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 |
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@attr(tags=['advanced', 'eip', 'advancedns', 'basic', 'sg'])
def test_deploy_vm_password_enabled(self):
'Test Deploy Virtual Machine with startVM=false & enabledpassword in\n template\n '
self.debug(('Deploying instance in the account: %s' % self.account.name))
self.virtual_machine = VirtualMa... | 5,462,064,970,451,302,000 | Test Deploy Virtual Machine with startVM=false & enabledpassword in
template | test/integration/component/test_stopped_vm.py | test_deploy_vm_password_enabled | ksowmya/cloudstack-1 | python | @attr(tags=['advanced', 'eip', 'advancedns', 'basic', 'sg'])
def test_deploy_vm_password_enabled(self):
'Test Deploy Virtual Machine with startVM=false & enabledpassword in\n template\n '
self.debug(('Deploying instance in the account: %s' % self.account.name))
self.virtual_machine = VirtualMa... |
@attr(tags=['advanced', 'eip', 'advancedns', 'basic', 'sg'])
def test_vm_per_account(self):
'Test VM limit per account\n '
self.debug(('Updating instance resource limit for account: %s' % self.account.name))
update_resource_limit(self.apiclient, 0, account=self.account.name, domainid=self.account.dom... | -7,472,055,634,946,024,000 | Test VM limit per account | test/integration/component/test_stopped_vm.py | test_vm_per_account | ksowmya/cloudstack-1 | python | @attr(tags=['advanced', 'eip', 'advancedns', 'basic', 'sg'])
def test_vm_per_account(self):
'\n '
self.debug(('Updating instance resource limit for account: %s' % self.account.name))
update_resource_limit(self.apiclient, 0, account=self.account.name, domainid=self.account.domainid, max=1)
self.de... |
@attr(tags=['advanced', 'eip', 'advancedns', 'basic', 'sg'])
def test_upload_attach_volume(self):
'Test Upload volume and attach to VM in stopped state\n '
self.debug(('Uploading the volume: %s' % self.services['volume']['diskname']))
try:
volume = Volume.upload(self.apiclient, self.services[... | 5,862,266,767,459,204,000 | Test Upload volume and attach to VM in stopped state | test/integration/component/test_stopped_vm.py | test_upload_attach_volume | ksowmya/cloudstack-1 | python | @attr(tags=['advanced', 'eip', 'advancedns', 'basic', 'sg'])
def test_upload_attach_volume(self):
'\n '
self.debug(('Uploading the volume: %s' % self.services['volume']['diskname']))
try:
volume = Volume.upload(self.apiclient, self.services['volume'], zoneid=self.zone.id, account=self.account... |
@attr(tags=['advanced', 'advancedns', 'simulator', 'api', 'basic', 'eip', 'sg'])
def test_deployVmOnGivenHost(self):
'Test deploy VM on specific host\n '
hosts = Host.list(self.apiclient, zoneid=self.zone.id, type='Routing', state='Up', listall=True)
self.assertEqual(isinstance(hosts, list), True, 'C... | 3,533,336,537,064,077,000 | Test deploy VM on specific host | test/integration/component/test_stopped_vm.py | test_deployVmOnGivenHost | ksowmya/cloudstack-1 | python | @attr(tags=['advanced', 'advancedns', 'simulator', 'api', 'basic', 'eip', 'sg'])
def test_deployVmOnGivenHost(self):
'\n '
hosts = Host.list(self.apiclient, zoneid=self.zone.id, type='Routing', state='Up', listall=True)
self.assertEqual(isinstance(hosts, list), True, 'CS should have atleast one host ... |
@staticmethod
def _get_workload_data(data: dict) -> dict:
'Return data for requested Workload.'
optimization_id = RequestDataProcessor.get_string_value(data, 'id')
optimization_data = OptimizationAPIInterface.get_optimization_details({'id': optimization_id})
return optimization_data | 1,312,375,586,110,402,800 | Return data for requested Workload. | neural_compressor/ux/web/service/optimization.py | _get_workload_data | intel/lp-opt-tool | python | @staticmethod
def _get_workload_data(data: dict) -> dict:
optimization_id = RequestDataProcessor.get_string_value(data, 'id')
optimization_data = OptimizationAPIInterface.get_optimization_details({'id': optimization_id})
return optimization_data |
def __init__(self, node) -> None:
' :param node: pyrlang.node.Node\n '
GenServer.__init__(self, node_name=node.node_name_, accepted_calls=['is_auth'])
node.register_name(self, Atom('net_kernel')) | 5,099,605,340,179,313,000 | :param node: pyrlang.node.Node | pyrlang/net_kernel.py | __init__ | AlexKovalevych/Pyrlang | python | def __init__(self, node) -> None:
' \n '
GenServer.__init__(self, node_name=node.node_name_, accepted_calls=['is_auth'])
node.register_name(self, Atom('net_kernel')) |
def sort_rings(index_rings: List[List[Tuple[(int, int)]]], vertices: npt.NDArray[np.float32]) -> SortedRingType:
'Sorts a list of index-rings.\n\n Takes a list of unsorted index rings and sorts them into\n "exterior" and "interior" components. Any doubly-nested rings\n are considered exterior rings.\n\n ... | -7,161,474,044,817,911,000 | Sorts a list of index-rings.
Takes a list of unsorted index rings and sorts them into
"exterior" and "interior" components. Any doubly-nested rings
are considered exterior rings.
Parameters
----------
index_rings : List[List[Tuple[int, int]]]
Unosorted list of list of mesh edges as specified by end node
index... | ocsmesh/mesh/mesh.py | sort_rings | noaa-ocs-modeling/OCSMesh | python | def sort_rings(index_rings: List[List[Tuple[(int, int)]]], vertices: npt.NDArray[np.float32]) -> SortedRingType:
'Sorts a list of index-rings.\n\n Takes a list of unsorted index rings and sorts them into\n "exterior" and "interior" components. Any doubly-nested rings\n are considered exterior rings.\n\n ... |
def _mesh_interpolate_worker(coords: npt.NDArray[np.float32], coords_crs: CRS, raster_path: Union[(str, Path)], chunk_size: Optional[int], method: Literal[('spline', 'linear', 'nearest')]='spline', filter_by_shape: bool=False):
"Interpolator worker function to be used in parallel calls\n\n Parameters\n ------... | -2,363,268,281,550,425,600 | Interpolator worker function to be used in parallel calls
Parameters
----------
coords : npt.NDArray[np.float32]
Mesh node coordinates.
coords_crs : CRS
Coordinate reference system of the input mesh coordinates.
raster_path : str or Path
Path to the raster temporary working file.
chunk_size : int or None
... | ocsmesh/mesh/mesh.py | _mesh_interpolate_worker | noaa-ocs-modeling/OCSMesh | python | def _mesh_interpolate_worker(coords: npt.NDArray[np.float32], coords_crs: CRS, raster_path: Union[(str, Path)], chunk_size: Optional[int], method: Literal[('spline', 'linear', 'nearest')]='spline', filter_by_shape: bool=False):
"Interpolator worker function to be used in parallel calls\n\n Parameters\n ------... |
def __init__(self, mesh: jigsaw_msh_t) -> None:
"Initialize Euclidean mesh object.\n\n Parameters\n ----------\n mesh : jigsaw_msh_t\n The underlying jigsaw_msh_t object to hold onto mesh data.\n\n Raises\n ------\n TypeError\n If input mesh is not of ... | 8,873,620,727,186,494,000 | Initialize Euclidean mesh object.
Parameters
----------
mesh : jigsaw_msh_t
The underlying jigsaw_msh_t object to hold onto mesh data.
Raises
------
TypeError
If input mesh is not of `jigsaw_msh_t` type.
ValueError
If input mesh's `mshID` is not equal to ``euclidean-mesh``.
If input mesh has `crs` pro... | ocsmesh/mesh/mesh.py | __init__ | noaa-ocs-modeling/OCSMesh | python | def __init__(self, mesh: jigsaw_msh_t) -> None:
"Initialize Euclidean mesh object.\n\n Parameters\n ----------\n mesh : jigsaw_msh_t\n The underlying jigsaw_msh_t object to hold onto mesh data.\n\n Raises\n ------\n TypeError\n If input mesh is not of ... |
def write(self, path: Union[(str, os.PathLike)], overwrite: bool=False, format: Literal[('grd', '2dm', 'msh', 'vtk')]='grd') -> None:
"Export the mesh object to the disk\n\n Parameters\n ----------\n path : path-like\n Path to which the mesh should be exported.\n overwrite : b... | -6,598,742,017,123,702,000 | Export the mesh object to the disk
Parameters
----------
path : path-like
Path to which the mesh should be exported.
overwrite : bool, default=False
Whether to overwrite, if a file already exists in `path`
format : { 'grd', '2dm', 'msh', 'vtk' }
Format of the export, SMS-2DM or GRD.
Returns
-------
None
... | ocsmesh/mesh/mesh.py | write | noaa-ocs-modeling/OCSMesh | python | def write(self, path: Union[(str, os.PathLike)], overwrite: bool=False, format: Literal[('grd', '2dm', 'msh', 'vtk')]='grd') -> None:
"Export the mesh object to the disk\n\n Parameters\n ----------\n path : path-like\n Path to which the mesh should be exported.\n overwrite : b... |
@property
def tria3(self):
'Reference to underlying mesh tirangle element structure'
return self.msh_t.tria3 | -3,376,814,324,331,499,500 | Reference to underlying mesh tirangle element structure | ocsmesh/mesh/mesh.py | tria3 | noaa-ocs-modeling/OCSMesh | python | @property
def tria3(self):
return self.msh_t.tria3 |
@property
def triangles(self):
'Reference to underlying mesh triangle element index array'
return self.msh_t.tria3['index'] | 2,047,632,625,657,273,300 | Reference to underlying mesh triangle element index array | ocsmesh/mesh/mesh.py | triangles | noaa-ocs-modeling/OCSMesh | python | @property
def triangles(self):
return self.msh_t.tria3['index'] |
@property
def quad4(self):
'Reference to underlying mesh quadrangle element structure'
return self.msh_t.quad4 | -9,157,907,240,058,236,000 | Reference to underlying mesh quadrangle element structure | ocsmesh/mesh/mesh.py | quad4 | noaa-ocs-modeling/OCSMesh | python | @property
def quad4(self):
return self.msh_t.quad4 |
@property
def quads(self):
'Reference to underlying mesh quadrangle element index array'
return self.msh_t.quad4['index'] | -2,822,426,976,579,586,600 | Reference to underlying mesh quadrangle element index array | ocsmesh/mesh/mesh.py | quads | noaa-ocs-modeling/OCSMesh | python | @property
def quads(self):
return self.msh_t.quad4['index'] |
@property
def crs(self):
'Reference to underlying mesh crs'
return self.msh_t.crs | -7,577,520,252,728,717,000 | Reference to underlying mesh crs | ocsmesh/mesh/mesh.py | crs | noaa-ocs-modeling/OCSMesh | python | @property
def crs(self):
return self.msh_t.crs |
@property
def hull(self):
'Reference to hull calculator helper object'
if (self._hull is None):
self._hull = Hull(self)
return self._hull | -1,355,562,474,230,493,700 | Reference to hull calculator helper object | ocsmesh/mesh/mesh.py | hull | noaa-ocs-modeling/OCSMesh | python | @property
def hull(self):
if (self._hull is None):
self._hull = Hull(self)
return self._hull |
@property
def nodes(self):
'Reference to node handler helper object'
if (self._nodes is None):
self._nodes = Nodes(self)
return self._nodes | -6,295,479,252,160,770,000 | Reference to node handler helper object | ocsmesh/mesh/mesh.py | nodes | noaa-ocs-modeling/OCSMesh | python | @property
def nodes(self):
if (self._nodes is None):
self._nodes = Nodes(self)
return self._nodes |
@property
def elements(self):
'Reference to element handler helper object'
if (self._elements is None):
self._elements = Elements(self)
return self._elements | 409,851,697,075,555,000 | Reference to element handler helper object | ocsmesh/mesh/mesh.py | elements | noaa-ocs-modeling/OCSMesh | python | @property
def elements(self):
if (self._elements is None):
self._elements = Elements(self)
return self._elements |
def __init__(self, mesh: jigsaw_msh_t) -> None:
'Initialize Euclidean 2D mesh object.\n\n Parameters\n ----------\n mesh : jigsaw_msh_t\n The underlying jigsaw_msh_t object to hold onto mesh data.\n\n Raises\n ------\n ValueError\n If number of mesh di... | 5,854,192,369,247,189,000 | Initialize Euclidean 2D mesh object.
Parameters
----------
mesh : jigsaw_msh_t
The underlying jigsaw_msh_t object to hold onto mesh data.
Raises
------
ValueError
If number of mesh dimensions is not equal to ``2``. | ocsmesh/mesh/mesh.py | __init__ | noaa-ocs-modeling/OCSMesh | python | def __init__(self, mesh: jigsaw_msh_t) -> None:
'Initialize Euclidean 2D mesh object.\n\n Parameters\n ----------\n mesh : jigsaw_msh_t\n The underlying jigsaw_msh_t object to hold onto mesh data.\n\n Raises\n ------\n ValueError\n If number of mesh di... |
def get_bbox(self, crs: Union[(str, CRS, None)]=None, output_type: Literal[(None, 'polygon', 'bbox')]=None) -> Union[(Polygon, Bbox)]:
"Get the bounding box of mesh elements.\n\n Parameters\n ----------\n crs : str or CRS or None, default=None\n CRS to transform the calculated boundi... | -2,025,501,107,320,347,000 | Get the bounding box of mesh elements.
Parameters
----------
crs : str or CRS or None, default=None
CRS to transform the calculated bounding box into before
returning
output_type : { None, 'polygon', 'bbox'}, default=None
Output type
Returns
-------
Polygon or Bbox
Bounding box of the mesh elements. | ocsmesh/mesh/mesh.py | get_bbox | noaa-ocs-modeling/OCSMesh | python | def get_bbox(self, crs: Union[(str, CRS, None)]=None, output_type: Literal[(None, 'polygon', 'bbox')]=None) -> Union[(Polygon, Bbox)]:
"Get the bounding box of mesh elements.\n\n Parameters\n ----------\n crs : str or CRS or None, default=None\n CRS to transform the calculated boundi... |
@property
def boundaries(self):
'Handle to boundaries calculator helper object'
if (self._boundaries is None):
self._boundaries = Boundaries(self)
return self._boundaries | 368,520,437,063,327,040 | Handle to boundaries calculator helper object | ocsmesh/mesh/mesh.py | boundaries | noaa-ocs-modeling/OCSMesh | python | @property
def boundaries(self):
if (self._boundaries is None):
self._boundaries = Boundaries(self)
return self._boundaries |
def tricontourf(self, **kwargs) -> Axes:
'Generate contour for the data of triangular elements of the mesh\n\n Parameters\n ----------\n **kwargs : dict, optional\n Passed to underlying `matplotlib` API.\n\n Returns\n -------\n Axes\n Axes on which the ... | -141,155,622,674,995,950 | Generate contour for the data of triangular elements of the mesh
Parameters
----------
**kwargs : dict, optional
Passed to underlying `matplotlib` API.
Returns
-------
Axes
Axes on which the filled contour is drawn. | ocsmesh/mesh/mesh.py | tricontourf | noaa-ocs-modeling/OCSMesh | python | def tricontourf(self, **kwargs) -> Axes:
'Generate contour for the data of triangular elements of the mesh\n\n Parameters\n ----------\n **kwargs : dict, optional\n Passed to underlying `matplotlib` API.\n\n Returns\n -------\n Axes\n Axes on which the ... |
def interpolate(self, raster: Union[(Raster, List[Raster])], method: Literal[('spline', 'linear', 'nearest')]='spline', nprocs: Optional[int]=None, info_out_path: Union[(pathlib.Path, str, None)]=None, filter_by_shape: bool=False) -> None:
"Interplate values from raster inputs to the mesh nodes.\n\n Paramete... | 9,192,351,401,424,472,000 | Interplate values from raster inputs to the mesh nodes.
Parameters
----------
raster : Raster or list of Raster
A single or a list of rasters from which values are
interpolated onto the mesh
method : {'spline', 'linear', 'nearest'}, default='spline'
Method of interpolation.
nprocs : int or None, default=No... | ocsmesh/mesh/mesh.py | interpolate | noaa-ocs-modeling/OCSMesh | python | def interpolate(self, raster: Union[(Raster, List[Raster])], method: Literal[('spline', 'linear', 'nearest')]='spline', nprocs: Optional[int]=None, info_out_path: Union[(pathlib.Path, str, None)]=None, filter_by_shape: bool=False) -> None:
"Interplate values from raster inputs to the mesh nodes.\n\n Paramete... |
def get_contour(self, level: float) -> LineString:
'Extract contour lines at the specified `level` from mesh values\n\n Parameters\n ----------\n level : float\n The level at which contour lines must be extracted.\n\n Returns\n -------\n LineString\n E... | 8,760,211,111,063,419,000 | Extract contour lines at the specified `level` from mesh values
Parameters
----------
level : float
The level at which contour lines must be extracted.
Returns
-------
LineString
Extracted and merged contour lines.
Raises
------
ValueError
If mesh has nodes that have null value `np.nan`. | ocsmesh/mesh/mesh.py | get_contour | noaa-ocs-modeling/OCSMesh | python | def get_contour(self, level: float) -> LineString:
'Extract contour lines at the specified `level` from mesh values\n\n Parameters\n ----------\n level : float\n The level at which contour lines must be extracted.\n\n Returns\n -------\n LineString\n E... |
def get_multipolygon(self, zmin: Optional[float]=None, zmax: Optional[float]=None) -> MultiPolygon:
'Calculate multipolygon covering mesh elements (hull)\n\n Parameters\n ----------\n zmin : float or None\n Minimum elevation to consider for multipolygon extraction\n zmax : flo... | -5,949,840,052,573,475,000 | Calculate multipolygon covering mesh elements (hull)
Parameters
----------
zmin : float or None
Minimum elevation to consider for multipolygon extraction
zmax : float or None
Maximum elevation to consider for multipolygon extraction
Returns
-------
MultiPolygon
Calculated multipolygon shape | ocsmesh/mesh/mesh.py | get_multipolygon | noaa-ocs-modeling/OCSMesh | python | def get_multipolygon(self, zmin: Optional[float]=None, zmax: Optional[float]=None) -> MultiPolygon:
'Calculate multipolygon covering mesh elements (hull)\n\n Parameters\n ----------\n zmin : float or None\n Minimum elevation to consider for multipolygon extraction\n zmax : flo... |
@property
def vert2(self):
'Reference to underlying mesh 2D vertices structure'
return self.msh_t.vert2 | 315,863,749,805,218,560 | Reference to underlying mesh 2D vertices structure | ocsmesh/mesh/mesh.py | vert2 | noaa-ocs-modeling/OCSMesh | python | @property
def vert2(self):
return self.msh_t.vert2 |
@property
def value(self):
'Reference to underlying mesh values'
return self.msh_t.value | 541,604,266,528,932,800 | Reference to underlying mesh values | ocsmesh/mesh/mesh.py | value | noaa-ocs-modeling/OCSMesh | python | @property
def value(self):
return self.msh_t.value |
@property
def bbox(self):
'Calculates and returns bounding box of the mesh hull.\n\n See Also\n --------\n get_bbox\n '
return self.get_bbox() | -1,139,814,721,322,906,000 | Calculates and returns bounding box of the mesh hull.
See Also
--------
get_bbox | ocsmesh/mesh/mesh.py | bbox | noaa-ocs-modeling/OCSMesh | python | @property
def bbox(self):
'Calculates and returns bounding box of the mesh hull.\n\n See Also\n --------\n get_bbox\n '
return self.get_bbox() |
def __new__(cls, mesh: jigsaw_msh_t) -> MeshType:
'Construct a concrete mesh object.\n\n Parameters\n ----------\n mesh : jigsaw_msh_t\n Input jigsaw mesh object\n\n Returns\n -------\n MeshType\n Mesh object created from the input\n\n Raises\n ... | 5,430,746,854,186,812,000 | Construct a concrete mesh object.
Parameters
----------
mesh : jigsaw_msh_t
Input jigsaw mesh object
Returns
-------
MeshType
Mesh object created from the input
Raises
------
TypeError
Input `mesh` is not a `jigsaw_msh_t` object.
NotImplementedError
Input `mesh` object cannot be used to create a Eucl... | ocsmesh/mesh/mesh.py | __new__ | noaa-ocs-modeling/OCSMesh | python | def __new__(cls, mesh: jigsaw_msh_t) -> MeshType:
'Construct a concrete mesh object.\n\n Parameters\n ----------\n mesh : jigsaw_msh_t\n Input jigsaw mesh object\n\n Returns\n -------\n MeshType\n Mesh object created from the input\n\n Raises\n ... |
@staticmethod
def open(path: Union[(str, Path)], crs: Optional[CRS]=None) -> MeshType:
'Read mesh from a file on disk\n\n Parameters\n ----------\n path : path-like\n Path to the file containig mesh.\n crs : CRS or None, default=None\n CRS of the mesh in the path. O... | -5,015,466,048,379,708,000 | Read mesh from a file on disk
Parameters
----------
path : path-like
Path to the file containig mesh.
crs : CRS or None, default=None
CRS of the mesh in the path. Overwrites any info read
from file, no transformation is done.
Returns
-------
MeshType
Mesh object created by reading the file.
Raises
--... | ocsmesh/mesh/mesh.py | open | noaa-ocs-modeling/OCSMesh | python | @staticmethod
def open(path: Union[(str, Path)], crs: Optional[CRS]=None) -> MeshType:
'Read mesh from a file on disk\n\n Parameters\n ----------\n path : path-like\n Path to the file containig mesh.\n crs : CRS or None, default=None\n CRS of the mesh in the path. O... |
def __init__(self, mesh: EuclideanMesh) -> None:
'Initializes the ring calculator object for the input `mesh`\n\n Parameters\n ----------\n mesh : EuclideanMesh\n Input mesh for which this object calculates rings.\n '
self.mesh = mesh | 918,915,827,880,436,700 | Initializes the ring calculator object for the input `mesh`
Parameters
----------
mesh : EuclideanMesh
Input mesh for which this object calculates rings. | ocsmesh/mesh/mesh.py | __init__ | noaa-ocs-modeling/OCSMesh | python | def __init__(self, mesh: EuclideanMesh) -> None:
'Initializes the ring calculator object for the input `mesh`\n\n Parameters\n ----------\n mesh : EuclideanMesh\n Input mesh for which this object calculates rings.\n '
self.mesh = mesh |
@lru_cache(maxsize=1)
def __call__(self) -> gpd.GeoDataFrame:
"Calcluates all the polygons of the mesh and extracts its rings.\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing all rings of the mesh hull polygon.\n Th... | 273,310,606,786,646,370 | Calcluates all the polygons of the mesh and extracts its rings.
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Dataframe containing all rings of the mesh hull polygon.
The rings are in the form of `shapely.geometry.LinearRing`.
Notes
-----
The result of this method is cached, so that multiple calls
t... | ocsmesh/mesh/mesh.py | __call__ | noaa-ocs-modeling/OCSMesh | python | @lru_cache(maxsize=1)
def __call__(self) -> gpd.GeoDataFrame:
"Calcluates all the polygons of the mesh and extracts its rings.\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing all rings of the mesh hull polygon.\n Th... |
def exterior(self) -> gpd.GeoDataFrame:
'Extracts the exterior ring from the results of `__call__`.\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing exterior ring of the mesh hull polygon.\n '
return self().loc[(self... | 8,858,591,886,772,095,000 | Extracts the exterior ring from the results of `__call__`.
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Dataframe containing exterior ring of the mesh hull polygon. | ocsmesh/mesh/mesh.py | exterior | noaa-ocs-modeling/OCSMesh | python | def exterior(self) -> gpd.GeoDataFrame:
'Extracts the exterior ring from the results of `__call__`.\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing exterior ring of the mesh hull polygon.\n '
return self().loc[(self... |
def interior(self) -> gpd.GeoDataFrame:
'Extracts the interior rings from the results of `__call__`.\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing interior rings of the mesh hull polygon.\n '
return self().loc[(se... | 8,726,658,138,874,750,000 | Extracts the interior rings from the results of `__call__`.
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Dataframe containing interior rings of the mesh hull polygon. | ocsmesh/mesh/mesh.py | interior | noaa-ocs-modeling/OCSMesh | python | def interior(self) -> gpd.GeoDataFrame:
'Extracts the interior rings from the results of `__call__`.\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing interior rings of the mesh hull polygon.\n '
return self().loc[(se... |
def __init__(self, mesh: EuclideanMesh) -> None:
'Initializes the edge calculator object for the input `mesh`\n\n Parameters\n ----------\n mesh : EuclideanMesh\n Input mesh for which boundary edges are calculated.\n '
self.mesh = mesh | 1,966,865,381,158,875,600 | Initializes the edge calculator object for the input `mesh`
Parameters
----------
mesh : EuclideanMesh
Input mesh for which boundary edges are calculated. | ocsmesh/mesh/mesh.py | __init__ | noaa-ocs-modeling/OCSMesh | python | def __init__(self, mesh: EuclideanMesh) -> None:
'Initializes the edge calculator object for the input `mesh`\n\n Parameters\n ----------\n mesh : EuclideanMesh\n Input mesh for which boundary edges are calculated.\n '
self.mesh = mesh |
@lru_cache(maxsize=1)
def __call__(self) -> gpd.GeoDataFrame:
"Calculates all boundary edges for the mesh.\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing all boundary edges of the mesh in\n the form of `shapely.geo... | -2,760,478,559,937,339,000 | Calculates all boundary edges for the mesh.
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Dataframe containing all boundary edges of the mesh in
the form of `shapely.geometry.LineString` for each
coordinate couple.
Notes
-----
The result of this method is cached, so that multiple calls
to it won... | ocsmesh/mesh/mesh.py | __call__ | noaa-ocs-modeling/OCSMesh | python | @lru_cache(maxsize=1)
def __call__(self) -> gpd.GeoDataFrame:
"Calculates all boundary edges for the mesh.\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing all boundary edges of the mesh in\n the form of `shapely.geo... |
def exterior(self) -> gpd.GeoDataFrame:
'Retruns exterior boundary edges from the results of `__call__`\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing exterior boundary edges of the mesh in\n the form of line strin... | 8,433,150,425,821,374,000 | Retruns exterior boundary edges from the results of `__call__`
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Dataframe containing exterior boundary edges of the mesh in
the form of line string couples. | ocsmesh/mesh/mesh.py | exterior | noaa-ocs-modeling/OCSMesh | python | def exterior(self) -> gpd.GeoDataFrame:
'Retruns exterior boundary edges from the results of `__call__`\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing exterior boundary edges of the mesh in\n the form of line strin... |
def interior(self) -> gpd.GeoDataFrame:
'Retruns interior boundary edges from the results of `__call__`\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing interior boundary edges of the mesh in\n the form of line strin... | 4,926,577,126,765,426,000 | Retruns interior boundary edges from the results of `__call__`
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Dataframe containing interior boundary edges of the mesh in
the form of line string couples. | ocsmesh/mesh/mesh.py | interior | noaa-ocs-modeling/OCSMesh | python | def interior(self) -> gpd.GeoDataFrame:
'Retruns interior boundary edges from the results of `__call__`\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing interior boundary edges of the mesh in\n the form of line strin... |
def __init__(self, mesh: EuclideanMesh) -> None:
'Initialize helper class for handling mesh hull calculations\n\n Parameters\n ----------\n mesh : EuclideanMesh\n Input mesh for which hull calculations are done.\n\n Notes\n -----\n This object holds onto the ring... | -3,874,946,093,954,868,700 | Initialize helper class for handling mesh hull calculations
Parameters
----------
mesh : EuclideanMesh
Input mesh for which hull calculations are done.
Notes
-----
This object holds onto the ring and edge calculator objects
as well as a reference to the input mesh. | ocsmesh/mesh/mesh.py | __init__ | noaa-ocs-modeling/OCSMesh | python | def __init__(self, mesh: EuclideanMesh) -> None:
'Initialize helper class for handling mesh hull calculations\n\n Parameters\n ----------\n mesh : EuclideanMesh\n Input mesh for which hull calculations are done.\n\n Notes\n -----\n This object holds onto the ring... |
@lru_cache(maxsize=1)
def __call__(self) -> gpd.GeoDataFrame:
"Calculates all polygons of the mesh including domain islands\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing all polygons of the mesh.\n\n See Also\n ... | -3,732,285,811,006,009,300 | Calculates all polygons of the mesh including domain islands
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Dataframe containing all polygons of the mesh.
See Also
--------
implode()
Dataframe with a single combined multipolygon.
multipolygon()
`shapely` multipolygon shape of combined mesh polygo... | ocsmesh/mesh/mesh.py | __call__ | noaa-ocs-modeling/OCSMesh | python | @lru_cache(maxsize=1)
def __call__(self) -> gpd.GeoDataFrame:
"Calculates all polygons of the mesh including domain islands\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing all polygons of the mesh.\n\n See Also\n ... |
def exterior(self) -> gpd.GeoDataFrame:
'Creates polygons from exterior rings of the mesh hull\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Polygons created from exterior rings of the mesh hull\n '
data = []
for exterior in self.... | -2,137,415,256,579,247,900 | Creates polygons from exterior rings of the mesh hull
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Polygons created from exterior rings of the mesh hull | ocsmesh/mesh/mesh.py | exterior | noaa-ocs-modeling/OCSMesh | python | def exterior(self) -> gpd.GeoDataFrame:
'Creates polygons from exterior rings of the mesh hull\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Polygons created from exterior rings of the mesh hull\n '
data = []
for exterior in self.... |
def interior(self) -> gpd.GeoDataFrame:
'Creates polygons from interior rings of the mesh hull\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Polygons created from interior rings of the mesh hull\n '
data = []
for interior in self.... | 4,092,416,981,451,248,600 | Creates polygons from interior rings of the mesh hull
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Polygons created from interior rings of the mesh hull | ocsmesh/mesh/mesh.py | interior | noaa-ocs-modeling/OCSMesh | python | def interior(self) -> gpd.GeoDataFrame:
'Creates polygons from interior rings of the mesh hull\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Polygons created from interior rings of the mesh hull\n '
data = []
for interior in self.... |
def implode(self) -> gpd.GeoDataFrame:
'Creates a dataframe from mesh polygons.\n\n Parameters\n ----------\n\n Returns\n ------\n gpd.GeoDataFrame\n Dataframe containing polygons of the mesh.\n\n See Also\n --------\n __call__()\n Datafr... | -1,110,060,583,713,869,800 | Creates a dataframe from mesh polygons.
Parameters
----------
Returns
------
gpd.GeoDataFrame
Dataframe containing polygons of the mesh.
See Also
--------
__call__()
Dataframe with multiple polygon and boundary ID entries
of the mesh polygons.
multipolygon()
`shapely` multipolygon shape of combined m... | ocsmesh/mesh/mesh.py | implode | noaa-ocs-modeling/OCSMesh | python | def implode(self) -> gpd.GeoDataFrame:
'Creates a dataframe from mesh polygons.\n\n Parameters\n ----------\n\n Returns\n ------\n gpd.GeoDataFrame\n Dataframe containing polygons of the mesh.\n\n See Also\n --------\n __call__()\n Datafr... |
def multipolygon(self) -> MultiPolygon:
'Returns mesh multi-polygons.\n\n Parameters\n ----------\n\n Returns\n ------\n MultiPolygon\n Combined shape of polygons of the mesh.\n\n See Also\n --------\n __call__()\n Dataframe with multiple... | -4,280,634,786,239,775,000 | Returns mesh multi-polygons.
Parameters
----------
Returns
------
MultiPolygon
Combined shape of polygons of the mesh.
See Also
--------
__call__()
Dataframe with multiple polygon and boundary ID entries
of the mesh polygons.
implode()
Dataframe with a single combined multipolygon of the mesh
pol... | ocsmesh/mesh/mesh.py | multipolygon | noaa-ocs-modeling/OCSMesh | python | def multipolygon(self) -> MultiPolygon:
'Returns mesh multi-polygons.\n\n Parameters\n ----------\n\n Returns\n ------\n MultiPolygon\n Combined shape of polygons of the mesh.\n\n See Also\n --------\n __call__()\n Dataframe with multiple... |
def triangulation(self) -> Triangulation:
'Create triangulation object from all the mesh elements.\n\n Parameters\n ----------\n\n Returns\n -------\n Triangulation\n The `matplotlib` triangulation object create from all\n the elements of the parent mesh.\n\n... | -3,367,212,795,462,451,700 | Create triangulation object from all the mesh elements.
Parameters
----------
Returns
-------
Triangulation
The `matplotlib` triangulation object create from all
the elements of the parent mesh.
Notes
-----
Currently only tria3 and quad4 elements are considered. | ocsmesh/mesh/mesh.py | triangulation | noaa-ocs-modeling/OCSMesh | python | def triangulation(self) -> Triangulation:
'Create triangulation object from all the mesh elements.\n\n Parameters\n ----------\n\n Returns\n -------\n Triangulation\n The `matplotlib` triangulation object create from all\n the elements of the parent mesh.\n\n... |
def __init__(self, mesh: EuclideanMesh) -> None:
'Initializes node handler helper object.\n\n Parameters\n ----------\n mesh : EuclideanMesh\n Input mesh for which this object handles nodes info.\n '
self.mesh = mesh
self._id_to_index = None
self._index_to_id = Non... | 7,583,967,430,143,226,000 | Initializes node handler helper object.
Parameters
----------
mesh : EuclideanMesh
Input mesh for which this object handles nodes info. | ocsmesh/mesh/mesh.py | __init__ | noaa-ocs-modeling/OCSMesh | python | def __init__(self, mesh: EuclideanMesh) -> None:
'Initializes node handler helper object.\n\n Parameters\n ----------\n mesh : EuclideanMesh\n Input mesh for which this object handles nodes info.\n '
self.mesh = mesh
self._id_to_index = None
self._index_to_id = Non... |
@lru_cache(maxsize=1)
def __call__(self) -> Dict[(int, int)]:
"Creates a mapping between node IDs and indexes.\n\n Parameters\n ----------\n\n Returns\n -------\n dict\n Mapping between node IDs and indexes.\n\n Notes\n -----\n The result of this me... | 971,225,677,000,335,900 | Creates a mapping between node IDs and indexes.
Parameters
----------
Returns
-------
dict
Mapping between node IDs and indexes.
Notes
-----
The result of this method is cached, so that multiple calls
to it won't result in multiple calculations. If the mesh
is modified and the cache is not properly clear the cal... | ocsmesh/mesh/mesh.py | __call__ | noaa-ocs-modeling/OCSMesh | python | @lru_cache(maxsize=1)
def __call__(self) -> Dict[(int, int)]:
"Creates a mapping between node IDs and indexes.\n\n Parameters\n ----------\n\n Returns\n -------\n dict\n Mapping between node IDs and indexes.\n\n Notes\n -----\n The result of this me... |
def id(self) -> List[int]:
'Retrives a list of element IDs.\n\n Parameters\n ----------\n\n Returns\n -------\n list of int\n List of node IDs as created by `__call__`\n '
return list(self().keys()) | -5,521,052,617,407,928,000 | Retrives a list of element IDs.
Parameters
----------
Returns
-------
list of int
List of node IDs as created by `__call__` | ocsmesh/mesh/mesh.py | id | noaa-ocs-modeling/OCSMesh | python | def id(self) -> List[int]:
'Retrives a list of element IDs.\n\n Parameters\n ----------\n\n Returns\n -------\n list of int\n List of node IDs as created by `__call__`\n '
return list(self().keys()) |
def index(self) -> npt.NDArray[int]:
'Retrives an array of element indexes.\n\n Parameters\n ----------\n\n Returns\n -------\n array-like\n Array of node indexes.\n '
return np.arange(len(self())) | 6,618,088,399,711,052,000 | Retrives an array of element indexes.
Parameters
----------
Returns
-------
array-like
Array of node indexes. | ocsmesh/mesh/mesh.py | index | noaa-ocs-modeling/OCSMesh | python | def index(self) -> npt.NDArray[int]:
'Retrives an array of element indexes.\n\n Parameters\n ----------\n\n Returns\n -------\n array-like\n Array of node indexes.\n '
return np.arange(len(self())) |
def coords(self) -> npt.NDArray[np.float32]:
'Retrieve the coordinates of mesh nodes\n\n Parameters\n ----------\n\n Returns\n -------\n array-like\n Coordinates of the mesh nodes as returned by `BaseMesh.coord`\n '
return self.mesh.coord | -5,574,595,264,128,667,000 | Retrieve the coordinates of mesh nodes
Parameters
----------
Returns
-------
array-like
Coordinates of the mesh nodes as returned by `BaseMesh.coord` | ocsmesh/mesh/mesh.py | coords | noaa-ocs-modeling/OCSMesh | python | def coords(self) -> npt.NDArray[np.float32]:
'Retrieve the coordinates of mesh nodes\n\n Parameters\n ----------\n\n Returns\n -------\n array-like\n Coordinates of the mesh nodes as returned by `BaseMesh.coord`\n '
return self.mesh.coord |
def values(self):
'Retrieve the values stored for mesh nodes\n\n Parameters\n ----------\n\n Returns\n -------\n array-like\n Values on the mesh nodes as returned by `BaseMesh.values`\n '
return self.mesh.values | 4,970,250,622,496,955,000 | Retrieve the values stored for mesh nodes
Parameters
----------
Returns
-------
array-like
Values on the mesh nodes as returned by `BaseMesh.values` | ocsmesh/mesh/mesh.py | values | noaa-ocs-modeling/OCSMesh | python | def values(self):
'Retrieve the values stored for mesh nodes\n\n Parameters\n ----------\n\n Returns\n -------\n array-like\n Values on the mesh nodes as returned by `BaseMesh.values`\n '
return self.mesh.values |
def get_index_by_id(self, node_id):
'Converts mesh ID to mesh index.\n\n Parameters\n ----------\n node_id : int\n ID of the node of interest\n\n Returns\n -------\n int\n Index of the node of interest\n '
return self.id_to_index[node_id] | -1,355,441,475,551,445,200 | Converts mesh ID to mesh index.
Parameters
----------
node_id : int
ID of the node of interest
Returns
-------
int
Index of the node of interest | ocsmesh/mesh/mesh.py | get_index_by_id | noaa-ocs-modeling/OCSMesh | python | def get_index_by_id(self, node_id):
'Converts mesh ID to mesh index.\n\n Parameters\n ----------\n node_id : int\n ID of the node of interest\n\n Returns\n -------\n int\n Index of the node of interest\n '
return self.id_to_index[node_id] |
def get_id_by_index(self, index: int):
'Converts mesh index to mesh ID.\n\n Parameters\n ----------\n index : int\n Index of the node of interest.\n\n Returns\n -------\n int\n ID of the node of interest\n '
return self.index_to_id[index] | 8,713,344,903,840,962,000 | Converts mesh index to mesh ID.
Parameters
----------
index : int
Index of the node of interest.
Returns
-------
int
ID of the node of interest | ocsmesh/mesh/mesh.py | get_id_by_index | noaa-ocs-modeling/OCSMesh | python | def get_id_by_index(self, index: int):
'Converts mesh index to mesh ID.\n\n Parameters\n ----------\n index : int\n Index of the node of interest.\n\n Returns\n -------\n int\n ID of the node of interest\n '
return self.index_to_id[index] |
@property
def id_to_index(self) -> Dict[(int, int)]:
'Read-only property returning the mapping of ID to index\n\n Notes\n -----\n Although the property is read-only, the return value object\n is a cached mutable dictionary object. Modifying the mesh\n without clearing the cache pr... | -5,844,648,040,583,783,000 | Read-only property returning the mapping of ID to index
Notes
-----
Although the property is read-only, the return value object
is a cached mutable dictionary object. Modifying the mesh
without clearing the cache properly or mutating the
returned object could result in undefined behavior | ocsmesh/mesh/mesh.py | id_to_index | noaa-ocs-modeling/OCSMesh | python | @property
def id_to_index(self) -> Dict[(int, int)]:
'Read-only property returning the mapping of ID to index\n\n Notes\n -----\n Although the property is read-only, the return value object\n is a cached mutable dictionary object. Modifying the mesh\n without clearing the cache pr... |
@property
def index_to_id(self) -> Dict[(int, int)]:
'Read-only property returning the mapping of index to ID\n\n Notes\n -----\n Although the property is read-only, the return value object\n is a cached mutable dictionary object. Modifying the mesh\n without clearing the cache pr... | 8,368,307,017,401,396,000 | Read-only property returning the mapping of index to ID
Notes
-----
Although the property is read-only, the return value object
is a cached mutable dictionary object. Modifying the mesh
without clearing the cache properly or mutating the
returned object could result in undefined behavior | ocsmesh/mesh/mesh.py | index_to_id | noaa-ocs-modeling/OCSMesh | python | @property
def index_to_id(self) -> Dict[(int, int)]:
'Read-only property returning the mapping of index to ID\n\n Notes\n -----\n Although the property is read-only, the return value object\n is a cached mutable dictionary object. Modifying the mesh\n without clearing the cache pr... |
def __init__(self, mesh: EuclideanMesh) -> None:
'Initialize the element handler helper object.\n\n Parameters\n ----------\n mesh : EuclideanMesh\n Input mesh for which this object handles elements info.\n '
self.mesh = mesh | 3,369,410,356,158,437,400 | Initialize the element handler helper object.
Parameters
----------
mesh : EuclideanMesh
Input mesh for which this object handles elements info. | ocsmesh/mesh/mesh.py | __init__ | noaa-ocs-modeling/OCSMesh | python | def __init__(self, mesh: EuclideanMesh) -> None:
'Initialize the element handler helper object.\n\n Parameters\n ----------\n mesh : EuclideanMesh\n Input mesh for which this object handles elements info.\n '
self.mesh = mesh |
@lru_cache(maxsize=1)
def __call__(self) -> Dict[(int, npt.NDArray[int])]:
"Creates a mapping between element IDs and associated node IDs.\n\n Parameters\n ----------\n\n Returns\n -------\n dict\n Mapping between element IDs and associated node Ids\n\n Notes\n ... | 5,356,765,634,915,342,000 | Creates a mapping between element IDs and associated node IDs.
Parameters
----------
Returns
-------
dict
Mapping between element IDs and associated node Ids
Notes
-----
The result of this method is cached, so that multiple calls
to it won't result in multiple calculations. If the mesh
is modified and the cache ... | ocsmesh/mesh/mesh.py | __call__ | noaa-ocs-modeling/OCSMesh | python | @lru_cache(maxsize=1)
def __call__(self) -> Dict[(int, npt.NDArray[int])]:
"Creates a mapping between element IDs and associated node IDs.\n\n Parameters\n ----------\n\n Returns\n -------\n dict\n Mapping between element IDs and associated node Ids\n\n Notes\n ... |
@lru_cache(maxsize=1)
def id(self) -> List[int]:
"Retrieves the list of element IDs as returned by `__call__`\n\n Parameters\n ----------\n\n Returns\n -------\n list of int\n List of element IDs.\n\n Notes\n -----\n The result of this method is cac... | 1,294,986,368,255,677,400 | Retrieves the list of element IDs as returned by `__call__`
Parameters
----------
Returns
-------
list of int
List of element IDs.
Notes
-----
The result of this method is cached, so that multiple calls
to it won't result in multiple calculations. If the mesh
is modified and the cache is not properly clear the c... | ocsmesh/mesh/mesh.py | id | noaa-ocs-modeling/OCSMesh | python | @lru_cache(maxsize=1)
def id(self) -> List[int]:
"Retrieves the list of element IDs as returned by `__call__`\n\n Parameters\n ----------\n\n Returns\n -------\n list of int\n List of element IDs.\n\n Notes\n -----\n The result of this method is cac... |
@lru_cache(maxsize=1)
def index(self) -> npt.NDArray[int]:
"Retrieves an array of element indices\n\n Parameters\n ----------\n\n Returns\n -------\n npt.NDArray\n 1D array of element indices.\n\n Notes\n -----\n The result of this method is cached,... | 6,401,692,417,557,761,000 | Retrieves an array of element indices
Parameters
----------
Returns
-------
npt.NDArray
1D array of element indices.
Notes
-----
The result of this method is cached, so that multiple calls
to it won't result in multiple calculations. If the mesh
is modified and the cache is not properly clear the calls
to this m... | ocsmesh/mesh/mesh.py | index | noaa-ocs-modeling/OCSMesh | python | @lru_cache(maxsize=1)
def index(self) -> npt.NDArray[int]:
"Retrieves an array of element indices\n\n Parameters\n ----------\n\n Returns\n -------\n npt.NDArray\n 1D array of element indices.\n\n Notes\n -----\n The result of this method is cached,... |
def array(self) -> npt.NDArray[int]:
"Retrieves a masked array of element node IDs.\n\n The return value is ``n x m`` where ``n`` is the number of\n elements and ``m`` is the maximum number of element nodes, e.g.\n if there are only trias, then it's 3, for trias and quads it\n is 4.\n\n ... | 7,271,535,590,505,444,000 | Retrieves a masked array of element node IDs.
The return value is ``n x m`` where ``n`` is the number of
elements and ``m`` is the maximum number of element nodes, e.g.
if there are only trias, then it's 3, for trias and quads it
is 4.
Parameters
----------
Returns
-------
npt.NDArray
Masked array where elements... | ocsmesh/mesh/mesh.py | array | noaa-ocs-modeling/OCSMesh | python | def array(self) -> npt.NDArray[int]:
"Retrieves a masked array of element node IDs.\n\n The return value is ``n x m`` where ``n`` is the number of\n elements and ``m`` is the maximum number of element nodes, e.g.\n if there are only trias, then it's 3, for trias and quads it\n is 4.\n\n ... |
@lru_cache(maxsize=1)
def triangles(self) -> npt.NDArray[int]:
"Retrieves an array of tria element node indices\n\n Parameters\n ----------\n\n Returns\n -------\n npt.NDArray\n 2D array of element nodes for triangle nodes\n\n Notes\n -----\n The re... | -5,246,321,069,616,125,000 | Retrieves an array of tria element node indices
Parameters
----------
Returns
-------
npt.NDArray
2D array of element nodes for triangle nodes
Notes
-----
The result of this method is cached, so that multiple calls
to it won't result in multiple calculations. If the mesh
is modified and the cache is not properly... | ocsmesh/mesh/mesh.py | triangles | noaa-ocs-modeling/OCSMesh | python | @lru_cache(maxsize=1)
def triangles(self) -> npt.NDArray[int]:
"Retrieves an array of tria element node indices\n\n Parameters\n ----------\n\n Returns\n -------\n npt.NDArray\n 2D array of element nodes for triangle nodes\n\n Notes\n -----\n The re... |
@lru_cache(maxsize=1)
def quads(self):
"Retrieves an array of quad element node indices\n\n Parameters\n ----------\n\n Returns\n -------\n npt.NDArray\n 2D array of element nodes for quadrangle nodes\n\n Notes\n -----\n The result of this method is... | -7,106,942,006,327,528,000 | Retrieves an array of quad element node indices
Parameters
----------
Returns
-------
npt.NDArray
2D array of element nodes for quadrangle nodes
Notes
-----
The result of this method is cached, so that multiple calls
to it won't result in multiple calculations. If the mesh
is modified and the cache is not proper... | ocsmesh/mesh/mesh.py | quads | noaa-ocs-modeling/OCSMesh | python | @lru_cache(maxsize=1)
def quads(self):
"Retrieves an array of quad element node indices\n\n Parameters\n ----------\n\n Returns\n -------\n npt.NDArray\n 2D array of element nodes for quadrangle nodes\n\n Notes\n -----\n The result of this method is... |
def triangulation(self) -> Triangulation:
'Create triangulation object from all the mesh elements.\n\n Parameters\n ----------\n\n Returns\n -------\n Triangulation\n The `matplotlib` triangulation object create from all\n the elements of the parent mesh.\n\n... | -1,068,129,071,886,108,900 | Create triangulation object from all the mesh elements.
Parameters
----------
Returns
-------
Triangulation
The `matplotlib` triangulation object create from all
the elements of the parent mesh.
Notes
-----
Currently only tria3 and quad4 elements are considered. | ocsmesh/mesh/mesh.py | triangulation | noaa-ocs-modeling/OCSMesh | python | def triangulation(self) -> Triangulation:
'Create triangulation object from all the mesh elements.\n\n Parameters\n ----------\n\n Returns\n -------\n Triangulation\n The `matplotlib` triangulation object create from all\n the elements of the parent mesh.\n\n... |
def geodataframe(self) -> gpd.GeoDataFrame:
'Create polygons for each element and return in dataframe\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe created from entries of `Polygon` type for\n each element.\n '
da... | -1,141,472,162,654,111,200 | Create polygons for each element and return in dataframe
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Dataframe created from entries of `Polygon` type for
each element. | ocsmesh/mesh/mesh.py | geodataframe | noaa-ocs-modeling/OCSMesh | python | def geodataframe(self) -> gpd.GeoDataFrame:
'Create polygons for each element and return in dataframe\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe created from entries of `Polygon` type for\n each element.\n '
da... |
def __init__(self, mesh: EuclideanMesh) -> None:
'Initialize boundary helper object\n\n Parameters\n ----------\n mesh : EuclideanMesh\n Input mesh for which this object calculates boundaries.\n '
self.mesh = mesh
self._ocean = gpd.GeoDataFrame()
self._land = gpd.G... | 6,331,604,948,413,647,000 | Initialize boundary helper object
Parameters
----------
mesh : EuclideanMesh
Input mesh for which this object calculates boundaries. | ocsmesh/mesh/mesh.py | __init__ | noaa-ocs-modeling/OCSMesh | python | def __init__(self, mesh: EuclideanMesh) -> None:
'Initialize boundary helper object\n\n Parameters\n ----------\n mesh : EuclideanMesh\n Input mesh for which this object calculates boundaries.\n '
self.mesh = mesh
self._ocean = gpd.GeoDataFrame()
self._land = gpd.G... |
@lru_cache(maxsize=1)
def _init_dataframes(self) -> None:
"Internal: Creates boundary dataframes based on boundary data\n\n Parameters\n ----------\n\n Returns\n -------\n None\n\n Notes\n -----\n This method doesn't have any return value, but it is cached\n ... | -1,658,431,923,495,654,700 | Internal: Creates boundary dataframes based on boundary data
Parameters
----------
Returns
-------
None
Notes
-----
This method doesn't have any return value, but it is cached
so that on re-execution it doesn't recalculate. | ocsmesh/mesh/mesh.py | _init_dataframes | noaa-ocs-modeling/OCSMesh | python | @lru_cache(maxsize=1)
def _init_dataframes(self) -> None:
"Internal: Creates boundary dataframes based on boundary data\n\n Parameters\n ----------\n\n Returns\n -------\n None\n\n Notes\n -----\n This method doesn't have any return value, but it is cached\n ... |
def ocean(self) -> gpd.GeoDataFrame:
'Retrieve the ocean boundary information dataframe\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing the geometry and information of\n ocean open boundary.\n '
self._ini... | -2,385,565,405,689,421,000 | Retrieve the ocean boundary information dataframe
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Dataframe containing the geometry and information of
ocean open boundary. | ocsmesh/mesh/mesh.py | ocean | noaa-ocs-modeling/OCSMesh | python | def ocean(self) -> gpd.GeoDataFrame:
'Retrieve the ocean boundary information dataframe\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing the geometry and information of\n ocean open boundary.\n '
self._ini... |
def land(self):
'Retrieve the land boundary information dataframe\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing the geometry and information of\n land boundary.\n '
self._init_dataframes()
return se... | 3,081,945,119,974,210,600 | Retrieve the land boundary information dataframe
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Dataframe containing the geometry and information of
land boundary. | ocsmesh/mesh/mesh.py | land | noaa-ocs-modeling/OCSMesh | python | def land(self):
'Retrieve the land boundary information dataframe\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing the geometry and information of\n land boundary.\n '
self._init_dataframes()
return se... |
def interior(self):
'Retrieve the island boundary information dataframe\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing the geometry and information of\n island boundary.\n '
self._init_dataframes()
r... | 2,270,183,772,908,834,600 | Retrieve the island boundary information dataframe
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Dataframe containing the geometry and information of
island boundary. | ocsmesh/mesh/mesh.py | interior | noaa-ocs-modeling/OCSMesh | python | def interior(self):
'Retrieve the island boundary information dataframe\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing the geometry and information of\n island boundary.\n '
self._init_dataframes()
r... |
@property
def data(self) -> Dict[(Optional[int], Any)]:
'Read-only property referencing the boundary data dictionary'
return self._data | -235,149,111,437,052,400 | Read-only property referencing the boundary data dictionary | ocsmesh/mesh/mesh.py | data | noaa-ocs-modeling/OCSMesh | python | @property
def data(self) -> Dict[(Optional[int], Any)]:
return self._data |
@lru_cache(maxsize=1)
def __call__(self) -> gpd.GeoDataFrame:
"Retrieve the dataframe for all boundaries information\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing information for all boundaries shape\n and type.\n... | 6,958,329,802,786,839,000 | Retrieve the dataframe for all boundaries information
Parameters
----------
Returns
-------
gpd.GeoDataFrame
Dataframe containing information for all boundaries shape
and type.
Notes
-----
The result of this method is cached, so that multiple calls
to it won't result in multiple calculations. If the mesh
is ... | ocsmesh/mesh/mesh.py | __call__ | noaa-ocs-modeling/OCSMesh | python | @lru_cache(maxsize=1)
def __call__(self) -> gpd.GeoDataFrame:
"Retrieve the dataframe for all boundaries information\n\n Parameters\n ----------\n\n Returns\n -------\n gpd.GeoDataFrame\n Dataframe containing information for all boundaries shape\n and type.\n... |
def __len__(self) -> int:
'Returns the number of boundary segments'
return len(self()) | -2,137,916,475,226,730,500 | Returns the number of boundary segments | ocsmesh/mesh/mesh.py | __len__ | noaa-ocs-modeling/OCSMesh | python | def __len__(self) -> int:
return len(self()) |
def auto_generate(self, threshold: float=0.0, land_ibtype: int=0, interior_ibtype: int=1):
'Automatically detect boundaries based on elevation data.\n\n Parameters\n ----------\n threshold : float, default=0\n Threshold above which nodes are considered dry nodes\n for ocea... | 3,523,008,198,753,785,300 | Automatically detect boundaries based on elevation data.
Parameters
----------
threshold : float, default=0
Threshold above which nodes are considered dry nodes
for ocean vs land boundary detection
land_ibtype : int, default=0
Value to assign to land boundary type
interior_ibtype : int, default=1
Value... | ocsmesh/mesh/mesh.py | auto_generate | noaa-ocs-modeling/OCSMesh | python | def auto_generate(self, threshold: float=0.0, land_ibtype: int=0, interior_ibtype: int=1):
'Automatically detect boundaries based on elevation data.\n\n Parameters\n ----------\n threshold : float, default=0\n Threshold above which nodes are considered dry nodes\n for ocea... |
def test_BasicDatasetProfiler_null_column():
'\n The profiler should determine that null columns are of null cardinality and of null type and\n not to generate expectations specific to types and cardinality categories.\n\n We verify this by running the basic profiler on a Pandas dataset with an empty colum... | -1,926,499,559,757,927,400 | The profiler should determine that null columns are of null cardinality and of null type and
not to generate expectations specific to types and cardinality categories.
We verify this by running the basic profiler on a Pandas dataset with an empty column
and asserting the number of successful results for the empty colu... | tests/profile/test_profile.py | test_BasicDatasetProfiler_null_column | AdamHepner/great_expectations | python | def test_BasicDatasetProfiler_null_column():
'\n The profiler should determine that null columns are of null cardinality and of null type and\n not to generate expectations specific to types and cardinality categories.\n\n We verify this by running the basic profiler on a Pandas dataset with an empty colum... |
def test_BasicDatasetProfiler_partially_null_column(dataset):
'\n Unit test to check the expectations that BasicDatasetProfiler creates for a partially null column.\n The test is executed against all the backends (Pandas, Spark, etc.), because it uses\n the fixture.\n\n "nulls" is the partially null col... | 1,171,748,317,385,277,400 | Unit test to check the expectations that BasicDatasetProfiler creates for a partially null column.
The test is executed against all the backends (Pandas, Spark, etc.), because it uses
the fixture.
"nulls" is the partially null column in the fixture dataset | tests/profile/test_profile.py | test_BasicDatasetProfiler_partially_null_column | AdamHepner/great_expectations | python | def test_BasicDatasetProfiler_partially_null_column(dataset):
'\n Unit test to check the expectations that BasicDatasetProfiler creates for a partially null column.\n The test is executed against all the backends (Pandas, Spark, etc.), because it uses\n the fixture.\n\n "nulls" is the partially null col... |
def test_BasicDatasetProfiler_non_numeric_low_cardinality(non_numeric_low_card_dataset):
'\n Unit test to check the expectations that BasicDatasetProfiler creates for a low cardinality\n non numeric column.\n The test is executed against all the backends (Pandas, Spark, etc.), because it uses\n the fixt... | 8,949,136,718,048,000,000 | Unit test to check the expectations that BasicDatasetProfiler creates for a low cardinality
non numeric column.
The test is executed against all the backends (Pandas, Spark, etc.), because it uses
the fixture. | tests/profile/test_profile.py | test_BasicDatasetProfiler_non_numeric_low_cardinality | AdamHepner/great_expectations | python | def test_BasicDatasetProfiler_non_numeric_low_cardinality(non_numeric_low_card_dataset):
'\n Unit test to check the expectations that BasicDatasetProfiler creates for a low cardinality\n non numeric column.\n The test is executed against all the backends (Pandas, Spark, etc.), because it uses\n the fixt... |
def test_BasicDatasetProfiler_non_numeric_high_cardinality(non_numeric_high_card_dataset):
'\n Unit test to check the expectations that BasicDatasetProfiler creates for a high cardinality\n non numeric column.\n The test is executed against all the backends (Pandas, Spark, etc.), because it uses\n the f... | -8,345,396,114,734,184,000 | Unit test to check the expectations that BasicDatasetProfiler creates for a high cardinality
non numeric column.
The test is executed against all the backends (Pandas, Spark, etc.), because it uses
the fixture. | tests/profile/test_profile.py | test_BasicDatasetProfiler_non_numeric_high_cardinality | AdamHepner/great_expectations | python | def test_BasicDatasetProfiler_non_numeric_high_cardinality(non_numeric_high_card_dataset):
'\n Unit test to check the expectations that BasicDatasetProfiler creates for a high cardinality\n non numeric column.\n The test is executed against all the backends (Pandas, Spark, etc.), because it uses\n the f... |
def test_BasicDatasetProfiler_numeric_high_cardinality(numeric_high_card_dataset):
'\n Unit test to check the expectations that BasicDatasetProfiler creates for a high cardinality\n numeric column.\n The test is executed against all the backends (Pandas, Spark, etc.), because it uses\n the fixture.\n ... | 4,874,539,107,115,850,000 | Unit test to check the expectations that BasicDatasetProfiler creates for a high cardinality
numeric column.
The test is executed against all the backends (Pandas, Spark, etc.), because it uses
the fixture. | tests/profile/test_profile.py | test_BasicDatasetProfiler_numeric_high_cardinality | AdamHepner/great_expectations | python | def test_BasicDatasetProfiler_numeric_high_cardinality(numeric_high_card_dataset):
'\n Unit test to check the expectations that BasicDatasetProfiler creates for a high cardinality\n numeric column.\n The test is executed against all the backends (Pandas, Spark, etc.), because it uses\n the fixture.\n ... |
def test_context_profiler(empty_data_context, filesystem_csv_2):
"This just validates that it's possible to profile using the datasource hook, and have\n validation results available in the DataContext"
empty_data_context.add_datasource('my_datasource', module_name='great_expectations.datasource', class_name... | -18,379,510,840,733,516 | This just validates that it's possible to profile using the datasource hook, and have
validation results available in the DataContext | tests/profile/test_profile.py | test_context_profiler | AdamHepner/great_expectations | python | def test_context_profiler(empty_data_context, filesystem_csv_2):
"This just validates that it's possible to profile using the datasource hook, and have\n validation results available in the DataContext"
empty_data_context.add_datasource('my_datasource', module_name='great_expectations.datasource', class_name... |
def test_BasicDatasetProfiler_on_titanic():
'\n A snapshot test for BasicDatasetProfiler.\n We are running the profiler on the Titanic dataset\n and comparing the EVRs to ones retrieved from a\n previously stored file.\n '
df = ge.read_csv('./tests/test_sets/Titanic.csv')
(suite, evrs) = df.p... | 8,954,095,020,677,000,000 | A snapshot test for BasicDatasetProfiler.
We are running the profiler on the Titanic dataset
and comparing the EVRs to ones retrieved from a
previously stored file. | tests/profile/test_profile.py | test_BasicDatasetProfiler_on_titanic | AdamHepner/great_expectations | python | def test_BasicDatasetProfiler_on_titanic():
'\n A snapshot test for BasicDatasetProfiler.\n We are running the profiler on the Titanic dataset\n and comparing the EVRs to ones retrieved from a\n previously stored file.\n '
df = ge.read_csv('./tests/test_sets/Titanic.csv')
(suite, evrs) = df.p... |
def concat_data(labelsfile, notes_file):
'\n INPUTS:\n labelsfile: sorted by hadm id, contains one label per line\n notes_file: sorted by hadm id, contains one note per line\n '
with open(labelsfile, 'r') as lf:
print('CONCATENATING')
with open(notes_file, 'r') as... | -3,154,365,080,102,165,500 | INPUTS:
labelsfile: sorted by hadm id, contains one label per line
notes_file: sorted by hadm id, contains one note per line | dataproc/concat_and_split.py | concat_data | franzbischoff/caml-mimic | python | def concat_data(labelsfile, notes_file):
'\n INPUTS:\n labelsfile: sorted by hadm id, contains one label per line\n notes_file: sorted by hadm id, contains one note per line\n '
with open(labelsfile, 'r') as lf:
print('CONCATENATING')
with open(notes_file, 'r') as... |
def next_labels(labelsfile):
'\n Generator for label sets from the label file\n '
labels_reader = csv.reader(labelsfile)
next(labels_reader)
first_label_line = next(labels_reader)
cur_subj = int(first_label_line[0])
cur_hadm = int(first_label_line[1])
cur_labels = [first_label_line... | 7,989,241,263,583,836,000 | Generator for label sets from the label file | dataproc/concat_and_split.py | next_labels | franzbischoff/caml-mimic | python | def next_labels(labelsfile):
'\n \n '
labels_reader = csv.reader(labelsfile)
next(labels_reader)
first_label_line = next(labels_reader)
cur_subj = int(first_label_line[0])
cur_hadm = int(first_label_line[1])
cur_labels = [first_label_line[2]]
for row in labels_reader:
s... |
def next_notes(notesfile):
'\n Generator for notes from the notes file\n This will also concatenate discharge summaries and their addenda, which have the same subject and hadm id\n '
nr = csv.reader(notesfile)
next(nr)
first_note = next(nr)
cur_subj = int(first_note[0])
cur_hadm... | 8,890,814,948,314,462,000 | Generator for notes from the notes file
This will also concatenate discharge summaries and their addenda, which have the same subject and hadm id | dataproc/concat_and_split.py | next_notes | franzbischoff/caml-mimic | python | def next_notes(notesfile):
'\n Generator for notes from the notes file\n This will also concatenate discharge summaries and their addenda, which have the same subject and hadm id\n '
nr = csv.reader(notesfile)
next(nr)
first_note = next(nr)
cur_subj = int(first_note[0])
cur_hadm... |
def _prediction_loop(self, dataloader: DataLoader, description: str, task_name: str, mode: str, prediction_loss_only: Optional[bool]=None) -> PredictionOutput:
'\n Prediction/evaluation loop, shared by `evaluate()` and `predict()`.\n Works both with or without labels.\n '
prediction_loss_on... | 4,250,822,879,790,479,400 | Prediction/evaluation loop, shared by `evaluate()` and `predict()`.
Works both with or without labels. | src/mtl_trainer.py | _prediction_loop | Daupler/CA-MTL | python | def _prediction_loop(self, dataloader: DataLoader, description: str, task_name: str, mode: str, prediction_loss_only: Optional[bool]=None) -> PredictionOutput:
'\n Prediction/evaluation loop, shared by `evaluate()` and `predict()`.\n Works both with or without labels.\n '
prediction_loss_on... |
def get_health(self, **kwargs):
'Get the health of an instance anytime during execution. Allow us to check if the instance is still healthy. # noqa: E501\n\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread... | 4,016,601,636,423,578,000 | Get the health of an instance anytime during execution. Allow us to check if the instance is still healthy. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_health(async_req=True)
>>> result = thread.get()
:par... | influxdb_client/service/health_service.py | get_health | rhajek/influxdb-client-python | python | def get_health(self, **kwargs):
'Get the health of an instance anytime during execution. Allow us to check if the instance is still healthy. # noqa: E501\n\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread... |
def get_health_with_http_info(self, **kwargs):
'Get the health of an instance anytime during execution. Allow us to check if the instance is still healthy. # noqa: E501\n\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n ... | 4,269,785,489,427,170,000 | Get the health of an instance anytime during execution. Allow us to check if the instance is still healthy. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_health_with_http_info(async_req=True)
>>> result = thr... | influxdb_client/service/health_service.py | get_health_with_http_info | rhajek/influxdb-client-python | python | def get_health_with_http_info(self, **kwargs):
'Get the health of an instance anytime during execution. Allow us to check if the instance is still healthy. # noqa: E501\n\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n ... |
def group_policies_gen(flat_policies, config):
'Filter policies using the following steps:\n 1. Apply prioritization among the policies that are sharing the same policy type and resource type\n 2. Remove redundant policies that may applicable across different types of resource\n 3. Filter policies based on... | 5,871,645,693,051,403,000 | Filter policies using the following steps:
1. Apply prioritization among the policies that are sharing the same policy type and resource type
2. Remove redundant policies that may applicable across different types of resource
3. Filter policies based on type and return
:param flat_policies: list of flat policies
:retur... | osdf/adapters/policy/utils.py | group_policies_gen | onap/optf-osdf | python | def group_policies_gen(flat_policies, config):
'Filter policies using the following steps:\n 1. Apply prioritization among the policies that are sharing the same policy type and resource type\n 2. Remove redundant policies that may applicable across different types of resource\n 3. Filter policies based on... |
def policy_name_as_regex(policy_name):
'Get the correct policy name as a regex\n (e.g. OOF_HAS_vCPE.cloudAttributePolicy ends up in policy as OOF_HAS_vCPE.Config_MS_cloudAttributePolicy.1.xml\n So, for now, we query it as OOF_HAS_vCPE..*aicAttributePolicy.*)\n :param policy_name: Example: OOF_HAS_vCPE.aicA... | 3,389,865,022,879,833,600 | Get the correct policy name as a regex
(e.g. OOF_HAS_vCPE.cloudAttributePolicy ends up in policy as OOF_HAS_vCPE.Config_MS_cloudAttributePolicy.1.xml
So, for now, we query it as OOF_HAS_vCPE..*aicAttributePolicy.*)
:param policy_name: Example: OOF_HAS_vCPE.aicAttributePolicy
:return: regexp for policy: Example: OOF_HAS... | osdf/adapters/policy/utils.py | policy_name_as_regex | onap/optf-osdf | python | def policy_name_as_regex(policy_name):
'Get the correct policy name as a regex\n (e.g. OOF_HAS_vCPE.cloudAttributePolicy ends up in policy as OOF_HAS_vCPE.Config_MS_cloudAttributePolicy.1.xml\n So, for now, we query it as OOF_HAS_vCPE..*aicAttributePolicy.*)\n :param policy_name: Example: OOF_HAS_vCPE.aicA... |
def retrieve_node(req_json, reference):
'\n Get the child node(s) from the dot-notation [reference] and parent [req_json].\n For placement and other requests, there are encoded JSONs inside the request or policy,\n so we need to expand it and then do a search over the parent plus expanded JSON.\n '
... | 8,100,758,929,228,773,000 | Get the child node(s) from the dot-notation [reference] and parent [req_json].
For placement and other requests, there are encoded JSONs inside the request or policy,
so we need to expand it and then do a search over the parent plus expanded JSON. | osdf/adapters/policy/utils.py | retrieve_node | onap/optf-osdf | python | def retrieve_node(req_json, reference):
'\n Get the child node(s) from the dot-notation [reference] and parent [req_json].\n For placement and other requests, there are encoded JSONs inside the request or policy,\n so we need to expand it and then do a search over the parent plus expanded JSON.\n '
... |
def read_pkl(path_pkl):
'\n Get a WaterFrame from a pickle file.\n\n Parameters\n ----------\n path_pkl: str\n Location of the pickle file.\n\n Returns\n -------\n wf_pkl: WaterFrame\n '
wf_pkl = WaterFrame()
pickle_dataset = pickle.load(open(path_pkl, 'rb'))
w... | -1,526,109,206,076,102,700 | Get a WaterFrame from a pickle file.
Parameters
----------
path_pkl: str
Location of the pickle file.
Returns
-------
wf_pkl: WaterFrame | mooda/input/read_pkl.py | read_pkl | rbardaji/mooda | python | def read_pkl(path_pkl):
'\n Get a WaterFrame from a pickle file.\n\n Parameters\n ----------\n path_pkl: str\n Location of the pickle file.\n\n Returns\n -------\n wf_pkl: WaterFrame\n '
wf_pkl = WaterFrame()
pickle_dataset = pickle.load(open(path_pkl, 'rb'))
w... |
@property
def color(self):
"\n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n ... | -9,075,663,790,309,021,000 | The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
... | packages/python/plotly/plotly/graph_objs/scatter3d/_textfont.py | color | 1abner1/plotly.py | python | @property
def color(self):
"\n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n ... |
@property
def colorsrc(self):
"\n Sets the source reference on Chart Studio Cloud for color .\n \n The 'colorsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['colorsrc'] | 4,662,598,374,469,181,000 | Sets the source reference on Chart Studio Cloud for color .
The 'colorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | packages/python/plotly/plotly/graph_objs/scatter3d/_textfont.py | colorsrc | 1abner1/plotly.py | python | @property
def colorsrc(self):
"\n Sets the source reference on Chart Studio Cloud for color .\n \n The 'colorsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['colorsrc'] |
@property
def family(self):
'\n HTML font family - the typeface that will be applied by the web\n browser. The web browser will only be able to apply a font if\n it is available on the system which it operates. Provide\n multiple font families, separated by commas, to indicate the\n ... | 3,791,649,582,837,001,000 | HTML font family - the typeface that will be applied by the web
browser. The web browser will only be able to apply a font if
it is available on the system which it operates. Provide
multiple font families, separated by commas, to indicate the
preference in which to apply fonts if they aren't available on
the system. T... | packages/python/plotly/plotly/graph_objs/scatter3d/_textfont.py | family | 1abner1/plotly.py | python | @property
def family(self):
'\n HTML font family - the typeface that will be applied by the web\n browser. The web browser will only be able to apply a font if\n it is available on the system which it operates. Provide\n multiple font families, separated by commas, to indicate the\n ... |
@property
def size(self):
"\n The 'size' property is a number and may be specified as:\n - An int or float in the interval [1, inf]\n - A tuple, list, or one-dimensional numpy array of the above\n\n Returns\n -------\n int|float|numpy.ndarray\n "
return self[... | 6,887,128,696,685,480,000 | The 'size' property is a number and may be specified as:
- An int or float in the interval [1, inf]
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
int|float|numpy.ndarray | packages/python/plotly/plotly/graph_objs/scatter3d/_textfont.py | size | 1abner1/plotly.py | python | @property
def size(self):
"\n The 'size' property is a number and may be specified as:\n - An int or float in the interval [1, inf]\n - A tuple, list, or one-dimensional numpy array of the above\n\n Returns\n -------\n int|float|numpy.ndarray\n "
return self[... |
@property
def sizesrc(self):
"\n Sets the source reference on Chart Studio Cloud for size .\n \n The 'sizesrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['sizesrc'] | 4,336,256,729,131,089,000 | Sets the source reference on Chart Studio Cloud for size .
The 'sizesrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | packages/python/plotly/plotly/graph_objs/scatter3d/_textfont.py | sizesrc | 1abner1/plotly.py | python | @property
def sizesrc(self):
"\n Sets the source reference on Chart Studio Cloud for size .\n \n The 'sizesrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['sizesrc'] |
def __init__(self, arg=None, color=None, colorsrc=None, family=None, size=None, sizesrc=None, **kwargs):
'\n Construct a new Textfont object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of\n ... | 5,340,964,613,872,540,000 | Construct a new Textfont object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.scatter3d.Textfont`
color
colorsrc
Sets the source reference on Chart Studio Cloud for
color .
family
HTML font family - the typeface that w... | packages/python/plotly/plotly/graph_objs/scatter3d/_textfont.py | __init__ | 1abner1/plotly.py | python | def __init__(self, arg=None, color=None, colorsrc=None, family=None, size=None, sizesrc=None, **kwargs):
'\n Construct a new Textfont object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of\n ... |
def testConvertHeadersValues(self):
'Tests the _ConvertHeadersValues function.'
plugin = msie_webcache.MsieWebCacheESEDBPlugin()
binary_value = b'HTTP/1.1 200 OK\r\nContent-Type: image/png\r\nX-Content-Type-Options: nosniff\r\nContent-Length: 2759\r\nX-XSS-Protection: 1; mode=block\r\nAlternate-Protocol: 80... | 3,312,209,579,307,669,500 | Tests the _ConvertHeadersValues function. | tests/parsers/esedb_plugins/msie_webcache.py | testConvertHeadersValues | ColdSmoke627/plaso | python | def testConvertHeadersValues(self):
plugin = msie_webcache.MsieWebCacheESEDBPlugin()
binary_value = b'HTTP/1.1 200 OK\r\nContent-Type: image/png\r\nX-Content-Type-Options: nosniff\r\nContent-Length: 2759\r\nX-XSS-Protection: 1; mode=block\r\nAlternate-Protocol: 80:quic\r\n\r\n'
expected_headers_value =... |
def testProcessOnDatabaseWithPartitionsTable(self):
'Tests the Process function on database with a Partitions table.'
plugin = msie_webcache.MsieWebCacheESEDBPlugin()
storage_writer = self._ParseESEDBFileWithPlugin(['WebCacheV01.dat'], plugin)
self.assertEqual(storage_writer.number_of_events, 1372)
... | -4,337,249,863,847,990,300 | Tests the Process function on database with a Partitions table. | tests/parsers/esedb_plugins/msie_webcache.py | testProcessOnDatabaseWithPartitionsTable | ColdSmoke627/plaso | python | def testProcessOnDatabaseWithPartitionsTable(self):
plugin = msie_webcache.MsieWebCacheESEDBPlugin()
storage_writer = self._ParseESEDBFileWithPlugin(['WebCacheV01.dat'], plugin)
self.assertEqual(storage_writer.number_of_events, 1372)
self.assertEqual(storage_writer.number_of_extraction_warnings, 0)... |
def testProcessOnDatabaseWithPartitionsExTable(self):
'Tests the Process function on database with a PartitionsEx table.'
plugin = msie_webcache.MsieWebCacheESEDBPlugin()
storage_writer = self._ParseESEDBFileWithPlugin(['PartitionsEx-WebCacheV01.dat'], plugin)
self.assertEqual(storage_writer.number_of_e... | 6,452,848,726,871,535,000 | Tests the Process function on database with a PartitionsEx table. | tests/parsers/esedb_plugins/msie_webcache.py | testProcessOnDatabaseWithPartitionsExTable | ColdSmoke627/plaso | python | def testProcessOnDatabaseWithPartitionsExTable(self):
plugin = msie_webcache.MsieWebCacheESEDBPlugin()
storage_writer = self._ParseESEDBFileWithPlugin(['PartitionsEx-WebCacheV01.dat'], plugin)
self.assertEqual(storage_writer.number_of_events, 4200)
self.assertEqual(storage_writer.number_of_extracti... |
def m_step_gaussian_mixture(data, gamma):
'% Performs the M-step of the EM algorithm for gaussain mixture model.\n %\n % @param data : n x d matrix with rows as d dimensional data points\n % @param gamma : n x k matrix of resposibilities\n %\n % @return pi : k x 1 array\n % @return mu : k... | 333,318,272,719,222,850 | % Performs the M-step of the EM algorithm for gaussain mixture model.
%
% @param data : n x d matrix with rows as d dimensional data points
% @param gamma : n x k matrix of resposibilities
%
% @return pi : k x 1 array
% @return mu : k x d matrix of maximized cluster centers
% @return sigma : cell array of maxi... | src/ML_Algorithms/ExpectationMaximization/m_step_gaussian_mixture.py | m_step_gaussian_mixture | leonardbj/AIMS | python | def m_step_gaussian_mixture(data, gamma):
'% Performs the M-step of the EM algorithm for gaussain mixture model.\n %\n % @param data : n x d matrix with rows as d dimensional data points\n % @param gamma : n x k matrix of resposibilities\n %\n % @return pi : k x 1 array\n % @return mu : k... |
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