linear boundary correction for 2d prior mask

getdist/analysis_defaults.ini

@@ -40,7 +40,7 @@ smooth_scale_1D =-1
 
 #0 is basic normalization correction
 #1 is linear boundary kernel (should get gradient correct)
-#2 is a higher order kernel, that also affects estimates way from the boundary (1D only)
+#2 is a higher order kernel, that also affects estimates away from the boundary (1D only)
 boundary_correction_order=1
 
 #Correct for (over-smoothing) biases using multiplicative bias correction

getdist/mcsamples.py

@@ -1794,12 +1794,9 @@ def get2DDensityGridData(self, j, j2, num_plot_contours=None, get_density=False,
             ix = a00 * bins2D > np.max(bins2D) * 1e-8
             a00 = a00[ix]
             normed = bins2D[ix] / a00
-            if boundary_correction_order == 0 or mask_function:
+            if boundary_correction_order == 0:
                 # simple boundary correction by normalization
                 bins2D[ix] = normed
-                if mask_function:
-                    bool_mask = prior_mask[winw:-winw, winw:-winw] < 1e-8
-                    bins2D[bool_mask] = 0
             elif boundary_correction_order == 1:
                 # linear boundary correction
                 indexes = np.arange(-winw, winw + 1)
@@ -1827,6 +1824,10 @@ def get2DDensityGridData(self, j, j2, num_plot_contours=None, get_density=False,
             else:
                 raise SettingError('unknown boundary_correction_order (expected 0 or 1)')
 
+        if mask_function:
+            bool_mask = prior_mask[winw:-winw, winw:-winw] < 1e-8
+            bins2D[bool_mask] = 0
+
         if mult_bias_correction_order and not mask_function:
             prior_mask = np.ones((ysize + 2 * winw, xsize + 2 * winw))
             self._setEdgeMask2D(parx, pary, prior_mask, winw, alledge=True)