{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from WD.utils import norm_area\n",
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "area = norm_area(\"5.6265\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b024f28b010>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TORCH311",
   "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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}