lattice/pyerrors
1
0
Fork 0
mirror of https://github.com/fjosw/pyerrors.git synced 2025-05-15 03:53:41 +02:00
Code Issues Projects Releases Packages Wiki Activity

Documentation updated

Browse source
This commit is contained in:
fjosw 2021-11-08 10:02:33 +00:00
parent ec4b7106d8
commit 6070ed0740
2 changed files with 25 additions and 12 deletions
Download patch file Download diff file Expand all files Collapse all files

35
docs/pyerrors/fits.html
View file

@ -173,7 +173,7 @@
<span class="k">def</span> <span class="nf">least_squares</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">priors</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">silent</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Performs a non-linear fit to y = func(x).</span>
<span class="sa">r</span><span class="sd">&#39;&#39;&#39;Performs a non-linear fit to y = func(x).</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
@ -184,14 +184,19 @@
<span class="sd"> func : object</span>
<span class="sd"> fit function, has to be of the form</span>
<span class="sd"> ```python</span>
<span class="sd"> def func(a, x):</span>
<span class="sd"> return a[0] + a[1] * x + a[2] * anp.sinh(x)</span>
<span class="sd"> y = a[0] + a[1] * x + a[2] * anp.sinh(x)</span>
<span class="sd"> return y</span>
<span class="sd"> ```</span>
<span class="sd"> For multiple x values func can be of the form</span>
<span class="sd"> ```python</span>
<span class="sd"> def func(a, x):</span>
<span class="sd"> (x1, x2) = x</span>
<span class="sd"> return a[0] * x1 ** 2 + a[1] * x2</span>
<span class="sd"> ```</span>
<span class="sd"> It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation</span>
<span class="sd"> will not work</span>
@ -220,7 +225,7 @@
<span class="sd"> corrected by effects caused by correlated input data.</span>
<span class="sd"> This can take a while as the full correlation matrix</span>
<span class="sd"> has to be calculated (default False).</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="k">if</span> <span class="n">priors</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">_prior_fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">priors</span><span class="p">,</span> <span class="n">silent</span><span class="o">=</span><span class="n">silent</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
@ -1049,7 +1054,7 @@ also accesible via indices.</li>
<details>
<summary>View Source</summary>
<div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">least_squares</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">priors</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">silent</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Performs a non-linear fit to y = func(x).</span>
<span class="sa">r</span><span class="sd">&#39;&#39;&#39;Performs a non-linear fit to y = func(x).</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
@ -1060,14 +1065,19 @@ also accesible via indices.</li>
<span class="sd"> func : object</span>
<span class="sd"> fit function, has to be of the form</span>
<span class="sd"> ```python</span>
<span class="sd"> def func(a, x):</span>
<span class="sd"> return a[0] + a[1] * x + a[2] * anp.sinh(x)</span>
<span class="sd"> y = a[0] + a[1] * x + a[2] * anp.sinh(x)</span>
<span class="sd"> return y</span>
<span class="sd"> ```</span>
<span class="sd"> For multiple x values func can be of the form</span>
<span class="sd"> ```python</span>
<span class="sd"> def func(a, x):</span>
<span class="sd"> (x1, x2) = x</span>
<span class="sd"> return a[0] * x1 ** 2 + a[1] * x2</span>
<span class="sd"> ```</span>
<span class="sd"> It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation</span>
<span class="sd"> will not work</span>
@ -1096,7 +1106,7 @@ also accesible via indices.</li>
<span class="sd"> corrected by effects caused by correlated input data.</span>
<span class="sd"> This can take a while as the full correlation matrix</span>
<span class="sd"> has to be calculated (default False).</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="sd"> &#39;&#39;&#39;</span>
<span class="k">if</span> <span class="n">priors</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">_prior_fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">priors</span><span class="p">,</span> <span class="n">silent</span><span class="o">=</span><span class="n">silent</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
@ -1117,14 +1127,17 @@ list of Obs.</li>
<li><p><strong>func</strong> (object):
fit function, has to be of the form</p>
<p>def func(a, x):
return a[0] + a[1] * x + a[2] * anp.sinh(x)</p>
<div class="codehilite"><pre><span></span><code><span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">a</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">anp</span><span class="o">.</span><span class="n">sinh</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">y</span>
</code></pre></div>
<p>For multiple x values func can be of the form</p>
<p>def func(a, x):
(x1, x2) = x
return a[0] * x1 ** 2 + a[1] * x2</p>
<div class="codehilite"><pre><span></span><code><span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span> <span class="o">=</span> <span class="n">x</span>
<span class="k">return</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">x1</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">x2</span>
</code></pre></div>
<p>It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
will not work</p></li>

2
docs/search.js
View file

File diff suppressed because one or more lines are too long