Merge branch 'enhancement/speed_up_nodatamask' into 'master'

The algorithm to compute the nodata mask is now much faster, especially for...

See merge request !13

geoarray/baseclasses.py

@@ -681,25 +681,41 @@ class GeoArray(object):
         :param overwrite: <bool> whether to overwrite existing nodata mask that has already been calculated
         :return:
         """
-
         if self._mask_nodata is None or overwrite:
             assert self.ndim in [2, 3], "Only 2D or 3D arrays are supported. Got a %sD array." % self.ndim
             arr = self[:, :, fromBand] if self.ndim == 3 and fromBand is not None else self[:]
 
-            min_v, max_v = np.min(arr), np.max(arr)
-            if (min_v == max_v == self.nodata) or (np.isnan(min_v) and np.isnan(max_v) and np.isnan(self.nodata)):
-                self.mask_nodata = np.full(arr.shape[:2], False)
+            if self.nodata is None:
+                mask = np.ones((self.rows, self.cols), np.bool)
+
+            elif np.isnan(self.nodata):
+                nanmask = np.isnan(arr)
+                nanbands = np.all(np.all(nanmask, axis=0), axis=0)
+
+                if np.all(nanbands):
+                    mask = np.full(arr.shape[:2], False)
+                elif arr.ndim == 2:
+                    mask = ~np.isnan(arr)
+                else:
+                    idx_1st_databand = np.argwhere(~nanbands)[0][0]
+                    mask = ~np.isnan(arr[:, :, idx_1st_databand])
+                    mask[~mask] = np.any(~np.isnan(arr[~mask]), axis=1)
+
             else:
-                if self.nodata is None:
-                    self.mask_nodata = np.ones((self.rows, self.cols), np.bool)
-                elif np.isnan(self.nodata):
-                    self.mask_nodata = \
-                        np.invert(np.isnan(arr)) if arr.ndim == 2 else \
-                        np.any(np.invert(np.isnan(arr)), axis=2)
+                bandmeans = np.mean(np.mean(arr, axis=0), axis=0)
+
+                if np.nanmean(bandmeans) == self.nodata:
+                    mask = np.full(arr.shape[:2], False)
+                elif arr.ndim == 2:
+                    mask = arr != self.nodata
                 else:
-                    self.mask_nodata = \
-                        np.ma.masked_not_equal(arr, self.nodata).mask if arr.ndim == 2 else \
-                        np.any(np.ma.masked_not_equal(arr, self.nodata).mask, axis=2)
+                    idx_1st_databand = np.argwhere(bandmeans != self.nodata)[0][0]
+                    mask = np.array(arr[:, :, idx_1st_databand] != self.nodata)
+                    mask[~mask] = np.any(arr[~mask] != self.nodata, axis=1)
+
+            self.mask_nodata = mask
+
+            return mask
 
     def find_noDataVal(self, bandIdx=0, sz=3):
         """Tries to derive no data value from homogenious corner pixels within 3x3 windows (by default).