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.


Combine data from different radar locations on one common set of locations

.. autosummary::
   :toctree: generated/

__all__ = ["extract_circle", "togrid", "compose_ko", "compose_weighted"]
__doc__ = __doc__.format("\n   ".join(__all__))

import numpy as np

[docs]def extract_circle(center, radius, coords): """Extract the indices of ``coords`` which fall within a circle \ defined by ``center`` and ``radius``. Parameters ---------- center : float radius : float coords : :class:`numpy:numpy.ndarray` array of float with shape (numpoints, 2) Returns ------- output : :class:`numpy:numpy.ndarray` 1-darray of integers, index array referring to the ``coords`` array """ return np.where(((coords - center) ** 2).sum(axis=-1) < radius**2)[0]
[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) *[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,[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,[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([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...")