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<a href="Scientific-module.html">Package Scientific</a> ::
<a href="Scientific.Statistics-module.html">Package Statistics</a> ::
<a href="Scientific.Statistics.Histogram-module.html">Module Histogram</a> ::
Class Histogram
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class Histogram</h1><p class="nomargin-top"></p>
<dl><dt>Known Subclasses:</dt>
<dd>
<ul class="subclass-list">
<li><a href="Scientific.Statistics.Histogram.WeightedHistogram-class.html">WeightedHistogram</a></li> </ul>
</dd></dl>
<hr />
<p>Histogram in one variable</p>
<p>The bin index and the number of points in a bin can be obtained by
indexing the histogram with the bin number. Application of len() yields
the number of bins. A histogram thus behaves like a sequence of bin index
- bin count pairs.</p>
<p>Here is an example on usage:</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>nsamples = 1000
<span class="py-prompt">>>> </span><span class="py-keyword">from</span> numpy <span class="py-keyword">import</span> random
<span class="py-prompt">>>> </span>data = random.normal(1.0, 0.5, nsamples)
<span class="py-prompt">>>> </span><span class="py-keyword">import</span> Scientific.Statistics <span class="py-keyword">as</span> S
<span class="py-prompt">>>> </span>S.mean(data)
<span class="py-output">0.9607056871982641</span>
<span class="py-output"></span><span class="py-prompt">>>> </span>S.standardDeviation(data)
<span class="py-output">0.50251811830486681</span>
<span class="py-output"></span><span class="py-prompt">>>> </span>S.median(data)
<span class="py-output">0.94853870756924152</span>
<span class="py-output"></span><span class="py-prompt">>>> </span>S.skewness(data) <span class="py-comment"># should be 0</span>
<span class="py-output">0.038940041870334556</span>
<span class="py-output"></span><span class="py-prompt">>>> </span>S.kurtosis(data) <span class="py-comment"># should be 3</span>
<span class="py-output">2.865582791273765</span>
<span class="py-output"></span><span class="py-prompt">>>> </span>
<span class="py-prompt">>>> </span><span class="py-keyword">from</span> Scientific.Statistics.Histogram <span class="py-keyword">import</span> Histogram
<span class="py-prompt">>>> </span>h = Histogram(data, 50) <span class="py-comment"># use 50 bins between min & max samples</span>
<span class="py-prompt">>>> </span>h.normalizeArea() <span class="py-comment"># make probabilities in histogram</span>
<span class="py-prompt">>>> </span>h[3] <span class="py-comment"># bin index and frequency in the 4th bin</span>
<span class="py-output">array([-0.45791018, 0.01553658])</span>
<span class="py-output"></span><span class="py-prompt">>>> </span>x = h.getBinIndices()
<span class="py-prompt">>>> </span>y = h.getBinCounts()
<span class="py-prompt">>>> </span><span class="py-comment"># can plot the y vector against the x vector (see below)</span>
<span class="py-prompt">>>> </span>
<span class="py-prompt">>>> </span><span class="py-comment"># add many more samples:</span>
<span class="py-prompt">>>> </span>nsamples2 = nsamples*100
<span class="py-prompt">>>> </span>data = random.normal(1.0, 0.5, nsamples2)
<span class="py-prompt">>>> </span>h.addData(data)
<span class="py-prompt">>>> </span>h.normalizeArea()
<span class="py-prompt">>>> </span>x2 = h.getBinIndices()
<span class="py-prompt">>>> </span>y2 = h.getBinCounts()</pre>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>plot (x,y) <span class="py-keyword">and</span> (x2,y2):
<span class="py-prompt">>>> </span><span class="py-keyword">import</span> Gnuplot
<span class="py-prompt">>>> </span>g = Gnuplot.Gnuplot(persist=1)
<span class="py-prompt">>>> </span>g.xlabel(<span class="py-string">'sample value'</span>); g.ylabel(<span class="py-string">'probability'</span>)
<span class="py-prompt">>>> </span>d1 = Gnuplot.Data(x, y, with=<span class="py-string">'lines'</span>,
<span class="py-more">... </span> title=<span class="py-string">'%d samples'</span> % nsamples)
<span class="py-prompt">>>> </span>d2 = Gnuplot.Data(x2, y2, with=<span class="py-string">'lines'</span>,
<span class="py-more">... </span> title=<span class="py-string">'%d samples'</span> % nsamples2)
<span class="py-prompt">>>> </span>g.plot(d1,d2)</pre>
<!-- ==================== INSTANCE METHODS ==================== -->
<a name="section-InstanceMethods"></a>
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<span class="table-header">Instance Methods</span></td>
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<span class="summary-type"> </span>
</td><td class="summary">
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<td><span class="summary-sig"><a href="Scientific.Statistics.Histogram.Histogram-class.html#__getitem__" class="summary-sig-name">__getitem__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">index</span>)</span><br />
Returns:
an array of shape (2,) containing the bin value and the bin count</td>
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</td>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a name="__getslice__"></a><span class="summary-sig-name">__getslice__</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">first</span>,
<span class="summary-sig-arg">last</span>)</span></td>
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</td>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Scientific.Statistics.Histogram.Histogram-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">data</span>,
<span class="summary-sig-arg">nbins</span>,
<span class="summary-sig-arg">range</span>=<span class="summary-sig-default">None</span>)</span></td>
<td align="right" valign="top">
</td>
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</td>
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<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"><code>int</code></span>
</td><td class="summary">
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<tr>
<td><span class="summary-sig"><a href="Scientific.Statistics.Histogram.Histogram-class.html#__len__" class="summary-sig-name">__len__</a>(<span class="summary-sig-arg">self</span>)</span><br />
Returns:
the number of bins</td>
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</td>
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</td>
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<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
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<tr>
<td><span class="summary-sig"><a href="Scientific.Statistics.Histogram.Histogram-class.html#addData" class="summary-sig-name">addData</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">data</span>)</span><br />
Add values to the originally supplied data sequence.</td>
<td align="right" valign="top">
</td>
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</td>
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<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
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<tr>
<td><span class="summary-sig"><a name="getBinCounts"></a><span class="summary-sig-name">getBinCounts</span>(<span class="summary-sig-arg">self</span>)</span><br />
Return an array of all the bin counts.</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
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<tr>
<td><span class="summary-sig"><a name="getBinIndices"></a><span class="summary-sig-name">getBinIndices</span>(<span class="summary-sig-arg">self</span>)</span><br />
Return an array of all the bin indices.</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Scientific.Statistics.Histogram.Histogram-class.html#normalize" class="summary-sig-name">normalize</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">norm</span>=<span class="summary-sig-default">1.0</span>)</span><br />
Scale all bin counts by the same factor</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
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<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Scientific.Statistics.Histogram.Histogram-class.html#normalizeArea" class="summary-sig-name">normalizeArea</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">norm</span>=<span class="summary-sig-default">1.0</span>)</span><br />
Scale all bin counts by the same factor</td>
<td align="right" valign="top">
</td>
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</td>
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<!-- ==================== METHOD DETAILS ==================== -->
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<span class="table-header">Method Details</span></td>
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<a name="__getitem__"></a>
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cellspacing="0" width="100%" bgcolor="white">
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<h3 class="epydoc"><span class="sig"><span class="sig-name">__getitem__</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">index</span>)</span>
<br /><em class="fname">(Indexing operator)</em>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>index</code></strong> (<code>int</code>) - a bin index</li>
</ul></dd>
<dt>Returns:</dt>
<dd>an array of shape (2,) containing the bin value and the bin count</dd>
</dl>
</td></tr></table>
</div>
<a name="__init__"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
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<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">data</span>,
<span class="sig-arg">nbins</span>,
<span class="sig-arg">range</span>=<span class="sig-default">None</span>)</span>
<br /><em class="fname">(Constructor)</em>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>data</code></strong> (<code>Numeric.array</code> of <code>float</code> or
<code>int</code>) - a sequence of data points</li>
<li><strong class="pname"><code>nbins</code></strong> (<code>int</code>) - the number of bins into which the data is to be sorted</li>
<li><strong class="pname"><code>range</code></strong> (<code>tuple</code> or <code>NoneType</code>) - a tuple of two values, specifying the lower and the upper end of
the interval spanned by the bins. Any data point outside this
interval will be ignored. If no range is given, the smallest and
largest data values are used to define the interval.</li>
</ul></dd>
</dl>
</td></tr></table>
</div>
<a name="__len__"></a>
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cellspacing="0" width="100%" bgcolor="white">
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<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">__len__</span>(<span class="sig-arg">self</span>)</span>
<br /><em class="fname">(Length operator)</em>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<dl class="fields">
<dt>Returns: <code>int</code></dt>
<dd>the number of bins</dd>
</dl>
</td></tr></table>
</div>
<a name="addData"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
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<table width="100%" cellpadding="0" cellspacing="0" border="0">
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<h3 class="epydoc"><span class="sig"><span class="sig-name">addData</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">data</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>Add values to the originally supplied data sequence. Use this method
to feed long data sequences in multiple parts to avoid memory
shortages.</p>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>data</code></strong> (<code>Numeric.array</code>) - a sequence of data points</li>
</ul></dd>
</dl>
<div class="fields"> <p><strong>Note:</strong>
this does not affect the default range of the histogram, which is
fixed when the histogram is created.
</p>
</div></td></tr></table>
</div>
<a name="normalize"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
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<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">normalize</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">norm</span>=<span class="sig-default">1.0</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>Scale all bin counts by the same factor</p>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>norm</code></strong> (<code>float</code> or <code>int</code>) - the sum of all bin counts after the rescaling</li>
</ul></dd>
</dl>
</td></tr></table>
</div>
<a name="normalizeArea"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
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<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">normalizeArea</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">norm</span>=<span class="sig-default">1.0</span>)</span>
</h3>
</td><td align="right" valign="top"
>
</td>
</tr></table>
<p>Scale all bin counts by the same factor</p>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>norm</code></strong> (<code>float</code> or <code>int</code>) - the area under the histogram after the rescaling</li>
</ul></dd>
</dl>
</td></tr></table>
</div>
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