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<li><a class="reference internal" href="#"><code class="docutils literal notranslate"><span class="pre">matplotlib.mlab</span></code></a><ul>
<li><a class="reference internal" href="#spectral-functions">Spectral functions</a></li>
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<div class="section" id="module-matplotlib.mlab">
<span id="matplotlib-mlab"></span><h1><code class="docutils literal notranslate"><span class="pre">matplotlib.mlab</span></code><a class="headerlink" href="#module-matplotlib.mlab" title="Permalink to this headline">¶</a></h1>
<p>Numerical python functions written for compatibility with MATLAB
commands with the same names. Most numerical python functions can be found in
the <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/index.html#module-numpy" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy</span></code></a> and <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/index.html#module-scipy" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy</span></code></a> libraries. What remains here is code for performing
spectral computations.</p>
<div class="section" id="spectral-functions">
<h2>Spectral functions<a class="headerlink" href="#spectral-functions" title="Permalink to this headline">¶</a></h2>
<dl class="docutils">
<dt><a class="reference internal" href="#matplotlib.mlab.cohere" title="matplotlib.mlab.cohere"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cohere</span></code></a></dt>
<dd>Coherence (normalized cross spectral density)</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.csd" title="matplotlib.mlab.csd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">csd</span></code></a></dt>
<dd>Cross spectral density using Welch's average periodogram</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.detrend" title="matplotlib.mlab.detrend"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend</span></code></a></dt>
<dd>Remove the mean or best fit line from an array</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.psd" title="matplotlib.mlab.psd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">psd</span></code></a></dt>
<dd>Power spectral density using Welch's average periodogram</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.specgram" title="matplotlib.mlab.specgram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">specgram</span></code></a></dt>
<dd>Spectrogram (spectrum over segments of time)</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.complex_spectrum" title="matplotlib.mlab.complex_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">complex_spectrum</span></code></a></dt>
<dd>Return the complex-valued frequency spectrum of a signal</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.magnitude_spectrum" title="matplotlib.mlab.magnitude_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">magnitude_spectrum</span></code></a></dt>
<dd>Return the magnitude of the frequency spectrum of a signal</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.angle_spectrum" title="matplotlib.mlab.angle_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">angle_spectrum</span></code></a></dt>
<dd>Return the angle (wrapped phase) of the frequency spectrum of a signal</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.phase_spectrum" title="matplotlib.mlab.phase_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">phase_spectrum</span></code></a></dt>
<dd>Return the phase (unwrapped angle) of the frequency spectrum of a signal</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_mean" title="matplotlib.mlab.detrend_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_mean</span></code></a></dt>
<dd>Remove the mean from a line.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_linear" title="matplotlib.mlab.detrend_linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_linear</span></code></a></dt>
<dd>Remove the best fit line from a line.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_none" title="matplotlib.mlab.detrend_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_none</span></code></a></dt>
<dd>Return the original line.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.stride_windows" title="matplotlib.mlab.stride_windows"><code class="xref py py-obj docutils literal notranslate"><span class="pre">stride_windows</span></code></a></dt>
<dd>Get all windows in an array in a memory-efficient manner</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.stride_repeat" title="matplotlib.mlab.stride_repeat"><code class="xref py py-obj docutils literal notranslate"><span class="pre">stride_repeat</span></code></a></dt>
<dd>Repeat an array in a memory-efficient manner</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.apply_window" title="matplotlib.mlab.apply_window"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply_window</span></code></a></dt>
<dd>Apply a window along a given axis</dd>
</dl>
<dl class="class">
<dt id="matplotlib.mlab.GaussianKDE">
<em class="property">class </em><code class="descclassname">matplotlib.mlab.</code><code class="descname">GaussianKDE</code><span class="sig-paren">(</span><em>dataset</em>, <em>bw_method=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#GaussianKDE"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.GaussianKDE" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
<p>Representation of a kernel-density estimate using Gaussian kernels.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>dataset</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array_like</span></dt>
<dd><p class="first last">Datapoints 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).</p>
</dd>
<dt><strong>bw_method</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str, scalar or callable, optional</span></dt>
<dd><p class="first last">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 <code class="xref py py-obj docutils literal notranslate"><span class="pre">kde.factor</span></code>. If a
callable, it should take a <a class="reference internal" href="#matplotlib.mlab.GaussianKDE" title="matplotlib.mlab.GaussianKDE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GaussianKDE</span></code></a> instance as only
parameter and return a scalar. If None (default), 'scott' is used.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Attributes:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>dataset</strong> <span class="classifier-delimiter">:</span> <span class="classifier">ndarray</span></dt>
<dd><p class="first last">The dataset with which <code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_kde</span></code> was initialized.</p>
</dd>
<dt><strong>dim</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of dimensions.</p>
</dd>
<dt><strong>num_dp</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of datapoints.</p>
</dd>
<dt><strong>factor</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The bandwidth factor, obtained from <code class="xref py py-obj docutils literal notranslate"><span class="pre">kde.covariance_factor</span></code>, with which
the covariance matrix is multiplied.</p>
</dd>
<dt><strong>covariance</strong> <span class="classifier-delimiter">:</span> <span class="classifier">ndarray</span></dt>
<dd><p class="first last">The covariance matrix of <code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset</span></code>, scaled by the calculated bandwidth
(<code class="xref py py-obj docutils literal notranslate"><span class="pre">kde.factor</span></code>).</p>
</dd>
<dt><strong>inv_cov</strong> <span class="classifier-delimiter">:</span> <span class="classifier">ndarray</span></dt>
<dd><p class="first last">The inverse of <code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance</span></code>.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Methods</p>
<table border="1" class="docutils">
<colgroup>
<col width="27%" />
<col width="73%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><strong>kde.evaluate(points)</strong></td>
<td>(ndarray) Evaluate the estimated pdf on a provided set of points.</td>
</tr>
<tr class="row-even"><td><strong>kde(points)</strong></td>
<td>(ndarray) Same as kde.evaluate(points)</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="matplotlib.mlab.GaussianKDE.covariance_factor">
<code class="descname">covariance_factor</code><span class="sig-paren">(</span><em>self</em><span class="sig-paren">)</span><a class="headerlink" href="#matplotlib.mlab.GaussianKDE.covariance_factor" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="matplotlib.mlab.GaussianKDE.evaluate">
<code class="descname">evaluate</code><span class="sig-paren">(</span><em>self</em>, <em>points</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#GaussianKDE.evaluate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.GaussianKDE.evaluate" title="Permalink to this definition">¶</a></dt>
<dd><p>Evaluate the estimated pdf on a set of points.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>points</strong> <span class="classifier-delimiter">:</span> <span class="classifier">(# of dimensions, # of points)-array</span></dt>
<dd><p class="first last">Alternatively, a (# of dimensions,) vector can be passed in and
treated as a single point.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first docutils">
<dt><strong>values</strong> <span class="classifier-delimiter">:</span> <span class="classifier">(# of points,)-array</span></dt>
<dd><p class="first last">The values at each point.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Raises:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>ValueError</strong> <span class="classifier-delimiter">:</span> <span class="classifier">if the dimensionality of the input points is different</span></dt>
<dd><p class="first last">than the dimensionality of the KDE.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.mlab.GaussianKDE.scotts_factor">
<code class="descname">scotts_factor</code><span class="sig-paren">(</span><em>self</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#GaussianKDE.scotts_factor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.GaussianKDE.scotts_factor" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="matplotlib.mlab.GaussianKDE.silverman_factor">
<code class="descname">silverman_factor</code><span class="sig-paren">(</span><em>self</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#GaussianKDE.silverman_factor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.GaussianKDE.silverman_factor" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.angle_spectrum">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">angle_spectrum</code><span class="sig-paren">(</span><em>x</em>, <em>Fs=None</em>, <em>window=None</em>, <em>pad_to=None</em>, <em>sides=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#angle_spectrum"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.angle_spectrum" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the angle of the frequency spectrum (wrapped phase spectrum) of
<em>x</em>. Data is padded to a length of <em>pad_to</em> and the windowing function
<em>window</em> is applied to the signal.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>x</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array or sequence</span></dt>
<dd><p class="first last">Array or sequence containing the data</p>
</dd>
<dt><strong>Fs</strong> <span class="classifier-delimiter">:</span> <span class="classifier">scalar</span></dt>
<dd><p class="first last">The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.</p>
</dd>
<dt><strong>window</strong> <span class="classifier-delimiter">:</span> <span class="classifier">callable or ndarray</span></dt>
<dd><p class="first last">A function or a vector of length <em>NFFT</em>. To create window vectors see
<a class="reference internal" href="#matplotlib.mlab.window_hanning" title="matplotlib.mlab.window_hanning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_hanning</span></code></a>, <a class="reference internal" href="#matplotlib.mlab.window_none" title="matplotlib.mlab.window_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_none</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.blackman.html#numpy.blackman" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.blackman</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.hamming.html#numpy.hamming" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.hamming</span></code></a>,
<a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.bartlett.html#numpy.bartlett" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.bartlett</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/signal.html#module-scipy.signal" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.signal</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.get_window.html#scipy.signal.get_window" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.signal.get_window</span></code></a>, etc. The
default is <a class="reference internal" href="#matplotlib.mlab.window_hanning" title="matplotlib.mlab.window_hanning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_hanning</span></code></a>. 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.</p>
</dd>
<dt><strong>sides</strong> <span class="classifier-delimiter">:</span> <span class="classifier">{'default', 'onesided', 'twosided'}</span></dt>
<dd><p class="first last">Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.</p>
</dd>
<dt><strong>pad_to</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">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 <em>n</em> parameter in the call to fft().
The default is None, which sets <em>pad_to</em> equal to the length of the
input signal (i.e. no padding).</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>spectrum</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array</span></dt>
<dd><p class="first last">The values for the angle spectrum in radians (real valued)</p>
</dd>
<dt><strong>freqs</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array</span></dt>
<dd><p class="first last">The frequencies corresponding to the elements in <em>spectrum</em></p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.mlab.complex_spectrum" title="matplotlib.mlab.complex_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">complex_spectrum</span></code></a></dt>
<dd>This function returns the angle value of <a class="reference internal" href="#matplotlib.mlab.complex_spectrum" title="matplotlib.mlab.complex_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">complex_spectrum</span></code></a>.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.magnitude_spectrum" title="matplotlib.mlab.magnitude_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">magnitude_spectrum</span></code></a></dt>
<dd>Returns the magnitudes of the corresponding frequencies.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.phase_spectrum" title="matplotlib.mlab.phase_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">phase_spectrum</span></code></a></dt>
<dd>Returns the phase (unwrapped angle) of the corresponding frequencies.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.specgram" title="matplotlib.mlab.specgram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">specgram</span></code></a></dt>
<dd>Can return the complex spectrum of segments within the signal.</dd>
</dl>
</div>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.apply_window">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">apply_window</code><span class="sig-paren">(</span><em>x</em>, <em>window</em>, <em>axis=0</em>, <em>return_window=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#apply_window"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.apply_window" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply the given window to the given 1D or 2D array along the given axis.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>x</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1D or 2D array or sequence</span></dt>
<dd><p class="first last">Array or sequence containing the data.</p>
</dd>
<dt><strong>window</strong> <span class="classifier-delimiter">:</span> <span class="classifier">function or array.</span></dt>
<dd><p class="first last">Either a function to generate a window or an array with length
<em>x</em>.shape[<em>axis</em>]</p>
</dd>
<dt><strong>axis</strong> <span class="classifier-delimiter">:</span> <span class="classifier">integer</span></dt>
<dd><p class="first last">The axis over which to do the repetition.
Must be 0 or 1. The default is 0</p>
</dd>
<dt><strong>return_window</strong> <span class="classifier-delimiter">:</span> <span class="classifier">bool</span></dt>
<dd><p class="first last">If true, also return the 1D values of the window that was applied</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.cohere">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">cohere</code><span class="sig-paren">(</span><em>x</em>, <em>y</em>, <em>NFFT=256</em>, <em>Fs=2</em>, <em>detrend=<function detrend_none at 0x7f9180c28680></em>, <em>window=<function window_hanning at 0x7f9180c63320></em>, <em>noverlap=0</em>, <em>pad_to=None</em>, <em>sides='default'</em>, <em>scale_by_freq=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#cohere"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.cohere" title="Permalink to this definition">¶</a></dt>
<dd><p>The coherence between <em>x</em> and <em>y</em>. Coherence is the normalized
cross spectral density:</p>
<div class="math notranslate nohighlight">
\[C_{xy} = \frac{|P_{xy}|^2}{P_{xx}P_{yy}}\]</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>x, y</strong></dt>
<dd><p class="first last">Array or sequence containing the data</p>
</dd>
<dt><strong>Fs</strong> <span class="classifier-delimiter">:</span> <span class="classifier">scalar</span></dt>
<dd><p class="first last">The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.</p>
</dd>
<dt><strong>window</strong> <span class="classifier-delimiter">:</span> <span class="classifier">callable or ndarray</span></dt>
<dd><p class="first last">A function or a vector of length <em>NFFT</em>. To create window vectors see
<a class="reference internal" href="#matplotlib.mlab.window_hanning" title="matplotlib.mlab.window_hanning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_hanning</span></code></a>, <a class="reference internal" href="#matplotlib.mlab.window_none" title="matplotlib.mlab.window_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_none</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.blackman.html#numpy.blackman" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.blackman</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.hamming.html#numpy.hamming" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.hamming</span></code></a>,
<a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.bartlett.html#numpy.bartlett" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.bartlett</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/signal.html#module-scipy.signal" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.signal</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.get_window.html#scipy.signal.get_window" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.signal.get_window</span></code></a>, etc. The
default is <a class="reference internal" href="#matplotlib.mlab.window_hanning" title="matplotlib.mlab.window_hanning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_hanning</span></code></a>. 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.</p>
</dd>
<dt><strong>sides</strong> <span class="classifier-delimiter">:</span> <span class="classifier">{'default', 'onesided', 'twosided'}</span></dt>
<dd><p class="first last">Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.</p>
</dd>
<dt><strong>pad_to</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">The number of points to which the data segment is padded when
performing the FFT. This can be different from <em>NFFT</em>, 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 <em>n</em> parameter
in the call to fft(). The default is None, which sets <em>pad_to</em>
equal to <em>NFFT</em></p>
</dd>
<dt><strong>NFFT</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">The number of data points used in each block for the FFT.
A power 2 is most efficient. The default value is 256.
This should <em>NOT</em> be used to get zero padding, or the scaling of the
result will be incorrect. Use <em>pad_to</em> for this instead.</p>
</dd>
<dt><strong>detrend</strong> <span class="classifier-delimiter">:</span> <span class="classifier">{'none', 'mean', 'linear'} or callable, default 'none'</span></dt>
<dd><p class="first last">The function applied to each segment before fft-ing, designed to
remove the mean or linear trend. Unlike in MATLAB, where the
<em>detrend</em> parameter is a vector, in Matplotlib is it a function.
The <a class="reference internal" href="#module-matplotlib.mlab" title="matplotlib.mlab"><code class="xref py py-mod docutils literal notranslate"><span class="pre">mlab</span></code></a> module defines <a class="reference internal" href="#matplotlib.mlab.detrend_none" title="matplotlib.mlab.detrend_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_none</span></code></a>,
<a class="reference internal" href="#matplotlib.mlab.detrend_mean" title="matplotlib.mlab.detrend_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_mean</span></code></a>, and <a class="reference internal" href="#matplotlib.mlab.detrend_linear" title="matplotlib.mlab.detrend_linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_linear</span></code></a>, but you can use a custom
function as well. You can also use a string to choose one of the
functions: 'none' calls <a class="reference internal" href="#matplotlib.mlab.detrend_none" title="matplotlib.mlab.detrend_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_none</span></code></a>. 'mean' calls <a class="reference internal" href="#matplotlib.mlab.detrend_mean" title="matplotlib.mlab.detrend_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_mean</span></code></a>.
'linear' calls <a class="reference internal" href="#matplotlib.mlab.detrend_linear" title="matplotlib.mlab.detrend_linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_linear</span></code></a>.</p>
</dd>
<dt><strong>scale_by_freq</strong> <span class="classifier-delimiter">:</span> <span class="classifier">bool, optional</span></dt>
<dd><p class="first last">Specifies 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.</p>
</dd>
<dt><strong>noverlap</strong> <span class="classifier-delimiter">:</span> <span class="classifier">integer</span></dt>
<dd><p class="first last">The number of points of overlap between blocks. The default value
is 0 (no overlap).</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt>The return value is the tuple (<em>Cxy</em>, <em>f</em>), where <em>f</em> are the</dt>
<dd></dd>
<dt>frequencies of the coherence vector. For cohere, scaling the</dt>
<dd></dd>
<dt>individual densities by the sampling frequency has no effect,</dt>
<dd></dd>
<dt>since the factors cancel out.</dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.mlab.psd" title="matplotlib.mlab.psd"><code class="xref py py-func docutils literal notranslate"><span class="pre">psd()</span></code></a>, <a class="reference internal" href="#matplotlib.mlab.csd" title="matplotlib.mlab.csd"><code class="xref py py-func docutils literal notranslate"><span class="pre">csd()</span></code></a></dt>
<dd>For information about the methods used to compute <span class="math notranslate nohighlight">\(P_{xy}\)</span>, <span class="math notranslate nohighlight">\(P_{xx}\)</span> and <span class="math notranslate nohighlight">\(P_{yy}\)</span>.</dd>
</dl>
</div>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.complex_spectrum">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">complex_spectrum</code><span class="sig-paren">(</span><em>x</em>, <em>Fs=None</em>, <em>window=None</em>, <em>pad_to=None</em>, <em>sides=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#complex_spectrum"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.complex_spectrum" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the complex-valued frequency spectrum of <em>x</em>. Data is padded to a
length of <em>pad_to</em> and the windowing function <em>window</em> is applied to the
signal.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>x</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array or sequence</span></dt>
<dd><p class="first last">Array or sequence containing the data</p>
</dd>
<dt><strong>Fs</strong> <span class="classifier-delimiter">:</span> <span class="classifier">scalar</span></dt>
<dd><p class="first last">The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.</p>
</dd>
<dt><strong>window</strong> <span class="classifier-delimiter">:</span> <span class="classifier">callable or ndarray</span></dt>
<dd><p class="first last">A function or a vector of length <em>NFFT</em>. To create window vectors see
<a class="reference internal" href="#matplotlib.mlab.window_hanning" title="matplotlib.mlab.window_hanning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_hanning</span></code></a>, <a class="reference internal" href="#matplotlib.mlab.window_none" title="matplotlib.mlab.window_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_none</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.blackman.html#numpy.blackman" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.blackman</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.hamming.html#numpy.hamming" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.hamming</span></code></a>,
<a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.bartlett.html#numpy.bartlett" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.bartlett</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/signal.html#module-scipy.signal" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.signal</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.get_window.html#scipy.signal.get_window" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.signal.get_window</span></code></a>, etc. The
default is <a class="reference internal" href="#matplotlib.mlab.window_hanning" title="matplotlib.mlab.window_hanning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_hanning</span></code></a>. 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.</p>
</dd>
<dt><strong>sides</strong> <span class="classifier-delimiter">:</span> <span class="classifier">{'default', 'onesided', 'twosided'}</span></dt>
<dd><p class="first last">Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.</p>
</dd>
<dt><strong>pad_to</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">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 <em>n</em> parameter in the call to fft().
The default is None, which sets <em>pad_to</em> equal to the length of the
input signal (i.e. no padding).</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>spectrum</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array</span></dt>
<dd><p class="first last">The values for the complex spectrum (complex valued)</p>
</dd>
<dt><strong>freqs</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array</span></dt>
<dd><p class="first last">The frequencies corresponding to the elements in <em>spectrum</em></p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.mlab.magnitude_spectrum" title="matplotlib.mlab.magnitude_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">magnitude_spectrum</span></code></a></dt>
<dd>Returns the absolute value of this function.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.angle_spectrum" title="matplotlib.mlab.angle_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">angle_spectrum</span></code></a></dt>
<dd>Returns the angle of this function.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.phase_spectrum" title="matplotlib.mlab.phase_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">phase_spectrum</span></code></a></dt>
<dd>Returns the phase (unwrapped angle) of this function.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.specgram" title="matplotlib.mlab.specgram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">specgram</span></code></a></dt>
<dd>Can return the complex spectrum of segments within the signal.</dd>
</dl>
</div>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.csd">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">csd</code><span class="sig-paren">(</span><em>x</em>, <em>y</em>, <em>NFFT=None</em>, <em>Fs=None</em>, <em>detrend=None</em>, <em>window=None</em>, <em>noverlap=None</em>, <em>pad_to=None</em>, <em>sides=None</em>, <em>scale_by_freq=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#csd"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.csd" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the cross-spectral density.</p>
<p>The cross spectral density <span class="math notranslate nohighlight">\(P_{xy}\)</span> by Welch's average
periodogram method. The vectors <em>x</em> and <em>y</em> are divided into
<em>NFFT</em> length segments. Each segment is detrended by function
<em>detrend</em> and windowed by function <em>window</em>. <em>noverlap</em> gives
the length of the overlap between segments. The product of
the direct FFTs of <em>x</em> and <em>y</em> are averaged over each segment
to compute <span class="math notranslate nohighlight">\(P_{xy}\)</span>, with a scaling to correct for power
loss due to windowing.</p>
<p>If len(<em>x</em>) < <em>NFFT</em> or len(<em>y</em>) < <em>NFFT</em>, they will be zero
padded to <em>NFFT</em>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>x, y</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D arrays or sequences</span></dt>
<dd><p class="first last">Arrays or sequences containing the data</p>
</dd>
<dt><strong>Fs</strong> <span class="classifier-delimiter">:</span> <span class="classifier">scalar</span></dt>
<dd><p class="first last">The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.</p>
</dd>
<dt><strong>window</strong> <span class="classifier-delimiter">:</span> <span class="classifier">callable or ndarray</span></dt>
<dd><p class="first last">A function or a vector of length <em>NFFT</em>. To create window vectors see
<a class="reference internal" href="#matplotlib.mlab.window_hanning" title="matplotlib.mlab.window_hanning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_hanning</span></code></a>, <a class="reference internal" href="#matplotlib.mlab.window_none" title="matplotlib.mlab.window_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_none</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.blackman.html#numpy.blackman" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.blackman</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.hamming.html#numpy.hamming" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.hamming</span></code></a>,
<a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.bartlett.html#numpy.bartlett" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.bartlett</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/signal.html#module-scipy.signal" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.signal</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.get_window.html#scipy.signal.get_window" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.signal.get_window</span></code></a>, etc. The
default is <a class="reference internal" href="#matplotlib.mlab.window_hanning" title="matplotlib.mlab.window_hanning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_hanning</span></code></a>. 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.</p>
</dd>
<dt><strong>sides</strong> <span class="classifier-delimiter">:</span> <span class="classifier">{'default', 'onesided', 'twosided'}</span></dt>
<dd><p class="first last">Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.</p>
</dd>
<dt><strong>pad_to</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">The number of points to which the data segment is padded when
performing the FFT. This can be different from <em>NFFT</em>, 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 <em>n</em> parameter
in the call to fft(). The default is None, which sets <em>pad_to</em>
equal to <em>NFFT</em></p>
</dd>
<dt><strong>NFFT</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">The number of data points used in each block for the FFT.
A power 2 is most efficient. The default value is 256.
This should <em>NOT</em> be used to get zero padding, or the scaling of the
result will be incorrect. Use <em>pad_to</em> for this instead.</p>
</dd>
<dt><strong>detrend</strong> <span class="classifier-delimiter">:</span> <span class="classifier">{'none', 'mean', 'linear'} or callable, default 'none'</span></dt>
<dd><p class="first last">The function applied to each segment before fft-ing, designed to
remove the mean or linear trend. Unlike in MATLAB, where the
<em>detrend</em> parameter is a vector, in Matplotlib is it a function.
The <a class="reference internal" href="#module-matplotlib.mlab" title="matplotlib.mlab"><code class="xref py py-mod docutils literal notranslate"><span class="pre">mlab</span></code></a> module defines <a class="reference internal" href="#matplotlib.mlab.detrend_none" title="matplotlib.mlab.detrend_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_none</span></code></a>,
<a class="reference internal" href="#matplotlib.mlab.detrend_mean" title="matplotlib.mlab.detrend_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_mean</span></code></a>, and <a class="reference internal" href="#matplotlib.mlab.detrend_linear" title="matplotlib.mlab.detrend_linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_linear</span></code></a>, but you can use a custom
function as well. You can also use a string to choose one of the
functions: 'none' calls <a class="reference internal" href="#matplotlib.mlab.detrend_none" title="matplotlib.mlab.detrend_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_none</span></code></a>. 'mean' calls <a class="reference internal" href="#matplotlib.mlab.detrend_mean" title="matplotlib.mlab.detrend_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_mean</span></code></a>.
'linear' calls <a class="reference internal" href="#matplotlib.mlab.detrend_linear" title="matplotlib.mlab.detrend_linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_linear</span></code></a>.</p>
</dd>
<dt><strong>scale_by_freq</strong> <span class="classifier-delimiter">:</span> <span class="classifier">bool, optional</span></dt>
<dd><p class="first last">Specifies 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.</p>
</dd>
<dt><strong>noverlap</strong> <span class="classifier-delimiter">:</span> <span class="classifier">integer</span></dt>
<dd><p class="first last">The number of points of overlap between segments.
The default value is 0 (no overlap).</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>Pxy</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array</span></dt>
<dd><p class="first last">The values for the cross spectrum <code class="xref py py-obj docutils literal notranslate"><span class="pre">P_{xy}</span></code> before scaling (real valued)</p>
</dd>
<dt><strong>freqs</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array</span></dt>
<dd><p class="first last">The frequencies corresponding to the elements in <em>Pxy</em></p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.mlab.psd" title="matplotlib.mlab.psd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">psd</span></code></a></dt>
<dd>equivalent to setting <code class="docutils literal notranslate"><span class="pre">y</span> <span class="pre">=</span> <span class="pre">x</span></code>.</dd>
</dl>
</div>
<p class="rubric">References</p>
<p>Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John
Wiley & Sons (1986)</p>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.demean">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">demean</code><span class="sig-paren">(</span><em>x</em>, <em>axis=0</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#demean"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.demean" title="Permalink to this definition">¶</a></dt>
<dd><p>[<em>Deprecated</em>] Return x minus its mean along the specified axis.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>x</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array or sequence</span></dt>
<dd><p class="first last">Array or sequence containing the data
Can have any dimensionality</p>
</dd>
<dt><strong>axis</strong> <span class="classifier-delimiter">:</span> <span class="classifier">integer</span></dt>
<dd><p class="first last">The axis along which to take the mean. See numpy.mean for a
description of this argument.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_mean" title="matplotlib.mlab.detrend_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_mean</span></code></a></dt>
<dd>Same as <a class="reference internal" href="#matplotlib.mlab.demean" title="matplotlib.mlab.demean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">demean</span></code></a> except for the default <em>axis</em>.</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<div class="deprecated">
<p><span class="versionmodified">Deprecated since version 3.1.</span></p>
</div>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.detrend">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">detrend</code><span class="sig-paren">(</span><em>x</em>, <em>key=None</em>, <em>axis=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#detrend"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.detrend" title="Permalink to this definition">¶</a></dt>
<dd><p>Return x with its trend removed.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>x</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array or sequence</span></dt>
<dd><p class="first last">Array or sequence containing the data.</p>
</dd>
<dt><strong>key</strong> <span class="classifier-delimiter">:</span> <span class="classifier">[ 'default' | 'constant' | 'mean' | 'linear' | 'none'] or function</span></dt>
<dd><p class="first last">Specifies the detrend algorithm to use. 'default' is 'mean', which is
the same as <a class="reference internal" href="#matplotlib.mlab.detrend_mean" title="matplotlib.mlab.detrend_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_mean</span></code></a>. 'constant' is the same. 'linear' is
the same as <a class="reference internal" href="#matplotlib.mlab.detrend_linear" title="matplotlib.mlab.detrend_linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_linear</span></code></a>. 'none' is the same as
<a class="reference internal" href="#matplotlib.mlab.detrend_none" title="matplotlib.mlab.detrend_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_none</span></code></a>. 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.</p>
</dd>
<dt><strong>axis</strong> <span class="classifier-delimiter">:</span> <span class="classifier">integer</span></dt>
<dd><p class="first last">The axis along which to do the detrending.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_mean" title="matplotlib.mlab.detrend_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_mean</span></code></a></dt>
<dd>Implementation of the 'mean' algorithm.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_linear" title="matplotlib.mlab.detrend_linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_linear</span></code></a></dt>
<dd>Implementation of the 'linear' algorithm.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_none" title="matplotlib.mlab.detrend_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_none</span></code></a></dt>
<dd>Implementation of the 'none' algorithm.</dd>
</dl>
</div>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.detrend_linear">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">detrend_linear</code><span class="sig-paren">(</span><em>y</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#detrend_linear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.detrend_linear" title="Permalink to this definition">¶</a></dt>
<dd><p>Return x minus best fit line; 'linear' detrending.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>y</strong> <span class="classifier-delimiter">:</span> <span class="classifier">0-D or 1-D array or sequence</span></dt>
<dd><p class="first last">Array or sequence containing the data</p>
</dd>
<dt><strong>axis</strong> <span class="classifier-delimiter">:</span> <span class="classifier">integer</span></dt>
<dd><p class="first last">The axis along which to take the mean. See numpy.mean for a
description of this argument.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_mean" title="matplotlib.mlab.detrend_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_mean</span></code></a></dt>
<dd>Another detrend algorithm.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_none" title="matplotlib.mlab.detrend_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_none</span></code></a></dt>
<dd>Another detrend algorithm.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.detrend" title="matplotlib.mlab.detrend"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend</span></code></a></dt>
<dd>A wrapper around all the detrend algorithms.</dd>
</dl>
</div>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.detrend_mean">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">detrend_mean</code><span class="sig-paren">(</span><em>x</em>, <em>axis=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#detrend_mean"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.detrend_mean" title="Permalink to this definition">¶</a></dt>
<dd><p>Return x minus the mean(x).</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>x</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array or sequence</span></dt>
<dd><p class="first last">Array or sequence containing the data
Can have any dimensionality</p>
</dd>
<dt><strong>axis</strong> <span class="classifier-delimiter">:</span> <span class="classifier">integer</span></dt>
<dd><p class="first last">The axis along which to take the mean. See numpy.mean for a
description of this argument.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_linear" title="matplotlib.mlab.detrend_linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_linear</span></code></a></dt>
<dd>Another detrend algorithm.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_none" title="matplotlib.mlab.detrend_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_none</span></code></a></dt>
<dd>Another detrend algorithm.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.detrend" title="matplotlib.mlab.detrend"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend</span></code></a></dt>
<dd>A wrapper around all the detrend algorithms.</dd>
</dl>
</div>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.detrend_none">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">detrend_none</code><span class="sig-paren">(</span><em>x</em>, <em>axis=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#detrend_none"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.detrend_none" title="Permalink to this definition">¶</a></dt>
<dd><p>Return x: no detrending.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>x</strong> <span class="classifier-delimiter">:</span> <span class="classifier">any object</span></dt>
<dd><p class="first last">An object containing the data</p>
</dd>
<dt><strong>axis</strong> <span class="classifier-delimiter">:</span> <span class="classifier">integer</span></dt>
<dd><p class="first last">This parameter is ignored.
It is included for compatibility with detrend_mean</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_mean" title="matplotlib.mlab.detrend_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_mean</span></code></a></dt>
<dd>Another detrend algorithm.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.detrend_linear" title="matplotlib.mlab.detrend_linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend_linear</span></code></a></dt>
<dd>Another detrend algorithm.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.detrend" title="matplotlib.mlab.detrend"><code class="xref py py-obj docutils literal notranslate"><span class="pre">detrend</span></code></a></dt>
<dd>A wrapper around all the detrend algorithms.</dd>
</dl>
</div>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.magnitude_spectrum">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">magnitude_spectrum</code><span class="sig-paren">(</span><em>x</em>, <em>Fs=None</em>, <em>window=None</em>, <em>pad_to=None</em>, <em>sides=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#magnitude_spectrum"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.magnitude_spectrum" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the magnitude (absolute value) of the frequency spectrum of
<em>x</em>. Data is padded to a length of <em>pad_to</em> and the windowing function
<em>window</em> is applied to the signal.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>x</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array or sequence</span></dt>
<dd><p class="first last">Array or sequence containing the data</p>
</dd>
<dt><strong>Fs</strong> <span class="classifier-delimiter">:</span> <span class="classifier">scalar</span></dt>
<dd><p class="first last">The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.</p>
</dd>
<dt><strong>window</strong> <span class="classifier-delimiter">:</span> <span class="classifier">callable or ndarray</span></dt>
<dd><p class="first last">A function or a vector of length <em>NFFT</em>. To create window vectors see
<a class="reference internal" href="#matplotlib.mlab.window_hanning" title="matplotlib.mlab.window_hanning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_hanning</span></code></a>, <a class="reference internal" href="#matplotlib.mlab.window_none" title="matplotlib.mlab.window_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_none</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.blackman.html#numpy.blackman" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.blackman</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.hamming.html#numpy.hamming" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.hamming</span></code></a>,
<a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.bartlett.html#numpy.bartlett" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.bartlett</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/signal.html#module-scipy.signal" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.signal</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.get_window.html#scipy.signal.get_window" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.signal.get_window</span></code></a>, etc. The
default is <a class="reference internal" href="#matplotlib.mlab.window_hanning" title="matplotlib.mlab.window_hanning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_hanning</span></code></a>. 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.</p>
</dd>
<dt><strong>sides</strong> <span class="classifier-delimiter">:</span> <span class="classifier">{'default', 'onesided', 'twosided'}</span></dt>
<dd><p class="first last">Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.</p>
</dd>
<dt><strong>pad_to</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">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 <em>n</em> parameter in the call to fft().
The default is None, which sets <em>pad_to</em> equal to the length of the
input signal (i.e. no padding).</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>spectrum</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array</span></dt>
<dd><p class="first last">The values for the magnitude spectrum (real valued)</p>
</dd>
<dt><strong>freqs</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array</span></dt>
<dd><p class="first last">The frequencies corresponding to the elements in <em>spectrum</em></p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.mlab.psd" title="matplotlib.mlab.psd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">psd</span></code></a></dt>
<dd>Returns the power spectral density.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.complex_spectrum" title="matplotlib.mlab.complex_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">complex_spectrum</span></code></a></dt>
<dd>This function returns the absolute value of <a class="reference internal" href="#matplotlib.mlab.complex_spectrum" title="matplotlib.mlab.complex_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">complex_spectrum</span></code></a>.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.angle_spectrum" title="matplotlib.mlab.angle_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">angle_spectrum</span></code></a></dt>
<dd>Returns the angles of the corresponding frequencies.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.phase_spectrum" title="matplotlib.mlab.phase_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">phase_spectrum</span></code></a></dt>
<dd>Returns the phase (unwrapped angle) of the corresponding frequencies.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.specgram" title="matplotlib.mlab.specgram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">specgram</span></code></a></dt>
<dd>Can return the complex spectrum of segments within the signal.</dd>
</dl>
</div>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.phase_spectrum">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">phase_spectrum</code><span class="sig-paren">(</span><em>x</em>, <em>Fs=None</em>, <em>window=None</em>, <em>pad_to=None</em>, <em>sides=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#phase_spectrum"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.phase_spectrum" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the phase of the frequency spectrum (unwrapped angle spectrum) of
<em>x</em>. Data is padded to a length of <em>pad_to</em> and the windowing function
<em>window</em> is applied to the signal.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>x</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array or sequence</span></dt>
<dd><p class="first last">Array or sequence containing the data</p>
</dd>
<dt><strong>Fs</strong> <span class="classifier-delimiter">:</span> <span class="classifier">scalar</span></dt>
<dd><p class="first last">The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.</p>
</dd>
<dt><strong>window</strong> <span class="classifier-delimiter">:</span> <span class="classifier">callable or ndarray</span></dt>
<dd><p class="first last">A function or a vector of length <em>NFFT</em>. To create window vectors see
<a class="reference internal" href="#matplotlib.mlab.window_hanning" title="matplotlib.mlab.window_hanning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_hanning</span></code></a>, <a class="reference internal" href="#matplotlib.mlab.window_none" title="matplotlib.mlab.window_none"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_none</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.blackman.html#numpy.blackman" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.blackman</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.hamming.html#numpy.hamming" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.hamming</span></code></a>,
<a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.bartlett.html#numpy.bartlett" title="(in NumPy v1.17)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.bartlett</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/signal.html#module-scipy.signal" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.signal</span></code></a>, <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.get_window.html#scipy.signal.get_window" title="(in SciPy v1.3.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.signal.get_window</span></code></a>, etc. The
default is <a class="reference internal" href="#matplotlib.mlab.window_hanning" title="matplotlib.mlab.window_hanning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">window_hanning</span></code></a>. 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.</p>
</dd>
<dt><strong>sides</strong> <span class="classifier-delimiter">:</span> <span class="classifier">{'default', 'onesided', 'twosided'}</span></dt>
<dd><p class="first last">Specifies which sides of the spectrum to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided
spectrum, while 'twosided' forces two-sided.</p>
</dd>
<dt><strong>pad_to</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">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 <em>n</em> parameter in the call to fft().
The default is None, which sets <em>pad_to</em> equal to the length of the
input signal (i.e. no padding).</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>spectrum</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array</span></dt>
<dd><p class="first last">The values for the phase spectrum in radians (real valued)</p>
</dd>
<dt><strong>freqs</strong> <span class="classifier-delimiter">:</span> <span class="classifier">1-D array</span></dt>
<dd><p class="first last">The frequencies corresponding to the elements in <em>spectrum</em></p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.mlab.complex_spectrum" title="matplotlib.mlab.complex_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">complex_spectrum</span></code></a></dt>
<dd>This function returns the phase value of <a class="reference internal" href="#matplotlib.mlab.complex_spectrum" title="matplotlib.mlab.complex_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">complex_spectrum</span></code></a>.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.magnitude_spectrum" title="matplotlib.mlab.magnitude_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">magnitude_spectrum</span></code></a></dt>
<dd>Returns the magnitudes of the corresponding frequencies.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.angle_spectrum" title="matplotlib.mlab.angle_spectrum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">angle_spectrum</span></code></a></dt>
<dd>Returns the angle (wrapped phase) of the corresponding frequencies.</dd>
<dt><a class="reference internal" href="#matplotlib.mlab.specgram" title="matplotlib.mlab.specgram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">specgram</span></code></a></dt>
<dd>Can return the complex spectrum of segments within the signal.</dd>
</dl>
</div>
</dd></dl>
<dl class="function">
<dt id="matplotlib.mlab.psd">
<code class="descclassname">matplotlib.mlab.</code><code class="descname">psd</code><span class="sig-paren">(</span><em>x</em>, <em>NFFT=None</em>, <em>Fs=None</em>, <em>detrend=None</em>, <em>window=None</em>, <em>noverlap=None</em>, <em>pad_to=None</em>, <em>sides=None</em>, <em>scale_by_freq=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/mlab.html#psd"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.mlab.psd" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the power spectral density.</p>
<p>The power spectral density <span class="math notranslate nohighlight">\(P_{xx}\)</span> by Welch's average
periodogram method. The vector <em>x</em> is divided into <em>NFFT</em> length
segments. Each segment is detrended by function <em>detrend</em> and
windowed by function <em>window</em>. <em>noverlap</em> gives the length of
the overlap between segments. The <span class="math notranslate nohighlight">\(|\mathrm{fft}(i)|^2\)</span>
of each segment <span class="math notranslate nohighlight">\(i\)</span> are averaged to compute <span class="math notranslate nohighlight">\(P_{xx}\)</span>.</p>
<p>If len(<em>x</em>) < <em>NFFT</em>, it will be zero padded to <em>NFFT</em>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />