{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "title",
   "metadata": {},
   "source": [
    "# Plotting the Search\n",
    "\n",
    "This notebook walks through the plotting helpers in `sklearn-genetic-opt`. It shows the default fitness view, richer history and logbook views, and two search-space plot styles that make it easier to understand how the optimizer explored the parameter space.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "setup",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-20T18:39:21.425258Z",
     "start_time": "2026-06-20T18:39:21.225944600Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-21T20:54:21.509277Z",
     "iopub.status.busy": "2026-06-21T20:54:21.508654Z",
     "iopub.status.idle": "2026-06-21T20:55:19.532231Z",
     "shell.execute_reply": "2026-06-21T20:55:19.530432Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " gen evals           avg          best     div  unique  stag     mut   sel             events\n",
      "---- ----- ------------- ------------- ------- ------- ----- ------- ----- ------------------\n",
      "   0     4       0.14741       0.30181   0.833   1.000     0       -     - -                 \n",
      "   1     8       0.20587       0.30181   0.583   1.000     1   0.050     3 -                 \n",
      "   2     8       0.25733       0.30181   0.583   0.750     2   0.050     3 dup=3             \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   3     8       0.27799       0.30181   0.333   0.750     3   0.050     3 dup=3             \n",
      "   4     8       0.26608       0.30181   0.250   0.500     4   0.050     3 dup=6             \n",
      "   5     8       0.26157       0.30181   0.417   0.750     5   0.050     3 dup=6             \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
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       "\n",
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       ".sk-global pre {\n",
       "  padding: 0;\n",
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       "  border: 1px dashed var(--sklearn-color-line);\n",
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       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
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       "\n",
       ".sk-global div.sk-container {\n",
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       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
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       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
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       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
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       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
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       "\n",
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       "  width: 0;\n",
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       "\n",
       "/* Serial-specific style estimator block */\n",
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       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
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       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
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       "\n",
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       "  /* Arrow on the left of the label */\n",
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       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
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       "\n",
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       "  display: none;\n",
       "  text-align: left;\n",
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       "  /* Expand drop-down */\n",
       "  display: block;\n",
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       "  overflow: visible;\n",
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       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
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       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "\n",
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       "/* Colorize estimator box */\n",
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       "\n",
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       "\n",
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       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
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       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
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       "\n",
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       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
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       "  text-align: center;\n",
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       "\n",
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       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
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       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
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       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
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       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>GASearchCV(crossover_probability=0.9, cv=2,\n",
       "           estimator=DecisionTreeRegressor(ccp_alpha=0.19121381309034374,\n",
       "                                           max_depth=2, min_samples_split=10,\n",
       "                                           random_state=42),\n",
       "           generations=5, mutation_probability=0.05, n_jobs=1,\n",
       "           param_grid={&#x27;ccp_alpha&#x27;: &lt;sklearn_genetic.space.space.Continuous object at 0x0000022C95420EC0&gt;,\n",
       "                       &#x27;criterion&#x27;: &lt;sklearn_genetic.space.space.Categorical object at 0x0000022C95421940&gt;,\n",
       "                       &#x27;max_depth&#x27;: &lt;sklearn_genetic.space.space.Integer object at 0x0000022C95421A90&gt;,\n",
       "                       &#x27;min_samples_split&#x27;: &lt;sklearn_genetic.space.space.Integer object at 0x0000022C954507D0&gt;},\n",
       "           population_size=4, return_train_score=True, scoring=&#x27;r2&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GASearchCV</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('estimator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">estimator</td>\n",
       "            <td class=\"value\">DecisionTreeR...ndom_state=42)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cv',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">cv</td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('param_grid',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">param_grid</td>\n",
       "            <td class=\"value\">{&#x27;ccp_alpha&#x27;: &lt;sklearn_gene...0022C95420EC0&gt;, &#x27;criterion&#x27;: &lt;sklearn_gene...0022C95421940&gt;, &#x27;max_depth&#x27;: &lt;sklearn_gene...0022C95421A90&gt;, &#x27;min_samples_split&#x27;: &lt;sklearn_gene...0022C954507D0&gt;}</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('scoring',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">scoring</td>\n",
       "            <td class=\"value\">&#x27;r2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('population_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">population_size</td>\n",
       "            <td class=\"value\">4</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('generations',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">generations</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('crossover_probability',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">crossover_probability</td>\n",
       "            <td class=\"value\">0.9</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('mutation_probability',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">mutation_probability</td>\n",
       "            <td class=\"value\">0.05</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('return_train_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">return_train_score</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tournament_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tournament_size</td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('elitism',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">elitism</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('keep_top_k',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">keep_top_k</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criteria',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">criteria</td>\n",
       "            <td class=\"value\">&#x27;max&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">algorithm</td>\n",
       "            <td class=\"value\">&#x27;eaMuPlusLambda&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('refit',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">refit</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">pre_dispatch</td>\n",
       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('error_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">error_score</td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('log_config',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">log_config</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('use_cache',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">use_cache</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start_configs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start_configs</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('evolution_config',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">evolution_config</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('population_config',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">population_config</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('runtime_config',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">runtime_config</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('optimization_config',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">optimization_config</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('parallel_backend',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">parallel_backend</td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('population_initializer',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">population_initializer</td>\n",
       "            <td class=\"value\">&#x27;smart&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('local_search',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">local_search</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('local_search_top_k',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">local_search_top_k</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('local_search_steps',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">local_search_steps</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('local_search_radius',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">local_search_radius</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('diversity_control',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">diversity_control</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('diversity_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">diversity_threshold</td>\n",
       "            <td class=\"value\">0.25</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('diversity_stagnation_generations',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">diversity_stagnation_generations</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('diversity_mutation_boost',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">diversity_mutation_boost</td>\n",
       "            <td class=\"value\">2.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_immigrants_fraction',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_immigrants_fraction</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('adaptive_selection',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">adaptive_selection</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('selection_pressure_min',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">selection_pressure_min</td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('selection_pressure_max',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">selection_pressure_max</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('offspring_diversity_retries',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">offspring_diversity_retries</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fitness_sharing',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fitness_sharing</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sharing_radius',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">sharing_radius</td>\n",
       "            <td class=\"value\">0.2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sharing_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">sharing_alpha</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('final_selection',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">final_selection</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('final_selection_top_k',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">final_selection_top_k</td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('final_selection_cv',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">final_selection_cv</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">X_</a></td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](296, 10)</td>\n",
       "           <td>[[ 0.01,-0.04,-0.03,...,-0.  , 0.01, 0.01],\n",
       " [-0.  ,-0.04, 0.05,..., 0.08, 0.08, 0.05],\n",
       " [ 0.01, 0.05,-0.01,..., 0.07, 0.04, 0.02],\n",
       " ...,\n",
       " [ 0.03,-0.04,-0.02,...,-0.04,-0.01,-0.  ],\n",
       " [-0.01,-0.04,-0.02,...,-0.  ,-0.04,-0.04],\n",
       " [-0.09,-0.04, 0.03,...,-0.04,-0.01,-0.  ]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">best_estimator_</a></td>\n",
       "           <td class=\"fitted-att-type\">DecisionTreeRegressor</td>\n",
       "           <td>DecisionTreeR...ndom_state=42)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">best_index_</a></td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>0</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">best_params_</a></td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;cc...ha&#x27;: 0.19121381309034374, &#x27;cr...on&#x27;: &#x27;sq...or&#x27;, &#x27;ma...th&#x27;: 2, &#x27;mi...it&#x27;: 10}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">best_score_</a></td>\n",
       "           <td class=\"fitted-att-type\">float</td>\n",
       "           <td>0.3018</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">cv_results_</a></td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;me...me&#x27;: [np.float64(0....6529083251953), np.float64(0....3884506225586), np.float64(0....9275588989258), np.float64(0....9248733520508), ...], &#x27;me...me&#x27;: [np.float64(0....8908386230469), np.float64(0....0425109863281), np.float64(0....6118392944336), np.float64(0....9141006469727), ...], &#x27;me...re&#x27;: [np.float64(0.3018126431428106), np.float64(0....2439934859743), np.float64(0....5429826555526), np.float64(0....5928029179815), ...], &#x27;me...re&#x27;: [np.float64(0....3243791290727), np.float64(0.4981076194721688), np.float64(0.9428519269019859), np.float64(0.738897812386645), ...], ...}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">estimator_</a></td>\n",
       "           <td class=\"fitted-att-type\">DecisionTreeRegressor</td>\n",
       "           <td>DecisionTreeR...ndom_state=42)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">final_selection_results_</a></td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;ca...es&#x27;: [], &#x27;changed&#x27;: False, &#x27;cv&#x27;: None, &#x27;enabled&#x27;: False, ...}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">fit_stats_</a></td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;ca...ts&#x27;: 1, &#x27;cr...ls&#x27;: 43, &#x27;du...es&#x27;: 0, &#x27;ev...es&#x27;: 44, ...}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">multimetric_</a></td>\n",
       "           <td class=\"fitted-att-type\">bool</td>\n",
       "           <td>False</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">n_features_in_</a></td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>10</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">n_splits_</a></td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>2</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">refit_time_</a></td>\n",
       "           <td class=\"fitted-att-type\">float</td>\n",
       "           <td>0.001968</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">scorer_</a></td>\n",
       "           <td class=\"fitted-att-type\">_Scorer</td>\n",
       "           <td>make_scorer(r...hod=&#x27;predict&#x27;)</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\"><a class=\"param-doc-link\" style=\"text-decoration:none;\">y_</a></td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](296,)</td>\n",
       "           <td>[154.,192.,116.,...,148., 64.,302.]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: DecisionTreeRegressor</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>DecisionTreeRegressor(ccp_alpha=0.19121381309034374, max_depth=2,\n",
       "                      min_samples_split=10, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_depth;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
       "            max_depth\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_depth;\">\n",
       "            max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.<br><br>For an example of how ``max_depth`` influences the model, see<br>:ref:`sphx_glr_auto_examples_tree_plot_tree_regression.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_split',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_samples_split;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
       "            min_samples_split\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_samples_split;\">\n",
       "            min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``&quot;best&quot;``. When ``max_features &lt; n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-ccp_alpha;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
       "            ccp_alpha\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-ccp_alpha;\">\n",
       "            ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.19121381309034374</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criterion',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-criterion;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=criterion,-%7B%22squared_error%22%2C%20%22absolute_error%22%2C%20%22poisson%22%7D%2C%20default%3D%22squared_error%22\">\n",
       "            criterion\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-criterion;\">\n",
       "            criterion: {&quot;squared_error&quot;, &quot;absolute_error&quot;, &quot;poisson&quot;}, default=&quot;squared_error&quot;<br><br>The function to measure the quality of a split. Supported criteria<br>are &quot;squared_error&quot; for the mean squared error, which is equal to<br>variance reduction as feature selection criterion and minimizes the L2<br>loss using the mean of each terminal node, &quot;absolute_error&quot; for the mean<br>absolute error, which minimizes the L1 loss using the median of each terminal<br>node, and &quot;poisson&quot; which uses reduction in Poisson deviance to find splits,<br>also using the mean of each terminal node.<br><br>.. versionadded:: 0.18<br>   Mean Absolute Error (MAE) criterion.<br><br>.. versionadded:: 0.24<br>    Poisson deviance criterion.<br><br>.. versionchanged:: 1.9<br>    Criterion `&quot;friedman_mse&quot;` was deprecated.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;squared_error&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('splitter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-splitter;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
       "            splitter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-splitter;\">\n",
       "            splitter: {&quot;best&quot;, &quot;random&quot;}, default=&quot;best&quot;<br><br>The strategy used to choose the split at each node. Supported<br>strategies are &quot;best&quot; to choose the best split and &quot;random&quot; to choose<br>the best random split.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_samples_leaf;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
       "            min_samples_leaf\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_samples_leaf;\">\n",
       "            min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_weight_fraction_leaf;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
       "            min_weight_fraction_leaf\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_weight_fraction_leaf;\">\n",
       "            min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_features;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
       "            max_features\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_features;\">\n",
       "            max_features: int, float or {&quot;sqrt&quot;, &quot;log2&quot;}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If &quot;sqrt&quot;, then `max_features=sqrt(n_features)`.<br>- If &quot;log2&quot;, then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_leaf_nodes;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
       "            max_leaf_nodes\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_leaf_nodes;\">\n",
       "            max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_impurity_decrease;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
       "            min_impurity_decrease\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_impurity_decrease;\">\n",
       "            min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-monotonic_cst;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
       "            monotonic_cst\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-monotonic_cst;\">\n",
       "            monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multioutput regressions (i.e. when `n_outputs_ &gt; 1`).<br><br>Read more in the :ref:`User Guide &lt;monotonic_cst_gbdt&gt;`.<br><br>.. versionadded:: 1.4</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_importances_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=feature_importances_,-ndarray%20of%20shape%20%28n_features%2C%29\">\n",
       "            feature_importances_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_importances_;\">\n",
       "            feature_importances_: ndarray of shape (n_features,)<br><br>The feature importances.<br>The higher, the more important the feature.<br>The importance of a feature is computed as the<br>(normalized) total reduction of the criterion brought<br>by that feature. It is also known as the Gini importance [4]_.<br><br>Warning: impurity-based feature importances can be misleading for<br>high cardinality features (many unique values). See<br>:func:`sklearn.inspection.permutation_importance` as an alternative.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](10,)</td>\n",
       "           <td>[0.  ,0.  ,0.84,...,0.  ,0.16,0.  ]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_features_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=max_features_,-int\">\n",
       "            max_features_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_features_;\">\n",
       "            max_features_: int<br><br>The inferred value of max_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>10</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>10</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_outputs_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=n_outputs_,-int\">\n",
       "            n_outputs_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_outputs_;\">\n",
       "            n_outputs_: int<br><br>The number of outputs when ``fit`` is performed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>1</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tree_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=tree_,-Tree%20instance\">\n",
       "            tree_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tree_;\">\n",
       "            tree_: Tree instance<br><br>The underlying Tree object. Please refer to<br>``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and<br>:ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`<br>for basic usage of these attributes.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">Tree</td>\n",
       "           <td>&lt;sklearn.tree...0022C954F4570&gt;</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-1');</script></body>"
      ],
      "text/plain": [
       "GASearchCV(crossover_probability=0.9, cv=2,\n",
       "           estimator=DecisionTreeRegressor(ccp_alpha=0.19121381309034374,\n",
       "                                           max_depth=2, min_samples_split=10,\n",
       "                                           random_state=42),\n",
       "           generations=5, mutation_probability=0.05, n_jobs=1,\n",
       "           param_grid={'ccp_alpha': <sklearn_genetic.space.space.Continuous object at 0x0000022C95420EC0>,\n",
       "                       'criterion': <sklearn_genetic.space.space.Categorical object at 0x0000022C95421940>,\n",
       "                       'max_depth': <sklearn_genetic.space.space.Integer object at 0x0000022C95421A90>,\n",
       "                       'min_samples_split': <sklearn_genetic.space.space.Integer object at 0x0000022C954507D0>},\n",
       "           population_size=4, return_train_score=True, scoring='r2')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "from sklearn_genetic import GASearchCV\n",
    "from sklearn_genetic.plots import plot_fitness_evolution, plot_history, plot_search_space\n",
    "from sklearn_genetic.space import Categorical, Continuous, Integer\n",
    "\n",
    "X, y = load_diabetes(return_X_y=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "\n",
    "search = GASearchCV(\n",
    "    DecisionTreeRegressor(random_state=42),\n",
    "    cv=2,\n",
    "    scoring='r2',\n",
    "    population_size=4,\n",
    "    generations=5,\n",
    "    tournament_size=3,\n",
    "    elitism=True,\n",
    "    crossover_probability=0.9,\n",
    "    mutation_probability=0.05,\n",
    "    param_grid={\n",
    "        'ccp_alpha': Continuous(0, 1),\n",
    "        'criterion': Categorical(['squared_error', 'absolute_error']),\n",
    "        'max_depth': Integer(2, 20),\n",
    "        'min_samples_split': Integer(2, 30),\n",
    "    },\n",
    "    criteria='max',\n",
    "    n_jobs=1,\n",
    ")\n",
    "\n",
    "search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "history-intro",
   "metadata": {},
   "source": [
    "## History and telemetry\n",
    "\n",
    "The fitted estimator stores generation-level telemetry in `history`. The newer fields track the adaptive behavior of the optimizer, including mutation pressure, diversity control, duplicate handling, and local refinement.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "history-table",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-20T18:39:21.515423Z",
     "start_time": "2026-06-20T18:39:21.438994500Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-21T20:55:19.536442Z",
     "iopub.status.busy": "2026-06-21T20:55:19.535637Z",
     "iopub.status.idle": "2026-06-21T20:55:19.578293Z",
     "shell.execute_reply": "2026-06-21T20:55:19.577171Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gen</th>\n",
       "      <th>fitness_best</th>\n",
       "      <th>fitness</th>\n",
       "      <th>fitness_max</th>\n",
       "      <th>unique_individual_ratio</th>\n",
       "      <th>genotype_diversity</th>\n",
       "      <th>mutation_probability</th>\n",
       "      <th>selection_pressure</th>\n",
       "      <th>random_immigrants</th>\n",
       "      <th>duplicate_replacements</th>\n",
       "      <th>local_refinements</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.301813</td>\n",
       "      <td>0.205867</td>\n",
       "      <td>0.301813</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.301813</td>\n",
       "      <td>0.257327</td>\n",
       "      <td>0.301813</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.301813</td>\n",
       "      <td>0.277993</td>\n",
       "      <td>0.301813</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.301813</td>\n",
       "      <td>0.266084</td>\n",
       "      <td>0.301813</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.301813</td>\n",
       "      <td>0.261574</td>\n",
       "      <td>0.301813</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gen  fitness_best   fitness  fitness_max  unique_individual_ratio  \\\n",
       "1    1      0.301813  0.205867     0.301813                     1.00   \n",
       "2    2      0.301813  0.257327     0.301813                     0.75   \n",
       "3    3      0.301813  0.277993     0.301813                     0.75   \n",
       "4    4      0.301813  0.266084     0.301813                     0.50   \n",
       "5    5      0.301813  0.261574     0.301813                     0.75   \n",
       "\n",
       "   genotype_diversity  mutation_probability  selection_pressure  \\\n",
       "1            0.583333                  0.05                 3.0   \n",
       "2            0.583333                  0.05                 3.0   \n",
       "3            0.333333                  0.05                 3.0   \n",
       "4            0.250000                  0.05                 3.0   \n",
       "5            0.416667                  0.05                 3.0   \n",
       "\n",
       "   random_immigrants  duplicate_replacements  local_refinements  \n",
       "1                  0                       0                  0  \n",
       "2                  0                       3                  0  \n",
       "3                  0                       3                  0  \n",
       "4                  0                       6                  0  \n",
       "5                  0                       6                  0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = pd.DataFrame(search.history)\n",
    "telemetry_columns = [\n",
    "    'gen',\n",
    "    'fitness_best',\n",
    "    'fitness',\n",
    "    'fitness_max',\n",
    "    'unique_individual_ratio',\n",
    "    'genotype_diversity',\n",
    "    'mutation_probability',\n",
    "    'selection_pressure',\n",
    "    'random_immigrants',\n",
    "    'duplicate_replacements',\n",
    "    'local_refinements',\n",
    "]\n",
    "history[[column for column in telemetry_columns if column in history.columns]].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fitness-intro",
   "metadata": {},
   "source": [
    "## Fitness plots\n",
    "\n",
    "`plot_fitness_evolution` accepts multiple metrics and an optional rolling window, which makes it easier to compare best-so-far fitness with the current population.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fitness-default",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-20T18:39:21.997459100Z",
     "start_time": "2026-06-20T18:39:21.529431200Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-21T20:55:19.581332Z",
     "iopub.status.busy": "2026-06-21T20:55:19.580940Z",
     "iopub.status.idle": "2026-06-21T20:55:20.013145Z",
     "shell.execute_reply": "2026-06-21T20:55:20.011469Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fitness_evolution(search)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fitness-multi",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-20T18:39:22.229822600Z",
     "start_time": "2026-06-20T18:39:21.999459400Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-21T20:55:20.016554Z",
     "iopub.status.busy": "2026-06-21T20:55:20.016106Z",
     "iopub.status.idle": "2026-06-21T20:55:20.480425Z",
     "shell.execute_reply": "2026-06-21T20:55:20.479414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fitness_evolution(\n",
    "    search,\n",
    "    metrics=['fitness_best', 'fitness', 'fitness_max'],\n",
    "    window=2,\n",
    "    kind='line',\n",
    "    title='Fitness comparison with smoothing',\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "history-plots-intro",
   "metadata": {},
   "source": [
    "## History plots\n",
    "\n",
    "`plot_history` can plot any fields from `history` or `logbook`. Use it to inspect fitness signals, diversity indicators, or optimizer-control events.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "history-plot",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-20T18:39:22.487345800Z",
     "start_time": "2026-06-20T18:39:22.231794200Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-21T20:55:20.482886Z",
     "iopub.status.busy": "2026-06-21T20:55:20.482540Z",
     "iopub.status.idle": "2026-06-21T20:55:20.843365Z",
     "shell.execute_reply": "2026-06-21T20:55:20.842077Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1100 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(\n",
    "    search,\n",
    "    fields=['fitness_best', 'fitness', 'unique_individual_ratio', 'genotype_diversity'],\n",
    "    kind='line',\n",
    "    subplots=True,\n",
    "    title='Optimizer history overview',\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "logbook-plot",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-20T18:39:22.725764600Z",
     "start_time": "2026-06-20T18:39:22.491346600Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-21T20:55:20.845950Z",
     "iopub.status.busy": "2026-06-21T20:55:20.845592Z",
     "iopub.status.idle": "2026-06-21T20:55:21.136597Z",
     "shell.execute_reply": "2026-06-21T20:55:21.135536Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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99dXqtaeccopyoH72s58p54xJpVL0rW99S22DufHGG5Uk/ZVXXqn+Pvnkk5Vj9Ytf/IKuvfZaimaan3ajz+qpSCDa/cLqEpx2Vjspm2ZJ9FI1KDmJ9BZP9mGlUS+VM9zNoG7KXzpHc0p+ZmdqbGo8gIa9uQb1jBjk3FA4CKl2YI+WwZz4hJxDHleZrFaDB0CtAWcq9GjpfnY59dRT6TOf+YyKNLFDxD/sUDHLli2jo48+Os/A4TRAhlP3OG3PzAknnEC//OUvjb+33XZbw5GS7bGjxk2ThZ6eHpXCEW01P82dknQGEC2Uapiu3seR3Uw2TZM1kuZXTBK9EHaypmiGepPDqhWAWW3Pjfqctf2dyqFrLlGMLk4ep5u1DffRLqiz8JTOkXwlP/mXI0VB9Jgyj0qJkvJ4ebVrEx22016+7hOwB6cGi/iEIHVTnNoLQK0BZyrkqAiJfV9KRYc4AsXpfn/729/oqquuoo985CN0+eWX09TUFM2fP7/o+9LpdJ6jxPDfo6O5IuXC9w4NDSnnjFP9zDQ2NlIUkcgU3xzS2axRw1bKKAXhhI106RcmN/paqH/j8cuiDmzssGhNKcyF/8+2rqYT9zzQtX1gyXNOj02Y+loVIpLKfH09vXklvf+Q/EUc4H5kSpT8tOMfTFNyLc1P7UGBwqTUTa2FMxV28QkeS7r4hCD3x+Gp2urlBwADZyrGHHzwwernm9/8Jt1999102WWXKTGJnXfemdrb24u+Z5dddlH1UGY2b96sUv9Kwe/hVMBjjjmG4oCxcsqrpVktwsGpfpB7jW6/MKk5ZGOtFiTReRFAOTJlJIrFgOWx/VzrGledKaUkKJLoZSLUmhBChlahbspzA5jTr0TJj9Fcaf9FWSQSZhb5YVjxka/PdX2dvu4PsEdbgfiEIOOpltpPACBEMw8LVOSnP/0p/fWvfzX+XrBgATU3N6uo0hlnnEGPPPIIrVmzRj03MzNDX/va1+jBBx9UYhX33nsvrV6tNfNkIYpbbrnFqL0qBr/nySefpCVLlqi/WY75//7v/+i6666LtJqfWe61VtLD4sSoXguSMJ3LVA2kbKoUvxKS6IVI/6kNA12ufT4vPrzWvZnSmVx9TsnP1x2tjpEB1z4fzIb7eZmV/Bg5L9M+N1pNTnHNVH6aH8OKfkyfXjcFwklLEfEJ83iaQF9GUIMgMlUlvPpKLs/7OWl0okwia6TC2OHtb387XXLJJSrFj9P0Nm7cSN/+9reVU8UpeZzyx2l5LF/e1tZGRxxxBB133HHqtazIJ8+xwh9v62Mf+1jJz+KIFNdnffKTn6S99tpLOWAsSHHDDTdQtOtsEnmrqYvm5Kc/gqj0C1OVQ+qxbDYT68agbOSs1KNCxRT0CpHjwNEJdmh2WrSVKxLc/clRPQ2o/C1GDGiOnGiSyltX/fmguDNlVvJj5N8ZvYGur32mOI2aVWGK1k1llIDKwTvu4et+AefiE2ResEqnISICag44Uw6Z3zRH1SOwOhgXtruJatmrO1OJbEI5VAx/Hn+uFVhsgqNPGzZsUNLlr3vd65QjJVx66aWqFxQ7S9tvvz3ttNNOxnNf+cpXlLPFz3FKIPeaEt785jfTfvvtN+vzPvvZzyonjFMEFy1aRHvvvTdFETYqi6UwsCrcTlS9oQn8jzDyPV4Mt6wuj76oeV5sC8NFwbCwt1MxRCKdVfce2/AqfeCwN7sqflGsx1Xh58ti0TMtq+m9Bx9X9eeD2bCjbFbyY2SBIePzAoOqmVKRqUSRtFNO9cvQc21r4EyFPDJVqLgo0uhZvXfZ1vMXBrSHAPgPnCmHcOPcK956npJ5dZvB8TGjHw4bI9stWKx+Z0fKasNe9d7GxqKOj7D11lurHzvPsWNldq7MbLnlluon6qlh5jobkUeXlDEQvX5hWlwqZ7iNTk7E1pni9LpKkuilJNJf7dxMHzis+n3gqALXbFVK8TM+v65O1U2t6Gmh9xKcKa8jU4KcGm1+m7B1b6kGqakpjEwxWvPeDOqmQgpHkFW/MtWIO39+MZ/NjpF+OFOgpoAzVQV88/HiBjSnoUkXQdAMHUgGBxDNkDQY/slmUVQbYVVGcY6FoYmx2KaTrbAgiV5KIr0nOUSpTKpial4lY2ttX4fah+Z6a2qemghFCnVTHkemzEp+jNmxGhgf9cWZ4n3gND/t8+tKjkUWy1B1fxFtrxFr8Qk1v+RShPPVObnXVFap/SFNE9QSmKlCjhQNAx+jGXqdDSPmBhSKoqvmJ46FGI9Dk/GMMnL61KYBTRK9nIpfOYn051rWVrUPa3rblWNUzNgq+fn667g3VffoUFWfD0op+Q3nKfkx2jWh4VejVRYnyHBnV7Uvs5+X/WODfVVvmy/7BOzVS/E8UWqxRkZUzxiuY1BbwJkKK5KCAWfKV0a5OFpPgTEb4F6kcwJv4eiu1qdNCu61x0cm49kHZVVPK6X0VeNKtUrFJNKZZ1s1hc9q66U4RbawpqJ83ZR2crjfFPBOyU8cV0GujYEJfxqt8qJUYbuCwv0Rh4rl+kG4aBnqKSo+IUgkEY17Qa0BZyq0iPpYTowC+BvNUGdBv2FIDRuIZs0UI/+OxrQPSr4kur2pXdK/1vd3Vr0PLIleytgq+fm6kc9pisC7eqlCqXy5JrhO1w+SslhlmltLjcU1fR2+7BOwTstQnxaZKnHupNcUC1AAUEvAmQop5qkKqX7+odL5srPPA5yp6DGsq9pJobsYAGzQxQ2eI4xGuUUK+606MzzO2fh2Aqf29Kp0stnF6VY/v32k39Fng9J0FlHyE+RvVoD0rWFvhcXBhoQ2Fngs8eIACL/4RFDjCYCwAGcqAqQz/vYBqWUkMlV4c0DT3mjBToWhGqZPc2JGjsYwZZMLwzl90aokeiHmaNZjG15ztA/KmcuIJLq9fZCUQG410ZMcdvT5oDidemSqGDK/ieCRP2l+egp1ieiGjAVuUYHoVHhoHSotPiFI5HMCmRyBOLvXPnEH/eGFB/3/cABnKgrMwJnyVxrdVGcjsJEHokNyesowICVQk4jxjV6l19mURC9EVpuXdW1yuA+bbUmiz3bmtPcs2bTK0eeD4qhoQpG+QOq4+1wTqiJTKvW2NKibCm8PO0nxK3V9y+Ns2KM8wV+eaVlFq3vb6cmNy2kkpiJLYQaRqQjADRWBv5EpMeyMyBScqchGGKUoWupD4hhldCKJXqpWpXt0UPX6sQMbTxxF4H2wKjwx6/N1Z46dMuAOfF56iyj5CX7XhKqaKQslwLm6qXbvdwrYE58oM8fIfZOdLog2+Qcf74fXLVO2Il9e/eOjPn46YOBMRQBOdwB+ihbkbgpigE/DmYpuvzBDTCQnzxwn2BDeMNClbqjmPkJ2MUukv9hmTyJ9XV+HkjZXSoIO+1SJ0lz7MOqm3IIdY6m5Laqep/874dMCg6j5lY9N5eqmesaGEeEICZv1yFShiIkZc+8y7jUF/FtMM1/rEADxHzhTYUaftOyuEgPnTqusphXW2UynkeYXtXRNKlhFlRs9G/1xYiVLouu1Sk02a5VmpVfpDg2njDiRRE/YkEQvpEF/Hzu7WFl1t16Kx0ax82JOy/IvzS+XXlgK2Vdeaa9WYRJUD8+ZXSODyg0ul0ZsbifSMQxnyi8eWrc0z04cnUaan984v/MCz+EpidcZ2Ehxwm233UYPPfSQmuAOOugg+vCHP0wLFmhd7js6OujGG2+ktWvX0g477EAf/OAH6eCDD1bPdXd30+9+9ztas2YNbbfddvSRj3yEXv/616vneHtLliyhhQsX0tKlS+lDH/oQvfnNb1bv+c1vfqO2t+uuu6rP2nfffSlKsCMled5GGoP+70waqZZRQgrd82w2nw1Hv1P8tH5N1a2Pcd0UG7Dr+rscSaLX1zlPM5S6KV5dXbJ5Fb3zgKMdbQcUyKKXUPJj5DGVHlRG8trd3m9ZqjRMpW6KBQ+ebVlNr9tmJ0/3C5Snbbi/oviEwGONayfRuNe/WjZucM3npzDDBvgHIlMO4RsCr9Z48cPG3ozph1Mw+HE7BZ3//ve/lUN0zjnn0EUXXURPP/00ffvb31bPDQ4O0vvf/34aHx+nT3ziE7TPPvso52fjxo3U3t5OZ511FiWTSfUcO1of+MAHlGPFDAwM0N///nfq6+tT291rr73Ua88991xKpVL06U9/mnbbbTc677zzaNWqVZFMDSOznLb+dyoLZyrK/cLyzmWMHGOeE0RFr1z6jVUkTZAXFvrGRiy9py85Qt1jQ7pkcnXrc2KovdaFuik36GBZ9DL3Dbk+eBz5scjAkSlR87M6FlahbioU9VKVxCcEmYf6xq3NH6A6Hlm/TGXV8HGXushkTHsphhlEphzAN56rH7+dNgx0u39GVDQqv1Gv1uCwjvbeegf60onvtnQjeuGFF+jAAw+kt771repvjix1dWmrzbfeeistXryYfvzjH6ttnXjiiXTSSSfRokWL6Ne//rVyhvg55oQTTqDOzk66/vrr6YYbblCPseH29a9/nebOnav+/u1vf0s77rgjfe9731N/H3fccdTb20u//OUv1fuiZ4Cb62z0gtoMGidHCbUyl81rGWacS17B82MV3g+4LxPnxytJ9CpS/IpFhx7ZsIzOOeQEy82CnUiiF6LSDNMpahvurWo7IL9hb6laOrkGePywAti2C7bw9NCJBLsVx1+l+qW57mvI030ClWkZ6q0oPiHI86jb8Z7hySQ937pWtc9pqG/Q7MYsUTKGirVhB86UQ/w0xKSQ3g6nnXaaihK9733vo1NOOUX9HHDAAeq5V199lY488si87yApefwcO0Nm+O9f/OIXxt/bbrut4UjJe1paWlS0S+DIVVNTE0WJMW7myrLoeXU2GmxcciqMU9lpEIDEvXKM64quwnO7gWrqi8IliZ5RNShujU2OLk2lZ2hZ5ybrzpRDSfRSdVPjM9M0ODFGW87V0pKBfTiroXestJIfYz5b/eMjnjpTvHouqoFSk1oOGc88724c6KI9t9rBs30DlZ0pJXBjoR5SUo0hz+093BNQiygnaE5Do6FUOx7DXophJ/rWRACwwcARIq/SIrj4WrsYEpTlPNhEgnZbvK0y/qwaKxxt+te//kV33nkn3XXXXXTdddfRhRdeSFdccQVNT0/TvHnzir4vnU7Peo4dp9HRnNRm4fMjIyN07LHHqu2baWxspEim+ZkOcS5NLEvTqRQ1NMGZisy5zPLqdu4xuXT4HPP11RQDQ91cL+XWAg9HMdiZ4qgGFzWXM6DYYF+t5+u74ZwW1k2dvv9RVW+zVuHUS6mjKFVLJ4IBvPDgtegHO1KzalIt1k0taVkNZyrk4hOzGkHDoPcUtj8f37hczdFcq6qucaOXYrwUa6MAnCmH8ITR3OCNs8BGyYzuOHH6jDxm11jab7/96Ktf/ar6uf/+++lzn/ucqoPaZZddqK2treh7OMWPa6fM8N+cxlcK3h6nEB522GEUtzobmZyUAT4zRfOamoPbQWD/XJquGfN5HZ4cp8URd6b4hrm+X5NEdyPFr1AinY3YVzrW01G77FPytev6O6uWRJ/9+fWUyaZU3RScKed0jpRX8hP4EmEfZ2B8jPyolzLXpFaCjfd0OkOre4rfr4D3tA33WRafMCs18oIM8A4WZtHSZrPUXN+U3+ogZu0/ogAEKCKCWanFCldeeSX9/ve/N/5mo5IjTKzmd+aZZ9IjjzxCK1asUM9xpOrSSy9VDtfZZ59N99xzD7322mvqOXa6WHDine98Z8nP4vc89dRT9Nhjj2n7mk7TD3/4Q7r66qspitGMvDqbgudBNBhhafQyK/JDE94ajn7ACk6cAsVjtKnBPWfKLJHO0aHKkbFsVZLohUh9D6tUAed0jg6UVfITpH6J6y+8JFmk91slZExxlBQEm+JnRXwiv844E7s2FGFs0sv3OK6XMh/7SRx330FkKiLYrdd517veRZ/5zGfob3/7m0rL48gRC0TMnz+fjjjiCLrkkkvo/PPPp5133lnJpL/pTW9SP3PmzKGPfvSjdMEFF6jnWHzi3e9+t5JHL8VRRx1FX/rSl+jzn/887bTTTtTT00O77757pMQncs5Sfp1NMQMdhD/9QW4m5kJ3c0oTR6aizgqpl1Kpce6ui4lE+tq+DguS6FqaiWtphroBzUXUI5NJWjRnvivbrTU6dSW/So6LPO91jYtK+1JpftbHidzzuMZx82AP7b7ldh7uISjG5kHr4hPmOTerN13edfE2OLAezP1do9r1PdeUIRXXXopRAM5UiDFPW0rO2cbZYrGJBx98UAlDTExM0J577qkcJYGdKXaYWAqde0lts01uwvvCF75AF198sYpKcXrflltuaTzHQhbSj8oMpw+yc8afx6qAnPoXNbTIFPfKoaIG+GgMDPBaQHrZMIWGpKQ0Rd0x1iTRWUXPWlG4o+hQSjOAB8ZHaKt5i2a9ZmB8VGsKqyTR3Ut55nPGqUIZ4rqp1fT2/Y5wbdu12LBXooylEAN5lAV4fJFFty7gZK6b4kbScKb8p9WG+ETunqk5U6w2CmfKfR7Wm/TytWFO8Zariuu7gb/AmYoI5u7WVqmrq6M99tij5PNbbLGF+ikGO0TSqNfMVlttpX6KwSmExd4TNQGKwhu9GOAoqI0Gko6Z0K8BM3Kbj3rKZsfogKpxcaO3UyUhiEc3vEbvOShf4dNtSfTZaYZ1lMmkaVnXJjhTVSr5VTKCZcFhfHrK80baWuTfXgRT6qZWom4qGPEJfcHETmYMp/2yeFY30jM9qWFbqYv+zKlvzK8LNppww5nyG9RMhR39OuE+AsB7OW2mMGVKbv7cyBREJ8LIJEopTUW8qaFq1CuS6CV6CFWLOGlLO/IFabyQRC8EdVPeK/kJcu5EttwrktNSk2qvZx/qpoKj1ab4RGGqX28SjXu9iEpJk96mEiJoThbfQXXAmQo5YsjbFaAA7tTZqHOg/w1nKnoRxllRRlmFj3hTQ6mXsloU7gQxnjq49qbg5sz1VCyAwfvglvBE3mcn6o1rLupRxCCb9VZS8ssvWp/2Ic3PWWRK5mhelQf+p/jZnWfkHsqpwMCbJr2sejo7i0YX/9Ab0wP/gDMVEXglAngHp7iUqrMR0LshakIis8+j3Hui7Eyx0buur0OvY/Cu75kY4ewwLe3Mj06xJDs3iHRTEt1MrlZRkwAGDmTRLSj5qWPtU50FR4M1+Ql7zhTvv0TXKqlLgmDFJwR5bdRrU8PG4xuW55r0FunjaW5M71UfVFAcOFMRgVcagHeIcV20zsanNBjgrsR9MeRmE2XHeE1vu1I30xwZ75wpHvfirD29eWXRFD+pb/Lms7XtFjpywJosOp8fq8danGYvV7NVZF8fM3aRcbiyp9WDPQOlaBnqMVJ57SC9prQ+SMC9Jr2v5TfpLcDcmN7rSDPIB85URLB6YwTV5POXqLPR/52cgdxolBr2BpnS5CXLTZLo9R7VSwnirBVKpK9UzpSWt+91miH3uQHOGvZakcyXBQa+x3h5XYzxHKtSwOy/VxxrlnsH4RafYGROmIBEt2twhF6yLprri9dKmaO+oxGvC44acKZCjlwacKa8JclpfhXqbNAIL1qRqaJpfvq/Ue3DwQaypqJnf7XYCWJEjUxNGI2OByfGqG2kX81JXqT4CSLpzavbqFe0p+TXkxyypOTHmMfR0IR3jXvFuGOlN6djYSqdgkPlo/iE1qfMnviEeUxxSxeurwTuNemtNzXpLQf36AP+AWcq7EgDPESmPEUz1rQbR4lTQFPpaBrgtQavgKtzWcTZkMfY4IyqSlt/ctQzSfRCOOVVDCOWSGdWeCSJPuuzUTflXMlPr7G128y5f9wb9TU2AiXq5WQRgN8j3+Vp1E35QsugluLnRORGHGaep3jxBbjXpLfcnCs9vmQBDPgHnKkIgeiUx5GpkpmU2vQ0HdFoRi1K3Kt0oiL5RHKriap0rFarpDkyXkmiF9JgSKRvMMmyeyOJXrpuapNnn1PLSn5msQ+mzyP1NV6sqiTwUwkZCyt7Wigs8KJMlOsvy8HptU7EJxjzqztG+l3ft1qjVJPesuIfkxD/8BM07a2CntFRGplwPy91cCJJEzNTyljh24/IEjfWzaGdFxdvsluM/v5+evbZZ6mxsZH22WcfWrNmDb3tbW+j6elpuvrqq+mSSy4p2bTXTOHrW1tbaeXKlWpbcSE5U1q2V+4jUY1m1BJ88x+ZlKa9RQwA/SGRjvXSGfCuXsp7R6awbmo6PUPtIwPqGmARAK7ZavIwKiVwehGLbWwe7PH8s2pRyU/g1/H1MDjuTRTB3NetUODHzljgInweh2GAj9fVj9+unI4DttuVTtzzQDpoh909Vdj0E/5ePE86+T6aAqPW9JvH46E7erKLVOtNeoujNabXMjSAX8CZqsKR+tAf/kbj0+5HK/hGWCxKMr+5if588fm03cKFlhygd73rXbTjjjvSvvvuS9tssw0tXbpUOUCdnZ30xz/+kT75yU9a2p/C1/f19RnbigvjqgdKToUoTtGMWoJXiSXFqb5Ymp9JOpZr4OY2NlFU4DovFoJgA6dUAbIXSHSDU7XuW/OSoWrppZJg7rPrjWgjLzDNbWz2/DPjo+Rnry9QJqv1sfG2x5SdvSpRN5Waoe7RIdp+4WIKkvaRfiUdzunfL3dsoGWdm2hB81w6bvf96bg9DqCdF21NUYVTMjXxCfv1UvlzbZZ6x4Zd379agmulKjXpNcMJGXwtJ6egPuwnSPNzCEek2JHiVKLG+npXfzh9p7G+Lu+HL6Tx6WlLkbCRkRG67bbbqLe3l0455RQ699xzlTN16KGHUiqVovvvv1+97p577qGOjnyVrkKKvV62xWzevJmefPJJSiaT9MADD9Cjjz6qHDk2Vp9//nn13qGhoVnb5SgZb2/ZsmWhqAfjNL9Ssr25xskopI1Gip82nopLx+rOVASl7tmR4siQk4JwtyTS71vzspHiJ8atl5jrpp5rWeP558VJyY9TgqzidZ2FWiVXl6XziKpWN5UoKtUfpKqmSqdM1KlFDnZG/7f6JfruA3+nHzx0Kz2+4bXIzTNM25Bz8YnCCGQ/GvdW2aR3TckmvcVBK5cgQGSqSjRlFXd9Up7EZjsYGUrxcoMFJiYm6KWXXlK/L1++nHbeeWfauHEj3XDDDXTSSScpB4bh1xx++OG00047ld6XTGbW69etW6e2xZGpF198kW666SaVSrjrrrvSK6+8QrvvvjstXryYxsbGaGBggL73ve/Rf//7XyOl8Lvf/S7973//o6OPPppWr16tPv+Xv/wlzZ07l4JC5fSrGwCVSQ3LRjI1rPZk0bVTVtwxzr9RbT2vcpQ3bPVSShLdg95O5WCDiiNTHB1inNRRVFM3xal+L3dupDfvfbDnn1lLSn6CnEuvUoOS0rC3yjEjqX5ckH/2QcdSkCzvyrUH4IgU3yunMyl1Dvha2TDQRZsGu+nWpU/QETvvTcfv8Xrad9udfVHhdCfFz5n4hCBZHkNQlfOsSW8xcn0xIY3uJ4hMxZDtt9+evvnNb6rfv/zlL9OZZ55pPNfU1KQeY/g1Bx54YNltWXn92rVr6Rvf+AZdf/319LOf/Yyee+452mOPPegPf/gD/eMf/1AX98MPP6xe+/jjj6sI1t13361ef9ddd9HU1BT99re/pcAjU2rKKhLNQFfxyKD6cJRJcTKrHYnUd1RQK+GZ6gwcp8jqNBtY/ONHvVThZ28e7PbtM6NKz9iwIyU/wwDT50Ev0/zcGAtB101xtGn9QKe6FkQIhiMxcxqaaGHzPFrQNFepbfJUNJGaVpG0a564nf7fvX+iu1c+55lqYhjEJwR5n9YbCXjRpLfocdf/jaswSlhBZCpKBJ8NVxSOQh111FHq9z333FP9e/LJJxvOGEfGuM6Kuffee2mXXXahxx57LM/541TBL3zhC4HsP980uB8GU2zV0NxVnFfmmy3kLYOAI1OJMqvg/Hg2S8MRUjvqGRui3uSwb5LopVKsjNQfH50pSSdkdSqu5WCD1Snr+jtV88sT9ng97b7ldhTHeik7Sn6CLDF4ZYCxM+XG/Uu+E48Dvh62nW9dkMlNWISFjVw+bsUWFng/5zU1G/cWrqvi1/M+37H8GbprxXO037Y704l7HkSH7rSnr4sTVthchfiEIPfSKKY5hq9Jb5PtazmqvRSjSriuYBBFX4oWmgQxJE96wYIFs5SimPb2dhofH1fRK6G5uVmlDwaFSt/Tj26xOgOzKtzY9BQtnpv7biCMzlRxVUZBq8Fh1b/oOFPr+7tMvZ38VwuTuileLeUVUj9TlXJ1U1l6vnWtUk1zstLOBuxr3ZtVCtaa3nb67tsuoLjRqUQD7Ks9ymulF5TbjFm4Lq0gY4/n7CWbV9FZrz+GAkvx00UBykUM+Lg2NjSoH5UGmE6pn5lsml7rbqGVPW00r7GZPnPcGSoFMAzwGOiuUnyisKcf0uMdNOldb69Jb+64a/+ywBLwDzhToGrs3LQ5isWRqh//+MehOfKaM1W6zmZWV/EtoqvSFHdGjUL30qhTnM2Xa45CI9aMAyPZTTgixJ9tR9jADdRn1tUpw+KVjg22nCk2Cu9e+Ty90L5OFXGLIicfzzjSNTJoW8mPESdnRo/Qe5Lml3XWsLcQcerZoQnCmeLju6JHq5ey42yoNMC6JprT2KTGMkcOeEzynHXbsifom6ecR6ETn6hi4UbONW9rdHqSFjUHVxMdySa9I5oqp/1MGL0vZhrOlJ/AmapB7Pb5cNoXpBhHHHEE/epXv1LiFBK9evXVV5VQxZvf/GYKAs2Vqlxnk9VXWEH0I1NG6lFEYHlhSd8KCr4Oqkmxc0MAY+OAtbqpgfFR+u+qF+iZzauUeAW/l407TpGcyWgCAaJKGCc69DQ/uxEF6W/N9VYicuK2wA/PoG4sBKi0z3RK9d8JAv7c4Ymkuh9UaqBaCj4/DU31qhE811R1jQ6FLMVPr82sYr4xv7drdIAWNYcj8ha3Jr3lIoLAPyBAUYNsueWWSn2PVfhWrVrl+uvLcf7559N2221HF1xwAf3pT3+iq666ij70oQ8VlU/3C20lt3ydDYppI+RMZctHGKOodqTqpWJo/FtFnAOucytXC8Cpm/9Y9iR9+4G/0ZMbl6uUJXYO2CBhUQBRxFKr5TFbGGEHkWvreDazq/aYaxmQNcR43E/z44U5NyJT2ndjJ4Sd5iAbZ1fbokDqAblWLSzGrxviE4xZKKd9OByNlqPWpJfHl91zIK9GX0x/gTNVJTzgU2mXfzIZtUKY/6MZ/FZbMnEd0tlnn21Ef3bbbTejyS4/9vOf/1z1o+KfShS+3rwt8+8MO138uSKDznDEiRsHiyDFLbfcQu973/to/fr1VF9frxoCc4PhoOAbhxaXKjdpRS+aUcuRqXJOhzwzHhG1Ix6fYiS7GSWOElrdlGbsv9i2btbzXOT+nxXP0rfuv5keXPuKasLN8yYbI/Ob5qq6lMKV9u6xQYoTnLqYa1ht05kyHRcvpKxl3nQjsmqu2eO6Kb9Z3rVZOehutAeQ78HjeoPFqKs/suhaPVi1iDx6b0zTar1u0utE7ErGpMwFwB+Q5ueQRXPn0LymRtW4N0Pu5pkrOYQiTtOcxgZaOKfZ0jbmz59PV155pfE3CzyYRR5YbU8U96xQ+HrZFqv4iZIfw72izJ/LfOpTn5q1bxdddBGFBb5xWK2zSUYomlGLiKhEWWdKf056JoUdLRqjrVr7Xa8UFrS6KS3V7+WO9XTcHgeoxzlK9eiGV+n+NS8rg11UOdmJaq5vVIs1hdsRMQuuL9p3m/ikHvH3caLkV0hfcoR23WIb1/ZLiS7o49etyKrUTb3WtZnOOOBo8gtOV+RUU45sznFB1ZXHIzuHfA9a1dOqFP7iID4h8PlOZ4n6Qi4FH6omvW1rVS2dk6gUI+/gMcrzpZ8N3msZOFMO2W7hQvrTxefTyIT7xvXgRFIZerlVK6157oLmZtpqwTxXP2vFihVlU/c48mR2luKIFpkq703Jiur4dDSiGbUI3zhEhreswlbEpGO1JqxaQbikBdUiUjfFK/icEvXU5pX0v1UvqkgK10XxJcz9WFhGuFzhvGbgZaknOUyxk0V3KFKiGfWaSp7bqXOi5Kc+x6WaP6mbah3uJd8l0VVUiuul3LkW63VnatNgDwVNq0viE4KMw8Fx96OdceTxjctVHZ3WpNdZfarRF5Oy6h7HtXnAe+BMVelQ8Y/b9CWb1QpYoTPF8Mqrmz0p2tra8mTKC5mZmYm9M6XU/CrV2ej/RiWaUYuwOp9I8JcXoKBIOVMsPpErCK9dpFZmaCJJ33ng76rxKTtXWi1ZnaqHsiIhrK7zLFF/ciR2suiaIeywyaqeSOl2M+tcip97aaoyFjhVl/fXr3YVrxmS6Jxq6M534QWAmYx2/sKR4le9+IQgx2hkKjptKAJt0rvBfpPeWZjsGLYj5zfNcW8nQUngTEUA85TGxoObcL2TueapFhE1v7IGuJEahshUWBnVb9iVjLac2pE3MtBu08NKfvr4DEoWPQywcSF1U1zvlHOimmwpXskiFTtlcaJzRFPyc5riJ06m282s2aDLLXK4g7nf1FObV9EZ+x/lryS6i73exGh224l1wubBHlfEJwTZhqbmCMrxfOsaR016Zx3zgrT37RYsxoGPuzOVTCapu7ubdtppJ5ozp7L3PDExoV6/ww47zHo9N4MtFFPgfkaLFi2iOOG2MwVyfabKK01JU8toRDNqEdXLxkgnKo04zalsOlJKfrUdl9IMs6b6RppKz6hjwcXZ7EQ5SWljRmKk5qcp+bHT7TwVNHdcxt2PGBeou7lZN/Vcy2pfnKnWoV5lnCpJ9Dr3TCepg+TvwpkPcxut1UV7l+Znr3+WlYULvmZBeZ7atFJFpepsNukt18olTnNc2AnMmWJZ7Ouuu07JbrMT9L3vfY9OP/30oq9lQ4KbvP7jH/+gbbfdVjlULKf9pS99yXjNZZddRj09PbTQlHb35S9/mU488USKBWrVMAuFFpfhsSXS6HVlxC3FBsBNIQKy6BVWVeWpTCbrSU8dt1FGsoo4IPedo1CicOXUMBeFsTitlvMYkYU2pyIlcjTdbmY9xo20PYDT3dkBaRvuV07Oojnu1hMX8lo3q/i5I4leyvhd1dNGh++8NwUBZ12oiK9L4hOMLABxaiSnVTtRp6sFOkYGaOMgC5tkXOrlp40oEWQCMXWmli1bRldffbWSyH79619Pjz76KH3hC19QDV056lTI7bffTv/73//o3nvvVc+vXbuWzjvvPNp///2VA8YX6urVq+kvf/kLHXTQQRRHZLJlKXbgHuJIMeUCU1ETLahFJEWikpGdi/BkVaSRZbPDPD6d9g6KK9VGN+T9rFwWK/GJKpX8cv3XpjyJGLsdWTWn+t218jm64PCTyEuWc72US5LoxRT9+N6+urc9MGeKxTzcFJ9gzHXf7PDvutg9lcg4wRL/IofuRk28vvbu2UIGmE0gd+f//Oc/qvcQO1LMSSedRAcccIByloqxYcMGuvDCCw1Ha5999qE3vOEN9NJLLxnPp1Ip2nvvvWnz5s00Ph5fb1zJeAPXMDunZets9H95JRSEt2bKUh820w2elcbCDNdRSDPP+hqul3KTOlPNXFzmUyWL7lDJrzBi57bITpIjXdmK3Sccp30yz7WuIS/heYLV9tjZsFOfZxVxgP1WJyyq5OeS+ARjFs1pG+lzZZtxgyPKz7WuNpwpV2rVpC+my1FmELLIFEeRjj46vzfEXnvtpR4vBqfrmUmn07Ru3To65phjDHnvhoYGOuuss9RA7OjooA9/+MOz3hcHpHkvcIesrpLIU48VAYoUnKnQN+ytVOZufpZltcNcoMv1UjxCa10W3U3kWuaxMjI5QYvnzqeo0zE6UJWSn/m4SE+zsEemGHZsOMLI1/6q3jbaf9tdyAtW9LSqhTdNEt19s0lSjbtHh2IjPiEkEnWUzWaoJ8DvFmaWd7fooi9ZaqpCeKKYmEwS6sPxjkyNjo7m1TYxCxYsUI9b4Xe/+50So3j3u9+t/h4aGlLy3bfeeivdf//9dNttt9HNN99M//73vylu8KTkliT61NSUZ6+PCtxvRsPaDQRdxcOe5pdbYbeyWhp2RTepl3K7eL+WMRv1XSGQo3azYW81TXENYZaM286UXsvowfg11y/dvaJ0i49q4ebAOUl097+H1LkFKRigyaJr0RE3ke3xXAbKp/i5V6umMR6jutCwE4gzxelUHF0ywxNVY2Pl4kSus/rDH/5Av/nNb2jxYm1F+YMf/KBysLbaaiv1N6cPvvOd71SOVVzIVXlY59prr6VTTjlFHQtOoTz33HPV4yzgccYZZyg1RSsUvv6+++4zthV1smrdP681Q1HEEEDNWtgFKCrXTOUpl4W8QNcsiw7cweyYhqG3jxtpQt1GXZ1zY8yY4zIZV1VjVZqfh2NYakzW9LV7kobNDgY361WORlnFV+fIdvm4u9002br4hDaG3BTXMDtTgyGQfg8bw5NJerVrk7Ir6uvsK5OWAq1caiTNb7vttqP+/v68x/jvrbfeuuz72IH6+9//Tn/9619V3ZSwceNGFenaZptcceMWW2xBmzZtotigVxSysWjFYGSFxF//+td0zTXXqPoyloiXtMjJyUn1Y5XC13ONW2GaZrTTJivf6I2u4lnthuf2DQe4leZXXkhEEP2ssDtTkEX3Bo5epimrGiJHnV4XlPwYuaVkdQdoC5fSH0c5MlWx9YRzeC7m65llpR9et5Tesd+Rrqe/ydzipiS6Ga1KKaEWTjil8IQ9tHpyv+BarazL4hOC2Cpu9y+LA8+1rqVUOq23enBzbMVPZCfsBBKZOvzww+mFF14w/uYo1csvv6zU/Epx5ZVX0t13361S+cyOFHP99dcraXUzzz//fGyV/SoVTbMgxzve8Q71+/e//33629/+phQTP/3pT6v0SIkqccTq8ccfL7utYq+XbTH33HOPUmL885//TG9/+9vpzDPPVI89++yzSnGRH+PzY2bVqlX0yU9+kt7ylrcoifunnnqKgk7zy1qODGZpEo17QwcbAuIYcY6+1Rs8G3phRin56c1pgXvI+Q8iCuBFvVS1Sn6MeUHJrePC+yVF8AmPzA0+l426A/DYhtc8qWnxQhK98DvU687mur4O8puWwV7XxScESbuGstzsa2PJ5pUqKsVjy8053mjlAvVh3wjkDv3e976XWlpalKHPRvdXv/pVmjdvHp166qnq+enpaVq5cqWRVvbb3/5WOVGXX345DQwMqOf4h4UmmIsvvpgefvhh9Tp20q644gq1/Y9+9KOeXgiTMzMe/aRoqsiPdJHnFbhy7Lbbbsq5YTg6xceBI0t87LjZMT/G8Gve+MY3lt1WsdfLthj+/aGHHlLng4//ySefTF/72tdUROyb3/wmfeUrX1GP83lm+H2clsmOM6drvu9976PPf/7zyvkNWoCiLKZIoNvSwaB6uP8Xq7MxVnL+cznlU6FeNOlNjkAW3QNkjLAASdRxQ8mvsJawb3zElX3j1gNSZ1rvYaaqqPrx4oPbIg6veSSJXogY0+3D+Vk7ftVLeSE+kdeKAIuQeWwa7KbOkUHlxLohh553zNHKpTbS/Dgdj1P1fvGLX6h+U9wv6o9//CM1NTUZKX9skHO06dBDD6Wnn35aOQg//elP87bD6WaXXnopHXLIIWp7vI0HH3xQSaSz82VO+3MTnnQ+8/d/0fKOLm+2z/8VCZXss/3WdMUZb1EpHc1Uur6MlQ2lfoybIrO4h3li48cYfo0c81JYff23vvUtmjt3Ll1wwQUqHfPjH/+4igzyz7777quiUZxmyA7ZwQcfrCJTzO67765UHG+88cZAUgfTXDOliqPLv8789OjkRKgV4GpbyS9nlJRFP+HJEDtTLI4h6pGQRXcXo2ZuKvqpR1z3Va2Sn/m48P1tYNyd+hZujGzrunQIR+Q4xZFX+e9a+Sx97A1vd2W7HO3ePKRJos/xuOGspGhyam9Q4hNeRN7kvPMiMFLkcyzZvNqISrmtEIlWLjXiTDGcqnfdddcVfW7HHXekO++80/ibnaRKHHbYYSW35wVBKmu5WRzsBttuu61ypBj5d5ddchK1zc3NhhIgO04vvvgiHX/88cbzHN0S5y8odcRcD/rKXerjYIDFDalpSFiOTIU/p5wLwiGL7g2SesTGflwa9rrR1FlmuaHJMfeU/Hy6Z/Lq/kRqml5u32BZiKYSXL8kjWy9kEQ3I30OlZS8S/tvBe4r5pX4RGErAhah2Hb+FlTrsFDK821rKK3XX3sVDZRsDRBjZyrK8EC94bz3uN6PQ+hLjipJy0KjkHOq+bMrpfn5DUfCCinVAJedKu4H9rnPfS7v8XqXi17t9u3SjrWVyqmsag4LQiiLrsK51m5Kcmm53aDUbWEByKJ7g5F6FPGaAl5Y4/491Sr5FfancUssQNVL6c6I1zTqzhT/vNSxno7c+XVVb3N5t7eS6MUiU+y8dYz0085beJNZU6xZr1fiE4z5qPH3gjNF9HL7eqWgaE5RdfeYQ33Yb+BMVXHTmWNByt0JcxobKJ1tmDV58yTLkx6vZlSD3VUQN1dNOK2PU/44mhU0fCz5mBqpCBWEPXRBRRqNwWp2bCNTCWvjNReZCq8x3ZPUnSnIoruOzK2spMU1PW5EdYKAa+pmXFDymyXM4lK/I61hb74cvVcoIYq6enU8/rfqxaqdKR4XK7s1SXQRuPB6/3lc8j2JI2J+OVNaip834hOF36tjZJAO3dH1j4gcS1qkt1SdJw6snEYeu35GOWuZaN5BapxqG8dy2h0zNjY2q9+XG68vxwc+8AFaunSp6hemcvMHBuiSSy6h7373u+Q35p5RVm4i8ppkDJsXx6VmyqoxIDeX6TA7U3qPKUCeph6NTEVXhKJzxB0lv1lznEsLRtp2/IlMMU16XdPGwe6qhYJYIECcQa8k0UvVF63v96Ye22/xicJxFYdWBNXSlxyh1b3tyv5g598Lcq1cskqcCXgPnKkIItGUavp8sRgES53/4x//cP315WAxiquuuop++ctfKon8E088UaUJfvGLXyS/4fQNIWGhB4rcaMZnEJkKZ5qf9dfL2ZZV/TCn+UEW3X3MTjcrakW6XsoFJT9BpkG3VC7HVCNt/s0fd4qjcxIFuXdVrv1KtZLobqRQWkGii+wk+wU7jaohsZdKhbqjH4dWBG5Epdj24DlInH+3MfeMm5gOb11wnECaXwz7TDGswPfkk08awg7cd4odF4ZvuqyqNzw8nKf0V4rC16dSKWNbp59+ulJVFLh5Mn+uKAAyv/rVr/JUALkXFTtm3Fh4/vz5RWuu/OwxxVi5jchrJNcZhIcxh5GpaqO8XsH7pRr26kplwF0k7Yyd1e6xQXr99rtFVhbdLSU/RrbDdUduIJEdv+BzykIUnL779OZV9N6Dc0JHzpwp7yXRZzkdaaJ+n5yO4cmk0X7By0b0IvgSh1YE1cDj6ZnNqyiV5dRi7yOBIgKz5bzKdh6oDjhTEUIkEqxEpvgiNdclcaqepOsJW2xhT1VHXs9iEbIt7kPFP6U+l1m8eLaMOL/O7ud76ZRamtT014RZtKCm0/yybIxYdKb0f/laCqNcL6tepVyshQHFDbw0ZVU6ZZRl0d1S8vMi/ZUXObQ0P//GcKPuTPE1tGmwh/bYcjtHTobUEs31WBLdjFzrvGA3k055riC4ob/LUCts8PCzcrV4tS3exOl9ylFWvaXKt6VxCx7Lu1LwNepxB85UBN0pN+souKHun/70p5LPv/3tb1c9pOKIFpXgJlPWXm9EpkJcZ1OraAXz9iNTfC1xM8kFzZqkf1jg5qOiTYjIlDdwWlM6y6lH7siA+w07293sTLmk5Ge+LtxSjNVkvnmRg3xDFfXX1avjc9eKZ+lzx59pexsrdOEJr52MQupM89KGgW7ab9udPf289QNdqj2I12qFsu3xGs/qWLJ5ld5byiPhCR0RE1E1oZPuiMmA8sCZimJoKqsVFroRIuYmu2effXbJ581Rp7ihBChs1dlox3sqxL2JahXu/cWn0npkKpHniIXOmRIlPx9U0GoVOa5+px51jQ7Sc61r6IQ9Xk9bzVtYVSG7m0p+5uuCHQnuhcMpc9Wn+Wl1R37C+83O1GvdLUr91q6z+ZqSRNfqFf3cd035rk4d/9W9bZ47U7nIlPdKiwxH22pVXY7rEF/p2KDqpTji6L26pagPw5nyAzhTEXYEGhLVr2xwzRL/1CJmAQoryNznVX8x4Aw2PFQ/GzYqHaQTsTG946JgmkaXVfLzqT9PrWKkHrnUU8kq96x6gZ5tWU33r3mZLj7qFDpyl32qVPJzL3qZi9hqKVlbz1vkQpqftUbabqIaoerG+5ObVtKb9zrI8nvZCVupmvVqRq/fsGPMn71xoNvTz2FnWVIZmz1OZZTzz5/Fi1eL5syjWuP5trU0ldZshyYf1CGlAbdcg8BbkIwfUbg/CnBLgMLqjV6vJ4DUaKjQUok0x9iqzaalQWgMTYQvzatXpflZb0IMnBt4PH78dpTZYGfp7l89cy/d9PwDNOUgdVjVS6moT51rq9zmrfQnqxNBYIcgqasC1vlsaqieU7oj9ODaV2y9l50YUTP0Srq6HCxMIBFMr1X8eByqVEaP6zLjop5ZDUs2r9SipIk6VXfuNYmA5rdaBc5UhDDf6MIs6RwnVcTiqQo49mFs2GuW4LVzPodDmFMukSnUS3lHLm3X3xrI/nFNPU0kvJ/atIK+/cDN1DbcZ1sWPeOyVJ65cWu1inJcH8NjWNsu+Y6kKHaMDNDwRNK2JHrCR0l0M9IKgQU0fEnxU9/TY2fKlK7cMdpPtQZf25v1KKCfNXjMhEttDkB54ExFFF7hANUeQ5vOlP6vqKyBMMmia+fHTu6/vDZsaRA8vvoMgxtTtNeRKRZb8OuaZsdNVornNDTRvMZmw3n+/kO3qCiKOCBWZNE18QB3PRXZ3ECVxnxSb1dgd5HDLbR6pzq1D3eufNa2JLpbvbvsItc8p+F5qRzLjYH9lH4XeXQW16lJ4YmMNqaqrUO0Sq4vJmq8/QB36qjhstpSLaMEKGwgxndYexPVdsNe+2INuTSIcDlT3NhSxhhk0b0joRutbGwPT/hTN8UOijhLfG45FW1+01z1O0e8/770cbr2iTt0dcrS8Pjo0hUf3Y6eyDw3XKUzJT2m7C5yuN1zinmhda2l93A0iKMIHEFo9KGupRjKidN/X9XT5sln8PfbMKBFpvyKfovTz8IpbsOCLt/435+pdchedNcPeKHm+da1hjPll4OeUx+GM+UHcKYihlwgMOirg1fkjBQUq8defyHfgOymCAJ/0vzsIOddxCtCleIHWXTPSRSkzPnBgIo4aqIRYlSxMTu/aQ411zeqOYkV6K6476+0srul5Hb6ksNKXEG93+XopRi9I1UKc6gIXDbYuj+pm+J94YhTJVZwVCqjRWuCEJ8wK/oxa/o6PPkMrsca1yOkfvXYk/E+aCPl0ioc0e0eG6Lblj1BYePVrs1KbZaXbZrq/etZJi75JJwpX4CaXxV0DYzSUNL9VW2ebDi8X5i+sWheM229haaCEydjfnJykn74wx/SZZddphr8bty4kZYtW0bvete7PPvMtKNiAzkfWZWuM1dP0QFhcKbsy/vm0iDClVPeC1l0H41WrW6JDbGDaHfPP5PrkNi/KIyi8u9zGpuUYcvjkY2va564k07d9zB6z0HHzTJ4O/RmvV70ITNUDquM2PIihXKlEhabonsAn18WkeAa4/+ufJ4O3H43S/VSQaX4CXxO0+mMUtvzgvX9nYYkul/OlDiIbjfu5fm/VY8mVlvn553whBaV8rM5vIxfv2tCaxU4U1U4Umd994+UnHQ/hMo3yWILevOaG+k3XzqLttlinu0UtTDT3d1Nt912G1166aXq79HRUWptbfX0M504o3Jvzeod6uFMhciZcuAbi/PF5zJMQBbdP0Q+2K86Dk7hLOf4cyPPhXVzlUPF6UH3rX5JNZD9zHFn0Lbzt8iXRXdZyU+QfRMlPqcklTPmfQ+jSnCEiZ2ptX0dZXtniSQ631v9qmup5Hh0j3ozLiXFz9e0s4Q746oQ7sfF3yWM9a9DE0la3tWij6lGXx10+aRptHLxBThTDuGIFDtSWidrdy8QnhgKjUNe2RifmqGR8SnlTMnkUY7+/n565pln1AV80EEH0W675a/KvfLKK7R27VraYYcd6LjjjsuT6+TI0Jo1a2i77baj448/3nhu3bp11NLSQrvuuistX76cjjjiCGO7sj1+7uijj7Yk/zkzM0P/+c9/1O///Oc/6bTTTqOFCxeqbTDr169XkaqjjjqKHn30UWpqaqKTTz5Z/fvkk0/S4OCg2nfez2LfrdS+OHFGzUYBp41U03ATuIdWX8JGm80Vev10hi0NQkvzC94IrQXYmExniQbH/ZHHHxjXaqbKnVuerzntbzo1o+odODrx7ftvpouOeAsdu/v+RpoWB9e9GCFGxLZKo1drWUCBo/Wc4vOcoQfWvkxn7H90SUEGXljhbx9UvZQgqZtaeph34hN+Rkok02bK5dYiXFcmPSMnQxaFebZ1tS4/n/C8l9csTI2SgffAmar2ANYn1Gqi+/U8RR5Paz0hmEqKTytWrKAPf/jD9MY3vpHmzp1LV1xxBX3pS1+iCy64QL2Xo0AvvPACHXvssbR69WpqbGykv/zlL+pffh07I/zc0qVLlXPzpz/9STX3ffnll+kXv/gFLViwgPbZZx/aeuutlTN1+eWX05IlS5Rjw9uZN28e3Xjjjep15eB9YeeM2bx5MyWTSbU/N9xwg0rz48//7W9/S83NzXTAAQfQ888/T3/84x9piy22UA4VO4w//vGP6d5776WtttIar1rZF558q7nPV1tPANyOTHEhdTSksSvRmxxSiyVNPvQiqXXEceDGzb7JomdZ3a6yG9TU0KjSvThKxUbi75+/n5Z1bqQPHnmK3rA344l0tyyeV1u4rqX5Bb8ooPWcqldRqcc3vFbSmcpJotcF3pJAxgcb4iyKseXc8vdRu/culUrsY71UoYATz7luOBc870uDZW3baeU8BFXvVrhvSsVPV4b0u3G1oT6chfqwHwQ/4oA9pEO9il5pCmbF+Mc//kFvfetb6Uc/+pH6+9lnn1WOCcOOxxNPPEH33XcfbbPNNsqxuP7662nTpk304osvKoflnnvuUc7JxMQEvf/976ff/OY3ysliOjs76X//+x/tueee6u+HHnpIOS933XWXcnJSqRR99KMfVe/hOqhysEP02c9+VkWn+LX8mexMmeHIFH+fQw45REXMzjnnHPrc5z6n3sefdcopp9AjjzxC733vey3vixaZsudOSaPXbAjTCWoZEaCwe7MycspD1ISZjSdplgolP++RMeN2HUfZyBRlqd7iWGVnaUHTXBU9ZWfg2dY1tK6/UzlXXij5mY3eale0RQI+aGeK4RQrPn69yRHqGh2gHRZqC29mlndvVveF+oDrpRit21dCjRUWIjluj9e7tu31eoqfatbro9Nhnp85rXbXxdtWvU0+n9o1pcH/cv3jLltsQ0HDqZScpsnHeq7fUSnT/Q1iZf4AZyrClFv147S3P/zhD7T//vvTW97yFjrmmGPUD8PpcvwYO1LSA+SLX/yi+v3qq6+md77znUaUh6Na7LywMyPO1KJFiwxHinnggQdo++23Vw6MwNEsdmrcYMstt1SOFLP77lqROKceMg0NDbTjjjuqCJWdfVECFE7qbBIJ5cSOwJkKDWIIi9S1/b5h4ak/7E+OGCmo6DHlPTl5fO9FSNg5kQhYfaLe1j5yfSZHEViYiIvsxTD1wuGWY8KOfbkFOys1U+r9FqJwXsORJj5WfG3dufw5+uQb3zGrlo2b+/L+NvqouFZJHCWdzdLa/k5XnakN/Z16fzLuw+VjDY/eEJrtlraRflecqVV6VEqWOfmWzmIUYXCmnlZRqbQ6zkFEysQ25Eg4X8t+RiFrEThTEUYpxJTIbfrEJz6hVPJ+97vfKaU8TpH7f//v/ymHih2P/fbbr+j7BgYGDKdLYOeEo1ECp/aZ6ejoUNGtDRs25Dlze++9N7mBOT1Pbuycclj4mJ19ca6GqE3aSX3VFQQLrzZLnrzTyFSYGmD3qPQbb1TawGzq9Do7rk/yGk7XMtoxODBi2SBjh4DT/tgp8GqM5IywrIracnPhatX8wkBTQ4OqiVrauWGWk7g8BJLopRT92oa1hUJ366WCSb+UxcjuEXeENVbp4hPa3K/VxXEz66DhNMYX29cpG03V7AVwEeQEs7I0OTNNC5rn+r4PtUQ4Zg3gCG7yWGriZ8EFroviHxaK4DojdrCeeuop5QwNDw8XfR87HizqUOhgcXSnFBzF4ujQt771LU/OpJ2JyOq+5AQo7BrgWi6B24pEwBmcbpnrF5ZwbDSGJc9eU/KbLZ0NvMEchfF69VbJolfZP4yzCFicIpVO646K+2NEtsnbH55M0pwFDp0pXVrd71qRUrCoxARNq8UXbvJ6zG77hU4S3YxEHXvHit+rnS4+tQ71qu/quyCCPhY4KYRrtqqFF0RX97Yb6otKlMulbVfLS+3rlQPD+Nlbyoz5fsj2Cpwpb8HSZ8QwT/N88y8F1zStWrVK/X7ggQcakarx8XE68sgjVc3U9HSuwPjBBx+kV199VSnf3X///cZzbGhy/dQb3vCGkp/F7+FaJbMTxuIWvB0rWFH9s4rVfRH1H7uLczJBjc8gMhUeJb+coelU6r5a5TK3YMOpktob8MZxYBljz2XR9YhINQa7ElRoaFCRFi8w71mfXr9nF743jevGZFjSVdVx01X67l/zsvE4L6SwIpyqlwpRNFjmM3ZKKwlOWWXzYI+SiVf1UgGcF3GsByeq7wfFKpeSIcK9xGTbfJ0FjRKeUL2lWO05+PS6oUl/1EprmeCXYoFjytV6cCrf97//fSUewWp2LPDwjne8Q0We3vOe99Att9yilP1YpIIFHh577DG6+eab6aKLLqI777yTPvCBD9Cpp55Kzz33HLW1tdFVV11V8rP4M26//Xb1Hlbg45RArllilT2rNVEsRMEqgWeffbajY2F3X5z26TK6ioesN1GtK/lpSR72MDssXHe1xdxc6mjwaX7hMerijHnMsELeNvMXeS4+EXZH2Vzb4tQw5cUJpxFjL2EHdGY6RZuHepQhzlE+TdAjHJLoxSJTHHHheq6dt8hPr68qxS+RCMRxlEWEYRfUcJUkuh5NZCEWuae7se1qYHGNtf0dan/mBBD9KyaY5ZXEPsiBO3aVpNJZlXLh7k9m1k+eIovUepSRvGRH6dprr1UXFKfpfepTn1J/Myx//ve//12p33EEh+un2IHaa6+9lAQ5N9Bl0YmhoSGllMeO2E477aTe+7rXvY7OOuusvM/i7f3tb3+jj3/84yp9kPtW3Xrrrcp5swLXP7H8OW+HI2IsbsGOEMO1TvI7w04Xf7fFixcbj7397W9X0Ter+8I3eSMy5VQ2GM5UqJT8ql3tH/RJGtvKjZgNhPoQFO3XAlLoL72b/JBFD0HrpYrItcR1XtUo+TFWZOD9dFC0VLMs/XfV86GTRC81Nlf0uNPEfsNApyHYEEQ6Y53uWJvHR3XOVO67iABR0Eq7z7SsNurvgkrxMzDUSqE+7DXhWYaJGIvnz6X5c5pU495pl3uiabLns5fa5zU30qJ5zcZqQyXJS07NK5WeN2fOHDrvvPOKPscKfqWeO/zww9VPIezksHPmFO5pxT+CqPcdeuih6se834X1UNxPy86+qKbI+u9Ooxlha/Ra20p+zlb7zSvwQa9mSrqRRAIgi+4fsn7LUUE/GvaGyWCvVNviNPVRpabp82uYIlN8zbOBy/P3M5tX0/sPOVE5U3wv5QWMsNRLCZwmlsmmlcx2tfB9TyJTQfWwk+NbbWYHCzyIxHuzXusqjtpkgK0u2LljZyoVkvEktuLoJJwpr4Ez5ZAdtlpI//n2h2ko6f4gHZxIKgncwsJddqS2XTxfK7SsSpHOP7i3FddilYKjUCJz7hd5x83uZKe/fCqFruLhSfMTUVz78Onn94ehCXNfcsS4tsNSZ1ILaBLUzqMwtiJTNnpMBYnsodPrIjk1qV1YAUVAysENfCdTWqPmF9vWqYgkn5fGuuAl0QvhRZUUpanDBUU//p7jekQoKJlsmdfY2ahG8GV9f6dafFK1X/o2ZJxlXGwKbJeVPW36ghg7rM6EW9xEHZNslpKo8fYcOFNVOlT844VRxfnclVSQxPAKM5xiaJYpLyd77hfVNLGTVdYwNXqtZbQ0P+d1KBKVCENOuaqX0uu/ohC9iAtGHYeHAhRsOPIiWVQc5Vz/LWeLhZzGFSZZdDNKFKCuXp2Tv778SOgk0WeJUKQ1JchqYQdEJNGDcqZygi9ZGhwfo20XbOHYaVEpfqbUTLGXsi42BXYkPKH38ApDXydDMGsqHAJLcSZ8swewTBScqZNPPln9hIlqInpiG/CqGAhPZMqp/LK2cqevpIdBFt2F+i9gD3FuvKwrYIlxmXci4UzpM53TFhCqZUGIxTZYSpudqZHJCTV3hEkS3Yyk+3KNbrXtGzhVMGj5d/Oncv8sp87UalN/Kfku5rTt1qE+350pXkBY2rlRLdby+ArDeJI94N50wFvCP6uDkheIW3KptUa6mpopU18aEDyaAZzfgNNPo9FNIIseDDJ2vGzE3Z+svsdUEMeE082doAQGqki/9RrVSFWPkIRNEt1MLtqSpY0D3VVtS6uX4qhJIiSCLwOOtsFZBK3DfcqZKlRfNLY95n/j3hda16o+XrwHQaQYlr+OUePtNeGcQUB5ZIJVQhXhvFmFGS3Nz+lxE2cq/PVqtaTm51QxLKfOGAJnKjlMmRCv5scVKVyf8jDaPMB9dfQUqzCsWFsWCkjNVJHmF96xrHpO6VGesEmil3I+VvW2O94O177x/MJzZdDpZzImnAq+rOlt19MViRoKInUybt1sdGyVZ1tXUzqTVpHnsESfIZjlH+E44yDWqX5hQ6XbODxsYgfxNuDIBj/2JTWrupopToMIfuWO8/x5TIVJSroWEAOMDSGvIs5c86Kciwg4Um6kM49NTar027A6U6RHD3j/tD5F4TWF6hOa87OpCkU/Ub4r5oAE1YyY1S0d10vpDXELo2y5xr3+Nqnlebt9uF8d46CPbx764XG6KAKsE94ZBFgC6Wb2cdqwlxHjgCdPHPtg4WiSiIk4VUiTtwUtdc/pIQO6mpwYT8DvoniigWT1hf7FYIUvlhqPCmKUcgTeyYJdUglXhNt5ZGN8YfNcmt80N9z7WVd9H7QN/Z2U1YURgkzzM0eChybtOzx8313V01oyXVEe4xpFP+HUw5m0thDTEKKxZAhmwZnyHDhTEcR8qcKgt4/RsNfBpCdvYfNiAr2mQqHkZ17tjOrNRqXgGLLo4bkZ1wLmo9055qyOw2qPqTBHaoouGlHWUU8gUfMLe5A1CmIvIkIhapBOkP5SoVKKdND7iFMD1bVUIjXTaNzrYf1j2cizuheFZzFMhjYEs7wHzlTEiYsztX79epqcnPQ1MuXkFmo2hrwsWAfW66WqaQwqN/agbzY58YloCBTEifyi+CFPe0xFJYXT7GA46b9lpN+GpHYkykj9DbfjcFLbyVHv1qHe0KSgybU27mAxcpVIorO8e5HGw7Jtv1uXKIEZ6atG4UH2ZiYmdmKYwUwXceIghNDV1UXvete7aHxc6/Vzzz330FlnneXZ56VdyrcJQ6PXWkYZbC41Bg16UUJk0cMq0Rx3xOjwonCdjT+p4QhLYbrdvod2jXf+YRBlrR41J+i/r3YgQrF5sEcZ06peKgTjz7yAZTdaVkwSPW/b+pHi9G8/RYX6ZLEkZPO3+XgAbwn+ygLOSMTnIpmamqKZmdxK0tve9ja69dZbPfksXj3KVBOZMt3YRkPQm6iWMSJTCRfEBwIWFMml+YXnRlxLiNHvJApTieHJcSVuUU1tX5Dpb1zvZQeO2Mu1FK51+ihHTjVTbU1fu+MUP95OGKLecq2ZBYSswLbOqt62slL2Zue926MoczH4GglJFmVxwSzK2T3AG4K/soAjZMoodYGwc/KDH/yA3vKWt9App5xCX/jCF6itrc14/qWXXqKLLrqI3vjGN9K73/1u+u9//2s89+KLL9IHP/hBOvbYY1XEiCNFwu23304f/ehH6bOf/SydcMIJxnPLli0ztnfOOefQAw88YOl7cDSKP4Ph/XzkkUfo/vvvp3PPPVc99p///Ic+9alP0a9//Ws68cQT1ff597//TY899hideeaZah+uvPLKvG2W25f84+X0Rq/nfHvY5BP40xjULCgiq+lBKflpalswPoNA0tHY8fHE0NJnjTDVU1gVCrDrYMq8qH1fmBhuIM7D5sFe2+/dMNBppMaFIWpinuM6R/otv69lqJfG9X6AjSWuI2ncy7TZ2Ha19CdHQtrWQoq8s4HXBccdzHQOYeNrYmrGm5/pGZqcTs36KbZyXioy9ac//YmWLl1Kf/zjH+nvf/871dfX07e//W31XGtrK334wx+mN73pTXTnnXfSF7/4RfrWt76lXr9ixQr13Mknn0x33HEHfeQjH6Gvfe1rtGTJEvXeVCpFTz75JB1++OF0yy23KIert7eXLr74YhVRuuuuu5Tz8/Wvf52eeuqpisdx3rx5ajvM3XffrfaJHcGJiQnj85544gnq7u5WTtT73vc+ta/sXP3sZz+jn/70p/TXv/6Vnn76afX6SvuSl+LncN6T+9GYUqwCwab5VbcNs6CI3KiDS/PLUn1Eamrihqxoj06Neyo+EaWzm3CojCY9ptQ2PNmz2kMiU7zoYgdeoNmgR6bCEJUqjHp22mjcu8qU4ldfZlHCqH8cGfQ3zS+EbS3yBLNC0P4jzgRfjRhB+KK58Ce30CvrO7zZPv9XxEg8cI/t6JpL3pG3ulQqMrVp0yaaP38+7bDDDtTU1KScjulp7WK67bbb6MADD6SPf/zj6u/tt99eOSO8isiOCkd02CFhOGrEkZ7f/OY3ynFiGhoalMPFDhpz9dVX09FHH62iQRJhOv/88+kPf/gDHX/88RW/79y5c41/ZZtmeL/+3//7f+p7sDPFTtQnPvEJ2muvvdTPvvvuS2vWrKHjjjuO/vznP5fdl2pk0QXNJMpSMkDjG+TU/NyITInRuOW8Bb4fWl4xHNINVsiiB4PMqV5c0yI+EQXluGLXht10ZiXMwz2mIvZ9o6DoZzdy2j06aAglBd2stzDqmaYsdY1ar1EUSfRKETZ2ptJZ502B41QTab6/cY33VvMWBro/cQbOlEPCco8oVedxwQUXKIeHHQhOj3vrW99Kp556qnqOHQ92psw0Nzerf9euXUsnnXRS3nOHHnoo3Xfffcbf7HyZnR5+j0SrBI4o8evcYNttt1WOFDNnzhz174477pi37+IoVtoXs/PpOMmPT36WIxmomQrcmcrm+rBUC0sP70HByqKHZfW41pCUtmkPVMC0yBRFDqcReJFFR1zKPWSOY6EcTrvccu4Cm816E+FypnSHZ2B8xPKCk0TYmisoEoqjNeRB/WMxhiaShoCROL1hhHthAe+AM+UAvlj/8pXzVOqdVyFjTjkqVEJqbqw3JgqeHHm1s1Sc5YADDqAHH3yQHnroIVWHxKlu//znP+n3v/+9ivSUymVvbGwsuupjFogQx8b8HNc4felLX8p73K18+VLRqmJU2hdOi6zWrpGjM+6jWhAoHZmqRjFMcux5OyNT/jZ6zEvxkzSwsKzS1BiGEEkmo1TGGl2UkDZk0SOWVZ+L1tmMTE1paX4YyS6eC/0/Pq4ru1vouD1eb7lZbzn1u6CQfbHaO2tdf6ehSFiqXkqQ6JBfjXs1JT/NLgjbYpj5/uZFPSjIAWeqikE6t7mRvGBuqpEylC5vJGqZZqqwUBliRV67cOFCJS7BP5yqx2IMnZ2dtPvuu6voVDH22GMPWrlyZd5j/Dc/Xgp+7tVXX1VphU5wc5KvtC8qMlXtMrG+vxMmBxMEt9JWrfyyCjRmg5O6l3opXnwOk8FTS8gYyurOzw4Lt3K9ZqquPlrnVlKE7NZaqIapEYzERaEXWjqbpbX9nZadKU3JLxOqqJTZ4bEaLTFS/BKlF4L9SNkthlK7DLF4kNzfIJjlLeFyo4EjitUBffnLX1bCEclkkjKZDD3//PO01VZb0TbbbKMiN88995xS8OMUOG6Y+853vlMJNrCKH0ey+Hd+joUnWKb8Ax/4QMnP55qk5cuX00033aTS7Vg1kFMMr7jiCkv7L6l7g4ODRrqeUyrtC9+MqkWmzEkHTQeBO3BahUQGq81Tl5tvUFL3vckh3b8P5824VpCj3+li4To7UVpkKnz1FJUwGqDaVAHjtEARoADuIVGPtmFrKnW8OMR1Q3wmwuZMyZxrNVV+VW+7undbWXByOm6ratgb4ppIcfLUIgfwjGjN7qAoqfTshqNf+cpXqK+vT4lGHHLIIUpi/Be/+IVK0dtzzz3p//7v/5RwxMEHH0wXXnghveMd71AOFacH/uQnP6Frr71WPccOGTtmHN0qxd57761EIdjp4voqlixn4YvLL7/cck0Uqwfy57P0ejVU2pec+mH1ogWQGg26XsqdXjby/mRA6owcmZI+MCAYzIZQl03FtErjdCYdrR5ThdfFTCZlP81PF6AA7iH1OFYbS28w6qWIGlxMW3WDnMOTsuQUtg33qfm+sa7Bcv0jLzL7odDKaX5mkeC4pOsCe4TrCgOWMd+mpPjRDAsu3HjjjUYdEddCmWHniX+KPXfGGWeon2LPnX322crpKYQFLvin2HsqfpdEQkmdy3s5IsbS5sxZZ51Fp512mvHaRYsWqR5ZogDIsFKfua6q3L4YDXtdaPQKZyo4Rl3sZSPncywgdUZDFh3GZ6CISmefRWPVCgMT+qp1xHpMMTIcuZ0EL0JZrQfRVsDDm/YUVWSeUwIfFhZfOMUvm82oiGi1qdBukzA5PBMzUzS3URPAKsbq3nZbTqH5uHSODtLeW+9AXtKvGvayLHo4YxNyNJKo8faUcJ59YItUiV5TQjnnxu5zLItudmTsbK8S8l7zZxT7PK6HMk9cnCZY7HOLPVaqL5eTySnIJq+1zlheZKo65P18U/cbThWVugHIogeLpOHZbVJbMQVIjdPouRZilLIzaGdVW2um7Z7KJsiPTPGCYNfooMVmveEMmZidO15MKseq3lZVqmDVKTQ37m0f7iM/GvaGejFMaryrLKEA5UFkKsrolYXFIlNhgVMLJUJWjNNPP51++MMf+rY/pfpyOZmcWPULBKnk504vG7nxTgbQ1FCU/CiESlC1Rq5J7bjLkSk2HqOX9mZ2/9jBXDRnXsX38Fge1R2v6LmP0RChYAdpeXcL7biotEgKL/S1DPZqUuIN3ghluaUy1zHcT7tvuV3J8bSqp822iIaIdXSPedu4V6Tqtc8M5/yNGm9/gDMVYUTQzxUHwSO4MTALQHgRybIL31hkpa6a27y8140GwKCaND93UonEyJ30qWDZDNc/QBY9HMiqt5v9WCQFKIriIprBq91j+pIjJQ3eQiNeanjDlloWB9hgz2TTqh6qHC2DPYaUeENIjXxxeMrVKPaMDWlqmEoSvcF2H6veMWt9rJzCjpTYFKF1poz7GyJTXgJnKga4kbrmFSx4UdiXKig4VSBHdb2Jwn7ca6VhrxvISAiiBk5T2wpfH5haRI6/m+meLJus1UwlIntM2BnkCJtdJb+ofuewp/qlKE3tFRT91ukNbvn8hTXira43dniSpR2elb1aVEo1HS7Sb7LstlVk2NvGvbzIENYeUwIEs/whnGcf2CLMkakwkRdJqkaAQv+Xb1ZwqIJt2OsGcuO1q1rmBr1jIosOQqMw5mL6rtFjKqSr1lYNsSGLzVXHTO0FwmpcRhmpFeaIZ+V6Kc0JCesijVxvg2Uc9dUqxc/+YpPdPlbVRp7DKovOoMbbHzDbWUTqGsJIWItMI+F06sfO3iGU6SlLU2k07g20ZsqNND99G0HUHkIWPTzIOOAIthviMnzPkJXr+oiKMYiBOGSxjsxQmvN4v2oVETngRsrFWqKIPbBBj0yF2aGVsVWqWTovVLKSHy+C2v0e4qixTL+XRCHybCwWosbbU8J7pYUEqekZH/d2haPaCAmoDE/O2YJjNzM9rR6fyMw4ULnipoPByGnXOqpmyqXeTLIJc02dX0AWPTyIAZbVFbqqhZtKS+poZCNT0jbA4go/q/65JQwDZiPjiA34TYPdRQ9R9+igob4Ytma9xfpBSZuLQlqGeozG7I02v4fRviSd8nQhXC2WZKPRohpZNN6CmqkKcP+ixYsXU09Pj/p73rx5nt8kZqamKTUzU7GAly/grF63MzmJhmyVmJyapDTLg2aJsomEcqQG+gZo7VgvpWykSprPCt8Itpm/yPJ7gTuMTo3r8stubE03orNZmkpNl+154iZcmzOqNwquD7HRU0uI4ELHyEBZtTTLKUC6mRXmCEE5ZMVd6x3lrzAMKK/ot7KnjV63zU6zXrPeaNabCLUzValf4ypTip/d+VFsJ85G4Xl2XtMc8oL+cXamwh15Nmq8sxlL/cmAM+BMWWCHHbSmb+JQeQ3fuHiCsTLkZdVlamAEF0kFOIo0kdJWuvhGwys17Ei9NlZ8hc+qUQ/8RckvG017qzdSzZFGXtH1y5mCLHq4kGgKj6/uMgpjtlKAdKM2qs6F2F1WI/AqMhWFZfqIi1Cwol+pyNSGfq2/VNhFbSTKxunV/FPo+JnFJ+x+D/P11j4yQPsUcTrdQBZMQttjynQseC7i49xoofExsA+OqgX4Qt5xxx1pu+22o5kZ72tk7li+hF5u30BzGsur4PGEKeH8L594Nm01f6Hn+xZlbl++hF5sW6cmFzaYObXPTkSqWI+MUikKwDtYwnzGRfll8xaGJsZpm/lbkB/0spJfxI3tuCFi4Hxu3Kmn0BySMBu1VlKxrCocsgAFfCmPzwlHOTNp6izRuHe9qpey15cpCMyLWAPJUdpu4WLjOV5M3qjXfTU3NFR1j2blQy+cKa6rlJ50YW64LlNPVq+1gzPlDXCmbKb88Y/XTFOGhlOTNJ0of1tiQ2xkRjPmB1MTtNOcbT3ftyjTP5WkwZkJqqc6StW50i+ZRj0ucAXllfx4ldbNfjrDk95K6Zrh6Adk0cNnqKbTGaMRpytKXxRdzLUnVqXRkebnLTLnDY7PHqMs5qC1Wwh3vRTDI0vm3faR/jxnal1/h9Enq9Gho5Jr3Ft9lLlc5JkJ82KJeaGOFzusNN8G9olmIjfIW31h+lxYSY07rooW6Mc9qde8gCCU/Fy8ienbkZVGvxr2ZrCMH8pITCmFMSdKX1Fs2CvI9VVKOa64ml+4jcuoI+lxrCRb2Gh8g1EvRdQQ8nQus0hJYZSN68E4usbfVeTg7SJZC17ZRqpeStUHRqcmcnjKWosDYJ/ArrZkMkn//Oc/qaWlhfbZZx9673vfayjnFWNoaIhuv/12am9vp1122UW9fuHChY63Fxdk9YX7mYDyjOlGuBvznpqns+xMQc3Pb1SdmloRdM9gk5SQER/TNqVmyqmxANxHjDs30nclMhVlcRFDLj6bUdLKlVKEWIpaoq3AG8zHlkUaDttpz7wUv6w4IRE4ByKmwQtLhf2l2K7hWiSnjnlCOZ1pzxr39iX5+g6/cqU582LUx8XCWiOQu/j09DRddNFF9Mgjj9BOO+1Et9xyC33mM58p+XoWfjj99NNp6dKltOuuu9KTTz5J73znO2lgYMDR9uKE0QfE407fUUelRLooWiCGPOcgg4AiUy7WoogYk5+CIlo6Dhvb4b0R1xpigIokc/XF6bx4E93za663sKLoJ+qUYTYuow4fW0n1W9vXXqRZb3TC3ZrDk9+EeHgySW0j/eqe3VjXUPW17EaUuXwab/jHutHTCzXe8XKm7rjjDhoeHqYbb7yRPvrRj9Jf/vIXeuWVV+jpp58u+vo//elPtP/++9N1111HH/rQh+i3v/0tLVq0SDlNTrYXJxIBpCdFEU6HSLkpWmA0T0RkKpgIo9s3MUnb9Od8snCMNJQMc/FyrSHX9XSquqa9vMgiDpkbdX1BYb7GOK3JqiBSHSoIPEXSyjYP9uQJIrQM9qrzEPYUv8K0WnagBG7U60aqotG416M5XdL8ooG9FgfAPoHM8k899RSdeOKJ1KCrtHC63lFHHaUiTsU49thj6Ytf/KLxN6fFcARqZGTE0fbihNzsoCrns2iB/u9ECpGpYGrfvDGi/XKmpFlvlPLtawFzWlup/jd2i9Mj7UzpKUJMfzIXPSjG5Mw0ZfQiwAgH4yJVN2UWV2gZ7DFEGxoiMuaKpdWq/lKZ6lMVjYWR9IwnjXu5sXdU+jbJLrIABfCGQK64rq4u5QyZ2X777amjo6Po60844QQ65JBDjL87OztpyZIl9MY3vtHR9uKE3LSSM7hI/BQtEBOjGoMLOINTidxeEZQRMelTpFFk0bWRFP6bca0gxlu2ysL1gYmckl8UjK1yyP5Xqj0ZM12XYuwDbxAH3ZyRYjTrVU1u6yJ1vUm6PF8zq/T+UtVmkOQa93LE1P15XdJ4o3Cs5R4zjsiUZwQyCiYnJ6mpKb+HEv/NtU+VYKGJz3/+8/TWt76VTjrppKq3F3VyF0n8v2uoRAv0zcCZCsgxdlkxTLY17lOkkQuuIYse7khMx0jxPj52DK2wF6dbQY5IMSnuWUp+bqtsgqJIHR43YR2aSOb1l3LS5DYoZD9nMinl9HCkjccZj6Nq+yHlN+7tJzeZTE0bKXNRiDzLsZhIoSzBKwIZBXPnzqWJiXy1JP57/vz5Zd/HdVFcE8UNdK+66qqqtxcHzKFs4J9oASJTwaGlhLhbM2WcT58ERdhoiFKheC0h80M1/WkkzS8OUcdc8Xr5ulyVQoQx7c85MUW0V/S0qLlEZNGjECkpFj3iBU+JSvF3q7ZPlnkho2Okj9ykT0/xi4wzpU9D4xDM8oxARsGee+6pJMzNtLa20u67717yPd3d3XTBBRfQQQcdpIQozLLnTrYXF4w+IJm0J3nBcUFbNXXPuJHJieWCgb+MTorEvZuRqVwRt39pfljBDyOSolZNmp8WmYrHfCwGLwu/lIPFJ+ISjQs7fHzlvKzr61SOvwjahL1Zrxnz/bhjZECrl+J2ES6NIRG46Bp1t9dUbrEkGnO4HOfJGSy6x8qZOvnkk5WMeW9vr/p7w4YN9OKLL9Ipp5xS9PVcE3X++efTaaedRt/85jdn9WWxu704IZMFT0Acegb+iBbI5JTKZHDIfYRXLbk2g6l3cfqS8+mHc8w3YRGgiNIqcq0gxtFgFe0mNGMrGoaW9Qbl5etytetSMzCB98jc0TbcRxv6O12L6PhJfvSon1b3cn+pjGvzoleNe6OWxiu7yI2egTcEop956qmn0t13301nn302HXHEEfTss8/SxRdfTAcccIB6nvtHXX311fSRj3yE9t57b/r617+uHmNBiW984xvGdg477DA655xzKm4vzpgvZC4Q3rmxOdD9qRU5bdlOOqvJrQN/4FQiicC6WjPlo3PMRum4XhAdhRSRWkMWqKrpT6OcqZj0EDPqCSuIs2jXpk87BYy5gxdmtHop9yI6fl9vacrSiu5WQ4ii0SWHUBr3Dup1ZW6n+UXnSEMwK5bOFF/s119/Pb3wwgvU3t6uGuzut99+eeIR7CgtWLBA/X3WWWepqFQhu+22m6Xt1Qq9Y0O086Ktg96NUIsWuJYapm+Gb2BRkUeNU+0beZTmx+cznXFvZbRcs17IokdHrtkOnCoqzTHrKTpRgoptICrUW0gqtWsiP6AskqHDizPr9Wa9DRGMdLMDmM5yA+IOwyGsd8mZksjUqMt9OCWNNyo1kYbQB8oSPCPQzm7cC4p/CmEniiNOwrvf/e6qtlcLudM8CfVV6ANSy7gtWiDbYUeKjafmhlwNH/C+X5jKVfdAgIK3zemy85vmkFeoFD9dnCAqN+NaQgwwp8Xa5h5TcVhkyYkclU+BzTXsjf53jlJkitPiukeH1NwVpRS/wugR92w05kWXrhujca/LrWOUM6XEPqJxvOVocm098IboLWOAWYhBxjfxOOGm2hkrUbkpWiBzPW8TtWr+oeoy9FoUL6TR/WhsyLLoGXYII5iSUwuYV3GdiPoY9RQR6UHjlsiRSqVWUXofd66GMc8f7FCpZr1VyokH2tstm9Wia/X1niwEuCXQxdvhGiy3RZC8xBgnqPH2jOjP9MC4UIYm3c0LDpIbnr6bvnX/X11pMscTtBjIdS4NeXNEQVSUgL/Nl72iGuEBK/Qkh9QNGeUl4SRXD5mhSQdNuQ2lr5g4y4VR+Ip9pmLwnaMWncpkuMltXdWNboNAxotqOMxlHnUN3kivu9SwlmsHRRUvKjWvMiokjR24TzRGArA0YVRTMB0meAV0eXeLkkr992tLqt4eO2SyKuXFQtJwhf4rwO3aN/fdKXOz1mGPFyW0NL/cdQvChfm8OFEBi1o9hVVjN1vhHiPS6RjX/iHGfJQXZszjhR1CL4SFmPZhd3pN5a7vKEWe5ThkacrBAhGoTFRGArAwYYzqktFRRymd6c7Ps61rqg7PS50NUyir74bx7bRQHTiPTHlhPMhN3EtnSpNF1+obonMjri3M1zYv6FRTMxUHzKZt3/hI0dfwareo/UmfLuA95vtZFFP8CmvseAp2O31bnDUn13L5hr3RqXk1L4hUUuUEzsCsFwMM6VpdbjnqSPNH+f3Vrk2upYa5KlpgKAXBmYqqkIgZ2Wal5qTV7n/UUkRqEa0onqOIQzXdYyrnXOp1uSVEjthAM0Q3ImJgxgHzHNIQ0fnEvHjR6GKKn7F9fevdo4OubE9qIjnLJSrXuHkvh6fiUw4SJqJ59YE85IKuJF0bFST3Xrhr5XMuNOzVV5I8mPykiSzwnlEWElHyue5v23COXcqtryyLHg0lqFok1+yzeCTGShpQfUQMLSvIV+FehpXm7KgU5ccBjm7PbWympvrGyEa6ed6dp75DPTV6EF2TSKmTa7kY/eO5yFRUMNs9IxMoS/CCaF59oOjKS1x6CPCN2dz9ceNAtyG76wROf9QKo8mT446wuX9IlNGLuoyEefx5qOSXk0UHYTc+7IqR8BwsaaJ1ifg4y3UVUmCTHkX/QWWa6htobmNTZKIkxeAUxbmNczz5DjJ23Wrc25/UFksiWw6CGm9PgDMVA2QhMJXJUCabiU2aX52ph9bdK593oTeR+6IFcUqvjAIjekqlpGF5EuH18Hxy2hhk0aNTx2FXXIYNNmnpECchBpk7SwlQqGiuh9F/AKpvwj3uamQqarWBclmOQn3YE6I1GkCF4sKcBHiU0b6D5vxw+gKzZPPKKtP83BctEJMBkSl/YFlmUSLyxlCtrlmrFXqN4mUQhTnV7nw6ELMeU7NTYCdK95jyIPoPgHuNe6tfJOO5W2qmonZ9GzXBHmZe1DLRGg2gKOaIC1/oUSfJaXnK+ckaOdQjUxO0sqfV0fa0G71Wp+KFgeGkFw1wWC+lu8SeCFDom/SyCbNS8lOrmrA6w4ycnwmbBpgytCSNM0bnWK63ZImorZZNEB85eBDPJtwSNa5mYXY6pZVTRK0mMpdJA2fKC+BMxQCzGg73sIk6Y6YbMxs1DXqh/n9WPFdFbyIvlHd0Zyomwh9hxyxB78WqYMLj85mTRXdPoh94g8wV0+m0rUjiwITegyZihpZlQ2xmskydq7YABkCYyGvcW2Wqn0rx0xdm6yImICQzkhsROjAb3NFjdrPjiz0uNVNi3Eqq37q+DppyEDWQmim3owFGJCONyJSfDXsTnhvQ3gi5jEyN05SxqompNwo1U1yDaic6JbLocXMp5NqY0mX9C+F0SESmQBgxR0vbhqpr3CuR5yjKrMRN9Tls4I4eE+TStqs+FUZUnQIbzfrF31DHpk2C0tkM3bPqRdvbk9Uo150p/ZhPI83PF4x+YRyJ9VDNL5VJkxdw1Biy6NHAPL56k8O2ja24pXHKtym10KBSsyNpYoK4k9e4d7S6xr19upIfby960Wdk0ngJnKm4Sde6JP8ZJNKzROYqnrRY/pV5ctNyW9tiw1gEBdxW36lkYIDoNOxl5OaYzmQ8c6Y41QSy6NFqVNtho9nnQAx7TOVdG9lM0bRHaWcRPQMT1AK5xr32m3DPVvKjSIJMGm+BMxUT5CbGQg1Rh1c5GfPqblNDgyE9vKG/y3ZqGOOVNHoq7U0kAxSvffOqLkPGBzs8XkSnepNcL6UZnDA6w4+cox6LBhg74ZIZEDXZZOtpj9miAi1aml+uTQcAYUJqVLlHVDVw5Fm1tohgBFb2eQppfp4Qrxm/hjGkfEtI10aFUpEk/l2EKO5Y8Yxt0QKtYNSb4e5VWhjwp1+YIL47f4YXcvcqzS+qy5o1iCzm9FlUSOWGthLVjJszZXb+h4pkP8h9x4v+bwC4tRgwOFldGUS/3tqiLoKrBsik8RbMfDFBDMyoN5BV+18ikiSpfqt6WpXMqWUxC2N73qW+AO8xVBm9cqZM2x3VmwO7Sa/uTMWtniauyPVttQ61P6Y9pgrpKxA54rlY2kNgbIO4Nu5lMRppPRPFxRIjkwaLv54QvREBiiI3MWlqGnUlP6Zw9YcjU2zwpjIZun/Ny4GLFsjW2MDHBOU9Y1Iz5YMzMuSykAunR/UkWYCCDe1oSerW+mr26KQ1A0wZWnpNXJxryAoVY7n3lFep1AC4aR9Vs9jMEdm07ohEsSZSrk22n4D7wJmKCYayXCYVD/GJIjdmsxDFYxteC1y0QLbJaWFo3Os9XA+o9WjyToBCzumwRQPaKpwCFtVmj7VKbjV7wpb4RFxr4oxI3Xh+mp85tRz900CYnamZTNpx416+vtkNiWKPKUUiF2FDurn7wJmK2Y0uk8lEOkoiqlDKVSlikIgzxakmrUO9FbfHYX3PylRM+zdRopklcAe+AWqRKZ60vJu25JSOuOxMKSU/vdkjIlPRMsAmLPa202TRKf6KsZPJItkE2tiOnwsJYmUfZbOOFY9V7WSE1ViNxd9sFgrEHgBnKmaTRdYDQzCQhr0lZite+ZR6hDuWP2NZtMALG8e8i3FQUQwzLAiRK+73/obj9vnsGRvSGw7HM2oRR+Q8cU2QlZXcwQgrfVlBvlXhtaH1BdRegbENQt+4d6TfufhEhCPPOYElXvyNdm19GIEzFRPMl7adJpNhQ92YKxgkzfWN6t/XujZXlCWXFB2pf3AT84Q6MgFnykvEKfY6lUjOqcjzuwVfk3IjBtGKxPBqtpVaC165jqrSVzWKsYV9AQEIc+PeTqfOFC+WRDjybLapJMsDuAecqZhgrvfglKKowkZspV5CmhCFlv/86MZXLfWZ8sqIlWM+Oh3daGAUUOmaJWrp3CRhTjd1EciiRw/zOOuusEDFDldce0wJMocWOpbsXHnZsgAAVxv3jg1V0bA3HgtiyKRxn3jO+jWKXORcKBlVtFXO8jdm/p6Neu3UQ2uXWlLz82qxWObVpIqoAa9QEcYytXRuIduecLmxYcdIvy6Ljik3igtUnSMDZV/LqdWpdLwFRuRYFF4bau6L8Io9qA0ko6HPYePePj3NL6rXtzafaUS5FCSs4M4eI+ps9kUJdc1UhVXOJj3Vj2tRukaHKjft9ciIlf10O5IBSkvc+2MwupdTztvqVTdijqpiyo3inMrzjNUeU5FU+rKAXHuTBYIcsgAGQJiRVH8nbS9Y1GtQF66I8oKYXMMjVfTbAsWJ7qgApYvnI7zqoBUzVw6lswhFfaJO3cLveG1J0ddwzy2Ro/aqmWSuxgbOlJdwjrcohnmJDBOrCm5WaB/WolK86Yb6eBracUWub2nWWYrBCa1eKs6dlsQYnSmoU1XOFDfTjuiKPagN7LY6KOwxxZLikXemsPjrGdEdFWAWci9zMlmEBTvFzE0NWnRqaeeGor0jzKIFXps4bqeFgWJpfn5kE7nf/LptuF+NT3PaGIhXtJ9Th3iu4dfH1amQr8WqmmJY5haSUDMFItK410HWgaqX0u8OoiYcRVCW4B3RHRWgjNpSdKMkoqJmJZLUqAtRTKVT9OSm5aVT/DycAL2qsQHFHWPvI1N682u9/sUNWof7IIse87SYARWZohppv5GlpEmEYszjZtoAuNq4N53OWwywglosiUVrC+cOJSgPnKkYpmFEtYcA5yWP606JlVC6WYjigTWvlFTy8xKZVgvrCIC7aOfSj5opjUqS+3ZoG+4zIlMgmnNqJSlhFv3RouDxPcfmqOqQqXGvLN4h6grCjNRN83XKaXv2I1PRn8NLKXKC6oEzFSPkQnEzRclP1AWuOz9Wb8xNujPFamncVK+UaIHX0uhTcKZ8iUx5VfsmyDhJZzOuOOK8QNAxMqCpQMVUmCDOGKILMzOVBSiUWmO0jS2r9OlS8RzB9bouFQA3SBQscNlBru9sTI6BmzXBQAPOVAxv/DMZdwzBoJT8yEbKCBuoIkRx54pn857Tmkt6m8sv9gOnGgJv+0wxnjtT+ljh64f7mFVL9+gQzehjoyHChcu1Sm5OTZWcU/lxLTIV7XoKO41Ppf2GCMOo52MclQPxGr/tFVodFMILtXz5R33BAIu/3hHfmb8GyRmCGVdrPnwXn7B5Y5bo1Itt6/IMHkkN83Y9SSJT0TvecU3/rAa5V/KIGXeh9pBXQCXXPs6GdlwR44nTNEvVoo5OT9RMdEbmZZGJzqX45fr4ABD28Vup1UHx1gfR7TElyO5PRjR7Kcxg9osRciPPRrRxr7oxO2jMKnVTHLp+rnVtEdEC75u8SvQBuI+59s3r1W/z9oddUMVU9VIxV3mLM+bxwFHGYgyOjxniKHF3mGUMD+vtN8byrk0Awo04/P02GvfyvV1qBOsT9bGYz6K42B524j3z1zC9ek57JBv22rwrKyGKOs2hunf1C7NFC7x0pkzRE+ANZlVGP1e/h11ofs2y6FFMuQUaZjn7rtHBooelj4vTjR5TiRrp1aM7U8acjcUCEKHGvSYBlUqoFF5ZMIj6gpgHarVAA85UjDDf0PoKxBgi07DXYSSpqUFzplqH+mhEnyg1BS5vFXhkX7n3CvAjMuWf8WxX8akQ3meWRecUsYaYRyxqIeLfkywemRpQkSl+XQyMrQok8uZqNCsH0byWZTHACn16PWQcIs+5xV/YK24T7ZEBSq68DLiwqh5EjymnNU4sQsETJb/3zuWaEMWID/1PxHbiVK5ijYNB9fihyuhF82tu9JrUjU4o+UUXGXOFaqGFK9e1cPUb0sp6+w12qrxOpQYgyL6QShZdmq5HfLFE9j8DZ8p14EzFDLlYpEA4SmgpI85uzPy9m+ob1e/Pta1Vk5/0hvH2Ri+KBdnIStKHHc2p8b5hryDjpVKjVispfuxk89YaIIse+dXsUnOqFKfXgkNh9DLU+9QoxVS1AAZAtBr3Ws0m4fqquFzf8g144RelCe4CZyquOe02coJDVzPlcNISVT/ezrMtq43J0ksFHrP626SN1S5gHal98/sakqiSU1qHe/XeQ3WRX9GsZQrrhAoZNHpM1dVOL8P0TMECGADRatzLmQN2ekzFSvGZslD0c5n4z/41hqwcjrog6+w3Kg9fD6c7QROi0NR2uOeUJN54KVpgdvy0vlbAbcamxvVz6Y/JJuc0qacyOaWdI1NZ5KbHZU4tJo3ORpYSoPA4nTgsyLUhPdh4wUGJ/GCxAMS0ca+W5heTMW76Dm60/gA54EzFDLdW1YMgpwzlfBtNDY1GHwknPauqQeSCgUc1U345U/rHSF2IU1gMhY3tqBct1zrl6iz4scmZGaNuM+6Yay44TUhzMOORAgXij5PGvSzmpXpMxWAeN1+lsFfcJfqjAxQ1BLnnUhQFKKptfJkTomCFPV49Tfig/pavcAU8qJmqImJpFzmj1aRtsiMmEYtaMLLjjIw7dh4KRWbUqrUeAY+DsWUnrZlrUlWfqRqJyoF4IPZF71jl9jFcBy0R6TjM42Z7ZQSLv64S/dEBijdli5gYAhsq47rxWk3tgVmIgg0fnje9NsJl+0jz84acKiP5G4moYkGCU/yUAhSLT9RHu9FjrSPGF88nhQqPUk/hZwQ8SMzfsX98zDA0EzAlQMTqpnghpBK5HnLxcKY03BFYAvnEZXSAghs/9xGIklT3OKtDGb2EqjNKGnUhCje2ZY3SNRWgeidb+j3V+TRdyYipRp2R8/E1Zz7+jVzjjvn8dY/lN+7lIvZaalprXtnuHBkwifwEulsA2LaRrPQRVEp+slgSk+tbvgbsFXeBMxUz5ILn1BNWtYuakh+5kDJSZxKi8HNyitLxjgrDk0lDxMEvtTS5hmaq6BLPzpR2E64NIzvOmB3irpHB2fUU+nmuFWQ8d4wO5ER+YrNqD2pGpMtCH0EtjTdeiyUyV0nrGOAOmAFjhvmm3jLUS5ETn3ApmjSnsVn19hG5dC+R/YU6jvsMjOsr/z7WpMj5rKZLvOox5WOdF/BnNbsnOVwkMhWdDAA3r488kR+McxAR7KRxm9N4Y9d4W+8VB9zBkXUyPT1N99xzD91www00OjqqHluyZIlLuwTcUqvZ0N8VmYOpQs4qzc+dFSA+BvOb5hjqfn4YF3a6qgNrqF4gfq/86x/FETEn/UU4NbFjRFu1R7PeeCBzUn9ypKixVY1oTlSPxcD4qJGaDUBUkChqKp2u2LiWr3c/23L4gXyTatVqQZXOVF9fH5155pl01VVX0a9//WtKJrW80+985zt000032d0c8AAplLTaRyEMiCpUFG2SnIJitEQ/ooAyVll62cc0C6OxYdZZY8Ou0UEjRTA+Rcu1Tak6C218etvLLmwkTMcibilQoLZKIdSCQBlyiyV1NdHqATjH9gi55ppr6JhjjqFHHnmEttpqK+Nxdq5+85vfxKZTdJSRC9+K9GdYSKqQc1RrD6qX0gbFGeQ0v2xQN1tnq3ec4id1NLUgl10LyJhgZUlhMjVt1EnWktMsNSfad4/qnA1qFa2UgCwtOGs9puIm/Q9nygts3wFeeeUVuuiii2atxB166KEq/W9oaMjN/QNVrKIOcIpUhHpMsdEcxfoDWZSdTiMy5TYDE3pkykeDLb+xYWXFp6JKfqSlfmHFPo4ORP6qda05UzLfpbNZZPmByGGOpHI6dim4pkiiN3G6vmGveIPtETJv3jxqa2ub9fiGDRsolUrR/Pnz3do3UGVkanxmMmICFNFc5ZR9rkZKGxSH0zBUlMfHNCKz/POwBfncUkp+ID7I+ONoVF7UVM1ZtSXAkLs6tKUvjHQQx+ydvpg25Daa0sNecRXbI+Tcc8+lb33rW3THHXfQzMwMrVmzhm655Rb6yEc+omqpmpqa3N1DYBsJSbMamRX5zzBJo0fTmdKYrkJKG8yGHRLlTAVxM5O6EJuRKd7n1iGtx1RDjG7AtY5E+2fSaUOqX2voWXs1Q0YarFowiOYCGKht5HruK1MzlVPyi1evwFxkCvaKm9jWjT7nnHOooaGBfv7zn9PAwAB9/OMfV9Go888/nz772c+6unPAGXLp861ufX8nHbbTXqE/lGNTmppfIoK5yWJcVFIGAvZIzkzRVEqb8P1WS+MriFclRybHbasPiuRsvY+9zoB/ResjkxO0eO58Xba/9pwJc00h/68eawYgomO4XBq3puTH9554RZ6N1h9p2Ctu4qgJz9lnn61+WMlvYmKCttlmG1d3Crghj15H6WyGNg50RcOZkshUhOesavoSgdloUalg0izUOMzqTr6Deil+O2TR44PZYWK1Rs2Zyq1c1xLm7ytKmwDErXGvRKayMb1+01k4U4E6U11dXao2yoy5hmrHHXek+nqsyAYNr+Sns0TtZQoswyZAwUSxX4sYE9KXCMaFO6iGqAGlWRhd4k2CA1Zo1euleDED4yA+SCofn9vO0UHaf7tddGcqbkpflSkc11Gcs0FtY9RAllHg7ddrpmK3WGL0UcyqH1y/ATlT73//+6m7u7voc1wv9eCDD9L222/vxr6Bqgss09QTAXl0To8b1ye1KPZzMPcl4u/SWO8o4AsKkHopvu/57ZgYXeJtiri0qXqpTPxuwECtZqcpaxStSw+0+lpzpgr+rrNfeg1AOBr3ZrTGvcWyCPqTWmQqbunaZnuFRbPmNkLnwA1sW33/+te/KG3KtcxkMtTb26sa+B599NFwpEKCWi1Nc2PF8MujqxoTXf0sikao2PlZXSEHzpQ7qJoUFZkKskv8tKPIVAOi87FDOdhZzcnn4u2RKa2erp7iZWxZU7vUagq1v4PeIwDsIdGYrF4btf3CLfOe5zlcU/OLl5LfLHtlZhrOVFDO1LbbbjvrsZ122omuvvpqestb3kIXXnghFP1CtPLCfRLCnnomSn5MNFNmEnmO4cLmuYHuTVwIosfU7DQQ6017ucGvFq2IV18SkG+AscLjoKnHVJjnVq9QfqU+aRf2nAQgCohIV+tQ/yxnanR6gqYDEj/yGvP9dGR6gractyDQ/YkLrs6Ck5OTSpTCDhzZsgs3By6EZdqnpqbyfpxsO27OFItQDDnolROI+ERUI1Om30emwn2so4Tq46NLT0ehF4f0l1LiE4hMxQ4ZhxyREqc5EcOVazvXR1TnbFDbaDWQ2nXbMdpfPMVPFxKKW5qfGSd9FIFLkambb755lsPEjsujjz5Ke+21F225Zb6HX4pHHnmEfvzjHyvxir333pu+973v0eGHH17xfdzf6g9/+APdeeedeY9zn6uXX345z/D6yU9+QqeddhrVIgnTysu6/g46et6+FFZUkb9a5oxmvxZp8srfgGWTgbtqfkHUpMgn2mnE3D7crwp6JQ0KxFMBjCPpRg+aGusxVShwNLuCCoDojOFMtnjjXiU+ITYJxTdNd1RPVQYBOFNLliyh/v58T577Th1xxBH00Y9+1NI2Nm3aRF/4whfoqquuopNPPpn+/ve/06c+9Sm6//77aYsttihbr/Xtb39bOV+FrFq1iv70pz/RkUceafcrxV4efdNgDx29a4idqamJyMuii+salSbJYWcmnaLhAGtSEqYmrVbhZr2G+mC0BzMol/qZmtGUJuOo9GXzWAQhDgOAG0j6Hi+MFCLiMnHrMVWYpjtqs/UHcNGZuuGGG6ha/vnPfyqxCokaXXzxxfTvf/+b7r33XjrvvPOKvudzn/ucijydeuqptGHDhrznWltbVbTs9a9/fejrg/yEc9nT6Qx1hFwePamanEbbMJHJiVetQfUMTiQDrUlx0otD9ZhS6k+1l/ZVS8YXN7vsTeZWrmuRXJpfbX5/EO/GvZqSn3oVxRHJpRnTW9IAH52pYul9xTj//PNpwYLyBW3Lly+ngw8+OO8xdoRee+21ku9585vfrNICOc2v0JlasWIFzZ8/X0XGli1bRttttx199rOfpfe85z1Uy8jNvzc5HPoeUzxxRbk9Xm5ygjPlqix6QDUp4r+lM9yLI1NRsp/ldTtHBtQYRrPeeBtffI57xobUnFVrsugC1wROpWcink0AahlJ2y3WmD3XY6ou1sqknBUEfHamiqX3FeN973tfxdcMDw/T4sWL8x5btGgRdXZ2Otru2rVr1fu/8Y1v0L777ksPPPAAfeUrX6FtttmG3vSmN1GtIgZgNAQooh6Z0iancThT7jfsDTAyxSeV07rmNTaXfX3X6CDNZLQoFpT84ol5fuL6OFXPV6PeBC8YLGyaiywQEH3F49RsQTOpiWyoj+f1Ld+K1Z6Bz86UG+l9Ag9SSeExP+ZUYpWjUPwjnH766fTQQw+pdMLadqYkxz/c8ugijS4rRVHE6EtUZGIGzsUnAhuzpj4kvHpXyZnKKfklkOYXU0Rsgs8z1/QxdYn4Kn1VApLoIMrIvSVd0LiXU7VFrbNSRkJkMZrSW2/9AVyumWI43W9gYMCQHledlKemaM2aNUpQolKa31ZbbaWiU2ZGRkZmRavsSKXX19erH2HnnXem559/nmoZmQgyegO6beeXFvcIEhVmZ0M0yikz+uQ0UUS2HzjsMRVg1qd5JHJO/XYLFlcUn8iogmWIT8QZXvBJU1ada/V3SBeoAADWG/f2jA3TTou2Mub7lL5YEtfIs3yryRnrarXAZWdKUugmJiaKNvQ9/vjjK27joIMOoldffTXvMf6b5c2d8PnPf145aD/60Y/y6qj22WcfqmXqTBKYa3s7wutMSZ+pCM9bTqS0QaUeU5xGVRe43D2LYVRCpX3pkSkQf0lwJYFfoz2mAIgD5jmeMwvEmTL3kKuLaY+pXB9FLP66he07we9//3ulwsdpdCwicf3119Ntt92mfv/Yxz6mnJpKsDDECy+8oEQtent71TY4UiXqfhLpstp099xzz6W77rqLHnzwQRUxu+mmm+i5555TKoG1jqy+bB7qpTALUER9lReTk7sY0rQBRiuNJq2T5Xtx8HzVqiv5NcC4rg0RCkjgAxB5pHEviwcJ/bpSZ5y7Bco8hsXfAJ2pnp4e5TTtsssudOihh9LGjRvVvz//+c9Vnycr7L777qoG69Zbb1UOFDs+7ABJeiA38j3qqKPU44VwKl9TU1PeY5xa+IMf/EA5ZW9729vovvvuU07fnnvuSbWO5LV3jFQWDwkCzlUe14sgo5yfbExOaUSmqoWdEhagCHpMyK20UmND3tdxJe/PkYp4rmQCDXMNX1hrUAEAdhWPR2Yt5MW5IbeRSQN7Jbg0P1bIS+uNLNlZEYdnxx13pPHxcRodHaWFCxdW3A4LQ5QSh9h1111npQEKH/jAB9RPIe9617vUDyg+WfSZJoswoYxQ6ScU4XUg2fPplJZrDZzDgg/SLDfInHVRaKzUiFlFpZQapaZyBuJLlKPnAIDi1/PAuNmZ4shUzI+UqWcecAfby76HHHIIXXvttSoNj6NHTzzxBD388MN0yy23qMfmzZvn0q4BN5Cak0qpSkEr+TFBpnS5NTnNZOBMuSI+oTsnQeasJ/KaSpembYiV/LReVHFdyQQ0a8Enyos/AIBcdHloYjwvMqXVRMb3+k6YMoNAQJEpliD//ve/r2qc9t9/fzrvvPPokksuUelk3OfJrKgHwiSPPmOp+Whg4hMRN05kz9MW6/xAaQZ08YnAR4R+7VR0pvR6qSiPX2A/MhXpxR8AgHE9j+l125LFE3S9rm+y8FnYK4E5Uw0NDXT11Vcbf19++eX06U9/WjlTlSTRgf+I88STQ+foIO28aOvQOVNaTD3a+cm5nhWYnFxp2KvGbrB1KVZFRTjNT2vwiIWkWilYZ8K2MAUAsEednpzFi81y/5Z63Tg3X5d7G5te5h5bwDm2RwvXJX3iE5+gO+64g8bGtEG3aNEiOFKhlv/ULpz1fZ0UxvqYqMuiM7L7HKHgCCBwjnSfDzptXcbkRJnGhlzzJ1K69TXcwLVWUHVxupEl/wIAot+4lxtxD02OGQuicV4skXsb32UndAEwUB22R8vPfvYzet3rXqfU+4477jj63Oc+R/feey9NTk5WuSvAc3n0wZ7QHWQthSoOKVKy/1nIjVbJoKgpBTwmcpGpmbIpfpqMLlFDfXxvviBnfM1vnkuLmuchpR2AmDXu7UvmekzFuYec+d7KdeugemyPFm64+9WvflX1meI+Uay8d91119Gxxx5Ll112GSWTlRtcAn+RSYHT/MLYY4pDzcHHIVzqP6MiGVjpqYYBTvPLZgNfGZTVu+kKzpSqlzJFgEH8iXJKMgAgv3GvpGtrSn56j6kaucaHLDSlBx7UTJk58MADVUSKB9/tt99OTz/9tFL0mz9/fjWbBS4jRilPFGFDE6AIPgpRLea9H50cp63mVW4PAIozoKfNBV0ALGNypoziUS4yVTs3XwAAiJN9xEIM3LiX616lx1QtOJF8nx2p0EcReORMzczM0LPPPkv333+/ik6x83TqqafST3/6U5X2BzW/8IayRyr0ywlSGr0u4s6UmeEQHueowB3ZlShJCAqArYiKtA33q8hUnFNCAAAgrkg/QW7c28TOVLSTZKyjvneWRidhrwTiTJ155pnU1dVFJ510En33u99VjXebmppc2RngbWSKG8qGTbllbEpT80tEXIZU0rx4VWsUKz1Vp/iFwpnS/2Vnqdh1w4/xaiaf8zBdUwAAAOw37m2sb1D3n1pYHBN7xSwLD3x0pr72ta/RG97wBjTnjWSRZZbahvppj622o7D1mYq4L2Ve6DEiK6Aa8Yng61JydXBZmpyZpgXNc/OeZ0dKUgCDdvwAAAA4t4+GJ8fVnM/zfX3M0/wY9RWzEKBwC9sWAEek5s2b59oOAO/hCUImjPUDnaEToKAQGM5u1tgkkeZXpSy6HukL2pkypZ6OFlm94xQ/qZeqhZVMAACIa++40akJGtJ7TNXVQJsLw17B4q8rwAKoEcSZahkKjzw6p0mN68p3QSu3uYHY/prcO3DesDd8giTFFI+Ukh+x6mDwjh8AAAD7SL02q/BySrd6rAbmc7nHQn3YHaJvwQJLiLPSPToUmiPGDU+lPiZsxrMztO8gDiKwz8C4VjOVDZls7nAJZ0rGLwAAgOhhTueWnoG1kGmQa0oPe8UN4j9iQIE8+mhojoi5tihoGWx3V3oQmXLKwISW5heWlUG50Q4XiIrwTZf7kvBKZkMN3HgBACCOmO81Rs/AkNx//GlKD2fKDWAF1NiEwXnBYZNFT8QkMiXzLyanantMhacAWMZl4XXDTt/EtHYTqoeSHwAARBJzw3XlTMXAFrGEYa+UbkoPrANnqsYiUzPpFE2H5OJRkSmVJhWPlSCZhLlXErBPJpsxapOkKDhoZFyOFThTrUNavRQ/C1l0AACIh+JxreCFvTI4MUY/evg2emLjcqo1wmGxAB8nC6LNQ72hOOJsoCpXKvp+lAZWeqqCpWlZlCRMUuMyNAsVj9qVkl9GLVLEYSEAAABqFfMcXivzuXxPXmB3i5fb19PmwR66dekTVGuEw2IB/sqj94dDHl1TvYtPWN1Y6UkjMuVUfEI7jlxDVxeqG06hQqPUSwEAAIhP3VR9DOq3rSDfUnolusHmoV51X2RRC1kYrRXCYbEAX1P92ob6QtNjiu3RuITWZT6eSdfWJOIWg0p8Qk/7pHDLx4qSXy2oPgEAQM04UxT/HlPme1s6k3Ftmy3KmeIE+Cz1J0eoloAlUIMTRvfYUGhqpsLYU8gp8j1ScKaqEp/ghcHQpFoUERXhlD/VXFilI9bGjRcAAOKKuUa3VhbI5BbLkSR2gKplYmaaukcHjaXxsJST+EVtjBqQF5liozVcan4hMZzdWulxYWKqRQa4Ya+aiROhLtJtU/VSuvhEPaZQAACIMmb12NAs5HmOfM+sKyIUrcNaip/QMdJPtQQsgRp0psz9nYJkbEpT84vN5GWs9GTQzLWKyFSYkLE5baqD4xQ/ox9JiBw/AAAA9uH2FnMbm2lOQ1N87BHLzYpn1wQ7oZUVbk3OVNdoODKg/ALOVA2uvnDBYRi6XmtpfuzkUSwQw5qjFm4WddYKg+McmeI0v7oQFulmZtVLKVeqRm68AAAQZ5rqG6i5oZFqBfOdy43+o5sHe/IWkcOSAeUX4bFagOeYDb+NA12hEKBg4mKQytfg6WQyBM5q1DDqkELkXcvYTJucY07zk8gUAAAAEGWGJ7X+jm6IT9Tp90U3thkl4EzVqjx6wM4Uy2aO6w5HmCIR1WBO+VIpjMAyEzNTRrQ0TOMhL9qYTqmfzpEBlY6IZr0AAACiiDlNfWRqvOr7d/fYUJ4oU63ZQOGxWoAviKHaPhysPPr49JQREo5j3cnwVG2tyrjRY0rqpcKkpmSONvKY7RwdDF1jYQAAAMDp/S1ZpePTOiSp70SN9fVGv01z2l/cgTVQY4gB2DM2HOh+mEUw6kKU1lX9So97Oci1xIDeYyoRMufavC+jU+PUbij5JULl9AEAAABO7m+jVTpTm/VmvebsJ/6b75m1AqyBGkMGOkcCwiGLHi7juWr04zum14MBO5Ep3SENaS3S0GSSWnUlv7oQ7ycAAABQCbmHJWeqc6ZahjTxCbblzGn6m4eCzYDyEzhTNYYM9PEqLx5XIlMqBBwvo1S+Sa3lC7slix42x9qcVz40Oa4p+YVMvh0AAACwi9xtx6tsl9MymBOfMEengi4n8RM4UzWGpNSlMplAU9HGpib0SATFCjG8OfIGrDOoGvaG00mRMTrCzpTeS6MBKX4AAACijH5zm5hx3rR3fGaKepLDyp4TUSZxprrGaqfXFJypGsO88r++vzOw/dCaxIUvElEtEmUTpUJgIzKVDadMvozRjpEBQ3GQmzwCAAAAUUXutpMp5/ZK61CvUe/coItPSAZUX8C1+X4CZ6rG0EKw2mnfNNATaI8pNp7jljLlVti81hjgyBRljcbSYUIcPHamMmoBILcCBwAAAEQRubdNVOFMbR7MiU/IwqNsd3CidlSN4UzVIGKwto/0B1ozFcYamWqRSWQq5TxsXmuw1PjQhCaIUqf3qAgTMkJ7uI+GyguvC2UEDQAAALCK2F/TVdgrHJnK6OITcl+UND+o+YFYI5EpNg6DV/OLp1E6CWfKMkMTSTUZmyfhUDrI6RljPwEAAIAoI3fbqXTK8TY2KyW/TN69u06P01STPhg1EJmqYREKLvoPCqV2p4eG44R8n7hMIk9tWklf+e9NtKG/y9seU/rEHsbeTYbDz2mp2Wwo9xEAAABwYq+kHDpT3Mi+NzmSJz5h3m46k6HJGqkfh1VQw5EpLqYPSkFNS/PjfaFYIYZ3XNL8nm9bo5zuW5Y+7tlnDHLPs4I0gTAhu8T1UuqmEcJURAAAAMAJrO7sPCqVLz7BSJSK75edowM1cVLgTNUgMtDT2QwNTyYDE6Bgwmg8V4N8m+kqwuZhomdsWKW2eVlfZ/SYCulYEAdZbhr19Zg2AQAARBu553KPKCcL6y1Ds8UnGPPf/JpaAFZBDWIe9usCkEdnwQGRDjd3y44FRtg8TVGHo2uSCspRTK8aESslvzCXIpmcvMKbBgAAABBFzAuFM5m0c2eKZmeVyKJ958gg1QIxs2RBFOTROc9WVkHiZpjKt0llo+9M9XIjPpOX83LHek8+Z2Bck0UP61gw71VYUxEBAAAAO8itjO/yTmqbNg9q4hP1ReqIxZni7JZaAM5UjSLOVBDy6FwvZexHzIqmjLB5JsyhFmt0Kynw3PdY3t3inQBFNhvasWB2nuBIAQAAiAPmBcyxKa30wo4d169S9LndTn1JG3NwYpRqAThTNYoYrn3J4QBl0eMbmeLQN6czRhleUTI3Vd406H4Uk50orWYqvCmf5jGKZr0AAADixpDN+nnuL1VMfKJw4XF4cpxqgXBaL8BzxHDlHj+BRKZUxCN+KVNGDjJlI6/opzWpzX0n5fS4XNzEjvV0KhXaHlOyX7Jn9SF1+AAAAAA7aDXAGiM2I1ObB3PiE3VF7t3y2PjMVE2cFFgGNYoM9ImU//LoHE5WrlQ4befqMH2piYhPIuxM8WQpDgRH2tpcTgtV4hOqXiqcPaYYvlnMb5pL8xqbi67AAQAAAFFEFrTtpvm1DPUY4hPlbMyZdDryWTpWCKf1AnyLTPHF0Dc+4usRT06zkxFewYFqMH8juys9YU3zq6/PCZa81LbO1c/IRbvCrZLHjl5jfUPQuwEAAAC4iHbfHbUdmSotPsGIzcA2RH/SXxszCOBM1SjmeqV1fR2+95hi+9lcjxNHRiKcK8ypmCIUwsWlDfqEuaav3dXPYel1iVLGLeUTAAAACDNy2zULg1WCHS8tq6S4+IQgd/Ra6DUFZ6pGMee5cu6rn/BFG2Yp7Gow9yEanYquM9WrK/lJ+p2k+rUP93sSmYrjWAAAAADCjNx7uWWNVVoqiE8YtlCAqtF+A2eqhqnTow0dIwMBqfkl4r3S41GTWz/o5hQ/acZHCSOUPzo1SdNpTTDCzR5TAAAAAPAXyQixU+NtNOstIT4hyHPcZiXuwJmqYWSg9/mcz6qcDP1CjCPiJCYjLEChxCc4eqhWl3I1U+z4rHCx35T0mIrrWAAAAADCitx5J2w07W0ZZPGJTMUF8Tr9vj6QjH+vKThTNYykbg1PJQNI8+MLjWKJOAYcgYsqvXpkKmv6TjJeXunY6HJkKrxKfgAAAEBcEYdoMjVtO82v0n27Tlrw2OxhFUVgwdQwsmowOTOjVhn8QiQ44x6NsLPSEzY4LK8my8TshrUbB7pc+YyZdIpG9LqyekxFAAAAgK/ILX4qba0vJt+zRXyioYz4hNnGjHLJg1XgTNUw5tStrlF/clq53wD3tjJ/fmxzkG0UdIYJdqKUM2Wqq2NkFYol011T8tN7nMXdsQYAAADCh3bvnUpZc6ZapV6KbYIKfRcT+n2dHTW/+5n6TTytWWBbeW59f6cvR40VYwwDOq4CFPq/4jRGDQ7JT6c0kYmGxGxniifGofGxqj+nn5X89OOFND8AAADAXxKm5rpWYPXnrAXxCUaeZ+cryurGVoAzVePIYN802OPL55l7GdTFtGgqYXOlJ2xw5EmJT6hzlFt54khinf7dnm9f61pkSkQuAAAAAOAfCVPWkHUlv8riE4z5NS1DfRRn4EzVOBIR6PJJHj0nix7jyJTNHOQwKvnlZNGLj5eV3W1Vf86gLose13EAAAAAhBlZyExnMq6KTzDm6FX7MJwpEGOkbqlvfNS/yJRK84tzNEL7XpIqF8XIlHJyikSM6vVIVetwrztpfvFOowYAAABCi9zhORWvUnRqeDKpZZRYEJ8QZLG0M+a9phCZqnFk1UBU1fxQ8lOuVCL+Kz1uNrcNIjJVDFmNGpxIUsbiSlbFND9EpgAAAADfyd1/sxUXgFuH+iyLTwgiYtXnknBVWIEzVeNIZIovIqs5s9WQVAp38Tag7eYghzIylc0WVVuUXlOcM72+v8sFAYpsbGvnAAAAgFCjL/7y8mlyRmtbU4rNgz2GbVBJfEKQ18W91xScqRpHBjobte3D/Z5/XnJ6QqV25drBxg9xFK3mIIcJ3ufeJKf5FVfYMzfvfblzvePP4dUtjkzFWSIfAAAACDNml2hkYsKS+ISj7KdJqPl5Anu3TzzxBN1888303HPPWX7f6Ogo3XHHHa5tr9YxFwiu80EenWumYi86oH81/p7sNEQJjhZJRE2cpkLEyVrb1+H4c1gmNaVLsZobAwMAAADAH8x10ZXKPTRnypr4hMAawMxkRFvFWKUhiA/lWovPfOYztGHDBjrqqKPoV7/6Fb397W+nK664ouz7ZmZm6Ctf+Qp1dnbSu9/97qq3BzTYmeILhJux+aXmJxLbcUQcRXbwp9MzNKehiaJCb3KoYu8nJUKRTlHXyKDjzxkwlPzy5dcBAAAA4K/Nwvfjcs7U0ETSSNVrsHHPTpjUAidnpmlOY3TsodA7U/fddx8tXbpU/btw4UJqb2+nM844g84++2w66KCDir5nzZo1yjlatWoV7bHHHlVvD+TQ0qwy1DXq3Di2ytiUpuaXiHGdjCz0sFOiJo8IOVNSL1Wu95NErMZnpmhyZormNDbb/pyBCVby01Udq95rAAAAADiBb/V8Ox6dmqgYlUrYdKbqTDVZXaMDtMdWO8TyJAVSrPDoo4/Sm9/8ZuX4MDvvvDMdeeSR9NBDD5V8z6WXXkpveMMb6Atf+IIr2wM5pGaFU7z8SfPjz4zvGTC7B6xeGCW6jR5T5SdHbSWL6OWODVX0mNLGQXwl8gEAAIBo2CyqdU0JWoZy4hN27tkJ3V5gNvuQ/VRTztTmzZtpt912y3tsl112oZaWlpLvufXWW+myyy6jpqYmV7YHZq8clFuVcAtxLmrFgB6JmDMlPabKTQ1KhEJPAXy1a7OjzxlQPabiLEMCAAAAhB+xx8aV2rJ74hOFNmZnFaUBYScQZ2piYoLmzp2b99icOXPU46VYsGCBq9sDsyNTM+kUTadmPDs0LGwwoRchxlnBTVuJId8cVDfpHh3UC0zLO7viTG0a7HH0OQOq8V/MhUgAAACAkJMw1bQXgxc+Nw/aF58odKZYKTiuBGLRNjc30/R0vrLH1NTULIcoqO3VGuacVi/DsLzqIc1g425Ey0pPlJwpbjIscuWllPwEeb4/OVJVZCrOTjUAAAAQFXullOIeC0+ItLmdeilB7vN8348rgVgynILX0ZEvq9zV1aVqncKwvVqWR98wUF0j1nKY83Hj36i1cg5y2OBVI5Fyr+TkKEU/jmZm0tQ5MuAwMlUL4wAAAAAIM9p9eGJmpnSKn66+68SZSuj25XCMe00F4kwdd9xx9Pjjj9PkpGZoDgwM0PPPP0/HH398KLZXi4jx7KU8usiiJ2oiMlU+bB5qJT/K1USVos7kgL/Yvs7W50ylZozjUikCBgAAAADv7ZWpEpEpdqay2QwlbIpPCGIrsAJwXAnEkjnrrLNoyy23pPPPP5+uvfZa+sAHPkAnnHACHXPMMer5kZER+v3vfz8r2uR0e8D6YO8eHfLscKkojchhx1yAQpzFcgWdYaOHlfxsKOxJdGpVb7ujFD+1DThTAAAAQOD2Ci90FqNlsMeQRa/GvpxJp1W/qTgSSJ8pVuS7+eab6T//+Y/qCfXFL35RNdkV0uk09fX1zaqDYg444ADVP8rO9oCNnNaJUU+V/JQrFW8/SiFfcWJmOlrOlO7sWqEhUUc89bYP9zkUn6gdVUcAAAAgjCRMddNFxSf0HlNN9fXOtp/Q7Eu+7/clh2n7hVtS3AjEmRK1vfe///1Fn+Mo0+WXX170Oe4fxT92tgesrxyoproekVRRmtpQcKtU0BnWND87aj3yOpZ/T6XT1GBxotUiU7rqIZwpAAAAIDDkPsw10IWwKNXopCak5aReiozSDk3krGWoL5bOFAoWQL48OsuXexRNSU5PKCO6NroLRdGZ4jS/LNVbdHBkzPDK1YqeVsufw5MzZNEBAACA4JEF7mIpeNWKTzDawqlmL3SM9FMcgTMFtIFgMqA3eqToxzVTtWJE5wo6vevb5SZcGCoNhqUWqhKqea8+QS7r3Gj5s9CwFwAAAAgJur3CTXmlnrlQfIIXT6vJJKnT39s1Fs/GvXCmgG/y6JxCqMtPUK0XdIaNXkPJL5e+ZwVZqVrX32n5PQPjYypCaXbgAQAAABCcvcI2QGHdlDTrrZY6/X4/kIxnryk4UyA3GPQoQ9uwN2FYJYfNBnsNGNG5gs7ZOchhpNskPmHH2RXHi1MErcIiJ3bSCQEAAADgDXIrZgtgwiRfzjYBt8uxU0tdyb7kBsBxBM4UMJCULTuGsf00P016O+6Iw5jKzFbHCXO9FK8e2XF2ZYKdTM3QsIVJknOyuWbKSmNgAAAAAHiLeQGVVZfzxCf0v53WS/kpchYksGbA7DCsbuy6jVyktRCZEqLSU0GU/OyixbG08/lS2/qKr2eHS44JnCkAAAAgPAxNjhu/b+b+UlWKTwhi902lZ2bVZcUBOFMgNxj0SIEXjWZTrBKoK9vVghEtE0c6GxVnitP87Du6SoRCj04t726p+HqJStmtzQIAAACA+yi1PX1RdMTkTLklPmFerOdF29HpXPQrLsCaAbnBoOffseNjDvW6ATtoshpRSwIU/J35eIYZ3kdVM8V1TA4cHFmxah3utSg+URuKjgAAAEAUEF/J7OhIs15Xtk+5e37rYGVbIWrAmQJFB/t6lxX9uF7KGHQ1UDRlLuic9Khvl1uMTI3T5MxMXt2cHcQBE0fJivgEGvYCAAAA4bL/kvpCOt/LW1wSnyhUjG4b7qO4AWcKFAx2bUhsHOh2XclP04qrjchUTs+Pv7v7aZNu10tp/b+s95gyIw4YpzRWGjeDuiw6AAAAAMKBpPHJwnf/+CgldbGIauulBLH9ujwSOQsSOFMgD5GrbndZHl1doCK9XQMCFImCyE8UxCfsiaIXd8Jf6igvQsETNDtu+UcIAAAAAEEzrmfSqKgUZVwRnxDq9AhXX3KY4gacKZA/IDySR+caLGVCJ2qpoFNDpEXDLoteTepdgz5Jru3rqChAwekDaNgLAAAAhAO590/omTQtQz36vbp68QlB7vuDE/HrNQVnChQd7G43VtNS3WpNeED7rqOhj0xJw17nSE5158hAydfwZ/SPjyinur4G6uYAAACAKCB3ZFFdbhnqc018QqgzFAPDvcDsBDhTIH9A6EaxWX3PDZLTEyrLT0vxqg1kMSfsqzCqZkpfgXJKfaLeqI2bSmliFoXwJC1CF7Ugjw8AAABEAVnonkxNK3tA9ZjKZo2sEzeo0+/7/BlxAxYNyB8Qpv5I3GDVzZopTeQgUXPH8n+rX6QH175CmRD2nOJ96kmyAEV1fZ/4u/K35e280rmh6GsGjHop9JgCAAAAwoKk8k2mZqhvfMToN+pElKpi/81MOvQqx3aBMwXyMIsQrOt3Tx59bErU/GrHmZrb2KycjOl0im5Z+jj96OF/UNfoIIUJrmGaSafyxEecoDXv1Sbd1zo3V+gxVVvjAAAAAAgzckeeTqWoZZAl0d0VnzAvMLMt2DVauiQgisCZAmXk0d1zpjj9i/P8akHJT+DjuKBpLjXXN6pw+YaBLvrOA3+je1a9QOlMJkSy6NpEWu0KlES2Ng52l+kxpYtz1NA4AAAAAEKNfk/mxVVu1stZJG6KT+SEubTttQy5qxgdNHCmQEmjeF1/p8tpfuxg1NYB58ljTmMTLWiaY0Sp/vXq0/S9h24JReO6bl18wg0HR/pN9SVHKkSmamwQAAAAACFG7sqpbIZa9Wa9XlCn2xmdiEyBuCNhXe4z4KY0OlOrEQmO+kiUild8eLL6/oO30B3Ln6FUJh3YfvWMDrnm4Ehkix3G3rHZfSQGVWSqdgRIAAAAgChgrmfaPOS++EShM+V2+52gQWQKlDSKWZWNew1UCzsLIrdZyypuuSjVXBXFmcmk6a4Vz9F37v+bUs4Jgm6V5pd1bZKUifKF9rUlIlO161ADAAAAYUQWVNmJ8kJ8QhAbcGBijOJE7Vq2oCRsEEvK1uMblld9pMwy60jx0tIo5zfNoTkNTcqR6RgdoB8+fBvdtvQJFdXxk97kkJo8q1HyMyPjZlVPW0k1v2qELgAAAADgLubbMtsEbKu5KT4hyGLqiMu9TIMGzhQoSkO9dhEt725xpV7KGHC1VjRVZkJpbmg0olQcvbtvzUt0xX1/dbVWrRxcaNqXHM1zgqpFVrJaC+rB+PuJ1H6d3pMKAAAAAGEgkedMqXYnHix81unbTOrRr7gAZwoURVYkepPDlMqkqlbyE8U4RKby4YjQgua5NFdFqbTjfdUj/6SbX36kZPNbt+gbHzV6X7mVfikRrpHJcZV7LQxNJI2CVplMAQAAABA85rty1sOemHWGamA6NKrGbgBnChSFIxU85NkAfr51dv2L7ciUMqQhiV2KpoZGWtg0VxV8csPkh9cto2/c9xda2dPq2QjlAlDp++R2mh+Pm9W9HQUpfiLBjmkHAAAACAuabLkG36u9EJ9gErqNwCn//ePFlX+jCKwaUPLCkujUsy1rqlby0/oL4WCXvRhVLdVc1exXHJBrHr+D7l/zsnfOlL5S5FY4XzXv1SfLVzo2GI+rYlOXJNgBAAAA4DKme7MX4hNkZChpbB50TzE6aOBMgZKIM8XNZqtBy41FfyGrNNU3qFoqiVLd/toSQ8DD9Ya9esTQTWQSXt9fGJnCGAAAAADCiFgCXolPMNqCquZ6dIzEp3EvnClQUYSC0/QGqgjHJqcnVJYfegzZuDDr6mhe4xz1+1R6hl7r2uxJZEpUe9xE0gO6RnN9JAYnNFl0AAAAAIQPyRrxSnyisG6qK0a9puBMgdKDI1FnDPpHN7zm+EixM4aoRHWplg+vX0aeRKZYqtxlhUVJ8+PeYmNTk0ZkKoPIFAAAABBKtEp5Hz4nkTDsgrgAZwqUpaGuQf27rHOT4yPFBrUuP4GjbZPGeu34r+ltd/XYTaamaUiXKq93WapcK2TVzvVL7euMhr0cmoI0PgAAABA+mhuaqLGunuY0NHr6OXX6gutQjBr3wpkCZZHISMfIAGUcyliyNLqIDwB78MQmUZ61fbkaJPfqpYjq692dBpQIhZ7qx33K+HMGJjQ1P7ck2AEAAADgHnzfntc0hxr0RVyvSOi2oLkHadSBZQMs1b9w09UVva1VpPmxIY2DXU2q34NrX/FAFj0XRfJi3Gwe7FHnfzql9SpDjykAAACgdqnTnSnupemFuFYQwJkClo35pzetdCyNLtsCzqNTK1zsOSX1Ul4VmkrqIEekckp+6DEFAAAA1DJ1us3BAlij05p9GHXgTIGKiDO12kHdDke0OEVNDTakeDlCQu6cLtky1EPuKfmRZ0iaXyqToZU9bYYEO+rmAAAAgNolYcqGaRvqozgAZwpYdqa4WHDcZo7r+PSUEcaFIe18FUcU8h5wKdVPaqa8ihbydsV5fq51jdG0GdFJAAAAoHZJKPtAsz1ah+FMgRpBpYJRQhnET9lM9TMXGELJrXpVv1dd6DfFTlQ310wpWXTv1lOkbqptuE+vzwIAAABArZPQLYKu0UGKA4hMAVt1Uy/qUtdW4dQ0LcELkalqaNQbKI9MjitHqBrYweWIISMRLy+QbWey3GFKRgEAAAAAapk6fbG1LzlMcQDOFLBlzG8e6rUfmZJ6GQhQOIZT5sQ5uX/1y66IT2iCEO72mDIj2+YiU45MQckPAAAAAHW6PTg4ofW7jDpwpoAtw5ilLO2IILCSn9TLAHdS/V7p3OCiLLrX6aEaShrfw5RCAAAAAESDOt06GJ2Emh+oURGExzcst9ewV0VB4E1Vi6RaDk6MVdU5XCn58TnxSBa9WPNeph5rNwAAAEDNU6fbk6L2HHWwVAws06Cn+i3vbrFXM5VldyoejdmChB0TCY3ft+blqpX8/ED6TTFI8wQAAABAQrdl0pm0yniKOnCmgO3ISG9ymFKZlOWaKa0+B5EpN1P9XmpfX3Wanx81THmRKaT5AQAAADVPnW5/8LJuZwwU/eBMActwml9CFxR4vtWaqt/YlKj5wZlyg8Y6zZnqS45Qcsp+rjGfu54kC1B4Kz5hdsCb6huosa4ekSkAAAAAkCoz0O3ClkF7wmZhBM4UcCSR/lzraus1Ux42h63F1Rz+4WjfA+vsN/DlWqtpPaRe78M54fM+t7GZ5jXN8fyzAAAAABCt6FTn6ABFHThTwBbiTK3v77KR5scXDQ60W86JRKesRgcL66UyescnPyJTAAAAAAClnCkuPYg6cKaAIxEKdpIGxkcsSaMziEy5fw66xwZpYmbamSy6x0p+AAAAAACVFP1YoTjqwJkC9gZMIqco9+iG18q+NpVJG7KXctEAt2rXEqr+6bENr9p6b7epxxQAAAAAQBAkdFtyeDL6jXth4QLbNOhpZss6N5V93fj0lCHBDePd5VQ/PTr1TIu12rU8WXTI1AMAAAAgQOp0Zyo5PRX58wBnCjium+oYGaBMhitwisOpgMZAQ9GUJxLpbcN9NJO2JlPP9CS1yBRkygEAAAAQtDM1k05TuowtGQXgTAHbNOj9gjiNb0Vva/mGvbrYASJT3qX6PbFphaX38PliSXVNEASXPgAAAACCIaHbIZwt02+hBj/MwKICVUmkP71pZfnIlErzg9hBUOfATP/4qLH6w84YAAAAAEAQJPQfpmUo2r2mYFEBR4ghv7q3vaySn3KloHXgaapfy1CPpRC5UvKjrC6LjksfAAAAAMGQUKrCmi3SPtwf6dMAiwpU5UxxE9hxU23UrIa9yniHN+VVuiUf2VQmY6mJshKfyMoEhnMCAAAAgODrproj3msKzhRwNnDYIKeEijw9VSLNTNVMZbV8WOBtqt/jG5db7zEF5xYAAAAAIXGm+sdHKcrAmQJVG/Ivtq8vWTOlpZUhCuJ1qt+G/q6yyooSmcrgfAAAAAAgBNTpaX5DE9HuNQVnCjhGeh1tHuop+vzYlKj5wZnyCnFoZzJpWtq50VLD3nrI1AMAAAAgYBJ6ZGpseoKiDJwp4Jh63ZCfSs0oEYSiNVOcVob6HG8b+Orn4ZENr5Z8HZ+jwYkx9Xt9Qns9AAAAAEDQaX5TqRm12BtV4EwB54MnkTAkth/fsLxEmh+/DgfZSxr0VL+1fR0lX9ObHDYmKij5AQAAACAszlQmm410dArOFKiKBj3Vb3l3S1FpdAaRKW+RyBSv7Kzobi2j5KfVr+F8AAAAACBoEqYykNahPooqgTlTK1eupE996lN02mmn0ec//3lqbW2t6vVf/epX6S1veUvez8MPP+zxtwBSs8ORj1QmZRyQVCZNE6npvAJD4L0YyMPrl5btMaVUGJF2CQAAAICASSQSRnSqdRjOlC36+vroQx/6EB188MF0zTXX0OLFi+niiy+mqakpx69/6qmn6LLLLqM///nPxs8b3/jG6s4yqAin+SX0EO3zreuMx8enp4y0MghQ+CcGsqqnrYwsug87AgAAAABgEbERu0cHKaoEEjK49dZbaa+99qLPfOYzdMABB9B3vvMdZXj/73//c/T67u5u6u/vpxNPPJF22WUX42fevHk+f7PajoqYG8dyvZRQh6Ipz2mo0+qmxmemaONA16znu1kWHWIgAAAAAAgRdXWaK9KbHKGoEogztXTpUjr88MNzO1FXR4cddhi9/PLLjl6/YsUK2mKLLegnP/kJvfOd76SPfexj9MILL/jwTQAjztT6/q78hr1qxQGRKT/gMHmDPiHdv/aVkml+kEUHAAAAQFio09P8RHE4igTiTHEUaeutt857bMstt6SBgQFHr2dnanp6mo455hi69tprlaPFaYHLl89WmAPeiVBwNGpA72KtIlMqrww1On7RqEenVnTli4GwYyuRQsiiAwAAACAs1OlpfqOTUPOzRTqdNsJ6xsGsq6NMJuPo9eeff74SmzjzzDNpn332oc9+9rN0wgkn0E033WRvx4AjWGBCVhYe2/CaoeSnXCnIovvu1I5OT1DHcH8RJT/IogMAAAAgPNTpImUiWhZFAolMLVq0iMbG8sN5o6Oj6nEnr+coFf+Y2X///am9vd31fQfla3aWdm7MNewlTYob+DchSd+v+9a+XCA+oUcJcT4AAAAAEBIS+qp7OpNWLV6iSCDO1H777Udr1qzJe4z/5sedvP7yyy+n6667Lu/5zZs3KxEK4A9Sr9MxMqAihqpmKsvuFCTk/KRRb+D7auem/MgUZNEBAAAAEDLqdGeKrcWuiCr6BeJMnXXWWfTYY4/RkiVL1N933303rV+/nk4//XRHrz/qqKPo5ptvpnXrNGnuRx99lB588EG68MILfftOtY6IUHB/qRW9rapGhw14REKCaeA7NJmkvuSwEZliJT+4tQAAAAAIW2QqoWfNtAz1UhTRlrF9hvtFfe1rX6NLLrmE6uvrac6cOfSzn/2Mtt12W/U8p+dddNFF9NOf/pSOPPLIiq8/55xzqK2tTf3b1NSkTswPfvADJUQB/JVIZ2fq6U0raSadVsa7FBYCf+BaQk73y2QzdP/al+n8w06ibj3NT6KHAAAAAABhik6ls1mV3RRFElnprBoArMA3PDxM22yzjZEzyaRSKerq6lLOUnNzc8XXm4UqWOFvq622Uk6XVbgei522F198kRYsWEBB8+cXH6KnN6+iuY257x4FONd1MjVNW85dQNsvWEyre9tULdW8pmh9j6gzlZqmydQMbT1vIf3k9Ivp0rt+RyNTE9Tc0ERzGhqD3j0AAAAAAIPx6UmayaTpiJ32ps8e/04KA3Z8g0AiUwJHkSS6ZKahoaFovVOp1wvsQJV7HviT6jc0MUbNDY1aZAqBqYDEQGaUTH3bcD9NpVLq8XpIKwIAAAAglIp+aRqY0NrrRA3k/QD3BpOe98pOlPSbKhZBBN6fB/7h83D7a0/rtWssi249WgsAAAAA4AcJ3VYcnhyP5AGHMwVcr5ti0noPMOkfAPw9D6Lqt7y7RYlPQBQdAAAAAGFW9EuqtjrRA5YucJVGvVYtndWcKaj5BavqxznIqmEvRw0RJQQAAABASJ2pmXRaCWhFDThTwFUKU8nqUDQVCBwRlMlJHFsAAAAAgLCR0LOYuCyhLzlCUQPOFHB3QCUSVK9fFGzKIzI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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(\n",
    "    search,\n",
    "    fields=['score', 'fit_time', 'score_time'],\n",
    "    source='logbook',\n",
    "    kind='area',\n",
    "    title='Logbook fields from candidate evaluations',\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "search-space-intro",
   "metadata": {},
   "source": [
    "## Search-space plots\n",
    "\n",
    "The search-space plots now have clearer modes. The pair plot shows relationships between sampled parameters, while the heatmap gives a quick correlation view.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "pair-plot",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-20T18:39:23.666004100Z",
     "start_time": "2026-06-20T18:39:22.728760300Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-21T20:55:21.139734Z",
     "iopub.status.busy": "2026-06-21T20:55:21.139301Z",
     "iopub.status.idle": "2026-06-21T20:55:22.529931Z",
     "shell.execute_reply": "2026-06-21T20:55:22.529022Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 743x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_search_space(\n",
    "    search,\n",
    "    features=['ccp_alpha', 'max_depth', 'min_samples_split', 'criterion'],\n",
    "    hue='criterion',\n",
    "    kind='pair',\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "heatmap-plot",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-20T18:40:10.848244700Z",
     "start_time": "2026-06-20T18:40:10.631758200Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-21T20:55:22.532943Z",
     "iopub.status.busy": "2026-06-21T20:55:22.532523Z",
     "iopub.status.idle": "2026-06-21T20:55:22.895710Z",
     "shell.execute_reply": "2026-06-21T20:55:22.894541Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_search_space(\n",
    "    search,\n",
    "    features=['ccp_alpha', 'max_depth', 'min_samples_split'],\n",
    "    kind='heatmap',\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "takeaways",
   "metadata": {},
   "source": [
    "## Takeaways\n",
    "\n",
    "- Use `plot_fitness_evolution` when you want a compact fitness trend.\n",
    "- Use `plot_history` when you want to inspect telemetry signals like diversity, stagnation, and control events.\n",
    "- Use `plot_search_space` when you want to understand how the sampled parameter values relate to each other or to the final score.\n"
   ]
  }
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