p3_1_pykernel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# То же самое в Python (Pylab) + нюансы netcdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pkg load netcdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ncread' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-be0194c2b4cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mncread\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'ncread' is not defined"
     ]
    }
   ],
   "source": [
    "ncread"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы прочитать netcdf в python есть несколько вариантов. В данном уроке рассмотрим низкоуровневую библиотеку netCDF4\n",
    "\n",
    "**Задание**: установите пакет netCDF4  в свое окружение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname4 = '/home/mizyuk/DataW2/AO_MexicanGulf_by_Dop5/SST_Reinolds_7D_AO_MexicanGulf_all_auto.mat.nc' # имя файла"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = Dataset(fname4) # инфо к nc файлу"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "загружаем перменные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Dataset'>\n",
       "root group (NETCDF4 data model, file format HDF5):\n",
       "    creation_time: 19-Nov-2018 16:55:09\n",
       "    product: SST_Reinolds_7D\n",
       "    creator: MHI, Remote Sensing Department, Kubryakov A.A.\n",
       "    source: Reinolds\n",
       "    grid_mapping: crs\n",
       "    dimensions(sizes): lat(56), lon(72), date(476)\n",
       "    variables(dimensions): float32 \u001b[4maa\u001b[0m(date,lon,lat), float32 \u001b[4mlon\u001b[0m(lon), float32 \u001b[4mlat\u001b[0m(lat), float32 \u001b[4mtime\u001b[0m(date)\n",
       "    groups: "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = f.variables['lon'] # долгота\n",
    "y = f.variables['lat'] # широта\n",
    "d = f.variables['time'] # дата\n",
    "aa = f.variables['aa'] # массив температуры"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "netCDF4._netCDF4.Variable"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "класс переменной netcdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Variable'>\n",
       "float32 lon(lon)\n",
       "    _FillValue: nan\n",
       "    longname: Longitude\n",
       "unlimited dimensions: \n",
       "current shape = (72,)\n",
       "filling on"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "класс array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В данно случае чтобы превратить переменную netcdf в array сделаем следующее"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[-97.875, -97.625, -97.375, -97.125, -96.875, -96.625,\n",
       "                   -96.375, -96.125, -95.875, -95.625, -95.375, -95.125,\n",
       "                   -94.875, -94.625, -94.375, -94.125, -93.875, -93.625,\n",
       "                   -93.375, -93.125, -92.875, -92.625, -92.375, -92.125,\n",
       "                   -91.875, -91.625, -91.375, -91.125, -90.875, -90.625,\n",
       "                   -90.375, -90.125, -89.875, -89.625, -89.375, -89.125,\n",
       "                   -88.875, -88.625, -88.375, -88.125, -87.875, -87.625,\n",
       "                   -87.375, -87.125, -86.875, -86.625, -86.375, -86.125,\n",
       "                   -85.875, -85.625, -85.375, -85.125, -84.875, -84.625,\n",
       "                   -84.375, -84.125, -83.875, -83.625, -83.375, -83.125,\n",
       "                   -82.875, -82.625, -82.375, -82.125, -81.875, -81.625,\n",
       "                   -81.375, -81.125, -80.875, -80.625, -80.375, -80.125],\n",
       "             mask=False,\n",
       "       fill_value=1e+20,\n",
       "            dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1919232"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size(aa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(476, 72, 56)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(aa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Некоторые функции для классов реализованны как свойства. Свойства классов вызываются через \".\" после имени переменной"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(476, 72, 56)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "мы получили такую размерность массива. Проверим чему они соответсвтуют."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Dataset'>\n",
       "root group (NETCDF4 data model, file format HDF5):\n",
       "    creation_time: 19-Nov-2018 16:55:09\n",
       "    product: SST_Reinolds_7D\n",
       "    creator: MHI, Remote Sensing Department, Kubryakov A.A.\n",
       "    source: Reinolds\n",
       "    grid_mapping: crs\n",
       "    dimensions(sizes): lat(56), lon(72), date(476)\n",
       "    variables(dimensions): float32 \u001b[4maa\u001b[0m(date,lon,lat), float32 \u001b[4mlon\u001b[0m(lon), float32 \u001b[4mlat\u001b[0m(lat), float32 \u001b[4mtime\u001b[0m(date)\n",
       "    groups: "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Примечание №1*: \n",
    "мы получили следующее положение размерностей:\n",
    "\n",
    "**T(время) X(долгота) Y(широта)**\n",
    "\n",
    "Однако, согласно [cf-конвенций](http://cfconventions.org/cf-conventions/cf-conventions#dimensions)\n",
    "их положение предлагается (хоть и не требуется!) таким:\n",
    "\n",
    "**T(время) [Z(глубина)] Y(широта) X(долгота)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим это на одном из массивов REMSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1 = Dataset('/home/mizyuk/DataW2/REMSS/testdatasets/REMSS/la_plata_20060101_20081231.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Dataset'>\n",
       "root group (NETCDF4 data model, file format HDF5):\n",
       "    dimensions(sizes): time(1096), lat(250), lon(205)\n",
       "    variables(dimensions): int64 \u001b[4mtime\u001b[0m(time), int16 \u001b[4manalysed_sst\u001b[0m(time,lat,lon)\n",
       "    groups: "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Увидим, к чему это приведет позже..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Математические операции определены только для класса array из пакета numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa = aa[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "в numpy для работы с массивами с пропущенными данными есть специальный подкласс masked_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(\n",
       "  data=[[[--, --, --, ..., --, --, --],\n",
       "         [--, --, --, ..., --, --, --],\n",
       "         [--, --, --, ..., --, --, --],\n",
       "         ...,\n",
       "         [27.780000686645508, 27.780000686645508, 27.790000915527344,\n",
       "          ..., 16.90999984741211, 15.850000381469727,\n",
       "          14.489999771118164],\n",
       "         [27.8799991607666, 27.8700008392334, 27.850000381469727, ...,\n",
       "          19.09000015258789, 17.799999237060547, 15.789999961853027],\n",
       "         [27.93000030517578, 27.920000076293945, 27.8700008392334, ...,\n",
       "          20.84000015258789, 19.510000228881836, 17.639999389648438]],\n",
       "\n",
       "        [[--, --, --, ..., --, --, --],\n",
       "         [--, --, --, ..., --, --, --],\n",
       "         [--, --, --, ..., --, --, --],\n",
       "         ...,\n",
       "         [27.34000015258789, 27.3799991607666, 27.3799991607666, ...,\n",
       "          17.860000610351562, 16.809999465942383, 15.65999984741211],\n",
       "         [27.31999969482422, 27.34000015258789, 27.3700008392334, ...,\n",
       "          19.6299991607666, 18.260000228881836, 16.690000534057617],\n",
       "         [27.299999237060547, 27.329999923706055, 27.329999923706055,\n",
       "          ..., 21.010000228881836, 19.700000762939453,\n",
       "          18.040000915527344]],\n",
       "\n",
       "        [[--, --, --, ..., --, --, --],\n",
       "         [--, --, --, ..., --, --, --],\n",
       "         [--, --, --, ..., --, --, --],\n",
       "         ...,\n",
       "         [27.260000228881836, 27.229999542236328, 27.190000534057617,\n",
       "          ..., 16.360000610351562, 15.279999732971191,\n",
       "          14.34000015258789],\n",
       "         [27.209999084472656, 27.170000076293945, 27.139999389648438,\n",
       "          ..., 18.219999313354492, 17.079999923706055,\n",
       "          15.800000190734863],\n",
       "         [27.1200008392334, 27.06999969482422, 27.049999237060547, ...,\n",
       "          20.040000915527344, 18.790000915527344, 17.6200008392334]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[--, --, --, ..., --, --, --],\n",
       "         [--, --, --, ..., --, --, --],\n",
       "         [--, --, --, ..., --, --, --],\n",
       "         ...,\n",
       "         [28.459999084472656, 28.549999237060547, 28.65999984741211,\n",
       "          ..., 18.110000610351562, 17.639999389648438, 16.8700008392334],\n",
       "         [28.3799991607666, 28.479999542236328, 28.540000915527344, ...,\n",
       "          19.790000915527344, 19.09000015258789, 18.09000015258789],\n",
       "         [28.31999969482422, 28.3700008392334, 28.40999984741211, ...,\n",
       "          21.709999084472656, 21.010000228881836, 19.84000015258789]],\n",
       "\n",
       "        [[--, --, --, ..., --, --, --],\n",
       "         [--, --, --, ..., --, --, --],\n",
       "         [--, --, --, ..., --, --, --],\n",
       "         ...,\n",
       "         [27.8700008392334, 27.709999084472656, 27.610000610351562, ...,\n",
       "          19.399999618530273, 18.579999923706055, 17.1299991607666],\n",
       "         [27.989999771118164, 27.829999923706055, 27.729999542236328,\n",
       "          ..., 21.329999923706055, 20.25, 18.450000762939453],\n",
       "         [28.049999237060547, 27.899999618530273, 27.81999969482422,\n",
       "          ..., 22.959999084472656, 21.959999084472656, 20.25]],\n",
       "\n",
       "        [[--, --, --, ..., --, --, --],\n",
       "         [--, --, --, ..., --, --, --],\n",
       "         [--, --, --, ..., --, --, --],\n",
       "         ...,\n",
       "         [27.850000381469727, 27.700000762939453, 27.600000381469727,\n",
       "          ..., 18.969999313354492, 17.8700008392334, 16.280000686645508],\n",
       "         [27.90999984741211, 27.75, 27.65999984741211, ...,\n",
       "          21.219999313354492, 19.969999313354492, 18.059999465942383],\n",
       "         [27.920000076293945, 27.780000686645508, 27.649999618530273,\n",
       "          ..., 23.18000030517578, 22.059999465942383,\n",
       "          20.139999389648438]]],\n",
       "  mask=[[[ True,  True,  True, ...,  True,  True,  True],\n",
       "         [ True,  True,  True, ...,  True,  True,  True],\n",
       "         [ True,  True,  True, ...,  True,  True,  True],\n",
       "         ...,\n",
       "         [False, False, False, ..., False, False, False],\n",
       "         [False, False, False, ..., False, False, False],\n",
       "         [False, False, False, ..., False, False, False]],\n",
       "\n",
       "        [[ True,  True,  True, ...,  True,  True,  True],\n",
       "         [ True,  True,  True, ...,  True,  True,  True],\n",
       "         [ True,  True,  True, ...,  True,  True,  True],\n",
       "         ...,\n",
       "         [False, False, False, ..., False, False, False],\n",
       "         [False, False, False, ..., False, False, False],\n",
       "         [False, False, False, ..., False, False, False]],\n",
       "\n",
       "        [[ True,  True,  True, ...,  True,  True,  True],\n",
       "         [ True,  True,  True, ...,  True,  True,  True],\n",
       "         [ True,  True,  True, ...,  True,  True,  True],\n",
       "         ...,\n",
       "         [False, False, False, ..., False, False, False],\n",
       "         [False, False, False, ..., False, False, False],\n",
       "         [False, False, False, ..., False, False, False]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ True,  True,  True, ...,  True,  True,  True],\n",
       "         [ True,  True,  True, ...,  True,  True,  True],\n",
       "         [ True,  True,  True, ...,  True,  True,  True],\n",
       "         ...,\n",
       "         [False, False, False, ..., False, False, False],\n",
       "         [False, False, False, ..., False, False, False],\n",
       "         [False, False, False, ..., False, False, False]],\n",
       "\n",
       "        [[ True,  True,  True, ...,  True,  True,  True],\n",
       "         [ True,  True,  True, ...,  True,  True,  True],\n",
       "         [ True,  True,  True, ...,  True,  True,  True],\n",
       "         ...,\n",
       "         [False, False, False, ..., False, False, False],\n",
       "         [False, False, False, ..., False, False, False],\n",
       "         [False, False, False, ..., False, False, False]],\n",
       "\n",
       "        [[ True,  True,  True, ...,  True,  True,  True],\n",
       "         [ True,  True,  True, ...,  True,  True,  True],\n",
       "         [ True,  True,  True, ...,  True,  True,  True],\n",
       "         ...,\n",
       "         [False, False, False, ..., False, False, False],\n",
       "         [False, False, False, ..., False, False, False],\n",
       "         [False, False, False, ..., False, False, False]]],\n",
       "  fill_value=nan,\n",
       "  dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "aa(aa==0)=nan; % 0all zeros to Nan (Not A Number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Воспользуемся функцией pcolor (или image) для визуализации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fae25a5e6d8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pcolormesh(aa[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Примечание №2*:  в python если вы итерируете по внешней размерности не обязательно указывать остальные. Это связано с важным свойством, которым мы будем пользоваться позже!\n",
    "\n",
    "Сейчас, возможно, кто-то заметил, какая проблема у нас возникла."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "picdir = 'Results/SST_Reinolds_7D/' # название директории"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'datevec' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-85cab178fba7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m## среднее за выбранный год\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatevec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# дата переводится в вектор (год, месяц, число, день, час, минута)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'datevec' is not defined"
     ]
    }
   ],
   "source": [
    "## среднее за выбранный год\n",
    "dv = datevec(d) # дата переводится в вектор (год, месяц, число, день, час, минута)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "функции datevec  у нас конежно же нет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime,timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = d[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext oct2py.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "%octave_push d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ans ="
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   2008      1      4      0      0      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   2008      1     11      0      0      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   2008      1     18      0      0      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   2008      1     25      0      0      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   2008      2      1      0      0      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   2008      2      8      0      0      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   2008      2     15      0      0      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   2008      2     22      0      0      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   2008      2     29      0      0      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   2008      3      7      0      0      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%octave\n",
    "datevec(d(1:10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Наши даты это количество дней от условного \"Рождества Христова\" (0-го года)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ans ="
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "   0   1   1   0   0   0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%octave\n",
    "datevec(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Замечание №3*: В matlab/octave придумана удобная система работы с датами. **Но**, к сожалению, она применима не везде, потому что ни в григорианском, ни в юлианском календарях нет [0-го года](https://ru.wikipedia.org/wiki/0_год)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "year 0 is out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-849746f63430>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: year 0 is out of range"
     ]
    }
   ],
   "source": [
    "datetime(0,1,1,0,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Понятно, что Арсений специально подготовил для нас массивы. Будем выкручиваться."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dv = [(datetime.fromordinal(d1)-timedelta(days=366)) for d1 in d]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dv = array(dv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = array([d.year for d in dv]) # год"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "yu1 = 2013 # будем строить за 2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa2013 = aa[year==2013] # выбираем только эти индексы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 72, 56)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa2013.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "aam = aa2013.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Dimensions of C (72, 56) are incompatible with X (72) and/or Y (56); see help(pcolor)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-aac7ab59583c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# figure(figsize=(8,6))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpcolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maam\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 2 рисунок\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/miniconda/envs/satocean/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mpcolor\u001b[0;34m(alpha, norm, cmap, vmin, vmax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2759\u001b[0m         \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2760\u001b[0m         \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2761\u001b[0;31m         **kwargs)\n\u001b[0m\u001b[1;32m   2762\u001b[0m     \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2763\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/miniconda/envs/satocean/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1808\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1809\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1810\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[0;32m/opt/miniconda/envs/satocean/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mpcolor\u001b[0;34m(self, alpha, norm, cmap, vmin, vmax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   5716\u001b[0m         \u001b[0malways\u001b[0m \u001b[0mdiscards\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mlast\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0mof\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5717\u001b[0m         \"\"\"\n\u001b[0;32m-> 5718\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pcolorargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pcolor'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallmatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5719\u001b[0m         \u001b[0mNy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/miniconda/envs/satocean/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36m_pcolorargs\u001b[0;34m(funcname, allmatch, *args)\u001b[0m\n\u001b[1;32m   5573\u001b[0m                 raise TypeError('Dimensions of C %s are incompatible with'\n\u001b[1;32m   5574\u001b[0m                                 ' X (%d) and/or Y (%d); see help(%s)' % (\n\u001b[0;32m-> 5575\u001b[0;31m                                     C.shape, Nx, Ny, funcname))\n\u001b[0m\u001b[1;32m   5576\u001b[0m             \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mNy\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mNx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5577\u001b[0m         \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msafe_masked_invalid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Dimensions of C (72, 56) are incompatible with X (72) and/or Y (56); see help(pcolor)"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure(figsize=(8,6))\n",
    "pcolor(x,y,aam) # 2 рисунок"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Очевидно, что массив надо транспонировать"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7fae22e027f0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(8,6))\n",
    "pcolor(x,y,aam.T) # 2 рисунок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mean SST in 2013 year')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(8,6))\n",
    "pcolor(x,y,aam.T)\n",
    "grid()\n",
    "title('Mean SST in '+str(yu1)+' year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(8,6))\n",
    "pcolor(x,y,aam.T,cmap='gist_ncar')\n",
    "colorbar()\n",
    "grid(linestyle='--')\n",
    "title('Mean SST in '+str(yu1)+' year')\n",
    "savefig('Results/SST_Reinolds_7D/from_python.png',dpi=300,bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SatOcean",
   "language": "python",
   "name": "satocean"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}