body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def grangercausalitytests(x, maxlag, addconst=True, verbose=True):
"four tests for granger non causality of 2 timeseries\n\n all four tests give similar results\n `params_ftest` and `ssr_ftest` are equivalent based on F test which is\n identical to lmtest:grangertest in R\n\n Parameters\n ----------\... | 2,497,703,576,321,163,000 | four tests for granger non causality of 2 timeseries
all four tests give similar results
`params_ftest` and `ssr_ftest` are equivalent based on F test which is
identical to lmtest:grangertest in R
Parameters
----------
x : array, 2d
data for test whether the time series in the second column Granger
causes the... | statsmodels/tsa/stattools.py | grangercausalitytests | josef-pkt/statsmodels | python | def grangercausalitytests(x, maxlag, addconst=True, verbose=True):
"four tests for granger non causality of 2 timeseries\n\n all four tests give similar results\n `params_ftest` and `ssr_ftest` are equivalent based on F test which is\n identical to lmtest:grangertest in R\n\n Parameters\n ----------\... |
def coint(y0, y1, trend='c', method='aeg', maxlag=None, autolag='aic', return_results=None):
'Test for no-cointegration of a univariate equation\n\n The null hypothesis is no cointegration. Variables in y0 and y1 are\n assumed to be integrated of order 1, I(1).\n\n This uses the augmented Engle-Granger two... | 3,271,704,490,787,290,600 | Test for no-cointegration of a univariate equation
The null hypothesis is no cointegration. Variables in y0 and y1 are
assumed to be integrated of order 1, I(1).
This uses the augmented Engle-Granger two-step cointegration test.
Constant or trend is included in 1st stage regression, i.e. in
cointegrating equation.
*... | statsmodels/tsa/stattools.py | coint | josef-pkt/statsmodels | python | def coint(y0, y1, trend='c', method='aeg', maxlag=None, autolag='aic', return_results=None):
'Test for no-cointegration of a univariate equation\n\n The null hypothesis is no cointegration. Variables in y0 and y1 are\n assumed to be integrated of order 1, I(1).\n\n This uses the augmented Engle-Granger two... |
def arma_order_select_ic(y, max_ar=4, max_ma=2, ic='bic', trend='c', model_kw={}, fit_kw={}):
"\n Returns information criteria for many ARMA models\n\n Parameters\n ----------\n y : array-like\n Time-series data\n max_ar : int\n Maximum number of AR lags to use. Default 4.\n max_ma :... | -3,637,179,301,659,311,600 | Returns information criteria for many ARMA models
Parameters
----------
y : array-like
Time-series data
max_ar : int
Maximum number of AR lags to use. Default 4.
max_ma : int
Maximum number of MA lags to use. Default 2.
ic : str, list
Information criteria to report. Either a single string or a list
... | statsmodels/tsa/stattools.py | arma_order_select_ic | josef-pkt/statsmodels | python | def arma_order_select_ic(y, max_ar=4, max_ma=2, ic='bic', trend='c', model_kw={}, fit_kw={}):
"\n Returns information criteria for many ARMA models\n\n Parameters\n ----------\n y : array-like\n Time-series data\n max_ar : int\n Maximum number of AR lags to use. Default 4.\n max_ma :... |
def has_missing(data):
"\n Returns True if 'data' contains missing entries, otherwise False\n "
return np.isnan(np.sum(data)) | -7,950,675,208,535,767,000 | Returns True if 'data' contains missing entries, otherwise False | statsmodels/tsa/stattools.py | has_missing | josef-pkt/statsmodels | python | def has_missing(data):
"\n \n "
return np.isnan(np.sum(data)) |
def kpss(x, regression='c', lags=None, store=False):
"\n Kwiatkowski-Phillips-Schmidt-Shin test for stationarity.\n\n Computes the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test for the null\n hypothesis that x is level or trend stationary.\n\n Parameters\n ----------\n x : array_like, 1d\n ... | -7,045,372,392,550,583,000 | Kwiatkowski-Phillips-Schmidt-Shin test for stationarity.
Computes the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test for the null
hypothesis that x is level or trend stationary.
Parameters
----------
x : array_like, 1d
Data series
regression : str{'c', 'ct'}
Indicates the null hypothesis for the KPSS test
... | statsmodels/tsa/stattools.py | kpss | josef-pkt/statsmodels | python | def kpss(x, regression='c', lags=None, store=False):
"\n Kwiatkowski-Phillips-Schmidt-Shin test for stationarity.\n\n Computes the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test for the null\n hypothesis that x is level or trend stationary.\n\n Parameters\n ----------\n x : array_like, 1d\n ... |
def _sigma_est_kpss(resids, nobs, lags):
'\n Computes equation 10, p. 164 of Kwiatkowski et al. (1992). This is the\n consistent estimator for the variance.\n '
s_hat = sum((resids ** 2))
for i in range(1, (lags + 1)):
resids_prod = np.dot(resids[i:], resids[:(nobs - i)])
s_hat += (... | -4,347,780,852,716,475,400 | Computes equation 10, p. 164 of Kwiatkowski et al. (1992). This is the
consistent estimator for the variance. | statsmodels/tsa/stattools.py | _sigma_est_kpss | josef-pkt/statsmodels | python | def _sigma_est_kpss(resids, nobs, lags):
'\n Computes equation 10, p. 164 of Kwiatkowski et al. (1992). This is the\n consistent estimator for the variance.\n '
s_hat = sum((resids ** 2))
for i in range(1, (lags + 1)):
resids_prod = np.dot(resids[i:], resids[:(nobs - i)])
s_hat += (... |
async def trigger_update(opp):
'Trigger a polling update by moving time forward.'
new_time = (dt.utcnow() + timedelta(seconds=(SCAN_INTERVAL + 1)))
async_fire_time_changed(opp, new_time)
(await opp.async_block_till_done()) | -1,536,932,550,561,218,800 | Trigger a polling update by moving time forward. | tests/components/smarttub/__init__.py | trigger_update | OpenPeerPower/core | python | async def trigger_update(opp):
new_time = (dt.utcnow() + timedelta(seconds=(SCAN_INTERVAL + 1)))
async_fire_time_changed(opp, new_time)
(await opp.async_block_till_done()) |
def __init__(self, dataclass_types: Union[(DataClassType, Iterable[DataClassType])], **kwargs):
'\n Args:\n dataclass_types:\n Dataclass type, or list of dataclass types for which we will "fill" instances with the parsed args.\n kwargs:\n (Optional) Passed ... | 5,540,399,526,315,942,000 | Args:
dataclass_types:
Dataclass type, or list of dataclass types for which we will "fill" instances with the parsed args.
kwargs:
(Optional) Passed to `argparse.ArgumentParser()` in the regular way. | toolbox/KGArgsParser.py | __init__ | LinXueyuanStdio/KGE-toolbox | python | def __init__(self, dataclass_types: Union[(DataClassType, Iterable[DataClassType])], **kwargs):
'\n Args:\n dataclass_types:\n Dataclass type, or list of dataclass types for which we will "fill" instances with the parsed args.\n kwargs:\n (Optional) Passed ... |
def parse_args_into_dataclasses(self, args=None, return_remaining_strings=False, look_for_args_file=True, args_filename=None) -> Tuple[(DataClass, ...)]:
'\n Parse command-line args into instances of the specified dataclass types.\n\n This relies on argparse\'s `ArgumentParser.parse_known_args`. See t... | 7,657,435,898,646,354,000 | Parse command-line args into instances of the specified dataclass types.
This relies on argparse's `ArgumentParser.parse_known_args`. See the doc at:
docs.python.org/3.7/library/argparse.html#argparse.ArgumentParser.parse_args
Args:
args:
List of strings to parse. The default is taken from sys.argv. (same... | toolbox/KGArgsParser.py | parse_args_into_dataclasses | LinXueyuanStdio/KGE-toolbox | python | def parse_args_into_dataclasses(self, args=None, return_remaining_strings=False, look_for_args_file=True, args_filename=None) -> Tuple[(DataClass, ...)]:
'\n Parse command-line args into instances of the specified dataclass types.\n\n This relies on argparse\'s `ArgumentParser.parse_known_args`. See t... |
def parse_json_file(self, json_file: str) -> Tuple[(DataClass, ...)]:
'\n Alternative helper method that does not use `argparse` at all, instead loading a json file and populating the\n dataclass types.\n '
data = json.loads(Path(json_file).read_text())
outputs = []
for dtype in sel... | -4,033,736,629,704,605,700 | Alternative helper method that does not use `argparse` at all, instead loading a json file and populating the
dataclass types. | toolbox/KGArgsParser.py | parse_json_file | LinXueyuanStdio/KGE-toolbox | python | def parse_json_file(self, json_file: str) -> Tuple[(DataClass, ...)]:
'\n Alternative helper method that does not use `argparse` at all, instead loading a json file and populating the\n dataclass types.\n '
data = json.loads(Path(json_file).read_text())
outputs = []
for dtype in sel... |
def parse_dict(self, args: dict) -> Tuple[(DataClass, ...)]:
'\n Alternative helper method that does not use `argparse` at all, instead uses a dict and populating the dataclass\n types.\n '
outputs = []
for dtype in self.dataclass_types:
keys = {f.name for f in dataclasses.field... | 3,798,765,331,785,445,000 | Alternative helper method that does not use `argparse` at all, instead uses a dict and populating the dataclass
types. | toolbox/KGArgsParser.py | parse_dict | LinXueyuanStdio/KGE-toolbox | python | def parse_dict(self, args: dict) -> Tuple[(DataClass, ...)]:
'\n Alternative helper method that does not use `argparse` at all, instead uses a dict and populating the dataclass\n types.\n '
outputs = []
for dtype in self.dataclass_types:
keys = {f.name for f in dataclasses.field... |
def __init__(self, pkg_dict: Dict[(str, Any)]):
'\n Class containing data that describes a package API\n\n :param pkg_dict: A dictionary representation of a\n software package, complying with the output format of\n doppel-describe.\n\n '
self._validate_pkg(pkg_dict)
... | -6,044,739,435,903,076,000 | Class containing data that describes a package API
:param pkg_dict: A dictionary representation of a
software package, complying with the output format of
doppel-describe. | doppel/PackageAPI.py | __init__ | franklinen/doppel-cli | python | def __init__(self, pkg_dict: Dict[(str, Any)]):
'\n Class containing data that describes a package API\n\n :param pkg_dict: A dictionary representation of a\n software package, complying with the output format of\n doppel-describe.\n\n '
self._validate_pkg(pkg_dict)
... |
@classmethod
def from_json(cls, filename: str) -> 'PackageAPI':
"\n Instantiate a Package object from a file.\n\n :param filename: Name of the JSON file\n that contains the description of the\n target package's API.\n\n "
_log_info(f'Creating package from {filename}')
... | -6,133,293,852,267,853,000 | Instantiate a Package object from a file.
:param filename: Name of the JSON file
that contains the description of the
target package's API. | doppel/PackageAPI.py | from_json | franklinen/doppel-cli | python | @classmethod
def from_json(cls, filename: str) -> 'PackageAPI':
"\n Instantiate a Package object from a file.\n\n :param filename: Name of the JSON file\n that contains the description of the\n target package's API.\n\n "
_log_info(f'Creating package from {filename}')
... |
def name(self) -> str:
'\n Get the name of the package.\n '
return self.pkg_dict['name'] | -4,031,569,820,273,227,000 | Get the name of the package. | doppel/PackageAPI.py | name | franklinen/doppel-cli | python | def name(self) -> str:
'\n \n '
return self.pkg_dict['name'] |
def num_functions(self) -> int:
'\n Get the number of exported functions in the package.\n '
return len(self.function_names()) | 358,728,798,804,206,400 | Get the number of exported functions in the package. | doppel/PackageAPI.py | num_functions | franklinen/doppel-cli | python | def num_functions(self) -> int:
'\n \n '
return len(self.function_names()) |
def function_names(self) -> List[str]:
'\n Get a list with the names of all exported functions\n in the package.\n '
return sorted(list(self.pkg_dict['functions'].keys())) | 3,015,912,207,251,559,000 | Get a list with the names of all exported functions
in the package. | doppel/PackageAPI.py | function_names | franklinen/doppel-cli | python | def function_names(self) -> List[str]:
'\n Get a list with the names of all exported functions\n in the package.\n '
return sorted(list(self.pkg_dict['functions'].keys())) |
def functions_with_args(self) -> Dict[(str, Dict[(str, Any)])]:
'\n Get a dictionary with all exported functions in the package\n and some details describing them.\n '
return self.pkg_dict['functions'] | -5,598,182,101,801,267,000 | Get a dictionary with all exported functions in the package
and some details describing them. | doppel/PackageAPI.py | functions_with_args | franklinen/doppel-cli | python | def functions_with_args(self) -> Dict[(str, Dict[(str, Any)])]:
'\n Get a dictionary with all exported functions in the package\n and some details describing them.\n '
return self.pkg_dict['functions'] |
def num_classes(self) -> int:
'\n Get the number of exported classes in the package.\n '
return len(self.class_names()) | -5,579,227,444,031,814,000 | Get the number of exported classes in the package. | doppel/PackageAPI.py | num_classes | franklinen/doppel-cli | python | def num_classes(self) -> int:
'\n \n '
return len(self.class_names()) |
def class_names(self) -> List[str]:
'\n Get a list with the names of all exported classes\n in the package.\n '
return sorted(list(self.pkg_dict['classes'].keys())) | -6,672,906,188,787,706,000 | Get a list with the names of all exported classes
in the package. | doppel/PackageAPI.py | class_names | franklinen/doppel-cli | python | def class_names(self) -> List[str]:
'\n Get a list with the names of all exported classes\n in the package.\n '
return sorted(list(self.pkg_dict['classes'].keys())) |
def public_methods(self, class_name: str) -> List[str]:
'\n Get a list with the names of all public methods for a class.\n\n :param class_name: Name of a class in the package\n '
return sorted(list(self.pkg_dict['classes'][class_name]['public_methods'].keys())) | 1,912,717,380,565,422,000 | Get a list with the names of all public methods for a class.
:param class_name: Name of a class in the package | doppel/PackageAPI.py | public_methods | franklinen/doppel-cli | python | def public_methods(self, class_name: str) -> List[str]:
'\n Get a list with the names of all public methods for a class.\n\n :param class_name: Name of a class in the package\n '
return sorted(list(self.pkg_dict['classes'][class_name]['public_methods'].keys())) |
def public_method_args(self, class_name: str, method_name: str) -> List[str]:
'\n Get a list of arguments for a public method from a class.\n\n :param class_name: Name of a class in the package\n :param method-name: Name of the method to get arguments for\n '
return list(self.pkg_dic... | 8,502,998,262,803,455,000 | Get a list of arguments for a public method from a class.
:param class_name: Name of a class in the package
:param method-name: Name of the method to get arguments for | doppel/PackageAPI.py | public_method_args | franklinen/doppel-cli | python | def public_method_args(self, class_name: str, method_name: str) -> List[str]:
'\n Get a list of arguments for a public method from a class.\n\n :param class_name: Name of a class in the package\n :param method-name: Name of the method to get arguments for\n '
return list(self.pkg_dic... |
@staticmethod
def get_placeholder(page, slot=None):
'\n Returns the named placeholder or, if no «slot» provided, the first\n editable, non-static placeholder or None.\n '
placeholders = page.get_placeholders()
if slot:
placeholders = placeholders.filter(slot=slot)
for ph in ... | 1,405,203,255,283,725,800 | Returns the named placeholder or, if no «slot» provided, the first
editable, non-static placeholder or None. | cms/forms/wizards.py | get_placeholder | rspeed/django-cms-contrib | python | @staticmethod
def get_placeholder(page, slot=None):
'\n Returns the named placeholder or, if no «slot» provided, the first\n editable, non-static placeholder or None.\n '
placeholders = page.get_placeholders()
if slot:
placeholders = placeholders.filter(slot=slot)
for ph in ... |
def clean(self):
'\n Validates that either the slug is provided, or that slugification from\n `title` produces a valid slug.\n :return:\n '
cleaned_data = super(CreateCMSPageForm, self).clean()
slug = cleaned_data.get('slug')
sub_page = cleaned_data.get('sub_page')
title ... | 13,318,186,756,332,642 | Validates that either the slug is provided, or that slugification from
`title` produces a valid slug.
:return: | cms/forms/wizards.py | clean | rspeed/django-cms-contrib | python | def clean(self):
'\n Validates that either the slug is provided, or that slugification from\n `title` produces a valid slug.\n :return:\n '
cleaned_data = super(CreateCMSPageForm, self).clean()
slug = cleaned_data.get('slug')
sub_page = cleaned_data.get('sub_page')
title ... |
def __init__(self, application, hostname, key):
"\n\t\t:param application: The application to associate this popup dialog with.\n\t\t:type application: :py:class:`.KingPhisherClientApplication`\n\t\t:param str hostname: The hostname associated with the key.\n\t\t:param key: The host's SSH key.\n\t\t:type key: :py:c... | -28,344,514,441,261,160 | :param application: The application to associate this popup dialog with.
:type application: :py:class:`.KingPhisherClientApplication`
:param str hostname: The hostname associated with the key.
:param key: The host's SSH key.
:type key: :py:class:`paramiko.pkey.PKey` | king_phisher/client/dialogs/ssh_host_key.py | __init__ | tanc7/king-phisher | python | def __init__(self, application, hostname, key):
"\n\t\t:param application: The application to associate this popup dialog with.\n\t\t:type application: :py:class:`.KingPhisherClientApplication`\n\t\t:param str hostname: The hostname associated with the key.\n\t\t:param key: The host's SSH key.\n\t\t:type key: :py:c... |
def __init__(self, application):
'\n\t\t:param application: The application which is using this policy.\n\t\t:type application: :py:class:`.KingPhisherClientApplication`\n\t\t'
self.application = application
self.logger = logging.getLogger(('KingPhisher.Client.' + self.__class__.__name__))
super(Missing... | -4,761,189,396,857,635,000 | :param application: The application which is using this policy.
:type application: :py:class:`.KingPhisherClientApplication` | king_phisher/client/dialogs/ssh_host_key.py | __init__ | tanc7/king-phisher | python | def __init__(self, application):
'\n\t\t:param application: The application which is using this policy.\n\t\t:type application: :py:class:`.KingPhisherClientApplication`\n\t\t'
self.application = application
self.logger = logging.getLogger(('KingPhisher.Client.' + self.__class__.__name__))
super(Missing... |
def generate_bubblesort(prefix, num_examples, debug=False, maximum=10000000000, debug_every=1000):
"\n Generates addition data with the given string prefix (i.e. 'train', 'test') and the specified\n number of examples.\n\n :param prefix: String prefix for saving the file ('train', 'test')\n :param num_e... | -4,308,264,678,087,419,000 | Generates addition data with the given string prefix (i.e. 'train', 'test') and the specified
number of examples.
:param prefix: String prefix for saving the file ('train', 'test')
:param num_examples: Number of examples to generate. | tasks/bubblesort/env/generate_data.py | generate_bubblesort | ford-core-ai/neural-programming-architectures | python | def generate_bubblesort(prefix, num_examples, debug=False, maximum=10000000000, debug_every=1000):
"\n Generates addition data with the given string prefix (i.e. 'train', 'test') and the specified\n number of examples.\n\n :param prefix: String prefix for saving the file ('train', 'test')\n :param num_e... |
def check():
' check that all paths are properly defined'
checked = True
print(f' - history tar files will be mounted on: {dirmounted_root}')
print(f' - ratarmount executable is in : {ratarmount}') | 7,019,680,802,621,906,000 | check that all paths are properly defined | paragridded/giga_tools.py | check | Mesharou/paragridded | python | def check():
' '
checked = True
print(f' - history tar files will be mounted on: {dirmounted_root}')
print(f' - ratarmount executable is in : {ratarmount}') |
def get_subdmap(directory):
'Reconstruct how netCDF files are stored in fused directory\n\n directory == dirgrid | dirhis '
_subdmap = {}
for subd in subdomains:
fs = glob.glob((directory.format(subd=subd) + '/*.nc'))
tiles = [int(f.split('.')[(- 2)]) for f in fs]
for t in tiles:
... | 5,205,752,084,815,372,000 | Reconstruct how netCDF files are stored in fused directory
directory == dirgrid | dirhis | paragridded/giga_tools.py | get_subdmap | Mesharou/paragridded | python | def get_subdmap(directory):
'Reconstruct how netCDF files are stored in fused directory\n\n directory == dirgrid | dirhis '
_subdmap = {}
for subd in subdomains:
fs = glob.glob((directory.format(subd=subd) + '/*.nc'))
tiles = [int(f.split('.')[(- 2)]) for f in fs]
for t in tiles:
... |
def mount_tar(source, tarfile, destdir):
'\n source: str, directory of the tar files\n template: str, template name for the tar file containing "{subd"\n subd: int, index of the subdomain (0<=subd<=13)\n destdir: str, directory where to archivemount\n\n '
srcfile = f'{source}/{tarfile}'
asser... | 4,785,231,440,578,954,000 | source: str, directory of the tar files
template: str, template name for the tar file containing "{subd"
subd: int, index of the subdomain (0<=subd<=13)
destdir: str, directory where to archivemount | paragridded/giga_tools.py | mount_tar | Mesharou/paragridded | python | def mount_tar(source, tarfile, destdir):
'\n source: str, directory of the tar files\n template: str, template name for the tar file containing "{subd"\n subd: int, index of the subdomain (0<=subd<=13)\n destdir: str, directory where to archivemount\n\n '
srcfile = f'{source}/{tarfile}'
asser... |
def mount(subd, grid=False, overwrite=True):
'Mount tar file `subd`'
if grid:
destdir = dirgrid.format(subd=subd)
srcdir = dirgridtar.format(subd=subd)
tarfile = targridtemplate.format(subd=subd)
else:
destdir = dirhis.format(subd=subd)
srcdir = dirgigaref.format(subd... | -7,433,501,504,231,536,000 | Mount tar file `subd` | paragridded/giga_tools.py | mount | Mesharou/paragridded | python | def mount(subd, grid=False, overwrite=True):
if grid:
destdir = dirgrid.format(subd=subd)
srcdir = dirgridtar.format(subd=subd)
tarfile = targridtemplate.format(subd=subd)
else:
destdir = dirhis.format(subd=subd)
srcdir = dirgigaref.format(subd=subd)
tarfile ... |
def mount_stats(grid=False):
' Print statistics on mounted tar files'
print(('-' * 40))
print(BB('statistics on mounted tar files'))
print(f'mounting point: {dirmounted}')
for subd in subdomains:
if grid:
destdir = dirgrid.format(subd=subd)
else:
destdir = dir... | 8,784,130,287,067,626,000 | Print statistics on mounted tar files | paragridded/giga_tools.py | mount_stats | Mesharou/paragridded | python | def mount_stats(grid=False):
' '
print(('-' * 40))
print(BB('statistics on mounted tar files'))
print(f'mounting point: {dirmounted}')
for subd in subdomains:
if grid:
destdir = dirgrid.format(subd=subd)
else:
destdir = dirhis.format(subd=subd)
if os.p... |
def umount(subd, grid=False):
' Unmount `subd` tar archive folder\n\n The command to unmount a fuse folder is fusermount -u'
if grid:
destdir = dirgrid.format(subd=subd)
else:
destdir = dirhis.format(subd=subd)
if (os.path.isdir(destdir) and (len(os.listdir(f'{destdir}')) != 0)):
... | -2,445,494,873,886,492,700 | Unmount `subd` tar archive folder
The command to unmount a fuse folder is fusermount -u | paragridded/giga_tools.py | umount | Mesharou/paragridded | python | def umount(subd, grid=False):
' Unmount `subd` tar archive folder\n\n The command to unmount a fuse folder is fusermount -u'
if grid:
destdir = dirgrid.format(subd=subd)
else:
destdir = dirhis.format(subd=subd)
if (os.path.isdir(destdir) and (len(os.listdir(f'{destdir}')) != 0)):
... |
def LLTP2domain(lowerleft, topright):
'Convert the two pairs of (lower, left), (top, right) in (lat, lon)\n into the four pairs of (lat, lon) of the corners '
(xa, ya) = lowerleft
(xb, yb) = topright
domain = [(xa, ya), (xa, yb), (xb, yb), (xb, ya)]
return domain | -7,237,054,415,005,802,000 | Convert the two pairs of (lower, left), (top, right) in (lat, lon)
into the four pairs of (lat, lon) of the corners | paragridded/giga_tools.py | LLTP2domain | Mesharou/paragridded | python | def LLTP2domain(lowerleft, topright):
'Convert the two pairs of (lower, left), (top, right) in (lat, lon)\n into the four pairs of (lat, lon) of the corners '
(xa, ya) = lowerleft
(xb, yb) = topright
domain = [(xa, ya), (xa, yb), (xb, yb), (xb, ya)]
return domain |
def find_tiles_inside(domain, corners):
'Determine which tiles are inside `domain`\n\n The function uses `corners` the list of corners for each tile\n '
p = Polygon(domain)
tileslist = []
for (tile, c) in corners.items():
q = Polygon(c)
if (p.overlaps(q) or p.contains(q)):
... | -2,155,202,173,137,227,300 | Determine which tiles are inside `domain`
The function uses `corners` the list of corners for each tile | paragridded/giga_tools.py | find_tiles_inside | Mesharou/paragridded | python | def find_tiles_inside(domain, corners):
'Determine which tiles are inside `domain`\n\n The function uses `corners` the list of corners for each tile\n '
p = Polygon(domain)
tileslist = []
for (tile, c) in corners.items():
q = Polygon(c)
if (p.overlaps(q) or p.contains(q)):
... |
def get_dates():
'\n Scan dirgiga for *tar files\n '
subd = 1
pattern = f'{dirgigaref}/*.{subd:02}.tar'.format(subd=subd)
files = glob.glob(pattern)
_dates_tar = [f.split('/')[(- 1)].split('.')[(- 3)] for f in files]
return sorted(_dates_tar) | 2,012,071,602,746,254,300 | Scan dirgiga for *tar files | paragridded/giga_tools.py | get_dates | Mesharou/paragridded | python | def get_dates():
'\n \n '
subd = 1
pattern = f'{dirgigaref}/*.{subd:02}.tar'.format(subd=subd)
files = glob.glob(pattern)
_dates_tar = [f.split('/')[(- 1)].split('.')[(- 3)] for f in files]
return sorted(_dates_tar) |
def set_default_time_zone(time_zone: dt.tzinfo) -> None:
'Set a default time zone to be used when none is specified.\n\n Async friendly.\n '
global DEFAULT_TIME_ZONE
assert isinstance(time_zone, dt.tzinfo)
DEFAULT_TIME_ZONE = time_zone | 8,305,351,147,355,129,000 | Set a default time zone to be used when none is specified.
Async friendly. | homeassistant/util/dt.py | set_default_time_zone | 854562/home-assistant | python | def set_default_time_zone(time_zone: dt.tzinfo) -> None:
'Set a default time zone to be used when none is specified.\n\n Async friendly.\n '
global DEFAULT_TIME_ZONE
assert isinstance(time_zone, dt.tzinfo)
DEFAULT_TIME_ZONE = time_zone |
def get_time_zone(time_zone_str: str) -> Optional[dt.tzinfo]:
'Get time zone from string. Return None if unable to determine.\n\n Async friendly.\n '
try:
return pytz.timezone(time_zone_str)
except pytzexceptions.UnknownTimeZoneError:
return None | 808,354,402,533,898,000 | Get time zone from string. Return None if unable to determine.
Async friendly. | homeassistant/util/dt.py | get_time_zone | 854562/home-assistant | python | def get_time_zone(time_zone_str: str) -> Optional[dt.tzinfo]:
'Get time zone from string. Return None if unable to determine.\n\n Async friendly.\n '
try:
return pytz.timezone(time_zone_str)
except pytzexceptions.UnknownTimeZoneError:
return None |
def utcnow() -> dt.datetime:
'Get now in UTC time.'
return dt.datetime.now(UTC) | -7,757,326,031,541,859,000 | Get now in UTC time. | homeassistant/util/dt.py | utcnow | 854562/home-assistant | python | def utcnow() -> dt.datetime:
return dt.datetime.now(UTC) |
def now(time_zone: Optional[dt.tzinfo]=None) -> dt.datetime:
'Get now in specified time zone.'
return dt.datetime.now((time_zone or DEFAULT_TIME_ZONE)) | -7,334,469,809,376,690,000 | Get now in specified time zone. | homeassistant/util/dt.py | now | 854562/home-assistant | python | def now(time_zone: Optional[dt.tzinfo]=None) -> dt.datetime:
return dt.datetime.now((time_zone or DEFAULT_TIME_ZONE)) |
def as_utc(dattim: dt.datetime) -> dt.datetime:
'Return a datetime as UTC time.\n\n Assumes datetime without tzinfo to be in the DEFAULT_TIME_ZONE.\n '
if (dattim.tzinfo == UTC):
return dattim
if (dattim.tzinfo is None):
dattim = DEFAULT_TIME_ZONE.localize(dattim)
return dattim.ast... | -256,635,588,040,750,370 | Return a datetime as UTC time.
Assumes datetime without tzinfo to be in the DEFAULT_TIME_ZONE. | homeassistant/util/dt.py | as_utc | 854562/home-assistant | python | def as_utc(dattim: dt.datetime) -> dt.datetime:
'Return a datetime as UTC time.\n\n Assumes datetime without tzinfo to be in the DEFAULT_TIME_ZONE.\n '
if (dattim.tzinfo == UTC):
return dattim
if (dattim.tzinfo is None):
dattim = DEFAULT_TIME_ZONE.localize(dattim)
return dattim.ast... |
def as_timestamp(dt_value: dt.datetime) -> float:
'Convert a date/time into a unix time (seconds since 1970).'
if hasattr(dt_value, 'timestamp'):
parsed_dt: Optional[dt.datetime] = dt_value
else:
parsed_dt = parse_datetime(str(dt_value))
if (parsed_dt is None):
raise ValueError('... | 7,903,070,737,980,607,000 | Convert a date/time into a unix time (seconds since 1970). | homeassistant/util/dt.py | as_timestamp | 854562/home-assistant | python | def as_timestamp(dt_value: dt.datetime) -> float:
if hasattr(dt_value, 'timestamp'):
parsed_dt: Optional[dt.datetime] = dt_value
else:
parsed_dt = parse_datetime(str(dt_value))
if (parsed_dt is None):
raise ValueError('not a valid date/time.')
return parsed_dt.timestamp() |
def as_local(dattim: dt.datetime) -> dt.datetime:
'Convert a UTC datetime object to local time zone.'
if (dattim.tzinfo == DEFAULT_TIME_ZONE):
return dattim
if (dattim.tzinfo is None):
dattim = UTC.localize(dattim)
return dattim.astimezone(DEFAULT_TIME_ZONE) | 2,996,560,705,096,557,600 | Convert a UTC datetime object to local time zone. | homeassistant/util/dt.py | as_local | 854562/home-assistant | python | def as_local(dattim: dt.datetime) -> dt.datetime:
if (dattim.tzinfo == DEFAULT_TIME_ZONE):
return dattim
if (dattim.tzinfo is None):
dattim = UTC.localize(dattim)
return dattim.astimezone(DEFAULT_TIME_ZONE) |
def utc_from_timestamp(timestamp: float) -> dt.datetime:
'Return a UTC time from a timestamp.'
return UTC.localize(dt.datetime.utcfromtimestamp(timestamp)) | -6,724,019,066,667,065,000 | Return a UTC time from a timestamp. | homeassistant/util/dt.py | utc_from_timestamp | 854562/home-assistant | python | def utc_from_timestamp(timestamp: float) -> dt.datetime:
return UTC.localize(dt.datetime.utcfromtimestamp(timestamp)) |
def start_of_local_day(dt_or_d: Union[(dt.date, dt.datetime, None)]=None) -> dt.datetime:
'Return local datetime object of start of day from date or datetime.'
if (dt_or_d is None):
date: dt.date = now().date()
elif isinstance(dt_or_d, dt.datetime):
date = dt_or_d.date()
return DEFAULT_T... | -5,787,161,904,655,488,000 | Return local datetime object of start of day from date or datetime. | homeassistant/util/dt.py | start_of_local_day | 854562/home-assistant | python | def start_of_local_day(dt_or_d: Union[(dt.date, dt.datetime, None)]=None) -> dt.datetime:
if (dt_or_d is None):
date: dt.date = now().date()
elif isinstance(dt_or_d, dt.datetime):
date = dt_or_d.date()
return DEFAULT_TIME_ZONE.localize(dt.datetime.combine(date, dt.time())) |
def parse_datetime(dt_str: str) -> Optional[dt.datetime]:
"Parse a string and return a datetime.datetime.\n\n This function supports time zone offsets. When the input contains one,\n the output uses a timezone with a fixed offset from UTC.\n Raises ValueError if the input is well formatted but not a valid ... | -1,937,966,146,818,874,600 | Parse a string and return a datetime.datetime.
This function supports time zone offsets. When the input contains one,
the output uses a timezone with a fixed offset from UTC.
Raises ValueError if the input is well formatted but not a valid datetime.
Returns None if the input isn't well formatted. | homeassistant/util/dt.py | parse_datetime | 854562/home-assistant | python | def parse_datetime(dt_str: str) -> Optional[dt.datetime]:
"Parse a string and return a datetime.datetime.\n\n This function supports time zone offsets. When the input contains one,\n the output uses a timezone with a fixed offset from UTC.\n Raises ValueError if the input is well formatted but not a valid ... |
def parse_date(dt_str: str) -> Optional[dt.date]:
'Convert a date string to a date object.'
try:
return dt.datetime.strptime(dt_str, DATE_STR_FORMAT).date()
except ValueError:
return None | -1,140,153,710,754,188,500 | Convert a date string to a date object. | homeassistant/util/dt.py | parse_date | 854562/home-assistant | python | def parse_date(dt_str: str) -> Optional[dt.date]:
try:
return dt.datetime.strptime(dt_str, DATE_STR_FORMAT).date()
except ValueError:
return None |
def parse_time(time_str: str) -> Optional[dt.time]:
'Parse a time string (00:20:00) into Time object.\n\n Return None if invalid.\n '
parts = str(time_str).split(':')
if (len(parts) < 2):
return None
try:
hour = int(parts[0])
minute = int(parts[1])
second = (int(par... | 4,760,396,034,145,555,000 | Parse a time string (00:20:00) into Time object.
Return None if invalid. | homeassistant/util/dt.py | parse_time | 854562/home-assistant | python | def parse_time(time_str: str) -> Optional[dt.time]:
'Parse a time string (00:20:00) into Time object.\n\n Return None if invalid.\n '
parts = str(time_str).split(':')
if (len(parts) < 2):
return None
try:
hour = int(parts[0])
minute = int(parts[1])
second = (int(par... |
def get_age(date: dt.datetime) -> str:
'\n Take a datetime and return its "age" as a string.\n\n The age can be in second, minute, hour, day, month or year. Only the\n biggest unit is considered, e.g. if it\'s 2 days and 3 hours, "2 days" will\n be returned.\n Make sure date is not in the future, or ... | -8,345,418,009,683,860,000 | Take a datetime and return its "age" as a string.
The age can be in second, minute, hour, day, month or year. Only the
biggest unit is considered, e.g. if it's 2 days and 3 hours, "2 days" will
be returned.
Make sure date is not in the future, or else it won't work. | homeassistant/util/dt.py | get_age | 854562/home-assistant | python | def get_age(date: dt.datetime) -> str:
'\n Take a datetime and return its "age" as a string.\n\n The age can be in second, minute, hour, day, month or year. Only the\n biggest unit is considered, e.g. if it\'s 2 days and 3 hours, "2 days" will\n be returned.\n Make sure date is not in the future, or ... |
def parse_time_expression(parameter: Any, min_value: int, max_value: int) -> List[int]:
'Parse the time expression part and return a list of times to match.'
if ((parameter is None) or (parameter == MATCH_ALL)):
res = list(range(min_value, (max_value + 1)))
elif (isinstance(parameter, str) and param... | 8,850,174,465,410,132,000 | Parse the time expression part and return a list of times to match. | homeassistant/util/dt.py | parse_time_expression | 854562/home-assistant | python | def parse_time_expression(parameter: Any, min_value: int, max_value: int) -> List[int]:
if ((parameter is None) or (parameter == MATCH_ALL)):
res = list(range(min_value, (max_value + 1)))
elif (isinstance(parameter, str) and parameter.startswith('/')):
parameter = int(parameter[1:])
... |
def find_next_time_expression_time(now: dt.datetime, seconds: List[int], minutes: List[int], hours: List[int]) -> dt.datetime:
'Find the next datetime from now for which the time expression matches.\n\n The algorithm looks at each time unit separately and tries to find the\n next one that matches for each. If... | -6,388,431,229,437,913,000 | Find the next datetime from now for which the time expression matches.
The algorithm looks at each time unit separately and tries to find the
next one that matches for each. If any of them would roll over, all
time units below that are reset to the first matching value.
Timezones are also handled (the tzinfo of the n... | homeassistant/util/dt.py | find_next_time_expression_time | 854562/home-assistant | python | def find_next_time_expression_time(now: dt.datetime, seconds: List[int], minutes: List[int], hours: List[int]) -> dt.datetime:
'Find the next datetime from now for which the time expression matches.\n\n The algorithm looks at each time unit separately and tries to find the\n next one that matches for each. If... |
def formatn(number: int, unit: str) -> str:
'Add "unit" if it\'s plural.'
if (number == 1):
return f'1 {unit}'
return f'{number:d} {unit}s' | 6,630,770,749,241,600,000 | Add "unit" if it's plural. | homeassistant/util/dt.py | formatn | 854562/home-assistant | python | def formatn(number: int, unit: str) -> str:
'Add "unit" if it\'s plural.'
if (number == 1):
return f'1 {unit}'
return f'{number:d} {unit}s' |
def q_n_r(first: int, second: int) -> Tuple[(int, int)]:
'Return quotient and remaining.'
return ((first // second), (first % second)) | -3,372,020,599,350,087,700 | Return quotient and remaining. | homeassistant/util/dt.py | q_n_r | 854562/home-assistant | python | def q_n_r(first: int, second: int) -> Tuple[(int, int)]:
return ((first // second), (first % second)) |
def _lower_bound(arr: List[int], cmp: int) -> Optional[int]:
'Return the first value in arr greater or equal to cmp.\n\n Return None if no such value exists.\n '
left = 0
right = len(arr)
while (left < right):
mid = ((left + right) // 2)
if (arr[mid] < cmp):
lef... | -4,479,979,004,816,162,300 | Return the first value in arr greater or equal to cmp.
Return None if no such value exists. | homeassistant/util/dt.py | _lower_bound | 854562/home-assistant | python | def _lower_bound(arr: List[int], cmp: int) -> Optional[int]:
'Return the first value in arr greater or equal to cmp.\n\n Return None if no such value exists.\n '
left = 0
right = len(arr)
while (left < right):
mid = ((left + right) // 2)
if (arr[mid] < cmp):
lef... |
def __init__(self, location: str=dataset_dir('MSLR10K'), split: str='train', fold: int=1, normalize: bool=True, filter_queries: Optional[bool]=None, download: bool=True, validate_checksums: bool=True):
'\n Args:\n location: Directory where the dataset is located.\n split: The data split... | 7,654,225,927,789,626,000 | Args:
location: Directory where the dataset is located.
split: The data split to load ("train", "test" or "vali")
fold: Which data fold to load (1...5)
normalize: Whether to perform query-level feature
normalization.
filter_queries: Whether to filter out queries that
have no relevant... | pytorchltr/datasets/svmrank/mslr10k.py | __init__ | SuperXiang/pytorchltr | python | def __init__(self, location: str=dataset_dir('MSLR10K'), split: str='train', fold: int=1, normalize: bool=True, filter_queries: Optional[bool]=None, download: bool=True, validate_checksums: bool=True):
'\n Args:\n location: Directory where the dataset is located.\n split: The data split... |
def tokenizeInput(self, token):
"\n Cleans and tokenizes the user's input.\n\n empty characters and spaces are trimmed to prevent\n matching all paths in the index.\n "
return list(filter(None, re.split(self.options.input_tokenizer, self.clean(token)))) | 5,647,962,165,047,563,000 | Cleans and tokenizes the user's input.
empty characters and spaces are trimmed to prevent
matching all paths in the index. | gooey/gui/components/filtering/prefix_filter.py | tokenizeInput | QuantumSpatialInc/Gooey | python | def tokenizeInput(self, token):
"\n Cleans and tokenizes the user's input.\n\n empty characters and spaces are trimmed to prevent\n matching all paths in the index.\n "
return list(filter(None, re.split(self.options.input_tokenizer, self.clean(token)))) |
def tokenizeChoice(self, choice):
"\n Splits the `choice` into a series of tokens based on\n the user's criteria.\n\n If suffix indexing is enabled, the individual tokens\n are further broken down and indexed by their suffix offsets. e.g.\n\n 'Banana', 'anana', 'nana', 'ana'\n... | 7,454,731,504,844,039,000 | Splits the `choice` into a series of tokens based on
the user's criteria.
If suffix indexing is enabled, the individual tokens
are further broken down and indexed by their suffix offsets. e.g.
'Banana', 'anana', 'nana', 'ana' | gooey/gui/components/filtering/prefix_filter.py | tokenizeChoice | QuantumSpatialInc/Gooey | python | def tokenizeChoice(self, choice):
"\n Splits the `choice` into a series of tokens based on\n the user's criteria.\n\n If suffix indexing is enabled, the individual tokens\n are further broken down and indexed by their suffix offsets. e.g.\n\n 'Banana', 'anana', 'nana', 'ana'\n... |
def decov(h, reduce='half_squared_sum'):
"Computes the DeCov loss of ``h``\n\n The output is a variable whose value depends on the value of\n the option ``reduce``. If it is ``'no'``, it holds a matrix\n whose size is same as the number of columns of ``y``.\n If it is ``'half_squared_sum'``, it holds th... | 6,244,738,837,472,731,000 | Computes the DeCov loss of ``h``
The output is a variable whose value depends on the value of
the option ``reduce``. If it is ``'no'``, it holds a matrix
whose size is same as the number of columns of ``y``.
If it is ``'half_squared_sum'``, it holds the half of the
squared Frobenius norm (i.e. squared of the L2 norm o... | chainer/functions/loss/decov.py | decov | Anyz01/chainer | python | def decov(h, reduce='half_squared_sum'):
"Computes the DeCov loss of ``h``\n\n The output is a variable whose value depends on the value of\n the option ``reduce``. If it is ``'no'``, it holds a matrix\n whose size is same as the number of columns of ``y``.\n If it is ``'half_squared_sum'``, it holds th... |
def _findFirstTraceInsideTensorFlowPyLibrary(self, op):
'Find the first trace of an op that belongs to the TF Python library.'
for trace in op.traceback:
if source_utils.guess_is_tensorflow_py_library(trace.filename):
return trace | -7,241,050,063,364,547,000 | Find the first trace of an op that belongs to the TF Python library. | tensorflow/python/debug/lib/source_remote_test.py | _findFirstTraceInsideTensorFlowPyLibrary | 05259/tensorflow | python | def _findFirstTraceInsideTensorFlowPyLibrary(self, op):
for trace in op.traceback:
if source_utils.guess_is_tensorflow_py_library(trace.filename):
return trace |
def testGRPCServerMessageSizeLimit(self):
'Assert gRPC debug server is started with unlimited message size.'
with test.mock.patch.object(grpc, 'server', wraps=grpc.server) as mock_grpc_server:
(_, _, _, server_thread, server) = grpc_debug_test_server.start_server_on_separate_thread(poll_server=True)
... | -3,176,832,388,540,558,000 | Assert gRPC debug server is started with unlimited message size. | tensorflow/python/debug/lib/source_remote_test.py | testGRPCServerMessageSizeLimit | 05259/tensorflow | python | def testGRPCServerMessageSizeLimit(self):
with test.mock.patch.object(grpc, 'server', wraps=grpc.server) as mock_grpc_server:
(_, _, _, server_thread, server) = grpc_debug_test_server.start_server_on_separate_thread(poll_server=True)
mock_grpc_server.assert_called_with(test.mock.ANY, options=[(... |
def list_combinations_generator(modalities: list):
'Generates combinations for items in the given list.\n\n Args:\n modalities: List of modalities available in the dataset.\n\n Returns:\n Combinations of items in the given list.\n '
modality_combinations = list()
for l... | 4,698,962,698,077,386,000 | Generates combinations for items in the given list.
Args:
modalities: List of modalities available in the dataset.
Returns:
Combinations of items in the given list. | codes/model_training_testing.py | list_combinations_generator | preetham-ganesh/multi-sensor-human-activity-recognition | python | def list_combinations_generator(modalities: list):
'Generates combinations for items in the given list.\n\n Args:\n modalities: List of modalities available in the dataset.\n\n Returns:\n Combinations of items in the given list.\n '
modality_combinations = list()
for l... |
def data_combiner(n_actions: int, subject_ids: list, n_takes: int, modalities: list, skeleton_pose_model: str):
'Combines skeleton point information for all actions, all takes, given list of subject ids and given list of\n modalities.\n\n Args:\n n_actions: Total number of actions in the origin... | -6,621,214,251,104,755,000 | Combines skeleton point information for all actions, all takes, given list of subject ids and given list of
modalities.
Args:
n_actions: Total number of actions in the original dataset.
subject_ids: List of subjects in the current set.
n_takes: Total number of takes in the original dataset.... | codes/model_training_testing.py | data_combiner | preetham-ganesh/multi-sensor-human-activity-recognition | python | def data_combiner(n_actions: int, subject_ids: list, n_takes: int, modalities: list, skeleton_pose_model: str):
'Combines skeleton point information for all actions, all takes, given list of subject ids and given list of\n modalities.\n\n Args:\n n_actions: Total number of actions in the origin... |
def calculate_metrics(actual_values: np.ndarray, predicted_values: np.ndarray):
'Using actual_values, predicted_values calculates metrics such as accuracy, balanced accuracy, precision, recall,\n and f1 scores.\n\n Args:\n actual_values: Actual action labels in the dataset\n predicte... | -4,365,684,577,823,481,300 | Using actual_values, predicted_values calculates metrics such as accuracy, balanced accuracy, precision, recall,
and f1 scores.
Args:
actual_values: Actual action labels in the dataset
predicted_values: Action labels predicted by the currently trained model
Returns:
Dictionary contains... | codes/model_training_testing.py | calculate_metrics | preetham-ganesh/multi-sensor-human-activity-recognition | python | def calculate_metrics(actual_values: np.ndarray, predicted_values: np.ndarray):
'Using actual_values, predicted_values calculates metrics such as accuracy, balanced accuracy, precision, recall,\n and f1 scores.\n\n Args:\n actual_values: Actual action labels in the dataset\n predicte... |
def retrieve_hyperparameters(current_model_name: str):
'Based on the current_model_name returns a list of hyperparameters used for optimizing the model (if necessary).\n\n Args:\n current_model_name: Name of the model currently expected to be trained\n\n Returns:\n A dictionary c... | 2,844,473,804,344,919,600 | Based on the current_model_name returns a list of hyperparameters used for optimizing the model (if necessary).
Args:
current_model_name: Name of the model currently expected to be trained
Returns:
A dictionary containing the hyperparameter name and the values that will be used to optimize the model | codes/model_training_testing.py | retrieve_hyperparameters | preetham-ganesh/multi-sensor-human-activity-recognition | python | def retrieve_hyperparameters(current_model_name: str):
'Based on the current_model_name returns a list of hyperparameters used for optimizing the model (if necessary).\n\n Args:\n current_model_name: Name of the model currently expected to be trained\n\n Returns:\n A dictionary c... |
def split_data_input_target(skeleton_data: pd.DataFrame):
'Splits skeleton_data into input and target datasets by filtering / selecting certain columns.\n\n Args:\n skeleton_data: Train / Validation / Test dataset used to split / filter certain columns.\n\n Returns:\n A tuple con... | -172,952,320,064,424,670 | Splits skeleton_data into input and target datasets by filtering / selecting certain columns.
Args:
skeleton_data: Train / Validation / Test dataset used to split / filter certain columns.
Returns:
A tuple containing 2 numpy ndarrays for the input and target datasets. | codes/model_training_testing.py | split_data_input_target | preetham-ganesh/multi-sensor-human-activity-recognition | python | def split_data_input_target(skeleton_data: pd.DataFrame):
'Splits skeleton_data into input and target datasets by filtering / selecting certain columns.\n\n Args:\n skeleton_data: Train / Validation / Test dataset used to split / filter certain columns.\n\n Returns:\n A tuple con... |
def video_based_model_testing(test_skeleton_information: pd.DataFrame, current_model: sklearn):
'Tests performance of the currently trained model on the validation or testing sets, where the performance is\n evaluated per video / file, instead of evaluating per frame.\n\n Args:\n test_skeleton_... | 3,758,275,840,654,412,000 | Tests performance of the currently trained model on the validation or testing sets, where the performance is
evaluated per video / file, instead of evaluating per frame.
Args:
test_skeleton_information: Pandas dataframe which contains skeleton point information for all actions,
... | codes/model_training_testing.py | video_based_model_testing | preetham-ganesh/multi-sensor-human-activity-recognition | python | def video_based_model_testing(test_skeleton_information: pd.DataFrame, current_model: sklearn):
'Tests performance of the currently trained model on the validation or testing sets, where the performance is\n evaluated per video / file, instead of evaluating per frame.\n\n Args:\n test_skeleton_... |
def model_training_testing(train_skeleton_information: pd.DataFrame, validation_skeleton_information: pd.DataFrame, test_skeleton_information: pd.DataFrame, current_model_name: str, parameters: dict):
'Trains and validates model for the current model name and hyperparameters on the train_skeleton_informaiton and\n ... | 8,992,935,888,324,668,000 | Trains and validates model for the current model name and hyperparameters on the train_skeleton_informaiton and
validation_skeleton_information.
Args:
train_skeleton_information: Pandas dataframe which contains skeleton point information for all actions,
subject_ids, and... | codes/model_training_testing.py | model_training_testing | preetham-ganesh/multi-sensor-human-activity-recognition | python | def model_training_testing(train_skeleton_information: pd.DataFrame, validation_skeleton_information: pd.DataFrame, test_skeleton_information: pd.DataFrame, current_model_name: str, parameters: dict):
'Trains and validates model for the current model name and hyperparameters on the train_skeleton_informaiton and\n ... |
def per_combination_results_export(combination_name: str, data_split: str, metrics_dataframe: pd.DataFrame):
'Exports the metrics_dataframe into a CSV format to the mentioned data_split folder. If the folder does not exist,\n then the folder is created.\n\n Args:\n combination_name: Name of the... | -7,363,266,707,070,975,000 | Exports the metrics_dataframe into a CSV format to the mentioned data_split folder. If the folder does not exist,
then the folder is created.
Args:
combination_name: Name of the current combination of modalities and skeleton pose model.
data_split: Name of the split the subset of the dataset belong... | codes/model_training_testing.py | per_combination_results_export | preetham-ganesh/multi-sensor-human-activity-recognition | python | def per_combination_results_export(combination_name: str, data_split: str, metrics_dataframe: pd.DataFrame):
'Exports the metrics_dataframe into a CSV format to the mentioned data_split folder. If the folder does not exist,\n then the folder is created.\n\n Args:\n combination_name: Name of the... |
def appends_parameter_metrics_combination(current_model_name: str, current_combination_name: str, current_split_metrics: dict, split_metrics_dataframe: pd.DataFrame):
'Appends the metrics for the current model and current parameter combination to the main dataframe.\n\n Args:\n current_model_name:... | 8,218,222,589,331,363,000 | Appends the metrics for the current model and current parameter combination to the main dataframe.
Args:
current_model_name: Name of the model currently being trained.
current_combination_name: Current combination of parameters used for training the model.
current_split_metrics: Metrics for the current par... | codes/model_training_testing.py | appends_parameter_metrics_combination | preetham-ganesh/multi-sensor-human-activity-recognition | python | def appends_parameter_metrics_combination(current_model_name: str, current_combination_name: str, current_split_metrics: dict, split_metrics_dataframe: pd.DataFrame):
'Appends the metrics for the current model and current parameter combination to the main dataframe.\n\n Args:\n current_model_name:... |
def per_combination_model_training_testing(train_subject_ids: list, validation_subject_ids: list, test_subject_ids: list, n_actions: int, n_takes: int, current_combination_modalities: list, skeleton_pose_model: str, model_names: list):
'Combines skeleton point information based on modality combination, and subject ... | -7,157,611,953,615,802,000 | Combines skeleton point information based on modality combination, and subject id group. Trains, validates, and
tests the list of classifier models. Calculates metrics for each data split, model and parameter combination.
Args:
train_subject_ids: List of subject ids in the training set.
validation_... | codes/model_training_testing.py | per_combination_model_training_testing | preetham-ganesh/multi-sensor-human-activity-recognition | python | def per_combination_model_training_testing(train_subject_ids: list, validation_subject_ids: list, test_subject_ids: list, n_actions: int, n_takes: int, current_combination_modalities: list, skeleton_pose_model: str, model_names: list):
'Combines skeleton point information based on modality combination, and subject ... |
def __init__(self, multi_scale=False, multi_image_sizes=(320, 352, 384, 416, 448, 480, 512, 544, 576, 608), misc_effect=None, visual_effect=None, batch_size=1, group_method='ratio', shuffle_groups=True, input_size=512, max_objects=100):
"\n Initialize Generator object.\n\n Args:\n batch_siz... | 1,858,173,405,841,341,700 | Initialize Generator object.
Args:
batch_size: The size of the batches to generate.
group_method: Determines how images are grouped together (defaults to 'ratio', one of ('none', 'random', 'ratio')).
shuffle_groups: If True, shuffles the groups each epoch.
input_size:
max_objects: | generators/common.py | __init__ | lbcsept/keras-CenterNet | python | def __init__(self, multi_scale=False, multi_image_sizes=(320, 352, 384, 416, 448, 480, 512, 544, 576, 608), misc_effect=None, visual_effect=None, batch_size=1, group_method='ratio', shuffle_groups=True, input_size=512, max_objects=100):
"\n Initialize Generator object.\n\n Args:\n batch_siz... |
def size(self):
'\n Size of the dataset.\n '
raise NotImplementedError('size method not implemented') | 2,519,970,720,400,613,400 | Size of the dataset. | generators/common.py | size | lbcsept/keras-CenterNet | python | def size(self):
'\n \n '
raise NotImplementedError('size method not implemented') |
def num_classes(self):
'\n Number of classes in the dataset.\n '
raise NotImplementedError('num_classes method not implemented') | 2,245,586,942,049,278,500 | Number of classes in the dataset. | generators/common.py | num_classes | lbcsept/keras-CenterNet | python | def num_classes(self):
'\n \n '
raise NotImplementedError('num_classes method not implemented') |
def has_label(self, label):
'\n Returns True if label is a known label.\n '
raise NotImplementedError('has_label method not implemented') | 8,231,604,603,183,398,000 | Returns True if label is a known label. | generators/common.py | has_label | lbcsept/keras-CenterNet | python | def has_label(self, label):
'\n \n '
raise NotImplementedError('has_label method not implemented') |
def has_name(self, name):
'\n Returns True if name is a known class.\n '
raise NotImplementedError('has_name method not implemented') | 5,509,816,958,451,983,000 | Returns True if name is a known class. | generators/common.py | has_name | lbcsept/keras-CenterNet | python | def has_name(self, name):
'\n \n '
raise NotImplementedError('has_name method not implemented') |
def name_to_label(self, name):
'\n Map name to label.\n '
raise NotImplementedError('name_to_label method not implemented') | -3,816,862,996,482,635,300 | Map name to label. | generators/common.py | name_to_label | lbcsept/keras-CenterNet | python | def name_to_label(self, name):
'\n \n '
raise NotImplementedError('name_to_label method not implemented') |
def label_to_name(self, label):
'\n Map label to name.\n '
raise NotImplementedError('label_to_name method not implemented') | 5,471,730,362,505,122,000 | Map label to name. | generators/common.py | label_to_name | lbcsept/keras-CenterNet | python | def label_to_name(self, label):
'\n \n '
raise NotImplementedError('label_to_name method not implemented') |
def image_aspect_ratio(self, image_index):
'\n Compute the aspect ratio for an image with image_index.\n '
raise NotImplementedError('image_aspect_ratio method not implemented') | 5,160,404,832,456,020,000 | Compute the aspect ratio for an image with image_index. | generators/common.py | image_aspect_ratio | lbcsept/keras-CenterNet | python | def image_aspect_ratio(self, image_index):
'\n \n '
raise NotImplementedError('image_aspect_ratio method not implemented') |
def load_image(self, image_index):
'\n Load an image at the image_index.\n '
raise NotImplementedError('load_image method not implemented') | -7,955,758,498,265,859,000 | Load an image at the image_index. | generators/common.py | load_image | lbcsept/keras-CenterNet | python | def load_image(self, image_index):
'\n \n '
raise NotImplementedError('load_image method not implemented') |
def load_annotations(self, image_index):
'\n Load annotations for an image_index.\n '
raise NotImplementedError('load_annotations method not implemented') | -7,710,779,890,866,678,000 | Load annotations for an image_index. | generators/common.py | load_annotations | lbcsept/keras-CenterNet | python | def load_annotations(self, image_index):
'\n \n '
raise NotImplementedError('load_annotations method not implemented') |
def load_annotations_group(self, group):
'\n Load annotations for all images in group.\n '
annotations_group = [self.load_annotations(image_index) for image_index in group]
for annotations in annotations_group:
assert isinstance(annotations, dict), "'load_annotations' should return a l... | 1,904,131,570,393,643,300 | Load annotations for all images in group. | generators/common.py | load_annotations_group | lbcsept/keras-CenterNet | python | def load_annotations_group(self, group):
'\n \n '
annotations_group = [self.load_annotations(image_index) for image_index in group]
for annotations in annotations_group:
assert isinstance(annotations, dict), "'load_annotations' should return a list of dictionaries, received: {}".format... |
def filter_annotations(self, image_group, annotations_group, group):
'\n Filter annotations by removing those that are outside of the image bounds or whose width/height < 0.\n '
for (index, (image, annotations)) in enumerate(zip(image_group, annotations_group)):
invalid_indices = np.where(... | -5,141,735,004,164,665,000 | Filter annotations by removing those that are outside of the image bounds or whose width/height < 0. | generators/common.py | filter_annotations | lbcsept/keras-CenterNet | python | def filter_annotations(self, image_group, annotations_group, group):
'\n \n '
for (index, (image, annotations)) in enumerate(zip(image_group, annotations_group)):
invalid_indices = np.where(((((((((annotations['bboxes'][:, 2] <= annotations['bboxes'][:, 0]) | (annotations['bboxes'][:, 3] <... |
def clip_transformed_annotations(self, image_group, annotations_group, group):
'\n Filter annotations by removing those that are outside of the image bounds or whose width/height < 0.\n '
filtered_image_group = []
filtered_annotations_group = []
for (index, (image, annotations)) in enumera... | 4,534,439,093,873,950,700 | Filter annotations by removing those that are outside of the image bounds or whose width/height < 0. | generators/common.py | clip_transformed_annotations | lbcsept/keras-CenterNet | python | def clip_transformed_annotations(self, image_group, annotations_group, group):
'\n \n '
filtered_image_group = []
filtered_annotations_group = []
for (index, (image, annotations)) in enumerate(zip(image_group, annotations_group)):
image_height = image.shape[0]
image_width =... |
def load_image_group(self, group):
'\n Load images for all images in a group.\n '
return [self.load_image(image_index) for image_index in group] | -208,212,597,319,730,600 | Load images for all images in a group. | generators/common.py | load_image_group | lbcsept/keras-CenterNet | python | def load_image_group(self, group):
'\n \n '
return [self.load_image(image_index) for image_index in group] |
def random_visual_effect_group_entry(self, image, annotations):
'\n Randomly transforms image and annotation.\n '
image = self.visual_effect(image)
return (image, annotations) | -7,949,354,188,122,564,000 | Randomly transforms image and annotation. | generators/common.py | random_visual_effect_group_entry | lbcsept/keras-CenterNet | python | def random_visual_effect_group_entry(self, image, annotations):
'\n \n '
image = self.visual_effect(image)
return (image, annotations) |
def random_visual_effect_group(self, image_group, annotations_group):
'\n Randomly apply visual effect on each image.\n '
assert (len(image_group) == len(annotations_group))
if (self.visual_effect is None):
return (image_group, annotations_group)
for index in range(len(image_group)... | 6,606,122,371,543,051,000 | Randomly apply visual effect on each image. | generators/common.py | random_visual_effect_group | lbcsept/keras-CenterNet | python | def random_visual_effect_group(self, image_group, annotations_group):
'\n \n '
assert (len(image_group) == len(annotations_group))
if (self.visual_effect is None):
return (image_group, annotations_group)
for index in range(len(image_group)):
(image_group[index], annotations... |
def random_transform_group_entry(self, image, annotations, transform=None):
'\n Randomly transforms image and annotation.\n '
if ((transform is not None) or self.transform_generator):
if (transform is None):
transform = adjust_transform_for_image(next(self.transform_generator),... | 3,619,452,415,224,716,000 | Randomly transforms image and annotation. | generators/common.py | random_transform_group_entry | lbcsept/keras-CenterNet | python | def random_transform_group_entry(self, image, annotations, transform=None):
'\n \n '
if ((transform is not None) or self.transform_generator):
if (transform is None):
transform = adjust_transform_for_image(next(self.transform_generator), image, self.transform_parameters.relativ... |
def random_transform_group(self, image_group, annotations_group):
'\n Randomly transforms each image and its annotations.\n '
assert (len(image_group) == len(annotations_group))
for index in range(len(image_group)):
(image_group[index], annotations_group[index]) = self.random_transform... | -6,856,956,971,389,891,000 | Randomly transforms each image and its annotations. | generators/common.py | random_transform_group | lbcsept/keras-CenterNet | python | def random_transform_group(self, image_group, annotations_group):
'\n \n '
assert (len(image_group) == len(annotations_group))
for index in range(len(image_group)):
(image_group[index], annotations_group[index]) = self.random_transform_group_entry(image_group[index], annotations_group[... |
def random_misc_group_entry(self, image, annotations):
'\n Randomly transforms image and annotation.\n '
assert (annotations['bboxes'].shape[0] != 0)
(image, boxes) = self.misc_effect(image, annotations['bboxes'])
annotations['bboxes'] = boxes
return (image, annotations) | -9,044,219,135,588,454,000 | Randomly transforms image and annotation. | generators/common.py | random_misc_group_entry | lbcsept/keras-CenterNet | python | def random_misc_group_entry(self, image, annotations):
'\n \n '
assert (annotations['bboxes'].shape[0] != 0)
(image, boxes) = self.misc_effect(image, annotations['bboxes'])
annotations['bboxes'] = boxes
return (image, annotations) |
def random_misc_group(self, image_group, annotations_group):
'\n Randomly transforms each image and its annotations.\n '
assert (len(image_group) == len(annotations_group))
if (self.misc_effect is None):
return (image_group, annotations_group)
for index in range(len(image_group)):
... | 3,756,365,868,291,788,000 | Randomly transforms each image and its annotations. | generators/common.py | random_misc_group | lbcsept/keras-CenterNet | python | def random_misc_group(self, image_group, annotations_group):
'\n \n '
assert (len(image_group) == len(annotations_group))
if (self.misc_effect is None):
return (image_group, annotations_group)
for index in range(len(image_group)):
(image_group[index], annotations_group[inde... |
def preprocess_group_entry(self, image, annotations):
'\n Preprocess image and its annotations.\n '
(image, scale, offset_h, offset_w) = self.preprocess_image(image)
annotations['bboxes'] *= scale
annotations['bboxes'][:, [0, 2]] += offset_w
annotations['bboxes'][:, [1, 3]] += offset_h... | -2,648,293,636,791,352,300 | Preprocess image and its annotations. | generators/common.py | preprocess_group_entry | lbcsept/keras-CenterNet | python | def preprocess_group_entry(self, image, annotations):
'\n \n '
(image, scale, offset_h, offset_w) = self.preprocess_image(image)
annotations['bboxes'] *= scale
annotations['bboxes'][:, [0, 2]] += offset_w
annotations['bboxes'][:, [1, 3]] += offset_h
return (image, annotations) |
def preprocess_group(self, image_group, annotations_group):
'\n Preprocess each image and its annotations in its group.\n '
assert (len(image_group) == len(annotations_group))
for index in range(len(image_group)):
(image_group[index], annotations_group[index]) = self.preprocess_group_e... | -3,169,902,642,537,334,300 | Preprocess each image and its annotations in its group. | generators/common.py | preprocess_group | lbcsept/keras-CenterNet | python | def preprocess_group(self, image_group, annotations_group):
'\n \n '
assert (len(image_group) == len(annotations_group))
for index in range(len(image_group)):
(image_group[index], annotations_group[index]) = self.preprocess_group_entry(image_group[index], annotations_group[index])
... |
def group_images(self):
'\n Order the images according to self.order and makes groups of self.batch_size.\n '
order = list(range(self.size()))
if (self.group_method == 'random'):
random.shuffle(order)
elif (self.group_method == 'ratio'):
order.sort(key=(lambda x: self.image... | -2,192,540,384,019,374,800 | Order the images according to self.order and makes groups of self.batch_size. | generators/common.py | group_images | lbcsept/keras-CenterNet | python | def group_images(self):
'\n \n '
order = list(range(self.size()))
if (self.group_method == 'random'):
random.shuffle(order)
elif (self.group_method == 'ratio'):
order.sort(key=(lambda x: self.image_aspect_ratio(x)))
self.groups = [[order[(x % len(order))] for x in range... |
def compute_inputs(self, image_group, annotations_group):
'\n Compute inputs for the network using an image_group.\n '
batch_images = np.zeros((len(image_group), self.input_size, self.input_size, 3), dtype=np.float32)
batch_hms = np.zeros((len(image_group), self.output_size, self.output_size, ... | -7,551,723,626,296,321,000 | Compute inputs for the network using an image_group. | generators/common.py | compute_inputs | lbcsept/keras-CenterNet | python | def compute_inputs(self, image_group, annotations_group):
'\n \n '
batch_images = np.zeros((len(image_group), self.input_size, self.input_size, 3), dtype=np.float32)
batch_hms = np.zeros((len(image_group), self.output_size, self.output_size, self.num_classes()), dtype=np.float32)
batch_hms... |
def compute_targets(self, image_group, annotations_group):
'\n Compute target outputs for the network using images and their annotations.\n '
return np.zeros((len(image_group),)) | -4,948,169,037,549,393,000 | Compute target outputs for the network using images and their annotations. | generators/common.py | compute_targets | lbcsept/keras-CenterNet | python | def compute_targets(self, image_group, annotations_group):
'\n \n '
return np.zeros((len(image_group),)) |
def compute_inputs_targets(self, group):
'\n Compute inputs and target outputs for the network.\n '
image_group = self.load_image_group(group)
annotations_group = self.load_annotations_group(group)
(image_group, annotations_group) = self.filter_annotations(image_group, annotations_group, g... | 3,504,434,119,494,047,000 | Compute inputs and target outputs for the network. | generators/common.py | compute_inputs_targets | lbcsept/keras-CenterNet | python | def compute_inputs_targets(self, group):
'\n \n '
image_group = self.load_image_group(group)
annotations_group = self.load_annotations_group(group)
(image_group, annotations_group) = self.filter_annotations(image_group, annotations_group, group)
(image_group, annotations_group) = self.... |
def __len__(self):
'\n Number of batches for generator.\n '
return len(self.groups) | 4,036,216,262,415,912,000 | Number of batches for generator. | generators/common.py | __len__ | lbcsept/keras-CenterNet | python | def __len__(self):
'\n \n '
return len(self.groups) |
def __getitem__(self, index):
'\n Keras sequence method for generating batches.\n '
group = self.groups[self.current_index]
if self.multi_scale:
if ((self.current_index % 10) == 0):
random_size_index = np.random.randint(0, len(self.multi_image_sizes))
self.image... | 2,202,590,093,009,340,400 | Keras sequence method for generating batches. | generators/common.py | __getitem__ | lbcsept/keras-CenterNet | python | def __getitem__(self, index):
'\n \n '
group = self.groups[self.current_index]
if self.multi_scale:
if ((self.current_index % 10) == 0):
random_size_index = np.random.randint(0, len(self.multi_image_sizes))
self.image_size = self.multi_image_sizes[random_size_in... |
def test_fas():
'\n Testing based upon the work provided in\n https://github.com/arkottke/notebooks/blob/master/effective_amp_spectrum.ipynb\n '
ddir = os.path.join('data', 'testdata')
datadir = pkg_resources.resource_filename('gmprocess', ddir)
fas_file = os.path.join(datadir, 'fas_greater_of_... | 5,979,378,271,937,405,000 | Testing based upon the work provided in
https://github.com/arkottke/notebooks/blob/master/effective_amp_spectrum.ipynb | tests/gmprocess/metrics/imt/fas_greater_of_two_test.py | test_fas | jrekoske-usgs/groundmotion-processing | python | def test_fas():
'\n Testing based upon the work provided in\n https://github.com/arkottke/notebooks/blob/master/effective_amp_spectrum.ipynb\n '
ddir = os.path.join('data', 'testdata')
datadir = pkg_resources.resource_filename('gmprocess', ddir)
fas_file = os.path.join(datadir, 'fas_greater_of_... |
def transcribe_audio(project_slug, creds, overwrite=False):
'\n project_slug: ./projects/audiostreams/filename.wav\n '
watson_jobs = []
audio_fn = (project_slug + '.wav')
print(('audio_filename:' + audio_fn))
time_slug = make_slug_from_path(audio_fn)
transcript_fn = (join(transcripts_dir(p... | 1,367,059,603,446,845,000 | project_slug: ./projects/audiostreams/filename.wav | watsoncloud/foo/high.py | transcribe_audio | audip/youtubeseek | python | def transcribe_audio(project_slug, creds, overwrite=False):
'\n \n '
watson_jobs = []
audio_fn = (project_slug + '.wav')
print(('audio_filename:' + audio_fn))
time_slug = make_slug_from_path(audio_fn)
transcript_fn = (join(transcripts_dir(project_slug), time_slug) + '.json')
print(('tr... |
@abstractmethod
def _plot_init(self):
'Setup MPL figure display with empty data.'
pass | -3,701,202,470,411,182,000 | Setup MPL figure display with empty data. | src/pymor/discretizers/builtin/gui/matplotlib.py | _plot_init | TreeerT/pymor | python | @abstractmethod
def _plot_init(self):
pass |
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