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fjosw 2022-02-24 16:14:09 +00:00
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37
docs/pyerrors/fits.html
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@ -756,7 +756,13 @@
<span class="k">def</span> <span class="nf">covariance_matrix</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns the covariance matrix of y.&quot;&quot;&quot;</span>
<span class="sd">&quot;&quot;&quot;Returns the covariance matrix of y.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> y : list or numpy.ndarray</span>
<span class="sd"> List or one dimensional array of Obs</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">cov</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">length</span><span class="p">,</span> <span class="n">length</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
@ -765,7 +771,12 @@
<span class="n">cov</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">item</span><span class="o">.</span><span class="n">dvalue</span> <span class="o">**</span> <span class="mi">2</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">cov</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">covariance</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="n">jtem</span><span class="p">)</span>
<span class="k">return</span> <span class="n">cov</span> <span class="o">+</span> <span class="n">cov</span><span class="o">.</span><span class="n">T</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">cov</span><span class="p">))</span>
<span class="n">cov</span> <span class="o">=</span> <span class="n">cov</span> <span class="o">+</span> <span class="n">cov</span><span class="o">.</span><span class="n">T</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">cov</span><span class="p">))</span>
<span class="n">eigenvalues</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigh</span><span class="p">(</span><span class="n">cov</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">eigenvalues</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">):</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Covariance matrix is not positive semi-definite&quot;</span><span class="p">,</span> <span class="ne">RuntimeWarning</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Eigenvalues of the covariance matrix:&quot;</span><span class="p">,</span> <span class="n">eigenvalues</span><span class="p">)</span>
<span class="k">return</span> <span class="n">cov</span>
<span class="k">def</span> <span class="nf">error_band</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">beta</span><span class="p">):</span>
@ -1452,7 +1463,13 @@ check if the residuals of the fit are gaussian distributed.</p>
<details>
<summary>View Source</summary>
<div class="pdoc-code codehilite"><pre><span></span><span class="k">def</span> <span class="nf">covariance_matrix</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns the covariance matrix of y.&quot;&quot;&quot;</span>
<span class="sd">&quot;&quot;&quot;Returns the covariance matrix of y.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> y : list or numpy.ndarray</span>
<span class="sd"> List or one dimensional array of Obs</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">cov</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">length</span><span class="p">,</span> <span class="n">length</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
@ -1461,12 +1478,24 @@ check if the residuals of the fit are gaussian distributed.</p>
<span class="n">cov</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">item</span><span class="o">.</span><span class="n">dvalue</span> <span class="o">**</span> <span class="mi">2</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">cov</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">covariance</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="n">jtem</span><span class="p">)</span>
<span class="k">return</span> <span class="n">cov</span> <span class="o">+</span> <span class="n">cov</span><span class="o">.</span><span class="n">T</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">cov</span><span class="p">))</span>
<span class="n">cov</span> <span class="o">=</span> <span class="n">cov</span> <span class="o">+</span> <span class="n">cov</span><span class="o">.</span><span class="n">T</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">cov</span><span class="p">))</span>
<span class="n">eigenvalues</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigh</span><span class="p">(</span><span class="n">cov</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">eigenvalues</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">):</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Covariance matrix is not positive semi-definite&quot;</span><span class="p">,</span> <span class="ne">RuntimeWarning</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Eigenvalues of the covariance matrix:&quot;</span><span class="p">,</span> <span class="n">eigenvalues</span><span class="p">)</span>
<span class="k">return</span> <span class="n">cov</span>
</pre></div>
</details>
<div class="docstring"><p>Returns the covariance matrix of y.</p>
<h6 id="parameters">Parameters</h6>
<ul>
<li><strong>y</strong> (list or numpy.ndarray):
List or one dimensional array of Obs</li>
</ul>
</div>

24
docs/pyerrors/obs.html
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@ -1652,10 +1652,10 @@
<span class="sd"> If abs(covariance(obs1, obs2)) &gt; obs1.dvalue * obs2.dvalue, the covariance</span>
<span class="sd"> is constrained to the maximum value.</span>
<span class="sd"> Keyword arguments</span>
<span class="sd"> -----------------</span>
<span class="sd"> correlation -- if true the correlation instead of the covariance is</span>
<span class="sd"> returned (default False)</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> correlation : bool</span>
<span class="sd"> if true the correlation instead of the covariance is returned (default False)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">expand_deltas</span><span class="p">(</span><span class="n">deltas</span><span class="p">,</span> <span class="n">idx</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">new_idx</span><span class="p">):</span>
@ -4841,10 +4841,10 @@ Second observable</li>
<span class="sd"> If abs(covariance(obs1, obs2)) &gt; obs1.dvalue * obs2.dvalue, the covariance</span>
<span class="sd"> is constrained to the maximum value.</span>
<span class="sd"> Keyword arguments</span>
<span class="sd"> -----------------</span>
<span class="sd"> correlation -- if true the correlation instead of the covariance is</span>
<span class="sd"> returned (default False)</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> correlation : bool</span>
<span class="sd"> if true the correlation instead of the covariance is returned (default False)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">expand_deltas</span><span class="p">(</span><span class="n">deltas</span><span class="p">,</span> <span class="n">idx</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">new_idx</span><span class="p">):</span>
@ -4972,10 +4972,12 @@ The gamma method has to be applied first to both observables.</p>
<p>If abs(covariance(obs1, obs2)) &gt; obs1.dvalue * obs2.dvalue, the covariance
is constrained to the maximum value.</p>
<h6 id="keyword-arguments">Keyword arguments</h6>
<h6 id="parameters">Parameters</h6>
<p>correlation -- if true the correlation instead of the covariance is
returned (default False)</p>
<ul>
<li><strong>correlation</strong> (bool):
if true the correlation instead of the covariance is returned (default False)</li>
</ul>
</div>

2
docs/search.js
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