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api/generated/hoi.metrics.DTC.html

@@ -471,7 +471,7 @@ <h1>hoi.metrics.DTC<a class="headerlink" href="#hoi-metrics-dtc" title="Link to
 </dd>
 </dl>
 <p class="rubric">References</p>
-<p>Te Sun, 1978 <span id="id1">[<a class="reference internal" href="../../theory.html#id50" title="Han Te Sun. Nonnegative entropy measures of multivariate symmetric correlations. Information and Control, 36:133–156, 1978.">29</a>]</span></p>
+<p>Te Sun, 1978 <span id="id1">[<a class="reference internal" href="../../theory.html#id50" title="Han Te Sun. Nonnegative entropy measures of multivariate symmetric correlations. Information and Control, 36:133–156, 1978.">30</a>]</span></p>
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api/generated/hoi.metrics.GradientOinfo.html

@@ -476,7 +476,7 @@ <h1>hoi.metrics.GradientOinfo<a class="headerlink" href="#hoi-metrics-gradientoi
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 </dl>
 <p class="rubric">References</p>
-<p>Scagliarini et al., 2023 <span id="id1">[<a class="reference internal" href="../../theory.html#id37" title="Tomas Scagliarini, Davide Nuzzi, Yuri Antonacci, Luca Faes, Fernando E Rosas, Daniele Marinazzo, and Sebastiano Stramaglia. Gradients of o-information: low-order descriptors of high-order dependencies. Physical Review Research, 5(1):013025, 2023.">23</a>]</span></p>
+<p>Scagliarini et al., 2023 <span id="id1">[<a class="reference internal" href="../../theory.html#id37" title="Tomas Scagliarini, Davide Nuzzi, Yuri Antonacci, Luca Faes, Fernando E Rosas, Daniele Marinazzo, and Sebastiano Stramaglia. Gradients of o-information: low-order descriptors of high-order dependencies. Physical Review Research, 5(1):013025, 2023.">24</a>]</span></p>
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api/generated/hoi.metrics.InfoTopo.html

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 <p class="rubric">References</p>
 <p>Baudot et al., 2019 <span id="id1">[<a class="reference internal" href="../../theory.html#id33" title="Pierre Baudot, Monica Tapia, Daniel Bennequin, and Jean-Marc Goaillard. Topological information data analysis. Entropy. An International and Interdisciplinary Journal of Entropy and Information Studies, 2019. Number: 869. URL: https://www.mdpi.com/1099-4300/21/9/869, doi:10.3390/e21090869.">4</a>]</span>; Tapia et al., 2018
-<span id="id2">[<a class="reference internal" href="../../theory.html#id34" title="Mónica Tapia, Pierre Baudot, Christine Formisano-Tréziny, Martial A Dufour, Simone Temporal, Manon Lasserre, Béatrice Marquèze-Pouey, Jean Gabert, Kazuto Kobayashi, and Jean-Marc Goaillard. Neurotransmitter identity and electrophysiological phenotype are genetically coupled in midbrain dopaminergic neurons. Scientific reports, 8(1):13637, 2018.">28</a>]</span></p>
+<span id="id2">[<a class="reference internal" href="../../theory.html#id34" title="Mónica Tapia, Pierre Baudot, Christine Formisano-Tréziny, Martial A Dufour, Simone Temporal, Manon Lasserre, Béatrice Marquèze-Pouey, Jean Gabert, Kazuto Kobayashi, and Jean-Marc Goaillard. Neurotransmitter identity and electrophysiological phenotype are genetically coupled in midbrain dopaminergic neurons. Scientific reports, 8(1):13637, 2018.">29</a>]</span></p>
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api/generated/hoi.metrics.Oinfo.html

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 <p class="rubric">References</p>
-<p>Rosas et al., 2019 <span id="id1">[<a class="reference internal" href="../../theory.html#id29" title="Fernando E. Rosas, Pedro A. M. Mediano, Michael Gastpar, and Henrik J. Jensen. Quantifying high-order interdependencies via multivariate extensions of the mutual information. Physical Review E, 100(3):032305, September 2019. Publisher: American Physical Society. URL: https://link.aps.org/doi/10.1103/PhysRevE.100.032305 (visited on 2022-12-17), doi:10.1103/PhysRevE.100.032305.">22</a>]</span></p>
+<p>Rosas et al., 2019 <span id="id1">[<a class="reference internal" href="../../theory.html#id29" title="Fernando E. Rosas, Pedro A. M. Mediano, Michael Gastpar, and Henrik J. Jensen. Quantifying high-order interdependencies via multivariate extensions of the mutual information. Physical Review E, 100(3):032305, September 2019. Publisher: American Physical Society. URL: https://link.aps.org/doi/10.1103/PhysRevE.100.032305 (visited on 2022-12-17), doi:10.1103/PhysRevE.100.032305.">23</a>]</span></p>
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api/generated/hoi.metrics.RSI.html

@@ -459,7 +459,7 @@ <h1>hoi.metrics.RSI<a class="headerlink" href="#hoi-metrics-rsi" title="Link to
 <p>The RSI has been referred to as the SynSum <span id="id1">[<a class="reference internal" href="../../theory.html#id39" title="Amir Globerson, Eran Stark, Eilon Vaadia, and Naftali Tishby. The minimum information principle and its application to neural code analysis. Proceedings of the National Academy of Sciences, 106(9):3490–3495, 2009.">9</a>]</span>,
 the WholeMinusSum synergy <span id="id2">[<a class="reference internal" href="../../theory.html#id41" title="Virgil Griffith and Christof Koch. Quantifying synergistic mutual information. In Guided self-organization: inception, pages 159–190. Springer, 2014.">11</a>]</span>, and
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-referred to as the redundancy <span id="id3">[<a class="reference internal" href="../../theory.html#id42" title="Elad Schneidman, Susanne Still, Michael J Berry, William Bialek, and others. Network information and connected correlations. Physical review letters, 91(23):238701, 2003.">24</a>]</span>.</p>
+referred to as the redundancy <span id="id3">[<a class="reference internal" href="../../theory.html#id42" title="Elad Schneidman, Susanne Still, Michael J Berry, William Bialek, and others. Network information and connected correlations. Physical review letters, 91(23):238701, 2003.">25</a>]</span>.</p>
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@@ -484,7 +484,7 @@ <h1>hoi.metrics.RSI<a class="headerlink" href="#hoi-metrics-rsi" title="Link to
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 <p class="rubric">References</p>
 <p>Chechik et al. 2001 <span id="id4">[<a class="reference internal" href="../../theory.html#id35" title="Gal Chechik, Amir Globerson, M Anderson, E Young, Israel Nelken, and Naftali Tishby. Group redundancy measures reveal redundancy reduction in the auditory pathway. Advances in neural information processing systems, 2001.">5</a>]</span>; Timme et al., 2014
-<span id="id5">[<a class="reference internal" href="../../theory.html#id38" title="Nicholas Timme, Wesley Alford, Benjamin Flecker, and John M Beggs. Synergy, redundancy, and multivariate information measures: an experimentalist’s perspective. Journal of computational neuroscience, 36:119–140, 2014.">30</a>]</span></p>
+<span id="id5">[<a class="reference internal" href="../../theory.html#id38" title="Nicholas Timme, Wesley Alford, Benjamin Flecker, and John M Beggs. Synergy, redundancy, and multivariate information measures: an experimentalist’s perspective. Journal of computational neuroscience, 36:119–140, 2014.">31</a>]</span></p>
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api/generated/hoi.metrics.TC.html

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 </dd>
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 <p class="rubric">References</p>
-<p>Watabe, 1960 <span id="id1">[<a class="reference internal" href="../../theory.html#id43" title="Satosi Watanabe. Information theoretical analysis of multivariate correlation. IBM Journal of research and development, 4(1):66–82, 1960.">34</a>]</span></p>
+<p>Watabe, 1960 <span id="id1">[<a class="reference internal" href="../../theory.html#id43" title="Satosi Watanabe. Information theoretical analysis of multivariate correlation. IBM Journal of research and development, 4(1):66–82, 1960.">35</a>]</span></p>
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auto_examples/it/plot_entropies.html

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auto_examples/it/plot_entropies_mvar.html

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auto_examples/it/plot_mi.html

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auto_examples/it/plot_tutorial_core.html

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auto_examples/it/sg_execution_times.html

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auto_examples/metrics/plot_infotopo.html

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 </pre></div>
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 <p>Now we can plot the landscape. This landscape show the values of HOI for
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+ 57%|█████▋    | Entropy H(4):  4/7 [00:00&lt;00:00,    8.51it/s]
+ 71%|███████▏  | Entropy H(5):  5/7 [00:00&lt;00:00,    8.79it/s]
+ 86%|████████▌ | Entropy H(6):  6/7 [00:00&lt;00:00,    9.00it/s]
+100%|██████████| Entropy H(7):  7/7 [00:00&lt;00:00,    9.16it/s]
 
 
   0%|          |  0/99 [00:00&lt;?,       ?it/s]
-100%|██████████| Mutual information:  99/99 [00:00&lt;00:00, 147981.50it/s]
+100%|██████████| Mutual information:  99/99 [00:00&lt;00:00, 149796.57it/s]
 </pre></div>
 </div>
 <p>Again, we can plot the landscape. This time, as we can see, there’s no more
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