radproc.core.load_years_and_resample

radproc.core.load_years_and_resample(HDFFile, year_start, year_end=0, freq='years')

Imports all months of the specified years, merges them together to one DataFrame and resamples the latter to [annual | monthly | daily | hourly] precipitation sums.

Parameters:
HDFFile : string
Path and name of the HDF5 file containing monthly datasets.
year_start : integer
First year for which data are to be loaded.
year_end : integer (optional, default: start_year)
Last year for which data are to be loaded.
freq : string (optional, default: “years”)

Target frequency. Available frequencies for downsampling:

“years”, “months”, “days”, “hours”
Returns:
df : pandas DataFrame
resampled to the target frequency and containing [annual | monthly | daily | hourly] precipitation sums.
Examples:

The mean annual precipitation sum can be calculated with the following syntax:

>>> import radproc as rp
>>> meanPrecip = rp.load_years_and_resample(r"C:\Data\RADOLAN.h5", 2010, 2015, "years").mean()
# The resulting pandas Series can be exported to an ESRI Grid:    
>>> rp.export_to_raster(series=meanPrecip, idRaster=rp.import_idarray_from_raster(r"C:\Data\idras"), outRaster=r"P:\GIS_data\N_mean10_15")

Note

All resampling functions set the label of aggregated intervals at the right, hence every label describes the precipitation accumulated in the previous interval period.