for z, var, mu, std, varcs, svarcs in zip(redshifts, conf_interval_variance, means, conf_interval_width, var_cumsum, std_cumsum):
output.write(f"{z:<12.4f} {var:<12.4f} {mu:<12.4f} {std:<12.4f} {varcs:<12.4f} {svarcs:<12.4f}\n")
if __name__ == "__main__":
colours = np.array(
list(
map(mpl.colors.to_hex, cmasher.rainforest(np.linspace(0.15, 0.80, 4)))
)
)
plot_style_dict = {
"RefL0100N1504": ("#000000", 3, "-"),
"RefL0025N0376": (colours[2], 2, ":"),
"RefL0025N0752": (colours[1], 2, "--"),
"RefL0050N0752": (colours[0], 2, ":"),
"RecalL0025N0752": (colours[3], 2, "--"),
}
def plot_statistic(data_files, stat_type, output_format=".png"):
colours = np.array(
list(
map(mpl.colors.to_hex,
cmasher.rainforest(np.linspace(0.15, 0.80, 4)))))
#colours = ["#FFB300", "#803E75", "#FF6800", "#A6BDD7", "#C10020", "#D39B85", "#817066"]
#color_list = ["#000000", "#FFB300", "#803E75", "#FF6800", "#A6BDD7", "#C10020", "#D39B85", "#817066"]
plot_style_dict = {
"RefL0100N1504": ("#000000", 3, "-"),
"RefL0025N0376": (colours[2], 2, ":"),
"RefL0025N0752": (colours[1], 2, "--"),
"RefL0050N0752": (colours[0], 2, ":"),
"RecalL0025N0752": (colours[3], 2, "--"),
#"NoAGNL0050N0752": (colours[0], 2, "-"),
#"AGNdT9L0050N0752": (colours[1], 2, "-"),
#"FBZL0050N0752": (colours[2], 2, "--"),
#"FBconstL0050N0752": (colours[3], 2, "--"),
#"FBsigmaL0050N0752": (colours[4], 2, "--"),
}
# plot_style_dict = {
# "RandGaussL0100": ("#000000", 3, "-"),
# "RandGaussL0025": (colours[1], 2, "--"),
# }
data_dict = {
"mean": {
"col":
None,
"output_name":
"All_Sims_mean_v_redshift",
"ylabel":
"$\langle \mathrm{DM_{cosmic}} \\rangle\ \left[\mathrm{pc\ cm^{-3}} \\right]$"
},
"median": {
"col":
2,
"output_name":
"All_Sims_median_v_redshift",
"ylabel":
"$\mathrm{Median\ DM_{cosmic}}\ \left[\mathrm{pc\ cm^{-3}} \\right]$"
},
"variance": {
"col": 3,
"output_name": "All_Sims_variance_v_redshift",
"ylabel": "$\sigma_{Var}^2\ \left[\mathrm{pc\ cm^{-3}} \\right]$"
},
"sigma_var": {
"col": 4,
"output_name": "All_Sims_sigma_var_v_redshift",
"ylabel":
"$\sigma_\mathrm{Var}\ \left[\mathrm{pc\ cm^{-3}} \\right]$"
},
"ci_width": {
"col":
7,
"output_name":
"All_Sims_ci_width_v_redshift",
"ylabel":
"\\textrm{Confidence Interval Width}\ $\left[\mathrm{pc\ cm^{-3}} \\right]$"
},
"sigma_ci": {
"col": 8,
"output_name": "All_Sims_sigma_ci_v_redshift",
"ylabel":
"$\sigma_\mathrm{CI}\ \left[\mathrm{pc\ cm^{-3}} \\right]$"
},
"f_ng": {
"col":
None,
"output_name":
"All_Sims_fng_v_redshift",
"ylabel":
"$f_\mathrm{NG} = 1 - \sigma_\mathrm{CI}/\sigma_\mathrm{Var}$",
},
"sigma_ci_mean": {
"col": None,
"output_name": "All_Sims_sigma_ci_mean_v_redshift",
"ylabel":
"$\sigma_\mathrm{CI}/\langle \mathrm{DM_{cosmic}} \\rangle$",
},
"sigma_var_mean": {
"col":
None,
"output_name":
"All_Sims_sigma_var_mean_v_redshift",
"ylabel":
"$\sigma_\mathrm{Var}/\langle \mathrm{DM_{cosmic}} \\rangle$",
},
"sigma_ci_median": {
"col": None,
"output_name": "All_Sims_sigma_ci_median_v_redshift",
"ylabel": "$\sigma_\mathrm{CI}/\mathrm{Median\ DM_{cosmic}}$",
},
"sigma_var_median": {
"col": None,
"output_name": "All_Sims_sigma_var_median_v_redshift",
"ylabel": "$\sigma_\mathrm{Var}/\mathrm{Median\ DM_{cosmic}}$",
},
}
plt.rcParams.update(set_rc_params(usetex=True))
fig, ax = plt.subplots(ncols=1, nrows=1, constrained_layout=True)
for filename in data_files:
sim_name = filename.split("_")[2]
if sim_name:
data = np.loadtxt(filename, unpack=True, skiprows=2)
L100stat = interpolate.interp1d(data[0],
data[1],
fill_value="extrapolate")
for filename in data_files:
sim_name = filename.split("_")[2]
data = np.loadtxt(filename, unpack=True, skiprows=2)
redshifts = data[0]
if (sim_name in ["RefL0025N0376"]) and (stat_type
in ["sigma_ci", "sigma_var"]):
ax.plot(redshifts,
stat * np.sqrt(2),
color="grey",
linewidth=1,
linestyle=":",
alpha=0.8)
ax.plot(redshifts,
stat * 2,
color="grey",
linewidth=1,
linestyle=":",
alpha=0.8)
if isinstance(data_dict[stat_type]["col"], int):
data_col = data_dict[stat_type]["col"]
stat = data[data_col]
else:
if stat_type == "mean":
print("TEST")
stat = data[1]
figg, axx = plt.subplots(ncols=1,
nrows=2,
constrained_layout=True)
lcolor, lwidth, lstyle = plot_style_dict[sim_name]
print(stat)
axx[0].plot(
redshifts,
stat,
color=lcolor,
linewidth=lwidth,
linestyle=lstyle,
label=f"\\textrm{{{sim_name}}}",
)
new_stat = L100stat(redshifts)
axx[1].plot(
redshifts,
(stat - new_stat) / new_stat,
color=lcolor,
linewidth=lwidth,
linestyle=lstyle,
)
lcolor, lwidth, lstyle = plot_style_dict[sim_name]
p13 = acosmo.Planck13
ax_twin = math_tools.cosmology.make_lookback_time_axis(
axx[0], cosmo=p13, z_range=(redshifts[0], redshifts[-1]))
ax_twin.set_xlabel("$\mathrm{Lookback\ Time\ [Gyr]}$")
axx[0].legend()
axx[1].set_xlabel("\\textrm{{Redshift}}")
axx[0].set_ylabel(data_dict[stat_type]["ylabel"])
print(
f"./analysis_plots/shuffled/TEST_{data_dict[stat_type]['output_name']}{output_format}"
)
plt.savefig(
f"./analysis_plots/shuffled/TEST_{data_dict[stat_type]['output_name']}{output_format}"
)
if stat_type == "f_ng":
stat = 1 - (data[data_dict["sigma_ci"]["col"]] /
data[data_dict["sigma_var"]["col"]])
ax.set_ylim(0, 1)
elif stat_type == "sigma_ci_mean":
stat = data[data_dict["sigma_ci"]["col"]] / data[
data_dict["mean"]["col"]]
elif stat_type == "sigma_var_mean":
stat = data[data_dict["sigma_var"]["col"]] / data[
data_dict["mean"]["col"]]
elif stat_type == "sigma_ci_median":
stat = data[data_dict["sigma_ci"]["col"]] / data[
data_dict["median"]["col"]]
elif stat_type == "sigma_var_median":
stat = data[data_dict["sigma_var"]["col"]] / data[
data_dict["median"]["col"]]
lcolor, lwidth, lstyle = plot_style_dict[sim_name]
ax.plot(
redshifts,
stat,
color=lcolor,
linewidth=lwidth,
linestyle=lstyle,
label=f"\\textrm{{{sim_name}}}",
)
p13 = acosmo.Planck13
ax_twin = math_tools.cosmology.make_lookback_time_axis(
ax, cosmo=p13, z_range=(redshifts[0], redshifts[-1]))
ax_twin.set_xlabel("$\mathrm{Lookback\ Time\ [Gyr]}$")
ax.legend()
ax.set_xlabel("\\textrm{{Redshift}}")
ax.set_ylabel(data_dict[stat_type]["ylabel"])
print(
f"./analysis_plots/shuffled/{data_dict[stat_type]['output_name']}{output_format}"
)
plt.savefig(
f"./analysis_plots/shuffled/{data_dict[stat_type]['output_name']}{output_format}"
)
def plot_resolution_tests():
file1 = "RefL0025N0376_Log_Normal_Fit_Mean_Std.txt"
file2 = "RefL0025N0752_Log_Normal_Fit_Mean_Std.txt"
file3 = "RecalL0025N0752_Log_Normal_Fit_Mean_Std.txt"
file4 = "RefL0100N1504_log_normal_fit_mean_std_percent.txt"
file5 = "RefL0050N0752_log_normal_fit_mean_std_percent.txt"
vartest = "/home/abatten/ADMIRE_ANALYSIS/ADMIRE_RefL0100N1504/all_snapshot_data/output/T4EOS/correlation_varience_test.txt"
var_data = np.loadtxt(vartest, skiprows=1, unpack=True)
redshift = var_data[0]
std_cumsum = var_data[5]
z1, mean1, std1, per1 = np.loadtxt(file1, unpack=True, skiprows=1)
z2, mean2, std2, per2 = np.loadtxt(file2, unpack=True, skiprows=1)
z3, mean3, std3, per3 = np.loadtxt(file3, unpack=True, skiprows=1)
z4, mean4, std4, per4 = np.loadtxt(file4, unpack=True, skiprows=1)
z5, mean5, std5, per5 = np.loadtxt(file5, unpack=True, skiprows=1)
var_data = np.loadtxt(vartest, skiprows=1, unpack=True)
redshift = var_data[0]
std_cumsum = var_data[5]
filename = "RefL0100N1504_log_normal_fit_mean_std_percent.txt"
redshift, mean, std, percent = np.loadtxt(filename,
unpack=True,
skiprows=1)
fig = plt.figure(figsize=(16, 12), constrained_layout=True)
gs = fig.add_gridspec(7, 1, wspace=0.0, hspace=0.0)
ax1 = fig.add_subplot(gs[0:4, 0])
ax2 = fig.add_subplot(gs[4:5, 0], sharex=ax1)
ax3 = fig.add_subplot(gs[5:6, 0], sharex=ax1)
ax4 = fig.add_subplot(gs[6:7, 0], sharex=ax1)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax2.get_xticklabels(), visible=False)
plt.setp(ax3.get_xticklabels(), visible=False)
#ax1.spines['bottom'].set_linewidth(0)
w_pad, h_pad, wspace, hspace = fig.get_constrained_layout_pads()
#fig.set_constrained_layout_pads(w_pad=0, h_pad=0, wspace=0, hspace=0)
axin = ax1.inset_axes([0.55, 0.1, 0.35, 0.35])
axin.set_xlim(2.6, 3)
axin.set_ylim(2600, 3000)
axin.xaxis.set_tick_params(labelsize=8)
axin.yaxis.set_tick_params(labelsize=8)
# Fit the Data with linear and Non-linear
#fit_linear = curve_fit(linear, redshift, mean, bounds=([0, 0], [1200, 1e-10]))
#fit_fz = curve_fit(f_function, redshift, mean)
colours = np.array(
list(
map(mpl.colors.to_hex,
cmasher.rainforest(np.linspace(0.20, 0.80, 4)))))[[3, 2, 1, 0]]
colours = np.append(colours, "#000000")
#mean_linear_fit = linear(redshift, fit_linear[0][0], fit_linear[0][1])
#mean_fz_fit = f_function(redshift, fit_fz[0][0])
#mean_linear_1000 = linear(redshift, 1000, 0)
#mean_linear_1200 = linear(redshift, 1200, 0)
#mean_linear_855 = linear(redshift, 855, 0)
ax1.plot(z1,
mean1,
color=colours[0],
label="$\mathrm{RefL0025N0376}\ <\mathrm{DM}>$",
linewidth=2)
ax1.plot(z2,
mean2,
color=colours[1],
label="$\mathrm{RefL0025N0752}\ <\mathrm{DM}>$",
linewidth=2)
ax1.plot(z5,
mean5,
color=colours[3],
label="$\mathrm{RefL0050N0752}\ <\mathrm{DM}>$",
linewidth=2)
ax1.plot(z3,
mean3,
color=colours[2],
label="$\mathrm{RecalL0025N0752}\ <\mathrm{DM}>$",
linewidth=2)
ax1.plot(z4,
mean4,
color=colours[4],
label="$\mathrm{RefL0100N1504}\ <\mathrm{DM}>$",
linewidth=2)
axin.plot(z1, mean1, color=colours[0], linewidth=2)
axin.plot(z2, mean2, color=colours[1], linewidth=2)
axin.plot(z3, mean3, color=colours[2], linewidth=2)
axin.plot(z4, mean4, color=colours[4], linewidth=2)
axin.plot(z5, mean5, color=colours[3], linewidth=2)
#colours = ["#67a9cf", "#ef8a62"]
#colours = ["#0571b0", "#ca0020"]
#ax1.plot(redshift, mean_fz_fit, color=colours[0], linewidth=3, label=r'$\mathrm{Non\textnormal{-}linear\ fit:\ <DM>} = \alpha F(z); \alpha=923.47$')
#ax1.plot(redshift, mean_linear_fit, colours[1], linewidth=3, label=r'$\mathrm{Linear\ fit:\ <DM>} = \beta z; \beta=1004.4$')
#ax1.plot(redshift, mean_linear_1000, colours[1], linestyle=":", linewidth=2, label=r'$\mathrm{<DM>} = 1000 z$')
#ax1.plot(redshift, mean_linear_1200, colours[1], linestyle="--", linewidth=1, label=r'$\mathrm{<DM>} = 1200 z$')
#ax1.plot(redshift, mean_linear_855, colours[1], linestyle="-.", linewidth=1, label=r'$\mathrm{<DM>} = 855 z$')
#axin.plot(redshift, mean_fz_fit, colours[0], linewidth=3)
#axin.plot(redshift, mean_linear_fit, colours[1], linewidth=3)
#axin.plot(redshift, mean_linear_1000, colours[1], linestyle=":", linewidth=2)
#axin.plot(redshift, mean_linear_1200, colours[1], linestyle="--", linewidth=1)
#axin.plot(redshift, mean_linear_855, colours[1], linestyle="-.", linewidth=1)
ax2.plot(np.linspace(-1, 4, 100),
np.zeros(100),
"black",
linestyle=":",
linewidth=1)
#ax2.plot(np.linspace(-1, 4, 100), np.zeros(100), "black", linewidth=1)
ax2.plot(redshift,
mean4 - mean1[::4],
color=colours[0],
linestyle='-',
linewidth=3,
label="RefL0100N1504 - RefL0025N0376")
ax2.plot(redshift,
mean4 - mean5[::2],
color=colours[3],
linestyle='-',
linewidth=3,
label="RefL0100N1504 - RefL0050N0752")
#ax2.plot(redshift, mean - mean_linear_fit, color=colours[1], linestyle="-", linewidth=3)
#ax2.plot(redshift, mean - mean_linear_1000, colours[1], linestyle=":", linewidth=2)
#ax2.plot(redshift, mean - mean_linear_1200, colours[1], linestyle="--", linewidth=1)
#ax2.plot(redshift, mean - mean_linear_855, colours[1], linestyle="-.", linewidth=1)
ax3.plot(np.linspace(-1, 4, 100),
np.zeros(100),
"black",
linestyle=":",
linewidth=1)
ax3.plot(z2, mean2 - mean1, color=colours[1], linestyle='-', linewidth=3)
ax4.plot(np.linspace(-1, 4, 100),
np.zeros(100),
"black",
linestyle=":",
linewidth=1)
ax4.plot(z3, mean3 - mean2, color=colours[2], linestyle='-', linewidth=3)
ax1.set_ylabel(
"$\mathrm{Expectation\ Value}$\n$<\mathrm{DM}>\ \left[\mathrm{pc\ cm^{-3}}\\right]$"
)
ax2.set_ylabel(
"$\mathrm{Residuals}$\n $\left[\mathrm{pc\ cm^{-3}}\\right]$",
multialignment='center')
ax3.set_ylabel(
"$\mathrm{Residuals}$\n $\left[\mathrm{pc\ cm^{-3}}\\right]$",
multialignment='center')
ax4.set_ylabel(
"$\mathrm{Residuals}$\n $\left[\mathrm{pc\ cm^{-3}}\\right]$",
multialignment='center')
ax4.set_xlabel("Redshift")
ax2.set_ylim(-200, 200)
ax3.set_ylim(-100, 100)
ax4.set_ylim(-100, 100)
ax2.set_xlim(-0.04, 3.04)
ax1.legend(frameon=False, fontsize=14)
ax2.legend(frameon=False, fontsize=14)
#plt.tight_layout()
ax1.indicate_inset_zoom(axin)
#plt.savefig('dmz_relation_RefL0100N1504_mean_fit_linear_fz.png', dpi=175)
# fig = plt.figure(constrained_layout=True, figsize=(8,6))
# gs = fig.add_gridspec(8, 16, wspace=0.0, hspace=0.0)
# ax1 = fig.add_subplot(gs[0:9, :9])
# ax2 = fig.add_subplot(gs[0:2, 9:], sharex=ax1)
# ax3 = fig.add_subplot(gs[3:5, 9:], sharex=ax2)
# ax4 = fig.add_subplot(gs[6:8, 9:], sharex=ax2)
#
# ax2_2 = ax2.twinx()
# ax3_2 = ax3.twinx()
# ax4_2 = ax4.twinx()
#
# plt.setp(ax2_2.get_yticklabels(), visible=False)
# plt.setp(ax3_2.get_yticklabels(), visible=False)
# plt.setp(ax4_2.get_yticklabels(), visible=False)
#
#
# #ax1.spines['bottom'].set_linewidth(0)
#
# ax1.set_xlabel("Redshift")
# ax1.set_ylabel(r"$\mathrm{<DM>\ [pc\ cm^{-3}]}$")
#
#
# ax2_2.set_ylabel("Volume Resolution")
# ax3_2.set_ylabel("Particle Resolution")
# ax4_2.set_ylabel("Physics Calibration")
#
#
# w_pad, h_pad, wspace, hspace = fig.get_constrained_layout_pads()
# fig.set_constrained_layout_pads(w_pad=0, h_pad=0, wspace=0, hspace=0)
#
#
# RefL25N752 = models[0]
# RecalL25N752 = models[1]
#
#
#plt.tight_layout()
plt.savefig("TESTING_RESOLUTION_TESTS.png")
file3 = "RecalL0025N0752_Log_Normal_Fit_Mean_Std.txt" file4 = "RefL0100N1504_log_normal_fit_mean_std_percent.txt" file5 = "RefL0050N0752_log_normal_fit_mean_std_percent.txt" z1, mean1, std1, per1 = np.loadtxt(file1, unpack=True, skiprows=1) z2, mean2, std2, per2 = np.loadtxt(file2, unpack=True, skiprows=1) z3, mean3, std3, per3 = np.loadtxt(file3, unpack=True, skiprows=1) z4, mean4, std4, per4 = np.loadtxt(file4, unpack=True, skiprows=1) z5, mean5, std5, per5 = np.loadtxt(file5, unpack=True, skiprows=1) z4 = z4 + math_tools.cosmology.cMpc_to_z(100) #colours = ['#ef8a62', '#67a9cf', '#666666'] #colours = ['#1b9e77','#d95f02','#7570b3'] colours = ['#1b9e77','#d95f02','#7570b3','#e7298a'] colours = np.array(list(map(mpl.colors.to_hex, cmasher.rainforest(np.linspace(0.20, 0.80, 4)))))[[3, 2, 1, 0]] colours = np.append(colours, "#000000") print(colours) ########################################################### ########################################################### # PLOT <DM> vs Redshift ########################################################### ########################################################### plt.plot(z1, mean1, label="RefL0025N0376", color=colours[0]) plt.plot(z2, mean2, label="RefL0025N0752", color=colours[1]) plt.plot(z5, mean5, label="RefL0050N0752", color=colours[3]) plt.plot(z3, mean3, label="RecalL0025N0752", color=colours[2]) plt.plot(z4, mean4, label="RefL0100N1504", color=colours[4], linewidth=2)
def plot_statistic(data_files, stat_type, output_format=".png"):
colours = np.array(
list(
map(mpl.colors.to_hex,
cmasher.rainforest(np.linspace(0.15, 0.80, 4)))))
#colours = ["#FFB300", "#803E75", "#FF6800", "#A6BDD7"]#, "#C10020", "#D39B85", "#817066"]
#color_list = ["#000000", "#FFB300", "#803E75", "#FF6800", "#A6BDD7", "#C10020", "#D39B85", "#817066"]
#plot_style_dict = {
# "RefL0100N1504": ("#000000", 3, "-"),
# "RefL0025N0376": (colours[2], 2, ":"),
# "RefL0025N0752": (colours[1], 2, "--"),
# "RefL0050N0752": (colours[0], 2, ":"),
# "RecalL0025N0752": (colours[3], 2, "--"),
#}
# plot_style_dict = {
# "RandGaussL0100": ("#000000", 3, "-"),
# "RandGaussL0025": (colours[1], 2, "--"),
#}
plot_style_dict = {
"Scrambled": ("#000000", 2, "-"),
"Transformed": (colours[1], 2, "--"),
}
data_dict = {
"mean": {
"col":
1,
"output_name":
"ALL_SIMS_mean_v_redshift",
"ylabel":
"$\langle \mathrm{DM_{cosmic}} \\rangle\ \left[\mathrm{pc\ cm^{-3}} \\right]$"
},
"median": {
"col":
2,
"output_name":
"ALL_SIMS_median_v_redshift",
"ylabel":
"$\mathrm{Median\ DM_{cosmic}}\ \left[\mathrm{pc\ cm^{-3}} \\right]$"
},
"variance": {
"col": 3,
"output_name": "shuffled_unshuffled_variance_v_redshift",
"ylabel": "$\sigma_G^2\ \left[\mathrm{pc\ cm^{-3}} \\right]$"
},
"sigma_g": {
"col": 4,
"output_name": "shuffled_unshuffled_sigma_var_v_redshift",
"ylabel":
"$\sigma_\mathrm{Var}\ \left[\mathrm{pc\ cm^{-3}} \\right]$"
},
"ci_width": {
"col":
7,
"output_name":
"shuffled_unshuffled_ci_width_v_redshift",
"ylabel":
"\\textrm{Confidence Interval Width}\ $\left[\mathrm{pc\ cm^{-3}} \\right]$"
},
"sigma_ci": {
"col": 8,
"output_name": "shuffled_unshuffled_sigma_ci_v_redshift",
"ylabel":
"$\sigma_\mathrm{CI}\ \left[\mathrm{pc\ cm^{-3}} \\right]$"
},
"sigma_ci_sigma_g": {
"col":
None,
"output_name":
"shuffled_unshuffled_fng_v_redshift",
"ylabel":
"$f_\mathrm{NG} = 1 - \sigma_\mathrm{CI}/\sigma_\mathrm{Var}$",
},
"sigma_ci_mean": {
"col": None,
"output_name": "shuffled_unshuffled_sigma_ci_mean_v_redshift",
"ylabel":
"$\sigma_\mathrm{CI}/\langle \mathrm{DM_{cosmic}} \\rangle$",
},
"sigma_g_mean": {
"col":
None,
"output_name":
"shuffled_unshuffled_sigma_var_mean_v_redshift",
"ylabel":
"$\sigma_\mathrm{Var}/\langle \mathrm{DM_{cosmic}} \\rangle$",
},
"sigma_ci_median": {
"col": None,
"output_name": "shuffled_unshuffled_sigma_ci_median_v_redshift",
"ylabel": "$\sigma_\mathrm{CI}/\mathrm{Median\ DM_{cosmic}}$",
},
"sigma_g_median": {
"col": None,
"output_name": "shuffled_unshuffled_sigma_var_median_v_redshift",
"ylabel": "$\sigma_\mathrm{Var}/\mathrm{Median\ DM_{cosmic}}$",
},
}
plt.rcParams.update(set_rc_params(usetex=True))
fig, ax = plt.subplots(ncols=1, nrows=1, constrained_layout=True)
for idx, filename in enumerate(data_files):
#sim_name = filename.split("_")[2]
if idx == 0:
sim_name = "Scrambled"
else:
sim_name = "Transformed"
data = np.loadtxt(filename, unpack=True, skiprows=2)
redshifts = data[0]
if isinstance(data_dict[stat_type]["col"], int):
data_col = data_dict[stat_type]["col"]
stat = data[data_col]
else:
if stat_type == "sigma_ci_sigma_g":
stat = 1 - (data[data_dict["sigma_ci"]["col"]] /
data[data_dict["sigma_g"]["col"]])
elif stat_type == "sigma_ci_mean":
stat = data[data_dict["sigma_ci"]["col"]] / data[
data_dict["mean"]["col"]]
elif stat_type == "sigma_g_mean":
stat = data[data_dict["sigma_g"]["col"]] / data[
data_dict["mean"]["col"]]
elif stat_type == "sigma_ci_median":
stat = data[data_dict["sigma_ci"]["col"]] / data[
data_dict["median"]["col"]]
elif stat_type == "sigma_g_median":
stat = data[data_dict["sigma_g"]["col"]] / data[
data_dict["median"]["col"]]
lcolor, lwidth, lstyle = plot_style_dict[sim_name]
ax.plot(
redshifts,
stat,
color=lcolor,
linewidth=lwidth,
linestyle=lstyle,
label=f"\\textrm{{{sim_name}}}$\ \mathrm{{Maps}}$",
)
plt.legend()
ax.set_xlabel("\\textrm{{Redshift}}")
ax.set_ylabel(data_dict[stat_type]["ylabel"])
plt.savefig(
f"analysis_plots/shuffled_vs_unshuffled/L0100N1504/{data_dict[stat_type]['output_name']}{output_format}"
)