Index: /issm/trunk-jpl/src/m/classes/stochasticforcing.py =================================================================== --- /issm/trunk-jpl/src/m/classes/stochasticforcing.py (revision 26759) +++ /issm/trunk-jpl/src/m/classes/stochasticforcing.py (revision 26760) @@ -43,7 +43,7 @@ def setdefaultparameters(self): # {{{ # Type of stabilization used - self.isstochasticforcing = 0 # stochasticforcing is turned off by default - self.fields = [] # Need to initialize to list to avoid "RuntimeError: object of type 'float' has no len()" on import of class - self.randomflag = 1 # true randomness is implemented by default + self.isstochasticforcing = 0 # stochasticforcing is turned off by default + self.fields = [] # Need to initialize to list to avoid "RuntimeError: object of type 'float' has no len()" on import of class + self.randomflag = 1 # true randomness is implemented by default return self #}}} @@ -63,5 +63,5 @@ # Check that all fields agree with the corresponding md class and if any field needs the default params - checkdefaults = False # Need to check defaults only if one of the fields does not have its own dimensionality + checkdefaults = False # Need to check defaults only if one of the fields does not have its own dimensionality structstoch = self.structstochforcing() for field in self.fields: @@ -91,24 +91,24 @@ if (type(md.friction).__name__ != mdname): raise TypeError('stochasticforcing field {} is only implemented for default friction'.format(field)) - if (md.friction.coupling!=0 and md.friction.coupling!=1 and md.friction.coupling!=2): + if md.friction.coupling not in[0, 1, 2]: raise TypeError('stochasticforcing field {} is only implemented for cases md.friction.coupling 0 or 1 or 2'.format(field)) - if (np.any(md.friction.q==0)): - raise TypeError('stochasticforcing field {} requires non-zero q exponent'.format(field)) + if (np.any(md.friction.q == 0)): + raise TypeError('stochasticforcing field {} requires non-zero q exponent'.format(field)) # Checking for specific dimensions - if not (field == 'SMBautoregression' or field == 'FrontalForcingsRignotAutoregression'): - checkdefaults = True # field with non-specific dimensionality + if field not in['SMBautoregression', 'FrontalForcingsRignotAutoregression']: + checkdefaults = True # field with non-specific dimensionality # Retrieve sum of all the field dimensionalities size_tot = self.defaultdimension * num_fields - indSMBar = -1 # About to check for index of SMBautoregression - indTFar = -1 # About to check for index of FrontalForcingsRignotAutoregression + indSMBar = -1 # About to check for index of SMBautoregression + indTFar = -1 # About to check for index of FrontalForcingsRignotAutoregression if ('SMBautoregression' in self.fields): size_tot = size_tot - self.defaultdimension + md.smb.num_basins - indSMBar = self.fields.index('SMBautoregression') # Index of SMBar, now check for consistency with TFar timestep (08Nov2021) + indSMBar = self.fields.index('SMBautoregression') # Index of SMBar, now check for consistency with TFar timestep (08Nov2021) if ('FrontalForcingsRignotAutoregression' in self.fields): size_tot = size_tot - self.defaultdimension + md.frontalforcings.num_basins - indTFar = self.fields.index('FrontalForcingsRignotAutoregression') # Index of TFar, now check for consistency with SMBar timestep (08Nov2021) - if (indSMBar != -1 and indTFar != -1): # Both autoregressive models are used: check autoregressive time step consistency + indTFar = self.fields.index('FrontalForcingsRignotAutoregression') # Index of TFar, now check for consistency with SMBar timestep (08Nov2021) + if (indSMBar != -1 and indTFar != -1): # Both autoregressive models are used: check autoregressive time step consistency covsum = self.covariance[np.sum(self.defaultdimensions[0:indSMBar]).astype(int):np.sum(self.defaultdimensions[0:indSMBar + 1]).astype(int), np.sum(self.defaultdimensions[0:indTFar]).astype(int):np.sum(self.defaultdimensions[0:indTFar + 1]).astype(int)] if((md.smb.ar_timestep != md.frontalforcings.ar_timestep) and np.any(covsum != 0)): @@ -121,5 +121,5 @@ if (checkdefaults): md = checkfield(md, 'fieldname', 'stochasticforcing.defaultdimension', 'numel', 1, 'NaN', 1, 'Inf', 1, '>', 0) - md = checkfield(md, 'fieldname', 'stochasticforcing.default_id','Inf',1,'NaN',1,'>=',0,'<=',self.defaultdimension,'size', [md.mesh.numberofelements]) + md = checkfield(md, 'fieldname', 'stochasticforcing.default_id', 'Inf', 1, 'NaN', 1, '>=', 0, '<=', self.defaultdimension, 'size', [md.mesh.numberofelements]) return md # }}} @@ -139,5 +139,5 @@ else: # Retrieve dimensionality of each field - dimensions = self.defaultdimension * np.ones((num_fields,)) + dimensions = self.defaultdimension * np.ones((num_fields)) for ind, field in enumerate(self.fields): # Checking for specific dimensions @@ -148,5 +148,5 @@ # Scaling covariance matrix (scale column-by-column and row-by-row) - scaledfields = ['DefaultCalving','FloatingMeltRate','SMBautoregression','SMBforcing'] # list of fields that need scaling * 1/yts + scaledfields = ['DefaultCalving', 'FloatingMeltRate', 'SMBautoregression', 'SMBforcing'] # list of fields that need scaling * 1/yts tempcovariance = np.copy(self.covariance) for i in range(num_fields): @@ -161,9 +161,9 @@ self.default_id = np.zeros(md.mesh.numberofelements) # Reshape dimensions as column array for marshalling - dimensions = dimensions.reshape(1,len(dimensions)) + dimensions = dimensions.reshape(1, len(dimensions)) WriteData(fid, prefix, 'data', num_fields, 'name', 'md.stochasticforcing.num_fields', 'format', 'Integer') WriteData(fid, prefix, 'object', self, 'fieldname', 'fields', 'format', 'StringArray') - WriteData(fid, prefix, 'data', dimensions, 'name', 'md.stochasticforcing.dimensions', 'format', 'IntMat','mattype',2) + WriteData(fid, prefix, 'data', dimensions, 'name', 'md.stochasticforcing.dimensions', 'format', 'IntMat', 'mattype', 2) WriteData(fid, prefix, 'object', self, 'fieldname', 'default_id', 'format', 'IntMat', 'mattype', 2) #12Nov2021 make sure this is zero-indexed! WriteData(fid, prefix, 'object', self, 'fieldname', 'defaultdimension', 'format', 'Integer')