few corrections

TestEsti.py

@@ -3,6 +3,7 @@ Test script for xQML
 
 Author: Vanneste
 """
+
 from __future__ import division
 
 import timeit
@@ -11,107 +12,135 @@ import healpy as hp
 from pylab import *
 import astropy.io.fits as fits
 
-from xqml import xQML
-from libcov import compute_ds_dcb
+import xqml
+import libcov
+import simulation
 from xqml_utils import progress_bar
-from simulation import getstokes, muKarcmin2var, GetBinningMatrix
+from simulation import muKarcmin2var, GetBinningMatrix
 from simulation import extrapolpixwin
-
-
-if __name__ == "__main__":
-    nside = 4
-    lmax = 3 * nside - 1
-    Slmax = 3 * nside - 1
-    deltal = 1
-    nsimu = 500
-    clth = np.array(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 = muKarcmin2var(muKarcmin, nside)
-    varmap = ones((2 * npix)) * pixvar
-    NoiseVar = np.diag(varmap)
-
-    cmb = np.array(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
-    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, S=S, 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)
-    fpixw = extrapolpixwin(nside, lmax+2, pixwining=True)
-    start = timeit.default_timer()
-    for n in np.arange(nsimu):
-        progress_bar(n, nsimu, timeit.default_timer() - start)
-        cmb = np.array(hp.synfast(clth[:, :len(fpixw)]*fpixw**2, nside,
-                       fwhm=deg2rad(fwhm), 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(3, 1, 1)
-    plot(lth, clth.transpose()[lth, 1: 3], '--k')
-    hcl = mean(allcl, 0).transpose()
-    scl = std(allcl, 0).transpose()
-    plot(ellval, hcl, 'b.')
-    plot(ellval, hcl + scl, 'r--', label=r"$\pm 1\sigma$")
-    plot(ellval, hcl - scl, 'r--')
-    ylabel(r"$C_\ell$")
-    semilogy()
-    legend(loc=4)
-    subplot(3, 1, 2)
-    cosmic = sqrt(2./(2 * lth + 1)) / mean(mask) * clth[1: 3, lth]
-    plot(lth, cosmic.transpose(), '--k')
-    plot(ellval, scl, 'r-', label=r"$\sigma_{\rm MC}$")
-    plot(ellval, sqrt(diag(V)).reshape(nder, -1).transpose(), 'b.')
-    ylabel(r"$\sigma(C_\ell)$")
-    semilogy()
-    legend(loc=4)
-    subplot(3, 1, 3)
-    plot(ellval, (hcl-clth[1: 3, lth].T)/(scl/sqrt(nsimu)), 'o')
-    ylabel(r"$R[C_\ell]$")
-    xlabel(r"$\ell$")
-    ylim(-3, 3)
-    show()
-
+ion()
+
+# if __name__ == "__main__":
+nside = 4
+lmax = 2 * nside - 1
+Slmax = 2 * nside - 1
+deltal = 1
+nsimu = 10000
+clth = np.array(hp.read_cl('planck_base_planck_2015_TTlowP.fits'))
+Clthshape = zeros(((6,)+shape(clth)[1:]))
+Clthshape[:4] = clth
+clth = Clthshape
+EB = 0.5
+clth[4] = EB*sqrt(clth[1]*clth[2])
+TB = 0.5
+clth[5] = TB*sqrt(clth[0]*clth[2])
+
+lth = arange(2, lmax+1)
+spec = ['EB', 'TE', 'TB']
+temp = True
+polar = True
+corr = False
+pixwin = False
+
+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)) < 60] = False
+npix = sum(mask)
+
+fwhm = 0.5
+bl = hp.gauss_beam(deg2rad(fwhm), lmax=Slmax + 1)
+
+stokes, spec, istokes, ispecs = simulation.getstokes(
+    spec=spec, polar=polar, temp=temp, corr=corr)
+print(stokes, spec, istokes, ispecs)
+nspec = len(spec)
+nstoke = len(stokes)
+
+# ############## Compute ds_dcb ###############
+ip = arange(hp.nside2npix(nside))
+ipok = ip[mask]
+
+Pl, S = libcov.compute_ds_dcb(ellbins, nside, ipok, bl, clth, Slmax, spec=spec,
+                              pixwin=pixwin, timing=True, MC=False)
+
+# ############## Compute spectra ###############
+
+muKarcmin = 1.0
+pixvar = muKarcmin2var(muKarcmin, nside)
+varmap = ones((nstoke * npix)) * pixvar
+NoiseVar = np.diag(varmap)
+
+cmb = np.array(hp.synfast(clth, nside, fwhm=deg2rad(fwhm), pixwin=pixwin,
+               new=True, verbose=False, lmax=Slmax))
+noise = (randn(len(varmap)) * varmap**0.5).reshape(nstoke, -1)
+dm = cmb[istokes][:, mask] + noise
+
+esti = xqml.xQML(mask, ellbins, clth, Pl=Pl, S=S, fwhm=fwhm,
+                 spec=spec, temp=temp, polar=polar, corr=corr)
+esti.construct_esti(NoiseVar, NoiseVar)
+cl = esti.get_spectra(dm, dm)
+V = esti.get_covariance()
+
+# ############## Construct MC ###############
+allcl = []
+# allcmb = []
+# esti = xqml.xQML(mask, ellbins, clth, Pl=Pl, fwhm=fwhm, spec=spec, temp=temp,
+#                    polar=polar, corr=corr)
+esti.construct_esti(NoiseVar, NoiseVar)
+fpixw = extrapolpixwin(nside, lmax+2, pixwin=pixwin)
+start = timeit.default_timer()
+for n in np.arange(nsimu):
+    progress_bar(n, nsimu, timeit.default_timer() - start)
+    cmb = np.array(hp.synfast(clth[:, :len(fpixw)]*(fpixw*bl)**2, nside,
+                   lmax=Slmax, fwhm=deg2rad(fwhm), 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)
+    # allcmb.append(cmbm)
+    allcl.append(esti.get_spectra(dmA, dmB))
+
+figure(figsize=[10, 8])
+clf()
+subplot(3, 1, 1)
+plot(lth, clth[ispecs][:, lth].T, '--k')
+hcl = mean(allcl, 0)
+scl = std(allcl, 0)
+[plot(ellval, hcl[s], 'o', color='C%i' % s, label=r"$%s$" % spec[s])
+    for s in np.arange(nspec)]
+[fill_between(ellval, (hcl - scl/sqrt(nsimu))[s], (hcl + scl/sqrt(nsimu))[s],
+              color='C%i' % s, alpha=0.2) for s in np.arange(nspec)]
+ylabel(r"$C_\ell$")
+semilogy()
+legend(loc=4)
+
+subplot(3, 1, 2)
+cosmic = sqrt(2./(2 * lth + 1)) / mean(mask) * clth[ispecs][:, lth]
+# plot(lth, cosmic.transpose(), '--k')
+[plot(ellval, scl[s], '--', color='C%i' % s, label=r"$\sigma^{%s}_{\rm MC}$" %
+      spec[s]) for s in np.arange(nspec)]
+[plot(ellval, sqrt(diag(V)).reshape(nspec, -1)[s], 'o', color='C%i' % s)
+    for s in np.arange(nspec)]
+ylabel(r"$\sigma(C_\ell)$")
+semilogy()
+legend(loc=4)
+
+subplot(3, 1, 3)
+[plot(ellval, (hcl[s]-clth[ispecs[s]][lth])/(scl[s]/sqrt(nsimu)), '--o',
+      color='C%i' % s) for s in np.arange(nspec)]
+ylabel(r"$R[C_\ell]$")
+xlabel(r"$\ell$")
+ylim(-3, 3)
+grid()
+show()
+
+# savefig("../Plots/Git/"+"test0.png")
 
 if __name__ == "__main__":
     """

estimators.py

@@ -42,7 +42,7 @@ def Pl(ds_dcb):
     return np.copy(ds_dcb).reshape(2 * (np.shape(ds_dcb)[1]), nnpix, nnpix)
 
 
-def CorrelationMatrix(Clth, Pl, ellbins, polar=True, temp=False, EBTB=False):
+def CorrelationMatrix(Clth, Pl, ellbins, polar=True, temp=False, corr=False):
     """
     Compute correlation matrix S = sum_l Pl*Cl
 
@@ -58,7 +58,7 @@ def CorrelationMatrix(Clth, Pl, ellbins, polar=True, temp=False, EBTB=False):
         If True, get Stokes parameters for polar (default: True)
     temp : bool
         If True, get Stokes parameters for temperature (default: False)
-    EBTB : bool
+    corr : bool
         If True, get Stokes parameters for EB and TB (default: False)
 
     Returns
@@ -76,7 +76,7 @@ def CorrelationMatrix(Clth, Pl, ellbins, polar=True, temp=False, EBTB=False):
     [[   0  280  560  840 1120]
      [1400 1680 1960 2240 2520]]
     """
-    if EBTB:
+    if corr:
         xx = ['TT', 'EE', 'BB', 'TE', 'EB', 'TB']
         ind = [0, 1, 2, 3, 4, 5]
     else:
@@ -85,7 +85,7 @@ def CorrelationMatrix(Clth, Pl, ellbins, polar=True, temp=False, EBTB=False):
 
     if not temp:
         allStoke = ['Q', 'U']
-        if EBTB:
+        if corr:
             xx = ['EE', 'BB', 'EB']
             ind = [1, 2, 5]
         else:

libangles.py

-"""
-Set of routines to ...
-
-Author: Vanneste
-"""
-from __future__ import division
-
-import numpy as np
-
-
-def polrotangle(ri, rj):
-    """
-    Computes cosine and sine of twice the angle between pixels i and j.
-
-    Parameters
-    ----------
-    ri : 3D array of floats
-        Coordinates of vector corresponding to input pixels i following
-        healpy.pix2vec(nside,ipix) output
-    rj : 3D array of floats
-        Coordinates of vector corresponding to input pixels j following
-        healpy.pix2vec(nside,jpix) output
-
-    Returns
-    ----------
-    cos2a : 1D array of floats
-        Cosine of twice the angle between pixels i and j
-    sin2a : 1D array of floats
-        Sine of twice the angle between pixels i and j
-
-    Example
-    ----------
-    >>> cos2a, sin2a = polrotangle([0.1,0.2,0.3], [0.4,0.5,0.6])
-    >>> print(round(cos2a,5),round(sin2a,5))
-    (0.06667, 0.37333)
-    """
-    z = np.array([0.0, 0.0, 1.0])
-
-    # Compute ri^rj : unit vector for the great circle connecting i and j
-    rij = np.cross(ri, rj)
-    norm = np.sqrt(np.dot(rij, np.transpose(rij)))
-
-    # case where pixels are identical or diametrically opposed on the sky
-    if norm <= 1e-15:
-        cos2a = 1.0
-        sin2a = 0.0
-        return cos2a, sin2a
-    rij = rij / norm
-
-    # Compute z^ri : unit vector for the meridian passing through pixel i
-    ris = np.cross(z, ri)
-    norm = np.sqrt(np.dot(ris, np.transpose(ris)))
-
-    # case where pixels is at the pole
-    if norm <= 1e-15:
-        cos2a = 1.0
-        sin2a = 0.0
-        return cos2a, sin2a
-    ris = ris / norm
-
-    # Now, the angle we want is that
-    # between these two great circles: defined by
-    cosa = np.dot(rij, np.transpose(ris))
-
-    # the sign is more subtle : see tegmark et de oliveira costa 2000 eq. A6
-    rijris = np.cross(rij, ris)
-    sina = np.dot(rijris, np.transpose(ri))
-
-    # so now we have directly cos2a and sin2a
-    cos2a = 2.0 * cosa * cosa - 1.0
-    sin2a = 2.0 * cosa * sina
-
-    return cos2a, sin2a
-
-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()

simulation.py

 """
-Set of routines to generate basic simulations 
+Set of routines to generate basic simulations
 
 Author: Vanneste
 """
@@ -96,62 +96,81 @@ def getNl(pixvar, nside, nbins):
     return Nl
 
 
-def getstokes(polar=True, temp=False, EBTB=False):
+def getstokes(spec=None, temp=False, polar=False, corr=False):
     """
     Get the Stokes parameters number and name(s)
 
     Parameters
     ----------
+    spec : bool
+        If True, get Stokes parameters for polar (default: True)
     polar : bool
         If True, get Stokes parameters for polar (default: True)
     temp : bool
         If True, get Stokes parameters for temperature (default: False)
-    EBTB : bool
+    corr : bool
         If True, get Stokes parameters for EB and TB (default: False)
 
     Returns
     ----------
-    allStoke : list of string
+    stokes : list of string
         Stokes variables names
     spec : int
         Spectra names
-    ind : list
+    istokes : list
         Indexes of power spectra
 
     Example
     ----------
-    >>> getstokes(polar=True, temp=False, EBTB=False)
+    >>> getstokes(polar=True, temp=False, corr=False)
     (['Q', 'U'], ['EE', 'BB'], [1, 2])
-    >>> getstokes(polar=True, temp=True, EBTB=False)
+    >>> getstokes(polar=True, temp=True, corr=False)
     (['I', 'Q', 'U'], ['TT', 'EE', 'BB', 'TE'], [0, 1, 2, 3])
-    >>> getstokes(polar=True, temp=True, EBTB=True)
+    >>> getstokes(polar=True, temp=True, corr=True)
     (['I', 'Q', 'U'], ['TT', 'EE', 'BB', 'TE', 'EB', 'TB'], [0, 1, 2, 3, 4, 5])
     """
-    allStoke = ['I', 'Q', 'U']
-    if EBTB:
-        spec = ['TT', 'EE', 'BB', 'TE', 'EB', 'TB']
-        ind = [0, 1, 2, 3, 4, 5]
+    if spec is not None:
+        _temp = "TT" in spec or "TE" in spec or "TB" in spec or temp
+        _polar = "EE" in spec or "BB" in spec or "TE" in spec or "TB" in \
+            spec or "EB" in spec or polar
+        _corr = "TE" in spec or "TB" in spec or "EB" in spec or corr
+        if not _temp and not _polar and not _corr:
+            print("invalid spectra list")
     else:
-        spec = ['TT', 'EE', 'BB', 'TE']
-        ind = [0, 1, 2, 3]
-    if not temp:
-        allStoke = ['Q', 'U']
-        if EBTB:
-            spec = ['EE', 'BB', 'EB']
-            ind = [1, 2, 4]
-        else:
-            spec = ['EE', 'BB']
-            ind = [1, 2]
-    if not polar:
-        allStoke = ['I']
-        spec = ['TT']
-        ind = [0]
-    return allStoke, spec, ind
+        _temp = temp
+        _polar = polar
+        _corr = corr
+
+    speclist = []
+    if _temp or (spec is None and corr):
+        speclist.extend(["TT"])
+    if _polar:
+        speclist.extend(["EE", "BB"])
+    if spec is not None and not corr:
+        if 'TE' in spec:
+            speclist.extend(["TE"])
+        if 'EB' in spec:
+            speclist.extend(["EB"])
+        if 'TB' in spec:
+            speclist.extend(["TB"])
+
+    elif _corr:
+        speclist.extend(["TE", "EB", "TB"])
+
+    stokes = []
+    if _temp:
+        stokes.extend(["I"])
+    if _polar:
+        stokes.extend(["Q", "U"])
+
+    ispecs = [['TT', 'EE', 'BB', 'TE', 'EB', 'TB'].index(s) for s in speclist]
+    istokes = [['I', 'Q', 'U'].index(s) for s in stokes]
+    return stokes, speclist, istokes, ispecs
 
 
 def GetBinningMatrix(
         ellbins, lmax, norm=False, polar=True,
-        temp=False, EBTB=False):
+        temp=False, corr=False):
     """
     Return P (m,n) and Q (n,m) binning matrices such that
     Cb = P.Cl and Vbb = P.Vll.Q with m the number of bins and
@@ -171,7 +190,7 @@ def GetBinningMatrix(
         If True, get Stokes parameters for polar (default: True)
     temp : bool
         If True, get Stokes parameters for temperature (default: False)
-    EBTB : bool
+    corr : bool
         If True, get Stokes parameters for EB and TB (default: False)
 
     Returns
@@ -229,8 +248,7 @@ def GetBinningMatrix(
     [ 3.  7.]
     """
     # ### define Stokes
-    allStoke, der, ind = getstokes(polar, temp, EBTB)
-    nder = len(der)
+    nspec = 1
 
     nbins = len(ellbins) - 1
     ellmin = np.array(ellbins[0: nbins])
@@ -248,10 +266,10 @@ def GetBinningMatrix(
     for i in np.arange(nbins):
         masklm.append(((ell[:-1] >= minell[i]) & (ell[:-1] <= maxell[i])))
 
-    allmasklm = nder*[list(masklm)]
+    allmasklm = nspec*[list(masklm)]
     masklM = np.array(sparse.block_diag(allmasklm[:]).toarray())
     binsnorm = np.array(
-        nder * [list(np.arange(minell[0], np.max(ellbins)))]).flatten()
+        nspec * [list(np.arange(minell[0], np.max(ellbins)))]).flatten()
 
     binsnorm = binsnorm*(binsnorm+1)/2./np.pi
     P = np.array(masklM)*1.
@@ -263,7 +281,7 @@ def GetBinningMatrix(
     return P, Q, ell, ellval
 
 
-def extrapolpixwin(nside, Slmax, pixwining=True):
+def extrapolpixwin(nside, Slmax, pixwin=True):
     '''
     Parameters
     ----------
@@ -271,7 +289,7 @@ def extrapolpixwin(nside, Slmax, pixwining=True):
         Healpix map resolution
     Slmax : int
         Maximum multipole value computed for the pixel covariance pixel matrix
-    pixwining : bool
+    pixwin : bool
         If True, multiplies the beam window function by the pixel
         window function. Default: True
 
@@ -293,7 +311,7 @@ def extrapolpixwin(nside, Slmax, pixwining=True):
     [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
       1.  1.]
     '''
-    if pixwining:
+    if pixwin:
         prepixwin = np.array(hp.pixwin(nside))
         poly = np.polyfit(np.arange(len(prepixwin)), np.log(prepixwin),
                           deg=3, w=np.sqrt(prepixwin))
@@ -318,4 +336,4 @@ if __name__ == "__main__":
     import doctest
     if np.__version__ >= "1.14.0":
         np.set_printoptions(legacy="1.13")
-    doctest.testmod()
+    doctest.testmod()
\ No newline at end of file

spin_functions.py

-"""
-Set of routines to compute the pixel covariance matrix using
-Legendre polynomials