Track memory leaks

TestMem.py

+#!/usr/bin/env python
+"""
+Test script for xQML
+
+Author: Vanneste
+"""
+
+from __future__ import division
+
+import numpy as np
+import healpy as hp
+from pylab import *
+import astropy.io.fits as fits
+import timeit
+import sys
+
+import xqml
+from xqml.xqml_utils import progress_bar, getstokes
+from xqml.simulation import muKarcmin2var
+from xqml.simulation import extrapolpixwin
+ion()
+show()
+
+exp = "Big"
+if len(sys.argv) > 1:
+    if sys.argv[1].lower()[0] == "s":
+        exp = "Small"
+
+if exp == "Big":
+    nside = 16
+    dell = 1
+    glat = 20
+elif exp == "Small":
+    nside = 64
+    dell = 10
+    glat = 80
+else:
+    print( "Need a patch !")
+
+#lmax = nside
+lmax = 2 * nside - 1
+nsimu = 10
+MODELFILE = 'planck_base_planck_2015_TTlowP.fits'
+Slmax = lmax
+
+s0 = timeit.default_timer()
+
+# provide list of specs to be computed, and/or options
+spec = ['EE','BB'] #'EB', 'TE', 'TB']
+pixwin = True
+ellbins = np.arange(2, lmax + 2, dell)
+ellbins[-1] = lmax+1
+
+muKarcmin = 1.0
+fwhm = 0.5
+
+
+
+##############################
+#input model
+clth = np.array(hp.read_cl(MODELFILE))
+Clthshape = zeros(((6,)+shape(clth)[1:]))
+Clthshape[:4] = clth
+clth = Clthshape
+lth = arange(2, lmax+1)
+##############################
+
+
+
+##############################
+# Create mask
+
+t, p = hp.pix2ang(nside, range(hp.nside2npix(nside)))
+mask = np.ones(hp.nside2npix(nside), bool)
+# import random
+# random.shuffle(mask)
+
+if exp == "Big":
+#    mask[abs(90 - rad2deg(t)) < glat] = False
+    mask[(90 - rad2deg(t)) < glat] = False
+elif exp == "Small":
+    mask[(90 - rad2deg(t)) < glat] = False
+
+fsky = np.mean(mask)
+npix = sum(mask)
+print("fsky=%.2g %% (npix=%d)" % (100*fsky,npix))
+toGB = 1024. * 1024. * 1024.
+emem = 8.*(npix*2*npix*2) * ( len(lth)*2 ) / toGB
+print("mem=%.2g Gb" % emem)
+##############################
+
+
+
+stokes, spec, istokes, ispecs = getstokes( spec=spec)
+print(stokes, spec, istokes, ispecs)
+nspec = len(spec)
+nstoke = len(stokes)
+
+
+# ############## Generate Noise ###############
+pixvar = muKarcmin2var(muKarcmin, nside)
+varmap = ones((nstoke * npix)) * pixvar
+NoiseVar = np.diag(varmap)
+
+noise = (randn(len(varmap)) * varmap**0.5).reshape(nstoke, -1)
+
+
+
+# ############## Initialise xqml class ###############
+esti = xqml.xQML(mask, ellbins, clth, lmax=lmax, fwhm=fwhm, spec=spec)
+s1 = timeit.default_timer()
+print( "Init: %d sec (%d)" % (s1-s0,s1-s0))
+
+esti.NA = NoiseVar
+esti.NB = NoiseVar
+
+invCa = xqml.xqml_utils.pd_inv(esti.S + esti.NA)
+invCb = xqml.xqml_utils.pd_inv(esti.S + esti.NB)
+s2 = timeit.default_timer()
+print( "Inv C: %d sec (%d)" % (s2-s0,s2-s1))
+s1 = s2
+
+esti.El = xqml.estimators.El(invCa, invCb, esti.Pl)
+s2 = timeit.default_timer()
+print( "Construct El: %d sec (%d)" % (s2-s0,s2-s1))
+s1 = s2
+
+#esti.bias = xqml.estimators.biasQuadEstimator(esti.NA, esti.El)
+#s2 = timeit.default_timer()
+#print( "Construct bias: %d sec (%d)" % (s2-s0,s2-s1))
+#s1 = s2
+
+#Wll = xqml.estimators.CrossWindowFunction(esti.El, esti.Pl)
+nl = len(esti.El)
+#Wll = np.asarray( [np.sum(E * P) for E in esti.El for P in esti.Pl] ).reshape(nl,nl)
+Wll = np.zeros((nl,nl))
+for l1 in range(nl):
+    for l2 in range(nl):
+        Wll[l1,l2] = np.sum( esti.El[l1] * esti.Pl[l2])
+s2 = timeit.default_timer()
+print( "Construct W: %d sec (%d)" % (s2-s0,s2-s1))
+s1=s2
+
+esti.invW = linalg.inv(Wll)
+s2 = timeit.default_timer()
+print( "inv W: %d sec (%d)" % (s2-s0,s2-s1))
+s1=s2
+
+#esti.construct_esti( NA=NoiseVar, NB=NoiseVar)
+
+s2 = timeit.default_timer()
+print( "Construct esti: %d sec (%d)" % (s2-s0,s2-s1))
+ellval = esti.lbin()
+
+
+# ############## Construct MC ###############
+allcla = []
+allcl = []
+t = []
+bl = hp.gauss_beam(deg2rad(fwhm), lmax=Slmax)
+fpixw = extrapolpixwin(nside, Slmax, pixwin=pixwin)
+for n in np.arange(nsimu):
+    progress_bar(n, nsimu)
+    cmb = np.array(hp.synfast(clth[:, :len(fpixw)]*(fpixw*bl)**2, nside,
+                   pixwin=False, lmax=Slmax, fwhm=0.0, new=True, verbose=False))
+    cmbm = cmb[istokes][:, mask]
+    dmA = cmbm + (randn(nstoke * npix) * sqrt(varmap)).reshape(nstoke, npix)
+    dmB = cmbm + (randn(nstoke * npix) * sqrt(varmap)).reshape(nstoke, npix)
+    s1 = timeit.default_timer()
+    allcl.append(esti.get_spectra(dmA, dmB))
+    t.append( timeit.default_timer() - s1)
+    allcla.append(esti.get_spectra(dmA))
+
+print( "get_spectra: %d (%d sec)" % (timeit.default_timer()-s0,mean(t)))
+hcl = mean(allcl, 0)
+scl = std(allcl, 0)
+hcla = mean(allcla, 0)
+scla = std(allcla, 0)
+
+
+
+
+
+## if __name__ == "__main__":
+##     """
+##     Run the doctest using
+
+##     python simulation.py
+
+##     If the tests are OK, the script should exit gracefuly, otherwise the
+##     failure(s) will be printed out.
+##     """
+##     import doctest
+##     if np.__version__ >= "1.14.0":
+##         np.set_printoptions(legacy="1.13")
+##     doctest.testmod()