Remove SimPEGLinearOperator as Scipy 16 has implemented this feature.

SimPEG/Maps.py

@@ -1,4 +1,5 @@
 import Utils, numpy as np, scipy.sparse as sp
+from scipy.sparse.linalg import LinearOperator
 from Tests import checkDerivative
 from PropMaps import PropMap, Property
 
@@ -289,7 +290,7 @@ class FullMap(IdentityMap):
     """
     FullMap
 
-    Given a scalar, the FullMap maps the value to the 
+    Given a scalar, the FullMap maps the value to the
     full model space.
     """
 
@@ -314,8 +315,8 @@ class FullMap(IdentityMap):
             :rtype: numpy.array
             :return: derivative of transformed model
         """
-        return np.ones([self.mesh.nC,1])     
-      
+        return np.ones([self.mesh.nC,1])
+
 
 class Vertical1DMap(IdentityMap):
     """Vertical1DMap
@@ -639,7 +640,7 @@ class ComplexMap(IdentityMap):
             return v[:nC] + v[nC:]*1j
         def adj(v):
             return np.r_[v.real,v.imag]
-        return Utils.SimPEGLinearOperator(shp,fwd,adj)
+        return LinearOperator(shp,matvec=fwd,rmatvec=adj)
 
     inverse = deriv
 

SimPEG/Tests/test_PropMaps.py

@@ -8,9 +8,9 @@ class MyPropMap(Maps.PropMap):
     mu    = Maps.Property("Mu", defaultVal=mu_0)
 
 class MyReciprocalPropMap(Maps.PropMap):
-    sigma = Maps.Property("Electrical Conductivity", defaultInvProp=True, propertyLink=('rho',   Maps.ReciprocalMap))
-    rho   = Maps.Property("Electrical Resistivity",                       propertyLink=('sigma', Maps.ReciprocalMap))
-    mu    = Maps.Property("Mu", defaultVal=mu_0, propertyLink=('mui',   Maps.ReciprocalMap))
+    sigma  = Maps.Property("Electrical Conductivity", defaultInvProp=True, propertyLink=('rho',   Maps.ReciprocalMap))
+    rho    = Maps.Property("Electrical Resistivity",                       propertyLink=('sigma', Maps.ReciprocalMap))
+    mu     = Maps.Property("Mu", defaultVal=mu_0,    propertyLink=('mui',  Maps.ReciprocalMap))
     mui    = Maps.Property("Mu", defaultVal=1./mu_0, propertyLink=('mu',   Maps.ReciprocalMap))
 
 

SimPEG/Utils/SolverUtils.py

@@ -110,9 +110,10 @@ def SolverWrapI(fun, checkAccuracy=True, accuracyTol=1e-5):
     return type(fun.__name__+'_Wrapped', (object,), {"__init__": __init__, "clean": clean, "__mul__": __mul__})
 
 
-Solver   = SolverWrapD(sp.linalg.spsolve, factorize=False)
-SolverLU = SolverWrapD(sp.linalg.splu, factorize=True)
-SolverCG = SolverWrapI(sp.linalg.cg)
+from scipy.sparse import linalg
+Solver   = SolverWrapD(linalg.spsolve, factorize=False)
+SolverLU = SolverWrapD(linalg.splu, factorize=True)
+SolverCG = SolverWrapI(linalg.cg)
 
 
 class SolverDiag(object):

SimPEG/Utils/matutils.py

@@ -342,10 +342,10 @@ def invPropertyTensor(M, tensor, returnMatrix=False):
 
 
 def diagEst(matFun, n, k=None, approach='Probing'):
-    """ 
+    """
     Estimate the diagonal of a matrix, A. Note that the matrix may be a function which returns A times a vector.
 
-    Three different approaches have been implemented, 
+    Three different approaches have been implemented,
         1. Probing : uses cyclic permutations of vectors with ones and zeros (default)
         2. Ones : random +/- 1 entries
         3. Random : random vectors
@@ -362,7 +362,7 @@ def diagEst(matFun, n, k=None, approach='Probing'):
 
     if type(matFun).__name__=='ndarray':
         A = matFun
-        matFun = lambda v: A.dot(v) 
+        matFun = lambda v: A.dot(v)
 
     if k is None:
         k = np.floor(n/10.)
@@ -397,10 +397,3 @@ def diagEst(matFun, n, k=None, approach='Probing'):
     return d
 
 
-from scipy.sparse.linalg import LinearOperator
-
-class SimPEGLinearOperator(LinearOperator):
-    """Extends scipy.sparse.linalg.LinearOperator to have a .T function."""
-    @property
-    def T(self):
-        return self.__class__((self.shape[1],self.shape[0]),self.rmatvec,rmatvec=self.matvec,matmat=self.matmat)