From a837bc2d61c1c0ebe550136f9840b3efee330a0f Mon Sep 17 00:00:00 2001
From: Philipp Horstenkamp <philipp@horstenkamp.de>
Date: Sun, 12 Feb 2023 14:46:39 +0100
Subject: [PATCH] Added a docstring to the board plot function

---
 main.ipynb | 35 +++++++++++++++++++----------------
 1 file changed, 19 insertions(+), 16 deletions(-)

diff --git a/main.ipynb b/main.ipynb
index ad5161d..5b76f0e 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -80,7 +80,10 @@
     "I chose to ignore player colors as far as I could instead a player perspective was used. Which allowed to change the perspective with a flipping of the sign. (multiplying with -1).\n",
     "The array format should also allow for data multiplication or the breaking of strikt sequences by flipping the game along one the for axis, (horizontal, vertical, transpose along both diagonals).\n",
     "\n",
-    "I wanted to implement different agents as classes that act on those game stacks."
+    "I wanted to implement different agents as classes that act on those game stacks.\n",
+    "\n",
+    "Since computation time is critical all computational have results are saved.\n",
+    "The analysis of those is then repeated in real time. If a recalculation of such a section is required the save file can be deleted and the code should be executed again."
    ],
    "metadata": {
     "collapsed": false
@@ -124,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,14 +171,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "array([[-1, -1],\n       [-1,  0],\n       [-1,  1],\n       [ 0, -1],\n       [ 0,  1],\n       [ 1, -1],\n       [ 1,  0],\n       [ 1,  1]])"
      },
-     "execution_count": 26,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -200,13 +203,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 5,
    "outputs": [
     {
      "data": {
       "text/plain": "array([[-1,  1],\n       [ 1, -1]])"
      },
-     "execution_count": 23,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,13 +315,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "<Figure size 300x300 with 1 Axes>",
-      "image/png": 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QMGQMzVBl26QOxUX0xRdfICYmBvHx8Zg5cyauXLmixrhIbd9D3TutAvJpIa3s9PQw9UB8VLyq0YOiB6G7qbuqGRQ8iorokUcewYYNG7B7924UFhbiyy+/xGOPPYa6Ot9/o51OJxwOR4uF7gJql5BHK7/u26/4qAbPEdjUOSg6xSMj4+bubkpKCh555BEMGDAA27dvR1ZW60e2Wq1WvPnmm4GNkoLPJS7HZDRpEq1VDgUuoH+W7rvvPvzgBz9ARUWFz3Xy8/Nht9u9i81mCySSgiVUXI7zhlOTaK1yKHABFVF9fT0uXbqEvn37+lzHZDLBbDa3WOguoNWvoZWcipoKuKVgH7zUkltyo6LG9z+QdHdRVES//vWvcfDgQVRWVuKTTz7BM888g9DQUEyfPl2t8ZFawqD+HVF9XLeowdmAy99cVjX6Us0lXreoE1FURFevXsX06dORmJiIn/zkJ4iMjMTRo0cRFRWl1vhITbFQ9ziifr6/vevMLlWPIyo5W6LKtkkdit6s3rp1q1rjIBEGQ76ekBqk5u37sOrgKuSOz1UlOiw0DIUHClXZNqmD55rpWU/IFzUL9l6RoXm7bVy5sby6HKXnSoO+V9TkakLpudLg3fSRNMEi0ruxCP7fgpDm7bZj3qZ5aHI1BfXs+yZXE+ZtmheU7ZF2WER6FwFgTJC3+Sg69EZ45beVyN2SG9Sz73O25PCysZ0Qi4iAJACjgrSthwAkdnz1osNFeHXHqwDg956R53mLdyzGusPr/NoGicWL55NsBOR7Xx2BfD0hJZ1ggPxP2qNQVEIeS3YtwdeOr7F8+nIYQ42KToZtcjWhydWEnC05LKFOjHtEdFMSgP8DENP8uL1XTJ7vxzQ/z48S8ig6XIQhrw/B/gv7AaDdN7E9399/YT+SX09mCXVy3COiliIAZODmfc2uotUTV2GGfJxQEO9rVvltJZ5a9pT3vmYZQzPuOEHWLblxqeYSSs6WoPBAIT8d6yJYRNS6npBfagGa3+m1vLoceVvzkIc83ulVJwxSsD477SCHwwGLxSI/uFfLZMj34ZYgv6QI11G26HzOXZ9zb5T/sNvt7Z5jKnaPqFFQrqTTbNH5nLs4ovPbIbaIuEekj3zOXZ9zV1B84oooHMAMjTOLIf9w9JYtOp9z1+fcN0Muwg7gx/dEJByLiIiEYxERkXAsIiISjkVERMKxiIhIOBYREQnHIiIi4RQX0VdffYVZs2YhMjIS4eHhGDZsGMrKytQYGxHphKIjq2tra5Gamoq0tDSUlJQgKioKX3zxBXr2DNJ1IIhIlxQV0dtvv43Y2FisX7/e+7WBAwcGfVBEpC+KXpp9+OGHeOihhzBt2jRER0djxIgRWLNmTZvPcTqdcDgcLRYiolspKqLLly+jsLAQDz74IPbs2YMFCxYgNzcXGzdu9Pkcq9UKi8XiXWJjYwMeNBF1LYqKyO12Y+TIkViyZAlGjBiBX/3qV/jlL3+JVatW+XxOfn4+7Ha7d7HZbAEPmoi6FkVF1LdvXwwZMqTF1wYPHowrV674fI7JZILZbG6xEBHdSlERpaam4uLFiy2+9vnnn2PAgAFBHRQR6YuiInrppZdw9OhRLFmyBBUVFSguLsbf/vY3ZGdnqzU+ItIBRUU0evRo7NixA1u2bMHQoUPx1ltvYdmyZZg5c6Za4yMiHVB8qdjMzExkZmaqMRYi0imea0ZEwrGIiEg4FhERCcciIiLhWEREJByLiIiEYxERkXAsIiISziBJkqRloMPhgMVikR/cq2Uy5PtwSwAMkO8Frpds0fmcuz7n3ij/Ybfb2z3ZXfGR1UHVKChX0mm26HzOXRzR+e0QW0TcI9JHPueuz7krKD5xRRQOYIbGmcWQfzh6yxadz7nrc+6bIRdhB/DNaiISjkVERMKxiIhIOBYREQnHIiIi4VhERCQci4iIhGMREZFwioooLi4OBoPhjoW3EyKiQCg6svrEiRNwuVzex2fPnsWECRMwbdq0oA+MiPRDURFFRUW1eLx06VIMGjQIjz/+eFAHRUT64ve5Zt9//z02bdqEl19+GQaDwed6TqcTTqfT+9jhcPgbSURdlN9vVn/wwQe4fv065syZ0+Z6VqsVFovFu8TGxvobSURdlN9FVFRUhIyMDMTExLS5Xn5+Pux2u3ex2Wz+RhJRF+XXS7N///vf2Lt3L95///121zWZTDCZTP7EEJFO+LVHtH79ekRHR2Py5MnBHg8R6ZDiInK73Vi/fj1mz54No1HsBR6JqGtQXER79+7FlStXMHfuXDXGQ0Q6pHiXJj09HRrf+IOIujiea0ZEwrGIiEg4FhERCcciIiLhWEREJByLiIiEYxERkXAGSeODghwOBywWi/zgXi2TwXugc+6cu5Ya5T/sdjvMZnObq4o9R6NRUK6k02zR+Zy7OKLz2yG2iLhHpI98zl2fc1dQfOKKKBzADI0ziyH/cPSWLTqfc9fn3DdDLsIO4JvVRCQci4iIhGMREZFwLCIiEo5FRETCsYiISDgWEREJxyIiIuEUFZHL5cJrr72GgQMHIjw8HIMGDcJbb73Fa1gTUUAUHVn99ttvo7CwEBs3bkRycjLKysrw/PPPw2KxIDc3V60xElEXp6iIPvnkE0ydOtV7Y8W4uDhs2bIFx48fV2VwRKQPil6aPfroo9i3bx8+//xzAMDp06dx+PBhZGRkqDI4ItIHRXtEixYtgsPhQFJSEkJDQ+FyuVBQUICZM2f6fI7T6YTT6fQ+djgc/o+WiLokRXtE27dvx+bNm1FcXIxPP/0UGzduxB//+Eds3LjR53OsVissFot3iY2NDXjQRNS1KCqi3/zmN1i0aBF+9rOfYdiwYfj5z3+Ol156CVar1edz8vPzYbfbvYvNZgt40ETUtSh6adbY2IiQkJbdFRoaCrfb7fM5JpMJJpPJv9ERkS4oKqIpU6agoKAA/fv3R3JyMj777DP8+c9/xty5c9UaHxHpgKIiWr58OV577TUsXLgQNTU1iImJwbx58/C73/1OrfERkQ4oKqKIiAgsW7YMy5YtU2k4RKRHPNeMiIRjERGRcCwiIhKORUREwrGIiEg4FhERCcciIiLhWEREJJxB0vg6r3a7Hffdd5/8IFzLZLS8D7eeskXnc+5iskXnN2dfv34dFoulzVUVHVkdDHV1dTcffOd7PdXpNVt0Pueuu/y6urp2i0jzPSK3242qqipERETAYDAoeq7D4UBsbCxsNhvMZrNKI7w78zl3/WWLzg80W5Ik1NXVISYm5o6rdtxO8z2ikJAQ9OvXL6BtmM1mIX8p7oZ8zl1/2aLzA8lub0/Ig29WE5FwLCIiEq5TFZHJZMLrr78u7IqPIvM5d/1li87XMlvzN6uJiG7XqfaIiKhrYhERkXAsIiISjkVERMJ1qiI6cuQIQkNDMXnyZM0y58yZA4PB4F0iIyMxceJE/Otf/9JsDNeuXUNOTg7i4+NhMpkQGxuLKVOmYN++farm3jr3sLAw3H///ZgwYQLWrVvX5r3s1Mi/dZk4caLq2W3lV1RUqJ597do15OXlISEhAd26dcP999+P1NRUFBYWorGxUbXcOXPm4Omnn77j6wcOHIDBYMD169dVye1URVRUVIScnBwcOnQIVVVVmuVOnDgR1dXVqK6uxr59+2A0GpGZmalJdmVlJUaNGoWPP/4Y77zzDs6cOYPdu3cjLS0N2dnZqud75l5ZWYmSkhKkpaUhLy8PmZmZuHHjhmb5ty5btmxRPbet/IEDB6qaefnyZYwYMQKlpaVYsmQJPvvsMxw5cgS//e1vsXPnTuzdu1fVfBE0P8XDX/X19di2bRvKyspw7do1bNiwAYsXL9Yk22QyoU+fPgCAPn36YNGiRXjsscfwzTffICoqStXshQsXwmAw4Pjx4+jevbv368nJyZrc2PLWuT/wwAMYOXIkfvjDH2L8+PHYsGEDfvGLX2iWL4KI/IULF8JoNKKsrKzF7zw+Ph5Tp05FVzziptPsEW3fvh1JSUlITEzErFmzsG7dOiG/kPr6emzatAkJCQmIjIxUNeu///0vdu/ejezs7BZ/IT28l1PR2BNPPIHhw4fj/fffF5Lflf3nP/9BaWmpz985AMUni3cGnaaIioqKMGvWLADy7rLdbsfBgwc1yd65cyd69OiBHj16ICIiAh9++CG2bdvW7hnFgaqoqIAkSUhKSlI1xx9JSUmorKxUPefWn71nWbJkieq5vvKnTZumap7nd56YmNji67179/aO4ZVXXlF1DK39zDMyMlTN7BQvzS5evIjjx49jx44dAACj0Yif/vSnKCoqwrhx41TPT0tLQ2FhIQCgtrYW7777LjIyMnD8+HEMGDBAtdy7eRdckiRN/mW+9Wfv0atXL9VzfeX72ktR2/Hjx+F2uzFz5kw4nU5Vs1r7mR87dsy7I6CGTlFERUVFuHHjBmJiYrxfkyQJJpMJK1as6PClBvzVvXt3JCQkeB+vXbsWFosFa9aswR/+8AfVch988EEYDAZcuHBBtQx/lZeXq/6mLXDnz15rWucnJCTAYDDg4sWLLb4eHx8PAAgPV/8yi63N+erVq6pm3vUvzW7cuIG///3v+NOf/oRTp055l9OnTyMmJkbTT1A8DAYDQkJC8N136l7yrlevXnjqqaewcuVKNDQ03PF9tT5Kbc/HH3+MM2fO4NlnnxWS35VFRkZiwoQJWLFiRau/867qrt8j2rlzJ2pra5GVlXXHns+zzz6LoqIizJ8/X9UxOJ1OXLt2DYD80mzFihWor6/HlClTVM0FgJUrVyI1NRUPP/wwfv/73yMlJQU3btzARx99hMLCQpSXl6ua75m7y+XC119/jd27d8NqtSIzMxPPPfecqtm35t/KaDSid+/eqmeL8u677yI1NRUPPfQQ3njjDaSkpCAkJAQnTpzAhQsXMGrUKNFDDD7pLpeZmSlNmjSp1e8dO3ZMAiCdPn1atfzZs2dLALxLRESENHr0aOm9995TLfN2VVVVUnZ2tjRgwADpnnvukR544AHpxz/+sbR//35Vc2+du9FolKKioqQnn3xSWrduneRyuVTNvj3/1iUxMVH1bE/+1KlTNcm6XVVVlfTCCy9IAwcOlMLCwqQePXpIDz/8sPTOO+9IDQ0NquX6mvP+/fslAFJtba0qubwMCBEJd9e/R0REXR+LiIiEYxERkXAsIiISjkVERMKxiIhIOBYREQnHIiIi4VhERCQci4iIhGMREZFwLCIiEu7/Adbt5Eva9bHJAAAAAElFTkSuQmCC\n"
+      "image/png": "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\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -329,11 +332,11 @@
     "    \"\"\"Plots a single otello board.\n",
     "\n",
     "    If a matplot axis object is given the board will be plotted into that axis. If not an axis object will be generated.\n",
+    "    The image generated will be shown directly.\n",
     "\n",
     "    Args:\n",
     "        board: The bord that should be plotted. Only a single games is allowed. A numpy array of the form 8x8 is expected.\n",
-    "        ax: If needed the\n",
-    "\n",
+    "        ax: If needed a matplotlib axis object can be defined that is used to place the board as a sublot into a bigger context.\n",
     "    \"\"\"\n",
     "    plot_all = False\n",
     "    if ax is None:\n",
@@ -341,7 +344,7 @@
     "        plot_all = True\n",
     "        fig, ax = plt.subplots(figsize=(fig_size, fig_size))\n",
     "\n",
-    "    ax.set_facecolor(\"#006400\")\n",
+    "    ax.set_facecolor(\"#66FF00\")\n",
     "    for i in range(BOARD_SIZE):\n",
     "        for j in range(BOARD_SIZE):\n",
     "            if board[i, j] == -1:\n",
@@ -691,7 +694,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "123 ms ± 4.08 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "110 ms ± 7.6 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     },
     {
@@ -731,12 +734,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10.8 s ± 339 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "9.34 s ± 430 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     },
     {
      "data": {
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-1.],\n          [-1., -1., -1., ..., -1., -1.,  1.],\n          [-1., -1.,  1., ..., -1., -1.,  1.],\n          ...,\n          [-1., -1., -1., ...,  1.,  1.,  1.],\n          [-1., -1., -1., ...,  1.,  1.,  1.],\n          [-1., -1., -1., ...,  1.,  1.,  1.]],\n \n         [[-1., -1., -1., ..., -1., -1., -1.],\n          [-1., -1.,  1., ..., -1., -1., -1.],\n          [-1.,  1., -1., ...,  1., -1., -1.],\n          ...,\n          [ 1.,  1.,  1., ..., -1.,  1.,  1.],\n          [-1., -1., -1., ...,  1.,  1.,  1.],\n          [-1., -1., -1., ..., -1., -1., -1.]],\n \n         ...,\n \n         [[-1., -1.,  1., ...,  1., -1., -1.],\n          [-1., -1., -1., ..., -1., -1., -1.],\n          [-1., -1.,  1., ...,  1., -1., -1.],\n          ...,\n          [-1., -1., -1., ..., -1., -1., -1.],\n          [-1., -1.,  1., ..., -1., -1., -1.],\n          [-1., -1., -1., ..., -1., -1., -1.]],\n \n         [[-1., -1., -1., ..., -1.,  1., -1.],\n          [ 1.,  1.,  1., ...,  1.,  1., -1.],\n          [-1.,  1.,  1., ..., -1.,  1., -1.],\n          ...,\n          [-1.,  1., -1., ..., -1.,  1., -1.],\n          [-1., -1.,  1., ...,  1., -1., -1.],\n          [-1., -1., -1., ..., -1., -1., -1.]],\n \n         [[ 1.,  1.,  1., ...,  1.,  1.,  1.],\n          [-1., -1., -1., ..., -1.,  1.,  1.],\n          [-1., -1., -1., ...,  1., -1.,  1.],\n          ...,\n          [-1., -1.,  1., ...,  1.,  1.,  1.],\n          [ 1.,  1.,  1., ..., -1.,  1.,  1.],\n          [ 1.,  1.,  1., ...,  1.,  1.,  1.]]],\n \n \n        [[[ 1.,  1.,  1., ...,  1.,  1., -1.],\n          [-1.,  1., -1., ...,  1.,  1., -1.],\n          [-1.,  1.,  1., ..., -1.,  1., -1.],\n          ...,\n          [-1.,  1., -1., ...,  1.,  1., -1.],\n          [-1., -1., -1., ..., -1.,  1., -1.],\n          [-1., -1., -1., ...,  1.,  1.,  1.]],\n \n         [[-1., -1., -1., ..., -1., -1., -1.],\n          [-1., -1., -1., ..., -1., -1.,  1.],\n          [-1., -1.,  1., ..., -1., -1.,  1.],\n          ...,\n          [-1., -1., -1., ...,  1.,  1.,  1.],\n          [-1., -1., -1., ...,  1.,  1.,  1.],\n          [-1., -1., -1., ...,  1.,  1.,  1.]],\n \n         [[-1., -1., -1., ..., -1., -1., -1.],\n          [-1., -1.,  1., ..., -1., -1., -1.],\n          [-1.,  1., -1., ...,  1., -1., -1.],\n          ...,\n          [ 1.,  1.,  1., ..., -1.,  1.,  1.],\n          [-1., -1., -1., ...,  1.,  1.,  1.],\n          [-1., -1., -1., ..., -1., -1., -1.]],\n \n         ...,\n \n         [[-1., -1.,  1., ...,  1., -1., -1.],\n          [-1., -1., -1., ..., -1., -1., -1.],\n          [-1., -1.,  1., ...,  1., -1., -1.],\n          ...,\n          [-1., -1., -1., ..., -1., -1., -1.],\n          [-1., -1.,  1., ..., -1., -1., -1.],\n          [-1., -1., -1., ..., -1., -1., -1.]],\n \n         [[-1., -1., -1., ..., -1.,  1., -1.],\n          [ 1.,  1.,  1., ...,  1.,  1., -1.],\n          [-1.,  1.,  1., ..., -1.,  1., -1.],\n          ...,\n          [-1.,  1., -1., ..., -1.,  1., -1.],\n          [-1., -1.,  1., ...,  1., -1., -1.],\n          [-1., -1., -1., ..., -1., -1., -1.]],\n \n         [[ 1.,  1.,  1., ...,  1.,  1.,  1.],\n          [-1., -1., -1., ..., -1.,  1.,  1.],\n          [-1., -1., -1., ...,  1., -1.,  1.],\n          ...,\n          [-1., -1.,  1., ...,  1.,  1.,  1.],\n          [ 1.,  1.,  1., ..., -1.,  1.,  1.],\n          [ 1.,  1.,  1., ...,  1.,  1.,  1.]]],\n \n \n        [[[ 1.,  1.,  1., ...,  1.,  1., -1.],\n          [-1.,  1., -1., ...,  1.,  1., -1.],\n          [-1.,  1.,  1., ..., -1.,  1., -1.],\n          ...,\n          [-1.,  1., -1., ...,  1.,  1., -1.],\n          [-1., -1., -1., ..., -1.,  1., -1.],\n          [-1., -1., -1., ...,  1.,  1.,  1.]],\n \n         [[-1., -1., -1., ..., -1., -1., -1.],\n          [-1., -1., -1., ..., -1., -1.,  1.],\n          [-1., -1.,  1., ..., -1., -1.,  1.],\n          ...,\n          [-1., -1., -1., ...,  1.,  1.,  1.],\n          [-1., -1., -1., ...,  1.,  1.,  1.],\n          [-1., -1., -1., ...,  1.,  1.,  1.]],\n \n         [[-1., -1., -1., ..., -1., -1., -1.],\n          [-1., -1.,  1., ..., -1., -1., -1.],\n          [-1.,  1., -1., ...,  1., -1., -1.],\n          ...,\n          [ 1.,  1.,  1., ..., -1.,  1.,  1.],\n          [-1., -1., -1., ...,  1.,  1.,  1.],\n          [-1., -1., -1., ..., -1., -1., -1.]],\n \n         ...,\n \n         [[-1., -1.,  1., ...,  1., -1., -1.],\n          [-1., -1., -1., ..., -1., -1., -1.],\n          [-1., -1.,  1., ...,  1., -1., -1.],\n          ...,\n          [-1., -1., -1., ..., -1., -1., -1.],\n          [-1., -1.,  1., ..., -1., -1., -1.],\n          [-1., -1., -1., ..., -1., -1., -1.]],\n \n         [[-1., -1., -1., ..., -1.,  1., -1.],\n          [ 1.,  1.,  1., ...,  1.,  1., -1.],\n          [-1.,  1.,  1., ..., -1.,  1., -1.],\n          ...,\n          [-1.,  1., -1., ..., -1.,  1., -1.],\n          [-1., -1.,  1., ...,  1., -1., -1.],\n          [-1., -1., -1., ..., -1., -1., -1.]],\n \n         [[ 1.,  1.,  1., ...,  1.,  1.,  1.],\n          [-1., -1., -1., ..., -1.,  1.,  1.],\n          [-1., -1., -1., ...,  1., -1.,  1.],\n          ...,\n          [-1., -1.,  1., ...,  1.,  1.,  1.],\n          [ 1.,  1.,  1., ..., -1.,  1.,  1.],\n          [ 1.,  1.,  1., ...,  1.,  1.,  1.]]]]),\n array([[[ 3.,  5.],\n         [ 5.,  3.],\n         [ 5.,  3.],\n         ...,\n         [ 5.,  3.],\n         [ 5.,  3.],\n         [ 3.,  5.]],\n \n        [[ 2.,  3.],\n         [ 5.,  4.],\n         [ 5.,  4.],\n         ...,\n         [ 5.,  4.],\n         [ 5.,  4.],\n         [ 4.,  5.]],\n \n        [[ 3.,  2.],\n         [ 6.,  5.],\n         [ 3.,  5.],\n         ...,\n         [ 2.,  5.],\n         [ 5.,  5.],\n         [ 5.,  6.]],\n \n        ...,\n \n        [[-1., -1.],\n         [-1., -1.],\n         [-1., -1.],\n         ...,\n         [-1., -1.],\n         [-1., -1.],\n         [-1., -1.]],\n \n        [[-1., -1.],\n         [-1., -1.],\n         [-1., -1.],\n         ...,\n         [-1., -1.],\n         [-1., 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      },
      "execution_count": 19,
      "metadata": {},
@@ -1040,7 +1043,7 @@
     {
      "data": {
       "text/plain": "<Figure size 1200x300 with 3 Axes>",
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\n"
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\n"
      },
      "metadata": {},
      "output_type": "display_data"