{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "61f49e0f",
   "metadata": {},
   "source": [
    "# Profiling the effect of input parameters on TARDIS runtime #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2c26cee6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iterations:          0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Packets:             0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tardis\n",
    "from tardis import run_tardis\n",
    "\n",
    "from tardis.io.config_reader import Configuration\n",
    "from tardis.io.atom_data.base import AtomData\n",
    "from tardis.simulation import Simulation\n",
    "from tardis import run_tardis\n",
    "from tardis.io.atom_data.util import download_atom_data\n",
    "download_atom_data('kurucz_cd23_chianti_H_He')\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "from astropy import units as u\n",
    "import csv\n",
    "import statistics\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dac6305a",
   "metadata": {},
   "source": [
    "### Functions for building the dictionaries and interacting with files ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a806b64e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_dictionary(fp):\n",
    "    \"\"\"\n",
    "    Uses the fp generated from open_file to return a dictionary. Then closes\n",
    "    the file at the end of execution.\n",
    "\n",
    "    'testing_param' : dict\n",
    "        'iteration' : time\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    fp : file\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    data_dict : dictionary\n",
    "    \"\"\"\n",
    "    \n",
    "    data_dict = {}\n",
    "    reader = csv.reader(fp)\n",
    "    for line in reader:\n",
    "        testing_param = int(line[0])\n",
    "        \n",
    "        iteration = int(line[1])\n",
    "        \n",
    "        time_of_run = float(line[2])\n",
    "        \n",
    "        if testing_param not in data_dict:\n",
    "            data_dict[testing_param] = {}\n",
    "        if iteration not in data_dict[testing_param]:\n",
    "            data_dict[testing_param][iteration] = time_of_run\n",
    "    fp.close()\n",
    "    return data_dict \n",
    "\n",
    "def add_to_dictionary(data_dict, info):\n",
    "    \"\"\"\n",
    "    Adds the data to the dictionary\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data_dict : dictionary\n",
    "    info : list\n",
    "        It is set up as [testing_param, iteration, run_time]\n",
    "    \"\"\"\n",
    "    \n",
    "    testing_param = info[0]\n",
    "    iteration = info[1]\n",
    "    time_of_run = info[2]\n",
    "    if testing_param not in data_dict:\n",
    "        data_dict[testing_param] = {}\n",
    "    data_dict[testing_param][iteration] = time_of_run\n",
    "\n",
    "\n",
    "def check_existance(data_dict, info):\n",
    "    \"\"\"\n",
    "    Checks to see if a data poitn already exists in data_dict.\n",
    "    Returns true if it exists, false if it does not.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    data_dict : dictionary\n",
    "    info : list\n",
    "        [testing_param, iteration]\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Boolean\n",
    "    \"\"\"\n",
    "    \n",
    "    testing_param = info[0]\n",
    "    iteration = info[1]\n",
    "    #Checks if it thread already exists\n",
    "    if testing_param not in data_dict:\n",
    "        return False\n",
    "    #If it does\n",
    "    elif (testing_param in data_dict):\n",
    "        #Does the iteration exist yet\n",
    "        if iteration not in data_dict[testing_param]:\n",
    "            return False\n",
    "    return True"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92662ad4",
   "metadata": {},
   "source": [
    "## Timing of a specific parameter in TARDIS ##"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a55336ba",
   "metadata": {},
   "source": [
    "### Number of Threads ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "411e12a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_data(thread_count, \n",
    "                  iteration_count, \n",
    "                  thread_dictionary, \n",
    "                  cpu_timing_dictionary, \n",
    "                  tardis_file, \n",
    "                  thread_file, \n",
    "                  cpu_file,):\n",
    "    \"\"\"\n",
    "    This function generates data for running a tardis file on different numbers of threads different times.\n",
    "    It writes the data to two dictionaries and their respecitve files.\n",
    "    \n",
    "    This function can be modified to write to as many or as few dictionaries and files as necessary.\n",
    "    In order to test different parameters, the index into the tardis_config must be adjusted. This must be done \n",
    "    manually in order to select the correct testing parameter.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    thread_count : int\n",
    "        Max number of threads to be tested\n",
    "    iteration_count : int\n",
    "        Amount of testing on each thread\n",
    "    thread_dictionary : dict\n",
    "    cpu_timing_dictionary : dict\n",
    "    tardis_file : str\n",
    "        TARDIS config file\n",
    "    thread_file : str\n",
    "        .csv file name\n",
    "    cpu_file : str\n",
    "        .csv file name\n",
    "    \"\"\"\n",
    "\n",
    "    tardis_config = Configuration.from_yaml(tardis_file)\n",
    "\n",
    "    for threads in range(1, thread_count+1):\n",
    "        for i in range(1, iteration_count+1):\n",
    "            #Continue if this data point already exists. If it exists in one it exists in both\n",
    "            if check_existance(thread_dictionary, [threads, i]):\n",
    "                continue\n",
    "\n",
    "            print(threads, \" : \", i)\n",
    "            tardis_config[\"montecarlo\"][\"nthreads\"] = threads\n",
    "\n",
    "            sim = Simulation.from_config(tardis_config)\n",
    "            start = time.monotonic()\n",
    "            sim.run_convergence()\n",
    "            sim.run_final()\n",
    "            end = time.monotonic()\n",
    "            run_time = end-start\n",
    "            \n",
    "            add_to_dictionary(thread_dictionary, [threads, i, run_time])\n",
    "            add_to_dictionary(cpu_timing_dictionary, [threads, i, (run_time * threads)])\n",
    "\n",
    "            with open(thread_file, \"a\") as data_file:\n",
    "                data_file.write(\"{}, {}, {}\\n\".format(threads, i, run_time))\n",
    "            with open(cpu_file, \"a\") as data_file:\n",
    "                data_file.write(\"{}, {}, {}\\n\".format(threads, i, run_time * threads))\n",
    "                \n",
    "    print(\"Completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6966653",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51ae63d85fc04cd2b68ca6192058bec4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "TqdmHBox(children=(HTML(value='Iterations:', layout=Layout(width='6%')), FloatProgress(value=0.0, layout=Layou…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "f7cb57fbd3674a03a38de20556cd98cf",
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       "version_minor": 0
      },
      "text/plain": [
       "TqdmHBox(children=(HTML(value='Packets:   ', layout=Layout(width='6%')), FloatProgress(value=0.0, layout=Layou…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed\n"
     ]
    }
   ],
   "source": [
    "TARDIS_FILE = 'tardis_example.yml'\n",
    "STORAGE_FILE = 'threads_dict_128_threads_1_node.csv'\n",
    "CPU_STORAGE_FILE = 'cpu_threads_dict_128_threads_1_node.csv'\n",
    "\n",
    "#Compile the numba functions\n",
    "run_tardis(TARDIS_FILE, log_level=\"warning\")\n",
    "\n",
    "#Opens file and builds the dictionary\n",
    "with open(STORAGE_FILE, \"r\") as threads_fp:\n",
    "    threads_dict = build_dictionary(threads_fp)\n",
    "\n",
    "with open(CPU_STORAGE_FILE, \"r\") as cpu_fp:\n",
    "    cpu_threads_dict = build_dictionary(cpu_fp)\n",
    "\n",
    "#Creates list of main data\n",
    "threads_numbers = [i for i in range(1, 129, 1)]\n",
    "cpu_threads_numbers = [i for i in range(1, 129, 1)]\n",
    "\n",
    "generate_data(128, 30, threads_dict, cpu_threads_dict, TARDIS_FILE, STORAGE_FILE, CPU_STORAGE_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7056ee6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_stat_dictionaries(main_dict):\n",
    "    \"\"\"\n",
    "    This builds two dictionaries, one that holds the mean of the data and the other that holds the stdev\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    main_dict : dict\n",
    "        Data dictionary, format of\n",
    "        testing_param : dictionary\n",
    "            iteration : time\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    main_dict_mean : dict\n",
    "    main_dict_stdev : dict\n",
    "    \"\"\"\n",
    "\n",
    "    main_dict_list = {}\n",
    "    for thread, iteration_dict in main_dict.items():\n",
    "        main_dict_list[thread] = [v for (k, v) in iteration_dict.items()]\n",
    "\n",
    "    main_dict_mean = {}\n",
    "    main_dict_stdev = {}\n",
    "    for thread, timing_list in main_dict_list.items():\n",
    "        main_dict_mean[thread] = statistics.mean(timing_list)\n",
    "        main_dict_stdev[thread] = statistics.stdev(timing_list)\n",
    "\n",
    "    return main_dict_mean, main_dict_stdev\n",
    "\n",
    "threads_dict_mean, threads_dict_stdev = create_stat_dictionaries(threads_dict)\n",
    "cpu_threads_dict_mean, cpu_threads_dict_stdev = create_stat_dictionaries(cpu_threads_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae1e8624",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def graph_data(testing_params, mean_dict, stdev_dict, x_axis_title, y_axis_title, graph_title, text_labels=False):\n",
    "    \"\"\"\n",
    "    Uses pyplot to graph the data given the mean and standard deviation of data.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    testing_params : list\n",
    "        This is a list of the testing parameters, which is used for the axis construction.\n",
    "    mean_dict : dict\n",
    "    stdev_dict : dict\n",
    "    x_axis_title : str\n",
    "    y_axis_title : str\n",
    "    graph_title : str\n",
    "    text_labels : bool\n",
    "        This is a boolean parameter for if the testing parameters are text. If they are text, then\n",
    "        it will set each tick mark on the x-axis to be a label in testing_params.\n",
    "    \"\"\"\n",
    "    \n",
    "    lists = sorted(mean_dict.items())\n",
    "    lists_stdev = sorted(stdev_dict.items())\n",
    "    x, y = zip(*lists)\n",
    "    x_stdev, y_stdev = zip(*lists_stdev)\n",
    "\n",
    "    fig = plt.figure(figsize=(20,10))\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    ax.set_xlabel(x_axis_title)\n",
    "    ax.set_ylabel(y_axis_title)\n",
    "    ax.set_title(graph_title)\n",
    "    ax.grid(True)\n",
    "    \n",
    "    if text_labels:\n",
    "        xtick_spacer = np.arange(0, len(testing_params), 1)\n",
    "        xticks = testing_params\n",
    "        ax.set_xticks(xtick_spacer)\n",
    "        ax.set_xticklabels(xticks, rotation='horizontal', fontsize=18)\n",
    "    elif not text_labels:\n",
    "        #If there are a lot of ticks, it will reduce for visibility.\n",
    "        interval = testing_params[1] - testing_params[0]\n",
    "        if len(testing_params) > 80:\n",
    "            xticks = np.arange(testing_params[0], testing_params[-1]+interval, interval*2)\n",
    "        else:\n",
    "            xticks = np.arange(testing_params[0], testing_params[-1]+interval, interval)\n",
    "        ax.set_xticks(xticks)\n",
    "    \n",
    "    ax.plot(x, y, color='tab:blue', label=\"{} vs {}\".format(x_axis_title, y_axis_title))\n",
    "    plt.errorbar(x, y, yerr=y_stdev, label='stdev')\n",
    "    leg = plt.legend(loc='best', ncol=1, mode='expanded', fancybox=False)\n",
    "    leg.get_frame().set_alpha(0.5)\n",
    "\n",
    "graph_data(threads_numbers, threads_dict_mean, threads_dict_stdev, \"threads\", \"time (sec)\", \"threads vs time\")\n",
    "graph_data(cpu_threads_numbers, cpu_threads_dict_mean, cpu_threads_dict_stdev, \"threads\", \"time (sec)\", \"threads vs cpu time\")"
   ]
  }
 ],
 "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": "3.8.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "c20eb03b6e6c860a5223c79131a5cfd0b7e61a9aa5d08c2b1400663bc6cc4e6d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}