bluenoise-raytracer/utk_experiments/plotting/.ipynb_checkpoints/plotter-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d0850083-6fec-4a51-8128-219c643397ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import os\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c65dafa-2226-4d12-a61e-f92a5f92bc97",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 3,
   "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": 4,
   "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": 5,
   "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": 6,
   "id": "c08e9726-cd2b-4d8a-a0ec-ef63fc8d7334",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pcfseries(folderpath):\n",
    "    pcffiles = os.listdir(folderpath)\n",
    "    values = {}\n",
    "\n",
    "    for  pcffilename in pcffiles:\n",
    "        cutoff = pcffilename.split(\"_\")[1].split('.txt')[0]\n",
    "        values[cutoff] = load_pcf(os.path.join(folderpath, pcffilename))\n",
    "\n",
    "\n",
    "    return values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63bb5f2d-d222-47b8-99c8-1ebb0e1839ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_heck_pcf(filepath):\n",
    "    xs = []\n",
    "    ys = []\n",
    "\n",
    "    with open(filepath, '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": 8,
   "id": "168ce96b-c7ba-49fd-b35f-f06529305a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_heck_pcfseries(folderpath):\n",
    "    pcffiles = os.listdir(folderpath)\n",
    "    values = {}\n",
    "\n",
    "    for pcffilename in pcffiles:\n",
    "        filename, file_extension = os.path.splitext(os.path.join(folderpath, pcffilename))\n",
    "        if file_extension != \".txt\":\n",
    "            continue\n",
    "        cutoff = pcffilename.split(\"_\")[1].split('.txt')[0]\n",
    "        values[cutoff] = load_heck_pcf(os.path.join(folderpath, pcffilename))\n",
    "\n",
    "\n",
    "    return values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f15def42-e258-4ff4-bcff-d8cd3a1e927d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_utk_rdfseries(folderpath):\n",
    "    rdffiles = os.listdir(folderpath)\n",
    "    values = {}\n",
    "\n",
    "    for rdffilename in rdffiles:\n",
    "        filename, file_extension = os.path.splitext(os.path.join(folderpath, rdffilename))\n",
    "        if file_extension != \".txt\":\n",
    "            continue\n",
    "        cutoff = rdffilename.split(\"_\")[1].split('.txt')[0]\n",
    "        values[cutoff] = load_radspec(os.path.join(folderpath, rdffilename))\n",
    "\n",
    "\n",
    "    return values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd9f363e-99f6-495d-a64c-793175509699",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import animation\n",
    "\n",
    "functions = load_utk_rdfseries(\"../result_data/rdffiles_Heck_UTK/\")\n",
    "\n",
    "keys = sorted(functions.keys())\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "plot = ax.plot(functions[keys[0]][0], functions[keys[0]][1])[0]\n",
    "ax.set(xlim=[0, 0.02], ylim=[-0.1, 0.1])\n",
    "\n",
    "def update(frame):\n",
    "    # determine maximum for plot scaling\n",
    "    maxx = max(functions[keys[frame]][0])\n",
    "    maxy = max(functions[keys[frame]][1])\n",
    "    \n",
    "    ax.set(xlim=[0, maxx * 1.05], ylim=[0, maxy * 1.05])\n",
    "    ax.set_title(\"max_distance: \" + keys[frame])\n",
    "    plot.set_xdata(functions[keys[frame]][0])\n",
    "    plot.set_ydata(functions[keys[frame]][1])\n",
    "\n",
    "anim = animation.FuncAnimation(fig=fig, func=update, frames=len(keys), interval=300)\n",
    "anim.save(\"animation_UTK_Heck.mp4\", dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "05a629ba-af59-4d1d-8bc8-7c55888db52c",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'load_utk_rdfseries' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 6\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m animation\n\u001b[1;32m      4\u001b[0m LINEWIDTH \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.6\u001b[39m\n\u001b[0;32m----> 6\u001b[0m utk_rdfdata \u001b[38;5;241m=\u001b[39m \u001b[43mload_utk_rdfseries\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../result_data/rdffiles_Heck_UTK/\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      7\u001b[0m psa_rdfdata \u001b[38;5;241m=\u001b[39m load_heck_pcfseries(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../result_data/pcffiles_PSA/\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      8\u001b[0m utk_pcfdata \u001b[38;5;241m=\u001b[39m load_pcfseries(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../result_data/pcffiles_UTK/\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'load_utk_rdfseries' is not defined"
     ]
    }
   ],
   "source": [
    "# Plotting of compound animation\n",
    "from matplotlib import animation\n",
    "\n",
    "LINEWIDTH = 0.6\n",
    "\n",
    "utk_rdfdata = load_utk_rdfseries(\"../result_data/rdffiles_Heck_UTK/\")\n",
    "psa_rdfdata = load_heck_pcfseries(\"../result_data/pcffiles_PSA/\")\n",
    "utk_pcfdata = load_pcfseries(\"../result_data/pcffiles_UTK/\")\n",
    "\n",
    "utk_rdf_keys = sorted(utk_rdfdata.keys())\n",
    "psa_keys = sorted(psa_rdfdata.keys())\n",
    "utk_pcf_keys = sorted(utk_pcfdata.keys())\n",
    "\n",
    "assert utk_rdf_keys == psa_keys == utk_pcf_keys\n",
    "\n",
    "fig, ax = plt.subplots(2,2, sharex=False, sharey=False)\n",
    "fig.tight_layout()\n",
    "\n",
    "plot_utk_rdf = ax[0][0].plot(utk_rdfdata[utk_rdf_keys[0]][0], utk_rdfdata[utk_rdf_keys[0]][1], linewidth=LINEWIDTH)[0]\n",
    "ax[0][0].set(xlim=[0, 0.02], ylim=[-0.1, 0.1])\n",
    "\n",
    "plot_psa_rdf = ax[0][1].plot(psa_rdfdata[psa_keys[0]][0], psa_rdfdata[psa_keys[0]][1], linewidth=LINEWIDTH)[0]\n",
    "ax[0][1].set(xlim=[0, 0.02], ylim=[-0.1, 0.1])\n",
    "\n",
    "plot_utk_pcf = ax[1][0].plot(utk_pcfdata[utk_pcf_keys[0]][0], utk_pcfdata[utk_pcf_keys[0]][1], linewidth=LINEWIDTH)[0]\n",
    "ax[1][0].set(xlim=[0, 0.02], ylim=[-0.1, 0.1])\n",
    "\n",
    "ax[1][1].set_axis_off()\n",
    "ax[1][1].set(xlim=[0, 1.0], ylim=[0, 1.0])\n",
    "ax[1][1].text(0.4, 0.4, \"max_distance: \\n\" + utk_pcf_keys[0])\n",
    "\n",
    "def update(frame):\n",
    "    # determine maximum for plot scaling\n",
    "    maxx_0 = max(utk_rdfdata[utk_rdf_keys[frame]][0])\n",
    "    maxy_0 = max(utk_rdfdata[utk_rdf_keys[frame]][1])\n",
    "\n",
    "    maxx_1 = max(psa_rdfdata[psa_keys[frame]][0])\n",
    "    maxy_1 = max(psa_rdfdata[psa_keys[frame]][1])\n",
    "\n",
    "    maxx_2 = max(utk_pcfdata[utk_pcf_keys[frame]][0])\n",
    "    maxy_2 = max(utk_pcfdata[utk_pcf_keys[frame]][1])\n",
    "    \n",
    "    ax[0][0].set(xlim=[0, maxx_0 * 1.05], ylim=[0, maxy_0 * 1.05])\n",
    "    ax[0][0].set_title(\"Heck in UTK\")\n",
    "    \n",
    "    plot_utk_rdf.set_xdata(utk_rdfdata[utk_rdf_keys[frame]][0])\n",
    "    plot_utk_rdf.set_ydata(utk_rdfdata[utk_rdf_keys[frame]][1])\n",
    "\n",
    "    ################\n",
    "    \n",
    "    ax[0][1].set(xlim=[0, maxx_1 * 1.05], ylim=[0, maxy_1 * 1.05])\n",
    "    ax[0][1].set_title(\"PSA implementation\")\n",
    "    \n",
    "    plot_psa_rdf.set_xdata(psa_rdfdata[psa_keys[frame]][0])\n",
    "    plot_psa_rdf.set_ydata(psa_rdfdata[psa_keys[frame]][1])\n",
    "\n",
    "    ################    \n",
    "    \n",
    "    ax[1][0].set(xlim=[0, maxx_2 * 1.05], ylim=[0, maxy_2 * 1.05])\n",
    "    ax[1][0].set_title(\"UTK implementation\")\n",
    "    \n",
    "    plot_utk_pcf.set_xdata(utk_pcfdata[utk_pcf_keys[frame]][0])\n",
    "    plot_utk_pcf.set_ydata(utk_pcfdata[utk_pcf_keys[frame]][1])\n",
    "\n",
    "    ################\n",
    "    ax[1][1].clear()\n",
    "    ax[1][1].set_axis_off()\n",
    "    ax[1][1].set(xlim=[0, 1.0], ylim=[0, 1.0])\n",
    "    ax[1][1].text(0.4, 0.4, \"max_distance: \\n\" + utk_pcf_keys[frame])\n",
    "\n",
    "maxkeylen = max(max(len(utk_rdf_keys), len(psa_keys)), len(utk_pcf_keys))\n",
    "\n",
    "anim = animation.FuncAnimation(fig=fig, func=update, frames=maxkeylen, interval=300)\n",
    "anim.save(\"animation_compound.mp4\", dpi=600, writer=\"ffmpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2a1e9a6e-a307-47c8-8aa6-de7c5a3166f3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '../cmake-build-debug/spectrum.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m spectrum \u001b[38;5;241m=\u001b[39m \u001b[43mload_spectrum_to_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../cmake-build-debug/spectrum.txt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m (px, py) \u001b[38;5;241m=\u001b[39m load_pointset(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../result_data/sampled.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m (pcfx, pcfy) \u001b[38;5;241m=\u001b[39m load_heck_pcf(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../psa/cmake-build-debug/sampled_rdf.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[0;32mIn[16], line 7\u001b[0m, in \u001b[0;36mload_spectrum_to_matrix\u001b[0;34m(freqpath)\u001b[0m\n\u001b[1;32m      3\u001b[0m maxfreq \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m\n\u001b[1;32m      5\u001b[0m freqs \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfreqpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m spectrum:\n\u001b[1;32m      8\u001b[0m     dimension \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(spectrum\u001b[38;5;241m.\u001b[39mreadline())\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m spectrum\u001b[38;5;241m.\u001b[39mreadlines():\n",
      "File \u001b[0;32m/nix/store/7km40f9z07gb81vd3041jm5l4v7xk4aa-python3-3.11.9-env/lib/python3.11/site-packages/IPython/core/interactiveshell.py:324\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    319\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    320\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    321\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    322\u001b[0m     )\n\u001b[0;32m--> 324\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../cmake-build-debug/spectrum.txt'"
     ]
    }
   ],
   "source": [
    "\n",
    "spectrum = load_spectrum_to_matrix(\"../cmake-build-debug/spectrum.txt\")\n",
    "\n",
    "(px, py) = load_pointset(\"../result_data/sampled.txt\")\n",
    "\n",
    "(pcfx, pcfy) = load_heck_pcf(\"../psa/cmake-build-debug/sampled_rdf.txt\")\n",
    "\n",
    "(specxs, specys) = load_radspec(\"../cmake-build-debug/radSpec.txt\")\n",
    "\n",
    "fig, ax = plt.subplots(2, 2)\n",
    "fig.tight_layout() \n",
    "plt.rcParams['figure.dpi'] = 800\n",
    "\n",
    "\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",
    "\n",
    "ax[0][1].imshow(spectrum, cmap=\"gray\")\n",
    "ax[0][1].set_title(\"Spectrum\")\n",
    "\n",
    "ax[1][0].plot(pcfx, pcfy, linewidth=0.5)\n",
    "ax[1][0].set_title(\"PCF\")\n",
    "\n",
    "ax[1][1].plot(specxs, specys, linewidth=0.5)\n",
    "ax[1][1].set_title(\"Power spectrum\")\n",
    "plt.savefig(\"fullfig_generated_utk.png\", dpi=800)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "41753602-44bb-44a9-a050-f1887589bd96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2)\n",
    "ax[0].set_aspect(1)\n",
    "#ax[1].set_aspect(1.0)\n",
    "\n",
    "pointset = load_pointset(\"../utk/cmake-build-debug/src/samplers/points.txt\")\n",
    "ax[0].scatter(pointset[0], pointset[1], s=0.1)\n",
    "\n",
    "rdf = load_radspec(\"../utk/cmake-build-debug/src/samplers/rdf.txt\")\n",
    "ax[1].set(xlim=[0.0, 0.26], ylim=[min(rdf[1]), max(rdf[1])])\n",
    "ax[1].plot(rdf[0], rdf[1], linewidth = 0.2)\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 400\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a217ce8d-fd36-4e2d-8c4a-90dba8c06cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation\n",
    "\n",
    "\n",
    "def generate_forceseries(path: str):\n",
    "    forces = []\n",
    "    for i in range(len(os.listdir(path))):\n",
    "        with open(os.path.join(path, \"force\" + str(i) + \".dat\"), 'r') as forcefile:\n",
    "            xs = []\n",
    "            ys = []\n",
    "            for line in forcefile.readlines():\n",
    "                x, y = line.split(\" \")\n",
    "                y.replace(\"\\n\", \"\")\n",
    "                xs.append(float(x))\n",
    "                ys.append(float(y))\n",
    "        forces.append((xs, ys)) \n",
    "    return forces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "122d231d-483c-46e8-8600-883a0e7b11fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forces = generate_forceseries(\"../result_data/forces_unmodified\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plot = plt.plot(forces[0][0], forces[0][1])[0]\n",
    "\n",
    "def update(frame):\n",
    "    maxx = max(forces[frame][0])\n",
    "    maxy = max(forces[frame][1])\n",
    "    \n",
    "    ax.set(xlim=[0.0, maxx * 0.2], ylim=[0.0, maxy * 0.2])\n",
    "    ax.set_title(\"frame \" + str(frame))\n",
    "    plot.set_xdata(forces[frame][0])\n",
    "    plot.set_ydata(forces[frame][1])\n",
    "\n",
    "anim = animation.FuncAnimation(fig=fig, func=update, frames=len(forces), interval=300)\n",
    "anim.save(\"animation_forces_16384.mp4\", dpi=600, writer=\"ffmpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da02f09d-7498-423a-af68-e2e1a729efd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def load_pointset_series(path: str):\n",
    "    pointsets = {}\n",
    "    for pointset in os.listdir(path):\n",
    "        key = float(pointset.replace(\"pointset_\", \"\").replace(\".txt\", \"\"))\n",
    "        pointset = load_pointset(os.path.join(path, pointset))\n",
    "        pointsets[key] = pointset\n",
    "\n",
    "    return pointsets\n",
    "\n",
    "def load_radspec_series(path: str):\n",
    "    radspecs = {}\n",
    "    for radspec in os.listdir(path):\n",
    "        key = float(radspec.replace(\"pointset_\", \"\").replace(\".rdf\", \"\"))\n",
    "        radspec = load_radspec(os.path.join(path, radspec))\n",
    "        radspecs[key] = radspec\n",
    "\n",
    "    return radspecs\n",
    "        \n",
    "\n",
    "pcfs = load_utk_rdfseries(\"../result_data/pointset_series/rdffiles/\")\n",
    "pointsets = load_pointset_series(\"../result_data/pointset_series/pointsets/\")\n",
    "radspecs = load_radspec_series(\"../result_data/pointset_series/radspecs/\")\n",
    "\n",
    "keys = sorted(pointsets.keys())\n",
    "\n",
    "fig, ax = plt.subplots(2,2)\n",
    "pointplot = ax[0][0].scatter(pointsets[keys[0]][0], pointsets[keys[0]][1], s=0.2)\n",
    "rdfplot = ax[0][1].plot(pcfs[str(keys[0])][0],pcfs[str(keys[0])][1], linewidth=0.2)[0]\n",
    "radspecplot = ax[1][0].plot(radspecs[keys[0]][0], radspecs[keys[0]][1], linewidth=0.2)[0]\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "\n",
    "def update(frame):\n",
    "\n",
    "    ax[0][0].clear()\n",
    "    pointplot = ax[0][0].scatter(pointsets[keys[frame]][0], pointsets[keys[frame]][1], s=0.2)\n",
    "    ax[0][0].set(xlim=[0, 1], ylim=[0.0, 1.0])\n",
    "    ax[0][0].set_title(\"cutoff: \" + str(keys[frame]))\n",
    "\n",
    "    maxx = float(max(pcfs[str(keys[frame])][0]))\n",
    "    maxy = float(max(pcfs[str(keys[frame])][1]))\n",
    "    \n",
    "    rdfplot.set_xdata(pcfs[str(keys[frame])][0])\n",
    "    rdfplot.set_ydata(pcfs[str(keys[frame])][1])\n",
    "\n",
    "    ax[0][1].set(xlim=[0, maxx], ylim=[0.0, 1.0e-7])\n",
    "    #ax[1].set_xbound(lower=-0.01, upper=maxx) \n",
    "    #ax[1].set_ybound(lower=0, upper=maxx)\n",
    "\n",
    "    radspecplot.set_xdata(radspecs[keys[frame]][0])\n",
    "    radspecplot.set_ydata(radspecs[keys[frame]][1])\n",
    "\n",
    "\n",
    "anim = animation.FuncAnimation(fig=fig, func=update, frames=len(keys), interval=300)\n",
    "anim.save(\"animation_cutoffs_with_radspec.mp4\", dpi=600, writer=\"ffmpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9130e7ed-06ba-40f9-afd5-0506022c4156",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '../utk/cmake-build-debug/src/samplers/pointset_10_rp.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[47], line 47\u001b[0m\n\u001b[1;32m     44\u001b[0m     ax[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mset_ylim([\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;241m2.0\u001b[39m])\n\u001b[1;32m     45\u001b[0m     ax[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mset_aspect(\u001b[38;5;241m57\u001b[39m)\n\u001b[0;32m---> 47\u001b[0m \u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;66;03m#anim = animation.FuncAnimation(fig=fig, func=update, frames=258, interval=200)\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;66;03m#anim.save(\"animation_gradients_no_cutoff.mp4\", dpi=600, writer=\"ffmpeg\")\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[47], line 41\u001b[0m, in \u001b[0;36mupdate\u001b[0;34m(frame)\u001b[0m\n\u001b[1;32m     38\u001b[0m ax[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mscatter(xs, ys, s\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2.0\u001b[39m)\n\u001b[1;32m     39\u001b[0m ax[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mset_aspect(\u001b[38;5;241m1.0\u001b[39m)\n\u001b[0;32m---> 41\u001b[0m (radspec_x, radspec_y) \u001b[38;5;241m=\u001b[39m \u001b[43mload_heck_pcf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m../utk/cmake-build-debug/src/samplers/pointset_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mframe\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_rp.txt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     42\u001b[0m radspec_y \u001b[38;5;241m=\u001b[39m scipy\u001b[38;5;241m.\u001b[39mndimage\u001b[38;5;241m.\u001b[39mgaussian_filter(radspec_y, \u001b[38;5;241m2.0\u001b[39m)\n\u001b[1;32m     43\u001b[0m ax[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mplot(radspec_x, radspec_y, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n",
      "Cell \u001b[0;32mIn[9], line 5\u001b[0m, in \u001b[0;36mload_heck_pcf\u001b[0;34m(filepath)\u001b[0m\n\u001b[1;32m      2\u001b[0m xs \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m      3\u001b[0m ys \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m specfile:\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m specfile\u001b[38;5;241m.\u001b[39mreadlines():\n\u001b[1;32m      7\u001b[0m         x, y \u001b[38;5;241m=\u001b[39m line\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/nix/store/zkknd8lr320pzg0ip9y95d7wcq5gryaf-python3-3.11.9-env/lib/python3.11/site-packages/IPython/core/interactiveshell.py:324\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    319\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    320\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    321\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    322\u001b[0m     )\n\u001b[0;32m--> 324\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../utk/cmake-build-debug/src/samplers/pointset_10_rp.txt'"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import animation\n",
    "\n",
    "\n",
    "def load_gradient(path: str):\n",
    "    xs, ys, us, vs, cs = [], [], [], [], []\n",
    "    with open(path, 'r') as gradfile:\n",
    "        for line in gradfile.readlines():\n",
    "            x, y, u, v = line.split(\" \")\n",
    "            x = float(x)\n",
    "            y = float(y)\n",
    "            u = float(u)\n",
    "            v = float(v)\n",
    "            \n",
    "            xs.append(x)\n",
    "            ys.append(y)\n",
    "            us.append(u)\n",
    "            vs.append(v)\n",
    "            cs.append(np.hypot(u,v))\n",
    "    \n",
    "    return (xs, ys, us, vs, cs)\n",
    "            \n",
    "\n",
    "fig, ax = plt.subplots(2,2)\n",
    "\n",
    "ax[0][0].set_xlim([-0.1, 1.1])\n",
    "ax[0][0].set_ylim([-0.1, 1.1])\n",
    "ax[1][0].set_xlim([-0.1, 1.1])\n",
    "ax[1][0].set_ylim([-0.1, 1.1])\n",
    "\n",
    "def update(frame):\n",
    "\n",
    "    for axis in ax:\n",
    "        for a in axis:\n",
    "            a.clear()\n",
    "    \n",
    "    (xs, ys, us, vs, cs) = load_gradient(f'../result_data/gradients_no_cutoff/forces_{str(frame)}.txt')\n",
    "    ax[0][0].quiver(xs, ys, us, vs, cs)\n",
    "    ax[0][1].scatter(xs, ys, s=2.0)\n",
    "    ax[0][1].set_aspect(1.0)\n",
    "\n",
    "    (radspec_x, radspec_y) = load_heck_pcf(f'../utk/cmake-build-debug/src/samplers/pointset_{str(frame)}_rp.txt')\n",
    "    radspec_y = scipy.ndimage.gaussian_filter(radspec_y, 2.0)\n",
    "    ax[1].plot(radspec_x, radspec_y, linewidth=0.5)\n",
    "    ax[1].set_ylim([-0.1, 2.0])\n",
    "    ax[1].set_aspect(57)\n",
    "\n",
    "update(10)\n",
    "    \n",
    "#anim = animation.FuncAnimation(fig=fig, func=update, frames=258, interval=200)\n",
    "#anim.save(\"animation_gradients_no_cutoff.mp4\", dpi=600, writer=\"ffmpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fb39474d-914c-40a0-adee-70d00c953794",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 233\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 234\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 235\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 236\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 237\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 238\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 239\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 240\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 241\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 242\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 243\n",
      "Config file 'psa.cfg' not found. Using defaults.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_223.txt'.\n",
      "mv: cannot stat './pointset_223_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_224.txt'.\n",
      "mv: cannot stat './pointset_224_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_225.txt'.\n",
      "mv: cannot stat './pointset_225_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_226.txt'.\n",
      "mv: cannot stat './pointset_226_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_227.txt'.\n",
      "mv: cannot stat './pointset_227_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_228.txt'.\n",
      "mv: cannot stat './pointset_228_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_229.txt'.\n",
      "mv: cannot stat './pointset_229_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_230.txt'.\n",
      "mv: cannot stat './pointset_230_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_231.txt'.\n",
      "mv: cannot stat './pointset_231_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_232.txt'.\n",
      "mv: cannot stat './pointset_232_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_233.txt'.\n",
      "mv: cannot stat './pointset_233_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_234.txt'.\n",
      "mv: cannot stat './pointset_234_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_235.txt'.\n",
      "mv: cannot stat './pointset_235_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_236.txt'.\n",
      "mv: cannot stat './pointset_236_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_237.txt'.\n",
      "mv: cannot stat './pointset_237_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_238.txt'.\n",
      "mv: cannot stat './pointset_238_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_239.txt'.\n",
      "mv: cannot stat './pointset_239_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_240.txt'.\n",
      "mv: cannot stat './pointset_240_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_241.txt'.\n",
      "mv: cannot stat './pointset_241_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_242.txt'.\n",
      "mv: cannot stat './pointset_242_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_243.txt'.\n",
      "mv: cannot stat './pointset_243_rp.txt': No such file or directory\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "converting file 244\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 245\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 246\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 247\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 248\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 249\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 250\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 251\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 252\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 253\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 254\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 255\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 256\n",
      "Config file 'psa.cfg' not found. Using defaults.\n",
      "converting file 257\n",
      "Config file 'psa.cfg' not found. Using defaults.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_244.txt'.\n",
      "mv: cannot stat './pointset_244_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_245.txt'.\n",
      "mv: cannot stat './pointset_245_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_246.txt'.\n",
      "mv: cannot stat './pointset_246_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_247.txt'.\n",
      "mv: cannot stat './pointset_247_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_248.txt'.\n",
      "mv: cannot stat './pointset_248_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_249.txt'.\n",
      "mv: cannot stat './pointset_249_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_250.txt'.\n",
      "mv: cannot stat './pointset_250_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_251.txt'.\n",
      "mv: cannot stat './pointset_251_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_252.txt'.\n",
      "mv: cannot stat './pointset_252_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_253.txt'.\n",
      "mv: cannot stat './pointset_253_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_254.txt'.\n",
      "mv: cannot stat './pointset_254_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_255.txt'.\n",
      "mv: cannot stat './pointset_255_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_256.txt'.\n",
      "mv: cannot stat './pointset_256_rp.txt': No such file or directory\n",
      "Cannot load '../utk/cmake-build-debug/src/samplers/pointset_257.txt'.\n",
      "mv: cannot stat './pointset_257_rp.txt': No such file or directory\n"
     ]
    }
   ],
   "source": [
    "def convert_to_pointset(path: str):\n",
    "    xs, ys = [], []\n",
    "    with open(path, 'r') as gradfile:\n",
    "        for line in gradfile.readlines():\n",
    "            x, y, _, _= line.split(\" \")\n",
    "            x = float(x)\n",
    "            y = float(y)\n",
    "\n",
    "            \n",
    "            xs.append(x)\n",
    "            ys.append(y)\n",
    "\n",
    "    with open(path.replace('.txt', '') + '_pointset.txt', 'w') as pointsetfile:\n",
    "        for i in range(len(xs)):\n",
    "            pointsetfile.write(str(xs[i]) + \" \" + str(ys[i]) + \"\\n\")\n",
    "\n",
    "for i in range(258):\n",
    "    print(f'converting file {i}')\n",
    "    #convert_to_pointset(f'../result_data/gradients_no_cutoff/forces_{i}.txt')\n",
    "    os.system('../psa/cmake-build-debug/psa --rp --raw ' + f'../utk/cmake-build-debug/src/samplers/pointset_{str(i)}.txt')\n",
    "    os.system('mv ' + f'./pointset_{str(i)}_rp.txt' + ' ' + f'../utk/cmake-build-debug/src/samplers/')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6e4e026-faee-42e8-b433-525974ebf411",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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