{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c65dafa-2226-4d12-a61e-f92a5f92bc97",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "def load_pointset(path: str) -> ([], []):\n",
    "    xs = []\n",
    "    ys = []\n",
    "    \n",
    "    with open(path, 'r') as points_file:\n",
    "        for line in points_file.readlines()[1:]:\n",
    "            x, y = line.split(\" \")\n",
    "            y.replace(\"\\n\", \"\")\n",
    "            \n",
    "            xs.append(float(x))\n",
    "            ys.append(float(y))\n",
    "    return (xs, ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b08c4be6-e653-499c-803b-e125f2e24f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_spectrum_to_matrix(freqpath: str) -> np.ndarray:\n",
    "    minfreq = 1.0e36\n",
    "    maxfreq = 0.0\n",
    "    \n",
    "    freqs = []\n",
    "    \n",
    "    with open(freqpath, 'r') as spectrum:\n",
    "        dimension = int(spectrum.readline())\n",
    "        for line in spectrum.readlines():\n",
    "            freqs.append(float(line))\n",
    "            minfreq = min(float(line), minfreq)\n",
    "            maxfreq = max(float(line), maxfreq)\n",
    "    \n",
    "    matrix = np.zeros((dimension, dimension))\n",
    "    \n",
    "    for row in range(dimension):\n",
    "        for col in range(dimension):\n",
    "            frequency = freqs[row * dimension + col]\n",
    "            # normalize\n",
    "            #frequency = (frequency - minfreq) / (maxfreq - minfreq)\n",
    "            frequency = math.sqrt(frequency / maxfreq);\n",
    "            matrix[row][col] = frequency\n",
    "    return matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3a4fff42-a5fe-4b80-93ba-54482e755c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pcf(pcfpath: str) -> ([],[]):\n",
    "    xs = []\n",
    "    ys = []\n",
    "    with open(pcfpath, 'r') as pcffile:\n",
    "        for line in pcffile.readlines():\n",
    "            ys.append(float(line))\n",
    "    xs = range(len(ys))\n",
    "\n",
    "    return (xs, ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4bc08371-8322-4f1a-b78a-8a0f94dd8a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_radspec(specpath: str) -> ([], []):\n",
    "    xs = []\n",
    "    ys = []\n",
    "\n",
    "    with open(specpath, 'r') as specfile:\n",
    "        for line in specfile.readlines():\n",
    "            x, y = line.split(\", \")\n",
    "            xs.append(float(x))\n",
    "            ys.append(float(y))\n",
    "    return (xs, ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a1e9a6e-a307-47c8-8aa6-de7c5a3166f3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "spectrum = load_spectrum_to_matrix(\"../result_data/spectrum.txt\")\n",
    "\n",
    "(px, py) = load_pointset(\"../result_data/sampled.txt\")\n",
    "\n",
    "(pcfx, pcfy) = load_pcf(\"../result_data/pcf.txt\")\n",
    "\n",
    "(specxs, specys) = load_radspec(\"../result_data/radSpec.txt\")\n",
    "\n",
    "fig, ax = plt.subplots(2, 2)\n",
    "plt.rcParams['figure.dpi'] = 800\n",
    "ax[0][0].set_aspect(1.0)\n",
    "ax[0][0].scatter(px, py, s=0.2)\n",
    "ax[0][0].set_title(\"Points\")\n",
    "ax[0][1].imshow(spectrum, cmap=\"gray\")\n",
    "ax[1][0].plot(pcfx, pcfy, linewidth=0.5)\n",
    "ax[1][1].plot(specxs, specys, linewidth=0.5)\n",
    "#plt.savefig(\"fullfig_utk.png\", dpi=800)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41753602-44bb-44a9-a050-f1887589bd96",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  }
 ],
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