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Source code for librosa.util.matching

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Matching functions"""

import numpy as np
import numba

from .exceptions import ParameterError
from .utils import valid_intervals
from .._typing import _SequenceLike

__all__ = ["match_intervals", "match_events"]


@numba.jit(nopython=True, cache=True)  # type: ignore
def __jaccard(int_a: np.ndarray, int_b: np.ndarray):  # pragma: no cover
    """Jaccard similarity between two intervals

    Parameters
    ----------
    int_a, int_b : np.ndarrays, shape=(2,)

    Returns
    -------
    Jaccard similarity between intervals
    """
    ends = [int_a[1], int_b[1]]
    if ends[1] < ends[0]:
        ends.reverse()

    starts = [int_a[0], int_b[0]]
    if starts[1] < starts[0]:
        starts.reverse()

    intersection = ends[0] - starts[1]
    if intersection < 0:
        intersection = 0.0

    union = ends[1] - starts[0]

    if union > 0:
        return intersection / union

    return 0.0


@numba.jit(nopython=True, cache=True)
def __match_interval_overlaps(query, intervals_to, candidates):  # pragma: no cover
    """Find the best Jaccard match from query to candidates"""

    best_score = -1
    best_idx = -1
    for idx in candidates:
        score = __jaccard(query, intervals_to[idx])

        if score > best_score:
            best_score, best_idx = score, idx
    return best_idx


@numba.jit(nopython=True, cache=True)  # type: ignore
def __match_intervals(
    intervals_from: np.ndarray, intervals_to: np.ndarray, strict: bool = True
) -> np.ndarray:  # pragma: no cover
    """Numba-accelerated interval matching algorithm."""
    # sort index of the interval starts
    start_index = np.argsort(intervals_to[:, 0])

    # sort index of the interval ends
    end_index = np.argsort(intervals_to[:, 1])

    # and sorted values of starts
    start_sorted = intervals_to[start_index, 0]
    # and ends
    end_sorted = intervals_to[end_index, 1]

    search_ends = np.searchsorted(start_sorted, intervals_from[:, 1], side="right")
    search_starts = np.searchsorted(end_sorted, intervals_from[:, 0], side="left")

    output = np.empty(len(intervals_from), dtype=numba.uint32)
    for i in range(len(intervals_from)):
        query = intervals_from[i]

        # Find the intervals that start after our query ends
        after_query = search_ends[i]
        # And the intervals that end after our query begins
        before_query = search_starts[i]

        # Candidates for overlapping have to (end after we start) and (begin before we end)
        candidates = set(start_index[:after_query]) & set(end_index[before_query:])

        # Proceed as before
        if len(candidates) > 0:
            output[i] = __match_interval_overlaps(query, intervals_to, candidates)
        elif strict:
            # Numba only lets us use compile-time constants in exception messages
            raise ParameterError
        else:
            # Find the closest interval
            # (start_index[after_query] - query[1]) is the distance to the next interval
            # (query[0] - end_index[before_query])
            dist_before = np.inf
            dist_after = np.inf
            if search_starts[i] > 0:
                dist_before = query[0] - end_sorted[search_starts[i] - 1]
            if search_ends[i] + 1 < len(intervals_to):
                dist_after = start_sorted[search_ends[i] + 1] - query[1]
            if dist_before < dist_after:
                output[i] = end_index[search_starts[i] - 1]
            else:
                output[i] = start_index[search_ends[i] + 1]
    return output


[docs]def match_intervals( intervals_from: np.ndarray, intervals_to: np.ndarray, strict: bool = True ) -> np.ndarray: """Match one set of time intervals to another. This can be useful for tasks such as mapping beat timings to segments. Each element ``[a, b]`` of ``intervals_from`` is matched to the element ``[c, d]`` of ``intervals_to`` which maximizes the Jaccard similarity between the intervals:: max(0, |min(b, d) - max(a, c)|) / |max(d, b) - min(a, c)| In ``strict=True`` mode, if there is no interval with positive intersection with ``[a,b]``, an exception is thrown. In ``strict=False`` mode, any interval ``[a, b]`` that has no intersection with any element of ``intervals_to`` is instead matched to the interval ``[c, d]`` which minimizes:: min(|b - c|, |a - d|) that is, the disjoint interval [c, d] with a boundary closest to [a, b]. .. note:: An element of ``intervals_to`` may be matched to multiple entries of ``intervals_from``. Parameters ---------- intervals_from : np.ndarray [shape=(n, 2)] The time range for source intervals. The ``i`` th interval spans time ``intervals_from[i, 0]`` to ``intervals_from[i, 1]``. ``intervals_from[0, 0]`` should be 0, ``intervals_from[-1, 1]`` should be the track duration. intervals_to : np.ndarray [shape=(m, 2)] Analogous to ``intervals_from``. strict : bool If ``True``, intervals can only match if they intersect. If ``False``, disjoint intervals can match. Returns ------- interval_mapping : np.ndarray [shape=(n,)] For each interval in ``intervals_from``, the corresponding interval in ``intervals_to``. See Also -------- match_events Raises ------ ParameterError If either array of input intervals is not the correct shape If ``strict=True`` and some element of ``intervals_from`` is disjoint from every element of ``intervals_to``. Examples -------- >>> ints_from = np.array([[3, 5], [1, 4], [4, 5]]) >>> ints_to = np.array([[0, 2], [1, 3], [4, 5], [6, 7]]) >>> librosa.util.match_intervals(ints_from, ints_to) array([2, 1, 2], dtype=uint32) >>> # [3, 5] => [4, 5] (ints_to[2]) >>> # [1, 4] => [1, 3] (ints_to[1]) >>> # [4, 5] => [4, 5] (ints_to[2]) The reverse matching of the above is not possible in ``strict`` mode because ``[6, 7]`` is disjoint from all intervals in ``ints_from``. With ``strict=False``, we get the following: >>> librosa.util.match_intervals(ints_to, ints_from, strict=False) array([1, 1, 2, 2], dtype=uint32) >>> # [0, 2] => [1, 4] (ints_from[1]) >>> # [1, 3] => [1, 4] (ints_from[1]) >>> # [4, 5] => [4, 5] (ints_from[2]) >>> # [6, 7] => [4, 5] (ints_from[2]) """ if len(intervals_from) == 0 or len(intervals_to) == 0: raise ParameterError("Attempting to match empty interval list") # Verify that the input intervals has correct shape and size valid_intervals(intervals_from) valid_intervals(intervals_to) try: # Suppress type check because of numba wrapper return __match_intervals(intervals_from, intervals_to, strict=strict) # type: ignore except ParameterError as exc: raise ParameterError(f"Unable to match intervals with strict={strict}") from exc
[docs]def match_events( events_from: _SequenceLike, events_to: _SequenceLike, left: bool = True, right: bool = True, ) -> np.ndarray: """Match one set of events to another. This is useful for tasks such as matching beats to the nearest detected onsets, or frame-aligned events to the nearest zero-crossing. .. note:: A target event may be matched to multiple source events. Examples -------- >>> # Sources are multiples of 7 >>> s_from = np.arange(0, 100, 7) >>> s_from array([ 0, 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, 84, 91, 98]) >>> # Targets are multiples of 10 >>> s_to = np.arange(0, 100, 10) >>> s_to array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90]) >>> # Find the matching >>> idx = librosa.util.match_events(s_from, s_to) >>> idx array([0, 1, 1, 2, 3, 3, 4, 5, 6, 6, 7, 8, 8, 9, 9]) >>> # Print each source value to its matching target >>> zip(s_from, s_to[idx]) [(0, 0), (7, 10), (14, 10), (21, 20), (28, 30), (35, 30), (42, 40), (49, 50), (56, 60), (63, 60), (70, 70), (77, 80), (84, 80), (91, 90), (98, 90)] Parameters ---------- events_from : ndarray [shape=(n,)] Array of events (eg, times, sample or frame indices) to match from. events_to : ndarray [shape=(m,)] Array of events (eg, times, sample or frame indices) to match against. left : bool right : bool If ``False``, then matched events cannot be to the left (or right) of source events. Returns ------- event_mapping : np.ndarray [shape=(n,)] For each event in ``events_from``, the corresponding event index in ``events_to``:: event_mapping[i] == arg min |events_from[i] - events_to[:]| See Also -------- match_intervals Raises ------ ParameterError If either array of input events is not the correct shape """ if len(events_from) == 0 or len(events_to) == 0: raise ParameterError("Attempting to match empty event list") # If we can't match left or right, then only strict equivalence # counts as a match. if not (left or right) and not np.all(np.in1d(events_from, events_to)): raise ParameterError( "Cannot match events with left=right=False " "and events_from is not contained " "in events_to" ) # If we can't match to the left, then there should be at least one # target event greater-equal to every source event if (not left) and max(events_to) < max(events_from): raise ParameterError( "Cannot match events with left=False " "and max(events_to) < max(events_from)" ) # If we can't match to the right, then there should be at least one # target event less-equal to every source event if (not right) and min(events_to) > min(events_from): raise ParameterError( "Cannot match events with right=False " "and min(events_to) > min(events_from)" ) # array of matched items output = np.empty_like(events_from, dtype=np.int32) # Suppress type check because of numba return __match_events_helper(output, events_from, events_to, left, right) # type: ignore
@numba.jit(nopython=True, cache=True) # type: ignore def __match_events_helper( output: np.ndarray, events_from: np.ndarray, events_to: np.ndarray, left: bool = True, right: bool = True, ): # pragma: no cover # mock dictionary for events from_idx = np.argsort(events_from) sorted_from = events_from[from_idx] to_idx = np.argsort(events_to) sorted_to = events_to[to_idx] # find the matching indices matching_indices = np.searchsorted(sorted_to, sorted_from) # iterate over indices in matching_indices for ind, middle_ind in enumerate(matching_indices): left_flag = False right_flag = False left_ind = -1 right_ind = len(matching_indices) left_diff = 0 right_diff = 0 mid_diff = 0 middle_ind = matching_indices[ind] sorted_from_num = sorted_from[ind] # Prevent oob from chosen index if middle_ind == len(sorted_to): middle_ind -= 1 # Permitted to look to the left if left and middle_ind > 0: left_ind = middle_ind - 1 left_flag = True # Permitted to look to right if right and middle_ind < len(sorted_to) - 1: right_ind = middle_ind + 1 right_flag = True mid_diff = abs(sorted_to[middle_ind] - sorted_from_num) if left and left_flag: left_diff = abs(sorted_to[left_ind] - sorted_from_num) if right and right_flag: right_diff = abs(sorted_to[right_ind] - sorted_from_num) if left_flag and ( not right and (sorted_to[middle_ind] > sorted_from_num) or (not right_flag and left_diff < mid_diff) or (left_diff < right_diff and left_diff < mid_diff) ): output[ind] = to_idx[left_ind] # Check if right should be chosen elif right_flag and (right_diff < mid_diff): output[ind] = to_idx[right_ind] # Selected index wins else: output[ind] = to_idx[middle_ind] # Undo sorting solutions = np.empty_like(output) solutions[from_idx] = output return solutions