test_itp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3c1b4f52-819e-4c63-8701-ab10523bbd04",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_probability as tfp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0918ce05-8b4e-4967-8c7a-a59ba8590039",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear1D_LinearGrid():\n",
    "    def __init__(self, values, binning):\n",
    "        \"\"\"\n",
    "        values  : tensor-like with shape (N,), as the reference values\n",
    "        binning : [lower, upper, n_points]\n",
    "        \"\"\"\n",
    "        self.ref_values = tf.convert_to_tensor(values, dtype=tf.float32)\n",
    "        self.lower, self.upper, self.n_points = binning\n",
    "    \n",
    "    def __call__(self, *args, **kwargs):\n",
    "        return self.interp(*args, **kwargs)\n",
    "    \n",
    "    def interp(self, points):\n",
    "        \"\"\"\n",
    "        points : tensor-like with shape (N, ), as the points to be interpolated.\n",
    "        \n",
    "        return : interpolated values at the points with shape (N,), weighted by the inverse of distance to 2^D nearest neighbors. \n",
    "        \"\"\"\n",
    "        points = tf.convert_to_tensor(points, dtype=tf.float32)\n",
    "        return tfp.math.interp_regular_1d_grid(points, self.lower, self.upper, self.ref_values, fill_value='constant_extension')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdab6666-ae16-4fb5-b4d1-7c36ec34717e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e6aa397-748b-43f6-8127-9694ffa01fdd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5c3685c-2873-4e34-94e2-56870f16b12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 5, 30)\n",
    "y = np.cos(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bd7e8fa2-786c-4623-b597-b54b5d7fc6b2",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "itpf = Linear1D_LinearGrid(y, [0, 5, 30])\n",
    "\n",
    "xx = np.linspace(-1, 6, 330)\n",
    "yy = itpf(xx)\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(xx, yy)\n",
    "# plt.xlim(4, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e549237b-bb8e-4462-8ad8-99337ebe00c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a878000-37c0-471b-a4dd-348b9f8b613c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ae0de6c-2352-450c-a930-b2b36b686685",
   "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": "3.10.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}