def get_fom(picke_file):
print "Loading pickle file...", pickle_file
(fit_params, stan_data,
other_inputs) = pickle.load(open(pickle_file, 'rb'))
for key in fit_params:
print "fit_params:", key
for key in other_inputs:
print "other_inputs:", key
if not other_inputs.has_key("jacobian_names"):
other_inputs["jacobian_names"] = " " * stan_data["nsys"]
other_inputs["redshifts"] = arange(stan_data["nred"]) * 0.1 + 0.05
print "Hack, but fixed in later versions!"
cmat = cov(transpose(fit_params["mus"]))
z_set = concatenate(
([0.05], arange(len(cmat) - 1, dtype=float64) * 0.05 + 0.125))
print "z_set", z_set
FoM = astro_functions.get_FoM(cmat,
z_set,
zp=0.3,
addcmb=1,
verbose=True,
shift_constraint=0.002)[0]
print "Final_FoM", FoM
return
def target_FoM_scale(coarse_z, coarse_mu_errs, target_FoM):
err_scales = arange(0.1, 3., 0.1)
FoMs_found = []
for err_scale in err_scales:
tmp_Cmat = diag(err_scale * coarse_mu_errs)**2.
print tmp_Cmat
FoM = astro_functions.get_FoM(tmp_Cmat,
coarse_z,
zp=0.3,
addcmb=1,
verbose=True,
shift_constraint=0.002)[0]
FoMs_found.append(FoM)
FoMs_found = array(FoMs_found)
print "scales ", err_scales, FoMs_found
inds = where(1 - isnan(FoMs_found))
FoMs_found = FoMs_found[inds]
err_scales = err_scales[inds]
FoMs_found = FoMs_found[::-1]
err_scales = err_scales[::-1]
print "scales ", err_scales, FoMs_found
interp_scale = interp1d(FoMs_found[inds], err_scales[inds],
kind='linear')(target_FoM)
print "interpolated to", interp_scale, "for", target_FoM
return interp_scale * coarse_mu_errs
def get_FoM_from_file(item):
f = pyfits.open(item)
cmat = f[0].data
f.close()
z_list = concatenate(([0.05], arange(len(cmat) - 1) * 0.05 + 0.125))
print z_list
FoM = get_FoM(cmat, z_list, adddesi=0)[0]
print "FoM", item, FoM
return FoM
#plt.subplot(1,2,1)
plt.imshow(sn_Cmat,
interpolation='nearest',
origin='lower',
cmap=plt.get_cmap('Greys'),
aspect='equal')
#plt.colorbar()
plt.xticks([0, 5, 10, 15], ["0.05", "0.55", "1.05", "1.55"])
#plt.xticklabels(["0.05", "0.55", "1.05", "1.55"])
plt.yticks([0, 5, 10, 15], ["0.05", "0.55", "1.05", "1.55"])
#plt.yicklabels(["0.05", "0.55", "1.05", "1.55"])
#plt.subplot(1,2,2)
#plt.plot(params["z_list"], sqrt(diag(sn_Cmat)*params["NSNe"]), color = 'k')
#plt.xlabel("Redshift")
#plt.ylabel("Total Distance Modulus Uncertainty per SN")
plt.title("Estimated Covariance Matrix, Binned in Redshift")
plt.xlabel("Redshift")
plt.ylabel("Redshift")
plt.savefig("Cmat.eps", bbox_inches='tight')
#for zp in arange(0, 1.001, 0.05):
FoM, uncertainties = get_FoM(sn_Cmat, params["z_list"], zp=0.3)
print "FoM", FoM
# print "zp, uncertainties ", zp, " ".join([str(item) for item in uncertainties])
if show_plots:
write_Cmat(params["orig_lines"],
sn_Cmat,
FoM,
flname="Cmat_" + params["suffix"] + ".txt")
def make_plots_from_dir(item):
print "Loading pickle file...", item
if item.count("/") > 0:
outputdir = item[:item.rfind("/")] + "/"
else:
outputdir = ""
(fit_params, stan_data, other_inputs) = pickle.load(open(item, 'rb'))
for key in fit_params:
print "fit_params:", key
for key in other_inputs:
print "other_inputs:", key
if not other_inputs.has_key("jacobian_names"):
other_inputs["jacobian_names"] = " " * stan_data["nsys"]
other_inputs["redshifts"] = arange(stan_data["nred"]) * 0.1 + 0.05
print "Hack, but fixed in later versions!"
cmat = cov(transpose(fit_params["mus"]))
save_img(cmat, outputdir + "cmat.fits")
z_set = concatenate(
([0.05], arange(len(cmat) - 1, dtype=float64) * 0.05 + 0.125))
print "z_set", z_set
FoM = astro_functions.get_FoM(cmat,
z_set,
zp=0.3,
addcmb=1,
verbose=True,
shift_constraint=0.002)[0]
print "Final_FoM", FoM
binned_points(cmat,
z_set,
bin_edges=[0., 0.1, 0.2, 0.55, 0.85, 1.15, 1.7],
target_FoM=FoM)
plot_RV_by_z(other_inputs=other_inputs, fit_params=fit_params)
mag_histogram(fit_params=fit_params)
plot_diag_mu_err(other_inputs=other_inputs,
stan_data=stan_data,
fit_params=fit_params)
plot_EBV_err(fit_params=fit_params)
systematics_histograms(other_inputs=other_inputs,
fit_params=fit_params,
stan_data=stan_data)
plot_d_mu_d_sys(other_inputs=other_inputs,
stan_data=stan_data,
fit_params=fit_params)
plot_d_mu_d_sys(other_inputs=other_inputs,
stan_data=stan_data,
offset=0.02,
fit_params=fit_params)
plot_d_mu_d_extinction(other_inputs=other_inputs,
fit_params=fit_params,
stan_data=stan_data)
plot_d_mu_d_RV_by_z(other_inputs=other_inputs,
stan_data=stan_data,
fit_params=fit_params)
plot_delta_vs_value(other_inputs["true_EBVs"], fit_params["true_EBV"],
"EBV_resid.pdf")
plot_delta_vs_value(other_inputs["true_mags"], fit_params["mags"],
"mag_resid.pdf")
if fit_params.has_key("true_dRV"):
plot_delta_vs_value(other_inputs["true_RVs"] - 3.1,
fit_params["true_dRV"],
"RV_resid.pdf",
diff=0)
plot_delta_vs_value(median(fit_params["mus"], axis=0), fit_params["mus"],
"mus_vs_mus.pdf")
print "Corner plots..."
make_corner(["RV_coeff", "log_EBV_star_coeff", "log_R_EBV_coeff"],
"EBV_params.pdf",
fit_params=fit_params)
make_multi_corner(
["mus", "RV_by_red", "log_EBV_star_by_red", "log_R_EBV_by_red"],
"RV_mus.pdf",
fit_params=fit_params,
stan_data=stan_data)
return None