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[READ-ONLY] Mirror of https://github.com/emilyhunt/ocelot. A toolbox for working with observations of star clusters. ocelot-docs.org
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var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 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0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 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white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: 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.jp-RenderedMermaid-Summary > pre { display: inline-block; white-space: normal; }</style><!-- End of mermaid configuration --><div class="jupyter-wrapper"><div class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light"><div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"></div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"><h1 id="binary-star-models">Binary star models<a class="anchor-link" href="#binary-star-models">¶</a></h1><p>ocelot includes a number of models, one of which is a binary star model. Let's look at what it contains, as an example!</p></div></div></div></div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"><div class="CodeMirror cm-s-jupyter"><div class="zeroclipboard-container"><clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-1"><div><span class="notice" hidden="">Copied!</span><svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20"><path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path></svg></div></clipboard-copy></div><div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span><span class="kn">from</span><span class="w"> </span><span class="nn">ocelot.model.binaries</span><span class="w"> </span><span class="kn">import</span> <span class="n">moe_distefano_17</span><span class="p">,</span> <span class="n">MoeDiStefanoMultiplicityRelation</span></pre></div><div class="clipboard-copy-txt" id="cell-1">import numpy as npimport matplotlib.pyplot as pltfrom ocelot.model.binaries import moe_distefano_17, MoeDiStefanoMultiplicityRelation</div></div></div></div></div></div></div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">In [27]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"><div class="CodeMirror cm-s-jupyter"><div class="zeroclipboard-container"><clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-2"><div><span class="notice" hidden="">Copied!</span><svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20"><path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path></svg></div></clipboard-copy></div><div class="highlight-ipynb hl-python"><pre><span></span><span class="n">masses</span><span class="p">,</span> <span class="n">periods</span><span class="p">,</span> <span class="n">periods_bounded</span><span class="p">,</span> <span class="n">frequencies</span> <span class="o">=</span> <span class="p">(</span> <span class="n">moe_distefano_17</span><span class="o">.</span><span class="n">mass</span><span class="p">,</span> <span class="n">moe_distefano_17</span><span class="o">.</span><span class="n">mass_ratio_periods</span><span class="p">,</span> <span class="n">moe_distefano_17</span><span class="o">.</span><span class="n">mass_ratio_periods_with_bounds</span><span class="p">,</span> <span class="n">moe_distefano_17</span><span class="o">.</span><span class="n">period_frequencies</span><span class="p">,</span><span class="p">)</span></pre></div><div class="clipboard-copy-txt" id="cell-2">masses, periods, periods_bounded, frequencies = ( moe_distefano_17.mass, moe_distefano_17.mass_ratio_periods, moe_distefano_17.mass_ratio_periods_with_bounds, moe_distefano_17.period_frequencies,)</div></div></div></div></div></div></div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"><div class="CodeMirror cm-s-jupyter"><div class="zeroclipboard-container"><clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-3"><div><span class="notice" hidden="">Copied!</span><svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20"><path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path></svg></div></clipboard-copy></div><div class="highlight-ipynb hl-python"><pre><span></span><span class="n">moe_distefano_17</span><span class="o">.</span><span class="n">_get_mass_ratio_distribution_parameters</span><span class="p">(</span> <span class="n">masses</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="n">masses</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span><span class="p">)</span></pre></div><div class="clipboard-copy-txt" id="cell-3">moe_distefano_17._get_mass_ratio_distribution_parameters( masses, np.full_like(masses, 8))</div></div></div></div></div><div class="jp-Cell-outputWrapper"><div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"></div><div class="jp-OutputArea jp-Cell-outputArea"><div class="jp-OutputArea-child jp-OutputArea-executeResult"><div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div><div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0"><pre>(array([-1.1, -1.1, -1.1, -2. , -2. , -2. , -2. ]), array([ 0.3, 0.3, 0.3, -1. , -1.5, -1.5, -1.5]), array([0.015, 0.015, 0.015, 0. , 0. , 0. , 0. ]))</pre></div></div></div></div></div></div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"><div class="CodeMirror cm-s-jupyter"><div class="zeroclipboard-container"><clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-4"><div><span class="notice" hidden="">Copied!</span><svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20"><path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path></svg></div></clipboard-copy></div><div class="highlight-ipynb hl-python"><pre><span></span><span class="n">x_pdf</span><span class="p">,</span> <span class="n">y_pdf</span> <span class="o">=</span> <span class="n">moe_distefano_17</span><span class="o">.</span><span class="n">_get_period_pdf</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="mf">16.0</span><span class="p">))</span><span class="n">ppf</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="n">moe_distefano_17</span><span class="o">.</span><span class="n">_get_period_percentile_point_function</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="mi">16</span><span class="p">]))</span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">ppf</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">x</span><span class="p">))</span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x_pdf</span><span class="p">,</span> <span class="n">y_pdf</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span></pre></div><div class="clipboard-copy-txt" id="cell-4">x_pdf, y_pdf = moe_distefano_17._get_period_pdf(np.atleast_1d(16.0))ppf, x = moe_distefano_17._get_period_percentile_point_function(np.asarray([16]))plt.plot(x, np.gradient(ppf.flatten(), x))plt.plot(x_pdf, y_pdf.flatten())</div></div></div></div></div><div class="jp-Cell-outputWrapper"><div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"></div><div class="jp-OutputArea jp-Cell-outputArea"><div class="jp-OutputArea-child jp-OutputArea-executeResult"><div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div><div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0"><pre>[<matplotlib.lines.Line2D at 0x7694def42ed0>]</pre></div></div><div class="jp-OutputArea-child"><div class="jp-OutputPrompt jp-OutputArea-prompt"></div><div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"><img alt="No description has been provided for this image" class="" 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"/></div></div></div></div></div></div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"><div class="CodeMirror cm-s-jupyter"><div class="zeroclipboard-container"><clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-5"><div><span class="notice" hidden="">Copied!</span><svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20"><path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path></svg></div></clipboard-copy></div><div class="highlight-ipynb hl-python"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">moe_distefano_17</span><span class="o">.</span><span class="n">_get_period_pdf</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="mf">16.0</span><span class="p">))</span><span class="n">ppf</span><span class="p">,</span> <span class="n">y1</span> <span class="o">=</span> <span class="n">moe_distefano_17</span><span class="o">.</span><span class="n">_get_period_percentile_point_function</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="mf">16.0</span><span class="p">))</span><span class="n">ppf</span> <span class="o">=</span> <span class="n">ppf</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="n">pp</span> <span class="o">=</span> <span class="n">moe_distefano_17</span><span class="o">.</span><span class="n">_sample_period</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="mi">100000</span><span class="p">,</span> <span class="mi">16</span><span class="p">))</span><span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">pp</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">ppf</span> <span class="o">/</span> <span class="mi">5</span><span class="p">)</span><span class="c1"># plt.plot(x, np.gradient(ppf, x))</span></pre></div><div class="clipboard-copy-txt" id="cell-5">x, y = moe_distefano_17._get_period_pdf(np.atleast_1d(16.0))ppf, y1 = moe_distefano_17._get_period_percentile_point_function(np.atleast_1d(16.0))ppf = ppf.flatten()pp = moe_distefano_17._sample_period(np.full(100000, 16))_ = plt.hist(pp, bins=25, density=True)plt.plot(x, y.flatten())plt.plot(x, ppf / 5)# plt.plot(x, np.gradient(ppf, x))</div></div></div></div></div><div class="jp-Cell-outputWrapper"><div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"></div><div class="jp-OutputArea jp-Cell-outputArea"><div class="jp-OutputArea-child jp-OutputArea-executeResult"><div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div><div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0"><pre>[<matplotlib.lines.Line2D at 0x7694de513d40>]</pre></div></div><div class="jp-OutputArea-child"><div class="jp-OutputPrompt jp-OutputArea-prompt"></div><div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"><img alt="No description has been provided for this image" class="" 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"/></div></div></div></div></div></div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"><div class="CodeMirror cm-s-jupyter"><div class="zeroclipboard-container"><clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-6"><div><span class="notice" hidden="">Copied!</span><svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20"><path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path></svg></div></clipboard-copy></div><div class="highlight-ipynb hl-python"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">moe_distefano_17</span><span class="o">.</span><span class="n">MoeDiStefanoMultiplicityRelation</span><span class="p">()</span></pre></div><div class="clipboard-copy-txt" id="cell-6">model = moe_distefano_17.MoeDiStefanoMultiplicityRelation()</div></div></div></div></div></div></div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"><div class="CodeMirror cm-s-jupyter"><div class="zeroclipboard-container"><clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-7"><div><span class="notice" hidden="">Copied!</span><svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20"><path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path></svg></div></clipboard-copy></div><div class="highlight-ipynb hl-python"><pre><span></span><span class="n">masses</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">100000</span><span class="p">)</span></pre></div><div class="clipboard-copy-txt" id="cell-7">masses = np.random.uniform(low=0.1, high=20, size=100000)</div></div></div></div></div></div></div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"><div class="CodeMirror cm-s-jupyter"><div class="zeroclipboard-container"><clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-8"><div><span class="notice" hidden="">Copied!</span><svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20"><path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path></svg></div></clipboard-copy></div><div class="highlight-ipynb hl-python"><pre><span></span><span class="n">mass_ratio</span><span class="p">,</span> <span class="n">period</span><span class="p">,</span> <span class="n">eccentricity</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">random_binary</span><span class="p">(</span><span class="n">masses</span><span class="p">)</span></pre></div><div class="clipboard-copy-txt" id="cell-8">mass_ratio, period, eccentricity = model.random_binary(masses)</div></div></div></div></div></div></div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"><div class="CodeMirror cm-s-jupyter"><div class="zeroclipboard-container"><clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-9"><div><span class="notice" hidden="">Copied!</span><svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20"><path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path></svg></div></clipboard-copy></div><div class="highlight-ipynb hl-python"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist2d</span><span class="p">(</span><span class="n">mass_ratio</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">period</span><span class="p">))</span></pre></div><div class="clipboard-copy-txt" id="cell-9">_ = plt.hist2d(mass_ratio, np.log10(period))</div></div></div></div></div><div class="jp-Cell-outputWrapper"><div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"></div><div class="jp-OutputArea jp-Cell-outputArea"><div class="jp-OutputArea-child"><div class="jp-OutputPrompt jp-OutputArea-prompt"></div><div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"><img alt="No description has been provided for this 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3qEJKbE10apVPQESWSthX2LT6Y0MjW+vvcc1lz0CDUb2Te+HabCtwAAYJIQFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIJlcYdHb2xvvfve7o729PebMmRPvf//7Y+vWrfWaDQBoMLnC4gc/+EH09PTExo0b41/+5V9iaGgo3vve98aePXvqNR8A0EBa8hy8bt26/a6vWbMm5syZE5s2bYrzzjsv6WAAQOPJFRa/bmBgICIiDj744AMeU6lUolKpjF0fHBys5ZQAwCRW9Ys3R0dH49prr42FCxfGiSeeeMDjent7o6OjY+zS3d1d7SkBgEmu6rDo6emJJ554Ivr6+t7wuKVLl8bAwMDYpb+/v9pTAgCTXFVPhVxzzTXx3e9+Nx566KE44ogj3vDYcrkc5XK5quEAgMaSKyyyLIs/+qM/invuuSc2bNgQxxxzTL3mAgAaUK6w6Onpibvvvjv++Z//Odrb2+P555+PiIiOjo6YPn16XQYEABpHrtdYrFixIgYGBuKCCy6Izs7Oscs//dM/1Ws+AKCB5H4qBADgQLxXCACQjLAAAJIRFgBAMsICAEimpvcKqUXHjtFomTZa1OlrNjqt6AnSKI0UPUHtpu0aLnqEJJorjfv18KtKw1PgRd6lUtETpDEV9mhpLnqCJF49rOgJajeyd3zHecQCAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSyR0WDz30UFx++eXR1dUVpVIp7r333jqMBQA0otxhsWfPnjj55JPjjjvuqMc8AEADa8n7AZdccklccskl9ZgFAGhwucMir0qlEpVKZez64OBgvU8JABSk7i/e7O3tjY6OjrFLd3d3vU8JABSk7o9YLF26NK6//vqx64ODg9Hd3R3ll4eipaW53qevm9HWqfEDNaPTSkWPULPmfaNFj5BE09DU2AP4DRr/W+24d6h7WJTL5SiXy/U+DQAwCUyN/3YDAJNC7kcsdu/eHdu2bRu7vnPnzti8eXMcfPDBceSRRyYdDgBoLLnD4tFHH40LL7xw7Pprr59YsmRJrFmzJtlgAEDjyR0WF1xwQWRZVo9ZAIAG5zUWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGRaJvqEWZZFRMTwcGWiT53UaNPUaLLRKBU9Qs1Kw8NFj5BE0/Bo0SOkMZoVPUHNmkZGih4hidJI439OlUamxtf3yN69RY9Qs9HKf+/w2r/jB1LK3uyIxJ599tno7u6eyFMCAIn09/fHEUccccA/n/CwGB0djeeeey7a29ujVGr8/y3/qsHBweju7o7+/v6YMWNG0eMkZbfGZLfGZLfGNJV3i/jvRyp27doVXV1d0fQGj9pP+FMhTU1Nb1g6U8GMGTOm5CdVhN0ald0ak90a01TeraOj402PmRovFAAAJgVhAQAkIywSKpfLceONN0a5XC56lOTs1pjs1pjs1pim8m55TPiLNwGAqcsjFgBAMsICAEhGWAAAyQgLACAZYZHTHXfcEUcffXS0tbXFWWedFT/+8Y8PeOzKlSvj3HPPjVmzZsWsWbNi0aJFb3h80fLstnbt2jjjjDNi5syZcdBBB8Upp5wS//AP/zCB0+aTZ7df1dfXF6VSKd7//vfXd8Aa5NltzZo1USqV9ru0tbVN4LT55L3fXnnllejp6YnOzs4ol8vxjne8I+67774JmjafPLtdcMEFr7vfSqVSXHbZZRM48fjlvd9uu+22OO6442L69OnR3d0d1113XeydpO+tkWe3oaGhuOmmm2LevHnR1tYWJ598cqxbt24Cpy1Ixrj19fVlra2t2de+9rVsy5Yt2Sc/+cls5syZ2QsvvPAbj//IRz6S3XHHHdl//Md/ZE899VR21VVXZR0dHdmzzz47wZO/uby7/du//Vu2du3a7Mknn8y2bduW3XbbbVlzc3O2bt26CZ78zeXd7TU7d+7MDj/88Ozcc8/NrrjiiokZNqe8u61evTqbMWNG9otf/GLs8vzzz0/w1OOTd7dKpZKdccYZ2aWXXpo9/PDD2c6dO7MNGzZkmzdvnuDJ31ze3V566aX97rMnnngia25uzlavXj2xg49D3t3uuuuurFwuZ3fddVe2c+fObP369VlnZ2d23XXXTfDkby7vbn/6p3+adXV1Zd/73vey7du3Z3feeWfW1taWPfbYYxM8+cQSFjmceeaZWU9Pz9j1kZGRrKurK+vt7R3Xxw8PD2ft7e3Z17/+9XqNWLVad8uyLDv11FOzP//zP6/HeDWpZrfh4eHs7LPPzv7+7/8+W7JkyaQNi7y7rV69Ouvo6Jig6WqTd7cVK1Zkc+fOzfbt2zdRI1at1q+3v/mbv8na29uz3bt312vEquXdraenJ7vooov2u+3666/PFi5cWNc5q5F3t87OzuxLX/rSfrf97u/+brZ48eK6zlk0T4WM0759+2LTpk2xaNGisduamppi0aJF8e///u/j+jteffXVGBoaioMPPrheY1al1t2yLIsHH3wwtm7dGuedd149R82t2t1uuummmDNnTnziE5+YiDGrUu1uu3fvjqOOOiq6u7vjiiuuiC1btkzEuLlUs9t3vvOdWLBgQfT09MShhx4aJ554YixbtixGJtlboKf4XrJq1ar40Ic+FAcddFC9xqxKNbudffbZsWnTprGnFHbs2BH33XdfXHrppRMy83hVs1ulUnndU43Tp0+Phx9+uK6zFm3C34SsUf3yl7+MkZGROPTQQ/e7/dBDD42f/OQn4/o7PvvZz0ZXV9d+n5iTQbW7DQwMxOGHHx6VSiWam5vjzjvvjIsvvrje4+ZSzW4PP/xwrFq1KjZv3jwBE1avmt2OO+64+NrXvhbz58+PgYGBuPXWW+Pss8+OLVu2TKo3B6xmtx07dsS//uu/xuLFi+O+++6Lbdu2xac//ekYGhqKG2+8cSLGHpdav5f8+Mc/jieeeCJWrVpVrxGrVs1uH/nIR+KXv/xlnHPOOZFlWQwPD8cf/uEfxp/92Z9NxMjjVs1uv/VbvxVf/OIX47zzzot58+bFgw8+GGvXrp10sZuaRywmyPLly6Ovry/uueeeSf1iuTza29tj8+bN8cgjj8Qtt9wS119/fWzYsKHosWqya9euuPLKK2PlypXxtre9rehxkluwYEF87GMfi1NOOSXOP//8WLt2bcyePTu+8pWvFD1azUZHR2POnDnx1a9+NU4//fT44Ac/GDfccEN8+ctfLnq0pFatWhUnnXRSnHnmmUWPksSGDRti2bJlceedd8Zjjz0Wa9euje9973tx8803Fz1azW6//fZ4+9vfHscff3y0trbGNddcEx//+Mff8C3HpwKPWIzT2972tmhubo4XXnhhv9tfeOGFOOyww97wY2+99dZYvnx5PPDAAzF//vx6jlmVandramqKY489NiIiTjnllHjqqaeit7c3LrjggnqOm0ve3bZv3x4/+9nP4vLLLx+7bXR0NCIiWlpaYuvWrTFv3rz6Dj1OtXxOvmbatGlx6qmnxrZt2+oxYtWq2a2zszOmTZsWzc3NY7edcMIJ8fzzz8e+ffuitbW1rjOPVy332549e6Kvry9uuummeo5YtWp2+4u/+Iu48sor4w/+4A8iIuKkk06KPXv2xNVXXx033HDDpPlHuJrdZs+eHffee2/s3bs3Xnrppejq6orPfe5zMXfu3IkYuTCT4x5rAK2trXH66afHgw8+OHbb6OhoPPjgg7FgwYIDftxf//Vfx8033xzr1q2LM844YyJGza3a3X7d6OhoVCqVeoxYtby7HX/88fH444/H5s2bxy6//du/HRdeeGFs3rw5uru7J3L8N5TifhsZGYnHH388Ojs76zVmVarZbeHChbFt27axEIyIePrpp6Ozs3PSREVEbffbN7/5zahUKvHRj3603mNWpZrdXn311dfFw2txmE2it7Kq5X5ra2uLww8/PIaHh+Pb3/52XHHFFfUet1gFv3i0ofT19WXlcjlbs2ZN9uSTT2ZXX311NnPmzLEf17vyyiuzz33uc2PHL1++PGttbc2+9a1v7fejYrt27SpqhQPKu9uyZcuy+++/P9u+fXv25JNPZrfeemvW0tKSrVy5sqgVDijvbr9uMv9USN7dvvCFL2Tr16/Ptm/fnm3atCn70Ic+lLW1tWVbtmwpaoUDyrvbM888k7W3t2fXXHNNtnXr1uy73/1uNmfOnOwv//Ivi1rhgKr9nDznnHOyD37wgxM9bi55d7vxxhuz9vb27Bvf+Ea2Y8eO7P7778/mzZuX/d7v/V5RKxxQ3t02btyYffvb3862b9+ePfTQQ9lFF12UHXPMMdnLL79c0AYTQ1jk9Hd/93fZkUcembW2tmZnnnlmtnHjxrE/O//887MlS5aMXT/qqKOyiHjd5cYbb5z4wcchz2433HBDduyxx2ZtbW3ZrFmzsgULFmR9fX0FTD0+eXb7dZM5LLIs327XXnvt2LGHHnpodumll07qn6nPe7/98Ic/zM4666ysXC5nc+fOzW655ZZseHh4gqcen7y7/eQnP8kiIrv//vsneNL88uw2NDSUff7zn8/mzZuXtbW1Zd3d3dmnP/3pSfuPb57dNmzYkJ1wwglZuVzODjnkkOzKK6/Mfv7znxcw9cTytukAQDJeYwEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkvl/bS8ggSS2/bAAAAAASUVORK5CYII="/></div></div></div></div></div></div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">In [49]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"><div class="CodeMirror cm-s-jupyter"><div class="zeroclipboard-container"><clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-10"><div><span class="notice" hidden="">Copied!</span><svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20"><path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path></svg></div></clipboard-copy></div><div class="highlight-ipynb hl-python"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist2d</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">period</span><span class="p">),</span> <span class="n">eccentricity</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span></pre></div><div class="clipboard-copy-txt" id="cell-10">_ = plt.hist2d(np.log10(period), eccentricity, bins=20)</div></div></div></div></div><div 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"/></div></div></div></div></div></div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">In [52]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"><div class="CodeMirror cm-s-jupyter"><div class="zeroclipboard-container"><clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-11"><div><span class="notice" hidden="">Copied!</span><svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20"><path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path></svg></div></clipboard-copy></div><div class="highlight-ipynb hl-python"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span><span class="n">model</span><span class="o">.</span><span class="n">random_binary</span><span class="p">(</span><span class="n">masses</span><span class="p">[:</span><span class="mi">10</span><span class="p">],</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span></pre></div><div class="clipboard-copy-txt" id="cell-11">np.random.seed(42)model.random_binary(masses[:10], seed=42)</div></div></div></div></div><div class="jp-Cell-outputWrapper"><div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"></div><div class="jp-OutputArea jp-Cell-outputArea"><div class="jp-OutputArea-child jp-OutputArea-executeResult"><div class="jp-OutputPrompt jp-OutputArea-prompt">Out[52]:</div><div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0"><pre>(array([0.94394365, 0.66991325, 0.35863824, 0.26508398, 0.98930322, 0.20365471, 0.28866418]), array([3.17552947e+06, 3.92321260e+04, 1.12824477e+07, 3.38937128e+05, 1.20609031e+01, 5.05059564e+07, 3.46602403e+05]), array([0.87715898, 0.33014873, 0.29591527, 0.32269941, 0.33187267, 0.69890144, 0.62707649]))</pre></div></div></div></div></div></div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell jp-CodeCell jp-Notebook-cell"><div class="jp-Cell-inputWrapper" tabindex="0"><div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"></div><div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">In [53]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"><div class="CodeMirror cm-s-jupyter"><div class="zeroclipboard-container"><clipboard-copy ,="" aria-label="Copy to Clipboard" for="cell-12"><div><span class="notice" hidden="">Copied!</span><svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20"><path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path></svg></div></clipboard-copy></div><div class="highlight-ipynb hl-python"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span><span class="n">model</span><span class="o">.</span><span class="n">random_binary</span><span class="p">(</span><span class="n">masses</span><span class="p">[:</span><span class="mi">10</span><span class="p">],</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span></pre></div><div class="clipboard-copy-txt" id="cell-12">np.random.seed(42)model.random_binary(masses[:10], seed=42)</div></div></div></div></div><div class="jp-Cell-outputWrapper"><div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"></div><div class="jp-OutputArea jp-Cell-outputArea"><div class="jp-OutputArea-child jp-OutputArea-executeResult"><div class="jp-OutputPrompt jp-OutputArea-prompt">Out[53]:</div><div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0"><pre>(array([0.94394365, 0.66991325, 0.35863824, 0.26508398, 0.98930322, 0.20365471, 0.28866418]), array([3.17552947e+06, 3.92321260e+04, 1.12824477e+07, 3.38937128e+05, 1.20609031e+01, 5.05059564e+07, 3.46602403e+05]), array([0.87715898, 0.33014873, 0.29591527, 0.32269941, 0.33187267, 0.69890144, 0.62707649]))</pre></div></div></div></div></div></div></div> <!-- jp-Notebook --></div> <!-- jupyter-wrapper -->
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