Signal Processing (scipy.signal
)#
The signal processing toolbox currently contains some filtering functions, a limited set of filter design tools, and a few Bspline interpolation algorithms for 1 and 2D data. While the Bspline algorithms could technically be placed under the interpolation category, they are included here because they only work with equallyspaced data and make heavy use of filtertheory and transferfunction formalism to provide a fast Bspline transform. To understand this section, you will need to understand that a signal in SciPy is an array of real or complex numbers.
Bsplines#
A Bspline is an approximation of a continuous function over a finite domain in terms of Bspline coefficients and knot points. If the knot points are equally spaced with spacing \(\Delta x\), then the Bspline approximation to a 1D function is the finitebasis expansion.
In two dimensions with knotspacing \(\Delta x\) and \(\Delta y\), the function representation is
In these expressions, \(\beta^{o}\left(\cdot\right)\) is the spacelimited Bspline basis function of order \(o\). The requirement of equallyspaced knotpoints and equallyspaced data points, allows the development of fast (inversefiltering) algorithms for determining the coefficients, \(c_{j}\), from samplevalues, \(y_{n}\). Unlike the general spline interpolation algorithms, these algorithms can quickly find the spline coefficients for large images.
The advantage of representing a set of samples via Bspline basis functions is that continuousdomain operators (derivatives, re sampling, integral, etc.), which assume that the data samples are drawn from an underlying continuous function, can be computed with relative ease from the spline coefficients. For example, the second derivative of a spline is
Using the property of Bsplines that
it can be seen that
If \(o=3\), then at the sample points:
Thus, the secondderivative signal can be easily calculated from the spline fit. If desired, smoothing splines can be found to make the second derivative less sensitive to random errors.
The savvy reader will have already noticed that the data samples are related to the knot coefficients via a convolution operator, so that simple convolution with the sampled Bspline function recovers the original data from the spline coefficients. The output of convolutions can change depending on how the boundaries are handled (this becomes increasingly more important as the number of dimensions in the dataset increases). The algorithms relating to Bsplines in the signalprocessing subpackage assume mirrorsymmetric boundary conditions. Thus, spline coefficients are computed based on that assumption, and datasamples can be recovered exactly from the spline coefficients by assuming them to be mirrorsymmetric also.
Currently the package provides functions for determining second and third
order cubic spline coefficients from equallyspaced samples in one and two
dimensions (qspline1d
, qspline2d
, cspline1d
,
cspline2d
). For large \(o\), the Bspline basis
function can be approximated well by a zeromean Gaussian function with
standarddeviation equal to \(\sigma_{o}=\left(o+1\right)/12\) :
A function to compute this Gaussian for arbitrary \(x\) and \(o\) is
also available ( gauss_spline
). The following code and figure use
splinefiltering to compute an edgeimage (the second derivative of a smoothed
spline) of a raccoon’s face, which is an array returned by the command scipy.datasets.face
.
The command sepfir2d
was used to apply a separable 2D FIR
filter with mirrorsymmetric boundary conditions to the spline coefficients.
This function is ideallysuited for reconstructing samples from spline
coefficients and is faster than convolve2d
, which convolves arbitrary
2D filters and allows for choosing mirrorsymmetric boundary
conditions.
>>> import numpy as np
>>> from scipy import signal, datasets
>>> import matplotlib.pyplot as plt
>>> image = datasets.face(gray=True).astype(np.float32)
>>> derfilt = np.array([1.0, 2, 1.0], dtype=np.float32)
>>> ck = signal.cspline2d(image, 8.0)
>>> deriv = (signal.sepfir2d(ck, derfilt, [1]) +
... signal.sepfir2d(ck, [1], derfilt))
Alternatively, we could have done:
laplacian = np.array([[0,1,0], [1,4,1], [0,1,0]], dtype=np.float32)
deriv2 = signal.convolve2d(ck,laplacian,mode='same',boundary='symm')
>>> plt.figure()
>>> plt.imshow(image)
>>> plt.gray()
>>> plt.title('Original image')
>>> plt.show()
>>> plt.figure()
>>> plt.imshow(deriv)
>>> plt.gray()
>>> plt.title('Output of spline edge filter')
>>> plt.show()
Filtering#
Filtering is a generic name for any system that modifies an input signal in some way. In SciPy, a signal can be thought of as a NumPy array. There are different kinds of filters for different kinds of operations. There are two broad kinds of filtering operations: linear and nonlinear. Linear filters can always be reduced to multiplication of the flattened NumPy array by an appropriate matrix resulting in another flattened NumPy array. Of course, this is not usually the best way to compute the filter, as the matrices and vectors involved may be huge. For example, filtering a \(512 \times 512\) image with this method would require multiplication of a \(512^2 \times 512^2\) matrix with a \(512^2\) vector. Just trying to store the \(512^2 \times 512^2\) matrix using a standard NumPy array would require \(68,719,476,736\) elements. At 4 bytes per element this would require \(256\textrm{GB}\) of memory. In most applications, most of the elements of this matrix are zero and a different method for computing the output of the filter is employed.
Convolution/Correlation#
Many linear filters also have the property of shiftinvariance. This means that the filtering operation is the same at different locations in the signal and it implies that the filtering matrix can be constructed from knowledge of one row (or column) of the matrix alone. In this case, the matrix multiplication can be accomplished using Fourier transforms.
Let \(x\left[n\right]\) define a 1D signal indexed by the integer \(n.\) Full convolution of two 1D signals can be expressed as
This equation can only be implemented directly if we limit the sequences to finitesupport sequences that can be stored in a computer, choose \(n=0\) to be the starting point of both sequences, let \(K+1\) be that value for which \(x\left[n\right]=0\) for all \(n\geq K+1\) and \(M+1\) be that value for which \(h\left[n\right]=0\) for all \(n\geq M+1\), then the discrete convolution expression is
For convenience, assume \(K\geq M.\) Then, more explicitly, the output of this operation is
Thus, the full discrete convolution of two finite sequences of lengths \(K+1\) and \(M+1\), respectively, results in a finite sequence of length \(K+M+1=\left(K+1\right)+\left(M+1\right)1.\)
1D convolution is implemented in SciPy with the function
convolve
. This function takes as inputs the signals \(x,\)
\(h\), and two optional flags ‘mode’ and ‘method’, and returns the signal
\(y.\)
The first optional flag, ‘mode’, allows for the specification of which part of the output signal to return. The default value of ‘full’ returns the entire signal. If the flag has a value of ‘same’, then only the middle \(K\) values are returned, starting at \(y\left[\left\lfloor \frac{M1}{2}\right\rfloor \right]\), so that the output has the same length as the first input. If the flag has a value of ‘valid’, then only the middle \(KM+1=\left(K+1\right)\left(M+1\right)+1\) output values are returned, where \(z\) depends on all of the values of the smallest input from \(h\left[0\right]\) to \(h\left[M\right].\) In other words, only the values \(y\left[M\right]\) to \(y\left[K\right]\) inclusive are returned.
The second optional flag, ‘method’, determines how the convolution is computed,
either through the Fourier transform approach with fftconvolve
or
through the direct method. By default, it selects the expected faster method.
The Fourier transform method has order \(O(N\log N)\), while the direct
method has order \(O(N^2)\). Depending on the big O constant and the value
of \(N\), one of these two methods may be faster. The default value, ‘auto’,
performs a rough calculation and chooses the expected faster method, while the
values ‘direct’ and ‘fft’ force computation with the other two methods.
The code below shows a simple example for convolution of 2 sequences:
>>> x = np.array([1.0, 2.0, 3.0])
>>> h = np.array([0.0, 1.0, 0.0, 0.0, 0.0])
>>> signal.convolve(x, h)
array([ 0., 1., 2., 3., 0., 0., 0.])
>>> signal.convolve(x, h, 'same')
array([ 2., 3., 0.])
This same function convolve
can actually take ND
arrays as inputs and will return the ND convolution of the
two arrays, as is shown in the code example below. The same input flags are
available for that case as well.
>>> x = np.array([[1., 1., 0., 0.], [1., 1., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]])
>>> h = np.array([[1., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 0.]])
>>> signal.convolve(x, h)
array([[ 1., 1., 0., 0., 0., 0., 0.],
[ 1., 1., 0., 0., 0., 0., 0.],
[ 0., 0., 1., 1., 0., 0., 0.],
[ 0., 0., 1., 1., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.]])
Correlation is very similar to convolution except that the minus sign becomes a plus sign. Thus,
is the (cross) correlation of the signals \(y\) and \(x.\) For finitelength signals with \(y\left[n\right]=0\) outside of the range \(\left[0,K\right]\) and \(x\left[n\right]=0\) outside of the range \(\left[0,M\right],\) the summation can simplify to
Assuming again that \(K\geq M\), this is
The SciPy function correlate
implements this operation. Equivalent
flags are available for this operation to return the full \(K+M+1\) length
sequence (‘full’) or a sequence with the same size as the largest sequence
starting at \(w\left[K+\left\lfloor \frac{M1}{2}\right\rfloor \right]\)
(‘same’) or a sequence where the values depend on all the values of the
smallest sequence (‘valid’). This final option returns the \(KM+1\)
values \(w\left[MK\right]\) to \(w\left[0\right]\) inclusive.
The function correlate
can also take arbitrary ND arrays as input
and return the ND convolution of the two arrays on output.
When \(N=2,\) correlate
and/or convolve
can be used
to construct arbitrary image filters to perform actions such as blurring,
enhancing, and edgedetection for an image.
>>> import numpy as np
>>> from scipy import signal, datasets
>>> import matplotlib.pyplot as plt
>>> image = datasets.face(gray=True)
>>> w = np.zeros((50, 50))
>>> w[0][0] = 1.0
>>> w[49][25] = 1.0
>>> image_new = signal.fftconvolve(image, w)
>>> plt.figure()
>>> plt.imshow(image)
>>> plt.gray()
>>> plt.title('Original image')
>>> plt.show()
>>> plt.figure()
>>> plt.imshow(image_new)
>>> plt.gray()
>>> plt.title('Filtered image')
>>> plt.show()
Calculating the convolution in the time domain as above is mainly used for
filtering when one of the signals is much smaller than the other ( \(K\gg
M\) ), otherwise linear filtering is more efficiently calculated in the
frequency domain provided by the function fftconvolve
. By default,
convolve
estimates the fastest method using choose_conv_method
.
If the filter function \(w[n,m]\) can be factored according to
convolution can be calculated by means of the function sepfir2d
. As an
example, we consider a Gaussian filter gaussian
which is often used for blurring.
>>> import numpy as np
>>> from scipy import signal, datasets
>>> import matplotlib.pyplot as plt
>>> image = np.asarray(datasets.ascent(), np.float64)
>>> w = signal.windows.gaussian(51, 10.0)
>>> image_new = signal.sepfir2d(image, w, w)
>>> plt.figure()
>>> plt.imshow(image)
>>> plt.gray()
>>> plt.title('Original image')
>>> plt.show()
>>> plt.figure()
>>> plt.imshow(image_new)
>>> plt.gray()
>>> plt.title('Filtered image')
>>> plt.show()
Differenceequation filtering#
A general class of linear 1D filters (that includes convolution filters) are filters described by the difference equation
where \(x\left[n\right]\) is the input sequence and \(y\left[n\right]\) is the output sequence. If we assume initial rest so that \(y\left[n\right]=0\) for \(n<0\), then this kind of filter can be implemented using convolution. However, the convolution filter sequence \(h\left[n\right]\) could be infinite if \(a_{k}\neq0\) for \(k\geq1.\) In addition, this general class of linear filter allows initial conditions to be placed on \(y\left[n\right]\) for \(n<0\) resulting in a filter that cannot be expressed using convolution.
The difference equation filter can be thought of as finding \(y\left[n\right]\) recursively in terms of its previous values
Often, \(a_{0}=1\) is chosen for normalization. The implementation in SciPy of this general difference equation filter is a little more complicated than would be implied by the previous equation. It is implemented so that only one signal needs to be delayed. The actual implementation equations are (assuming \(a_{0}=1\) ):
where \(K=\max\left(N,M\right).\) Note that \(b_{K}=0\) if \(K>M\) and \(a_{K}=0\) if \(K>N.\) In this way, the output at time \(n\) depends only on the input at time \(n\) and the value of \(z_{0}\) at the previous time. This can always be calculated as long as the \(K\) values \(z_{0}\left[n1\right]\ldots z_{K1}\left[n1\right]\) are computed and stored at each time step.
The differenceequation filter is called using the command lfilter
in
SciPy. This command takes as inputs the vector \(b,\) the vector,
\(a,\) a signal \(x\) and returns the vector \(y\) (the same
length as \(x\) ) computed using the equation given above. If \(x\) is
ND, then the filter is computed along the axis provided.
If desired, initial conditions providing the values of
\(z_{0}\left[1\right]\) to \(z_{K1}\left[1\right]\) can be provided
or else it will be assumed that they are all zero. If initial conditions are
provided, then the final conditions on the intermediate variables are also
returned. These could be used, for example, to restart the calculation in the
same state.
Sometimes, it is more convenient to express the initial conditions in terms of the signals \(x\left[n\right]\) and \(y\left[n\right].\) In other words, perhaps you have the values of \(x\left[M\right]\) to \(x\left[1\right]\) and the values of \(y\left[N\right]\) to \(y\left[1\right]\) and would like to determine what values of \(z_{m}\left[1\right]\) should be delivered as initial conditions to the differenceequation filter. It is not difficult to show that, for \(0\leq m<K,\)
Using this formula, we can find the initialcondition vector
\(z_{0}\left[1\right]\) to \(z_{K1}\left[1\right]\) given initial
conditions on \(y\) (and \(x\) ). The command lfiltic
performs
this function.
As an example, consider the following system:
The code calculates the signal \(y[n]\) for a given signal \(x[n]\);
first for initial conditions \(y[1] = 0\) (default case), then for
\(y[1] = 2\) by means of lfiltic
.
>>> import numpy as np
>>> from scipy import signal
>>> x = np.array([1., 0., 0., 0.])
>>> b = np.array([1.0/2, 1.0/4])
>>> a = np.array([1.0, 1.0/3])
>>> signal.lfilter(b, a, x)
array([0.5, 0.41666667, 0.13888889, 0.0462963])
>>> zi = signal.lfiltic(b, a, y=[2.])
>>> signal.lfilter(b, a, x, zi=zi)
(array([ 1.16666667, 0.63888889, 0.21296296, 0.07098765]), array([0.02366]))
Note that the output signal \(y[n]\) has the same length as the length as the input signal \(x[n]\).
Analysis of Linear Systems#
Linear system described a lineardifference equation can be fully described by the coefficient vectors \(a\) and \(b\) as was done above; an alternative representation is to provide a factor \(k\), \(N_z\) zeros \(z_k\) and \(N_p\) poles \(p_k\), respectively, to describe the system by means of its transfer function \(H(z)\), according to
This alternative representation can be obtained with the scipy function
tf2zpk
; the inverse is provided by zpk2tf
.
For the above example we have
>>> b = np.array([1.0/2, 1.0/4])
>>> a = np.array([1.0, 1.0/3])
>>> signal.tf2zpk(b, a)
(array([0.5]), array([ 0.33333333]), 0.5)
i.e., the system has a zero at \(z=1/2\) and a pole at \(z=1/3\).
The scipy function freqz
allows calculation of the frequency response
of a system described by the coefficients \(a_k\) and \(b_k\). See the
help of the freqz
function for a comprehensive example.
Filter Design#
Timediscrete filters can be classified into finite response (FIR) filters and infinite response (IIR) filters. FIR filters can provide a linear phase response, whereas IIR filters cannot. SciPy provides functions for designing both types of filters.
FIR Filter#
The function firwin
designs filters according to the window method.
Depending on the provided arguments, the function returns different filter
types (e.g., lowpass, bandpass…).
The example below designs a lowpass and a bandstop filter, respectively.
>>> import numpy as np
>>> import scipy.signal as signal
>>> import matplotlib.pyplot as plt
>>> b1 = signal.firwin(40, 0.5)
>>> b2 = signal.firwin(41, [0.3, 0.8])
>>> w1, h1 = signal.freqz(b1)
>>> w2, h2 = signal.freqz(b2)
>>> plt.title('Digital filter frequency response')
>>> plt.plot(w1, 20*np.log10(np.abs(h1)), 'b')
>>> plt.plot(w2, 20*np.log10(np.abs(h2)), 'r')
>>> plt.ylabel('Amplitude Response (dB)')
>>> plt.xlabel('Frequency (rad/sample)')
>>> plt.grid()
>>> plt.show()
Note that firwin
uses, per default, a normalized frequency defined such
that the value \(1\) corresponds to the Nyquist frequency, whereas the
function freqz
is defined such that the value \(\pi\) corresponds
to the Nyquist frequency.
The function firwin2
allows design of almost arbitrary frequency
responses by specifying an array of corner frequencies and corresponding
gains, respectively.
The example below designs a filter with such an arbitrary amplitude response.
>>> import numpy as np
>>> import scipy.signal as signal
>>> import matplotlib.pyplot as plt
>>> b = signal.firwin2(150, [0.0, 0.3, 0.6, 1.0], [1.0, 2.0, 0.5, 0.0])
>>> w, h = signal.freqz(b)
>>> plt.title('Digital filter frequency response')
>>> plt.plot(w, np.abs(h))
>>> plt.title('Digital filter frequency response')
>>> plt.ylabel('Amplitude Response')
>>> plt.xlabel('Frequency (rad/sample)')
>>> plt.grid()
>>> plt.show()
Note the linear scaling of the yaxis and the different definition of the
Nyquist frequency in firwin2
and freqz
(as explained above).
IIR Filter#
SciPy provides two functions to directly design IIR iirdesign
and
iirfilter
, where the filter type (e.g., elliptic) is passed as an
argument and several more filter design functions for specific filter types,
e.g., ellip
.
The example below designs an elliptic lowpass filter with defined passband and stopband ripple, respectively. Note the much lower filter order (order 4) compared with the FIR filters from the examples above in order to reach the same stopband attenuation of \(\approx 60\) dB.
>>> import numpy as np
>>> import scipy.signal as signal
>>> import matplotlib.pyplot as plt
>>> b, a = signal.iirfilter(4, Wn=0.2, rp=5, rs=60, btype='lowpass', ftype='ellip')
>>> w, h = signal.freqz(b, a)
>>> plt.title('Digital filter frequency response')
>>> plt.plot(w, 20*np.log10(np.abs(h)))
>>> plt.title('Digital filter frequency response')
>>> plt.ylabel('Amplitude Response [dB]')
>>> plt.xlabel('Frequency (rad/sample)')
>>> plt.grid()
>>> plt.show()
Filter Coefficients#
Filter coefficients can be stored in several different formats:
‘ba’ or ‘tf’ = transfer function coefficients
‘zpk’ = zeros, poles, and overall gain
‘ss’ = statespace system representation
‘sos’ = transfer function coefficients of secondorder sections
Functions, such as tf2zpk
and zpk2ss
, can convert between them.
Transfer function representation#
The ba
or tf
format is a 2tuple (b, a)
representing a transfer
function, where b is a length M+1
array of coefficients of the Morder
numerator polynomial, and a is a length N+1
array of coefficients of the
Norder denominator, as positive, descending powers of the transfer function
variable. So the tuple of \(b = [b_0, b_1, ..., b_M]\) and
\(a =[a_0, a_1, ..., a_N]\) can represent an analog filter of the form:
or a discretetime filter of the form:
This “positive powers” form is found more commonly in controls engineering. If M and N are equal (which is true for all filters generated by the bilinear transform), then this happens to be equivalent to the “negative powers” discretetime form preferred in DSP:
Although this is true for common filters, remember that this is not true in the general case. If M and N are not equal, the discretetime transfer function coefficients must first be converted to the “positive powers” form before finding the poles and zeros.
This representation suffers from numerical error at higher orders, so other formats are preferred when possible.
Zeros and poles representation#
The zpk
format is a 3tuple (z, p, k)
, where z is an Mlength
array of the complex zeros of the transfer function
\(z = [z_0, z_1, ..., z_{M1}]\), p is an Nlength array of the
complex poles of the transfer function \(p = [p_0, p_1, ..., p_{N1}]\),
and k is a scalar gain. These represent the digital transfer function:
or the analog transfer function:
Although the sets of roots are stored as ordered NumPy arrays, their ordering
does not matter: ([1, 2], [3, 4], 1)
is the same filter as
([2, 1], [4, 3], 1)
.
Statespace system representation#
The ss
format is a 4tuple of arrays (A, B, C, D)
representing the
statespace of an Norder digital/discretetime system of the form:
or a continuous/analog system of the form:
with P inputs, Q outputs and N state variables, where:
x is the state vector
y is the output vector of length Q
u is the input vector of length P
A is the state matrix, with shape
(N, N)
B is the input matrix with shape
(N, P)
C is the output matrix with shape
(Q, N)
D is the feedthrough or feedforward matrix with shape
(Q, P)
. (In cases where the system does not have a direct feedthrough, all values in D are zero.)
Statespace is the most general representation and the only one that allows
for multipleinput, multipleoutput (MIMO) systems. There are multiple
statespace representations for a given transfer function. Specifically, the
“controllable canonical form” and “observable canonical form” have the same
coefficients as the tf
representation, and, therefore, suffer from the same
numerical errors.
Secondorder sections representation#
The sos
format is a single 2D array of shape (n_sections, 6)
,
representing a sequence of secondorder transfer functions which, when
cascaded in series, realize a higherorder filter with minimal numerical
error. Each row corresponds to a secondorder tf
representation, with
the first three columns providing the numerator coefficients and the last
three providing the denominator coefficients:
The coefficients are typically normalized, such that \(a_0\) is always 1. The section order is usually not important with floatingpoint computation; the filter output will be the same, regardless of the order.
Filter transformations#
The IIR filter design functions first generate a prototype analog lowpass filter with a normalized cutoff frequency of 1 rad/sec. This is then transformed into other frequencies and band types using the following substitutions:
Type 
Transformation 

\(s \rightarrow \frac{s}{\omega_0}\) 

\(s \rightarrow \frac{\omega_0}{s}\) 

\(s \rightarrow \frac{s^2 + {\omega_0}^2}{s \cdot \mathrm{BW}}\) 

\(s \rightarrow \frac{s \cdot \mathrm{BW}}{s^2 + {\omega_0}^2}\) 
Here, \(\omega_0\) is the new cutoff or center frequency, and \(\mathrm{BW}\) is the bandwidth. These preserve symmetry on a logarithmic frequency axis.
To convert the transformed analog filter into a digital filter, the
bilinear
transform is used, which makes the following substitution:
where T is the sampling time (the inverse of the sampling frequency).
Other filters#
The signal processing package provides many more filters as well.
Median Filter#
A median filter is commonly applied when noise is markedly nonGaussian or
when it is desired to preserve edges. The median filter works by sorting all
of the array pixel values in a rectangular region surrounding the point of
interest. The sample median of this list of neighborhood pixel values is used
as the value for the output array. The sample median is the middlearray value
in a sorted list of neighborhood values. If there are an even number of
elements in the neighborhood, then the average of the middle two values is
used as the median. A general purpose median filter that works on
ND arrays is medfilt
. A specialized version that works
only for 2D arrays is available as medfilt2d
.
Order Filter#
A median filter is a specific example of a more general class of filters
called order filters. To compute the output at a particular pixel, all order
filters use the array values in a region surrounding that pixel. These array
values are sorted and then one of them is selected as the output value. For
the median filter, the sample median of the list of array values is used as
the output. A generalorder filter allows the user to select which of the
sorted values will be used as the output. So, for example, one could choose to
pick the maximum in the list or the minimum. The order filter takes an
additional argument besides the input array and the region mask that specifies
which of the elements in the sorted list of neighbor array values should be
used as the output. The command to perform an order filter is
order_filter
.
Wiener filter#
The Wiener filter is a simple deblurring filter for denoising images. This is not the Wiener filter commonly described in imagereconstruction problems but, instead, it is a simple, localmean filter. Let \(x\) be the input signal, then the output is
where \(m_{x}\) is the local estimate of the mean and \(\sigma_{x}^{2}\) is the local estimate of the variance. The window for these estimates is an optional input parameter (default is \(3\times3\) ). The parameter \(\sigma^{2}\) is a threshold noise parameter. If \(\sigma\) is not given, then it is estimated as the average of the local variances.
Hilbert filter#
The Hilbert transform constructs the complexvalued analytic signal from a real signal. For example, if \(x=\cos\omega n\), then \(y=\textrm{hilbert}\left(x\right)\) would return (except near the edges) \(y=\exp\left(j\omega n\right).\) In the frequency domain, the hilbert transform performs
where \(H\) is \(2\) for positive frequencies, \(0\) for negative frequencies, and \(1\) for zerofrequencies.
Analog Filter Design#
The functions iirdesign
, iirfilter
, and the filter design
functions for specific filter types (e.g., ellip
) all have a flag
analog, which allows the design of analog filters as well.
The example below designs an analog (IIR) filter, obtains via tf2zpk
the poles and zeros and plots them in the complex splane. The zeros at
\(\omega \approx 150\) and \(\omega \approx 300\) can be clearly seen
in the amplitude response.
>>> import numpy as np
>>> import scipy.signal as signal
>>> import matplotlib.pyplot as plt
>>> b, a = signal.iirdesign(wp=100, ws=200, gpass=2.0, gstop=40., analog=True)
>>> w, h = signal.freqs(b, a)
>>> plt.title('Analog filter frequency response')
>>> plt.plot(w, 20*np.log10(np.abs(h)))
>>> plt.ylabel('Amplitude Response [dB]')
>>> plt.xlabel('Frequency')
>>> plt.grid()
>>> plt.show()
>>> z, p, k = signal.tf2zpk(b, a)
>>> plt.plot(np.real(z), np.imag(z), 'ob', markerfacecolor='none')
>>> plt.plot(np.real(p), np.imag(p), 'xr')
>>> plt.legend(['Zeros', 'Poles'], loc=2)
>>> plt.title('Pole / Zero Plot')
>>> plt.xlabel('Real')
>>> plt.ylabel('Imaginary')
>>> plt.grid()
>>> plt.show()
Spectral Analysis#
Periodogram Measurements#
The scipy function periodogram
provides a method to estimate the
spectral density using the periodogram method.
The example below calculates the periodogram of a sine signal in white Gaussian noise.
>>> import numpy as np
>>> import scipy.signal as signal
>>> import matplotlib.pyplot as plt
>>> fs = 10e3
>>> N = 1e5
>>> amp = 2*np.sqrt(2)
>>> freq = 1270.0
>>> noise_power = 0.001 * fs / 2
>>> time = np.arange(N) / fs
>>> x = amp*np.sin(2*np.pi*freq*time)
>>> x += np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
>>> f, Pper_spec = signal.periodogram(x, fs, 'flattop', scaling='spectrum')
>>> plt.semilogy(f, Pper_spec)
>>> plt.xlabel('frequency [Hz]')
>>> plt.ylabel('PSD')
>>> plt.grid()
>>> plt.show()
Spectral Analysis using Welch’s Method#
An improved method, especially with respect to noise immunity, is Welch’s
method, which is implemented by the scipy function welch
.
The example below estimates the spectrum using Welch’s method and uses the same parameters as the example above. Note the much smoother noise floor of the spectrogram.
>>> import numpy as np
>>> import scipy.signal as signal
>>> import matplotlib.pyplot as plt
>>> fs = 10e3
>>> N = 1e5
>>> amp = 2*np.sqrt(2)
>>> freq = 1270.0
>>> noise_power = 0.001 * fs / 2
>>> time = np.arange(N) / fs
>>> x = amp*np.sin(2*np.pi*freq*time)
>>> x += np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
>>> f, Pwelch_spec = signal.welch(x, fs, scaling='spectrum')
>>> plt.semilogy(f, Pwelch_spec)
>>> plt.xlabel('frequency [Hz]')
>>> plt.ylabel('PSD')
>>> plt.grid()
>>> plt.show()
LombScargle Periodograms (lombscargle
)#
Leastsquares spectral analysis (LSSA) [1] [2] is a method of estimating a frequency spectrum, based on a leastsquares fit of sinusoids to data samples, similar to Fourier analysis. Fourier analysis, the most used spectral method in science, generally boosts longperiodic noise in longgapped records; LSSA mitigates such problems.
The LombScargle method performs spectral analysis on unevenlysampled data and is known to be a powerful way to find, and test the significance of, weak periodic signals.
For a time series comprising \(N_{t}\) measurements \(X_{j}\equiv X(t_{j})\) sampled at times \(t_{j}\), where \((j = 1, \ldots, N_{t})\), assumed to have been scaled and shifted, such that its mean is zero and its variance is unity, the normalized LombScargle periodogram at frequency \(f\) is
Here, \(\omega \equiv 2\pi f\) is the angular frequency. The frequencydependent time offset \(\tau\) is given by
The lombscargle
function calculates the periodogram using a slightly
modified algorithm due to Townsend [3], which allows the periodogram to be
calculated using only a single pass through the input arrays for each
frequency.
The equation is refactored as:
and
Here,
while the sums are
This requires \(N_{f}(2N_{t}+3)\) trigonometric function evaluations giving a factor of \(\sim 2\) speed increase over the straightforward implementation.
ShortTime Fourier Transform#
This section gives some background information on using the ShortTimeFFT
class: The shorttime Fourier transform (STFT) can be utilized to analyze the
spectral properties of signals over time. It divides a signal into overlapping
chunks by utilizing a sliding window and calculates the Fourier transform
of each chunk. For a continuoustime complexvalued signal \(x(t)\) the
STFT is defined [4] as
where \(w(t)\) is a complexvalued window function with its complex
conjugate being \(\conj{w(t)}\). It can be interpreted as determining the
scalar product of \(x\) with the window \(w\) which is translated by
the time \(t\) and then modulated (i.e., frequencyshifted) by the
frequency \(f\).
For working with sampled signals \(x[k] := x(kT)\), \(k\in\IZ\), with
sampling interval \(T\) (being the inverse of the sampling frequency fs
),
the discrete version, i.e., only evaluating the STFT at discrete grid points
\(S[q, p] := S(q \Delta f, p\Delta t)\), \(q,p\in\IZ\), needs to be
used. It can be formulated as
with p representing the time index of \(S\) with time interval
\(\Delta t := h T\), \(h\in\IN\) (see delta_t
), which can be
expressed as the hop
size of \(h\) samples. \(q\) represents the
frequency index of \(S\) with step size \(\Delta f := 1 / (N T)\)
(see delta_f
), which makes it FFT compatible. \(w[m] := w(mT)\),
\(m\in\IZ\) is the sampled window function.
To be more aligned to the implementation of ShortTimeFFT
, it makes sense to
reformulate Eq. (1) as a twostep process:
Extract the \(p\)th slice by windowing with the window \(w[m]\) made up of \(M\) samples (see
m_num
) centered at \(t[p] := p \Delta t = h T\) (seedelta_t
), i.e.,(2)#\[x_p[m] = x\!\big[m  \lfloor M/2\rfloor + h p\big]\, \conj{w[m]}\ , \quad m = 0, \ldots M1\ ,\]where the integer \(\lfloor M/2\rfloor\) represents
M//2
, i.e, it is the mid point of the window (m_num_mid
). For notational convenience, \(x[k]:=0\) for \(k\not\in\{0, 1, \ldots, N1\}\) is assumed. In the subsection Sliding Windows the indexing of the slices is discussed in more detail.Then perform a discrete Fourier transform (i.e., an FFT) of \(x_p[m]\).
(3)#\[S[q, p] = \sum_{m=0}^{M1} x_p[m] \exp\!\big\{% 2\jj\pi (q + \phi_m)\, m / M\big\}\ .\]Note that a linear phase \(\phi_m\) (see
phase_shift
) can be specified, which corresponds to shifting the input by \(\phi_m\) samples. The default is \(\phi_m = \lfloor M/2\rfloor\) (corresponds per definition tophase_shift = 0
), which suppresses linear phase components for unshifted signals. Furthermore, the FFT may be oversampled by padding \(w[m]\) with zeros. This can be achieved by specifyingmfft
to be larger than the window lengthm_num
—this sets \(M\) tomfft
(implying that also \(w[m]:=0\) for \(m\not\in\{0, 1, \ldots, M1\}\) holds).
The inverse shorttime Fourier transform (istft
) is implemented by reversing
these two steps:
Perform the inverse discrete Fourier transform, i.e.,
\[x_p[m] = \frac{1}{M}\sum_{q=0}^M S[q, p]\, \exp\!\big\{ 2\jj\pi (q + \phi_m)\, m / M\big\}\ .\]Sum the shifted slices weighted by \(w_d[m]\) to reconstruct the original signal, i.e.,
\[x[k] = \sum_p x_p\!\big[\mu_p(k)\big]\, w_d\!\big[\mu_p(k)\big]\ ,\quad \mu_p(k) = k + \lfloor M/2\rfloor  h p\]for \(k \in [0, \ldots, n1]\). \(w_d[m]\) is the socalled canonical dual window of \(w[m]\) and is also made up of \(M\) samples.
Note that an inverse STFT does not necessarily exist for all windows and hop sizes. For a given
window \(w[m]\) the hop size \(h\) must be small enough to ensure that
every sample of \(x[k]\) is touched by a nonzero value of at least one
window slice. This is sometimes referred as the “nonzero overlap condition”
(see check_NOLA
). Some more details are
given in the subsection Inverse STFT and Dual Windows.
Sliding Windows#
This subsection discusses how the sliding window is indexed in the
ShortTimeFFT
by means of an example: Consider a window of length 6 with a
hop
interval of two and a sampling interval T
of one, e.g., ShortTimeFFT
(np.ones(6), 2, fs=1)
. The following image schematically depicts the first
four window positions also named time slices:
The xaxis denotes the time \(t\), which corresponds to the sample index
k indicated by the bottom row of blue boxes. The yaxis denotes the time
slice index \(p\). The signal \(x[k]\) starts at index \(k=0\) and
is marked by a light blue background. Per definition the zeroth slice
(\(p=0\)) is centered at \(t=0\). The center of each slice
(m_num_mid
), here being the sample 6//2=3
, is marked by the text “mid”.
By default the stft
calculates all slices which have some overlap with the
signal. Hence the first slice is at p_min
= 1 with the lowest sample index
being k_min
= 5. The first sample index unaffected by a slice not sticking
out to the left of the signal is \(p_{lb} = 2\) and the first sample index
unaffected by border effects is \(k_{lb} = 5\). The property
lower_border_end
returns the tuple \((k_{lb}, p_{lb})\).
The behavior at the end of the signal is depicted for a signal with \(n=50\) samples below, as indicated by the blue background:
Here the last slice has index \(p=26\). Hence, following Python
convention of the end index being outside the range, p_max
= 27 indicates the
first slice not touching the signal. The corresponding sample index is
k_max
= 55. The first slice, which sticks out to the right is
\(p_{ub} = 24\) with its first sample at \(k_{ub}=45\). The function
upper_border_begin
returns the tuple \((k_{ub}, p_{ub})\).
Inverse STFT and Dual Windows#
The term dual window stems from frame theory [5] where a frame is a series expansion which can represent any function in a given Hilbert space. There the expansions \(\{g_k\}\) and \(\{h_k\}\) are dual frames if for all functions \(f\) in the given Hilbert space \(\mathcal{H}\)
holds, where \(\langle ., .\rangle\) denotes the scalar product of \(\mathcal{H}\). All frames have dual frames [5].
An STFT evaluated only at discrete grid
points \(S(q \Delta f, p\Delta t)\) is called a “Gabor frame” in
literature [4] [5].
Since the support of the window \(w[m]\) is limited to a finite interval,
the ShortTimeFFT
falls into the class of the socalled “painless
nonorthogonal expansions” [4]. In this case the dual windows always have the
same support and can be calculated by means of inverting a diagonal matrix. A
rough derivation only requiring some understanding of manipulating matrices
will be sketched out in the following:
Since the STFT given in Eq. (1) is a linear mapping in
\(x[k]\), it can be expressed in vectormatrix notation. This allows us to
express the inverse via the formal solution of the linear least squares method
(as in lstsq
), which leads to a beautiful and
simple result.
We begin by reformulating the windowing of Eq. (2)
where the \(M\times N\) matrix \(\vb{W}_{\!p}\) has only nonzeros entries on the \((ph)\)th minor diagonal, i.e.,
with \(\delta_{k,l}\) being the Kronecker Delta. Eq. (3) can be expressed as
which allows the STFT of the \(p\)th slice to be written as
Note that \(\vb{F}\) is unitary, i.e., the inverse equals its conjugate transpose meaning \(\conjT{\vb{F}}\vb{F} = \vb{I}\).
To obtain a single vectormatrix equation for the STFT, the slices are stacked into one vector, i.e.,
where \(P\) is the number of columns of the resulting STFT. To invert this equation the MoorePenrose inverse \(\vb{G}^\dagger\) can be utilized
which exists if
is invertible. Then \(\vb{x} = \vb{G}^\dagger\vb{G}\,\vb{x} = \inv{\conjT{\vb{G}}\vb{G}}\,\conjT{\vb{G}}\vb{G}\,\vb{x}\) obviously holds. \(\vb{D}\) is always a diagonal matrix with nonnegative diagonal entries. This becomes clear, when simplifying \(\vb{D}\) further to
due to \(\vb{F}\) being unitary. Furthermore
shows that \(\vb{D}_p\) is a diagonal matrix with nonnegative entries. Hence, summing \(\vb{D}_p\) preserves that property. This allows to simplify Eq. (7) further, i.e,
Utilizing Eq. (5), (8), (9), \(\vb{U}_p=\vb{W}_{\!p}\vb{D}^{1}\) can be expressed as
This shows \(\vb{U}_p\) has the identical structure as \(\vb{W}_p\) in Eq. (5), i.e., having only nonzero entries on the \((ph)\)th minor diagonal. The sum term in the inverse can be interpreted as sliding \(w[\mu]^2\) over \(w[m]\) (with an incorporated inversion), so only components overlapping with \(w[m]\) have an effect. Hence, all \(U_p[m, k]\) far enough from the border are identical windows. To circumvent border effects, \(x[k]\) is padded with zeros, enlarging \(\vb{U}\) so all slices which touch \(x[k]\) contain the identical dual window
Since \(w[m] = 0\) holds for \(m \not\in\{0, \ldots, M1\}\), it is only required to sum over the indexes \(\eta\) fulfilling \(\eta < M/h\). The name dual window can be justified by inserting Eq. (6) into Eq. (10), i.e.,
showing that \(\vb{U}_p\) and \(\vb{W}_{\!p}\) are interchangeable. Hence, \(w_d[m]\) is also a valid window with dual window \(w[m]\). Note that \(w_d[m]\) is not a unique dual window, due to \(\vb{s}\) typically having more entries than \(\vb{x}\). It can be shown, that \(w_d[m]\) has the minimal energy (or \(L_2\) norm) [4], which is the reason for being named the “canonical dual window”.
Comparison with Legacy Implementation#
The functions stft
, istft
, and the spectrogram
predate the
ShortTimeFFT
implementation. This section discusses the key differences
between the older “legacy” and the newer ShortTimeFFT
implementations. The
main motivation for a rewrite was the insight that integrating dual
windows could not be done in a sane way without
breaking compatibility. This opened the opportunity for rethinking the code
structure and the parametrization, thus making some implicit behavior more
explicit.
The following example compares the two STFTs of a complex valued chirp signal with a negative slope:
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from scipy.fft import fftshift
>>> from scipy.signal import stft, istft, spectrogram, ShortTimeFFT
...
>>> fs, N = 200, 1001 # 200 Hz sampling rate for 5 s signal
>>> t_z = np.arange(N) / fs # time indexes for signal
>>> z = np.exp(2j*np.pi*70 * (t_z  0.2*t_z**2)) # complexvalued chirp
...
>>> nperseg, noverlap = 50, 40
>>> win = ('gaussian', 1e2 * fs) # Gaussian with 0.01 s standard dev.
...
>>> # Legacy STFT:
>>> f0_u, t0, Sz0_u = stft(z, fs, win, nperseg, noverlap,
... return_onesided=False, scaling='spectrum')
>>> f0, Sz0 = fftshift(f0_u), fftshift(Sz0_u, axes=0)
...
>>> # New STFT:
>>> SFT = ShortTimeFFT.from_window(win, fs, nperseg, noverlap,
... fft_mode='centered',
... scale_to='magnitude', phase_shift=None)
>>> Sz1 = SFT.stft(z)
...
>>> # Plot results:
>>> fig1, axx = plt.subplots(2, 1, sharex='all', sharey='all',
... figsize=(6., 5.)) # enlarge figure a bit
>>> t_lo, t_hi, f_lo, f_hi = SFT.extent(N, center_bins=True)
>>> t_str0 = r"Legacy stft() produces $%d\times%d$ points" % Sz0.T.shape
>>> t_str1 = r"ShortTimeFFT produces $%d\times%d$ points" % Sz1.T.shape
>>> _ = axx[0].set(title=t_str0, xlim=(t_lo, t_hi), ylim=(f_lo, f_hi))
>>> _ = axx[1].set(title=t_str1, xlabel="Time $t$ in seconds " +
... rf"($\Delta t= %g\,$s)" % SFT.delta_t)
...
>>> # Calculate extent of plot with centered bins since imshow
... # does not interpolate by default:
... dt2 = (npersegnoverlap) / fs / 2 # equals SFT.delta_t / 2
>>> df2 = fs / nperseg / 2 # equals SFT.delta_f / 2
>>> extent0 = (dt2, t0[1] + dt2, f0[0]  df2, f0[1]  df2)
>>> extent1 = SFT.extent(N, center_bins=True)
...
>>> kw = dict(origin='lower', aspect='auto', cmap='viridis')
>>> im1a = axx[0].imshow(abs(Sz0), extent=extent0, **kw)
>>> im1b = axx[1].imshow(abs(Sz1), extent=extent1, **kw)
>>> fig1.colorbar(im1b, ax=axx, label="Magnitude $S_z(t, f)$")
>>> _ = fig1.supylabel(rf"Frequency $f$ in Hertz ($\Delta f = %g\,$Hz)" %
... SFT.delta_f)
>>> plt.show()
That the ShortTimeFFT
produces 3 more time slices than the legacy version is
the main difference. As laid out in the Sliding Windows
section, all slices which touch the signal are incorporated in the new version.
This has the advantage that the STFT can be sliced and reassembled as shown in
the ShortTimeFFT
code example. Furthermore, using all touching slices makes
the ISTFT more robust in the case of windows that are zero somewhere.
Note that the slices with identical time stamps produce equal results (up to numerical accuracy), i.e.:
>>> np.allclose(Sz0, Sz1[:, 2:1])
True
Generally, those additional slices contain nonzero values. Due to the large overlap in our example, they are quite small. E.g.:
>>> abs(Sz1[:, 1]).min(), abs(Sz1[:, 1]).max()
(6.925060911593139e07, 8.00271269218721e07)
The ISTFT can be utilized to reconstruct the original signal:
>>> t0_r, z0_r = istft(Sz0_u, fs, win, nperseg, noverlap,
... input_onesided=False, scaling='spectrum')
>>> z1_r = SFT.istft(Sz1, k1=N)
...
>>> len(z0_r), len(z)
(1010, 1001)
>>> np.allclose(z0_r[:N], z)
True
>>> np.allclose(z1_r, z)
True
Note that the legacy implementation returns a signal which is longer than the
original. On the other hand, the new istft
allows to explicitly specify the
start index k0 and the end index k1 of the reconstructed signal. The length
discrepancy in the old implementation is caused by the fact that the signal
length is not a multiple of the slices.
Further differences between the new and legacy versions in this example are:
The parameter
fft_mode='centered'
ensures that the zero frequency is vertically centered for twosided FFTs in the plot. With the legacy implementation,fftshift
needs to be utilized.fft_mode='twosided'
produces the same behavior as the old version.The parameter
phase_shift=None
ensures identical phases of the two versions.ShortTimeFFT
’s default value of0
produces STFT slices with an additional linear phase term.
A spectrogram is defined as the absolute square of the STFT [4]. The
spectrogram
provided by the ShortTimeFFT
sticks to that definition, i.e.:
>>> np.allclose(SFT.spectrogram(z), abs(Sz1)**2)
True
On the other hand, the legacy spectrogram
provides another STFT
implementation with the key difference being the different handling of the
signal borders. The following example shows how to use the ShortTimeFFT
to
obtain an identical SFT as produced with the legacy spectrogram
:
>>> # Legacy spectrogram (detrending for complex signals not useful):
>>> f2_u, t2, Sz2_u = spectrogram(z, fs, win, nperseg, noverlap,
... detrend=None, return_onesided=False,
... scaling='spectrum', mode='complex')
>>> f2, Sz2 = fftshift(f2_u), fftshift(Sz2_u, axes=0)
...
>>> # New STFT:
... SFT = ShortTimeFFT.from_window(win, fs, nperseg, noverlap,
... fft_mode='centered',
... scale_to='magnitude', phase_shift=None)
>>> Sz3 = SFT.stft(z, p0=0, p1=(Nnoverlap)//SFT.hop, k_offset=nperseg//2)
>>> t3 = SFT.t(N, p0=0, p1=(Nnoverlap)//SFT.hop, k_offset=nperseg//2)
...
>>> np.allclose(t2, t3)
True
>>> np.allclose(f2, SFT.f)
True
>>> np.allclose(Sz2, Sz3)
True
The difference from the other STFTs is that the time slices do not start at 0 but
at nperseg//2
, i.e.:
>>> t2
array([0.125, 0.175, 0.225, 0.275, 0.325, 0.375, 0.425, 0.475, 0.525,
...
4.625, 4.675, 4.725, 4.775, 4.825, 4.875])
Furthermore, only slices which do not stick out to the right are returned,
centering the last slice at 4.875 s, which makes it shorter than with the default
stft
parametrization.
Using the mode
parameter, the legacy spectrogram
can also return the
‘angle’, ‘phase’, ‘psd’ or the ‘magnitude’. The scaling
behavior of the
legacy spectrogram
is not straightforward, since it depends on the
parameters mode
, scaling
and return_onesided
. There is no direct
correspondence for all combinations in the ShortTimeFFT
, since it provides
only ‘magnitude’, ‘psd’ or no scaling
of the window at all. The following
table shows those correspondences:
Legacy 


mode 
scaling 
return_onesided 

psd 
density 
True 
onesided2X 
psd 

psd 
density 
False 
twosided 
psd 

magnitude 
spectrum 
True 
onesided 
magnitude 

magnitude 
spectrum 
False 
twosided 
magnitude 

complex 
spectrum 
True 
onesided 
magnitude 

complex 
spectrum 
False 
twosided 
magnitude 

psd 
spectrum 
True 
— 
— 

psd 
spectrum 
False 
— 
— 

complex 
density 
True 
— 
— 

complex 
density 
False 
— 
— 

magnitude 
density 
True 
— 
— 

magnitude 
density 
False 
— 
— 

— 
— 
— 


When using onesided
output on complexvalued input signals, the old
spectrogram
switches to twosided
mode. The ShortTimeFFT
raises
a TypeError
, since the utilized rfft
function only accepts
realvalued inputs.
This comment of
Github issue 14903 discusses
variations of the old spectrogram
parameters for a single cosine input.
Detrend#
SciPy provides the function detrend
to remove a constant or linear
trend in a data series in order to see effect of higher order.
The example below removes the constant and linear trend of a secondorder polynomial time series and plots the remaining signal components.
>>> import numpy as np
>>> import scipy.signal as signal
>>> import matplotlib.pyplot as plt
>>> t = np.linspace(10, 10, 20)
>>> y = 1 + t + 0.01*t**2
>>> yconst = signal.detrend(y, type='constant')
>>> ylin = signal.detrend(y, type='linear')
>>> plt.plot(t, y, 'rx')
>>> plt.plot(t, yconst, 'bo')
>>> plt.plot(t, ylin, 'k+')
>>> plt.grid()
>>> plt.legend(['signal', 'const. detrend', 'linear detrend'])
>>> plt.show()
References
Some further reading and related software: