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docs/pyerrors/obs.html
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@ -293,6 +293,9 @@
<li>
<a class="function" href="#load_object">load_object</a>
</li>
<li>
<a class="function" href="#import_jackknife">import_jackknife</a>
</li>
<li>
<a class="function" href="#merge_obs">merge_obs</a>
</li>
@ -1881,6 +1884,25 @@
<span class="k">return</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">import_jackknife</span><span class="p">(</span><span class="n">jacks</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Imports jackknife samples and returns an Obs</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> jacks : numpy.ndarray</span>
<span class="sd"> numpy array containing the mean value as zeroth entry and</span>
<span class="sd"> the N jackknife samples as first to Nth entry.</span>
<span class="sd"> name : str</span>
<span class="sd"> name of the ensemble the samples are defined on.</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">jacks</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">prj</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</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="o">-</span> <span class="p">(</span><span class="n">length</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">length</span><span class="p">))</span>
<span class="n">samples</span> <span class="o">=</span> <span class="n">jacks</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">@</span> <span class="n">prj</span>
<span class="n">new_obs</span> <span class="o">=</span> <span class="n">Obs</span><span class="p">([</span><span class="n">samples</span><span class="p">],</span> <span class="p">[</span><span class="n">name</span><span class="p">])</span>
<span class="n">new_obs</span><span class="o">.</span><span class="n">_value</span> <span class="o">=</span> <span class="n">jacks</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">return</span> <span class="n">new_obs</span>
<span class="k">def</span> <span class="nf">merge_obs</span><span class="p">(</span><span class="n">list_of_obs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Combine all observables in list_of_obs into one new observable</span>
@ -5177,6 +5199,52 @@ path to the file</li>
</div>
</section>
<section id="import_jackknife">
<div class="attr function"><a class="headerlink" href="#import_jackknife">#&nbsp;&nbsp</a>
<span class="def">def</span>
<span class="name">import_jackknife</span><span class="signature">(jacks, name)</span>:
</div>
<details>
<summary>View Source</summary>
<div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">import_jackknife</span><span class="p">(</span><span class="n">jacks</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Imports jackknife samples and returns an Obs</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> jacks : numpy.ndarray</span>
<span class="sd"> numpy array containing the mean value as zeroth entry and</span>
<span class="sd"> the N jackknife samples as first to Nth entry.</span>
<span class="sd"> name : str</span>
<span class="sd"> name of the ensemble the samples are defined on.</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">jacks</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">prj</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</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="o">-</span> <span class="p">(</span><span class="n">length</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">length</span><span class="p">))</span>
<span class="n">samples</span> <span class="o">=</span> <span class="n">jacks</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">@</span> <span class="n">prj</span>
<span class="n">new_obs</span> <span class="o">=</span> <span class="n">Obs</span><span class="p">([</span><span class="n">samples</span><span class="p">],</span> <span class="p">[</span><span class="n">name</span><span class="p">])</span>
<span class="n">new_obs</span><span class="o">.</span><span class="n">_value</span> <span class="o">=</span> <span class="n">jacks</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">return</span> <span class="n">new_obs</span>
</pre></div>
</details>
<div class="docstring"><p>Imports jackknife samples and returns an Obs</p>
<h6 id="parameters">Parameters</h6>
<ul>
<li><strong>jacks</strong> (numpy.ndarray):
numpy array containing the mean value as zeroth entry and
the N jackknife samples as first to Nth entry.</li>
<li><strong>name</strong> (str):
name of the ensemble the samples are defined on.</li>
</ul>
</div>
</section>
<section id="merge_obs">
<div class="attr function"><a class="headerlink" href="#merge_obs">#&nbsp;&nbsp</a>

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