART,
sync=sync,
)
job.wait()
expected_hyperparameter_tuning_job = _get_hyperparameter_tuning_job_proto()
create_hyperparameter_tuning_job_mock.assert_called_once_with(
parent=_TEST_PARENT,
hyperparameter_tuning_job=expected_hyperparameter_tuning_job,
)
assert job._gca_resource.state == gca_job_state_compat.JobState.JOB_STATE_FAILED
@pytest.mark.usefixtures("create_hyperparameter_tuning_job_mock_fail")
def test_run_hyperparameter_tuning_job_with_fail_at_creation(self):
aiplatform.init(
project=_TEST_PROJECT,
location=_TEST_LOCATION,
staging_bucket=_TEST_STAGING_BUCKET,
encryption_spec_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME,
)
custom_job = aiplatform.CustomJob(
display_name=test_custom_job._TEST_DISPLAY_NAME,
worker_pool_specs=test_custom_job._TEST_WORKER_POOL_SPEC,
base_output_dir=test_custom_job._TEST_BASE_OUTPUT_DIR,
)
job = aiplatform.HyperparameterTuningJob(
display_name=_TEST_DISPLAY_NAME,
custom_job=custom_job,
metric_spec={_TEST_METRIC_SPEC_KEY: _TEST_METRIC_SPEC_VALUE},
parameter_spec={
"lr": hpt.DoubleParameterSpec(min=0.001, max=0.1, scale="log"),
"units": hpt.IntegerParameterSpec(min=4, max=1028, scale="linear"),
"activation": hpt.CategoricalParameterSpec(
values=["relu", "sigmoid", "elu", "selu", "tanh"]
),
"batch_size": hpt.DiscreteParameterSpec(
values=[16, 32], scale="linear"
),
},
parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
max_trial_count=_TEST_MAX_TRIAL_COUNT,
max_failed_trial_count=_TEST_MAX_FAILED_TRIAL_COUNT,
search_algorithm=_TEST_SEARCH_ALGORITHM,
measurement_selection=_TEST_MEASUREMENT_SELECTION,
)
job.run(
service_account=_TEST_SERVICE_ACCOUNT,
network=_TEST_NETWORK,
timeout=_TEST_TIMEOUT,
restart_job_on_worker_restart=_TEST_RESTART_JOB_ON_WORKER_RESTART,
sync=False,
)
with pytest.raises(RuntimeError) as e:
job.wait_for_resource_creation()
assert e.match("Mock fail")
with pytest.raises(RuntimeError) as e:
job.resource_name
assert e.match(
"HyperparameterTuningJob resource has not been created. Resource failed with: Mock fail"
)
with pytest.raises(RuntimeError) as e:
job.network
assert e.match(
"HyperparameterTuningJob resource has not been created. Resource failed with: Mock fail"
)
with pytest.raises(RuntimeError) as e:
job.trials
assert e.match(
"HyperparameterTuningJob resource has not been created. Resource failed with: Mock fail"
)
def test_hyperparameter_tuning_job_get_state_raises_without_run(self):
aiplatform.init(
project=_TEST_PROJECT,
location=_TEST_LOCATION,
staging_bucket=_TEST_STAGING_BUCKET,
encryption_spec_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME,
)
custom_job = aiplatform.CustomJob(
display_name=test_custom_job._TEST_DISPLAY_NAME,
worker_pool_specs=test_custom_job._TEST_WORKER_POOL_SPEC,
base_output_dir=test_custom_job._TEST_BASE_OUTPUT_DIR,
)
job = aiplatform.HyperparameterTuningJob(
display_name=_TEST_DISPLAY_NAME,
custom_job=custom_job,
metric_spec={_TEST_METRIC_SPEC_KEY: _TEST_METRIC_SPEC_VALUE},
parameter_spec={
"lr": hpt.DoubleParameterSpec(min=0.001, max=0.1, scale="log"),
"units": hpt.IntegerParameterSpec(min=4, max=1028, scale="linear"),
"activation": hpt.CategoricalParameterSpec(
values=["relu", "sigmoid", "elu", "selu", "tanh"]
),
"batch_size": hpt.DiscreteParameterSpec(
values=[16, 32, 64], scale="linear"
),
},
parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
max_trial_count=_TEST_MAX_TRIAL_COUNT,
max_failed_trial_count=_TEST_MAX_FAILED_TRIAL_COUNT,
search_algorithm=_TEST_SEARCH_ALGORITHM,
measurement_selection=_TEST_MEASUREMENT_SELECTION,
)
with pytest.raises(RuntimeError):
print(job.state)
def test_get_hyperparameter_tuning_job(self, get_hyperparameter_tuning_job_mock):
job = aiplatform.HyperparameterTuningJob.get(
_TEST_HYPERPARAMETERTUNING_JOB_NAME
)
get_hyperparameter_tuning_job_mock.assert_called_once_with(
name=_TEST_HYPERPARAMETERTUNING_JOB_NAME, retry=base._DEFAULT_RETRY
)
assert (
job._gca_resource.state == gca_job_state_compat.JobState.JOB_STATE_PENDING
)
@pytest.mark.parametrize("sync", [True, False])
def test_create_hyperparameter_tuning_job_with_tensorboard(
self,
create_hyperparameter_tuning_job_mock_with_tensorboard,
get_hyperparameter_tuning_job_mock,
sync,
):
aiplatform.init(
project=_TEST_PROJECT,
location=_TEST_LOCATION,
staging_bucket=_TEST_STAGING_BUCKET,
encryption_spec_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME,
)
custom_job = aiplatform.CustomJob(
display_name=test_custom_job._TEST_DISPLAY_NAME,
worker_pool_specs=test_custom_job._TEST_WORKER_POOL_SPEC,
base_output_dir=test_custom_job._TEST_BASE_OUTPUT_DIR,
)
job = aiplatform.HyperparameterTuningJob(
display_name=_TEST_DISPLAY_NAME,
custom_job=custom_job,
metric_spec={_TEST_METRIC_SPEC_KEY: _TEST_METRIC_SPEC_VALUE},
parameter_spec={
"lr": hpt.DoubleParameterSpec(min=0.001, max=0.1, scale="log"),
"units": hpt.IntegerParameterSpec(min=4, max=1028, scale="linear"),
"activation": hpt.CategoricalParameterSpec(
values=["relu", "sigmoid", "elu", "selu", "tanh"]
),
"batch_size": hpt.DiscreteParameterSpec(
values=[16, 32], scale="linear"
),
},
parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
max_trial_count=_TEST_MAX_TRIAL_COUNT,
max_failed_trial_count=_TEST_MAX_FAILED_TRIAL_COUNT,
search_algorithm=_TEST_SEARCH_ALGORITHM,
measurement_selection=_TEST_MEASUREMENT_SELECTION,
labels=_TEST_LABELS,
)
job.run(
service_account=_TEST_SERVICE_ACCOUNT,
network=_TEST_NETWORK,
timeout=_TEST_TIMEOUT,
restart_job_on_worker_restart=_TEST_RESTART_JOB_ON_WORKER_RESTART,
tensorboard=test_custom_job._TEST_TENSORBOARD_NAME,
sync=sync,
)
job.wait()
expected_hyperparameter_tuning_job = _get_hyperparameter_tuning_job_proto()
expected_hyperparameter_tuning_job.trial_job_spec.tensorboard = (
test_custom_job._TEST_TENSORBOARD_NAME
)
create_hyperparameter_tuning_job_mock_with_tensorboard.assert_called_once_with(
parent=_TEST_PARENT,
hyperparameter_tuning_job=expected_hyperparameter_tuning_job,
)
assert (
job._gca_resource.state == gca_job_state_compat.JobState.JOB_STATE_SUCCEEDED
)
@pytest.mark.parametrize("sync", [True, False])
def test_create_hyperparameter_tuning_job_with_enable_web_access(
self,
create_hyperparameter_tuning_job_mock_with_enable_web_access,
get_hyperparameter_tuning_job_mock_with_enable_web_access,
sync,
caplog,
):
caplog.set_level(logging.INFO)
aiplatform.init(
project=_TEST_PROJECT,
location=_TEST_LOCATION,
staging_bucket=_TEST_STAGING_BUCKET,
encryption_spec_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME,
)
custom_job = aiplatform.CustomJob(
display_name=test_custom_job._TEST_DISPLAY_NAME,
worker_pool_specs=test_custom_job._TEST_WORKER_POOL_SPEC,
base_output_dir=test_custom_job._TEST_BASE_OUTPUT_DIR,
)
job = aiplatform.HyperparameterTuningJob(
display_name=_TEST_DISPLAY_NAME,
custom_job=custom_job,
metric_spec={_TEST_METRIC_SPEC_KEY: _TEST_METRIC_SPEC_VALUE},
parameter_spec={
"lr": hpt.DoubleParameterSpec(min=0.001, max=0.1, scale="log"),
"units": hpt.IntegerParameterSpec(min=4, max=1028, scale="linear"),
"activation": hpt.CategoricalParameterSpec(
values=["relu", "sigmoid", "elu", "selu", "tanh"]
),
"batch_size": hpt.DiscreteParameterSpec(
values=[16, 32], scale="linear"
),
},
parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
max_trial_count=_TEST_MAX_TRIAL_COUNT,
max_failed_trial_count=_TEST_MAX_FAILED_TRIAL_COUNT,
search_algorithm=_TEST_SEARCH_ALGORITHM,
measurement_selection=_TEST_MEASUREMENT_SELECTION,
labels=_TEST_LABELS,
)
job.run(
service_account=_TEST_SERVICE_ACCOUNT,
network=_TEST_NETWORK,
timeout=_TEST_TIMEOUT,
restart_job_on_worker_restart=_TEST_RESTART_JOB_ON_WORKER_RESTART,
enable_web_access=test_custom_job._TEST_ENABLE_WEB_ACCESS,
sync=sync,
)
job.wait()
assert "workerpool0-0" in caplog.text
expected_hyperparameter_tuning_job = (
_get_hyperparameter_tuning_job_proto_with_enable_web_access()
)
create_hyperparameter_tuning_job_mock_with_enable_web_access.assert_called_once_with(
parent=_TEST_PARENT,
hyperparameter_tuning_job=expected_hyperparameter_tuning_job,
)
assert job.state == gca_job_state_compat.JobState.JOB_STATE_SUCCEEDED
assert job.network == _TEST_NETWORK
assert job.trials == []
caplog.clear()
def test_log_enable_web_access_after_get_hyperparameter_tuning_job(
self, get_hyperparameter_tuning_job_mock_with_enable_web_access,
):
hp_job = aiplatform.HyperparameterTuningJob.get(
_TEST_HYPERPARAMETERTUNING_JOB_NAME
)
hp_job._block_until_complete()
assert hp_job._logged_web_access_uris == set(
test_custom_job._TEST_WEB_ACCESS_URIS.values()
)
| 39.464525 | 121 | 0.672897 | 3,210 | 29,480 | 5.61838 | 0.085358 | 0.128639 | 0.146659 | 0.072082 | 0.830829 | 0.806432 | 0.782534 | 0.741059 | 0.724258 | 0.688384 | 0 | 0.00739 | 0.261024 | 29,480 | 746 | 122 | 39.517426 | 0.820473 | 0.019844 | 0 | 0.669826 | 0 | 0 | 0.045779 | 0.017972 | 0 | 0 | 0 | 0 | 0.031596 | 1 | 0.031596 | false | 0 | 0.020537 | 0 | 0.058452 | 0.00158 | 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 |
16144a6d1208bab3e129824da1e2163adc757d0e | 4,681 | py | Python | code/score.py | broadinstitute/TCRP | 9e580dbf0c9d0ec5e5b1a949087df5a3724fa35b | [
"MIT"
] | null | null | null | code/score.py | broadinstitute/TCRP | 9e580dbf0c9d0ec5e5b1a949087df5a3724fa35b | [
"MIT"
] | null | null | null | code/score.py | broadinstitute/TCRP | 9e580dbf0c9d0ec5e5b1a949087df5a3724fa35b | [
"MIT"
] | null | null | null | import numpy as np
import torch
from torch.autograd import Variable
from utils import *
from scipy import stats
# Helper methods for evaluating a classification network
def forward_pass(net, in_, target, weights=None):
# Forward in_ through the net, return loss and output
input_var = Variable(in_)
target_var = Variable(target)
# Second output is hidden
out, _ = net.net_forward(input_var, weights)
# Here loss is MSE
loss = net.loss_fn(out, target_var)
return loss, out
def evaluate_new_PDX(net, loader, train_flag, weights=None):
#evaluate the net on the data in the loader
net.eval()
test_predict = torch.zeros(0,0)
test_label = torch.zeros(0,0)
cat_test_label = torch.zeros(0,0)
total_loss = 0
for i, (in_, target, cat_target) in enumerate(loader):
input_var = Variable(in_)
target_var = Variable(target)
# Second output is hidden
out, _ = net.net_forward(input_var, weights)
# Here loss is MSE
l = net.loss_fn(out, target_var)
test_predict = torch.cat([test_predict, out.data], 0)
test_label = torch.cat([test_label, target_var.data], 0)
cat_test_label = torch.cat([cat_test_label, cat_target], 0)
#print l.data.cpu().numpy().shape
total_loss += l.data.cpu().numpy()
if test_predict.size()[0] <= 1:
pear_corr = -1
else:
pear_corr = pearson_corr(test_predict, test_label)
rho, pval = stats.spearmanr( test_predict.data.cpu().numpy() ,test_label.data.cpu().numpy() )
predict_data = {}
predict_data['test_predict'] = test_predict.data.cpu().numpy()
predict_data['test_label'] = test_label.data.cpu().numpy()
predict_data['cat_test_label'] = cat_test_label.cpu().numpy()
return float(total_loss) / test_label.size()[0], pear_corr, rho, predict_data
def evaluate_new(net, loader, train_flag, weights=None):
#evaluate the net on the data in the loader
net.eval()
test_predict = torch.zeros(0,0)
test_label = torch.zeros(0,0)
total_loss = 0
for i, (in_, target) in enumerate(loader):
input_var = Variable(in_)
target_var = Variable(target)
# Second output is hidden
out, _ = net.net_forward(input_var, weights)
# Here loss is MSE
l = net.loss_fn(out, target_var)
test_predict = torch.cat([test_predict, out.data], 0)
test_label = torch.cat([test_label, target_var.data], 0)
#print l.data.cpu().numpy().shape
total_loss += l.data.cpu().numpy()
if test_predict.size()[0] <= 1:
pear_corr = -1
else:
pear_corr = pearson_corr(test_predict, test_label)
rho, pval = stats.spearmanr( test_predict.data.cpu().numpy() ,test_label.data.cpu().numpy() )
return float(total_loss) / test_label.size()[0], pear_corr, rho, test_predict, test_label
def evaluate_cv(net, loader, weights=None):
#evaluate the net on the data in the loader
net.eval()
test_predict = torch.zeros(0,0)
test_label = torch.zeros(0,0)
total_loss = 0
#print 'In size evaluate'
for i, (in_, target) in enumerate(loader):
input_var = Variable(in_)
target_var = Variable(target)
# Second output is hidden
out, _ = net.net_forward(input_var, weights)
# Here loss is MSE
l = net.loss_fn(out, target_var)
test_predict = torch.cat([test_predict, out.data], 0)
test_label = torch.cat([test_label, target_var.data], 0)
total_loss += l.data.cpu().numpy()
pear_corr = pearson_corr(test_predict, test_label)
return float(total_loss) / test_label.size()[0], pear_corr, test_predict
def evaluate(net, loader, train_flag, weights=None):
#evaluate the net on the data in the loader
net.eval()
test_predict = torch.zeros(0,0)
test_label = torch.zeros(0,0)
total_loss = 0
#print 'In size evaluate'
for i, (in_, target) in enumerate(loader):
input_var = Variable(in_)
target_var = Variable(target)
# Second output is hidden
out, _ = net.net_forward(input_var, weights)
# Here loss is MSE
l = net.loss_fn(out, target_var)
test_predict = torch.cat([test_predict, out.data], 0)
test_label = torch.cat([test_label, target_var.data], 0)
#total_loss += l.data.cpu().numpy()[0]
total_loss += l.data.cpu().numpy()
#aa, bb = out.data.cpu().numpy(), target_var.data.cpu().numpy()
#print np.mean(np.square(aa - bb)), l.data.cpu().numpy()[0]
if test_predict.size()[0] <= 3:
pear_corr = -1
else:
pear_corr = pearson_corr(test_predict, test_label)
#print test_predict.cpu().numpy()[:,0]
#print test_label.cpu().numpy()[:,0]
#print 'finish evaluate'
'''
test_predict = test_predict.cpu().numpy()
for i in range(test_predict.shape[0]):
print test_predict[i,0],
print ''
test_label = test_label.cpu().numpy()
for i in range(test_label.shape[0]):
print test_label[i,0],
print ''
'''
return float(total_loss) / test_label.size()[0], pear_corr
| 26.902299 | 94 | 0.706687 | 756 | 4,681 | 4.15873 | 0.108466 | 0.100191 | 0.061069 | 0.034351 | 0.789758 | 0.762405 | 0.755725 | 0.720102 | 0.707697 | 0.707697 | 0 | 0.013121 | 0.153386 | 4,681 | 173 | 95 | 27.057803 | 0.780217 | 0.18009 | 0 | 0.727273 | 0 | 0 | 0.010087 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.056818 | false | 0.011364 | 0.056818 | 0 | 0.170455 | 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 |
1641678a316d421426a266e67df5df8dd5fd3c76 | 26,684 | py | Python | src/geocat/f2py/linint2_wrapper.py | NCAR/geocat-f2py | fee07e680f61ca2ebfbb33f1554d9d85271fa32a | [
"Apache-2.0"
] | 4 | 2021-02-20T20:02:11.000Z | 2021-11-24T13:35:32.000Z | src/geocat/f2py/linint2_wrapper.py | NCAR/geocat-f2py | fee07e680f61ca2ebfbb33f1554d9d85271fa32a | [
"Apache-2.0"
] | 27 | 2020-12-07T17:00:05.000Z | 2022-03-24T16:42:17.000Z | src/geocat/f2py/linint2_wrapper.py | NCAR/geocat-f2py | fee07e680f61ca2ebfbb33f1554d9d85271fa32a | [
"Apache-2.0"
] | 4 | 2021-01-07T01:50:11.000Z | 2021-07-07T13:05:42.000Z | import warnings
import numpy as np
import xarray as xr
from dask.array.core import map_blocks
from .errors import ChunkError, CoordinateError
from .fortran import dlinint1, dlinint2, dlinint2pts
from .missing_values import fort2py_msg, py2fort_msg
# Dask Wrappers _<funcname>()
# These Wrapper are executed within dask processes, and should do anything that
# can benefit from parallel excution.
def _linint1(xi, fi, xo, icycx, msg_py, shape):
# ''' signature : fo = dlinint1(xi,fi,xo,[icycx,xmsg,iopt])
# missing value handling
fi, msg_py, msg_fort = py2fort_msg(fi, msg_py=msg_py)
# fortran call
fo = dlinint1(
xi,
fi,
xo,
icycx=icycx,
xmsg=msg_fort,
)
# numpy and reshape
fo = np.asarray(fo)
fo = fo.reshape(shape)
# missing value handling
fi, msg_fort, msg_py = fort2py_msg(fi, msg_fort=msg_fort, msg_py=msg_py)
fo, msg_fort, msg_py = fort2py_msg(fo, msg_fort=msg_fort, msg_py=msg_py)
return fo
def _linint2(xi, yi, fi, xo, yo, icycx, msg_py, shape):
# ''' signature : fo = dlinint2(xi,yi,fi,xo,yo,[icycx,xmsg,iopt])
# missing value handling
fi, msg_py, msg_fort = py2fort_msg(fi, msg_py=msg_py)
# fortran call
fo = dlinint2(
xi,
yi,
fi,
xo,
yo,
icycx=icycx,
xmsg=msg_fort,
)
# numpy and reshape
fo = np.asarray(fo)
fo = fo.reshape(shape)
# missing value handling
fort2py_msg(fi, msg_fort=msg_fort, msg_py=msg_py)
fort2py_msg(fo, msg_fort=msg_fort, msg_py=msg_py)
return fo
def _linint2pts(xi, yi, fi, xo, yo, icycx, msg_py, shape):
# ''' signature : fo = dlinint2pts(xi,yi,fi,xo,yo,[icycx,xmsg])
# missing value handling
fi, msg_py, msg_fort = py2fort_msg(fi, msg_py=msg_py)
# fortran call
fo, error_code = dlinint2pts(xi, yi, fi, xo, yo, icycx=icycx, xmsg=msg_fort)
# Catch warnings
if error_code == 1:
warnings.warn(
"WARNING linint2pts: Not enough points in input arrays or output coordinates!"
)
elif error_code == 2:
warnings.warn(
"WARNING linint2pts: x_in should be a monotonically increasing array !"
)
elif error_code == 3:
warnings.warn(
"WARNING linint2pts: y_in should be a monotonically increasing array !"
)
# numpy and reshape
fo = np.asarray(fo)
fo = fo.reshape(shape)
# missing value handling
fort2py_msg(fi, msg_fort=msg_fort, msg_py=msg_py)
fort2py_msg(fo, msg_fort=msg_fort, msg_py=msg_py)
return fo
# Outer Wrappers <funcname>()
# These Wrappers are excecuted in the __main__ python process, and should be
# used for any tasks which would not benefit from parallel execution.
def linint1(fi, xo, xi=None, icycx=0, msg_py=None):
# ''' signature : fo = dlinint1(xi,fi,xo,[icycx,xmsg,iopt])
"""Interpolates from one series to another using piecewise linear
interpolation across the rightmost dimension.
linint1 uses piecewise linear interpolation to interpolate from
one series to another. The series may be cyclic in the X
direction.
If missing values are present, then linint1 will perform the
piecewise linear interpolation at all points possible, but
will return missing values at coordinates which could not
be used.
If any of the output coordinates xo are outside those of the
input coordinates xi, the fo values at those coordinates will
be set to missing (i.e. no extrapolation is performed).
Parameters
----------
fi : :class:`xarray.DataArray` or :class:`numpy.ndarray`:
An array of one or more dimensions. If xi is passed in as an
argument, then the size of the rightmost dimension of fi
must match the rightmost dimension of xi.
If missing values are present, then linint1 will perform the
piecewise linear interpolation at all points possible, but
will return missing values at coordinates which could not be
used.
Note:
This variable must be
supplied as a :class:`xarray.DataArray` in order to copy
the dimension names to the output. Otherwise, default
names will be used.
xo : :class:`xarray.DataArray` or :class:`numpy.ndarray`:
A one-dimensional array that specifies the X coordinates of
the return array. It must be strictly monotonically
increasing or decreasing, but may be unequally spaced.
If the output coordinates (xo) are outside those of the
input coordinates (xi), then the fo values at those
coordinates will be set to missing (i.e. no extrapolation is
performed).
xi (:class:`numpy.ndarray`):
An array that specifies the X coordinates of the fi array.
Most frequently, this array is one-dimensional. It must be
strictly monotonically increasing or decreasing, but can be
unequally spaced.
If xi is multi-dimensional, then its
dimensions must be the same as fi's dimensions. If it is
one-dimensional, its length must be the same as the rightmost
(fastest varying) dimension of fi.
Note:
If fi is of type :class:`xarray.DataArray` and xi is
left unspecified, then the rightmost coordinate
dimension of fi will be used. If fi is not of type
:class:`xarray.DataArray`, then xi becomes a mandatory
parameter. This parameter must be specified as a keyword
argument.
icycx : :obj:`bool`:
An option to indicate whether the rightmost dimension of fi
is cyclic. This should be set to True only if you have
global data, but your longitude values don't quite wrap all
the way around the globe. For example, if your longitude
values go from, say, -179.75 to 179.75, or 0.5 to 359.5,
then you would set this to True.
msg_py : :obj:`numpy.number`:
A numpy scalar value that represent a missing value in fi.
This argument allows a user to use a missing value scheme
other than NaN or masked arrays, similar to what NCL allows.
Returns
-------
fo : :class:`xarray.DataArray`:
The interpolated series. The returned value will have the same
dimensions as fi, except for the rightmost dimension which
will have the same dimension size as the length of xo.
The return type will be double if fi is double, and float
otherwise.
Examples
--------
Example 1: Using linint1 with :class:`xarray.DataArray` input
.. code-block:: python
import numpy as np
import xarray as xr
import geocat.comp
fi_np = np.random.rand(80) # random 80-element array
# xi does not have to be equally spaced, but it is
# in this example
xi = np.arange(80)
# create target coordinate array, in this case use the same
# min/max values as xi, but with different spacing
xo = np.linspace(xi.min(), xi.max(), 100)
# create :class:`xarray.DataArray` and chunk it using the
# full shape of the original array.
# note that xi is attached as a coordinate array
fi = xr.DataArray(fi_np,
dims=['x'],
coords={'x': xi}
).chunk(fi_np.shape)
fo = geocat.comp.linint1(fi, xo, icycx=0)
"""
# ''' Start of boilerplate
if not isinstance(fi, xr.DataArray):
if (xi is None):
raise CoordinateError(
"linint2: Argument xi must be provided explicitly unless fi is an xarray.DataArray."
)
fi = xr.DataArray(fi,)
fi_chunk = dict([(k, v) for (k, v) in zip(list(fi.dims), list(fi.shape))
])
fi = xr.DataArray(
fi.data,
coords={
fi.dims[-1]: xi,
},
dims=fi.dims,
).chunk(fi_chunk)
xi = fi.coords[fi.dims[-1]]
# ensure rightmost dimensions of input are not chunked
if fi.chunks == None:
fi.chunk()
if list(fi.chunks)[-1:] != [xi.shape]:
raise Exception("fi must be unchunked along the last dimension")
# fi data structure elements and autochunking
fi_chunks = list(fi.dims)
fi_chunks[:-1] = [
(k, 1) for (k, v) in zip(list(fi.dims)[:-1],
list(fi.chunks)[:-1])
]
fi_chunks[-1:] = [
(k, v[0]) for (k, v) in zip(list(fi.dims)[-1:],
list(fi.chunks)[-1:])
]
fi_chunks = dict(fi_chunks)
fi = fi.chunk(fi_chunks)
# fo datastructure elements
fo_chunks = list(fi.chunks)
fo_chunks[-1:] = (xo.shape,)
fo_chunks = tuple(fo_chunks)
fo_shape = tuple(a[0] for a in list(fo_chunks))
fo_coords = {k: v for (k, v) in fi.coords.items()}
fo_coords[fi.dims[-1]] = xo
# ''' end of boilerplate
fo = map_blocks(
_linint1,
xi,
fi.data,
xo,
icycx,
msg_py,
fo_shape,
chunks=fo_chunks,
dtype=fi.dtype,
drop_axis=[fi.ndim - 1],
new_axis=[fi.ndim - 1],
)
fo = xr.DataArray(fo.compute(),
attrs=fi.attrs,
dims=fi.dims,
coords=fo_coords)
return fo
def linint2(fi, xo, yo, xi=None, yi=None, icycx=0, msg_py=None):
"""Interpolates a regular grid to a rectilinear one using bi-linear
interpolation.
linint2 uses bilinear interpolation to interpolate from one
rectilinear grid to another. The input grid may be cyclic in the x
direction. The interpolation is first performed in the x direction,
and then in the y direction.
Parameters
----------
fi : :class:`xarray.DataArray` or :class:`numpy.ndarray`:
An array of two or more dimensions. If xi is passed in as an
argument, then the size of the rightmost dimension of fi
must match the rightmost dimension of xi. Similarly, if yi
is passed in as an argument, then the size of the second-
rightmost dimension of fi must match the rightmost dimension
of yi.
If missing values are present, then linint2 will perform the
bilinear interpolation at all points possible, but will
return missing values at coordinates which could not be
used.
Note:
This variable must be
supplied as a :class:`xarray.DataArray` in order to copy
the dimension names to the output. Otherwise, default
names will be used.
xo : :class:`xarray.DataArray` or :class:`numpy.ndarray`:
A one-dimensional array that specifies the X coordinates of
the return array. It must be strictly monotonically
increasing, but may be unequally spaced.
For geo-referenced data, xo is generally the longitude
array.
If the output coordinates (xo) are outside those of the
input coordinates (xi), then the fo values at those
coordinates will be set to missing (i.e. no extrapolation is
performed).
yo : :class:`xarray.DataArray` or :class:`numpy.ndarray`:
A one-dimensional array that specifies the Y coordinates of
the return array. It must be strictly monotonically
increasing, but may be unequally spaced.
For geo-referenced data, yo is generally the latitude array.
If the output coordinates (yo) are outside those of the
input coordinates (yi), then the fo values at those
coordinates will be set to missing (i.e. no extrapolation is
performed).
xi (:class:`numpy.ndarray`):
An array that specifies the X coordinates of the fi array.
Most frequently, this is a 1D strictly monotonically
increasing array that may be unequally spaced. In some
cases, xi can be a multi-dimensional array (see next
paragraph). The rightmost dimension (call it nxi) must have
at least two elements, and is the last (fastest varying)
dimension of fi.
If xi is a multi-dimensional array, then each nxi subsection
of xi must be strictly monotonically increasing, but may be
unequally spaced. All but its rightmost dimension must be
the same size as all but fi's rightmost two dimensions.
For geo-referenced data, xi is generally the longitude
array.
Note:
If fi is of type :class:`xarray.DataArray` and xi is
left unspecified, then the rightmost coordinate
dimension of fi will be used. If fi is not of type
:class:`xarray.DataArray`, then xi becomes a mandatory
parameter. This parameter must be specified as a keyword
argument.
yi (:class:`numpy.ndarray`):
An array that specifies the Y coordinates of the fi array.
Most frequently, this is a 1D strictly monotonically
increasing array that may be unequally spaced. In some
cases, yi can be a multi-dimensional array (see next
paragraph). The rightmost dimension (call it nyi) must have
at least two elements, and is the second-to-last dimension
of fi.
If yi is a multi-dimensional array, then each nyi subsection
of yi must be strictly monotonically increasing, but may be
unequally spaced. All but its rightmost dimension must be
the same size as all but fi's rightmost two dimensions.
For geo-referenced data, yi is generally the latitude array.
Note:
If fi is of type :class:`xarray.DataArray` and xi is
left unspecified, then the second-to-rightmost
coordinate dimension of fi will be used. If fi is not of
type :class:`xarray.DataArray`, then xi becomes a
mandatory parameter. This parameter must be specified as
a keyword argument.
icycx : :obj:`bool`:
An option to indicate whether the rightmost dimension of fi
is cyclic. This should be set to True only if you have
global data, but your longitude values don't quite wrap all
the way around the globe. For example, if your longitude
values go from, say, -179.75 to 179.75, or 0.5 to 359.5,
then you would set this to True.
msg_py : :obj:`numpy.number`:
A numpy scalar value that represent a missing value in fi.
This argument allows a user to use a missing value scheme
other than NaN or masked arrays, similar to what NCL allows.
Returns
-------
fo : :class:`xarray.DataArray`:
The interpolated grid. If the *meta*
parameter is True, then the result will include named dimensions
matching the input array. The returned value will have the same
dimensions as fi, except for the rightmost two dimensions which
will have the same dimension sizes as the lengths of yo and xo.
The return type will be double if fi is double, and float
otherwise.
Examples
--------
Example 1: Using linint2 with :class:`xarray.DataArray` input
.. code-block:: python
import numpy as np
import xarray as xr
import geocat.comp
fi_np = np.random.rand(30, 80) # random 30x80 array
# xi and yi do not have to be equally spaced, but they are
# in this example
xi = np.arange(80)
yi = np.arange(30)
# create target coordinate arrays, in this case use the same
# min/max values as xi and yi, but with different spacing
xo = np.linspace(xi.min(), xi.max(), 100)
yo = np.linspace(yi.min(), yi.max(), 50)
# create :class:`xarray.DataArray` and chunk it using the
# full shape of the original array.
# note that xi and yi are attached as coordinate arrays
fi = xr.DataArray(fi_np,
dims=['lat', 'lon'],
coords={'lat': yi, 'lon': xi}
).chunk(fi_np.shape)
fo = geocat.comp.linint2(fi, xo, yo, icycx=0)
"""
# ''' Start of boilerplate
if not isinstance(fi, xr.DataArray):
if (xi is None) | (yi is None):
raise CoordinateError(
"linint2: Arguments xi and yi must be provided explicitly unless fi is an xarray.DataArray."
)
fi = xr.DataArray(fi,)
fi_chunk = dict([(k, v) for (k, v) in zip(list(fi.dims), list(fi.shape))
])
fi = xr.DataArray(
fi.data,
coords={
fi.dims[-1]: xi,
fi.dims[-2]: yi,
},
dims=fi.dims,
).chunk(fi_chunk)
xi = fi.coords[fi.dims[-1]]
yi = fi.coords[fi.dims[-2]]
# ensure rightmost dimensions of input are not chunked
if fi.chunks == None:
fi.chunk()
if list(fi.chunks)[-2:] != [yi.shape, xi.shape]:
raise ChunkError(
"linint2: fi must be unchunked along the rightmost two dimensions")
# fi data structure elements and autochunking
fi_chunks = list(fi.dims)
fi_chunks[:-2] = [
(k, 1) for (k, v) in zip(list(fi.dims)[:-2],
list(fi.chunks)[:-2])
]
fi_chunks[-2:] = [
(k, v[0]) for (k, v) in zip(list(fi.dims)[-2:],
list(fi.chunks)[-2:])
]
fi_chunks = dict(fi_chunks)
fi = fi.chunk(fi_chunks)
# fo datastructure elements
fo_chunks = list(fi.chunks)
fo_chunks[-2:] = (yo.shape, xo.shape)
fo_chunks = tuple(fo_chunks)
fo_shape = tuple(a[0] for a in list(fo_chunks))
fo_coords = {k: v for (k, v) in fi.coords.items()}
fo_coords[fi.dims[-1]] = xo
fo_coords[fi.dims[-2]] = yo
# ''' end of boilerplate
fo = map_blocks(
_linint2,
yi,
xi,
fi.data,
yo,
xo,
icycx,
msg_py,
fo_shape,
chunks=fo_chunks,
dtype=fi.dtype,
drop_axis=[fi.ndim - 2, fi.ndim - 1],
new_axis=[fi.ndim - 2, fi.ndim - 1],
)
fo = xr.DataArray(fo.compute(),
attrs=fi.attrs,
dims=fi.dims,
coords=fo_coords)
return fo
def linint2pts(fi, xo, yo, icycx=False, msg_py=None, xi=None, yi=None):
"""Interpolates from a rectilinear grid to an unstructured grid or
locations using bilinear interpolation.
Parameters
----------
fi : :class:`xarray.DataArray` or :class:`numpy.ndarray`:
An array of two or more dimensions. The two rightmost
dimensions (nyi x nxi) are the dimensions to be used in
the interpolation. If user-defined missing values are
present (other than NaNs), the value of `msg_py` must be
set appropriately.
xo : :class:`xarray.DataArray` or :class:`numpy.ndarray`:
A one-dimensional array that specifies the X (longitude)
coordinates of the unstructured grid.
yo : :class:`xarray.DataArray` or :class:`numpy.ndarray`:
A one-dimensional array that specifies the Y (latitude)
coordinates of the unstructured grid. It must be the same
length as `xo`.
icycx : :obj:`bool`:
An option to indicate whether the rightmost dimension of fi
is cyclic. Default valus is 0. This should be set to True
only if you have global data, but your longitude values
don't quite wrap all the way around the globe. For example,
if your longitude values go from, say, -179.75 to 179.75,
or 0.5 to 359.5, then you would set this to True.
msg_py : :obj:`numpy.number`:
A numpy scalar value that represent a missing value in fi.
This argument allows a user to use a missing value scheme
other than NaN or masked arrays, similar to what NCL allows.
xi : :class:`xarray.DataArray` or :class:`numpy.ndarray`:
A strictly monotonically increasing array that specifies
the X [longitude] coordinates of the `fi` array. `xi` might
be defined as the coordinates of `fi` when `fi` is of type
`xarray.DataArray`; in this case `xi` may not be explicitly
given as a function argument.
yi : :class:`xarray.DataArray` or :class:`numpy.ndarray`:
A strictly monotonically increasing array that specifies
the Y [latitude] coordinates of the `fi` array. ``yi` might
be defined as the coordinates of `fi` when `fi` is of type
`xarray.DataArray`; in this case `yi` may not be explicitly
given as a function argument.
Returns
-------
fo: :class:`numpy.ndarray`:
The returned value will have the same dimensions as `fi`,
except for the rightmost dimension which will have the same
dimension size as the length of `yo` and `xo`. The return
type will be double if `fi` is double, and float otherwise.
Description
-----------
The `linint2pts` function uses bilinear interpolation to interpolate
from a rectilinear grid to an unstructured grid.
If missing values are present, then `linint2pts` will perform the
piecewise linear interpolation at all points possible, but will return
missing values at coordinates which could not be used. If one or more
of the four closest grid points to a particular (xo, yo) coordinate
pair are missing, then the return value for this coordinate pair will
be missing.
If the user inadvertently specifies output coordinates (xo, yo) that
are outside those of the input coordinates (xi, yi), the output value
at this coordinate pair will be set to missing as no extrapolation
is performed.
`linint2pts` is different from `linint2` in that `xo` and `yo` are
coordinate pairs, and need not be monotonically increasing. It is
also different in the dimensioning of the return array.
This function could be used if the user wanted to interpolate gridded
data to, say, the location of rawinsonde sites or buoy/xbt locations.
Warning: if `xi` contains longitudes, then the `xo` values must be in the
same range. In addition, if the `xi` values span 0 to 360, then the `xo`
values must also be specified in this range (i.e. -180 to 180 will not work).
Examples
--------
Example 1: Using linint2pts with :class:`xarray.DataArray` input
.. code-block:: python
import numpy as np
import xarray as xr
import geocat.comp
fi_np = np.random.rand(30, 80) # random 30x80 array
# xi and yi do not have to be equally spaced, but they are
# in this example
xi = np.arange(80)
yi = np.arange(30)
# create target coordinate arrays, in this case use the same
# min/max values as xi and yi, but with different spacing
xo = np.linspace(xi.min(), xi.max(), 100)
yo = np.linspace(yi.min(), yi.max(), 50)
# create :class:`xarray.DataArray` and chunk it using the
# full shape of the original array.
# note that xi and yi are attached as coordinate arrays
fi = xr.DataArray(fi_np,
dims=['lat', 'lon'],
coords={'lat': yi, 'lon': xi}
).chunk(fi_np.shape)
fo = geocat.comp.linint2pts(fi, xo, yo, 0)
"""
# ''' Start of boilerplate
# If a Numpy input is given, convert it to Xarray and chunk it just
# with its dims
if not isinstance(fi, xr.DataArray):
if (xi is None) | (yi is None):
raise CoordinateError(
"linint2pts: Arguments xi and yi must be provided explicitly unless fi is an xarray.DataArray."
)
fi = xr.DataArray(fi)
fi_chunk = dict([(k, v) for (k, v) in zip(list(fi.dims), list(fi.shape))
])
fi = xr.DataArray(
fi.data,
coords={
fi.dims[-1]: xi,
fi.dims[-2]: yi,
},
dims=fi.dims,
).chunk(fi_chunk)
# Xarray input
else:
# If an unchunked Xarray input is given, chunk it just with its dims
if (fi.chunks is None):
fi_chunk = dict([
(k, v) for (k, v) in zip(list(fi.dims), list(fi.shape))
])
data = fi.chunk(fi_chunk)
xi = fi.coords[fi.dims[-1]]
yi = fi.coords[fi.dims[-2]]
# Ensure the rightmost dimension of input is not chunked
if fi.chunks == None:
fi.chunk()
if list(fi.chunks)[-2:] != [yi.shape, xi.shape]:
raise ChunkError(
"ERROR linint2pts: fi must be unchunked along the rightmost two dimensions"
)
if xo.shape != yo.shape:
raise Exception("ERROR linint2pts xo and yo must be of equal length")
# fi data structure elements and autochunking
fi_chunks = list(fi.dims)
fi_chunks[:-2] = [
(k, 1) for (k, v) in zip(list(fi.dims)[:-2],
list(fi.chunks)[:-2])
]
fi_chunks[-2:] = [
(k, v[0]) for (k, v) in zip(list(fi.dims)[-2:],
list(fi.chunks)[-2:])
]
fi_chunks = dict(fi_chunks)
fi = fi.chunk(fi_chunks)
# fo datastructure elements
fo_chunks = list(fi.chunks)
fo_chunks[-2:] = (xo.shape,)
fo_chunks = tuple(fo_chunks)
fo_shape = tuple(a[0] for a in list(fo_chunks))
fo_coords = {k: v for (k, v) in fi.coords.items()}
# fo_coords.remove(fi.dims[-1]) # this dimension dissapears
fo_coords[fi.dims[-1]] = xo # remove this line omce dims are figured out
fo_coords[fi.dims[-2]] = yo # maybe replace with 'pts'
# ''' end of boilerplate
fo = map_blocks(
_linint2pts,
yi,
xi,
fi.data,
yo,
xo,
icycx,
msg_py,
fo_shape,
chunks=fo_chunks,
dtype=fi.dtype,
drop_axis=[fi.ndim - 2, fi.ndim - 1],
new_axis=[fi.ndim - 2],
)
fo = xr.DataArray(fo.compute(), attrs=fi.attrs)
return fo
# Transparent wrappers for geocat.ncomp backwards compatibility
def linint2_points(fi, xo, yo, icycx, msg=None, meta=False, xi=None, yi=None):
warnings.warn(
"linint2_points function name and signature will be deprecated soon "
"in a future version. Use `linint2pts` instead!",
PendingDeprecationWarning)
return linint2pts(fi, xo, yo, icycx=icycx, msg_py=msg, xi=xi, yi=yi)
| 38.728592 | 111 | 0.614825 | 3,777 | 26,684 | 4.299179 | 0.103786 | 0.011393 | 0.032024 | 0.005604 | 0.803178 | 0.768629 | 0.742086 | 0.718869 | 0.701749 | 0.672312 | 0 | 0.01237 | 0.30018 | 26,684 | 688 | 112 | 38.784884 | 0.857181 | 0.629291 | 0 | 0.666667 | 0 | 0 | 0.096138 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.028112 | false | 0 | 0.028112 | 0 | 0.084337 | 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 |
1642a4017a552d4b9dc924a992b094f782a18a24 | 23,757 | py | Python | sdk/python/pulumi_azure_native/digitaltwins/_inputs.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | 31 | 2020-09-21T09:41:01.000Z | 2021-02-26T13:21:59.000Z | sdk/python/pulumi_azure_native/digitaltwins/_inputs.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | 231 | 2020-09-21T09:38:45.000Z | 2021-03-01T11:16:03.000Z | sdk/python/pulumi_azure_native/digitaltwins/_inputs.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | 4 | 2020-09-29T14:14:59.000Z | 2021-02-10T20:38:16.000Z | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from .. import _utilities, _tables
from ._enums import *
__all__ = [
'ConnectionPropertiesPrivateLinkServiceConnectionStateArgs',
'DigitalTwinsIdentityArgs',
'EventGridArgs',
'EventHubArgs',
'PrivateEndpointConnectionArgs',
'PrivateEndpointConnectionPropertiesArgs',
'ServiceBusArgs',
]
@pulumi.input_type
class ConnectionPropertiesPrivateLinkServiceConnectionStateArgs:
def __init__(__self__, *,
description: pulumi.Input[str],
status: pulumi.Input[Union[str, 'PrivateLinkServiceConnectionStatus']],
actions_required: Optional[pulumi.Input[str]] = None):
"""
:param pulumi.Input[str] description: The description for the current state of a private endpoint connection.
:param pulumi.Input[Union[str, 'PrivateLinkServiceConnectionStatus']] status: The status of a private endpoint connection.
:param pulumi.Input[str] actions_required: Actions required for a private endpoint connection.
"""
pulumi.set(__self__, "description", description)
pulumi.set(__self__, "status", status)
if actions_required is not None:
pulumi.set(__self__, "actions_required", actions_required)
@property
@pulumi.getter
def description(self) -> pulumi.Input[str]:
"""
The description for the current state of a private endpoint connection.
"""
return pulumi.get(self, "description")
@description.setter
def description(self, value: pulumi.Input[str]):
pulumi.set(self, "description", value)
@property
@pulumi.getter
def status(self) -> pulumi.Input[Union[str, 'PrivateLinkServiceConnectionStatus']]:
"""
The status of a private endpoint connection.
"""
return pulumi.get(self, "status")
@status.setter
def status(self, value: pulumi.Input[Union[str, 'PrivateLinkServiceConnectionStatus']]):
pulumi.set(self, "status", value)
@property
@pulumi.getter(name="actionsRequired")
def actions_required(self) -> Optional[pulumi.Input[str]]:
"""
Actions required for a private endpoint connection.
"""
return pulumi.get(self, "actions_required")
@actions_required.setter
def actions_required(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "actions_required", value)
@pulumi.input_type
class DigitalTwinsIdentityArgs:
def __init__(__self__, *,
type: Optional[pulumi.Input[Union[str, 'DigitalTwinsIdentityType']]] = None):
"""
The managed identity for the DigitalTwinsInstance.
:param pulumi.Input[Union[str, 'DigitalTwinsIdentityType']] type: The type of Managed Identity used by the DigitalTwinsInstance. Only SystemAssigned is supported.
"""
if type is not None:
pulumi.set(__self__, "type", type)
@property
@pulumi.getter
def type(self) -> Optional[pulumi.Input[Union[str, 'DigitalTwinsIdentityType']]]:
"""
The type of Managed Identity used by the DigitalTwinsInstance. Only SystemAssigned is supported.
"""
return pulumi.get(self, "type")
@type.setter
def type(self, value: Optional[pulumi.Input[Union[str, 'DigitalTwinsIdentityType']]]):
pulumi.set(self, "type", value)
@pulumi.input_type
class EventGridArgs:
def __init__(__self__, *,
access_key1: pulumi.Input[str],
endpoint_type: pulumi.Input[str],
topic_endpoint: pulumi.Input[str],
access_key2: Optional[pulumi.Input[str]] = None,
authentication_type: Optional[pulumi.Input[Union[str, 'AuthenticationType']]] = None,
dead_letter_secret: Optional[pulumi.Input[str]] = None,
dead_letter_uri: Optional[pulumi.Input[str]] = None):
"""
Properties related to EventGrid.
:param pulumi.Input[str] access_key1: EventGrid secondary accesskey. Will be obfuscated during read.
:param pulumi.Input[str] endpoint_type: The type of Digital Twins endpoint
Expected value is 'EventGrid'.
:param pulumi.Input[str] topic_endpoint: EventGrid Topic Endpoint
:param pulumi.Input[str] access_key2: EventGrid secondary accesskey. Will be obfuscated during read.
:param pulumi.Input[Union[str, 'AuthenticationType']] authentication_type: Specifies the authentication type being used for connecting to the endpoint.
:param pulumi.Input[str] dead_letter_secret: Dead letter storage secret for key-based authentication. Will be obfuscated during read.
:param pulumi.Input[str] dead_letter_uri: Dead letter storage URL for identity-based authentication.
"""
pulumi.set(__self__, "access_key1", access_key1)
pulumi.set(__self__, "endpoint_type", 'EventGrid')
pulumi.set(__self__, "topic_endpoint", topic_endpoint)
if access_key2 is not None:
pulumi.set(__self__, "access_key2", access_key2)
if authentication_type is not None:
pulumi.set(__self__, "authentication_type", authentication_type)
if dead_letter_secret is not None:
pulumi.set(__self__, "dead_letter_secret", dead_letter_secret)
if dead_letter_uri is not None:
pulumi.set(__self__, "dead_letter_uri", dead_letter_uri)
@property
@pulumi.getter(name="accessKey1")
def access_key1(self) -> pulumi.Input[str]:
"""
EventGrid secondary accesskey. Will be obfuscated during read.
"""
return pulumi.get(self, "access_key1")
@access_key1.setter
def access_key1(self, value: pulumi.Input[str]):
pulumi.set(self, "access_key1", value)
@property
@pulumi.getter(name="endpointType")
def endpoint_type(self) -> pulumi.Input[str]:
"""
The type of Digital Twins endpoint
Expected value is 'EventGrid'.
"""
return pulumi.get(self, "endpoint_type")
@endpoint_type.setter
def endpoint_type(self, value: pulumi.Input[str]):
pulumi.set(self, "endpoint_type", value)
@property
@pulumi.getter(name="topicEndpoint")
def topic_endpoint(self) -> pulumi.Input[str]:
"""
EventGrid Topic Endpoint
"""
return pulumi.get(self, "topic_endpoint")
@topic_endpoint.setter
def topic_endpoint(self, value: pulumi.Input[str]):
pulumi.set(self, "topic_endpoint", value)
@property
@pulumi.getter(name="accessKey2")
def access_key2(self) -> Optional[pulumi.Input[str]]:
"""
EventGrid secondary accesskey. Will be obfuscated during read.
"""
return pulumi.get(self, "access_key2")
@access_key2.setter
def access_key2(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "access_key2", value)
@property
@pulumi.getter(name="authenticationType")
def authentication_type(self) -> Optional[pulumi.Input[Union[str, 'AuthenticationType']]]:
"""
Specifies the authentication type being used for connecting to the endpoint.
"""
return pulumi.get(self, "authentication_type")
@authentication_type.setter
def authentication_type(self, value: Optional[pulumi.Input[Union[str, 'AuthenticationType']]]):
pulumi.set(self, "authentication_type", value)
@property
@pulumi.getter(name="deadLetterSecret")
def dead_letter_secret(self) -> Optional[pulumi.Input[str]]:
"""
Dead letter storage secret for key-based authentication. Will be obfuscated during read.
"""
return pulumi.get(self, "dead_letter_secret")
@dead_letter_secret.setter
def dead_letter_secret(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "dead_letter_secret", value)
@property
@pulumi.getter(name="deadLetterUri")
def dead_letter_uri(self) -> Optional[pulumi.Input[str]]:
"""
Dead letter storage URL for identity-based authentication.
"""
return pulumi.get(self, "dead_letter_uri")
@dead_letter_uri.setter
def dead_letter_uri(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "dead_letter_uri", value)
@pulumi.input_type
class EventHubArgs:
def __init__(__self__, *,
endpoint_type: pulumi.Input[str],
authentication_type: Optional[pulumi.Input[Union[str, 'AuthenticationType']]] = None,
connection_string_primary_key: Optional[pulumi.Input[str]] = None,
connection_string_secondary_key: Optional[pulumi.Input[str]] = None,
dead_letter_secret: Optional[pulumi.Input[str]] = None,
dead_letter_uri: Optional[pulumi.Input[str]] = None,
endpoint_uri: Optional[pulumi.Input[str]] = None,
entity_path: Optional[pulumi.Input[str]] = None):
"""
Properties related to EventHub.
:param pulumi.Input[str] endpoint_type: The type of Digital Twins endpoint
Expected value is 'EventHub'.
:param pulumi.Input[Union[str, 'AuthenticationType']] authentication_type: Specifies the authentication type being used for connecting to the endpoint.
:param pulumi.Input[str] connection_string_primary_key: PrimaryConnectionString of the endpoint for key-based authentication. Will be obfuscated during read.
:param pulumi.Input[str] connection_string_secondary_key: SecondaryConnectionString of the endpoint for key-based authentication. Will be obfuscated during read.
:param pulumi.Input[str] dead_letter_secret: Dead letter storage secret for key-based authentication. Will be obfuscated during read.
:param pulumi.Input[str] dead_letter_uri: Dead letter storage URL for identity-based authentication.
:param pulumi.Input[str] endpoint_uri: The URL of the EventHub namespace for identity-based authentication. It must include the protocol sb://
:param pulumi.Input[str] entity_path: The EventHub name in the EventHub namespace for identity-based authentication.
"""
pulumi.set(__self__, "endpoint_type", 'EventHub')
if authentication_type is not None:
pulumi.set(__self__, "authentication_type", authentication_type)
if connection_string_primary_key is not None:
pulumi.set(__self__, "connection_string_primary_key", connection_string_primary_key)
if connection_string_secondary_key is not None:
pulumi.set(__self__, "connection_string_secondary_key", connection_string_secondary_key)
if dead_letter_secret is not None:
pulumi.set(__self__, "dead_letter_secret", dead_letter_secret)
if dead_letter_uri is not None:
pulumi.set(__self__, "dead_letter_uri", dead_letter_uri)
if endpoint_uri is not None:
pulumi.set(__self__, "endpoint_uri", endpoint_uri)
if entity_path is not None:
pulumi.set(__self__, "entity_path", entity_path)
@property
@pulumi.getter(name="endpointType")
def endpoint_type(self) -> pulumi.Input[str]:
"""
The type of Digital Twins endpoint
Expected value is 'EventHub'.
"""
return pulumi.get(self, "endpoint_type")
@endpoint_type.setter
def endpoint_type(self, value: pulumi.Input[str]):
pulumi.set(self, "endpoint_type", value)
@property
@pulumi.getter(name="authenticationType")
def authentication_type(self) -> Optional[pulumi.Input[Union[str, 'AuthenticationType']]]:
"""
Specifies the authentication type being used for connecting to the endpoint.
"""
return pulumi.get(self, "authentication_type")
@authentication_type.setter
def authentication_type(self, value: Optional[pulumi.Input[Union[str, 'AuthenticationType']]]):
pulumi.set(self, "authentication_type", value)
@property
@pulumi.getter(name="connectionStringPrimaryKey")
def connection_string_primary_key(self) -> Optional[pulumi.Input[str]]:
"""
PrimaryConnectionString of the endpoint for key-based authentication. Will be obfuscated during read.
"""
return pulumi.get(self, "connection_string_primary_key")
@connection_string_primary_key.setter
def connection_string_primary_key(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "connection_string_primary_key", value)
@property
@pulumi.getter(name="connectionStringSecondaryKey")
def connection_string_secondary_key(self) -> Optional[pulumi.Input[str]]:
"""
SecondaryConnectionString of the endpoint for key-based authentication. Will be obfuscated during read.
"""
return pulumi.get(self, "connection_string_secondary_key")
@connection_string_secondary_key.setter
def connection_string_secondary_key(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "connection_string_secondary_key", value)
@property
@pulumi.getter(name="deadLetterSecret")
def dead_letter_secret(self) -> Optional[pulumi.Input[str]]:
"""
Dead letter storage secret for key-based authentication. Will be obfuscated during read.
"""
return pulumi.get(self, "dead_letter_secret")
@dead_letter_secret.setter
def dead_letter_secret(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "dead_letter_secret", value)
@property
@pulumi.getter(name="deadLetterUri")
def dead_letter_uri(self) -> Optional[pulumi.Input[str]]:
"""
Dead letter storage URL for identity-based authentication.
"""
return pulumi.get(self, "dead_letter_uri")
@dead_letter_uri.setter
def dead_letter_uri(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "dead_letter_uri", value)
@property
@pulumi.getter(name="endpointUri")
def endpoint_uri(self) -> Optional[pulumi.Input[str]]:
"""
The URL of the EventHub namespace for identity-based authentication. It must include the protocol sb://
"""
return pulumi.get(self, "endpoint_uri")
@endpoint_uri.setter
def endpoint_uri(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "endpoint_uri", value)
@property
@pulumi.getter(name="entityPath")
def entity_path(self) -> Optional[pulumi.Input[str]]:
"""
The EventHub name in the EventHub namespace for identity-based authentication.
"""
return pulumi.get(self, "entity_path")
@entity_path.setter
def entity_path(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "entity_path", value)
@pulumi.input_type
class PrivateEndpointConnectionArgs:
def __init__(__self__, *,
properties: pulumi.Input['PrivateEndpointConnectionPropertiesArgs']):
"""
The private endpoint connection of a Digital Twin.
"""
pulumi.set(__self__, "properties", properties)
@property
@pulumi.getter
def properties(self) -> pulumi.Input['PrivateEndpointConnectionPropertiesArgs']:
return pulumi.get(self, "properties")
@properties.setter
def properties(self, value: pulumi.Input['PrivateEndpointConnectionPropertiesArgs']):
pulumi.set(self, "properties", value)
@pulumi.input_type
class PrivateEndpointConnectionPropertiesArgs:
def __init__(__self__, *,
group_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
private_link_service_connection_state: Optional[pulumi.Input['ConnectionPropertiesPrivateLinkServiceConnectionStateArgs']] = None):
"""
:param pulumi.Input[Sequence[pulumi.Input[str]]] group_ids: The list of group ids for the private endpoint connection.
"""
if group_ids is not None:
pulumi.set(__self__, "group_ids", group_ids)
if private_link_service_connection_state is not None:
pulumi.set(__self__, "private_link_service_connection_state", private_link_service_connection_state)
@property
@pulumi.getter(name="groupIds")
def group_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
The list of group ids for the private endpoint connection.
"""
return pulumi.get(self, "group_ids")
@group_ids.setter
def group_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "group_ids", value)
@property
@pulumi.getter(name="privateLinkServiceConnectionState")
def private_link_service_connection_state(self) -> Optional[pulumi.Input['ConnectionPropertiesPrivateLinkServiceConnectionStateArgs']]:
return pulumi.get(self, "private_link_service_connection_state")
@private_link_service_connection_state.setter
def private_link_service_connection_state(self, value: Optional[pulumi.Input['ConnectionPropertiesPrivateLinkServiceConnectionStateArgs']]):
pulumi.set(self, "private_link_service_connection_state", value)
@pulumi.input_type
class ServiceBusArgs:
def __init__(__self__, *,
endpoint_type: pulumi.Input[str],
authentication_type: Optional[pulumi.Input[Union[str, 'AuthenticationType']]] = None,
dead_letter_secret: Optional[pulumi.Input[str]] = None,
dead_letter_uri: Optional[pulumi.Input[str]] = None,
endpoint_uri: Optional[pulumi.Input[str]] = None,
entity_path: Optional[pulumi.Input[str]] = None,
primary_connection_string: Optional[pulumi.Input[str]] = None,
secondary_connection_string: Optional[pulumi.Input[str]] = None):
"""
Properties related to ServiceBus.
:param pulumi.Input[str] endpoint_type: The type of Digital Twins endpoint
Expected value is 'ServiceBus'.
:param pulumi.Input[Union[str, 'AuthenticationType']] authentication_type: Specifies the authentication type being used for connecting to the endpoint.
:param pulumi.Input[str] dead_letter_secret: Dead letter storage secret for key-based authentication. Will be obfuscated during read.
:param pulumi.Input[str] dead_letter_uri: Dead letter storage URL for identity-based authentication.
:param pulumi.Input[str] endpoint_uri: The URL of the ServiceBus namespace for identity-based authentication. It must include the protocol sb://
:param pulumi.Input[str] entity_path: The ServiceBus Topic name for identity-based authentication
:param pulumi.Input[str] primary_connection_string: PrimaryConnectionString of the endpoint for key-based authentication. Will be obfuscated during read.
:param pulumi.Input[str] secondary_connection_string: SecondaryConnectionString of the endpoint for key-based authentication. Will be obfuscated during read.
"""
pulumi.set(__self__, "endpoint_type", 'ServiceBus')
if authentication_type is not None:
pulumi.set(__self__, "authentication_type", authentication_type)
if dead_letter_secret is not None:
pulumi.set(__self__, "dead_letter_secret", dead_letter_secret)
if dead_letter_uri is not None:
pulumi.set(__self__, "dead_letter_uri", dead_letter_uri)
if endpoint_uri is not None:
pulumi.set(__self__, "endpoint_uri", endpoint_uri)
if entity_path is not None:
pulumi.set(__self__, "entity_path", entity_path)
if primary_connection_string is not None:
pulumi.set(__self__, "primary_connection_string", primary_connection_string)
if secondary_connection_string is not None:
pulumi.set(__self__, "secondary_connection_string", secondary_connection_string)
@property
@pulumi.getter(name="endpointType")
def endpoint_type(self) -> pulumi.Input[str]:
"""
The type of Digital Twins endpoint
Expected value is 'ServiceBus'.
"""
return pulumi.get(self, "endpoint_type")
@endpoint_type.setter
def endpoint_type(self, value: pulumi.Input[str]):
pulumi.set(self, "endpoint_type", value)
@property
@pulumi.getter(name="authenticationType")
def authentication_type(self) -> Optional[pulumi.Input[Union[str, 'AuthenticationType']]]:
"""
Specifies the authentication type being used for connecting to the endpoint.
"""
return pulumi.get(self, "authentication_type")
@authentication_type.setter
def authentication_type(self, value: Optional[pulumi.Input[Union[str, 'AuthenticationType']]]):
pulumi.set(self, "authentication_type", value)
@property
@pulumi.getter(name="deadLetterSecret")
def dead_letter_secret(self) -> Optional[pulumi.Input[str]]:
"""
Dead letter storage secret for key-based authentication. Will be obfuscated during read.
"""
return pulumi.get(self, "dead_letter_secret")
@dead_letter_secret.setter
def dead_letter_secret(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "dead_letter_secret", value)
@property
@pulumi.getter(name="deadLetterUri")
def dead_letter_uri(self) -> Optional[pulumi.Input[str]]:
"""
Dead letter storage URL for identity-based authentication.
"""
return pulumi.get(self, "dead_letter_uri")
@dead_letter_uri.setter
def dead_letter_uri(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "dead_letter_uri", value)
@property
@pulumi.getter(name="endpointUri")
def endpoint_uri(self) -> Optional[pulumi.Input[str]]:
"""
The URL of the ServiceBus namespace for identity-based authentication. It must include the protocol sb://
"""
return pulumi.get(self, "endpoint_uri")
@endpoint_uri.setter
def endpoint_uri(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "endpoint_uri", value)
@property
@pulumi.getter(name="entityPath")
def entity_path(self) -> Optional[pulumi.Input[str]]:
"""
The ServiceBus Topic name for identity-based authentication
"""
return pulumi.get(self, "entity_path")
@entity_path.setter
def entity_path(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "entity_path", value)
@property
@pulumi.getter(name="primaryConnectionString")
def primary_connection_string(self) -> Optional[pulumi.Input[str]]:
"""
PrimaryConnectionString of the endpoint for key-based authentication. Will be obfuscated during read.
"""
return pulumi.get(self, "primary_connection_string")
@primary_connection_string.setter
def primary_connection_string(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "primary_connection_string", value)
@property
@pulumi.getter(name="secondaryConnectionString")
def secondary_connection_string(self) -> Optional[pulumi.Input[str]]:
"""
SecondaryConnectionString of the endpoint for key-based authentication. Will be obfuscated during read.
"""
return pulumi.get(self, "secondary_connection_string")
@secondary_connection_string.setter
def secondary_connection_string(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "secondary_connection_string", value)
| 43.510989 | 170 | 0.686661 | 2,682 | 23,757 | 5.863162 | 0.056301 | 0.090238 | 0.081908 | 0.067154 | 0.838665 | 0.750652 | 0.720191 | 0.673959 | 0.638728 | 0.614054 | 0 | 0.001175 | 0.211727 | 23,757 | 545 | 171 | 43.590826 | 0.838522 | 0.251968 | 0 | 0.557927 | 1 | 0 | 0.161329 | 0.071416 | 0 | 0 | 0 | 0 | 0 | 1 | 0.204268 | false | 0 | 0.018293 | 0.006098 | 0.335366 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
16997c58bb6f47eb6ccc77fc88fb686901b5a836 | 257 | py | Python | tests/test_action_delays.py | mehrdad-shokri/InstaPy | 76b38265d8ef312ba05ee7a94b820363c38e6599 | [
"MIT"
] | null | null | null | tests/test_action_delays.py | mehrdad-shokri/InstaPy | 76b38265d8ef312ba05ee7a94b820363c38e6599 | [
"MIT"
] | null | null | null | tests/test_action_delays.py | mehrdad-shokri/InstaPy | 76b38265d8ef312ba05ee7a94b820363c38e6599 | [
"MIT"
] | null | null | null | from instapy import util
def test_default_values_returned():
assert util.get_action_delay("like") == 2
assert util.get_action_delay("comment") == 2
assert util.get_action_delay("follow") == 3
assert util.get_action_delay("unfollow") == 10
| 28.555556 | 50 | 0.723735 | 37 | 257 | 4.72973 | 0.540541 | 0.228571 | 0.297143 | 0.434286 | 0.56 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0.023041 | 0.155642 | 257 | 8 | 51 | 32.125 | 0.78341 | 0 | 0 | 0 | 0 | 0 | 0.097276 | 0 | 0 | 0 | 0 | 0 | 0.666667 | 1 | 0.166667 | true | 0 | 0.166667 | 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 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
16ae1a5d6b5d2bae1bfdd283993dfa7bffa6ba08 | 72 | py | Python | mission/finite_state_machine/tests/src/sm_classes/test_gate_search_state.py | theBadMusician/Vortex-AUV | a2450f295b1288c0914f9505512bd8f34869b62c | [
"MIT"
] | 1 | 2021-02-02T13:21:19.000Z | 2021-02-02T13:21:19.000Z | mission/finite_state_machine/tests/src/sm_classes/test_gate_search_state.py | oyssolbo/Vortex-AUV | 27477110d733a064d318037129d628938c8950de | [
"MIT"
] | null | null | null | mission/finite_state_machine/tests/src/sm_classes/test_gate_search_state.py | oyssolbo/Vortex-AUV | 27477110d733a064d318037129d628938c8950de | [
"MIT"
] | null | null | null | import pytest
def test_execute():
#do stuff
assert True == True | 14.4 | 23 | 0.666667 | 10 | 72 | 4.7 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.25 | 72 | 5 | 23 | 14.4 | 0.87037 | 0.111111 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.333333 | 1 | 0.333333 | true | 0 | 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 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
16c091375f12ebded3df0385226f0a5ec871e465 | 272 | py | Python | openapi/script/1662/get_dcs_token.py | ProjectJinBao/nirvana7 | 0fe614afe94358bad4fa0bbda8013d8040e96456 | [
"Apache-2.0"
] | null | null | null | openapi/script/1662/get_dcs_token.py | ProjectJinBao/nirvana7 | 0fe614afe94358bad4fa0bbda8013d8040e96456 | [
"Apache-2.0"
] | null | null | null | openapi/script/1662/get_dcs_token.py | ProjectJinBao/nirvana7 | 0fe614afe94358bad4fa0bbda8013d8040e96456 | [
"Apache-2.0"
] | 1 | 2020-06-16T09:25:14.000Z | 2020-06-16T09:25:14.000Z | def get_dcs_token():
return "LkANIIDy39vXgDwFHSE7xLGHIe7JoWQnZxNn2vlrYzMpNUTZfeBYJFnsdbnaJ0cLIgVGT_2IESE67Zg8-ePI2XGjSXyp4Z1gcpC8OOFeWcprEbj9D-KcDHVMXbVcdoutR-SN_fAfwHHMRD1c5CtJig=="
def demo(name, sex):
def demo2(name1, sex1):
pass
return 'hello' | 34 | 162 | 0.790441 | 22 | 272 | 9.590909 | 0.863636 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.085106 | 0.136029 | 272 | 8 | 163 | 34 | 0.812766 | 0 | 0 | 0 | 0 | 0 | 0.575092 | 0.556777 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0.166667 | 0 | 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 | 1 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 6 |
16c319a46b96c9cf5c505a365836b5e79cab8791 | 140 | py | Python | leonardo/utils/email.py | timgates42/django-leonardo | c155f97fee9e2be1e0f508d47a1c205028253ecc | [
"BSD-3-Clause"
] | 102 | 2015-04-30T12:27:14.000Z | 2021-10-31T18:21:16.000Z | leonardo/utils/email.py | timgates42/django-leonardo | c155f97fee9e2be1e0f508d47a1c205028253ecc | [
"BSD-3-Clause"
] | 158 | 2015-04-30T22:42:34.000Z | 2019-09-07T15:37:22.000Z | leonardo/utils/email.py | timgates42/django-leonardo | c155f97fee9e2be1e0f508d47a1c205028253ecc | [
"BSD-3-Clause"
] | 64 | 2015-05-10T12:00:39.000Z | 2021-07-29T19:47:27.000Z |
import warnings
from .emails import *
warnings.warn(
'Please import email utils from leonardo.utils.emails instead of this location')
| 20 | 84 | 0.771429 | 19 | 140 | 5.684211 | 0.684211 | 0.259259 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.164286 | 140 | 6 | 85 | 23.333333 | 0.923077 | 0 | 0 | 0 | 0 | 0 | 0.553957 | 0.151079 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.75 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 6 |
bc54ca0e9067020ebddd787b592b7634ecbe5c11 | 4,394 | py | Python | Server/tests/test_coupon.py | getballaena/Get-Ballaena-Server | 7c04e31017f13608fab5a0b490d78f79336a2866 | [
"MIT"
] | null | null | null | Server/tests/test_coupon.py | getballaena/Get-Ballaena-Server | 7c04e31017f13608fab5a0b490d78f79336a2866 | [
"MIT"
] | null | null | null | Server/tests/test_coupon.py | getballaena/Get-Ballaena-Server | 7c04e31017f13608fab5a0b490d78f79336a2866 | [
"MIT"
] | null | null | null | from unittest import TestCase
from unittest.mock import MagicMock, patch
from bson import ObjectId
from app import create_app
from tests.request import check_status_code, coupon_list_request, coupon_delete_request
def create_coupon_mock_list():
return [MagicMock(id=i, coupon_name=f'coupon {i}') for i in range(10)]
class TestCouponList(TestCase):
def setUp(self):
self.client = create_app(test=True).test_client()
@patch('view.coupon.CouponView.get_current_user', return_value=MagicMock())
@patch('model.CouponModel.get_coupons_by_user', return_value=create_coupon_mock_list())
@check_status_code(200)
def test_success(self,
get_coupons_by_user_mock: MagicMock,
get_current_user_mock: MagicMock):
res = coupon_list_request(self)
get_current_user_mock.assert_called_once_with()
get_coupons_by_user_mock.assert_called_once_with(user=get_current_user_mock.return_value)
for i, coupon in enumerate(res.json):
expect = {
'coupon_id': str(i),
'coupon_name': f'coupon {i}'
}
self.assertDictEqual(expect, coupon)
return res
@patch('view.coupon.CouponView.get_current_user', return_value=MagicMock())
@patch('model.CouponModel.get_coupons_by_user', return_value=None)
@check_status_code(200)
def test_empty(self,
get_coupons_by_user_mock: MagicMock,
get_current_user_mock: MagicMock):
res = coupon_list_request(self)
get_current_user_mock.assert_called_once_with()
get_coupons_by_user_mock.assert_called_once_with(user=get_current_user_mock.return_value)
self.assertEqual(res.json, [])
return res
class TestCouponRedemption(TestCase):
def setUp(self):
self.client = create_app(test=True).test_client()
@patch('view.coupon.CouponView.get_current_user', return_value=MagicMock())
@patch('view.coupon.CouponView.get_coupon_id', return_value=ObjectId())
@patch('model.CouponModel.get_coupon_by_coupon_id_and_user', return_value=MagicMock())
@check_status_code(200)
def test_success(self,
get_coupon_by_coupon_id_and_user_mock: MagicMock,
get_coupon_id_mock: MagicMock,
get_current_user_mock: MagicMock):
res = coupon_delete_request(self)
get_current_user_mock.assert_called_once_with()
get_coupon_id_mock.assert_called_once_with()
get_coupon_by_coupon_id_and_user_mock(
get_coupon_id_mock.return_value,
get_current_user_mock.return_value,
)
return res
@patch('view.coupon.CouponView.get_current_user', return_value=MagicMock())
@patch('view.coupon.CouponView.get_coupon_id', return_value=ObjectId())
@patch('model.CouponModel.get_coupon_by_coupon_id_and_user', return_value=None)
@check_status_code(204)
def test_wrong_coupon_id(self,
get_coupon_by_coupon_id_and_user_mock: MagicMock,
get_coupon_id_mock: MagicMock,
get_current_user_mock: MagicMock):
res = coupon_delete_request(self)
get_current_user_mock.assert_called_once_with()
get_coupon_id_mock.assert_called_once_with()
get_coupon_by_coupon_id_and_user_mock(
get_coupon_id_mock.return_value,
get_current_user_mock.return_value,
)
return res
@patch('view.coupon.CouponView.get_current_user', return_value=MagicMock())
@patch('view.coupon.CouponView.get_coupon_id', return_value=ObjectId())
@patch('model.CouponModel.get_coupon_by_coupon_id_and_user', return_value=MagicMock())
@check_status_code(403)
def test_wrong_staff_code(self,
get_coupon_by_coupon_id_and_user_mock: MagicMock,
get_coupon_id_mock: MagicMock,
get_current_user_mock: MagicMock):
res = coupon_delete_request(self, staff_code='wrong')
get_current_user_mock.assert_called_once_with()
get_coupon_id_mock.assert_called_once_with()
get_coupon_by_coupon_id_and_user_mock(
get_coupon_id_mock.return_value,
get_current_user_mock.return_value,
)
return res
| 37.555556 | 97 | 0.688894 | 563 | 4,394 | 4.895204 | 0.119005 | 0.072569 | 0.101597 | 0.097968 | 0.829826 | 0.829826 | 0.812409 | 0.798621 | 0.798621 | 0.780842 | 0 | 0.005027 | 0.230314 | 4,394 | 116 | 98 | 37.87931 | 0.809876 | 0 | 0 | 0.681818 | 0 | 0 | 0.130178 | 0.119936 | 0 | 0 | 0 | 0 | 0.136364 | 1 | 0.090909 | false | 0 | 0.056818 | 0.011364 | 0.238636 | 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 |
bc7227f7c6ecb8297c32df6473fbec6c02d78dff | 5,091 | py | Python | DarkFAST-main/saya_gans/ngewe/__init__.py | Zusyaku/Termux-And-Lali-Linux-V2 | b1a1b0841d22d4bf2cc7932b72716d55f070871e | [
"Apache-2.0"
] | 2 | 2021-11-17T03:35:03.000Z | 2021-12-08T06:00:31.000Z | DarkFAST-main/saya_gans/ngewe/__init__.py | Zusyaku/Termux-And-Lali-Linux-V2 | b1a1b0841d22d4bf2cc7932b72716d55f070871e | [
"Apache-2.0"
] | null | null | null | DarkFAST-main/saya_gans/ngewe/__init__.py | Zusyaku/Termux-And-Lali-Linux-V2 | b1a1b0841d22d4bf2cc7932b72716d55f070871e | [
"Apache-2.0"
] | 2 | 2021-11-05T18:07:48.000Z | 2022-02-24T21:25:07.000Z | # hallo bro :v
from modul import *
aap_gans=[]
class gadag_user:
def __init__(self,url,cookie):
self.url=url
self.cookies=cookie
def followers(self,link,what=False):
try:
if what is True:
link=req.get(link,cookies=self.cookies).text
_=re.findall('" \/>\<div\ class\=\".."\>\<a\ href\=\"/(.*?)"\>\<span\>(.*?)</span\>',link)
for __ in _:
___=re.search("id=(\d*)" if "profile.php" in __[0] else "(.*?)\?",__[0])
aap_gans.append(___.group(1)+"(Aap Gans)"+__[1] if ___ is not None else __[0]+"(Aap Gans)"+__[1])
print(f"\r * mengumpulkan {len(aap_gans)} user, ctrl+c stop",end="")
if "Lihat Selengkapnya" in link:
self.followers(self.url+parser(link,"html.parser").find("a",string="Lihat Selengkapnya")["href"],True)
return aap_gans
except: return aap_gans
def fl(self,link,what=False):
try:
if what is True:
link=req.get(link,cookies=self.cookies).text
_=re.findall('style\=\"vertical-align: middle"\>\<a\ class\=\"..\" href\=\"/(.*?)"\>(.*?)</a\>',link)
for __ in _:
aap_gans.append(re.search("id=(\d*)" if "profile.php" in __ [0] else "(.*?)\?",__[0]).group(1)+"(Aap Gans)"+__[1])
print(f"\r * mengumpulkan {len(aap_gans)} user, ctrl+c stop",end="")
if "Lihat Teman Lain" in link:
self.fl(self.url+parser(link,"html.parser").find("a",string="Lihat Teman Lain")["href"],True)
return aap_gans
except: return aap_gans
def grup(self,link,why,what=False):
try:
if what is True:
link=req.get(link,cookies=self.cookies).text
_=re.findall('\<h3\>\<a\ class\=\"..\"\ href\=\"\/(.*?)\"\>(.*?)<\/a\>',link)
for __ in _:
___=re.search("id=(\d*)" if "profile.php" in __[0] else "Aap Afandi Ganteng:v",__[0])
aap_gans.append(___.group(1)+"(Aap Gans)"+__[1] if ___ is not None else __[0]+"(Aap Gans)"+__[1])
print(f"\r * mengumpulkan {len(aap_gans)} user, ctrl+c stop",end="")
if "Lihat Selengkapnya" in link:
self.grup(self.url+parser(link,"html.parser").find("a",string="Lihat Selengkapnya")["href"],why,True)
else:
self.get_post(f"{self.url}/groups/{why}")
return aap_gans
except: return aap_gans
def get_post(self,link):
try:
link=req.get(link,cookies=self.cookies).text
_=re.findall('\<h3\ class\=\".*?">\<span>\<strong>\<a\ href\=\"/(.*?)\">(.*?)</a\>\</strong\>',link)
for __ in _:
if "profile.php" in __[0]:
___=re.search("profile.php\?id=(\d*)",__[0]).group(1)
if ___ in aap_gans:
continue
else:
aap_gans.append(___+"(Aap Gans)"+__[1])
else:
___=re.search("(.*?)\?refid",__[0]).group(1)
if ___ in aap_gans:
continue
else:
aap_gans.append(___+"(Aap Gans)"+__[1])
print(f"\r * mengumpulkan {len(aap_gans)} user, ctrl+c stop",end="")
if "Lihat Postingan Lainnya" in link:
self.get_post(self.url+parser(link,"html.parser").find("a",string="Lihat Postingan Lainnya")["href"])
except: pass
def cari(self,link,jumlah,what=False,why=False):
try:
if what is True:
link=req.get(link,cookies=self.cookies).text
_=re.findall('picture" \/>\<\/a\>\<\/td\>\<td\ class\=\".*?"\>\<a\ href\=\"\/(.*?)"\>\<div\ class\=\"..\"\>\<div\ class\=\"..\"\>(.*?)<\/div>',link)
for __ in _:
aap_gans.append(re.search("id=(\d*)" if "profile.php" in __[0] else "(.*?)\?refid=",__[0]).group(1)+"(Aap Gans)"+__[1])
print(f"\r * mengumpulkan {len(aap_gans)} user, ctrl+c stop",end="")
if len(aap_gans)==jumlah:
why=True;break
if why is False:
if "Lihat Hasil Selanjutnya" in link:
self.cari(parser(link,"html.parser").find("a",string="Lihat Hasil Selanjutnya")["href"],jumlah,True)
return aap_gans
except: return aap_gans
def request(self,link,what=False):
try:
if what is True:
link=req.get(link,cookies=self.cookies).text
_=re.findall('middle\"\>\<a\ class\=\"..\"\ href\=\"(.*?)\"\>(.*?)\<\/a\>',link)
for __ in _:
aap_gans.append(re.search("uid=(\d*)" if "?uid" in __[0] else "\/(.*?)\?fref",__[0]).group(1)+"(Aap Gans)"+__[1])
print(f"\r * mengumpulkan {len(aap_gans)} user, ctrl+c stop",end="")
if "Lihat selengkapnya" in link:
self.request(self.url+parser(link,"html.parser").find("a",string="Lihat selengkapnya")["href"],True)
return aap_gans
except: return aap_gans
def like_post(self,link,jumlah,what=False,why=False):
try:
if what is True:
link=req.get(link,cookies=self.cookies).text
_=re.findall('\<h3\ class\=\".."\>\<a\ href\=\"/(.*?)"\>(.*?)<\/a\>',link)
for __ in _:
aap_gans.append(re.search("id=(\d*)",__[0]).group(1)+"(Aap Gans)"+__[1] if "profile.php" in __[0] else __[0]+"(Aap Gans)"+__[1])
print(f"\r * mengumpulkan {len(aap_gans)} user, ctrl+c stop",end="")
if len(aap_gans)==jumlah:
why=True;break
if why is False:
if "Lihat Selengkapnya" in link:
self.like_post(self.url+parser(link,"html.parser").find("a",string="Lihat Selengkapnya")["href"],jumlah,True)
return aap_gans
except: return aap_gans
| 41.390244 | 152 | 0.591436 | 737 | 5,091 | 3.862958 | 0.130258 | 0.105725 | 0.054795 | 0.034422 | 0.807517 | 0.801194 | 0.788198 | 0.784334 | 0.759396 | 0.753073 | 0 | 0.009488 | 0.171872 | 5,091 | 123 | 153 | 41.390244 | 0.665797 | 0.002357 | 0 | 0.614679 | 0 | 0.009174 | 0.32385 | 0.078087 | 0 | 0 | 0 | 0 | 0 | 1 | 0.073395 | false | 0.009174 | 0.009174 | 0 | 0.146789 | 0.06422 | 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 |
bcc5308fcf8ac1193b3de3b7c76fd8a095f05e0f | 2,458 | py | Python | warp/yul/extract_block.py | Glitch18/warp | ae3d1b3d0236a2c29f1114135ee8b04563987329 | [
"Apache-2.0"
] | null | null | null | warp/yul/extract_block.py | Glitch18/warp | ae3d1b3d0236a2c29f1114135ee8b04563987329 | [
"Apache-2.0"
] | null | null | null | warp/yul/extract_block.py | Glitch18/warp | ae3d1b3d0236a2c29f1114135ee8b04563987329 | [
"Apache-2.0"
] | null | null | null | from __future__ import annotations
from typing import Callable
import yul.yul_ast as ast
from yul.Scope import get_scope
def extract_block_as_function(
block: ast.Block, name: str, has_leave: bool = False
) -> tuple[ast.FunctionDefinition, ast.Statement]:
read_vars = get_scope(block).read_variables
if has_leave:
# If there is a leave in the block, some subset of modified
# variables will also be read at the time of "leaving". We
# play safe and mark all of the modified variables as read. An
# opportunity of optimization.
read_vars |= get_scope(block).modified_variables
read_vars = sorted(read_vars)
mod_vars = sorted(get_scope(block).modified_variables)
typed_read_vars = [ast.TypedName(x.name) for x in read_vars]
typed_mod_vars = [ast.TypedName(x.name) for x in mod_vars]
fun_def = ast.FunctionDefinition(
name=name,
parameters=typed_read_vars,
return_variables=typed_mod_vars,
body=block,
)
fun_call = ast.FunctionCall(ast.Identifier(name), read_vars)
fun_stmt = ast.Assignment(variable_names=mod_vars, value=fun_call)
return fun_def, fun_stmt
DUMMY_CALL = ast.Assignment([], ast.FunctionCall(ast.Identifier("__WARP_DUMMY"), []))
def extract_rec_block_as_function(
rec_block: Callable[[ast.Statement], ast.Block], name: str, has_leave: bool = False
) -> tuple[ast.FunctionDefinition, ast.Statement]:
stubbed_body = rec_block(DUMMY_CALL)
read_vars = get_scope(stubbed_body).read_variables
if has_leave:
# If there is a leave in the block, some subset of modified
# variables will also be read at the time of "leaving". We
# play safe and mark all of the modified variables as read. An
# opportunity of optimization.
read_vars |= get_scope(stubbed_body).modified_variables
read_vars = sorted(read_vars)
mod_vars = sorted(get_scope(stubbed_body).modified_variables)
typed_read_vars = [ast.TypedName(x.name) for x in read_vars]
typed_mod_vars = [ast.TypedName(x.name) for x in mod_vars]
fun_call = ast.FunctionCall(ast.Identifier(name), read_vars)
fun_stmt = ast.Assignment(variable_names=mod_vars, value=fun_call)
real_body = rec_block(fun_stmt)
fun_def = ast.FunctionDefinition(
name=name,
parameters=typed_read_vars,
return_variables=typed_mod_vars,
body=real_body,
)
return fun_def, fun_stmt
| 39.645161 | 87 | 0.716029 | 357 | 2,458 | 4.666667 | 0.210084 | 0.076831 | 0.026411 | 0.038415 | 0.816327 | 0.77491 | 0.745498 | 0.745498 | 0.745498 | 0.745498 | 0 | 0 | 0.199349 | 2,458 | 61 | 88 | 40.295082 | 0.846545 | 0.166395 | 0 | 0.533333 | 0 | 0 | 0.005882 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.044444 | false | 0 | 0.088889 | 0 | 0.177778 | 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 |
bcc7434c3b77fa2cea3fbf059a33486f18ab2f09 | 5,525 | py | Python | subnetCal.py | christivn/subnet-calculator-cidr | 82a8ec4eec5f96908cee2df91610558079733cd8 | [
"Apache-2.0"
] | 2 | 2020-09-11T11:30:49.000Z | 2021-07-01T22:06:25.000Z | subnetCal.py | christivn/subnet-calculator-cidr | 82a8ec4eec5f96908cee2df91610558079733cd8 | [
"Apache-2.0"
] | null | null | null | subnetCal.py | christivn/subnet-calculator-cidr | 82a8ec4eec5f96908cee2df91610558079733cd8 | [
"Apache-2.0"
] | null | null | null | def printCalculate(cidr):
s_cidr=cidr.split("/")
ip=s_cidr[0]
binary_ip=""
s_ip=ip.split(".")
for i in range(4):
int_ip=int(s_ip[i])
str_binary=str(decimalToBinary(int_ip))
zeros=""
for i in range(8-len(str_binary)):
zeros+="0"
binary_ip+=zeros+str_binary+"."
binary_ip=binary_ip[:-1]
binary_mask=""
binary_wildcard=""
for i in range(32):
if(i%8==0 and i!=0):
binary_mask+="."
binary_wildcard+="."
if(i<int(s_cidr[1])):
binary_mask+="1"
binary_wildcard+="0"
else:
binary_mask+="0"
binary_wildcard+="1"
mask=""
s_binary_mask=binary_mask.split(".")
for i in range(4):
mask+=str(int(s_binary_mask[i], 2))
if(i<3):
mask+="."
aux_binary_min_host=""
change=False
changeIndex=0
for i in range(len(binary_ip)):
if(binary_ip[34-i]=="0" and change==False):
change=True
changeIndex=34-i
aux_binary_min_host+=binary_ip[i]
else:
aux_binary_min_host+=binary_ip[i]
binary_min_host=""
for i in range(len(aux_binary_min_host)):
if(i==changeIndex):
binary_min_host+="1"
else:
binary_min_host+=aux_binary_min_host[i]
binary_max_host=""
for i in range(len(binary_mask)):
if(binary_mask[i]=="0"):
if(i==changeIndex):
binary_max_host+="0"
else:
binary_max_host+="1"
else:
binary_max_host+=binary_ip[i]
min_host=""
s_binary_min_host=binary_min_host.split(".")
for i in range(4):
min_host+=str(int(s_binary_min_host[i], 2))
if(i<3):
min_host+="."
max_host=""
s_binary_max_host=binary_max_host.split(".")
for i in range(4):
max_host+=str(int(s_binary_max_host[i], 2))
if(i<3):
max_host+="."
zero_bits=0
for i in range(4):
zero_bits+=s_binary_mask[i].count("0")
total_host=pow(2,zero_bits)-2
print("""\033[34m+--------------------------------------------------------+\033[0m
\033[32m\033[01mNETWORK:\033[0m """+cidr+"""
\033[32m\033[01mIP:\033[0m """+ip+"""
\033[32m\033[01mMASK:\033[0m """+mask+"""
\033[32m\033[01mRANGE:\033[0m """+min_host+""" / """+max_host+"""
\033[34m+--------------------------------------------------------+\033[0m
\033[36m\033[01mBINARY IP:\033[0m """+binary_ip+"""
\033[36m\033[01mBINARY MASK:\033[0m """+binary_mask+"""
\033[36m\033[01mBINARY WILDCARD:\033[0m """+binary_wildcard+"""
\033[36m\033[01mBINARY MIN HOST:\033[0m """+binary_min_host+"""
\033[36m\033[01mBINARY MAX HOST:\033[0m """+binary_max_host+"""
\033[34m+--------------------------------------------------------+\033[0m
\033[95m\033[01mMIN HOST:\033[0m """+min_host+"""
\033[95m\033[01mMAX HOST:\033[0m """+max_host+"""
\033[95m\033[01mTOTAL NUMBER OF HOSTS:\033[0m """+str(total_host)+"""
\033[34m+--------------------------------------------------------+\033[0m """)
def simpleCalculate(cidr):
s_cidr=cidr.split("/")
ip=s_cidr[0]
binary_ip=""
s_ip=ip.split(".")
for i in range(4):
int_ip=int(s_ip[i])
str_binary=str(decimalToBinary(int_ip))
zeros=""
for i in range(8-len(str_binary)):
zeros+="0"
binary_ip+=zeros+str_binary+"."
binary_ip=binary_ip[:-1]
binary_mask=""
binary_wildcard=""
for i in range(32):
if(i%8==0 and i!=0):
binary_mask+="."
binary_wildcard+="."
if(i<int(s_cidr[1])):
binary_mask+="1"
binary_wildcard+="0"
else:
binary_mask+="0"
binary_wildcard+="1"
mask=""
s_binary_mask=binary_mask.split(".")
for i in range(4):
mask+=str(int(s_binary_mask[i], 2))
if(i<3):
mask+="."
aux_binary_min_host=""
change=False
changeIndex=0
for i in range(len(binary_ip)):
if(binary_ip[34-i]=="0" and change==False):
change=True
changeIndex=34-i
aux_binary_min_host+=binary_ip[i]
else:
aux_binary_min_host+=binary_ip[i]
binary_min_host=""
for i in range(len(aux_binary_min_host)):
if(i==changeIndex):
binary_min_host+="1"
else:
binary_min_host+=aux_binary_min_host[i]
binary_max_host=""
for i in range(len(binary_mask)):
if(binary_mask[i]=="0"):
if(i==changeIndex):
binary_max_host+="0"
else:
binary_max_host+="1"
else:
binary_max_host+=binary_ip[i]
min_host=""
s_binary_min_host=binary_min_host.split(".")
for i in range(4):
min_host+=str(int(s_binary_min_host[i], 2))
if(i<3):
min_host+="."
max_host=""
s_binary_max_host=binary_max_host.split(".")
for i in range(4):
max_host+=str(int(s_binary_max_host[i], 2))
if(i<3):
max_host+="."
zero_bits=0
for i in range(4):
zero_bits+=s_binary_mask[i].count("0")
total_host=pow(2,zero_bits)-2
return [cidr,ip,mask,min_host,max_host,total_host]
def decimalToBinary(number):
if number<0:
return 'Not positive'
i = 0
result = ''
while number>>i:
result = ('1' if number>>i&1 else '0') + result
i += 1
return result | 28.188776 | 86 | 0.527783 | 779 | 5,525 | 3.490372 | 0.084724 | 0.084958 | 0.109967 | 0.080912 | 0.785215 | 0.774549 | 0.774549 | 0.759103 | 0.759103 | 0.759103 | 0 | 0.071482 | 0.270769 | 5,525 | 196 | 87 | 28.188776 | 0.603376 | 0 | 0 | 0.847059 | 0 | 0 | 0.146761 | 0.097177 | 0 | 0 | 0 | 0 | 0 | 1 | 0.017647 | false | 0 | 0 | 0 | 0.035294 | 0.011765 | 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 |
bcdc2b5e72f8f9890b666ef76d9cc819c638224b | 43 | py | Python | src/spark/goodbyes/__init__.py | simonhodder/spark | 1062e3092996f82f7bc2c852d6444c2be2a1e296 | [
"MIT"
] | null | null | null | src/spark/goodbyes/__init__.py | simonhodder/spark | 1062e3092996f82f7bc2c852d6444c2be2a1e296 | [
"MIT"
] | null | null | null | src/spark/goodbyes/__init__.py | simonhodder/spark | 1062e3092996f82f7bc2c852d6444c2be2a1e296 | [
"MIT"
] | null | null | null |
from .goodbye_plugins import GoodbyeModel
| 14.333333 | 41 | 0.860465 | 5 | 43 | 7.2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.116279 | 43 | 2 | 42 | 21.5 | 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 |
bcdff503ac9158ffa7cf55e169f3d67720de6b8b | 33,469 | py | Python | Wrappers/Python/test/test_BlockDataContainer.py | mhquah/CCPi-Framework | 35f11db30ef5453cfeaae296ed45ea780e42733a | [
"Apache-2.0"
] | 9 | 2019-09-05T10:21:49.000Z | 2021-04-25T19:33:56.000Z | Wrappers/Python/test/test_BlockDataContainer.py | mhquah/CCPi-Framework | 35f11db30ef5453cfeaae296ed45ea780e42733a | [
"Apache-2.0"
] | 580 | 2018-06-01T13:19:43.000Z | 2021-05-07T10:28:57.000Z | Wrappers/Python/test/test_BlockDataContainer.py | mhquah/CCPi-Framework | 35f11db30ef5453cfeaae296ed45ea780e42733a | [
"Apache-2.0"
] | 12 | 2018-11-29T12:15:59.000Z | 2021-11-29T07:13:21.000Z | # -*- coding: utf-8 -*-
# This work is part of the Core Imaging Library (CIL) developed by CCPi
# (Collaborative Computational Project in Tomographic Imaging), with
# substantial contributions by UKRI-STFC and University of Manchester.
# 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
import numpy
from cil.framework import ImageGeometry, AcquisitionGeometry
from cil.framework import ImageData, AcquisitionData
from cil.framework import BlockDataContainer, DataContainer
import functools
from cil.optimisation.operators import GradientOperator, IdentityOperator, BlockOperator
class BDCUnittest(unittest.TestCase):
def assertBlockDataContainerEqual(self, container1, container2):
print ("assert Block Data Container Equal")
self.assertTrue(issubclass(container1.__class__, container2.__class__))
for col in range(container1.shape[0]):
if issubclass(container1.get_item(col).__class__, DataContainer):
print ("Checking col ", col)
self.assertNumpyArrayEqual(
container1.get_item(col).as_array(),
container2.get_item(col).as_array()
)
else:
self.assertBlockDataContainerEqual(container1.get_item(col),container2.get_item(col))
def assertNumpyArrayEqual(self, first, second):
res = True
try:
numpy.testing.assert_array_equal(first, second)
except AssertionError as err:
res = False
print(err)
self.assertTrue(res)
def assertBlockDataContainerAlmostEqual(self, container1, container2, decimal=7):
print ("assert Block Data Container Equal")
self.assertTrue(issubclass(container1.__class__, container2.__class__))
for col in range(container1.shape[0]):
if issubclass(container1.get_item(col).__class__, DataContainer):
print ("Checking col ", col)
self.assertNumpyArrayAlmostEqual(
container1.get_item(col).as_array(),
container2.get_item(col).as_array(),
decimal=decimal
)
else:
self.assertBlockDataContainerAlmostEqual(container1.get_item(col),container2.get_item(col), decimal=decimal)
def assertNumpyArrayAlmostEqual(self, first, second, decimal):
numpy.testing.assert_array_almost_equal(first, second, decimal)
class TestBlockDataContainer(BDCUnittest):
def skiptest_BlockDataContainerShape(self):
print ("test block data container")
ig0 = ImageGeometry(12,42,55,32)
ig1 = ImageGeometry(12,42,55,32)
data0 = ImageData(geometry=ig0)
data1 = ImageData(geometry=ig1) + 1
data2 = ImageData(geometry=ig0) + 2
data3 = ImageData(geometry=ig1) + 3
cp0 = BlockDataContainer(data0,data1)
cp1 = BlockDataContainer(data2,data3)
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp0.T.shape == transpose_shape)
def skiptest_BlockDataContainerShapeArithmetic(self):
print ("test block data container")
ig0 = ImageGeometry(2,3,4)
ig1 = ImageGeometry(2,3,4)
data0 = ImageData(geometry=ig0)
data1 = ImageData(geometry=ig1) + 1
data2 = ImageData(geometry=ig0) + 2
data3 = ImageData(geometry=ig1) + 3
cp0 = BlockDataContainer(data0,data1)
#cp1 = BlockDataContainer(data2,data3)
cp1 = cp0 + 1
self.assertTrue(cp1.shape == cp0.shape)
cp1 = cp0.T + 1
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T - 1
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = (cp0.T + 1)*2
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = (cp0.T + 1)/2
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T.power(2.2)
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T.maximum(3)
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T.abs()
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T.sign()
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T.sqrt()
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
cp1 = cp0.T.conjugate()
transpose_shape = (cp0.shape[1], cp0.shape[0])
self.assertTrue(cp1.shape == transpose_shape)
def test_BlockDataContainer(self):
print ("test block data container")
ig0 = ImageGeometry(2,3,4)
ig1 = ImageGeometry(2,3,5)
# data0 = ImageData(geometry=ig0)
# data1 = ImageData(geometry=ig1) + 1
data0 = ig0.allocate(0.)
data1 = ig1.allocate(1.)
# data2 = ImageData(geometry=ig0) + 2
# data3 = ImageData(geometry=ig1) + 3
data2 = ig0.allocate(2.)
data3 = ig1.allocate(3.)
cp0 = BlockDataContainer(data0,data1)
cp1 = BlockDataContainer(data2,data3)
cp2 = BlockDataContainer(data0+1, data2+1)
d = cp2 + data0
self.assertEqual(d.get_item(0).as_array()[0][0][0], 1)
try:
d = cp2 + data1
self.assertTrue(False)
except ValueError as ve:
print (ve)
self.assertTrue(True)
d = cp2 - data0
self.assertEqual(d.get_item(0).as_array()[0][0][0], 1)
try:
d = cp2 - data1
self.assertTrue(False)
except ValueError as ve:
print (ve)
self.assertTrue(True)
d = cp2 * data2
self.assertEqual(d.get_item(0).as_array()[0][0][0], 2)
try:
d = cp2 * data1
self.assertTrue(False)
except ValueError as ve:
print (ve)
self.assertTrue(True)
a = [ (el, ot) for el,ot in zip(cp0.containers,cp1.containers)]
print (a[0][0].shape)
#cp2 = BlockDataContainer(*a)
cp2 = cp0.add(cp1)
self.assertEqual (cp2.get_item(0).as_array()[0][0][0] , 2.)
self.assertEqual (cp2.get_item(1).as_array()[0][0][0] , 4.)
cp2 = cp0 + cp1
self.assertTrue (cp2.get_item(0).as_array()[0][0][0] == 2.)
self.assertTrue (cp2.get_item(1).as_array()[0][0][0] == 4.)
cp2 = cp0 + 1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 1. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 2., decimal = 5)
cp2 = cp0 + [1 ,2]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 1. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 3., decimal = 5)
cp2 += cp1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , +3. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , +6., decimal = 5)
cp2 += 1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , +4. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , +7., decimal = 5)
cp2 += [-2,-1]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 2. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 6., decimal = 5)
cp2 = cp0.subtract(cp1)
assert (cp2.get_item(0).as_array()[0][0][0] == -2.)
assert (cp2.get_item(1).as_array()[0][0][0] == -2.)
cp2 = cp0 - cp1
assert (cp2.get_item(0).as_array()[0][0][0] == -2.)
assert (cp2.get_item(1).as_array()[0][0][0] == -2.)
cp2 = cp0 - 1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , -1. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 0, decimal = 5)
cp2 = cp0 - [1 ,2]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , -1. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , -1., decimal = 5)
cp2 -= cp1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , -3. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , -4., decimal = 5)
cp2 -= 1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , -4. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , -5., decimal = 5)
cp2 -= [-2,-1]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , -2. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , -4., decimal = 5)
cp2 = cp0.multiply(cp1)
assert (cp2.get_item(0).as_array()[0][0][0] == 0.)
assert (cp2.get_item(1).as_array()[0][0][0] == 3.)
cp2 = cp0 * cp1
assert (cp2.get_item(0).as_array()[0][0][0] == 0.)
assert (cp2.get_item(1).as_array()[0][0][0] == 3.)
cp2 = cp0 * 2
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 2, decimal = 5)
cp2 = 2 * cp0
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 2, decimal = 5)
cp2 = cp0 * [3 ,2]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 2., decimal = 5)
cp2 = cp0 * numpy.asarray([3 ,2])
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 2., decimal = 5)
cp2 = [3,2] * cp0
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 2., decimal = 5)
cp2 = numpy.asarray([3,2]) * cp0
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 2., decimal = 5)
try:
cp2 = [3,2,3] * cp0
#numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
#numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 2., decimal = 5)
self.assertTrue(False)
except ValueError as ve:
print (ve)
self.assertTrue(True)
cp2 *= cp1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0 , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , +6., decimal = 5)
cp2 *= 1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , +6., decimal = 5)
cp2 *= [-2,-1]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , -6., decimal = 5)
try:
cp2 *= [2,3,5]
self.assertTrue(False)
except ValueError as ve:
print (ve)
self.assertTrue(True)
cp2 = cp0.divide(cp1)
assert (cp2.get_item(0).as_array()[0][0][0] == 0.)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0], 1./3., decimal=4)
cp2 = cp0/cp1
assert (cp2.get_item(0).as_array()[0][0][0] == 0.)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0], 1./3., decimal=4)
cp2 = cp0 / 2
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 0.5, decimal = 5)
cp2 = cp0 / [3 ,2]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 0.5, decimal = 5)
cp2 = cp0 / numpy.asarray([3 ,2])
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 0.5, decimal = 5)
cp3 = numpy.asarray([3 ,2]) / (cp0+1)
numpy.testing.assert_almost_equal(cp3.get_item(0).as_array()[0][0][0] , 3. , decimal=5)
numpy.testing.assert_almost_equal(cp3.get_item(1).as_array()[0][0][0] , 1, decimal = 5)
cp2 += 1
cp2 /= cp1
# TODO fix inplace division
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 1./2 , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 1.5/3., decimal = 5)
cp2 /= 1
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0.5 , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 0.5, decimal = 5)
cp2 /= [-2,-1]
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , -0.5/2. , decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , -0.5, decimal = 5)
####
cp2 = cp0.power(cp1)
assert (cp2.get_item(0).as_array()[0][0][0] == 0.)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0], 1., decimal=4)
cp2 = cp0**cp1
assert (cp2.get_item(0).as_array()[0][0][0] == 0.)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0], 1., decimal=4)
cp2 = cp0 ** 2
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0] , 0., decimal=5)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0] , 1., decimal = 5)
cp2 = cp0.maximum(cp1)
assert (cp2.get_item(0).as_array()[0][0][0] == cp1.get_item(0).as_array()[0][0][0])
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0], cp2.get_item(1).as_array()[0][0][0], decimal=4)
cp2 = cp0.abs()
numpy.testing.assert_almost_equal(cp2.get_item(0).as_array()[0][0][0], 0., decimal=4)
numpy.testing.assert_almost_equal(cp2.get_item(1).as_array()[0][0][0], 1., decimal=4)
cp2 = cp0.subtract(cp1)
s = cp2.sign()
numpy.testing.assert_almost_equal(s.get_item(0).as_array()[0][0][0], -1., decimal=4)
numpy.testing.assert_almost_equal(s.get_item(1).as_array()[0][0][0], -1., decimal=4)
cp2 = cp0.add(cp1)
s = cp2.sqrt()
numpy.testing.assert_almost_equal(s.get_item(0).as_array()[0][0][0], numpy.sqrt(2), decimal=4)
numpy.testing.assert_almost_equal(s.get_item(1).as_array()[0][0][0], numpy.sqrt(4), decimal=4)
s = cp0.sum()
size = functools.reduce(lambda x,y: x*y, data1.shape, 1)
print ("size" , size)
numpy.testing.assert_almost_equal(s, 0 + size, decimal=4)
s0 = 1
s1 = 1
for i in cp0.get_item(0).shape:
s0 *= i
for i in cp0.get_item(1).shape:
s1 *= i
#numpy.testing.assert_almost_equal(s[1], cp0.get_item(0,0).as_array()[0][0][0]*s0 +cp0.get_item(1,0).as_array()[0][0][0]*s1, decimal=4)
def test_Nested_BlockDataContainer(self):
print ("test_Nested_BlockDataContainer")
ig0 = ImageGeometry(2,3,4)
ig1 = ImageGeometry(2,3,4)
# data0 = ImageData(geometry=ig0)
# data1 = ImageData(geometry=ig1) + 1
# data2 = ImageData(geometry=ig0) + 2
# data3 = ImageData(geometry=ig1) + 3
data0 = ig0.allocate(0.)
data1 = ig1.allocate(1.)
data2 = ig0.allocate(2.)
data3 = ig1.allocate(3.)
cp0 = BlockDataContainer(data0,data1)
cp1 = BlockDataContainer(data2,data3)
nbdc = BlockDataContainer(cp0, cp1)
nbdc2 = nbdc + 2
numpy.testing.assert_almost_equal(nbdc2.get_item(0).get_item(0).as_array()[0][0][0] , 2. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(0).get_item(1).as_array()[0][0][0] , 3. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(1).get_item(0).as_array()[0][0][0] , 4. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(1).get_item(1).as_array()[0][0][0] , 5. , decimal=5)
nbdc2 = 2 + nbdc
numpy.testing.assert_almost_equal(nbdc2.get_item(0).get_item(0).as_array()[0][0][0] , 2. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(0).get_item(1).as_array()[0][0][0] , 3. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(1).get_item(0).as_array()[0][0][0] , 4. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(1).get_item(1).as_array()[0][0][0] , 5. , decimal=5)
nbdc2 = nbdc * 2
numpy.testing.assert_almost_equal(nbdc2.get_item(0).get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(0).get_item(1).as_array()[0][0][0] , 2. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(1).get_item(0).as_array()[0][0][0] , 4. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(1).get_item(1).as_array()[0][0][0] , 6. , decimal=5)
nbdc2 = 2 * nbdc
numpy.testing.assert_almost_equal(nbdc2.get_item(0).get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(0).get_item(1).as_array()[0][0][0] , 2. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(1).get_item(0).as_array()[0][0][0] , 4. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(1).get_item(1).as_array()[0][0][0] , 6. , decimal=5)
nbdc2 = nbdc / 2
numpy.testing.assert_almost_equal(nbdc2.get_item(0).get_item(0).as_array()[0][0][0] , 0. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(0).get_item(1).as_array()[0][0][0] , .5 , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(1).get_item(0).as_array()[0][0][0] , 1. , decimal=5)
numpy.testing.assert_almost_equal(nbdc2.get_item(1).get_item(1).as_array()[0][0][0] , 3./2 , decimal=5)
c5 = nbdc.get_item(0).power(2).sum()
c5a = nbdc.power(2).sum()
print ("sum", c5a, c5)
cp0 = BlockDataContainer(data0,data2)
a = cp0 * data2
b = data2 * cp0
self.assertBlockDataContainerEqual(a,b)
print ("test_Nested_BlockDataContainer OK")
def test_NestedBlockDataContainer2(self):
M, N = 2, 3
ig = ImageGeometry(voxel_num_x = M, voxel_num_y = N)
ag = ig
u = ig.allocate(1)
op1 = GradientOperator(ig)
op2 = IdentityOperator(ig, ag)
operator = BlockOperator(op1, op2, shape=(2,1))
d1 = op1.direct(u)
d2 = op2.direct(u)
d = operator.direct(u)
dd = operator.domain_geometry()
ww = operator.range_geometry()
print(d.get_item(0).get_item(0).as_array())
print(d.get_item(0).get_item(1).as_array())
print(d.get_item(1).as_array())
c1 = d + d
c2 = 2*d
c3 = d / (d+0.0001)
c5 = d.get_item(0).power(2).sum()
def test_BlockDataContainer_fill(self):
print ("test block data container")
ig0 = ImageGeometry(2,3,4)
ig1 = ImageGeometry(2,3,5)
data0 = ImageData(geometry=ig0)
data1 = ImageData(geometry=ig1) + 1
data2 = ImageData(geometry=ig0) + 2
data3 = ImageData(geometry=ig1) + 3
cp0 = BlockDataContainer(data0,data1)
#cp1 = BlockDataContainer(data2,data3)
cp2 = BlockDataContainer(data0+1, data1+1)
data0.fill(data2)
self.assertNumpyArrayEqual(data0.as_array(), data2.as_array())
data0 = ImageData(geometry=ig0)
for el,ot in zip(cp0, cp2):
print (el.shape, ot.shape)
cp0.fill(cp2)
self.assertBlockDataContainerEqual(cp0, cp2)
def test_NestedBlockDataContainer(self):
ig0 = ImageGeometry(2,3,4)
ig1 = ImageGeometry(2,3,5)
data0 = ig0.allocate(0)
data2 = ig0.allocate(1)
cp0 = BlockDataContainer(data0,data2)
#cp1 = BlockDataContainer(data2,data3)
nested = BlockDataContainer(cp0, data2, data2)
out = BlockDataContainer(BlockDataContainer(data0 , data0), data0, data0)
nested.divide(data2,out=out)
self.assertBlockDataContainerEqual(out, nested)
def test_axpby(self):
# test axpby between BlockDataContainers
ig0 = ImageGeometry(2,3,4)
ig1 = ImageGeometry(2,3,5)
data0 = ig0.allocate(-1)
data2 = ig0.allocate(1)
data1 = ig0.allocate(2)
data3 = ig0.allocate(3)
cp0 = BlockDataContainer(data0,data2)
cp1 = BlockDataContainer(data1,data3)
out = cp0 * 0. - 10
cp0.axpby(3,-2,cp1,out, num_threads=4)
# operation should be [ 3 * -1 + (-2) * 2 , 3 * 1 + (-2) * 3 ]
# output should be [ -7 , -3 ]
res0 = ig0.allocate(-7)
res2 = ig0.allocate(-3)
res = BlockDataContainer(res0, res2)
print ("res0", res0.as_array())
print ("res2", res2.as_array())
print ("###############################")
print ("out_0", out.get_item(0).as_array())
print ("out_1", out.get_item(1).as_array())
self.assertBlockDataContainerEqual(out, res)
def test_axpby2(self):
# test axpby with BlockDataContainer and DataContainer
ig0 = ImageGeometry(2,3,4)
# ig1 = ImageGeometry(2,3,5)
data0 = ig0.allocate(-1)
data2 = ig0.allocate(1)
data1 = ig0.allocate(2)
# data3 = ig1.allocate(3)
cp0 = BlockDataContainer(data0,data2)
# cp1 = BlockDataContainer(data1,data3)
out = cp0 * 0. - 10
cp0.axpby(3,-2,data1,out)
# operation should be [ 3 * -1 + (-2) * 2 , 3 * 1 + (-2) * 2 ]
# output should be [ -7 , -1 ]
res0 = ig0.allocate(-7)
res2 = ig0.allocate(-1)
res = BlockDataContainer(res0, res2)
print ("res0", res0.as_array())
print ("res2", res2.as_array())
print ("###############################")
print ("out_0", out.get_item(0).as_array())
print ("out_1", out.get_item(1).as_array())
self.assertBlockDataContainerEqual(out, res)
def test_axpby3(self):
# test axpby with nested BlockDataContainer
ig0 = ImageGeometry(2,3,4)
ig1 = ImageGeometry(2,3,5)
data0 = ig0.allocate(-1)
data2 = ig0.allocate(1)
# data1 = ig0.allocate(2)
data3 = ig1.allocate(3)
cp0 = BlockDataContainer(data0,data2)
cp1 = BlockDataContainer(cp0 *0. + [2, -2], data3)
print (cp1.get_item(0).get_item(0).as_array())
print (cp1.get_item(0).get_item(1).as_array())
print (cp1.get_item(1).as_array())
print ("###############################")
out = cp1 * 0.
cp2 = out + [1,3]
print (cp2.get_item(0).get_item(0).as_array())
print (cp2.get_item(0).get_item(1).as_array())
print (cp2.get_item(1).as_array())
cp2.axpby(3,-2, cp1 ,out)
# output should be [ [ -1 , 7 ] , 3]
res0 = ig0.allocate(-1)
res2 = ig0.allocate(7)
res3 = ig1.allocate(3)
res = BlockDataContainer(BlockDataContainer(res0, res2), res3)
# print ("res0", res0.as_array())
# print ("res2", res2.as_array())
print ("###############################")
# print ("out_0", out.get_item(0).as_array())
# print ("out_1", out.get_item(1).as_array())
self.assertBlockDataContainerEqual(out, res)
def test_axpby4(self):
# test axpby with nested BlockDataContainer
ig0 = ImageGeometry(2,3,4)
ig1 = ImageGeometry(2,3,5)
data0 = ig0.allocate(-1)
data2 = ig0.allocate(1)
# data1 = ig0.allocate(2)
data3 = ig1.allocate(3)
cp0 = BlockDataContainer(data0,data2)
cp1 = BlockDataContainer(cp0 *0. + [2, -2], data3)
print (cp1.get_item(0).get_item(0).as_array())
print (cp1.get_item(0).get_item(1).as_array())
print (cp1.get_item(1).as_array())
print ("###############################")
out = cp1 * 0.
cp2 = out + [1,3]
print (cp2.get_item(0).get_item(0).as_array())
print (cp2.get_item(0).get_item(1).as_array())
print (cp2.get_item(1).as_array())
cp2.axpby(3,-2, cp1 ,out, num_threads=4)
# output should be [ [ -1 , 7 ] , 3]
res0 = ig0.allocate(-1)
res2 = ig0.allocate(7)
res3 = ig1.allocate(3)
res = BlockDataContainer(BlockDataContainer(res0, res2), res3)
# print ("res0", res0.as_array())
# print ("res2", res2.as_array())
print ("###############################")
# print ("out_0", out.get_item(0).as_array())
# print ("out_1", out.get_item(1).as_array())
self.assertBlockDataContainerEqual(out, res)
class TestOutParameter(BDCUnittest):
def setUp(self):
ig0 = ImageGeometry(2,3,4)
ig1 = ImageGeometry(2,3,5)
data0 = ig0.allocate(-1)
data2 = ig1.allocate(1)
# data1 = ig0.allocate(2)
# data3 = ig1.allocate(3)
cp0 = BlockDataContainer(data0,data2)
self.ig0 = ig0
self.ig1 = ig1
self.cp0 = cp0
def test_binary_add(self):
# test axpby with nested BlockDataContainer
cp0 = self.cp0
cp1 = cp0 * 0
cp0.add(1 , out = cp1)
res = BlockDataContainer(self.ig0.allocate(0), self.ig1.allocate(2))
self.assertBlockDataContainerEqual(cp1, res)
def test_binary_subtract(self):
# test axpby with nested BlockDataContainer
cp0 = self.cp0
cp1 = cp0 * 0
cp0.subtract(1 , out = cp1)
res = BlockDataContainer(self.ig0.allocate(-1-1), self.ig1.allocate(1-1))
self.assertBlockDataContainerEqual(cp1, res)
def test_binary_multiply(self):
# test axpby with nested BlockDataContainer
cp0 = self.cp0
cp1 = cp0 * 0
cp0.multiply(2 , out = cp1)
res = BlockDataContainer(self.ig0.allocate(-1*2), self.ig1.allocate(1*2))
self.assertBlockDataContainerAlmostEqual(cp1, res)
def test_binary_divide(self):
# test axpby with nested BlockDataContainer
cp0 = self.cp0
cp1 = cp0 * 0
cp0.divide(2 , out = cp1)
res = BlockDataContainer(self.ig0.allocate(-1/2), self.ig1.allocate(1/2))
self.assertBlockDataContainerAlmostEqual(cp1, res)
def test_binary_power(self):
# test axpby with nested BlockDataContainer
cp0 = self.cp0
cp1 = cp0 * 0
cp0.power(2 , out = cp1)
res = BlockDataContainer(self.ig0.allocate((-1)**2), self.ig1.allocate((1)**2))
self.assertBlockDataContainerAlmostEqual(cp1, res)
def test_binary_maximum(self):
# test axpby with nested BlockDataContainer
cp0 = self.cp0
cp1 = cp0 * 10
cp0.maximum(0 , out = cp1)
res = BlockDataContainer(self.ig0.allocate(0), self.ig1.allocate(1))
self.assertBlockDataContainerAlmostEqual(cp1, res)
def test_binary_minimum(self):
# test axpby with nested BlockDataContainer
cp0 = self.cp0
cp1 = cp0 * 10
cp0.minimum(0 , out = cp1)
res = BlockDataContainer(self.ig0.allocate(-1), self.ig1.allocate(0))
self.assertBlockDataContainerAlmostEqual(cp1, res)
def test_unary_abs(self):
# test axpby with nested BlockDataContainer
cp0 = self.cp0
cp0.abs(out = cp0)
res = BlockDataContainer(self.ig0.allocate(1), self.ig1.allocate(1))
self.assertBlockDataContainerAlmostEqual(res, cp0)
def test_unary_sign(self):
# test axpby with nested BlockDataContainer
cp0 = self.cp0
cp1 = cp0.sign()
res = BlockDataContainer(self.ig0.allocate(-1), self.ig1.allocate(1))
self.assertBlockDataContainerAlmostEqual(res, cp1)
def test_unary_sign2(self):
# test axpby with nested BlockDataContainer
cp0 = self.cp0
cp0.sign(out=cp0)
res = BlockDataContainer(self.ig0.allocate(-1), self.ig1.allocate(1))
self.assertBlockDataContainerAlmostEqual(res, cp0)
def test_unary_sqrt(self):
# test axpby with nested BlockDataContainer
data0 = self.ig0.allocate(4)
data2 = self.ig1.allocate(8)
# data1 = ig0.allocate(2)
# data3 = ig1.allocate(3)
cp0 = BlockDataContainer(data0,data2)
cp1 = cp0.sqrt()
res = BlockDataContainer(self.ig0.allocate(numpy.sqrt(4)), self.ig1.allocate(numpy.sqrt(8)))
self.assertBlockDataContainerAlmostEqual(res, cp1)
def test_unary_sqrt2(self):
# test axpby with nested BlockDataContainer
data0 = self.ig0.allocate(4)
data2 = self.ig1.allocate(8)
# data1 = ig0.allocate(2)
# data3 = ig1.allocate(3)
cp0 = BlockDataContainer(data0,data2)
cp0.sqrt(out=cp0)
res = BlockDataContainer(self.ig0.allocate(numpy.sqrt(4)), self.ig1.allocate(numpy.sqrt(8)))
self.assertBlockDataContainerAlmostEqual(res, cp0)
def test_unary_conjugate(self):
# test axpby with nested BlockDataContainer
data0 = self.ig0.allocate(4+3j, dtype=numpy.complex64)
data2 = self.ig1.allocate(1-1j, dtype=numpy.complex64)
# data1 = ig0.allocate(2)
# data3 = ig1.allocate(3)
cp0 = BlockDataContainer(data0,data2)
cp1 = cp0.conjugate()
res = BlockDataContainer(self.ig0.allocate(4-3j, dtype=numpy.complex64), self.ig1.allocate(1+1j, dtype=numpy.complex64))
self.assertBlockDataContainerAlmostEqual(res, cp1)
def test_unary_conjugate2(self):
# test axpby with nested BlockDataContainer
data0 = self.ig0.allocate(4+3j, dtype=numpy.complex64)
data2 = self.ig1.allocate(1-1j, dtype=numpy.complex64)
# data1 = ig0.allocate(2)
# data3 = ig1.allocate(3)
cp0 = BlockDataContainer(data0,data2)
cp0.conjugate(out=cp0)
res = BlockDataContainer(self.ig0.allocate(4-3j, dtype=numpy.complex64), self.ig1.allocate(1+1j, dtype=numpy.complex64))
self.assertBlockDataContainerAlmostEqual(res, cp0)
def test_unary_abs1(self):
# test axpby with nested BlockDataContainer
cp0 = self.cp0
cp1 = cp0.abs()
res = BlockDataContainer(self.ig0.allocate(1), self.ig1.allocate(1))
self.assertBlockDataContainerAlmostEqual(res, cp1) | 41.370828 | 144 | 0.578266 | 4,439 | 33,469 | 4.22077 | 0.057896 | 0.027327 | 0.022897 | 0.05332 | 0.818745 | 0.811272 | 0.795741 | 0.777968 | 0.764998 | 0.753363 | 0 | 0.077652 | 0.271624 | 33,469 | 809 | 145 | 41.370828 | 0.69091 | 0.097583 | 0 | 0.557348 | 0 | 0 | 0.016511 | 0.008392 | 0 | 0 | 0 | 0.001236 | 0.299283 | 1 | 0.055556 | false | 0 | 0.012545 | 0 | 0.073477 | 0.087814 | 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 |
4c4bf3665ccc39ab7aebf360e55c9daf226f6ce0 | 10,685 | py | Python | tests/test_summary.py | capsian/pymfe_MOCAI | 929c8034748846e92cddf1962dc9ba0dd5aa36c1 | [
"MIT"
] | 86 | 2019-03-21T23:56:22.000Z | 2022-02-06T23:18:33.000Z | tests/test_summary.py | capsian/pymfe_MOCAI | 929c8034748846e92cddf1962dc9ba0dd5aa36c1 | [
"MIT"
] | 100 | 2019-03-21T18:32:30.000Z | 2021-03-19T16:38:41.000Z | tests/test_summary.py | capsian/pymfe_MOCAI | 929c8034748846e92cddf1962dc9ba0dd5aa36c1 | [
"MIT"
] | 24 | 2019-04-22T17:10:56.000Z | 2021-06-01T14:26:49.000Z | """Test module for General class metafeatures."""
import typing as t
import pytest
import pymfe._internal
import pymfe._summary
import pymfe.mfe
import numpy as np
def test_get_summary():
assert not set(pymfe.mfe.MFE.valid_summary()).symmetric_difference(
pymfe._internal.VALID_SUMMARY
)
def test_sum_histogram():
mf = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
aux = pymfe._summary.sum_histogram(mf, bins=5)
assert np.allclose(np.array([0.2, 0.2, 0.2, 0.2, 0.2]), aux)
def test_sum_quantiles():
mf = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
aux = pymfe._summary.sum_quantiles(mf, package="numpy")
assert np.allclose(np.array([1.0, 3.25, 5.5, 7.75, 10.0]), aux)
with pytest.raises(ValueError):
pymfe._summary.sum_quantiles(mf, package="asd")
aux = pymfe._summary.sum_quantiles(mf, package="scipy")
assert np.allclose(np.array([1.0, 2.95, 5.5, 8.05, 10.0]), aux)
def test_sum_skewness():
mf = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
aux = pymfe._summary.sum_skewness(mf)
assert np.allclose(0.0, aux)
with pytest.raises(ValueError):
pymfe._summary.sum_skewness(mf, method=4)
aux = pymfe._summary.sum_skewness([])
assert aux is np.nan
mf = [10.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
aux = pymfe._summary.sum_skewness(mf, method=2)
assert np.allclose(-0.15146310708295876, aux)
def test_sum_kurtosis():
mf = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
aux = pymfe._summary.sum_kurtosis(mf)
assert np.allclose(-1.5616363636363637, aux)
with pytest.raises(ValueError):
pymfe._summary.sum_kurtosis(mf, method=4)
aux = pymfe._summary.sum_kurtosis([])
assert aux is np.nan
mf = [10.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
aux = pymfe._summary.sum_kurtosis(mf, method=2)
assert np.allclose(-1.356984911550468, aux)
def test_ddof():
sing_val = [1.0]
assert np.isclose(0.0, pymfe._summary.sum_std(sing_val, ddof=0))
assert np.isclose(0.0, pymfe._summary.sum_var(sing_val, ddof=0))
assert np.isnan(pymfe._summary.sum_std(sing_val, ddof=1))
assert np.isnan(pymfe._summary.sum_var(sing_val, ddof=1))
assert np.isnan(pymfe._summary.sum_std(sing_val, ddof=2))
assert np.isnan(pymfe._summary.sum_var(sing_val, ddof=2))
assert np.isnan(pymfe._summary.sum_nanstd(sing_val, ddof=1))
assert np.isnan(pymfe._summary.sum_nanvar(sing_val, ddof=1))
assert np.isnan(pymfe._summary.sum_nanstd(sing_val, ddof=2))
assert np.isnan(pymfe._summary.sum_nanvar(sing_val, ddof=2))
@pytest.mark.parametrize(
"summary_func",
[
"nanmean",
"nansd",
"nanvar",
"nanhistogram",
"naniq_range",
"nankurtosis",
"nanmax",
"nanmedian",
"nanmin",
"nanquantiles",
"nanrange",
"nanskewness",
],
)
def test_nansummary(summary_func):
values = np.array(
[
1,
np.nan,
np.nan,
2,
-4,
np.nan,
9,
-11,
1,
5,
6.4,
2.3,
4.5,
np.nan,
0,
]
)
clean_values = values[~np.isnan(values)]
summary_nan = pymfe._summary.SUMMARY_METHODS[summary_func]
summary_reg = pymfe._summary.SUMMARY_METHODS[summary_func[3:]]
assert np.allclose(
summary_nan(list(values)), summary_reg(list(clean_values))
)
def test_nancount():
values = np.array(
[
1,
np.nan,
np.nan,
2,
-4,
np.nan,
9,
-11,
1,
5,
6.4,
2.3,
4.5,
np.nan,
0,
]
)
summary_nan = pymfe._summary.SUMMARY_METHODS["nancount"]
summary_reg = pymfe._summary.SUMMARY_METHODS["count"]
assert np.allclose(
summary_nan(list(values)),
summary_reg(list(values)) - np.count_nonzero(np.isnan(values)),
)
@pytest.mark.parametrize("p", [-1, 0, 1, 2, 3, 4])
def test_powersum_scalar(p: t.Union[int, float]):
values = [0, 0, -1, 10, -10, -5, 8, 2.5, 0.1, -0.2]
res_a = pymfe._summary.sum_powersum(values, p)
res_b = np.sum(np.power(values, p))
assert np.isclose(res_a, res_b)
@pytest.mark.parametrize("p", [-1, 0, 1, 2, 3, 4])
def test_nanpowersum_scalar(p: t.Union[int, float]):
values = [0, np.nan, -1, np.nan, -10, -5, 8, 2.5, 0.1, -0.2, np.nan]
res_a = pymfe._summary.sum_nanpowersum(values, p)
res_b = np.nansum(np.power(pymfe._summary._remove_nan(values), p))
assert np.isclose(res_a, res_b)
@pytest.mark.parametrize("p", [[2], [-1, 0], [1, 2, 3, 4]])
def test_powersum_array(p: t.Sequence[t.Union[int, float]]):
values = [0, 0, -1, 10, -10, -5, 8, 2.5, 0.1, -0.2]
res_a = pymfe._summary.sum_powersum(values, p)
res_b = [np.sum(np.power(values, cur_p)) for cur_p in p]
assert len(res_a) == len(p) and np.allclose(res_a, res_b)
@pytest.mark.parametrize("p", [[2], [-1, 0], [1, 2, 3, 4]])
def test_nanpowersum_array(p: t.Sequence[t.Union[int, float]]):
values = [0, np.nan, -1, np.nan, -10, -5, 8, 2.5, 0.1, -0.2, np.nan]
res_a = pymfe._summary.sum_nanpowersum(values, p)
res_b = [
np.nansum(np.power(pymfe._summary._remove_nan(values), cur_p))
for cur_p in p
]
assert len(res_a) == len(p) and np.allclose(res_a, res_b)
@pytest.mark.parametrize("p", [-1, 0, 1, 2, 3, 4])
def test_pnorm_scalar(p: t.Union[int, float]):
values = [0, 0, -1, 10, -10, -5, 8, 2.5, 0.1, -0.2]
res_a = pymfe._summary.sum_pnorm(values, p)
res_b = np.linalg.norm(values, p) if p >= 0 else np.nan
assert np.isclose(res_a, res_b, equal_nan=True)
@pytest.mark.parametrize("p", [-1, 0, 1, 2, 3, 4])
def test_nanpnorm_scalar(p: t.Union[int, float]):
values = [0, np.nan, -1, np.nan, -10, -5, 8, 2.5, 0.1, -0.2, np.nan]
res_a = pymfe._summary.sum_nanpnorm(values, p)
res_b = (
np.linalg.norm(pymfe._summary._remove_nan(values), p)
if p >= 0
else np.nan
)
assert np.isclose(res_a, res_b, equal_nan=True)
@pytest.mark.parametrize("p", [[2], [-1, 0], [1, 2, 3, 4]])
def test_pnorm_array(p: t.Sequence[t.Union[int, float]]):
values = [0, 0, -1, 10, -10, -5, 8, 2.5, 0.1, -0.2]
res_a = pymfe._summary.sum_pnorm(values, p)
res_b = [
np.linalg.norm(values, cur_p) if cur_p >= 0 else np.nan for cur_p in p
]
assert len(res_a) == len(p) and np.allclose(res_a, res_b, equal_nan=True)
@pytest.mark.parametrize("p", [[2], [-1, 0], [1, 2, 3, 4]])
def test_nanpnorm_array(p: t.Sequence[t.Union[int, float]]):
values = [0, np.nan, -1, np.nan, -10, -5, 8, 2.5, 0.1, -0.2, np.nan]
res_a = pymfe._summary.sum_nanpnorm(values, p)
res_b = [
np.linalg.norm(pymfe._summary._remove_nan(values), cur_p)
if cur_p >= 0
else np.nan
for cur_p in p
]
assert len(res_a) == len(p) and np.allclose(res_a, res_b, equal_nan=True)
def test_sum_sum():
values = [0, 0, -1, 10, -10, -5, 8, 2.5, 0.1, -0.2]
assert np.isclose(sum(values), pymfe._summary.sum_sum(values))
def test_sum_nansum():
values = [0, np.nan, -1, np.nan, -10, -5, 8, 2.5, 0.1, -0.2, np.nan]
assert np.isclose(np.nansum(values), pymfe._summary.sum_nansum(values))
@pytest.mark.parametrize(
"summary, sum_args, exp_len",
(
("mean", None, 1),
("nanmean", None, 1),
("sd", None, 1),
("nansd", None, 1),
("var", None, 1),
("nanvar", None, 1),
("histogram", {"bins": 7}, 7),
("nanhistogram", {"bins": 7}, 7),
("iq_range", None, 1),
("naniq_range", None, 1),
("kurtosis", None, 1),
("nankurtosis", None, 1),
("max", None, 1),
("nanmax", None, 1),
("median", None, 1),
("nanmedian", None, 1),
("min", None, 1),
("nanmin", None, 1),
("quantiles", None, 5),
("nanquantiles", None, 5),
("range", None, 1),
("nanrange", None, 1),
("skewness", None, 1),
("nanskewness", None, 1),
("sum", None, 1),
("nansum", None, 1),
("powersum", None, 1),
("powersum", {"p": [-1, 0, 1, 2]}, 4),
("pnorm", None, 1),
("pnorm", {"p": [-1, 0, 1, 2]}, 4),
("nanpowersum", None, 1),
("nanpowersum", {"p": [-1, 0, 1, 2]}, 4),
("nanpnorm", None, 1),
("nanpnorm", {"p": [-1, 0, 1, 2]}, 4),
),
)
def test_summary_empty_slice(
summary: str, sum_args: t.Dict[str, t.Any], exp_len: int
):
if sum_args is None:
sum_args = {}
X = np.asarray([1, 2, 3], dtype=str)
extractor = pymfe.mfe.MFE(features="mean", summary=summary).fit(
X, transform_cat=None
)
res = extractor.extract(suppress_warnings=True, **{summary: sum_args})[1]
assert len(res) == exp_len and np.all(np.isnan(res))
@pytest.mark.parametrize(
"summary, sum_args, exp_len",
(
("mean", None, 1),
("nanmean", None, 1),
("sd", None, 1),
("nansd", None, 1),
("var", None, 1),
("nanvar", None, 1),
("histogram", {"bins": 7}, 7),
("nanhistogram", {"bins": 7}, 7),
("iq_range", None, 1),
("naniq_range", None, 1),
("kurtosis", None, 1),
("nankurtosis", None, 1),
("max", None, 1),
("nanmax", None, 1),
("median", None, 1),
("nanmedian", None, 1),
("min", None, 1),
("nanmin", None, 1),
("quantiles", None, 5),
("nanquantiles", None, 5),
("range", None, 1),
("nanrange", None, 1),
("skewness", None, 1),
("nanskewness", None, 1),
("sum", None, 1),
("nansum", None, 1),
("powersum", None, 1),
("powersum", {"p": [-1, 0, 1, 2]}, 4),
("pnorm", None, 1),
("pnorm", {"p": [-1, 0, 1, 2]}, 4),
("nanpowersum", None, 1),
("nanpowersum", {"p": [-1, 0, 1, 2]}, 4),
("nanpnorm", None, 1),
("nanpnorm", {"p": [-1, 0, 1, 2]}, 4),
),
)
def test_summary_all_nan(
summary: str, sum_args: t.Dict[str, t.Any], exp_len: int
):
if sum_args is None:
sum_args = {}
X = np.full(5, fill_value=np.nan)
extractor = pymfe.mfe.MFE(features="mean", summary=summary)
extractor.fit(X, transform_cat=None, transform_num=None)
res = extractor.extract(suppress_warnings=True, **{summary: sum_args})[1]
assert len(res) == exp_len and np.all(np.isnan(res))
| 29.035326 | 78 | 0.550304 | 1,656 | 10,685 | 3.410628 | 0.091787 | 0.046034 | 0.084986 | 0.011331 | 0.836402 | 0.820467 | 0.776735 | 0.743803 | 0.717953 | 0.679001 | 0 | 0.071997 | 0.259055 | 10,685 | 367 | 79 | 29.114441 | 0.641405 | 0.004024 | 0 | 0.592466 | 0 | 0 | 0.068071 | 0 | 0 | 0 | 0 | 0 | 0.116438 | 1 | 0.068493 | false | 0 | 0.020548 | 0 | 0.089041 | 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 |
4c55c7e57b6f7336bc568e5218a1436b1976fc33 | 106 | py | Python | main.py | i2nes/app-engine-blog | 94cdc25674c946ad643f7f140cbedf095773de3f | [
"MIT"
] | null | null | null | main.py | i2nes/app-engine-blog | 94cdc25674c946ad643f7f140cbedf095773de3f | [
"MIT"
] | null | null | null | main.py | i2nes/app-engine-blog | 94cdc25674c946ad643f7f140cbedf095773de3f | [
"MIT"
] | null | null | null | from app import create_app
from config import config, blog_config
app = create_app(config, blog_config)
| 17.666667 | 38 | 0.811321 | 17 | 106 | 4.823529 | 0.352941 | 0.219512 | 0.390244 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.141509 | 106 | 5 | 39 | 21.2 | 0.901099 | 0 | 0 | 0 | 0 | 0 | 0 | 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 |
4c5b6592193f186d7272b9bbc85b01ac42d5989b | 29,962 | py | Python | vault/tests/test_vault.py | tdimnet/integrations-core | a78133a3b71a1b8377fa214d121a98647031ab06 | [
"BSD-3-Clause"
] | 663 | 2016-08-23T05:23:45.000Z | 2022-03-29T00:37:23.000Z | vault/tests/test_vault.py | tdimnet/integrations-core | a78133a3b71a1b8377fa214d121a98647031ab06 | [
"BSD-3-Clause"
] | 6,642 | 2016-06-09T16:29:20.000Z | 2022-03-31T22:24:09.000Z | vault/tests/test_vault.py | tdimnet/integrations-core | a78133a3b71a1b8377fa214d121a98647031ab06 | [
"BSD-3-Clause"
] | 1,222 | 2017-01-27T15:51:38.000Z | 2022-03-31T18:17:51.000Z | # (C) Datadog, Inc. 2018-present
# All rights reserved
# Licensed under a 3-clause BSD style license (see LICENSE)
import re
import mock
import pytest
import requests
from datadog_checks.dev.http import MockResponse
from datadog_checks.dev.utils import get_metadata_metrics
from datadog_checks.vault import Vault
from datadog_checks.vault.errors import ApiUnreachable
from datadog_checks.vault.vault import Leader
from .common import INSTANCES, auth_required, noauth_required
pytestmark = pytest.mark.usefixtures('dd_environment')
class TestVault:
def test_bad_config(self, aggregator, dd_run_check):
instance = INSTANCES['invalid']
c = Vault(Vault.CHECK_NAME, {}, [instance])
with pytest.raises(Exception):
dd_run_check(c)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, count=0)
def test_unsupported_api_version_fallback(self, aggregator, dd_run_check):
instance = INSTANCES['unsupported_api']
c = Vault(Vault.CHECK_NAME, {}, [instance])
assert not instance['api_url'].endswith(Vault.DEFAULT_API_VERSION)
dd_run_check(c)
assert c._api_url.endswith(Vault.DEFAULT_API_VERSION)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.OK, count=1)
def test_service_check_connect_ok(self, aggregator, dd_run_check):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
dd_run_check(c, dd_run_check)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.OK, count=1)
def test_service_check_connect_ok_all_tags(self, aggregator, dd_run_check, global_tags):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/leader':
return MockResponse(
json_data={'ha_enabled': False, 'is_self': True, 'leader_address': '', 'leader_cluster_address': ''}
)
elif url == instance['api_url'] + '/sys/health':
return MockResponse(
json_data={
'cluster_id': '9e25ccdb-09ea-8bd8-0521-34cf3ef7a4cc',
'cluster_name': 'vault-cluster-f5f44063',
'initialized': True,
'replication_dr_mode': 'disabled',
'replication_performance_mode': 'disabled',
'sealed': False,
'server_time_utc': 1529357080,
'standby': False,
'version': '0.10.2',
}
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.OK, tags=global_tags, count=1)
def test_service_check_connect_fail(self, aggregator, dd_run_check):
instance = INSTANCES['bad_url']
c = Vault(Vault.CHECK_NAME, {}, [instance])
with pytest.raises(
Exception,
match=r'^Vault endpoint `{}.+?` timed out after 1\.0 seconds$'.format(re.escape(instance['api_url'])),
):
dd_run_check(c, extract_message=True)
aggregator.assert_service_check(
Vault.SERVICE_CHECK_CONNECT,
status=Vault.CRITICAL,
tags=['instance:foobar', 'api_url:http://1.2.3.4:555/v1'],
count=1,
)
def test_service_check_500_fail(self, aggregator, dd_run_check, global_tags):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
with mock.patch('requests.get', return_value=MockResponse(status_code=500)):
with pytest.raises(
Exception, match=r'^The Vault endpoint `{}.+?` returned 500$'.format(re.escape(instance['api_url']))
):
dd_run_check(c, extract_message=True)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.CRITICAL, tags=global_tags, count=1)
def test_api_unreachable(self):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
with pytest.raises(ApiUnreachable, match=r"Error accessing Vault endpoint.*"):
c.access_api("http://foo.bar", ignore_status_codes=None)
def test_service_check_unsealed_ok(self, aggregator, dd_run_check):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
dd_run_check(c)
aggregator.assert_service_check(Vault.SERVICE_CHECK_UNSEALED, status=Vault.OK, count=1)
def test_service_check_unsealed_ok_all_tags(self, aggregator, dd_run_check, global_tags):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/leader':
return MockResponse(
json_data={'ha_enabled': False, 'is_self': True, 'leader_address': '', 'leader_cluster_address': ''}
)
elif url == instance['api_url'] + '/sys/health':
return MockResponse(
json_data={
'cluster_id': '9e25ccdb-09ea-8bd8-0521-34cf3ef7a4cc',
'cluster_name': 'vault-cluster-f5f44063',
'initialized': True,
'replication_dr_mode': 'disabled',
'replication_performance_mode': 'disabled',
'sealed': False,
'server_time_utc': 1529357080,
'standby': False,
'version': '0.10.2',
}
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
expected_tags = [
'is_leader:true',
'cluster_name:vault-cluster-f5f44063',
'vault_cluster:vault-cluster-f5f44063',
'vault_version:0.10.2',
]
expected_tags.extend(global_tags)
aggregator.assert_service_check(Vault.SERVICE_CHECK_UNSEALED, status=Vault.OK, tags=expected_tags, count=1)
def test_service_check_unsealed_fail(self, aggregator, dd_run_check):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/health':
return MockResponse(
json_data={
'cluster_id': '9e25ccdb-09ea-8bd8-0521-34cf3ef7a4cc',
'cluster_name': 'vault-cluster-f5f44063',
'initialized': False,
'replication_dr_mode': 'disabled',
'replication_performance_mode': 'disabled',
'sealed': True,
'server_time_utc': 1529357080,
'standby': False,
'version': '0.10.2',
},
status_code=503,
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
aggregator.assert_service_check(Vault.SERVICE_CHECK_UNSEALED, status=Vault.CRITICAL, count=1)
def test_service_check_initialized_ok(self, aggregator, dd_run_check):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
dd_run_check(c)
aggregator.assert_service_check(Vault.SERVICE_CHECK_INITIALIZED, status=Vault.OK, count=1)
def test_service_check_initialized_ok_all_tags(self, aggregator, dd_run_check, global_tags):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/leader':
return MockResponse(
json_data={'ha_enabled': False, 'is_self': True, 'leader_address': '', 'leader_cluster_address': ''}
)
elif url == instance['api_url'] + '/sys/health':
return MockResponse(
json_data={
'cluster_id': '9e25ccdb-09ea-8bd8-0521-34cf3ef7a4cc',
'cluster_name': 'vault-cluster-f5f44063',
'initialized': True,
'replication_dr_mode': 'disabled',
'replication_performance_mode': 'disabled',
'sealed': False,
'server_time_utc': 1529357080,
'standby': False,
'version': '0.10.2',
}
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
expected_tags = [
'is_leader:true',
'cluster_name:vault-cluster-f5f44063',
'vault_cluster:vault-cluster-f5f44063',
'vault_version:0.10.2',
]
expected_tags.extend(global_tags)
aggregator.assert_service_check(Vault.SERVICE_CHECK_INITIALIZED, status=Vault.OK, tags=expected_tags, count=1)
def test_service_check_initialized_fail(self, aggregator, dd_run_check):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/health':
return MockResponse(
json_data={
'cluster_id': '9e25ccdb-09ea-8bd8-0521-34cf3ef7a4cc',
'cluster_name': 'vault-cluster-f5f44063',
'initialized': False,
'replication_dr_mode': 'disabled',
'replication_performance_mode': 'disabled',
'sealed': False,
'server_time_utc': 1529357080,
'standby': False,
'version': '0.10.2',
},
status_code=501,
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
aggregator.assert_service_check(Vault.SERVICE_CHECK_INITIALIZED, status=Vault.CRITICAL, count=1)
def test_disable_legacy_cluster_tag(self, aggregator, dd_run_check, global_tags):
instance = INSTANCES['main']
instance['disable_legacy_cluster_tag'] = True
c = Vault(Vault.CHECK_NAME, {}, [instance])
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/leader':
return MockResponse(
json_data={'ha_enabled': False, 'is_self': True, 'leader_address': '', 'leader_cluster_address': ''}
)
elif url == instance['api_url'] + '/sys/health':
return MockResponse(
json_data={
'cluster_id': '9e25ccdb-09ea-8bd8-0521-34cf3ef7a4cc',
'cluster_name': 'vault-cluster-f5f44063',
'initialized': True,
'replication_dr_mode': 'disabled',
'replication_performance_mode': 'disabled',
'sealed': False,
'server_time_utc': 1529357080,
'standby': False,
'version': '0.10.2',
}
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
expected_tags = [
'is_leader:true',
'vault_cluster:vault-cluster-f5f44063',
'vault_version:0.10.2',
]
expected_tags.extend(global_tags)
aggregator.assert_service_check(Vault.SERVICE_CHECK_INITIALIZED, status=Vault.OK, tags=expected_tags, count=1)
def test_replication_dr_mode(self, aggregator, dd_run_check):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
c.log.debug = mock.MagicMock()
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/health':
return MockResponse(
json_data={
'cluster_id': '9e25ccdb-09ea-8bd8-0521-34cf3ef7a4cc',
'cluster_name': 'vault-cluster-f5f44063',
'initialized': False,
'replication_dr_mode': 'secondary',
'replication_performance_mode': 'primary',
'sealed': False,
'server_time_utc': 1529357080,
'standby': True,
'performance_standby': False,
'version': '0.10.2',
},
status_code=200,
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
c.log.debug.assert_called_with(
"Detected vault in replication DR secondary mode, skipping Prometheus metric collection."
)
aggregator.assert_metric('vault.is_leader', 1)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.OK, count=1)
assert_all_metrics(aggregator)
def test_replication_dr_mode_changed(self, aggregator, dd_run_check):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
c.log.debug = mock.MagicMock()
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/health':
if getattr(mock_requests_get, 'first_health_call', True):
mock_requests_get.first_health_call = False
replication_dr_mode = 'primary'
else:
replication_dr_mode = 'secondary'
return MockResponse(
json_data={
'cluster_id': '9e25ccdb-09ea-8bd8-0521-34cf3ef7a4cc',
'cluster_name': 'vault-cluster-f5f44063',
'initialized': False,
'replication_dr_mode': replication_dr_mode,
'replication_performance_mode': 'primary',
'sealed': False,
'server_time_utc': 1529357080,
'standby': True,
'performance_standby': False,
'version': '0.10.2',
},
status_code=200,
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
assert not c._replication_dr_secondary_mode
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.OK, count=1)
aggregator.assert_metric('vault.is_leader', 1)
assert_all_metrics(aggregator)
aggregator.reset()
dd_run_check(c)
c.log.debug.assert_called_with(
"Detected vault in replication DR secondary mode, skipping Prometheus metric collection."
)
assert c._replication_dr_secondary_mode
aggregator.assert_metric('vault.is_leader', 1)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.OK, count=1)
assert_all_metrics(aggregator)
@pytest.mark.parametrize("cluster", [True, False])
def test_event_leader_change(self, aggregator, dd_run_check, cluster):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
next_leader = None
if cluster:
c._previous_leader = Leader('', 'foo')
next_leader = Leader('', 'bar')
else:
c._previous_leader = Leader('foo', '')
next_leader = Leader('bar', '')
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/leader':
return MockResponse(
json_data={
'ha_enabled': False,
'is_self': True,
'leader_address': next_leader.leader_addr,
'leader_cluster_address': next_leader.leader_cluster_addr,
}
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
assert len(aggregator.events) > 0
event = aggregator.events[0]
assert event['event_type'] == Vault.EVENT_LEADER_CHANGE
assert event['msg_title'] == 'Leader change'
if cluster:
assert event['msg_text'] == 'Leader cluster address changed from `foo` to `bar`.'
else:
assert event['msg_text'] == 'Leader address changed from `foo` to `bar`.'
assert event['alert_type'] == 'info'
assert event['source_type_name'] == Vault.CHECK_NAME
assert event['host'] == c.hostname
assert 'is_leader:true' in event['tags']
assert c._previous_leader == next_leader
def test_leader_change_not_self(self, aggregator, dd_run_check):
"""The agent should only submit a leader change event when the monitored vault is the leader."""
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
c._previous_leader = Leader('foo', '')
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/leader':
return MockResponse(
json_data={
'ha_enabled': False,
'is_self': False,
'leader_address': 'bar',
'leader_cluster_address': '',
}
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
assert len(aggregator.events) == 0
def test_is_leader_metric_true(self, aggregator, dd_run_check):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/leader':
return MockResponse(
json_data={
'ha_enabled': False,
'is_self': True,
'leader_address': 'bar',
'leader_cluster_address': '',
}
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
aggregator.assert_metric('vault.is_leader', 1)
def test_is_leader_metric_false(self, aggregator, dd_run_check):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/leader':
return MockResponse(
json_data={
'ha_enabled': False,
'is_self': False,
'leader_address': 'bar',
'leader_cluster_address': '',
}
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
aggregator.assert_metric('vault.is_leader', 0)
@pytest.mark.parametrize('status_code', [200, 429, 472, 473, 501, 503])
def test_sys_health_non_standard_status_codes(self, aggregator, dd_run_check, status_code):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/health':
return MockResponse(
json_data={
'cluster_id': '9e25ccdb-09ea-8bd8-0521-34cf3ef7a4cc',
'cluster_name': 'vault-cluster-f5f44063',
'initialized': False,
'replication_dr_mode': 'disabled',
'replication_performance_mode': 'disabled',
'sealed': False,
'server_time_utc': 1529357080,
'standby': True,
'performance_standby': False,
'version': '0.10.2',
},
status_code=status_code,
)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
aggregator.assert_metric('vault.is_leader', 1)
assert_all_metrics(aggregator)
def test_sys_leader_non_standard_status_codes(self, aggregator, dd_run_check):
instance = INSTANCES['main']
c = Vault(Vault.CHECK_NAME, {}, [instance])
# Keep a reference for use during mock
requests_get = requests.get
def mock_requests_get(url, *args, **kwargs):
if url == instance['api_url'] + '/sys/leader':
return MockResponse(json_data={'errors': ["Vault is sealed"]}, status_code=503)
return requests_get(url, *args, **kwargs)
with mock.patch('requests.get', side_effect=mock_requests_get, autospec=True):
dd_run_check(c)
aggregator.assert_metric('vault.is_leader', count=0)
@auth_required
def test_token_renewal(self, caplog, aggregator, dd_run_check, instance, global_tags):
instance = instance()
instance['token_renewal_wait'] = 1
c = Vault(Vault.CHECK_NAME, {}, [instance])
renew_client_token = c.renew_client_token
dd_run_check(c)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.OK, count=1, tags=global_tags)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.WARNING, count=0)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.CRITICAL, count=0)
assert 'Permission denied, refreshing the client token...' not in caplog.text
c.set_client_token('foo')
c.renew_client_token = lambda: None
aggregator.reset()
dd_run_check(c)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.OK, count=0)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.WARNING, count=1, tags=global_tags)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.CRITICAL, count=0)
assert 'Permission denied, refreshing the client token...' in caplog.text
aggregator.reset()
with pytest.raises(Exception, match='^403 Client Error: Forbidden for url'):
dd_run_check(c, extract_message=True)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.OK, count=0)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.WARNING, count=0)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.CRITICAL, count=1, tags=global_tags)
renew_client_token()
aggregator.reset()
dd_run_check(c)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.OK, count=1, tags=global_tags)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.WARNING, count=0)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.CRITICAL, count=0)
@auth_required
def test_auth_needed_but_no_token(self, aggregator, dd_run_check, instance, global_tags):
instance = instance()
instance['no_token'] = True
c = Vault(Vault.CHECK_NAME, {}, [instance])
with pytest.raises(Exception, match='^400 Client Error: Bad Request for url'):
dd_run_check(c, extract_message=True)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.OK, count=0)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.WARNING, count=0)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.CRITICAL, count=1, tags=global_tags)
@noauth_required
def test_noauth_needed(self, aggregator, dd_run_check, no_token_instance, global_tags):
c = Vault(Vault.CHECK_NAME, {}, [no_token_instance])
dd_run_check(c, extract_message=True)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.OK, count=1, tags=global_tags)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.WARNING, count=0)
aggregator.assert_service_check(Vault.SERVICE_CHECK_CONNECT, status=Vault.CRITICAL, count=0)
def test_route_transform(self, aggregator, no_token_instance, global_tags):
c = Vault(Vault.CHECK_NAME, {}, [no_token_instance])
c.parse_config()
content = (
'# HELP vault_route_create_foobar_ vault_route_create_foobar_\n'
'# TYPE vault_route_create_foobar_ summary\n'
'vault_route_create_foobar_{quantile="0.5"} 1\n'
'vault_route_create_foobar_{quantile="0.9"} 2\n'
'vault_route_create_foobar_{quantile="0.99"} 3\n'
'vault_route_create_foobar__sum 571.073808670044\n'
'vault_route_create_foobar__count 18\n'
'# HELP vault_route_rollback_sys_ vault_route_rollback_sys_\n'
'# TYPE vault_route_rollback_sys_ summary\n'
'vault_route_rollback_sys_{quantile="0.5"} 3\n'
'vault_route_rollback_sys_{quantile="0.9"} 3\n'
'vault_route_rollback_sys_{quantile="0.99"} 4\n'
'vault_route_rollback_sys__sum 3.2827999591827393\n'
'vault_route_rollback_sys__count 1'
)
def iter_lines(**_):
for elt in content.split("\n"):
yield elt
with mock.patch('datadog_checks.base.utils.http.requests') as r:
r.get.return_value = mock.MagicMock(status_code=200, content=content, iter_lines=iter_lines)
c.process(c._scraper_config, c._metric_transformers)
for quantile in [0.5, 0.9, 0.99]:
quantile_tag = 'quantile:{}'.format(quantile)
aggregator.assert_metric('vault.vault.route.rollback.sys.quantile', tags=global_tags + [quantile_tag])
aggregator.assert_metric(
'vault.route.rollback.quantile', tags=global_tags + [quantile_tag, 'mountpoint:sys']
)
aggregator.assert_metric(
'vault.route.rollback.quantile', tags=global_tags + [quantile_tag, 'mountpoint:sys']
)
aggregator.assert_metric(
'vault.route.create.quantile', tags=global_tags + [quantile_tag, 'mountpoint:foobar']
)
aggregator.assert_metric('vault.vault.route.rollback.sys.sum', tags=global_tags)
aggregator.assert_metric('vault.vault.route.rollback.sys.count', tags=global_tags)
aggregator.assert_metric('vault.route.rollback.sum', tags=global_tags + ['mountpoint:sys'])
aggregator.assert_metric('vault.route.rollback.count', tags=global_tags + ['mountpoint:sys'])
aggregator.assert_metric('vault.route.create.sum', tags=global_tags + ['mountpoint:foobar'])
aggregator.assert_metric('vault.route.create.count', tags=global_tags + ['mountpoint:foobar'])
assert_all_metrics(aggregator)
def assert_all_metrics(aggregator):
aggregator.assert_all_metrics_covered()
aggregator.assert_metrics_using_metadata(get_metadata_metrics())
aggregator.assert_no_duplicate_metrics()
| 43.868228 | 120 | 0.59215 | 3,259 | 29,962 | 5.160479 | 0.081006 | 0.056903 | 0.031514 | 0.056606 | 0.848615 | 0.817874 | 0.792603 | 0.758116 | 0.739089 | 0.723332 | 0 | 0.025741 | 0.301148 | 29,962 | 682 | 121 | 43.932551 | 0.777449 | 0.023964 | 0 | 0.655678 | 0 | 0 | 0.179009 | 0.068886 | 0 | 0 | 0 | 0 | 0.144689 | 1 | 0.076923 | false | 0 | 0.018315 | 0 | 0.155678 | 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 |
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