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<span class="sd"> Returns</span>
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<span class="sd"> -------</span>
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<span class="sd"> np.ndarray</span>
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<span class="sd"> numpy.ndarray</span>
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<span class="sd"> Returns a numpy array of length N + 1 where N is the number of samples</span>
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<span class="sd"> for the given ensemble and replicum. The zeroth entry of the array contains</span>
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<span class="sd"> the mean value of the Obs, entries 1 to N contain the N jackknife samples</span>
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<span class="sd"> Returns</span>
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<span class="sd"> -------</span>
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<span class="sd"> np.ndarray</span>
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<span class="sd"> numpy.ndarray</span>
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<span class="sd"> Returns a numpy array of length N + 1 where N is the number of samples</span>
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<span class="sd"> for the given ensemble and replicum. The zeroth entry of the array contains</span>
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<span class="sd"> the mean value of the Obs, entries 1 to N contain the N jackknife samples</span>
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@ -3629,7 +3629,7 @@ specifies a custom path for the file (default '.')</li>
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<span class="sd"> Returns</span>
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<span class="sd"> -------</span>
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<span class="sd"> np.ndarray</span>
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<span class="sd"> numpy.ndarray</span>
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<span class="sd"> Returns a numpy array of length N + 1 where N is the number of samples</span>
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<span class="sd"> for the given ensemble and replicum. The zeroth entry of the array contains</span>
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<span class="sd"> the mean value of the Obs, entries 1 to N contain the N jackknife samples</span>
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@ -3659,7 +3659,7 @@ specifies a custom path for the file (default '.')</li>
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<h6 id="returns">Returns</h6>
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<ul>
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<li><strong>np.ndarray</strong>: Returns a numpy array of length N + 1 where N is the number of samples
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<li><strong>numpy.ndarray</strong>: Returns a numpy array of length N + 1 where N is the number of samples
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for the given ensemble and replicum. The zeroth entry of the array contains
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the mean value of the Obs, entries 1 to N contain the N jackknife samples
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derived from the Obs. The current implementation only works for observables
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