mirror of
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Add pynbody and other support (#92)
* Simplify box units * Move old scripts * Add printing * Update readers * Disable boundscheck * Add new ordering * Clean up imports * Enforce dtype and add mass to quijote * Simplify print statements * Fix little typos * Fix key bug * Bug fixing * Delete boring comments * Improve ultimate clumps for PHEW * Delete boring comments * Add basic reading * Remove 0th index HID * Add flipping of X and Z * Updates to halo catalogues * Add ordered caching * Fix flipping * Add new flags * Fix PHEW empty clumps * Stop over-wrriting * Little improvements to angular neighbours * Add catalogue masking * Change if-else statements * Cache only filtered data * Add PHEW cats * Add comments * Sort imports * Get Quijote workign * Docs * Add HMF calculation * Move to old * Fix angular * Add great circle distance * Update imports * Update impotrts * Update docs * Remove unused import * Fix a quick bug * Update compatibility * Rename files * Renaming * Improve compatiblity * Rename snapsht * Fix snapshot bug * Update interface * Finish updating interface * Update all paths * Add old scripts * Add basic halo * Update imports * Improve snapshot processing * Update ordering * Fix how CM positions accessed * Add merger paths * Add imports * Add merger reading * Add making a merger tree * Add a basic merger tree reader * Add imports * Add main branch walking + comments + debuggin * Get tree running * Add working merger tree walking along main branch * Add units conversion for merger data * Add hid_to_array_index * Update merger tree * Add mergertree mass to PHEWcat * Edit comments * Add this to track changes... * Fix a little bug * Add mergertree mass * Add cache clearing * Improve summing substructure code * Littbe bug * Little updates to the merger tree reader * Update .giignore * Add box selection * Add optional deletingf of a group * add to keep track of changes * Update changes * Remove * Add manual tracker * Fix bug * Add m200c_to_r200c * Add manual halo tracking * Remove skipped snapshots * update cosmo params to match csiborg * remove old comments * Add SDSSxALFALFA * Fix bugs * Rename * Edit paths * Updates * Add comments * Add comment * Add hour conversion * Add imports * Add new observation class * Add selection * Add imports * Fix small bug * Add field copying for safety * Add matching to survey without masking * Add P(k) calculation * Add nb * Edit comment * Move files * Remove merger import * Edit setup.yp * Fix typo * Edit import warnigns * update nb * Update README * Update README * Update README * Add skeleton * Add skeleton
This commit is contained in:
Richard Stiskalek
GitHub
parent
5500fbd2b9
commit
e972f8e3f2
53 changed files with 4627 additions and 1774 deletions
2
.gitignore
vendored
2
.gitignore
vendored
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@ -24,3 +24,5 @@ scripts_plots/submit.sh
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scripts_plots/*.out
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scripts_plots/*.sh
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notebooks/test.ipynb
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scripts/mgtree.py
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scripts/makemerger.py
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21
README.md
21
README.md
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@ -1,3 +1,22 @@
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# CSiBORG Tools
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A compendium of tools for analysing the suite of Constrained Simulations in BORG (CSiBORG) simulations.
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Tools for analysing the suite of Constrained Simulations in BORG (CSiBORG) simulations. The interface is designed to work with the following suites of simulations:
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- CSiBORG1 dark matter-only RAMSES simulations (full support),
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- CSiBORG2 dark matter-only Gadget4 simulations (planned full support),
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- Quijote dark matter-only Gadget2 simulations (partial support),
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however with little effort it can support other simulations as well.
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## TODO
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- [ ] Add full support for CSiBORG2 suite of simulations.
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- [ ] Add SPH field calculation from cosmotools.
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## Adding a new simulation suite
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box units
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paths
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readsim
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halo_cat
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@ -12,12 +12,12 @@
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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from csiborgtools import clustering, field, match, read, summary # noqa
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from .utils import (center_of_mass, delta2ncells, number_counts, # noqa
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periodic_distance, periodic_distance_two_points, # noqa
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binned_statistic, cosine_similarity) # noqa
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from csiborgtools import clustering, field, halo, match, read, summary # noqa
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from .utils import (center_of_mass, delta2ncells, number_counts, # noqa
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periodic_distance, periodic_distance_two_points, # noqa
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binned_statistic, cosine_similarity, fprint, # noqa
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hms_to_degrees, dms_to_degrees, great_circle_distance) # noqa
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# Arguments to csiborgtools.read.Paths.
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paths_glamdring = {"srcdir": "/mnt/extraspace/hdesmond/",
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@ -46,5 +46,34 @@ class SDSS:
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(lambda x: cls[x] < 155, ("DIST", ))
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]
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def __call__(self):
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return read.SDSS(h=1, sel_steps=self.steps)
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def __call__(self, fpath=None, apply_selection=True):
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if fpath is None:
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fpath = "/mnt/extraspace/rstiskalek/catalogs/nsa_v1_0_1.fits"
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sel_steps = self.steps if apply_selection else None
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return read.SDSS(fpath, h=1, sel_steps=sel_steps)
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class SDSSxALFALFA:
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@staticmethod
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def steps(cls):
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return [(lambda x: cls[x], ("IN_DR7_LSS",)),
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(lambda x: cls[x] < 17.6, ("ELPETRO_APPMAG_r", )),
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(lambda x: cls[x] < 155, ("DIST", ))
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]
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def __call__(self, fpath=None, apply_selection=True):
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if fpath is None:
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fpath = "/mnt/extraspace/rstiskalek/catalogs/5asfullmatch.fits"
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sel_steps = self.steps if apply_selection else None
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return read.SDSS(fpath, h=1, sel_steps=sel_steps)
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###############################################################################
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# Clusters #
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###############################################################################
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clusters = {"Virgo": read.ObservedCluster(RA=hms_to_degrees(12, 27),
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dec=dms_to_degrees(12, 43),
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dist=16.5 * 0.7,
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name="Virgo"),
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}
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@ -15,12 +15,11 @@
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from warnings import warn
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try:
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import MAS_library as MASL # noqa
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from .density import (DensityField, PotentialField, # noqa
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TidalTensorField, VelocityField)
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from .interp import (evaluate_cartesian, evaluate_sky, field2rsp, # noqa
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fill_outside, make_sky, observer_vobs)
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from .utils import nside2radec, smoothen_field # noqa
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import MAS_library as MASL # noqa
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from .density import (DensityField, PotentialField, TidalTensorField, # noqa
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VelocityField, power_spectrum) # noqa
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from .interp import (evaluate_cartesian, evaluate_sky, field2rsp, # noqa
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fill_outside, make_sky, observer_vobs) # noqa
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from .utils import nside2radec, smoothen_field # noqa
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except ImportError:
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warn("MAS_library not found, `DensityField` will not be available", UserWarning) # noqa
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warn("MAS_library not found, `DensityField` and related Pylians-based routines will not be available") # noqa
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@ -18,6 +18,7 @@ Density field and cross-correlation calculations.
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from abc import ABC
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import MAS_library as MASL
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import Pk_library as PKL
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import numpy
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from numba import jit
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from tqdm import trange
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@ -33,13 +34,7 @@ class BaseField(ABC):
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@property
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def box(self):
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"""
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Simulation box information and transformations.
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Returns
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-------
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:py:class:`csiborgtools.units.CSiBORGBox`
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"""
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"""Simulation box information and transformations."""
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return self._box
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@box.setter
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@ -52,13 +47,7 @@ class BaseField(ABC):
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@property
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def MAS(self):
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"""
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Mass-assignment scheme.
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Returns
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-------
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str
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"""
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"""Mass-assignment scheme."""
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if self._MAS is None:
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raise ValueError("`MAS` is not set.")
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return self._MAS
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@ -103,7 +92,6 @@ class DensityField(BaseField):
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Calculate the overdensity field from the density field.
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Defined as :math:`\rho/ <\rho> - 1`. Overwrites the input array.
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Parameters
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----------
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delta : 3-dimensional array of shape `(grid, grid, grid)`
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@ -117,7 +105,7 @@ class DensityField(BaseField):
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delta -= 1
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return delta
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def __call__(self, parts, grid, flip_xz=True, nbatch=30, verbose=True):
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def __call__(self, pos, mass, grid, nbatch=30, verbose=True):
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"""
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Calculate the density field using a Pylians routine [1, 2].
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Iteratively loads the particles into memory, flips their `x` and `z`
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@ -126,13 +114,12 @@ class DensityField(BaseField):
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Parameters
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----------
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parts : 2-dimensional array of shape `(n_parts, 7)`
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Particle positions, velocities and masses.
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Columns are: `x`, `y`, `z`, `vx`, `vy`, `vz`, `M`.
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pos : 2-dimensional array of shape `(n_parts, 3)`
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Particle positions
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mass : 1-dimensional array of shape `(n_parts,)`
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Particle masses
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grid : int
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Grid size.
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flip_xz : bool, optional
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Whether to flip the `x` and `z` coordinates.
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nbatch : int, optional
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Number of batches to split the particle loading into.
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verbose : bool, optional
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@ -150,24 +137,20 @@ class DensityField(BaseField):
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"""
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rho = numpy.zeros((grid, grid, grid), dtype=numpy.float32)
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nparts = parts.shape[0]
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nparts = pos.shape[0]
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batch_size = nparts // nbatch
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start = 0
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for __ in trange(nbatch + 1, disable=not verbose,
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desc="Loading particles for the density field"):
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end = min(start + batch_size, nparts)
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pos = parts[start:end]
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pos, vel, mass = pos[:, :3], pos[:, 3:6], pos[:, 6]
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batch_pos = pos[start:end]
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batch_mass = mass[start:end]
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pos = force_single_precision(pos)
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vel = force_single_precision(vel)
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mass = force_single_precision(mass)
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if flip_xz:
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pos[:, [0, 2]] = pos[:, [2, 0]]
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vel[:, [0, 2]] = vel[:, [2, 0]]
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batch_pos = force_single_precision(batch_pos)
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batch_mass = force_single_precision(batch_mass)
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MASL.MA(pos, rho, 1., self.MAS, W=mass, verbose=False)
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MASL.MA(batch_pos, rho, 1., self.MAS, W=batch_mass, verbose=False)
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if end == nparts:
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break
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start = end
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@ -178,8 +161,105 @@ class DensityField(BaseField):
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return rho
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# class SPHDensityVelocity(BaseField):
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# r"""
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# Density field calculation. Based primarily on routines of Pylians [1].
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#
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# Parameters
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# ----------
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# box : :py:class:`csiborgtools.read.CSiBORGBox`
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# The simulation box information and transformations.
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# MAS : str
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# Mass assignment scheme. Options are Options are: 'NGP' (nearest grid
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# point), 'CIC' (cloud-in-cell), 'TSC' (triangular-shape cloud), 'PCS'
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# (piecewise cubic spline).
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# paths : :py:class:`csiborgtools.read.Paths`
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# The simulation paths.
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#
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# References
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# ----------
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# [1] https://pylians3.readthedocs.io/
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# """
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#
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# def __init__(self, box, MAS):
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# self.box = box
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# self.MAS = MAS
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#
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# def overdensity_field(self, delta):
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# r"""
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# Calculate the overdensity field from the density field.
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# Defined as :math:`\rho/ <\rho> - 1`. Overwrites the input array.
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#
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# Parameters
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# ----------
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# delta : 3-dimensional array of shape `(grid, grid, grid)`
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# The density field.
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#
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# Returns
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# -------
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# 3-dimensional array of shape `(grid, grid, grid)`.
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# """
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# delta /= delta.mean()
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# delta -= 1
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# return delta
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#
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# def __call__(self, pos, mass, grid, nbatch=30, verbose=True):
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# """
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# Calculate the density field using a Pylians routine [1, 2].
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# Iteratively loads the particles into memory, flips their `x` and `z`
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# coordinates. Particles are assumed to be in box units, with positions
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# in [0, 1]
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#
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# Parameters
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# ----------
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# pos : 2-dimensional array of shape `(n_parts, 3)`
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# Particle positions
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# mass : 1-dimensional array of shape `(n_parts,)`
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# Particle masses
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# grid : int
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# Grid size.
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# nbatch : int, optional
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# Number of batches to split the particle loading into.
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# verbose : bool, optional
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# Verbosity flag.
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#
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# Returns
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# -------
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# 3-dimensional array of shape `(grid, grid, grid)`.
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#
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# References
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# ----------
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# [1] https://pylians3.readthedocs.io/
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# [2] https://github.com/franciscovillaescusa/Pylians3/blob/master
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# /library/MAS_library/MAS_library.pyx
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# """
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# rho = numpy.zeros((grid, grid, grid), dtype=numpy.float32)
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#
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# nparts = pos.shape[0]
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# batch_size = nparts // nbatch
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# start = 0
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#
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# for __ in trange(nbatch + 1, disable=not verbose,
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# desc="Loading particles for the density field"):
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# end = min(start + batch_size, nparts)
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# batch_pos = pos[start:end]
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# batch_mass = mass[start:end]
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#
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# batch_pos = force_single_precision(batch_pos)
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# batch_mass = force_single_precision(batch_mass)
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#
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# MASL.MA(batch_pos, rho, 1., self.MAS, W=batch_mass, verbose=False)
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# if end == nparts:
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# break
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# start = end
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#
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# # Divide by the cell volume in (kpc / h)^3
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# rho /= (self.box.boxsize / grid * 1e3)**3
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#
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# return rho
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###############################################################################
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# Density field calculation #
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# Velocity field calculation #
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###############################################################################
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@ -242,7 +322,7 @@ class VelocityField(BaseField):
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/ numpy.sqrt(px**2 + py**2 + pz**2))
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return radvel
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def __call__(self, parts, grid, flip_xz=True, nbatch=30,
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def __call__(self, pos, vel, mass, grid, flip_xz=True, nbatch=30,
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verbose=True):
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"""
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Calculate the velocity field using a Pylians routine [1, 2].
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@ -251,9 +331,12 @@ class VelocityField(BaseField):
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Parameters
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----------
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parts : 2-dimensional array of shape `(n_parts, 7)`
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Particle positions, velocities and masses.
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Columns are: `x`, `y`, `z`, `vx`, `vy`, `vz`, `M`.
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pos : 2-dimensional array of shape `(n_parts, 3)`
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Particle positions.
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vel : 2-dimensional array of shape `(n_parts, 3)`
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Particle velocities.
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mass : 1-dimensional array of shape `(n_parts,)`
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Particle masses.
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grid : int
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Grid size.
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flip_xz : bool, optional
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|
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@ -273,26 +356,26 @@ class VelocityField(BaseField):
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[2] https://github.com/franciscovillaescusa/Pylians3/blob/master
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/library/MAS_library/MAS_library.pyx
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"""
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rho_velx = numpy.zeros((grid, grid, grid), dtype=numpy.float32)
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rho_vely = numpy.zeros((grid, grid, grid), dtype=numpy.float32)
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rho_velz = numpy.zeros((grid, grid, grid), dtype=numpy.float32)
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rho_vel = [rho_velx, rho_vely, rho_velz]
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rho_vel = [numpy.zeros((grid, grid, grid), dtype=numpy.float32),
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numpy.zeros((grid, grid, grid), dtype=numpy.float32),
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numpy.zeros((grid, grid, grid), dtype=numpy.float32),
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]
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cellcounts = numpy.zeros((grid, grid, grid), dtype=numpy.float32)
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nparts = parts.shape[0]
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nparts = pos.shape[0]
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batch_size = nparts // nbatch
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start = 0
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for __ in trange(nbatch + 1) if verbose else range(nbatch + 1):
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end = min(start + batch_size, nparts)
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pos = parts[start:end]
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pos, vel, mass = pos[:, :3], pos[:, 3:6], pos[:, 6]
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pos = force_single_precision(pos)
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vel = force_single_precision(vel)
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mass = force_single_precision(mass)
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if flip_xz:
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pos[:, [0, 2]] = pos[:, [2, 0]]
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vel[:, [0, 2]] = vel[:, [2, 0]]
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batch_pos = pos[start:end]
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batch_vel = vel[start:end]
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batch_mass = mass[start:end]
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batch_pos = force_single_precision(batch_pos)
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batch_vel = force_single_precision(batch_vel)
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batch_mass = force_single_precision(batch_mass)
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vel *= mass.reshape(-1, 1)
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for i in range(3):
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@ -308,7 +391,7 @@ class VelocityField(BaseField):
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for i in range(3):
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divide_nonzero(rho_vel[i], cellcounts)
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|
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return numpy.stack([rho_velx, rho_vely, rho_velz])
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return numpy.stack(rho_vel)
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|
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###############################################################################
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|
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@ -505,3 +588,35 @@ def eigenvalues_to_environment(eigvals, th):
|
|||
else:
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env[i, j, k] = 3
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return env
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Power spectrum calculation #
|
||||
###############################################################################
|
||||
|
||||
|
||||
def power_spectrum(delta, boxsize, MAS, threads=1, verbose=True):
|
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"""
|
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Calculate the monopole power spectrum of the density field.
|
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|
||||
Parameters
|
||||
----------
|
||||
delta : 3-dimensional array of shape `(grid, grid, grid)`
|
||||
The over-density field.
|
||||
boxsize : float
|
||||
The simulation box size in `Mpc / h`.
|
||||
MAS : str
|
||||
Mass assignment scheme used to calculate the density field.
|
||||
threads : int, optional
|
||||
Number of threads to use.
|
||||
verbose : bool, optional
|
||||
Verbosity flag.
|
||||
|
||||
Returns
|
||||
-------
|
||||
k, Pk : 1-dimensional arrays of shape `(grid,)`
|
||||
The wavenumbers and the power spectrum.
|
||||
"""
|
||||
axis = 2 # Axis along which compute the quadrupole and hexadecapole
|
||||
Pk = PKL.Pk(delta, boxsize, axis, MAS, threads, verbose)
|
||||
return Pk.k3D, Pk.Pk[:, 0]
|
||||
|
|
|
|||
|
|
@ -98,9 +98,12 @@ def evaluate_sky(*fields, pos, mpc2box, smooth_scales=None, verbose=False):
|
|||
-------
|
||||
(list of) 1-dimensional array of shape `(n_samples, len(smooth_scales))`
|
||||
"""
|
||||
pos = force_single_precision(pos)
|
||||
# Make a copy of the positions to avoid modifying the input.
|
||||
pos = numpy.copy(pos)
|
||||
|
||||
pos = force_single_precision(pos)
|
||||
pos[:, 0] *= mpc2box
|
||||
|
||||
cart_pos = radec_to_cartesian(pos) + 0.5
|
||||
|
||||
if smooth_scales is not None:
|
||||
|
|
|
|||
16
csiborgtools/halo/__init__.py
Normal file
16
csiborgtools/halo/__init__.py
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
# 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.
|
||||
|
||||
from .prop import density_profile # noqa
|
||||
46
csiborgtools/halo/prop.py
Normal file
46
csiborgtools/halo/prop.py
Normal file
|
|
@ -0,0 +1,46 @@
|
|||
# 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.
|
||||
|
||||
import numpy
|
||||
from scipy.stats import binned_statistic
|
||||
|
||||
from ..utils import periodic_distance
|
||||
|
||||
|
||||
def density_profile(pos, mass, center, nbins, boxsize):
|
||||
"""
|
||||
Calculate a density profile.
|
||||
"""
|
||||
raise NotImplementedError("Not implemented yet..")
|
||||
|
||||
rdist = periodic_distance(pos, center, boxsize)
|
||||
rmin, rmax = numpy.min(rdist), numpy.max(rdist)
|
||||
|
||||
bin_edges = numpy.logspace(numpy.log10(rmin), numpy.log10(rmax), nbins)
|
||||
|
||||
|
||||
rho, __, __ = binned_statistic(rdist, mass, statistic='sum',
|
||||
bins=bin_edges)
|
||||
|
||||
rho /= 4. / 3 * numpy.pi * (bin_edges[1:]**3 - bin_edges[:-1]**3)
|
||||
|
||||
print(bin_edges)
|
||||
|
||||
r = 0.5 * (bin_edges[1:] + bin_edges[:-1])
|
||||
|
||||
# r = numpy.sqrt(bin_edges[:1] * bin_edges[:-1])
|
||||
|
||||
return r, rho
|
||||
|
||||
|
|
@ -12,7 +12,5 @@
|
|||
# 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.
|
||||
from .match import (ParticleOverlap, RealisationsMatcher, # noqa
|
||||
calculate_overlap, calculate_overlap_indxs, pos2cell, # noqa
|
||||
find_neighbour, get_halo_cell_limits, # noqa
|
||||
matching_max) # noqa
|
||||
from .match import (ParticleOverlap, RealisationsMatcher, calculate_overlap, # noqa
|
||||
pos2cell, find_neighbour, matching_max) # noqa
|
||||
|
|
|
|||
|
|
@ -13,7 +13,8 @@
|
|||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
"""
|
||||
Support for matching halos between CSiBORG IC realisations.
|
||||
Support for matching halos between CSiBORG IC realisations based on their
|
||||
Lagrangian patch overlap.
|
||||
"""
|
||||
from abc import ABC
|
||||
from datetime import datetime
|
||||
|
|
@ -21,30 +22,19 @@ from functools import lru_cache
|
|||
from math import ceil
|
||||
|
||||
import numpy
|
||||
from scipy.ndimage import gaussian_filter
|
||||
|
||||
from numba import jit
|
||||
from scipy.ndimage import gaussian_filter
|
||||
from tqdm import tqdm, trange
|
||||
|
||||
from ..read import load_halo_particles
|
||||
|
||||
|
||||
class BaseMatcher(ABC):
|
||||
"""
|
||||
Base class for `RealisationsMatcher` and `ParticleOverlap`.
|
||||
"""
|
||||
"""Base class for `RealisationsMatcher` and `ParticleOverlap`."""
|
||||
_box_size = None
|
||||
_bckg_halfsize = None
|
||||
|
||||
@property
|
||||
def box_size(self):
|
||||
"""
|
||||
Number of cells in the box.
|
||||
|
||||
Returns
|
||||
-------
|
||||
box_size : int
|
||||
"""
|
||||
"""Number of cells in the box."""
|
||||
if self._box_size is None:
|
||||
raise RuntimeError("`box_size` has not been set.")
|
||||
return self._box_size
|
||||
|
|
@ -64,10 +54,6 @@ class BaseMatcher(ABC):
|
|||
grid distance from the center of the box to each side over which to
|
||||
evaluate the background density field. Must be less than or equal to
|
||||
half the box size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
bckg_halfsize : int
|
||||
"""
|
||||
if self._bckg_halfsize is None:
|
||||
raise RuntimeError("`bckg_halfsize` has not been set.")
|
||||
|
|
@ -130,10 +116,6 @@ class RealisationsMatcher(BaseMatcher):
|
|||
"""
|
||||
Multiplier of the sum of the initial Lagrangian patch sizes of a halo
|
||||
pair. Determines the range within which neighbors are returned.
|
||||
|
||||
Returns
|
||||
-------
|
||||
nmult : float
|
||||
"""
|
||||
return self._nmult
|
||||
|
||||
|
|
@ -148,10 +130,6 @@ class RealisationsMatcher(BaseMatcher):
|
|||
"""
|
||||
Tolerance on the absolute logarithmic mass difference of potential
|
||||
matches.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
return self._dlogmass
|
||||
|
||||
|
|
@ -166,10 +144,6 @@ class RealisationsMatcher(BaseMatcher):
|
|||
"""
|
||||
Mass kind whose similarity is to be checked. Must be a valid key in the
|
||||
halo catalogue.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
return self._mass_kind
|
||||
|
||||
|
|
@ -181,17 +155,10 @@ class RealisationsMatcher(BaseMatcher):
|
|||
|
||||
@property
|
||||
def overlapper(self):
|
||||
"""
|
||||
The overlapper object.
|
||||
|
||||
Returns
|
||||
-------
|
||||
:py:class:`csiborgtools.match.ParticleOverlap`
|
||||
"""
|
||||
"""The overlapper object."""
|
||||
return self._overlapper
|
||||
|
||||
def cross(self, cat0, catx, particles0, particlesx, halo_map0, halo_mapx,
|
||||
delta_bckg, cache_size=10000, verbose=True):
|
||||
def cross(self, cat0, catx, delta_bckg, cache_size=10000, verbose=True):
|
||||
r"""
|
||||
Find all neighbours whose CM separation is less than `nmult` times the
|
||||
sum of their initial Lagrangian patch sizes and calculate their
|
||||
|
|
@ -204,16 +171,6 @@ class RealisationsMatcher(BaseMatcher):
|
|||
Halo catalogue of the reference simulation.
|
||||
catx : instance of :py:class:`csiborgtools.read.BaseCatalogue`
|
||||
Halo catalogue of the cross simulation.
|
||||
particles0 : 2-dimensional array
|
||||
Particles archive file of the reference simulation. The columns
|
||||
must be `x`, `y`, `z` and `M`.
|
||||
particlesx : 2-dimensional array
|
||||
Particles archive file of the cross simulation. The columns must be
|
||||
`x`, `y`, `z` and `M`.
|
||||
halo_map0 : 2-dimensional array
|
||||
Halo map of the reference simulation.
|
||||
halo_mapx : 2-dimensional array
|
||||
Halo map of the cross simulation.
|
||||
delta_bckg : 3-dimensional array
|
||||
Summed background density field of the reference and cross
|
||||
simulations calculated with particles assigned to halos at the
|
||||
|
|
@ -250,14 +207,11 @@ class RealisationsMatcher(BaseMatcher):
|
|||
aratio = numpy.abs(numpy.log10(catx[p][indx] / cat0[p][i]))
|
||||
match_indxs[i] = match_indxs[i][aratio < self.dlogmass]
|
||||
|
||||
hid2map0 = {hid: i for i, hid in enumerate(halo_map0[:, 0])}
|
||||
hid2mapx = {hid: i for i, hid in enumerate(halo_mapx[:, 0])}
|
||||
|
||||
# We will cache the halos from the cross simulation to speed up the I/O
|
||||
@lru_cache(maxsize=cache_size)
|
||||
def load_cached_halox(hid):
|
||||
return load_processed_halo(hid, particlesx, halo_mapx, hid2mapx,
|
||||
nshift=0, ncells=self.box_size)
|
||||
return load_processed_halo(hid, catx, nshift=0,
|
||||
ncells=self.box_size)
|
||||
|
||||
iterator = tqdm(
|
||||
cat0["index"],
|
||||
|
|
@ -273,8 +227,7 @@ class RealisationsMatcher(BaseMatcher):
|
|||
# Next, we find this halo's particles, total mass, minimum and
|
||||
# maximum cells and convert positions to cells.
|
||||
pos0, mass0, totmass0, mins0, maxs0 = load_processed_halo(
|
||||
k0, particles0, halo_map0, hid2map0, nshift=0,
|
||||
ncells=self.box_size)
|
||||
k0, cat0, nshift=0, ncells=self.box_size)
|
||||
|
||||
# We now loop over matches of this halo and calculate their
|
||||
# overlap, storing them in `_cross`.
|
||||
|
|
@ -298,9 +251,8 @@ class RealisationsMatcher(BaseMatcher):
|
|||
cross = numpy.asanyarray(cross, dtype=object)
|
||||
return match_indxs, cross
|
||||
|
||||
def smoothed_cross(self, cat0, catx, particles0, particlesx, halo_map0,
|
||||
halo_mapx, delta_bckg, match_indxs, smooth_kwargs,
|
||||
cache_size=10000, verbose=True):
|
||||
def smoothed_cross(self, cat0, catx, delta_bckg, match_indxs,
|
||||
smooth_kwargs, cache_size=10000, verbose=True):
|
||||
r"""
|
||||
Calculate the smoothed overlaps for pairs previously identified via
|
||||
`self.cross(...)` to have a non-zero NGP overlap.
|
||||
|
|
@ -311,16 +263,6 @@ class RealisationsMatcher(BaseMatcher):
|
|||
Halo catalogue of the reference simulation.
|
||||
catx : instance of :py:class:`csiborgtools.read.BaseCatalogue`
|
||||
Halo catalogue of the cross simulation.
|
||||
particles0 : 2-dimensional array
|
||||
Particles archive file of the reference simulation. The columns
|
||||
must be `x`, `y`, `z` and `M`.
|
||||
particlesx : 2-dimensional array
|
||||
Particles archive file of the cross simulation. The columns must be
|
||||
`x`, `y`, `z` and `M`.
|
||||
halo_map0 : 2-dimensional array
|
||||
Halo map of the reference simulation.
|
||||
halo_mapx : 2-dimensional array
|
||||
Halo map of the cross simulation.
|
||||
delta_bckg : 3-dimensional array
|
||||
Smoothed summed background density field of the reference and cross
|
||||
simulations calculated with particles assigned to halos at the
|
||||
|
|
@ -339,13 +281,11 @@ class RealisationsMatcher(BaseMatcher):
|
|||
overlaps : 1-dimensional array of arrays
|
||||
"""
|
||||
nshift = read_nshift(smooth_kwargs)
|
||||
hid2map0 = {hid: i for i, hid in enumerate(halo_map0[:, 0])}
|
||||
hid2mapx = {hid: i for i, hid in enumerate(halo_mapx[:, 0])}
|
||||
|
||||
@lru_cache(maxsize=cache_size)
|
||||
def load_cached_halox(hid):
|
||||
return load_processed_halo(hid, particlesx, halo_mapx, hid2mapx,
|
||||
nshift=nshift, ncells=self.box_size)
|
||||
return load_processed_halo(hid, catx, nshift=nshift,
|
||||
ncells=self.box_size)
|
||||
|
||||
iterator = tqdm(
|
||||
cat0["index"],
|
||||
|
|
@ -355,8 +295,7 @@ class RealisationsMatcher(BaseMatcher):
|
|||
cross = [numpy.asanyarray([], dtype=numpy.float32)] * match_indxs.size
|
||||
for i, k0 in enumerate(iterator):
|
||||
pos0, mass0, __, mins0, maxs0 = load_processed_halo(
|
||||
k0, particles0, halo_map0, hid2map0, nshift=nshift,
|
||||
ncells=self.box_size)
|
||||
k0, cat0, nshift=nshift, ncells=self.box_size)
|
||||
|
||||
# Now loop over the matches and calculate the smoothed overlap.
|
||||
_cross = numpy.full(match_indxs[i].size, numpy.nan, numpy.float32)
|
||||
|
|
@ -396,8 +335,7 @@ class ParticleOverlap(BaseMatcher):
|
|||
self.box_size = box_size
|
||||
self.bckg_halfsize = bckg_halfsize
|
||||
|
||||
def make_bckg_delta(self, particles, halo_map, hid2map, halo_cat,
|
||||
delta=None, verbose=False):
|
||||
def make_bckg_delta(self, cat, delta=None, verbose=False):
|
||||
"""
|
||||
Calculate a NGP density field of particles belonging to halos of a
|
||||
halo catalogue `halo_cat`. Particles are only counted within the
|
||||
|
|
@ -406,15 +344,8 @@ class ParticleOverlap(BaseMatcher):
|
|||
|
||||
Parameters
|
||||
----------
|
||||
particles : 2-dimensional array
|
||||
Particles archive file. The columns must be `x`, `y`, `z` and `M`.
|
||||
halo_map : 2-dimensional array
|
||||
Array containing start and end indices in the particle array
|
||||
corresponding to each halo.
|
||||
hid2map : dict
|
||||
Dictionary mapping halo IDs to `halo_map` array positions.
|
||||
halo_cat : instance of :py:class:`csiborgtools.read.BaseCatalogue`
|
||||
Halo catalogue.
|
||||
cat : instance of :py:class:`csiborgtools.read.BaseCatalogue`
|
||||
Halo catalogue of the reference simulation.
|
||||
delta : 3-dimensional array, optional
|
||||
Array to store the density field. If `None` a new array is
|
||||
created.
|
||||
|
|
@ -436,16 +367,17 @@ class ParticleOverlap(BaseMatcher):
|
|||
& (delta.dtype == numpy.float32))
|
||||
|
||||
iterator = tqdm(
|
||||
halo_cat["index"],
|
||||
cat["index"],
|
||||
desc=f"{datetime.now()} Calculating the background field",
|
||||
disable=not verbose
|
||||
)
|
||||
for hid in iterator:
|
||||
pos = load_halo_particles(hid, particles, halo_map, hid2map)
|
||||
pos = cat.halo_particles(hid, "pos", in_initial=True)
|
||||
if pos is None:
|
||||
continue
|
||||
|
||||
pos, mass = pos[:, :3], pos[:, 3]
|
||||
mass = cat.halo_particles(hid, "mass", in_initial=True)
|
||||
|
||||
pos = pos2cell(pos, self.box_size)
|
||||
|
||||
# We mask out particles outside the cubical high-resolution region
|
||||
|
|
@ -874,7 +806,7 @@ def calculate_overlap_indxs(delta1, delta2, cellmins, delta_bckg, nonzero,
|
|||
return intersect / (mass1 + mass2 - intersect)
|
||||
|
||||
|
||||
def load_processed_halo(hid, particles, halo_map, hid2map, ncells, nshift):
|
||||
def load_processed_halo(hid, cat, ncells, nshift):
|
||||
"""
|
||||
Load a processed halo from the `.h5` file. This is to be wrapped by a
|
||||
cacher.
|
||||
|
|
@ -883,14 +815,8 @@ def load_processed_halo(hid, particles, halo_map, hid2map, ncells, nshift):
|
|||
----------
|
||||
hid : int
|
||||
Halo ID.
|
||||
particles : 2-dimensional array
|
||||
Array of particles in box units. The columns must be `x`, `y`, `z`
|
||||
and `M`.
|
||||
halo_map : 2-dimensional array
|
||||
Array containing start and end indices in the particle array
|
||||
corresponding to each halo.
|
||||
hid2map : dict
|
||||
Dictionary mapping halo IDs to `halo_map` array positions.
|
||||
cat : instance of :py:class:`csiborgtools.read.BaseCatalogue`
|
||||
Halo catalogue.
|
||||
ncells : int
|
||||
Number of cells in the box density field.
|
||||
nshift : int
|
||||
|
|
@ -909,8 +835,8 @@ def load_processed_halo(hid, particles, halo_map, hid2map, ncells, nshift):
|
|||
maxs : len-3 tuple
|
||||
Maximum cell indices of the halo.
|
||||
"""
|
||||
pos = load_halo_particles(hid, particles, halo_map, hid2map)
|
||||
pos, mass = pos[:, :3], pos[:, 3]
|
||||
pos = cat.halo_particles(hid, "pos", in_initial=True)
|
||||
mass = cat.halo_particles(hid, "mass", in_initial=True)
|
||||
|
||||
pos = pos2cell(pos, ncells)
|
||||
mins, maxs = get_halo_cell_limits(pos, ncells=ncells, nshift=nshift)
|
||||
|
|
|
|||
|
|
@ -12,9 +12,12 @@
|
|||
# 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.
|
||||
from .box_units import CSiBORGBox, QuijoteBox # noqa
|
||||
from .halo_cat import CSiBORGHaloCatalogue, QuijoteHaloCatalogue, fiducial_observers # noqa
|
||||
from .obs import SDSS, MCXCClusters, PlanckClusters, TwoMPPGalaxies, TwoMPPGroups # noqa
|
||||
from .paths import Paths # noqa
|
||||
from .readsim import MmainReader, CSiBORGReader, QuijoteReader, halfwidth_mask, load_halo_particles # noqa
|
||||
from .utils import cols_to_structured, read_h5 # noqa
|
||||
from .box_units import CSiBORGBox, QuijoteBox # noqa
|
||||
from .halo_cat import (CSiBORGCatalogue, QuijoteCatalogue, # noqa
|
||||
CSiBORGPHEWCatalogue, fiducial_observers) # noqa
|
||||
from .obs import (SDSS, MCXCClusters, PlanckClusters, TwoMPPGalaxies, # noqa
|
||||
TwoMPPGroups, ObservedCluster, match_array_to_no_masking) # noqa
|
||||
from .paths import Paths # noqa
|
||||
from .readsim import (CSiBORGReader, QuijoteReader, load_halo_particles, # noqa
|
||||
make_halomap_dict) # noqa
|
||||
from .utils import cols_to_structured, read_h5 # noqa
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ Simulation box unit transformations.
|
|||
"""
|
||||
from abc import ABC, abstractmethod, abstractproperty
|
||||
|
||||
import numpy
|
||||
from astropy import constants, units
|
||||
from astropy.cosmology import LambdaCDM
|
||||
|
||||
|
|
@ -28,80 +29,39 @@ from .readsim import CSiBORGReader, QuijoteReader
|
|||
|
||||
|
||||
class BaseBox(ABC):
|
||||
"""
|
||||
Base class for box units.
|
||||
"""
|
||||
_name = "box_units"
|
||||
_cosmo = None
|
||||
|
||||
@property
|
||||
def cosmo(self):
|
||||
"""
|
||||
The box cosmology.
|
||||
|
||||
Returns
|
||||
-------
|
||||
cosmo : `astropy.cosmology.LambdaCDM`
|
||||
"""
|
||||
if self._cosmo is None:
|
||||
raise ValueError("Cosmology not set.")
|
||||
return self._cosmo
|
||||
|
||||
@property
|
||||
def H0(self):
|
||||
r"""
|
||||
The Hubble parameter at the time of the snapshot in units of
|
||||
:math:`\mathrm{km} \mathrm{s}^{-1} \mathrm{Mpc}^{-1}`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
H0 : float
|
||||
"""
|
||||
r"""Present Hubble parameter in :math:`\mathrm{km} \mathrm{s}^{-1}`"""
|
||||
return self.cosmo.H0.value
|
||||
|
||||
@property
|
||||
def rho_crit0(self):
|
||||
r"""
|
||||
Present-day critical density in :math:`M_\odot h^2 / \mathrm{cMpc}^3`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
rho_crit0 : float
|
||||
"""
|
||||
"""Present-day critical density in M_sun h^2 / cMpc^3."""
|
||||
rho_crit0 = self.cosmo.critical_density0
|
||||
return rho_crit0.to_value(units.solMass / units.Mpc**3)
|
||||
|
||||
@property
|
||||
def h(self):
|
||||
r"""
|
||||
The little 'h' parameter at the time of the snapshot.
|
||||
|
||||
Returns
|
||||
-------
|
||||
h : float
|
||||
"""
|
||||
"""The little 'h' parameter at the time of the snapshot."""
|
||||
return self._h
|
||||
|
||||
@property
|
||||
def Om0(self):
|
||||
r"""
|
||||
The matter density parameter.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Om0 : float
|
||||
"""
|
||||
"""The present time matter density parameter."""
|
||||
return self.cosmo.Om0
|
||||
|
||||
@abstractproperty
|
||||
def boxsize(self):
|
||||
"""
|
||||
Box size in cMpc.
|
||||
|
||||
Returns
|
||||
-------
|
||||
boxsize : float
|
||||
"""
|
||||
"""Box size in cMpc."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
|
|
@ -116,8 +76,7 @@ class BaseBox(ABC):
|
|||
|
||||
Returns
|
||||
-------
|
||||
length : float
|
||||
Length in box units.
|
||||
float
|
||||
"""
|
||||
pass
|
||||
|
||||
|
|
@ -133,8 +92,7 @@ class BaseBox(ABC):
|
|||
|
||||
Returns
|
||||
-------
|
||||
length : float
|
||||
Length in :math:`\mathrm{cMpc} / h`
|
||||
float
|
||||
"""
|
||||
pass
|
||||
|
||||
|
|
@ -150,8 +108,7 @@ class BaseBox(ABC):
|
|||
|
||||
Returns
|
||||
-------
|
||||
mass : float
|
||||
Mass in box units.
|
||||
float
|
||||
"""
|
||||
pass
|
||||
|
||||
|
|
@ -167,8 +124,23 @@ class BaseBox(ABC):
|
|||
|
||||
Returns
|
||||
-------
|
||||
mass : float
|
||||
Mass in :math:`M_\odot / h`.
|
||||
float
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def m200c_to_r200c(self, m200c):
|
||||
"""
|
||||
Convert M200c to R200c in units of cMpc / h.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
m200c : float
|
||||
M200c in units of M_sun / h.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
pass
|
||||
|
||||
|
|
@ -248,6 +220,12 @@ class CSiBORGBox(BaseBox):
|
|||
def boxsize(self):
|
||||
return self.box2mpc(1.)
|
||||
|
||||
def m200c_to_r200c(self, m200c):
|
||||
rho_crit = self.cosmo.critical_density(1 / self._aexp - 1)
|
||||
rho_crit = rho_crit.to_value(units.solMass / units.Mpc**3)
|
||||
r200c = (3 * m200c / (4 * numpy.pi * 200 * rho_crit))**(1 / 3)
|
||||
return r200c / self._aexp
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Quijote fiducial cosmology box #
|
||||
|
|
@ -256,7 +234,7 @@ class CSiBORGBox(BaseBox):
|
|||
|
||||
class QuijoteBox(BaseBox):
|
||||
"""
|
||||
Quijote fiducial cosmology box.
|
||||
Quijote cosmology box.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -289,33 +267,10 @@ class QuijoteBox(BaseBox):
|
|||
return length / self.boxsize
|
||||
|
||||
def solarmass2box(self, mass):
|
||||
r"""
|
||||
Convert mass from :math:`M_\odot / h` to box units.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mass : float
|
||||
Mass in :math:`M_\odot`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mass : float
|
||||
Mass in box units.
|
||||
"""
|
||||
return mass / self._info["TotMass"]
|
||||
|
||||
def box2solarmass(self, mass):
|
||||
r"""
|
||||
Convert mass from box units to :math:`M_\odot / h`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mass : float
|
||||
Mass in box units.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mass : float
|
||||
Mass in :math:`M_\odot / h`.
|
||||
"""
|
||||
return mass * self._info["TotMass"]
|
||||
|
||||
def m200c_to_r200c(self, m200c):
|
||||
raise ValueError("Not implemented for Quijote boxes.")
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load diff
|
|
@ -383,6 +383,9 @@ class FitsSurvey(ABC):
|
|||
return out
|
||||
return out[self.selection_mask]
|
||||
|
||||
def __len__(self):
|
||||
return self.size
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Planck clusters #
|
||||
|
|
@ -560,8 +563,7 @@ class SDSS(FitsSurvey):
|
|||
Parameters
|
||||
----------
|
||||
fpath : str, optional
|
||||
Path to the FITS file. By default
|
||||
`/mnt/extraspace/rstiskalek/catalogs/nsa_v1_0_1.fits`.
|
||||
Path to the FITS file.
|
||||
h : float, optional
|
||||
Little h. By default `h = 1`. The catalogue assumes this value.
|
||||
The routine properties should take care of little h conversion.
|
||||
|
|
@ -581,9 +583,7 @@ class SDSS(FitsSurvey):
|
|||
"""
|
||||
name = "SDSS"
|
||||
|
||||
def __init__(self, fpath=None, h=1, Om0=0.3175, sel_steps=None):
|
||||
if fpath is None:
|
||||
fpath = "/mnt/extraspace/rstiskalek/catalogs/nsa_v1_0_1.fits"
|
||||
def __init__(self, fpath, h=1, Om0=0.3175, sel_steps=None):
|
||||
self._file = fits.open(fpath, memmap=False)
|
||||
self.h = h
|
||||
|
||||
|
|
@ -719,3 +719,114 @@ class SDSS(FitsSurvey):
|
|||
Get `IN_DR7_LSS` and turn to a boolean array.
|
||||
"""
|
||||
return self.get_fitsitem("IN_DR7_LSS").astype(bool)
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Individual observations #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class BaseSingleObservation(ABC):
|
||||
"""
|
||||
Base class to hold information about a single object.
|
||||
"""
|
||||
def __init__(self):
|
||||
self._spherical_pos = None
|
||||
self._name = None
|
||||
|
||||
@property
|
||||
def spherical_pos(self):
|
||||
"""
|
||||
Spherical position of the observation in dist/RA/dec in Mpc / h and
|
||||
degrees, respectively.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array of shape (3,)
|
||||
"""
|
||||
if self._spherical_pos is None:
|
||||
raise ValueError("`spherical_pos` is not set!")
|
||||
return self._spherical_pos
|
||||
|
||||
@spherical_pos.setter
|
||||
def spherical_pos(self, pos):
|
||||
if isinstance(pos, (list, tuple)):
|
||||
pos = numpy.array(pos)
|
||||
|
||||
if not pos.shape == (3,):
|
||||
raise ValueError("`spherical_pos` must be a of shape (3,).")
|
||||
|
||||
self._spherical_pos = pos
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
"""
|
||||
Observated object name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
if self._name is None:
|
||||
raise ValueError("`name` is not set!")
|
||||
return self._name
|
||||
|
||||
@name.setter
|
||||
def name(self, name):
|
||||
if not isinstance(name, str):
|
||||
raise ValueError("`name` must be a string.")
|
||||
self._name = name
|
||||
|
||||
|
||||
class ObservedCluster(BaseSingleObservation):
|
||||
"""
|
||||
Class to hold information about an observed cluster.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
RA : float
|
||||
Right ascension in degrees.
|
||||
dec : float
|
||||
Declination in degrees.
|
||||
dist : float
|
||||
Distance in Mpc / h.
|
||||
name : str
|
||||
Cluster name.
|
||||
"""
|
||||
def __init__(self, RA, dec, dist, name):
|
||||
super().__init__()
|
||||
self.name = name
|
||||
self.spherical_pos = [dist, RA, dec]
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Utility functions #
|
||||
###############################################################################
|
||||
|
||||
def match_array_to_no_masking(arr, surv):
|
||||
"""
|
||||
Match an array to a survey without masking.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
arr : n-dimensional array
|
||||
Array to match.
|
||||
surv : survey class
|
||||
Survey class.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : n-dimensional array
|
||||
"""
|
||||
dtype = arr.dtype
|
||||
if arr.ndim > 1:
|
||||
shape = arr.shape
|
||||
out = numpy.full((surv.selection_mask.size, *shape[1:]), numpy.nan,
|
||||
dtype=dtype)
|
||||
else:
|
||||
out = numpy.full(surv.selection_mask.size, numpy.nan, dtype=dtype)
|
||||
|
||||
for i, indx in enumerate(surv["INDEX"]):
|
||||
out[indx] = arr[i]
|
||||
|
||||
return out
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@
|
|||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
"""CSiBORG paths manager."""
|
||||
from glob import glob
|
||||
from glob import glob, iglob
|
||||
from os import makedirs
|
||||
from os.path import isdir, join
|
||||
from warnings import warn
|
||||
|
|
@ -61,13 +61,7 @@ class Paths:
|
|||
|
||||
@property
|
||||
def srcdir(self):
|
||||
"""
|
||||
Path to the folder where CSiBORG simulations are stored.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Path to the folder where CSiBORG simulations are stored."""
|
||||
if self._srcdir is None:
|
||||
raise ValueError("`srcdir` is not set!")
|
||||
return self._srcdir
|
||||
|
|
@ -81,13 +75,7 @@ class Paths:
|
|||
|
||||
@property
|
||||
def borg_dir(self):
|
||||
"""
|
||||
Path to the folder where BORG MCMC chains are stored.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Path to the folder where BORG MCMC chains are stored."""
|
||||
if self._borg_dir is None:
|
||||
raise ValueError("`borg_dir` is not set!")
|
||||
return self._borg_dir
|
||||
|
|
@ -101,13 +89,7 @@ class Paths:
|
|||
|
||||
@property
|
||||
def quijote_dir(self):
|
||||
"""
|
||||
Path to the folder where Quijote simulations are stored.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Path to the folder where Quijote simulations are stored."""
|
||||
if self._quijote_dir is None:
|
||||
raise ValueError("`quijote_dir` is not set!")
|
||||
return self._quijote_dir
|
||||
|
|
@ -121,13 +103,7 @@ class Paths:
|
|||
|
||||
@property
|
||||
def postdir(self):
|
||||
"""
|
||||
Path to the folder where post-processed files are stored.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Path to the folder where post-processed files are stored."""
|
||||
if self._postdir is None:
|
||||
raise ValueError("`postdir` is not set!")
|
||||
return self._postdir
|
||||
|
|
@ -139,19 +115,6 @@ class Paths:
|
|||
check_directory(path)
|
||||
self._postdir = path
|
||||
|
||||
@property
|
||||
def temp_dumpdir(self):
|
||||
"""
|
||||
Path to a temporary dumping folder.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
fpath = join(self.postdir, "temp")
|
||||
try_create_directory(fpath)
|
||||
return fpath
|
||||
|
||||
@staticmethod
|
||||
def quijote_fiducial_nsim(nsim, nobs=None):
|
||||
"""
|
||||
|
|
@ -167,7 +130,7 @@ class Paths:
|
|||
|
||||
Returns
|
||||
-------
|
||||
id : str
|
||||
str
|
||||
"""
|
||||
if nobs is None:
|
||||
assert isinstance(nsim, str)
|
||||
|
|
@ -190,36 +153,14 @@ class Paths:
|
|||
"""
|
||||
return join(self.borg_dir, "mcmc", f"mcmc_{nsim}.h5")
|
||||
|
||||
def fof_membership(self, nsim, simname, sorted=False):
|
||||
"""
|
||||
Path to the file containing the FoF particle membership.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
simname : str
|
||||
Simulation name. Must be one of `csiborg` or `quijote`.
|
||||
sorted : bool, optional
|
||||
Whether to return path to the file that is sorted in the same
|
||||
order as the PHEW output.
|
||||
"""
|
||||
assert simname in ["csiborg", "quijote"]
|
||||
if simname == "quijote":
|
||||
raise RuntimeError("Quijote FoF membership is in the FoF cats.")
|
||||
fdir = join(self.postdir, "FoF_membership", )
|
||||
try_create_directory(fdir)
|
||||
fout = join(fdir, f"fof_membership_{nsim}.npy")
|
||||
if sorted:
|
||||
fout = fout.replace(".npy", "_sorted.npy")
|
||||
return fout
|
||||
|
||||
def fof_cat(self, nsim, simname, from_quijote_backup=False):
|
||||
def fof_cat(self, nsnap, nsim, simname, from_quijote_backup=False):
|
||||
r"""
|
||||
Path to the :math:`z = 0` FoF halo catalogue.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsnap : int
|
||||
Snapshot index.
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
simname : str
|
||||
|
|
@ -228,15 +169,15 @@ class Paths:
|
|||
Whether to return the path to the Quijote FoF catalogue from the
|
||||
backup.
|
||||
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
if simname == "csiborg":
|
||||
fdir = join(self.postdir, "FoF_membership", )
|
||||
fdir = join(self.postdir, "halo_maker", f"ramses_{nsim}",
|
||||
f"output_{str(nsnap).zfill(5)}", "FOF")
|
||||
try_create_directory(fdir)
|
||||
return join(fdir, f"halo_catalog_{nsim}_FOF.txt")
|
||||
return join(fdir, "fort.132")
|
||||
elif simname == "quijote":
|
||||
if from_quijote_backup:
|
||||
return join(self.quijote_dir, "halos_backup", str(nsim))
|
||||
|
|
@ -245,57 +186,6 @@ class Paths:
|
|||
else:
|
||||
raise ValueError(f"Unknown simulation name `{simname}`.")
|
||||
|
||||
def mmain(self, nsnap, nsim):
|
||||
"""
|
||||
Path to the `mmain` CSiBORG files of summed substructure.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsnap : int
|
||||
Snapshot index.
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
fdir = join(self.postdir, "mmain")
|
||||
try_create_directory(fdir)
|
||||
return join(
|
||||
fdir, f"mmain_{str(nsim).zfill(5)}_{str(nsnap).zfill(5)}.npz")
|
||||
|
||||
def initmatch(self, nsim, simname, kind):
|
||||
"""
|
||||
Path to the `initmatch` files where the halo match between the
|
||||
initial and final snapshot of a CSiBORG realisaiton is stored.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
simname : str
|
||||
Simulation name. Must be one of `csiborg` or `quijote`.
|
||||
kind : str
|
||||
Type of match. Must be one of `particles` or `fit`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
assert kind in ["particles", "fit"]
|
||||
ftype = "npy" if kind == "fit" else "h5"
|
||||
|
||||
if simname == "csiborg":
|
||||
fdir = join(self.postdir, "initmatch")
|
||||
elif simname == "quijote":
|
||||
fdir = join(self.quijote_dir, "initmatch")
|
||||
else:
|
||||
raise ValueError(f"Unknown simulation name `{simname}`.")
|
||||
|
||||
try_create_directory(fdir)
|
||||
return join(fdir, f"{kind}_{str(nsim).zfill(5)}.{ftype}")
|
||||
|
||||
def get_ics(self, simname, from_quijote_backup=False):
|
||||
"""
|
||||
Get available IC realisation IDs for either the CSiBORG or Quijote
|
||||
|
|
@ -411,7 +301,7 @@ class Paths:
|
|||
|
||||
Returns
|
||||
-------
|
||||
snapstr
|
||||
str
|
||||
"""
|
||||
simpath = self.snapshots(nsim, simname, tonew=nsnap == 1)
|
||||
if simname == "csiborg":
|
||||
|
|
@ -422,7 +312,27 @@ class Paths:
|
|||
nsnap = str(nsnap).zfill(3)
|
||||
return join(simpath, f"snapdir_{nsnap}", f"snap_{nsnap}")
|
||||
|
||||
def particles(self, nsim, simname):
|
||||
def merger_tree_file(self, nsnap, nsim):
|
||||
"""
|
||||
Path to the CSiBORG on-the-fly generated merger tree file.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsnap : int
|
||||
Snapshot index.
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
nsim = str(nsim)
|
||||
nsnap = str(nsnap).zfill(5)
|
||||
return join(self.srcdir, f"ramses_out_{nsim}",
|
||||
f"output_{nsnap}", f"mergertree_{nsnap}.dat")
|
||||
|
||||
def processed_output(self, nsim, simname, halo_finder):
|
||||
"""
|
||||
Path to the files containing all particles of a CSiBORG realisation at
|
||||
:math:`z = 0`.
|
||||
|
|
@ -433,22 +343,80 @@ class Paths:
|
|||
IC realisation index.
|
||||
simname : str
|
||||
Simulation name. Must be one of `csiborg` or `quijote`.
|
||||
halo_finder : str
|
||||
Halo finder name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
if simname == "csiborg":
|
||||
fdir = join(self.postdir, "particles")
|
||||
fdir = join(self.postdir, "processed_output")
|
||||
elif simname == "quijote":
|
||||
fdir = join(self.quijote_dir, "Particles_fiducial")
|
||||
else:
|
||||
raise ValueError(f"Unknown simulation name `{simname}`.")
|
||||
|
||||
try_create_directory(fdir)
|
||||
fname = f"parts_{str(nsim).zfill(5)}.h5"
|
||||
fname = f"parts_{halo_finder}_{str(nsim).zfill(5)}.hdf5"
|
||||
return join(fdir, fname)
|
||||
|
||||
def processed_phew(self, nsim):
|
||||
"""
|
||||
Path to the files containing PHEW CSiBORG catalogues.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
fdir = join(self.postdir, "processed_output")
|
||||
try_create_directory(fdir)
|
||||
return join(fdir, f"phew_{str(nsim).zfill(5)}.hdf5")
|
||||
|
||||
def processed_merger_tree(self, nsim):
|
||||
"""
|
||||
Path to the files containing the processed original merger tree files.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
fdir = join(self.postdir, "processed_output")
|
||||
try_create_directory(fdir)
|
||||
return join(fdir, f"merger_{str(nsim).zfill(5)}.hdf5")
|
||||
|
||||
def halomaker_particle_membership(self, nsnap, nsim, halo_finder):
|
||||
"""
|
||||
Path to the HaloMaker particle membership file (CSiBORG only).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsnap : int
|
||||
Snapshot index.
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
halo_finder : str
|
||||
Halo finder name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
fdir = join(self.postdir, "halo_maker", f"ramses_{nsim}",
|
||||
f"output_{str(nsnap).zfill(5)}", halo_finder)
|
||||
fpath = join(fdir, "*particle_membership*")
|
||||
return next(iglob(fpath, recursive=True), None)
|
||||
|
||||
def ascii_positions(self, nsim, kind):
|
||||
"""
|
||||
Path to ASCII files containing the positions of particles or halos.
|
||||
|
|
@ -469,35 +437,6 @@ class Paths:
|
|||
|
||||
return join(fdir, fname)
|
||||
|
||||
def structfit(self, nsnap, nsim, simname):
|
||||
"""
|
||||
Path to the halo catalogue from `fit_halos.py`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsnap : int
|
||||
Snapshot index.
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
simname : str
|
||||
Simulation name. Must be one of `csiborg` or `quijote`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
if simname == "csiborg":
|
||||
fdir = join(self.postdir, "structfit")
|
||||
elif simname == "quijote":
|
||||
fdir = join(self.quijote_dir, "structfit")
|
||||
else:
|
||||
raise ValueError(f"Unknown simulation name `{simname}`.")
|
||||
|
||||
try_create_directory(fdir)
|
||||
|
||||
fname = f"out_{str(nsim).zfill(5)}_{str(nsnap).zfill(5)}.npy"
|
||||
return join(fdir, fname)
|
||||
|
||||
def overlap(self, simname, nsim0, nsimx, min_logmass, smoothed):
|
||||
"""
|
||||
Path to the overlap files between two CSiBORG simulations.
|
||||
|
|
@ -688,31 +627,6 @@ class Paths:
|
|||
fname = f"obs_vp_{MAS}_{str(nsim).zfill(5)}_{grid}.npz"
|
||||
return join(fdir, fname)
|
||||
|
||||
def halo_counts(self, simname, nsim, from_quijote_backup=False):
|
||||
"""
|
||||
Path to the files containing the binned halo counts.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
simname : str
|
||||
Simulation name. Must be `csiborg`, `quijote` or `quijote_full`.
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
from_quijote_backup : bool, optional
|
||||
Whether to return the path to the Quijote halo counts from the
|
||||
backup catalogues.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
fdir = join(self.postdir, "HMF")
|
||||
try_create_directory(fdir)
|
||||
fname = f"halo_counts_{simname}_{str(nsim).zfill(5)}.npz"
|
||||
if from_quijote_backup:
|
||||
fname = fname.replace("halo_counts", "halo_counts_backup")
|
||||
return join(fdir, fname)
|
||||
|
||||
def cross_nearest(self, simname, run, kind, nsim=None, nobs=None):
|
||||
"""
|
||||
Path to the files containing distance from a halo in a reference
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load diff
|
|
@ -15,6 +15,7 @@
|
|||
"""Collection of stand-off utility functions used in the scripts."""
|
||||
import numpy
|
||||
from numba import jit
|
||||
from datetime import datetime
|
||||
|
||||
###############################################################################
|
||||
# Positions #
|
||||
|
|
@ -87,7 +88,7 @@ def periodic_distance_two_points(p1, p2, boxsize):
|
|||
return dist**0.5
|
||||
|
||||
|
||||
@jit(nopython=True)
|
||||
@jit(nopython=True, boundscheck=False)
|
||||
def periodic_wrap_grid(pos, boxsize=1):
|
||||
"""Wrap positions in a periodic box."""
|
||||
for n in range(pos.shape[0]):
|
||||
|
|
@ -139,17 +140,34 @@ def radec_to_cartesian(X):
|
|||
"""
|
||||
dist, ra, dec = X[:, 0], X[:, 1], X[:, 2]
|
||||
|
||||
ra *= numpy.pi / 180
|
||||
dec *= numpy.pi / 180
|
||||
cdec = numpy.cos(dec)
|
||||
|
||||
cdec = numpy.cos(dec * numpy.pi / 180)
|
||||
return numpy.vstack([
|
||||
dist * cdec * numpy.cos(ra),
|
||||
dist * cdec * numpy.sin(ra),
|
||||
dist * numpy.sin(dec)
|
||||
dist * cdec * numpy.cos(ra * numpy.pi / 180),
|
||||
dist * cdec * numpy.sin(ra * numpy.pi / 180),
|
||||
dist * numpy.sin(dec * numpy.pi / 180)
|
||||
]).T
|
||||
|
||||
|
||||
@jit(nopython=True, fastmath=True, boundscheck=False)
|
||||
def great_circle_distance(x1, x2):
|
||||
"""
|
||||
Great circle distance between two points on a sphere, defined by RA and
|
||||
dec, both in degrees.
|
||||
"""
|
||||
ra1, dec1 = x1
|
||||
ra2, dec2 = x2
|
||||
|
||||
ra1 *= numpy.pi / 180
|
||||
dec1 *= numpy.pi / 180
|
||||
ra2 *= numpy.pi / 180
|
||||
dec2 *= numpy.pi / 180
|
||||
|
||||
return 180 / numpy.pi * numpy.arccos(
|
||||
numpy.sin(dec1) * numpy.sin(dec2)
|
||||
+ numpy.cos(dec1) * numpy.cos(dec2) * numpy.cos(ra1 - ra2)
|
||||
)
|
||||
|
||||
|
||||
def cosine_similarity(x, y):
|
||||
r"""
|
||||
Calculate the cosine similarity between two Cartesian vectors. Defined
|
||||
|
|
@ -179,6 +197,36 @@ def cosine_similarity(x, y):
|
|||
return out[0] if out.size == 1 else out
|
||||
|
||||
|
||||
def hms_to_degrees(hours, minutes=None, seconds=None):
|
||||
"""
|
||||
Convert hours, minutes and seconds to degrees.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
hours, minutes, seconds : float
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
return hours * 15 + (minutes or 0) / 60 * 15 + (seconds or 0) / 3600 * 15
|
||||
|
||||
|
||||
def dms_to_degrees(degrees, arcminutes=None, arcseconds=None):
|
||||
"""
|
||||
Convert degrees, arcminutes and arcseconds to decimal degrees.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
degrees, arcminutes, arcseconds : float
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
return degrees + (arcminutes or 0) / 60 + (arcseconds or 0) / 3600
|
||||
|
||||
|
||||
def real2redshift(pos, vel, observer_location, observer_velocity, box,
|
||||
periodic_wrap=True, make_copy=True):
|
||||
r"""
|
||||
|
|
@ -262,3 +310,9 @@ def binned_statistic(x, y, left_edges, bin_width, statistic):
|
|||
if numpy.any(mask):
|
||||
out[i] = statistic(y[mask])
|
||||
return out
|
||||
|
||||
|
||||
def fprint(msg, verbose=True):
|
||||
"""Print and flush a message with a timestamp."""
|
||||
if verbose:
|
||||
print(f"{datetime.now()}: {msg}", flush=True)
|
||||
|
|
|
|||
721
notebooks/powerspectrum_test.ipynb
Normal file
721
notebooks/powerspectrum_test.ipynb
Normal file
File diff suppressed because one or more lines are too long
|
|
@ -66,7 +66,7 @@ jobs = csiborgtools.utils.split_jobs(nsims, nproc)[rank]
|
|||
for n in jobs:
|
||||
print(f"Rank {rank} at {datetime.now()}: saving {n}th delta.", flush=True)
|
||||
nsim = ics[n]
|
||||
particles = reader.read_particle(max(paths.get_snapshots(nsim, "csiborg")),
|
||||
particles = reader.read_snapshot(max(paths.get_snapshots(nsim, "csiborg")),
|
||||
nsim, ["x", "y", "z", "M"], verbose=False)
|
||||
# Halfwidth -- particle selection
|
||||
if args.halfwidth < 0.5:
|
||||
14
old/cluster_crosspk.sh
Normal file
14
old/cluster_crosspk.sh
Normal file
|
|
@ -0,0 +1,14 @@
|
|||
nthreads=20
|
||||
memory=40
|
||||
queue="berg"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python"
|
||||
file="cluster_crosspk.py"
|
||||
grid=1024
|
||||
halfwidth=0.13
|
||||
|
||||
cm="addqueue -q $queue -n $nthreads -m $memory $env $file --grid $grid --halfwidth $halfwidth"
|
||||
|
||||
echo "Submitting:"
|
||||
echo $cm
|
||||
echo
|
||||
$cm
|
||||
27
old/cluster_knn_auto.sh
Normal file
27
old/cluster_knn_auto.sh
Normal file
|
|
@ -0,0 +1,27 @@
|
|||
nthreads=4
|
||||
memory=4
|
||||
queue="cmb"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python"
|
||||
file="cluster_knn_auto.py"
|
||||
Rmax=219.8581560283688
|
||||
verbose="true"
|
||||
|
||||
|
||||
|
||||
simname="quijote"
|
||||
nsims="0 1 2"
|
||||
# simname="csiborg"
|
||||
# nsims="7444 7900 9052"
|
||||
|
||||
run="mass003"
|
||||
|
||||
pythoncm="$env $file --run $run --simname $simname --nsims $nsims --Rmax $Rmax --verbose $verbose"
|
||||
|
||||
echo $pythoncm
|
||||
$pythoncm
|
||||
|
||||
# cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm"
|
||||
# echo "Submitting:"
|
||||
# echo $cm
|
||||
# echo
|
||||
# $cm
|
||||
18
old/cluster_knn_cross.sh
Normal file
18
old/cluster_knn_cross.sh
Normal file
|
|
@ -0,0 +1,18 @@
|
|||
nthreads=151
|
||||
memory=4
|
||||
queue="cmb"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python"
|
||||
file="knn_cross.py"
|
||||
|
||||
runs="mass001"
|
||||
|
||||
pythoncm="$env $file --runs $runs"
|
||||
|
||||
echo $pythoncm
|
||||
$pythoncm
|
||||
|
||||
# cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm"
|
||||
# echo "Submitting:"
|
||||
# echo $cm
|
||||
# echo
|
||||
# $cm
|
||||
26
old/cluster_tpcf_auto.sh
Normal file
26
old/cluster_tpcf_auto.sh
Normal file
|
|
@ -0,0 +1,26 @@
|
|||
nthreads=26
|
||||
memory=7
|
||||
queue="cmb"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python"
|
||||
file="cluster_tpcf_auto.py"
|
||||
Rmax=219.8581560283688
|
||||
verbose="true"
|
||||
|
||||
# simname="quijote"
|
||||
# nsims="0 1 2"
|
||||
simname="csiborg"
|
||||
nsims="7444 7900 9052"
|
||||
|
||||
|
||||
run="mass003"
|
||||
|
||||
pythoncm="$env $file --run $run --simname $simname --nsims $nsims --Rmax $Rmax --verbose $verbose"
|
||||
|
||||
echo $pythoncm
|
||||
$pythoncm
|
||||
|
||||
# cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm"
|
||||
# echo "Submitting:"
|
||||
# echo $cm
|
||||
# echo
|
||||
# $cm
|
||||
24
old/fit_hmf.sh
Executable file
24
old/fit_hmf.sh
Executable file
|
|
@ -0,0 +1,24 @@
|
|||
nthreads=11
|
||||
memory=2
|
||||
queue="berg"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python"
|
||||
file="fit_hmf.py"
|
||||
|
||||
simname="quijote_full"
|
||||
nsims="-1"
|
||||
verbose=True
|
||||
lower_lim=12.0
|
||||
upper_lim=16.0
|
||||
Rmax=155
|
||||
from_quijote_backup="true"
|
||||
bw=0.2
|
||||
|
||||
pythoncm="$env $file --simname $simname --nsims $nsims --Rmax $Rmax --lims $lower_lim $upper_lim --bw $bw --from_quijote_backup $from_quijote_backup --verbose $verbose"
|
||||
|
||||
$pythoncm
|
||||
|
||||
# cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm"
|
||||
# echo "Submitting:"
|
||||
# echo $cm
|
||||
# echo
|
||||
# $cm
|
||||
686
old/merger.py
Normal file
686
old/merger.py
Normal file
|
|
@ -0,0 +1,686 @@
|
|||
# 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.
|
||||
"""
|
||||
Support for reading the PHEW/ACACIA CSiBORG merger trees. However, note that
|
||||
the merger trees are very unreliable.
|
||||
"""
|
||||
from abc import ABC
|
||||
from datetime import datetime
|
||||
from gc import collect
|
||||
|
||||
import numpy
|
||||
from h5py import File
|
||||
from tqdm import tqdm, trange
|
||||
from treelib import Tree
|
||||
|
||||
from ..utils import periodic_distance
|
||||
from .paths import Paths
|
||||
|
||||
###############################################################################
|
||||
# Utility functions. #
|
||||
###############################################################################
|
||||
|
||||
|
||||
def clump_identifier(clump, nsnap):
|
||||
"""
|
||||
Generate a unique identifier for a clump at a given snapshot.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
clump : int
|
||||
Clump ID.
|
||||
nsnap : int
|
||||
Snapshot index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
return f"{str(clump).rjust(9, 'x')}__{str(nsnap).rjust(4, 'x')}"
|
||||
|
||||
|
||||
def extract_identifier(identifier):
|
||||
"""
|
||||
Extract the clump ID and snapshot index from a identifier generated by
|
||||
`clump_identifier`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
identifier : str
|
||||
Identifier.
|
||||
|
||||
Returns
|
||||
-------
|
||||
clump, nsnap : int
|
||||
Clump ID and snapshot index.
|
||||
"""
|
||||
clump, nsnap = identifier.split('__')
|
||||
return int(clump.lstrip('x')), int(nsnap.lstrip('x'))
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Merger tree reader class. #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class BaseMergerReader(ABC):
|
||||
"""
|
||||
Base class for the CSiBORG merger tree reader.
|
||||
"""
|
||||
_paths = None
|
||||
_nsim = None
|
||||
_min_snap = None
|
||||
_cache = {}
|
||||
|
||||
@property
|
||||
def paths(self):
|
||||
"""Paths manager."""
|
||||
if self._paths is None:
|
||||
raise ValueError("`paths` is not set.")
|
||||
return self._paths
|
||||
|
||||
@paths.setter
|
||||
def paths(self, paths):
|
||||
assert isinstance(paths, Paths)
|
||||
self._paths = paths
|
||||
|
||||
@property
|
||||
def nsim(self):
|
||||
"""Simulation index."""
|
||||
if self._nsim is None:
|
||||
raise ValueError("`nsim` is not set.")
|
||||
return self._nsim
|
||||
|
||||
@nsim.setter
|
||||
def nsim(self, nsim):
|
||||
assert isinstance(nsim, (int, numpy.integer))
|
||||
self._nsim = nsim
|
||||
|
||||
@property
|
||||
def min_snap(self):
|
||||
"""Minimum snapshot index to read."""
|
||||
return self._min_snap
|
||||
|
||||
@min_snap.setter
|
||||
def min_snap(self, min_snap):
|
||||
if min_snap is not None:
|
||||
assert isinstance(min_snap, (int, numpy.integer))
|
||||
self._min_snap = int(min_snap)
|
||||
|
||||
def cache_length(self):
|
||||
"""Length of the cache."""
|
||||
return len(self._cache)
|
||||
|
||||
def cache_clear(self):
|
||||
"""Clear the cache."""
|
||||
self._cache = {}
|
||||
collect()
|
||||
|
||||
def __getitem__(self, key):
|
||||
try:
|
||||
return self._cache[key]
|
||||
except KeyError:
|
||||
fname = self.paths.processed_merger_tree(self.nsim)
|
||||
|
||||
nsnap, kind = key.split("__")
|
||||
|
||||
with File(fname, "r") as f:
|
||||
if kind == "clump_to_array":
|
||||
cl = self[f"{nsnap}__clump"]
|
||||
|
||||
x = {}
|
||||
for i, c in enumerate(cl):
|
||||
if c in x:
|
||||
x[c] += (i,)
|
||||
else:
|
||||
x[c] = (i,)
|
||||
else:
|
||||
x = f[f"{str(nsnap)}/{kind}"][:]
|
||||
|
||||
# Cache it
|
||||
self._cache[key] = x
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class MergerReader(BaseMergerReader):
|
||||
"""
|
||||
Merger tree reader.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsim : int
|
||||
Simulation index.
|
||||
paths : Paths
|
||||
Paths manager.
|
||||
min_snap : int
|
||||
Minimum snapshot index. Trees below this snapshot will not be read.
|
||||
"""
|
||||
def __init__(self, nsim, paths, min_snap=None):
|
||||
self.nsim = nsim
|
||||
self.paths = paths
|
||||
self.min_snap = min_snap
|
||||
|
||||
def get_info(self, current_clump, current_snap, is_main=None):
|
||||
"""
|
||||
Make a list of information about a clump at a given snapshot.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
current_clump : int
|
||||
Clump ID.
|
||||
current_snap : int
|
||||
Snapshot index.
|
||||
is_main : bool
|
||||
Whether this is the main progenitor.
|
||||
|
||||
Returns
|
||||
-------
|
||||
list
|
||||
"""
|
||||
if current_clump < 0:
|
||||
raise ValueError("Clump ID must be positive.")
|
||||
|
||||
if is_main is not None and not isinstance(is_main, bool):
|
||||
raise ValueError("`is_main` must be a boolean.")
|
||||
|
||||
k = self[f"{current_snap}__clump_to_array"][current_clump][0]
|
||||
|
||||
out = [self[f"{current_snap}__desc_mass"][k],
|
||||
*self[f"{current_snap}__desc_pos"][k][::-1]] # TODO REMOVE LATER
|
||||
|
||||
if is_main is not None:
|
||||
return [is_main,] + out
|
||||
|
||||
return out
|
||||
|
||||
def get_mass(self, clump, snap):
|
||||
"""
|
||||
Get the mass of a clump at a given snapshot.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
clump : int
|
||||
Clump ID.
|
||||
snap : int
|
||||
Snapshot index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
if clump < 0:
|
||||
raise ValueError("Clump ID must be positive.")
|
||||
k = self[f"{snap}__clump_to_array"][clump][0]
|
||||
return self[f"{snap}__desc_mass"][k]
|
||||
|
||||
def get_pos(self, clump, snap):
|
||||
if clump < 0:
|
||||
raise ValueError("Clump ID must be positive.")
|
||||
k = self[f"{snap}__clump_to_array"][clump][0]
|
||||
return self[f"{snap}__desc_pos"][k]
|
||||
|
||||
def find_main_progenitor(self, clump, nsnap):
|
||||
"""
|
||||
Find the main progenitor of a clump at a given snapshot. Cases are:
|
||||
- `clump > 0`, `progenitor > 0`: main progenitor is in the adjacent
|
||||
snapshot,
|
||||
- `clump > 0`, `progenitor < 0`: main progenitor is not in the
|
||||
adjacent snapshot.
|
||||
- `clump < 0`, `progenitor = 0`: no progenitor, newly formed clump.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
clump : int
|
||||
Clump ID.
|
||||
nsnap : int
|
||||
Snapshot index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
progenitor : int
|
||||
Main progenitor clump ID.
|
||||
progenitor_snap : int
|
||||
Main progenitor snapshot index.
|
||||
"""
|
||||
if not clump > 0:
|
||||
raise ValueError("Clump ID must be positive.")
|
||||
|
||||
cl2array = self[f"{nsnap}__clump_to_array"]
|
||||
if clump in cl2array:
|
||||
k = cl2array[clump]
|
||||
else:
|
||||
raise ValueError("Clump ID not found.")
|
||||
|
||||
if len(k) > 1:
|
||||
raise ValueError("Found more than one main progenitor.")
|
||||
k = k[0]
|
||||
|
||||
progenitor = abs(self[f"{nsnap}__progenitor"][k])
|
||||
progenitor_snap = self[f"{nsnap}__progenitor_outputnr"][k]
|
||||
|
||||
if (self.min_snap is not None) and (nsnap < self.min_snap):
|
||||
return 0, numpy.nan
|
||||
|
||||
return progenitor, progenitor_snap
|
||||
|
||||
def find_minor_progenitors(self, clump, nsnap):
|
||||
"""
|
||||
Find the minor progenitors of a clump at a given snapshot. This means
|
||||
that `clump < 0`, `progenitor > 0`, i.e. this clump also has another
|
||||
main progenitor.
|
||||
|
||||
If there are no minor progenitors, return `None` for both lists.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
clump : int
|
||||
Clump ID.
|
||||
nsnap : int
|
||||
Snapshot index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
prog : list
|
||||
List of minor progenitor clump IDs.
|
||||
prog_snap : list
|
||||
List of minor progenitor snapshot indices.
|
||||
"""
|
||||
if not clump > 0:
|
||||
raise ValueError("Clump ID must be positive.")
|
||||
|
||||
try:
|
||||
ks = self[f"{nsnap}__clump_to_array"][-clump]
|
||||
except KeyError:
|
||||
return None, None
|
||||
|
||||
prog = [self[f"{nsnap}__progenitor"][k] for k in ks]
|
||||
prog_nsnap = [self[f"{nsnap}__progenitor_outputnr"][k] for k in ks]
|
||||
|
||||
if (self.min_snap is not None) and (nsnap < self.min_snap):
|
||||
return None, None
|
||||
|
||||
return prog, prog_nsnap
|
||||
|
||||
def find_progenitors(self, clump, nsnap):
|
||||
"""
|
||||
Find all progenitors of a clump at a given snapshot. The main
|
||||
progenitor is the first element of the list.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
clump : int
|
||||
Clump ID.
|
||||
nsnap : int
|
||||
Snapshot index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
prog : list
|
||||
List of progenitor clump IDs.
|
||||
prog_nsnap : list
|
||||
List of progenitor snapshot indices.
|
||||
"""
|
||||
main_prog, main_prog_nsnap = self.find_main_progenitor(clump, nsnap)
|
||||
min_prog, min_prog_nsnap = self.find_minor_progenitors(clump, nsnap)
|
||||
|
||||
# Check that if the main progenitor is not in the adjacent snapshot,
|
||||
# then the minor progenitor are also in that snapshot (if any).
|
||||
if (min_prog is not None) and (main_prog_nsnap != nsnap - 1) and not all(prog_nsnap == mprog for mprog in min_prog_nsnap): # noqa
|
||||
raise ValueError(f"For clump {clump} at snapshot {nsnap} we have "
|
||||
f"main progenitor at {main_prog_nsnap} and "
|
||||
"minor progenitors at {min_prog_nsnap}.")
|
||||
|
||||
if min_prog is None:
|
||||
prog = [main_prog,]
|
||||
prog_nsnap = [main_prog_nsnap,]
|
||||
else:
|
||||
prog = [main_prog,] + min_prog
|
||||
prog_nsnap = [main_prog_nsnap,] + min_prog_nsnap
|
||||
|
||||
if prog[0] == 0 and len(prog) > 1:
|
||||
raise ValueError("No main progenitor but minor progenitors "
|
||||
"found for clump {clump} at snapshot {nsnap}.")
|
||||
|
||||
return prog, prog_nsnap
|
||||
|
||||
def tree_mass_at_snapshot(self, clump, nsnap, target_snap):
|
||||
"""
|
||||
Calculate the total mass of nodes in a tree at a given snapshot.
|
||||
"""
|
||||
# If clump is 0 (i.e., we've reached the end of the tree), return 0
|
||||
if clump == 0:
|
||||
return 0
|
||||
|
||||
# Find the progenitors for the given clump and nsnap
|
||||
prog, prog_nsnap = self.find_progenitors(clump, nsnap)
|
||||
|
||||
if prog[0] == 0:
|
||||
print(prog)
|
||||
return 0
|
||||
|
||||
# Sum the mass of the current clump's progenitors
|
||||
tot = 0
|
||||
for p, psnap in zip(prog, prog_nsnap):
|
||||
if psnap == target_snap:
|
||||
tot += self.get_mass(p, psnap)
|
||||
|
||||
# Recursively sum the mass of each progenitor's progenitors
|
||||
for p, psnap in zip(prog, prog_nsnap):
|
||||
# print("P ", p, psnap)
|
||||
tot += self.mass_all_progenitor2(p, psnap, target_snap)
|
||||
|
||||
return tot
|
||||
|
||||
def is_jumper(self, clump, nsnap, nsnap_descendant):
|
||||
pass
|
||||
|
||||
def make_tree(self, current_clump, current_nsnap,
|
||||
above_clump=None, above_nsnap=None,
|
||||
tree=None, is_main=None, verbose=False):
|
||||
"""
|
||||
Make a merger tree for a clump at a given snapshot.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
current_clump : int
|
||||
Clump ID of the descendant clump.
|
||||
current_nsnap : int
|
||||
Snapshot index of the descendent clump.
|
||||
above_clump : int, optional
|
||||
Clump ID of a clump above the current clump in the tree.
|
||||
above_nsnap : int, optional
|
||||
Snapshot index of a clump above the current clump in the tree.
|
||||
tree : treelib.Tree, optional
|
||||
Tree to add to.
|
||||
is_main : bool, optional
|
||||
Whether this is the main progenitor.
|
||||
verbose : bool, optional
|
||||
Verbosity flag.
|
||||
|
||||
Returns
|
||||
-------
|
||||
treelib.Tree
|
||||
Tree with the current clump as the root.
|
||||
"""
|
||||
if verbose:
|
||||
print(f"{datetime.now()}: Node of a clump {current_clump} at "
|
||||
f"snapshot {current_nsnap}.", flush=True)
|
||||
|
||||
# Terminate if we are at the end of the tree
|
||||
if current_clump == 0:
|
||||
return
|
||||
|
||||
# Create the root node or add a new node
|
||||
if tree is None:
|
||||
tree = Tree()
|
||||
tree.create_node(
|
||||
"root",
|
||||
identifier=clump_identifier(current_clump, current_nsnap),
|
||||
data=self.get_info(current_clump, current_nsnap, True),
|
||||
)
|
||||
else:
|
||||
tree.create_node(
|
||||
identifier=clump_identifier(current_clump, current_nsnap),
|
||||
parent=clump_identifier(above_clump, above_nsnap),
|
||||
data=self.get_info(current_clump, current_nsnap, is_main),
|
||||
)
|
||||
|
||||
# This returns a list of progenitors and their snapshots. The first
|
||||
# element is the main progenitor.
|
||||
prog, prog_nsnap = self.find_progenitors(current_clump, current_nsnap)
|
||||
|
||||
for i, (p, psnap) in enumerate(zip(prog, prog_nsnap)):
|
||||
self.make_tree(p, psnap, current_clump, current_nsnap, tree,
|
||||
is_main=i == 0, verbose=verbose)
|
||||
|
||||
return tree
|
||||
|
||||
def walk_main_progenitor(self, main_clump, main_nsnap, verbose=False):
|
||||
"""
|
||||
Walk the main progenitor branch of a clump.
|
||||
|
||||
Each snapshot contains information about the clump at that snapshot.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
clump : int
|
||||
Clump ID.
|
||||
nsnap : int
|
||||
Snapshot index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
structured array
|
||||
"""
|
||||
out = []
|
||||
|
||||
pbar = tqdm(disable=not verbose)
|
||||
while True:
|
||||
prog, prog_nsnap = self.find_progenitors(main_clump, main_nsnap)
|
||||
|
||||
# Unpack the main and minor progenitor
|
||||
mainprog, mainprog_nsnap = prog[0], prog_nsnap[0]
|
||||
if len(prog) > 1:
|
||||
minprog, minprog_nsnap = prog[1:], prog_nsnap[1:]
|
||||
else:
|
||||
minprog, minprog_nsnap = None, None
|
||||
|
||||
# If there is no progenitor, then set the main progenitor mass to 0
|
||||
if mainprog == 0:
|
||||
mainprog_mass = numpy.nan
|
||||
else:
|
||||
mainprog_mass = self.get_mass(mainprog, mainprog_nsnap)
|
||||
|
||||
totprog_mass = mainprog_mass
|
||||
|
||||
# Unpack masses of the progenitors
|
||||
if minprog is not None:
|
||||
minprog, minprog_nsnap = prog[1:], prog_nsnap[1:]
|
||||
minprog_masses = [self.get_mass(c, n)
|
||||
for c, n in zip(minprog, minprog_nsnap)]
|
||||
|
||||
max_minprog_mass = max(minprog_masses)
|
||||
minprog_totmass = sum(minprog_masses)
|
||||
totprog_mass += minprog_totmass
|
||||
else:
|
||||
minprog_totmass = numpy.nan
|
||||
max_minprog_mass = numpy.nan
|
||||
|
||||
out += [
|
||||
[main_nsnap,]
|
||||
+ self.get_info(main_clump, main_nsnap)
|
||||
+ [mainprog_nsnap, totprog_mass, mainprog_mass, minprog_totmass, max_minprog_mass / mainprog_mass] # noqa
|
||||
]
|
||||
|
||||
pbar.update(1)
|
||||
pbar.set_description(f"Clump {main_clump} ({main_nsnap})")
|
||||
|
||||
if mainprog == 0:
|
||||
pbar.close()
|
||||
break
|
||||
|
||||
main_clump = mainprog
|
||||
main_nsnap = mainprog_nsnap
|
||||
|
||||
# Convert output to a structured array. We store integers as float
|
||||
# to avoid errors because of converting NaNs to integers.
|
||||
out = numpy.vstack(out)
|
||||
dtype = [("desc_snapshot_index", numpy.float32),
|
||||
("desc_mass", numpy.float32),
|
||||
("desc_x", numpy.float32),
|
||||
("desc_y", numpy.float32),
|
||||
("desc_z", numpy.float32),
|
||||
("prog_snapshot_index", numpy.float32),
|
||||
("prog_totmass", numpy.float32),
|
||||
("mainprog_mass", numpy.float32),
|
||||
("minprog_totmass", numpy.float32),
|
||||
("merger_ratio", numpy.float32),
|
||||
]
|
||||
|
||||
return numpy.array([tuple(row) for row in out], dtype=dtype)
|
||||
|
||||
def match_mass_to_phewcat(self, phewcat):
|
||||
"""
|
||||
For each clump mass in the PHEW catalogue, find the corresponding
|
||||
clump mass in the merger tree file. If no match is found returns NaN.
|
||||
These are not equal because the PHEW catalogue mass is the mass without
|
||||
unbinding.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
phewcat : csiborgtools.read.CSiBORGPEEWReader
|
||||
PHEW catalogue reader.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mass : float
|
||||
"""
|
||||
if phewcat.nsim != self.nsim:
|
||||
raise ValueError("Simulation indices do not match.")
|
||||
|
||||
nsnap = phewcat.nsnap
|
||||
indxs = phewcat["index"]
|
||||
mergertree_mass = numpy.full(len(indxs), numpy.nan,
|
||||
dtype=numpy.float32)
|
||||
|
||||
for i, ind in enumerate(indxs):
|
||||
try:
|
||||
mergertree_mass[i] = self.get_mass(ind, nsnap)
|
||||
except KeyError:
|
||||
continue
|
||||
|
||||
return mergertree_mass
|
||||
|
||||
def match_pos_to_phewcat(self, phewcat):
|
||||
"""
|
||||
For each clump mass in the PHEW catalogue, find the corresponding
|
||||
clump mass in the merger tree file. If no match is found returns NaN.
|
||||
These are not equal because the PHEW catalogue mass is the mass without
|
||||
unbinding.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
phewcat : csiborgtools.read.CSiBORGPEEWReader
|
||||
PHEW catalogue reader.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mass : float
|
||||
"""
|
||||
if phewcat.nsim != self.nsim:
|
||||
raise ValueError("Simulation indices do not match.")
|
||||
|
||||
nsnap = phewcat.nsnap
|
||||
indxs = phewcat["index"]
|
||||
mergertree_pos = numpy.full((len(indxs), 3), numpy.nan,
|
||||
dtype=numpy.float32)
|
||||
|
||||
for i, ind in enumerate(indxs):
|
||||
try:
|
||||
mergertree_pos[i] = self.get_pos(ind, nsnap)
|
||||
except KeyError:
|
||||
continue
|
||||
|
||||
return mergertree_pos[:, ::-1] # TODO later remove
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Manual halo tracking. #
|
||||
###############################################################################
|
||||
|
||||
|
||||
def track_halo_manually(cats, hid, maxdist=0.15, max_dlogm=0.35):
|
||||
"""
|
||||
Manually track a halo without using the merger tree. Searches for nearby
|
||||
halo of similar mass in adjacent snapshots. Supports only main haloes and
|
||||
can only work for the most massive haloes in a simulation, however even
|
||||
then significant care should be taken.
|
||||
|
||||
Selects the most massive halo within a search radius to be a match.
|
||||
|
||||
In case a progenitor is not found in the adjacent snapshot, the search
|
||||
continues in the next snapshot. Occasionally some haloes disappear..
|
||||
|
||||
Parameters
|
||||
----------
|
||||
cats : dict
|
||||
Dictionary of halo catalogues, keys are snapshot indices.
|
||||
hid : int
|
||||
Halo ID.
|
||||
maxdist : float, optional
|
||||
Maximum comoving distance for a halo to move between adjacent
|
||||
snapshots.
|
||||
max_dlogm : float, optional
|
||||
Maximum |log mass ratio| for a halo to be considered a progenitor.
|
||||
|
||||
Returns
|
||||
-------
|
||||
hist : structured array
|
||||
History of the halo.
|
||||
"""
|
||||
nsnap0 = max(cats.keys())
|
||||
k = cats[nsnap0]["hid_to_array_index"][hid]
|
||||
pos = cats[nsnap0]["cartesian_pos"][k]
|
||||
mass = cats[nsnap0]["summed_mass"][k]
|
||||
|
||||
if not cats[nsnap0]["is_main"][k]:
|
||||
raise ValueError("Only main haloes are supported.")
|
||||
|
||||
if not mass > 1e13:
|
||||
raise ValueError("Only the most massive haloes are supported.")
|
||||
|
||||
if not cats[nsnap0]["dist"][k] < 155.5:
|
||||
raise ValueError("Only high-resolution region haloes are supported.")
|
||||
|
||||
dtype = [("snapshot_index", numpy.float32),
|
||||
("x", numpy.float32),
|
||||
("y", numpy.float32),
|
||||
("z", numpy.float32),
|
||||
("mass", numpy.float32),
|
||||
("desc_dist", numpy.float32),
|
||||
]
|
||||
hist = numpy.full(len(cats), numpy.nan, dtype=dtype)
|
||||
hist["snapshot_index"][0] = nsnap0
|
||||
hist["x"][0], hist["y"][0], hist["z"][0] = pos
|
||||
hist["mass"][0] = mass
|
||||
|
||||
for n in trange(1, len(cats), desc="Tracking halo"):
|
||||
nsnap = nsnap0 - n
|
||||
hist["snapshot_index"][n] = nsnap
|
||||
|
||||
# Find indices of all main haloes that are within a box of width
|
||||
indxs = cats[nsnap].select_in_box(pos, 2 * maxdist)
|
||||
|
||||
if len(indxs) == 0:
|
||||
continue
|
||||
|
||||
nearby_pos = cats[nsnap]["cartesian_pos"][indxs]
|
||||
nearby_mass = cats[nsnap]["summed_mass"][indxs]
|
||||
|
||||
# Distance from the previous position and |log mass ratio|
|
||||
dist = periodic_distance(nearby_pos, pos, cats[nsnap].box.boxsize)
|
||||
dlogm = numpy.abs(numpy.log10(nearby_mass / mass))
|
||||
k = numpy.argmin(dlogm)
|
||||
|
||||
if (dlogm[k] < max_dlogm) & (dist[k] < maxdist):
|
||||
hist["x"][n], hist["y"][n], hist["z"][n] = nearby_pos[k]
|
||||
hist["mass"][n] = nearby_mass[k]
|
||||
hist["desc_dist"][n] = dist[k]
|
||||
|
||||
pos = nearby_pos[k]
|
||||
mass = nearby_mass[k]
|
||||
|
||||
return hist
|
||||
|
|
@ -98,7 +98,7 @@ def sort_fofid(nsim, verbose=True):
|
|||
|
||||
reader = csiborgtools.read.CSiBORGReader(paths)
|
||||
pars_extract = ["x"] # Dummy variable
|
||||
__, pids = reader.read_particle(nsnap, nsim, pars_extract,
|
||||
__, pids = reader.read_snapshot(nsnap, nsim, pars_extract,
|
||||
return_structured=False, verbose=verbose)
|
||||
del __
|
||||
collect()
|
||||
17
old/mv_fofmembership.sh
Normal file
17
old/mv_fofmembership.sh
Normal file
|
|
@ -0,0 +1,17 @@
|
|||
nthreads=1
|
||||
memory=100
|
||||
queue="berg"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python"
|
||||
file="mv_fofmembership.py"
|
||||
nsims="5511"
|
||||
|
||||
pythoncm="$env $file --nsims $nsims"
|
||||
|
||||
# echo $pythoncm
|
||||
# $pythoncm
|
||||
|
||||
cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm"
|
||||
echo "Submitting:"
|
||||
echo $cm
|
||||
echo
|
||||
$cm
|
||||
|
|
@ -12,7 +12,7 @@
|
|||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
r"""
|
||||
Script to load in the simulation particles, sort them by their FoF halo ID and
|
||||
Script to load in the simulation particles, sort them by their halo ID and
|
||||
dump into a HDF5 file. Stores the first and last index of each halo in the
|
||||
particle array. This can be used for fast slicing of the array to acces
|
||||
particles of a single clump.
|
||||
|
|
@ -108,7 +108,7 @@ def main(nsim, simname, verbose):
|
|||
pars_extract = ['x', 'y', 'z', 'vx', 'vy', 'vz', 'M', "ID"]
|
||||
else:
|
||||
pars_extract = None
|
||||
parts, pids = partreader.read_particle(
|
||||
parts, pids = partreader.read_snapshot(
|
||||
nsnap, nsim, pars_extract, return_structured=False, verbose=verbose)
|
||||
|
||||
# In case of CSiBORG, we need to convert the mass and velocities from
|
||||
18
old/pre_dumppart.sh
Normal file
18
old/pre_dumppart.sh
Normal file
|
|
@ -0,0 +1,18 @@
|
|||
nthreads=1
|
||||
memory=40
|
||||
queue="berg"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python"
|
||||
file="pre_dumppart.py"
|
||||
simname="csiborg"
|
||||
nsims="5511"
|
||||
|
||||
pythoncm="$env $file --nsims $nsims --simname $simname"
|
||||
|
||||
# echo $pythoncm
|
||||
# $pythoncm
|
||||
|
||||
cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm"
|
||||
echo "Submitting:"
|
||||
echo $cm
|
||||
echo
|
||||
$cm
|
||||
|
|
@ -67,14 +67,13 @@ def sort_particle_membership(nsim, nsnap, method):
|
|||
fout = fpath + "_sorted.hdf5"
|
||||
print(f"{datetime.now()}: saving the sorted data to ... `{fout}`")
|
||||
|
||||
header = """
|
||||
This dataset represents halo indices for each particle.
|
||||
- The particles are ordered as they appear in the simulation snapshot.
|
||||
- Unassigned particles are given a halo index of 0.
|
||||
"""
|
||||
with h5py.File(fout, 'w') as hdf:
|
||||
dset = hdf.create_dataset('hids_dataset', data=hids)
|
||||
dset.attrs['header'] = header
|
||||
dset = hdf.create_dataset('hids', data=hids)
|
||||
dset.attrs['header'] = """
|
||||
This dataset represents (sub)halo indices for each particle.
|
||||
- The particles are ordered as they appear in the simulation snapshot.
|
||||
- Unassigned particles are given an index of 0.
|
||||
"""
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
19
old/sort_halomaker.sh
Executable file
19
old/sort_halomaker.sh
Executable file
|
|
@ -0,0 +1,19 @@
|
|||
nthreads=1
|
||||
memory=64
|
||||
queue="berg"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python"
|
||||
file="sort_halomaker.py"
|
||||
|
||||
method="FOF"
|
||||
nsim="7444"
|
||||
|
||||
pythoncm="$env $file --method $method --nsim $nsim"
|
||||
|
||||
# echo $pythoncm
|
||||
# $pythoncm
|
||||
|
||||
cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm"
|
||||
echo "Submitting:"
|
||||
echo $cm
|
||||
echo
|
||||
$cm
|
||||
|
|
@ -61,13 +61,13 @@ def positions_to_ascii(positions, output_filename, boxsize=None,
|
|||
out_file.write(chunk_str + "\n")
|
||||
|
||||
|
||||
def extract_positions(nsim, paths, kind):
|
||||
def extract_positions(nsim, simname, paths, kind):
|
||||
"""
|
||||
Extract either the particle or halo positions.
|
||||
"""
|
||||
if kind == "particles":
|
||||
fname = paths.particles(nsim, args.simname)
|
||||
return h5py.File(fname, 'r')["particles"]
|
||||
fname = paths.processed_output(nsim, simname, "FOF")
|
||||
return h5py.File(fname, 'r')["snapshot_final/pos"][:]
|
||||
|
||||
if kind == "particles_rsp":
|
||||
raise NotImplementedError("RSP of particles is not implemented yet.")
|
||||
|
|
@ -75,23 +75,23 @@ def extract_positions(nsim, paths, kind):
|
|||
fpath = paths.observer_peculiar_velocity("PCS", 512, nsim)
|
||||
vpec_observer = numpy.load(fpath)["observer_vp"][0, :]
|
||||
cat = csiborgtools.read.CSiBORGHaloCatalogue(
|
||||
nsim, paths, bounds={"dist": (0, 155.5)}, load_fitted=True,
|
||||
load_initial=False, observer_velocity=vpec_observer, )
|
||||
nsim, paths, "halo_catalogue", "FOF", bounds={"dist": (0, 155.5)},
|
||||
observer_velocity=vpec_observer)
|
||||
|
||||
if kind == "halos":
|
||||
return cat.position()
|
||||
return cat["cartesian_pos"]
|
||||
|
||||
if kind == "halos_rsp":
|
||||
return cat.redshift_space_position()
|
||||
return cat["cartesian_redshift_pos"]
|
||||
|
||||
raise ValueError(f"Unknown kind `{kind}`. Allowed values are: "
|
||||
"`particles`, `particles_rsp`, `halos`, `halos_rsp`.")
|
||||
|
||||
|
||||
def main(nsim, paths, kind):
|
||||
boxsize = 677.7 if "particles" in kind else None
|
||||
pos = extract_positions(nsim, paths, kind)
|
||||
output_filename = paths.ascii_positions(nsim, kind)
|
||||
def main(args, paths):
|
||||
boxsize = 677.7 if "particles" in args.kind else None
|
||||
pos = extract_positions(args.nsim, args.simname, paths, args.kind)
|
||||
output_filename = paths.ascii_positions(args.nsim, args.kind)
|
||||
positions_to_ascii(pos, output_filename, boxsize=boxsize)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -28,6 +28,16 @@ from taskmaster import work_delegation
|
|||
import csiborgtools
|
||||
from utils import get_nsims
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Cosmotool SPH density & velocity field #
|
||||
###############################################################################
|
||||
|
||||
def cosmotool_sph(nsim, parser_args):
|
||||
pass
|
||||
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Density field #
|
||||
###############################################################################
|
||||
|
|
@ -40,13 +50,15 @@ def density_field(nsim, parser_args, to_save=True):
|
|||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
nsnap = max(paths.get_snapshots(nsim, "csiborg"))
|
||||
box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)
|
||||
fname = paths.processed_output(nsim, "csiborg", "halo_catalogue")
|
||||
|
||||
if not parser_args.in_rsp:
|
||||
parts = csiborgtools.read.read_h5(paths.particles(nsim, "csiborg"))
|
||||
parts = parts["particles"]
|
||||
snap = csiborgtools.read.read_h5(fname)["snapshot_final"]
|
||||
pos = snap["pos"]
|
||||
mass = snap["mass"]
|
||||
|
||||
gen = csiborgtools.field.DensityField(box, parser_args.MAS)
|
||||
field = gen(parts, parser_args.grid, verbose=parser_args.verbose)
|
||||
field = gen(pos, mass, parser_args.grid, verbose=parser_args.verbose)
|
||||
else:
|
||||
field = numpy.load(paths.field(
|
||||
"density", parser_args.MAS, parser_args.grid, nsim, False))
|
||||
|
|
@ -83,12 +95,15 @@ def velocity_field(nsim, parser_args, to_save=True):
|
|||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
nsnap = max(paths.get_snapshots(nsim, "csiborg"))
|
||||
box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)
|
||||
fname = paths.processed_output(nsim, "csiborg", "halo_catalogue")
|
||||
|
||||
parts = csiborgtools.read.read_h5(paths.particles(nsim, "csiborg"))
|
||||
parts = parts["particles"]
|
||||
snap = csiborgtools.read.read_h5(fname)["snapshot_final"]
|
||||
pos = snap["pos"]
|
||||
vel = snap["vel"]
|
||||
mass = snap["mass"]
|
||||
|
||||
gen = csiborgtools.field.VelocityField(box, parser_args.MAS)
|
||||
field = gen(parts, parser_args.grid, verbose=parser_args.verbose)
|
||||
field = gen(pos, vel, mass, parser_args.grid, verbose=parser_args.verbose)
|
||||
|
||||
if to_save:
|
||||
fout = paths.field("velocity", parser_args.MAS, parser_args.grid,
|
||||
|
|
@ -247,6 +262,7 @@ if __name__ == "__main__":
|
|||
parser.add_argument("--verbose", type=lambda x: bool(strtobool(x)),
|
||||
help="Verbosity flag for reading in particles.")
|
||||
parser.add_argument("--simname", type=str, default="csiborg",
|
||||
choices=["csiborg", "csiborg2"],
|
||||
help="Verbosity flag for reading in particles.")
|
||||
parser_args = parser.parse_args()
|
||||
comm = MPI.COMM_WORLD
|
||||
|
|
|
|||
|
|
@ -53,12 +53,20 @@ def open_galaxy_positions(survey_name, comm):
|
|||
|
||||
if rank == 0:
|
||||
if survey_name == "SDSS":
|
||||
survey = csiborgtools.read.SDSS(
|
||||
h=1, sel_steps=lambda cls: steps(cls, survey_name))
|
||||
survey = csiborgtools.SDSS()()
|
||||
pos = numpy.vstack([survey["DIST_UNCORRECTED"],
|
||||
survey["RA"],
|
||||
survey["DEC"]],
|
||||
).T
|
||||
pos = pos.astype(numpy.float32)
|
||||
indxs = survey["INDEX"]
|
||||
if survey_name == "SDSSxALFALFA":
|
||||
survey = csiborgtools.SDSSxALFALFA()()
|
||||
pos = numpy.vstack([survey["DIST_UNCORRECTED"],
|
||||
survey["RA_1"],
|
||||
survey["DEC_1"]],
|
||||
).T
|
||||
pos = pos.astype(numpy.float32)
|
||||
indxs = survey["INDEX"]
|
||||
elif survey_name == "GW170817":
|
||||
samples = File("/mnt/extraspace/rstiskalek/GWLSS/H1L1V1-EXTRACT_POSTERIOR_GW170817-1187008600-400.hdf", 'r')["samples"] # noqa
|
||||
|
|
@ -110,7 +118,7 @@ def evaluate_field(field, pos, nrand, smooth_scales=None, seed=42,
|
|||
field_smoothed = csiborgtools.field.smoothen_field(
|
||||
field, scale * MPC2BOX, boxsize=1, make_copy=True)
|
||||
else:
|
||||
field_smoothed = field
|
||||
field_smoothed = numpy.copy(field)
|
||||
|
||||
val[:, i] = csiborgtools.field.evaluate_sky(
|
||||
field_smoothed, pos=pos, mpc2box=MPC2BOX)
|
||||
|
|
@ -164,7 +172,7 @@ if __name__ == "__main__":
|
|||
parser.add_argument("--nsims", type=int, nargs="+", default=None,
|
||||
help="IC realisations. If `-1` processes all.")
|
||||
parser.add_argument("--survey", type=str, required=True,
|
||||
choices=["SDSS", "GW170817"],
|
||||
choices=["SDSS", "SDSSxALFALFA", "GW170817"],
|
||||
help="Galaxy survey")
|
||||
parser.add_argument("--smooth_scales", type=float, nargs="+", default=None,
|
||||
help="Smoothing scales in Mpc / h.")
|
||||
|
|
@ -189,12 +197,6 @@ if __name__ == "__main__":
|
|||
|
||||
pos, indxs = open_galaxy_positions(args.survey, MPI.COMM_WORLD)
|
||||
|
||||
if MPI.COMM_WORLD.Get_rank() == 0 and args.survey != "GW170817":
|
||||
fout = f"/mnt/extraspace/rstiskalek/CSiBORG/ascii_positions/{args.survey}_positions.npz" # noqa
|
||||
pos = csiborgtools.utils.radec_to_cartesian(pos) + 677.7 / 2
|
||||
print(f"Saving to ... `{fout}`.")
|
||||
numpy.savez(fout, pos=pos, indxs=indxs)
|
||||
|
||||
def _main(nsim):
|
||||
main(nsim, args, pos, indxs, paths,
|
||||
verbose=MPI.COMM_WORLD.Get_size() == 1)
|
||||
|
|
|
|||
|
|
@ -1,118 +0,0 @@
|
|||
# Copyright (C) 2022 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.
|
||||
"""
|
||||
Script to calculate the particle centre of mass, Lagrangian patch size in the
|
||||
initial snapshot.
|
||||
|
||||
The initial snapshot particles are read from the sorted files.
|
||||
"""
|
||||
from argparse import ArgumentParser
|
||||
from datetime import datetime
|
||||
|
||||
import numpy
|
||||
from mpi4py import MPI
|
||||
from taskmaster import work_delegation
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils import get_nsims
|
||||
|
||||
try:
|
||||
import csiborgtools
|
||||
except ModuleNotFoundError:
|
||||
import sys
|
||||
|
||||
sys.path.append("../")
|
||||
import csiborgtools
|
||||
|
||||
|
||||
def _main(nsim, simname, verbose):
|
||||
"""
|
||||
Calculate the Lagrangian halo centre of mass and Lagrangian patch size in
|
||||
the initial snapshot.
|
||||
"""
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
cols = [("index", numpy.int32),
|
||||
("x", numpy.float32),
|
||||
("y", numpy.float32),
|
||||
("z", numpy.float32),
|
||||
("lagpatch_size", numpy.float32),
|
||||
("lagpatch_ncells", numpy.int32),]
|
||||
|
||||
fname = paths.initmatch(nsim, simname, "particles")
|
||||
parts = csiborgtools.read.read_h5(fname)
|
||||
parts = parts['particles']
|
||||
halo_map = csiborgtools.read.read_h5(paths.particles(nsim, simname))
|
||||
halo_map = halo_map["halomap"]
|
||||
|
||||
if simname == "csiborg":
|
||||
cat = csiborgtools.read.CSiBORGHaloCatalogue(
|
||||
nsim, paths, bounds=None, load_fitted=False, load_initial=False)
|
||||
else:
|
||||
cat = csiborgtools.read.QuijoteHaloCatalogue(
|
||||
nsim, paths, nsnap=4, load_fitted=False, load_initial=False)
|
||||
hid2map = {hid: i for i, hid in enumerate(halo_map[:, 0])}
|
||||
|
||||
# Initialise the overlapper.
|
||||
if simname == "csiborg":
|
||||
kwargs = {"box_size": 2048, "bckg_halfsize": 512}
|
||||
else:
|
||||
kwargs = {"box_size": 512, "bckg_halfsize": 256}
|
||||
overlapper = csiborgtools.match.ParticleOverlap(**kwargs)
|
||||
|
||||
out = csiborgtools.read.cols_to_structured(len(cat), cols)
|
||||
for i, hid in enumerate(tqdm(cat["index"]) if verbose else cat["index"]):
|
||||
out["index"][i] = hid
|
||||
part = csiborgtools.read.load_halo_particles(hid, parts, halo_map,
|
||||
hid2map)
|
||||
|
||||
# Skip if the halo has no particles or is too small.
|
||||
if part is None or part.size < 40:
|
||||
continue
|
||||
|
||||
pos, mass = part[:, :3], part[:, 3]
|
||||
# Calculate the centre of mass and the Lagrangian patch size.
|
||||
cm = csiborgtools.center_of_mass(pos, mass, boxsize=1.0)
|
||||
distances = csiborgtools.periodic_distance(pos, cm, boxsize=1.0)
|
||||
out["x"][i], out["y"][i], out["z"][i] = cm
|
||||
out["lagpatch_size"][i] = numpy.percentile(distances, 99)
|
||||
|
||||
# Calculate the number of cells with > 0 density.
|
||||
delta = overlapper.make_delta(pos, mass, subbox=True)
|
||||
out["lagpatch_ncells"][i] = csiborgtools.delta2ncells(delta)
|
||||
|
||||
# Now save it
|
||||
fout = paths.initmatch(nsim, simname, "fit")
|
||||
if verbose:
|
||||
print(f"{datetime.now()}: dumping fits to .. `{fout}`.", flush=True)
|
||||
with open(fout, "wb") as f:
|
||||
numpy.save(f, out)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = ArgumentParser()
|
||||
parser.add_argument("--simname", type=str, default="csiborg",
|
||||
choices=["csiborg", "quijote"],
|
||||
help="Simulation name")
|
||||
parser.add_argument("--nsims", type=int, nargs="+", default=None,
|
||||
help="IC realisations. If `-1` processes all.")
|
||||
args = parser.parse_args()
|
||||
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
nsims = get_nsims(args, paths)
|
||||
|
||||
def main(nsim):
|
||||
_main(nsim, args.simname, MPI.COMM_WORLD.Get_size() == 1)
|
||||
|
||||
work_delegation(main, nsims, MPI.COMM_WORLD)
|
||||
|
|
@ -69,7 +69,7 @@ def pair_match_max(nsim0, nsimx, simname, min_logmass, mult, verbose):
|
|||
raise ValueError(f"Unknown simulation `{simname}`.")
|
||||
|
||||
reader = csiborgtools.summary.PairOverlap(cat0, catx, paths, min_logmass,
|
||||
maxdist=maxdist)
|
||||
maxdist=maxdist)
|
||||
out = csiborgtools.match.matching_max(
|
||||
cat0, catx, mass_kind, mult=mult, periodic=periodic,
|
||||
overlap=reader.overlap(from_smoothed=True),
|
||||
|
|
@ -106,54 +106,36 @@ def pair_match(nsim0, nsimx, simname, min_logmass, sigma, verbose):
|
|||
"""
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
smooth_kwargs = {"sigma": sigma, "mode": "constant", "cval": 0}
|
||||
bounds = {"lagpatch_size": (0, None)}
|
||||
|
||||
if simname == "csiborg":
|
||||
overlapper_kwargs = {"box_size": 2048, "bckg_halfsize": 512}
|
||||
mass_kind = "fof_totpartmass"
|
||||
bounds = {"dist": (0, 155), mass_kind: (10**min_logmass, None)}
|
||||
|
||||
cat0 = csiborgtools.read.CSiBORGHaloCatalogue(
|
||||
nsim0, paths, bounds=bounds, load_fitted=False,
|
||||
with_lagpatch=True)
|
||||
catx = csiborgtools.read.CSiBORGHaloCatalogue(
|
||||
nsimx, paths, bounds=bounds, load_fitted=False,
|
||||
with_lagpatch=True)
|
||||
bounds |= {"dist": (0, 155), mass_kind: (10**min_logmass, None)}
|
||||
cat0 = csiborgtools.read.CSiBORGCatalogue(
|
||||
nsim0, paths, "halo_catalogue", "FOF", mass_kind, bounds)
|
||||
catx = csiborgtools.read.CSiBORGCatalogue(
|
||||
nsimx, paths, "halo_catalogue", "FOF", mass_kind, bounds)
|
||||
elif simname == "quijote":
|
||||
overlapper_kwargs = {"box_size": 512, "bckg_halfsize": 256}
|
||||
mass_kind = "group_mass"
|
||||
bounds = {mass_kind: (10**min_logmass, None)}
|
||||
bounds |= {mass_kind: (10**min_logmass, None)}
|
||||
|
||||
cat0 = csiborgtools.read.QuijoteHaloCatalogue(
|
||||
nsim0, paths, 4, bounds=bounds, load_fitted=False,
|
||||
with_lagpatch=True)
|
||||
catx = csiborgtools.read.QuijoteHaloCatalogue(
|
||||
nsimx, paths, 4, bounds=bounds, load_fitted=False,
|
||||
with_lagpatch=True)
|
||||
cat0 = csiborgtools.read.QuijoteCatalogue(
|
||||
nsim0, paths, "halo_catalogue", "FOF", mass_kind, bounds=bounds)
|
||||
catx = csiborgtools.read.QuijoteCatalogue(
|
||||
nsimx, paths, "halo_catalogue", "FOF", mass_kind, bounds=bounds)
|
||||
else:
|
||||
raise ValueError(f"Unknown simulation name: `{simname}`.")
|
||||
|
||||
halomap0 = csiborgtools.read.read_h5(
|
||||
paths.particles(nsim0, simname))["halomap"]
|
||||
parts0 = csiborgtools.read.read_h5(
|
||||
paths.initmatch(nsim0, simname, "particles"))["particles"]
|
||||
hid2map0 = {hid: i for i, hid in enumerate(halomap0[:, 0])}
|
||||
|
||||
halomapx = csiborgtools.read.read_h5(
|
||||
paths.particles(nsimx, simname))["halomap"]
|
||||
partsx = csiborgtools.read.read_h5(
|
||||
paths.initmatch(nsimx, simname, "particles"))["particles"]
|
||||
hid2mapx = {hid: i for i, hid in enumerate(halomapx[:, 0])}
|
||||
|
||||
overlapper = csiborgtools.match.ParticleOverlap(**overlapper_kwargs)
|
||||
delta_bckg = overlapper.make_bckg_delta(parts0, halomap0, hid2map0, cat0,
|
||||
delta_bckg = overlapper.make_bckg_delta(cat0, verbose=verbose)
|
||||
delta_bckg = overlapper.make_bckg_delta(catx, delta=delta_bckg,
|
||||
verbose=verbose)
|
||||
delta_bckg = overlapper.make_bckg_delta(partsx, halomapx, hid2mapx, catx,
|
||||
delta=delta_bckg, verbose=verbose)
|
||||
|
||||
matcher = csiborgtools.match.RealisationsMatcher(
|
||||
mass_kind=mass_kind, **overlapper_kwargs)
|
||||
match_indxs, ngp_overlap = matcher.cross(cat0, catx, parts0, partsx,
|
||||
halomap0, halomapx, delta_bckg,
|
||||
matcher = csiborgtools.match.RealisationsMatcher(mass_kind=mass_kind,
|
||||
**overlapper_kwargs)
|
||||
match_indxs, ngp_overlap = matcher.cross(cat0, catx, delta_bckg,
|
||||
verbose=verbose)
|
||||
|
||||
# We want to store the halo IDs of the matches, not their array positions
|
||||
|
|
@ -177,8 +159,7 @@ def pair_match(nsim0, nsimx, simname, min_logmass, sigma, verbose):
|
|||
gaussian_filter(delta_bckg, output=delta_bckg, **smooth_kwargs)
|
||||
|
||||
# We calculate the smoothed overlap for the pairs whose NGP overlap is > 0.
|
||||
smoothed_overlap = matcher.smoothed_cross(cat0, catx, parts0, partsx,
|
||||
halomap0, halomapx, delta_bckg,
|
||||
smoothed_overlap = matcher.smoothed_cross(cat0, catx, delta_bckg,
|
||||
match_indxs, smooth_kwargs,
|
||||
verbose=verbose)
|
||||
|
||||
979
scripts/mergertree_extract.py
Normal file
979
scripts/mergertree_extract.py
Normal file
|
|
@ -0,0 +1,979 @@
|
|||
# Copyright (C) 2023 Mladen Ivkovic, 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.
|
||||
|
||||
import copy
|
||||
import os
|
||||
from os.path import exists, join
|
||||
from os import makedirs
|
||||
from sys import argv
|
||||
from datetime import datetime
|
||||
|
||||
import numpy as np
|
||||
from joblib import dump, load
|
||||
from tqdm import trange
|
||||
|
||||
errmsg = """
|
||||
|
||||
------------------------------------
|
||||
mergertree-extract.py
|
||||
------------------------------------
|
||||
|
||||
|
||||
---------------
|
||||
Usage
|
||||
---------------
|
||||
|
||||
This script extracts the masses of clumps and haloes written by the mergertree
|
||||
patch.
|
||||
It needs output_XXXXX/mergertree_XXXXX.txtYYYYY and
|
||||
output_XXXXX/clump_XXXXX.txtYYYYY files to work.
|
||||
You need to run it from the directory where the output_XXXXX directories are
|
||||
in.
|
||||
|
||||
|
||||
There are three working modes defined:
|
||||
|
||||
1) do for one clump only.
|
||||
You need to provide the clump ID you want it done for.
|
||||
You can provide a starting directory, but by default the script will
|
||||
search for the directory where z = 0.
|
||||
|
||||
run with `python3 mergertree-extract.py <clumpid> [--options] `
|
||||
|
||||
this creates the file mergertree_XXXXX_halo-<halo-ID>.txt. Its contents are
|
||||
discussed below.
|
||||
|
||||
|
||||
2) do for one halo.
|
||||
You need to provide the halo ID you want it done for, and the flag
|
||||
-c or --children.
|
||||
The script will by itself find all the child clumps and walk through
|
||||
their main branches as well, and write them down.
|
||||
|
||||
run with `python3 mergertree-extract.py <haloid> -c [--options]`
|
||||
or `python3 mergertree-extract.py <haloid> --children [--options]`
|
||||
|
||||
this creates the hollowing files:
|
||||
|
||||
- halo_hierarchy_XXXXX-<halo-ID>.txt
|
||||
contains the halo ID, how many children it has, and the children
|
||||
IDs
|
||||
|
||||
- mergertree_XXXXX_halo-<halo-ID>.txt
|
||||
mergertree data for halo that you chose.
|
||||
|
||||
- mergertree_XXXXX_subhalo-<child-ID>.txt
|
||||
mergertree data for subhalos of the halo you chose. One file will
|
||||
be created for each subhalo.
|
||||
|
||||
The contents of the mergertree_XXXXX* files are discussed below.
|
||||
|
||||
|
||||
3) do for all haloes
|
||||
The script will just walk off all haloes in the z = 0 directory. Note:
|
||||
Haloes, not clumps!
|
||||
run with `python3 mergertree-extract.py -a [--options]`
|
||||
or `python3 mergertree-extract.py --all [--options]`
|
||||
|
||||
This will create the same type of files as in mode (2), just for all
|
||||
haloes.
|
||||
|
||||
|
||||
If only an integer is given as cmdline arg, mode (1) [one clump only] will be
|
||||
run. If no cmd line argument is given, mode (3) [--all] will be run.
|
||||
|
||||
|
||||
|
||||
---------------
|
||||
Output
|
||||
---------------
|
||||
|
||||
the mergertree_XXXXX* files have 6 columns:
|
||||
|
||||
snapshot The snapshot from which this data is taken from
|
||||
|
||||
redshift The redshift of that snapshot
|
||||
|
||||
clump_ID The clump ID of the clump at that snapshot
|
||||
|
||||
mass The mass of the clump at that snapshot, based on what's in
|
||||
the output_XXXXX/mergertree_XXXXX.txtYYYYY files, not the
|
||||
output_XXXXX/clump_XXXXX.txtYYYYY files.
|
||||
|
||||
mass_from_mergers how much mass has been merged into this clump in this
|
||||
snapshot, i.e. the sum of all the clump masses that have
|
||||
been found to merge with this clump at this snapshot. This
|
||||
does not include the mass of clumps which only seem to
|
||||
merge with this clump, but re-emerge later.
|
||||
|
||||
mass_from_jumpers The mass of all clumps that seem to merge with this clump,
|
||||
but re-emerge at a later time.
|
||||
|
||||
|
||||
----------------
|
||||
Options
|
||||
----------------
|
||||
|
||||
List of all flags:
|
||||
|
||||
Running modes
|
||||
|
||||
-a, --all: make trees for all clumps in output where z = 0
|
||||
-c --children: make trees for a halo and all its subhaloes. You need to
|
||||
specify which halo via its halo ID.
|
||||
-h, --help: print this help and exit.
|
||||
|
||||
Options:
|
||||
--start-at=INT don't start at z = 0 snapshot, but with the specified
|
||||
directory output_00INT.
|
||||
--prefix=some/path/ path where you want your output written to.
|
||||
-v, --verbose: be more verbose about what you're doing
|
||||
|
||||
|
||||
|
||||
|
||||
-----------------
|
||||
Requirements
|
||||
-----------------
|
||||
|
||||
It needs output_XXXXX/mergertree_XXXXX.txtYYYYY and
|
||||
output_XXXXX/clump_XXXXX.txtYYYYY files to work, which are created using the
|
||||
mergertree patch in ramses.
|
||||
|
||||
Also needs numpy.
|
||||
"""
|
||||
|
||||
###############################################################################
|
||||
# Clump data #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class ClumpData:
|
||||
"""
|
||||
Data from clump_XXXXX.txt
|
||||
|
||||
Parameters
|
||||
----------
|
||||
par : params object
|
||||
"""
|
||||
def __init__(self, par):
|
||||
self.clumpids = np.zeros(1) # clump ID
|
||||
self.parent = np.zeros(1) # parent ID
|
||||
self.level = np.zeros(1) # clump level
|
||||
|
||||
def read_clumpdata(self, par):
|
||||
"""Reads in the clump data for the z = 0 directory."""
|
||||
if par.verbose:
|
||||
print("Reading clump data.")
|
||||
|
||||
out = p.z0
|
||||
|
||||
raw_data = [None for i in range(par.ncpu)]
|
||||
dirnrstr = str(par.outputnrs[out]).zfill(5)
|
||||
dirname = 'output_' + dirnrstr
|
||||
|
||||
i = 0
|
||||
for cpu in range(1):
|
||||
fname = join(par.workdir, dirname, 'clump_' + dirnrstr + '.dat')
|
||||
new_data = np.loadtxt(fname, dtype='int', skiprows=1,
|
||||
usecols=[0, 1, 2])
|
||||
if new_data.ndim == 2:
|
||||
raw_data[i] = new_data
|
||||
i += 1
|
||||
elif new_data.shape[0] == 3: # if only 1 row is present in file
|
||||
raw_data[i] = np.atleast_2d(new_data)
|
||||
i += 1
|
||||
|
||||
fulldata = np.concatenate(raw_data[:i], axis=0)
|
||||
self.clumpids = fulldata[:, 0]
|
||||
self.level = fulldata[:, 1]
|
||||
self.parent = fulldata[:, 2]
|
||||
|
||||
def cleanup_clumpdata(self, par, mtd):
|
||||
"""
|
||||
The particle unbinding can remove entire clumps from the catalogue.
|
||||
If the option isn't set in the namelist, the clumpfinder output will
|
||||
still be made not based on the clumpfinder. If that is the case, the
|
||||
clumpfinder catalogue will contain clumps which the mergertree data
|
||||
doesn't have, leading to problems. So remove those here.
|
||||
"""
|
||||
for i, c in enumerate(self.clumpids):
|
||||
if c not in mtd.descendants[par.z0]:
|
||||
self.clumpids[i] = 0
|
||||
self.level[i] = 0
|
||||
self.parent[i] = -1 # don't make it the same as clumpid
|
||||
|
||||
def find_children(self, clumpid):
|
||||
"""Find the children for given clump ID."""
|
||||
children = []
|
||||
last_added = [clumpid]
|
||||
|
||||
loopcounter = 0
|
||||
while True:
|
||||
loopcounter += 1
|
||||
this_level_parents = copy.copy(last_added)
|
||||
children += this_level_parents
|
||||
last_added = []
|
||||
for i, cid in enumerate(self.clumpids):
|
||||
if self.parent[i] in this_level_parents and cid != clumpid:
|
||||
last_added.append(cid)
|
||||
|
||||
if len(last_added) == 0:
|
||||
break
|
||||
|
||||
if loopcounter == 100:
|
||||
print("Finished 100 iterations, we shouldn't be this deep")
|
||||
break
|
||||
|
||||
return children[1:] # don't return top level parent
|
||||
|
||||
def write_children(self, par, clumpid, children):
|
||||
"""Write the children to file."""
|
||||
hfile = join(par.outdir, f"{par.halofilename}-{str(clumpid)}.txt")
|
||||
|
||||
with open(hfile, 'w') as f:
|
||||
f.write("# {0:>18} {1:>18} {2:>18}\n".format("halo", "nr_of_children", "children")) # noqa
|
||||
nc = len(children)
|
||||
dumpstring = " {0:18d} {1:18d}".format(clumpid, nc)
|
||||
dumpstring = "".join([dumpstring] + [" {0:18d}".format(c) for c in children] + ['\n']) # noqa
|
||||
f.write(dumpstring)
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Constants object #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class Constants:
|
||||
"""
|
||||
Class holding constants.
|
||||
"""
|
||||
def __init__(self):
|
||||
self.Mpc = 3.086e24 # cm
|
||||
self.M_Sol = 1.98855e33 # g
|
||||
self.Gyr = (24 * 3600 * 365 * 1e9) # s
|
||||
self.G = 4.492e-15 # Mpc^3/(M_sol Gyr^2)
|
||||
|
||||
self.H0 = 100 # km/s/Mpc
|
||||
self.omega_m = 0.307000011205673
|
||||
self.omega_l = 0.693000018596649
|
||||
self.omega_k = 0.0
|
||||
self.omega_b = 0.0
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Params object #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class Params:
|
||||
"""
|
||||
Global parameters to be stored
|
||||
"""
|
||||
def __init__(self):
|
||||
# self.workdir = f"/mnt/extraspace/hdesmond/ramses_out_{self.nsim}"
|
||||
# self.outdir = f"/mnt/extraspace/rstiskalek/CSiBORG/cleaned_mtree/ramses_out_{self.nsim}" # noqa
|
||||
# if not exists(self.outdir):
|
||||
# makedirs(self.outdir)
|
||||
self.lastdir = "" # last output_XXXXX directory
|
||||
self.lastdirnr = -1 # XXXX from lastdir
|
||||
self.ncpu = 1 # Number of CPUs used
|
||||
self.noutput = 1 # how many output_XXXXX dirs exist
|
||||
self.nout = 1 # how many outputs we're gonna deal with. (Some might not have merger tree data) # noqa
|
||||
self.outputnrs = None # numpy array of output numbers
|
||||
self.output_lowest = 0 # lowest snapshot number that we're dealing with (>= 1) # noqa
|
||||
self.z0 = 0 # index of z=0 snapshot (or whichever you want to start with) # noqa
|
||||
|
||||
# NOTE: params.nout will be defined such that you can easily loop
|
||||
|
||||
self.verbose = False # verbosity
|
||||
self.start_at = 0 # output dir to start with, if given
|
||||
|
||||
self.output_prefix = "" # user given prefix for output files
|
||||
self.outputfilename = "" # output filename. Stores prefix/mergertree_XXXXX part of name only # noqa
|
||||
self.halofilename = "" # output filename for halo hierarchy. Stores prefix/halo_hierarchy_XXXXX part of filename only # noqa
|
||||
|
||||
self.one_halo_only = False # do the tree for one clump only
|
||||
self.halo_and_children = False # do the tree for one halo, including subhaloes # noqa
|
||||
self.do_all = False # do for all clumps at z=0 output
|
||||
|
||||
self.clumpid = 0 # which clump ID to work for.
|
||||
self.nsim = None
|
||||
|
||||
# Dictionnary of accepted keyword command line arguments
|
||||
self.accepted_flags = {
|
||||
'-a': self.set_do_all,
|
||||
'--all': self.set_do_all,
|
||||
'-r': self.set_halo_and_children,
|
||||
'--recursive': self.set_halo_and_children,
|
||||
'-c': self.set_halo_and_children,
|
||||
'--children': self.set_halo_and_children,
|
||||
'-h': self.get_help,
|
||||
'--help': self.get_help,
|
||||
'-v': self.set_verbose,
|
||||
'--verbose': self.set_verbose,
|
||||
}
|
||||
|
||||
self.accepted_flags_with_args = {
|
||||
"--nsim": self.set_nsim,
|
||||
'--start-at': self.set_startnr,
|
||||
'--prefix': self.set_prefix,
|
||||
}
|
||||
|
||||
# -----------------------------
|
||||
# Setter methods
|
||||
# -----------------------------
|
||||
|
||||
def set_do_all(self):
|
||||
self.do_all = True
|
||||
return
|
||||
|
||||
def set_halo_and_children(self):
|
||||
self.halo_and_children = True
|
||||
return
|
||||
|
||||
def get_help(self):
|
||||
print(errmsg)
|
||||
quit()
|
||||
return
|
||||
|
||||
def set_verbose(self):
|
||||
self.verbose = True
|
||||
return
|
||||
|
||||
def set_startnr(self, arg):
|
||||
flag, startnr = arg.split("=")
|
||||
try:
|
||||
self.start_at = int(startnr)
|
||||
except ValueError:
|
||||
print("given value for --start-at=INT isn't an integer?")
|
||||
|
||||
def set_prefix(self, arg):
|
||||
flag, prefix = arg.split("=")
|
||||
# try:
|
||||
self.output_prefix = prefix
|
||||
try:
|
||||
os.makedirs(self.output_prefix)
|
||||
except FileExistsError:
|
||||
pass
|
||||
return
|
||||
|
||||
def set_nsim(self, arg):
|
||||
flag, nsim = arg.split("=")
|
||||
try:
|
||||
self.nsim = int(nsim)
|
||||
except ValueError:
|
||||
print("given value for --nsim=INT isn't an integer?")
|
||||
|
||||
def read_cmdlineargs(self):
|
||||
"""
|
||||
Reads in the command line arguments and store them in the
|
||||
global_params object.
|
||||
"""
|
||||
nargs = len(argv)
|
||||
i = 1 # first cmdlinearg is filename of this file, so skip it
|
||||
|
||||
while i < nargs:
|
||||
arg = argv[i]
|
||||
arg = arg.strip()
|
||||
if arg in self.accepted_flags.keys():
|
||||
self.accepted_flags[arg]()
|
||||
else:
|
||||
for key in self.accepted_flags_with_args.keys():
|
||||
if arg.startswith(key):
|
||||
self.accepted_flags_with_args[key](arg)
|
||||
break
|
||||
else:
|
||||
try:
|
||||
self.clumpid = int(arg)
|
||||
except ValueError:
|
||||
print(f"I didn't recognize the argument '{arg}'. Use "
|
||||
"mergertre-extract.py -h or --help to print "
|
||||
"help message.")
|
||||
quit()
|
||||
|
||||
i += 1
|
||||
|
||||
if self.nsim is None:
|
||||
raise ValueError("nsim not set. Use --nsim=INT to set it.")
|
||||
|
||||
@property
|
||||
def workdir(self):
|
||||
return f"/mnt/extraspace/hdesmond/ramses_out_{self.nsim}"
|
||||
|
||||
@property
|
||||
def outdir(self):
|
||||
fname = f"/mnt/extraspace/rstiskalek/CSiBORG/cleaned_mtree/ramses_out_{self.nsim}" # noqa
|
||||
if not exists(fname):
|
||||
makedirs(fname)
|
||||
return fname
|
||||
|
||||
def get_output_info(self):
|
||||
"""
|
||||
Read in the output info based on the files in the current working
|
||||
directory. Reads in last directory, ncpu, noutputs. Doesn't read
|
||||
infofiles.
|
||||
"""
|
||||
# self.workdir = os.getcwd()
|
||||
filelist = os.listdir(self.workdir)
|
||||
|
||||
outputlist = []
|
||||
for filename in filelist:
|
||||
if filename.startswith('output_'):
|
||||
outputlist.append(filename)
|
||||
|
||||
if len(outputlist) < 1:
|
||||
print("I didn't find any output_XXXXX directories in current "
|
||||
"working directory. Are you in the correct workdir? "
|
||||
"Use mergertree-extract.py -h or --help to print help "
|
||||
"message.")
|
||||
quit()
|
||||
|
||||
outputlist.sort()
|
||||
|
||||
self.lastdir = outputlist[-1]
|
||||
self.lastdirnr = int(self.lastdir[-5:])
|
||||
self.noutput = len(outputlist)
|
||||
|
||||
if (self.start_at > 0):
|
||||
# check that directory exists
|
||||
startnrstr = str(self.start_at).zfill(5)
|
||||
if 'output_' + startnrstr not in outputlist:
|
||||
print("Didn't find specified starting directory "
|
||||
f"output_{startnrstr} use mergertree-extract.py -h or "
|
||||
"--help to print help message.")
|
||||
quit()
|
||||
|
||||
# read ncpu from infofile in last output directory
|
||||
infofile = join(self.workdir, self.lastdir,
|
||||
f"info_{self.lastdir[-5:]}.txt")
|
||||
with open(infofile, 'r') as f:
|
||||
ncpuline = f.readline()
|
||||
line = ncpuline.split()
|
||||
self.ncpu = int(line[-1])
|
||||
|
||||
def setup_and_checks(self, sd):
|
||||
"""
|
||||
Do checks and additional setups once you have all the cmd line args and
|
||||
output infos
|
||||
|
||||
Parameters
|
||||
----------
|
||||
sd: snapshotdata object
|
||||
"""
|
||||
# set running mode
|
||||
if not self.do_all:
|
||||
if self.clumpid <= 0:
|
||||
print("No or wrong clump id given. Setting the --all mode.")
|
||||
self.set_do_all()
|
||||
else:
|
||||
if not self.halo_and_children:
|
||||
self.one_halo_only = True
|
||||
|
||||
# generate list of outputdirnumbers
|
||||
startnr = self.lastdirnr
|
||||
self.outputnrs = np.array(range(startnr, startnr - self.noutput, -1))
|
||||
|
||||
# find starting output directory
|
||||
self.z0 = np.argmin(np.absolute(sd.redshift))
|
||||
|
||||
if self.start_at > 0:
|
||||
# replace z0 dir with starting dir
|
||||
self.z0 = self.lastdirnr - self.start_at
|
||||
|
||||
# generate output filename
|
||||
dirnrstr = str(self.outputnrs[self.z0]).zfill(5)
|
||||
fname = "mergertree_" + dirnrstr
|
||||
self.outputfilename = join(self.output_prefix, fname)
|
||||
|
||||
# generate halo output filename
|
||||
fname = "halo_hierarchy_" + dirnrstr
|
||||
self.halofilename = join(self.output_prefix, fname)
|
||||
|
||||
# rename output_prefix to something if it wasn't set
|
||||
if self.output_prefix == "":
|
||||
self.output_prefix = os.path.relpath(self.workdir)
|
||||
|
||||
# find self.nout; i.e. how many outputs we are actually going to have
|
||||
for out in range(self.noutput - 1, -1, -1):
|
||||
dirnrstr = str(self.outputnrs[out]).zfill(5)
|
||||
mtreefile = join(self.workdir,
|
||||
f"output_{dirnrstr}",
|
||||
f"mergertree_{dirnrstr}.dat")
|
||||
|
||||
if os.path.exists(mtreefile):
|
||||
print("Loading mergertree data from ", mtreefile)
|
||||
# if there is a file, this is lowest snapshot number directory
|
||||
# that we'll be dealing with, and hence will have the highest
|
||||
# index number in the arrays I'm using
|
||||
|
||||
# NOTE: params.nout will be defined such that you can easily
|
||||
# loop for out in range(p.z0, p.nout)
|
||||
self.nout = out + 1
|
||||
break
|
||||
|
||||
def print_params(self):
|
||||
"""Prints out the parameters that are set."""
|
||||
if self.do_all:
|
||||
print("Working mode: all clumps")
|
||||
else:
|
||||
if self.halo_and_children:
|
||||
print("Working mode: halo", self.clumpid, "and its children") # noqa
|
||||
else:
|
||||
print("Working mode: clump ", self.clumpid)
|
||||
|
||||
print("workdir: ", self.workdir)
|
||||
print("snapshot of tree root: ", self.outputnrs[self.z0])
|
||||
print("p.one_halo_only ", p.one_halo_only)
|
||||
print("p.do_all ", p.do_all)
|
||||
print("p.halo_and_children ", p.halo_and_children)
|
||||
print("p.one_halo_only ", p.one_halo_only)
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Merger tree data #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class MTreeData:
|
||||
"""
|
||||
Merger tree data lists
|
||||
|
||||
Parameters
|
||||
----------
|
||||
par : params object
|
||||
"""
|
||||
def __init__(self, par):
|
||||
self.progenitors = [np.zeros(1) for i in range(par.noutput)] # progenitor IDs # noqa
|
||||
self.descendants = [np.zeros(1) for i in range(par.noutput)] # descendant IDs # noqa
|
||||
self.progenitor_outputnrs = [np.zeros(1) for i in range(par.noutput)] # snapshot number of progenitor # noqa
|
||||
self.mass = [np.zeros(1) for i in range(par.noutput)] # descendant mass # noqa
|
||||
self.mass_to_remove = [np.zeros(1) for i in range(par.noutput)] # descendant mass # noqa
|
||||
|
||||
def read_mergertree_data(self, par, sd):
|
||||
"""Reads in mergertree data."""
|
||||
|
||||
if par.verbose:
|
||||
print("Reading in mergertree data")
|
||||
|
||||
# Preparation
|
||||
|
||||
# define new datatype for mergertree output
|
||||
mtree = np.dtype([('clump', 'i4'),
|
||||
('prog', 'i4'),
|
||||
('prog_outnr', 'i4'),
|
||||
('mass', 'f8'),
|
||||
('npart', 'f8'),
|
||||
('x', 'f8'),
|
||||
('y', 'f8'),
|
||||
('z', 'f8'),
|
||||
('vx', 'f8'),
|
||||
('vy', 'f8'),
|
||||
('vz', 'f8')
|
||||
])
|
||||
|
||||
# ---------------------------
|
||||
# Loop over directories
|
||||
# ---------------------------
|
||||
|
||||
startnr = par.lastdirnr
|
||||
# READ THE ONES BEFORE z0 TOO!
|
||||
for output in trange(par.nout, desc="Reading merger"):
|
||||
dirnr = str(startnr - output).zfill(5)
|
||||
srcdir = 'output_' + dirnr
|
||||
|
||||
fnames = [srcdir + '/' + "mergertree_" + dirnr + '.dat']
|
||||
fnames[0] = join(par.workdir, fnames[0])
|
||||
|
||||
datalist = [np.zeros((1, 3)) for i in range(par.ncpu)]
|
||||
i = 0
|
||||
nofile = 0
|
||||
for f in fnames:
|
||||
if os.path.exists(f):
|
||||
datalist[i] = np.atleast_1d(np.genfromtxt(f, dtype=mtree,
|
||||
skip_header=1))
|
||||
i += 1
|
||||
else:
|
||||
nofile += 1
|
||||
|
||||
if nofile == p.ncpu:
|
||||
print("Didn't find any mergertree data in", srcdir)
|
||||
|
||||
# ---------------------------------
|
||||
# Sort out data
|
||||
# ---------------------------------
|
||||
if i > 0:
|
||||
fulldata = np.concatenate(datalist[:i], axis=0)
|
||||
|
||||
self.descendants[output] = fulldata[:]['clump']
|
||||
self.progenitors[output] = fulldata[:]['prog']
|
||||
self.progenitor_outputnrs[output] = fulldata[:]['prog_outnr']
|
||||
self.mass[output] = fulldata[:]['mass']
|
||||
# self.npart[output] = fulldata[:]['npart']
|
||||
# self.x[output] = fulldata[:]['x']
|
||||
# self.y[output] = fulldata[:]['y']
|
||||
# self.z[output] = fulldata[:]['z']
|
||||
# self.vx[output] = fulldata[:]['vx']
|
||||
# self.vy[output] = fulldata[:]['vy']
|
||||
# self.vz[output] = fulldata[:]['vz']
|
||||
|
||||
# --------------------------------------
|
||||
# Transform units to physical units
|
||||
# --------------------------------------
|
||||
|
||||
# transform units to physical units
|
||||
for i in range(len(self.descendants)):
|
||||
self.mass[i] *= sd.unit_m[i]
|
||||
# self.x[i] *= sd.unit_l[i] # only transform later when needed; Need to check for periodicity first! # noqa
|
||||
# self.y[i] *= sd.unit_l[i]
|
||||
# self.z[i] *= sd.unit_l[i]
|
||||
# self.vx[i] *= sd.unit_l[i]/sd.unit_t[i]
|
||||
# self.vy[i] *= sd.unit_l[i]/sd.unit_t[i]
|
||||
# self.vz[i] *= sd.unit_l[i]/sd.unit_t[i]
|
||||
|
||||
def clean_up_jumpers(self, par):
|
||||
"""
|
||||
Remove jumpers from the merger list. Take note of how much mass should
|
||||
be removed from the descendant because the jumper is to be removed.
|
||||
"""
|
||||
# First initialize mass_to_remove arrays
|
||||
self.mass_to_remove = [np.zeros(self.descendants[out].shape)
|
||||
for out in range(par.noutput)]
|
||||
nreplaced = 0
|
||||
for out in trange(par.nout + par.z0 - 1, desc="Cleaning jumpers"):
|
||||
for i, pr in enumerate(self.progenitors[out]):
|
||||
if pr < 0:
|
||||
# Subtract 1 here from snapind:
|
||||
# progenitor_outputnrs gives the snapshot number where the
|
||||
# jumper was a descendant for the last time
|
||||
# so you need to overwrite the merging one snapshot later,
|
||||
# where the clump is the progenitor
|
||||
snapind = get_snap_ind(p, self.progenitor_outputnrs[out][i]) - 1 # noqa
|
||||
|
||||
# NOTE bottleneck
|
||||
jumpind = self.progenitors[snapind] == -pr
|
||||
|
||||
# NOTE bottleneck
|
||||
# find index of descendant into which this clump will
|
||||
# appearingly merge into
|
||||
mergerind = self.descendants[snapind] == - self.descendants[snapind][jumpind] # noqa
|
||||
# overwrite merging event so it won't count
|
||||
self.descendants[snapind][jumpind] = 0
|
||||
|
||||
# find mass of jumper in previous snapshot
|
||||
jumpmassind = self.descendants[snapind + 1] == -pr
|
||||
# note how much mass might need to be removed for whatever
|
||||
# you need it
|
||||
self.mass_to_remove[snapind][mergerind] += self.mass[snapind + 1][jumpmassind] # noqa
|
||||
|
||||
nreplaced += 1
|
||||
|
||||
print("Cleaned out", nreplaced, "jumpers")
|
||||
|
||||
def get_tree(self, par, tree, sd, clumpid):
|
||||
"""Follow the main branch down."""
|
||||
if par.verbose:
|
||||
print("Computing tree for clump", clumpid)
|
||||
|
||||
dind = self.descendants[par.z0] == clumpid
|
||||
desc_snap_ind = p.z0
|
||||
desc = self.descendants[p.z0][dind]
|
||||
prog = self.progenitors[p.z0][dind]
|
||||
|
||||
def get_prog_indices(prog, desc_snap_ind):
|
||||
"""
|
||||
Compute snapshot index at which given progenitor has been a
|
||||
descendant and its index in the array
|
||||
|
||||
prog: progenitor ID
|
||||
desc_snap_ind: snapshot index of descendant of given prog
|
||||
|
||||
returns:
|
||||
p_snap_ind: snapshot index of the progenitor
|
||||
pind: progenitor index (np.array mask) of progenitor in
|
||||
array where it is descendant
|
||||
"""
|
||||
if prog > 0: # if progenitor isn't jumper
|
||||
# find progenitor's index in previous snapshot
|
||||
p_snap_ind = desc_snap_ind + 1
|
||||
pind = self.descendants[p_snap_ind] == prog
|
||||
|
||||
elif prog < 0:
|
||||
p_snap_ind = get_snap_ind(
|
||||
par, self.progenitor_outputnrs[desc_snap_ind][dind])
|
||||
pind = self.descendants[p_snap_ind] == -prog
|
||||
|
||||
return p_snap_ind, pind
|
||||
|
||||
while True:
|
||||
# first calculate merger mass
|
||||
mergers = self.descendants[desc_snap_ind] == -desc
|
||||
mergermass = 0.0
|
||||
if mergers.any():
|
||||
for m in self.progenitors[desc_snap_ind][mergers]:
|
||||
# find mass of merger. That's been written down at the
|
||||
# place where merger was descendant.
|
||||
m_snap_ind, mergerind = get_prog_indices(m, desc_snap_ind)
|
||||
mergermass += self.mass[m_snap_ind][mergerind]
|
||||
|
||||
# add the descendant to the tree
|
||||
tree.add_snap(par.outputnrs[desc_snap_ind],
|
||||
sd.redshift[desc_snap_ind], desc,
|
||||
self.mass[desc_snap_ind][dind], mergermass,
|
||||
self.mass_to_remove[desc_snap_ind][dind])
|
||||
|
||||
# now descend down the main branch
|
||||
if prog != 0:
|
||||
p_snap_ind, pind = get_prog_indices(prog, desc_snap_ind)
|
||||
else:
|
||||
# stop at progenitor = 0
|
||||
break
|
||||
|
||||
# prepare for next round
|
||||
desc_snap_ind = p_snap_ind
|
||||
dind = pind
|
||||
desc = abs(prog)
|
||||
prog = self.progenitors[p_snap_ind][pind]
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Snapshot data #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class SnapshotData():
|
||||
"""Snapshot specific data"""
|
||||
def __init__(self, par):
|
||||
# read in
|
||||
self.aexp = np.zeros(par.noutput)
|
||||
self.unit_l = np.zeros(par.noutput)
|
||||
self.unit_m = np.zeros(par.noutput)
|
||||
self.unit_t = np.zeros(par.noutput)
|
||||
self.unit_dens = np.zeros(par.noutput)
|
||||
# to be computed
|
||||
self.redshift = np.zeros(par.noutput) # z
|
||||
|
||||
def read_infofiles(self, par, const):
|
||||
"""Read the info_XXXXX.txt files."""
|
||||
if par.verbose:
|
||||
print("Reading info files.")
|
||||
|
||||
startnr = par.lastdirnr
|
||||
|
||||
for output in range(p.noutput):
|
||||
# Start with last directory (e.g. output_00060),
|
||||
# work your way to first directory (e.g. output_00001)
|
||||
# p.z0 isn't decided yet, so just read in everything here.
|
||||
dirnr = str(startnr - output).zfill(5)
|
||||
srcdir = 'output_' + dirnr
|
||||
|
||||
try:
|
||||
# ------------------------------------------------------
|
||||
# get time, redshift, and units even for output_00001
|
||||
# ------------------------------------------------------
|
||||
fileloc = srcdir + '/info_' + dirnr + '.txt'
|
||||
fileloc = join(par.workdir, fileloc)
|
||||
infofile = open(fileloc)
|
||||
for i in range(9):
|
||||
infofile.readline() # skip first 9 lines
|
||||
|
||||
# get expansion factor
|
||||
aline = infofile.readline()
|
||||
astring, equal, aval = aline.partition("=")
|
||||
afloat = float(aval)
|
||||
sd.aexp[output] = afloat
|
||||
|
||||
for i in range(5):
|
||||
infofile.readline() # skip 5 lines
|
||||
|
||||
# get unit_l
|
||||
unitline = infofile.readline()
|
||||
unitstring, equal, unitval = unitline.partition("=")
|
||||
unitfloat = float(unitval)
|
||||
sd.unit_l[output] = unitfloat
|
||||
|
||||
# get unit_dens
|
||||
unitline = infofile.readline()
|
||||
unitstring, equal, unitval = unitline.partition("=")
|
||||
unitfloat = float(unitval)
|
||||
sd.unit_dens[output] = unitfloat
|
||||
|
||||
# get unit_t
|
||||
unitline = infofile.readline()
|
||||
unitstring, equal, unitval = unitline.partition("=")
|
||||
unitfloat = float(unitval)
|
||||
sd.unit_t[output] = unitfloat
|
||||
|
||||
infofile.close()
|
||||
|
||||
except IOError: # If file doesn't exist
|
||||
print("Didn't find any info data in ", srcdir)
|
||||
break
|
||||
|
||||
self.unit_m = self.unit_dens * self.unit_l ** 3 / const.M_Sol
|
||||
self.unit_l /= const.Mpc
|
||||
self.unit_t /= const.Gyr
|
||||
|
||||
self.redshift = 1. / self.aexp - 1
|
||||
|
||||
###############################################################################
|
||||
# Tree object #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class Tree:
|
||||
"""
|
||||
Holds tree result data. It's not really a tree, it's just the values along
|
||||
the main branch, but let's call it a tree anyway. Sue me.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nelements : int
|
||||
Estimate for how many snapshots you need to allocate space for.
|
||||
"""
|
||||
def __init__(self, nelements):
|
||||
self.n = 0 # number of elements in tree # noqa
|
||||
self.snapshotnr = -np.ones(nelements, dtype=int) # snapshot number of array values # noqa
|
||||
self.redshift = -np.ones(nelements, dtype=float) # redshift at that snapshot # noqa
|
||||
self.clumpids = -np.ones(nelements, dtype=int) # clump id of halo in that snapshot # noqa
|
||||
self.mass = np.zeros(nelements, dtype=float) # mass at that snapshot # noqa
|
||||
self.mergermass = np.zeros(nelements, dtype=float) # sum of mass of swallowed up clumps # noqa
|
||||
self.mass_to_remove = np.zeros(nelements, dtype=float) # sum of mass of swallowed up clumps # noqa
|
||||
|
||||
def add_snap(self, nr, z, ID, m, mm, mdel):
|
||||
"""Add new result."""
|
||||
n = self.n
|
||||
self.snapshotnr[n] = nr
|
||||
self.redshift[n] = z
|
||||
self.clumpids[n] = ID
|
||||
self.mass[n] = m
|
||||
self.mergermass[n] = mm
|
||||
self.mass_to_remove[n] = mdel
|
||||
self.n += 1
|
||||
|
||||
def write_tree(self, par, case='halo'):
|
||||
"""Write the results to file."""
|
||||
resfile = join(
|
||||
par.outdir,
|
||||
f"{par.outputfilename}_{case}-{str(self.clumpids[0])}.txt")
|
||||
|
||||
with open(resfile, 'w') as f:
|
||||
f.write('# {0:>12} {1:>12} {2:>16} {3:>18} {4:>18} {5:>18}\n'.format( # noqa
|
||||
"snapshot", "redshift", "clump_ID", "mass[M_sol]",
|
||||
"mass_from_mergers", "mass_from_jumpers"))
|
||||
|
||||
for i in range(self.n):
|
||||
f.write(' {0:12d} {1:12.4f} {2:16d} {3:18.6e} {4:18.6e} {5:18.6e}\n'.format( # noqa
|
||||
self.snapshotnr[i], self.redshift[i], self.clumpids[i],
|
||||
self.mass[i], self.mergermass[i], self.mass_to_remove[i]))
|
||||
|
||||
return
|
||||
|
||||
|
||||
def get_snap_ind(p, snap):
|
||||
"""
|
||||
Computes the snapshot index in mtreedata/halodata/snapshotdata arrays for a
|
||||
given snapshot number snap
|
||||
"""
|
||||
return (p.noutput - snap).item()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
p = Params()
|
||||
c = Constants()
|
||||
|
||||
# Read cmdlineargs, available output, get global parameters
|
||||
p.read_cmdlineargs()
|
||||
p.get_output_info()
|
||||
|
||||
sd = SnapshotData(p)
|
||||
sd.read_infofiles(p, c)
|
||||
|
||||
# finish setup
|
||||
p.setup_and_checks(sd)
|
||||
p.print_params()
|
||||
|
||||
# now read in mergertree data
|
||||
fname = join(p.outdir, "mtreedata.p")
|
||||
if exists(fname):
|
||||
print(f"{datetime.now()}: loading mergertree data from `{fname}`.",
|
||||
flush=True)
|
||||
mtd = load(fname)
|
||||
print(f"{datetime.now()}: finished loading mergertree data from `{fname}`.", # noqa
|
||||
flush=True)
|
||||
else:
|
||||
print("Generating mergertree data.", flush=True)
|
||||
mtd = MTreeData(p)
|
||||
mtd.read_mergertree_data(p, sd)
|
||||
# clean up jumpers
|
||||
mtd.clean_up_jumpers(p)
|
||||
|
||||
print("Saving mergertree data.", flush=True)
|
||||
dump(mtd, fname)
|
||||
|
||||
# read in clump data if required
|
||||
if p.do_all or p.halo_and_children:
|
||||
cd = ClumpData(p)
|
||||
cd.read_clumpdata(p)
|
||||
|
||||
# clean up halo catalogue
|
||||
cd.cleanup_clumpdata(p, mtd)
|
||||
|
||||
# find children, and write them down
|
||||
if p.verbose:
|
||||
print("Searching for child clumps.")
|
||||
|
||||
if p.halo_and_children:
|
||||
children = cd.find_children(p.clumpid)
|
||||
cd.write_children(p, p.clumpid, children)
|
||||
|
||||
if p.do_all:
|
||||
is_halo = cd.clumpids == cd.parent
|
||||
childlist = [None for c in cd.clumpids[is_halo]]
|
||||
for i, halo in enumerate(cd.clumpids[is_halo]):
|
||||
children = cd.find_children(halo)
|
||||
cd.write_children(p, halo, children)
|
||||
childlist[i] = children
|
||||
|
||||
# finally, get the bloody tree
|
||||
|
||||
if p.one_halo_only:
|
||||
newtree = Tree(p.nout)
|
||||
mtd.get_tree(p, newtree, sd, p.clumpid)
|
||||
newtree.write_tree(p, 'halo')
|
||||
|
||||
if p.halo_and_children:
|
||||
newtree = Tree(p.nout)
|
||||
mtd.get_tree(p, newtree, sd, p.clumpid)
|
||||
newtree.write_tree(p, 'halo')
|
||||
|
||||
for c in children:
|
||||
newtree = Tree(p.nout)
|
||||
mtd.get_tree(p, newtree, sd, c)
|
||||
newtree.write_tree(p, 'subhalo')
|
||||
|
||||
if p.do_all:
|
||||
for i, halo in enumerate(cd.clumpids[is_halo]):
|
||||
newtree = Tree(p.nout)
|
||||
mtd.get_tree(p, newtree, sd, halo)
|
||||
newtree.write_tree(p, 'halo')
|
||||
|
||||
for c in childlist[i]:
|
||||
newtree = Tree(p.nout)
|
||||
mtd.get_tree(p, newtree, sd, c)
|
||||
newtree.write_tree(p, 'subhalo')
|
||||
|
||||
print('Finished.')
|
||||
457
scripts/process_snapshot.py
Normal file
457
scripts/process_snapshot.py
Normal file
|
|
@ -0,0 +1,457 @@
|
|||
# Copyright (C) 2022 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.
|
||||
r"""
|
||||
Script to process simulation files and create a single HDF5 file, in which
|
||||
particles are sorted by the particle halo IDs.
|
||||
"""
|
||||
from argparse import ArgumentParser
|
||||
from gc import collect
|
||||
|
||||
import h5py
|
||||
import numpy
|
||||
from mpi4py import MPI
|
||||
|
||||
import csiborgtools
|
||||
from csiborgtools import fprint
|
||||
from numba import jit
|
||||
from taskmaster import work_delegation
|
||||
from tqdm import trange, tqdm
|
||||
from utils import get_nsims
|
||||
|
||||
|
||||
@jit(nopython=True, boundscheck=False)
|
||||
def minmax_halo(hid, halo_ids, start_loop=0):
|
||||
"""
|
||||
Find the start and end index of a halo in a sorted array of halo IDs.
|
||||
This is much faster than using `numpy.where` and then `numpy.min` and
|
||||
`numpy.max`.
|
||||
"""
|
||||
start = None
|
||||
end = None
|
||||
|
||||
for i in range(start_loop, halo_ids.size):
|
||||
n = halo_ids[i]
|
||||
if n == hid:
|
||||
if start is None:
|
||||
start = i
|
||||
end = i
|
||||
elif n > hid:
|
||||
break
|
||||
return start, end
|
||||
|
||||
|
||||
def process_snapshot(nsim, simname, halo_finder, verbose):
|
||||
"""
|
||||
Read in the snapshot particles, sort them by their halo ID and dump
|
||||
into a HDF5 file. Stores the first and last index of each halo in the
|
||||
particle array for fast slicing of the array to acces particles of a single
|
||||
halo.
|
||||
"""
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
nsnap = max(paths.get_snapshots(nsim, simname))
|
||||
|
||||
if simname == "csiborg":
|
||||
partreader = csiborgtools.read.CSiBORGReader(paths)
|
||||
box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)
|
||||
else:
|
||||
partreader = csiborgtools.read.QuijoteReader(paths)
|
||||
box = None
|
||||
|
||||
desc = {"hid": f"Halo finder ID ({halo_finder})of the particle.",
|
||||
"pos": "DM particle positions in box units.",
|
||||
"vel": "DM particle velocity in km / s.",
|
||||
"mass": "DM particle mass in Msun / h.",
|
||||
"pid": "DM particle ID",
|
||||
}
|
||||
|
||||
fname = paths.processed_output(nsim, simname, halo_finder)
|
||||
|
||||
fprint(f"loading HIDs of IC {nsim}.", verbose)
|
||||
hids = partreader.read_halo_id(nsnap, nsim, halo_finder, verbose)
|
||||
collect()
|
||||
|
||||
fprint(f"sorting HIDs of IC {nsim}.")
|
||||
sort_indxs = numpy.argsort(hids)
|
||||
|
||||
with h5py.File(fname, "w") as f:
|
||||
group = f.create_group("snapshot_final")
|
||||
group.attrs["header"] = "Snapshot data at z = 0."
|
||||
|
||||
fprint("dumping halo IDs.", verbose)
|
||||
dset = group.create_dataset("halo_ids", data=hids[sort_indxs])
|
||||
dset.attrs["header"] = desc["hid"]
|
||||
del hids
|
||||
collect()
|
||||
|
||||
fprint("reading, sorting and dumping the snapshot particles.", verbose)
|
||||
for kind in ["pos", "vel", "mass", "pid"]:
|
||||
x = partreader.read_snapshot(nsnap, nsim, kind)[sort_indxs]
|
||||
|
||||
if simname == "csiborg" and kind == "vel":
|
||||
x = box.box2vel(x) if simname == "csiborg" else x
|
||||
|
||||
if simname == "csiborg" and kind == "mass":
|
||||
x = box.box2solarmass(x) if simname == "csiborg" else x
|
||||
|
||||
dset = f["snapshot_final"].create_dataset(kind, data=x)
|
||||
dset.attrs["header"] = desc[kind]
|
||||
del x
|
||||
collect()
|
||||
|
||||
del sort_indxs
|
||||
collect()
|
||||
|
||||
fprint(f"creating a halo map for IC {nsim}.")
|
||||
with h5py.File(fname, "r") as f:
|
||||
part_hids = f["snapshot_final"]["halo_ids"][:]
|
||||
# We loop over the unique halo IDs and remove the 0 halo ID
|
||||
unique_halo_ids = numpy.unique(part_hids)
|
||||
unique_halo_ids = unique_halo_ids[unique_halo_ids != 0]
|
||||
halo_map = numpy.full((unique_halo_ids.size, 3), numpy.nan,
|
||||
dtype=numpy.uint64)
|
||||
start_loop, niters = 0, unique_halo_ids.size
|
||||
for i in trange(niters, disable=not verbose):
|
||||
hid = unique_halo_ids[i]
|
||||
k0, kf = minmax_halo(hid, part_hids, start_loop=start_loop)
|
||||
halo_map[i, :] = hid, k0, kf
|
||||
start_loop = kf
|
||||
|
||||
# Dump the halo mapping.
|
||||
with h5py.File(fname, "r+") as f:
|
||||
dset = f["snapshot_final"].create_dataset("halo_map", data=halo_map)
|
||||
dset.attrs["header"] = """
|
||||
Halo to particle mapping. Columns are HID, start index, end index.
|
||||
"""
|
||||
f.close()
|
||||
|
||||
del part_hids
|
||||
collect()
|
||||
|
||||
# Add the halo finder catalogue
|
||||
with h5py.File(fname, "r+") as f:
|
||||
group = f.create_group("halofinder_catalogue")
|
||||
group.attrs["header"] = f"Original {halo_finder} halo catalogue."
|
||||
cat = partreader.read_catalogue(nsnap, nsim, halo_finder)
|
||||
|
||||
hid2pos = {hid: i for i, hid in enumerate(unique_halo_ids)}
|
||||
|
||||
for key in cat.dtype.names:
|
||||
x = numpy.full(unique_halo_ids.size, numpy.nan,
|
||||
dtype=cat[key].dtype)
|
||||
for i in range(len(cat)):
|
||||
j = hid2pos[cat["index"][i]]
|
||||
x[j] = cat[key][i]
|
||||
group.create_dataset(key, data=x)
|
||||
f.close()
|
||||
|
||||
# Lastly create the halo catalogue
|
||||
with h5py.File(fname, "r+") as f:
|
||||
group = f.create_group("halo_catalogue")
|
||||
group.attrs["header"] = f"{halo_finder} halo catalogue."
|
||||
group.create_dataset("index", data=unique_halo_ids)
|
||||
f.close()
|
||||
|
||||
|
||||
def add_initial_snapshot(nsim, simname, halo_finder, verbose):
|
||||
"""
|
||||
Sort the initial snapshot particles according to their final snapshot and
|
||||
add them to the final snapshot's HDF5 file.
|
||||
"""
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
fname = paths.processed_output(nsim, simname, halo_finder)
|
||||
|
||||
if simname == "csiborg":
|
||||
partreader = csiborgtools.read.CSiBORGReader(paths)
|
||||
else:
|
||||
partreader = csiborgtools.read.QuijoteReader(paths)
|
||||
|
||||
fprint(f"processing simulation `{nsim}`.", verbose)
|
||||
if simname == "csiborg":
|
||||
nsnap0 = 1
|
||||
elif simname == "quijote":
|
||||
nsnap0 = -1
|
||||
else:
|
||||
raise ValueError(f"Unknown simulation `{simname}`.")
|
||||
|
||||
fprint("loading and sorting the initial PID.", verbose)
|
||||
sort_indxs = numpy.argsort(partreader.read_snapshot(nsnap0, nsim, "pid"))
|
||||
|
||||
fprint("loading the final particles.", verbose)
|
||||
with h5py.File(fname, "r") as f:
|
||||
sort_indxs_final = f["snapshot_final/pid"][:]
|
||||
f.close()
|
||||
|
||||
fprint("sorting the particles according to the final snapshot.", verbose)
|
||||
sort_indxs_final = numpy.argsort(numpy.argsort(sort_indxs_final))
|
||||
sort_indxs = sort_indxs[sort_indxs_final]
|
||||
|
||||
del sort_indxs_final
|
||||
collect()
|
||||
|
||||
fprint("loading and sorting the initial particle position.", verbose)
|
||||
pos = partreader.read_snapshot(nsnap0, nsim, "pos")[sort_indxs]
|
||||
|
||||
del sort_indxs
|
||||
collect()
|
||||
|
||||
# In Quijote some particles are position precisely at the edge of the
|
||||
# box. Move them to be just inside.
|
||||
if simname == "quijote":
|
||||
mask = pos >= 1
|
||||
if numpy.any(mask):
|
||||
spacing = numpy.spacing(pos[mask])
|
||||
assert numpy.max(spacing) <= 1e-5
|
||||
pos[mask] -= spacing
|
||||
|
||||
fprint(f"dumping particles for `{nsim}` to `{fname}`.", verbose)
|
||||
with h5py.File(fname, "r+") as f:
|
||||
if "snapshot_initial" in f.keys():
|
||||
del f["snapshot_initial"]
|
||||
group = f.create_group("snapshot_initial")
|
||||
group.attrs["header"] = "Initial snapshot data."
|
||||
dset = group.create_dataset("pos", data=pos)
|
||||
dset.attrs["header"] = "DM particle positions in box units."
|
||||
|
||||
f.close()
|
||||
|
||||
|
||||
def calculate_initial(nsim, simname, halo_finder, verbose):
|
||||
"""Calculate the Lagrangian patch centre of mass and size."""
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
|
||||
fname = paths.processed_output(nsim, simname, halo_finder)
|
||||
fprint("loading the particle information.", verbose)
|
||||
f = h5py.File(fname, "r")
|
||||
pos = f["snapshot_initial/pos"]
|
||||
mass = f["snapshot_final/mass"]
|
||||
hid = f["halo_catalogue/index"][:]
|
||||
hid2map = csiborgtools.read.make_halomap_dict(
|
||||
f["snapshot_final/halo_map"][:])
|
||||
|
||||
if simname == "csiborg":
|
||||
kwargs = {"box_size": 2048, "bckg_halfsize": 512}
|
||||
else:
|
||||
kwargs = {"box_size": 512, "bckg_halfsize": 256}
|
||||
overlapper = csiborgtools.match.ParticleOverlap(**kwargs)
|
||||
|
||||
lagpatch_pos = numpy.full((len(hid), 3), numpy.nan, dtype=numpy.float32)
|
||||
lagpatch_size = numpy.full(len(hid), numpy.nan, dtype=numpy.float32)
|
||||
lagpatch_ncells = numpy.full(len(hid), numpy.nan, dtype=numpy.int32)
|
||||
|
||||
for i in trange(len(hid), disable=not verbose):
|
||||
h = hid[i]
|
||||
# These are unasigned particles.
|
||||
if h == 0:
|
||||
continue
|
||||
|
||||
parts_pos = csiborgtools.read.load_halo_particles(h, pos, hid2map)
|
||||
parts_mass = csiborgtools.read.load_halo_particles(h, mass, hid2map)
|
||||
|
||||
# Skip if the halo has no particles or is too small.
|
||||
if parts_pos is None or parts_pos.size < 5:
|
||||
continue
|
||||
|
||||
cm = csiborgtools.center_of_mass(parts_pos, parts_mass, boxsize=1.0)
|
||||
sep = csiborgtools.periodic_distance(parts_pos, cm, boxsize=1.0)
|
||||
delta = overlapper.make_delta(parts_pos, parts_mass, subbox=True)
|
||||
|
||||
lagpatch_pos[i] = cm
|
||||
lagpatch_size[i] = numpy.percentile(sep, 99)
|
||||
lagpatch_ncells[i] = csiborgtools.delta2ncells(delta)
|
||||
|
||||
f.close()
|
||||
collect()
|
||||
|
||||
with h5py.File(fname, "r+") as f:
|
||||
grp = f["halo_catalogue"]
|
||||
dset = grp.create_dataset("lagpatch_pos", data=lagpatch_pos)
|
||||
dset.attrs["header"] = "Lagrangian patch centre of mass in box units."
|
||||
|
||||
dset = grp.create_dataset("lagpatch_size", data=lagpatch_size)
|
||||
dset.attrs["header"] = "Lagrangian patch size in box units."
|
||||
|
||||
dset = grp.create_dataset("lagpatch_ncells", data=lagpatch_ncells)
|
||||
dset.attrs["header"] = f"Lagrangian patch number of cells on a {kwargs['box_size']}^3 grid." # noqa
|
||||
|
||||
f.close()
|
||||
|
||||
|
||||
def make_phew_halo_catalogue(nsim, verbose):
|
||||
"""
|
||||
Process the PHEW halo catalogue for a CSiBORG simulation at all snapshots.
|
||||
"""
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
snapshots = paths.get_snapshots(nsim, "csiborg")
|
||||
reader = csiborgtools.read.CSiBORGReader(paths)
|
||||
keys_write = ["index", "x", "y", "z", "mass_cl", "parent",
|
||||
"ultimate_parent", "summed_mass"]
|
||||
|
||||
# Create a HDF5 file to store all this.
|
||||
fname = paths.processed_phew(nsim)
|
||||
with h5py.File(fname, "w") as f:
|
||||
f.close()
|
||||
|
||||
for nsnap in tqdm(snapshots, disable=not verbose, desc="Snapshot"):
|
||||
try:
|
||||
data = reader.read_phew_clumps(nsnap, nsim, verbose=False)
|
||||
except FileExistsError:
|
||||
continue
|
||||
|
||||
with h5py.File(fname, "r+") as f:
|
||||
if str(nsnap) in f:
|
||||
print(f"Group {nsnap} already exists. Deleting.", flush=True)
|
||||
del f[str(nsnap)]
|
||||
grp = f.create_group(str(nsnap))
|
||||
for key in keys_write:
|
||||
grp.create_dataset(key, data=data[key])
|
||||
|
||||
grp.attrs["header"] = f"CSiBORG PHEW clumps at snapshot {nsnap}."
|
||||
f.close()
|
||||
|
||||
# Now write the redshifts
|
||||
scale_factors = numpy.full(len(snapshots), numpy.nan, dtype=numpy.float32)
|
||||
for i, nsnap in enumerate(snapshots):
|
||||
box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)
|
||||
scale_factors[i] = box._aexp
|
||||
|
||||
redshifts = scale_factors[-1] / scale_factors - 1
|
||||
|
||||
with h5py.File(fname, "r+") as f:
|
||||
grp = f.create_group("info")
|
||||
grp.create_dataset("redshift", data=redshifts)
|
||||
grp.create_dataset("snapshots", data=snapshots)
|
||||
grp.create_dataset("Om0", data=[box.Om0])
|
||||
grp.create_dataset("boxsize", data=[box.boxsize])
|
||||
f.close()
|
||||
|
||||
|
||||
def make_merger_tree_file(nsim, verbose):
|
||||
"""
|
||||
Process the `.dat` merger tree files and dump them into a HDF5 file.
|
||||
"""
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
reader = csiborgtools.read.CSiBORGReader(paths)
|
||||
snaps = paths.get_snapshots(nsim, "csiborg")
|
||||
|
||||
fname = paths.processed_merger_tree(nsim)
|
||||
with h5py.File(fname, "w") as f:
|
||||
f.close()
|
||||
|
||||
for nsnap in tqdm(snaps, desc="Loading merger files",
|
||||
disable=not verbose):
|
||||
try:
|
||||
data = reader.read_merger_tree(nsnap, nsim)
|
||||
except FileExistsError:
|
||||
continue
|
||||
|
||||
with h5py.File(fname, "r+") as f:
|
||||
grp = f.create_group(str(nsnap))
|
||||
|
||||
grp.create_dataset("clump",
|
||||
data=data[:, 0].astype(numpy.int32))
|
||||
grp.create_dataset("progenitor",
|
||||
data=data[:, 1].astype(numpy.int32))
|
||||
grp.create_dataset("progenitor_outputnr",
|
||||
data=data[:, 2].astype(numpy.int32))
|
||||
grp.create_dataset("desc_mass",
|
||||
data=data[:, 3].astype(numpy.float32))
|
||||
grp.create_dataset("desc_npart",
|
||||
data=data[:, 4].astype(numpy.int32))
|
||||
grp.create_dataset("desc_pos",
|
||||
data=data[:, 5:8].astype(numpy.float32))
|
||||
grp.create_dataset("desc_vel",
|
||||
data=data[:, 8:11].astype(numpy.float32))
|
||||
f.close()
|
||||
|
||||
|
||||
def append_merger_tree_mass_to_phew_catalogue(nsim, verbose):
|
||||
"""
|
||||
Append mass of haloes from mergertree files to the PHEW catalogue. The
|
||||
difference between this and the PHEW value is that the latter is written
|
||||
before unbinding is performed.
|
||||
|
||||
Note that currently only does this for the highest snapshot.
|
||||
"""
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
snapshots = paths.get_snapshots(nsim, "csiborg")
|
||||
merger_reader = csiborgtools.read.MergerReader(nsim, paths)
|
||||
|
||||
for nsnap in tqdm(snapshots, disable=not verbose, desc="Snapshot"):
|
||||
# TODO do this for all later
|
||||
if nsnap < 930:
|
||||
continue
|
||||
try:
|
||||
phewcat = csiborgtools.read.CSiBORGPHEWCatalogue(nsnap, nsim,
|
||||
paths)
|
||||
except ValueError:
|
||||
phewcat.close()
|
||||
continue
|
||||
|
||||
mergertree_mass = merger_reader.match_mass_to_phewcat(phewcat)
|
||||
phewcat.close()
|
||||
|
||||
fname = paths.processed_phew(nsim)
|
||||
with h5py.File(fname, "r+") as f:
|
||||
grp = f[str(nsnap)]
|
||||
grp.create_dataset("mergertree_mass_new", data=mergertree_mass)
|
||||
f.close()
|
||||
|
||||
|
||||
def main(nsim, args):
|
||||
if args.make_final:
|
||||
process_snapshot(nsim, args.simname, args.halofinder, True)
|
||||
|
||||
if args.make_initial:
|
||||
add_initial_snapshot(nsim, args.simname, args.halofinder, True)
|
||||
calculate_initial(nsim, args.simname, args.halofinder, True)
|
||||
|
||||
if args.make_phew:
|
||||
make_phew_halo_catalogue(nsim, True)
|
||||
|
||||
if args.make_merger:
|
||||
make_merger_tree_file(nsim, True)
|
||||
|
||||
if args.append_merger_mass:
|
||||
append_merger_tree_mass_to_phew_catalogue(nsim, True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = ArgumentParser()
|
||||
parser.add_argument("--simname", type=str, default="csiborg",
|
||||
choices=["csiborg", "quijote"],
|
||||
help="Simulation name")
|
||||
parser.add_argument("--nsims", type=int, nargs="+", default=None,
|
||||
help="IC realisations. If `-1` processes all.")
|
||||
parser.add_argument("--halofinder", type=str, help="Halo finder")
|
||||
parser.add_argument("--make_final", action="store_true", default=False,
|
||||
help="Process the final snapshot.")
|
||||
parser.add_argument("--make_initial", action="store_true", default=False,
|
||||
help="Process the initial snapshot.")
|
||||
parser.add_argument("--make_phew", action="store_true", default=False,
|
||||
help="Process the PHEW halo catalogue.")
|
||||
parser.add_argument("--make_merger", action="store_true", default=False,
|
||||
help="Process the merger tree files.")
|
||||
parser.add_argument("--append_merger_mass", action="store_true",
|
||||
default=False,
|
||||
help="Append the merger tree mass to the PHEW cat.")
|
||||
|
||||
args = parser.parse_args()
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
nsims = get_nsims(args, paths)
|
||||
|
||||
def _main(nsim):
|
||||
main(nsim, args)
|
||||
|
||||
work_delegation(_main, nsims, MPI.COMM_WORLD)
|
||||
|
|
@ -1,114 +0,0 @@
|
|||
# Copyright (C) 2022 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.
|
||||
r"""
|
||||
Script to sort the initial snapshot particles according to their final
|
||||
snapshot ordering, which is sorted by the halo IDs.
|
||||
|
||||
Ensures the following units:
|
||||
- Positions in box units.
|
||||
- Masses in :math:`M_\odot / h`.
|
||||
"""
|
||||
from argparse import ArgumentParser
|
||||
from datetime import datetime
|
||||
from gc import collect
|
||||
|
||||
import h5py
|
||||
import numpy
|
||||
from mpi4py import MPI
|
||||
from taskmaster import work_delegation
|
||||
|
||||
import csiborgtools
|
||||
from utils import get_nsims
|
||||
|
||||
|
||||
def _main(nsim, simname, verbose):
|
||||
"""
|
||||
Sort the initial snapshot particles according to their final snapshot
|
||||
ordering and dump them into a HDF5 file.
|
||||
"""
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
if simname == "csiborg":
|
||||
partreader = csiborgtools.read.CSiBORGReader(paths)
|
||||
else:
|
||||
partreader = csiborgtools.read.QuijoteReader(paths)
|
||||
|
||||
print(f"{datetime.now()}: processing simulation `{nsim}`.", flush=True)
|
||||
# We first load the particle IDs in the final snapshot.
|
||||
pidf = csiborgtools.read.read_h5(paths.particles(nsim, simname))
|
||||
pidf = pidf["particle_ids"]
|
||||
# Then we load the particles in the initil snapshot and make sure that
|
||||
# their particle IDs are sorted as in the final snapshot. Again, because of
|
||||
# precision this must be read as structured.
|
||||
if simname == "csiborg":
|
||||
pars_extract = ["x", "y", "z", "M", "ID"]
|
||||
# CSiBORG's initial snapshot ID
|
||||
nsnap = 1
|
||||
else:
|
||||
pars_extract = None
|
||||
# Use this to point the reader to the ICs snapshot
|
||||
nsnap = -1
|
||||
part0, pid0 = partreader.read_particle(
|
||||
nsnap, nsim, pars_extract, return_structured=False, verbose=verbose)
|
||||
|
||||
# In CSiBORG we need to convert particle masses from box units.
|
||||
if simname == "csiborg":
|
||||
box = csiborgtools.read.CSiBORGBox(
|
||||
max(paths.get_snapshots(nsim, simname)), nsim, paths)
|
||||
part0[:, 3] = box.box2solarmass(part0[:, 3])
|
||||
|
||||
# Quijote's initial snapshot information also contains velocities but we
|
||||
# don't need those.
|
||||
if simname == "quijote":
|
||||
part0 = part0[:, [0, 1, 2, 6]]
|
||||
# In Quijote some particles are position precisely at the edge of the
|
||||
# box. Move them to be just inside.
|
||||
pos = part0[:, :3]
|
||||
mask = pos >= 1
|
||||
if numpy.any(mask):
|
||||
spacing = numpy.spacing(pos[mask])
|
||||
assert numpy.max(spacing) <= 1e-5
|
||||
pos[mask] -= spacing
|
||||
|
||||
# First enforce them to already be sorted and then apply reverse
|
||||
# sorting from the final snapshot.
|
||||
part0 = part0[numpy.argsort(pid0)]
|
||||
del pid0
|
||||
collect()
|
||||
part0 = part0[numpy.argsort(numpy.argsort(pidf))]
|
||||
fout = paths.initmatch(nsim, simname, "particles")
|
||||
if verbose:
|
||||
print(f"{datetime.now()}: dumping particles for `{nsim}` to `{fout}`",
|
||||
flush=True)
|
||||
with h5py.File(fout, "w") as f:
|
||||
f.create_dataset("particles", data=part0)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Argument parser
|
||||
parser = ArgumentParser()
|
||||
parser.add_argument("--simname", type=str, default="csiborg",
|
||||
choices=["csiborg", "quijote"],
|
||||
help="Simulation name")
|
||||
parser.add_argument("--nsims", type=int, nargs="+", default=None,
|
||||
help="IC realisations. If `-1` processes all.")
|
||||
args = parser.parse_args()
|
||||
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
nsims = get_nsims(args, paths)
|
||||
|
||||
def main(nsim):
|
||||
_main(nsim, args.simname, MPI.COMM_WORLD.Get_size() == 1)
|
||||
|
||||
work_delegation(main, nsims, MPI.COMM_WORLD)
|
||||
39
setup.py
39
setup.py
|
|
@ -1,52 +1,28 @@
|
|||
from setuptools import find_packages, setup
|
||||
|
||||
# List of dependencies:
|
||||
# - Corrfunc -> To be moved to a separate package.
|
||||
# - NumPy
|
||||
# - SciPy
|
||||
# - Numba
|
||||
# - Pylians
|
||||
# - tqdm
|
||||
# - healpy
|
||||
# - astropy
|
||||
# - scikit-learn
|
||||
# - joblib
|
||||
# - h5py
|
||||
# - MPI
|
||||
# - pyyaml
|
||||
# - taskmaster
|
||||
# - matplotlib
|
||||
# - scienceplots
|
||||
# - cache_to_disk
|
||||
|
||||
|
||||
BUILD_REQ = ["numpy", "scipy"]
|
||||
INSTALL_REQ = BUILD_REQ
|
||||
INSTALL_REQ += ["Corrfunc",
|
||||
"Pylians",
|
||||
INSTALL_REQ += [
|
||||
"numba",
|
||||
"tqdm",
|
||||
"healpy",
|
||||
"astropy",
|
||||
"scikit-learn",
|
||||
"h5py",
|
||||
"matplotlib",
|
||||
"scienceplots",
|
||||
"mpi4py",
|
||||
"pyyaml",
|
||||
"joblib",]
|
||||
|
||||
"pynbody",
|
||||
"joblib",
|
||||
]
|
||||
|
||||
setup(
|
||||
name="csiborgtools",
|
||||
version="0.2",
|
||||
version="0.3",
|
||||
description="CSiBORG analysis tools",
|
||||
url="https://github.com/Richard-Sti/csiborgtools",
|
||||
author="Richard Stiskalek",
|
||||
author_email="richard.stiskalek@protonmail.com",
|
||||
license="GPL-3.0",
|
||||
packages=find_packages(),
|
||||
python_requires=">=3.8",
|
||||
python_requires=">=3.6",
|
||||
build_requires=BUILD_REQ,
|
||||
setup_requires=BUILD_REQ,
|
||||
install_requires=INSTALL_REQ,
|
||||
|
|
@ -55,5 +31,6 @@ setup(
|
|||
"Intended Audience :: Science/Research",
|
||||
"Operating System :: POSIX :: Linux",
|
||||
"Programming Language :: Python :: 3.8",
|
||||
"Programming Language :: Python :: 3.9"]
|
||||
"Programming Language :: Python :: 3.9"
|
||||
]
|
||||
)
|
||||
|
|
|
|||
Loading…
Reference in a new issue