CalculateTides-Broader.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the reviewer, looking at the tides beyond the one point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arrow\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import xarray as xr\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "basedir = '/results2/SalishSea/nowcast-green.202111/'\n",
    "iY = 344; iX = 276; iZ = 10 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "60\n",
      "70\n",
      "80\n",
      "90\n",
      "100\n",
      "110\n",
      "120\n",
      "130\n",
      "140\n",
      "150\n",
      "160\n",
      "170\n",
      "180\n",
      "190\n",
      "200\n",
      "210\n",
      "220\n",
      "230\n",
      "240\n",
      "250\n",
      "260\n",
      "270\n",
      "280\n",
      "290\n",
      "300\n",
      "310\n",
      "320\n",
      "330\n",
      "340\n",
      "350\n",
      "360\n"
     ]
    }
   ],
   "source": [
    "year = 2015\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "timerange = arrow.Arrow.range('day', start, endtime)\n",
    "for i, day in enumerate(timerange):\n",
    "    dir1 = day.format('DDMMMYY').lower()\n",
    "    ymd = day.format('YYYYMMDD')\n",
    "    filename = 'SalishSea_1h_'+ymd+'_'+ymd+'_grid_U.nc'\n",
    "    fullfile = os.path.join(basedir, dir1, filename)\n",
    "    u_vel = xr.open_dataset(fullfile)\n",
    "    velocity = u_vel['vozocrtx'].isel(y=iY, x=iX).sel(depthu=iZ, method='nearest')\n",
    "    u_vel.close()\n",
    "    if i == 0:\n",
    "        velocity_year = velocity.copy(deep=True)\n",
    "        velocity.close()\n",
    "    else:\n",
    "        velocity_year = xr.concat([velocity_year, velocity], dim='time_counter')\n",
    "        velocity.close()\n",
    "    if i % 10 == 0:\n",
    "        print (i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total velocity, v component too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "60\n",
      "70\n",
      "80\n",
      "90\n",
      "100\n",
      "110\n",
      "120\n",
      "130\n",
      "140\n",
      "150\n",
      "160\n",
      "170\n",
      "180\n",
      "190\n",
      "200\n",
      "210\n",
      "220\n",
      "230\n",
      "240\n",
      "250\n",
      "260\n",
      "270\n",
      "280\n",
      "290\n",
      "300\n",
      "310\n",
      "320\n",
      "330\n",
      "340\n",
      "350\n",
      "360\n"
     ]
    }
   ],
   "source": [
    "year = 2015\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "timerange = arrow.Arrow.range('day', start, endtime)\n",
    "for i, day in enumerate(timerange):\n",
    "    dir1 = day.format('DDMMMYY').lower()\n",
    "    ymd = day.format('YYYYMMDD')\n",
    "    filename = 'SalishSea_1h_'+ymd+'_'+ymd+'_grid_U.nc'\n",
    "    fullfile = os.path.join(basedir, dir1, filename)\n",
    "    u_vel = xr.open_dataset(fullfile)\n",
    "    velocity = u_vel['vozocrtx'].isel(y=iY, x=iX).sel(depthu=iZ, method='nearest')\n",
    "    u_vel.close()\n",
    "    filename = 'SalishSea_1h_'+ymd+'_'+ymd+'_grid_V.nc'\n",
    "    fullfile = os.path.join(basedir, dir1, filename)\n",
    "    v_vel = xr.open_dataset(fullfile)\n",
    "    v_velocity = v_vel['vomecrty'].isel(y=[iY-1, iY], x=iX).sel(depthv=iZ, method='nearest').mean(axis=1)\n",
    "    vv = velocity**2 + v_velocity**2\n",
    "    if i == 0:\n",
    "        velocity_year = velocity.copy(deep=True)\n",
    "        vv_year = vv.copy(deep=True)\n",
    "        velocity.close()\n",
    "        v_velocity.close()\n",
    "        vv.close()\n",
    "    else:\n",
    "        velocity_year = xr.concat([velocity_year, velocity], dim='time_counter')\n",
    "        vv_year = xr.concat([vv_year, vv], dim='time_counter')\n",
    "        velocity.close()\n",
    "        v_velocity.close()\n",
    "        vv.close()\n",
    "    if i % 10 == 0:\n",
    "        print (i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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(velocity_year**2, vv_year, bins=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.99388073]\n",
      " [0.99388073 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>   0.991</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th>  <td>   0.991</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>           <td>1.019e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 26 Jun 2024</td> <th>  Prob (F-statistic):</th>            <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:15:24</td>     <th>  Log-Likelihood:    </th>           <td>  9533.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  8760</td>      <th>  AIC:               </th>          <td>-1.906e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  8759</td>      <th>  BIC:               </th>          <td>-1.906e+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>    1.0527</td> <td>    0.001</td> <td> 1009.298</td> <td> 0.000</td> <td>    1.051</td> <td>    1.055</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4390.688</td> <th>  Durbin-Watson:     </th> <td>   1.112</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>46320.621</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 2.163</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>13.402</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):                   0.991\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.991\n",
       "Method:                 Least Squares   F-statistic:                          1.019e+06\n",
       "Date:                Wed, 26 Jun 2024   Prob (F-statistic):                        0.00\n",
       "Time:                        16:15:24   Log-Likelihood:                          9533.0\n",
       "No. Observations:                8760   AIC:                                 -1.906e+04\n",
       "Df Residuals:                    8759   BIC:                                 -1.906e+04\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1             1.0527      0.001   1009.298      0.000       1.051       1.055\n",
       "==============================================================================\n",
       "Omnibus:                     4390.688   Durbin-Watson:                   1.112\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            46320.621\n",
       "Skew:                           2.163   Prob(JB):                         0.00\n",
       "Kurtosis:                      13.402   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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = vv_year.values\n",
    "x = velocity_year.values**2\n",
    "\n",
    "nansInArray = (np.isnan(y) | np.isnan(x))\n",
    "print (np.corrcoef(x[~nansInArray], y[~nansInArray]))\n",
    "\n",
    "model11 = sm.OLS(y, x, missing='drop').fit()\n",
    "model11.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "\n",
    "Highly correlated (R2 = 0.991) with a coefficient of 1.053pm0.002"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max each day instead of the average of the square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "60\n",
      "70\n",
      "80\n",
      "90\n",
      "100\n",
      "110\n",
      "120\n",
      "150\n",
      "160\n",
      "170\n",
      "180\n",
      "190\n",
      "200\n",
      "210\n",
      "220\n",
      "230\n",
      "240\n",
      "250\n",
      "260\n",
      "270\n",
      "280\n",
      "290\n",
      "300\n",
      "310\n",
      "320\n",
      "330\n",
      "340\n",
      "350\n",
      "360\n"
     ]
    }
   ],
   "source": [
    "year = 2015\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "timerange = arrow.Arrow.range('day', start, endtime)\n",
    "for i, day in enumerate(timerange):\n",
    "    dir1 = day.format('DDMMMYY').lower()\n",
    "    ymd = day.format('YYYYMMDD')\n",
    "    filename = 'SalishSea_1h_'+ymd+'_'+ymd+'_grid_U.nc'\n",
    "    fullfile = os.path.join(basedir, dir1, filename)\n",
    "    u_vel = xr.open_dataset(fullfile)\n",
    "    velocity = u_vel['vozocrtx'].isel(y=iY, x=iX).sel(depthu=iZ, method='nearest')\n",
    "    u_vel.close()\n",
    "    filename = 'SalishSea_1h_'+ymd+'_'+ymd+'_grid_V.nc'\n",
    "    fullfile = os.path.join(basedir, dir1, filename)\n",
    "    v_vel = xr.open_dataset(fullfile)\n",
    "    v_velocity = v_vel['vomecrty'].isel(y=[iY-1, iY], x=iX).sel(depthv=iZ, method='nearest').mean(axis=1)\n",
    "    vv = (velocity**2 + v_velocity**2).max(axis=0)\n",
    "    if i == 0:\n",
    "        velocity_year = velocity.copy(deep=True)\n",
    "        vv_year = vv.copy(deep=True)\n",
    "        velocity.close()\n",
    "        v_velocity.close()\n",
    "        vv.close()\n",
    "    else:\n",
    "        velocity_year = xr.concat([velocity_year, velocity], dim='time_counter')\n",
    "        vv_year = xr.concat([vv_year, vv], dim='time_counter')\n",
    "        velocity.close()\n",
    "        v_velocity.close()\n",
    "        vv.close()\n",
    "    if i % 10 == 0:\n",
    "        print (i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAKn0lEQVR4nO3d/6tkd33H8dfbzWp0TappUxuSYAotUgnUyDb+ECg0tRI1pP3RQPylwlJoIdKC1B/9B8RfSmHR0BatQYyBklJrxIQQ0MTdmJjETUU0pSHCmqZi0pasSd794c6abze5s/XOzDs7jwdc9s6d2Tnvw7JPDp85557q7gAw1xs2PQAAr02oAYYTaoDhhBpgOKEGGO6cVbzpG+tNfW4OreKtAc5KT+W/nujuC3d7biWhPjeH8r76w1W8NcBZ6ev95X9/tecsfQAMJ9QAwwk1wHBCDTCcUAMMJ9QAwwk1wHBCDTCcUAMMJ9QAwwk1wHBCDTCcUAMMJ9QAwwk1wHBL/T7qqno0yVNJnkvybHcfXuVQALzgTG4c8Afd/cTKJgFgV5Y+AIZbNtSd5GtVdbyqjuz2gqo6UlXHqurYz/PM/k0IsOWWXfq4qrsfr6pfT3J7VT3S3Xe9+AXdfTTJ0SQ5vy7ofZ4TYGstdUTd3Y8v/jyZ5NYkV65yKABesGeoq+pQVZ13+vskH0jy0KoHA2DHMksf70hya1Wdfv0/dvdXVzoVAL+wZ6i7+4dJfncNswCwC6fnAQwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDncldyMerAwe2arvPnzq1ke0C6+WIGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGWDnVVHaiq71TVbascCICXOpMj6huTnFjVIADsbqlQV9UlST6c5LOrHQeAl1v2nomfSfKJJOe92guq6kiSI0lybt7ySw/2//GG3/mtjWy3v/+jjWwX2A57HlFX1bVJTnb38dd6XXcf7e7D3X34YN60bwMCbLtllj6uSnJdVT2a5OYkV1fV51c6FQC/sGeou/uT3X1Jd1+W5CNJvtHdN6x8MgCSOI8aYLxlP0xMknT3nUnuXMkkAOzKETXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMNwZ/ZrT6Z587wUb2e7b3TMRWCFH1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMNyeoa6qc6vq3qp6oKoerqpPrWMwAHYsc+OAZ5Jc3d1PV9XBJHdX1b9097dWPBsAWSLU3d1Jnl48PLj46lUOBcALllqjrqoDVXV/kpNJbu/ue3Z5zZGqOlZVx36eZ/Z5TIDttdQ9E7v7uSTvqaq3Jbm1qi7v7ode9pqjSY4myfl1wUaOuJ85vzax2Tx/6tRGtgtshzM666O7f5rkziTXrGIYAF5pmbM+LlwcSaeq3pzk/UkeWfFcACwss/RxUZK/r6oD2Qn7l7r7ttWOBcBpy5z18d0kV6xhFgB24cpEgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmC4pW7Fdabq4MGc8xsXr+KtX9N/X+qeu8DZxxE1wHBCDTCcUAMMJ9QAwwk1wHBCDTCcUAMMJ9QAwwk1wHBCDTCcUAMMJ9QAwwk1wHBCDTCcUAMMJ9QAw+0Z6qq6tKruqKoTVfVwVd24jsEA2LHMHV6eTfJX3X1fVZ2X5HhV3d7d31vxbABkiSPq7v5xd9+3+P6pJCeSrP8+WwBb6ozumVhVlyW5Isk9uzx3JMmRJDnnV96eH/3pO/djvjPyqw+6ZyJw9ln6w8SqemuSW5J8vLt/9vLnu/todx/u7sMH3nJoP2cE2GpLhbqqDmYn0l/o7q+sdiQAXmyZsz4qyeeSnOjuT69+JABebJkj6quSfDTJ1VV1/+LrQyueC4CFPT9M7O67k9QaZgFgF65MBBhOqAGGE2qA4YQaYDihBhhOqAGGE2qA4YQaYDihBhhOqAGGE2qA4YQaYDihBhhOqAGGO6N7Ji7r8gt/knv/7G9X8dav6YOX/d7at5kkz29kq8C2cEQNMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADD7Rnqqrqpqk5W1UPrGAiAl1rmiPrvklyz4jkAeBV7hrq770ry5BpmAWAX+7ZGXVVHqupYVR37yX8+t19vC7D19u2eid19NMnRJDn/0MV9zXU37NdbLz/DqQfXvk2AVXPWB8BwQg0w3DKn530xyTeTvKuqHquqj61+LABO23ONuruvX8cgAOzO0gfAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwwn1ADDCTXAcEINMJxQAwy3b/dMfIn/+d/0MfcvBNgPjqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmA4oQYYTqgBhhNqgOGEGmC4pUJdVddU1b9V1Q+q6q9XPRQAL9gz1FV1IMnfJPlgkncnub6q3r3qwQDYscwR9ZVJftDdP+zuU0luTvLHqx0LgNOWuRXXxUn+40WPH0vyvpe/qKqOJDmyePjM1/vLD/3y471u/FqSJzY9xJrZ5+1gn9fnna/2xDKhrl1+1q/4QffRJEeTpKqOdffhpcd7ndu2/U3s87awzzMss/TxWJJLX/T4kiSPr2YcAF5umVB/O8lvV9VvVtUbk3wkyT+tdiwATttz6aO7n62qv0jyr0kOJLmpux/e468d3Y/hXke2bX8T+7wt7PMA1f2K5WYABnFlIsBwQg0w3L6GetsuNa+qm6rqZFVtzTnjVXVpVd1RVSeq6uGqunHTM61aVZ1bVfdW1QOLff7Upmdah6o6UFXfqarbNj3LulTVo1X1YFXdX1XHNj3Pafu2Rr241Pz7Sf4oO6f0fTvJ9d39vX3ZwEBV9ftJnk7yD919+abnWYequijJRd19X1Wdl+R4kj85y/+dK8mh7n66qg4muTvJjd39rQ2PtlJV9ZdJDic5v7uv3fQ861BVjyY53N2jLvLZzyPqrbvUvLvvSvLkpudYp+7+cXfft/j+qSQnsnP16lmrdzy9eHhw8XVWfwpfVZck+XCSz256FvY31Ltdan5W/wfedlV1WZIrktyz4VFWbrEMcH+Sk0lu7+6zfZ8/k+QTSZ7f8Bzr1km+VlXHF78WY4T9DPVSl5pzdqiqtya5JcnHu/tnm55n1br7ue5+T3auzL2yqs7apa6qujbJye4+vulZNuCq7n5vdn5b6J8vljc3bj9D7VLzLbFYp70lyRe6+yubnmeduvunSe5Mcs1mJ1mpq5Jct1ivvTnJ1VX1+c2OtB7d/fjiz5NJbs3Oku7G7WeoXWq+BRYfrH0uyYnu/vSm51mHqrqwqt62+P7NSd6f5JGNDrVC3f3J7r6kuy/Lzv/jb3T3DRsea+Wq6tDiA/JU1aEkH0gy4oyufQt1dz+b5PSl5ieSfGmJS81f16rqi0m+meRdVfVYVX1s0zOtwVVJPpqdo6z7F18f2vRQK3ZRkjuq6rvZOSC5vbu35pS1LfKOJHdX1QNJ7k3yz9391Q3PlMQl5ADjuTIRYDihBhhOqAGGE2qA4YQaYDihBhhOqAGG+z9Kpj50vOtumAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(velocity_year**2, marker= 'o')\n",
    "#plt.plot(24*np.arange(365), vv_year, marker= 'o')\n",
    "daily = (velocity_year**2).resample(time_counter='1D').mean()\n",
    "#plt.plot(24*np.arange(365), daily, marker = 'o')\n",
    "#plt.plot(daily, vv_year, 'o')\n",
    "day_avg_tide_pd = daily.to_dataframe()\n",
    "previous = day_avg_tide_pd.rolling(4, center=True).mean()\n",
    "new_pd = vv_year.to_dataframe(name='vv_year')\n",
    "new = new_pd.rolling(4, center=True).mean()\n",
    "plt.hist2d(previous.vozocrtx, new.vv_year, bins=np.arange(0, 6, 0.5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.91955293]\n",
      " [0.91955293 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>   0.978</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.978</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>1.596e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 26 Jun 2024</td> <th>  Prob (F-statistic):</th>          <td>6.93e-301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>18:01:48</td>     <th>  Log-Likelihood:    </th>          <td> -100.88</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   362</td>      <th>  AIC:               </th>          <td>   203.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   361</td>      <th>  BIC:               </th>          <td>   207.6</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>    3.6310</td> <td>    0.029</td> <td>  126.346</td> <td> 0.000</td> <td>    3.574</td> <td>    3.688</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.348</td> <th>  Durbin-Watson:     </th> <td>   0.250</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.042</td> <th>  Jarque-Bera (JB):  </th> <td>   8.397</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.127</td> <th>  Prob(JB):          </th> <td>  0.0150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.702</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):                   0.978\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.978\n",
       "Method:                 Least Squares   F-statistic:                          1.596e+04\n",
       "Date:                Wed, 26 Jun 2024   Prob (F-statistic):                   6.93e-301\n",
       "Time:                        18:01:48   Log-Likelihood:                         -100.88\n",
       "No. Observations:                 362   AIC:                                      203.8\n",
       "Df Residuals:                     361   BIC:                                      207.6\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1             3.6310      0.029    126.346      0.000       3.574       3.688\n",
       "==============================================================================\n",
       "Omnibus:                        6.348   Durbin-Watson:                   0.250\n",
       "Prob(Omnibus):                  0.042   Jarque-Bera (JB):                8.397\n",
       "Skew:                          -0.127   Prob(JB):                       0.0150\n",
       "Kurtosis:                       3.702   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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = new.vv_year.values\n",
    "x = previous.vozocrtx.values\n",
    "\n",
    "nansInArray = (np.isnan(y) | np.isnan(x))\n",
    "print (np.corrcoef(x[~nansInArray], y[~nansInArray]))\n",
    "\n",
    "model11 = sm.OLS(y, x, missing='drop').fit()\n",
    "model11.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "\n",
    "Daily maximum velocity rolling averaged over 4 days is well correlated with the mean velocity squared rolling average over 4 days. However, the coefficient is 3.63pm0.06"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Depth Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "meshmask = xr.open_dataset('/home/sallen/MEOPAR/grid/mesh_mask202108.nc')\n",
    "umask = meshmask.umask[0]\n",
    "meshmask.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "60\n",
      "70\n",
      "80\n",
      "90\n",
      "100\n",
      "110\n",
      "120\n",
      "130\n",
      "140\n",
      "150\n",
      "160\n",
      "170\n",
      "180\n",
      "190\n",
      "200\n",
      "210\n",
      "220\n",
      "230\n",
      "240\n",
      "250\n",
      "260\n",
      "270\n",
      "280\n",
      "290\n",
      "300\n",
      "310\n",
      "320\n",
      "330\n",
      "340\n",
      "350\n",
      "360\n"
     ]
    }
   ],
   "source": [
    "year = 2015\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "timerange = arrow.Arrow.range('day', start, endtime)\n",
    "for i, day in enumerate(timerange):\n",
    "    dir1 = day.format('DDMMMYY').lower()\n",
    "    ymd = day.format('YYYYMMDD')\n",
    "    filename = 'SalishSea_1h_'+ymd+'_'+ymd+'_grid_U.nc'\n",
    "    fullfile = os.path.join(basedir, dir1, filename)\n",
    "    u_vel = xr.open_dataset(fullfile)\n",
    "    velocity = u_vel['vozocrtx'].isel(y=iY, x=iX)\n",
    "    u_vel.close()\n",
    "    filename = 'SalishSea_1h_'+ymd+'_'+ymd+'_grid_V.nc'\n",
    "    fullfile = os.path.join(basedir, dir1, filename)\n",
    "    v_vel = xr.open_dataset(fullfile)\n",
    "    v_velocity = v_vel['vomecrty'].isel(y=[iY-1, iY], x=iX).mean(axis=2)\n",
    "    v_vel.close()\n",
    "    filename = 'SalishSea_1h_'+ymd+'_'+ymd+'_grid_T.nc'\n",
    "    fullfile = os.path.join(basedir, dir1, filename)\n",
    "    gridT = xr.open_dataset(fullfile)\n",
    "    e3u = gridT['e3t'].isel(y=iY, x=[iX-1, iX]).min(axis=2)\n",
    "    e3u = e3u.rename({'deptht': 'z'})\n",
    "    velocity = velocity.rename({'depthu': 'z'})\n",
    "    v_velocity = v_velocity.rename({'depthv': 'z'})\n",
    "    vs = (velocity**2 + v_velocity**2)\n",
    "    va = (vs * e3u).where(umask[:, iY, iX] == 1).sum(axis=1) / (e3u.where(umask[:, iY, iX] == 1).sum(axis=1))\n",
    "    vv = va.max(axis=0)\n",
    "    if i == 0:\n",
    "        vv_year = vv.copy(deep=True)\n",
    "        velocity.close()\n",
    "        v_velocity.close()\n",
    "        vv.close()\n",
    "    else:\n",
    "        vv_year = xr.concat([vv_year, vv], dim='time_counter')\n",
    "        velocity.close()\n",
    "        v_velocity.close()\n",
    "        vv.close()\n",
    "    if i % 10 == 0:\n",
    "        print (i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "daily = (velocity_year**2).resample(time_counter='1D').mean()\n",
    "day_avg_tide_pd = daily.to_dataframe()\n",
    "previous = day_avg_tide_pd.rolling(4, center=True).mean()\n",
    "new_pd = vv_year.to_dataframe(name='vv_year')\n",
    "new = new_pd.rolling(4, center=True).mean()\n",
    "plt.hist2d(previous.vozocrtx, new.vv_year, bins=np.arange(0, 6, 0.5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.89790163]\n",
      " [0.89790163 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>   0.972</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.972</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>1.253e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 18 Jun 2024</td> <th>  Prob (F-statistic):</th>          <td>2.33e-282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:42:06</td>     <th>  Log-Likelihood:    </th>          <td> -27.453</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   362</td>      <th>  AIC:               </th>          <td>   56.91</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   361</td>      <th>  BIC:               </th>          <td>   60.80</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>    2.6261</td> <td>    0.023</td> <td>  111.926</td> <td> 0.000</td> <td>    2.580</td> <td>    2.672</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.869</td> <th>  Durbin-Watson:     </th> <td>   0.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.648</td> <th>  Jarque-Bera (JB):  </th> <td>   0.928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.028</td> <th>  Prob(JB):          </th> <td>   0.629</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.758</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):                   0.972\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.972\n",
       "Method:                 Least Squares   F-statistic:                          1.253e+04\n",
       "Date:                Tue, 18 Jun 2024   Prob (F-statistic):                   2.33e-282\n",
       "Time:                        13:42:06   Log-Likelihood:                         -27.453\n",
       "No. Observations:                 362   AIC:                                      56.91\n",
       "Df Residuals:                     361   BIC:                                      60.80\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1             2.6261      0.023    111.926      0.000       2.580       2.672\n",
       "==============================================================================\n",
       "Omnibus:                        0.869   Durbin-Watson:                   0.183\n",
       "Prob(Omnibus):                  0.648   Jarque-Bera (JB):                0.928\n",
       "Skew:                          -0.028   Prob(JB):                        0.629\n",
       "Kurtosis:                       2.758   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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = new.vv_year.values\n",
    "x = previous.vozocrtx.values\n",
    "\n",
    "nansInArray = (np.isnan(y) | np.isnan(x))\n",
    "print (np.corrcoef(x[~nansInArray], y[~nansInArray]))\n",
    "\n",
    "model12 = sm.OLS(y, x, missing='drop').fit()\n",
    "model12.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer ###\n",
    "\n",
    "Daily maximum depth-averaged velocity rolling averaged over 4 days is well correlated (0.90) with the mean u-velocity squared rolling average over 4 days. The coefficient is 2.63pm0.04\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now Look at a Point in Haro Strait ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "umask[0, 200:400, 100:300].plot()\n",
    "plt.plot(130, 110, 'rx');\n",
    "jY = 200+110; jX = 100+130;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "60\n",
      "70\n",
      "80\n",
      "90\n",
      "100\n",
      "110\n",
      "120\n",
      "130\n",
      "140\n",
      "150\n",
      "160\n",
      "170\n",
      "180\n",
      "190\n",
      "200\n",
      "210\n",
      "220\n",
      "230\n",
      "240\n",
      "250\n",
      "260\n",
      "270\n",
      "280\n",
      "290\n",
      "300\n",
      "320\n",
      "330\n",
      "340\n",
      "350\n",
      "360\n"
     ]
    }
   ],
   "source": [
    "year = 2015\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "timerange = arrow.Arrow.range('day', start, endtime)\n",
    "for i, day in enumerate(timerange):\n",
    "    dir1 = day.format('DDMMMYY').lower()\n",
    "    ymd = day.format('YYYYMMDD')\n",
    "    filename = 'SalishSea_1h_'+ymd+'_'+ymd+'_grid_U.nc'\n",
    "    fullfile = os.path.join(basedir, dir1, filename)\n",
    "    u_vel = xr.open_dataset(fullfile)\n",
    "    velocity = u_vel['vozocrtx'].isel(y=jY, x=jX)\n",
    "    u_vel.close()\n",
    "    filename = 'SalishSea_1h_'+ymd+'_'+ymd+'_grid_V.nc'\n",
    "    fullfile = os.path.join(basedir, dir1, filename)\n",
    "    v_vel = xr.open_dataset(fullfile)\n",
    "    v_velocity = v_vel['vomecrty'].isel(y=[jY-1, jY], x=jX).mean(axis=2)\n",
    "    v_vel.close()\n",
    "    filename = 'SalishSea_1h_'+ymd+'_'+ymd+'_grid_T.nc'\n",
    "    fullfile = os.path.join(basedir, dir1, filename)\n",
    "    gridT = xr.open_dataset(fullfile)\n",
    "    e3u = gridT['e3t'].isel(y=jY, x=[jX-1, jX]).min(axis=2)\n",
    "    e3u = e3u.rename({'deptht': 'z'})\n",
    "    velocity = velocity.rename({'depthu': 'z'})\n",
    "    v_velocity = v_velocity.rename({'depthv': 'z'})\n",
    "    vs = (velocity**2 + v_velocity**2)\n",
    "    va = (vs * e3u).where(umask[:, jY, jX] == 1).sum(axis=1) / (e3u.where(umask[:, jY, jX] == 1).sum(axis=1))\n",
    "    vh = va.max(axis=0)\n",
    "    if i == 0:\n",
    "        vh_year = vh.copy(deep=True)\n",
    "        velocity.close()\n",
    "        v_velocity.close()\n",
    "        vh.close()\n",
    "    else:\n",
    "        vh_year = xr.concat([vh_year, vh], dim='time_counter')\n",
    "        velocity.close()\n",
    "        v_velocity.close()\n",
    "        vh.close()\n",
    "    if i % 10 == 0:\n",
    "        print (i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": [
    "new_pd = vh_year.to_dataframe(name='vh_year')\n",
    "new = new_pd.rolling(4, center=True).mean()\n",
    "plt.hist2d(previous.vozocrtx, new.vh_year, bins=np.arange(0, 2, 0.1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.86823385]\n",
      " [0.86823385 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>   0.957</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.957</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   8108.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 19 Jun 2024</td> <th>  Prob (F-statistic):</th>          <td>1.94e-249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:58:02</td>     <th>  Log-Likelihood:    </th>          <td>  223.78</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   362</td>      <th>  AIC:               </th>          <td>  -445.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   361</td>      <th>  BIC:               </th>          <td>  -441.7</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>    1.0555</td> <td>    0.012</td> <td>   90.045</td> <td> 0.000</td> <td>    1.032</td> <td>    1.079</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>19.941</td> <th>  Durbin-Watson:     </th> <td>   0.185</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  22.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.606</td> <th>  Prob(JB):          </th> <td>1.44e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.917</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):                   0.957\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.957\n",
       "Method:                 Least Squares   F-statistic:                              8108.\n",
       "Date:                Wed, 19 Jun 2024   Prob (F-statistic):                   1.94e-249\n",
       "Time:                        09:58:02   Log-Likelihood:                          223.78\n",
       "No. Observations:                 362   AIC:                                     -445.6\n",
       "Df Residuals:                     361   BIC:                                     -441.7\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1             1.0555      0.012     90.045      0.000       1.032       1.079\n",
       "==============================================================================\n",
       "Omnibus:                       19.941   Durbin-Watson:                   0.185\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               22.292\n",
       "Skew:                          -0.606   Prob(JB):                     1.44e-05\n",
       "Kurtosis:                       2.917   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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = new.vh_year.values\n",
    "x = previous.vozocrtx.values\n",
    "\n",
    "nansInArray = (np.isnan(y) | np.isnan(x))\n",
    "print (np.corrcoef(x[~nansInArray], y[~nansInArray]))\n",
    "\n",
    "model13 = sm.OLS(y, x, missing='drop').fit()\n",
    "model13.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:        (time_counter: 35064)\n",
       "Coordinates:\n",
       "    nav_lat        float32 48.74564\n",
       "    depthu         float32 9.502433\n",
       "    nav_lon        float32 -123.106224\n",
       "    time_centered  (time_counter) datetime64[ns] 2015-01-01T00:30:00 ... 2018-12-31T23:30:00\n",
       "  * time_counter   (time_counter) datetime64[ns] 2015-01-01T00:30:00 ... 2018-12-31T23:30:00\n",
       "Data variables:\n",
       "    vozocrtx       (time_counter) float32 1.5254081 1.7519335 ... 1.4587202"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "velsquared = velocity * velocity\n",
    "velsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "day_avg_tide_vel = velsquared.resample(time_counter='1D').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "day_avg_tide_vel.vozocrtx.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f380a681588>"
      ]
     },
     "execution_count": 25,
     "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": [
    "day_avg_tide_pd = day_avg_tide_vel.to_dataframe()\n",
    "day_avg_tide_pd = day_avg_tide_pd.drop('depthu', 1)\n",
    "day_avg_tide_pd = day_avg_tide_pd.drop('nav_lat', 1)\n",
    "day_avg_tide_pd = day_avg_tide_pd.drop('nav_lon', 1)\n",
    "day_avg_tide_pd.to_csv('day_avg_tide_pd.csv')\n",
    "day_avg_tide_pd.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f380a1917f0>"
      ]
     },
     "execution_count": 30,
     "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": [
    "low_pass_tide = day_avg_tide_pd.rolling(4, center=True).mean()\n",
    "low_pass_tide.to_csv('low_pass_tide.csv')\n",
    "low_pass_tide.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "velocity2015.close()\n",
    "velocity2016.close()\n",
    "velocity2017.close()\n",
    "velocity2018.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "12px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}