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installed required libs
2021-02-17 12:26:31 +05:30
# Author: Travis Oliphant
# 1999 -- 2002
import operator
import math
import timeit
from scipy.spatial import cKDTree
from . import sigtools, dlti
from ._upfirdn import upfirdn, _output_len, _upfirdn_modes
from scipy import linalg, fft as sp_fft
from scipy.fft._helper import _init_nd_shape_and_axes
from scipy._lib._util import prod as _prod
import numpy as np
from scipy.special import lambertw
from .windows import get_window
from ._arraytools import axis_slice, axis_reverse, odd_ext, even_ext, const_ext
from .filter_design import cheby1, _validate_sos
from .fir_filter_design import firwin
from ._sosfilt import _sosfilt
import warnings
__all__ = ['correlate', 'correlation_lags', 'correlate2d',
'convolve', 'convolve2d', 'fftconvolve', 'oaconvolve',
'order_filter', 'medfilt', 'medfilt2d', 'wiener', 'lfilter',
'lfiltic', 'sosfilt', 'deconvolve', 'hilbert', 'hilbert2',
'cmplx_sort', 'unique_roots', 'invres', 'invresz', 'residue',
'residuez', 'resample', 'resample_poly', 'detrend',
'lfilter_zi', 'sosfilt_zi', 'sosfiltfilt', 'choose_conv_method',
'filtfilt', 'decimate', 'vectorstrength']
_modedict = {'valid': 0, 'same': 1, 'full': 2}
_boundarydict = {'fill': 0, 'pad': 0, 'wrap': 2, 'circular': 2, 'symm': 1,
'symmetric': 1, 'reflect': 4}
def _valfrommode(mode):
try:
return _modedict[mode]
except KeyError as e:
raise ValueError("Acceptable mode flags are 'valid',"
" 'same', or 'full'.") from e
def _bvalfromboundary(boundary):
try:
return _boundarydict[boundary] << 2
except KeyError as e:
raise ValueError("Acceptable boundary flags are 'fill', 'circular' "
"(or 'wrap'), and 'symmetric' (or 'symm').") from e
def _inputs_swap_needed(mode, shape1, shape2, axes=None):
"""Determine if inputs arrays need to be swapped in `"valid"` mode.
If in `"valid"` mode, returns whether or not the input arrays need to be
swapped depending on whether `shape1` is at least as large as `shape2` in
every calculated dimension.
This is important for some of the correlation and convolution
implementations in this module, where the larger array input needs to come
before the smaller array input when operating in this mode.
Note that if the mode provided is not 'valid', False is immediately
returned.
"""
if mode != 'valid':
return False
if not shape1:
return False
if axes is None:
axes = range(len(shape1))
ok1 = all(shape1[i] >= shape2[i] for i in axes)
ok2 = all(shape2[i] >= shape1[i] for i in axes)
if not (ok1 or ok2):
raise ValueError("For 'valid' mode, one must be at least "
"as large as the other in every dimension")
return not ok1
def correlate(in1, in2, mode='full', method='auto'):
r"""
Cross-correlate two N-dimensional arrays.
Cross-correlate `in1` and `in2`, with the output size determined by the
`mode` argument.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear cross-correlation
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
method : str {'auto', 'direct', 'fft'}, optional
A string indicating which method to use to calculate the correlation.
``direct``
The correlation is determined directly from sums, the definition of
correlation.
``fft``
The Fast Fourier Transform is used to perform the correlation more
quickly (only available for numerical arrays.)
``auto``
Automatically chooses direct or Fourier method based on an estimate
of which is faster (default). See `convolve` Notes for more detail.
.. versionadded:: 0.19.0
Returns
-------
correlate : array
An N-dimensional array containing a subset of the discrete linear
cross-correlation of `in1` with `in2`.
See Also
--------
choose_conv_method : contains more documentation on `method`.
correlation_lags : calculates the lag / displacement indices array for 1D
cross-correlation.
Notes
-----
The correlation z of two d-dimensional arrays x and y is defined as::
z[...,k,...] = sum[..., i_l, ...] x[..., i_l,...] * conj(y[..., i_l - k,...])
This way, if x and y are 1-D arrays and ``z = correlate(x, y, 'full')``
then
.. math::
z[k] = (x * y)(k - N + 1)
= \sum_{l=0}^{||x||-1}x_l y_{l-k+N-1}^{*}
for :math:`k = 0, 1, ..., ||x|| + ||y|| - 2`
where :math:`||x||` is the length of ``x``, :math:`N = \max(||x||,||y||)`,
and :math:`y_m` is 0 when m is outside the range of y.
``method='fft'`` only works for numerical arrays as it relies on
`fftconvolve`. In certain cases (i.e., arrays of objects or when
rounding integers can lose precision), ``method='direct'`` is always used.
When using "same" mode with even-length inputs, the outputs of `correlate`
and `correlate2d` differ: There is a 1-index offset between them.
Examples
--------
Implement a matched filter using cross-correlation, to recover a signal
that has passed through a noisy channel.
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> sig = np.repeat([0., 1., 1., 0., 1., 0., 0., 1.], 128)
>>> sig_noise = sig + np.random.randn(len(sig))
>>> corr = signal.correlate(sig_noise, np.ones(128), mode='same') / 128
>>> clock = np.arange(64, len(sig), 128)
>>> fig, (ax_orig, ax_noise, ax_corr) = plt.subplots(3, 1, sharex=True)
>>> ax_orig.plot(sig)
>>> ax_orig.plot(clock, sig[clock], 'ro')
>>> ax_orig.set_title('Original signal')
>>> ax_noise.plot(sig_noise)
>>> ax_noise.set_title('Signal with noise')
>>> ax_corr.plot(corr)
>>> ax_corr.plot(clock, corr[clock], 'ro')
>>> ax_corr.axhline(0.5, ls=':')
>>> ax_corr.set_title('Cross-correlated with rectangular pulse')
>>> ax_orig.margins(0, 0.1)
>>> fig.tight_layout()
>>> plt.show()
Compute the cross-correlation of a noisy signal with the original signal.
>>> x = np.arange(128) / 128
>>> sig = np.sin(2 * np.pi * x)
>>> sig_noise = sig + np.random.randn(len(sig))
>>> corr = signal.correlate(sig_noise, sig)
>>> lags = signal.correlation_lags(len(sig), len(sig_noise))
>>> corr /= np.max(corr)
>>> fig, (ax_orig, ax_noise, ax_corr) = plt.subplots(3, 1, figsize=(4.8, 4.8))
>>> ax_orig.plot(sig)
>>> ax_orig.set_title('Original signal')
>>> ax_orig.set_xlabel('Sample Number')
>>> ax_noise.plot(sig_noise)
>>> ax_noise.set_title('Signal with noise')
>>> ax_noise.set_xlabel('Sample Number')
>>> ax_corr.plot(lags, corr)
>>> ax_corr.set_title('Cross-correlated signal')
>>> ax_corr.set_xlabel('Lag')
>>> ax_orig.margins(0, 0.1)
>>> ax_noise.margins(0, 0.1)
>>> ax_corr.margins(0, 0.1)
>>> fig.tight_layout()
>>> plt.show()
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if in1.ndim == in2.ndim == 0:
return in1 * in2.conj()
elif in1.ndim != in2.ndim:
raise ValueError("in1 and in2 should have the same dimensionality")
# Don't use _valfrommode, since correlate should not accept numeric modes
try:
val = _modedict[mode]
except KeyError as e:
raise ValueError("Acceptable mode flags are 'valid',"
" 'same', or 'full'.") from e
# this either calls fftconvolve or this function with method=='direct'
if method in ('fft', 'auto'):
return convolve(in1, _reverse_and_conj(in2), mode, method)
elif method == 'direct':
# fastpath to faster numpy.correlate for 1d inputs when possible
if _np_conv_ok(in1, in2, mode):
return np.correlate(in1, in2, mode)
# _correlateND is far slower when in2.size > in1.size, so swap them
# and then undo the effect afterward if mode == 'full'. Also, it fails
# with 'valid' mode if in2 is larger than in1, so swap those, too.
# Don't swap inputs for 'same' mode, since shape of in1 matters.
swapped_inputs = ((mode == 'full') and (in2.size > in1.size) or
_inputs_swap_needed(mode, in1.shape, in2.shape))
if swapped_inputs:
in1, in2 = in2, in1
if mode == 'valid':
ps = [i - j + 1 for i, j in zip(in1.shape, in2.shape)]
out = np.empty(ps, in1.dtype)
z = sigtools._correlateND(in1, in2, out, val)
else:
ps = [i + j - 1 for i, j in zip(in1.shape, in2.shape)]
# zero pad input
in1zpadded = np.zeros(ps, in1.dtype)
sc = tuple(slice(0, i) for i in in1.shape)
in1zpadded[sc] = in1.copy()
if mode == 'full':
out = np.empty(ps, in1.dtype)
elif mode == 'same':
out = np.empty(in1.shape, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
if swapped_inputs:
# Reverse and conjugate to undo the effect of swapping inputs
z = _reverse_and_conj(z)
return z
else:
raise ValueError("Acceptable method flags are 'auto',"
" 'direct', or 'fft'.")
def correlation_lags(in1_len, in2_len, mode='full'):
r"""
Calculates the lag / displacement indices array for 1D cross-correlation.
Parameters
----------
in1_size : int
First input size.
in2_size : int
Second input size.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output.
See the documentation `correlate` for more information.
See Also
--------
correlate : Compute the N-dimensional cross-correlation.
Returns
-------
lags : array
Returns an array containing cross-correlation lag/displacement indices.
Indices can be indexed with the np.argmax of the correlation to return
the lag/displacement.
Notes
-----
Cross-correlation for continuous functions :math:`f` and :math:`g` is
defined as:
.. math ::
\left ( f\star g \right )\left ( \tau \right )
\triangleq \int_{t_0}^{t_0 +T}
\overline{f\left ( t \right )}g\left ( t+\tau \right )dt
Where :math:`\tau` is defined as the displacement, also known as the lag.
Cross correlation for discrete functions :math:`f` and :math:`g` is
defined as:
.. math ::
\left ( f\star g \right )\left [ n \right ]
\triangleq \sum_{-\infty}^{\infty}
\overline{f\left [ m \right ]}g\left [ m+n \right ]
Where :math:`n` is the lag.
Examples
--------
Cross-correlation of a signal with its time-delayed self.
>>> from scipy import signal
>>> rng = np.random.RandomState(0)
>>> x = rng.standard_normal(1000)
>>> y = np.concatenate([rng.standard_normal(100), x])
>>> correlation = signal.correlate(x, y, mode="full")
>>> lags = signal.correlation_lags(x.size, y.size, mode="full")
>>> lag = lags[np.argmax(correlation)]
"""
# calculate lag ranges in different modes of operation
if mode == "full":
# the output is the full discrete linear convolution
# of the inputs. (Default)
lags = np.arange(-in2_len + 1, in1_len)
elif mode == "same":
# the output is the same size as `in1`, centered
# with respect to the 'full' output.
# calculate the full output
lags = np.arange(-in2_len + 1, in1_len)
# determine the midpoint in the full output
mid = lags.size // 2
# determine lag_bound to be used with respect
# to the midpoint
lag_bound = in1_len // 2
# calculate lag ranges for even and odd scenarios
if in1_len % 2 == 0:
lags = lags[(mid-lag_bound):(mid+lag_bound)]
else:
lags = lags[(mid-lag_bound):(mid+lag_bound)+1]
elif mode == "valid":
# the output consists only of those elements that do not
# rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
# must be at least as large as the other in every dimension.
# the lag_bound will be either negative or positive
# this let's us infer how to present the lag range
lag_bound = in1_len - in2_len
if lag_bound >= 0:
lags = np.arange(lag_bound + 1)
else:
lags = np.arange(lag_bound, 1)
return lags
def _centered(arr, newshape):
# Return the center newshape portion of the array.
newshape = np.asarray(newshape)
currshape = np.array(arr.shape)
startind = (currshape - newshape) // 2
endind = startind + newshape
myslice = [slice(startind[k], endind[k]) for k in range(len(endind))]
return arr[tuple(myslice)]
def _init_freq_conv_axes(in1, in2, mode, axes, sorted_axes=False):
"""Handle the axes argument for frequency-domain convolution.
Returns the inputs and axes in a standard form, eliminating redundant axes,
swapping the inputs if necessary, and checking for various potential
errors.
Parameters
----------
in1 : array
First input.
in2 : array
Second input.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output.
See the documentation `fftconvolve` for more information.
axes : list of ints
Axes over which to compute the FFTs.
sorted_axes : bool, optional
If `True`, sort the axes.
Default is `False`, do not sort.
Returns
-------
in1 : array
The first input, possible swapped with the second input.
in2 : array
The second input, possible swapped with the first input.
axes : list of ints
Axes over which to compute the FFTs.
"""
s1 = in1.shape
s2 = in2.shape
noaxes = axes is None
_, axes = _init_nd_shape_and_axes(in1, shape=None, axes=axes)
if not noaxes and not len(axes):
raise ValueError("when provided, axes cannot be empty")
# Axes of length 1 can rely on broadcasting rules for multipy,
# no fft needed.
axes = [a for a in axes if s1[a] != 1 and s2[a] != 1]
if sorted_axes:
axes.sort()
if not all(s1[a] == s2[a] or s1[a] == 1 or s2[a] == 1
for a in range(in1.ndim) if a not in axes):
raise ValueError("incompatible shapes for in1 and in2:"
" {0} and {1}".format(s1, s2))
# Check that input sizes are compatible with 'valid' mode.
if _inputs_swap_needed(mode, s1, s2, axes=axes):
# Convolution is commutative; order doesn't have any effect on output.
in1, in2 = in2, in1
return in1, in2, axes
def _freq_domain_conv(in1, in2, axes, shape, calc_fast_len=False):
"""Convolve two arrays in the frequency domain.
This function implements only base the FFT-related operations.
Specifically, it converts the signals to the frequency domain, multiplies
them, then converts them back to the time domain. Calculations of axes,
shapes, convolution mode, etc. are implemented in higher level-functions,
such as `fftconvolve` and `oaconvolve`. Those functions should be used
instead of this one.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
axes : array_like of ints
Axes over which to compute the FFTs.
shape : array_like of ints
The sizes of the FFTs.
calc_fast_len : bool, optional
If `True`, set each value of `shape` to the next fast FFT length.
Default is `False`, use `axes` as-is.
Returns
-------
out : array
An N-dimensional array containing the discrete linear convolution of
`in1` with `in2`.
"""
if not len(axes):
return in1 * in2
complex_result = (in1.dtype.kind == 'c' or in2.dtype.kind == 'c')
if calc_fast_len:
# Speed up FFT by padding to optimal size.
fshape = [
sp_fft.next_fast_len(shape[a], not complex_result) for a in axes]
else:
fshape = shape
if not complex_result:
fft, ifft = sp_fft.rfftn, sp_fft.irfftn
else:
fft, ifft = sp_fft.fftn, sp_fft.ifftn
sp1 = fft(in1, fshape, axes=axes)
sp2 = fft(in2, fshape, axes=axes)
ret = ifft(sp1 * sp2, fshape, axes=axes)
if calc_fast_len:
fslice = tuple([slice(sz) for sz in shape])
ret = ret[fslice]
return ret
def _apply_conv_mode(ret, s1, s2, mode, axes):
"""Calculate the convolution result shape based on the `mode` argument.
Returns the result sliced to the correct size for the given mode.
Parameters
----------
ret : array
The result array, with the appropriate shape for the 'full' mode.
s1 : list of int
The shape of the first input.
s2 : list of int
The shape of the second input.
mode : str {'full', 'valid', 'same'}
A string indicating the size of the output.
See the documentation `fftconvolve` for more information.
axes : list of ints
Axes over which to compute the convolution.
Returns
-------
ret : array
A copy of `res`, sliced to the correct size for the given `mode`.
"""
if mode == "full":
return ret.copy()
elif mode == "same":
return _centered(ret, s1).copy()
elif mode == "valid":
shape_valid = [ret.shape[a] if a not in axes else s1[a] - s2[a] + 1
for a in range(ret.ndim)]
return _centered(ret, shape_valid).copy()
else:
raise ValueError("acceptable mode flags are 'valid',"
" 'same', or 'full'")
def fftconvolve(in1, in2, mode="full", axes=None):
"""Convolve two N-dimensional arrays using FFT.
Convolve `in1` and `in2` using the fast Fourier transform method, with
the output size determined by the `mode` argument.
This is generally much faster than `convolve` for large arrays (n > ~500),
but can be slower when only a few output values are needed, and can only
output float arrays (int or object array inputs will be cast to float).
As of v0.19, `convolve` automatically chooses this method or the direct
method based on an estimation of which is faster.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
axes : int or array_like of ints or None, optional
Axes over which to compute the convolution.
The default is over all axes.
Returns
-------
out : array
An N-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
See Also
--------
convolve : Uses the direct convolution or FFT convolution algorithm
depending on which is faster.
oaconvolve : Uses the overlap-add method to do convolution, which is
generally faster when the input arrays are large and
significantly different in size.
Examples
--------
Autocorrelation of white noise is an impulse.
>>> from scipy import signal
>>> sig = np.random.randn(1000)
>>> autocorr = signal.fftconvolve(sig, sig[::-1], mode='full')
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_mag) = plt.subplots(2, 1)
>>> ax_orig.plot(sig)
>>> ax_orig.set_title('White noise')
>>> ax_mag.plot(np.arange(-len(sig)+1,len(sig)), autocorr)
>>> ax_mag.set_title('Autocorrelation')
>>> fig.tight_layout()
>>> fig.show()
Gaussian blur implemented using FFT convolution. Notice the dark borders
around the image, due to the zero-padding beyond its boundaries.
The `convolve2d` function allows for other types of image boundaries,
but is far slower.
>>> from scipy import misc
>>> face = misc.face(gray=True)
>>> kernel = np.outer(signal.windows.gaussian(70, 8),
... signal.windows.gaussian(70, 8))
>>> blurred = signal.fftconvolve(face, kernel, mode='same')
>>> fig, (ax_orig, ax_kernel, ax_blurred) = plt.subplots(3, 1,
... figsize=(6, 15))
>>> ax_orig.imshow(face, cmap='gray')
>>> ax_orig.set_title('Original')
>>> ax_orig.set_axis_off()
>>> ax_kernel.imshow(kernel, cmap='gray')
>>> ax_kernel.set_title('Gaussian kernel')
>>> ax_kernel.set_axis_off()
>>> ax_blurred.imshow(blurred, cmap='gray')
>>> ax_blurred.set_title('Blurred')
>>> ax_blurred.set_axis_off()
>>> fig.show()
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if in1.ndim == in2.ndim == 0: # scalar inputs
return in1 * in2
elif in1.ndim != in2.ndim:
raise ValueError("in1 and in2 should have the same dimensionality")
elif in1.size == 0 or in2.size == 0: # empty arrays
return np.array([])
in1, in2, axes = _init_freq_conv_axes(in1, in2, mode, axes,
sorted_axes=False)
s1 = in1.shape
s2 = in2.shape
shape = [max((s1[i], s2[i])) if i not in axes else s1[i] + s2[i] - 1
for i in range(in1.ndim)]
ret = _freq_domain_conv(in1, in2, axes, shape, calc_fast_len=True)
return _apply_conv_mode(ret, s1, s2, mode, axes)
def _calc_oa_lens(s1, s2):
"""Calculate the optimal FFT lengths for overlapp-add convolution.
The calculation is done for a single dimension.
Parameters
----------
s1 : int
Size of the dimension for the first array.
s2 : int
Size of the dimension for the second array.
Returns
-------
block_size : int
The size of the FFT blocks.
overlap : int
The amount of overlap between two blocks.
in1_step : int
The size of each step for the first array.
in2_step : int
The size of each step for the first array.
"""
# Set up the arguments for the conventional FFT approach.
fallback = (s1+s2-1, None, s1, s2)
# Use conventional FFT convolve if sizes are same.
if s1 == s2 or s1 == 1 or s2 == 1:
return fallback
if s2 > s1:
s1, s2 = s2, s1
swapped = True
else:
swapped = False
# There cannot be a useful block size if s2 is more than half of s1.
if s2 >= s1/2:
return fallback
# Derivation of optimal block length
# For original formula see:
# https://en.wikipedia.org/wiki/Overlap-add_method
#
# Formula:
# K = overlap = s2-1
# N = block_size
# C = complexity
# e = exponential, exp(1)
#
# C = (N*(log2(N)+1))/(N-K)
# C = (N*log2(2N))/(N-K)
# C = N/(N-K) * log2(2N)
# C1 = N/(N-K)
# C2 = log2(2N) = ln(2N)/ln(2)
#
# dC1/dN = (1*(N-K)-N)/(N-K)^2 = -K/(N-K)^2
# dC2/dN = 2/(2*N*ln(2)) = 1/(N*ln(2))
#
# dC/dN = dC1/dN*C2 + dC2/dN*C1
# dC/dN = -K*ln(2N)/(ln(2)*(N-K)^2) + N/(N*ln(2)*(N-K))
# dC/dN = -K*ln(2N)/(ln(2)*(N-K)^2) + 1/(ln(2)*(N-K))
# dC/dN = -K*ln(2N)/(ln(2)*(N-K)^2) + (N-K)/(ln(2)*(N-K)^2)
# dC/dN = (-K*ln(2N) + (N-K)/(ln(2)*(N-K)^2)
# dC/dN = (N - K*ln(2N) - K)/(ln(2)*(N-K)^2)
#
# Solve for minimum, where dC/dN = 0
# 0 = (N - K*ln(2N) - K)/(ln(2)*(N-K)^2)
# 0 * ln(2)*(N-K)^2 = N - K*ln(2N) - K
# 0 = N - K*ln(2N) - K
# 0 = N - K*(ln(2N) + 1)
# 0 = N - K*ln(2Ne)
# N = K*ln(2Ne)
# N/K = ln(2Ne)
#
# e^(N/K) = e^ln(2Ne)
# e^(N/K) = 2Ne
# 1/e^(N/K) = 1/(2*N*e)
# e^(N/-K) = 1/(2*N*e)
# e^(N/-K) = K/N*1/(2*K*e)
# N/K*e^(N/-K) = 1/(2*e*K)
# N/-K*e^(N/-K) = -1/(2*e*K)
#
# Using Lambert W function
# https://en.wikipedia.org/wiki/Lambert_W_function
# x = W(y) It is the solution to y = x*e^x
# x = N/-K
# y = -1/(2*e*K)
#
# N/-K = W(-1/(2*e*K))
#
# N = -K*W(-1/(2*e*K))
overlap = s2-1
opt_size = -overlap*lambertw(-1/(2*math.e*overlap), k=-1).real
block_size = sp_fft.next_fast_len(math.ceil(opt_size))
# Use conventional FFT convolve if there is only going to be one block.
if block_size >= s1:
return fallback
if not swapped:
in1_step = block_size-s2+1
in2_step = s2
else:
in1_step = s2
in2_step = block_size-s2+1
return block_size, overlap, in1_step, in2_step
def oaconvolve(in1, in2, mode="full", axes=None):
"""Convolve two N-dimensional arrays using the overlap-add method.
Convolve `in1` and `in2` using the overlap-add method, with
the output size determined by the `mode` argument.
This is generally much faster than `convolve` for large arrays (n > ~500),
and generally much faster than `fftconvolve` when one array is much
larger than the other, but can be slower when only a few output values are
needed or when the arrays are very similar in shape, and can only
output float arrays (int or object array inputs will be cast to float).
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
axes : int or array_like of ints or None, optional
Axes over which to compute the convolution.
The default is over all axes.
Returns
-------
out : array
An N-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
See Also
--------
convolve : Uses the direct convolution or FFT convolution algorithm
depending on which is faster.
fftconvolve : An implementation of convolution using FFT.
Notes
-----
.. versionadded:: 1.4.0
Examples
--------
Convolve a 100,000 sample signal with a 512-sample filter.
>>> from scipy import signal
>>> sig = np.random.randn(100000)
>>> filt = signal.firwin(512, 0.01)
>>> fsig = signal.oaconvolve(sig, filt)
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_mag) = plt.subplots(2, 1)
>>> ax_orig.plot(sig)
>>> ax_orig.set_title('White noise')
>>> ax_mag.plot(fsig)
>>> ax_mag.set_title('Filtered noise')
>>> fig.tight_layout()
>>> fig.show()
References
----------
.. [1] Wikipedia, "Overlap-add_method".
https://en.wikipedia.org/wiki/Overlap-add_method
.. [2] Richard G. Lyons. Understanding Digital Signal Processing,
Third Edition, 2011. Chapter 13.10.
ISBN 13: 978-0137-02741-5
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if in1.ndim == in2.ndim == 0: # scalar inputs
return in1 * in2
elif in1.ndim != in2.ndim:
raise ValueError("in1 and in2 should have the same dimensionality")
elif in1.size == 0 or in2.size == 0: # empty arrays
return np.array([])
elif in1.shape == in2.shape: # Equivalent to fftconvolve
return fftconvolve(in1, in2, mode=mode, axes=axes)
in1, in2, axes = _init_freq_conv_axes(in1, in2, mode, axes,
sorted_axes=True)
s1 = in1.shape
s2 = in2.shape
if not axes:
ret = in1 * in2
return _apply_conv_mode(ret, s1, s2, mode, axes)
# Calculate this now since in1 is changed later
shape_final = [None if i not in axes else
s1[i] + s2[i] - 1 for i in range(in1.ndim)]
# Calculate the block sizes for the output, steps, first and second inputs.
# It is simpler to calculate them all together than doing them in separate
# loops due to all the special cases that need to be handled.
optimal_sizes = ((-1, -1, s1[i], s2[i]) if i not in axes else
_calc_oa_lens(s1[i], s2[i]) for i in range(in1.ndim))
block_size, overlaps, \
in1_step, in2_step = zip(*optimal_sizes)
# Fall back to fftconvolve if there is only one block in every dimension.
if in1_step == s1 and in2_step == s2:
return fftconvolve(in1, in2, mode=mode, axes=axes)
# Figure out the number of steps and padding.
# This would get too complicated in a list comprehension.
nsteps1 = []
nsteps2 = []
pad_size1 = []
pad_size2 = []
for i in range(in1.ndim):
if i not in axes:
pad_size1 += [(0, 0)]
pad_size2 += [(0, 0)]
continue
if s1[i] > in1_step[i]:
curnstep1 = math.ceil((s1[i]+1)/in1_step[i])
if (block_size[i] - overlaps[i])*curnstep1 < shape_final[i]:
curnstep1 += 1
curpad1 = curnstep1*in1_step[i] - s1[i]
else:
curnstep1 = 1
curpad1 = 0
if s2[i] > in2_step[i]:
curnstep2 = math.ceil((s2[i]+1)/in2_step[i])
if (block_size[i] - overlaps[i])*curnstep2 < shape_final[i]:
curnstep2 += 1
curpad2 = curnstep2*in2_step[i] - s2[i]
else:
curnstep2 = 1
curpad2 = 0
nsteps1 += [curnstep1]
nsteps2 += [curnstep2]
pad_size1 += [(0, curpad1)]
pad_size2 += [(0, curpad2)]
# Pad the array to a size that can be reshaped to the desired shape
# if necessary.
if not all(curpad == (0, 0) for curpad in pad_size1):
in1 = np.pad(in1, pad_size1, mode='constant', constant_values=0)
if not all(curpad == (0, 0) for curpad in pad_size2):
in2 = np.pad(in2, pad_size2, mode='constant', constant_values=0)
# Reshape the overlap-add parts to input block sizes.
split_axes = [iax+i for i, iax in enumerate(axes)]
fft_axes = [iax+1 for iax in split_axes]
# We need to put each new dimension before the corresponding dimension
# being reshaped in order to get the data in the right layout at the end.
reshape_size1 = list(in1_step)
reshape_size2 = list(in2_step)
for i, iax in enumerate(split_axes):
reshape_size1.insert(iax, nsteps1[i])
reshape_size2.insert(iax, nsteps2[i])
in1 = in1.reshape(*reshape_size1)
in2 = in2.reshape(*reshape_size2)
# Do the convolution.
fft_shape = [block_size[i] for i in axes]
ret = _freq_domain_conv(in1, in2, fft_axes, fft_shape, calc_fast_len=False)
# Do the overlap-add.
for ax, ax_fft, ax_split in zip(axes, fft_axes, split_axes):
overlap = overlaps[ax]
if overlap is None:
continue
ret, overpart = np.split(ret, [-overlap], ax_fft)
overpart = np.split(overpart, [-1], ax_split)[0]
ret_overpart = np.split(ret, [overlap], ax_fft)[0]
ret_overpart = np.split(ret_overpart, [1], ax_split)[1]
ret_overpart += overpart
# Reshape back to the correct dimensionality.
shape_ret = [ret.shape[i] if i not in fft_axes else
ret.shape[i]*ret.shape[i-1]
for i in range(ret.ndim) if i not in split_axes]
ret = ret.reshape(*shape_ret)
# Slice to the correct size.
slice_final = tuple([slice(islice) for islice in shape_final])
ret = ret[slice_final]
return _apply_conv_mode(ret, s1, s2, mode, axes)
def _numeric_arrays(arrays, kinds='buifc'):
"""
See if a list of arrays are all numeric.
Parameters
----------
ndarrays : array or list of arrays
arrays to check if numeric.
numeric_kinds : string-like
The dtypes of the arrays to be checked. If the dtype.kind of
the ndarrays are not in this string the function returns False and
otherwise returns True.
"""
if type(arrays) == np.ndarray:
return arrays.dtype.kind in kinds
for array_ in arrays:
if array_.dtype.kind not in kinds:
return False
return True
def _conv_ops(x_shape, h_shape, mode):
"""
Find the number of operations required for direct/fft methods of
convolution. The direct operations were recorded by making a dummy class to
record the number of operations by overriding ``__mul__`` and ``__add__``.
The FFT operations rely on the (well-known) computational complexity of the
FFT (and the implementation of ``_freq_domain_conv``).
"""
if mode == "full":
out_shape = [n + k - 1 for n, k in zip(x_shape, h_shape)]
elif mode == "valid":
out_shape = [abs(n - k) + 1 for n, k in zip(x_shape, h_shape)]
elif mode == "same":
out_shape = x_shape
else:
raise ValueError("Acceptable mode flags are 'valid',"
" 'same', or 'full', not mode={}".format(mode))
s1, s2 = x_shape, h_shape
if len(x_shape) == 1:
s1, s2 = s1[0], s2[0]
if mode == "full":
direct_ops = s1 * s2
elif mode == "valid":
direct_ops = (s2 - s1 + 1) * s1 if s2 >= s1 else (s1 - s2 + 1) * s2
elif mode == "same":
direct_ops = (s1 * s2 if s1 < s2 else
s1 * s2 - (s2 // 2) * ((s2 + 1) // 2))
else:
if mode == "full":
direct_ops = min(_prod(s1), _prod(s2)) * _prod(out_shape)
elif mode == "valid":
direct_ops = min(_prod(s1), _prod(s2)) * _prod(out_shape)
elif mode == "same":
direct_ops = _prod(s1) * _prod(s2)
full_out_shape = [n + k - 1 for n, k in zip(x_shape, h_shape)]
N = _prod(full_out_shape)
fft_ops = 3 * N * np.log(N) # 3 separate FFTs of size full_out_shape
return fft_ops, direct_ops
def _fftconv_faster(x, h, mode):
"""
See if using fftconvolve or convolve is faster.
Parameters
----------
x : np.ndarray
Signal
h : np.ndarray
Kernel
mode : str
Mode passed to convolve
Returns
-------
fft_faster : bool
Notes
-----
See docstring of `choose_conv_method` for details on tuning hardware.
See pull request 11031 for more detail:
https://github.com/scipy/scipy/pull/11031.
"""
fft_ops, direct_ops = _conv_ops(x.shape, h.shape, mode)
offset = -1e-3 if x.ndim == 1 else -1e-4
constants = {
"valid": (1.89095737e-9, 2.1364985e-10, offset),
"full": (1.7649070e-9, 2.1414831e-10, offset),
"same": (3.2646654e-9, 2.8478277e-10, offset)
if h.size <= x.size
else (3.21635404e-9, 1.1773253e-8, -1e-5),
} if x.ndim == 1 else {
"valid": (1.85927e-9, 2.11242e-8, offset),
"full": (1.99817e-9, 1.66174e-8, offset),
"same": (2.04735e-9, 1.55367e-8, offset),
}
O_fft, O_direct, O_offset = constants[mode]
return O_fft * fft_ops < O_direct * direct_ops + O_offset
def _reverse_and_conj(x):
"""
Reverse array `x` in all dimensions and perform the complex conjugate
"""
reverse = (slice(None, None, -1),) * x.ndim
return x[reverse].conj()
def _np_conv_ok(volume, kernel, mode):
"""
See if numpy supports convolution of `volume` and `kernel` (i.e. both are
1D ndarrays and of the appropriate shape). NumPy's 'same' mode uses the
size of the larger input, while SciPy's uses the size of the first input.
Invalid mode strings will return False and be caught by the calling func.
"""
if volume.ndim == kernel.ndim == 1:
if mode in ('full', 'valid'):
return True
elif mode == 'same':
return volume.size >= kernel.size
else:
return False
def _timeit_fast(stmt="pass", setup="pass", repeat=3):
"""
Returns the time the statement/function took, in seconds.
Faster, less precise version of IPython's timeit. `stmt` can be a statement
written as a string or a callable.
Will do only 1 loop (like IPython's timeit) with no repetitions
(unlike IPython) for very slow functions. For fast functions, only does
enough loops to take 5 ms, which seems to produce similar results (on
Windows at least), and avoids doing an extraneous cycle that isn't
measured.
"""
timer = timeit.Timer(stmt, setup)
# determine number of calls per rep so total time for 1 rep >= 5 ms
x = 0
for p in range(0, 10):
number = 10**p
x = timer.timeit(number) # seconds
if x >= 5e-3 / 10: # 5 ms for final test, 1/10th that for this one
break
if x > 1: # second
# If it's macroscopic, don't bother with repetitions
best = x
else:
number *= 10
r = timer.repeat(repeat, number)
best = min(r)
sec = best / number
return sec
def choose_conv_method(in1, in2, mode='full', measure=False):
"""
Find the fastest convolution/correlation method.
This primarily exists to be called during the ``method='auto'`` option in
`convolve` and `correlate`. It can also be used to determine the value of
``method`` for many different convolutions of the same dtype/shape.
In addition, it supports timing the convolution to adapt the value of
``method`` to a particular set of inputs and/or hardware.
Parameters
----------
in1 : array_like
The first argument passed into the convolution function.
in2 : array_like
The second argument passed into the convolution function.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
measure : bool, optional
If True, run and time the convolution of `in1` and `in2` with both
methods and return the fastest. If False (default), predict the fastest
method using precomputed values.
Returns
-------
method : str
A string indicating which convolution method is fastest, either
'direct' or 'fft'
times : dict, optional
A dictionary containing the times (in seconds) needed for each method.
This value is only returned if ``measure=True``.
See Also
--------
convolve
correlate
Notes
-----
Generally, this method is 99% accurate for 2D signals and 85% accurate
for 1D signals for randomly chosen input sizes. For precision, use
``measure=True`` to find the fastest method by timing the convolution.
This can be used to avoid the minimal overhead of finding the fastest
``method`` later, or to adapt the value of ``method`` to a particular set
of inputs.
Experiments were run on an Amazon EC2 r5a.2xlarge machine to test this
function. These experiments measured the ratio between the time required
when using ``method='auto'`` and the time required for the fastest method
(i.e., ``ratio = time_auto / min(time_fft, time_direct)``). In these
experiments, we found:
* There is a 95% chance of this ratio being less than 1.5 for 1D signals
and a 99% chance of being less than 2.5 for 2D signals.
* The ratio was always less than 2.5/5 for 1D/2D signals respectively.
* This function is most inaccurate for 1D convolutions that take between 1
and 10 milliseconds with ``method='direct'``. A good proxy for this
(at least in our experiments) is ``1e6 <= in1.size * in2.size <= 1e7``.
The 2D results almost certainly generalize to 3D/4D/etc because the
implementation is the same (the 1D implementation is different).
All the numbers above are specific to the EC2 machine. However, we did find
that this function generalizes fairly decently across hardware. The speed
tests were of similar quality (and even slightly better) than the same
tests performed on the machine to tune this function's numbers (a mid-2014
15-inch MacBook Pro with 16GB RAM and a 2.5GHz Intel i7 processor).
There are cases when `fftconvolve` supports the inputs but this function
returns `direct` (e.g., to protect against floating point integer
precision).
.. versionadded:: 0.19
Examples
--------
Estimate the fastest method for a given input:
>>> from scipy import signal
>>> img = np.random.rand(32, 32)
>>> filter = np.random.rand(8, 8)
>>> method = signal.choose_conv_method(img, filter, mode='same')
>>> method
'fft'
This can then be applied to other arrays of the same dtype and shape:
>>> img2 = np.random.rand(32, 32)
>>> filter2 = np.random.rand(8, 8)
>>> corr2 = signal.correlate(img2, filter2, mode='same', method=method)
>>> conv2 = signal.convolve(img2, filter2, mode='same', method=method)
The output of this function (``method``) works with `correlate` and
`convolve`.
"""
volume = np.asarray(in1)
kernel = np.asarray(in2)
if measure:
times = {}
for method in ['fft', 'direct']:
times[method] = _timeit_fast(lambda: convolve(volume, kernel,
mode=mode, method=method))
chosen_method = 'fft' if times['fft'] < times['direct'] else 'direct'
return chosen_method, times
# for integer input,
# catch when more precision required than float provides (representing an
# integer as float can lose precision in fftconvolve if larger than 2**52)
if any([_numeric_arrays([x], kinds='ui') for x in [volume, kernel]]):
max_value = int(np.abs(volume).max()) * int(np.abs(kernel).max())
max_value *= int(min(volume.size, kernel.size))
if max_value > 2**np.finfo('float').nmant - 1:
return 'direct'
if _numeric_arrays([volume, kernel], kinds='b'):
return 'direct'
if _numeric_arrays([volume, kernel]):
if _fftconv_faster(volume, kernel, mode):
return 'fft'
return 'direct'
def convolve(in1, in2, mode='full', method='auto'):
"""
Convolve two N-dimensional arrays.
Convolve `in1` and `in2`, with the output size determined by the
`mode` argument.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
method : str {'auto', 'direct', 'fft'}, optional
A string indicating which method to use to calculate the convolution.
``direct``
The convolution is determined directly from sums, the definition of
convolution.
``fft``
The Fourier Transform is used to perform the convolution by calling
`fftconvolve`.
``auto``
Automatically chooses direct or Fourier method based on an estimate
of which is faster (default). See Notes for more detail.
.. versionadded:: 0.19.0
Returns
-------
convolve : array
An N-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
See Also
--------
numpy.polymul : performs polynomial multiplication (same operation, but
also accepts poly1d objects)
choose_conv_method : chooses the fastest appropriate convolution method
fftconvolve : Always uses the FFT method.
oaconvolve : Uses the overlap-add method to do convolution, which is
generally faster when the input arrays are large and
significantly different in size.
Notes
-----
By default, `convolve` and `correlate` use ``method='auto'``, which calls
`choose_conv_method` to choose the fastest method using pre-computed
values (`choose_conv_method` can also measure real-world timing with a
keyword argument). Because `fftconvolve` relies on floating point numbers,
there are certain constraints that may force `method=direct` (more detail
in `choose_conv_method` docstring).
Examples
--------
Smooth a square pulse using a Hann window:
>>> from scipy import signal
>>> sig = np.repeat([0., 1., 0.], 100)
>>> win = signal.windows.hann(50)
>>> filtered = signal.convolve(sig, win, mode='same') / sum(win)
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_win, ax_filt) = plt.subplots(3, 1, sharex=True)
>>> ax_orig.plot(sig)
>>> ax_orig.set_title('Original pulse')
>>> ax_orig.margins(0, 0.1)
>>> ax_win.plot(win)
>>> ax_win.set_title('Filter impulse response')
>>> ax_win.margins(0, 0.1)
>>> ax_filt.plot(filtered)
>>> ax_filt.set_title('Filtered signal')
>>> ax_filt.margins(0, 0.1)
>>> fig.tight_layout()
>>> fig.show()
"""
volume = np.asarray(in1)
kernel = np.asarray(in2)
if volume.ndim == kernel.ndim == 0:
return volume * kernel
elif volume.ndim != kernel.ndim:
raise ValueError("volume and kernel should have the same "
"dimensionality")
if _inputs_swap_needed(mode, volume.shape, kernel.shape):
# Convolution is commutative; order doesn't have any effect on output
volume, kernel = kernel, volume
if method == 'auto':
method = choose_conv_method(volume, kernel, mode=mode)
if method == 'fft':
out = fftconvolve(volume, kernel, mode=mode)
result_type = np.result_type(volume, kernel)
if result_type.kind in {'u', 'i'}:
out = np.around(out)
return out.astype(result_type)
elif method == 'direct':
# fastpath to faster numpy.convolve for 1d inputs when possible
if _np_conv_ok(volume, kernel, mode):
return np.convolve(volume, kernel, mode)
return correlate(volume, _reverse_and_conj(kernel), mode, 'direct')
else:
raise ValueError("Acceptable method flags are 'auto',"
" 'direct', or 'fft'.")
def order_filter(a, domain, rank):
"""
Perform an order filter on an N-D array.
Perform an order filter on the array in. The domain argument acts as a
mask centered over each pixel. The non-zero elements of domain are
used to select elements surrounding each input pixel which are placed
in a list. The list is sorted, and the output for that pixel is the
element corresponding to rank in the sorted list.
Parameters
----------
a : ndarray
The N-dimensional input array.
domain : array_like
A mask array with the same number of dimensions as `a`.
Each dimension should have an odd number of elements.
rank : int
A non-negative integer which selects the element from the
sorted list (0 corresponds to the smallest element, 1 is the
next smallest element, etc.).
Returns
-------
out : ndarray
The results of the order filter in an array with the same
shape as `a`.
Examples
--------
>>> from scipy import signal
>>> x = np.arange(25).reshape(5, 5)
>>> domain = np.identity(3)
>>> x
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])
>>> signal.order_filter(x, domain, 0)
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 1., 2., 0.],
[ 0., 5., 6., 7., 0.],
[ 0., 10., 11., 12., 0.],
[ 0., 0., 0., 0., 0.]])
>>> signal.order_filter(x, domain, 2)
array([[ 6., 7., 8., 9., 4.],
[ 11., 12., 13., 14., 9.],
[ 16., 17., 18., 19., 14.],
[ 21., 22., 23., 24., 19.],
[ 20., 21., 22., 23., 24.]])
"""
domain = np.asarray(domain)
size = domain.shape
for k in range(len(size)):
if (size[k] % 2) != 1:
raise ValueError("Each dimension of domain argument "
" should have an odd number of elements.")
return sigtools._order_filterND(a, domain, rank)
def medfilt(volume, kernel_size=None):
"""
Perform a median filter on an N-dimensional array.
Apply a median filter to the input array using a local window-size
given by `kernel_size`. The array will automatically be zero-padded.
Parameters
----------
volume : array_like
An N-dimensional input array.
kernel_size : array_like, optional
A scalar or an N-length list giving the size of the median filter
window in each dimension. Elements of `kernel_size` should be odd.
If `kernel_size` is a scalar, then this scalar is used as the size in
each dimension. Default size is 3 for each dimension.
Returns
-------
out : ndarray
An array the same size as input containing the median filtered
result.
Warns
-----
UserWarning
If array size is smaller than kernel size along any dimension
See Also
--------
scipy.ndimage.median_filter
Notes
-------
The more general function `scipy.ndimage.median_filter` has a more
efficient implementation of a median filter and therefore runs much faster.
"""
volume = np.atleast_1d(volume)
if kernel_size is None:
kernel_size = [3] * volume.ndim
kernel_size = np.asarray(kernel_size)
if kernel_size.shape == ():
kernel_size = np.repeat(kernel_size.item(), volume.ndim)
for k in range(volume.ndim):
if (kernel_size[k] % 2) != 1:
raise ValueError("Each element of kernel_size should be odd.")
if any(k > s for k, s in zip(kernel_size, volume.shape)):
warnings.warn('kernel_size exceeds volume extent: the volume will be '
'zero-padded.')
domain = np.ones(kernel_size)
numels = np.prod(kernel_size, axis=0)
order = numels // 2
return sigtools._order_filterND(volume, domain, order)
def wiener(im, mysize=None, noise=None):
"""
Perform a Wiener filter on an N-dimensional array.
Apply a Wiener filter to the N-dimensional array `im`.
Parameters
----------
im : ndarray
An N-dimensional array.
mysize : int or array_like, optional
A scalar or an N-length list giving the size of the Wiener filter
window in each dimension. Elements of mysize should be odd.
If mysize is a scalar, then this scalar is used as the size
in each dimension.
noise : float, optional
The noise-power to use. If None, then noise is estimated as the
average of the local variance of the input.
Returns
-------
out : ndarray
Wiener filtered result with the same shape as `im`.
Examples
--------
>>> from scipy.misc import face
>>> from scipy.signal.signaltools import wiener
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> img = np.random.random((40, 40)) #Create a random image
>>> filtered_img = wiener(img, (5, 5)) #Filter the image
>>> f, (plot1, plot2) = plt.subplots(1, 2)
>>> plot1.imshow(img)
>>> plot2.imshow(filtered_img)
>>> plt.show()
Notes
-----
This implementation is similar to wiener2 in Matlab/Octave.
For more details see [1]_
References
----------
.. [1] Lim, Jae S., Two-Dimensional Signal and Image Processing,
Englewood Cliffs, NJ, Prentice Hall, 1990, p. 548.
"""
im = np.asarray(im)
if mysize is None:
mysize = [3] * im.ndim
mysize = np.asarray(mysize)
if mysize.shape == ():
mysize = np.repeat(mysize.item(), im.ndim)
# Estimate the local mean
lMean = correlate(im, np.ones(mysize), 'same') / np.prod(mysize, axis=0)
# Estimate the local variance
lVar = (correlate(im ** 2, np.ones(mysize), 'same') /
np.prod(mysize, axis=0) - lMean ** 2)
# Estimate the noise power if needed.
if noise is None:
noise = np.mean(np.ravel(lVar), axis=0)
res = (im - lMean)
res *= (1 - noise / lVar)
res += lMean
out = np.where(lVar < noise, lMean, res)
return out
def convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
"""
Convolve two 2-dimensional arrays.
Convolve `in1` and `in2` with output size determined by `mode`, and
boundary conditions determined by `boundary` and `fillvalue`.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
boundary : str {'fill', 'wrap', 'symm'}, optional
A flag indicating how to handle boundaries:
``fill``
pad input arrays with fillvalue. (default)
``wrap``
circular boundary conditions.
``symm``
symmetrical boundary conditions.
fillvalue : scalar, optional
Value to fill pad input arrays with. Default is 0.
Returns
-------
out : ndarray
A 2-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
Examples
--------
Compute the gradient of an image by 2D convolution with a complex Scharr
operator. (Horizontal operator is real, vertical is imaginary.) Use
symmetric boundary condition to avoid creating edges at the image
boundaries.
>>> from scipy import signal
>>> from scipy import misc
>>> ascent = misc.ascent()
>>> scharr = np.array([[ -3-3j, 0-10j, +3 -3j],
... [-10+0j, 0+ 0j, +10 +0j],
... [ -3+3j, 0+10j, +3 +3j]]) # Gx + j*Gy
>>> grad = signal.convolve2d(ascent, scharr, boundary='symm', mode='same')
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_mag, ax_ang) = plt.subplots(3, 1, figsize=(6, 15))
>>> ax_orig.imshow(ascent, cmap='gray')
>>> ax_orig.set_title('Original')
>>> ax_orig.set_axis_off()
>>> ax_mag.imshow(np.absolute(grad), cmap='gray')
>>> ax_mag.set_title('Gradient magnitude')
>>> ax_mag.set_axis_off()
>>> ax_ang.imshow(np.angle(grad), cmap='hsv') # hsv is cyclic, like angles
>>> ax_ang.set_title('Gradient orientation')
>>> ax_ang.set_axis_off()
>>> fig.show()
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if not in1.ndim == in2.ndim == 2:
raise ValueError('convolve2d inputs must both be 2-D arrays')
if _inputs_swap_needed(mode, in1.shape, in2.shape):
in1, in2 = in2, in1
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
out = sigtools._convolve2d(in1, in2, 1, val, bval, fillvalue)
return out
def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
"""
Cross-correlate two 2-dimensional arrays.
Cross correlate `in1` and `in2` with output size determined by `mode`, and
boundary conditions determined by `boundary` and `fillvalue`.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear cross-correlation
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
boundary : str {'fill', 'wrap', 'symm'}, optional
A flag indicating how to handle boundaries:
``fill``
pad input arrays with fillvalue. (default)
``wrap``
circular boundary conditions.
``symm``
symmetrical boundary conditions.
fillvalue : scalar, optional
Value to fill pad input arrays with. Default is 0.
Returns
-------
correlate2d : ndarray
A 2-dimensional array containing a subset of the discrete linear
cross-correlation of `in1` with `in2`.
Notes
-----
When using "same" mode with even-length inputs, the outputs of `correlate`
and `correlate2d` differ: There is a 1-index offset between them.
Examples
--------
Use 2D cross-correlation to find the location of a template in a noisy
image:
>>> from scipy import signal
>>> from scipy import misc
>>> face = misc.face(gray=True) - misc.face(gray=True).mean()
>>> template = np.copy(face[300:365, 670:750]) # right eye
>>> template -= template.mean()
>>> face = face + np.random.randn(*face.shape) * 50 # add noise
>>> corr = signal.correlate2d(face, template, boundary='symm', mode='same')
>>> y, x = np.unravel_index(np.argmax(corr), corr.shape) # find the match
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_template, ax_corr) = plt.subplots(3, 1,
... figsize=(6, 15))
>>> ax_orig.imshow(face, cmap='gray')
>>> ax_orig.set_title('Original')
>>> ax_orig.set_axis_off()
>>> ax_template.imshow(template, cmap='gray')
>>> ax_template.set_title('Template')
>>> ax_template.set_axis_off()
>>> ax_corr.imshow(corr, cmap='gray')
>>> ax_corr.set_title('Cross-correlation')
>>> ax_corr.set_axis_off()
>>> ax_orig.plot(x, y, 'ro')
>>> fig.show()
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if not in1.ndim == in2.ndim == 2:
raise ValueError('correlate2d inputs must both be 2-D arrays')
swapped_inputs = _inputs_swap_needed(mode, in1.shape, in2.shape)
if swapped_inputs:
in1, in2 = in2, in1
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
out = sigtools._convolve2d(in1, in2.conj(), 0, val, bval, fillvalue)
if swapped_inputs:
out = out[::-1, ::-1]
return out
def medfilt2d(input, kernel_size=3):
"""
Median filter a 2-dimensional array.
Apply a median filter to the `input` array using a local window-size
given by `kernel_size` (must be odd). The array is zero-padded
automatically.
Parameters
----------
input : array_like
A 2-dimensional input array.
kernel_size : array_like, optional
A scalar or a list of length 2, giving the size of the
median filter window in each dimension. Elements of
`kernel_size` should be odd. If `kernel_size` is a scalar,
then this scalar is used as the size in each dimension.
Default is a kernel of size (3, 3).
Returns
-------
out : ndarray
An array the same size as input containing the median filtered
result.
See also
--------
scipy.ndimage.median_filter
Notes
-------
The more general function `scipy.ndimage.median_filter` has a more
efficient implementation of a median filter and therefore runs much faster.
"""
image = np.asarray(input)
if kernel_size is None:
kernel_size = [3] * 2
kernel_size = np.asarray(kernel_size)
if kernel_size.shape == ():
kernel_size = np.repeat(kernel_size.item(), 2)
for size in kernel_size:
if (size % 2) != 1:
raise ValueError("Each element of kernel_size should be odd.")
return sigtools._medfilt2d(image, kernel_size)
def lfilter(b, a, x, axis=-1, zi=None):
"""
Filter data along one-dimension with an IIR or FIR filter.
Filter a data sequence, `x`, using a digital filter. This works for many
fundamental data types (including Object type). The filter is a direct
form II transposed implementation of the standard difference equation
(see Notes).
The function `sosfilt` (and filter design using ``output='sos'``) should be
preferred over `lfilter` for most filtering tasks, as second-order sections
have fewer numerical problems.
Parameters
----------
b : array_like
The numerator coefficient vector in a 1-D sequence.
a : array_like
The denominator coefficient vector in a 1-D sequence. If ``a[0]``
is not 1, then both `a` and `b` are normalized by ``a[0]``.
x : array_like
An N-dimensional input array.
axis : int, optional
The axis of the input data array along which to apply the
linear filter. The filter is applied to each subarray along
this axis. Default is -1.
zi : array_like, optional
Initial conditions for the filter delays. It is a vector
(or array of vectors for an N-dimensional input) of length
``max(len(a), len(b)) - 1``. If `zi` is None or is not given then
initial rest is assumed. See `lfiltic` for more information.
Returns
-------
y : array
The output of the digital filter.
zf : array, optional
If `zi` is None, this is not returned, otherwise, `zf` holds the
final filter delay values.
See Also
--------
lfiltic : Construct initial conditions for `lfilter`.
lfilter_zi : Compute initial state (steady state of step response) for
`lfilter`.
filtfilt : A forward-backward filter, to obtain a filter with linear phase.
savgol_filter : A Savitzky-Golay filter.
sosfilt: Filter data using cascaded second-order sections.
sosfiltfilt: A forward-backward filter using second-order sections.
Notes
-----
The filter function is implemented as a direct II transposed structure.
This means that the filter implements::
a[0]*y[n] = b[0]*x[n] + b[1]*x[n-1] + ... + b[M]*x[n-M]
- a[1]*y[n-1] - ... - a[N]*y[n-N]
where `M` is the degree of the numerator, `N` is the degree of the
denominator, and `n` is the sample number. It is implemented using
the following difference equations (assuming M = N)::
a[0]*y[n] = b[0] * x[n] + d[0][n-1]
d[0][n] = b[1] * x[n] - a[1] * y[n] + d[1][n-1]
d[1][n] = b[2] * x[n] - a[2] * y[n] + d[2][n-1]
...
d[N-2][n] = b[N-1]*x[n] - a[N-1]*y[n] + d[N-1][n-1]
d[N-1][n] = b[N] * x[n] - a[N] * y[n]
where `d` are the state variables.
The rational transfer function describing this filter in the
z-transform domain is::
-1 -M
b[0] + b[1]z + ... + b[M] z
Y(z) = -------------------------------- X(z)
-1 -N
a[0] + a[1]z + ... + a[N] z
Examples
--------
Generate a noisy signal to be filtered:
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> t = np.linspace(-1, 1, 201)
>>> x = (np.sin(2*np.pi*0.75*t*(1-t) + 2.1) +
... 0.1*np.sin(2*np.pi*1.25*t + 1) +
... 0.18*np.cos(2*np.pi*3.85*t))
>>> xn = x + np.random.randn(len(t)) * 0.08
Create an order 3 lowpass butterworth filter:
>>> b, a = signal.butter(3, 0.05)
Apply the filter to xn. Use lfilter_zi to choose the initial condition of
the filter:
>>> zi = signal.lfilter_zi(b, a)
>>> z, _ = signal.lfilter(b, a, xn, zi=zi*xn[0])
Apply the filter again, to have a result filtered at an order the same as
filtfilt:
>>> z2, _ = signal.lfilter(b, a, z, zi=zi*z[0])
Use filtfilt to apply the filter:
>>> y = signal.filtfilt(b, a, xn)
Plot the original signal and the various filtered versions:
>>> plt.figure
>>> plt.plot(t, xn, 'b', alpha=0.75)
>>> plt.plot(t, z, 'r--', t, z2, 'r', t, y, 'k')
>>> plt.legend(('noisy signal', 'lfilter, once', 'lfilter, twice',
... 'filtfilt'), loc='best')
>>> plt.grid(True)
>>> plt.show()
"""
a = np.atleast_1d(a)
if len(a) == 1:
# This path only supports types fdgFDGO to mirror _linear_filter below.
# Any of b, a, x, or zi can set the dtype, but there is no default
# casting of other types; instead a NotImplementedError is raised.
b = np.asarray(b)
a = np.asarray(a)
if b.ndim != 1 and a.ndim != 1:
raise ValueError('object of too small depth for desired array')
x = _validate_x(x)
inputs = [b, a, x]
if zi is not None:
# _linear_filter does not broadcast zi, but does do expansion of
# singleton dims.
zi = np.asarray(zi)
if zi.ndim != x.ndim:
raise ValueError('object of too small depth for desired array')
expected_shape = list(x.shape)
expected_shape[axis] = b.shape[0] - 1
expected_shape = tuple(expected_shape)
# check the trivial case where zi is the right shape first
if zi.shape != expected_shape:
strides = zi.ndim * [None]
if axis < 0:
axis += zi.ndim
for k in range(zi.ndim):
if k == axis and zi.shape[k] == expected_shape[k]:
strides[k] = zi.strides[k]
elif k != axis and zi.shape[k] == expected_shape[k]:
strides[k] = zi.strides[k]
elif k != axis and zi.shape[k] == 1:
strides[k] = 0
else:
raise ValueError('Unexpected shape for zi: expected '
'%s, found %s.' %
(expected_shape, zi.shape))
zi = np.lib.stride_tricks.as_strided(zi, expected_shape,
strides)
inputs.append(zi)
dtype = np.result_type(*inputs)
if dtype.char not in 'fdgFDGO':
raise NotImplementedError("input type '%s' not supported" % dtype)
b = np.array(b, dtype=dtype)
a = np.array(a, dtype=dtype, copy=False)
b /= a[0]
x = np.array(x, dtype=dtype, copy=False)
out_full = np.apply_along_axis(lambda y: np.convolve(b, y), axis, x)
ind = out_full.ndim * [slice(None)]
if zi is not None:
ind[axis] = slice(zi.shape[axis])
out_full[tuple(ind)] += zi
ind[axis] = slice(out_full.shape[axis] - len(b) + 1)
out = out_full[tuple(ind)]
if zi is None:
return out
else:
ind[axis] = slice(out_full.shape[axis] - len(b) + 1, None)
zf = out_full[tuple(ind)]
return out, zf
else:
if zi is None:
return sigtools._linear_filter(b, a, x, axis)
else:
return sigtools._linear_filter(b, a, x, axis, zi)
def lfiltic(b, a, y, x=None):
"""
Construct initial conditions for lfilter given input and output vectors.
Given a linear filter (b, a) and initial conditions on the output `y`
and the input `x`, return the initial conditions on the state vector zi
which is used by `lfilter` to generate the output given the input.
Parameters
----------
b : array_like
Linear filter term.
a : array_like
Linear filter term.
y : array_like
Initial conditions.
If ``N = len(a) - 1``, then ``y = {y[-1], y[-2], ..., y[-N]}``.
If `y` is too short, it is padded with zeros.
x : array_like, optional
Initial conditions.
If ``M = len(b) - 1``, then ``x = {x[-1], x[-2], ..., x[-M]}``.
If `x` is not given, its initial conditions are assumed zero.
If `x` is too short, it is padded with zeros.
Returns
-------
zi : ndarray
The state vector ``zi = {z_0[-1], z_1[-1], ..., z_K-1[-1]}``,
where ``K = max(M, N)``.
See Also
--------
lfilter, lfilter_zi
"""
N = np.size(a) - 1
M = np.size(b) - 1
K = max(M, N)
y = np.asarray(y)
if x is None:
result_type = np.result_type(np.asarray(b), np.asarray(a), y)
if result_type.kind in 'bui':
result_type = np.float64
x = np.zeros(M, dtype=result_type)
else:
x = np.asarray(x)
result_type = np.result_type(np.asarray(b), np.asarray(a), y, x)
if result_type.kind in 'bui':
result_type = np.float64
x = x.astype(result_type)
L = np.size(x)
if L < M:
x = np.r_[x, np.zeros(M - L)]
y = y.astype(result_type)
zi = np.zeros(K, result_type)
L = np.size(y)
if L < N:
y = np.r_[y, np.zeros(N - L)]
for m in range(M):
zi[m] = np.sum(b[m + 1:] * x[:M - m], axis=0)
for m in range(N):
zi[m] -= np.sum(a[m + 1:] * y[:N - m], axis=0)
return zi
def deconvolve(signal, divisor):
"""Deconvolves ``divisor`` out of ``signal`` using inverse filtering.
Returns the quotient and remainder such that
``signal = convolve(divisor, quotient) + remainder``
Parameters
----------
signal : array_like
Signal data, typically a recorded signal
divisor : array_like
Divisor data, typically an impulse response or filter that was
applied to the original signal
Returns
-------
quotient : ndarray
Quotient, typically the recovered original signal
remainder : ndarray
Remainder
Examples
--------
Deconvolve a signal that's been filtered:
>>> from scipy import signal
>>> original = [0, 1, 0, 0, 1, 1, 0, 0]
>>> impulse_response = [2, 1]
>>> recorded = signal.convolve(impulse_response, original)
>>> recorded
array([0, 2, 1, 0, 2, 3, 1, 0, 0])
>>> recovered, remainder = signal.deconvolve(recorded, impulse_response)
>>> recovered
array([ 0., 1., 0., 0., 1., 1., 0., 0.])
See Also
--------
numpy.polydiv : performs polynomial division (same operation, but
also accepts poly1d objects)
"""
num = np.atleast_1d(signal)
den = np.atleast_1d(divisor)
N = len(num)
D = len(den)
if D > N:
quot = []
rem = num
else:
input = np.zeros(N - D + 1, float)
input[0] = 1
quot = lfilter(num, den, input)
rem = num - convolve(den, quot, mode='full')
return quot, rem
def hilbert(x, N=None, axis=-1):
"""
Compute the analytic signal, using the Hilbert transform.
The transformation is done along the last axis by default.
Parameters
----------
x : array_like
Signal data. Must be real.
N : int, optional
Number of Fourier components. Default: ``x.shape[axis]``
axis : int, optional
Axis along which to do the transformation. Default: -1.
Returns
-------
xa : ndarray
Analytic signal of `x`, of each 1-D array along `axis`
Notes
-----
The analytic signal ``x_a(t)`` of signal ``x(t)`` is:
.. math:: x_a = F^{-1}(F(x) 2U) = x + i y
where `F` is the Fourier transform, `U` the unit step function,
and `y` the Hilbert transform of `x`. [1]_
In other words, the negative half of the frequency spectrum is zeroed
out, turning the real-valued signal into a complex signal. The Hilbert
transformed signal can be obtained from ``np.imag(hilbert(x))``, and the
original signal from ``np.real(hilbert(x))``.
Examples
---------
In this example we use the Hilbert transform to determine the amplitude
envelope and instantaneous frequency of an amplitude-modulated signal.
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.signal import hilbert, chirp
>>> duration = 1.0
>>> fs = 400.0
>>> samples = int(fs*duration)
>>> t = np.arange(samples) / fs
We create a chirp of which the frequency increases from 20 Hz to 100 Hz and
apply an amplitude modulation.
>>> signal = chirp(t, 20.0, t[-1], 100.0)
>>> signal *= (1.0 + 0.5 * np.sin(2.0*np.pi*3.0*t) )
The amplitude envelope is given by magnitude of the analytic signal. The
instantaneous frequency can be obtained by differentiating the
instantaneous phase in respect to time. The instantaneous phase corresponds
to the phase angle of the analytic signal.
>>> analytic_signal = hilbert(signal)
>>> amplitude_envelope = np.abs(analytic_signal)
>>> instantaneous_phase = np.unwrap(np.angle(analytic_signal))
>>> instantaneous_frequency = (np.diff(instantaneous_phase) /
... (2.0*np.pi) * fs)
>>> fig = plt.figure()
>>> ax0 = fig.add_subplot(211)
>>> ax0.plot(t, signal, label='signal')
>>> ax0.plot(t, amplitude_envelope, label='envelope')
>>> ax0.set_xlabel("time in seconds")
>>> ax0.legend()
>>> ax1 = fig.add_subplot(212)
>>> ax1.plot(t[1:], instantaneous_frequency)
>>> ax1.set_xlabel("time in seconds")
>>> ax1.set_ylim(0.0, 120.0)
References
----------
.. [1] Wikipedia, "Analytic signal".
https://en.wikipedia.org/wiki/Analytic_signal
.. [2] Leon Cohen, "Time-Frequency Analysis", 1995. Chapter 2.
.. [3] Alan V. Oppenheim, Ronald W. Schafer. Discrete-Time Signal
Processing, Third Edition, 2009. Chapter 12.
ISBN 13: 978-1292-02572-8
"""
x = np.asarray(x)
if np.iscomplexobj(x):
raise ValueError("x must be real.")
if N is None:
N = x.shape[axis]
if N <= 0:
raise ValueError("N must be positive.")
Xf = sp_fft.fft(x, N, axis=axis)
h = np.zeros(N)
if N % 2 == 0:
h[0] = h[N // 2] = 1
h[1:N // 2] = 2
else:
h[0] = 1
h[1:(N + 1) // 2] = 2
if x.ndim > 1:
ind = [np.newaxis] * x.ndim
ind[axis] = slice(None)
h = h[tuple(ind)]
x = sp_fft.ifft(Xf * h, axis=axis)
return x
def hilbert2(x, N=None):
"""
Compute the '2-D' analytic signal of `x`
Parameters
----------
x : array_like
2-D signal data.
N : int or tuple of two ints, optional
Number of Fourier components. Default is ``x.shape``
Returns
-------
xa : ndarray
Analytic signal of `x` taken along axes (0,1).
References
----------
.. [1] Wikipedia, "Analytic signal",
https://en.wikipedia.org/wiki/Analytic_signal
"""
x = np.atleast_2d(x)
if x.ndim > 2:
raise ValueError("x must be 2-D.")
if np.iscomplexobj(x):
raise ValueError("x must be real.")
if N is None:
N = x.shape
elif isinstance(N, int):
if N <= 0:
raise ValueError("N must be positive.")
N = (N, N)
elif len(N) != 2 or np.any(np.asarray(N) <= 0):
raise ValueError("When given as a tuple, N must hold exactly "
"two positive integers")
Xf = sp_fft.fft2(x, N, axes=(0, 1))
h1 = np.zeros(N[0], 'd')
h2 = np.zeros(N[1], 'd')
for p in range(2):
h = eval("h%d" % (p + 1))
N1 = N[p]
if N1 % 2 == 0:
h[0] = h[N1 // 2] = 1
h[1:N1 // 2] = 2
else:
h[0] = 1
h[1:(N1 + 1) // 2] = 2
exec("h%d = h" % (p + 1), globals(), locals())
h = h1[:, np.newaxis] * h2[np.newaxis, :]
k = x.ndim
while k > 2:
h = h[:, np.newaxis]
k -= 1
x = sp_fft.ifft2(Xf * h, axes=(0, 1))
return x
def cmplx_sort(p):
"""Sort roots based on magnitude.
Parameters
----------
p : array_like
The roots to sort, as a 1-D array.
Returns
-------
p_sorted : ndarray
Sorted roots.
indx : ndarray
Array of indices needed to sort the input `p`.
Examples
--------
>>> from scipy import signal
>>> vals = [1, 4, 1+1.j, 3]
>>> p_sorted, indx = signal.cmplx_sort(vals)
>>> p_sorted
array([1.+0.j, 1.+1.j, 3.+0.j, 4.+0.j])
>>> indx
array([0, 2, 3, 1])
"""
p = np.asarray(p)
indx = np.argsort(abs(p))
return np.take(p, indx, 0), indx
def unique_roots(p, tol=1e-3, rtype='min'):
"""Determine unique roots and their multiplicities from a list of roots.
Parameters
----------
p : array_like
The list of roots.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. Refer to Notes about
the details on roots grouping.
rtype : {'max', 'maximum', 'min', 'minimum', 'avg', 'mean'}, optional
How to determine the returned root if multiple roots are within
`tol` of each other.
- 'max', 'maximum': pick the maximum of those roots
- 'min', 'minimum': pick the minimum of those roots
- 'avg', 'mean': take the average of those roots
When finding minimum or maximum among complex roots they are compared
first by the real part and then by the imaginary part.
Returns
-------
unique : ndarray
The list of unique roots.
multiplicity : ndarray
The multiplicity of each root.
Notes
-----
If we have 3 roots ``a``, ``b`` and ``c``, such that ``a`` is close to
``b`` and ``b`` is close to ``c`` (distance is less than `tol`), then it
doesn't necessarily mean that ``a`` is close to ``c``. It means that roots
grouping is not unique. In this function we use "greedy" grouping going
through the roots in the order they are given in the input `p`.
This utility function is not specific to roots but can be used for any
sequence of values for which uniqueness and multiplicity has to be
determined. For a more general routine, see `numpy.unique`.
Examples
--------
>>> from scipy import signal
>>> vals = [0, 1.3, 1.31, 2.8, 1.25, 2.2, 10.3]
>>> uniq, mult = signal.unique_roots(vals, tol=2e-2, rtype='avg')
Check which roots have multiplicity larger than 1:
>>> uniq[mult > 1]
array([ 1.305])
"""
if rtype in ['max', 'maximum']:
reduce = np.max
elif rtype in ['min', 'minimum']:
reduce = np.min
elif rtype in ['avg', 'mean']:
reduce = np.mean
else:
raise ValueError("`rtype` must be one of "
"{'max', 'maximum', 'min', 'minimum', 'avg', 'mean'}")
p = np.asarray(p)
points = np.empty((len(p), 2))
points[:, 0] = np.real(p)
points[:, 1] = np.imag(p)
tree = cKDTree(points)
p_unique = []
p_multiplicity = []
used = np.zeros(len(p), dtype=bool)
for i in range(len(p)):
if used[i]:
continue
group = tree.query_ball_point(points[i], tol)
group = [x for x in group if not used[x]]
p_unique.append(reduce(p[group]))
p_multiplicity.append(len(group))
used[group] = True
return np.asarray(p_unique), np.asarray(p_multiplicity)
def invres(r, p, k, tol=1e-3, rtype='avg'):
"""Compute b(s) and a(s) from partial fraction expansion.
If `M` is the degree of numerator `b` and `N` the degree of denominator
`a`::
b(s) b[0] s**(M) + b[1] s**(M-1) + ... + b[M]
H(s) = ------ = ------------------------------------------
a(s) a[0] s**(N) + a[1] s**(N-1) + ... + a[N]
then the partial-fraction expansion H(s) is defined as::
r[0] r[1] r[-1]
= -------- + -------- + ... + --------- + k(s)
(s-p[0]) (s-p[1]) (s-p[-1])
If there are any repeated roots (closer together than `tol`), then H(s)
has terms like::
r[i] r[i+1] r[i+n-1]
-------- + ----------- + ... + -----------
(s-p[i]) (s-p[i])**2 (s-p[i])**n
This function is used for polynomials in positive powers of s or z,
such as analog filters or digital filters in controls engineering. For
negative powers of z (typical for digital filters in DSP), use `invresz`.
Parameters
----------
r : array_like
Residues corresponding to the poles. For repeated poles, the residues
must be ordered to correspond to ascending by power fractions.
p : array_like
Poles. Equal poles must be adjacent.
k : array_like
Coefficients of the direct polynomial term.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. See `unique_roots`
for further details.
rtype : {'avg', 'min', 'max'}, optional
Method for computing a root to represent a group of identical roots.
Default is 'avg'. See `unique_roots` for further details.
Returns
-------
b : ndarray
Numerator polynomial coefficients.
a : ndarray
Denominator polynomial coefficients.
See Also
--------
residue, invresz, unique_roots
"""
r = np.atleast_1d(r)
p = np.atleast_1d(p)
k = np.trim_zeros(np.atleast_1d(k), 'f')
unique_poles, multiplicity = _group_poles(p, tol, rtype)
factors, denominator = _compute_factors(unique_poles, multiplicity,
include_powers=True)
if len(k) == 0:
numerator = 0
else:
numerator = np.polymul(k, denominator)
for residue, factor in zip(r, factors):
numerator = np.polyadd(numerator, residue * factor)
return numerator, denominator
def _compute_factors(roots, multiplicity, include_powers=False):
"""Compute the total polynomial divided by factors for each root."""
current = np.array([1])
suffixes = [current]
for pole, mult in zip(roots[-1:0:-1], multiplicity[-1:0:-1]):
monomial = np.array([1, -pole])
for _ in range(mult):
current = np.polymul(current, monomial)
suffixes.append(current)
suffixes = suffixes[::-1]
factors = []
current = np.array([1])
for pole, mult, suffix in zip(roots, multiplicity, suffixes):
monomial = np.array([1, -pole])
block = []
for i in range(mult):
if i == 0 or include_powers:
block.append(np.polymul(current, suffix))
current = np.polymul(current, monomial)
factors.extend(reversed(block))
return factors, current
def _compute_residues(poles, multiplicity, numerator):
denominator_factors, _ = _compute_factors(poles, multiplicity)
numerator = numerator.astype(poles.dtype)
residues = []
for pole, mult, factor in zip(poles, multiplicity,
denominator_factors):
if mult == 1:
residues.append(np.polyval(numerator, pole) /
np.polyval(factor, pole))
else:
numer = numerator.copy()
monomial = np.array([1, -pole])
factor, d = np.polydiv(factor, monomial)
block = []
for _ in range(mult):
numer, n = np.polydiv(numer, monomial)
r = n[0] / d[0]
numer = np.polysub(numer, r * factor)
block.append(r)
residues.extend(reversed(block))
return np.asarray(residues)
def residue(b, a, tol=1e-3, rtype='avg'):
"""Compute partial-fraction expansion of b(s) / a(s).
If `M` is the degree of numerator `b` and `N` the degree of denominator
`a`::
b(s) b[0] s**(M) + b[1] s**(M-1) + ... + b[M]
H(s) = ------ = ------------------------------------------
a(s) a[0] s**(N) + a[1] s**(N-1) + ... + a[N]
then the partial-fraction expansion H(s) is defined as::
r[0] r[1] r[-1]
= -------- + -------- + ... + --------- + k(s)
(s-p[0]) (s-p[1]) (s-p[-1])
If there are any repeated roots (closer together than `tol`), then H(s)
has terms like::
r[i] r[i+1] r[i+n-1]
-------- + ----------- + ... + -----------
(s-p[i]) (s-p[i])**2 (s-p[i])**n
This function is used for polynomials in positive powers of s or z,
such as analog filters or digital filters in controls engineering. For
negative powers of z (typical for digital filters in DSP), use `residuez`.
See Notes for details about the algorithm.
Parameters
----------
b : array_like
Numerator polynomial coefficients.
a : array_like
Denominator polynomial coefficients.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. See `unique_roots`
for further details.
rtype : {'avg', 'min', 'max'}, optional
Method for computing a root to represent a group of identical roots.
Default is 'avg'. See `unique_roots` for further details.
Returns
-------
r : ndarray
Residues corresponding to the poles. For repeated poles, the residues
are ordered to correspond to ascending by power fractions.
p : ndarray
Poles ordered by magnitude in ascending order.
k : ndarray
Coefficients of the direct polynomial term.
See Also
--------
invres, residuez, numpy.poly, unique_roots
Notes
-----
The "deflation through subtraction" algorithm is used for
computations --- method 6 in [1]_.
The form of partial fraction expansion depends on poles multiplicity in
the exact mathematical sense. However there is no way to exactly
determine multiplicity of roots of a polynomial in numerical computing.
Thus you should think of the result of `residue` with given `tol` as
partial fraction expansion computed for the denominator composed of the
computed poles with empirically determined multiplicity. The choice of
`tol` can drastically change the result if there are close poles.
References
----------
.. [1] J. F. Mahoney, B. D. Sivazlian, "Partial fractions expansion: a
review of computational methodology and efficiency", Journal of
Computational and Applied Mathematics, Vol. 9, 1983.
"""
b = np.asarray(b)
a = np.asarray(a)
if (np.issubdtype(b.dtype, np.complexfloating)
or np.issubdtype(a.dtype, np.complexfloating)):
b = b.astype(complex)
a = a.astype(complex)
else:
b = b.astype(float)
a = a.astype(float)
b = np.trim_zeros(np.atleast_1d(b), 'f')
a = np.trim_zeros(np.atleast_1d(a), 'f')
if a.size == 0:
raise ValueError("Denominator `a` is zero.")
poles = np.roots(a)
if b.size == 0:
return np.zeros(poles.shape), cmplx_sort(poles)[0], np.array([])
if len(b) < len(a):
k = np.empty(0)
else:
k, b = np.polydiv(b, a)
unique_poles, multiplicity = unique_roots(poles, tol=tol, rtype=rtype)
unique_poles, order = cmplx_sort(unique_poles)
multiplicity = multiplicity[order]
residues = _compute_residues(unique_poles, multiplicity, b)
index = 0
for pole, mult in zip(unique_poles, multiplicity):
poles[index:index + mult] = pole
index += mult
return residues / a[0], poles, k
def residuez(b, a, tol=1e-3, rtype='avg'):
"""Compute partial-fraction expansion of b(z) / a(z).
If `M` is the degree of numerator `b` and `N` the degree of denominator
`a`::
b(z) b[0] + b[1] z**(-1) + ... + b[M] z**(-M)
H(z) = ------ = ------------------------------------------
a(z) a[0] + a[1] z**(-1) + ... + a[N] z**(-N)
then the partial-fraction expansion H(z) is defined as::
r[0] r[-1]
= --------------- + ... + ---------------- + k[0] + k[1]z**(-1) ...
(1-p[0]z**(-1)) (1-p[-1]z**(-1))
If there are any repeated roots (closer than `tol`), then the partial
fraction expansion has terms like::
r[i] r[i+1] r[i+n-1]
-------------- + ------------------ + ... + ------------------
(1-p[i]z**(-1)) (1-p[i]z**(-1))**2 (1-p[i]z**(-1))**n
This function is used for polynomials in negative powers of z,
such as digital filters in DSP. For positive powers, use `residue`.
See Notes of `residue` for details about the algorithm.
Parameters
----------
b : array_like
Numerator polynomial coefficients.
a : array_like
Denominator polynomial coefficients.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. See `unique_roots`
for further details.
rtype : {'avg', 'min', 'max'}, optional
Method for computing a root to represent a group of identical roots.
Default is 'avg'. See `unique_roots` for further details.
Returns
-------
r : ndarray
Residues corresponding to the poles. For repeated poles, the residues
are ordered to correspond to ascending by power fractions.
p : ndarray
Poles ordered by magnitude in ascending order.
k : ndarray
Coefficients of the direct polynomial term.
See Also
--------
invresz, residue, unique_roots
"""
b = np.asarray(b)
a = np.asarray(a)
if (np.issubdtype(b.dtype, np.complexfloating)
or np.issubdtype(a.dtype, np.complexfloating)):
b = b.astype(complex)
a = a.astype(complex)
else:
b = b.astype(float)
a = a.astype(float)
b = np.trim_zeros(np.atleast_1d(b), 'b')
a = np.trim_zeros(np.atleast_1d(a), 'b')
if a.size == 0:
raise ValueError("Denominator `a` is zero.")
elif a[0] == 0:
raise ValueError("First coefficient of determinant `a` must be "
"non-zero.")
poles = np.roots(a)
if b.size == 0:
return np.zeros(poles.shape), cmplx_sort(poles)[0], np.array([])
b_rev = b[::-1]
a_rev = a[::-1]
if len(b_rev) < len(a_rev):
k_rev = np.empty(0)
else:
k_rev, b_rev = np.polydiv(b_rev, a_rev)
unique_poles, multiplicity = unique_roots(poles, tol=tol, rtype=rtype)
unique_poles, order = cmplx_sort(unique_poles)
multiplicity = multiplicity[order]
residues = _compute_residues(1 / unique_poles, multiplicity, b_rev)
index = 0
powers = np.empty(len(residues), dtype=int)
for pole, mult in zip(unique_poles, multiplicity):
poles[index:index + mult] = pole
powers[index:index + mult] = 1 + np.arange(mult)
index += mult
residues *= (-poles) ** powers / a_rev[0]
return residues, poles, k_rev[::-1]
def _group_poles(poles, tol, rtype):
if rtype in ['max', 'maximum']:
reduce = np.max
elif rtype in ['min', 'minimum']:
reduce = np.min
elif rtype in ['avg', 'mean']:
reduce = np.mean
else:
raise ValueError("`rtype` must be one of "
"{'max', 'maximum', 'min', 'minimum', 'avg', 'mean'}")
unique = []
multiplicity = []
pole = poles[0]
block = [pole]
for i in range(1, len(poles)):
if abs(poles[i] - pole) <= tol:
block.append(pole)
else:
unique.append(reduce(block))
multiplicity.append(len(block))
pole = poles[i]
block = [pole]
unique.append(reduce(block))
multiplicity.append(len(block))
return np.asarray(unique), np.asarray(multiplicity)
def invresz(r, p, k, tol=1e-3, rtype='avg'):
"""Compute b(z) and a(z) from partial fraction expansion.
If `M` is the degree of numerator `b` and `N` the degree of denominator
`a`::
b(z) b[0] + b[1] z**(-1) + ... + b[M] z**(-M)
H(z) = ------ = ------------------------------------------
a(z) a[0] + a[1] z**(-1) + ... + a[N] z**(-N)
then the partial-fraction expansion H(z) is defined as::
r[0] r[-1]
= --------------- + ... + ---------------- + k[0] + k[1]z**(-1) ...
(1-p[0]z**(-1)) (1-p[-1]z**(-1))
If there are any repeated roots (closer than `tol`), then the partial
fraction expansion has terms like::
r[i] r[i+1] r[i+n-1]
-------------- + ------------------ + ... + ------------------
(1-p[i]z**(-1)) (1-p[i]z**(-1))**2 (1-p[i]z**(-1))**n
This function is used for polynomials in negative powers of z,
such as digital filters in DSP. For positive powers, use `invres`.
Parameters
----------
r : array_like
Residues corresponding to the poles. For repeated poles, the residues
must be ordered to correspond to ascending by power fractions.
p : array_like
Poles. Equal poles must be adjacent.
k : array_like
Coefficients of the direct polynomial term.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. See `unique_roots`
for further details.
rtype : {'avg', 'min', 'max'}, optional
Method for computing a root to represent a group of identical roots.
Default is 'avg'. See `unique_roots` for further details.
Returns
-------
b : ndarray
Numerator polynomial coefficients.
a : ndarray
Denominator polynomial coefficients.
See Also
--------
residuez, unique_roots, invres
"""
r = np.atleast_1d(r)
p = np.atleast_1d(p)
k = np.trim_zeros(np.atleast_1d(k), 'b')
unique_poles, multiplicity = _group_poles(p, tol, rtype)
factors, denominator = _compute_factors(unique_poles, multiplicity,
include_powers=True)
if len(k) == 0:
numerator = 0
else:
numerator = np.polymul(k[::-1], denominator[::-1])
for residue, factor in zip(r, factors):
numerator = np.polyadd(numerator, residue * factor[::-1])
return numerator[::-1], denominator
def resample(x, num, t=None, axis=0, window=None, domain='time'):
"""
Resample `x` to `num` samples using Fourier method along the given axis.
The resampled signal starts at the same value as `x` but is sampled
with a spacing of ``len(x) / num * (spacing of x)``. Because a
Fourier method is used, the signal is assumed to be periodic.
Parameters
----------
x : array_like
The data to be resampled.
num : int
The number of samples in the resampled signal.
t : array_like, optional
If `t` is given, it is assumed to be the equally spaced sample
positions associated with the signal data in `x`.
axis : int, optional
The axis of `x` that is resampled. Default is 0.
window : array_like, callable, string, float, or tuple, optional
Specifies the window applied to the signal in the Fourier
domain. See below for details.
domain : string, optional
A string indicating the domain of the input `x`:
``time`` Consider the input `x` as time-domain (Default),
``freq`` Consider the input `x` as frequency-domain.
Returns
-------
resampled_x or (resampled_x, resampled_t)
Either the resampled array, or, if `t` was given, a tuple
containing the resampled array and the corresponding resampled
positions.
See Also
--------
decimate : Downsample the signal after applying an FIR or IIR filter.
resample_poly : Resample using polyphase filtering and an FIR filter.
Notes
-----
The argument `window` controls a Fourier-domain window that tapers
the Fourier spectrum before zero-padding to alleviate ringing in
the resampled values for sampled signals you didn't intend to be
interpreted as band-limited.
If `window` is a function, then it is called with a vector of inputs
indicating the frequency bins (i.e. fftfreq(x.shape[axis]) ).
If `window` is an array of the same length as `x.shape[axis]` it is
assumed to be the window to be applied directly in the Fourier
domain (with dc and low-frequency first).
For any other type of `window`, the function `scipy.signal.get_window`
is called to generate the window.
The first sample of the returned vector is the same as the first
sample of the input vector. The spacing between samples is changed
from ``dx`` to ``dx * len(x) / num``.
If `t` is not None, then it is used solely to calculate the resampled
positions `resampled_t`
As noted, `resample` uses FFT transformations, which can be very
slow if the number of input or output samples is large and prime;
see `scipy.fft.fft`.
Examples
--------
Note that the end of the resampled data rises to meet the first
sample of the next cycle:
>>> from scipy import signal
>>> x = np.linspace(0, 10, 20, endpoint=False)
>>> y = np.cos(-x**2/6.0)
>>> f = signal.resample(y, 100)
>>> xnew = np.linspace(0, 10, 100, endpoint=False)
>>> import matplotlib.pyplot as plt
>>> plt.plot(x, y, 'go-', xnew, f, '.-', 10, y[0], 'ro')
>>> plt.legend(['data', 'resampled'], loc='best')
>>> plt.show()
"""
if domain not in ('time', 'freq'):
raise ValueError("Acceptable domain flags are 'time' or"
" 'freq', not domain={}".format(domain))
x = np.asarray(x)
Nx = x.shape[axis]
# Check if we can use faster real FFT
real_input = np.isrealobj(x)
if domain == 'time':
# Forward transform
if real_input:
X = sp_fft.rfft(x, axis=axis)
else: # Full complex FFT
X = sp_fft.fft(x, axis=axis)
else: # domain == 'freq'
X = x
# Apply window to spectrum
if window is not None:
if callable(window):
W = window(sp_fft.fftfreq(Nx))
elif isinstance(window, np.ndarray):
if window.shape != (Nx,):
raise ValueError('window must have the same length as data')
W = window
else:
W = sp_fft.ifftshift(get_window(window, Nx))
newshape_W = [1] * x.ndim
newshape_W[axis] = X.shape[axis]
if real_input:
# Fold the window back on itself to mimic complex behavior
W_real = W.copy()
W_real[1:] += W_real[-1:0:-1]
W_real[1:] *= 0.5
X *= W_real[:newshape_W[axis]].reshape(newshape_W)
else:
X *= W.reshape(newshape_W)
# Copy each half of the original spectrum to the output spectrum, either
# truncating high frequences (downsampling) or zero-padding them
# (upsampling)
# Placeholder array for output spectrum
newshape = list(x.shape)
if real_input:
newshape[axis] = num // 2 + 1
else:
newshape[axis] = num
Y = np.zeros(newshape, X.dtype)
# Copy positive frequency components (and Nyquist, if present)
N = min(num, Nx)
nyq = N // 2 + 1 # Slice index that includes Nyquist if present
sl = [slice(None)] * x.ndim
sl[axis] = slice(0, nyq)
Y[tuple(sl)] = X[tuple(sl)]
if not real_input:
# Copy negative frequency components
if N > 2: # (slice expression doesn't collapse to empty array)
sl[axis] = slice(nyq - N, None)
Y[tuple(sl)] = X[tuple(sl)]
# Split/join Nyquist component(s) if present
# So far we have set Y[+N/2]=X[+N/2]
if N % 2 == 0:
if num < Nx: # downsampling
if real_input:
sl[axis] = slice(N//2, N//2 + 1)
Y[tuple(sl)] *= 2.
else:
# select the component of Y at frequency +N/2,
# add the component of X at -N/2
sl[axis] = slice(-N//2, -N//2 + 1)
Y[tuple(sl)] += X[tuple(sl)]
elif Nx < num: # upsampling
# select the component at frequency +N/2 and halve it
sl[axis] = slice(N//2, N//2 + 1)
Y[tuple(sl)] *= 0.5
if not real_input:
temp = Y[tuple(sl)]
# set the component at -N/2 equal to the component at +N/2
sl[axis] = slice(num-N//2, num-N//2 + 1)
Y[tuple(sl)] = temp
# Inverse transform
if real_input:
y = sp_fft.irfft(Y, num, axis=axis)
else:
y = sp_fft.ifft(Y, axis=axis, overwrite_x=True)
y *= (float(num) / float(Nx))
if t is None:
return y
else:
new_t = np.arange(0, num) * (t[1] - t[0]) * Nx / float(num) + t[0]
return y, new_t
def resample_poly(x, up, down, axis=0, window=('kaiser', 5.0),
padtype='constant', cval=None):
"""
Resample `x` along the given axis using polyphase filtering.
The signal `x` is upsampled by the factor `up`, a zero-phase low-pass
FIR filter is applied, and then it is downsampled by the factor `down`.
The resulting sample rate is ``up / down`` times the original sample
rate. By default, values beyond the boundary of the signal are assumed
to be zero during the filtering step.
Parameters
----------
x : array_like
The data to be resampled.
up : int
The upsampling factor.
down : int
The downsampling factor.
axis : int, optional
The axis of `x` that is resampled. Default is 0.
window : string, tuple, or array_like, optional
Desired window to use to design the low-pass filter, or the FIR filter
coefficients to employ. See below for details.
padtype : string, optional
`constant`, `line`, `mean`, `median`, `maximum`, `minimum` or any of
the other signal extension modes supported by `scipy.signal.upfirdn`.
Changes assumptions on values beyond the boundary. If `constant`,
assumed to be `cval` (default zero). If `line` assumed to continue a
linear trend defined by the first and last points. `mean`, `median`,
`maximum` and `minimum` work as in `np.pad` and assume that the values
beyond the boundary are the mean, median, maximum or minimum
respectively of the array along the axis.
.. versionadded:: 1.4.0
cval : float, optional
Value to use if `padtype='constant'`. Default is zero.
.. versionadded:: 1.4.0
Returns
-------
resampled_x : array
The resampled array.
See Also
--------
decimate : Downsample the signal after applying an FIR or IIR filter.
resample : Resample up or down using the FFT method.
Notes
-----
This polyphase method will likely be faster than the Fourier method
in `scipy.signal.resample` when the number of samples is large and
prime, or when the number of samples is large and `up` and `down`
share a large greatest common denominator. The length of the FIR
filter used will depend on ``max(up, down) // gcd(up, down)``, and
the number of operations during polyphase filtering will depend on
the filter length and `down` (see `scipy.signal.upfirdn` for details).
The argument `window` specifies the FIR low-pass filter design.
If `window` is an array_like it is assumed to be the FIR filter
coefficients. Note that the FIR filter is applied after the upsampling
step, so it should be designed to operate on a signal at a sampling
frequency higher than the original by a factor of `up//gcd(up, down)`.
This function's output will be centered with respect to this array, so it
is best to pass a symmetric filter with an odd number of samples if, as
is usually the case, a zero-phase filter is desired.
For any other type of `window`, the functions `scipy.signal.get_window`
and `scipy.signal.firwin` are called to generate the appropriate filter
coefficients.
The first sample of the returned vector is the same as the first
sample of the input vector. The spacing between samples is changed
from ``dx`` to ``dx * down / float(up)``.
Examples
--------
By default, the end of the resampled data rises to meet the first
sample of the next cycle for the FFT method, and gets closer to zero
for the polyphase method:
>>> from scipy import signal
>>> x = np.linspace(0, 10, 20, endpoint=False)
>>> y = np.cos(-x**2/6.0)
>>> f_fft = signal.resample(y, 100)
>>> f_poly = signal.resample_poly(y, 100, 20)
>>> xnew = np.linspace(0, 10, 100, endpoint=False)
>>> import matplotlib.pyplot as plt
>>> plt.plot(xnew, f_fft, 'b.-', xnew, f_poly, 'r.-')
>>> plt.plot(x, y, 'ko-')
>>> plt.plot(10, y[0], 'bo', 10, 0., 'ro') # boundaries
>>> plt.legend(['resample', 'resamp_poly', 'data'], loc='best')
>>> plt.show()
This default behaviour can be changed by using the padtype option:
>>> import numpy as np
>>> from scipy import signal
>>> N = 5
>>> x = np.linspace(0, 1, N, endpoint=False)
>>> y = 2 + x**2 - 1.7*np.sin(x) + .2*np.cos(11*x)
>>> y2 = 1 + x**3 + 0.1*np.sin(x) + .1*np.cos(11*x)
>>> Y = np.stack([y, y2], axis=-1)
>>> up = 4
>>> xr = np.linspace(0, 1, N*up, endpoint=False)
>>> y2 = signal.resample_poly(Y, up, 1, padtype='constant')
>>> y3 = signal.resample_poly(Y, up, 1, padtype='mean')
>>> y4 = signal.resample_poly(Y, up, 1, padtype='line')
>>> import matplotlib.pyplot as plt
>>> for i in [0,1]:
... plt.figure()
... plt.plot(xr, y4[:,i], 'g.', label='line')
... plt.plot(xr, y3[:,i], 'y.', label='mean')
... plt.plot(xr, y2[:,i], 'r.', label='constant')
... plt.plot(x, Y[:,i], 'k-')
... plt.legend()
>>> plt.show()
"""
x = np.asarray(x)
if up != int(up):
raise ValueError("up must be an integer")
if down != int(down):
raise ValueError("down must be an integer")
up = int(up)
down = int(down)
if up < 1 or down < 1:
raise ValueError('up and down must be >= 1')
if cval is not None and padtype != 'constant':
raise ValueError('cval has no effect when padtype is ', padtype)
# Determine our up and down factors
# Use a rational approximation to save computation time on really long
# signals
g_ = math.gcd(up, down)
up //= g_
down //= g_
if up == down == 1:
return x.copy()
n_in = x.shape[axis]
n_out = n_in * up
n_out = n_out // down + bool(n_out % down)
if isinstance(window, (list, np.ndarray)):
window = np.array(window) # use array to force a copy (we modify it)
if window.ndim > 1:
raise ValueError('window must be 1-D')
half_len = (window.size - 1) // 2
h = window
else:
# Design a linear-phase low-pass FIR filter
max_rate = max(up, down)
f_c = 1. / max_rate # cutoff of FIR filter (rel. to Nyquist)
half_len = 10 * max_rate # reasonable cutoff for sinc-like function
h = firwin(2 * half_len + 1, f_c, window=window)
h *= up
# Zero-pad our filter to put the output samples at the center
n_pre_pad = (down - half_len % down)
n_post_pad = 0
n_pre_remove = (half_len + n_pre_pad) // down
# We should rarely need to do this given our filter lengths...
while _output_len(len(h) + n_pre_pad + n_post_pad, n_in,
up, down) < n_out + n_pre_remove:
n_post_pad += 1
h = np.concatenate((np.zeros(n_pre_pad, dtype=h.dtype), h,
np.zeros(n_post_pad, dtype=h.dtype)))
n_pre_remove_end = n_pre_remove + n_out
# Remove background depending on the padtype option
funcs = {'mean': np.mean, 'median': np.median,
'minimum': np.amin, 'maximum': np.amax}
upfirdn_kwargs = {'mode': 'constant', 'cval': 0}
if padtype in funcs:
background_values = funcs[padtype](x, axis=axis, keepdims=True)
elif padtype in _upfirdn_modes:
upfirdn_kwargs = {'mode': padtype}
if padtype == 'constant':
if cval is None:
cval = 0
upfirdn_kwargs['cval'] = cval
else:
raise ValueError(
'padtype must be one of: maximum, mean, median, minimum, ' +
', '.join(_upfirdn_modes))
if padtype in funcs:
x = x - background_values
# filter then remove excess
y = upfirdn(h, x, up, down, axis=axis, **upfirdn_kwargs)
keep = [slice(None), ]*x.ndim
keep[axis] = slice(n_pre_remove, n_pre_remove_end)
y_keep = y[tuple(keep)]
# Add background back
if padtype in funcs:
y_keep += background_values
return y_keep
def vectorstrength(events, period):
'''
Determine the vector strength of the events corresponding to the given
period.
The vector strength is a measure of phase synchrony, how well the
timing of the events is synchronized to a single period of a periodic
signal.
If multiple periods are used, calculate the vector strength of each.
This is called the "resonating vector strength".
Parameters
----------
events : 1D array_like
An array of time points containing the timing of the events.
period : float or array_like
The period of the signal that the events should synchronize to.
The period is in the same units as `events`. It can also be an array
of periods, in which case the outputs are arrays of the same length.
Returns
-------
strength : float or 1D array
The strength of the synchronization. 1.0 is perfect synchronization
and 0.0 is no synchronization. If `period` is an array, this is also
an array with each element containing the vector strength at the
corresponding period.
phase : float or array
The phase that the events are most strongly synchronized to in radians.
If `period` is an array, this is also an array with each element
containing the phase for the corresponding period.
References
----------
van Hemmen, JL, Longtin, A, and Vollmayr, AN. Testing resonating vector
strength: Auditory system, electric fish, and noise.
Chaos 21, 047508 (2011);
:doi:`10.1063/1.3670512`.
van Hemmen, JL. Vector strength after Goldberg, Brown, and von Mises:
biological and mathematical perspectives. Biol Cybern.
2013 Aug;107(4):385-96. :doi:`10.1007/s00422-013-0561-7`.
van Hemmen, JL and Vollmayr, AN. Resonating vector strength: what happens
when we vary the "probing" frequency while keeping the spike times
fixed. Biol Cybern. 2013 Aug;107(4):491-94.
:doi:`10.1007/s00422-013-0560-8`.
'''
events = np.asarray(events)
period = np.asarray(period)
if events.ndim > 1:
raise ValueError('events cannot have dimensions more than 1')
if period.ndim > 1:
raise ValueError('period cannot have dimensions more than 1')
# we need to know later if period was originally a scalar
scalarperiod = not period.ndim
events = np.atleast_2d(events)
period = np.atleast_2d(period)
if (period <= 0).any():
raise ValueError('periods must be positive')
# this converts the times to vectors
vectors = np.exp(np.dot(2j*np.pi/period.T, events))
# the vector strength is just the magnitude of the mean of the vectors
# the vector phase is the angle of the mean of the vectors
vectormean = np.mean(vectors, axis=1)
strength = abs(vectormean)
phase = np.angle(vectormean)
# if the original period was a scalar, return scalars
if scalarperiod:
strength = strength[0]
phase = phase[0]
return strength, phase
def detrend(data, axis=-1, type='linear', bp=0, overwrite_data=False):
"""
Remove linear trend along axis from data.
Parameters
----------
data : array_like
The input data.
axis : int, optional
The axis along which to detrend the data. By default this is the
last axis (-1).
type : {'linear', 'constant'}, optional
The type of detrending. If ``type == 'linear'`` (default),
the result of a linear least-squares fit to `data` is subtracted
from `data`.
If ``type == 'constant'``, only the mean of `data` is subtracted.
bp : array_like of ints, optional
A sequence of break points. If given, an individual linear fit is
performed for each part of `data` between two break points.
Break points are specified as indices into `data`. This parameter
only has an effect when ``type == 'linear'``.
overwrite_data : bool, optional
If True, perform in place detrending and avoid a copy. Default is False
Returns
-------
ret : ndarray
The detrended input data.
Examples
--------
>>> from scipy import signal
>>> randgen = np.random.RandomState(9)
>>> npoints = 1000
>>> noise = randgen.randn(npoints)
>>> x = 3 + 2*np.linspace(0, 1, npoints) + noise
>>> (signal.detrend(x) - noise).max() < 0.01
True
"""
if type not in ['linear', 'l', 'constant', 'c']:
raise ValueError("Trend type must be 'linear' or 'constant'.")
data = np.asarray(data)
dtype = data.dtype.char
if dtype not in 'dfDF':
dtype = 'd'
if type in ['constant', 'c']:
ret = data - np.expand_dims(np.mean(data, axis), axis)
return ret
else:
dshape = data.shape
N = dshape[axis]
bp = np.sort(np.unique(np.r_[0, bp, N]))
if np.any(bp > N):
raise ValueError("Breakpoints must be less than length "
"of data along given axis.")
Nreg = len(bp) - 1
# Restructure data so that axis is along first dimension and
# all other dimensions are collapsed into second dimension
rnk = len(dshape)
if axis < 0:
axis = axis + rnk
newdims = np.r_[axis, 0:axis, axis + 1:rnk]
newdata = np.reshape(np.transpose(data, tuple(newdims)),
(N, _prod(dshape) // N))
if not overwrite_data:
newdata = newdata.copy() # make sure we have a copy
if newdata.dtype.char not in 'dfDF':
newdata = newdata.astype(dtype)
# Find leastsq fit and remove it for each piece
for m in range(Nreg):
Npts = bp[m + 1] - bp[m]
A = np.ones((Npts, 2), dtype)
A[:, 0] = np.cast[dtype](np.arange(1, Npts + 1) * 1.0 / Npts)
sl = slice(bp[m], bp[m + 1])
coef, resids, rank, s = linalg.lstsq(A, newdata[sl])
newdata[sl] = newdata[sl] - np.dot(A, coef)
# Put data back in original shape.
tdshape = np.take(dshape, newdims, 0)
ret = np.reshape(newdata, tuple(tdshape))
vals = list(range(1, rnk))
olddims = vals[:axis] + [0] + vals[axis:]
ret = np.transpose(ret, tuple(olddims))
return ret
def lfilter_zi(b, a):
"""
Construct initial conditions for lfilter for step response steady-state.
Compute an initial state `zi` for the `lfilter` function that corresponds
to the steady state of the step response.
A typical use of this function is to set the initial state so that the
output of the filter starts at the same value as the first element of
the signal to be filtered.
Parameters
----------
b, a : array_like (1-D)
The IIR filter coefficients. See `lfilter` for more
information.
Returns
-------
zi : 1-D ndarray
The initial state for the filter.
See Also
--------
lfilter, lfiltic, filtfilt
Notes
-----
A linear filter with order m has a state space representation (A, B, C, D),
for which the output y of the filter can be expressed as::
z(n+1) = A*z(n) + B*x(n)
y(n) = C*z(n) + D*x(n)
where z(n) is a vector of length m, A has shape (m, m), B has shape
(m, 1), C has shape (1, m) and D has shape (1, 1) (assuming x(n) is
a scalar). lfilter_zi solves::
zi = A*zi + B
In other words, it finds the initial condition for which the response
to an input of all ones is a constant.
Given the filter coefficients `a` and `b`, the state space matrices
for the transposed direct form II implementation of the linear filter,
which is the implementation used by scipy.signal.lfilter, are::
A = scipy.linalg.companion(a).T
B = b[1:] - a[1:]*b[0]
assuming `a[0]` is 1.0; if `a[0]` is not 1, `a` and `b` are first
divided by a[0].
Examples
--------
The following code creates a lowpass Butterworth filter. Then it
applies that filter to an array whose values are all 1.0; the
output is also all 1.0, as expected for a lowpass filter. If the
`zi` argument of `lfilter` had not been given, the output would have
shown the transient signal.
>>> from numpy import array, ones
>>> from scipy.signal import lfilter, lfilter_zi, butter
>>> b, a = butter(5, 0.25)
>>> zi = lfilter_zi(b, a)
>>> y, zo = lfilter(b, a, ones(10), zi=zi)
>>> y
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
Another example:
>>> x = array([0.5, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0])
>>> y, zf = lfilter(b, a, x, zi=zi*x[0])
>>> y
array([ 0.5 , 0.5 , 0.5 , 0.49836039, 0.48610528,
0.44399389, 0.35505241])
Note that the `zi` argument to `lfilter` was computed using
`lfilter_zi` and scaled by `x[0]`. Then the output `y` has no
transient until the input drops from 0.5 to 0.0.
"""
# FIXME: Can this function be replaced with an appropriate
# use of lfiltic? For example, when b,a = butter(N,Wn),
# lfiltic(b, a, y=numpy.ones_like(a), x=numpy.ones_like(b)).
#
# We could use scipy.signal.normalize, but it uses warnings in
# cases where a ValueError is more appropriate, and it allows
# b to be 2D.
b = np.atleast_1d(b)
if b.ndim != 1:
raise ValueError("Numerator b must be 1-D.")
a = np.atleast_1d(a)
if a.ndim != 1:
raise ValueError("Denominator a must be 1-D.")
while len(a) > 1 and a[0] == 0.0:
a = a[1:]
if a.size < 1:
raise ValueError("There must be at least one nonzero `a` coefficient.")
if a[0] != 1.0:
# Normalize the coefficients so a[0] == 1.
b = b / a[0]
a = a / a[0]
n = max(len(a), len(b))
# Pad a or b with zeros so they are the same length.
if len(a) < n:
a = np.r_[a, np.zeros(n - len(a))]
elif len(b) < n:
b = np.r_[b, np.zeros(n - len(b))]
IminusA = np.eye(n - 1, dtype=np.result_type(a, b)) - linalg.companion(a).T
B = b[1:] - a[1:] * b[0]
# Solve zi = A*zi + B
zi = np.linalg.solve(IminusA, B)
# For future reference: we could also use the following
# explicit formulas to solve the linear system:
#
# zi = np.zeros(n - 1)
# zi[0] = B.sum() / IminusA[:,0].sum()
# asum = 1.0
# csum = 0.0
# for k in range(1,n-1):
# asum += a[k]
# csum += b[k] - a[k]*b[0]
# zi[k] = asum*zi[0] - csum
return zi
def sosfilt_zi(sos):
"""
Construct initial conditions for sosfilt for step response steady-state.
Compute an initial state `zi` for the `sosfilt` function that corresponds
to the steady state of the step response.
A typical use of this function is to set the initial state so that the
output of the filter starts at the same value as the first element of
the signal to be filtered.
Parameters
----------
sos : array_like
Array of second-order filter coefficients, must have shape
``(n_sections, 6)``. See `sosfilt` for the SOS filter format
specification.
Returns
-------
zi : ndarray
Initial conditions suitable for use with ``sosfilt``, shape
``(n_sections, 2)``.
See Also
--------
sosfilt, zpk2sos
Notes
-----
.. versionadded:: 0.16.0
Examples
--------
Filter a rectangular pulse that begins at time 0, with and without
the use of the `zi` argument of `scipy.signal.sosfilt`.
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> sos = signal.butter(9, 0.125, output='sos')
>>> zi = signal.sosfilt_zi(sos)
>>> x = (np.arange(250) < 100).astype(int)
>>> f1 = signal.sosfilt(sos, x)
>>> f2, zo = signal.sosfilt(sos, x, zi=zi)
>>> plt.plot(x, 'k--', label='x')
>>> plt.plot(f1, 'b', alpha=0.5, linewidth=2, label='filtered')
>>> plt.plot(f2, 'g', alpha=0.25, linewidth=4, label='filtered with zi')
>>> plt.legend(loc='best')
>>> plt.show()
"""
sos = np.asarray(sos)
if sos.ndim != 2 or sos.shape[1] != 6:
raise ValueError('sos must be shape (n_sections, 6)')
if sos.dtype.kind in 'bui':
sos = sos.astype(np.float64)
n_sections = sos.shape[0]
zi = np.empty((n_sections, 2), dtype=sos.dtype)
scale = 1.0
for section in range(n_sections):
b = sos[section, :3]
a = sos[section, 3:]
zi[section] = scale * lfilter_zi(b, a)
# If H(z) = B(z)/A(z) is this section's transfer function, then
# b.sum()/a.sum() is H(1), the gain at omega=0. That's the steady
# state value of this section's step response.
scale *= b.sum() / a.sum()
return zi
def _filtfilt_gust(b, a, x, axis=-1, irlen=None):
"""Forward-backward IIR filter that uses Gustafsson's method.
Apply the IIR filter defined by `(b,a)` to `x` twice, first forward
then backward, using Gustafsson's initial conditions [1]_.
Let ``y_fb`` be the result of filtering first forward and then backward,
and let ``y_bf`` be the result of filtering first backward then forward.
Gustafsson's method is to compute initial conditions for the forward
pass and the backward pass such that ``y_fb == y_bf``.
Parameters
----------
b : scalar or 1-D ndarray
Numerator coefficients of the filter.
a : scalar or 1-D ndarray
Denominator coefficients of the filter.
x : ndarray
Data to be filtered.
axis : int, optional
Axis of `x` to be filtered. Default is -1.
irlen : int or None, optional
The length of the nonnegligible part of the impulse response.
If `irlen` is None, or if the length of the signal is less than
``2 * irlen``, then no part of the impulse response is ignored.
Returns
-------
y : ndarray
The filtered data.
x0 : ndarray
Initial condition for the forward filter.
x1 : ndarray
Initial condition for the backward filter.
Notes
-----
Typically the return values `x0` and `x1` are not needed by the
caller. The intended use of these return values is in unit tests.
References
----------
.. [1] F. Gustaffson. Determining the initial states in forward-backward
filtering. Transactions on Signal Processing, 46(4):988-992, 1996.
"""
# In the comments, "Gustafsson's paper" and [1] refer to the
# paper referenced in the docstring.
b = np.atleast_1d(b)
a = np.atleast_1d(a)
order = max(len(b), len(a)) - 1
if order == 0:
# The filter is just scalar multiplication, with no state.
scale = (b[0] / a[0])**2
y = scale * x
return y, np.array([]), np.array([])
if axis != -1 or axis != x.ndim - 1:
# Move the axis containing the data to the end.
x = np.swapaxes(x, axis, x.ndim - 1)
# n is the number of samples in the data to be filtered.
n = x.shape[-1]
if irlen is None or n <= 2*irlen:
m = n
else:
m = irlen
# Create Obs, the observability matrix (called O in the paper).
# This matrix can be interpreted as the operator that propagates
# an arbitrary initial state to the output, assuming the input is
# zero.
# In Gustafsson's paper, the forward and backward filters are not
# necessarily the same, so he has both O_f and O_b. We use the same
# filter in both directions, so we only need O. The same comment
# applies to S below.
Obs = np.zeros((m, order))
zi = np.zeros(order)
zi[0] = 1
Obs[:, 0] = lfilter(b, a, np.zeros(m), zi=zi)[0]
for k in range(1, order):
Obs[k:, k] = Obs[:-k, 0]
# Obsr is O^R (Gustafsson's notation for row-reversed O)
Obsr = Obs[::-1]
# Create S. S is the matrix that applies the filter to the reversed
# propagated initial conditions. That is,
# out = S.dot(zi)
# is the same as
# tmp, _ = lfilter(b, a, zeros(), zi=zi) # Propagate ICs.
# out = lfilter(b, a, tmp[::-1]) # Reverse and filter.
# Equations (5) & (6) of [1]
S = lfilter(b, a, Obs[::-1], axis=0)
# Sr is S^R (row-reversed S)
Sr = S[::-1]
# M is [(S^R - O), (O^R - S)]
if m == n:
M = np.hstack((Sr - Obs, Obsr - S))
else:
# Matrix described in section IV of [1].
M = np.zeros((2*m, 2*order))
M[:m, :order] = Sr - Obs
M[m:, order:] = Obsr - S
# Naive forward-backward and backward-forward filters.
# These have large transients because the filters use zero initial
# conditions.
y_f = lfilter(b, a, x)
y_fb = lfilter(b, a, y_f[..., ::-1])[..., ::-1]
y_b = lfilter(b, a, x[..., ::-1])[..., ::-1]
y_bf = lfilter(b, a, y_b)
delta_y_bf_fb = y_bf - y_fb
if m == n:
delta = delta_y_bf_fb
else:
start_m = delta_y_bf_fb[..., :m]
end_m = delta_y_bf_fb[..., -m:]
delta = np.concatenate((start_m, end_m), axis=-1)
# ic_opt holds the "optimal" initial conditions.
# The following code computes the result shown in the formula
# of the paper between equations (6) and (7).
if delta.ndim == 1:
ic_opt = linalg.lstsq(M, delta)[0]
else:
# Reshape delta so it can be used as an array of multiple
# right-hand-sides in linalg.lstsq.
delta2d = delta.reshape(-1, delta.shape[-1]).T
ic_opt0 = linalg.lstsq(M, delta2d)[0].T
ic_opt = ic_opt0.reshape(delta.shape[:-1] + (M.shape[-1],))
# Now compute the filtered signal using equation (7) of [1].
# First, form [S^R, O^R] and call it W.
if m == n:
W = np.hstack((Sr, Obsr))
else:
W = np.zeros((2*m, 2*order))
W[:m, :order] = Sr
W[m:, order:] = Obsr
# Equation (7) of [1] says
# Y_fb^opt = Y_fb^0 + W * [x_0^opt; x_{N-1}^opt]
# `wic` is (almost) the product on the right.
# W has shape (m, 2*order), and ic_opt has shape (..., 2*order),
# so we can't use W.dot(ic_opt). Instead, we dot ic_opt with W.T,
# so wic has shape (..., m).
wic = ic_opt.dot(W.T)
# `wic` is "almost" the product of W and the optimal ICs in equation
# (7)--if we're using a truncated impulse response (m < n), `wic`
# contains only the adjustments required for the ends of the signal.
# Here we form y_opt, taking this into account if necessary.
y_opt = y_fb
if m == n:
y_opt += wic
else:
y_opt[..., :m] += wic[..., :m]
y_opt[..., -m:] += wic[..., -m:]
x0 = ic_opt[..., :order]
x1 = ic_opt[..., -order:]
if axis != -1 or axis != x.ndim - 1:
# Restore the data axis to its original position.
x0 = np.swapaxes(x0, axis, x.ndim - 1)
x1 = np.swapaxes(x1, axis, x.ndim - 1)
y_opt = np.swapaxes(y_opt, axis, x.ndim - 1)
return y_opt, x0, x1
def filtfilt(b, a, x, axis=-1, padtype='odd', padlen=None, method='pad',
irlen=None):
"""
Apply a digital filter forward and backward to a signal.
This function applies a linear digital filter twice, once forward and
once backwards. The combined filter has zero phase and a filter order
twice that of the original.
The function provides options for handling the edges of the signal.
The function `sosfiltfilt` (and filter design using ``output='sos'``)
should be preferred over `filtfilt` for most filtering tasks, as
second-order sections have fewer numerical problems.
Parameters
----------
b : (N,) array_like
The numerator coefficient vector of the filter.
a : (N,) array_like
The denominator coefficient vector of the filter. If ``a[0]``
is not 1, then both `a` and `b` are normalized by ``a[0]``.
x : array_like
The array of data to be filtered.
axis : int, optional
The axis of `x` to which the filter is applied.
Default is -1.
padtype : str or None, optional
Must be 'odd', 'even', 'constant', or None. This determines the
type of extension to use for the padded signal to which the filter
is applied. If `padtype` is None, no padding is used. The default
is 'odd'.
padlen : int or None, optional
The number of elements by which to extend `x` at both ends of
`axis` before applying the filter. This value must be less than
``x.shape[axis] - 1``. ``padlen=0`` implies no padding.
The default value is ``3 * max(len(a), len(b))``.
method : str, optional
Determines the method for handling the edges of the signal, either
"pad" or "gust". When `method` is "pad", the signal is padded; the
type of padding is determined by `padtype` and `padlen`, and `irlen`
is ignored. When `method` is "gust", Gustafsson's method is used,
and `padtype` and `padlen` are ignored.
irlen : int or None, optional
When `method` is "gust", `irlen` specifies the length of the
impulse response of the filter. If `irlen` is None, no part
of the impulse response is ignored. For a long signal, specifying
`irlen` can significantly improve the performance of the filter.
Returns
-------
y : ndarray
The filtered output with the same shape as `x`.
See Also
--------
sosfiltfilt, lfilter_zi, lfilter, lfiltic, savgol_filter, sosfilt
Notes
-----
When `method` is "pad", the function pads the data along the given axis
in one of three ways: odd, even or constant. The odd and even extensions
have the corresponding symmetry about the end point of the data. The
constant extension extends the data with the values at the end points. On
both the forward and backward passes, the initial condition of the
filter is found by using `lfilter_zi` and scaling it by the end point of
the extended data.
When `method` is "gust", Gustafsson's method [1]_ is used. Initial
conditions are chosen for the forward and backward passes so that the
forward-backward filter gives the same result as the backward-forward
filter.
The option to use Gustaffson's method was added in scipy version 0.16.0.
References
----------
.. [1] F. Gustaffson, "Determining the initial states in forward-backward
filtering", Transactions on Signal Processing, Vol. 46, pp. 988-992,
1996.
Examples
--------
The examples will use several functions from `scipy.signal`.
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
First we create a one second signal that is the sum of two pure sine
waves, with frequencies 5 Hz and 250 Hz, sampled at 2000 Hz.
>>> t = np.linspace(0, 1.0, 2001)
>>> xlow = np.sin(2 * np.pi * 5 * t)
>>> xhigh = np.sin(2 * np.pi * 250 * t)
>>> x = xlow + xhigh
Now create a lowpass Butterworth filter with a cutoff of 0.125 times
the Nyquist frequency, or 125 Hz, and apply it to ``x`` with `filtfilt`.
The result should be approximately ``xlow``, with no phase shift.
>>> b, a = signal.butter(8, 0.125)
>>> y = signal.filtfilt(b, a, x, padlen=150)
>>> np.abs(y - xlow).max()
9.1086182074789912e-06
We get a fairly clean result for this artificial example because
the odd extension is exact, and with the moderately long padding,
the filter's transients have dissipated by the time the actual data
is reached. In general, transient effects at the edges are
unavoidable.
The following example demonstrates the option ``method="gust"``.
First, create a filter.
>>> b, a = signal.ellip(4, 0.01, 120, 0.125) # Filter to be applied.
>>> np.random.seed(123456)
`sig` is a random input signal to be filtered.
>>> n = 60
>>> sig = np.random.randn(n)**3 + 3*np.random.randn(n).cumsum()
Apply `filtfilt` to `sig`, once using the Gustafsson method, and
once using padding, and plot the results for comparison.
>>> fgust = signal.filtfilt(b, a, sig, method="gust")
>>> fpad = signal.filtfilt(b, a, sig, padlen=50)
>>> plt.plot(sig, 'k-', label='input')
>>> plt.plot(fgust, 'b-', linewidth=4, label='gust')
>>> plt.plot(fpad, 'c-', linewidth=1.5, label='pad')
>>> plt.legend(loc='best')
>>> plt.show()
The `irlen` argument can be used to improve the performance
of Gustafsson's method.
Estimate the impulse response length of the filter.
>>> z, p, k = signal.tf2zpk(b, a)
>>> eps = 1e-9
>>> r = np.max(np.abs(p))
>>> approx_impulse_len = int(np.ceil(np.log(eps) / np.log(r)))
>>> approx_impulse_len
137
Apply the filter to a longer signal, with and without the `irlen`
argument. The difference between `y1` and `y2` is small. For long
signals, using `irlen` gives a significant performance improvement.
>>> x = np.random.randn(5000)
>>> y1 = signal.filtfilt(b, a, x, method='gust')
>>> y2 = signal.filtfilt(b, a, x, method='gust', irlen=approx_impulse_len)
>>> print(np.max(np.abs(y1 - y2)))
1.80056858312e-10
"""
b = np.atleast_1d(b)
a = np.atleast_1d(a)
x = np.asarray(x)
if method not in ["pad", "gust"]:
raise ValueError("method must be 'pad' or 'gust'.")
if method == "gust":
y, z1, z2 = _filtfilt_gust(b, a, x, axis=axis, irlen=irlen)
return y
# method == "pad"
edge, ext = _validate_pad(padtype, padlen, x, axis,
ntaps=max(len(a), len(b)))
# Get the steady state of the filter's step response.
zi = lfilter_zi(b, a)
# Reshape zi and create x0 so that zi*x0 broadcasts
# to the correct value for the 'zi' keyword argument
# to lfilter.
zi_shape = [1] * x.ndim
zi_shape[axis] = zi.size
zi = np.reshape(zi, zi_shape)
x0 = axis_slice(ext, stop=1, axis=axis)
# Forward filter.
(y, zf) = lfilter(b, a, ext, axis=axis, zi=zi * x0)
# Backward filter.
# Create y0 so zi*y0 broadcasts appropriately.
y0 = axis_slice(y, start=-1, axis=axis)
(y, zf) = lfilter(b, a, axis_reverse(y, axis=axis), axis=axis, zi=zi * y0)
# Reverse y.
y = axis_reverse(y, axis=axis)
if edge > 0:
# Slice the actual signal from the extended signal.
y = axis_slice(y, start=edge, stop=-edge, axis=axis)
return y
def _validate_pad(padtype, padlen, x, axis, ntaps):
"""Helper to validate padding for filtfilt"""
if padtype not in ['even', 'odd', 'constant', None]:
raise ValueError(("Unknown value '%s' given to padtype. padtype "
"must be 'even', 'odd', 'constant', or None.") %
padtype)
if padtype is None:
padlen = 0
if padlen is None:
# Original padding; preserved for backwards compatibility.
edge = ntaps * 3
else:
edge = padlen
# x's 'axis' dimension must be bigger than edge.
if x.shape[axis] <= edge:
raise ValueError("The length of the input vector x must be greater "
"than padlen, which is %d." % edge)
if padtype is not None and edge > 0:
# Make an extension of length `edge` at each
# end of the input array.
if padtype == 'even':
ext = even_ext(x, edge, axis=axis)
elif padtype == 'odd':
ext = odd_ext(x, edge, axis=axis)
else:
ext = const_ext(x, edge, axis=axis)
else:
ext = x
return edge, ext
def _validate_x(x):
x = np.asarray(x)
if x.ndim == 0:
raise ValueError('x must be at least 1-D')
return x
def sosfilt(sos, x, axis=-1, zi=None):
"""
Filter data along one dimension using cascaded second-order sections.
Filter a data sequence, `x`, using a digital IIR filter defined by
`sos`.
Parameters
----------
sos : array_like
Array of second-order filter coefficients, must have shape
``(n_sections, 6)``. Each row corresponds to a second-order
section, with the first three columns providing the numerator
coefficients and the last three providing the denominator
coefficients.
x : array_like
An N-dimensional input array.
axis : int, optional
The axis of the input data array along which to apply the
linear filter. The filter is applied to each subarray along
this axis. Default is -1.
zi : array_like, optional
Initial conditions for the cascaded filter delays. It is a (at
least 2D) vector of shape ``(n_sections, ..., 2, ...)``, where
``..., 2, ...`` denotes the shape of `x`, but with ``x.shape[axis]``
replaced by 2. If `zi` is None or is not given then initial rest
(i.e. all zeros) is assumed.
Note that these initial conditions are *not* the same as the initial
conditions given by `lfiltic` or `lfilter_zi`.
Returns
-------
y : ndarray
The output of the digital filter.
zf : ndarray, optional
If `zi` is None, this is not returned, otherwise, `zf` holds the
final filter delay values.
See Also
--------
zpk2sos, sos2zpk, sosfilt_zi, sosfiltfilt, sosfreqz
Notes
-----
The filter function is implemented as a series of second-order filters
with direct-form II transposed structure. It is designed to minimize
numerical precision errors for high-order filters.
.. versionadded:: 0.16.0
Examples
--------
Plot a 13th-order filter's impulse response using both `lfilter` and
`sosfilt`, showing the instability that results from trying to do a
13th-order filter in a single stage (the numerical error pushes some poles
outside of the unit circle):
>>> import matplotlib.pyplot as plt
>>> from scipy import signal
>>> b, a = signal.ellip(13, 0.009, 80, 0.05, output='ba')
>>> sos = signal.ellip(13, 0.009, 80, 0.05, output='sos')
>>> x = signal.unit_impulse(700)
>>> y_tf = signal.lfilter(b, a, x)
>>> y_sos = signal.sosfilt(sos, x)
>>> plt.plot(y_tf, 'r', label='TF')
>>> plt.plot(y_sos, 'k', label='SOS')
>>> plt.legend(loc='best')
>>> plt.show()
"""
x = _validate_x(x)
sos, n_sections = _validate_sos(sos)
x_zi_shape = list(x.shape)
x_zi_shape[axis] = 2
x_zi_shape = tuple([n_sections] + x_zi_shape)
inputs = [sos, x]
if zi is not None:
inputs.append(np.asarray(zi))
dtype = np.result_type(*inputs)
if dtype.char not in 'fdgFDGO':
raise NotImplementedError("input type '%s' not supported" % dtype)
if zi is not None:
zi = np.array(zi, dtype) # make a copy so that we can operate in place
if zi.shape != x_zi_shape:
raise ValueError('Invalid zi shape. With axis=%r, an input with '
'shape %r, and an sos array with %d sections, zi '
'must have shape %r, got %r.' %
(axis, x.shape, n_sections, x_zi_shape, zi.shape))
return_zi = True
else:
zi = np.zeros(x_zi_shape, dtype=dtype)
return_zi = False
axis = axis % x.ndim # make positive
x = np.moveaxis(x, axis, -1)
zi = np.moveaxis(zi, [0, axis + 1], [-2, -1])
x_shape, zi_shape = x.shape, zi.shape
x = np.reshape(x, (-1, x.shape[-1]))
x = np.array(x, dtype, order='C') # make a copy, can modify in place
zi = np.ascontiguousarray(np.reshape(zi, (-1, n_sections, 2)))
sos = sos.astype(dtype, copy=False)
_sosfilt(sos, x, zi)
x.shape = x_shape
x = np.moveaxis(x, -1, axis)
if return_zi:
zi.shape = zi_shape
zi = np.moveaxis(zi, [-2, -1], [0, axis + 1])
out = (x, zi)
else:
out = x
return out
def sosfiltfilt(sos, x, axis=-1, padtype='odd', padlen=None):
"""
A forward-backward digital filter using cascaded second-order sections.
See `filtfilt` for more complete information about this method.
Parameters
----------
sos : array_like
Array of second-order filter coefficients, must have shape
``(n_sections, 6)``. Each row corresponds to a second-order
section, with the first three columns providing the numerator
coefficients and the last three providing the denominator
coefficients.
x : array_like
The array of data to be filtered.
axis : int, optional
The axis of `x` to which the filter is applied.
Default is -1.
padtype : str or None, optional
Must be 'odd', 'even', 'constant', or None. This determines the
type of extension to use for the padded signal to which the filter
is applied. If `padtype` is None, no padding is used. The default
is 'odd'.
padlen : int or None, optional
The number of elements by which to extend `x` at both ends of
`axis` before applying the filter. This value must be less than
``x.shape[axis] - 1``. ``padlen=0`` implies no padding.
The default value is::
3 * (2 * len(sos) + 1 - min((sos[:, 2] == 0).sum(),
(sos[:, 5] == 0).sum()))
The extra subtraction at the end attempts to compensate for poles
and zeros at the origin (e.g. for odd-order filters) to yield
equivalent estimates of `padlen` to those of `filtfilt` for
second-order section filters built with `scipy.signal` functions.
Returns
-------
y : ndarray
The filtered output with the same shape as `x`.
See Also
--------
filtfilt, sosfilt, sosfilt_zi, sosfreqz
Notes
-----
.. versionadded:: 0.18.0
Examples
--------
>>> from scipy.signal import sosfiltfilt, butter
>>> import matplotlib.pyplot as plt
Create an interesting signal to filter.
>>> n = 201
>>> t = np.linspace(0, 1, n)
>>> np.random.seed(123)
>>> x = 1 + (t < 0.5) - 0.25*t**2 + 0.05*np.random.randn(n)
Create a lowpass Butterworth filter, and use it to filter `x`.
>>> sos = butter(4, 0.125, output='sos')
>>> y = sosfiltfilt(sos, x)
For comparison, apply an 8th order filter using `sosfilt`. The filter
is initialized using the mean of the first four values of `x`.
>>> from scipy.signal import sosfilt, sosfilt_zi
>>> sos8 = butter(8, 0.125, output='sos')
>>> zi = x[:4].mean() * sosfilt_zi(sos8)
>>> y2, zo = sosfilt(sos8, x, zi=zi)
Plot the results. Note that the phase of `y` matches the input, while
`y2` has a significant phase delay.
>>> plt.plot(t, x, alpha=0.5, label='x(t)')
>>> plt.plot(t, y, label='y(t)')
>>> plt.plot(t, y2, label='y2(t)')
>>> plt.legend(framealpha=1, shadow=True)
>>> plt.grid(alpha=0.25)
>>> plt.xlabel('t')
>>> plt.show()
"""
sos, n_sections = _validate_sos(sos)
x = _validate_x(x)
# `method` is "pad"...
ntaps = 2 * n_sections + 1
ntaps -= min((sos[:, 2] == 0).sum(), (sos[:, 5] == 0).sum())
edge, ext = _validate_pad(padtype, padlen, x, axis,
ntaps=ntaps)
# These steps follow the same form as filtfilt with modifications
zi = sosfilt_zi(sos) # shape (n_sections, 2) --> (n_sections, ..., 2, ...)
zi_shape = [1] * x.ndim
zi_shape[axis] = 2
zi.shape = [n_sections] + zi_shape
x_0 = axis_slice(ext, stop=1, axis=axis)
(y, zf) = sosfilt(sos, ext, axis=axis, zi=zi * x_0)
y_0 = axis_slice(y, start=-1, axis=axis)
(y, zf) = sosfilt(sos, axis_reverse(y, axis=axis), axis=axis, zi=zi * y_0)
y = axis_reverse(y, axis=axis)
if edge > 0:
y = axis_slice(y, start=edge, stop=-edge, axis=axis)
return y
def decimate(x, q, n=None, ftype='iir', axis=-1, zero_phase=True):
"""
Downsample the signal after applying an anti-aliasing filter.
By default, an order 8 Chebyshev type I filter is used. A 30 point FIR
filter with Hamming window is used if `ftype` is 'fir'.
Parameters
----------
x : array_like
The signal to be downsampled, as an N-dimensional array.
q : int
The downsampling factor. When using IIR downsampling, it is recommended
to call `decimate` multiple times for downsampling factors higher than
13.
n : int, optional
The order of the filter (1 less than the length for 'fir'). Defaults to
8 for 'iir' and 20 times the downsampling factor for 'fir'.
ftype : str {'iir', 'fir'} or ``dlti`` instance, optional
If 'iir' or 'fir', specifies the type of lowpass filter. If an instance
of an `dlti` object, uses that object to filter before downsampling.
axis : int, optional
The axis along which to decimate.
zero_phase : bool, optional
Prevent phase shift by filtering with `filtfilt` instead of `lfilter`
when using an IIR filter, and shifting the outputs back by the filter's
group delay when using an FIR filter. The default value of ``True`` is
recommended, since a phase shift is generally not desired.
.. versionadded:: 0.18.0
Returns
-------
y : ndarray
The down-sampled signal.
See Also
--------
resample : Resample up or down using the FFT method.
resample_poly : Resample using polyphase filtering and an FIR filter.
Notes
-----
The ``zero_phase`` keyword was added in 0.18.0.
The possibility to use instances of ``dlti`` as ``ftype`` was added in
0.18.0.
"""
x = np.asarray(x)
q = operator.index(q)
if n is not None:
n = operator.index(n)
if ftype == 'fir':
if n is None:
half_len = 10 * q # reasonable cutoff for our sinc-like function
n = 2 * half_len
b, a = firwin(n+1, 1. / q, window='hamming'), 1.
elif ftype == 'iir':
if n is None:
n = 8
system = dlti(*cheby1(n, 0.05, 0.8 / q))
b, a = system.num, system.den
elif isinstance(ftype, dlti):
system = ftype._as_tf() # Avoids copying if already in TF form
b, a = system.num, system.den
else:
raise ValueError('invalid ftype')
result_type = x.dtype
if result_type.kind in 'bui':
result_type = np.float64
b = np.asarray(b, dtype=result_type)
a = np.asarray(a, dtype=result_type)
sl = [slice(None)] * x.ndim
a = np.asarray(a)
if a.size == 1: # FIR case
b = b / a
if zero_phase:
y = resample_poly(x, 1, q, axis=axis, window=b)
else:
# upfirdn is generally faster than lfilter by a factor equal to the
# downsampling factor, since it only calculates the needed outputs
n_out = x.shape[axis] // q + bool(x.shape[axis] % q)
y = upfirdn(b, x, up=1, down=q, axis=axis)
sl[axis] = slice(None, n_out, None)
else: # IIR case
if zero_phase:
y = filtfilt(b, a, x, axis=axis)
else:
y = lfilter(b, a, x, axis=axis)
sl[axis] = slice(None, None, q)
return y[tuple(sl)]