Overlap reader (#28)

* Edit output path

* remove matching of many sims

* Add a class skeleton

* Add a setter for only nsim

* Deletei mport

* Simplify cross and delete combined halo catalogue

* Simplify reading in catalogues

* Add distance calculation

* Add mass ratio

* Clean up the submission script

* Add the summer overlap

* Docs

* Add array copying

* Add import

* Add resampling mean

csiborgtools/match/match.py

@@ -16,10 +16,10 @@
 import numpy
 from scipy.ndimage import gaussian_filter
 from tqdm import (tqdm, trange)
+from datetime import datetime
 from astropy.coordinates import SkyCoord
 from numba import jit
-from gc import collect
+from ..read import (concatenate_clumps, clumps_pos2cell)
-from ..read import (CombinedHaloCatalogue, concatenate_clumps, clumps_pos2cell)  # noqa
 
 
 def brute_spatial_separation(c1, c2, angular=False, N=None, verbose=False):
@@ -77,58 +77,118 @@ class RealisationsMatcher:
     neighbours in all remaining IC realisations that are within some mass
     range of it.
 
-
     Parameters
     ----------
-    cats : :py:class`csiborgtools.read.CombinedHaloCatalogue`
+    nmult : float or int, optional
-        Combined halo catalogue to search.
+        Multiple of the sum of pair initial Lagrangian patch sizes
-    """
+        within which to return neighbours. By default 1.
-    _cats = None
+    dlogmass : float, optional
+        Tolerance on the absolute logarithmic mass difference of potential
+        matches. By default 2.
+    mass_kind : str, optional
+        The mass kind whose similarity is to be checked. Must be a valid
+        catalogue key. By default `totpartmass`, i.e. the total particle
+        mass associated with a halo.
+    overlapper_kwargs : dict, optional
+        Keyword arguments passed to `ParticleOverlapper`.
+    remove_nooverlap : bool, optional
+        Whether to remove pairs with exactly zero overlap. By default `True`.
 
-    def __init__(self, cats):
+    """
-        self.cats = cats
+    _nmult = None
+    _dlogmass = None
+    _mass_kind = None
+    _overlapper = None
+    _remove_nooverlap = None
+
+    def __init__(self, nmult=1., dlogmass=2., mass_kind="totpartmass",
+                 overlapper_kwargs={}, remove_nooverlap=True):
+        self.nmult = nmult
+        self.dlogmass = dlogmass
+        self.mass_kind = mass_kind
+        self._overlapper = ParticleOverlap(**overlapper_kwargs)
+        self.remove_nooverlap = remove_nooverlap
 
     @property
-    def cats(self):
+    def nmult(self):
         """
-        Combined catalogues.
+        Multiple of the sum of pair initial Lagrangian patch sizes within which
+        to return neighbours.
 
         Returns
         -------
-        cats : :py:class`csiborgtools.read.CombinedHaloCatalogue`
+        nmult : float
-            Combined halo catalogue.
         """
-        return self._cats
+        return self._nmult
 
-    @cats.setter
+    @nmult.setter
-    def cats(self, cats):
+    def nmult(self, nmult):
-        """
+        """Set `nmult`."""
-        Sets `cats`, ensures that this is a `CombinedHaloCatalogue` object.
+        assert nmult > 0
-        """
+        self._nmult = nmult
-        if not isinstance(cats, CombinedHaloCatalogue):
-            raise TypeError("`cats` must be of type `CombinedHaloCatalogue`.")
-        self._cats = cats
 
-    def search_sim_indices(self, n_sim):
+    @property
+    def dlogmass(self):
         """
-        Return indices of all other IC realisations but of `n_sim`.
+        Tolerance on the absolute logarithmic mass difference of potential
-
+        matches.
-        Parameters
-        ----------
-        n_sim : int
-            IC realisation index of `self.cats` to be skipped.
 
         Returns
         -------
-        indxs : list of int
+        dlogmass : float
-            The remaining IC indices.
         """
-        return [i for i in range(self.cats.N) if i != n_sim]
+        return self._dlogmass
 
-    def _check_masskind(self, mass_kind):
+    @dlogmass.setter
-        """Check that `mass_kind` is a valid key."""
+    def dlogmass(self, dlogmass):
-        if mass_kind not in self.cats[0].keys:
+        """Set `dlogmass`."""
-            raise ValueError("Invalid mass kind `{}`.".format(mass_kind))
+        assert dlogmass > 0
+        self._dlogmass = dlogmass
+
+    @property
+    def mass_kind(self):
+        """
+        The mass kind whose similarity is to be checked.
+
+        Returns
+        -------
+        mass_kind : str
+        """
+        return self._mass_kind
+
+    @mass_kind.setter
+    def mass_kind(self, mass_kind):
+        """Set `mass_kind`."""
+        assert isinstance(mass_kind, str)
+        self._mass_kind = mass_kind
+
+    @property
+    def remove_nooverlap(self):
+        """
+        Whether to remove pairs with exactly zero overlap.
+
+        Returns
+        -------
+        remove_nooverlap : bool
+        """
+        return self._remove_nooverlap
+
+    @remove_nooverlap.setter
+    def remove_nooverlap(self, remove_nooverlap):
+        """Set `remove_nooverlap`."""
+        assert isinstance(remove_nooverlap, bool)
+        self._remove_nooverlap = remove_nooverlap
+
+    @property
+    def overlapper(self):
+        """
+        The overlapper object.
+
+        Returns
+        -------
+        overlapper : :py:class:`csiborgtools.match.ParticleOverlap`
+        """
+        return self._overlapper
 
     @staticmethod
     def _cat2clump_mapping(cat_indxs, clump_indxs):
@@ -154,264 +214,111 @@ class RealisationsMatcher:
         mapping[ind2] = ind1
         return mapping
 
-    def cross_knn_position_single(self, n_sim, nmult=1, dlogmass=None,
+    def cross(self, nsim0, nsimx,  cat0, catx, overlap=False, verbose=True):
-                                  mass_kind="totpartmass", overlap=False,
-                                  overlapper_kwargs={}, select_initial=True,
-                                  remove_nooverlap=True, fast_neighbours=False,
-                                  verbose=True):
         r"""
-        Find all neighbours within a multiple of the sum of either the initial
+        Find all neighbours whose CM separation is less than `nmult` times the
-        Lagrangian patch sizes (distance at :math:`z = 70`) or :math:`R_{200c}`
+        sum of their initial Lagrangian patch sizes. Enforces that the
-        (distance at :math:`z = 0`). Enforces that the neighbours' are similar
+        neighbours' are similar in mass up to `dlogmass` dex.
-        in mass up to `dlogmass` dex and optionally calculates their overlap.
 
         Parameters
         ----------
-        n_sim : int
+        nsim0, nsimx : int
-            Index of an IC realisation in `self.cats` whose halos' neighbours
+            The reference and cross simulation IDs.
-            in the remaining simulations to search for.
+        cat0, catx: :py:class:`csiborgtools.read.HaloCatalogue`
-        nmult : float or int, optional
+            Halo catalogues corresponding to `nsim0` and `nsimx`, respectively.
-            Multiple of the sum of pair Lagrangian patch sizes or
-            :math:`R_{200c}` within which to return neighbours. By default 1.
-        dlogmass : float, optional
-            Tolerance on mass logarithmic mass difference. By default `None`.
-        mass_kind : str, optional
-            The mass kind whose similarity is to be checked. Must be a valid
-            catalogue key. By default `totpartmass`, i.e. the total particle
-            mass associated with a halo.
         overlap : bool, optional
-            Whether to calculate overlap between clumps in the initial
+            whether to calculate overlap between clumps in the initial
-            snapshot. By default `False`. This operation is slow.
+            snapshot. by default `false`. this operation is slow.
-        overlapper_kwargs : dict, optional
-            Keyword arguments passed to `ParticleOverlapper`.
-        select_initial : bool, optional
-            Whether to select nearest neighbour at the initial or final
-            snapshot. By default `True`, i.e. at the initial snapshot.
-        remove_nooverlap : bool, optional
-            Whether to remove pairs with exactly zero overlap. By default
-            `True`.
-        fast_neighbours : bool, optional
-            Whether to calculate neighbours within a fixed radius of each
-            clump. Note that this will result in missing some matches. If
-            `True` then `nmult` is a multiple of either the initial patch size
-            of :math:`R_{200c}`.
         verbose : bool, optional
-            Iterator verbosity flag. By default `True`.
+            iterator verbosity flag. by default `true`.
 
         Returns
         -------
-        matches : composite array
+        indxs : 1-dimensional array of shape `(nhalos, )`
-            Array, indices are `(n_sims - 1, 5, n_halos, n_matches)`. The
+            Indices of halos in the reference catalogue.
-            2nd axis is `index` of the neighbouring halo in its catalogue,
+        match_indxs : 1-dimensional array of arrays
-            `dist` is the 3D distance to the halo whose neighbours are
+            Indices of halo counterparts in the cross catalogue.
-            searched, `dist0` is the separation of the initial CMs, and
+        overlaps : 1-dimensional array of arrays
-            `overlap` is the overlap over the initial clumps, respectively.
+            Overlaps with the cross catalogue.
         """
-        self._check_masskind(mass_kind)
+        assert (nsim0 == cat0.paths.n_sim) & (nsimx == catx.paths.n_sim)
-        # Halo properties of this simulation
-        logmass = numpy.log10(self.cats[n_sim][mass_kind])
-        pos = self.cats[n_sim].positions      # Grav potential minimum
-        pos0 = self.cats[n_sim].positions0    # CM positions
-        if select_initial:
-            R = self.cats[n_sim]["lagpatch"]  # Initial Lagrangian patch size
-        else:
-            R = self.cats[n_sim]["r200"]      # R200c at z = 0
 
-        if overlap:
+        # Query the KNN
-            overlapper = ParticleOverlap(**overlapper_kwargs)
         if verbose:
-                print("Loading initial clump particles for `n_sim = {}`."
+            print("{}: querying the KNN.".format(datetime.now()), flush=True)
-                      .format(n_sim), flush=True)
+        match_indxs = radius_neighbours(
-            # Grab a paths object. What it is set to is unimportant
+            catx.knn0, cat0.positions0, radiusX=cat0["lagpatch"],
-            paths = self.cats[0].paths
+            radiusKNN=catx["lagpatch"], nmult=self.nmult, enforce_in32=True,
-            with open(paths.clump0_path(self.cats.n_sims[n_sim]), "rb") as f:
+            verbose=verbose)
+
+        # Remove neighbours whose mass is too large/small
+        if self.dlogmass is not None:
+            for j, indx in enumerate(match_indxs):
+                # |log(M1 / M2)|
+                p = self.mass_kind
+                aratio = numpy.abs(numpy.log10(catx[p][indx] / cat0[p][j]))
+                match_indxs[j] = match_indxs[j][aratio < self.dlogmass]
+
+        # Initialise the array outside in case `overlap` is `False`
+        cross = [numpy.asanyarray([], dtype=numpy.float32)] * match_indxs.size
+        if overlap:
+            if verbose:
+                print("Loading the clump particles", flush=True)
+            with open(cat0.paths.clump0_path(nsim0), "rb") as f:
                 clumps0 = numpy.load(f, allow_pickle=True)
-            clumps_pos2cell(clumps0, overlapper)
+            with open(catx.paths.clump0_path(nsimx), 'rb') as f:
-            # Precalculate min and max cell along each axis
-            mins0, maxs0 = get_clumplims(clumps0,
-                                         ncells=overlapper.inv_clength,
-                                         nshift=overlapper.nshift)
-            cat2clumps0 = self._cat2clump_mapping(self.cats[n_sim]["index"],
-                                                  clumps0["ID"])
-
-        matches = [None] * (self.cats.N - 1)
-        # Verbose iterator
-        if verbose:
-            iters = enumerate(tqdm(self.search_sim_indices(n_sim)))
-        else:
-            iters = enumerate(self.search_sim_indices(n_sim))
-        iters = enumerate(self.search_sim_indices(n_sim))
-        # Search for neighbours in the other simulations at z = 70
-        for count, i in iters:
-            if verbose:
-                print("Querying the KNN for `n_sim = {}`.".format(n_sim),
-                      flush=True)
-            # Query the KNN either fast (miss some) or slow (get all)
-            if select_initial:
-                if fast_neighbours:
-                    dist0, indxs = self.cats[i].radius_neigbours(
-                        pos0, R * nmult, select_initial=True)
-                else:
-                    dist0, indxs = radius_neighbours(
-                        self.cats[i].knn0, pos0, radiusX=R,
-                        radiusKNN=self.cats[i]["lagpatch"], nmult=nmult,
-                        verbose=verbose)
-            else:
-                # Will switch dist0 <-> dist at the end
-                if fast_neighbours:
-                    dist0, indxs = self.cats[i].radius_neigbours(
-                        pos, R * nmult, select_initial=False)
-                else:
-                    dist0, indxs = radius_neighbours(
-                        self.cats[i].knn, pos, radiusX=R,
-                        radiusKNN=self.cats[i]["r200"], nmult=nmult,
-                        verbose=verbose)
-            # Enforce int32 and float32
-            for n in range(dist0.size):
-                dist0[n] = dist0[n].astype(numpy.float32)
-                indxs[n] = indxs[n].astype(numpy.int32)
-
-            # Get rid of neighbors whose mass is too off
-            if dlogmass is not None:
-                for j, indx in enumerate(indxs):
-                    match_logmass = numpy.log10(self.cats[i][mass_kind][indx])
-                    mask = numpy.abs(match_logmass - logmass[j]) < dlogmass
-                    dist0[j] = dist0[j][mask]
-                    indxs[j] = indx[mask]
-
-            # Find the distance at z = 0 (or z = 70 dep. on `search_initial``)
-            dist = [numpy.asanyarray([], dtype=numpy.float32)] * dist0.size
-            with_neigbours = numpy.where([ii.size > 0 for ii in indxs])[0]
-            # Fill the pre-allocated array on positions with neighbours
-            for k in with_neigbours:
-                if select_initial:
-                    dist[k] = numpy.linalg.norm(
-                        pos[k] - self.cats[i].positions[indxs[k]], axis=1)
-                else:
-                    dist[k] = numpy.linalg.norm(
-                        pos0[k] - self.cats[i].positions0[indxs[k]], axis=1)
-
-            # Calculate the initial snapshot overlap
-            cross = [numpy.asanyarray([], dtype=numpy.float32)] * dist0.size
-            if overlap:
-                if verbose:
-                    print("Loading initial clump particles for `n_sim = {}` "
-                          "to compare against `n_sim = {}`.".format(i, n_sim),
-                          flush=True)
-                with open(paths.clump0_path(self.cats.n_sims[i]), 'rb') as f:
                 clumpsx = numpy.load(f, allow_pickle=True)
-                clumps_pos2cell(clumpsx, overlapper)
+
+            # Convert 3D positions to particle IDs
+            clumps_pos2cell(clumps0, self.overlapper)
+            clumps_pos2cell(clumpsx, self.overlapper)
 
             # Calculate the particle field
             if verbose:
-                    print("Loading and smoothing the crossed total field.",
+                print("Creating and smoothing the crossed field.", flush=True)
-                          flush=True)
+            delta = self.overlapper.make_delta(concatenate_clumps(clumpsx),
-                particles = concatenate_clumps(clumpsx)
+                                               to_smooth=False)
-                delta = overlapper.make_delta(particles, to_smooth=False)
+            delta = self.overlapper.smooth_highres(delta)
-                del particles; collect()  # noqa - no longer needed
+
-                delta = overlapper.smooth_highres(delta)
+            # Min and max cells along each axis for each halo
-                if verbose:
+            limkwargs = {"ncells": self.overlapper.inv_clength,
-                    print("Smoothed up the field.", flush=True)
+                         "nshift": self.overlapper.nshift}
-                # Precalculate min and max cell along each axis
+            mins0, maxs0 = get_clumplims(clumps0, **limkwargs)
-                minsx, maxsx = get_clumplims(clumpsx,
+            minsx, maxsx = get_clumplims(clumpsx, **limkwargs)
-                                             ncells=overlapper.inv_clength,
+
-                                             nshift=overlapper.nshift)
+            # Mapping from a catalogue halo index to the list of clumps
+            cat2clumps0 = self._cat2clump_mapping(cat0["index"], clumps0["ID"])
+            cat2clumpsx = self._cat2clump_mapping(catx["index"], clumpsx["ID"])
 
-                cat2clumpsx = self._cat2clump_mapping(self.cats[i]["index"],
-                                                      clumpsx["ID"])
             # Loop only over halos that have neighbours
-                for k in tqdm(with_neigbours) if verbose else with_neigbours:
+            wneigbours = numpy.where([ii.size > 0 for ii in match_indxs])[0]
-                    # Find which clump matches index of this halo from cat
+            for k in tqdm(wneigbours) if verbose else wneigbours:
-                    match0 = cat2clumps0[k]
+                match0 = cat2clumps0[k]  # Clump pos matching this halo
-
+                # The clump, its mass and mins & maxs
-                    # Unpack this clum, its mamss and mins and maxs
                 cl0 = clumps0["clump"][match0]
                 mass0 = numpy.sum(cl0['M'])
                 mins0_current, maxs0_current = mins0[match0], maxs0[match0]
-                    # Preallocate this array.
-                    crosses = numpy.full(indxs[k].size, numpy.nan,
-                                         numpy.float32)
 
-                    # Loop over the ones we cross-correlate with
+                # Array to store overlaps of this halo
-                    for ii, ind in enumerate(indxs[k]):
+                crosses = numpy.full(match_indxs[k].size, numpy.nan,
-                        # Again which cross clump to this index
+                                     numpy.float32)
-                        matchx = cat2clumpsx[ind]
+                # Loop over matches of this halo from the other simulation
+                for ii, ind in enumerate(match_indxs[k]):
+                    matchx = cat2clumpsx[ind]  # Clump pos matching this halo
                     clx = clumpsx["clump"][matchx]
-                        crosses[ii] = overlapper(
+                    crosses[ii] = self.overlapper(
                         cl0, clx, delta, mins0_current, maxs0_current,
                         minsx[matchx], maxsx[matchx],
                         mass1=mass0, mass2=numpy.sum(clx['M']))
-
                 cross[k] = crosses
-                    # Optionally remove points whose overlap is exaclt zero
+
-                    if remove_nooverlap:
+                # Optionally remove matches with exactly 0 overlap
+                if self.remove_nooverlap:
                     mask = cross[k] > 0
-                        indxs[k] = indxs[k][mask]
+                    match_indxs[k] = match_indxs[k][mask]
-                        dist[k] = dist[k][mask]
-                        dist0[k] = dist0[k][mask]
                     cross[k] = cross[k][mask]
 
-            # Append as a composite array. Flip dist order if not select_init
+        return cat0["index"], match_indxs, cross
-            if select_initial:
-                matches[count] = numpy.asarray(
-                    [indxs, dist, dist0, cross], dtype=object)
-            else:
-                matches[count] = numpy.asarray(
-                    [indxs, dist0, dist, cross], dtype=object)
-
-        return numpy.asarray(matches, dtype=object)
-
-    def cross_knn_position_all(self, nmult=1, dlogmass=None,
-                               mass_kind="totpartmass", overlap=False,
-                               overlapper_kwargs={},
-                               select_initial=True, remove_nooverlap=True,
-                               verbose=True):
-        r"""
-        Find all counterparts of halos in all simulations listed in
-        `self.cats`. See `self.cross_knn_position_single` for more details.
-
-        Parameters
-        ----------
-        nmult : float or int, optional
-            Multiple of the sum of pair Lagrangian patch sizes or
-            :math:`R_{200c}` within which to return neighbours. By default 1.
-        dlogmass : float, optional
-            Tolerance on mass logarithmic mass difference. By default `None`.
-        mass_kind : str, optional
-            The mass kind whose similarity is to be checked. Must be a valid
-            catalogue key. By default `totpartmass`, i.e. the total particle
-            mass associated with a halo.
-        overlap : bool, optional
-            Whether to calculate overlap between clumps in the initial
-            snapshot. By default `False`. Note that this operation is
-            substantially slower.
-        overlapper_kwargs : dict, optional
-            Keyword arguments passed to `ParticleOverlapper`.
-        select_initial : bool, optional
-            Whether to select nearest neighbour at the initial or final
-            snapshot. By default `True`, i.e. at the initial snapshot.
-        remove_nooverlap : bool, optional
-            Whether to remove pairs with exactly zero overlap. By default
-            `True`.
-        verbose : bool, optional
-            Iterator verbosity flag. By default `True`.
-
-        Returns
-        -------
-        matches : list of composite arrays
-            List whose length is `n_sims`. For description of its elements see
-            `self.cross_knn_position_single(...)`.
-        """
-        N = self.cats.N  # Number of catalogues
-        matches = [None] * N
-        # Loop over each catalogue
-        for i in trange(N) if verbose else range(N):
-            matches[i] = self.cross_knn_position_single(
-                i, nmult, dlogmass, mass_kind=mass_kind, overlap=overlap,
-                overlapper_kwargs=overlapper_kwargs,
-                select_initial=select_initial,
-                remove_nooverlap=remove_nooverlap, verbose=verbose)
-        return matches
 
 
 ###############################################################################
@@ -1002,7 +909,8 @@ def dist_percentile(dist, qs, distmax=0.075):
     return x
 
 
-def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1., verbose=True):
+def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1., enforce_in32=False,
+                      verbose=True):
     """
     Find all neigbours of a trained KNN model whose center of mass separation
     is less than `nmult` times the sum of their respective radii.
@@ -1020,13 +928,14 @@ def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1., verbose=True):
         Patch radii corresponding to clumps used to train `knn`.
     nmult : float, optional
         Multiple of the sum of two radii below which to consider a match.
+    enforce_int32 : bool, optional
+        Whether to enforce 32-bit integer precision of the indices. By default
+        `False`.
     verbose : bool, optional
         Verbosity flag.
 
     Returns
     -------
-    dists : 1-dimensional array `(n_samples,)` of arrays
-        Distance from `X` to matches from `knn`.
     indxs : 1-dimensional array `(n_samples,)` of arrays
         Matches to `X` from `knn`.
     """
@@ -1035,7 +944,6 @@ def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1., verbose=True):
     assert radiusKNN.size == knn.n_samples_fit_  # patchknn matches the knn?
 
     nsamples = X.shape[0]
-    dists = [None] * nsamples            # Initiate lists
     indxs = [None] * nsamples
     patchknn_max = numpy.max(radiusKNN)  # Maximum for completeness
 
@@ -1046,9 +954,8 @@ def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1., verbose=True):
         # Note that `dist` and `indx` are wrapped in 1-element arrays
         # so we take the first item where appropriate
         mask = (dist[0] / (radiusX[i] + radiusKNN[indx[0]])) < nmult
-        dists[i] = dist[0][mask]
         indxs[i] = indx[0][mask]
+        if enforce_in32:
+            indxs[i] = indxs[i].astype(numpy.int32)
 
-    dists = numpy.asarray(dists, dtype=object)  # Turn into array of arrays
+    return numpy.asarray(indxs, dtype=object)
-    indxs = numpy.asarray(indxs, dtype=object)
-    return dists, indxs

csiborgtools/read/__init__.py

@@ -14,9 +14,8 @@
 # 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
 from .readsim import (CSiBORGPaths, ParticleReader, read_mmain, read_initcm, halfwidth_select)  # noqa
-from .make_cat import (HaloCatalogue, CombinedHaloCatalogue, concatenate_clumps,  # noqa
+from .make_cat import (HaloCatalogue, concatenate_clumps, clumps_pos2cell)  # noqa
-                       clumps_pos2cell)  # noqa
 from .readobs import (PlanckClusters, MCXCClusters, TwoMPPGalaxies,  # noqa
                       TwoMPPGroups, SDSS)  # noqa
 from .outsim import (dump_split, combine_splits, make_ascii_powmes)  # noqa
-from .summaries import PKReader  # noqa
+from .summaries import (PKReader, OverlapReader, binned_resample_mean) # noqa

csiborgtools/read/make_cat.py

@@ -17,10 +17,8 @@ Functions to read in the particle and clump files.
 """
 import numpy
 from os.path import join
-from tqdm import trange
-from copy import deepcopy
 from sklearn.neighbors import NearestNeighbors
-from .readsim import (read_mmain, read_initcm)
+from .readsim import (CSiBORGPaths, read_mmain, read_initcm)
 from ..utils import (flip_cols, add_columns)
 from ..units import (BoxUnits, cartesian_to_radec)
 
@@ -47,7 +45,11 @@ class HaloCatalogue:
     _positions = None
     _positions0 = None
 
-    def __init__(self, paths, min_m500=None, max_dist=None):
+    def __init__(self, nsim, min_m500=None, max_dist=None):
+        # Set up paths
+        paths = CSiBORGPaths(n_sim=nsim)
+        paths.n_snap = paths.get_maximum_snapshot()
+        self._paths = paths
         self._box = BoxUnits(paths)
         min_m500 = 0 if min_m500 is None else min_m500
         max_dist = numpy.infty if max_dist is None else max_dist
@@ -378,99 +380,6 @@ class HaloCatalogue:
         return self._data[key]
 
 
-class CombinedHaloCatalogue:
-    r"""
-    A combined halo catalogue, containing `HaloCatalogue` for each IC
-    realisation at the latest redshift.
-
-    Parameters
-    ----------
-    paths : py:class`csiborgtools.read.CSiBORGPaths`
-        CSiBORG paths-handling object. Doest not have to have set set `n_sim`
-        and `n_snap`.
-    min_m500 : float, optional
-        The minimum :math:`M_{rm 500c} / M_\odot` mass. By default no
-        threshold.
-    max_dist : float, optional
-        The maximum comoving distance of a halo. By default no upper limit.
-    verbose : bool, optional
-        Verbosity flag for reading the catalogues.
-    """
-    _n_sims = None
-    _n_snaps = None
-    _cats = None
-
-    def __init__(self, paths, min_m500=None, max_dist=None, verbose=True):
-        # Read simulations and their maximum snapshots
-        # NOTE later change this back to all simulations
-        # self._n_sims = [7468, 7588, 8020, 8452, 8836]
-        self._n_sims = [7468, 7588]
-#        self._n_sims = paths.ic_ids
-        n_snaps = [paths.get_maximum_snapshot(i) for i in self._n_sims]
-        self._n_snaps = numpy.asanyarray(n_snaps)
-
-        cats = [None] * self.N
-        for i in trange(self.N) if verbose else range(self.N):
-            paths = deepcopy(paths)
-            paths.set_info(self.n_sims[i], self.n_snaps[i])
-            cats[i] = HaloCatalogue(paths, min_m500, max_dist)
-        self._cats = cats
-
-    @property
-    def N(self):
-        """
-        Number of IC realisations in this combined catalogue.
-
-        Returns
-        -------
-        N : int
-            Number of catalogues.
-        """
-        return len(self.n_sims)
-
-    @property
-    def n_sims(self):
-        """
-        IC realisations CSiBORG identifiers.
-
-        Returns
-        -------
-        ids : 1-dimensional array
-            Array of IDs.
-        """
-        return self._n_sims
-
-    @property
-    def n_snaps(self):
-        """
-        Snapshot numbers corresponding to `self.n_sims`.
-
-        Returns
-        -------
-        n_snaps : 1-dimensional array
-            Array of snapshot numbers.
-        """
-        return self._n_snaps
-
-    @property
-    def cats(self):
-        """
-        Catalogues associated with this object.
-
-        Returns
-        -------
-        cats : list of `HaloCatalogue`
-            Catalogues.
-        """
-        return self._cats
-
-    def __getitem__(self, n):
-        if n > self.N:
-            raise ValueError("Catalogue count is {}, requested catalogue {}."
-                             .format(self.N, n))
-        return self.cats[n]
-
-
 def concatenate_clumps(clumps):
     """
     Concatenate an array of clumps to a single array containing all particles.

csiborgtools/read/readsim.py

@@ -88,7 +88,8 @@ class CSiBORGPaths:
         self.to_new = to_new
         if n_sim is not None and n_snap is not None:
             self.set_info(n_sim, n_snap)
-        # "/mnt/extraspace/rstiskalek/csiborg/initmatch/clump_cm_7468.npy"
+        if n_sim is not None:
+            self.n_sim = n_sim
 
     @property
     def srcdir(self):

csiborgtools/read/summaries.py

@@ -166,3 +166,268 @@ class PKReader:
                 k += 1
 
         return ks, xpks
+
+
+class OverlapReader:
+    """
+    TODO: docs
+
+    """
+
+    def __init__(self, nsim0, nsimx, cat0, catx, fskel=None):
+        if fskel is None:
+            fskel = "/mnt/extraspace/rstiskalek/csiborg/overlap/"
+            fskel += "cross_{}_{}.npz"
+        self._data = numpy.load(fskel.format(nsim0, nsimx), allow_pickle=True)
+        self._set_cats(nsim0, nsimx, cat0, catx)
+
+    @property
+    def nsim0(self):
+        """
+        The reference simulation ID.
+
+        Returns
+        -------
+        nsim0 : int
+        """
+        return self._nsim0
+
+    @property
+    def nsimx(self):
+        """
+        The cross simulation ID.
+
+        Returns
+        -------
+        nsimx : int
+        """
+        return self._nsimx
+
+    @property
+    def cat0(self):
+        """
+        The reference halo catalogue.
+
+        Returns
+        -------
+        cat0 : :py:class:`csiborgtools.read.HaloCatalogue`
+        """
+        return self._cat0
+
+    @property
+    def catx(self):
+        """
+        The cross halo catalogue.
+
+        Returns
+        -------
+        catx : :py:class:`csiborgtools.read.HaloCatalogue`
+        """
+        return self._catx
+
+    def _set_cats(self, nsim0, nsimx, cat0, catx):
+        """
+        Set the simulation IDs and catalogues.
+
+        Parameters
+        ----------
+        nsim0, nsimx : int
+            The reference and cross simulation IDs.
+        cat0, catx: :py:class:`csiborgtools.read.HaloCatalogue`
+            Halo catalogues corresponding to `nsim0` and `nsimx`, respectively.
+
+        Returns
+        -------
+        None
+        """
+        assert (nsim0 == cat0.paths.n_sim) & (nsimx == catx.paths.n_sim)
+        self._nsim0 = nsim0
+        self._nsimx = nsimx
+        self._cat0 = cat0
+        self._catx = catx
+
+    @property
+    def indxs(self):
+        """
+        Indices of halos from the reference catalogue.
+
+        Returns
+        -------
+        indxs : 1-dimensional array
+        """
+        return self._data["indxs"]
+
+    @property
+    def match_indxs(self):
+        """
+        Indices of halos from the cross catalogue.
+
+        Returns
+        -------
+        match_indxs : array of 1-dimensional arrays of shape `(nhalos, )`
+        """
+        return self._data["match_indxs"]
+
+    @property
+    def overlap(self):
+        """
+        Pair overlap of halos between the reference and cross simulations.
+
+        Returns
+        -------
+        ovelap : array of 1-dimensional arrays of shape `(nhalos, )`
+        """
+        return self._data["cross"]
+
+    def dist(self, in_initial, norm=None):
+        """
+        Final snapshot pair distances.
+
+        Parameters
+        ----------
+        in_initial : bool
+            Whether to calculate separation in the initial or final snapshot.
+
+        Returns
+        -------
+        dist : array of 1-dimensional arrays of shape `(nhalos, )`
+        """
+        assert norm is None or norm in ("r200", "ref_patch", "sum_patch")
+        # Positions either in the initial or final snapshot
+        if in_initial:
+            pos0 = self.cat0.positions0
+            posx = self.catx.positions0
+        else:
+            pos0 = self.cat0.positions
+            posx = self.catx.positions
+
+        dist = [None] * self.indxs.size
+        for n, ind in enumerate(self.match_indxs):
+            dist[n] = numpy.linalg.norm(pos0[n, :] - posx[ind, :], axis=1)
+
+            # Normalisation
+            if norm == "r200":
+                dist[n] /= self.cat0["r200"][n]
+            if norm == "ref_patch":
+                dist[n] /= self.cat0["lagpatch"][n]
+            if norm == "sum_patch":
+                dist[n] /= (self.cat0["lagpatch"][n]
+                            + self.catx["lagpatch"][ind])
+        return numpy.array(dist, dtype=object)
+
+    def mass_ratio(self, mass_kind="totpartmass", in_log=True, in_abs=True):
+        """
+        Pair mass ratio.
+
+        Parameters
+        ----------
+        mass_kind : str, optional
+            The mass kind whose ratio is to be calculated. Must be a valid
+            catalogue key. By default `totpartmass`, i.e. the total particle
+            mass associated with a halo.
+        in_log : bool, optional
+            Whether to return logarithm of the ratio. By default `True`.
+        in_abs : bool, optional
+            Whether to return absolute value of the ratio. By default `True`.
+
+        Returns
+        -------
+        ratio : array of 1-dimensional arrays of shape `(nhalos, )`
+        """
+        mass0 = self.cat0[mass_kind]
+        massx = self.catx[mass_kind]
+
+        ratio = [None] * self.indxs.size
+        for n, ind in enumerate(self.match_indxs):
+            ratio[n] = mass0[n] / massx[ind]
+            if in_log:
+                ratio[n] = numpy.log10(ratio[n])
+            if in_abs:
+                ratio[n] = numpy.abs(ratio[n])
+        return numpy.array(ratio, dtype=object)
+
+    def summed_overlap(self):
+        """
+        Summed overlap of each halo in the reference simulation with the cross
+        simulation.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        summed_overlap : 1-dimensional array of shape `(nhalos, )`
+        """
+        return numpy.array([numpy.sum(cross) for cross in self._data["cross"]])
+    
+    def copy_per_match(self, par):
+        """
+        Make an array like `self.match_indxs` where each of its element is an
+        equal value array of the pair clump property from the reference
+        catalogue.
+
+        Parameters
+        ----------
+        par : str
+            Property to be copied over.
+        
+        Returns
+        -------
+        out : 1-dimensional array of shape `(nhalos, )`
+        """
+        out = [None] * self.indxs.size
+        for n, ind in enumerate(self.match_indxs):
+            out[n] = numpy.ones(ind.size) * self.cat0[par][n]
+        return numpy.array(out, dtype=object)
+
+
+def binned_resample_mean(x, y, prob, bins, nresample=50, seed=42):
+    """
+    Calculate binned average of `y` by MC resampling. Each point is kept with
+    probability `prob`.
+
+    Parameters
+    ----------
+    x : 1-dimensional array
+        Independent variable.
+    y : 1-dimensional array
+        Dependent variable.
+    prob : 1-dimensional array
+        Sample probability.
+    bins : 1-dimensional array
+        Bin edges to bin `x`.
+    nresample : int, optional
+        Number of MC resamples. By default 50.
+    seed : int, optional
+        Random seed.
+    
+    Returns
+    -------
+    bin_centres : 1-dimensional array
+        Bin centres.
+    stat : 2-dimensional array
+        Mean and its standard deviation from MC resampling.
+    """
+    assert (x.ndim == 1) & (x.shape == y.shape == prob.shape)
+
+    gen = numpy.random.RandomState(seed)
+
+    loop_stat = numpy.full(nresample, numpy.nan)      # Preallocate loop arr 
+    stat = numpy.full((bins.size - 1, 2), numpy.nan)  # Preallocate output 
+
+    for i in range(bins.size - 1):
+        mask = (x > bins[i]) & (x <= bins[i + 1])
+        nsamples = numpy.sum(mask)
+
+        loop_stat[:] = numpy.nan  # Clear it
+        for j in range(nresample):
+            loop_stat[j] = numpy.mean(y[mask][gen.rand(nsamples) < prob[mask]])
+        
+        stat[i, 0] = numpy.mean(loop_stat)
+        stat[i, 1] = numpy.std(loop_stat)
+    
+    bin_centres = (bins[1:] + bins[:-1]) / 2
+
+    return bin_centres, stat
+    

scripts/run_singlematch.py

@@ -32,35 +32,25 @@ import utils
 parser = ArgumentParser()
 parser.add_argument("--nmult", type=float)
 parser.add_argument("--overlap", type=lambda x: bool(strtobool(x)))
-parser.add_argument("--select_initial", type=lambda x: bool(strtobool(x)))
-parser.add_argument("--fast_neighbours", type=lambda x: bool(strtobool(x)))
 args = parser.parse_args()
 
 # File paths
-ic = 7468
+nsim0 = 7468
-fperm = join(utils.dumpdir, "overlap", "cross_{}.npy")
+nsimx = 7588
-
+fperm = join(utils.dumpdir, "overlap", "cross_{}_{}.npy")
-paths = csiborgtools.read.CSiBORGPaths(to_new=False)
-paths.set_info(ic, paths.get_maximum_snapshot(ic))
 
 print("{}: loading catalogues.".format(datetime.now()), flush=True)
-cat = csiborgtools.read.CombinedHaloCatalogue(paths)
+cat0 = csiborgtools.read.HaloCatalogue(nsim0)
-
+catx = csiborgtools.read.HaloCatalogue(nsimx)
-
-matcher = csiborgtools.match.RealisationsMatcher(cat)
-nsim0 = cat.n_sims[0]
-nsimx = cat.n_sims[1]
 
+matcher = csiborgtools.match.RealisationsMatcher()
 print("{}: crossing the simulations.".format(datetime.now()), flush=True)
-
+indxs, match_indxs, cross = matcher.cross(
-out = matcher.cross_knn_position_single(
+    nsim0, nsimx, cat0, catx, overlap=False)
-    0, nmult=args.nmult, dlogmass=2., overlap=args.overlap,
-    select_initial=args.select_initial, fast_neighbours=args.fast_neighbours)
-
 # Dump the result
-fout = fperm.format(nsim0)
+fout = fperm.format(nsim0, nsimx)
 print("Saving results to `{}`.".format(fout), flush=True)
 with open(fout, "wb") as f:
-    numpy.save(fout, out)
+    numpy.savez(fout, indxs=indxs, match_indxs=match_indxs, cross=cross)
 
 print("All finished.", flush=True)