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docs/pyerrors/roots.html
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@ -69,23 +69,34 @@
<details>
<summary>View Source</summary>
<div class="codehilite"><pre><span></span><span class="ch">#!/usr/bin/env python</span>
<span class="c1"># coding: utf-8</span>
<span class="kn">import</span> <span class="nn">scipy.optimize</span>
<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">scipy.optimize</span>
<span class="kn">from</span> <span class="nn">autograd</span> <span class="kn">import</span> <span class="n">jacobian</span>
<span class="kn">from</span> <span class="nn">.obs</span> <span class="kn">import</span> <span class="n">derived_observable</span><span class="p">,</span> <span class="n">pseudo_Obs</span>
<span class="k">def</span> <span class="nf">find_root</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">guess</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Finds the root of the function func(x, d) where d is an Obs.</span>
<span class="sa">r</span><span class="sd">&#39;&#39;&#39;Finds the root of the function func(x, d) where d is an `Obs`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> -----------------</span>
<span class="sd"> d -- Obs passed to the function.</span>
<span class="sd"> func -- Function to be minimized. Any numpy functions have to use the autograd.numpy wrapper</span>
<span class="sd"> guess -- Initial guess for the minimization.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="sd"> d : Obs</span>
<span class="sd"> Obs passed to the function.</span>
<span class="sd"> func : object</span>
<span class="sd"> Function to be minimized. Any numpy functions have to use the autograd.numpy wrapper.</span>
<span class="sd"> Example:</span>
<span class="sd"> ```python</span>
<span class="sd"> import autograd.numpy as anp</span>
<span class="sd"> def root_func(x, d):</span>
<span class="sd"> return anp.exp(-x ** 2) - d</span>
<span class="sd"> ```</span>
<span class="sd"> guess : float</span>
<span class="sd"> Initial guess for the minimization.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> Obs</span>
<span class="sd"> `Obs` valued root of the function.</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="n">root</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">optimize</span><span class="o">.</span><span class="n">fsolve</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">guess</span><span class="p">,</span> <span class="n">d</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
<span class="c1"># Error propagation as detailed in arXiv:1809.01289</span>
@ -110,14 +121,28 @@
<details>
<summary>View Source</summary>
<div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">find_root</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">guess</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Finds the root of the function func(x, d) where d is an Obs.</span>
<span class="sa">r</span><span class="sd">&#39;&#39;&#39;Finds the root of the function func(x, d) where d is an `Obs`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> -----------------</span>
<span class="sd"> d -- Obs passed to the function.</span>
<span class="sd"> func -- Function to be minimized. Any numpy functions have to use the autograd.numpy wrapper</span>
<span class="sd"> guess -- Initial guess for the minimization.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="sd"> d : Obs</span>
<span class="sd"> Obs passed to the function.</span>
<span class="sd"> func : object</span>
<span class="sd"> Function to be minimized. Any numpy functions have to use the autograd.numpy wrapper.</span>
<span class="sd"> Example:</span>
<span class="sd"> ```python</span>
<span class="sd"> import autograd.numpy as anp</span>
<span class="sd"> def root_func(x, d):</span>
<span class="sd"> return anp.exp(-x ** 2) - d</span>
<span class="sd"> ```</span>
<span class="sd"> guess : float</span>
<span class="sd"> Initial guess for the minimization.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> Obs</span>
<span class="sd"> `Obs` valued root of the function.</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="n">root</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">optimize</span><span class="o">.</span><span class="n">fsolve</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">guess</span><span class="p">,</span> <span class="n">d</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
<span class="c1"># Error propagation as detailed in arXiv:1809.01289</span>
@ -130,14 +155,29 @@
</details>
<div class="docstring"><p>Finds the root of the function func(x, d) where d is an Obs.</p>
<div class="docstring"><p>Finds the root of the function func(x, d) where d is an <code>Obs</code>.</p>
<h6 id="parameters">Parameters</h6>
<ul>
<li><strong>d -- Obs passed to the function.</strong></li>
<li><strong>func -- Function to be minimized. Any numpy functions have to use the autograd.numpy wrapper</strong></li>
<li><strong>guess -- Initial guess for the minimization.</strong></li>
<li><strong>d</strong> (Obs):
Obs passed to the function.</li>
<li><strong>func</strong> (object):
Function to be minimized. Any numpy functions have to use the autograd.numpy wrapper.
Example:
<code>python
import autograd.numpy as anp
def root_func(x, d):
return anp.exp(-x ** 2) - d
</code></li>
<li><strong>guess</strong> (float):
Initial guess for the minimization.</li>
</ul>
<h6 id="returns">Returns</h6>
<ul>
<li><strong>Obs</strong>: <code>Obs</code> valued root of the function.</li>
</ul>
</div>

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