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<h1>Source code for matplotlib.scale</h1><div class="highlight"><pre>
<span></span><span class="sd">"""</span>
<span class="sd">Scales define the distribution of data values on an axis, e.g. a log scaling.</span>
<span class="sd">They are attached to an `~.axis.Axis` and hold a `.Transform`, which is</span>
<span class="sd">responsible for the actual data transformation.</span>
<span class="sd">See also `.axes.Axes.set_xscale` and the scales examples in the documentation.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">inspect</span>
<span class="kn">import</span> <span class="nn">textwrap</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">ma</span>
<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">_api</span><span class="p">,</span> <span class="n">docstring</span>
<span class="kn">from</span> <span class="nn">matplotlib.ticker</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">NullFormatter</span><span class="p">,</span> <span class="n">ScalarFormatter</span><span class="p">,</span> <span class="n">LogFormatterSciNotation</span><span class="p">,</span> <span class="n">LogitFormatter</span><span class="p">,</span>
<span class="n">NullLocator</span><span class="p">,</span> <span class="n">LogLocator</span><span class="p">,</span> <span class="n">AutoLocator</span><span class="p">,</span> <span class="n">AutoMinorLocator</span><span class="p">,</span>
<span class="n">SymmetricalLogLocator</span><span class="p">,</span> <span class="n">LogitLocator</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">matplotlib.transforms</span> <span class="kn">import</span> <span class="n">Transform</span><span class="p">,</span> <span class="n">IdentityTransform</span>
<div class="viewcode-block" id="ScaleBase"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.ScaleBase">[docs]</a><span class="k">class</span> <span class="nc">ScaleBase</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> The base class for all scales.</span>
<span class="sd"> Scales are separable transformations, working on a single dimension.</span>
<span class="sd"> Any subclasses will want to override:</span>
<span class="sd"> - :attr:`name`</span>
<span class="sd"> - :meth:`get_transform`</span>
<span class="sd"> - :meth:`set_default_locators_and_formatters`</span>
<span class="sd"> And optionally:</span>
<span class="sd"> - :meth:`limit_range_for_scale`</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__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="sa">r</span><span class="sd">"""</span>
<span class="sd"> Construct a new scale.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> The following note is for scale implementors.</span>
<span class="sd"> For back-compatibility reasons, scales take an `~matplotlib.axis.Axis`</span>
<span class="sd"> object as first argument. However, this argument should not</span>
<span class="sd"> be used: a single scale object should be usable by multiple</span>
<span class="sd"> `~matplotlib.axis.Axis`\es at the same time.</span>
<span class="sd"> """</span>
<div class="viewcode-block" id="ScaleBase.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.ScaleBase.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 the :class:`~matplotlib.transforms.Transform` object</span>
<span class="sd"> associated with this scale.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
<div class="viewcode-block" id="ScaleBase.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.ScaleBase.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 of *axis* to instances suitable for</span>
<span class="sd"> this scale.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
<div class="viewcode-block" id="ScaleBase.limit_range_for_scale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.ScaleBase.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"> Return the range *vmin*, *vmax*, restricted to the</span>
<span class="sd"> domain supported by this scale (if any).</span>
<span class="sd"> *minpos* should be the minimum positive value in the data.</span>
<span class="sd"> This is used by log scales to determine a minimum value.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span></div></div>
<div class="viewcode-block" id="LinearScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LinearScale">[docs]</a><span class="k">class</span> <span class="nc">LinearScale</span><span class="p">(</span><span class="n">ScaleBase</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> The default linear scale.</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'linear'</span>
<span class="k">def</span> <span class="fm">__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="c1"># This method is present only to prevent inheritance of the base class'</span>
<span class="c1"># constructor docstring, which would otherwise end up interpolated into</span>
<span class="c1"># the docstring of Axis.set_scale.</span>
<span class="sd">"""</span>
<span class="sd"> """</span>
<div class="viewcode-block" id="LinearScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LinearScale.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="c1"># docstring inherited</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">AutoLocator</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">ScalarFormatter</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>
<span class="c1"># update the minor locator for x and y axis based on rcParams</span>
<span class="k">if</span> <span class="p">(</span><span class="n">axis</span><span class="o">.</span><span class="n">axis_name</span> <span class="o">==</span> <span class="s1">'x'</span> <span class="ow">and</span> <span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'xtick.minor.visible'</span><span class="p">]</span> <span class="ow">or</span>
<span class="n">axis</span><span class="o">.</span><span class="n">axis_name</span> <span class="o">==</span> <span class="s1">'y'</span> <span class="ow">and</span> <span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'ytick.minor.visible'</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">AutoMinorLocator</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_minor_locator</span><span class="p">(</span><span class="n">NullLocator</span><span class="p">())</span></div>
<div class="viewcode-block" id="LinearScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LinearScale.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 the transform for linear scaling, which is just the</span>
<span class="sd"> `~matplotlib.transforms.IdentityTransform`.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">IdentityTransform</span><span class="p">()</span></div></div>
<div class="viewcode-block" id="FuncTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncTransform">[docs]</a><span class="k">class</span> <span class="nc">FuncTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> A simple transform that takes and arbitrary function for the</span>
<span class="sd"> forward and inverse transform.</span>
<span class="sd"> """</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">forward</span><span class="p">,</span> <span class="n">inverse</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> forward : callable</span>
<span class="sd"> The forward function for the transform. This function must have</span>
<span class="sd"> an inverse and, for best behavior, be monotonic.</span>
<span class="sd"> It must have the signature::</span>
<span class="sd"> def forward(values: array-like) -> array-like</span>
<span class="sd"> inverse : callable</span>
<span class="sd"> The inverse of the forward function. Signature as ``forward``.</span>
<span class="sd"> """</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="k">if</span> <span class="n">callable</span><span class="p">(</span><span class="n">forward</span><span class="p">)</span> <span class="ow">and</span> <span class="n">callable</span><span class="p">(</span><span class="n">inverse</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_forward</span> <span class="o">=</span> <span class="n">forward</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_inverse</span> <span class="o">=</span> <span class="n">inverse</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'arguments to FuncTransform must be functions'</span><span class="p">)</span>
<div class="viewcode-block" id="FuncTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">values</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward</span><span class="p">(</span><span class="n">values</span><span class="p">)</span></div>
<div class="viewcode-block" id="FuncTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">FuncTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_inverse</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="FuncScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScale">[docs]</a><span class="k">class</span> <span class="nc">FuncScale</span><span class="p">(</span><span class="n">ScaleBase</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Provide an arbitrary scale with user-supplied function for the axis.</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'function'</span>
<span class="k">def</span> <span class="fm">__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="n">functions</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> axis : `~matplotlib.axis.Axis`</span>
<span class="sd"> The axis for the scale.</span>
<span class="sd"> functions : (callable, callable)</span>
<span class="sd"> two-tuple of the forward and inverse functions for the scale.</span>
<span class="sd"> The forward function must be monotonic.</span>
<span class="sd"> Both functions must have the signature::</span>
<span class="sd"> def forward(values: array-like) -> array-like</span>
<span class="sd"> """</span>
<span class="n">forward</span><span class="p">,</span> <span class="n">inverse</span> <span class="o">=</span> <span class="n">functions</span>
<span class="n">transform</span> <span class="o">=</span> <span class="n">FuncTransform</span><span class="p">(</span><span class="n">forward</span><span class="p">,</span> <span class="n">inverse</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">transform</span>
<div class="viewcode-block" id="FuncScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScale.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">"""Return the `.FuncTransform` associated with this scale."""</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="FuncScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScale.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="c1"># docstring inherited</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">AutoLocator</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">ScalarFormatter</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>
<span class="c1"># update the minor locator for x and y axis based on rcParams</span>
<span class="k">if</span> <span class="p">(</span><span class="n">axis</span><span class="o">.</span><span class="n">axis_name</span> <span class="o">==</span> <span class="s1">'x'</span> <span class="ow">and</span> <span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'xtick.minor.visible'</span><span class="p">]</span> <span class="ow">or</span>
<span class="n">axis</span><span class="o">.</span><span class="n">axis_name</span> <span class="o">==</span> <span class="s1">'y'</span> <span class="ow">and</span> <span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'ytick.minor.visible'</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">AutoMinorLocator</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_minor_locator</span><span class="p">(</span><span class="n">NullLocator</span><span class="p">())</span></div></div>
<div class="viewcode-block" id="LogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogTransform">[docs]</a><span class="k">class</span> <span class="nc">LogTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">rename_parameter</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="s2">"nonpos"</span><span class="p">,</span> <span class="s2">"nonpositive"</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">nonpositive</span><span class="o">=</span><span class="s1">'clip'</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="k">if</span> <span class="n">base</span> <span class="o"><=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">base</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'The log base cannot be <= 0 or == 1'</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">=</span> <span class="n">base</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_clip</span> <span class="o">=</span> <span class="n">_api</span><span class="o">.</span><span class="n">check_getitem</span><span class="p">(</span>
<span class="p">{</span><span class="s2">"clip"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s2">"mask"</span><span class="p">:</span> <span class="kc">False</span><span class="p">},</span> <span class="n">nonpositive</span><span class="o">=</span><span class="n">nonpositive</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">(base=</span><span class="si">{}</span><span class="s2">, nonpositive=</span><span class="si">{!r}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span> <span class="s2">"clip"</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clip</span> <span class="k">else</span> <span class="s2">"mask"</span><span class="p">)</span>
<div class="viewcode-block" id="LogTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="c1"># Ignore invalid values due to nans being passed to the transform.</span>
<span class="k">with</span> <span class="n">np</span><span class="o">.</span><span class="n">errstate</span><span class="p">(</span><span class="n">divide</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">,</span> <span class="n">invalid</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">):</span>
<span class="n">log</span> <span class="o">=</span> <span class="p">{</span><span class="n">np</span><span class="o">.</span><span class="n">e</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">,</span> <span class="mi">10</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">}</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">)</span>
<span class="k">if</span> <span class="n">log</span><span class="p">:</span> <span class="c1"># If possible, do everything in a single call to NumPy.</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">log</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">out</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clip</span><span class="p">:</span>
<span class="c1"># SVG spec says that conforming viewers must support values up</span>
<span class="c1"># to 3.4e38 (C float); however experiments suggest that</span>
<span class="c1"># Inkscape (which uses cairo for rendering) runs into cairo's</span>
<span class="c1"># 24-bit limit (which is apparently shared by Agg).</span>
<span class="c1"># Ghostscript (used for pdf rendering appears to overflow even</span>
<span class="c1"># earlier, with the max value around 2 ** 15 for the tests to</span>
<span class="c1"># pass. On the other hand, in practice, we want to clip beyond</span>
<span class="c1"># np.log10(np.nextafter(0, 1)) ~ -323</span>
<span class="c1"># so 1000 seems safe.</span>
<span class="n">out</span><span class="p">[</span><span class="n">a</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1000</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="LogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">InvertedLogTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="InvertedLogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLogTransform">[docs]</a><span class="k">class</span> <span class="nc">InvertedLogTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">=</span> <span class="n">base</span>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">(base=</span><span class="si">{}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">)</span>
<div class="viewcode-block" id="InvertedLogTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLogTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="k">return</span> <span class="n">ma</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span></div>
<div class="viewcode-block" id="InvertedLogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLogTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">LogTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="LogScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogScale">[docs]</a><span class="k">class</span> <span class="nc">LogScale</span><span class="p">(</span><span class="n">ScaleBase</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> A standard logarithmic scale. Care is taken to only plot positive values.</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'log'</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"scale.LogTransform"</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">LogTransform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">LogTransform</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"scale.InvertedLogTransform"</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">InvertedLogTransform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">InvertedLogTransform</span>
<span class="k">def</span> <span class="fm">__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="sd">"""</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> axis : `~matplotlib.axis.Axis`</span>
<span class="sd"> The axis for the scale.</span>
<span class="sd"> base : float, default: 10</span>
<span class="sd"> The base of the logarithm.</span>
<span class="sd"> nonpositive : {'clip', 'mask'}, default: 'clip'</span>
<span class="sd"> Determines the behavior for non-positive values. They can either</span>
<span class="sd"> be masked as invalid, or clipped to a very small positive number.</span>
<span class="sd"> subs : sequence of int, default: None</span>
<span class="sd"> Where to place the subticks between each major tick. For example,</span>
<span class="sd"> in a log10 scale, ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place 8</span>
<span class="sd"> logarithmically spaced minor ticks between each major tick.</span>
<span class="sd"> """</span>
<span class="c1"># After the deprecation, the whole (outer) __init__ can be replaced by</span>
<span class="c1"># def __init__(self, axis, *, base=10, subs=None, nonpositive="clip")</span>
<span class="c1"># The following is to emit the right warnings depending on the axis</span>
<span class="c1"># used, as the *old* kwarg names depended on the axis.</span>
<span class="n">axis_name</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="s2">"axis_name"</span><span class="p">,</span> <span class="s2">"x"</span><span class="p">)</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">rename_parameter</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"base</span><span class="si">{</span><span class="n">axis_name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="s2">"base"</span><span class="p">)</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">rename_parameter</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"subs</span><span class="si">{</span><span class="n">axis_name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="s2">"subs"</span><span class="p">)</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">rename_parameter</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"nonpos</span><span class="si">{</span><span class="n">axis_name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="s2">"nonpositive"</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="p">,</span> <span class="n">base</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">subs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nonpositive</span><span class="o">=</span><span class="s2">"clip"</span><span class="p">):</span>
<span class="k">return</span> <span class="n">base</span><span class="p">,</span> <span class="n">subs</span><span class="p">,</span> <span class="n">nonpositive</span>
<span class="n">base</span><span class="p">,</span> <span class="n">subs</span><span class="p">,</span> <span class="n">nonpositive</span> <span class="o">=</span> <span class="fm">__init__</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">_transform</span> <span class="o">=</span> <span class="n">LogTransform</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">nonpositive</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">subs</span> <span class="o">=</span> <span class="n">subs</span>
<span class="n">base</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="k">lambda</span> <span class="bp">self</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">base</span><span class="p">)</span>
<div class="viewcode-block" id="LogScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogScale.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="c1"># docstring inherited</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">LogLocator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</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">LogFormatterSciNotation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</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">LogLocator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">subs</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">LogFormatterSciNotation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span>
<span class="n">labelOnlyBase</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">subs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)))</span></div>
<div class="viewcode-block" id="LogScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogScale.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">"""Return the `.LogTransform` associated with this scale."""</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="LogScale.limit_range_for_scale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogScale.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">"""Limit the domain to positive values."""</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">minpos</span><span class="p">):</span>
<span class="n">minpos</span> <span class="o">=</span> <span class="mf">1e-300</span> <span class="c1"># Should rarely (if ever) have a visible effect.</span>
<span class="k">return</span> <span class="p">(</span><span class="n">minpos</span> <span class="k">if</span> <span class="n">vmin</span> <span class="o"><=</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">vmin</span><span class="p">,</span>
<span class="n">minpos</span> <span class="k">if</span> <span class="n">vmax</span> <span class="o"><=</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">vmax</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="FuncScaleLog"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScaleLog">[docs]</a><span class="k">class</span> <span class="nc">FuncScaleLog</span><span class="p">(</span><span class="n">LogScale</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Provide an arbitrary scale with user-supplied function for the axis and</span>
<span class="sd"> then put on a logarithmic axes.</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'functionlog'</span>
<span class="k">def</span> <span class="fm">__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="n">functions</span><span class="p">,</span> <span class="n">base</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> axis : `matplotlib.axis.Axis`</span>
<span class="sd"> The axis for the scale.</span>
<span class="sd"> functions : (callable, callable)</span>
<span class="sd"> two-tuple of the forward and inverse functions for the scale.</span>
<span class="sd"> The forward function must be monotonic.</span>
<span class="sd"> Both functions must have the signature::</span>
<span class="sd"> def forward(values: array-like) -> array-like</span>
<span class="sd"> base : float, default: 10</span>
<span class="sd"> Logarithmic base of the scale.</span>
<span class="sd"> """</span>
<span class="n">forward</span><span class="p">,</span> <span class="n">inverse</span> <span class="o">=</span> <span class="n">functions</span>
<span class="bp">self</span><span class="o">.</span><span class="n">subs</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_transform</span> <span class="o">=</span> <span class="n">FuncTransform</span><span class="p">(</span><span class="n">forward</span><span class="p">,</span> <span class="n">inverse</span><span class="p">)</span> <span class="o">+</span> <span class="n">LogTransform</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">base</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span><span class="o">.</span><span class="n">_b</span><span class="o">.</span><span class="n">base</span> <span class="c1"># Base of the LogTransform.</span>
<div class="viewcode-block" id="FuncScaleLog.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScaleLog.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">"""Return the `.Transform` associated with this scale."""</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span></div></div>
<div class="viewcode-block" id="SymmetricalLogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogTransform">[docs]</a><span class="k">class</span> <span class="nc">SymmetricalLogTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">linthresh</span><span class="p">,</span> <span class="n">linscale</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="k">if</span> <span class="n">base</span> <span class="o"><=</span> <span class="mf">1.0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"'base' must be larger than 1"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">linthresh</span> <span class="o"><=</span> <span class="mf">0.0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"'linthresh' must be positive"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">linscale</span> <span class="o"><=</span> <span class="mf">0.0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"'linscale' must be positive"</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">=</span> <span class="n">base</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span> <span class="o">=</span> <span class="n">linthresh</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linscale</span> <span class="o">=</span> <span class="n">linscale</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_linscale_adj</span> <span class="o">=</span> <span class="p">(</span><span class="n">linscale</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">**</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_log_base</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
<div class="viewcode-block" id="SymmetricalLogTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="n">abs_a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="k">with</span> <span class="n">np</span><span class="o">.</span><span class="n">errstate</span><span class="p">(</span><span class="n">divide</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">,</span> <span class="n">invalid</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span> <span class="o">*</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_linscale_adj</span> <span class="o">+</span>
<span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">abs_a</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_log_base</span><span class="p">)</span>
<span class="n">inside</span> <span class="o">=</span> <span class="n">abs_a</span> <span class="o"><=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span>
<span class="n">out</span><span class="p">[</span><span class="n">inside</span><span class="p">]</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="n">inside</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_linscale_adj</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="SymmetricalLogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">InvertedSymmetricalLogTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linscale</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="InvertedSymmetricalLogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedSymmetricalLogTransform">[docs]</a><span class="k">class</span> <span class="nc">InvertedSymmetricalLogTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">linthresh</span><span class="p">,</span> <span class="n">linscale</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="n">symlog</span> <span class="o">=</span> <span class="n">SymmetricalLogTransform</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">linthresh</span><span class="p">,</span> <span class="n">linscale</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">=</span> <span class="n">base</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span> <span class="o">=</span> <span class="n">linthresh</span>
<span class="bp">self</span><span class="o">.</span><span class="n">invlinthresh</span> <span class="o">=</span> <span class="n">symlog</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">linthresh</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linscale</span> <span class="o">=</span> <span class="n">linscale</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_linscale_adj</span> <span class="o">=</span> <span class="p">(</span><span class="n">linscale</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">**</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
<div class="viewcode-block" id="InvertedSymmetricalLogTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedSymmetricalLogTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="n">abs_a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="k">with</span> <span class="n">np</span><span class="o">.</span><span class="n">errstate</span><span class="p">(</span><span class="n">divide</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">,</span> <span class="n">invalid</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span> <span class="o">*</span> <span class="p">(</span>
<span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span>
<span class="n">abs_a</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_linscale_adj</span><span class="p">))</span>
<span class="n">inside</span> <span class="o">=</span> <span class="n">abs_a</span> <span class="o"><=</span> <span class="bp">self</span><span class="o">.</span><span class="n">invlinthresh</span>
<span class="n">out</span><span class="p">[</span><span class="n">inside</span><span class="p">]</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="n">inside</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_linscale_adj</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="InvertedSymmetricalLogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedSymmetricalLogTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">SymmetricalLogTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">linscale</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="SymmetricalLogScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogScale">[docs]</a><span class="k">class</span> <span class="nc">SymmetricalLogScale</span><span class="p">(</span><span class="n">ScaleBase</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> The symmetrical logarithmic scale is logarithmic in both the</span>
<span class="sd"> positive and negative directions from the origin.</span>
<span class="sd"> Since the values close to zero tend toward infinity, there is a</span>
<span class="sd"> need to have a range around zero that is linear. The parameter</span>
<span class="sd"> *linthresh* allows the user to specify the size of this range</span>
<span class="sd"> (-*linthresh*, *linthresh*).</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> base : float, default: 10</span>
<span class="sd"> The base of the logarithm.</span>
<span class="sd"> linthresh : float, default: 2</span>
<span class="sd"> Defines the range ``(-x, x)``, within which the plot is linear.</span>
<span class="sd"> This avoids having the plot go to infinity around zero.</span>
<span class="sd"> subs : sequence of int</span>
<span class="sd"> Where to place the subticks between each major tick.</span>
<span class="sd"> For example, in a log10 scale: ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place</span>
<span class="sd"> 8 logarithmically spaced minor ticks between each major tick.</span>
<span class="sd"> linscale : float, optional</span>
<span class="sd"> This allows the linear range ``(-linthresh, linthresh)`` to be</span>
<span class="sd"> stretched relative to the logarithmic range. Its value is the number of</span>
<span class="sd"> decades to use for each half of the linear range. For example, when</span>
<span class="sd"> *linscale* == 1.0 (the default), the space used for the positive and</span>
<span class="sd"> negative halves of the linear range will be equal to one decade in</span>
<span class="sd"> the logarithmic range.</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'symlog'</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"scale.SymmetricalLogTransform"</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">SymmetricalLogTransform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">SymmetricalLogTransform</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span>
<span class="s2">"3.3"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"scale.InvertedSymmetricalLogTransform"</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">InvertedSymmetricalLogTransform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">InvertedSymmetricalLogTransform</span>
<span class="k">def</span> <span class="fm">__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="n">axis_name</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="s2">"axis_name"</span><span class="p">,</span> <span class="s2">"x"</span><span class="p">)</span>
<span class="c1"># See explanation in LogScale.__init__.</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">rename_parameter</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"base</span><span class="si">{</span><span class="n">axis_name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="s2">"base"</span><span class="p">)</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">rename_parameter</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"linthresh</span><span class="si">{</span><span class="n">axis_name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="s2">"linthresh"</span><span class="p">)</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">rename_parameter</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"subs</span><span class="si">{</span><span class="n">axis_name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="s2">"subs"</span><span class="p">)</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">rename_parameter</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"linscale</span><span class="si">{</span><span class="n">axis_name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="s2">"linscale"</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="p">,</span> <span class="n">base</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">linthresh</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">subs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">linscale</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="k">return</span> <span class="n">base</span><span class="p">,</span> <span class="n">linthresh</span><span class="p">,</span> <span class="n">subs</span><span class="p">,</span> <span class="n">linscale</span>
<span class="n">base</span><span class="p">,</span> <span class="n">linthresh</span><span class="p">,</span> <span class="n">subs</span><span class="p">,</span> <span class="n">linscale</span> <span class="o">=</span> <span class="fm">__init__</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">_transform</span> <span class="o">=</span> <span class="n">SymmetricalLogTransform</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">linthresh</span><span class="p">,</span> <span class="n">linscale</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">subs</span> <span class="o">=</span> <span class="n">subs</span>
<span class="n">base</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="k">lambda</span> <span class="bp">self</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">base</span><span class="p">)</span>
<span class="n">linthresh</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="k">lambda</span> <span class="bp">self</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">linthresh</span><span class="p">)</span>
<span class="n">linscale</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="k">lambda</span> <span class="bp">self</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">linscale</span><span class="p">)</span>
<div class="viewcode-block" id="SymmetricalLogScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogScale.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="c1"># docstring inherited</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">SymmetricalLogLocator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_transform</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">LogFormatterSciNotation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</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">SymmetricalLogLocator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_transform</span><span class="p">(),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">subs</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="SymmetricalLogScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogScale.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">"""Return the `.SymmetricalLogTransform` associated with this scale."""</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span></div></div>
<div class="viewcode-block" id="LogitTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitTransform">[docs]</a><span class="k">class</span> <span class="nc">LogitTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">rename_parameter</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="s2">"nonpos"</span><span class="p">,</span> <span class="s2">"nonpositive"</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nonpositive</span><span class="o">=</span><span class="s1">'mask'</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="n">_api</span><span class="o">.</span><span class="n">check_in_list</span><span class="p">([</span><span class="s1">'mask'</span><span class="p">,</span> <span class="s1">'clip'</span><span class="p">],</span> <span class="n">nonpositive</span><span class="o">=</span><span class="n">nonpositive</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_nonpositive</span> <span class="o">=</span> <span class="n">nonpositive</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_clip</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"clip"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s2">"mask"</span><span class="p">:</span> <span class="kc">False</span><span class="p">}[</span><span class="n">nonpositive</span><span class="p">]</span>
<div class="viewcode-block" id="LogitTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="sd">"""logit transform (base 10), masked or clipped"""</span>
<span class="k">with</span> <span class="n">np</span><span class="o">.</span><span class="n">errstate</span><span class="p">(</span><span class="n">divide</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">,</span> <span class="n">invalid</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">a</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">a</span><span class="p">))</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clip</span><span class="p">:</span> <span class="c1"># See LogTransform for choice of clip value.</span>
<span class="n">out</span><span class="p">[</span><span class="n">a</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1000</span>
<span class="n">out</span><span class="p">[</span><span class="mi">1</span> <span class="o"><=</span> <span class="n">a</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="LogitTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">LogisticTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_nonpositive</span><span class="p">)</span></div>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">(</span><span class="si">{!r}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nonpositive</span><span class="p">)</span></div>
<div class="viewcode-block" id="LogisticTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogisticTransform">[docs]</a><span class="k">class</span> <span class="nc">LogisticTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">rename_parameter</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="s2">"nonpos"</span><span class="p">,</span> <span class="s2">"nonpositive"</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nonpositive</span><span class="o">=</span><span class="s1">'mask'</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_nonpositive</span> <span class="o">=</span> <span class="n">nonpositive</span>
<div class="viewcode-block" id="LogisticTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogisticTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="sd">"""logistic transform (base 10)"""</span>
<span class="k">return</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="n">a</span><span class="p">))</span></div>
<div class="viewcode-block" id="LogisticTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogisticTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">LogitTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_nonpositive</span><span class="p">)</span></div>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">(</span><span class="si">{!r}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nonpositive</span><span class="p">)</span></div>
<div class="viewcode-block" id="LogitScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitScale">[docs]</a><span class="k">class</span> <span class="nc">LogitScale</span><span class="p">(</span><span class="n">ScaleBase</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Logit scale for data between zero and one, both excluded.</span>
<span class="sd"> This scale is similar to a log scale close to zero and to one, and almost</span>
<span class="sd"> linear around 0.5. It maps the interval ]0, 1[ onto ]-infty, +infty[.</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'logit'</span>
<span class="nd">@_api</span><span class="o">.</span><span class="n">rename_parameter</span><span class="p">(</span><span class="s2">"3.3"</span><span class="p">,</span> <span class="s2">"nonpos"</span><span class="p">,</span> <span class="s2">"nonpositive"</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__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="n">nonpositive</span><span class="o">=</span><span class="s1">'mask'</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span>
<span class="n">one_half</span><span class="o">=</span><span class="sa">r</span><span class="s2">"\frac</span><span class="si">{1}{2}</span><span class="s2">"</span><span class="p">,</span> <span class="n">use_overline</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> axis : `matplotlib.axis.Axis`</span>
<span class="sd"> Currently unused.</span>
<span class="sd"> nonpositive : {'mask', 'clip'}</span>
<span class="sd"> Determines the behavior for values beyond the open interval ]0, 1[.</span>
<span class="sd"> They can either be masked as invalid, or clipped to a number very</span>
<span class="sd"> close to 0 or 1.</span>
<span class="sd"> use_overline : bool, default: False</span>
<span class="sd"> Indicate the usage of survival notation (\overline{x}) in place of</span>
<span class="sd"> standard notation (1-x) for probability close to one.</span>
<span class="sd"> one_half : str, default: r"\frac{1}{2}"</span>
<span class="sd"> The string used for ticks formatter to represent 1/2.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_transform</span> <span class="o">=</span> <span class="n">LogitTransform</span><span class="p">(</span><span class="n">nonpositive</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_use_overline</span> <span class="o">=</span> <span class="n">use_overline</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_one_half</span> <span class="o">=</span> <span class="n">one_half</span>
<div class="viewcode-block" id="LogitScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitScale.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">"""Return the `.LogitTransform` associated with this scale."""</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="LogitScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitScale.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="c1"># docstring inherited</span>
<span class="c1"># ..., 0.01, 0.1, 0.5, 0.9, 0.99, ...</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">LogitLocator</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">LogitFormatter</span><span class="p">(</span>
<span class="n">one_half</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_one_half</span><span class="p">,</span>
<span class="n">use_overline</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_use_overline</span>
<span class="p">)</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">LogitLocator</span><span class="p">(</span><span class="n">minor</span><span class="o">=</span><span class="kc">True</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">LogitFormatter</span><span class="p">(</span>
<span class="n">minor</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">one_half</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_one_half</span><span class="p">,</span>
<span class="n">use_overline</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_use_overline</span>
<span class="p">)</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="LogitScale.limit_range_for_scale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitScale.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 values between 0 and 1 (excluded).</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">minpos</span><span class="p">):</span>
<span class="n">minpos</span> <span class="o">=</span> <span class="mf">1e-7</span> <span class="c1"># Should rarely (if ever) have a visible effect.</span>
<span class="k">return</span> <span class="p">(</span><span class="n">minpos</span> <span class="k">if</span> <span class="n">vmin</span> <span class="o"><=</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">vmin</span><span class="p">,</span>
<span class="mi">1</span> <span class="o">-</span> <span class="n">minpos</span> <span class="k">if</span> <span class="n">vmax</span> <span class="o">>=</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">vmax</span><span class="p">)</span></div></div>
<span class="n">_scale_mapping</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'linear'</span><span class="p">:</span> <span class="n">LinearScale</span><span class="p">,</span>
<span class="s1">'log'</span><span class="p">:</span> <span class="n">LogScale</span><span class="p">,</span>
<span class="s1">'symlog'</span><span class="p">:</span> <span class="n">SymmetricalLogScale</span><span class="p">,</span>
<span class="s1">'logit'</span><span class="p">:</span> <span class="n">LogitScale</span><span class="p">,</span>
<span class="s1">'function'</span><span class="p">:</span> <span class="n">FuncScale</span><span class="p">,</span>
<span class="s1">'functionlog'</span><span class="p">:</span> <span class="n">FuncScaleLog</span><span class="p">,</span>
<span class="p">}</span>
<div class="viewcode-block" id="get_scale_names"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.get_scale_names">[docs]</a><span class="k">def</span> <span class="nf">get_scale_names</span><span class="p">():</span>
<span class="sd">"""Return the names of the available scales."""</span>
<span class="k">return</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">_scale_mapping</span><span class="p">)</span></div>
<div class="viewcode-block" id="scale_factory"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.scale_factory">[docs]</a><span class="k">def</span> <span class="nf">scale_factory</span><span class="p">(</span><span class="n">scale</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="sd">"""</span>
<span class="sd"> Return a scale class by name.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> scale : {%(names)s}</span>
<span class="sd"> axis : `matplotlib.axis.Axis`</span>
<span class="sd"> """</span>
<span class="n">scale</span> <span class="o">=</span> <span class="n">scale</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<span class="n">_api</span><span class="o">.</span><span class="n">check_in_list</span><span class="p">(</span><span class="n">_scale_mapping</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">)</span>
<span class="k">return</span> <span class="n">_scale_mapping</span><span class="p">[</span><span class="n">scale</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></div>
<span class="k">if</span> <span class="n">scale_factory</span><span class="o">.</span><span class="vm">__doc__</span><span class="p">:</span>
<span class="n">scale_factory</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">scale_factory</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">%</span> <span class="p">{</span>
<span class="s2">"names"</span><span class="p">:</span> <span class="s2">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">repr</span><span class="p">,</span> <span class="n">get_scale_names</span><span class="p">()))}</span>
<div class="viewcode-block" id="register_scale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.register_scale">[docs]</a><span class="k">def</span> <span class="nf">register_scale</span><span class="p">(</span><span class="n">scale_class</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Register a new kind of scale.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> scale_class : subclass of `ScaleBase`</span>
<span class="sd"> The scale to register.</span>
<span class="sd"> """</span>
<span class="n">_scale_mapping</span><span class="p">[</span><span class="n">scale_class</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">scale_class</span></div>
<span class="k">def</span> <span class="nf">_get_scale_docs</span><span class="p">():</span>
<span class="sd">"""</span>
<span class="sd"> Helper function for generating docstrings related to scales.</span>
<span class="sd"> """</span>
<span class="n">docs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">scale_class</span> <span class="ow">in</span> <span class="n">_scale_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">docs</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span>
<span class="sa">f</span><span class="s2">" </span><span class="si">{</span><span class="n">name</span><span class="si">!r}</span><span class="s2">"</span><span class="p">,</span>
<span class="s2">""</span><span class="p">,</span>
<span class="n">textwrap</span><span class="o">.</span><span class="n">indent</span><span class="p">(</span><span class="n">inspect</span><span class="o">.</span><span class="n">getdoc</span><span class="p">(</span><span class="n">scale_class</span><span class="o">.</span><span class="fm">__init__</span><span class="p">),</span> <span class="s2">" "</span> <span class="o">*</span> <span class="mi">8</span><span class="p">),</span>
<span class="s2">""</span>
<span class="p">])</span>
<span class="k">return</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">docs</span><span class="p">)</span>
<span class="n">docstring</span><span class="o">.</span><span class="n">interpd</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
<span class="n">scale_type</span><span class="o">=</span><span class="s1">'{</span><span class="si">%s</span><span class="s1">}'</span> <span class="o">%</span> <span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">repr</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">get_scale_names</span><span class="p">()]),</span>
<span class="n">scale_docs</span><span class="o">=</span><span class="n">_get_scale_docs</span><span class="p">()</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(),</span>
<span class="p">)</span>
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