[cig-commits] [commit] validate_MT_params: Parameter validation for modified tait (3c66047)

[cig_noreply at geodynamics.org]

Repository : https://github.com/geodynamics/burnman

On branch  : validate_MT_params
Link       : https://github.com/geodynamics/burnman/compare/0000000000000000000000000000000000000000...3c66047445484f7df5dc2264c36bac2422f94b3f

>---------------------------------------------------------------

commit 3c66047445484f7df5dc2264c36bac2422f94b3f
Author: Bob Myhill <myhill.bob at gmail.com>
Date:   Fri Dec 12 17:23:03 2014 -0800

    Parameter validation for modified tait


>---------------------------------------------------------------

3c66047445484f7df5dc2264c36bac2422f94b3f
 burnman/eos/modified_tait.py | 58 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 58 insertions(+)

diff --git a/burnman/eos/modified_tait.py b/burnman/eos/modified_tait.py
index f11687d..5598336 100644
--- a/burnman/eos/modified_tait.py
+++ b/burnman/eos/modified_tait.py
@@ -287,3 +287,61 @@ class MT(eos.EquationOfState):
         at ambient pressure [Pa]
         """
         return (params['Cp'][0]*np.log(temperature) + params['Cp'][1]*temperature - 0.5*params['Cp'][2]/np.power(temperature,2.) - 2.0*params['Cp'][3]/np.sqrt(temperature)) - (params['Cp'][0]*np.log(T_0) + params['Cp'][1]*T_0 - 0.5*params['Cp'][2]/(T_0*T_0) - 2.0*params['Cp'][3]/np.sqrt(T_0))
+
+
+    def validate_parameters(self, params):
+        """
+        Check for existence and validity of the parameters
+        """
+
+        #if G and Gprime are not included this is presumably deliberate,
+        #as we can model density and bulk modulus just fine without them,
+        #so just add them to the dictionary as nans
+        if 'H_0' not in params:
+            params['H_0'] = float('nan')
+        if 'S_0' not in params:
+            params['S_0'] = float('nan')
+        if 'G_0' not in params:
+            params['G_0'] = float('nan')
+        if 'Gprime_0' not in params:
+            params['Gprime_0'] = float('nan')
+  
+        #check that all the required keys are in the dictionary
+        expected_keys = ['H_0', 'S_0', 'V_0', 'Cp', 'a_0', 'K_0', 'Kprime_0', 'Kdprime_0', 'n', 'molar_mass']
+        for k in expected_keys:
+            if k not in params:
+                raise KeyError('params object missing parameter : ' + k)
+        
+        #now check that the values are reasonable.  I mostly just
+        #made up these values from experience, and we are only 
+        #raising a warning.  Better way to do this? [IR]
+        if params['G_0'] is not float('nan') and (params['G_0'] < 0. or params['G_0'] > 1.e13):
+            warnings.warn( 'Unusual value for G_0', stacklevel=2 )
+        if params['Gprime_0'] is not float('nan') and (params['Gprime_0'] < -5. or params['Gprime_0'] > 10.):
+            warnings.warn( 'Unusual value for Gprime_0', stacklevel=2 )
+
+        # no test for H_0
+        if params['S_0'] is not float('nan') and params['S_0'] < 0.:
+            warnings.warn( 'Unusual value for S_0', stacklevel=2 )
+        if params['V_0'] < 1.e-7 or params['V_0'] > 1.e-3:
+            warnings.warn( 'Unusual value for V_0', stacklevel=2 )
+
+            
+        if self.heat_capacity_p0(T_0,params) < 0.:
+            warnings.warn( 'Negative heat capacity at T_0', stacklevel=2 )
+        if self.heat_capacity_p0(2000.,params) < 0.:
+            warnings.warn( 'Negative heat capacity at 2000K', stacklevel=2 )
+ 
+        if params['a_0'] < 0. or params['a_0'] > 1.e-3:
+            warnings.warn( 'Unusual value for a_0', stacklevel=2 )
+        if params['K_0'] < 1.e9 or params['K_0'] > 1.e13:
+            warnings.warn( 'Unusual value for K_0', stacklevel=2 )
+        if params['Kprime_0'] < 0. or params['Kprime_0'] > 10.:
+            warnings.warn( 'Unusual value for Kprime_0', stacklevel=2 )
+        # no test for Kdprime_0
+
+        if params['n'] < 1. or params['n'] > 100. or not float(params['n']).is_integer():
+            warnings.warn( 'Unusual value for n', stacklevel=2 )
+        if params['molar_mass'] < 0.001 or params['molar_mass'] > 1.:
+            warnings.warn( 'Unusual value for molar_mass', stacklevel=2 )
+