Grids for new prams Images.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from matplotlib.colors import Normalize\n",
    "\n",
    "Test_Data = pd.DataFrame({\n",
    "    **{'L0_' +  str(i): np.full(10, 4) if i in [] else np.full(10, 1) if i in [5] else np.full(10, 15) for i in range(1, 10)},\n",
    "    **{'L1_' + str(i): np.full(10, 2) if i in [18, 19, 17, 20] else (np.full(10, 5) if i in [15,16, 21, 22] else np.full(10, 15)) for i in range(1, 37)},\n",
    "    **{'L2_' + str(i): np.full(10, 19) if i in [] else (np.full(10, 3) if i in [18,19,17, 20] else (np.full(10, 7) if i in [16, 21] else np.full(10, 17))) for i in range(1, 37)},\n",
    "    **{'L3_' + str(i): np.full(10, 4) if i in [] else np.full(10, 8) if i in [5] else np.full(10, 15) for i in range(1, 10)},\n",
    "    **{'Had_' + str(i): np.full(10, 9) if i in [2, 4, 6, 8] else np.full(10, 18) if i in [5] else np.full(10, 6) for i in range(1, 10)}\n",
    "})\n",
    "\n",
    "\n",
    "# Function to reshape columns into a matrix\n",
    "def reshape_to_matrix(columns, shape):\n",
    "    data = Test_Data[columns]\n",
    "    matrices = data.values.reshape(-1, *shape)\n",
    "    return list(matrices)\n",
    "\n",
    "# Reshape the data into matrices and store them in the DataFrame\n",
    "Test_Data['L0_matrix'] = reshape_to_matrix(['L0_' + str(i) for i in range(1, 10)], (3, 3))\n",
    "Test_Data['L1_matrix'] = reshape_to_matrix(['L1_' + str(i) for i in range(1, 37)], (3, 12))\n",
    "Test_Data['L2_matrix'] = reshape_to_matrix(['L2_' + str(i) for i in range(1, 37)], (3, 12))\n",
    "Test_Data['L3_matrix'] = reshape_to_matrix(['L3_' + str(i) for i in range(1, 10)], (3, 3))\n",
    "Test_Data['Had_matrix'] = reshape_to_matrix(['Had_' + str(i) for i in range(1, 10)], (3, 3))\n",
    "\n",
    "# Function to plot a matrix with a square grid aspect ratio\n",
    "def plot_square_grid_matrix(matrix, title, matrix_type):\n",
    "    fig, ax = plt.subplots(figsize=(6, 6))  # Set figure size to be square\n",
    "\n",
    "    # Determine the aspect ratio based on matrix type\n",
    "    if matrix_type in ['L0', 'L3', 'Had']:\n",
    "        aspect = 1.0  # Square cells for 3x3 matrices\n",
    "    elif matrix_type in ['L1', 'L2']:\n",
    "        aspect = 12 / 3  # Compress the width for L1 and L2\n",
    "\n",
    "    # Plot the matrix\n",
    "    norm = Normalize(vmin=0, vmax=19)\n",
    "    cax = ax.matshow(matrix, cmap='tab20', norm=norm, aspect=aspect)\n",
    "    plt.title(title)\n",
    "\n",
    "    # Adding grid lines based on the matrix size\n",
    "    nrows, ncols = matrix.shape\n",
    "    for i in range(nrows-1):\n",
    "        ax.axhline(i+0.5, color='white', linestyle='-', linewidth=2)\n",
    "    for i in range(ncols-1):\n",
    "        ax.axvline(i+0.5, color='white', linestyle='-', linewidth=2)\n",
    "\n",
    "    # Turn off the axis labels\n",
    "    ax.axis('off')\n",
    "\n",
    "    # Adjust layout\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "# Plot the first matrix from each type with the new aspect ratio\n",
    "plot_square_grid_matrix(Test_Data['L0_matrix'][0], 'L0 Matrix', 'L0')\n",
    "plot_square_grid_matrix(Test_Data['L1_matrix'][0], 'L1 Matrix', 'L1')\n",
    "plot_square_grid_matrix(Test_Data['L2_matrix'][0], 'L2 Matrix', 'L2')\n",
    "plot_square_grid_matrix(Test_Data['L3_matrix'][0], 'L3 Matrix', 'L3')\n",
    "plot_square_grid_matrix(Test_Data['Had_matrix'][0], 'Had Matrix', 'Had')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAJOCAYAAAAXj2WDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAASk0lEQVR4nO3da2iehd3H8f/9pE2qdWqdqWWe65xRJ04mvvDFrIdprUfcuqmsti+KshXtnJ3yKFoL1YKKOjtXQSGefVEFabHqFBTGFEXs8yCbo1M8oFtpnV2k67m5nhcPZpZkNtHmF4OfDxTa6/7fuf+Bm/DNdV1JW03TNAUAEPJfI70AAPD1Ij4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPuBr7pBDDqlZs2aN9BpfyLvvvlutVqvuv//+kV4FGALxAV8h999/f7VarXrttdcGfHzKlCn13e9+N7zV/2u1WtVqtWr27NkDPn7dddf1zXz00UdD/vgrVqyoG2+88UtuCYwG4gMYtHHjxtUTTzxRW7Zs6ffYY489VuPGjfvCH3vFihW1YMGCIT3n4IMPro0bN9aMGTO+8OsCeeIDGLSpU6fWJ598Uk8//fQOx1966aV655136qyzzorssW3bttqyZUu1Wq0aN25ctbW1RV4X2DXEB4xy3d3ddcopp9TEiROro6OjjjrqqFqyZEm/uaZpauHChXXAAQfU7rvvXieffHL96U9/GtJr7b///vWDH/ygHn300R2OP/LII3XMMccMeEnoD3/4Q02fPr0OOuig6ujoqAMPPLCuvPLK2rhxY9/MrFmz6u67766qf1/eabVaVfXv+zpuu+22uvPOO+uwww6rjo6O+vOf/9zvno81a9ZUZ2dnTZkypT77H3a/9dZbNX78+PrpT386pM8XGB5jRnoBoL+enp4B75vYunVrv2NLliypo48+us4999waM2ZMLV++vH7xi19Ub29vzZkzp2/uhhtuqIULF9a0adNq2rRp9frrr9fpp58+4CWUz3PxxRfX3Llza/369bXHHnvUtm3baunSpfWrX/2qNm3a1G9+6dKltWHDhvr5z39e3/zmN+vVV1+txYsX1wcffFBLly6tqqrLLrus/va3v9Vzzz1XDz300ICv293dXZs2bapLL720Ojo6ap999qne3t4dZiZOnFhLliyp6dOn1+LFi+uKK66o3t7emjVrVn3jG9+o3/3ud0P6XIFh0gBfGd3d3U1Vfe6fo48+eofnbNiwod/HOeOMM5rJkyf3/XvNmjVNe3t7c9ZZZzW9vb19x6+99tqmqpqZM2fudLeqaubMmdN8/PHHTXt7e/PQQw81TdM0Tz31VNNqtZp33323mT9/flNVzdq1az93v0WLFjWtVqt57733+o7NmTOnGehL0jvvvNNUVbPnnns2a9asGfCx7u7uHY5fdNFFze67796sWrWqufXWW5uqap588smdfo5AhjMf8BV0991313e+851+x6+66qravn37Dsd22223vr/39PTU1q1b66STTqpnn322enp6aq+99qrnn3++tmzZUpdffnnf5Yyqql/+8pd18803D2m3CRMm1NSpU+uxxx6rn/3sZ/Xoo4/WiSeeWAcffPCA85/d71//+ldt3LixTjzxxGqaplauXFkHHXTQoF73Rz/6UXV2dg5q9re//W29+OKL9eMf/7hWrVpVM2bMqPPOO29QzwWGn/iAr6ATTjihjj/++H7HJ0yY0O9yzB//+MeaP39+vfzyy7Vhw4YdHvs0Pt57772qqjr88MN3eLyzs7MmTJgw5P0uvvjimjFjRr3//vv15JNP1i233PIfZ99///264YYbatmyZbVu3bp++w3WoYceOujZffbZp+66666aPn167bfffnXXXXcN+rnA8BMfMIq9/fbbdeqpp1ZXV1fdfvvtdeCBB1Z7e3utWLGi7rjjjn73ROwq5557bnV0dNTMmTNr8+bN9ZOf/GTAue3bt9cPf/jD+vjjj+uaa66prq6uGj9+fH344Yc1a9asIe332TMog/Hss89WVdW6devqgw8+qL333ntIzweGj/iAUWz58uW1efPmWrZs2Q6XL1544YUd5j69JPLXv/61Jk+e3Hd87dq1/c5GDMZuu+1W559/fj388MN15pln1r777jvg3BtvvFGrVq2qBx54oC655JK+488991y/2c9eDvqynnnmmbrvvvvq6quvrkceeaRmzpxZr7zySo0Z40sefBX4UVsYxT79/RbNZ36stKenp7q7u3eYO+2002rs2LG1ePHiHWbvvPPOL/za8+bNq/nz59f1118/pP2apqnf/OY3/WbHjx9fVVX//Oc/v/BOnz5/9uzZdcIJJ9TNN99c9913X73++utDvrcFGD6+DYBR7PTTT6/29vY655xz6rLLLqv169fXvffeWxMnTqy///3vfXOdnZ01b968WrRoUZ199tk1bdq0WrlyZT399NP/8azFzhx77LF17LHHfu5MV1dXHXbYYTVv3rz68MMPa88996wnnnhiwLMt3//+96uq6oorrqgzzjij2tra6sILLxzyXnPnzq1//OMf9fzzz1dbW1tNnTq1Zs+eXQsXLqzzzjtvpzsDw8+ZDxjFjjjiiHr88cer1WrVvHnz6p577qlLL7205s6d22924cKFtWDBglq5cmX9+te/rrfffrt+//vf951xGA5jx46t5cuX1/e+971atGhRLViwoA4//PB68MEH+81ecMEFdfnll9czzzxTM2bMqIsuumjIr7ds2bJ68MEH66abbqqurq6+47fffnt961vfqpkzZw74u1KArFbz2fOhAADDzJkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgKgxgx18s+vI4dwDhsW3X3yhxk6aVFtXr663ppw80uvAoHjfMpod+Zc3dzrjzAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUa2maZrBDG5dvXq4d4FdbkxnZ7Xa2qrZvr22rV070uvAoHjfMpqNnTRppzODjg8AgF1hzGAHnflgNPIdJKOR9y2j2S498/Fm15FfeiFI+/aLL9TYSZNq6+rV9daUk0d6HRgU71tGsyP/8uZOZ9xwCgBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIKrVNE0zmMGtq1cP9y6wy43p7KxWW1s127fXtrVrR3odGBTvW0azsZMm7XRm0PEBALArjBnsoDMfjEZj9tuvWq1WNU1TW7ZsGel1YFDa29ur1WpVb29TG3o2j/Q6MCR7TBi305lBn/l4s+vIL70QpE3+3/+pjo6O2rx5c61cuXKk14FBOe6446qjo6PWr9tUD/z3SyO9DgzJnHtO2emMG04BgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABR4gMAiBIfAECU+AAAosQHABAlPgCAKPEBAESJDwAgSnwAAFHiAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIgSHwBAlPgAAKLEBwAQJT4AgCjxAQBEiQ8AIEp8AABRraZpmpFeAgD4+nDmAwCIEh8AQJT4AACixAcAECU+AIAo8QEARIkPACBKfAAAUeIDAIj6Px8NsZU5Vu4pAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from matplotlib.colors import Normalize\n",
    "\n",
    "Test_Data = pd.DataFrame({\n",
    "    **{'L0_' +  str(i): np.full(10, 6) if i in [5] else np.full(10, 15) for i in range(1, 10)},\n",
    "    **{'L1_' + str(i): np.full(10, 6) if i in [18, 19, 17, 16, 21, 20] else np.full(10,8) if i in [30,29,22,7,31,32,9,15,5] else np.full(10, 15) for i in range(1, 37)},\n",
    "    **{'L2_' + str(i): np.full(10, 6) if i in [18,19,20,17,21,16,15,30] else np.full(10,8) if i in [6,4,31,5,7,8,33,27,22,29,32,9,28,34] else np.full(10, 15)for i in range(1, 37)},\n",
    "    **{'L3_' + str(i): np.full(10, 6) if i in [5]  else np.full(10,8) if i in [2,8] else np.full(10, 15) for i in range(1, 10)},\n",
    "    **{'Had_' + str(i): np.full(10, 6) if i in [5,2,4,6,3,7,1] else np.full(10,8) if i  in [9] else np.full(10, 15) for i in range(1, 10)}\n",
    "})\n",
    "\n",
    "\n",
    "# Function to reshape columns into a matrix\n",
    "def reshape_to_matrix(columns, shape):\n",
    "    data = Test_Data[columns]\n",
    "    matrices = data.values.reshape(-1, *shape)\n",
    "    return list(matrices)\n",
    "\n",
    "# Reshape the data into matrices and store them in the DataFrame\n",
    "Test_Data['L0_matrix'] = reshape_to_matrix(['L0_' + str(i) for i in range(1, 10)], (3, 3))\n",
    "Test_Data['L1_matrix'] = reshape_to_matrix(['L1_' + str(i) for i in range(1, 37)], (3, 12))\n",
    "Test_Data['L2_matrix'] = reshape_to_matrix(['L2_' + str(i) for i in range(1, 37)], (3, 12))\n",
    "Test_Data['L3_matrix'] = reshape_to_matrix(['L3_' + str(i) for i in range(1, 10)], (3, 3))\n",
    "Test_Data['Had_matrix'] = reshape_to_matrix(['Had_' + str(i) for i in range(1, 10)], (3, 3))\n",
    "\n",
    "# Function to plot a matrix with a square grid aspect ratio\n",
    "def plot_square_grid_matrix(matrix, title, matrix_type):\n",
    "    fig, ax = plt.subplots(figsize=(6, 6))  # Set figure size to be square\n",
    "\n",
    "    # Determine the aspect ratio based on matrix type\n",
    "    if matrix_type in ['L0', 'L3', 'Had']:\n",
    "        aspect = 1.0  # Square cells for 3x3 matrices\n",
    "    elif matrix_type in ['L1', 'L2']:\n",
    "        aspect = 12 / 3  # Compress the width for L1 and L2\n",
    "\n",
    "    # Plot the matrix\n",
    "    norm = Normalize(vmin=0, vmax=19)\n",
    "    cax = ax.matshow(matrix, cmap='tab20', norm=norm, aspect=aspect)\n",
    "    plt.title(title)\n",
    "\n",
    "    # Adding grid lines based on the matrix size\n",
    "    nrows, ncols = matrix.shape\n",
    "    for i in range(nrows-1):\n",
    "        ax.axhline(i+0.5, color='white', linestyle='-', linewidth=2)\n",
    "    for i in range(ncols-1):\n",
    "        ax.axvline(i+0.5, color='white', linestyle='-', linewidth=2)\n",
    "\n",
    "    # Turn off the axis labels\n",
    "    ax.axis('off')\n",
    "\n",
    "    # Adjust layout\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "# Plot the first matrix from each type with the new aspect ratio\n",
    "plot_square_grid_matrix(Test_Data['L0_matrix'][0], 'L0 Matrix', 'L0')\n",
    "plot_square_grid_matrix(Test_Data['L1_matrix'][0], 'L1 Matrix', 'L1')\n",
    "plot_square_grid_matrix(Test_Data['L2_matrix'][0], 'L2 Matrix', 'L2')\n",
    "plot_square_grid_matrix(Test_Data['L3_matrix'][0], 'L3 Matrix', 'L3')\n",
    "plot_square_grid_matrix(Test_Data['Had_matrix'][0], 'Had Matrix', 'Had')\n"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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