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  <div class="section" id="pylab-examples-example-code-histogram-demo-extended-py">
<span id="pylab-examples-histogram-demo-extended"></span><h1>pylab_examples example code: histogram_demo_extended.py<a class="headerlink" href="#pylab-examples-example-code-histogram-demo-extended-py" title="Permalink to this headline">ΒΆ</a></h1>
<p>(<a class="reference external" href="../../mpl_examples/pylab_examples/histogram_demo_extended.py">Source code</a>)</p>
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<div class="highlight-python"><div class="highlight"><pre><span class="c">#!/usr/bin/env python</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pylab</span> <span class="kn">as</span> <span class="nn">P</span>

<span class="c">#</span>
<span class="c"># The hist() function now has a lot more options</span>
<span class="c">#</span>

<span class="c">#</span>
<span class="c"># first create a single histogram</span>
<span class="c">#</span>
<span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span> <span class="o">=</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">25</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">+</span> <span class="n">sigma</span><span class="o">*</span><span class="n">P</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="c"># the histogram of the data with histtype=&#39;step&#39;</span>
<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">histtype</span><span class="o">=</span><span class="s">&#39;stepfilled&#39;</span><span class="p">)</span>
<span class="n">P</span><span class="o">.</span><span class="n">setp</span><span class="p">(</span><span class="n">patches</span><span class="p">,</span> <span class="s">&#39;facecolor&#39;</span><span class="p">,</span> <span class="s">&#39;g&#39;</span><span class="p">,</span> <span class="s">&#39;alpha&#39;</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">)</span>

<span class="c"># add a line showing the expected distribution</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">normpdf</span><span class="p">(</span> <span class="n">bins</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">)</span>
<span class="n">l</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bins</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s">&#39;k--&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>


<span class="c">#</span>
<span class="c"># create a histogram by providing the bin edges (unequally spaced)</span>
<span class="c">#</span>
<span class="n">P</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>

<span class="n">bins</span> <span class="o">=</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span><span class="mi">125</span><span class="p">,</span><span class="mi">150</span><span class="p">,</span><span class="mi">160</span><span class="p">,</span><span class="mi">170</span><span class="p">,</span><span class="mi">180</span><span class="p">,</span><span class="mi">190</span><span class="p">,</span><span class="mi">200</span><span class="p">,</span><span class="mi">210</span><span class="p">,</span><span class="mi">220</span><span class="p">,</span><span class="mi">230</span><span class="p">,</span><span class="mi">240</span><span class="p">,</span><span class="mi">250</span><span class="p">,</span><span class="mi">275</span><span class="p">,</span><span class="mi">300</span><span class="p">]</span>
<span class="c"># the histogram of the data with histtype=&#39;step&#39;</span>
<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">histtype</span><span class="o">=</span><span class="s">&#39;bar&#39;</span><span class="p">,</span> <span class="n">rwidth</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>

<span class="c">#</span>
<span class="c"># now we create a cumulative histogram of the data</span>
<span class="c">#</span>
<span class="n">P</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>

<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">histtype</span><span class="o">=</span><span class="s">&#39;step&#39;</span><span class="p">,</span> <span class="n">cumulative</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

<span class="c"># add a line showing the expected distribution</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">normpdf</span><span class="p">(</span> <span class="n">bins</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">)</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span>
<span class="n">y</span> <span class="o">/=</span> <span class="n">y</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">l</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bins</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s">&#39;k--&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>

<span class="c"># create a second data-set with a smaller standard deviation</span>
<span class="n">sigma2</span> <span class="o">=</span> <span class="mf">15.</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">+</span> <span class="n">sigma2</span><span class="o">*</span><span class="n">P</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="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">histtype</span><span class="o">=</span><span class="s">&#39;step&#39;</span><span class="p">,</span> <span class="n">cumulative</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

<span class="c"># add a line showing the expected distribution</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">normpdf</span><span class="p">(</span> <span class="n">bins</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">sigma2</span><span class="p">)</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span>
<span class="n">y</span> <span class="o">/=</span> <span class="n">y</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">l</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bins</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s">&#39;r--&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>

<span class="c"># finally overplot a reverted cumulative histogram</span>
<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
    <span class="n">histtype</span><span class="o">=</span><span class="s">&#39;step&#39;</span><span class="p">,</span> <span class="n">cumulative</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>


<span class="n">P</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span>
<span class="n">P</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">)</span>


<span class="c">#</span>
<span class="c"># histogram has the ability to plot multiple data in parallel ...</span>
<span class="c"># Note the new color kwarg, used to override the default, which</span>
<span class="c"># uses the line color cycle.</span>
<span class="c">#</span>
<span class="n">P</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>

<span class="c"># create a new data-set</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">+</span> <span class="n">sigma</span><span class="o">*</span><span class="n">P</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>

<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">histtype</span><span class="o">=</span><span class="s">&#39;bar&#39;</span><span class="p">,</span>
                            <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s">&#39;crimson&#39;</span><span class="p">,</span> <span class="s">&#39;burlywood&#39;</span><span class="p">,</span> <span class="s">&#39;chartreuse&#39;</span><span class="p">],</span>
                            <span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="s">&#39;Crimson&#39;</span><span class="p">,</span> <span class="s">&#39;Burlywood&#39;</span><span class="p">,</span> <span class="s">&#39;Chartreuse&#39;</span><span class="p">])</span>
<span class="n">P</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>

<span class="c">#</span>
<span class="c"># ... or we can stack the data</span>
<span class="c">#</span>
<span class="n">P</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>

<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">histtype</span><span class="o">=</span><span class="s">&#39;barstacked&#39;</span><span class="p">)</span>

<span class="c">#</span>
<span class="c"># finally: make a multiple-histogram of data-sets with different length</span>
<span class="c">#</span>
<span class="n">x0</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">+</span> <span class="n">sigma</span><span class="o">*</span><span class="n">P</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="n">x1</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">+</span> <span class="n">sigma</span><span class="o">*</span><span class="n">P</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">7000</span><span class="p">)</span>
<span class="n">x2</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">+</span> <span class="n">sigma</span><span class="o">*</span><span class="n">P</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3000</span><span class="p">)</span>

<span class="c"># and exercise the weights option by arbitrarily giving the first half</span>
<span class="c"># of each series only half the weight of the others:</span>

<span class="n">w0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">x0</span><span class="p">)</span>
<span class="n">w0</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">x0</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">w1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">x1</span><span class="p">)</span>
<span class="n">w1</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">x1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">w2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">x2</span><span class="p">)</span>
<span class="n">w0</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">x2</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span>



<span class="n">P</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>

<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span> <span class="p">[</span><span class="n">x0</span><span class="p">,</span><span class="n">x1</span><span class="p">,</span><span class="n">x2</span><span class="p">],</span> <span class="mi">10</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="n">w0</span><span class="p">,</span> <span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">],</span> <span class="n">histtype</span><span class="o">=</span><span class="s">&#39;bar&#39;</span><span class="p">)</span>

<span class="n">P</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<p>Keywords: python, matplotlib, pylab, example, codex (see <a class="reference internal" href="../../faq/howto_faq.html#how-to-search-examples"><em>Search examples</em></a>)</p>
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