From 914eae9713e626ca6f774a1bef9f058c11dd20a5 Mon Sep 17 00:00:00 2001
From: Peter Swain <peter.swain@ed.ac.uk>
Date: Fri, 28 Apr 2023 12:30:30 +0100
Subject: [PATCH] streamlined indexing simplifying broadcasting
---
src/agora/utils/indexing.py | 78 +++++++++++++++++++------------------
1 file changed, 40 insertions(+), 38 deletions(-)
diff --git a/src/agora/utils/indexing.py b/src/agora/utils/indexing.py
index 24fa004e..0e50be02 100644
--- a/src/agora/utils/indexing.py
+++ b/src/agora/utils/indexing.py
@@ -17,9 +17,9 @@ def validate_association(
match_column: t.Optional[int] = None,
) -> t.Tuple[np.ndarray, np.ndarray]:
"""
- Identify matches between two arrays by matching rows.
+ Identify matches between two arrays by comparing rows.
- We use broadcasting for fast multi-indexing, generalising for lineage dynamics.
+ We use broadcasting for speed.
Parameters
----------
@@ -30,68 +30,72 @@ def validate_association(
indices : np.ndarray
a 2D array where each column is a different level, such as (trap_id, cell_label). This should not include mother_label.
match_column: int
- int indicating a specific column that is required to match (i.e. 0-1 for target-source when trying to merge tracklets or mother-bud for lineage)
- must be present in indices.
- If None, one match suffices for the resultant indices
- vector to be True.
+ If 0, match mothers; if 1 match daughters.
+ If None, match both mothers and daughters.
Returns
-------
valid_association: boolean np.ndarray
- 1D array indicating valid elements in association.
+ 1D array indicating elements in association with matches.
valid_indices: boolean np.ndarray
- 1D array indicating valid elements in indices.
+ 1D array indicating elements in indices with matches.
Examples
--------
>>> import numpy as np
>>> from agora.utils.indexing import validate_association
- >>> association = np.array([ [[0, 1], [0, 3]], [[0, 1], [0, 4]] ])
+ >>> association = np.array([ [[0, 1], [0, 3]], [[0, 1], [0, 4]], [[0, 1], [0, 6]], [[0, 4], [0, 7]] ])
>>> indices = np.array([ [0, 1], [0, 2], [0, 3]])
>>> print(indices.T)
- >>> valid_associations, valid_indices = validate_association(merges, indices)
+ >>> valid_associations, valid_indices = validate_association(association, indices)
>>> print(valid_associations)
- array([ True, False])
+ array([ True, False, False, False])
>>> print(valid_indices)
array([ True, False, True])
"""
if association.ndim == 2:
# reshape into 3D array for broadcasting
- # [trap, mother, daughter] becomes [[trap, mother], [trap, daughter]] for each trap
+ # for each trap, [trap, mother, daughter] becomes
+ # [[trap, mother], [trap, daughter]]
association = _assoc_indices_to_3d(association)
- # compare existing association with available indices
- # swap trap and cell_label axes for the indices array to correctly broadcast
- # compare [[trap, mother], [trap, daughter]] with [trap, cell_label] for all traps in association and for all [trap, cell_label] pairs in indices
+ # use broadcasting to compare association with indices
+ # swap trap and cell_label axes for correct broadcasting
+ indicesT = indices.T
+ # compare each of [[trap, mother], [trap, daughter]] for all traps
+ # in association with [trap, cell_label] for all traps in indices
valid_ndassociation = (
- association[..., np.newaxis] == indices.T[np.newaxis, ...]
+ association[..., np.newaxis] == indicesT[np.newaxis, ...]
)
- # broadcasting is confusing (but efficient):
- # first, we check the dimension across trap and cell id to ensure both match
- # 1. find only those comparisons with both trap_ids and cell labels matching - they are now marked as True
+ # make True comparisons have both trap_ids and cell labels matching
valid_cell_ids = valid_ndassociation.all(axis=2)
if match_column is None:
- # then, we check the merge tuples to check which have both target and source
- # 2. keep only those comparisons that match at least one row in indices
- # 3. keep those that have a match for both mother and daughter in association
- valid_association = valid_cell_ids.any(axis=2).all(axis=1)
- # finally, we check the dimension that crosses all indices to ensure the pair
- # is present in a valid merge event
- valid_indices = (
- valid_ndassociation[valid_association].all(axis=2).any(axis=(0, 1))
- )
- myversion = valid_cell_ids.any(axis=1).any(axis=0)
- 1 / 0
+ # 1. find matches in association
+ # make True comparisons match at least one row in indices
+ va_intermediate = valid_cell_ids.any(axis=2)
+ # make True comparisons have both mother and daughter matching rows in indices
+ valid_association = va_intermediate.all(axis=1)
+ # 2. find matches in indices
+ # make True comparisons match for at least one mother or daughter in association
+ ind_intermediate = valid_cell_ids.any(axis=1)
+ # make True comparisons match for at least one row in association
+ valid_indices = ind_intermediate.any(axis=0)
+ # OLD
+ # valid_indices = (
+ # valid_ndassociation[valid_association].all(axis=2).any(axis=(0, 1))
+ # )
else:
- # we fetch specific indices if we aim for the ones with one present
+ # match_column selects mothers if 0 and daughters if 1
+ # make True match at least one row in indices
+ valid_association = valid_cell_ids[:, match_column].any(axis=1)
+ # make True match at least one row in association
valid_indices = valid_cell_ids[:, match_column].any(axis=0)
- # calid association then becomes a boolean array: True means that there is a
- # match (match_column) between that cell and the index
- valid_association = (
- valid_cell_ids[:, match_column] & valid_indices
- ).any(axis=1)
+ # OLD
+ # valid_association = (
+ # valid_cell_ids[:, match_column] & valid_indices
+ # ).any(axis=1)
return valid_association, valid_indices
@@ -105,8 +109,6 @@ def _assoc_indices_to_3d(ndarray: np.ndarray):
[ [0, 1, 3], [0, 1, 4] ]
becomes
[ [[0, 1], [0, 3]], [[0, 1], [0, 4]] ]
-
- This is used to convert a signal MultiIndex before comparing association.
"""
result = ndarray
if len(ndarray) and ndarray.ndim > 1:
--
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