radproc 0.1.4

Description:

radproc 0.1.4

Radproc is an open source Python library intended to facilitate precipitation data processing and analysis for GIS-users.
It provides functions for processing, analysis and export of RADOLAN (Radar Online Adjustment) composites and rain gauge data in MR90 format.
The German Weather Service (DWD) provides the RADOLAN RW composites for free in the Climate Data Center
but the data processing represents a big challenge for many potential users.
Radproc’s goal is to lower the barrier for using these data, especially in conjunction with ArcGIS.
Therefore, radproc provides an automated ArcGIS-compatible data processing workflow based on pandas DataFrames and HDF5.
Moreover, radproc’s arcgis module includes a collection of functions for data exchange between pandas and ArcGIS.

Note
Please cite radproc as Kreklow, J. (2018): Radproc - A GIS-compatible Python-Package for automated RADOLAN Composite Processing and Analysis. Zenodo. http://doi.org/10.5281/zenodo.1313701


Radproc’s Main Features

Raw Data Processing


Support for the reanalyzed RADOLAN products RW (60 min), YW and RY (both 5 min. resolution)
Automatically reading in all binary RADOLAN composites from a predefined directory structure
Optionally clipping the composites to a study area in order to reduce data size
Default data structure: Monthly pandas DataFrames with full support for time series analysis and spatial location of each pixel
Efficient data storage in HDF5 format with fast data access and optional data compression
Easy downsampling of time series
Reading in DWD rain gauge data in MR90 format into the same data structure as RADOLAN.




Data Exchange with ArcGIS


Export of single RADOLAN composites or analysis results into projected raster datasets or ESRI grids for your study area
Export of all DataFrame rows into raster datasets in a new file geodatabase, optionally including several statistics rasters
Import of dbf tables (stand-alone or attribute tables of feature classes) into pandas DataFrames
Joining DataFrame columns to attribute tables
Extended value extraction from rasters to points (optionally including the eight surrounding cells)
Extended zonal statistics




Analysis


Calculation of precipitation sums for arbitrary periods of time
Heavy rainfall analysis, e.g. identification, counting and export of rainfall intervals exceeding defined thresholds
Data quality assessment
Comparison of RADOLAN and rain gauge data
In preparation: Erosivity analysis, e.g. calculation of monthly, seasonal or annual R-factors





Documentation
The full documentation for the latest radproc version is available at http://www.pgweb.uni-hannover.de/
Most of the docs are also hosted at https://radproc.readthedocs.io which will provide support for docs of older versions in future,
but unfortunately Readthedocs doesn’t seem to support sphinx autodocs for the arcpy module which is not hosted at PyPI.
Consequently, the docs for the radproc.arcgis module are missing here.
If you have any idea how to fix this issue, please let me know.