Add kurtosis as metric to evaluate performance of standardization

examples/advanced/plot_standardization.py

@@ -45,7 +45,7 @@
 maxc_arr = np.maximum(np.abs(planc_arr), np.abs(profc_arr))
 
 # Remove large outliers
-dh_arr[np.abs(dh_arr) > 4 * xdem.spatialstats.nmad(dh_arr)] = np.nan
+dh_arr[np.abs(dh_arr) > 7 * xdem.spatialstats.nmad(dh_arr)] = np.nan
 
 # Define bins for 2D binning
 custom_bin_slope = np.unique(
@@ -97,6 +97,12 @@
 z_dh.data[mask_glacier.data] = np.nan
 z_dh.data[np.abs(z_dh.data) > 4] = np.nan
 
+from scipy.stats import kurtosis
+kbef = kurtosis(dh_arr, nan_policy="omit")
+kaft = kurtosis(z_dh.flatten(), nan_policy="omit")
+print("Excess kurtosis before standardization: {}".format(kbef))
+print("Excess kurtosis after standardization: {}".format(kaft))
+
 # %%
 # We perform a scale-correction for the standardization, to ensure that the spread of the data is exactly 1.
 # The NMAD is used as a robust measure for the spread (see :ref:`robuststats-nmad`).