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<article id="content">
<header>
<h1 class="title"><code>analysis.simulatedAnnealing</code> module</h1>
</header>
<section id="section-intro">
<p>Plots the evolution of the simulated annealing algorithm from a log file</p>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">&#34;&#34;&#34;Plots the evolution of the simulated annealing algorithm from a log file&#34;&#34;&#34;

import matplotlib.pyplot as plt
import numpy as np
import pickle
import pandas
from scipy.signal import savgol_filter

from analysis import utils

def deroll(arr, limits, start=0):
    &#34;&#34;&#34;Derolls a log array. It returns a likely guess of what an array would
    have been before applying a mod operator to bring it into the limits
    region. Example, for limits = [0, 1] the array [0.5, 0.7, 0.9, 0.1] would
    return [0.5, 0.7, 0.9, 1.1].

    Parameters:
        arr (array): array to deroll
        limit (tuple): (lower limit, upper limit)
        start (int): the first start values of the array will not be derolled

    Returns:
        The derolled array.
    &#34;&#34;&#34;
    for i in range(start,len(arr)-1): # Do not deroll before start
        if np.abs(arr[i+1]-arr[i]) &gt; (limits[1]-limits[0])/2:
            # Continue the array in the closest possible way
            if arr[i+1]&gt;arr[i]: arr[i+1:] -= (limits[1]-limits[0])
            else: arr[i+1:] += (limits[1]-limits[0])
    return arr

def returnBars(arr, n):
    &#34;&#34;&#34;Calculates the 10th-90th percentile running confidence interval

    Parameters:
        arr (arr): The array to calculate error bars for
        n (int): The smoothing of the confidence interval

    Returns:
        The smoothed running confidence interval
    &#34;&#34;&#34;
    r = pandas.Series(arr).rolling(window = n, center = False)
    s1, s2 =  r.quantile(.90), r.quantile(.1)
    return savgol_filter(s1[n:], 101, 3), savgol_filter(s2[n:], 101, 3)

# Load the data saved by the simulating annealing algorithm
log = pickle.load(open(&#39;data/logs/log.pickle&#39;, &#34;rb&#34; ) )[:1400]

# Define the parameters we wish to plot
mask = [0,1,2,3,5,7,8] #Non-fixed parameters
limits = np.array([[0, np.pi], [-np.pi, np.pi], 
    [0, np.pi], [-np.pi, np.pi], 
    [.5,1.0], [.55,.8], [.55,.8]])
labels = [r&#39;$i_1$ / rad&#39;, r&#39;$\omega_1$ / rad&#39;, 
    r&#39;$i_2$ / rad&#39;, r&#39;$\omega_2$ / rad&#39;, 
    &#39;e&#39;, r&#39;$R_1$&#39;, r&#39;$R_2$&#39;, r&#39;$\mu$&#39;]
ticks = [[0, np.pi], [-np.pi,0, np.pi],
    [0, np.pi],[-np.pi, 0, np.pi],
    [.5, 1.0], [.55, .8], [.55, .8]]
ticklabels = [[0,r&#39;$\pi$&#39;],[r&#39;$-\pi$&#39;,0,r&#39;$\pi$&#39;],
    [0,r&#39;$\pi$&#39;],[r&#39;$-\pi$&#39;,0,r&#39;$\pi$&#39;],
    [&#39;.5&#39;,&#39;1.0&#39;], [&#39;.55&#39;,&#39;.8&#39;], [&#39;.55&#39;,&#39;.8&#39;]]

# Mask away the parameters we don&#39;t want to plot
scores = np.array([l[0] for l in log])
paramss = np.array([l[1] for l in log])[:,mask]
# Fix conventions for inclination
paramss[:,0] = paramss[:,0] - np.pi
paramss[:,2] = np.pi - paramss[:,2]

# Start plotting
i = np.arange(len(log))
f, axs = plt.subplots(1+len(paramss[0]), 1,  figsize=(10, 10), 
    sharex=True, gridspec_kw = {&#39;height_ratios&#39;:[2., 1., 1, 1, 1, 1, 1, 1]})
plt.tight_layout()
utils.stylizePlot(axs)

# Plot metric
axs[0].scatter(i, scores, marker=&#39;x&#39;, c=&#39;black&#39;, s=5, linewidth=.5)
axs[0].fill_between(i[20:], *returnBars(scores, 20), color=&#39;r&#39;, alpha=.2)
utils.setSize(axs[0], x=(0, None), y=(0.8, None))
utils.setAxes(axs[0], y=&#39;Metric&#39;)
# Get twin axis to mark temperature in it
ax2 = axs[0].twiny()
ax2.set_xscale(&#39;log&#39;)
ax2.set_xlabel(&#39;Temperature&#39;, fontsize=14)
ax2.invert_xaxis()
ax2.set_xlim((.25, 0.015129))
ax2.set_xticks([.2, .1, .09, .08, .07, .06, .05, .04, .03, .02, .01])
ax2.set_xticklabels([str(i) 
    for i in [.2, .1, .09, .08, .07, .06, .05, .04, .03, .02, .01]])

# Plot parameters one by one
for j in range(0, len(paramss[0])):
    utils.setSize(axs[j+1], x=(0, len(log)), y=limits[j])
    axs[j+1].set_ylabel(labels[j], fontsize=14)
    axs[j+1].set_yticks(ticks[j])
    axs[j+1].set_yticklabels(ticklabels[j])
    # Be careful plotting cyclic parameters
    derolled = deroll(paramss[:,j], limits[j], start=500)
    bar1, bar2 = returnBars(paramss[:,j], 20)
    for k in range(-3, 3): #
        # Plot the confidence intervals an data multiple times
        # to deal with cyclic parameters
        axs[j+1].scatter(i, derolled + k*(limits[j][1]-limits[j][0]), 
            marker=&#39;x&#39;, c=&#39;black&#39;, s=5, linewidth=.5)
        axs[j+1].fill_between(i[20:], bar1 + k*(limits[j][1]-limits[j][0]),
            bar2 + k*(limits[j][1]-limits[j][0]), color=&#39;r&#39;, alpha=.2)

f.align_ylabels(axs[:])
axs[-1].set_xlabel(&#39;Iteration&#39;, fontsize=14)
plt.show()</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="analysis.simulatedAnnealing.deroll"><code class="name flex">
<span>def <span class="ident">deroll</span></span>(<span>arr, limits, start=0)</span>
</code></dt>
<dd>
<section class="desc"><p>Derolls a log array. It returns a likely guess of what an array would
have been before applying a mod operator to bring it into the limits
region. Example, for limits = [0, 1] the array [0.5, 0.7, 0.9, 0.1] would
return [0.5, 0.7, 0.9, 1.1].</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>arr</code></strong> :&ensp;<code>array</code></dt>
<dd>array to deroll</dd>
<dt><strong><code>limit</code></strong> :&ensp;<code>tuple</code></dt>
<dd>(lower limit, upper limit)</dd>
<dt><strong><code>start</code></strong> :&ensp;<code>int</code></dt>
<dd>the first start values of the array will not be derolled</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>The derolled array.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def deroll(arr, limits, start=0):
    &#34;&#34;&#34;Derolls a log array. It returns a likely guess of what an array would
    have been before applying a mod operator to bring it into the limits
    region. Example, for limits = [0, 1] the array [0.5, 0.7, 0.9, 0.1] would
    return [0.5, 0.7, 0.9, 1.1].

    Parameters:
        arr (array): array to deroll
        limit (tuple): (lower limit, upper limit)
        start (int): the first start values of the array will not be derolled

    Returns:
        The derolled array.
    &#34;&#34;&#34;
    for i in range(start,len(arr)-1): # Do not deroll before start
        if np.abs(arr[i+1]-arr[i]) &gt; (limits[1]-limits[0])/2:
            # Continue the array in the closest possible way
            if arr[i+1]&gt;arr[i]: arr[i+1:] -= (limits[1]-limits[0])
            else: arr[i+1:] += (limits[1]-limits[0])
    return arr</code></pre>
</details>
</dd>
<dt id="analysis.simulatedAnnealing.returnBars"><code class="name flex">
<span>def <span class="ident">returnBars</span></span>(<span>arr, n)</span>
</code></dt>
<dd>
<section class="desc"><p>Calculates the 10th-90th percentile running confidence interval</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>arr</code></strong> :&ensp;<code>arr</code></dt>
<dd>The array to calculate error bars for</dd>
<dt><strong><code>n</code></strong> :&ensp;<code>int</code></dt>
<dd>The smoothing of the confidence interval</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>The smoothed running confidence interval</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def returnBars(arr, n):
    &#34;&#34;&#34;Calculates the 10th-90th percentile running confidence interval

    Parameters:
        arr (arr): The array to calculate error bars for
        n (int): The smoothing of the confidence interval

    Returns:
        The smoothed running confidence interval
    &#34;&#34;&#34;
    r = pandas.Series(arr).rolling(window = n, center = False)
    s1, s2 =  r.quantile(.90), r.quantile(.1)
    return savgol_filter(s1[n:], 101, 3), savgol_filter(s2[n:], 101, 3)</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="analysis.simulatedAnnealing.deroll" href="#analysis.simulatedAnnealing.deroll">deroll</a></code></li>
<li><code><a title="analysis.simulatedAnnealing.returnBars" href="#analysis.simulatedAnnealing.returnBars">returnBars</a></code></li>
</ul>
</li>
</ul>
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