plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d9e818c",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'table.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 9\u001b[0m\n\u001b[1;32m      7\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minline\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Read the CSV file into a DataFrame\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtable.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;66;03m# Extract the data columns\u001b[39;00m\n\u001b[1;32m     12\u001b[0m mpi_processes \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#MPI Processes\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[0;32m/opt/homebrew/anaconda3/lib/python3.10/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\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",
      "File \u001b[0;32m/opt/homebrew/anaconda3/lib/python3.10/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\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",
      "File \u001b[0;32m/opt/homebrew/anaconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    936\u001b[0m     dialect,\n\u001b[1;32m    937\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    946\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m    947\u001b[0m )\n\u001b[1;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/homebrew/anaconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\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[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m/opt/homebrew/anaconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1439\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/homebrew/anaconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1736\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1737\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1738\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1739\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1740\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1741\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1742\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1743\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1744\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1746\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[0;32m/opt/homebrew/anaconda3/lib/python3.10/site-packages/pandas/io/common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    851\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    852\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    853\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    854\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    855\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    857\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    858\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    859\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    860\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    861\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    862\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    863\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    864\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    865\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'table.csv'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "# Read the CSV file into a DataFrame\n",
    "data = pd.read_csv('table.csv')\n",
    "\n",
    "# Extract the data columns\n",
    "mpi_processes = data['#MPI Processes']\n",
    "openmp_threads = data['#OpenMP Threads']\n",
    "elapsed_time = data['Elapsed Time (s)']\n",
    "\n",
    "# Create a 3D surface plot\n",
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot_trisurf(mpi_processes, openmp_threads, elapsed_time, cmap='viridis')\n",
    "ax.set_xlabel('#MPI Processes')\n",
    "ax.set_ylabel('#OpenMP Threads')\n",
    "ax.set_zlabel('Elapsed Time (s)')\n",
    "ax.set_title('3D Surface Plot')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a85a8c41",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Create a line plot for each #MPI Processes\n",
    "plt.figure()\n",
    "for mpi_process in sorted(set(mpi_processes)):\n",
    "    plt.plot(openmp_threads[mpi_processes == mpi_process], elapsed_time[mpi_processes == mpi_process], marker='o', label=f'#MPI Processes = {mpi_process}')\n",
    "plt.xlabel('#OpenMP Threads')\n",
    "plt.ylabel('Elapsed Time (s)')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Create a combination of bar plots for each #OpenMP Threads\n",
    "plt.figure()\n",
    "for openmp_thread in sorted(set(openmp_threads)):\n",
    "    plt.bar(mpi_processes[openmp_threads == openmp_thread], elapsed_time[openmp_threads == openmp_thread], label=f'#OpenMP Threads = {openmp_thread}')\n",
    "plt.xlabel('#MPI Processes')\n",
    "plt.ylabel('Elapsed Time (s)')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "473e73ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a line plot for each #MPI Processes\n",
    "plt.figure(figsize=(10, 6))  # Adjust the figure size\n",
    "colors = ['#524582', '#367BC3', '#38BFA7', '#8FE1A2']\n",
    "\n",
    "for i, mpi_process in enumerate(sorted(set(mpi_processes))):\n",
    "    plt.plot(\n",
    "        openmp_threads[mpi_processes == mpi_process], \n",
    "        elapsed_time[mpi_processes == mpi_process], \n",
    "        marker='o', \n",
    "        label=f'#MPI Processes = {mpi_process}',\n",
    "        color=colors[i % len(colors)]  # Rotate through the color palette\n",
    "    )\n",
    "\n",
    "plt.xlabel('#OpenMP Threads', fontsize=12)  # Increase the font size\n",
    "plt.ylabel('Elapsed Time (s)', fontsize=12)  # Increase the font size\n",
    "#plt.title('Elapsed Time vs. OpenMP Threads', fontsize=14)  # Add a title\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True)  # Add grid lines\n",
    "plt.tight_layout()  # Improve spacing between elements\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Create a combination of bar plots for each #OpenMP Threads\n",
    "plt.figure(figsize=(10, 6))  # Adjust the figure size\n",
    "\n",
    "for i, openmp_thread in enumerate(sorted(set(openmp_threads))):\n",
    "    plt.bar(\n",
    "        mpi_processes[openmp_threads == openmp_thread], \n",
    "        elapsed_time[openmp_threads == openmp_thread], \n",
    "        label=f'#OpenMP Threads = {openmp_thread}',\n",
    "        color=colors[i % len(colors)]  # Rotate through the color palette\n",
    "    )\n",
    "\n",
    "plt.xlabel('#MPI Processes', fontsize=12)  # Increase the font size\n",
    "plt.ylabel('Elapsed Time (s)', fontsize=12)  # Increase the font size\n",
    "#plt.title('Elapsed Time vs. MPI Processes', fontsize=14)  # Add a title\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(False)  # Remove grid lines\n",
    "plt.tight_layout()  # Improve spacing between elements\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "679f415c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "data = [\n",
    "    [2, 2, 3366.906596],\n",
    "    [2, 4, 1758.610779],\n",
    "    [2, 8, 941.896197],\n",
    "    [2, 16, 458.678493],\n",
    "    [4, 2, 1720.058628],\n",
    "    [4, 4, 899.002651],\n",
    "    [4, 8, 465.599454],\n",
    "    [4, 16, 234.584873],\n",
    "    [8, 2, 888.602859],\n",
    "    [8, 4, 456.913649],\n",
    "    [8, 8, 233.238874],\n",
    "    [8, 16, 118.009458],\n",
    "    [16, 2, 443.307552],\n",
    "    [16, 4, 227.840907],\n",
    "    [16, 8, 117.521767],\n",
    "    [16, 16, 62.826288]\n",
    "]\n",
    "\n",
    "# Extract the data into separate arrays\n",
    "mpi_processes = np.array([row[0] for row in data])\n",
    "openmp_threads = np.array([row[1] for row in data])\n",
    "elapsed_time = np.array([row[2] for row in data])\n",
    "\n",
    "# Calculate the execution time with one processor for each combination\n",
    "one_processor_time = []\n",
    "for p in set(mpi_processes):\n",
    "    match = (mpi_processes == p) & (openmp_threads == 1)\n",
    "    if np.any(match):\n",
    "        one_processor_time.append(elapsed_time[match][0])\n",
    "    else:\n",
    "        one_processor_time.append(np.nan)\n",
    "one_processor_time = np.array(one_processor_time)\n",
    "\n",
    "# Calculate the speedup and efficiency\n",
    "speedup = one_processor_time / elapsed_time\n",
    "efficiency = speedup / mpi_processes\n",
    "\n",
    "# Print the speedup and efficiency values\n",
    "print(\"Speedup:\")\n",
    "for i in range(len(data)):\n",
    "    print(f\"MPI Processes={mpi_processes[i]}, OpenMP Threads={openmp_threads[i]}: {speedup[i]}\")\n",
    "\n",
    "print(\"\\nEfficiency:\")\n",
    "for i in range(len(data)):\n",
    "    print(f\"MPI Processes={mpi_processes[i]}, OpenMP Threads={openmp_threads[i]}: {efficiency[i]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc968b02",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "data = {\n",
    "    'MPI processes': [2, 2, 2, 4, 4, 4, 4, 8, 8, 8, 8, 16, 16, 16, 16],\n",
    "    'OpenMP threads': [4, 8, 16, 2, 4, 8, 16, 2, 4, 8, 16, 2, 4, 8, 16],\n",
    "    'Speedup': [1.914, 3.574, 7.340, 1.956, 3.745, 7.229, 14.345, 3.788, 7.367, 14.424, 28.502, 7.593, 14.769, 28.642, 53.563],\n",
    "    'Efficiency': [0.239, 0.447, 0.459, 0.489, 0.936, 0.903, 0.897, 0.474, 0.921, 0.901, 0.891, 0.237, 0.461, 0.448, 0.334],\n",
    "}\n",
    "\n",
    "# Convert to pandas DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Pivot the data for speedup and efficiency\n",
    "speedup_pivot = df.pivot(\"MPI processes\", \"OpenMP threads\", \"Speedup\")\n",
    "efficiency_pivot = df.pivot(\"MPI processes\", \"OpenMP threads\", \"Efficiency\")\n",
    "\n",
    "# Create a figure with two subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
    "\n",
    "# Plot Heatmaps\n",
    "sns.heatmap(speedup_pivot, annot=True, cmap=\"YlGnBu\", ax=ax1)\n",
    "sns.heatmap(efficiency_pivot, annot=True, cmap=\"YlGnBu\", ax=ax2)\n",
    "\n",
    "# Set titles\n",
    "ax1.set_title(\"Speedup\")\n",
    "ax2.set_title(\"Efficiency\")\n",
    "\n",
    "plt.show()\n"
   ]
  }
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