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* Update nb * Update script * Update script * Rename * Update script * Update script * Remove warning * Ignore minors when extracting MAH * Fix paths bug * Move notebooks * Move files * Rename and delete things * Rename file * Move file * Rename things * Remove old print statement * Add basic MAH plot * Add random MAH path * Output snapshot numbers * Add MAH random extraction * Fix redshift bug * Edit script * Add extracting random MAH * Little updates * Add CB2 redshift * Add some caching * Add diagnostic plots * Add caching * Minor updates * Update nb * Update notebook * Update script * Add Sorce randoms * Add CB2 varysmall * Update nb * Update nb * Update nb * Use catalogue HMF * Move definition of radec2galactic * Update nb * Update import * Update import * Add galatic coords to catalogues * Update nb
390 KiB
390 KiB
In [1]:
import sys
from os.path import join
import numpy as np
import matplotlib.pyplot as plt
from numba import jit
from tqdm import tqdm, trange
import joblib
sys.path.append("../")
import csiborgtools
%matplotlib widget
%load_ext autoreload
%autoreload 2
In [2]:
srcdir = "/cosma8/data/dp016/dc-stis1/csiborg_new"
dumpdir = "/cosma8/data/dp016/dc-stis1/csiborg_dump"
paths = csiborgtools.read.CSiBORGPaths(srcdir=srcdir, dumpdir=dumpdir)
reader = csiborgtools.read.ParticleReader(paths)
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part = reader.read_particle(1, 7444, ["x", "y", "z", "M", "ID"], verbose=True)
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/cosma8/data/dp016/dc-stis1/csiborg_new/ramses_out_7444_new/output_00001/info_00001.txt
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!cat /cosma8/data/dp016/dc-stis1/csiborg_new/ramses_out_7444/output_00001/info_00001.txt
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!cat /cosma8/data/dp016/dc-stis1/csiborg_new/ramses_out_7444_new/output_00001/info_00001.txt
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paths.dumpdir
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len(paths.get_ics(tonew=True))
Out[8]:
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fname = "ramses_out_{}_{}.npy".format(
str(self.nsim).zfill(5), str(self.nsnap).zfill(5))
data = numpy.load(join(self.paths.dumpdir, fname))
In [2]:
cat0 = csiborgtools.read.ClumpsCatalogue(7468)
catx = csiborgtools.read.ClumpsCatalogue(7588)
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reader = csiborgtools.read.PairOverlap(cat0, catx, max_dist=150 / 0.705)
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ks = np.argsort(reader.cat0("totpartmass"))[::-1]
k = ks[1]
plt.figure()
plt.scatter(reader.dist(False, "r200")[k], reader.mass_ratio()[k], c=reader.overlap(False)[k])
plt.colorbar(label="Overlap")
plt.title(r"$\log M_{{\rm tot}} / M_\odot = {:.4f}$".format(np.log10(reader.cat0("totpartmass")[k])))
plt.xlabel(r"$\Delta r_i / R_{200c}$")
plt.ylabel(r"$|\log \dfrac{M_i}{M_{\rm tot}}|$")
plt.tight_layout()
plt.show()
In [32]:
print("Starting: {}.".format(datetime.now()))
clumps0 = np.load("/mnt/extraspace/rstiskalek/csiborg/initmatch/clump_7468_particles.npy", allow_pickle=True)
print("Loaded `clump0`: {}.".format(datetime.now()))
clumpsx = np.load("/mnt/extraspace/rstiskalek/csiborg/initmatch/clump_7588_particles.npy", allow_pickle=True)
print("Loaded `clumpx`: {}.".format(datetime.now()))
overlapper = csiborgtools.match.ParticleOverlap()
hid2clumps0 = {hid: n for n, hid in enumerate(clumps0["ID"])}
hid2clumpsx = {hid: n for n, hid in enumerate(clumpsx["ID"])}
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# Convert positions to cell IDs
overlapper.clumps_pos2cell(clumps0)
overlapper.clumps_pos2cell(clumpsx)
mins0, maxs0 = csiborgtools.match.get_halolims(clumps0, overlapper.inv_clength, overlapper.nshift)
minsx, maxsx = csiborgtools.match.get_halolims(clumpsx, overlapper.inv_clength, overlapper.nshift)
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delta_bckg = overlapper.make_bckg_delta(clumps0)
delta_bckg = overlapper.make_bckg_delta(clumpsx, delta=delta_bckg)
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smooth_kwargs = {"sigma": 1, "truncate": 4, "mode": "constant", "cval": 0.0}
delta_bckg_smooth = gaussian_filter(delta_bckg, **smooth_kwargs)
In [363]:
# k = 24734 # skull!
ks = np.argsort(reader.cat0("totpartmass"))[::-1]
# k = ks[1]
k = 331
n = 0
print("Ratio is ", summed_ratio[k])
print("Original overlap is ", overlap_raw[k][n])
print("Smoothed overlap is ", overlap_smoothed[k][n])
index_cl0 = hid2clumps0[reader.cat0("index", k)]
cl0 = clumps0[index_cl0][0]
mins_cl0, maxs_cl0 = mins0[index_cl0], maxs0[index_cl0]
index_clx = hid2clumpsx[reader.catx("index", reader["match_indxs"][k][n])]
clx = clumpsx[index_clx][0]
mins_clx, maxs_clx = minsx[index_clx], maxsx[index_clx]
delta1, delta2, cellmins, nonzero = overlapper.make_deltas(
cl0, clx, mins_cl0, maxs_cl0, mins_clx, maxs_clx, smooth_kwargs=smooth_kwargs)
csiborgtools.match.calculate_overlap(delta1, delta2, cellmins, delta_bckg_smooth)
Out[363]:
In [364]:
xs = []
for n in range(reader["match_indxs"][k].size):
index_clx = hid2clumpsx[reader.catx("index", reader["match_indxs"][k][n])]
clx = clumpsx[index_clx][0]
mins_clx, maxs_clx = minsx[index_clx], maxsx[index_clx]
print("NGP/smoothed overlap ", overlap_raw[k][n], overlap_smoothed[k][n])
delta1, delta2, cellmins, nonzero1 = overlapper.make_deltas(
cl0, clx, mins_cl0, maxs_cl0, mins_clx, maxs_clx, smooth_kwargs=smooth_kwargs)
x = csiborgtools.match.calculate_overlap(delta1, delta2, cellmins, delta_bckg_smooth)
print(x)
xs.append(x)
print("Sum is ", sum(xs))
print("Originally NGP/smoothed was ", summed_raw[k], summed_smoothed[k])
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dlogm = [None] * len(indxs)
mass = [None] * len(indxs)
for k in trange(len(indxs)):
dlogm[k] = np.abs(np.log10(cat[0]["totpartmass"][k]) - np.log10(cat[1]["totpartmass"][indxs[k]]))
mass[k] = np.ones(indxs[k].size) * cat[0]["totpartmass"][k]
dlogm = np.asanyarray(dlogm)
mass = np.asanyarray(mass)
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plt.figure()
plt.scatter(np.concatenate(dlogm), np.concatenate(overlap), s=1, rasterized=True)
t = np.linspace(0, 2)
plt.plot(t, 10**(-t), c="red", label=r"$10^{-|\log M_1 / M_2|}$")
plt.xlabel(r"$|\log M_1 / M_2|$")
plt.ylabel(r"$\mathcal{O}$")
plt.legend()
plt.tight_layout()
# plt.savefig("../plots/mass_comparison.png", dpi=450)
plt.show()
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for k in trange(len(indxs)):
if np.any((dlogm[k] > 1.75) & (overlap[k] > 0.15)):
print(k)
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k = 97788
print(dlogm[k])
print(overlap[k])
n = np.argmax(overlap[k])
index_cl0 = [cl[1] for cl in clumps0].index(cat[0][k]["index"])
cl0 = clumps0[index_cl0][0]
mins_cl0, maxs_cl0 = mins0[index_cl0], maxs0[index_cl0]
index_clx = [cl[1] for cl in clumpsx].index(cat[1]["index"][indxs[k]][n])
clx = clumpsx[index_clx][0]
mins_clx, maxs_clx = minsx[index_clx], maxsx[index_clx]
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delta1, delta2, cellmins = overlapper.make_deltas(cl0, clx, mins_cl0, maxs_cl0, mins_clx, maxs_clx)
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overlapper.overlap(delta1, delta2, cellmins, delta)
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delta1.sum() / delta2.sum()
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plt.figure()
plt.imshow(np.sum(delta1, axis=2))
plt.show()
plt.figure()
plt.imshow(np.sum(delta2, axis=2))
plt.show()
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ncounter = len(indxs[k])
true_overlap = np.full(ncounter, np.nan)
spherical_overlap = np.full(ncounter, np.nan)
for n in trange(len(indxs[k])):
clx = clumpsx[[cl[1] for cl in clumpsx].index(cat[1]["index"][indxs[k]][n])][0]
R1 = (3 * cl0.size / (4 * np.pi))**(1./3) * 1 / 2048
R2 = (3 * clx.size / (4 * np.pi))**(1./3) * 1 / 2048
d = np.linalg.norm([np.mean(cl0[p]) - np.mean(clx[p]) for p in ('x', 'y', 'z')])
spherical_overlap[n] = csiborgtools.match.spherical_overlap(R1, R2, d)
true_overlap[n] = overlapper(cl0, clx, delta)
# print(true_overlap, spherical_overlap)
In [ ]:
plt.figure()
plt.scatter(true_overlap, spherical_overlap)
t = np.linspace(0, 1, 100)
plt.plot(t, t, c="k", ls="--")
plt.xlabel("True overlap")
plt.ylabel("Spherical overlap")
# plt.xscale("log")
# plt.yscale("log")
plt.show()
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R1 = (3 * cl0.size / (4 * np.pi))**(1./3) * 1 / 2048
R2 = (3 * clx.size / (4 * np.pi))**(1./3) * 1 / 2048
d = np.linalg.norm([np.mean(cl0[p]) - np.mean(clx[p]) for p in ('x', 'y', 'z')])
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box = cat[0].box
maverage = box.box2solarmass(clumps0[2][0]["M"][0])
cell = box.box2mpc(1/2048)
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n_sim = 0
import numpy
R = (3 * cat.cats[n_sim]["npart"] / (4 * numpy.pi))**(1./3) * 1 / 2048
R = cat.cats[n_sim].box.box2mpc(R)
In [ ]:
# dlogm = [None] * len(indxs)
# for k in trange(len(indxs)):
# dlogm[k] = np.abs(np.log10(cat[0]["totpartmass"][k]) - np.log10(cat[1]["totpartmass"][indxs[k]]))
# dlogm = np.asanyarray(dlogm)
normdist = [None] * len(indxs)
masses = [None] * len(indxs)
for k in trange(len(indxs)):
normdist[k] = dist0[k] / ((3 * cat[0]["totpartmass"][k] / (4 * np.pi * maverage))**(1/3) * cell)
masses[k] = np.log10(np.ones(indxs[k].size) * cat[0]["totpartmass"][k])
normdist = np.asanyarray(normdist)
masses = np.asanyarray(masses)
In [ ]:
plt.figure()
# plt.scatter(np.concatenate(normdist), np.concatenate(overlap), c=np.concatenate(masses), s=4)
plt.scatter(np.concatenate(normdist), np.concatenate(masses), c=np.concatenate(overlap), s=4)
plt.colorbar()
# plt.xlabel(r"$z = 0$ normalised separation by $\hat{R}$")
# plt.xlabel(r"Absolute difference in total mass [dex]")
# plt.xscale("log")
# plt.ylabel(r"$\mathcal{O}$")
plt.xscale("log")
plt.tight_layout()
# plt.savefig("../plots/another_view.png", dpi=450)
plt.show()
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cl0 = clumps0[[cl[1] for cl in clumps0].index(cat[0][k]["index"])][0]
clx = clumpsx[[cl[1] for cl in clumpsx].index(cat[1]["index"][indxs[k]][n])][0]
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