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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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<title>11. Class reference — astroML 0.2 documentation</title>
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<p style="text-align: center">This documentation is
for astroML <strong>version 0.2</strong>
<!-- — <a href="http://scikit-learn.org/stable/support.html#documentation-resources">Other versions</a> -->
</p>
<h3>This page</h3>
<ul>
<li><a class="reference internal" href="#">11. Class reference</a><ul>
<li><a class="reference internal" href="#module-astroML.plotting">11.1. Plotting Functions: <tt class="docutils literal"><span class="pre">astroML.plotting</span></tt></a><ul>
<li><a class="reference internal" href="#functions">11.1.1. Functions</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#classes">11.1.2. Classes</a><ul>
</ul>
</li>
</ul>
</li>
<li><a class="reference internal" href="#module-astroML.density_estimation">11.2. Density Estimation & Histograms: <tt class="docutils literal"><span class="pre">astroML.density_estimation</span></tt></a><ul>
<li><a class="reference internal" href="#histogram-tools">11.2.1. Histogram Tools</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#density-estimation">11.2.2. Density Estimation</a><ul>
</ul>
</li>
</ul>
</li>
<li><a class="reference internal" href="#module-astroML.linear_model">11.3. Linear Regression & Fitting: <tt class="docutils literal"><span class="pre">astroML.linear_model</span></tt></a><ul>
<li><a class="reference internal" href="#linear-regression">11.3.1. Linear Regression</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#id2">11.3.2. Functions</a><ul>
</ul>
</li>
</ul>
</li>
<li><a class="reference internal" href="#module-astroML.datasets">11.4. Loading of Datasets: <tt class="docutils literal"><span class="pre">astroML.datasets</span></tt></a><ul>
<li><a class="reference internal" href="#astronomy-datasets">11.4.1. Astronomy Datasets</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-astroML.time_series">11.5. Time Series Analysis: <tt class="docutils literal"><span class="pre">astroML.time_series</span></tt></a><ul>
<li><a class="reference internal" href="#periodic-time-series">11.5.1. Periodic Time Series</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#aperiodic-time-series">11.5.2. Aperiodic Time Series</a><ul>
</ul>
</li>
</ul>
</li>
<li><a class="reference internal" href="#module-astroML.stats">11.6. Statistical Functions: <tt class="docutils literal"><span class="pre">astroML.stats</span></tt></a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-astroML.dimensionality">11.7. Dimensionality Reduction: <tt class="docutils literal"><span class="pre">astroML.dimensionality</span></tt></a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-astroML.correlation">11.8. Correlation Functions: <tt class="docutils literal"><span class="pre">astroML.correlation</span></tt></a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-astroML.filters">11.9. Filters: <tt class="docutils literal"><span class="pre">astroML.filters</span></tt></a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-astroML.fourier">11.10. Fourier and Wavelet Transforms: <tt class="docutils literal"><span class="pre">astroML.fourier</span></tt></a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-astroML.lumfunc">11.11. Luminosity Functions: <tt class="docutils literal"><span class="pre">astroML.lumfunc</span></tt></a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-astroML.classification">11.12. Classification: <tt class="docutils literal"><span class="pre">astroML.classification</span></tt></a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-astroML.resample">11.13. Resampling: <tt class="docutils literal"><span class="pre">astroML.resample</span></tt></a><ul>
</ul>
</li>
<li><a class="reference internal" href="#addon-functions-astroml-addons">11.14. Addon Functions <tt class="docutils literal"><span class="pre">astroML_addons</span></tt></a><ul>
</ul>
</li>
</ul>
</li>
</ul>
<h3>Links</h3>
<p><a href="https://groups.google.com/forum/#!forum/astroml-general">astroML Mailing List</a></p>
<p><a href="http://github.com/astroML/astroML/issues">GitHub Issue Tracker</a></p>
<h3>Videos</h3>
<p><a href="http://pyvideo.org/video/1218/astroml-data-mining-and-machine-learning-for-ast">Scipy 2012</a> (15 minute talk)</p>
<p><a href="http://pyvideo.org/video/2035/opening-up-astronomy-with-python-and-astroml-sci">Scipy 2013</a> (20 minute talk)</p>
<h3>Citing</h3>
<p>If you use the software, please consider
<!-- hard-coded link here because pathto() adds an extra # -->
<a href="/index.html#citing-astroml">citing astroML</a>.</p>
</div>
<div class="content">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body">
<div class="section" id="class-reference">
<h1>11. Class reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h1>
<p>This is a list of modules, classes, and functions available in <tt class="docutils literal"><span class="pre">astroML</span></tt>.
For more details, please refer to the <a class="reference internal" href="../user_guide/index.html#user-guide"><em>user guide</em></a> or
the <a class="reference internal" href="../user_guide/introduction.html#textbook"><em>text book</em></a>. Examples of the use of <tt class="docutils literal"><span class="pre">astroML</span></tt>
can also be found in the <a class="reference internal" href="../examples/index.html#example-root"><em>code examples</em></a>, the
<a class="reference internal" href="../book_figures/index.html#book-fig-root"><em>text book figures</em></a> and the
<a class="reference internal" href="../paper_figures/index.html#paper-fig-root"><em>paper figures</em></a>.</p>
<span class="target" id="module-astroML"></span><div class="section" id="module-astroML.plotting">
<span id="plotting-functions-astroml-plotting"></span><h2>11.1. Plotting Functions: <a class="reference internal" href="#module-astroML.plotting" title="astroML.plotting"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.plotting</span></tt></a><a class="headerlink" href="#module-astroML.plotting" title="Permalink to this headline">¶</a></h2>
<div class="section" id="functions">
<h3>11.1.1. Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.plotting.hist.html#astroML.plotting.hist" title="astroML.plotting.hist"><tt class="xref py py-obj docutils literal"><span class="pre">plotting.hist</span></tt></a>(x[, bins, range])</td>
<td>Enhanced histogram</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.plotting.scatter_contour.html#astroML.plotting.scatter_contour" title="astroML.plotting.scatter_contour"><tt class="xref py py-obj docutils literal"><span class="pre">plotting.scatter_contour</span></tt></a>(x, y[, levels, ...])</td>
<td>Scatter plot with contour over dense regions</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="classes">
<h3>11.1.2. Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.plotting.MultiAxes.html#astroML.plotting.MultiAxes" title="astroML.plotting.MultiAxes"><tt class="xref py py-obj docutils literal"><span class="pre">plotting.MultiAxes</span></tt></a>(ndim[, inner_labels, ...])</td>
<td>Visualize Multiple-dimensional data</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-astroML.density_estimation">
<span id="density-estimation-histograms-astroml-density-estimation"></span><h2>11.2. Density Estimation & Histograms: <a class="reference internal" href="#module-astroML.density_estimation" title="astroML.density_estimation"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.density_estimation</span></tt></a><a class="headerlink" href="#module-astroML.density_estimation" title="Permalink to this headline">¶</a></h2>
<div class="section" id="histogram-tools">
<h3>11.2.1. Histogram Tools<a class="headerlink" href="#histogram-tools" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.density_estimation.histogram.html#astroML.density_estimation.histogram" title="astroML.density_estimation.histogram"><tt class="xref py py-obj docutils literal"><span class="pre">density_estimation.histogram</span></tt></a>(a[, bins, range])</td>
<td>Enhanced histogram</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.density_estimation.bayesian_blocks.html#astroML.density_estimation.bayesian_blocks" title="astroML.density_estimation.bayesian_blocks"><tt class="xref py py-obj docutils literal"><span class="pre">density_estimation.bayesian_blocks</span></tt></a>(t[, x, ...])</td>
<td>Bayesian Blocks Implementation</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.density_estimation.knuth_bin_width.html#astroML.density_estimation.knuth_bin_width" title="astroML.density_estimation.knuth_bin_width"><tt class="xref py py-obj docutils literal"><span class="pre">density_estimation.knuth_bin_width</span></tt></a>(data[, ...])</td>
<td>Return the optimal histogram bin width using Knuth’s rule [R2]</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.density_estimation.scotts_bin_width.html#astroML.density_estimation.scotts_bin_width" title="astroML.density_estimation.scotts_bin_width"><tt class="xref py py-obj docutils literal"><span class="pre">density_estimation.scotts_bin_width</span></tt></a>(data[, ...])</td>
<td>Return the optimal histogram bin width using Scott’s rule:</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.density_estimation.freedman_bin_width.html#astroML.density_estimation.freedman_bin_width" title="astroML.density_estimation.freedman_bin_width"><tt class="xref py py-obj docutils literal"><span class="pre">density_estimation.freedman_bin_width</span></tt></a>(data)</td>
<td>Return the optimal histogram bin width using the Freedman-Diaconis rule</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="density-estimation">
<h3>11.2.2. Density Estimation<a class="headerlink" href="#density-estimation" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.density_estimation.XDGMM.html#astroML.density_estimation.XDGMM" title="astroML.density_estimation.XDGMM"><tt class="xref py py-obj docutils literal"><span class="pre">density_estimation.XDGMM</span></tt></a>(n_components[, ...])</td>
<td>Extreme Deconvolution</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.density_estimation.KDE.html#astroML.density_estimation.KDE" title="astroML.density_estimation.KDE"><tt class="xref py py-obj docutils literal"><span class="pre">density_estimation.KDE</span></tt></a>([metric, h])</td>
<td>Kernel Density Estimate</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.density_estimation.KNeighborsDensity.html#astroML.density_estimation.KNeighborsDensity" title="astroML.density_estimation.KNeighborsDensity"><tt class="xref py py-obj docutils literal"><span class="pre">density_estimation.KNeighborsDensity</span></tt></a>([...])</td>
<td>K-neighbors density estimation</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.density_estimation.EmpiricalDistribution.html#astroML.density_estimation.EmpiricalDistribution" title="astroML.density_estimation.EmpiricalDistribution"><tt class="xref py py-obj docutils literal"><span class="pre">density_estimation.EmpiricalDistribution</span></tt></a>(data)</td>
<td>Empirically learn a distribution from one-dimensional data</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.density_estimation.FunctionDistribution.html#astroML.density_estimation.FunctionDistribution" title="astroML.density_estimation.FunctionDistribution"><tt class="xref py py-obj docutils literal"><span class="pre">density_estimation.FunctionDistribution</span></tt></a>(...)</td>
<td>Generate random variables distributed according to an arbitrary function</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-astroML.linear_model">
<span id="linear-regression-fitting-astroml-linear-model"></span><h2>11.3. Linear Regression & Fitting: <a class="reference internal" href="#module-astroML.linear_model" title="astroML.linear_model"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.linear_model</span></tt></a><a class="headerlink" href="#module-astroML.linear_model" title="Permalink to this headline">¶</a></h2>
<div class="section" id="linear-regression">
<h3>11.3.1. Linear Regression<a class="headerlink" href="#linear-regression" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.linear_model.LinearRegression.html#astroML.linear_model.LinearRegression" title="astroML.linear_model.LinearRegression"><tt class="xref py py-obj docutils literal"><span class="pre">linear_model.LinearRegression</span></tt></a>([fit_intercept])</td>
<td>Simple Linear Regression with errors in y</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.linear_model.PolynomialRegression.html#astroML.linear_model.PolynomialRegression" title="astroML.linear_model.PolynomialRegression"><tt class="xref py py-obj docutils literal"><span class="pre">linear_model.PolynomialRegression</span></tt></a>([degree, ...])</td>
<td>Polynomial Regression with errors in y</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.linear_model.BasisFunctionRegression.html#astroML.linear_model.BasisFunctionRegression" title="astroML.linear_model.BasisFunctionRegression"><tt class="xref py py-obj docutils literal"><span class="pre">linear_model.BasisFunctionRegression</span></tt></a>([...])</td>
<td>Basis Function with errors in y</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.linear_model.NadarayaWatson.html#astroML.linear_model.NadarayaWatson" title="astroML.linear_model.NadarayaWatson"><tt class="xref py py-obj docutils literal"><span class="pre">linear_model.NadarayaWatson</span></tt></a>([kernel, h])</td>
<td>Nadaraya-Watson Kernel Regression</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="id2">
<h3>11.3.2. Functions<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.linear_model.TLS_logL.html#astroML.linear_model.TLS_logL" title="astroML.linear_model.TLS_logL"><tt class="xref py py-obj docutils literal"><span class="pre">linear_model.TLS_logL</span></tt></a>(v, X, dX)</td>
<td>Compute the total least squares log-likelihood</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-astroML.datasets">
<span id="loading-of-datasets-astroml-datasets"></span><h2>11.4. Loading of Datasets: <a class="reference internal" href="#module-astroML.datasets" title="astroML.datasets"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.datasets</span></tt></a><a class="headerlink" href="#module-astroML.datasets" title="Permalink to this headline">¶</a></h2>
<div class="section" id="astronomy-datasets">
<h3>11.4.1. Astronomy Datasets<a class="headerlink" href="#astronomy-datasets" title="Permalink to this headline">¶</a></h3>
</div>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.datasets.fetch_sdss_spectrum.html#astroML.datasets.fetch_sdss_spectrum" title="astroML.datasets.fetch_sdss_spectrum"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_sdss_spectrum</span></tt></a>(plate, mjd, fiber)</td>
<td>Fetch an SDSS spectrum from the Data Archive Server</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.datasets.fetch_sdss_corrected_spectra.html#astroML.datasets.fetch_sdss_corrected_spectra" title="astroML.datasets.fetch_sdss_corrected_spectra"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_sdss_corrected_spectra</span></tt></a>([...])</td>
<td>Loader for Iterative PCA pre-processed galaxy spectra</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.datasets.fetch_sdss_S82standards.html#astroML.datasets.fetch_sdss_S82standards" title="astroML.datasets.fetch_sdss_S82standards"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_sdss_S82standards</span></tt></a>([...])</td>
<td>Loader for SDSS stripe82 standard star catalog</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.datasets.fetch_dr7_quasar.html#astroML.datasets.fetch_dr7_quasar" title="astroML.datasets.fetch_dr7_quasar"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_dr7_quasar</span></tt></a>([data_home, ...])</td>
<td>Loader for SDSS DR7 quasar catalog</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.datasets.fetch_moving_objects.html#astroML.datasets.fetch_moving_objects" title="astroML.datasets.fetch_moving_objects"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_moving_objects</span></tt></a>([data_home, ...])</td>
<td>Loader for SDSS moving objects datasets</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.datasets.fetch_sdss_galaxy_colors.html#astroML.datasets.fetch_sdss_galaxy_colors" title="astroML.datasets.fetch_sdss_galaxy_colors"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_sdss_galaxy_colors</span></tt></a>([...])</td>
<td>Loader for SDSS galaxy colors.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.datasets.fetch_nasa_atlas.html#astroML.datasets.fetch_nasa_atlas" title="astroML.datasets.fetch_nasa_atlas"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_nasa_atlas</span></tt></a>([data_home, ...])</td>
<td>Loader for NASA galaxy atlas data</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.datasets.fetch_sdss_sspp.html#astroML.datasets.fetch_sdss_sspp" title="astroML.datasets.fetch_sdss_sspp"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_sdss_sspp</span></tt></a>([data_home, ...])</td>
<td>Loader for SDSS SEGUE Stellar Parameter Pipeline data</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.datasets.fetch_sdss_specgals.html#astroML.datasets.fetch_sdss_specgals" title="astroML.datasets.fetch_sdss_specgals"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_sdss_specgals</span></tt></a>([data_home, ...])</td>
<td>Loader for SDSS Galaxies with spectral information</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.datasets.fetch_great_wall.html#astroML.datasets.fetch_great_wall" title="astroML.datasets.fetch_great_wall"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_great_wall</span></tt></a>([data_home, ...])</td>
<td>Get the 2D SDSS “Great Wall” distribution, following Cowan et al 2008</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.datasets.fetch_imaging_sample.html#astroML.datasets.fetch_imaging_sample" title="astroML.datasets.fetch_imaging_sample"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_imaging_sample</span></tt></a>([data_home, ...])</td>
<td>Loader for SDSS Imaging sample data</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.datasets.fetch_wmap_temperatures.html#astroML.datasets.fetch_wmap_temperatures" title="astroML.datasets.fetch_wmap_temperatures"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_wmap_temperatures</span></tt></a>([masked, ...])</td>
<td>Loader for WMAP temperature map data</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.datasets.fetch_rrlyrae_mags.html#astroML.datasets.fetch_rrlyrae_mags" title="astroML.datasets.fetch_rrlyrae_mags"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_rrlyrae_mags</span></tt></a>([data_home, ...])</td>
<td>Loader for RR-Lyrae data</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.datasets.fetch_rrlyrae_combined.html#astroML.datasets.fetch_rrlyrae_combined" title="astroML.datasets.fetch_rrlyrae_combined"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_rrlyrae_combined</span></tt></a>([data_home, ...])</td>
<td>Loader for RR-Lyrae combined data</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.datasets.fetch_LINEAR_sample.html#astroML.datasets.fetch_LINEAR_sample" title="astroML.datasets.fetch_LINEAR_sample"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_LINEAR_sample</span></tt></a>([data_home, ...])</td>
<td>Loader for LINEAR data sample</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.datasets.fetch_LINEAR_geneva.html#astroML.datasets.fetch_LINEAR_geneva" title="astroML.datasets.fetch_LINEAR_geneva"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_LINEAR_geneva</span></tt></a>([data_home, ...])</td>
<td>Loader for LINEAR geneva data.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.datasets.fetch_LIGO_bigdog.html#astroML.datasets.fetch_LIGO_bigdog" title="astroML.datasets.fetch_LIGO_bigdog"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_LIGO_bigdog</span></tt></a>([data_home, ...])</td>
<td>Loader for LIGO bigdog event</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.datasets.fetch_LIGO_large.html#astroML.datasets.fetch_LIGO_large" title="astroML.datasets.fetch_LIGO_large"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_LIGO_large</span></tt></a>([data_home, ...])</td>
<td>Loader for LIGO large dataset</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.datasets.fetch_hogg2010test.html#astroML.datasets.fetch_hogg2010test" title="astroML.datasets.fetch_hogg2010test"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_hogg2010test</span></tt></a>([structured])</td>
<td>Fetch the Hogg et al 2010 test data</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.datasets.fetch_rrlyrae_templates.html#astroML.datasets.fetch_rrlyrae_templates" title="astroML.datasets.fetch_rrlyrae_templates"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_rrlyrae_templates</span></tt></a>([...])</td>
<td>Loader for RR-Lyrae template data</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.datasets.fetch_sdss_filter.html#astroML.datasets.fetch_sdss_filter" title="astroML.datasets.fetch_sdss_filter"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_sdss_filter</span></tt></a>(fname[, ...])</td>
<td>Loader for SDSS Filter profiles</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.datasets.fetch_vega_spectrum.html#astroML.datasets.fetch_vega_spectrum" title="astroML.datasets.fetch_vega_spectrum"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.fetch_vega_spectrum</span></tt></a>([data_home, ...])</td>
<td>Loader for Vega reference spectrum</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.datasets.generate_mu_z.html#astroML.datasets.generate_mu_z" title="astroML.datasets.generate_mu_z"><tt class="xref py py-obj docutils literal"><span class="pre">datasets.generate_mu_z</span></tt></a>([size, z0, dmu_0, ...])</td>
<td>Generate a dataset of distance modulus vs redshift.</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-astroML.time_series">
<span id="time-series-analysis-astroml-time-series"></span><h2>11.5. Time Series Analysis: <a class="reference internal" href="#module-astroML.time_series" title="astroML.time_series"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.time_series</span></tt></a><a class="headerlink" href="#module-astroML.time_series" title="Permalink to this headline">¶</a></h2>
<div class="section" id="periodic-time-series">
<h3>11.5.1. Periodic Time Series<a class="headerlink" href="#periodic-time-series" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.time_series.lomb_scargle.html#astroML.time_series.lomb_scargle" title="astroML.time_series.lomb_scargle"><tt class="xref py py-obj docutils literal"><span class="pre">time_series.lomb_scargle</span></tt></a></td>
<td>(Generalized) Lomb-Scargle Periodogram with Floating Mean</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.time_series.lomb_scargle_bootstrap.html#astroML.time_series.lomb_scargle_bootstrap" title="astroML.time_series.lomb_scargle_bootstrap"><tt class="xref py py-obj docutils literal"><span class="pre">time_series.lomb_scargle_bootstrap</span></tt></a>(t, y, dy, ...)</td>
<td>Use a bootstrap analysis to compute Lomb-Scargle significance</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.time_series.multiterm_periodogram.html#astroML.time_series.multiterm_periodogram" title="astroML.time_series.multiterm_periodogram"><tt class="xref py py-obj docutils literal"><span class="pre">time_series.multiterm_periodogram</span></tt></a>(t, y, dy, ...)</td>
<td>Perform a multiterm periodogram at each omega</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.time_series.search_frequencies.html#astroML.time_series.search_frequencies" title="astroML.time_series.search_frequencies"><tt class="xref py py-obj docutils literal"><span class="pre">time_series.search_frequencies</span></tt></a>(t, y, dy[, ...])</td>
<td>Utility Routine to find the best frequencies</td>
</tr>
</tbody>
</table>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.time_series.MultiTermFit.html#astroML.time_series.MultiTermFit" title="astroML.time_series.MultiTermFit"><tt class="xref py py-obj docutils literal"><span class="pre">time_series.MultiTermFit</span></tt></a>(omega, n_terms)</td>
<td>Multi-term Fourier fit to a light curve</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="aperiodic-time-series">
<h3>11.5.2. Aperiodic Time Series<a class="headerlink" href="#aperiodic-time-series" title="Permalink to this headline">¶</a></h3>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.time_series.ACF_scargle.html#astroML.time_series.ACF_scargle" title="astroML.time_series.ACF_scargle"><tt class="xref py py-obj docutils literal"><span class="pre">time_series.ACF_scargle</span></tt></a>(t, y, dy[, n_omega, ...])</td>
<td>Compute the Auto-correlation function via Scargle’s method</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.time_series.ACF_EK.html#astroML.time_series.ACF_EK" title="astroML.time_series.ACF_EK"><tt class="xref py py-obj docutils literal"><span class="pre">time_series.ACF_EK</span></tt></a>(t, y, dy[, bins])</td>
<td>Auto-correlation function via the Edelson-Krolik method</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.time_series.generate_power_law.html#astroML.time_series.generate_power_law" title="astroML.time_series.generate_power_law"><tt class="xref py py-obj docutils literal"><span class="pre">time_series.generate_power_law</span></tt></a>(N, dt, beta)</td>
<td>Generate a power-law light curve</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.time_series.generate_damped_RW.html#astroML.time_series.generate_damped_RW" title="astroML.time_series.generate_damped_RW"><tt class="xref py py-obj docutils literal"><span class="pre">time_series.generate_damped_RW</span></tt></a>(t_rest[, ...])</td>
<td>Generate a damped random walk light curve</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="module-astroML.stats">
<span id="statistical-functions-astroml-stats"></span><h2>11.6. Statistical Functions: <a class="reference internal" href="#module-astroML.stats" title="astroML.stats"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.stats</span></tt></a><a class="headerlink" href="#module-astroML.stats" title="Permalink to this headline">¶</a></h2>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.stats.binned_statistic.html#astroML.stats.binned_statistic" title="astroML.stats.binned_statistic"><tt class="xref py py-obj docutils literal"><span class="pre">stats.binned_statistic</span></tt></a>(x, values[, ...])</td>
<td>Compute a binned statistic for a set of data.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.stats.binned_statistic_2d.html#astroML.stats.binned_statistic_2d" title="astroML.stats.binned_statistic_2d"><tt class="xref py py-obj docutils literal"><span class="pre">stats.binned_statistic_2d</span></tt></a>(x, y, values[, ...])</td>
<td>Compute a bidimensional binned statistic for a set of data.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.stats.binned_statistic_dd.html#astroML.stats.binned_statistic_dd" title="astroML.stats.binned_statistic_dd"><tt class="xref py py-obj docutils literal"><span class="pre">stats.binned_statistic_dd</span></tt></a>(sample, values[, ...])</td>
<td>Compute a multidimensional binned statistic for a set of data.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.stats.sigmaG.html#astroML.stats.sigmaG" title="astroML.stats.sigmaG"><tt class="xref py py-obj docutils literal"><span class="pre">stats.sigmaG</span></tt></a>(a[, axis, overwrite_input, ...])</td>
<td>Compute the rank-based estimate of the standard deviation</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.stats.median_sigmaG.html#astroML.stats.median_sigmaG" title="astroML.stats.median_sigmaG"><tt class="xref py py-obj docutils literal"><span class="pre">stats.median_sigmaG</span></tt></a>(a[, axis, ...])</td>
<td>Compute median and rank-based estimate of the standard deviation</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.stats.mean_sigma.html#astroML.stats.mean_sigma" title="astroML.stats.mean_sigma"><tt class="xref py py-obj docutils literal"><span class="pre">stats.mean_sigma</span></tt></a>(a[, axis, dtype, ddof, ...])</td>
<td>Compute mean and standard deviation for an array</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.stats.fit_bivariate_normal.html#astroML.stats.fit_bivariate_normal" title="astroML.stats.fit_bivariate_normal"><tt class="xref py py-obj docutils literal"><span class="pre">stats.fit_bivariate_normal</span></tt></a>(x, y[, robust])</td>
<td>Fit bivariate normal parameters to a 2D distribution of points</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.stats.bivariate_normal.html#astroML.stats.bivariate_normal" title="astroML.stats.bivariate_normal"><tt class="xref py py-obj docutils literal"><span class="pre">stats.bivariate_normal</span></tt></a>([mu, sigma_1, ...])</td>
<td>Sample points from a 2D normal distribution</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.stats.trunc_exp.html#astroML.stats.trunc_exp" title="astroML.stats.trunc_exp"><tt class="xref py py-obj docutils literal"><span class="pre">stats.trunc_exp</span></tt></a></td>
<td>A truncated positive exponential continuous random variable.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.stats.linear.html#astroML.stats.linear" title="astroML.stats.linear"><tt class="xref py py-obj docutils literal"><span class="pre">stats.linear</span></tt></a></td>
<td>A truncated positive exponential continuous random variable.</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-astroML.dimensionality">
<span id="dimensionality-reduction-astroml-dimensionality"></span><h2>11.7. Dimensionality Reduction: <a class="reference internal" href="#module-astroML.dimensionality" title="astroML.dimensionality"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.dimensionality</span></tt></a><a class="headerlink" href="#module-astroML.dimensionality" title="Permalink to this headline">¶</a></h2>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.dimensionality.iterative_pca.html#astroML.dimensionality.iterative_pca" title="astroML.dimensionality.iterative_pca"><tt class="xref py py-obj docutils literal"><span class="pre">dimensionality.iterative_pca</span></tt></a>(X, M[, n_ev, ...])</td>
<td><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"></td>
</tr>
</tbody>
</table>
</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-astroML.correlation">
<span id="correlation-functions-astroml-correlation"></span><h2>11.8. Correlation Functions: <a class="reference internal" href="#module-astroML.correlation" title="astroML.correlation"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.correlation</span></tt></a><a class="headerlink" href="#module-astroML.correlation" title="Permalink to this headline">¶</a></h2>
<p>Tools for computing two-point correlation functions.</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.correlation.two_point.html#astroML.correlation.two_point" title="astroML.correlation.two_point"><tt class="xref py py-obj docutils literal"><span class="pre">correlation.two_point</span></tt></a>(data, bins[, method, ...])</td>
<td>Two-point correlation function</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.correlation.two_point_angular.html#astroML.correlation.two_point_angular" title="astroML.correlation.two_point_angular"><tt class="xref py py-obj docutils literal"><span class="pre">correlation.two_point_angular</span></tt></a>(ra, dec, bins)</td>
<td>Angular two-point correlation function</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.correlation.bootstrap_two_point.html#astroML.correlation.bootstrap_two_point" title="astroML.correlation.bootstrap_two_point"><tt class="xref py py-obj docutils literal"><span class="pre">correlation.bootstrap_two_point</span></tt></a>(data, bins)</td>
<td>Bootstrapped two-point correlation function</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.correlation.bootstrap_two_point_angular.html#astroML.correlation.bootstrap_two_point_angular" title="astroML.correlation.bootstrap_two_point_angular"><tt class="xref py py-obj docutils literal"><span class="pre">correlation.bootstrap_two_point_angular</span></tt></a>(ra, ...)</td>
<td>Angular two-point correlation function</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-astroML.filters">
<span id="filters-astroml-filters"></span><h2>11.9. Filters: <a class="reference internal" href="#module-astroML.filters" title="astroML.filters"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.filters</span></tt></a><a class="headerlink" href="#module-astroML.filters" title="Permalink to this headline">¶</a></h2>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.filters.savitzky_golay.html#astroML.filters.savitzky_golay" title="astroML.filters.savitzky_golay"><tt class="xref py py-obj docutils literal"><span class="pre">filters.savitzky_golay</span></tt></a>(y, window_size, order)</td>
<td>Smooth (and optionally differentiate) data with a Savitzky-Golay filter</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.filters.wiener_filter.html#astroML.filters.wiener_filter" title="astroML.filters.wiener_filter"><tt class="xref py py-obj docutils literal"><span class="pre">filters.wiener_filter</span></tt></a>(t, h[, signal, noise, ...])</td>
<td>Compute a Wiener-filtered time-series</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.filters.min_component_filter.html#astroML.filters.min_component_filter" title="astroML.filters.min_component_filter"><tt class="xref py py-obj docutils literal"><span class="pre">filters.min_component_filter</span></tt></a>(x, y, feature_mask)</td>
<td>Minimum component filtering</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-astroML.fourier">
<span id="fourier-and-wavelet-transforms-astroml-fourier"></span><h2>11.10. Fourier and Wavelet Transforms: <a class="reference internal" href="#module-astroML.fourier" title="astroML.fourier"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.fourier</span></tt></a><a class="headerlink" href="#module-astroML.fourier" title="Permalink to this headline">¶</a></h2>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.fourier.FT_continuous.html#astroML.fourier.FT_continuous" title="astroML.fourier.FT_continuous"><tt class="xref py py-obj docutils literal"><span class="pre">fourier.FT_continuous</span></tt></a>(t, h[, axis, method])</td>
<td>Approximate a continuous 1D Fourier Transform with sampled data.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.fourier.IFT_continuous.html#astroML.fourier.IFT_continuous" title="astroML.fourier.IFT_continuous"><tt class="xref py py-obj docutils literal"><span class="pre">fourier.IFT_continuous</span></tt></a>(f, H[, axis, method])</td>
<td>Approximate a continuous 1D Inverse Fourier Transform with sampled data.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.fourier.PSD_continuous.html#astroML.fourier.PSD_continuous" title="astroML.fourier.PSD_continuous"><tt class="xref py py-obj docutils literal"><span class="pre">fourier.PSD_continuous</span></tt></a>(t, h[, axis, method])</td>
<td>Approximate a continuous 1D Power Spectral Density of sampled data.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.fourier.wavelet_PSD.html#astroML.fourier.wavelet_PSD" title="astroML.fourier.wavelet_PSD"><tt class="xref py py-obj docutils literal"><span class="pre">fourier.wavelet_PSD</span></tt></a>(t, h, f0[, Q])</td>
<td>Compute the wavelet PSD as a function of f0 and t</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.fourier.sinegauss.html#astroML.fourier.sinegauss" title="astroML.fourier.sinegauss"><tt class="xref py py-obj docutils literal"><span class="pre">fourier.sinegauss</span></tt></a>(t, t0, f0, Q)</td>
<td>Sine-gaussian wavelet</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.fourier.sinegauss_FT.html#astroML.fourier.sinegauss_FT" title="astroML.fourier.sinegauss_FT"><tt class="xref py py-obj docutils literal"><span class="pre">fourier.sinegauss_FT</span></tt></a>(f, t0, f0, Q)</td>
<td>Fourier transform of the sine-gaussian wavelet.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.fourier.sinegauss_PSD.html#astroML.fourier.sinegauss_PSD" title="astroML.fourier.sinegauss_PSD"><tt class="xref py py-obj docutils literal"><span class="pre">fourier.sinegauss_PSD</span></tt></a>(f, t0, f0, Q)</td>
<td>Compute the PSD of the sine-gaussian function at frequency f</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-astroML.lumfunc">
<span id="luminosity-functions-astroml-lumfunc"></span><h2>11.11. Luminosity Functions: <a class="reference internal" href="#module-astroML.lumfunc" title="astroML.lumfunc"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.lumfunc</span></tt></a><a class="headerlink" href="#module-astroML.lumfunc" title="Permalink to this headline">¶</a></h2>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
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</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.lumfunc.Cminus.html#astroML.lumfunc.Cminus" title="astroML.lumfunc.Cminus"><tt class="xref py py-obj docutils literal"><span class="pre">lumfunc.Cminus</span></tt></a>(x, y, xmax, ymax)</td>
<td>Lynden-Bell’s C-minus method</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.lumfunc.binned_Cminus.html#astroML.lumfunc.binned_Cminus" title="astroML.lumfunc.binned_Cminus"><tt class="xref py py-obj docutils literal"><span class="pre">lumfunc.binned_Cminus</span></tt></a>(x, y, xmax, ymax, ...)</td>
<td>Compute the binned distributions using the Cminus method</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.lumfunc.bootstrap_Cminus.html#astroML.lumfunc.bootstrap_Cminus" title="astroML.lumfunc.bootstrap_Cminus"><tt class="xref py py-obj docutils literal"><span class="pre">lumfunc.bootstrap_Cminus</span></tt></a>(x, y, xmax, ymax, ...)</td>
<td>Compute the binned distributions using the Cminus method, with</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-astroML.classification">
<span id="classification-astroml-classification"></span><h2>11.12. Classification: <a class="reference internal" href="#module-astroML.classification" title="astroML.classification"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.classification</span></tt></a><a class="headerlink" href="#module-astroML.classification" title="Permalink to this headline">¶</a></h2>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.classification.GMMBayes.html#astroML.classification.GMMBayes" title="astroML.classification.GMMBayes"><tt class="xref py py-obj docutils literal"><span class="pre">classification.GMMBayes</span></tt></a>([n_components])</td>
<td>GMM Bayes Classifier</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="module-astroML.resample">
<span id="resampling-astroml-resample"></span><h2>11.13. Resampling: <a class="reference internal" href="#module-astroML.resample" title="astroML.resample"><tt class="xref py py-mod docutils literal"><span class="pre">astroML.resample</span></tt></a><a class="headerlink" href="#module-astroML.resample" title="Permalink to this headline">¶</a></h2>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML.resample.bootstrap.html#astroML.resample.bootstrap" title="astroML.resample.bootstrap"><tt class="xref py py-obj docutils literal"><span class="pre">resample.bootstrap</span></tt></a>(data, n_bootstraps, ...)</td>
<td>Compute bootstraped statistics of a dataset.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="generated/astroML.resample.jackknife.html#astroML.resample.jackknife" title="astroML.resample.jackknife"><tt class="xref py py-obj docutils literal"><span class="pre">resample.jackknife</span></tt></a>(data, user_statistic[, ...])</td>
<td>Compute first-order jackknife statistics of the data.</td>
</tr>
</tbody>
</table>
<p>..automodule:: astroML_addons</p>
</div>
<div class="section" id="addon-functions-astroml-addons">
<h2>11.14. Addon Functions <tt class="xref py py-mod docutils literal"><span class="pre">astroML_addons</span></tt><a class="headerlink" href="#addon-functions-astroml-addons" title="Permalink to this headline">¶</a></h2>
<p>These functions should not be called directly: when they are installed, they
are used automatically by <a class="reference internal" href="#module-astroML" title="astroML"><tt class="xref py py-mod docutils literal"><span class="pre">astroML</span></tt></a>.</p>
<span class="target" id="module-astroML_addons.periodogram"></span><table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="generated/astroML_addons.periodogram.lomb_scargle.html#astroML_addons.periodogram.lomb_scargle" title="astroML_addons.periodogram.lomb_scargle"><tt class="xref py py-obj docutils literal"><span class="pre">periodogram.lomb_scargle</span></tt></a></td>
<td>(Generalized) Lomb-Scargle Periodogram with Floating Mean</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
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