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Source code for librosa.util.utils
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Utility functions"""
import scipy.ndimage
import scipy.sparse
import six
import numpy as np
import numba
from numpy.lib.stride_tricks import as_strided
from .._cache import cache
from .exceptions import ParameterError
from .decorators import deprecated
# Constrain STFT block sizes to 256 KB
MAX_MEM_BLOCK = 2**8 * 2**10
__all__ = ['MAX_MEM_BLOCK',
'frame', 'pad_center', 'fix_length',
'valid_audio', 'valid_int', 'valid_intervals',
'fix_frames',
'axis_sort', 'localmax', 'normalize',
'peak_pick',
'sparsify_rows',
'roll_sparse',
'shear', 'stack',
'fill_off_diagonal',
'index_to_slice',
'sync',
'softmask',
'buf_to_float',
'tiny',
'cyclic_gradient']
[docs]def frame(x, frame_length=2048, hop_length=512, axis=-1):
'''Slice a data array into (overlapping) frames.
This implementation uses low-level stride manipulation to avoid
making a copy of the data. The resulting frame representation
is a new view of the input data.
For example, a one-dimensional input `x = [0, 1, 2, 3, 4, 5, 6]`
can be framed with frame length 3 and hop length 2 in two ways.
The first (`axis=-1`), results in the array `x_frames`:
[[0, 2, 4],
[1, 3, 5],
[2, 4, 6]]
where each column `x_frames[:, i]` contains a contiguous slice of
the input `x[i * hop_length : i * hop_length + frame_length]`.
The second way (`axis=0`) results in the array `x_frames`:
[[0, 1, 2],
[2, 3, 4],
[4, 5, 6]]
where each row `x_frames[i]` contains a contiguous slice of the input.
This generalizes to higher dimensional inputs, as shown in the examples below.
In general, the framing operation increments by 1 the number of dimensions,
adding a new "frame axis" either to the end of the array (`axis=-1`)
or the beginning of the array (`axis=0`).
Parameters
----------
x : np.ndarray
Time series to frame. Must be contiguous in memory, see the "Raises"
section below for more information.
frame_length : int > 0 [scalar]
Length of the frame
hop_length : int > 0 [scalar]
Number of steps to advance between frames
axis : 0 or -1
The axis along which to frame.
If `axis=-1` (the default), then `x` is framed along its last dimension.
`x` must be "F-contiguous" in this case.
If `axis=0`, then `x` is framed along its first dimension.
`x` must be "C-contiguous" in this case.
Returns
-------
x_frames : np.ndarray [shape=(..., frame_length, N_FRAMES) or (N_FRAMES, frame_length, ...)]
A framed view of `x`, for example with `axis=-1` (framing on the last dimension):
`x_frames[..., j] == x[..., j * hop_length : j * hop_length + frame_length]`
If `axis=0` (framing on the first dimension), then:
`x_frames[j] = x[j * hop_length : j * hop_length + frame_length]`
Raises
------
ParameterError
If `x` is not contiguous in memory or not an `np.ndarray`.
If `x.shape[axis] < frame_length`, there is not enough data to fill one frame.
If `hop_length < 1`, frames cannot advance.
If `axis` is not 0 or -1. Framing is only supported along the first or last axis.
If `axis=-1` (the default), then `x` must be "F-contiguous".
If `axis=0`, then `x` must be "C-contiguous".
If the contiguity of `x` is incompatible with the framing axis.
See Also
--------
np.asfortranarray : Convert data to F-contiguous representation
np.ascontiguousarray : Convert data to C-contiguous representation
np.ndarray.flags : information about the memory layout of a numpy `ndarray`.
Examples
--------
Extract 2048-sample frames from monophonic `y` with a hop of 64 samples per frame
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> frames = librosa.util.frame(y, frame_length=2048, hop_length=64)
>>> frames
array([[ 0.000e+00, 0.000e+00, ..., -2.448e-06, -6.789e-07],
[ 0.000e+00, 0.000e+00, ..., -1.399e-05, 1.004e-06],
...,
[-7.352e-04, 5.162e-03, ..., 0.000e+00, 0.000e+00],
[ 2.168e-03, 4.870e-03, ..., 0.000e+00, 0.000e+00]],
dtype=float32)
>>> y.shape
(1355168,)
>>> frames.shape
(2048, 21143)
Or frame along the first axis instead of the last:
>>> frames = librosa.util.frame(y, frame_length=2048, hop_length=64, axis=0)
>>> frames.shape
(21143, 2048)
Frame a stereo signal:
>>> y, sr = librosa.load(librosa.util.example_audio_file(), mono=False)
>>> y.shape
(2, 1355168)
>>> frames = librosa.util.frame(y, frame_length=2048, hop_length=64)
(2, 2048, 21143)
Carve an STFT into fixed-length patches of 32 frames with 50% overlap
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> S = np.abs(librosa.stft(y))
>>> S.shape
(1025, 2647)
>>> S_patch = librosa.util.frame(S, frame_length=32, hop_length=16)
>>> S_patch.shape
(1025, 32, 82)
>>> # The first patch contains the first 32 frames of S
>>> np.allclose(S_patch[:, :, 0], S[:, :32])
True
>>> # The second patch contains frames 16 to 16+32=48, and so on
>>> np.allclose(S_patch[:, :, 1], S[:, 16:48])
True
'''
if not isinstance(x, np.ndarray):
raise ParameterError('Input must be of type numpy.ndarray, '
'given type(x)={}'.format(type(x)))
if x.shape[axis] < frame_length:
raise ParameterError('Input is too short (n={:d})'
' for frame_length={:d}'.format(x.shape[axis], frame_length))
if hop_length < 1:
raise ParameterError('Invalid hop_length: {:d}'.format(hop_length))
n_frames = 1 + (x.shape[axis] - frame_length) // hop_length
strides = np.asarray(x.strides)
new_stride = np.prod(strides[strides > 0] // x.itemsize) * x.itemsize
if axis == -1:
if not x.flags['F_CONTIGUOUS']:
raise ParameterError('Input array must be F-contiguous '
'for framing along axis={}'.format(axis))
shape = list(x.shape)[:-1] + [frame_length, n_frames]
strides = list(strides) + [hop_length * new_stride]
elif axis == 0:
if not x.flags['C_CONTIGUOUS']:
raise ParameterError('Input array must be C-contiguous '
'for framing along axis={}'.format(axis))
shape = [n_frames, frame_length] + list(x.shape)[1:]
strides = [hop_length * new_stride] + list(strides)
else:
raise ParameterError('Frame axis={} must be either 0 or -1'.format(axis))
return as_strided(x, shape=shape, strides=strides)
[docs]@cache(level=20)
def valid_audio(y, mono=True):
'''Validate whether a variable contains valid, mono audio data.
Parameters
----------
y : np.ndarray
The input data to validate
mono : bool
Whether or not to force monophonic audio
Returns
-------
valid : bool
True if all tests pass
Raises
------
ParameterError
In any of these cases:
- `type(y)` is not `np.ndarray`
- `y.dtype` is not floating-point
- `mono == True` and `y.ndim` is not 1
- `mono == False` and `y.ndim` is not 1 or 2
- `np.isfinite(y).all()` is False
- `y.flags["F_CONTIGUOUS"]` is False
Notes
-----
This function caches at level 20.
Examples
--------
>>> # By default, valid_audio allows only mono signals
>>> filepath = librosa.util.example_audio_file()
>>> y_mono, sr = librosa.load(filepath, mono=True)
>>> y_stereo, _ = librosa.load(filepath, mono=False)
>>> librosa.util.valid_audio(y_mono), librosa.util.valid_audio(y_stereo)
True, False
>>> # To allow stereo signals, set mono=False
>>> librosa.util.valid_audio(y_stereo, mono=False)
True
See also
--------
stack
numpy.asfortranarray
numpy.float32
'''
if not isinstance(y, np.ndarray):
raise ParameterError('Audio data must be of type numpy.ndarray')
if not np.issubdtype(y.dtype, np.floating):
raise ParameterError('Audio data must be floating-point')
if mono and y.ndim != 1:
raise ParameterError('Invalid shape for monophonic audio: '
'ndim={:d}, shape={}'.format(y.ndim, y.shape))
elif y.ndim > 2 or y.ndim == 0:
raise ParameterError('Audio data must have shape (samples,) or (channels, samples). '
'Received shape={}'.format(y.shape))
if not np.isfinite(y).all():
raise ParameterError('Audio buffer is not finite everywhere')
if not y.flags["F_CONTIGUOUS"]:
raise ParameterError('Audio buffer is not Fortran-contiguous. '
'Use numpy.asfortranarray to ensure Fortran contiguity.')
return True
[docs]def valid_int(x, cast=None):
'''Ensure that an input value is integer-typed.
This is primarily useful for ensuring integrable-valued
array indices.
Parameters
----------
x : number
A scalar value to be cast to int
cast : function [optional]
A function to modify `x` before casting.
Default: `np.floor`
Returns
-------
x_int : int
`x_int = int(cast(x))`
Raises
------
ParameterError
If `cast` is provided and is not callable.
'''
if cast is None:
cast = np.floor
if not six.callable(cast):
raise ParameterError('cast parameter must be callable')
return int(cast(x))
[docs]def valid_intervals(intervals):
'''Ensure that an array is a valid representation of time intervals:
- intervals.ndim == 2
- intervals.shape[1] == 2
- intervals[i, 0] <= intervals[i, 1] for all i
Parameters
----------
intervals : np.ndarray [shape=(n, 2)]
set of time intervals
Returns
-------
valid : bool
True if `intervals` passes validation.
'''
if intervals.ndim != 2 or intervals.shape[-1] != 2:
raise ParameterError('intervals must have shape (n, 2)')
if np.any(intervals[:, 0] > intervals[:, 1]):
raise ParameterError('intervals={} must have non-negative durations'.format(intervals))
return True
[docs]def pad_center(data, size, axis=-1, **kwargs):
'''Wrapper for np.pad to automatically center an array prior to padding.
This is analogous to `str.center()`
Examples
--------
>>> # Generate a vector
>>> data = np.ones(5)
>>> librosa.util.pad_center(data, 10, mode='constant')
array([ 0., 0., 1., 1., 1., 1., 1., 0., 0., 0.])
>>> # Pad a matrix along its first dimension
>>> data = np.ones((3, 5))
>>> librosa.util.pad_center(data, 7, axis=0)
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.]])
>>> # Or its second dimension
>>> librosa.util.pad_center(data, 7, axis=1)
array([[ 0., 1., 1., 1., 1., 1., 0.],
[ 0., 1., 1., 1., 1., 1., 0.],
[ 0., 1., 1., 1., 1., 1., 0.]])
Parameters
----------
data : np.ndarray
Vector to be padded and centered
size : int >= len(data) [scalar]
Length to pad `data`
axis : int
Axis along which to pad and center the data
kwargs : additional keyword arguments
arguments passed to `np.pad()`
Returns
-------
data_padded : np.ndarray
`data` centered and padded to length `size` along the
specified axis
Raises
------
ParameterError
If `size < data.shape[axis]`
See Also
--------
numpy.pad
'''
kwargs.setdefault('mode', 'constant')
n = data.shape[axis]
lpad = int((size - n) // 2)
lengths = [(0, 0)] * data.ndim
lengths[axis] = (lpad, int(size - n - lpad))
if lpad < 0:
raise ParameterError(('Target size ({:d}) must be '
'at least input size ({:d})').format(size, n))
return np.pad(data, lengths, **kwargs)
[docs]def fix_length(data, size, axis=-1, **kwargs):
'''Fix the length an array `data` to exactly `size`.
If `data.shape[axis] < n`, pad according to the provided kwargs.
By default, `data` is padded with trailing zeros.
Examples
--------
>>> y = np.arange(7)
>>> # Default: pad with zeros
>>> librosa.util.fix_length(y, 10)
array([0, 1, 2, 3, 4, 5, 6, 0, 0, 0])
>>> # Trim to a desired length
>>> librosa.util.fix_length(y, 5)
array([0, 1, 2, 3, 4])
>>> # Use edge-padding instead of zeros
>>> librosa.util.fix_length(y, 10, mode='edge')
array([0, 1, 2, 3, 4, 5, 6, 6, 6, 6])
Parameters
----------
data : np.ndarray
array to be length-adjusted
size : int >= 0 [scalar]
desired length of the array
axis : int, <= data.ndim
axis along which to fix length
kwargs : additional keyword arguments
Parameters to `np.pad()`
Returns
-------
data_fixed : np.ndarray [shape=data.shape]
`data` either trimmed or padded to length `size`
along the specified axis.
See Also
--------
numpy.pad
'''
kwargs.setdefault('mode', 'constant')
n = data.shape[axis]
if n > size:
slices = [slice(None)] * data.ndim
slices[axis] = slice(0, size)
return data[tuple(slices)]
elif n < size:
lengths = [(0, 0)] * data.ndim
lengths[axis] = (0, size - n)
return np.pad(data, lengths, **kwargs)
return data
[docs]def fix_frames(frames, x_min=0, x_max=None, pad=True):
'''Fix a list of frames to lie within [x_min, x_max]
Examples
--------
>>> # Generate a list of frame indices
>>> frames = np.arange(0, 1000.0, 50)
>>> frames
array([ 0., 50., 100., 150., 200., 250., 300., 350.,
400., 450., 500., 550., 600., 650., 700., 750.,
800., 850., 900., 950.])
>>> # Clip to span at most 250
>>> librosa.util.fix_frames(frames, x_max=250)
array([ 0, 50, 100, 150, 200, 250])
>>> # Or pad to span up to 2500
>>> librosa.util.fix_frames(frames, x_max=2500)
array([ 0, 50, 100, 150, 200, 250, 300, 350, 400,
450, 500, 550, 600, 650, 700, 750, 800, 850,
900, 950, 2500])
>>> librosa.util.fix_frames(frames, x_max=2500, pad=False)
array([ 0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500,
550, 600, 650, 700, 750, 800, 850, 900, 950])
>>> # Or starting away from zero
>>> frames = np.arange(200, 500, 33)
>>> frames
array([200, 233, 266, 299, 332, 365, 398, 431, 464, 497])
>>> librosa.util.fix_frames(frames)
array([ 0, 200, 233, 266, 299, 332, 365, 398, 431, 464, 497])
>>> librosa.util.fix_frames(frames, x_max=500)
array([ 0, 200, 233, 266, 299, 332, 365, 398, 431, 464, 497,
500])
Parameters
----------
frames : np.ndarray [shape=(n_frames,)]
List of non-negative frame indices
x_min : int >= 0 or None
Minimum allowed frame index
x_max : int >= 0 or None
Maximum allowed frame index
pad : boolean
If `True`, then `frames` is expanded to span the full range
`[x_min, x_max]`
Returns
-------
fixed_frames : np.ndarray [shape=(n_fixed_frames,), dtype=int]
Fixed frame indices, flattened and sorted
Raises
------
ParameterError
If `frames` contains negative values
'''
frames = np.asarray(frames)
if np.any(frames < 0):
raise ParameterError('Negative frame index detected')
if pad and (x_min is not None or x_max is not None):
frames = np.clip(frames, x_min, x_max)
if pad:
pad_data = []
if x_min is not None:
pad_data.append(x_min)
if x_max is not None:
pad_data.append(x_max)
frames = np.concatenate((pad_data, frames))
if x_min is not None:
frames = frames[frames >= x_min]
if x_max is not None:
frames = frames[frames <= x_max]
return np.unique(frames).astype(int)
[docs]def axis_sort(S, axis=-1, index=False, value=None):
'''Sort an array along its rows or columns.
Examples
--------
Visualize NMF output for a spectrogram S
>>> # Sort the columns of W by peak frequency bin
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> S = np.abs(librosa.stft(y))
>>> W, H = librosa.decompose.decompose(S, n_components=32)
>>> W_sort = librosa.util.axis_sort(W)
Or sort by the lowest frequency bin
>>> W_sort = librosa.util.axis_sort(W, value=np.argmin)
Or sort the rows instead of the columns
>>> W_sort_rows = librosa.util.axis_sort(W, axis=0)
Get the sorting index also, and use it to permute the rows of H
>>> W_sort, idx = librosa.util.axis_sort(W, index=True)
>>> H_sort = H[idx, :]
>>> import matplotlib.pyplot as plt
>>> plt.figure()
>>> plt.subplot(2, 2, 1)
>>> librosa.display.specshow(librosa.amplitude_to_db(W, ref=np.max),
... y_axis='log')
>>> plt.title('W')
>>> plt.subplot(2, 2, 2)
>>> librosa.display.specshow(H, x_axis='time')
>>> plt.title('H')
>>> plt.subplot(2, 2, 3)
>>> librosa.display.specshow(librosa.amplitude_to_db(W_sort,
... ref=np.max),
... y_axis='log')
>>> plt.title('W sorted')
>>> plt.subplot(2, 2, 4)
>>> librosa.display.specshow(H_sort, x_axis='time')
>>> plt.title('H sorted')
>>> plt.tight_layout()
>>> plt.show()
Parameters
----------
S : np.ndarray [shape=(d, n)]
Array to be sorted
axis : int [scalar]
The axis along which to compute the sorting values
- `axis=0` to sort rows by peak column index
- `axis=1` to sort columns by peak row index
index : boolean [scalar]
If true, returns the index array as well as the permuted data.
value : function
function to return the index corresponding to the sort order.
Default: `np.argmax`.
Returns
-------
S_sort : np.ndarray [shape=(d, n)]
`S` with the columns or rows permuted in sorting order
idx : np.ndarray (optional) [shape=(d,) or (n,)]
If `index == True`, the sorting index used to permute `S`.
Length of `idx` corresponds to the selected `axis`.
Raises
------
ParameterError
If `S` does not have exactly 2 dimensions (`S.ndim != 2`)
'''
if value is None:
value = np.argmax
if S.ndim != 2:
raise ParameterError('axis_sort is only defined for 2D arrays')
bin_idx = value(S, axis=np.mod(1-axis, S.ndim))
idx = np.argsort(bin_idx)
sort_slice = [slice(None)] * S.ndim
sort_slice[axis] = idx
if index:
return S[tuple(sort_slice)], idx
else:
return S[tuple(sort_slice)]
[docs]@cache(level=40)
def normalize(S, norm=np.inf, axis=0, threshold=None, fill=None):
'''Normalize an array along a chosen axis.
Given a norm (described below) and a target axis, the input
array is scaled so that
`norm(S, axis=axis) == 1`
For example, `axis=0` normalizes each column of a 2-d array
by aggregating over the rows (0-axis).
Similarly, `axis=1` normalizes each row of a 2-d array.
This function also supports thresholding small-norm slices:
any slice (i.e., row or column) with norm below a specified
`threshold` can be left un-normalized, set to all-zeros, or
filled with uniform non-zero values that normalize to 1.
Note: the semantics of this function differ from
`scipy.linalg.norm` in two ways: multi-dimensional arrays
are supported, but matrix-norms are not.
Parameters
----------
S : np.ndarray
The matrix to normalize
norm : {np.inf, -np.inf, 0, float > 0, None}
- `np.inf` : maximum absolute value
- `-np.inf` : mininum absolute value
- `0` : number of non-zeros (the support)
- float : corresponding l_p norm
See `scipy.linalg.norm` for details.
- None : no normalization is performed
axis : int [scalar]
Axis along which to compute the norm.
threshold : number > 0 [optional]
Only the columns (or rows) with norm at least `threshold` are
normalized.
By default, the threshold is determined from
the numerical precision of `S.dtype`.
fill : None or bool
If None, then columns (or rows) with norm below `threshold`
are left as is.
If False, then columns (rows) with norm below `threshold`
are set to 0.
If True, then columns (rows) with norm below `threshold`
are filled uniformly such that the corresponding norm is 1.
.. note:: `fill=True` is incompatible with `norm=0` because
no uniform vector exists with l0 "norm" equal to 1.
Returns
-------
S_norm : np.ndarray [shape=S.shape]
Normalized array
Raises
------
ParameterError
If `norm` is not among the valid types defined above
If `S` is not finite
If `fill=True` and `norm=0`
See Also
--------
scipy.linalg.norm
Notes
-----
This function caches at level 40.
Examples
--------
>>> # Construct an example matrix
>>> S = np.vander(np.arange(-2.0, 2.0))
>>> S
array([[-8., 4., -2., 1.],
[-1., 1., -1., 1.],
[ 0., 0., 0., 1.],
[ 1., 1., 1., 1.]])
>>> # Max (l-infinity)-normalize the columns
>>> librosa.util.normalize(S)
array([[-1. , 1. , -1. , 1. ],
[-0.125, 0.25 , -0.5 , 1. ],
[ 0. , 0. , 0. , 1. ],
[ 0.125, 0.25 , 0.5 , 1. ]])
>>> # Max (l-infinity)-normalize the rows
>>> librosa.util.normalize(S, axis=1)
array([[-1. , 0.5 , -0.25 , 0.125],
[-1. , 1. , -1. , 1. ],
[ 0. , 0. , 0. , 1. ],
[ 1. , 1. , 1. , 1. ]])
>>> # l1-normalize the columns
>>> librosa.util.normalize(S, norm=1)
array([[-0.8 , 0.667, -0.5 , 0.25 ],
[-0.1 , 0.167, -0.25 , 0.25 ],
[ 0. , 0. , 0. , 0.25 ],
[ 0.1 , 0.167, 0.25 , 0.25 ]])
>>> # l2-normalize the columns
>>> librosa.util.normalize(S, norm=2)
array([[-0.985, 0.943, -0.816, 0.5 ],
[-0.123, 0.236, -0.408, 0.5 ],
[ 0. , 0. , 0. , 0.5 ],
[ 0.123, 0.236, 0.408, 0.5 ]])
>>> # Thresholding and filling
>>> S[:, -1] = 1e-308
>>> S
array([[ -8.000e+000, 4.000e+000, -2.000e+000,
1.000e-308],
[ -1.000e+000, 1.000e+000, -1.000e+000,
1.000e-308],
[ 0.000e+000, 0.000e+000, 0.000e+000,
1.000e-308],
[ 1.000e+000, 1.000e+000, 1.000e+000,
1.000e-308]])
>>> # By default, small-norm columns are left untouched
>>> librosa.util.normalize(S)
array([[ -1.000e+000, 1.000e+000, -1.000e+000,
1.000e-308],
[ -1.250e-001, 2.500e-001, -5.000e-001,
1.000e-308],
[ 0.000e+000, 0.000e+000, 0.000e+000,
1.000e-308],
[ 1.250e-001, 2.500e-001, 5.000e-001,
1.000e-308]])
>>> # Small-norm columns can be zeroed out
>>> librosa.util.normalize(S, fill=False)
array([[-1. , 1. , -1. , 0. ],
[-0.125, 0.25 , -0.5 , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0.125, 0.25 , 0.5 , 0. ]])
>>> # Or set to constant with unit-norm
>>> librosa.util.normalize(S, fill=True)
array([[-1. , 1. , -1. , 1. ],
[-0.125, 0.25 , -0.5 , 1. ],
[ 0. , 0. , 0. , 1. ],
[ 0.125, 0.25 , 0.5 , 1. ]])
>>> # With an l1 norm instead of max-norm
>>> librosa.util.normalize(S, norm=1, fill=True)
array([[-0.8 , 0.667, -0.5 , 0.25 ],
[-0.1 , 0.167, -0.25 , 0.25 ],
[ 0. , 0. , 0. , 0.25 ],
[ 0.1 , 0.167, 0.25 , 0.25 ]])
'''
# Avoid div-by-zero
if threshold is None:
threshold = tiny(S)
elif threshold <= 0:
raise ParameterError('threshold={} must be strictly '
'positive'.format(threshold))
if fill not in [None, False, True]:
raise ParameterError('fill={} must be None or boolean'.format(fill))
if not np.all(np.isfinite(S)):
raise ParameterError('Input must be finite')
# All norms only depend on magnitude, let's do that first
mag = np.abs(S).astype(np.float)
# For max/min norms, filling with 1 works
fill_norm = 1
if norm == np.inf:
length = np.max(mag, axis=axis, keepdims=True)
elif norm == -np.inf:
length = np.min(mag, axis=axis, keepdims=True)
elif norm == 0:
if fill is True:
raise ParameterError('Cannot normalize with norm=0 and fill=True')
length = np.sum(mag > 0, axis=axis, keepdims=True, dtype=mag.dtype)
elif np.issubdtype(type(norm), np.number) and norm > 0:
length = np.sum(mag**norm, axis=axis, keepdims=True)**(1./norm)
if axis is None:
fill_norm = mag.size**(-1./norm)
else:
fill_norm = mag.shape[axis]**(-1./norm)
elif norm is None:
return S
else:
raise ParameterError('Unsupported norm: {}'.format(repr(norm)))
# indices where norm is below the threshold
small_idx = length < threshold
Snorm = np.empty_like(S)
if fill is None:
# Leave small indices un-normalized
length[small_idx] = 1.0
Snorm[:] = S / length
elif fill:
# If we have a non-zero fill value, we locate those entries by
# doing a nan-divide.
# If S was finite, then length is finite (except for small positions)
length[small_idx] = np.nan
Snorm[:] = S / length
Snorm[np.isnan(Snorm)] = fill_norm
else:
# Set small values to zero by doing an inf-divide.
# This is safe (by IEEE-754) as long as S is finite.
length[small_idx] = np.inf
Snorm[:] = S / length
return Snorm
[docs]def localmax(x, axis=0):
"""Find local maxima in an array `x`.
An element `x[i]` is considered a local maximum if the following
conditions are met:
- `x[i] > x[i-1]`
- `x[i] >= x[i+1]`
Note that the first condition is strict, and that the first element
`x[0]` will never be considered as a local maximum.
Examples
--------
>>> x = np.array([1, 0, 1, 2, -1, 0, -2, 1])
>>> librosa.util.localmax(x)
array([False, False, False, True, False, True, False, True], dtype=bool)
>>> # Two-dimensional example
>>> x = np.array([[1,0,1], [2, -1, 0], [2, 1, 3]])
>>> librosa.util.localmax(x, axis=0)
array([[False, False, False],
[ True, False, False],
[False, True, True]], dtype=bool)
>>> librosa.util.localmax(x, axis=1)
array([[False, False, True],
[False, False, True],
[False, False, True]], dtype=bool)
Parameters
----------
x : np.ndarray [shape=(d1,d2,...)]
input vector or array
axis : int
axis along which to compute local maximality
Returns
-------
m : np.ndarray [shape=x.shape, dtype=bool]
indicator array of local maximality along `axis`
"""
paddings = [(0, 0)] * x.ndim
paddings[axis] = (1, 1)
x_pad = np.pad(x, paddings, mode='edge')
inds1 = [slice(None)] * x.ndim
inds1[axis] = slice(0, -2)
inds2 = [slice(None)] * x.ndim
inds2[axis] = slice(2, x_pad.shape[axis])
return (x > x_pad[tuple(inds1)]) & (x >= x_pad[tuple(inds2)])
[docs]def peak_pick(x, pre_max, post_max, pre_avg, post_avg, delta, wait):
'''Uses a flexible heuristic to pick peaks in a signal.
A sample n is selected as an peak if the corresponding x[n]
fulfills the following three conditions:
1. `x[n] == max(x[n - pre_max:n + post_max])`
2. `x[n] >= mean(x[n - pre_avg:n + post_avg]) + delta`
3. `n - previous_n > wait`
where `previous_n` is the last sample picked as a peak (greedily).
This implementation is based on [1]_ and [2]_.
.. [1] Boeck, Sebastian, Florian Krebs, and Markus Schedl.
"Evaluating the Online Capabilities of Onset Detection Methods." ISMIR.
2012.
.. [2] https://github.com/CPJKU/onset_detection/blob/master/onset_program.py
Parameters
----------
x : np.ndarray [shape=(n,)]
input signal to peak picks from
pre_max : int >= 0 [scalar]
number of samples before `n` over which max is computed
post_max : int >= 1 [scalar]
number of samples after `n` over which max is computed
pre_avg : int >= 0 [scalar]
number of samples before `n` over which mean is computed
post_avg : int >= 1 [scalar]
number of samples after `n` over which mean is computed
delta : float >= 0 [scalar]
threshold offset for mean
wait : int >= 0 [scalar]
number of samples to wait after picking a peak
Returns
-------
peaks : np.ndarray [shape=(n_peaks,), dtype=int]
indices of peaks in `x`
Raises
------
ParameterError
If any input lies outside its defined range
Examples
--------
>>> y, sr = librosa.load(librosa.util.example_audio_file(), duration=15)
>>> onset_env = librosa.onset.onset_strength(y=y, sr=sr,
... hop_length=512,
... aggregate=np.median)
>>> peaks = librosa.util.peak_pick(onset_env, 3, 3, 3, 5, 0.5, 10)
>>> peaks
array([ 4, 23, 73, 102, 142, 162, 182, 211, 261, 301, 320,
331, 348, 368, 382, 396, 411, 431, 446, 461, 476, 491,
510, 525, 536, 555, 570, 590, 609, 625, 639])
>>> import matplotlib.pyplot as plt
>>> times = librosa.times_like(onset_env, sr=sr, hop_length=512)
>>> plt.figure()
>>> ax = plt.subplot(2, 1, 2)
>>> D = librosa.stft(y)
>>> librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max),
... y_axis='log', x_axis='time')
>>> plt.subplot(2, 1, 1, sharex=ax)
>>> plt.plot(times, onset_env, alpha=0.8, label='Onset strength')
>>> plt.vlines(times[peaks], 0,
... onset_env.max(), color='r', alpha=0.8,
... label='Selected peaks')
>>> plt.legend(frameon=True, framealpha=0.8)
>>> plt.axis('tight')
>>> plt.tight_layout()
>>> plt.show()
'''
if pre_max < 0:
raise ParameterError('pre_max must be non-negative')
if pre_avg < 0:
raise ParameterError('pre_avg must be non-negative')
if delta < 0:
raise ParameterError('delta must be non-negative')
if wait < 0:
raise ParameterError('wait must be non-negative')
if post_max <= 0:
raise ParameterError('post_max must be positive')
if post_avg <= 0:
raise ParameterError('post_avg must be positive')
if x.ndim != 1:
raise ParameterError('input array must be one-dimensional')
# Ensure valid index types
pre_max = valid_int(pre_max, cast=np.ceil)
post_max = valid_int(post_max, cast=np.ceil)
pre_avg = valid_int(pre_avg, cast=np.ceil)
post_avg = valid_int(post_avg, cast=np.ceil)
wait = valid_int(wait, cast=np.ceil)
# Get the maximum of the signal over a sliding window
max_length = pre_max + post_max
max_origin = np.ceil(0.5 * (pre_max - post_max))
# Using mode='constant' and cval=x.min() effectively truncates
# the sliding window at the boundaries
mov_max = scipy.ndimage.filters.maximum_filter1d(x, int(max_length),
mode='constant',
origin=int(max_origin),
cval=x.min())
# Get the mean of the signal over a sliding window
avg_length = pre_avg + post_avg
avg_origin = np.ceil(0.5 * (pre_avg - post_avg))
# Here, there is no mode which results in the behavior we want,
# so we'll correct below.
mov_avg = scipy.ndimage.filters.uniform_filter1d(x, int(avg_length),
mode='nearest',
origin=int(avg_origin))
# Correct sliding average at the beginning
n = 0
# Only need to correct in the range where the window needs to be truncated
while n - pre_avg < 0 and n < x.shape[0]:
# This just explicitly does mean(x[n - pre_avg:n + post_avg])
# with truncation
start = n - pre_avg
start = start if start > 0 else 0
mov_avg[n] = np.mean(x[start:n + post_avg])
n += 1
# Correct sliding average at the end
n = x.shape[0] - post_avg
# When post_avg > x.shape[0] (weird case), reset to 0
n = n if n > 0 else 0
while n < x.shape[0]:
start = n - pre_avg
start = start if start > 0 else 0
mov_avg[n] = np.mean(x[start:n + post_avg])
n += 1
# First mask out all entries not equal to the local max
detections = x * (x == mov_max)
# Then mask out all entries less than the thresholded average
detections = detections * (detections >= (mov_avg + delta))
# Initialize peaks array, to be filled greedily
peaks = []
# Remove onsets which are close together in time
last_onset = -np.inf
for i in np.nonzero(detections)[0]:
# Only report an onset if the "wait" samples was reported
if i > last_onset + wait:
peaks.append(i)
# Save last reported onset
last_onset = i
return np.array(peaks)
[docs]@cache(level=40)
def sparsify_rows(x, quantile=0.01):
'''
Return a row-sparse matrix approximating the input `x`.
Parameters
----------
x : np.ndarray [ndim <= 2]
The input matrix to sparsify.
quantile : float in [0, 1.0)
Percentage of magnitude to discard in each row of `x`
Returns
-------
x_sparse : `scipy.sparse.csr_matrix` [shape=x.shape]
Row-sparsified approximation of `x`
If `x.ndim == 1`, then `x` is interpreted as a row vector,
and `x_sparse.shape == (1, len(x))`.
Raises
------
ParameterError
If `x.ndim > 2`
If `quantile` lies outside `[0, 1.0)`
Notes
-----
This function caches at level 40.
Examples
--------
>>> # Construct a Hann window to sparsify
>>> x = scipy.signal.hann(32)
>>> x
array([ 0. , 0.01 , 0.041, 0.09 , 0.156, 0.236, 0.326,
0.424, 0.525, 0.625, 0.72 , 0.806, 0.879, 0.937,
0.977, 0.997, 0.997, 0.977, 0.937, 0.879, 0.806,
0.72 , 0.625, 0.525, 0.424, 0.326, 0.236, 0.156,
0.09 , 0.041, 0.01 , 0. ])
>>> # Discard the bottom percentile
>>> x_sparse = librosa.util.sparsify_rows(x, quantile=0.01)
>>> x_sparse
<1x32 sparse matrix of type '<type 'numpy.float64'>'
with 26 stored elements in Compressed Sparse Row format>
>>> x_sparse.todense()
matrix([[ 0. , 0. , 0. , 0.09 , 0.156, 0.236, 0.326,
0.424, 0.525, 0.625, 0.72 , 0.806, 0.879, 0.937,
0.977, 0.997, 0.997, 0.977, 0.937, 0.879, 0.806,
0.72 , 0.625, 0.525, 0.424, 0.326, 0.236, 0.156,
0.09 , 0. , 0. , 0. ]])
>>> # Discard up to the bottom 10th percentile
>>> x_sparse = librosa.util.sparsify_rows(x, quantile=0.1)
>>> x_sparse
<1x32 sparse matrix of type '<type 'numpy.float64'>'
with 20 stored elements in Compressed Sparse Row format>
>>> x_sparse.todense()
matrix([[ 0. , 0. , 0. , 0. , 0. , 0. , 0.326,
0.424, 0.525, 0.625, 0.72 , 0.806, 0.879, 0.937,
0.977, 0.997, 0.997, 0.977, 0.937, 0.879, 0.806,
0.72 , 0.625, 0.525, 0.424, 0.326, 0. , 0. ,
0. , 0. , 0. , 0. ]])
'''
if x.ndim == 1:
x = x.reshape((1, -1))
elif x.ndim > 2:
raise ParameterError('Input must have 2 or fewer dimensions. '
'Provided x.shape={}.'.format(x.shape))
if not 0.0 <= quantile < 1:
raise ParameterError('Invalid quantile {:.2f}'.format(quantile))
x_sparse = scipy.sparse.lil_matrix(x.shape, dtype=x.dtype)
mags = np.abs(x)
norms = np.sum(mags, axis=1, keepdims=True)
mag_sort = np.sort(mags, axis=1)
cumulative_mag = np.cumsum(mag_sort / norms, axis=1)
threshold_idx = np.argmin(cumulative_mag < quantile, axis=1)
for i, j in enumerate(threshold_idx):
idx = np.where(mags[i] >= mag_sort[i, j])
x_sparse[i, idx] = x[i, idx]
return x_sparse.tocsr()
[docs]@deprecated('0.7.1', '0.8.0')
def roll_sparse(x, shift, axis=0):
'''Sparse matrix roll
This operation is equivalent to ``numpy.roll``, but operates on sparse matrices.
.. warning:: This function is deprecated in version 0.7.1.
It will be removed in version 0.8.0.
Parameters
----------
x : scipy.sparse.spmatrix or np.ndarray
The sparse matrix input
shift : int
The number of positions to roll the specified axis
axis : (0, 1, -1)
The axis along which to roll.
Returns
-------
x_rolled : same type as `x`
The rolled matrix, with the same format as `x`
See Also
--------
numpy.roll
Examples
--------
>>> # Generate a random sparse binary matrix
>>> X = scipy.sparse.lil_matrix(np.random.randint(0, 2, size=(5,5)))
>>> X_roll = roll_sparse(X, 2, axis=0) # Roll by 2 on the first axis
>>> X_dense_r = roll_sparse(X.toarray(), 2, axis=0) # Equivalent dense roll
>>> np.allclose(X_roll, X_dense_r.toarray())
True
'''
if not scipy.sparse.isspmatrix(x):
return np.roll(x, shift, axis=axis)
# shift-mod-length lets us have shift > x.shape[axis]
if axis not in [0, 1, -1]:
raise ParameterError('axis must be one of (0, 1, -1)')
shift = np.mod(shift, x.shape[axis])
if shift == 0:
return x.copy()
fmt = x.format
if axis == 0:
x = x.tocsc()
elif axis in (-1, 1):
x = x.tocsr()
# lil matrix to start
x_r = scipy.sparse.lil_matrix(x.shape, dtype=x.dtype)
idx_in = [slice(None)] * x.ndim
idx_out = [slice(None)] * x_r.ndim
idx_in[axis] = slice(0, -shift)
idx_out[axis] = slice(shift, None)
x_r[tuple(idx_out)] = x[tuple(idx_in)]
idx_out[axis] = slice(0, shift)
idx_in[axis] = slice(-shift, None)
x_r[tuple(idx_out)] = x[tuple(idx_in)]
return x_r.asformat(fmt)
[docs]def buf_to_float(x, n_bytes=2, dtype=np.float32):
"""Convert an integer buffer to floating point values.
This is primarily useful when loading integer-valued wav data
into numpy arrays.
See Also
--------
buf_to_float
Parameters
----------
x : np.ndarray [dtype=int]
The integer-valued data buffer
n_bytes : int [1, 2, 4]
The number of bytes per sample in `x`
dtype : numeric type
The target output type (default: 32-bit float)
Returns
-------
x_float : np.ndarray [dtype=float]
The input data buffer cast to floating point
"""
# Invert the scale of the data
scale = 1./float(1 << ((8 * n_bytes) - 1))
# Construct the format string
fmt = '<i{:d}'.format(n_bytes)
# Rescale and format the data buffer
return scale * np.frombuffer(x, fmt).astype(dtype)
[docs]def index_to_slice(idx, idx_min=None, idx_max=None, step=None, pad=True):
'''Generate a slice array from an index array.
Parameters
----------
idx : list-like
Array of index boundaries
idx_min : None or int
idx_max : None or int
Minimum and maximum allowed indices
step : None or int
Step size for each slice. If `None`, then the default
step of 1 is used.
pad : boolean
If `True`, pad `idx` to span the range `idx_min:idx_max`.
Returns
-------
slices : list of slice
``slices[i] = slice(idx[i], idx[i+1], step)``
Additional slice objects may be added at the beginning or end,
depending on whether ``pad==True`` and the supplied values for
`idx_min` and `idx_max`.
See Also
--------
fix_frames
Examples
--------
>>> # Generate slices from spaced indices
>>> librosa.util.index_to_slice(np.arange(20, 100, 15))
[slice(20, 35, None), slice(35, 50, None), slice(50, 65, None), slice(65, 80, None),
slice(80, 95, None)]
>>> # Pad to span the range (0, 100)
>>> librosa.util.index_to_slice(np.arange(20, 100, 15),
... idx_min=0, idx_max=100)
[slice(0, 20, None), slice(20, 35, None), slice(35, 50, None), slice(50, 65, None),
slice(65, 80, None), slice(80, 95, None), slice(95, 100, None)]
>>> # Use a step of 5 for each slice
>>> librosa.util.index_to_slice(np.arange(20, 100, 15),
... idx_min=0, idx_max=100, step=5)
[slice(0, 20, 5), slice(20, 35, 5), slice(35, 50, 5), slice(50, 65, 5), slice(65, 80, 5),
slice(80, 95, 5), slice(95, 100, 5)]
'''
# First, normalize the index set
idx_fixed = fix_frames(idx, idx_min, idx_max, pad=pad)
# Now convert the indices to slices
return [slice(start, end, step) for (start, end) in zip(idx_fixed, idx_fixed[1:])]
[docs]@cache(level=40)
def sync(data, idx, aggregate=None, pad=True, axis=-1):
"""Synchronous aggregation of a multi-dimensional array between boundaries
.. note::
In order to ensure total coverage, boundary points may be added
to `idx`.
If synchronizing a feature matrix against beat tracker output, ensure
that frame index numbers are properly aligned and use the same hop length.
Parameters
----------
data : np.ndarray
multi-dimensional array of features
idx : iterable of ints or slices
Either an ordered array of boundary indices, or
an iterable collection of slice objects.
aggregate : function
aggregation function (default: `np.mean`)
pad : boolean
If `True`, `idx` is padded to span the full range `[0, data.shape[axis]]`
axis : int
The axis along which to aggregate data
Returns
-------
data_sync : ndarray
`data_sync` will have the same dimension as `data`, except that the `axis`
coordinate will be reduced according to `idx`.
For example, a 2-dimensional `data` with `axis=-1` should satisfy
`data_sync[:, i] = aggregate(data[:, idx[i-1]:idx[i]], axis=-1)`
Raises
------
ParameterError
If the index set is not of consistent type (all slices or all integers)
Notes
-----
This function caches at level 40.
Examples
--------
Beat-synchronous CQT spectra
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr, trim=False)
>>> C = np.abs(librosa.cqt(y=y, sr=sr))
>>> beats = librosa.util.fix_frames(beats, x_max=C.shape[1])
By default, use mean aggregation
>>> C_avg = librosa.util.sync(C, beats)
Use median-aggregation instead of mean
>>> C_med = librosa.util.sync(C, beats,
... aggregate=np.median)
Or sub-beat synchronization
>>> sub_beats = librosa.segment.subsegment(C, beats)
>>> sub_beats = librosa.util.fix_frames(sub_beats, x_max=C.shape[1])
>>> C_med_sub = librosa.util.sync(C, sub_beats, aggregate=np.median)
Plot the results
>>> import matplotlib.pyplot as plt
>>> beat_t = librosa.frames_to_time(beats, sr=sr)
>>> subbeat_t = librosa.frames_to_time(sub_beats, sr=sr)
>>> plt.figure()
>>> plt.subplot(3, 1, 1)
>>> librosa.display.specshow(librosa.amplitude_to_db(C,
... ref=np.max),
... x_axis='time')
>>> plt.title('CQT power, shape={}'.format(C.shape))
>>> plt.subplot(3, 1, 2)
>>> librosa.display.specshow(librosa.amplitude_to_db(C_med,
... ref=np.max),
... x_coords=beat_t, x_axis='time')
>>> plt.title('Beat synchronous CQT power, '
... 'shape={}'.format(C_med.shape))
>>> plt.subplot(3, 1, 3)
>>> librosa.display.specshow(librosa.amplitude_to_db(C_med_sub,
... ref=np.max),
... x_coords=subbeat_t, x_axis='time')
>>> plt.title('Sub-beat synchronous CQT power, '
... 'shape={}'.format(C_med_sub.shape))
>>> plt.tight_layout()
>>> plt.show()
"""
if aggregate is None:
aggregate = np.mean
shape = list(data.shape)
if np.all([isinstance(_, slice) for _ in idx]):
slices = idx
elif np.all([np.issubdtype(type(_), np.integer) for _ in idx]):
slices = index_to_slice(np.asarray(idx), 0, shape[axis], pad=pad)
else:
raise ParameterError('Invalid index set: {}'.format(idx))
agg_shape = list(shape)
agg_shape[axis] = len(slices)
data_agg = np.empty(agg_shape, order='F' if np.isfortran(data) else 'C', dtype=data.dtype)
idx_in = [slice(None)] * data.ndim
idx_agg = [slice(None)] * data_agg.ndim
for (i, segment) in enumerate(slices):
idx_in[axis] = segment
idx_agg[axis] = i
data_agg[tuple(idx_agg)] = aggregate(data[tuple(idx_in)], axis=axis)
return data_agg
[docs]def softmask(X, X_ref, power=1, split_zeros=False):
'''Robustly compute a softmask operation.
`M = X**power / (X**power + X_ref**power)`
Parameters
----------
X : np.ndarray
The (non-negative) input array corresponding to the positive mask elements
X_ref : np.ndarray
The (non-negative) array of reference or background elements.
Must have the same shape as `X`.
power : number > 0 or np.inf
If finite, returns the soft mask computed in a numerically stable way
If infinite, returns a hard (binary) mask equivalent to `X > X_ref`.
Note: for hard masks, ties are always broken in favor of `X_ref` (`mask=0`).
split_zeros : bool
If `True`, entries where `X` and X`_ref` are both small (close to 0)
will receive mask values of 0.5.
Otherwise, the mask is set to 0 for these entries.
Returns
-------
mask : np.ndarray, shape=`X.shape`
The output mask array
Raises
------
ParameterError
If `X` and `X_ref` have different shapes.
If `X` or `X_ref` are negative anywhere
If `power <= 0`
Examples
--------
>>> X = 2 * np.ones((3, 3))
>>> X_ref = np.vander(np.arange(3.0))
>>> X
array([[ 2., 2., 2.],
[ 2., 2., 2.],
[ 2., 2., 2.]])
>>> X_ref
array([[ 0., 0., 1.],
[ 1., 1., 1.],
[ 4., 2., 1.]])
>>> librosa.util.softmask(X, X_ref, power=1)
array([[ 1. , 1. , 0.667],
[ 0.667, 0.667, 0.667],
[ 0.333, 0.5 , 0.667]])
>>> librosa.util.softmask(X_ref, X, power=1)
array([[ 0. , 0. , 0.333],
[ 0.333, 0.333, 0.333],
[ 0.667, 0.5 , 0.333]])
>>> librosa.util.softmask(X, X_ref, power=2)
array([[ 1. , 1. , 0.8],
[ 0.8, 0.8, 0.8],
[ 0.2, 0.5, 0.8]])
>>> librosa.util.softmask(X, X_ref, power=4)
array([[ 1. , 1. , 0.941],
[ 0.941, 0.941, 0.941],
[ 0.059, 0.5 , 0.941]])
>>> librosa.util.softmask(X, X_ref, power=100)
array([[ 1.000e+00, 1.000e+00, 1.000e+00],
[ 1.000e+00, 1.000e+00, 1.000e+00],
[ 7.889e-31, 5.000e-01, 1.000e+00]])
>>> librosa.util.softmask(X, X_ref, power=np.inf)
array([[ True, True, True],
[ True, True, True],
[False, False, True]], dtype=bool)
'''
if X.shape != X_ref.shape:
raise ParameterError('Shape mismatch: {}!={}'.format(X.shape,
X_ref.shape))
if np.any(X < 0) or np.any(X_ref < 0):
raise ParameterError('X and X_ref must be non-negative')
if power <= 0:
raise ParameterError('power must be strictly positive')
# We're working with ints, cast to float.
dtype = X.dtype
if not np.issubdtype(dtype, np.floating):
dtype = np.float32
# Re-scale the input arrays relative to the larger value
Z = np.maximum(X, X_ref).astype(dtype)
bad_idx = (Z < np.finfo(dtype).tiny)
Z[bad_idx] = 1
# For finite power, compute the softmask
if np.isfinite(power):
mask = (X / Z)**power
ref_mask = (X_ref / Z)**power
good_idx = ~bad_idx
mask[good_idx] /= mask[good_idx] + ref_mask[good_idx]
# Wherever energy is below energy in both inputs, split the mask
if split_zeros:
mask[bad_idx] = 0.5
else:
mask[bad_idx] = 0.0
else:
# Otherwise, compute the hard mask
mask = X > X_ref
return mask
[docs]def tiny(x):
'''Compute the tiny-value corresponding to an input's data type.
This is the smallest "usable" number representable in `x`'s
data type (e.g., float32).
This is primarily useful for determining a threshold for
numerical underflow in division or multiplication operations.
Parameters
----------
x : number or np.ndarray
The array to compute the tiny-value for.
All that matters here is `x.dtype`.
Returns
-------
tiny_value : float
The smallest positive usable number for the type of `x`.
If `x` is integer-typed, then the tiny value for `np.float32`
is returned instead.
See Also
--------
numpy.finfo
Examples
--------
For a standard double-precision floating point number:
>>> librosa.util.tiny(1.0)
2.2250738585072014e-308
Or explicitly as double-precision
>>> librosa.util.tiny(np.asarray(1e-5, dtype=np.float64))
2.2250738585072014e-308
Or complex numbers
>>> librosa.util.tiny(1j)
2.2250738585072014e-308
Single-precision floating point:
>>> librosa.util.tiny(np.asarray(1e-5, dtype=np.float32))
1.1754944e-38
Integer
>>> librosa.util.tiny(5)
1.1754944e-38
'''
# Make sure we have an array view
x = np.asarray(x)
# Only floating types generate a tiny
if np.issubdtype(x.dtype, np.floating) or np.issubdtype(x.dtype, np.complexfloating):
dtype = x.dtype
else:
dtype = np.float32
return np.finfo(dtype).tiny
def fill_off_diagonal(x, radius, value=0):
"""Sets all cells of a matrix to a given ``value``
if they lie outside a constraint region.
In this case, the constraint region is the
Sakoe-Chiba band which runs with a fixed ``radius``
along the main diagonal.
When ``x.shape[0] != x.shape[1]``, the radius will be
expanded so that ``x[-1, -1] = 1`` always.
``x`` will be modified in place.
Parameters
----------
x : np.ndarray [shape=(N, M)]
Input matrix, will be modified in place.
radius : float
The band radius (1/2 of the width) will be
``int(radius*min(x.shape))``.
value : int
``x[n, m] = value`` when ``(n, m)`` lies outside the band.
Examples
--------
>>> x = np.ones((8, 8))
>>> librosa.util.fill_off_diagonal(x, 0.25)
>>> x
array([[1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 1, 1]])
>>> x = np.ones((8, 12))
>>> librosa.util.fill_off_diagonal(x, 0.25)
>>> x
array([[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]])
"""
nx, ny = x.shape
# Calculate the radius in indices, rather than proportion
radius = np.round(radius * np.min(x.shape))
nx, ny = x.shape
offset = np.abs((x.shape[0] - x.shape[1]))
if nx < ny:
idx_u = np.triu_indices_from(x, k=radius + offset)
idx_l = np.tril_indices_from(x, k=-radius)
else:
idx_u = np.triu_indices_from(x, k=radius)
idx_l = np.tril_indices_from(x, k=-radius - offset)
# modify input matrix
x[idx_u] = value
x[idx_l] = value
[docs]def cyclic_gradient(data, edge_order=1, axis=-1):
'''Estimate the gradient of a function over a uniformly sampled,
periodic domain.
This is essentially the same as `np.gradient`, except that edge effects
are handled by wrapping the observations (i.e. assuming periodicity)
rather than extrapolation.
Parameters
----------
data : np.ndarray
The function values observed at uniformly spaced positions on
a periodic domain
edge_order: {1, 2}
The order of the difference approximation used for estimating
the gradient
axis : int
The axis along which gradients are calculated.
Returns
-------
grad : np.ndarray like `data`
The gradient of `data` taken along the specified axis.
See Also
--------
np.gradient
Examples
--------
This example estimates the gradient of cosine (-sine) from 64
samples using direct (aperiodic) and periodic gradient
calculation.
>>> import matplotlib.pyplot as plt
>>> x = 2 * np.pi * np.linspace(0, 1, num=64, endpoint=False)
>>> y = np.cos(x)
>>> grad = np.gradient(y)
>>> cyclic_grad = librosa.util.cyclic_gradient(y)
>>> true_grad = -np.sin(x) * 2 * np.pi / len(x)
>>> plt.plot(x, true_grad, label='True gradient', linewidth=5,
... alpha=0.35)
>>> plt.plot(x, cyclic_grad, label='cyclic_gradient')
>>> plt.plot(x, grad, label='np.gradient', linestyle=':')
>>> plt.legend()
>>> # Zoom into the first part of the sequence
>>> plt.xlim([0, np.pi/16])
>>> plt.ylim([-0.025, 0.025])
>>> plt.show()
'''
# Wrap-pad the data along the target axis by `edge_order` on each side
padding = [(0, 0)] * data.ndim
padding[axis] = (edge_order, edge_order)
data_pad = np.pad(data, padding, mode='wrap')
# Compute the gradient
grad = np.gradient(data_pad, edge_order=edge_order, axis=axis)
# Remove the padding
slices = [slice(None)] * data.ndim
slices[axis] = slice(edge_order, -edge_order)
return grad[tuple(slices)]
@numba.jit(nopython=True, cache=True)
def __shear_dense(X, factor=+1, axis=-1):
'''Numba-accelerated shear for dense (ndarray) arrays'''
if axis == 0:
X = X.T
X_shear = np.empty_like(X)
for i in range(X.shape[1]):
X_shear[:, i] = np.roll(X[:, i], factor * i)
if axis == 0:
X_shear = X_shear.T
return X_shear
def __shear_sparse(X, factor=+1, axis=-1):
'''Fast shearing for sparse matrices
Shearing is performed using CSC array indices,
and the result is converted back to whatever sparse format
the data was originally provided in.
'''
fmt = X.format
if axis == 0:
X = X.T
# Now we're definitely rolling on the correct axis
X_shear = X.tocsc(copy=True)
# The idea here is to repeat the shear amount (factor * range)
# by the number of non-zeros for each column.
# The number of non-zeros is computed by diffing the index pointer array
roll = np.repeat(factor * np.arange(X_shear.shape[1]), np.diff(X_shear.indptr))
# In-place roll
np.mod(X_shear.indices + roll, X_shear.shape[0], out=X_shear.indices)
if axis == 0:
X_shear = X_shear.T
# And convert back to the input format
return X_shear.asformat(fmt)
[docs]def shear(X, factor=1, axis=-1):
'''Shear a matrix by a given factor.
The `n`th column `X[:, n]` will be displaced (rolled)
by `factor * n`.
This is primarily useful for converting between lag and recurrence
representations: shearing with `factor=-1` converts the main diagonal
to a horizontal. Shearing with `factor=1` converts a horizontal to
a diagonal.
Parameters
----------
X : np.ndarray [ndim=2] or scipy.sparse matrix
The array to be sheared
factor : integer
The shear factor: `X[:, n] -> np.roll(X[:, n], factor * n)`
axis : integer
The axis along which to shear
Returns
-------
X_shear : same type as `X`
The sheared matrix
Examples
--------
>>> E = np.eye(3)
>>> librosa.util.shear(E, factor=-1, axis=-1)
array([[1., 1., 1.],
[0., 0., 0.],
[0., 0., 0.]])
>>> librosa.util.shear(E, factor=-1, axis=0)
array([[1., 0., 0.],
[1., 0., 0.],
[1., 0., 0.]])
>>> librosa.util.shear(E, factor=1, axis=-1)
array([[1., 0., 0.],
[0., 0., 1.],
[0., 1., 0.]])
'''
if not np.issubdtype(type(factor), np.integer):
raise ParameterError('factor={} must be integer-valued'.format(factor))
if scipy.sparse.isspmatrix(X):
return __shear_sparse(X, factor=factor, axis=axis)
else:
return __shear_dense(X, factor=factor, axis=axis)
[docs]def stack(arrays, axis=0):
'''Stack one or more arrays along a target axis.
This function is similar to `np.stack`, except that memory contiguity is
retained when stacking along the first dimension.
This is useful when combining multiple monophonic audio signals into a
multi-channel signal, or when stacking multiple feature representations
to form a multi-dimensional array.
Parameters
----------
arrays : list
one or more `np.ndarray`
axis : integer
The target axis along which to stack. `axis=0` creates a new first axis,
and `axis=-1` creates a new last axis.
Returns
-------
arr_stack : np.ndarray [shape=(len(arrays), array_shape) or shape=(array_shape, len(arrays))]
The input arrays, stacked along the target dimension.
If `axis=0`, then `arr_stack` will be F-contiguous.
Otherwise, `arr_stack` will be C-contiguous by default, as computed by
`np.stack`.
Raises
------
ParameterError
- If `arrays` do not all have the same shape
- If no `arrays` are given
See Also
--------
np.stack
np.ndarray.flags
frame
Examples
--------
Combine two buffers into a contiguous arrays
>>> y_left = np.ones(5)
>>> y_right = -np.ones(5)
>>> y_stereo = librosa.util.stack([y_left, y_right], axis=0)
>>> y_stereo
array([[ 1., 1., 1., 1., 1.],
[-1., -1., -1., -1., -1.]])
>>> y_stereo.flags
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
Or along the trailing axis
>>> y_stereo = librosa.util.stack([y_left, y_right], axis=-1)
>>> y_stereo
array([[ 1., -1.],
[ 1., -1.],
[ 1., -1.],
[ 1., -1.],
[ 1., -1.]])
>>> y_stereo.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
'''
shapes = {arr.shape for arr in arrays}
if len(shapes) > 1:
raise ParameterError('all input arrays must have the same shape')
elif len(shapes) < 1:
raise ParameterError('at least one input array must be provided for stack')
shape_in = shapes.pop()
if axis != 0:
return np.stack(arrays, axis=axis)
else:
# If axis is 0, enforce F-ordering
shape = tuple([len(arrays)] + list(shape_in))
# Find the common dtype for all inputs
dtype = np.find_common_type([arr.dtype for arr in arrays], [])
# Allocate an empty array of the right shape and type
result = np.empty(shape, dtype=dtype, order='F')
# Stack into the preallocated buffer
np.stack(arrays, axis=axis, out=result)
return result