Changeset 25090 for issm/trunk-jpl/src/m/classes/qmu/normal_uncertain.py

Timestamp:

06/21/20 21:48:26 (5 years ago)

Author:

jdquinn

Message:

CHG: Cleanup; saving progress

File:

• issm/trunk-jpl/src/m/classes/qmu/normal_uncertain.py (modified) (7 diffs)

issm/trunk-jpl/src/m/classes/qmu/normal_uncertain.py

@@ -14 +14 @@
 
     Usage:
-        nuv = normal_uncertain('descriptor',descriptor,'mean',mean,'stddev',stddev,'partition',partition)
-        where nuv is the normal_uncertain object returned by the constructor, mean and stddev are self
-        explanatory.  partition is the partition vector for distributed variables. Can be a partition
-        vector over elements or vertices.
+        [nuv] = normal_uncertain(
+            'descriptor', descriptor,
+            'mean', mean,
+            'stddev', stddev,
+            'partition', partition
+            )
+
+        where nuv is the normal_uncertain object returned by the constructor, 
+        mean and stddev are self explanatory, and partition is the partition 
+        vector for distributed variables. Can be a partition vector over 
+        elements or vertices.
 
     Example:
@@ -32 +39 @@
             )
     '''
-    def __init__(self):
+    def __init__(self): #{{{
         self.descriptor = ''
         self.mean       = np.NaN
@@ -38 +45 @@
         self.partition  = []
         self.nsteps     = 0
+    #}}}
 
     @staticmethod
@@ -52 +60 @@
                 nuv = args[0]
             else:
-                raise RuntimeError('Object ' + str(args[0]) + ' is a ' + str(type(args[0])) + ' class object, not "normal_uncertain".')
+                raise Exception('Object ' + str(args[0]) + ' is a ' + str(type(args[0])) + ' class object, not "normal_uncertain".')
 
         # create the object from the input
         else:
-            # lines differ here in other classes / tests; see asizec problem in notes
             nuv = normal_uncertain()
 
@@ -67 +74 @@
             nuv.stddev     = options.getfieldvalue('stddev')
 
-            #if the variable is scaled, a partition vector should have been supplied, and
-            #that partition vector should have as many partitions as the mean and stddev
-            #vectors:
+            #if the variable is scaled, a partition vector should have been 
+            #supplied, and that partition vector should have as many partitions 
+            #as the mean and stddev vectors:
             if nuv.isscaled():
                 nuv.partition = options.getfieldvalue('partition')
@@ -75 +82 @@
                 npart = qmupart2npart(nuv.partition)
                 if npart != nuv.mean.shape[0]:
-                    raise RuntimeError("normal_uncertain constructor: for the scaled variable %s the row size of the mean field should be identical to the number of partitions" % nuv.descriptor)
+                    raise Exception("normal_uncertain constructor: for the scaled variable %s the row size of the mean field should be identical to the number of partitions" % nuv.descriptor)
                 if npart != nuv.stddev.shape[0]:
-                    raise RuntimeError("normal_uncertain constructor: for the scaled variable %s the row size of the stddev field should be identical to the number of partitions" % nuv.descriptor)
+                    raise Exception("normal_uncertain constructor: for the scaled variable %s the row size of the stddev field should be identical to the number of partitions" % nuv.descriptor)
                 if nuv.nsteps != nuv.mean.shape[1]:
-                    raise RuntimeError("normal_uncertain constructor: for the scaled variable %s the col size of the mean field should be identical to the number of time steps" % nuv.descriptor)
+                    raise Exception("normal_uncertain constructor: for the scaled variable %s the col size of the mean field should be identical to the number of time steps" % nuv.descriptor)
                 if nuv.nsteps != nuv.stddev.shape[1]:
-                    raise RuntimeError("normal_uncertain constructor: for the scaled variable %s the col size of the stddev field should be identical to the number of time steps" % nuv.descriptor)
+                    raise Exception("normal_uncertain constructor: for the scaled variable %s the col size of the stddev field should be identical to the number of time steps" % nuv.descriptor)
 
         return [nuv] # Always return a list, so we have something akin to a MATLAB single row matrix
@@ -107 +114 @@
     def checkconsistency(self, md, solution, analyses): #{{{
         md = checkfield(md, 'field', self.mean, 'fieldname', 'normal_uncertain.mean', 'NaN', 1, 'Inf', 1, '>=', 0)
-        md = checkfield(md, 'field', self.stddev, 'fieldname', 'normal_uncertain.stddev', 'NaN', 1, 'Inf', 1, '>=', 0, 'numel', len(self.mean))
+        md = checkfield(md, 'field', self.stddev, 'fieldname', 'normal_uncertain.stddev', 'NaN', 1, 'Inf', 1, '>=', 0)
         if self.isscaled():
             if self.partition == []:
-                raise RuntimeError("normal_uncertain is a scaled variable, but it's missing a partition vector")
+                raise Exception("normal_uncertain is a scaled variable, but it's missing a partition vector")
             #better have a partition vector that has as many partitions as stddev's size:
             if self.stddev.shape[0] != partition_npart(self.partititon):
-                raise RuntimeError("normal_uncertain error message: row size of stddev and partition size should be identical")
+                raise Exception("normal_uncertain error message: row size of stddev and partition size should be identical")
             if self.mean.shape[0] != partition_npart(self.partition):
-                raise RuntimeError("normal_uncertain error message: row size of mean and partition size should be identical")
+                raise Exception("normal_uncertain error message: row size of mean and partition size should be identical")
             #we need as many steps in stddev and mean as there are in time steps
             if self.stddev.shape[1] != self.nsteps:
-                raise RuntimeError("normal_uncertain error message: col size of stddev and partition size should be identical")
+                raise Exception("normal_uncertain error message: col size of stddev and partition size should be identical")
             if self.mean.shape[1] != self.nsteps:
-                raise RuntimeError("normal_uncertain error message: col size of mean and partition size should be identical")
+                raise Exception("normal_uncertain error message: col size of mean and partition size should be identical")
             md = checkfield(md, 'field', self.partition, 'fieldname', 'normal_uncertain.partition', 'NaN', 1, 'Inf', 1, '>=', -1, 'numel', [md.mesh.numberofvertices, md.mesh.numberofvertices])
             if self.partition.shape[1] > 1:
-                raise RuntimeError("normal_uncertain error message: partition should be a column vector")
+                raise Exception("normal_uncertain error message: partition should be a column vector")
             partcheck = np.unique(self.partition)
             partmin = min(partcheck)
             partmax = max(partcheck)
             if partmax < -1:
-                raise RuntimeError("normal_uncertain error message: partition vector's min value should be -1 (for no partition), or start at 0")
+                raise Exception("normal_uncertain error message: partition vector's min value should be -1 (for no partition), or start at 0")
             nmax = max(md.mesh.numberofelements, md.mesh.numberofvertices)
             if partmax > nmax:
-                raise RuntimeError("normal_uncertain error message: partition vector's values cannot go over the number of vertices or elements")
+                raise Exception("normal_uncertain error message: partition vector's values cannot go over the number of vertices or elements")
     #}}}