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<div class="section" id="sigma-clip">
<h1>sigma_clip<a class="headerlink" href="#sigma-clip" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="astropy.stats.funcs.sigma_clip">
<tt class="descclassname">astropy.stats.funcs.</tt><tt class="descname">sigma_clip</tt><big>(</big><em>data</em>, <em>sig=3</em>, <em>iters=1</em>, <em>cenfunc=<function median at 0x14d4c08></em>, <em>varfunc=<function var at 0x13490c8></em>, <em>maout=False</em><big>)</big><a class="reference external" href="http://github.com/astropy/astropy/tree/v0.2.4/astropy/stats/funcs.py#L16" title="Push the Edit button on the next page"><span class="edit-on-github viewcode-link"> [edit on github]</span></a><a class="reference internal" href="../_modules/astropy/stats/funcs.html#sigma_clip"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#astropy.stats.funcs.sigma_clip" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform sigma-clipping on the provided data.</p>
<p>This performs the sigma clipping algorithm - i.e. the data will be iterated
over, each time rejecting points that are more than a specified number of
standard deviations discrepant.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last"><tt class="xref py py-obj docutils literal"><span class="pre">scipy.stats.sigmaclip</span></tt> provides a subset of the functionality in this
function.</p>
</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"><p class="first"><strong>data</strong> : array-like</p>
<blockquote>
<div><p>The data to be sigma-clipped (any shape).</p>
</div></blockquote>
<p><strong>sig</strong> : float</p>
<blockquote>
<div><p>The number of standard deviations (<em>not</em> variances) to use as the
clipping limit.</p>
</div></blockquote>
<p><strong>iters</strong> : int or None</p>
<blockquote>
<div><p>The number of iterations to perform clipping for, or None to clip until
convergence is achieved (i.e. continue until the last iteration clips
nothing).</p>
</div></blockquote>
<p><strong>cenfunc</strong> : callable</p>
<blockquote>
<div><p>The technique to compute the center for the clipping. Must be a
callable that takes in a 1D data array and outputs the central value.
Defaults to the median.</p>
</div></blockquote>
<p><strong>varfunc</strong> : callable</p>
<blockquote>
<div><p>The technique to compute the variance about the center. Must be a
callable that takes in a 1D data array and outputs the width estimator
that will be interpreted as a variance. Defaults to the variance.</p>
</div></blockquote>
<p><strong>maout</strong> : bool or ‘copy’</p>
<blockquote>
<div><p>If True, a masked array will be returned. If the special string
‘inplace’, the masked array will contain the same array as <tt class="xref py py-obj docutils literal"><span class="pre">data</span></tt>,
otherwise the array data will be copied.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first"><strong>filtereddata</strong> : <tt class="xref py py-obj docutils literal"><span class="pre">numpy.ndarray</span></tt> or <tt class="xref py py-obj docutils literal"><span class="pre">numpy.masked.MaskedArray</span></tt></p>
<blockquote>
<div><p>If <tt class="xref py py-obj docutils literal"><span class="pre">maout</span></tt> is True, this is a masked array with a shape matching the
input that is masked where the algorithm has rejected those values.
Otherwise, a 1D array of values including only those that are not
clipped.</p>
</div></blockquote>
<p><strong>mask</strong> : boolean array</p>
<blockquote class="last">
<div><p>Only present if <tt class="xref py py-obj docutils literal"><span class="pre">maout</span></tt> is False. A boolean array with a shape matching
the input <tt class="xref py py-obj docutils literal"><span class="pre">data</span></tt> that is False for rejected values and True for all
others.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<p>This will generate random variates from a Gaussian distribution and return
only the points that are within 2 <em>sample</em> standard deviation from the
median:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">astropy.stats</span> <span class="kn">import</span> <span class="n">sigma_clip</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">randn</span>
<span class="gp">>>> </span><span class="n">randvar</span> <span class="o">=</span> <span class="n">randn</span><span class="p">(</span><span class="mi">10000</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">data</span><span class="p">,</span><span class="n">mask</span> <span class="o">=</span> <span class="n">sigma_clip</span><span class="p">(</span><span class="n">randvar</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>This will clipping on a similar distribution, but for 3 sigma relative to
the sample <em>mean</em>, will clip until converged, and produces a
<tt class="xref py py-obj docutils literal"><span class="pre">numpy.masked.MaskedArray</span></tt>:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">astropy.stats</span> <span class="kn">import</span> <span class="n">sigma_clip</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">randn</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">mean</span>
<span class="gp">>>> </span><span class="n">randvar</span> <span class="o">=</span> <span class="n">randn</span><span class="p">(</span><span class="mi">10000</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">maskedarr</span> <span class="o">=</span> <span class="n">sigma_clip</span><span class="p">(</span><span class="n">randvar</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">maout</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
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