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More plotting (#74)

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* Add a new plot

* Add a binned trend

* Fix bug

* Improve plot further

* Add new plotting

* add max overlap

* edit get_overlap

* Add max overlap plot

* Update plot

* Add max overlap key

* add max dist flag

* Improve plotting
This commit is contained in:
Richard Stiskalek 2023-07-03 15:35:10 +01:00 • committed by GitHub
parent fbf9c2a4b7
commit 28e93e917f
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3 changed files with 690 additions and 95 deletions
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123
csiborgtools/read/overlap_summary.py
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@ -38,18 +38,21 @@ class PairOverlap:
Halo catalogue corresponding to the cross simulation.
paths : py:class`csiborgtools.read.Paths`
CSiBORG paths object.
maxdist : float, optional
Maximum halo distance in :math:`\mathrm{Mpc} / h` from the centre of
the high-resolution region. Removes overlaps of haloes outside it.
"""
_cat0 = None
_catx = None
_data = None
def __init__(self, cat0, catx, paths):
def __init__(self, cat0, catx, paths, maxdist=None):
self._cat0 = cat0
self._catx = catx
self.load(cat0, catx, paths)
self.load(cat0, catx, paths, maxdist)
def load(self, cat0, catx, paths):
"""
def load(self, cat0, catx, paths, maxdist=None):
r"""
Load overlap calculation results. Matches the results back to the two
catalogues in question.
@ -61,6 +64,9 @@ class PairOverlap:
Halo catalogue corresponding to the cross simulation.
paths : py:class`csiborgtools.read.Paths`
CSiBORG paths object.
maxdist : float, optional
Maximum halo distance in :math:`\mathrm{Mpc} / h` from the centre
of the high-resolution region.
Returns
-------
@ -125,6 +131,14 @@ class PairOverlap:
match_indxs, ngp_overlap, smoothed_overlap = self._invert_match(
match_indxs, ngp_overlap, smoothed_overlap, len(catx),)
if maxdist is not None:
dist = cat0.radial_distance(in_initial=False)
for i in range(len(cat0)):
if dist[i] > maxdist:
match_indxs[i] = numpy.array([], dtype=int)
ngp_overlap[i] = numpy.array([], dtype=float)
smoothed_overlap[i] = numpy.array([], dtype=float)
self._data = {"match_indxs": match_indxs,
"ngp_overlap": ngp_overlap,
"smoothed_overlap": smoothed_overlap,
@ -228,7 +242,11 @@ class PairOverlap:
summed_overlap : 1-dimensional array of shape `(nhalos, )`
"""
overlap = self.overlap(from_smoothed)
return numpy.array([numpy.sum(cross)for cross in overlap])
out = numpy.full(len(overlap), numpy.nan, dtype=numpy.float32)
for i in range(len(overlap)):
if len(overlap[i]) > 0:
out[i] = numpy.sum(overlap[i])
return out
def prob_nomatch(self, from_smoothed):
"""
@ -246,8 +264,11 @@ class PairOverlap:
prob_nomatch : 1-dimensional array of shape `(nhalos, )`
"""
overlap = self.overlap(from_smoothed)
return numpy.array([numpy.product(numpy.subtract(1, cross))
for cross in overlap])
out = numpy.full(len(overlap), numpy.nan, dtype=numpy.float32)
for i in range(len(overlap)):
if len(overlap[i]) > 0:
out[i] = numpy.product(numpy.subtract(1, overlap[i]))
return out
def dist(self, in_initial, norm_kind=None):
"""
@ -326,6 +347,35 @@ class PairOverlap:
ratio[i] = numpy.abs(ratio[i])
return numpy.array(ratio, dtype=object)
def max_overlap_key(self, key, from_smoothed):
"""
Calculate the maximum overlap mass of each halo in the reference
simulation from the cross simulation.
Parameters
----------
key : str
Key to the maximum overlap statistic to calculate.
from_smoothed : bool
Whether to use the smoothed overlap or not.
mass_kind : str, optional
The mass kind whose ratio is to be calculated.
Returns
-------
out : 1-dimensional array of shape `(nhalos, )`
"""
out = numpy.full(len(self), numpy.nan, dtype=numpy.float32)
y = self.catx(key)
overlap = self.overlap(from_smoothed)
for i, match_ind in enumerate(self["match_indxs"]):
# Skip if no match
if len(match_ind) == 0:
continue
out[i] = y[match_ind][numpy.argmax(overlap[i])]
return out
def counterpart_mass(self, from_smoothed, overlap_threshold=0.,
in_log=False, mass_kind="totpartmass"):
"""
@ -359,7 +409,7 @@ class PairOverlap:
for i, match_ind in enumerate(self["match_indxs"]):
# Skip if no match
if match_ind.size == 0:
if len(match_ind) == 0:
continue
massx_ = massx[match_ind] # Again just create references
@ -527,6 +577,63 @@ class NPairsOverlap:
self._pairs = pairs
def max_overlap(self, from_smoothed, verbose=True):
"""
Calculate maximum overlap of each halo in the reference simulation with
the cross simulations.
Parameters
----------
from_smoothed : bool
Whether to use the smoothed overlap or not.
verbose : bool, optional
Verbosity flag.
Returns
-------
max_overlap : 2-dimensional array of shape `(nhalos, ncatxs)`
"""
out = [None] * len(self)
if verbose:
print("Calculating maximum overlap...", flush=True)
def get_max(y_):
if len(y_) == 0:
return numpy.nan
return numpy.max(y_)
for i, pair in enumerate(tqdm(self.pairs) if verbose else self.pairs):
out[i] = numpy.asanyarray([get_max(y_)
for y_ in pair.overlap(from_smoothed)])
return numpy.vstack(out).T
def max_overlap_key(self, key, from_smoothed, verbose=True):
"""
Calculate maximum overlap mass of each halo in the reference
simulation with the cross simulations.
Parameters
----------
key : str
Key to the maximum overlap statistic to calculate.
from_smoothed : bool
Whether to use the smoothed overlap or not.
verbose : bool, optional
Verbosity flag.
Returns
-------
out : 2-dimensional array of shape `(nhalos, ncatxs)`
"""
out = [None] * len(self)
if verbose:
print(f"Calculating maximum overlap {key}...", flush=True)
for i, pair in enumerate(tqdm(self.pairs) if verbose else self.pairs):
out[i] = pair.max_overlap_key(key, from_smoothed)
return numpy.vstack(out).T
def summed_overlap(self, from_smoothed, verbose=True):
"""
Calculate summed overlap of each halo in the reference simulation with