{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at oscillation index for 2015 at Deep Bay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy.linalg as la\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import glob\n",
    "from salishsea_tools import (nc_tools, geo_tools, tidetools) \n",
    "import netCDF4 as nc\n",
    "import numpy.ma as ma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Jan = sorted(glob.glob('/results/SalishSea/hindcast/*jan15/*1h*grid_T*'))\n",
    "Feb = sorted(glob.glob('/results/SalishSea/hindcast/*feb15/*1h*grid_T*'))\n",
    "Mar = sorted(glob.glob('/results/SalishSea/hindcast/*mar15/*1h*grid_T*'))\n",
    "Apr = sorted(glob.glob('/results/SalishSea/hindcast/*apr15/*1h*grid_T*'))\n",
    "May = sorted(glob.glob('/results/SalishSea/hindcast/*may15/*1h*grid_T*'))\n",
    "Jun = sorted(glob.glob('/results/SalishSea/hindcast/*jun15/*1h*grid_T*'))\n",
    "Jul = sorted(glob.glob('/results/SalishSea/hindcast/*jul15/*1h*grid_T*'))\n",
    "Aug = sorted(glob.glob('/results/SalishSea/hindcast/*aug15/*1h*grid_T*'))\n",
    "Sep = sorted(glob.glob('/results/SalishSea/hindcast/*sep15/*1h*grid_T*'))\n",
    "Oct = sorted(glob.glob('/results/SalishSea/hindcast/*oct15/*1h*grid_T*'))\n",
    "Nov = sorted(glob.glob('/results/SalishSea/hindcast/*nov15/*1h*grid_T*'))\n",
    "Dec = sorted(glob.glob('/results/SalishSea/hindcast/*dec15/*1h*grid_T*'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Index for January"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "start_time=0\n",
    "end_time= -1\n",
    "depth=0\n",
    "lon=558\n",
    "lat=155\n",
    "\n",
    "with nc_tools.scDataset(Jan) as f:\n",
    "        t = f.variables['votemper'][start_time:end_time,depth,lon,lat]\n",
    "        s = f.variables['vosaline'][start_time:end_time,depth,lon,lat]\n",
    "        ssh = f.variables['sossheig'][start_time:end_time,lon,lat]\n",
    "        lows_and_highs = []\n",
    "        start_point = 0\n",
    "        interval = 9\n",
    "        target = 'low'\n",
    "        while (start_point) < (ssh.shape[0]-1):\n",
    "            if target == 'low':\n",
    "                low = np.argmin(ssh[start_point:start_point + interval])\n",
    "                lows_and_highs.append(low + start_point)\n",
    "                start_point = low + start_point \n",
    "                target = 'high'\n",
    "            elif target == 'high':\n",
    "                high = np.argmax(ssh[start_point:start_point + interval])\n",
    "                lows_and_highs.append(high + start_point)\n",
    "                start_point = high + start_point \n",
    "                target = 'low'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "list_of_index = ma.masked_array(np.zeros([ssh.shape[0]]), True)\n",
    "for n in range(len(lows_and_highs)-2):\n",
    "    a = np.array([(s[lows_and_highs[n]],t[lows_and_highs[n]])])\n",
    "    b = np.array([(s[lows_and_highs[n+1]],t[lows_and_highs[n+1]])])\n",
    "    c = np.array([(s[lows_and_highs[n+2]],t[lows_and_highs[n+2]])])\n",
    "    dist_ab = la.norm(b - a)\n",
    "    dist_ac = la.norm(c - a)\n",
    "    dist_bc = la.norm(c - b)\n",
    "    OscIndex = (dist_ab + dist_bc) / dist_ac \n",
    "    list_of_index[lows_and_highs[n+1]] = OscIndex\n",
    "    list_of_index.mask[lows_and_highs[n+1]] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0078f6ac18>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAHiCAYAAAA597/kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVKsTEucAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0079039940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(8,8))\n",
    "x = range(len(list_of_index))\n",
    "ax.plot(list_of_index, 'r.')\n",
    "ax.grid('on')\n",
    "ax.set_title('OscIndex for January')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "start_time=0\n",
    "end_time= -1\n",
    "depth=6\n",
    "lon=558\n",
    "lat=155\n",
    "year = Jan + Feb + Mar + Apr + May + Jun + Jul + Aug + Sep + Oct + Nov + Dec\n",
    "\n",
    "with nc_tools.scDataset(year) as f:\n",
    "        t = f.variables['votemper'][start_time:end_time,depth,lon,lat]\n",
    "        s = f.variables['vosaline'][start_time:end_time,depth,lon,lat]\n",
    "        ssh = f.variables['sossheig'][start_time:end_time,lon,lat]\n",
    "        lows_and_highs = []\n",
    "        start_point = 0\n",
    "        interval = 9\n",
    "        target = 'low'\n",
    "        while (start_point) < (ssh.shape[0]-1):\n",
    "            if target == 'low':\n",
    "                low = np.argmin(ssh[start_point:start_point + interval])\n",
    "                lows_and_highs.append(low + start_point)\n",
    "                start_point = low + start_point \n",
    "                target = 'high'\n",
    "            elif target == 'high':\n",
    "                high = np.argmax(ssh[start_point:start_point + interval])\n",
    "                lows_and_highs.append(high + start_point)\n",
    "                start_point = high + start_point \n",
    "                target = 'low'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f00793994a8>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAHiCAYAAAA597/kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0078e79898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "list_of_index = ma.masked_array(np.zeros([ssh.shape[0]]), True)\n",
    "for n in range(len(lows_and_highs)-2):\n",
    "    a = np.array([(s[lows_and_highs[n]],t[lows_and_highs[n]])])\n",
    "    b = np.array([(s[lows_and_highs[n+1]],t[lows_and_highs[n+1]])])\n",
    "    c = np.array([(s[lows_and_highs[n+2]],t[lows_and_highs[n+2]])])\n",
    "    dist_ab = la.norm(b - a)\n",
    "    dist_ac = la.norm(c - a)\n",
    "    dist_bc = la.norm(c - b)\n",
    "    OscIndex = (dist_ab + dist_bc) / dist_ac \n",
    "    list_of_index[lows_and_highs[n+1]] = OscIndex\n",
    "    list_of_index.mask[lows_and_highs[n+1]] = False\n",
    "\n",
    "fig,ax = plt.subplots(1,1,figsize=(8,8))    \n",
    "x = range(len(list_of_index))\n",
    "ax.plot(list_of_index ,'r.')\n",
    "ax.grid('on')\n",
    "ax.set_title('OscIndex for 2015')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}