move geometry to geometry2d and add new geometry module (3d)
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<html xmlns="http://www.w3.org/1999/xhtml">
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<title>pylib.numerical.fit module — pylib 2019.12.21 documentation</title>
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<div class="section" id="module-pylib.numerical.fit">
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<span id="pylib-numerical-fit-module"></span><h1>pylib.numerical.fit module<a class="headerlink" href="#module-pylib.numerical.fit" title="Permalink to this headline">¶</a></h1>
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<p>Function and approximation.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Date</dt>
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<dd class="field-odd"><p>2019-10-15</p>
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</dd>
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</dl>
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<span class="target" id="module-fit"></span><dl class="function">
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<dt id="pylib.numerical.fit.gauss">
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<code class="sig-name descname">gauss</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">*p</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pylib/numerical/fit.html#gauss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pylib.numerical.fit.gauss" title="Permalink to this definition">¶</a></dt>
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<dd><p>Gauss distribution function.</p>
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<div class="math notranslate nohighlight">
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\[f(x)=ae^{-(x-b)^{2}/(2c^{2})}\]</div>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>x</strong> (<em>int</em><em> or </em><em>float</em><em> or </em><em>list</em><em> or </em><em>numpy.ndarray</em>) – positions where the gauss function will be calculated</p></li>
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<li><p><strong>p</strong> (<em>list</em>) – <p>gauss parameters [a, b, c, d]:</p>
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<ul>
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<li><p>a – amplitude (<span class="math notranslate nohighlight">\(\int y \,\mathrm{d}x=1 \Leftrightarrow a=1/(c\sqrt{2\pi})\)</span> )</p></li>
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<li><p>b – expected value <span class="math notranslate nohighlight">\(\mu\)</span> (position of maximum, default = 0)</p></li>
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<li><p>c – standard deviation <span class="math notranslate nohighlight">\(\sigma\)</span> (variance <span class="math notranslate nohighlight">\(\sigma^2=c^2\)</span>)</p></li>
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<li><p>d – vertical offset (default = 0)</p></li>
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</ul>
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</p></li>
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</ul>
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</dd>
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<dt class="field-even">Returns</dt>
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<dd class="field-even"><p>gauss values at given positions x</p>
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</dd>
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<dt class="field-odd">Return type</dt>
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<dd class="field-odd"><p>numpy.ndarray</p>
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</dd>
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</dl>
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</dd></dl>
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<dl class="function">
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<dt id="pylib.numerical.fit.gauss_fit">
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<code class="sig-name descname">gauss_fit</code><span class="sig-paren">(</span><em class="sig-param">x</em>, <em class="sig-param">y</em>, <em class="sig-param">e=None</em>, <em class="sig-param">x_fit=None</em>, <em class="sig-param">verbose=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pylib/numerical/fit.html#gauss_fit"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pylib.numerical.fit.gauss_fit" title="Permalink to this definition">¶</a></dt>
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<dd><p>Fit Gauss distribution function to data.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>x</strong> (<em>int</em><em> or </em><em>float</em><em> or </em><em>list</em><em> or </em><em>numpy.ndarray</em>) – positions</p></li>
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<li><p><strong>y</strong> (<em>int</em><em> or </em><em>float</em><em> or </em><em>list</em><em> or </em><em>numpy.ndarray</em>) – values</p></li>
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<li><p><strong>e</strong> (<em>int</em><em> or </em><em>float</em><em> or </em><em>list</em><em> or </em><em>numpy.ndarray</em>) – error values (default = None)</p></li>
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<li><p><strong>x_fit</strong> (<em>int</em><em> or </em><em>float</em><em> or </em><em>list</em><em> or </em><em>numpy.ndarray</em>) – positions of fitted function (default = None, if None then x
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is used)</p></li>
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<li><p><strong>verbose</strong> (<em>bool</em>) – verbose information (default = False)</p></li>
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</ul>
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</dd>
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<dt class="field-even">Returns</dt>
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<dd class="field-even"><p><ul class="simple">
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<li><p>numpy.ndarray – fitted values (y_fit)</p></li>
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<li><p>numpy.ndarray – parameters of gauss distribution function (popt:
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amplitude a, expected value <span class="math notranslate nohighlight">\(\mu\)</span>, standard deviation
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<span class="math notranslate nohighlight">\(\sigma\)</span>, vertical offset d)</p></li>
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<li><p>numpy.float64 – full width at half maximum (FWHM)</p></li>
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</ul>
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</p>
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</dd>
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<dt class="field-odd">Return type</dt>
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<dd class="field-odd"><p>tuple</p>
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</dd>
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</dl>
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<div class="admonition seealso">
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<p class="admonition-title">See also</p>
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<p><a class="reference internal" href="#pylib.numerical.fit.gauss" title="pylib.numerical.fit.gauss"><code class="xref py py-meth docutils literal notranslate"><span class="pre">gauss()</span></code></a></p>
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