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    18. 

  18. <article id="content">
    19. 

  19. <header>
    20. 

  20. <h1 class="title"><code>analysis.simulatedAnnealing</code> module</h1>
    21. 

  21. </header>
    22. 

  22. <section id="section-intro">
    23. 

  23. <p>Plots the evolution of the simulated annealing algorithm from a log file</p>
    24. 

  24. <details class="source">
    25. 

  25. <summary>Source code</summary>
    26. 

  26. <pre><code class="python">&#34;&#34;&#34;Plots the evolution of the simulated annealing algorithm from a log file&#34;&#34;&#34;
    27. 

  27. 

  28. import matplotlib.pyplot as plt
    29. 

  29. import numpy as np
    30. 

  30. import pickle
    31. 

  31. import pandas
    32. 

  32. from scipy.signal import savgol_filter
    33. 

  33. 

  34. from analysis import utils
    35. 

  35. 

  36. def deroll(arr, limits, start=0):
    37. 

  37.     &#34;&#34;&#34;Derolls a log array. It returns a likely guess of what an array would
    38. 

  38.     have been before applying a mod operator to bring it into the limits
    39. 

  39.     region. Example, for limits = [0, 1] the array [0.5, 0.7, 0.9, 0.1] would
    40. 

  40.     return [0.5, 0.7, 0.9, 1.1].
    41. 

  41. 

  42.     Parameters:
    43. 

  43.         arr (array): array to deroll
    44. 

  44.         limit (tuple): (lower limit, upper limit)
    45. 

  45.         start (int): the first start values of the array will not be derolled
    46. 

  46. 

  47.     Returns:
    48. 

  48.         The derolled array.
    49. 

  49.     &#34;&#34;&#34;
    50. 

  50.     for i in range(start,len(arr)-1): # Do not deroll before start
    51. 

  51.         if np.abs(arr[i+1]-arr[i]) &gt; (limits[1]-limits[0])/2:
    52. 

  52.             # Continue the array in the closest possible way
    53. 

  53.             if arr[i+1]&gt;arr[i]: arr[i+1:] -= (limits[1]-limits[0])
    54. 

  54.             else: arr[i+1:] += (limits[1]-limits[0])
    55. 

  55.     return arr
    56. 

  56. 

  57. def returnBars(arr, n):
    58. 

  58.     &#34;&#34;&#34;Calculates the 10th-90th percentile running confidence interval
    59. 

  59. 

  60.     Parameters:
    61. 

  61.         arr (arr): The array to calculate error bars for
    62. 

  62.         n (int): The smoothing of the confidence interval
    63. 

  63. 

  64.     Returns:
    65. 

  65.         The smoothed running confidence interval
    66. 

  66.     &#34;&#34;&#34;
    67. 

  67.     r = pandas.Series(arr).rolling(window = n, center = False)
    68. 

  68.     s1, s2 =  r.quantile(.90), r.quantile(.1)
    69. 

  69.     return savgol_filter(s1[n:], 101, 3), savgol_filter(s2[n:], 101, 3)
    70. 

  70. 

  71. # Load the data saved by the simulating annealing algorithm
    72. 

  72. log = pickle.load(open(&#39;data/logs/log.pickle&#39;, &#34;rb&#34; ) )[:1400]
    73. 

  73. 

  74. # Define the parameters we wish to plot
    75. 

  75. mask = [0,1,2,3,5,7,8] #Non-fixed parameters
    76. 

  76. limits = np.array([[0, np.pi], [-np.pi, np.pi], 
    77. 

  77.     [0, np.pi], [-np.pi, np.pi], 
    78. 

  78.     [.5,1.0], [.55,.8], [.55,.8]])
    79. 

  79. labels = [r&#39;$i_1$ / rad&#39;, r&#39;$\omega_1$ / rad&#39;, 
    80. 

  80.     r&#39;$i_2$ / rad&#39;, r&#39;$\omega_2$ / rad&#39;, 
    81. 

  81.     &#39;e&#39;, r&#39;$R_1$&#39;, r&#39;$R_2$&#39;, r&#39;$\mu$&#39;]
    82. 

  82. ticks = [[0, np.pi], [-np.pi,0, np.pi],
    83. 

  83.     [0, np.pi],[-np.pi, 0, np.pi],
    84. 

  84.     [.5, 1.0], [.55, .8], [.55, .8]]
    85. 

  85. ticklabels = [[0,r&#39;$\pi$&#39;],[r&#39;$-\pi$&#39;,0,r&#39;$\pi$&#39;],
    86. 

  86.     [0,r&#39;$\pi$&#39;],[r&#39;$-\pi$&#39;,0,r&#39;$\pi$&#39;],
    87. 

  87.     [&#39;.5&#39;,&#39;1.0&#39;], [&#39;.55&#39;,&#39;.8&#39;], [&#39;.55&#39;,&#39;.8&#39;]]
    88. 

  88. 

  89. # Mask away the parameters we don&#39;t want to plot
    90. 

  90. scores = np.array([l[0] for l in log])
    91. 

  91. paramss = np.array([l[1] for l in log])[:,mask]
    92. 

  92. # Fix conventions for inclination
    93. 

  93. paramss[:,0] = paramss[:,0] - np.pi
    94. 

  94. paramss[:,2] = np.pi - paramss[:,2]
    95. 

  95. 

  96. # Start plotting
    97. 

  97. i = np.arange(len(log))
    98. 

  98. f, axs = plt.subplots(1+len(paramss[0]), 1,  figsize=(10, 10), 
    99. 

  99.     sharex=True, gridspec_kw = {&#39;height_ratios&#39;:[2., 1., 1, 1, 1, 1, 1, 1]})
    100. 

  100. plt.tight_layout()
    101. 

  101. utils.stylizePlot(axs)
    102. 

  102. 

  103. # Plot metric
    104. 

  104. axs[0].scatter(i, scores, marker=&#39;x&#39;, c=&#39;black&#39;, s=5, linewidth=.5)
    105. 

  105. axs[0].fill_between(i[20:], *returnBars(scores, 20), color=&#39;r&#39;, alpha=.2)
    106. 

  106. utils.setSize(axs[0], x=(0, None), y=(0.8, None))
    107. 

  107. utils.setAxes(axs[0], y=&#39;Metric&#39;)
    108. 

  108. # Get twin axis to mark temperature in it
    109. 

  109. ax2 = axs[0].twiny()
    110. 

  110. ax2.set_xscale(&#39;log&#39;)
    111. 

  111. ax2.set_xlabel(&#39;Temperature&#39;, fontsize=14)
    112. 

  112. ax2.invert_xaxis()
    113. 

  113. ax2.set_xlim((.25, 0.015129))
    114. 

  114. ax2.set_xticks([.2, .1, .09, .08, .07, .06, .05, .04, .03, .02, .01])
    115. 

  115. ax2.set_xticklabels([str(i) 
    116. 

  116.     for i in [.2, .1, .09, .08, .07, .06, .05, .04, .03, .02, .01]])
    117. 

  117. 

  118. # Plot parameters one by one
    119. 

  119. for j in range(0, len(paramss[0])):
    120. 

  120.     utils.setSize(axs[j+1], x=(0, len(log)), y=limits[j])
    121. 

  121.     axs[j+1].set_ylabel(labels[j], fontsize=14)
    122. 

  122.     axs[j+1].set_yticks(ticks[j])
    123. 

  123.     axs[j+1].set_yticklabels(ticklabels[j])
    124. 

  124.     # Be careful plotting cyclic parameters
    125. 

  125.     derolled = deroll(paramss[:,j], limits[j], start=500)
    126. 

  126.     bar1, bar2 = returnBars(paramss[:,j], 20)
    127. 

  127.     for k in range(-3, 3): #
    128. 

  128.         # Plot the confidence intervals an data multiple times
    129. 

  129.         # to deal with cyclic parameters
    130. 

  130.         axs[j+1].scatter(i, derolled + k*(limits[j][1]-limits[j][0]), 
    131. 

  131.             marker=&#39;x&#39;, c=&#39;black&#39;, s=5, linewidth=.5)
    132. 

  132.         axs[j+1].fill_between(i[20:], bar1 + k*(limits[j][1]-limits[j][0]),
    133. 

  133.             bar2 + k*(limits[j][1]-limits[j][0]), color=&#39;r&#39;, alpha=.2)
    134. 

  134. 

  135. f.align_ylabels(axs[:])
    136. 

  136. axs[-1].set_xlabel(&#39;Iteration&#39;, fontsize=14)
    137. 

  137. plt.show()</code></pre>
    138. 

  138. </details>
    139. 

  139. </section>
    140. 

  140. <section>
    141. 

  141. </section>
    142. 

  142. <section>
    143. 

  143. </section>
    144. 

  144. <section>
    145. 

  145. <h2 class="section-title" id="header-functions">Functions</h2>
    146. 

  146. <dl>
    147. 

  147. <dt id="analysis.simulatedAnnealing.deroll"><code class="name flex">
    148. 

  148. <span>def <span class="ident">deroll</span></span>(<span>arr, limits, start=0)</span>
    149. 

  149. </code></dt>
    150. 

  150. <dd>
    151. 

  151. <section class="desc"><p>Derolls a log array. It returns a likely guess of what an array would
    152. 

  152. have been before applying a mod operator to bring it into the limits
    153. 

  153. region. Example, for limits = [0, 1] the array [0.5, 0.7, 0.9, 0.1] would
    154. 

  154. return [0.5, 0.7, 0.9, 1.1].</p>
    155. 

  155. <h2 id="parameters">Parameters</h2>
    156. 

  156. <dl>
    157. 

  157. <dt><strong><code>arr</code></strong> :&ensp;<code>array</code></dt>
    158. 

  158. <dd>array to deroll</dd>
    159. 

  159. <dt><strong><code>limit</code></strong> :&ensp;<code>tuple</code></dt>
    160. 

  160. <dd>(lower limit, upper limit)</dd>
    161. 

  161. <dt><strong><code>start</code></strong> :&ensp;<code>int</code></dt>
    162. 

  162. <dd>the first start values of the array will not be derolled</dd>
    163. 

  163. </dl>
    164. 

  164. <h2 id="returns">Returns</h2>
    165. 

  165. <p>The derolled array.</p></section>
    166. 

  166. <details class="source">
    167. 

  167. <summary>Source code</summary>
    168. 

  168. <pre><code class="python">def deroll(arr, limits, start=0):
    169. 

  169.     &#34;&#34;&#34;Derolls a log array. It returns a likely guess of what an array would
    170. 

  170.     have been before applying a mod operator to bring it into the limits
    171. 

  171.     region. Example, for limits = [0, 1] the array [0.5, 0.7, 0.9, 0.1] would
    172. 

  172.     return [0.5, 0.7, 0.9, 1.1].
    173. 

  173. 

  174.     Parameters:
    175. 

  175.         arr (array): array to deroll
    176. 

  176.         limit (tuple): (lower limit, upper limit)
    177. 

  177.         start (int): the first start values of the array will not be derolled
    178. 

  178. 

  179.     Returns:
    180. 

  180.         The derolled array.
    181. 

  181.     &#34;&#34;&#34;
    182. 

  182.     for i in range(start,len(arr)-1): # Do not deroll before start
    183. 

  183.         if np.abs(arr[i+1]-arr[i]) &gt; (limits[1]-limits[0])/2:
    184. 

  184.             # Continue the array in the closest possible way
    185. 

  185.             if arr[i+1]&gt;arr[i]: arr[i+1:] -= (limits[1]-limits[0])
    186. 

  186.             else: arr[i+1:] += (limits[1]-limits[0])
    187. 

  187.     return arr</code></pre>
    188. 

  188. </details>
    189. 

  189. </dd>
    190. 

  190. <dt id="analysis.simulatedAnnealing.returnBars"><code class="name flex">
    191. 

  191. <span>def <span class="ident">returnBars</span></span>(<span>arr, n)</span>
    192. 

  192. </code></dt>
    193. 

  193. <dd>
    194. 

  194. <section class="desc"><p>Calculates the 10th-90th percentile running confidence interval</p>
    195. 

  195. <h2 id="parameters">Parameters</h2>
    196. 

  196. <dl>
    197. 

  197. <dt><strong><code>arr</code></strong> :&ensp;<code>arr</code></dt>
    198. 

  198. <dd>The array to calculate error bars for</dd>
    199. 

  199. <dt><strong><code>n</code></strong> :&ensp;<code>int</code></dt>
    200. 

  200. <dd>The smoothing of the confidence interval</dd>
    201. 

  201. </dl>
    202. 

  202. <h2 id="returns">Returns</h2>
    203. 

  203. <p>The smoothed running confidence interval</p></section>
    204. 

  204. <details class="source">
    205. 

  205. <summary>Source code</summary>
    206. 

  206. <pre><code class="python">def returnBars(arr, n):
    207. 

  207.     &#34;&#34;&#34;Calculates the 10th-90th percentile running confidence interval
    208. 

  208. 

  209.     Parameters:
    210. 

  210.         arr (arr): The array to calculate error bars for
    211. 

  211.         n (int): The smoothing of the confidence interval
    212. 

  212. 

  213.     Returns:
    214. 

  214.         The smoothed running confidence interval
    215. 

  215.     &#34;&#34;&#34;
    216. 

  216.     r = pandas.Series(arr).rolling(window = n, center = False)
    217. 

  217.     s1, s2 =  r.quantile(.90), r.quantile(.1)
    218. 

  218.     return savgol_filter(s1[n:], 101, 3), savgol_filter(s2[n:], 101, 3)</code></pre>
    219. 

  219. </details>
    220. 

  220. </dd>
    221. 

  221. </dl>
    222. 

  222. </section>
    223. 

  223. <section>
    224. 

  224. </section>
    225. 

  225. </article>
    226. 

  226. <nav id="sidebar">
    227. 

  227. <h1>Index</h1>
    228. 

  228. <div class="toc">
    229. 

  229. <ul></ul>
    230. 

  230. </div>
    231. 

  231. <ul id="index">
    232. 

  232. <li><h3><a href="#header-functions">Functions</a></h3>
    233. 

  233. <ul class="">
    234. 

  234. <li><code><a title="analysis.simulatedAnnealing.deroll" href="#analysis.simulatedAnnealing.deroll">deroll</a></code></li>
    235. 

  235. <li><code><a title="analysis.simulatedAnnealing.returnBars" href="#analysis.simulatedAnnealing.returnBars">returnBars</a></code></li>
    236. 

  236. </ul>
    237. 

  237. </li>
    238. 

  238. </ul>
    239. 

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    240. 

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    241. 

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