Clean up old functions (#8)

* update nbs

* rm unused functions

* simplify loading

* optionally return counts as well

* update TODO

* update TODO
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Richard Stiskalek 2022-11-06 21:02:24 +00:00 committed by GitHub
parent a0f187d660
commit f0821c54bf
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8 changed files with 7454 additions and 138 deletions

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@ -1,17 +1,15 @@
# CSiBORG tools # CSiBORG tools
## :scroll: Short-term TODO ## :scroll: Short-term TODO
- [ ] Make a nice plot comparing the SZ clusters and their number density - [ ] Add half-mass radius and its corresponding concentration.
- [x] Compare empirical $M_{500c}$ to the NFW expectation. - [ ] Model the Planck SZ selection function.
- [ ] Calculate catalogues for all realisations. - [ ] Calculate catalogues for all realisations.
- [x] Add shortcut function for loading a catalogue
## :hourglass: Long-term TODO ## :hourglass: Long-term TODO
- [ ] Calculate the halo spin - [ ] Calculate the halo spin.
- [ ] Model the Planck SZ selection function.
- [ ] Calculate the cross-correlation in CSiBORG. Should see the scale of the constraints? - [ ] Calculate the cross-correlation in CSiBORG. Should see the scale of the constraints?
- [ ] Improve file naming system - [ ] Calculate DM environmental properties.
## :bulb: Open questions ## :bulb: Open questions

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@ -51,7 +51,7 @@ def binned_counts(x, bins):
return centres, out return centres, out
def number_density(data, feat, bins, max_dist, to_log10): def number_density(data, feat, bins, max_dist, to_log10, return_counts=False):
""" """
Calculate volume-limited number density of a feature `feat` from array Calculate volume-limited number density of a feature `feat` from array
`data`, normalised also by the bin width. `data`, normalised also by the bin width.
@ -70,6 +70,9 @@ def number_density(data, feat, bins, max_dist, to_log10):
to_log10 : bool to_log10 : bool
Whether to take a logarithm of base 10 of the feature. If so, then the Whether to take a logarithm of base 10 of the feature. If so, then the
bins must also be logarithmic. bins must also be logarithmic.
return_counts : bool, optional
Whether to also return number counts in each bin. By default `False`.
Returns Returns
------- -------
@ -80,6 +83,9 @@ def number_density(data, feat, bins, max_dist, to_log10):
Number density of shape `(n_edges - 1, )`. Number density of shape `(n_edges - 1, )`.
nd_err : 1-dimensional array nd_err : 1-dimensional array
Poissonian uncertainty of `nd` of shape `(n_edges - 1, )`. Poissonian uncertainty of `nd` of shape `(n_edges - 1, )`.
counts: 1-dimensional array, optional
Counts in each bin of shape `(n_edges - 1, )`. Returned only if
`return_counts`.
""" """
# Extract the param and optionally convert to log10 # Extract the param and optionally convert to log10
x = data[feat] x = data[feat]
@ -104,4 +110,8 @@ def number_density(data, feat, bins, max_dist, to_log10):
# Convert bins to linear space if log10 # Convert bins to linear space if log10
if to_log10: if to_log10:
bin_centres = 10**bin_centres bin_centres = 10**bin_centres
return bin_centres, nd, nd_err
out = (bin_centres, nd, nd_err)
if return_counts:
out += counts
return out

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@ -13,6 +13,5 @@
# with this program; if not, write to the Free Software Foundation, Inc., # with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
from .transforms import (cartesian_to_radec, convert_mass_cols, # noqa from .transforms import cartesian_to_radec # noqa
convert_position_cols) # noqa
from .box_units import (BoxUnits, convert_from_boxunits) # noqa from .box_units import (BoxUnits, convert_from_boxunits) # noqa

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@ -16,15 +16,9 @@
Various coordinate transformations. Various coordinate transformations.
""" """
import numpy import numpy
little_h = 0.705
BOXSIZE = 677.7 / little_h # Mpc. Otherwise positions in [0, 1].
BOXMASS = 3.749e19 # Msun
def cartesian_to_radec(arr, xpar="peak_x", ypar="peak_y", zpar="peak_z"): def cartesian_to_radec(arr, xpar="peak_x", ypar="peak_y", zpar="peak_z"):
r""" r"""
Extract `x`, `y`, and `z` coordinates from a record array `arr` and Extract `x`, `y`, and `z` coordinates from a record array `arr` and
@ -61,50 +55,3 @@ def cartesian_to_radec(arr, xpar="peak_x", ypar="peak_y", zpar="peak_z"):
ra[ra < 0] += 360 ra[ra < 0] += 360
return dist, ra, dec return dist, ra, dec
def convert_mass_cols(arr, cols):
r"""
Convert mass columns from box units to :math:`M_{\odot}`. `arr` is passed
by reference and is not explicitly returned back.
Parameters
----------
arr : structured array
The array whose columns are to be converted.
cols : str or list of str
The mass columns to be converted.
Returns
-------
None
"""
cols = [cols] if isinstance(cols, str) else cols
for col in cols:
arr[col] *= BOXMASS
def convert_position_cols(arr, cols, zero_centered=True):
r"""
Convert position columns from box units to :math:`\mathrm{Mpc}`. `arr` is
passed by reference and is not explicitly returned back.
Parameters
----------
arr : structured array
The array whose columns are to be converted.
cols : str or list of str
The mass columns to be converted.
zero_centered : bool, optional
Whether to translate the well-resolved origin in the centre of the
simulation to the :math:`(0, 0 , 0)` point. By default `True`.
Returns
-------
None
"""
cols = [cols] if isinstance(cols, str) else cols
for col in cols:
arr[col] *= BOXSIZE
if zero_centered:
arr[col] -= BOXSIZE / 2

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@ -19,7 +19,6 @@ Notebook utility functions.
import numpy import numpy
from os.path import join from os.path import join
from tqdm import trange
from astropy.cosmology import FlatLambdaCDM from astropy.cosmology import FlatLambdaCDM
try: try:
@ -33,40 +32,6 @@ Nsplits = 200
dumpdir = "/mnt/extraspace/rstiskalek/csiborg/" dumpdir = "/mnt/extraspace/rstiskalek/csiborg/"
def load_mmain_convert(n):
srcdir = "/users/hdesmond/Mmain"
arr = csiborgtools.io.read_mmain(n, srcdir)
csiborgtools.utils.convert_mass_cols(arr, "mass_cl")
csiborgtools.utils.convert_position_cols(
arr, ["peak_x", "peak_y", "peak_z"])
csiborgtools.utils.flip_cols(arr, "peak_x", "peak_z")
d, ra, dec = csiborgtools.utils.cartesian_to_radec(arr)
arr = csiborgtools.utils.add_columns(
arr, [d, ra, dec], ["dist", "ra", "dec"])
return arr
def load_mmains(N=None, verbose=True):
ids = csiborgtools.io.get_csiborg_ids("/mnt/extraspace/hdesmond")
N = ids.size if N is None else N
if N > ids.size:
raise ValueError("`N` cannot be larger than 101.")
# If N less than num of CSiBORG, then radomly choose
if N == ids.size:
choices = numpy.arange(N)
else:
choices = numpy.random.choice(ids.size, N, replace=False)
out = [None] * N
iters = trange(N) if verbose else range(N)
for i in iters:
j = choices[i]
out[i] = load_mmain_convert(ids[j])
return out
def load_processed(Nsim, Nsnap): def load_processed(Nsim, Nsnap):
simpath = csiborgtools.io.get_sim_path(Nsim) simpath = csiborgtools.io.get_sim_path(Nsim)
outfname = join( outfname = join(
@ -76,6 +41,7 @@ def load_processed(Nsim, Nsnap):
# Add mmain # Add mmain
mmain = csiborgtools.io.read_mmain(Nsim, "/mnt/zfsusers/hdesmond/Mmain") mmain = csiborgtools.io.read_mmain(Nsim, "/mnt/zfsusers/hdesmond/Mmain")
data = csiborgtools.io.merge_mmain_to_clumps(data, mmain) data = csiborgtools.io.merge_mmain_to_clumps(data, mmain)
csiborgtools.utils.flip_cols(data, "peak_x", "peak_z")
# Cut on numbre of particles and finite m200 # Cut on numbre of particles and finite m200
data = data[(data["npart"] > 100) & numpy.isfinite(data["m200"])] data = data[(data["npart"] > 100) & numpy.isfinite(data["m200"])]
@ -85,6 +51,10 @@ def load_processed(Nsim, Nsnap):
"r200", "r500", "Rs", "rho0", "peak_x", "peak_y", "peak_z"] "r200", "r500", "Rs", "rho0", "peak_x", "peak_y", "peak_z"]
data = csiborgtools.units.convert_from_boxunits( data = csiborgtools.units.convert_from_boxunits(
data, convert_cols, boxunits) data, convert_cols, boxunits)
# Now calculate spherical coordinates
d, ra, dec = csiborgtools.units.cartesian_to_radec(data)
data = csiborgtools.utils.add_columns(
data, [d, ra, dec], ["dist", "ra", "dec"])
return data return data