{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the key lines from Transport-Analysis to Give Figure 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arrow\n",
    "import cmocean.cm as cm\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import statsmodels.api as sm\n",
    "import xarray as xr\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.size'] = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh = nc.Dataset('/home/sallen/MEOPAR/grid/mesh_mask201702.nc')\n",
    "#mesh = nc.Dataset('../../../myResults/mesh_mask201702.nc')\n",
    "gdepw = mesh.variables['gdepw_1d'][0]\n",
    "bathy = nc.Dataset('/home/sallen/MEOPAR/grid/bathymetry_201702.nc')\n",
    "#bathy = nc.Dataset('../../../myResults/bathymetry_201702.nc')\n",
    "lats = bathy.variables['nav_lat'][:]\n",
    "lons = bathy.variables['nav_lon'][:]\n",
    "gdept = mesh.variables['gdept_1d'][0]\n",
    "mesh.close()\n",
    "bathy.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_nan(ds):\n",
    "    for j, index in enumerate(ds.index):\n",
    "        if ds.transport[j] != ds.transport[j]:\n",
    "            print (index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Density Forcing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma = pd.read_csv('sigma_2015_201806.csv', index_col=0)\n",
    "m2015 = sigma.south - sigma.north\n",
    "m2015.index = pd.to_datetime(m2015.index, format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma = pd.read_csv('sigma_2016_201806.csv', index_col=0)\n",
    "m2016 = sigma.south - sigma.north\n",
    "m2016.index = pd.to_datetime(m2016.index, format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma = pd.read_csv('sigma_2017_201806.csv', index_col=0)\n",
    "m2017 = sigma.south - sigma.north\n",
    "m2017.index = pd.to_datetime(m2017.index, format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma = pd.read_csv('sigma_2018_201806.csv', index_col=0)\n",
    "m2018 = sigma.south - sigma.north\n",
    "m2018.index = pd.to_datetime(m2018.index, format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "goverrho=9.81/1000.\n",
    "densitydiff = pd.concat([m2015, m2016, m2017, m2018])\n",
    "densityforcing = np.sqrt(goverrho*densitydiff[:])\n",
    "densityforcing.index = densityforcing.index.tz_convert(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "low_pass_tide = pd.read_csv('low_pass_tide.csv', index_col=0)\n",
    "low_pass_tide.index = pd.to_datetime(low_pass_tide.index, format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Winds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>wind</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-6.719084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>-2.420515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>-1.188265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>9.409819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>10.242579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-27</th>\n",
       "      <td>-1.132697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>4.772913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-29</th>\n",
       "      <td>9.158186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-30</th>\n",
       "      <td>-2.748346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>-5.677968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1461 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 wind\n",
       "time                 \n",
       "2015-01-01  -6.719084\n",
       "2015-01-02  -2.420515\n",
       "2015-01-03  -1.188265\n",
       "2015-01-04   9.409819\n",
       "2015-01-05  10.242579\n",
       "...               ...\n",
       "2018-12-27  -1.132697\n",
       "2018-12-28   4.772913\n",
       "2018-12-29   9.158186\n",
       "2018-12-30  -2.748346\n",
       "2018-12-31  -5.677968\n",
       "\n",
       "[1461 rows x 1 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wind = pd.read_csv('day_avg_wind.csv', index_col=0)\n",
    "wind.index = pd.to_datetime(wind.index, format=\"%Y-%m-%d\")\n",
    "wind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_setup = (0.002 * 1.2 / 1000. * wind.wind * np.abs(wind.wind) / (0.02 * 50) * 100e3/50).rolling(window=30, center=True).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Richardson Number/Froude Number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "smooth = densityforcing.rolling(window=4, center=True).mean()\n",
    "smooth.index = smooth.index.tz_localize('UTC').tz_convert(None)\n",
    "myfroude = low_pass_tide.vozocrtx/smooth**2\n",
    "Ri = 1/myfroude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_flux = pd.read_csv('deepflux.csv', index_col=0)\n",
    "shallow_flux = pd.read_csv('shallowflux.csv', index_col=0)\n",
    "deep_flux.index = pd.to_datetime(deep_flux.index, format=\"%Y-%m-%d\")\n",
    "shallow_flux.index = pd.to_datetime(shallow_flux.index, format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "baroclinic_flux = shallow_flux.transport + deep_flux.transport\n",
    "baroclinic_flux.index = baroclinic_flux.index.tz_convert(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "depthwidth = 50*10e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(14, 14))\n",
    "axs[0, 0].hist2d(densityforcing, baroclinic_flux[:]/depthwidth, range=[[0, 2.], [0, 0.3]], bins=40, cmap='gist_heat_r')\n",
    "axs[0, 0].set_title(\"Baroclinic Velocity vs Density Forcing\")\n",
    "axs[0, 0].set_xlabel('Density Forcing (m s$^{-1}$)')\n",
    "axs[0, 0].set_ylabel('Velocity from Flux (m s$^{-1}$)')\n",
    "axs[0, 0].set_xlim(0.8, 2.)\n",
    "axs[0, 0].set_ylim(0, 0.3)\n",
    "axs[0, 1].hist2d(Ri, baroclinic_flux/depthwidth/densityforcing, range=[[0, 10], [0, 0.15]], bins=40, cmap='gist_heat_r');\n",
    "axs[0, 1].set_title(\"Baroclinic Velocity/Density Forcing vs Ri\")\n",
    "axs[0, 1].set_xlabel('Richardson Number')\n",
    "axs[0, 1].set_ylabel('Baroclinic Flux/Density Forcing')\n",
    "axs[1, 0].hist2d(0.0325 * densityforcing.values * depthwidth*np.sqrt(Ri.values)/1e3, baroclinic_flux/1e3, range=[[0, 150], \n",
    "                                                                                                                 [0, 150]],\n",
    "                 bins=40, cmap='gist_heat_r')\n",
    "axs[1, 0].plot(np.arange(0, 150, 5), np.arange(0, 150, 5), 'k')\n",
    "axs[1, 0].set_title(\"Baroclinic Flux vs Density and Richardson Number Fit\")\n",
    "axs[1, 0].set_xlabel('Function of Density and Richardson Number (mSv)')\n",
    "axs[1, 0].set_ylabel('Baroclinic Flux (mSv)')\n",
    "\n",
    "axs[1, 1].hist2d(((0.0339 * densityforcing.values - 0.0575 * w_setup.values) * depthwidth * np.sqrt(Ri).values)/1e3, baroclinic_flux/1e3, \n",
    "              range=[[0, 150], \n",
    "                                                                                                                 [0, 150]],\n",
    "                 bins=40, cmap='gist_heat_r');\n",
    "axs[1, 1].plot(np.arange(0, 150, 5), np.arange(0, 150, 5), 'k')\n",
    "\n",
    "axs[1, 1].set_title(\"Baroclinic Flux vs Density, Richardson Number and Wind Fit\")\n",
    "axs[1, 1].set_xlabel('Function of Density,Richardson Number and Wind (mSv)')\n",
    "axs[1, 1].set_ylabel('Baroclinic Flux (mSv)')\n",
    "\n",
    "fig.tight_layout()\n",
    "#fig.savefig('/home/sallen/MEOPAR/estuarine_flux_paper/density_forcing.pdf')\n",
    "#fig.savefig('/home/sallen/MEOPAR/estuarine_flux_paper/density_forcing.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n",
    "fig, axs = plt.subplots(2, 2, figsize=(14, 6), gridspec_kw={'width_ratios': [3, 1]}, sharex='col', sharey='row', )\n",
    "axs[0, 0].plot((densityforcing * depthwidth * 0.0325 * np.sqrt(Ri).values)/1e3, label='Theory');\n",
    "axs[0, 0].plot(baroclinic_flux/1e3, label='Model')\n",
    "axs[0, 0].set_ylabel('Baroclinic Flux (mSv)')\n",
    "axs[0, 0].legend(loc='upper right')\n",
    "fig.text(0.07, 0.9, 'Without Wind')\n",
    "hist, xed, yed, colours = axs[0, 1].hist2d(0.0325 * densityforcing.values * depthwidth*np.sqrt(Ri.values)/1e3, baroclinic_flux/1e3, \n",
    "                 range=[[0, 150], [0, 150]], bins=25, cmap='gist_heat_r');\n",
    "axs[0, 1].plot(np.arange(0, 150, 5), np.arange(0, 150, 5), 'k')\n",
    "fig.colorbar(colours, ax=axs[0, 1]);\n",
    "\n",
    "axs[1, 0].plot((0.0339 * densityforcing.values - 0.0575 * w_setup) * depthwidth * np.sqrt(Ri).values/1e3, label='Theory w Wind');\n",
    "axs[1, 0].plot(baroclinic_flux/1e3, label='Model')\n",
    "axs[1, 0].set_ylabel('Baroclinic Flux (mSv)')\n",
    "#axs[1, 0].legend(loc='lower left')\n",
    "axs[1, 0].set_xlabel('Date')\n",
    "fig.text(0.07, 0.45, 'With Wind')\n",
    "hist, xed, yed, colours = axs[1, 1].hist2d(((0.0339 * densityforcing.values - 0.0575 * w_setup.values) * depthwidth * np.sqrt(Ri).values)/1e3, \n",
    "                                           baroclinic_flux/1e3, \n",
    "                 range=[[0, 150], [0, 150]], bins=25, cmap='gist_heat_r');\n",
    "axs[1, 1].plot(np.arange(0, 150, 5), np.arange(0, 150, 5), 'k')\n",
    "fig.colorbar(colours, ax=axs[1, 1]);\n",
    "axs[1, 1].set_xlabel('Theoretical Flux (mSv)')\n",
    "\n",
    "fig.tight_layout();\n",
    "#fig.savefig('/home/sallen/MEOPAR/estuarine_flux_paper/theory_fit.pdf')\n",
    "#fig.savefig('/home/sallen/MEOPAR/estuarine_flux_paper/theory_fit.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From transport_analysis: Rsquared or variance explained is 95.1% and 97%, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x252 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n",
    "fig, axs = plt.subplots(1, 2, figsize=(14, 3.5), gridspec_kw={'width_ratios': [3, 1]}, sharex='col', sharey='row', )\n",
    "axs[0].plot((densityforcing * depthwidth * 0.0335 * np.sqrt(Ri).values)/1e3, label='Theory');\n",
    "axs[0].plot(baroclinic_flux/1e3, label='SSC')\n",
    "axs[0].set_ylabel('Baroclinic Flux (mSv)')\n",
    "axs[0].legend(loc=[0.45, 0.7], fontsize=14)\n",
    "hist, xed, yed, colours = axs[1].hist2d(0.0335 * densityforcing.values * depthwidth*np.sqrt(Ri.values)/1e3, baroclinic_flux/1e3, \n",
    "                 range=[[0, 150], [0, 150]], bins=25, cmap='gist_heat_r');\n",
    "axs[1].plot(np.arange(0, 150, 5), np.arange(0, 150, 5), 'k')\n",
    "fig.colorbar(colours, ax=axs[1]);\n",
    "\n",
    "axs[0].set_xlabel('Date')\n",
    "axs[1].set_xlabel('Theoretical Flux (mSv)')\n",
    "\n",
    "fig.tight_layout();\n",
    "fig.savefig('/home/sallen/MEOPAR/estuarine_flux_paper/theory_fit_nw.pdf')\n",
    "fig.savefig('/home/sallen/MEOPAR/estuarine_flux_paper/theory_fit_nw.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the yearly variation in the flux to compare to the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015    59110.980707\n",
      "2016    59329.579881\n",
      "2017    59652.381287\n",
      "2018    57539.710212\n",
      "Name: transport, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.03586380845552159"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_estimate = baroclinic_flux.groupby(baroclinic_flux.index.year).mean()\n",
    "print (annual_estimate)\n",
    "(annual_estimate[2017]-annual_estimate[2018])/annual_estimate.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015 57.64999999999999\n",
      "2016 57.900000000000006\n"
     ]
    }
   ],
   "source": [
    "# Sanity Check\n",
    "print ('2015', 0.5*(23.3+23.2+2.4+2.4+23.9+24.0+8.1+8.0))\n",
    "print ('2016', 0.5*(24+23.7+2.1+2.1+24.2+24.0+7.9+7.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>transport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>26683.563787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>26785.802757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>26999.600567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>26532.935924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         transport\n",
       "2015  26683.563787\n",
       "2016  26785.802757\n",
       "2017  26999.600567\n",
       "2018  26532.935924"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deep_flux.groupby(deep_flux.index.year).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015 25.65\n",
      "2016 25.950000000000003\n"
     ]
    }
   ],
   "source": [
    "# Sanity Check\n",
    "print ('2015', 0.5*(23.3+23.2+2.4+2.4))\n",
    "print ('2016', 0.5*(24+23.7+2.1+2.1))"
   ]
  },
  {
   "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",
       "    }\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>transport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>32392.318331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>32543.777124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>32652.780719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>31006.774288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         transport\n",
       "2015  32392.318331\n",
       "2016  32543.777124\n",
       "2017  32652.780719\n",
       "2018  31006.774288"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shallow_flux.groupby(shallow_flux.index.year).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015 32.0\n",
      "2016 31.95\n"
     ]
    }
   ],
   "source": [
    "# Sanity Check\n",
    "print ('2015', 0.5*(23.9+24.0+8.1+8.0))\n",
    "print ('2016', 0.5*(24.2+24.0+7.9+7.8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also see some calculations in Transport-Analysis: basically fixing the time scale adds a bit of flux to the mean.  However the rank is correct.  2015-2017 similar.  2018 smaller."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-01             NaN\n",
       "2015-01-02             NaN\n",
       "2015-01-03    36315.756510\n",
       "2015-01-04    34503.584711\n",
       "2015-01-05    32994.653380\n",
       "                  ...     \n",
       "2018-12-27    26438.613041\n",
       "2018-12-28    30095.643638\n",
       "2018-12-29    34561.361321\n",
       "2018-12-30    43205.573577\n",
       "2018-12-31             NaN\n",
       "Length: 1461, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theory = (densityforcing * depthwidth * 0.0335 * np.sqrt(Ri).values)\n",
    "theory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_annual_theory = theory.groupby(theory.index.year).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-01 00:00:00+00:00    1.845435\n",
       "2015-01-02 00:00:00+00:00    1.918297\n",
       "2015-01-03 00:00:00+00:00    1.930509\n",
       "2015-01-04 00:00:00+00:00    1.859343\n",
       "2015-01-05 00:00:00+00:00    1.752873\n",
       "                               ...   \n",
       "2018-12-27 00:00:00+00:00    1.423626\n",
       "2018-12-28 00:00:00+00:00    1.485563\n",
       "2018-12-29 00:00:00+00:00    1.496672\n",
       "2018-12-30 00:00:00+00:00    1.721671\n",
       "2018-12-31 00:00:00+00:00    1.886177\n",
       "Length: 1461, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "densitydiff*goverrho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_annual = densityforcing.groupby(densityforcing.index.year).mean()\n",
    "df2_annual = goverrho*(densitydiff.groupby(densityforcing.index.year)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "low_pass_tide['vel'] = np.sqrt(low_pass_tide.vozocrtx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "tide_annual = low_pass_tide.groupby(low_pass_tide.index.year).vozocrtx.mean()\n",
    "tideH_annual = low_pass_tide.groupby(low_pass_tide.index.year).vel.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    54991.743525\n",
       "2016    54436.085745\n",
       "2017    56492.930680\n",
       "2018    54344.530947\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depthwidth * 0.0335 * df_annual**2/np.sqrt(tide_annual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    57091.885533\n",
       "2016    56814.561642\n",
       "2017    58683.057195\n",
       "2018    56604.851827\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_theory = depthwidth * 0.0335 * df_annual**2/np.sqrt(tide_annual)\n",
    "annual_theory2 = depthwidth * 0.0335 * df2_annual/tideH_annual\n",
    "annual_theory2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.026138509748409155\n",
      "0.039014766680320924\n",
      "0.03626974770329467\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "annual_theory.plot()\n",
    "raw_annual_theory.plot()\n",
    "annual_estimate.plot()\n",
    "annual_theory2.plot()\n",
    "print ((raw_annual_theory[2017]-raw_annual_theory[2018])/raw_annual_theory.mean())\n",
    "print ((annual_theory[2017]-annual_theory[2018])/annual_theory.mean())\n",
    "print ((annual_theory2[2017]-annual_theory2[2018])/annual_theory2.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8c6af404f0>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(annual_estimate, raw_annual_theory, 'o')\n",
    "plt.plot([57500, 61000], [57500, 61000])\n",
    "#plt.plot(annual_estimate, raw_annual_theory, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f623859c280>]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_annual/1.54, annual_estimate, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f62275a3400>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tide_annual/0.521, annual_estimate, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "tags": []
   },
   "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>time_counter</th>\n",
       "      <th>sossheig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.056298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.001114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.028283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1456</td>\n",
       "      <td>-0.001184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1457</td>\n",
       "      <td>-0.012295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1458</td>\n",
       "      <td>-0.041369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1459</td>\n",
       "      <td>-0.080571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>1460</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1461 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      time_counter  sossheig\n",
       "0                0       NaN\n",
       "1                1       NaN\n",
       "2                2 -0.056298\n",
       "3                3  0.001114\n",
       "4                4  0.028283\n",
       "...            ...       ...\n",
       "1456          1456 -0.001184\n",
       "1457          1457 -0.012295\n",
       "1458          1458 -0.041369\n",
       "1459          1459 -0.080571\n",
       "1460          1460       NaN\n",
       "\n",
       "[1461 rows x 2 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh = pd.read_csv('low_pass_ssh.csv')\n",
    "ssh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "wcum = np.cumsum(wind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barotropic_flux = (shallow_flux.transport - deep_flux.transport)\n",
    "slow_flux = barotropic_flux.rolling(window=30, center=True).mean()\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 5))\n",
    "#ax.plot(barotropic_flux)\n",
    "#ax.plot(slow_flux);\n",
    "ax2 = ax.twinx();\n",
    "#ax2.plot(w_setup, 'g');\n",
    "ax.plot(\n",
    "    (baroclinic_flux - 0.0325 * densityforcing.values * depthwidth*np.sqrt(Ri.values)),\n",
    "    linewidth=3)\n",
    "#ax.plot(baroclinic_flux - (0.0339 * densityforcing.values - 0.0575 * w_setup) * depthwidth * np.sqrt(Ri).values)\n",
    "ax2.plot(shallow_flux.index, shallow_flux, 'r');\n",
    "ax.grid()\n",
    "#ax.set_xlim(datetime.datetime(2016, 1, 1), datetime.datetime(2017, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(barotropic_flux/1e3, -0.0325 * densityforcing.values * depthwidth*np.sqrt(Ri.values)/1e3 + baroclinic_flux/1e3, 'o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(barotropic_flux/1e3, -0.0325 * densityforcing.values * depthwidth*np.sqrt(Ri.values)/1e3 + baroclinic_flux/1e3, range=[[-10, 15], [-50, 50]], bins=40);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6eef7766d0>]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(w_setup, -0.0325 * densityforcing.values * depthwidth*np.sqrt(Ri.values)/1e3 + baroclinic_flux/1e3, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.966</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.966</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>2.002e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 10 Nov 2021</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:35:47</td>     <th>  Log-Likelihood:    </th>          <td> -15459.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1429</td>      <th>  AIC:               </th>          <td>3.092e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1427</td>      <th>  BIC:               </th>          <td>3.093e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>-5.658e+04</td> <td> 2708.954</td> <td>  -20.884</td> <td> 0.000</td> <td>-6.19e+04</td> <td>-5.13e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td>    0.0318</td> <td>    0.000</td> <td>  170.019</td> <td> 0.000</td> <td>    0.031</td> <td>    0.032</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>12.553</td> <th>  Durbin-Watson:     </th> <td>   0.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.002</td> <th>  Jarque-Bera (JB):  </th> <td>  18.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.045</td> <th>  Prob(JB):          </th> <td>0.000106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.547</td> <th>  Cond. No.          </th> <td>1.61e+07</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[3] The condition number is large, 1.61e+07. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.966\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.966\n",
       "Method:                 Least Squares   F-statistic:                          2.002e+04\n",
       "Date:                Wed, 10 Nov 2021   Prob (F-statistic):                        0.00\n",
       "Time:                        16:35:47   Log-Likelihood:                         -15459.\n",
       "No. Observations:                1429   AIC:                                  3.092e+04\n",
       "Df Residuals:                    1427   BIC:                                  3.093e+04\n",
       "Df Model:                           2                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1         -5.658e+04   2708.954    -20.884      0.000   -6.19e+04   -5.13e+04\n",
       "x2             0.0318      0.000    170.019      0.000       0.031       0.032\n",
       "==============================================================================\n",
       "Omnibus:                       12.553   Durbin-Watson:                   0.204\n",
       "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               18.302\n",
       "Skew:                           0.045   Prob(JB):                     0.000106\n",
       "Kurtosis:                       3.547   Cond. No.                     1.61e+07\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[3] The condition number is large, 1.61e+07. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = baroclinic_flux.values\n",
    "x = ssh.sossheig.rolling(window=30, center=True).mean()\n",
    "z = densityforcing.values * np.sqrt(Ri) * depthwidth\n",
    "X = np.column_stack((x, z))\n",
    "\n",
    "model11 = sm.OLS(y, X, missing='drop').fit()\n",
    "model11.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.958</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.958</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>1.672e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 10 Nov 2021</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:16:21</td>     <th>  Log-Likelihood:    </th>          <td> -15847.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1453</td>      <th>  AIC:               </th>          <td>3.170e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1451</td>      <th>  BIC:               </th>          <td>3.171e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>   -0.3295</td> <td>    0.030</td> <td>  -11.110</td> <td> 0.000</td> <td>   -0.388</td> <td>   -0.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td>    0.0342</td> <td>    0.000</td> <td>  175.539</td> <td> 0.000</td> <td>    0.034</td> <td>    0.035</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>32.818</td> <th>  Durbin-Watson:     </th> <td>   0.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  38.537</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.303</td> <th>  Prob(JB):          </th> <td>4.28e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.518</td> <th>  Cond. No.          </th> <td>    162.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.958\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.958\n",
       "Method:                 Least Squares   F-statistic:                          1.672e+04\n",
       "Date:                Wed, 10 Nov 2021   Prob (F-statistic):                        0.00\n",
       "Time:                        16:16:21   Log-Likelihood:                         -15847.\n",
       "No. Observations:                1453   AIC:                                  3.170e+04\n",
       "Df Residuals:                    1451   BIC:                                  3.171e+04\n",
       "Df Model:                           2                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            -0.3295      0.030    -11.110      0.000      -0.388      -0.271\n",
       "x2             0.0342      0.000    175.539      0.000       0.034       0.035\n",
       "==============================================================================\n",
       "Omnibus:                       32.818   Durbin-Watson:                   0.201\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               38.537\n",
       "Skew:                           0.303   Prob(JB):                     4.28e-09\n",
       "Kurtosis:                       3.518   Cond. No.                         162.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = baroclinic_flux.values\n",
    "x = barotropic_flux.values\n",
    "z = densityforcing.values * np.sqrt(Ri) * depthwidth\n",
    "X = np.column_stack((x, z))\n",
    "\n",
    "model11 = sm.OLS(y, X, missing='drop').fit()\n",
    "model11.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "714048790.8615748"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmean((baroclinic_flux.values - np.nanmean(baroclinic_flux.values))**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "483666177.36044043\n"
     ]
    }
   ],
   "source": [
    "fit = 0.0342*densityforcing.values * np.sqrt(Ri.values) * depthwidth - 0.3295*barotropic_flux.values\n",
    "print (np.nanmean((fit - np.nanmean(fit))**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0006946478804702028\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.951</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.951</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>2.801e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 10 Nov 2021</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:12:24</td>     <th>  Log-Likelihood:    </th>          <td>  3822.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1453</td>      <th>  AIC:               </th>          <td>  -7643.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1452</td>      <th>  BIC:               </th>          <td>  -7637.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>    0.0325</td> <td>    0.000</td> <td>  167.368</td> <td> 0.000</td> <td>    0.032</td> <td>    0.033</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 7.775</td> <th>  Durbin-Watson:     </th> <td>   0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.020</td> <th>  Jarque-Bera (JB):  </th> <td>   9.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.086</td> <th>  Prob(JB):          </th> <td> 0.00996</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.351</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.951\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.951\n",
       "Method:                 Least Squares   F-statistic:                          2.801e+04\n",
       "Date:                Wed, 10 Nov 2021   Prob (F-statistic):                        0.00\n",
       "Time:                        16:12:24   Log-Likelihood:                          3822.3\n",
       "No. Observations:                1453   AIC:                                     -7643.\n",
       "Df Residuals:                    1452   BIC:                                     -7637.\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1             0.0325      0.000    167.368      0.000       0.032       0.033\n",
       "==============================================================================\n",
       "Omnibus:                        7.775   Durbin-Watson:                   0.155\n",
       "Prob(Omnibus):                  0.020   Jarque-Bera (JB):                9.217\n",
       "Skew:                           0.086   Prob(JB):                      0.00996\n",
       "Kurtosis:                       3.351   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = baroclinic_flux.values/(densityforcing.values * depthwidth)\n",
    "print (np.nanmean((y - np.nanmean(y))**2))\n",
    "x = barotropic_flux.values\n",
    "z = np.sqrt(Ri.values)\n",
    "X = z\n",
    "\n",
    "model11 = sm.OLS(y, X, missing='drop').fit()\n",
    "model11.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00036340217417041765\n",
      "0.5231458763315207\n"
     ]
    }
   ],
   "source": [
    "fit = 0.0325*np.sqrt(Ri.values)\n",
    "print (np.nanmean((fit - np.nanmean(fit))**2))\n",
    "print (np.nanmean((fit - np.nanmean(fit))**2)/np.nanmean((y - np.nanmean(y))**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "413041551.42613953\n"
     ]
    }
   ],
   "source": [
    "fit = 1.673e4*densityforcing.values * np.sqrt(Ri.values)\n",
    "print (np.nanmean((fit - np.nanmean(fit))**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5784500398075039\n"
     ]
    }
   ],
   "source": [
    "print (413041551/714048790)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "58% of the variance explained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "628005660.6140531\n"
     ]
    }
   ],
   "source": [
    "fit = 1.834e4*densityforcing.values * np.sqrt(Ri.values) - 1.3324*barotropic_flux.values\n",
    "print (np.nanmean((fit - np.nanmean(fit))**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8794996487564947\n"
     ]
    }
   ],
   "source": [
    "print (628005660/714048790)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "88% of the variance explained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.968</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.968</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>2.157e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 10 Nov 2021</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:05:43</td>     <th>  Log-Likelihood:    </th>          <td> -15441.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1432</td>      <th>  AIC:               </th>          <td>3.089e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1430</td>      <th>  BIC:               </th>          <td>3.090e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>-6.339e+04</td> <td> 2650.225</td> <td>  -23.919</td> <td> 0.000</td> <td>-6.86e+04</td> <td>-5.82e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td> 1.676e+04</td> <td>   81.177</td> <td>  206.417</td> <td> 0.000</td> <td> 1.66e+04</td> <td> 1.69e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.586</td> <th>  Durbin-Watson:     </th> <td>   0.233</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.005</td> <th>  Jarque-Bera (JB):  </th> <td>  14.307</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.064</td> <th>  Prob(JB):          </th> <td>0.000782</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.473</td> <th>  Cond. No.          </th> <td>    32.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.968\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.968\n",
       "Method:                 Least Squares   F-statistic:                          2.157e+04\n",
       "Date:                Wed, 10 Nov 2021   Prob (F-statistic):                        0.00\n",
       "Time:                        16:05:43   Log-Likelihood:                         -15441.\n",
       "No. Observations:                1432   AIC:                                  3.089e+04\n",
       "Df Residuals:                    1430   BIC:                                  3.090e+04\n",
       "Df Model:                           2                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1         -6.339e+04   2650.225    -23.919      0.000   -6.86e+04   -5.82e+04\n",
       "x2          1.676e+04     81.177    206.417      0.000    1.66e+04    1.69e+04\n",
       "==============================================================================\n",
       "Omnibus:                       10.586   Durbin-Watson:                   0.233\n",
       "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               14.307\n",
       "Skew:                          -0.064   Prob(JB):                     0.000782\n",
       "Kurtosis:                       3.473   Cond. No.                         32.6\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = baroclinic_flux.values\n",
    "x = w_setup.values\n",
    "z = densityforcing.values * np.sqrt(Ri)\n",
    "X = np.column_stack((x, z))\n",
    "\n",
    "model11 = sm.OLS(y, X, missing='drop').fit()\n",
    "model11.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "652150905.1084213\n"
     ]
    }
   ],
   "source": [
    "fit = 1.676e4*densityforcing.values * np.sqrt(Ri.values) - 6.339e4*w_setup.values\n",
    "print (np.nanmean((fit - np.nanmean(fit))**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9133142078428562\n"
     ]
    }
   ],
   "source": [
    "print (652150905/714048790)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the correlations with density difference and with the winds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.        0.8073329]\n",
      " [0.8073329 1.       ]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.898</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.898</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>1.280e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 17 Jun 2024</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:40:31</td>     <th>  Log-Likelihood:    </th>          <td> -16509.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1454</td>      <th>  AIC:               </th>          <td>3.302e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1453</td>      <th>  BIC:               </th>          <td>3.303e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td> 3.941e+04</td> <td>  348.321</td> <td>  113.149</td> <td> 0.000</td> <td> 3.87e+04</td> <td> 4.01e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>200.686</td> <th>  Durbin-Watson:     </th> <td>   0.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 287.712</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.033</td>  <th>  Prob(JB):          </th> <td>3.34e-63</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.692</td>  <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.898\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.898\n",
       "Method:                 Least Squares   F-statistic:                          1.280e+04\n",
       "Date:                Mon, 17 Jun 2024   Prob (F-statistic):                        0.00\n",
       "Time:                        17:40:31   Log-Likelihood:                         -16509.\n",
       "No. Observations:                1454   AIC:                                  3.302e+04\n",
       "Df Residuals:                    1453   BIC:                                  3.303e+04\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1          3.941e+04    348.321    113.149      0.000    3.87e+04    4.01e+04\n",
       "==============================================================================\n",
       "Omnibus:                      200.686   Durbin-Watson:                   0.066\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              287.712\n",
       "Skew:                           1.033   Prob(JB):                     3.34e-63\n",
       "Kurtosis:                       3.692   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = baroclinic_flux.values\n",
    "X = densityforcing.values\n",
    "\n",
    "modeln1 = sm.OLS(y, X, missing='drop').fit()\n",
    "\n",
    "plt.hist2d(X, y, range=[[0, 2], [0, 150000]], bins=25, cmap='gist_heat_r');\n",
    "#plt.plot(X, y, 'o')\n",
    "nansInArray = np.isnan(y) \n",
    "print (np.corrcoef(X[~nansInArray], y[~nansInArray]))\n",
    "modeln1.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.         -0.29698463]\n",
      " [-0.29698463  1.        ]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.636</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.635</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   2534.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 17 Jun 2024</td> <th>  Prob (F-statistic):</th>          <td>1.01e-320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:43:23</td>     <th>  Log-Likelihood:    </th>          <td> -17424.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1453</td>      <th>  AIC:               </th>          <td>3.485e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1452</td>      <th>  BIC:               </th>          <td>3.485e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td> 9.199e+04</td> <td> 1827.365</td> <td>   50.341</td> <td> 0.000</td> <td> 8.84e+04</td> <td> 9.56e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>38.771</td> <th>  Durbin-Watson:     </th> <td>   0.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  41.332</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.408</td> <th>  Prob(JB):          </th> <td>1.06e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.865</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.636\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.635\n",
       "Method:                 Least Squares   F-statistic:                              2534.\n",
       "Date:                Mon, 17 Jun 2024   Prob (F-statistic):                   1.01e-320\n",
       "Time:                        17:43:23   Log-Likelihood:                         -17424.\n",
       "No. Observations:                1453   AIC:                                  3.485e+04\n",
       "Df Residuals:                    1452   BIC:                                  3.485e+04\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1          9.199e+04   1827.365     50.341      0.000    8.84e+04    9.56e+04\n",
       "==============================================================================\n",
       "Omnibus:                       38.771   Durbin-Watson:                   0.071\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               41.332\n",
       "Skew:                           0.408   Prob(JB):                     1.06e-09\n",
       "Kurtosis:                       2.865   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = baroclinic_flux.values\n",
    "X = low_pass_tide.values[:, 0]\n",
    "modeln2 = sm.OLS(y, X, missing='drop').fit()\n",
    "nansInArray = (np.isnan(y) | np.isnan(X))\n",
    "print (np.corrcoef(X[~nansInArray], y[~nansInArray]))\n",
    "#plt.plot(X, y, 'o')\n",
    "plt.hist2d(X, y, range=[[0, 1.3], [0, 150000]], bins=25, cmap='gist_heat_r');\n",
    "modeln2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(a & b).any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlations after Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rivers\n",
    "r2015 = pd.read_csv('SoG_runoff_2015.csv', parse_dates=True, index_col=0)\n",
    "r2016 = pd.read_csv('SoG_runoff_2016.csv', index_col=0, parse_dates=True)\n",
    "r2017 = pd.read_csv('SoG_runoff_2017.csv', index_col=0, parse_dates=True)\n",
    "r2018 = pd.read_csv('SoG_runoff_2018.csv', index_col=0, parse_dates=True)\n",
    "SoGrunoff = pd.concat([r2015, r2016, r2017, r2018])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SSH\n",
    "ssh = pd.read_csv('low_pass_ssh.csv')\n",
    "base = datetime.datetime(2015, 1, 1, tzinfo=datetime.timezone.utc)\n",
    "date_list = [base + datetime.timedelta(days=x) for x in range(1461)]\n",
    "ssh['time'] = date_list\n",
    "ssh.set_index('time', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "residual = baroclinic_flux - densityforcing * depthwidth * 0.0335 * np.sqrt(Ri).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.072</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.071</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   112.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 28 Jun 2024</td> <th>  Prob (F-statistic):</th>          <td>1.94e-25</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:03:58</td>     <th>  Log-Likelihood:    </th>          <td> -15852.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1453</td>      <th>  AIC:               </th>          <td>3.171e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1452</td>      <th>  BIC:               </th>          <td>3.171e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td> -663.7018</td> <td>   62.478</td> <td>  -10.623</td> <td> 0.000</td> <td> -786.258</td> <td> -541.146</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>20.146</td> <th>  Durbin-Watson:     </th> <td>   0.229</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  23.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.221</td> <th>  Prob(JB):          </th> <td>9.22e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.432</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.072\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.071\n",
       "Method:                 Least Squares   F-statistic:                              112.8\n",
       "Date:                Fri, 28 Jun 2024   Prob (F-statistic):                    1.94e-25\n",
       "Time:                        13:03:58   Log-Likelihood:                         -15852.\n",
       "No. Observations:                1453   AIC:                                  3.171e+04\n",
       "Df Residuals:                    1452   BIC:                                  3.171e+04\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1          -663.7018     62.478    -10.623      0.000    -786.258    -541.146\n",
       "==============================================================================\n",
       "Omnibus:                       20.146   Durbin-Watson:                   0.229\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               23.188\n",
       "Skew:                           0.221   Prob(JB):                     9.22e-06\n",
       "Kurtosis:                       3.432   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = residual.values\n",
    "X = wind.wind.values\n",
    "modeln2 = sm.OLS(y, X, missing='drop').fit()\n",
    "#nansInArray = (np.isnan(y) | np.isnan(X))\n",
    "#print (np.corrcoef(X[~nansInArray], y[~nansInArray]))\n",
    "plt.plot(X, y, 'o')\n",
    "#plt.hist2d(X, y, range=[[0, 1.3], [0, 150000]], bins=25, cmap='gist_heat_r');\n",
    "modeln2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2262964ca0>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(15, 4))\n",
    "plt.plot(-X)\n",
    "plt.plot(-wind.shift(-20).wind.values)\n",
    "plt.plot(y/4000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no lag -0.265101831034956\n",
      "-3 -0.3760583231887586\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "min = 0\n",
    "for lag in range(-60, 1):\n",
    "    value = residual.corr(wind.wind.shift(lag))\n",
    "    if lag == 0:\n",
    "        print ('no lag', value)\n",
    "    if value < min:\n",
    "        min = value\n",
    "        mlag = lag\n",
    "    plt.plot(lag, value, 'x')\n",
    "print (mlag, min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no lag -0.30831992754822674\n",
      "-5 -0.39320176965480763\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "min = 0\n",
    "soss = ssh.tz_localize(None)\n",
    "res = residual.tz_localize(None)\n",
    "for lag in range(-60, 1):\n",
    "    value = res.corr(soss.sossheig.shift(lag))\n",
    "    if lag == 0:\n",
    "        print ('no lag', value)\n",
    "    if value < min:\n",
    "        min = value\n",
    "        mlag = lag\n",
    "    plt.plot(lag, value, 'x')\n",
    "print (mlag, min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no lag 0.19310495474871878\n",
      "-78 -0.3696220867471902\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "min = 0\n",
    "run = SoGrunoff.tz_localize(None)\n",
    "res = residual.tz_localize(None)\n",
    "for lag in range(-90, 1):\n",
    "    value = res.corr(run.rorunoff.shift(lag))\n",
    "    if lag == 0:\n",
    "        print ('no lag', value)\n",
    "    if value < min:\n",
    "        min = value\n",
    "        mlag = lag\n",
    "    plt.plot(lag, value, 'x')\n",
    "print (mlag, min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.955</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.955</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>3.072e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 28 Jun 2024</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:21:23</td>     <th>  Log-Likelihood:    </th>          <td> -15907.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1453</td>      <th>  AIC:               </th>          <td>3.182e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1452</td>      <th>  BIC:               </th>          <td>3.182e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td> 1.673e+04</td> <td>   95.442</td> <td>  175.266</td> <td> 0.000</td> <td> 1.65e+04</td> <td> 1.69e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>31.020</td> <th>  Durbin-Watson:     </th> <td>   0.165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  34.941</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.308</td> <th>  Prob(JB):          </th> <td>2.59e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.445</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.955\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.955\n",
       "Method:                 Least Squares   F-statistic:                          3.072e+04\n",
       "Date:                Fri, 28 Jun 2024   Prob (F-statistic):                        0.00\n",
       "Time:                        13:21:23   Log-Likelihood:                         -15907.\n",
       "No. Observations:                1453   AIC:                                  3.182e+04\n",
       "Df Residuals:                    1452   BIC:                                  3.182e+04\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1          1.673e+04     95.442    175.266      0.000    1.65e+04    1.69e+04\n",
       "==============================================================================\n",
       "Omnibus:                       31.020   Durbin-Watson:                   0.165\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               34.941\n",
       "Skew:                           0.308   Prob(JB):                     2.59e-08\n",
       "Kurtosis:                       3.445   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = baroclinic_flux.values\n",
    "z = densityforcing.values * np.sqrt(Ri)\n",
    "X = z\n",
    "\n",
    "model11 = sm.OLS(y, X, missing='drop').fit()\n",
    "model11.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Add wind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.958</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.958</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>1.661e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 28 Jun 2024</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:21:53</td>     <th>  Log-Likelihood:    </th>          <td> -15852.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1453</td>      <th>  AIC:               </th>          <td>3.171e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1451</td>      <th>  BIC:               </th>          <td>3.172e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td> -667.7614</td> <td>   62.622</td> <td>  -10.663</td> <td> 0.000</td> <td> -790.601</td> <td> -544.922</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td> 1.666e+04</td> <td>   92.151</td> <td>  180.807</td> <td> 0.000</td> <td> 1.65e+04</td> <td> 1.68e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>21.150</td> <th>  Durbin-Watson:     </th> <td>   0.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  24.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.231</td> <th>  Prob(JB):          </th> <td>5.53e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.431</td> <th>  Cond. No.          </th> <td>    1.48</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.958\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.958\n",
       "Method:                 Least Squares   F-statistic:                          1.661e+04\n",
       "Date:                Fri, 28 Jun 2024   Prob (F-statistic):                        0.00\n",
       "Time:                        13:21:53   Log-Likelihood:                         -15852.\n",
       "No. Observations:                1453   AIC:                                  3.171e+04\n",
       "Df Residuals:                    1451   BIC:                                  3.172e+04\n",
       "Df Model:                           2                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1          -667.7614     62.622    -10.663      0.000    -790.601    -544.922\n",
       "x2          1.666e+04     92.151    180.807      0.000    1.65e+04    1.68e+04\n",
       "==============================================================================\n",
       "Omnibus:                       21.150   Durbin-Watson:                   0.228\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               24.209\n",
       "Skew:                           0.231   Prob(JB):                     5.53e-06\n",
       "Kurtosis:                       3.431   Cond. No.                         1.48\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = baroclinic_flux.values\n",
    "x = wind.wind.values\n",
    "z = densityforcing.values * np.sqrt(Ri)\n",
    "X = np.column_stack((x, z))\n",
    "\n",
    "model11 = sm.OLS(y, X, missing='drop').fit()\n",
    "model11.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Add Sea surface height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.959</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.959</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>1.706e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 28 Jun 2024</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:24:13</td>     <th>  Log-Likelihood:    </th>          <td> -15833.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1453</td>      <th>  AIC:               </th>          <td>3.167e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1451</td>      <th>  BIC:               </th>          <td>3.168e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>-2.757e+04</td> <td> 2215.787</td> <td>  -12.440</td> <td> 0.000</td> <td>-3.19e+04</td> <td>-2.32e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td> 1.631e+04</td> <td>   96.891</td> <td>  168.290</td> <td> 0.000</td> <td> 1.61e+04</td> <td> 1.65e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.419</td> <th>  Durbin-Watson:     </th> <td>   0.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.005</td> <th>  Jarque-Bera (JB):  </th> <td>  11.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.147</td> <th>  Prob(JB):          </th> <td> 0.00320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.321</td> <th>  Cond. No.          </th> <td>    24.4</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.959\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.959\n",
       "Method:                 Least Squares   F-statistic:                          1.706e+04\n",
       "Date:                Fri, 28 Jun 2024   Prob (F-statistic):                        0.00\n",
       "Time:                        13:24:13   Log-Likelihood:                         -15833.\n",
       "No. Observations:                1453   AIC:                                  3.167e+04\n",
       "Df Residuals:                    1451   BIC:                                  3.168e+04\n",
       "Df Model:                           2                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1         -2.757e+04   2215.787    -12.440      0.000   -3.19e+04   -2.32e+04\n",
       "x2          1.631e+04     96.891    168.290      0.000    1.61e+04    1.65e+04\n",
       "==============================================================================\n",
       "Omnibus:                       10.419   Durbin-Watson:                   0.192\n",
       "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               11.487\n",
       "Skew:                           0.147   Prob(JB):                      0.00320\n",
       "Kurtosis:                       3.321   Cond. No.                         24.4\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = baroclinic_flux.values\n",
    "x = ssh.sossheig.values\n",
    "z = densityforcing.values * np.sqrt(Ri)\n",
    "X = np.column_stack((x, z))\n",
    "\n",
    "model11 = sm.OLS(y, X, missing='drop').fit()\n",
    "model11.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "### freshwater"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.955</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.955</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>1.539e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 28 Jun 2024</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:25:09</td>     <th>  Log-Likelihood:    </th>          <td> -15905.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1453</td>      <th>  AIC:               </th>          <td>3.181e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1451</td>      <th>  BIC:               </th>          <td>3.182e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>    0.2996</td> <td>    0.153</td> <td>    1.964</td> <td> 0.050</td> <td>    0.000</td> <td>    0.599</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td> 1.635e+04</td> <td>  216.263</td> <td>   75.587</td> <td> 0.000</td> <td> 1.59e+04</td> <td> 1.68e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>40.849</td> <th>  Durbin-Watson:     </th> <td>   0.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  46.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.367</td> <th>  Prob(JB):          </th> <td>8.26e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.479</td> <th>  Cond. No.          </th> <td>3.21e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[3] The condition number is large, 3.21e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.955\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.955\n",
       "Method:                 Least Squares   F-statistic:                          1.539e+04\n",
       "Date:                Fri, 28 Jun 2024   Prob (F-statistic):                        0.00\n",
       "Time:                        13:25:09   Log-Likelihood:                         -15905.\n",
       "No. Observations:                1453   AIC:                                  3.181e+04\n",
       "Df Residuals:                    1451   BIC:                                  3.182e+04\n",
       "Df Model:                           2                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1             0.2996      0.153      1.964      0.050       0.000       0.599\n",
       "x2          1.635e+04    216.263     75.587      0.000    1.59e+04    1.68e+04\n",
       "==============================================================================\n",
       "Omnibus:                       40.849   Durbin-Watson:                   0.161\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               46.433\n",
       "Skew:                           0.367   Prob(JB):                     8.26e-11\n",
       "Kurtosis:                       3.479   Cond. No.                     3.21e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[3] The condition number is large, 3.21e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = baroclinic_flux.values\n",
    "x = SoGrunoff.rorunoff.values\n",
    "z = densityforcing.values * np.sqrt(Ri)\n",
    "X = np.column_stack((x, z))\n",
    "\n",
    "model11 = sm.OLS(y, X, missing='drop').fit()\n",
    "model11.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2265127b20>]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Ri/3.6**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f22644a7f40>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(densityforcing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2264f4fb80>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(low_pass_tide)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}