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colour each ring according to the initial distance. It plots a
prograde equalmass …" />
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<header>
<h1 class="title"><code>analysis.ringPlot</code> module</h1>
</header>
<section id="section-intro">
<p>Make a plot of an encountering with rings (fig 1, fig 3) and
colour each ring according to the initial distance. It plots a
prograde equalmass encounter by default.</p>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">&#34;&#34;&#34;Make a plot of an encountering with rings (fig 1, fig 3) and 
colour each ring according to the initial distance. It plots a
prograde equalmass encounter by default.
&#34;&#34;&#34;

import matplotlib.pyplot as plt
from scipy.interpolate import splprep, splev, interp1d
from matplotlib import markers
import numpy as np

from analysis.colormaps import parula_map
from analysis import utils

def plotRings(ax, data, n):
    &#34;&#34;&#34;Make a ring plot of the data.

    Parameters:
        ax: the matplotlib axis where the data should be plotted
        data: a data dictionary in the format saved by the simulation
        n (int): the number of particles to plot. These will be interpolated
            and can be much higher than those originally in the simulation.
    &#34;&#34;&#34;
    # Identify all the rings
    rings = np.unique(data[&#39;type&#39;][:,1])
    rings = rings[rings!=&#39;0&#39;]
    # Plot each ring
    for ring, color, in zip(rings, 
                     parula_map(np.linspace(1.0, 0., len(rings)))):
        xy = data[&#39;r_vec&#39;][data[&#39;type&#39;][:,1]==ring] #data for this ring
        # Interpolate data (to use opacity to plot local density)
        tck, u = splprep(xy[:,:2].T, u=None, s=0.0, per=1) 
        f = interp1d(np.linspace(0,1,len(u)), u, kind=&#39;cubic&#39;) 
        x_new, y_new = splev(f(np.linspace(0,1,n)), tck, der=0)
        #Plot data
        ax.scatter(x_new, y_new, s=2, c=[color], alpha=0.01)
    utils.plotCOM(ax)
    utils.plotCenterMasses(ax, data)
    utils.plotTracks(ax, data[&#39;tracks&#39;]) 
    # Styling
    utils.setAxes(ax, mode=&#39;hide&#39;)

####################################
####################################

# List of times to plot
ts = [2000, 3000, 3500, 4000, 4900]
figsize = (12, 4)
fileName = &#39;prograde_equalmass&#39;

f, axs = plt.subplots(1, len(ts),  figsize=figsize, sharey=False)
for t, ax in zip(ts, axs):
    data = utils.loadData(fileName, t)
    plotRings(ax, data, n=10000)
    height, width = 3.0, 3 * 1/len(ts) * figsize[0]/figsize[1]
    utils.setSize(ax, x=(-width, width), y=(-height, height), mode=&#39;square&#39;)
f.subplots_adjust(hspace=0, wspace=0)
plt.show()</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="analysis.ringPlot.plotRings"><code class="name flex">
<span>def <span class="ident">plotRings</span></span>(<span>ax, data, n)</span>
</code></dt>
<dd>
<section class="desc"><p>Make a ring plot of the data.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>ax</code></strong></dt>
<dd>the matplotlib axis where the data should be plotted</dd>
<dt><strong><code>data</code></strong></dt>
<dd>a data dictionary in the format saved by the simulation</dd>
<dt><strong><code>n</code></strong> :&ensp;<code>int</code></dt>
<dd>the number of particles to plot. These will be interpolated
and can be much higher than those originally in the simulation.</dd>
</dl></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def plotRings(ax, data, n):
    &#34;&#34;&#34;Make a ring plot of the data.

    Parameters:
        ax: the matplotlib axis where the data should be plotted
        data: a data dictionary in the format saved by the simulation
        n (int): the number of particles to plot. These will be interpolated
            and can be much higher than those originally in the simulation.
    &#34;&#34;&#34;
    # Identify all the rings
    rings = np.unique(data[&#39;type&#39;][:,1])
    rings = rings[rings!=&#39;0&#39;]
    # Plot each ring
    for ring, color, in zip(rings, 
                     parula_map(np.linspace(1.0, 0., len(rings)))):
        xy = data[&#39;r_vec&#39;][data[&#39;type&#39;][:,1]==ring] #data for this ring
        # Interpolate data (to use opacity to plot local density)
        tck, u = splprep(xy[:,:2].T, u=None, s=0.0, per=1) 
        f = interp1d(np.linspace(0,1,len(u)), u, kind=&#39;cubic&#39;) 
        x_new, y_new = splev(f(np.linspace(0,1,n)), tck, der=0)
        #Plot data
        ax.scatter(x_new, y_new, s=2, c=[color], alpha=0.01)
    utils.plotCOM(ax)
    utils.plotCenterMasses(ax, data)
    utils.plotTracks(ax, data[&#39;tracks&#39;]) 
    # Styling
    utils.setAxes(ax, mode=&#39;hide&#39;)</code></pre>
</details>
</dd>
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</section>
<section>
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<h1>Index</h1>
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<ul id="index">
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="analysis.ringPlot.plotRings" href="#analysis.ringPlot.plotRings">plotRings</a></code></li>
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