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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

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<p class="last">Click <a class="reference internal" href="#sphx-glr-download-gallery-api-power-norm-py"><span class="std std-ref">here</span></a> to download the full example code</p>

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<span id="sphx-glr-gallery-api-power-norm-py"></span><h1>Exploring normalizations<a class="headerlink" href="#exploring-normalizations" title="Permalink to this headline">¶</a></h1>

<p>Various normalization on a multivariate normal distribution.</p>

<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>

<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="kn">as</span> <span class="nn">mcolors</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>

<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.multivariate_normal.html#numpy.random.multivariate_normal" title="View documentation for numpy.random.multivariate_normal"><span class="n">multivariate_normal</span></a>

<span class="n">data</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.vstack.html#numpy.vstack" title="View documentation for numpy.vstack"><span class="n">np</span><span class="o">.</span><span class="n">vstack</span></a><span class="p">([</span>

<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.multivariate_normal.html#numpy.random.multivariate_normal" title="View documentation for numpy.random.multivariate_normal"><span class="n">multivariate_normal</span></a><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="p">[[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]],</span> <span class="n">size</span><span class="o">=</span><span class="mi">100000</span><span class="p">),</span>

<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.multivariate_normal.html#numpy.random.multivariate_normal" title="View documentation for numpy.random.multivariate_normal"><span class="n">multivariate_normal</span></a><span class="p">([</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">],</span> <span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]],</span> <span class="n">size</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>

<span class="n">gammas</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">]</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">gamma</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">flat</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">gammas</span><span class="p">):</span>

<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">r</span><span class="s1">'Power law $(\gamma=</span><span class="si">%1.1f</span><span class="s1">)$'</span> <span class="o">%</span> <span class="n">gamma</span><span class="p">)</span>

<span class="n">bins</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><a href="../../api/_as_gen/matplotlib.colors.PowerNorm.html#matplotlib.colors.PowerNorm" title="View documentation for matplotlib.colors.PowerNorm"><span class="n">mcolors</span><span class="o">.</span><span class="n">PowerNorm</span></a><span class="p">(</span><span class="n">gamma</span><span class="p">))</span>

<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>

</pre></div>

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<a class="reference download internal" download="" href="../../_downloads/8c1b41d308bdc375a89828dcf01099ff/power_norm.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">power_norm.py</span></code></a></div>

<a class="reference download internal" download="" href="../../_downloads/0f32ee5cde8d6e8cafea62015db50550/power_norm.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">power_norm.ipynb</span></code></a></div>

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