for j in range(nbins_m):
mask = (mass_long > mass_bins[j]) & (mass_long <
mass_bins[j + 1])
plot_data[f'minT_{minT[i]}_med'][j] = np.nanmedian(
np.log10(ew_no_uvb[mask]) -
np.log10(ew_with_uvb[mask]))
plot_data[f'minT_{minT[i]}_per25'][j] = np.nanpercentile(
np.log10(ew_no_uvb[mask]) -
np.log10(ew_with_uvb[mask]), 25.)
plot_data[f'minT_{minT[i]}_per75'][j] = np.nanpercentile(
np.log10(ew_no_uvb[mask]) -
np.log10(ew_with_uvb[mask]), 75.)
write_dict_to_h5(plot_data, median_file)
for i in range(len(minT)):
ax[l].plot(plot_data['mass'],
plot_data[f'minT_{minT[i]}_med'],
ls='-',
c=colors[i],
label=r'$T_{{\rm min}} = {{{}}}$'.format(minT[i]))
if i == len(minT) - 2:
ax[l].fill_between(plot_data['mass'],
plot_data[f'minT_{minT[i]}_per25'],
plot_data[f'minT_{minT[i]}_per75'],
color=colors[i],
alpha=0.4)
if l == 0:
f'ew_wave_{fr200[j]}r200'].flatten()
plot_data[f'minT_{minT[i]}_{bin_label}_med'][
j] = np.nanmedian(
np.log10(ew_no_uvb[mask]) -
np.log10(ew_with_uvb[mask]))
plot_data[f'minT_{minT[i]}_{bin_label}_per25'][
j] = np.nanpercentile(
np.log10(ew_no_uvb[mask]) -
np.log10(ew_with_uvb[mask]), 25.)
plot_data[f'minT_{minT[i]}_{bin_label}_per75'][
j] = np.nanpercentile(
np.log10(ew_no_uvb[mask]) -
np.log10(ew_with_uvb[mask]), 75)
write_dict_to_h5(plot_data, profile_file)
for i in range(len(minT)):
ax[l].plot(plot_data['fr200'],
plot_data[f'minT_{minT[i]}_{bin_label}_med'],
ls='-',
c=colors[i],
label=r'$T_{{\rm min}} = {{{}}}$'.format(minT[i]),
lw=1.5)
if minT[i] == '5.0':
ax[l].fill_between(
plot_data['fr200'],
plot_data[f'minT_{minT[i]}_{bin_label}_per75'],
plot_data[f'minT_{minT[i]}_{bin_label}_per25'],
alpha=0.3,