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io.read_EDGE_netcdf: set an range_lim default value of 200km avoid errors in case range_lim is greater than the maximum range of the data
io.read_EDGE_netcdf: included the toctree added an range limitation asinput argument rounding of theta0 instead of cuting the decimals rolling the azimuths instead of sorting added max_range to the returned attributes
renamed SUB2dBZ to read_EDGE_netcdf and modified and enhanced the function. It still needs to be clarified whether the data array needs to flipped.
modified project function in georef.py so it can also do inverse projection
minor edit in georef_example.py
added convenience function projected_bincoords_from_radarspecs to documentation minor edit in projected_bincoords_from_radarspecs (now returns ravelled coordinate arrays)
added the missing import
added io.SUB2dBZ for reading philipinean s-band radar data
added tutorials and some minor edits
added colormap argument to vis.polar_plot
correctAttenuationHJ in atten.py processes even if the input gateset contains NaNs; in this case all corresponding k(attenuation)-beams are set to NaN, too; but the remaining PIA will be calculated;
if this approves, the code for the other to correction functions should also be...
atten.py: moved the NaN-check before first calculations and memory allocations
change in make.bat: deleting source/generated before each built
Fixed bug that occurred if only one point is passed to the IDW Interpolator
removed superfluous calculations estimated speedup is roughly by a factor of 1.7
removed superfluous call to np.where
added _z2rEnhanced_mdcorr function same result as z2rEnhanced but roughly 50 times faster.
Removed the trafo.rvp2r function: for clarity such processchains should be executed in a more basic way e.g. zr.z2r(trafo.idecibel(trafo.rvp2dBZ(x))), in order to avoid undue code and import errors (caused by endless cross-referencing).
Fixed a refencing bug when nnearest equals 1 in idw
Added convenience function ipol.interpolate
This function should not only offer a uniform interface for all interpolation classes. Particularly, it allows to efficiently deal with missing values in the interpolation source values (i.e. np.nan values) if your input value array contains missing values...
changed warning message
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