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def process_meta_item(self, item, absolute_path=True, **kwargs):
'Process single meta data item\n\n Parameters\n ----------\n item : MetaDataItem\n Meta data item\n\n absolute_path : bool\n Convert file paths to be absolute\n Default value True\n\n '
if absolute_path:
item.filename = self.relative_to_absolute_path(item.filename)
else:
item.filename = self.absolute_to_relative_path(item.filename)
(raw_path, raw_filename) = os.path.split(item.filename)
item.identifier = os.path.splitext(raw_filename)[0]
item.source_label = 'mixture'
| -3,948,935,948,919,123,000
|
Process single meta data item
Parameters
----------
item : MetaDataItem
Meta data item
absolute_path : bool
Convert file paths to be absolute
Default value True
|
dcase_util/datasets/tut.py
|
process_meta_item
|
ankitshah009/dcase_util
|
python
|
def process_meta_item(self, item, absolute_path=True, **kwargs):
'Process single meta data item\n\n Parameters\n ----------\n item : MetaDataItem\n Meta data item\n\n absolute_path : bool\n Convert file paths to be absolute\n Default value True\n\n '
if absolute_path:
item.filename = self.relative_to_absolute_path(item.filename)
else:
item.filename = self.absolute_to_relative_path(item.filename)
(raw_path, raw_filename) = os.path.split(item.filename)
item.identifier = os.path.splitext(raw_filename)[0]
item.source_label = 'mixture'
|
def prepare(self):
'Prepare dataset for the usage.\n\n Returns\n -------\n self\n\n '
if (not self.meta_container.exists()):
meta_data = MetaDataContainer()
annotation_files = Path().file_list(path=os.path.join(self.local_path, 'meta'), extensions=['ann'])
for annotation_filename in annotation_files:
scene_label = os.path.split(os.path.split(annotation_filename)[0])[1]
identifier = os.path.splitext(os.path.split(annotation_filename)[1])[0]
audio_filename = os.path.join('audio', scene_label, (identifier + '.wav'))
data = MetaDataContainer(filename=annotation_filename).load()
for item in data:
item.filename = audio_filename
item.scene_label = scene_label
self.process_meta_item(item=item, absolute_path=False)
meta_data += data
meta_data.save(filename=self.meta_file)
self.load()
return self
| -7,578,620,383,298,427,000
|
Prepare dataset for the usage.
Returns
-------
self
|
dcase_util/datasets/tut.py
|
prepare
|
ankitshah009/dcase_util
|
python
|
def prepare(self):
'Prepare dataset for the usage.\n\n Returns\n -------\n self\n\n '
if (not self.meta_container.exists()):
meta_data = MetaDataContainer()
annotation_files = Path().file_list(path=os.path.join(self.local_path, 'meta'), extensions=['ann'])
for annotation_filename in annotation_files:
scene_label = os.path.split(os.path.split(annotation_filename)[0])[1]
identifier = os.path.splitext(os.path.split(annotation_filename)[1])[0]
audio_filename = os.path.join('audio', scene_label, (identifier + '.wav'))
data = MetaDataContainer(filename=annotation_filename).load()
for item in data:
item.filename = audio_filename
item.scene_label = scene_label
self.process_meta_item(item=item, absolute_path=False)
meta_data += data
meta_data.save(filename=self.meta_file)
self.load()
return self
|
def __init__(self, storage_name='TUT-sound-events-2016-evaluation', data_path=None, included_content_types=None, **kwargs):
"\n Constructor\n\n Parameters\n ----------\n\n storage_name : str\n Name to be used when storing dataset on disk\n Default value 'TUT-sound-events-2016-evaluation'\n\n data_path : str\n Root path where the dataset is stored. If None, os.path.join(tempfile.gettempdir(), 'dcase_util_datasets')\n is used.\n Default value None\n\n included_content_types : list of str or str\n Indicates what content type should be processed. One or multiple from ['all', 'audio', 'meta', 'code',\n 'documentation']. If None given, ['all'] is used. Parameter can be also comma separated string.\n Default value None\n\n "
kwargs['included_content_types'] = included_content_types
kwargs['data_path'] = data_path
kwargs['storage_name'] = storage_name
kwargs['dataset_group'] = 'event'
kwargs['dataset_meta'] = {'authors': 'Annamaria Mesaros, Toni Heittola, and Tuomas Virtanen', 'title': 'TUT Sound Events 2016, evaluation dataset', 'url': 'http://www.cs.tut.fi/sgn/arg/dcase2016/download/', 'audio_source': 'Field recording', 'audio_type': 'Natural', 'recording_device_model': 'Roland Edirol R-09', 'microphone_model': 'Soundman OKM II Klassik/studio A3 electret microphone', 'licence': 'free non-commercial'}
kwargs['crossvalidation_folds'] = None
source_url = 'https://zenodo.org/record/996424/files/'
kwargs['package_list'] = [{'content_type': 'documentation', 'remote_file': (source_url + 'TUT-sound-events-2016-evaluation.doc.zip'), 'remote_bytes': 69834, 'remote_md5': '0644b54d96f4cefd0ecb2c7ea9161aa9', 'filename': 'TUT-sound-events-2016-evaluation.doc.zip'}, {'content_type': 'meta', 'remote_file': (source_url + 'TUT-sound-events-2016-evaluation.meta.zip'), 'remote_bytes': 41608, 'remote_md5': '91c266b0780ac619a0d74298a3805e9e', 'filename': 'TUT-sound-events-2016-evaluation.meta.zip'}, {'content_type': 'audio', 'remote_file': (source_url + 'TUT-sound-events-2016-evaluation.audio.zip'), 'remote_bytes': 471072452, 'remote_md5': '29434e8c53bd51206df0234e6cf2238c', 'filename': 'TUT-sound-events-2016-evaluation.audio.zip'}]
kwargs['audio_paths'] = [os.path.join('audio', 'home'), os.path.join('audio', 'residential_area')]
super(TUTSoundEvents_2016_EvaluationSet, self).__init__(**kwargs)
| 5,616,250,338,590,877,000
|
Constructor
Parameters
----------
storage_name : str
Name to be used when storing dataset on disk
Default value 'TUT-sound-events-2016-evaluation'
data_path : str
Root path where the dataset is stored. If None, os.path.join(tempfile.gettempdir(), 'dcase_util_datasets')
is used.
Default value None
included_content_types : list of str or str
Indicates what content type should be processed. One or multiple from ['all', 'audio', 'meta', 'code',
'documentation']. If None given, ['all'] is used. Parameter can be also comma separated string.
Default value None
|
dcase_util/datasets/tut.py
|
__init__
|
ankitshah009/dcase_util
|
python
|
def __init__(self, storage_name='TUT-sound-events-2016-evaluation', data_path=None, included_content_types=None, **kwargs):
"\n Constructor\n\n Parameters\n ----------\n\n storage_name : str\n Name to be used when storing dataset on disk\n Default value 'TUT-sound-events-2016-evaluation'\n\n data_path : str\n Root path where the dataset is stored. If None, os.path.join(tempfile.gettempdir(), 'dcase_util_datasets')\n is used.\n Default value None\n\n included_content_types : list of str or str\n Indicates what content type should be processed. One or multiple from ['all', 'audio', 'meta', 'code',\n 'documentation']. If None given, ['all'] is used. Parameter can be also comma separated string.\n Default value None\n\n "
kwargs['included_content_types'] = included_content_types
kwargs['data_path'] = data_path
kwargs['storage_name'] = storage_name
kwargs['dataset_group'] = 'event'
kwargs['dataset_meta'] = {'authors': 'Annamaria Mesaros, Toni Heittola, and Tuomas Virtanen', 'title': 'TUT Sound Events 2016, evaluation dataset', 'url': 'http://www.cs.tut.fi/sgn/arg/dcase2016/download/', 'audio_source': 'Field recording', 'audio_type': 'Natural', 'recording_device_model': 'Roland Edirol R-09', 'microphone_model': 'Soundman OKM II Klassik/studio A3 electret microphone', 'licence': 'free non-commercial'}
kwargs['crossvalidation_folds'] = None
source_url = 'https://zenodo.org/record/996424/files/'
kwargs['package_list'] = [{'content_type': 'documentation', 'remote_file': (source_url + 'TUT-sound-events-2016-evaluation.doc.zip'), 'remote_bytes': 69834, 'remote_md5': '0644b54d96f4cefd0ecb2c7ea9161aa9', 'filename': 'TUT-sound-events-2016-evaluation.doc.zip'}, {'content_type': 'meta', 'remote_file': (source_url + 'TUT-sound-events-2016-evaluation.meta.zip'), 'remote_bytes': 41608, 'remote_md5': '91c266b0780ac619a0d74298a3805e9e', 'filename': 'TUT-sound-events-2016-evaluation.meta.zip'}, {'content_type': 'audio', 'remote_file': (source_url + 'TUT-sound-events-2016-evaluation.audio.zip'), 'remote_bytes': 471072452, 'remote_md5': '29434e8c53bd51206df0234e6cf2238c', 'filename': 'TUT-sound-events-2016-evaluation.audio.zip'}]
kwargs['audio_paths'] = [os.path.join('audio', 'home'), os.path.join('audio', 'residential_area')]
super(TUTSoundEvents_2016_EvaluationSet, self).__init__(**kwargs)
|
def prepare(self):
'Prepare dataset for the usage.\n\n Returns\n -------\n self\n\n '
if ((not self.meta_container.exists()) and os.path.isdir(os.path.join(self.local_path, 'meta'))):
meta_data = MetaDataContainer()
annotation_files = Path().file_list(path=os.path.join(self.local_path, 'meta'), extensions=['ann'])
for annotation_filename in annotation_files:
scene_label = os.path.split(os.path.split(annotation_filename)[0])[1]
identifier = os.path.splitext(os.path.split(annotation_filename)[1])[0]
audio_filename = os.path.join('audio', scene_label, (identifier + '.wav'))
data = MetaDataContainer(filename=annotation_filename).load(decimal='comma')
for item in data:
item.filename = audio_filename
item.scene_label = scene_label
self.process_meta_item(item=item, absolute_path=False)
meta_data += data
meta_data.save(filename=self.meta_file)
self.load()
return self
| 1,593,265,729,418,320,000
|
Prepare dataset for the usage.
Returns
-------
self
|
dcase_util/datasets/tut.py
|
prepare
|
ankitshah009/dcase_util
|
python
|
def prepare(self):
'Prepare dataset for the usage.\n\n Returns\n -------\n self\n\n '
if ((not self.meta_container.exists()) and os.path.isdir(os.path.join(self.local_path, 'meta'))):
meta_data = MetaDataContainer()
annotation_files = Path().file_list(path=os.path.join(self.local_path, 'meta'), extensions=['ann'])
for annotation_filename in annotation_files:
scene_label = os.path.split(os.path.split(annotation_filename)[0])[1]
identifier = os.path.splitext(os.path.split(annotation_filename)[1])[0]
audio_filename = os.path.join('audio', scene_label, (identifier + '.wav'))
data = MetaDataContainer(filename=annotation_filename).load(decimal='comma')
for item in data:
item.filename = audio_filename
item.scene_label = scene_label
self.process_meta_item(item=item, absolute_path=False)
meta_data += data
meta_data.save(filename=self.meta_file)
self.load()
return self
|
def __init__(self, storage_name='TUT-SED-synthetic-2016', data_path=None, included_content_types=None, **kwargs):
"\n Constructor\n\n Parameters\n ----------\n\n storage_name : str\n Name to be used when storing dataset on disk\n Default value 'TUT-SED-synthetic-2016'\n\n data_path : str\n Root path where the dataset is stored. If None, os.path.join(tempfile.gettempdir(), 'dcase_util_datasets')\n is used.\n Default value None\n\n included_content_types : list of str or str\n Indicates what content type should be processed. One or multiple from ['all', 'audio', 'meta', 'code',\n 'documentation']. If None given, ['all'] is used. Parameter can be also comma separated string.\n Default value None\n\n "
kwargs['included_content_types'] = included_content_types
kwargs['data_path'] = data_path
kwargs['storage_name'] = storage_name
kwargs['dataset_group'] = 'event'
kwargs['dataset_meta'] = {'authors': 'Emre Cakir', 'title': 'TUT-SED Synthetic 2016', 'url': 'http://www.cs.tut.fi/sgn/arg/taslp2017-crnn-sed/tut-sed-synthetic-2016', 'audio_source': 'Field recording', 'audio_type': 'Synthetic', 'recording_device_model': 'Unknown', 'microphone_model': 'Unknown'}
kwargs['crossvalidation_folds'] = 1
source_url = 'http://www.cs.tut.fi/sgn/arg/taslp2017-crnn-sed/datasets/TUT-SED-synthetic-2016/'
kwargs['package_list'] = [{'content_type': 'meta', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.meta.zip'), 'remote_bytes': 973618, 'remote_md5': 'e2ae895bdf39f2a359a97bb0bcf76101', 'filename': 'TUT-SED-synthetic-2016.meta.zip'}, {'content_type': 'audio', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.audio.1.zip'), 'remote_bytes': 1026369647, 'remote_md5': 'ede8b9c6d1b0d1d64bfc5791404f58fb', 'filename': 'TUT-SED-synthetic-2016.audio.1.zip'}, {'content_type': 'audio', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.audio.2.zip'), 'remote_bytes': 1018650039, 'remote_md5': 'cde647a377a58fc74e3012139d65c447', 'filename': 'TUT-SED-synthetic-2016.audio.2.zip'}, {'content_type': 'audio', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.audio.3.zip'), 'remote_bytes': 1070239392, 'remote_md5': '5fc2824dcce442f441f4c6a975881789', 'filename': 'TUT-SED-synthetic-2016.audio.3.zip'}, {'content_type': 'audio', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.audio.4.zip'), 'remote_bytes': 1040622610, 'remote_md5': '4ba016d949171ccc8493d3d274009825', 'filename': 'TUT-SED-synthetic-2016.audio.4.zip'}, {'content_type': 'audio', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.audio.5.zip'), 'remote_bytes': 264812997, 'remote_md5': '6a44578dd7738bd4ba044d5d2b9a5448', 'filename': 'TUT-SED-synthetic-2016.audio.5.zip'}, {'content_type': 'features', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.features.zip'), 'remote_bytes': 480894082, 'remote_md5': '66bc0abc19a276986964a6d4a2d2f6bc', 'filename': 'TUT-SED-synthetic-2016.features.zip'}]
kwargs['audio_paths'] = ['audio']
super(TUT_SED_Synthetic_2016, self).__init__(**kwargs)
| -5,004,241,706,890,039,000
|
Constructor
Parameters
----------
storage_name : str
Name to be used when storing dataset on disk
Default value 'TUT-SED-synthetic-2016'
data_path : str
Root path where the dataset is stored. If None, os.path.join(tempfile.gettempdir(), 'dcase_util_datasets')
is used.
Default value None
included_content_types : list of str or str
Indicates what content type should be processed. One or multiple from ['all', 'audio', 'meta', 'code',
'documentation']. If None given, ['all'] is used. Parameter can be also comma separated string.
Default value None
|
dcase_util/datasets/tut.py
|
__init__
|
ankitshah009/dcase_util
|
python
|
def __init__(self, storage_name='TUT-SED-synthetic-2016', data_path=None, included_content_types=None, **kwargs):
"\n Constructor\n\n Parameters\n ----------\n\n storage_name : str\n Name to be used when storing dataset on disk\n Default value 'TUT-SED-synthetic-2016'\n\n data_path : str\n Root path where the dataset is stored. If None, os.path.join(tempfile.gettempdir(), 'dcase_util_datasets')\n is used.\n Default value None\n\n included_content_types : list of str or str\n Indicates what content type should be processed. One or multiple from ['all', 'audio', 'meta', 'code',\n 'documentation']. If None given, ['all'] is used. Parameter can be also comma separated string.\n Default value None\n\n "
kwargs['included_content_types'] = included_content_types
kwargs['data_path'] = data_path
kwargs['storage_name'] = storage_name
kwargs['dataset_group'] = 'event'
kwargs['dataset_meta'] = {'authors': 'Emre Cakir', 'title': 'TUT-SED Synthetic 2016', 'url': 'http://www.cs.tut.fi/sgn/arg/taslp2017-crnn-sed/tut-sed-synthetic-2016', 'audio_source': 'Field recording', 'audio_type': 'Synthetic', 'recording_device_model': 'Unknown', 'microphone_model': 'Unknown'}
kwargs['crossvalidation_folds'] = 1
source_url = 'http://www.cs.tut.fi/sgn/arg/taslp2017-crnn-sed/datasets/TUT-SED-synthetic-2016/'
kwargs['package_list'] = [{'content_type': 'meta', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.meta.zip'), 'remote_bytes': 973618, 'remote_md5': 'e2ae895bdf39f2a359a97bb0bcf76101', 'filename': 'TUT-SED-synthetic-2016.meta.zip'}, {'content_type': 'audio', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.audio.1.zip'), 'remote_bytes': 1026369647, 'remote_md5': 'ede8b9c6d1b0d1d64bfc5791404f58fb', 'filename': 'TUT-SED-synthetic-2016.audio.1.zip'}, {'content_type': 'audio', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.audio.2.zip'), 'remote_bytes': 1018650039, 'remote_md5': 'cde647a377a58fc74e3012139d65c447', 'filename': 'TUT-SED-synthetic-2016.audio.2.zip'}, {'content_type': 'audio', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.audio.3.zip'), 'remote_bytes': 1070239392, 'remote_md5': '5fc2824dcce442f441f4c6a975881789', 'filename': 'TUT-SED-synthetic-2016.audio.3.zip'}, {'content_type': 'audio', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.audio.4.zip'), 'remote_bytes': 1040622610, 'remote_md5': '4ba016d949171ccc8493d3d274009825', 'filename': 'TUT-SED-synthetic-2016.audio.4.zip'}, {'content_type': 'audio', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.audio.5.zip'), 'remote_bytes': 264812997, 'remote_md5': '6a44578dd7738bd4ba044d5d2b9a5448', 'filename': 'TUT-SED-synthetic-2016.audio.5.zip'}, {'content_type': 'features', 'remote_file': (source_url + 'TUT-SED-synthetic-2016.features.zip'), 'remote_bytes': 480894082, 'remote_md5': '66bc0abc19a276986964a6d4a2d2f6bc', 'filename': 'TUT-SED-synthetic-2016.features.zip'}]
kwargs['audio_paths'] = ['audio']
super(TUT_SED_Synthetic_2016, self).__init__(**kwargs)
|
def prepare(self):
'Prepare dataset for the usage.\n\n Returns\n -------\n self\n\n '
if (not self.meta_container.exists()):
meta_files = Path().file_list(path=os.path.join(self.local_path, 'meta'), extensions=['txt'])
meta_data = MetaDataContainer()
for meta_filename in meta_files:
audio_filename = os.path.join('audio', os.path.split(meta_filename)[1].replace('.txt', '.wav'))
data = MetaDataContainer(filename=meta_filename).load()
for item in data:
item.filename = audio_filename
item.scene_label = 'synthetic'
item.source_label = 'm'
self.process_meta_item(item=item, absolute_path=False)
meta_data += data
meta_data.save(filename=self.meta_file)
self.load()
return self
| -6,557,052,325,882,180,000
|
Prepare dataset for the usage.
Returns
-------
self
|
dcase_util/datasets/tut.py
|
prepare
|
ankitshah009/dcase_util
|
python
|
def prepare(self):
'Prepare dataset for the usage.\n\n Returns\n -------\n self\n\n '
if (not self.meta_container.exists()):
meta_files = Path().file_list(path=os.path.join(self.local_path, 'meta'), extensions=['txt'])
meta_data = MetaDataContainer()
for meta_filename in meta_files:
audio_filename = os.path.join('audio', os.path.split(meta_filename)[1].replace('.txt', '.wav'))
data = MetaDataContainer(filename=meta_filename).load()
for item in data:
item.filename = audio_filename
item.scene_label = 'synthetic'
item.source_label = 'm'
self.process_meta_item(item=item, absolute_path=False)
meta_data += data
meta_data.save(filename=self.meta_file)
self.load()
return self
|
def file_features(self, filename):
'Pre-calculated acoustic features for given file\n\n Parameters\n ----------\n filename : str\n File name\n\n Returns\n -------\n data : numpy.ndarray\n Matrix containing acoustic features\n\n '
filename_ = self.absolute_to_relative_path(filename).replace('audio/', 'features/')
filename_ = (os.path.splitext(filename_)[0] + '.cpickle')
if os.path.isfile(os.path.join(self.local_path, filename_)):
feature_data = pickle.load(open(os.path.join(self.local_path, filename_), 'rb'))
return feature_data['feat']
else:
return None
| 428,636,534,373,538,800
|
Pre-calculated acoustic features for given file
Parameters
----------
filename : str
File name
Returns
-------
data : numpy.ndarray
Matrix containing acoustic features
|
dcase_util/datasets/tut.py
|
file_features
|
ankitshah009/dcase_util
|
python
|
def file_features(self, filename):
'Pre-calculated acoustic features for given file\n\n Parameters\n ----------\n filename : str\n File name\n\n Returns\n -------\n data : numpy.ndarray\n Matrix containing acoustic features\n\n '
filename_ = self.absolute_to_relative_path(filename).replace('audio/', 'features/')
filename_ = (os.path.splitext(filename_)[0] + '.cpickle')
if os.path.isfile(os.path.join(self.local_path, filename_)):
feature_data = pickle.load(open(os.path.join(self.local_path, filename_), 'rb'))
return feature_data['feat']
else:
return None
|
def _reset(self, new_min_lr=None, new_max_lr=None, new_base_epochs=None, new_mul_epochs=None):
'Resets cycle iterations.'
if (new_min_lr != None):
self.min_lr = new_min_lr
if (new_max_lr != None):
self.max_lr = new_max_lr
if (new_base_epochs != None):
self.base_epochs = new_base_epochs
if (new_mul_epochs != None):
self.mul_epochs = new_mul_epochs
self.cycles = 0.0
self.cycle_iterations = 0.0
| -8,164,284,948,579,837,000
|
Resets cycle iterations.
|
sgdr_callback.py
|
_reset
|
Callidior/semantic-embeddings
|
python
|
def _reset(self, new_min_lr=None, new_max_lr=None, new_base_epochs=None, new_mul_epochs=None):
if (new_min_lr != None):
self.min_lr = new_min_lr
if (new_max_lr != None):
self.max_lr = new_max_lr
if (new_base_epochs != None):
self.base_epochs = new_base_epochs
if (new_mul_epochs != None):
self.mul_epochs = new_mul_epochs
self.cycles = 0.0
self.cycle_iterations = 0.0
|
@pytest.fixture(scope='session')
def selenium_patcher():
'Add custom .'
add_custom_commands()
| 1,832,878,463,908,379,400
|
Add custom .
|
pytest_selenium_enhancer/plugin.py
|
selenium_patcher
|
popescunsergiu/pytest-selenium-enhancer
|
python
|
@pytest.fixture(scope='session')
def selenium_patcher():
add_custom_commands()
|
def _check_cache_entry(self, entry):
'Assert validity of the cache entry.'
self.assertIsInstance(entry.site, BaseSite)
self.assertIsInstance(entry.site._loginstatus, int)
self.assertIsInstance(entry.site._username, list)
if (entry.site._loginstatus >= 1):
self.assertIsNotNone(entry.site._username[0])
self.assertIsInstance(entry._params, dict)
self.assertIsNotNone(entry._params)
| -5,294,106,942,538,693,000
|
Assert validity of the cache entry.
|
tests/cache_tests.py
|
_check_cache_entry
|
Annie201/pywikibot-core
|
python
|
def _check_cache_entry(self, entry):
self.assertIsInstance(entry.site, BaseSite)
self.assertIsInstance(entry.site._loginstatus, int)
self.assertIsInstance(entry.site._username, list)
if (entry.site._loginstatus >= 1):
self.assertIsNotNone(entry.site._username[0])
self.assertIsInstance(entry._params, dict)
self.assertIsNotNone(entry._params)
|
def test_cache(self):
'Test the apicache by doing _check_cache_entry over each entry.'
cache.process_entries(_cache_dir, self._check_cache_entry)
| -1,055,730,274,172,869,800
|
Test the apicache by doing _check_cache_entry over each entry.
|
tests/cache_tests.py
|
test_cache
|
Annie201/pywikibot-core
|
python
|
def test_cache(self):
cache.process_entries(_cache_dir, self._check_cache_entry)
|
def setup_argparse_only():
'Wrapper for ``setup_argparse()`` that only returns the parser.\n\n Only used in sphinx documentation via ``sphinx-argparse``.\n '
return setup_argparse()[0]
| 1,517,469,603,038,058,800
|
Wrapper for ``setup_argparse()`` that only returns the parser.
Only used in sphinx documentation via ``sphinx-argparse``.
|
varfish_cli/__main__.py
|
setup_argparse_only
|
bihealth/varfish-cli
|
python
|
def setup_argparse_only():
'Wrapper for ``setup_argparse()`` that only returns the parser.\n\n Only used in sphinx documentation via ``sphinx-argparse``.\n '
return setup_argparse()[0]
|
def setup_argparse():
'Create argument parser.'
parser = argparse.ArgumentParser(prog='varfish-cli')
parser.add_argument('--verbose', action='store_true', default=False, help='Increase verbosity.')
parser.add_argument('--version', action='version', version=('%%(prog)s %s' % __version__))
group = parser.add_argument_group('Basic Configuration')
group.add_argument('--no-verify-ssl', dest='verify_ssl', default=True, action='store_false', help='Disable HTTPS SSL verification')
group.add_argument('--config', default=os.environ.get('VARFISH_CONFIG_PATH', None), help='Path to configuration file.')
group.add_argument('--varfish-server-url', default=os.environ.get('VARFISH_SERVER_URL', None), help='VarFish server URL key to use, defaults to env VARFISH_SERVER_URL.')
group.add_argument('--varfish-api-token', default=os.environ.get('VARFISH_API_TOKEN', None), help='VarFish API token to use, defaults to env VARFISH_API_TOKEN.')
subparsers = parser.add_subparsers(dest='cmd')
setup_argparse_case(subparsers.add_parser('case', help='Work with cases.'))
return (parser, subparsers)
| -638,400,639,551,440,800
|
Create argument parser.
|
varfish_cli/__main__.py
|
setup_argparse
|
bihealth/varfish-cli
|
python
|
def setup_argparse():
parser = argparse.ArgumentParser(prog='varfish-cli')
parser.add_argument('--verbose', action='store_true', default=False, help='Increase verbosity.')
parser.add_argument('--version', action='version', version=('%%(prog)s %s' % __version__))
group = parser.add_argument_group('Basic Configuration')
group.add_argument('--no-verify-ssl', dest='verify_ssl', default=True, action='store_false', help='Disable HTTPS SSL verification')
group.add_argument('--config', default=os.environ.get('VARFISH_CONFIG_PATH', None), help='Path to configuration file.')
group.add_argument('--varfish-server-url', default=os.environ.get('VARFISH_SERVER_URL', None), help='VarFish server URL key to use, defaults to env VARFISH_SERVER_URL.')
group.add_argument('--varfish-api-token', default=os.environ.get('VARFISH_API_TOKEN', None), help='VarFish API token to use, defaults to env VARFISH_API_TOKEN.')
subparsers = parser.add_subparsers(dest='cmd')
setup_argparse_case(subparsers.add_parser('case', help='Work with cases.'))
return (parser, subparsers)
|
def main(argv=None):
'Main entry point before parsing command line arguments.'
(parser, subparsers) = setup_argparse()
args = parser.parse_args(argv)
if args.verbose:
level = logging.DEBUG
else:
formatter = logzero.LogFormatter(fmt='%(color)s[%(levelname)1.1s %(asctime)s]%(end_color)s %(message)s')
logzero.formatter(formatter)
level = logging.INFO
logzero.loglevel(level=level)
if args.config:
config_paths = (args.config,)
else:
config_paths = GLOBAL_CONFIG_PATHS
for config_path in config_paths:
config_path = os.path.expanduser(os.path.expandvars(config_path))
if os.path.exists(config_path):
with open(config_path, 'rt') as tomlf:
toml_config = toml.load(tomlf)
break
else:
toml_config = None
logger.info('Could not find any of the global configuration files %s.', config_paths)
config = CommonConfig.create(args, toml_config)
cmds = {None: run_nocmd, 'case': run_case}
res = cmds[args.cmd](config, toml_config, args, parser, (subparsers.choices[args.cmd] if args.cmd else None))
if (not res):
logger.info('All done. Have a nice day!')
else:
logger.error('Something did not work out correctly.')
return res
| 3,123,301,595,510,910,000
|
Main entry point before parsing command line arguments.
|
varfish_cli/__main__.py
|
main
|
bihealth/varfish-cli
|
python
|
def main(argv=None):
(parser, subparsers) = setup_argparse()
args = parser.parse_args(argv)
if args.verbose:
level = logging.DEBUG
else:
formatter = logzero.LogFormatter(fmt='%(color)s[%(levelname)1.1s %(asctime)s]%(end_color)s %(message)s')
logzero.formatter(formatter)
level = logging.INFO
logzero.loglevel(level=level)
if args.config:
config_paths = (args.config,)
else:
config_paths = GLOBAL_CONFIG_PATHS
for config_path in config_paths:
config_path = os.path.expanduser(os.path.expandvars(config_path))
if os.path.exists(config_path):
with open(config_path, 'rt') as tomlf:
toml_config = toml.load(tomlf)
break
else:
toml_config = None
logger.info('Could not find any of the global configuration files %s.', config_paths)
config = CommonConfig.create(args, toml_config)
cmds = {None: run_nocmd, 'case': run_case}
res = cmds[args.cmd](config, toml_config, args, parser, (subparsers.choices[args.cmd] if args.cmd else None))
if (not res):
logger.info('All done. Have a nice day!')
else:
logger.error('Something did not work out correctly.')
return res
|
def get_train_hooks(name_list, use_tpu=False, **kwargs):
'Factory for getting a list of TensorFlow hooks for training by name.\n\n Args:\n name_list: a list of strings to name desired hook classes. Allowed:\n LoggingTensorHook, ProfilerHook, ExamplesPerSecondHook, which are defined\n as keys in HOOKS\n use_tpu: Boolean of whether computation occurs on a TPU. This will disable\n hooks altogether.\n **kwargs: a dictionary of arguments to the hooks.\n\n Returns:\n list of instantiated hooks, ready to be used in a classifier.train call.\n\n Raises:\n ValueError: if an unrecognized name is passed.\n '
if (not name_list):
return []
if use_tpu:
tf.logging.warning('hooks_helper received name_list `{}`, but a TPU is specified. No hooks will be used.'.format(name_list))
return []
train_hooks = []
for name in name_list:
hook_name = HOOKS.get(name.strip().lower())
if (hook_name is None):
raise ValueError('Unrecognized training hook requested: {}'.format(name))
else:
train_hooks.append(hook_name(**kwargs))
return train_hooks
| 8,321,067,302,129,089,000
|
Factory for getting a list of TensorFlow hooks for training by name.
Args:
name_list: a list of strings to name desired hook classes. Allowed:
LoggingTensorHook, ProfilerHook, ExamplesPerSecondHook, which are defined
as keys in HOOKS
use_tpu: Boolean of whether computation occurs on a TPU. This will disable
hooks altogether.
**kwargs: a dictionary of arguments to the hooks.
Returns:
list of instantiated hooks, ready to be used in a classifier.train call.
Raises:
ValueError: if an unrecognized name is passed.
|
official/utils/logs/hooks_helper.py
|
get_train_hooks
|
Mithilesh1609/assembled-cnn
|
python
|
def get_train_hooks(name_list, use_tpu=False, **kwargs):
'Factory for getting a list of TensorFlow hooks for training by name.\n\n Args:\n name_list: a list of strings to name desired hook classes. Allowed:\n LoggingTensorHook, ProfilerHook, ExamplesPerSecondHook, which are defined\n as keys in HOOKS\n use_tpu: Boolean of whether computation occurs on a TPU. This will disable\n hooks altogether.\n **kwargs: a dictionary of arguments to the hooks.\n\n Returns:\n list of instantiated hooks, ready to be used in a classifier.train call.\n\n Raises:\n ValueError: if an unrecognized name is passed.\n '
if (not name_list):
return []
if use_tpu:
tf.logging.warning('hooks_helper received name_list `{}`, but a TPU is specified. No hooks will be used.'.format(name_list))
return []
train_hooks = []
for name in name_list:
hook_name = HOOKS.get(name.strip().lower())
if (hook_name is None):
raise ValueError('Unrecognized training hook requested: {}'.format(name))
else:
train_hooks.append(hook_name(**kwargs))
return train_hooks
|
def get_logging_tensor_hook(every_n_iter=100, tensors_to_log=None, **kwargs):
'Function to get LoggingTensorHook.\n\n Args:\n every_n_iter: `int`, print the values of `tensors` once every N local\n steps taken on the current worker.\n tensors_to_log: List of tensor names or dictionary mapping labels to tensor\n names. If not set, log _TENSORS_TO_LOG by default.\n **kwargs: a dictionary of arguments to LoggingTensorHook.\n\n Returns:\n Returns a LoggingTensorHook with a standard set of tensors that will be\n printed to stdout.\n '
if (tensors_to_log is None):
tensors_to_log = _TENSORS_TO_LOG
return tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=every_n_iter)
| -2,488,998,155,569,033,700
|
Function to get LoggingTensorHook.
Args:
every_n_iter: `int`, print the values of `tensors` once every N local
steps taken on the current worker.
tensors_to_log: List of tensor names or dictionary mapping labels to tensor
names. If not set, log _TENSORS_TO_LOG by default.
**kwargs: a dictionary of arguments to LoggingTensorHook.
Returns:
Returns a LoggingTensorHook with a standard set of tensors that will be
printed to stdout.
|
official/utils/logs/hooks_helper.py
|
get_logging_tensor_hook
|
Mithilesh1609/assembled-cnn
|
python
|
def get_logging_tensor_hook(every_n_iter=100, tensors_to_log=None, **kwargs):
'Function to get LoggingTensorHook.\n\n Args:\n every_n_iter: `int`, print the values of `tensors` once every N local\n steps taken on the current worker.\n tensors_to_log: List of tensor names or dictionary mapping labels to tensor\n names. If not set, log _TENSORS_TO_LOG by default.\n **kwargs: a dictionary of arguments to LoggingTensorHook.\n\n Returns:\n Returns a LoggingTensorHook with a standard set of tensors that will be\n printed to stdout.\n '
if (tensors_to_log is None):
tensors_to_log = _TENSORS_TO_LOG
return tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=every_n_iter)
|
def get_profiler_hook(model_dir, save_steps=1000, **kwargs):
'Function to get ProfilerHook.\n\n Args:\n model_dir: The directory to save the profile traces to.\n save_steps: `int`, print profile traces every N steps.\n **kwargs: a dictionary of arguments to ProfilerHook.\n\n Returns:\n Returns a ProfilerHook that writes out timelines that can be loaded into\n profiling tools like chrome://tracing.\n '
return tf.train.ProfilerHook(save_steps=save_steps, output_dir=model_dir)
| -3,053,697,511,662,991,400
|
Function to get ProfilerHook.
Args:
model_dir: The directory to save the profile traces to.
save_steps: `int`, print profile traces every N steps.
**kwargs: a dictionary of arguments to ProfilerHook.
Returns:
Returns a ProfilerHook that writes out timelines that can be loaded into
profiling tools like chrome://tracing.
|
official/utils/logs/hooks_helper.py
|
get_profiler_hook
|
Mithilesh1609/assembled-cnn
|
python
|
def get_profiler_hook(model_dir, save_steps=1000, **kwargs):
'Function to get ProfilerHook.\n\n Args:\n model_dir: The directory to save the profile traces to.\n save_steps: `int`, print profile traces every N steps.\n **kwargs: a dictionary of arguments to ProfilerHook.\n\n Returns:\n Returns a ProfilerHook that writes out timelines that can be loaded into\n profiling tools like chrome://tracing.\n '
return tf.train.ProfilerHook(save_steps=save_steps, output_dir=model_dir)
|
def get_examples_per_second_hook(every_n_steps=100, batch_size=128, warm_steps=5, **kwargs):
'Function to get ExamplesPerSecondHook.\n\n Args:\n every_n_steps: `int`, print current and average examples per second every\n N steps.\n batch_size: `int`, total batch size used to calculate examples/second from\n global time.\n warm_steps: skip this number of steps before logging and running average.\n **kwargs: a dictionary of arguments to ExamplesPerSecondHook.\n\n Returns:\n Returns a ProfilerHook that writes out timelines that can be loaded into\n profiling tools like chrome://tracing.\n '
return hooks.ExamplesPerSecondHook(batch_size=batch_size, every_n_steps=every_n_steps, warm_steps=warm_steps, metric_logger=logger.get_benchmark_logger())
| 683,162,254,790,537,200
|
Function to get ExamplesPerSecondHook.
Args:
every_n_steps: `int`, print current and average examples per second every
N steps.
batch_size: `int`, total batch size used to calculate examples/second from
global time.
warm_steps: skip this number of steps before logging and running average.
**kwargs: a dictionary of arguments to ExamplesPerSecondHook.
Returns:
Returns a ProfilerHook that writes out timelines that can be loaded into
profiling tools like chrome://tracing.
|
official/utils/logs/hooks_helper.py
|
get_examples_per_second_hook
|
Mithilesh1609/assembled-cnn
|
python
|
def get_examples_per_second_hook(every_n_steps=100, batch_size=128, warm_steps=5, **kwargs):
'Function to get ExamplesPerSecondHook.\n\n Args:\n every_n_steps: `int`, print current and average examples per second every\n N steps.\n batch_size: `int`, total batch size used to calculate examples/second from\n global time.\n warm_steps: skip this number of steps before logging and running average.\n **kwargs: a dictionary of arguments to ExamplesPerSecondHook.\n\n Returns:\n Returns a ProfilerHook that writes out timelines that can be loaded into\n profiling tools like chrome://tracing.\n '
return hooks.ExamplesPerSecondHook(batch_size=batch_size, every_n_steps=every_n_steps, warm_steps=warm_steps, metric_logger=logger.get_benchmark_logger())
|
def get_logging_metric_hook(tensors_to_log=None, every_n_secs=600, **kwargs):
'Function to get LoggingMetricHook.\n\n Args:\n tensors_to_log: List of tensor names or dictionary mapping labels to tensor\n names. If not set, log _TENSORS_TO_LOG by default.\n every_n_secs: `int`, the frequency for logging the metric. Default to every\n 10 mins.\n\n Returns:\n Returns a LoggingMetricHook that saves tensor values in a JSON format.\n '
if (tensors_to_log is None):
tensors_to_log = _TENSORS_TO_LOG
return metric_hook.LoggingMetricHook(tensors=tensors_to_log, metric_logger=logger.get_benchmark_logger(), every_n_secs=every_n_secs)
| -5,095,878,965,021,634,000
|
Function to get LoggingMetricHook.
Args:
tensors_to_log: List of tensor names or dictionary mapping labels to tensor
names. If not set, log _TENSORS_TO_LOG by default.
every_n_secs: `int`, the frequency for logging the metric. Default to every
10 mins.
Returns:
Returns a LoggingMetricHook that saves tensor values in a JSON format.
|
official/utils/logs/hooks_helper.py
|
get_logging_metric_hook
|
Mithilesh1609/assembled-cnn
|
python
|
def get_logging_metric_hook(tensors_to_log=None, every_n_secs=600, **kwargs):
'Function to get LoggingMetricHook.\n\n Args:\n tensors_to_log: List of tensor names or dictionary mapping labels to tensor\n names. If not set, log _TENSORS_TO_LOG by default.\n every_n_secs: `int`, the frequency for logging the metric. Default to every\n 10 mins.\n\n Returns:\n Returns a LoggingMetricHook that saves tensor values in a JSON format.\n '
if (tensors_to_log is None):
tensors_to_log = _TENSORS_TO_LOG
return metric_hook.LoggingMetricHook(tensors=tensors_to_log, metric_logger=logger.get_benchmark_logger(), every_n_secs=every_n_secs)
|
@property
def _constructor(self):
"\n Class constructor (for this class it's just `__class__`.\n "
return type(self)
| 1,696,908,129,312,269,800
|
Class constructor (for this class it's just `__class__`.
|
pandas/core/base.py
|
_constructor
|
BryanRacic/pandas
|
python
|
@property
def _constructor(self):
"\n \n "
return type(self)
|
def __repr__(self) -> str:
'\n Return a string representation for a particular object.\n '
return object.__repr__(self)
| 3,016,805,634,138,606,600
|
Return a string representation for a particular object.
|
pandas/core/base.py
|
__repr__
|
BryanRacic/pandas
|
python
|
def __repr__(self) -> str:
'\n \n '
return object.__repr__(self)
|
def _reset_cache(self, key: (str | None)=None) -> None:
'\n Reset cached properties. If ``key`` is passed, only clears that key.\n '
if (not hasattr(self, '_cache')):
return
if (key is None):
self._cache.clear()
else:
self._cache.pop(key, None)
| 8,692,063,033,057,934,000
|
Reset cached properties. If ``key`` is passed, only clears that key.
|
pandas/core/base.py
|
_reset_cache
|
BryanRacic/pandas
|
python
|
def _reset_cache(self, key: (str | None)=None) -> None:
'\n \n '
if (not hasattr(self, '_cache')):
return
if (key is None):
self._cache.clear()
else:
self._cache.pop(key, None)
|
def __sizeof__(self) -> int:
'\n Generates the total memory usage for an object that returns\n either a value or Series of values\n '
memory_usage = getattr(self, 'memory_usage', None)
if memory_usage:
mem = memory_usage(deep=True)
return int((mem if is_scalar(mem) else mem.sum()))
return super().__sizeof__()
| -6,205,035,213,557,224,000
|
Generates the total memory usage for an object that returns
either a value or Series of values
|
pandas/core/base.py
|
__sizeof__
|
BryanRacic/pandas
|
python
|
def __sizeof__(self) -> int:
'\n Generates the total memory usage for an object that returns\n either a value or Series of values\n '
memory_usage = getattr(self, 'memory_usage', None)
if memory_usage:
mem = memory_usage(deep=True)
return int((mem if is_scalar(mem) else mem.sum()))
return super().__sizeof__()
|
def _freeze(self):
'\n Prevents setting additional attributes.\n '
object.__setattr__(self, '__frozen', True)
| -7,029,115,408,159,700,000
|
Prevents setting additional attributes.
|
pandas/core/base.py
|
_freeze
|
BryanRacic/pandas
|
python
|
def _freeze(self):
'\n \n '
object.__setattr__(self, '__frozen', True)
|
def _gotitem(self, key, ndim: int, subset=None):
'\n sub-classes to define\n return a sliced object\n\n Parameters\n ----------\n key : str / list of selections\n ndim : {1, 2}\n requested ndim of result\n subset : object, default None\n subset to act on\n '
raise AbstractMethodError(self)
| -4,390,008,202,310,129,000
|
sub-classes to define
return a sliced object
Parameters
----------
key : str / list of selections
ndim : {1, 2}
requested ndim of result
subset : object, default None
subset to act on
|
pandas/core/base.py
|
_gotitem
|
BryanRacic/pandas
|
python
|
def _gotitem(self, key, ndim: int, subset=None):
'\n sub-classes to define\n return a sliced object\n\n Parameters\n ----------\n key : str / list of selections\n ndim : {1, 2}\n requested ndim of result\n subset : object, default None\n subset to act on\n '
raise AbstractMethodError(self)
|
def transpose(self: _T, *args, **kwargs) -> _T:
'\n Return the transpose, which is by definition self.\n\n Returns\n -------\n %(klass)s\n '
nv.validate_transpose(args, kwargs)
return self
| -278,207,969,909,020,400
|
Return the transpose, which is by definition self.
Returns
-------
%(klass)s
|
pandas/core/base.py
|
transpose
|
BryanRacic/pandas
|
python
|
def transpose(self: _T, *args, **kwargs) -> _T:
'\n Return the transpose, which is by definition self.\n\n Returns\n -------\n %(klass)s\n '
nv.validate_transpose(args, kwargs)
return self
|
@property
def shape(self) -> Shape:
'\n Return a tuple of the shape of the underlying data.\n '
return self._values.shape
| -5,094,044,862,570,053,000
|
Return a tuple of the shape of the underlying data.
|
pandas/core/base.py
|
shape
|
BryanRacic/pandas
|
python
|
@property
def shape(self) -> Shape:
'\n \n '
return self._values.shape
|
@property
def ndim(self) -> int:
'\n Number of dimensions of the underlying data, by definition 1.\n '
return 1
| -6,934,603,568,630,411,000
|
Number of dimensions of the underlying data, by definition 1.
|
pandas/core/base.py
|
ndim
|
BryanRacic/pandas
|
python
|
@property
def ndim(self) -> int:
'\n \n '
return 1
|
def item(self):
'\n Return the first element of the underlying data as a Python scalar.\n\n Returns\n -------\n scalar\n The first element of %(klass)s.\n\n Raises\n ------\n ValueError\n If the data is not length-1.\n '
if (len(self) == 1):
return next(iter(self))
raise ValueError('can only convert an array of size 1 to a Python scalar')
| 7,207,348,110,767,913,000
|
Return the first element of the underlying data as a Python scalar.
Returns
-------
scalar
The first element of %(klass)s.
Raises
------
ValueError
If the data is not length-1.
|
pandas/core/base.py
|
item
|
BryanRacic/pandas
|
python
|
def item(self):
'\n Return the first element of the underlying data as a Python scalar.\n\n Returns\n -------\n scalar\n The first element of %(klass)s.\n\n Raises\n ------\n ValueError\n If the data is not length-1.\n '
if (len(self) == 1):
return next(iter(self))
raise ValueError('can only convert an array of size 1 to a Python scalar')
|
@property
def nbytes(self) -> int:
'\n Return the number of bytes in the underlying data.\n '
return self._values.nbytes
| -8,601,156,375,750,126,000
|
Return the number of bytes in the underlying data.
|
pandas/core/base.py
|
nbytes
|
BryanRacic/pandas
|
python
|
@property
def nbytes(self) -> int:
'\n \n '
return self._values.nbytes
|
@property
def size(self) -> int:
'\n Return the number of elements in the underlying data.\n '
return len(self._values)
| 5,262,765,579,070,325,000
|
Return the number of elements in the underlying data.
|
pandas/core/base.py
|
size
|
BryanRacic/pandas
|
python
|
@property
def size(self) -> int:
'\n \n '
return len(self._values)
|
@property
def array(self) -> ExtensionArray:
"\n The ExtensionArray of the data backing this Series or Index.\n\n Returns\n -------\n ExtensionArray\n An ExtensionArray of the values stored within. For extension\n types, this is the actual array. For NumPy native types, this\n is a thin (no copy) wrapper around :class:`numpy.ndarray`.\n\n ``.array`` differs ``.values`` which may require converting the\n data to a different form.\n\n See Also\n --------\n Index.to_numpy : Similar method that always returns a NumPy array.\n Series.to_numpy : Similar method that always returns a NumPy array.\n\n Notes\n -----\n This table lays out the different array types for each extension\n dtype within pandas.\n\n ================== =============================\n dtype array type\n ================== =============================\n category Categorical\n period PeriodArray\n interval IntervalArray\n IntegerNA IntegerArray\n string StringArray\n boolean BooleanArray\n datetime64[ns, tz] DatetimeArray\n ================== =============================\n\n For any 3rd-party extension types, the array type will be an\n ExtensionArray.\n\n For all remaining dtypes ``.array`` will be a\n :class:`arrays.NumpyExtensionArray` wrapping the actual ndarray\n stored within. If you absolutely need a NumPy array (possibly with\n copying / coercing data), then use :meth:`Series.to_numpy` instead.\n\n Examples\n --------\n For regular NumPy types like int, and float, a PandasArray\n is returned.\n\n >>> pd.Series([1, 2, 3]).array\n <PandasArray>\n [1, 2, 3]\n Length: 3, dtype: int64\n\n For extension types, like Categorical, the actual ExtensionArray\n is returned\n\n >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))\n >>> ser.array\n ['a', 'b', 'a']\n Categories (2, object): ['a', 'b']\n "
raise AbstractMethodError(self)
| 7,608,311,211,353,715,000
|
The ExtensionArray of the data backing this Series or Index.
Returns
-------
ExtensionArray
An ExtensionArray of the values stored within. For extension
types, this is the actual array. For NumPy native types, this
is a thin (no copy) wrapper around :class:`numpy.ndarray`.
``.array`` differs ``.values`` which may require converting the
data to a different form.
See Also
--------
Index.to_numpy : Similar method that always returns a NumPy array.
Series.to_numpy : Similar method that always returns a NumPy array.
Notes
-----
This table lays out the different array types for each extension
dtype within pandas.
================== =============================
dtype array type
================== =============================
category Categorical
period PeriodArray
interval IntervalArray
IntegerNA IntegerArray
string StringArray
boolean BooleanArray
datetime64[ns, tz] DatetimeArray
================== =============================
For any 3rd-party extension types, the array type will be an
ExtensionArray.
For all remaining dtypes ``.array`` will be a
:class:`arrays.NumpyExtensionArray` wrapping the actual ndarray
stored within. If you absolutely need a NumPy array (possibly with
copying / coercing data), then use :meth:`Series.to_numpy` instead.
Examples
--------
For regular NumPy types like int, and float, a PandasArray
is returned.
>>> pd.Series([1, 2, 3]).array
<PandasArray>
[1, 2, 3]
Length: 3, dtype: int64
For extension types, like Categorical, the actual ExtensionArray
is returned
>>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
>>> ser.array
['a', 'b', 'a']
Categories (2, object): ['a', 'b']
|
pandas/core/base.py
|
array
|
BryanRacic/pandas
|
python
|
@property
def array(self) -> ExtensionArray:
"\n The ExtensionArray of the data backing this Series or Index.\n\n Returns\n -------\n ExtensionArray\n An ExtensionArray of the values stored within. For extension\n types, this is the actual array. For NumPy native types, this\n is a thin (no copy) wrapper around :class:`numpy.ndarray`.\n\n ``.array`` differs ``.values`` which may require converting the\n data to a different form.\n\n See Also\n --------\n Index.to_numpy : Similar method that always returns a NumPy array.\n Series.to_numpy : Similar method that always returns a NumPy array.\n\n Notes\n -----\n This table lays out the different array types for each extension\n dtype within pandas.\n\n ================== =============================\n dtype array type\n ================== =============================\n category Categorical\n period PeriodArray\n interval IntervalArray\n IntegerNA IntegerArray\n string StringArray\n boolean BooleanArray\n datetime64[ns, tz] DatetimeArray\n ================== =============================\n\n For any 3rd-party extension types, the array type will be an\n ExtensionArray.\n\n For all remaining dtypes ``.array`` will be a\n :class:`arrays.NumpyExtensionArray` wrapping the actual ndarray\n stored within. If you absolutely need a NumPy array (possibly with\n copying / coercing data), then use :meth:`Series.to_numpy` instead.\n\n Examples\n --------\n For regular NumPy types like int, and float, a PandasArray\n is returned.\n\n >>> pd.Series([1, 2, 3]).array\n <PandasArray>\n [1, 2, 3]\n Length: 3, dtype: int64\n\n For extension types, like Categorical, the actual ExtensionArray\n is returned\n\n >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))\n >>> ser.array\n ['a', 'b', 'a']\n Categories (2, object): ['a', 'b']\n "
raise AbstractMethodError(self)
|
def to_numpy(self, dtype: (npt.DTypeLike | None)=None, copy: bool=False, na_value=lib.no_default, **kwargs) -> np.ndarray:
'\n A NumPy ndarray representing the values in this Series or Index.\n\n Parameters\n ----------\n dtype : str or numpy.dtype, optional\n The dtype to pass to :meth:`numpy.asarray`.\n copy : bool, default False\n Whether to ensure that the returned value is not a view on\n another array. Note that ``copy=False`` does not *ensure* that\n ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that\n a copy is made, even if not strictly necessary.\n na_value : Any, optional\n The value to use for missing values. The default value depends\n on `dtype` and the type of the array.\n\n .. versionadded:: 1.0.0\n\n **kwargs\n Additional keywords passed through to the ``to_numpy`` method\n of the underlying array (for extension arrays).\n\n .. versionadded:: 1.0.0\n\n Returns\n -------\n numpy.ndarray\n\n See Also\n --------\n Series.array : Get the actual data stored within.\n Index.array : Get the actual data stored within.\n DataFrame.to_numpy : Similar method for DataFrame.\n\n Notes\n -----\n The returned array will be the same up to equality (values equal\n in `self` will be equal in the returned array; likewise for values\n that are not equal). When `self` contains an ExtensionArray, the\n dtype may be different. For example, for a category-dtype Series,\n ``to_numpy()`` will return a NumPy array and the categorical dtype\n will be lost.\n\n For NumPy dtypes, this will be a reference to the actual data stored\n in this Series or Index (assuming ``copy=False``). Modifying the result\n in place will modify the data stored in the Series or Index (not that\n we recommend doing that).\n\n For extension types, ``to_numpy()`` *may* require copying data and\n coercing the result to a NumPy type (possibly object), which may be\n expensive. When you need a no-copy reference to the underlying data,\n :attr:`Series.array` should be used instead.\n\n This table lays out the different dtypes and default return types of\n ``to_numpy()`` for various dtypes within pandas.\n\n ================== ================================\n dtype array type\n ================== ================================\n category[T] ndarray[T] (same dtype as input)\n period ndarray[object] (Periods)\n interval ndarray[object] (Intervals)\n IntegerNA ndarray[object]\n datetime64[ns] datetime64[ns]\n datetime64[ns, tz] ndarray[object] (Timestamps)\n ================== ================================\n\n Examples\n --------\n >>> ser = pd.Series(pd.Categorical([\'a\', \'b\', \'a\']))\n >>> ser.to_numpy()\n array([\'a\', \'b\', \'a\'], dtype=object)\n\n Specify the `dtype` to control how datetime-aware data is represented.\n Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp`\n objects, each with the correct ``tz``.\n\n >>> ser = pd.Series(pd.date_range(\'2000\', periods=2, tz="CET"))\n >>> ser.to_numpy(dtype=object)\n array([Timestamp(\'2000-01-01 00:00:00+0100\', tz=\'CET\'),\n Timestamp(\'2000-01-02 00:00:00+0100\', tz=\'CET\')],\n dtype=object)\n\n Or ``dtype=\'datetime64[ns]\'`` to return an ndarray of native\n datetime64 values. The values are converted to UTC and the timezone\n info is dropped.\n\n >>> ser.to_numpy(dtype="datetime64[ns]")\n ... # doctest: +ELLIPSIS\n array([\'1999-12-31T23:00:00.000000000\', \'2000-01-01T23:00:00...\'],\n dtype=\'datetime64[ns]\')\n '
if is_extension_array_dtype(self.dtype):
return self.array.to_numpy(dtype, copy=copy, na_value=na_value, **kwargs)
elif kwargs:
bad_keys = list(kwargs.keys())[0]
raise TypeError(f"to_numpy() got an unexpected keyword argument '{bad_keys}'")
result = np.asarray(self._values, dtype=dtype)
if (copy or (na_value is not lib.no_default)):
result = result.copy()
if (na_value is not lib.no_default):
result[self.isna()] = na_value
return result
| 4,357,615,367,760,797,000
|
A NumPy ndarray representing the values in this Series or Index.
Parameters
----------
dtype : str or numpy.dtype, optional
The dtype to pass to :meth:`numpy.asarray`.
copy : bool, default False
Whether to ensure that the returned value is not a view on
another array. Note that ``copy=False`` does not *ensure* that
``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
a copy is made, even if not strictly necessary.
na_value : Any, optional
The value to use for missing values. The default value depends
on `dtype` and the type of the array.
.. versionadded:: 1.0.0
**kwargs
Additional keywords passed through to the ``to_numpy`` method
of the underlying array (for extension arrays).
.. versionadded:: 1.0.0
Returns
-------
numpy.ndarray
See Also
--------
Series.array : Get the actual data stored within.
Index.array : Get the actual data stored within.
DataFrame.to_numpy : Similar method for DataFrame.
Notes
-----
The returned array will be the same up to equality (values equal
in `self` will be equal in the returned array; likewise for values
that are not equal). When `self` contains an ExtensionArray, the
dtype may be different. For example, for a category-dtype Series,
``to_numpy()`` will return a NumPy array and the categorical dtype
will be lost.
For NumPy dtypes, this will be a reference to the actual data stored
in this Series or Index (assuming ``copy=False``). Modifying the result
in place will modify the data stored in the Series or Index (not that
we recommend doing that).
For extension types, ``to_numpy()`` *may* require copying data and
coercing the result to a NumPy type (possibly object), which may be
expensive. When you need a no-copy reference to the underlying data,
:attr:`Series.array` should be used instead.
This table lays out the different dtypes and default return types of
``to_numpy()`` for various dtypes within pandas.
================== ================================
dtype array type
================== ================================
category[T] ndarray[T] (same dtype as input)
period ndarray[object] (Periods)
interval ndarray[object] (Intervals)
IntegerNA ndarray[object]
datetime64[ns] datetime64[ns]
datetime64[ns, tz] ndarray[object] (Timestamps)
================== ================================
Examples
--------
>>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
>>> ser.to_numpy()
array(['a', 'b', 'a'], dtype=object)
Specify the `dtype` to control how datetime-aware data is represented.
Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp`
objects, each with the correct ``tz``.
>>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
>>> ser.to_numpy(dtype=object)
array([Timestamp('2000-01-01 00:00:00+0100', tz='CET'),
Timestamp('2000-01-02 00:00:00+0100', tz='CET')],
dtype=object)
Or ``dtype='datetime64[ns]'`` to return an ndarray of native
datetime64 values. The values are converted to UTC and the timezone
info is dropped.
>>> ser.to_numpy(dtype="datetime64[ns]")
... # doctest: +ELLIPSIS
array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'],
dtype='datetime64[ns]')
|
pandas/core/base.py
|
to_numpy
|
BryanRacic/pandas
|
python
|
def to_numpy(self, dtype: (npt.DTypeLike | None)=None, copy: bool=False, na_value=lib.no_default, **kwargs) -> np.ndarray:
'\n A NumPy ndarray representing the values in this Series or Index.\n\n Parameters\n ----------\n dtype : str or numpy.dtype, optional\n The dtype to pass to :meth:`numpy.asarray`.\n copy : bool, default False\n Whether to ensure that the returned value is not a view on\n another array. Note that ``copy=False`` does not *ensure* that\n ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that\n a copy is made, even if not strictly necessary.\n na_value : Any, optional\n The value to use for missing values. The default value depends\n on `dtype` and the type of the array.\n\n .. versionadded:: 1.0.0\n\n **kwargs\n Additional keywords passed through to the ``to_numpy`` method\n of the underlying array (for extension arrays).\n\n .. versionadded:: 1.0.0\n\n Returns\n -------\n numpy.ndarray\n\n See Also\n --------\n Series.array : Get the actual data stored within.\n Index.array : Get the actual data stored within.\n DataFrame.to_numpy : Similar method for DataFrame.\n\n Notes\n -----\n The returned array will be the same up to equality (values equal\n in `self` will be equal in the returned array; likewise for values\n that are not equal). When `self` contains an ExtensionArray, the\n dtype may be different. For example, for a category-dtype Series,\n ``to_numpy()`` will return a NumPy array and the categorical dtype\n will be lost.\n\n For NumPy dtypes, this will be a reference to the actual data stored\n in this Series or Index (assuming ``copy=False``). Modifying the result\n in place will modify the data stored in the Series or Index (not that\n we recommend doing that).\n\n For extension types, ``to_numpy()`` *may* require copying data and\n coercing the result to a NumPy type (possibly object), which may be\n expensive. When you need a no-copy reference to the underlying data,\n :attr:`Series.array` should be used instead.\n\n This table lays out the different dtypes and default return types of\n ``to_numpy()`` for various dtypes within pandas.\n\n ================== ================================\n dtype array type\n ================== ================================\n category[T] ndarray[T] (same dtype as input)\n period ndarray[object] (Periods)\n interval ndarray[object] (Intervals)\n IntegerNA ndarray[object]\n datetime64[ns] datetime64[ns]\n datetime64[ns, tz] ndarray[object] (Timestamps)\n ================== ================================\n\n Examples\n --------\n >>> ser = pd.Series(pd.Categorical([\'a\', \'b\', \'a\']))\n >>> ser.to_numpy()\n array([\'a\', \'b\', \'a\'], dtype=object)\n\n Specify the `dtype` to control how datetime-aware data is represented.\n Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp`\n objects, each with the correct ``tz``.\n\n >>> ser = pd.Series(pd.date_range(\'2000\', periods=2, tz="CET"))\n >>> ser.to_numpy(dtype=object)\n array([Timestamp(\'2000-01-01 00:00:00+0100\', tz=\'CET\'),\n Timestamp(\'2000-01-02 00:00:00+0100\', tz=\'CET\')],\n dtype=object)\n\n Or ``dtype=\'datetime64[ns]\'`` to return an ndarray of native\n datetime64 values. The values are converted to UTC and the timezone\n info is dropped.\n\n >>> ser.to_numpy(dtype="datetime64[ns]")\n ... # doctest: +ELLIPSIS\n array([\'1999-12-31T23:00:00.000000000\', \'2000-01-01T23:00:00...\'],\n dtype=\'datetime64[ns]\')\n '
if is_extension_array_dtype(self.dtype):
return self.array.to_numpy(dtype, copy=copy, na_value=na_value, **kwargs)
elif kwargs:
bad_keys = list(kwargs.keys())[0]
raise TypeError(f"to_numpy() got an unexpected keyword argument '{bad_keys}'")
result = np.asarray(self._values, dtype=dtype)
if (copy or (na_value is not lib.no_default)):
result = result.copy()
if (na_value is not lib.no_default):
result[self.isna()] = na_value
return result
|
def max(self, axis=None, skipna: bool=True, *args, **kwargs):
"\n Return the maximum value of the Index.\n\n Parameters\n ----------\n axis : int, optional\n For compatibility with NumPy. Only 0 or None are allowed.\n skipna : bool, default True\n Exclude NA/null values when showing the result.\n *args, **kwargs\n Additional arguments and keywords for compatibility with NumPy.\n\n Returns\n -------\n scalar\n Maximum value.\n\n See Also\n --------\n Index.min : Return the minimum value in an Index.\n Series.max : Return the maximum value in a Series.\n DataFrame.max : Return the maximum values in a DataFrame.\n\n Examples\n --------\n >>> idx = pd.Index([3, 2, 1])\n >>> idx.max()\n 3\n\n >>> idx = pd.Index(['c', 'b', 'a'])\n >>> idx.max()\n 'c'\n\n For a MultiIndex, the maximum is determined lexicographically.\n\n >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])\n >>> idx.max()\n ('b', 2)\n "
nv.validate_minmax_axis(axis)
nv.validate_max(args, kwargs)
return nanops.nanmax(self._values, skipna=skipna)
| 4,268,672,646,825,609,700
|
Return the maximum value of the Index.
Parameters
----------
axis : int, optional
For compatibility with NumPy. Only 0 or None are allowed.
skipna : bool, default True
Exclude NA/null values when showing the result.
*args, **kwargs
Additional arguments and keywords for compatibility with NumPy.
Returns
-------
scalar
Maximum value.
See Also
--------
Index.min : Return the minimum value in an Index.
Series.max : Return the maximum value in a Series.
DataFrame.max : Return the maximum values in a DataFrame.
Examples
--------
>>> idx = pd.Index([3, 2, 1])
>>> idx.max()
3
>>> idx = pd.Index(['c', 'b', 'a'])
>>> idx.max()
'c'
For a MultiIndex, the maximum is determined lexicographically.
>>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
>>> idx.max()
('b', 2)
|
pandas/core/base.py
|
max
|
BryanRacic/pandas
|
python
|
def max(self, axis=None, skipna: bool=True, *args, **kwargs):
"\n Return the maximum value of the Index.\n\n Parameters\n ----------\n axis : int, optional\n For compatibility with NumPy. Only 0 or None are allowed.\n skipna : bool, default True\n Exclude NA/null values when showing the result.\n *args, **kwargs\n Additional arguments and keywords for compatibility with NumPy.\n\n Returns\n -------\n scalar\n Maximum value.\n\n See Also\n --------\n Index.min : Return the minimum value in an Index.\n Series.max : Return the maximum value in a Series.\n DataFrame.max : Return the maximum values in a DataFrame.\n\n Examples\n --------\n >>> idx = pd.Index([3, 2, 1])\n >>> idx.max()\n 3\n\n >>> idx = pd.Index(['c', 'b', 'a'])\n >>> idx.max()\n 'c'\n\n For a MultiIndex, the maximum is determined lexicographically.\n\n >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])\n >>> idx.max()\n ('b', 2)\n "
nv.validate_minmax_axis(axis)
nv.validate_max(args, kwargs)
return nanops.nanmax(self._values, skipna=skipna)
|
@doc(op='max', oppose='min', value='largest')
def argmax(self, axis=None, skipna: bool=True, *args, **kwargs) -> int:
"\n Return int position of the {value} value in the Series.\n\n If the {op}imum is achieved in multiple locations,\n the first row position is returned.\n\n Parameters\n ----------\n axis : {{None}}\n Dummy argument for consistency with Series.\n skipna : bool, default True\n Exclude NA/null values when showing the result.\n *args, **kwargs\n Additional arguments and keywords for compatibility with NumPy.\n\n Returns\n -------\n int\n Row position of the {op}imum value.\n\n See Also\n --------\n Series.arg{op} : Return position of the {op}imum value.\n Series.arg{oppose} : Return position of the {oppose}imum value.\n numpy.ndarray.arg{op} : Equivalent method for numpy arrays.\n Series.idxmax : Return index label of the maximum values.\n Series.idxmin : Return index label of the minimum values.\n\n Examples\n --------\n Consider dataset containing cereal calories\n\n >>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0,\n ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}})\n >>> s\n Corn Flakes 100.0\n Almond Delight 110.0\n Cinnamon Toast Crunch 120.0\n Cocoa Puff 110.0\n dtype: float64\n\n >>> s.argmax()\n 2\n >>> s.argmin()\n 0\n\n The maximum cereal calories is the third element and\n the minimum cereal calories is the first element,\n since series is zero-indexed.\n "
delegate = self._values
nv.validate_minmax_axis(axis)
skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
if isinstance(delegate, ExtensionArray):
if ((not skipna) and delegate.isna().any()):
return (- 1)
else:
return delegate.argmax()
else:
return nanops.nanargmax(delegate, skipna=skipna)
| 2,756,451,465,106,003,500
|
Return int position of the {value} value in the Series.
If the {op}imum is achieved in multiple locations,
the first row position is returned.
Parameters
----------
axis : {{None}}
Dummy argument for consistency with Series.
skipna : bool, default True
Exclude NA/null values when showing the result.
*args, **kwargs
Additional arguments and keywords for compatibility with NumPy.
Returns
-------
int
Row position of the {op}imum value.
See Also
--------
Series.arg{op} : Return position of the {op}imum value.
Series.arg{oppose} : Return position of the {oppose}imum value.
numpy.ndarray.arg{op} : Equivalent method for numpy arrays.
Series.idxmax : Return index label of the maximum values.
Series.idxmin : Return index label of the minimum values.
Examples
--------
Consider dataset containing cereal calories
>>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0,
... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}})
>>> s
Corn Flakes 100.0
Almond Delight 110.0
Cinnamon Toast Crunch 120.0
Cocoa Puff 110.0
dtype: float64
>>> s.argmax()
2
>>> s.argmin()
0
The maximum cereal calories is the third element and
the minimum cereal calories is the first element,
since series is zero-indexed.
|
pandas/core/base.py
|
argmax
|
BryanRacic/pandas
|
python
|
@doc(op='max', oppose='min', value='largest')
def argmax(self, axis=None, skipna: bool=True, *args, **kwargs) -> int:
"\n Return int position of the {value} value in the Series.\n\n If the {op}imum is achieved in multiple locations,\n the first row position is returned.\n\n Parameters\n ----------\n axis : {{None}}\n Dummy argument for consistency with Series.\n skipna : bool, default True\n Exclude NA/null values when showing the result.\n *args, **kwargs\n Additional arguments and keywords for compatibility with NumPy.\n\n Returns\n -------\n int\n Row position of the {op}imum value.\n\n See Also\n --------\n Series.arg{op} : Return position of the {op}imum value.\n Series.arg{oppose} : Return position of the {oppose}imum value.\n numpy.ndarray.arg{op} : Equivalent method for numpy arrays.\n Series.idxmax : Return index label of the maximum values.\n Series.idxmin : Return index label of the minimum values.\n\n Examples\n --------\n Consider dataset containing cereal calories\n\n >>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0,\n ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}})\n >>> s\n Corn Flakes 100.0\n Almond Delight 110.0\n Cinnamon Toast Crunch 120.0\n Cocoa Puff 110.0\n dtype: float64\n\n >>> s.argmax()\n 2\n >>> s.argmin()\n 0\n\n The maximum cereal calories is the third element and\n the minimum cereal calories is the first element,\n since series is zero-indexed.\n "
delegate = self._values
nv.validate_minmax_axis(axis)
skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
if isinstance(delegate, ExtensionArray):
if ((not skipna) and delegate.isna().any()):
return (- 1)
else:
return delegate.argmax()
else:
return nanops.nanargmax(delegate, skipna=skipna)
|
def min(self, axis=None, skipna: bool=True, *args, **kwargs):
"\n Return the minimum value of the Index.\n\n Parameters\n ----------\n axis : {None}\n Dummy argument for consistency with Series.\n skipna : bool, default True\n Exclude NA/null values when showing the result.\n *args, **kwargs\n Additional arguments and keywords for compatibility with NumPy.\n\n Returns\n -------\n scalar\n Minimum value.\n\n See Also\n --------\n Index.max : Return the maximum value of the object.\n Series.min : Return the minimum value in a Series.\n DataFrame.min : Return the minimum values in a DataFrame.\n\n Examples\n --------\n >>> idx = pd.Index([3, 2, 1])\n >>> idx.min()\n 1\n\n >>> idx = pd.Index(['c', 'b', 'a'])\n >>> idx.min()\n 'a'\n\n For a MultiIndex, the minimum is determined lexicographically.\n\n >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])\n >>> idx.min()\n ('a', 1)\n "
nv.validate_minmax_axis(axis)
nv.validate_min(args, kwargs)
return nanops.nanmin(self._values, skipna=skipna)
| 4,895,894,055,981,468,000
|
Return the minimum value of the Index.
Parameters
----------
axis : {None}
Dummy argument for consistency with Series.
skipna : bool, default True
Exclude NA/null values when showing the result.
*args, **kwargs
Additional arguments and keywords for compatibility with NumPy.
Returns
-------
scalar
Minimum value.
See Also
--------
Index.max : Return the maximum value of the object.
Series.min : Return the minimum value in a Series.
DataFrame.min : Return the minimum values in a DataFrame.
Examples
--------
>>> idx = pd.Index([3, 2, 1])
>>> idx.min()
1
>>> idx = pd.Index(['c', 'b', 'a'])
>>> idx.min()
'a'
For a MultiIndex, the minimum is determined lexicographically.
>>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
>>> idx.min()
('a', 1)
|
pandas/core/base.py
|
min
|
BryanRacic/pandas
|
python
|
def min(self, axis=None, skipna: bool=True, *args, **kwargs):
"\n Return the minimum value of the Index.\n\n Parameters\n ----------\n axis : {None}\n Dummy argument for consistency with Series.\n skipna : bool, default True\n Exclude NA/null values when showing the result.\n *args, **kwargs\n Additional arguments and keywords for compatibility with NumPy.\n\n Returns\n -------\n scalar\n Minimum value.\n\n See Also\n --------\n Index.max : Return the maximum value of the object.\n Series.min : Return the minimum value in a Series.\n DataFrame.min : Return the minimum values in a DataFrame.\n\n Examples\n --------\n >>> idx = pd.Index([3, 2, 1])\n >>> idx.min()\n 1\n\n >>> idx = pd.Index(['c', 'b', 'a'])\n >>> idx.min()\n 'a'\n\n For a MultiIndex, the minimum is determined lexicographically.\n\n >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])\n >>> idx.min()\n ('a', 1)\n "
nv.validate_minmax_axis(axis)
nv.validate_min(args, kwargs)
return nanops.nanmin(self._values, skipna=skipna)
|
def tolist(self):
'\n Return a list of the values.\n\n These are each a scalar type, which is a Python scalar\n (for str, int, float) or a pandas scalar\n (for Timestamp/Timedelta/Interval/Period)\n\n Returns\n -------\n list\n\n See Also\n --------\n numpy.ndarray.tolist : Return the array as an a.ndim-levels deep\n nested list of Python scalars.\n '
if (not isinstance(self._values, np.ndarray)):
return list(self._values)
return self._values.tolist()
| 4,623,667,165,696,130,000
|
Return a list of the values.
These are each a scalar type, which is a Python scalar
(for str, int, float) or a pandas scalar
(for Timestamp/Timedelta/Interval/Period)
Returns
-------
list
See Also
--------
numpy.ndarray.tolist : Return the array as an a.ndim-levels deep
nested list of Python scalars.
|
pandas/core/base.py
|
tolist
|
BryanRacic/pandas
|
python
|
def tolist(self):
'\n Return a list of the values.\n\n These are each a scalar type, which is a Python scalar\n (for str, int, float) or a pandas scalar\n (for Timestamp/Timedelta/Interval/Period)\n\n Returns\n -------\n list\n\n See Also\n --------\n numpy.ndarray.tolist : Return the array as an a.ndim-levels deep\n nested list of Python scalars.\n '
if (not isinstance(self._values, np.ndarray)):
return list(self._values)
return self._values.tolist()
|
def __iter__(self):
'\n Return an iterator of the values.\n\n These are each a scalar type, which is a Python scalar\n (for str, int, float) or a pandas scalar\n (for Timestamp/Timedelta/Interval/Period)\n\n Returns\n -------\n iterator\n '
if (not isinstance(self._values, np.ndarray)):
return iter(self._values)
else:
return map(self._values.item, range(self._values.size))
| 6,172,453,236,682,056,000
|
Return an iterator of the values.
These are each a scalar type, which is a Python scalar
(for str, int, float) or a pandas scalar
(for Timestamp/Timedelta/Interval/Period)
Returns
-------
iterator
|
pandas/core/base.py
|
__iter__
|
BryanRacic/pandas
|
python
|
def __iter__(self):
'\n Return an iterator of the values.\n\n These are each a scalar type, which is a Python scalar\n (for str, int, float) or a pandas scalar\n (for Timestamp/Timedelta/Interval/Period)\n\n Returns\n -------\n iterator\n '
if (not isinstance(self._values, np.ndarray)):
return iter(self._values)
else:
return map(self._values.item, range(self._values.size))
|
@cache_readonly
def hasnans(self) -> bool:
'\n Return if I have any nans; enables various perf speedups.\n '
return bool(isna(self).any())
| -3,629,634,497,472,234,000
|
Return if I have any nans; enables various perf speedups.
|
pandas/core/base.py
|
hasnans
|
BryanRacic/pandas
|
python
|
@cache_readonly
def hasnans(self) -> bool:
'\n \n '
return bool(isna(self).any())
|
def _reduce(self, op, name: str, *, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds):
'\n Perform the reduction type operation if we can.\n '
func = getattr(self, name, None)
if (func is None):
raise TypeError(f'{type(self).__name__} cannot perform the operation {name}')
return func(skipna=skipna, **kwds)
| -6,248,915,611,062,733,000
|
Perform the reduction type operation if we can.
|
pandas/core/base.py
|
_reduce
|
BryanRacic/pandas
|
python
|
def _reduce(self, op, name: str, *, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds):
'\n \n '
func = getattr(self, name, None)
if (func is None):
raise TypeError(f'{type(self).__name__} cannot perform the operation {name}')
return func(skipna=skipna, **kwds)
|
@final
def _map_values(self, mapper, na_action=None):
"\n An internal function that maps values using the input\n correspondence (which can be a dict, Series, or function).\n\n Parameters\n ----------\n mapper : function, dict, or Series\n The input correspondence object\n na_action : {None, 'ignore'}\n If 'ignore', propagate NA values, without passing them to the\n mapping function\n\n Returns\n -------\n Union[Index, MultiIndex], inferred\n The output of the mapping function applied to the index.\n If the function returns a tuple with more than one element\n a MultiIndex will be returned.\n "
if is_dict_like(mapper):
if (isinstance(mapper, dict) and hasattr(mapper, '__missing__')):
dict_with_default = mapper
mapper = (lambda x: dict_with_default[x])
else:
mapper = create_series_with_explicit_dtype(mapper, dtype_if_empty=np.float64)
if isinstance(mapper, ABCSeries):
if is_categorical_dtype(self.dtype):
cat = cast('Categorical', self._values)
return cat.map(mapper)
values = self._values
indexer = mapper.index.get_indexer(values)
new_values = algorithms.take_nd(mapper._values, indexer)
return new_values
if (is_extension_array_dtype(self.dtype) and hasattr(self._values, 'map')):
values = self._values
if (na_action is not None):
raise NotImplementedError
map_f = (lambda values, f: values.map(f))
else:
values = self._values.astype(object)
if (na_action == 'ignore'):
map_f = (lambda values, f: lib.map_infer_mask(values, f, isna(values).view(np.uint8)))
elif (na_action is None):
map_f = lib.map_infer
else:
msg = f"na_action must either be 'ignore' or None, {na_action} was passed"
raise ValueError(msg)
new_values = map_f(values, mapper)
return new_values
| 4,348,222,714,382,488,000
|
An internal function that maps values using the input
correspondence (which can be a dict, Series, or function).
Parameters
----------
mapper : function, dict, or Series
The input correspondence object
na_action : {None, 'ignore'}
If 'ignore', propagate NA values, without passing them to the
mapping function
Returns
-------
Union[Index, MultiIndex], inferred
The output of the mapping function applied to the index.
If the function returns a tuple with more than one element
a MultiIndex will be returned.
|
pandas/core/base.py
|
_map_values
|
BryanRacic/pandas
|
python
|
@final
def _map_values(self, mapper, na_action=None):
"\n An internal function that maps values using the input\n correspondence (which can be a dict, Series, or function).\n\n Parameters\n ----------\n mapper : function, dict, or Series\n The input correspondence object\n na_action : {None, 'ignore'}\n If 'ignore', propagate NA values, without passing them to the\n mapping function\n\n Returns\n -------\n Union[Index, MultiIndex], inferred\n The output of the mapping function applied to the index.\n If the function returns a tuple with more than one element\n a MultiIndex will be returned.\n "
if is_dict_like(mapper):
if (isinstance(mapper, dict) and hasattr(mapper, '__missing__')):
dict_with_default = mapper
mapper = (lambda x: dict_with_default[x])
else:
mapper = create_series_with_explicit_dtype(mapper, dtype_if_empty=np.float64)
if isinstance(mapper, ABCSeries):
if is_categorical_dtype(self.dtype):
cat = cast('Categorical', self._values)
return cat.map(mapper)
values = self._values
indexer = mapper.index.get_indexer(values)
new_values = algorithms.take_nd(mapper._values, indexer)
return new_values
if (is_extension_array_dtype(self.dtype) and hasattr(self._values, 'map')):
values = self._values
if (na_action is not None):
raise NotImplementedError
map_f = (lambda values, f: values.map(f))
else:
values = self._values.astype(object)
if (na_action == 'ignore'):
map_f = (lambda values, f: lib.map_infer_mask(values, f, isna(values).view(np.uint8)))
elif (na_action is None):
map_f = lib.map_infer
else:
msg = f"na_action must either be 'ignore' or None, {na_action} was passed"
raise ValueError(msg)
new_values = map_f(values, mapper)
return new_values
|
def value_counts(self, normalize: bool=False, sort: bool=True, ascending: bool=False, bins=None, dropna: bool=True):
"\n Return a Series containing counts of unique values.\n\n The resulting object will be in descending order so that the\n first element is the most frequently-occurring element.\n Excludes NA values by default.\n\n Parameters\n ----------\n normalize : bool, default False\n If True then the object returned will contain the relative\n frequencies of the unique values.\n sort : bool, default True\n Sort by frequencies.\n ascending : bool, default False\n Sort in ascending order.\n bins : int, optional\n Rather than count values, group them into half-open bins,\n a convenience for ``pd.cut``, only works with numeric data.\n dropna : bool, default True\n Don't include counts of NaN.\n\n Returns\n -------\n Series\n\n See Also\n --------\n Series.count: Number of non-NA elements in a Series.\n DataFrame.count: Number of non-NA elements in a DataFrame.\n DataFrame.value_counts: Equivalent method on DataFrames.\n\n Examples\n --------\n >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])\n >>> index.value_counts()\n 3.0 2\n 1.0 1\n 2.0 1\n 4.0 1\n dtype: int64\n\n With `normalize` set to `True`, returns the relative frequency by\n dividing all values by the sum of values.\n\n >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])\n >>> s.value_counts(normalize=True)\n 3.0 0.4\n 1.0 0.2\n 2.0 0.2\n 4.0 0.2\n dtype: float64\n\n **bins**\n\n Bins can be useful for going from a continuous variable to a\n categorical variable; instead of counting unique\n apparitions of values, divide the index in the specified\n number of half-open bins.\n\n >>> s.value_counts(bins=3)\n (0.996, 2.0] 2\n (2.0, 3.0] 2\n (3.0, 4.0] 1\n dtype: int64\n\n **dropna**\n\n With `dropna` set to `False` we can also see NaN index values.\n\n >>> s.value_counts(dropna=False)\n 3.0 2\n 1.0 1\n 2.0 1\n 4.0 1\n NaN 1\n dtype: int64\n "
return value_counts(self, sort=sort, ascending=ascending, normalize=normalize, bins=bins, dropna=dropna)
| 2,223,745,085,199,082,000
|
Return a Series containing counts of unique values.
The resulting object will be in descending order so that the
first element is the most frequently-occurring element.
Excludes NA values by default.
Parameters
----------
normalize : bool, default False
If True then the object returned will contain the relative
frequencies of the unique values.
sort : bool, default True
Sort by frequencies.
ascending : bool, default False
Sort in ascending order.
bins : int, optional
Rather than count values, group them into half-open bins,
a convenience for ``pd.cut``, only works with numeric data.
dropna : bool, default True
Don't include counts of NaN.
Returns
-------
Series
See Also
--------
Series.count: Number of non-NA elements in a Series.
DataFrame.count: Number of non-NA elements in a DataFrame.
DataFrame.value_counts: Equivalent method on DataFrames.
Examples
--------
>>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
>>> index.value_counts()
3.0 2
1.0 1
2.0 1
4.0 1
dtype: int64
With `normalize` set to `True`, returns the relative frequency by
dividing all values by the sum of values.
>>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
>>> s.value_counts(normalize=True)
3.0 0.4
1.0 0.2
2.0 0.2
4.0 0.2
dtype: float64
**bins**
Bins can be useful for going from a continuous variable to a
categorical variable; instead of counting unique
apparitions of values, divide the index in the specified
number of half-open bins.
>>> s.value_counts(bins=3)
(0.996, 2.0] 2
(2.0, 3.0] 2
(3.0, 4.0] 1
dtype: int64
**dropna**
With `dropna` set to `False` we can also see NaN index values.
>>> s.value_counts(dropna=False)
3.0 2
1.0 1
2.0 1
4.0 1
NaN 1
dtype: int64
|
pandas/core/base.py
|
value_counts
|
BryanRacic/pandas
|
python
|
def value_counts(self, normalize: bool=False, sort: bool=True, ascending: bool=False, bins=None, dropna: bool=True):
"\n Return a Series containing counts of unique values.\n\n The resulting object will be in descending order so that the\n first element is the most frequently-occurring element.\n Excludes NA values by default.\n\n Parameters\n ----------\n normalize : bool, default False\n If True then the object returned will contain the relative\n frequencies of the unique values.\n sort : bool, default True\n Sort by frequencies.\n ascending : bool, default False\n Sort in ascending order.\n bins : int, optional\n Rather than count values, group them into half-open bins,\n a convenience for ``pd.cut``, only works with numeric data.\n dropna : bool, default True\n Don't include counts of NaN.\n\n Returns\n -------\n Series\n\n See Also\n --------\n Series.count: Number of non-NA elements in a Series.\n DataFrame.count: Number of non-NA elements in a DataFrame.\n DataFrame.value_counts: Equivalent method on DataFrames.\n\n Examples\n --------\n >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])\n >>> index.value_counts()\n 3.0 2\n 1.0 1\n 2.0 1\n 4.0 1\n dtype: int64\n\n With `normalize` set to `True`, returns the relative frequency by\n dividing all values by the sum of values.\n\n >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])\n >>> s.value_counts(normalize=True)\n 3.0 0.4\n 1.0 0.2\n 2.0 0.2\n 4.0 0.2\n dtype: float64\n\n **bins**\n\n Bins can be useful for going from a continuous variable to a\n categorical variable; instead of counting unique\n apparitions of values, divide the index in the specified\n number of half-open bins.\n\n >>> s.value_counts(bins=3)\n (0.996, 2.0] 2\n (2.0, 3.0] 2\n (3.0, 4.0] 1\n dtype: int64\n\n **dropna**\n\n With `dropna` set to `False` we can also see NaN index values.\n\n >>> s.value_counts(dropna=False)\n 3.0 2\n 1.0 1\n 2.0 1\n 4.0 1\n NaN 1\n dtype: int64\n "
return value_counts(self, sort=sort, ascending=ascending, normalize=normalize, bins=bins, dropna=dropna)
|
def nunique(self, dropna: bool=True) -> int:
"\n Return number of unique elements in the object.\n\n Excludes NA values by default.\n\n Parameters\n ----------\n dropna : bool, default True\n Don't include NaN in the count.\n\n Returns\n -------\n int\n\n See Also\n --------\n DataFrame.nunique: Method nunique for DataFrame.\n Series.count: Count non-NA/null observations in the Series.\n\n Examples\n --------\n >>> s = pd.Series([1, 3, 5, 7, 7])\n >>> s\n 0 1\n 1 3\n 2 5\n 3 7\n 4 7\n dtype: int64\n\n >>> s.nunique()\n 4\n "
uniqs = self.unique()
if dropna:
uniqs = remove_na_arraylike(uniqs)
return len(uniqs)
| 8,887,803,906,589,405,000
|
Return number of unique elements in the object.
Excludes NA values by default.
Parameters
----------
dropna : bool, default True
Don't include NaN in the count.
Returns
-------
int
See Also
--------
DataFrame.nunique: Method nunique for DataFrame.
Series.count: Count non-NA/null observations in the Series.
Examples
--------
>>> s = pd.Series([1, 3, 5, 7, 7])
>>> s
0 1
1 3
2 5
3 7
4 7
dtype: int64
>>> s.nunique()
4
|
pandas/core/base.py
|
nunique
|
BryanRacic/pandas
|
python
|
def nunique(self, dropna: bool=True) -> int:
"\n Return number of unique elements in the object.\n\n Excludes NA values by default.\n\n Parameters\n ----------\n dropna : bool, default True\n Don't include NaN in the count.\n\n Returns\n -------\n int\n\n See Also\n --------\n DataFrame.nunique: Method nunique for DataFrame.\n Series.count: Count non-NA/null observations in the Series.\n\n Examples\n --------\n >>> s = pd.Series([1, 3, 5, 7, 7])\n >>> s\n 0 1\n 1 3\n 2 5\n 3 7\n 4 7\n dtype: int64\n\n >>> s.nunique()\n 4\n "
uniqs = self.unique()
if dropna:
uniqs = remove_na_arraylike(uniqs)
return len(uniqs)
|
@property
def is_unique(self) -> bool:
'\n Return boolean if values in the object are unique.\n\n Returns\n -------\n bool\n '
return (self.nunique(dropna=False) == len(self))
| -1,875,521,501,258,616,300
|
Return boolean if values in the object are unique.
Returns
-------
bool
|
pandas/core/base.py
|
is_unique
|
BryanRacic/pandas
|
python
|
@property
def is_unique(self) -> bool:
'\n Return boolean if values in the object are unique.\n\n Returns\n -------\n bool\n '
return (self.nunique(dropna=False) == len(self))
|
@property
def is_monotonic(self) -> bool:
'\n Return boolean if values in the object are\n monotonic_increasing.\n\n Returns\n -------\n bool\n '
from pandas import Index
return Index(self).is_monotonic
| -1,426,646,453,358,816,000
|
Return boolean if values in the object are
monotonic_increasing.
Returns
-------
bool
|
pandas/core/base.py
|
is_monotonic
|
BryanRacic/pandas
|
python
|
@property
def is_monotonic(self) -> bool:
'\n Return boolean if values in the object are\n monotonic_increasing.\n\n Returns\n -------\n bool\n '
from pandas import Index
return Index(self).is_monotonic
|
@property
def is_monotonic_increasing(self) -> bool:
'\n Alias for is_monotonic.\n '
return self.is_monotonic
| 7,444,097,157,233,163,000
|
Alias for is_monotonic.
|
pandas/core/base.py
|
is_monotonic_increasing
|
BryanRacic/pandas
|
python
|
@property
def is_monotonic_increasing(self) -> bool:
'\n \n '
return self.is_monotonic
|
@property
def is_monotonic_decreasing(self) -> bool:
'\n Return boolean if values in the object are\n monotonic_decreasing.\n\n Returns\n -------\n bool\n '
from pandas import Index
return Index(self).is_monotonic_decreasing
| -4,775,814,088,260,608,000
|
Return boolean if values in the object are
monotonic_decreasing.
Returns
-------
bool
|
pandas/core/base.py
|
is_monotonic_decreasing
|
BryanRacic/pandas
|
python
|
@property
def is_monotonic_decreasing(self) -> bool:
'\n Return boolean if values in the object are\n monotonic_decreasing.\n\n Returns\n -------\n bool\n '
from pandas import Index
return Index(self).is_monotonic_decreasing
|
def _memory_usage(self, deep: bool=False) -> int:
'\n Memory usage of the values.\n\n Parameters\n ----------\n deep : bool, default False\n Introspect the data deeply, interrogate\n `object` dtypes for system-level memory consumption.\n\n Returns\n -------\n bytes used\n\n See Also\n --------\n numpy.ndarray.nbytes : Total bytes consumed by the elements of the\n array.\n\n Notes\n -----\n Memory usage does not include memory consumed by elements that\n are not components of the array if deep=False or if used on PyPy\n '
if hasattr(self.array, 'memory_usage'):
return self.array.memory_usage(deep=deep)
v = self.array.nbytes
if (deep and is_object_dtype(self) and (not PYPY)):
values = cast(np.ndarray, self._values)
v += lib.memory_usage_of_objects(values)
return v
| 2,268,990,164,839,207,200
|
Memory usage of the values.
Parameters
----------
deep : bool, default False
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption.
Returns
-------
bytes used
See Also
--------
numpy.ndarray.nbytes : Total bytes consumed by the elements of the
array.
Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False or if used on PyPy
|
pandas/core/base.py
|
_memory_usage
|
BryanRacic/pandas
|
python
|
def _memory_usage(self, deep: bool=False) -> int:
'\n Memory usage of the values.\n\n Parameters\n ----------\n deep : bool, default False\n Introspect the data deeply, interrogate\n `object` dtypes for system-level memory consumption.\n\n Returns\n -------\n bytes used\n\n See Also\n --------\n numpy.ndarray.nbytes : Total bytes consumed by the elements of the\n array.\n\n Notes\n -----\n Memory usage does not include memory consumed by elements that\n are not components of the array if deep=False or if used on PyPy\n '
if hasattr(self.array, 'memory_usage'):
return self.array.memory_usage(deep=deep)
v = self.array.nbytes
if (deep and is_object_dtype(self) and (not PYPY)):
values = cast(np.ndarray, self._values)
v += lib.memory_usage_of_objects(values)
return v
|
def _construct_result(self, result, name):
'\n Construct an appropriately-wrapped result from the ArrayLike result\n of an arithmetic-like operation.\n '
raise AbstractMethodError(self)
| 7,152,423,457,140,417,000
|
Construct an appropriately-wrapped result from the ArrayLike result
of an arithmetic-like operation.
|
pandas/core/base.py
|
_construct_result
|
BryanRacic/pandas
|
python
|
def _construct_result(self, result, name):
'\n Construct an appropriately-wrapped result from the ArrayLike result\n of an arithmetic-like operation.\n '
raise AbstractMethodError(self)
|
def test_random_grid_search_for_glm():
'\n Create and instantiate classes, call test methods to test randomize grid search for GLM Gaussian\n or Binomial families.\n\n :return: None\n '
test_glm_gaussian_random_grid = Test_glm_random_grid_search('gaussian')
test_glm_gaussian_random_grid.test1_glm_random_grid_search_model_number('mse(xval=True)')
test_glm_gaussian_random_grid.test2_glm_random_grid_search_max_model()
test_glm_gaussian_random_grid.test3_glm_random_grid_search_max_runtime_secs()
test_glm_gaussian_random_grid.test4_glm_random_grid_search_metric('MSE', False)
if (test_glm_gaussian_random_grid.test_failed > 0):
sys.exit(1)
else:
pyunit_utils.remove_files(os.path.join(test_glm_gaussian_random_grid.current_dir, test_glm_gaussian_random_grid.json_filename))
| -2,897,046,621,495,929,300
|
Create and instantiate classes, call test methods to test randomize grid search for GLM Gaussian
or Binomial families.
:return: None
|
h2o-py/dynamic_tests/testdir_algos/glm/pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py
|
test_random_grid_search_for_glm
|
13927729580/h2o-3
|
python
|
def test_random_grid_search_for_glm():
'\n Create and instantiate classes, call test methods to test randomize grid search for GLM Gaussian\n or Binomial families.\n\n :return: None\n '
test_glm_gaussian_random_grid = Test_glm_random_grid_search('gaussian')
test_glm_gaussian_random_grid.test1_glm_random_grid_search_model_number('mse(xval=True)')
test_glm_gaussian_random_grid.test2_glm_random_grid_search_max_model()
test_glm_gaussian_random_grid.test3_glm_random_grid_search_max_runtime_secs()
test_glm_gaussian_random_grid.test4_glm_random_grid_search_metric('MSE', False)
if (test_glm_gaussian_random_grid.test_failed > 0):
sys.exit(1)
else:
pyunit_utils.remove_files(os.path.join(test_glm_gaussian_random_grid.current_dir, test_glm_gaussian_random_grid.json_filename))
|
def __init__(self, family):
'\n Constructor.\n\n :param family: distribution family for tests\n :return: None\n '
self.setup_data()
self.setup_grid_params()
| 5,059,522,531,481,194,000
|
Constructor.
:param family: distribution family for tests
:return: None
|
h2o-py/dynamic_tests/testdir_algos/glm/pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py
|
__init__
|
13927729580/h2o-3
|
python
|
def __init__(self, family):
'\n Constructor.\n\n :param family: distribution family for tests\n :return: None\n '
self.setup_data()
self.setup_grid_params()
|
def setup_data(self):
'\n This function performs all initializations necessary:\n load the data sets and set the training set indices and response column index\n '
self.sandbox_dir = pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, True)
self.training1_data = h2o.import_file(path=pyunit_utils.locate(self.training1_filename))
self.y_index = (self.training1_data.ncol - 1)
self.x_indices = list(range(self.y_index))
pyunit_utils.remove_csv_files(self.current_dir, '.csv', action='copy', new_dir_path=self.sandbox_dir)
| -6,597,030,469,623,763,000
|
This function performs all initializations necessary:
load the data sets and set the training set indices and response column index
|
h2o-py/dynamic_tests/testdir_algos/glm/pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py
|
setup_data
|
13927729580/h2o-3
|
python
|
def setup_data(self):
'\n This function performs all initializations necessary:\n load the data sets and set the training set indices and response column index\n '
self.sandbox_dir = pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, True)
self.training1_data = h2o.import_file(path=pyunit_utils.locate(self.training1_filename))
self.y_index = (self.training1_data.ncol - 1)
self.x_indices = list(range(self.y_index))
pyunit_utils.remove_csv_files(self.current_dir, '.csv', action='copy', new_dir_path=self.sandbox_dir)
|
def setup_grid_params(self):
'\n This function setup the randomized gridsearch parameters that will be used later on:\n\n 1. It will first try to grab all the parameters that are griddable and parameters used by GLM.\n 2. It will find the intersection of parameters that are both griddable and used by GLM.\n 3. There are several extra parameters that are used by GLM that are denoted as griddable but actually is not.\n These parameters have to be discovered manually and they These are captured in self.exclude_parameter_lists.\n 4. We generate the gridsearch hyper-parameter. For numerical parameters, we will generate those randomly.\n For enums, we will include all of them.\n\n :return: None\n '
model = H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds)
model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data)
self.one_model_time = pyunit_utils.find_grid_runtime([model])
print('Time taken to build a base barebone model is {0}'.format(self.one_model_time))
(self.gridable_parameters, self.gridable_types, self.gridable_defaults) = pyunit_utils.get_gridables(model._model_json['parameters'])
self.hyper_params = {}
self.hyper_params['fold_assignment'] = ['AUTO', 'Random', 'Modulo']
self.hyper_params['missing_values_handling'] = ['MeanImputation', 'Skip']
(self.hyper_params, self.gridable_parameters, self.gridable_types, self.gridable_defaults) = pyunit_utils.gen_grid_search(model.full_parameters.keys(), self.hyper_params, self.exclude_parameter_lists, self.gridable_parameters, self.gridable_types, self.gridable_defaults, random.randint(1, self.max_int_number), self.max_int_val, self.min_int_val, random.randint(1, self.max_real_number), self.max_real_val, self.min_real_val)
if ('lambda' in list(self.hyper_params)):
self.hyper_params['lambda'] = [(self.lambda_scale * x) for x in self.hyper_params['lambda']]
time_scale = (self.max_runtime_scale * self.one_model_time)
if ('max_runtime_secs' in list(self.hyper_params)):
self.hyper_params['max_runtime_secs'] = [(time_scale * x) for x in self.hyper_params['max_runtime_secs']]
self.possible_number_models = pyunit_utils.count_models(self.hyper_params)
pyunit_utils.write_hyper_parameters_json(self.current_dir, self.sandbox_dir, self.json_filename, self.hyper_params)
| 8,059,243,211,549,357,000
|
This function setup the randomized gridsearch parameters that will be used later on:
1. It will first try to grab all the parameters that are griddable and parameters used by GLM.
2. It will find the intersection of parameters that are both griddable and used by GLM.
3. There are several extra parameters that are used by GLM that are denoted as griddable but actually is not.
These parameters have to be discovered manually and they These are captured in self.exclude_parameter_lists.
4. We generate the gridsearch hyper-parameter. For numerical parameters, we will generate those randomly.
For enums, we will include all of them.
:return: None
|
h2o-py/dynamic_tests/testdir_algos/glm/pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py
|
setup_grid_params
|
13927729580/h2o-3
|
python
|
def setup_grid_params(self):
'\n This function setup the randomized gridsearch parameters that will be used later on:\n\n 1. It will first try to grab all the parameters that are griddable and parameters used by GLM.\n 2. It will find the intersection of parameters that are both griddable and used by GLM.\n 3. There are several extra parameters that are used by GLM that are denoted as griddable but actually is not.\n These parameters have to be discovered manually and they These are captured in self.exclude_parameter_lists.\n 4. We generate the gridsearch hyper-parameter. For numerical parameters, we will generate those randomly.\n For enums, we will include all of them.\n\n :return: None\n '
model = H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds)
model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data)
self.one_model_time = pyunit_utils.find_grid_runtime([model])
print('Time taken to build a base barebone model is {0}'.format(self.one_model_time))
(self.gridable_parameters, self.gridable_types, self.gridable_defaults) = pyunit_utils.get_gridables(model._model_json['parameters'])
self.hyper_params = {}
self.hyper_params['fold_assignment'] = ['AUTO', 'Random', 'Modulo']
self.hyper_params['missing_values_handling'] = ['MeanImputation', 'Skip']
(self.hyper_params, self.gridable_parameters, self.gridable_types, self.gridable_defaults) = pyunit_utils.gen_grid_search(model.full_parameters.keys(), self.hyper_params, self.exclude_parameter_lists, self.gridable_parameters, self.gridable_types, self.gridable_defaults, random.randint(1, self.max_int_number), self.max_int_val, self.min_int_val, random.randint(1, self.max_real_number), self.max_real_val, self.min_real_val)
if ('lambda' in list(self.hyper_params)):
self.hyper_params['lambda'] = [(self.lambda_scale * x) for x in self.hyper_params['lambda']]
time_scale = (self.max_runtime_scale * self.one_model_time)
if ('max_runtime_secs' in list(self.hyper_params)):
self.hyper_params['max_runtime_secs'] = [(time_scale * x) for x in self.hyper_params['max_runtime_secs']]
self.possible_number_models = pyunit_utils.count_models(self.hyper_params)
pyunit_utils.write_hyper_parameters_json(self.current_dir, self.sandbox_dir, self.json_filename, self.hyper_params)
|
def tear_down(self):
'\n This function performs teardown after the dynamic test is completed. If all tests\n passed, it will delete all data sets generated since they can be quite large. It\n will move the training/validation/test data sets into a Rsandbox directory so that\n we can re-run the failed test.\n '
if self.test_failed:
self.sandbox_dir = pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, True)
pyunit_utils.move_files(self.sandbox_dir, self.training1_data_file, self.training1_filename)
json_file = os.path.join(self.sandbox_dir, self.json_filename)
with open(json_file, 'wb') as test_file:
json.dump(self.hyper_params, test_file)
else:
pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, False)
| 551,565,696,271,934,300
|
This function performs teardown after the dynamic test is completed. If all tests
passed, it will delete all data sets generated since they can be quite large. It
will move the training/validation/test data sets into a Rsandbox directory so that
we can re-run the failed test.
|
h2o-py/dynamic_tests/testdir_algos/glm/pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py
|
tear_down
|
13927729580/h2o-3
|
python
|
def tear_down(self):
'\n This function performs teardown after the dynamic test is completed. If all tests\n passed, it will delete all data sets generated since they can be quite large. It\n will move the training/validation/test data sets into a Rsandbox directory so that\n we can re-run the failed test.\n '
if self.test_failed:
self.sandbox_dir = pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, True)
pyunit_utils.move_files(self.sandbox_dir, self.training1_data_file, self.training1_filename)
json_file = os.path.join(self.sandbox_dir, self.json_filename)
with open(json_file, 'wb') as test_file:
json.dump(self.hyper_params, test_file)
else:
pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, False)
|
def test1_glm_random_grid_search_model_number(self, metric_name):
'\n This test is used to make sure the randomized gridsearch will generate all models specified in the\n hyperparameters if no stopping condition is given in the search criterion.\n\n :param metric_name: string to denote what grid search model should be sort by\n\n :return: None\n '
print('*******************************************************************************************')
print(('test1_glm_random_grid_search_model_number for GLM ' + self.family))
h2o.cluster_info()
search_criteria = {'strategy': 'RandomDiscrete', 'stopping_rounds': 0, 'seed': round(time.time())}
print('GLM Gaussian grid search_criteria: {0}'.format(search_criteria))
random_grid_model = H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria)
random_grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data)
if (not (len(random_grid_model) == self.possible_number_models)):
self.test_failed += 1
self.test_failed_array[self.test_num] = 1
print('test1_glm_random_grid_search_model_number for GLM: failed, number of models generatedpossible model number {0} and randomized gridsearch model number {1} are not equal.'.format(self.possible_number_models, len(random_grid_model)))
else:
self.max_grid_runtime = pyunit_utils.find_grid_runtime(random_grid_model)
if (self.test_failed_array[self.test_num] == 0):
print('test1_glm_random_grid_search_model_number for GLM: passed!')
self.test_num += 1
sys.stdout.flush()
| 1,333,334,887,265,819,400
|
This test is used to make sure the randomized gridsearch will generate all models specified in the
hyperparameters if no stopping condition is given in the search criterion.
:param metric_name: string to denote what grid search model should be sort by
:return: None
|
h2o-py/dynamic_tests/testdir_algos/glm/pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py
|
test1_glm_random_grid_search_model_number
|
13927729580/h2o-3
|
python
|
def test1_glm_random_grid_search_model_number(self, metric_name):
'\n This test is used to make sure the randomized gridsearch will generate all models specified in the\n hyperparameters if no stopping condition is given in the search criterion.\n\n :param metric_name: string to denote what grid search model should be sort by\n\n :return: None\n '
print('*******************************************************************************************')
print(('test1_glm_random_grid_search_model_number for GLM ' + self.family))
h2o.cluster_info()
search_criteria = {'strategy': 'RandomDiscrete', 'stopping_rounds': 0, 'seed': round(time.time())}
print('GLM Gaussian grid search_criteria: {0}'.format(search_criteria))
random_grid_model = H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria)
random_grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data)
if (not (len(random_grid_model) == self.possible_number_models)):
self.test_failed += 1
self.test_failed_array[self.test_num] = 1
print('test1_glm_random_grid_search_model_number for GLM: failed, number of models generatedpossible model number {0} and randomized gridsearch model number {1} are not equal.'.format(self.possible_number_models, len(random_grid_model)))
else:
self.max_grid_runtime = pyunit_utils.find_grid_runtime(random_grid_model)
if (self.test_failed_array[self.test_num] == 0):
print('test1_glm_random_grid_search_model_number for GLM: passed!')
self.test_num += 1
sys.stdout.flush()
|
def test2_glm_random_grid_search_max_model(self):
'\n This test is used to test the stopping condition max_model_number in the randomized gridsearch. The\n max_models parameter is randomly generated. If it is higher than the actual possible number of models\n that can be generated with the current hyper-space parameters, randomized grid search should generate\n all the models. Otherwise, grid search shall return a model that equals to the max_model setting.\n '
print('*******************************************************************************************')
print(('test2_glm_random_grid_search_max_model for GLM ' + self.family))
h2o.cluster_info()
self.max_model_number = random.randint(1, int((self.allowed_scaled_model_number * self.possible_number_models)))
search_criteria = {'strategy': 'RandomDiscrete', 'max_models': self.max_model_number, 'seed': round(time.time())}
print('GLM Gaussian grid search_criteria: {0}'.format(search_criteria))
print('Possible number of models built is {0}'.format(self.possible_number_models))
grid_model = H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria)
grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data)
number_model_built = len(grid_model)
print('Maximum model limit is {0}. Number of models built is {1}'.format(search_criteria['max_models'], number_model_built))
if (self.possible_number_models >= self.max_model_number):
if (not (number_model_built == self.max_model_number)):
print('test2_glm_random_grid_search_max_model: failed. Number of model built {0} does not match stopping condition number{1}.'.format(number_model_built, self.max_model_number))
self.test_failed += 1
self.test_failed_array[self.test_num] = 1
else:
print('test2_glm_random_grid_search_max_model for GLM: passed.')
elif (not (number_model_built == self.possible_number_models)):
self.test_failed += 1
self.test_failed_array[self.test_num] = 1
print('test2_glm_random_grid_search_max_model: failed. Number of model built {0} does not equal to possible model number {1}.'.format(number_model_built, self.possible_number_models))
else:
print('test2_glm_random_grid_search_max_model for GLM: passed.')
self.test_num += 1
sys.stdout.flush()
| 3,782,168,255,454,180,400
|
This test is used to test the stopping condition max_model_number in the randomized gridsearch. The
max_models parameter is randomly generated. If it is higher than the actual possible number of models
that can be generated with the current hyper-space parameters, randomized grid search should generate
all the models. Otherwise, grid search shall return a model that equals to the max_model setting.
|
h2o-py/dynamic_tests/testdir_algos/glm/pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py
|
test2_glm_random_grid_search_max_model
|
13927729580/h2o-3
|
python
|
def test2_glm_random_grid_search_max_model(self):
'\n This test is used to test the stopping condition max_model_number in the randomized gridsearch. The\n max_models parameter is randomly generated. If it is higher than the actual possible number of models\n that can be generated with the current hyper-space parameters, randomized grid search should generate\n all the models. Otherwise, grid search shall return a model that equals to the max_model setting.\n '
print('*******************************************************************************************')
print(('test2_glm_random_grid_search_max_model for GLM ' + self.family))
h2o.cluster_info()
self.max_model_number = random.randint(1, int((self.allowed_scaled_model_number * self.possible_number_models)))
search_criteria = {'strategy': 'RandomDiscrete', 'max_models': self.max_model_number, 'seed': round(time.time())}
print('GLM Gaussian grid search_criteria: {0}'.format(search_criteria))
print('Possible number of models built is {0}'.format(self.possible_number_models))
grid_model = H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria)
grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data)
number_model_built = len(grid_model)
print('Maximum model limit is {0}. Number of models built is {1}'.format(search_criteria['max_models'], number_model_built))
if (self.possible_number_models >= self.max_model_number):
if (not (number_model_built == self.max_model_number)):
print('test2_glm_random_grid_search_max_model: failed. Number of model built {0} does not match stopping condition number{1}.'.format(number_model_built, self.max_model_number))
self.test_failed += 1
self.test_failed_array[self.test_num] = 1
else:
print('test2_glm_random_grid_search_max_model for GLM: passed.')
elif (not (number_model_built == self.possible_number_models)):
self.test_failed += 1
self.test_failed_array[self.test_num] = 1
print('test2_glm_random_grid_search_max_model: failed. Number of model built {0} does not equal to possible model number {1}.'.format(number_model_built, self.possible_number_models))
else:
print('test2_glm_random_grid_search_max_model for GLM: passed.')
self.test_num += 1
sys.stdout.flush()
|
def test3_glm_random_grid_search_max_runtime_secs(self):
'\n This function will test the stopping criteria max_runtime_secs. For each model built, the field\n run_time actually denote the time in ms used to build the model. We will add up the run_time from all\n models and check against the stopping criteria max_runtime_secs. Since each model will check its run time\n differently, there is some inaccuracies in the actual run time. For example, if we give a model 10 ms to\n build. The GLM may check and see if it has used up all the time for every 10 epochs that it has run. On\n the other hand, deeplearning may check the time it has spent after every epoch of training.\n\n If we are able to restrict the runtime to not exceed the specified max_runtime_secs by a certain\n percentage, we will consider the test a success.\n\n :return: None\n '
print('*******************************************************************************************')
print(('test3_glm_random_grid_search_max_runtime_secs for GLM ' + self.family))
h2o.cluster_info()
if ('max_runtime_secs' in list(self.hyper_params)):
del self.hyper_params['max_runtime_secs']
self.possible_number_models = pyunit_utils.count_models(self.hyper_params)
max_run_time_secs = random.uniform(self.one_model_time, (self.allowed_scaled_time * self.max_grid_runtime))
search_criteria = {'strategy': 'RandomDiscrete', 'max_runtime_secs': max_run_time_secs, 'seed': round(time.time())}
print('GLM Gaussian grid search_criteria: {0}'.format(search_criteria))
grid_model = H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria)
grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data)
actual_run_time_secs = pyunit_utils.find_grid_runtime(grid_model)
print('Maximum time limit is {0}. Time taken to build all model is {1}'.format(search_criteria['max_runtime_secs'], actual_run_time_secs))
print('Maximum model number is {0}. Actual number of models built is {1}'.format(self.possible_number_models, len(grid_model)))
if (actual_run_time_secs <= (search_criteria['max_runtime_secs'] * (1 + self.allowed_diff))):
print('test3_glm_random_grid_search_max_runtime_secs: passed!')
if (len(grid_model) > self.possible_number_models):
self.test_failed += 1
self.test_failed_array[self.test_num] = 1
print('test3_glm_random_grid_search_max_runtime_secs: failed. Generated {0} models which exceeds maximum possible model number {1}'.format(len(grid_model), self.possible_number_models))
elif (len(grid_model) == 1):
print('test3_glm_random_grid_search_max_runtime_secs: passed!')
else:
self.test_failed += 1
self.test_failed_array[self.test_num] = 1
print('test3_glm_random_grid_search_max_runtime_secs: failed. Model takes time {0} seconds which exceeds allowed time {1}'.format(actual_run_time_secs, (max_run_time_secs * (1 + self.allowed_diff))))
self.test_num += 1
sys.stdout.flush()
| 6,804,544,480,565,740,000
|
This function will test the stopping criteria max_runtime_secs. For each model built, the field
run_time actually denote the time in ms used to build the model. We will add up the run_time from all
models and check against the stopping criteria max_runtime_secs. Since each model will check its run time
differently, there is some inaccuracies in the actual run time. For example, if we give a model 10 ms to
build. The GLM may check and see if it has used up all the time for every 10 epochs that it has run. On
the other hand, deeplearning may check the time it has spent after every epoch of training.
If we are able to restrict the runtime to not exceed the specified max_runtime_secs by a certain
percentage, we will consider the test a success.
:return: None
|
h2o-py/dynamic_tests/testdir_algos/glm/pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py
|
test3_glm_random_grid_search_max_runtime_secs
|
13927729580/h2o-3
|
python
|
def test3_glm_random_grid_search_max_runtime_secs(self):
'\n This function will test the stopping criteria max_runtime_secs. For each model built, the field\n run_time actually denote the time in ms used to build the model. We will add up the run_time from all\n models and check against the stopping criteria max_runtime_secs. Since each model will check its run time\n differently, there is some inaccuracies in the actual run time. For example, if we give a model 10 ms to\n build. The GLM may check and see if it has used up all the time for every 10 epochs that it has run. On\n the other hand, deeplearning may check the time it has spent after every epoch of training.\n\n If we are able to restrict the runtime to not exceed the specified max_runtime_secs by a certain\n percentage, we will consider the test a success.\n\n :return: None\n '
print('*******************************************************************************************')
print(('test3_glm_random_grid_search_max_runtime_secs for GLM ' + self.family))
h2o.cluster_info()
if ('max_runtime_secs' in list(self.hyper_params)):
del self.hyper_params['max_runtime_secs']
self.possible_number_models = pyunit_utils.count_models(self.hyper_params)
max_run_time_secs = random.uniform(self.one_model_time, (self.allowed_scaled_time * self.max_grid_runtime))
search_criteria = {'strategy': 'RandomDiscrete', 'max_runtime_secs': max_run_time_secs, 'seed': round(time.time())}
print('GLM Gaussian grid search_criteria: {0}'.format(search_criteria))
grid_model = H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria)
grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data)
actual_run_time_secs = pyunit_utils.find_grid_runtime(grid_model)
print('Maximum time limit is {0}. Time taken to build all model is {1}'.format(search_criteria['max_runtime_secs'], actual_run_time_secs))
print('Maximum model number is {0}. Actual number of models built is {1}'.format(self.possible_number_models, len(grid_model)))
if (actual_run_time_secs <= (search_criteria['max_runtime_secs'] * (1 + self.allowed_diff))):
print('test3_glm_random_grid_search_max_runtime_secs: passed!')
if (len(grid_model) > self.possible_number_models):
self.test_failed += 1
self.test_failed_array[self.test_num] = 1
print('test3_glm_random_grid_search_max_runtime_secs: failed. Generated {0} models which exceeds maximum possible model number {1}'.format(len(grid_model), self.possible_number_models))
elif (len(grid_model) == 1):
print('test3_glm_random_grid_search_max_runtime_secs: passed!')
else:
self.test_failed += 1
self.test_failed_array[self.test_num] = 1
print('test3_glm_random_grid_search_max_runtime_secs: failed. Model takes time {0} seconds which exceeds allowed time {1}'.format(actual_run_time_secs, (max_run_time_secs * (1 + self.allowed_diff))))
self.test_num += 1
sys.stdout.flush()
|
def test4_glm_random_grid_search_metric(self, metric_name, bigger_is_better):
'\n This function will test the last stopping condition using metrics.\n\n :param metric_name: metric we want to use to test the last stopping condition\n :param bigger_is_better: higher metric value indicates better model performance\n\n :return: None\n '
print('*******************************************************************************************')
print(((('test4_glm_random_grid_search_metric using ' + metric_name) + ' for family ') + self.family))
h2o.cluster_info()
search_criteria = {'strategy': 'RandomDiscrete', 'stopping_metric': metric_name, 'stopping_tolerance': random.uniform(1e-08, self.max_tolerance), 'stopping_rounds': random.randint(1, self.max_stopping_rounds), 'seed': round(time.time())}
print('GLM Gaussian grid search_criteria: {0}'.format(search_criteria))
self.hyper_params['max_runtime_secs'] = [0.3]
grid_model = H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria)
grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data)
stopped_correctly = pyunit_utils.evaluate_metrics_stopping(grid_model.models, metric_name, bigger_is_better, search_criteria, self.possible_number_models)
if stopped_correctly:
print((('test4_glm_random_grid_search_metric ' + metric_name) + ': passed. '))
else:
self.test_failed += 1
self.test_failed_array[self.test_num] = 1
print((('test4_glm_random_grid_search_metric ' + metric_name) + ': failed. '))
self.test_num += 1
| -2,631,604,389,071,402,500
|
This function will test the last stopping condition using metrics.
:param metric_name: metric we want to use to test the last stopping condition
:param bigger_is_better: higher metric value indicates better model performance
:return: None
|
h2o-py/dynamic_tests/testdir_algos/glm/pyunit_glm_gaussian_gridsearch_randomdiscrete_large.py
|
test4_glm_random_grid_search_metric
|
13927729580/h2o-3
|
python
|
def test4_glm_random_grid_search_metric(self, metric_name, bigger_is_better):
'\n This function will test the last stopping condition using metrics.\n\n :param metric_name: metric we want to use to test the last stopping condition\n :param bigger_is_better: higher metric value indicates better model performance\n\n :return: None\n '
print('*******************************************************************************************')
print(((('test4_glm_random_grid_search_metric using ' + metric_name) + ' for family ') + self.family))
h2o.cluster_info()
search_criteria = {'strategy': 'RandomDiscrete', 'stopping_metric': metric_name, 'stopping_tolerance': random.uniform(1e-08, self.max_tolerance), 'stopping_rounds': random.randint(1, self.max_stopping_rounds), 'seed': round(time.time())}
print('GLM Gaussian grid search_criteria: {0}'.format(search_criteria))
self.hyper_params['max_runtime_secs'] = [0.3]
grid_model = H2OGridSearch(H2OGeneralizedLinearEstimator(family=self.family, nfolds=self.nfolds), hyper_params=self.hyper_params, search_criteria=search_criteria)
grid_model.train(x=self.x_indices, y=self.y_index, training_frame=self.training1_data)
stopped_correctly = pyunit_utils.evaluate_metrics_stopping(grid_model.models, metric_name, bigger_is_better, search_criteria, self.possible_number_models)
if stopped_correctly:
print((('test4_glm_random_grid_search_metric ' + metric_name) + ': passed. '))
else:
self.test_failed += 1
self.test_failed_array[self.test_num] = 1
print((('test4_glm_random_grid_search_metric ' + metric_name) + ': failed. '))
self.test_num += 1
|
def expect_cpp_code_generic_compile_error(expected_error_regex, tmppy_source, module_ir2, module_ir1, cxx_source):
"\n Tests that the given source produces the expected error during compilation.\n\n :param expected_error_regex: A regex used to match the _py2tmp error type,\n e.g. 'NoBindingFoundForAbstractClassError<ScalerImpl>'.\n :param cxx_source: The second part of the source code. This will be dedented.\n "
expected_error_regex = expected_error_regex.replace(' ', '')
def check_error(e, error_message_lines, error_message_head, normalized_error_message_lines):
for line in normalized_error_message_lines:
if re.search(expected_error_regex, line):
return
pytest.fail(textwrap.dedent(' Expected error {expected_error} but the compiler output did not contain that.\n Compiler command line: {compiler_command}\n Error message was:\n {error_message}\n\n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source:\n {cxx_source}\n ').format(expected_error=expected_error_regex, compiler_command=e.command, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=error_message_head), pytrace=False)
expect_cpp_code_compile_error_helper(check_error, tmppy_source, module_ir2, module_ir1, cxx_source)
| -7,943,035,485,855,268,000
|
Tests that the given source produces the expected error during compilation.
:param expected_error_regex: A regex used to match the _py2tmp error type,
e.g. 'NoBindingFoundForAbstractClassError<ScalerImpl>'.
:param cxx_source: The second part of the source code. This will be dedented.
|
_py2tmp/testing/utils.py
|
expect_cpp_code_generic_compile_error
|
DalavanCloud/tmppy
|
python
|
def expect_cpp_code_generic_compile_error(expected_error_regex, tmppy_source, module_ir2, module_ir1, cxx_source):
"\n Tests that the given source produces the expected error during compilation.\n\n :param expected_error_regex: A regex used to match the _py2tmp error type,\n e.g. 'NoBindingFoundForAbstractClassError<ScalerImpl>'.\n :param cxx_source: The second part of the source code. This will be dedented.\n "
expected_error_regex = expected_error_regex.replace(' ', )
def check_error(e, error_message_lines, error_message_head, normalized_error_message_lines):
for line in normalized_error_message_lines:
if re.search(expected_error_regex, line):
return
pytest.fail(textwrap.dedent(' Expected error {expected_error} but the compiler output did not contain that.\n Compiler command line: {compiler_command}\n Error message was:\n {error_message}\n\n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source:\n {cxx_source}\n ').format(expected_error=expected_error_regex, compiler_command=e.command, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=error_message_head), pytrace=False)
expect_cpp_code_compile_error_helper(check_error, tmppy_source, module_ir2, module_ir1, cxx_source)
|
def expect_cpp_code_compile_error(expected_py2tmp_error_regex, expected_py2tmp_error_desc_regex, tmppy_source, module_ir2, module_ir1, cxx_source):
"\n Tests that the given source produces the expected error during compilation.\n\n :param expected_py2tmp_error_regex: A regex used to match the _py2tmp error type,\n e.g. 'NoBindingFoundForAbstractClassError<ScalerImpl>'.\n :param expected_py2tmp_error_desc_regex: A regex used to match the _py2tmp error description,\n e.g. 'No explicit binding was found for C, and C is an abstract class'.\n :param source_code: The C++ source code. This will be dedented.\n :param ignore_deprecation_warnings: A boolean. If True, deprecation warnings will be ignored.\n "
if ('\n' in expected_py2tmp_error_regex):
raise Exception('expected_py2tmp_error_regex should not contain newlines')
if ('\n' in expected_py2tmp_error_desc_regex):
raise Exception('expected_py2tmp_error_desc_regex should not contain newlines')
expected_py2tmp_error_regex = expected_py2tmp_error_regex.replace(' ', '')
def check_error(e, error_message_lines, error_message_head, normalized_error_message_lines):
for (line_number, line) in enumerate(normalized_error_message_lines):
match = re.search('tmppy::impl::(.*Error<.*>)', line)
if match:
actual_py2tmp_error_line_number = line_number
actual_py2tmp_error = match.groups()[0]
if (config.CXX_COMPILER_NAME == 'MSVC'):
try:
replacement_lines = []
if (normalized_error_message_lines[(line_number + 1)].strip() == 'with'):
for line in itertools.islice(normalized_error_message_lines, (line_number + 3), None):
line = line.strip()
if (line == ']'):
break
if line.endswith(','):
line = line[:(- 1)]
replacement_lines.append(line)
for replacement_line in replacement_lines:
match = re.search('([A-Za-z0-9_-]*)=(.*)', replacement_line)
if (not match):
raise Exception(('Failed to parse replacement line: %s' % replacement_line)) from e
(type_variable, type_expression) = match.groups()
actual_py2tmp_error = re.sub((('\\b' + type_variable) + '\\b'), type_expression, actual_py2tmp_error)
except Exception:
raise Exception('Failed to parse MSVC template type arguments')
break
else:
pytest.fail(textwrap.dedent(' Expected error {expected_error} but the compiler output did not contain user-facing _py2tmp errors.\n Compiler command line: {compiler_command}\n Error message was:\n {error_message}\n\n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source code:\n {cxx_source}\n ').format(expected_error=expected_py2tmp_error_regex, compiler_command=e.command, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=error_message_head), pytrace=False)
for (line_number, line) in enumerate(error_message_lines):
match = re.search(py2tmp_error_message_extraction_regex, line)
if match:
actual_static_assert_error_line_number = line_number
actual_static_assert_error = match.groups()[0]
break
else:
pytest.fail(textwrap.dedent(' Expected error {expected_error} but the compiler output did not contain static_assert errors.\n Compiler command line: {compiler_command}\n Error message was:\n {error_message}\n \n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source code:\n {cxx_source}\n ').format(expected_error=expected_py2tmp_error_regex, compiler_command=e.command, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=error_message_head), pytrace=False)
try:
regex_search_result = re.search(expected_py2tmp_error_regex, actual_py2tmp_error)
except Exception as e:
raise Exception(("re.search() failed for regex '%s'" % expected_py2tmp_error_regex)) from e
if (not regex_search_result):
pytest.fail(textwrap.dedent(' The compilation failed as expected, but with a different error type.\n Expected _py2tmp error type: {expected_py2tmp_error_regex}\n Error type was: {actual_py2tmp_error}\n Expected static assert error: {expected_py2tmp_error_desc_regex}\n Static assert was: {actual_static_assert_error}\n \n Error message was:\n {error_message}\n \n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source code:\n {cxx_source}\n '.format(expected_py2tmp_error_regex=expected_py2tmp_error_regex, actual_py2tmp_error=actual_py2tmp_error, expected_py2tmp_error_desc_regex=expected_py2tmp_error_desc_regex, actual_static_assert_error=actual_static_assert_error, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=error_message_head)), pytrace=False)
try:
regex_search_result = re.search(expected_py2tmp_error_desc_regex, actual_static_assert_error)
except Exception as e:
raise Exception(("re.search() failed for regex '%s'" % expected_py2tmp_error_desc_regex)) from e
if (not regex_search_result):
pytest.fail(textwrap.dedent(' The compilation failed as expected, but with a different error message.\n Expected _py2tmp error type: {expected_py2tmp_error_regex}\n Error type was: {actual_py2tmp_error}\n Expected static assert error: {expected_py2tmp_error_desc_regex}\n Static assert was: {actual_static_assert_error}\n \n Error message:\n {error_message}\n \n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source code:\n {cxx_source}\n '.format(expected_py2tmp_error_regex=expected_py2tmp_error_regex, actual_py2tmp_error=actual_py2tmp_error, expected_py2tmp_error_desc_regex=expected_py2tmp_error_desc_regex, actual_static_assert_error=actual_static_assert_error, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=error_message_head)), pytrace=False)
if ((actual_py2tmp_error_line_number > 6) or (actual_static_assert_error_line_number > 6)):
pytest.fail(textwrap.dedent(' The compilation failed with the expected message, but the error message contained too many lines before the relevant ones.\n The error type was reported on line {actual_py2tmp_error_line_number} of the message (should be <=6).\n The static assert was reported on line {actual_static_assert_error_line_number} of the message (should be <=6).\n Error message:\n {error_message}\n\n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source code:\n {cxx_source}\n '.format(actual_py2tmp_error_line_number=actual_py2tmp_error_line_number, actual_static_assert_error_line_number=actual_static_assert_error_line_number, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=error_message_head)), pytrace=False)
for line in error_message_lines[:max(actual_py2tmp_error_line_number, actual_static_assert_error_line_number)]:
if re.search('tmppy::impl', line):
pytest.fail(('The compilation failed with the expected message, but the error message contained some metaprogramming types in the output (besides Error). Error message:\n%s' + error_message_head), pytrace=False)
expect_cpp_code_compile_error_helper(check_error, tmppy_source, module_ir2, module_ir1, cxx_source)
| -3,876,706,337,203,248,600
|
Tests that the given source produces the expected error during compilation.
:param expected_py2tmp_error_regex: A regex used to match the _py2tmp error type,
e.g. 'NoBindingFoundForAbstractClassError<ScalerImpl>'.
:param expected_py2tmp_error_desc_regex: A regex used to match the _py2tmp error description,
e.g. 'No explicit binding was found for C, and C is an abstract class'.
:param source_code: The C++ source code. This will be dedented.
:param ignore_deprecation_warnings: A boolean. If True, deprecation warnings will be ignored.
|
_py2tmp/testing/utils.py
|
expect_cpp_code_compile_error
|
DalavanCloud/tmppy
|
python
|
def expect_cpp_code_compile_error(expected_py2tmp_error_regex, expected_py2tmp_error_desc_regex, tmppy_source, module_ir2, module_ir1, cxx_source):
"\n Tests that the given source produces the expected error during compilation.\n\n :param expected_py2tmp_error_regex: A regex used to match the _py2tmp error type,\n e.g. 'NoBindingFoundForAbstractClassError<ScalerImpl>'.\n :param expected_py2tmp_error_desc_regex: A regex used to match the _py2tmp error description,\n e.g. 'No explicit binding was found for C, and C is an abstract class'.\n :param source_code: The C++ source code. This will be dedented.\n :param ignore_deprecation_warnings: A boolean. If True, deprecation warnings will be ignored.\n "
if ('\n' in expected_py2tmp_error_regex):
raise Exception('expected_py2tmp_error_regex should not contain newlines')
if ('\n' in expected_py2tmp_error_desc_regex):
raise Exception('expected_py2tmp_error_desc_regex should not contain newlines')
expected_py2tmp_error_regex = expected_py2tmp_error_regex.replace(' ', )
def check_error(e, error_message_lines, error_message_head, normalized_error_message_lines):
for (line_number, line) in enumerate(normalized_error_message_lines):
match = re.search('tmppy::impl::(.*Error<.*>)', line)
if match:
actual_py2tmp_error_line_number = line_number
actual_py2tmp_error = match.groups()[0]
if (config.CXX_COMPILER_NAME == 'MSVC'):
try:
replacement_lines = []
if (normalized_error_message_lines[(line_number + 1)].strip() == 'with'):
for line in itertools.islice(normalized_error_message_lines, (line_number + 3), None):
line = line.strip()
if (line == ']'):
break
if line.endswith(','):
line = line[:(- 1)]
replacement_lines.append(line)
for replacement_line in replacement_lines:
match = re.search('([A-Za-z0-9_-]*)=(.*)', replacement_line)
if (not match):
raise Exception(('Failed to parse replacement line: %s' % replacement_line)) from e
(type_variable, type_expression) = match.groups()
actual_py2tmp_error = re.sub((('\\b' + type_variable) + '\\b'), type_expression, actual_py2tmp_error)
except Exception:
raise Exception('Failed to parse MSVC template type arguments')
break
else:
pytest.fail(textwrap.dedent(' Expected error {expected_error} but the compiler output did not contain user-facing _py2tmp errors.\n Compiler command line: {compiler_command}\n Error message was:\n {error_message}\n\n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source code:\n {cxx_source}\n ').format(expected_error=expected_py2tmp_error_regex, compiler_command=e.command, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=error_message_head), pytrace=False)
for (line_number, line) in enumerate(error_message_lines):
match = re.search(py2tmp_error_message_extraction_regex, line)
if match:
actual_static_assert_error_line_number = line_number
actual_static_assert_error = match.groups()[0]
break
else:
pytest.fail(textwrap.dedent(' Expected error {expected_error} but the compiler output did not contain static_assert errors.\n Compiler command line: {compiler_command}\n Error message was:\n {error_message}\n \n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source code:\n {cxx_source}\n ').format(expected_error=expected_py2tmp_error_regex, compiler_command=e.command, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=error_message_head), pytrace=False)
try:
regex_search_result = re.search(expected_py2tmp_error_regex, actual_py2tmp_error)
except Exception as e:
raise Exception(("re.search() failed for regex '%s'" % expected_py2tmp_error_regex)) from e
if (not regex_search_result):
pytest.fail(textwrap.dedent(' The compilation failed as expected, but with a different error type.\n Expected _py2tmp error type: {expected_py2tmp_error_regex}\n Error type was: {actual_py2tmp_error}\n Expected static assert error: {expected_py2tmp_error_desc_regex}\n Static assert was: {actual_static_assert_error}\n \n Error message was:\n {error_message}\n \n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source code:\n {cxx_source}\n '.format(expected_py2tmp_error_regex=expected_py2tmp_error_regex, actual_py2tmp_error=actual_py2tmp_error, expected_py2tmp_error_desc_regex=expected_py2tmp_error_desc_regex, actual_static_assert_error=actual_static_assert_error, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=error_message_head)), pytrace=False)
try:
regex_search_result = re.search(expected_py2tmp_error_desc_regex, actual_static_assert_error)
except Exception as e:
raise Exception(("re.search() failed for regex '%s'" % expected_py2tmp_error_desc_regex)) from e
if (not regex_search_result):
pytest.fail(textwrap.dedent(' The compilation failed as expected, but with a different error message.\n Expected _py2tmp error type: {expected_py2tmp_error_regex}\n Error type was: {actual_py2tmp_error}\n Expected static assert error: {expected_py2tmp_error_desc_regex}\n Static assert was: {actual_static_assert_error}\n \n Error message:\n {error_message}\n \n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source code:\n {cxx_source}\n '.format(expected_py2tmp_error_regex=expected_py2tmp_error_regex, actual_py2tmp_error=actual_py2tmp_error, expected_py2tmp_error_desc_regex=expected_py2tmp_error_desc_regex, actual_static_assert_error=actual_static_assert_error, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=error_message_head)), pytrace=False)
if ((actual_py2tmp_error_line_number > 6) or (actual_static_assert_error_line_number > 6)):
pytest.fail(textwrap.dedent(' The compilation failed with the expected message, but the error message contained too many lines before the relevant ones.\n The error type was reported on line {actual_py2tmp_error_line_number} of the message (should be <=6).\n The static assert was reported on line {actual_static_assert_error_line_number} of the message (should be <=6).\n Error message:\n {error_message}\n\n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source code:\n {cxx_source}\n '.format(actual_py2tmp_error_line_number=actual_py2tmp_error_line_number, actual_static_assert_error_line_number=actual_static_assert_error_line_number, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=error_message_head)), pytrace=False)
for line in error_message_lines[:max(actual_py2tmp_error_line_number, actual_static_assert_error_line_number)]:
if re.search('tmppy::impl', line):
pytest.fail(('The compilation failed with the expected message, but the error message contained some metaprogramming types in the output (besides Error). Error message:\n%s' + error_message_head), pytrace=False)
expect_cpp_code_compile_error_helper(check_error, tmppy_source, module_ir2, module_ir1, cxx_source)
|
def expect_cpp_code_success(tmppy_source, module_ir2, module_ir1, cxx_source):
'\n Tests that the given source compiles and runs successfully.\n\n :param source_code: The C++ source code. This will be dedented.\n '
if ('main(' not in cxx_source):
cxx_source += textwrap.dedent('\n int main() {\n }\n ')
source_file_name = _create_temporary_file(cxx_source, file_name_suffix='.cpp')
executable_suffix = {'posix': '', 'nt': '.exe'}[os.name]
output_file_name = _create_temporary_file('', executable_suffix)
e = None
try:
compiler.compile_and_link(source=source_file_name, include_dirs=[config.MPYL_INCLUDE_DIR], output_file_name=output_file_name, args=[])
except CommandFailedException as e1:
e = e1
if e:
pytest.fail(textwrap.dedent(' The generated C++ source did not compile.\n Compiler command line: {compiler_command}\n Error message was:\n {error_message}\n \n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source:\n {cxx_source}\n ').format(compiler_command=e.command, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=_cap_to_lines(e.stderr, 40)), pytrace=False)
try:
run_compiled_executable(output_file_name)
except CommandFailedException as e1:
e = e1
if e:
pytest.fail(textwrap.dedent(' The generated C++ executable did not run successfully.\n stderr was:\n {error_message}\n\n TMPPy source:\n {tmppy_source}\n \n C++ source:\n {cxx_source}\n ').format(tmppy_source=add_line_numbers(tmppy_source), cxx_source=add_line_numbers(cxx_source), error_message=_cap_to_lines(e.stderr, 40)), pytrace=False)
try_remove_temporary_file(source_file_name)
try_remove_temporary_file(output_file_name)
| 3,384,454,379,031,476,700
|
Tests that the given source compiles and runs successfully.
:param source_code: The C++ source code. This will be dedented.
|
_py2tmp/testing/utils.py
|
expect_cpp_code_success
|
DalavanCloud/tmppy
|
python
|
def expect_cpp_code_success(tmppy_source, module_ir2, module_ir1, cxx_source):
'\n Tests that the given source compiles and runs successfully.\n\n :param source_code: The C++ source code. This will be dedented.\n '
if ('main(' not in cxx_source):
cxx_source += textwrap.dedent('\n int main() {\n }\n ')
source_file_name = _create_temporary_file(cxx_source, file_name_suffix='.cpp')
executable_suffix = {'posix': , 'nt': '.exe'}[os.name]
output_file_name = _create_temporary_file(, executable_suffix)
e = None
try:
compiler.compile_and_link(source=source_file_name, include_dirs=[config.MPYL_INCLUDE_DIR], output_file_name=output_file_name, args=[])
except CommandFailedException as e1:
e = e1
if e:
pytest.fail(textwrap.dedent(' The generated C++ source did not compile.\n Compiler command line: {compiler_command}\n Error message was:\n {error_message}\n \n TMPPy source:\n {tmppy_source}\n \n TMPPy IR2:\n {tmppy_ir2}\n \n TMPPy IR1:\n {tmppy_ir1}\n \n C++ source:\n {cxx_source}\n ').format(compiler_command=e.command, tmppy_source=add_line_numbers(tmppy_source), tmppy_ir2=str(module_ir2), tmppy_ir1=str(module_ir1), cxx_source=add_line_numbers(cxx_source), error_message=_cap_to_lines(e.stderr, 40)), pytrace=False)
try:
run_compiled_executable(output_file_name)
except CommandFailedException as e1:
e = e1
if e:
pytest.fail(textwrap.dedent(' The generated C++ executable did not run successfully.\n stderr was:\n {error_message}\n\n TMPPy source:\n {tmppy_source}\n \n C++ source:\n {cxx_source}\n ').format(tmppy_source=add_line_numbers(tmppy_source), cxx_source=add_line_numbers(cxx_source), error_message=_cap_to_lines(e.stderr, 40)), pytrace=False)
try_remove_temporary_file(source_file_name)
try_remove_temporary_file(output_file_name)
|
def test_commit(shared_instance, dbapi_database):
'Test committing a transaction with several statements.'
want_row = (1, 'updated-first-name', 'last-name', 'example@example.com')
conn = Connection(shared_instance, dbapi_database)
cursor = conn.cursor()
cursor.execute("\nINSERT INTO contacts (contact_id, first_name, last_name, email)\nVALUES (1, 'first-name', 'last-name', 'example@example.com')\n ")
cursor.execute("\nUPDATE contacts\nSET first_name = 'updated-first-name'\nWHERE first_name = 'first-name'\n")
cursor.execute("\nUPDATE contacts\nSET email = 'example@example.com'\nWHERE email = 'example@example.com'\n")
conn.commit()
cursor.execute('SELECT * FROM contacts')
got_rows = cursor.fetchall()
conn.commit()
assert (got_rows == [want_row])
cursor.close()
conn.close()
| 8,524,424,343,788,117,000
|
Test committing a transaction with several statements.
|
tests/system/test_dbapi.py
|
test_commit
|
jpburbank/python-spanner
|
python
|
def test_commit(shared_instance, dbapi_database):
want_row = (1, 'updated-first-name', 'last-name', 'example@example.com')
conn = Connection(shared_instance, dbapi_database)
cursor = conn.cursor()
cursor.execute("\nINSERT INTO contacts (contact_id, first_name, last_name, email)\nVALUES (1, 'first-name', 'last-name', 'example@example.com')\n ")
cursor.execute("\nUPDATE contacts\nSET first_name = 'updated-first-name'\nWHERE first_name = 'first-name'\n")
cursor.execute("\nUPDATE contacts\nSET email = 'example@example.com'\nWHERE email = 'example@example.com'\n")
conn.commit()
cursor.execute('SELECT * FROM contacts')
got_rows = cursor.fetchall()
conn.commit()
assert (got_rows == [want_row])
cursor.close()
conn.close()
|
def test_rollback(shared_instance, dbapi_database):
'Test rollbacking a transaction with several statements.'
want_row = (2, 'first-name', 'last-name', 'example@example.com')
conn = Connection(shared_instance, dbapi_database)
cursor = conn.cursor()
cursor.execute("\nINSERT INTO contacts (contact_id, first_name, last_name, email)\nVALUES (2, 'first-name', 'last-name', 'example@example.com')\n ")
conn.commit()
cursor.execute("\nUPDATE contacts\nSET first_name = 'updated-first-name'\nWHERE first_name = 'first-name'\n")
cursor.execute("\nUPDATE contacts\nSET email = 'example@example.com'\nWHERE email = 'example@example.com'\n")
conn.rollback()
cursor.execute('SELECT * FROM contacts')
got_rows = cursor.fetchall()
conn.commit()
assert (got_rows == [want_row])
cursor.close()
conn.close()
| 787,733,936,744,369,300
|
Test rollbacking a transaction with several statements.
|
tests/system/test_dbapi.py
|
test_rollback
|
jpburbank/python-spanner
|
python
|
def test_rollback(shared_instance, dbapi_database):
want_row = (2, 'first-name', 'last-name', 'example@example.com')
conn = Connection(shared_instance, dbapi_database)
cursor = conn.cursor()
cursor.execute("\nINSERT INTO contacts (contact_id, first_name, last_name, email)\nVALUES (2, 'first-name', 'last-name', 'example@example.com')\n ")
conn.commit()
cursor.execute("\nUPDATE contacts\nSET first_name = 'updated-first-name'\nWHERE first_name = 'first-name'\n")
cursor.execute("\nUPDATE contacts\nSET email = 'example@example.com'\nWHERE email = 'example@example.com'\n")
conn.rollback()
cursor.execute('SELECT * FROM contacts')
got_rows = cursor.fetchall()
conn.commit()
assert (got_rows == [want_row])
cursor.close()
conn.close()
|
def test_autocommit_mode_change(shared_instance, dbapi_database):
'Test auto committing a transaction on `autocommit` mode change.'
want_row = (2, 'updated-first-name', 'last-name', 'example@example.com')
conn = Connection(shared_instance, dbapi_database)
cursor = conn.cursor()
cursor.execute("\nINSERT INTO contacts (contact_id, first_name, last_name, email)\nVALUES (2, 'first-name', 'last-name', 'example@example.com')\n ")
cursor.execute("\nUPDATE contacts\nSET first_name = 'updated-first-name'\nWHERE first_name = 'first-name'\n")
conn.autocommit = True
cursor.execute('SELECT * FROM contacts')
got_rows = cursor.fetchall()
assert (got_rows == [want_row])
cursor.close()
conn.close()
| -5,353,696,721,925,448,000
|
Test auto committing a transaction on `autocommit` mode change.
|
tests/system/test_dbapi.py
|
test_autocommit_mode_change
|
jpburbank/python-spanner
|
python
|
def test_autocommit_mode_change(shared_instance, dbapi_database):
want_row = (2, 'updated-first-name', 'last-name', 'example@example.com')
conn = Connection(shared_instance, dbapi_database)
cursor = conn.cursor()
cursor.execute("\nINSERT INTO contacts (contact_id, first_name, last_name, email)\nVALUES (2, 'first-name', 'last-name', 'example@example.com')\n ")
cursor.execute("\nUPDATE contacts\nSET first_name = 'updated-first-name'\nWHERE first_name = 'first-name'\n")
conn.autocommit = True
cursor.execute('SELECT * FROM contacts')
got_rows = cursor.fetchall()
assert (got_rows == [want_row])
cursor.close()
conn.close()
|
def test_rollback_on_connection_closing(shared_instance, dbapi_database):
"\n When closing a connection all the pending transactions\n must be rollbacked. Testing if it's working this way.\n "
want_row = (1, 'first-name', 'last-name', 'example@example.com')
conn = Connection(shared_instance, dbapi_database)
cursor = conn.cursor()
cursor.execute("\nINSERT INTO contacts (contact_id, first_name, last_name, email)\nVALUES (1, 'first-name', 'last-name', 'example@example.com')\n ")
conn.commit()
cursor.execute("\nUPDATE contacts\nSET first_name = 'updated-first-name'\nWHERE first_name = 'first-name'\n")
conn.close()
conn = Connection(shared_instance, dbapi_database)
cursor = conn.cursor()
cursor.execute('SELECT * FROM contacts')
got_rows = cursor.fetchall()
conn.commit()
assert (got_rows == [want_row])
cursor.close()
conn.close()
| -7,109,604,337,055,367,000
|
When closing a connection all the pending transactions
must be rollbacked. Testing if it's working this way.
|
tests/system/test_dbapi.py
|
test_rollback_on_connection_closing
|
jpburbank/python-spanner
|
python
|
def test_rollback_on_connection_closing(shared_instance, dbapi_database):
"\n When closing a connection all the pending transactions\n must be rollbacked. Testing if it's working this way.\n "
want_row = (1, 'first-name', 'last-name', 'example@example.com')
conn = Connection(shared_instance, dbapi_database)
cursor = conn.cursor()
cursor.execute("\nINSERT INTO contacts (contact_id, first_name, last_name, email)\nVALUES (1, 'first-name', 'last-name', 'example@example.com')\n ")
conn.commit()
cursor.execute("\nUPDATE contacts\nSET first_name = 'updated-first-name'\nWHERE first_name = 'first-name'\n")
conn.close()
conn = Connection(shared_instance, dbapi_database)
cursor = conn.cursor()
cursor.execute('SELECT * FROM contacts')
got_rows = cursor.fetchall()
conn.commit()
assert (got_rows == [want_row])
cursor.close()
conn.close()
|
def test_results_checksum(shared_instance, dbapi_database):
'Test that results checksum is calculated properly.'
conn = Connection(shared_instance, dbapi_database)
cursor = conn.cursor()
cursor.execute("\nINSERT INTO contacts (contact_id, first_name, last_name, email)\nVALUES\n(1, 'first-name', 'last-name', 'example@example.com'),\n(2, 'first-name2', 'last-name2', 'example@example.com')\n ")
assert (len(conn._statements) == 1)
conn.commit()
cursor.execute('SELECT * FROM contacts')
got_rows = cursor.fetchall()
assert (len(conn._statements) == 1)
conn.commit()
checksum = hashlib.sha256()
checksum.update(pickle.dumps(got_rows[0]))
checksum.update(pickle.dumps(got_rows[1]))
assert (cursor._checksum.checksum.digest() == checksum.digest())
| 3,821,948,948,630,807,600
|
Test that results checksum is calculated properly.
|
tests/system/test_dbapi.py
|
test_results_checksum
|
jpburbank/python-spanner
|
python
|
def test_results_checksum(shared_instance, dbapi_database):
conn = Connection(shared_instance, dbapi_database)
cursor = conn.cursor()
cursor.execute("\nINSERT INTO contacts (contact_id, first_name, last_name, email)\nVALUES\n(1, 'first-name', 'last-name', 'example@example.com'),\n(2, 'first-name2', 'last-name2', 'example@example.com')\n ")
assert (len(conn._statements) == 1)
conn.commit()
cursor.execute('SELECT * FROM contacts')
got_rows = cursor.fetchall()
assert (len(conn._statements) == 1)
conn.commit()
checksum = hashlib.sha256()
checksum.update(pickle.dumps(got_rows[0]))
checksum.update(pickle.dumps(got_rows[1]))
assert (cursor._checksum.checksum.digest() == checksum.digest())
|
def test_DDL_autocommit(shared_instance, dbapi_database):
'Check that DDLs in autocommit mode are immediately executed.'
conn = Connection(shared_instance, dbapi_database)
conn.autocommit = True
cur = conn.cursor()
cur.execute('\n CREATE TABLE Singers (\n SingerId INT64 NOT NULL,\n Name STRING(1024),\n ) PRIMARY KEY (SingerId)\n ')
conn.close()
conn = Connection(shared_instance, dbapi_database)
cur = conn.cursor()
cur.execute('DROP TABLE Singers')
conn.commit()
| 7,331,400,359,012,387,000
|
Check that DDLs in autocommit mode are immediately executed.
|
tests/system/test_dbapi.py
|
test_DDL_autocommit
|
jpburbank/python-spanner
|
python
|
def test_DDL_autocommit(shared_instance, dbapi_database):
conn = Connection(shared_instance, dbapi_database)
conn.autocommit = True
cur = conn.cursor()
cur.execute('\n CREATE TABLE Singers (\n SingerId INT64 NOT NULL,\n Name STRING(1024),\n ) PRIMARY KEY (SingerId)\n ')
conn.close()
conn = Connection(shared_instance, dbapi_database)
cur = conn.cursor()
cur.execute('DROP TABLE Singers')
conn.commit()
|
def test_DDL_commit(shared_instance, dbapi_database):
'Check that DDLs in commit mode are executed on calling `commit()`.'
conn = Connection(shared_instance, dbapi_database)
cur = conn.cursor()
cur.execute('\n CREATE TABLE Singers (\n SingerId INT64 NOT NULL,\n Name STRING(1024),\n ) PRIMARY KEY (SingerId)\n ')
conn.commit()
conn.close()
conn = Connection(shared_instance, dbapi_database)
cur = conn.cursor()
cur.execute('DROP TABLE Singers')
conn.commit()
| 3,273,943,874,377,342,000
|
Check that DDLs in commit mode are executed on calling `commit()`.
|
tests/system/test_dbapi.py
|
test_DDL_commit
|
jpburbank/python-spanner
|
python
|
def test_DDL_commit(shared_instance, dbapi_database):
conn = Connection(shared_instance, dbapi_database)
cur = conn.cursor()
cur.execute('\n CREATE TABLE Singers (\n SingerId INT64 NOT NULL,\n Name STRING(1024),\n ) PRIMARY KEY (SingerId)\n ')
conn.commit()
conn.close()
conn = Connection(shared_instance, dbapi_database)
cur = conn.cursor()
cur.execute('DROP TABLE Singers')
conn.commit()
|
def test_ping(shared_instance, dbapi_database):
'Check connection validation method.'
conn = Connection(shared_instance, dbapi_database)
conn.validate()
conn.close()
| 427,163,684,188,578,200
|
Check connection validation method.
|
tests/system/test_dbapi.py
|
test_ping
|
jpburbank/python-spanner
|
python
|
def test_ping(shared_instance, dbapi_database):
conn = Connection(shared_instance, dbapi_database)
conn.validate()
conn.close()
|
def _last_stack_str():
"Print stack trace from call that didn't originate from here"
stack = extract_stack()
for s in stack[::(- 1)]:
if (op.join('vispy', 'gloo', 'buffer.py') not in __file__):
break
return format_list([s])[0]
| 615,006,874,114,146,700
|
Print stack trace from call that didn't originate from here
|
vispy/gloo/buffer.py
|
_last_stack_str
|
CVandML/vispy
|
python
|
def _last_stack_str():
stack = extract_stack()
for s in stack[::(- 1)]:
if (op.join('vispy', 'gloo', 'buffer.py') not in __file__):
break
return format_list([s])[0]
|
@property
def nbytes(self):
' Buffer size in bytes '
return self._nbytes
| -2,619,702,808,926,269,000
|
Buffer size in bytes
|
vispy/gloo/buffer.py
|
nbytes
|
CVandML/vispy
|
python
|
@property
def nbytes(self):
' '
return self._nbytes
|
def set_subdata(self, data, offset=0, copy=False):
' Set a sub-region of the buffer (deferred operation).\n\n Parameters\n ----------\n\n data : ndarray\n Data to be uploaded\n offset: int\n Offset in buffer where to start copying data (in bytes)\n copy: bool\n Since the operation is deferred, data may change before\n data is actually uploaded to GPU memory.\n Asking explicitly for a copy will prevent this behavior.\n '
data = np.array(data, copy=copy)
nbytes = data.nbytes
if (offset < 0):
raise ValueError('Offset must be positive')
elif ((offset + nbytes) > self._nbytes):
raise ValueError('Data does not fit into buffer')
if ((nbytes == self._nbytes) and (offset == 0)):
self._glir.command('SIZE', self._id, nbytes)
self._glir.command('DATA', self._id, offset, data)
| -5,062,661,572,289,726,000
|
Set a sub-region of the buffer (deferred operation).
Parameters
----------
data : ndarray
Data to be uploaded
offset: int
Offset in buffer where to start copying data (in bytes)
copy: bool
Since the operation is deferred, data may change before
data is actually uploaded to GPU memory.
Asking explicitly for a copy will prevent this behavior.
|
vispy/gloo/buffer.py
|
set_subdata
|
CVandML/vispy
|
python
|
def set_subdata(self, data, offset=0, copy=False):
' Set a sub-region of the buffer (deferred operation).\n\n Parameters\n ----------\n\n data : ndarray\n Data to be uploaded\n offset: int\n Offset in buffer where to start copying data (in bytes)\n copy: bool\n Since the operation is deferred, data may change before\n data is actually uploaded to GPU memory.\n Asking explicitly for a copy will prevent this behavior.\n '
data = np.array(data, copy=copy)
nbytes = data.nbytes
if (offset < 0):
raise ValueError('Offset must be positive')
elif ((offset + nbytes) > self._nbytes):
raise ValueError('Data does not fit into buffer')
if ((nbytes == self._nbytes) and (offset == 0)):
self._glir.command('SIZE', self._id, nbytes)
self._glir.command('DATA', self._id, offset, data)
|
def set_data(self, data, copy=False):
' Set data in the buffer (deferred operation).\n\n This completely resets the size and contents of the buffer.\n\n Parameters\n ----------\n data : ndarray\n Data to be uploaded\n copy: bool\n Since the operation is deferred, data may change before\n data is actually uploaded to GPU memory.\n Asking explicitly for a copy will prevent this behavior.\n '
data = np.array(data, copy=copy)
nbytes = data.nbytes
if (nbytes != self._nbytes):
self.resize_bytes(nbytes)
else:
self._glir.command('SIZE', self._id, nbytes)
if nbytes:
self._glir.command('DATA', self._id, 0, data)
| 768,647,042,963,925,500
|
Set data in the buffer (deferred operation).
This completely resets the size and contents of the buffer.
Parameters
----------
data : ndarray
Data to be uploaded
copy: bool
Since the operation is deferred, data may change before
data is actually uploaded to GPU memory.
Asking explicitly for a copy will prevent this behavior.
|
vispy/gloo/buffer.py
|
set_data
|
CVandML/vispy
|
python
|
def set_data(self, data, copy=False):
' Set data in the buffer (deferred operation).\n\n This completely resets the size and contents of the buffer.\n\n Parameters\n ----------\n data : ndarray\n Data to be uploaded\n copy: bool\n Since the operation is deferred, data may change before\n data is actually uploaded to GPU memory.\n Asking explicitly for a copy will prevent this behavior.\n '
data = np.array(data, copy=copy)
nbytes = data.nbytes
if (nbytes != self._nbytes):
self.resize_bytes(nbytes)
else:
self._glir.command('SIZE', self._id, nbytes)
if nbytes:
self._glir.command('DATA', self._id, 0, data)
|
def resize_bytes(self, size):
' Resize this buffer (deferred operation). \n \n Parameters\n ----------\n size : int\n New buffer size in bytes.\n '
self._nbytes = size
self._glir.command('SIZE', self._id, size)
for view in self._views:
if (view() is not None):
view()._valid = False
self._views = []
| -7,041,053,181,543,419,000
|
Resize this buffer (deferred operation).
Parameters
----------
size : int
New buffer size in bytes.
|
vispy/gloo/buffer.py
|
resize_bytes
|
CVandML/vispy
|
python
|
def resize_bytes(self, size):
' Resize this buffer (deferred operation). \n \n Parameters\n ----------\n size : int\n New buffer size in bytes.\n '
self._nbytes = size
self._glir.command('SIZE', self._id, size)
for view in self._views:
if (view() is not None):
view()._valid = False
self._views = []
|
def set_subdata(self, data, offset=0, copy=False, **kwargs):
' Set a sub-region of the buffer (deferred operation).\n\n Parameters\n ----------\n\n data : ndarray\n Data to be uploaded\n offset: int\n Offset in buffer where to start copying data (in bytes)\n copy: bool\n Since the operation is deferred, data may change before\n data is actually uploaded to GPU memory.\n Asking explicitly for a copy will prevent this behavior.\n **kwargs : dict\n Additional keyword arguments.\n '
data = self._prepare_data(data, **kwargs)
offset = (offset * self.itemsize)
Buffer.set_subdata(self, data=data, offset=offset, copy=copy)
| 2,903,474,374,651,890,700
|
Set a sub-region of the buffer (deferred operation).
Parameters
----------
data : ndarray
Data to be uploaded
offset: int
Offset in buffer where to start copying data (in bytes)
copy: bool
Since the operation is deferred, data may change before
data is actually uploaded to GPU memory.
Asking explicitly for a copy will prevent this behavior.
**kwargs : dict
Additional keyword arguments.
|
vispy/gloo/buffer.py
|
set_subdata
|
CVandML/vispy
|
python
|
def set_subdata(self, data, offset=0, copy=False, **kwargs):
' Set a sub-region of the buffer (deferred operation).\n\n Parameters\n ----------\n\n data : ndarray\n Data to be uploaded\n offset: int\n Offset in buffer where to start copying data (in bytes)\n copy: bool\n Since the operation is deferred, data may change before\n data is actually uploaded to GPU memory.\n Asking explicitly for a copy will prevent this behavior.\n **kwargs : dict\n Additional keyword arguments.\n '
data = self._prepare_data(data, **kwargs)
offset = (offset * self.itemsize)
Buffer.set_subdata(self, data=data, offset=offset, copy=copy)
|
def set_data(self, data, copy=False, **kwargs):
' Set data (deferred operation)\n\n Parameters\n ----------\n data : ndarray\n Data to be uploaded\n copy: bool\n Since the operation is deferred, data may change before\n data is actually uploaded to GPU memory.\n Asking explicitly for a copy will prevent this behavior.\n **kwargs : dict\n Additional arguments.\n '
data = self._prepare_data(data, **kwargs)
self._dtype = data.dtype
self._stride = data.strides[(- 1)]
self._itemsize = self._dtype.itemsize
Buffer.set_data(self, data=data, copy=copy)
| 2,841,027,101,147,999,000
|
Set data (deferred operation)
Parameters
----------
data : ndarray
Data to be uploaded
copy: bool
Since the operation is deferred, data may change before
data is actually uploaded to GPU memory.
Asking explicitly for a copy will prevent this behavior.
**kwargs : dict
Additional arguments.
|
vispy/gloo/buffer.py
|
set_data
|
CVandML/vispy
|
python
|
def set_data(self, data, copy=False, **kwargs):
' Set data (deferred operation)\n\n Parameters\n ----------\n data : ndarray\n Data to be uploaded\n copy: bool\n Since the operation is deferred, data may change before\n data is actually uploaded to GPU memory.\n Asking explicitly for a copy will prevent this behavior.\n **kwargs : dict\n Additional arguments.\n '
data = self._prepare_data(data, **kwargs)
self._dtype = data.dtype
self._stride = data.strides[(- 1)]
self._itemsize = self._dtype.itemsize
Buffer.set_data(self, data=data, copy=copy)
|
@property
def dtype(self):
' Buffer dtype '
return self._dtype
| 4,078,825,277,664,268,000
|
Buffer dtype
|
vispy/gloo/buffer.py
|
dtype
|
CVandML/vispy
|
python
|
@property
def dtype(self):
' '
return self._dtype
|
@property
def offset(self):
' Buffer offset (in bytes) relative to base '
return 0
| 8,895,473,055,360,589,000
|
Buffer offset (in bytes) relative to base
|
vispy/gloo/buffer.py
|
offset
|
CVandML/vispy
|
python
|
@property
def offset(self):
' '
return 0
|
@property
def stride(self):
' Stride of data in memory '
return self._stride
| 4,803,607,196,136,743,000
|
Stride of data in memory
|
vispy/gloo/buffer.py
|
stride
|
CVandML/vispy
|
python
|
@property
def stride(self):
' '
return self._stride
|
@property
def size(self):
' Number of elements in the buffer '
return self._size
| -2,399,418,111,054,706,700
|
Number of elements in the buffer
|
vispy/gloo/buffer.py
|
size
|
CVandML/vispy
|
python
|
@property
def size(self):
' '
return self._size
|
@property
def itemsize(self):
' The total number of bytes required to store the array data '
return self._itemsize
| 2,928,576,800,103,052,000
|
The total number of bytes required to store the array data
|
vispy/gloo/buffer.py
|
itemsize
|
CVandML/vispy
|
python
|
@property
def itemsize(self):
' '
return self._itemsize
|
@property
def glsl_type(self):
' GLSL declaration strings required for a variable to hold this data.\n '
if (self.dtype is None):
return None
dtshape = self.dtype[0].shape
n = (dtshape[0] if dtshape else 1)
if (n > 1):
dtype = ('vec%d' % n)
else:
dtype = ('float' if ('f' in self.dtype[0].base.kind) else 'int')
return ('attribute', dtype)
| 6,848,344,529,212,954,000
|
GLSL declaration strings required for a variable to hold this data.
|
vispy/gloo/buffer.py
|
glsl_type
|
CVandML/vispy
|
python
|
@property
def glsl_type(self):
' \n '
if (self.dtype is None):
return None
dtshape = self.dtype[0].shape
n = (dtshape[0] if dtshape else 1)
if (n > 1):
dtype = ('vec%d' % n)
else:
dtype = ('float' if ('f' in self.dtype[0].base.kind) else 'int')
return ('attribute', dtype)
|
def resize_bytes(self, size):
' Resize the buffer (in-place, deferred operation)\n\n Parameters\n ----------\n size : integer\n New buffer size in bytes\n\n Notes\n -----\n This clears any pending operations.\n '
Buffer.resize_bytes(self, size)
self._size = (size // self.itemsize)
| 3,280,132,657,365,361,700
|
Resize the buffer (in-place, deferred operation)
Parameters
----------
size : integer
New buffer size in bytes
Notes
-----
This clears any pending operations.
|
vispy/gloo/buffer.py
|
resize_bytes
|
CVandML/vispy
|
python
|
def resize_bytes(self, size):
' Resize the buffer (in-place, deferred operation)\n\n Parameters\n ----------\n size : integer\n New buffer size in bytes\n\n Notes\n -----\n This clears any pending operations.\n '
Buffer.resize_bytes(self, size)
self._size = (size // self.itemsize)
|
def __getitem__(self, key):
' Create a view on this buffer. '
view = DataBufferView(self, key)
self._views.append(weakref.ref(view))
return view
| -1,744,978,608,335,112,700
|
Create a view on this buffer.
|
vispy/gloo/buffer.py
|
__getitem__
|
CVandML/vispy
|
python
|
def __getitem__(self, key):
' '
view = DataBufferView(self, key)
self._views.append(weakref.ref(view))
return view
|
def __setitem__(self, key, data):
' Set data (deferred operation) '
if isinstance(key, string_types):
raise ValueError('Cannot set non-contiguous data on buffer')
elif isinstance(key, int):
if (key < 0):
key += self.size
if ((key < 0) or (key > self.size)):
raise IndexError('Buffer assignment index out of range')
(start, stop, step) = (key, (key + 1), 1)
elif isinstance(key, slice):
(start, stop, step) = key.indices(self.size)
if (stop < start):
(start, stop) = (stop, start)
elif (key == Ellipsis):
(start, stop, step) = (0, self.size, 1)
else:
raise TypeError('Buffer indices must be integers or strings')
if (step != 1):
raise ValueError('Cannot set non-contiguous data on buffer')
if (not isinstance(data, np.ndarray)):
data = np.array(data, dtype=self.dtype, copy=False)
if (data.size < (stop - start)):
data = np.resize(data, (stop - start))
elif (data.size > (stop - start)):
raise ValueError('Data too big to fit GPU data.')
offset = start
self.set_subdata(data=data, offset=offset, copy=True)
| -7,364,368,203,983,513,000
|
Set data (deferred operation)
|
vispy/gloo/buffer.py
|
__setitem__
|
CVandML/vispy
|
python
|
def __setitem__(self, key, data):
' '
if isinstance(key, string_types):
raise ValueError('Cannot set non-contiguous data on buffer')
elif isinstance(key, int):
if (key < 0):
key += self.size
if ((key < 0) or (key > self.size)):
raise IndexError('Buffer assignment index out of range')
(start, stop, step) = (key, (key + 1), 1)
elif isinstance(key, slice):
(start, stop, step) = key.indices(self.size)
if (stop < start):
(start, stop) = (stop, start)
elif (key == Ellipsis):
(start, stop, step) = (0, self.size, 1)
else:
raise TypeError('Buffer indices must be integers or strings')
if (step != 1):
raise ValueError('Cannot set non-contiguous data on buffer')
if (not isinstance(data, np.ndarray)):
data = np.array(data, dtype=self.dtype, copy=False)
if (data.size < (stop - start)):
data = np.resize(data, (stop - start))
elif (data.size > (stop - start)):
raise ValueError('Data too big to fit GPU data.')
offset = start
self.set_subdata(data=data, offset=offset, copy=True)
|
@property
def offset(self):
' Buffer offset (in bytes) relative to base '
return self._offset
| 8,690,617,661,463,767,000
|
Buffer offset (in bytes) relative to base
|
vispy/gloo/buffer.py
|
offset
|
CVandML/vispy
|
python
|
@property
def offset(self):
' '
return self._offset
|
@property
def base(self):
'Buffer base if this buffer is a view on another buffer. '
return self._base
| 5,915,824,618,440,580,000
|
Buffer base if this buffer is a view on another buffer.
|
vispy/gloo/buffer.py
|
base
|
CVandML/vispy
|
python
|
@property
def base(self):
' '
return self._base
|
def start():
'\n This function is run once every time the start button is pressed\n '
global max_speed
global show_triggers
global show_joysticks
print('Start function called')
rc.set_update_slow_time(0.5)
rc.drive.stop()
max_speed = 0.25
show_triggers = False
show_joysticks = False
assert (rc_utils.remap_range(5, 0, 10, 0, 50) == 25)
assert (rc_utils.remap_range(5, 0, 20, 1000, 900) == 975)
assert (rc_utils.remap_range(2, 0, 1, (- 10), 10) == 30)
assert (rc_utils.remap_range(2, 0, 1, (- 10), 10, True) == 10)
assert (rc_utils.clamp(3, 0, 10) == 3)
assert (rc_utils.clamp((- 2), 0, 10) == 0)
assert (rc_utils.clamp(11, 0, 10) == 10)
print('>> Test Utils: A testing program for the racecar_utils library.\n\nControls:\n Right trigger = accelerate forward\n Left trigger = accelerate backward\n Left joystick = turn front wheels\n A button = Take a color image and crop it to the top left\n B button = Take a color image and identify the largest red contour\n X button = Take a depth image and print several statistics\n Y button = Take a lidar scan and print several statistics\n')
| -7,655,744,681,631,914,000
|
This function is run once every time the start button is pressed
|
labs/test_utils.py
|
start
|
MITLLRacecar/racecar-allison-aj
|
python
|
def start():
'\n \n '
global max_speed
global show_triggers
global show_joysticks
print('Start function called')
rc.set_update_slow_time(0.5)
rc.drive.stop()
max_speed = 0.25
show_triggers = False
show_joysticks = False
assert (rc_utils.remap_range(5, 0, 10, 0, 50) == 25)
assert (rc_utils.remap_range(5, 0, 20, 1000, 900) == 975)
assert (rc_utils.remap_range(2, 0, 1, (- 10), 10) == 30)
assert (rc_utils.remap_range(2, 0, 1, (- 10), 10, True) == 10)
assert (rc_utils.clamp(3, 0, 10) == 3)
assert (rc_utils.clamp((- 2), 0, 10) == 0)
assert (rc_utils.clamp(11, 0, 10) == 10)
print('>> Test Utils: A testing program for the racecar_utils library.\n\nControls:\n Right trigger = accelerate forward\n Left trigger = accelerate backward\n Left joystick = turn front wheels\n A button = Take a color image and crop it to the top left\n B button = Take a color image and identify the largest red contour\n X button = Take a depth image and print several statistics\n Y button = Take a lidar scan and print several statistics\n')
|
def update():
'\n After start() is run, this function is run every frame until the back button\n is pressed\n '
if rc.controller.was_pressed(rc.controller.Button.A):
image = rc.camera.get_color_image()
cropped = rc_utils.crop(image, (0, 0), ((rc.camera.get_height() // 2), (rc.camera.get_width() // 2)))
rc.display.show_color_image(cropped)
if rc.controller.was_pressed(rc.controller.Button.B):
image = rc.camera.get_color_image()
contours = rc_utils.find_contours(image, RED[0], RED[1])
largest_contour = rc_utils.get_largest_contour(contours)
if (largest_contour is not None):
center = rc_utils.get_contour_center(largest_contour)
area = rc_utils.get_contour_area(largest_contour)
print('Largest red contour: center={}, area={:.2f}'.format(center, area))
rc_utils.draw_contour(image, largest_contour, rc_utils.ColorBGR.green.value)
rc_utils.draw_circle(image, center, rc_utils.ColorBGR.yellow.value)
rc.display.show_color_image(image)
else:
print('No red contours found')
if rc.controller.was_pressed(rc.controller.Button.X):
depth_image = rc.camera.get_depth_image()
left_distance = rc_utils.get_pixel_average_distance(depth_image, ((rc.camera.get_height() // 2), (rc.camera.get_width() // 4)))
center_distance = rc_utils.get_depth_image_center_distance(depth_image)
center_distance_raw = rc_utils.get_depth_image_center_distance(depth_image, 1)
right_distance = rc_utils.get_pixel_average_distance(depth_image, ((rc.camera.get_height() // 2), ((3 * rc.camera.get_width()) // 4)))
print(f'Depth image left distance: {left_distance:.2f} cm')
print(f'Depth image center distance: {center_distance:.2f} cm')
print(f'Depth image raw center distance: {center_distance_raw:.2f} cm')
print(f'Depth image right distance: {right_distance:.2f} cm')
upper_left_distance = rc_utils.get_pixel_average_distance(depth_image, (2, 1), 11)
lower_right_distance = rc_utils.get_pixel_average_distance(depth_image, ((rc.camera.get_height() - 2), (rc.camera.get_width() - 5)), 13)
print(f'Depth image upper left distance: {upper_left_distance:.2f} cm')
print(f'Depth image lower right distance: {lower_right_distance:.2f} cm')
cropped = rc_utils.crop(depth_image, (0, 0), (((rc.camera.get_height() * 2) // 3), rc.camera.get_width()))
closest_point = rc_utils.get_closest_pixel(cropped)
closest_distance = cropped[closest_point[0]][closest_point[1]]
print(f'Depth image closest point (upper half): (row={closest_point[0]}, col={closest_point[1]}), distance={closest_distance:.2f} cm')
rc.display.show_depth_image(cropped, points=[closest_point])
if rc.controller.was_pressed(rc.controller.Button.Y):
lidar = rc.lidar.get_samples()
front_distance = rc_utils.get_lidar_average_distance(lidar, 0)
right_distance = rc_utils.get_lidar_average_distance(lidar, 90)
back_distance = rc_utils.get_lidar_average_distance(lidar, 180)
left_distance = rc_utils.get_lidar_average_distance(lidar, 270)
print(f'Front LIDAR distance: {front_distance:.2f} cm')
print(f'Right LIDAR distance: {right_distance:.2f} cm')
print(f'Back LIDAR distance: {back_distance:.2f} cm')
print(f'Left LIDAR distance: {left_distance:.2f} cm')
closest_sample = rc_utils.get_lidar_closest_point(lidar)
print(f'Closest LIDAR point: {closest_sample[0]:.2f} degrees, {closest_sample[1]:.2f} cm')
rc.display.show_lidar(lidar, highlighted_samples=[closest_sample])
(rjoy_x, rjoy_y) = rc.controller.get_joystick(rc.controller.Joystick.RIGHT)
if ((abs(rjoy_x) > 0) or (abs(rjoy_y) > 0)):
lidar = rc.lidar.get_samples()
angle = (((math.atan2(rjoy_x, rjoy_y) * 180) / math.pi) % 360)
distance = rc_utils.get_lidar_average_distance(lidar, angle)
print(f'LIDAR distance at angle {angle:.2f} = {distance:.2f} cm')
left_trigger = rc.controller.get_trigger(rc.controller.Trigger.LEFT)
right_trigger = rc.controller.get_trigger(rc.controller.Trigger.RIGHT)
left_joystick = rc.controller.get_joystick(rc.controller.Joystick.LEFT)
rc.drive.set_speed_angle((right_trigger - left_trigger), left_joystick[0])
| 5,690,716,355,874,481,000
|
After start() is run, this function is run every frame until the back button
is pressed
|
labs/test_utils.py
|
update
|
MITLLRacecar/racecar-allison-aj
|
python
|
def update():
'\n After start() is run, this function is run every frame until the back button\n is pressed\n '
if rc.controller.was_pressed(rc.controller.Button.A):
image = rc.camera.get_color_image()
cropped = rc_utils.crop(image, (0, 0), ((rc.camera.get_height() // 2), (rc.camera.get_width() // 2)))
rc.display.show_color_image(cropped)
if rc.controller.was_pressed(rc.controller.Button.B):
image = rc.camera.get_color_image()
contours = rc_utils.find_contours(image, RED[0], RED[1])
largest_contour = rc_utils.get_largest_contour(contours)
if (largest_contour is not None):
center = rc_utils.get_contour_center(largest_contour)
area = rc_utils.get_contour_area(largest_contour)
print('Largest red contour: center={}, area={:.2f}'.format(center, area))
rc_utils.draw_contour(image, largest_contour, rc_utils.ColorBGR.green.value)
rc_utils.draw_circle(image, center, rc_utils.ColorBGR.yellow.value)
rc.display.show_color_image(image)
else:
print('No red contours found')
if rc.controller.was_pressed(rc.controller.Button.X):
depth_image = rc.camera.get_depth_image()
left_distance = rc_utils.get_pixel_average_distance(depth_image, ((rc.camera.get_height() // 2), (rc.camera.get_width() // 4)))
center_distance = rc_utils.get_depth_image_center_distance(depth_image)
center_distance_raw = rc_utils.get_depth_image_center_distance(depth_image, 1)
right_distance = rc_utils.get_pixel_average_distance(depth_image, ((rc.camera.get_height() // 2), ((3 * rc.camera.get_width()) // 4)))
print(f'Depth image left distance: {left_distance:.2f} cm')
print(f'Depth image center distance: {center_distance:.2f} cm')
print(f'Depth image raw center distance: {center_distance_raw:.2f} cm')
print(f'Depth image right distance: {right_distance:.2f} cm')
upper_left_distance = rc_utils.get_pixel_average_distance(depth_image, (2, 1), 11)
lower_right_distance = rc_utils.get_pixel_average_distance(depth_image, ((rc.camera.get_height() - 2), (rc.camera.get_width() - 5)), 13)
print(f'Depth image upper left distance: {upper_left_distance:.2f} cm')
print(f'Depth image lower right distance: {lower_right_distance:.2f} cm')
cropped = rc_utils.crop(depth_image, (0, 0), (((rc.camera.get_height() * 2) // 3), rc.camera.get_width()))
closest_point = rc_utils.get_closest_pixel(cropped)
closest_distance = cropped[closest_point[0]][closest_point[1]]
print(f'Depth image closest point (upper half): (row={closest_point[0]}, col={closest_point[1]}), distance={closest_distance:.2f} cm')
rc.display.show_depth_image(cropped, points=[closest_point])
if rc.controller.was_pressed(rc.controller.Button.Y):
lidar = rc.lidar.get_samples()
front_distance = rc_utils.get_lidar_average_distance(lidar, 0)
right_distance = rc_utils.get_lidar_average_distance(lidar, 90)
back_distance = rc_utils.get_lidar_average_distance(lidar, 180)
left_distance = rc_utils.get_lidar_average_distance(lidar, 270)
print(f'Front LIDAR distance: {front_distance:.2f} cm')
print(f'Right LIDAR distance: {right_distance:.2f} cm')
print(f'Back LIDAR distance: {back_distance:.2f} cm')
print(f'Left LIDAR distance: {left_distance:.2f} cm')
closest_sample = rc_utils.get_lidar_closest_point(lidar)
print(f'Closest LIDAR point: {closest_sample[0]:.2f} degrees, {closest_sample[1]:.2f} cm')
rc.display.show_lidar(lidar, highlighted_samples=[closest_sample])
(rjoy_x, rjoy_y) = rc.controller.get_joystick(rc.controller.Joystick.RIGHT)
if ((abs(rjoy_x) > 0) or (abs(rjoy_y) > 0)):
lidar = rc.lidar.get_samples()
angle = (((math.atan2(rjoy_x, rjoy_y) * 180) / math.pi) % 360)
distance = rc_utils.get_lidar_average_distance(lidar, angle)
print(f'LIDAR distance at angle {angle:.2f} = {distance:.2f} cm')
left_trigger = rc.controller.get_trigger(rc.controller.Trigger.LEFT)
right_trigger = rc.controller.get_trigger(rc.controller.Trigger.RIGHT)
left_joystick = rc.controller.get_joystick(rc.controller.Joystick.LEFT)
rc.drive.set_speed_angle((right_trigger - left_trigger), left_joystick[0])
|
def shrink(coords: np.ndarray, dist: np.ndarray) -> tuple[np.ndarray]:
'Shrinks a 2D polygon by a given distance.\n\n The coordinates of the polygon are expected as an N x 2-matrix,\n and a positive distance results in inward shrinking.\n \n An empty set is returned if the shrinking operation removes all\n original elements.\n\n Args:\n coords: A matrix of coordinates.\n dist: The distance to shrink by.\n\n Returns:\n A tuple containing the x, y coordinates of the original set, as\n well as the x and y coordinates of the shrunken set, in that\n order.\n '
my_polygon = geometry.Polygon(coords)
xy = my_polygon.exterior.xy
my_polygon_shrunken = my_polygon.buffer((- dist))
try:
xys = my_polygon_shrunken.exterior.xy
except AttributeError:
xys = ([0], [0])
return (*xy, *xys)
| 9,125,759,857,284,818,000
|
Shrinks a 2D polygon by a given distance.
The coordinates of the polygon are expected as an N x 2-matrix,
and a positive distance results in inward shrinking.
An empty set is returned if the shrinking operation removes all
original elements.
Args:
coords: A matrix of coordinates.
dist: The distance to shrink by.
Returns:
A tuple containing the x, y coordinates of the original set, as
well as the x and y coordinates of the shrunken set, in that
order.
|
geometry_tools.py
|
shrink
|
helkebir/Reachable-Set-Inner-Approximation
|
python
|
def shrink(coords: np.ndarray, dist: np.ndarray) -> tuple[np.ndarray]:
'Shrinks a 2D polygon by a given distance.\n\n The coordinates of the polygon are expected as an N x 2-matrix,\n and a positive distance results in inward shrinking.\n \n An empty set is returned if the shrinking operation removes all\n original elements.\n\n Args:\n coords: A matrix of coordinates.\n dist: The distance to shrink by.\n\n Returns:\n A tuple containing the x, y coordinates of the original set, as\n well as the x and y coordinates of the shrunken set, in that\n order.\n '
my_polygon = geometry.Polygon(coords)
xy = my_polygon.exterior.xy
my_polygon_shrunken = my_polygon.buffer((- dist))
try:
xys = my_polygon_shrunken.exterior.xy
except AttributeError:
xys = ([0], [0])
return (*xy, *xys)
|
def hausdorff(A: np.ndarray, B: np.ndarray) -> float:
'Computes the Hausdorff distance between two 2D polygons.\n\n Args:\n A: A matrix defining the first polygon.\n B: A matrix defining the second polygon.\n \n Returns:\n A float representing the Hausdorff distance.\n '
return geometry.Polygon(A).hausdorff_distance(geometry.Polygon(B))
| 5,987,260,360,704,853,000
|
Computes the Hausdorff distance between two 2D polygons.
Args:
A: A matrix defining the first polygon.
B: A matrix defining the second polygon.
Returns:
A float representing the Hausdorff distance.
|
geometry_tools.py
|
hausdorff
|
helkebir/Reachable-Set-Inner-Approximation
|
python
|
def hausdorff(A: np.ndarray, B: np.ndarray) -> float:
'Computes the Hausdorff distance between two 2D polygons.\n\n Args:\n A: A matrix defining the first polygon.\n B: A matrix defining the second polygon.\n \n Returns:\n A float representing the Hausdorff distance.\n '
return geometry.Polygon(A).hausdorff_distance(geometry.Polygon(B))
|
def read_polygon(file: str) -> np.ndarray:
'Reads a polygon from a table.\n\n Args:\n file: Path to a file containing a plain text, tab-separated\n table with scalars.\n \n Returns:\n A matrix containing the data in the file.\n '
return np.genfromtxt(file)
| 2,876,362,114,396,736,500
|
Reads a polygon from a table.
Args:
file: Path to a file containing a plain text, tab-separated
table with scalars.
Returns:
A matrix containing the data in the file.
|
geometry_tools.py
|
read_polygon
|
helkebir/Reachable-Set-Inner-Approximation
|
python
|
def read_polygon(file: str) -> np.ndarray:
'Reads a polygon from a table.\n\n Args:\n file: Path to a file containing a plain text, tab-separated\n table with scalars.\n \n Returns:\n A matrix containing the data in the file.\n '
return np.genfromtxt(file)
|
def deserialize_args(args):
'Try to deserialize given args. Return input if not serialized'
deserialized = parse_qs(args)
if (deserialized == {}):
return args
else:
return deserialized
| 5,916,436,655,583,058,000
|
Try to deserialize given args. Return input if not serialized
|
dartui/utils.py
|
deserialize_args
|
cjlucas/DarTui
|
python
|
def deserialize_args(args):
deserialized = parse_qs(args)
if (deserialized == {}):
return args
else:
return deserialized
|
def get_disk_usage(path):
"Return disk usage statistics about the given path.\n\n Returned valus is a named tuple with attributes 'total', 'used' and\n 'free', which are the amount of total, used and free space, in bytes.\n \n Source: http://stackoverflow.com/a/7285483/975118\n "
st = os.statvfs(path)
free = (st.f_bavail * st.f_frsize)
total = (st.f_blocks * st.f_frsize)
used = ((st.f_blocks - st.f_bfree) * st.f_frsize)
return _ntuple_diskusage(total, used, free)
| -6,989,553,908,795,418,000
|
Return disk usage statistics about the given path.
Returned valus is a named tuple with attributes 'total', 'used' and
'free', which are the amount of total, used and free space, in bytes.
Source: http://stackoverflow.com/a/7285483/975118
|
dartui/utils.py
|
get_disk_usage
|
cjlucas/DarTui
|
python
|
def get_disk_usage(path):
"Return disk usage statistics about the given path.\n\n Returned valus is a named tuple with attributes 'total', 'used' and\n 'free', which are the amount of total, used and free space, in bytes.\n \n Source: http://stackoverflow.com/a/7285483/975118\n "
st = os.statvfs(path)
free = (st.f_bavail * st.f_frsize)
total = (st.f_blocks * st.f_frsize)
used = ((st.f_blocks - st.f_bfree) * st.f_frsize)
return _ntuple_diskusage(total, used, free)
|
def get_torrent_files(f):
'\n Input:\n f -- cgi.FileStorage object \n Returns:\n torrent_files -- a list of TorrentFile objects\n '
torrent_files = []
if f.filename.lower().endswith('.zip'):
z = zipfile.ZipFile(f.file)
torrent_files = [TorrentFile(name=zi.filename, data=z.open(zi).read()) for zi in z.infolist() if zi.filename.lower().endswith('.torrent')]
elif f.filename.lower().endswith('.torrent'):
torrent_files = [TorrentFile(name=f.filename, data=f.file.read())]
return torrent_files
| -3,494,092,591,716,452,000
|
Input:
f -- cgi.FileStorage object
Returns:
torrent_files -- a list of TorrentFile objects
|
dartui/utils.py
|
get_torrent_files
|
cjlucas/DarTui
|
python
|
def get_torrent_files(f):
'\n Input:\n f -- cgi.FileStorage object \n Returns:\n torrent_files -- a list of TorrentFile objects\n '
torrent_files = []
if f.filename.lower().endswith('.zip'):
z = zipfile.ZipFile(f.file)
torrent_files = [TorrentFile(name=zi.filename, data=z.open(zi).read()) for zi in z.infolist() if zi.filename.lower().endswith('.torrent')]
elif f.filename.lower().endswith('.torrent'):
torrent_files = [TorrentFile(name=f.filename, data=f.file.read())]
return torrent_files
|
def __init__(self, value=None, defaultFormat='%a[SHORT], %d %b[SHORT] %Y %H:%M:%S %Z'):
' The value should be in the LOCAL timezone.\n\t\t'
self.ourValue = value
self.defaultFormat = defaultFormat
| -1,599,152,371,501,616,000
|
The value should be in the LOCAL timezone.
|
lib/pubtal/DateContext.py
|
__init__
|
owlfish/pubtal
|
python
|
def __init__(self, value=None, defaultFormat='%a[SHORT], %d %b[SHORT] %Y %H:%M:%S %Z'):
' \n\t\t'
self.ourValue = value
self.defaultFormat = defaultFormat
|
def setUp(self):
'set up the test\n '
pymel.core.newFile(force=True)
self.sm = pymel.core.PyNode('sequenceManager1')
| -1,835,430,997,191,037,400
|
set up the test
|
tests/previs/test_sequence_manager_extension.py
|
setUp
|
Khosiyat/anima
|
python
|
def setUp(self):
'\n '
pymel.core.newFile(force=True)
self.sm = pymel.core.PyNode('sequenceManager1')
|
def test_from_xml_path_argument_skipped(self):
'testing if a TypeError will be raised when the path argument is\n skipped\n '
sm = pymel.core.PyNode('sequenceManager1')
with self.assertRaises(TypeError) as cm:
sm.from_xml()
self.assertEqual(cm.exception.message, 'from_xml() takes exactly 2 arguments (1 given)')
| -9,032,480,269,813,086,000
|
testing if a TypeError will be raised when the path argument is
skipped
|
tests/previs/test_sequence_manager_extension.py
|
test_from_xml_path_argument_skipped
|
Khosiyat/anima
|
python
|
def test_from_xml_path_argument_skipped(self):
'testing if a TypeError will be raised when the path argument is\n skipped\n '
sm = pymel.core.PyNode('sequenceManager1')
with self.assertRaises(TypeError) as cm:
sm.from_xml()
self.assertEqual(cm.exception.message, 'from_xml() takes exactly 2 arguments (1 given)')
|
def test_from_xml_path_argument_is_not_a_string(self):
'testing if a TypeError will be raised when the path argument is not\n a string\n '
sm = pymel.core.PyNode('sequenceManager1')
with self.assertRaises(TypeError) as cm:
sm.from_xml(30)
self.assertEqual(cm.exception.message, 'path argument in SequenceManager.from_xml should be a string, not int')
| 2,190,324,599,069,293,000
|
testing if a TypeError will be raised when the path argument is not
a string
|
tests/previs/test_sequence_manager_extension.py
|
test_from_xml_path_argument_is_not_a_string
|
Khosiyat/anima
|
python
|
def test_from_xml_path_argument_is_not_a_string(self):
'testing if a TypeError will be raised when the path argument is not\n a string\n '
sm = pymel.core.PyNode('sequenceManager1')
with self.assertRaises(TypeError) as cm:
sm.from_xml(30)
self.assertEqual(cm.exception.message, 'path argument in SequenceManager.from_xml should be a string, not int')
|
def test_from_xml_path_argument_is_not_a_valid_path(self):
'testing if a IOError will be raised when the path argument is not\n a valid path\n '
sm = pymel.core.PyNode('sequenceManager1')
with self.assertRaises(IOError) as cm:
sm.from_xml('not a valid path')
self.assertEqual(cm.exception.message, 'Please supply a valid path to an XML file!')
| 1,832,061,779,750,431,200
|
testing if a IOError will be raised when the path argument is not
a valid path
|
tests/previs/test_sequence_manager_extension.py
|
test_from_xml_path_argument_is_not_a_valid_path
|
Khosiyat/anima
|
python
|
def test_from_xml_path_argument_is_not_a_valid_path(self):
'testing if a IOError will be raised when the path argument is not\n a valid path\n '
sm = pymel.core.PyNode('sequenceManager1')
with self.assertRaises(IOError) as cm:
sm.from_xml('not a valid path')
self.assertEqual(cm.exception.message, 'Please supply a valid path to an XML file!')
|
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