# matplotlib.mlab¶

Numerical python functions written for compatibility with MATLAB commands with the same names. Most numerical python functions can be found in the numpy and scipy libraries. What remains here is code for performing spectral computations.

## Spectral functions¶

cohere
Coherence (normalized cross spectral density)
csd
Cross spectral density using Welch's average periodogram
detrend
Remove the mean or best fit line from an array
psd
Power spectral density using Welch's average periodogram
specgram
Spectrogram (spectrum over segments of time)
complex_spectrum
Return the complex-valued frequency spectrum of a signal
magnitude_spectrum
Return the magnitude of the frequency spectrum of a signal
angle_spectrum
Return the angle (wrapped phase) of the frequency spectrum of a signal
phase_spectrum
Return the phase (unwrapped angle) of the frequency spectrum of a signal
detrend_mean
Remove the mean from a line.
detrend_linear
Remove the best fit line from a line.
detrend_none
Return the original line.
stride_windows
Get all windows in an array in a memory-efficient manner
stride_repeat
Repeat an array in a memory-efficient manner
apply_window
Apply a window along a given axis
class matplotlib.mlab.GaussianKDE(dataset, bw_method=None)[source]

Bases: object

Representation of a kernel-density estimate using Gaussian kernels.

Parameters: datasetarray-likeDatapoints to estimate from. In case of univariate data this is a 1-D array, otherwise a 2-D array with shape (# of dims, # of data). bw_methodstr, scalar or callable, optionalThe method used to calculate the estimator bandwidth. This can be 'scott', 'silverman', a scalar constant or a callable. If a scalar, this will be used directly as kde.factor. If a callable, it should take a GaussianKDE instance as only parameter and return a scalar. If None (default), 'scott' is used. datasetndarrayThe dataset with which gaussian_kde was initialized. dimintNumber of dimensions. num_dpintNumber of datapoints. factorfloatThe bandwidth factor, obtained from kde.covariance_factor, with which the covariance matrix is multiplied. covariancendarrayThe covariance matrix of dataset, scaled by the calculated bandwidth (kde.factor). inv_covndarrayThe inverse of covariance.

Methods

 kde.evaluate(points) (ndarray) Evaluate the estimated pdf on a provided set of points. kde(points) (ndarray) Same as kde.evaluate(points)
covariance_factor(self)
evaluate(self, points)[source]

Evaluate the estimated pdf on a set of points.

Parameters: points(# of dimensions, # of points)-arrayAlternatively, a (# of dimensions,) vector can be passed in and treated as a single point. (# of points,)-arrayThe values at each point. ValueErrorif the dimensionality of the input points is differentthan the dimensionality of the KDE.
scotts_factor(self)[source]
silverman_factor(self)[source]
matplotlib.mlab.angle_spectrum(x, Fs=None, window=None, pad_to=None, sides=None)

Compute the angle of the frequency spectrum (wrapped phase spectrum) of x. Data is padded to a length of pad_to and the windowing function window is applied to the signal.

Parameters: x1-D array or sequenceArray or sequence containing the data Fsfloat, default: 2The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. windowcallable or ndarray, default: window_hanningA function or a vector of length NFFT. To create window vectors see window_hanning, window_none, numpy.blackman, numpy.hamming, numpy.bartlett, scipy.signal, scipy.signal.get_window, etc. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. sides{'default', 'onesided', 'twosided'}, optionalWhich sides of the spectrum to return. 'default' is one-sided for real data and two-sided for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. pad_toint, optionalThe number of points to which the data segment is padded when performing the FFT. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to the length of the input signal (i.e. no padding). spectrum1-D arrayThe angle of the frequency spectrum (wrapped phase spectrum). freqs1-D arrayThe frequencies corresponding to the elements in spectrum.

psd
Returns the power spectral density.
complex_spectrum
Returns the complex-valued frequency spectrum.
magnitude_spectrum
Returns the absolute value of the complex_spectrum.
angle_spectrum
Returns the angle of the complex_spectrum.
phase_spectrum
Returns the phase (unwrapped angle) of the complex_spectrum.
specgram
Can return the complex spectrum of segments within the signal.
matplotlib.mlab.apply_window(x, window, axis=0, return_window=None)[source]

[Deprecated] Apply the given window to the given 1D or 2D array along the given axis.

Parameters: x1D or 2D array or sequenceArray or sequence containing the data. windowfunction or array.Either a function to generate a window or an array with length x.shape[axis] axisintThe axis over which to do the repetition. Must be 0 or 1. The default is 0 return_windowboolIf true, also return the 1D values of the window that was applied

Notes

Deprecated since version 3.2.

matplotlib.mlab.cohere(x, y, NFFT=256, Fs=2, detrend=<function detrend_none at 0x7fcc164d63a0>, window=<function window_hanning at 0x7fcc164d2ca0>, noverlap=0, pad_to=None, sides='default', scale_by_freq=None)[source]

The coherence between x and y. Coherence is the normalized cross spectral density:

$C_{xy} = \frac{|P_{xy}|^2}{P_{xx}P_{yy}}$
Parameters: x, yArray or sequence containing the data Fsfloat, default: 2The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. windowcallable or ndarray, default: window_hanningA function or a vector of length NFFT. To create window vectors see window_hanning, window_none, numpy.blackman, numpy.hamming, numpy.bartlett, scipy.signal, scipy.signal.get_window, etc. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. sides{'default', 'onesided', 'twosided'}, optionalWhich sides of the spectrum to return. 'default' is one-sided for real data and two-sided for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. pad_toint, optionalThe number of points to which the data segment is padded when performing the FFT. This can be different from NFFT, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to NFFT NFFTint, default: 256The number of data points used in each block for the FFT. A power 2 is most efficient. This should NOT be used to get zero padding, or the scaling of the result will be incorrect; use pad_to for this instead. detrend{'none', 'mean', 'linear'} or callable, default 'none'The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the detrend parameter is a vector, in Matplotlib is it a function. The mlab module defines detrend_none, detrend_mean, and detrend_linear, but you can use a custom function as well. You can also use a string to choose one of the functions: 'none' calls detrend_none. 'mean' calls detrend_mean. 'linear' calls detrend_linear. scale_by_freqbool, default: TrueWhether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility. noverlapintThe number of points of overlap between blocks. The default value is 0 (no overlap). The return value is the tuple (Cxy, f), where f are the frequencies of the coherence vector. For cohere, scaling the individual densities by the sampling frequency has no effect, since the factors cancel out.

psd(), csd()
For information about the methods used to compute $$P_{xy}$$, $$P_{xx}$$ and $$P_{yy}$$.
matplotlib.mlab.complex_spectrum(x, Fs=None, window=None, pad_to=None, sides=None)

Compute the complex-valued frequency spectrum of x. Data is padded to a length of pad_to and the windowing function window is applied to the signal.

Parameters: x1-D array or sequenceArray or sequence containing the data Fsfloat, default: 2The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. windowcallable or ndarray, default: window_hanningA function or a vector of length NFFT. To create window vectors see window_hanning, window_none, numpy.blackman, numpy.hamming, numpy.bartlett, scipy.signal, scipy.signal.get_window, etc. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. sides{'default', 'onesided', 'twosided'}, optionalWhich sides of the spectrum to return. 'default' is one-sided for real data and two-sided for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. pad_toint, optionalThe number of points to which the data segment is padded when performing the FFT. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to the length of the input signal (i.e. no padding). spectrum1-D arrayThe complex-valued frequency spectrum. freqs1-D arrayThe frequencies corresponding to the elements in spectrum.

psd
Returns the power spectral density.
complex_spectrum
Returns the complex-valued frequency spectrum.
magnitude_spectrum
Returns the absolute value of the complex_spectrum.
angle_spectrum
Returns the angle of the complex_spectrum.
phase_spectrum
Returns the phase (unwrapped angle) of the complex_spectrum.
specgram
Can return the complex spectrum of segments within the signal.
matplotlib.mlab.csd(x, y, NFFT=None, Fs=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None)[source]

Compute the cross-spectral density.

The cross spectral density $$P_{xy}$$ by Welch's average periodogram method. The vectors x and y are divided into NFFT length segments. Each segment is detrended by function detrend and windowed by function window. noverlap gives the length of the overlap between segments. The product of the direct FFTs of x and y are averaged over each segment to compute $$P_{xy}$$, with a scaling to correct for power loss due to windowing.

If len(x) < NFFT or len(y) < NFFT, they will be zero padded to NFFT.

Parameters: x, y1-D arrays or sequencesArrays or sequences containing the data Fsfloat, default: 2The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. windowcallable or ndarray, default: window_hanningA function or a vector of length NFFT. To create window vectors see window_hanning, window_none, numpy.blackman, numpy.hamming, numpy.bartlett, scipy.signal, scipy.signal.get_window, etc. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. sides{'default', 'onesided', 'twosided'}, optionalWhich sides of the spectrum to return. 'default' is one-sided for real data and two-sided for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. pad_toint, optionalThe number of points to which the data segment is padded when performing the FFT. This can be different from NFFT, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to NFFT NFFTint, default: 256The number of data points used in each block for the FFT. A power 2 is most efficient. This should NOT be used to get zero padding, or the scaling of the result will be incorrect; use pad_to for this instead. detrend{'none', 'mean', 'linear'} or callable, default 'none'The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the detrend parameter is a vector, in Matplotlib is it a function. The mlab module defines detrend_none, detrend_mean, and detrend_linear, but you can use a custom function as well. You can also use a string to choose one of the functions: 'none' calls detrend_none. 'mean' calls detrend_mean. 'linear' calls detrend_linear. scale_by_freqbool, default: TrueWhether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility. noverlapintThe number of points of overlap between segments. The default value is 0 (no overlap). Pxy1-D arrayThe values for the cross spectrum $$P_{xy}$$ before scaling (real valued) freqs1-D arrayThe frequencies corresponding to the elements in Pxy

psd
equivalent to setting y = x.

References

Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)

matplotlib.mlab.detrend(x, key=None, axis=None)[source]

Return x with its trend removed.

Parameters: xarray or sequenceArray or sequence containing the data. key{'default', 'constant', 'mean', 'linear', 'none'} or functionThe detrending algorithm to use. 'default', 'mean', and 'constant' are the same as detrend_mean. 'linear' is the same as detrend_linear. 'none' is the same as detrend_none. The default is 'mean'. See the corresponding functions for more details regarding the algorithms. Can also be a function that carries out the detrend operation. axisintThe axis along which to do the detrending.

detrend_mean
Implementation of the 'mean' algorithm.
detrend_linear
Implementation of the 'linear' algorithm.
detrend_none
Implementation of the 'none' algorithm.
matplotlib.mlab.detrend_linear(y)[source]

Return x minus best fit line; 'linear' detrending.

Parameters: y0-D or 1-D array or sequenceArray or sequence containing the data axisintThe axis along which to take the mean. See numpy.mean for a description of this argument.

detrend_mean
Another detrend algorithm.
detrend_none
Another detrend algorithm.
detrend
A wrapper around all the detrend algorithms.
matplotlib.mlab.detrend_mean(x, axis=None)[source]

Return x minus the mean(x).

Parameters: xarray or sequenceArray or sequence containing the data Can have any dimensionality axisintThe axis along which to take the mean. See numpy.mean for a description of this argument.

detrend_linear
Another detrend algorithm.
detrend_none
Another detrend algorithm.
detrend
A wrapper around all the detrend algorithms.
matplotlib.mlab.detrend_none(x, axis=None)[source]

Return x: no detrending.

Parameters: xany objectAn object containing the data axisintThis parameter is ignored. It is included for compatibility with detrend_mean

detrend_mean
Another detrend algorithm.
detrend_linear
Another detrend algorithm.
detrend
A wrapper around all the detrend algorithms.
matplotlib.mlab.magnitude_spectrum(x, Fs=None, window=None, pad_to=None, sides=None)

Compute the magnitude (absolute value) of the frequency spectrum of x. Data is padded to a length of pad_to and the windowing function window is applied to the signal.

Parameters: x1-D array or sequenceArray or sequence containing the data Fsfloat, default: 2The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. windowcallable or ndarray, default: window_hanningA function or a vector of length NFFT. To create window vectors see window_hanning, window_none, numpy.blackman, numpy.hamming, numpy.bartlett, scipy.signal, scipy.signal.get_window, etc. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. sides{'default', 'onesided', 'twosided'}, optionalWhich sides of the spectrum to return. 'default' is one-sided for real data and two-sided for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. pad_toint, optionalThe number of points to which the data segment is padded when performing the FFT. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to the length of the input signal (i.e. no padding). spectrum1-D arrayThe magnitude (absolute value) of the frequency spectrum. freqs1-D arrayThe frequencies corresponding to the elements in spectrum.

psd
Returns the power spectral density.
complex_spectrum
Returns the complex-valued frequency spectrum.
magnitude_spectrum
Returns the absolute value of the complex_spectrum.
angle_spectrum
Returns the angle of the complex_spectrum.
phase_spectrum
Returns the phase (unwrapped angle) of the complex_spectrum.
specgram
Can return the complex spectrum of segments within the signal.
matplotlib.mlab.phase_spectrum(x, Fs=None, window=None, pad_to=None, sides=None)

Compute the phase of the frequency spectrum (unwrapped phase spectrum) of x. Data is padded to a length of pad_to and the windowing function window is applied to the signal.

Parameters: x1-D array or sequenceArray or sequence containing the data Fsfloat, default: 2The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. windowcallable or ndarray, default: window_hanningA function or a vector of length NFFT. To create window vectors see window_hanning, window_none, numpy.blackman, numpy.hamming, numpy.bartlett, scipy.signal, scipy.signal.get_window, etc. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. sides{'default', 'onesided', 'twosided'}, optionalWhich sides of the spectrum to return. 'default' is one-sided for real data and two-sided for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. pad_toint, optionalThe number of points to which the data segment is padded when performing the FFT. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to the length of the input signal (i.e. no padding). spectrum1-D arrayThe phase of the frequency spectrum (unwrapped phase spectrum). freqs1-D arrayThe frequencies corresponding to the elements in spectrum.

psd
Returns the power spectral density.
complex_spectrum
Returns the complex-valued frequency spectrum.
magnitude_spectrum
Returns the absolute value of the complex_spectrum.
angle_spectrum
Returns the angle of the complex_spectrum.
phase_spectrum
Returns the phase (unwrapped angle) of the complex_spectrum.
specgram
Can return the complex spectrum of segments within the signal.
matplotlib.mlab.psd(x, NFFT=None, Fs=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None)[source]

Compute the power spectral density.

The power spectral density $$P_{xx}$$ by Welch's average periodogram method. The vector x is divided into NFFT length segments. Each segment is detrended by function detrend and windowed by function window. noverlap gives the length of the overlap between segments. The $$|\mathrm{fft}(i)|^2$$ of each segment $$i$$ are averaged to compute $$P_{xx}$$.

If len(x) < NFFT, it will be zero padded to NFFT.

Parameters: x1-D array or sequenceArray or sequence containing the data Fsfloat, default: 2The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. windowcallable or ndarray, default: window_hanningA function or a vector of length NFFT. To create window vectors see window_hanning, window_none, numpy.blackman, numpy.hamming, numpy.bartlett, scipy.signal, scipy.signal.get_window, etc. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. sides{'default', 'onesided', 'twosided'}, optionalWhich sides of the spectrum to return. 'default' is one-sided for real data and two-sided for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. pad_toint, optionalThe number of points to which the data segment is padded when performing the FFT. This can be different from NFFT, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to NFFT NFFTint, default: 256The number of data points used in each block for the FFT. A power 2 is most efficient. This should NOT be used to get zero padding, or the scaling of the result will be incorrect; use pad_to for this instead. detrend{'none', 'mean', 'linear'} or callable, default 'none'The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the detrend parameter is a vector, in Matplotlib is it a function. The mlab module defines detrend_none, detrend_mean, and detrend_linear, but you can use a custom function as well. You can also use a string to choose one of the functions: 'none' calls detrend_none. 'mean' calls detrend_mean. 'linear' calls detrend_linear. scale_by_freqbool, default: TrueWhether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility. noverlapintThe number of points of overlap between segments. The default value is 0 (no overlap). Pxx1-D arrayThe values for the power spectrum $$P_{xx}$$ (real valued) freqs1-D arrayThe frequencies corresponding to the elements in Pxx

specgram
specgram differs in the default overlap; in not returning the mean of the segment periodograms; and in returning the times of the segments.
magnitude_spectrum
returns the magnitude spectrum.
csd
returns the spectral density between two signals.

References

Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)

matplotlib.mlab.specgram(x, NFFT=None, Fs=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, mode=None)[source]

Compute a spectrogram.

Compute and plot a spectrogram of data in x. Data are split into NFFT length segments and the spectrum of each section is computed. The windowing function window is applied to each segment, and the amount of overlap of each segment is specified with noverlap.

Parameters: xarray-like1-D array or sequence. Fsfloat, default: 2The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. windowcallable or ndarray, default: window_hanningA function or a vector of length NFFT. To create window vectors see window_hanning, window_none, numpy.blackman, numpy.hamming, numpy.bartlett, scipy.signal, scipy.signal.get_window, etc. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. sides{'default', 'onesided', 'twosided'}, optionalWhich sides of the spectrum to return. 'default' is one-sided for real data and two-sided for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. pad_toint, optionalThe number of points to which the data segment is padded when performing the FFT. This can be different from NFFT, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to NFFT NFFTint, default: 256The number of data points used in each block for the FFT. A power 2 is most efficient. This should NOT be used to get zero padding, or the scaling of the result will be incorrect; use pad_to for this instead. detrend{'none', 'mean', 'linear'} or callable, default 'none'The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the detrend parameter is a vector, in Matplotlib is it a function. The mlab module defines detrend_none, detrend_mean, and detrend_linear, but you can use a custom function as well. You can also use a string to choose one of the functions: 'none' calls detrend_none. 'mean' calls detrend_mean. 'linear' calls detrend_linear. scale_by_freqbool, default: TrueWhether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility. noverlapint, optionalThe number of points of overlap between blocks. The default value is 128. modestr, default: 'psd' What sort of spectrum to use: 'psd'Returns the power spectral density. 'complex'Returns the complex-valued frequency spectrum. 'magnitude'Returns the magnitude spectrum. 'angle'Returns the phase spectrum without unwrapping. 'phase'Returns the phase spectrum with unwrapping. spectrumarray-like2-D array, columns are the periodograms of successive segments. freqsarray-like1-D array, frequencies corresponding to the rows in spectrum. tarray-like1-D array, the times corresponding to midpoints of segments (i.e the columns in spectrum).

psd
differs in the overlap and in the return values.
complex_spectrum
similar, but with complex valued frequencies.
magnitude_spectrum
similar single segment when mode is 'magnitude'.
angle_spectrum
similar to single segment when mode is 'angle'.
phase_spectrum
similar to single segment when mode is 'phase'.

Notes

detrend and scale_by_freq only apply when mode is set to 'psd'.

matplotlib.mlab.stride_repeat(x, n, axis=0)[source]

[Deprecated] Repeat the values in an array in a memory-efficient manner. Array x is stacked vertically n times.

Warning

It is not safe to write to the output array. Multiple elements may point to the same piece of memory, so modifying one value may change others.

Parameters: x1D array or sequenceArray or sequence containing the data. nintThe number of time to repeat the array. axisintThe axis along which the data will run.

Notes

Deprecated since version 3.2.

References

stackoverflow: Repeat NumPy array without replicating data?

matplotlib.mlab.stride_windows(x, n, noverlap=None, axis=0)[source]

Get all windows of x with length n as a single array, using strides to avoid data duplication.

Warning

It is not safe to write to the output array. Multiple elements may point to the same piece of memory, so modifying one value may change others.

Parameters: x1D array or sequenceArray or sequence containing the data. nintThe number of data points in each window. noverlapintThe overlap between adjacent windows. Default is 0 (no overlap) axisintThe axis along which the windows will run.

References

matplotlib.mlab.window_hanning(x)[source]

Return x times the hanning window of len(x).

window_none
matplotlib.mlab.window_none(x)[source]
window_hanning