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<spanclass="sig-prename descclassname"><spanclass="pre">results.</span></span><spanclass="sig-name descname"><spanclass="pre">bat</span></span><aclass="headerlink" href="#MDAnalysis.analysis.bat.BAT.results.bat" title="Link to this definition"></a></dt>
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<dd><p>Contains the time series of the Bond-Angle-Torsion coordinates as a
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(nframes, 3N) <aclass="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.3)"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">numpy.ndarray</span></code></a> array. Each row corresponds to
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(nframes, 3N) <aclass="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.4)"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">numpy.ndarray</span></code></a> array. Each row corresponds to
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a frame in the trajectory. In each column, the first six elements
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describe external degrees of freedom. The first three are the center
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of mass of the initial atom. The next three specify the external angles
<ddclass="field-odd"><p><strong>bat_frame</strong> (<aclass="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.3)"><em>numpy.ndarray</em></a>) – an array with dimensions (3N,) with external then internal
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<ddclass="field-odd"><p><strong>bat_frame</strong> (<aclass="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.4)"><em>numpy.ndarray</em></a>) – an array with dimensions (3N,) with external then internal
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degrees of freedom based on the root atoms, followed by the bond,
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angle, and (proper and improper) torsion coordinates.</p>
<spanclass="sig-prename descclassname"><spanclass="pre">MDAnalysis.analysis.contacts.</span></span><spanclass="sig-name descname"><spanclass="pre">hard_cut_q</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">r</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">cutoff</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal" href="../../_modules/MDAnalysis/analysis/contacts.html#hard_cut_q"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink" href="#MDAnalysis.analysis.contacts.hard_cut_q" title="Link to this definition"></a></dt>
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<dd><p>Calculate fraction of native contacts <em>Q</em> for a hard cut off.</p>
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<p>The cutoff can either be a float or a <aclass="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.3)"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">ndarray</span></code></a> of the same
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<p>The cutoff can either be a float or a <aclass="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.4)"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">ndarray</span></code></a> of the same
<li><p><strong>grid</strong> (<em>array_like</em>) – histogram or density, typically a <aclass="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.3)"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">numpy.ndarray</span></code></a></p></li>
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<li><p><strong>grid</strong> (<em>array_like</em>) – histogram or density, typically a <aclass="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.4)"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">numpy.ndarray</span></code></a></p></li>
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<li><p><strong>edges</strong> (<aclass="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.14)"><em>list</em></a>) – list of arrays, the lower and upper bin edges along the axes</p></li>
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<li><p><strong>parameters</strong> (<aclass="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.14)"><em>dict</em></a>) – <p>dictionary of class parameters; saved with
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<aclass="reference internal" href="#MDAnalysis.analysis.density.Density.save" title="MDAnalysis.analysis.density.Density.save"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">Density.save()</span></code></a>. The following keys are meaningful to
<spanclass="sig-name descname"><spanclass="pre">edges</span></span><aclass="headerlink" href="#MDAnalysis.analysis.density.Density.edges" title="Link to this definition"></a></dt>
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<dd><p>The boundaries of each cell in <cite>grid</cite> along all axes (equivalent
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to what <aclass="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.histogramdd.html#numpy.histogramdd" title="(in NumPy v2.3)"><codeclass="xref py py-func docutils literal notranslate"><spanclass="pre">numpy.histogramdd()</span></code></a> returns).</p>
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to what <aclass="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.histogramdd.html#numpy.histogramdd" title="(in NumPy v2.4)"><codeclass="xref py py-func docutils literal notranslate"><spanclass="pre">numpy.histogramdd()</span></code></a> returns).</p>
<p>If the input histogram consists of counts per cell then the
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<aclass="reference internal" href="#MDAnalysis.analysis.density.Density.make_density" title="MDAnalysis.analysis.density.Density.make_density"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">Density.make_density()</span></code></a> method converts the grid to a physical density. For
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a probability density, divide it by <codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">Density.grid.sum()</span></code> or use <codeclass="docutils literal notranslate"><spanclass="pre">density=True</span></code>
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right away in <aclass="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.histogramdd.html#numpy.histogramdd" title="(in NumPy v2.3)"><codeclass="xref py py-func docutils literal notranslate"><spanclass="pre">histogramdd()</span></code></a>.</p>
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right away in <aclass="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.histogramdd.html#numpy.histogramdd" title="(in NumPy v2.4)"><codeclass="xref py py-func docutils literal notranslate"><spanclass="pre">histogramdd()</span></code></a>.</p>
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<p>The user <em>should</em> set the <em>parameters</em> keyword (see docs for the
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constructor); in particular, if the data are already a density, one must
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set <codeclass="docutils literal notranslate"><spanclass="pre">isDensity=True</span></code> because there is no reliable way to detect if
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