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Function _multi_distance_profile

stumpy/mstump.py:442–551  ·  view source on GitHub ↗

Multi-dimensional wrapper to compute the multi-dimensional distance profile for a given query window within the times series or sequence that is denoted by the `query_idx` index. Essentially, this is a convenience wrapper around `_multi_mass`. Parameters ---------- query_id

(
    query_idx,
    T_A,
    T_B,
    m,
    M_T,
    Σ_T,
    μ_Q,
    σ_Q,
    T_subseq_isconstant,
    Q_subseq_isconstant,
    include=None,
    discords=False,
    excl_zone=None,
)

Source from the content-addressed store, hash-verified

440
441
442def _multi_distance_profile(
443 query_idx,
444 T_A,
445 T_B,
446 m,
447 M_T,
448 Σ_T,
449 μ_Q,
450 σ_Q,
451 T_subseq_isconstant,
452 Q_subseq_isconstant,
453 include=None,
454 discords=False,
455 excl_zone=None,
456):
457 """
458 Multi-dimensional wrapper to compute the multi-dimensional distance profile for a
459 given query window within the times series or sequence that is denoted by the
460 `query_idx` index. Essentially, this is a convenience wrapper around `_multi_mass`.
461
462 Parameters
463 ----------
464 query_idx : int
465 The start index of the (multi-dimensional) query subsequence in `T_B`
466
467 T_A : numpy.ndarray
468 The time series or sequence for which the multi-dimensional distance profile
469 is computed
470
471 T_B : numpy.ndarray
472 The time series or sequence that contains your query subsequences
473
474 m : int
475 Window size
476
477 M_T : numpy.ndarray
478 Sliding mean for `T_A`
479
480 Σ_T : numpy.ndarray
481 Sliding standard deviation for `T_A`
482
483 μ_Q : numpy.ndarray
484 Sliding mean for the query subsequence `T_B`
485
486 σ_Q : numpy.ndarray
487 Sliding standard deviation for the query subsequence `T_B`
488
489 T_subseq_isconstant : numpy.ndarray
490 A boolean array that indicates whether a subsequence in `T_A` is constant (True)
491
492 Q_subseq_isconstant : numpy.ndarray
493 A boolean array that indicates whether the subsequence in `T_B` is
494 constant (True)
495
496 include : numpy.ndarray, default None
497 A list of (zero-based) indices corresponding to the dimensions in `T` that
498 must be included in the constrained multidimensional motif search.
499 For more information, see Section IV D in:

Callers 2

multi_distance_profileFunction · 0.85

Calls 1

_multi_massFunction · 0.85

Tested by

no test coverage detected