{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ac8ede50",
   "metadata": {},
   "source": [
    "# Mesh2d Basics\n",
    "\n",
    "This is the basic introduction for using the `meshkernel` library.\n",
    "\n",
    "`meshkernel` can be used for creating and manipulating various kinds of meshes.\n",
    "\n",
    "The most common case deals with unstructured, two-dimensional meshes which is why this tutorial focuses on these."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb76801",
   "metadata": {},
   "source": [
    "At the very beginning, the necessary libraries have to be imported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "651f8535",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from meshkernel import GeometryList, MakeGridParameters, MeshKernel, ProjectionType"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c86a232",
   "metadata": {},
   "source": [
    "`meshkernel` provides a set of convenience methods for creating common meshes.\n",
    "\n",
    "We use the curvilinear_compute_rectangular_grid method to create a simple curvilinear grid. You can look at the documentation in order to find all its parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87d93674",
   "metadata": {},
   "outputs": [],
   "source": [
    "mk = MeshKernel()\n",
    "\n",
    "make_grid_parameters = MakeGridParameters()\n",
    "make_grid_parameters.num_columns = 3\n",
    "make_grid_parameters.num_rows = 2\n",
    "make_grid_parameters.angle = 0.0\n",
    "make_grid_parameters.origin_x = 0.0\n",
    "make_grid_parameters.origin_y = 0.0\n",
    "make_grid_parameters.block_size_x = 1.0\n",
    "make_grid_parameters.block_size_y = 1.0\n",
    "\n",
    "mk.curvilinear_compute_rectangular_grid(make_grid_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27bea1f6",
   "metadata": {},
   "source": [
    "We convert the curvilinear grid to an unstructured mesh and get the resulting mesh2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9776cb5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mk.curvilinear_convert_to_mesh2d()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f8a7ef32",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh2d_input = mk.mesh2d_get()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49a198a",
   "metadata": {},
   "source": [
    "`Mesh2D` has three mandatory attributes, which are enough to fully describe any unstructured mesh.\n",
    "\n",
    "The first two are `node_x` and `node_y`.\n",
    "They are one-dimensional `double` arrays, which describe the position of the nodes as can be seen below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7cd78e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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rr+ijjz7SBx98EFhHTk6OsrOz1b9/f6WkpGj69OkqKysL3DUEAACat5ADy8aNGzVw4MDA6+NjSbKzszVnzhwdOHBAX3/9dWB+RUWF/vCHP2jfvn1q1aqVevfurQ8//DBoHSNGjNA333yjKVOmyOv1qk+fPlq+fPkpA3EBAEDz5DLGmHAXcbb8fr88Ho98Pp9iYmLCXQ4AAKiFUL6/+VtCAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrhRxYVq9erWHDhikxMVEul0tLliw5bf+33npL1157rdq1a6eYmBilpaXp/fffD+rz4IMPyuVyBU09evQItTQAANBEhRxYysrKlJycrPz8/Fr1X716ta699lq9++672rRpkwYOHKhhw4Zpy5YtQf169uypAwcOBKY1a9aEWhoAAGiiWoS6wJAhQzRkyJBa958+fXrQ60cffVRLly7V22+/rb59+54opEULxcfHh1oOAABoBhp8DEtVVZWOHj2q2NjYoPYdO3YoMTFRXbp00c0336yvv/66xnWUl5fL7/cHTQAAoOlq8MDy1FNPqbS0VMOHDw+0paamas6cOVq+fLlefPFFFRcX66qrrtLRo0erXUdeXp48Hk9gSkpKaqjyAQBAGLiMMabOC7tcWrx4sbKysmrV//XXX9cdd9yhpUuXKiMjo8Z+R44cUceOHTVt2jSNGTPmlPnl5eUqLy8PvPb7/UpKSpLP51NMTEzI2wEAABqe3++Xx+Op1fd3yGNY6mr+/PkaO3asFi5ceNqwIkkXXnihunXrpp07d1Y73+12y+12n4syAQCAhRrkktC8efM0evRozZs3T0OHDj1j/9LSUu3atUsJCQkNUB0AALBdyGdYSktLg858FBcXq6ioSLGxserQoYNyc3O1b98+zZ07V5JzGSg7O1vPPvusUlNT5fV6JUktW7aUx+ORJN13330aNmyYOnbsqP3792vq1KmKjIzUyJEj62MbAQBAIxfyGZaNGzeqb9++gVuSc3Jy1LdvX02ZMkWSdODAgaA7fF566SX9+OOPGj9+vBISEgLTvffeG+izd+9ejRw5Ut27d9fw4cN10UUXad26dWrXrt3Zbh8AAGgCzmrQrS1CGbQDAADsEMr3N39LCAAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYaukvf5FcLumKK8JdCRqrlSudY6i6ad26cFeHxm7zZum666TYWKlVK+ez6rnnwl0VGrPbbqv5M8vlkvbta9h6WjTs2zVOe/dKjz4qnX9+uCtBU/D730tXXhnc1rVreGpB0/DBB9KwYVLfvtLkydIFF0i7djmfXUBd3XWXlJER3GaMdPfdUqdO0sUXN2w9BJZauO8+6Re/kCorpUOHwl0NGrurrpJuuCHcVaCp8PulUaOkoUOlN9+UIjhvjnqSluZMP7VmjfTdd9LNNzd8PSEf2qtXr9awYcOUmJgol8ulJUuWnHGZlStX6uc//7ncbre6du2qOXPmnNInPz9fnTp1UnR0tFJTU7Vhw4ZQSzsnVq92PgSmTw93JWhKjh6Vfvwx3FWgKXj9damkxLlsHREhlZVJVVXhrgpN1euvO5eD/uf/bPj3DjmwlJWVKTk5Wfn5+bXqX1xcrKFDh2rgwIEqKirSxIkTNXbsWL3//vuBPgsWLFBOTo6mTp2qzZs3Kzk5WZmZmTp48GCo5dWrykrpnnuksWOlXr3CWgqakNGjpZgYKTpaGjhQ2rgx3BWhMfvwQ+d42rdP6t7duRwUEyONGycdOxbu6tCU/PCD9MYb0oABziWhhhbyJaEhQ4ZoyJAhte4/Y8YMde7cWU8//bQk6Wc/+5nWrFmjZ555RpmZmZKkadOm6Y477tDo0aMDyyxbtkyzZs3SpEmTQi2x3syYIe3e7XwgAGcrKkr693+XfvtbqW1b6YsvpKeeci4RrV3rjD8AQrVjh3O27vrrpTFjpLw8Z4D3889LR45I8+aFu0I0Fe+/Lx0+HJ7LQVIDjGEpLCxUxkmjdjIzMzVx4kRJUkVFhTZt2qTc3NzA/IiICGVkZKiwsLDadZaXl6u8vDzw2u/313vdhw9LU6Y4A9jatav31aMZGjDAmY677jpnLEvv3lJurrR8efhqQ+NVWuqMKbj77hN3Bf3bv0kVFdJf/yo99JB02WXhrRFNw+uvS+edJw0fHp73P+fDs7xer+Li4oLa4uLi5Pf79f333+vQoUOqrKysto/X6612nXl5efJ4PIEpKSmp3uv+05+c2wPvuafeVw0EdO3q/Gb88cfOJUggVC1bOj9HjgxuPz7GoIbf+4CQlJZKS5dKmZnSRReFp4ZGOZ48NzdXPp8vMO3Zs6de179jh/TSS87tp/v3S1995UzHjjnX8L76SvrnP+v1LdGMJSU5vw2XlYW7EjRGiYnOz5N+51P79s7Pb79t2HrQNC1ZEr67g44754ElPj5eJSUlQW0lJSWKiYlRy5Yt1bZtW0VGRlbbJz4+vtp1ut1uxcTEBE31ad8+Z5T9738vde58Ylq/XvryS+e/H3qoXt8Szdg//uEMwL3ggnBXgsaoXz/n58kP8dq/3/nJJW3Uh//8T+cz6rrrwlfDOQ8saWlpKigoCGpbsWKF0v51c3dUVJT69esX1KeqqkoFBQWBPg3tiiukxYtPnXr2lDp0cP57zJiwlIZG7JtvTm37f/9P+q//kgYN4vkZqJvj4wlmzgxuf+UVqUUL6eqrG7wkNDHffOPcfPK73zlPUQ6XkAfdlpaWaufOnYHXxcXFKioqUmxsrDp06KDc3Fzt27dPc+fOlSTdfffdeuGFF/TAAw/o9ttv10cffaQ33nhDy5YtC6wjJydH2dnZ6t+/v1JSUjR9+nSVlZUF7hpqaG3bSllZp7YffxZLdfOAMxkxwhlvMGCAc7r+iy+cS4+tWkmPPRbu6tBY9e0r3X67NGuWc7dQerpzl9DChc5g7uOXjIC6WrDAObbCeTlIkmRC9PHHHxtJp0zZ2dnGGGOys7NNenr6Kcv06dPHREVFmS5dupjZs2efst7nn3/edOjQwURFRZmUlBSzbt26Wtfk8/mMJOPz+ULdnJCkpxvTs+c5fQs0Yc8+a0xKijGxsca0aGFMQoIxt9xizI4d4a4MjV1FhTEPPmhMx47GnHeeMV27GvPMM+GuCk3FL35hTPv2xvz4Y/2vO5Tvb5cxxoQxL9ULv98vj8cjn89X7+NZAADAuRHK9zdXzQEAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1qtTYMnPz1enTp0UHR2t1NRUbdiwoca+V199tVwu1ynT0KFDA31uu+22U+YPHjy4LqUBAIAmqEWoCyxYsEA5OTmaMWOGUlNTNX36dGVmZmr79u1q3779Kf3feustVVRUBF4fPnxYycnJuvHGG4P6DR48WLNnzw68drvdoZYGAACaqJDPsEybNk133HGHRo8ercsvv1wzZsxQq1atNGvWrGr7x8bGKj4+PjCtWLFCrVq1OiWwuN3uoH5t2rSp2xYBAIAmJ6TAUlFRoU2bNikjI+PECiIilJGRocLCwlqtY+bMmbrpppt0/vnnB7WvXLlS7du3V/fu3TVu3DgdPny4xnWUl5fL7/cHTQAAoOkKKbAcOnRIlZWViouLC2qPi4uT1+s94/IbNmzQ1q1bNXbs2KD2wYMHa+7cuSooKNDjjz+uVatWaciQIaqsrKx2PXl5efJ4PIEpKSkplM0AAACNTMhjWM7GzJkz1atXL6WkpAS133TTTYH/7tWrl3r37q1LL71UK1eu1DXXXHPKenJzc5WTkxN47ff7CS0AADRhIZ1hadu2rSIjI1VSUhLUXlJSovj4+NMuW1ZWpvnz52vMmDFnfJ8uXbqobdu22rlzZ7Xz3W63YmJigiYAANB0hRRYoqKi1K9fPxUUFATaqqqqVFBQoLS0tNMuu3DhQpWXl+uWW2454/vs3btXhw8fVkJCQijlAQCAJirku4RycnL08ssv69VXX9W2bds0btw4lZWVafTo0ZKkUaNGKTc395TlZs6cqaysLF100UVB7aWlpbr//vu1bt06ffXVVyooKND111+vrl27KjMzs46bBQAAmpKQx7CMGDFC33zzjaZMmSKv16s+ffpo+fLlgYG4X3/9tSIignPQ9u3btWbNGn3wwQenrC8yMlKffvqpXn31VR05ckSJiYkaNGiQHn74YZ7FAgAAJEkuY4wJdxFny+/3y+PxyOfzMZ4FAIBGIpTvb/6WEAAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAenUKLPn5+erUqZOio6OVmpqqDRs21Nh3zpw5crlcQVN0dHRQH2OMpkyZooSEBLVs2VIZGRnasWNHXUoDAABNUMiBZcGCBcrJydHUqVO1efNmJScnKzMzUwcPHqxxmZiYGB04cCAw7d69O2j+E088oeeee04zZszQ+vXrdf755yszM1PHjh0LfYsAAECTE3JgmTZtmu644w6NHj1al19+uWbMmKFWrVpp1qxZNS7jcrkUHx8fmOLi4gLzjDGaPn26/vSnP+n6669X7969NXfuXO3fv19Lliyp00YBAICmJaTAUlFRoU2bNikjI+PECiIilJGRocLCwhqXKy0tVceOHZWUlKTrr79en3/+eWBecXGxvF5v0Do9Ho9SU1NrXGd5ebn8fn/QBAAAmq6QAsuhQ4dUWVkZdIZEkuLi4uT1eqtdpnv37po1a5aWLl2qv/3tb6qqqtKAAQO0d+9eSQosF8o68/Ly5PF4AlNSUlIomwEAABqZc36XUFpamkaNGqU+ffooPT1db731ltq1a6e//vWvdV5nbm6ufD5fYNqzZ089VgwAAGwTUmBp27atIiMjVVJSEtReUlKi+Pj4Wq3jvPPOU9++fbVz505JCiwXyjrdbrdiYmKCJgAA0HSFFFiioqLUr18/FRQUBNqqqqpUUFCgtLS0Wq2jsrJSn332mRISEiRJnTt3Vnx8fNA6/X6/1q9fX+t1AgCApq1FqAvk5OQoOztb/fv3V0pKiqZPn66ysjKNHj1akjRq1ChdfPHFysvLkyQ99NBD+sUvfqGuXbvqyJEjevLJJ7V7926NHTtWknMH0cSJE/XII4/osssuU+fOnTV58mQlJiYqKyur/rYUAAA0WiEHlhEjRuibb77RlClT5PV61adPHy1fvjwwaPbrr79WRMSJEzfffvut7rjjDnm9XrVp00b9+vXT2rVrdfnllwf6PPDAAyorK9Odd96pI0eO6Fe/+pWWL19+ygPmAABA8+QyxphwF3G2/H6/PB6PfD4f41kAAGgkQvn+5m8JAQAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsV6fAkp+fr06dOik6OlqpqanasGFDjX1ffvllXXXVVWrTpo3atGmjjIyMU/rfdtttcrlcQdPgwYPrUhoAAGiCQg4sCxYsUE5OjqZOnarNmzcrOTlZmZmZOnjwYLX9V65cqZEjR+rjjz9WYWGhkpKSNGjQIO3bty+o3+DBg3XgwIHANG/evLptEQAAaHJcxhgTygKpqam68sor9cILL0iSqqqqlJSUpHvuuUeTJk064/KVlZVq06aNXnjhBY0aNUqSc4blyJEjWrJkSehbIMnv98vj8cjn8ykmJqZO6wAAAA0rlO/vkM6wVFRUaNOmTcrIyDixgogIZWRkqLCwsFbr+O677/TDDz8oNjY2qH3lypVq3769unfvrnHjxunw4cM1rqO8vFx+vz9oAgAATVdIgeXQoUOqrKxUXFxcUHtcXJy8Xm+t1vHHP/5RiYmJQaFn8ODBmjt3rgoKCvT4449r1apVGjJkiCorK6tdR15enjweT2BKSkoKZTMAAEAj06Ih3+yxxx7T/PnztXLlSkVHRwfab7rppsB/9+rVS71799all16qlStX6pprrjllPbm5ucrJyQm89vv9hBYAAJqwkM6wtG3bVpGRkSopKQlqLykpUXx8/GmXfeqpp/TYY4/pgw8+UO/evU/bt0uXLmrbtq127txZ7Xy3262YmJigCQAANF0hBZaoqCj169dPBQUFgbaqqioVFBQoLS2txuWeeOIJPfzww1q+fLn69+9/xvfZu3evDh8+rISEhFDKAwAATVTItzXn5OTo5Zdf1quvvqpt27Zp3LhxKisr0+jRoyVJo0aNUm5ubqD/448/rsmTJ2vWrFnq1KmTvF6vvF6vSktLJUmlpaW6//77tW7dOn311VcqKCjQ9ddfr65duyozM7OeNhMAADRmIY9hGTFihL755htNmTJFXq9Xffr00fLlywMDcb/++mtFRJzIQS+++KIqKip0ww03BK1n6tSpevDBBxUZGalPP/1Ur776qo4cOaLExEQNGjRIDz/8sNxu91luHgAAaApCfg6LjXgOCwAAjc85ew4LAABAOBBYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQJLDcrLpT/+UUpMlFq2lFJTpRUrwl0VGrvSUmnqVGnwYCk2VnK5pDlzwl0VGru//12aMEHq2VM6/3ypQwdp+HDpyy/DXRkas88/l268UerSRWrVSmrbVvr1r6W33w5PPQSWGtx2mzRtmnTzzdKzz0qRkdJvfyutWRPuytCYHTokPfSQtG2blJwc7mrQVDz+uLRokXTNNc7n1Z13SqtXSz//ubR1a7irQ2O1e7d09KiUne0cV5MnO+3XXSe99FLD1+MyxpiGf9v65ff75fF45PP5FBMTc9br27DBOaPy5JPSffc5bceOSVdcIbVvL61de9ZvgWaqvFz69lspPl7auFG68kpp9mwnIAN1tXat1L+/FBV1om3HDqlXL+mGG6S//S18taFpqayU+vVzvhP/+7/Pfn2hfH/X6QxLfn6+OnXqpOjoaKWmpmrDhg2n7b9w4UL16NFD0dHR6tWrl959992g+cYYTZkyRQkJCWrZsqUyMjK0Y8eOupRWL9580zmjcuedJ9qio6UxY6TCQmnPnrCVhkbO7XbCClCfBgwIDiuSdNllziWibdvCUxOapshIKSlJOnKk4d875MCyYMEC5eTkaOrUqdq8ebOSk5OVmZmpgwcPVtt/7dq1GjlypMaMGaMtW7YoKytLWVlZ2vqT85RPPPGEnnvuOc2YMUPr16/X+eefr8zMTB07dqzuW3YWtmyRunWTTg57KSnOz6KiBi8JAEJijFRS4ow7AM5GWZlzOXvXLumZZ6T33nMuPzY4E6KUlBQzfvz4wOvKykqTmJho8vLyqu0/fPhwM3To0KC21NRUc9dddxljjKmqqjLx8fHmySefDMw/cuSIcbvdZt68ebWqyefzGUnG5/OFujnV6tnTmN/85tT2zz83RjJmxox6eRs0c3//u3M8zZ4d7krQFL32mnN8zZwZ7krQ2N11l3MsScZERBhzww3G/POf9bPuUL6/QzrDUlFRoU2bNikjIyPQFhERoYyMDBUWFla7TGFhYVB/ScrMzAz0Ly4ultfrDerj8XiUmppa4zrLy8vl9/uDpvr0/ffOqfuTRUefmA8Atvrv/5bGj5fS0pwBk8DZmDjRuUv21VelIUOccSwVFQ1fR0iB5dChQ6qsrFRcXFxQe1xcnLxeb7XLeL3e0/Y//jOUdebl5cnj8QSmpKSkUDbjjFq2dAZHnuz4FaqWLev17QCg3ni90tChksdzYjwecDZ69JAyMqRRo6R33nEezzBsmHPOpSE1ytuac3Nz5fP5AtOeeh4Fm5AgHThwavvxtsTEen07AKgXPp/zG/CRI9Ly5XxW4dy44Qbn2T8N/ZyfkAJL27ZtFRkZqZKSkqD2kpISxddw60N8fPxp+x//Gco63W63YmJigqb61KeP8z/i5CtN69efmA8ANjl2zPmt98svnd+CL7883BWhqTo+LMLna9j3DSmwREVFqV+/fiooKAi0VVVVqaCgQGlpadUuk5aWFtRfklasWBHo37lzZ8XHxwf18fv9Wr9+fY3rPNduuMG5RvfTB+OUlzvPy0hNdW7pAgBbVFZKI0Y4j11YuNAZuwKcrepu/v3hB2nuXGdoREOH4hahLpCTk6Ps7Gz1799fKSkpmj59usrKyjR69GhJ0qhRo3TxxRcrLy9PknTvvfcqPT1dTz/9tIYOHar58+dr48aNeulfacDlcmnixIl65JFHdNlll6lz586aPHmyEhMTlZWVVX9bGoLUVOdxxLm5zv+wrl2dwUZffSXNnBmWktCEvPCCc8p+/37n9dtvS3v3Ov99zz3O2AMgFH/4g/Rf/+WcYfnnP099UNwtt4SnLjRud93lXGn49a+liy92xkf95386g7qfflq64IIGLqgutyE9//zzpkOHDiYqKsqkpKSYdevWBealp6eb7OzsoP5vvPGG6datm4mKijI9e/Y0y5YtC5pfVVVlJk+ebOLi4ozb7TbXXHON2b59e63rqe/bmo0x5vvvjbnvPmPi441xu4258kpjli+vt9WjGevY8cQtgidPxcXhrg6NUXp6zcdU3T7lAWPmzTMmI8OYuDhjWrQwpk0b5/XSpfX3HqF8f/NofgAAEBbn/NH8AAAADYnAAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYL+S/JWSj4w/r9Z/855UBAIC1jn9v1+ah+00isBw9elSSlMSfUQYAoNE5evSoPGf4y69N4m8JVVVVaf/+/WrdurVcLle9rtvv9yspKUl79uzh7xSdAfuq9thXtce+Cg37q/bYV7V3rvaVMUZHjx5VYmKiIiJOP0qlSZxhiYiI0CWXXHJO3yMmJoYDupbYV7XHvqo99lVo2F+1x76qvXOxr850ZuU4Bt0CAADrEVgAAID1CCxn4Ha7NXXqVLnd7nCXYj32Ve2xr2qPfRUa9lftsa9qz4Z91SQG3QIAgKaNMywAAMB6BBYAAGA9AgsAALAegQUAAFiPwCIpPz9fnTp1UnR0tFJTU7Vhw4bT9l+4cKF69Oih6Oho9erVS++++24DVRp+oeyrOXPmyOVyBU3R0dENWG34rF69WsOGDVNiYqJcLpeWLFlyxmVWrlypn//853K73eratavmzJlzzuu0Qaj7auXKlaccVy6XS16vt2EKDqO8vDxdeeWVat26tdq3b6+srCxt3779jMs1x8+suuyr5vqZ9eKLL6p3796Bh8KlpaXpvffeO+0y4Timmn1gWbBggXJycjR16lRt3rxZycnJyszM1MGDB6vtv3btWo0cOVJjxozRli1blJWVpaysLG3durWBK294oe4ryXkq4oEDBwLT7t27G7Di8CkrK1NycrLy8/Nr1b+4uFhDhw7VwIEDVVRUpIkTJ2rs2LF6//33z3Gl4Rfqvjpu+/btQcdW+/btz1GF9li1apXGjx+vdevWacWKFfrhhx80aNAglZWV1bhMc/3Mqsu+kprnZ9Yll1yixx57TJs2bdLGjRv1m9/8Rtdff70+//zzavuH7ZgyzVxKSooZP3584HVlZaVJTEw0eXl51fYfPny4GTp0aFBbamqqueuuu85pnTYIdV/Nnj3beDyeBqrOXpLM4sWLT9vngQceMD179gxqGzFihMnMzDyHldmnNvvq448/NpLMt99+2yA12ezgwYNGklm1alWNfZrzZ9ZP1WZf8Zl1Qps2bcwrr7xS7bxwHVPN+gxLRUWFNm3apIyMjEBbRESEMjIyVFhYWO0yhYWFQf0lKTMzs8b+TUVd9pUklZaWqmPHjkpKSjptYm/umutxdTb69OmjhIQEXXvttfrkk0/CXU5Y+Hw+SVJsbGyNfTi2HLXZVxKfWZWVlZo/f77KysqUlpZWbZ9wHVPNOrAcOnRIlZWViouLC2qPi4ur8Xq41+sNqX9TUZd91b17d82aNUtLly7V3/72N1VVVWnAgAHau3dvQ5TcqNR0XPn9fn3//fdhqspOCQkJmjFjhhYtWqRFixYpKSlJV199tTZv3hzu0hpUVVWVJk6cqF/+8pe64oorauzXXD+zfqq2+6o5f2Z99tlnuuCCC+R2u3X33Xdr8eLFuvzyy6vtG65jqkn8tWbYKS0tLSihDxgwQD/72c/017/+VQ8//HAYK0Nj1r17d3Xv3j3wesCAAdq1a5eeeeYZvfbaa2GsrGGNHz9eW7du1Zo1a8JdivVqu6+a82dW9+7dVVRUJJ/PpzfffFPZ2dlatWpVjaElHJr1GZa2bdsqMjJSJSUlQe0lJSWKj4+vdpn4+PiQ+jcVddlXJzvvvPPUt29f7dy581yU2KjVdFzFxMSoZcuWYaqq8UhJSWlWx9WECRP0zjvv6OOPP9Yll1xy2r7N9TPruFD21cma02dWVFSUunbtqn79+ikvL0/Jycl69tlnq+0brmOqWQeWqKgo9evXTwUFBYG2qqoqFRQU1HjtLi0tLai/JK1YsaLG/k1FXfbVySorK/XZZ58pISHhXJXZaDXX46q+FBUVNYvjyhijCRMmaPHixfroo4/UuXPnMy7TXI+tuuyrkzXnz6yqqiqVl5dXOy9sx9Q5HdLbCMyfP9+43W4zZ84c88UXX5g777zTXHjhhcbr9RpjjLn11lvNpEmTAv0/+eQT06JFC/PUU0+Zbdu2malTp5rzzjvPfPbZZ+HahAYT6r7685//bN5//32za9cus2nTJnPTTTeZ6Oho8/nnn4drExrM0aNHzZYtW8yWLVuMJDNt2jSzZcsWs3v3bmOMMZMmTTK33nproP8//vEP06pVK3P//febbdu2mfz8fBMZGWmWL18erk1oMKHuq2eeecYsWbLE7Nixw3z22Wfm3nvvNREREebDDz8M1yY0mHHjxhmPx2NWrlxpDhw4EJi+++67QB8+sxx12VfN9TNr0qRJZtWqVaa4uNh8+umnZtKkScblcpkPPvjAGGPPMdXsA4sxxjz//POmQ4cOJioqyqSkpJh169YF5qWnp5vs7Oyg/m+88Ybp1q2biYqKMj179jTLli1r4IrDJ5R9NXHixEDfuLg489vf/tZs3rw5DFU3vOO33p48Hd8/2dnZJj09/ZRl+vTpY6KiokyXLl3M7NmzG7zucAh1Xz3++OPm0ksvNdHR0SY2NtZcffXV5qOPPgpP8Q2suv0kKehY4TPLUZd91Vw/s26//XbTsWNHExUVZdq1a2euueaaQFgxxp5jymWMMef2HA4AAMDZadZjWAAAQONAYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9f4/DJMICesC1g0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot white points only for scaling the plot\n",
    "ax.plot(mesh2d_input.node_x, mesh2d_input.node_y, \"ow\")\n",
    "\n",
    "# Numbering the nodes\n",
    "for i in range(mesh2d_input.node_x.size):\n",
    "    ax.annotate(\n",
    "        int(i),\n",
    "        xy=(mesh2d_input.node_x[i], mesh2d_input.node_y[i]),\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        fontsize=12,\n",
    "        color=\"blue\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c0aa00",
   "metadata": {},
   "source": [
    "The third mandatory attribute is `edge_nodes`.\n",
    "It describes the indices of the nodes that make up the edges.\n",
    "\n",
    "Two indices describe one edge.\n",
    "So in our case the indices 0-4, 1-5, 2-6, ... each describe one edge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6c17a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  4,  1,  5,  2,  6,  3,  7,  4,  8,  5,  9,  6, 10,  7, 11,  0,\n",
       "        1,  1,  2,  2,  3,  4,  5,  5,  6,  6,  7,  8,  9,  9, 10, 10, 11],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mesh2d_input.edge_nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9868b5fb",
   "metadata": {},
   "source": [
    "With all three parameters together we can plot the mesh."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e3c50fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "mesh2d_input.plot_edges(ax, color=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35f31c20",
   "metadata": {},
   "source": [
    "In order to interact with the `meshkernel` library, we create a new instance of the `MeshKernel` class.\n",
    "\n",
    "The `projection` parameter of its constructor describes whether the mesh is cartesian (`ProjextionType.CARTESIAN`) or spherical (`ProjextionType.SPHERICAL`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c29964a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mk = MeshKernel(projection=ProjectionType.CARTESIAN)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7954ccae",
   "metadata": {},
   "source": [
    "Each instance holds it own state.\n",
    "This state can be accessed with the corresponding getter and setter methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "19aa165f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mk.mesh2d_set(mesh2d_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a874eb76",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh2d_output_0 = mk.mesh2d_get()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d8bc297",
   "metadata": {},
   "source": [
    "We have now set mesh2d and immediately got it again, without asking `meshkernel` to execute any operations in between.\n",
    "\n",
    "After we set the mesh2d, meshkernel calculated the face data and edge coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a4d8b8d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "mesh2d_output_0.plot_faces(ax)\n",
    "\n",
    "# Draw face index at the face's center\n",
    "for face_index, (face_x, face_y) in enumerate(\n",
    "    zip(mesh2d_output_0.face_x, mesh2d_output_0.face_y)\n",
    "):\n",
    "    ax.text(face_x, face_y, face_index, ha=\"center\", va=\"center\", fontsize=22)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d338acf6",
   "metadata": {},
   "source": [
    "`meshkernel` also searches for the middle point of edges and adds them as parameters to the `Mesh2D` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f87ac27a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(mesh2d_output_0.edge_x, mesh2d_output_0.edge_y, \".\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff70a3ab",
   "metadata": {},
   "source": [
    "Until now everything *looked* structured, so let us add a few nodes to change that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "73f609b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "node_index_0 = mk.mesh2d_insert_node(4.0, 1.5)\n",
    "node_index_1 = mk.mesh2d_insert_node(4.0, 2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b881cfc5",
   "metadata": {},
   "source": [
    "We also have to connect our nodes, otherwise `meshkernel` will garbage collect them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b1f99c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "edge_index_0 = mk.mesh2d_insert_edge(7, node_index_0)\n",
    "edge_index_1 = mk.mesh2d_insert_edge(node_index_0, node_index_1)\n",
    "edge_index_3 = mk.mesh2d_insert_edge(node_index_1, 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f446957",
   "metadata": {},
   "source": [
    "Let us get the new state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f542b38f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh2d_output_1 = mk.mesh2d_get()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3b677f8",
   "metadata": {},
   "source": [
    "If we plot the output, we can also see that `meshkernel` immediately found a new face."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a67c2dde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "mesh2d_output_1.plot_faces(ax)\n",
    "\n",
    "# Draw face index at the face's center\n",
    "for face_index, (face_x, face_y) in enumerate(\n",
    "    zip(mesh2d_output_1.face_x, mesh2d_output_1.face_y)\n",
    "):\n",
    "    ax.text(face_x, face_y, face_index, ha=\"center\", va=\"center\", fontsize=22)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e6cef40",
   "metadata": {},
   "source": [
    "We can also delete nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "64c0fbdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "mk.mesh2d_delete_node(node_index_1)\n",
    "mesh2d_output_2 = mk.mesh2d_get()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82acc708",
   "metadata": {},
   "source": [
    "We are back to six faces, but one hanging edge remains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ef64cb2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/dqz02muSwxHQsozDERYAAALk0089p4DWrJHsdunllz0X1xJWLkRgAQCgmdXUSE8/7Xn3z+HDUs+e0ubN0tSpnAK6GE4JAQDQjE6ckCZOlAoKPPO//KXnKbZt2wa2LtMRWAAAaCbFxZ6AcvSoFBkpvfSS9Otfc1SlPjglBABAE6uulp58UvrZzzxhpXdv6bPPPM9bIazUD0dYAABoQk6n9KtfSYWFnvn0dCknR2rdOrB1BRsCCwAATaSwULrnHqm8XGrVynMXUHp6oKsKTpwSAgCgkVVXS3PnSrff7gkrN9zgOQVEWGk4vwPLhg0bNGbMGMXFxclmsyk/P7/O9u+9955uv/12derUSVFRUUpOTtaHH37o0+bxxx+XzWbzmXr37u1vaQAABNzRo9KIEdITT3ieYDt5sueR+336BLqy4OZ3YHG73UpISFBOTk692m/YsEG333671q5dq9LSUg0fPlxjxozRjh07fNr17dtXx44d804bN270tzQAAALqww+lxETP3UBt2khvv+15am2rVoGuLPj5fQ3LqFGjNGrUqHq3nz9/vs/8f/3Xf+n999/X3//+dw0YMOCHQlq0UExMjL/lAAAQcN9/Lz32mOdhcJKUkCCtXCldf31g6wolzX4NS01Njc6cOaMOHTr4LN+7d6/i4uLUvXt33XPPPTp06NBF91FVVSWXy+UzAQAQCIcPS8OG/RBWpk71PLWWsNK4mj2wPPfcc6qsrNTdd9/tXZaUlKS8vDwVFBRo4cKFOnDggG677TadOXOm1n1kZ2fL4XB4p/j4+OYqHwAArw8+8JwC+vRTKSrK8x6gl1/2PBQOjatZA8uyZcv0pz/9SStXrlTnzp29y0eNGqWxY8eqf//+Sk1N1dq1a3X69GmtXLmy1v1kZWWpoqLCOx0+fLi5ugAAgL77Tpo5U/r5z6V//UsaOFDavl360d/iaGTN9hyW5cuXa/LkyVq1apVSUlLqbNuuXTtdf/312rdvX63r7Xa77HZ7U5QJAECdDh6Uxo/33PkjSb/7nfTss563LaPpNMsRlnfeeUcZGRl65513NHr06Eu2r6ys1P79+xUbG9sM1QEAUD/5+dKAAZ6w0q6d9N570oIFhJXm4HdgqaysVFlZmcrKyiRJBw4cUFlZmfci2aysLE2cONHbftmyZZo4caKef/55JSUlyel0yul0qqKiwttm5syZKi4u1sGDB7Vp0ybdddddCg8P14QJEy6zewAAXL6qKmnGDOmuu6TTp6XBg6UdOzzzaB5+B5Zt27ZpwIAB3luSMzMzNWDAAM2ZM0eSdOzYMZ87fF599VV9//33mjZtmmJjY73T9OnTvW2OHDmiCRMmqFevXrr77rt19dVXa/PmzerUqdPl9g8AgMvyz39Kt9ziOZIiSQ8/LP33f0tduwa0rCuOzbIsK9BFXC6XyyWHw6GKigpFRUU12n7dbs+DfySpspIXVQUjxjC4MX7BL9jH8K9/9bxR2eWSOnSQ8vKkMWMCXVXo8Of7m3cJAQBwnm+/laZNk8aO9YSVW26RysoIK4FEYAEA4Ef27pWSkz3PU5Gk2bOlTz6ReORXYDXbbc0AAJjunXek++/3nL7q2FF6801p5MhAVwWJIywAAOjf//YElV/+0hNWfvpTzykgwoo5CCwAgCva7t2e25Rfe02y2TwvMSwslK65JtCV4cc4JQQAuGK98YbnZYXffCNFR0tvvSVd4mHsCBCOsAAArjhut5SRIaWne8LKz37mOQVEWDEXgQUAcEXZtUu66SbPM1XCwqQnnpDWrZNiYgJdGerCKSEAwBXBsqQlS6SHHvJcZBsbKy1bJg0bFujKUB8EFgBAyDtzxnOtyttve+ZTUz3Xr3TuHNi6UH+cEgIAhLT/+R9p0CBPWAkPl7KzpbVrCSvBhiMsAICQZFnSK6943rJcVSV16eJ5MNyttwa6MjQEgQUAEHJcLmnKFGnlSs/86NHS0qXS1VcHti40HKeEAAAhpbRUuvFGT1hp0UJ67jnpb38jrAQ7jrAAAEKCZUkvvSTNnCmdPStde620fLl0882BrgyNgcACAAh6X38tTZokrV7tmU9L89zC3L59QMtCI+KUEAAgqG3d6jkFtHq1dNVV0oIF0nvvEVZCDYEFABCULEuaN0+65Rbp4EGpe3dp0ybpd7/zvMQQoYVTQgCAoPOvf0n33Sf9/e+e+V/8Qlq0SHI4AloWmhBHWAAAQWXTJikx0RNW7Hbp5Zc9dwQRVkIbgQUAEBRqaqRnnpF++lPp8GGpZ09p82bPI/c5BRT6OCUEADDeiRNSerr0j3945idM8DzFtm3bwNaF5kNgAQAYbcMGT0A5elSKjJT+8hfPLcwcVbmycEoIAGCk6mrpqaek4cM9YaV3b88tzJMnE1auRBxhAQAYp7xc+tWvpI8+8sxPnCjl5Eht2gS2LgQOgQUAYJSPP5buuUdyOqVWrTx3AaWnB7oqBBqnhAAARqiulubOlVJSPGGlb1/ps88IK/DgCAsAIOCOHvUcVSkq8sxPnux5xH6rVgEtCwYhsAAAAmrdOs/1KidOeK5ReeUV6Ze/DHRVMA2nhAAAAfH999If/yilpnrCSkKCVFpKWEHtOMICAGh2R454nq2ycaNn/oEHpBde8DxnBaiN30dYNmzYoDFjxiguLk42m035+fmX3KaoqEg33nij7Ha7evTooby8vAva5OTkqGvXroqMjFRSUpK2bt3qb2kAgCCwdq3nXUAbN3qeVLtihbRwIWEFdfM7sLjdbiUkJCgnJ6de7Q8cOKDRo0dr+PDhKisr04wZMzR58mR9+OGH3jYrVqxQZmam5s6dq+3btyshIUGpqak6fvy4v+UBAAz13XfSrFnS6NHSqVPSjTdKO3ZId98d6MoQDGyWZVkN3thm0+rVq5WWlnbRNn/4wx/0wQcfaNeuXd5l48eP1+nTp1VQUCBJSkpK0k033aSXXnpJklRTU6P4+Hg99NBDmj179iXrcLlccjgcqqioUFRUVEO7cwG3+4eHFJWXS61bN9qu0Uzcbik62vMzYxh8GL/g9+MxHDzY86RaSXroIenPf/a8bRlXLn++v5v8GpaSkhKlpKT4LEtNTdWMGTMkSWfPnlVpaamysrK868PCwpSSkqKSkpJa91lVVaWqqirvvMvlavzCz3PuA4fgxRgGN8Yv+G3dKrVrJy1ZIt11V6CrQbBp8ruEnE6nos/7lyY6Oloul0v//ve/dfLkSVVXV9faxul01rrP7OxsORwO7xQfH99k9QMAGsegQZ5TQIQVNERQ3iWUlZWlzMxM77zL5Wry0MLh6ODEKYXgxvgFvx+P4bp1Uvv2ga0HwavJA0tMTIzKy8t9lpWXlysqKkotW7ZUeHi4wsPDa20TExNT6z7tdrvszXzis3Vr/rEMdoxhcGP8gl9ERKArQDBr8lNCycnJKiws9Fm2fv16JScnS5IiIiI0cOBAnzY1NTUqLCz0tgEAAFc2vwNLZWWlysrKVFZWJslz23JZWZkOHTokyXO6ZuLEid72DzzwgP75z39q1qxZ+r//+z+9/PLLWrlypX7/+99722RmZuq1117T0qVLtXv3bk2dOlVut1sZGRmX2T0AABAK/D4ltG3bNg0fPtw7f+5akvT0dOXl5enYsWPe8CJJ3bp10wcffKDf//73WrBggbp06aJFixYpNTXV22bcuHE6ceKE5syZI6fTqcTERBUUFFxwIS4AALgyXdZzWEzRHM9hqazk/HkwYgyDG+MX/BhD1MWf729efggAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMF6DAktOTo66du2qyMhIJSUlaevWrRdtO2zYMNlstgum0aNHe9vcd999F6wfOXJkQ0oDAAAhqIW/G6xYsUKZmZnKzc1VUlKS5s+fr9TUVO3Zs0edO3e+oP17772ns2fPeudPnTqlhIQEjR071qfdyJEj9frrr3vn7Xa7v6UBAIAQ5fcRlnnz5mnKlCnKyMhQnz59lJubq1atWmnJkiW1tu/QoYNiYmK80/r169WqVasLAovdbvdp1759+4b1CAAAhBy/AsvZs2dVWlqqlJSUH3YQFqaUlBSVlJTUax+LFy/W+PHj1bp1a5/lRUVF6ty5s3r16qWpU6fq1KlTF91HVVWVXC6XzwQAAEKXX4Hl5MmTqq6uVnR0tM/y6OhoOZ3OS26/detW7dq1S5MnT/ZZPnLkSL3xxhsqLCzUM888o+LiYo0aNUrV1dW17ic7O1sOh8M7xcfH+9MNAAAQZPy+huVyLF68WP369dPgwYN9lo8fP977c79+/dS/f39dd911Kioq0ogRIy7YT1ZWljIzM73zLpeL0AIAQAjz6whLx44dFR4ervLycp/l5eXliomJqXNbt9ut5cuXa9KkSZf8Pd27d1fHjh21b9++Wtfb7XZFRUX5TAAAIHT5FVgiIiI0cOBAFRYWepfV1NSosLBQycnJdW67atUqVVVV6Ve/+tUlf8+RI0d06tQpxcbG+lMeAAAIUX7fJZSZmanXXntNS5cu1e7duzV16lS53W5lZGRIkiZOnKisrKwLtlu8eLHS0tJ09dVX+yyvrKzUI488os2bN+vgwYMqLCzUnXfeqR49eig1NbWB3QIAAKHE72tYxo0bpxMnTmjOnDlyOp1KTExUQUGB90LcQ4cOKSzMNwft2bNHGzdu1Lp16y7YX3h4uD7//HMtXbpUp0+fVlxcnO644w49+eSTPIsFAABIkmyWZVmBLuJyuVwuORwOVVRUNOr1LG631KaN5+fKSum8O7ERBBjD4Mb4BT/GEHXx5/ubdwkBAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIzXoMCSk5Ojrl27KjIyUklJSdq6detF2+bl5clms/lMkZGRPm0sy9KcOXMUGxurli1bKiUlRXv37m1IaQAAIAT5HVhWrFihzMxMzZ07V9u3b1dCQoJSU1N1/Pjxi24TFRWlY8eOeacvv/zSZ/2zzz6rF198Ubm5udqyZYtat26t1NRUffvtt/73CAAAhBy/A8u8efM0ZcoUZWRkqE+fPsrNzVWrVq20ZMmSi25js9kUExPjnaKjo73rLMvS/Pnz9eijj+rOO+9U//799cYbb+jo0aPKz89vUKcAAEBo8SuwnD17VqWlpUpJSflhB2FhSklJUUlJyUW3q6ys1LXXXqv4+Hjdeeed+uKLL7zrDhw4IKfT6bNPh8OhpKSki+6zqqpKLpfLZwIAAKHLr8By8uRJVVdX+xwhkaTo6Gg5nc5at+nVq5eWLFmi999/X2+99ZZqamo0ZMgQHTlyRJK82/mzz+zsbDkcDu8UHx/vTzcAAECQafK7hJKTkzVx4kQlJiZq6NCheu+999SpUye98sorDd5nVlaWKioqvNPhw4cbsWIAAGAavwJLx44dFR4ervLycp/l5eXliomJqdc+rrrqKg0YMED79u2TJO92/uzTbrcrKirKZwIAAKHLr8ASERGhgQMHqrCw0LuspqZGhYWFSk5Ortc+qqurtXPnTsXGxkqSunXrppiYGJ99ulwubdmypd77BAAAoa2FvxtkZmYqPT1dgwYN0uDBgzV//ny53W5lZGRIkiZOnKhrrrlG2dnZkqQnnnhCN998s3r06KHTp0/rz3/+s7788ktNnjxZkucOohkzZuipp55Sz5491a1bNz322GOKi4tTWlpa4/UUAAAELb8Dy7hx43TixAnNmTNHTqdTiYmJKigo8F40e+jQIYWF/XDg5uuvv9aUKVPkdDrVvn17DRw4UJs2bVKfPn28bWbNmiW32637779fp0+f1q233qqCgoILHjAHAACuTDbLsqxAF3G5XC6XHA6HKioqGvV6FrdbatPG83NlpdS6daPtGs2EMQxujF/wYwxRF3++v3mXEAAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeA0KLDk5OeratasiIyOVlJSkrVu3XrTta6+9pttuu03t27dX+/btlZKSckH7++67TzabzWcaOXJkQ0oDAAAhyO/AsmLFCmVmZmru3Lnavn27EhISlJqaquPHj9favqioSBMmTNAnn3yikpISxcfH64477tBXX33l027kyJE6duyYd3rnnXca1iMAABBy/A4s8+bN05QpU5SRkaE+ffooNzdXrVq10pIlS2pt//bbb+u3v/2tEhMT1bt3by1atEg1NTUqLCz0aWe32xUTE+Od2rdv37AeAQCAkONXYDl79qxKS0uVkpLyww7CwpSSkqKSkpJ67eObb77Rd999pw4dOvgsLyoqUufOndWrVy9NnTpVp06duug+qqqq5HK5fCYAABC6/AosJ0+eVHV1taKjo32WR0dHy+l01msff/jDHxQXF+cTekaOHKk33nhDhYWFeuaZZ1RcXKxRo0apurq61n1kZ2fL4XB4p/j4eH+6AQAAgkyL5vxlTz/9tJYvX66ioiJFRkZ6l48fP977c79+/dS/f39dd911Kioq0ogRIy7YT1ZWljIzM73zLpeL0AIAQAjz6whLx44dFR4ervLycp/l5eXliomJqXPb5557Tk8//bTWrVun/v3719m2e/fu6tixo/bt21frervdrqioKJ8JAACELr8CS0REhAYOHOhzwey5C2iTk5Mvut2zzz6rJ598UgUFBRo0aNAlf8+RI0d06tQpxcbG+lMeAAAIUX7fJZSZmanXXntNS5cu1e7duzV16lS53W5lZGRIkiZOnKisrCxv+2eeeUaPPfaYlixZoq5du8rpdMrpdKqyslKSVFlZqUceeUSbN2/WwYMHVVhYqDvvvFM9evRQampqI3UTAAAEM7+vYRk3bpxOnDihOXPmyOl0KjExUQUFBd4LcQ8dOqSwsB9y0MKFC3X27Fn94he/8NnP3Llz9fjjjys8PFyff/65li5dqtOnTysuLk533HGHnnzySdnt9svsHgAACAU2y7KsQBdxuVwulxwOhyoqKhr1eha3W2rTxvNzZaXUunWj7RrNhDEMboxf8GMMURd/vr95lxAAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgNCiw5OTnq2rWrIiMjlZSUpK1bt9bZftWqVerdu7ciIyPVr18/rV271me9ZVmaM2eOYmNj1bJlS6WkpGjv3r0NKQ0AAIQgvwPLihUrlJmZqblz52r79u1KSEhQamqqjh8/Xmv7TZs2acKECZo0aZJ27NihtLQ0paWladeuXd42zz77rF588UXl5uZqy5Ytat26tVJTU/Xtt982vGcAACBk2CzLsvzZICkpSTfddJNeeuklSVJNTY3i4+P10EMPafbs2Re0HzdunNxut9asWeNddvPNNysxMVG5ubmyLEtxcXF6+OGHNXPmTElSRUWFoqOjlZeXp/Hjx1+yJpfLJYfDoYqKCkVFRfnTnTq53VKbNp6fy8ul1q0bbddoJm63FB3t+ZkxDD6MX/D78RhWVjKG8OXP93cLf3Z89uxZlZaWKisry7ssLCxMKSkpKikpqXWbkpISZWZm+ixLTU1Vfn6+JOnAgQNyOp1KSUnxrnc4HEpKSlJJSUmtgaWqqkpVVVXeeZfL5U83GuTcBw7BizEMbowfcGXz65TQyZMnVV1drejz/uWIjo6W0+msdRun01ln+3P/9Wef2dnZcjgc3ik+Pt6fbtRb69aSf8efAAC1sSyOruDy+HWExRRZWVk+R21cLleThRbJcxgTAAAEjl+BpWPHjgoPD1d5ebnP8vLycsXExNS6TUxMTJ3tz/23vLxcsbGxPm0SExNr3afdbpfdbven9MvCXwUAAASWX6eEIiIiNHDgQBUWFnqX1dTUqLCwUMnJybVuk5yc7NNektavX+9t361bN8XExPi0cblc2rJly0X3CQAArix+nxLKzMxUenq6Bg0apMGDB2v+/Plyu93KyMiQJE2cOFHXXHONsrOzJUnTp0/X0KFD9fzzz2v06NFavny5tm3bpldffVWSZLPZNGPGDD311FPq2bOnunXrpscee0xxcXFKS0trvJ4CAICg5XdgGTdunE6cOKE5c+bI6XQqMTFRBQUF3otmDx06pLCwHw7cDBkyRMuWLdOjjz6qP/7xj+rZs6fy8/N1ww03eNvMmjVLbrdb999/v06fPq1bb71VBQUFioyMbIQuAgCAYOf3c1hM1FTPYQEAAE3Hn+9v3iUEAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIzn96P5TXTuYb0ulyvAlQAAgPo6971dn4fuh0RgOXPmjCQpPj4+wJUAAAB/nTlzRg6Ho842IfEuoZqaGh09elRt27aVzWZr1H27XC7Fx8fr8OHDIfmeolDvnxT6faR/wS/U+xjq/ZNCv49N1T/LsnTmzBnFxcX5vDi5NiFxhCUsLExdunRp0t8RFRUVkv8TnhPq/ZNCv4/0L/iFeh9DvX9S6PexKfp3qSMr53DRLQAAMB6BBQAAGI/Acgl2u11z586V3W4PdClNItT7J4V+H+lf8Av1PoZ6/6TQ76MJ/QuJi24BAEBo4wgLAAAwHoEFAAAYj8ACAACMR2ABAADGI7BIysnJUdeuXRUZGamkpCRt3bq1zvarVq1S7969FRkZqX79+mnt2rXNVGnD+NO/vLw82Ww2nykyMrIZq/XPhg0bNGbMGMXFxclmsyk/P/+S2xQVFenGG2+U3W5Xjx49lJeX1+R1Xg5/+1hUVHTBGNpsNjmdzuYp2E/Z2dm66aab1LZtW3Xu3FlpaWnas2fPJbcLls9hQ/oXTJ/DhQsXqn///t4HiiUnJ+sf//hHndsEy9id428fg2n8avP000/LZrNpxowZdbZr7nG84gPLihUrlJmZqblz52r79u1KSEhQamqqjh8/Xmv7TZs2acKECZo0aZJ27NihtLQ0paWladeuXc1cef342z/J8yTDY8eOeacvv/yyGSv2j9vtVkJCgnJycurV/sCBAxo9erSGDx+usrIyzZgxQ5MnT9aHH37YxJU2nL99PGfPnj0+49i5c+cmqvDyFBcXa9q0adq8ebPWr1+v7777TnfccYfcbvdFtwmmz2FD+icFz+ewS5cuevrpp1VaWqpt27bpZz/7me6880598cUXtbYPprE7x98+SsEzfuf77LPP9Morr6h///51tgvIOFpXuMGDB1vTpk3zzldXV1txcXFWdnZ2re3vvvtua/To0T7LkpKSrN/85jdNWmdD+du/119/3XI4HM1UXeOSZK1evbrONrNmzbL69u3rs2zcuHFWampqE1bWeOrTx08++cSSZH399dfNUlNjO378uCXJKi4uvmibYPsc/lh9+hfMn0PLsqz27dtbixYtqnVdMI/dj9XVx2AdvzNnzlg9e/a01q9fbw0dOtSaPn36RdsGYhyv6CMsZ8+eVWlpqVJSUrzLwsLClJKSopKSklq3KSkp8WkvSampqRdtH0gN6Z8kVVZW6tprr1V8fPwl/4oINsE0fpcrMTFRsbGxuv322/Xpp58Gupx6q6iokCR16NDhom2CeRzr0z8pOD+H1dXVWr58udxut5KTk2ttE8xjJ9Wvj1Jwjt+0adM0evToC8anNoEYxys6sJw8eVLV1dWKjo72WR4dHX3R8/1Op9Ov9oHUkP716tVLS5Ys0fvvv6+33npLNTU1GjJkiI4cOdIcJTe5i42fy+XSv//97wBV1bhiY2OVm5urd999V++++67i4+M1bNgwbd++PdClXVJNTY1mzJihW265RTfccMNF2wXT5/DH6tu/YPsc7ty5U23atJHdbtcDDzyg1atXq0+fPrW2Ddax86ePwTZ+krR8+XJt375d2dnZ9WofiHEMibc1o/EkJyf7/NUwZMgQ/cd//IdeeeUVPfnkkwGsDPXVq1cv9erVyzs/ZMgQ7d+/Xy+88ILefPPNAFZ2adOmTdOuXbu0cePGQJfSJOrbv2D7HPbq1UtlZWWqqKjQX//6V6Wnp6u4uPiiX+jByJ8+Btv4HT58WNOnT9f69euNvjj4ig4sHTt2VHh4uMrLy32Wl5eXKyYmptZtYmJi/GofSA3p3/muuuoqDRgwQPv27WuKEpvdxcYvKipKLVu2DFBVTW/w4MHGh4AHH3xQa9as0YYNG9SlS5c62wbT5/Acf/p3PtM/hxEREerRo4ckaeDAgfrss8+0YMECvfLKKxe0Dcaxk/zr4/lMH7/S0lIdP35cN954o3dZdXW1NmzYoJdeeklVVVUKDw/32SYQ43hFnxKKiIjQwIEDVVhY6F1WU1OjwsLCi56bTE5O9mkvSevXr6/zXGagNKR/56uurtbOnTsVGxvbVGU2q2Aav8ZUVlZm7BhalqUHH3xQq1ev1scff6xu3bpdcptgGseG9O98wfY5rKmpUVVVVa3rgmns6lJXH89n+viNGDFCO3fuVFlZmXcaNGiQ7rnnHpWVlV0QVqQAjWOTXc4bJJYvX27Z7XYrLy/P+t///V/r/vvvt9q1a2c5nU7Lsizr3nvvtWbPnu1t/+mnn1otWrSwnnvuOWv37t3W3LlzrauuusrauXNnoLpQJ3/796c//cn68MMPrf3791ulpaXW+PHjrcjISOuLL74IVBfqdObMGWvHjh3Wjh07LEnWvHnzrB07dlhffvmlZVmWNXv2bOvee+/1tv/nP/9ptWrVynrkkUes3bt3Wzk5OVZ4eLhVUFAQqC5ckr99fOGFF6z8/Hxr79691s6dO63p06dbYWFh1kcffRSoLtRp6tSplsPhsIqKiqxjx455p2+++cbbJpg/hw3pXzB9DmfPnm0VFxdbBw4csD7//HNr9uzZls1ms9atW2dZVnCP3Tn+9jGYxu9izr9LyIRxvOIDi2VZ1l/+8hfrJz/5iRUREWENHjzY2rx5s3fd0KFDrfT0dJ/2K1eutK6//norIiLC6tu3r/XBBx80c8X+8ad/M2bM8LaNjo62/vM//9Pavn17AKqun3O38J4/netTenq6NXTo0Au2SUxMtCIiIqzu3btbr7/+erPX7Q9/+/jMM89Y1113nRUZGWl16NDBGjZsmPXxxx8Hpvh6qK1vknzGJZg/hw3pXzB9Dn/9619b1157rRUREWF16tTJGjFihPeL3LKCe+zO8bePwTR+F3N+YDFhHG2WZVlNd/wGAADg8l3R17AAAIDgQGABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPH+PzbZVR5E/UQqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "mesh2d_output_2.plot_edges(ax, color=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfba291d",
   "metadata": {},
   "source": [
    "Quite often, hanging edges are unwanted.\n",
    "That is why `meshkernel` provides methods to deal with them.\n",
    "For once we can it can count hanging edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "306a116a",
   "metadata": {},
   "outputs": [],
   "source": [
    "hanging_edges = mk.mesh2d_get_hanging_edges()\n",
    "assert hanging_edges.size == 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac05eb75",
   "metadata": {},
   "source": [
    "`meshkernel` can also find and delete them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fdfcee68",
   "metadata": {},
   "outputs": [],
   "source": [
    "mk.mesh2d_delete_hanging_edges()\n",
    "mesh2d_output_3 = mk.mesh2d_get()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d58d8bb",
   "metadata": {},
   "source": [
    "After we have deleted the hanging edges, we are back at the original state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7d8f1b3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "mesh2d_output_3.plot_edges(ax, color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf0e2560-1d1f-4f90-ac47-61ddc4c5bd71",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}