Added directive to save dpred after every iteration.

SimPEG/Directives.py

@@ -207,6 +207,39 @@ class SaveOutputEveryIteration(_SaveEveryIteration):
         f.close()
 
 
+class SaveOutputDictEveryIteration(_SaveEveryIteration):
+    """SaveOutputDictEveryIteration
+    A directive that saves some relevant information from the inversion run to a numpy .npz dictionary file (see numpy.savez function for further info).
+    """
+
+    def initialize(self):
+        print "SimPEG.SaveOutputDictEveryIteration will save your inversion progress as dictionary: '###-%s.npz'"%self.fileName
+
+    def endIter(self):
+        # Save the data.
+        ms = self.reg.Ws * ( self.reg.mapping * (self.invProb.curModel - self.reg.mref) )
+        phi_ms = 0.5*ms.dot(ms)
+        if self.reg.smoothModel == True:
+            mref = self.reg.mref
+        else:
+            mref = 0
+        mx = self.reg.Wx * ( self.reg.mapping * (self.invProb.curModel - mref) )
+        phi_mx = 0.5 * mx.dot(mx)
+        if self.prob.mesh.dim==2:
+            my = self.reg.Wy * ( self.reg.mapping * (self.invProb.curModel - mref) )
+            phi_my = 0.5 * my.dot(my)
+        else:
+            phi_my = 'NaN'
+        if self.prob.mesh.dim==3:
+            mz = self.reg.Wz * ( self.reg.mapping * (self.invProb.curModel - mref) )
+            phi_mz = 0.5 * mz.dot(mz)
+        else:
+            phi_mz = 'NaN'
+
+
+        # Save the file as a npz
+        np.savez('{:03d}-{:s}'.format(self.opt.iter,self.fileName), iter=self.opt.iter, beta=self.invProb.beta, phi_d=self.invProb.phi_d, phi_m=self.invProb.phi_m, phi_ms=phi_ms, phi_mx=phi_mx, phi_my=phi_my, phi_mz=phi_mz,f=self.opt.f, m=self.invProb.curModel,dpred=self.invProb.dpred)
+
 
 
 # class UpdateReferenceModel(Parameter):

SimPEG/MT/BaseMT.py

@@ -66,6 +66,7 @@ class BaseMTProblem(BaseFDEMProblem):
                     # v should be of size 2*nE (for 2 polarizations)
                     PDeriv_u = lambda t: rx.projectFieldsDeriv(src, self.mesh, u, t) # wrt u, we don't have have PDeriv wrt m
                     Jv[src, rx] = PDeriv_u(mkvc(du_dm))
+            dA_duI.clean()
         # Return the vectorized sensitivities
         return mkvc(Jv)
 
@@ -112,5 +113,6 @@ class BaseMTProblem(BaseFDEMProblem):
                         Jtv +=  -du_dmT.real
                     else:
                         raise Exception('Must be real or imag')
-
+            # Clean the factorization, clear memory.
+            ATinv.clean()
         return Jtv