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8d34b832af
* pep8 * style issue * Documentation edits * Remove old files * Remove more old files * Doc & stop setting snap and nsim in paths * add nsnap, nsim support * Remove blank space * docs * Docs edits * Reduce redudant code * docs * Documentation * Docs * Simplify * add nsnap nsim arguments * Remove redundant docs * Fix typos * Shorten catalogue * Remove import * Remove blank line * Docs only edits * Rearrange imports * Remove blank space * Rearrange imports * Rearrange imports * Remove blank space * Update submission scritp * Edit docs * Remove blank line * new paths * Update submission scripts * Update file handling * Remove blank line * Move things around * Ne paths * Edit submission script * edit paths * Fix typo * Fix bug * Remove import * Update nb
28 KiB
28 KiB
In [1]:
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
try:
import csiborgtools
except ModuleNotFoundError:
print("not found")
import sys
sys.path.append("../")
import csiborgtools
import utils
import joblib
from os.path import join
%load_ext autoreload
%autoreload 2
from gc import collect
In [2]:
surv = utils.SDSS()()
X = np.vstack([surv[p] for p in ("DIST", "RA", "DEC")]).T
X = X.astype(np.float32)
In [3]:
particles = np.load("/mnt/extraspace/rstiskalek/csiborg/scratch/particles.npy")
In [4]:
grid = 256
length = 677.05
paths = csiborgtools.read.CSiBORGPaths()
paths.set_info(7444, paths.get_maximum_snapshot(7444))
box = csiborgtools.units.BoxUnits(paths)
MAS = "CIC"
field = csiborgtools.field.DensityField(particles, length, box, MAS)
In [27]:
x0 = field.potential_field(grid, 2)
In [28]:
x1 = field.potential_fieldal_field(grid)
In [34]:
plt.figure()
plt.imshow(np.mean(x0, axis=2) - np.mean(x1, axis=2))
plt.show()
In [33]:
plt.figure()
plt.imshow(np.mean(x1, axis=2))
plt.show()
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In [ ]:
delta = field.overdensity_field(grid, verbose=True)
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field.evaluate_sky(delta, pos=X, isdeg=True)
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dtype = {"names": ['x', 'y'], "formats": [float, float]}
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z = np.zeros((2, 2), dtype=dtype)
In [ ]:
z['x']
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T = field.tensor_field(grid)
In [ ]:
Teval = field.evaluate_sky(T.T00, T.T01, T.T02, T.T11, T.T12, T.T22, pos=X, isdeg=True)
In [ ]:
field.tensor_field_eigvals(*Teval)
In [ ]:
In [ ]:
np.sort()
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Teval[0].shape
In [ ]:
a
In [ ]:
Z = np.zeros((N, 3, 3), dtype=np.float32)
Z[:, 0, 0] = Teval[0]
Z[:, 0, 1] = Teval[1]
Z[:, 0, 2] = Teval[2]
Z[:, 1, 1] = Teval[3]
Z[:, 1, 2] = Teval[4]
Z[:, 2, 2] = Teval[5]
Zall = np.zeros((N, 3, 3), dtype=np.float32)
Zall[:, 0, 0] = Teval[0]
Zall[:, 0, 1] = Teval[1]
Zall[:, 1, 0] = Teval[1]
Zall[:, 0, 2] = Teval[2]
Zall[:, 2, 0] = Teval[2]
Zall[:, 1, 1] = Teval[3]
Zall[:, 1, 2] = Teval[4]
Zall[:, 2, 1] = Teval[4]
Zall[:, 2, 2] = Teval[5]
In [ ]:
np.linalg.eigvalsh(Z[120, ...], 'U')
In [ ]:
np.linalg.eigvals(Zall[120, ...])
In [ ]:
out = field.evaluate_sky(g.gx, g.gy, g.gz, pos=X)
In [ ]:
out
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plt.figure()
plt.hist(np.log10(np.linalg.norm(out, axis=0)), bins="auto")
plt.show()
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