{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "from salishsea_tools import viz_tools, places, visualisations\n",
    "from matplotlib import pyplot as plt, dates\n",
    "from datetime import datetime, timedelta\n",
    "from calendar import month_name\n",
    "from scipy.io import loadmat\n",
    "from tqdm.notebook import tqdm\n",
    "from salishsea_tools import nc_tools\n",
    "from dask.diagnostics import ProgressBar\n",
    "import cmocean\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 12, 'axes.titlesize': 'medium'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/ocean/ksuchy/MOAD/observe/NPGO.csv', index_col=0,header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index.name = \"YEAR\"\n",
    "df = df.apply(pd.to_numeric) # convert all columns of DataFrame\n",
    "#df = df[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
       "      <th>JUN</th>\n",
       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>-0.243309</td>\n",
       "      <td>-0.729927</td>\n",
       "      <td>-0.182149</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.144928</td>\n",
       "      <td>1.230000</td>\n",
       "      <td>1.230000</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>0.110011</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>0.137363</td>\n",
       "      <td>0.218341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.179856</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>1.370000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>2.140000</td>\n",
       "      <td>2.230000</td>\n",
       "      <td>1.770000</td>\n",
       "      <td>1.970000</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>0.138313</td>\n",
       "      <td>0.110011</td>\n",
       "      <td>0.302115</td>\n",
       "      <td>0.240385</td>\n",
       "      <td>0.180180</td>\n",
       "      <td>0.270270</td>\n",
       "      <td>0.178571</td>\n",
       "      <td>0.139276</td>\n",
       "      <td>0.102669</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>1.060000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>2.060000</td>\n",
       "      <td>1.840000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>1.870000</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.610000</td>\n",
       "      <td>0.101215</td>\n",
       "      <td>0.128370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.310559</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.167785</td>\n",
       "      <td>1.390000</td>\n",
       "      <td>1.110000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>1.330000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.267380</td>\n",
       "      <td>0.757576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.119904</td>\n",
       "      <td>0.115875</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>1.930000</td>\n",
       "      <td>1.630000</td>\n",
       "      <td>1.940000</td>\n",
       "      <td>1.860000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>1.870000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1.230000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.155521</td>\n",
       "      <td>0.210084</td>\n",
       "      <td>0.117371</td>\n",
       "      <td>0.137552</td>\n",
       "      <td>0.221729</td>\n",
       "      <td>0.195312</td>\n",
       "      <td>0.256410</td>\n",
       "      <td>-0.680272</td>\n",
       "      <td>-0.124224</td>\n",
       "      <td>-1.450000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>-0.256410</td>\n",
       "      <td>-0.374532</td>\n",
       "      <td>-0.220264</td>\n",
       "      <td>-0.161812</td>\n",
       "      <td>0.440529</td>\n",
       "      <td>-0.239808</td>\n",
       "      <td>-0.746269</td>\n",
       "      <td>-0.183486</td>\n",
       "      <td>-0.128700</td>\n",
       "      <td>0.235849</td>\n",
       "      <td>0.320513</td>\n",
       "      <td>-0.280899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>-0.181488</td>\n",
       "      <td>-1.270000</td>\n",
       "      <td>-1.400000</td>\n",
       "      <td>-1.380000</td>\n",
       "      <td>-0.138889</td>\n",
       "      <td>-1.250000</td>\n",
       "      <td>-1.470000</td>\n",
       "      <td>-1.930000</td>\n",
       "      <td>-2.100000</td>\n",
       "      <td>-1.310000</td>\n",
       "      <td>-2.250000</td>\n",
       "      <td>-1.110000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.106724</td>\n",
       "      <td>0.436681</td>\n",
       "      <td>0.211864</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.146628</td>\n",
       "      <td>-0.104384</td>\n",
       "      <td>-0.205339</td>\n",
       "      <td>-0.183824</td>\n",
       "      <td>-0.129199</td>\n",
       "      <td>0.284091</td>\n",
       "      <td>1.160000</td>\n",
       "      <td>-1.540000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1.622642</td>\n",
       "      <td>-0.149701</td>\n",
       "      <td>-0.112108</td>\n",
       "      <td>-0.884956</td>\n",
       "      <td>-0.219298</td>\n",
       "      <td>-0.341297</td>\n",
       "      <td>-0.225225</td>\n",
       "      <td>-0.188324</td>\n",
       "      <td>-0.190114</td>\n",
       "      <td>-1.410000</td>\n",
       "      <td>-2.060000</td>\n",
       "      <td>-2.690000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-1.500000</td>\n",
       "      <td>-2.230000</td>\n",
       "      <td>-2.030000</td>\n",
       "      <td>-2.060000</td>\n",
       "      <td>-1.920000</td>\n",
       "      <td>-1.940000</td>\n",
       "      <td>-1.570000</td>\n",
       "      <td>-2.120000</td>\n",
       "      <td>-2.320000</td>\n",
       "      <td>-2.340000</td>\n",
       "      <td>-1.770000</td>\n",
       "      <td>-1.020000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>-0.116293</td>\n",
       "      <td>-2.560000</td>\n",
       "      <td>-1.990000</td>\n",
       "      <td>-2.160000</td>\n",
       "      <td>-1.860000</td>\n",
       "      <td>-2.350000</td>\n",
       "      <td>-2.160000</td>\n",
       "      <td>-2.060000</td>\n",
       "      <td>-2.520000</td>\n",
       "      <td>-2.580000</td>\n",
       "      <td>-3.370000</td>\n",
       "      <td>-2.540000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>-1.520000</td>\n",
       "      <td>-2.320000</td>\n",
       "      <td>-1.830000</td>\n",
       "      <td>-1.880000</td>\n",
       "      <td>-1.360000</td>\n",
       "      <td>-1.890000</td>\n",
       "      <td>-2.040000</td>\n",
       "      <td>-1.360000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>-1.830000</td>\n",
       "      <td>-2.590000</td>\n",
       "      <td>-1.330000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           JAN       FEB       MAR       APR       MAY       JUN       JUL  \\\n",
       "YEAR                                                                         \n",
       "2007 -0.243309 -0.729927 -0.182149  0.384615  0.144928  1.230000  1.230000   \n",
       "2008  0.179856  1.140000  1.370000  1.400000  1.740000  1.280000  1.430000   \n",
       "2009  0.138313  0.110011  0.302115  0.240385  0.180180  0.270270  0.178571   \n",
       "2010  2.060000  1.840000  1.650000  1.180000  1.870000  1.240000  1.150000   \n",
       "2011  0.310559  0.181818  1.200000  0.167785  1.390000  1.110000  1.040000   \n",
       "2012  0.119904  0.115875  1.120000  1.930000  1.630000  1.940000  1.860000   \n",
       "2013  1.230000  1.200000  0.155521  0.210084  0.117371  0.137552  0.221729   \n",
       "2014 -0.256410 -0.374532 -0.220264 -0.161812  0.440529 -0.239808 -0.746269   \n",
       "2015 -0.181488 -1.270000 -1.400000 -1.380000 -0.138889 -1.250000 -1.470000   \n",
       "2016  0.106724  0.436681  0.211864 -0.250000 -0.146628 -0.104384 -0.205339   \n",
       "2017  1.622642 -0.149701 -0.112108 -0.884956 -0.219298 -0.341297 -0.225225   \n",
       "2018 -1.500000 -2.230000 -2.030000 -2.060000 -1.920000 -1.940000 -1.570000   \n",
       "2019 -0.116293 -2.560000 -1.990000 -2.160000 -1.860000 -2.350000 -2.160000   \n",
       "2020 -1.520000 -2.320000 -1.830000 -1.880000 -1.360000 -1.890000 -2.040000   \n",
       "\n",
       "           AUG       SEP       OCT       NOV       DEC  Unnamed: 13  \\\n",
       "YEAR                                                                  \n",
       "2007  1.430000  0.110011  1.270000  0.137363  0.218341          NaN   \n",
       "2008  2.140000  2.230000  1.770000  1.970000  0.227273          NaN   \n",
       "2009  0.139276  0.102669  1.150000  1.050000  1.060000          NaN   \n",
       "2010  1.080000  1.000000  1.610000  0.101215  0.128370          NaN   \n",
       "2011  1.040000  1.330000  1.200000  0.267380  0.757576          NaN   \n",
       "2012  1.730000  1.360000  1.870000  1.400000  1.200000          NaN   \n",
       "2013  0.195312  0.256410 -0.680272 -0.124224 -1.450000          NaN   \n",
       "2014 -0.183486 -0.128700  0.235849  0.320513 -0.280899          NaN   \n",
       "2015 -1.930000 -2.100000 -1.310000 -2.250000 -1.110000          NaN   \n",
       "2016 -0.183824 -0.129199  0.284091  1.160000 -1.540000          NaN   \n",
       "2017 -0.188324 -0.190114 -1.410000 -2.060000 -2.690000          NaN   \n",
       "2018 -2.120000 -2.320000 -2.340000 -1.770000 -1.020000          NaN   \n",
       "2019 -2.060000 -2.520000 -2.580000 -3.370000 -2.540000          NaN   \n",
       "2020 -1.360000  1.250000 -1.830000 -2.590000 -1.330000          NaN   \n",
       "\n",
       "      Unnamed: 14  \n",
       "YEAR               \n",
       "2007          NaN  \n",
       "2008          NaN  \n",
       "2009          NaN  \n",
       "2010          NaN  \n",
       "2011          NaN  \n",
       "2012          NaN  \n",
       "2013          NaN  \n",
       "2014          NaN  \n",
       "2015          NaN  \n",
       "2016          NaN  \n",
       "2017          NaN  \n",
       "2018          NaN  \n",
       "2019          NaN  \n",
       "2020          NaN  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2007:2020]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT',\n",
       "       'NOV', 'DEC', 'Unnamed: 13', 'Unnamed: 14'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['Unnamed: 13', 'Unnamed: 14'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "NPGO = df.loc[2007:2020].to_numpy().flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('/ocean/ksuchy/MOAD/observe/CentralSoG_0-10mNitrateAnomalies.csv', index_col=0)\n",
    "\n",
    "#df2 = pd.read_csv('/ocean/ksuchy/MOAD/observe/CentralSoG_NitrateAnomalies.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.index.name = \"YEAR\"\n",
    "df2 = df2.apply(pd.to_numeric) # convert all columns of DataFrame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
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       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>0.568147</td>\n",
       "      <td>1.051579</td>\n",
       "      <td>2.377032</td>\n",
       "      <td>2.414698</td>\n",
       "      <td>0.237077</td>\n",
       "      <td>0.132123</td>\n",
       "      <td>1.528375</td>\n",
       "      <td>-0.668343</td>\n",
       "      <td>-0.697072</td>\n",
       "      <td>1.511975</td>\n",
       "      <td>0.743682</td>\n",
       "      <td>1.161057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1.276618</td>\n",
       "      <td>0.479209</td>\n",
       "      <td>1.319208</td>\n",
       "      <td>-1.097002</td>\n",
       "      <td>1.878009</td>\n",
       "      <td>0.518437</td>\n",
       "      <td>0.618601</td>\n",
       "      <td>2.267914</td>\n",
       "      <td>2.672396</td>\n",
       "      <td>-1.678422</td>\n",
       "      <td>0.497572</td>\n",
       "      <td>0.403591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>0.779868</td>\n",
       "      <td>0.430057</td>\n",
       "      <td>2.087796</td>\n",
       "      <td>4.853228</td>\n",
       "      <td>1.664037</td>\n",
       "      <td>0.246908</td>\n",
       "      <td>0.905363</td>\n",
       "      <td>0.360774</td>\n",
       "      <td>-1.045085</td>\n",
       "      <td>0.595109</td>\n",
       "      <td>2.490391</td>\n",
       "      <td>1.573691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.034209</td>\n",
       "      <td>1.255441</td>\n",
       "      <td>1.802995</td>\n",
       "      <td>3.074134</td>\n",
       "      <td>1.325453</td>\n",
       "      <td>0.726371</td>\n",
       "      <td>0.762110</td>\n",
       "      <td>1.089319</td>\n",
       "      <td>0.088487</td>\n",
       "      <td>1.137430</td>\n",
       "      <td>0.201821</td>\n",
       "      <td>0.694923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.560476</td>\n",
       "      <td>0.772615</td>\n",
       "      <td>2.546247</td>\n",
       "      <td>9.392093</td>\n",
       "      <td>0.847004</td>\n",
       "      <td>-0.471765</td>\n",
       "      <td>-0.582232</td>\n",
       "      <td>-0.948415</td>\n",
       "      <td>0.565439</td>\n",
       "      <td>-0.134362</td>\n",
       "      <td>-0.006921</td>\n",
       "      <td>0.394563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.789796</td>\n",
       "      <td>1.147739</td>\n",
       "      <td>3.256987</td>\n",
       "      <td>4.216038</td>\n",
       "      <td>0.147361</td>\n",
       "      <td>0.936165</td>\n",
       "      <td>-0.418391</td>\n",
       "      <td>-0.520689</td>\n",
       "      <td>-2.380251</td>\n",
       "      <td>0.515971</td>\n",
       "      <td>0.416087</td>\n",
       "      <td>1.410105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.216742</td>\n",
       "      <td>0.035722</td>\n",
       "      <td>0.997229</td>\n",
       "      <td>-2.721693</td>\n",
       "      <td>0.434487</td>\n",
       "      <td>0.117015</td>\n",
       "      <td>0.132615</td>\n",
       "      <td>0.691013</td>\n",
       "      <td>1.132842</td>\n",
       "      <td>0.705337</td>\n",
       "      <td>-0.054270</td>\n",
       "      <td>-0.598873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>-0.168980</td>\n",
       "      <td>0.088505</td>\n",
       "      <td>-0.472435</td>\n",
       "      <td>-2.550886</td>\n",
       "      <td>-1.914227</td>\n",
       "      <td>-0.344112</td>\n",
       "      <td>-0.414754</td>\n",
       "      <td>-0.995434</td>\n",
       "      <td>-2.113837</td>\n",
       "      <td>-0.514631</td>\n",
       "      <td>-1.249320</td>\n",
       "      <td>-1.407372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>-2.075749</td>\n",
       "      <td>-1.507267</td>\n",
       "      <td>-6.402064</td>\n",
       "      <td>-5.363631</td>\n",
       "      <td>-0.790012</td>\n",
       "      <td>0.466801</td>\n",
       "      <td>1.025258</td>\n",
       "      <td>2.547623</td>\n",
       "      <td>4.371997</td>\n",
       "      <td>0.614517</td>\n",
       "      <td>-0.078646</td>\n",
       "      <td>0.494163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>-0.754900</td>\n",
       "      <td>-1.356740</td>\n",
       "      <td>0.465542</td>\n",
       "      <td>-4.617599</td>\n",
       "      <td>0.367258</td>\n",
       "      <td>-0.221639</td>\n",
       "      <td>-0.017881</td>\n",
       "      <td>1.801608</td>\n",
       "      <td>1.597828</td>\n",
       "      <td>1.803464</td>\n",
       "      <td>-0.713561</td>\n",
       "      <td>-1.271169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>-1.682356</td>\n",
       "      <td>-1.640867</td>\n",
       "      <td>0.514737</td>\n",
       "      <td>1.776615</td>\n",
       "      <td>-1.944853</td>\n",
       "      <td>-0.714686</td>\n",
       "      <td>-1.145170</td>\n",
       "      <td>-1.623345</td>\n",
       "      <td>-2.310101</td>\n",
       "      <td>-0.724774</td>\n",
       "      <td>0.381249</td>\n",
       "      <td>-0.969339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>-0.754947</td>\n",
       "      <td>-0.677864</td>\n",
       "      <td>-4.683838</td>\n",
       "      <td>-2.127948</td>\n",
       "      <td>-1.830622</td>\n",
       "      <td>-0.717698</td>\n",
       "      <td>-0.960148</td>\n",
       "      <td>-1.950949</td>\n",
       "      <td>-0.838416</td>\n",
       "      <td>-2.117258</td>\n",
       "      <td>-0.501826</td>\n",
       "      <td>0.210814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.165554</td>\n",
       "      <td>-0.378386</td>\n",
       "      <td>-3.366277</td>\n",
       "      <td>-2.951009</td>\n",
       "      <td>0.622692</td>\n",
       "      <td>-0.518994</td>\n",
       "      <td>-0.645146</td>\n",
       "      <td>-1.551466</td>\n",
       "      <td>-0.555656</td>\n",
       "      <td>-0.434903</td>\n",
       "      <td>-1.859252</td>\n",
       "      <td>-1.216886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.045522</td>\n",
       "      <td>0.300256</td>\n",
       "      <td>-0.443162</td>\n",
       "      <td>-4.297039</td>\n",
       "      <td>-1.043665</td>\n",
       "      <td>-0.154926</td>\n",
       "      <td>-0.788602</td>\n",
       "      <td>-0.499612</td>\n",
       "      <td>-0.488570</td>\n",
       "      <td>-1.279451</td>\n",
       "      <td>-0.267004</td>\n",
       "      <td>-0.879268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Jan       Feb       Mar       Apr       May       Jun       Jul  \\\n",
       "YEAR                                                                         \n",
       "2007  0.568147  1.051579  2.377032  2.414698  0.237077  0.132123  1.528375   \n",
       "2008  1.276618  0.479209  1.319208 -1.097002  1.878009  0.518437  0.618601   \n",
       "2009  0.779868  0.430057  2.087796  4.853228  1.664037  0.246908  0.905363   \n",
       "2010  1.034209  1.255441  1.802995  3.074134  1.325453  0.726371  0.762110   \n",
       "2011  0.560476  0.772615  2.546247  9.392093  0.847004 -0.471765 -0.582232   \n",
       "2012  0.789796  1.147739  3.256987  4.216038  0.147361  0.936165 -0.418391   \n",
       "2013  0.216742  0.035722  0.997229 -2.721693  0.434487  0.117015  0.132615   \n",
       "2014 -0.168980  0.088505 -0.472435 -2.550886 -1.914227 -0.344112 -0.414754   \n",
       "2015 -2.075749 -1.507267 -6.402064 -5.363631 -0.790012  0.466801  1.025258   \n",
       "2016 -0.754900 -1.356740  0.465542 -4.617599  0.367258 -0.221639 -0.017881   \n",
       "2017 -1.682356 -1.640867  0.514737  1.776615 -1.944853 -0.714686 -1.145170   \n",
       "2018 -0.754947 -0.677864 -4.683838 -2.127948 -1.830622 -0.717698 -0.960148   \n",
       "2019  0.165554 -0.378386 -3.366277 -2.951009  0.622692 -0.518994 -0.645146   \n",
       "2020  0.045522  0.300256 -0.443162 -4.297039 -1.043665 -0.154926 -0.788602   \n",
       "\n",
       "           Aug       Sep       Oct       Nov       Dec  \n",
       "YEAR                                                    \n",
       "2007 -0.668343 -0.697072  1.511975  0.743682  1.161057  \n",
       "2008  2.267914  2.672396 -1.678422  0.497572  0.403591  \n",
       "2009  0.360774 -1.045085  0.595109  2.490391  1.573691  \n",
       "2010  1.089319  0.088487  1.137430  0.201821  0.694923  \n",
       "2011 -0.948415  0.565439 -0.134362 -0.006921  0.394563  \n",
       "2012 -0.520689 -2.380251  0.515971  0.416087  1.410105  \n",
       "2013  0.691013  1.132842  0.705337 -0.054270 -0.598873  \n",
       "2014 -0.995434 -2.113837 -0.514631 -1.249320 -1.407372  \n",
       "2015  2.547623  4.371997  0.614517 -0.078646  0.494163  \n",
       "2016  1.801608  1.597828  1.803464 -0.713561 -1.271169  \n",
       "2017 -1.623345 -2.310101 -0.724774  0.381249 -0.969339  \n",
       "2018 -1.950949 -0.838416 -2.117258 -0.501826  0.210814  \n",
       "2019 -1.551466 -0.555656 -0.434903 -1.859252 -1.216886  \n",
       "2020 -0.499612 -0.488570 -1.279451 -0.267004 -0.879268  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "Nitrate_Anom=df2.to_numpy().flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.68146998e-01,  1.05157894e+00,  2.37703222e+00,  2.41469847e+00,\n",
       "        2.37076790e-01,  1.32123051e-01,  1.52837541e+00, -6.68342631e-01,\n",
       "       -6.97071613e-01,  1.51197457e+00,  7.43681864e-01,  1.16105666e+00,\n",
       "        1.27661808e+00,  4.79209169e-01,  1.31920849e+00, -1.09700244e+00,\n",
       "        1.87800895e+00,  5.18436663e-01,  6.18600847e-01,  2.26791377e+00,\n",
       "        2.67239635e+00, -1.67842230e+00,  4.97571606e-01,  4.03590932e-01,\n",
       "        7.79868031e-01,  4.30057381e-01,  2.08779567e+00,  4.85322797e+00,\n",
       "        1.66403734e+00,  2.46908417e-01,  9.05363427e-01,  3.60774480e-01,\n",
       "       -1.04508507e+00,  5.95108725e-01,  2.49039109e+00,  1.57369104e+00,\n",
       "        1.03420928e+00,  1.25544086e+00,  1.80299535e+00,  3.07413424e+00,\n",
       "        1.32545266e+00,  7.26370614e-01,  7.62110383e-01,  1.08931918e+00,\n",
       "        8.84867780e-02,  1.13742996e+00,  2.01821384e-01,  6.94922968e-01,\n",
       "        5.60475691e-01,  7.72615336e-01,  2.54624739e+00,  9.39209283e+00,\n",
       "        8.47004141e-01, -4.71765293e-01, -5.82231833e-01, -9.48414964e-01,\n",
       "        5.65439204e-01, -1.34361576e-01, -6.92094700e-03,  3.94562750e-01,\n",
       "        7.89796373e-01,  1.14773927e+00,  3.25698656e+00,  4.21603837e+00,\n",
       "        1.47361248e-01,  9.36165497e-01, -4.18390629e-01, -5.20688752e-01,\n",
       "       -2.38025140e+00,  5.15971374e-01,  4.16086935e-01,  1.41010512e+00,\n",
       "        2.16742057e-01,  3.57217600e-02,  9.97228796e-01, -2.72169262e+00,\n",
       "        4.34486776e-01,  1.17014636e-01,  1.32615273e-01,  6.91012707e-01,\n",
       "        1.13284154e+00,  7.05336681e-01, -5.42703520e-02, -5.98873171e-01,\n",
       "       -1.68980149e-01,  8.85050050e-02, -4.72434545e-01, -2.55088564e+00,\n",
       "       -1.91422712e+00, -3.44111588e-01, -4.14753567e-01, -9.95433646e-01,\n",
       "       -2.11383725e+00, -5.14630849e-01, -1.24932009e+00, -1.40737185e+00,\n",
       "       -2.07574867e+00, -1.50726665e+00, -6.40206363e+00, -5.36363145e+00,\n",
       "       -7.90011519e-01,  4.66801299e-01,  1.02525846e+00,  2.54762336e+00,\n",
       "        4.37199676e+00,  6.14516751e-01, -7.86461490e-02,  4.94163064e-01,\n",
       "       -7.54899971e-01, -1.35674001e+00,  4.65542370e-01, -4.61759877e+00,\n",
       "        3.67257973e-01, -2.21639077e-01, -1.78812820e-02,  1.80160841e+00,\n",
       "        1.59782790e+00,  1.80346363e+00, -7.13561317e-01, -1.27116939e+00,\n",
       "       -1.68235614e+00, -1.64086653e+00,  5.14737431e-01,  1.77661531e+00,\n",
       "       -1.94485306e+00, -7.14686053e-01, -1.14516991e+00, -1.62334515e+00,\n",
       "       -2.31010071e+00, -7.24774061e-01,  3.81248943e-01, -9.69339283e-01,\n",
       "       -7.54947191e-01, -6.77863755e-01, -4.68383759e+00, -2.12794834e+00,\n",
       "       -1.83062162e+00, -7.17697714e-01, -9.60148189e-01, -1.95094855e+00,\n",
       "       -8.38416384e-01, -2.11725814e+00, -5.01826326e-01,  2.10814486e-01,\n",
       "        1.65554013e-01, -3.78386428e-01, -3.36627651e+00, -2.95100897e+00,\n",
       "        6.22692351e-01, -5.18994005e-01, -6.45145911e-01, -1.55146575e+00,\n",
       "       -5.55655881e-01, -4.34903450e-01, -1.85925242e+00, -1.21688569e+00,\n",
       "        4.55215950e-02,  3.00255645e-01, -4.43161994e-01, -4.29703898e+00,\n",
       "       -1.04366491e+00, -1.54926445e-01, -7.88602482e-01, -4.99612472e-01,\n",
       "       -4.88570239e-01, -1.27945130e+00, -2.67004223e-01, -8.79267635e-01])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Nitrate_Anom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.392092825"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Nitrate_Anom.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = []\n",
    "for year in range(2007, 2021):\n",
    "    for month in range(1, 13):\n",
    "        index = df.index == str(year)\n",
    "        date.append(datetime(year, month, 1))\n",
    "        \n",
    "        \n",
    "\n",
    "date = np.array(date)\n",
    "#NPGO = df.loc[2007:2020].to_numpy().flatten()\n",
    "#Nitrate_Anom = df2.to_numpy().flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "168\n",
      "168\n",
      "168\n"
     ]
    }
   ],
   "source": [
    "print(len(NPGO))\n",
    "print(len(Nitrate_Anom))\n",
    "print(len(date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "ax1.bar(date[NPGO>0], NPGO[NPGO>0], width=20, color='royalblue',label='NPGO+ (cold)')\n",
    "ax1.bar(date[NPGO<0], NPGO[NPGO<0], width=20, color='r',label='NPGO- (warm)')\n",
    "ax1.set_ylim(-5,5)\n",
    "ax2.set_ylim(-10,10)\n",
    "ax2.plot(date,Nitrate_Anom,color='k',linewidth=2.5,label='0-10 m Nitrate Anomalies')\n",
    "ax1.set_title('',fontsize=18)\n",
    "ax1.set_ylabel('NPGO Index',fontsize=14)\n",
    "ax2.set_ylabel('Nitrate (mmol N m$^{-3}$)',fontsize=14)\n",
    "ax1.legend(frameon=False,loc=2,fontsize=12)\n",
    "ax2.legend(frameon=False,loc=1,fontsize=12)\n",
    "ax1.xaxis.set_tick_params(labelsize=14)\n",
    "ax1.yaxis.set_tick_params(labelsize=14)\n",
    "\n",
    "#plt.savefig('SuppFigureS4_revised.png', bbox_inches='tight',dpi=1000,transparent=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (py39)",
   "language": "python",
   "name": "py39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}