{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sandy Cove Analysis  Aug 19, 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import arrow\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib\n",
    "from matplotlib.colors import LogNorm\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "from salishsea_tools import data_tools, places\n",
    "import shared\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rmse(predictions, targets):\n",
    "    return np.sqrt(np.nanmean((predictions - targets) ** 2))\n",
    "def bias(predictions, targets):\n",
    "    return np.nanmean(predictions - targets)\n",
    "def willmott(predictions, targets):\n",
    "    xbar = np.nanmean(targets)\n",
    "    return (1 - (np.nansum((predictions - targets)**2))  / \n",
    "                             np.nansum((np.abs(predictions - xbar)\n",
    "                                     + np.abs(targets - xbar))**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set-up Location and Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = 'SandyCove'\n",
    "place = 'Sandy Cove'\n",
    "mean_sea_lvl = places.PLACES[place]['mean sea lvl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "start = datetime(2017, 8, 21)\n",
    "end = datetime(2018, 5, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Model Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ssh = xr.open_dataset('/results/SalishSea/nowcast-green/20aug17/'+filename+'.nc')\n",
    "for aday in arrow.Arrow.range('day', start, end):\n",
    "    day = aday.format('DDMMMYY').lower()\n",
    "    newdata = xr.open_dataset('/results/SalishSea/nowcast-green/'+day+'/'+filename+'.nc')\n",
    "    ssh = xr.concat([ssh, newdata], dim='time_counter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Save just in case\n",
    "ssh.to_netcdf('sandycove.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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kYnTRw3Z/QCLSuTv+Of5bOlwztnw5NlirzCftuaLfbeM9H7t64zaO+venLFjtru8lkytx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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe13c9e82b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ssh.sossheig.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-9ef6b0405ed4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m             \u001b[0marrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mssh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime_counter\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m             \u001b[0mtimedelta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseconds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m             \u001b[0marrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mssh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime_counter\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         )\n\u001b[1;32m      8\u001b[0m obs_10min_avg = xr.DataArray(\n",
      "\u001b[0;32m/ocean/sallen/allen/research/Meopar/Tools/SalishSeaTools/salishsea_tools/data_tools.py\u001b[0m in \u001b[0;36mget_chs_tides\u001b[0;34m(data_type, stn_id, begin, end)\u001b[0m\n\u001b[1;32m    486\u001b[0m             \u001b[0mend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'YYYY-MM-DD HH:mm:ss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    487\u001b[0m             \u001b[0mfirst_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'station_id={}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstn_number\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 488\u001b[0;31m             \u001b[0morder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    489\u001b[0m         )\n\u001b[1;32m    490\u001b[0m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzeep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhelpers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserialize_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/zeep/client.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     43\u001b[0m         return self._proxy._binding.send(\n\u001b[1;32m     44\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_proxy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_client\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_proxy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_binding_options\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m             self._op_name, args, kwargs)\n\u001b[0m\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/zeep/wsdl/bindings/soap.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, client, options, operation, args, kwargs)\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         response = client.transport.post_xml(\n\u001b[0;32m--> 113\u001b[0;31m             options['address'], envelope, http_headers)\n\u001b[0m\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0moperation_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/zeep/transports.py\u001b[0m in \u001b[0;36mpost_xml\u001b[0;34m(self, address, envelope, headers)\u001b[0m\n\u001b[1;32m     93\u001b[0m         \"\"\"\n\u001b[1;32m     94\u001b[0m         \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0metree_to_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menvelope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maddress\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/zeep/transports.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(self, address, message, headers)\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m             timeout=self.operation_timeout)\n\u001b[0m\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misEnabledFor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDEBUG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(self, url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m    533\u001b[0m         \"\"\"\n\u001b[1;32m    534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 535\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'POST'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    486\u001b[0m         }\n\u001b[1;32m    487\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 488\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/requests/models.py\u001b[0m in \u001b[0;36mcontent\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    779\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 781\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_content\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCONTENT_CHUNK_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    782\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_content_consumed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/requests/models.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m()\u001b[0m\n\u001b[1;32m    701\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'stream'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    704\u001b[0m                         \u001b[0;32myield\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mProtocolError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/requests/packages/urllib3/response.py\u001b[0m in \u001b[0;36mstream\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m    426\u001b[0m         \"\"\"\n\u001b[1;32m    427\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunked\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msupports_chunked_reads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_chunked\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecode_content\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/requests/packages/urllib3/response.py\u001b[0m in \u001b[0;36mread_chunked\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m    591\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk_left\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m                 \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_chunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    594\u001b[0m                 decoded = self._decode(chunk, decode_content=decode_content,\n\u001b[1;32m    595\u001b[0m                                        flush_decoder=False)\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/requests/packages/urllib3/response.py\u001b[0m in \u001b[0;36m_handle_chunk\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    556\u001b[0m             \u001b[0mreturned_chunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# amt > self.chunk_left\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 558\u001b[0;31m             \u001b[0mreturned_chunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_safe_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk_left\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    559\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_safe_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Toss the CRLF at the end of the chunk.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk_left\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_safe_read\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    610\u001b[0m         \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    611\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mamt\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 612\u001b[0;31m             \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMAXAMOUNT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    613\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    614\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mIncompleteRead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mb''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/requests/packages/urllib3/contrib/pyopenssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    254\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    257\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mOpenSSL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSysCallError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Unexpected EOF'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/sallen/anaconda/envs/py36/lib/python3.6/site-packages/OpenSSL/SSL.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1332\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL_peek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1333\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1334\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1335\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_ssl_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "obs_1min = data_tools.get_chs_tides(\n",
    "            'obs',\n",
    "            place,\n",
    "            arrow.get(str(ssh.time_counter[0].values)) -\n",
    "            timedelta(seconds=5 * 60),\n",
    "            arrow.get(str(ssh.time_counter[-1].values))\n",
    "        )\n",
    "obs_10min_avg = xr.DataArray(\n",
    "            obs_1min.resample('10min', loffset='5min').mean()\n",
    "        )\n",
    "obs = xr.Dataset({\n",
    "            'water_level': obs_10min_avg.rename({\n",
    "                'dim_0': 'time'\n",
    "            })\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save just in case\n",
    "obs.to_netcdf('SCobc.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read it in for speed\n",
    "obs = xr.open_dataset('SCobc.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LP+3A3upFrL1zJ+eVPH1KzcIsq2sGx5av2ciNr8xkU1WB5O13QFf5hbLEJtZurGTF2mjD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\noihJ1CgoiqIoSdQoKIqiKEn+Hx64NEiaOzRBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe1393efba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ssh.sossheig.plot()\n",
    "(obs.water_level - mean_sea_lvl).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Base Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE 0.105347847994\n",
      "bias 0.0307859186572\n",
      "Willmott 0.996905645365\n"
     ]
    }
   ],
   "source": [
    "#plt.plot(obs.water_level, ssh.sossheig.data[:, 0, 0], 'o')\n",
    "print('RMSE', rmse(ssh.sossheig.data[:, 0, 0], obs.water_level - mean_sea_lvl))\n",
    "print('bias', bias(ssh.sossheig.data[:, 0, 0], obs.water_level - mean_sea_lvl))\n",
    "print('Willmott', willmott(ssh.sossheig.data[:, 0, 0], obs.water_level - mean_sea_lvl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qhiOW/vOED7aPCb9QryD03/iQMHwynzIvou7Xb9ZbPed9hjCp1gpgDSEwOrae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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe13ca25c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ssh_obs = np.array(obs.water_level) - mean_sea_lvl\n",
    "ssh_model = np.array(ssh.sossheig.data[:, 0, 0])\n",
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "fig, ax = plt.subplots(1, 1, figsize = (8.5, 7))\n",
    "c, xedge, yedge, im = ax.hist2d(ssh_obs[~np.isnan(ssh_obs)],\n",
    "                                ssh_model[~np.isnan(ssh_obs)], bins = 100, norm=LogNorm(), cmap='copper')\n",
    "im\n",
    "ax.plot([-3.2, 2.2], [-3.2, 2.2], 'w')\n",
    "ax.set_xlim((-3.2, 2.2))\n",
    "ax.set_ylim((-3.2, 2.2))\n",
    "ax.set_xlabel('Observations (m)')\n",
    "ax.set_ylabel('Raw Model (m)')\n",
    "ax.set_title('Sea Surface Height at Sandy Cove,\\n Aug 20, 2017- May 8, 2018')\n",
    "cb = fig.colorbar(im, ax=ax)\n",
    "cb.set_label('Point Density')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get tidal Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tidal_predictions = '/results/nowcast-sys/SalishSeaNowcast/tidal_predictions/'\n",
    "model_ssh_period = slice(\n",
    "        str(ssh.time_centered[0].values), \n",
    "        str(ssh.time_centered[-1].values))\n",
    "\n",
    "# Predicted tide water levels dataset from ttide\n",
    "ttide = shared.get_tides(place, tidal_predictions)\n",
    "ttide.rename(columns={' pred_noshallow ': 'pred_noshallow'}, inplace=True)\n",
    "ttide.index = ttide.time\n",
    "ttide_full = xr.Dataset.from_dataframe(ttide)\n",
    "ttide_ds = ttide_full.sel(time=model_ssh_period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save just in case\n",
    "ttide_ds.to_netcdf('extract_pred.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 200)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4BsEvEsasgtzVqDHjKay3sP2UJxhq67Oh1ylMTgllXnYU09PDyYi8dZaPXQun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iAAAE\nwUlEQVTEbyKiLvd3UaHjVOFFRO2I+rErIjoj4nPWFp0WEd+LiOaIeGHE2Jj1JCK+mJvL7I+IdxQm\nahXCGLnyjYj4Q0TsjojNEbEwN35xRPSOqDHfKVzkKoQx8mXMfY+1ZfYaI1d+MiJPjkbErty4tWUW\nO8d35imbt+TtdLOIKAYOALcC9cAzwIdSSr/PSwCa9iJiObA8pbQzIuYBzwLvBT4InEopfbOgAWpa\niYijwIaUUsuIsa8DbSmlr+Ua0YtSSl8oVIyafnL7ogbgTcB9WFsERMSNwCngP1JKV+fGRq0nEfEG\n4EfA9cAKYCuwNqWUKVD4yqMxcuU24LcppaGI+GeAXK5cDGw5/TzNPmPky5cZZd9jbZndRsuVV/z7\nA0BHSukr1pbZ7Rzfme9liuYt+TyS6HrgYErpcEppAPgxcFcet69pLqXUmFLambvfBewDVhY2Ks0w\ndwEP5e4/xHDBlEa6GTiUUjpW6EA0faSUtgFtrxgeq57cBfw4pdSfUjoCHGR4jqNZYLRcSSk9llIa\nyj18GliV98A0LY1RW8ZibZnFzpUrEREM/2j+o7wGpWnpHN+Zp2zeks8m0UrgpRGP67EBoDHkOuRv\nBP4vN/SZ3GHc3/MUIuUkYGtEPBsRn8iNLU0pNebuNwFLCxOaprF7OHuSZW3RWMaqJ85ndC4fB341\n4vEludNBHo+ITYUKStPOaPsea4vGsgk4nlKqGzFmbdErvzNP2bzFhas17UREFfDfwOdSSp3AvwCX\nAuuBRuCBAoan6eOGlNJ64A7g/txhumek4XNpvXyjzoiIMuA9wM9yQ9YWjYv1ROMREV8ChoAf5IYa\ngQtz+6q/Bn4YEfMLFZ+mDfc9mqgPcfYPXNYWjfad+YzJzlvy2SRqAFaPeLwqNyadERGlDCf7D1JK\nPwdIKR1PKWVSSlng3/DQWwEppYbc32ZgM8N5cTx3nu7p83WbCxehpqE7gJ0ppeNgbdFrGqueOJ/R\nq0TEvcCdwEdyk3Nyh/a35u4/CxwC1hYsSE0L59j3WFv0KhFRArwf+MnpMWuLRvvOzBTOW/LZJHoG\nWBMRl+R+zb0HeDiP29c0lzvf9rvAvpTSt0aMLx/xtPcBL7zytZpdIqIyt1AbEVEJ3MZwXjwMfCz3\ntI8BvyhMhJqmzvolztqi1zBWPXkYuCciyiPiEmANsL0A8WmaiIjbgc8D70kp9YwYX5JbLJ+IuJTh\nXDlcmCg1XZxj32Nt0WhuAf6QUqo/PWBtmd3G+s7MFM5bSqY25LHlrvjwaeDXQDHwvZTS3nxtXzPC\nW4A/B/acvsQj8LfAhyJiPcOHzB0F/qow4WkaWQpsHq6RlAA/TCk9GhHPAD+NiL8AjjG8yJ90upl4\nK2fXj69bWwQQET8C3gpUR0Q98I/A1xilnqSU9kbET4HfM3xq0f1efWj2GCNXvgiUA7/J7ZeeTil9\nErgR+EpEDAJZ4JMppfEuYqzXgTHy5a2j7XusLbPbaLmSUvour15LEawts91Y35mnbN4SuSNiJUmS\nJEmSNIu5cLUkSZIkSZJsEkmSJEmSJMkmkSRJkiRJkrBJJEmSJEmSJGwSSZIkSZIkCZtEkiRJkiRJ\nwiaRJEmSJEmSsEkkSZIkSZIk4P8Bzs9TvFqrlCgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe12c69ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(20, 4))\n",
    "ax.plot(ssh.sossheig[:, 0, 0])\n",
    "ax.plot(obs.water_level - mean_sea_lvl)\n",
    "ax.plot(ttide_ds.pred_all)\n",
    "ax.set_xlim((0, 200))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correct the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh_correction = ttide_ds.pred_noshallow - ttide_ds.pred_8\n",
    "ssh_corrected = np.array(ssh.sossheig[:, 0, 0].values) + np.array(ssh_correction.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE 0.0834871724631\n",
      "bias 0.0309168653373\n",
      "Willmott 0.998085656952\n"
     ]
    }
   ],
   "source": [
    "#plt.plot(obs.water_level, ssh_corrected, 'o')\n",
    "print('RMSE', rmse(ssh_corrected, obs.water_level - mean_sea_lvl))\n",
    "print('bias', bias(ssh_corrected, obs.water_level - mean_sea_lvl))\n",
    "print('Willmott', willmott(ssh_corrected, obs.water_level - mean_sea_lvl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+sH1O+IV6JaH/xieE4ZP5lHkWDb9+s95ynLcNYVKthcASQmB0ZI60txICoQ3z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\no464zVEtTSoFM7OTzGyymU2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AY3egL/A48GH0eEdgDvBwqQqVxZ3AH8xsfXd/E8DMBkXP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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe133d1cba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ssh_obs = np.array(obs.water_level) - mean_sea_lvl\n",
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "fig, ax = plt.subplots(1, 1, figsize = (8.5, 7))\n",
    "c, xedge, yedge, im = ax.hist2d(ssh_obs[~np.isnan(ssh_obs)],\n",
    "                                ssh_corrected[~np.isnan(ssh_obs)], bins = 100, norm=LogNorm(), cmap='copper')\n",
    "im\n",
    "ax.plot([-3.2, 2.2], [-3.2, 2.2], 'w')\n",
    "ax.set_xlim((-3.2, 2.2))\n",
    "ax.set_ylim((-3.2, 2.2))\n",
    "ax.set_xlabel('Observations (m)')\n",
    "ax.set_ylabel('Corrected Model (m)')\n",
    "ax.set_title('Sea Surface Height at Sandy Cove,\\n Aug 20, 2017- May 8, 2018')\n",
    "cb = fig.colorbar(im, ax=ax)\n",
    "cb.set_label('Point Density')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Look at Residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "residual_model = ssh_corrected - np.array(ttide_ds.pred_all.values)\n",
    "residual_obs = np.array(obs.water_level) - mean_sea_lvl - np.array(ttide_ds.pred_all.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE 0.0834871724631\n",
      "bias 0.0309168653373\n",
      "Willmott 0.947058353941\n"
     ]
    }
   ],
   "source": [
    "print('RMSE', rmse(residual_model, residual_obs))\n",
    "print('bias', bias(residual_model, residual_obs))\n",
    "print('Willmott', willmott(residual_model, residual_obs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CMwllhSkshmEYRl4QkUj5xpP2jpRXrdtYenzt6ElVIpNRfTGFxTAMwzAKi1Yi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\nYBEREZGip4BFREREit7/B54OIcgljf9eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe13983f748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "fig, ax = plt.subplots(1, 1, figsize = (8.5, 7))\n",
    "c, xedge, yedge, im = ax.hist2d(residual_obs[~np.isnan(residual_obs)], \n",
    "                                residual_model[~np.isnan(residual_obs)], bins = 100, norm=LogNorm(), cmap='copper')\n",
    "im\n",
    "ax.plot([-0.48, 0.72], [-0.48, 0.72], 'w')\n",
    "ax.set_xlim((-0.48, 0.72))\n",
    "ax.set_ylim((-0.48, 0.72))\n",
    "ax.set_xlabel('Observations (m)')\n",
    "ax.set_ylabel('Corrected Model (m)')\n",
    "ax.set_title('Tidal Residuals at Sandy Cove,\\n Aug 20, 2017- May 8, 2018')\n",
    "cb = fig.colorbar(im, ax=ax)\n",
    "cb.set_label('Point Density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
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}