mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-22 23:18:01 +00:00
1d847cbd06
* Rename paths object * Remove redshift calculation * Explicit keywrod arg * Rename box units * Basic renaming * Little docs * Rename paths * add imports * Sort imports * Add Quijote cat * Split boxes * add Quijote path * Add origin argument * Update nbs
25 KiB
25 KiB
In [1]:
import numpy as np
import matplotlib.pyplot as plt
import joblib
from glob import glob
from tqdm import tqdm
try:
import csiborgtools
except ModuleNotFoundError:
print("not found")
import sys
sys.path.append("../")
import csiborgtools
%matplotlib notebook
%load_ext autoreload
%autoreload 2
In [13]:
knnreader = csiborgtools.read.kNNCDFReader()
auto_files = glob("/mnt/extraspace/rstiskalek/csiborg/knn/auto/*")
ks = [0, 3, 7]
rs, cdf, mass_thresholds = knnreader.read(auto_files, ks, rmin=0.1)
In [14]:
mu = np.mean(cdf, axis=0)
std = np.std(cdf, axis=0)
cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
fig, axs = plt.subplots(ncols=3, figsize=(6.4 * 1.5, 4.8 * 0.75))
for ki, k in enumerate(ks):
for n in list(range(3))[::-1]:
label = r"$\log M_{{\rm tot}} / M_\odot \geq {}$".format(np.log10(mass_thresholds[n]))
axs[ki].fill_between(rs, 1 - std[n, ki, :] / mu[n, ki, :], 1 + std[n, ki, :] / mu[n, ki, :],
color=cols[n], label=label)
axs[ki].set_title(r"$k \leq {}$".format(k))
axs[ki].axvline(2.65 / 0.705, c="red", ls="--", label="BORG constrained scale", alpha=0.5)
axs[ki].set_xscale("log")
axs[ki].set_xlabel(r"$r~\left[\mathrm{Mpc}\right]$")
axs[0].set_ylabel(r"CDF ratio to mean")
axs[0].legend(frameon=False)
plt.tight_layout(w_pad=0)
fig.savefig("../plots/knn_auto.png", dpi=450)
fig.show()
In [16]:
knnreader = csiborgtools.read.kNNCDFReader()
cross_files = glob("/mnt/extraspace/rstiskalek/csiborg/knn/cross/knncdf_9004*")
cross_files = knnreader.cross_files(7444, "/mnt/extraspace/rstiskalek/csiborg/knn/cross/")
ks = [0, 3, 7]
rs, corr, mass_thresholds = knnreader.read(cross_files, ks, rmin=1)
In [17]:
cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
fig, axs = plt.subplots(ncols=3, figsize=(6.4 * 1.5, 4.8 * 0.75))
for ki, k in enumerate(ks):
for n in range(3):
for i in range(35):
axs[ki].plot(rs, corr[i, n, ki, :], c=cols[n], lw=0.5)
axs[ki].plot(rs, np.median(corr[:, n, ki, :], axis=0), c=cols[n],
label=r"$\log M_{{\rm tot}} / M_\odot \geq {}$".format(np.log10(mass_thresholds[n])))
axs[ki].axvline(2.65 / 0.705, c="red", ls="--", label="BORG constrained scale", alpha=0.5)
axs[ki].set_xscale("log")
axs[ki].set_title(r"$k \leq {}$".format(k))
axs[ki].set_xlabel(r"$r~\left[\mathrm{Mpc}\right]$")
axs[0].legend(frameon=False)
plt.tight_layout()
fig.savefig("../plots/knn_cross.png", dpi=450)
fig.show()
In [ ]:
In [ ]:
In [ ]: