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File organisation (#41)

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* Split summary files

* Rename script

* Cosmetics

* Minor changes

* Update TODO
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Richard Stiskalek 2023-04-09 21:12:19 +01:00 committed by GitHub
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2
README.md
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@ -18,7 +18,7 @@
- [x] Add normalised marks to the submission scripts.
- [x] Verify analytical formula for the kNN of a uniform field.
- [x] For the cross-correlation try making the second field randoms.
- [ ] Clean up the reader code.
- [x] Clean up the reader code.
- [x] Correct the crossing script.
- [ ] Get started with the 2PCF calculation.

9
csiborgtools/read/__init__.py
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@ -14,9 +14,10 @@
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
from .readsim import (CSiBORGPaths, ParticleReader, read_mmain, read_initcm, halfwidth_select) # noqa
from .make_cat import (HaloCatalogue, concatenate_clumps) # noqa
from .readobs import (PlanckClusters, MCXCClusters, TwoMPPGalaxies, # noqa
from .halo_cat import (HaloCatalogue, concatenate_clumps) # noqa
from .obs import (PlanckClusters, MCXCClusters, TwoMPPGalaxies, # noqa
TwoMPPGroups, SDSS) # noqa
from .outsim import (dump_split, combine_splits) # noqa
from .summaries import (PKReader, kNNCDFReader, PairOverlap, NPairsOverlap, # noqa
binned_resample_mean) # noqa
from .overlap_summary import (PairOverlap, NPairsOverlap, binned_resample_mean) # noqa
from .knn_summary import kNNCDFReader # noqa
from .pk_summary import PKReader # noqa

0
csiborgtools/read/make_cat.py → csiborgtools/read/halo_cat.py
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221
csiborgtools/read/knn_summary.py Normal file
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@ -0,0 +1,221 @@
# Copyright (C) 2023 Richard Stiskalek
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""kNN-CDF reader."""
from os.path import join
from glob import glob
import numpy
from scipy.special import factorial
import joblib
class kNNCDFReader:
"""
Shortcut object to read in the kNN CDF data.
"""
def read(self, run, folder, rmin=None, rmax=None, to_clip=True):
"""
Read the auto- or cross-correlation kNN-CDF data. Infers the type from
the data files.
Parameters
----------
run : str
Run ID to read in.
folder : str
Path to the folder where the auto-correlation kNN-CDF is stored.
rmin : float, optional
Minimum separation. By default ignored.
rmax : float, optional
Maximum separation. By default ignored.
to_clip : bool, optional
Whether to clip the auto-correlation CDF. Ignored for
cross-correlation.
Returns
-------
rs : 1-dimensional array of shape `(neval, )`
Separations where the CDF is evaluated.
out : 3-dimensional array of shape `(len(files), len(ks), neval)`
Array of CDFs or cross-correlations.
"""
run += ".p"
files = [f for f in glob(join(folder, "*")) if run in f]
if len(files) == 0:
raise RuntimeError("No files found for run `{}`.".format(run[:-2]))
for i, file in enumerate(files):
data = joblib.load(file)
if i == 0: # Initialise the array
if "corr" in data.keys():
kind = "corr"
isauto = False
else:
kind = "cdf"
isauto = True
out = numpy.full((len(files), *data[kind].shape), numpy.nan,
dtype=numpy.float32)
rs = data["rs"]
out[i, ...] = data[kind]
if isauto and to_clip:
out[i, ...] = self.clipped_cdf(out[i, ...])
# Apply separation cuts
mask = (rs >= rmin if rmin is not None else rs > 0)
mask &= (rs <= rmax if rmax is not None else rs < numpy.infty)
rs = rs[mask]
out = out[..., mask]
return rs, out
@staticmethod
def peaked_cdf(cdf, make_copy=True):
"""
Transform the CDF to a peaked CDF.
Parameters
----------
cdf : 1- or 2- or 3-dimensional array
CDF to be transformed along the last axis.
make_copy : bool, optional
Whether to make a copy of the CDF before transforming it to avoid
overwriting it.
Returns
-------
peaked_cdf : 1- or 2- or 3-dimensional array
"""
cdf = numpy.copy(cdf) if make_copy else cdf
cdf[cdf > 0.5] = 1 - cdf[cdf > 0.5]
return cdf
@staticmethod
def clipped_cdf(cdf):
"""
Clip the CDF, setting values where the CDF is either 0 or after the
first occurence of 1 to `numpy.nan`.
Parameters
----------
cdf : 2- or 3-dimensional array
CDF to be clipped.
Returns
-------
clipped_cdf : 2- or 3-dimensional array
The clipped CDF.
"""
cdf = numpy.copy(cdf)
if cdf.ndim == 2:
cdf = cdf.reshape(1, *cdf.shape)
nknns, nneighbours, __ = cdf.shape
for i in range(nknns):
for k in range(nneighbours):
ns = numpy.where(cdf[i, k, :] == 1.)[0]
if ns.size > 1:
cdf[i, k, ns[1]:] = numpy.nan
cdf[cdf == 0] = numpy.nan
cdf = cdf[0, ...] if nknns == 1 else cdf # Reshape if necessary
return cdf
@staticmethod
def prob_k(cdf):
r"""
Calculate the PDF that a spherical volume of radius :math:`r` contains
:math:`k` objects, i.e. :math:`P(k | V = 4 \pi r^3 / 3)`.
Parameters
----------
cdf : 3-dimensional array of shape `(len(files), len(ks), len(rs))`
Array of CDFs
Returns
-------
pk : 3-dimensional array of shape `(len(files), len(ks)- 1, len(rs))`
"""
out = numpy.full_like(cdf[..., 1:, :], numpy.nan, dtype=numpy.float32)
nks = cdf.shape[-2]
out[..., 0, :] = 1 - cdf[..., 0, :]
for k in range(1, nks - 1):
out[..., k, :] = cdf[..., k - 1, :] - cdf[..., k, :]
return out
def mean_prob_k(self, cdf):
r"""
Calculate the mean PDF that a spherical volume of radius :math:`r`
contains :math:`k` objects, i.e. :math:`P(k | V = 4 \pi r^3 / 3)`,
averaged over the IC realisations.
Parameters
----------
cdf : 3-dimensional array of shape `(len(files), len(ks), len(rs))`
Array of CDFs
Returns
-------
out : 3-dimensional array of shape `(len(ks) - 1, len(rs), 2)`
Mean :math:`P(k | V = 4 \pi r^3 / 3) and its standard deviation,
stored along the last dimension, respectively.
"""
pk = self.prob_k(cdf)
return numpy.stack([numpy.mean(pk, axis=0), numpy.std(pk, axis=0)],
axis=-1)
def poisson_prob_k(self, rs, k, ndensity):
r"""
Calculate the analytical PDF that a spherical volume of
radius :math:`r` contains :math:`k` objects, i.e.
:math:`P(k | V = 4 \pi r^3 / 3)`, assuming a Poisson field (uniform
distribution of points).
Parameters
----------
rs : 1-dimensional array
Array of separations.
k : int
Number of objects.
ndensity : float
Number density of objects.
Returns
-------
pk : 1-dimensional array
The PDF that a spherical volume of radius :math:`r` contains
:math:`k` objects.
"""
V = 4 * numpy.pi / 3 * rs**3
return (ndensity * V)**k / factorial(k) * numpy.exp(-ndensity * V)
@staticmethod
def cross_files(ic, folder):
"""
Return the file paths corresponding to the cross-correlation of a given
IC.
Parameters
----------
ic : int
The desired IC.
folder : str
The folder containing the cross-correlation files.
Returns
-------
filepath : list of str
"""
return [file for file in glob(join(folder, "*")) if str(ic) in file]

19
csiborgtools/read/readobs.py → csiborgtools/read/obs.py
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@ -15,19 +15,16 @@
"""
Scripts to read in observation.
"""
import numpy
from abc import ABC, abstractproperty
from os.path import join
from warnings import warn
import numpy
from scipy import constants
from astropy.io import fits
from astropy.coordinates import SkyCoord
from astropy import units
from scipy import constants
from warnings import warn
from ..utils import (cols_to_structured)
F64 = numpy.float64
###############################################################################
# Text survey base class #
@ -112,8 +109,9 @@ class TwoMPPGalaxies(TextSurvey):
cat = numpy.genfromtxt(fpath, delimiter="|", )
cat = cat[cat[:, 12] == 0, :]
# Pre=allocate array and fillt it
cols = [("RA", F64), ("DEC", F64), ("Ksmag", F64), ("ZCMB", F64),
("DIST", F64)]
cols = [("RA", numpy.float64), ("DEC", numpy.float64),
("Ksmag", numpy.float64), ("ZCMB", numpy.float64),
("DIST", numpy.float64)]
data = cols_to_structured(cat.shape[0], cols)
data["RA"] = cat[:, 1]
data["DEC"] = cat[:, 2]
@ -158,8 +156,9 @@ class TwoMPPGroups(TextSurvey):
"""
cat = numpy.genfromtxt(fpath, delimiter="|", )
# Pre-allocate and fill the array
cols = [("RA", F64), ("DEC", F64), ("K2mag", F64),
("Rich", numpy.int64), ("sigma", F64)]
cols = [("RA", numpy.float64), ("DEC", numpy.float64),
("K2mag", numpy.float64), ("Rich", numpy.int64),
("sigma", numpy.float64e)]
data = cols_to_structured(cat.shape[0], cols)
data["K2mag"] = cat[:, 3]
data["Rich"] = cat[:, 4]

367
csiborgtools/read/summaries.py → csiborgtools/read/overlap_summary.py
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@ -16,377 +16,10 @@
Tools for summarising various results.
"""
from os.path import (join, isfile)
from glob import glob
import numpy
from scipy.special import factorial
import joblib
from tqdm import tqdm
###############################################################################
# PKReader #
###############################################################################
class PKReader:
"""
A shortcut object for reading in the power spectrum files.
Parameters
----------
ic_ids : list of int
IC IDs to be read.
hw : float
Box half-width.
fskel : str, optional
The skeleton path. By default
`/mnt/extraspace/rstiskalek/csiborg/crosspk/out_{}_{}_{}.p`, where
the formatting options are `ic0, ic1, hw`.
dtype : dtype, optional
Output precision. By default `numpy.float32`.
"""
def __init__(self, ic_ids, hw, fskel=None, dtype=numpy.float32):
self.ic_ids = ic_ids
self.hw = hw
if fskel is None:
fskel = "/mnt/extraspace/rstiskalek/csiborg/crosspk/out_{}_{}_{}.p"
self.fskel = fskel
self.dtype = dtype
@staticmethod
def _set_klim(kmin, kmax):
"""
Sets limits on the wavenumber to 0 and infinity if `None`s provided.
"""
if kmin is None:
kmin = 0
if kmax is None:
kmax = numpy.infty
return kmin, kmax
def read_autos(self, kmin=None, kmax=None):
"""
Read in the autocorrelation power spectra.
Parameters
----------
kmin : float, optional
The minimum wavenumber. By default `None`, i.e. 0.
kmin : float, optional
The maximum wavenumber. By default `None`, i.e. infinity.
Returns
-------
ks : 1-dimensional array
Array of wavenumbers.
pks : 2-dimensional array of shape `(len(self.ic_ids), ks.size)`
Autocorrelation of each simulation.
"""
kmin, kmax = self._set_klim(kmin, kmax)
ks, pks, sel = None, None, None
for i, nsim in enumerate(self.ic_ids):
pk = joblib.load(self.fskel.format(nsim, nsim, self.hw))
# Get cuts and pre-allocate arrays
if i == 0:
x = pk.k3D
sel = (kmin < x) & (x < kmax)
ks = x[sel].astype(self.dtype)
pks = numpy.full((len(self.ic_ids), numpy.sum(sel)), numpy.nan,
dtype=self.dtype)
pks[i, :] = pk.Pk[sel, 0, 0]
return ks, pks
def read_single_cross(self, ic0, ic1, kmin=None, kmax=None):
"""
Read cross-correlation between IC IDs `ic0` and `ic1`.
Parameters
----------
ic0 : int
The first IC ID.
ic1 : int
The second IC ID.
kmin : float, optional
The minimum wavenumber. By default `None`, i.e. 0.
kmin : float, optional
The maximum wavenumber. By default `None`, i.e. infinity.
Returns
-------
ks : 1-dimensional array
Array of wavenumbers.
xpk : 1-dimensional array of shape `(ks.size, )`
Cross-correlation.
"""
if ic0 == ic1:
raise ValueError("Requested cross correlation for the same ICs.")
kmin, kmax = self._set_klim(kmin, kmax)
# Check their ordering. The latter must be larger.
ics = (ic0, ic1)
if ic0 > ic1:
ics = ics[::-1]
pk = joblib.load(self.fskel.format(*ics, self.hw))
ks = pk.k3D
sel = (kmin < ks) & (ks < kmax)
ks = ks[sel].astype(self.dtype)
xpk = pk.XPk[sel, 0, 0].astype(self.dtype)
return ks, xpk
def read_cross(self, kmin=None, kmax=None):
"""
Read cross-correlation between all IC pairs.
Parameters
----------
kmin : float, optional
The minimum wavenumber. By default `None`, i.e. 0.
kmin : float, optional
The maximum wavenumber. By default `None`, i.e. infinity.
Returns
-------
ks : 1-dimensional array
Array of wavenumbers.
xpks : 3-dimensional array of shape (`nics, nics - 1, ks.size`)
Cross-correlations. The first column is the the IC and is being
cross-correlated with the remaining ICs, in the second column.
"""
nics = len(self.ic_ids)
ks, xpks = None, None
for i, ic0 in enumerate(tqdm(self.ic_ids)):
k = 0
for ic1 in self.ic_ids:
# We don't want cross-correlation
if ic0 == ic1:
continue
x, y = self.read_single_cross(ic0, ic1, kmin, kmax)
# If in the first iteration pre-allocate arrays
if ks is None:
ks = x
xpks = numpy.full((nics, nics - 1, ks.size), numpy.nan,
dtype=self.dtype)
xpks[i, k, :] = y
# Bump up the iterator
k += 1
return ks, xpks
###############################################################################
# PKReader #
###############################################################################
class kNNCDFReader:
"""
Shortcut object to read in the kNN CDF data.
"""
def read(self, run, folder, rmin=None, rmax=None, to_clip=True):
"""
Read the auto- or cross-correlation kNN-CDF data. Infers the type from
the data files.
Parameters
----------
run : str
Run ID to read in.
folder : str
Path to the folder where the auto-correlation kNN-CDF is stored.
rmin : float, optional
Minimum separation. By default ignored.
rmax : float, optional
Maximum separation. By default ignored.
to_clip : bool, optional
Whether to clip the auto-correlation CDF. Ignored for
cross-correlation.
Returns
-------
rs : 1-dimensional array of shape `(neval, )`
Separations where the CDF is evaluated.
out : 3-dimensional array of shape `(len(files), len(ks), neval)`
Array of CDFs or cross-correlations.
"""
run += ".p"
files = [f for f in glob(join(folder, "*")) if run in f]
if len(files) == 0:
raise RuntimeError("No files found for run `{}`.".format(run[:-2]))
for i, file in enumerate(files):
data = joblib.load(file)
if i == 0: # Initialise the array
if "corr" in data.keys():
kind = "corr"
isauto = False
else:
kind = "cdf"
isauto = True
out = numpy.full((len(files), *data[kind].shape), numpy.nan,
dtype=numpy.float32)
rs = data["rs"]
out[i, ...] = data[kind]
if isauto and to_clip:
out[i, ...] = self.clipped_cdf(out[i, ...])
# Apply separation cuts
mask = (rs >= rmin if rmin is not None else rs > 0)
mask &= (rs <= rmax if rmax is not None else rs < numpy.infty)
rs = rs[mask]
out = out[..., mask]
return rs, out
@staticmethod
def peaked_cdf(cdf, make_copy=True):
"""
Transform the CDF to a peaked CDF.
Parameters
----------
cdf : 1- or 2- or 3-dimensional array
CDF to be transformed along the last axis.
make_copy : bool, optional
Whether to make a copy of the CDF before transforming it to avoid
overwriting it.
Returns
-------
peaked_cdf : 1- or 2- or 3-dimensional array
"""
cdf = numpy.copy(cdf) if make_copy else cdf
cdf[cdf > 0.5] = 1 - cdf[cdf > 0.5]
return cdf
@staticmethod
def clipped_cdf(cdf):
"""
Clip the CDF, setting values where the CDF is either 0 or after the
first occurence of 1 to `numpy.nan`.
Parameters
----------
cdf : 2- or 3-dimensional array
CDF to be clipped.
Returns
-------
clipped_cdf : 2- or 3-dimensional array
The clipped CDF.
"""
cdf = numpy.copy(cdf)
if cdf.ndim == 2:
cdf = cdf.reshape(1, *cdf.shape)
nknns, nneighbours, __ = cdf.shape
for i in range(nknns):
for k in range(nneighbours):
ns = numpy.where(cdf[i, k, :] == 1.)[0]
if ns.size > 1:
cdf[i, k, ns[1]:] = numpy.nan
cdf[cdf == 0] = numpy.nan
cdf = cdf[0, ...] if nknns == 1 else cdf # Reshape if necessary
return cdf
@staticmethod
def prob_k(cdf):
r"""
Calculate the PDF that a spherical volume of radius :math:`r` contains
:math:`k` objects, i.e. :math:`P(k | V = 4 \pi r^3 / 3)`.
Parameters
----------
cdf : 3-dimensional array of shape `(len(files), len(ks), len(rs))`
Array of CDFs
Returns
-------
pk : 3-dimensional array of shape `(len(files), len(ks)- 1, len(rs))`
"""
out = numpy.full_like(cdf[..., 1:, :], numpy.nan, dtype=numpy.float32)
nks = cdf.shape[-2]
out[..., 0, :] = 1 - cdf[..., 0, :]
for k in range(1, nks - 1):
out[..., k, :] = cdf[..., k - 1, :] - cdf[..., k, :]
return out
def mean_prob_k(self, cdf):
"""
Calculate the mean PDF that a spherical volume of radius :math:`r`
contains :math:`k` objects, i.e. :math:`P(k | V = 4 \pi r^3 / 3)`,
averaged over the IC realisations.
Parameters
----------
cdf : 3-dimensional array of shape `(len(files), len(ks), len(rs))`
Array of CDFs
Returns
-------
out : 3-dimensional array of shape `(len(ks) - 1, len(rs), 2)`
Mean :math:`P(k | V = 4 \pi r^3 / 3) and its standard deviation,
stored along the last dimension, respectively.
"""
pk = self.prob_k(cdf)
return numpy.stack([numpy.mean(pk, axis=0), numpy.std(pk, axis=0)],
axis=-1)
def poisson_prob_k(self, rs, k, ndensity):
"""
Calculate the analytical PDF that a spherical volume of
radius :math:`r` contains :math:`k` objects, i.e.
:math:`P(k | V = 4 \pi r^3 / 3)`, assuming a Poisson field (uniform
distribution of points).
Parameters
----------
rs : 1-dimensional array
Array of separations.
k : int
Number of objects.
ndensity : float
Number density of objects.
Returns
-------
pk : 1-dimensional array
The PDF that a spherical volume of radius :math:`r` contains
:math:`k` objects.
"""
V = 4 * numpy.pi / 3 * rs**3
return (ndensity * V)**k / factorial(k) * numpy.exp(-ndensity * V)
@staticmethod
def cross_files(ic, folder):
"""
Return the file paths corresponding to the cross-correlation of a given
IC.
Parameters
----------
ic : int
The desired IC.
folder : str
The folder containing the cross-correlation files.
Returns
-------
filepath : list of str
"""
return [file for file in glob(join(folder, "*")) if str(ic) in file]
###############################################################################
# PKReader #
###############################################################################
class PairOverlap:
r"""
A shortcut object for reading in the results of matching two simulations.

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csiborgtools/read/pk_summary.py Normal file
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@ -0,0 +1,166 @@
# Copyright (C) 2022 Richard Stiskalek, Harry Desmond
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""Power spectrum reader."""
import numpy
import joblib
from tqdm import tqdm
class PKReader:
"""
A shortcut object for reading in the power spectrum files.
Parameters
----------
ic_ids : list of int
IC IDs to be read.
hw : float
Box half-width.
fskel : str, optional
The skeleton path. By default
`/mnt/extraspace/rstiskalek/csiborg/crosspk/out_{}_{}_{}.p`, where
the formatting options are `ic0, ic1, hw`.
dtype : dtype, optional
Output precision. By default `numpy.float32`.
"""
def __init__(self, ic_ids, hw, fskel=None, dtype=numpy.float32):
self.ic_ids = ic_ids
self.hw = hw
if fskel is None:
fskel = "/mnt/extraspace/rstiskalek/csiborg/crosspk/out_{}_{}_{}.p"
self.fskel = fskel
self.dtype = dtype
@staticmethod
def _set_klim(kmin, kmax):
"""
Sets limits on the wavenumber to 0 and infinity if `None`s provided.
"""
if kmin is None:
kmin = 0
if kmax is None:
kmax = numpy.infty
return kmin, kmax
def read_autos(self, kmin=None, kmax=None):
"""
Read in the autocorrelation power spectra.
Parameters
----------
kmin : float, optional
The minimum wavenumber. By default `None`, i.e. 0.
kmin : float, optional
The maximum wavenumber. By default `None`, i.e. infinity.
Returns
-------
ks : 1-dimensional array
Array of wavenumbers.
pks : 2-dimensional array of shape `(len(self.ic_ids), ks.size)`
Autocorrelation of each simulation.
"""
kmin, kmax = self._set_klim(kmin, kmax)
ks, pks, sel = None, None, None
for i, nsim in enumerate(self.ic_ids):
pk = joblib.load(self.fskel.format(nsim, nsim, self.hw))
# Get cuts and pre-allocate arrays
if i == 0:
x = pk.k3D
sel = (kmin < x) & (x < kmax)
ks = x[sel].astype(self.dtype)
pks = numpy.full((len(self.ic_ids), numpy.sum(sel)), numpy.nan,
dtype=self.dtype)
pks[i, :] = pk.Pk[sel, 0, 0]
return ks, pks
def read_single_cross(self, ic0, ic1, kmin=None, kmax=None):
"""
Read cross-correlation between IC IDs `ic0` and `ic1`.
Parameters
----------
ic0 : int
The first IC ID.
ic1 : int
The second IC ID.
kmin : float, optional
The minimum wavenumber. By default `None`, i.e. 0.
kmin : float, optional
The maximum wavenumber. By default `None`, i.e. infinity.
Returns
-------
ks : 1-dimensional array
Array of wavenumbers.
xpk : 1-dimensional array of shape `(ks.size, )`
Cross-correlation.
"""
if ic0 == ic1:
raise ValueError("Requested cross correlation for the same ICs.")
kmin, kmax = self._set_klim(kmin, kmax)
# Check their ordering. The latter must be larger.
ics = (ic0, ic1)
if ic0 > ic1:
ics = ics[::-1]
pk = joblib.load(self.fskel.format(*ics, self.hw))
ks = pk.k3D
sel = (kmin < ks) & (ks < kmax)
ks = ks[sel].astype(self.dtype)
xpk = pk.XPk[sel, 0, 0].astype(self.dtype)
return ks, xpk
def read_cross(self, kmin=None, kmax=None):
"""
Read cross-correlation between all IC pairs.
Parameters
----------
kmin : float, optional
The minimum wavenumber. By default `None`, i.e. 0.
kmin : float, optional
The maximum wavenumber. By default `None`, i.e. infinity.
Returns
-------
ks : 1-dimensional array
Array of wavenumbers.
xpks : 3-dimensional array of shape (`nics, nics - 1, ks.size`)
Cross-correlations. The first column is the the IC and is being
cross-correlated with the remaining ICs, in the second column.
"""
nics = len(self.ic_ids)
ks, xpks = None, None
for i, ic0 in enumerate(tqdm(self.ic_ids)):
k = 0
for ic1 in self.ic_ids:
# We don't want cross-correlation
if ic0 == ic1:
continue
x, y = self.read_single_cross(ic0, ic1, kmin, kmax)
# If in the first iteration pre-allocate arrays
if ks is None:
ks = x
xpks = numpy.full((nics, nics - 1, ks.size), numpy.nan,
dtype=self.dtype)
xpks[i, k, :] = y
# Bump up the iterator
k += 1
return ks, xpks

5
csiborgtools/read/readsim.py
View File

@ -594,6 +594,11 @@ class ParticleReader:
return out
###############################################################################
# Supplementary reading functions #
###############################################################################
def read_mmain(nsim, srcdir, fname="Mmain_{}.npy"):
"""
Read `mmain` numpy arrays of central halos whose mass contains their