Updated uw for python3 compatibility.

python/uw/data/dataman.py

@@ -11,7 +11,7 @@
 import collections
 import glob
 import warnings
-from cPickle import dump,load
+from pickle import dump,load
 
 import numpy as np
 from astropy.io import fits as pyfits
@@ -536,7 +536,7 @@ def _make_ltcube(self):
                 print ('Constructing all-sky livetime cube')
             else:
                 print('Constructing livetime cube about RA,Dec = ({0:0.3f},{1:0.3f}) with a radius of {2:0.3f} deg.'.format(roi_dir.ra(),roi_dir.dec(),exp_radius))
-        for i in xrange(1+self.use_weighted_livetime):
+        for i in range(1+self.use_weighted_livetime):
             #print('on iteration {0}'.format(i))
             sys.stdout.flush()
             lt = skymaps.LivetimeCube(

python/uw/data/dssman.py

@@ -195,7 +195,7 @@ def __init__(self,fits_name,header_key='EVENTS'):
         indeces = sorted(list(set([int(k[-1]) for k in keys])))
         kdeque,vdeque = deque(),deque()
         counter = 0 # index of list of DSS indeces
-        for i in xrange(len(keys)):
+        for i in range(len(keys)):
             if int(keys[i][-1])!=indeces[counter]:
                 self.append(DSSFactory(kdeque,vdeque)) 
                 kdeque.clear(); vdeque.clear()
@@ -228,7 +228,7 @@ def delete(self,index):
         """ Delete a DSS entry and re-index the remaining ones."""
         ret = self.pop(index)
         if index < len(self)-1:
-            for i in xrange(index,len(self)):
+            for i in range(index,len(self)):
                 self[i]['index'] = i+1
         return ret
 
@@ -259,7 +259,7 @@ def roi_info(self,tol=0.01,delete_duplicates=False):
             optionally delete them."""
         roi_info = None
         offset = 0
-        for i in xrange(len(self)):
+        for i in range(len(self)):
             i += offset
             d = self[i]
             r = d.roi_info()
@@ -306,7 +306,7 @@ def process_pixeldata(pd):
     ptlvars = ['zenithcut','thetacut','emin','emax']
     indices = [1,1,0,1]
 
-    for i in xrange(len(ptlvars)):
+    for i in range(len(ptlvars)):
         # check the appropriate simple cut
         ptl_var = pd.__dict__[ptlvars[i]]
         dss = dsse.get_simple_entry(colnames[i])

python/uw/irfs/psfman.py

@@ -121,7 +121,7 @@ def _calculate_weights(self,event_type, skydir=None):
 
         dummy     = skydir or SkyDir() 
         weights  = np.zeros([len(elo),len(clo)])
-        for i in xrange(len(elo)):                   # iterator over energies
+        for i in range(len(elo)):                   # iterator over energies
             em = (elo[i]*ehi[i])**0.5
             for k,(c0,c1) in enumerate(zip(clo,chi)):# iterator over cos(theta) on-axis to edge
                 if c0 < 0.35: continue               # exclude bins below cos(theta)=0.4

python/uw/like/Models.py

@@ -666,7 +666,7 @@ def __str__(self,absolute=False, indent=''):
         l=[]
         if (not self.background and np.any(lo_p[0:-2]!=0)) or \
                  (self.background and np.any(lo_p!=0)): #if statistical errors are present    
-            for i in xrange(len(pnames)):
+            for i in range(len(pnames)):
                 t_n = '%-10s' % pnames[i]
                 if i < self.npar:
                     # if free is empty (shouldn't happen normally) treat as all False
@@ -685,7 +685,7 @@ def __str__(self,absolute=False, indent=''):
                     l+=[t_n+': %.3g + %.3g - %.3g (avg = %.3g) %s'%(p[i],hi_p[i],lo_p[i],(hi_p[i]*lo_p[i])**0.5,frozen)]
             return indent+ ('\n'+indent).join(l)
         else: #if no errors are present
-            for i in xrange(len(pnames)):
+            for i in range(len(pnames)):
                 t_n = '%-10s' % pnames[i]
                 if i < self.npar:
                     frozen = '' if self.free[i] else '(FROZEN)'
@@ -904,7 +904,7 @@ def __flux_derivs__(self,*args):
         errs = np.asarray([delta] * len(self._p) )
         hi,lo = self.copy(),self.copy()
         derivs = []
-        for i in xrange(len(self._p)):
+        for i in range(len(self._p)):
             hi.setp(i,hi._p[i] + errs[i],internal=True)
             lo.setp(i,lo._p[i] - errs[i],internal=True)
             derivs  += [(hi.i_flux(*args) - lo.i_flux(*args))/(2*errs[i])]
@@ -2006,7 +2006,7 @@ def __call__(self,e):
         f = lambda x: kv(5./3,x)
         if hasattr(e,'__len__'):
             rvals = np.empty(len(e),dtype=float)
-            for i in xrange(len(rvals)):
+            for i in range(len(rvals)):
                 rvals[i] = n0*quad(f,e[i]/cutoff,np.inf)[0]
         else:
             rvals = n0*quad(f,float(e)/cutoff,np.inf)[0]

python/uw/like/counts_plotter.py

@@ -14,7 +14,7 @@
 
 def counts(r,integral=False):
 
-    groupings = [deque() for x in xrange(len(r.bin_centers))]
+    groupings = [deque() for x in range(len(r.bin_centers))]
 
     #group slw by energy
     for i,ei in enumerate(r.bin_centers):
@@ -30,7 +30,7 @@ def counts(r,integral=False):
     src = np.asarray([ np.asarray([band.phase_factor*band.ps_counts*band.overlaps for band in g]).sum(axis=0) for g in groupings])
 
     if integral:
-        for i in xrange(len(iso)):
+        for i in range(len(iso)):
             #iso[i] = iso[i:].sum()
             #gal[i] = gal[i:].sum()
             dif[i] = dif[i:].sum(axis=0)
@@ -61,14 +61,14 @@ def get_counts(roi, merge_non_free=True, merge_all=False, integral=False):
         free_mask = np.asarray([np.any(m.free) for m in roi.psm.models]) 
         new_src    = np.zeros([len(en),free_mask.sum()+1])
         counter = 0
-        for i in xrange(len(free_mask)):
+        for i in range(len(free_mask)):
             if free_mask[i] :
                 new_src[:,counter] = src[:,i]
                 counter += 1
             else:
                 new_src[:,-1] += src[:,i]
         src        = new_src
-        ps_names = [ps_names[i] for i in xrange(len(ps_names)) if free_mask[i] ]
+        ps_names = [ps_names[i] for i in range(len(ps_names)) if free_mask[i] ]
         ps_names += ['Other Point Sources' ]
     models =  zip(bg_names+ps_names, np.hstack((dif,src)).T)
     return dict(energies=en, observed=obs, models=models, total=tot)

python/uw/like/likelihood_fit.py

@@ -112,7 +112,7 @@ def find_logl_change(self,initial_value,delta_logl):
         hi = initial_value
         ll_0 = self.function(hi)
         if ll_0-self.function(lo)>delta_logl:
-            for i in xrange(20):
+            for i in range(20):
                 avg = .5*(hi+lo)
                 ll = self.function(avg)
                 if ll_0-ll<delta_logl: hi = avg
@@ -124,7 +124,7 @@ def find_logl_change(self,initial_value,delta_logl):
         lo = initial_value
         hi = initial_value*10
         while ll_0-self.function(hi)<delta_logl: hi+=1
-        for i in xrange(20):
+        for i in range(20):
             avg = .5*(lo+hi)
             ll = self.function(avg)
             if ll_0-ll<delta_logl: lo = avg

python/uw/like/mapplots.py

@@ -686,7 +686,7 @@ def ppf(prob,mean):
             return n.ppf(prob)        
         d = poisson(mean)
         prev = 0
-        for i in xrange(1,200):        
+        for i in range(1,200):        
             new = d.cdf(i)
             if new >= prob: break
             prev = new

python/uw/like/pointspec2.py

@@ -86,7 +86,7 @@ def __init__(self, data_specification, **kwargs):
         """
         if not isinstance(data_specification,dataman.DataSpec):
             if os.path.exists(data_specification):
-                from cPickle import load
+                from pickle import load
                 try:
                     data_specification = load(file(data_specification))
                 except UnpicklingError:

python/uw/like/pointspec_helpers.py

@@ -25,7 +25,7 @@ def __init__(self,skydir,name,model=None,free_parameters=True,leave_parameters=F
         self.model  = PowerLaw() if model is None else model
         #if not free_parameters:
         if not leave_parameters:
-            for i in xrange(len(self.model.free)): self.model.free[i] = free_parameters
+            for i in range(len(self.model.free)): self.model.free[i] = free_parameters
         self.duplicate = False
     def __str__(self):
         return '\n'.join(['\n',

python/uw/like/pypsf.py

@@ -11,7 +11,6 @@
 import numpy as np
 from os.path import join
 import os
-from cPickle import load
 from skymaps import ExposureWeighter,SkyDir,PySkyFunction,Hep3Vector,\
     WeightedSkyDirList,PythonUtilities,PythonPsf
 from scipy.integrate import quad,simps
@@ -98,7 +97,7 @@ def __calc_weights__(self,livetimefile='',skydir=None):
         elo,ehi,clo,chi = self.e_los,self.e_his,self.c_los[::-1],self.c_his[::-1]
 
         weights  = np.zeros([2,len(elo),len(clo)])
-        for i in xrange(len(elo)):                            # iterator over energies
+        for i in range(len(elo)):                            # iterator over energies
             em = (elo[i]*ehi[i])**0.5
             for j in [0,1]:                                      # iterate over conversion types
                 for k,(c0,c1) in enumerate(zip(clo,chi)):# iterator over cos(theta) on-axis to edge
@@ -169,7 +168,7 @@ def _norm_pars(self):
         ens = (self.e_los*self.e_his)**0.5
         for ct in [0,1]: # iterate through conversion type
             scale_func = self.scale_func[ct]
-            for i in xrange(self.tables.shape[2]): # iterate through energy
+            for i in range(self.tables.shape[2]): # iterate through energy
                 sf = scale_func(ens[i])
                 # vector operations in incidence angle
                 nc,nt,gc,gt,sc,st = self.tables[ct,:,i,:]
@@ -310,7 +309,7 @@ def __init__(self,psf,band,**kwargs):
 
             # calculate the actual PSF integral over sub-bands
             fopt = np.zeros_like(dom)
-            for i in xrange(len(b.sp_points)):
+            for i in range(len(b.sp_points)):
                 n = psf(b.sp_points[i],b.ct,dom,density=True)*b.sp_vector[i]*b.sp_points[i]**-index
                 fopt += n
             n = (b.sp_vector*f(b.sp_points)).sum()

python/uw/like/roi_analysis.py

@@ -326,10 +326,10 @@ def check_gradient(self,tol=1e-3,get_approx_gradient=False):
         p0 = self.get_parameters().copy()
         grad = np.empty_like(p0)
         max_iter = 40
-        for i in xrange(len(p0)):
+        for i in range(len(p0)):
             delta = 1e-2
             prev_grad = np.inf
-            for j in xrange(max_iter):
+            for j in range(max_iter):
                 pwork = p0.copy()
                 pwork[i] += delta
                 lhi = self.logLikelihood(pwork)
@@ -444,7 +444,7 @@ def fit(self,method='simplex', tolerance = 0.01, save_values = True,
             from uw.utilities.minuit import Minuit
             temp_params = self.parameters()
             npars = self.parameters().shape[0]
-            param_names = ['p%i'%i for i in xrange(npars)]
+            param_names = ['p%i'%i for i in range(npars)]
 
             if use_gradient:
                 gradient         = self.gradient
@@ -476,7 +476,7 @@ def fit(self,method='simplex', tolerance = 0.01, save_values = True,
             ll_0 = self.logLikelihood(self.parameters())
             if use_gradient:
                 f0 = fmin_bfgs(self.logLikelihood,self.parameters(),self.gradient,full_output=1,maxiter=500,gtol=gtol,disp=0)
-                for i in xrange(10):
+                for i in range(10):
                     f = self._save_bfgs = fmin_bfgs(self.logLikelihood,self.parameters(),self.gradient,full_output=1,maxiter=500,gtol=gtol,disp=0)
                     if abs(f0[1] - f[1]) < tolerance: break # note absolute tolerance
                     if not self.quiet:

python/uw/like/roi_diffuse.py

@@ -250,7 +250,7 @@ def sub_energy_binning(band,nsimps):
 
     def initialize_counts(self,bands,roi_dir=None):
         rd = self.roi_dir if roi_dir is None else roi_dir
-        self.bands = [SmallBand() for i in xrange(len(bands))]
+        self.bands = [SmallBand() for i in range(len(bands))]
 
         for iband,(myband,band) in enumerate(zip(self.bands,bands)):
             if not self.quiet: 
@@ -342,7 +342,7 @@ def gradient(self,bands,model_index):
         for myband,band in zip(self.bands,bands):
             pts = sm.gradient(myband.bg_points)
             cp = 0
-            for j in xrange(npar):
+            for j in range(npar):
                 if not sm.free[j]: continue
                 apterm = band.phase_factor*(myband.ap_evals * pts[j,:]).sum()
                 if band.has_pixels:

python/uw/like/roi_extended.py

@@ -126,7 +126,7 @@ def set_state(self,band):
 
     def initialize_counts(self,bands,roi_dir=None):
         rd = self.roi_dir if roi_dir is None else roi_dir
-        self.bands = [SmallBand() for i in xrange(len(bands))]
+        self.bands = [SmallBand() for i in range(len(bands))]
 
         es = self.extended_source
         sm = es.model

python/uw/like/roi_image.py

@@ -210,7 +210,7 @@ def process_filedata(roi,selected_bands):
 
         data = get_fields(ft1files,['RA','DEC','TIME','ENERGY','CONVERSION_TYPE'],cuts)
         # convert into skydirs
-        skydirs = [ SkyDir(float(data['RA'][i]),float(data['DEC'][i])) for i in xrange(len(data['RA']))]
+        skydirs = [ SkyDir(float(data['RA'][i]),float(data['DEC'][i])) for i in range(len(data['RA']))]
 
         # apply the same gti cut used to read in the initial WSDL.
         gti=roi.sa.pixeldata.gti

python/uw/like/roi_localize.py

@@ -111,7 +111,7 @@ def localize(self):
             print (('\t'+7*'%10.4f')% (diff,diff, l.par[0],l.par[1],l.par[3],l.par[4], l.par[6]))
 
         old_sigma=1.0
-        for i in xrange(self.max_iteration):
+        for i in range(self.max_iteration):
             try:
 
                 l.fit(update=True)

python/uw/like/roi_modify.py

@@ -118,7 +118,7 @@ def modify_model(roi,which,model,free=None,keep_old_flux=True):
             free=np.asarray([free]*len(model.get_all_parameters()))
 
         assert(len(free)==len(model.get_all_parameters()))
-        for i in xrange(len(free)):
+        for i in range(len(free)):
             model.freeze(i,freeze=not free[i])
 
     roi.__update_state__()

python/uw/like/roi_save.py

@@ -6,7 +6,7 @@
 author: Joshua Lande
 """
 import os
-import cPickle
+import pickle
 import collections
 import numpy as N
 
@@ -75,7 +75,7 @@ def save(roi,filename):
 
     d['LATEXTDIR']=os.environ['LATEXTDIR'] if os.environ.has_key('LATEXTDIR') else None
 
-    cPickle.dump(d,open(path.expand(filename),'w'))
+    pickle.dump(d,open(path.expand(filename),'w'))
 
 def load(filename,**kwargs):
     """ Factory method to return a ROIAnalysis object
@@ -84,7 +84,7 @@ def load(filename,**kwargs):
         Any additional kwargs is used to modify DataSpecification, SpectralAnalysis,
         and ROIAnalysis objects."""
     if isinstance(filename, basestring):
-        d=cPickle.load(open(path.expand(filename),'r'))
+        d=pickle.load(open(path.expand(filename),'r'))
     elif isinstance(filename, dict):
         d=filename
     else:
@@ -100,7 +100,7 @@ def load(filename,**kwargs):
               hasattr(ds.spatial_model,'skyfun') \
               and ds.spatial_model.skyfun is None 
               for ds in d['diffuse_sources']]):
-        d=cPickle.load(open(path.expand(filename),'r'))
+        d=pickle.load(open(path.expand(filename),'r'))
 
     from . pointspec import DataSpecification,SpectralAnalysis
     from . roi_analysis import ROIAnalysis