create_subplots.ipynb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8b434f0-4ac2-414b-8b30-8e6cacabff04",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f4e6e73d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aP368/vCHP8jr9WrVqlUdbjdkyBANHTq05dX6iaMAAHRWSPE6deqUysrKlJmZGbA8MzNTO3fu7HDba665RsnJybrxxhv17rvvhj5SAAD+S1woK9fW1qqxsVEejydgucfj0bFjx4Juk5ycrOeee05paWlqaGjQv//7v+vGG2/U1q1b9a1vfSvoNg0NDWpoaGj52u/3hzJMAEAvF1K8mjkcjoCvjTFtljW7/PLLdfnll7d8PWXKFFVVVel3v/tdu/EqKChQfn5+OEMDAPQBIX1smJiYqNjY2DZnWTU1NW3Oxjpy3XXX6dNPP233+3l5efL5fC2vqqqqUIYJAOjlQopXfHy80tLSVFJSErC8pKREU6dO7fT7lJeXKzk5ud3vO51OuVyugBcAAM1C/thw8eLFmj9/vtLT0zVlyhQ999xzqqysVHZ2tqSzZ01HjhzRiy++KEn6wx/+oFGjRmnChAk6deqU1q9fr9dee02vvfZa984EANBnhByvefPmqa6uTsuXL1d1dbUmTpyo4uJipaSkSJKqq6sD/ubr1KlTevjhh3XkyBH1799fEyZM0J/+9CfNmjWr+2YBAOhTHMYYE+1BnA+PwQbQV3H8C457GwIArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6YcWrsLBQqampSkhIUFpamkpLSztcf9u2bUpLS1NCQoJGjx6tZ599NqzBAgAghRGvoqIi5eTkaMmSJSovL9f06dM1c+ZMVVZWBl3/wIEDmjVrlqZPn67y8nI98sgjevDBB/Xaa691efAAgL7JYYwxoWxw7bXXavLkyVq1alXLsvHjx2v27NkqKChos/4vfvELvfnmm9q7d2/LsuzsbH300Ud6//33O7VPv98vt9stn88nl8sVynABwGoc/4IL6czr1KlTKisrU2ZmZsDyzMxM7dy5M+g277//fpv1b7nlFu3atUunT58OcbgAAEhxoaxcW1urxsZGeTyegOUej0fHjh0Lus2xY8eCrn/mzBnV1tYqOTm5zTYNDQ1qaGho+drn80k6+28gANCXNB/3QvyQrNcLKV7NHA5HwNfGmDbLzrd+sOXNCgoKlJ+f32a51+sNdagA0CvU19fL7XZHexgXjJDilZiYqNjY2DZnWTU1NW3OrpoNHTo06PpxcXG69NJLg26Tl5enxYsXt3zd1NSkEydO6NJLL+0wkgDQ2xhjVF9fr2HDhkV7KBeUkOIVHx+vtLQ0lZSUaM6cOS3LS0pKdPvttwfdZsqUKdq8eXPAsi1btig9PV39+vULuo3T6ZTT6QxYNmjQoFCGCgC9BmdcbYV8qfzixYv1/PPPa82aNdq7d68eeughVVZWKjs7W9LZs6YFCxa0rJ+dna1Dhw5p8eLF2rt3r9asWaPVq1fr4Ycf7r5ZAAD6lJB/5zVv3jzV1dVp+fLlqq6u1sSJE1VcXKyUlBRJUnV1dcDffKWmpqq4uFgPPfSQVq5cqWHDhumpp57SHXfc0X2zAAD0KSH/nRcAANHGvQ0BANYhXgAA6xAvAIB1iBcAwDrECwBgnbDj9d577+m2227TsGHD5HA49MYbb5x3G57rBQC9S7RaEHa8vvjiC02aNEnPPPNMp9bnuV4A0PtEqwXd8ndeDodDr7/+umbPnt3uOt3xXC8AwIUrki2I2O+8uvJcr4aGBi1btizgMSl9CfNn/syf+feW+XfXMx4jduY1duxYLVq0SI888kjLsp07d2ratGk6evRo0Od6SWf/wR0/flwpKSnas2ePkpKS2ty0t7fz+/3yer2qqqrqk09SZf7Mvy/P3+fzaeTIkfrP//zPTt2gvKmpSUePHtXFF18c1lM4jDGqq6vT4MGDFRPz/89vgt0wvbWebEFrYT3PK1yhPtdLCny214QJE3pucBbo688zY/7Mvy+rrq7uVLyOHj3aIz+rpUuXatmyZd3yXuG0oLWIxSuc53pJZ+9SP3fuXE2YMEEffPCBRo0axZlXH8P8mX9fnv+RI0d0xRVX6KKLLurU+hdffLEkhf3zav55V1ZWBjyKpbuOu+G2oLWIxSuc53pJZ39gSUlJkqRRo0a1/Pe+xOl0aunSpX3yI1OJ+TP/vj3/5t91JSQkdGr95rMXl8vVpdi73e4e+ZeFcFvQhglTfX29KS8vN+Xl5UaSWbFihSkvLzeHDh0yxhiTm5tr5s+f37L+/v37zYABA8xDDz1kKioqzOrVq02/fv3Mpk2bzrsvn89nJBmfzxfucAHASqEe/7p6vAx1+0i24Fxhx+vdd981ktq8Fi5caIwxZuHChWbGjBkB22zdutVcc801Jj4+3owaNcqsWrWqU/siXgD6qgs9XpFswbmseJ6X3++X2+2Wz+frk595A+i7Qj3+dfV4acvxlnsbAgCsQ7wAANYhXgAA6xAvAIB1iBcAhKCpqUmTJk3SrFmzWpZ9+9vfDunuEO+++64cDoeKi4t7Yoh9AvECgBCsW7dOf/3rX7t0q6Trr79eM2bM0M9+9jM1NjZ23+D6EOIFAJ3U2Nio/Px8zZgxQxkZGV16r4cfflgVFRXauHFjN42ubyFeANBJxcXFqqys1Pz587v8Xt/5zneUlJTEE+XDRLwAoJPWrVsnh8OhO+64I+j3v/76a/385z+X1+tVQkKCrrzySq1ZsybounFxcZo9e7Z27NihTz/9tCeH3SsRLwDoBGOMtm7dqnHjxrX7aJLvfe97Kioq0ve+9z394Ac/UE1Nje677z4VFBQEXX/KlCmSpHfeeaenht1rES8A6IS9e/fqxIkTmjx5crvr7N+/X//3//5frVixQk8//bQ++ugjJScn69FHH9X+/fvbrJ+Wlibp7MMYERriBQCdcPjwYUmSx+Npd50lS5a0PE9LOvvsqsWLF+vMmTN66aWX2qzf/F7N743OI14A0Al1dXWSpEsuuaTddaZPn97ust27d7f53uDBgyVJtbW13TDCvoV4AUAn9O/fX5L01VdftbvOkCFD2ixrPrvy+Xxtvtf8XgMGDOiOIfYpxAsAOqH5Ke4nTpxod52ampo2y44fPy7p7JOJW2t+r774hPiuIl4A0AkTJkxQTExMh5e1l5aWtrvs6quvbvO9jz/+WJJ05ZVXds8g+xDiBQCdMGjQIF111VXatWuX2nuG7+OPP676+vqWr48fP64VK1YoLi5Od999d5v1//KXv0iSZsyY0TOD7sWIFwB00uzZs+Xz+fThhx8G/f7o0aM1ceJELV68WA888ICuuuoqVVdXKz8/X6NHj26zfklJiS655BJ961vf6umh9zrECwA6KSsrS7GxsVq/fn3Q77/yyiuaO3euXnnlFT333HNKSkrS888/r0ceeaTNuocOHdKOHTu0cOFCJSQk9PTQex2Hae/89wLi9/vldrvl8/nkcrmiPRwAfdjdd9+tLVu26NChQxo4cGDY7/Poo4/q17/+tfbu3avLLrus3fVCPf519Xhpy/GWMy8ACMHjjz+ukydPauXKlWG/x+eff66nnnpK//2///cOw4X2xUV7AABgk9TUVL3wwgtd+sPigwcPKicnRw888EA3jqxvIV4AEKJ58+Z1afurr7466KXz6Dw+NgQAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDpdildhYaFSU1OVkJCgtLQ0lZaWdrj+hg0bNGnSJA0YMEDJycm65557VFdX15UhAACiLBotCDteRUVFysnJ0ZIlS1ReXq7p06dr5syZqqysDLr+9u3btWDBAt13333as2ePXn31VX344YfKysoKdwgAgCiLWgtMmDIyMkx2dnbAsnHjxpnc3Nyg6//2t781o0ePDlj21FNPmREjRpx3Xz6fz0gyPp8v3OECgJVCPf519XgZ6vaRbMG5wjrzOnXqlMrKypSZmRmwPDMzUzt37gy6zdSpU3X48GEVFxfLGKPjx49r06ZNuvXWW8MZAgAgyqLZgrDiVVtbq8bGRnk8noDlHo9Hx44da3fAGzZs0Lx58xQfH6+hQ4dq0KBBevrppzvcV0NDg/x+vyTJ7/eroaEhnCEDAELg9/sDXsGOvZFsQWtdumDD4XAEfG2MabOsWUVFhR588EE9+uijKisr09tvv60DBw4oOzu7w30UFBTI6/VKkrxerwoKCroyZABAJ3i9Xrnd7pZXR8feSLSgtbiQ1v4viYmJio2NbVPWmpqaNgVuVlBQoGnTpulnP/uZJOmqq67SwIEDNX36dD322GNKTk4Oul1eXp6ysrLk9XpVVVWlpKSkcIYMAAhBVVWVXC5Xy9dOp7PNOpFsQWthnXnFx8crLS1NJSUlActLSko0derUoNt8+eWXiokJ3F1sbKyks5Vuj9PpbPkBulyuoD9AAED3crlcAa9gx95ItqCNkC7vOMfLL79s+vXrZ1avXm0qKipMTk6OGThwoDl48KAxxpjc3Fwzf/78lvXXrl1r4uLiTGFhodm3b5/Zvn27SU9PNxkZGefdF1cbAuirLvSrDSPZgnOF9bGhJM2bN091dXVavny5qqurNXHiRBUXFyslJUWSVF1dHXCd/6JFi1RfX69nnnlGP/3pTzVo0CDdcMMNeuKJJ8IdAgAgyqLVAocxoZynRYff75fb7ZbP5wv4DBYAertQj39dPV7acrzl3oYAAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwTpfiVVhYqNTUVCUkJCgtLU2lpaUdrt/Q0KAlS5YoJSVFTqdTl112mdasWdOVIQAAoiwaLYgLd7BFRUXKyclRYWGhpk2bpn/913/VzJkzVVFRoZEjRwbdZu7cuTp+/LhWr16tb3zjG6qpqdGZM2fCHQIAIMqi1gITpoyMDJOdnR2wbNy4cSY3Nzfo+m+99ZZxu92mrq4u5H35fD4jyfh8vrDGCgC2CvX419XjZajbR7IF5wrrY8NTp06prKxMmZmZAcszMzO1c+fOoNu8+eabSk9P129+8xsNHz5cY8eO1cMPP6yvvvoqnCEAAKIsmi0I62PD2tpaNTY2yuPxBCz3eDw6duxY0G3279+v7du3KyEhQa+//rpqa2t1//3368SJEx1+1tnQ0CC/3y9J8vv9cjqdcjqd4QwbANBJzcfdZsGOvZFsQWtdumDD4XAEfG2MabOsWVNTkxwOhzZs2KCMjAzNmjVLK1as0Lp16zosbkFBgbxeryTJ6/WqoKCgK0MGAHSC1+uV2+1ueXV07I1EC1oL68wrMTFRsbGxbcpaU1PTpsDNkpOTNXz4cLnd7pZl48ePlzFGhw8f1pgxY4Jul5eXp6ysLHm9XlVVVSkpKSmcIQMAQlBVVSWXy9XydbBPvCLZgtbCOvOKj49XWlqaSkpKApaXlJRo6tSpQbeZNm2ajh49qpMnT7Ys++STTxQTE6MRI0a0uy+n09nyA3S5XHxkCAAR4HK5Al7Bjr2RbEEb4V7p8fLLL5t+/fqZ1atXm4qKCpOTk2MGDhxoDh48aIwxJjc318yfP79l/fr6ejNixAhz5513mj179pht27aZMWPGmKysrPPui6sNAfRVF/rVhpFswbnC/juvefPmqa6uTsuXL1d1dbUmTpyo4uJipaSkSJKqq6tVWVnZsv5FF12kkpISPfDAA0pPT9ell16quXPn6rHHHgt3CACAKItWCxzGGNOtM+kBfr9fbrdbPp8v4DNYAOjtQj3+dfV4acvxlnsbAgCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDpdildhYaFSU1OVkJCgtLQ0lZaWdmq7HTt2KC4uTldffXVXdg8AuABEowVhx6uoqEg5OTlasmSJysvLNX36dM2cOVOVlZUdbufz+bRgwQLdeOON4e4aAHCBiFYLHMYYE86G1157rSZPnqxVq1a1LBs/frxmz56tgoKCdre76667NGbMGMXGxuqNN97Q7t27z7svv98vt9stn88nl8sVznABwEqhHv+6erwMdftItuBcYZ15nTp1SmVlZcrMzAxYnpmZqZ07d7a73dq1a7Vv3z4tXbo0nN0CAC4g0WxBXDgb1dbWqrGxUR6PJ2C5x+PRsWPHgm7z6aefKjc3V6WlpYqL6/xuGxoa5Pf7JZ39NwKn0ymn0xnOsAEAndR83G0W7NgbyRa01qULNhwOR8DXxpg2yySpsbFRd999t/Lz8zV27NiQ9lFQUCCv1ytJ8nq9HZ6GAgC6h9frldvtbnl1dOyNRAtaCyt7iYmJio2NbVPWmpqaNgWWpPr6eu3atUvl5eX68Y9/LElqamqSMUZxcXHasmWLbrjhhqD7ysvLU1ZWlrxer6qqqpSUlBTOkAEAIaiqqgr4nVewT7wi2YLWwopXfHy80tLSVFJSojlz5rQsLykp0e23395mfZfLpb/97W8BywoLC/XOO+9o06ZNSk1NbXdfTqez5Qfocrn4yBAAIsDlcp33go1ItqC1sD9wXLx4sebPn6/09HRNmTJFzz33nCorK5WdnS3p7BnTkSNH9OKLLyomJkYTJ04M2H7IkCFKSEhosxwAYI9otSDseM2bN091dXVavny5qqurNXHiRBUXFyslJUWSVF1dfd7r/AEAdotWC8L+O69I4u+8APRVF/rfeUUL9zYEAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdboUr8LCQqWmpiohIUFpaWkqLS1td90//vGPuvnmm5WUlCSXy6UpU6boz3/+c1d2DwC4AESjBWHHq6ioSDk5OVqyZInKy8s1ffp0zZw5U5WVlUHXf++993TzzTeruLhYZWVluv7663XbbbepvLw83CEAAKIsai0wYcrIyDDZ2dkBy8aNG2dyc3M7/R5XXHGFyc/PP+96Pp/PSDI+ny/kcQKAzUI9/nX1eBnq9pFswbnCOvM6deqUysrKlJmZGbA8MzNTO3fu7NR7NDU1qb6+XoMHDw5nCACAKItmC+JCWvu/1NbWqrGxUR6PJ2C5x+PRsWPHOvUev//97/XFF19o7ty5Ha7X0NAgv98vSfL7/XI6nXI6neEMGwDQSc3H3WbBjr2RbEFrXbpgw+FwBHxtjGmzLJiNGzdq2bJlKioq0pAhQzpct6CgQF6vV5Lk9XpVUFAQ/oABAJ3i9XrldrtbXh0deyPRgtbCOvNKTExUbGxsm7LW1NS0KXBrRUVFuu+++/Tqq6/qpptuOu++8vLylJWVJa/Xq6qqKiUlJYUzZABACKqqquRyuVq+DvaJVyRb0FpYZ17x8fFKS0tTSUlJwPKSkhJNnTq13e02btyoRYsW6aWXXtKtt97aqX05nc6WH6DL5eIjQwCIAJfLFfAKduyNZAtaC+vMS5IWL16s+fPnKz09XVOmTNFzzz2nyspKZWdnSzp7xnTkyBG9+OKLLYNdsGCBnnzySV133XUtpe7fv7/cbne4wwAARFHUWhDStYmtrFy50qSkpJj4+HgzefJks23btpbvLVy40MyYMaPl6xkzZhhJbV4LFy487364VB5AX3WhXypvTORacC6HMcZ0Q3x7lN/vl9vtls/nC/gMFgB6u1CPf109XtpyvOXehgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALBOl+JVWFio1NRUJSQkKC0tTaWlpR2uv23bNqWlpSkhIUGjR4/Ws88+25XdAwAuANFoQdjxKioqUk5OjpYsWaLy8nJNnz5dM2fOVGVlZdD1Dxw4oFmzZmn69OkqLy/XI488ogcffFCvvfZauEMAAERZ1FpgwpSRkWGys7MDlo0bN87k5uYGXf/nP/+5GTduXMCyH/7wh+a666477758Pp+RZHw+X7jDBQArhXr86+rxMtTtI9mCc4V15nXq1CmVlZUpMzMzYHlmZqZ27twZdJv333+/zfq33HKLdu3apdOnT3e4v4aGhoD/7GsaGhq0bNky5s/8oz2UqGD+F+7xL9ItOFdc6MOVamtr1djYKI/HE7Dc4/Ho2LFjQbc5duxY0PXPnDmj2tpaJScnB92uoaFBn332mSTp4MGDkiSn0xnOsK3l9/uVn5+vrKwsuVyuaA8n4pg/8+/L86+trZUkff31151a3xgj6ezPLRzN2/l8voDlTqezzbE3ki1oLax4NXM4HAFfG2PaLDvf+sGWn6ugoED5+fmSpIyMjHCH2it4vd5oDyGqmD/z78tOnjzZqfXq6uokdf3nNXLkyICvly5dqmXLlgVdNxItaC2seCUmJio2NrZNWWtqatoUtdnQoUODrh8XF6dLL7203X3l5eXp3nvvVUpKivbs2aOkpKQ+eebl9XpVVVXVJ//Nk/kz/748f5/Pp5EjR3b6jGTw4MGSpMrKSrnd7rD3d+jQIQ0aNKhlebDjbiRb0FpY8YqPj1daWppKSko0Z86cluUlJSW6/fbbg24zZcoUbd68OWDZli1blJ6ern79+rW7L6fTKY/Ho6VLl+qyyy7rc+GSzv4Mli5d2ifDLTF/5s/8ly5dqv79+3dq/ZiYs5cyuN3uLsV+0KBB590+ki1oI6TLO87x8ssvm379+pnVq1ebiooKk5OTYwYOHGgOHjxojDEmNzfXzJ8/v2X9/fv3mwEDBpiHHnrIVFRUmNWrV5t+/fqZTZs2hTsEAEArkb7aMFotCDtexhizcuVKk5KSYuLj483kyZPNtm3bWr63cOFCM2PGjID1t27daq655hoTHx9vRo0aZVatWtWV3QMAWol0vIyJTgscxvzXb8oAANZraGhQQUGB8vLywvqYtavbRwrxAgBYhxvzAgCsQ7wAANYhXgAA6xAvAIB1wo7Xe++9p9tuu03Dhg2Tw+HQG2+8cd5teJ4XAPQu0WpB2PH64osvNGnSJD3zzDOdWp/neQFA7xOtFnTLpfIOh0Ovv/66Zs+e3e46v/jFL/Tmm29q7969Lcuys7P10Ucf6f333+/qEAAAURbJFkTsd15dfZ5XX3+eD/Nn/syf+fcG3fU8r4ideY0dO1aLFi3SI4880rJs586dmjZtmo4ePdrh87yOHz/OXeX78F21mT/z78vzb77L+3/+538G3OW9PU1NTTp69KguvvjikB4x0swYo7q6Og0ePLjlJr9S8Od5tdaTLWitS8/zClVXn+c1YcKEnhucBfr684yYP/Pvy6qrqzsVr6NHj/bIz6qj53mFKmrP8wpHV57nNXfuXE2YMEEffPCBRo0axZlXH8P8mX9fnv+RI0d0xRVX6KKLLurU+hdffLEkhf3zav55t34eWHcdd6P6PK9wdOV5XklJSZKkUaNGtfz3voTnGTF/5t9359/8u66EhIROrd989uJyuboU+64+D6w9UX+eV319vSkvLzfl5eVGklmxYoUpLy83hw4dMsZ07zNcunqLfwCwVajHv0g/EiWSLThX2PF69913jaQ2r4ULFxpjuvcZLsQLQF91occrki04lxWPRPH7/XK73fL5fH3yM28AfVeox7+uHi9tOd5yb0MAgHWIFwDAOsQLAGAd4gUAsA7xAoAQNDU1adKkSZo1a1bI2545c0bf+MY3NHfu3B4YWd9CvAAgBOvWrdNf//rXsG6VFBcXpyVLlujVV1/Vzp07u39wfQjxAoBOamxsVH5+vmbMmKGMjIyw3mP+/PnyeDx69NFHu3l0fQvxAoBOKi4uVmVlpebPnx/2e8TFxemuu+7SO++8o08//bQbR9e3EC8A6KR169bJ4XDojjvuaPO9+vp6LV++XFdddZUGDhwot9uta665Rv/yL//S5jlVc+fOlTFG69ati9DIex/iBQCdYIzR1q1bNW7cuDaPJqmtrdV1112npUuXKjY2VtnZ2br33ns1dOhQPfHEE/riiy8C1k9LS1N8fLzeeeedCM6gd4no87wAwFZ79+7ViRMnNHPmzDbfu//++1VRUaFHHnlEjz/+eMD3jh8/3uZxJk6nUxMmTFBZWZkaGhr65N3yu4ozLwDohMOHD0uSPB5PwPLjx49r06ZNuuyyy4JegejxeBQX1/Y8wePx6PTp06qpqemR8fZ2xAsAOqGurk6SdMkllwQs37Vrl4wxuv7660N6HtXgwYMlnf3IEaEjXgDQCf3795ckffXVVwHLP//8c0nS8OHDQ3q/5vcZMGBA1wfXBxEvAOiE5qe4nzhxImB588UbR44cCen9mt+nLz4dvjsQLwDohAkTJigmJqbN32alp6crJiZG7777bptL4jvy8ccfa9iwYS0fHyI0xAsAOmHQoEG66qqrWn7H1czj8eiOO+7Qvn37lJ+f32a7mpoanTlzJmBZZWWljh07phkzZvT4uHsr4gUAnTR79mz5fD59+OGHAcsLCws1fvx4Pf7445o8ebIefvhh/fSnP9Vtt90mr9erkydPBqxfUlLS8n4ID/ECgE7KyspSbGys1q9fH7A8MTFR//Ef/6F/+Zd/0VdffaVnnnlGq1ev1uHDh5Wbm6uBAwcGrL9+/XoNGTKEeHWBw5x7/nuB8vv9crvd8vl8crlc0R4OgD7s7rvv1pYtW3To0KE2UeqMf/zjH7r88su1dOnSTt2cN9TjX1ePl7YcbznzAoAQPP744zp58qRWrlwZ1va/+tWv5PF49NOf/rSbR9a3EC8ACEFqaqpeeOGFsM66zpw5ozFjxujf//3fw9oe/x/3NgSAEM2bNy+s7eLi4vTLX/6ym0fTN3HmBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdboUr8LCQqWmpiohIUFpaWkqLS3tcP0NGzZo0qRJGjBggJKTk3XPPfeorq6uK0MAAERZNFoQdryKioqUk5OjJUuWqLy8XNOnT9fMmTNVWVkZdP3t27drwYIFuu+++7Rnzx69+uqr+vDDD5WVlRXuEAAAURa1FpgwZWRkmOzs7IBl48aNM7m5uUHX/+1vf2tGjx4dsOypp54yI0aMOO++fD6fkWR8Pl+4wwUAK4V6/Ovq8TLU7SPZgnOFdeZ16tQplZWVKTMzM2B5Zmamdu7cGXSbqVOn6vDhwyouLpYxRsePH9emTZt06623hjMEAECURbMFYcWrtrZWjY2N8ng8Acs9Ho+OHTvW7oA3bNigefPmKT4+XkOHDtWgQYP09NNPd7ivhoYG+f1+SZLf71dDQ0M4QwYAhMDv9we8gh17I9mC1rp0wYbD4Qj42hjTZlmziooKPfjgg3r00UdVVlamt99+WwcOHFB2dnaH+ygoKJDX65Ukeb1eFRQUdGXIAIBO8Hq9crvdLa+Ojr2RaEFrcSGt/V8SExMVGxvbpqw1NTVtCtysoKBA06ZN089+9jNJ0lVXXaWBAwdq+vTpeuyxx5ScnBx0u7y8PGVlZcnr9aqqqkpJSUnhDBkAEIKqqiq5XK6Wr51OZ5t1ItmC1sI684qPj1daWppKSkoClpeUlGjq1KlBt/nyyy8VExO4u9jYWElnK90ep9PZ8gN0uVxBf4AAgO7lcrkCXsGOvZFsQRshXd5xjpdfftn069fPrF692lRUVJicnBwzcOBAc/DgQWOMMbm5uWb+/Pkt669du9bExcWZwsJCs2/fPrN9+3aTnp5uMjIyzrsvrjYE0Fdd6FcbRrIF5wrrY0NJmjdvnurq6rR8+XJVV1dr4sSJKi4uVkpKiiSpuro64Dr/RYsWqb6+Xs8884x++tOfatCgQbrhhhv0xBNPhDsEAECURasFDmNCOU+LDr/fL7fbLZ/PF/AZLAD0dqEe/7p6vLTleMu9DQEA1iFeAADrEC8AgHWIFwDAOsQLAGAd4gUAsA7xAgBYh3gBAKxDvAAA1iFeAADrEC8AgHWIFwDAOsQLAGAd4gUAsA7xAgBYh3gBAKxDvAAA1iFeAADrEC8AgHWIFwDAOsQLAGAd4gUAsA7xAgBYh3gBAKxDvAAA1iFeAADrEC8AgHWIFwDAOsQLAGAd4gUAsA7xAgBYh3gBAKxDvAAA1iFeAADrEC8AgHWIFwDAOsQLAGAd4gUAsA7xAgBYh3gBAKxDvAAA1iFeAADrEC8AgHWIFwDAOsQLAGAd4gUAsA7xAgBYh3gBAKxDvAAA1iFeAADrEC8AgHWIFwDAOsQLAGCdLsWrsLBQqampSkhIUFpamkpLSztcv6GhQUuWLFFKSoqcTqcuu+wyrVmzpitDAABEWTRaEBfuYIuKipSTk6PCwkJNmzZN//qv/6qZM2eqoqJCI0eODLrN3Llzdfz4ca1evVrf+MY3VFNTozNnzoQ7BABAlEWtBSZMGRkZJjs7O2DZuHHjTG5ubtD133rrLeN2u01dXV3I+/L5fEaS8fl8YY0VAGwV6vGvq8fLULePZAvOFdbHhqdOnVJZWZkyMzMDlmdmZmrnzp1Bt3nzzTeVnp6u3/zmNxo+fLjGjh2rhx9+WF999VU4QwAARFk0WxDWx4a1tbVqbGyUx+MJWO7xeHTs2LGg2+zfv1/bt29XQkKCXn/9ddXW1ur+++/XiRMnOvyss6GhQX6/X5Lk9/vldDrldDrDGTYAoJOaj7vNgh17I9mC1rp0wYbD4Qj42hjTZlmzpqYmORwObdiwQRkZGZo1a5ZWrFihdevWdVjcgoICeb1eSZLX61VBQUFXhgwA6ASv1yu3293y6ujYG4kWtBbWmVdiYqJiY2PblLWmpqZNgZslJydr+PDhcrvdLcvGjx8vY4wOHz6sMWPGBN0uLy9PWVlZ8nq9qqqqUlJSUjhDBgCEoKqqSi6Xq+XrYJ94RbIFrYV15hUfH6+0tDSVlJQELC8pKdHUqVODbjNt2jQdPXpUJ0+ebFn2ySefKCYmRiNGjGh3X06ns+UH6HK5+MgQACLA5XIFvIIdeyPZgjbCvdLj5ZdfNv369TOrV682FRUVJicnxwwcONAcPHjQGGNMbm6umT9/fsv69fX1ZsSIEebOO+80e/bsMdu2bTNjxowxWVlZ590XVxsC6Ksu9KsNI9mCc4X9d17z5s1TXV2dli9frurqak2cOFHFxcVKSUmRJFVXV6uysrJl/YsuukglJSV64IEHlJ6erksvvVRz587VY489Fu4QAABRFq0WOIwxpltn0gP8fr/cbrd8Pl/AZ7AA0NuFevzr6vHSluMt9zYEAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdboUr8LCQqWmpiohIUFpaWkqLS3t1HY7duxQXFycrr766q7sHgBwAYhGC8KOV1FRkXJycrRkyRKVl5dr+vTpmjlzpiorKzvczufzacGCBbrxxhvD3TUA4AIRrRY4jDEmnA2vvfZaTZ48WatWrWpZNn78eM2ePVsFBQXtbnfXXXdpzJgxio2N1RtvvKHdu3efd19+v19ut1s+n08ulyuc4QKAlUI9/nX1eBnq9pFswbnCOvM6deqUysrKlJmZGbA8MzNTO3fubHe7tWvXat++fVq6dGk4uwUAXECi2YK4cDaqra1VY2OjPB5PwHKPx6Njx44F3ebTTz9Vbm6uSktLFRfX+d02NDTI7/dLOvtvBE6nU06nM5xhAwA6qfm42yzYsTeSLWitSxdsOByOgK+NMW2WSVJjY6Puvvtu5efna+zYsSHto6CgQF6vV5Lk9Xo7PA0FAHQPr9crt9vd8uro2BuJFrQWVvYSExMVGxvbpqw1NTVtCixJ9fX12rVrl8rLy/XjH/9YktTU1CRjjOLi4rRlyxbdcMMNQfeVl5enrKwseb1eVVVVKSkpKZwhAwBCUFVVFfA7r2CfeEWyBa2FFa/4+HilpaWppKREc+bMaVleUlKi22+/vc36LpdLf/vb3wKWFRYW6p133tGmTZuUmpra7r6cTmfLD9DlcvGRIQBEgMvlOu8FG5FsQWthf+C4ePFizZ8/X+np6ZoyZYqee+45VVZWKjs7W9LZM6YjR47oxRdfVExMjCZOnBiw/ZAhQ5SQkNBmOQDAHtFqQdjxmjdvnurq6rR8+XJVV1dr4sSJKi4uVkpKiiSpurr6vNf5AwDsFq0WhP13XpHE33kB6Ksu9L/zihbubQgAsA7xAgBYh3gBAKxDvAAA1iFeAADrEC8AgHWIFwDAOsQLAGAd4gUAsA7xAgBYh3gBAKxDvAAA1iFeAADrEC8AgHWIFwDAOsQLAGAd4gUAsA7xAgBYh3gBAKxDvAAA1iFeAADrEC8AgHWIFwDAOsQLAGAd4gUAsA7xAgBYh3gBAKxDvAAA1iFeAADrEC8AgHWIFwDAOsQLAGAd4gUAsA7xAgBYh3gBAKxDvAAA1iFeAADrEC8AgHWIFwDAOsQLAGAd4gUAsA7xAgBYh3gBAKxDvAAA1iFeAADrEC8AgHWIFwDAOsQLAGAd4gUAsA7xAgBYh3gBAKxDvAAA1iFeAADrdClehYWFSk1NVUJCgtLS0lRaWtruun/84x918803KykpSS6XS1OmTNGf//znruweAHABiEYLwo5XUVGRcnJytGTJEpWXl2v69OmaOXOmKisrg67/3nvv6eabb1ZxcbHKysp0/fXX67bbblN5eXm4QwAARFnUWmDClJGRYbKzswOWjRs3zuTm5nb6Pa644gqTn59/3vV8Pp+RZHw+X8jjBACbhXr86+rxMtTtI9mCc4V15nXq1CmVlZUpMzMzYHlmZqZ27tzZqfdoampSfX29Bg8eHM4QAABRFs0WxIW09n+pra1VY2OjPB5PwHKPx6Njx4516j1+//vf64svvtDcuXM7XK+hoUF+v1+S5Pf75XQ65XQ6wxk2AKCTmo+7zYIdeyPZgta6dMGGw+EI+NoY02ZZMBs3btSyZctUVFSkIUOGdLhuQUGBvF6vJMnr9aqgoCD8AQMAOsXr9crtdre8Ojr2RqIFrYV15pWYmKjY2Ng2Za2pqWlT4NaKiop033336dVXX9VNN9103n3l5eUpKytLXq9XVVVVSkpKCmfIAIAQVFVVyeVytXwd7BOvSLagtbDOvOLj45WWlqaSkpKA5SUlJZo6dWq7223cuFGLFi3SSy+9pFtvvbVT+3I6nS0/QJfLxUeGABABLpcr4BXs2BvJFrQW1pmXJC1evFjz589Xenq6pkyZoueee06VlZXKzs6WdPaM6ciRI3rxxRdbBrtgwQI9+eSTuu6661pK3b9/f7nd7nCHAQCIoqi1IKRrE1tZuXKlSUlJMfHx8Wby5Mlm27ZtLd9buHChmTFjRsvXM2bMMJLavBYuXHje/XCpPIC+6kK/VN6YyLXgXA5jjOmG+PYov98vt9stn88X8BksAPR2oR7/unq8tOV4y70NAQDWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYB3iBQCwDvECAFiHeAEArEO8AADWIV4AAOsQLwCAdYgXAMA6xAsAYJ0uxauwsFCpqalKSEhQWlqaSktLO1x/27ZtSktLU0JCgkaPHq1nn322K7sHAFwAotGCsONVVFSknJwcLVmyROXl5Zo+fbpmzpypysrKoOsfOHBAs2bN0vTp01VeXq5HHnlEDz74oF577bVwhwAAiLKotcCEKSMjw2RnZwcsGzdunMnNzQ26/s9//nMzbty4gGU//OEPzXXXXXfeffl8PiPJ+Hy+cIcLAFYK9fjX1eNlqNtHsgXnCuvM69SpUyorK1NmZmbA8szMTO3cuTPoNu+//36b9W+55Rbt2rVLp0+f7nB/DQ0NAf/Z1zQ0NGjZsmXMn/lHeyhRwfwv3ONfpFtwrrjQhyvV1taqsbFRHo8nYLnH49GxY8eCbnPs2LGg6585c0a1tbVKTk4Oul1DQ4M+++wzSdLBgwclSU6nM5xhW8vv9ys/P19ZWVlyuVzRHk7EMX/m35fnX1tbK0n6+uuvO7W+MUbS2Z9bOJq38/l8AcudTmebY28kW9BaWPFq5nA4Ar42xrRZdr71gy0/V0FBgfLz8yVJGRkZ4Q61V/B6vdEeQlQxf+bfl508ebJT69XV1Unq+s9r5MiRAV8vXbpUy5YtC7puJFrQWljxSkxMVGxsbJuy1tTUtClqs6FDhwZdPy4uTpdeemm7+8rLy9O9996rlJQU7dmzR0lJSX3yzMvr9aqqqqpP/psn82f+fXn+Pp9PI0eO7PQZyeDBgyVJlZWVcrvdYe/v0KFDGjRoUMvyYMfdSLagtbDiFR8fr7S0NJWUlGjOnDkty0tKSnT77bcH3WbKlCnavHlzwLItW7YoPT1d/fr1a3dfTqdTHo9HS5cu1WWXXdbnwiWd/RksXbq0T4ZbYv7Mn/kvXbpU/fv379T6MTFnL2Vwu91div2gQYPOu30kW9BGSJd3nOPll182/fr1M6tXrzYVFRUmJyfHDBw40Bw8eNAYY0xubq6ZP39+y/r79+83AwYMMA899JCpqKgwq1evNv369TObNm0KdwgAgFYifbVhtFoQdryMMWblypUmJSXFxMfHm8mTJ5tt27a1fG/hwoVmxowZAetv3brVXHPNNSY+Pt6MGjXKrFq1qiu7BwC0Eul4GROdFjiM+a/flAEArNfQ0KCCggLl5eWF9TFrV7ePFOIFALAON+YFAFjngolXX7/Jbyjz/+Mf/6ibb75ZSUlJcrlcmjJliv785z9HcLTdL9R//s127NihuLg4XX311T07wB4W6vwbGhq0ZMkSpaSkyOl06rLLLtOaNWsiNNruF+r8N2zYoEmTJmnAgAFKTk7WPffc0/L3TTZ57733dNttt2nYsGFyOBx64403zrtNbzv2hS3k35L1gOarVf7t3/7NVFRUmJ/85Cdm4MCB5tChQ0HXb75a5Sc/+YmpqKgw//Zv/2b1lYuhzv8nP/mJeeKJJ8wHH3xgPvnkE5OXl2f69etn/s//+T8RHnn3CHX+zT7//HMzevRok5mZaSZNmhSZwfaAcOb/3e9+11x77bWmpKTEHDhwwPzlL38xO3bsiOCou0+o8y8tLTUxMTHmySefNPv37zelpaVmwoQJZvbs2REeedcVFxebJUuWmNdee81IMq+//nqH6/e2Y19XXBDxitaNHS8Uoc4/mCuuuMLk5+d399AiItz5z5s3z/zyl780S5cutTpeoc7/rbfeMm6329TV1UVieD0u1Pn/9re/NaNHjw5Y9tRTT5kRI0b02BgjoTPx6m3Hvq6I+seG0byx44UgnPm31tTUpPr6+pa/rLdJuPNfu3at9u3bp6VLl/b0EHtUOPN/8803lZ6ert/85jcaPny4xo4dq4cfflhfffVVJIbcrcKZ/9SpU3X48GEVFxfLGKPjx49r06ZNuvXWWyMx5KjqTce+rurSvQ27QzRv7HghCGf+rf3+97/XF198oblz5/bEEHtUOPP/9NNPlZubq9LSUsXFRf1/wl0Szvz379+v7du3KyEhQa+//rpqa2t1//3368SJE9b93iuc+U+dOlUbNmzQvHnz9PXXX+vMmTP67ne/q6effjoSQ46q3nTs66qon3k1i8aNHS8koc6/2caNG7Vs2TIVFRVpyJAhPTW8HtfZ+Tc2Nuruu+9Wfn6+xo4dG6nh9bhQ/vk3NTXJ4XBow4YNysjI0KxZs7RixQqtW7fOyrMvKbT5V1RU6MEHH9Sjjz6qsrIyvf322zpw4ICys7MjMdSo623HvnBF/V9bo3ljxwtBOPNvVlRUpPvuu0+vvvqqbrrppp4cZo8Jdf719fXatWuXysvL9eMf/1jS2YO5MUZxcXHasmWLbrjhhoiMvTuE888/OTlZw4cPD7jp6vjx42WM0eHDhzVmzJgeHXN3Cmf+BQUFmjZtmn72s59Jkq666ioNHDhQ06dP12OPPdarzz5607Gvq6J+5nXujR3PVVJSoqlTpwbdZsqUKW3WD+vGjheAcOYvnT3jWrRokV566SWrP+sPdf4ul0t/+9vftHv37pZXdna2Lr/8cu3evVvXXnttpIbeLcL55z9t2jQdPXo04BEZn3zyiWJiYjRixIgeHW93C2f+X375ZcvNZ5vFxsZK+v9nIb1Vbzr2dVmULhQJ0Ndv8hvq/F966SUTFxdnVq5caaqrq1ten3/+ebSm0CWhzr812682DHX+9fX1ZsSIEebOO+80e/bsMdu2bTNjxowxWVlZ0ZpCl4Q6/7Vr15q4uDhTWFho9u3bZ7Zv327S09NNRkZGtKYQtvr6elNeXm7Ky8uNJLNixQpTXl7e8mcCvf3Y1xUXRLyM4Sa/ocx/xowZRlKb18KFCyM/8G4S6j//c9keL2NCn//evXvNTTfdZPr3729GjBhhFi9ebL788ssIj7r7hDr/p556ylxxxRWmf//+Jjk52Xz/+983hw8fjvCou+7dd9/t8P/LfeHYFy7ubQgAsE7Uf+cFAECoiBcAwDrECwBgHeIFALAO8QIAWId4AQCsQ7wAANYhXgAA6xAvAIB1iBcAwDrECwBgHeIFALDO/wPCZfaMvq9v3gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax1 = fig.add_axes([0, 0, 0.4, 0.4])\n",
    "ax2 = fig.add_axes([0, 0.42, 0.4, 0.4])\n",
    "cbar_ax1 = fig.add_axes([ax1.get_position().x1+0.015, ax1.get_position().y0, 0.02, ax1.get_position().height])\n",
    "cbar_ax2 = fig.add_axes([ax2.get_position().x1+0.015, ax2.get_position().y0, 0.02, ax2.get_position().height])\n",
    "rmsd_ax = fig.add_axes([ax2.get_position().x0, ax2.get_position().y1+0.02, ax2.get_position().width, 0.1])\n",
    "\n",
    "ax1.tick_params(direction='inout', labelbottom=True, top=True, bottom=True)\n",
    "ax2.tick_params(direction='inout', labelbottom=False, top=True, bottom=True)\n",
    "cbar_ax1.tick_params(left=False, labelleft=False, right=True, labelright=True, labelbottom=False)\n",
    "cbar_ax2.tick_params(left=False, labelleft=False, right=True, labelright=True, labelbottom=False)\n",
    "rmsd_ax.tick_params(bottom=False, labelbottom=False, top=True, labeltop=False)\n",
    "\n",
    "rmsd_ax.annotate(text='(a)', xy=(0.90, 0.91), xycoords='axes fraction', ha='left', va='top', fontsize=14)\n",
    "ax2.annotate(text='(b)', xy=(0.90, 0.97), xycoords='axes fraction', ha='left', va='top', fontsize=14)\n",
    "ax1.annotate(text='(c)', xy=(0.90, 0.97), xycoords='axes fraction', ha='left', va='top', fontsize=14)\n",
    "\n",
    "plt.title('Your title', fontsize=16, y=1, pad=10)\n",
    "plt.savefig('fig.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e75d1b70",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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