- class wradlib.adjust.AdjustMultiply(obs_coords, raw_coords, nnear_raws=9, stat='median', mingages=5, minval=0.0, mfb_args=None, ipclass=<class 'wradlib.ipol.Idw'>, **ipargs)[source]#
Gage adjustment using a multiplicative error model
First, an instance of AdjustMultiply has to be created. Calling this instance then does the actual adjustment. The motivation behind this performance. In case the observation points are always the same for different time steps, the computation of neighbours and inverse distance weights only needs to be performed once during initialisation.
AdjustMultiply automatically takes care of invalid gage or radar observations (e.g. NaN, Inf or other typical missing data flags such as -9999). However, in case e.g. the observation data contain missing values, the computation of the inverse distance weights needs to be repeated in
__call__which is at the expense of performance.
For a complete overview of parameters for the initialisation of adjustment objects, as well as an extensive example, please see
numpy.ndarray) – array of adjusted radar values
- __init__(obs_coords, raw_coords, nnear_raws=9, stat='median', mingages=5, minval=0.0, mfb_args=None, ipclass=<class 'wradlib.ipol.Idw'>, **ipargs)#
__init__(obs_coords, raw_coords[, ...])
Leave-One-Out Cross Validation, applicable to all gage adjustment classes.