def format_toepliz(coupling, l_toep, lmax):
"""take a matrix and apply the toepliz appoximation
Parameters
----------
toepliz_array: array
consist of an array where the upper part is the exact matrix and
the lower part is the diagonal. We will feed the off diagonal
of the lower part using the measurement of the correlation from the exact computatio
l_toep: integer
the l at which we start the approx
lmax: integer
the maximum multipole of the array
"""
toepliz_array = coupling.copy() * 0
diag = np.sqrt(np.diag(coupling))
corr = so_cov.cov2corr(coupling, remove_diag=False)
for ell in range(0, l_toep - 2):
toepliz_array[ell:ell + lmax - (l_toep - 2),
ell] = corr[l_toep - 2:lmax, l_toep - 2]
for ell in range(l_toep - 2, lmax):
pix = ell - (l_toep - 2)
toepliz_array[ell:, ell] = corr[l_toep - 2:lmax - pix, l_toep - 2]
toepliz_array = toepliz_array * np.outer(diag, diag)
toepliz_array = toepliz_array + toepliz_array.T - np.diag(
np.diag(toepliz_array))
return toepliz_array
def toeplitz(coupling, lmax):
toepliz_array = coupling.copy() * 0
diag = np.sqrt(np.diag(coupling))
corr = so_cov.cov2corr(coupling, remove_diag=False)
last_column = corr[:, lmax - 1]
for ell in range(lmax):
toepliz_array[:ell + 1, ell] = last_column[lmax - 1 - ell:lmax]
toepliz_array = toepliz_array * np.outer(diag, diag)
toepliz_array = toepliz_array + toepliz_array.T - np.diag(
np.diag(toepliz_array))
return toepliz_array
plt.plot(lb[1:],
var[1:] / analytic_var[1:],
label="MC/Analytic %sx%s" % (bl[:2], bl[2:4]))
if count == 1:
plt.ylabel(r"Ratio $Cov_{i,i,\ell}$", fontsize=22)
if count == 4:
plt.xlabel(r"$\ell$", fontsize=22)
plt.legend()
count += 1
plt.savefig("%s/covariance_pseudo_diagonal_ratio_%s_%s.png" %
(cov_plot_dir, spec1, spec2),
bbox_inches="tight")
plt.clf()
plt.close()
analytic_corr = so_cov.cov2corr(analytic_cov)
mc_corr = so_cov.cov2corr(mc_cov)
plt.figure(figsize=(15, 15))
plt.suptitle(
"%s %s (press c/v to switch between covariance matrix elements)" %
(spec1, spec2),
fontsize=30)
count = 1
for bl in [
"TTTT", "TETE", "ETET", "EEEE", "TTTE", "TTEE", "TTET", "TEET",
"TEEE", "ETEE", "EETE", "EEET", "ETTE", "ETTT", "EETT", "TETT"
]:
mc_corr_sub = so_cov.selectblock(mc_corr, ["TT", "TE", "ET", "EE"],
n_bins,
cl_00 = compute_master(f0, f0, w00)
n_ell = len(cl_00[0])
cw = nmt.NmtCovarianceWorkspace()
cw.compute_coupling_coefficients(f0, f0, f0, f0)
covar_00_00 = nmt.gaussian_covariance(cw,
0,
0,
0,
0, [cl_tt], [cl_tt], [cl_tt], [cl_tt],
w00,
wb=w00).reshape([n_ell, 1, n_ell, 1])
cov_namaster = covar_00_00[:, 0, :, 0]
corr_pspy = so_cov.cov2corr(cov_pspy)
plt.matshow(corr_pspy, vmin=-0.3, vmax=0.3)
plt.savefig("%s/corr_pspsy.png" % (test_dir))
plt.clf()
plt.close()
corr_namaster = so_cov.cov2corr(cov_namaster)
plt.matshow(corr_namaster, vmin=-0.3, vmax=0.3)
plt.savefig("%s/corr_namaster.png" % (test_dir))
plt.clf()
plt.close()
print("cov mat sim")
nsims = 1000
Db_list = []
for iii in range(nsims):
from pspy import so_dict, so_cov, pspy_utils
import cov_plot_utils
import numpy as np
import sys
d = so_dict.so_dict()
d.read_from_file(sys.argv[1])
run_name = d["run_name"]
plot_dir = "plot_%s" % run_name
pspy_utils.create_directory(plot_dir)
coupling_dir = "coupling_%s" % run_name
name_list = ["00", "02", "20", "++", "--"]
mcm_dict = {}
cov_plot_utils.residual_plot(plot_dir,
coupling_dir,
d["clfile"],
9998,
vmax=5 * 10**-6)
for id_mcm, name in enumerate(name_list):
coupling = np.load("%s/coupling_exact_%s.npy" % (coupling_dir, name))
corr = so_cov.cov2corr(coupling, remove_diag=False)
cov_plot_utils.toepliz_plot(plot_dir, corr, name, lsplit=2500)