{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import copy\n",
    "from benchmark_nerfacto import table_rows\n",
    "\n",
    "# methods sorted in the same order we want in the paper tables\n",
    "methods = [row[0] for row in table_rows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def csv_filename_to_results(csv_filename, num_decimals=2):\n",
    "    # code to read the csv file\n",
    "    # and return a dictionary\n",
    "\n",
    "    df = pd.read_csv(csv_filename)\n",
    "\n",
    "    column_titles = list(df.columns.values)\n",
    "    last_row = list(df.values[-1])\n",
    "\n",
    "    # print(len(column_titles))\n",
    "    # print(len(last_row))\n",
    "\n",
    "    from collections import defaultdict\n",
    "    results = defaultdict(dict)\n",
    "    for column_title, last_row_value in zip(column_titles, last_row):\n",
    "        if column_title == \"Step\" or \"__MAX\" in column_title or \"__MIN\" in column_title:\n",
    "            continue\n",
    "        name = column_title.split(\" - \")[0]\n",
    "        capture_name, method = name.split(\"_\")\n",
    "        metric = float(last_row_value)\n",
    "        # format to have 2 decimal places\n",
    "        if num_decimals == 2:\n",
    "            metric = f\"{metric:.2f}\"\n",
    "        elif num_decimals == 3:\n",
    "            metric = f\"{metric:.3f}\"\n",
    "        results[capture_name][method] = metric\n",
    "\n",
    "    # import pprint\n",
    "    # pprint.pprint(results)\n",
    "    # print(len(results))\n",
    "\n",
    "    results = dict(results)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the csv filenames\n",
    "# these are generated from exporting CSVs from wandb graphs\n",
    "# csv_filename_psrn = \"/home/ethanweber/nerfactory/wandb-psnr.csv\"\n",
    "# csv_filename_ssim = \"/home/ethanweber/nerfactory/wandb-ssim.csv\"\n",
    "# csv_filename_lpips = \"/home/ethanweber/nerfactory/wandb-lpips.csv\"\n",
    "csv_filename_psrn = \"/home/ethanweber/nerfactory/wandb-jan9-psnr.csv\"\n",
    "csv_filename_ssim = \"/home/ethanweber/nerfactory/wandb-jan9-ssim.csv\"\n",
    "csv_filename_lpips = \"/home/ethanweber/nerfactory/wandb-jan9-lpips.csv\"\n",
    "\n",
    "results_psrn = csv_filename_to_results(csv_filename_psrn)\n",
    "results_ssim = csv_filename_to_results(csv_filename_ssim, num_decimals=3)\n",
    "results_lpips = csv_filename_to_results(csv_filename_lpips, num_decimals=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_row(method, capture_names):\n",
    "    # remove spaces from method\n",
    "    method_text = method.replace(\"-\", \" \")\n",
    "    string = f\"{method_text}\"\n",
    "    for idx, capture_name in enumerate(capture_names):\n",
    "        psnr = results_psrn[capture_name][method]\n",
    "        ssim = results_ssim[capture_name][method]\n",
    "        lpips = results_lpips[capture_name][method]\n",
    "        if idx == len(capture_names) - 1:\n",
    "            string += \" & \\\\multicolumn{1}{r|}{\" + f\"{psnr} / {ssim} / {lpips}\" + \"}\"\n",
    "        else:\n",
    "            string += \" & \\\\multicolumn{1}{r}{\" + f\"{psnr} / {ssim} / {lpips}\" + \"}\"\n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Egypt', 'person', 'kitchen', 'plane', 'dozer']\n",
      "no contraction & \\multicolumn{1}{r}{18.06 / 0.520 / 0.151} & \\multicolumn{1}{r}{21.96 / 0.511 / 0.125} & \\multicolumn{1}{r}{19.35 / 0.764 / 0.078} & \\multicolumn{1}{r}{18.45 / 0.519 / 0.161} & \\multicolumn{1}{r|}{19.53 / 0.786 / 0.081} \\\\ \n",
      "synthetic on real & \\multicolumn{1}{r}{18.51 / 0.546 / 0.119} & \\multicolumn{1}{r}{24.68 / 0.667 / 0.099} & \\multicolumn{1}{r}{20.98 / 0.745 / 0.074} & \\multicolumn{1}{r}{19.32 / 0.578 / 0.102} & \\multicolumn{1}{r|}{21.41 / 0.758 / 0.080} \\\\ \\hline\n",
      "\n",
      "['floating-tree', 'aspen', 'stump', 'sculpture', 'Giannini-Hall']\n",
      "no contraction & \\multicolumn{1}{r}{18.98 / 0.679 / 0.088} & \\multicolumn{1}{r}{15.80 / 0.330 / 0.342} & \\multicolumn{1}{r}{20.69 / 0.704 / 0.075} & \\multicolumn{1}{r}{21.43 / 0.720 / 0.073} & \\multicolumn{1}{r|}{17.75 / 0.596 / 0.134} \\\\ \n",
      "synthetic on real & \\multicolumn{1}{r}{21.13 / 0.726 / 0.072} & \\multicolumn{1}{r}{15.53 / 0.301 / 0.332} & \\multicolumn{1}{r}{23.86 / 0.786 / 0.053} & \\multicolumn{1}{r}{22.62 / 0.706 / 0.076} & \\multicolumn{1}{r|}{18.41 / 0.576 / 0.133} \\\\ \\hline\n"
     ]
    }
   ],
   "source": [
    "capture_names_row1 = [\n",
    "    \"Egypt\",\n",
    "    \"person\",\n",
    "    \"kitchen\",\n",
    "    \"plane\",\n",
    "    \"dozer\"\n",
    "]\n",
    "capture_names_row2 = [\n",
    "    \"floating-tree\",\n",
    "    \"aspen\",\n",
    "    \"stump\",\n",
    "    \"sculpture\",\n",
    "    \"Giannini-Hall\"\n",
    "]\n",
    "\n",
    "print(capture_names_row1)\n",
    "for method in methods[:-1]:\n",
    "    # print(generate_row(method, capture_names_row1) + ' \\\\\\ \\cline{1-1}')\n",
    "    print(generate_row(method, capture_names_row1) + ' \\\\\\ ')\n",
    "print(generate_row(methods[-1], capture_names_row1) + ' \\\\\\ \\hline')\n",
    "\n",
    "print()\n",
    "\n",
    "print(capture_names_row2)\n",
    "for method in methods[:-1]:\n",
    "    # print(generate_row(method, capture_names_row2) + ' \\\\\\ \\cline{1-1}')\n",
    "    print(generate_row(method, capture_names_row2) + ' \\\\\\ ')\n",
    "print(generate_row(methods[-1], capture_names_row2) + ' \\\\\\ \\hline')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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