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dmo_analysis.py
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import numpy as np
import matplotlib.pyplot as plt
import splashback as sp
import determine_radius as dr
plt.style.use("mnras.mplstyle")
box = "L2800N5040"
def dmo_stacking(data, split, split_bins, bootstrap=False):
"""
Stacks dark matter only profiles.
Parameters
----------
data : obj
Simulation data class.
split : string
Name of criteria to use for stacking.
split_bins : array
Bin edges for stacking criteria.
bootstrap : bool, optional
Whether to calculate sampling errors on the location of the minima.
The default is False.
Returns
-------
None.
"""
if split == "mass":
split_data = np.log10(data.M200m)
else:
split_data = getattr(data, split)
not_nan = np.where(np.isfinite(split_data)==True)[0]
#will return 0 or len for values outside the range
bins_sort = np.digitize(split_data[not_nan], split_bins)
N_bins = len(split_bins) - 1
stacked_data = np.zeros((N_bins, data.N_rad))
for i in range(N_bins):
bin_mask = np.where(bins_sort == i+1)[0]
stacked_data[i,:] = sp.stack_data(data.DM_density_3D[not_nan,:][bin_mask,:])
log = sp.log_gradients(data.rad_mid, stacked_data)
R_SP_DM, second, depth_DM, second_depth = dr.depth_cut(data.rad_mid, log,
cut=-2.5,
depth_value="y",
second_caustic="y")
setattr(data, split+ "_density_DM", stacked_data)
setattr(data, split+ "_log_DM", log)
setattr(data, "R_DM_"+split, R_SP_DM)
setattr(data, "2_DM_"+split, second)
setattr(data, "depth_DM_"+split, depth_DM)
if bootstrap:
Rsp_error, _ = sp.bootstrap_errors(data, data.DM_density_3D, split, split_data, split_bins)
setattr(data, "error_R_DM_"+split, Rsp_error[0,:])
def second_caustic(data, split):
"""
Identifies which minima is the second caustic and splashback radius in
dark matter profiles.
Parameters
----------
data : obj
Simulation data.
split : str
Name of criteria used for profile stacking.
Returns
-------
None.
"""
second_all = getattr(data, "2_DM_"+split)
R_sp = getattr(data, "R_DM_"+split)
second_mask = np.where(np.isfinite(second_all))[0]
for i in range(len(second_mask)):
index = second_mask[i]
if R_sp[index] < second_all[index]:
larger = second_all[index]
smaller = R_sp[index]
R_sp[index] = larger
second_all[index] = smaller
setattr(data, "R_DM_"+split, R_sp)
setattr(data, "2_DM_"+split, second_all)
def plot_profiles(flm, dmo, bins):
"""
Compares all profiles for three different runs and three standard stacking
criteria.
Parameters
----------
flm_HF : obj
"Standard" run data.
flm_HWA : obj
First alternative run.
flm_HSA : obj
Second alternative run.
bins : numpy array
Bins used to stack profiles for all three stacking criteria.
(3,N_bins) In order: accretion rate, mass, energy ratio.
quantity : str, optional
Plot either gas or DM profiles. The default is "DM".
Returns
-------
None.
"""
bin_type = np.array(["accretion", "mass"])
labels = np.array(["$\Gamma$", "$\log M_{\\rm{200m}}$"])
N_bins = bins.shape[1] -1
fig, ax = plt.subplots(nrows=2, ncols=2,
figsize=(4,4),
sharex=True, sharey=True,
gridspec_kw={'hspace' : 0, 'wspace' : 0})
cm1 = plt.cm.autumn(np.linspace(0,0.95,N_bins))
cm2 = plt.cm.winter(np.linspace(0,1,N_bins))
lw = 0.8
location = 'upper left'
for i in range(0, N_bins):
for j in range(2):
if i == 0:
label = str(np.round(bins[j,0],2)) + "$<$" + labels[j] \
+ "$<$" + str(np.round(bins[j,1],2))
elif i == N_bins-1:
label = labels[j] + "$>$" + str(np.round(bins[j,i],2))
else:
label = str(np.round(bins[j,i],2)) \
+ "$<$" + labels[j] + "$<$" \
+ str(np.round(bins[j,i+1],2))
if j == 0:
cm = cm1
elif j==1:
cm = cm2
ax[0,j].semilogx(flm.rad_mid, getattr(flm, bin_type[j] + "_log_DM")[i,:],
color=cm[i], linewidth=lw, label=label)
ax[1,j].semilogx(dmo.rad_mid, getattr(dmo, bin_type[j] + "_log_DM")[i,:],
color=cm[i], linewidth=lw)
ax[0,j].legend(loc=location)
ylim = (-4.2, 0.9)
ax[0,0].set_ylim(ylim)
ax[0,0].text(0.05, 0.05, "HYDRO", transform=ax[0,0].transAxes)
ax[1,0].text(0.05, 0.05, "DMO", transform=ax[1,0].transAxes)
#plt.subplots_adjust(left=0.5)
#plt.subplots_adjust(bottom=0.1)
plt.text(0.5, 0.04, "$r/R_{\\rm{200m}}$", transform=fig.transFigure)
plt.text(0.0, 0.45, r"$d \log \rho_{\rm{DM}} / d \log r$",
transform=fig.transFigure, rotation="vertical")
filename = "splashback_data/flamingo/plots/dmo_v_hydro_profiles.png"
plt.savefig(filename, dpi=300)
plt.show()
def compare_rsp(flm, dmo):
"""
Compares locations of minima in dark matter only and hydro runs.
Parameters
----------
flm : obj
Hydro simulation data.
dmo : obj
Dark matter only simulation data.
Returns
-------
None.
"""
fig, ax = plt.subplots(nrows=1, ncols=1)
ax.scatter(flm.R_DM_accretion, dmo.R_DM_accretion,
edgecolor="k", marker="o", color="red", s=75,
label="$\Gamma$")
ax.scatter(flm.R_DM_mass, dmo.R_DM_mass,
edgecolor="k", marker="^", color="blue", s=75,
label="$M_{\\rm{200m}}$")
xlim = ax.get_xlim()
ylim = ax.get_ylim()
ax.plot(xlim, xlim, color="k", linestyle="--", alpha=0.6)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
plt.xlabel(r"$R_{\rm{SP,hydro}}/R_{\rm{200m}}$")
plt.ylabel(r"$R_{\rm{SP,DMO}}/R_{\rm{200m}}$")
plt.legend()
# plt.savefig("splashback_data/flamingo/plots/dmo_v_hydro_rsp.png", dpi=300)
plt.show()
def plot_difference(flm, dmo):
"""
Plots differences between the minima in hydro and dark matter only
simulations.
Parameters
----------
flm : obj
Hydro simulation data.
dmo : TYPE
Dark matter only simulation data.
Returns
-------
None.
"""
delta_accretion = (flm.R_DM_accretion - dmo.R_DM_accretion)/ flm.R_DM_accretion
error1 = dmo.R_DM_accretion / flm.R_DM_accretion
error2 = dmo.error_R_DM_accretion / dmo.R_DM_accretion
error3 = flm.error_R_DM_accretion / flm.R_DM_accretion
acc_error = error1 * np.sqrt(error2**2 + error3**2)
weights_acc = 1 / acc_error**2
mean_acc = np.nansum(delta_accretion * weights_acc) / np.nansum(weights_acc)
delta_mass = (flm.R_DM_mass - dmo.R_DM_mass) / flm.R_DM_mass
error1 = dmo.R_DM_mass / flm.R_DM_mass
error2 = dmo.error_R_DM_mass / dmo.R_DM_mass
error3 = flm.error_R_DM_mass / flm.R_DM_mass
mass_error = error1 * np.sqrt(error2**2 + error3**2)
weights_mass = 1 / mass_error**2
mean_mass = np.nansum(delta_mass * weights_mass) / np.nansum(weights_mass)
fig, ax = plt.subplots(nrows=1, ncols=2, sharey=True,
gridspec_kw={'hspace' : 0, 'wspace' : 0},
figsize=(3.3,2))
ax[0].errorbar(mass_mid, delta_mass, yerr=mass_error,
fmt="o",
color="cyan",
capsize=2,
markersize=2)
ax[0].set_xscale('log')
xlim = ax[0].get_xlim()
ax[0].semilogx(xlim, (mean_mass, mean_mass),
linestyle="--", color="grey",
label="Weighted mean")
ax[0].semilogx(xlim, (0,0),
color="k", label="Zero")
ax[0].set_xlim(xlim)
ax[0].set_xlabel("$M_{\\rm{200m}} / \\rm{M_{\odot}}$")
ax[0].set_ylabel("$(R_{\\rm{SP,hydro}} - R_{\\rm{SP, DMO}}) / R_{\\rm{SP,hydro}}$")
ax[0].legend()
ax[1].errorbar(accretion_mid, delta_accretion, yerr=acc_error,
fmt="o",
color="r",
capsize=2,
markersize=2)
xlim = ax[1].get_xlim()
ax[1].plot(xlim, (mean_acc, mean_acc),
linestyle="--", color="grey")
ax[1].plot(xlim, (0,0),
color="k")
ax[1].set_xlim(xlim)
ax[1].set_xlabel("$\Gamma$")
plt.subplots_adjust(left=0.15, right=0.99, bottom=0.17,top=0.99)
filename = "splashback_data/flamingo/plots/dmo_differences.png"
plt.savefig(filename, dpi=300)
plt.show()
dmo = sp.flamingo(box, "DMO")
dmo.read_properties()
flm = sp.flamingo(box, "HF")
flm.read_properties()
N_bins = 10
mass_bins = np.linspace(14, 15.4, N_bins+1)
accretion_bins = np.linspace(0, 4.2, N_bins+1)
mass_mid = 10**((mass_bins[:-1] + mass_bins[1:])/2)
accretion_mid = (accretion_bins[:-1] + accretion_bins[1:])/2
bins = np.vstack((accretion_bins, mass_bins))
sp.stack_and_find_3D(flm, "accretion", accretion_bins, bootstrap=True)
sp.stack_and_find_3D(flm, "mass", mass_bins, bootstrap=True)
dmo_stacking(dmo, "accretion", accretion_bins, bootstrap=True)
dmo_stacking(dmo, "mass", mass_bins, bootstrap=True)
second_caustic(dmo, "accretion")
second_caustic(flm, "accretion")
# plot_profiles(flm, dmo, bins)
#compare_rsp(flm, dmo, bins)
plot_difference(flm, dmo)