{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas\n",
    "import logging\n",
    "import matplotlib.style\n",
    "matplotlib.style.use('ggplot')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models   = ('tappy', 'tidegui', 't_tide', 'utide', 'getijsys', 'fes2012')\n",
    "stations = ('HVH', 'Honolulu')\n",
    "def n_comments(fname, comment):\n",
    "    with open(fname, 'r') as f:\n",
    "        n_lines = 0\n",
    "        pattern = re.compile(\"^\\s*{0}\".format(comment))\n",
    "        for l in f:\n",
    "            if pattern.search(l) is None:\n",
    "                break\n",
    "            else:\n",
    "                n_lines += 1\n",
    "    return n_lines\n",
    "n_comments('tidegui/HVH_noos_2007_2007_tidegui_prediction.txt', '#')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "kwargs = {\n",
    "    'tappy': dict(delim_whitespace=True, names=(\"time\", \"waterlevel\"), parse_dates=[\"time\"]),\n",
    "    'tidegui': dict(delim_whitespace=True, names=(\"time\", \"waterlevel\"), parse_dates=[\"time\"]),\n",
    "    't_tide': dict(delim_whitespace=True, names=(\"time\", \"waterlevel\"), parse_dates=[\"time\"]),\n",
    "    'utide': dict(delim_whitespace=True, names=(\"time\", \"waterlevel\"), parse_dates=[\"time\"]),\n",
    "    'hatyan': dict(delim_whitespace=True, names=(\"time\", \"waterlevel\"), parse_dates=[\"time\"]),\n",
    "    'fes2012': dict(delim_whitespace=True, names=(\"time\", \"waterlevel\"), parse_dates=[\"time\"]),\n",
    "    'measurements': dict(delim_whitespace=True, names=(\"time\", \"waterlevel\"), parse_dates=[\"time\"])\n",
    "}\n",
    "dfs = []\n",
    "for station in stations:\n",
    "    for from_year in (2007, 2013):\n",
    "        model    = 'measurements'\n",
    "        filename = '{model}/{station}_noos_{from_year}_{model}.txt'.format(**locals())\n",
    "        df = pandas.read_csv(filename, skiprows=n_comments(filename, '#'), **kwargs[model])\n",
    "        df['model']     = 'measurements'\n",
    "        df['from_year'] = from_year\n",
    "        df['to_year']   = from_year\n",
    "        df['station']   = station\n",
    "        dfs.append(df)\n",
    "for station in ('HVH', 'Honolulu'):\n",
    "    for from_year in (2007, 2013):\n",
    "        for to_year in (2007, 2013):\n",
    "            for model in models:\n",
    "                filename = '{model}/{station}_noos_{from_year}_{to_year}_{model}_prediction.txt'.format(**locals())\n",
    "                exists = os.path.exists(filename)\n",
    "                \n",
    "                if exists:\n",
    "                    df = pandas.read_csv(filename, skiprows=n_comments(filename, '#'), **kwargs[model])\n",
    "                    df['model']     = model\n",
    "                    df['from_year'] = from_year\n",
    "                    df['to_year']   = to_year\n",
    "                    df['station']   = station\n",
    "                    dfs.append(df)\n",
    "predictions = pandas.concat(dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fes2012', 'measurements', 't_tide', 'tappy', 'tidegui', 'utide'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(predictions['model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014776"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'time', u'waterlevel', u'model', u'from_year', u'to_year', u'station'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(734972.0, 734974.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jzaGiu3P3A78F7lNKDQVQSo1SSl1u/ztfKTXRDhxqgDb7C6zSsFOBj2qtmztc63FghlLq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1346ad850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from matplotlib.dates import MonthLocator, WeekdayLocator, DateFormatter\n",
    "import datetime\n",
    "\n",
    "def plot(label, df):\n",
    "    months = MonthLocator(range(1, 13), bymonthday=1, interval=3)\n",
    "    monthsFmt = DateFormatter(\"%b '%y\")\n",
    "    ax.plot([x for x in df.time], df.waterlevel, label=label, alpha=0.5)\n",
    "    ax.legend(loc='upper left')\n",
    "    ax.xaxis.set_major_locator(months)\n",
    "    ax.xaxis.set_major_formatter(monthsFmt)\n",
    "    ax.set_ylim(-.3, .3)\n",
    "    \n",
    "from IPython.html.widgets import interactive\n",
    "fig, ax = plt.subplots(figsize=(13,6))\n",
    "groups = predictions[np.logical_and.reduce([\n",
    "        predictions['from_year'] == 2013,\n",
    "        predictions['to_year'] == 2013,\n",
    "        predictions['station'] == 'Honolulu',\n",
    "        np.in1d(predictions['model'],('tappy','t_tide', 'utide','tidegui', 'fes2012', 'measurements'))\n",
    "    ])].groupby('model')\n",
    "for key, group in groups:\n",
    "    plot(key, group)\n",
    "ax.set_xlim(datetime.datetime(2013,4,14),datetime.datetime(2013,4,16))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"20\" halign=\"left\">waterlevel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th colspan=\"20\" halign=\"left\">HVH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th colspan=\"2\" halign=\"left\">fes2012</th>\n",
       "      <th colspan=\"2\" halign=\"left\">measurements</th>\n",
       "      <th colspan=\"4\" halign=\"left\">t_tide</th>\n",
       "      <th colspan=\"4\" halign=\"left\">tappy</th>\n",
       "      <th colspan=\"4\" halign=\"left\">tidegui</th>\n",
       "      <th colspan=\"4\" halign=\"left\">utide</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>from_year</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2007</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2013</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2007</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2013</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2007</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2013</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2007</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2013</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to_year</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:00:00</th>\n",
       "      <td> 0.673</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.93595</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.89792</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.108425</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.000825</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.1281</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.9494</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.04187</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.99510</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:10:00</th>\n",
       "      <td> 0.670</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.96567</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.93251</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.139520</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.036491</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.1587</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.9864</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.07142</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.02889</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:20:00</th>\n",
       "      <td> 0.674</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.98585</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.95778</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.159541</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.060812</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.1731</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.0081</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.09155</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.05307</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:30:00</th>\n",
       "      <td> 0.683</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.99742</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.97443</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.169529</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.074457</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.1727</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.0150</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.10326</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.06846</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:40:00</th>\n",
       "      <td> 0.695</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.00136</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 0.98322</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.170635</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.078294</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.1600</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.0085</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.10755</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 1.07599</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    waterlevel                                                \\\n",
       "station                    HVH                                                 \n",
       "model                  fes2012      measurements        t_tide                 \n",
       "from_year                 2007 2013         2007 2013     2007          2013   \n",
       "to_year                   2007 2013         2007 2013     2007 2013     2007   \n",
       "time                                                                           \n",
       "2007-01-01 00:00:00      0.673  NaN         0.95  NaN  0.93595  NaN  0.89792   \n",
       "2007-01-01 00:10:00      0.670  NaN         0.91  NaN  0.96567  NaN  0.93251   \n",
       "2007-01-01 00:20:00      0.674  NaN         0.86  NaN  0.98585  NaN  0.95778   \n",
       "2007-01-01 00:30:00      0.683  NaN         0.85  NaN  0.99742  NaN  0.97443   \n",
       "2007-01-01 00:40:00      0.695  NaN         0.81  NaN  1.00136  NaN  0.98322   \n",
       "\n",
       "                                                                             \\\n",
       "station                                                                       \n",
       "model                        tappy                     tidegui                \n",
       "from_year                     2007           2013         2007         2013   \n",
       "to_year             2013      2007 2013      2007 2013    2007 2013    2007   \n",
       "time                                                                          \n",
       "2007-01-01 00:00:00  NaN  1.108425  NaN  1.000825  NaN  1.1281  NaN  0.9494   \n",
       "2007-01-01 00:10:00  NaN  1.139520  NaN  1.036491  NaN  1.1587  NaN  0.9864   \n",
       "2007-01-01 00:20:00  NaN  1.159541  NaN  1.060812  NaN  1.1731  NaN  1.0081   \n",
       "2007-01-01 00:30:00  NaN  1.169529  NaN  1.074457  NaN  1.1727  NaN  1.0150   \n",
       "2007-01-01 00:40:00  NaN  1.170635  NaN  1.078294  NaN  1.1600  NaN  1.0085   \n",
       "\n",
       "                                                      \n",
       "station                                               \n",
       "model                       utide                     \n",
       "from_year                    2007          2013       \n",
       "to_year             2013     2007 2013     2007 2013  \n",
       "time                                                  \n",
       "2007-01-01 00:00:00  NaN  1.04187  NaN  0.99510  NaN  \n",
       "2007-01-01 00:10:00  NaN  1.07142  NaN  1.02889  NaN  \n",
       "2007-01-01 00:20:00  NaN  1.09155  NaN  1.05307  NaN  \n",
       "2007-01-01 00:30:00  NaN  1.10326  NaN  1.06846  NaN  \n",
       "2007-01-01 00:40:00  NaN  1.10755  NaN  1.07599  NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HVH = predictions.query('station == \"HVH\"').pivot_table(index='time',columns=['station', 'model', 'from_year', 'to_year'], values=['waterlevel']) # \n",
    "HVH.head()\n",
    "\n",
    "#import numpy as np\n",
    "#np.sqrt(np.mean((df['waterlevel_measured'] - df['waterlevel_tappy']) ** 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"20\" halign=\"left\">waterlevel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <th colspan=\"20\" halign=\"left\">Honolulu</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th colspan=\"2\" halign=\"left\">fes2012</th>\n",
       "      <th colspan=\"2\" halign=\"left\">measurements</th>\n",
       "      <th colspan=\"4\" halign=\"left\">t_tide</th>\n",
       "      <th colspan=\"4\" halign=\"left\">tappy</th>\n",
       "      <th colspan=\"4\" halign=\"left\">tidegui</th>\n",
       "      <th colspan=\"4\" halign=\"left\">utide</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>from_year</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2007</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2013</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2007</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2013</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2007</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2013</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2007</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2013</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to_year</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "      <th>2007</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:00:00</th>\n",
       "      <td>-0.090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.04654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.07660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.045872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.060161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.04415</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.07973</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:10:00</th>\n",
       "      <td>-0.097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>    NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.04902</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.08023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.048330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.064394</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0610</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0860</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.04667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.08350</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:20:00</th>\n",
       "      <td>-0.105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>    NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.05254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.08483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.051783</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.069576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0910</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.05024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.08827</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:30:00</th>\n",
       "      <td>-0.114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>    NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.05709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.09042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.056243</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.075686</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0719</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.05488</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.09402</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:40:00</th>\n",
       "      <td>-0.124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>    NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.06267</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.09697</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.061709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.082698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.1032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.06058</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.10073</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    waterlevel                                                \\\n",
       "station               Honolulu                                                 \n",
       "model                  fes2012      measurements        t_tide                 \n",
       "from_year                 2007 2013         2007 2013     2007          2013   \n",
       "to_year                   2007 2013         2007 2013     2007 2013     2007   \n",
       "time                                                                           \n",
       "2007-01-01 00:00:00     -0.090  NaN      -0.0339  NaN -0.04654  NaN -0.07660   \n",
       "2007-01-01 00:10:00     -0.097  NaN          NaN  NaN -0.04902  NaN -0.08023   \n",
       "2007-01-01 00:20:00     -0.105  NaN          NaN  NaN -0.05254  NaN -0.08483   \n",
       "2007-01-01 00:30:00     -0.114  NaN          NaN  NaN -0.05709  NaN -0.09042   \n",
       "2007-01-01 00:40:00     -0.124  NaN          NaN  NaN -0.06267  NaN -0.09697   \n",
       "\n",
       "                                                                             \\\n",
       "station                                                                       \n",
       "model                        tappy                     tidegui                \n",
       "from_year                     2007           2013         2007         2013   \n",
       "to_year             2013      2007 2013      2007 2013    2007 2013    2007   \n",
       "time                                                                          \n",
       "2007-01-01 00:00:00  NaN -0.045872  NaN -0.060161  NaN -0.0573  NaN -0.0816   \n",
       "2007-01-01 00:10:00  NaN -0.048330  NaN -0.064394  NaN -0.0610  NaN -0.0860   \n",
       "2007-01-01 00:20:00  NaN -0.051783  NaN -0.069576  NaN -0.0658  NaN -0.0910   \n",
       "2007-01-01 00:30:00  NaN -0.056243  NaN -0.075686  NaN -0.0719  NaN -0.0967   \n",
       "2007-01-01 00:40:00  NaN -0.061709  NaN -0.082698  NaN -0.0793  NaN -0.1032   \n",
       "\n",
       "                                                      \n",
       "station                                               \n",
       "model                       utide                     \n",
       "from_year                    2007          2013       \n",
       "to_year             2013     2007 2013     2007 2013  \n",
       "time                                                  \n",
       "2007-01-01 00:00:00  NaN -0.04415  NaN -0.07973  NaN  \n",
       "2007-01-01 00:10:00  NaN -0.04667  NaN -0.08350  NaN  \n",
       "2007-01-01 00:20:00  NaN -0.05024  NaN -0.08827  NaN  \n",
       "2007-01-01 00:30:00  NaN -0.05488  NaN -0.09402  NaN  \n",
       "2007-01-01 00:40:00  NaN -0.06058  NaN -0.10073  NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Honolulu = predictions.query('station == \"Honolulu\"').pivot_table(index='time',columns=['station', 'model', 'from_year', 'to_year'], values=['waterlevel']) # \n",
    "Honolulu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>from_year</th>\n",
       "      <th>to_year</th>\n",
       "      <th>model</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2007</td>\n",
       "      <td>        tappy</td>\n",
       "      <td> 0.065592</td>\n",
       "      <td> 0.256110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2007</td>\n",
       "      <td>      tidegui</td>\n",
       "      <td> 0.063843</td>\n",
       "      <td> 0.252672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 </th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2007</td>\n",
       "      <td>       t_tide</td>\n",
       "      <td> 0.080183</td>\n",
       "      <td> 0.283166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 </th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2007</td>\n",
       "      <td>        utide</td>\n",
       "      <td> 0.067264</td>\n",
       "      <td> 0.259353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 </th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2007</td>\n",
       "      <td>      fes2012</td>\n",
       "      <td> 0.250352</td>\n",
       "      <td> 0.500352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 </th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>        tappy</td>\n",
       "      <td> 0.082594</td>\n",
       "      <td> 0.287392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>   tappy_self</td>\n",
       "      <td> 0.025333</td>\n",
       "      <td> 0.159164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>      tidegui</td>\n",
       "      <td> 0.062017</td>\n",
       "      <td> 0.249032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td> tidegui_self</td>\n",
       "      <td> 0.010669</td>\n",
       "      <td> 0.103292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>       t_tide</td>\n",
       "      <td> 0.083658</td>\n",
       "      <td> 0.289236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>  t_tide_self</td>\n",
       "      <td> 0.020989</td>\n",
       "      <td> 0.144876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>        utide</td>\n",
       "      <td> 0.075981</td>\n",
       "      <td> 0.275646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>   utide_self</td>\n",
       "      <td> 0.016298</td>\n",
       "      <td> 0.127662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>        tappy</td>\n",
       "      <td> 0.087740</td>\n",
       "      <td> 0.296209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>   tappy_self</td>\n",
       "      <td> 0.022710</td>\n",
       "      <td> 0.150697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>      tidegui</td>\n",
       "      <td> 0.073736</td>\n",
       "      <td> 0.271543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td> tidegui_self</td>\n",
       "      <td> 0.009893</td>\n",
       "      <td> 0.099461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>       t_tide</td>\n",
       "      <td> 0.100705</td>\n",
       "      <td> 0.317341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>  t_tide_self</td>\n",
       "      <td> 0.020549</td>\n",
       "      <td> 0.143350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>        utide</td>\n",
       "      <td> 0.087281</td>\n",
       "      <td> 0.295433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>   utide_self</td>\n",
       "      <td> 0.016154</td>\n",
       "      <td> 0.127098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2013</td>\n",
       "      <td>        tappy</td>\n",
       "      <td> 0.057465</td>\n",
       "      <td> 0.239718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2013</td>\n",
       "      <td>      tidegui</td>\n",
       "      <td> 0.051348</td>\n",
       "      <td> 0.226600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2013</td>\n",
       "      <td>       t_tide</td>\n",
       "      <td> 0.062265</td>\n",
       "      <td> 0.249530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2013</td>\n",
       "      <td>        utide</td>\n",
       "      <td> 0.058555</td>\n",
       "      <td> 0.241980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>      HVH</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2013</td>\n",
       "      <td>      fes2012</td>\n",
       "      <td> 0.266190</td>\n",
       "      <td> 0.515936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2007</td>\n",
       "      <td>        tappy</td>\n",
       "      <td> 0.002272</td>\n",
       "      <td> 0.047669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2007</td>\n",
       "      <td>      tidegui</td>\n",
       "      <td> 0.001865</td>\n",
       "      <td> 0.043185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2007</td>\n",
       "      <td>       t_tide</td>\n",
       "      <td> 0.002265</td>\n",
       "      <td> 0.047596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2007</td>\n",
       "      <td>        utide</td>\n",
       "      <td> 0.002276</td>\n",
       "      <td> 0.047705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2007</td>\n",
       "      <td>      fes2012</td>\n",
       "      <td> 0.004439</td>\n",
       "      <td> 0.066630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>        tappy</td>\n",
       "      <td> 0.003540</td>\n",
       "      <td> 0.059501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>   tappy_self</td>\n",
       "      <td> 0.000579</td>\n",
       "      <td> 0.024061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>      tidegui</td>\n",
       "      <td> 0.001893</td>\n",
       "      <td> 0.043507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td> tidegui_self</td>\n",
       "      <td> 0.000662</td>\n",
       "      <td> 0.025738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>       t_tide</td>\n",
       "      <td> 0.003428</td>\n",
       "      <td> 0.058553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>  t_tide_self</td>\n",
       "      <td> 0.000514</td>\n",
       "      <td> 0.022674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>        utide</td>\n",
       "      <td> 0.003427</td>\n",
       "      <td> 0.058537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2007</td>\n",
       "      <td> 2013</td>\n",
       "      <td>   utide_self</td>\n",
       "      <td> 0.000637</td>\n",
       "      <td> 0.025242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>        tappy</td>\n",
       "      <td> 0.002867</td>\n",
       "      <td> 0.053543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>   tappy_self</td>\n",
       "      <td> 0.000609</td>\n",
       "      <td> 0.024671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>      tidegui</td>\n",
       "      <td> 0.002459</td>\n",
       "      <td> 0.049590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td> tidegui_self</td>\n",
       "      <td> 0.000594</td>\n",
       "      <td> 0.024376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>       t_tide</td>\n",
       "      <td> 0.002787</td>\n",
       "      <td> 0.052789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>  t_tide_self</td>\n",
       "      <td> 0.000521</td>\n",
       "      <td> 0.022832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>        utide</td>\n",
       "      <td> 0.002882</td>\n",
       "      <td> 0.053684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2007</td>\n",
       "      <td>   utide_self</td>\n",
       "      <td> 0.000646</td>\n",
       "      <td> 0.025411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2013</td>\n",
       "      <td>        tappy</td>\n",
       "      <td> 0.002963</td>\n",
       "      <td> 0.054435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2013</td>\n",
       "      <td>      tidegui</td>\n",
       "      <td> 0.001230</td>\n",
       "      <td> 0.035077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2013</td>\n",
       "      <td>       t_tide</td>\n",
       "      <td> 0.002914</td>\n",
       "      <td> 0.053986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2013</td>\n",
       "      <td>        utide</td>\n",
       "      <td> 0.002931</td>\n",
       "      <td> 0.054142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td> Honolulu</td>\n",
       "      <td> 2013</td>\n",
       "      <td> 2013</td>\n",
       "      <td>      fes2012</td>\n",
       "      <td> 0.004429</td>\n",
       "      <td> 0.066547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     station  from_year  to_year         model       MSE      RMSE\n",
       "0        HVH       2007     2007         tappy  0.065592  0.256110\n",
       "1        HVH       2007     2007       tidegui  0.063843  0.252672\n",
       "2        HVH       2007     2007        t_tide  0.080183  0.283166\n",
       "3        HVH       2007     2007         utide  0.067264  0.259353\n",
       "4        HVH       2007     2007       fes2012  0.250352  0.500352\n",
       "5        HVH       2007     2013         tappy  0.082594  0.287392\n",
       "6        HVH       2007     2013    tappy_self  0.025333  0.159164\n",
       "7        HVH       2007     2013       tidegui  0.062017  0.249032\n",
       "8        HVH       2007     2013  tidegui_self  0.010669  0.103292\n",
       "9        HVH       2007     2013        t_tide  0.083658  0.289236\n",
       "10       HVH       2007     2013   t_tide_self  0.020989  0.144876\n",
       "11       HVH       2007     2013         utide  0.075981  0.275646\n",
       "12       HVH       2007     2013    utide_self  0.016298  0.127662\n",
       "13       HVH       2013     2007         tappy  0.087740  0.296209\n",
       "14       HVH       2013     2007    tappy_self  0.022710  0.150697\n",
       "15       HVH       2013     2007       tidegui  0.073736  0.271543\n",
       "16       HVH       2013     2007  tidegui_self  0.009893  0.099461\n",
       "17       HVH       2013     2007        t_tide  0.100705  0.317341\n",
       "18       HVH       2013     2007   t_tide_self  0.020549  0.143350\n",
       "19       HVH       2013     2007         utide  0.087281  0.295433\n",
       "20       HVH       2013     2007    utide_self  0.016154  0.127098\n",
       "21       HVH       2013     2013         tappy  0.057465  0.239718\n",
       "22       HVH       2013     2013       tidegui  0.051348  0.226600\n",
       "23       HVH       2013     2013        t_tide  0.062265  0.249530\n",
       "24       HVH       2013     2013         utide  0.058555  0.241980\n",
       "25       HVH       2013     2013       fes2012  0.266190  0.515936\n",
       "26  Honolulu       2007     2007         tappy  0.002272  0.047669\n",
       "27  Honolulu       2007     2007       tidegui  0.001865  0.043185\n",
       "28  Honolulu       2007     2007        t_tide  0.002265  0.047596\n",
       "29  Honolulu       2007     2007         utide  0.002276  0.047705\n",
       "30  Honolulu       2007     2007       fes2012  0.004439  0.066630\n",
       "31  Honolulu       2007     2013         tappy  0.003540  0.059501\n",
       "32  Honolulu       2007     2013    tappy_self  0.000579  0.024061\n",
       "33  Honolulu       2007     2013       tidegui  0.001893  0.043507\n",
       "34  Honolulu       2007     2013  tidegui_self  0.000662  0.025738\n",
       "35  Honolulu       2007     2013        t_tide  0.003428  0.058553\n",
       "36  Honolulu       2007     2013   t_tide_self  0.000514  0.022674\n",
       "37  Honolulu       2007     2013         utide  0.003427  0.058537\n",
       "38  Honolulu       2007     2013    utide_self  0.000637  0.025242\n",
       "39  Honolulu       2013     2007         tappy  0.002867  0.053543\n",
       "40  Honolulu       2013     2007    tappy_self  0.000609  0.024671\n",
       "41  Honolulu       2013     2007       tidegui  0.002459  0.049590\n",
       "42  Honolulu       2013     2007  tidegui_self  0.000594  0.024376\n",
       "43  Honolulu       2013     2007        t_tide  0.002787  0.052789\n",
       "44  Honolulu       2013     2007   t_tide_self  0.000521  0.022832\n",
       "45  Honolulu       2013     2007         utide  0.002882  0.053684\n",
       "46  Honolulu       2013     2007    utide_self  0.000646  0.025411\n",
       "47  Honolulu       2013     2013         tappy  0.002963  0.054435\n",
       "48  Honolulu       2013     2013       tidegui  0.001230  0.035077\n",
       "49  Honolulu       2013     2013        t_tide  0.002914  0.053986\n",
       "50  Honolulu       2013     2013         utide  0.002931  0.054142\n",
       "51  Honolulu       2013     2013       fes2012  0.004429  0.066547"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = predictions.pivot_table(index='time',columns=['station', 'model', 'from_year', 'to_year'], values=['waterlevel']) # \n",
    "results = []\n",
    "for station in ('HVH', 'Honolulu'):\n",
    "    for from_year in (2007, 2013):\n",
    "        for to_year in (2007, 2013):\n",
    "            for model in models:\n",
    "                try:\n",
    "                    SE = (np.array(table['waterlevel'][station]['measurements'][to_year][to_year]) - np.array(table['waterlevel'][station][model][from_year][to_year]))**2\n",
    "                except KeyError:\n",
    "                    continue\n",
    "                MSE = np.mean(SE[~np.isnan(SE)])\n",
    "                results.append(dict(station=station, from_year=from_year, to_year=to_year, model=model, RMSE=np.sqrt(MSE), MSE=MSE))\n",
    "                if from_year == to_year:\n",
    "                    continue\n",
    "                try:\n",
    "                    SE = (np.array(table['waterlevel'][station][model][to_year][to_year]) - np.array(table['waterlevel'][station][model][from_year][to_year]))**2\n",
    "                except KeyError:\n",
    "                    continue\n",
    "                MSE = np.mean(SE[~np.isnan(SE)])\n",
    "                results.append(dict(station=station, from_year=from_year, to_year=to_year, model=model + '_self', RMSE=np.sqrt(MSE), MSE=MSE))\n",
    "results = pandas.DataFrame(data=results, columns=('station', 'from_year', 'to_year', 'model', 'MSE', 'RMSE'))\n",
    "results.sort()    \n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "# O2007 - P2007  O2007 - P2013  P2013 - P2007\n",
    "# Cross prediction error =  constituent error + meteo error\n",
    "0.073736 - 0.009893 == 0.063843\n",
    "0.002459 - 0.000594 == 0.001865"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import lxml.etree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:getijsys/HVH_noos_2007_getijsys_analysis.xml does not exist\n",
      "WARNING:root:getijsys/HVH_noos_2013_getijsys_analysis.xml does not exist\n",
      "WARNING:root:getijsys/Honolulu_noos_2007_getijsys_analysis.xml does not exist\n",
      "WARNING:root:getijsys/Honolulu_noos_2013_getijsys_analysis.xml does not exist\n",
      "WARNING:root:fes2012/HVH_noos_2007_fes2012_analysis.xml does not exist\n",
      "WARNING:root:fes2012/HVH_noos_2013_fes2012_analysis.xml does not exist\n",
      "WARNING:root:fes2012/Honolulu_noos_2007_fes2012_analysis.xml does not exist\n",
      "WARNING:root:fes2012/Honolulu_noos_2013_fes2012_analysis.xml does not exist\n"
     ]
    }
   ],
   "source": [
    "tree     = lxml.etree.ElementTree()\n",
    "records = []\n",
    "files   = []\n",
    "\n",
    "for model in models:\n",
    "    for station in stations:\n",
    "        for from_year in (2007, 2013):\n",
    "            filename = os.path.join(model, '{station}_noos_{from_year}_{model}_analysis.xml'.format(**locals()))\n",
    "            files.append(filename)\n",
    "substitutes = {\n",
    "    \"A0\": \"Z0\",\n",
    "    \"THE1\": \"THETA1\"\n",
    "}\n",
    "def normalize(name):\n",
    "    name = name.upper()\n",
    "    name = name.replace('(', '').replace(')','')\n",
    "    name = substitutes.get(name, name)\n",
    "    return name\n",
    "for name in files:\n",
    "    if not os.path.exists(name):\n",
    "        logging.warn('%s %s', name, 'does not exist')\n",
    "        continue\n",
    "    doc = tree.parse(open(name))\n",
    "    for element in doc.findall('.//Harmonic'):\n",
    "        row = {\"file\": name}\n",
    "        for sub in element.getchildren():\n",
    "            row[sub.tag] = sub.text.strip()\n",
    "            if sub.tag == 'name':\n",
    "                row['normalized'] = normalize(sub.text.strip())\n",
    "            \n",
    "        records.append(row)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "df = pandas.DataFrame.from_records(records).sort('speed')\n",
    "df_sel = df[df['file'].apply(lambda x:'2013' in x and 'HVH' in x)]\n",
    "a = df_sel.pivot(index='normalized', columns='file', values='speed')\n",
    "a.to_csv('speeds.csv')\n",
    "\n",
    "a = df_sel.pivot(index='normalized', columns='file', values=('amplitude'))\n",
    "a.to_csv('amplitudes.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# does not work in python 3\n",
    "df.to_excel('constituents.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import dateutil.parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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