Index: sm/trunk-jpl/src/m/sampling.py
===================================================================
--- /issm/trunk-jpl/src/m/sampling.py	(revision 26015)
+++ 	(revision )
@@ -1,106 +1,0 @@
-import numpy as np
-
-from checkfield import checkfield
-from fielddisplay import fielddisplay
-from project3d import project3d
-from WriteData import WriteData
-
-
-class sampling(object):
-    """SAMPLING class definition
-
-    Usage:
-        sampling = sampling()
-    """
-
-    def __init__(self):  # {{{
-        self.kappa = float('NaN')
-        self.tau = 0
-        self.beta = float('NaN')
-        self.hydrostatic_adjustment = 0
-        self.phi = 0
-        self.alpha = 0
-        self.robin = 0
-        self.seed = 0
-        self.requested_outputs = []
-
-        # Set defaults
-        self.setdefaultparameters()
-
-    #}}}
-
-    def __repr__(self):  # {{{
-        s = '   Sampling parameters::\n'
-        s += '{}\n'.format(fielddisplay(self,'kappa','coefficient of the identity operator'))
-        s += '{}\n'.format(fielddisplay(self,'tau','scaling coefficient of the solution (default 1.0)'))
-        s += '{}\n'.format(fielddisplay(self,'alpha','exponent in PDE operator, (default 2.0, BiLaplacian covariance operator)'))
-        s += '{}\n'.format(disp(sprintf('\n      %s','Parameters of Robin boundary conditions nabla () \cdot normvec + beta ():')))
-        s += '{}\n'.format(fielddisplay(self,'beta','Coefficient in Robin boundary conditions (to be defined for robin = 1)'))
-        s += '{}\n'.format(fielddisplay(self,'phi','Temporal correlation factor (|phi|<1 for stationary process, phi = 1 for random walk process) (default 0)'))
-        s += '{}\n'.format(fielddisplay(self,'seed','Seed for pseudorandom number generator (given seed if >=0 and random seed if <0) (default -1)'))
-        s += '{}\n'.format(fielddisplay(self, 'requested_outputs', 'additional outputs requested (not implemented yet)'))
-        return s
-    #}}}
-
-    def defaultoutputs(self, md):  # {{{
-        return []
-
-    #}}}
-    def setdefaultparameters(self):  # {{{
-        # Scaling coefficient
-        self.tau = 1
-        # Apply Robin boundary conditions
-        self.robin = 0
-        # Temporal correlation factor
-        self.phi = 0
-        # Exponent in fraction SPDE (default=2, biLaplacian covariance operator)
-        self.alpha = 2 
-        # Seed for pseudorandom number generator (default -1 for random seed)
-        self.alpha = -1 
-        # Default output
-        self.requested_outputs = ['default']
-        return self
-    #}}}
-
-    def checkconsistency(self, md, solution, analyses):  # {{{
-        # Early return
-        if (SamplingAnalysis' not in analyses):
-            return md
-
-        md = checkfield(md,'fieldname','sampling.kappa','NaN',1,'Inf',1,'size',[md.mesh.numberofvertices 1],'>',0)
-        md = checkfield(md,'fieldname','sampling.tau','NaN',1,'Inf',1,'numel',1,'>',0)
-        md = checkfield(md,'fieldname','sampling.beta','NaN',1,'Inf',1,'size',[md.mesh.numberofvertices 1],'>',0)
-        md = checkfield(md,'fieldname','sampling.phi','NaN',1,'Inf',1,'numel',1,'>=',0)
-        md = checkfield(md,'fieldname','sampling.alpha','NaN',1,'Inf',1,'numel',1,'>',0)
-        md = checkfield(md,'fieldname','sampling.robin','numel',1,'values',[0 1])
-        md = checkfield(md,'fieldname','sampling.seed','NaN',1,'Inf',1,'numel',1)
-        md = checkfield(md,'fieldname','sampling.requested_outputs','stringrow',1)
-
-        return md
-    # }}}
-
-    def marshall(self, prefix, md, fid):  # {{{
-        WriteData(fid,prefix,'object',self,'fieldname','kappa','format','DoubleMat','mattype',1)
-        WriteData(fid,prefix,'object',self,'fieldname','tau','format','Double')
-        WriteData(fid,prefix,'object',self,'fieldname','beta','format','DoubleMat','mattype',1)
-        WriteData(fid,prefix,'object',self,'fieldname','phi','format','Double')
-        WriteData(fid,prefix,'object',self,'fieldname','alpha','format','Integer')
-        WriteData(fid,prefix,'object',self,'fieldname','robin','format','Boolean')
-        WriteData(fid,prefix,'object',self,'fieldname','seed','format','Integer')
-
-        # Process requested outputs
-        outputs = self.requested_outputs
-        indices = [i for i, x in enumerate(outputs) if x == 'default']
-        if len(indices) > 0:
-            outputscopy = outputs[0:max(0, indices[0] - 1)] + self.defaultoutputs(md) + outputs[indices[0] + 1:]
-            outputs = outputscopy
-        WriteData(fid, prefix, 'data', outputs, 'name', 'md.masstransport.requested_outputs', 'format', 'StringArray')
-
-        # Process requested outputs
-		outputs = self.requested_outputs
-		indices = [i for i, x in enumerate(outputs) if x == 'default']
-		   if len(indices) > 0:
-            outputscopy = outputs[0:max(0, indices[0] - 1)] + self.defaultoutputs(md) + outputs[indices[0] + 1:]
-            outputs = outputscopy
-		WriteData(fid,prefix,'data',outputs,'name','md.sampling.requested_outputs','format','StringArray')
-    # }}}