def make_corr_plots(filename):
corrobj = corr21cm.Corr21cm()
T_b_sim = corrobj.T_b(1420. / 800. - 1)
print T_b_sim
master = shelve.open(filename, "r")
nlag = 15
nmode = 11
nsim = 9
# load the autocorr simulations --------------------------------------------
#splt.process_batch_correlations(autocorr_sim_param, cross_power=False,
# multiplier=1.)
autocorr_sim_data = splt.batch_correlations_statistics(
autocorr_sim_param, randtoken="rand", include_signal=False)
lagl = autocorr_sim_data[0]
lagc = autocorr_sim_data[1]
lagr = autocorr_sim_data[2]
autocorr_sim = autocorr_sim_data[3]
autocorr_sim_err = autocorr_sim_data[4]
autocorr_sim_cov = autocorr_sim_data[5]
# now convert from the 1e-3 Omega_HI in simulations to 0.5e-3
#autocorr_sim /= 2.
#autocorr_sim_err /= 2.
# calibrate to xcorr: 0.56126646473 0.229801269703
autocorr_sim_low = autocorr_sim * (0.56 - 0.23)
autocorr_sim_high = autocorr_sim * (0.56 + 0.23)
autocorr_sim_center = autocorr_sim * 0.56
for correntry in zip(lagl, lagc, lagr, autocorr_sim_low,
autocorr_sim_center, autocorr_sim_high):
print "%5.3g %5.3g %5.3g %5.3g %5.3g %5.3g" % correntry
# treat the autocorr and loss simulations----------------------------------
lags = np.zeros((3, nlag))
autocorr = np.zeros((nmode, nlag))
autocorr_err = np.zeros((nmode, nlag))
autocorr_sim = np.zeros((nsim, nmode, nlag))
autocorr_sim_avg = np.zeros((nmode, nlag))
compensated_autocorr = np.zeros((nmode, nlag))
compensated_autocorr_err = np.zeros((nmode, nlag))
mode_compensation = np.zeros((nmode, nlag))
for mode_index in range(0, nmode):
mode_num = mode_index * 5
autocorr_name = "autocorr" + repr(mode_num) + "_mode"
entry = master[autocorr_name]
autocorr[mode_index, :] = entry["corr1D"]
autocorr_err[mode_index, :] = entry["corr1D_std"]
lags[0, :] = entry["x_axis"][0]
lags[1, :] = entry["x_axis"][1]
lags[2, :] = entry["x_axis"][2]
for sim_index in range(0, nsim):
sim_name = "sim" + repr(sim_index + 1) + "_mode" + repr(mode_num)
entry = master[sim_name]
autocorr_sim[sim_index, mode_index, :] = entry["corr1D"]
autocorr_sim_avg = np.mean(autocorr_sim, axis=0) / 1000.
zero_modes = autocorr_sim_avg[0, :]
mode_compensation = autocorr_sim_avg / zero_modes[None, :]
compensated_autocorr = autocorr / mode_compensation
compensated_autocorr_err = autocorr_err / mode_compensation
print "-" * 80
for lagind in range(0, nlag):
lag_bounds = splt.fancy_vector(lags[:, lagind], "%5.2g")
modes = splt.fancy_vector(autocorr[:, lagind], "%5.2g")
modes_err = splt.fancy_vector(autocorr_err[:, lagind], "%5.2g")
print lag_bounds + modes + modes_err
print "-" * 80
for lagind in range(0, nlag):
lag_bounds = splt.fancy_vector(lags[:, lagind], "%5.2g")
modes = splt.fancy_vector(mode_compensation[:, lagind], "%5.2g")
print lag_bounds + modes
print "-" * 80
for lagind in range(0, nlag):
lag_bounds = splt.fancy_vector(lags[:, lagind], "%5.2g")
modes = splt.fancy_vector(autocorr_sim_avg[:, lagind], "%5.2g")
print lag_bounds + modes
print "-" * 80
for lagind in range(0, nlag):
lag_bounds = splt.fancy_vector(lags[:, lagind], "%5.2g")
modes = splt.fancy_vector(compensated_autocorr[:, lagind], "%5.2g")
modes_err = splt.fancy_vector(compensated_autocorr_err[:, lagind],
"%5.2g")
print lag_bounds + modes + modes_err
master.close()
def make_corr_plots(filename):
corrobj = corr21cm.Corr21cm()
T_b_sim = corrobj.T_b(1420./800.-1)
print T_b_sim
master = shelve.open(filename, "r")
nlag = 15
nmode = 11
nsim = 9
# load the autocorr simulations --------------------------------------------
#splt.process_batch_correlations(autocorr_sim_param, cross_power=False,
# multiplier=1.)
autocorr_sim_data = splt.batch_correlations_statistics(autocorr_sim_param,
randtoken="rand",
include_signal=False)
lagl = autocorr_sim_data[0]
lagc = autocorr_sim_data[1]
lagr = autocorr_sim_data[2]
autocorr_sim = autocorr_sim_data[3]
autocorr_sim_err = autocorr_sim_data[4]
autocorr_sim_cov = autocorr_sim_data[5]
# now convert from the 1e-3 Omega_HI in simulations to 0.5e-3
#autocorr_sim /= 2.
#autocorr_sim_err /= 2.
# calibrate to xcorr: 0.56126646473 0.229801269703
autocorr_sim_low = autocorr_sim*(0.56-0.23)
autocorr_sim_high = autocorr_sim*(0.56+0.23)
autocorr_sim_center = autocorr_sim*0.56
for correntry in zip(lagl, lagc, lagr,
autocorr_sim_low, autocorr_sim_center, autocorr_sim_high):
print "%5.3g %5.3g %5.3g %5.3g %5.3g %5.3g" % correntry
sys.exit()
# treat the autocorr and loss simulations----------------------------------
lags = np.zeros((3, nlag))
autocorr = np.zeros((nmode, nlag))
autocorr_err = np.zeros((nmode, nlag))
autocorr_sim = np.zeros((nsim, nmode, nlag))
autocorr_sim_avg = np.zeros((nmode, nlag))
compensated_autocorr = np.zeros((nmode, nlag))
compensated_autocorr_err = np.zeros((nmode, nlag))
mode_compensation = np.zeros((nmode, nlag))
for mode_index in range(0, nmode):
mode_num = mode_index*5
autocorr_name = "autocorr" + repr(mode_num) + "_mode"
entry = master[autocorr_name]
autocorr[mode_index, :] = entry["corr1D"]
autocorr_err[mode_index, :] = entry["corr1D_std"]
lags[0, :] = entry["x_axis"][0]
lags[1, :] = entry["x_axis"][1]
lags[2, :] = entry["x_axis"][2]
for sim_index in range(0, nsim):
sim_name = "sim" + repr(sim_index+1) + "_mode" + repr(mode_num)
entry = master[sim_name]
autocorr_sim[sim_index, mode_index, :] = entry["corr1D"]
autocorr_sim_avg = np.mean(autocorr_sim, axis=0)/1000.
zero_modes = autocorr_sim_avg[0,:]
mode_compensation = autocorr_sim_avg/zero_modes[None, :]
compensated_autocorr = autocorr/mode_compensation
compensated_autocorr_err = autocorr_err/mode_compensation
print mode_compensation
for lagind in range(0, nlag):
lag_bounds = splt.fancy_vector(lags[:,lagind], "%5.2g")
modes = splt.fancy_vector(autocorr[:,lagind], "%5.2g")
modes_err = splt.fancy_vector(autocorr_err[:,lagind], "%5.2g")
print lag_bounds + modes + modes_err
for lagind in range(0, nlag):
lag_bounds = splt.fancy_vector(lags[:,lagind], "%5.2g")
modes = splt.fancy_vector(autocorr_sim_avg[:,lagind], "%5.2g")
print lag_bounds + modes
for lagind in range(0, nlag):
lag_bounds = splt.fancy_vector(lags[:,lagind], "%5.2g")
modes = splt.fancy_vector(compensated_autocorr[:,lagind], "%5.2g")
modes_err = splt.fancy_vector(compensated_autocorr_err[:,lagind], "%5.2g")
print lag_bounds + modes + modes_err
master.close()
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
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
