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Documentation updated
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3 changed files with 95 additions and 64 deletions
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@ -205,6 +205,9 @@ The standard value for the parameter $S$ of this automatic windowing procedure i
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<p>The integrated autocorrelation time $\tau_\mathrm{int}$ and the autocorrelation function $\rho(W)$ can be monitored via the methods <code><a href="pyerrors/obs.html#Obs.plot_tauint">pyerrors.obs.Obs.plot_tauint</a></code> and <code><a href="pyerrors/obs.html#Obs.plot_tauint">pyerrors.obs.Obs.plot_tauint</a></code>.</p>
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<p>If the parameter $S$ is set to zero it is assumed that dataset does not exhibit any autocorrelation and the windowsize is chosen to be zero.
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In this case the error estimate is identical to the sample standard error.</p>
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<h3 id="exponential-tails">Exponential tails</h3>
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<p>Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\rho$ as suggested in <a href="https://arxiv.org/abs/1009.5228">arXiv:1009.5228</a>. The longest autocorrelation time in the history, $\tau_\mathrm{exp}$, can be passed to the <code>gamma_method</code> as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.</p>
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@ -454,6 +457,9 @@ See <code><a href="pyerrors/obs.html#Obs.export_jackknife">pyerrors.obs.Obs.expo
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<span class="sd">The integrated autocorrelation time $\tau_\mathrm{int}$ and the autocorrelation function $\rho(W)$ can be monitored via the methods `pyerrors.obs.Obs.plot_tauint` and `pyerrors.obs.Obs.plot_tauint`.</span>
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<span class="sd">If the parameter $S$ is set to zero it is assumed that dataset does not exhibit any autocorrelation and the windowsize is chosen to be zero.</span>
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<span class="sd">In this case the error estimate is identical to the sample standard error.</span>
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<span class="sd">### Exponential tails</span>
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<span class="sd">Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\rho$ as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228). The longest autocorrelation time in the history, $\tau_\mathrm{exp}$, can be passed to the `gamma_method` as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.</span>
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@ -472,20 +472,21 @@
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<span class="k">return</span> <span class="n">res</span>
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<span class="k">def</span> <span class="nf">gamma_method</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="sd">"""Calculate the error and related properties of the Obs.</span>
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<span class="sd">"""Estimate the error and related properties of the Obs.</span>
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<span class="sd"> Parameters</span>
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<span class="sd"> ----------</span>
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<span class="sd"> S : float</span>
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<span class="sd"> specifies a custom value for the parameter S (default 2.0), can be</span>
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<span class="sd"> a float or an array of floats for different ensembles</span>
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<span class="sd"> specifies a custom value for the parameter S (default 2.0).</span>
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<span class="sd"> If set to 0 it is assumed that the data exhibits no</span>
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<span class="sd"> autocorrelation. In this case the error estimates coincides</span>
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<span class="sd"> with the sample standard error.</span>
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<span class="sd"> tau_exp : float</span>
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<span class="sd"> positive value triggers the critical slowing down analysis</span>
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<span class="sd"> (default 0.0), can be a float or an array of floats for different</span>
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<span class="sd"> ensembles</span>
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<span class="sd"> (default 0.0).</span>
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<span class="sd"> N_sigma : float</span>
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<span class="sd"> number of standard deviations from zero until the tail is</span>
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<span class="sd"> attached to the autocorrelation function (default 1)</span>
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<span class="sd"> attached to the autocorrelation function (default 1).</span>
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<span class="sd"> fft : bool</span>
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<span class="sd"> determines whether the fft algorithm is used for the computation</span>
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<span class="sd"> of the autocorrelation function (default True)</span>
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@ -597,10 +598,17 @@
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<span class="bp">self</span><span class="o">.</span><span class="n">e_ddvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_dvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">n</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">/</span> <span class="n">e_N</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">e_windowsize</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">n</span>
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<span class="k">break</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">e_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">e_dtauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">e_dvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">e_gamma</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">e_N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">e_ddvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_dvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">0.5</span> <span class="o">/</span> <span class="n">e_N</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">e_windowsize</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="c1"># Standard automatic windowing procedure</span>
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<span class="n">g_w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_n_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_n_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
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<span class="n">g_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span> <span class="o">/</span> <span class="n">g_w</span><span class="p">)</span> <span class="o">-</span> <span class="n">g_w</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span> <span class="o">*</span> <span class="n">e_N</span><span class="p">)</span>
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<span class="n">tau</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_n_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_n_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
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<span class="n">g_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span> <span class="o">/</span> <span class="n">tau</span><span class="p">)</span> <span class="o">-</span> <span class="n">tau</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span> <span class="o">*</span> <span class="n">e_N</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">):</span>
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<span class="k">if</span> <span class="n">n</span> <span class="o"><</span> <span class="n">w_max</span> <span class="o">//</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="p">:</span>
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<span class="n">_compute_drho</span><span class="p">(</span><span class="n">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
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@ -2061,20 +2069,21 @@
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<span class="k">return</span> <span class="n">res</span>
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<span class="k">def</span> <span class="nf">gamma_method</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="sd">"""Calculate the error and related properties of the Obs.</span>
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<span class="sd">"""Estimate the error and related properties of the Obs.</span>
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<span class="sd"> Parameters</span>
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<span class="sd"> ----------</span>
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<span class="sd"> S : float</span>
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<span class="sd"> specifies a custom value for the parameter S (default 2.0), can be</span>
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<span class="sd"> a float or an array of floats for different ensembles</span>
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<span class="sd"> specifies a custom value for the parameter S (default 2.0).</span>
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<span class="sd"> If set to 0 it is assumed that the data exhibits no</span>
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<span class="sd"> autocorrelation. In this case the error estimates coincides</span>
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<span class="sd"> with the sample standard error.</span>
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<span class="sd"> tau_exp : float</span>
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<span class="sd"> positive value triggers the critical slowing down analysis</span>
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<span class="sd"> (default 0.0), can be a float or an array of floats for different</span>
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<span class="sd"> ensembles</span>
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<span class="sd"> (default 0.0).</span>
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<span class="sd"> N_sigma : float</span>
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<span class="sd"> number of standard deviations from zero until the tail is</span>
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<span class="sd"> attached to the autocorrelation function (default 1)</span>
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<span class="sd"> attached to the autocorrelation function (default 1).</span>
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<span class="sd"> fft : bool</span>
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<span class="sd"> determines whether the fft algorithm is used for the computation</span>
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<span class="sd"> of the autocorrelation function (default True)</span>
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@ -2186,10 +2195,17 @@
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<span class="bp">self</span><span class="o">.</span><span class="n">e_ddvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_dvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">n</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">/</span> <span class="n">e_N</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">e_windowsize</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">n</span>
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<span class="k">break</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">e_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">e_dtauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">e_dvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">e_gamma</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">e_N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">e_ddvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_dvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">0.5</span> <span class="o">/</span> <span class="n">e_N</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">e_windowsize</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="c1"># Standard automatic windowing procedure</span>
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<span class="n">g_w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_n_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_n_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
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<span class="n">g_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span> <span class="o">/</span> <span class="n">g_w</span><span class="p">)</span> <span class="o">-</span> <span class="n">g_w</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span> <span class="o">*</span> <span class="n">e_N</span><span class="p">)</span>
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<span class="n">tau</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_n_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_n_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="n">g_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span> <span class="o">/</span> <span class="n">tau</span><span class="p">)</span> <span class="o">-</span> <span class="n">tau</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span> <span class="o">*</span> <span class="n">e_N</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">n</span> <span class="o"><</span> <span class="n">w_max</span> <span class="o">//</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">_compute_drho</span><span class="p">(</span><span class="n">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
|
||||
|
|
@ -3010,20 +3026,21 @@ already subtracted from the samples</li>
|
|||
<details>
|
||||
<summary>View Source</summary>
|
||||
<div class="codehilite"><pre><span></span> <span class="k">def</span> <span class="nf">gamma_method</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""Calculate the error and related properties of the Obs.</span>
|
||||
<span class="sd">"""Estimate the error and related properties of the Obs.</span>
|
||||
|
||||
<span class="sd"> Parameters</span>
|
||||
<span class="sd"> ----------</span>
|
||||
<span class="sd"> S : float</span>
|
||||
<span class="sd"> specifies a custom value for the parameter S (default 2.0), can be</span>
|
||||
<span class="sd"> a float or an array of floats for different ensembles</span>
|
||||
<span class="sd"> specifies a custom value for the parameter S (default 2.0).</span>
|
||||
<span class="sd"> If set to 0 it is assumed that the data exhibits no</span>
|
||||
<span class="sd"> autocorrelation. In this case the error estimates coincides</span>
|
||||
<span class="sd"> with the sample standard error.</span>
|
||||
<span class="sd"> tau_exp : float</span>
|
||||
<span class="sd"> positive value triggers the critical slowing down analysis</span>
|
||||
<span class="sd"> (default 0.0), can be a float or an array of floats for different</span>
|
||||
<span class="sd"> ensembles</span>
|
||||
<span class="sd"> (default 0.0).</span>
|
||||
<span class="sd"> N_sigma : float</span>
|
||||
<span class="sd"> number of standard deviations from zero until the tail is</span>
|
||||
<span class="sd"> attached to the autocorrelation function (default 1)</span>
|
||||
<span class="sd"> attached to the autocorrelation function (default 1).</span>
|
||||
<span class="sd"> fft : bool</span>
|
||||
<span class="sd"> determines whether the fft algorithm is used for the computation</span>
|
||||
<span class="sd"> of the autocorrelation function (default True)</span>
|
||||
|
|
@ -3135,10 +3152,17 @@ already subtracted from the samples</li>
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">e_ddvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_dvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">n</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">/</span> <span class="n">e_N</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">e_windowsize</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">n</span>
|
||||
<span class="k">break</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">e_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">e_dtauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">e_dvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">e_gamma</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">e_N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">e_ddvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_dvalue</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">0.5</span> <span class="o">/</span> <span class="n">e_N</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">e_windowsize</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="c1"># Standard automatic windowing procedure</span>
|
||||
<span class="n">g_w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_n_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_n_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="n">g_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span> <span class="o">/</span> <span class="n">g_w</span><span class="p">)</span> <span class="o">-</span> <span class="n">g_w</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span> <span class="o">*</span> <span class="n">e_N</span><span class="p">)</span>
|
||||
<span class="n">tau</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">S</span><span class="p">[</span><span class="n">e_name</span><span class="p">]</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_n_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">e_n_tauint</span><span class="p">[</span><span class="n">e_name</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="n">g_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span> <span class="o">/</span> <span class="n">tau</span><span class="p">)</span> <span class="o">-</span> <span class="n">tau</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span> <span class="o">*</span> <span class="n">e_N</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w_max</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">n</span> <span class="o"><</span> <span class="n">w_max</span> <span class="o">//</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">_compute_drho</span><span class="p">(</span><span class="n">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
|
||||
|
|
@ -3163,21 +3187,22 @@ already subtracted from the samples</li>
|
|||
|
||||
</details>
|
||||
|
||||
<div class="docstring"><p>Calculate the error and related properties of the Obs.</p>
|
||||
<div class="docstring"><p>Estimate the error and related properties of the Obs.</p>
|
||||
|
||||
<h6 id="parameters">Parameters</h6>
|
||||
|
||||
<ul>
|
||||
<li><strong>S</strong> (float):
|
||||
specifies a custom value for the parameter S (default 2.0), can be
|
||||
a float or an array of floats for different ensembles</li>
|
||||
specifies a custom value for the parameter S (default 2.0).
|
||||
If set to 0 it is assumed that the data exhibits no
|
||||
autocorrelation. In this case the error estimates coincides
|
||||
with the sample standard error.</li>
|
||||
<li><strong>tau_exp</strong> (float):
|
||||
positive value triggers the critical slowing down analysis
|
||||
(default 0.0), can be a float or an array of floats for different
|
||||
ensembles</li>
|
||||
(default 0.0).</li>
|
||||
<li><strong>N_sigma</strong> (float):
|
||||
number of standard deviations from zero until the tail is
|
||||
attached to the autocorrelation function (default 1)</li>
|
||||
attached to the autocorrelation function (default 1).</li>
|
||||
<li><strong>fft</strong> (bool):
|
||||
determines whether the fft algorithm is used for the computation
|
||||
of the autocorrelation function (default True)</li>
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
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Add table
Add a link
Reference in a new issue