notebooks/map2grid_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eeb50c2b-7774-4552-b695-8cb02278846c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import paths\n",
    "import numpy as np\n",
    "import h5py\n",
    "from map2grid import compute_map, map2grid\n",
    "from sdfs.geom_mrst import GeomMRST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f944845-2a27-4d9f-9643-6123778b96fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a59b440-77bf-475f-8fae-122697e2fb36",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('text', usetex=True)\n",
    "plt.rc('image', cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5238c15b-dc7a-45c3-97f0-ab61beb7f08c",
   "metadata": {},
   "outputs": [],
   "source": [
    "geom_filename = '../data/geom_1x.mat'\n",
    "geom = GeomMRST(geom_filename)\n",
    "map = compute_map(geom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77d5a8a2-a331-434e-ba8e-c190b2e0593a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File('../data/hanford_data_100_uncond.h5','r') as f:\n",
    "    u_ens = f['u_ens'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffd95c3d-a807-4fbe-a609-3c1bdc81446b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x12f43b1f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 411,
       "width": 539
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_mapped = map2grid(u_ens[0], map)\n",
    "plt.pcolor(data_mapped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f56dc21d-68b7-4412-bc07-947e372e1331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69, 54)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mapped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a035eb65-9c1c-4b09-a3d2-81667a57f64d",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}