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@ -215,7 +215,7 @@ The standard value for the parameter $S$ of this automatic windowing procedure i
</code></pre>
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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>
<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_rho">pyerrors.obs.Obs.plot_rho</a></code>.</p>
<p>If the parameter $S$ is set to zero it is assumed that the dataset does not exhibit any autocorrelation and the window size is chosen to be zero.
In this case the error estimate is identical to the sample standard error.</p>
@ -737,7 +737,7 @@ The following entries are optional:</li>
</span><span id="L-106"><a href="#L-106"><span class="linenos">106</span></a>
</span><span id="L-107"><a href="#L-107"><span class="linenos">107</span></a><span class="sd">```</span>
</span><span id="L-108"><a href="#L-108"><span class="linenos">108</span></a>
</span><span id="L-109"><a href="#L-109"><span class="linenos">109</span></a><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>
</span><span id="L-109"><a href="#L-109"><span class="linenos">109</span></a><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_rho`.</span>
</span><span id="L-110"><a href="#L-110"><span class="linenos">110</span></a>
</span><span id="L-111"><a href="#L-111"><span class="linenos">111</span></a><span class="sd">If the parameter $S$ is set to zero it is assumed that the dataset does not exhibit any autocorrelation and the window size is chosen to be zero.</span>
</span><span id="L-112"><a href="#L-112"><span class="linenos">112</span></a><span class="sd">In this case the error estimate is identical to the sample standard error.</span>