ruins.processing.sdm#
Module Contents#
Functions#
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relative scaled distribution mapping assuming a gamma distributed parameter (with lower limit zero) |
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absolute scaled distribution mapping assuming a normal distributed parameter |
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scaled distribution mapping - wrapper to relative and absolute bias correction functions |
- ruins.processing.sdm.relSDM(obs, mod, sce, cdf_threshold=0.9999999, lower_limit=0.1)#
relative scaled distribution mapping assuming a gamma distributed parameter (with lower limit zero) rewritten from pyCAT for 1D data
obs :: observed variable time series mod :: modelled variable for same time series as obs sce :: to unbias modelled time series cdf_threshold :: upper and lower threshold of CDF lower_limit :: lower limit of data signal (values below will be masked!)
returns corrected timeseries tested with pandas series.
- ruins.processing.sdm.absSDM(obs, mod, sce, cdf_threshold=0.9999999)#
absolute scaled distribution mapping assuming a normal distributed parameter rewritten from pyCAT for 1D data
obs :: observed variable time series mod :: modelled variable for same time series as obs sce :: to unbias modelled time series cdf_threshold :: upper and lower threshold of CDF
returns corrected timeseries tested with pandas series.
- ruins.processing.sdm.SDM(obs, mod, sce, meth='rel', cdf_threshold=0.9999999, lower_limit=0.1)#
scaled distribution mapping - wrapper to relative and absolute bias correction functions rewritten from pyCAT for 1D data
obs :: observed variable time series mod :: modelled variable for same time series as obs sce :: to unbias modelled time series meth :: ‘rel’ for relative SDM, else absolute SDM will be performed cdf_threshold :: upper and lower threshold of CDF lower_limit :: lower limit of data signal (values below will be masked when meth != ‘rel’)
The original authors suggest to use the absolute SDM for air temperature and the relative SDM for precipitation and radiation series.
returns corrected timeseries tested with pandas series.