{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = '/home/notebook/shared-ns1000k/inputs/SEAICE_M/'\n",
    "\n",
    "FILE = 'ci.mon.mean.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_seaice_xarray(filepath, shift_lons=True):\n",
    "    import iris, xarray\n",
    "    cube = iris.load_cube(DATA_DIR + FILE)\n",
    "    if shift_lons:\n",
    "        cube = cube.intersection(longitude=(-180, 180))\n",
    "    return xarray.DataArray.from_iris(cube)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1932: UserWarning: Ignoring netCDF variable 'ci' invalid units '(0 - 1)'\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'ci' (time: 408, latitude: 241, longitude: 480)>\n",
       "dask.array<filled, shape=(408, 241, 480), dtype=float64, chunksize=(136, 241, 240), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1979-01-01 1979-02-01 ... 2012-12-01\n",
       "  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0\n",
       "  * longitude  (longitude) float64 -180.0 -179.2 -178.5 ... 177.8 178.5 179.2\n",
       "Attributes:\n",
       "    long_name:      Sea-ice cover\n",
       "    invalid_units:  (0 - 1)\n",
       "    Conventions:    CF-1.0\n",
       "    history:        2013-10-08 12:17:20 GMT by mars2netcdf-0.92"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xarr = load_seaice_xarray(DATA_DIR + FILE)\n",
    "xarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xarr[0].plot(figsize=(18, 8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ci</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1979-01-01</th>\n",
       "      <td>0.887362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-02-01</th>\n",
       "      <td>0.897902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-03-01</th>\n",
       "      <td>0.893445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-04-01</th>\n",
       "      <td>0.897780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-05-01</th>\n",
       "      <td>0.882448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-01</th>\n",
       "      <td>0.462465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-01</th>\n",
       "      <td>0.450493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-01</th>\n",
       "      <td>0.581409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-01</th>\n",
       "      <td>0.731711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-01</th>\n",
       "      <td>0.790916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ci\n",
       "time                \n",
       "1979-01-01  0.887362\n",
       "1979-02-01  0.897902\n",
       "1979-03-01  0.893445\n",
       "1979-04-01  0.897780\n",
       "1979-05-01  0.882448\n",
       "...              ...\n",
       "2012-08-01  0.462465\n",
       "2012-09-01  0.450493\n",
       "2012-10-01  0.581409\n",
       "2012-11-01  0.731711\n",
       "2012-12-01  0.790916\n",
       "\n",
       "[408 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arctic_subset = xarr.sel(latitude=slice(90, 66))\n",
    "sea_ice_timeseries = arctic_subset.mean(('latitude', 'longitude'))\n",
    "df = sea_ice_timeseries.to_dataframe()\n",
    "df # check groubpy pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ci</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1979-04-01</th>\n",
       "      <td>0.897780</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-05-01</th>\n",
       "      <td>0.882448</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-06-01</th>\n",
       "      <td>0.832372</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-07-01</th>\n",
       "      <td>0.730873</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-08-01</th>\n",
       "      <td>0.641400</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-01</th>\n",
       "      <td>0.816096</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-01</th>\n",
       "      <td>0.705867</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <td>0.506345</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-01</th>\n",
       "      <td>0.462465</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-01</th>\n",
       "      <td>0.450493</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ci  month\n",
       "time                       \n",
       "1979-04-01  0.897780      4\n",
       "1979-05-01  0.882448      5\n",
       "1979-06-01  0.832372      6\n",
       "1979-07-01  0.730873      7\n",
       "1979-08-01  0.641400      8\n",
       "...              ...    ...\n",
       "2012-05-01  0.816096      5\n",
       "2012-06-01  0.705867      6\n",
       "2012-07-01  0.506345      7\n",
       "2012-08-01  0.462465      8\n",
       "2012-09-01  0.450493      9\n",
       "\n",
       "[204 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df['month'] = df.index.month\n",
    "mask = df.month.between(4,9)\n",
    "only_summer = df[mask]\n",
    "only_summer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ci</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>0.767332</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>0.765320</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>0.756767</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>0.768070</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>0.754272</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>0.740740</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>0.737155</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>0.764738</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>0.765342</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>0.751974</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>0.765815</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>0.732723</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>0.749251</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>0.760599</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>0.742980</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>0.755424</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>0.735277</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>0.768912</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>0.761290</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>0.760728</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>0.755153</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>0.752014</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>0.759274</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>0.736615</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>0.741784</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.740296</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>0.729770</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>0.727268</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>0.706418</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.732276</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>0.696743</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.669539</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.661307</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.631010</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ci  month\n",
       "time                 \n",
       "1979  0.767332    6.5\n",
       "1980  0.765320    6.5\n",
       "1981  0.756767    6.5\n",
       "1982  0.768070    6.5\n",
       "1983  0.754272    6.5\n",
       "1984  0.740740    6.5\n",
       "1985  0.737155    6.5\n",
       "1986  0.764738    6.5\n",
       "1987  0.765342    6.5\n",
       "1988  0.751974    6.5\n",
       "1989  0.765815    6.5\n",
       "1990  0.732723    6.5\n",
       "1991  0.749251    6.5\n",
       "1992  0.760599    6.5\n",
       "1993  0.742980    6.5\n",
       "1994  0.755424    6.5\n",
       "1995  0.735277    6.5\n",
       "1996  0.768912    6.5\n",
       "1997  0.761290    6.5\n",
       "1998  0.760728    6.5\n",
       "1999  0.755153    6.5\n",
       "2000  0.752014    6.5\n",
       "2001  0.759274    6.5\n",
       "2002  0.736615    6.5\n",
       "2003  0.741784    6.5\n",
       "2004  0.740296    6.5\n",
       "2005  0.729770    6.5\n",
       "2006  0.727268    6.5\n",
       "2007  0.706418    6.5\n",
       "2008  0.732276    6.5\n",
       "2009  0.696743    6.5\n",
       "2010  0.669539    6.5\n",
       "2011  0.661307    6.5\n",
       "2012  0.631010    6.5"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summer_mean = only_summer.groupby(only_summer.index.year).mean()\n",
    "summer_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = summer_mean['ci'].plot(figsize=(8, 8), ylim=(0, 1))\n",
    "ax.set_title('Blaaaaa');"
   ]
  }
 ],
 "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",
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