{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>max (TH/J)</th>\n",
       "      <th>useless</th>\n",
       "      <th>archaicity (TH/J)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-03</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-04</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-05</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5132</th>\n",
       "      <td>2023-01-20</td>\n",
       "      <td>0.046500</td>\n",
       "      <td>0.046500</td>\n",
       "      <td>0.046500</td>\n",
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       "    <tr>\n",
       "      <th>5133</th>\n",
       "      <td>2023-01-21</td>\n",
       "      <td>0.046500</td>\n",
       "      <td>0.046500</td>\n",
       "      <td>0.046500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5134</th>\n",
       "      <td>2023-01-22</td>\n",
       "      <td>0.046500</td>\n",
       "      <td>0.046500</td>\n",
       "      <td>0.046500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5135</th>\n",
       "      <td>2023-01-23</td>\n",
       "      <td>0.046500</td>\n",
       "      <td>0.046500</td>\n",
       "      <td>0.046500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5136</th>\n",
       "      <td>2023-01-24</td>\n",
       "      <td>0.046500</td>\n",
       "      <td>0.046500</td>\n",
       "      <td>0.046500</td>\n",
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       "</table>\n",
       "<p>5137 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  max (TH/J)   useless  archaicity (TH/J)\n",
       "0     2009-01-01    0.000001  0.000001           0.000001\n",
       "1     2009-01-02    0.000001  0.000001           0.000001\n",
       "2     2009-01-03    0.000001  0.000001           0.000001\n",
       "3     2009-01-04    0.000001  0.000001           0.000001\n",
       "4     2009-01-05    0.000001  0.000001           0.000001\n",
       "...          ...         ...       ...                ...\n",
       "5132  2023-01-20    0.046500  0.046500           0.046500\n",
       "5133  2023-01-21    0.046500  0.046500           0.046500\n",
       "5134  2023-01-22    0.046500  0.046500           0.046500\n",
       "5135  2023-01-23    0.046500  0.046500           0.046500\n",
       "5136  2023-01-24    0.046500  0.046500           0.046500\n",
       "\n",
       "[5137 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pmaxv1 = pd.read_csv('pmaxv1/pmaxv1.csv') # rows are date,max (TH/J),useless,archaicity (TH/J)\n",
    "pmaxv1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmaxv1.index = pd.to_datetime(pmaxv1['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_efficiency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>2.010000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-02</th>\n",
       "      <td>2.010000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>2.010000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>2.010000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>2.010000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-27</th>\n",
       "      <td>5.410000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28</th>\n",
       "      <td>5.410000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-29</th>\n",
       "      <td>5.410000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-30</th>\n",
       "      <td>5.410000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>5.410000e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4748 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            max_efficiency\n",
       "date                      \n",
       "2011-01-01    2.010000e+06\n",
       "2011-01-02    2.010000e+06\n",
       "2011-01-03    2.010000e+06\n",
       "2011-01-04    2.010000e+06\n",
       "2011-01-05    2.010000e+06\n",
       "...                    ...\n",
       "2023-12-27    5.410000e+10\n",
       "2023-12-28    5.410000e+10\n",
       "2023-12-29    5.410000e+10\n",
       "2023-12-30    5.410000e+10\n",
       "2023-12-31    5.410000e+10\n",
       "\n",
       "[4748 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pmaxv2 = pd.read_csv('../hardwarelist/pmaxv2.csv') # date\tmax_efficiency\n",
    "pmaxv2['date'] = pd.to_datetime(pmaxv2['date'])\n",
    "pmaxv2 = pmaxv2.set_index('date')\n",
    "pmaxv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Timothe\\AppData\\Local\\Temp\\ipykernel_46252\\2826866675.py:6: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  pmaxv3 = pd.concat([pmaxv3, pd.DataFrame([[date, max(max1, max2)]], columns=['date', 'max_efficiency'])])\n"
     ]
    }
   ],
   "source": [
    "# for each date in pmaxv2, get the max between the two dataframes\n",
    "pmaxv3 = pd.DataFrame(columns=['date', 'max_efficiency'])\n",
    "for date in pmaxv2.index:\n",
    "    max1 = pmaxv1[pmaxv1.index <= date].iloc[-1]['max (TH/J)']*1e12\n",
    "    max2 = pmaxv2.loc[date]['max_efficiency']\n",
    "    pmaxv3 = pd.concat([pmaxv3, pd.DataFrame([[date, max(max1, max2)]], columns=['date', 'max_efficiency'])])\n",
    "\n",
    "pmaxv3.to_csv('../hardwarelist/pmaxv3.csv', index=False)"
   ]
  }
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