syncopy.shared.tools.best_match¶

syncopy.shared.tools.
best_match
(source, selection, span=False, tol=None, squash_duplicates=False)[source]¶ Find matching elements in a given 1darray/list
 Parameters
source (NumPy 1darray/list) – Reference array whose elements are to be matched by selection
selection (NumPy 1darray/list) – Array of queryvalues whose closest matches are to be found in source. Note that source and selection need not be the same length.
span (bool) – If True, selection is interpreted as (closed) interval
[lo, hi]
and source is queried for all elements contained in the interval, i.e.,lo <= src <= hi for src in source
(typically used for toilim/foilimlike selections).tol (None or float) – If None for each component of selection the closest value in source is selected, e.g., for
source = [10, 20]
andselection = [50, 0, 50]
the closest values are [10, 10, 20]. If not None, ensures values in selection do not deviate further than tol from source. If any element sel of selection is outside a tolneighborhood around source, i.e.,np.abs(sel  source).max() >= tol
, aSPYValueError
is raised.squash_duplicates (bool) – If True, identical matches are removed from the result.
 Returns
values (NumPy 1darray) – Values of source that most closely match given elements in selection
idx (NumPy 1darray) – Indices of values with respect to source, such that,
source[idx] == values
Notes
This is an auxiliary method that is intended purely for internal use. Thus, no error checking is performed.
Examples
Exact matching, ordered source and selection:
>>> best_match(np.arange(10), [2,5]) (array([2, 5]), array([2, 5]))
Inexact matching, ordered source and selection:
>>> source = np.arange(10) >>> selection = np.array([1.5, 1.5, 2.2, 6.2, 8.8]) >>> best_match(source, selection) (array([2, 2, 2, 6, 9]), array([2, 2, 2, 6, 9]))
Inexact matching, unordered source and selection:
>>> source = np.array([2.2, 1.5, 1.5, 6.2, 8.8]) >>> selection = np.array([1.9, 9., 1., 0.4, 1.2, 0.2, 9.3]) >>> best_match(source, selection) (array([2.2, 8.8, 1.5, 1.5, 1.5, 1.5, 8.8]), array([0, 4, 1, 1, 1, 1, 4]))
Same as above, but ignore duplicate matches
>>> best_match(source, selection, squash_duplicates=True) (array([2.2, 8.8, 1.5]), array([0, 4, 1]))
Intervalmatching:
>>> best_match(np.arange(10), [2.9, 6.1], span=True) (array([3, 4, 5, 6]), array([3, 4, 5, 6]))