PEP8ified the test script

TestEsti.py

@@ -13,95 +13,87 @@ from pylab import *
 import astropy.io.fits as fits
 
 from xqml import xQML
-from simulation import getstokes, muKarcmin2var, GetBinningMatrix
 from libcov import compute_ds_dcb
 from xqml_utils import progress_bar
+from simulation import getstokes, muKarcmin2var, GetBinningMatrix
 
 if __name__ == "__main__":
-	nside = 8
-	lmax = 3*nside-1
-	Slmax = 3*nside-1
-	deltal = 1
-	clth = hp.read_cl('planck_base_planck_2015_TTlowP.fits')
-	lth = arange(2,lmax+1)
-
-	ellbins = arange(2,lmax+2,deltal)
-	ellbins[-1] = lmax+1
-
-	P, Q, ell, ellval = GetBinningMatrix(ellbins, lmax)
-	nbins=len(ellbins)-1
-
-
-	#create mask
-	t,p=hp.pix2ang( nside, range(hp.nside2npix(nside)))
-	mask = np.ones(hp.nside2npix(nside),bool)
-	mask[abs(90-rad2deg(t)) < 10] = False
-	npix = sum(mask)
-
-	fwhm = 0.5
-	bl=hp.gauss_beam(deg2rad(fwhm), lmax=Slmax+1)
-
-	allStoke, der, ind = getstokes(polar=True,temp=False,EBTB=False)
-	nder=len(der)
-
-	muKarcmin = 0.1
-
-
-	pixvar = 2 * muKarcmin2var(muKarcmin, nside)
-	varmap = ones((2*npix))*pixvar
-	NoiseVar = np.diag(varmap)
-
-	cmb = hp.synfast( clth, nside, fwhm=deg2rad(fwhm), pixwin=True, new=True, verbose=False)
-	noise = (randn(len(varmap)) * varmap**.5).reshape(2,-1)
-	dm = cmb[1:,mask] + noise
-
-
-	###################################### Compute ds_dcb ######################################
-	ip=arange(hp.nside2npix(nside))
-	ipok=ip[mask]
-
-	Pl, S = compute_ds_dcb(ellbins,nside,ipok,bl,clth,Slmax,polar=True,temp=False, EBTB=False, pixwining=True, timing=True, MC=False)
-	Pl = Pl.reshape((nder)*(np.shape(Pl)[1]), 2*npix, 2*npix)
-
-
-
-
-
-
-	###################################### Compute spectra ######################################
-	esti = xQML(mask,ellbins, clth, Pl=Pl, fwhm=fwhm)
-	esti.construct_esti( NoiseVar, NoiseVar)
-	cl = esti.get_spectra( dm, dm)
-	V = esti.get_covariance()
-
-
-	###################################### Construct MC ######################################
-	allcl = []
-	esti = xQML(mask,ellbins, clth, Pl=Pl, fwhm=fwhm)
-	esti.construct_esti( NoiseVar, NoiseVar)
-	start = timeit.default_timer()
-	for n in np.arange(100):
-		progress_bar( n, 100,timeit.default_timer()-start)
-		cmb = hp.synfast( clth, nside, fwhm=deg2rad(fwhm), pixwin=True, new=True, verbose=False)
-		dm = cmb[1:,mask] + (randn(2*npix)*sqrt(varmap)).reshape( 2, npix)
-		allcl.append(esti.get_spectra( dm, dm))
-
-
-	figure()
-	subplot( 2,1,1)
-	plot( lth, clth.transpose()[lth,1:3], 'k')
-	plot( ellval, mean( allcl,0).transpose(), 'r')
-	plot( ellval, mean( allcl,0).transpose() + std( allcl,0).transpose(), 'r--')
-	plot( ellval, mean( allcl,0).transpose() - std( allcl,0).transpose(), 'r--')
-	semilogy()
-	subplot( 2,1,2)
-	cosmic = sqrt( 2./(2*lth+1))/mean(mask) * clth[1:3,lth]
-	plot( lth, cosmic.transpose(), 'k')
-	plot( ellval, std( allcl,0).transpose(), 'r')
-	plot( ellval, sqrt(diag(V)).reshape(nder,-1).transpose(), 'b')
-	semilogy()
-
-
-
-
-	show()
+    nside = 8
+    lmax = 3 * nside - 1
+    Slmax = 3 * nside - 1
+    deltal = 1
+    clth = hp.read_cl('planck_base_planck_2015_TTlowP.fits')
+    lth = arange(2, lmax+1)
+
+    ellbins = arange(2, lmax + 2, deltal)
+    ellbins[-1] = lmax + 1
+
+    P, Q, ell, ellval = GetBinningMatrix(ellbins, lmax)
+    nbins = len(ellbins) - 1
+
+    # Create mask
+    t, p = hp.pix2ang(nside, range(hp.nside2npix(nside)))
+    mask = np.ones(hp.nside2npix(nside), bool)
+    mask[abs(90 - rad2deg(t)) < 10] = False
+    npix = sum(mask)
+
+    fwhm = 0.5
+    bl = hp.gauss_beam(deg2rad(fwhm), lmax=Slmax + 1)
+
+    allStoke, der, ind = getstokes(polar=True, temp=False, EBTB=False)
+    nder = len(der)
+
+    muKarcmin = 0.1
+
+    pixvar = 2 * muKarcmin2var(muKarcmin, nside)
+    varmap = ones((2 * npix)) * pixvar
+    NoiseVar = np.diag(varmap)
+
+    cmb = hp.synfast(
+        clth, nside, fwhm=deg2rad(fwhm), pixwin=True, new=True, verbose=False)
+    noise = (randn(len(varmap)) * varmap**0.5).reshape(2, -1)
+    dm = cmb[1:, mask] + noise
+
+    ############### Compute ds_dcb ###############
+    ip = arange(hp.nside2npix(nside))
+    ipok = ip[mask]
+
+    Pl, S = compute_ds_dcb(
+        ellbins, nside, ipok, bl, clth, Slmax,
+        polar=True, temp=False, EBTB=False,
+        pixwining=True, timing=True, MC=False)
+    Pl = Pl.reshape((nder)*(np.shape(Pl)[1]), 2 * npix, 2 * npix)
+
+    ############### Compute spectra ###############
+    esti = xQML(mask, ellbins, clth, Pl=Pl, fwhm=fwhm)
+    esti.construct_esti(NoiseVar, NoiseVar)
+    cl = esti.get_spectra(dm, dm)
+    V = esti.get_covariance()
+
+    ############### Construct MC ###############
+    allcl = []
+    esti = xQML(mask, ellbins, clth, Pl=Pl, fwhm=fwhm)
+    esti.construct_esti(NoiseVar, NoiseVar)
+    start = timeit.default_timer()
+    for n in np.arange(100):
+        progress_bar(n, 100, timeit.default_timer() - start)
+        cmb = hp.synfast(
+            clth, nside, fwhm=deg2rad(fwhm),
+            pixwin=True, new=True, verbose=False)
+        dm = cmb[1:, mask] + (randn(2 * npix) * sqrt(varmap)).reshape(2, npix)
+        allcl.append(esti.get_spectra(dm, dm))
+
+    figure()
+    subplot(2, 1, 1)
+    plot(lth, clth.transpose()[lth, 1: 3], 'k')
+    plot(ellval, mean(allcl, 0).transpose(), 'r')
+    plot(ellval, mean(allcl, 0).transpose() + std(allcl, 0).transpose(), 'r--')
+    plot(ellval, mean(allcl, 0).transpose() - std(allcl, 0).transpose(), 'r--')
+    semilogy()
+    subplot(2, 1, 2)
+    cosmic = sqrt(2./(2 * lth + 1)) / mean(mask) * clth[1: 3, lth]
+    plot(lth, cosmic.transpose(), 'k')
+    plot(ellval, std(allcl, 0).transpose(), 'r')
+    plot(ellval, sqrt(diag(V)).reshape(nder, -1).transpose(), 'b')
+    semilogy()
+    show()