mirror of
https://github.com/Richard-Sti/csiborgtools.git
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5784011de0
* Add seperate autoknn script & config file * edit ics * Edit submission script * Add threshold values * Edit batch sizign * Remove print * edit * Rename files * Rename * Update nb * edit runs * Edit submit * Add median threshold * add new auto reader * editt submit * edit submit * Edit submit * Add mean prk * Edit runs * Remove correlation file * Move split to clutering * Add init * Remove import * Add the file * Add correlation reading * Edit scripts * Add below and above median permutation for cross * Update imports * Move rvs_in_sphere * Create utils * Split * Add import * Add normalised marks * Add import * Edit readme * Clean up submission file * Stop tracking submit files * Update gitignore * Add poisson field analytical expression * Add abstract generators * Add generators * Pass in the generator * Add a check for if there are any files * Start saving average density * Update nb * Update readme * Update units * Edit jobs * Update submits * Update reader * Add random crossing * Update crossing script * Add crossing with random * Update readme * Update notebook
717 KiB
717 KiB
In [1]:
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors
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
import yaml
%matplotlib notebook
%load_ext autoreload
%autoreload 2
In [44]:
with open('../scripts/knn_auto.yml', 'r') as file:
config = yaml.safe_load(file)
paths = csiborgtools.read.CSiBORGPaths()
knnreader = csiborgtools.read.kNNCDFReader()
auto_folder = "/mnt/extraspace/rstiskalek/csiborg/knn/auto/"
cross_folder = "/mnt/extraspace/rstiskalek/csiborg/knn/cross/"
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rs, corr = knnreader.read("mass001", cross_folder, rmin=1, rmax=50)
rs, corr_rand = knnreader.read("mass001_random", auto_folder, rmin=1, rmax=50)
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cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
plt.figure()
for k in range(64):
plt.plot(rs, corr[0, k, :], c=cols[0], lw=0.5)
# plt.plot(rs, np.abs(corr_rand[0, k, :]), c=cols[1], lw=0.5)
plt.xscale("log")
plt.axvline(2.65 / 0.705, c="red", ls="--")
plt.show()
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rs, cdf = knnreader.read_auto("mass001_spinloww", folder, rmin=1, rmax=50)
pk = knnreader.mean_prob_k(cdf)
# rs, cdf = knnreader.read_auto("mass001_spinhigh", folder, rmin=1, rmax=50)
# pk_perm = knnreader.mean_prob_k(cdf)
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pk.shape
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cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
plt.figure()
# plt.plot(rs, np.nansum(pk, axis=0)[:, 0], c=cols[k])
# plt.plot(rs, np.nansum(pk_perm, axis=0)[:, 0], c=cols[k], ls="--")
for k in range(1, 2):
plt.plot(rs, pk[k, :, 0], c=cols[k])
# plt.plot(rs, pk_perm[k, :, 0], c=cols[k], ls="--")
# plt.fill_between(rs, pk[k, :, 0] - pk[k, :, 1], pk[k, :, 0] + pk[k, :, 1])
# plt.plot(rs, np.sum(pk, axis=0)[:, 0])
# plt.xscale("log")
plt.show()
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plt.figure()
k = 2
rs, mu, std = mean_auto(k, "mass_003_spinhigh")
rs, mu_perm, std_perm = mean_auto(k, "mass003_spinlow")
z = mu / mu_perm
deltaz = z * np.sqrt((std / mu)**2 + (std_perm / mu_perm)**2)
plt.plot(rs, z, c=cols[0])
plt.fill_between(rs, z - deltaz, z + deltaz, color=cols[0], alpha=0.3)
# rs, mu, std = mean_auto(k, "mass001_spinhigh")
# rs, mu_perm, std_perm = mean_auto(k, "mass001_spinhigh_perm")
# z = mu / mu_perm
# deltaz = z * np.sqrt((std / mu)**2 + (std_perm / mu_perm)**2)
# plt.plot(rs, z, c=cols[1])
# plt.fill_between(rs, z - deltaz, z + deltaz, color=cols[1], alpha=0.3)
plt.axhline(1, c="black", ls="--")
# plt.fill_between(rs, mu - std, mu + std, color=cols[2], alpha=0.3)
plt.xscale("log")
# plt.yscale("log")
plt.show()
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# rs, corr = knnreader.read_auto("mass001_spinmedian_cross", folder, rmin=0.5)
# rs, corr_perm = knnreader.read_auto("mass001_spinmedian_cross_perm", folder, rmin=0.5)
# rs, cdf_low = knnreader.read_auto("mass001_spinmedian_cross_perm", folder, rmin=0.5)
rs, cdf_high = knnreader.read_auto("mass001_spinmedian_cross_perm", folder, rmin=0.5)
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plt.figure()
rs, mu, std = mean_auto(0, "mass001_spinlow")
plt.plot(rs, mu, c=cols[0])
plt.fill_between(rs, mu - std, mu + std, color=cols[0], alpha=0.5)
rs, mu, std = mean_auto(0, "mass001_spinlow_perm")
plt.plot(rs, mu, c=cols[1])
plt.fill_between(rs, mu - std, mu + std, color=cols[1], alpha=0.5)
plt.show()
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prk_low = knnreader.prk(rs, cdf_low)
prk_high = knnreader.prk(rs, cdf_high)
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k = 0
plt.figure()
for i in range(101):
plt.plot(rs, prk_low[i, k, :], lw=0.1, c=cols[0])
plt.plot(rs, prk_high[i, k, :], lw=0.1, c=cols[1])
rs, mu, std = mean_auto(0, "mass001")
plt.plot(rs, mu, c=cols[2])
plt.fill_between(rs, mu - std, mu + std, color=cols[2], alpha=0.5)
plt.show()
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rs, corr = knnreader.read_auto("mass001_spinmedian_cross", folder, rmin=0.5)
rs, corrperm = knnreader.read_auto("mass001_spinmedian_cross_perm", folder, rmin=0.5)
# rs, corr_low = knnreader.read_auto("mass001_spinlow_cross_perm", folder, rmin=0.5)
# rs, corr_high = knnreader.read_auto("mass001_spinhigh_cross_perm", folder, rmin=0.5)
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k = 2
plt.figure()
for i in range(101):
plt.plot(rs, corr[i, k, :], lw=0.1, c=cols[0])
plt.plot(rs, corrperm[i, k, :], lw=0.1, c=cols[1])
# plt.plot(rs, corr[i, k, :] - corrperm[i, k, :], lw=0.1, c=cols[0])
# plt.plot(rs, , lw=0.1, c=cols[1])
plt.xscale("log")
plt.yscale("log")
plt.show()
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k = 0
plt.figure()
for i in range(101):
plt.plot(rs, corr[i, k, :], c=cols[0], lw=0.1)
for i in range(101):
plt.plot(rs, corr_perm[i, k, :], c=cols[1], lw=0.1)
plt.xscale("log")
plt.yscale("log")
plt.show()
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mean_prk.shape
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cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
plt.figure()
rs, cdf = knnreader.read_auto("mass001_spinlow", folder, rmin=0.5)
pk = knnreader.prob_kvolume(cdf, rs, True)
for i in range(101):
plt.plot(rs, pk[i, 5, :], lw=0.1, c=cols[0])
rs, cdf = knnreader.read_auto("mass001_spinhigh", folder, rmin=0.5)
pk = knnreader.prob_kvolume(cdf, rs, True)
for i in range(101):
plt.plot(rs, pk[i, 5, :], lw=0.1, c=cols[1])
rs, cdf = knnreader.read_auto("mass001_spinhigh_perm", folder, rmin=0.5)
pk = knnreader.prob_kvolume(cdf, rs, True)
for i in range(101):
plt.plot(rs, pk[i, 5, :], lw=0.1, c=cols[2])
# plt.xscale("log")
plt.show()
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cat = csiborgtools.read.HaloCatalogue(7444, paths, min_mass=1e12, max_dist=155/0.705)
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x = ""
for key in auto_config.keys():
x += key + " "
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x
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auto_config
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auto_folder = "/mnt/extraspace/rstiskalek/csiborg/knn/auto/"
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np.log10(cat["totpartmass"].min())
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cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
plt.figure()
rs, cdf = knnreader.read_auto("004", auto_folder)
pk = knnreader.prob_kvolume(cdf, rs, normalise=True)
for i in range(101):
plt.plot(rs, pk[i, 0, :], c=cols[0], lw=0.1)
rs, cdf = knnreader.read_auto("005", auto_folder)
pk = knnreader.prob_kvolume(cdf, rs, normalise=True)
for i in range(101):
plt.plot(rs, pk[i, 0, :], c=cols[1], lw=0.1)
rs, cdf = knnreader.read_auto("001", auto_folder)
pk = knnreader.prob_kvolume(cdf, rs, normalise=True)
for i in range(101):
plt.plot(rs, pk[i, 0, :], c=cols[2], lw=0.1)
# plt.xscale("log")
# plt.yscale("log")
plt.show()
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cat = csiborgtools.read.HaloCatalogue(7444, paths)
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from tqdm import trange
x = np.full((len(ics), 3), np.nan)
for i in trange(len(ics)):
cat = csiborgtools.read.HaloCatalogue(ics[i], paths, max_dist=155 / 0.705)
for j, th in enumerate([1e12, 1e13, 1e14]):
mask = cat["totpartmass"] > th
x[i, j] = np.nanmedian(cat["lambda200c"][mask])
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np.mean(x[:, 2]), np.std(x[:, 2])
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cdf.shape
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np.nanmedian()
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plt.figure()
plt.scatter(cat["m200"], cat["lambda200c"], s=1)
plt.xscale("log")
plt.yscale("log")
plt.show()
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files = glob("/mnt/extraspace/rstiskalek/csiborg/knn/auto/*")
ks = [0, 1, 2, 3, 4, 5, 6, 7]
rs, cdf, thresholds = knnreader.read(files, ks, rmin=0.01, rmax=100)
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pk = knnreader.prob_kvolume(cdf, rs, True)
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cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
plt.figure()
n = 1
for k in range(7):
plt.plot(rs, np.mean(pk[:, n, k, :], axis=0), c=cols[k], label=r"$k = {}$".format(k))
for i in range(101):
plt.plot(rs, pk[i, n, k, :], c=cols[k], lw=0.05)
plt.legend(frameon=False)
plt.xlabel(r"$r~\left[\mathrm{Mpc}\right]$")
plt.ylabel(r"$P\left(k | V = 4 \pi r^3 / 3\right)$")
# plt.savefig("../plots/test.png", dpi=450)
plt.show()
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n = 2
k = 1
x = cdf[:, n, k - 1, :] - cdf[:, n, k, :]
plt.figure()
for i in range(101):
plt.plot(rs, x[i, :])
plt.xscale("log")
plt.show()
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files = knnreader.cross_files(7444, "/mnt/extraspace/rstiskalek/csiborg/knn/cross/")
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ks = [0, 1, 2, 3, 4, 5, 6, 7]
rs, cross, threshold = knnreader.read(files, ks, rmin=0.5)
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n = 0
k = 1
plt.figure()
for i in range(100):
plt.plot(rs, cross[i, n, k - 1, :] - cross[i, n, k, :])
plt.xscale("log")
plt.axvline(2.65 / 0.705)
plt.show()
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"/mnt/extraspace/hdesmond/ramses_out_7444/output_00950/clump_00950.dat"
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cat1 = csiborgtools.read.HaloCatalogue(7444, min_mass=1e13, max_dist=155 / 0.705)
cat2 = csiborgtools.read.HaloCatalogue(7468, min_mass=1e13, max_dist=155 / 0.705)
In [ ]:
knncdf = csiborgtools.match.kNN_CDF()
knn1 = NearestNeighbors()
knn1.fit(cat1.positions)
knn2 = NearestNeighbors()
knn2.fit(cat2.positions)
# rs, cdf = knncdf(knn, nneighbours=2, Rmax=155 / 0.705, rmin=0.01, rmax=100,
# nsamples=int(1e6), neval=int(1e4), random_state=42, batch_size=int(1e6))
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!ls /mnt/extraspace/rstiskalek/csiborg/knn/cross/knncdf_7444_7468.p
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from glob import glob
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files = glob("/mnt/extraspace/rstiskalek/csiborg/knn/cross/*")
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cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
plt.figure()
for file in files:
d = joblib.load(file)
mask = d["rs"] > 0.1
plt.plot(d["rs"][mask], d["corr_0"][0, mask], c=cols[0], lw=0.4)
plt.xscale("log")
plt.axvline(2.65 / 0.705, lw=0.8, c="red", ls="--")
# plt.yscale("log")
plt.show()
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5500 / comb(5, 3)
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plt.figure()
plt.plot(d["rs"], d["corr_0"][1, :])
plt.plot(d["rs"], d["corr_1"][1, :])
plt.plot(d["rs"], d["corr_2"][1, :])
# plt.yscale("log")
# plt.xscale("log")
plt.show()
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# rs, cdf = knncdf(knn1, nneighbours=2, Rmax=155 / 0.705, rmin=0.01, rmax=100,
# nsamples=int(1e6), neval=int(1e4), random_state=42, batch_size=int(1e6))
rs, cdf0, cdf1, joint_cdf = knncdf.joint(knn1, knn2, nneighbours=8, Rmax=155 / 0.705,
rmin=0.01, rmax=100, nsamples=int(1e6), neval=int(1e4),
random_state=42, batch_size=int(1e6))
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cdf0 = knncdf.clipped_cdf(cdf0)
cdf1 = knncdf.clipped_cdf(cdf1)
joint_cdf = knncdf.clipped_cdf(joint_cdf)
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corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)
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ics = [7444, 7468, 7492, 7516, 7540, 7564, 7588, 7612, 7636, 7660, 7684,
7708, 7732, 7756, 7780, 7804, 7828, 7852, 7876, 7900, 7924, 7948,
7972, 7996, 8020, 8044, 8068, 8092, 8116, 8140, 8164, 8188, 8212,
8236, 8260, 8284, 8308, 8332, 8356, 8380, 8404, 8428, 8452, 8476,
8500, 8524, 8548, 8572, 8596, 8620, 8644, 8668, 8692, 8716, 8740,
8764, 8788, 8812, 8836, 8860, 8884, 8908, 8932, 8956, 8980, 9004,
9028, 9052, 9076, 9100, 9124, 9148, 9172, 9196, 9220, 9244, 9268,
9292, 9316, 9340, 9364, 9388, 9412, 9436, 9460, 9484, 9508, 9532,
9556, 9580, 9604, 9628, 9652, 9676, 9700, 9724, 9748, 9772, 9796,
9820, 9844]
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from scipy.special import comb
from itertools import combinations
# for subset in itertools.combinations(stuff, L):
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list(combinations(ics, 2))
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comb()
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plt.figure()
# plt.plot(rs, knncdf.peaked_cdf(cdf0[0, :]))
# plt.plot(rs, knncdf.peaked_cdf(cdf1[0, :]))
# plt.plot(rs, knncdf.peaked_cdf(joint_cdf[0, :]))
for i in range(8):
plt.plot(rs, corr[i, :])
# plt.yscale("log")
# plt.xscale("log")
plt.axvline(2.65 / 0.705, c="red", ls="--")
plt.show()
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dist1, dist2 = knncdf.joint(knn1, knn2, nneighbours=2, Rmax=155 / 0.705, rmin=0.01, rmax=100,
nsamples=int(1e6), neval=int(1e4), random_state=42, batch_size=int(1e6))
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plt.figure()
plt.plot(rs, knncdf.peaked_cdf(cdf[0, :]))
plt.yscale("log" )
plt.xscale("log")
plt.show()
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mask
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dist
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m1 = (rs > 1) & (rs < 35)
fig, axs = plt.subplots(ncols=3, figsize=(6.4 * 1.5, 4.8), sharey=True)
fig.subplots_adjust(wspace=0)
for k in range(3):
for n in range(len(ics)):
m = m1 & (cdfs[n, k, :] > 1e-3)
axs[k].plot(rs[m], cdfs[n, k, m], c="black", lw=0.05)
axs[k].set_xscale("log")
axs[k].set_yscale("log")
axs[k].set_title(r"$k = {}$".format(k))
axs[k].set_xlabel(r"$r~\left[\mathrm{Mpc}\right]$")
axs[0].set_ylabel(r"Peaked CDF")
plt.tight_layout(w_pad=0)
fig.savefig("../plots/peaked_cdf.png", dpi=450)
fig.show()
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m = (rs > 0.5) & (rs < 35)
fig, axs = plt.subplots(ncols=3, figsize=(6.4 * 1.5, 4.8), sharey=True)
fig.subplots_adjust(wspace=0)
for k in range(3):
mu = np.nanmean(cdfs[:, k, :], axis=0)
for n in range(len(ics)):
axs[k].plot(rs[m], (cdfs[n, k, :] / mu)[m], c="black", lw=0.1)
axs[k].set_ylim(0.5, 1.5)
axs[k].axhline(1, ls="--", c="red", zorder=0)
axs[k].axvline(2.65 / 0.705, ls="--", c="red", zorder=0)
axs[k].set_xscale("log")
axs[k].set_xlabel(r"$r~\left[\mathrm{Mpc}\right]$")
axs[k].set_title(r"$k = {}$".format(k))
axs[0].set_ylabel(r"Relative peaked CDF")
plt.tight_layout(w_pad=0)
fig.savefig("../plots/peaked_cdf_ratios.png", dpi=450)
fig.show()
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plt.figure()
k = 2
mu = np.nanmean(cdfs[:, k, :], axis=0)
# plt.plot(rs, mu, c="black")
for i in range(len(ics)):
plt.plot(rs, cdfs[i, k, :] / mu)
plt.ylim(0.75, 1.25)
plt.axhline(1, ls="--", c="black")
plt.xscale("log")
# plt.yscale("log")
plt.show()
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x.shape
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dist0, __ = knn0.kneighbors(X, 3)
distx, __ = knnx.kneighbors(X, 3)
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x0, y0 = knncdf.peaked_cdf_from_samples(dist0[:, 0], 0.5, 20, neval=10000)
xx, yx = knncdf.peaked_cdf_from_samples(distx[:, 0], 0.5, 20, neval=10000)
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distx[:, 0].min()
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plt.figure()
plt.plot(x0, y0)
plt.plot(xx, yx)
plt.yscale("log")
plt.xscale("log")
plt.show()
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plt.figure()
for i in range(3):
plt.plot(*knncdf.cdf_from_samples(dist0[:, i], 1, 25))
plt.plot(*knncdf.cdf_from_samples(distx[:, i], 1, 25))
# plt.xlim(0.5, 25)
plt.yscale("log")
plt.xscale("log")
plt.xlabel(r"$r~\left[\mathrm{Mpc}\right]$")
plt.show()
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x = dist[:, 0]
q = np.linspace(0, 100, int(x.size / 5))
p = np.percentile(x, q)
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y = np.sort(x)
yy = np.arange(y.size) / y.size
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plt.figure()
plt.plot(p, q / 100)
plt.plot(y, yy)
# plt.yscale("log")
plt.show()
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plt.figure()
plt.hist(dist[:, 0], bins="auto", histtype="step")
plt.hist(dist[:, 1], bins="auto", histtype="step")
plt.hist(dist[:, 2], bins="auto", histtype="step")
plt.show()
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plt.figure()
plt.hist(cat0["dec"], bins="auto")
plt.show()
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gen = np.random.default_rng(22)
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gen.normal()
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theta = np.linspace( t, np.pi, 100)
plt.figure()
plt.plot(theta, np.sin(theta))
plt.show()
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X = np.array([-3.9514747, -0.6966991, 2.97158]).reshape(1, -1)
X
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dist, indxs = knn0.kneighbors(X, n_neighbors=1)
dist, indxs
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cat0.positions[indxs]
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