Visualiztion240720.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9bf50f5b-a912-40bd-aa08-dd95c4302361",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a99286b6-8b3c-4fd8-99bb-7d3a6a6a2b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_excel('Results240719.xlsx', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a9c283c-d244-4f7b-9682-9332fa4d6564",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting the relevant columns for histogram plotting\n",
    "columns_to_plot = ['Basein', 'Baseout', 'Saain', 'Saaout', 'Detin', 'Detout']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4341e624-1f94-499b-a2ac-b0b9a0edfb35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned = df.copy()\n",
    "for col in df_cleaned.columns:\n",
    "    df_cleaned[col] = pd.to_numeric(df_cleaned[col], errors='coerce')\n",
    "\n",
    "# Verifying the conversion\n",
    "df_cleaned['Detout'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ee0743c-565a-409f-b76f-0932967b8788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NsrKyHFXU8pq7DiorK6Outi6HFQEAAABA2yc8J29VVVXF6SPPjaXLVzVrXM+SrvHAhF+3iQA9k3Ww+tNV8dGChbF1bW0OKwMAAACAtk14Tt5KpVKxdPmqKNv3xOjWs3eTxqxc+nFUTX00UqlUmwjPM1kHi2fPisoP74k1dcJzAAAAAMiU8Jy8161n7yjt1a/Jy1flsJbW0px1sGLJohxXAwAAAABtnxOGAgAAAABAGuE5AAAAAACkEZ4DAAAAAEAa4TkAAAAAAKQRngMAAAAAQBrhOQAAAAAApBGeAwAAAABAGuE5AAAAAACkEZ4DAAAAAECajMLzuXPnZrsOACAP6PEAUFj0bgDInYzC82233TaGDh0aDzzwQKxevTrbNQEArUSPB4DConcDQO5kFJ6/+eabsdtuu8Xll18effr0ifPPPz9effXVbNcGALQwPR4ACoveDQC5k1F4Pnjw4Bg/fnwsWLAgJkyYEIsWLYoDDjggBg0aFOPHj4+qqqps1wkAtAA9HgAKi94NALmzUScM7dixYxx//PHxu9/9Lm6++eaYPXt2XHHFFdGvX78YMWJELFy4MFt1AgAtSI8HgMKidwNA9m1UeP7666/HhRdeGH379o3x48fHFVdcEbNnz47nn38+FixYEMOHD89WnQBAC9LjAaCw6N0AkH0dMxk0fvz4mDBhQrz77rtx9NFHx/333x9HH310bLLJZ1n8wIED41e/+lXssMMOWS0WAMgtPR4ACoveDQC5k1F4fvvtt8fZZ58dI0eOjD59+qx3ma233jruvvvujSoOAGhZejwAFBa9GwByJ6Pw/P333//SZTp37hxnnnlmJg8PALQSPR4ACoveDQC5k9Fvnk+YMCEeeeSRdW5/5JFH4r777tvoogCA1qHHA0Bh0bsBIHcyCs9vuumm2HLLLde5vVevXnHjjTdudFEAQOvQ4wGgsOjdAJA7GYXnlZWVMXDgwHVuHzBgQMyfP3+jiwIAWoceDwCFRe8GgNzJKDzv1atXvPXWW+vc/uabb8YWW2yx0UUBAK1DjweAwqJ3A0DuZBSen3zyyXHppZfGCy+8EGvWrIk1a9bE888/H6NGjYqTTz452zUCAC1EjweAwqJ3A0DudMxk0A9/+MOorKyMQw45JDp2/Owh6uvrY8SIEX5TDQAKmB4PAIVF7waA3MkoPO/cuXM8/PDDcf3118ebb74ZXbp0iZ122ikGDBiQ7foAgBakxwNAYdG7ASB3MgrP19p+++1j++23z1YtAECe0OMBoLDo3QCQfRmF52vWrIl77703nnvuuVi8eHHU19c3uv/555/PSnEAQMvS4wGgsOjdAJA7GYXno0aNinvvvTeGDRsWgwcPjqKiomzXBQC0Aj0eAAqL3g0AuZNReP7b3/42fve738XRRx+d7XoAgFakx/N5VVVVkUqlmjWmtLQ0ysrKcvo8lZWVUVdb16znyHfWAZApvRtoTY5haOsyPmHotttum+1a1mvBggVx1VVXxZNPPhmffvppbL/99nH33XfHHnvs0SLPDwDtSUv2ePJbVVVVnD7y3Fi6fFWzxvUs6RoPTPh1kwP0TJ5n9aer4qMFC2Pr2tpm1ZavrANgY+jdQGtxDEN7kFF4fvnll8fPfvazuO2223L6J2GffPJJ7L///jF06NB48skno1evXjF79uzYfPPNc/acANCetVSPJ/+lUqlYunxVlO17YnTr2btJY1Yu/Tiqpj4aqVSqyeF5Js+zePasqPzwnlhT1zY+dFkHwMbQu4HW4hiG9iCj8Pyll16KF154IZ588skYNGhQdOrUqdH9jz32WFaKu/nmm6N///4xYcKEhtu22WabrDw2ALCulurxFI5uPXtHaa9+TV6+qgWeZ8WSRRk+S36zDoBM6N1Aa3MMQ1uWUXi++eabx/HHH5/tWtYxceLEOOKII+Jb3/pWTJkyJbbaaqu48MIL47zzztvgmOrq6qiurm643tzf6YSmaqnfgSWitqYmKisrmzXGuobMtFSPBwCyQ+8GgNzJKDz//DfBc2nOnDlx++23x5gxY+Lqq6+OV199NS699NIoLi6OESNGrHdMRUVFXHfddS1SH+1XS/0OLBHVK5bFvLlzYvTV46K4uLjJ46xryExL9XgAIDv0bgDInYzC84iIurq6mDx5csyePTtOPfXUKCkpiX/+859RWloam222WVaKq6+vjz333DNuvPHGiIjYbbfd4u23347bb799g+H52LFjY8yYMQ3XU6lU9O/fPyv1wFot9TuwRNRWfxr1RR1jy31OiC3KBzRpjHUNG6clejwAkD16NwDkRkbheWVlZRx55JExf/78qK6ujsMOOyxKSkriRz/6UaxevTruuOOOrBTXt2/f2HHHHRvd9rWvfS0effTRDY4pLi5u1rdTYWO01O/AEtG1R5l1DS2gpXo8AJAdejcA5M4mmQwaNWpU7LnnnvHJJ59Ely5dGm4//vjj47nnnstacfvvv3+8++67jW577733YsCApn37FABonpbq8QBAdujdAJA7GX3z/KWXXoq//vWv0blz50a3DxgwIBYsWJCVwiIiLrvssthvv/3ixhtvjG9/+9vx6quvxp133hl33nln1p4DAPiPlurxAEB26N0AkDsZffO8vr4+1qxZs87tH330UZSUlGx0UWvttdde8fjjj8dDDz0UgwcPjuuvvz5uvfXWOO2007L2HADAf7RUjwcAskPvBoDcySg8P+yww+LWW29tuF5UVBQrVqyIa6+9No4++uhs1RYREcccc0zMnDkzVq9eHe+8806cd955WX18AOA/WrLHAwAbT+8GgNzJ6Gdbbrnllhg6dGjsuOOOsXr16jj11FPj/fffjy233DIeeuihbNcIALQQPR4ACoveDQC5k1F4Xl5eHjNmzIiHHnoopk+fHvX19XHOOefEaaed1ugEJQBAYdHjAaCw6N0AkDsZhecREV26dImzzz47zj777GzWAwC0Mj0eAAqL3g0AuZFReH7//fd/4f0jRozIqBgAoHXp8QBQWPRuAMidjMLzUaNGNbpeW1sbq1atis6dO0fXrl01ZwAoUHo8ABQWvRsAcmeTTAZ98sknjS4rVqyId999Nw444AAnJAGAAqbHA0Bh0bsBIHcyCs/XZ7vttoubbrppnf/1BgAKmx4PAIVF7waA7MhaeB4R0aFDh/jnP/+ZzYcEAPKAHg8AhUXvBoCNl9Fvnk+cOLHR9SRJYuHChXHbbbfF/vvvn5XCAICWp8cDQGHRuwEgdzIKz4877rhG14uKiqKsrCwOPvjg+OlPf5qNugCAVqDHA0Bh0bsBIHcyCs/r6+uzXQcAkAf0eAAoLHo3AOROVn/zHAAAAAAA2oKMvnk+ZsyYJi87fvz4TJ4CAGgFejwAFBa9GwByJ6Pw/I033ojp06dHXV1d/Nd//VdERLz33nvRoUOH2H333RuWKyoqyk6VAECL0OMBoLDo3QCQOxmF58cee2yUlJTEfffdFz169IiIiE8++SRGjhwZ3/jGN+Lyyy/PapEAQMvQ4wGgsOjdAJA7Gf3m+U9/+tOoqKhoaMwRET169Igf/vCHzuYNAAVMjweAwqJ3A0DuZBSep1Kp+Pjjj9e5ffHixbF8+fKNLgoAaB16PAAUFr0bAHIno/D8+OOPj5EjR8bvf//7+Oijj+Kjjz6K3//+93HOOefECSeckO0aAYAWoscDQGHRuwEgdzL6zfM77rgjrrjiijj99NOjtrb2swfq2DHOOeec+PGPf5zVAgGAlqPHA0Bh0bsBIHcyCs+7du0av/zlL+PHP/5xzJ49O5IkiW233Ta6deuW7foAgBakxwNAYdG7ASB3MvrZlrUWLlwYCxcujO233z66desWSZJkqy4AoBXp8QBQWPRuAMi+jMLzJUuWxCGHHBLbb799HH300bFw4cKIiDj33HPj8ssvz2qBAEDL0eMBoLDo3QCQOxmF55dddll06tQp5s+fH127dm24/aSTToqnnnoqa8UBAC1LjweAwqJ3A0DuZPSb588880w8/fTT0a9fv0a3b7fddlFZWZmVwgCAlqfHA0Bh0bsBIHcy+ub5ypUrG/2P9lr/+te/ori4eKOLAgBahx4PAIVF7waA3MkoPD/wwAPj/vvvb7heVFQU9fX18eMf/ziGDh2ateIAgJalxwNAYdG7ASB3MvrZlh//+McxZMiQeP3116Ompia+853vxNtvvx1Lly6Nv/71r9muEQBoIXo8ABQWvRsAciejb57vuOOO8dZbb8XXv/71OOyww2LlypVxwgknxBtvvBFf/epXs10jANBC9HgAKCx6NwDkTrO/eV5bWxuHH354/OpXv4rrrrsuFzVBi6uqqopUKtXk5SsrK6Outq7Zz1NbU9Osk/Zk+jwtpaXWW1vT3PUWEVFaWhplZWU5qgg+o8cDQGHRuwEgt5odnnfq1ClmzZoVRUVFuagHWlxVVVWcPvLcWLp8VZPHrP50VXy0YGFsXVvb5DHVK5bFvLlzYvTV45p84p5MnqeltNR6a2syWW8RET1LusYDE34tQCenctXjKyoq4uqrr45Ro0bFrbfemtXHBoD2TO8GgNzK6DfPR4wYEXfffXfcdNNN2a4HWlwqlYqly1dF2b4nRreevZs0ZvHsWVH54T2xpq7pIXBt9adRX9QxttznhNiifEDOnqeltNR6a2syWW8rl34cVVMfjVQqJTwn57Ld41977bW48847Y+edd87K4wEAjendAJA7GYXnNTU18etf/zomTZoUe+65Z3Tr1q3R/ePHj89KcdCSuvXsHaW9+jVp2RVLFmX8PF17lLXI87SUllpvbU1z1ltERFUOa4HPy2aPX7FiRZx22mlx1113xQ9/+MNslwoAhN4NALnUrPB8zpw5sc0228SsWbNi9913j4iI9957r9Eyfs4FAApPLnr8RRddFMOGDYtDDz30Sz+AV1dXR3V1dcP15p4XIF9kck6Dmpqa6Ny5c87HOHcCmWruOVsiWm6/bmuvn0zeQyK8vtsrvZvW1tbO5dQS/a6lzp8W0TI9Mt/Pa9ZS6y2f92s2XrPC8+222y4WLlwYL7zwQkREnHTSSfHzn/88evdu2k8PAAD5Kds9/re//W1Mnz49XnvttSYtX1FRUfAnOsvknAa1NTWxYH5l9BswMDp2atphWSZjIpw7gcxkcs6Wltqv29rrJ9PzokR4fbdXejetqa2dy6ml+l1LnT+tpXpkPp/XrKXWW0T+7tdkR7PC8yRJGl1/8sknY+XKlVktCABoedns8R9++GGMGjUqnnnmmdh0002bNGbs2LExZsyYhuupVCr69++f0fO3lkzPBTFn3j3R4+vDm3U+jOaOce4EMpXpOVtaYr9ua6+fTN5DWrI+8o/eTWtqa+dyasl+11LnT2upvpqv5zVrqfWWz/s12ZHRb56vld6sAYC2YWN6/LRp02Lx4sWxxx57NNy2Zs2aePHFF+O2226L6urq6NChQ6MxxcXFTf5GSL7L5FwQmZwPozljIpw7gY2T6300n8dEtOzrp7nnRYnw+uYzejetoa2dy6ml+lA+19bWztPW1o4TaHnNCs+LiorW+c00v3EOAIUvmz3+kEMOiZkzZza6beTIkbHDDjvEVVddtc6HbwCg+fRuAMi9Zv9sy1lnndXwv8urV6+OCy64YJ2zeT/22GPZqxAAyLls9viSkpIYPHhwo9u6desWW2yxxTq3AwCZ0bsBIPeaFZ6feeaZja6ffvrpWS0GAGgdejwAFBa9GwByr1nh+YQJE3JVBwDQinLd4ydPnpzTxweA9kbvBoDc26S1CwAAAAAAgHwjPAcAAAAAgDTCcwAAAAAASCM8BwAAAACANMJzAAAAAABIIzwHAAAAAIA0wnMAAAAAAEgjPAcAAAAAgDTCcwAAAAAASCM8BwAAAACANMJzAAAAAABIIzwHAAAAAIA0wnMAAAAAAEgjPAcAAAAAgDTCcwAAAAAASCM8BwAAAACANMJzAAAAAABIIzwHAAAAAIA0wnMAAAAAAEgjPAcAAAAAgDTCcwAAAAAASFNQ4XlFRUUUFRXF6NGjW7sUAAAAAADasIIJz1977bW48847Y+edd27tUgAAAAAAaOM6tnYBTbFixYo47bTT4q677oof/vCHX7hsdXV1VFdXN1xPpVK5Lo8mqqqqatb2qKysjLraumY/T21NTVRWVub8edqatrbemjufiIjS0tIoKyvLUUUAAAAAFJKCCM8vuuiiGDZsWBx66KFfGp5XVFTEdddd10KV0VRVVVVx+shzY+nyVU0es/rTVfHRgoWxdW1tk8dUr1gW8+bOidFXj4vi4uKcPU9b09bWWybziYjoWdI1HpjwawE6AAAAAPkfnv/2t7+N6dOnx2uvvdak5ceOHRtjxoxpuJ5KpaJ///65Ko8mSqVSsXT5qijb98To1rN3k8Ysnj0rKj+8J9bUNT2cra3+NOqLOsaW+5wQW5QPyNnztDVtbb1lMp+VSz+OqqmPRiqVEp4DAAAAkN/h+YcffhijRo2KZ555JjbddNMmjSkuLm7WN01pWd169o7SXv2atOyKJYsyfp6uPcpa5Hnamra23pozn4iIqhzWAgAAAEBhyevwfNq0abF48eLYY489Gm5bs2ZNvPjii3HbbbdFdXV1dOjQoRUrBAAAAACgLcrr8PyQQw6JmTNnNrpt5MiRscMOO8RVV10lOAcAAAAAICfyOjwvKSmJwYMHN7qtW7duscUWW6xzOwAAAAAAZMsmrV0AAAAAAADkm7z+5vn6TJ48ubVLAAAAAACgjfPNcwAAAAAASCM8BwAAAACANMJzAAAAAABIU3C/eQ4AQP6qramJysrKJi9fWVkZdbV1OazoP1qqtnxeBwBkR1VVVaRSqWaNqampic6dOzdrTGlpaZSVlTVrDMBambxXed9pTHgOAEBWVK9YFvPmzonRV4+L4uLiJo1Z/emq+GjBwti6trZN1JbP6wCA7KiqqorTR54bS5evavKY2pqaWDC/MvoNGBgdOzU9iulZ0jUemPBrQRbQbJm8V0V430knPAcAICtqqz+N+qKOseU+J8QW5QOaNGbx7FlR+eE9saYut8FxS9WWz+sAgOxIpVKxdPmqKNv3xOjWs3eTxiyePSvmzLsnenx9eJP7w8qlH0fV1EcjlUoJsYBmy+S9yvvOuoTnAABkVdceZVHaq1+Tll2xZFGOq2mspWrL53UAQHZ069m72e/1zekPERFVGVUG8B/Nea+K8L6TzglDAQAAAAAgjfAcAAAAAADSCM8BAAAAACCN8BwAAAAAANIIzwEAAAAAII3wHAAAAAAA0gjPAQAAAAAgjfAcAAAAAADSCM8BAAAAACCN8BwAAAAAANIIzwEAAAAAII3wHAAAAAAA0gjPAQAAAAAgjfAcAAAAAADSCM8BAAAAACCN8BwAAAAAANIIzwEAAAAAII3wHAAAAAAA0gjPAQAAAAAgjfAcAAAAAADSdGztAmh9VVVVkUqlmjWmpqYmOnfu3OTlKysro662rrmlAQAAAAC0CuF5O1dVVRWnjzw3li5f1eQxtTU1sWB+ZfQbMDA6dmraLrT601Xx0YKFsXVtbaalAgAAAAC0GOF5O5dKpWLp8lVRtu+J0a1n7yaNWTx7VsyZd0/0+Prw2KJ8QJPHVH54T6ypE54DAAAAAPlPeE5ERHTr2TtKe/Vr0rIrliyKiIiuPcqaPQYAAAAAoBA4YSgAAAAAAKQRngMAAAAAQBrhOQAAAAAApBGeAwAAAABAGuE5AAAAAACkEZ4DAAAAAEAa4TkAAAAAAKQRngMAAAAAQBrhOQAAAAAApBGeAwAAAABAGuE5AAAAAACkEZ4DAAAAAEAa4TkAAAAAAKQRngMAAAAAQBrhOQCQVRUVFbHXXntFSUlJ9OrVK4477rh49913W7ssAOAL6N8AsC7hOQCQVVOmTImLLroo/va3v8WkSZOirq4uDj/88Fi5cmVrlwYAbID+DQDr6tjaBQAAbctTTz3V6PqECROiV69eMW3atDjwwAPXO6a6ujqqq6sbrqdSqazWVFVV1ezHLC0tjbKysqzW0Zpqa2qisrKyyctXVlZGXW1dDiuCwtHc109ERE1NTXTu3LnJy3vN0dqa279z3bsjWq5/N/d58v31ms/HPY5HaIta4jghkzGZvn4ymU9LvPdm+jwbS3gOAOTUsmXLIiKiZ8+eG1ymoqIirrvuupw8f1VVVZw+8txYunxVs8b1LOkaD0z4dZsI0KtXLIt5c+fE6KvHRXFxcZPGrP50VXy0YGFsXVub4+ogv2Xy+qmtqYkF8yuj34CB0bFT0z5yec2Rb76sf+eyd0e0XP/O5Hny+fWaz8c9jkdoi1rqOKGlji0ymU9Ey7z3ZvI82SA8BwByJkmSGDNmTBxwwAExePDgDS43duzYGDNmTMP1VCoV/fv3z0oNqVQqli5fFWX7nhjdevZu0piVSz+OqqmPRiqVahPheW31p1Ff1DG23OeE2KJ8QJPGLJ49Kyo/vCfW1PmwSvuW6etnzrx7osfXh3vNUZCa0r9z2bvXPl5L9O9MniefX6/5fNzjeIS2qCWPE1ri2CKT+bTUe29rfUYTngMAOXPxxRfHW2+9FS+99NIXLldcXNysbzZkolvP3lHaq1+Tl6/KYS2tpWuPsiavgxVLFuW4Gigsmbx+vOYoVE3p3y3RuyNarn8353kK4fWaz8c93htpi1rqOKGlXj/NeZ6Ilnnv3Zjn2RjCcwAgJy655JKYOHFivPjii9GvX9MPiACA1qN/A8B/CM8BgKxKkiQuueSSePzxx2Py5MkxcODA1i4JAPgS+jcArEt4DgBk1UUXXRQPPvhg/OEPf4iSkpJYtOizPxfs3r17dOnSpZWrAwDWR/8GgHVt0toFAABty+233x7Lli2LIUOGRN++fRsuDz/8cGuXBgBsgP4NAOvyzXMAIKuSJGntEgCAZtK/AWBdvnkOAAAAAABphOcAAAAAAJBGeA4AAAAAAGmE5wAAAAAAkEZ4DgAAAAAAafI6PK+oqIi99torSkpKolevXnHcccfFu+++29plAQAAAADQxuV1eD5lypS46KKL4m9/+1tMmjQp6urq4vDDD4+VK1e2dmkAAAAAALRhHVu7gC/y1FNPNbo+YcKE6NWrV0ybNi0OPPDA9Y6prq6O6urqhuupVCqnNQJtR21NTVRWVjZrTE1NTXTu3LnJy1dWVkZdbV1zS8uottLS0igrK2v2cwEAAACQ5+F5umXLlkVERM+ePTe4TEVFRVx33XUtVRLQRlSvWBbz5s6J0VePi+Li4iaNqa2piQXzK6PfgIHRsVPT3k5Xf7oqPlqwMLaurc1pbRERPUu6xgMTfi1ABwAAAMhAwYTnSZLEmDFj4oADDojBgwdvcLmxY8fGmDFjGq6nUqno379/S5QIFLDa6k+jvqhjbLnPCbFF+YAmjVk8e1bMmXdP9Pj68GaNqfzwnlhT1/TwPJPaVi79OKqmPhqpVEp4DgAAAJCBggnPL7744njrrbfipZde+sLliouLm/XNTIDP69qjLEp79WvSsiuWLMp4TK5ri4ioyviZAAAAACiI8PySSy6JiRMnxosvvhj9+jU9OAIAAAAAgEzkdXieJElccskl8fjjj8fkyZNj4MCBrV0SAAAAAADtQF6H5xdddFE8+OCD8Yc//CFKSkpi0aLPfu6ge/fu0aVLl1auDgAAAACAtmqT1i7gi9x+++2xbNmyGDJkSPTt27fh8vDDD7d2aQAAAAAAtGF5/c3zJElauwQAAAAAANqhvP7mOQAAAAAAtAbhOQAAAAAApBGeAwAAAABAGuE5AAAAAACkEZ4DAAAAAEAa4TkAAAAAAKQRngMAAAAAQBrhOQAAAAAApBGeAwAAAABAGuE5AAAAAACkEZ4DAAAAAEAa4TkAAAAAAKQRngMAAAAAQBrhOQAAAAAApBGeAwAAAABAGuE5AAAAAACkEZ4DAAAAAEAa4TkAAAAAAKQRngMAAAAAQJqOrV0AAAAAmautqYnKyspmjampqYnOnTs3a0xpaWmUlZU1a0xLqaqqilQq1awx+TwfACgkzT0WqaysjLrauhxWlD3CcwAAgAJVvWJZzJs7J0ZfPS6Ki4ubNKa2piYWzK+MfgMGRsdOTf9I2LOkazww4dd5FzhXVVXF6SPPjaXLVzVrXL7OBwAKSSbHIqs/XRUfLVgYW9fW5ri6jSc8BwAAKFC11Z9GfVHH2HKfE2KL8gFNGrN49qyYM++e6PH14U0es3Lpx1E19dFIpVJ5FzanUqlYunxVlO17YnTr2btJY/J5PgBQSDI9Fqn88J5YUyc8BwAAIMe69iiL0l79mrTsiiWLmj0mIqIqo8paTreevdvUfACgkGRyLFIInDAUAAAAAADSCM8BAAAAACCN8BwAAAAAANIIzwEAAAAAII0ThmagqqoqUqlUs8aUlpY2+yzuLfU8AGtl8r5TU1MTnTt3btaYfH6v8t4LAAAARAjPm62qqipOH3luLF2+qlnjepZ0jQcm/LrJ4UpLPQ/AWpm879TW1MSC+ZXRb8DA6Nip6S0lX9+rvPcCAAAAawnPmymVSsXS5auibN8To1vP3k0as3Lpx1E19dFIpVJNDlZa6nkA1srkfWfx7FkxZ9490ePrw2OL8gFNGpPP71XeewEAAIC1hOcZ6tazd5T26tfk5avy/HkA1mrO+86KJYsiIqJrj7I29V7lvRcAAABwwlAAAAAAAEgjPAcAAAAAgDTCcwAAAAAASCM8BwAAAACANMJzAAAAAABIIzwHAAAAAIA0wnMAAAAAAEgjPAcAAAAAgDTCcwAAAAAASCM8BwAAAACANMJzAAAAAABIIzwHAAAAAIA0wnMAAAAAAEgjPAcAAAAAgDTCcwAAAAAASCM8BwAAAACANMJzAAAAAABIIzwHAAAAAIA0wnMAAAAAAEgjPAcAAAAAgDTCcwAAAAAASCM8BwAAAACANMJzAAAAAABIIzwHAAAAAIA0wnMAAAAAAEgjPAcAAAAAgDTCcwAAAAAASCM8BwAAAACANMJzAAAAAABIUxDh+S9/+csYOHBgbLrpprHHHnvEX/7yl9YuCQD4Evo3ABQWvRsAGsv78Pzhhx+O0aNHx/e+971444034hvf+EYcddRRMX/+/NYuDQDYAP0bAAqL3g0A68r78Hz8+PFxzjnnxLnnnhtf+9rX4tZbb43+/fvH7bff3tqlAQAboH8DQGHRuwFgXR1bu4AvUlNTE9OmTYvvfve7jW4//PDD4+WXX17vmOrq6qiurm64vmzZsoiISKVSWalp+fLlsaauLv69cF7Url7VpDErP1kc1Z9+Gn//+99j+fLlTRrz4YcfRs3q1Xn5PKnFH0VSXx+pRR9Gx6ImDTHGGGNaeEw+vx9kUltLyfS9d01dXSxfvjwrvWbtYyRJstGP1VryrX/nc+/O5/cRY4wxJv/HtORz6d8bpnd/pj199s7n150swRhjjGmrY1rts3eSxxYsWJBERPLXv/610e033HBDsv322693zLXXXptEhIuLi4uLS0FfPvzww5ZotTmhf7u4uLi4tMeL3u3i4uLi4lJ4ly/r33n9zfO1iooa/xdEkiTr3LbW2LFjY8yYMQ3X6+vrY+nSpbHFFltscExLSaVS0b9///jwww+jtLS0VWtpaeZu7u1p7u113hHmno25J0kSy5cvj/Ly8ixW1zryrX+3t/2zvc03wpzNuW1qb/ONKLw5692fyafP3tlSaPtiobBec8N6zQ3rNTfyYb02tX/ndXi+5ZZbRocOHWLRokWNbl+8eHH07t17vWOKi4ujuLi40W2bb755rkrMSGlpabt9wZm7ubcn7XXeEea+sXPv3r17lqppHfnev9vb/tne5hthzu1Fe5tze5tvRGHNWe/+TL599s6WQtoXC4n1mhvWa25Yr7nR2uu1Kf07r08Y2rlz59hjjz1i0qRJjW6fNGlS7Lfffq1UFQDwRfRvACgsejcArF9ef/M8ImLMmDFxxhlnxJ577hn77rtv3HnnnTF//vy44IILWrs0AGAD9G8AKCx6NwCsK+/D85NOOimWLFkSP/jBD2LhwoUxePDg+POf/xwDBgxo7dKarbi4OK699tp1/rStPTB3c29P2uu8I8y9vc59ffKxf7e3bdTe5hthzu1Fe5tze5tvRPuccz7Ix97d2uyLuWG95ob1mhvWa24U0notSpIkae0iAAAAAAAgn+T1b54DAAAAAEBrEJ4DAAAAAEAa4TkAAAAAAKQRngMAAAAAQBrh+QZUVFREUVFRjB49OiIiamtr46qrroqddtopunXrFuXl5TFixIj45z//2WjckCFDoqioqNHl5JNPbrTMJ598EmeccUZ07949unfvHmeccUb8+9//brTM/Pnz49hjj41u3brFlltuGZdeemnU1NQ0WmbmzJlx0EEHRZcuXWKrrbaKH/zgB5GN87+mzz0iYty4cbHDDjtEt27dokePHnHooYfGK6+80mhcdXV1XHLJJbHllltGt27d4pvf/GZ89NFH7WLubXW7f975558fRUVFceuttza6va1u98/b0NwLfbuvb95nnXXWOnPaZ599Go1rq9u8KXMv9G2ezyoqKmKvvfaKkpKS6NWrVxx33HHx7rvvrrPcO++8E9/85jeje/fuUVJSEvvss0/Mnz+/4f5C2j+zNec777wzhgwZEqWlpVFUVLTOXNranJcuXRqXXHJJ/Nd//Vd07do1tt5667j00ktj2bJleTfnbG3j888/P7761a9Gly5doqysLIYPHx7/+Mc/8m6+2ZzzWkmSxFFHHRVFRUXxxBNPtOk5F0qPyeY2njp1ahx88MHRrVu32HzzzWPIkCHx6aef5tV8KRzrO75LkiTGjRsX5eXl0aVLlxgyZEi8/fbbjcYV0rFDa2jPnxmybdy4ceustz59+jTcb3/NzJetV/tr5hYsWBCnn356bLHFFtG1a9fYddddY9q0aQ33t5t9NmEdr776arLNNtskO++8czJq1KgkSZLk3//+d3LooYcmDz/8cPKPf/wjmTp1arL33nsne+yxR6OxBx10UHLeeeclCxcubLj8+9//brTMkUcemQwePDh5+eWXk5dffjkZPHhwcswxxzTcX1dXlwwePDgZOnRoMn369GTSpElJeXl5cvHFFzcss2zZsqR3797JySefnMycOTN59NFHk5KSkuQnP/lJ1ueeJEnym9/8Jpk0aVIye/bsZNasWck555yTlJaWJosXL25Y5oILLki22mqrZNKkScn06dOToUOHJrvssktSV1fX5ufeVrf7Wo8//niyyy67JOXl5cktt9zS6L62ut2bMvdC3u4bmveZZ56ZHHnkkY3mtGTJkkZj2+o2b8rcC3mb57sjjjgimTBhQjJr1qxkxowZybBhw5Ktt946WbFiRcMyH3zwQdKzZ8/kyiuvTKZPn57Mnj07+eMf/5h8/PHHDcsU0v6ZrTnfcsstSUVFRVJRUZFERPLJJ5+s81xtac4zZ85MTjjhhGTixInJBx98kDz33HPJdtttl5x44ol5N+dsbeNf/epXyZQpU5K5c+cm06ZNS4499tikf//+bXq/Xmv8+PHJUUcdlURE8vjjjze6r63NuVB6TLbm+/LLLyelpaVJRUVFMmvWrOS9995LHnnkkWT16tV5NV8Kw4aO72666aakpKQkefTRR5OZM2cmJ510UtK3b98klUo1LFNIxw4trT1/ZsiFa6+9Nhk0aFCj9fb5XMH+mpkvW6/218wsXbo0GTBgQHLWWWclr7zySjJ37tzk2WefTT744IOGZdrLPis8T7N8+fJku+22SyZNmpQcdNBB6w3T1nr11VeTiEgqKysbbvuyMX//+9+TiEj+9re/Ndw2derUJCKSf/zjH0mSJMmf//znZJNNNkkWLFjQsMxDDz2UFBcXJ8uWLUuSJEl++ctfJt27d290cFlRUZGUl5cn9fX1zZ12kiTNm/uyZcuSiEieffbZJEk++8+FTp06Jb/97W8bllmwYEGyySabJE899VSbnnuStO3t/tFHHyVbbbVVMmvWrGTAgAGNAuS2vt2/aO5JUrjb/YvmfeaZZybDhw/f4Ni2vM2/bO5JUrjbvBAtXrw4iYhkypQpDbeddNJJyemnn77BMYW+f2Yy58974YUX1huet+U5r/W73/0u6dy5c1JbW5skSf7OOVvzffPNN5OIaPjwkq/zTZKNm/OMGTOSfv36JQsXLlwnPG+Lcy7UHpPpfPfee+/k+9///gbvz9f5kn82dHxXX1+f9OnTJ7npppsall29enXSvXv35I477kiSpPCPHXKpPX9myJVrr7022WWXXdZ7n/01c1+0XpPE/pqpq666KjnggAM2eH972mf9bEuaiy66KIYNGxaHHnroly67bNmyKCoqis0337zR7b/5zW9iyy23jEGDBsUVV1wRy5cvb7hv6tSp0b1799h7770bbttnn32ie/fu8fLLLzcsM3jw4CgvL29Y5ogjjojq6uqGP4+YOnVqHHTQQVFcXNxomX/+858xb968TKbe5LnX1NTEnXfeGd27d49ddtklIiKmTZsWtbW1cfjhhzcsV15eHoMHD240r7Y497Xa4navr6+PM844I6688soYNGjQOve35e3+ZXNfqxC3+5ft75MnT45evXrF9ttvH+edd14sXry44b62vM2/bO5rFeI2L0Rrf4KjZ8+eEfHZa/JPf/pTbL/99nHEEUdEr169Yu+99270Mw6Fvn9mMuemaA9zXrZsWZSWlkbHjh3zes7ZmO/KlStjwoQJMXDgwOjfv39ez3dj5rxq1ao45ZRT4rbbbmv0p9drtcU5RxRmj8lkvosXL45XXnklevXqFfvtt1/07t07DjrooHjppZfyfr7knw0d382dOzcWLVrU6LiguLg4DjrooIZ9qNCPHXKpPX9myKX3338/ysvLY+DAgXHyySfHnDlzIsL+urE2tF7Xsr8238SJE2PPPfeMb33rW9GrV6/Ybbfd4q677mq4vz3ts8Lzz/ntb38b06dPj4qKii9ddvXq1fHd7343Tj311CgtLW24/bTTTouHHnooJk+eHNdcc008+uijccIJJzTcv2jRoujVq9c6j9erV69YtGhRwzK9e/dudH+PHj2ic+fOX7jM2utrl2mOpsz9j3/8Y2y22Wax6aabxi233BKTJk2KLbfcsuE5O3fuHD169Finps/X3BbnHtF2t/vNN98cHTt2jEsvvXS997fl7f5lc48ozO3+ZfM+6qij4je/+U08//zz8dOf/jRee+21OPjgg6O6urrh+drqNv+yuUcU5jYvREmSxJgxY+KAAw6IwYMHR8RnQcuKFSvipptuiiOPPDKeeeaZOP744+OEE06IKVOmRERh75+Zzrkp2vqclyxZEtdff32cf/75eT3njZ3vL3/5y9hss81is802i6eeeiomTZoUnTt3ztv5buycL7vssthvv/1i+PDh633stjjnQuwxmc53bagxbty4OO+88+Kpp56K3XffPQ455JB4//3383a+5J8vOr5bu33Xt/0/v38U6rFDLrXnzwy5tPfee8f9998fTz/9dNx1112xaNGi2G+//WLJkiX2143wRes1wv6aqTlz5sTtt98e2223XTz99NNxwQUXxKWXXhr3339/RLSv99iOGzW6Dfnwww9j1KhR8cwzz8Smm276hcvW1tbGySefHPX19fHLX/6y0X3nnXdew78HDx4c2223Xey5554xffr02H333SMioqioaJ3HTJKk0e2ZLJP8vx/BX9/YL9LUuQ8dOjRmzJgR//rXv+Kuu+6Kb3/72w3fGNmQbMyrKcu09tzb4nafNm1a/OxnP4vp06c3+7ELfbs3de6Ftt2bsr+fdNJJjea05557xoABA+JPf/pTow/wza03W8vk8rXelLkX2jYvVBdffHG89dZbjb6BWF9fHxERw4cPj8suuywiInbdddd4+eWX44477oiDDjpog4+X7/tnRPbnnK6tzjmVSsWwYcNixx13jGuvvbbRffk2542d72mnnRaHHXZYLFy4MH7yk5/Et7/97fjrX//a8J6Wb/PdmDlPnDgxnn/++XjjjTe+8PHb0pwjCrPHZDrftcucf/75MXLkyIiI2G233eK5556Le+65pyGwy7f5kl+a+llufdv/y7Z9IRw75Ep7/syQa0cddVTDv3faaafYd99946tf/Wrcd999DSewtL823xet1zFjxthfM1RfXx977rln3HjjjRHxWZ9+++234/bbb48RI0Y0LNce9lnfPP9/pk2bFosXL4499tgjOnbsGB07dowpU6bEz3/+8+jYsWOsWbMmIj4Lzr/97W/H3LlzY9KkSY2+db4+u+++e3Tq1KnhGxR9+vSJjz/+eJ3lqqqqGv5HpE+fPuv8r8gnn3wStbW1X7jM2j87Sf+flmzNvVu3brHtttvGPvvsE3fffXd07Ngx7r777oZ6ampq4pNPPlmnps/X3Bbnvj5tYbtPnjw5Fi9eHFtvvXXD/ZWVlXH55ZfHNtts01BPW9zuTZn7+uT7dm/q/v55ffv2jQEDBjSaU1vc5k2Z+/rk+zYvRJdccklMnDgxXnjhhejXr1/D7VtuuWV07Ngxdtxxx0bLf+1rX4v58+dHROHunxsz56Zoq3Nevnx5HHnkkbHZZpvF448/Hp06dcrbOWdjvt27d4/tttsuDjzwwPj9738f//jHP+Lxxx/Py/lu7Jyff/75mD17dmy++eYN79kRESeeeGIMGTKkTc55ffK9x2zMfPv27RsR8aXv6fk0X/LPlx3fbegbh+nHBYV47JBL7fkzQ0vr1q1b7LTTTvH+++83/ESZ/XXjfX69ro/9tWn69u37pX06on3ss8Lz/+eQQw6JmTNnxowZMxoue+65Z5x22mkxY8aM6NChQ0Nw/v7778ezzz4bW2yxxZc+7ttvvx21tbUNB4j77rtvLFu2LF599dWGZV555ZVYtmxZ7Lfffg3LzJo1KxYuXNiwzDPPPBPFxcWxxx57NCzz4osvRk1NTaNlysvLvzDcy3Tu65MkScOfueyxxx7RqVOnmDRpUsP9CxcujFmzZjWaV1uc+/q0he1+1llnxVtvvdXo/vLy8rjyyivj6aefjoi2u92bMvf1yfftnsn+vmTJkvjwww8b5tRWt3lT5r4++b7NC0mSJHHxxRfHY489Fs8//3wMHDiw0f2dO3eOvfbaK959991Gt7/33nsxYMCAiCi8/TMbc26KtjjnVCoVhx9+eHTu3DkmTpy4zjfj8mXOudzGnz8WyZf5ZmvO3/3ud9fpwxERt9xyS0yYMKFNznl98rXHZGO+22yzTZSXl3/hMvkyX/LXlx3ffeUrX4k+ffo0Oi6oqamJKVOmNOxDhXbs0BLa82eGllZdXR3vvPNO9O3bNwYOHGh/zZLPr9f1sb82zf777/+Ffbpd7bPNObtoe/P5M0rX1tYm3/zmN5N+/folM2bMSBYuXNhwqa6uTpIkST744IPkuuuuS1577bVk7ty5yZ/+9Kdkhx12SHbbbbekrq6u4XGPPPLIZOedd06mTp2aTJ06Ndlpp52SY445puH+urq6ZPDgwckhhxySTJ8+PXn22WeTfv36JRdffHHDMv/+97+T3r17J6ecckoyc+bM5LHHHktKS0uTn/zkJ1mf+4oVK5KxY8cmU6dOTebNm5dMmzYtOeecc5Li4uJk1qxZDWMuuOCCpF+/fsmzzz6bTJ8+PTn44IOTXXbZpc3Pva1u9/UZMGBAcssttzS6rS1u96bMva1s98/Pe/ny5cnll1+evPzyy8ncuXOTF154Idl3332TrbbaKkmlUg1j2uI2b8rc28o2z1f/8z//k3Tv3j2ZPHlyox67atWqhmUee+yxpFOnTsmdd96ZvP/++8kvfvGLpEOHDslf/vKXhmUKaf/M1pwXLlyYvPHGG8ldd92VRETy4osvJm+88UayZMmSNjnnVCqV7L333slOO+2UfPDBB40eJ9+2czbmO3v27OTGG29MXn/99aSysjJ5+eWXk+HDhyc9e/ZMPv7447yab7bmvD4RkTz++OONbmtLcy6kHpOtbXzLLbckpaWlySOPPJK8//77yfe///1k0003TT744IO8mi+FJf2Y/qabbkq6d++ePPbYY8nMmTOTU045Jenbt2+bPLbNpfb8mSGbLr/88mTy5MnJnDlzkr/97W/JMccck5SUlCTz5s1LksT+mqkvWq/218y9+uqrSceOHZMbbrghef/995Pf/OY3SdeuXZMHHnigYZn2ss8Kz7/A5xvE3Llzk4hY7+WFF15IkiRJ5s+fnxx44IFJz549k86dOydf/epXk0svvbTRh9ckSZIlS5Ykp512WlJSUpKUlJQkp512WvLJJ580WqaysjIZNmxY0qVLl6Rnz57JxRdfnKxevbrRMm+99VbyjW98IykuLk769OmTjBs3Lqmvr8/63D/99NPk+OOPT8rLy5POnTsnffv2Tb75zW8mr776aqMxn376aXLxxRcnPXv2TLp06ZIcc8wxyfz589v83Nvqdl+f9YXnbXG7r0/63NvKdv/8vFetWpUcfvjhSVlZWdKpU6dk6623Ts4888x1tmdb3OZNmXtb2eb5akM9dsKECY2Wu/vuu5Ntt9022XTTTZNddtkleeKJJxrdX0j7Z7bmfO21137p47SlOb/wwgsbfJy5c+fm1ZyzMd8FCxYkRx11VNKrV6+kU6dOSb9+/ZJTTz01+cc//tHoMfJhvtma84YeNz08b0tzLqQek81tXFFRkfTr1y/p2rVrsu+++67zHyj5MF8KS/oxfX19fXLttdcmffr0SYqLi5MDDzwwmTlzZqMxhXTs0Fra82eGbDrppJOSvn37Jp06dUrKy8uTE044IXn77bcb7re/ZuaL1qv9deP8f//f/5cMHjw4KS4uTnbYYYfkzjvvbHR/e9lni5Lk//16OgAAAAAAEBF+8xwAAAAAANYhPAcAAAAAgDTCcwAAAAAASCM8BwAAAACANMJzAAAAAABIIzwHAAAAAIA0wnMAAAAAAEgjPAcAAAAAgDTCcwAAIIqKiuKJJ55o7TIAACBvCM+hjVu8eHGcf/75sfXWW0dxcXH06dMnjjjiiJg6dWprl9bIuHHjYtddd23tMgAgb5111llRVFQURUVF0alTp+jdu3ccdthhcc8990R9fX2TH2dDPXfhwoVx1FFHZbFiAGhfct2rs2HIkCExevTonDw2tEXCc2jjTjzxxHjzzTfjvvvui/feey8mTpwYQ4YMiaVLl7Z2aQBAMx155JGxcOHCmDdvXjz55JMxdOjQGDVqVBxzzDFRV1e3UY/dp0+fKC4uzlKlANA+5bJXAy1PeA5t2L///e946aWX4uabb46hQ4fGgAED4utf/3qMHTs2hg0bFhER48ePj5122im6desW/fv3jwsvvDBWrFjR8BhLliyJU045Jfr16xddu3aNnXbaKR566KFGz1NdXR2XXnpp9OrVKzbddNM44IAD4rXXXmu4/957743NN9+80ZgnnngiioqKGu6/7rrr4s0332z4X/p77703NysFAArY2r8i22qrrWL33XePq6++Ov7whz/Ek08+2dA7ly1bFv/93/8dvXr1itLS0jj44IPjzTffjIgv7rmf/9mWefPmRVFRUTz22GMxdOjQ6Nq1a+yyyy5595drAJBvctmr58+fH8OHD4/NNtssSktL49vf/nZ8/PHHDc991llnxXHHHdeontGjR8eQIUMa7p8yZUr87Gc/a3jsefPm5XiNQGETnkMbttlmm8Vmm20WTzzxRFRXV693mU022SR+/vOfx6xZs+K+++6L559/Pr7zne803L969erYY4894o9//GPMmjUr/vu//zvOOOOMeOWVVxqW+c53vhOPPvpo3HfffTF9+vTYdttt44gjjmjyt9tPOumkuPzyy2PQoEGxcOHCWLhwYZx00kkbN3kAaCcOPvjg2GWXXeKxxx6LJEli2LBhsWjRovjzn/8c06ZNi9133z0OOeSQWLp0abN77ve+97244oorYsaMGbH99tvHKaec4ltzANBM2ejVSZLEcccdF0uXLo0pU6bEpEmTYvbs2c367Pyzn/0s9t133zjvvPMaHrt///45nDkUPuE5tGEdO3aMe++9N+67777YfPPNY//994+rr7463nrrrYZlRo8eHUOHDo2BAwfGwQcfHNdff3387ne/a7h/q622iiuuuCJ23XXX+MpXvhKXXHJJHHHEEfHII49ERMTKlSvj9ttvjx//+Mdx1FFHxY477hh33XVXdOnSJe6+++4m1dmlS5fYbLPNomPHjtGnT5/o06dPdOnSJbsrAwDasB122CHmzZsXL7zwQsycOTMeeeSR2HPPPWO77baLn/zkJ7H55pvH73//+2b33CuuuCKGDRsW22+/fVx33XVRWVkZH3zwQQvODADaho3t1c8++2y89dZb8eCDD8Yee+wRe++9d/zf//1fTJkypdFffn+R7t27R+fOnaNr164Nj92hQ4cczxwKm/Ac2rgTTzwx/vnPf8bEiRPjiCOOiMmTJ8fuu+/e8GdfL7zwQhx22GGx1VZbRUlJSYwYMSKWLFkSK1eujIiINWvWxA033BA777xzbLHFFrHZZpvFM888E/Pnz4+IiNmzZ0dtbW3sv//+Dc/ZqVOn+PrXvx7vvPNOi88XANqjJEmiqKgopk2bFitWrGjo2Wsvc+fOjdmzZzf7cXfeeeeGf/ft2zciPjsZOQDQPBvbq995553o379/o2+K77jjjrH55pv77A051LG1CwByb9NNN43DDjssDjvssPjf//3fOPfcc+Paa6+NoUOHxtFHHx0XXHBBXH/99dGzZ8946aWX4pxzzona2tqIiPjpT38at9xyS9x6660Nv40+evToqKmpiYjPDgAiouH3y9dae2AQ8dlPw6xdbq21jw8AbLx33nknBg4cGPX19dG3b9+YPHnyOsukn3+kKTp16tTw77V9vb6+PtMyAaDd2the/fnP2Bu63WdvyD7hObRDO+64YzzxxBPx+uuvR11dXfz0pz+NTTb57A9RPv+TLRERf/nLX2L48OFx+umnR8RnH5jff//9+NrXvhYREdtuu2107tw5XnrppTj11FMj4rPm/Prrr8fo0aMjIqKsrCyWL18eK1eujG7dukVExIwZMxo9T+fOnWPNmjW5mjIAtFnPP/98zJw5My677LLo169fLFq0KDp27BjbbLPNepfXcwGgZWWjV++4444xf/78+PDDDxu+ff73v/89li1b1vD5vKysLGbNmtVo3IwZMxr9Z7jjAGgeP9sCbdiSJUvi4IMPjgceeCDeeuutmDt3bjzyyCPxox/9KIYPHx5f/epXo66uLn7xi1/EnDlz4v/+7//ijjvuaPQY2267bUyaNClefvnleOedd+L888+PRYsWNdzfrVu3+J//+Z+48sor46mnnoq///3vcd5558WqVavinHPOiYiIvffeO7p27RpXX311fPDBB/Hggw82/GzMWttss03MnTs3ZsyYEf/61782eIJTAGjPqqurY9GiRbFgwYKYPn163HjjjTF8+PA45phjYsSIEXHooYfGvvvuG8cdd1w8/fTTMW/evHj55Zfj+9//frz++usRoecCQC7lqlcfeuihsfPOO8dpp50W06dPj1dffTVGjBgRBx10UOy5554R8dmJSV9//fW4//774/33349rr712nTB9m222iVdeeSXmzZsX//rXv/xFGXwJ4Tm0YZtttlnsvffeccstt8SBBx4YgwcPjmuuuSbOO++8uO2222LXXXeN8ePHx8033xyDBw+O3/zmN1FRUdHoMa655prYfffd44gjjoghQ4ZEnz594rjjjmu0zE033RQnnnhinHHGGbH77rvHBx98EE8//XT06NEjIiJ69uwZDzzwQPz5z3+OnXbaKR566KEYN25co8c48cQT48gjj4yhQ4dGWVlZPPTQQ7lcNQBQkJ566qno27dvbLPNNnHkkUfGCy+8ED//+c/jD3/4Q3To0CGKioriz3/+cxx44IFx9tlnx/bbbx8nn3xyzJs3L3r37h0Rei4A5FKuenVRUVE88cQT0aNHjzjwwAPj0EMPja985Svx8MMPNzz3EUccEddcc0185zvfib322iuWL18eI0aMaFTfFVdcER06dIgdd9wxysrKGs5nBqxfUZL+Y0gAAAAAANDO+eY5AAAAAACkEZ4DAAAAAEAa4TkAAAAAAKQRngMAAAAAQBrhOQAAAAAApBGeAwAAAABAGuE5AAAAAACkEZ4DAAAAAEAa4TkAAAAAAKQRngMAAAAAQBrhOQAAAAAApPn/AZSUkWbzRtkUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "for i, column in enumerate(columns_to_plot, 1):\n",
    "    plt.subplot(2, 3, i)\n",
    "    plt.hist(df_cleaned[column].dropna(), bins=30, edgecolor='k', alpha=0.7)\n",
    "    plt.title(f'Histogram of {column}')\n",
    "    plt.xlabel(column)\n",
    "    plt.ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "159918b4-02b3-441b-8cd1-363662e59060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting histograms for 'Basein' and 'Baseout' in the same figure with contrasting colors\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.hist(df_cleaned['Basein'].dropna(), bins=30, edgecolor='k', alpha=0.7, color='blue', label='Basein')\n",
    "plt.hist(df_cleaned['Baseout'].dropna(), bins=30, edgecolor='k', alpha=0.7, color='orange', label='Baseout')\n",
    "\n",
    "plt.title('Histogram of Basein and Baseout')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.savefig(\"base.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "afadeb6f-591a-4d08-8ec2-37f66f38a38a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pairwise(strategy1, strategy2):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    plt.hist(df_cleaned[strategy1].dropna(), bins=30, edgecolor='k', alpha=0.7, color='blue', label= f'{strategy1}')\n",
    "    plt.hist(df_cleaned[strategy2].dropna(), bins=30, edgecolor='k', alpha=0.7, color='orange', label=f'{strategy2}')\n",
    "\n",
    "    plt.title(f'Histogram of {strategy1} and {strategy2}')\n",
    "    plt.xlabel('Cost', fontsize=16)\n",
    "    plt.ylabel('Frequency', fontsize=16)\n",
    "       \n",
    "    # Set the font size for tick labels\n",
    "    plt.xticks(fontsize=14)\n",
    "    plt.yticks(fontsize=14)\n",
    "\n",
    "    # Set the legend with increased font size\n",
    "    plt.legend(fontsize=14)\n",
    "    \n",
    "    filename = f\"{strategy1}.png\"\n",
    "\n",
    "# Save the figure to a file with the dynamic filename\n",
    "    plt.savefig(filename)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51cbf58a-2e58-448e-b8f2-76dcce111bde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pairwise('Basein', 'Baseout')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c51580ce-c51f-4132-9ac2-aace5c1df714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pairwise('Saain', 'Saaout')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1025a980-040b-4fe0-8892-6e72f2cf56b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pairwise('Detin', 'Detout')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9740a7f7-6645-4f04-9e13-0b0e9a30996e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int= pd.DataFrame()\n",
    "\n",
    "for col in columns_to_plot:\n",
    "    df_int[col]= df_cleaned[col].dropna().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc2c4531-1126-4993-9842-a8e31f032fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Basein</th>\n",
       "      <th>Baseout</th>\n",
       "      <th>Saain</th>\n",
       "      <th>Saaout</th>\n",
       "      <th>Detin</th>\n",
       "      <th>Detout</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>713833.312500</td>\n",
       "      <td>761335.812500</td>\n",
       "      <td>390423.825</td>\n",
       "      <td>432153.671137</td>\n",
       "      <td>261645.0</td>\n",
       "      <td>407749.038401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>784800.281250</td>\n",
       "      <td>799082.781250</td>\n",
       "      <td>372239.450</td>\n",
       "      <td>436651.008373</td>\n",
       "      <td>261260.0</td>\n",
       "      <td>467491.899762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>833089.843750</td>\n",
       "      <td>777404.243750</td>\n",
       "      <td>384348.500</td>\n",
       "      <td>446931.909204</td>\n",
       "      <td>262868.0</td>\n",
       "      <td>607448.405210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>798526.614583</td>\n",
       "      <td>799194.114583</td>\n",
       "      <td>383664.400</td>\n",
       "      <td>423773.169317</td>\n",
       "      <td>263559.0</td>\n",
       "      <td>492308.126219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>809961.812500</td>\n",
       "      <td>821170.062500</td>\n",
       "      <td>386739.250</td>\n",
       "      <td>445950.415885</td>\n",
       "      <td>261903.0</td>\n",
       "      <td>475631.124427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Basein        Baseout       Saain         Saaout     Detin  \\\n",
       "0  713833.312500  761335.812500  390423.825  432153.671137  261645.0   \n",
       "1  784800.281250  799082.781250  372239.450  436651.008373  261260.0   \n",
       "2  833089.843750  777404.243750  384348.500  446931.909204  262868.0   \n",
       "3  798526.614583  799194.114583  383664.400  423773.169317  263559.0   \n",
       "4  809961.812500  821170.062500  386739.250  445950.415885  261903.0   \n",
       "\n",
       "          Detout  \n",
       "0  407749.038401  \n",
       "1  467491.899762  \n",
       "2  607448.405210  \n",
       "3  492308.126219  \n",
       "4  475631.124427  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "add20c11-dc89-4b4e-a9f3-4a0ab5aa9f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_statistics(df, columns):\n",
    "    \"\"\"\n",
    "    Computes the mean, standard deviation, 75th percentile, 95th percentile, \n",
    "    and coefficient of variance for specified columns in a DataFrame.\n",
    "\n",
    "    Parameters:\n",
    "    df (pd.DataFrame): The DataFrame containing the data.\n",
    "    columns (list): List of columns to compute statistics for.\n",
    "\n",
    "    Returns:\n",
    "    pd.DataFrame: A DataFrame containing the computed statistics for each column.\n",
    "    \"\"\"\n",
    "    statistics = {\n",
    "        'Mean': [],\n",
    "        'Standard Deviation': [],\n",
    "        '75th Percentile': [],\n",
    "        '95th Percentile': [],\n",
    "        'Coefficient of Variance': []\n",
    "    }\n",
    "\n",
    "    for column in columns:\n",
    "        col_data = df[column].dropna()\n",
    "        mean = col_data.mean()\n",
    "        std_dev = col_data.std()\n",
    "        p75 = col_data.quantile(0.75)\n",
    "        p95 = col_data.quantile(0.95)\n",
    "        cov = 100*std_dev / mean if mean != 0 else float('inf')\n",
    "        \n",
    "        statistics['Mean'].append(mean)\n",
    "        statistics['Standard Deviation'].append(std_dev)\n",
    "        statistics['75th Percentile'].append(p75)\n",
    "        statistics['95th Percentile'].append(p95)\n",
    "        statistics['Coefficient of Variance'].append(cov)\n",
    "\n",
    "    return pd.DataFrame(statistics, index=columns)\n",
    "\n",
    "# Example usage with the cleaned DataFrame and specified columns\n",
    "columns_to_analyze = ['Basein', 'Baseout', 'Saain', 'Saaout', 'Detin', 'Detout']\n",
    "stats_df = compute_statistics(df_int, columns_to_analyze)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1f69c7c7-7d5d-473e-a0ea-d3f9bdae4fef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>75th Percentile</th>\n",
       "      <th>95th Percentile</th>\n",
       "      <th>Coefficient of Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Basein</th>\n",
       "      <td>771878.599479</td>\n",
       "      <td>45696.065375</td>\n",
       "      <td>804103.658854</td>\n",
       "      <td>851082.100000</td>\n",
       "      <td>5.920110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baseout</th>\n",
       "      <td>771873.682229</td>\n",
       "      <td>48687.652434</td>\n",
       "      <td>802315.023958</td>\n",
       "      <td>848452.073437</td>\n",
       "      <td>6.307723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saain</th>\n",
       "      <td>382723.689500</td>\n",
       "      <td>5444.316016</td>\n",
       "      <td>386746.775000</td>\n",
       "      <td>390930.365000</td>\n",
       "      <td>1.422519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saaout</th>\n",
       "      <td>437729.540472</td>\n",
       "      <td>5618.797678</td>\n",
       "      <td>441202.474634</td>\n",
       "      <td>446386.043130</td>\n",
       "      <td>1.283623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detin</th>\n",
       "      <td>262758.240000</td>\n",
       "      <td>1384.228492</td>\n",
       "      <td>263751.750000</td>\n",
       "      <td>264976.180000</td>\n",
       "      <td>0.526807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detout</th>\n",
       "      <td>497939.676458</td>\n",
       "      <td>52913.169170</td>\n",
       "      <td>534584.496384</td>\n",
       "      <td>584610.505210</td>\n",
       "      <td>10.626422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Mean  Standard Deviation  75th Percentile  95th Percentile  \\\n",
       "Basein   771878.599479        45696.065375    804103.658854    851082.100000   \n",
       "Baseout  771873.682229        48687.652434    802315.023958    848452.073437   \n",
       "Saain    382723.689500         5444.316016    386746.775000    390930.365000   \n",
       "Saaout   437729.540472         5618.797678    441202.474634    446386.043130   \n",
       "Detin    262758.240000         1384.228492    263751.750000    264976.180000   \n",
       "Detout   497939.676458        52913.169170    534584.496384    584610.505210   \n",
       "\n",
       "         Coefficient of Variance  \n",
       "Basein                  5.920110  \n",
       "Baseout                 6.307723  \n",
       "Saain                   1.422519  \n",
       "Saaout                  1.283623  \n",
       "Detin                   0.526807  \n",
       "Detout                 10.626422  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8eb3a681-5899-422d-8ac4-31a2a91c8e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_df.to_csv('stats_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "12d72e30-a3e6-4c8a-98f5-37f3663b18aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the bar chart with coefficient of variance on a secondary axis as a bar plot\n",
    "fig, ax1 = plt.subplots(figsize=(14, 8))\n",
    "\n",
    "# Plotting the mean, standard deviation, 75th percentile, and 95th percentile\n",
    "stats_df[['Mean', 'Standard Deviation', '75th Percentile', '95th Percentile']].plot(kind='bar', ax=ax1)\n",
    "ax1.set_xlabel('Columns', fontsize=16)\n",
    "ax1.set_ylabel('Values', fontsize=16)\n",
    "ax1.set_title('Statistics: Mean, Standard Deviation, 75th Percentile, 95th Percentile, Coefficient of Variance', fontsize=16)\n",
    "ax1.legend(loc='upper left', fontsize=16)\n",
    "\n",
    "# Increase the font size of the x-axis labels\n",
    "ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=16, rotation=0)\n",
    "\n",
    "# Creating a secondary axis for coefficient of variance\n",
    "ax2 = ax1.twinx()\n",
    "stats_df['Coefficient of Variance'].plot(kind='bar', color='magenta', ax=ax2, alpha=0.7, width=0.1, position= 0)\n",
    "ax2.set_ylabel('Coefficient of Variance', fontsize=16)\n",
    "ax2.legend(['Coefficient of Variance'], loc='upper right', fontsize=16)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"summary.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "71484a6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Basein', 'Baseout', 'Saain', 'Saaout', 'Detin', 'Detout'], dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e18a6ea1-903a-4938-b4ad-46e0167cb1ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the box plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "df_int.boxplot(grid=False)\n",
    "\n",
    "# Setting the title and labels with increased font size\n",
    "# plt.title('', fontsize=16)\n",
    "plt.ylabel('Cost', fontsize=14)\n",
    "# plt.xlabel('Columns', fontsize=14)\n",
    "\n",
    "# Setting the font size for tick labels\n",
    "plt.xticks(rotation=45, fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
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