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8 changes: 8 additions & 0 deletions 010_api_reference.html
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<li class="toctree-l1 current"><a class="current reference internal" href="#">API Reference</a><ul>
<li class="toctree-l2"><a class="reference internal" href="_autosummary/ibllib.html">ibllib</a></li>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/iblatlas.html#module-iblatlas" title="iblatlas"><code class="xref py py-obj docutils literal notranslate"><span class="pre">iblatlas</span></code></a></p></td>
<td><p>A package for working with brain atlases.</p></td>
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<td><p>(DEPRECATED) The psychofit toolbox contains tools to fit two-alternative psychometric data. The fitting is done using maximal likelihood estimation: one assumes that the responses of the subject are given by a binomial distribution whose mean is given by the psychometric function. The data can be expressed in fraction correct (from .5 to 1) or in fraction of one specific choice (from 0 to 1). To fit them you can use these functions: - weibull50: Weibull function from 0.5 to 1, with lapse rate - weibull: Weibull function from 0 to 1, with lapse rate - erf_psycho: erf function from 0 to 1, with lapse rate - erf_psycho_2gammas: erf function from 0 to 1, with two lapse rates Functions in the toolbox are: - mle_fit_psycho: Maximumum likelihood fit of psychometric function - neg_likelihood: Negative likelihood of a psychometric function For more info, see: - Examples: Examples of use of psychofit toolbox Matteo Carandini, 2000-2015.</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="brainbox.behavior.training.html#module-brainbox.behavior.training" title="brainbox.behavior.training"><code class="xref py py-obj docutils literal notranslate"><span class="pre">brainbox.behavior.training</span></code></a></p></td>
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<td><p>Computing and testing IBL training status criteria.</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="brainbox.behavior.wheel.html#module-brainbox.behavior.wheel" title="brainbox.behavior.wheel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">brainbox.behavior.wheel</span></code></a></p></td>
<td><p>Set of functions to handle wheel data.</p></td>
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<section id="brainbox-behavior-training-trials-keys">
<h1>brainbox.behavior.training.TRIALS_KEYS<a class="headerlink" href="#brainbox-behavior-training-trials-keys" title="Link to this heading"></a></h1>
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<dt class="sig sig-object py" id="brainbox.behavior.training.TRIALS_KEYS">
<span class="sig-name descname"><span class="pre">TRIALS_KEYS</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">['contrastLeft',</span> <span class="pre">'contrastRight',</span> <span class="pre">'feedbackType',</span> <span class="pre">'probabilityLeft',</span> <span class="pre">'choice',</span> <span class="pre">'response_times',</span> <span class="pre">'stimOn_times']</span></em><a class="headerlink" href="#brainbox.behavior.training.TRIALS_KEYS" title="Link to this definition"></a></dt>
<dd><p>The required keys in the trials object for computing training status.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>list of str</p>
</dd>
</dl>
</dd></dl>

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