lalinference: use range instead of xrange

lalinference/python/cbcBayesMCMC2pos.py

@@ -22,6 +22,8 @@
 #       Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
 #       MA 02110-1301, USA.
 
+from six.moves import range
+
 import matplotlib
 matplotlib.use('Agg') #sets backend to not need to open windows
 
@@ -128,7 +130,7 @@ def downsample_and_evidence(data_hdf5, deltaLogP=None, fixedBurnin=None, nDownsa
 			'Temperature ladder spread over multiple hdf5 files. Use cbcBayesCombinePTMCMCh5s.py to combine.'
 
 	highTchains = []
-	for i in xrange(1,int(posterior_samples['nTemps'][0])):
+	for i in range(1,int(posterior_samples['nTemps'][0])):
 		ps, samps = peparser.parse(data_hdf5, deltaLogP=deltaLogP, fixedBurnins=fixedBurnin,
 			nDownsample=nDownsample, tablename='chain_'+str('%02.f' %i))
 		highTchains.append(apt.Table(samps, names=ps))
@@ -140,7 +142,7 @@ def downsample_and_evidence(data_hdf5, deltaLogP=None, fixedBurnin=None, nDownsa
 	betas[0] = 1./np.median(posterior_samples['temperature'])
 	logls[0] = np.median(posterior_samples['logl'])
 
-	for i in xrange(len(highTchains)):
+	for i in range(len(highTchains)):
 		betas[i+1] = 1./np.median(highTchains[i]['temperature'])
 		logls[i+1] = np.median(highTchains[i]['logl'])
 
@@ -185,7 +187,7 @@ def weight_and_combine(pos_chains, verbose=False):
 	log_evs = np.zeros(len(pos_chains))
 	log_noise_evs = np.zeros_like(log_evs)
 
-	for i in xrange(len(pos_chains)):
+	for i in range(len(pos_chains)):
 		log_evs[i] = pos_chains[i]['chain_log_evidence'][0]
 		log_noise_evs[i] = pos_chains[i]['chain_log_noise_evidence'][0]
 	if verbose: print('Computed log_evidences: %s'%(str(log_evs)))
@@ -195,14 +197,14 @@ def weight_and_combine(pos_chains, verbose=False):
 	fracs=[np.exp(log_ev-max_log_ev) for log_ev in log_evs]
 	if verbose: print('Relative weights of input files: %s'%(str(fracs)))
 
-	Ns=[fracs[i]/len(pos_chains[i]) for i in xrange(len(fracs))]
+	Ns=[fracs[i]/len(pos_chains[i]) for i in range(len(fracs))]
 	Ntot=max(Ns)
 	fracs=[n/Ntot for n in Ns]
 	if verbose: print('Relative weights of input files taking into account their length: %s'%(str(fracs)))
 
 	final_posterior = pos_chains[0][np.random.uniform(size=len(pos_chains[0]))<fracs[0]]
 
-	for i in xrange(1,len(pos_chains)):
+	for i in range(1,len(pos_chains)):
 		final_posterior = apt.vstack([final_posterior,
 			pos_chains[i][np.random.uniform(size=len(pos_chains[i]))<fracs[i]]])
 
@@ -269,7 +271,7 @@ if __name__ == '__main__':
 
 	chain_posteriors = []
 
-	for i in xrange(len(datafiles)):
+	for i in range(len(datafiles)):
 		chain_posteriors.append(downsample_and_evidence(datafiles[i],
 			deltaLogP=opts.deltaLogP, fixedBurnin=fixedBurnins[i], nDownsample=opts.downsample, verbose=opts.verbose))
 

lalinference/python/cbcBayesPosToSimBurst.py

@@ -30,6 +30,7 @@
 Populate a sim_inspiral table with random draws from an ASCII table.
 """
 from optparse import Option, OptionParser
+from six.moves import range
 import numpy as np
 from glue.ligolw import ligolw
 from glue.ligolw import lsctables
@@ -176,7 +177,7 @@ if __name__ == "__main__":
       injections['time_geocent_gps_ns'] = np.modf(est_time)[0] * 10**9
       
     # Populate structured array
-    injections['waveform'] = [opts.approx for i in xrange(N)]
+    injections['waveform'] = [opts.approx for i in range(N)]
     try:
       injections['frequency'] = samples['frequency']
     except:
@@ -205,7 +206,7 @@ if __name__ == "__main__":
     xmldoc.childNodes[0].appendChild(sim_table)
 
     # Add empty rows to the sim_inspiral table
-    for inj in xrange(N):
+    for inj in range(N):
         row = sim_table.RowType()
         for slot in row.__slots__: setattr(row, slot, 0)
         sim_table.append(row)

lalinference/python/cbcBayesPosToSimInspiral.py

@@ -26,6 +26,7 @@
 Populate a sim_inspiral table with random draws from an ASCII table.
 """
 from optparse import Option, OptionParser
+from six.moves import range
 import numpy as np
 from glue.ligolw import ligolw
 from glue.ligolw import lsctables
@@ -249,11 +250,11 @@ if __name__ == "__main__":
         else:  # executed if no exception is raised
             print(('f_low given in both input file and command line.'
                   ' Using command line argument: %r' % opts.flow))
-        flow = [opts.flow for i in xrange(N)]
+        flow = [opts.flow for i in range(N)]
 
     # Populate structured array
-    injections['waveform'] = [opts.approx for i in xrange(N)]
-    injections['taper'] = [opts.taper for i in xrange(N)]
+    injections['waveform'] = [opts.approx for i in range(N)]
+    injections['taper'] = [opts.taper for i in range(N)]
     injections['f_lower'] = flow
     injections['mchirp'] = mc
     injections['eta'] = eta
@@ -273,8 +274,8 @@ if __name__ == "__main__":
     injections['spin2x'] = s2x
     injections['spin2y'] = s2y
     injections['spin2z'] = s2z
-    injections['amp_order'] = [opts.amporder for i in xrange(N)]
-    injections['numrel_data'] = [ "" for _ in xrange(N)]
+    injections['amp_order'] = [opts.amporder for i in range(N)]
+    injections['numrel_data'] = [ "" for _ in range(N)]
 
     # Create a new XML document
     xmldoc = ligolw.Document()
@@ -283,7 +284,7 @@ if __name__ == "__main__":
     xmldoc.childNodes[0].appendChild(sim_table)
 
     # Add empty rows to the sim_inspiral table
-    for inj in xrange(N):
+    for inj in range(N):
         row = sim_table.RowType()
         for slot in row.__slots__: setattr(row, slot, 0)
         sim_table.append(row)

lalinference/python/lalinference/lalinference_pipe_utils.py

@@ -19,6 +19,7 @@ from itertools import permutations
 import shutil
 import numpy as np
 import math
+from six.moves import range
 
 # We use the GLUE pipeline utilities to construct classes for each
 # type of job. Each class has inputs and outputs, which are used to
@@ -1240,7 +1241,7 @@ class LALInferencePipelineDAG(pipeline.CondorDAG):
        combinenodes[i].set_pos_output_file(input_file[:input_file_split_index]+'combine_'+input_file[input_file_split_index:])
        combinenodes[i].add_var_arg(input_file)
        number_of_mpi_jobs = self.config.getint('mpi','mpi_task_count')
-       for j in xrange(1,number_of_mpi_jobs):
+       for j in range(1,number_of_mpi_jobs):
           combinenodes[i].add_var_arg(input_file+".%02d" % j)
        self.add_node(combinenodes[i])
     mergenode=MergeNode(self.merge_job,parents=combinenodes,engine='mcmc')

lalinference/python/lalinference/rapid_pe/effectiveFisher.py

@@ -19,6 +19,8 @@ Module of routines to compute an effective Fisher matrix and related utilities,
 such as finding a region of interest and laying out a grid over it
 """
 
+from six.moves import range
+
 from . import lalsimutils as lsu
 import numpy as np
 from scipy.optimize import leastsq, brentq
@@ -61,7 +63,7 @@ def evaluate_ip_on_grid(hfSIG, P, IP, param_names, grid):
     Npts = len(grid)
     assert len(grid[0])==Nparams
     return np.array([update_params_ip(hfSIG, P, IP, param_names, grid[i])
-            for i in xrange(Npts)])
+            for i in range(Npts)])
 
 
 def update_params_ip(hfSIG, P, IP, param_names, vals):
@@ -276,7 +278,7 @@ def multi_dim_flatgrid(*arrs):
         [2,4,6,2,4,6,2,4,6]
     """
     outarrs = multi_dim_meshgrid(*arrs)
-    return tuple([ outarrs[i].flatten() for i in xrange(len(outarrs)) ])
+    return tuple([ outarrs[i].flatten() for i in range(len(outarrs)) ])
 
 def multi_dim_grid(*arrs):
     """

lalinference/python/lalinference/rapid_pe/lalsimutils.py

@@ -25,6 +25,8 @@ import copy
 import types
 from math import cos, sin, sqrt
 
+from six.moves import range
+
 import numpy as np
 from scipy import interpolate
 from scipy import signal
@@ -335,7 +337,7 @@ class InnerProduct(object):
             WFD.data.data[:] = np.sqrt(self.weights) # W_FD is 1/sqrt(S_n(f))
             WFD.data.data[0] = WFD.data.data[-1] = 0. # zero 0, f_Nyq bins
             lal.REAL8FreqTimeFFT(WTD, WFD, revplan) # IFFT to TD
-            for i in xrange(N_spec/2, self.len2side - N_spec/2):
+            for i in range(N_spec/2, self.len2side - N_spec/2):
                 WTD.data.data[i] = 0. # Zero all but T_spec/2 ends of W_TD
             lal.REAL8TimeFreqFFT(WFD, WTD, fwdplan) # FFT back to FD
             WFD.data.data[0] = WFD.data.data[-1] = 0. # zero 0, f_Nyq bins

lalinference/python/lalinference/rapid_pe/statutils.py

@@ -15,6 +15,8 @@
 # 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import math
 
+from six.moves import range
+
 import numpy
 from lal.rate import BinnedDensity, NDBins, IrregularBins, LinearBins
 
@@ -79,7 +81,7 @@ def get_adaptive_binning(samples, edges, nbins=100, bintype='linear'):
 
     ordering = numpy.argsort(samples)
     stride = len(samples) / nbins
-    bins = [samples[ordering[i]] for i in xrange(stride, nbins*stride, stride)]
+    bins = [samples[ordering[i]] for i in range(stride, nbins*stride, stride)]
     bins.insert(0, edges[0])
     bins.append(edges[1])
     return BinnedArray(NDBins((IrregularBins(bins),)))

lalinference/python/lalinference/tiger/postproc.py

@@ -48,6 +48,7 @@ from sys import exit, stdout, version_info, float_info
 from time import time
 from numpy import sqrt, array, empty, hstack, min, max, reshape, shape, loadtxt, vstack, append, arange, random, column_stack, concatenate, savetxt, log, exp, size, zeros, argmax, argsort, sort, sum, subtract, array_split
 from re import findall
+from six.moves import range
 
 py_version = version_info[:2]
 if py_version < (2, 7):
@@ -182,7 +183,7 @@ def TigerPostProcess(configfile):
 
 	# FETCHING DATA
 	tigerruns = []
-	for i in xrange(len(fetch_type)):
+	for i in range(len(fetch_type)):
 		if fetch_type[i] == 'source':
 			# READING LOCATION FILES
 			locfp = open(fetch_loc[i],'r')
@@ -204,7 +205,7 @@ def TigerPostProcess(configfile):
 	# SAVING DATA BEFORE CUT
 	html_files_sources = []
 	stdout.write('Saving data\n')
-	for i in xrange(len(tigerruns)):
+	for i in range(len(tigerruns)):
 		html_files_sources.append(path.join(localdest,tigerruns[i].label+'.pickle'))
 		tigerruns[i].savetopickle(html_files_sources[-1])
 		stdout.write('... Saving data: %s\n' % html_files_sources[-1])
@@ -225,7 +226,7 @@ def TigerPostProcess(configfile):
 
 	# SAVING DATA AFTER CUT
 	#print 'Saving data - after cut'
-	#for i in xrange(len(tigerruns)):
+	#for i in range(len(tigerruns)):
 		#html_files_sources.append(path.join(localdest,tigerruns[i].label+'_cut'+'.pickle'))
 		#tigerruns[i].savetopickle(html_files_sources[-1])
 		#html_files_sources.append(path.join(localdest,tigerruns[i].label+'_cut'+'.dat'))
@@ -305,7 +306,7 @@ def TigerPostProcess(configfile):
 	html_files_cumfreq = [[]]*len(tigerruns) 
 	if plot_cumfreq == True:
 		stdout.write("... Cumulative frequency\n")
-		for i in xrange(len(tigerruns)):
+		for i in range(len(tigerruns)):
 			html_files_cumfreq[i] = []
 			clf()
 			fig = figure()
@@ -321,11 +322,11 @@ def TigerPostProcess(configfile):
 	stdout.write("... Cumulative Bayes\n")
 	if plot_cumbayes == True:
 		N_sources_cats = array(N_sources[N_sources!=1])
-		for i in xrange(len(tigerruns)):
+		for i in range(len(tigerruns)):
 			html_files_cumbayes[i] = [[]]*len(N_sources_cats)
-			for j in xrange(len(N_sources_cats)):
+			for j in range(len(N_sources_cats)):
 				html_files_cumbayes[i][j] = []
-				for k in xrange(tigerruns[i].nsources/N_sources_cats[j]):
+				for k in range(tigerruns[i].nsources/N_sources_cats[j]):
 					clf()
 					fig = figure()
 					ax = fig.add_subplot(111)
@@ -391,11 +392,11 @@ def TigerPostProcess(configfile):
 		htmltxt+='</tr>'
 
 		background = 0
-		for i in xrange(len(tigerruns)):
+		for i in range(len(tigerruns)):
 			if i != background: # hardcoded for the time being
 				htmltxt+='<tr>'
 				htmltxt+='<td>'+tigerruns[i].label+'</td>'
-				for j in xrange(len(N_sources)):
+				for j in range(len(N_sources)):
 					if i < background:
 						effstr="%.2f "%html_data_efficiencies[j,i,0]+' | '+ '%.2f'% html_data_efficiencies[j,i,1]
 					else:
@@ -417,8 +418,8 @@ def TigerPostProcess(configfile):
 		<td>p value</td>
 		</tr>
 		"""
-		for i in xrange(len(tigerruns)-1):
-			for j in xrange(len(N_sources)):
+		for i in range(len(tigerruns)-1):
+			for j in range(len(N_sources)):
 				htmltxt+='<tr>'
 				htmltxt+='<td>'+'N='+str(N_sources[j])+'</td>'
 				htmltxt+='<td>'+'%.2f'%html_data_ks[i][j][0]+'</td>'
@@ -451,7 +452,7 @@ def TigerPostProcess(configfile):
 	"""
 	<h2>Individual runs</h2>
 	"""
-	for i in xrange(len(tigerruns)):
+	for i in range(len(tigerruns)):
 		htmltxt+="<a href='"+tigerruns[i].label+".html"+"'>"+tigerruns[i].label+"</a>&nbsp;&nbsp;&nbsp;&nbsp;"
 
 	# CLOSE
@@ -462,7 +463,7 @@ def TigerPostProcess(configfile):
 	"""
 
 	# CREATE PAGES FOR INIDVIDUAL RUNS
-	for i in xrange(len(tigerruns)):
+	for i in range(len(tigerruns)):
 		indhtmlfile=open(path.join(localdest,tigerruns[i].label+'.html'), 'w')
 		indhtmltxt=\
 		"""
@@ -494,7 +495,7 @@ def TigerPostProcess(configfile):
 		<h2>Cumulative Bayes factors (per catalog)</h2>
 		"""
 		N_sources_cats = array(N_sources[N_sources!=1])
-		for j in xrange(len(html_files_cumbayes[i])):
+		for j in range(len(html_files_cumbayes[i])):
 			indhtmltxt+="<h2>"+str(N_sources_cats[j])+" sources per catalog"+"</h2>"
 			for item in html_files_cumbayes[i][j]:
 				if ".png" in item:
@@ -610,7 +611,7 @@ class TigerRun:
 
 		# GET SNR FILES
 		#snrfilename = "snr_%s_%.1f.dat"%(self.bayesfiles[k].split('_')[1],int(self.gps[k]))
-		#self.snrfiles = array(["snr_%s_%.1f.dat"%(self.bayesfiles[k].split('_')[1],int(self.gps[k])) for k in xrange(len(self.bayesfiles))])
+		#self.snrfiles = array(["snr_%s_%.1f.dat"%(self.bayesfiles[k].split('_')[1],int(self.gps[k])) for k in range(len(self.bayesfiles))])
 		#self.snrfiles = array(["snr_%s_%.1f.dat"%(x,int(y)) for x,y in zip(self.detectors, self.gps)])
 		self.snrfiles = array([k.replace('_','-').replace('-B.txt','_snr.txt').replace('posterior','lalinferencenest-%s'%(k.split('-')[1].split('.')[0])).replace('%s'%(k.split('-')[0].split('_')[-1]),'%.1f'%(float(k.split('-')[0].split('_')[-1]))) for k in self.bayesfiles])
 		#self.snrfiles = array(["snr_H1L1V1_"+item+".0.dat" for item in self.gps])
@@ -673,7 +674,7 @@ class TigerRun:
 				self.snr = zeros(self.nsources)
 				return
 			count = 0
-			for k in xrange(len(self.gps)):
+			for k in range(len(self.gps)):
 				ndet = len(self.detectors[k])/2
 				if ndet == 1:
 					self.snr.append(float(snrrawdata[count+ndet-1].strip('%s:'%(self.detectors[k])).strip()))
@@ -755,7 +756,7 @@ class TigerSet:
 		# CALCULATING SUBHYPOTHESES
 		self.testcoef = testcoef # testing coefficients
 		self.subhyp=[]
-		for i in xrange(len(self.testcoef)):
+		for i in range(len(self.testcoef)):
 			s = combinations(testcoef, i+1)
 			self.subhyp.extend(["".join(j) for j in s])
 		self.subhyp.insert(0,'GR')
@@ -824,7 +825,7 @@ class TigerSet:
 		CALCULATE THE TOTAL ODDS FROM DATA STRUCTURE
 		"""
 		odds = empty(0)
-		for i in xrange(self.nsources/N):
+		for i in range(self.nsources/N):
 			odds = append(odds,OddsFromBayes(self.bayes[i*N:(i+1)*N,:], len(self.testcoef)))
 		return odds
 
@@ -973,7 +974,7 @@ def OddsFromBayes(matrix, Nhyp):
 
 	TotalOdds = -float_info.max
 	# ONE SMALLER THAN THE WIDTH OF THE MATRIX FOR GR
-	for j in xrange(shape(matrix)[1]-1):
+	for j in range(shape(matrix)[1]-1):
 		TotalOdds = lnPLUS(TotalOdds, sum(matrix[:, j+1]-matrix[:, 0]))
 	TotalOdds -= log(pow(2,Nhyp)-1)
 
@@ -983,7 +984,7 @@ def TigerCreateHistogram(list_tigerruns, axis, N=1, xlim=None,bins=50):
 	"""
 	CREATE TIGER HISTOGRAMS FROM A LIST OF TIGERRUNS
 	"""
-	for i in xrange(len(list_tigerruns)):
+	for i in range(len(list_tigerruns)):
 		if N == 1:
 			# PLOT HISTOGRAMS
 			axis.hist(list_tigerruns[i].odds(N), \
@@ -1026,7 +1027,7 @@ def TigerCalculateEfficiency(list_tigerruns, N=1, beta=[0.95], background=0):
 	efficiencies = []
 	OddsBeta=[mquantiles(list_tigerruns[background].odds(N),prob=[b]) for b in beta]
 	efficiencies = empty((len(list_tigerruns)-1,len(beta)))
-	for i in xrange(len(list_tigerruns)):
+	for i in range(len(list_tigerruns)):
 		if N>list_tigerruns[i].nsources:
 			stdout.write("... Warning: Not sufficient events (%s) to calculate the efficiency for %s sources. Writing zeros\n"%(list_tigerruns[i].nsources,N))
 			if i < background:
@@ -1036,7 +1037,7 @@ def TigerCalculateEfficiency(list_tigerruns, N=1, beta=[0.95], background=0):
 			continue
 		if i != background:
 			tmp = list_tigerruns[i].odds(N)
-			for j in xrange(len(OddsBeta)):
+			for j in range(len(OddsBeta)):
 				msk = tmp>OddsBeta[j]
 				nmsk = tmp<OddsBeta[j]
 				nabovebeta=len(tmp[msk])
@@ -1070,7 +1071,7 @@ def TigerCreateSNRvsOdds(list_tigerruns, axis):
 	axis.set_xlabel("$\mathrm{SNR}$")
 	axis.set_ylabel("$\ln{O_{\mathrm{GR}}^{\mathrm{modGR}}}$")
 
-	for i in xrange(len(list_tigerruns)):
+	for i in range(len(list_tigerruns)):
 		axis.scatter(list_tigerruns[i].snr, list_tigerruns[i].odds(), \
 				color=colors[i], \
 				marker=markers[i], \
@@ -1095,12 +1096,12 @@ def TigerCreateCumFreq(tigerrun, axis):
 	nsnrsteps = len(snrrange) 
 	freqs = zeros((nsnrsteps,tigerrun.nsubhyp)) 
 
-	for i in xrange(nsnrsteps):
+	for i in range(nsnrsteps):
 		if snrrange[i] > min(tigerrun.snr):
 			snrmask = tigerrun.snr<snrrange[i]
 			bayesmasked = tigerrun.bayes[snrmask,:]
 			maxarray = list(argmax(bayesmasked, axis=1))
-			for j in xrange(tigerrun.nsubhyp):
+			for j in range(tigerrun.nsubhyp):
 					freqs[i,j] = maxarray.count(j)
 
 	# CHECK IF TOTAL ADDS UP TO TOTAL NUMBER OF SOURCES
@@ -1113,7 +1114,7 @@ def TigerCreateCumFreq(tigerrun, axis):
 	markerstyles = cycle(['+','*','.','<','d', '^', 'x', 's'])
 	colorstyles = cycle(["b", "g","r","c","m","y","k"])
 
-	for i in xrange(tigerrun.nsubhyp):
+	for i in range(tigerrun.nsubhyp):
 			if tigerrun.subhyp[i].split("_")[-1] =="GR":
 				axis.plot(snrrange, freqs[:,i], label='$\mathcal{H}_{\mathrm{GR}}$', color="black", linewidth=3.0)
 			else:
@@ -1155,8 +1156,8 @@ def TigerCreateCumBayes(tigerrun,axis,nthcat,nsourcespercat):
 	sortorder = argsort(snrcat)
 	snrcatsorted = sort(snrcat)
 	bayescatsorted = bayescat[sortorder,:]
-	cumodds = array([OddsFromBayes(bayescatsorted[:i+1,:],len(tigerrun.testcoef)) for i in xrange(nsourcespercat)])
-	cumbayes = array([sum(subtract(bayescatsorted[:i+1,1:].T,bayescatsorted[:i+1,0]).T,axis=0) for i in xrange(nsourcespercat)])
+	cumodds = array([OddsFromBayes(bayescatsorted[:i+1,:],len(tigerrun.testcoef)) for i in range(nsourcespercat)])
+	cumbayes = array([sum(subtract(bayescatsorted[:i+1,1:].T,bayescatsorted[:i+1,0]).T,axis=0) for i in range(nsourcespercat)])
 
 	markerstyles = cycle(['+','*','.','<','d', '^', 'x', 's'])
 
@@ -1164,7 +1165,7 @@ def TigerCreateCumBayes(tigerrun,axis,nthcat,nsourcespercat):
 	axis.set_ylabel("$\ln{{}^{(cat)}B^{i_1 i_2 \ldots i_k}_{\mathrm{GR}}}$")
 
 	# PLOT BAYESFACTORS
-	for i in xrange(tigerrun.nsubhyp-1):
+	for i in range(tigerrun.nsubhyp-1):
 
 		if tigerrun.engine == 'lalnest':
 			curlabel = "$H_{"+''.join(tigerrun.subhyp[i+1].split("_")[-1].split('dphi'))+"}$"

lalinference/python/rapidpe_compute_intrinsic_fisher.py

@@ -27,6 +27,8 @@ import stat
 from functools import partial
 from argparse import ArgumentParser
 
+from six.moves import range
+
 import numpy as np
 
 import lal
@@ -312,7 +314,7 @@ else: # do random, uniform placement
 	cart_grid, sph_grid = eff.uniform_random_ellipsoid(Nrandpts, r1, r2)
 # Rotate to get coordinates in parameter basis
 cart_grid = np.array([ np.real( np.dot(rot, cart_grid[i]))
-    for i in xrange(len(cart_grid)) ])
+    for i in range(len(cart_grid)) ])
 # Put in convenient units,
 # change from parameter differential (i.e. dtheta)
 # to absolute parameter value (i.e. theta = theta_true + dtheta)
@@ -353,7 +355,7 @@ if opts.save_ellipsoid_data:
 
 # Convert to m1, m2
 m1m2_grid = np.array([lsu.m1m2(cart_grid[i][0], cart_grid[i][1])
-        for i in xrange(len(cart_grid))])
+        for i in range(len(cart_grid))])
 m1m2_grid /= lal.MSUN_SI
 
 if opts.mass_points_xml: