avni.tools.xarray module#
- avni.tools.xarray.xarray_to_epix(data: Union[xarray.core.dataarray.DataArray, xarray.core.dataset.Dataset], latname: str = 'latitude', lonname: str = 'longitude') numpy.ndarray[source]#
Convert multi-dimensional pixel grid in xarray to extended pixel (.epix) format
- Parameters
- datatp.Union[xr.DataArray,xr.Dataset]
Multi-dimensional pixel grid in xarray formats
- latnamestr, optional
Name to use for latitude column in named numpy array, by default ‘latitude’
- lonnamestr, optional
Name to use for longitude column in named numpy array, by default ‘longitude’
- Returns
- np.ndarray
Array containing (latitude, longitude, pixel_size, value)
- Authors
Raj Moulik (moulik@caa.columbia.edu)
- Last Modified
2023.02.16 5.00
- avni.tools.xarray.epix_to_xarray(epixarr: numpy.ndarray, latname: str = 'latitude', lonname: str = 'longitude') xarray.core.dataarray.DataArray[source]#
Convert extended pixel (.epix) to multi-dimensional pixel grid in xarray format
- Parameters
- epixarrnp.ndarray
Array containing (latitude, longitude, pixel_size, value)
- latnamestr, optional
Name to use for latitude column in named numpy array, by default ‘latitude’
- lonnamestr, optional
Name to use for longitude column in named numpy array, by default ‘longitude’
- Returns
- xr.DataArray
Multi-dimensional pixel grid in xarray formats
- Authors
Raj Moulik (moulik@caa.columbia.edu)
- Last Modified
2023.02.16 5.00
- avni.tools.xarray.tree3D(treefile: str, latitude: Union[None, list, tuple, numpy.ndarray] = None, longitude: Union[None, list, tuple, numpy.ndarray] = None, radius_in_km: Union[None, list, tuple, numpy.ndarray] = None)[source]#
Build a KD-tree at specific locations
- Parameters
- treefilestr
Name of the file where tree is (or will be) stored
- latitudetp.Union[list,tuple,np.ndarray], optional
Latitudes of locations queried, by default None
- longitudetp.Union[list,tuple,np.ndarray], optional
Longitudes of locations queried, by default None
- radius_in_kmtp.Union[list,tuple,np.ndarray], optional
Radii of locations queried, by default None
- Returns
- tree
A
scipy.spatial.cKDTree()
- Authors
Raj Moulik (moulik@caa.columbia.edu)
- Last Modified
2023.02.16 5.00
- avni.tools.xarray.querytree3D(tree, latitude: Union[list, tuple, numpy.ndarray], longitude: Union[list, tuple, numpy.ndarray], radius_in_km: Union[list, tuple, numpy.ndarray], values: Union[None, scipy.sparse.csc.csc_matrix, scipy.sparse.csr.csr_matrix, list, tuple, numpy.ndarray] = None, nearest: int = 1)[source]#
Query a KD-tree for values at specific locations
- Parameters
- tree
A
scipy.spatial.cKDTree()- latitudetp.Union[list,tuple,np.ndarray], optional
Latitudes of locations queried, by default None
- longitudetp.Union[list,tuple,np.ndarray], optional
Longitudes of locations queried, by default None
- radius_in_kmtp.Union[list,tuple,np.ndarray], optional
Radii of locations queried, by default None
- valuestp.Union[None,sparse.csc_matrix,sparse.csr_matrix,list,tuple,np.ndarray], optional
Values at each KD-tree point, by default None
- nearestint, optional
Number of nearest values in the KD-tree to interpolated from, by default 1 so nearest
- Returns
- inds or interp, inds
indices of the nearest points in te KD-tree and the interporlated value (if values is not None)
- Authors
Raj Moulik (moulik@caa.columbia.edu)
- Last Modified
2023.02.16 5.00
- avni.tools.xarray.get_stride(resolution: str) int[source]#
Get the stride to use for various resolution of plotting. This dictates downsampling before interpolation.
- Parameters
- resolutionstr
Resolution of boundary database to use in Basemap. Can be c (crude), l (low), i (intermediate), h (high), f (full)
- Returns
- int
stride to use for resolution
- Authors
Raj Moulik (moulik@caa.columbia.edu)
- Last Modified
2023.02.16 5.00
- avni.tools.xarray.ncfile2tree3D(ncfile: str, treefile: str, lonlatdepth: list = ['longitude', 'latitude', 'depth'], resolution: str = 'h', radius_in_km: Union[None, list, tuple, numpy.ndarray] = None)[source]#
Read or write a pickle interpolant with KD-tree
- Parameters
- ncfilestr
Name of the topography file in NETCDF4 format
- treefilestr
Name of the file where tree is (or will be) stored
- lonlatdepthlist, optional
A list of variable names of the longitude, latitude, depth (in km) arrays, by default [‘longitude’,’latitude’,’depth’]
- resolutionstr, optional
Dictates downsampling before interpolation, by default ‘h’
- radius_in_kmtp.Union[None,list,tuple,np.ndarray], optional
Radius in kilometer when a 2D surface. Ignores the 3rd field in lonlatdepth, by default None
- Returns
- tree
A
scipy.spatial.cKDTree()
- Authors
Raj Moulik (moulik@caa.columbia.edu)
- Last Modified
2023.02.16 5.00
- avni.tools.xarray.readtopography(model: Union[None, str] = None, resolution: str = 'h', field: str = 'z', latitude: str = 'lat', longitude: str = 'lon', latitude_limits: list = [- 90, 90], longitude_limits: list = [- 180, 180], dbs_path: Union[None, str] = None)[source]#
Read standard topography file in NETCDF4 format specified in
constants()- Parameters
- modelstr, optional
Name of the topography file in NETCDF4 format, by default
constants.topography()- resolutionstr, optional
Dictates downsampling before interpolation, by default ‘h’
- fieldstr, optional
Field name in the NETCDF4 file to use, by default ‘z’
- latitudestr, optional
Name to use for latitude column in named numpy array, by default ‘lat’
- longitudestr, optional
Name to use for longitude column in named numpy array, by default ‘lon’
- latitude_limitslist, optional
Limit for restricting the domain for reading topography, by default [-90,90]
- longitude_limitslist, optional
Limit for restricting the domain for reading topography, by default [-180,180]
- dbs_pathtp.Union[None,str], optional
Database path to the folder contianing topography file, by default None so takes the value in
constants.topofolder()
- Returns
- xr.Dataset
Multi-dimensional pixel grid in xarray formats
- Authors
Raj Moulik (moulik@caa.columbia.edu)
- Last Modified
2023.02.16 5.00
- avni.tools.xarray.checkxarray(data: Union[xarray.core.dataarray.DataArray, xarray.core.dataset.Dataset], latname: str = 'latitude', lonname: str = 'longitude', Dataset=True) Tuple[list, float, tuple][source]#
Checks whether the data input is a DataArray and the coordinates are compatible
- Parameters
- datatp.Union[xr.DataArray,xr.Dataset]
Multi-dimensional pixel grid in xarray formats
- latnamestr, optional
Name to use for latitude column in named numpy array, by default ‘latitude’
- lonnamestr, optional
Name to use for longitude column in named numpy array, by default ‘longitude’
- Datasetbool, optional
Allow Dataset or not, by default True
- Returns
- tp.Tuple[list,float,tuple]
First element is a list of error warnings while performing checks
Second element is size of the uniform pixel.
Third element is a tuple containing the shape of the grid
- Authors
Raj Moulik (moulik@caa.columbia.edu)
- Last Modified
2023.02.16 5.00
- avni.tools.xarray.areaxarray(data: Union[xarray.core.dataarray.DataArray, xarray.core.dataset.Dataset], latname: str = 'latitude', lonname: str = 'longitude', pix_width: Union[None, numpy.ndarray] = None) xarray.core.dataarray.DataArray[source]#
Calculate area for multi-dimensional pixel grid in xarray formats
- Parameters
- datatp.Union[xr.DataArray,xr.Dataset]
Multi-dimensional pixel grid in xarray formats
- latnamestr, optional
Name to use for latitude column, by default ‘latitude’
- lonnamestr, optional
Name to use for longitude column, by default ‘longitude’
- pix_widthtp.Union[None,np.ndarray], optional
Width of pixels if not the default derived from data, by default None so is derived
- Returns
- xr.DataArray
A DataArray object with area of each pixel
- Authors
Raj Moulik (moulik@caa.columbia.edu)
- Last Modified
2023.02.16 5.00
- avni.tools.xarray.meanxarray(data: Union[xarray.core.dataarray.DataArray, xarray.core.dataset.Dataset], area: Union[None, xarray.core.dataarray.DataArray] = None, latname: str = 'latitude', lonname: str = 'longitude', pix_width: Union[None, numpy.ndarray] = None) Tuple[float, xarray.core.dataarray.DataArray, float][source]#
Calculate geographically weighted average of a multi-dimensional pixel grid in xarray formats
- Parameters
- datatp.Union[xr.DataArray,xr.Dataset]
Multi-dimensional pixel grid in xarray formats to average over
- areatp.Union[None,xr.DataArray], optional
Area of each pixel, by default None so calculated on the fly
- latnamestr, optional
Name to use for latitude column, by default ‘latitude’
- lonnamestr, optional
Name to use for longitude column, by default ‘longitude’
- pix_widthtp.Union[None,np.ndarray], optional
Width of pixels if not the default derived from data, by default None so is derived
- Returns
- tp.Tuple[float,xr.DataArray,float]
First element is the global average Second element is a DataArray containing area weights for each pixel Third element is the percentage of global area covered by this basis set
- Authors
Raj Moulik (moulik@caa.columbia.edu)
- Last Modified
2023.02.16 5.00