DM-22470: Remove all uses of past and future from fgcm

fgcm/_version.py

@@ -1,5 +1,5 @@
 from __future__ import division, absolute_import, print_function
 
-__version__ = '2.4.0'
+__version__ = '2.4.1'
 
 __version_info__ = __version__.split('.')

fgcm/fgcmApertureCorrection.py

@@ -1,5 +1,6 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
+
+from builtins import range
 
 import numpy as np
 import os
@@ -96,7 +97,7 @@ def computeApertureCorrections(self):
 
         expIndexUse,=np.where(self.fgcmPars.expFlag == 0)
 
-        for i in xrange(self.fgcmPars.nBands):
+        for i in range(self.fgcmPars.nBands):
             use,=np.where((self.fgcmPars.expBandIndex[expIndexUse] == i) &
                           (self.fgcmPars.expSeeingVariable[expIndexUse] > self.illegalValue) &
                           (np.isfinite(self.fgcmPars.expSeeingVariable[expIndexUse])))

fgcm/fgcmApplyZeropoints.py

@@ -1,5 +1,4 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
 
 import numpy as np
 import os

fgcm/fgcmAtmosphereTable.py

@@ -1,6 +1,6 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
 
+from builtins import range
 
 import numpy as np
 import scipy.interpolate as interpolate
@@ -356,10 +356,10 @@ def generateTable(self):
         self.fgcmLog.info("Generating %d*%d=%d PWV atmospheres..." % (pwvPlus.size,zenithPlus.size,pwvPlus.size*zenithPlus.size))
         self.pwvAtmTable = np.zeros((pwvPlus.size,zenithPlus.size,self.atmLambda.size))
 
-        for i in xrange(pwvPlus.size):
+        for i in range(pwvPlus.size):
             sys.stdout.write('%d' % (i))
             sys.stdout.flush()
-            for j in xrange(zenithPlus.size):
+            for j in range(zenithPlus.size):
                 sys.stdout.write('.')
                 sys.stdout.flush()
                 atm=self.modGen(pwv=pwvPlus[i],zenith=zenithPlus[j],
@@ -370,10 +370,10 @@ def generateTable(self):
         self.fgcmLog.info("\nGenerating %d*%d=%d O3 atmospheres..." % (o3Plus.size,zenithPlus.size,o3Plus.size*zenithPlus.size))
         self.o3AtmTable = np.zeros((o3Plus.size, zenithPlus.size, self.atmLambda.size))
 
-        for i in xrange(o3Plus.size):
+        for i in range(o3Plus.size):
             sys.stdout.write('%d' % (i))
             sys.stdout.flush()
-            for j in xrange(zenithPlus.size):
+            for j in range(zenithPlus.size):
                 sys.stdout.write('.')
                 sys.stdout.flush()
                 atm=self.modGen(o3=o3Plus[i],zenith=zenithPlus[j],
@@ -386,7 +386,7 @@ def generateTable(self):
         self.o2AtmTable = np.zeros((zenithPlus.size, self.atmLambda.size))
         self.rayleighAtmTable = np.zeros((zenithPlus.size, self.atmLambda.size))
 
-        for j in xrange(zenithPlus.size):
+        for j in range(zenithPlus.size):
             sys.stdout.write('.')
             sys.stdout.flush()
             atm=self.modGen(zenith=zenithPlus[j],

fgcm/fgcmBasemap.py

@@ -1,5 +1,4 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
 
 """
 Adapted from Alex Drlica-Wagner

fgcm/fgcmBrightObs.py

@@ -1,5 +1,6 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
+
+from builtins import range
 
 import numpy as np
 import os
@@ -140,7 +141,7 @@ def brightestObsMeanMag(self,debug=False,computeSEDSlopes=False):
             #  see also fgcmChisq.py
             # splitValues is the first of the goodStars in each list
             splitValues = np.zeros(nSections-1,dtype='i4')
-            for i in xrange(1,nSections):
+            for i in range(1,nSections):
                 splitValues[i-1] = goodStarsList[i][0]
 
             # get the indices from the goodStarsSub matched list

fgcm/fgcmChisq.py

@@ -1,5 +1,5 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
+from builtins import range
 
 import numpy as np
 import os
@@ -344,7 +344,7 @@ def __call__(self,fitParams,fitterUnits=False,computeDerivatives=False,computeSE
             proc = None
 
             self.totalHandleDict = {}
-            for thisCore in xrange(self.nCore):
+            for thisCore in range(self.nCore):
                 self.totalHandleDict[workerIndex + thisCore] = (
                     snmm.createArray(self.nSums,dtype='f8'))
 
@@ -359,7 +359,7 @@ def __call__(self,fitParams,fitterUnits=False,computeDerivatives=False,computeSE
             #  see also fgcmBrightObs.py
             # splitValues is the first of the goodStars in each list
             splitValues = np.zeros(nSections-1,dtype='i4')
-            for i in xrange(1,nSections):
+            for i in range(1,nSections):
                 splitValues[i-1] = goodStarsList[i][0]
 
             # get the indices from the goodStarsSub matched list (matched to goodStars)
@@ -398,7 +398,7 @@ def __call__(self,fitParams,fitterUnits=False,computeDerivatives=False,computeSE
 
             # sum up the partial sums from the different jobs
             partialSums = np.zeros(self.nSums,dtype='f8')
-            for thisCore in xrange(self.nCore):
+            for thisCore in range(self.nCore):
                 partialSums[:] += snmm.getArray(
                     self.totalHandleDict[workerIndex + thisCore])[:]
 

fgcm/fgcmComputeStepUnits.py

@@ -1,5 +1,5 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
+from builtins import range
 
 import numpy as np
 import os
@@ -128,7 +128,7 @@ def run(self, fitParams):
         proc = None
 
         self.totalHandleDict = {}
-        for thisCore in xrange(self.nCore):
+        for thisCore in range(self.nCore):
             self.totalHandleDict[workerIndex + thisCore] = (
                 snmm.createArray(self.nSums,dtype='f8'))
 
@@ -138,7 +138,7 @@ def run(self, fitParams):
         goodStarsList = np.array_split(goodStars,nSections)
 
         splitValues = np.zeros(nSections-1,dtype='i4')
-        for i in xrange(1,nSections):
+        for i in range(1,nSections):
             splitValues[i-1] = goodStarsList[i][0]
 
         splitIndices = np.searchsorted(goodStars[goodStarsSub], splitValues)
@@ -161,7 +161,7 @@ def run(self, fitParams):
 
         # sum up the partial sums from the different jobs
         partialSums = np.zeros(self.nSums,dtype='f8')
-        for thisCore in xrange(self.nCore):
+        for thisCore in range(self.nCore):
             partialSums[:] += snmm.getArray(
                 self.totalHandleDict[workerIndex + thisCore])[:]
 

fgcm/fgcmConfig.py

@@ -1,5 +1,5 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
+from builtins import range
 
 import numpy as np
 import os
@@ -440,7 +440,7 @@ def __init__(self, configDict, lutIndex, lutStd, expInfo, ccdOffsets, checkFiles
 
         if self.epochNames is None:
             self.epochNames = []
-            for i in xrange(self.epochMJDs.size):
+            for i in range(self.epochMJDs.size):
                 self.epochNames.append('epoch%d' % (i))
 
         # are they sorted?

fgcm/fgcmConnectivity.py

@@ -1,5 +1,4 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
 
 import numpy as np
 import esutil

fgcm/fgcmExposureSelector.py

@@ -1,6 +1,4 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
-
 
 import numpy as np
 import os

fgcm/fgcmFitCycle.py

@@ -1,5 +1,4 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
 
 import numpy as np
 import os

fgcm/fgcmFlagVariables.py

@@ -1,5 +1,4 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
 
 import numpy as np
 import os

fgcm/fgcmGray.py

@@ -1,5 +1,5 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
+from builtins import range
 
 import numpy as np
 import os
@@ -215,7 +215,7 @@ def computeExpGrayForInitialSelection(self):
                          (expNGoodStarForInitialSelection > self.minStarPerExp) &
                          (expGrayForInitialSelection > self.expGrayInitialCut))
 
-        for i in xrange(self.fgcmPars.nBands):
+        for i in range(self.fgcmPars.nBands):
             self.fgcmLog.debug('Making EXP_GRAY (initial) histogram for %s band' %
                                (self.fgcmPars.bands[i]))
             inBand, = np.where(self.fgcmPars.expBandIndex[expUse] == i)
@@ -594,7 +594,7 @@ def computeExpGrayColorSplit(self):
         expGrayNGoodStarsColorSplit[:, :] = 0
         expGrayWtColorSplit = np.zeros_like(expGrayColorSplit)
 
-        for c in xrange(gmiCutLow.size):
+        for c in range(gmiCutLow.size):
             use, = np.where((gmiGO > gmiCutLow[c]) &
                             (gmiGO < gmiCutHigh[c]))
 
@@ -704,7 +704,7 @@ def makeExpGrayPlots(self):
         expUse,=np.where((self.fgcmPars.expFlag == 0) &
                          (expNGoodStars > self.minStarPerExp))
 
-        for i in xrange(self.fgcmPars.nBands):
+        for i in range(self.fgcmPars.nBands):
             inBand, = np.where(self.fgcmPars.expBandIndex[expUse] == i)
 
             if (inBand.size == 0) :
@@ -888,7 +888,7 @@ def computeExpGrayCuts(self):
         expGrayPhotometricCut[:] = [float(f) for f in self.expGrayPhotometricCut]
         expGrayHighCut[:] = [float(f) for f in self.expGrayHighCut]
 
-        for i in xrange(self.fgcmPars.nBands):
+        for i in range(self.fgcmPars.nBands):
             inBand, = np.where(self.fgcmPars.expBandIndex[expUse] == i)
 
             if inBand.size == 0:
@@ -931,7 +931,7 @@ def computeExpGrayCutsFromRepeatability(self):
         expGrayPhotometricCut[:] = [float(f) for f in self.expGrayPhotometricCut]
         expGrayHighCut[:] = [float(f) for f in self.expGrayHighCut]
 
-        for i in xrange(self.fgcmPars.nBands):
+        for i in range(self.fgcmPars.nBands):
             delta = np.clip(self.autoPhotometricCutNSig * self.fgcmPars.compReservedRawCrunchedRepeatability[i], 0.001, 1e5)
 
             cut = -1 * int(np.ceil(delta / self.autoPhotometricCutStep)) * self.autoPhotometricCutStep

fgcm/fgcmLUT.py

@@ -1,5 +1,5 @@
 from __future__ import division, absolute_import, print_function
-from past.builtins import xrange
+from builtins import range
 
 import numpy as np
 import scipy.interpolate as interpolate
@@ -169,7 +169,7 @@ def setThroughputs(self, throughputDict):
                                              ('THROUGHPUT_AVG','f4'),
                                              ('THROUGHPUT_CCD','f4',self.nCCD)])
             tput['LAMBDA'][:] = lam
-            for ccdIndex in xrange(self.nCCD):
+            for ccdIndex in range(self.nCCD):
                 try:
                     tput['THROUGHPUT_CCD'][:,ccdIndex] = throughputDict[filterName][ccdIndex]
                 except:
@@ -180,7 +180,7 @@ def setThroughputs(self, throughputDict):
                 tput['THROUGHPUT_AVG'][:] = throughputDict[filterName]['AVG']
             else:
                 self.fgcmLog.info("Average throughput not found in throughputDict for filter %s.  Computing now..." % (filterName))
-                for i in xrange(lam.size):
+                for i in range(lam.size):
                     use,=np.where(tput['THROUGHPUT_CCD'][i,:] > 0.0)
                     if (use.size > 0):
                         tput['THROUGHPUT_AVG'][i] = np.mean(tput['THROUGHPUT_CCD'][i,use])
@@ -328,15 +328,15 @@ def makeLUT(self):
         # get the filters over the same lambda ranges...
         self.fgcmLog.info("\nInterpolating filters...")
         self.throughputs = []
-        for i in xrange(len(self.filterNames)):
+        for i in range(len(self.filterNames)):
             inLam = self.inThroughputs[i]['LAMBDA']
 
             tput = np.zeros(self.atmLambda.size, dtype=[('LAMBDA','f4'),
                                                         ('THROUGHPUT_AVG','f4'),
                                                         ('THROUGHPUT_CCD','f4',self.nCCD)])
             tput['LAMBDA'][:] = self.atmLambda
 
-            for ccdIndex in xrange(self.nCCD):
+            for ccdIndex in range(self.nCCD):
                 ifunc = interpolate.interp1d(inLam, self.inThroughputs[i]['THROUGHPUT_CCD'][:,ccdIndex])
                 tput['THROUGHPUT_CCD'][:,ccdIndex] = np.clip(ifunc(self.atmLambda),
                                                              0.0,
@@ -349,15 +349,15 @@ def makeLUT(self):
         # and now we can get the standard atmosphere and lambda_b
         self.fgcmLog.info("Computing lambdaB")
         self.lambdaB = np.zeros(len(self.filterNames))
-        for i in xrange(len(self.filterNames)):
+        for i in range(len(self.filterNames)):
             num = integrate.simps(self.atmLambda * self.throughputs[i]['THROUGHPUT_AVG'] / self.atmLambda, self.atmLambda)
             denom = integrate.simps(self.throughputs[i]['THROUGHPUT_AVG'] / self.atmLambda, self.atmLambda)
             self.lambdaB[i] = num / denom
             self.fgcmLog.info("Filter: %s, lambdaB = %.3f" % (self.filterNames[i], self.lambdaB[i]))
 
         self.fgcmLog.info("Computing lambdaStdFilter")
         self.lambdaStdFilter = np.zeros(len(self.filterNames))
-        for i in xrange(len(self.filterNames)):
+        for i in range(len(self.filterNames)):
             num = integrate.simps(self.atmLambda * self.throughputs[i]['THROUGHPUT_AVG'] * self.atmStdTrans / self.atmLambda, self.atmLambda)
             denom = integrate.simps(self.throughputs[i]['THROUGHPUT_AVG'] * self.atmStdTrans / self.atmLambda, self.atmLambda)
             self.lambdaStdFilter[i] = num / denom
@@ -380,7 +380,7 @@ def makeLUT(self):
         self.I1Std = np.zeros(len(self.filterNames))
         self.I2Std = np.zeros(len(self.filterNames))
 
-        for i in xrange(len(self.filterNames)):
+        for i in range(len(self.filterNames)):
             self.I0Std[i] = integrate.simps(self.throughputs[i]['THROUGHPUT_AVG'] * self.atmStdTrans / self.atmLambda, self.atmLambda)
             self.I1Std[i] = integrate.simps(self.throughputs[i]['THROUGHPUT_AVG'] * self.atmStdTrans * (self.atmLambda - self.lambdaStd[i]) / self.atmLambda, self.atmLambda)
             self.I2Std[i] = integrate.simps(self.throughputs[i]['THROUGHPUT_AVG'] * self.atmStdTrans * (self.atmLambda - self.lambdaStd[i])**2. / self.atmLambda, self.atmLambda)
@@ -409,17 +409,17 @@ def makeLUT(self):
         self.pmbFactor = pmbFactorPlus[:-1]
 
         # this set of nexted for loops could probably be vectorized in some way
-        for i in xrange(len(self.filterNames)):
+        for i in range(len(self.filterNames)):
             self.fgcmLog.info("Working on filter %s" % (self.filterNames[i]))
-            for j in xrange(pwvPlus.size):
+            for j in range(pwvPlus.size):
                 self.fgcmLog.info("  and on pwv #%d" % (j))
-                for k in xrange(o3Plus.size):
+                for k in range(o3Plus.size):
                     self.fgcmLog.info("   and on o3 #%d" % (k))
-                    for m in xrange(tauPlus.size):
-                        for n in xrange(alphaPlus.size):
-                            for o in xrange(zenithPlus.size):
+                    for m in range(tauPlus.size):
+                        for n in range(alphaPlus.size):
+                            for o in range(zenithPlus.size):
                                 aerosolTauLambda = np.exp(-1.0*tauPlus[m]*airmassPlus[o]*(self.atmLambda/self.lambdaNorm)**(-alphaPlus[n]))
-                                for p in xrange(self.nCCDStep):
+                                for p in range(self.nCCDStep):
                                     if (p == self.nCCD):
                                         Sb = self.throughputs[i]['THROUGHPUT_AVG'] * o2AtmTable[o,:] * rayleighAtmTable[o,:] * pwvAtmTable[j,o,:] * o3AtmTable[k,o,:] * aerosolTauLambda
                                     else:
@@ -484,14 +484,14 @@ def makeLUT(self):
 
         ## FIXME: figure out PMB derivative?
 
-        for i in xrange(len(self.filterNames)):
+        for i in range(len(self.filterNames)):
             self.fgcmLog.info("Working on filter %s" % (self.filterNames[i]))
-            for j in xrange(self.pwv.size):
-                for k in xrange(self.o3.size):
-                    for m in xrange(self.tau.size):
-                        for n in xrange(self.alpha.size):
-                            for o in xrange(self.zenith.size):
-                                for p in xrange(self.nCCDStep):
+            for j in range(self.pwv.size):
+                for k in range(self.o3.size):
+                    for m in range(self.tau.size):
+                        for n in range(self.alpha.size):
+                            for o in range(self.zenith.size):
+                                for p in range(self.nCCDStep):
                                     self.lutDeriv['D_LNPWV'][i,j,k,m,n,o,p] = (
                                         ((lutPlus['I0'][i,j+1,k,m,n,o,p] -
                                           lutPlus['I0'][i,j,k,m,n,o,p]) /
@@ -572,7 +572,7 @@ def makeLUT(self):
 
             # now do it...looping is no problem since there aren't that many.
 
-            for i in xrange(nTemplates):
+            for i in range(nTemplates):
                 data = fits[extNames[i]].read()
 
                 templateLambda = data['LAMBDA']
@@ -598,14 +598,14 @@ def makeLUT(self):
                     fnu[hi] = intFunc(self.atmLambda[good[-1]])
 
                 # compute synthetic mags
-                for j in xrange(len(self.filterNames)):
+                for j in range(len(self.filterNames)):
                     num = integrate.simps(fnu * self.throughputs[j]['THROUGHPUT_AVG'][:] * self.atmStdTrans / self.atmLambda, self.atmLambda)
                     denom = integrate.simps(self.throughputs[j]['THROUGHPUT_AVG'][:] * self.atmStdTrans / self.atmLambda, self.atmLambda)
 
                     self.sedLUT['SYNTHMAG'][i,j] = -2.5*np.log10(num/denom)
 
                 # and compute fprimes
-                for j in xrange(len(self.filterNames)):
+                for j in range(len(self.filterNames)):
                     use,=np.where((templateLambda >= (self.lambdaStd[j]-delta)) &
                                   (templateLambda <= (self.lambdaStd[j]+delta)))