example_usage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5d4dbefe-88fb-4c7e-81e1-afc3487355ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyinterpx.Interpolation import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "04e83118-dc0a-41b0-a2ad-82aa500b94c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The interpolation took 0.15858 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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53//7fw95/lb+/+962NBuvfXWuOyyy4ZsO/300+Mv//IvR12j1WrFV77yldh9992HbP/MZz6zznEjfRTuF77whZgxY8ao9nv22WfHgQceOOo+gfSEBQAj2HHHHeOkk04a9e37+vrine9855BtCxYsqPyFZvvss0+85z3vGfXt+/v743Of+9yw7V/+8pcr7R8AAKrq9WPpqs4444whP8+cOTPe/va3j7nO1KlT43/+z/85ZNtFF1005jrtdjv++Z//eczj1mX69OkjvltgXc4555xh29b3PONf//Vfh20799xzo90e/ctQBxxwwLB3WT/77LNx/vnnj7rGhtRqteKYY44Zsm2sX/Cbwprn8c4771wpcOrr64uPf/zjQ7ZddtllsXz58hFv/9vf/jYuueSSIdv22muvYe9QWJdWqzXi+QZsOMICgBEcf/zxY3ogGxGx3377DdtW9W2nJ5xwwqiuuFndG97whth6662HbLvyyitj5cqVlXoAAIAqev1Yuop58+bFnDlzhmw74YQTKtd74xvfOKz+ww8/PKYar3nNa2L77bev3MNIjjnmmFF9ZNHq9txzzzjggAOGbLvnnnti/vz5I95+/vz5w47dPvvsM6p3Ta/pgx/84LBtP/3pT8dcZ0PZZZddhvx8++23b9DnY8uWLRv25eDHHXfcqL8Ae01veMMbhtW/+eabR7ztT3/601i1atWQbVWe1+61116xzz77jK1RIJlOrxsAaKJDDz10zGN22mmnYdvW91mea/OWt7xlzGPa7XYcddRRce65576wbdmyZXHnnXdWemAOAABV9PqxdBXXXnvtsG0HHXRQ5Xo77LDDsG133HFHbLfddqOuMZaPPxqtKs8zIiJmzZoVN91005BtN998c2yzzTbDbvvf//3fw7a97W1vq7TffffdN3bYYYeYO3fuC9tuuummKMtyzC9CV7FkyZK47rrr4uc//3nce++98eSTT8bTTz8dzz777IjfD7dkyZIhPy9fvjwee+yxYRd1vVh+9rOfDbvyv855vPHGG8fUqVOH3BfvuOOOEe/jI4UIs2bNqrTfWbNmxW233VZpLFCPsABgBGteETIaI32+ZZUnONOnT48ttthizOMinrsKY01z5swRFgAAsMH08rF0VSO9wF31hfW1eeKJJ8Z0+5Ee29fR6XTiFa94RaWxa3ueMdLHNN1+++3DttW5UnyfffYZEhY89dRT8dBDD8XOO+9cueb6zJkzJ84888y45JJLan/Z7uLFizdYWDDSefzBD34w+vv7K9dc/Qu/I9Z+Hq/5zpw6z2v33nvvSuOA+oQFACPYeOONxzymr69v2LYqbzndddddxzzmebvtttuwbRv6s14BAMhbLx9LV7VgwYJh2+66666k+3jyySfHdPvNN9886f633XbbMX8E0fPG8jxjpBeT1/yi3LHYY489RtzHixEWrFy5Mj70oQ/Fl770pRHfOVDFhgy9RjqPf/nLXybdx9rO4zXPh9TPa4ENQ1gAMIKRnqxsKCNdVVVn7OLFi2t0AwAAY9PLx9JVjfWF/CrGeoX6lClTku5/Qz3PWLRo0bBt06ZNq7zvjTbaaNi23//+95Xrrc3KlSvj7W9/e1x88cXJ624ovTyP1zwfUp9vwIYhLABomIkTJyYd+8wzz9RpBwAAXvJGeoG71zqdtC/Z1HmeMWHChGHb1vY8Y83trVZrxPGjtaGe45xxxhkjBgXTp0+Po446Kg488MDYaaedYuutt47JkyfHwMBAjBs3bshtr7nmmhfluyZGq5fn8ZrHJPXzWmDDEBYANMyzzz6bdOzkyZPrtAMAAC9548ePH7Zt6dKllT+2p4nqPM9Y83PrI9b+PGPN7WVZxh/+8IfKgcGGeI7z+OOPx+mnnz5kW6fTiTPPPDNOPvnkUQc3db/foK6RzuNf/vKXG+RjfSZPnjzk3QWpn9cCG0a71w0AMFSdz7QcaWydt/yO1oZ8ay0AAKS26aabDtv2YnzUTS9tqOcZI31sUJ2PRh1pbJXvxViXSy65ZFggcsYZZ8Tf/u3fjukdHr0+Z3p5Hq95PqQ+34ANQ1gA0DC/+tWvKo+9//77h21b1xejpfoiuQ3x2ZgAAPBi2WKLLYZte/jhh3vQyYvnkUceieXLl1caO5bnGZttttmwbXW+ZPfee+8dtm2kF8Xr+OlPfzrk54022ij+5m/+Zsx1fv3rX6dqqZJensdrng8jnTOjdd9999VtB6hIWADQMAsWLIjHH3+80tg5c+YM27b33nuv9fYjfWna008/Peb9Pvjgg2MeAwAATbH//vsP23bdddf1oJMXz6pVq+Kuu+6qNHYszzP22muvYdtuu+22SvuNiLj11luH/Dxt2rTYaaedKtcbyfz584f8vP/++1f6ou6bbropVUuV9PI8XvN8ePTRR+Oxxx6rVGuk8w3YMIQFAA30n//5n2Me0+12h30h18DAQLzyla9c65iR3jpc5WqYa6+9dsxjIkb+0rbBwcFKtQAAyM+ajyerPpY84ogjhm278MILK9VqsirPMyJGXosZM2aMeNsDDzxw2Lbvf//7lfY7Z86cmDt37rD9tlqtSvXW5oknnhjyc5WPOXriiSfi6quvrrT/VOfxYYcdNqzWj370ow3ysbEjnQ8XXXRRpVovxfse/LEQFgA00Ne//vUxj7nssstiwYIFQ7Ydfvjh67wiZtdddx227ZZbbhnTfgcHB+O8884b05jnjfTFZEuWLKlUCwCA/Kz5eLLqY8k99tgjdtlllyHbbrnllrjqqqsq99ZEF1xwwZg/iuiee+4ZdsX8n/zJn8Q222wz4u232Wab2H333Ydsu+222ypdLf7FL35x2LbXvva1Y66zPhMnThzy85rhwWice+65sWzZskr7T3UeT5kyJWbOnDlk24IFC+KCCy6oVG8sjjjiiGFBxde//vUoy3JMde68885a70QB6hEWADTQrbfeOqYHdCtXroyPfOQjw7afeOKJ6xy3+eabx9Zbbz1k23e/+90xXcly7rnnDrvaZ7RG+vKzXn/OJwAAfzzWfDw5d+7cMb84+by///u/H7bthBNO6PmX1qa0YMGC+OxnPzumMaeccsqwbet7nnHSSScN23byySeP6djccsstMXv27CHbJk6cGO9973tHXWO0ttxyyyE/33jjjfHss8+Oevw999wTp59+euX9r3keL168OBYtWlSp1mmnnTZs20c+8pEX/XnWlltuGW9+85uHbJszZ05885vfHHWNsiwrfVcEkI6wAKChTj755Lj99tvXe7uyLON973vfsC+B2nnnneMNb3jDescfeeSRQ35+5JFH4uyzzx5Vj1deeWX8r//1v0Z125Hsueeew7ZdeumllesBAJCXP/3TPx3y81NPPRU33nhjpVrvfve7Y7fddhuybe7cufGGN7whfvOb31Sq+fTTT8dnP/vZ+Pa3v11p/Ivhk5/8ZFx22WWjuu1pp5027N0VG220URxzzDHrHHfssccO+yifm2++edQvBD/88MPxtre9Lbrd7pDt73vf+0b8KNW6Xv3qVw/5ecmSJfFP//RPoxo7b968ePOb31z5y6Mjhp/HEdWfFx166KHDPlZr0aJF8frXv77yF00vW7YsvvKVr8TnP//5dd5upGDplFNOGfa9E2vzkY98JG644YZKPQJpCAsAGmZgYCAinnti8T/+x/9Y5xOL3/72t/HWt7512LsQWq1WfPWrX412e/2/5k844YRh2z72sY/FV77ylbVe+bNs2bI444wz4sgjj4zly5e/0PNYbbzxxsOekJ1//vlx1llnxVNPPVWpJgAA+Rjp8/GPP/74uPrqq4e90Lw+RVHE9773vWEfCfOzn/0sXvWqV8UXv/jFUX3MzKpVq+KKK66I97///bHtttvGxz72sfjd7343pl5eDM8/Zl+5cmXMmjUrzjrrrLWu0VNPPRV/9Vd/FZ/+9KeH/b+zzjorpk6dus59TZo0Kb70pS8N237uuefG29/+9nV+8e2ll14aBx988LAvHd5+++3j//yf/7PO/VZ19NFHD3vudOaZZ8Y//MM/xKpVq9Y67t///d/jgAMOeOGq/SlTplTa/4wZM4bt/8Mf/nBcfPHFlb5vYPbs2bHVVlsN2fbAAw/EfvvtF6effvqonmuVZRk33nhjfOhDH4rtt98+TjzxxPW+O+HQQw+NY489dsi2Z555Jl772teu8x0GTz75ZLz73e9+IYwYP378evsDXhzDv1kSgJ6aNWtW3HPPPXHXXXfFokWL4phjjolPf/rT8da3vjV22mmnmDRpUvzud7+LG264IX784x/H0qVLh9X4m7/5mzjssMNGtb/99tsvjjrqqCFfjjw4OBgnnnhinHvuufHWt741dt555+jv74+FCxfGnDlz4tJLL43HH3/8hdufffbZ630r8tocf/zxQ96dMDg4GKeeemp8+MMfjq233jqmTp0aRVEMGXPiiSdW3h8AAC8d+++/f+yxxx5x7733vrDtV7/6VbzmNa+J8ePHx9Zbbx0TJkwYNu7SSy8d9mJqxHPvfP23f/u3mDVr1pAXiR9//PE46aST4u///u/j0EMPjf333z8233zzmDp1ajz77LOxePHieOSRR2LOnDlxxx13xNNPP/3iTLiG/fffPyZOnBiXXnppLFu2LE499dQ4++yz4+ijj47ddtstpk2bFo8//njceuutcfHFF4/4gvKb3/zmYS8Gr8073vGOuPzyy+Mb3/jGkO3f//7348c//nEceeSR8epXvzpe9rKXxbJly2Lu3LlxySWXxJ133jmsVl9fX3z7299eb0hR1ctf/vJ497vfHd/61reGbP/Upz4Vs2fPjre97W3xZ3/2ZzFp0qT4/e9/H/fff39ccskl8dBDD71w2wkTJsQZZ5wRf/3Xfz3m/W+55Zbx+te/fsi7CR577LF4y1veEv39/bHNNtvExIkTh32x89e//vXYZ599htXbaqut4uKLL46ZM2cO+TilJUuWxMc//vH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      "text/plain": [
       "<Figure size 1920x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Parameters for the interpolation\n",
    "centering = True\n",
    "factor = 8\n",
    "channels = 25\n",
    "\n",
    "# Setting up Interpolation class\n",
    "interpolation = interp(\n",
    "    num_points=6,\n",
    "    max_degree=3,\n",
    "    num_channels=channels,\n",
    "    learnable=False,\n",
    "    factor=factor,\n",
    "    align_corners=centering,\n",
    ")\n",
    "\n",
    "# Creating inputdata using sinusoid\n",
    "length = 10\n",
    "\n",
    "# Initializing a tensor of random values to represent the grid\n",
    "x = torch.zeros(2, channels, length, length, length)\n",
    "\n",
    "# Preparing input positions for the sinusoidal function\n",
    "input_positions = torch.zeros(length, length, length, 3)\n",
    "for i in range(x.shape[2]):\n",
    "    for j in range(x.shape[3]):\n",
    "        for k in range(x.shape[4]):\n",
    "            input_positions[i, j, k] = torch.tensor([i, j, k])\n",
    "            pos = dx * np.array([i, j, k])\n",
    "            x[:, :, i, j, k] = sinusoidal_function(*pos)\n",
    "\n",
    "# Interpolating data\n",
    "time1 = time.time()\n",
    "interpolated = interpolation(x)\n",
    "print(f\"The interpolation took {(time.time() - time1):.5f} seconds\")\n",
    "\n",
    "\n",
    "# Plotting\n",
    "plt.figure(dpi=300)\n",
    "plt.subplot(121)\n",
    "plt.title(\"input\")\n",
    "x_shape = x.shape\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.imshow(x[0, 0, x_shape[2] // 2, :, :])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title(\"interpolated\")\n",
    "interp_shape = interpolated.shape\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.imshow(interpolated[0, 0, interp_shape[2] // 2, :, :])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87f30848-280c-443e-bee0-90ec564fab85",
   "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": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}