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wradlib Documentation, Release 0.1.1 >>> mult_adjusted = mult_adjuster(obs, radar,0.) >>> # adjust the radar observation by MFB >>> mfb_adjuster = adjust.AdjustMFB(obs_coords, radar_coords, nnear_raws=1) >>> mfb_adjusted = mfb_adjuster(obs, radar,0.) >>> # adjust the radar observation by AdjustMixed >>> mixed_adjuster = adjust.AdjustMixed(obs_coords, radar_coords, nnear_raws=1) >>> mixed_adjusted = mixed_adjuster(obs, radar) >>> line1 = pl.plot(radar_coords, radar, ’k-’, label="raw radar") >>> line2 = pl.plot(obs_coords, obs, ’ro’, label="gage obs") >>> line3 = pl.plot(radar_coords, add_adjusted, ’-’, color="red", label="adjusted by AdjustAdd") >>> line4 = pl.plot(radar_coords, mult_adjusted, ’-’, color="green", label="adjusted by AdjustMu >>> line5 = pl.plot(radar_coords, mfb_adjusted, ’-’, color="orange", label="adjusted by AdjustMF >>> line6 = pl.plot(radar_coords, mixed_adjusted, ’-’, color="blue", label="adjusted by AdjustMi >>> pl.legend() >>> pl.show() Methods xvalidate(obs, raw) Leave-One-Out Cross Validation, applicable to all gage adjustment classes. wradlib.adjust.AdjustMFB.xvalidate AdjustMFB.xvalidate(obs, raw) Leave-One-Out Cross Validation, applicable to all gage adjustment classes. This method will be inherited to other Adjust classes. It should thus be applicable to all adjustment procedures without any modification. This way, the actual adjustment procedure has only to be defined once in the __call__ method. The output of this method can be evaluated by using the verify.ErrorMetrics class. Parameters obs : array of floats raw : array of floats Returns obs : array of floats valid observations at those locations which have a valid radar observation estatobs : array of floats estimated values at the valid observation locations __call__(obs, raw[, targets, rawatobs, ix]) xvalidate(obs, raw) Return the field of raw values adjusted by obs. Leave-One-Out Cross Validation, applicable to all gage adjustment classes. wradlib.adjust.AdjustMFB.__call__ AdjustMFB.__call__(obs, raw, targets=None, rawatobs=None, ix=None) Return the field of raw values adjusted by obs. Parameters obs : array of floats Gage observations raw : array of floats 68 Chapter 3. Library Reference
wradlib Documentation, Release 0.1.1 Raw unadjusted radar rainfall targets : (INTERNAL) array of floats Coordinate pairs for locations on which the final adjustment product is interpolated Defaults to None. In this case, the output locations will be identical to the radar coordinates rawatobs : (INTERNAL) array of floats For internal use from AdjustBase.xvalidate only (defaults to None) ix : (INTERNAL) array of integers For internal use from AdjustBase.xvalidate only (defaults to None) wradlib.adjust.AdjustMFB.xvalidate AdjustMFB.xvalidate(obs, raw) Leave-One-Out Cross Validation, applicable to all gage adjustment classes. This method will be inherited to other Adjust classes. It should thus be applicable to all adjustment procedures without any modification. This way, the actual adjustment procedure has only to be defined once in the __call__ method. The output of this method can be evaluated by using the verify.ErrorMetrics class. Parameters obs : array of floats raw : array of floats Returns obs : array of floats valid observations at those locations which have a valid radar observation estatobs : array of floats estimated values at the valid observation locations wradlib.adjust.AdjustMultiply class wradlib.adjust.AdjustMultiply(obs_coords, raw_coords, nnear_raws=9, stat=’median’, mingages=5, minval=0.0, Ipclass=, **ipargs) 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 invserse 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. Parameters obs_coords : array of float coordinate pairs of observations points raw_coords : array of float coordinate pairs of raw (unadjusted) field nnear_raws : integer 3.13. Gage adjustment 69
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