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===================================================================================================
 Radproc - A GIS-compatible Python-Package for automated RADOLAN Composite Processing and Analysis
===================================================================================================

:Release: |release|
:Date: |today|

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1313701.svg
   :target: https://doi.org/10.5281/zenodo.1313701

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-Online RW composites for free in the Climate Data Center (ftp://ftp-cdc.dwd.de/pub/CDC/grids_germany/hourly/radolan/)
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*

.. toctree::
   :maxdepth: 3
   
   gettingstarted
   notebooks
   reference
   releasenotes
   
Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`