{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 海面水温\n",
    "\n",
    "地図だけでなく値を描画してみよう。ここでは海面水温データ[HadISST1](http://www.metoffice.gov.uk/hadobs/hadisst/)を使う。\n",
    "\n",
    "[download page](http://www.metoffice.gov.uk/hadobs/hadisst/data/download.html)から`HadISST1_SST_update.nc.gz`をダウンロードする。拡張子`.gz`（[gzip](http://www.gzip.org)圧縮）が付いているが，圧縮されていない場合は，拡張子`.gz`削除する。圧縮されている場合は解凍する。Windowsにはgzipを扱う機能はないので，[7-Zip](https://sevenzip.osdn.jp)などを用いる。\n",
    "\n",
    "### NetCDF\n",
    "\n",
    "`nc`（[NetCDF](https://www.unidata.ucar.edu/software/netcdf/)）を読むには[netCDF4 python](http://unidata.github.io/netcdf4-python/)を用いる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import netCDF4 as nc4\n",
    "\n",
    "dataset = nc4.Dataset('HadISST1_SST_update.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`netcdf_dataset(ファイル名)`は`Dataset`を作成する。`Dataset`は座標軸や変数などをまとめたものである。`Dataset`から変数を読むには`variables`メソッドを用いる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sst = dataset.variables['sst']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1月のデータを描画してみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ySm7cND8TYnsnsuBLoJkQIueMKGudydyzbFVYB8heBG5jHSDrLYQ496DzFLpv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bdae518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "lat = dataset.variables['latitude'][:]\n",
    "lon = dataset.variables['longitude'][:]\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "\n",
    "plt.contourf(lon, lat, sst[0,], 60, transform=ccrs.PlateCarree())\n",
    "plt.colorbar()\n",
    "\n",
    "ax.coastlines()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "おかしな図になってしまった。[データ形式](http://www.metoffice.gov.uk/hadobs/hadisst/data/Read_instructions_sst.txt)を確認すると，海氷に覆われているところに-1000が入っている。確認してみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data = [-1000.0 -1000.0 -1.7999999523162842 -1.7999999523162842\n",
       " -1.7999999523162842 -1.7999999523162842 -1.7999999523162842\n",
       " -1.7999999523162842 -1.7999999523162842 -1.3447265625 -0.15625\n",
       " 1.0224127769470215 1.7339876890182495 2.135985851287842 1.9301509857177734\n",
       " 1.3141674995422363 1.8001981973648071 2.6573405265808105 3.610403299331665\n",
       " 4.4060893058776855 5.157680988311768 5.751012802124023 6.087249755859375\n",
       " 6.243302345275879 6.439548492431641 6.810280799865723 7.357912063598633\n",
       " 7.942025184631348 8.291414260864258 8.346809387207031 8.331205368041992\n",
       " 8.400185585021973 8.414026260375977 8.284310340881348 8.080513000488281\n",
       " 7.885481357574463 7.802855491638184 -- -- 9.766984939575195\n",
       " 9.766984939575195 -- -- -- -- -- -- -- -- 14.950439453125\n",
       " 15.034799575805664 15.309090614318848 15.588996887207031\n",
       " 15.678974151611328 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
       " -- -- -- -- -- -- -- -- -- -- -- -- 28.66602897644043 28.807947158813477\n",
       " 28.998897552490234 28.993816375732422 28.805492401123047 28.4868221282959\n",
       " 28.14432716369629 27.855770111083984 27.61625099182129 27.427505493164062\n",
       " 27.250234603881836 27.02727508544922 26.753498077392578 26.424318313598633\n",
       " 26.01759910583496 25.558128356933594 25.080873489379883 24.617101669311523\n",
       " 24.203371047973633 23.833818435668945 23.51828384399414 23.262577056884766\n",
       " 23.099180221557617 23.022619247436523 22.979427337646484 22.97173500061035\n",
       " 22.976743698120117 22.99199867248535 23.07520866394043 23.2158145904541\n",
       " 23.322978973388672 23.351940155029297 23.316436767578125\n",
       " 23.211238861083984 23.045005798339844 22.852628707885742\n",
       " 22.544879913330078 22.095966339111328 21.5771484375 20.952518463134766\n",
       " 20.155269622802734 19.183979034423828 18.14966583251953 17.092430114746094\n",
       " 16.00191307067871 14.901739120483398 13.715605735778809 12.461091995239258\n",
       " 11.428731918334961 10.658792495727539 9.816112518310547 8.862934112548828\n",
       " 7.930225849151611 6.979014873504639 5.906191825866699 4.732023239135742\n",
       " 3.70619797706604 2.8906478881835938 2.273542642593384 1.8709700107574463\n",
       " 1.5515344142913818 1.2728630304336548 1.0218982696533203\n",
       " 0.7814406156539917 0.595293402671814 0.4671807587146759 0.3404085636138916\n",
       " 0.29420292377471924 0.3925720453262329 0.4885733127593994\n",
       " 0.4826304614543915 0.4230322539806366 0.2793792486190796\n",
       " 0.0003629326820373535 -0.2482202649116516 -0.36572265625 -- -- -- -- -- --\n",
       " -- -- -- -- -- -- -- -- -- -- -- -- -- --],\n",
       "             mask = [False False False False False False False False False False False False\n",
       " False False False False False False False False False False False False\n",
       " False False False False False False False False False False False False\n",
       " False  True  True False False  True  True  True  True  True  True  True\n",
       "  True False False False False False  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       " False False False False False False False False False False False False\n",
       " False False False False False False False False False False False False\n",
       " False False False False False False False False False False False False\n",
       " False False False False False False False False False False False False\n",
       " False False False False False False False False False False False False\n",
       " False False False False False False False False False False False False\n",
       " False False False False  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True],\n",
       "       fill_value = -1e+30)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst[0,:,180]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "確かに北極に近いところに-1000が入っている。`sst`はデータが格納されている`data`だけでなく，`True, False`が格納されている`mask`から構成されている。このような配列を[Masked Array](https://docs.scipy.org/doc/numpy/reference/maskedarray.html)という。-1000もマスクすれば絵が描けそうだ。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7mxtvvDFkWo/Hw6pVq7j3sbkoRivu+pYDDn5JpJQFQEHD52ohxFagg5RyMRAx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e138320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy.ma as ma\n",
    "import netCDF4 as nc4\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "dataset = nc4.Dataset('HadISST1_SST_update.nc')\n",
    "\n",
    "ice_covered = -1000\n",
    "sst = ma.masked_values(dataset.variables['sst'][0, :, :], ice_covered)\n",
    "lat = dataset.variables['latitude'][:]\n",
    "lon = dataset.variables['longitude'][:]\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "\n",
    "plt.contourf(lon, lat, sst, 60, transform=ccrs.PlateCarree(), vmin = -10.0, vmax = 35.0)\n",
    "plt.colorbar()\n",
    "\n",
    "ax.coastlines()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### テキストデータ\n",
    "\n",
    "NetCDFは軸の情報などが付加されていて便利であるが，HadISSTにはテキストデータで提供されているものがある。2004年のデータを読んでみよう。取得し解凍してから，1行読んでみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'     1     1  2004   180 rows    360 columns\\n'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin = open('HadISST1_SST_2004.txt', 'rt')\n",
    "fin.readline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[データ形式](http://www.metoffice.gov.uk/hadobs/hadisst/data/Read_instructions_sst.txt)のとおり，最初の行はヘッダのようだ。次の行は北極に一番近い緯度円（89.5N）のデータ。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000\\n'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin.readline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "全て海氷だ。少し読み飛ばしてみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'-32768-32768  -180  -180  -180  -180  -180  -180  -180  -180  -180  -180  -132   -58-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768  -180  -180  -180  -180  -180  -180  -180  -180-32768-32768  -180  -180  -180  -180  -180  -180  -180-32768-32768  -180  -180-32768-32768-32768-32768-32768-32768  -180 -1000-32768-32768-32768-32768  -180  -180  -180 -1000 -1000 -1000  -180 -1000-32768-32768-32768-32768-32768-32768  -180  -180 -1000 -1000 -1000  -180  -180  -180  -180  -180  -167   -64    15    34    38    18-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768 -1000 -1000  -180    59    82   149   211   247   259   261   268   275   275   264   245   217   186   185   213   248   289   332   373   417   464   515   569   592   587   592   604   623   645   657   662   665   668   670   666   654   633   609   591   578-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768   235   210   189   174   161   113    53    63    77    76    74     9    68     3-32768-32768  -141  -125-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768  -151  -134-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768-32768\\n'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for j in range(20):\n",
    "    fin.readline()\n",
    "fin.readline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-32768は陸地で海面水温のないところ。NetCDFでは`mask`が`True`となっていた。面倒なことに-32768の前には空白が省略されている。-1000に置き換えることにする。テキストの置き換えには，標準モジュール`re`を使う。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' -1000 -1000 -1000 -1000 -1000  -180  -180  -180  -180  -180  -180  -180  -170   -98   -58   -11 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000  -180 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000  -180  -180 -1000 -1000 -1000 -1000  -180  -180 -1000  -180 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000  -180  -180  -180  -160  -123   -12    43    59 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000  -180  -180  -180  -117   -59   -16    93   205   220   303   350   366   369   366   364   356   347   339   320   293   254   210   202   232   271   317   366   412   455   494   540   592   619   623   632   647   665   687   701   705   705   702   696   679   654   627   598   583 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000   104    94    67 -1000    -7    -7   -27 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000  -133 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000 -1000\\n'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "land = -32768\n",
    "seaice = -1000\n",
    "re.sub(str(land), ' '+str(seaice), fin.readline())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "緯度円上のデータが一つの文字列になっている。空白で区切り文字列をリストに分解する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['-1000',\n",
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       " '-1000',\n",
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       " '-1000',\n",
       " '-1000',\n",
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       " '-1000',\n",
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       " '-1000',\n",
       " '-1000',\n",
       " '-1000',\n",
       " '-1000',\n",
       " '-1000']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.sub(str(land), ' '+str(seaice), fin.readline()).split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "リストを`numpy`の整数の配列に型変換する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -62,    13,    88, -1000, -1000, -1000, -1000, -1000,   -82,\n",
       "         -90,  -115,   -90,  -132, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000,  -166,  -180,  -180,\n",
       "       -1000, -1000,  -180,  -180,  -180, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000,  -180,  -180, -1000, -1000,\n",
       "        -180,  -180,  -180,  -108,   -17,    57,   110,   135,   134,\n",
       "         124, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000,   193,   293,   343,\n",
       "         357,   282,   251,   315,   387,   437,   459,   488,   537,\n",
       "         581,   589,   563,   536, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000,   447,   430,   404,   385,   381,\n",
       "         386,   395,   423,   467,   506,   538,   571,   607,   638,\n",
       "         665,   692,   716,   737,   757,   768,   769,   757,   734,\n",
       "         711,   687,   659, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000,   209,   197,    64, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,    -8,\n",
       "          -4,    90,    87,    85, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000,\n",
       "       -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.array(re.sub(str(land), ' '+str(seaice), fin.readline()).split()).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上をまとめて関数にする。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "\n",
    "nx = 360; ny = 180\n",
    "def read_hadisst(fname, m):\n",
    "    fin = open(fname, 'rt')\n",
    "    sst = ma.zeros((m, ny, nx))\n",
    "    land = -32768\n",
    "    seaice = -1000\n",
    "    for k in range(m):\n",
    "        fin.readline()\n",
    "        for j in range(ny):\n",
    "            line = re.sub(str(land), ' '+str(seaice), fin.readline())\n",
    "            sst[k, j, :] = ma.masked_equal(np.array(line.split()).astype(int), seaice) * 0.01\n",
    "    fin.close()\n",
    "    return sst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sst = read_hadisst('HadISST1_SST_2004.txt', 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 180, 360)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KCqiqqiIkJIQOHTp4yx89ehQpJVarFYvFQlBQkP/jdFa5r50w4VJtVFZWAm4DwWwOJb/A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\nDFZJasxglaTGDFZJasxglaTGDFZJauxv8LNg3ga6aZ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e610eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "\n",
    "plt.contourf(lon, lat, sst[0,:,:], 60, transform=ccrs.PlateCarree(), vmin = -10.0, vmax = 35.0)\n",
    "plt.colorbar()\n",
    "\n",
    "ax.coastlines()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}