{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import salishsea_tools.river_202108 as rivers\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Johnson', 'Jimmycomelately', 'SalmonSnow', 'Chimacum', 'Thorndike', 'Torboo', 'LittleBigQuilcene', 'Dosewalips', 'Duckabush', 'Fulton', 'Waketick', 'HammaHamma', 'Jorsted', 'Eagle', 'Lilliwaup', 'Finch', 'Skokomish', 'Rendsland', 'Tahuya', 'Mission', 'Union', 'Coulter', 'Minter', 'Burley', 'Olalla', 'Blackjack', 'ClearBarker', 'BigValley', 'BigBear', 'Swaback', 'Stavis', 'Anderson', 'Dewatta', 'Sherwood', 'DeerJohnsGoldboroughMill', 'Skookum', 'KennedySchneider', 'PerryMcClane', 'Deschutes', 'Woodward', 'Woodland', 'Chambers', 'NisquallyMcAllister', 'Puyallup', 'Hylebas', 'Duwamish1', 'Duwamish2', 'CedarSammamish'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rivers_puget = rivers.prop_dict['puget']\n",
    "rivers_puget.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('/ocean/cstang/MOAD/rivers_data/river_dailies_to_ts_rivers_20171210_20221231.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Johnson [kg/m2/s]</th>\n",
       "      <th>Jimmycomelately [kg/m2/s]</th>\n",
       "      <th>SalmonSnow [kg/m2/s]</th>\n",
       "      <th>Chimacum [kg/m2/s]</th>\n",
       "      <th>Thorndike [kg/m2/s]</th>\n",
       "      <th>Torboo [kg/m2/s]</th>\n",
       "      <th>LittleBigQuilcene [kg/m2/s]</th>\n",
       "      <th>Dosewalips [kg/m2/s]</th>\n",
       "      <th>Duckabush [kg/m2/s]</th>\n",
       "      <th>Fulton [kg/m2/s]</th>\n",
       "      <th>...</th>\n",
       "      <th>Deschutes [kg/m2/s]</th>\n",
       "      <th>Woodward [kg/m2/s]</th>\n",
       "      <th>Woodland [kg/m2/s]</th>\n",
       "      <th>Chambers [kg/m2/s]</th>\n",
       "      <th>NisquallyMcAllister [kg/m2/s]</th>\n",
       "      <th>Puyallup [kg/m2/s]</th>\n",
       "      <th>Hylebas [kg/m2/s]</th>\n",
       "      <th>Duwamish1 [kg/m2/s]</th>\n",
       "      <th>Duwamish2 [kg/m2/s]</th>\n",
       "      <th>CedarSammamish [kg/m2/s]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.014361</td>\n",
       "      <td>0.014347</td>\n",
       "      <td>0.071620</td>\n",
       "      <td>0.057317</td>\n",
       "      <td>0.014253</td>\n",
       "      <td>0.014272</td>\n",
       "      <td>0.099892</td>\n",
       "      <td>0.056994</td>\n",
       "      <td>0.039894</td>\n",
       "      <td>0.005701</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059857</td>\n",
       "      <td>0.008521</td>\n",
       "      <td>0.008524</td>\n",
       "      <td>0.056353</td>\n",
       "      <td>0.424030</td>\n",
       "      <td>0.559204</td>\n",
       "      <td>0.002810</td>\n",
       "      <td>0.141188</td>\n",
       "      <td>0.141189</td>\n",
       "      <td>0.283189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.014065</td>\n",
       "      <td>0.014052</td>\n",
       "      <td>0.070146</td>\n",
       "      <td>0.056137</td>\n",
       "      <td>0.013960</td>\n",
       "      <td>0.013979</td>\n",
       "      <td>0.097836</td>\n",
       "      <td>0.055820</td>\n",
       "      <td>0.039073</td>\n",
       "      <td>0.005584</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058625</td>\n",
       "      <td>0.008345</td>\n",
       "      <td>0.008348</td>\n",
       "      <td>0.055193</td>\n",
       "      <td>0.415300</td>\n",
       "      <td>0.547691</td>\n",
       "      <td>0.002752</td>\n",
       "      <td>0.138281</td>\n",
       "      <td>0.138282</td>\n",
       "      <td>0.277359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.013733</td>\n",
       "      <td>0.013720</td>\n",
       "      <td>0.068488</td>\n",
       "      <td>0.054810</td>\n",
       "      <td>0.013630</td>\n",
       "      <td>0.013648</td>\n",
       "      <td>0.095523</td>\n",
       "      <td>0.054501</td>\n",
       "      <td>0.038149</td>\n",
       "      <td>0.005452</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057239</td>\n",
       "      <td>0.008148</td>\n",
       "      <td>0.008151</td>\n",
       "      <td>0.053889</td>\n",
       "      <td>0.405483</td>\n",
       "      <td>0.534745</td>\n",
       "      <td>0.002687</td>\n",
       "      <td>0.135013</td>\n",
       "      <td>0.135013</td>\n",
       "      <td>0.270803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.013474</td>\n",
       "      <td>0.013462</td>\n",
       "      <td>0.067199</td>\n",
       "      <td>0.053778</td>\n",
       "      <td>0.013373</td>\n",
       "      <td>0.013391</td>\n",
       "      <td>0.093725</td>\n",
       "      <td>0.053475</td>\n",
       "      <td>0.037431</td>\n",
       "      <td>0.005349</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056162</td>\n",
       "      <td>0.007995</td>\n",
       "      <td>0.007997</td>\n",
       "      <td>0.052874</td>\n",
       "      <td>0.397852</td>\n",
       "      <td>0.524681</td>\n",
       "      <td>0.002636</td>\n",
       "      <td>0.132472</td>\n",
       "      <td>0.132472</td>\n",
       "      <td>0.265706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.013252</td>\n",
       "      <td>0.013240</td>\n",
       "      <td>0.066093</td>\n",
       "      <td>0.052894</td>\n",
       "      <td>0.013153</td>\n",
       "      <td>0.013171</td>\n",
       "      <td>0.092184</td>\n",
       "      <td>0.052596</td>\n",
       "      <td>0.036815</td>\n",
       "      <td>0.005261</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055238</td>\n",
       "      <td>0.007863</td>\n",
       "      <td>0.007866</td>\n",
       "      <td>0.052005</td>\n",
       "      <td>0.391308</td>\n",
       "      <td>0.516050</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>0.130293</td>\n",
       "      <td>0.130293</td>\n",
       "      <td>0.261335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>0.037919</td>\n",
       "      <td>0.037884</td>\n",
       "      <td>0.189112</td>\n",
       "      <td>0.151344</td>\n",
       "      <td>0.037635</td>\n",
       "      <td>0.037686</td>\n",
       "      <td>0.263764</td>\n",
       "      <td>0.150491</td>\n",
       "      <td>0.105339</td>\n",
       "      <td>0.015054</td>\n",
       "      <td>...</td>\n",
       "      <td>0.158051</td>\n",
       "      <td>0.022499</td>\n",
       "      <td>0.022507</td>\n",
       "      <td>0.148800</td>\n",
       "      <td>1.119644</td>\n",
       "      <td>1.476569</td>\n",
       "      <td>0.007419</td>\n",
       "      <td>0.372805</td>\n",
       "      <td>0.372806</td>\n",
       "      <td>0.747756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844</th>\n",
       "      <td>0.036420</td>\n",
       "      <td>0.036386</td>\n",
       "      <td>0.181635</td>\n",
       "      <td>0.145361</td>\n",
       "      <td>0.036147</td>\n",
       "      <td>0.036196</td>\n",
       "      <td>0.253337</td>\n",
       "      <td>0.144542</td>\n",
       "      <td>0.101175</td>\n",
       "      <td>0.014459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151803</td>\n",
       "      <td>0.021609</td>\n",
       "      <td>0.021617</td>\n",
       "      <td>0.142918</td>\n",
       "      <td>1.075381</td>\n",
       "      <td>1.418195</td>\n",
       "      <td>0.007126</td>\n",
       "      <td>0.358067</td>\n",
       "      <td>0.358068</td>\n",
       "      <td>0.718194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845</th>\n",
       "      <td>0.028637</td>\n",
       "      <td>0.028611</td>\n",
       "      <td>0.142821</td>\n",
       "      <td>0.114299</td>\n",
       "      <td>0.028423</td>\n",
       "      <td>0.028461</td>\n",
       "      <td>0.199201</td>\n",
       "      <td>0.113654</td>\n",
       "      <td>0.079555</td>\n",
       "      <td>0.011369</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119364</td>\n",
       "      <td>0.016992</td>\n",
       "      <td>0.016997</td>\n",
       "      <td>0.112377</td>\n",
       "      <td>0.845580</td>\n",
       "      <td>1.115138</td>\n",
       "      <td>0.005603</td>\n",
       "      <td>0.281551</td>\n",
       "      <td>0.281552</td>\n",
       "      <td>0.564722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>0.026237</td>\n",
       "      <td>0.026213</td>\n",
       "      <td>0.130852</td>\n",
       "      <td>0.104719</td>\n",
       "      <td>0.026041</td>\n",
       "      <td>0.026076</td>\n",
       "      <td>0.182506</td>\n",
       "      <td>0.104129</td>\n",
       "      <td>0.072887</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109360</td>\n",
       "      <td>0.015568</td>\n",
       "      <td>0.015573</td>\n",
       "      <td>0.102959</td>\n",
       "      <td>0.774713</td>\n",
       "      <td>1.021679</td>\n",
       "      <td>0.005133</td>\n",
       "      <td>0.257954</td>\n",
       "      <td>0.257955</td>\n",
       "      <td>0.517393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1847</th>\n",
       "      <td>0.025001</td>\n",
       "      <td>0.024978</td>\n",
       "      <td>0.124684</td>\n",
       "      <td>0.099784</td>\n",
       "      <td>0.024813</td>\n",
       "      <td>0.024847</td>\n",
       "      <td>0.173904</td>\n",
       "      <td>0.099221</td>\n",
       "      <td>0.069452</td>\n",
       "      <td>0.009925</td>\n",
       "      <td>...</td>\n",
       "      <td>0.104205</td>\n",
       "      <td>0.014834</td>\n",
       "      <td>0.014839</td>\n",
       "      <td>0.098106</td>\n",
       "      <td>0.738199</td>\n",
       "      <td>0.973525</td>\n",
       "      <td>0.004891</td>\n",
       "      <td>0.245796</td>\n",
       "      <td>0.245797</td>\n",
       "      <td>0.493007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1848 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Johnson [kg/m2/s]  Jimmycomelately [kg/m2/s]  SalmonSnow [kg/m2/s]  \\\n",
       "0              0.014361                   0.014347              0.071620   \n",
       "1              0.014065                   0.014052              0.070146   \n",
       "2              0.013733                   0.013720              0.068488   \n",
       "3              0.013474                   0.013462              0.067199   \n",
       "4              0.013252                   0.013240              0.066093   \n",
       "...                 ...                        ...                   ...   \n",
       "1843           0.037919                   0.037884              0.189112   \n",
       "1844           0.036420                   0.036386              0.181635   \n",
       "1845           0.028637                   0.028611              0.142821   \n",
       "1846           0.026237                   0.026213              0.130852   \n",
       "1847           0.025001                   0.024978              0.124684   \n",
       "\n",
       "      Chimacum [kg/m2/s]  Thorndike [kg/m2/s]  Torboo [kg/m2/s]  \\\n",
       "0               0.057317             0.014253          0.014272   \n",
       "1               0.056137             0.013960          0.013979   \n",
       "2               0.054810             0.013630          0.013648   \n",
       "3               0.053778             0.013373          0.013391   \n",
       "4               0.052894             0.013153          0.013171   \n",
       "...                  ...                  ...               ...   \n",
       "1843            0.151344             0.037635          0.037686   \n",
       "1844            0.145361             0.036147          0.036196   \n",
       "1845            0.114299             0.028423          0.028461   \n",
       "1846            0.104719             0.026041          0.026076   \n",
       "1847            0.099784             0.024813          0.024847   \n",
       "\n",
       "      LittleBigQuilcene [kg/m2/s]  Dosewalips [kg/m2/s]  Duckabush [kg/m2/s]  \\\n",
       "0                        0.099892              0.056994             0.039894   \n",
       "1                        0.097836              0.055820             0.039073   \n",
       "2                        0.095523              0.054501             0.038149   \n",
       "3                        0.093725              0.053475             0.037431   \n",
       "4                        0.092184              0.052596             0.036815   \n",
       "...                           ...                   ...                  ...   \n",
       "1843                     0.263764              0.150491             0.105339   \n",
       "1844                     0.253337              0.144542             0.101175   \n",
       "1845                     0.199201              0.113654             0.079555   \n",
       "1846                     0.182506              0.104129             0.072887   \n",
       "1847                     0.173904              0.099221             0.069452   \n",
       "\n",
       "      Fulton [kg/m2/s]  ...  Deschutes [kg/m2/s]  Woodward [kg/m2/s]  \\\n",
       "0             0.005701  ...             0.059857            0.008521   \n",
       "1             0.005584  ...             0.058625            0.008345   \n",
       "2             0.005452  ...             0.057239            0.008148   \n",
       "3             0.005349  ...             0.056162            0.007995   \n",
       "4             0.005261  ...             0.055238            0.007863   \n",
       "...                ...  ...                  ...                 ...   \n",
       "1843          0.015054  ...             0.158051            0.022499   \n",
       "1844          0.014459  ...             0.151803            0.021609   \n",
       "1845          0.011369  ...             0.119364            0.016992   \n",
       "1846          0.010416  ...             0.109360            0.015568   \n",
       "1847          0.009925  ...             0.104205            0.014834   \n",
       "\n",
       "      Woodland [kg/m2/s]  Chambers [kg/m2/s]  NisquallyMcAllister [kg/m2/s]  \\\n",
       "0               0.008524            0.056353                       0.424030   \n",
       "1               0.008348            0.055193                       0.415300   \n",
       "2               0.008151            0.053889                       0.405483   \n",
       "3               0.007997            0.052874                       0.397852   \n",
       "4               0.007866            0.052005                       0.391308   \n",
       "...                  ...                 ...                            ...   \n",
       "1843            0.022507            0.148800                       1.119644   \n",
       "1844            0.021617            0.142918                       1.075381   \n",
       "1845            0.016997            0.112377                       0.845580   \n",
       "1846            0.015573            0.102959                       0.774713   \n",
       "1847            0.014839            0.098106                       0.738199   \n",
       "\n",
       "      Puyallup [kg/m2/s]  Hylebas [kg/m2/s]  Duwamish1 [kg/m2/s]  \\\n",
       "0               0.559204           0.002810             0.141188   \n",
       "1               0.547691           0.002752             0.138281   \n",
       "2               0.534745           0.002687             0.135013   \n",
       "3               0.524681           0.002636             0.132472   \n",
       "4               0.516050           0.002593             0.130293   \n",
       "...                  ...                ...                  ...   \n",
       "1843            1.476569           0.007419             0.372805   \n",
       "1844            1.418195           0.007126             0.358067   \n",
       "1845            1.115138           0.005603             0.281551   \n",
       "1846            1.021679           0.005133             0.257954   \n",
       "1847            0.973525           0.004891             0.245796   \n",
       "\n",
       "      Duwamish2 [kg/m2/s]  CedarSammamish [kg/m2/s]  \n",
       "0                0.141189                  0.283189  \n",
       "1                0.138282                  0.277359  \n",
       "2                0.135013                  0.270803  \n",
       "3                0.132472                  0.265706  \n",
       "4                0.130293                  0.261335  \n",
       "...                   ...                       ...  \n",
       "1843             0.372806                  0.747756  \n",
       "1844             0.358068                  0.718194  \n",
       "1845             0.281552                  0.564722  \n",
       "1846             0.257955                  0.517393  \n",
       "1847             0.245797                  0.493007  \n",
       "\n",
       "[1848 rows x 48 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:,3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f135eb5a1a0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "puget_rivers_avg = np.sum(data.iloc[:,3:],axis=1)\n",
    "\n",
    "plt.plot(puget_rivers_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "puget_rivers_avg.index = data['date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "puget_rivers_avg\n",
    "\n",
    "puget_rivers_avg.to_csv('puget_rivers_avg.csv',index_label='Date')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 ('analysis-camryn')",
   "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": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "45037fb5df905fe0ec215a1f079d5355b535b26f32fb585b4920c91cfb7d4d84"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}