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    <title>statserver &mdash; SIDEKIT  documentation</title>
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    <link rel="top" title="SIDEKIT  documentation" href="../index.html" />
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  <h1>Source code for statserver</h1><div class="highlight"><pre>
<span class="c"># -*- coding: utf-8 -*-</span>
<span class="c">#</span>
<span class="c"># This file is part of SIDEKIT.</span>
<span class="c">#</span>
<span class="c"># SIDEKIT is a python package for speaker verification.</span>
<span class="c"># Home page: http://www-lium.univ-lemans.fr/sidekit/</span>
<span class="c">#</span>
<span class="c"># SIDEKIT is a python package for speaker verification.</span>
<span class="c"># Home page: http://www-lium.univ-lemans.fr/sidekit/</span>
<span class="c">#    </span>
<span class="c"># SIDEKIT is free software: you can redistribute it and/or modify</span>
<span class="c"># it under the terms of the GNU LLesser General Public License as </span>
<span class="c"># published by the Free Software Foundation, either version 3 of the License, </span>
<span class="c"># or (at your option) any later version.</span>
<span class="c">#</span>
<span class="c"># SIDEKIT is distributed in the hope that it will be useful,</span>
<span class="c"># but WITHOUT ANY WARRANTY; without even the implied warranty of</span>
<span class="c"># MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span>
<span class="c"># GNU Lesser General Public License for more details.</span>
<span class="c">#</span>
<span class="c"># You should have received a copy of the GNU Lesser General Public License</span>
<span class="c"># along with SIDEKIT.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd">Copyright 2014-2016 Anthony Larcher</span>
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<span class="sd">:mod:`statserver` provides methods to manage zero and first statistics.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">copy</span>
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<span class="kn">import</span> <span class="nn">ctypes</span>
<span class="kn">import</span> <span class="nn">h5py</span>
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<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">multiprocessing</span>
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<span class="kn">import</span> <span class="nn">numpy</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">scipy</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">warnings</span>
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<span class="kn">from</span> <span class="nn">sidekit.bosaris</span> <span class="kn">import</span> <span class="n">IdMap</span>
<span class="kn">from</span> <span class="nn">sidekit.mixture</span> <span class="kn">import</span> <span class="n">Mixture</span>
<span class="kn">from</span> <span class="nn">sidekit.features_server</span> <span class="kn">import</span> <span class="n">FeaturesServer</span>
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<span class="kn">from</span> <span class="nn">sidekit.sidekit_wrappers</span> <span class="kn">import</span> <span class="n">process_parallel_lists</span><span class="p">,</span> <span class="n">deprecated</span><span class="p">,</span> <span class="n">check_path_existance</span>
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<span class="kn">import</span> <span class="nn">sidekit.frontend</span>
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<span class="kn">from</span> <span class="nn">sidekit</span> <span class="kn">import</span> <span class="n">STAT_TYPE</span>
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<span class="n">ct</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_double</span>
<span class="k">if</span> <span class="n">STAT_TYPE</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">:</span>
    <span class="n">ct</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_float</span>
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<span class="n">__license__</span> <span class="o">=</span> <span class="s">&quot;LGPL&quot;</span>
<span class="n">__author__</span> <span class="o">=</span> <span class="s">&quot;Anthony Larcher&quot;</span>
<span class="n">__copyright__</span> <span class="o">=</span> <span class="s">&quot;Copyright 2014-2016 Anthony Larcher&quot;</span>
<span class="n">__maintainer__</span> <span class="o">=</span> <span class="s">&quot;Anthony Larcher&quot;</span>
<span class="n">__email__</span> <span class="o">=</span> <span class="s">&quot;anthony.larcher@univ-lemans.fr&quot;</span>
<span class="n">__status__</span> <span class="o">=</span> <span class="s">&quot;Production&quot;</span>
<span class="n">__docformat__</span> <span class="o">=</span> <span class="s">&#39;reStructuredText&#39;</span>
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<span class="k">def</span> <span class="nf">compute_llk</span><span class="p">(</span><span class="n">stat</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">U</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
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    <span class="c"># Compute Likelihood</span>
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    <span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span> <span class="o">=</span> <span class="n">stat</span><span class="o">.</span><span class="n">stat1</span><span class="o">.</span><span class="n">shape</span>
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    <span class="n">centered_data</span> <span class="o">=</span> <span class="n">stat</span><span class="o">.</span><span class="n">stat1</span> <span class="o">-</span> <span class="n">stat</span><span class="o">.</span><span class="n">get_mean_stat1</span><span class="p">()</span>
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    <span class="k">if</span> <span class="n">sigma</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
        <span class="n">sigma_tot</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">V</span><span class="p">,</span> <span class="n">V</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">+</span> <span class="n">sigma</span>
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    <span class="k">else</span><span class="p">:</span>
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        <span class="n">sigma_tot</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">V</span><span class="p">,</span> <span class="n">V</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">+</span> <span class="n">numpy</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">sigma</span><span class="p">)</span>
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    <span class="k">if</span> <span class="n">U</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
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        <span class="n">sigma_tot</span> <span class="o">+=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">U</span><span class="p">,</span> <span class="n">U</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
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    <span class="n">E</span><span class="p">,</span> <span class="n">junk</span> <span class="o">=</span> <span class="n">scipy</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">sigma_tot</span><span class="p">)</span>
    <span class="n">log_det</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">E</span><span class="p">))</span>

    <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">n</span> <span class="o">*</span> <span class="n">d</span> <span class="o">*</span> <span class="n">numpy</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="n">numpy</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">+</span> <span class="n">n</span> <span class="o">*</span> <span class="n">log_det</span> <span class="o">+</span>
                    <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">centered_data</span><span class="p">,</span>
                                                  <span class="n">scipy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">sigma_tot</span><span class="p">))</span> <span class="o">*</span> <span class="n">centered_data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))))</span>
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<span class="k">def</span> <span class="nf">sum_log_probabilities</span><span class="p">(</span><span class="n">lp</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Sum log probabilities in a secure manner to avoid extreme values</span>

<span class="sd">    :param lp: ndarray of log-probabilities to sum</span>
<span class="sd">    &quot;&quot;&quot;</span>
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    <span class="n">pp_max</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">lp</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">log_lk</span> <span class="o">=</span> <span class="n">pp_max</span> \
        <span class="o">+</span> <span class="n">numpy</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">exp</span><span class="p">((</span><span class="n">lp</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">-</span> <span class="n">pp_max</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">()),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
    <span class="n">ind</span> <span class="o">=</span> <span class="o">~</span><span class="n">numpy</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">pp_max</span><span class="p">)</span>
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    <span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">ind</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
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        <span class="n">log_lk</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">=</span> <span class="n">pp_max</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span>
    <span class="n">pp</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">exp</span><span class="p">((</span><span class="n">lp</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">-</span> <span class="n">log_lk</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">())</span>
    <span class="k">return</span> <span class="n">pp</span><span class="p">,</span> <span class="n">log_lk</span>
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<span class="nd">@process_parallel_lists</span>
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<span class="k">def</span> <span class="nf">fa_model_loop</span><span class="p">(</span><span class="n">batch_start</span><span class="p">,</span>
                  <span class="n">mini_batch_indices</span><span class="p">,</span>
                  <span class="n">r</span><span class="p">,</span>
                  <span class="n">phi_white</span><span class="p">,</span>
                  <span class="n">phi</span><span class="p">,</span>
                  <span class="n">sigma</span><span class="p">,</span>
                  <span class="n">stat0</span><span class="p">,</span>
                  <span class="n">stat1</span><span class="p">,</span>
                  <span class="n">e_h</span><span class="p">,</span>
                  <span class="n">e_hh</span><span class="p">,</span>
                  <span class="n">num_thread</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
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    <span class="sd">&quot;&quot;&quot;</span>
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<span class="sd">    :param batch_start: index to start at in the list</span>
<span class="sd">    :param mini_batch_indices: indices of the elements in the list (should start at zero)</span>
<span class="sd">    :param r: rank of the matrix</span>
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<span class="sd">    :param phi_white: whitened version of the factor matrix</span>
<span class="sd">    :param phi: non-whitened version of the factor matrix</span>
<span class="sd">    :param sigma: covariance matrix</span>
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<span class="sd">    :param stat0: matrix of zero order statistics</span>
<span class="sd">    :param stat1: matrix of first order statistics</span>
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<span class="sd">    :param e_h: accumulator</span>
<span class="sd">    :param e_hh: accumulator</span>
<span class="sd">    :param num_thread: number of parallel process to run</span>
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<span class="sd">    &quot;&quot;&quot;</span>
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    <span class="k">if</span> <span class="n">sigma</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
        <span class="n">A</span> <span class="o">=</span> <span class="n">phi</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">sigma</span><span class="p">,</span> <span class="n">phi</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
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    <span class="n">tmp</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">phi</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">phi</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
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    <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">mini_batch_indices</span><span class="p">:</span>
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        <span class="k">if</span> <span class="n">sigma</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="n">inv_lambda</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">r</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">phi_white</span><span class="o">.</span><span class="n">T</span> <span class="o">*</span> <span class="n">stat0</span><span class="p">[</span><span class="n">idx</span> <span class="o">+</span> <span class="n">batch_start</span><span class="p">,</span> <span class="p">:])</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">phi_white</span><span class="p">))</span>
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        <span class="k">else</span><span class="p">:</span>
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            <span class="n">inv_lambda</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">stat0</span><span class="p">[</span><span class="n">idx</span> <span class="o">+</span> <span class="n">batch_start</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">A</span> <span class="o">+</span> <span class="n">numpy</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
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        <span class="n">Aux</span> <span class="o">=</span> <span class="n">phi_white</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">stat1</span><span class="p">[</span><span class="n">idx</span> <span class="o">+</span> <span class="n">batch_start</span><span class="p">,</span> <span class="p">:])</span>
        <span class="n">numpy</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">Aux</span><span class="p">,</span> <span class="n">inv_lambda</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">e_h</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
        <span class="n">e_hh</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">inv_lambda</span> <span class="o">+</span> <span class="n">numpy</span><span class="o">.</span><span class="n">outer</span><span class="p">(</span><span class="n">e_h</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">e_h</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">tmp</span><span class="p">)</span>
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<span class="nd">@process_parallel_lists</span>
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<span class="k">def</span> <span class="nf">fa_distribution_loop</span><span class="p">(</span><span class="n">distrib_indices</span><span class="p">,</span> <span class="n">_A</span><span class="p">,</span> <span class="n">stat0</span><span class="p">,</span> <span class="n">batch_start</span><span class="p">,</span> <span class="n">batch_stop</span><span class="p">,</span> <span class="n">e_hh</span><span class="p">,</span> <span class="n">num_thread</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
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    <span class="sd">&quot;&quot;&quot;</span>
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<span class="sd">    :param distrib_indices: indices of the distributions to iterate on</span>
<span class="sd">    :param _A: accumulator</span>
<span class="sd">    :param stat0: matrix of zero order statistics</span>
<span class="sd">    :param batch_start: index of the first session to process</span>
<span class="sd">    :param batch_stop: index of the last session to process</span>
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<span class="sd">    :param e_hh: accumulator</span>
<span class="sd">    :param num_thread: number of parallel process to run</span>
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<span class="sd">    &quot;&quot;&quot;</span>
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    <span class="n">tmp</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">e_hh</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">e_hh</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
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    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">distrib_indices</span><span class="p">:</span>
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        <span class="n">_A</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s">&#39;ijk,i-&gt;jk&#39;</span><span class="p">,</span> <span class="n">e_hh</span><span class="p">,</span> <span class="n">stat0</span><span class="p">[</span><span class="n">batch_start</span><span class="p">:</span><span class="n">batch_stop</span><span class="p">,</span> <span class="n">c</span><span class="p">],</span> <span class="n">out</span><span class="o">=</span><span class="n">tmp</span><span class="p">)</span>
        <span class="c"># The line abov is equivalent to the two lines below:</span>
        <span class="c"># tmp = (E_hh.T * stat0[batch_start:batch_stop, c]).T</span>
        <span class="c"># _A[c] += numpy.sum(tmp, axis=0)</span>
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<span class="k">def</span> <span class="nf">load_existing_statistics_hdf5</span><span class="p">(</span><span class="n">statserver</span><span class="p">,</span> <span class="n">statserver_file_name</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Load required statistics into the StatServer by reading from a file</span>
<span class="sd">        in hdf5 format.</span>
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<span class="sd">    :param statserver: sidekit.StatServer to fill</span>
<span class="sd">    :param statserver_file_name: name of the file to read from</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">assert</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">statserver_file_name</span><span class="p">),</span> <span class="s">&quot;statserver_file_name does not exist&quot;</span>

    <span class="c"># Load the StatServer</span>
    <span class="n">ss</span> <span class="o">=</span> <span class="n">StatServer</span><span class="p">(</span><span class="n">statserver_file_name</span><span class="p">)</span>

    <span class="c"># Check dimension consistency with current Stat_Server</span>
    <span class="n">ok</span> <span class="o">=</span> <span class="bp">True</span>
    <span class="k">if</span> <span class="n">statserver</span><span class="o">.</span><span class="n">stat0</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
        <span class="n">ok</span> <span class="o">&amp;=</span> <span class="p">(</span><span class="n">ss</span><span class="o">.</span><span class="n">stat0</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">statserver</span><span class="o">.</span><span class="n">stat0</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
        <span class="n">ok</span> <span class="o">&amp;=</span> <span class="p">(</span><span class="n">ss</span><span class="o">.</span><span class="n">stat1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">statserver</span><span class="o">.</span><span class="n">stat1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">statserver</span><span class="o">.</span><span class="n">stat0</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">statserver</span><span class="o">.</span> <span class="n">modelset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ss</span><span class="o">.</span><span class="n">stat0</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
        <span class="n">statserver</span><span class="o">.</span><span class="n">stat1</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">statserver</span><span class="o">.</span> <span class="n">modelset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ss</span><span class="o">.</span><span class="n">stat1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">ok</span><span class="p">:</span>
        <span class="c"># For each segment, load statistics if they exist</span>
        <span class="c"># Get the lists of existing segments</span>
        <span class="n">seg_idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">statserver</span><span class="o">.</span> <span class="n">segset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">if</span> <span class="n">statserver</span><span class="o">.</span><span class="n">segset</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="ow">in</span> <span class="n">ss</span><span class="o">.</span><span class="n">segset</span><span class="p">]</span>
        <span class="n">stat_idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">ss</span><span class="o">.</span><span class="n">segset</span> <span class="o">==</span> <span class="n">seg</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">seg</span> <span class="ow">in</span> <span class="n">statserver</span><span class="o">.</span><span class="n">segset</span> <span class="k">if</span> <span class="n">seg</span> <span class="ow">in</span> <span class="n">ss</span><span class="o">.</span><span class="n">segset</span><span class="p">]</span>

        <span class="c"># Copy statistics</span>
        <span class="n">statserver</span><span class="o">.</span><span class="n">stat0</span><span class="p">[</span><span class="n">seg_idx</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">stat0</span><span class="p">[</span><span class="n">stat_idx</span><span class="p">,</span> <span class="p">:]</span>
        <span class="n">statserver</span><span class="o">.</span><span class="n">stat1</span><span class="p">[</span><span class="n">seg_idx</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">stat1</span><span class="p">[</span><span class="n">stat_idx</span><span class="p">,</span> <span class="p">:]</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s">&#39;Mismatched statistic dimensions&#39;</span><span class="p">)</span>
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<div class="viewcode-block" id="StatServer"><a class="viewcode-back" href="../statserver.html#statserver.StatServer">[docs]</a><span class="k">class</span> <span class="nc">StatServer</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;A class for statistic storage and processing</span>

<span class="sd">    :attr modelset: list of model IDs for each session as an array of strings</span>
<span class="sd">    :attr segset: the list of session IDs as an array of strings</span>
<span class="sd">    :attr start: index of the first frame of the segment</span>
<span class="sd">    :attr stop: index of the last frame of the segment</span>
<span class="sd">    :attr stat0: a ndarray of float64. Each line contains 0-order statistics </span>
<span class="sd">        from the corresponding session</span>
<span class="sd">    :attr stat1: a ndarray of float64. Each line contains 1-order statistics </span>
<span class="sd">        from the corresponding session</span>
<span class="sd">    </span>
<span class="sd">    &quot;&quot;&quot;</span>
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    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">statserver_file_name</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">ubm</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Initialize an empty StatServer or load a StatServer from an existing</span>
<span class="sd">        file.</span>
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<span class="sd">        :param statserver_file_name: name of the file to read from. If filename</span>
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<span class="sd">                is an empty string, the StatServer is initialized empty. </span>
<span class="sd">                If filename is an IdMap object, the StatServer is initialized </span>
<span class="sd">                to match the structure of the IdMap.</span>
<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="bp">self</span><span class="o">.</span><span class="n">modelset</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">&quot;|O&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">segset</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">&quot;|O&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">start</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">&quot;|O&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">stop</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">&quot;|O&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">statserver_file_name</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
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            <span class="k">pass</span>
        <span class="c"># initialize</span>
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        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">statserver_file_name</span><span class="p">,</span> <span class="n">IdMap</span><span class="p">):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">modelset</span> <span class="o">=</span> <span class="n">statserver_file_name</span><span class="o">.</span><span class="n">leftids</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">segset</span> <span class="o">=</span> <span class="n">statserver_file_name</span><span class="o">.</span><span class="n">rightids</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">start</span> <span class="o">=</span> <span class="n">statserver_file_name</span><span class="o">.</span><span class="n">start</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">stop</span> <span class="o">=</span> <span class="n">statserver_file_name</span><span class="o">.</span><span class="n">stop</span>
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            <span class="k">if</span> <span class="n">ubm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>            
                <span class="c"># Initialize stat0 and stat1 given the size of the UBM</span>
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                <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span> <span class="n">segset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ubm</span><span class="o">.</span><span class="n">distrib_nb</span><span class="p">()),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span> <span class="n">segset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ubm</span><span class="o">.</span><span class="n">sv_size</span><span class="p">()),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
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                <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">():</span>
                    <span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s">&#39;ignore&#39;</span><span class="p">,</span> <span class="ne">RuntimeWarning</span><span class="p">)</span>
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                    <span class="n">tmp_stat0</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">Array</span><span class="p">(</span><span class="n">ct</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ctypeslib</span><span class="o">.</span><span class="n">as_array</span><span class="p">(</span><span class="n">tmp_stat0</span><span class="o">.</span><span class="n">get_obj</span><span class="p">())</span>
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                    <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ubm</span><span class="o">.</span><span class="n">distrib_nb</span><span class="p">())</span>
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                    <span class="n">tmp_stat1</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">Array</span><span class="p">(</span><span class="n">ct</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ctypeslib</span><span class="o">.</span><span class="n">as_array</span><span class="p">(</span><span class="n">tmp_stat1</span><span class="o">.</span><span class="n">get_obj</span><span class="p">())</span>
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                    <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ubm</span><span class="o">.</span><span class="n">sv_size</span><span class="p">())</span>
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        <span class="c"># initialize by reading an existing StatServer</span>
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        <span class="k">else</span><span class="p">:</span>
            <span class="n">tmp</span> <span class="o">=</span> <span class="n">StatServer</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">statserver_file_name</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">modelset</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">modelset</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">segset</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">segset</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">start</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">start</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">stop</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">stop</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">stat0</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">stat1</span>
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    <span class="k">def</span> <span class="nf">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">ch</span> <span class="o">=</span> <span class="s">&#39;-&#39;</span> <span class="o">*</span> <span class="mi">30</span> <span class="o">+</span> <span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span>
        <span class="n">ch</span> <span class="o">+=</span> <span class="s">&#39;modelset: &#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">modelset</span><span class="o">.</span><span class="n">__repr__</span><span class="p">()</span> <span class="o">+</span> <span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span>
        <span class="n">ch</span> <span class="o">+=</span> <span class="s">&#39;segset: &#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="o">.</span><span class="n">__repr__</span><span class="p">()</span> <span class="o">+</span> <span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span>
        <span class="n">ch</span> <span class="o">+=</span> <span class="s">&#39;seg start:&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">start</span><span class="o">.</span><span class="n">__repr__</span><span class="p">()</span> <span class="o">+</span> <span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span>
        <span class="n">ch</span> <span class="o">+=</span> <span class="s">&#39;seg stop:&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="o">.</span><span class="n">__repr__</span><span class="p">()</span> <span class="o">+</span> <span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span>
        <span class="n">ch</span> <span class="o">+=</span> <span class="s">&#39;stat0:&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="o">.</span><span class="n">__repr__</span><span class="p">()</span> <span class="o">+</span> <span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span>
        <span class="n">ch</span> <span class="o">+=</span> <span class="s">&#39;stat1:&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="o">.</span><span class="n">__repr__</span><span class="p">()</span> <span class="o">+</span> <span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span>
        <span class="n">ch</span> <span class="o">+=</span> <span class="s">&#39;-&#39;</span> <span class="o">*</span> <span class="mi">30</span> <span class="o">+</span> <span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span>
        <span class="k">return</span> <span class="n">ch</span>
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<div class="viewcode-block" id="StatServer.validate"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.validate">[docs]</a>    <span class="k">def</span> <span class="nf">validate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">warn</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Validate the structure and content of the StatServer. </span>
<span class="sd">        Check consistency between the different attributes of </span>
<span class="sd">        the StatServer:</span>
<span class="sd">        - dimension of the modelset</span>
<span class="sd">        - dimension of the segset</span>
<span class="sd">        - length of the modelset and segset</span>
<span class="sd">        - consistency of stat0 and stat1</span>
<span class="sd">        </span>
<span class="sd">        :param warn: bollean optional, if True, display possible warning</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">ok</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">modelset</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span> \
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            <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">modelset</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">start</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> \
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            <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">modelset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> \
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            <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="nb">bool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
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        <span class="k">if</span> <span class="n">warn</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span>
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                <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s">&#39;Duplicated segments in StatServer&#39;</span><span class="p">)</span>
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        <span class="k">return</span> <span class="n">ok</span>
</div>
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<div class="viewcode-block" id="StatServer.merge"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.merge">[docs]</a>    <span class="k">def</span> <span class="nf">merge</span><span class="p">(</span><span class="o">*</span><span class="n">arg</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Merge a variable number of StatServers into one.</span>
<span class="sd">        If a pair segmentID is duplicated, keep ony one</span>
<span class="sd">        of them and raises a WARNING</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">line_number</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">ss</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">arg</span><span class="p">):</span>
            <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">ss</span><span class="p">,</span> <span class="n">sidekit</span><span class="o">.</span><span class="n">StatServer</span><span class="p">)</span> <span class="ow">and</span> <span class="n">ss</span><span class="o">.</span><span class="n">validate</span><span class="p">()),</span> <span class="s">&quot;Arguments must be proper StatServers&quot;</span>
            
            <span class="c"># Check consistency of StatServers (dimension of the stat0 and stat1)</span>
            <span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">dim_stat0</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">stat0</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
                <span class="n">dim_stat1</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">stat1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>            
            <span class="k">else</span><span class="p">:</span>
                <span class="k">assert</span><span class="p">(</span><span class="n">dim_stat0</span> <span class="o">==</span> <span class="n">ss</span><span class="o">.</span><span class="n">stat0</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">and</span> 
                       <span class="n">dim_stat1</span> <span class="o">==</span> <span class="n">ss</span><span class="o">.</span><span class="n">stat1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="s">&quot;Stat dimensions are not consistent&quot;</span>
    
            <span class="n">line_number</span> <span class="o">+=</span> <span class="n">ss</span><span class="o">.</span><span class="n">modelset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    
        <span class="c"># Get a list of unique modelID-segmentID    </span>
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        <span class="n">id_list</span> <span class="o">=</span> <span class="p">[]</span>
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        <span class="k">for</span> <span class="n">ss</span> <span class="ow">in</span> <span class="n">arg</span><span class="p">:</span>
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            <span class="n">id_list</span> <span class="o">+=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ss</span><span class="o">.</span><span class="n">segset</span><span class="p">)</span>
        <span class="n">id_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">id_list</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">line_number</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">id_set</span><span class="p">):</span>
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            <span class="k">print</span><span class="p">(</span><span class="s">&quot;WARNING: duplicated segmentID in input StatServers&quot;</span><span class="p">)</span>
        
        <span class="c"># Initialize the new StatServer with unique set of segmentID</span>
        <span class="n">new_stat_server</span> <span class="o">=</span> <span class="n">sidekit</span><span class="o">.</span><span class="n">StatServer</span><span class="p">()</span>
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        <span class="n">new_stat_server</span><span class="o">.</span><span class="n">modelset</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">id_set</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s">&#39;object&#39;</span><span class="p">)</span>
        <span class="n">new_stat_server</span><span class="o">.</span><span class="n">segset</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">id_set</span><span class="p">))</span>
        <span class="n">new_stat_server</span><span class="o">.</span><span class="n">start</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">id_set</span><span class="p">),</span> <span class="s">&#39;object&#39;</span><span class="p">)</span>
        <span class="n">new_stat_server</span><span class="o">.</span><span class="n">stop</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">id_set</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s">&#39;object&#39;</span><span class="p">)</span>
        <span class="n">new_stat_server</span><span class="o">.</span><span class="n">stat0</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">id_set</span><span class="p">),</span> <span class="n">dim_stat0</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
        <span class="n">new_stat_server</span><span class="o">.</span><span class="n">stat1</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">id_set</span><span class="p">),</span> <span class="n">dim_stat1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
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        <span class="k">for</span> <span class="n">ss</span> <span class="ow">in</span> <span class="n">arg</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">segment</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ss</span><span class="o">.</span><span class="n">segset</span><span class="p">):</span>
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                <span class="n">new_idx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">new_stat_server</span><span class="o">.</span><span class="n">segset</span> <span class="o">==</span> <span class="n">segment</span><span class="p">)</span>
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                <span class="n">new_stat_server</span><span class="o">.</span><span class="n">modelset</span><span class="p">[</span><span class="n">new_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">modelset</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
                <span class="n">new_stat_server</span><span class="o">.</span><span class="n">start</span><span class="p">[</span><span class="n">new_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">start</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
                <span class="n">new_stat_server</span><span class="o">.</span><span class="n">stop</span><span class="p">[</span><span class="n">new_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">stop</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
                <span class="n">new_stat_server</span><span class="o">.</span><span class="n">stat0</span><span class="p">[</span><span class="n">new_idx</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">stat0</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="p">:]</span>
                <span class="n">new_stat_server</span><span class="o">.</span><span class="n">stat1</span><span class="p">[</span><span class="n">new_idx</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">stat1</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="p">:]</span>
                
        <span class="k">assert</span><span class="p">(</span><span class="n">new_stat_server</span><span class="o">.</span><span class="n">validate</span><span class="p">()),</span> <span class="s">&quot;Problem in StatServer Merging&quot;</span>
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        <span class="k">return</span> <span class="n">new_stat_server</span>
</div>
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    <span class="nd">@staticmethod</span>
<div class="viewcode-block" id="StatServer.read"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.read">[docs]</a>    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="n">statserver_file_name</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s">&#39;&#39;</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Read StatServer in hdf5 format</span>
<span class="sd">        </span>
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<span class="sd">        :param statserver_file_name: name of the file to read from</span>
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<span class="sd">        :param prefix: prefixe of the dataset to read from in HDF5 file</span>
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<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="n">statserver_file_name</span><span class="p">,</span> <span class="s">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
            <span class="n">statserver</span> <span class="o">=</span> <span class="n">StatServer</span><span class="p">()</span>
            <span class="n">statserver</span><span class="o">.</span><span class="n">modelset</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;modelset&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">value</span>
            <span class="n">statserver</span><span class="o">.</span><span class="n">segset</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;segset&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">value</span>
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            <span class="c"># if running python 3, need a conversion to unicode</span>
            <span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
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                <span class="n">statserver</span><span class="o">.</span><span class="n">modelset</span> <span class="o">=</span> <span class="n">statserver</span><span class="o">.</span><span class="n">modelset</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">&#39;U&#39;</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
                <span class="n">statserver</span><span class="o">.</span><span class="n">segset</span> <span class="o">=</span> <span class="n">statserver</span><span class="o">.</span><span class="n">segset</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">&#39;U&#39;</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
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            <span class="n">tmpstart</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;start&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">value</span>
            <span class="n">tmpstop</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;stop&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">value</span>
            <span class="n">statserver</span><span class="o">.</span><span class="n">start</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;start&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s">&#39;|O&#39;</span><span class="p">)</span>
            <span class="n">statserver</span><span class="o">.</span><span class="n">stop</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">f</span><span class="p">[</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;stop&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s">&#39;|O&#39;</span><span class="p">)</span>
            <span class="n">statserver</span><span class="o">.</span><span class="n">start</span><span class="p">[</span><span class="n">tmpstart</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmpstart</span><span class="p">[</span><span class="n">tmpstart</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
            <span class="n">statserver</span><span class="o">.</span><span class="n">stop</span><span class="p">[</span><span class="n">tmpstop</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmpstop</span><span class="p">[</span><span class="n">tmpstop</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
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            <span class="n">statserver</span><span class="o">.</span><span class="n">stat0</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;stat0&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
            <span class="n">statserver</span><span class="o">.</span><span class="n">stat1</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;stat1&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
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            <span class="k">assert</span> <span class="n">statserver</span><span class="o">.</span><span class="n">validate</span><span class="p">(),</span> <span class="s">&quot;Error: wrong StatServer format&quot;</span>
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            <span class="k">return</span> <span class="n">statserver</span>
</div>
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    <span class="nd">@check_path_existance</span>
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    <span class="k">def</span> <span class="nf">write</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_file_name</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s">&#39;&#39;</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Write the StatServer to disk in hdf5 format.</span>
<span class="sd">        </span>
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<span class="sd">        :param output_file_name: name of the file to write in.</span>
<span class="sd">        :param prefix:</span>
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<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">validate</span><span class="p">(),</span> <span class="s">&quot;Error: wrong StatServer format&quot;</span>
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        <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="n">output_file_name</span><span class="p">,</span> <span class="s">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
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            <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;modelset&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">modelset</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">&#39;S&#39;</span><span class="p">),</span>
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                             <span class="n">maxshape</span><span class="o">=</span><span class="p">(</span><span class="bp">None</span><span class="p">,),</span>
                             <span class="n">compression</span><span class="o">=</span><span class="s">&quot;gzip&quot;</span><span class="p">,</span>
                             <span class="n">fletcher32</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
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            <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;segset&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">&#39;S&#39;</span><span class="p">),</span>
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                             <span class="n">maxshape</span><span class="o">=</span><span class="p">(</span><span class="bp">None</span><span class="p">,),</span>
                             <span class="n">compression</span><span class="o">=</span><span class="s">&quot;gzip&quot;</span><span class="p">,</span>
                             <span class="n">fletcher32</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
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            <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;stat0&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="p">,</span>
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                             <span class="n">maxshape</span><span class="o">=</span><span class="p">(</span><span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">),</span>
                             <span class="n">compression</span><span class="o">=</span><span class="s">&quot;gzip&quot;</span><span class="p">,</span>
                             <span class="n">fletcher32</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
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            <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;stat1&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="p">,</span>
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                             <span class="n">maxshape</span><span class="o">=</span><span class="p">(</span><span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">),</span>
                             <span class="n">compression</span><span class="o">=</span><span class="s">&quot;gzip&quot;</span><span class="p">,</span>
                             <span class="n">fletcher32</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

            <span class="n">start</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">start</span><span class="p">)</span>
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            <span class="n">start</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">start</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">&#39;float&#39;</span><span class="p">))]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
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            <span class="n">start</span> <span class="o">=</span> <span class="n">start</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">&#39;int8&#39;</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>

            <span class="n">stop</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="p">)</span>
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            <span class="n">stop</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">&#39;float&#39;</span><span class="p">))]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
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            <span class="n">stop</span> <span class="o">=</span> <span class="n">stop</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">&#39;int8&#39;</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
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            <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;start&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">start</span><span class="p">,</span>
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                             <span class="n">maxshape</span><span class="o">=</span><span class="p">(</span><span class="bp">None</span><span class="p">,),</span>
                             <span class="n">compression</span><span class="o">=</span><span class="s">&quot;gzip&quot;</span><span class="p">,</span>
                             <span class="n">fletcher32</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
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            <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s">&quot;stop&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">stop</span><span class="p">,</span>
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                             <span class="n">maxshape</span><span class="o">=</span><span class="p">(</span><span class="bp">None</span><span class="p">,),</span>
                             <span class="n">compression</span><span class="o">=</span><span class="s">&quot;gzip&quot;</span><span class="p">,</span>
                             <span class="n">fletcher32</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
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<div class="viewcode-block" id="StatServer.get_model_stat0"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.get_model_stat0">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_stat0</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mod_id</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Return zero-order statistics of a given model</span>
<span class="sd">        </span>
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<span class="sd">        :param mod_id: ID of the model which stat0 will be returned</span>
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<span class="sd">          </span>
<span class="sd">        :return: a matrix of zero-order statistics as a ndarray</span>
<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="n">S</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span> <span class="n">modelset</span> <span class="o">==</span> <span class="n">mod_id</span><span class="p">,</span> <span class="p">:]</span>
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        <span class="k">return</span> <span class="n">S</span>
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<div class="viewcode-block" id="StatServer.get_model_stat1"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.get_model_stat1">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_stat1</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mod_id</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Return first-order statistics of a given model</span>
<span class="sd">        </span>
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<span class="sd">        :param mod_id: string, ID of the model which stat1 will be returned</span>
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<span class="sd">          </span>
<span class="sd">        :return: a matrix of first-order statistics as a ndarray</span>
<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">modelset</span> <span class="o">==</span> <span class="n">mod_id</span><span class="p">,</span> <span class="p">:]</span>
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<div class="viewcode-block" id="StatServer.get_model_stat0_by_index"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.get_model_stat0_by_index">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_stat0_by_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mod_idx</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Return zero-order statistics of model number modIDX</span>
<span class="sd">        </span>
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<span class="sd">        :param mod_idx: integer, index of the unique model which stat0 will be</span>
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<span class="sd">            returned</span>
<span class="sd">        </span>
<span class="sd">        :return: a matrix of zero-order statistics as a ndarray</span>
<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">modelset</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">modelset</span><span class="p">)[</span><span class="n">mod_idx</span><span class="p">]),</span> <span class="p">:]</span>
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<div class="viewcode-block" id="StatServer.get_model_stat1_by_index"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.get_model_stat1_by_index">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_stat1_by_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mod_idx</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Return first-order statistics of model number modIDX</span>
<span class="sd">        </span>
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<span class="sd">        :param mod_idx: integer, index of the unique model which stat1 will be</span>
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<span class="sd">              returned</span>
<span class="sd">        </span>
<span class="sd">        :return: a matrix of first-order statistics as a ndarray</span>
<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="n">selectSeg</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">modelset</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">modelset</span><span class="p">)[</span><span class="n">mod_idx</span><span class="p">])</span>
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        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="p">[</span><span class="n">selectSeg</span><span class="p">,</span> <span class="p">:]</span>
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<div class="viewcode-block" id="StatServer.get_segment_stat0"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.get_segment_stat0">[docs]</a>    <span class="k">def</span> <span class="nf">get_segment_stat0</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">seg_id</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Return zero-order statistics of segment which ID is segID</span>
<span class="sd">        </span>
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<span class="sd">        :param seg_id: string, ID of the segment which stat0 will be</span>
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<span class="sd">              returned</span>
<span class="sd">        </span>
<span class="sd">        :return: a matrix of zero-order statistics as a ndarray</span>
<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">segset</span> <span class="o">==</span> <span class="n">seg_id</span><span class="p">,</span> <span class="p">:]</span>
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<div class="viewcode-block" id="StatServer.get_segment_stat1"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.get_segment_stat1">[docs]</a>    <span class="k">def</span> <span class="nf">get_segment_stat1</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">seg_id</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Return first-order statistics of segment which ID is segID</span>
<span class="sd">        </span>
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<span class="sd">        :param seg_id: string, ID of the segment which stat1 will be</span>
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<span class="sd">              returned</span>
<span class="sd">        </span>
<span class="sd">        :return: a matrix of first-order statistics as a ndarray</span>
<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">segset</span> <span class="o">==</span> <span class="n">seg_id</span><span class="p">,</span> <span class="p">:]</span>
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<div class="viewcode-block" id="StatServer.get_segment_stat0_by_index"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.get_segment_stat0_by_index">[docs]</a>    <span class="k">def</span> <span class="nf">get_segment_stat0_by_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">seg_idx</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Return zero-order statistics of segment number segIDX</span>
<span class="sd">        </span>
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<span class="sd">        :param seg_idx: integer, index of the unique segment which stat0 will be</span>
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<span class="sd">              returned</span>
<span class="sd">        </span>
<span class="sd">        :return: a matrix of zero-order statistics as a ndarray</span>
<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="p">[</span><span class="n">seg_idx</span><span class="p">,</span> <span class="p">:]</span>
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<div class="viewcode-block" id="StatServer.get_segment_stat1_by_index"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.get_segment_stat1_by_index">[docs]</a>    <span class="k">def</span> <span class="nf">get_segment_stat1_by_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">seg_idx</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Return first-order statistics of segment number segIDX</span>
<span class="sd">        </span>
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<span class="sd">        :param seg_idx: integer, index of the unique segment which stat1 will be</span>
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<span class="sd">              returned</span>
<span class="sd">        </span>
<span class="sd">        :return: a matrix of first-order statistics as a ndarray</span>
<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="p">[</span><span class="n">seg_idx</span><span class="p">,</span> <span class="p">:]</span>
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<div class="viewcode-block" id="StatServer.get_model_segments"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.get_model_segments">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_segments</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mod_id</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Return the list of segments belonging to model modID</span>
<span class="sd">        </span>
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<span class="sd">        :param mod_id: string, ID of the model which belonging segments will be</span>
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<span class="sd">              returned</span>
<span class="sd">        </span>
<span class="sd">        :return: a list of segments belonging to the model</span>
<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">modelset</span> <span class="o">==</span> <span class="n">mod_id</span><span class="p">]</span>
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<div class="viewcode-block" id="StatServer.get_model_segments_by_index"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.get_model_segments_by_index">[docs]</a>    <span class="k">def</span> <span class="nf">get_model_segments_by_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mod_idx</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Return the list of segments belonging to model number modIDX</span>
<span class="sd">        </span>
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<span class="sd">        :param mod_idx: index of the model which list of segments will be</span>
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<span class="sd">            returned</span>
<span class="sd">        </span>
<span class="sd">        :return: a list of segments belonging to the model</span>
<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="n">select_seg</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">modelset</span> <span class="o">==</span> <span class="n">numpy</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">modelset</span><span class="p">)[</span><span class="n">mod_idx</span><span class="p">])</span>
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        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="p">[</span><span class="n">select_seg</span><span class="p">,</span> <span class="p">:]</span>
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<div class="viewcode-block" id="StatServer.align_segments"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.align_segments">[docs]</a>    <span class="k">def</span> <span class="nf">align_segments</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">segment_list</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Align segments of the current StatServer to match a list of segment </span>
<span class="sd">            provided as input parameter. The size of the StatServer might be </span>
<span class="sd">            reduced to match the input list of segments.</span>
<span class="sd">        </span>
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<span class="sd">        :param segment_list: ndarray of strings, list of segments to match</span>
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<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="n">indx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">numpy</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">segset</span> <span class="o">==</span> <span class="n">v</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">segment_list</span><span class="p">])</span>
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        <span class="bp">self</span><span class="o">.</span><span class="n">segset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="p">[</span><span class="n">indx</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modelset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">modelset</span><span class="p">[</span><span class="n">indx</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">start</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">start</span><span class="p">[</span><span class="n">indx</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">stop</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="p">[</span><span class="n">indx</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="p">[</span><span class="n">indx</span><span class="p">,</span> <span class="p">:]</span>
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        <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="p">[</span><span class="n">indx</span><span class="p">,</span> <span class="p">:]</span>
        </div>
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<div class="viewcode-block" id="StatServer.align_models"><a class="viewcode-back" href="../statserver.html#statserver.StatServer.align_models">[docs]</a>    <span class="k">def</span> <span class="nf">align_models</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_list</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Align models of the current StatServer to match a list of models </span>
<span class="sd">            provided as input parameter. The size of the StatServer might be </span>
<span class="sd">            reduced to match the input list of models.</span>
<span class="sd">        </span>
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<span class="sd">        :param model_list: ndarray of strings, list of models to match</span>
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<span class="sd">        &quot;&quot;&quot;</span>
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        <span class="n">indx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">numpy</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">modelset</span> <span class="o">==</span> <span class="n">v</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">model_list</span><span class="p">])</span>
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        <span class="bp">self</span><span class="o">.</span><span class="n">segset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="p">[</span><span class="n">indx</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">modelset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">modelset</span><span class="p">[</span><span class="n">indx</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">start</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">start</span><span class="p">[</span><span class="n">indx</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">stop</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="p">[</span><span class="n">indx</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="p">[</span><span class="n">indx</span><span class="p">,</span> <span class="p">:]</span>
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        <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="p">[</span><span class="n">indx</span><span class="p">,</span> <span class="p">:]</span>
</div>
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    <span class="nd">@process_parallel_lists</span>
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    <span class="k">def</span> <span class="nf">accumulate_stat</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ubm</span><span class="p">,</span> <span class="n">feature_server</span><span class="p">,</span> <span class="n">seg_indices</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">channel_extension</span><span class="o">=</span><span class="p">(</span><span class="s">&quot;&quot;</span><span class="p">,</span> <span class="s">&quot;_b&quot;</span><span class="p">),</span> <span class="n">num_thread</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
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        <span class="sd">&quot;&quot;&quot;Compute statistics for a list of sessions which indices </span>
<span class="sd">            are given in segIndices.</span>
<span class="sd">        </span>
<span class="sd">        :param ubm: a Mixture object used to compute the statistics</span>
<span class="sd">        :param feature_server: featureServer object</span>
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<span class="sd">        :param seg_indices: list of indices of segments to process</span>
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<span class="sd">              if segIndices is an empty list, process all segments.</span>
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<span class="sd">        :param channel_extension: tuple of strings, extension of first and second channel for stereo files, default</span>
<span class="sd">        is (&quot;&quot;, &quot;_b&quot;)</span>
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<span class="sd">        :param num_thread: number of parallel process to run</span>
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<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ubm</span><span class="p">,</span> <span class="n">Mixture</span><span class="p">),</span> <span class="s">&#39;First parameter has to be a Mixture&#39;</span>
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        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">feature_server</span><span class="p">,</span> <span class="n">FeaturesServer</span><span class="p">),</span> <span class="s">&#39;Second parameter has to be a FeaturesServer&#39;</span>
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        <span class="k">if</span> <span class="p">(</span><span class="n">seg_indices</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">)</span> \
                <span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stat0</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> \
                <span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stat1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">stat0</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ubm</span><span class="o">.</span><span class="n">distrib_nb</span><span class="p">()),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">stat1</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ubm</span><span class="o">.</span><span class="n">sv_size</span><span class="p">()),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">STAT_TYPE</span><span class="p">)</span>
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            <span class="n">seg_indices</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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        <span class="n">feature_server</span><span class="o">.</span><span class="n">keep_all_features</span> <span class="o">=</span> <span class="bp">True</span>
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        <span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">seg_indices</span><span class="p">):</span>
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            <span class="n">logging</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s">&#39;Compute statistics for {}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="p">[</span><span class="n">idx</span><span class="p">]))</span>

            <span class="n">show</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">segset</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>

            <span class="c"># If using a FeaturesExtractor, get the channel number by checking the extension of the show</span>
            <span class="n">channel</span> <span class="o">=</span> <span class="mi">0</span>
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            <span class="k">if</span> <span class="n">feature_server</span><span class="o">.</span><span class="n">features_extractor</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span> <span class="ow">and</span> <span class="n">show</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="n">channel_extension</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
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                <span class="n">channel</span> <span class="o">=</span> <span class="mi">1</span>
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            <span class="n">show</span> <span class="o">=</span> <span class="n">show</span><span class="p">[:</span><span class="n">show</span><span class="o">.</span><span class="n">rfind</span><span class="p">(</span><span class="n">channel_extension</span><span class="p">[</span><span class="n">channel</span><span class="p">])]</span>
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            <span class="n">cep</span><span class="p">,</span> <span class="n">vad</span> <span class="o">=</span> <span class="n">feature_server</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">show</span><span class="p">,</span> <span class="n">channel</span><span class="o">=</span><span class="n">channel</span><span class="p">)</span>
            <span class="n">stop</span> <span class="o">=</span> <span class="n">vad</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="ow">is</span> <span class="bp">None</span> <span class="k">else</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stop</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">vad</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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