altitude_trial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  77.3917,   34.2349, 3929.    ],\n",
       "       [  77.4083,   34.2349, 4139.    ],\n",
       "       [  77.425 ,   34.2349, 4259.    ],\n",
       "       ...,\n",
       "       [  79.6917,   31.7186, 4307.    ],\n",
       "       [  79.7083,   31.7186, 4275.    ],\n",
       "       [  79.725 ,   31.7186, 4255.    ]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data = np.loadtxt('elevation_data.txt'); #Load data\n",
    "data #Display a few rows of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.interpolate import griddata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25521, 3)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "Long = data[:,0]; Lat = data[:,1]; Elev = data[:,2]; #Variables\n",
    "print(data.shape)\n",
    "pts=100000; #Input the desired number of points here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(316, 316) (316, 316) (316, 316)\n"
     ]
    }
   ],
   "source": [
    "[x,y]=np.meshgrid(np.linspace(np.min(Long),np.max(Long),int(np.sqrt(pts))),np.linspace(np.min(Lat),np.max(Lat),int(np.sqrt(pts))))\n",
    "z = griddata((Long, Lat), Elev, (x, y), method='linear')\n",
    "#x = np.matrix.flatten(x); #Gridded longitude\n",
    "#y = np.matrix.flatten(y); #Gridded latitude\n",
    "#z = np.matrix.flatten(z); #Gridded elevation\n",
    "print(x.shape,y.shape,z.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1728x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(24,12))\n",
    "plt.contourf(x,y,z,cmap='jet')\n",
    "plt.colorbar(label='Elevation above sea level [m]')\n",
    "plt.plot(78.964,32.779,color='black',marker='o',markersize=10)\n",
    "\n",
    "plt.xlabel('Longitude [°]')\n",
    "plt.ylabel('Latitude [°]')\n",
    "plt.gca().set_aspect('equal')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Tanmay Singh\\AppData\\Local\\Temp\\ipykernel_10820\\2312560097.py:3: UserWarning: No contour levels were found within the data range.\n",
      "  plt.contour([x,y], cmap = \"viridis\",\n",
      "c:\\Users\\Tanmay Singh\\anaconda3\\lib\\site-packages\\matplotlib\\colorbar.py:1240: RuntimeWarning: invalid value encountered in true_divide\n",
      "  y = y / (self._boundaries[self._inside][-1] -\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 1 is out of bounds for axis 0 with size 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32md:\\OneDrive - IIT Delhi\\Intern\\RRI\\altitude_trial.ipynb Cell 6'\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/OneDrive%20-%20IIT%20Delhi/Intern/RRI/altitude_trial.ipynb#ch0000007?line=2'>3</a>\u001b[0m plt\u001b[39m.\u001b[39mcontour([x,y], cmap \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mviridis\u001b[39m\u001b[39m\"\u001b[39m, \n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/OneDrive%20-%20IIT%20Delhi/Intern/RRI/altitude_trial.ipynb#ch0000007?line=3'>4</a>\u001b[0m             levels \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(\u001b[39mrange\u001b[39m(\u001b[39m0\u001b[39m, \u001b[39m5000\u001b[39m, \u001b[39m100\u001b[39m)))\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/OneDrive%20-%20IIT%20Delhi/Intern/RRI/altitude_trial.ipynb#ch0000007?line=4'>5</a>\u001b[0m plt\u001b[39m.\u001b[39mtitle(\u001b[39m\"\u001b[39m\u001b[39mElevation Contours of Ladakh\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/OneDrive%20-%20IIT%20Delhi/Intern/RRI/altitude_trial.ipynb#ch0000007?line=5'>6</a>\u001b[0m cbar \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39;49mcolorbar()\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/OneDrive%20-%20IIT%20Delhi/Intern/RRI/altitude_trial.ipynb#ch0000007?line=6'>7</a>\u001b[0m plt\u001b[39m.\u001b[39mgca()\u001b[39m.\u001b[39mset_aspect(\u001b[39m'\u001b[39m\u001b[39mequal\u001b[39m\u001b[39m'\u001b[39m, adjustable\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbox\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/OneDrive%20-%20IIT%20Delhi/Intern/RRI/altitude_trial.ipynb#ch0000007?line=7'>8</a>\u001b[0m plt\u001b[39m.\u001b[39mshow()\n",
      "File \u001b[1;32mc:\\Users\\Tanmay Singh\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py:2088\u001b[0m, in \u001b[0;36mcolorbar\u001b[1;34m(mappable, cax, ax, **kw)\u001b[0m\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/pyplot.py?line=2082'>2083</a>\u001b[0m     \u001b[39mif\u001b[39;00m mappable \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/pyplot.py?line=2083'>2084</a>\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39mNo mappable was found to use for colorbar \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/pyplot.py?line=2084'>2085</a>\u001b[0m                            \u001b[39m'\u001b[39m\u001b[39mcreation. First define a mappable such as \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/pyplot.py?line=2085'>2086</a>\u001b[0m                            \u001b[39m'\u001b[39m\u001b[39man image (with imshow) or a contour set (\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/pyplot.py?line=2086'>2087</a>\u001b[0m                            \u001b[39m'\u001b[39m\u001b[39mwith contourf).\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m-> <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/pyplot.py?line=2087'>2088</a>\u001b[0m ret \u001b[39m=\u001b[39m gcf()\u001b[39m.\u001b[39mcolorbar(mappable, cax\u001b[39m=\u001b[39mcax, ax\u001b[39m=\u001b[39max, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkw)\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/pyplot.py?line=2088'>2089</a>\u001b[0m \u001b[39mreturn\u001b[39;00m ret\n",
      "File \u001b[1;32mc:\\Users\\Tanmay Singh\\anaconda3\\lib\\site-packages\\matplotlib\\figure.py:1158\u001b[0m, in \u001b[0;36mFigureBase.colorbar\u001b[1;34m(self, mappable, cax, ax, use_gridspec, **kw)\u001b[0m\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/figure.py?line=1153'>1154</a>\u001b[0m NON_COLORBAR_KEYS \u001b[39m=\u001b[39m [\u001b[39m'\u001b[39m\u001b[39mfraction\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mpad\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mshrink\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39maspect\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39manchor\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/figure.py?line=1154'>1155</a>\u001b[0m                      \u001b[39m'\u001b[39m\u001b[39mpanchor\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/figure.py?line=1155'>1156</a>\u001b[0m cb_kw \u001b[39m=\u001b[39m {k: v \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kw\u001b[39m.\u001b[39mitems() \u001b[39mif\u001b[39;00m k \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m NON_COLORBAR_KEYS}\n\u001b[1;32m-> <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/figure.py?line=1157'>1158</a>\u001b[0m cb \u001b[39m=\u001b[39m cbar\u001b[39m.\u001b[39mColorbar(cax, mappable, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcb_kw)\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/figure.py?line=1159'>1160</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m userax:\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/figure.py?line=1160'>1161</a>\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msca(current_ax)\n",
      "File \u001b[1;32mc:\\Users\\Tanmay Singh\\anaconda3\\lib\\site-packages\\matplotlib\\colorbar.py:495\u001b[0m, in \u001b[0;36mColorbar.__init__\u001b[1;34m(self, ax, mappable, cmap, norm, alpha, values, boundaries, orientation, ticklocation, extend, spacing, ticks, format, drawedges, filled, extendfrac, extendrect, label)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=492'>493</a>\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=493'>494</a>\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mformatter \u001b[39m=\u001b[39m \u001b[39mformat\u001b[39m  \u001b[39m# Assume it is a Formatter or None\u001b[39;00m\n\u001b[1;32m--> <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=494'>495</a>\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdraw_all()\n\u001b[0;32m    <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=496'>497</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(mappable, contour\u001b[39m.\u001b[39mContourSet) \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m mappable\u001b[39m.\u001b[39mfilled:\n\u001b[0;32m    <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=497'>498</a>\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_lines(mappable)\n",
      "File \u001b[1;32mc:\\Users\\Tanmay Singh\\anaconda3\\lib\\site-packages\\matplotlib\\colorbar.py:579\u001b[0m, in \u001b[0;36mColorbar.draw_all\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=576'>577</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvmin, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvmax \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_boundaries[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_inside][[\u001b[39m0\u001b[39m, \u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]]\n\u001b[0;32m    <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=577'>578</a>\u001b[0m \u001b[39m# Compute the X/Y mesh.\u001b[39;00m\n\u001b[1;32m--> <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=578'>579</a>\u001b[0m X, Y, extendlen \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_mesh()\n\u001b[0;32m    <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=579'>580</a>\u001b[0m \u001b[39m# draw the extend triangles, and shrink the inner axes to accommodate.\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=580'>581</a>\u001b[0m \u001b[39m# also adds the outline path to self.outline spine:\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=581'>582</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_do_extends(extendlen)\n",
      "File \u001b[1;32mc:\\Users\\Tanmay Singh\\anaconda3\\lib\\site-packages\\matplotlib\\colorbar.py:1138\u001b[0m, in \u001b[0;36mColorbar._mesh\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=1135'>1136</a>\u001b[0m norm\u001b[39m.\u001b[39mvmin \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvmin\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=1136'>1137</a>\u001b[0m norm\u001b[39m.\u001b[39mvmax \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvmax\n\u001b[1;32m-> <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=1137'>1138</a>\u001b[0m y, extendlen \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_proportional_y()\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=1138'>1139</a>\u001b[0m \u001b[39m# invert:\u001b[39;00m\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=1139'>1140</a>\u001b[0m \u001b[39mif\u001b[39;00m (\u001b[39misinstance\u001b[39m(norm, (colors\u001b[39m.\u001b[39mBoundaryNorm, colors\u001b[39m.\u001b[39mNoNorm)) \u001b[39mor\u001b[39;00m\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=1140'>1141</a>\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mboundaries \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m):\n",
      "File \u001b[1;32mc:\\Users\\Tanmay Singh\\anaconda3\\lib\\site-packages\\matplotlib\\colorbar.py:1262\u001b[0m, in \u001b[0;36mColorbar._proportional_y\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=1258'>1259</a>\u001b[0m yscaled \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mma\u001b[39m.\u001b[39mfilled(norm(yscaled), np\u001b[39m.\u001b[39mnan)\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=1259'>1260</a>\u001b[0m \u001b[39m# make the lower and upper extend lengths proportional to the lengths\u001b[39;00m\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=1260'>1261</a>\u001b[0m \u001b[39m# of the first and last boundary spacing (if extendfrac='auto'):\u001b[39;00m\n\u001b[1;32m-> <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=1261'>1262</a>\u001b[0m automin \u001b[39m=\u001b[39m yscaled[\u001b[39m1\u001b[39;49m] \u001b[39m-\u001b[39m yscaled[\u001b[39m0\u001b[39m]\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=1262'>1263</a>\u001b[0m automax \u001b[39m=\u001b[39m yscaled[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m] \u001b[39m-\u001b[39m yscaled[\u001b[39m-\u001b[39m\u001b[39m2\u001b[39m]\n\u001b[0;32m   <a href='file:///c%3A/Users/Tanmay%20Singh/anaconda3/lib/site-packages/matplotlib/colorbar.py?line=1263'>1264</a>\u001b[0m extendlength \u001b[39m=\u001b[39m [\u001b[39m0\u001b[39m, \u001b[39m0\u001b[39m]\n",
      "\u001b[1;31mIndexError\u001b[0m: index 1 is out of bounds for axis 0 with size 1"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Tanmay Singh\\anaconda3\\lib\\site-packages\\matplotlib\\transforms.py:2663: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  y_scale = 1.0 / inh\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.contour([x,y], cmap = \"viridis\", \n",
    "            levels = list(range(0, 5000, 100)))\n",
    "plt.title(\"Elevation Contours of Ladakh\")\n",
    "cbar = plt.colorbar()\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "9dcd7a6aed1a88230ea0db2f0b36c2e82ff920ac80878a2df90092e469e00d12"
  },
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}