source: issm/oecreview/Archive/25834-26739/ISSM-26665-26666.diff

../trunk-jpl/src/m/classes/stochasticforcing.py

@@ -34 +34 @@
         s += '{}\n'.format(fielddisplay(self, 'randomflag', 'whether to apply real randomness (true) or pseudo-randomness with fixed seed (false)'))
         s += 'Available fields:\n'
         s += '   DefaultCalving\n'
+        s += '   FloatingMeltRate\n'
         s += '   SMBautoregression\n'
         s += '   FrontalForcingsRignotAutoregression (thermal forcing)\n'
         return s
@@ -57 +58 @@
         # Check that covariance matrix is positive definite (this is done internally by linalg)
         try:
             np.linalg.cholesky(self.covariance)
-        except LinAlgError:
+        except:
             error('md.stochasticforcing.covariance is not positive definite')
 
         # Check that all fields agree with the corresponding md class and if any field needs the default params