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 and kernel density estimations.  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     classmatplotlib.mlab.GaussianKDE(dataset, bw_method=None)[source]
 
Bases: object Representation of a kernel-density estimate using Gaussian kernels.  Parameters 
 
datasetarray-like


Datapoints to estimate from. In case of univariate data this is a 1-D array, otherwise a 2D array with shape (# of dims, # of data).  
bw_methodstr, scalar or callable, optional


The 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.    Attributes 
 
datasetndarray


The dataset with which gaussian_kde was initialized.  
dimint


Number of dimensions.  
num_dpint


Number of datapoints.  
factorfloat


The bandwidth factor, obtained from kde.covariance_factor, with which the covariance matrix is multiplied.  
covariancendarray


The covariance matrix of dataset, scaled by the calculated bandwidth (kde.factor).  
inv_covndarray


The 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()[source]

   evaluate(points)[source]
 
Evaluate the estimated pdf on a set of points.  Parameters 
 
points(# of dimensions, # of points)-array


Alternatively, a (# of dimensions,) vector can be passed in and treated as a single point.    Returns 
 (# of points,)-array

The values at each point.    Raises 
 
ValueErrorif the dimensionality of the input points is different


than the dimensionality of the KDE.     
   scotts_factor()[source]

   silverman_factor()[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 sequence


Array or sequence containing the data  
Fsfloat, default: 2


The 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_hanning


A 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'}, optional


Which 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, optional


The 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).    Returns 
 
spectrum1-D array


The angle of the frequency spectrum (wrapped phase spectrum).  
freqs1-D array


The frequencies corresponding to the elements in spectrum.      See also  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.cohere(x, y, NFFT=256, Fs=2, detrend=<function detrend_none>, window=<function window_hanning>, 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, y

Array or sequence containing the data  
Fsfloat, default: 2


The 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_hanning


A 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'}, optional


Which 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, optional


The 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: 256


The 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 it is 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: True


Whether 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, default: 0 (no overlap)


The number of points of overlap between segments.    Returns 
 
Cxy1-D array


The coherence vector.  
freqs1-D array


The frequencies for the elements in Cxy.      See also  
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 sequence


Array or sequence containing the data  
Fsfloat, default: 2


The 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_hanning


A 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'}, optional


Which 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, optional


The 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).    Returns 
 
spectrum1-D array


The complex-valued frequency spectrum.  
freqs1-D array


The frequencies corresponding to the elements in spectrum.      See also  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 sequences


Arrays or sequences containing the data  
Fsfloat, default: 2


The 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_hanning


A 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'}, optional


Which 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, optional


The 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: 256


The 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 it is 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: True


Whether 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, default: 0 (no overlap)


The number of points of overlap between segments.    Returns 
 
Pxy1-D array


The values for the cross spectrum \(P_{xy}\) before scaling (real valued)  
freqs1-D array


The frequencies corresponding to the elements in Pxy      See also  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 sequence


Array or sequence containing the data.  
key{'default', 'constant', 'mean', 'linear', 'none'} or function


The 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.  
axisint


The axis along which to do the detrending.      See also  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 sequence


Array or sequence containing the data      See also  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 sequence


Array or sequence containing the data Can have any dimensionality  
axisint


The axis along which to take the mean. See numpy.mean for a description of this argument.      See also  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 object


An object containing the data  
axisint


This parameter is ignored. It is included for compatibility with detrend_mean      See also  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 sequence


Array or sequence containing the data  
Fsfloat, default: 2


The 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_hanning


A 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'}, optional


Which 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, optional


The 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).    Returns 
 
spectrum1-D array


The magnitude (absolute value) of the frequency spectrum.  
freqs1-D array


The frequencies corresponding to the elements in spectrum.      See also  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 sequence


Array or sequence containing the data  
Fsfloat, default: 2


The 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_hanning


A 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'}, optional


Which 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, optional


The 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).    Returns 
 
spectrum1-D array


The phase of the frequency spectrum (unwrapped phase spectrum).  
freqs1-D array


The frequencies corresponding to the elements in spectrum.      See also  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 sequence


Array or sequence containing the data  
Fsfloat, default: 2


The 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_hanning


A 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'}, optional


Which 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, optional


The 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: 256


The 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 it is 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: True


Whether 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, default: 0 (no overlap)


The number of points of overlap between segments.    Returns 
 
Pxx1-D array


The values for the power spectrum \(P_{xx}\) (real valued)  
freqs1-D array


The frequencies corresponding to the elements in Pxx      See also  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-like


1-D array or sequence.  
Fsfloat, default: 2


The 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_hanning


A 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'}, optional


Which 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, optional


The 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: 256


The 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 it is 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: True


Whether 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, default: 128


The number of points of overlap between blocks.  
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.        Returns 
 
spectrumarray-like


2D array, columns are the periodograms of successive segments.  
freqsarray-like


1-D array, frequencies corresponding to the rows in spectrum.  
tarray-like


1-D array, the times corresponding to midpoints of segments (i.e the columns in spectrum).      See also  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_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 sequence


Array or sequence containing the data.  
nint


The number of data points in each window.  
noverlapint, default: 0 (no overlap)


The overlap between adjacent windows.  
axisint


The axis along which the windows will run.     References stackoverflow: Rolling window for 1D arrays in Numpy? stackoverflow: Using strides for an efficient moving average filter 
   matplotlib.mlab.window_hanning(x)[source]
 
Return x times the hanning window of len(x).  See also  window_none

Another window algorithm.    
   matplotlib.mlab.window_none(x)[source]
 
No window function; simply return x.  See also  window_hanning

Another window algorithm.