Possibly deal with a good chunk of the old_divs

SimPEG/Examples/Inversion_IRLS.py

@@ -6,7 +6,6 @@ from future import standard_library
 standard_library.install_aliases()
 from builtins import str
 from builtins import range
-from past.utils import old_div
 from SimPEG import *
 
 
@@ -56,10 +55,10 @@ def run(N=100, plotIt=True):
 
     # Distance weighting
     wr = np.sum(prob.G**2.,axis=0)**0.5
-    wr = ( old_div(wr,np.max(wr)) )
+    wr = ( wr/np.max(wr))
 
     dmis = DataMisfit.l2_DataMisfit(survey)
-    dmis.Wd = old_div(1.,wd)
+    dmis.Wd = 1./wd
 
     betaest = Directives.BetaEstimate_ByEig()
 
@@ -68,15 +67,15 @@ def run(N=100, plotIt=True):
     reg.cell_weights = wr
 
     reg.mref = np.zeros(mesh.nC)
-    
+
 
     opt = Optimization.ProjectedGNCG(maxIter=100 ,lower=-2.,upper=2., maxIterLS = 20, maxIterCG= 10, tolCG = 1e-3)
     invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
     update_Jacobi = Directives.Update_lin_PreCond()
-    
+
     # Set the IRLS directive, penalize the lowest 25 percentile of model values
     # Start with an l2-l2, then switch to lp-norms
-    norms   = [0., 0., 2., 2.]    
+    norms   = [0., 0., 2., 2.]
     IRLS = Directives.Update_IRLS( norms=norms, prctile = 25, maxIRLSiter = 15, minGNiter=3)
 
     inv = Inversion.BaseInversion(invProb, directiveList=[IRLS,betaest,update_Jacobi])