{
 "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>-0.580000</td>\n",
       "      <td>-1.200000</td>\n",
       "      <td>-0.860000</td>\n",
       "      <td>-1.890000</td>\n",
       "      <td>-0.860000</td>\n",
       "      <td>-0.550000</td>\n",
       "      <td>-0.690000</td>\n",
       "      <td>-0.960000</td>\n",
       "      <td>-0.670000</td>\n",
       "      <td>-0.060000</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>-1.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>-1.350000</td>\n",
       "      <td>-1.850000</td>\n",
       "      <td>-1.220000</td>\n",
       "      <td>-0.670000</td>\n",
       "      <td>-0.290000</td>\n",
       "      <td>-0.520000</td>\n",
       "      <td>-0.320000</td>\n",
       "      <td>-0.390000</td>\n",
       "      <td>-0.670000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "2021 -0.580000 -1.200000 -0.860000 -1.890000 -0.860000 -0.550000 -0.690000   \n",
       "2022 -1.350000 -1.850000 -1.220000 -0.670000 -0.290000 -0.520000 -0.320000   \n",
       "\n",
       "           AUG       SEP       OCT       NOV       DEC  \n",
       "YEAR                                                    \n",
       "2007  1.430000  0.110011  1.270000  0.137363  0.218341  \n",
       "2008  2.140000  2.230000  1.770000  1.970000  0.227273  \n",
       "2009  0.139276  0.102669  1.150000  1.050000  1.060000  \n",
       "2010  1.080000  1.000000  1.610000  0.101215  0.128370  \n",
       "2011  1.040000  1.330000  1.200000  0.267380  0.757576  \n",
       "2012  1.730000  1.360000  1.870000  1.400000  1.200000  \n",
       "2013  0.195312  0.256410 -0.680272 -0.124224 -1.450000  \n",
       "2014 -0.183486 -0.128700  0.235849  0.320513 -0.280899  \n",
       "2015 -1.930000 -2.100000 -1.310000 -2.250000 -1.110000  \n",
       "2016 -0.183824 -0.129199  0.284091  1.160000 -1.540000  \n",
       "2017 -0.188324 -0.190114 -1.410000 -2.060000 -2.690000  \n",
       "2018 -2.120000 -2.320000 -2.340000 -1.770000 -1.020000  \n",
       "2019 -2.060000 -2.520000 -2.580000 -3.370000 -2.540000  \n",
       "2020 -1.360000  1.250000 -1.830000 -2.590000 -1.330000  \n",
       "2021 -0.960000 -0.670000 -0.060000 -0.300000 -1.990000  \n",
       "2022 -0.390000 -0.670000  0.000000  0.000000  0.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2007:2023]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "NPGO = df.loc[2007:2020].to_numpy().flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </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",
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       "  </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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Nitrate_Anom=df2.to_numpy().flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Nitrate_Anom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.392092825"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Nitrate_Anom.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 38,
   "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 \\n(umol 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fef062f4e20>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "\n",
    "ax.bar(date[NPGO>0], NPGO[NPGO>0], width=20, color='royalblue',label='NPGO positive')\n",
    "ax.bar(date[NPGO<0], NPGO[NPGO<0], width=20, color='r',label='NPGO negative')\n",
    "ax.set_ylim(-3.5,3.5)\n",
    "#ax.plot(date,Nitrate_Anom,color='k',linewidth=2.5,label='0-25 m Nitrate Anomalies')\n",
    "ax.set_title('',fontsize=18)\n",
    "ax.set_ylabel('NPGO Index',fontsize=16)\n",
    "\n",
    "ax.legend(frameon=False,loc=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.read_csv('/ocean/ksuchy/MOAD/observe/CentralSoG_DiatomAnomalies.csv', index_col=0,header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',\n",
       "       'Nov', 'Dec'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Current_NPGO = df.loc[2007:2023].to_numpy().flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_date = []\n",
    "for year in range(2007, 2023):\n",
    "    for month in range(1, 13):\n",
    "        index = df.index == str(year)\n",
    "        current_date.append(datetime(year, month, 1))\n",
    "        \n",
    "        \n",
    "\n",
    "current_date = np.array(current_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(192,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_date.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fef068a3640>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "\n",
    "ax.bar(current_date[Current_NPGO>0], Current_NPGO[Current_NPGO>0], width=20, color='royalblue',label='NPGO positive')\n",
    "ax.bar(current_date[Current_NPGO<0], Current_NPGO[Current_NPGO<0], width=20, color='r',label='NPGO negative')\n",
    "ax.set_ylim(-6,6)\n",
    "#ax.plot(date,Nitrate_Anom,color='k',linewidth=2.5,label='0-25 m Nitrate Anomalies')\n",
    "ax.set_title('',fontsize=18)\n",
    "ax.set_ylabel('NPGO Index', fontsize=16)\n",
    "ax.legend(frameon=False,loc=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Annual_NPGO_index=np.array([0.416655983,1.406427404,0.410149171,1.242465357,0.832926488,1.356314911,0.122457003,\\\n",
    "                   -0.132940854,-1.315864758,-0.030001073,-0.570698404,-1.901666667,-2.188857716,-1.558333333]) #,-0.884166667,-0.606666667"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Jun_Diatom_Anom=np.array([3.2,6.2,1.8,3.1,4.9,7.3,-0.9,-7.1,-0.6,-3.1,-2.2,-4.0,-3.2,-5.6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Years=np.array([2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020]) #,2021,2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.0, 10.0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "colors=['b','b','b','b','b','b','r','r','r','r','r','r','r','r','r','r',]\n",
    "\n",
    "ax.bar(Years, Annual_NPGO_index, width=.5, color=colors,label='NPGO Index')\n",
    "ax.plot(Years,Jun_Diatom_Anom,color='k',linewidth=2.5,label='0-100 m Diatom Anomalies')\n",
    "ax.set_ylim(-10,10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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