Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1299 lines
47 KiB
1299 lines
47 KiB
"""
|
|
Functions for identifying peaks in signals.
|
|
"""
|
|
import math
|
|
import numpy as np
|
|
|
|
from scipy.signal.wavelets import cwt, ricker
|
|
from scipy.stats import scoreatpercentile
|
|
|
|
from ._peak_finding_utils import (
|
|
_local_maxima_1d,
|
|
_select_by_peak_distance,
|
|
_peak_prominences,
|
|
_peak_widths
|
|
)
|
|
|
|
|
|
__all__ = ['argrelmin', 'argrelmax', 'argrelextrema', 'peak_prominences',
|
|
'peak_widths', 'find_peaks', 'find_peaks_cwt']
|
|
|
|
|
|
def _boolrelextrema(data, comparator, axis=0, order=1, mode='clip'):
|
|
"""
|
|
Calculate the relative extrema of `data`.
|
|
|
|
Relative extrema are calculated by finding locations where
|
|
``comparator(data[n], data[n+1:n+order+1])`` is True.
|
|
|
|
Parameters
|
|
----------
|
|
data : ndarray
|
|
Array in which to find the relative extrema.
|
|
comparator : callable
|
|
Function to use to compare two data points.
|
|
Should take two arrays as arguments.
|
|
axis : int, optional
|
|
Axis over which to select from `data`. Default is 0.
|
|
order : int, optional
|
|
How many points on each side to use for the comparison
|
|
to consider ``comparator(n,n+x)`` to be True.
|
|
mode : str, optional
|
|
How the edges of the vector are treated. 'wrap' (wrap around) or
|
|
'clip' (treat overflow as the same as the last (or first) element).
|
|
Default 'clip'. See numpy.take.
|
|
|
|
Returns
|
|
-------
|
|
extrema : ndarray
|
|
Boolean array of the same shape as `data` that is True at an extrema,
|
|
False otherwise.
|
|
|
|
See also
|
|
--------
|
|
argrelmax, argrelmin
|
|
|
|
Examples
|
|
--------
|
|
>>> testdata = np.array([1,2,3,2,1])
|
|
>>> _boolrelextrema(testdata, np.greater, axis=0)
|
|
array([False, False, True, False, False], dtype=bool)
|
|
|
|
"""
|
|
if((int(order) != order) or (order < 1)):
|
|
raise ValueError('Order must be an int >= 1')
|
|
|
|
datalen = data.shape[axis]
|
|
locs = np.arange(0, datalen)
|
|
|
|
results = np.ones(data.shape, dtype=bool)
|
|
main = data.take(locs, axis=axis, mode=mode)
|
|
for shift in range(1, order + 1):
|
|
plus = data.take(locs + shift, axis=axis, mode=mode)
|
|
minus = data.take(locs - shift, axis=axis, mode=mode)
|
|
results &= comparator(main, plus)
|
|
results &= comparator(main, minus)
|
|
if(~results.any()):
|
|
return results
|
|
return results
|
|
|
|
|
|
def argrelmin(data, axis=0, order=1, mode='clip'):
|
|
"""
|
|
Calculate the relative minima of `data`.
|
|
|
|
Parameters
|
|
----------
|
|
data : ndarray
|
|
Array in which to find the relative minima.
|
|
axis : int, optional
|
|
Axis over which to select from `data`. Default is 0.
|
|
order : int, optional
|
|
How many points on each side to use for the comparison
|
|
to consider ``comparator(n, n+x)`` to be True.
|
|
mode : str, optional
|
|
How the edges of the vector are treated.
|
|
Available options are 'wrap' (wrap around) or 'clip' (treat overflow
|
|
as the same as the last (or first) element).
|
|
Default 'clip'. See numpy.take.
|
|
|
|
Returns
|
|
-------
|
|
extrema : tuple of ndarrays
|
|
Indices of the minima in arrays of integers. ``extrema[k]`` is
|
|
the array of indices of axis `k` of `data`. Note that the
|
|
return value is a tuple even when `data` is 1-D.
|
|
|
|
See Also
|
|
--------
|
|
argrelextrema, argrelmax, find_peaks
|
|
|
|
Notes
|
|
-----
|
|
This function uses `argrelextrema` with np.less as comparator. Therefore, it
|
|
requires a strict inequality on both sides of a value to consider it a
|
|
minimum. This means flat minima (more than one sample wide) are not detected.
|
|
In case of 1-D `data` `find_peaks` can be used to detect all
|
|
local minima, including flat ones, by calling it with negated `data`.
|
|
|
|
.. versionadded:: 0.11.0
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.signal import argrelmin
|
|
>>> x = np.array([2, 1, 2, 3, 2, 0, 1, 0])
|
|
>>> argrelmin(x)
|
|
(array([1, 5]),)
|
|
>>> y = np.array([[1, 2, 1, 2],
|
|
... [2, 2, 0, 0],
|
|
... [5, 3, 4, 4]])
|
|
...
|
|
>>> argrelmin(y, axis=1)
|
|
(array([0, 2]), array([2, 1]))
|
|
|
|
"""
|
|
return argrelextrema(data, np.less, axis, order, mode)
|
|
|
|
|
|
def argrelmax(data, axis=0, order=1, mode='clip'):
|
|
"""
|
|
Calculate the relative maxima of `data`.
|
|
|
|
Parameters
|
|
----------
|
|
data : ndarray
|
|
Array in which to find the relative maxima.
|
|
axis : int, optional
|
|
Axis over which to select from `data`. Default is 0.
|
|
order : int, optional
|
|
How many points on each side to use for the comparison
|
|
to consider ``comparator(n, n+x)`` to be True.
|
|
mode : str, optional
|
|
How the edges of the vector are treated.
|
|
Available options are 'wrap' (wrap around) or 'clip' (treat overflow
|
|
as the same as the last (or first) element).
|
|
Default 'clip'. See `numpy.take`.
|
|
|
|
Returns
|
|
-------
|
|
extrema : tuple of ndarrays
|
|
Indices of the maxima in arrays of integers. ``extrema[k]`` is
|
|
the array of indices of axis `k` of `data`. Note that the
|
|
return value is a tuple even when `data` is 1-D.
|
|
|
|
See Also
|
|
--------
|
|
argrelextrema, argrelmin, find_peaks
|
|
|
|
Notes
|
|
-----
|
|
This function uses `argrelextrema` with np.greater as comparator. Therefore,
|
|
it requires a strict inequality on both sides of a value to consider it a
|
|
maximum. This means flat maxima (more than one sample wide) are not detected.
|
|
In case of 1-D `data` `find_peaks` can be used to detect all
|
|
local maxima, including flat ones.
|
|
|
|
.. versionadded:: 0.11.0
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.signal import argrelmax
|
|
>>> x = np.array([2, 1, 2, 3, 2, 0, 1, 0])
|
|
>>> argrelmax(x)
|
|
(array([3, 6]),)
|
|
>>> y = np.array([[1, 2, 1, 2],
|
|
... [2, 2, 0, 0],
|
|
... [5, 3, 4, 4]])
|
|
...
|
|
>>> argrelmax(y, axis=1)
|
|
(array([0]), array([1]))
|
|
"""
|
|
return argrelextrema(data, np.greater, axis, order, mode)
|
|
|
|
|
|
def argrelextrema(data, comparator, axis=0, order=1, mode='clip'):
|
|
"""
|
|
Calculate the relative extrema of `data`.
|
|
|
|
Parameters
|
|
----------
|
|
data : ndarray
|
|
Array in which to find the relative extrema.
|
|
comparator : callable
|
|
Function to use to compare two data points.
|
|
Should take two arrays as arguments.
|
|
axis : int, optional
|
|
Axis over which to select from `data`. Default is 0.
|
|
order : int, optional
|
|
How many points on each side to use for the comparison
|
|
to consider ``comparator(n, n+x)`` to be True.
|
|
mode : str, optional
|
|
How the edges of the vector are treated. 'wrap' (wrap around) or
|
|
'clip' (treat overflow as the same as the last (or first) element).
|
|
Default is 'clip'. See `numpy.take`.
|
|
|
|
Returns
|
|
-------
|
|
extrema : tuple of ndarrays
|
|
Indices of the maxima in arrays of integers. ``extrema[k]`` is
|
|
the array of indices of axis `k` of `data`. Note that the
|
|
return value is a tuple even when `data` is 1-D.
|
|
|
|
See Also
|
|
--------
|
|
argrelmin, argrelmax
|
|
|
|
Notes
|
|
-----
|
|
|
|
.. versionadded:: 0.11.0
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.signal import argrelextrema
|
|
>>> x = np.array([2, 1, 2, 3, 2, 0, 1, 0])
|
|
>>> argrelextrema(x, np.greater)
|
|
(array([3, 6]),)
|
|
>>> y = np.array([[1, 2, 1, 2],
|
|
... [2, 2, 0, 0],
|
|
... [5, 3, 4, 4]])
|
|
...
|
|
>>> argrelextrema(y, np.less, axis=1)
|
|
(array([0, 2]), array([2, 1]))
|
|
|
|
"""
|
|
results = _boolrelextrema(data, comparator,
|
|
axis, order, mode)
|
|
return np.nonzero(results)
|
|
|
|
|
|
def _arg_x_as_expected(value):
|
|
"""Ensure argument `x` is a 1-D C-contiguous array of dtype('float64').
|
|
|
|
Used in `find_peaks`, `peak_prominences` and `peak_widths` to make `x`
|
|
compatible with the signature of the wrapped Cython functions.
|
|
|
|
Returns
|
|
-------
|
|
value : ndarray
|
|
A 1-D C-contiguous array with dtype('float64').
|
|
"""
|
|
value = np.asarray(value, order='C', dtype=np.float64)
|
|
if value.ndim != 1:
|
|
raise ValueError('`x` must be a 1-D array')
|
|
return value
|
|
|
|
|
|
def _arg_peaks_as_expected(value):
|
|
"""Ensure argument `peaks` is a 1-D C-contiguous array of dtype('intp').
|
|
|
|
Used in `peak_prominences` and `peak_widths` to make `peaks` compatible
|
|
with the signature of the wrapped Cython functions.
|
|
|
|
Returns
|
|
-------
|
|
value : ndarray
|
|
A 1-D C-contiguous array with dtype('intp').
|
|
"""
|
|
value = np.asarray(value)
|
|
if value.size == 0:
|
|
# Empty arrays default to np.float64 but are valid input
|
|
value = np.array([], dtype=np.intp)
|
|
try:
|
|
# Safely convert to C-contiguous array of type np.intp
|
|
value = value.astype(np.intp, order='C', casting='safe',
|
|
subok=False, copy=False)
|
|
except TypeError:
|
|
raise TypeError("cannot safely cast `peaks` to dtype('intp')")
|
|
if value.ndim != 1:
|
|
raise ValueError('`peaks` must be a 1-D array')
|
|
return value
|
|
|
|
|
|
def _arg_wlen_as_expected(value):
|
|
"""Ensure argument `wlen` is of type `np.intp` and larger than 1.
|
|
|
|
Used in `peak_prominences` and `peak_widths`.
|
|
|
|
Returns
|
|
-------
|
|
value : np.intp
|
|
The original `value` rounded up to an integer or -1 if `value` was
|
|
None.
|
|
"""
|
|
if value is None:
|
|
# _peak_prominences expects an intp; -1 signals that no value was
|
|
# supplied by the user
|
|
value = -1
|
|
elif 1 < value:
|
|
# Round up to a positive integer
|
|
if not np.can_cast(value, np.intp, "safe"):
|
|
value = math.ceil(value)
|
|
value = np.intp(value)
|
|
else:
|
|
raise ValueError('`wlen` must be larger than 1, was {}'
|
|
.format(value))
|
|
return value
|
|
|
|
|
|
def peak_prominences(x, peaks, wlen=None):
|
|
"""
|
|
Calculate the prominence of each peak in a signal.
|
|
|
|
The prominence of a peak measures how much a peak stands out from the
|
|
surrounding baseline of the signal and is defined as the vertical distance
|
|
between the peak and its lowest contour line.
|
|
|
|
Parameters
|
|
----------
|
|
x : sequence
|
|
A signal with peaks.
|
|
peaks : sequence
|
|
Indices of peaks in `x`.
|
|
wlen : int, optional
|
|
A window length in samples that optionally limits the evaluated area for
|
|
each peak to a subset of `x`. The peak is always placed in the middle of
|
|
the window therefore the given length is rounded up to the next odd
|
|
integer. This parameter can speed up the calculation (see Notes).
|
|
|
|
Returns
|
|
-------
|
|
prominences : ndarray
|
|
The calculated prominences for each peak in `peaks`.
|
|
left_bases, right_bases : ndarray
|
|
The peaks' bases as indices in `x` to the left and right of each peak.
|
|
The higher base of each pair is a peak's lowest contour line.
|
|
|
|
Raises
|
|
------
|
|
ValueError
|
|
If a value in `peaks` is an invalid index for `x`.
|
|
|
|
Warns
|
|
-----
|
|
PeakPropertyWarning
|
|
For indices in `peaks` that don't point to valid local maxima in `x`,
|
|
the returned prominence will be 0 and this warning is raised. This
|
|
also happens if `wlen` is smaller than the plateau size of a peak.
|
|
|
|
Warnings
|
|
--------
|
|
This function may return unexpected results for data containing NaNs. To
|
|
avoid this, NaNs should either be removed or replaced.
|
|
|
|
See Also
|
|
--------
|
|
find_peaks
|
|
Find peaks inside a signal based on peak properties.
|
|
peak_widths
|
|
Calculate the width of peaks.
|
|
|
|
Notes
|
|
-----
|
|
Strategy to compute a peak's prominence:
|
|
|
|
1. Extend a horizontal line from the current peak to the left and right
|
|
until the line either reaches the window border (see `wlen`) or
|
|
intersects the signal again at the slope of a higher peak. An
|
|
intersection with a peak of the same height is ignored.
|
|
2. On each side find the minimal signal value within the interval defined
|
|
above. These points are the peak's bases.
|
|
3. The higher one of the two bases marks the peak's lowest contour line. The
|
|
prominence can then be calculated as the vertical difference between the
|
|
peaks height itself and its lowest contour line.
|
|
|
|
Searching for the peak's bases can be slow for large `x` with periodic
|
|
behavior because large chunks or even the full signal need to be evaluated
|
|
for the first algorithmic step. This evaluation area can be limited with the
|
|
parameter `wlen` which restricts the algorithm to a window around the
|
|
current peak and can shorten the calculation time if the window length is
|
|
short in relation to `x`.
|
|
However, this may stop the algorithm from finding the true global contour
|
|
line if the peak's true bases are outside this window. Instead, a higher
|
|
contour line is found within the restricted window leading to a smaller
|
|
calculated prominence. In practice, this is only relevant for the highest set
|
|
of peaks in `x`. This behavior may even be used intentionally to calculate
|
|
"local" prominences.
|
|
|
|
.. versionadded:: 1.1.0
|
|
|
|
References
|
|
----------
|
|
.. [1] Wikipedia Article for Topographic Prominence:
|
|
https://en.wikipedia.org/wiki/Topographic_prominence
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.signal import find_peaks, peak_prominences
|
|
>>> import matplotlib.pyplot as plt
|
|
|
|
Create a test signal with two overlayed harmonics
|
|
|
|
>>> x = np.linspace(0, 6 * np.pi, 1000)
|
|
>>> x = np.sin(x) + 0.6 * np.sin(2.6 * x)
|
|
|
|
Find all peaks and calculate prominences
|
|
|
|
>>> peaks, _ = find_peaks(x)
|
|
>>> prominences = peak_prominences(x, peaks)[0]
|
|
>>> prominences
|
|
array([1.24159486, 0.47840168, 0.28470524, 3.10716793, 0.284603 ,
|
|
0.47822491, 2.48340261, 0.47822491])
|
|
|
|
Calculate the height of each peak's contour line and plot the results
|
|
|
|
>>> contour_heights = x[peaks] - prominences
|
|
>>> plt.plot(x)
|
|
>>> plt.plot(peaks, x[peaks], "x")
|
|
>>> plt.vlines(x=peaks, ymin=contour_heights, ymax=x[peaks])
|
|
>>> plt.show()
|
|
|
|
Let's evaluate a second example that demonstrates several edge cases for
|
|
one peak at index 5.
|
|
|
|
>>> x = np.array([0, 1, 0, 3, 1, 3, 0, 4, 0])
|
|
>>> peaks = np.array([5])
|
|
>>> plt.plot(x)
|
|
>>> plt.plot(peaks, x[peaks], "x")
|
|
>>> plt.show()
|
|
>>> peak_prominences(x, peaks) # -> (prominences, left_bases, right_bases)
|
|
(array([3.]), array([2]), array([6]))
|
|
|
|
Note how the peak at index 3 of the same height is not considered as a
|
|
border while searching for the left base. Instead, two minima at 0 and 2
|
|
are found in which case the one closer to the evaluated peak is always
|
|
chosen. On the right side, however, the base must be placed at 6 because the
|
|
higher peak represents the right border to the evaluated area.
|
|
|
|
>>> peak_prominences(x, peaks, wlen=3.1)
|
|
(array([2.]), array([4]), array([6]))
|
|
|
|
Here, we restricted the algorithm to a window from 3 to 7 (the length is 5
|
|
samples because `wlen` was rounded up to the next odd integer). Thus, the
|
|
only two candidates in the evaluated area are the two neighboring samples
|
|
and a smaller prominence is calculated.
|
|
"""
|
|
x = _arg_x_as_expected(x)
|
|
peaks = _arg_peaks_as_expected(peaks)
|
|
wlen = _arg_wlen_as_expected(wlen)
|
|
return _peak_prominences(x, peaks, wlen)
|
|
|
|
|
|
def peak_widths(x, peaks, rel_height=0.5, prominence_data=None, wlen=None):
|
|
"""
|
|
Calculate the width of each peak in a signal.
|
|
|
|
This function calculates the width of a peak in samples at a relative
|
|
distance to the peak's height and prominence.
|
|
|
|
Parameters
|
|
----------
|
|
x : sequence
|
|
A signal with peaks.
|
|
peaks : sequence
|
|
Indices of peaks in `x`.
|
|
rel_height : float, optional
|
|
Chooses the relative height at which the peak width is measured as a
|
|
percentage of its prominence. 1.0 calculates the width of the peak at
|
|
its lowest contour line while 0.5 evaluates at half the prominence
|
|
height. Must be at least 0. See notes for further explanation.
|
|
prominence_data : tuple, optional
|
|
A tuple of three arrays matching the output of `peak_prominences` when
|
|
called with the same arguments `x` and `peaks`. This data are calculated
|
|
internally if not provided.
|
|
wlen : int, optional
|
|
A window length in samples passed to `peak_prominences` as an optional
|
|
argument for internal calculation of `prominence_data`. This argument
|
|
is ignored if `prominence_data` is given.
|
|
|
|
Returns
|
|
-------
|
|
widths : ndarray
|
|
The widths for each peak in samples.
|
|
width_heights : ndarray
|
|
The height of the contour lines at which the `widths` where evaluated.
|
|
left_ips, right_ips : ndarray
|
|
Interpolated positions of left and right intersection points of a
|
|
horizontal line at the respective evaluation height.
|
|
|
|
Raises
|
|
------
|
|
ValueError
|
|
If `prominence_data` is supplied but doesn't satisfy the condition
|
|
``0 <= left_base <= peak <= right_base < x.shape[0]`` for each peak,
|
|
has the wrong dtype, is not C-contiguous or does not have the same
|
|
shape.
|
|
|
|
Warns
|
|
-----
|
|
PeakPropertyWarning
|
|
Raised if any calculated width is 0. This may stem from the supplied
|
|
`prominence_data` or if `rel_height` is set to 0.
|
|
|
|
Warnings
|
|
--------
|
|
This function may return unexpected results for data containing NaNs. To
|
|
avoid this, NaNs should either be removed or replaced.
|
|
|
|
See Also
|
|
--------
|
|
find_peaks
|
|
Find peaks inside a signal based on peak properties.
|
|
peak_prominences
|
|
Calculate the prominence of peaks.
|
|
|
|
Notes
|
|
-----
|
|
The basic algorithm to calculate a peak's width is as follows:
|
|
|
|
* Calculate the evaluation height :math:`h_{eval}` with the formula
|
|
:math:`h_{eval} = h_{Peak} - P \\cdot R`, where :math:`h_{Peak}` is the
|
|
height of the peak itself, :math:`P` is the peak's prominence and
|
|
:math:`R` a positive ratio specified with the argument `rel_height`.
|
|
* Draw a horizontal line at the evaluation height to both sides, starting at
|
|
the peak's current vertical position until the lines either intersect a
|
|
slope, the signal border or cross the vertical position of the peak's
|
|
base (see `peak_prominences` for an definition). For the first case,
|
|
intersection with the signal, the true intersection point is estimated
|
|
with linear interpolation.
|
|
* Calculate the width as the horizontal distance between the chosen
|
|
endpoints on both sides. As a consequence of this the maximal possible
|
|
width for each peak is the horizontal distance between its bases.
|
|
|
|
As shown above to calculate a peak's width its prominence and bases must be
|
|
known. You can supply these yourself with the argument `prominence_data`.
|
|
Otherwise, they are internally calculated (see `peak_prominences`).
|
|
|
|
.. versionadded:: 1.1.0
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy.signal import chirp, find_peaks, peak_widths
|
|
>>> import matplotlib.pyplot as plt
|
|
|
|
Create a test signal with two overlayed harmonics
|
|
|
|
>>> x = np.linspace(0, 6 * np.pi, 1000)
|
|
>>> x = np.sin(x) + 0.6 * np.sin(2.6 * x)
|
|
|
|
Find all peaks and calculate their widths at the relative height of 0.5
|
|
(contour line at half the prominence height) and 1 (at the lowest contour
|
|
line at full prominence height).
|
|
|
|
>>> peaks, _ = find_peaks(x)
|
|
>>> results_half = peak_widths(x, peaks, rel_height=0.5)
|
|
>>> results_half[0] # widths
|
|
array([ 64.25172825, 41.29465463, 35.46943289, 104.71586081,
|
|
35.46729324, 41.30429622, 181.93835853, 45.37078546])
|
|
>>> results_full = peak_widths(x, peaks, rel_height=1)
|
|
>>> results_full[0] # widths
|
|
array([181.9396084 , 72.99284945, 61.28657872, 373.84622694,
|
|
61.78404617, 72.48822812, 253.09161876, 79.36860878])
|
|
|
|
Plot signal, peaks and contour lines at which the widths where calculated
|
|
|
|
>>> plt.plot(x)
|
|
>>> plt.plot(peaks, x[peaks], "x")
|
|
>>> plt.hlines(*results_half[1:], color="C2")
|
|
>>> plt.hlines(*results_full[1:], color="C3")
|
|
>>> plt.show()
|
|
"""
|
|
x = _arg_x_as_expected(x)
|
|
peaks = _arg_peaks_as_expected(peaks)
|
|
if prominence_data is None:
|
|
# Calculate prominence if not supplied and use wlen if supplied.
|
|
wlen = _arg_wlen_as_expected(wlen)
|
|
prominence_data = _peak_prominences(x, peaks, wlen)
|
|
return _peak_widths(x, peaks, rel_height, *prominence_data)
|
|
|
|
|
|
def _unpack_condition_args(interval, x, peaks):
|
|
"""
|
|
Parse condition arguments for `find_peaks`.
|
|
|
|
Parameters
|
|
----------
|
|
interval : number or ndarray or sequence
|
|
Either a number or ndarray or a 2-element sequence of the former. The
|
|
first value is always interpreted as `imin` and the second, if supplied,
|
|
as `imax`.
|
|
x : ndarray
|
|
The signal with `peaks`.
|
|
peaks : ndarray
|
|
An array with indices used to reduce `imin` and / or `imax` if those are
|
|
arrays.
|
|
|
|
Returns
|
|
-------
|
|
imin, imax : number or ndarray or None
|
|
Minimal and maximal value in `argument`.
|
|
|
|
Raises
|
|
------
|
|
ValueError :
|
|
If interval border is given as array and its size does not match the size
|
|
of `x`.
|
|
|
|
Notes
|
|
-----
|
|
|
|
.. versionadded:: 1.1.0
|
|
"""
|
|
try:
|
|
imin, imax = interval
|
|
except (TypeError, ValueError):
|
|
imin, imax = (interval, None)
|
|
|
|
# Reduce arrays if arrays
|
|
if isinstance(imin, np.ndarray):
|
|
if imin.size != x.size:
|
|
raise ValueError('array size of lower interval border must match x')
|
|
imin = imin[peaks]
|
|
if isinstance(imax, np.ndarray):
|
|
if imax.size != x.size:
|
|
raise ValueError('array size of upper interval border must match x')
|
|
imax = imax[peaks]
|
|
|
|
return imin, imax
|
|
|
|
|
|
def _select_by_property(peak_properties, pmin, pmax):
|
|
"""
|
|
Evaluate where the generic property of peaks confirms to an interval.
|
|
|
|
Parameters
|
|
----------
|
|
peak_properties : ndarray
|
|
An array with properties for each peak.
|
|
pmin : None or number or ndarray
|
|
Lower interval boundary for `peak_properties`. ``None`` is interpreted as
|
|
an open border.
|
|
pmax : None or number or ndarray
|
|
Upper interval boundary for `peak_properties`. ``None`` is interpreted as
|
|
an open border.
|
|
|
|
Returns
|
|
-------
|
|
keep : bool
|
|
A boolean mask evaluating to true where `peak_properties` confirms to the
|
|
interval.
|
|
|
|
See Also
|
|
--------
|
|
find_peaks
|
|
|
|
Notes
|
|
-----
|
|
|
|
.. versionadded:: 1.1.0
|
|
"""
|
|
keep = np.ones(peak_properties.size, dtype=bool)
|
|
if pmin is not None:
|
|
keep &= (pmin <= peak_properties)
|
|
if pmax is not None:
|
|
keep &= (peak_properties <= pmax)
|
|
return keep
|
|
|
|
|
|
def _select_by_peak_threshold(x, peaks, tmin, tmax):
|
|
"""
|
|
Evaluate which peaks fulfill the threshold condition.
|
|
|
|
Parameters
|
|
----------
|
|
x : ndarray
|
|
A 1-D array which is indexable by `peaks`.
|
|
peaks : ndarray
|
|
Indices of peaks in `x`.
|
|
tmin, tmax : scalar or ndarray or None
|
|
Minimal and / or maximal required thresholds. If supplied as ndarrays
|
|
their size must match `peaks`. ``None`` is interpreted as an open
|
|
border.
|
|
|
|
Returns
|
|
-------
|
|
keep : bool
|
|
A boolean mask evaluating to true where `peaks` fulfill the threshold
|
|
condition.
|
|
left_thresholds, right_thresholds : ndarray
|
|
Array matching `peak` containing the thresholds of each peak on
|
|
both sides.
|
|
|
|
Notes
|
|
-----
|
|
|
|
.. versionadded:: 1.1.0
|
|
"""
|
|
# Stack thresholds on both sides to make min / max operations easier:
|
|
# tmin is compared with the smaller, and tmax with the greater thresold to
|
|
# each peak's side
|
|
stacked_thresholds = np.vstack([x[peaks] - x[peaks - 1],
|
|
x[peaks] - x[peaks + 1]])
|
|
keep = np.ones(peaks.size, dtype=bool)
|
|
if tmin is not None:
|
|
min_thresholds = np.min(stacked_thresholds, axis=0)
|
|
keep &= (tmin <= min_thresholds)
|
|
if tmax is not None:
|
|
max_thresholds = np.max(stacked_thresholds, axis=0)
|
|
keep &= (max_thresholds <= tmax)
|
|
|
|
return keep, stacked_thresholds[0], stacked_thresholds[1]
|
|
|
|
|
|
def find_peaks(x, height=None, threshold=None, distance=None,
|
|
prominence=None, width=None, wlen=None, rel_height=0.5,
|
|
plateau_size=None):
|
|
"""
|
|
Find peaks inside a signal based on peak properties.
|
|
|
|
This function takes a 1-D array and finds all local maxima by
|
|
simple comparison of neighboring values. Optionally, a subset of these
|
|
peaks can be selected by specifying conditions for a peak's properties.
|
|
|
|
Parameters
|
|
----------
|
|
x : sequence
|
|
A signal with peaks.
|
|
height : number or ndarray or sequence, optional
|
|
Required height of peaks. Either a number, ``None``, an array matching
|
|
`x` or a 2-element sequence of the former. The first element is
|
|
always interpreted as the minimal and the second, if supplied, as the
|
|
maximal required height.
|
|
threshold : number or ndarray or sequence, optional
|
|
Required threshold of peaks, the vertical distance to its neighboring
|
|
samples. Either a number, ``None``, an array matching `x` or a
|
|
2-element sequence of the former. The first element is always
|
|
interpreted as the minimal and the second, if supplied, as the maximal
|
|
required threshold.
|
|
distance : number, optional
|
|
Required minimal horizontal distance (>= 1) in samples between
|
|
neighbouring peaks. Smaller peaks are removed first until the condition
|
|
is fulfilled for all remaining peaks.
|
|
prominence : number or ndarray or sequence, optional
|
|
Required prominence of peaks. Either a number, ``None``, an array
|
|
matching `x` or a 2-element sequence of the former. The first
|
|
element is always interpreted as the minimal and the second, if
|
|
supplied, as the maximal required prominence.
|
|
width : number or ndarray or sequence, optional
|
|
Required width of peaks in samples. Either a number, ``None``, an array
|
|
matching `x` or a 2-element sequence of the former. The first
|
|
element is always interpreted as the minimal and the second, if
|
|
supplied, as the maximal required width.
|
|
wlen : int, optional
|
|
Used for calculation of the peaks prominences, thus it is only used if
|
|
one of the arguments `prominence` or `width` is given. See argument
|
|
`wlen` in `peak_prominences` for a full description of its effects.
|
|
rel_height : float, optional
|
|
Used for calculation of the peaks width, thus it is only used if `width`
|
|
is given. See argument `rel_height` in `peak_widths` for a full
|
|
description of its effects.
|
|
plateau_size : number or ndarray or sequence, optional
|
|
Required size of the flat top of peaks in samples. Either a number,
|
|
``None``, an array matching `x` or a 2-element sequence of the former.
|
|
The first element is always interpreted as the minimal and the second,
|
|
if supplied as the maximal required plateau size.
|
|
|
|
.. versionadded:: 1.2.0
|
|
|
|
Returns
|
|
-------
|
|
peaks : ndarray
|
|
Indices of peaks in `x` that satisfy all given conditions.
|
|
properties : dict
|
|
A dictionary containing properties of the returned peaks which were
|
|
calculated as intermediate results during evaluation of the specified
|
|
conditions:
|
|
|
|
* 'peak_heights'
|
|
If `height` is given, the height of each peak in `x`.
|
|
* 'left_thresholds', 'right_thresholds'
|
|
If `threshold` is given, these keys contain a peaks vertical
|
|
distance to its neighbouring samples.
|
|
* 'prominences', 'right_bases', 'left_bases'
|
|
If `prominence` is given, these keys are accessible. See
|
|
`peak_prominences` for a description of their content.
|
|
* 'width_heights', 'left_ips', 'right_ips'
|
|
If `width` is given, these keys are accessible. See `peak_widths`
|
|
for a description of their content.
|
|
* 'plateau_sizes', left_edges', 'right_edges'
|
|
If `plateau_size` is given, these keys are accessible and contain
|
|
the indices of a peak's edges (edges are still part of the
|
|
plateau) and the calculated plateau sizes.
|
|
|
|
.. versionadded:: 1.2.0
|
|
|
|
To calculate and return properties without excluding peaks, provide the
|
|
open interval ``(None, None)`` as a value to the appropriate argument
|
|
(excluding `distance`).
|
|
|
|
Warns
|
|
-----
|
|
PeakPropertyWarning
|
|
Raised if a peak's properties have unexpected values (see
|
|
`peak_prominences` and `peak_widths`).
|
|
|
|
Warnings
|
|
--------
|
|
This function may return unexpected results for data containing NaNs. To
|
|
avoid this, NaNs should either be removed or replaced.
|
|
|
|
See Also
|
|
--------
|
|
find_peaks_cwt
|
|
Find peaks using the wavelet transformation.
|
|
peak_prominences
|
|
Directly calculate the prominence of peaks.
|
|
peak_widths
|
|
Directly calculate the width of peaks.
|
|
|
|
Notes
|
|
-----
|
|
In the context of this function, a peak or local maximum is defined as any
|
|
sample whose two direct neighbours have a smaller amplitude. For flat peaks
|
|
(more than one sample of equal amplitude wide) the index of the middle
|
|
sample is returned (rounded down in case the number of samples is even).
|
|
For noisy signals the peak locations can be off because the noise might
|
|
change the position of local maxima. In those cases consider smoothing the
|
|
signal before searching for peaks or use other peak finding and fitting
|
|
methods (like `find_peaks_cwt`).
|
|
|
|
Some additional comments on specifying conditions:
|
|
|
|
* Almost all conditions (excluding `distance`) can be given as half-open or
|
|
closed intervals, e.g., ``1`` or ``(1, None)`` defines the half-open
|
|
interval :math:`[1, \\infty]` while ``(None, 1)`` defines the interval
|
|
:math:`[-\\infty, 1]`. The open interval ``(None, None)`` can be specified
|
|
as well, which returns the matching properties without exclusion of peaks.
|
|
* The border is always included in the interval used to select valid peaks.
|
|
* For several conditions the interval borders can be specified with
|
|
arrays matching `x` in shape which enables dynamic constrains based on
|
|
the sample position.
|
|
* The conditions are evaluated in the following order: `plateau_size`,
|
|
`height`, `threshold`, `distance`, `prominence`, `width`. In most cases
|
|
this order is the fastest one because faster operations are applied first
|
|
to reduce the number of peaks that need to be evaluated later.
|
|
* While indices in `peaks` are guaranteed to be at least `distance` samples
|
|
apart, edges of flat peaks may be closer than the allowed `distance`.
|
|
* Use `wlen` to reduce the time it takes to evaluate the conditions for
|
|
`prominence` or `width` if `x` is large or has many local maxima
|
|
(see `peak_prominences`).
|
|
|
|
.. versionadded:: 1.1.0
|
|
|
|
Examples
|
|
--------
|
|
To demonstrate this function's usage we use a signal `x` supplied with
|
|
SciPy (see `scipy.misc.electrocardiogram`). Let's find all peaks (local
|
|
maxima) in `x` whose amplitude lies above 0.
|
|
|
|
>>> import matplotlib.pyplot as plt
|
|
>>> from scipy.misc import electrocardiogram
|
|
>>> from scipy.signal import find_peaks
|
|
>>> x = electrocardiogram()[2000:4000]
|
|
>>> peaks, _ = find_peaks(x, height=0)
|
|
>>> plt.plot(x)
|
|
>>> plt.plot(peaks, x[peaks], "x")
|
|
>>> plt.plot(np.zeros_like(x), "--", color="gray")
|
|
>>> plt.show()
|
|
|
|
We can select peaks below 0 with ``height=(None, 0)`` or use arrays matching
|
|
`x` in size to reflect a changing condition for different parts of the
|
|
signal.
|
|
|
|
>>> border = np.sin(np.linspace(0, 3 * np.pi, x.size))
|
|
>>> peaks, _ = find_peaks(x, height=(-border, border))
|
|
>>> plt.plot(x)
|
|
>>> plt.plot(-border, "--", color="gray")
|
|
>>> plt.plot(border, ":", color="gray")
|
|
>>> plt.plot(peaks, x[peaks], "x")
|
|
>>> plt.show()
|
|
|
|
Another useful condition for periodic signals can be given with the
|
|
`distance` argument. In this case, we can easily select the positions of
|
|
QRS complexes within the electrocardiogram (ECG) by demanding a distance of
|
|
at least 150 samples.
|
|
|
|
>>> peaks, _ = find_peaks(x, distance=150)
|
|
>>> np.diff(peaks)
|
|
array([186, 180, 177, 171, 177, 169, 167, 164, 158, 162, 172])
|
|
>>> plt.plot(x)
|
|
>>> plt.plot(peaks, x[peaks], "x")
|
|
>>> plt.show()
|
|
|
|
Especially for noisy signals peaks can be easily grouped by their
|
|
prominence (see `peak_prominences`). E.g., we can select all peaks except
|
|
for the mentioned QRS complexes by limiting the allowed prominence to 0.6.
|
|
|
|
>>> peaks, properties = find_peaks(x, prominence=(None, 0.6))
|
|
>>> properties["prominences"].max()
|
|
0.5049999999999999
|
|
>>> plt.plot(x)
|
|
>>> plt.plot(peaks, x[peaks], "x")
|
|
>>> plt.show()
|
|
|
|
And, finally, let's examine a different section of the ECG which contains
|
|
beat forms of different shape. To select only the atypical heart beats, we
|
|
combine two conditions: a minimal prominence of 1 and width of at least 20
|
|
samples.
|
|
|
|
>>> x = electrocardiogram()[17000:18000]
|
|
>>> peaks, properties = find_peaks(x, prominence=1, width=20)
|
|
>>> properties["prominences"], properties["widths"]
|
|
(array([1.495, 2.3 ]), array([36.93773946, 39.32723577]))
|
|
>>> plt.plot(x)
|
|
>>> plt.plot(peaks, x[peaks], "x")
|
|
>>> plt.vlines(x=peaks, ymin=x[peaks] - properties["prominences"],
|
|
... ymax = x[peaks], color = "C1")
|
|
>>> plt.hlines(y=properties["width_heights"], xmin=properties["left_ips"],
|
|
... xmax=properties["right_ips"], color = "C1")
|
|
>>> plt.show()
|
|
"""
|
|
# _argmaxima1d expects array of dtype 'float64'
|
|
x = _arg_x_as_expected(x)
|
|
if distance is not None and distance < 1:
|
|
raise ValueError('`distance` must be greater or equal to 1')
|
|
|
|
peaks, left_edges, right_edges = _local_maxima_1d(x)
|
|
properties = {}
|
|
|
|
if plateau_size is not None:
|
|
# Evaluate plateau size
|
|
plateau_sizes = right_edges - left_edges + 1
|
|
pmin, pmax = _unpack_condition_args(plateau_size, x, peaks)
|
|
keep = _select_by_property(plateau_sizes, pmin, pmax)
|
|
peaks = peaks[keep]
|
|
properties["plateau_sizes"] = plateau_sizes
|
|
properties["left_edges"] = left_edges
|
|
properties["right_edges"] = right_edges
|
|
properties = {key: array[keep] for key, array in properties.items()}
|
|
|
|
if height is not None:
|
|
# Evaluate height condition
|
|
peak_heights = x[peaks]
|
|
hmin, hmax = _unpack_condition_args(height, x, peaks)
|
|
keep = _select_by_property(peak_heights, hmin, hmax)
|
|
peaks = peaks[keep]
|
|
properties["peak_heights"] = peak_heights
|
|
properties = {key: array[keep] for key, array in properties.items()}
|
|
|
|
if threshold is not None:
|
|
# Evaluate threshold condition
|
|
tmin, tmax = _unpack_condition_args(threshold, x, peaks)
|
|
keep, left_thresholds, right_thresholds = _select_by_peak_threshold(
|
|
x, peaks, tmin, tmax)
|
|
peaks = peaks[keep]
|
|
properties["left_thresholds"] = left_thresholds
|
|
properties["right_thresholds"] = right_thresholds
|
|
properties = {key: array[keep] for key, array in properties.items()}
|
|
|
|
if distance is not None:
|
|
# Evaluate distance condition
|
|
keep = _select_by_peak_distance(peaks, x[peaks], distance)
|
|
peaks = peaks[keep]
|
|
properties = {key: array[keep] for key, array in properties.items()}
|
|
|
|
if prominence is not None or width is not None:
|
|
# Calculate prominence (required for both conditions)
|
|
wlen = _arg_wlen_as_expected(wlen)
|
|
properties.update(zip(
|
|
['prominences', 'left_bases', 'right_bases'],
|
|
_peak_prominences(x, peaks, wlen=wlen)
|
|
))
|
|
|
|
if prominence is not None:
|
|
# Evaluate prominence condition
|
|
pmin, pmax = _unpack_condition_args(prominence, x, peaks)
|
|
keep = _select_by_property(properties['prominences'], pmin, pmax)
|
|
peaks = peaks[keep]
|
|
properties = {key: array[keep] for key, array in properties.items()}
|
|
|
|
if width is not None:
|
|
# Calculate widths
|
|
properties.update(zip(
|
|
['widths', 'width_heights', 'left_ips', 'right_ips'],
|
|
_peak_widths(x, peaks, rel_height, properties['prominences'],
|
|
properties['left_bases'], properties['right_bases'])
|
|
))
|
|
# Evaluate width condition
|
|
wmin, wmax = _unpack_condition_args(width, x, peaks)
|
|
keep = _select_by_property(properties['widths'], wmin, wmax)
|
|
peaks = peaks[keep]
|
|
properties = {key: array[keep] for key, array in properties.items()}
|
|
|
|
return peaks, properties
|
|
|
|
|
|
def _identify_ridge_lines(matr, max_distances, gap_thresh):
|
|
"""
|
|
Identify ridges in the 2-D matrix.
|
|
|
|
Expect that the width of the wavelet feature increases with increasing row
|
|
number.
|
|
|
|
Parameters
|
|
----------
|
|
matr : 2-D ndarray
|
|
Matrix in which to identify ridge lines.
|
|
max_distances : 1-D sequence
|
|
At each row, a ridge line is only connected
|
|
if the relative max at row[n] is within
|
|
`max_distances`[n] from the relative max at row[n+1].
|
|
gap_thresh : int
|
|
If a relative maximum is not found within `max_distances`,
|
|
there will be a gap. A ridge line is discontinued if
|
|
there are more than `gap_thresh` points without connecting
|
|
a new relative maximum.
|
|
|
|
Returns
|
|
-------
|
|
ridge_lines : tuple
|
|
Tuple of 2 1-D sequences. `ridge_lines`[ii][0] are the rows of the
|
|
ii-th ridge-line, `ridge_lines`[ii][1] are the columns. Empty if none
|
|
found. Each ridge-line will be sorted by row (increasing), but the
|
|
order of the ridge lines is not specified.
|
|
|
|
References
|
|
----------
|
|
.. [1] Bioinformatics (2006) 22 (17): 2059-2065.
|
|
:doi:`10.1093/bioinformatics/btl355`
|
|
|
|
Examples
|
|
--------
|
|
>>> data = np.random.rand(5,5)
|
|
>>> ridge_lines = _identify_ridge_lines(data, 1, 1)
|
|
|
|
Notes
|
|
-----
|
|
This function is intended to be used in conjunction with `cwt`
|
|
as part of `find_peaks_cwt`.
|
|
|
|
"""
|
|
if(len(max_distances) < matr.shape[0]):
|
|
raise ValueError('Max_distances must have at least as many rows '
|
|
'as matr')
|
|
|
|
all_max_cols = _boolrelextrema(matr, np.greater, axis=1, order=1)
|
|
# Highest row for which there are any relative maxima
|
|
has_relmax = np.nonzero(all_max_cols.any(axis=1))[0]
|
|
if(len(has_relmax) == 0):
|
|
return []
|
|
start_row = has_relmax[-1]
|
|
# Each ridge line is a 3-tuple:
|
|
# rows, cols,Gap number
|
|
ridge_lines = [[[start_row],
|
|
[col],
|
|
0] for col in np.nonzero(all_max_cols[start_row])[0]]
|
|
final_lines = []
|
|
rows = np.arange(start_row - 1, -1, -1)
|
|
cols = np.arange(0, matr.shape[1])
|
|
for row in rows:
|
|
this_max_cols = cols[all_max_cols[row]]
|
|
|
|
# Increment gap number of each line,
|
|
# set it to zero later if appropriate
|
|
for line in ridge_lines:
|
|
line[2] += 1
|
|
|
|
# XXX These should always be all_max_cols[row]
|
|
# But the order might be different. Might be an efficiency gain
|
|
# to make sure the order is the same and avoid this iteration
|
|
prev_ridge_cols = np.array([line[1][-1] for line in ridge_lines])
|
|
# Look through every relative maximum found at current row
|
|
# Attempt to connect them with existing ridge lines.
|
|
for ind, col in enumerate(this_max_cols):
|
|
# If there is a previous ridge line within
|
|
# the max_distance to connect to, do so.
|
|
# Otherwise start a new one.
|
|
line = None
|
|
if(len(prev_ridge_cols) > 0):
|
|
diffs = np.abs(col - prev_ridge_cols)
|
|
closest = np.argmin(diffs)
|
|
if diffs[closest] <= max_distances[row]:
|
|
line = ridge_lines[closest]
|
|
if(line is not None):
|
|
# Found a point close enough, extend current ridge line
|
|
line[1].append(col)
|
|
line[0].append(row)
|
|
line[2] = 0
|
|
else:
|
|
new_line = [[row],
|
|
[col],
|
|
0]
|
|
ridge_lines.append(new_line)
|
|
|
|
# Remove the ridge lines with gap_number too high
|
|
# XXX Modifying a list while iterating over it.
|
|
# Should be safe, since we iterate backwards, but
|
|
# still tacky.
|
|
for ind in range(len(ridge_lines) - 1, -1, -1):
|
|
line = ridge_lines[ind]
|
|
if line[2] > gap_thresh:
|
|
final_lines.append(line)
|
|
del ridge_lines[ind]
|
|
|
|
out_lines = []
|
|
for line in (final_lines + ridge_lines):
|
|
sortargs = np.array(np.argsort(line[0]))
|
|
rows, cols = np.zeros_like(sortargs), np.zeros_like(sortargs)
|
|
rows[sortargs] = line[0]
|
|
cols[sortargs] = line[1]
|
|
out_lines.append([rows, cols])
|
|
|
|
return out_lines
|
|
|
|
|
|
def _filter_ridge_lines(cwt, ridge_lines, window_size=None, min_length=None,
|
|
min_snr=1, noise_perc=10):
|
|
"""
|
|
Filter ridge lines according to prescribed criteria. Intended
|
|
to be used for finding relative maxima.
|
|
|
|
Parameters
|
|
----------
|
|
cwt : 2-D ndarray
|
|
Continuous wavelet transform from which the `ridge_lines` were defined.
|
|
ridge_lines : 1-D sequence
|
|
Each element should contain 2 sequences, the rows and columns
|
|
of the ridge line (respectively).
|
|
window_size : int, optional
|
|
Size of window to use to calculate noise floor.
|
|
Default is ``cwt.shape[1] / 20``.
|
|
min_length : int, optional
|
|
Minimum length a ridge line needs to be acceptable.
|
|
Default is ``cwt.shape[0] / 4``, ie 1/4-th the number of widths.
|
|
min_snr : float, optional
|
|
Minimum SNR ratio. Default 1. The signal is the value of
|
|
the cwt matrix at the shortest length scale (``cwt[0, loc]``), the
|
|
noise is the `noise_perc`th percentile of datapoints contained within a
|
|
window of `window_size` around ``cwt[0, loc]``.
|
|
noise_perc : float, optional
|
|
When calculating the noise floor, percentile of data points
|
|
examined below which to consider noise. Calculated using
|
|
scipy.stats.scoreatpercentile.
|
|
|
|
References
|
|
----------
|
|
.. [1] Bioinformatics (2006) 22 (17): 2059-2065.
|
|
:doi:`10.1093/bioinformatics/btl355`
|
|
|
|
"""
|
|
num_points = cwt.shape[1]
|
|
if min_length is None:
|
|
min_length = np.ceil(cwt.shape[0] / 4)
|
|
if window_size is None:
|
|
window_size = np.ceil(num_points / 20)
|
|
|
|
window_size = int(window_size)
|
|
hf_window, odd = divmod(window_size, 2)
|
|
|
|
# Filter based on SNR
|
|
row_one = cwt[0, :]
|
|
noises = np.zeros_like(row_one)
|
|
for ind, val in enumerate(row_one):
|
|
window_start = max(ind - hf_window, 0)
|
|
window_end = min(ind + hf_window + odd, num_points)
|
|
noises[ind] = scoreatpercentile(row_one[window_start:window_end],
|
|
per=noise_perc)
|
|
|
|
def filt_func(line):
|
|
if len(line[0]) < min_length:
|
|
return False
|
|
snr = abs(cwt[line[0][0], line[1][0]] / noises[line[1][0]])
|
|
if snr < min_snr:
|
|
return False
|
|
return True
|
|
|
|
return list(filter(filt_func, ridge_lines))
|
|
|
|
|
|
def find_peaks_cwt(vector, widths, wavelet=None, max_distances=None,
|
|
gap_thresh=None, min_length=None,
|
|
min_snr=1, noise_perc=10, window_size=None):
|
|
"""
|
|
Find peaks in a 1-D array with wavelet transformation.
|
|
|
|
The general approach is to smooth `vector` by convolving it with
|
|
`wavelet(width)` for each width in `widths`. Relative maxima which
|
|
appear at enough length scales, and with sufficiently high SNR, are
|
|
accepted.
|
|
|
|
Parameters
|
|
----------
|
|
vector : ndarray
|
|
1-D array in which to find the peaks.
|
|
widths : sequence
|
|
1-D array of widths to use for calculating the CWT matrix. In general,
|
|
this range should cover the expected width of peaks of interest.
|
|
wavelet : callable, optional
|
|
Should take two parameters and return a 1-D array to convolve
|
|
with `vector`. The first parameter determines the number of points
|
|
of the returned wavelet array, the second parameter is the scale
|
|
(`width`) of the wavelet. Should be normalized and symmetric.
|
|
Default is the ricker wavelet.
|
|
max_distances : ndarray, optional
|
|
At each row, a ridge line is only connected if the relative max at
|
|
row[n] is within ``max_distances[n]`` from the relative max at
|
|
``row[n+1]``. Default value is ``widths/4``.
|
|
gap_thresh : float, optional
|
|
If a relative maximum is not found within `max_distances`,
|
|
there will be a gap. A ridge line is discontinued if there are more
|
|
than `gap_thresh` points without connecting a new relative maximum.
|
|
Default is the first value of the widths array i.e. widths[0].
|
|
min_length : int, optional
|
|
Minimum length a ridge line needs to be acceptable.
|
|
Default is ``cwt.shape[0] / 4``, ie 1/4-th the number of widths.
|
|
min_snr : float, optional
|
|
Minimum SNR ratio. Default 1. The signal is the value of
|
|
the cwt matrix at the shortest length scale (``cwt[0, loc]``), the
|
|
noise is the `noise_perc`th percentile of datapoints contained within a
|
|
window of `window_size` around ``cwt[0, loc]``.
|
|
noise_perc : float, optional
|
|
When calculating the noise floor, percentile of data points
|
|
examined below which to consider noise. Calculated using
|
|
`stats.scoreatpercentile`. Default is 10.
|
|
window_size : int, optional
|
|
Size of window to use to calculate noise floor.
|
|
Default is ``cwt.shape[1] / 20``.
|
|
|
|
Returns
|
|
-------
|
|
peaks_indices : ndarray
|
|
Indices of the locations in the `vector` where peaks were found.
|
|
The list is sorted.
|
|
|
|
See Also
|
|
--------
|
|
cwt
|
|
Continuous wavelet transform.
|
|
find_peaks
|
|
Find peaks inside a signal based on peak properties.
|
|
|
|
Notes
|
|
-----
|
|
This approach was designed for finding sharp peaks among noisy data,
|
|
however with proper parameter selection it should function well for
|
|
different peak shapes.
|
|
|
|
The algorithm is as follows:
|
|
1. Perform a continuous wavelet transform on `vector`, for the supplied
|
|
`widths`. This is a convolution of `vector` with `wavelet(width)` for
|
|
each width in `widths`. See `cwt`.
|
|
2. Identify "ridge lines" in the cwt matrix. These are relative maxima
|
|
at each row, connected across adjacent rows. See identify_ridge_lines
|
|
3. Filter the ridge_lines using filter_ridge_lines.
|
|
|
|
.. versionadded:: 0.11.0
|
|
|
|
References
|
|
----------
|
|
.. [1] Bioinformatics (2006) 22 (17): 2059-2065.
|
|
:doi:`10.1093/bioinformatics/btl355`
|
|
|
|
Examples
|
|
--------
|
|
>>> from scipy import signal
|
|
>>> xs = np.arange(0, np.pi, 0.05)
|
|
>>> data = np.sin(xs)
|
|
>>> peakind = signal.find_peaks_cwt(data, np.arange(1,10))
|
|
>>> peakind, xs[peakind], data[peakind]
|
|
([32], array([ 1.6]), array([ 0.9995736]))
|
|
|
|
"""
|
|
widths = np.asarray(widths)
|
|
|
|
if gap_thresh is None:
|
|
gap_thresh = np.ceil(widths[0])
|
|
if max_distances is None:
|
|
max_distances = widths / 4.0
|
|
if wavelet is None:
|
|
wavelet = ricker
|
|
|
|
cwt_dat = cwt(vector, wavelet, widths, window_size=window_size)
|
|
ridge_lines = _identify_ridge_lines(cwt_dat, max_distances, gap_thresh)
|
|
filtered = _filter_ridge_lines(cwt_dat, ridge_lines, min_length=min_length,
|
|
window_size=window_size, min_snr=min_snr,
|
|
noise_perc=noise_perc)
|
|
max_locs = np.asarray([x[1][0] for x in filtered])
|
|
max_locs.sort()
|
|
|
|
return max_locs
|
|
|