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https://github.com/DifferentiableUniverseInitiative/JaxPM.git
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356 KiB
356 KiB
In [1]:
import numpy as np
import matplotlib.pyplot as plt
from h5py import File
import csiborgtools
%load_ext autoreload
%autoreload 2
%matplotlib inline
In [69]:
def read_key(key, stationary_observer=False):
nsims = range(51)
bfs = [None] * len(nsims)
for nsim in nsims:
if stationary_observer:
fname = f"/mnt/extraspace/rstiskalek/quijote/BulkFlow_fiducial/sBF_nsim_{nsim}.hdf5"
else:
fname = f"/mnt/extraspace/rstiskalek/quijote/BulkFlow_fiducial/BF_nsim_{nsim}.hdf5"
with File(fname, 'r') as f:
if nsim == 0:
print("Keys are: ", list(f.keys()))
distances = f['distances'][...]
bfs[nsim] = f[key][...]
bfs = np.asarray(bfs)
ndist = len(distances)
bx = np.asarray([bfs[..., i, 0].reshape(-1, ) for i in range(ndist)]).T
by = np.asarray([bfs[..., i, 1].reshape(-1, ) for i in range(ndist)]).T
bz = np.asarray([bfs[..., i, 2].reshape(-1, ) for i in range(ndist)]).T
return distances, np.stack([bx, by, bz], axis=-1)
Bulk flow in the cosmic (simulation) rest frame¶
In [73]:
distances, bf_volume_part = read_key("bf_volume_part")
distances, bf_vrad_weighted_part = read_key("bf_vrad_weighted_part")
plt.figure()
plt.title("Bulk flow magnitude comparison")
cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
# LCDM expectation
Rs,mean,std,mode,p05,p16,p84,p95 = np.load("/mnt/users/rstiskalek/csiborgtools/data/BulkFlowPlot.npy")
m = Rs < 200
plt.plot(Rs[m], p05[m], color="black", alpha=0.25)
plt.plot(Rs[m], p95[m], color="black", alpha=0.25)
plt.plot(Rs[m], p16[m], color="black", alpha=0.5)
plt.plot(Rs[m], p84[m], color="black", alpha=0.5)
plt.plot(Rs[m], mode[m], color="black", label=r"$\Lambda\mathrm{CDM}$")
# Volume averaging
y = np.linalg.norm(bf_volume_part, axis=-1)
ylowlow, ylow, ymed, yhigh, yhighhigh = np.percentile(y, [5, 16, 50, 84, 95], axis=0)
plt.fill_between(distances, ylowlow, yhighhigh, alpha=0.25, color=cols[0])
plt.fill_between(distances, ylow, yhigh, alpha=0.5, color=cols[0])
plt.plot(distances, ymed, label=r"$B_i = \langle v_i \rangle_R$", color=cols[0])
# 1 / r^2
y = np.linalg.norm(bf_vrad_weighted_part, axis=-1)
ylowlow, ylow, ymed, yhigh, yhighhigh = np.percentile(y, [5, 16, 50, 84, 95], axis=0)
plt.fill_between(distances, ylowlow, yhighhigh, alpha=0.25, color=cols[1])
plt.fill_between(distances, ylow, yhigh, alpha=0.5, color=cols[1])
plt.plot(distances, ymed, label=r"$B_i = R^2 \langle v_r \hat{n}_i / r^2 \rangle_R$", color=cols[1])
plt.legend()
plt.xlim(distances[2], distances.max())
plt.ylim(0, 1000)
plt.xlabel(r"$R ~ [\mathrm{Mpc} / h]$")
plt.ylabel(r"$\left\| \mathbf{B} \right\| ~ [\mathrm{km} / s]$")
plt.tight_layout()
plt.savefig("../../plots/bf_comparison.png", dpi=450)
plt.show()
Component differences¶
In [67]:
dx = bf_volume_part - bf_vrad_weighted_part
fig, axs = plt.subplots(1, 3, figsize=(12, 4), sharey=True)
fig.suptitle("Bulk flow component difference")
cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
for i in range(3):
y = dx[..., i]
ylowlow, ylow, ymed, yhigh, yhighhigh = np.percentile(y, [5, 16, 50, 84, 95], axis=0)
axs[i].fill_between(distances, ylowlow, yhighhigh, alpha=0.25, color=cols[0])
axs[i].fill_between(distances, ylow, yhigh, alpha=0.5, color=cols[0])
axs[i].plot(distances, ymed, color=cols[0])
axs[i].set_xlabel(r"$R ~ [\mathrm{Mpc} / h]$")
axs[i].set_xlim(distances[2], distances.max())
axs[i].axhline(0, color="black", linestyle="--", zorder=0)
axs[0].set_ylabel(r"$\Delta B_i ~ [\mathrm{km} / s]$")
fig.tight_layout()
fig.savefig("../../plots/bf_difference.png", dpi=450)
fig.show()
Bulk flow in the observer rest frame¶
In [75]:
distances, bf_volume_part = read_key("bf_volume_part", True)
distances, bf_vrad_weighted_part = read_key("bf_vrad_weighted_part", True)
plt.figure()
plt.title("Bulk flow magnitude comparison (observer rest frame)")
cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
# Volume averaging
y = np.linalg.norm(bf_volume_part, axis=-1)
ylowlow, ylow, ymed, yhigh, yhighhigh = np.percentile(y, [5, 16, 50, 84, 95], axis=0)
plt.fill_between(distances, ylowlow, yhighhigh, alpha=0.25, color=cols[0])
plt.fill_between(distances, ylow, yhigh, alpha=0.5, color=cols[0])
plt.plot(distances, ymed, label=r"$B_i = \langle v_i \rangle_R$", color=cols[0])
# 1 / r^2
y = np.linalg.norm(bf_vrad_weighted_part, axis=-1)
ylowlow, ylow, ymed, yhigh, yhighhigh = np.percentile(y, [5, 16, 50, 84, 95], axis=0)
plt.fill_between(distances, ylowlow, yhighhigh, alpha=0.25, color=cols[1])
plt.fill_between(distances, ylow, yhigh, alpha=0.5, color=cols[1])
plt.plot(distances, ymed, label=r"$B_i = R^2 \langle v_r \hat{n}_i / r^2 \rangle_R$", color=cols[1])
plt.legend()
plt.xlim(distances[2], distances.max())
plt.ylim(0, 1000)
plt.xlabel(r"$R ~ [\mathrm{Mpc} / h]$")
plt.ylabel(r"$\left\| \mathbf{B} \right\| ~ [\mathrm{km} / s]$")
plt.tight_layout()
plt.savefig("../../plots/bf_comparison_observer.png", dpi=450)
plt.show()
