test_mf6_algomesh.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from shapely.geometry import Polygon\n",
    "import geopandas as gpd\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make simple small square domain for algomesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 10.0, 20.0, 30, 30.0, 30.0, 30.0, 20.0, 10.0, 0.0, 0.0, 0.0, 0.0]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[0.0, 0.0, 0.0, 0.0, 10.0, 20.0, 30.0, 30.0, 30.0, 30.0, 20.0, 10.0, 0.0]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y = [],[]\n",
    "y += [0.]*4\n",
    "x += [0.,10.,20.,30]\n",
    "x += [30.]*3\n",
    "y += [10.,20.,30.]\n",
    "y += [30.]*3\n",
    "x += [20.,10.,0.]\n",
    "y += [20.,10.,0.]\n",
    "x += [0.]*3\n",
    "x\n",
    "y\n",
    "    \n",
    "poly = Polygon(zip(x, y))\n",
    "poly = gpd.GeoDataFrame(index=[0], geometry=[poly])  \n",
    "poly.plot()\n",
    "#poly.to_file(\"test_mf6_algomesh_domain.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 15.0, 30, 30.0, 30.0, 15.0, 0.0, 0.0, 0.0]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[0.0, 0.0, 0.0, 15.0, 30.0, 30.0, 30.0, 15.0, 0.0]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y = [],[]\n",
    "y += [0.]*3\n",
    "x += [0.,15.,30]\n",
    "x += [30.]*2\n",
    "y += [15.,30.]\n",
    "y += [30.]*2\n",
    "x += [15.,0.]\n",
    "y += [15.,0.]\n",
    "x += [0.]*2\n",
    "x\n",
    "y\n",
    "    \n",
    "poly = Polygon(zip(x, y))\n",
    "poly = gpd.GeoDataFrame(index=[0], geometry=[poly])  \n",
    "poly.plot()\n",
    "poly.to_file(\"test_mf6_algomesh_domain.shp\")\n",
    "\n",
    "pts = gpd.points_from_xy(x, y)\n",
    "pts = gpd.GeoDataFrame(index=list(range(len(x))), geometry=pts)\n",
    "pts.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 30.0, 30.0, 30.0, 0.0, 0.0, 0.0]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[0.0, 0.0, 15.0, 30.0, 30.0, 15.0, 0.0]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAMC0lEQVR4nO3df6jd9X3H8eerNq6lCupyCEHNYq2s5I81yiWzVKSzs7j0DxXKqH+U/CGkFAWF7o/QwWZhf9gx9a/iiCgNw9m6VlE22ZqFgBSG7sbGmBi6qERmiMkVJ9p/tiW+98f5Bi7Zvb0n93zPuaf9PB9wON/zPd97v2++5JnzIyfnm6pC0m+/T6z1AJKmw9ilRhi71Ahjlxph7FIjPjnNna1fv742b948zV1KTTlw4MB7VTVY6r6pxr5582bm5+enuUupKUneXu4+n8ZLjTB2qRHGLjXC2KVGGLvUiBVjT/KpJC8neTXJkSTf69Zfk+SlJG8k+XGSiyc/rqTVGuWR/b+BW6rqC8BW4LYkNwLfBx6pqs8B/wXcPbEpJY1txdhr6FfdzXXdpYBbgJ906/cAd0xiQEn9GOlDNUkuAg4AnwN+ALwJfFBVZ7pN3gGuXOZndwI7ATZt2jTSUJt3/dNI20ktOf7g18b6+ZHeoKuqs1W1FbgK2AZ8ftQdVNXuqpqrqrnBYMlP8Umaggt6N76qPgD2A18ELkty7pnBVcCJfkeT1KdR3o0fJLmsW/40cCtwlGH0X+822wE8N6EZJfVglNfsG4E93ev2TwBPV9U/Jnkd+FGSvwJ+ATw+wTkljWnF2KvqEHD9EuvfYvj6XdJvAD9BJzXC2KVGGLvUCGOXGmHsUiOMXWqEsUuNMHapEcYuNcLYpUYYu9QIY5caYexSI4xdaoSxS40wdqkRxi41wtilRhi71Ahjlxph7FIjjF1qhLFLjTB2qRGjnP7p6iT7k7ye5EiS+7r1DyQ5keRgd9k++XElrdYop386A3ynql5JcilwIMne7r5HqupvJjeepL6Mcvqnk8DJbvmjJEdZ5lzskmbXBb1mT7KZ4XnfXupW3ZvkUJInkly+zM/sTDKfZH5hYWG8aSWt2sixJ7kE+Clwf1V9CDwKXAtsZfjI/9BSP1dVu6tqrqrmBoPB+BNLWpWRYk+yjmHoT1bVMwBVdaqqzlbVx8BjeEZXaaaN8m58GJ57/WhVPbxo/cZFm90JHO5/PEl9GeXd+C8B3wReS3KwW/dd4K4kW4ECjgPfmsB8knoyyrvxPweyxF0v9D+OpEnxE3RSI4xdaoSxS40wdqkRxi41wtilRhi71Ahjlxph7FIjjF1qhLFLjTB2qRHGLjXC2KVGGLvUCGOXGmHsUiOMXWqEsUuNMHapEcYuNcLYpUYYu9QIY5caMcrpn65Osj/J60mOJLmvW39Fkr1JjnXXS57FVdJsGOWR/QzwnaraAtwI3JNkC7AL2FdV1wH7utuSZtSKsVfVyap6pVv+CDgKXAncDuzpNtsD3DGhGSX14IJesyfZDFwPvARsqKqT3V3vAhuW+ZmdSeaTzC8sLIwzq6QxjBx7kksYnqP9/qr6cPF9VVUMz+b6/1TV7qqaq6q5wWAw1rCSVm+k2JOsYxj6k1X1TLf61LlztHfXpyczoqQ+jPJufIDHgaNV9fCiu54HdnTLO4Dn+h9PUl9WPD878CXgm8BrSQ52674LPAg8neRu4G3gTycyoaRerBh7Vf0cyDJ3f6XfcSRNip+gkxph7FIjjF1qhLFLjTB2qRHGLjXC2KVGGLvUCGOXGmHsUiOMXWqEsUuNMHapEcYuNcLYpUYYu9QIY5caYexSI4xdaoSxS40wdqkRxi41wtilRhi71IhRTv/0RJLTSQ4vWvdAkhNJDnaX7ZMdU9K4Rnlk/yFw2xLrH6mqrd3lhX7HktS3FWOvqheB96cwi6QJGuc1+71JDnVP8y9fbqMkO5PMJ5lfWFgYY3eSxrHa2B8FrgW2AieBh5bbsKp2V9VcVc0NBoNV7k7SuFYVe1WdqqqzVfUx8Biwrd+xJPVtVbEn2bjo5p3A4eW2lTQbVjw/e5KngC8D65O8A/wl8OUkW4ECjgPfmtyIkvqwYuxVddcSqx+fwCySJshP0EmNMHapEcYuNcLYpUYYu9QIY5caYexSI4xdaoSxS40wdqkRxi41wtilRhi71Ahjlxph7FIjjF1qhLFLjTB2qRHGLjXC2KVGGLvUCGOXGmHsUiOMXWrEirF3Z2k9neTwonVXJNmb5Fh3vexZXCXNhlEe2X8I3Hbeul3Avqq6DtjX3ZY0w1aMvapeBN4/b/XtwJ5ueQ9wR79jSerbal+zb6iqk93yu8CG5TZMsjPJfJL5hYWFVe5O0rjGfoOuqorh2VyXu393Vc1V1dxgMBh3d5JWabWxnzp3jvbu+nR/I0mahNXG/jywo1veATzXzziSJmWUf3p7Cvg34PeTvJPkbuBB4NYkx4A/7m5LmmGfXGmDqrprmbu+0vMskibIT9BJjTB2qRHGLjXC2KVGGLvUCGOXGmHsUiOMXWqEsUuNMHapEcYuNcLYpUYYu9QIY5caYexSI4xdaoSxS40wdqkRxi41wtilRhi71Ahjlxph7FIjjF1qxIonifh1khwHPgLOAmeqaq6PoST1b6zYO39UVe/18HskTZBP46VGjBt7AT9LciDJzqU2SLIzyXyS+YWFhTF3J2m1xo39pqq6AfgT4J4kN5+/QVXtrqq5qpobDAZj7k7Sao0Ve1Wd6K5PA88C2/oYSlL/Vh17ks8kufTcMvBV4HBfg0nq1zjvxm8Ank1y7vf8fVX9cy9TSerdqmOvqreAL/Q4i6QJ8p/epEYYu9QIY5caYexSI4xdaoSxS40wdqkRxi41wtilRhi71Ahjlxph7FIjjF1qhLFLjTB2qRHGLjXC2KVGGLvUCGOXGmHsUiOMXWqEsUuNMHapEcYuNWKs2JPcluSXSd5IsquvoST1b5xzvV0E/IDhGVy3AHcl2dLXYJL6Nc4j+zbgjap6q6r+B/gRcHs/Y0nq2zgndrwS+M9Ft98B/vD8jZLsBHYCbNq0aaRffPzBr40xlqSlTPwNuqraXVVzVTU3GAwmvTtJyxgn9hPA1YtuX9WtkzSDxon934HrklyT5GLgG8Dz/YwlqW/jnJ/9TJJ7gX8BLgKeqKojvU0mqVfjvEFHVb0AvNDTLJImyE/QSY0wdqkRxi41wtilRqSqprezZAF4e4RN1wPvTXgcZ3CG38YZfq+qlvz02lRjH1WS+aqacwZncIb+ZvBpvNQIY5caMaux717rAXCGc5xh6Dd+hpl8zS6pf7P6yC6pZ8YuNWKmYp+FL7BMcjzJa0kOJpmf4n6fSHI6yeFF665IsjfJse768inv/4EkJ7pjcTDJ9kntv9vf1Un2J3k9yZEk93Xrp3kclpthasciyaeSvJzk1W6G73Xrr0nyUtfHj7v/Wj66qpqJC8P/Jvsm8FngYuBVYMsazHEcWL8G+70ZuAE4vGjdXwO7uuVdwPenvP8HgD+b4jHYCNzQLV8K/AfDLzOd5nFYboapHQsgwCXd8jrgJeBG4GngG936vwW+fSG/d5Ye2Zv+AsuqehF4/7zVtwN7uuU9wB1T3v9UVdXJqnqlW/4IOMrwuw6neRyWm2FqauhX3c113aWAW4CfdOsv+DjMUuxLfYHlVA9yp4CfJTnQfVnmWtpQVSe75XeBDWsww71JDnVP8yf29Pl8STYD1zN8VFuT43DeDDDFY5HkoiQHgdPAXobPej+oqjPdJhfcxyzFPituqqobGH4f/j1Jbl7rgWD4tz3Dv4im6VHgWmArcBJ4aBo7TXIJ8FPg/qr6cPF90zoOS8ww1WNRVWeraivD73bcBnx+3N85S7HPxBdYVtWJ7vo08CzDA71WTiXZCNBdn57mzqvqVPeH7mPgMaZwLJKsYxjZk1X1TLd6qsdhqRnW4lh0+/0A2A98Ebgsyblvl7rgPmYp9jX/Asskn0ly6bll4KvA4V//UxP1PLCjW94BPDfNnZ8LrHMnEz4WSQI8DhytqocX3TW147DcDNM8FkkGSS7rlj8N3MrwvYP9wNe7zS78OEzj3cULeBdyO8N3P98E/nwN9v9Zhv8K8CpwZJozAE8xfHr4vwxfj90N/C6wDzgG/CtwxZT3/3fAa8AhhsFtnPAxuInhU/RDwMHusn3Kx2G5GaZ2LIA/AH7R7esw8BeL/ny+DLwB/APwOxfye/24rNSIWXoaL2mCjF1qhLFLjTB2qRHGLjXC2KVGGLvUiP8DCsPrm0oBS+IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y = [],[]\n",
    "y += [0.]*2\n",
    "x += [0.,30.]\n",
    "x += [30.]*2\n",
    "y += [15.,30.]\n",
    "y += [30.]\n",
    "x += [0.]\n",
    "y += [15.,0.]\n",
    "x += [0.]*2\n",
    "x\n",
    "y\n",
    "    \n",
    "poly = Polygon(zip(x, y))\n",
    "poly = gpd.GeoDataFrame(index=[0], geometry=[poly])  \n",
    "poly.plot()\n",
    "poly.to_file(\"test_mf6_algomesh_domain.shp\")\n",
    "\n",
    "pts = gpd.points_from_xy(x, y)\n",
    "pts = gpd.GeoDataFrame(index=list(range(len(x))), geometry=pts)\n",
    "pts.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 30.0, 30.0, 0.0, 0.0]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[0.0, 0.0, 30.0, 30.0, 0.0]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this poly bombs algomesh\n",
    "\n",
    "x,y = [],[]\n",
    "y += [0.]*2\n",
    "x += [0.,30.]\n",
    "x += [30.]\n",
    "y += [30.]\n",
    "y += [30.]\n",
    "x += [0.]\n",
    "y += [0.]\n",
    "x += [0.]\n",
    "x\n",
    "y\n",
    "    \n",
    "poly = Polygon(zip(x, y))\n",
    "poly = gpd.GeoDataFrame(index=[0], geometry=[poly])  \n",
    "poly.plot()\n",
    "poly.to_file(\"test_mf6_algomesh_domain.shp\")\n",
    "\n",
    "pts = gpd.points_from_xy(x, y)\n",
    "pts = gpd.GeoDataFrame(index=list(range(len(x))), geometry=pts)\n",
    "pts.plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### this is the final poly I used to build with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 30.0, 30.0, 30.0, 0.0, 0.0]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[0.0, 0.0, 15.0, 30.0, 30.0, 0.0]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y = [],[]\n",
    "y += [0.]*2\n",
    "x += [0.,30.]\n",
    "x += [30.]*2\n",
    "y += [15.,30.]\n",
    "y += [30.]\n",
    "x += [0.]\n",
    "y += [0.]\n",
    "x += [0.]\n",
    "x\n",
    "y\n",
    "    \n",
    "poly = Polygon(zip(x, y))\n",
    "poly = gpd.GeoDataFrame(index=[0], geometry=[poly])\n",
    "poly[\"top\"] = 10.\n",
    "poly[\"bot\"] = 0.\n",
    "poly[\"kh\"] = 1.\n",
    "poly[\"kv\"] = .1\n",
    "poly.plot()\n",
    "poly.to_file(\"test_mf6_algomesh_domain.shp\")\n",
    "\n",
    "pts = gpd.points_from_xy(x, y)\n",
    "pts = gpd.GeoDataFrame(index=list(range(len(x))), geometry=pts)\n",
    "pts.plot()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}