def plot_power_spectrum_grid(ps_data_dir,
output_dir,
ps_data=None,
scales='large',
sim_data_sets=None,
black_background=False,
system=None,
high_z_only=False,
plot_ps_normalized=False):
if system == 'Lux' or system == 'Summit': matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig_height = 5
fig_width = 8
fig_dpi = 300
if high_z_only: fig_height = 8
label_size = 18
figure_text_size = 18
legend_font_size = 16
tick_label_size_major = 15
tick_label_size_minor = 13
tick_size_major = 5
tick_size_minor = 3
tick_width_major = 1.5
tick_width_minor = 1
border_width = 1
if system == 'Lux':
prop = matplotlib.font_manager.FontProperties(fname=os.path.join(
'/home/brvillas/fonts', "Helvetica.ttf"),
size=12)
if system == 'Shamrock':
prop = matplotlib.font_manager.FontProperties(fname=os.path.join(
'/home/bruno/fonts/Helvetica', "Helvetica.ttf"),
size=12)
dir_boss = ps_data_dir + 'data_power_spectrum_boss/'
data_filename = dir_boss + 'data_table.py'
data_boss = load_data_boss(data_filename)
data_z_boss = data_boss['z_vals']
data_filename = ps_data_dir + 'data_power_spectrum_walther_2019/data_table.txt'
data_walther = load_power_spectrum_table(data_filename)
data_z_w = data_walther['z_vals']
dir_data_boera = ps_data_dir + 'data_power_spectrum_boera_2019/'
data_boera = load_tabulated_data_boera(dir_data_boera)
data_z_b = data_boera['z_vals']
data_dir_viel = ps_data_dir + 'data_power_spectrum_viel_2013/'
data_viel = load_tabulated_data_viel(data_dir_viel)
data_z_v = data_viel['z_vals']
dir_irsic = ps_data_dir + 'data_power_spectrum_irsic_2017/'
data_filename = dir_irsic + 'data_table.py'
data_irsic = load_data_irsic(data_filename)
data_z_irsic = data_irsic['z_vals']
z_vals_small_scale = [
2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 4.2, 4.6, 5.0, 5.4
]
z_vals_large_scale = [
2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4
]
z_vals_middle_scale = [3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4]
z_vals_small_scale_walther = [
2.0,
2.2,
2.4,
2.6,
2.8,
3.0,
3.2,
3.4,
]
z_vals_all = [
2.2,
2.4,
2.6,
2.8,
3.0,
3.2,
3.4,
3.6,
3.8,
4.0,
4.2,
4.4,
]
z_vals_small_highz = [
4.2,
4.6,
5.0,
]
z_high = [5.0, 5.4]
z_large_middle = [
3.0,
3.2,
3.4,
3.6,
3.8,
4.0,
]
z_vals_large_reduced = [
2.6,
2.8,
3.0,
3.2,
3.4,
3.6,
3.8,
4.0,
]
z_vals_small_reduced = [4.2, 4.6, 5.0]
if scales == 'large': z_vals = z_vals_large_scale
elif scales == 'small': z_vals = z_vals_small_scale
elif scales == 'middle': z_vals = z_vals_middle_scale
elif scales == 'small_walther': z_vals = z_vals_small_scale_walther
elif scales == 'small_highz': z_vals = z_vals_small_highz
elif scales == 'all': z_vals = z_vals_all
elif scales == 'large_middle': z_vals = z_large_middle
elif scales == 'large_reduced': z_vals = z_vals_large_reduced
elif scales == 'small_reduced': z_vals = z_vals_small_reduced
else:
print("ERROR: Scales = large, small of middle ")
return
if high_z_only: z_vals = z_high
nrows = 3
ncols = 4
if scales == 'small_walther': nrows = 2
if high_z_only: nrows, ncols = 1, 2
if scales == 'large_middle': ncols, nrows = 3, 2
if scales == 'middle':
nrows = 2
flags = np.zeros((nrows, ncols))
if scales == 'small_highz': nrows, ncols = 1, 3
if scales == 'large_reduced': nrows, ncols = 2, 4
if scales == 'small_reduced': nrows, ncols = 1, 3
plot_boss, plot_walther, plot_boera, plot_viel, plot_irsic = False, False, False, False, False
if scales == 'large': plot_boss = True
if scales == 'all': plot_boss, plot_boera, plot_irsic = True, True, True
if scales == 'middle': plot_boss, plot_irsic = True, True,
if scales == 'small_highz': plot_boss, plot_boera = True, True,
if scales == 'large_middle': plot_boss, plot_irsic = True, True
if scales == 'large_reduced': plot_boss = True
if scales == 'small_reduced':
plot_boera = True
fig_height *= 1.4
fig, ax_l = plt.subplots(nrows=nrows,
ncols=ncols,
figsize=(2 * fig_width, fig_height * nrows))
plt.subplots_adjust(hspace=0.02, wspace=0.02)
c_pchw18 = pylab.cm.viridis(.7)
c_hm12 = pylab.cm.cool(.3)
c_boss = pylab.cm.viridis(.3)
c_walther = pylab.cm.viridis(.3)
c_viel = 'C1'
c_boera = pylab.cm.Purples(.7)
c_irsic = pylab.cm.Purples(.7)
text_color = 'black'
color_line = c_pchw18
if scales == 'middle':
c_walther = 'C3'
text_color = 'black'
if black_background:
text_color = 'white'
c_boss = 'C1'
c_irsic = 'C9'
c_boera = yellows[0]
blue = blues[4]
color_line = blue
for index, current_z in enumerate(z_vals):
indx_j = index % ncols
indx_i = index // ncols
if nrows > 1: ax = ax_l[indx_i][indx_j]
else: ax = ax_l[indx_j]
if scales == 'middle': flags[indx_i, indx_j] = 1
if ps_data:
for sim_id in ps_data:
data_sim = ps_data[sim_id]
label = data_sim['label']
sim_z_vals = data_sim['z_vals']
diff = np.abs(sim_z_vals - current_z)
diff_min = diff.min()
index = np.where(diff == diff_min)[0][0]
data = data_sim[index]
k = data['k_vals']
ps = data['ps_mean']
delta = ps * k / np.pi
if current_z == 4.6: delta *= 1.1
if current_z == 5.0: delta *= 1.1
ax.plot(k,
delta,
linewidth=3,
label=label,
zorder=1,
c=color_line)
if sim_data_sets:
for sim_data in sim_data_sets:
# sim_z_vals = sim_data['z']
# diff = np.abs( sim_z_vals - current_z )
# diff_min = diff.min()
# index = np.where( diff == diff_min )[0][0]
# # print( index )
# if diff_min < 0.08:
# k = sim_data['ps_kvals'][index]
# ps = sim_data['ps_mean'][index]
# delta = ps * k / np.pi
# ax.plot( k, delta, linewidth=3, label=sim_data['plot_label'], zorder=1 )
# # ax.plot( k, delta, c=color_line, linewidth=3, label=sim_data['plot_label'] )
#
if plot_ps_normalized:
ps_data = sim_data['power_spectrum_normalized']
name = ps_data['normalization_key']
else:
ps_data = sim_data['power_spectrum']
sim_z_vals = ps_data['z']
diff = np.abs(sim_z_vals - current_z)
diff_min = diff.min()
index = np.where(diff == diff_min)[0][0]
# print( index )
if diff_min < 0.08:
k = ps_data['k_vals'][index]
ps = ps_data['ps_mean'][index]
delta = ps * k / np.pi
ax.plot(k,
delta,
linewidth=3,
label=sim_data['plot_label'],
zorder=1)
# ax.plot( k, delta, c=color_line, linewidth=3, label=sim_data['plot_label'] )
text_pos_x = 0.85
if scales == 'large_reduced': text_pos_x = 0.15
ax.text(text_pos_x,
0.95,
r'$z={0:.1f}$'.format(current_z),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=figure_text_size,
color=text_color)
if plot_boss:
# Add Boss data
z_diff = np.abs(data_z_boss - current_z)
diff_min = z_diff.min()
if diff_min < 1e-1:
data_index = np.where(z_diff == diff_min)[0][0]
data_z_local = data_z_boss[data_index]
data_k = data_boss[data_index]['k_vals']
data_delta_power = data_boss[data_index]['delta_power']
data_delta_power_error = data_boss[data_index][
'delta_power_error']
label_boss = 'eBOSS (2019)'
d_boss = ax.errorbar(data_k,
data_delta_power,
yerr=data_delta_power_error,
fmt='o',
c=c_boss,
label=label_boss,
zorder=2)
if plot_walther:
# Add Walther data
z_diff = np.abs(data_z_w - current_z)
diff_min = z_diff.min()
if diff_min < 1e-1:
data_index = np.where(z_diff == diff_min)[0][0]
data_z_local = data_z_w[data_index]
data_k = data_walther[data_index]['k_vals']
data_delta_power = data_walther[data_index]['delta_power']
data_delta_power_error = data_walther[data_index][
'delta_power_error']
label_walther = 'Walther et al. (2018)'
d_walther = ax.errorbar(data_k,
data_delta_power,
yerr=data_delta_power_error,
fmt='o',
c=c_walther,
label=label_walther,
zorder=2)
# Add Irsic data
if plot_irsic:
z_diff = np.abs(data_z_irsic - current_z)
diff_min = z_diff.min()
if diff_min < 1e-1:
data_index = np.where(z_diff == diff_min)[0][0]
data_z_local = data_z_irsic[data_index]
data_k = data_irsic[data_index]['k_vals']
data_delta_power = data_irsic[data_index]['delta_power']
data_delta_power_error = data_irsic[data_index][
'delta_power_error']
label_irsic = 'Irsic et al. (2017)'
d_irsic = ax.errorbar(data_k,
data_delta_power,
yerr=data_delta_power_error,
fmt='o',
c=c_irsic,
label=label_irsic,
zorder=2)
if plot_boera:
# Add Boera data
z_diff = np.abs(data_z_b - current_z)
diff_min = z_diff.min()
if diff_min < 1e-1:
data_index = np.where(z_diff == diff_min)[0][0]
data_z_local = data_z_b[data_index]
data_k = data_boera[data_index]['k_vals']
data_delta_power = data_boera[data_index]['delta_power']
data_delta_power_error = data_boera[data_index][
'delta_power_error']
label_boera = 'Boera et al. (2019)'
d_boera = ax.errorbar(data_k,
data_delta_power,
yerr=data_delta_power_error,
fmt='o',
c=c_boera,
label=label_boera,
zorder=2)
if plot_viel:
# Add Viel data
z_diff = np.abs(data_z_v - current_z)
diff_min = z_diff.min()
if diff_min < 1e-1:
data_index = np.where(z_diff == diff_min)[0][0]
data_z_local = data_z_v[data_index]
data_k = data_viel[data_index]['k_vals']
data_delta_power = data_viel[data_index]['delta_power']
data_delta_power_error = data_viel[data_index][
'delta_power_error']
label_viel = 'Viel et al. (2013)'
d_viel = ax.errorbar(data_k,
data_delta_power,
yerr=data_delta_power_error,
fmt='o',
c=c_viel,
label=label_viel,
zorder=2)
legend_loc = 3
if indx_i == nrows - 1 and nrows != 2: legend_loc = 2
if scales == 'large': legend_loc = 2
if scales == 'middle': legend_loc = 2
if scales == 'small_highz': legend_loc = 3
if scales == 'large_middle': legend_loc = 2
if scales == 'large_reduced': legend_loc = 4
label_bars = r'1$\sigma$ skewers $P\,(\Delta_F^2)$'
add_legend = False
if indx_j == 0: add_legend = True
# if scales == 'middle' and indx_i == nrows-1 and indx_j == ncols-1: add_legend = True
if add_legend:
# leg = ax.legend( loc=legend_loc, frameon=False, fontsize=12)
leg = ax.legend(loc=legend_loc, frameon=False, prop=prop)
for text in leg.get_texts():
plt.setp(text, color=text_color)
x_min, x_max = 4e-3, 2.5e-1
if indx_i == 0: y_min, y_max = 1e-3, 9e-2
if indx_i == 1: y_min, y_max = 5e-3, 2e-1
if indx_i == 2: y_min, y_max = 5e-2, 3
if scales == 'large':
x_min, x_max = 2e-3, 2.3e-2
if indx_i == 0: y_min, y_max = 1e-2, 1.2e-1
if indx_i == 1: y_min, y_max = 2e-2, 2.5e-1
if indx_i == 2: y_min, y_max = 5e-2, 7e-1
if scales == 'middle':
x_min, x_max = 2e-3, 7e-2
if indx_i == 0: y_min, y_max = 1.8e-2, 2.5e-1
if indx_i == 1: y_min, y_max = 5e-2, 7e-1
if scales == 'all':
x_min, x_max = 4e-3, 2.5e-1
if indx_i == 0: y_min, y_max = 1e-3, 9e-2
if indx_i == 1: y_min, y_max = 5e-3, 2e-1
if indx_i == 2: y_min, y_max = 5e-2, 3
if scales == 'small_highz':
x_min, x_max = 2e-3, 3e-1
if indx_i == 0: y_min, y_max = 5e-3, 1e0
if scales == 'large_middle':
x_min, x_max = 2e-3, 7e-2
if indx_i == 0: y_min, y_max = 2e-2, 1.5e-1
if indx_i == 1: y_min, y_max = 4e-2, 3.5e-1
if scales == 'large_reduced':
x_min, x_max = 2e-3, 2.5e-2
if indx_i == 0: y_min, y_max = 1.2e-2, 1.3e-1
if indx_i == 1: y_min, y_max = 2.5e-2, 4e-1
if scales == 'large_reduced':
x_min, x_max = 2e-3, 2.5e-2
if indx_i == 0: y_min, y_max = 1.2e-2, 1.3e-1
if indx_i == 1: y_min, y_max = 2.5e-2, 4e-1
if scales == 'small_reduced':
x_min, x_max = 4e-3, 2e-1
if indx_i == 0: y_min, y_max = 4e-2, 7e-1
if high_z_only: y_min, y_max = 5e-2, 3
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xscale('log')
ax.set_yscale('log')
[sp.set_linewidth(border_width) for sp in ax.spines.values()]
ax.tick_params(axis='both',
which='major',
color=text_color,
labelcolor=text_color,
labelsize=tick_label_size_major,
size=tick_size_major,
width=tick_width_major,
direction='in')
ax.tick_params(axis='both',
which='minor',
color=text_color,
labelcolor=text_color,
labelsize=tick_label_size_minor,
size=tick_size_minor,
width=tick_width_minor,
direction='in')
if indx_i != nrows - 1: ax.set_xticklabels([])
if indx_j > 0:
ax.set_yticklabels([])
ax.tick_params(axis='y', which='minor', labelsize=0)
if indx_j == 0:
ax.set_ylabel(r' $\Delta_F^2(k)$',
fontsize=label_size,
color=text_color)
if indx_i == nrows - 1:
ax.set_xlabel(r'$ k \,\,\, [\mathrm{s}\,\mathrm{km}^{-1}] $',
fontsize=label_size,
color=text_color)
if black_background:
fig.patch.set_facecolor('black')
ax.set_facecolor('k')
[
spine.set_edgecolor(text_color)
for spine in list(ax.spines.values())
]
if scales == 'middle':
for i in range(nrows):
for j in range(ncols):
if not flags[i, j]:
ax = ax_l[i][j].axis('off')
fileName = output_dir + f'flux_ps_grid_{scales}'
if plot_ps_normalized: fileName += f'_{name}'
if high_z_only: fileName += '_highZ'
fileName += '.png'
# fileName += '.pdf'
fig.savefig(fileName,
pad_inches=0.1,
bbox_inches='tight',
dpi=fig_dpi,
facecolor=fig.get_facecolor())
print('Saved Image: ', fileName)
def Get_Comparable_Power_Spectrum(ps_data_dir,
z_min,
z_max,
data_sets,
ps_range,
log_ps=False,
rescaled_walther=False,
rescale_walter_file=None):
print(f'Loading P(k) Data:')
dir_boss = ps_data_dir + 'data_power_spectrum_boss/'
data_filename = dir_boss + 'data_table.py'
data_boss = load_data_boss(data_filename)
dir_irsic = ps_data_dir + 'data_power_spectrum_irsic_2017/'
data_filename = dir_irsic + 'data_table.py'
data_irsic = load_data_irsic(data_filename)
data_filename = ps_data_dir + 'data_power_spectrum_walther_2019/data_table.txt'
data_walther = load_power_spectrum_table(data_filename)
dir_data_boera = ps_data_dir + 'data_power_spectrum_boera_2019/'
data_boera = load_tabulated_data_boera(dir_data_boera)
data_dir_viel = ps_data_dir + 'data_power_spectrum_viel_2013/'
data_viel = load_tabulated_data_viel(data_dir_viel)
data_dir = {
'Boss': data_boss,
'Walther': data_walther,
'Boera': data_boera,
'Viel': data_viel,
'Irsic': data_irsic
}
data_kvals, data_ps, data_ps_sigma, data_indices, data_z = [], [], [], [], []
log_data_ps, log_data_ps_sigma = [], []
sim_z, sim_kmin, sim_kmax = ps_range['z'], ps_range['k_min'], ps_range[
'k_max']
ps_data = {}
data_id = 0
for data_index, data_name in enumerate(data_sets):
print(f' Loading P(k) Data: {data_name}')
data_set = data_dir[data_name]
keys = data_set.keys()
n_indices = len(keys) - 1
if data_name == 'Walther':
if rescaled_walther:
print(
f" Loading Walther rescale values: {rescale_walter_file}")
rescale_walter_alphas = Load_Pickle_Directory(
rescale_walter_file)
for index in range(n_indices):
data = data_set[index]
z = data['z']
if z >= z_min and z <= z_max:
diff = np.abs(sim_z - z)
id_min = np.where(diff == diff.min())[0][0]
z_sim = sim_z[id_min]
kmin = sim_kmin[id_min]
kmax = sim_kmax[id_min]
k_vals = data['k_vals']
k_indices = np.where((k_vals >= kmin) & (k_vals <= kmax))
k_vals = k_vals[k_indices]
delta_ps = data['delta_power'][k_indices]
delta_ps_sigma = data['delta_power_error'][k_indices]
log_delta_ps = np.log(delta_ps)
log_delta_ps_sigma = 1 / delta_ps * delta_ps_sigma
if data_name == 'Walther' and rescaled_walther:
rescale_z = rescale_walter_alphas[index]['z']
rescale_alpha = rescale_walter_alphas[index]['alpha']
print(
f' Rescaling z={rescale_z:.1f} alpha={rescale_alpha:.3f} '
)
delta_ps *= rescale_alpha
ps_data[data_id] = {
'z': z,
'k_vals': k_vals,
'delta_ps': delta_ps,
'delta_ps_sigma': delta_ps_sigma
}
data_z.append(z)
data_kvals.append(k_vals)
data_ps.append(delta_ps)
data_ps_sigma.append(delta_ps_sigma)
log_data_ps.append(log_delta_ps)
log_data_ps_sigma.append(log_delta_ps_sigma)
data_id += 1
k_vals_all = np.concatenate(data_kvals)
delta_ps_all = np.concatenate(data_ps)
delta_ps_sigma_all = np.concatenate(data_ps_sigma)
log_delta_ps_all = np.concatenate(log_data_ps)
log_delta_ps_sigma_all = np.concatenate(log_data_ps_sigma)
ps_data_out = {'P(k)': {}, 'separate': ps_data}
ps_data_out['P(k)']['k_vals'] = k_vals_all
if log_ps:
ps_data_out['P(k)']['mean'] = log_delta_ps_all
ps_data_out['P(k)']['sigma'] = log_delta_ps_sigma_all
else:
ps_data_out['P(k)']['mean'] = delta_ps_all
ps_data_out['P(k)']['sigma'] = delta_ps_sigma_all
n_data_points = len(k_vals_all)
print(f' N data points: {n_data_points}')
return ps_data_out
#Box parameters
Lbox = 50.0 #Mpc/h
nPoints = 2048
nx = nPoints
ny = nPoints
nz = nPoints
ncells = nx * ny * nz
dir_boss = 'data/data_power_spectrum_boss/'
data_filename = dir_boss + 'data_table.py'
data_boss = load_data_boss(data_filename)
data_z_boss = data_boss['z_vals']
data_filename = 'data/data_power_spectrum_walther_2019/data_table.txt'
data_walther = load_power_spectrum_table(data_filename)
data_z_w = data_walther['z_vals']
dir_data_boera = 'data/data_power_spectrum_boera_2019/'
data_boera = load_tabulated_data_boera(dir_data_boera)
data_z_b = data_boera['z_vals']
data_dir_viel = 'data/data_power_spectrum_viel_2013/'
data_viel = load_tabulated_data_viel(data_dir_viel)
data_z_v = data_viel['z_vals']
snapshots_indices = [
83,
90,
96,
102,
def Get_Comparable_Power_Spectrum(ps_data_dir, z_min, z_max, data_sets,
ps_range):
print(f'Loading P(k) Data:')
dir_boss = ps_data_dir + 'data_power_spectrum_boss/'
data_filename = dir_boss + 'data_table.py'
data_boss = load_data_boss(data_filename)
data_filename = ps_data_dir + 'data_power_spectrum_walther_2019/data_table.txt'
data_walther = load_power_spectrum_table(data_filename)
dir_data_boera = ps_data_dir + 'data_power_spectrum_boera_2019/'
data_boera = load_tabulated_data_boera(dir_data_boera)
data_dir_viel = ps_data_dir + 'data_power_spectrum_viel_2013/'
data_viel = load_tabulated_data_viel(data_dir_viel)
data_dir = {
'Boss': data_boss,
'Walther': data_walther,
'Boera': data_boera,
'Viel': data_viel
}
data_kvals, data_ps, data_ps_sigma, data_indices, data_z = [], [], [], [], []
sim_z, sim_kmin, sim_kmax = ps_range['z'], ps_range['k_min'], ps_range[
'k_max']
ps_data = {}
data_id = 0
for data_index, data_name in enumerate(data_sets):
print(f' Loading P(k) Data: {data_name}')
data_set = data_dir[data_name]
keys = data_set.keys()
n_indices = len(keys) - 1
for index in range(n_indices):
data = data_set[index]
z = data['z']
if z >= z_min and z <= z_max:
diff = np.abs(sim_z - z)
id_min = np.where(diff == diff.min())[0][0]
z_sim = sim_z[id_min]
# print( f'data_z: {z:.1f} sim_z: {z_sim:.1f}')
kmin = sim_kmin[id_min]
kmax = sim_kmax[id_min]
k_vals = data['k_vals']
k_indices = np.where((k_vals >= kmin) & (k_vals <= kmax))
k_vals = k_vals[k_indices]
delta_ps = data['delta_power'][k_indices]
delta_ps_sigma = data['delta_power_error'][k_indices]
ps_data[data_id] = {
'z': z,
'k_vals': k_vals,
'delta_ps': delta_ps,
'delta_ps_sigma': delta_ps_sigma
}
data_z.append(z)
data_kvals.append(k_vals)
data_ps.append(delta_ps)
data_ps_sigma.append(delta_ps_sigma)
data_id += 1
k_vals_all = np.concatenate(data_kvals)
delta_ps_all = np.concatenate(data_ps)
delta_ps_sigma_all = np.concatenate(data_ps_sigma)
ps_data_out = {'P(k)': {}, 'separate': ps_data}
ps_data_out['P(k)']['k_vals'] = k_vals_all
ps_data_out['P(k)']['mean'] = delta_ps_all
ps_data_out['P(k)']['sigma'] = delta_ps_sigma_all
n_data_points = len(k_vals_all)
print(f' N data points: {n_data_points}')
return ps_data_out