fixed rel scoring

src/ILAMB/ConfSoilMoisture.py

@@ -1340,35 +1340,35 @@ def _composeSubset(a_dep, a_ind):
                                   m.color,
                                   os.path.join(self.output_path,"%s_%s_rel_func_%s.png" % (m.name,region,ind_name)))
 
-                # Score the distribution
-                score_list = []
-                for ref_dist, com_dist in zip(ref_dist_list, com_dist_list):
-                    score = _scoreDistribution(ref_dist,com_dist)
-                    score_list.append(score)
-                score = np.sum(np.array(score_list)*(self.depths[:,1] - self.depths[:,0])) / \
-                        (self.depths[-1,1] - self.depths[0,0])
-                sname = "%s Hellinger Distance %s" % (longname,region)
-                if sname in scalars.variables:
-                    scalars.variables[sname][0] = score
-                else:
-                    Variable(name = sname,
-                             unit = "1",
-                             data = score).toNetCDF4(results,group="Relationships")
-
-                # Score the functional response
-                score_list = []
-                for ref_mean, com_mean in zip(ref_mean_list, com_mean_list):
-                    score = _scoreFunction(ref_mean,com_mean)
-                    score_list.append(score)
-                score = np.sum(np.array(score_list)*(self.depths[:,1] - self.depths[:,0])) / \
-                        (self.depths[-1,1] - self.depths[0,0])
-                sname = "%s RMSE Score %s" % (longname,region)
-                if sname in scalars.variables:
-                    scalars.variables[sname][0] = score
-                else:
-                    Variable(name = sname,
-                             unit = "1",
-                             data = score).toNetCDF4(results,group="Relationships")
+                    # Score the distribution
+                    score_list = []
+                    for ref_dist, com_dist in zip(ref_dist_list, com_dist_list):
+                        score = _scoreDistribution(ref_dist,com_dist)
+                        score_list.append(score)
+                    score = np.sum(np.array(score_list)*(self.depths[:,1] - self.depths[:,0])) / \
+                            (self.depths[-1,1] - self.depths[0,0])
+                    sname = "%s Hellinger Distance %s" % (longname,region)
+                    if sname in scalars.variables:
+                        scalars.variables[sname][0] = score
+                    else:
+                        Variable(name = sname,
+                                 unit = "1",
+                                 data = score).toNetCDF4(results,group="Relationships")
+
+                    # Score the functional response
+                    score_list = []
+                    for ref_mean, com_mean in zip(ref_mean_list, com_mean_list):
+                        score = _scoreFunction(ref_mean,com_mean)
+                        score_list.append(score)
+                    score = np.sum(np.array(score_list)*(self.depths[:,1] - self.depths[:,0])) / \
+                            (self.depths[-1,1] - self.depths[0,0])
+                    sname = "%s RMSE Score %s" % (longname,region)
+                    if sname in scalars.variables:
+                        scalars.variables[sname][0] = score
+                    else:
+                        Variable(name = sname,
+                                 unit = "1",
+                                 data = score).toNetCDF4(results,group="Relationships")
 
 
     def _sensitivity(self, m):

src/ILAMB/ModelResult.py

@@ -418,6 +418,7 @@ def derivedVariable(self,variable_name,expression,lats=None,lons=None,initial_ti
 
         np.seterr(divide='ignore',invalid='ignore')
         result,unit = il.SympifyWithArgsUnits(expression,args,units)
+        result = result.astype(var.data.data.dtype)
         np.seterr(divide='raise',invalid='raise')
         mask  += np.isnan(result)
         result = np.ma.masked_array(np.nan_to_num(result),mask=mask)