def execute(self, processes):
summary_outname = self.params["outdir"] + "theory_fit.dat"
print "opening: ", self.params["summary_file"]
print "to summary: ", summary_outname
cross_summary = h5py.File(self.params["summary_file"], "r")
# this has: kvec, power_1d, power_1d_cov
print "using col:%d of %s" % (self.params["pk_column"],
self.params["theory_curve"])
theory_curve = np.genfromtxt(self.params["theory_curve"])
theory_curve = theory_curve[:, self.params["pk_column"]]
#print theory_curve
bin_center = cross_summary["kvec"]["k_1d_center"].value
(kmin, kmax) = tuple(self.params["krange"])
restrict = np.where(np.logical_and(bin_center > kmin,
bin_center < kmax))
res_slice = slice(min(restrict[0]), max(restrict[0]) + 1)
#restrict_alt = np.where(restrict)[0][np.newaxis, :]
#restricted_cov = covmock[restrict_alt][0]
summary_outfile = open(summary_outname, "w")
for treatment in cross_summary["power_1d"]:
pwr = cross_summary["power_1d"][treatment].value
cov = cross_summary["power_1d_cov"][treatment].value
amplitude = utils.ampfit(pwr[res_slice],
cov[res_slice, res_slice],
theory_curve[res_slice])
nmodes = float(treatment.split("modes")[0])
snr = amplitude['amp'] / amplitude['error']
outstr = "%d " % nmodes
outstr += "%(amp)10.5f %(error)10.5f " % amplitude
outstr += "%(chi2)10.5f %(dof)10.5f %(pte)10.5f " % amplitude
outstr += "%10.5f" % snr
print outstr
summary_outfile.write(outstr + "\n")
def execute(self, processes):
summary_outname = self.params["outdir"] + "theory_fit.dat"
print "opening: ", self.params["summary_file"]
print "to summary: ", summary_outname
cross_summary = h5py.File(self.params["summary_file"], "r")
# this has: kvec, power_1d, power_1d_cov
print "using col:%d of %s" % (self.params["pk_column"],
self.params["theory_curve"])
theory_curve = np.genfromtxt(self.params["theory_curve"])
theory_curve = theory_curve[:, self.params["pk_column"]]
#print theory_curve
bin_center = cross_summary["kvec"]["k_1d_center"].value
(kmin, kmax) = tuple(self.params["krange"])
restrict = np.where(
np.logical_and(bin_center > kmin, bin_center < kmax))
res_slice = slice(min(restrict[0]), max(restrict[0]) + 1)
#restrict_alt = np.where(restrict)[0][np.newaxis, :]
#restricted_cov = covmock[restrict_alt][0]
summary_outfile = open(summary_outname, "w")
for treatment in cross_summary["power_1d"]:
pwr = cross_summary["power_1d"][treatment].value
cov = cross_summary["power_1d_cov"][treatment].value
amplitude = utils.ampfit(pwr[res_slice], cov[res_slice, res_slice],
theory_curve[res_slice])
nmodes = float(treatment.split("modes")[0])
snr = amplitude['amp'] / amplitude['error']
outstr = "%d " % nmodes
outstr += "%(amp)10.5f %(error)10.5f " % amplitude
outstr += "%(chi2)10.5f %(dof)10.5f %(pte)10.5f " % amplitude
outstr += "%10.5f" % snr
print outstr
summary_outfile.write(outstr + "\n")
def make_xcorr_plotdata():
# find the mean brightness used in simulations
corrobj = corr21cm.Corr21cm()
T_b_sim = corrobj.T_b(1420. / 800. - 1)
print T_b_sim
#splt.process_batch_correlations(xcorr_real_param,
# multiplier=1., cross_power=True)
##splt.process_batch_correlations(xcorr_sim_param, cross_power=True,
## multiplier=1./(T_b_sim/1.e3))
#splt.process_batch_correlations(xcorr_sim_param, cross_power=True,
# multiplier=1./(T_b_sim))
#splt.process_batch_correlations(xcorr_loss_sim_param,
# multiplier=1./T_b_sim*1.e-3, cross_power=True)
#splt.process_batch_correlations(xcorr_variants_param,
# multiplier=1., cross_power=True)
#splt.plot_batch_correlations(xcorr_real_param,
# dir_prefix="plots/xcorr_real/",
# color_range=[-0.04, 0.04], cross_power=True)
#splt.plot_batch_correlations(xcorr_sim_param,
# dir_prefix="plots/xcorr_sim/",
# color_range=[-0.04, 0.04], cross_power=True)
#splt.plot_batch_correlations(xcorr_loss_param,
# dir_prefix="plots/xcorr_loss/",
# color_range=[-0.04, 0.04], cross_power=True)
#splt.plot_batch_correlations(xcorr_loss_sim_param,
# dir_prefix="plots/xcorr_loss_sim/",
# color_range=[-0.04, 0.04], cross_power=True)
#splt.plot_batch_correlations(xcorr_variants_param,
# dir_prefix="plots/xcorr_variants/",
# color_range=[-0.04, 0.04], cross_power=True)
# find the real xcorr signal with errors
xcorr_data = splt.batch_correlations_statistics(xcorr_real_param,
randtoken="rand",
include_signal=True)
# find the simulated xcorr signal with errors
xcorr_sim_data = splt.batch_correlations_statistics(xcorr_sim_param,
randtoken="rand",
include_signal=False)
# find the compensation function from simulations
compmode = splt.batch_compensation_function(xcorr_loss_sim_param)
lagl = xcorr_data[0]
lagc = xcorr_data[1]
lagr = xcorr_data[2]
xcorr_null = xcorr_data[3]
xcorr_signal = xcorr_data[6]
xcorr_cov = xcorr_data[5]
xcorr_err = xcorr_data[4]
xcorr_sim = xcorr_sim_data[3]
xcorr_sim_err = xcorr_sim_data[4]
xcorr_sim_cov = xcorr_sim_data[5]
# 0=0, 5=1, 10=2, 15=3, etc.
compensation = compmode[3, :]
(amp, amp_err) = utils.ampfit(xcorr_signal, xcorr_cov + xcorr_sim_cov,
xcorr_sim)
print amp, amp_err
# now convert from the 1e-3 Omega_HI in simulations to 0.5e-3
#xcorr_sim /= 2.
#xcorr_sim_err /= 2.
xcorr_sim *= amp
xcorr_sim_err *= amp
for correntry in zip(lagl, lagc, lagr, xcorr_signal, xcorr_null, xcorr_err,
xcorr_sim, xcorr_sim_err, compensation):
print "%5.3g %5.3g %5.3g %5.3g %5.3g %5.3g %5.3g %5.3g %5.3g" % correntry