pynbody.bridge.RamsesBugOrderBridge

pynbody.bridge.RamsesBugOrderBridge#

class pynbody.bridge.RamsesBugOrderBridge(start, end, order_array='iord', monotonic=False, allow_family_change=False, only_families=None)[source]#

Bases: OrderBridge

Methods

__call__(s)

Map from a SubSnap at one end of the bridge, to the corresponding SubSnap at the other end

catalog_transfer_matrix([min_index, ...])

Deprecated interface to count_particles_in_common().

count_particles_in_common(halos_1, halos_2, /)

Return a matrix with the number of particles transferred from groups_1 to groups_2.

fuzzy_match_catalog([min_index, max_index, ...])

Deprecated alternative to fuzzy_match_halos().

fuzzy_match_halos(halos_1, halos_2, /[, ...])

Given halo catalogues, return possible matches within the second catalogue for each halo in the first.

match_catalog([min_index, max_index, ...])

Deprecated alternative to match_halos().

match_halos(halos_1, halos_2, /[, ...])

Given halo catalogues, identify the likely halo number in the second catalogue for each halo in the first.
__init__(start, end, order_array='iord', monotonic=False, allow_family_change=False, only_families=None)[source]#

A special case of OrderBridge for tracking between Ramses snapshots affected by int32 iord truncation bug.

In this bug, the iord array is truncated to int32 when transmitted from one CPU to another. We first truncate all iords to int32, then use heuristics to try to disambiguate collisions that occur due to this bit loss. The heuristics are that star particles must always map onto star particles, and DM particles must map onto DM particles of the same level.

catalog_transfer_matrix(min_index=1, max_index=30, groups_1=None, groups_2=None, use_family=None, only_family=None)#

Deprecated interface to count_particles_in_common(). Use that method instead.

count_particles_in_common(halos_1, halos_2, /, max_num_halos=None, use_family=None) → ndarray#

Return a matrix with the number of particles transferred from groups_1 to groups_2.

Normally, match_catalog() (or fuzzy_match_catalog()) are easier to use, but this routine provides the maximal information.

Warning

This routine returns results in terms of halo indexes, rather than halo numbers. This is because halo indexes are guaranteed to be continuous starting at zero, while halo numbers may have gaps. If you need to convert from halo indexes to halo numbers, you can use number_mapper() attribute of HaloCatalogue. Alternatively, use the match_catalog() method which returns results in terms of halo numbers.

Parameters:

  • halos_1 (pynbody.halo.HaloCatalogue) – The HaloCatalogue for the first snapshot (the one at the start of the bridge).

  • halos_2 (pynbody.halo.HaloCatalogue) – The HaloCatalogue for the second snapshot (the one at the end of the bridge).

  • max_num_halos (int, optional) – The maximum number of halos

  • use_family (str) – Only match particles of this family. Default is None, in which case all particles are matched.

Returns:

A matrix with the number of particles transferred from each halo in the first catalogue to each halo in the second. The size of the matrix is determined by the maximum number of halos in either catalogue, or by the value of max_num_halos if specified.

Return type:

numpy.ndarray

fuzzy_match_catalog(min_index=1, max_index=30, threshold=0.01, groups_1=None, groups_2=None, use_family=None, only_family=None)#

Deprecated alternative to fuzzy_match_halos(). Use that method instead.

fuzzy_match_halos(halos_1, halos_2, /, threshold=0.01, use_family=None, use_halo_indexes=False) → dict[int, list[tuple[int, float]]]#

Given halo catalogues, return possible matches within the second catalogue for each halo in the first.

For details about the parameters to this function, see the documentation for match_catalog().

Parameters:

  • halos_1 (pynbody.halo.HaloCatalogue) – The HaloCatalogue for the source snapshot

  • halos_2 (pynbody.halo.HaloCatalogue) – The HaloCatalogue for the destination snapshot

  • threshold (float) – The minimum fraction of particles in common for a match to be considered. Default is 0.01.

  • use_family (str) – Only match particles of this family. Default is None, in which case all particles are matched.

  • use_halo_indexes (bool) – If True, instead of returning halo numbers, return halo indexes. Default is False.

Returns:

Maps from halo number in the first catalogue to a list of tuples. Each tuple contains the halo number in the second catalogue and the fraction of particles in the first halo that are in the second halo. If use_halo_indexes is True, the halo numbers are replaced by halo indexes.

Return type:

dict[int, list[tuple[int, float]]]

match_catalog(min_index=1, max_index=30, threshold=0.5, groups_1=None, groups_2=None, use_family=None)#

Deprecated alternative to match_halos(). Use that method instead.

match_halos(halos_1, halos_2, /, threshold=0.5, use_family=None, fill_value=-1, use_halo_indexes=False)#

Given halo catalogues, identify the likely halo number in the second catalogue for each halo in the first.

For example, if a bridge b links snapshot f1 (high redshift) to f2 (low redshift) and we perform:

>>> h1 = f1.halos()
>>> h2 = f2.halos()
>>> cat = b.match_halos(h1, h2)

then cat is now a dictionary such that f1.halos()[i] is the major progenitor for``f2.halos()[cat[i]]``, assuming cat[i] is positive.

Halos which cannot be matched because they have too few particles in common give the result fill_value, default -1. This is determined by the given threshold fraction of particles in common (specified by threshold, default 50%)

Parameters:

  • halos_1 (pynbody.halo.HaloCatalogue) – The HaloCatalogue for the first snapshot.

  • halos_2 (pynbody.halo.HaloCatalogue) – The HaloCatalogue for the second snapshot.

  • threshold (float) – The minimum fraction of particles in common for a match to be considered. Default is 0.5.

  • use_family (str) – Only match particles of this family. Default is None, in which case all particles are matched. Setting this to a family name can be useful if matching between two different simulations where the relationship between DM particles is known, but perhaps the relationship between gas particles is not (e.g. a Ramses simulation where actually the gas ‘particles’ are cells)

  • use_halo_indexes (bool) – If True, instead of returning a dictionary mapping to halo numbers, return a numpy array that matches halo IDs (which are zero-based indexes into the full list of halos; for more information see the nomenclature guide in the documentation for HaloCatalogue. The default is False, so that halo numbers are used throughout.

  • fill_value (int) – The value to use for halos that cannot be matched. Default is -1.

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