03 - NumPy arrays.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NumPy arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nikolay Koldunov\n",
    "\n",
    "koldunovn@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "================"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img  height=\"100\" src=\"files/numpy.png\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-    a powerful N-dimensional array object\n",
    "-    sophisticated (broadcasting) functions\n",
    "-    tools for integrating C/C++ and Fortran code\n",
    "-    useful linear algebra, Fourier transform, and random number capabilities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(precision=3 , suppress= True) # this is just to make the output look better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data in to a variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = np.loadtxt('Ham_3column.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1891.,     1.,     1.,   -72.],\n",
       "       [ 1891.,     1.,     2.,   -43.],\n",
       "       [ 1891.,     1.,     3.,   -32.],\n",
       "       ..., \n",
       "       [ 2014.,     8.,    29.,   216.],\n",
       "       [ 2014.,     8.,    30.,   198.],\n",
       "       [ 2014.,     8.,    31.,   184.]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45168, 4)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img  height=\"100\" src=\"files/anatomyarray.png\" >"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180672"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it's a *row-major* order. Matlab and Fortran use *column-major* order for arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy arrays are statically typed, which allow faster operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can't assign value of different type to element of the numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Year'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-c2dbfa0bc312>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtemp\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'Year'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Year'"
     ]
    }
   ],
   "source": [
    "temp[0,0] = 'Year'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slicing works similarly to Matlab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1891.,     1.,     1.,   -72.],\n",
       "       [ 1891.,     1.,     2.,   -43.],\n",
       "       [ 1891.,     1.,     3.,   -32.],\n",
       "       [ 1891.,     1.,     4.,    12.],\n",
       "       [ 1891.,     1.,     5.,   -29.]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[0:5,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2014.,     8.,    27.,   219.],\n",
       "       [ 2014.,     8.,    28.,   234.],\n",
       "       [ 2014.,     8.,    29.,   216.],\n",
       "       [ 2014.,     8.,    30.,   198.]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[-5:-1,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can look at the data. This is done by matplotlib module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119c69cc0>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DMIzP4RkBwzAM4zosCBiGYRIY9iNgGIbxOawaYhiG8TnsUMYwDMO4DgsChmGS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c56240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as plt\n",
    "plt.plot(temp[:,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general it is similar to Matlab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First 12 elements of **second** column (months). Remember that indexing starts with 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,\n",
       "        12.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[0:12,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First raw:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1891.,     1.,     1.,   -72.])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    " - Plot only first 1000 values\n",
    " - Plot last 1000 values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119405b00>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lJmLmZKEpHeHVv7YuEcNvLp6NufsPCxyzqiH4EbTQ9+rBW+noGogApkaQiDMM\nqE84Mk11E4xYoY8a1GhHi5ltSM28EZ2zMcq1ESQ8TBVixepXX0e9bkG1pUpFlNuLa7RUcZ+GzYv5\n6fkz8JmZZh6scDrPX27+NnaYd/gheX73KdNG4sSDRmr2zi/9dhYTepZt3ovP/uY1fMkqEeDnuHv4\n3c14+F3vVApRzVI80MkYM80HqmlIWUXqEsL87JZPXH0cXl29Q/veezecikcXb8Z//32pMjb/kgni\nbYObq+JcJrJyRDYRZE1DHKmMmfinFiTjGre6zoGfiMWQyhimszhPv1VdPIbetOHqbmavYH2+Rr3H\ntrTr+ySUmijBCOJaOASBlBToh/jMQfsNlPJ0nD+geNajXj61jDUQ3c+QK6QRFIi7X1mLjt60ncUb\nRqVXy0ULRCMZsUJMxE0fgbqAyBhOu7LuK9UJSmba6BZccZzedDSwIamdmOTV8NJN7Xh1lVOQyKaj\natIK5IlHblGYNgwk4zEMqHdeS87dk5UuLDdpXVtVI7g8oGOcH6KHgXp/iWgUP4GjXrMhHrkJpSYX\ny6MjO5yLfIlwGoH8m6nXMWZrBNEkga4el26xUAhIIygwwsYaJpSvuT6hdQypeQSJWAxpw3BXFbUT\nz5hLKAgG+oSCBvHpI8YgzhiWb92Le15ZB8AZBXP2L8z6SHLEijzGDOdVc8NpncUwzXOJOHPVCDK4\nOx5cqxHEY1ZosPOe+c7Z0/CdgD7SXrQ0JLBrX59rwhLRTn635ufvcjbIaaqPFhJZLKJEpYldZf+H\n2BacF2P+Lwtp9XeVKwBEQfYpCaKGoOYKaQQFRqwC2/b2aLNK5VWiuooUiJo1MUkVTWt8BGJyEjei\nbqWnRqpEoSEZxwVHjnOEovplygJO261fV69KQ28agmUaiuFjBzjrY3FwlyDQRZUkYgwpw7CcxfkZ\n6ycPN23ZDUnn4273ipDuk87eNHZ29sKLcs0HySlqSBYEED4C/8+I6y7P/aovZ9roFgDA+CFNkcbz\n+WMmuLYVSyMgQVAgxK1h1z5Z3oaTbn7etZ88oXiZbX79wmoA2bDORCxmmYbMzx431XQA205l66rq\nVnoD+qERCOSVaipj4I21u/DdR5Y6tgkMRSOoFpwagWoaYq7fmXN3YpDO/puIxcC5aXKLUsbYj2+c\nNBV//8qxOHxcq2P7h1Ygg3yfnH3bS5j1/fmexyrXDmdRmu2IPeVzEX8GOosN56JMx2VzJ+Hhq+Zi\n7pTgwAxIj/LNAAAgAElEQVSZCUOb8fBVcx3bomYn5woJggIjrzo27HI72uQJRXXmqYibIhlnpmnI\nmm9njh/sOFbWCahzFvdf1ZT9HX1pAxf85jXc+1o2Pn631EvBUa65TCeRXJBPJWsa4pZpKOYK/eTc\nrRHoTUPm53rTRqjGQGGIxRiOGD/YNan87S0zpFk+F1HszOtala9GkMNnNFFDQYfR+QgA4JI52dV8\nIs5cQjcsxXIOq5AgKDBBq2D5gQtKJbfLUIuEMuvYjVZtE7XpiZ9f4tyZrhYQoZEfAl2jlfW7srV3\n5BVWuU4iueDII5AKs6UyZrE49bfXaQQ6Z7G4xr2pTN5Xg16tR4V5b/3OffY2r+igctXq5Ouxwioh\n4YXOR2DY1zDc86pem2vPPNj+O5dcD4EwF04Y2oT3bzwt5+NEpVp8d2WHeOaCJj/5ZuxOZfCJGaNx\n8KiB+PGTH7r2zWoEVoih9VkRg65GHaQ87Pe68gNRiDtMQ+7zW9XWiSMnDgHgTJArV7NCTmhOhQPo\nSRvaCf6Ol9Y42ogC+rIfIqlI5J/kE6+FgRDm66XSx3u6Uhg72L1vuWp18rhOveXFUJ/p05QACc6U\n1wd/NCTjGDagDjs6+/rVoJtJ/j2/CL98Q4KgwARNfrKg6OpLo7ku7rmisMNHY8JZbG4f3FSHoycN\nwdmHOTt+dvaWpj3kz55eYdcnkldY1ZRdrDuX9q6UZ0vHjp60bYoRJDVmgKhlCaKgu6/qEjG73lBa\n8uZ7tRYt12voNSyvFf76nftwy/wV9uuwCWV21JCuDpQlxPsjLAdak/90y+FcLMg0VGCCbgpZUHT1\nZtBYF/d0RIkHORGPoasvI1UlBe7/4hxcPGeiY/9C1Sg58aARvu/v6Oy1uz85wkfLdDWZC7pTCepH\nvMOKxhG+Ad2cky8HsQ6dRjCoMWmvjGXtzmsREXCKJcNrAtfdcxwcyzY7M/hDh4/aGoH7vVsvPALz\npgzD8AG551qMaGnAg1+eg5+cNyPnY+QCCYIC49eRCnDewF2pDJrrEp62YbF9mVWG4q6X1wLwLjsw\npMnd2yAfjBgYfKOL05Kfw+oSBNHPRTTnmWR1g9P5hPpjXw5Ct4od1Ji071H5+uhi2oHy9fN4DUvr\n0+BmTSjn58NpBOL8db/lUZOG4L4rjvYt0hiGWROG2H6/YkGCoECIrMKgloCyRpAxOBpDmIZWt3UC\nAN7dYBYw83ICnnNE7g5hP+oDopsA6cHSRGZUA1HCFQX7rFX2rRcdjiuPn4xjJg9x7bO3JzsB/+mK\no3MfoAZ5gSF6TAxqTGJPVwrH/3gBXpGywkVzlJEtTqFfCc5ix3aPdVhaUW3C3pteUUOVDvkICsy+\nPn87vbrCaqqLezr1xIMshIuw43rdlGpSU75QE5N0ZDQrrOrSCLzf+9LH9D2ehalu7OAmfFuKMnHs\nI2kJQ/thYtAhh6uKe6nV6mL20a4uPC91LZu/vA1b2rtdwQDleg29NBWd4OJw++7C5BH0pjO48R/L\nAISrFFBJkEZQYIIWGuoN2VyX8NQIhP1YLTVQ7MWJV5ObeVICja5FZblOIrngN2EcP1WfSCQmeb9Y\n8cuPy9YUynf4aItU9kIce5DU4H6XlP8BmBqMGnlWrlqdp2lI5yPg3FE6XGwz//f+jt37stpasRK9\nigUJghKj3qiNdXHPpC9x833qiDGOmkH7FMfepw4fjW+dekCeR5rFyychl68QD5r8YJWrWQEwQ15v\nfvrD0CYfP5lW76ExCWeynx+gqS6B82aNBdC/ulA65B7GQoscKF0zNbggleEuB7g6gZYL3qYhvUag\nVs3NagTe3yHf9l7m2EqFBEGJyBgc//7Xxa7+A011cU/Ti7wKkR/gFqVJ+c8vPAJfPXFqHkfrzbgh\njfbfrdLqUmg6lRI19MU/LMJtz63C5vaeUPv7CYy6uF6Qp9JmsllQKe7vf+oQPPa1eRjZou8qlyvy\nfXXyNLPG/eBm74CCvrSBVMbA5fMm4a9fnAOgnDWC8KahR97d7PCHyJ8Pe34FDO4qCVV2OpXDim0d\nePDtjfjan99xbG+qS3iWmpAFwX5W68kDRw7EwaOKG3MsI5fa/a/TD7L/zlheOmdmcfHGFRURAy7C\nXoPwmzC8NIKUR7MZlYZkHIeMGRRqHFGQBdCNn5yOl//rBAz3iQB7edUOGNzUJERce7kKc697y2u8\nTysNdoKioQyDO3JESCMgQhF0n3jdoKZGoBcEsklh6oiBALKVDovNrRcejge+NAejLIH03L9/zLG6\nFBqB/ICly1gSCG1mj0+3Lhn1VOQIIC8fQJ9H1nExufmCGXjwy3OQjMcwdnCT7beYpymQ9pOnzOz2\nZDxmC7ByMu9d9/B7uPSeNwB4C+aw2ex2uLPH/vctXI//eGCJ/Zqihoi84HWDNvnED8uryWEDzUm3\nuUT14c+xShv/8nMz8ezybZg8fIDj/VTa7LtrcGBocx127utzOSPLicFWzsXuHDUCWXj7lQ4utZPx\n3JljHa93Wtfk6ElD8PIqfXe6ZJzZE19/8wgMg4Ox3Fpuqtz3+kfZ43oIAq8yK65xeRSda+9OYcaN\nTzvMnkD1RQ2RICgQOWsE9QlPh5U8iYhuU1726GIxbEA9PnvkeNf2T/yf2aSmIRnDgSMHYue+PmwJ\naX8vBSIJKKwzVJ13GiQHv1+F10KWkMgFkQV74H4DPfepS0gaQT+VusnffgInHDgc91x6VP8OpOCV\nrxM2u94rfHTjbrP+UlhNsVIh01CJ8FrBDGxIeDqLZdOQMDGU+8KkJ2XgoP1aEI8xbC1jQSB+xrA2\ncLdGkL1mfuGhhSwhkQuXHDsR911+NE6dvp/nPsl4zL7PMpyjJ5XBNQ8ucZQbj8KCD/X1mPrDl+57\nW7s9jCCYN2UYbv/8TABOAb+jsxffeXipx6eqC9IIioxo4O618mxpSKLHoxy1rI6K+aoSVNQxgxvR\n0pDAnu7yNQ0JwtrAVXkhm4b8BEGpTUMq8RjDPI+8B0EyHgNjDDFmmnYeensT/vLmBsRjDD/49KFF\nGml4RKtWAJ7Pksx9Uga3HA120z8/wNsf7cn/AMuQ8lqe1ADiPlPjmGW8bMzydtFarxJ8ViMG1mNQ\nYxLt3aWphhqFsDZwDm8fgV+tmXIzDYVBjDkeY8hwbt9zpcgpWNXWEdhvQK6FFaXwYowFN6apVmpe\nEPSkMjnVjQlG/8CLFefbH+12vSdWi16rxkZpsgnTMq9cGNCQsARB+dpZmWT6CIOfRiA467BRLjNf\nuWkEMv9ytNvXIxNjzOqlnFsEUT6es5NvftG338DQ5jpHYqPo1hcGxpjD5FdtIaJ+1LQg2NHZi4O+\n8yRuePT9on2nuNF+Pn+l6z1x43mFGMrbbdNQBdyrzXUJtJS5IBCE1QiE7fggy8kqzEHy3PF/Fx2B\nJdc7u0yVOnzUjx9++lBt0yJxzyZiDH0ZwzZHRk0uC6rEmyuygMlw7hhXZI1AOqUyc+cUlBo6VTci\nnPHe19cH7Nk/ZHOAXyi9uPHClCL+zKyxGNPaiAs1ETvlRnN9Ai0NSXT2BAuCnZ29vhVbDYPjh08s\nx7od+zz36Q9RE6buuHg2/nD5URhshRfKEwljzGXmS1SAaeiyuZMcr4UVszdt4J5X1tmmmaihpEH9\nGrxIZQz8z2PLPJ3TcmG81sak4xpEEQSmRuB8XSvUtCAQD32+LUPLt+x1PCR1jpW895cJjSCM+WBM\nayNeueZEjBvS1I+R5p/jNRVPm+riaKyLh1LTZ31/Pv7lztc939/e2Ys7XlyDM297qV/jVLGjhiLe\nC4Makzhu6nDfGjWPfW2e/Xe8ApaZ3/2Es6ihuGdF7ssdL64BEP236vPQCFZv7/Tt1/3PpVtx18tr\n8YMnluuPmzEwprURE4c2Yca4VofJKoyzWMDg1C7INFQDvLF2V0ESnD7Yuhdn3PoS7l+0wd4m24/F\nQ3XURHcteqFyV/JK5AZlEgFMjaAxGQ/szSDwi9QQwrsrwgMehairXGY9QX6r3UPGDMJ4S2AnK8GW\nB+Cmc7PRQOI3OXqS856N+lvpTEOpjIGTfvYCvvonffgnkC1XIvcQkCfsvrSB3rSBOfsPQ4wxh8AJ\ne88Bpg9EPqNy9ufkm5oUBJxzXPCb1/C53y7s97GWbd5rN4gBgC173LHyA6QqksI0pHO0jR7U6NpW\naUwePgDvfOcUnCX1T262NIJ8tM6Marp556PdWNUW3Aie5egAFc76oAxWOfKmEpDrJYluWfde7kwC\ni+oj0GkEK7eZTZZeWa3PagayGru8QJIFb286g55UBg3JGGKMOfotR+nbzZhTuFVCIEa+qElBECWS\nIIgzb3sJn/rlK/Zr3UQlRzGoarbgx+cdht9flt9sy1KhVrQcNqAeDUlTEDia2Rscd7+81tcsoBJV\nEHz6V6/i5Ju9o0wEYlxRjy/mdTExqatmgQidrZTJRWRKD2pM4uzDRgNwZ0xH/a10gkCY+PwS7WxB\nIG078Lon7b9Xt+1DZ28aI1saEGPOyf83lhkrDOkMx92vrLXPq0Jkdl6oSUGQr6buulwA3SqpWRIE\nGXvCcX72gtnj7IqiOj45Y3SuwywJV8wzHY5HTxqCWIyhMRkH51nzwOY93Tj91hfxvceW4efPrgh9\n3EIXPQtj7tCtGoUgOOGgEdrPiMb1Ybq7lQOnH7Iffv7Zw7HoupM9tZjoUUPez52fphT0LZ+/y9Ts\nxw9p6peg7csYMDjwN8usWynaWz6ojLsyz+RLEOgSanTzSHOd20cQNhln5vhW/Pyzh+O2i47IbZAl\n4ojxg7HuprNwv1XHvtGaAIXN9tqH3sMKyyygNtbxQ56EN+3p9t03Stx6FNNQShLiYt4Rq92g8NBb\nPnt46DGVEsYYPnXEGNf5/EK6D8NqBNs7evHtv7/nMNmo+AoCnUqgYWRLfahM+5svmOH7vtAoKtlX\nF5W8CALG2N2MsTbG2FJp2xDG2DOMsZXW/4Ot7YwxdhtjbBVjbAljbGY+xhCF7oA+wmFJaWJB/TKG\nAclHEPIh+vXFs/CpAjWhLybCzqxz8kapvyOb1Obe9By27fWuX9QRQcCIySbMZZHDFW0fgXXd/SqP\nAkBrk3cjmEpgulT2PGw46I+f/AB/WvgR/v7OJs99/FbyYcV5c30i0JxTn4hhxrhW332YHb0X8our\ngHyd6u8AnK5suwbAs5zzqQCetV4DwBkAplr/rgRwe57GEJruvvz4CHSrelnbmDC0CV88fjIOkCo7\nevkIvGBBy6AKQawst7R34811uzBU8iMIR6pf5IhAFaDvb2733HdvhAQ2u/pkiOsiR6+ICUzYz+XO\ncdWI3MgmbBFBsdr3TygM/t2DnoXmuoSjjLhOKMcYQzJg4SG+hcJHI8I5fxHALmXzOQB+b/39ewCf\nkrbfy01eB9DKGBuFIpI305CkEUy85nG889FuR7ja2MGNuPbMgx2rHYNzvLBiO9aGTIiqFjOlmAw+\nc/trOP/Xrznaaz749iZkDI7HlmwJPI4qCLZ39Ibe1/e4wncTwjTU5xAE5v/fPOUA/MdpB+ITFebL\nicrAhux1W719HyZe83hgVzcRPu0nmFN+plI7ash/bAPqE3heqmwq1xwSPjazF4L/ccT7lVDQMV8U\ncvkyknO+BQA451sYY8KLNgbABmm/jda24FkgT3TlyTSkagR/XbQRY1qzDt9XVu0EALuLF2Cahi65\n+43Q31EtdkrVBiyb0Hbt68ODb20MdRx1om7bmx9BYJuGQmkE2X3E9WmuT+CqE6Z4fuaxr80rWO5D\nqVm+pQNz9h/q+b4ov7FX8hGIKryCtI+ZSS3w50VzfQKtjUn7d5ZvFeGkZ8r2p75xvOfxKiXCKx+U\nwgqm+3VdV5oxdiVjbBFjbNH27fmtXx4lycQPVRAMqI9rm698/ugJdivAP76RLWdx2NjgvrTVsihR\n1ew/LvzI8XpnyOQ+daL+2TMrsNKjGmWU6EYhNMIIj1wSEQ8ZMwhHeYSWVjv1lkbQIZUY6U0baJea\nvaR8fndhRg16FFRTUMqRgGb+H48xNFld/T45Y7S2IY9tGqqWhy8EhRQE24TJx/q/zdq+EcA4ab+x\nADarH+ac38E5n805nz18uLtsQX8QSSwqW9t7Ij3kag/eprqE1m4aizE7bX/j7mykSxhBUC0+giA1\nWw1F9Ir40flWTvGoRhklvDHjk+incvYvXg593FpgpZKwt3F3F9q7U+hJZbCqrTOrEUimoUvufgMz\nvve0/dpPIxDJemEW6D3Wvg9fNVcbuBGPMQwbUI9HvzoXPz7vMN9j1ZBCUFBB8CiAS6y/LwHwiLT9\nC1b00DEA2oUJqVj87Bl93Pox//ssjv7h/NDHUSel19bsxC4Pe+kBIwciHmOOCS5MtAyrksiFIMeb\nGkK61yPUMEpZg0KZhggn333kfSzZmM2un/ejBTjz1pfw7b+/h5NvfsEuFidf04VrnS7F/ZWe1zJZ\njcD7Hvq3kw8AkNX2x7Q22n21gazJQazyDxvbqi0dDkhRQzUkCfIVPvpnAK8BOJAxtpExdjmAmwCc\nwhhbCeAU6zUAPAFgDYBVAO4E8JV8jMGLBR+04bRbXgx0aAl8nVaufZ0rjjfW7sI7H+3BmFazVMQ5\nhzsdh/WJmMO5GabKaLXcivGAqpvqStzLCRw22gqIqBFEcBYTbkROyGKr3MqmPd141fKRhelV7dcz\nWTjn/eZlUdVVCILm+ji+c/Y0u9SJuBei2P1ryTSUF2cx5/wij7dO0uzLAVyVj+8Nw18XbcCH2zrw\n0sodvhEduawEvZLCjpw4GC9ecILLvl+fiGFfb9Y/4dfJSlAtDqug1dU2ZbLw8uNEmaj9Sn679rUO\nGxQa/46moVCtMaa10ZXMJ7KGz5HKrQibfVDiH+CvvWVCLM5ECPKEoc1Yu2MfGhJxxGLMEaYMhJvc\nxa3qFahRjfKhSgwP3oiJ9E8LP8LyLXs9bc9hnZUyXqvTpvoE4jHmupHqE3FHxFIixjB7wmB8+eP7\ne35HlciBwAfw4XedbiKv3zaSaSiS0AhnGvr0r14Nfcxq5e9fORaThzc7tunqRQlBsKU9WBD4aXri\nPd8S7paZ9f4rj8E9lx5p+6TUhVSYhZXYw2uuePSr87TbK5mqFwQi9Oy1NTtxxq0veWZD+sWje+Hl\n4GrysD3WJ2OOEMJEnOGBLx+L/zr9IM/vqBZnsU4QxBhw/qyx2v29ftsopqFIeQQGmYbCMqKlAb+/\n1FkgsSeVcQlRUXaj06e0hMBPAAsB8NdF3iHGYtIe0dKAEw7M1nuy7zvr8PloDFQtizOZqhcEqnnA\nq11eWB+CjKdGUOchCBJOQSBnaaqcO9N0dFVAH5NQ6AUBw0/On4GWBreFMh8aQZRaQwY5iyOhNkTq\nTmWwR0kYE8K8K0S4tp+ADyPQZZOrjHrfhXIAM4Y9XX34/uP6RjjV6DuokmnGG1Wd7LVKUA8b4LQd\n7u6K3k/Xq65QY53e9VKfiGOfZBry6z/wo88choXfPslV+rdS0ankYpuuLLiX/8VrwtAlCUbRCAxy\nFveL7j7DdQ2EaTTMT5oxOB56eyNOufkFtCn1o8JcE68kUXGPiesbdjX/lzc3eL5XLX47mRoQBM7X\nwjSkrmiuClHnRsVrojn+gGHa7e9tanc8FMMGeGsEyXgMI1u8y1JXGvIq6pRpIwFkH0pd8T41R0Pg\nZSee87/PucocR5nUhRkjao19wqQ7lXEJ7yjzZcbg+L8Fq7CyrdNVfiWMs3ifhyAQ8RgiGjBUyDb8\no/WqUCGofkGgJi33WmqqGk2QC7pQ06kjBmD6aH2i2MgW58Tf6GFCqkbkUFnR6P26sw4GoF8xqpPK\nM8u24a31uzwn6vbuFB5b7ExHkY8blE0u6k+FLQ8OAIuuOzn0vtVOTyrj0taiCgJxbdXjhNIIPE1D\n5hQnFoBh6gcx5r/qr5ayLzJVLwjUeUP4CKaNatHsHQ3d5PL41cd57n/vZUc7XtcHlCyuJtQHa91N\nZ+HiORM991cng3+9dxE+c/trvrZkVbDKQuOg7zyJ9zZ6VyoVgiCojLhcwriWrp+OBd/6uP13d1/G\npcVFMaFkOLe1PfkaLPiwDY++6yo84OIyqxGSynkzx2JQY9LO6QlTWpqB+Qqxakw0q/o72eUjsATB\nlJH6BBbVd+DFvt40PtjqrnGT9IlKGKA4ResrpFtVPpBNQ2HK2HuahjSCYH8rlDFoJfnWerVAbhZR\nmtyv5g3g7CwX1ISm2pk0LBtCum7nPnzjL+863o8iCNIZww7skLWyS+95M1Ro9yFj9Fr4+KFNWHz9\nqXaSZz4m8Wr0EVR38XRofASWIBiiaRByyrSReHt9cMLQvt40pl//lPY9P7VRzSSuFkdwGGRBECbj\n18sEpE7287/5MdTFYzj+JwtcPXHVqCE/q4/Q7oKa0GcMs5rsJw8fXfMagYxuURRmvjz+gOHoTWXQ\n3p2yr5duETC4KWkHdJw3ayye/7ANOzqjR/o1h+gXEWwaivy1ZU/V38nqZCAcirrVeHNdHKmMgb60\ngY//ZAGeWbZNe8yvKyufsKhhZ7U0kUQVBF6lPtTEpSkjBtiJS6ogUDUPnTaRzhi47dmVdnvC4A5z\nHIeNHYRrzzi4Km3FUXn/xtNwdIiqql69mm/97OEY09qIzt60vWjTXXuxaPrs7HH46fkz8Mo1J2LJ\nDaeGHufh41rx9ZOm4uchW4X6uRKqsU9B1WsE6pwjJhLdJNxcn0Da4Njd1Yd1O7vwzfvfxXs3nuba\nz6vMQND94dYIakgQMNk0FEYj0E/IuvaTWUGgRA2FcDre+dJa3CwVIfQzDT25dAtWtnVg/xHNnvvU\nGs31CQz1MKfKgre5LoGelHsF31QfR3N9wlGVV6cRxGMMq35whr2gqE/EUZ+I4+BRLRioyUNRYYzh\n3045IHA/wIwY8pvsq9FHUPWCQF19ijr4OrNMc30CqYxhrwq9et6qxzzhwOEYPrAePz7Pvym2qhHU\n0opSLjrXH41A1+jeFgTKal79Hp0AUnse+5mGvnSfGWJcjTbi/uBVxVMWvI11cUBpyjeoMYn6RNxV\n+l137WMxfW2uf37dOzgjVxirvfDRmhMEL6wwm9zoVuMNyThSGa5NcHIe0/n6HiXd3osoTdqrjega\nQXhBIBz06gSiXnudaUid1L2c1HLJi2rMLO0PjR6CQO4Nrnvexg42Hbh7e9SMZPd12rQ7uF5RvmDw\n72dZjQu4qp+ZvBafusbWotTBb19a43vMKOWNZWp5ApFPPczv4GWr15qGrJWiWj5EFSa60FN1KF6a\nSKf0vet3dmn3qVW8Sqrs6MzW79JFWP3g04cCcD9POmFc7Dw/Xx9BFT7GNSsIdCuUViuSyC+9HHCu\nLHUCxQvZR/Afpx0Y+nPVgGx7v+GT07X7TJbCEaNoBIwx1MVjLmexSyPQ3AyqLVg9hmBvd/Z789Xz\nuloY6pMhL1AFwbfPPAiHWzkZN517mGNxEKUnSKHwM/9V44Ku6gWB1+q9XqPOtjYmQx2zR5oskhFu\nCnnSuWyuPgGmWhG/7XVnHYwRA/WlM2Sh6pU4phME4rMuQaDM6TrhIj/vDcmYp2lINl9QGQonI5Ti\niYdqYvrVBZNcVXfckCbH4sCvbWWx8HuqyTRUgXiZGOo0qmprUzhBIE8E44fmFkFSjasKP5rrE1h3\n01m44rjJnvvIq8YPt3Zg4jWP46WV2x37dHiUNK5LxNCX8a81pIsakld+TXUJz9VoezcJAi9Uwa57\njsTqf+Z483+18m59PHgRUCw4uG+uQDU+ulXvLNY5fhkzHYyMmaajJ64+Dr3pTKgwNJm6RAy/vWR2\nTuOqNUEQBjkrW+RwPPLuZhw3dbi9XRQXO2rSEMybki3uF48x1wStOof1zuLs343JuMtxKZD7VVCF\nUidHThqMeVOG4eVVOwDo/QHjBjdi3U1nIWNwPPX+VpxxyH6O92WNISiXo9BwrlYoc1KNUWNVrxH0\nSLHlFx8zAYDpHxB2ZQCYMLQJR4wfbPsIghD+ha+dMMVOXY8KyQE3ckivqP2jxmyLuvN/vOJoXH3S\nVHt7nGkEgUdWuYxTI4h7TkKyIIjSArMWqE/Ecd8V2TpaukXOIEtLiMcYzjx0lMu8IguPKIX/CoHB\ng7qhVd/DW/2CQCoMJ6IbxIRz7kyzO5a4sINC+gimjBiACUObcNUJU3IeVzXaGftLUuN4F34V8XOJ\nblfqqszUCJyfVVfuuqZE8nVorIt7TkI79mUFgZcfgTDR1dsKera4tAZPGQaeWbYNX/njW3kfWxg4\nuG+Domp8dGvKNCTUT/H//5wzHf9x2oF2Qoyq0nr1C8gYHAftN7AqU81LybwpQ/HiCqdPQFySOGNI\nc54tJ6z89KYgUJ3FwYJAPk5dPIa0YU4C6rXtle6jMvBlliXC1NqsacwUJAjkS2UYHP9676J8Dy80\nBvf3A5FpqAKRNQIx0YtVZSIewxCPvgSnThvpqR6mMkZNJ4cVitkTh2DF98/A6EFZ56MwDamre1Wj\niseYq6icWiZcbVwDOB9qcX/oGuXIQsSr/EWt88fLj8ZvLp6lzSsIFATSxFtqZzE49y1QSIKgwuCc\nOx7ghWt3Asjan/0Y1Jj0DGPLGLwq7YSlJs4Y6hIxR1+BeCwGzrkjH0T30+s0gh2dvY5iZ2JVv6er\nD7fOX4lUxnCECQpNMZXhuP351WjryJaf6E1n7GNR1JCeY6cMw2nT99O+1xKoEWR/01L/vgb373dd\njY9+VQsC1RQwbnCTx55uWhqTniuTtMGR8Ok7QOSGEK6yiS4eczt9dSsynbN4e0evI0xR3A93vLgG\nt8xfgYfe3uiw94rvXbxhD3705Af45v2L7ff60gYGWCWMSz1RlTs6/1eQRjBaCroo9e/LOfcdQzX6\n96paEKimges/oc9o1ZGMx7wbqGe4q5Io0X90E/wT723Frc+uDNxP5yzeua8PQ5tlQWDeDwMbzEnp\ngzZa0q4AABJCSURBVK0djs/UJZhjv91d2WJofWnDrmVP4aPRCeq9cczkoXjkqrkYPrBeOwn/+vMz\nCzU0FwavvWtc1YIAMCuDCsL0CJ47ZSgAM/JBZysGhEZQ9T9d0dFpWZv2dOM2SxCIFbnuuuhMQ71p\nw1FKRGgE4tIZBtd2HBNJZfKE1Js27DyTz1jRZkQwB49qwcJvnxRq3xnjWpHU5IMAwIgWfTZ6Pjls\nrJkRzaHPOalmqno2a22qC10ZVPC7S4/Csu+dhp37+sC5vvdAxjBy1ghu/9xM3H/lMTl9ttoJcsKJ\niVi3WItpnMXpjIFkPIafnT8Dowc12D6CLqsnBYfTMXngfmb7UhFMIL7nqj++jRdWbEdjMo6lN56G\n751zSNRTq1kSMYaRESbxmCUIXCXb8z0wDb+35or/eWxZzUWGVbUgyIVkPIamugQeW2w2zL5fU4Au\nncndWXzGoaNw9OSh/RpjtRK3cwb0v21Lg7edOaHRCIQv5zOzxuLISUNsk48QBD2pDB5/bwsAYN1N\nZ9nJgR1WdrEwD4h96hNxDKhPUKBABKJmCSdiDBnOXQ7ZycMG5HFUeuQ8FrVcSbVT9XkEAPDQV47F\nUCtM9NGvzg3Vt/S6s6bhPx9cYq8SZdIGr/nG5YVAJCIdOqYFy7fsdb3vVwJE5yxOZbgd5lufiNmm\nIVG4bl9fxlFSWmSai3pGBueO6JEolWYJk6ihoLEYQ9rglnZofvaGT0yzM5MLiSx7frlgdcG/r5yo\niTt75vjBmGAVhztsbCv2Hx68ujh1+kgAejNE2jBoVVgAmqxEpO+dcwh++wV3DSdfQRBjrtIPpmko\n29pQCIK91kSvVjIVwl30PDAM7uhDoCtUSPgTNQIoEWMwFNNQsZ61aswPCAvd2R6IKAddNmraoKih\nQiASkRqScZx08AjX+wN9TEPxGHOVfpCd+vWJGHqtKLLVbZ0AgJ2dzhaJwlktTEMGz9Y2ksdH+HPk\nRLOZ/UH7DcRvLp4V6bMxJmsE1rYiPWs1LAdqwzSUC8IMoGajGoaZ3ESZxflHjvBhjLkqivppBDpn\ncSpj2P0i6pOmaYhzjrU7zOa5qyyBIBArfqExZAxuVzsFgpOiCJOzDhuFIyeelFOkTyLu1giKtVKv\nZUFAs5kH8RhDMs5cGoEIXaSEsvzjKhuhvPbTCLTO4kw28a8+EUfa4GjvTqE7lUEyzuwM85svmGEe\nQwiCbqERcHT3ZRcCLRHLlNcyuYZ7xi2NwGEaKtIMTaYhQksqw/GH19Y7tokVKvkICo+qdPlqBMyd\nUJY2DHtyFxreRqsJumiUAmRbLQqhIfwCGYPbEUbm95NGUGiEFig/XkUzDWm27T/c9C1+8XjvhkrV\nAAmCADoVh6JINiIfQeFRzW9+K3LhZJRJZXjWNKQIgoP2a7H3ExFlyZgzaqg3bTj6E0dtXEREJysI\nZGdxcb7bK2MdAD49c0xxBlEi6M4OQTqTXVkKh2ODpucxkRs/O38G1u/qcm1XZW2jpryxQOsslq6b\ncP5v7zT7Ckwclm0xKirQ2hqBFFUkawQjWoKbtBP9QwiCcvERCE2y2gvOkiAIQXcqg4HWhCImBoog\nyR+fmaUv2aCW8fCL44/FmKs4XUoqDjjAWs1/5+GlAIAJQ7IFCIUgsMNHraihtMHxjfvftfc7fNzg\nwHMh+kciFkN3JuOMGiqaIHB/z6/+ZRbufW0dDtLkE1UTJTMNMcZOZ4x9yBhbxRi7plTj8OO/zzwY\nAGyHYSpj4I8LTZ9BI2kEBUedABp8BIFhmNFAjrr2GcM29wxT+k5MHJYVBNnGROb37ZO0gL60gcnD\nm7H4+lM9e1cQ+cNOKJMudSn9ceOHNuG6s6dVfROqkggCxlgcwC8BnAFgGoCLGGPTSjEWP8SDL7SA\nXy1YjTtfWgsAaCCNoOCofhg/c5woA/HCSrPDmWFwGDxr7hkywDmJj9b0mvYqJFgXj4VuY0r0Dzuh\nzKERlHBANUKpNIKjAKzinK/hnPcB+AuAc0o0Fk+E+UeEGa5o68i+RxpBwWlVygqE8csI7W3nPjNZ\nTJh75HLUgF6jS0ozzmFjB+GUaWZ2uVyGgigsIqFMNgPWclhnsSiVIBgDQK7mttHaVlaIstVCI9i9\nr8/1HlE4RFlggZ857vGr5wEAdlrO4CN/MB9ANlpomKQRnD9rrG0PllebskYwsCGBS4+dCADo0bS4\nJAqD0Ajk/gXFNA2dfPBIHDZ2ED4xYzReu/bEon1vqSmVs1h3ZR2uPsbYlQCuBIDx48cXY0wuhGno\n/c3tmDVhsKPFJfkICo/cXQyAo+2kiggH3bC7G+9tbLe3i4qlYuJvbUriJ+ebCWSvX3uS7RcAnEmC\nLQ1J1FvfV2M9SkpKIs6QyhhoacxOTcXUCH57ibvGleD2z83Epj3dRRtLMSmVINgIYJz0eiyAzfIO\nnPM7ANwBALNnzy7Jo3jomEEYWJ/Aym1mKQI5KoU0gsKjlp32Mw2JTPA7XlyDO15ckz2GNKEsueFU\nx6Sy3yBn9msy5tQIgrpqEflnYEMCHb1phw+nXBy1Zxw6qtRDKBilMg29CWAqY2wSY6wOwIUAHi3R\nWDxhjKGxLp6tqS4tDUkjKDxqJm+9j0YAQFsaXD5GS0PS7nKm/7yiEVDZ6aLT0pBER0/K8dsXq8RE\nLVMSjYBznmaMfRXAUwDiAO7mnL9firEEUZeIoc+qN0QaQXGRV/NAsLNYl+0dpR6+bItubUqSRlAC\nBjYk0JMyHDH9ZaIQVDUlSyjjnD8B4IlSfX9Y6uIx9FkaAZfcGA00SRQcVSMI+s11CWdHjG/V7KlH\nnnyOnTKMGtGUAFHhVe4VUS6moWqG7vQAHBqBlGZON2fhUWv7JAMqvqqmoWvPOMi3vaUfI1sayDRU\nAkS+xs59vfY2KvBYeKjERAB1iZjtI1B7ExCFRZ3EGWNYfP2pSHv0wVVLg3sliIWhuS5OGkEJmGTV\ngJJ7RVAeQeEhQRBAUjIN9aSqvPJUmaGrNuqX4atqBP2pENtYFycnZQkQxQBl1w4pBIWHBEEAdfEY\nelJmZ6uUx0qUKAxRO4KpPYX7M4/XxWO2z2CMphwFURiaNRVmyTRUeEgQBPDamp0AgEnXPoFhA+px\n1KQh+K/TDyrxqGqDqDZ61TTUn0QwIQT+cPlROGBkdVeeLCfiMebwywFkGioGZASNgME5Dhg5ALMm\nUDniYsAYw6NfnRt6f3XCMPKQEnzc1OEYmWPbRSI31BwdaghUeOgXjkBGqYpIFJ7DxoYP/1Tn/Qgp\nBDa/uXiWq6k9UVwak3G0W32jAX+/EJEfSBBEwOBc27yCKCy3f24mwszpGWXm5zloBKdN3w+nTY/8\nMSKPqMma1Cu68JBpKIDvnJ1tk2AoLfSI4nDGoaNwZog6L3t7Uo7XVCyuMlEzyOmZKzwkCAI4cmLW\nH2BwuinLGdGLYMY405yUDx8BUXwapZpSk6Xe0kThIEEQgBybnuG8XyGJRGE5dfp+AIAxraZzNxcf\nAVF6hGloxrhWPPetj5d2MDUC+QgCkLNLUxmDnMVlzPfOmY7zZo3B/OVtAEgjqFRE1BA9acWDNIIA\n5CQlzimmuZxJxmOYNWGInYmai7OYKD3CR0BW2OJBgiAAtd4MFZsrf0SNoiZNlipR/tgaAS26igY9\nKQGoZQvINFT+XDp3EuIxhovnTCj1UIgcED4CetKKBwmCAJKqRkB3Z9lTl4jhiuMml3oYRI402qYh\netiKBZmGAlBr4JNpiCAKS1AnOiL/kCAIoD4Rx/xvHm+/pjwCgigswjRE/T+KBwmCEEwZMRBNdRTJ\nQBDFQPjl+jIU9VUsSBCEpIHslgRRFEQ5cYMyAosGCYKQNFhOYxIEBFFYEjHzWctQHkjRIEEQkuFW\nTXryERBEYREtRtVqskThIEEQkrGDzXaFJAcIorAI0xAJguJBgiAkg5vMbFW6NwmisCQsZzEJguJB\ngiAkwm5JDewJorAI0xAVDSweJAhCImoOpWmVQhAFRQgCetaKBwmCkIgM41SaNAKCKCQUPlp8SBCE\nRDSoSdHNSRAFhcJHiw8JgpDYgoB8BARRUChqqPiQIAiJbbckQUAQBUVoBGQaKh4kCEKS1Qjo5iSI\nQiI0AnIWFw8SBCERzuI+0ggIoqBQZnHxIUEQknFDmgAAk4Y2l3gkBFHdkLO4+FCHspB8/MARuP/K\nY3DkxCGlHgpBVDVC+6ZqLsWDBEEEjp48tNRDIIiqR/gGDh0zqMQjqR1IEBAEUVYcMHIgvvixybhs\n7qRSD6VmIEFAEERZEY8xXHvGwaUeRk3RL2cxY+x8xtj7jDGDMTZbee9axtgqxtiHjLHTpO2nW9tW\nMcau6c/3EwRBEP2nv1FDSwGcC+BFeSNjbBqACwFMB3A6gF8xxuKMsTiAXwI4A8A0ABdZ+xIEQRAl\nol+mIc75cgBg7vaN5wD4C+e8F8BaxtgqAEdZ763inK+xPvcXa99l/RkHQRAEkTuFyiMYA2CD9Hqj\ntc1rO0EQBFEiAjUCxth8APtp3vpvzvkjXh/TbOPQCx5t1ghj7EoAVwLA+PHjg4ZJEARB5EigIOCc\nn5zDcTcCGCe9Hgtgs/W313b1e+8AcAcAzJ49m1IMCYIgCkShTEOPAriQMVbPGJsEYCqANwC8CWAq\nY2wSY6wOpkP50QKNgSAIgghBv5zFjLFPA/gFgOEAHmeMvcs5P41z/j5j7K8wncBpAFdxzjPWZ74K\n4CkAcQB3c87f79cZEARBEP2C8Qoo7MQY2w5gfT8OMQzAjjwNp1Kgc65+au18ATrnqEzgnA8P2qki\nBEF/YYwt4pzPDt6zeqBzrn5q7XwBOudCQWWoCYIgahwSBARBEDVOrQiCO0o9gBJA51z91Nr5AnTO\nBaEmfAQEQRCEN7WiERAEQRAeVLUgqNaS14yxcYyxBYyx5VYZ8K9b24cwxp5hjK20/h9sbWeMsdus\n32EJY2xmac8gd6wqtu8wxh6zXk9ijC20zvl+K1ERVjLj/dY5L2SMTSzluHOFMdbKGHuAMfaBdb3n\nVPt1Zoz9m3VfL2WM/Zkx1lBt15kxdjdjrI0xtlTaFvm6MsYusfZfyRi7JNfxVK0gqPKS12kA/845\nPxjAMQCuss7tGgDPcs6nAnjWeg2Yv8FU69+VAG4v/pDzxtcBLJde/wjALdY57wZwubX9cgC7OedT\nANxi7VeJ3ArgSc75QQBmwDz3qr3OjLExAK4GMJtzfgjMxNMLUX3X+XcwS/TLRLqujLEhAK4HcDTM\n6s7XC+ERGc55Vf4DMAfAU9LrawFcW+pxFehcHwFwCoAPAYyyto0C8KH1928AXCTtb+9XSf9g1qZ6\nFsCJAB6DWdxwB4CEes1hZq/Psf5OWPuxUp9DxPNtAbBWHXc1X2dkKxQPsa7bYwBOq8brDGAigKW5\nXlcAFwH4jbTdsV+Uf1WrEaBGSl5bqvARABYCGMk53wIA1v8jrN2q5bf4OYD/BGBYr4cC2MM5T1uv\n5fOyz9l6v93av5KYDGA7gHssc9hvGWPNqOLrzDnfBOCnAD4CsAXmdXsL1X2dBVGva96udzULAq9S\n2FUDY2wAgAcBfINzvtdvV822ivotGGNnA2jjnL8lb9bsykO8VykkAMwEcDvn/AgA+5A1F+io+HO2\nTBvnAJgEYDSAZpimEZVqus5BeJ1j3s69mgWBXynsiocxloQpBP7IOX/I2ryNMTbKen8UgDZrezX8\nFnMBfJIxtg7AX2Cah34OoJUxJoonyudln7P1/iAAu4o54DywEcBGzvlC6/UDMAVDNV/nkwGs5Zxv\n55ynADwE4FhU93UWRL2uebve1SwIqrbkNWOMAbgLwHLO+c3SW48CEJEDl8D0HYjtX7CiD44B0C5U\n0EqBc34t53ws53wizGv5HOf8cwAWADjP2k09Z/FbnGftX1ErRc75VgAbGGMHWptOglnRt2qvM0yT\n0DGMsSbrPhfnXLXXWSLqdX0KwKmMscGWJnWqtS06pXaYFNgZcyaAFQBWw+yoVvIx5em85sFUAZcA\neNf6dyZM2+izAFZa/w+x9mcwI6hWA3gPZkRGyc+jH+f/cQCPWX9PhtnrYhWAvwGot7Y3WK9XWe9P\nLvW4czzXwwEssq71wwAGV/t1BnAjgA8ALAXwBwD11XadAfwZpg8kBXNlf3ku1xXAZda5rwJwaa7j\nocxigiCIGqeaTUMEQRBECEgQEARB1DgkCAiCIGocEgQEQRA1DgkCgiCIGocEAUEQRI1DgoAgCKLG\nIUFAEARR4/x/2BK38MrTvvUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c2c588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(temp[-1000:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create mask, selecting all raws where values in third raw (days) equals 10:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (temp[:,2]==10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we apply this mask and show only first 5 raws of the array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1891.,     1.,    10.,   -89.],\n",
       "       [ 1891.,     2.,    10.,   -19.],\n",
       "       [ 1891.,     3.,    10.,    32.],\n",
       "       [ 1891.,     4.,    10.,    84.],\n",
       "       [ 1891.,     5.,    10.,   188.],\n",
       "       [ 1891.,     6.,    10.,   130.],\n",
       "       [ 1891.,     7.,    10.,   161.],\n",
       "       [ 1891.,     8.,    10.,   171.],\n",
       "       [ 1891.,     9.,    10.,   221.],\n",
       "       [ 1891.,    10.,    10.,   181.],\n",
       "       [ 1891.,    11.,    10.,    80.],\n",
       "       [ 1891.,    12.,    10.,   107.],\n",
       "       [ 1892.,     1.,    10.,    -4.],\n",
       "       [ 1892.,     2.,    10.,    36.],\n",
       "       [ 1892.,     3.,    10.,    16.],\n",
       "       [ 1892.,     4.,    10.,   146.],\n",
       "       [ 1892.,     5.,    10.,   195.],\n",
       "       [ 1892.,     6.,    10.,   205.],\n",
       "       [ 1892.,     7.,    10.,   209.],\n",
       "       [ 1892.,     8.,    10.,   155.]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[mask][:20,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You don't have to create separate variable for mask, but apply it directly. Here instead of first five rows I show five last rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2014.,     4.,    10.,   116.],\n",
       "       [ 2014.,     5.,    10.,    27.],\n",
       "       [ 2014.,     6.,    10.,   300.],\n",
       "       [ 2014.,     7.,    10.,   277.],\n",
       "       [ 2014.,     8.,    10.,   259.]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[temp[:,2]==10][-5:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can combine conditions. In this case we select days from 10 to 12 (only first 10 elements are shown):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1891.,     1.,    10.,   -89.],\n",
       "       [ 1891.,     1.,    11.,    16.],\n",
       "       [ 1891.,     1.,    12.,    21.],\n",
       "       [ 1891.,     2.,    10.,   -19.],\n",
       "       [ 1891.,     2.,    11.,    36.],\n",
       "       [ 1891.,     2.,    12.,    31.],\n",
       "       [ 1891.,     3.,    10.,    32.],\n",
       "       [ 1891.,     3.,    11.,    46.],\n",
       "       [ 1891.,     3.,    12.,    46.],\n",
       "       [ 1891.,     4.,    10.,    84.]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[(temp[:,2]>=10)&(temp[:,2]<=12)][0:10,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "    Select only summer months\n",
    "    Select only first half of the year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create example array from first 12 values of second column and perform some basic operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,\n",
       "        12.])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days = temp[0:12,2]\n",
    "days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,\n",
       "        22.])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days+10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  20.,   40.,   60.,   80.,  100.,  120.,  140.,  160.,  180.,\n",
       "        200.,  220.,  240.])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.,    4.,    9.,   16.,   25.,   36.,   49.,   64.,   81.,\n",
       "        100.,  121.,  144.])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days*days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's wrong with this figure?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11950cdd8>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NOg2GnWbZIgihz26ESa9VUkBLbdEtzbdOoK/OLUOqhYSgDUmlOB4/twKthuHe\no3M5RzKeW/JjW48Nem31H5Eem0Ep8plc9IEx4IoBe9rzxoxgcrVE4knc/OXH8Mn7TpS0/7xHuiPM\n7pOUi+FOc1WuIW84XvfUUcF4j+T2+52rhwGk1RZkBYynVgJYC8WLBoqBdZeXVw6KB6OlxQhyBYvF\nbOFzSxvnYudDtMwoZhEAUsB4elWyCES1vNWog92oK7kDqbiBIYuAaDpOLfiwGojhI2/YAc6Brz9+\nYcM+Z5b8SvuIaknvNzS54MN4jzXjwtBjV7cxnS8cRySewr1H5/DwZP5KWYGwCEaKWATSPpYqXUOx\nureXEFw16sSjH78Jr9kp9fLKN8tY1A9cVyRQDKwHwbMtAkuBrCFTnqyhY3OSEIh5F6WgtJcoEiMA\npDjBtCu4oaNuObMallugqhggIWhLHjsr9fh7+6EteMs1w/j+s5ewnNZ6NxRLYNYdrrqKVpDegfTU\ngg97Bjsyn7caFNeRGojMD72W4VP3ncBasLC1MecJw6DVlHRXN9JphisYU+50y0UaStMYFgEAjKe5\n/jrlwsDspmvPXnRjoMNUUurweivq9RiBQacpaFma9VrEEqmMIPWiN4wlXxSMSRZJqbiDhdtLpLO1\n2wJ/JIF5TxijaTcB/R3GkoWgFdpLACQEbcljZ1ewb9iBHpsRH7hpAvFkCt/8zUXleWGKqykEnlAc\nq4Eo5j1h7B1ybHjeH0moVlTkDUsX6Y/evAvecAyfuf9kwf3n18IYdJpKascsrIb5HO60UvCE6995\nNB+MMQw6TBkWAeccz1504dB4V0kT1bI7kIaiSdiKFFrlmkkg3EKHt/dgxhVCLFFavGe1SMO5dNJF\nMMMisJdeXUxCQDQl3nAcL1zyKO6A8R4r7jgwhO8+PaPctYuMoWJ59aXSY5e+lL89J+Wo7xnKsgjk\nL5GryJ17qfhli+DQeBf+9PU78ODxRTx0Kv9Q8nlPuKT4AFB9Cqk3FG+I1NF8ZE8uW/BGsOSL4uBY\naWPFTXotzHqtEiNY8kUKuoWA9SB9ervvl2a90GsZ7jwwhGSKZzSIK4QrEIPVoN3Q1yoXoh01gA2u\noWW/NKtBrOtzP53MmW694peslmKVzI0OCUGb8cTUKpIpjpt2rc94+M+v24FEkuMvf3wSnHOcWw7A\noNNkfFGqQdQKPH52BQA2uoZEGwqVRlr6IpJF4DDr8J9esx1Oix6/PrOSd//5tdKFQLhHKokTcM4l\ni6CBhWCYjnyrAAAgAElEQVTQYcKib/3YTszlbr1RCKdFD3cwhr99cBK/mFxSbjrycdOuPhh1Gjx4\nfEHZdmzWg92DHcpNQ6lxAlcwWpJbCJDOpTBytmRYBEbEkxxrcgrsVx+dwjd+c3FDvyxAqirushhU\nSaqoJ829eqJsHjuzgg6TDleNOpVtE302fOTmnfj5qct44NgCzlz2Y6LXplqKY69sETx+bhW9duMG\nM1o0pnNVMSQlHdHGocOkh04rpQlmZ8IIoglpUlWx1FFBr80IY4W1BMFYEskUb5isoVwMOMxY8kaV\nu+GT815oNQy7s8S7EA6zHj95cR7f/O1FvPfGMfzXu64suL/NqMPrrujDz05cRjLFkUxxHJ/z4MCI\nE9t7bWCs9MwhdzBW8t25UafFkMMMvZYp8xmA9VkNl30RBKMJPPKylHCwkKMhXysUkwEkBG0F5xyP\nnV3Bq3b0Qpd1B3P3q7fh6i1O/NX9p3Bi3otdA+rEB4DMxnN7hzZeUNYtgtyuoRcvreHw53+VM801\nFyJYLFoEZPu901n0SNtLtQgYYxWnkIoiq0bJGsrFoMOEWDIFt7zWE/Ne7OizleRqEXRaDEikOD59\n22789R17SrqhOLJ/CKuBKJ656ML5lQCCsSSuGnXCbNBi2GkuOWC8GogpNxalMN5jxUinJWONoiZg\n2RfFr15eRkSuel7M8fkjISCajjNLflz2RXKa6loNw5fedgDRRBLuYEy11FEgsz1vtlso/fl8mUMP\nnVrCvCeMB44t5Hw+G184Ab2WwaSXPt4DDlPeAiER9C3VIgAqTyFVOo82tEUgagmkDpwns4b1lMLH\nb9mFe/7jIfzxq7eVFGAGgNde0QuzXosHjy/iJTlQfEC2Wnf02Up3DQWiiiuyFP7itt34wlv3Z2xL\nry5+8PgC7CYp2J3rZoKEgGg6npGHwLxyR0/O57f12vDnt14BIPcFu1KsRp2SGZIdKAakFgNWgzZv\nLcGzcp+bdB9yIfyRODpMeuUiNOgwwxOK56xeVWoIirSXSGeks7Lq4kbqPJqP9OriRW8ErmAM+0bK\nE4JXbO0sGhfIxmLQ4fW7+/Dzk5fxwswa7EYdtslZPRN9NpxfCRQdoxlPpuAOFu88ms6eoY4NhXIi\njfj8SgCPnlnBW68ZgVGn2dCZlXNOQkA0HyIrZ6BAOfx7bxzDfX9yI169o7wvcjFE5lA+gZGKyja6\nhsKxJI7PedFtNeDkvC9j4Hg+fJFERi+f7LGM6cx5wmAMRXvopDPSaYa7glqCRhlKU4j06mIxo+HK\nMi2CSjmyfwjuYAw/fnEe+0cdSjrvRJ8NsUSqqDvuwkoQiRSvOu3ZoNOg22rAvUfnEEukcMcBqSFf\ndozAF04glkw1fVUxQELQVgQiCdiMuoL58owxXLOls6Sc+nLothphMWiVro/Z9NiMObOGXry0hkSK\n4+O37AJQmlXgC8fRYVrPXV+/y914Fz+/Fka/3ZR3wlouRpUU0vKsgvXOo40bI+ixGqHTMCx6I0qg\nWE3rsBA37eqF1aBFNJHKSmaQLuzF3EOnFjb2saqU/g4T1kJxDDpMuHq0E4MO84YYwUpAEgayCIim\nwh+JK/7OzeaaLZ147a6+vALTYzPkzBp65qIbGgYc2T+IV2ztxIPHF4u+ly8Sz+gln+73zmbeEyor\nPgCkzyUoL2Asqm0b2TWk0UgZNJe9kYoCxdVg0mtx855+AMCBkcysNgA4V0QIJhd8MOo0GYVildLf\nIV3cb983CI2GYdC5MeGgVdpLACQEm8JqIIq/vv8kfvj8bM5c5M0iEE0UrfKsFX91xx589Z3X5H0+\nvR9ROs9cdGHvkAN2kx5H9g/i5ct+TC37C76XLxxHh3n9OLMHtadTTjGZYKRCi8AbjsOg0yhB7EZF\ncoOEcWLOu2luIcG7btiKnf22DL+9w6xHr91Y1CKYXPThigH7hoy4ShAppEfkwUxDDjOWfJGMjrYi\nviR6NDUzjf2JbAE45/jkv57APU/N4BP3Hse1n/sl7vrqE3h4cqmsuahq4I8k6mYRFKPbZsRaKJbx\nRYsmknjxkke5KNy2bxCMoahV4I8k0JFmEVgMOjjM+g0WQTLFseiJlG0R9NgMci1BeRaBqCouNZOm\nXgw4TDg175MCxZssBK/Y2oVffOQ1Gya4FcscEgOPciUjVMIbdvfjjgNDOCAHygedJqT4uhUASD2Q\nDFpNRp+iZoWEoMbc98I8fnl6CZ++bTd++uFX4uNv3Al/JI4//s7zeO//eg4XymioVS3+aAK2Esfv\nbTa9NgM4lwqCBCfmvIgmUooQ9HeYcGisCw8eXywoor5IfMPgl1y1BMv+CBIpXrZFwBirKHPIE2qc\nFtSFGHSY4JcD4ZttEeRjos+G88uBvOd9wRuBJxTHniF11vuGPf34x3dcrYj2kNKZdf2cTy0FMN5j\nVcUCqTfNfwQNzKI3jM/+2ylcO9aJ//jKcewdcuBDr9uBh/7Lq/GXt+/GCzNruOUrjysNtmpNPWME\nxchVSyDaH1+bNhDlyIEhTC0HcHoxt3somkgiEk9lBIsBuZYgSwhKHUiTi9Gu8msJGq3zaD7E5DIN\nUzeNuBom+mzwRxN5m8GJPkW1Wu+gU3IVLXjWP0NTKwFMqFhvU09ICGoE5xyfuPc4EkmOL77tQEbl\nol6rwR+9aht++uFXIZ7kODpT+gSmaghEErDXKUZQjPUh9+sWwbMX3djZb8toGXBk3yCMOg3+9zMz\nOV/HL/cZyh48nssiEMVkI2VaBICoJSg3WByHo4EzhgQiy2pHnx3mIg3jNgsRMM7nHppc2DjwSE1E\nHEDcTETiSVxyhzDRS0JAFOC3U6v4zblVfOq2K/I2bxvulJpeidYDtSYQbdwYgbAIxCSzRDKFozNr\nG4p9Oq0G3HXVEH78wrzS4TIdpc+QOcsi6DBjNRDNaGc8V41F0GnBWiiurLcUvKFYU7iGRHC9UdxC\nQLoQ5LYEJxe9GO+2wlqjG50Okw5WgxYLsmvowkoQnK+vq9khIagRYtTdTTv78u6j1TB0mPRKoVEt\nSSRTCMWSsBkb80Ik+sOI6uLTi34EogkcyjEV6903jCEcT+JHz89ueE5YBB05LAIgcyj5vCeMLquh\n4BjFfByekKqzf1HCBDRBo3ceFYzKvXdesbW01tObQa/NiA6TLm8K6eSiD7tVChTngjGGQadZ6U0l\neh+p2YqlnpAQ1IiofOdpLJIq2GnRYy1UeyEIRqX2CrYGtQhsRh0MOo3iGrr/pXkwBlyXY07ulcMO\nHNzaie8+PaN0yRSIhnPZweJcKaTltJ/OZu9QB8Z7rCW3vYglJCFu5FkEgl67Ef/+p6/Cfzg4Uu+l\nKDDGsKPfntM15A3HMesOq1JIVgjJvShZBFNLfmgYVKlZaARICAA8N+1WPXsnmpAuvMYiFasOi2FT\nXEPrHTkbUwgYY+iVq4tnXEHc89Q0fveakYz2wOm8+8YxzLhCeOxs5pwBX7iwRSDiBJxzTC0HMvrQ\nl7veI/sH8dR5V0m1Id4maC+Rzs5+dfLx1WSiN3cK6enF2gaKBUMOs9JmYmpF+uwYdY0RQ6mWxjrT\ndeKjP3wJX/7lOVVfU7SuLfZB6bToc/q61SYgpwM2arAYkNxDK4Eo/t+fvQy9VoM/k9tK5OLWvQPo\nsxtxz1PTGdvzCV56Dx0AuOQOYd4T3hCDKIfb9w8ixYGfnyxe7SwCy7320nsaEZlM9NngCsY2zKBW\nMoZqbRE4TUqcaWo5oLS+aAVICCD1wV/xlzasulRKtQicZr0yCamW5MumaSR6bEa8eMmDn5+6jA+8\nZrvSFz4XBp0Gv3/dFvz6zEpGIzp/HteQ3aSHzahTLIInz0sdTQ9PbIxBlMqufjsm+mz4txLaXrxw\nSUoRvnqLs8ieRD5Eqmb2bILJRR96bEb01VhkhxxmcC7Fli6uBlsmUAyQECAcSyIcT2YUMqlBNJGC\nQasp2rzNaTEozchqSSAqvUejxggASQgC0QQGHSb80au2Fd3/Drn8//m09FtfOAENA6w50h7Tawme\nmFpFn92I7VWk/wn30HPT7owgdC5emFnDsNOc19VFFEekamZPKzu14Kt5fABYtyqfueBCPMlJCFoJ\ncTfuytHnphoi8WRRawCQfMb+SCKjtUK13P/SPD7zk5MZ29YtggYWArlV9Z/fekVJ+esi0Js+uUxU\nFedq4yBqCVIpjqfOu3B4oqfqdg9H9g+Cc+BnJ/JbBZxzPD/jbqgsnGZk2GmGWa/NiBNEE0lMLftr\n7hYCgCG5qOzxc1JcagcJQesghGAtFNuQgVIN0USqaMYQII31A6BqnODB44u49+hcxjZFCBo4RvDm\nq4fx0Zt34k75Tr8YJr0WPTZjphCE4xsCxYIBuavmmSU/XMEYbtxeuVtIMNFnxxUD9oL9jxa8ESz5\noiQEVaLRMGzvs2a4hp6fXkM8yfGKLbX/vxVFZU9MSW7F7SQErcNaULoApzhUzeePxlMlZRSILBI1\nU0hnXEGE48mMiVzNECOY6LPjw6/fUdYshGGnSakQBqShNPmsnkGHCcv+CH4j39HdOJF7Ulu5HNk/\niKMza/jSL87knIImKsdJCKpnoteGqaX1orLHzq7AoNXgBhVEvRhWow4dJh28YWlOQb06+daCthcC\nd1qg1p2jH36lRBPJkiwCp2IRqOOaSqU4ZlxShkr6sQWicWg1rOFbIJfLkNOc0TpCjKnMxYDDjBQH\nfvLiAsa6LRXXEGTz3sPjuOuqIfzjr6bw+i/9Gv+e5SZ6YWYNZr22Zu0P2omJPhsWvBFlOtxjZ1Zw\n7XhnzSqKsxmSPzOtFB8A6igEjLFbGWNnGGNTjLFP1msd6aloasYJIqVaBHJ2i7BMquWyL6IUs7nT\njscvTydr9BbI5TLkNGPBE1a6UvrCiQ3tJQSilmBy0aeaNQBIxXB///ar8cP33wCHxYAPfO+FjPqG\nozNruGrU2XB5+c2ISNk8vxLAojeMM0v+sucjV4P4DJEQqABjTAvgqwDeBGAPgHcwxvbUYy1rGRaB\nekIQTSRLuvsWMQK13FLTrvVUygyLoIFnEVTDkNOMUCypxFh8BS2C9Yydw9vVEwLBofEu3P/Bwxh2\nmvEPj5wD5xyhWAKTiz5yC6mEMq1sKYDHzkhie9Ou/G1c1GaQLAJVOQRginN+gXMeA/ADAHfVYyFr\nwRjETfKqqkKQKilryCHHCNSqLhZuISDT1eWv43SyWjIsZ3KIOIE0nSy3EAymCUGtfMoGnQb/6TXb\ncHRmDU9fcOP4nBfJFCchUImt3RboNAxTKwE8dnYFgw7TpmbvDKV1Zm0l6iUEwwDSO4bNyds2HXco\nrgydcKvoGorGkyW5hjpMOmg1TLVagmlXUBE2V4ZrKP+dcjMzpKSQSmMEg7FkXsvHYdbDpNdg92BH\nRmtrtXnbwVH02o3474+eUwLFVEimDnqtNJP45UUffntuFa/Z2bup7s5rx7qwrceK3YOtJQT1ukXM\ndeYycjcZY3cDuBsAtmzZUrOFrAVj6OswwheJqxwsLs0iYIzBoWJ18cxqCNt6rJh2hTJeMxBN1Lzy\nsh4MpdUSiDYa+QSPMYa7Dgxj/2ht2yub9Fq8/9Xb8Hc/PS31rO+zbRi9SFTORJ8ND08uIZHiuGnX\n5sUHAOC6bd341cdv2tT33AzqZRHMARhNezwCIKONI+f865zzg5zzg729tTvZa6EYuiwG9NiMcKns\nGjLpS2tI5TSr14p62hXEeI8VnRZDRsyjkecVV0O31QCDToMFT3i94VyBDp9f+N39eOd1W2u+rt+/\nbgs6LXrMusObkuPeTkz02ZBIceg0TNWgfztTLyF4DsAOxtg4Y8wA4O0AHqjHQtaCMXRaDeiyGtQN\nFpdYWQxItQRqxAg455h2BbG124puqyHDNRSItGaMgDGGYacZ857wegvqBhA8i0GH971yHADVD6iN\nCNRes7WzJd2d9aAu3xjOeYIx9iEADwHQAvgW5/xUPdbiDsWkDqBWA2bd5Y0eLESkxMpiQKolKNar\nphSW/VFE4imMdVswuWDIcA1JFkFrfmkGHSbZIsjdcK5e/OHhcUQTKdy6b6DeS2kpRKB2M9NGW526\nJTZzzn/GOd/JOd/OOf9cPdYQjkmDzjutBukOWnWLoETXkEWvSrBYdOHc2m1FV9rxRBNJxJKplnQN\nAaKWINJwMxesRh0+9sZddNeqMrsH7fh/3rwP77qh9i6+dqExvjF1Qtwxd1kMCEQScAelfkPltDjI\nhxQjKNEiMKsznEakjo7JQiBcXc3QcK4ahpxmLPkjcAeFa4guvK0MYwy/f13tEkjakbYudRQXSqfF\ngG6bEckUV+4qqyGRTCGR4iVbBJ0WPYKxZMZg9Uq46ApCr2UYcprQZTXAG44jkUwhIAtBK8YIAKmW\ngHPgrNyDplFcQwTRLLS1ECgWgewaAqCKe0iZV1xGsBgAPFX2G5pxBTHaaYFOq0G3zQAuN9Lzt7gQ\niBTSly/7wFhjd1gliEakzYVAuvvvsuqVAiM1MoeEEJScPioaz1UZJ5heDWFrtzSDV7SucAdj8EeF\n77w175SFEJy57IfNoFPFtUcQ7UR7C4F80e+0GBQhUKPxXKljKgVqtKLmnGNGTh0FoFg47mCs9WME\ncmX4Wih/ewmCIPLT1kIg7v4dZj26bepZBMrg+hKDxUrjuSoCxquBGIKxJMZki6Ar7XgCLS4EZoNW\nEfJWPUaCqCVtLQRroRgcZj10Wk2aa6j6NhPrFkFpriGHWTSeq9wimJG7jm7tkSyCLst6zEMMdG/V\nGAGwPkaQLAKCKJ82F4K4IgBGnRZ2ow6rariG4iJGUKJFYBWtqCt/74tyDcG47BoSr7kWjCk9eBp5\ncH21CPcQpY4SRPm07pWhBNaCMcU/D0juFDWDxaVaBFaDFjoNqypGMOMKQathGO6ULoh6rQYdJh3c\nwRiMeg0MOk3J62lGRMC4EdpLEESz0dYWgTsYU1woAFTrNxSJlxcsZozBaTFU5Rq6sBrASKcZ+rQp\nWKK62B9JtPwFUoydJNcQQZRPWwvBWiimuFAAqNZmolyLAKi+8dyxWS/2DWe2V+6yGiTXUIs2nEuH\nLAKCqJy2F4L0ASWSRaBesLicQfFOc+X9hha9Ycx7wrgmq91xl9WoBItbOT4AULCYIKqhbYVANJzL\niBFYjXAHY8og9EoRweLyLAJDxcNpXpjxANjY7rjLqoc7GEUgmoDd2NoXyPEeK6wGLbb1Wuu9FIJo\nOlr7NrEA7rSGc4IemwHxJIc/mqgq+yQi0kfLsQgsepxaqMwiODqzBpNegz1DHRnbu6xGrAXj6LQk\nMNplqei1mwWnxYAX/upmGLRte29DEBXTtt8apao4yzUEVF9drKSPlmERdFoqH1d59NIa9o84MwLF\ngBTziCVTWPRG2qLQyqjTbur8WoJoFdpXCELr7SUEahWVKcHisiwCAyLxlJJxVCqReBKTC96cU7CE\nyHnDcWrERhBEXtpWCESaaJd13QXUbTUCqN4iEBfzctwUIlbhLXN28Yl5L+JJnnMubneatdOqDecI\ngqiethWC9IZzgi6V+g1FEykYtJqyumA6zXIlcJnuoaMzawCk+a3ZpGdEtXrWEEEQldO2QuAOxcHY\nep8fAKrNJIgmkmW5hQApRgCU32/o6MwatvVYMy76gq4Mi4CEgCCI3LStEHhCMXSYpIZzApNeC6tB\nq4pFUG47B4ciBKW/N+ccL8ys4eocbiEgyyKgGAFBEHloWyFwB2O576JV6DcUiSdLbi8hWG9FXbpF\nMOMKwRWM5QwUA4DFoFXWQc3YCILIR9sKwVooprhj0umzmzC/Fq7qtaOJVNmuIREsdpdhEYj4QD4h\nYIwpYkcxAoIg8tG2QuAOxjMCxYKrR504NudR2kRUQjSeKquGAAAsBh3Mei3cZWQsvXBpDXajDjv6\nbHn3UYSAXEMEQeShbYXAk9VwTnBovAvRRAon5rwVv3YlwWIA6C7DLTW3FsL9Ly3g8ERPwewkmtxF\nEEQx2lIIVgNRXPZFsCVH24Vrx7oAAM9cdFf8+tF4quwYASBlLa2WIASpFMcn7j0Ozjk+ffvugvsq\nQtDivYYIgqicthSCX51eBufA667o2/Bcp9WAXf12PFuNECSSMOnLHwLTbTPCFShe1fzdp2fw5HkX\nPnNkT9EeQhQjIAiiGG15dfjF5BKGnWbszWrSJjg03oUfvziPRDKVkV5aKlL6aGUWweSCr+A+F1eD\n+Py/v4ybdvXi964dLfqadx4Ygt2kh7aM4jaCINqLtrMIwrEkfju1gjfs7svboOzQeBcC0QROL/qV\nbZzzkttTS+mj5VsEInW10Pt89oFTMOg0+MJb95fUYO3qLZ346M07y14LQRDtQ9sJwW/OrSAST+Hm\nPQN59zk0LuIELmXbX/z4JP7gm8+U9B6VWgQ9ViNiyRT88rD5bBLJFJ6+4MJbrxlBf4ep7NcnCILI\nRdsJwcOTS7CbdLhuW1feffo7TBjrtihxgpdmPfj+s5dw5rI/79+kE02kKowRFG6DfW45gGgihf0j\njpzPEwRBVEJbCUEyxfGrl5fx2l19G3r3Z3NovAvPTbuRSnH87YOTAAB/JPedejbRCiqLASlYDORv\ng31iXkppvXKYhIAgCPVoKyF48dIaXMEYbt7TX3TfQ+PdWAvF8ZVHzimN3aKJFGLyrIFCRCqoLAbW\nm96t5rEITs57pXGMPTSOkSAI9WgrIXh4cgl6LcNrdvUW3fc6OU7wD4+cwxUDdvzB9VsBAIE8/ntB\nIplCMsUrChYXcw2dmPdi75CjrPbWBEEQxWg7Ibh+W3dJDdhGOs0YdEgB2c8c2aO0q/ZHCjeFE9PJ\nTBVYBIUmpCWSKZxe9JFbiCAI1WkbIfBF4riwGsSN23tK2p8xhndetwV/cP0WHJ7oUVo0FIsTiOlk\nlVgERp0WdqMup2toaiWASDyFfSO5ax8IgiAqpW0KyhY8UkfR0S5zyX/zodftUH63lSgEyrziCoLF\ngOQeyjUYR/Q+2kcWAUEQKtM2FoEQgiFn6UKQjnAnFYsRrLuGyrcIgPxtJk7Oe2ExaDHek7/TKEEQ\nRCXUTAgYY59ljM0zxl6S/92W9tynGGNTjLEzjLFbarWGdOY9EQDAcIVCINo4F48RCNdQZf+1Xdbc\nHUilQHEHtYogCEJ1am0RfJlzfpX872cAwBjbA+DtAPYCuBXAPzHGKrt9LoMFTxh6LUOvnKtfLiJG\nUMwiiMRl11AFwWIA6LEZNsQIEskUJilQTBBEjaiHa+guAD/gnEc55xcBTAE4VOs3XfCE0d9hqjj1\nsuQYQRXBYgDothqxFoohlVrvN3R+JSgFikkICIKoAbUWgg8xxo4zxr7FGBPzFIcBzKbtMydvy4Ax\ndjdj7HnG2PMrKytVL2TRE6k4PgBIF3aDVlNysLiS9FFAcg0lUxze8LoLSlQUkxAQBFELqhICxtgv\nGWMnc/y7C8DXAGwHcBWARQBfEn+W46U2tNvknH+dc36Qc36wt7d4AVgx5j3hiuMDArtJh0C0tDqC\nii0CUVSWVksgAsXbeilQTBCE+lSVPso5f0Mp+zHGvgHgQfnhHID0RvojABaqWUcxkimOy74IhpzV\ndey0mXRl1BFUGiOQYhirgRgm5Lk5J+a92DNIgWKCIGpDLbOGBtMevhnASfn3BwC8nTFmZIyNA9gB\n4NlarQMAlv0RJFO8KtcQIFsEJbuGqrMIROZQIpnC5IIP+6jjKEEQNaKWBWX/H2PsKkhun2kA7wcA\nzvkpxtgPAUwCSAD4IOc8WcN1VF1DILAZi1sEaqSPAlBqCc4uBRCOJ3HVqLOi1yMIgihGzYSAc/6u\nAs99DsDnavXe2VRbQyCwm/SYWwsX3EdJH60wRtBlyexAemzOAwAkBARB1Iy2qCwWFoFoIlcpdqOu\n9IKyCrOGdFoNOi16xTX00iUPnBY9thQZUk8QBFEpbSMEHSYd7CV0HS2ElDVUrI6gul5DgOQeEllD\nx+Y8ODDiLGk+MUEQRCW0jRBUGx8A1rOGCg2XjyZSMOg0VV24u21GrAZiCEYTOLvkxwFyCxEEUUPa\nQgjmPZGq4wOAFCNIprgSB8hFpMIxlen02AxwBaI4Me9FigNXkxAQBFFD2kIIVLMISmg8F02kKg4U\nC0TjuWOzUqCYhtUTBFFLWl4IAtEEvOG4KkKgDKcpECeIJpIVt5cQSP2G4jg6s4bRLrMy1J4gCKIW\ntLwQLCo1BNVlDAEoaUpZNJ5SxTUEAE9MreLACLmFCIKoLS0vBAtedWoIAMBmlIfTFBKCRLJq15Cw\nAIIxKiQjCKL2tL4QqFRVDKTPJCgcI6jWNSSqiwEqJCMIova0hRBoNQx99ur97CJY7CvqGqrOIhCu\nIa2GYe8QBYoJgqgtLS8E854wBjpM0GmrP1RlbnEBIYgkkhVXFQu6rZJo7eq3w2yo+fA2giDanJYX\nAil1tPpAMQBYjdJFudbBYodZD4NWg6u2kFuIIIjaU8vuow3BgieCq1W6oOq0GlgM2iIxgmTFLagF\nGg3DN997EDv77VW9DkEQRCm0tEWQSnEsetUpJhMUa0UtFZRV/9/6qh296O9Qx5IhCIIoREsLwWog\niniy+oE06dhNuoIFZVKLCfLrEwTRPLS0a6jTasAvPvLqjHTMarGZ9JtiERAEQWwWLS0Eeq1GdT97\nh0mHQJFeQ9XGCAiCIDYTunUtk0IxgngyhWSKk0VAEERTQVesMik0nEYMrq+2joAgCGIzoStWmdiM\n+WME0bg0ppJcQwRBNBMkBGUiLIJUauOUMsUiINcQQRBNBF2xykQ0ngvGNloFEdkioPRRgiCaCRKC\nMlmfUrZRCMgiIAiiGaErVpnYReO5HAFjIQQUIyAIopkgISgTmyn/3OKo4hqi/1aCIJoHumKVSaFx\nlRFKHyUIogmhK1aZ2AvFCChYTBBEE0JCUCalxQjov5UgiOaBrlhlUihGQOmjBEE0IyQEZWI1aMFY\n7rNIJ7cAAAakSURBVHGVYQoWEwTRhNAVq0wYY7AZdTkH2D8xtYo+uxHdNmMdVkYQBFEZJAQV0GHS\nb4gR+CNxPHpmBbftG4RWw+q0MoIgiPIhIagAqRV1Zozgl6eXEEukcGT/YJ1WRRAEURkkBBWQqxX1\ng8cWMegw4ZotnXVaFUEQRGWQEFSAzaTLCBZ7w3E8fm4Ft+8bhIbcQgRBNBkkBBWQPaXsF6cuI57k\nOHJgqI6rIgiCqIyqhIAx9jbG2CnGWIoxdjDruU8xxqYYY2cYY7ekbb9V3jbFGPtkNe9fL+wmPVzB\nGNaCMQDAg8cXMdJpxoERR51XRhAEUT7VWgQnAbwFwOPpGxljewC8HcBeALcC+CfGmJYxpgXwVQBv\nArAHwDvkfZuK1+zsQSCawGu/9Gt84/ELeGJqFbfvHwRj5BYiCKL5qEoIOOenOedncjx1F4AfcM6j\nnPOLAKYAHJL/TXHOL3DOYwB+IO/bVNx65SB++uFXYle/HZ/72WkkUhx37Ce3EEEQzYmuRq87DODp\ntMdz8jYAmM3afl2N1lBTrhjowA/uvh4/PbGI88tB7B3qqPeSCIIgKqKoEDDGfglgIMdTn+ac35/v\nz3Js48htgWwc/iu9790A7gaALVu2FFtmXWCM4QhZAgRBNDlFhYBz/oYKXncOwGja4xEAC/Lv+bZn\nv+/XAXwdAA4ePJhTLAiCIIjqqVX66AMA3s4YMzLGxgHsAPAsgOcA7GCMjTPGDJACyg/UaA0EQRBE\nCVQVI2CMvRnAPwLoBfBTxthLnPNbOOenGGM/BDAJIAHgg5zzpPw3HwLwEAAtgG9xzk9VdQQEQRBE\nVTDOG9/rcvDgQf7888/XexkEQRBNBWPsKOf8YLH9qLKYIAiizSEhIAiCaHNICAiCINocEgKCIIg2\npymCxYyxFQAzVbxED4BVlZbTLLTjMQPtedzteMxAex53uce8lXPeW2ynphCCamGMPV9K5LyVaMdj\nBtrzuNvxmIH2PO5aHTO5hgiCINocEgKCIIg2p12E4Ov1XkAdaMdjBtrzuNvxmIH2PO6aHHNbxAgI\ngiCI/LSLRUAQBEHkoaWFoBXmI5cCY2yUMfYoY+y0PEP6T+XtXYyxhxlj5+SfnfVeq9rII1BfZIw9\nKD8eZ4w9Ix/zv8hdblsKxpiTMXYvY+xl+Zzf0OrnmjH2EfmzfZIx9n3GmKkVzzVj7FuMsWXG2Mm0\nbTnPLZP4B/n6dpwxdk2l79uyQtAq85FLJAHgY5zz3QCuB/BB+Vg/CeARzvkOAI/Ij1uNPwVwOu3x\nFwB8WT7mNQDvq8uqasvfA/g55/wKAAcgHX/LnmvG2DCADwM4yDm/ElLn4rejNc/1tyHNeU8n37l9\nE6QW/zsgDfH6WqVv2rJCgBaZj1wKnPNFzvkL8u9+SBeGYUjHe4+82z0Afqc+K6wNjLERALcD+Gf5\nMQPwOgD3yru04jF3AHg1gG8CAOc8xjn3oMXPNaSW+WbGmA6ABcAiWvBcc84fB+DO2pzv3N4F4Dtc\n4mkATsbYYCXv28pCMIyN85GH8+zbMjDGxgBcDeAZAP2c80VAEgsAffVbWU34CoBPAEjJj7sBeDjn\nCflxK57zbQBWAPwv2SX2z4wxK1r4XHPO5wF8EcAlSALgBXAUrX+uBfnOrWrXuFYWgnxzk1sWxpgN\nwL8C+C+cc1+911NLGGNHACxzzo+mb86xa6udcx2AawB8jXN+NYAgWsgNlAvZJ34XgHEAQwCskNwi\n2bTauS6Gap/3VhaCQnOTWw7GmB6SCHyPc36fvHlJmIryz+V6ra8GHAZwJ2NsGpLb73WQLASn7D4A\nWvOczwGY45w/Iz++F5IwtPK5fgOAi5zzFc55HMB9AG5E659rQb5zq9o1rpWFoG3mI8u+8W8COM05\n/29pTz0A4D3y7+8BcP9mr61WcM4/xTkf4ZyPQTq3v+KcvxPAowB+V96tpY4ZADjnlwHMMsZ2yZte\nD2kkbMuea0guoesZYxb5sy6OuaXPdRr5zu0DAN4tZw9dD8ArXEhlwzlv2X8AbgNwFsB5AJ+u93pq\neJyvhGQSHgfwkvzvNkg+80cAnJN/dtV7rTU6/psAPCj/vg3AswCmAPwIgLHe66vB8V4F4Hn5fP8E\nQGern2sAfwPgZQAnAXwXgLEVzzWA70OKg8Qh3fG/L9+5heQa+qp8fTsBKauqovelymKCIIg2p5Vd\nQwRBEEQJkBAQBEG0OSQEBEEQbQ4JAUEQRJtDQkAQBNHmkBAQBEG0OSQEBEEQbQ4JAUEQRJvzfwG4\nOxf33FH9nQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c568d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(temp[:100,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "- Create new array that will contain only temperatures\n",
    "\n",
    "- Convert temperature to deg C\n",
    "\n",
    "- Convert all temperatures to deg F\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create *temp_values* that will contain only data values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -7.2,  -4.3,  -3.2, ...,  21.6,  19.8,  18.4])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_values = temp[:,3]/10.\n",
    "temp_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-14.6"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_values.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.299999999999997"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_values.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.488779667020898"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_values.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0535876992993405"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_values.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "564093.19999999995"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use *sum* function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "564093.19999999995"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(temp_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can make operations on the subsets:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "    Calculate mean for first 1000 values of temperature\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can save your data as a text file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('temp_only_values.csv',temp[:, 3]/10., fmt='%.4f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Head of resulting file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-7.2000\r\n",
      "-4.3000\r\n",
      "-3.2000\r\n",
      "1.2000\r\n",
      "-2.9000\r\n",
      "-4.3000\r\n",
      "-3.7000\r\n",
      "-9.7000\r\n",
      "-9.9000\r\n",
      "-8.9000\r\n"
     ]
    }
   ],
   "source": [
    "!head temp_only_values.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also save it as binary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=open('temp_only_values.bin', 'w')\n",
    "temp[:,3].tofile(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "* Select and plot only data for October\n",
    "* Calculate monthly means for years from 1990 to 1999 and plot them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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