body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1
value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
a63258b796c598eef48c87700a5ef16bfbc688d45dc46e3ba78419c4cdbc27b8 | def getWordSet(b):
"build a dict of word objects\n keeps all apostrophes, even though a closing apos might be a close\n single quote. later, try to replace the apos with a letter (typ. 'g')\n to make a word.\n "
a = b[:]
wo = dict()
for (i, _) in enumerate(a):
t33 = a[i]
t33 ... | build a dict of word objects
keeps all apostrophes, even though a closing apos might be a close
single quote. later, try to replace the apos with a letter (typ. 'g')
to make a word. | pgspell.py | getWordSet | asylumcs/pgspell | 0 | python | def getWordSet(b):
"build a dict of word objects\n keeps all apostrophes, even though a closing apos might be a close\n single quote. later, try to replace the apos with a letter (typ. 'g')\n to make a word.\n "
a = b[:]
wo = dict()
for (i, _) in enumerate(a):
t33 = a[i]
t33 ... | def getWordSet(b):
"build a dict of word objects\n keeps all apostrophes, even though a closing apos might be a close\n single quote. later, try to replace the apos with a letter (typ. 'g')\n to make a word.\n "
a = b[:]
wo = dict()
for (i, _) in enumerate(a):
t33 = a[i]
t33 ... |
91e0627062122ca384ff2357b66257cc03d32a5a01ab43c70b36dbd9065e712e | @pytest.mark.parametrize('factorization', ['CP', 'Tucker', 'TT'])
def test_FactorizedTensor(factorization):
'Test for FactorizedTensor'
shape = (4, 3, 2, 5)
fact_tensor = FactorizedTensor.new(shape=shape, rank='same', factorization=factorization)
fact_tensor.normal_()
assert (fact_tensor._name.lower... | Test for FactorizedTensor | tltorch/factorized_tensors/tests/test_factorizations.py | test_FactorizedTensor | cassiofragadantas/torch | 0 | python | @pytest.mark.parametrize('factorization', ['CP', 'Tucker', 'TT'])
def test_FactorizedTensor(factorization):
shape = (4, 3, 2, 5)
fact_tensor = FactorizedTensor.new(shape=shape, rank='same', factorization=factorization)
fact_tensor.normal_()
assert (fact_tensor._name.lower() == factorization.lower()... | @pytest.mark.parametrize('factorization', ['CP', 'Tucker', 'TT'])
def test_FactorizedTensor(factorization):
shape = (4, 3, 2, 5)
fact_tensor = FactorizedTensor.new(shape=shape, rank='same', factorization=factorization)
fact_tensor.normal_()
assert (fact_tensor._name.lower() == factorization.lower()... |
e7f9f93aeb212dbf4bfe8a5adfa200a1c1815a468e0ba1983a9199d4c2c04c69 | @pytest.mark.parametrize('factorization', ['CP', 'TT'])
def test_transduction(factorization):
'Test for transduction'
shape = (3, 4, 5)
new_dim = 2
for mode in range(3):
fact_tensor = FactorizedTensor.new(shape=shape, rank=6, factorization=factorization)
fact_tensor.normal_()
ori... | Test for transduction | tltorch/factorized_tensors/tests/test_factorizations.py | test_transduction | cassiofragadantas/torch | 0 | python | @pytest.mark.parametrize('factorization', ['CP', 'TT'])
def test_transduction(factorization):
shape = (3, 4, 5)
new_dim = 2
for mode in range(3):
fact_tensor = FactorizedTensor.new(shape=shape, rank=6, factorization=factorization)
fact_tensor.normal_()
original_rec = fact_tensor... | @pytest.mark.parametrize('factorization', ['CP', 'TT'])
def test_transduction(factorization):
shape = (3, 4, 5)
new_dim = 2
for mode in range(3):
fact_tensor = FactorizedTensor.new(shape=shape, rank=6, factorization=factorization)
fact_tensor.normal_()
original_rec = fact_tensor... |
60ce443d5f90ca2fe0a3ec972856285104a4c2372dc249449f76ba3f2b656187 | @pytest.mark.parametrize('unsqueezed_init', ['average', 1.2])
def test_tucker_init_unsqueezed_modes(unsqueezed_init):
'Test for Tucker Factorization init from tensor with unsqueezed_modes\n '
tensor = FactorizedTensor.new((4, 4, 4), rank=(4, 1, 4), factorization='tucker')
mat = torch.randn((4, 4))
te... | Test for Tucker Factorization init from tensor with unsqueezed_modes | tltorch/factorized_tensors/tests/test_factorizations.py | test_tucker_init_unsqueezed_modes | cassiofragadantas/torch | 0 | python | @pytest.mark.parametrize('unsqueezed_init', ['average', 1.2])
def test_tucker_init_unsqueezed_modes(unsqueezed_init):
'\n '
tensor = FactorizedTensor.new((4, 4, 4), rank=(4, 1, 4), factorization='tucker')
mat = torch.randn((4, 4))
tensor.init_from_tensor(mat, unsqueezed_modes=[1], unsqueezed_init=uns... | @pytest.mark.parametrize('unsqueezed_init', ['average', 1.2])
def test_tucker_init_unsqueezed_modes(unsqueezed_init):
'\n '
tensor = FactorizedTensor.new((4, 4, 4), rank=(4, 1, 4), factorization='tucker')
mat = torch.randn((4, 4))
tensor.init_from_tensor(mat, unsqueezed_modes=[1], unsqueezed_init=uns... |
3b818c35a3ad7e21c57122bf281d6fe7007560709e63626f97146662cc293ba0 | def word_matches(word):
' True when the word before the cursor matches. '
if self.ignore_case:
word = word.lower()
if self.match_middle:
return (word_before_cursor in word)
else:
return word.startswith(word_before_cursor) | True when the word before the cursor matches. | questionary/completer.py | word_matches | ahmed-agiza/questionary | 0 | python | def word_matches(word):
' '
if self.ignore_case:
word = word.lower()
if self.match_middle:
return (word_before_cursor in word)
else:
return word.startswith(word_before_cursor) | def word_matches(word):
' '
if self.ignore_case:
word = word.lower()
if self.match_middle:
return (word_before_cursor in word)
else:
return word.startswith(word_before_cursor)<|docstring|>True when the word before the cursor matches.<|endoftext|> |
e62885cbd35581b7b43b4a0bac264fea2e8181243078b247716326b59bf8df2f | def pullFile(self, path, relative=True, timeout=5.0, encoding=None):
' Downloads a file from a given path and returns its content as string.\n The path can be either relative to the remote repo, or absolute.\n '
if relative:
path = path.replace('\\', '/')
path = urljoin(self.remote_rep... | Downloads a file from a given path and returns its content as string.
The path can be either relative to the remote repo, or absolute. | filmatyk/updater.py | pullFile | Noiredd/Filmatyk | 2 | python | def pullFile(self, path, relative=True, timeout=5.0, encoding=None):
' Downloads a file from a given path and returns its content as string.\n The path can be either relative to the remote repo, or absolute.\n '
if relative:
path = path.replace('\\', '/')
path = urljoin(self.remote_rep... | def pullFile(self, path, relative=True, timeout=5.0, encoding=None):
' Downloads a file from a given path and returns its content as string.\n The path can be either relative to the remote repo, or absolute.\n '
if relative:
path = path.replace('\\', '/')
path = urljoin(self.remote_rep... |
4f8fb5d27f677a468b5ae67d2c3bf25925d4982409ac31fc19b1747ad8d713a6 | def getExistingFiles(self):
' Reads the current version file and returns the dict of files. '
with open(self.local_meta_file_path, 'r') as current_ver_file:
current_ver_data = json.loads(current_ver_file.read())
return current_ver_data['files'] | Reads the current version file and returns the dict of files. | filmatyk/updater.py | getExistingFiles | Noiredd/Filmatyk | 2 | python | def getExistingFiles(self):
' '
with open(self.local_meta_file_path, 'r') as current_ver_file:
current_ver_data = json.loads(current_ver_file.read())
return current_ver_data['files'] | def getExistingFiles(self):
' '
with open(self.local_meta_file_path, 'r') as current_ver_file:
current_ver_data = json.loads(current_ver_file.read())
return current_ver_data['files']<|docstring|>Reads the current version file and returns the dict of files.<|endoftext|> |
81a4dd30d4186df00b7c53ab63bd2b3d2b8458b056815eefdc2d7fc8da5ebb94 | def getDownloadFiles(self, existing):
' Returns dict of files to be downloaded (new or changed ones). '
download_list = []
for (new_file, new_sum) in self.updated_files.items():
if (not (new_file in existing.keys())):
download_list.append((new_file, new_sum))
elif (new_sum != exi... | Returns dict of files to be downloaded (new or changed ones). | filmatyk/updater.py | getDownloadFiles | Noiredd/Filmatyk | 2 | python | def getDownloadFiles(self, existing):
' '
download_list = []
for (new_file, new_sum) in self.updated_files.items():
if (not (new_file in existing.keys())):
download_list.append((new_file, new_sum))
elif (new_sum != existing[new_file]):
download_list.append((new_file,... | def getDownloadFiles(self, existing):
' '
download_list = []
for (new_file, new_sum) in self.updated_files.items():
if (not (new_file in existing.keys())):
download_list.append((new_file, new_sum))
elif (new_sum != existing[new_file]):
download_list.append((new_file,... |
bf96e47669e7c98d322592ce24cf75d546586cbf8acf0265fd3c05c704e1e4c9 | def getDeletionFiles(self, existing):
' Returns list of files that are removed in the new version. '
return [ex_file for ex_file in existing.keys() if (ex_file not in self.updated_files.keys())] | Returns list of files that are removed in the new version. | filmatyk/updater.py | getDeletionFiles | Noiredd/Filmatyk | 2 | python | def getDeletionFiles(self, existing):
' '
return [ex_file for ex_file in existing.keys() if (ex_file not in self.updated_files.keys())] | def getDeletionFiles(self, existing):
' '
return [ex_file for ex_file in existing.keys() if (ex_file not in self.updated_files.keys())]<|docstring|>Returns list of files that are removed in the new version.<|endoftext|> |
6a5a5baf5783a414045de4c080fb07c638fa7f3fac9fa5c6ab1029058c0b1791 | def downloadFile(self, path, checksum, attempt=1):
" Downloads a file from a repo-relative path to a temporary location.\n Retries if the checksum doesn't match (up to 3 attempts)."
data = self.pullFile(path)
target_path = os.path.join(self.temporary_directory, path.replace('\\', '--'))
with open... | Downloads a file from a repo-relative path to a temporary location.
Retries if the checksum doesn't match (up to 3 attempts). | filmatyk/updater.py | downloadFile | Noiredd/Filmatyk | 2 | python | def downloadFile(self, path, checksum, attempt=1):
" Downloads a file from a repo-relative path to a temporary location.\n Retries if the checksum doesn't match (up to 3 attempts)."
data = self.pullFile(path)
target_path = os.path.join(self.temporary_directory, path.replace('\\', '--'))
with open... | def downloadFile(self, path, checksum, attempt=1):
" Downloads a file from a repo-relative path to a temporary location.\n Retries if the checksum doesn't match (up to 3 attempts)."
data = self.pullFile(path)
target_path = os.path.join(self.temporary_directory, path.replace('\\', '--'))
with open... |
ea500bc455a87ddbc634987fd7e22176f9006d8139887fadc5fa81e9513df0c9 | def removeOldBackups(self, path=Paths.local_repo_path):
' Recursively traverses the app directory and removes any .bak files. '
folders = []
for item in os.listdir(path):
ipath = os.path.join(path, item)
if os.path.isdir(ipath):
folders.append(ipath)
elif ipath.endswith('... | Recursively traverses the app directory and removes any .bak files. | filmatyk/updater.py | removeOldBackups | Noiredd/Filmatyk | 2 | python | def removeOldBackups(self, path=Paths.local_repo_path):
' '
folders = []
for item in os.listdir(path):
ipath = os.path.join(path, item)
if os.path.isdir(ipath):
folders.append(ipath)
elif ipath.endswith('.bak'):
os.remove(ipath)
for folder in folders:
... | def removeOldBackups(self, path=Paths.local_repo_path):
' '
folders = []
for item in os.listdir(path):
ipath = os.path.join(path, item)
if os.path.isdir(ipath):
folders.append(ipath)
elif ipath.endswith('.bak'):
os.remove(ipath)
for folder in folders:
... |
61820be706e8798fe2dfa612d5e45de8828c806cad56a9665f4672ab0368d441 | def applyFile(self, path):
' Moves a file from a temp location overwriting the target file.\n Accepts repo-relative paths. Backs up the original file first.'
source_path = os.path.join(self.temporary_directory, path.replace('\\', '--'))
target_path = os.path.join('..', (path if (not self.linuxMode) e... | Moves a file from a temp location overwriting the target file.
Accepts repo-relative paths. Backs up the original file first. | filmatyk/updater.py | applyFile | Noiredd/Filmatyk | 2 | python | def applyFile(self, path):
' Moves a file from a temp location overwriting the target file.\n Accepts repo-relative paths. Backs up the original file first.'
source_path = os.path.join(self.temporary_directory, path.replace('\\', '--'))
target_path = os.path.join('..', (path if (not self.linuxMode) e... | def applyFile(self, path):
' Moves a file from a temp location overwriting the target file.\n Accepts repo-relative paths. Backs up the original file first.'
source_path = os.path.join(self.temporary_directory, path.replace('\\', '--'))
target_path = os.path.join('..', (path if (not self.linuxMode) e... |
d90e8eada25a649f885a38ebc329cb05d71397ec576015896e79c32e400f7a97 | def _decode_os_value(self, value):
'Return the value of a dictionary based on its keys'
if (not (self.use_os_keys and isinstance(value, dict))):
return value
os_keys = ['windows', 'linux', 'mac']
if (not [v_key for v_key in value.keys() if (v_key not in os_keys)]):
if IS_WIN:
... | Return the value of a dictionary based on its keys | yamiconfig/__init__.py | _decode_os_value | mtik00/yamiconfig | 0 | python | def _decode_os_value(self, value):
if (not (self.use_os_keys and isinstance(value, dict))):
return value
os_keys = ['windows', 'linux', 'mac']
if (not [v_key for v_key in value.keys() if (v_key not in os_keys)]):
if IS_WIN:
return value['windows']
elif IS_MAC:
... | def _decode_os_value(self, value):
if (not (self.use_os_keys and isinstance(value, dict))):
return value
os_keys = ['windows', 'linux', 'mac']
if (not [v_key for v_key in value.keys() if (v_key not in os_keys)]):
if IS_WIN:
return value['windows']
elif IS_MAC:
... |
7a807f505eba35ffa58f8d8c09fc445bcf1dfc9bdd38861d7c271975326695c7 | def _validate(self, yaml_data):
'\n Make sure the types of the data are the same types as the default.\n '
if self.schema:
self.schema.validate(yaml_data) | Make sure the types of the data are the same types as the default. | yamiconfig/__init__.py | _validate | mtik00/yamiconfig | 0 | python | def _validate(self, yaml_data):
'\n \n '
if self.schema:
self.schema.validate(yaml_data) | def _validate(self, yaml_data):
'\n \n '
if self.schema:
self.schema.validate(yaml_data)<|docstring|>Make sure the types of the data are the same types as the default.<|endoftext|> |
2213cdc2c96a3983617a5ce3856b718be6ae618432e7dafe8fa14ec995ee4015 | def reset(self, path=None):
'\n Resets all configuration settings to the default, ignoring any current\n user settings.\n\n :param str path: The path to the configuration file to write, if any\n '
self._calculated = copy.deepcopy(self._default)
self.extra_data.clear()
if path... | Resets all configuration settings to the default, ignoring any current
user settings.
:param str path: The path to the configuration file to write, if any | yamiconfig/__init__.py | reset | mtik00/yamiconfig | 0 | python | def reset(self, path=None):
'\n Resets all configuration settings to the default, ignoring any current\n user settings.\n\n :param str path: The path to the configuration file to write, if any\n '
self._calculated = copy.deepcopy(self._default)
self.extra_data.clear()
if path... | def reset(self, path=None):
'\n Resets all configuration settings to the default, ignoring any current\n user settings.\n\n :param str path: The path to the configuration file to write, if any\n '
self._calculated = copy.deepcopy(self._default)
self.extra_data.clear()
if path... |
345090865048949048a56f1af679c203f03dd2ae07343ba390d7d35dd1164db1 | def load_configs(self):
'Find all of the config files and load them in'
self._default = self.loads(self._default_raw)
self._calculated = copy.deepcopy(self._default)
for fpath in self.user_files:
temp = self.load_file(fpath)
if temp:
self._calculated.update(temp) | Find all of the config files and load them in | yamiconfig/__init__.py | load_configs | mtik00/yamiconfig | 0 | python | def load_configs(self):
self._default = self.loads(self._default_raw)
self._calculated = copy.deepcopy(self._default)
for fpath in self.user_files:
temp = self.load_file(fpath)
if temp:
self._calculated.update(temp) | def load_configs(self):
self._default = self.loads(self._default_raw)
self._calculated = copy.deepcopy(self._default)
for fpath in self.user_files:
temp = self.load_file(fpath)
if temp:
self._calculated.update(temp)<|docstring|>Find all of the config files and load them in<|... |
3541a11d6510cd6db56d57b520a310f849e8a1e4642d9fed75bb1987633fd70e | def load_file(self, path):
'Load and validate a file, and return the data.'
if os.path.isfile(path):
with open(path) as fh:
text = fh.read()
data = (YAML().load(text) or {})
try:
self._validate(data)
except SchemaError:
print(('ERROR: Configura... | Load and validate a file, and return the data. | yamiconfig/__init__.py | load_file | mtik00/yamiconfig | 0 | python | def load_file(self, path):
if os.path.isfile(path):
with open(path) as fh:
text = fh.read()
data = (YAML().load(text) or {})
try:
self._validate(data)
except SchemaError:
print(('ERROR: Configuration file [%s] did not validate' % path))
... | def load_file(self, path):
if os.path.isfile(path):
with open(path) as fh:
text = fh.read()
data = (YAML().load(text) or {})
try:
self._validate(data)
except SchemaError:
print(('ERROR: Configuration file [%s] did not validate' % path))
... |
81520140f7c423767213708befc36207ea3abc286d811c5981291b67ebd05a43 | def loads(self, yaml_string):
'Load a configuration from a string'
data = (YAML().load(yaml_string) or {})
try:
self._validate(data)
except SchemaError:
raise
return data | Load a configuration from a string | yamiconfig/__init__.py | loads | mtik00/yamiconfig | 0 | python | def loads(self, yaml_string):
data = (YAML().load(yaml_string) or {})
try:
self._validate(data)
except SchemaError:
raise
return data | def loads(self, yaml_string):
data = (YAML().load(yaml_string) or {})
try:
self._validate(data)
except SchemaError:
raise
return data<|docstring|>Load a configuration from a string<|endoftext|> |
5a210386e856706ba18547afe52457bc60398857c10d2b69be199f73f8aab8d3 | def dump(self, obj=None):
'\n Return the *calculated* configuration as a YAML-formatted string.\n\n NOTE: This only includes keys that are part of the default config.\n '
obj = (obj or self._calculated)
d = StringIO()
YAML().dump(obj, d)
return d.getvalue() | Return the *calculated* configuration as a YAML-formatted string.
NOTE: This only includes keys that are part of the default config. | yamiconfig/__init__.py | dump | mtik00/yamiconfig | 0 | python | def dump(self, obj=None):
'\n Return the *calculated* configuration as a YAML-formatted string.\n\n NOTE: This only includes keys that are part of the default config.\n '
obj = (obj or self._calculated)
d = StringIO()
YAML().dump(obj, d)
return d.getvalue() | def dump(self, obj=None):
'\n Return the *calculated* configuration as a YAML-formatted string.\n\n NOTE: This only includes keys that are part of the default config.\n '
obj = (obj or self._calculated)
d = StringIO()
YAML().dump(obj, d)
return d.getvalue()<|docstring|>Return th... |
574c994339b919264e3e776bd9085ae2f09e9ca63f2152f1283198b366083044 | def is_default(self, key):
'Returns True if the key has not been modified from the default'
return bool(((key in self._calculated) and (key in self._default) and (self._calculated[key] == self._default[key]))) | Returns True if the key has not been modified from the default | yamiconfig/__init__.py | is_default | mtik00/yamiconfig | 0 | python | def is_default(self, key):
return bool(((key in self._calculated) and (key in self._default) and (self._calculated[key] == self._default[key]))) | def is_default(self, key):
return bool(((key in self._calculated) and (key in self._default) and (self._calculated[key] == self._default[key])))<|docstring|>Returns True if the key has not been modified from the default<|endoftext|> |
48bd9108f39e41c26a155d0c26d16e2e058c2a5e8ecd5782e2549af46bd2eadb | def store_config(self, fpath):
'Stores the current configuration to the YAML file'
with open(fpath, 'wb') as fh:
fh.write(self.dump()) | Stores the current configuration to the YAML file | yamiconfig/__init__.py | store_config | mtik00/yamiconfig | 0 | python | def store_config(self, fpath):
with open(fpath, 'wb') as fh:
fh.write(self.dump()) | def store_config(self, fpath):
with open(fpath, 'wb') as fh:
fh.write(self.dump())<|docstring|>Stores the current configuration to the YAML file<|endoftext|> |
6bdc94e1d1546910eea5dc713ac197b0a494491a851e6afefafda858b5555ec5 | def store_defaults(self, fpath):
'Creates a new file with all default settings commented out'
if self.default_file:
default_text_lines = open(self.default_file).readlines()
else:
default_text_lines = self._default_raw.split('\n')
new_lines = [USER_CONFIG_HEADER]
for line in default_t... | Creates a new file with all default settings commented out | yamiconfig/__init__.py | store_defaults | mtik00/yamiconfig | 0 | python | def store_defaults(self, fpath):
if self.default_file:
default_text_lines = open(self.default_file).readlines()
else:
default_text_lines = self._default_raw.split('\n')
new_lines = [USER_CONFIG_HEADER]
for line in default_text_lines:
line = line.strip()
if (line and ... | def store_defaults(self, fpath):
if self.default_file:
default_text_lines = open(self.default_file).readlines()
else:
default_text_lines = self._default_raw.split('\n')
new_lines = [USER_CONFIG_HEADER]
for line in default_text_lines:
line = line.strip()
if (line and ... |
45f9e9d7e506eed20f5136e033db0cbb9d64d7a26fb23433ed3e9a40f7a24446 | def load_settings(self) -> Optional[dict]:
'\n Load the settings.\n '
if (not file_m.does_exist(os_path=self.settings_file_full_path)):
self.mac_logger.info('The settings file %s does not exist. Creating a new one...', self.settings_file_full_path)
if (not file_m.does_exist(os_path... | Load the settings. | src/perspective_settings.py | load_settings | jmacgrillen/perspective | 0 | python | def load_settings(self) -> Optional[dict]:
'\n \n '
if (not file_m.does_exist(os_path=self.settings_file_full_path)):
self.mac_logger.info('The settings file %s does not exist. Creating a new one...', self.settings_file_full_path)
if (not file_m.does_exist(os_path=self.settings_fil... | def load_settings(self) -> Optional[dict]:
'\n \n '
if (not file_m.does_exist(os_path=self.settings_file_full_path)):
self.mac_logger.info('The settings file %s does not exist. Creating a new one...', self.settings_file_full_path)
if (not file_m.does_exist(os_path=self.settings_fil... |
b35ef9377dd26eab3fe7629708b2761b6ee1333e1819917e12ee060832e6c44d | def save_settings(self) -> None:
'\n Save all the settings back to the settings file.\n '
try:
self.mac_logger.debug('Saving settings to {0}'.format(self.settings_file_full_path))
with open(file=self.settings_file_full_path, mode='w') as yml_file:
yaml.dump(data=self.ap... | Save all the settings back to the settings file. | src/perspective_settings.py | save_settings | jmacgrillen/perspective | 0 | python | def save_settings(self) -> None:
'\n \n '
try:
self.mac_logger.debug('Saving settings to {0}'.format(self.settings_file_full_path))
with open(file=self.settings_file_full_path, mode='w') as yml_file:
yaml.dump(data=self.app_settings, stream=yml_file, indent=4, default_f... | def save_settings(self) -> None:
'\n \n '
try:
self.mac_logger.debug('Saving settings to {0}'.format(self.settings_file_full_path))
with open(file=self.settings_file_full_path, mode='w') as yml_file:
yaml.dump(data=self.app_settings, stream=yml_file, indent=4, default_f... |
889372b978d6afdca652d86f0be4ca2e707c51a5328caeb3a92bd0df17fdb923 | def key_exists(self, key_name: str) -> bool:
'\n Check whether the key exists.\n '
if (key_name in self.app_settings.keys()):
return True
return False | Check whether the key exists. | src/perspective_settings.py | key_exists | jmacgrillen/perspective | 0 | python | def key_exists(self, key_name: str) -> bool:
'\n \n '
if (key_name in self.app_settings.keys()):
return True
return False | def key_exists(self, key_name: str) -> bool:
'\n \n '
if (key_name in self.app_settings.keys()):
return True
return False<|docstring|>Check whether the key exists.<|endoftext|> |
66f2a9ad489289cc784f66964fa14da0883e87602ddc0910ee2956643ab3844a | def upper_codon_inframe(text_file, search_sequence=['tgg', 'cag', 'cga', 'caa']):
'\n # TODO: Docstring\n '
file_path = text_file
l = list(os.path.splitext(file_path))
l.insert(1, '_parsed')
list_res = list()
f = open(file_path, 'r+')
s = f.read()
f.close()
for i in range(0, (l... | # TODO: Docstring | sequenceParser.py | upper_codon_inframe | FrancoisCzarny/BaseEditorSequenceParser | 0 | python | def upper_codon_inframe(text_file, search_sequence=['tgg', 'cag', 'cga', 'caa']):
'\n \n '
file_path = text_file
l = list(os.path.splitext(file_path))
l.insert(1, '_parsed')
list_res = list()
f = open(file_path, 'r+')
s = f.read()
f.close()
for i in range(0, (len(s) - 1), 3):
... | def upper_codon_inframe(text_file, search_sequence=['tgg', 'cag', 'cga', 'caa']):
'\n \n '
file_path = text_file
l = list(os.path.splitext(file_path))
l.insert(1, '_parsed')
list_res = list()
f = open(file_path, 'r+')
s = f.read()
f.close()
for i in range(0, (len(s) - 1), 3):
... |
e1060e8ede1daf978ee0df8c9b14589e33af4c2fc8c1b58fd88c996999b0f22b | def main():
'\n Advbox demo which demonstrate how to use advbox.\n '
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='i... | Advbox demo which demonstrate how to use advbox. | tutorials/mnist_tutorial_jsma.py | main | StijnMatsHendriks/adversarial_attack_demo | 819 | python | def main():
'\n \n '
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cos... | def main():
'\n \n '
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = mnist_cnn_model(img)
cos... |
d20eea5f878c311a7b598d363fdd8ee9d359f96727e796c4622c37788ebb1336 | def __init__(self, image: numpy.ndarray):
'\n Initializes the object that segments a given image into its main colours.\n\n Args:\n image: A three-dimensional numpy array, representing the image to be segmented, which entries are in 0...255\n range and the channels are BGR... | Initializes the object that segments a given image into its main colours.
Args:
image: A three-dimensional numpy array, representing the image to be segmented, which entries are in 0...255
range and the channels are BGR. | colour_segmentation/segmentator.py | __init__ | mmunar97/colour-segmentation | 0 | python | def __init__(self, image: numpy.ndarray):
'\n Initializes the object that segments a given image into its main colours.\n\n Args:\n image: A three-dimensional numpy array, representing the image to be segmented, which entries are in 0...255\n range and the channels are BGR... | def __init__(self, image: numpy.ndarray):
'\n Initializes the object that segments a given image into its main colours.\n\n Args:\n image: A three-dimensional numpy array, representing the image to be segmented, which entries are in 0...255\n range and the channels are BGR... |
b5e7840172bef22be479b7f7d325f1d8a9cafacc6b73a1f0d55e1d9db96bb2a0 | def segment(self, method: SegmentationAlgorithm, **kwargs) -> SegmentationResult:
'\n Segments the image with the selected method.\n\n Args:\n method: A SegmentationAlgorithm value, representing the method to be used.\n\n Returns:\n A SegmentationResult object, containing ... | Segments the image with the selected method.
Args:
method: A SegmentationAlgorithm value, representing the method to be used.
Returns:
A SegmentationResult object, containing the classification of each pixel and the elapsed time. | colour_segmentation/segmentator.py | segment | mmunar97/colour-segmentation | 0 | python | def segment(self, method: SegmentationAlgorithm, **kwargs) -> SegmentationResult:
'\n Segments the image with the selected method.\n\n Args:\n method: A SegmentationAlgorithm value, representing the method to be used.\n\n Returns:\n A SegmentationResult object, containing ... | def segment(self, method: SegmentationAlgorithm, **kwargs) -> SegmentationResult:
'\n Segments the image with the selected method.\n\n Args:\n method: A SegmentationAlgorithm value, representing the method to be used.\n\n Returns:\n A SegmentationResult object, containing ... |
d1aebc2fff830e3a59c60e9c46f4eeb46418a14f19448f41dfbe055de24e7ebd | def __segment_with_amante_trapezoidal(self, **kwargs) -> SegmentationResult:
'\n Segments the image with the Amante-Fonseca fuzzy sets.\n '
fuzzy_set_amante_segmentator = AmanteTrapezoidalSegmentator(image=self.__image)
return fuzzy_set_amante_segmentator.segment(**kwargs) | Segments the image with the Amante-Fonseca fuzzy sets. | colour_segmentation/segmentator.py | __segment_with_amante_trapezoidal | mmunar97/colour-segmentation | 0 | python | def __segment_with_amante_trapezoidal(self, **kwargs) -> SegmentationResult:
'\n \n '
fuzzy_set_amante_segmentator = AmanteTrapezoidalSegmentator(image=self.__image)
return fuzzy_set_amante_segmentator.segment(**kwargs) | def __segment_with_amante_trapezoidal(self, **kwargs) -> SegmentationResult:
'\n \n '
fuzzy_set_amante_segmentator = AmanteTrapezoidalSegmentator(image=self.__image)
return fuzzy_set_amante_segmentator.segment(**kwargs)<|docstring|>Segments the image with the Amante-Fonseca fuzzy sets.<|endoft... |
6910a8f7317aeb9bc9b0712bbe968f9984b0677b485b5588d56be6614ab31321 | def __segment_with_chamorro_trapezoidal(self, **kwargs) -> SegmentationResult:
'\n Segments the image with the Chamorro et al fuzzy sets.\n '
fuzzy_set_chamorro_segmentator = ChamorroTrapezoidalSegmentator(image=self.__image)
return fuzzy_set_chamorro_segmentator.segment(**kwargs) | Segments the image with the Chamorro et al fuzzy sets. | colour_segmentation/segmentator.py | __segment_with_chamorro_trapezoidal | mmunar97/colour-segmentation | 0 | python | def __segment_with_chamorro_trapezoidal(self, **kwargs) -> SegmentationResult:
'\n \n '
fuzzy_set_chamorro_segmentator = ChamorroTrapezoidalSegmentator(image=self.__image)
return fuzzy_set_chamorro_segmentator.segment(**kwargs) | def __segment_with_chamorro_trapezoidal(self, **kwargs) -> SegmentationResult:
'\n \n '
fuzzy_set_chamorro_segmentator = ChamorroTrapezoidalSegmentator(image=self.__image)
return fuzzy_set_chamorro_segmentator.segment(**kwargs)<|docstring|>Segments the image with the Chamorro et al fuzzy sets.... |
b8f9a7b50790d3d1585d8c4d5632baa5bfa7eab7ee13d23f935bcb8c71ebb923 | def __segment_with_liu_trapezoidal(self, **kwargs) -> SegmentationResult:
'\n Segments the image with the Liu-Wang fuzzy sets.\n '
fuzzy_set_liu_segmentator = LiuWangTrapezoidalSegmentator(image=self.__image)
return fuzzy_set_liu_segmentator.segment(**kwargs) | Segments the image with the Liu-Wang fuzzy sets. | colour_segmentation/segmentator.py | __segment_with_liu_trapezoidal | mmunar97/colour-segmentation | 0 | python | def __segment_with_liu_trapezoidal(self, **kwargs) -> SegmentationResult:
'\n \n '
fuzzy_set_liu_segmentator = LiuWangTrapezoidalSegmentator(image=self.__image)
return fuzzy_set_liu_segmentator.segment(**kwargs) | def __segment_with_liu_trapezoidal(self, **kwargs) -> SegmentationResult:
'\n \n '
fuzzy_set_liu_segmentator = LiuWangTrapezoidalSegmentator(image=self.__image)
return fuzzy_set_liu_segmentator.segment(**kwargs)<|docstring|>Segments the image with the Liu-Wang fuzzy sets.<|endoftext|> |
4b962f4091f78ece0dc7ac35bc26acb17e8402ff0fce83b81ea7ba29448ec8ca | def __segment_with_shamir_triangular(self, **kwargs) -> SegmentationResult:
'\n Segments the image with the Shamir fuzzy sets.\n '
fuzzy_set_shamir_segmentator = ShamirTriangularSegmentator(image=self.__image)
return fuzzy_set_shamir_segmentator.segment(**kwargs) | Segments the image with the Shamir fuzzy sets. | colour_segmentation/segmentator.py | __segment_with_shamir_triangular | mmunar97/colour-segmentation | 0 | python | def __segment_with_shamir_triangular(self, **kwargs) -> SegmentationResult:
'\n \n '
fuzzy_set_shamir_segmentator = ShamirTriangularSegmentator(image=self.__image)
return fuzzy_set_shamir_segmentator.segment(**kwargs) | def __segment_with_shamir_triangular(self, **kwargs) -> SegmentationResult:
'\n \n '
fuzzy_set_shamir_segmentator = ShamirTriangularSegmentator(image=self.__image)
return fuzzy_set_shamir_segmentator.segment(**kwargs)<|docstring|>Segments the image with the Shamir fuzzy sets.<|endoftext|> |
fc2a4f1e1ca1244581c6ae5bfbecbce0afb4fa6eafa090abb89fe1d6d3ed0c50 | def stft(y, n_fft, hop_length, win_length):
'\n Wrapper of the official torch.stft for single-channel and multi-channel\n\n Args:\n y: single- or multi-channel speech with shape of [B, C, T] or [B, T]\n n_fft: num of FFT\n hop_length: hop length\n win_length: hanning window size\n\... | Wrapper of the official torch.stft for single-channel and multi-channel
Args:
y: single- or multi-channel speech with shape of [B, C, T] or [B, T]
n_fft: num of FFT
hop_length: hop length
win_length: hanning window size
Shapes:
mag: [B, F, T] if dims of input is [B, T], whereas [B, C, F, T] if dim... | audio_zen/acoustics/feature.py | stft | ShkarupaDC/FullSubNet | 219 | python | def stft(y, n_fft, hop_length, win_length):
'\n Wrapper of the official torch.stft for single-channel and multi-channel\n\n Args:\n y: single- or multi-channel speech with shape of [B, C, T] or [B, T]\n n_fft: num of FFT\n hop_length: hop length\n win_length: hanning window size\n\... | def stft(y, n_fft, hop_length, win_length):
'\n Wrapper of the official torch.stft for single-channel and multi-channel\n\n Args:\n y: single- or multi-channel speech with shape of [B, C, T] or [B, T]\n n_fft: num of FFT\n hop_length: hop length\n win_length: hanning window size\n\... |
064d3c049e8439e1271c046e2c8920605686914656ca711faf213fc2466da3cf | def istft(features, n_fft, hop_length, win_length, length=None, input_type='complex'):
'\n Wrapper of the official torch.istft\n\n Args:\n features: [B, F, T] (complex) or ([B, F, T], [B, F, T]) (mag and phase)\n n_fft: num of FFT\n hop_length: hop length\n win_length: hanning wind... | Wrapper of the official torch.istft
Args:
features: [B, F, T] (complex) or ([B, F, T], [B, F, T]) (mag and phase)
n_fft: num of FFT
hop_length: hop length
win_length: hanning window size
length: expected length of istft
use_mag_phase: use mag and phase as the input ("features")
Returns:
si... | audio_zen/acoustics/feature.py | istft | ShkarupaDC/FullSubNet | 219 | python | def istft(features, n_fft, hop_length, win_length, length=None, input_type='complex'):
'\n Wrapper of the official torch.istft\n\n Args:\n features: [B, F, T] (complex) or ([B, F, T], [B, F, T]) (mag and phase)\n n_fft: num of FFT\n hop_length: hop length\n win_length: hanning wind... | def istft(features, n_fft, hop_length, win_length, length=None, input_type='complex'):
'\n Wrapper of the official torch.istft\n\n Args:\n features: [B, F, T] (complex) or ([B, F, T], [B, F, T]) (mag and phase)\n n_fft: num of FFT\n hop_length: hop length\n win_length: hanning wind... |
ab97aee558a030a7a4fd84abaee5663b2cfe3ecc173991e096428db82e5fc19e | def aligned_subsample(data_a, data_b, sub_sample_length):
'\n Start from a random position and take a fixed-length segment from two speech samples\n\n Notes\n Only support one-dimensional speech signal (T,) and two-dimensional spectrogram signal (F, T)\n\n Only support subsample in the last axis... | Start from a random position and take a fixed-length segment from two speech samples
Notes
Only support one-dimensional speech signal (T,) and two-dimensional spectrogram signal (F, T)
Only support subsample in the last axis. | audio_zen/acoustics/feature.py | aligned_subsample | ShkarupaDC/FullSubNet | 219 | python | def aligned_subsample(data_a, data_b, sub_sample_length):
'\n Start from a random position and take a fixed-length segment from two speech samples\n\n Notes\n Only support one-dimensional speech signal (T,) and two-dimensional spectrogram signal (F, T)\n\n Only support subsample in the last axis... | def aligned_subsample(data_a, data_b, sub_sample_length):
'\n Start from a random position and take a fixed-length segment from two speech samples\n\n Notes\n Only support one-dimensional speech signal (T,) and two-dimensional spectrogram signal (F, T)\n\n Only support subsample in the last axis... |
90ee9ebf26c1c7fdb32c3c81a29bccbbeb56b351447e8bbe6539c7c1036d5f55 | def subsample(data, sub_sample_length, start_position: int=(- 1), return_start_position=False):
'\n Randomly select fixed-length data from \n\n Args:\n data: **one-dimensional data**\n sub_sample_length: how long\n start_position: If start index smaller than 0, randomly generate one index... | Randomly select fixed-length data from
Args:
data: **one-dimensional data**
sub_sample_length: how long
start_position: If start index smaller than 0, randomly generate one index | audio_zen/acoustics/feature.py | subsample | ShkarupaDC/FullSubNet | 219 | python | def subsample(data, sub_sample_length, start_position: int=(- 1), return_start_position=False):
'\n Randomly select fixed-length data from \n\n Args:\n data: **one-dimensional data**\n sub_sample_length: how long\n start_position: If start index smaller than 0, randomly generate one index... | def subsample(data, sub_sample_length, start_position: int=(- 1), return_start_position=False):
'\n Randomly select fixed-length data from \n\n Args:\n data: **one-dimensional data**\n sub_sample_length: how long\n start_position: If start index smaller than 0, randomly generate one index... |
8642993e13ed3ba98f6f078ff7d48bfacb73187de2b6d770df1f56b5ad81cdf6 | def overlap_cat(chunk_list, dim=(- 1)):
'\n 按照 50% 的 overlap 沿着最后一个维度对 chunk_list 进行拼接\n\n Args:\n dim: 需要拼接的维度\n chunk_list(list): [[B, T], [B, T], ...]\n\n Returns:\n overlap 拼接后\n '
overlap_output = []
for (i, chunk) in enumerate(chunk_list):
(first_half, last_hal... | 按照 50% 的 overlap 沿着最后一个维度对 chunk_list 进行拼接
Args:
dim: 需要拼接的维度
chunk_list(list): [[B, T], [B, T], ...]
Returns:
overlap 拼接后 | audio_zen/acoustics/feature.py | overlap_cat | ShkarupaDC/FullSubNet | 219 | python | def overlap_cat(chunk_list, dim=(- 1)):
'\n 按照 50% 的 overlap 沿着最后一个维度对 chunk_list 进行拼接\n\n Args:\n dim: 需要拼接的维度\n chunk_list(list): [[B, T], [B, T], ...]\n\n Returns:\n overlap 拼接后\n '
overlap_output = []
for (i, chunk) in enumerate(chunk_list):
(first_half, last_hal... | def overlap_cat(chunk_list, dim=(- 1)):
'\n 按照 50% 的 overlap 沿着最后一个维度对 chunk_list 进行拼接\n\n Args:\n dim: 需要拼接的维度\n chunk_list(list): [[B, T], [B, T], ...]\n\n Returns:\n overlap 拼接后\n '
overlap_output = []
for (i, chunk) in enumerate(chunk_list):
(first_half, last_hal... |
3c9878cce86dca4180cdff4e6bc3e9f7ac09682b297da9e3dbc3b4d5b46060ed | def activity_detector(audio, fs=16000, activity_threshold=0.13, target_level=(- 25), eps=1e-06):
'\n Return the percentage of the time the audio signal is above an energy threshold\n\n Args:\n audio:\n fs:\n activity_threshold:\n target_level:\n eps:\n\n Returns:\n\n '... | Return the percentage of the time the audio signal is above an energy threshold
Args:
audio:
fs:
activity_threshold:
target_level:
eps:
Returns: | audio_zen/acoustics/feature.py | activity_detector | ShkarupaDC/FullSubNet | 219 | python | def activity_detector(audio, fs=16000, activity_threshold=0.13, target_level=(- 25), eps=1e-06):
'\n Return the percentage of the time the audio signal is above an energy threshold\n\n Args:\n audio:\n fs:\n activity_threshold:\n target_level:\n eps:\n\n Returns:\n\n '... | def activity_detector(audio, fs=16000, activity_threshold=0.13, target_level=(- 25), eps=1e-06):
'\n Return the percentage of the time the audio signal is above an energy threshold\n\n Args:\n audio:\n fs:\n activity_threshold:\n target_level:\n eps:\n\n Returns:\n\n '... |
45acdcaab6547c4101d47d5ef8d0fba4836ec28f3b348f19ce01fcb2f23eb0db | def batch_shuffle_frequency(tensor, indices=None):
'\n\n Randomly shuffle frequency of a spectrogram and return shuffle indices.\n\n Args:\n tensor: input tensor with batch dim\n indices:\n\n Examples:\n input =\n tensor([[[[1., 1., 1.],\n [2., 2., 2.],\... | Randomly shuffle frequency of a spectrogram and return shuffle indices.
Args:
tensor: input tensor with batch dim
indices:
Examples:
input =
tensor([[[[1., 1., 1.],
[2., 2., 2.],
[3., 3., 3.],
[4., 4., 4.]]],
[[[1., 1., 1.],
... | audio_zen/acoustics/feature.py | batch_shuffle_frequency | ShkarupaDC/FullSubNet | 219 | python | def batch_shuffle_frequency(tensor, indices=None):
'\n\n Randomly shuffle frequency of a spectrogram and return shuffle indices.\n\n Args:\n tensor: input tensor with batch dim\n indices:\n\n Examples:\n input =\n tensor([[[[1., 1., 1.],\n [2., 2., 2.],\... | def batch_shuffle_frequency(tensor, indices=None):
'\n\n Randomly shuffle frequency of a spectrogram and return shuffle indices.\n\n Args:\n tensor: input tensor with batch dim\n indices:\n\n Examples:\n input =\n tensor([[[[1., 1., 1.],\n [2., 2., 2.],\... |
ade4e9b96d0fa96aef35727abd6d5e96984d35a4cf75780acbddfb7db5d1b444 | def drop_band(input, num_groups=2):
'\n Reduce computational complexity of the sub-band part in the FullSubNet model.\n\n Shapes:\n input: [B, C, F, T]\n return: [B, C, F // num_groups, T]\n '
(batch_size, _, num_freqs, _) = input.shape
assert (batch_size > num_groups), f'Batch size =... | Reduce computational complexity of the sub-band part in the FullSubNet model.
Shapes:
input: [B, C, F, T]
return: [B, C, F // num_groups, T] | audio_zen/acoustics/feature.py | drop_band | ShkarupaDC/FullSubNet | 219 | python | def drop_band(input, num_groups=2):
'\n Reduce computational complexity of the sub-band part in the FullSubNet model.\n\n Shapes:\n input: [B, C, F, T]\n return: [B, C, F // num_groups, T]\n '
(batch_size, _, num_freqs, _) = input.shape
assert (batch_size > num_groups), f'Batch size =... | def drop_band(input, num_groups=2):
'\n Reduce computational complexity of the sub-band part in the FullSubNet model.\n\n Shapes:\n input: [B, C, F, T]\n return: [B, C, F // num_groups, T]\n '
(batch_size, _, num_freqs, _) = input.shape
assert (batch_size > num_groups), f'Batch size =... |
5c91a770efb4884b54cce8ba797333550f2e58a490a35a74533c4b934e7e5a85 | def forward(self, x):
'\n x: BS x N x K\n '
if (x.dim() != 3):
raise RuntimeError('{} accept 3D tensor as input'.format(self.__name__))
x = torch.transpose(x, 1, 2)
x = super(ChannelWiseLayerNorm, self).forward(x)
x = torch.transpose(x, 1, 2)
return x | x: BS x N x K | audio_zen/acoustics/feature.py | forward | ShkarupaDC/FullSubNet | 219 | python | def forward(self, x):
'\n \n '
if (x.dim() != 3):
raise RuntimeError('{} accept 3D tensor as input'.format(self.__name__))
x = torch.transpose(x, 1, 2)
x = super(ChannelWiseLayerNorm, self).forward(x)
x = torch.transpose(x, 1, 2)
return x | def forward(self, x):
'\n \n '
if (x.dim() != 3):
raise RuntimeError('{} accept 3D tensor as input'.format(self.__name__))
x = torch.transpose(x, 1, 2)
x = super(ChannelWiseLayerNorm, self).forward(x)
x = torch.transpose(x, 1, 2)
return x<|docstring|>x: BS x N x K<|endoftex... |
75ec17ba2b1c7ab9da5585029e6ce5aca30b634c1d2c3d3c6f19389dbde96453 | def compute_ipd(self, phase):
'\n Args\n phase: phase of shape [B, M, F, K]\n Returns\n IPD of shape [B, I, F, K]\n '
cos_ipd = torch.cos((phase[(:, self.ipd_left)] - phase[(:, self.ipd_right)]))
sin_ipd = torch.sin((phase[(:, self.ipd_left)] - phase[(:, self.ipd_... | Args
phase: phase of shape [B, M, F, K]
Returns
IPD of shape [B, I, F, K] | audio_zen/acoustics/feature.py | compute_ipd | ShkarupaDC/FullSubNet | 219 | python | def compute_ipd(self, phase):
'\n Args\n phase: phase of shape [B, M, F, K]\n Returns\n IPD of shape [B, I, F, K]\n '
cos_ipd = torch.cos((phase[(:, self.ipd_left)] - phase[(:, self.ipd_right)]))
sin_ipd = torch.sin((phase[(:, self.ipd_left)] - phase[(:, self.ipd_... | def compute_ipd(self, phase):
'\n Args\n phase: phase of shape [B, M, F, K]\n Returns\n IPD of shape [B, I, F, K]\n '
cos_ipd = torch.cos((phase[(:, self.ipd_left)] - phase[(:, self.ipd_right)]))
sin_ipd = torch.sin((phase[(:, self.ipd_left)] - phase[(:, self.ipd_... |
f8ee583d9436b5212441a45170a47f1b9cdcd0c3b5dd24f9cb2c69ec24eabd86 | def forward(self, magnitude, phase, real, imag):
'\n Args:\n y: input mixture waveform with shape [B, M, T]\n\n Notes:\n B - batch_size\n M - num_channels\n C - num_speakers\n F - num_freqs\n T - seq_len or num_samples\n K - ... | Args:
y: input mixture waveform with shape [B, M, T]
Notes:
B - batch_size
M - num_channels
C - num_speakers
F - num_freqs
T - seq_len or num_samples
K - num_frames
I - IPD feature_size
Returns:
Spatial features and directional features of shape [B, ?, K] | audio_zen/acoustics/feature.py | forward | ShkarupaDC/FullSubNet | 219 | python | def forward(self, magnitude, phase, real, imag):
'\n Args:\n y: input mixture waveform with shape [B, M, T]\n\n Notes:\n B - batch_size\n M - num_channels\n C - num_speakers\n F - num_freqs\n T - seq_len or num_samples\n K - ... | def forward(self, magnitude, phase, real, imag):
'\n Args:\n y: input mixture waveform with shape [B, M, T]\n\n Notes:\n B - batch_size\n M - num_channels\n C - num_speakers\n F - num_freqs\n T - seq_len or num_samples\n K - ... |
4e521fef8c9dd1dff744f1cabb4c8bc29afe3274be07c35e251b5f1a3824bffb | def compute_ipd(self, phase):
'\n Args\n phase: phase of shape [B, M, F, K]\n Returns\n IPD pf shape [B, I, F, K]\n '
cos_ipd = torch.cos((phase[(:, self.ipd_left)] - phase[(:, self.ipd_right)]))
sin_ipd = torch.sin((phase[(:, self.ipd_left)] - phase[(:, self.ipd_... | Args
phase: phase of shape [B, M, F, K]
Returns
IPD pf shape [B, I, F, K] | audio_zen/acoustics/feature.py | compute_ipd | ShkarupaDC/FullSubNet | 219 | python | def compute_ipd(self, phase):
'\n Args\n phase: phase of shape [B, M, F, K]\n Returns\n IPD pf shape [B, I, F, K]\n '
cos_ipd = torch.cos((phase[(:, self.ipd_left)] - phase[(:, self.ipd_right)]))
sin_ipd = torch.sin((phase[(:, self.ipd_left)] - phase[(:, self.ipd_... | def compute_ipd(self, phase):
'\n Args\n phase: phase of shape [B, M, F, K]\n Returns\n IPD pf shape [B, I, F, K]\n '
cos_ipd = torch.cos((phase[(:, self.ipd_left)] - phase[(:, self.ipd_right)]))
sin_ipd = torch.sin((phase[(:, self.ipd_left)] - phase[(:, self.ipd_... |
705bef48087c5e5dda8cd05033a57cbce266313f53bde52c342c4f10c2c909e3 | def forward(self, y):
'\n Args:\n y: input mixture waveform with shape [B, M, T]\n\n Notes:\n B - batch_size\n M - num_channels\n C - num_speakers\n F - num_freqs\n T - seq_len or num_samples\n K - num_frames\n I -... | Args:
y: input mixture waveform with shape [B, M, T]
Notes:
B - batch_size
M - num_channels
C - num_speakers
F - num_freqs
T - seq_len or num_samples
K - num_frames
I - IPD feature_size
Returns:
Spatial features and directional features of shape [B, ?, K] | audio_zen/acoustics/feature.py | forward | ShkarupaDC/FullSubNet | 219 | python | def forward(self, y):
'\n Args:\n y: input mixture waveform with shape [B, M, T]\n\n Notes:\n B - batch_size\n M - num_channels\n C - num_speakers\n F - num_freqs\n T - seq_len or num_samples\n K - num_frames\n I -... | def forward(self, y):
'\n Args:\n y: input mixture waveform with shape [B, M, T]\n\n Notes:\n B - batch_size\n M - num_channels\n C - num_speakers\n F - num_freqs\n T - seq_len or num_samples\n K - num_frames\n I -... |
48f4b9a89b4a0c3be7846c6ebfd6811e9581cfabd45d3efd401c1b3552fc6cfe | def find_path(start, goal, neighbors_fnct, reversePath=False, heuristic_cost_estimate_fnct=(lambda a, b: Infinite), distance_between_fnct=(lambda a, b: 1.0), is_goal_reached_fnct=(lambda a, b: (a == b))):
'A non-class version of the path finding algorithm'
class FindPath(AStar):
def heuristic_cost_est... | A non-class version of the path finding algorithm | astar/__init__.py | find_path | kopp/python-astar | 133 | python | def find_path(start, goal, neighbors_fnct, reversePath=False, heuristic_cost_estimate_fnct=(lambda a, b: Infinite), distance_between_fnct=(lambda a, b: 1.0), is_goal_reached_fnct=(lambda a, b: (a == b))):
class FindPath(AStar):
def heuristic_cost_estimate(self, current, goal):
return heur... | def find_path(start, goal, neighbors_fnct, reversePath=False, heuristic_cost_estimate_fnct=(lambda a, b: Infinite), distance_between_fnct=(lambda a, b: 1.0), is_goal_reached_fnct=(lambda a, b: (a == b))):
class FindPath(AStar):
def heuristic_cost_estimate(self, current, goal):
return heur... |
b45de3d6f32dd357093d7eb869541a7ebb9f77f01e6b372d6a0515faae014447 | @abstractmethod
def heuristic_cost_estimate(self, current, goal):
'Computes the estimated (rough) distance between a node and the goal, this method must be implemented in a subclass. The second parameter is always the goal.'
raise NotImplementedError | Computes the estimated (rough) distance between a node and the goal, this method must be implemented in a subclass. The second parameter is always the goal. | astar/__init__.py | heuristic_cost_estimate | kopp/python-astar | 133 | python | @abstractmethod
def heuristic_cost_estimate(self, current, goal):
raise NotImplementedError | @abstractmethod
def heuristic_cost_estimate(self, current, goal):
raise NotImplementedError<|docstring|>Computes the estimated (rough) distance between a node and the goal, this method must be implemented in a subclass. The second parameter is always the goal.<|endoftext|> |
757b805ddeea1ea45bd2aafc56735e3165b7c14de27d9bf230e87e700a96834b | @abstractmethod
def distance_between(self, n1, n2):
"Gives the real distance between two adjacent nodes n1 and n2 (i.e n2 belongs to the list of n1's neighbors).\n n2 is guaranteed to belong to the list returned by the call to neighbors(n1).\n This method must be implemented in a subclass."
... | Gives the real distance between two adjacent nodes n1 and n2 (i.e n2 belongs to the list of n1's neighbors).
n2 is guaranteed to belong to the list returned by the call to neighbors(n1).
This method must be implemented in a subclass. | astar/__init__.py | distance_between | kopp/python-astar | 133 | python | @abstractmethod
def distance_between(self, n1, n2):
"Gives the real distance between two adjacent nodes n1 and n2 (i.e n2 belongs to the list of n1's neighbors).\n n2 is guaranteed to belong to the list returned by the call to neighbors(n1).\n This method must be implemented in a subclass."
... | @abstractmethod
def distance_between(self, n1, n2):
"Gives the real distance between two adjacent nodes n1 and n2 (i.e n2 belongs to the list of n1's neighbors).\n n2 is guaranteed to belong to the list returned by the call to neighbors(n1).\n This method must be implemented in a subclass."
... |
9f23d473d0109103e5cfd9f125e397dd8e4ba4d28473e17b9d91a22504a529e0 | @abstractmethod
def neighbors(self, node):
'For a given node, returns (or yields) the list of its neighbors. this method must be implemented in a subclass'
raise NotImplementedError | For a given node, returns (or yields) the list of its neighbors. this method must be implemented in a subclass | astar/__init__.py | neighbors | kopp/python-astar | 133 | python | @abstractmethod
def neighbors(self, node):
raise NotImplementedError | @abstractmethod
def neighbors(self, node):
raise NotImplementedError<|docstring|>For a given node, returns (or yields) the list of its neighbors. this method must be implemented in a subclass<|endoftext|> |
3d42068a4ccb4ff69e085722fdd3278977c08c4706f61f30109c431247288177 | def is_goal_reached(self, current, goal):
" returns true when we can consider that 'current' is the goal"
return (current == goal) | returns true when we can consider that 'current' is the goal | astar/__init__.py | is_goal_reached | kopp/python-astar | 133 | python | def is_goal_reached(self, current, goal):
" "
return (current == goal) | def is_goal_reached(self, current, goal):
" "
return (current == goal)<|docstring|>returns true when we can consider that 'current' is the goal<|endoftext|> |
3396c99399eeff61077160f8b431274a437baff968518dba51612e4e99befa92 | def clip(image: np.ndarray, k: int, lb: int, ub: int, b: int, c: str) -> np.ndarray:
"Return the Netpbm image mod k.\n\n Args:\n image (np.ndarray): Image to mod.\n k (int): Number of gradients.\n lb (int): Lower bound of gradients to show.\n ub (int): Upper bound of gradients to show... | Return the Netpbm image mod k.
Args:
image (np.ndarray): Image to mod.
k (int): Number of gradients.
lb (int): Lower bound of gradients to show.
ub (int): Upper bound of gradients to show.
b (int): Width of the border.
c (str): Color of the border {'white', 'black'}
Returns:
np.ndarray: Nu... | clip/src.py | clip | henryrobbins/artwork | 0 | python | def clip(image: np.ndarray, k: int, lb: int, ub: int, b: int, c: str) -> np.ndarray:
"Return the Netpbm image mod k.\n\n Args:\n image (np.ndarray): Image to mod.\n k (int): Number of gradients.\n lb (int): Lower bound of gradients to show.\n ub (int): Upper bound of gradients to show... | def clip(image: np.ndarray, k: int, lb: int, ub: int, b: int, c: str) -> np.ndarray:
"Return the Netpbm image mod k.\n\n Args:\n image (np.ndarray): Image to mod.\n k (int): Number of gradients.\n lb (int): Lower bound of gradients to show.\n ub (int): Upper bound of gradients to show... |
03a2b130317bfe02b8dbbe595dc799086033d13b1964ebc3b97fa52384c91c9f | def batch_call(calls):
"\n Similar interface but block height as last param. Uses JSON-RPC batch.\n\n [[contract, 'func', arg, block_identifier]]\n "
jsonrpc_batch = []
fn_list = []
ids = count()
for (contract, fn_name, *fn_inputs, block) in calls:
fn = getattr(contract, fn_name)
... | Similar interface but block height as last param. Uses JSON-RPC batch.
[[contract, 'func', arg, block_identifier]] | yearn/multicall2.py | batch_call | poolpitako/yearn-exporter | 1 | python | def batch_call(calls):
"\n Similar interface but block height as last param. Uses JSON-RPC batch.\n\n [[contract, 'func', arg, block_identifier]]\n "
jsonrpc_batch = []
fn_list = []
ids = count()
for (contract, fn_name, *fn_inputs, block) in calls:
fn = getattr(contract, fn_name)
... | def batch_call(calls):
"\n Similar interface but block height as last param. Uses JSON-RPC batch.\n\n [[contract, 'func', arg, block_identifier]]\n "
jsonrpc_batch = []
fn_list = []
ids = count()
for (contract, fn_name, *fn_inputs, block) in calls:
fn = getattr(contract, fn_name)
... |
e4eb148d7a1bf5cfff52d2d1e622f9367ecf9adb7c1eadfaee43f2ea983272f9 | def set_base_yaml(self):
'Set the base yaml for the alarm, specifics for alarms will be updated on their class'
self.template = f'''
{self.resource_unique_name}:
Type: AWS::CloudWatch::Alarm
Properties:
AlarmDescription: Instance={self.instance_name} Metric={self.metric} AlertLev... | Set the base yaml for the alarm, specifics for alarms will be updated on their class | app/src/references/old/alarms_ec2.py | set_base_yaml | dwbelliston/cloudwedge | 0 | python | def set_base_yaml(self):
self.template = f'
{self.resource_unique_name}:
Type: AWS::CloudWatch::Alarm
Properties:
AlarmDescription: Instance={self.instance_name} Metric={self.metric} AlertLevel={self.alert_level} Type=EC2 AlertOwner={self.alert_owner}
Namespace: AWS/... | def set_base_yaml(self):
self.template = f'
{self.resource_unique_name}:
Type: AWS::CloudWatch::Alarm
Properties:
AlarmDescription: Instance={self.instance_name} Metric={self.metric} AlertLevel={self.alert_level} Type=EC2 AlertOwner={self.alert_owner}
Namespace: AWS/... |
08f41045ab12cf17674fe956c24956856fa6195902bfcb556a138a870f9b2271 | def test_to_bool(self):
'\n Verify we can convert properly the boolean strings\n '
self.assertRaises(ValueError, SimpleConfigParser.to_bool, None)
self.assertRaises(ValueError, SimpleConfigParser.to_bool, True)
self.assertRaises(ValueError, SimpleConfigParser.to_bool, False)
self.asser... | Verify we can convert properly the boolean strings | test/TestReadConfig.py | test_to_bool | lemaslab/redi | 7 | python | def test_to_bool(self):
'\n \n '
self.assertRaises(ValueError, SimpleConfigParser.to_bool, None)
self.assertRaises(ValueError, SimpleConfigParser.to_bool, True)
self.assertRaises(ValueError, SimpleConfigParser.to_bool, False)
self.assertTrue(SimpleConfigParser.to_bool('true'))
self... | def test_to_bool(self):
'\n \n '
self.assertRaises(ValueError, SimpleConfigParser.to_bool, None)
self.assertRaises(ValueError, SimpleConfigParser.to_bool, True)
self.assertRaises(ValueError, SimpleConfigParser.to_bool, False)
self.assertTrue(SimpleConfigParser.to_bool('true'))
self... |
085f437315d7117ea5ea29534c52c8ea69c0446418b5120d7275a885e40e9c52 | def __setup_bambou():
' Avoid having bad behavior when using importlib.import_module method\n '
import pkg_resources
from bambou import BambouConfig, NURESTModelController
default_attrs = pkg_resources.resource_filename(__name__, '/resources/attrs_defaults.ini')
BambouConfig.set_default_values_co... | Avoid having bad behavior when using importlib.import_module method | tests/base/sdk/python/tdldk/v1_0/__init__.py | __setup_bambou | edwinfeener/monolithe | 18 | python | def __setup_bambou():
' \n '
import pkg_resources
from bambou import BambouConfig, NURESTModelController
default_attrs = pkg_resources.resource_filename(__name__, '/resources/attrs_defaults.ini')
BambouConfig.set_default_values_config_file(default_attrs)
NURESTModelController.register_model(G... | def __setup_bambou():
' \n '
import pkg_resources
from bambou import BambouConfig, NURESTModelController
default_attrs = pkg_resources.resource_filename(__name__, '/resources/attrs_defaults.ini')
BambouConfig.set_default_values_config_file(default_attrs)
NURESTModelController.register_model(G... |
d0325d0b54911211e8e13f3386c8bf8c5aadacce22985b48e07bbd69187a3945 | def plot_string(self, ax=None, frame=None, plot_kwargs=None):
"\n Plot the string at an input frame. Here, frame is a dump of a step in the run. If `fts_job´ is the name\n of the fts job, the number of dumps can be specified by the user while submitting the job, as:\n\n >>> fts_job.set_outp... | Plot the string at an input frame. Here, frame is a dump of a step in the run. If `fts_job´ is the name
of the fts job, the number of dumps can be specified by the user while submitting the job, as:
>>> fts_job.set_output_whitelist(**{'calc_static_centroids': {'energy_pot': 20}})
and run the job. Here, it dumps (... | pyiron_contrib/protocol/compound/finite_temperature_string.py | plot_string | pyiron/pyiron_contrib | 5 | python | def plot_string(self, ax=None, frame=None, plot_kwargs=None):
"\n Plot the string at an input frame. Here, frame is a dump of a step in the run. If `fts_job´ is the name\n of the fts job, the number of dumps can be specified by the user while submitting the job, as:\n\n >>> fts_job.set_outp... | def plot_string(self, ax=None, frame=None, plot_kwargs=None):
"\n Plot the string at an input frame. Here, frame is a dump of a step in the run. If `fts_job´ is the name\n of the fts job, the number of dumps can be specified by the user while submitting the job, as:\n\n >>> fts_job.set_outp... |
94a8597023a2dc3dff565e27b0b73a9c0230a58563da1b1210a5fc6c2ebf2285 | def get_forward_barrier(self, frame=None, use_minima=False):
'\n Get the energy barrier from the 0th image to the highest energy (saddle state).\n\n Args:\n frame (int): A particular dump. (Default is None, the final dump.)\n use_minima (bool): Whether to use the minima of the en... | Get the energy barrier from the 0th image to the highest energy (saddle state).
Args:
frame (int): A particular dump. (Default is None, the final dump.)
use_minima (bool): Whether to use the minima of the energies to compute tha barrier. (Default is
False, use the 0th value.)
Returns:
(float): the... | pyiron_contrib/protocol/compound/finite_temperature_string.py | get_forward_barrier | pyiron/pyiron_contrib | 5 | python | def get_forward_barrier(self, frame=None, use_minima=False):
'\n Get the energy barrier from the 0th image to the highest energy (saddle state).\n\n Args:\n frame (int): A particular dump. (Default is None, the final dump.)\n use_minima (bool): Whether to use the minima of the en... | def get_forward_barrier(self, frame=None, use_minima=False):
'\n Get the energy barrier from the 0th image to the highest energy (saddle state).\n\n Args:\n frame (int): A particular dump. (Default is None, the final dump.)\n use_minima (bool): Whether to use the minima of the en... |
243dfddbef4719b5c6a0ef5b7eec9e61c637d7806f134f2a827282a463e14f53 | def get_reverse_barrier(self, frame=None, use_minima=False):
'\n Get the energy barrier from the final image to the highest energy (saddle state).\n\n Args:\n frame (int): A particular dump. (Default is None, the final dump.)\n use_minima (bool): Whether to use the minima of the ... | Get the energy barrier from the final image to the highest energy (saddle state).
Args:
frame (int): A particular dump. (Default is None, the final dump.)
use_minima (bool): Whether to use the minima of the energies to compute tha barrier. (Default is
False, use the nth value.)
Returns:
(float): t... | pyiron_contrib/protocol/compound/finite_temperature_string.py | get_reverse_barrier | pyiron/pyiron_contrib | 5 | python | def get_reverse_barrier(self, frame=None, use_minima=False):
'\n Get the energy barrier from the final image to the highest energy (saddle state).\n\n Args:\n frame (int): A particular dump. (Default is None, the final dump.)\n use_minima (bool): Whether to use the minima of the ... | def get_reverse_barrier(self, frame=None, use_minima=False):
'\n Get the energy barrier from the final image to the highest energy (saddle state).\n\n Args:\n frame (int): A particular dump. (Default is None, the final dump.)\n use_minima (bool): Whether to use the minima of the ... |
c52cb9b58345fab85dc3c2073c590a7caf483f239c3c5b028d1403f7818b4617 | def __init__(self, nnef_graph, custom_operations=None, batch_normalization_momentum=0.1, tensor_hooks=None):
'\n nnef_graph might be modified by this class if training and write_nnef is used\n '
super(NNEFModule, self).__init__()
self._nnef_graph = nnef_graph
for nnef_tensor in self._n... | nnef_graph might be modified by this class if training and write_nnef is used | nnef_tools/backend/pytorch/nnef_module.py | __init__ | Alena19971993/NNEF-Tools | 0 | python | def __init__(self, nnef_graph, custom_operations=None, batch_normalization_momentum=0.1, tensor_hooks=None):
'\n \n '
super(NNEFModule, self).__init__()
self._nnef_graph = nnef_graph
for nnef_tensor in self._nnef_graph.tensors:
if nnef_tensor.is_constant:
np_array =... | def __init__(self, nnef_graph, custom_operations=None, batch_normalization_momentum=0.1, tensor_hooks=None):
'\n \n '
super(NNEFModule, self).__init__()
self._nnef_graph = nnef_graph
for nnef_tensor in self._nnef_graph.tensors:
if nnef_tensor.is_constant:
np_array =... |
df50172c20f5bd0586557de096920832ba8f1b16bcd067b1a6765195e0a1ee62 | def reset_parameters(self):
'\n This method provides a very simple initialization that was enough for out experiments\n If you need something more nuanced, please do the initialization externally\n '
biases = set()
for op in self._nnef_graph.operations:
if (op.name in ('conv', '... | This method provides a very simple initialization that was enough for out experiments
If you need something more nuanced, please do the initialization externally | nnef_tools/backend/pytorch/nnef_module.py | reset_parameters | Alena19971993/NNEF-Tools | 0 | python | def reset_parameters(self):
'\n This method provides a very simple initialization that was enough for out experiments\n If you need something more nuanced, please do the initialization externally\n '
biases = set()
for op in self._nnef_graph.operations:
if (op.name in ('conv', '... | def reset_parameters(self):
'\n This method provides a very simple initialization that was enough for out experiments\n If you need something more nuanced, please do the initialization externally\n '
biases = set()
for op in self._nnef_graph.operations:
if (op.name in ('conv', '... |
c1f91cc500c4fe34d0bd8630ed8e106c2b21a5f07ae5432ba67339d8c31db48c | def list_of_array_equal(s, t):
'\n Compare two lists of ndarrays\n\n s, t: lists of numpy.ndarrays\n\n '
eq_(len(s), len(t))
all((assert_array_equal(x, y) for (x, y) in zip(s, t))) | Compare two lists of ndarrays
s, t: lists of numpy.ndarrays | quantecon/tests/test_graph_tools.py | list_of_array_equal | chenxulong/quanteco | 9 | python | def list_of_array_equal(s, t):
'\n Compare two lists of ndarrays\n\n s, t: lists of numpy.ndarrays\n\n '
eq_(len(s), len(t))
all((assert_array_equal(x, y) for (x, y) in zip(s, t))) | def list_of_array_equal(s, t):
'\n Compare two lists of ndarrays\n\n s, t: lists of numpy.ndarrays\n\n '
eq_(len(s), len(t))
all((assert_array_equal(x, y) for (x, y) in zip(s, t)))<|docstring|>Compare two lists of ndarrays
s, t: lists of numpy.ndarrays<|endoftext|> |
636dc9aba8cba6ec70a162b5fe7e4f0b5b008c179633c5192a9c24b1b5f27b07 | @raises(ValueError)
def test_raises_value_error_non_sym():
'Test with non symmetric input'
g = DiGraph(np.array([[0.4, 0.6]])) | Test with non symmetric input | quantecon/tests/test_graph_tools.py | test_raises_value_error_non_sym | chenxulong/quanteco | 9 | python | @raises(ValueError)
def test_raises_value_error_non_sym():
g = DiGraph(np.array([[0.4, 0.6]])) | @raises(ValueError)
def test_raises_value_error_non_sym():
g = DiGraph(np.array([[0.4, 0.6]]))<|docstring|>Test with non symmetric input<|endoftext|> |
7818b73d33613d396f5cd9c18d43fa737a68f74716f718d9e963710f1991c553 | def setUp(self):
'Setup Digraph instances'
self.graphs = Graphs()
for graph_dict in self.graphs.graph_dicts:
try:
weighted = graph_dict['weighted']
except:
weighted = False
graph_dict['g'] = DiGraph(graph_dict['A'], weighted=weighted) | Setup Digraph instances | quantecon/tests/test_graph_tools.py | setUp | chenxulong/quanteco | 9 | python | def setUp(self):
self.graphs = Graphs()
for graph_dict in self.graphs.graph_dicts:
try:
weighted = graph_dict['weighted']
except:
weighted = False
graph_dict['g'] = DiGraph(graph_dict['A'], weighted=weighted) | def setUp(self):
self.graphs = Graphs()
for graph_dict in self.graphs.graph_dicts:
try:
weighted = graph_dict['weighted']
except:
weighted = False
graph_dict['g'] = DiGraph(graph_dict['A'], weighted=weighted)<|docstring|>Setup Digraph instances<|endoftext|> |
c10815b6396091204a457a9393d8a95a2ac0465d84dcef952a32810825d2f2f1 | async def test_aspirate_implementation(decoy: Decoy, equipment: EquipmentHandler, movement: MovementHandler, pipetting: PipettingHandler, run_control: RunControlHandler) -> None:
'An Aspirate should have an execution implementation.'
subject = AspirateImplementation(equipment=equipment, movement=movement, pipet... | An Aspirate should have an execution implementation. | api/tests/opentrons/protocol_engine/commands/test_aspirate.py | test_aspirate_implementation | y3rsh/opentrons | 235 | python | async def test_aspirate_implementation(decoy: Decoy, equipment: EquipmentHandler, movement: MovementHandler, pipetting: PipettingHandler, run_control: RunControlHandler) -> None:
subject = AspirateImplementation(equipment=equipment, movement=movement, pipetting=pipetting, run_control=run_control)
location ... | async def test_aspirate_implementation(decoy: Decoy, equipment: EquipmentHandler, movement: MovementHandler, pipetting: PipettingHandler, run_control: RunControlHandler) -> None:
subject = AspirateImplementation(equipment=equipment, movement=movement, pipetting=pipetting, run_control=run_control)
location ... |
4eadd56a04f9e4473d180fb2a0b273900ba291051998338f0ac68c21ad9a5f9c | def gray2color(gray, color):
' \n transform a gray image (2d array) to a color image given the color (1x3 vector) \n untested\n '
return np.stack(((gray * c) for c in color), (- 1)) | transform a gray image (2d array) to a color image given the color (1x3 vector)
untested | voxelmorph/voxelmorph/tf/external/pytools-lib/pynd/imutils.py | gray2color | Noodles-321/Registration | 107 | python | def gray2color(gray, color):
' \n transform a gray image (2d array) to a color image given the color (1x3 vector) \n untested\n '
return np.stack(((gray * c) for c in color), (- 1)) | def gray2color(gray, color):
' \n transform a gray image (2d array) to a color image given the color (1x3 vector) \n untested\n '
return np.stack(((gray * c) for c in color), (- 1))<|docstring|>transform a gray image (2d array) to a color image given the color (1x3 vector)
untested<|endoftext|> |
ede850614af4af880dd0dec16fd8c55cbc37358596c39a2c983be167c18d8ff1 | def rgb2gray(rgb, mixing=[0.2989, 0.587, 0.114], keepdims=False):
' \n transform a rgb image (i.e. array with last dimension of 3) to grayscale\n (which reduces the last dimension)\n '
gray = np.dot(rgb[(..., :3)], mixing)
if keepdims:
gray = gray[(..., np.newaxis)]
return gray | transform a rgb image (i.e. array with last dimension of 3) to grayscale
(which reduces the last dimension) | voxelmorph/voxelmorph/tf/external/pytools-lib/pynd/imutils.py | rgb2gray | Noodles-321/Registration | 107 | python | def rgb2gray(rgb, mixing=[0.2989, 0.587, 0.114], keepdims=False):
' \n transform a rgb image (i.e. array with last dimension of 3) to grayscale\n (which reduces the last dimension)\n '
gray = np.dot(rgb[(..., :3)], mixing)
if keepdims:
gray = gray[(..., np.newaxis)]
return gray | def rgb2gray(rgb, mixing=[0.2989, 0.587, 0.114], keepdims=False):
' \n transform a rgb image (i.e. array with last dimension of 3) to grayscale\n (which reduces the last dimension)\n '
gray = np.dot(rgb[(..., :3)], mixing)
if keepdims:
gray = gray[(..., np.newaxis)]
return gray<|docstri... |
4f75b57a4ae689d6c8d2b5fe8de2a1d592cf05bb6754becec7d9c4ee22441359 | def main() -> None:
'Read the Real Python article feed.'
args = [a for a in sys.argv[1:] if (not a.startswith('-'))]
opts = [o for o in sys.argv[1:] if o.startswith('-')]
if (('-h' in opts) or ('--help' in opts)):
viewer.show(__doc__)
raise SystemExit()
show_links = (('-l' in opts) o... | Read the Real Python article feed. | reader/__main__.py | main | finage/realpython_reader | 100 | python | def main() -> None:
args = [a for a in sys.argv[1:] if (not a.startswith('-'))]
opts = [o for o in sys.argv[1:] if o.startswith('-')]
if (('-h' in opts) or ('--help' in opts)):
viewer.show(__doc__)
raise SystemExit()
show_links = (('-l' in opts) or ('--show-links' in opts))
url ... | def main() -> None:
args = [a for a in sys.argv[1:] if (not a.startswith('-'))]
opts = [o for o in sys.argv[1:] if o.startswith('-')]
if (('-h' in opts) or ('--help' in opts)):
viewer.show(__doc__)
raise SystemExit()
show_links = (('-l' in opts) or ('--show-links' in opts))
url ... |
99805efc580e87d5cb9ad8a86fdd6f9d48746642d912028b507696acb7184124 | def method_foo(self):
'\n Method of Parent Class A\n '
print('AAA') | Method of Parent Class A | super/example_super.py | method_foo | firemanxbr/python-examples | 2 | python | def method_foo(self):
'\n \n '
print('AAA') | def method_foo(self):
'\n \n '
print('AAA')<|docstring|>Method of Parent Class A<|endoftext|> |
27bc5c00eb766889ffb1349f5b8bb933622cb8b3a96738e4e301222eb8a7386a | def method_bar(self):
'\n Method of Sub Class B\n '
super(SubB, self).method_foo()
print('BBB') | Method of Sub Class B | super/example_super.py | method_bar | firemanxbr/python-examples | 2 | python | def method_bar(self):
'\n \n '
super(SubB, self).method_foo()
print('BBB') | def method_bar(self):
'\n \n '
super(SubB, self).method_foo()
print('BBB')<|docstring|>Method of Sub Class B<|endoftext|> |
dfd0d74aab31c82a8e159ba0024ca48676a1d0b08e7bdd1f94ad0f8d07d1ff97 | def method_foo(self):
'\n Method of Sub Class X\n '
print('XXX') | Method of Sub Class X | super/example_super.py | method_foo | firemanxbr/python-examples | 2 | python | def method_foo(self):
'\n \n '
print('XXX') | def method_foo(self):
'\n \n '
print('XXX')<|docstring|>Method of Sub Class X<|endoftext|> |
c6a3d20eddf59558b6bf84e7cfa7d9724611ab2b83da1cd1e6ddbd5684eb2e39 | def _parse_args():
'return a parser with arguments and values'
parser = argparse.ArgumentParser()
_init_general_parsers(parser)
_init_subparsers(parser)
if (len(sys.argv) == 1):
parser.print_help()
sys.exit(1)
return parser.parse_args() | return a parser with arguments and values | wow_addon_manager/cli.py | _parse_args | qwezarty/wow-addon-manager | 0 | python | def _parse_args():
parser = argparse.ArgumentParser()
_init_general_parsers(parser)
_init_subparsers(parser)
if (len(sys.argv) == 1):
parser.print_help()
sys.exit(1)
return parser.parse_args() | def _parse_args():
parser = argparse.ArgumentParser()
_init_general_parsers(parser)
_init_subparsers(parser)
if (len(sys.argv) == 1):
parser.print_help()
sys.exit(1)
return parser.parse_args()<|docstring|>return a parser with arguments and values<|endoftext|> |
d9b5933595569cb9cb8688b9d16353eabeb9c56bbaaeb0df4c419ef968c40a29 | def _init_general_parsers(parser):
'initialize global cli arguments'
parser.add_argument('-v', '--version', action='version', version='%(prog)s 0.0.1', help='show version and exit.') | initialize global cli arguments | wow_addon_manager/cli.py | _init_general_parsers | qwezarty/wow-addon-manager | 0 | python | def _init_general_parsers(parser):
parser.add_argument('-v', '--version', action='version', version='%(prog)s 0.0.1', help='show version and exit.') | def _init_general_parsers(parser):
parser.add_argument('-v', '--version', action='version', version='%(prog)s 0.0.1', help='show version and exit.')<|docstring|>initialize global cli arguments<|endoftext|> |
199e9082c70e872b4e76ff9a7c08b1b579d496e54f7b891330dbd7fe75285440 | def _init_subparsers(parent):
'initialize cli sub-positional arguments'
subparsers = parent.add_subparsers()
parser_install = subparsers.add_parser('install', help='install a specific addon.')
parser_install.set_defaults(func=install)
parser_install.add_argument('addon', help='the addon you want to ... | initialize cli sub-positional arguments | wow_addon_manager/cli.py | _init_subparsers | qwezarty/wow-addon-manager | 0 | python | def _init_subparsers(parent):
subparsers = parent.add_subparsers()
parser_install = subparsers.add_parser('install', help='install a specific addon.')
parser_install.set_defaults(func=install)
parser_install.add_argument('addon', help='the addon you want to install.')
parser_search = subparsers... | def _init_subparsers(parent):
subparsers = parent.add_subparsers()
parser_install = subparsers.add_parser('install', help='install a specific addon.')
parser_install.set_defaults(func=install)
parser_install.add_argument('addon', help='the addon you want to install.')
parser_search = subparsers... |
97024d16483f12d79444c8a988d77658187681f8f797885cafdef16244738450 | def create(self, validated_data):
'\n Create / Update Configurations\n :param validated_data: Validated data\n :return: upserted configurations object\n '
workspace = validated_data['workspace']
(configuration, _) = Configuration.objects.update_or_create(workspace_id=workspace, d... | Create / Update Configurations
:param validated_data: Validated data
:return: upserted configurations object | apps/workspaces/serializers.py | create | fylein/fyle-netsuite-api | 1 | python | def create(self, validated_data):
'\n Create / Update Configurations\n :param validated_data: Validated data\n :return: upserted configurations object\n '
workspace = validated_data['workspace']
(configuration, _) = Configuration.objects.update_or_create(workspace_id=workspace, d... | def create(self, validated_data):
'\n Create / Update Configurations\n :param validated_data: Validated data\n :return: upserted configurations object\n '
workspace = validated_data['workspace']
(configuration, _) = Configuration.objects.update_or_create(workspace_id=workspace, d... |
4f1e3de59b5b820e46e29441a5fdb25ef959322bcab671a448c9aec7cad6ecd4 | def validate(self, attrs):
'\n Validate auto create destination entity\n :param attrs: Non-validated data\n :return: upserted general settings object\n '
if self.partial:
return attrs
if ((not attrs['auto_map_employees']) and attrs['auto_create_destination_entity']):
... | Validate auto create destination entity
:param attrs: Non-validated data
:return: upserted general settings object | apps/workspaces/serializers.py | validate | fylein/fyle-netsuite-api | 1 | python | def validate(self, attrs):
'\n Validate auto create destination entity\n :param attrs: Non-validated data\n :return: upserted general settings object\n '
if self.partial:
return attrs
if ((not attrs['auto_map_employees']) and attrs['auto_create_destination_entity']):
... | def validate(self, attrs):
'\n Validate auto create destination entity\n :param attrs: Non-validated data\n :return: upserted general settings object\n '
if self.partial:
return attrs
if ((not attrs['auto_map_employees']) and attrs['auto_create_destination_entity']):
... |
8401d1486f56c37247b72ebd7def50486aec5011080c1a401336169017f51cdc | def gaussian_log(x, x0, xsig):
'\n\tfunction to calculate the gaussian probability (its normed to Pmax and given in log)\n\t\n\tINPUT:\n\t\n\t x = where is the data point or parameter value\n\t\n\t x0 = mu\n\t\n\t xsig = sigma\n\t'
return (- np.divide(((x - x0) * (x - x0)), ((2 * xsig) * xsig))) | function to calculate the gaussian probability (its normed to Pmax and given in log)
INPUT:
x = where is the data point or parameter value
x0 = mu
xsig = sigma | Chempy/cem_function.py | gaussian_log | jan-rybizki/Chempy | 25 | python | def gaussian_log(x, x0, xsig):
'\n\tfunction to calculate the gaussian probability (its normed to Pmax and given in log)\n\t\n\tINPUT:\n\t\n\t x = where is the data point or parameter value\n\t\n\t x0 = mu\n\t\n\t xsig = sigma\n\t'
return (- np.divide(((x - x0) * (x - x0)), ((2 * xsig) * xsig))) | def gaussian_log(x, x0, xsig):
'\n\tfunction to calculate the gaussian probability (its normed to Pmax and given in log)\n\t\n\tINPUT:\n\t\n\t x = where is the data point or parameter value\n\t\n\t x0 = mu\n\t\n\t xsig = sigma\n\t'
return (- np.divide(((x - x0) * (x - x0)), ((2 * xsig) * xsig)))<|docstrin... |
75404432c6b243d29a9225eecd5f9cd0ba1dde67d6c31bac69d13e3eba620c81 | def lognorm_log(x, mu, factor):
'\n\tthis function provides Prior probability distribution where the factor away from the mean behaves like the sigma deviation in normal_log \n\t\n\tfor example if mu = 1 and factor = 2 \n\t\n\tfor\t1 it returns 0\n\t\n\tfor 0,5 and 2 it returns -0.5\n\t\n\tfor 0.25 and 4 it returns... | this function provides Prior probability distribution where the factor away from the mean behaves like the sigma deviation in normal_log
for example if mu = 1 and factor = 2
for 1 it returns 0
for 0,5 and 2 it returns -0.5
for 0.25 and 4 it returns -2.0
and so forth
Can be used to specify the prior on the y... | Chempy/cem_function.py | lognorm_log | jan-rybizki/Chempy | 25 | python | def lognorm_log(x, mu, factor):
'\n\tthis function provides Prior probability distribution where the factor away from the mean behaves like the sigma deviation in normal_log \n\t\n\tfor example if mu = 1 and factor = 2 \n\t\n\tfor\t1 it returns 0\n\t\n\tfor 0,5 and 2 it returns -0.5\n\t\n\tfor 0.25 and 4 it returns... | def lognorm_log(x, mu, factor):
'\n\tthis function provides Prior probability distribution where the factor away from the mean behaves like the sigma deviation in normal_log \n\t\n\tfor example if mu = 1 and factor = 2 \n\t\n\tfor\t1 it returns 0\n\t\n\tfor 0,5 and 2 it returns -0.5\n\t\n\tfor 0.25 and 4 it returns... |
575d3f0c5337ed3d7eb4c09109c902053a0ba450dfdaeb42eb4063acd8b0b7f2 | def gaussian(x, x0, xsig):
'\n\tfunction to calculate the gaussian probability (its normed to Pmax and given in log)\n\t\n\tINPUT:\n\t\n\t x = where is the data point or parameter value\n\t\n\t x0 = mu\n\t\n\t xsig = sigma\n\t'
factor = (1.0 / np.sqrt((((xsig * xsig) * 2.0) * np.pi)))
exponent = (- np... | function to calculate the gaussian probability (its normed to Pmax and given in log)
INPUT:
x = where is the data point or parameter value
x0 = mu
xsig = sigma | Chempy/cem_function.py | gaussian | jan-rybizki/Chempy | 25 | python | def gaussian(x, x0, xsig):
'\n\tfunction to calculate the gaussian probability (its normed to Pmax and given in log)\n\t\n\tINPUT:\n\t\n\t x = where is the data point or parameter value\n\t\n\t x0 = mu\n\t\n\t xsig = sigma\n\t'
factor = (1.0 / np.sqrt((((xsig * xsig) * 2.0) * np.pi)))
exponent = (- np... | def gaussian(x, x0, xsig):
'\n\tfunction to calculate the gaussian probability (its normed to Pmax and given in log)\n\t\n\tINPUT:\n\t\n\t x = where is the data point or parameter value\n\t\n\t x0 = mu\n\t\n\t xsig = sigma\n\t'
factor = (1.0 / np.sqrt((((xsig * xsig) * 2.0) * np.pi)))
exponent = (- np... |
13fb197208bf2a75833d70d922e99c9b34637e1109925b72c1b27cb29d6c3119 | def lognorm(x, mu, factor):
'\n\tthis function provides Prior probability distribution where the factor away from the mean behaves like the sigma deviation in normal_log \n\tBEWARE: this function is not a properly normalized probability distribution. It only provides relative values.\n\t\n\tINPUT:\n\n\t x = where... | this function provides Prior probability distribution where the factor away from the mean behaves like the sigma deviation in normal_log
BEWARE: this function is not a properly normalized probability distribution. It only provides relative values.
INPUT:
x = where to evaluate the function, can be an array
mu ... | Chempy/cem_function.py | lognorm | jan-rybizki/Chempy | 25 | python | def lognorm(x, mu, factor):
'\n\tthis function provides Prior probability distribution where the factor away from the mean behaves like the sigma deviation in normal_log \n\tBEWARE: this function is not a properly normalized probability distribution. It only provides relative values.\n\t\n\tINPUT:\n\n\t x = where... | def lognorm(x, mu, factor):
'\n\tthis function provides Prior probability distribution where the factor away from the mean behaves like the sigma deviation in normal_log \n\tBEWARE: this function is not a properly normalized probability distribution. It only provides relative values.\n\t\n\tINPUT:\n\n\t x = where... |
ac8eeb9732ad729bfafb18ac61fd03e43a79c07079f235efefad57ba6f360329 | def shorten_sfr(a):
'\n\tThis function crops the SFR to the length of the age of the star and ensures that enough stars are formed at the stellar birth epoch\n\n\tINPUT:\n\n\t a = Modelparameters\n\n\tOUTPUT:\n\t\n\t the function will update the modelparameters, such that the simulation will end when the star i... | This function crops the SFR to the length of the age of the star and ensures that enough stars are formed at the stellar birth epoch
INPUT:
a = Modelparameters
OUTPUT:
the function will update the modelparameters, such that the simulation will end when the star is born and it will also check whether there is ... | Chempy/cem_function.py | shorten_sfr | jan-rybizki/Chempy | 25 | python | def shorten_sfr(a):
'\n\tThis function crops the SFR to the length of the age of the star and ensures that enough stars are formed at the stellar birth epoch\n\n\tINPUT:\n\n\t a = Modelparameters\n\n\tOUTPUT:\n\t\n\t the function will update the modelparameters, such that the simulation will end when the star i... | def shorten_sfr(a):
'\n\tThis function crops the SFR to the length of the age of the star and ensures that enough stars are formed at the stellar birth epoch\n\n\tINPUT:\n\n\t a = Modelparameters\n\n\tOUTPUT:\n\t\n\t the function will update the modelparameters, such that the simulation will end when the star i... |
b742578b6eac6cb8260b50132807d1228a560332a74726ac34a900539ec0533e | def cem(changing_parameter, a):
"\n\tThis is the function calculating the chemical evolution for a specific parameter set (changing_parameter) and for a specific observational constraint specified in a (e.g. 'solar_norm' calculates the likelihood of solar abundances coming out of the model). It returns the posterio... | This is the function calculating the chemical evolution for a specific parameter set (changing_parameter) and for a specific observational constraint specified in a (e.g. 'solar_norm' calculates the likelihood of solar abundances coming out of the model). It returns the posterior and a list of blobs. It can be used by ... | Chempy/cem_function.py | cem | jan-rybizki/Chempy | 25 | python | def cem(changing_parameter, a):
"\n\tThis is the function calculating the chemical evolution for a specific parameter set (changing_parameter) and for a specific observational constraint specified in a (e.g. 'solar_norm' calculates the likelihood of solar abundances coming out of the model). It returns the posterio... | def cem(changing_parameter, a):
"\n\tThis is the function calculating the chemical evolution for a specific parameter set (changing_parameter) and for a specific observational constraint specified in a (e.g. 'solar_norm' calculates the likelihood of solar abundances coming out of the model). It returns the posterio... |
bdb3d34f57b6f6a279ed92cea70cb41eefd67d3bc576a1e5a36af278ba334687 | def cem_real(changing_parameter, a):
'\n\treal chempy function. description can be found in cem\n\t'
a = extract_parameters_and_priors(changing_parameter, a)
basic_solar = solar_abundances()
getattr(basic_solar, a.solar_abundance_name)()
elements_to_trace = a.elements_to_trace
directory = 'model... | real chempy function. description can be found in cem | Chempy/cem_function.py | cem_real | jan-rybizki/Chempy | 25 | python | def cem_real(changing_parameter, a):
'\n\t\n\t'
a = extract_parameters_and_priors(changing_parameter, a)
basic_solar = solar_abundances()
getattr(basic_solar, a.solar_abundance_name)()
elements_to_trace = a.elements_to_trace
directory = 'model_temp/'
if a.calculate_model:
(cube, abun... | def cem_real(changing_parameter, a):
'\n\t\n\t'
a = extract_parameters_and_priors(changing_parameter, a)
basic_solar = solar_abundances()
getattr(basic_solar, a.solar_abundance_name)()
elements_to_trace = a.elements_to_trace
directory = 'model_temp/'
if a.calculate_model:
(cube, abun... |
48d677ca78dfc3678cd9ab1d9dee7ac13ade81ba6fa68659b65e5ea67935da71 | def cem2(a):
"\n\tThis is the function calculating the chemical evolution for a specific parameter set (changing_parameter) and for a specific observational constraint specified in a (e.g. 'solar_norm' calculates the likelihood of solar abundances coming out of the model). It returns the posterior and a list of blo... | This is the function calculating the chemical evolution for a specific parameter set (changing_parameter) and for a specific observational constraint specified in a (e.g. 'solar_norm' calculates the likelihood of solar abundances coming out of the model). It returns the posterior and a list of blobs. It can be used by ... | Chempy/cem_function.py | cem2 | jan-rybizki/Chempy | 25 | python | def cem2(a):
"\n\tThis is the function calculating the chemical evolution for a specific parameter set (changing_parameter) and for a specific observational constraint specified in a (e.g. 'solar_norm' calculates the likelihood of solar abundances coming out of the model). It returns the posterior and a list of blo... | def cem2(a):
"\n\tThis is the function calculating the chemical evolution for a specific parameter set (changing_parameter) and for a specific observational constraint specified in a (e.g. 'solar_norm' calculates the likelihood of solar abundances coming out of the model). It returns the posterior and a list of blo... |
4d2cfc9f5030c3e23a50a6a193613625ace455f08123f4ca12fffce20803928d | def cem_real2(a):
'\n\treal chempy function. description can be found in cem2\n\t'
a = shorten_sfr(a)
basic_solar = solar_abundances()
getattr(basic_solar, a.solar_abundance_name)()
elements_to_trace = list(a.elements_to_trace)
directory = 'model_temp/'
if a.calculate_model:
(cube, a... | real chempy function. description can be found in cem2 | Chempy/cem_function.py | cem_real2 | jan-rybizki/Chempy | 25 | python | def cem_real2(a):
'\n\t\n\t'
a = shorten_sfr(a)
basic_solar = solar_abundances()
getattr(basic_solar, a.solar_abundance_name)()
elements_to_trace = list(a.elements_to_trace)
directory = 'model_temp/'
if a.calculate_model:
(cube, abundances) = Chempy(a)
cube1 = cube.cube
... | def cem_real2(a):
'\n\t\n\t'
a = shorten_sfr(a)
basic_solar = solar_abundances()
getattr(basic_solar, a.solar_abundance_name)()
elements_to_trace = list(a.elements_to_trace)
directory = 'model_temp/'
if a.calculate_model:
(cube, abundances) = Chempy(a)
cube1 = cube.cube
... |
2a90758227c8feac7ba15f958a3794336adfb4aefbd79759622cc6ec0af33fb8 | def posterior_function(changing_parameter, a):
"\n\tThe posterior function is the interface between the optimizing function and Chempy. Usually the likelihood will be calculated with respect to a so called 'stellar wildcard'.\n\tWildcards can be created according to the tutorial 6. A few wildcards are already store... | The posterior function is the interface between the optimizing function and Chempy. Usually the likelihood will be calculated with respect to a so called 'stellar wildcard'.
Wildcards can be created according to the tutorial 6. A few wildcards are already stored in the input folder. Chempy will try the current folder f... | Chempy/cem_function.py | posterior_function | jan-rybizki/Chempy | 25 | python | def posterior_function(changing_parameter, a):
"\n\tThe posterior function is the interface between the optimizing function and Chempy. Usually the likelihood will be calculated with respect to a so called 'stellar wildcard'.\n\tWildcards can be created according to the tutorial 6. A few wildcards are already store... | def posterior_function(changing_parameter, a):
"\n\tThe posterior function is the interface between the optimizing function and Chempy. Usually the likelihood will be calculated with respect to a so called 'stellar wildcard'.\n\tWildcards can be created according to the tutorial 6. A few wildcards are already store... |
9339705e0a47ae1f9ae901326fc46e1c3e4789ba2bc821662fc3f377e3421306 | def posterior_function_real(changing_parameter, a):
'\n\tThis is the actual posterior function. But the functionality is explained in posterior_function.\n\t'
a = extract_parameters_and_priors(changing_parameter, a)
prior = sum(np.log(a.prior))
backup = (a.end, a.time_steps, a.total_mass)
if (a.stel... | This is the actual posterior function. But the functionality is explained in posterior_function. | Chempy/cem_function.py | posterior_function_real | jan-rybizki/Chempy | 25 | python | def posterior_function_real(changing_parameter, a):
'\n\t\n\t'
a = extract_parameters_and_priors(changing_parameter, a)
prior = sum(np.log(a.prior))
backup = (a.end, a.time_steps, a.total_mass)
if (a.stellar_identifier is 'prior'):
likelihood = 0.0
abundance_list = 0
else:
... | def posterior_function_real(changing_parameter, a):
'\n\t\n\t'
a = extract_parameters_and_priors(changing_parameter, a)
prior = sum(np.log(a.prior))
backup = (a.end, a.time_steps, a.total_mass)
if (a.stellar_identifier is 'prior'):
likelihood = 0.0
abundance_list = 0
else:
... |
e5820d16e8380e1f17cc241629fa8c7b2efea17072696984225a5b6b88762a8b | def posterior_function_for_minimization(changing_parameter, a):
'\n\tcalls the posterior function but just returns the negative log posterior instead of posterior and blobs\n\t'
(posterior, blobs) = posterior_function(changing_parameter, a)
return (- posterior) | calls the posterior function but just returns the negative log posterior instead of posterior and blobs | Chempy/cem_function.py | posterior_function_for_minimization | jan-rybizki/Chempy | 25 | python | def posterior_function_for_minimization(changing_parameter, a):
'\n\t\n\t'
(posterior, blobs) = posterior_function(changing_parameter, a)
return (- posterior) | def posterior_function_for_minimization(changing_parameter, a):
'\n\t\n\t'
(posterior, blobs) = posterior_function(changing_parameter, a)
return (- posterior)<|docstring|>calls the posterior function but just returns the negative log posterior instead of posterior and blobs<|endoftext|> |
d5250f32c9ba76273e94899da0cd52429b1644d94c6aa267470d61ecc4de5a9b | def posterior_function_returning_predictions(args):
'\n\tcalls the posterior function but just returns the negative log posterior instead of posterior and blobs\n\t'
(changing_parameter, a) = args
(posterior, abundance_list, element_list) = posterior_function_predictions(changing_parameter, a)
return (a... | calls the posterior function but just returns the negative log posterior instead of posterior and blobs | Chempy/cem_function.py | posterior_function_returning_predictions | jan-rybizki/Chempy | 25 | python | def posterior_function_returning_predictions(args):
'\n\t\n\t'
(changing_parameter, a) = args
(posterior, abundance_list, element_list) = posterior_function_predictions(changing_parameter, a)
return (abundance_list, element_list) | def posterior_function_returning_predictions(args):
'\n\t\n\t'
(changing_parameter, a) = args
(posterior, abundance_list, element_list) = posterior_function_predictions(changing_parameter, a)
return (abundance_list, element_list)<|docstring|>calls the posterior function but just returns the negative log... |
6bcf48ff551d53d8beaeea98fe6806dfe52b7bcfcab72c4fb0e3b6554f526931 | def posterior_function_predictions(changing_parameter, a):
'\n\tThis is like posterior_function_real. But returning the predicted elements as well.\n\t'
start_time = time.time()
a = extract_parameters_and_priors(changing_parameter, a)
prior = sum(np.log(a.prior))
precalculation = time.time()
bac... | This is like posterior_function_real. But returning the predicted elements as well. | Chempy/cem_function.py | posterior_function_predictions | jan-rybizki/Chempy | 25 | python | def posterior_function_predictions(changing_parameter, a):
'\n\t\n\t'
start_time = time.time()
a = extract_parameters_and_priors(changing_parameter, a)
prior = sum(np.log(a.prior))
precalculation = time.time()
backup = (a.end, a.time_steps, a.total_mass)
(abundance_list, elements_to_trace) =... | def posterior_function_predictions(changing_parameter, a):
'\n\t\n\t'
start_time = time.time()
a = extract_parameters_and_priors(changing_parameter, a)
prior = sum(np.log(a.prior))
precalculation = time.time()
backup = (a.end, a.time_steps, a.total_mass)
(abundance_list, elements_to_trace) =... |
32c37175e2309416e6164f7924cd0b4f2c6d901541d56a958e62092ac967cccf | def get_prior(changing_parameter, a):
'\n\tThis function calculates the prior probability\n\n\tINPUT:\n\n\t changing_parameter = the values of the parameter vector\n\n\t a = the model parameters including the names of the parameters (which is needed to identify them with the prescribed priors in parameters.py)\... | This function calculates the prior probability
INPUT:
changing_parameter = the values of the parameter vector
a = the model parameters including the names of the parameters (which is needed to identify them with the prescribed priors in parameters.py)
OUTPUT:
the log prior is returned | Chempy/cem_function.py | get_prior | jan-rybizki/Chempy | 25 | python | def get_prior(changing_parameter, a):
'\n\tThis function calculates the prior probability\n\n\tINPUT:\n\n\t changing_parameter = the values of the parameter vector\n\n\t a = the model parameters including the names of the parameters (which is needed to identify them with the prescribed priors in parameters.py)\... | def get_prior(changing_parameter, a):
'\n\tThis function calculates the prior probability\n\n\tINPUT:\n\n\t changing_parameter = the values of the parameter vector\n\n\t a = the model parameters including the names of the parameters (which is needed to identify them with the prescribed priors in parameters.py)\... |
f12e022a9a0d8413be5d851d29547c23cd1f662e796e3f123b8b5748ea318f0f | def global_optimization(changing_parameter, result):
'\n\tThis function is a buffer function if global_optimization_real fails and it only returns the negative posterior\n\t'
try:
(posterior, error_list, elements) = global_optimization_real(changing_parameter, result)
return posterior
except... | This function is a buffer function if global_optimization_real fails and it only returns the negative posterior | Chempy/cem_function.py | global_optimization | jan-rybizki/Chempy | 25 | python | def global_optimization(changing_parameter, result):
'\n\t\n\t'
try:
(posterior, error_list, elements) = global_optimization_real(changing_parameter, result)
return posterior
except Exception as ex:
import traceback
traceback.print_exc()
return np.inf | def global_optimization(changing_parameter, result):
'\n\t\n\t'
try:
(posterior, error_list, elements) = global_optimization_real(changing_parameter, result)
return posterior
except Exception as ex:
import traceback
traceback.print_exc()
return np.inf<|docstring|>This fun... |
d11dfc95053d00c8bc366b3363134fea346a1f39f4d12107572a52916a3cc1d3 | def global_optimization_error_returned(changing_parameter, result):
'\n\tthis is a buffer function preventing failures from global_optimization_real and returning all its output including the best model error\n\t'
try:
(posterior, error_list, elements) = global_optimization_real(changing_parameter, resu... | this is a buffer function preventing failures from global_optimization_real and returning all its output including the best model error | Chempy/cem_function.py | global_optimization_error_returned | jan-rybizki/Chempy | 25 | python | def global_optimization_error_returned(changing_parameter, result):
'\n\t\n\t'
try:
(posterior, error_list, elements) = global_optimization_real(changing_parameter, result)
return ((- posterior), error_list, elements)
except Exception as ex:
import traceback
traceback.print_e... | def global_optimization_error_returned(changing_parameter, result):
'\n\t\n\t'
try:
(posterior, error_list, elements) = global_optimization_real(changing_parameter, result)
return ((- posterior), error_list, elements)
except Exception as ex:
import traceback
traceback.print_e... |
c0f42cb0fbfb6554e5b7af889f8ce507db2eaec75df4b7542a9ff5824b68c957 | def global_optimization_real(changing_parameter, result):
'\n\tThis function calculates the predictions from several Chempy zones in parallel. It also calculates the likelihood for common model errors\n\tBEWARE: Model parameters are called as saved in parameters.py!!!\n\n\tINPUT:\n\n\t changing_parameter = the gl... | This function calculates the predictions from several Chempy zones in parallel. It also calculates the likelihood for common model errors
BEWARE: Model parameters are called as saved in parameters.py!!!
INPUT:
changing_parameter = the global SSP parameters (parameters that all stars share)
result = the complet... | Chempy/cem_function.py | global_optimization_real | jan-rybizki/Chempy | 25 | python | def global_optimization_real(changing_parameter, result):
'\n\tThis function calculates the predictions from several Chempy zones in parallel. It also calculates the likelihood for common model errors\n\tBEWARE: Model parameters are called as saved in parameters.py!!!\n\n\tINPUT:\n\n\t changing_parameter = the gl... | def global_optimization_real(changing_parameter, result):
'\n\tThis function calculates the predictions from several Chempy zones in parallel. It also calculates the likelihood for common model errors\n\tBEWARE: Model parameters are called as saved in parameters.py!!!\n\n\tINPUT:\n\n\t changing_parameter = the gl... |
be961f875722eff274fdf83c75d919a5a93ae9a2396081917c7726066cb34839 | def extract_parameters_and_priors(changing_parameter, a):
'\n\tThis function extracts the parameters from changing parameters and writes them into the ModelParamaters (a), so that Chempy can evaluate the changed parameter settings\n\t'
for (i, item) in enumerate(a.to_optimize):
setattr(a, item, changing... | This function extracts the parameters from changing parameters and writes them into the ModelParamaters (a), so that Chempy can evaluate the changed parameter settings | Chempy/cem_function.py | extract_parameters_and_priors | jan-rybizki/Chempy | 25 | python | def extract_parameters_and_priors(changing_parameter, a):
'\n\t\n\t'
for (i, item) in enumerate(a.to_optimize):
setattr(a, item, changing_parameter[i])
val = getattr(a, item)
prior_names = []
prior = []
for name in a.to_optimize:
(mean, std, functional_form) = a.priors.get(na... | def extract_parameters_and_priors(changing_parameter, a):
'\n\t\n\t'
for (i, item) in enumerate(a.to_optimize):
setattr(a, item, changing_parameter[i])
val = getattr(a, item)
prior_names = []
prior = []
for name in a.to_optimize:
(mean, std, functional_form) = a.priors.get(na... |
73677c9e7b092eedbcc32e6d69dcf5781687aefab21dc53ef1915afe284408f9 | def posterior_function_local_for_minimization(changing_parameter, stellar_identifier, global_parameters, errors, elements):
'\n\tcalls the local posterior function but just returns the negative log posterior instead of posterior and blobs\n\t'
(posterior, blobs) = posterior_function_local(changing_parameter, st... | calls the local posterior function but just returns the negative log posterior instead of posterior and blobs | Chempy/cem_function.py | posterior_function_local_for_minimization | jan-rybizki/Chempy | 25 | python | def posterior_function_local_for_minimization(changing_parameter, stellar_identifier, global_parameters, errors, elements):
'\n\t\n\t'
(posterior, blobs) = posterior_function_local(changing_parameter, stellar_identifier, global_parameters, errors, elements)
return (- posterior) | def posterior_function_local_for_minimization(changing_parameter, stellar_identifier, global_parameters, errors, elements):
'\n\t\n\t'
(posterior, blobs) = posterior_function_local(changing_parameter, stellar_identifier, global_parameters, errors, elements)
return (- posterior)<|docstring|>calls the local p... |
8eb16e036de8c01c1cbbc3e069b0b14938432177f8900f4ad9dba2db665a187a | def posterior_function_local(changing_parameter, stellar_identifier, global_parameters, errors, elements):
"\n\tThe posterior function is the interface between the optimizing function and Chempy. Usually the likelihood will be calculated with respect to a so called 'stellar wildcard'.\n\tWildcards can be created ac... | The posterior function is the interface between the optimizing function and Chempy. Usually the likelihood will be calculated with respect to a so called 'stellar wildcard'.
Wildcards can be created according to the tutorial 6 from the github page. A few wildcards are already stored in the input folder. Chempy will try... | Chempy/cem_function.py | posterior_function_local | jan-rybizki/Chempy | 25 | python | def posterior_function_local(changing_parameter, stellar_identifier, global_parameters, errors, elements):
"\n\tThe posterior function is the interface between the optimizing function and Chempy. Usually the likelihood will be calculated with respect to a so called 'stellar wildcard'.\n\tWildcards can be created ac... | def posterior_function_local(changing_parameter, stellar_identifier, global_parameters, errors, elements):
"\n\tThe posterior function is the interface between the optimizing function and Chempy. Usually the likelihood will be calculated with respect to a so called 'stellar wildcard'.\n\tWildcards can be created ac... |
9e1502600590bf6939496d6d027f170553244ffd09a9f60f18af46b1cc713a53 | def posterior_function_local_real(changing_parameter, stellar_identifier, global_parameters, errors, elements):
'\n\tThis is the actual posterior function. But the functionality is explained in posterior_function.\n\t'
from .parameter import ModelParameters
a = ModelParameters()
a.stellar_identifier = s... | This is the actual posterior function. But the functionality is explained in posterior_function. | Chempy/cem_function.py | posterior_function_local_real | jan-rybizki/Chempy | 25 | python | def posterior_function_local_real(changing_parameter, stellar_identifier, global_parameters, errors, elements):
'\n\t\n\t'
from .parameter import ModelParameters
a = ModelParameters()
a.stellar_identifier = stellar_identifier
start_time = time.time()
changing_parameter = np.hstack((global_parame... | def posterior_function_local_real(changing_parameter, stellar_identifier, global_parameters, errors, elements):
'\n\t\n\t'
from .parameter import ModelParameters
a = ModelParameters()
a.stellar_identifier = stellar_identifier
start_time = time.time()
changing_parameter = np.hstack((global_parame... |
cc49756192aaf179532427e5b43eae886ecb14d517fda50f68c238cba653f875 | def posterior_function_many_stars(changing_parameter, error_list, elements):
"\n\tThe posterior function is the interface between the optimizing function and Chempy. Usually the likelihood will be calculated with respect to a so called 'stellar wildcard'.\n\tWildcards can be created according to the tutorial 6. A f... | The posterior function is the interface between the optimizing function and Chempy. Usually the likelihood will be calculated with respect to a so called 'stellar wildcard'.
Wildcards can be created according to the tutorial 6. A few wildcards are already stored in the input folder. Chempy will try the current folder f... | Chempy/cem_function.py | posterior_function_many_stars | jan-rybizki/Chempy | 25 | python | def posterior_function_many_stars(changing_parameter, error_list, elements):
"\n\tThe posterior function is the interface between the optimizing function and Chempy. Usually the likelihood will be calculated with respect to a so called 'stellar wildcard'.\n\tWildcards can be created according to the tutorial 6. A f... | def posterior_function_many_stars(changing_parameter, error_list, elements):
"\n\tThe posterior function is the interface between the optimizing function and Chempy. Usually the likelihood will be calculated with respect to a so called 'stellar wildcard'.\n\tWildcards can be created according to the tutorial 6. A f... |
a1ad9b290f0daceacf4ce7da0709be6823abf98dc3b88dd189d74a538e24a120 | def posterior_function_many_stars_real(changing_parameter, error_list, error_element_list):
'\n\tThis is the actual posterior function for many stars. But the functionality is explained in posterior_function_many_stars.\n\t'
import numpy.ma as ma
from .cem_function import get_prior, posterior_function_retur... | This is the actual posterior function for many stars. But the functionality is explained in posterior_function_many_stars. | Chempy/cem_function.py | posterior_function_many_stars_real | jan-rybizki/Chempy | 25 | python | def posterior_function_many_stars_real(changing_parameter, error_list, error_element_list):
'\n\t\n\t'
import numpy.ma as ma
from .cem_function import get_prior, posterior_function_returning_predictions
from .data_to_test import likelihood_evaluation, read_out_wildcard
from .parameter import ModelPa... | def posterior_function_many_stars_real(changing_parameter, error_list, error_element_list):
'\n\t\n\t'
import numpy.ma as ma
from .cem_function import get_prior, posterior_function_returning_predictions
from .data_to_test import likelihood_evaluation, read_out_wildcard
from .parameter import ModelPa... |
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