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<!DOCTYPE html>
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<li class="toctree-l1"><a class="reference internal" href="../../tutorial/getting_started.html">Getting started with <code class="docutils literal"><span class="pre">mpl-probscale</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorial/closer_look_at_viz.html">A closer look at probability plots</a></li>
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<h1>Source code for probscale.probscale</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span>
<span class="kn">from</span> <span class="nn">matplotlib.scale</span> <span class="kn">import</span> <span class="n">ScaleBase</span>
<span class="kn">from</span> <span class="nn">matplotlib.ticker</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">FixedLocator</span><span class="p">,</span>
<span class="n">NullLocator</span><span class="p">,</span>
<span class="n">NullFormatter</span><span class="p">,</span>
<span class="n">FuncFormatter</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">.transforms</span> <span class="kn">import</span> <span class="n">ProbTransform</span>
<span class="kn">from</span> <span class="nn">.formatters</span> <span class="kn">import</span> <span class="n">PctFormatter</span><span class="p">,</span> <span class="n">ProbFormatter</span>
<span class="k">class</span> <span class="nc">_minimal_norm</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> A basic implmentation of a normal distribution, minimally</span>
<span class="sd"> API-complient with scipt.stats.norm</span>
<span class="sd"> """</span>
<span class="n">_A</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="mi">8</span> <span class="o">*</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">pi</span> <span class="o">-</span> <span class="mf">3.0</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mf">3.0</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">pi</span> <span class="o">-</span> <span class="mf">4.0</span><span class="p">)))</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">_approx_erf</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="sd">""" Approximate solution to the error function</span>
<span class="sd"> http://en.wikipedia.org/wiki/Error_function</span>
<span class="sd"> """</span>
<span class="n">guts</span> <span class="o">=</span> <span class="o">-</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="mf">4.0</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pi</span> <span class="o">+</span> <span class="n">cls</span><span class="o">.</span><span class="n">_A</span> <span class="o">*</span> <span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">+</span> <span class="n">cls</span><span class="o">.</span><span class="n">_A</span> <span class="o">*</span> <span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">numpy</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">guts</span><span class="p">))</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">_approx_inv_erf</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">z</span><span class="p">):</span>
<span class="sd">""" Approximate solution to the inverse error function</span>
<span class="sd"> http://en.wikipedia.org/wiki/Error_function</span>
<span class="sd"> """</span>
<span class="n">_b</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="n">cls</span><span class="o">.</span><span class="n">_A</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mf">0.5</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">z</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
<span class="n">_c</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">z</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">cls</span><span class="o">.</span><span class="n">_A</span>
<span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">_b</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">_c</span><span class="p">)</span> <span class="o">-</span> <span class="n">_b</span><span class="p">)</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">ppf</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">q</span><span class="p">):</span>
<span class="sd">""" Percent point function (inverse of cdf)</span>
<span class="sd"> Wikipedia: https://goo.gl/Rtxjme</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">cls</span><span class="o">.</span><span class="n">_approx_inv_erf</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">q</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="sd">""" Cumulative density function</span>
<span class="sd"> Wikipedia: https://goo.gl/ciUNLx</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">cls</span><span class="o">.</span><span class="n">_approx_erf</span><span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)))</span>
<div class="viewcode-block" id="ProbScale"><a class="viewcode-back" href="../../api/probscale.html#probscale.probscale.ProbScale">[docs]</a><span class="k">class</span> <span class="nc">ProbScale</span><span class="p">(</span><span class="n">ScaleBase</span><span class="p">):</span>
<span class="sd">""" A probability scale for matplotlib Axes.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> axis : a matplotlib axis artist</span>
<span class="sd"> The axis whose scale will be set.</span>
<span class="sd"> dist : scipy.stats probability distribution, optional</span>
<span class="sd"> The distribution whose ppf/cdf methods should be used to compute</span>
<span class="sd"> the tick positions. By default, a minimal implimentation of the</span>
<span class="sd"> ``scipy.stats.norm`` class is used so that scipy is not a</span>
<span class="sd"> requirement.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> The most basic use:</span>
<span class="sd"> .. plot::</span>
<span class="sd"> :context: close-figs</span>
<span class="sd"> >>> from matplotlib import pyplot</span>
<span class="sd"> >>> import probscale</span>
<span class="sd"> >>> fig, ax = pyplot.subplots(figsize=(4, 7))</span>
<span class="sd"> >>> ax.set_ylim(bottom=0.5, top=99.5)</span>
<span class="sd"> >>> ax.set_yscale('prob')</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'prob'</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dist</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'dist'</span><span class="p">,</span> <span class="n">_minimal_norm</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">as_pct</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'as_pct'</span><span class="p">,</span> <span class="bp">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nonpos</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'nonpos'</span><span class="p">,</span> <span class="s1">'mask'</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_transform</span> <span class="o">=</span> <span class="n">ProbTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dist</span><span class="p">,</span> <span class="n">as_pct</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">as_pct</span><span class="p">)</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">_get_probs</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">nobs</span><span class="p">,</span> <span class="n">as_pct</span><span class="p">):</span>
<span class="sd">""" Returns the x-axis labels for a probability plot based on</span>
<span class="sd"> the number of observations (`nobs`).</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">as_pct</span><span class="p">:</span>
<span class="n">factor</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">factor</span> <span class="o">=</span> <span class="mf">100.0</span>
<span class="n">order</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">nobs</span><span class="p">)))</span>
<span class="n">base_probs</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">70</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">90</span><span class="p">])</span>
<span class="n">axis_probs</span> <span class="o">=</span> <span class="n">base_probs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">order</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n</span> <span class="o"><=</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">lower_fringe</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
<span class="n">upper_fringe</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">])</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">lower_fringe</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">])</span>
<span class="n">upper_fringe</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">9</span><span class="p">])</span>
<span class="n">new_lower</span> <span class="o">=</span> <span class="n">lower_fringe</span> <span class="o">/</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
<span class="n">new_upper</span> <span class="o">=</span> <span class="n">upper_fringe</span> <span class="o">/</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="o">+</span> <span class="n">axis_probs</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="n">axis_probs</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">new_lower</span><span class="p">,</span> <span class="n">axis_probs</span><span class="p">,</span> <span class="n">new_upper</span><span class="p">])</span>
<span class="n">locs</span> <span class="o">=</span> <span class="n">axis_probs</span> <span class="o">/</span> <span class="n">factor</span>
<span class="k">return</span> <span class="n">locs</span>
<div class="viewcode-block" id="ProbScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/probscale.html#probscale.probscale.ProbScale.set_default_locators_and_formatters">[docs]</a> <span class="k">def</span> <span class="nf">set_default_locators_and_formatters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Set the locators and formatters to specialized versions for</span>
<span class="sd"> log scaling.</span>
<span class="sd"> """</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">FixedLocator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_probs</span><span class="p">(</span><span class="mf">1e8</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_pct</span><span class="p">)))</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">as_pct</span><span class="p">:</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">FuncFormatter</span><span class="p">(</span><span class="n">PctFormatter</span><span class="p">()))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">FuncFormatter</span><span class="p">(</span><span class="n">ProbFormatter</span><span class="p">()))</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span><span class="n">NullLocator</span><span class="p">())</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_formatter</span><span class="p">(</span><span class="n">NullFormatter</span><span class="p">())</span>
</div>
<div class="viewcode-block" id="ProbScale.get_transform"><a class="viewcode-back" href="../../api/probscale.html#probscale.probscale.ProbScale.get_transform">[docs]</a> <span class="k">def</span> <span class="nf">get_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Return a :class:`~matplotlib.transforms.Transform` instance</span>
<span class="sd"> appropriate for the given logarithm base.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span>
</div>
<div class="viewcode-block" id="ProbScale.limit_range_for_scale"><a class="viewcode-back" href="../../api/probscale.html#probscale.probscale.ProbScale.limit_range_for_scale">[docs]</a> <span class="k">def</span> <span class="nf">limit_range_for_scale</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="p">,</span> <span class="n">minpos</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Limit the domain to positive values.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="p">(</span><span class="n">vmin</span> <span class="o"><=</span> <span class="mf">0.0</span> <span class="ow">and</span> <span class="n">minpos</span> <span class="ow">or</span> <span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span> <span class="o"><=</span> <span class="mf">0.0</span> <span class="ow">and</span> <span class="n">minpos</span> <span class="ow">or</span> <span class="n">vmax</span><span class="p">)</span></div></div>
</pre></div>
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