Source code for wradlib.comp
#!/usr/bin/env python
# Copyright (c) 2011-2020, wradlib developers.
# Distributed under the MIT License. See LICENSE.txt for more info.
"""
Composition
^^^^^^^^^^^
Combine data from different radar locations on one common set of locations
.. autosummary::
:nosignatures:
:toctree: generated/
{}
"""
__all__ = ["extract_circle", "togrid", "compose_ko", "compose_weighted"]
__doc__ = __doc__.format("\n ".join(__all__))
import numpy as np
[docs]def togrid(src, trg, radius, center, data, interpol, *args, **kwargs):
"""Interpolate data from a radar location to the composite grid or set of \
locations
Parameters
----------
src : :class:`numpy:numpy.ndarray`
array of float of shape (numpoints, ndim),
cartesian x / y coordinates of the radar bins
trg : :class:`numpy:numpy.ndarray`
array of float of shape (numpoints, ndim),
cartesian x / y coordinates of the composite
radius : float
the radius of the radar circle (same units as src and trg)
center : :class:`numpy:numpy.ndarray`
array of float, the location coordinates of the radar
data : :class:`numpy:numpy.ndarray`
array of float, the data that should be transferred to composite
interpol : :class:`~wradlib.ipol.IpolBase`
an interpolation class name from :mod:`wradlib.ipol`
e.g. :class:`~wradlib.ipol.Nearest` or :class:`~wradlib.ipol.Idw`
Other Parameters
----------------
*args : dict
arguments of Interpolator (see class documentation)
Keyword Arguments
-----------------
**kwargs : dict
keyword arguments of Interpolator (see class documentation)
Returns
-------
output : :class:`numpy:numpy.ndarray`
array of float, data of the radar circle which is interpolated on
the composite grid
Note
----
Keyword arguments to be used while calling the interpolator can be issued as
`call_kwargs`, eg. togrid(..., call_kwargs=dict(maxdist=10))
Examples
--------
See :ref:`/notebooks/basics/wradlib_workflow.ipynb#Gridding`.
"""
# get indices to select the subgrid from the composite grid
ix = extract_circle(center, radius, trg)
call_kwargs = kwargs.pop("call_kwargs", {})
# interpolate on subgrid
ip = interpol(src, trg[ix], *args, **kwargs)
data_on_subgrid = ip(data, **call_kwargs).reshape(len(ix))
# create container for entire grid
composegridshape = [len(trg)]
composegridshape.extend(data.shape[1:])
compose_grid = np.repeat(np.nan, len(trg) * np.prod(data.shape[1:])).reshape(
composegridshape
)
# push subgrid results into the large grid
compose_grid[ix] = data_on_subgrid
return compose_grid
[docs]def compose_ko(radargrids, qualitygrids):
"""Composes grids according to quality information using quality \
information as a knockout criterion.
The value of the composed pixel is taken from the radargrid whose
quality grid has the highest value.
Parameters
----------
radargrids : list
radar data to be composited. Each item in the list corresponds to the
data of one radar location. All items must have the same shape.
qualitygrids : list
quality data to decide upon which radar site will contribute its pixel
to the composite. Then length of this list must be the same as that
of `radargrids`. All items must have the same shape and be aligned with
the items in `radargrids`.
Returns
-------
composite : :class:`numpy:numpy.ndarray`
"""
# first add a fallback array for all pixels having missing values in all
# radargrids
radarfallback = np.repeat(np.nan, np.prod(radargrids[0].shape)).reshape(
radargrids[0].shape
)
radargrids.append(radarfallback)
radarinfo = np.array(radargrids)
# then do the same for the quality grids
qualityfallback = np.repeat(-np.inf, np.prod(radargrids[0].shape)).reshape(
radargrids[0].shape
)
qualitygrids.append(qualityfallback)
qualityinfo = np.array(qualitygrids)
select = np.nanargmax(qualityinfo, axis=0)
composite = radarinfo.reshape((radarinfo.shape[0], -1))[
select.ravel(), np.arange(np.prod(radarinfo.shape[1:]))
].reshape(radarinfo.shape[1:])
radargrids.pop()
qualitygrids.pop()
return composite
[docs]def compose_weighted(radargrids, qualitygrids):
"""Composes grids according to quality information using a weighted \
averaging approach.
The value of the composed pixel is the weighted average of all radar
pixels with the quality values being the weights.
Parameters
----------
radargrids : list
list of arrays
qualitygrids : list
list of arrays
Returns
-------
composite : :class:`numpy:numpy.ndarray`
Examples
--------
See :ref:`/notebooks/workflow/recipe1.ipynb`.
See Also
--------
:func:`~wradlib.comp.compose_ko`
"""
radarinfo = np.array(radargrids)
qualityinfo = np.array(qualitygrids)
# overall nanmask
nanmask = np.all(np.isnan(radarinfo), axis=0)
# quality grids must contain values only where radarinfo does
qualityinfo[np.isnan(radarinfo)] = np.nan
qualityinfo /= np.nansum(qualityinfo, axis=0)
composite = np.nansum(radarinfo * qualityinfo, axis=0)
composite[nanmask] = np.nan
return composite
if __name__ == "__main__":
print("wradlib: Calling module <comp> as main...")
