fixed calculation of resolution to get rid of warning

cerinterp/resampler.py

@@ -337,9 +337,20 @@ class Resampler(object):
 #                 resolution = numpy.amin(abs(numpy.diff(srclat)))
                 resolution = 1
             else:
+                # estimate resolution from latitude
                 y, x = srclat.shape
-                resolution = abs(srclat[y / 2, x / 2] - srclat[y / 2 + 1, x / 2])
+                #contiguous_lat = numpy.ma.flatnotmasked_contiguous(srclat[:, x / 2])
+                #if contiguous_lat is list:
+                #    sublats = 
+                resolution = numpy.ma.abs(
+                    srclat[0:-1, x / 2] - srclat[1:, x / 2]
+                    ).mean()
+                if resolution is numpy.ma.masked:
+                    logging.warning(
+                        "Data spatial resolution could not be estimated"
+                        )
             radius = resolution * 100000. / 2. * 1.6
+            
         logging.debug("Radius for resampling : %s", radius)
         return source_def, target_def, radius
 
@@ -347,7 +358,7 @@ class Resampler(object):
     def __get_closest_neighbour_info(cls,
                                      source_def,
                                      target_def,
-                                     radius=None,
+                                     radius,
                                      ):
         """Returns the indexes for a closest neighbour resampling.
 
@@ -463,7 +474,8 @@ class Resampler(object):
         target_lats = target.get_lat().astype('float32')
         if isinstance(target, Grid) and len(target_lons.shape) == 1:
             target_lons, target_lats = numpy.meshgrid(target_lons, target_lats)
-        source_def, target_def, radius = Resampler.__get_pyresample_def(
+
+        source_def, target_def, calc_radius = Resampler.__get_pyresample_def(
             source_lats,
             source_lons,
             source.__class__.__name__,
@@ -488,7 +500,7 @@ class Resampler(object):
                     index_array, distance_array\
                     = Resampler.__get_closest_neighbour_info(source_def,
                                                              target_def,
-                                                             radius)
+                                                             calc_radius)
                 first = False
                 if not valid_input_index.any():
                     # return None if no source data can be resampled on the