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Table 1

Radproc development goals and decisions for implementation

Development goalsImplementation
  • ➣ Open source

 
  • ✓ Python as programming language

 
  • ➣ Maximum compatibility with GIS to reduce data amount by clipping to study area and allow for custom visualisation and geoprocessing

 
 
  • ➣ Availability of tools for statistical analysis and visualisation to allow for custom analyses beyond radproc's functions

 
  • ✓ DataFrames from the pandas Python package as primary data structure

 
  • ➣ Availability of a flexible, widely used and well-documented data format with support for statistical analyses and time series which is equally suitable for radar and rain gauge data

 
 
  • ➣ Structured, compressible data storage format enabling fast data access

 
  • ✓ Data storage in HDF5 with one group per year and therein monthly pandas DataFrames as datasets

 
  • ➣ Storage of processed data in a uniform data format on which all analysis and export functions are built in order to enable extensibility and interfacing to other precipitation data formats

 
 
  • ➣ Widely used, stable and well-documented GIS as a basis for all spatial analysis and visualisation tasks

 
  • ✓ Choice of ArcGIS as the most mature and widely used GIS

 
Development goalsImplementation
  • ➣ Open source

 
  • ✓ Python as programming language

 
  • ➣ Maximum compatibility with GIS to reduce data amount by clipping to study area and allow for custom visualisation and geoprocessing

 
 
  • ➣ Availability of tools for statistical analysis and visualisation to allow for custom analyses beyond radproc's functions

 
  • ✓ DataFrames from the pandas Python package as primary data structure

 
  • ➣ Availability of a flexible, widely used and well-documented data format with support for statistical analyses and time series which is equally suitable for radar and rain gauge data

 
 
  • ➣ Structured, compressible data storage format enabling fast data access

 
  • ✓ Data storage in HDF5 with one group per year and therein monthly pandas DataFrames as datasets

 
  • ➣ Storage of processed data in a uniform data format on which all analysis and export functions are built in order to enable extensibility and interfacing to other precipitation data formats

 
 
  • ➣ Widely used, stable and well-documented GIS as a basis for all spatial analysis and visualisation tasks

 
  • ✓ Choice of ArcGIS as the most mature and widely used GIS

 
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