TestingTrainedNetwork.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tf2onnx\n",
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      "Installing collected packages: protobuf, onnx, tf2onnx\n",
      "  Attempting uninstall: protobuf\n",
      "    Found existing installation: protobuf 3.14.0\n",
      "    Uninstalling protobuf-3.14.0:\n",
      "      Successfully uninstalled protobuf-3.14.0\n",
      "\n",
      "[notice] A new release of pip available: 22.1.2 -> 23.2.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Consider using the `--user` option or check the permissions.\n",
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      "Requirement already satisfied: stable-baselines[mpi] in c:\\python37\\lib\\site-packages (2.9.0)\n",
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      "Installing collected packages: protobuf, onnx, tf2onnx\n",
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      "\n",
      "[notice] A new release of pip available: 22.1.2 -> 23.2.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
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      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "pip install stable-baselines[mpi] tf2onnx\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "type object 'FullyConvPolicySmallMap' has no attribute 'observation_space'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_14416\\2921505919.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;31m# Define a dummy observation to trace the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkeras_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobservation_space\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;31m# Create a function to trace the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: type object 'FullyConvPolicySmallMap' has no attribute 'observation_space'"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from stable_baselines import PPO2\n",
    "\n",
    "# Load your trained PPO2 model\n",
    "import model\n",
    "model.FullyConvPolicy = model.FullyConvPolicySmallMap\n",
    "# from model import FullyConvPolicySmallMap as FullyConvPolicy\n",
    "\n",
    "ppo2_model = PPO2.load(\"my_models/sokoban/wide/model_1.pkl\")\n",
    "\n",
    "# Get the underlying Keras model from the PPO2 model\n",
    "keras_model = ppo2_model.policy\n",
    "\n",
    "# Define a dummy observation to trace the model\n",
    "obs = keras_model.observation_space.sample().reshape(1, -1)\n",
    "\n",
    "# Create a function to trace the model\n",
    "@tf.function(input_signature=[tf.TensorSpec(shape=obs.shape, dtype=tf.float32)])\n",
    "def serve(obs):\n",
    "    action, _ = keras_model(obs)\n",
    "    return action\n",
    "\n",
    "# Save the Keras model in SavedModel format\n",
    "tf.saved_model.save(keras_model, 'saved_model', signatures={'serving_default': serve})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:\n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\common\\tf_util.py:58: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\common\\tf_util.py:67: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\common\\policies.py:115: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\common\\input.py:25: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\a2c\\utils.py:136: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\common\\distributions.py:326: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\common\\distributions.py:327: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\ppo2\\ppo2.py:194: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\ppo2\\ppo2.py:202: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\ops\\math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\ppo2\\ppo2.py:210: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\ppo2\\ppo2.py:244: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\stable_baselines\\ppo2\\ppo2.py:246: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pip install tensorflow==1.15\n",
    "#Install stable-baselines as described in the documentation\n",
    "\n",
    "import gym\n",
    "import gym_pcgrl\n",
    "from gym_pcgrl import wrappers\n",
    "\n",
    "from utils import get_exp_name, max_exp_idx, load_model, make_vec_envs\n",
    "from stable_baselines import PPO2\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "import model\n",
    "model.FullyConvPolicy = model.FullyConvPolicySmallMap\n",
    "# from model import FullyConvPolicySmallMap as FullyConvPolicy\n",
    "\n",
    "model = PPO2.load(\"my_models/sokoban/wide/model_1.pkl\")\n",
    "# model.save(\"tests/zelda_turtle_onlymap_3_test\", cloudpickle=False)\n",
    "# model = PPO2.load(\"runs/binary_narrow_no_visits_no_heatmap_no_changes_1_log/latest_model.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'player': 2, 'crate': 1, 'target': 1, 'regions': 1, 'dist-win': 250, 'sol-length': 0, 'iterations': 250, 'changes': 3, 'max_iterations': 250, 'max_changes': 10, 'terminal_observation': array([[[0., 1., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0.]],\n",
      "\n",
      "       [[1., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [0., 0., 1., 0., 0.]],\n",
      "\n",
      "       [[0., 0., 0., 0., 1.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [0., 0., 1., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.]],\n",
      "\n",
      "       [[0., 1., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0.]],\n",
      "\n",
      "       [[0., 1., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 1., 0.],\n",
      "        [1., 0., 0., 0., 0.]]])}]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "def show_state(env, l, c, r, step=0, name=\"\", info=\"\"):\n",
    "    fig = plt.figure(10)\n",
    "    plt.clf()\n",
    "    \n",
    "#     plt.title(\"{} | Step: {} Path: {} Changes: {} Regions: {}\".format(name, step, l[-1], c[-1], r[-1]))\n",
    "    \n",
    "#     ax1 = fig.add_subplot(1,4,1)\n",
    "    ax1 = plt.imshow(env.render(mode='rgb_array'))\n",
    "    plt.axis('off')\n",
    "    \n",
    "#     ax2 = fig.add_subplot(1,4,2)\n",
    "#     ax2 = plt.plot(l)\n",
    "    \n",
    "#     ax3 = fig.add_subplot(1,4,3)\n",
    "#     ax3 = plt.plot(c)\n",
    "    \n",
    "#     ax4 = fig.add_subplot(1,4,4)\n",
    "#     ax4 = plt.plot(r)\n",
    "    \n",
    "    \n",
    "#     fig.set_figwidth(15)\n",
    "#     plt.tight_layout()\n",
    "    \n",
    "    display.clear_output(wait=True)\n",
    "    display.display(plt.gcf())\n",
    "\n",
    "def get_env_fun(p_name, r_name, crop_size, ch_perc):\n",
    "    def get_env():\n",
    "        if r_name != \"wide\":\n",
    "            env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), crop_size, random_tile=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        else:\n",
    "            env = wrappers.ActionMapImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "env = DummyVecEnv([get_env_fun('sokoban', 'wide', 28, 0.4)])\n",
    "# model = PPO2(CustomPolicy, env, verbose=1, tensorboard_log=\"./runs\")\n",
    "\n",
    "# Enjoy trained agent\n",
    "path_length = []\n",
    "changes = []\n",
    "regions = []\n",
    "obs = env.reset()\n",
    "obs = env.reset()\n",
    "fixed_array = None\n",
    "if fixed_array is not None:\n",
    "    env.get_attr('pcgrl_env')[0]._rep._map = np.array(fixed_array)\n",
    "    env.get_attr('pcgrl_env')[0]._rep._old_map = np.array(fixed_array)\n",
    "frames = [env.get_attr('pcgrl_env')[0]._rep.get_observation()['map']]\n",
    "dones = False\n",
    "i = 0\n",
    "while not dones:\n",
    "    i += 1\n",
    "    action, _states = model.predict(obs)\n",
    "    #action = np.array([env.action_space.sample()])\n",
    "    frames.append(env.get_attr('pcgrl_env')[0]._rep.get_observation()['map'])\n",
    "    obs, rewards, dones, info = env.step(action)\n",
    "#     path_length.append(info[0]['path-length'])\n",
    "#     changes.append(rewards[0])\n",
    "#     regions.append(info[0]['regions'])\n",
    "    if dones:\n",
    "#         print(\"YAAAAAAAAAAAAAY\")\n",
    "        break\n",
    "    show_state(env, path_length, changes, regions, i)\n",
    "plt.show()\n",
    "print(info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Sokoban Model Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: netron in c:\\python37\\lib\\site-packages (7.2.1)Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python37\\lib\\site-packages)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "[notice] A new release of pip available: 22.1.2 -> 23.2.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install netron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "!netron F:\\learning\\master\\PCGRL\\gym-pcgrl\\my_models\\binary\\narrow "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnpicklingError",
     "evalue": "A load persistent id instruction was encountered,\nbut no persistent_load function was specified.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnpicklingError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_14416\\4032323898.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# Load the model from the pickle file using dill\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'my_models/binary/narrow/model_1.pkl'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdill\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m# Now you can proceed with the conversion to ONNX and visualization steps\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\dill\\_dill.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(file, ignore, **kwds)\u001b[0m\n\u001b[0;32m    285\u001b[0m     \u001b[0mSee\u001b[0m \u001b[1;33m:\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;31m`\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkeyword\u001b[0m \u001b[0marguments\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    286\u001b[0m     \"\"\"\n\u001b[1;32m--> 287\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mUnpickler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mignore\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    289\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\dill\\_dill.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    440\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    441\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m#NOTE: if settings change, need to update attributes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 442\u001b[1;33m         \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStockUnpickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    443\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_main_module\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'__name__'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'__main__'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    444\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ignore\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mUnpicklingError\u001b[0m: A load persistent id instruction was encountered,\nbut no persistent_load function was specified."
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import tensorflow as tf\n",
    "\n",
    "import dill\n",
    "\n",
    "# Load the model from the pickle file using dill\n",
    "with open('my_models/binary/narrow/model_1.pkl', 'rb') as file:\n",
    "    model = dill.load(file)\n",
    "\n",
    "# Now you can proceed with the conversion to ONNX and visualization steps\n",
    "\n",
    "import tf2onnx\n",
    "\n",
    "# Convert the model to ONNX format\n",
    "onnx_model = tf2onnx.convert.from_sklearn(model)\n",
    "\n",
    "# Save the ONNX model to a file\n",
    "with open('model.onnx', 'wb') as file:\n",
    "    file.write(onnx_model.SerializeToString())\n",
    "\n",
    "# Convert the loaded model to H5 format\n",
    "model.save('model.h5')\n",
    "\n",
    "print(\"Model has been saved in H5 format.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\ops\\distributions\\distribution.py:265: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\n",
      "Instructions for updating:\n",
      "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n",
      "WARNING:tensorflow:From c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\ops\\distributions\\bernoulli.py:169: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\n",
      "Instructions for updating:\n",
      "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:Internal Python error in the inspect module.\n",
      "Below is the traceback from this internal error.\n",
      "\n",
      "ERROR:root:Internal Python error in the inspect module.\n",
      "Below is the traceback from this internal error.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3553, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"C:\\Users\\Maryam\\AppData\\Local\\Temp\\ipykernel_8060\\1572482140.py\", line 5, in <module>\n",
      "    import model\n",
      "  File \"f:\\learning\\master\\PCGRL\\gym-pcgrl\\model.py\", line 1, in <module>\n",
      "    import tensorflow as tf\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 99, in <module>\n",
      "    from tensorflow_core import *\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\__init__.py\", line 28, in <module>\n",
      "    from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import\n",
      "  File \"<frozen importlib._bootstrap>\", line 1019, in _handle_fromlist\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 50, in __getattr__\n",
      "    module = self._load()\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 44, in _load\n",
      "    module = _importlib.import_module(self.__name__)\n",
      "  File \"c:\\Python37\\lib\\importlib\\__init__.py\", line 127, in import_module\n",
      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\__init__.py\", line 83, in <module>\n",
      "    from tensorflow.python import keras\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\__init__.py\", line 26, in <module>\n",
      "    from tensorflow.python.keras import activations\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\__init__.py\", line 26, in <module>\n",
      "    from tensorflow.python.keras import activations\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\activations.py\", line 23, in <module>\n",
      "    from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\__init__.py\", line 38, in <module>\n",
      "    from tensorflow.python.keras.utils.multi_gpu_utils import multi_gpu_model\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\multi_gpu_utils.py\", line 22, in <module>\n",
      "    from tensorflow.python.keras.engine.training import Model\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\", line 47, in <module>\n",
      "    from tensorflow.python.keras.engine import training_arrays\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_arrays.py\", line 41, in <module>\n",
      "    from scipy.sparse import issparse  # pylint: disable=g-import-not-at-top\n",
      "  File \"c:\\Python37\\lib\\site-packages\\scipy\\__init__.py\", line 136, in <module>\n",
      "    from . import _distributor_init\n",
      "  File \"c:\\Python37\\lib\\site-packages\\scipy\\_distributor_init.py\", line 61, in <module>\n",
      "    WinDLL(os.path.abspath(filename))\n",
      "  File \"c:\\Python37\\lib\\ctypes\\__init__.py\", line 364, in __init__\n",
      "    self._handle = _dlopen(self._name, mode)\n",
      "KeyboardInterrupt\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2099, in showtraceback\n",
      "    stb = value._render_traceback_()\n",
      "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1101, in get_records\n",
      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 248, in wrapped\n",
      "    return f(*args, **kwargs)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 281, in _fixed_getinnerframes\n",
      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
      "  File \"c:\\Python37\\lib\\inspect.py\", line 1502, in getinnerframes\n",
      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
      "  File \"c:\\Python37\\lib\\inspect.py\", line 1460, in getframeinfo\n",
      "    filename = getsourcefile(frame) or getfile(frame)\n",
      "  File \"c:\\Python37\\lib\\inspect.py\", line 696, in getsourcefile\n",
      "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
      "  File \"c:\\Python37\\lib\\inspect.py\", line 733, in getmodule\n",
      "    if ismodule(module) and hasattr(module, '__file__'):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 50, in __getattr__\n",
      "    module = self._load()\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 44, in _load\n",
      "    module = _importlib.import_module(self.__name__)\n",
      "  File \"c:\\Python37\\lib\\importlib\\__init__.py\", line 127, in import_module\n",
      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
      "  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\n",
      "  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\n",
      "  File \"<frozen importlib._bootstrap>\", line 953, in _find_and_load_unlocked\n",
      "  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
      "  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\n",
      "  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\n",
      "  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\n",
      "  File \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\n",
      "  File \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\n",
      "  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\__init__.py\", line 36, in <module>\n",
      "    from tensorflow._api.v1 import compat\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\_api\\v1\\compat\\__init__.py\", line 23, in <module>\n",
      "    from tensorflow._api.v1.compat import v1\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\_api\\v1\\compat\\v1\\__init__.py\", line 41, in <module>\n",
      "    from tensorflow._api.v1.compat.v1 import feature_column\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\_api\\v1\\compat\\v1\\feature_column\\__init__.py\", line 10, in <module>\n",
      "    from tensorflow.python.feature_column.feature_column import input_layer\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\feature_column\\feature_column.py\", line 147, in <module>\n",
      "    from tensorflow.python.keras.engine import training\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\", line 40, in <module>\n",
      "    from tensorflow.python.keras import backend as K\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\__init__.py\", line 27, in <module>\n",
      "    from tensorflow.python.keras import applications\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\applications\\__init__.py\", line 26, in <module>\n",
      "    from tensorflow.python.keras import layers\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\layers\\__init__.py\", line 29, in <module>\n",
      "    from tensorflow.python.keras.layers.advanced_activations import LeakyReLU\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\layers\\advanced_activations.py\", line 27, in <module>\n",
      "    from tensorflow.python.keras.utils import tf_utils\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\__init__.py\", line 38, in <module>\n",
      "    from tensorflow.python.keras.utils.multi_gpu_utils import multi_gpu_model\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\multi_gpu_utils.py\", line 22, in <module>\n",
      "    from tensorflow.python.keras.engine.training import Model\n",
      "ImportError: cannot import name 'Model' from 'tensorflow.python.keras.engine.training' (c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py)\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3553, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"C:\\Users\\Maryam\\AppData\\Local\\Temp\\ipykernel_8060\\1572482140.py\", line 5, in <module>\n",
      "    import model\n",
      "  File \"f:\\learning\\master\\PCGRL\\gym-pcgrl\\model.py\", line 1, in <module>\n",
      "    import tensorflow as tf\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 99, in <module>\n",
      "    from tensorflow_core import *\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\__init__.py\", line 28, in <module>\n",
      "    from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import\n",
      "  File \"<frozen importlib._bootstrap>\", line 1019, in _handle_fromlist\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 50, in __getattr__\n",
      "    module = self._load()\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 44, in _load\n",
      "    module = _importlib.import_module(self.__name__)\n",
      "  File \"c:\\Python37\\lib\\importlib\\__init__.py\", line 127, in import_module\n",
      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\__init__.py\", line 83, in <module>\n",
      "    from tensorflow.python import keras\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\__init__.py\", line 26, in <module>\n",
      "    from tensorflow.python.keras import activations\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\__init__.py\", line 26, in <module>\n",
      "    from tensorflow.python.keras import activations\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\activations.py\", line 23, in <module>\n",
      "    from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\__init__.py\", line 38, in <module>\n",
      "    from tensorflow.python.keras.utils.multi_gpu_utils import multi_gpu_model\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\multi_gpu_utils.py\", line 22, in <module>\n",
      "    from tensorflow.python.keras.engine.training import Model\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\", line 47, in <module>\n",
      "    from tensorflow.python.keras.engine import training_arrays\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_arrays.py\", line 41, in <module>\n",
      "    from scipy.sparse import issparse  # pylint: disable=g-import-not-at-top\n",
      "  File \"c:\\Python37\\lib\\site-packages\\scipy\\__init__.py\", line 136, in <module>\n",
      "    from . import _distributor_init\n",
      "  File \"c:\\Python37\\lib\\site-packages\\scipy\\_distributor_init.py\", line 61, in <module>\n",
      "    WinDLL(os.path.abspath(filename))\n",
      "  File \"c:\\Python37\\lib\\ctypes\\__init__.py\", line 364, in __init__\n",
      "    self._handle = _dlopen(self._name, mode)\n",
      "KeyboardInterrupt\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2099, in showtraceback\n",
      "    stb = value._render_traceback_()\n",
      "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3473, in run_ast_nodes\n",
      "    if (await self.run_code(code, result,  async_=asy)):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3575, in run_code\n",
      "    self.showtraceback(running_compiled_code=True)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2102, in showtraceback\n",
      "    value, tb, tb_offset=tb_offset)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1368, in structured_traceback\n",
      "    self, etype, value, tb, tb_offset, number_of_lines_of_context)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1268, in structured_traceback\n",
      "    self, etype, value, tb, tb_offset, number_of_lines_of_context\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1125, in structured_traceback\n",
      "    tb_offset)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1082, in format_exception_as_a_whole\n",
      "    last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 382, in find_recursion\n",
      "    return len(records), 0\n",
      "TypeError: object of type 'NoneType' has no len()\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2099, in showtraceback\n",
      "    stb = value._render_traceback_()\n",
      "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1101, in get_records\n",
      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 248, in wrapped\n",
      "    return f(*args, **kwargs)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 281, in _fixed_getinnerframes\n",
      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
      "  File \"c:\\Python37\\lib\\inspect.py\", line 1502, in getinnerframes\n",
      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
      "  File \"c:\\Python37\\lib\\inspect.py\", line 1460, in getframeinfo\n",
      "    filename = getsourcefile(frame) or getfile(frame)\n",
      "  File \"c:\\Python37\\lib\\inspect.py\", line 696, in getsourcefile\n",
      "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
      "  File \"c:\\Python37\\lib\\inspect.py\", line 733, in getmodule\n",
      "    if ismodule(module) and hasattr(module, '__file__'):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 50, in __getattr__\n",
      "    module = self._load()\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 44, in _load\n",
      "    module = _importlib.import_module(self.__name__)\n",
      "  File \"c:\\Python37\\lib\\importlib\\__init__.py\", line 127, in import_module\n",
      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
      "  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\n",
      "  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\n",
      "  File \"<frozen importlib._bootstrap>\", line 953, in _find_and_load_unlocked\n",
      "  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
      "  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\n",
      "  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\n",
      "  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\n",
      "  File \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\n",
      "  File \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\n",
      "  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\__init__.py\", line 36, in <module>\n",
      "    from tensorflow._api.v1 import compat\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\_api\\v1\\compat\\__init__.py\", line 23, in <module>\n",
      "    from tensorflow._api.v1.compat import v1\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\_api\\v1\\compat\\v1\\__init__.py\", line 41, in <module>\n",
      "    from tensorflow._api.v1.compat.v1 import feature_column\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\_api\\v1\\compat\\v1\\feature_column\\__init__.py\", line 10, in <module>\n",
      "    from tensorflow.python.feature_column.feature_column import input_layer\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\feature_column\\feature_column.py\", line 147, in <module>\n",
      "    from tensorflow.python.keras.engine import training\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\", line 40, in <module>\n",
      "    from tensorflow.python.keras import backend as K\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\__init__.py\", line 27, in <module>\n",
      "    from tensorflow.python.keras import applications\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\applications\\__init__.py\", line 26, in <module>\n",
      "    from tensorflow.python.keras import layers\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\layers\\__init__.py\", line 29, in <module>\n",
      "    from tensorflow.python.keras.layers.advanced_activations import LeakyReLU\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\layers\\advanced_activations.py\", line 27, in <module>\n",
      "    from tensorflow.python.keras.utils import tf_utils\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\__init__.py\", line 38, in <module>\n",
      "    from tensorflow.python.keras.utils.multi_gpu_utils import multi_gpu_model\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\multi_gpu_utils.py\", line 22, in <module>\n",
      "    from tensorflow.python.keras.engine.training import Model\n",
      "ImportError: cannot import name 'Model' from 'tensorflow.python.keras.engine.training' (c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:Internal Python error in the inspect module.\n",
      "Below is the traceback from this internal error.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3553, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"C:\\Users\\Maryam\\AppData\\Local\\Temp\\ipykernel_8060\\1572482140.py\", line 5, in <module>\n",
      "    import model\n",
      "  File \"f:\\learning\\master\\PCGRL\\gym-pcgrl\\model.py\", line 1, in <module>\n",
      "    import tensorflow as tf\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 99, in <module>\n",
      "    from tensorflow_core import *\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\__init__.py\", line 28, in <module>\n",
      "    from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import\n",
      "  File \"<frozen importlib._bootstrap>\", line 1019, in _handle_fromlist\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 50, in __getattr__\n",
      "    module = self._load()\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 44, in _load\n",
      "    module = _importlib.import_module(self.__name__)\n",
      "  File \"c:\\Python37\\lib\\importlib\\__init__.py\", line 127, in import_module\n",
      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\__init__.py\", line 83, in <module>\n",
      "    from tensorflow.python import keras\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\__init__.py\", line 26, in <module>\n",
      "    from tensorflow.python.keras import activations\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\__init__.py\", line 26, in <module>\n",
      "    from tensorflow.python.keras import activations\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\activations.py\", line 23, in <module>\n",
      "    from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\__init__.py\", line 38, in <module>\n",
      "    from tensorflow.python.keras.utils.multi_gpu_utils import multi_gpu_model\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\multi_gpu_utils.py\", line 22, in <module>\n",
      "    from tensorflow.python.keras.engine.training import Model\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\", line 47, in <module>\n",
      "    from tensorflow.python.keras.engine import training_arrays\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_arrays.py\", line 41, in <module>\n",
      "    from scipy.sparse import issparse  # pylint: disable=g-import-not-at-top\n",
      "  File \"c:\\Python37\\lib\\site-packages\\scipy\\__init__.py\", line 136, in <module>\n",
      "    from . import _distributor_init\n",
      "  File \"c:\\Python37\\lib\\site-packages\\scipy\\_distributor_init.py\", line 61, in <module>\n",
      "    WinDLL(os.path.abspath(filename))\n",
      "  File \"c:\\Python37\\lib\\ctypes\\__init__.py\", line 364, in __init__\n",
      "    self._handle = _dlopen(self._name, mode)\n",
      "KeyboardInterrupt\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2099, in showtraceback\n",
      "    stb = value._render_traceback_()\n",
      "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3473, in run_ast_nodes\n",
      "    if (await self.run_code(code, result,  async_=asy)):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3575, in run_code\n",
      "    self.showtraceback(running_compiled_code=True)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2102, in showtraceback\n",
      "    value, tb, tb_offset=tb_offset)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1368, in structured_traceback\n",
      "    self, etype, value, tb, tb_offset, number_of_lines_of_context)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1268, in structured_traceback\n",
      "    self, etype, value, tb, tb_offset, number_of_lines_of_context\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1125, in structured_traceback\n",
      "    tb_offset)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1082, in format_exception_as_a_whole\n",
      "    last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 382, in find_recursion\n",
      "    return len(records), 0\n",
      "TypeError: object of type 'NoneType' has no len()\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2099, in showtraceback\n",
      "    stb = value._render_traceback_()\n",
      "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3030, in _run_cell\n",
      "    return runner(coro)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 78, in _pseudo_sync_runner\n",
      "    coro.send(None)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3258, in run_cell_async\n",
      "    interactivity=interactivity, compiler=compiler, result=result)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3492, in run_ast_nodes\n",
      "    self.showtraceback()\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2102, in showtraceback\n",
      "    value, tb, tb_offset=tb_offset)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1368, in structured_traceback\n",
      "    self, etype, value, tb, tb_offset, number_of_lines_of_context)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1268, in structured_traceback\n",
      "    self, etype, value, tb, tb_offset, number_of_lines_of_context\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1143, in structured_traceback\n",
      "    chained_exceptions_tb_offset)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1082, in format_exception_as_a_whole\n",
      "    last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 382, in find_recursion\n",
      "    return len(records), 0\n",
      "TypeError: object of type 'NoneType' has no len()\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2099, in showtraceback\n",
      "    stb = value._render_traceback_()\n",
      "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1101, in get_records\n",
      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 248, in wrapped\n",
      "    return f(*args, **kwargs)\n",
      "  File \"c:\\Python37\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 281, in _fixed_getinnerframes\n",
      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
      "  File \"c:\\Python37\\lib\\inspect.py\", line 1502, in getinnerframes\n",
      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
      "  File \"c:\\Python37\\lib\\inspect.py\", line 1460, in getframeinfo\n",
      "    filename = getsourcefile(frame) or getfile(frame)\n",
      "  File \"c:\\Python37\\lib\\inspect.py\", line 696, in getsourcefile\n",
      "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
      "  File \"c:\\Python37\\lib\\inspect.py\", line 733, in getmodule\n",
      "    if ismodule(module) and hasattr(module, '__file__'):\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 50, in __getattr__\n",
      "    module = self._load()\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow\\__init__.py\", line 44, in _load\n",
      "    module = _importlib.import_module(self.__name__)\n",
      "  File \"c:\\Python37\\lib\\importlib\\__init__.py\", line 127, in import_module\n",
      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
      "  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\n",
      "  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\n",
      "  File \"<frozen importlib._bootstrap>\", line 953, in _find_and_load_unlocked\n",
      "  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
      "  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\n",
      "  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\n",
      "  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\n",
      "  File \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\n",
      "  File \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\n",
      "  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\__init__.py\", line 36, in <module>\n",
      "    from tensorflow._api.v1 import compat\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\_api\\v1\\compat\\__init__.py\", line 23, in <module>\n",
      "    from tensorflow._api.v1.compat import v1\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\_api\\v1\\compat\\v1\\__init__.py\", line 41, in <module>\n",
      "    from tensorflow._api.v1.compat.v1 import feature_column\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\_api\\v1\\compat\\v1\\feature_column\\__init__.py\", line 10, in <module>\n",
      "    from tensorflow.python.feature_column.feature_column import input_layer\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\feature_column\\feature_column.py\", line 147, in <module>\n",
      "    from tensorflow.python.keras.engine import training\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\", line 40, in <module>\n",
      "    from tensorflow.python.keras import backend as K\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\__init__.py\", line 27, in <module>\n",
      "    from tensorflow.python.keras import applications\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\applications\\__init__.py\", line 26, in <module>\n",
      "    from tensorflow.python.keras import layers\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\layers\\__init__.py\", line 29, in <module>\n",
      "    from tensorflow.python.keras.layers.advanced_activations import LeakyReLU\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\layers\\advanced_activations.py\", line 27, in <module>\n",
      "    from tensorflow.python.keras.utils import tf_utils\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\__init__.py\", line 38, in <module>\n",
      "    from tensorflow.python.keras.utils.multi_gpu_utils import multi_gpu_model\n",
      "  File \"c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\multi_gpu_utils.py\", line 22, in <module>\n",
      "    from tensorflow.python.keras.engine.training import Model\n",
      "ImportError: cannot import name 'Model' from 'tensorflow.python.keras.engine.training' (c:\\Python37\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py)\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import gym_pcgrl\n",
    "from gym_pcgrl import wrappers\n",
    "\n",
    "import model\n",
    "from model import FullyConvPolicySmallMap,FullyConvPolicyBigMap\n",
    "from utils import get_exp_name, max_exp_idx, load_model, make_vec_envs\n",
    "from stable_baselines import PPO2\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import zlib\n",
    "import math\n",
    "import os\n",
    "from sklearn.decomposition import PCA\n",
    "from gym_pcgrl.envs.helper import run_dikjstra, _get_certain_tiles, get_tile_locations\n",
    "\n",
    "binary_lambdas = {\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'iterations': lambda info: info['iterations'],#/ (1.0 * info['max_iterations']),\n",
    "    'changes': lambda info: info['changes'],# / (1.0 * info['max_changes']),\n",
    "    'pathlength_const': lambda info: [0, 1][info['path-length'] >= 48],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'success': lambda info: [0, 1][info['path-length'] >= 48 and info['regions'] == 1]\n",
    "}\n",
    "binaryimp_lambdas = {\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'path-imp': lambda info: info['path-imp'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'iterations': lambda info: info['iterations'],#/ (1.0 * info['max_iterations']),\n",
    "    'changes': lambda info: info['changes'],# / (1.0 * info['max_changes']),\n",
    "    'pathimp_const': lambda info: [0, 1][info['path-imp'] >= 20],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'success': lambda info: [0, 1][info['path-imp'] >= 20 and info['regions'] == 1]\n",
    "}\n",
    "zelda_lambdas = {\n",
    "    'player': lambda info: info['player'],\n",
    "    'key': lambda info: info['key'],\n",
    "    'door': lambda info: info['door'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'nearestenemy': lambda info: info['nearest-enemy'],\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'iterations': lambda info: info['iterations'],\n",
    "    'changes': lambda info: info['changes'],\n",
    "    'player_const': lambda info: [0, 1][info['player'] == 1],\n",
    "    'key_const': lambda info: [0, 1][info['key'] == 1],\n",
    "    'door_const': lambda info: [0, 1][info['door'] == 1],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'nearestenemy_const': lambda info: [0, 1][info['nearest-enemy'] >= 4],\n",
    "    'pathlength_const': lambda info: [0, 1][info['path-length'] >= 16],\n",
    "    'success': lambda info: [0, 1][info['path-length'] >= 16 and info['nearest-enemy'] >= 4]\n",
    "}\n",
    "sokoban_lambdas = {\n",
    "    'player': lambda info: info['player'],\n",
    "    'crate': lambda info: info['crate'],\n",
    "    'target': lambda info: info['target'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'sollength': lambda info: info['sol-length'],\n",
    "    'iterations': lambda info: info['iterations'],\n",
    "    'changes': lambda info: info['changes'],\n",
    "    'player_const': lambda info: [0, 1][info['player'] == 1],\n",
    "    'ratio_const': lambda info: [0, 1][info['crate'] == info['target'] and info['crate'] > 0],\n",
    "    'has_sol': lambda info: [0, 1][info['sol-length'] >= 1],\n",
    "    'success': lambda info: [0, 1][info['sol-length'] >= 18]\n",
    "}\n",
    "lambdas = {\n",
    "    'binary': binary_lambdas,\n",
    "    'binaryimp': binaryimp_lambdas,\n",
    "    'zelda': zelda_lambdas,\n",
    "    'sokoban': sokoban_lambdas\n",
    "}\n",
    "\n",
    "def calc_tp_count(lvl, p_size):\n",
    "    padded_lvl = np.pad(lvl, p_size, constant_values=1)\n",
    "    pattern_dict = {}\n",
    "    for (y, x), v in np.ndenumerate(lvl):\n",
    "        sy, sx = y+p_size-p_size//2, x+p_size-p_size//2\n",
    "        pattern = str(padded_lvl[sy:sy+p_size,sx:sx+p_size])\n",
    "        if not (pattern in pattern_dict):\n",
    "            pattern_dict[pattern] = 0\n",
    "        pattern_dict[pattern] += 1\n",
    "    return pattern_dict\n",
    "        \n",
    "def calc_tp_kldiv(lvl1, lvl2, p_size=3, epsilon = 1e-6):\n",
    "    p_dict = calc_tp_count(lvl1, p_size)\n",
    "    q_dict = calc_tp_count(lvl2, p_size)\n",
    "    t_patterns = set()\n",
    "    t_patterns.update(p_dict.keys())\n",
    "    t_patterns.update(q_dict.keys())\n",
    "    total_p = sum(p_dict.values())\n",
    "    total_q = sum(q_dict.values())\n",
    "    value = 0\n",
    "    for x in t_patterns:\n",
    "        p_dash = epsilon/((total_p + epsilon) * (1 + epsilon))\n",
    "        q_dash = epsilon/((total_q + epsilon) * (1 + epsilon))\n",
    "        if x in p_dict:\n",
    "            p_dash = (p_dict[x] + epsilon) / ((total_p + epsilon) * (1 + epsilon))\n",
    "        if x in q_dict:\n",
    "            q_dash = (q_dict[x] + epsilon) / ((total_p + epsilon) * (1 + epsilon))\n",
    "        value += p_dash * math.log(p_dash/q_dash)\n",
    "    return value\n",
    "\n",
    "def calc_longest_path(map):\n",
    "    map_locations = get_tile_locations(map, [0, 1])\n",
    "    empty_tiles = _get_certain_tiles(map_locations, passable_values)\n",
    "    final_visited_map = np.zeros((len(map), len(map[0])))\n",
    "    final_value = 0\n",
    "    final_loc = (0,0)\n",
    "    final_map = None\n",
    "    for (x,y) in empty_tiles:\n",
    "        if final_visited_map[y][x] > 0:\n",
    "            continue\n",
    "        dikjstra_map, visited_map = run_dikjstra(x, y, map, [0])\n",
    "        final_visited_map += visited_map\n",
    "        (my,mx) = np.unravel_index(np.argmax(dikjstra_map, axis=None), dikjstra_map.shape)\n",
    "        dikjstra_map, _ = run_dikjstra(mx, my, map, passable_values)\n",
    "        (fy, fx) = np.unravel_index(np.argmax(dikjstra_map, axis=None), dikjstra_map.shape)\n",
    "        max_value = np.max(dikjstra_map)\n",
    "        if max_value > final_value:\n",
    "            final_value = max_value\n",
    "            final_loc = (fx, fy)\n",
    "            final_map = dikjstra_map\n",
    "    path = [final_loc]\n",
    "    current = final_loc\n",
    "    while final_map[current[1]][current[0]] != 0:\n",
    "        small = (current[0], current[1])\n",
    "        for (dx,dy) in [(0,1), (0,-1), (1,0), (-1,0)]:\n",
    "            new = (current[0] + dx, current[1] + dy)\n",
    "            if final_map[new[1]][new[0]] == -1 or new[0] < 0 or new[1] < 0 or new[0] >= final_map.shape[1] or new[1] >= final_map.shape[0]:\n",
    "                continue\n",
    "            if final_map[new[1]][new[0]] < final_map[small[1]][small[0]]:\n",
    "                small = new\n",
    "        path.append(small)\n",
    "        current = small\n",
    "    return path\n",
    "\n",
    "def calc_hamming(lvl1, lvl2):\n",
    "    return np.clip(abs(lvl1 - lvl2), 0, 1).sum()/(lvl1.shape[0] * lvl1.shape[1])\n",
    "\n",
    "def calc_compression(lvl1, lvl2):\n",
    "    c_1 = zlib.compress(lvl1.tobytes(), level=9)\n",
    "    c_2 = zlib.compress(lvl2.tobytes(), level=9)\n",
    "    cb = zlib.compress(lvl1.tobytes() + lvl2.tobytes(), level=9)\n",
    "    return (len(cb) - min(len(c_1), len(c_2))) / max(len(c_1), len(c_2))\n",
    "\n",
    "div_func = {\n",
    "    'hamming': lambda lvl1, lvl2: calc_hamming(lvl1, lvl2),\n",
    "    'tpkldiv': lambda lvl1, lvl2: calc_tp_kldiv(lvl1, lvl2),\n",
    "    'compression': lambda lvl1, lvl2: calc_compression(lvl1, lvl2)\n",
    "}\n",
    "\n",
    "def get_pca_diversity(lvls):\n",
    "    pca2d = PCA(2)\n",
    "    temp_reshaped_lvls = np.array(lvls).reshape((len(lvls), -1))\n",
    "    projected_lvls = pca2d.fit_transform(temp_reshaped_lvls)\n",
    "    variance = np.var(projected_lvls, axis=0)\n",
    "    return [np.mean(variance)]\n",
    "\n",
    "def get_svd_diversity(lvls):\n",
    "    normalized = []\n",
    "    for i in range(len(lvls)):\n",
    "        normalized.append(lvls[i] / np.linalg.norm(lvls[i]))\n",
    "    temp_reshaped_lvls = np.array(normalized).reshape((len(normalized), -1))\n",
    "    U, s, V = np.linalg.svd(temp_reshaped_lvls)\n",
    "    return s\n",
    "\n",
    "def get_diversity(lvls, d_type=\"hamming\"):\n",
    "    diversity = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_diversity = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                lvl_diversity.append(div_func[d_type](lvl1, lvl2))\n",
    "        diversity.append(np.min(lvl_diversity))\n",
    "    return diversity\n",
    "\n",
    "def get_env_func(p_name, r_name, ch_perc=0.2, crop_size=10):\n",
    "    def get_env():\n",
    "        if r_name != \"wide\":\n",
    "            env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), crop_size, add_visits=False, add_heatmap=False, add_changes=False, random_tile=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        else:\n",
    "            env = wrappers.ActionMapImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), add_visits=False, add_heatmap=False, add_changes=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "def sample_data(sample_size, env_func, model, lambdas):\n",
    "    sample_info = {}\n",
    "    lvls = []\n",
    "    for name in lambdas:\n",
    "        sample_info[name] = []\n",
    "    env = DummyVecEnv([env_func])\n",
    "    for i in range(sample_size):\n",
    "        done = False\n",
    "        obs = env.reset()\n",
    "        while not done:\n",
    "            action, _ = model.predict(obs)\n",
    "            last_lvl = env.get_attr('pcgrl_env')[0]._rep.get_observation()['map']\n",
    "            obs, rewards, done, info = env.step(action)\n",
    "        lvls.append(last_lvl)\n",
    "        for name in lambdas:\n",
    "            sample_info[name].append(lambdas[name](info[0]))\n",
    "    sample_info['hdiversity'] = get_diversity(lvls, \"hamming\")\n",
    "#     sample_info['cdiversity'] = get_diversity(lvls, \"compression\")\n",
    "#     sample_info['pdiversity'] = get_pca_diversity(lvls)\n",
    "#     sample_info['sdiversity'] = get_svd_diversity(lvls)\n",
    "    return sample_info\n",
    "\n",
    "p_name = \"sokoban\"\n",
    "crop_size = 10\n",
    "sample_size = 100\n",
    "rep_names = [\"narrow\", \"turtle\", \"wide\"]\n",
    "exp_name = '1'\n",
    "number_of_models = 1\n",
    "ch_perc_array = np.arange(0, 1.01, 0.1)\n",
    "result = {}\n",
    "for i in range(len(rep_names)):\n",
    "    r_name = rep_names[i]\n",
    "    if isinstance(exp_name,list):\n",
    "        e_name = exp_name[i]\n",
    "    else:\n",
    "        e_name = exp_name\n",
    "    for m_index in range(number_of_models):\n",
    "       # m_name = \"{}_{}_{}\".format(p_name, r_name, e_name)\n",
    "        m_name = \"model_1\"\n",
    "        print(\"Loading model {}\".format(m_name))\n",
    "        add_index = \"\"\n",
    "        if number_of_models > 1:\n",
    "            add_index = \"_\" + str(m_index + 1)\n",
    "        e_format = 'pkl'\n",
    "        if not os.path.exists('my_models/sokoban/{}{}.{}'.format(m_name, add_index, e_format)):\n",
    "            e_format = 'pkl'\n",
    "        dir = 'my_models/sokoban/{}/{}{}.{}'.format(r_name,m_name, add_index, e_format)\n",
    "        print(dir)\n",
    "        model = PPO2.load(dir)\n",
    "        if m_name not in result:\n",
    "            result[m_name] = {}\n",
    "        for ch_index in range(len(ch_perc_array)):\n",
    "            ch_perc = ch_perc_array[ch_index]\n",
    "            print(\"Testing {}_{} at change percentage of {}\".format(m_name, m_index+1, ch_perc))\n",
    "            env_func = get_env_func(p_name, r_name, ch_perc, crop_size)\n",
    "            temp_result = sample_data(sample_size, env_func, model, lambdas[p_name])\n",
    "            for name in temp_result:\n",
    "                if not(name in result[m_name]):\n",
    "                    result[m_name][name] = []\n",
    "                if number_of_models > 1:\n",
    "                    if m_index == 0:\n",
    "                        result[m_name][name].append([np.mean(temp_result[name])])\n",
    "                    else:\n",
    "                        result[m_name][name][ch_index].append(np.mean(temp_result[name]))\n",
    "                else:\n",
    "                    result[m_name][name].append(np.mean(temp_result[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overcoming the serialization issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import gym_pcgrl\n",
    "from gym_pcgrl import wrappers\n",
    "\n",
    "import model\n",
    "from model import FullyConvPolicySmallMap,FullyConvPolicyBigMap\n",
    "from utils import get_exp_name, max_exp_idx, load_model, make_vec_envs\n",
    "from stable_baselines import PPO2\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import zlib\n",
    "import math\n",
    "import os\n",
    "# from sklearn.decomposition import PCA\n",
    "from gym_pcgrl.envs.helper import run_dikjstra, _get_certain_tiles, get_tile_locations\n",
    "\n",
    "binary_lambdas = {\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'iterations': lambda info: info['iterations'],#/ (1.0 * info['max_iterations']),\n",
    "    'changes': lambda info: info['changes'],# / (1.0 * info['max_changes']),\n",
    "    'pathlength_const': lambda info: [0, 1][info['path-length'] >= 48],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'success': lambda info: [0, 1][info['path-length'] >= 48 and info['regions'] == 1]\n",
    "}\n",
    "binaryimp_lambdas = {\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'path-imp': lambda info: info['path-imp'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'iterations': lambda info: info['iterations'],#/ (1.0 * info['max_iterations']),\n",
    "    'changes': lambda info: info['changes'],# / (1.0 * info['max_changes']),\n",
    "    'pathimp_const': lambda info: [0, 1][info['path-imp'] >= 20],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'success': lambda info: [0, 1][info['path-imp'] >= 20 and info['regions'] == 1]\n",
    "}\n",
    "zelda_lambdas = {\n",
    "    'player': lambda info: info['player'],\n",
    "    'key': lambda info: info['key'],\n",
    "    'door': lambda info: info['door'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'nearestenemy': lambda info: info['nearest-enemy'],\n",
    "    'pathlength': lambda info: info['path-length'],\n",
    "    'iterations': lambda info: info['iterations'],\n",
    "    'changes': lambda info: info['changes'],\n",
    "    'player_const': lambda info: [0, 1][info['player'] == 1],\n",
    "    'key_const': lambda info: [0, 1][info['key'] == 1],\n",
    "    'door_const': lambda info: [0, 1][info['door'] == 1],\n",
    "    'regions_const': lambda info: [0, 1][info['regions'] == 1],\n",
    "    'nearestenemy_const': lambda info: [0, 1][info['nearest-enemy'] >= 4],\n",
    "    'pathlength_const': lambda info: [0, 1][info['path-length'] >= 16],\n",
    "    'has_sol': lambda info: [0, 1][info['path-length'] >= 1 and info['nearest-enemy'] >= 4],\n",
    "    'success': lambda info: [0, 1][info['path-length'] >= 16 and info['nearest-enemy'] >= 4]\n",
    "}\n",
    "sokoban_lambdas = {\n",
    "    'player': lambda info: info['player'],\n",
    "    'crate': lambda info: info['crate'],\n",
    "    'target': lambda info: info['target'],\n",
    "    'regions': lambda info: info['regions'],\n",
    "    'sollength': lambda info: info['sol-length'],\n",
    "    'iterations': lambda info: info['iterations'],\n",
    "    'changes': lambda info: info['changes'],\n",
    "    'player_const': lambda info: [0, 1][info['player'] == 1],\n",
    "    'ratio_const': lambda info: [0, 1][info['crate'] == info['target'] and info['crate'] > 0],\n",
    "    'has_sol': lambda info: [0, 1][info['sol-length'] >= 1],\n",
    "    'success': lambda info: [0, 1][info['sol-length'] >= 18]\n",
    "}\n",
    "lambdas = {\n",
    "    'binary': binaryimp_lambdas,\n",
    "    'binaryimp': binaryimp_lambdas,\n",
    "    'zelda': zelda_lambdas,\n",
    "    'sokoban': sokoban_lambdas\n",
    "}\n",
    "\n",
    "def calc_tp_count(lvl, p_size):\n",
    "    padded_lvl = np.pad(lvl, p_size, constant_values=1)\n",
    "    pattern_dict = {}\n",
    "    for (y, x), v in np.ndenumerate(lvl):\n",
    "        sy, sx = y+p_size-p_size//2, x+p_size-p_size//2\n",
    "        pattern = str(padded_lvl[sy:sy+p_size,sx:sx+p_size])\n",
    "        if not (pattern in pattern_dict):\n",
    "            pattern_dict[pattern] = 0\n",
    "        pattern_dict[pattern] += 1\n",
    "    return pattern_dict\n",
    "        \n",
    "def calc_tp_kldiv(lvl1, lvl2, p_size=3, epsilon = 1e-6):\n",
    "    p_dict = calc_tp_count(lvl1, p_size)\n",
    "    q_dict = calc_tp_count(lvl2, p_size)\n",
    "    t_patterns = set()\n",
    "    t_patterns.update(p_dict.keys())\n",
    "    t_patterns.update(q_dict.keys())\n",
    "    total_p = sum(p_dict.values())\n",
    "    total_q = sum(q_dict.values())\n",
    "    value = 0\n",
    "    for x in t_patterns:\n",
    "        p_dash = epsilon/((total_p + epsilon) * (1 + epsilon))\n",
    "        q_dash = epsilon/((total_q + epsilon) * (1 + epsilon))\n",
    "        if x in p_dict:\n",
    "            p_dash = (p_dict[x] + epsilon) / ((total_p + epsilon) * (1 + epsilon))\n",
    "        if x in q_dict:\n",
    "            q_dash = (q_dict[x] + epsilon) / ((total_p + epsilon) * (1 + epsilon))\n",
    "        value += p_dash * math.log(p_dash/q_dash)\n",
    "    return value\n",
    "\n",
    "def calc_longest_path(map):\n",
    "    map_locations = get_tile_locations(map, [0, 1])\n",
    "    empty_tiles = _get_certain_tiles(map_locations, passable_values)\n",
    "    final_visited_map = np.zeros((len(map), len(map[0])))\n",
    "    final_value = 0\n",
    "    final_loc = (0,0)\n",
    "    final_map = None\n",
    "    for (x,y) in empty_tiles:\n",
    "        if final_visited_map[y][x] > 0:\n",
    "            continue\n",
    "        dikjstra_map, visited_map = run_dikjstra(x, y, map, [0])\n",
    "        final_visited_map += visited_map\n",
    "        (my,mx) = np.unravel_index(np.argmax(dikjstra_map, axis=None), dikjstra_map.shape)\n",
    "        dikjstra_map, _ = run_dikjstra(mx, my, map, passable_values)\n",
    "        (fy, fx) = np.unravel_index(np.argmax(dikjstra_map, axis=None), dikjstra_map.shape)\n",
    "        max_value = np.max(dikjstra_map)\n",
    "        if max_value > final_value:\n",
    "            final_value = max_value\n",
    "            final_loc = (fx, fy)\n",
    "            final_map = dikjstra_map\n",
    "    path = [final_loc]\n",
    "    current = final_loc\n",
    "    while final_map[current[1]][current[0]] != 0:\n",
    "        small = (current[0], current[1])\n",
    "        for (dx,dy) in [(0,1), (0,-1), (1,0), (-1,0)]:\n",
    "            new = (current[0] + dx, current[1] + dy)\n",
    "            if final_map[new[1]][new[0]] == -1 or new[0] < 0 or new[1] < 0 or new[0] >= final_map.shape[1] or new[1] >= final_map.shape[0]:\n",
    "                continue\n",
    "            if final_map[new[1]][new[0]] < final_map[small[1]][small[0]]:\n",
    "                small = new\n",
    "        path.append(small)\n",
    "        current = small\n",
    "    return path\n",
    "\n",
    "def calc_hamming(lvl1, lvl2):\n",
    "    return np.clip(abs(lvl1 - lvl2), 0, 1).sum()/(lvl1.shape[0] * lvl1.shape[1])\n",
    "\n",
    "def calc_compression(lvl1, lvl2):\n",
    "    c_1 = zlib.compress(lvl1.tobytes(), level=9)\n",
    "    c_2 = zlib.compress(lvl2.tobytes(), level=9)\n",
    "    cb = zlib.compress(lvl1.tobytes() + lvl2.tobytes(), level=9)\n",
    "    return (len(cb) - min(len(c_1), len(c_2))) / max(len(c_1), len(c_2))\n",
    "\n",
    "div_func = {\n",
    "    'hamming': lambda lvl1, lvl2: calc_hamming(lvl1, lvl2),\n",
    "    'tpkldiv': lambda lvl1, lvl2: calc_tp_kldiv(lvl1, lvl2),\n",
    "    'compression': lambda lvl1, lvl2: calc_compression(lvl1, lvl2)\n",
    "}\n",
    "\n",
    "def get_pca_diversity(lvls):\n",
    "    pca2d = PCA(2)\n",
    "    temp_reshaped_lvls = np.array(lvls).reshape((len(lvls), -1))\n",
    "    projected_lvls = pca2d.fit_transform(temp_reshaped_lvls)\n",
    "    variance = np.var(projected_lvls, axis=0)\n",
    "    return [np.mean(variance)]\n",
    "\n",
    "def get_svd_diversity(lvls):\n",
    "    normalized = []\n",
    "    for i in range(len(lvls)):\n",
    "        normalized.append(lvls[i] / np.linalg.norm(lvls[i]))\n",
    "    temp_reshaped_lvls = np.array(normalized).reshape((len(normalized), -1))\n",
    "    U, s, V = np.linalg.svd(temp_reshaped_lvls)\n",
    "    return s\n",
    "\n",
    "def get_diversity(lvls, d_type=\"hamming\"):\n",
    "    diversity = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_diversity = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                lvl_diversity.append(div_func[d_type](lvl1, lvl2))\n",
    "        diversity.append(np.min(lvl_diversity))\n",
    "    return diversity\n",
    "\n",
    "def get_env_func(p_name, r_name, ch_perc=0.2, crop_size=10):\n",
    "    def get_env():\n",
    "        if r_name != \"wide\":\n",
    "            env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), crop_size, add_visits=False, add_heatmap=False, add_changes=False, random_tile=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        else:\n",
    "            env = wrappers.ActionMapImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), add_visits=False, add_heatmap=False, add_changes=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "def sample_data(sample_size, env_func, model, lambdas):\n",
    "    sample_info = {}\n",
    "    lvls = []\n",
    "    for name in lambdas:\n",
    "        sample_info[name] = []\n",
    "    env = DummyVecEnv([env_func])\n",
    "    for i in range(sample_size):\n",
    "        done = False\n",
    "        obs = env.reset()\n",
    "        while not done:\n",
    "            if model is None:\n",
    "                action = [env.action_space.sample()]\n",
    "            else:\n",
    "                action, _ = model.predict(obs)\n",
    "            last_lvl = env.get_attr('pcgrl_env')[0]._rep.get_observation()['map']\n",
    "            obs, rewards, done, info = env.step(action)\n",
    "        lvls.append(last_lvl)\n",
    "        for name in lambdas:\n",
    "            sample_info[name].append(lambdas[name](info[0]))\n",
    "    sample_info['hdiversity'] = get_diversity(lvls, \"hamming\")\n",
    "#     sample_info['cdiversity'] = get_diversity(lvls, \"compression\")\n",
    "#     sample_info['pdiversity'] = get_pca_diversity(lvls)\n",
    "#     sample_info['sdiversity'] = get_svd_diversity(lvls)\n",
    "    return sample_info\n",
    "\n",
    "def get_result_dict(ch_perc_array, lambdafuncs):\n",
    "    result = {}\n",
    "    for name in lambdafuncs:\n",
    "        result[name] = []\n",
    "        for _ in ch_perc_array:\n",
    "            result[name].append([])\n",
    "    result['hdiversity'] = []        \n",
    "    for _ in ch_perc_array:\n",
    "        result['hdiversity'].append([])\n",
    "    return result\n",
    "\n",
    "def get_change_percentage_data(model, p_name, r_name, crop_size, sample_size, ch_perc_array, lambdafuncs, result):\n",
    "    for ch_index in range(len(ch_perc_array)):\n",
    "        ch_perc = ch_perc_array[ch_index]\n",
    "        print(\"Testing {}_{} at change percentage of {}\".format(p_name, r_name, ch_perc))\n",
    "        env_func = get_env_func(p_name, r_name, ch_perc, crop_size)\n",
    "        temp_result = sample_data(sample_size, env_func, model, lambdafuncs)\n",
    "        for name in temp_result:\n",
    "            result[name][ch_index].append(np.mean(temp_result[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_name = \"sokoban\"\n",
    "ch_perc_array = np.arange(0, 1.01, 0.1)\n",
    "lambdafuncs = lambdas[p_name]\n",
    "\n",
    "result = {}\n",
    "result['{}_narrow'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)\n",
    "result['{}_turtle'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)\n",
    "result['{}_wide'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sokoban_narrow': {'player': [[1.2], [1.2], [1.15], [1.05], [0.975], [1.0], [1.0], [1.0], [0.95], [1.0], [1.0]], 'crate': [[1.5], [1.3], [1.425], [1.5], [1.325], [1.325], [1.5], [1.5], [1.425], [1.425], [1.325]], 'target': [[1.425], [1.375], [1.425], [1.475], [1.325], [1.275], [1.5], [1.5], [1.425], [1.425], [1.325]], 'regions': [[2.175], [1.65], [1.1], [1.025], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0]], 'sollength': [[0.275], [1.075], [3.55], [6.15], [7.35], [11.075], [9.825], [9.525], [8.75], [9.05], [8.7]], 'iterations': [[3.55], [8.25], [34.075], [74.025], [186.025], [201.55], [322.85], [381.575], [441.475], [497.475], [582.675]], 'changes': [[1.0], [2.0], [4.7], [6.225], [7.925], [7.825], [7.825], [8.0], [7.8], [7.775], [7.575]], 'player_const': [[0.425], [0.825], [0.775], [0.9], [0.975], [1.0], [1.0], [1.0], [0.95], [0.9], [1.0]], 'ratio_const': [[0.275], [0.6], [0.8], [0.975], [1.0], [0.975], [1.0], [1.0], [1.0], [1.0], [1.0]], 'has_sol': [[0.025], [0.1], [0.35], [0.65], [0.875], [0.95], [0.925], [0.875], [0.85], [0.9], [0.925]], 'success': [[0.0], [0.025], [0.025], [0.1], [0.05], [0.225], [0.15], [0.125], [0.125], [0.1], [0.075]], 'hdiversity': [[0.426], [0.438], [0.395], [0.368], [0.305], [0.324], [0.31], [0.28900000000000003], [0.30500000000000005], [0.29400000000000004], [0.29900000000000004]]}, 'sokoban_turtle': {'player': [[1.275], [1.125], [1.05], [1.025], [1.05], [0.975], [1.0], [0.975], [1.0], [1.025], [1.0]], 'crate': [[1.275], [1.1], [1.425], [1.525], [1.375], [1.575], [1.25], [1.625], [1.375], [1.45], [1.45]], 'target': [[1.4], [1.35], [1.475], [1.45], [1.35], [1.5], [1.25], [1.625], [1.375], [1.425], [1.425]], 'regions': [[2.1], [1.425], [1.025], [1.0], [1.05], [1.0], [1.0], [1.0], [1.05], [1.0], [1.0]], 'sollength': [[0.125], [0.975], [5.625], [7.775], [8.975], [8.65], [9.5], [10.825], [9.875], [11.35], [9.325]], 'iterations': [[3.275], [9.25], [45.5], [117.125], [193.025], [264.125], [339.825], [369.25], [419.4], [435.45], [569.1]], 'changes': [[1.0], [1.975], [4.525], [5.725], [6.725], [6.1], [6.3], [6.375], [7.175], [6.5], [6.75]], 'player_const': [[0.6], [0.85], [0.95], [0.975], [0.95], [0.975], [1.0], [0.975], [0.95], [0.975], [1.0]], 'ratio_const': [[0.35], [0.475], [0.825], [0.95], [0.975], [0.925], [1.0], [0.95], [0.925], [0.975], [0.975]], 'has_sol': [[0.025], [0.125], [0.475], [0.725], [0.8], [0.775], [1.0], [0.875], [0.825], [0.85], [0.9]], 'success': [[0.0], [0.0], [0.075], [0.075], [0.125], [0.175], [0.1], [0.175], [0.15], [0.225], [0.1]], 'hdiversity': [[0.433], [0.4109999999999999], [0.421], [0.37], [0.34500000000000003], [0.36500000000000005], [0.347], [0.378], [0.35], [0.34500000000000003], [0.34400000000000003]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425], [1.2, 1.05, 1.05], [0.8, 0.725, 0.725], [0.675, 0.675, 0.7], [0.675, 0.525, 0.475], [0.475, 0.625, 0.625], [0.55, 0.475, 0.525], [0.525, 0.5, 0.475], [0.525, 0.5, 0.575], [0.425, 0.4, 0.4], [0.425, 0.6, 0.45]], 'crate': [[1.175, 1.05, 1.1], [1.25, 0.875, 1.35], [0.95, 1.15, 1.125], [0.9, 1.2, 1.2], [0.825, 0.8, 0.925], [0.675, 0.65, 0.525], [0.3, 0.475, 0.55], [0.4, 0.425, 0.725], [0.275, 0.475, 0.275], [0.375, 0.225, 0.35], [0.375, 0.475, 0.375]], 'target': [[1.2, 1.2, 1.45], [1.625, 1.35, 1.5], [1.175, 1.2, 1.275], [1.05, 1.125, 1.225], [0.875, 0.925, 0.85], [0.8, 0.725, 0.675], [0.4, 0.55, 0.7], [0.5, 0.45, 0.775], [0.275, 0.45, 0.325], [0.4, 0.225, 0.425], [0.35, 0.425, 0.375]], 'regions': [[1.95, 1.9, 1.725], [1.425, 1.15, 1.425], [1.0, 1.0, 1.025], [1.025, 1.0, 1.05], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.05]], 'sollength': [[0.275, 0.0, 0.0], [0.15, 0.15, 0.0], [0.925, 0.45, 0.25], [1.725, 0.475, 1.075], [0.775, 0.4, 1.275], [2.525, 0.575, 2.275], [0.85, 1.45, 0.4], [0.725, 1.425, 2.375], [1.275, 1.675, 0.875], [0.625, 0.0, 0.675], [0.525, 2.45, 0.0]], 'iterations': [[1.0, 1.025, 1.025], [2.175, 2.05, 2.375], [6.475, 8.1, 13.525], [19.725, 13.6, 18.35], [54.25, 47.2, 38.275], [110.6, 98.95, 81.8], [214.15, 168.825, 167.775], [202.95, 230.725, 258.525], [414.7, 351.45, 396.05], [442.8, 469.35, 432.2], [549.075, 479.95, 541.35]], 'changes': [[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [5.0, 4.925, 5.0], [6.825, 7.0, 6.7], [9.85, 9.875, 9.95], [11.225, 11.775, 11.375], [13.95, 13.75, 14.575], [15.65, 14.925, 14.625], [15.55, 16.575, 16.6], [17.225, 16.875, 18.025], [18.2, 17.8, 18.45]], 'player_const': [[0.5, 0.45, 0.275], [0.375, 0.575, 0.45], [0.7, 0.675, 0.675], [0.675, 0.575, 0.65], [0.575, 0.525, 0.475], [0.475, 0.625, 0.625], [0.5, 0.475, 0.525], [0.475, 0.5, 0.475], [0.525, 0.5, 0.575], [0.425, 0.4, 0.4], [0.425, 0.6, 0.45]], 'ratio_const': [[0.175, 0.225, 0.175], [0.2, 0.125, 0.3], [0.45, 0.4, 0.475], [0.525, 0.375, 0.475], [0.475, 0.375, 0.45], [0.3, 0.3, 0.35], [0.2, 0.3, 0.225], [0.3, 0.3, 0.4], [0.225, 0.225, 0.225], [0.225, 0.125, 0.275], [0.275, 0.25, 0.225]], 'has_sol': [[0.025, 0.0, 0.0], [0.025, 0.025, 0.0], [0.125, 0.025, 0.05], [0.125, 0.075, 0.075], [0.075, 0.05, 0.075], [0.15, 0.125, 0.175], [0.125, 0.075, 0.075], [0.05, 0.125, 0.15], [0.075, 0.075, 0.125], [0.075, 0.0, 0.075], [0.1, 0.15, 0.0]], 'success': [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.025, 0.0], [0.05, 0.0, 0.05], [0.0, 0.0, 0.025], [0.075, 0.0, 0.075], [0.025, 0.075, 0.0], [0.025, 0.05, 0.075], [0.05, 0.075, 0.0], [0.025, 0.0, 0.025], [0.0, 0.1, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006], [0.43499999999999994, 0.40800000000000003, 0.418], [0.354, 0.365, 0.358], [0.309, 0.321, 0.329], [0.23199999999999998, 0.2, 0.24000000000000005], [0.146, 0.161, 0.182], [0.082, 0.093, 0.10600000000000001], [0.08, 0.091, 0.10800000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004], [0.05700000000000001, 0.04, 0.049], [0.049, 0.08900000000000001, 0.06399999999999999]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open('{}_results.json'.format(p_name)) as fp:\n",
    "    result = json.load(fp)\n",
    "    \n",
    "print(result)\n",
    "#result['{}_random'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)\n",
    "result['{}_turtle random'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)\n",
    "# result['{}_wide random'.format(p_name)] = get_result_dict(ch_perc_array, lambdafuncs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing sokoban_narrow\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing sokoban_narrow at change percentage of 0.0\n",
      "Testing sokoban_narrow at change percentage of 0.1\n",
      "Testing sokoban_narrow at change percentage of 0.2\n",
      "Testing sokoban_narrow at change percentage of 0.30000000000000004\n",
      "Testing sokoban_narrow at change percentage of 0.4\n",
      "Testing sokoban_narrow at change percentage of 0.5\n",
      "Testing sokoban_narrow at change percentage of 0.6000000000000001\n",
      "Testing sokoban_narrow at change percentage of 0.7000000000000001\n",
      "Testing sokoban_narrow at change percentage of 0.8\n",
      "Testing sokoban_narrow at change percentage of 0.9\n",
      "Testing sokoban_narrow at change percentage of 1.0\n",
      "Testing sokoban_turtle\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "Testing sokoban_turtle at change percentage of 0.0\n",
      "Testing sokoban_turtle at change percentage of 0.1\n",
      "Testing sokoban_turtle at change percentage of 0.2\n",
      "Testing sokoban_turtle at change percentage of 0.30000000000000004\n",
      "Testing sokoban_turtle at change percentage of 0.4\n",
      "Testing sokoban_turtle at change percentage of 0.5\n",
      "Testing sokoban_turtle at change percentage of 0.6000000000000001\n",
      "Testing sokoban_turtle at change percentage of 0.7000000000000001\n",
      "Testing sokoban_turtle at change percentage of 0.8\n",
      "Testing sokoban_turtle at change percentage of 0.9\n",
      "Testing sokoban_turtle at change percentage of 1.0\n",
      "Testing sokoban_wide\n",
      "Loading a model without an environment, this model cannot be trained until it has a valid environment.\n",
      "Testing sokoban_wide at change percentage of 0.0\n",
      "Testing sokoban_wide at change percentage of 0.1\n",
      "Testing sokoban_wide at change percentage of 0.2\n",
      "Testing sokoban_wide at change percentage of 0.30000000000000004\n",
      "Testing sokoban_wide at change percentage of 0.4\n",
      "Testing sokoban_wide at change percentage of 0.5\n",
      "Testing sokoban_wide at change percentage of 0.6000000000000001\n",
      "Testing sokoban_wide at change percentage of 0.7000000000000001\n",
      "Testing sokoban_wide at change percentage of 0.8\n",
      "Testing sokoban_wide at change percentage of 0.9\n",
      "Testing sokoban_wide at change percentage of 1.0\n"
     ]
    }
   ],
   "source": [
    "crop_size = 10\n",
    "sample_size = 100\n",
    "r_names = [\"narrow\", \"turtle\", \"wide\"]\n",
    "for i in range(len(r_names)):\n",
    "    r_name = r_names[i]\n",
    "    print(\"Testing {}_{}\".format(p_name, r_name))\n",
    "    model = None\n",
    "    model = PPO2.load('my_models/{}/{}/model_1.pkl'.format(p_name, r_name))\n",
    "    res_name = r_name\n",
    "    if model is None:\n",
    "        res_name = \"random\"\n",
    "    get_change_percentage_data(model, p_name, r_name, crop_size, sample_size, ch_perc_array, lambdafuncs, result['{}_{}'.format(p_name, res_name)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sokoban\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'crate': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'target': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'regions': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'iterations': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'changes': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'player_const': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'has_sol': [[0.025, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03], [0.125, 0.025, 0.05, 0.11], [0.125, 0.075, 0.075, 0.08], [0.075, 0.05, 0.075, 0.14], [0.15, 0.125, 0.175, 0.08], [0.125, 0.075, 0.075, 0.05], [0.05, 0.125, 0.15, 0.06], [0.075, 0.075, 0.125, 0.05], [0.075, 0.0, 0.075, 0.06], [0.1, 0.15, 0.0, 0.02]], 'success': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "print(p_name)\n",
    "with open('{}_results.json'.format(p_name), 'w') as fp:\n",
    "    json.dump(result, fp)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single Model Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "def get_data(results, name):\n",
    "    output = {}\n",
    "    for n in results:\n",
    "        output[n] = results[n][name]\n",
    "    return output\n",
    "\n",
    "def plt_dict(p_dict, y_title, file_name):\n",
    "    plt.figure()\n",
    "    names = []\n",
    "    for name in p_dict:\n",
    "        plt.plot(np.array(ch_perc_array),p_dict[name])\n",
    "        names.append(name)\n",
    "    plt.legend(names)\n",
    "    plt.xlim(0.0,1.0)\n",
    "    plt.xticks(np.array(ch_perc_array), rotation=90)\n",
    "    plt.xlabel('change percentage')\n",
    "    plt.ylabel(y_title)\n",
    "    plt.savefig('graphs/' + file_name + \".pdf\")\n",
    "    for n in lambdas[p_name]:\n",
    "        plt_dict(get_data(result, n), n, '{}_{}_{}'.format(p_name, exp_name, n))\n",
    "    plt_dict(get_data(result, 'hdiversity'), 'hamming diversity', '{}_{}_{}'.format(p_name,exp_name,'hammingdiversity'))\n",
    "    plt_dict(get_data(result, 'cdiversity'), 'compression diversity', '{}_{}_{}'.format(p_name,exp_name,'compressiondiversity'))\n",
    "    plt_dict(get_data(result, 'pdiversity'), 'pca diversity', '{}_{}_{}'.format(p_name,exp_name,'pcadiversity'))\n",
    "    plt_dict(get_data(result, 'sdiversity'), 'svd diversity', '{}_{}_{}'.format(p_name,exp_name,'svddiversity'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paper Graphs with multi models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'crate': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'target': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'regions': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'iterations': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'changes': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'player_const': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'has_sol': [[0.025, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03], [0.125, 0.025, 0.05, 0.11], [0.125, 0.075, 0.075, 0.08], [0.075, 0.05, 0.075, 0.14], [0.15, 0.125, 0.175, 0.08], [0.125, 0.075, 0.075, 0.05], [0.05, 0.125, 0.15, 0.06], [0.075, 0.075, 0.125, 0.05], [0.075, 0.0, 0.075, 0.06], [0.1, 0.15, 0.0, 0.02]], 'success': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} player\n",
      "sokoban_narrow {'sokoban_narrow': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'sokoban_turtle': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'sokoban_wide': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'sokoban_turtle': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'sokoban_wide': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'sokoban_turtle': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'sokoban_wide': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'crate': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'target': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'regions': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'iterations': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'changes': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'player_const': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'has_sol': [[0.025, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03], [0.125, 0.025, 0.05, 0.11], [0.125, 0.075, 0.075, 0.08], [0.075, 0.05, 0.075, 0.14], [0.15, 0.125, 0.175, 0.08], [0.125, 0.075, 0.075, 0.05], [0.05, 0.125, 0.15, 0.06], [0.075, 0.075, 0.125, 0.05], [0.075, 0.0, 0.075, 0.06], [0.1, 0.15, 0.0, 0.02]], 'success': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} crate\n",
      "sokoban_narrow {'sokoban_narrow': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'sokoban_turtle': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'sokoban_wide': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'sokoban_turtle': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'sokoban_wide': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'sokoban_turtle': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'sokoban_wide': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'crate': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'target': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'regions': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'iterations': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'changes': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'player_const': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'has_sol': [[0.025, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03], [0.125, 0.025, 0.05, 0.11], [0.125, 0.075, 0.075, 0.08], [0.075, 0.05, 0.075, 0.14], [0.15, 0.125, 0.175, 0.08], [0.125, 0.075, 0.075, 0.05], [0.05, 0.125, 0.15, 0.06], [0.075, 0.075, 0.125, 0.05], [0.075, 0.0, 0.075, 0.06], [0.1, 0.15, 0.0, 0.02]], 'success': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} target\n",
      "sokoban_narrow {'sokoban_narrow': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'sokoban_turtle': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'sokoban_wide': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'sokoban_turtle': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'sokoban_wide': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'sokoban_turtle': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'sokoban_wide': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'crate': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'target': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'regions': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'iterations': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'changes': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'player_const': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'has_sol': [[0.025, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03], [0.125, 0.025, 0.05, 0.11], [0.125, 0.075, 0.075, 0.08], [0.075, 0.05, 0.075, 0.14], [0.15, 0.125, 0.175, 0.08], [0.125, 0.075, 0.075, 0.05], [0.05, 0.125, 0.15, 0.06], [0.075, 0.075, 0.125, 0.05], [0.075, 0.0, 0.075, 0.06], [0.1, 0.15, 0.0, 0.02]], 'success': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} regions\n",
      "sokoban_narrow {'sokoban_narrow': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sokoban_turtle': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sokoban_wide': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sokoban_turtle': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sokoban_wide': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sokoban_turtle': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sokoban_wide': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'crate': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'target': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'regions': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'iterations': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'changes': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'player_const': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'has_sol': [[0.025, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03], [0.125, 0.025, 0.05, 0.11], [0.125, 0.075, 0.075, 0.08], [0.075, 0.05, 0.075, 0.14], [0.15, 0.125, 0.175, 0.08], [0.125, 0.075, 0.075, 0.05], [0.05, 0.125, 0.15, 0.06], [0.075, 0.075, 0.125, 0.05], [0.075, 0.0, 0.075, 0.06], [0.1, 0.15, 0.0, 0.02]], 'success': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} sollength\n",
      "sokoban_narrow {'sokoban_narrow': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'sokoban_turtle': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'sokoban_wide': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'sokoban_turtle': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'sokoban_wide': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'sokoban_turtle': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'sokoban_wide': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'crate': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'target': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'regions': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'iterations': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'changes': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'player_const': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'has_sol': [[0.025, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03], [0.125, 0.025, 0.05, 0.11], [0.125, 0.075, 0.075, 0.08], [0.075, 0.05, 0.075, 0.14], [0.15, 0.125, 0.175, 0.08], [0.125, 0.075, 0.075, 0.05], [0.05, 0.125, 0.15, 0.06], [0.075, 0.075, 0.125, 0.05], [0.075, 0.0, 0.075, 0.06], [0.1, 0.15, 0.0, 0.02]], 'success': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} iterations\n",
      "sokoban_narrow {'sokoban_narrow': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'sokoban_turtle': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'sokoban_wide': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'sokoban_turtle': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'sokoban_wide': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'sokoban_turtle': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'sokoban_wide': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'crate': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'target': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'regions': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'iterations': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'changes': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'player_const': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'has_sol': [[0.025, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03], [0.125, 0.025, 0.05, 0.11], [0.125, 0.075, 0.075, 0.08], [0.075, 0.05, 0.075, 0.14], [0.15, 0.125, 0.175, 0.08], [0.125, 0.075, 0.075, 0.05], [0.05, 0.125, 0.15, 0.06], [0.075, 0.075, 0.125, 0.05], [0.075, 0.0, 0.075, 0.06], [0.1, 0.15, 0.0, 0.02]], 'success': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} changes\n",
      "sokoban_narrow {'sokoban_narrow': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'sokoban_turtle': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'sokoban_wide': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'sokoban_turtle': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'sokoban_wide': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'sokoban_turtle': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'sokoban_wide': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'crate': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'target': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'regions': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'iterations': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'changes': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'player_const': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'has_sol': [[0.025, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03], [0.125, 0.025, 0.05, 0.11], [0.125, 0.075, 0.075, 0.08], [0.075, 0.05, 0.075, 0.14], [0.15, 0.125, 0.175, 0.08], [0.125, 0.075, 0.075, 0.05], [0.05, 0.125, 0.15, 0.06], [0.075, 0.075, 0.125, 0.05], [0.075, 0.0, 0.075, 0.06], [0.1, 0.15, 0.0, 0.02]], 'success': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} player_const\n",
      "sokoban_narrow {'sokoban_narrow': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'sokoban_turtle': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'sokoban_wide': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'sokoban_turtle': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'sokoban_wide': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'sokoban_turtle': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'sokoban_wide': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'crate': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'target': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'regions': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'iterations': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'changes': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'player_const': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'has_sol': [[0.025, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03], [0.125, 0.025, 0.05, 0.11], [0.125, 0.075, 0.075, 0.08], [0.075, 0.05, 0.075, 0.14], [0.15, 0.125, 0.175, 0.08], [0.125, 0.075, 0.075, 0.05], [0.05, 0.125, 0.15, 0.06], [0.075, 0.075, 0.125, 0.05], [0.075, 0.0, 0.075, 0.06], [0.1, 0.15, 0.0, 0.02]], 'success': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} ratio_const\n",
      "sokoban_narrow {'sokoban_narrow': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'sokoban_turtle': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'sokoban_wide': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'sokoban_turtle': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'sokoban_wide': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'sokoban_turtle': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'sokoban_wide': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'crate': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'target': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'regions': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'iterations': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'changes': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'player_const': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'has_sol': [[0.025, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03], [0.125, 0.025, 0.05, 0.11], [0.125, 0.075, 0.075, 0.08], [0.075, 0.05, 0.075, 0.14], [0.15, 0.125, 0.175, 0.08], [0.125, 0.075, 0.075, 0.05], [0.05, 0.125, 0.15, 0.06], [0.075, 0.075, 0.125, 0.05], [0.075, 0.0, 0.075, 0.06], [0.1, 0.15, 0.0, 0.02]], 'success': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} success\n",
      "sokoban_narrow {'sokoban_narrow': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'sokoban_turtle': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'sokoban_wide': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'sokoban_turtle': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'sokoban_wide': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'sokoban_turtle': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'sokoban_wide': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "{'sokoban_narrow': {'player': [[1.2, 1.41], [1.2, 1.31], [1.15, 1.03], [1.05, 0.98], [0.975, 0.97], [1.0, 0.97], [1.0, 0.98], [1.0, 0.98], [0.95, 1.01], [1.0, 0.97], [1.0, 0.98]], 'crate': [[1.5, 1.27], [1.3, 1.29], [1.425, 1.3], [1.5, 1.34], [1.325, 1.44], [1.325, 1.55], [1.5, 1.51], [1.5, 1.44], [1.425, 1.41], [1.425, 1.43], [1.325, 1.39]], 'target': [[1.425, 1.36], [1.375, 1.37], [1.425, 1.43], [1.475, 1.36], [1.325, 1.44], [1.275, 1.56], [1.5, 1.51], [1.5, 1.46], [1.425, 1.42], [1.425, 1.43], [1.325, 1.4]], 'regions': [[2.175, 2.15], [1.65, 1.73], [1.1, 1.03], [1.025, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.275, 0.08], [1.075, 0.34], [3.55, 5.31], [6.15, 7.72], [7.35, 9.16], [11.075, 8.84], [9.825, 8.36], [9.525, 8.53], [8.75, 9.18], [9.05, 9.54], [8.7, 9.42]], 'iterations': [[3.55, 4.34], [8.25, 8.17], [34.075, 32.51], [74.025, 74.14], [186.025, 186.45], [201.55, 259.44], [322.85, 340.22], [381.575, 387.66], [441.475, 454.28], [497.475, 492.95], [582.675, 540.09]], 'changes': [[1.0, 1.0], [2.0, 2.0], [4.7, 4.72], [6.225, 6.13], [7.925, 7.32], [7.825, 7.84], [7.825, 8.07], [8.0, 7.76], [7.8, 8.16], [7.775, 8.29], [7.575, 7.72]], 'player_const': [[0.425, 0.5], [0.825, 0.73], [0.775, 0.97], [0.9, 0.98], [0.975, 0.97], [1.0, 0.95], [1.0, 0.98], [1.0, 0.98], [0.95, 0.99], [0.9, 0.97], [1.0, 0.98]], 'ratio_const': [[0.275, 0.3], [0.6, 0.43], [0.8, 0.82], [0.975, 0.96], [1.0, 0.98], [0.975, 0.99], [1.0, 0.98], [1.0, 0.98], [1.0, 0.99], [1.0, 1.0], [1.0, 0.99]], 'has_sol': [[0.025, 0.01], [0.1, 0.04], [0.35, 0.55], [0.65, 0.72], [0.875, 0.85], [0.95, 0.87], [0.925, 0.84], [0.875, 0.87], [0.85, 0.93], [0.9, 0.9], [0.925, 0.92]], 'success': [[0.0, 0.0], [0.025, 0.0], [0.025, 0.07], [0.1, 0.15], [0.05, 0.11], [0.225, 0.07], [0.15, 0.1], [0.125, 0.09], [0.125, 0.1], [0.1, 0.11], [0.075, 0.15]], 'hdiversity': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]]}, 'sokoban_turtle': {'player': [[1.275, 1.26], [1.125, 1.17], [1.05, 1.01], [1.025, 1.0], [1.05, 0.99], [0.975, 1.03], [1.0, 1.0], [0.975, 0.99], [1.0, 1.01], [1.025, 1.02], [1.0, 1.02]], 'crate': [[1.275, 1.37], [1.1, 1.39], [1.425, 1.4], [1.525, 1.5], [1.375, 1.5], [1.575, 1.45], [1.25, 1.42], [1.625, 1.48], [1.375, 1.49], [1.45, 1.54], [1.45, 1.43]], 'target': [[1.4, 1.36], [1.35, 1.44], [1.475, 1.38], [1.45, 1.5], [1.35, 1.55], [1.5, 1.42], [1.25, 1.42], [1.625, 1.43], [1.375, 1.48], [1.425, 1.5], [1.425, 1.38]], 'regions': [[2.1, 2.27], [1.425, 1.65], [1.025, 1.03], [1.0, 1.0], [1.05, 1.01], [1.0, 1.0], [1.0, 1.0], [1.0, 1.02], [1.05, 1.0], [1.0, 1.0], [1.0, 1.0]], 'sollength': [[0.125, 0.07], [0.975, 0.86], [5.625, 6.29], [7.775, 8.24], [8.975, 9.04], [8.65, 9.77], [9.5, 9.85], [10.825, 9.18], [9.875, 9.89], [11.35, 10.19], [9.325, 9.95]], 'iterations': [[3.275, 3.19], [9.25, 7.88], [45.5, 55.93], [117.125, 123.67], [193.025, 217.47], [264.125, 257.52], [339.825, 329.88], [369.25, 392.35], [419.4, 443.11], [435.45, 488.21], [569.1, 539.77]], 'changes': [[1.0, 1.0], [1.975, 1.93], [4.525, 4.44], [5.725, 5.07], [6.725, 6.05], [6.1, 6.64], [6.3, 6.23], [6.375, 6.22], [7.175, 6.76], [6.5, 6.81], [6.75, 6.64]], 'player_const': [[0.6, 0.69], [0.85, 0.76], [0.95, 0.97], [0.975, 0.96], [0.95, 0.97], [0.975, 0.97], [1.0, 1.0], [0.975, 0.96], [0.95, 0.97], [0.975, 0.96], [1.0, 0.98]], 'ratio_const': [[0.35, 0.36], [0.475, 0.42], [0.825, 0.83], [0.95, 0.91], [0.975, 0.97], [0.925, 0.96], [1.0, 0.97], [0.95, 0.96], [0.925, 0.99], [0.975, 0.96], [0.975, 0.95]], 'has_sol': [[0.025, 0.01], [0.125, 0.08], [0.475, 0.59], [0.725, 0.77], [0.8, 0.82], [0.775, 0.84], [1.0, 0.89], [0.875, 0.83], [0.825, 0.87], [0.85, 0.84], [0.9, 0.9]], 'success': [[0.0, 0.0], [0.0, 0.02], [0.075, 0.08], [0.075, 0.11], [0.125, 0.12], [0.175, 0.16], [0.1, 0.14], [0.175, 0.1], [0.15, 0.12], [0.225, 0.14], [0.1, 0.16]], 'hdiversity': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]]}, 'sokoban_wide': {'player': [[1.2, 1.5, 1.425, 1.29], [1.2, 1.05, 1.05, 0.88], [0.8, 0.725, 0.725, 0.82], [0.675, 0.675, 0.7, 0.69], [0.675, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.63], [0.55, 0.475, 0.525, 0.55], [0.525, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'crate': [[1.175, 1.05, 1.1, 1.21], [1.25, 0.875, 1.35, 1.33], [0.95, 1.15, 1.125, 1.17], [0.9, 1.2, 1.2, 1.18], [0.825, 0.8, 0.925, 0.77], [0.675, 0.65, 0.525, 0.67], [0.3, 0.475, 0.55, 0.61], [0.4, 0.425, 0.725, 0.42], [0.275, 0.475, 0.275, 0.37], [0.375, 0.225, 0.35, 0.22], [0.375, 0.475, 0.375, 0.34]], 'target': [[1.2, 1.2, 1.45, 1.41], [1.625, 1.35, 1.5, 1.35], [1.175, 1.2, 1.275, 1.23], [1.05, 1.125, 1.225, 1.17], [0.875, 0.925, 0.85, 0.93], [0.8, 0.725, 0.675, 0.85], [0.4, 0.55, 0.7, 0.6], [0.5, 0.45, 0.775, 0.4], [0.275, 0.45, 0.325, 0.32], [0.4, 0.225, 0.425, 0.25], [0.35, 0.425, 0.375, 0.33]], 'regions': [[1.95, 1.9, 1.725, 1.79], [1.425, 1.15, 1.425, 1.5], [1.0, 1.0, 1.025, 1.03], [1.025, 1.0, 1.05, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.01], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.05, 1.0]], 'sollength': [[0.275, 0.0, 0.0, 0.0], [0.15, 0.15, 0.0, 0.39], [0.925, 0.45, 0.25, 1.22], [1.725, 0.475, 1.075, 0.63], [0.775, 0.4, 1.275, 1.61], [2.525, 0.575, 2.275, 0.87], [0.85, 1.45, 0.4, 0.69], [0.725, 1.425, 2.375, 0.93], [1.275, 1.675, 0.875, 0.77], [0.625, 0.0, 0.675, 1.24], [0.525, 2.45, 0.0, 0.08]], 'iterations': [[1.0, 1.025, 1.025, 1.0], [2.175, 2.05, 2.375, 2.85], [6.475, 8.1, 13.525, 9.3], [19.725, 13.6, 18.35, 28.22], [54.25, 47.2, 38.275, 54.5], [110.6, 98.95, 81.8, 95.56], [214.15, 168.825, 167.775, 184.01], [202.95, 230.725, 258.525, 274.84], [414.7, 351.45, 396.05, 366.86], [442.8, 469.35, 432.2, 478.19], [549.075, 479.95, 541.35, 542.02]], 'changes': [[1.0, 1.0, 1.0, 1.0], [2.0, 2.0, 2.0, 1.97], [5.0, 4.925, 5.0, 4.96], [6.825, 7.0, 6.7, 6.89], [9.85, 9.875, 9.95, 9.7], [11.225, 11.775, 11.375, 11.47], [13.95, 13.75, 14.575, 13.97], [15.65, 14.925, 14.625, 15.15], [15.55, 16.575, 16.6, 17.1], [17.225, 16.875, 18.025, 16.82], [18.2, 17.8, 18.45, 19.62]], 'player_const': [[0.5, 0.45, 0.275, 0.36], [0.375, 0.575, 0.45, 0.43], [0.7, 0.675, 0.675, 0.74], [0.675, 0.575, 0.65, 0.67], [0.575, 0.525, 0.475, 0.66], [0.475, 0.625, 0.625, 0.61], [0.5, 0.475, 0.525, 0.55], [0.475, 0.5, 0.475, 0.44], [0.525, 0.5, 0.575, 0.47], [0.425, 0.4, 0.4, 0.43], [0.425, 0.6, 0.45, 0.32]], 'ratio_const': [[0.175, 0.225, 0.175, 0.17], [0.2, 0.125, 0.3, 0.31], [0.45, 0.4, 0.475, 0.41], [0.525, 0.375, 0.475, 0.52], [0.475, 0.375, 0.45, 0.43], [0.3, 0.3, 0.35, 0.27], [0.2, 0.3, 0.225, 0.24], [0.3, 0.3, 0.4, 0.14], [0.225, 0.225, 0.225, 0.19], [0.225, 0.125, 0.275, 0.12], [0.275, 0.25, 0.225, 0.19]], 'has_sol': [[0.025, 0.0, 0.0, 0.0], [0.025, 0.025, 0.0, 0.03], [0.125, 0.025, 0.05, 0.11], [0.125, 0.075, 0.075, 0.08], [0.075, 0.05, 0.075, 0.14], [0.15, 0.125, 0.175, 0.08], [0.125, 0.075, 0.075, 0.05], [0.05, 0.125, 0.15, 0.06], [0.075, 0.075, 0.125, 0.05], [0.075, 0.0, 0.075, 0.06], [0.1, 0.15, 0.0, 0.02]], 'success': [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.02], [0.0, 0.025, 0.0, 0.02], [0.05, 0.0, 0.05, 0.0], [0.0, 0.0, 0.025, 0.04], [0.075, 0.0, 0.075, 0.02], [0.025, 0.075, 0.0, 0.02], [0.025, 0.05, 0.075, 0.03], [0.05, 0.075, 0.0, 0.03], [0.025, 0.0, 0.025, 0.04], [0.0, 0.1, 0.0, 0.0]], 'hdiversity': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]]}, 'sokoban_turtle random': {'player': [[], [], [], [], [], [], [], [], [], [], []], 'crate': [[], [], [], [], [], [], [], [], [], [], []], 'target': [[], [], [], [], [], [], [], [], [], [], []], 'regions': [[], [], [], [], [], [], [], [], [], [], []], 'sollength': [[], [], [], [], [], [], [], [], [], [], []], 'iterations': [[], [], [], [], [], [], [], [], [], [], []], 'changes': [[], [], [], [], [], [], [], [], [], [], []], 'player_const': [[], [], [], [], [], [], [], [], [], [], []], 'ratio_const': [[], [], [], [], [], [], [], [], [], [], []], 'has_sol': [[], [], [], [], [], [], [], [], [], [], []], 'success': [[], [], [], [], [], [], [], [], [], [], []], 'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]}} hdiversity\n",
      "sokoban_narrow {'sokoban_narrow': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]], 'sokoban_turtle': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]], 'sokoban_wide': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_turtle {'sokoban_narrow': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]], 'sokoban_turtle': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]], 'sokoban_wide': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n",
      "sokoban_wide {'sokoban_narrow': [[0.426, 0.4115999999999999], [0.438, 0.3972], [0.395, 0.3632], [0.368, 0.3396], [0.305, 0.2932], [0.324, 0.28359999999999996], [0.31, 0.27359999999999995], [0.28900000000000003, 0.27759999999999996], [0.30500000000000005, 0.26280000000000003], [0.29400000000000004, 0.26], [0.29900000000000004, 0.27520000000000006]], 'sokoban_turtle': [[0.433, 0.39519999999999994], [0.4109999999999999, 0.39759999999999995], [0.421, 0.36479999999999996], [0.37, 0.3544], [0.34500000000000003, 0.3332], [0.36500000000000005, 0.3236], [0.347, 0.31720000000000004], [0.378, 0.31200000000000006], [0.35, 0.3112], [0.34500000000000003, 0.31079999999999997], [0.34400000000000003, 0.31679999999999997]], 'sokoban_wide': [[0.404, 0.43200000000000005, 0.43800000000000006, 0.39679999999999993], [0.43499999999999994, 0.40800000000000003, 0.418, 0.38959999999999995], [0.354, 0.365, 0.358, 0.33559999999999995], [0.309, 0.321, 0.329, 0.2964], [0.23199999999999998, 0.2, 0.24000000000000005, 0.1972], [0.146, 0.161, 0.182, 0.156], [0.082, 0.093, 0.10600000000000001, 0.09759999999999999], [0.08, 0.091, 0.10800000000000001, 0.06480000000000001], [0.06899999999999999, 0.08499999999999999, 0.051000000000000004, 0.052000000000000005], [0.05700000000000001, 0.04, 0.049, 0.04120000000000001], [0.049, 0.08900000000000001, 0.06399999999999999, 0.0376]], 'sokoban_turtle random': [[], [], [], [], [], [], [], [], [], [], []]}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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P/3Yip+ZYoC4fmk3QJ6WkwlNLjdeGqb6YAT88hggGyD/xLtwJ2loSWxr28HTeBzj8Lq4a/ltOyTmjU+tLJJgSSLGkoG8+1uIgdFmiEEJYAZ2U0h66fyrwV2ABcAXwSOjnx6FTFgA3CyHeRmvEbuiL7ROff/55T4egKEoXc7h9vL6ikFeW5lPW4GZAooV/nDeGCydlYnDVQO1utJp5TUAGKXZW4gi4iKzZTf8fH0fqjOSfdA+e2AwCMsCHJT/yYclPpJkTuXviHQxMOvCYKJPORHpUOhaj5YDHdqQrSxSpwEehbGcA3pRSfimEWAW8K4S4GigALgod/zla19hdaN1jr+zC2LrMv/71L0wmE7feeiu33347GzZs4Pvvv+f7779n7ty5LFmyhNWrV5OUlMTDDz/MK6+8QkpKCllZWUycOBGAvLw8brrpJqqqqrBYLLzwwgsMHz68h1+ZoigHUuPwMG9pPm+uKKSm0cvQ1Cj+eOo4zh2fgS7oBVshuOpanOMN+ih0VuAJ+rBUbKf/oicImKLJP+FOfFEp1HgaeCrvA7bbCzguaTxXjr4ac1RSh3EIBEmRSSRHJh/Sinb767JEIaXcDbTqyCulrAFOamO7BG46nDE8uvJRttduP/CBB2F4wnDunnJ3u/unT5/Ov//9b2699VZWr16Nx+PB5/OxaNEiZsyYwZIlSwBYs2YNb7/9NuvXr8fv9zNhwoSmRHHttdfy3HPPMWTIEFasWMGNN97I999/f1hfh6Ioh4eUkpI6Fy8tzeeDNcU0uHyMzojhwXNGccaYfuj8bqjPB3cDzUsRAI1+F8WuKvwyQFTJerKWPIM3KoWCE+7EHxnPmrodzNn9Eb5ggJtyzmd69kwwx3QYT6QhknRrOmaD+bC9RjUy+zCbOHEia9aswWazYTKZmDBhAqtXr2bRokU8+eST/OMf/wBg0aJFnHfeeVgsWpHwnHPOAcDhcLB06VIuvPDCpmvuneNJUZTwEQhK8qrsvLwknwXrS2n0Bpg0IJ7rjsvhpOEp6Lw2rYrJ23qyT4Bar41ydy0SSUzBcjKXv4g7PouC4+7AbYzkzYIv+KJ8OQMtadw6+FekJ4/qMEno0JFiSSExMvGwv9Y+nSg6+ubfVYxGI9nZ2cybN49jjjmGsWPHsnDhQnbt2tWpeZaCwSBxcXGsX7++64NVFOWguX0BtpfbmbdkD19sLsfjD3JMTiLXzhjE9MFJ6D31UL0D/K42z5dSUu6uodanJZD4XQtJW/0azuShFM64jZKAkye3vMEeZxkzU4/i0v6nYoxKg8i4dmOKMkaRbk3H2Hz22MNILYXaBaZPn85jjz3GjBkzmD59Os899xzjx49vUVc4Y8YM5s+fj8vlwm6388knnwAQExNDdnY27733HqC9qTZs2NAjr0NRlH3sbh/L8qq5/Z31/GrOUhZsKOWYnETevOYoXp09keMzQF+1FeoL2k0S/mCAAmd5U5JI3PY56atfxZE+loLj7uCnhp3cu+k5qrz13Dn0EmYPPAOjNRWsCW1ezyAMZEZlMiBmQJclCejjJYqeMn36dB5++GGmTp2K1WrFbDYzffr0FsdMmDCBiy++mHHjxpGSksLkyZOb9r3xxhvccMMNPPTQQ/h8Pn79618zbtzhm7dFUZTOCQYl9S4f6wrreGN5IT/8XIlOCE4ekcpV07MZn27F6KqG6m0turq2xel3U+Kuxhv0gZSkbPqQ5K2f0tB/CnmTruClgs/4sXo9w6L7c0vOr0gyxTatTteW2IhY+ln7HZZJ/w5ETTN+BFG/G0XpHF8gSI3Dy6o9tby9qpCleTUYDTpmjurH7GMGMirFiMldE+rB1PFnqDY+oo4ab0NoQ5B+a98gcef31A46juUjT+WJvA8oc9dwXsYMLsg4Dr3QgykWYvq1ut4vGTi3lxBCTTOuKIpyKFzeANUOD8t31/DOqiJWF9RhidDzq4mZXHZ0f4bFC8yeGqizde56AQ+lrmrcQa+2IRggY+VLxOUvpWrYabyels1rW+cSZYjkvuFXMCpWG1hHRDREt04SieZEUiwpXTKfU0dUolAU5YgmpcTm8lPlcLMsr5Z3VxexqaSBaLOBy44ewMUTM8mJ8WPxVoLN2elrVnsbqPLUI0MlDr3bRvqqV4gpWcuu0WfzjwgvKws+Jzd2CDfknEvs3jmYjFaISYdmwx8O18C5Q9UnE4WU8rAMMulLenMVo6J0hUBQUtvopcrhZumuGt5dXcTPFQ4SrBFcfWw2541PIzvSjdVXCo2d76LuCXgpcVfjCniIsJcTXbyO6JJ1WKp3IZB8O/YsHvbkU+e0c1n/Uzmj39R9JQSjBWIympLE4R44d6j6XKIwm83U1NSQmJiokkWIlJKamhrM5sM3AEdReiuPP0CNw0uV3cPindW8t6aI/BonqTEmbjw+hzNHJdPf3Eh0oBjc/oO6do2nHlvpGqKL15Jesg6zrRQAV1x/KkadzSsWI29WryHJFMuDI69mcFTmvpMNZi1J6LTPra4YOHeo+lyiyMzMpLi4mKqqqp4OJayYzWYyMzMPfKCi9FEOj59qu4faRi8/7Kjk/TXFlDa4yYqP5PaTh3La8DjSDQ5iZDH4gp2/cMCLv3g1rrzviCleQ6K7Hil0NCYPo2zw8dgzxlMVYeaZXR+yqTqPoxNGcW32OViaJwC9CWIyQafr0oFzh6rPJYq9A94URVFA68FUXOeixuHhm60VfLC2hGqHh0HJVu6ZOZzjBllI09mIFcWIzuYHjx2KVkD+YmTRCgw+FxaDCUe/Mdgzx+NIG0vApLU5bGnYw1M7XqbR7+Z32WdzUvLElrUdugiIzQS9nmhjNGnWtC4dE3Eo+lyiUBRF2auu0UtRnZNPNpTy3ppi6p0+RqTFcNMJOUxN15OqtxOnt9GpWmpHJeQvhoIlULoeZICAOZaGrCnYM8fTmDoS2ewDPiiDfFjyIx+U/EiaOZF7h/+WAZb9ejIJA8RmYjCY6WftR6wp9rC+/sNFJQpFUfocXyBIab2L9YX1PPX9LnZVORiXGctdp2YxPjlAiq6BeKNsWna0TVJqczXtTQ7VP2vbY7PwjD6P8n4jcCQMhDa6qtZ57Tyd9wFbbHuYnjSOqweeiVlvanmQ0ENcFrGWpG4bOHeowjcyRVGUQ9Dg9LG72sEbywv5cF0xMWYjd586lOMzJcm6auJNOvTtJYigH8o3Qf4SKFgM9nJAQOpImHItgQHHUGa20uBztPv8GxvyeGbXB7iDXq4fdC7HJ49vfZDQY4zPJj1uUKdWp+tpKlEoitIn+ANBSuvdLN5VzdPf76S0wc0pI1K4ZkI0A0x1JFoMGNoqQvhcULxaKzkULgOPDfRGyJgIuZfBgKlgScThd1HqqsbXTpIIyADvF//A/NJFZEQm8+fBs8m0pLQ+UOhITB5NSnx2tw+cO1QqUSiK0us1uHz8XGHnxUV7+GpLOf1iTPx9ZibHprhJtjqINO7XOOyqg4KlWsmhZDUEvGCKhqyjYeCxkDVZG9OA1tZQ4apumsivLc0XFzoheQKzB5yOSR/R6jizzkRa+iQsUamH9fV3NZUoFEXptQJBSWm9iy82lfHcj7upd3m5YEwCV46EjGgPcZER+xqqG4q1UkP+YqjYAkiISoURZ8OAaZA2FvZrJ3AGPJS4qrSJ/Nqxrv5nns37CG/Qz805F3Bs0tiWB+iMoI8gLjKJtITB6Mzh2WDdEZUoFEXplexuHxuLG3j6+10s213DoAQTf50Rx6R+kBhlwqgTWoN04XJYMw+qdmgnJg6GiVdoySFxMG11eZJSUumpo8Zra5qCY3/+YIB3ir/jk7IlDLD047Zhl5Iela6NiTBEgD4C9CaEThsXkRTZ8fKl4UwlCkVRehWtFOHk3dXFvLRkD15/kKvGWbl0lIG0GBOWCL2WIIpXw+q5ULkNotNh6s1atVIbk+015w54KXFV7ZvIrwUB+giqfA6e3PE6O235nJx1ApePupIIQ+uqJoMwkBmdidVoPUyvvmeoRKEoSq/h8PhZuaeG/36zk40lDYxNMXLHZDNj0yKJ31vNVLoeVr8E5Ru1qqUZd8LQma2qlfYnpaTG20Clpx4phDalRqhUgD6iqZSwumI1czbMISiD3DbhNqamT23zema9mf7R/cNu8NyhUIlCUZSwFwxKiuqdvLQon7dWFmDUwe2TzfxqRCQp0WaMeqG1O6yeCyVrwZII034Pw8/QPuTbI/RgMOMVOkp8DThNFrDEg6H1h7s/6OeNra/wxZ4vGBQ7iFsn3Eo/a9ulk5iIGDKiMnpNr6YDUYlCUZSw1ujxs3BHJf/5ajt5NS6mZRq4fbKF4almoiIMWtvD6pe0KTUi4+Hom2DkOWBoNsBN6LReTAZz6GYCYyTojdS6a6lorCAY0f7kexWNFTyx9gl2N+xm5sCZXDri0nZLCqmW1F7dHtEWlSgURQlLwaCkoLaRp7/ZxkcbK4kzCf4yLZJzhluJj4xAV5enJYiCJWCKgSnXwqjztATQRIA1SauC2u+D3Rf0UWorwNHB4DmA5aXL+d/G/6ETOu6YeAdT0qa0eZxe6MmMyuwVA+gOlkoUiqKEHafXz6dr8/nPd7soswc4fZCRWydbyUkyE2ErgmXzYPdCiLDCpKtg9AXa/SZCq36KStXaFvbT4GmgrLGMQAfrXHsDXl7f+jpfF3xNTlwOt024jZS2BtChtUdkRWcR0VE1Vy+mEoWiKGFDSsnO0moe/3IzX+50kh6l4/GTrJySYyXaUw4/vgJ532lVR+Mvg7EXawPlmgiwJEBUvzYThD/op6yxDJu346VMyxxlPLH2CfJt+Zw56EwuGX5Ju3MxxRhjyIjuO+0RbenyRCGE0AOrgRIp5VlCiGzgbSARWAP8VkrpFUKYgFeBiUANcLGUMr+r41MUJTw4Gx28u2QLTy6tod4juXhEBDdPjiJD1KFb8T/4+Utt8NqYiyD312COa3a20Nonovu1bJtoxu61U+ooxS87XoxoSckSXtj4AgadgT9O/iMTUye2e2xKZArJluRDeLW9S3eUKG4DtgExocePAv+RUr4thHgOuBqYE/pZJ6UcLIT4dei4i7shPkVRepD0udmet5uHvi1iSbGfwfE6/nliFNOTnZg2Pgs7PtcGxY06D3J/o1UpNRcZr5UgjO03Rte76ylpLOkwDm/Ay7wt8/i+8HuGxQ/jlgm3tNsorRd6MqIyiI6IbnN/X9OliUIIkQmcCTwM3CG01TpOBH4TOuQV4AG0RDErdB/gfeBpIYSQarFnRemb/F6cdSW8vDifZ9Y68Qfh2lwzN4z0Ef/zPPhxgTZwbviZkHspRO3XPmCOg+i0DhMEaAmgrLGsw2NKHCX8d81/KbIXMWvwLC4cemG7VU0mnYmsmCxM+08b3od1dYniv8BdwN60mwjUS9lU9isGMkL3M4AiACmlXwjREDq+uvkFhRDXAtcC9O/fvytjVxSlKwR84Khg3a5C/vKDjY1VAXJT9Dw4JciYinfQfTwfgj5tkNyE32rJoDlzbChBRLZ5+eaklBQ7ignS/tJ1PxX/xNxNczHpTdw75V7GpYxr99hoYzQZURnodfrOvto+ocsShRDiLKBSSrlGCHH84bqulPJ54HmASZMmqdKGovQWAT80VuKoLefJ5XW8vNGNSQ/3TwrwW92nGH/8CPweGHwyTLhcWx60OVOMliAiLJ1+yipXFS6/q819br+blze/zI/FPzIiYQS3TLiFBHNCu9dKjkxut9dTX9eVJYppwDlCiDMAM1obxRNAnBDCECpVZAJ7Kw5LgCygWAhhAGLRGrUVRenNggFtGdHGKhbn2/nLjzby6oOcmuHlHynfkfjzh+BthEEnwMTZED+g5fmmGK2ROuLg5kty+pxUuara3FdkK+K/a/9LqaOUC4ZcwPlDzm+3lKAXetKj0omJiGlz/5GgyxKFlPJe4F6AUIniTinlpUKI94BfofV8ugL4OHTKgtDjZaH936v2CUXpxYJBcFaDo4J6p5tHFjfw7jYPaSYPHw9dyNiK+YgamzZR38QrITGn5fkR0VqCMB38ALZAMECxo7jVdiklC4sWMm/zPCKNkfzp6D8xJmlMu9eJ0EXQP6b/EdUe0ZaeGEdxN/C2EOIhYB0wN7R9LvCaEGIXUAv8ugdiUxTll5ISnLXgKEf6vXy5q5EHf7JR1+jh3/0WMsv1MbrCem2RoElXQvKwludHRIUSxKH3KCptLMW33xoSLr+LuZvmsrhkMWOSxnDT+JuIM8W1e40jtT2iLd2SKKSUPwA/hO7vBlqNgZdSuoELuyMeRVG6iKsO7OUEvW7Wl7v4zwo7K4pcXG9ZyE0xCzDV12pLjE66ClJHtTz3MCQI0LrC7j+grsBWwH/X/JfyxnIuGnYR5w4+t8MBcknmJFKtvWsVuq6kRmYrivLLeexgKyPgaWRXjYsnVzpYuNvBbww/8HTUZ8T6qyFhLEy6H9JzW55rtGoJwvzL2wDa6gpb667lb8v+hlFn5M9T/8zIxJHtnq9DR3pUOrGm3rcKXVdSiUJRlEPndYK9DL+rgeJ6D/9b6+D7HTVcIr7h0civiQraIHE0TLxHK0k0X03OaAkliMPzodxWV9igDPLchufwBrw8OO1BMqIy2j0/QhdBVnQWZkPH4zKORCpRKIpy8PwesJXic9ZRYfPw+sZGvt1axiXBz/hLxPeYpQeZORVyL4F++60hbYjUEkRk3GENqdJZ2aor7Nf5X7OxaiNXjb6qwyQRZYwiIyqj3UF2Rzr1W1EUpfMCPrCX47NXU+1w89F2Jz9uzONC/yfcqV+CXi+ROSdB7iWIhEEtzzWYQwki/rCH1ehrpNrdYmwuxfZi3tj2BuNTxnPKgFPaPTfRnEiqJRXRxtrZikYlCkVRDiwYAEcFXlsFtXYX3+1xs2zdRma5P+ZG/Vr8RhNy2DmI3IsR+69JbTBr031b2h/M9ksEggFKHC3ncfIH/Ty97mnMBjPXjb2uzSSg2iM6TyUKRVHaFwxCYxWehnLqHE5WFbtYt2Y5Mxvnc6luB+6IKJwjLyMy91eIFlVJQmt7sCQelkbqjrTVFfa9He+Rb8vnzkl3EtdillmNUWckKzqLSMOBpwFRVKJQFKUtobEQrrpi6u1Otlc0smP1Qo63fczZuiJspiTqRl1P1LhzMJuaTamhN2nJwZLQakW5rlDnrmvVFXZbzTYW5C3gxKwTmdRvUqtzrAYrmdGZqj3iIKjflKIoLbnqcNaUUGezU1Jjo2D1FxxT/wkniGoqzFmUjf4DcaNPJca8d7Ry95UemvMEPJQ3lrfY5vQ5eWb9M6RYUrh81OWtzlHtEYdGJQpFUTQeO43VRdTW11NfV0vFmo+ZWPs5U4SDPNNwdo65geThxxJnDSWIptJDIui796NESkmJvaTVrLAvb36ZWnctDx7zYIturgJBujW9zWoo5cBUolCUI53Xib2qkPq6Glx1ZTSs/ZDRtd8yRnhYZ5pI/pgLSR+SS2KUCYNO1yOlh/1VOitxBVp2hV1WuoxFJYu4YMgFDIkf0mJfiiVFJYlfQCUKRTlCSZ9bSxC1Vfird+Pd8D6D6xYhJSyOmIZ+7K/Izh5CUnQEkWZLj5Ue9tdWV9haVy0vbnqRnLgczhtyXot9FoOl3ZXqlM7p1F9cCPFP4CHABXwJjAVul1K+3oWxKYrSBaTfS0NVMfXV5egqNqLb/D6D6lfhlCYWGE7FOPZ8Rg1MJzHaRGxcEliTfvH8S4eLP+in2N5yVtigDDJnwxz8QT83597copFah67DgXZK53T2q8GpUsq7hBDnAfnA+cBPgEoUitJLBP1+6quKaaguxlSygpit75Fg20atjOIF3QWYxp7DUdkJxEVHkZichiEqucdLD/src5Thb1ogU/Plni/ZVL2J3435HWlRLVfDS7WmEqGP6M4Q+6TOvgv2Hncm8J6UskH1GlCU3iEQCFBXVUJDeQHWwu9I3vEBUY2FFMsknucKIkeeximDY7DEJJDaL4PI6LieDrlNde46bL6WXWGLbEW8tf0tJqRM4KT+J7XYF22M7nDFOqXzOpsoPhVCbEererpBCJEMuLsuLEVRfqlgIEh1dRn20p3E7P6UzJ3zMbmr2BHMYq68AcvQ4zl3ZByRsUmkpKYTH935JUa7W1tdYX0BH0+tewqLwcJ141qOvjYIQ6vShXLoOpUopJT3hNopGqSUASFEIzCra0NTFOVQeT0eyneswLrlTQbs/hSDz87q4DDmBGYTlT2FX49PJjYhhbj4BFJjzOh14VtD0F5X2Hd3vEuhvZA/Tv5jq2k4+ln7YdR1/YC/I8XBVEAOBwaG1rPe69XDHI+iKL+Qw16PbcWbpK98BIO3gYVyIk95zyam/xh+OzmdlJRULJEmMuIiMRvDf/W2CmdFq66wW6q38OnuTzm5/8lMTJ3YYl9sRKyav+kw62yvp9eAHGA9EAhtlqhEoShhpbYsH374B2k73qFAZHCD5x5MqUOZPXUAORkpGPSCtFgzcZbe0cDb6Gukxl3Tatuz658l1ZrKZSMva7HPIAykWVWV0+HW2RLFJGCklFJ2ZTCKohwaGfBTtWMZUd/fi6V6E1/qj+cu1+XcfNpYpg5OQQhIjIogJTq8q5maa6srLMBLm16izlPHX4/5a6tFhtQa112js4liM9APKDvQgYqidC+/u5Ha5a+TsOzvCL+LxyJu5DnHdP7vzBFMGpCA1aQnvZdUMzXXVlfYJSVLWFK6hAuHXsjg+MEt9iWYEoiKiOrOEI8YnU0UScBWIcRKwLN3o5TynC6JSlGUTnHXleP++q8kb3sTT1Qmt+nv59v6NO49fThTshPIjLMQa+l9jbptdYWtdlUzd9NchsQN4dzB57bYF6GLINWa2o0RHlk6myge6MogFEU5SMEg9j2r0H9xJ3HVG6nLPJFr7L9jbQ3cddowjspOpH+ChWhz70sSbXWFDcogc9bPISAD3DT+phbVSwJBRlQGOqHr7lCPGJ3tHvujECIVmBzatFJKWdl1YSmK0i6/h4aVb2D98UF0fhcluXdwU9HxbKx0cMcpw5g2OInUWFOvTBLtdYX9fPfnbKnZwrVjr6WfteUKeonmRCzG8B0D0hd0KgULIS4CVgIXAhcBK4QQv+rKwBRFaS3oqMY+/05ivr6DQEQ0u06Yw62lJ7GhxMFtJw3huKHJxFmMpESbD3yxMNRWV9gCWwFv73ibyamTOSHrhBb7IvWRpFhSujPEI1Jnq57+D5i8txQRGpn9LfB+VwWmKEozUuItXk9wwW1EV23AlnUSBVPu529LXKwprOXmEwZz4vBUIiN0ZMT1zuU9HV5Hq66w3oCXp9c9jdVo5Zqx17QYfS0QpEelq0WIukFnK/V0+1U11RzoXCGEWQixUgixQQixRQjxYGh7thBihRBilxDiHSFERGi7KfR4V2j/wEN5QYrS5wR8uFe/ge71c4mo3U75hD+Qf9yT/GO5m5X5tVx/XA6njeqHQS/on2BF10u6vzbnD/opcZS02v7Ojncoshdx/djriTG1XP8ixZLSqnus0jU6myi+FEJ8JYSYLYSYDXwGfH6AczzAiVLKcUAuMFMIcTTwKPAfKeVgoA64OnT81UBdaPt/QscpypHN1YD7kz9i+uwWgsYoCk7+H+Vjb+KxH0tYmlfD747N5swxaQgB/RMsRBh6Z4NuW11hN1Vv4rPdn3HqgFMZnzq+xT61xkT36tS7Skr5R+B5tHUoxgLPSynvPsA5UkrpCD00hm4SOJF9VVavAOeG7s8KPSa0/yShypTKEUyWb8Y7bxbm9S9jzzyOvDPewZZ5Ak8u3M1PO6u58piBzMrV1lpIj4vEagqvKcE7q9Zd26orrMPrYM76OaRb07l05KUt9qk1Jrpfp99ZUsoPgA8O5uJCCD2wBhgMPAPkAfVSNn11KAb2/sUzgKLQc/mFEA1AIlC93zWvBa4F6N+//8GEoyi9QzBAYOMH8OVdGLyNlE+4g+ox1xEwmHlm4S6+31HJZUf15/wJmQAkREWQYO0dU3LszxPwUNFY0WKblJK5m+fS4GngD9P+gElvarG/n7WfWmOimx2onWFx6KddCGFrdrMLIWwdnQsgpQxIKXOBTGAK2sSCv4iU8nkp5SQp5aTk5ORfejlFCS9uO/7P7kI3/zoCBisFp7xI1fhbCRrMPPdjHl9vreDiyVlcPFn7kmQx6UmP7Z319O11hV1SsoRlpcv41dBfkROX02JftDGaeHN8d4apcIAShZTy2NDPX7QOopSyXgixEJgKxAkhDKFSRSawtwWrBMgCikMz1MaiNZorypGhcgf++TdgKF2DLfMESqb9A390OlJKXly8hy82l3PBhAwunaIlCaNBMCDB0mt7/bTVFbbKWcXczXMZFj+MWYNbrmSg1pjoOZ0dR/FaZ7bttz9ZCBEXuh8JnAJsAxYCe8dgXAF8HLq/IPSY0P7v1SSEyhEhGIRNHxB8eSa68k2UT7iDwpP/15Qk5i3NZ8GGUs4Zl84VUwcihEAIGJBgxaDvnY3XbXWFDcogz65/FiklN42/qdVIa7XGRM/pbBvFqOYPQt/4J7Zz7F5pwCuhdgod8K6U8lMhxFbgbSHEQ8A6YG7o+LnAa0KIXUAt8OtOxqYovZfPRfCbvyBWvYDfmkbJqU/gyJgBoVLCGysK+XBdCWeMSeN3x2Y3lR6y4i1ERvSuSf72aq8r7Kd5n7KtdhvXj7u+1SA6tcZEz+owUQgh7gX+BEQ2a5MQgBetF1S7pJQbgfFtbN+N1l6x/3Y32shvRTkyVO8i+NH16EpWYc88nuLpj+JvtpbC26sKeWd1EaeOTOW6GYOakkRytKlXTvS3V6mjtFVX2PyGfN7Z8Q5T+k3huMzjWuxTa0z0vAO1UfwD+IcQ4h9Synu7KSZF6dukhK0LkJ/dDm4b5RPuoGrcjdCsJ8/7a4p5Y0UhJw5P4aYTBqMLJYlos4F+vbTxGrSusHafvcW2vaOvYyJi+N3Y37Vqc1FrTPS8zk4KeK8QIh4YApibbf+pqwJTlD7J74FvH0SumIPP0o/iU1+mMWN6U1UTwPz1JbyyLJ8ZQ5K59cQhTUnCZNSRldB7J79rqysswFvb36LYUcy9U+4lJqLl6Gu1xkR46OxSqL8DbkPrpbQeOBpYhjZ4TlGUzqjLR354DaJoJY7M4yme/i/8+62h8NnGUuYu3sMxOYncccrQptXodDpt5HVvWZ1uf1JKiu3FrbrCbqzayBd7vmDmwJmMSxnXYp9aYyJ8dLbLxG1oU4wXSClPQGt7qO+qoBSlz9n2KfKFk6BkHeUT7yT/lLmtksRXW8p57qfdHJWdwB9PHdYiKWQlWHrdCnXNVTgrcAfcLbbZvXbmrJ9DRlQGvxnxmxb71BoT4aWzvZ7cUkq31i1PmKSU24UQw7o0MkXpCwI++PYB5PJn8Vv7UXjaqzgzprU67LttFTyzcBeTBsRz98zhLbq9psaaiOmFa0vs1VZXWCklL256EZvXxl1T7mo10lqtMRFeOpsoikNjIuYD3wgh6oCCrgpKUfqE+iL44HdQtBxH5vEUzfg3AUvr2QR+2FHJE9/tZFxWHPeePgJjsyTRm9eWAG350kpn6zXOFpUsYkXZCi4ZfgnZsdkt9qk1JsJPZxuzzwvdfSA0wjoW+LLLolKU3kxK2PElfHIL0lVPxcQ7qRp7I+hb/7st2VXNf779mdEZsfzfGSNazP7am9eW8Aa8lDhKcPqdrfZVOit5efPLDE8Yztk5Z7fYp9aYCE8HTBShAXNbpJTDQVsWtcujUpTeKBiEmp3w47+QWz4gYO1H4czXaEw/ps3Dl++u4V9f72BYvxj+fObIFm0Qel3vXVuixlVDpbOyVcM1aKOvn1n/DAA35t7Yqg1CrTERng6YKKSUASHEDiFEfyllYXcEpSi9SsAP9lJY8TyseRnpc+LIOYuiKQ8QsLS9ZsLq/Foe/XI7g5OjeODskS1GWQsBAxJ739oSvoCPEkcJjf7Gdo9ZkLeAHbU7uDH3xlbVS2qNifDV2TaKeGCLEGIl0PQukFKe0yVRKUpvEPCBvQK2zoflz4KtBH/6ZIrG34Gj3zEtxkY0t66wjr9/sY2BiVYeOGcUloiW/4ZpseZet7ZEnbuOCmcFARlo95g9DXt4b8d7HJ12NNMzprfYp9aYCG+dfTf+uUujUJTexO8BRwUULodlz0DZemTcQOpP+hclWbOQHVSdbCyu56HPtpEZb+Gvs0YRtV9CSIiKIDHK1M7Z4ccX9FHqKMXhc3R4nCfg4al1T2mjr8e0Hn2t1pgIb51tzP5RCDEAGCKl/FYIYQF6b6duRTkUPhfYy6EmD1a9AD9/BeYYfMfcTvHgS3AYU9stRQBsKW3gr59upV+smb/NGk30fl1ee9vaEvXuesqd5R2WIvZ6c9ublDpK+b+j/q/VSGu1xkT46+zI7GvQVpVLAHLQVqN7Djip60JTlDDhcWglCEcFbHwXNrwFwQCMvRjbuKspMgwgqO/4A357uY0HP9lKUpSJh84dTWxkyyTRm9aW8Af9lDnKWi1f2p71lev5Kv8rTs8+nTHJY1rsU2tM9A6drXq6CW3G1xUAUsqdQgjV0Vnp29wN4KgEjw12fqOVIhqrIfs4glOupyx6NLUceOrrXZUOHliwhTiLkYfPHU28pWUVS29aW6LB00B5Y3mr2V/bU9FYwXMbniMzOpNLhl/Sar9aY6J36Gyi8EgpvXu/7YTWo1CLCil9k6tOSxA+J5RthGVPQ/XPkDwMTrofT/rRFAYTcQcP/AG3p9rBnz/ejNVk4OFzx7TZ/pAZHxn2a0v4g37KG8tp8DZ06nhPwMP8XfP5NO9TDDoD946/t1UbhFpjovfobKL4UQixd12KU4AbgU+6LixF6WZSgrNWq14KeMBWAiv+B3t+AmsynPAnGHIadcZUSrwWOrP2YkFNI/fN34zZqOfh88aQHN06SSRHm4izhHcjrt1rb3MNibZIKVlRtoLXtr5GjbuGaenTuHTEpSREJrQ4Tq0x0bt0NlHcA1wNbAKuAz6XUr7QZVEpSncJBsFZrZUggj7w2GHd67D5Q9DpYdJVMPYiApYUSmUS9Z7OXbaozsl98zdj0Ot4+NzR9Itp3YYR7mtLBIIByp3l1HvqO3V8kb2IeZvnsaVmCwNjBnLLhFsYnjC8zWPVGhO9S2cTxS1SyieApuQghLgttE1Rep+AHxqrtCQR9Gu3bZ/A6pe1ZDF0Jky+GqL74YpMo9BlwutvPdK41WWDkqV51by4aA8IePjc0aS3MQ1HuK8t4fA6KG0sxRf0HfDYRl8j7//8Pl/lf4XFYOGq0Vdx8oCT2535Va0x0ft0NlFcAeyfFGa3sU1RwlvAp5UenNUgg1qVU9EKWD4H6gsgLRem3gRJQ8AcR7UhhXK7Hyk7ThJOr59vtlawYEMplXYPGXGR3Hv6cDLjWyeDcF5bIiiDVDRWUOup7dSxPxT9wNvb38butXPSgJO4eNjFREdEt3uOWmOidzrQmtmXAL8BsoUQC5rtigYO/E5SlHCxd5Ccs5amfhi1u2HZs1CyGmIz4dSHYMA00EcQiMmg2BWBzdZxvXy1w8MnG0r5aks5jd4Ao9JjuGb6IKZkJzStTLe/cF1bwulzUuIowRv0HvDYXXW7eHnzy+Q15DEsfhj3HnVvq1lg96fWmOi9DlSiWAqUAUnAv5tttwMbuyooRTls9g6SczfQlCCctVoV047PIMIKU2+GkbNAb4TIBBrNqRQ1ePH5208SeVUO5q8rYdGuaqSUTBucxLm5GQxNbf/bNITn2hJBGaTSWdlqzYi21HvqeXv72/xQ9APxpnhuzr2ZaRnTOjX+Q60x0Xt1mCiklAVo605M7Z5wFOUw2TtIztNsUJjfA5vfh3VvaPdHnQcTrgBzDOgjIDaTSp+JylpPm72aglKyOr+O+etL2FTSQKRRz1lj0jhnXDopbTRW7y8c15Zw+pyUOkrxBDtupfcH/Xyd/zXv/fwe3oCXs3PO5vwh5xNp6Nw06GqNid7tQFVPi6WUxwoh7LQcNyEAKaWMaedURekZfg/UF4K32dxDUsLuhVp3V0eFVr101HUQ11/bb0nEb02jqMGLw936A9PrD7JwRyXz15dQXOciKSqCK48ZyGmj+nV68r5wW1tCStlUipAHGBK1uXoz87bMo9hezLjkcVwx6grSo9I7/VxqjYne70AlimNDPzsuTytKOPC5tHmYmvfUqdyqTdxXsQUSc+C4uyBjorZPb4K4LBxEUlTtxB9o+YHZ4PLx+aYyPttURoPLR06ylT+cMpRjBycd1CjqcFtbwuV3UeoobbWG9f6qXdW8tvU1VpStIMWSwp2T7mRi6sSD+sDXCz39LP3UGhO9XO+ay1hR2uN1Qm2e1s0VtHaJlS9A3ncQmQAz7oKhp2ljIwCsycjoNCodPiptLddPKKpz8vH6UhZur8QbCDJ5YDzn5WYwOiP2oL8Vh9PaElJKql3VVLmqOixFeANePsn7hI93fQzARcMu4qxBZx3U7K56oSfBnECiOVGNl+gDuixRCCGygFeBVLRqq+ellE8IIRKAd4CBQD5wkZSyTmj/gU8AZwBOYLaUcm1Xxaf0IR6H1oNJBrSEsf5N2PSutm/8b2HcJRARakQ1mCGuPz59JEU1Tho92synUko2lzTw0foSVuXXEaHXccLwFGblppPVRhfXzgqXtSU8AQ8l9hJcAVe7x0gpWV2xmte2vkals5Kj047mspGXHdRiQipB9E1d+Q72A3+QUq4VQkQDa4QQ36CNv/hOSvmIEOIetFHfdwOnA0NCt6OAOaGfitI+tw3q9mhjIuoL4ct7wFYKg0+GKddC1N4GVKHdj07D5vFTXO0gEJT4A0EW76pm/voS8qoaiY008psp/TljTFqrGV4PVrzVGBZrS1S7qql0VnZYiihxlPDKllfYWLWRzOhM7jv6PkYnje70c6gE0bd1WaKQUpahda1FSmkXQmxDm558FnB86LBXgB/QEsUs4FUppQSWCyHihBBpoesoSmuueqjLBySUbYCv7wOhh7OfhLSx+44zREJcf6QxknKbm2q7l0aPn6+2lPPJxlKqHV4y4yO5+YTBHD8sGZPhl3/QWU36Hm+89gV9FNuLcfqd7R7j9Dn5cOeHfLHnC0x6E1eMuoJTB5za6Q97lSCODN1SJhZCDATGo01Tntrsw78crWoKtCRS1Oy04tC2FolCCHEt2toY9O/fv+uCVsKbs1YrQSBh59fw4z8hJh1mPqL9BEBAdD+ISsUbkBRWNVJQ3ciCDaV8vbUCly/A2IxYbjx+MBMHxLc7QK49QoDJoMNk0GMy6vbdN+h6vOHaF/CRb8tvd/BcUAZZXLKYN7e9SYOngeOzjufXw3/d6dlcVYI4snR5ohBCRAEfAL+XUtqaNwZKKaUQ4qCmK5dSPg88DzBp0iQ11fmRqLEGGgq1bq9rX4E187SpN079G5hCHfSMVojLAmMkDS4f322r4MO1JSzNq0YIwfTBSczKzWBwyoHnHNLpwGzUt0oKEXpdWHb59Aa85Nvy252naU/DHl7a/BI763aSE5fDHyf/kZy4nE5de2+CSDAnYND1fNuL0j269C8thDCiJYk3pJQfhjZX7K1SEkKkAZWh7SVAVrPTM0PbFGUfRxXYirU5m356DHZ+BUNOgxl3aiOrEVqJwpqMPyh5b2Uhry0rYGuZDWuEnnNzMzh7XDpJbbQdGA2iqURgMugwGfWYDbpesaDQXp6Ah/yG/DanBLd5bbyz/R2+L/yemIgYrh93PTMyZ3RqSg2VII5sXdnrSQBzgW1Syseb7VqANsngI6GfHzfbfrMQ4m20RuwG1T6htGAvB3uZNrvr13+GsvXaNODjf6vVA+lNED8QFybeWprP3EW7Kal3kxJt4nfHZnPKyFSsJkPYVhf9Um6/mwJbQaskEQgG+LbwW97d8S4uv4uZ2TO5cOiFnZpOQy/0xJviSYxMVAniCNaVf/lpwG+BTUKI9aFtf0JLEO8KIa5Gmx7kotC+z9G6xu5C6x57ZRfGpvQ2tlJtVLWtFL64W0saJ/wfDDlF22+KgfiBvLaymMe+2kGDy8fwftE8eE4Op45qmSD6IpffRaGtsFWS2FazjXlb5lFgK2BU4ihmj55NVnRWO1fZRyUIpbmu7PW0GG2qj7ac1MbxEm1tbkVpqb5Imxa8Ygt89SetbeLMxyBtHFqDdRpEp/L+mmL+PH8zx+QkcvspQ5k0ID4s2xAON6fPSaG9kIAMNG3zB/3M3TSXhUULSYpM4vaJtzOl35QD/j506LRGapUglGbUO0EJX1JqPZtctbD7B1j4sLYs6cxHtYZqnQHiB4IpmkU7q7jng41MG5zIy7OnhMVI6O7QVpJo9DXy+OrH2VKzhXNyzuGCoRdg0nc8nkMlCKUj6h2hhCcptTESrjrY8BasfB5SR8NpD4E5DiKitCShN7K11Mb1r68hJyWKOZdNPGKSRKOvkUJbIUH2LapU7arm0ZWPUuoo5cbcG5mROaPDa6gEoXSGemco4ScY1EZbu2ph8X9h+6cw6AQ4/h4wmMCaovVsEoKyBhdXzVtFtMnIvCsnh91aD13F4XVQZC9qkSQKbYU8svIRXH4X90y5hzHJY9o9XyUI5WCod4gSXoJBbd4mRxl884C2+lzuZTD5KtAZtSqnyHgAbG4fV7y0EofHz/s3TCUtNnym8e5KNq+NYntxiyk5Nldv5t+r/41Zb+aBYx5gQMyANs9VCUI5FOqdooSPYEBLErV58MU9WvvEjLtg+BnaNBzxA8GoTVft9Qe54bU17KluZN6VUxje78hYGqXB00CJo6RFklhcvJg5G+aQFpXGPVPuaXMSPx064s3xJEUmqQShHDT1jlHCQ8CvJYjSdfDlvdoCRKc/CpmTtBJEbH9tiDTaLKf3fLCRJXk1PH7ROKYN7vzspr3Z/klCSsmCvAW8tf0tRiaO5A+T/oDVaG1xjkoQyuGg3jlKzwv4oGYX7PoWvn8IzLFw5r8hYRDEZoK1ZSL499c/8+G6Eu48dSjnT8jsoaC7V527jtLG0qbHQRnk5c0v803BNxyTfgw3jLsBo75l+0yMMYa0qDSVIJRfTL2DlJ7l90L1TtjwprYSXfIwOO3v2mR+8dn71pEIeXNFIU8v3MWvJ2dx0wmDeyjo7lXrrqWscd8kBZ6AhyfXPsmaijWcnXM2lwy/pNU0HKmW1INaR0JROqIShdJzfG6o3gGL/wNbPoKB0+HE/9N6NcUNAH3Lt+fC7ZX8+ePNHD80mYfOHX1EDKarcdVQ7ixvemzz2Pjnqn+SV5/H7FGzmZk9s8XxeqEnMyqTqIgDT3aoKJ2lEoXSM3wuKN8E3/wZCpfD2ItgynVaVVN0v1aHbypu4KY31zKiXzTPXDqhV03Ud6iqXdVUOCuaHpc3lvPIikeocddoI63TprQ43qw3kxWddVBLlipKZ6hEoXQ/byMUrYTP/6g1YB97O4y+QCtFmFv3XiqqdXLlvJXEWyJ46crJYbG0aFerdFZS5apqeryzbif/XPVPkPDnqX9maPzQFsfHRMSQEZXRqZlgFeVg9f3/OCW8eBzaQkNf3AVeB5z2D8g5Uev6amj9Tbje6WX2yyvxBSRvXzuZlGhz98fczSoaK6h2Vzc9Xl2+mifXPkmcOY57ptxDelR60z6BIMWSotojlC6lEoXSfdw2bTqOb/8CEVY45ynoPxViMrRpwvc/3BfgmldXU1Tr4vXfHcXglOgeCLp7lTeWU+OuaXr8TcE3vLTpJQbFDuKuKXe1WIHOIAxkRGWo9gily6lEoXQPVz0seQKW/Ffr9nr6o5AxsWmU9f6CQckf3tvAqvw6nrpkPFOyE7o13J5Q5iij1lMLaGMk3t7xNh/v+pgJKRO4dcKtmA37SlOqPULpTipRKF2vsVqbHnzjO5B1NJz2sDbBn7H9aqRHvtzOZxvLuPf04Zw9Lr3d4/qKEkcJ9Z56QJsi/LkNz7G4ZDEn9T+Jq0Zf1WJd6tiIWNKj0lV7hNJtVKJQulZ9Mcy/AfJ/gpHnwkl/hvhBTaOs2/LK0nye/2k3l08dwLUzBnVfrD1ASkmJo4QGbwOgTRv++JrH2Vy9mYuHXcy5g89t6gas2iOUnqIShdJ1KrbB+1dC1XaYejMccxtEp3R4yldbynngky2cMjKVv5w9qk+PlZBSUmwvxuazAdqYiUdXPkqJo6TVFOEGYSAzOrPVFB2K0h1UolC6Rv5ieP8qrW1i5t9hwhVaA3YH1hbWcetb6xiXGceTvx6PvpevYd0RKSVF9iLsPjsARbYi/rHyH7j8Lu6ecjdjk8c2HRupjyQrOqvVFB2K0l1UolAOv80fwIJbQB8BF8yFYae3GmW9v/zqRn43bzX9YszMvWISkRF9c21r0OZpKrIX4fA5ANhSvYV/r/43Jr2p1RThcaY40qxpqj1C6VEqUSiH15In4dsHtHUjLpirzf56ADUOD1e8vBKAeVdNITGq42U7e7OgDFJoK6TR3wjA4pLFzFk/hzRrGvcctW+KcIEg1ZJKYmRiT4arKIBKFMrhEvDBl/fAqhchczL8ah7EHXhmV5c3wO9eWU15g5s3rzma7KS+WwcfCAYotBfi9DuRUvJJ3ie8uf1NRiSM4A+T/tA0HsIgDGRFZ2ExWg5wRUXpHipRKL9c1Q6Yf6O2Gt3IWTBrDpgO/IEfCEpue3sd64vrmXPpRCYOaHtMRV8QCAYosBfg8rsIyiDzNs/j64KvW00RHmmIJCtKtUco4UUlCuXQBYOw5mVtpHXAByc9ANNu67Dr615SSv726Va+3lrBX84eyczRrScC7Cv8QT+FtkJcAReegIen1j7F6orVnD3obC4ZsW+K8DhTHOnW9D7d00vpnVSiUA6NvQI+uwO2fwrJw+HcOZAxodOnv7hoD/OW5vO7Y7O5clp2Fwbas3xBH4W2QtwBd7tThAsE/az9SDD3/dHnSu+kEoVycKSEHV/C538AexlMugpOeahTVU17fbqxlIc/38aZY9L40xkjujDYnuUL+ihoKMAT9LQ7Rbhqj1B6gy7rcyeEeEkIUSmE2NxsW4IQ4hshxM7Qz/jQdiGEeFIIsUsIsVEI0fmvpkr38Ti0qcHf+Q0EA3DRa3Dm4weVJFbuqeWOdzYwaWA8/75oHLo+OlbCF/CR35CPJ+hhV90u7l9yPw6fg/uOvq8pSUQaIhkUN0glCSXsdWXn7HnAzP223QN8J6UcAnwXegxwOjAkdLsWmNOFcSmHong1vHQqrHoBhpwC1yyEEWe1Oetre3ZV2rnm1dVkJkTy4uWTMBv73liJoAxS7aomryEPb9DLmoo1/HXZXzEbzPx12l8ZljAMgHhTPNkx2Rh1qtFaCX9dVvUkpfxJCDFwv82zgOND918BfgDuDm1/VUopgeVCiDghRJqUsgylZ/k8sPwZ+OmfIHQw8xGYci3oDu5DvtLuZvbLqzDqBa9cOYU4S9+a9VRKSZ2njipnFX7pB+Dbgm+Zu2ku2bHZ3DXlLuJMcQgEadY04s19t4eX0vd0dxtFarMP/3IgNXQ/AyhqdlxxaFurRCGEuBat1EH//v27LlIFqvPg8ztg9w+QlqutH5E29kBntdLo8XP1vFXUOLy8c93RZCX0raqWenc9Va4qvEEvoJUq3t3xLvN3zWd8ynhum3AbZoNZtUcovVaPNWZLKaUQQh7Cec8DzwNMmjTpoM9XOiHgg03vwzf3gbMOjr4JTvi/g2qL2MsfCHLzm2vZUmrjhcsnMTYz7vDH20NsXhtVzircATegjZVYUrqE+TvnU9pYyon9T+Tq0Vej1+mxGCxkRmeqqialV+ruRFGxt0pJCJEGVIa2lwBZzY7LDG1Tupu9Ar69Hza8DbFZ8OtnYMipnRobsT8pJX/+eAsLd1Tx8HmjOWlE6oFP6gUafY1UOCtw+V2ANk7ip+Kf+HjXx1Q4K+gf3Z/fT/g9R6UdhRCCBFMC/az91PgIpdfq7kSxALgCeCT08+Nm228WQrwNHAU0qPaJbhYMwO6F2lrWNXna2hEzH4GYtEO+5LM/5PHWykJuPD6HS48acOATwpzT56TSWdk0T5Mv4GNh0UIW5C2g2lXNoNhB3DnpTiakTkAndKo9QukzuixRCCHeQmu4ThJCFAN/QUsQ7wohrgYKgItCh38OnAHsApzAlV0Vl9IGV4O2ROmyp8Fo0bq8jv8tGA69wfmjdcX866sdnJubzh9PG3b4Yu0Bbr+bKmdV07oR3oCX7wq/45O8T6h11zIkbghXj76a3JTcplKDUWckMypTtUcofUJX9nq6pJ1dJ7VxrARu6qpYlHYEg1C+Ab64G4pWaMuUnvlv6Df6F1126a5q7np/I1MHJfLPX43rtVUu3oCXKldV0xKlbr+bbwq+4dPdn9LgaWBEwghuGHcDo5NGt0gQieZE4kxxLZYvVZTeTI3MPlJ5G2H9G/D9Q+BzwfQ/wLTbwRz9iy67vdzGda+tITvJynO/nUiEofeto+AL+qh2VlPnqUMicfqcfJX/FZ/v/hy7z86YpDGcP+F8RiTuG1UeqY8kMTKRmIiYXpsYFaU9KlEcaaSE2t3amhHbFkDiYJj5KOSccNBjI/ZX3uDmypdXYTHpmXflFGIje1cPn0AwQLWrmlp3LUGCOLwOvsz/ki/2fEGjr5Hc5FzOH3o+Q+OHNp0TbYwmMTJRLVGq9GkqURxJfG74+Uv4+j5oKIZxl8CJ90Ns+i++9OaSBu58bwM2l493r59KelzkYQi4ewRlkFp3LdWuagIygM1r4/Pdn/NV/le4/C4mpU7ivCHnkROXA4AOHbGmWBIjEzHp++4iS4qyl0oURwpbGSx+HFbNBUsCzHoaRv8KjOZDvqTbF+CLzWW8urSAdUX1WCL0PHfZREalxx7GwLuOlLIpQfiln3pPPZ/lfcbXBV/jDXg5Ku0ozhtyXtPSpAZhIN4cT4I5AYNO/esoRw71bu/r/F6tofrr/4OyDTDoeDj1YUgZeUhjIwCKap28vqKAd1cVUef0kZ1k5f6zRnLBhExiLb2juqneXU+lqxJf0Eetq5ZPdn/CdwXf4Qv6OCb9GM4bch6Z0doKfRG6CBIjtQZqtXa1ciRSiaIva6yBta/AosdABrXR1ZOvAcvB9+sPBCU//lzJq8sK+PHnKnRCcMqIFH47dSDH5CT2mgZcm9dGZWMlnqCHalc1H+/6mIVFCwnKINMzpjNr8CzSo7SqOIvB0tRArShHMpUo+qKADyq2wPd/g13fQuooOPXvMOCYgx4bUePw8O7qYt5YXkBxvYvkKBO3nDiES6ZkkRbbe9ohHF4Hla5KXH4XFY0VfLzrY34s/hGA47KOY1bOLFKtqQgE0RHRJJoT1RgIRQlRiaKvcdVrq85991dorIKJV8KMOyE2s9OXkFKyrqie15YV8OnGUnwByVHZCdx7xghOHZWKUd97ql+aj6YudZQyf9d8FpcsRi/0nNT/JM4ZfA5JkUno0DW1P0To+9bMtoryS6lE0VcEA1q318X/hQ1vQnQ/OPc5GH4mmKI6dQmn18+C9aW8uqyArWU2rCY9v5nSn0uPHsDQ1F82vqK7NR9NXWQv4qOdH7GsdBlGnZGZA2dyVs5ZWqO0MJAYmUi8KV4NkFOUdqhE0dtJCa46rcH6279A1Q4YdobWHpEyolNjI3ZVOnhjeQHvry3G7vYzLDWah88bzbm5GVhNvest4vQ5qXXX0uBtIL8hnw93fsjK8pWY9CbOyjmLswadRawpFrPeTKI5kVhTbK9pX1GUntK7PgWUfYJBcNZAbR5s/lBrtNZHwCl/g9xLwZrY4en+QJBvtlbw6rIClu2uwaATnDEmjcunDmDigPhe8+EZlEEcPgcOrwO7145f+smrz+PDnR+ypmINkYZIzhtyHmdkn0F0RDRWg5WkyCSiIjpXylIURSWK3icYgMZqrQSx6T3Y+TX43ZB1FJxwH2RN6XBsRIXNzdsri3hjRQGVdg9psWb+eNowLpqURXJ07xg85gv6mhJDo6+RgAxQaC9kfeV61lasZUfdDqxGKxcOvZCZ2TOJMkYRExFDYmQikYbe0wCvKOFCJYreIuADezls+wQ2vw8la0BvhJyTYfT50H8qxGS0OTZCSsny3bW8uiyfb7ZW4A9KZgxJ4u/njeGE4SnodeFfenD73di9duxeO66AC7ffzebqzayrXMe6ynXUumsBGBgzkN8M/w0nDziZ6Iho4kxxJJoTMep7x/gORQlHKlGEO78Xqn/WJvDb8hHYy8CarI2HGHUeJAwCS2Kb3V5tbh8frS3hlWX57K5qJDbSyJXTBnLpUQMYmBTecxNJKWn0NWrJwWfHF/RR6ihlfeV61lWuY1vtNvxBP5GGSMYkjWF8ynjGpYwjwZyAUWckwZygGqgV5TBRiSJc+dxQsATWzNtXvZQ2Do6+QevJZE0Bc9sDwbaW2nhteT7z15Xi8gUYmxnLYxeO46yxaZiN4fvB6Q/6tSoln1al5PK72FazjXWV61hfuZ5yZzkAGVEZzBw4k/Ep4xmWMAyDzoBZb8ZqtBIdEY3FYOk1bSyK0huoRBFu3Dat7WHd61C6VmugHnwy5P5Gq16KjG+zJ5PHH+DLzeW8sjSftYX1mAw6zhmXzm+nDgjrdaq9AS82r02rUvK7qHJVadVJFevYUrMFT8BDhC6CUUmjOGPQGeSm5JJiScEgDFiNVqKMUVgjrGotakXpQipRhIv6Qlj5Imx6R2uLsKbAUdfB+MshMQeMrRth6xq9rNhTw5JdNXy6sZQ6p4/+CRbuO3MEv5qYSZwlPAeOOX1ObF4bDq+DRn8jP9f93NTWUGwvBiDFksLxWceTm5LLqMRRmPQmLAYLURFRWI1W1SitKN1IJYqeVrgCls+Bn78IVS/lwrF/0BqoLYnQrArF5vaxcnctS/OqWZpXw45yOxIwG3VMH5LM5VMHMC0nCV2YNU4HZRC7197UjbXaVc36Kq2tYWPVRlx+F3qhZ0TiCE7IOoHxKeNJs6Zh0puaSgxWg1W1NyhKD1GJoicE/FrPpZXPh3ovRcDQmTDpahg4TevNBDR6/KzKr2VpXg1LdlWzrcxGUILJoGN8Vhy3nzKUY3ISGZsZF3YryfkCPuw+e1NPpV31u5raGnY37AYg3hTP1LSpjE8dz+ik0VpSMFixRmhVSmoqDUUJDypRdKfGGljxP1j3ila9FJUKx94OE6+C+P64vAHW7K5j2e5qluyqYXNJA/6gxKATjMuK4+YThzB1UCLj+8eFXaN0IBjAE/A0lRqqXFVsrNrI2sq1bKjagN1rRyAYGj+Ui4ddzITUCfSP7t9UnRRljCLSEKkaoRUlDKlE0R3KNsHSJ2Hbx+D3QMZEOOkBPMPPZV2pk2Wra1iat5T1RfX4AhK9EIzOjOG64wYxdVASEwfEExnR84nBF/DhDXrxBkK3oLdpm91rp8JZwfrK9ayvXM/PdT8jkURHRDMueRwTUiYwNnkscaa4psRgNVrVAkCK0guo/9KuEvBrg+OWPwvFK8FgIjj8HLZn/5bv6tNZurKGte//gMcfRCdgZHoMV03L5uicRCYPTCCqB+ZYCspgUwLwBrQk4Alo6zZUOCuocddQ666l1lWr/XTXUuOuoc5dh8vvarrOoNhBnD/kfHJTchkSN6SpATrKGIXZcOgr6imK0jNUojjcnLWw+mVY9QLYy/Ba+rFm4E286j2BHzcGcK52AD8zvF80lx41gKk5iUzJTiA2snu6d/qCPq0UEPDi8rsobyyn3FlORWMFVa6qpgTQ/OYP+ltcQyd0xJniSDAnkBmVybhkbaBbYmQiIxNGkmpJbSo1WIwWtSqcovRyKlEcLuWbkcueRW5+H13AwzbTWObIS/m0NpdgrY7BKRFcODGRqTmJHJWdSLy1axpqpZRaVZDHTomjREsEjeVUuiqpdFZqpYJQiaDeU49EtjjfqDOSaE4k3hzP0PihxJvjSTQnkmBOIMGcQFJkEomRiUToIzAIA3qdHr3QY9AZmsY2qOkyFKVvUYniYAQDSI8NT6MNj6Mer9OGu2o3hnXzSKtfi5sIPvIfyyuBU/FGjWDq8ESeGJTI0YMSf/GEe76gjzpXHXWeuqZv+nWeOhrcDTR4G6h11VLpqqTGpVUPOXyOVtewGq1NH/xZMVkkmhNJikwiOTKZZEsyqZGpxJvjMegNTR/+eqFHr9M3JQVVOlCUI09YJQohxEzgCUAPvCilfORQriOlxOMP4vQGcHp8uF2NuBsb8DbW43fa8btsBN3aTXps6DwOhNeBzufA4HNg9DswBhyYAk4ig42Ygy4sOLHgQQDm0G2vomAyz0RcQeXgC8kdls0rg5LoF9t2Xbwv4KPB20CDR7vVubUP/zp3HfWeeuo99dg8Nhq8Ddg8tqa5jpq3AbT6vSGIMcWQaE4k1ZrKqMRRJFuSSY5MJtWaSoolhVRLKjGmmFalAEVRlAMRUsoDH9UNhBB64GfgFKAYWAVcIqXc2t45OVkp8rGbz8Dkb8QUdGIKNhIZdGKVTqy4iRIuonBhFIEDPr9f6nAICw5hoVFE4tJbcOkjceut+IwWPEYrAYMFv8lC0GhFmswQYUWao4kdOIgIs6/pw7/B09D0oV/vqafB04DNa8PmseEOuNuNQSd0RBujiY6IJiYiRvtpiiEmQrvFmmKJM8URY4oh3hRPrCmWeHM80cZojHqj+ravKEqnCCHWSCkndfb4cPpKOQXYJaXcDSCEeBuYBbSbKGw6G5/ELsUnDPiFDr/QERB6/MJEUBdJQCQQFIKAEASFIAgEBQSR2n2CBJEEZJCgDOCXexOKBBpDt+rWT+wP3RpDj8ta7tYLPbGm2KYP+H7WfgxLGEacKY7YCO3DPs4UR6xZux9riiU2Ihar0arGESiKEnbCKVFkAEXNHhcDR+1/kBDiWuBagOgB0fj7H41ep8MkDOiErqlaZW/dul5o9eoGXWj/fvv0Qo9Op8PQ1vmh43QitL+N4ww6A9ER0cRGxDZ941cDxxRF6UvCKVF0ipTyeeB5gEmTJslXz3i1hyNSFEXp28KpUrsEyGr2ODO0TVEURelB4ZQoVgFDhBDZQogI4NfAgh6OSVEU5YgXNlVPUkq/EOJm4Cu07rEvSSm39HBYiqIoR7ywSRQAUsrPgc97Og5FURRln3CqelIURVHCkEoUiqIoSodUolAURVE6pBKFoiiK0qGwmevpUAgh7MCOno4DSKLNuT6OuBggPOJQMewTDnGEQwwQHnGEQwwAw6SU0Z09OKx6PR2CHQczsVVXEUKs7uk4wiGGcIlDxRBecYRDDOESRzjEsDeOgzleVT0piqIoHVKJQlEURelQb08Uz/d0ACHhEEc4xADhEYeKYZ9wiCMcYoDwiCMcYoCDjKNXN2YriqIoXa+3lygURVGULqYShaIoitIhlSgURVGUDvXKRCGESBBCJPR0HEp4EkJM6OkYlPAjhEjq6Rh6q16TKIQQ/YUQbwshqoAVwEohRGVo28AeDu+II4QYLoT4QgjxmRAiRwgxTwhRL4RYKYQY0Y1xTNjvNhFYIIQYf6QnDCFEjBBiohAivqdj6W5CiNOFEHuEEItD74UtwAohRLEQ4qQeiCdeCBHT3c972Egpe8UNWAZcDOibbdOjrYS3PAzi29SNz5UFvA0sAv4EGJvtm99NMfwEnA1cAhSE/g4itO27bvxdBIGlwMJmN1fo5/fdFMNVze5nAt8B9aG4hnbj7+J1ICl0/zSgEPg29Pe5sJtiqAVeBE4i1KuyJ27AemAEMBWoAY4ObR8BrO2mGNKBV4EGIBD6exQCDzT/n+3G30kqMCF0Sz2oc3vqD3kIL3Lnoew7zDGc387tAqCqG38X3wDXA7nAU6EPpMTQvnXdFMO6Zvd37bevW/4RQ891AfAjcHqzbXu66/n3f73Au8C1aKX187o5aW5qdn8pMDB0PwnY0E0x7ABuBpagrXn/xN4P6R78mxTtt299N8XwPXB86P75wH8AK/AQ8Hw3/i5ygeXAttAXh2+B7aFtEzpzjd4019MaIcSzwCtAUWhbFnAFsK6bYngHeANoa/CJuZtiAEiWUj4Xun+LEOIy4CchxDm0HVtX0De7//h++yK6KQaklB8IIb4C/iaEuAr4A933O2jLUCnlRaH7Hwkh7u/G59YJIWKklDa0klYhgJSyWgjRXf/rjVLKp4GnhRD90Uqazwoh4oC3pZR/6qY46oUQ1wExQJ0Q4na0JH4y4OimGBKllD8ASCk/FEL8n5SyEbhPCLG9m2IAmAdcJ6Vc0XyjEOJo4GVg3IEu0JsSxeXA1cCDQEZoWzHwCTC3m2LYCDwmpdy8/w4hxMndFAOAUQhhllK6AaSUrwshytHWG7d2UwzPCCGipJQOKeWzezcKIQajfWPpNlJKB3C7EGI82heJqO58fiBTCPEkWtVbshDCKKX0hfYZuzGOB4GFQohn0L7RvyeEWACcAHzZTTGIvXeklIXAP4F/CiGGo1Udd5crgPvQEuapaFWkX6FVw13TTTFUhb7ELUQrUeQDCCEE3ds+bN0/SQBIKZcLITr1eaFGZh8EIcR0oCD0D7D/vklSyoOakfEXxHE7WtH6x/22jwf+KaU8pTviCEehf8Lo0Lfq7nrOK/bbtEBKWSeE6Afc2o3fovcm6muAoWhfBIvR2q2+6qbnf1xKeUd3PFe4C5WoHgNGorWZ/FFKWSaESESrkvqgm+J4EshBay9pXhtzOVo17c0HvEZfSBRCiLOklJ/2dByKJlz+HuEShxJejsT3hRDidGAW+2pjStC+0HzemfN7TffYA5jc0wEIIc7q6RggbOLo8b9HSI/HESZ/j7CIIxxiCDni3hdSyi+klNdLKc8O3a7vbJKA3tVGQaies62s+Jeei6rJZCAcvqV0Wxzh8vcIlzjaccS9L8IlBvW+ODAhxLVSygPOJNtrqp6EEHejNUi9jVbvClqf9V+j9aZ4pJviaO/Nt607nj9c4gijv0e4xKHeF+EVg3pfdIIQ4jop5f8OeFwvShQ/A6Oa9SbZuz0C2CKlHNINMYTLm6/H4wiHv0e4xBEOf49wiSMcYgjFod4XnSCEuFJK+fIBD+yuQR+/9IY2QGRAG9sHoK2d3R0x/EwbIyrRxg10y6C/cIkjHP4e4RJHOPw9wiWOcIhBvS8OKsbCzhzXm9oofg98J4TYyb4uXv2BwWgjQbtDEG1YfsF+29NC+7pLOMTxe3r+7xEucYTD3yNc4giHGEC9L5oIITa2twttWo8D6jWJQkr5pRBiKDCFlvV9q6SUgW4K4/f0/JsvLOIIk79HuMTxe9T7IpxiUO+LllLR5v6q22+7QJvq5YB6TRtFuBBC6OjhD8dwikPRhMvfIxziCIcYwkU4/C6EEHOBl6WUi9vY96aU8jcHvIZKFIqiKEpH+sqAO0VRFKWLqEShKIqidEglCqVXEdpKer/q6TjCiRDi90IIS0/HofRdKlEoSg84zOtD/B5QiULpMipRKGFLCHG5EGKjEGKDEOK1ZrtmCCGWCiF27y1dCCGihBDfCSHWCiE2CSFmhbYPFEJsE0K8IITYIoT4WggRGdo3OXT99UKIfwkhNoe260OPV4X2X9dGbAOFENuFEG+Erv/+3m/1Qlun+kchxBohxFdCiLTQ9h+EEP8VQqwGbgs9/9LQ61sphIhu77mFEMeHzn+/2fMKIcStaH31FwohFoaOnSOEWB16vQ82i/mM0LlrhBBPCiE+DW23CiFeCsWwbu/vTlGa9PTIQHVTt7ZuwCi0ka1714BOCP2cB7yH9iVnJKFlWNHGBMWE7icBu9D6iQ8E/EBuaN+7wGWh+5uBqaH7jwCbQ/evBe4L3TcBq4Hs/eIbiLaS3rTQ45eAO9EWKlqKtgohaIv1vBS6/wPwbOh+BLAbmBx6HBN6DW0+N3A82trLmaHXvgw4NnRc/t7f036/K33oOceircBYtPd1AG8Bn4bu/73Z7yQu9Hu39vR7QN3C59ZrBtwpR5wTgfeklNUAUsraZvvmSymDwFYhxN6RpQL4uxBiBtqo1wz2jTrdI6VcH7q/BhgotKU5o6WUy0Lb3wT2Tv18KjC2WVtILDAE2LNfjEVSyiWh+68Dt6KtJDca+EYIAdqHdVmzc94J/RwGlEkpV4Venw1ACNHec3uBlVLK4tBx69GSVau+8cBFQohr0RJPGlpC1QG7pZR7X8NbaElp7+s9RwhxZ+ixGW1gWFhMXKf0PJUolN7I0+z+3qU3LwWSgYlSSp8QIp9965g3Pz4ARB7g+gK4RR54Vbj9ByHJ0LlbpJRT2zmn8VCeWwhxPK1fR6v/XyFENlrJZrLUVtmbx4HXcxfABVLKHQc4TjlCqTYKJVx9D1wotGUjEUIkHOD4WKAylCROQJv8rV1SynrALoQ4KrTp1812fwXcIIQwhp57qGh7beH+Qoi9CeE3aN/ud6Ctmz01dK5RCDGqjXN3AGlCiMmh46JDDdydfe7m7EB06H4MWjJqCJW2Tm/2fIOEEANDj5uvX/0VcIsIFYGEtqSuojRRJQolLEkptwghHgZ+FEIEgHXA7A5OeQP4RAixCa1ef3snnuZq4AUhRBD4Ea0NAOBFtGqdtaEPzyrg3DbO3wHcJIR4CdgKzJFSekPVRk8KIWLR/sf+C2zZ7/V5hRAXA0+FGtddwMkH8dzNPQ98KYQolVKeIIRYF3r9RcCS0PO5hBA3ho5rBFY1O/9voRg3Cm3KiT3sq4ZTFDWFh3LkEkJESSkdofv3AGlSyts6ee5AtMbg0V0Y4mG19/WGEtAzaFNd/6en41LCn6p6Uo5kZ4a6xm4GpgMP9XRAXeyaUCP4FrSqugOubKYooEoUiqIoygGoEoWiKIrSIZUoFEVRlA6pRKEoiqJ0SCUKRVEUpUMqUSiKoigd+n+uAfbjtcmWpwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NFBcXk56ezjPPPMOwYcMS/M2Uir9Yk8WWyi3M2jyLL4q+IGIijOs5jvMHn88xecccVnORx+Fp6JfIcGe0+d3xklmnShoPfvUga8rWtOp7DusyjF+O/2WL2ydNmsQjjzzCzTffTEFBAX6/n2AwyPz585k8eTJffPEFAIWFhfzjH/9g2bJlhEIhRo8e3ZA0pk2bxlNPPcXQoUNZtGgRN9xwA3Pnzm3V76FUMoslWdT3V8zaNIuVpStJcaZw9oCzOXfQuTFPAFjPgcPqwLZrE211NXZ71KmSRiKMGTOGwsJCqqqqSElJYfTo0RQUFDB//nwee+wx/vCHPwAwf/58vvvd75Kebl05+u1vfxuAmpoaFixYwKWXXtrwnvVzTinV0RljKPOVUeItaTFZ+MN+Ptv+Ge9vfp9dtbvoktqFHw3/EWf2P/OQLpTrCMNh46FTJY0D1QjaitvtZtCgQcycOZNTTjmFE044gU8++YQNGzbENPdTJBIhNzeXZcuWtX2wSiUJYwzl/nKK64pbTBZN+yuG5Aw5pP4Kl7hId6eT5c4iw5PRIYbDxkOnShqJMmnSJB5++GGef/55jj/+eG699VbGjBmzz5nM5MmTmTp1Kr/61a8IhUK89957XHfddWRnZzNo0CDeeOMNLr30UowxfP3114wcOTKB30iptlGfLEq8JS3eq2Jz5WZmbZrFgp0LGvorLhh8AUfnHX3Q2oFTnOR4Gud10trEodOkEQeTJk3i97//PRMmTCAjI4PU1FQmTZq0T5nRo0dz+eWXM3LkSLp37864ceMatr3yyitcf/313HfffQSDQa644gpNGqpDMcZQ4a+g2FvcbLKo76/476b/sqp0FanO1EPqr8hwZZCXmkeWJ6tTd2K3Bp0avZPS30Ulg/pkUeItIRAJ7LfdF/JZ11dsnsXu2t3kp+Zz7qBzY+qvcDvc5KbkkpuSqx3ZB6BToyulkp4xhkp/JcXe4maTRZmvjA+3fMhHWz+iNlgbc3+FIGR7sslNyW3T+2R3Zpo0lFJxc7BksblyM//d9F8W7lx4SP0Vac40clNzyfHkxP32p51Np0gaxhjt8IrSkZokVftQF6yjKlBFpb9yv9FQzfVXTBk4hXMHnkuPjB4tvmf9zYpyU3I73VXZidThk0ZqaiqlpaXk5+dr4sBKGKWlpaSm6j8y1bb8YT+V/koq/ZUx91fEcn1FpjuT3JRcsj3Z+m86AdosaYjI88CFwF5jzAh73d3AtUCxXexOY8x+ky+JyLnAo4ATeNYY88DhxtG3b1927NhBcXHxwQt3EqmpqfTt2zfRYagOKBgJUumvpMpfhTfsbbZMmdfur9hm91fkDuHm0TczvmfL/RUeh6ehU9vt1OspEqktaxozgSeAvzdZ/2djzMMt7SQiTuCvwNnADmCxiLxrjFl1OEHUX1ynlGob4UiY6kA1Ff4KakO1zZbxh/0s27uMhTsXsnj34pj6Kxw4yE6xOrWT/RaonUmbJQ1jzDwRGXgYu44HNhhjNgGIyD+Ai4HDShpKqdZnjKE6WE2lv5KaQE2z992uTxRf7vqSJXuW4A/7yfZkH7S/Is2VRl5KHtmebO3UTkKJ6NO4SUR+AhQAvzDGlDfZ3gfYHvV6B3BSS28mItOAaQD9+/dv5VCVUtFqg7VW81OgqtmbHUXXKJbuXdqQKCb1mcTJvU9meJfhzSaC+k7tvNQ8Upwp8fgq6jDFO2n8DbgXMPbzI8BPj+QNjTEzgBlgXdx3pAEqpfblDXkbOrSbmweqxUTRdxIn92o5UQhCljuLnNQcstxZ2qndTsQ1aRhj9tQvi8gzwH+aKVYE9It63ddep5SKk0A40JAo/JH9Z1X2h/0s3bOUL3d92ZAocjw5B00UACmOFOuaipQcnSSwHYpr0hCRXsaYXfbL7wIrmim2GBgqIoOwksUVwA/jFKJSnVYoErISRaASb2j/kU9HkihSnalkebLI8mSR5kpr66+i2lBbDrl9DTgd6CoiO4DfAqeLyCis5qktwHV22d5YQ2vPN8aEROQm4EOsIbfPG2NWtlWcSnVmEROhOmB3aAdrMOzbwnugRDGh1wSG5w9vdgLA+luiZnmyyPZk6zDZDqTDT1iolNqXMYaaYA2V/kqqA9X7jXzyhXwNo56iE8W4XuMOmCgcOMh0Z5LlySLTk3nY9+BW8aUTFiqlmuUNeanwVVAVqNqvQ7ulRDG572Sr6amFROESF5keO1G4M3Xa8U5Ak4ZSnUCFr4KdtTv3aX463EThcXgamp30RkadjyYNpTq4vXV7KfZa0+jUJ4qFuxaybO+ymBNFmjOtoSNbJwfs3DRpKNVBGWPYWbuTCn8Fe+v28urqV1myZwmBSOCgiUI7slVLNGko1QGFI2F21OygJljDuvJ1PLz4YYKRIKf1O63FROHAQaYnk2xPNpnuTJ3CQzVLk4ZSHUwwEmR71Xa8YS+fF33O08ufJj81n9vH307vzN77lHWJq6HZKcOdoR3Z6qA0aSjVgfhCPrZVbyMQDvDmujd5a/1bDO8ynFvH3kqWJwvYtyM73Z2e4IhVe6NJQ6kOojZYy/bq7XhDXv62/G8s3LmQ0/udzjXHX4Pb4SY/NZ+clBztyFZHRJOGUh1Apb+Sopoiyn3l/LHgj2yq2MSPhv+ICwdfiMvhom9mXzI9mYkOU3UAmjSUaudKvCXsqdvD1qqtPPTVQ9QEa7h17K2M6zmOVGcq/bL64XF6Eh2m6iA0aSjVThlj2F27mzJ/GYV7Cnl8yeOku9O5+5S7GZQziGx3Nn2y+mjntmpVmjSUaociJsKO6h1UBaqYtXkWL696mUE5g7ht3G10Se1Ct7RudE/vnugwVQekSUOpdiYUCbGtahvVwWpeWPECH2/7mPE9x3PjiTeS5kyjT1Yfsj3ZiQ5TdVCaNJRqR/xhP9uqtlHmK+PPhX9mZelKvnPUd7jsmMsa+i90dJRqS5o0lGon6oJ1bK/ezvbq7Ty0+CH21u3lhlE3MLnvZDLdmfTN7KtXcas2p0lDqXag0l/JzpqdrChdwZ8K/oSI8OsJv2ZYl2Hkp+bTI72Hzjar4kKThlJJrn5I7SfbPuHZb56lZ0ZPbh93O70yetE7szc5KTmJDlF1Ipo0lEpiu2t3U+wt5rXVr/Hepvc4odsJ/Hz0z8lJyaFfVj+937aKu7a8R/jzwIXAXmPMCHvdH4GLgACwEfgfY0xFM/tuAaqBMBA6lFsRKtURREyEouoi9nr38sTSJyjYU8CUAVO46riryPJk0S+rn95OVSVEW171MxM4t8m6OcAIY8wJwDrgVwfY/wxjzChNGKqzCUVCbK3ayuaqzdy94G4K9xQy9bip/PT4n9ItrRsDswdqwlAJ02Z/ecaYeSIysMm62VEvvwS+31afr1R7FAgH2Fa1jVVlq3h48cP4wj5uH387o7uPpldGL/JS8xIdourkEnm68lPg9Ra2GWC2iBjgaWPMjJbeRESmAdMA+vfv3+pBKhUv9UNqPy/6nCeXPUlOSg73nHQPg3IG0S+rn05jrpJCQpKGiPw/IAS80kKRicaYIhHpDswRkTXGmHnNFbQTygyAsWPHmjYJWKk2Vh2oZnvVdv614V+8vvZ1js47ml+M/QU9M3rSL6sfbofeblUlh7gnDRGZitVB/i1jTLMHeWNMkf28V0TeBsYDzSYNpdq7Ml8Z26u28/TXTzO/aD4T+0xk2gnT6J7enV4ZvXTCQZVU4po0RORc4HbgNGNMXQtlMgCHMabaXp4C3BPHMJWKmz21e9hUuYlHCh9hbdlaLjvmMi456hJ6ZPSga1rXRIen1H7acsjta8DpQFcR2QH8Fmu0VApWkxPAl8aY6SLSG3jWGHM+0AN4297uAl41xnzQVnEqlQjGGIpqilhZupKHFj9Eua+cW0bfwqQ+k+ib1ZcMd0aiQ1SqWW05euoHzax+roWyO4Hz7eVNwMi2ikupRAtHwmyv3s6CnQv4y5K/4HF6+O2E3zKi6wi9YZJKejrYW6k4CoaDbKvexrsb32Xmypn0zezL7eNvZ3DOYPpk6g2TVPLTpKFUnHhDXjZXbOaFlS/wwZYPGNNjDDedeBMDsgbQLb1bosNTKiYxJQ0RORVYZoypFZErgdHAo8aYrW0anVIdRE2ghrXla/lz4Z9ZXrycCwZfwI+P/TH9s/qT5clKdHhKxSzWmsbfgJEiMhL4BfAs8HfgtLYKTKmOIBwJU+ItYXXZah5c/CC7anZx7fHXcv6g8+mX3Y8UZ0qiQ1TqkMSaNELGGCMiFwNPGGOeE5Gr2zIwpdozYwzl/nKK64r5uuRr/lL4F8ImzK9O+hUTek2gT2YfvWGSapdiTRrVIvIr4MfAJBFxAHqJqlLNqA5Us6d2DxsqNvD62tcp2FNAz/Se3D7+dk7oegI9MnokOkSlDlusSeNy4IfAT40xu0WkP/DHtgtLqfbHF/Kxp85KFm+ue5Mvir4g1ZXK5cdczgWDLmBw7mC9YZJq92JKGnaieAsYaq8qAd5us6iUakeCkSDFdcVsrNzIv9b/i0+2fYJTnFw05CK+PeTbdEvrRq/MXnrDJNUhxDp66lqsmWS7AEOAPsBTwLfaLjSlklvERCj1lrK5cjPvbHiHD7Z8QMRE+Fb/b/Hdod+ld2ZvuqV109FRqkOJtXnqRqxJAxcBGGPW2zPQKtUpVfgq2Fq9lX9v+Df/2fQffCEfE/tM5PtHf59BOYPoltaNTE9mosNUqtXFmjT8xpiAPR8UIuLCuueFUp1KXbCObVXbeHfTu7yz4R2qA9WM6zmOy465jGF5w+iW3k3njVIdWqxJ4zMRuRNIE5GzgRuA99ouLKWSSyAcYGf1Tt7b/B5vrnuTMl8Zx3c9nsuPuZxR3UfRLa2b3iRJdQqxJo07gKuBb4DrgFlYF/gp1aGFI2H21O1h1uZZvL72dXbX7uao3KO4YdQNTOg1ga5pXTVZqE4l1tFTEeAZ+6FUh2eModRbyuyts3l1zatsrdpKv6x+3Db2Ns7odwbd0rvpaCjVKcU6euob9u/DqAQKgPuMMaWtHZhSiVIVqGLutrm8vOpl1pavpXt6d24adRPnDjqXHuk9SHWlJjpEpRIm1uap94Ew8Kr9+gogHdgNzAQuavXIlIozb8jLF0VfMHPlTJYXLycvJY9rjr+G7xz1HXpm9NR5opQi9qRxljFmdNTrb0RkiTFmtD3rrVLtVjAcpGBPAc+teI5FuxaR6c7kyuFXctkxl9Ens4/eFEmpKLEmDaeIjDfGfAUgIuOA+tnWQm0SmVJtLGIirCxZyYxvZvDZ9s9IcabwvaHf48rhVzIgewBup06vplRTsSaNa4DnRSQTEKAKuEZEMoA/tLSTiDwPXAjsNcaMsNd1AV4HBgJbgMuMMeXN7HsVcJf98j5jzIsxxqrUQW2s2MiMr2cwe+tsBOH8QeczdcRUhuQOwe3QZKFUS8SY2K/RE5EcAGNMZYzlJwM1wN+jksZDQJkx5gERuQPIM8b8ssl+XbA62cdidcAXAmOaSy7Rxo4dawoKCmL+Pqrz2VWzi2e+eYZ3N75LMBLk9L6nc/XxVzM8f7gmC9UpiUihMWZsrOVjHT2VAnwPq3bgqr8y3Bhzz4H2M8bME5GBTVZfDJxuL78IfAr8skmZc4A5xpgy+/PnAOcCr8USr1JNVfgrePbrZ3lz/ZvUBms5pfcpTDt+GiO7j8Tl0LseKxWrWP+1/BtriG0h4D/Cz+xhjNllL+8Gmru5QB9ge9TrHfa6/YjINKzJFOnfv/8RhqY6Gm/Qy99X/Z2XV79Mhb+C0d1HM33kdMb3HK83QVLqMMSaNPoaY85t7Q+37wZ4RHNYGWNmADPAap5qlcBUh7C8eDm3fnore+v2MrzLcO455R4m952syUKpIxBr0lggIscbY75phc/cIyK9jDG7RKQXsLeZMkU0NmEB9MVqxlIqJoW7C7lx7o2kOdN4cNKDnDPwHE0WSrUCR4zlJgKFIrJWRL4WkW9E5OvD/Mx3gavs5auwmr6a+hCYIiJ5IpIHTLHXKXVQX+78kus/vp5MdyYzz5vJ+YPP14ShVCuJtaZx3uG8uYi8hlVj6CoiO4DfAg8A/xSRq4GtwGV22bHAdGPMNcaYMhG5F1hsv9U99Z3iSh3IvB3zuPXTW8lPzef5c56nT1azXWFKqcN0qENuuwMNE+8YY7a1RVCHS4fcdm4fb/2Y/5v3f/TM6Mnz5zxPz4yeiQ5JqaR3qENuY2qeEpFvi8h6YDPwGdZFee8fVoRKtYEPNn/AbfNuo29mX14890VNGEq1kVj7NO4FTgbWGWMGYd0b/Ms2i0qpQ/Dexve4Y/4dDMweyMxzZ9ItvVuiQ1Kqw4o1aQTt6c8dIuIwxnyCdbW2Ugn11rq3uOuLuxiaN5SZ586kS1qXRIekVIcWa0d4hT3v1DzgFRHZC9S2XVhKHdw/1vyD+xfdz7H5x/LMlGfI8mQlOiSlOrxYaxoXA17gf4EPgI3oPTRUAr248kXuX3Q/I7uN5LlzntOEoVScxHq71+hahc42qxLqma+f4bGljzG2x1j+dtbf9E56SsVRrKOnLhGR9SJSKSJVIlItIlVtHZxSTf116V95bOljnNzrZJ466ylNGErFWax9Gg8BFxljVrdlMEodyJ8K/sQLK19gYp+JPHrGo3pHPaUSINaksUcThkoUYwwPfvUgr6x5hTP7ncnDpz2sd9VTKkEOmDRE5BJ7sUBEXgfeIWpqdGPMv9ouNKWshHHfl/fxz3X/5JyB5/DgpAd1HimlEuhgNY36EVIGqMOaOJCodZo0VJsxxvDrL37Nvzf+m4sGX8R9E+/DIbEO+FNKtYUDJg1jzP8AiMiLwC3GmAr7dR7wSJtHpzqtcCTMrz7/Fe9vfp9LjrqEu0+5m/o7RiqlEifWPo0T6hMGgDGmXERObJuQVGcXCof4v3n/x0fbPuKKY67gzpPu1IShOgRjDBEDEWOIGIPL4cDpaF9/27EmDYeI5BljygFEpMsh7KtUzALhALd+eiuf7fiMnxz7E24be5smDBVXkYghbB/UTcMB3no2ERq27bc9YvYtay+HI41lm5tU3ONykOp2kOZ2kuJ2kuZ24nElbzNsrAf+R4CFIvKG/fpS4PdtE5LqrPwhPzd/cjMLdi7gmuOv4ZbRtyQ6JJXkjDGE7YO8sQ/Q4aiDezhiHbzDUQf2cKTxoN9w1h+h4cDeVjGG7M9tWI4Y0j3WITgQilDlDTXs43BAqp1AGp8dSXECFesV4X8XkQLgTHvVJcaYVW0XlupsvCEvN350I4v3LOaGkTdw/ajrEx1Su2GMdRCqPxiFw4ZgJEKtP0RtIESdP0ytP4Q/FMHpEDwuBy772e1w4HY58DgFt8uJyyGIgCA4BLCXrXUgYq0Xa0Oz6+uPa9HvIyJ2Oetsu+FsPfrMveGATsPBNfqAHo4q4w9G8IXC+EMRAqEIgXCEoP0c/dofihAMRwiEDYFQmEDYNJQL2++1z8M0HtBbOtCHIva+BsLhiJ2cINywft/3jBwkEWWnuuienUqPrJR9nrtnpdA9K5U0j7Ph90xxOUi1E0l97cTljG+tJOYmJjtJaKJQra4mUMMNH9/A0r1L+fnon3P18Ve32nsHQhEixux3AKt/jidjDP5QBG8gjC8Utp6DEbzBMD77UesPUxcIURewnr2BMHVBq2x9OWv/CL5gGH8wgt8+eNYfQP2hMMHw4Z0yC+ByCi6HlVhcTsHpcOB2Ck6H2OvsbdHL9eWaLDvryzjt8g6H9TvEcJAPhiItljuSCoEAbjtxOkVwOu1nR+PD5RAc9vb6ZbdTcLkdOMRlfccm+1jv5cAp2OscLb5Xfflqf5C9VX72VvvYUlrHV1vK9vt/d7CkkpXm2qc2kup2kuJqu1qJ9kuohKr2V3PdR9exomQFt4+9nR8f9+NWed9AKMKeKh8VdcEDlhOJOiN2NJ4pW32T+54lO0QIhiPU+IPU+MNU+4LU+MJU+4NU+4JUeUNU+0NU+0JUea11ld4gVb4QdYEQvmDksL6LQyDF7STV5cDjcpDisg4KKS4HGSlOumR6Gl6nuKz28Iay7saybqeDUNg6Uw6GrbPgYDhir7PWhyLGem2fiQcj+y6HI1Hlw1biql+ur+0E7c8IR71vc0nM43LgcdoPV2ONp/51hsdtlWm2XONrj9Ne17Be8LiceFyCO7qc/RtYtanEN/M0J2IMlXVB9lT72Fvlb3g+1KTSIzuVfl3S6J+fTpeMlIZmrtbodI970hCRY4DXo1YNBn5jjPlLVJnTgX9j3SkQ4F/GmHviFKKKkwpfBdPmTGNN2RruPOlOrhh2xRG/Zzhi2Fvto7Qm0GL7dDhiqAtYB/caf4ga+7naH/06aK3zhaj1N5b1hw584M9IcZKd6iYnzXr0yk0jL91DZop9JujZv506Nep1msdJqstJqsfRsM7dxs0Ppr6Ttn4Z7NeNnbcNy03K0GRbQ4evtaGhXMRYSSUYNjjEOmt3SFRTVnSzVsN/9l9ff7Cvb+qqL9NQvnGxIdk3rpeG5X2/f9SyXYepX2f2KWeaWcc+BU3U1ljeIxLVlBWJQCgSISPFSY+cVEKRCJEmf25HmlR65abRN9dKJgPyMxjUNZ1DFfekYYxZC4wCEBEnUAS83UzR+caYC+MYmoqjUm8p186+lk2Vm7j7lLu5ZOglB9/pAIwxlNYG2Fvlb+j8XLa9grlr9lJhn/XXJ4TaQPiA75XicpCZ4rIeqS56ZKdyVPf6126yUl3kpXvIz3CTn5FCt6wUumal0DUzpd0NnwT74NoQdvuLv6Nr2ueyb3/Jvn0wgXCEstoAOyu87Kr0safq4EnlUCW6eepbwEZjzNYEx6HiaG/tXq6Zcw3bqrZx76n3ctGQI7s1S2VdkN1VPgIhqxll4aZS3izczsbiWnLS3PTMTiUv3UO/vHQyU62Df5b9XJ8IMlNcZNlJov7M3ukQUqLaiOuf2/rMX6lo9X0mhyPSJNEEwxFKavxsL/NSVF5HUaWXOx48tPdMdNK4AnithW0TRGQ5sBO4zRizsrlCIjINmAbQv3//NglStZ5dNbu4ZvY17KrdxQOTHuDcQece9nvV+kPsqvThDYQJhiPMXbOXt5bsYFeljz65afzszKM445juBz3Iu5yyT1Ko7z+I96gUpVqbwyE4ENxR07Xlpns4qnvjTcvuOMT3FNNSw28bExEPVkI4zhizp8m2bCBijKkRkfOBR40xQw/2nmPHjjUFBQVtE7A6Yjuqd3DN7GsorivmockP8a0B3zqs9/GHwuyu9FHltTqYP1ixm38v20lZXYCjumXy/TF9OXlw/n5nZ26XNHQiR9ce2mOTklKtRUQKjTFjYy2fyJrGecCSpgkDwBhTFbU8S0SeFJGuxpiSuEaoWs2Wyi1cO/taKvwV/PmMPzO57+RDfo9QOMLeaj9ltQHKagO8t3wns77ZRW0gzKh+udx69tGc0DenobM0PcVJbpqbNI+TFJcmB6VaQyKTxg9ooWlKRHpi3cPDiMh4rDsMlsYzONV6NlRs4LrZ11EdrObRMx/llN6nHNL+kYihpNZPcbWfnRU+3l5axEer9hAMRzhlSD7fG92XoT2s6rbLKeSle8hNd5Pq1inUlWptCUkaIpIBnA1cF7VuOoAx5ing+8D1IhICvMAVJlHtaOqIrCldw/SPpuML+/jrmX9lXK9xh7R/eW2APdU+1u2u4a0lO5i/vhiHCGcO684lJ/alT14aIpCV6iI33UN2qitpx+CrBIpEIFAN/hoI1ECwDhwucHrA6baf65dT7OVEd/kmp4T8KsaYWiC/ybqnopafAJ6Id1yqda0oWcH1H11P2IR56qynGNV9VMz7VvuC7K70Uri1gjcLd1CwtZw0t5OLR/Xh4pG9yc9MIcXtIDfdTV66R0c0qX0ZYyWH+iQRqAUTgaoi2LMKKrdDag5kdIX0fEjvCuldwJXS+B7iAIfbWrdfYrGXO+EJiqZS1SYW7VrErZ/eikMczDh7BiO6johpP18wTFGFl0/X7OXNwh2s3l1NdqqLK08ewAUjepGV5iInzU2XDA8ZKfrnq2zGWLWHhiRRA75KKF5jJYm9q2DvavBXHfh9UrIak0hGfTLJb0wuGV0hrYuVOMBKKs3WVjwdtrbS8b6RSqhKfyWPLXmMf234F9mebJ4+62mG5Q876H7BcISi8jreXb6LNwt3sK2sju5ZKUyfPJhvDe9BXoaHLhkectLc2qGtLEGvnSSqwVsBpRvs5GAniIptdkGBvAEwcCJ0PxZ6HAu5/a3aR10p1Jbs+1xXCnUlULTNWjbNXAyamtt8UolONmm5UcmjhWYwh5PGmR/bx9+1Jg3VKowxvLfpPR5d8ih76/Yyue9k7hh3B/2y+x1wv0jEsL28jlcXbeNfS4sorvYzoEs6vzj7aE47uhvdslPIS/ccfqd2KAAYq6kBsZ7b0T9QFSXkb0wSZZth99dWcti7CorXQshnlUvNtZLDUWdbCaLbMPBkWP/vXan2IwUiYcgbaD1HQhAOWs/RE3+YiFVjaUgoJVDb5Ll0A3jLrbLRxAFpeU2SSZPE4vJY/S0mYiWnhmX7gbHiM2bf9c0+7HKRcJN19nL0ezeUOfDsCM3RpKGO2KaKTdy/6H4W7V5En8w+PHLaI5w14KwD3s/bGMPmklpe+GIL/15WRJUvxPBe2Vx/2hDOOKYbXTJSyE47zE7toNc68/RVNB5ImtOQSKSZpBK9Tfbd1mK5JvtEv67vdHXoiK6YhYPgr4baYihaAruX201Nq6F2r1XG4YL8oTDsAitRdB8OWb3AnWYlh/pnVyq4U2P73KZJJBKGSP1yCMKhxuX6JBMJWX9z0clkn1rMXihebSWXRBKn9TcoDrvP5tD/HjVpqMPmC/l4evnTvLT6JYwxTD12KtNHTifDk3HA/dbtqebpzzby32924QtGGDsgjx+M78+koV3JTfcc3l3LArXWGaG3AsL+2PapPzOM57g8cdjJwx3VXBG17HB3yHbwmETC4KuCPSthx1ewa7lViyjd2HhGnNUTeo6wkkP3Y6HbcKsZqGmCcBzBwAiHfWCN7hQ/kH2SSPDASSfotRJHXYmVUMLBxoN39IFcnFZCdLit1053499Gw3pX499O/d9T9DqnJ2qEmL3c3O9y56GdmHXSv051pObtmMcDXz3A9urtjOs5jl+N+xVDuxz4ov1VOyt5fO4G5qzaQ8QYJh/djasmDGTcoC5kHk6ntr/Gqk34KiEc2H97OAil6+2zwfozf+zagsOeKjWqNtD0ObpWcUT7ORr/MYNd+zlIDcjRTEJpODh0kA7WiD2aadtC2LHYThJRndXuNKtpaeQV0GME9D4RcvqAK82qNbjSkuN3cLoOLY5wqDGJQGOykOjEkbyjAZPgF1ftyZ7aPTzw1QN8tO0juqV1475T7+PbQ759wGakgi1lPD53A/PWFeN2OrjghF5cM3EQx/bOObRObWOs5gpfpfWINHOvjJAPti+GzfNg2wKrBpJM6jtAXfa1APXPDcvR2zz2a8/+612p4M6w2urdaeDJtEb+uNOt55RsSMm0ysV6QGuYBz28b5t3dBv5Puvrm2rCja8bzqxDdvn6M+9w43uVrIeiQqu5ab/O6knQ6wToMwa6HwcpGVZycHna7H9J3B1qkkky7TdyFVcRE+HvK//OU18/hS/k4/JjLueWE28hKyWr2fLGGOavL+HRj9dTuLWcjBQnV50ykGsmDqJvl0OYw98Y68zTW2E915+dRQvUwNaFsHk+bF9kNU+lZMPAydD/ZOvAiom6AUTUszFAxG6iMlFNVpFmyrWwX32HZYv7Ray4QwGrRhTyRz37o9b7GmtN+5QJNP+9Y+Vw2snD0xhfQzIw+3aWxktqLvQ4DoZdCH3HQp+xkNndilMHKSQ1TRrqoJbvXc49X97DuvJ1jOg6gl+f/GuOzT+22bKRiOGDFbt5bO561uyupmumh1vPHsr/nDqIrFR3bB8YiVgJwldhtXE3N8LDWwFbP7cSRVGhdVBNz4djzoNBk62zVUdH+fMW+6w9qiM2HLDbzOsTjv06HJ2YghD27ftaxG4Gkf07RfdrU7df01ybe3P7OPZtanE03Ves5ex+0O2Ydn223Znp/zXVoip/FQ8XPMw7G94hJyWHX5/0ay495tJmm6KC4QjvLC3iyU82sLm0jr55adx78QguG9eXFFcMIzQi4cZmJ39V82e9NXthy+dW09Pur60yWb1gxPesRNF9eGM/wmGL6pOA5vssmpbbrzz7r2t6kJWoA+lBD8B65q2ShyYNtR9jDO9seIc/F/6ZCn8FFw25iNvG3kZeat5+ZX3BMP/4ahtPzdvE7kofR3XP5NErRnHhCb0P3l9Rnyi85VZfRXPDmCp3WLWJzfOsIYtgja0/8Uqr/Tv/qOYPqu50a5qIlKzGM+uDJQI9OCt1UJo01D42lm/k7oV3s6x4GUNzh/LYmY81O2dUtS/Iiwu38PznWyirDXBC3xx+/50RnDms+4GvrQiH7BpFRfOJwhgo32wlic3zoGyTtb7bMTDuWhg0ybqatzmeTCtRpOZ2rI5TpZKIJg0FgDfk5YmlT/Dq6lfxOD3cNvY2rhx+Jc4mF/+U1QZ4dv4mXlq4lWp/iAmD87nlrKGcNKhLy8kiHLQvtqu0Oq33SxQRa46g+hpFVREg0PN4mHCjVaPI6tnMG4tVk0jNhdTsxiGtSqk2o0lDMXfbXO5fdD976vZw9oCzuXP8nXRN77pPmV2VXp7+bBP/WLwNXzDCWcO7c/O3hnJC39zm3zQctJqdvBUQbGbYayQEu7+xEsWW+dZVv+KEPqOtcfkDTrVmHW1KHNbIqNQc66FXWCsVV5o0OrFdNbu458t7+Lzoc/pl9eOZKc9wcq+T9ymzuaSWJz/ZwNtLizAGLhrZi5vOPGqfeww3MMaqTdSVNt/0FA5A0VLYMs/q0PZVWsNA+423mp4GTLBqDk05XI2JIiU7qS98Uqqj06TRCQUjQV745gVmfDMDgBtH3cjVI67GHdW8s3pXFY/PXc8HK3bjcji4fFw/pp82hH7NXWMR9IG3DOrK9r/gLuiF7V9ZtYmtC61ahzsd+k+w+if6jbdeN+VwW0kiLdfqq9BOaqWSgiaNTmbx7sX8buHv2Fq1lUl9JnHXyXfRO7N3w/bCrWU89vEGPltXTIbHyTWTBnPNpEF0z2oy2VskYnVm15Xa/RRRfBWwbZFVm9j+VePFdoNPsxJFnzFWDaMpZ4qVJFJz7AvylFLJRpNGJ1HqLeWhxQ8xa/Mseqb35LEzHuOM/mcAjVdvPz53PYu3lJOT5ubWs47mqlMGkpPepHM5UGclCm9540V39SOeti605hHasxIw1tTPB7vYrn5obGqONR2GUiqpJSxpiMgWoBoIAyFjzNgm2wV4FDgfqAOmGmOWxDvO9i4cCfPGujd4dMmj+EI+fnrcT5k+ajpprjQiEcPsVbt5fO4GVu6sontWCr++YDg/OKk/6Z6oP41I2Gp68pZZd0cD6wrjncusJLFtIdTssdZ3PRpG/wQGnAJdhzZ/sZ0OjVWq3Up0TeMMY0xJC9vOA4baj5OAv9nPKkarSldx94K7WV22mjHdx/CbU37D4JzBBMMR3ircwV8/3cCm4lr65aXxwCXH893Rffa9ettfbdUqfJXWsNjaYqvZadtCa+qOkM+aK6jvWCtR9D/ZmspjP9FDY3N0+gil2rFk/td7MfB3Y4wBvhSRXBHpZYzZlejAkl11oJpHlzzKG2vfICclhz9M/AMXDL4AfyjCSwu38LfPNrKzwscxPTJ57Acncv6Inricdo0gHLRqFXWlEPJC8brG2kTJOqtMZg+r2an/BOg1svn7DjhcUTUKHRqrVEeRyKRhgNkiYoCnjTEzmmzvA2yPer3DXqdJ4wBWlKzgxo9vpMJXwWXHXMbNo2+GSCpPfbaJZz/fRGlNgFH9crnvOyM44xj76m1j7LuOlUL1HigqaEwU3nKrian7sTB+mpUo8gbuO5pJnFbfhDsNPOnWcqw3sFFKtSuJTBoTjTFFItIdmCMia4wx8w71TURkGjANoH//FqaX6CSW7l3K9R9dT44nh1cvfJVeqUOZ8elmXlywhSpfiIlHdeWmM49qvHo75LcSxe5vrJFO2xZa/RSRoDV6qd9JVpLoN96qLYCVQOoThNtOELHeRlMp1e4lLGkYY4rs570i8jYwHohOGkVAv6jXfe11Td9nBjADYOzYsfG8cWdSWbx7MTd+fCPd0rrxwClP8taCWl77ai7eYJhzjuvBjWccZV29HbH7JjZ9Bhs/thJF+RbrTXL7w4hLrETRc4R1rURDDSKj8Xaaes2EUp1WQpKGiGQADmNMtb08BbinSbF3gZtE5B9YHeCV2p/RvC+KvuDmT26mb2ZfLul1L5c8vpKIgYtH9ub604cwtEcWVBbBl6/Dhjmw/Uurk1uc0HukdSOc/hOs0U771CDSNEEopfaRqJpGD+Bte4I7F/CqMeYDEZkOYIx5CpiFNdx2A9aQ2/9JUKxJ7ZNtn/CLz37BoJxBnJx2J799ezsTBufz0PeOp19oK6x6HP4927r/solYI5gGnGpdOzHodOtuaZ5065aaOj2HUuogxBqc1DGMHTvWFBQUJDqMuPlwy4f8ct4vOSZvGH0Dt/DW4jKuOD6L3+e8i3P9+9a9KMCqQQw+HYZOgX4nW6OaNEEopQARKWx6ndyBJPOQW3UA7218j7u+uIvj8o/Hufda3lpbxl0nObl628+QjTus6cQn3ATHnA95AxIdrlKqg9Ck0Q79a/2/uHvB3YzsOprSTVeyZmcNL5xazhnf/NIa6jr1v9aFdkop1co0abQzr615jfsX3c+JXU9m44rvUVYT5MNxSxla+LB1j+wf/hNy+x38jZRS6jBo0mhHZq6YySOFjzAy/xSWFVxMhoT5Yug/6fL1OzD8Ivju0zo7rFKqTWnSaCeeXv40Tyx7guNzJ/PVl+dxQraXVzL+Qsqmr+G0X8Hpv9ThsUqpNqdJI8kZY3h86eM8880zDMs8nYVfns2lPYt5IHA/jopquOwlOPbbiQ5TKdVJaNJIYsYY/ljwR15a9RIDPWeyePFZ/Lr/Kn5a+giS0RWu/si6clsppeJEk0aSipgI9y+6n9fXvk5PzmLl8jN4ecAHTNzzsnWtxRWvQEbXRIeplOpkNGkkoXAkzO8W/o63N7xNbmAKuzedxKd9nqb/ns9h9FVw/sN68yKlVEJo0kgyoUiI//f5/2PW5lmk1Z6LZ+dwFubfT2bZNjjvj3DStESHqJTqxDRpJJFgOMjt827no20f4ag4n1Hl3Xky/be4Qg748dsw+LREh6iU6uQ0aSQJf9jPrZ/eyrwd8wgXX8jV/gC3yO+R7CHWBXtdBiU6RKWU0qSRDLwhLzfPvZkvd31JaPdF/CWynnOCs2HoOfD956z7ayulVBLQpJFgdcE6bvj4Bgr3LMG58wLe4lOO9q+Eib+AM+/S2WiVUklFk0YCVQeqmTZ7OitKV9Cl6EzeMW+RG6mE7z0Hx38/0eEppdR+NGkkSKW/kqs/vJa1ZesYtnMcr4Zew5Wei/zwQ+h9YqLDU0qpZmnSSIAyXxlT37+azZWbmbJrMI8E3oA+Y+CKVyGrZ6LDU0qpFmnSiLPiumKu/O//sLNmJ9ftyeYm/8dwwhVw0aPgTk10eEopdUBx72UVkX4i8omIrBKRlSJySzNlTheRShFZZj9+E+8428Lu2t1c9u6V7KrZyb17gtzo/wam3AfffUoThlKqXUhETSME/MIYs0REsoBCEZljjFnVpNx8Y8yFCYivTeyo3sEV711Fta+cJ/eWcUokhPzwDRh6VqJDU0qpmMU9aRhjdgG77OVqEVkN9AGaJo0OY3PlZq54dyrBYDUv7d7JiIxeOH70T+g6NNGhKaXUIUnoRQAiMhA4EVjUzOYJIrJcRN4XkePiG1nrWVO6ju+/cyUmUMlru7Yxos/JOKbN1YShlGqXEtYRLiKZwFvAz40xVU02LwEGGGNqROR84B2g2aOsiEwDpgH079+/7QI+DEt2r+TqD64mI1zH33cXMWjMNOTse8Gp4w+UUu1TQmoaIuLGShivGGP+1XS7MabKGFNjL88C3CLS7M0jjDEzjDFjjTFju3Xr1qZxH4p5Wwv56ftXkReu4aXdexh8/mPIuX/QhKGUatfifgQTEQGeA1YbY/7UQpmewB5jjBGR8VjJrTSOYR6Rd9d8wW8W3kTPsJ+/lfkZ9JP3oN/4RIellFJHLBGnvacCPwa+EZFl9ro7gf4AxpingO8D14tICPACVxhjTAJiPWQzC+fw6Ne30Tfk5wl/HgOvexNy+iQ6LKWUahWJGD31OSAHKfME8ER8Imod64uLeWLhP5hXOoOBoQCPpo2m/1XPgSc90aEppVSr0Qb2I/D1zh08X/A2q/b8lz3uIiICxwX9/Hngj+h19m9ADpgblVKq3dGkcYi+3LqJlwteZ23ZbPa4SzACfSTED6uDTM49gZO+9TMcQ85IdJhKKdUmNGkchDGGuRtW8+aSl1hfNY89Hmt08GCCXFUtnN5jAqNP/REycBK4PAmOViml2pYmjWaEwxH+u3YJ/136IhvqFrHX4wVguAlwUW0a3+p/FiNGXwm9RmoTlFKqU9GkYQuFI7z19Xw+/nom64NfU+IOIMYwMhLgEm9Xpgy9iKGjfwh5AxIdqlJKJUynThq+YJB/Fr7PvNWvsC68lnJ3GKcxjAkFuZy+nH/cZfQfeSmkd0l0qEoplRQ6XdKoDfh4dcHrLNz4JuvYSqXL4BbDuFCY8e5juGD0j+k5/AKdqlwppZrRKZJGeV0N/5j3LIu2/4e1zj3UOCHNEWGcz8G41JFcdNI15A+eDI6Ezt+olFJJr8Mmjd0Ve/nnZ09SuOdjVrvL8TqELGeEcYEUTsqfwEUTbyC7V7udPFcppRKiQyWNQNDPI2/8nGXlC1nlriXgELq4wpwayOGUXmdw4eQbScvVKT2UUupwdaiksaFqAzProLszwumh7kzqfx4XTJ6OOy070aEppVSH0KGSRrbx8NDgn3POhJ/gcLkTHY5SSnU4HSpp9O06lPMmXZ3oMJRSqsPS4UJKKaVipklDKaVUzDRpKKWUipkmDaWUUjHTpKGUUipmmjSUUkrFTJOGUkqpmGnSUEopFTMxxiQ6hlYjItXA2gSH0RUoSXAMkBxxaAyNkiGOZIgBkiOOZIgBkiOOY4wxWbEW7lBXhANrjTFjExmAiBQkOoZkiUNjSK44kiGGZIkjGWJIljhEpOBQymvzlFJKqZhp0lBKKRWzjpY0ZiQ6AJIjBkiOODSGRskQRzLEAMkRRzLEAMkRxyHF0KE6wpVSSrWtjlbTUEop1YY0aSillIqZJg2llFIxa/dJQ0S6iEiXRMehkpOIjE50DMlARLJFZIyI5CU6lmQgIl0T/Pl5IpKdyBgOV7tMGiLSX0T+ISLFwCLgKxHZa68bmODwABCRb+L0Of3s7z1fRO4UEXfUtnfiEYP9WcNE5H0R+a+IDBGRmSJSISJficjwOMUwusljDPCuiJwYz+QhIj+NWu4rIh/bv8UCETk6TjG8XH9gFJFzgBXAg8AyEbk0HjHYn10mIs+KyLdEROL1uU1iOE9ENovI5/bfwkpgkYjsEJFvxTGO3iLydxGpxLoKfIWIbBORu6P/3cYxnh5R/1Z6xLyjMabdPYCFwOWAM2qdE7gC+DKOcVzSwuN7QHGcYpgDTAdGAY8DC4B8e9vSOP4W84CLgB8AW+3/F2Kv+zhOMUTs7/9J1MNrP8+N42+xJGr5n8A0rBO078bxt/gmankBMNBe7gosj+NvsRa4CfgCKAIeBU6O1+fbMSwDhgMTgNL6z7fXLYljHHOB0+3lS4A/AxnAfcCMOMYxCvgSWA18ZD/W2OtGH3T/eP7Pa8Uvvf5wtrVBHEFgJvBCM4/qOMWwrMnrK4GVwJA4/4NYGrW8ocm2uMRhJ+vPgPOi1m2O12/Q3Pdt5v/P0jjFsBLItpc/BxzR2xL0W/QHbgeWAJuA+xMQw/Ym25bFIwb7s5Y3eV0YtbwmjnEsA05qZv3JsZxQtNe5pwpF5EngRWC7va4fcBWwNI5xfA08bIxZ0XSDiJwVpxjcIpJqjPEBGGNeFpHdwIdYZzHx4oxa/lOTbZ54BGCMeUtEPgTutZuIfgEk4kKkviLyGFZNq5uIuI0xQXtbvJohfgd8IiJ/xTrLf0NE3gXOAD6IUwxg/QYAGGO2AQ8BD4nIMKzWgnioEJHrgGygXET+F6sGeBZQE6cYAIpF5Eqsmu8lwBYAu9kunl0FGcaYRU1XGmO+FJGDHjPaa9L4CXA11j+MPva6HcB7wHNxjOPnQFUL274bpxieBU7COsMGwBjzkd1u/VCcYgD4q4hkGmNqjDFP1q8UkaOwqr9xYYypAf5XRE7EOqnIjNdnR/m/qOUCO4ZyEekJvBuPAIwx/xSRJcC1wNFY/9ZPBl4zxnwYjxhsn7QQ3xqsf7/xcBVwF1bz5RSsJtQPsZpRr41TDAA/BR4G7sA627/JXt8F+FUc43hfRP4L/J19T7p/QgwnFHpFuOqw7DO4LGNMS4ldqU5JRM4DLqbxpLsIeNcYM+tg+7bL0VMHIiIXJjoGSI44kiEGSFwcxlKVyBiaSoY4kiEGSI44kiEGiH8cxpj3jTHTjTEX2Y/psSQM6IBJAxiX6ABsyRBHMsQAyRFHMsQAyRFHMsQAyRFHMsQASRKHiEw7aJn22jxld6Q1V71a3dniSIYYkiWOZIghWeJIhhiSJY5kiCGZ4miJiFxnjHn6QGXaZU1DRH4J/ANrZMZX9kOA10Tkjs4URzLEkCxxJEMMyRJHMsSQLHEkQwzJFMdBBA5WoF3WNERkHXBc1DDG+vUerDHoQztLHMkQQ7LEkQwxJEscyRBDssSRDDEkUxwHIiLbjDH9D1SmXdY0sIbO9W5mfS97W2eKIxliSJY4kiGGZIkjGWJIljiSIYakiUNEvm7h8Q1w0OlE2ut1Gj8HPhaR9TSOM+4PHEXj2OfOEkcyxJAscSRDDMkSRzLEkCxxJEMMyRRHD+AcoLzJesGacuaA2mXzFICIOIDx7NuhtNgYE+5scSRDDMkSRzLEkCxxJEMMyRJHMsSQLHGIyHPAC8aYz5vZ9qox5ocH3L+9Jg2llFLx1177NJRSSiWAJg2llFIx06Sh2jWxbvb0/UTHkUxE5Ocikp7oOFTHpElDqSQgIq05kvHngCYN1SY0aah2Q0R+Yo8nXy4iL0VtmizWrVQ31dc6RCRTrNusLhGRb0TkYnv9QBFZLSLPiMhKEZktImn2tnH2+y8TkT+KyAp7vdN+vdjefl0zsQ0UkTUi8or9/m/Wn+2LdW/uz0SkUEQ+FJFe9vpPReQvIlIA3GJ//gL7+30lIlktfbaInG7v/2bU54qI3Ix1LcAnIvKJXfZvIlJgf9/fRcV8vr1voYg8JiL/sddniMjzdgxL6387pYD2eec+fXS+B3AcsA7oar/uYj/PBN7AOgE6FvuugVjXINXfua4rsAFrHPpAIASMsrf9E7jSXl4BTLCXHwBW2MvTgLvs5RSse2QMahLfQKwbPp1qv34euA3rpksLgG72+suB5+3lT4En7WUP1t3sxtmvs+3v0OxnA6cDlUBf+7svBCba5bbU/05Nfiun/ZknAKlY1woMsre9BvzHXr4/6jfJtX/3jET/DegjOR7t9eI+1fmcCbxhjCkBMMaURW17xxgTAVaJSP0VrQLcLyKTsa627UPj1a6bjTHL7OVCYKCI5GLde2Ohvf5VoH666inACVF9JznAUGBzkxi3G2O+sJdfBm7GuqnNCGCOiIB14N4Vtc/r9vMxwC5jzGL7+9VP6d7SZweAr4wxO+xyy7AS135j74HLxJq91IV19fGxWIlmkzGm/ju8hpWg6r/vt0XkNvt1KtZFaEkxqZ5KLE0aqiPwRy3X3170R0A3YIwxJigiW7AOfk3Lh4G0g7y/AD8zB7/jXdOLnoy970pjzIQW9qk9nM8WkdPZ/3vs9+9ZRAZh1XjGGWPKRWQmjb/DgT7ze8aYtQcppzoh7dNQ7cVc4FIRyQcQkS4HKZ8D7LUTxhnAgAMVNsZUANUicpK96oqozR8C14uI2/7so6X5eyn3F5H65PBDrLP+tVj3CZ9g7+sWkeOa2Xct0EtExtnlsuzO8Vg/O1o1kGUvZ2Mlpkq7FnZe1OcNFpGB9uvo+3V/CPxM7KqRWLfOVQrQmoZqJ4wxK0Xk98BnIhIGlgJTD7DLK8B7Yk3CVgCsieFjrgaeEZEI1j3XK+31z2I1/SyxD6TFwHea2X8tcKOIPA+sAv5mjAnYTUuPiUgO1r+5vwArm3y/gIhcDjxud8x7gbMO4bOjzQA+EJGdxpgzRGSp/f23A1/Yn+cVkRvscrXA4qj977Vj/FqsaS8209hUpzo5nUZEKZuIZBpjauzlO4BexphbYtx3IFZH8og2DLFV1X9fOxn9FVhvjPlzouNSyU2bp5RqdIE93HYFMAm4L9EBtbFr7Q70lVjNeQe8Y5tSoDUNpZRSh0BrGkoppWKmSUMppVTMNGkopZSKmSYNpZRSMdOkoZRSKmaaNJRSSsXs/wP8DCfkXV0QLwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "import os\n",
    "def get_data(results, name):\n",
    "    output = {}\n",
    "    print(results, name)\n",
    "    for n in results:\n",
    "        output[n] = results[n][name]\n",
    "    return output\n",
    "\n",
    "def getMeanSTD(inputarray):\n",
    "    mean = []\n",
    "    std = []\n",
    "    for i in range(len(inputarray)):\n",
    "        mean.append(np.mean(inputarray[i]))\n",
    "        std.append(np.std(inputarray[i]))\n",
    "    return np.array(mean), np.array(std)\n",
    "\n",
    "def plt_dict(p_dict, y_title, file_name):\n",
    "    plt.figure()\n",
    "    names = []\n",
    "    for t_name in [\"narrow\", \"turtle\", \"wide\"]:\n",
    "        name = p_name + \"_\" + t_name\n",
    "        print(name, p_dict)\n",
    "        mean, std = getMeanSTD(p_dict[name])\n",
    "        plt.plot(np.array(ch_perc_array), mean)\n",
    "        plt.fill_between(np.array(ch_perc_array), mean - std, mean + std, alpha=0.2)\n",
    "        names.append(name.split(\"_\")[1])\n",
    "    plt.legend(names, loc='upper left')\n",
    "    plt.xlim(0.0,1.0)\n",
    "    plt.xticks(np.array(ch_perc_array), rotation=90)\n",
    "    plt.xlabel('change percentage')\n",
    "    plt.ylabel(y_title)\n",
    "    if not os.path.exists('graphs'):\n",
    "        os.makedirs('graphs')\n",
    "\n",
    "    plt.savefig('graphs/' + file_name + \".pdf\", bbox_inches='tight')\n",
    "\n",
    "for n in lambdas[p_name]:\n",
    "    if n == \"has_sol\":\n",
    "        continue\n",
    "    plt_dict(get_data(result, n), n, '{}_{}'.format(p_name, n))\n",
    "plt_dict(get_data(result, 'hdiversity'), 'Hamming Diversity', '{}_{}'.format(p_name,'hammingdiversity'))\n",
    "# plt_dict(get_data(result, 'cdiversity'), 'compression diversity', '{}_{}_{}'.format(p_name,exp_name,'compressiondiversity'))\n",
    "# plt_dict(get_data(result, 'pdiversity'), 'pca diversity', '{}_{}_{}'.format(p_name,exp_name,'pcadiversity'))\n",
    "# plt_dict(get_data(result, 'sdiversity'), 'svd diversity', '{}_{}_{}'.format(p_name,exp_name,'svddiversity'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing solid probability with 0.5 change rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Error: the file binary_narrow_noaction_heatmap_changes_100M could not be found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_6908\\2425268192.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     49\u001b[0m     \u001b[0mregions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[0mboth_perc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m     \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPPO2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0msolid_perc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.01\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.05\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Testing {} at solid percentage of {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msolid_perc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\base_class.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(cls, load_path, env, custom_objects, **kwargs)\u001b[0m\n\u001b[0;32m    873\u001b[0m         \u001b[1;33m:\u001b[0m\u001b[0mparam\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mextra\u001b[0m \u001b[0marguments\u001b[0m \u001b[0mto\u001b[0m \u001b[0mchange\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0mloading\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    874\u001b[0m         \"\"\"\n\u001b[1;32m--> 875\u001b[1;33m         \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_from_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    877\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'policy_kwargs'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'policy_kwargs'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'policy_kwargs'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\base_class.py\u001b[0m in \u001b[0;36m_load_from_file\u001b[1;34m(load_path, load_data, custom_objects)\u001b[0m\n\u001b[0;32m    606\u001b[0m                     \u001b[0mload_path\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;34m\".zip\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    607\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 608\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Error: the file {} could not be found\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    610\u001b[0m         \u001b[1;31m# Open the zip archive and load data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error: the file binary_narrow_noaction_heatmap_changes_100M could not be found"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import gym_pcgrl\n",
    "from gym_pcgrl import wrappers\n",
    "\n",
    "from stable_baselines.common.policies import MlpPolicy, CnnPolicy, FeedForwardPolicy\n",
    "from stable_baselines.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "from stable_baselines.a2c.utils import conv, linear, conv_to_fc\n",
    "from stable_baselines.results_plotter import load_results, ts2xy\n",
    "from stable_baselines import PPO2\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "import pdb\n",
    "\n",
    "p_name = \"sokoban\"\n",
    "rep_names = [\"narrow\", \"turtle\"]\n",
    "exp_names = [\"noaction_heatmap_changes_100M\", \"heatmap_changes_100M\"]\n",
    "\n",
    "def get_env_func(p_name, r_name, solid_perc=0.2):\n",
    "    def get_env():\n",
    "        env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), 28, random_tile=False, change_percentage=0.4, probs={\"empty\":1-solid_perc, \"solid\":solid_perc})\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "def get_action(env, model, action_type=True):\n",
    "    action = None\n",
    "    if action_type == 0:\n",
    "        action, _ = model.predict(obs)\n",
    "    elif action_type == 1:\n",
    "        action_prob = model.action_probability(obs)[0]\n",
    "        action = np.random.choice(a=list(range(len(action_prob))), size=1, p=action_prob)\n",
    "    else:\n",
    "        action = np.array([env.action_space.sample()])\n",
    "    return action\n",
    "\n",
    "pathlength_perc = {}\n",
    "pathlength={}\n",
    "regions_perc = {}\n",
    "regions={}\n",
    "both_perc = {}\n",
    "for i in range(len(exp_names)):\n",
    "    r_name = rep_names[i]\n",
    "    e_name = exp_names[i]\n",
    "    m_name = \"{}_{}_{}\".format(p_name, r_name, e_name)\n",
    "    pathlength_perc[m_name] = []\n",
    "    pathlength[m_name] = []\n",
    "    regions_perc[m_name] = []\n",
    "    regions[m_name] = []\n",
    "    both_perc[m_name] = []\n",
    "    model = PPO2.load(m_name)\n",
    "    for solid_perc in np.arange(0.0, 1.01, 0.05):\n",
    "        print(\"Testing {} at solid percentage of {}\".format(m_name,solid_perc))\n",
    "        temp_path = []\n",
    "        temp_length = []\n",
    "        temp_regions = []\n",
    "        temp_number = []\n",
    "        temp_both = []\n",
    "        for i in range(20):\n",
    "            env = DummyVecEnv([get_env_func(p_name, r_name, ch_perc)])\n",
    "            done = False\n",
    "            obs = env.reset()\n",
    "            while not done:\n",
    "                action = get_action(env, model, 0)\n",
    "                obs, rewards, done, info = env.step(action)\n",
    "            temp_path.append([0,1][info[0]['path-length']>=48])\n",
    "            temp_length.append(info[0]['path-length'])\n",
    "            temp_regions.append([0,1][info[0]['regions']==1])\n",
    "            temp_number.append(info[0]['regions'])\n",
    "            temp_both.append([0,1][temp_path[-1] == 1 and temp_regions[-1] == 1])\n",
    "        pathlength_perc[m_name].append(np.mean(temp_path))\n",
    "        pathlength[m_name].append(np.mean(temp_length))\n",
    "        regions_perc[m_name].append(np.mean(temp_regions))\n",
    "        regions[m_name].append(np.mean(temp_number))\n",
    "        both_perc[m_name].append(np.mean(temp_both))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (21,) and (0,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3900\\2121413545.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mnames\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mregions_perc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1.01\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.05\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mregions_perc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mnames\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2840\u001b[0m     return gca().plot(\n\u001b[0;32m   2841\u001b[0m         \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2842\u001b[1;33m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2843\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2844\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1741\u001b[0m         \"\"\"\n\u001b[0;32m   1742\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1743\u001b[1;33m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1744\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1745\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    271\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    272\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 273\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    274\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    275\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    397\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    398\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 399\u001b[1;33m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[0;32m    400\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[0;32m    401\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (21,) and (0,)"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "plt.figure()\n",
    "names = []\n",
    "for name in regions_perc:\n",
    "    plt.plot(np.array(np.arange(0.0,1.01,0.05)),regions_perc[name])\n",
    "    names.append(name)\n",
    "plt.legend(names)\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.xticks(np.array(np.arange(0.0,1.01,0.05)), rotation=90)\n",
    "plt.title('Region == 1')\n",
    "plt.savefig('const_regions.pdf')\n",
    "\n",
    "plt.figure()\n",
    "names = []\n",
    "for name in pathlength_perc:\n",
    "    plt.plot(np.array(np.arange(0.0,1.01,0.05)),pathlength_perc[name])\n",
    "    names.append(name)\n",
    "plt.legend(names)\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.xticks(np.array(np.arange(0.0,1.01,0.05)), rotation=90)\n",
    "plt.title('Path >= 48')\n",
    "plt.savefig('const_path.pdf')\n",
    "\n",
    "plt.figure()\n",
    "names = []\n",
    "for name in pathlength:\n",
    "    plt.plot(np.array(np.arange(0.0,1.01,0.05)),pathlength[name])\n",
    "    names.append(name)\n",
    "plt.legend(names)\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.xticks(np.array(np.arange(0.0,1.01,0.05)), rotation=90)\n",
    "plt.title('Path Length')\n",
    "plt.savefig('pathlength.pdf')\n",
    "\n",
    "plt.figure()\n",
    "names = []\n",
    "for name in regions:\n",
    "    plt.plot(np.array(np.arange(0.0,1.01,0.05)),regions[name])\n",
    "    names.append(name)\n",
    "plt.legend(names)\n",
    "plt.xlim(0.0,1.0)\n",
    "plt.xticks(np.array(np.arange(0.0,1.01,0.05)), rotation=90)\n",
    "plt.title('Number of Regions')\n",
    "plt.savefig('regions.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = wrappers.CroppedImagePCGRLWrapper(\"zelda-turtle-v0\", 22, random_tile=False, change_percentage=0.2)\n",
    "env.reset().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2280549686.py, line 15)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\Maryam\\AppData\\Local\\Temp\\ipykernel_3900\\2280549686.py\"\u001b[1;36m, line \u001b[1;32m15\u001b[0m\n\u001b[1;33m    'hamming': lambda lvl1, lvl2: return np.clip(abs(lvl1 - lvl2), 0, 1).sum()\u001b[0m\n\u001b[1;37m                                       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "def get_tpkldiv_diversity(lvls):\n",
    "    hamming = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_hamming = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                diff = np.clip(abs(lvl1 - lvl2), 0, 1)\n",
    "                lvl_hamming.append(diff.sum())\n",
    "        hamming.append(np.mean(lvl_hamming) / (lvls[0].shape[0] * lvls[0].shape[1]))\n",
    "    return hamming\n",
    "\n",
    "diversity={\n",
    "    'hamming': lambda lvl1, lvl2: return np.clip(abs(lvl1 - lvl2), 0, 1).sum()\n",
    "    'tpkldiv': lambda lvl1, lvl2: return calc_tp_kldiv(lvl1, lvl2, 3)\n",
    "    ''\n",
    "}\n",
    "\n",
    "def get_diversity(lvls, d_type):\n",
    "    result = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_hamming = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                if d_type == \"hamming\":\n",
    "                    \n",
    "        result.append(np.mean(lvl_hamming) / (lvls[0].shape[0] * lvls[0].shape[1]))\n",
    "    return result\n",
    "\n",
    "def get_hamming_diversity(lvls):\n",
    "    hamming = []\n",
    "    for i in range(len(lvls)):\n",
    "        lvl1 = lvls[i]\n",
    "        lvl_hamming = []\n",
    "        for j in range(len(lvls)):\n",
    "            lvl2 = lvls[j]\n",
    "            if i != j:\n",
    "                diff = np.clip(abs(lvl1 - lvl2), 0, 1)\n",
    "                lvl_hamming.append(diff.sum())\n",
    "        hamming.append(np.mean(lvl_hamming) / (lvls[0].shape[0] * lvls[0].shape[1]))\n",
    "    return hamming\n",
    "\n",
    "env_func = get_env_func(\"sokoban\", \"wide\", 1, 10)\n",
    "lvls = []\n",
    "for i in range(5):\n",
    "    env = DummyVecEnv([env_func])\n",
    "    done = False\n",
    "    obs = env.reset()\n",
    "    while not done:\n",
    "        action, _ = model.predict(obs)\n",
    "        obs, rewards, done, info = env.step(action)\n",
    "    lvls.append(env.get_attr('pcgrl_env')[0]._rep.get_observation()['map'])\n",
    "diversity = get_hamming_diversity(lvls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'lvls' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3900\\3401017067.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mcomp_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mcomp_2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mcomp_both_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlvls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mlvls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'lvls' is not defined"
     ]
    }
   ],
   "source": [
    "import zlib\n",
    "\n",
    "\n",
    "comp_1 = zlib.compress(lvls[0].tobytes(), level=9)\n",
    "comp_2 = zlib.compress(lvls[1].tobytes(), level=9)\n",
    "comp_both_1 = zlib.compress(lvls[0].tobytes() + lvls[1].tobytes(), level=9)\n",
    "result_1 = (len(comp_both_1) - min(len(comp_1), len(comp_2))) / max(len(comp_1), len(comp_2))\n",
    "comp_both_2 = zlib.compress(lvls[1].tobytes() + lvls[0].tobytes(), level=9)\n",
    "result_2 = (len(comp_both_2) - min(len(comp_1), len(comp_2))) / max(len(comp_1), len(comp_2))\n",
    "print((result_1 + result_2)/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binary_narrow_noaction_heatmap_changes_100M\n",
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      "  [1.]]], (28, 28, 1), float32)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Error: the file tests/binary_narrow_noaction_heatmap_changes_100M.pkl could not be found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3900\\138669907.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mget_env_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mch_perc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobservation_space\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPPO2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'tests/{}.{}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me_format\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m \u001b[0menv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDummyVecEnv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mget_env_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mch_perc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m \u001b[0mobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\base_class.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(cls, load_path, env, custom_objects, **kwargs)\u001b[0m\n\u001b[0;32m    873\u001b[0m         \u001b[1;33m:\u001b[0m\u001b[0mparam\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mextra\u001b[0m \u001b[0marguments\u001b[0m \u001b[0mto\u001b[0m \u001b[0mchange\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0mloading\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    874\u001b[0m         \"\"\"\n\u001b[1;32m--> 875\u001b[1;33m         \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_from_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    877\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'policy_kwargs'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'policy_kwargs'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'policy_kwargs'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Python37\\lib\\site-packages\\stable_baselines\\common\\base_class.py\u001b[0m in \u001b[0;36m_load_from_file\u001b[1;34m(load_path, load_data, custom_objects)\u001b[0m\n\u001b[0;32m    606\u001b[0m                     \u001b[0mload_path\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;34m\".zip\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    607\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 608\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Error: the file {} could not be found\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    610\u001b[0m         \u001b[1;31m# Open the zip archive and load data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error: the file tests/binary_narrow_noaction_heatmap_changes_100M.pkl could not be found"
     ]
    }
   ],
   "source": [
    "def get_action(env, model, action_type=True):\n",
    "    action = None\n",
    "    if action_type == 0:\n",
    "        action, _ = model.predict(obs)\n",
    "    elif action_type == 1:\n",
    "        action_prob = model.action_probability(obs)[0]\n",
    "        action = np.random.choice(a=list(range(len(action_prob))), size=1, p=action_prob)\n",
    "    else:\n",
    "        action = np.array([env.action_space.sample()])\n",
    "    return action\n",
    "\n",
    "def get_env_func(p_name, r_name, ch_perc=0.2, crop_size=10):\n",
    "    def get_env():\n",
    "        if r_name != \"wide\":\n",
    "            env = wrappers.CroppedImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), crop_size, add_visits=False, add_heatmap=True, add_changes=True, random_tile=False, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        else:\n",
    "            env = wrappers.ActionMapImagePCGRLWrapper(\"{}-{}-v0\".format(p_name, r_name), add_visits=False, add_heatmap=True, add_changes=True, change_percentage=min(0.2, ch_perc))\n",
    "            env.pcgrl_env.adjust_param(change_percentage=ch_perc)\n",
    "        return env\n",
    "    return get_env\n",
    "\n",
    "print(m_name)\n",
    "print(p_name)\n",
    "print(r_name)\n",
    "print(get_env_func(p_name, r_name, ch_perc)().observation_space)\n",
    "model = PPO2.load('tests/{}.{}'.format(m_name, e_format))\n",
    "env = DummyVecEnv([get_env_func(p_name, r_name, ch_perc)])\n",
    "obs = env.reset()\n",
    "print(env.observation_space)\n",
    "action = get_action(env, model, 0)\n",
    "obs, rewards, done, info = env.step(action)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Box([[[0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]]\n",
       "\n",
       " [[0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]]\n",
       "\n",
       " [[0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]]\n",
       "\n",
       " [[0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]]\n",
       "\n",
       " [[0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]\n",
       "  [0. 0. 0. 0. 0.]]], [[[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]\n",
       "\n",
       " [[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]\n",
       "\n",
       " [[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]\n",
       "\n",
       " [[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]\n",
       "\n",
       " [[1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]\n",
       "  [1. 1. 1. 1. 1.]]], (5, 5, 5), float32)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_env_func(p_name, r_name)().observation_space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sokoban_narrow': {'player': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'crate': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'target': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'regions': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'sollength': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'iterations': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'changes': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'player_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'ratio_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'has_sol': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'success': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]},\n",
       " 'sokoban_turtle': {'player': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'crate': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'target': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'regions': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'sollength': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'iterations': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'changes': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'player_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'ratio_const': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'has_sol': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'success': [[], [], [], [], [], [], [], [], [], [], []],\n",
       "  'hdiversity': [[], [], [], [], [], [], [], [], [], [], []]},\n",
       " 'sokoban_wide': {'player': [[1.2],\n",
       "   [1.2],\n",
       "   [0.8],\n",
       "   [0.675],\n",
       "   [0.675],\n",
       "   [0.475],\n",
       "   [0.55],\n",
       "   [0.525],\n",
       "   [0.525],\n",
       "   [0.425],\n",
       "   [0.425]],\n",
       "  'crate': [[1.175],\n",
       "   [1.25],\n",
       "   [0.95],\n",
       "   [0.9],\n",
       "   [0.825],\n",
       "   [0.675],\n",
       "   [0.3],\n",
       "   [0.4],\n",
       "   [0.275],\n",
       "   [0.375],\n",
       "   [0.375]],\n",
       "  'target': [[1.2],\n",
       "   [1.625],\n",
       "   [1.175],\n",
       "   [1.05],\n",
       "   [0.875],\n",
       "   [0.8],\n",
       "   [0.4],\n",
       "   [0.5],\n",
       "   [0.275],\n",
       "   [0.4],\n",
       "   [0.35]],\n",
       "  'regions': [[1.95],\n",
       "   [1.425],\n",
       "   [1.0],\n",
       "   [1.025],\n",
       "   [1.0],\n",
       "   [1.0],\n",
       "   [1.0],\n",
       "   [1.0],\n",
       "   [1.0],\n",
       "   [1.0],\n",
       "   [1.0]],\n",
       "  'sollength': [[0.275],\n",
       "   [0.15],\n",
       "   [0.925],\n",
       "   [1.725],\n",
       "   [0.775],\n",
       "   [2.525],\n",
       "   [0.85],\n",
       "   [0.725],\n",
       "   [1.275],\n",
       "   [0.625],\n",
       "   [0.525]],\n",
       "  'iterations': [[1.0],\n",
       "   [2.175],\n",
       "   [6.475],\n",
       "   [19.725],\n",
       "   [54.25],\n",
       "   [110.6],\n",
       "   [214.15],\n",
       "   [202.95],\n",
       "   [414.7],\n",
       "   [442.8],\n",
       "   [549.075]],\n",
       "  'changes': [[1.0],\n",
       "   [2.0],\n",
       "   [5.0],\n",
       "   [6.825],\n",
       "   [9.85],\n",
       "   [11.225],\n",
       "   [13.95],\n",
       "   [15.65],\n",
       "   [15.55],\n",
       "   [17.225],\n",
       "   [18.2]],\n",
       "  'player_const': [[0.5],\n",
       "   [0.375],\n",
       "   [0.7],\n",
       "   [0.675],\n",
       "   [0.575],\n",
       "   [0.475],\n",
       "   [0.5],\n",
       "   [0.475],\n",
       "   [0.525],\n",
       "   [0.425],\n",
       "   [0.425]],\n",
       "  'ratio_const': [[0.175],\n",
       "   [0.2],\n",
       "   [0.45],\n",
       "   [0.525],\n",
       "   [0.475],\n",
       "   [0.3],\n",
       "   [0.2],\n",
       "   [0.3],\n",
       "   [0.225],\n",
       "   [0.225],\n",
       "   [0.275]],\n",
       "  'has_sol': [[0.025],\n",
       "   [0.025],\n",
       "   [0.125],\n",
       "   [0.125],\n",
       "   [0.075],\n",
       "   [0.15],\n",
       "   [0.125],\n",
       "   [0.05],\n",
       "   [0.075],\n",
       "   [0.075],\n",
       "   [0.1]],\n",
       "  'success': [[0.0],\n",
       "   [0.0],\n",
       "   [0.0],\n",
       "   [0.05],\n",
       "   [0.0],\n",
       "   [0.075],\n",
       "   [0.025],\n",
       "   [0.025],\n",
       "   [0.05],\n",
       "   [0.025],\n",
       "   [0.0]],\n",
       "  'hdiversity': [[0.404],\n",
       "   [0.43499999999999994],\n",
       "   [0.354],\n",
       "   [0.309],\n",
       "   [0.23199999999999998],\n",
       "   [0.146],\n",
       "   [0.082],\n",
       "   [0.08],\n",
       "   [0.06899999999999999],\n",
       "   [0.05700000000000001],\n",
       "   [0.049]]}}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 2],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 1, 0, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_start = [\n",
    "       [0, 4, 0, 1, 0],\n",
    "       [2, 1, 1, 1, 1],\n",
    "       [0, 1, 1, 1, 0],\n",
    "       [0, 0, 1, 0, 0],\n",
    "       [1, 2, 0, 0, 0]]\n",
    "\n",
    "narrow_temp = [\n",
    "       [0, 4, 0, 4, 0],\n",
    "       [2, 3, 1, 1, 1],\n",
    "       [0, 0, 0, 0, 0],\n",
    "       [0, 3, 1, 0, 0],\n",
    "       [1, 0, 0, 0, 0]]\n",
    "\n",
    "turtle_temp = [\n",
    "       [0, 4, 0, 0, 0],\n",
    "       [0, 1, 3, 1, 0],\n",
    "       [0, 0, 4, 3, 0],\n",
    "       [0, 0, 1, 0, 0],\n",
    "       [1, 2, 0, 0, 0]]\n",
    "\n",
    "wide_temp = [\n",
    "       [0, 0, 0, 0, 0],\n",
    "       [4, 1, 1, 2, 1],\n",
    "       [0, 0, 0, 3, 0],\n",
    "       [0, 0, 1, 0, 0],\n",
    "       [0, 0, 0, 0, 0]]\n",
    "\n",
    "second_start = [\n",
    "       [1, 0, 0, 3, 3],\n",
    "       [0, 0, 0, 1, 1],\n",
    "       [1, 0, 3, 0, 0],\n",
    "       [1, 0, 1, 0, 1],\n",
    "       [0, 0, 0, 0, 0]]\n",
    "\n",
    "narrow_last = [\n",
    "       [1, 0, 0, 0, 0],\n",
    "       [0, 0, 0, 2, 1],\n",
    "       [0, 0, 3, 0, 0],\n",
    "       [1, 0, 1, 0, 1],\n",
    "       [4, 0, 0, 0, 0]]\n",
    "\n",
    "turtle_last = [\n",
    "       [1, 0, 0, 3, 4],\n",
    "       [0, 0, 0, 2, 1],\n",
    "       [1, 0, 3, 0, 0],\n",
    "       [1, 0, 1, 0, 1],\n",
    "       [0, 4, 0, 0, 0]]\n",
    "\n",
    "wide_last = [\n",
    "       [0, 0, 0, 0, 0],\n",
    "       [0, 0, 4, 1, 1],\n",
    "       [1, 0, 0, 0, 2],\n",
    "       [1, 3, 1, 0, 1],\n",
    "       [0, 0, 0, 0, 0]]\n",
    "\n",
    "third_start = [\n",
    "       [4, 0, 1, 0, 0],\n",
    "       [1, 0, 2, 2, 0],\n",
    "       [1, 1, 0, 4, 0],\n",
    "       [0, 1, 1, 1, 0],\n",
    "       [0, 1, 0, 0, 1]]\n",
    "\n",
    "narrow_last = [\n",
    "       [4, 0, 0, 0, 0],\n",
    "       [1, 0, 0, 2, 0],\n",
    "       [1, 3, 0, 4, 0],\n",
    "       [0, 3, 0, 1, 0],\n",
    "       [0, 0, 0, 0, 1]]\n",
    "\n",
    "turtle_last = [\n",
    "       [4, 0, 0, 0, 0],\n",
    "       [1, 0, 2, 3, 0],\n",
    "       [0, 3, 0, 4, 0],\n",
    "       [0, 1, 1, 1, 0],\n",
    "       [0, 0, 0, 0, 0]]\n",
    "\n",
    "wide_last = [\n",
    "       [4, 0, 0, 0, 0],\n",
    "       [1, 0, 2, 0, 0],\n",
    "       [1, 1, 0, 3, 0],\n",
    "       [0, 1, 0, 1, 0],\n",
    "       [0, 0, 0, 0, 0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 2],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info[0]['terminal_observation'].argmax(axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.525"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(result['sokoban_wide']['sollength'][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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