{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1> Tömbök (numpy) </h1></center>\n",
    "<br>\n",
    "\n",
    "\n",
    "Ebben a Notebookban bemutatjuk az tömböket illetve hogyan kell a pythonban adatfájlokat beolvasni."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adattömbök, matematikai eszközök, adatfájlok kezelése\n",
    "\n",
    "A pythonban a **`numpy`** csomag felel az adatok beolvasásáért, matematikai operációk elvégezésért (mint a gyökvonás, szinusz ...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tömbök\n",
    "\n",
    "A programozásban létrehozhatunk mátrixokat, melyeket adattömböknek hívunk. Ezek bármennyi dimenziósak lehetnek."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(3,)\n"
     ]
    }
   ],
   "source": [
    "a = array([1, 2, 3])      # hozzunk létre egy 1 dimenziós tömböt\n",
    "print(type(a))            # Írassuk ki \"a\" típusát: \"<type 'numpy.ndarray'>\"\n",
    "print(a.shape)            # Nézzük meg a formáját: \"(3,)\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A python és a legtöbb programozási nyelv nullától kezdi a tömbök indexelését. Azaz az első elem indexe 0, a másodiké 1 és így tovább. Egy 10 elemű tömbnek az utolsó eleme 9-es indexet visel (Elemszám-1)!**\n",
    "\n",
    "Így kell elképzelni az adattömböket:\n",
    "\n",
    "<img src=\"https://1.bp.blogspot.com/-7a-mSPtfScw/Vze66QWv_MI/AAAAAAAAC7c/1PaKQQzwGwY6GIo4RX3u-rsDuCoFlkkaACLcB/s1600/numpy_2D_slicing_diagram.jpg\">\n",
    "<br><br><br>\n",
    "<img src=\"http://www.scipy-lectures.org/_images/numpy_fancy_indexing.png\" width=500>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 3\n",
      "[5 2 3]\n"
     ]
    }
   ],
   "source": [
    "print(a[0], a[1], a[2])   # Írassuk ki a tömb, első (0.-ik), második, ás harmadik elemét\n",
    "a[0] = 5                  # Átírjuk az első (nulladik) elem értékét\n",
    "print(a)                  # Nézzük meg a megváltozott \"a\" vektort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n",
      "1 2 4\n"
     ]
    }
   ],
   "source": [
    "b = array([[1,2,3],[4,5,6]])    # Készítsünk 2 dimenziós tömböt\n",
    "print(b.shape)                     # Kiíratjuk az alakját\n",
    "print(b[0, 0], b[0, 1], b[1, 0])   # Írjunk ki pár elemet a tömbből"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \"Almátrixok\"\n",
    "\n",
    "A pythonban ki tudunk egy tömbből venni résztömböket. De vigyázzunk, mert a résztömbön végzett művelet a szülőtömb adott elemein is elvégződik."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "77\n"
     ]
    }
   ],
   "source": [
    "    # Készítsük els következő 2 dimenziós (3 sor, 4 oszlop) tömböt\n",
    "    # [[ 1  2  3  4]\n",
    "    #  [ 5  6  7  8]\n",
    "    #  [ 9 10 11 12]]\n",
    "a = array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
    "print(a[0, 1])   # Írassuk ki a 0. sor, 1. oszlopának elem(eit): 2\n",
    "\n",
    "    # Hozzuk létre a mátrix következő almátrixát (ami 2*2 nagyságú)\n",
    "    # [[2 3]\n",
    "    #  [6 7]]\n",
    "    # Azaz a mátrixból a 0-tól 2-ig levő adatsorok, illetve függőlegesen 1-tól 3-ig levő adatoszlopok kivétele\n",
    "    # Vegyük észre, hogy a felső határ már nincs benne az adatsorban!\n",
    "b = a[0:2, 1:3]\n",
    "\n",
    "    # Változtassuk meg a b mátrix [0,0]-ás elemének értékét\n",
    "b[0, 0] = 77\n",
    "\n",
    "    # Ellenőrizzük az a mátrixban a \"b[0,0]\" helyen levő értéket (a[0,1]-as hely)\n",
    "print(a[0, 1])   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Számsorok generálása és tömbbe rendezése\n",
    "\n",
    "Lehetőség van adott hosszú sorokat gyártani, melyekből tömb készíthető. Nézzük néhány példát erre:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n"
     ]
    }
   ],
   "source": [
    "a = arange(15) # Készítsünk egy 15 elemű listát \n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = arange(15)   # Készítsünk egy 15 elemű listát \n",
    "a = a.reshape(3, 5) # Rendezzük a listát 3*5-s tömbbe\n",
    "a                   # Nézzük meg az eredményt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size # Megadja mekkora a tömb mérete (mennyi adat van benne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'int64'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype.name # Megnézi milyen tipusú adatok vannak a tömbben"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.25, -0.25,  8.  ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = array([6.25, -0.25, 8.])\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'float64'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.dtype.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.+0.j,  2.+0.j],\n",
       "       [ 3.+0.j,  4.+0.j]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = array( [ [1,2], [3,4] ], dtype=complex )\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'complex128'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.dtype.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeros( (3,4) ) #Készítsünk egy 3*4-es tömböt, mely tele van nullával"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 1, 1],\n",
       "        [1, 1, 1, 1],\n",
       "        [1, 1, 1, 1]],\n",
       "\n",
       "       [[1, 1, 1, 1],\n",
       "        [1, 1, 1, 1],\n",
       "        [1, 1, 1, 1]]], dtype=int16)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones( (2,3,4), dtype=int16 ) # Készítsünk egy 3 dimenziós tömböt, csupa egyesből"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95]\n"
     ]
    }
   ],
   "source": [
    "a = arange(15, 100, 5) # Készítsünk egy listát 15-től, 100-ig, 5-sével \n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30, 25, 20, 15])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arange( 30, 10, -5 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arange( 10, 30, -5 ) #Ez üres lista, de NINCS hibaüzenet!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.3,  0.6,  0.9,  1.2,  1.5,  1.8])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arange( 0, 2, 0.3 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ,  1.25,  1.5 ,  1.75,  2.  ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linspace( 0, 2, 9 ) # Készítsünk egy 9 elemű listát 0 és 2 között. Egyenlő lépésközzel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matematikai függvények\n",
    "\n",
    "Bővebb leírás itt található: https://docs.scipy.org/doc/numpy/reference/routines.math.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.06346652,  0.12693304,  0.19039955,  0.25386607,\n",
       "        0.31733259,  0.38079911,  0.44426563,  0.50773215,  0.57119866,\n",
       "        0.63466518,  0.6981317 ,  0.76159822,  0.82506474,  0.88853126,\n",
       "        0.95199777,  1.01546429,  1.07893081,  1.14239733,  1.20586385,\n",
       "        1.26933037,  1.33279688,  1.3962634 ,  1.45972992,  1.52319644,\n",
       "        1.58666296,  1.65012947,  1.71359599,  1.77706251,  1.84052903,\n",
       "        1.90399555,  1.96746207,  2.03092858,  2.0943951 ,  2.15786162,\n",
       "        2.22132814,  2.28479466,  2.34826118,  2.41172769,  2.47519421,\n",
       "        2.53866073,  2.60212725,  2.66559377,  2.72906028,  2.7925268 ,\n",
       "        2.85599332,  2.91945984,  2.98292636,  3.04639288,  3.10985939,\n",
       "        3.17332591,  3.23679243,  3.30025895,  3.36372547,  3.42719199,\n",
       "        3.4906585 ,  3.55412502,  3.61759154,  3.68105806,  3.74452458,\n",
       "        3.8079911 ,  3.87145761,  3.93492413,  3.99839065,  4.06185717,\n",
       "        4.12532369,  4.1887902 ,  4.25225672,  4.31572324,  4.37918976,\n",
       "        4.44265628,  4.5061228 ,  4.56958931,  4.63305583,  4.69652235,\n",
       "        4.75998887,  4.82345539,  4.88692191,  4.95038842,  5.01385494,\n",
       "        5.07732146,  5.14078798,  5.2042545 ,  5.26772102,  5.33118753,\n",
       "        5.39465405,  5.45812057,  5.52158709,  5.58505361,  5.64852012,\n",
       "        5.71198664,  5.77545316,  5.83891968,  5.9023862 ,  5.96585272,\n",
       "        6.02931923,  6.09278575,  6.15625227,  6.21971879,  6.28318531])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = linspace( 0, 2*pi, 100 ) # A pi-nek a neve \"pi\"\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5b48d6b240>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7hfPvlZmUVtisjqOc4GjxWV7fkM1NQ2JJiOxgdZxm0cKglAsRER6amMyJM5Us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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b540cc208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = sin(x) # vegyük az előbb generált adatsornak a szinuszát\n",
    "plot(f)   # majd ábárzoljuk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5b47836240>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xv9VRg7s5cKyIjzYc4KaLYunQOtDqOMrBtDCoJuPn48XDI7uSkl3I1ymHrY6j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\nlFJKneP/AWS85fope0bxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b540cc0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(cos(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5b48b45278>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Abg96+1vM7D5gK1AC7tAInWipnQ9fwzJF4qOhwHf3AvD+Mzz3OeBzjby/NM+b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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b4785ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(tan(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5b45f704e0>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b48b582e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(tanh(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5b48724320>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zu4JdRPpTuEeZzXuquevx3Vw8LZf7btJQjIicnsI9irx8qJavPbqDRUXZrLupRMEuIgNS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\nRHxI4S4i4kMKdxERH/r/Ze62ewiwo0cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b45f92668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(arctan(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = arange(3)\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  2.71828183,  7.3890561 ])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp(B) # Emeljük \"e\"-adra a B elemeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  1.        ,  1.41421356])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqrt(B) # Gyökvonás neve 'sqrt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  0.,  6.])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C = array([2., -1., 4.])\n",
    "add(B, C) # Össze is adhatunk elemenként 2 tömböt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kétváltozós függvények ábrázolása (felületi ábrák)\n",
    "\n",
    "Ha kétváltozós függvényt szeretnénk ábrázolni, akkor ahhoz a mintavételezést a numpy csomag meshgrid() függvényével tehetjük meg az alábbi szintaxis szerint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "xrange=linspace(-3,3,100)   # határok és pontok száma az x irányba\n",
    "yrange=linspace(-3,3,100)   # határok és pontok száma az y irányba\n",
    "x,y=meshgrid(xrange,yrange) # mintavételezés az x és y síkban"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Két változós függvényt a pcolormesh() matplotlib függvény segítségével tudunk ábrázolni. \n",
    "\n",
    "A fent definiált x és y tömbök segítségével például az\n",
    "$\n",
    "f(x,y)=e^{-(x^2+y^2)}\n",
    "$\n",
    "kétdimenziós Gauss-görbét az alábbi módon ábrázolhatjuk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f5b477edfd0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YP/jAeibPraP7aRmp9DfQ9vEkVfY3aJ5/F7N7cd9pcO9+yF9wj070FVLsr87TGVV3pusL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b6cf78c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pcolormesh(x,y,exp(-(x**2+y**2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hogy egy adott szín mit jelent, azt a colorbar() függvény segítségével tudhatjuk meg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f5b45efbe80>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P3dyW2ar1ACAicdX6B+f2+9cAPgDgp9s0SkVMCNkJWi6VdG1cbT78nJ1rprhq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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b540fae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pcolormesh(x,y,exp(-(x**2+y**2)))\n",
    "colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kétváltozós függvényeket szokás kontúrok segítségével is ábrázolni. Egy kontúrvonal azokat a pontokat tartalmazza, ahol az adott két változós függvény ugyanazt az értéket veszi fel. A contour() függvény segítségével tudunk kontúrábrákat generálni:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f5b45ee7fd0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j4eY9jO3eifE94qy23VuWyxtHtjAlqiuP9LrUarrGaNHzzclX2Vu5juGBM7gp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\nRsEuIhIYBbuISGAU7CIigfkPn2WEhrsPW80AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b47857630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contour(x,y,exp(-(x**2+y**2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ha csak megadott kontúrértékekre vagyunk kíváncsiak, akkor azt a levels kulcsszó használatával tehetjük meg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f5b4393f908>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4dhIZ6T8Mcy+vaePzDUfZeagEZDImj4xlwfRU0lPCkctldHTp2HK4iC1ZhWfC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\nBxYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b47deb160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contour(x,y,exp(-(x**2+y**2)), levels=linspace(0,1,20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ábra mentése\n",
    "A legenerált ábrákat a savefig() paranccsal tudjuk kimenteni képfile-okba."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b438fcd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x,y)\n",
    "savefig('az_en_abram.pdf')"
   ]
  }
 ],
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