{
 "metadata": {
  "name": "",
  "signature": "sha256:0971794448c6a1ced39960c071a19ff1dc88aab3df197b2cf89b7de81b578e67"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "from pylab import *\n",
      "%matplotlib inline\n",
      "\n",
      "# nastavitve za izris grafov (http://matplotlib.org/1.3.1/users/customizing.html)\n",
      "rc('text', usetex=True)\n",
      "rc('font', size=12, family='serif', serif=['Computer Modern'])\n",
      "rc('xtick', labelsize='small')\n",
      "rc('ytick', labelsize='small')\n",
      "rc('legend', frameon=False, fontsize='medium')\n",
      "rc('figure', figsize=(5, 3))\n",
      "rc('lines', linewidth=2.0)\n",
      "rc('axes', color_cycle=['k'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Histogrami"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Histogram je vrsta grafa, ki se uporablja v statistiki za prikaz porazdelitve neke koli\u010dine. Histogram nari\u0161emo tako, da na vodoravno os nana\u0161amo predal\u010dke, tj.  razli\u010dne vrednosti koli\u010dine ali pa intervale teh vrednosti, na navpi\u010dno os pa s pokon\u010dnimi stolpci predstavimo ustrezne frekvence - tj. kako pogosto vrednosti padejo v vsakega od predal\u010dkov. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Najprej definirajmo nekaj funkcij, s katerimi bomo nara\u010dunali podatke, ki jih bomo prikazali na histogramu. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# met ene kocke\n",
      "def met():\n",
      "    # izra\u010dunaj naklju\u010dno celo \u0161tevilo med 1 in 6\n",
      "    return randint(1, 7)\n",
      "\n",
      "# met N kock\n",
      "def N_metov(N):\n",
      "    # izra\u010dunaj N naklju\u010dnih celih \u0161tevil med 1 in 6\n",
      "    return randint(1, 7, N)\n",
      "\n",
      "# dogodek = vsota metov N kock\n",
      "N=100\n",
      "def dogodek():\n",
      "    # izra\u010dunaj vsoto N naklju\u010dnih celih \u0161tevil med 1 in 6\n",
      "    return sum(randint(1, 7, N))\n",
      "\n",
      "# N dogodkov\n",
      "def N_dogodkov(N):\n",
      "    # izra\u010dunaj N naklju\u010dnih dogodkov\n",
      "    return [dogodek() for i in xrange(N)]\n",
      "\n",
      "print 'met:', met()\n",
      "print '10 metov:', N_metov(10)\n",
      "print 'dogodek:', dogodek()\n",
      "print '10 dogodkov:', N_dogodkov(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "met: 1\n",
        "10 metov: [3 4 1 5 2 5 3 6 4 6]\n",
        "dogodek: 382\n",
        "10 dogodkov: [360, 339, 327, 342, 354, 312, 359, 331, 348, 350]\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Nara\u010dunajmo 1000 dogodkov,"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dogodki=N_dogodkov(1000)\n",
      "\n",
      "plot(dogodki, '.');\n",
      "xlabel(r'Zaporedna \\v st. dogodka')\n",
      "ylabel(r'Vsota 100 metov kocke');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VkIa9t57HSWX9DpfRuR/fiS9duqT93Wg02mQeyhaKZPeQCWy36Ao/J37boPOS0ue88sor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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1029dbf90>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "in nari\u0161imo histograma z razli\u010dnima \u0161teviloma predal\u010dkov, 4 in 100. Opazimo, da je v prvem primeru predal\u010dkov premalo, da bi uspe\u0161no prikazali porazdelitev, v drugem pa jih je preve\u010d in se posledi\u010dno porazdelitev izgubi v \u0161umu."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(5, 6))\n",
      "subplot(211)\n",
      "hist(dogodki, 4, alpha=0.5);\n",
      "title(r'Premalo predal\\v ckov')\n",
      "xlabel(r'Vsota 100 metov kocke');\n",
      "ylabel(r'\\v Stevilo dogodkov');\n",
      "subplot(212)\n",
      "hist(dogodki, 100, alpha=0.5);\n",
      "title(r'Preve\\v c predal\\v ckov')\n",
      "xlabel(r'Vsota 100 metov kocke');\n",
      "ylabel(r'\\v Stevilo dogodkov');\n",
      "tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OXddFP2+vGXo5ay8RjTNHEm4oFMJnn30GoHnDbW5uDsVikbP2EtG1MnTC1XUdsizj9evX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\nn34KoHmTLRaLQVEU8fn5+fm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       "text": [
        "<matplotlib.figure.Figure at 0x1049e2b10>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\u0160tevilo predal\u010dkov"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "O optimalnem \u0161tevilu predal\u010dkov je te\u017eko govoriti, saj z razli\u010dnimi velikostmi predal\u010dkov poudarimo razli\u010dne zna\u010dilnosti podatkov. Vseeno je bilo predlaganih nekaj <a href=\"http://en.wikipedia.org/wiki/Histogram#Number_of_bins_and_width\">pravil</a>, ki pod dolo\u010denimi pogoji (histogram ima en sam vrh, ...) dajo optimalne rezultate. Poglejmo si dve:\n",
      "<OL>\n",
      "<LI> <b>Sturgesova formula</b>: $N_\\mathrm{p}=[1+\\log_2(N)]$, kjer je $N_\\mathrm{p}$ \u0161tevilo predal\u010dkov, $N$ \u0161tevilo podatkov in $[x]$ prvo celo \u0161tevilo, ki je ve\u010dje od $x$.\n",
      "<LI> <b>Pravilo Freedmana in Diaconisa</b>: $w=\\frac{2\\mathrm{IQR(x)}}{\\sqrt[3]{N}}$, kjer je $w$ \u0161irina predal\u010dka, $N$ \u0161tevilo podatkov in $\\mathrm{IQR}(x)$ razlika med zgornjim in spodnjim kvartilom podatkov $x$. Spodnji kvartil je vrednost, pri kateri je 25% podatkov manj\u0161ih od te vrednosti. Zgornji kvartil pa je vrednost, pri kateri je 75% podatkov manj\u0161ih od te vrednosti. $\\mathrm{IQR}(x)$ torej podaja zna\u010dilno \u0161irino porazdelitve vrednosti podatkov. \n",
      "</OL>\n",
      "Poglejmo si, kak\u0161no \u0161tevilo predal\u010dkov predlagata ti dve pravili:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n=len(dogodki)\n",
      "print '\u0160tevilo podatkov:', n\n",
      "\n",
      "# Sturgesova formula\n",
      "np=int(ceil(1 + log2(n)))\n",
      "print \"\u0160tevilo predal\u010dkov po Sturgesovi formuli:\", np\n",
      "\n",
      "# pravilo Freedmana in Diaconisa\n",
      "q1=percentile(dogodki, 25)\n",
      "q3=percentile(dogodki, 75)\n",
      "iqr = q3 - q1\n",
      "print 'IQR(x):', iqr\n",
      "sirina = 2 * iqr * n ** (-1. / 3.)\n",
      "np=int((max(dogodki) - min(dogodki)) / sirina)\n",
      "print \"\u0160tevilo predal\u010dkov po pravilu Freedmana in Diaconisa:\", np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u0160tevilo podatkov: 1000\n",
        "\u0160tevilo predal\u010dkov po Sturgesovi formuli: 11\n",
        "IQR(x): 24.0\n",
        "\u0160tevilo predal\u010dkov po pravilu Freedmana in Diaconisa: 26\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Nari\u0161imo histogram s \u0161tevilom predal\u010dkov po pravilu Freedmana in Diakonisa:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hist(dogodki, np, alpha=0.5);\n",
      "xlabel(r'Vsota 100 metov kocke');\n",
      "ylabel(r'\\v Stevilo dogodkov');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MOkP3fD6PaDSKSqUCQRAAAKFQCLVaLajwiIhuLPCLTZZlYX5+HolE4sY90L29Pef3lZUV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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104a11f90>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Namesto \u0161tevila dogodkov v vsakem od predal\u010dkov lahko s histogramom prika\u017eemo verjetnostno gostoto. V tem primeru je plo\u0161\u010dina pod celotnim histogramom enaka 1, plo\u0161\u010dina histograma na nekem intervalu pa predstavlja verjetnost, da je vsota metov znotraj tega intervala. Pri tem na\u010dinu prestavitve je bolj smiselno, da histogram nari\u0161emo s \u010drto namesto s stolpci."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hist(dogodki, np, normed=True, histtype='step');\n",
      "xlabel(r'Vsota 100 metov kocke');\n",
      "ylabel(r'Verjetnostna gostota');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dbgiCgDNnzhS08ksJBAIYGhoyuxHK1dnb24vFxUVze37/eHEd5eKn5rhpYmJi\notlBUOvp7u6Gpmnr/kDn5+dx9epVXLlyxZwwuru7G/Pz87h+/ToSiQREUcTXvvY1AMD169cRj8fR\n2dkJj8eDTCaDpaUlfPrTn8b169cxNzeH3bt3w+12m6+haRqi0Sjefvtt3HHHHfB4PNi+fTs+/vGP\nI5VKIZVKYe/evXC73WW3F4vH44hEIub8olNTUzAMA3v37sX09DT+8pe/YGBgAF/4whcQj8exvLyM\nK1eu4Pr167j99tvXHUelY87NsFZuEud4PI7JyUns2bMH3/jGNyBJEm699VY8+eSTJeu8++67kUgk\nzPf9+vXruHLlSsk6Dh48WDJ+ag7OpkVEZBN2ERAR2YQJlojIJkywREQ2YYIlIrIJEywRkU2YYImI\nbMIES0RkEyZYIiKbMMESEdnk/wBeYQQ/PpmPWQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104a113d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Kumulativni histogram"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "V kumulativnem histogramu je vrednost v predal\u010dku enaka vsoti \u0161tevila dogodkov v tem predal\u010dku in v vseh predal\u010dkih levo od njega. Vrednost v zadnjem predal\u010dku je torej enaka \u0161tevilu vseh dogodkov."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n, bins, patches=hist(dogodki, np, cumulative=True, histtype='step');\n",
      "patches[0].set_xy(patches[0].get_xy()[:-1]);\n",
      "xlabel(r'Vsota 100 metov kocke');\n",
      "ylabel(r'Kumulativno \\v stevilo dogodkov');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TOBxGKBTC6OhoVbssy2btVkEQkM1mMTo6WjXkzmQyGBsbA/BhEqt8REUkEkEg\nEIDP5wOAukPU2jIU09PTZlUnoHSd0+Vy1W3f64Zaeb/FYnHXMQCY13JdLhdcLpdZpLj2c9T7zB6P\nBw6HA4IgYHl5uao3X08gEEAwGDQvETTap8fjwcbGhtleeb27dh+N4id7PLCwsLBgdxDUG0ZHR6Gq\n6q7/KNfW1vDvf/8bt2/fNossj46OYm1tDTs7O9A0DaIo4mtf+xoAYGdnB+l0GkNDQ3A6nSgUCtjc\n3MRnPvMZ7OzsIJlMYmRkBA6HwzyGqqpYWVnBP/7xDzzyyCNwOp148MEH8YlPfALZbBbZbBZPPvkk\nHA5Hw/Za6XQa8XjcrM8ZiURgGAaefPJJRKNR/OlPf4Lf78fjjz+OdDqNmzdv4vbt29jZ2cHDDz+8\n63Ps9ZnLlcsaFT1Op9NYXFzE5z73OXzjG9+Az+fDQw89hB/84Ad19/mFL3wBmqaZf/ednR3cvn27\n7j7Onz9fN36yB6tUERG1EYf/RERtxKRKRNRGTKpERG3EpEpE1EZMqkREbcSkSkTURkyqRERtxKRK\nRNRGTKpERG30f/jfI+eLzNEAAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104f6c890>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Tudi s kumulativnim histogramom lahko predstavimo verjetnostno gostoto. Vrednost v predal\u010dku je v tem primeru enaka verjetnosti, da bo dogodek v tem predal\u010dku ali pa v enem od predal\u010dkov levo od njega. Vrednost v zadnjem predal\u010dku je enaka 1."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n, bins, patches=hist(dogodki, np, normed=True, cumulative=True, histtype='step');\n",
      "patches[0].set_xy(patches[0].get_xy()[:-1]);\n",
      "ylim([0,1])\n",
      "xlabel('Vsota 100 metov kocke');\n",
      "ylabel('Kumulativna verjetnost');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cTGhw+9GjR6smVtvYqYiIqB0I7WkGAoGqPc1oNNr0QERETiZ8eL5x3vNCodD0QERETiZU\nNDs6OjA+Pm5eQc9kMlhaWrIkGBGREwkVzatXryIcDpuNO69evWpJKCIipxIqmv39/VXDjDi4nYja\njVDRlGUZiUQCHR0dKJfLUFUVly9ftiobEZHjCHdur0zLWy6Xkc/nrcpFRORIQkVzaGioap5zDjki\nonYjVDQB4NatW5BlGeVyGclkkq3hqOkePnyIixcvYm1tre5z/vznPwtNJ03ULEJT+G7uranrOpaX\nly0JVsEpfNvP7du38fWvfx3j4+PbPu+FF17AM88806JUdNDstrYIFU1VVasGt2ezWcv/b8+i2X5u\n376N3t5e3L592+4odIC1ZN7zjQUT2NmQo0QiAa/XC13XEYlEai4H1gfKT09Pi8QhImo5S/tpZrNZ\nGIaBYDAIwzCQSqUQCoWq1qcoCjweD/L5PBKJRM3CSkTkFEJdjmKxGHRdh67rmJ+fb9hPU9M08xyo\n1+vF/Px81XJd16GqqrmcQ5iIyOks7aeZz+fNc55utxvFYrFq+ca9SlVV8fLLL4vEISJqOUf009R1\nHR0dHRgcHGzK+oiIrGJpP83K3UMAYBgGZFmu+bx4PL7t7ZgTExPmv/v6+tDX17fz0EREABYXF7G4\nuLjn9expyFEjuVwOqqpibGwMc3NzeOSRRzA4OAjDMCBJEoD1ghkOh+F2u7dcKAI45KgdccgRtcJu\na0vDw/ONjYY3F8zr169v+7eVC0WapmF1ddU8/K6sR1VVjI6OwuPxQJZlrK6uiqUnImqxhnuaw8PD\nmJ2d3fJ4qVSCoiiWNyHmnmb74Z4mtYJlg9vL5TIWFhZw+vRp8zFN0xCNRs1DbCIRb775Jt577726\ny0ulUgvTEInZ0TnNQqEAwzDg9XoRiUSg6zqSySSOHDlieeHknubB8/zzz+Opp57CV77ylbrP8Xg8\n7KJFlrL0NkqPxwNd1xEMBhEOh2serhOJGB0dxfPPP293DCJhDS8EaZoGYP2OnWQyWXUxaGFhwbpk\nREQO1HBP8/z58+jp6TF3Y3VdN8dqcjZKImo3DYumx+NBIBBAuVyGy+VCIBAwzwVsvi2SiOiga1g0\nX3vtNXg8nprL+vv7mx6IiMjJGp7TrFcwGy0jIjqIhBp2EBG1OxZNIiIBLJpERAKEp/Al2s6NGzfw\n+uuvb/ucmzdvtigNUfOxaFJT/elPf8KNGzfw/e9/v+5z/H4/urq6WpiKqHlYNKnpvvGNb+CVV16x\nOwaRJXhOk4hIAIsmEZEAFk0iIgEsmkREAlg0iYgE8Oo57djDhw/x8ssv45///Gfd53z00UdVU6MQ\nHTRCU/jagdNdOIdhGHjyySfxxhtvbPu8b37zmzh27FiLUhHtzm5rC4sm7ZhhGDh+/DgMw7A7CtGe\nWTbvORER/R8WTSIiAbwQRKZz585te77y888/x//+7/+2MBGR81i+p5lIJKBpGhKJxK6WO8ni4qLd\nEUxWZPnggw/w85//HKqq1vy5du0abty40ZIsu+GUHACz1OOkLLtl6Z5mNpuFYRgIBoMwDAOpVAqh\nUGjHy51mcXERfX19dscAIJ5ldXUVf/zjH7d9zu3bt/HEE0+Ys41alcUqTskBMEs9TsqyW5YWTU3T\nzP8AvV4vZmZmqopio+W0M+VyGT/+8Y+3var97rvv4t69e9u2bHv66adx8uRJKyISHRiWFs18Po9A\nIAAAcLvdW6b8bbTcSteuXcPdu3eF/ub999/H7373O/P3mzdv4t///jdcLlez41V566238NFHH0GS\nJPOxTz75xMxSLpfx8ccf47e//W3ddfzoRz/C008/DZ/PZ2lWogOvbKFoNFpWVbVcLpfL+Xy+PDQ0\nJLS8XC6XfT5fGQB/+MMf/jT1x+fz7aquWbqn6fP5zENGwzAgy7LQcgBYXl62MiIRkRBLr54rigJd\n1wEAuq5jYGAAAMxCWW85EZFTWVo0/X4/gPULPqurqxgcHASwXiy3W05E5FSOuPe8MkYzk8lgenoa\nwPpwpEwmAwAYHh6G2+1GIpGA1+uFruuIRCIty5JKpQAAxWLRfN1WZInH4/D5fEgmk2aWWq9rZxag\n+rOyK0vF+fPnEYvFbM1ix7ZbK4dd221Fo+/CzizA7rdb22+j1DQNiqIgEolAkiTzDU1OTiISiUCW\nZaiqilwuZ47plGXZ3CCszpLL5SBJEkKhELq7u5FKpVqSJZfLIZfLIRgMAkDd17UrS73Pyo4sFaqq\nQtM0ANVjgFuVpXI3Vau33Xrbih3bbUWj78KuLKqq7nm7tb1o6roOVVUBrI/VzOfzSKVS6OnpAQCE\nQiGEQiGoqlo1pnN+fr4lWSRJQiwWQ6lUgq7rCAQCLcni9/tx+fJlM1d/fz/m5+e3vK5dWTZ+Vj6f\nD/l83rYsAFAqldDR0WFeTLQji6IomJuba/m2W+szsWu7BXb2XdiVpVAo7Hm7tb1oRiIRc3dYVVX0\n9vZiaWkJKysr0DQN4+PjAGAWMMC6MZ0bs8zPz6O3txcejwd+vx8ejwe6rsPj8bQkC7D+hU9NTWF4\neBiHDx+Gruvm60qShGKx2PIsQ0NDOHz48JbPqqenx7YswPq2UzlHDqDqs2plFru23c05PB4PAoGA\nLdvtdt9Fq7fbzVmasd3aXjQrdF1HR0eHeTHI5XIhGAyip6cHr732WsuzHD16FIODgzAMA0ePHkUy\nmcTFixeRy+ValsPtdmNsbAzpdNo8vLBLJUsmk6nKUvneWnkn1+YsuVzOvEmi1TZncblctmy7m3NU\n9rBavd3a+V1stl2WvWy3julyFI/HzUOMjXetSJKEdDqNnp6ehmM6rciSTCYRjUZx+PBhZDIZxGKx\nHY0v3atsNguXywW/34+uri7MzMxUfQarq6uQZdm2LJXzZ5u/NzuyhMNhc5mu69A0zbYsldMFQOu2\n3Vo5dF23ZbutDCGs9120crvdnGVhYcGcimUv260j9jTj8Th+9rOfAVg/ia0oCvL5PID1D7m3t7dl\nYzo3Zpmbm0OpVDK7O3s8Hvh8vpZk0TTNPEyo9RkUCgUMDAzYlgWo/b3ZkaVy7jAUCkGSJASDQVu/\no1Zvu5tz9PT02LbdNvouWrndbs6ysWDuZbu1vWiqqorR0VF4PB7IsozV1VXzS06lUkin0zh37lxL\nxnRuzmIYBs6dO4d4PI5UKoVEIoFoNNqSLCMjIzAMA4lEAoVCYctnUCwWMTg4aFuWWt+bXVkq4vE4\nCoUCFhYWbMtix7a7OcfY2Jht221Fve+ildttrSzN2G4dMU6TiGi/sH1Pk4hoP2HRJCISwKJJRCSA\nRZOISACLJhGRABbNA05VVfh8PgwPD6NUKpmPz83Nobu7G9evX29Zlmw2u2XW0bm5OWiaZjYBafS4\nU6mqis7OTtvXQdZj0TzgFEUx74F2u93m4z6fD3Nzc3jmmWeE17mb6ZY1TcPk5GTV5G+Vph/BYBCh\nUMhs3VXv8WZr5rTRiqJUzeFk1zrIeiyabSASiUBV1ao9TV3Xcfz4ceF1GYaBmZkZ4b8LBoNVtxgC\n63tWG4uEJEnI5XJ1H2+m3b4PIhbNNjE8PIzZ2Vnz941FKZvNmj0OK3ulwPqemKZp5mEysF5sK3PU\nbyxklb6R4+PjKBQKO8pUaSpRIcsydF2v+/hmlcPZSsbR0VHkcjmzw9DGfFNTU9A0zbxrpt772Pye\nVVWFLMvmaYxoNIqpqalt35eqquju7sbCwkLdzxFYv1Ol0nBk8/8UNq9jc36yj2MadpC1otEohoaG\nEIlEkEqlqrq7zM7Ooqenp6qF1tzcHACYjTnGx8fh9XoRCATM5rYbxeNxsyjHYrEtHdX3qtY0yYqi\nwOv1wufz4fjx48hms0in02YD4KtXr8Lv9yMej5vvJRgMYmBgAG+//faW91HrPYfDYYyMjGBlZQXA\n+v98KstrKZVKKJVKSKfTdddZ6dWq6zpGRkZQKBSqPrPN66iXn+zBPc024ff76x7mTk5OQtd1dHd3\nm4esGxuzAkBHR4c5hUMtsVjMvN96pzafvysWi/B6vXUfr2djV5rKHmq5XDbPn2azWfO953K5ug0Z\nar3ndDqNaDRqNvTdrgNOpVP7xsP+euusdD8C1htqVApmrXXsND+1BotmG4lGo4hEIujq6qp6PJFI\nmH07JUlCoVBAV1dX1SFxPp9Hd3c3gP8rUhunEIjFYgiFQuakebUOITe3ORgeHjY7AgHr5xn9fn/N\nx7e7YFVZb7lc3vIaAMxzqX6/H36/32xCu/l91HrPPT098Hg8kCQJs7OzVXvjtYRCIQwNDZmH8PXW\n2dPTg6WlJfPxjeebN6+jXn6yx/9MTExM2B2CWqOrqwuapm35j25hYQF3797FnTt3zAa6XV1dWFhY\nwNraGnK5HGRZxgsvvAAAWFtbQzabxZEjR+D1elEqlZDJZPDkk09ibW0N8/Pz6OzshMfjMV9D0zQk\nk0n89a9/xbFjx+D1enHo0CE89thjKBQKKBQKeO655+DxeOo+vlk2m0U8Hjf7M8ZiMRiGgeeeew7T\n09N49913MTAwgGeffRbZbBa3bt3CnTt3sLa2hieeeGLL+9juPVe6XtVrapvNZjE5OYmvfe1rePHF\nF6EoCr7whS/gpz/9ac11njx5Erlczvzc19bWcOfOnZrrOHv2bM38ZA92OSIiEsDDcyIiASyaREQC\nWDSJiASwaBIRCWDRJCISwKJJRCSARZOISACLJhGRABZNIiIB/x/8ATzyTadRjQAAAABJRU5ErkJg\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1049ed7d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Kumulativni histogram je \u0161e posebej priro\u010den za ocenjevanje verjetnosti, da je vrednost prikazane koli\u010dine v nekem intervalu. Na spodnjem primeru iz kumulativnega histograma (a) brez te\u017eav ocenimo verjetnost, da je vsota 100 metov manj\u0161a od 330, na pribli\u017eno 20%. Ustrezna koli\u010dina na obi\u010dajnem histogramu (b) je plo\u0161\u010dina histograma na tem intervalu in je vizualno ne moremo tako natan\u010dno oceniti."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(5, 6))\n",
      "subplot(211)\n",
      "n, bins, patches=hist(dogodki, np, normed=True, cumulative=True, histtype='step');\n",
      "patches[0].set_xy(patches[0].get_xy()[:-1]);\n",
      "xlabel(r'Vsota 100 metov kocke');\n",
      "ylabel(r'Kumulativna verjetnostna gostota');\n",
      "grid();\n",
      "axvline(330, color='r', linewidth=2.0, ls='--')\n",
      "annotate(r'(a)', xy=(0.9,0.9), xycoords='axes fraction')\n",
      "subplot(212)\n",
      "hist(dogodki, np, normed=True, histtype='step');\n",
      "xlabel(r'Vsota 100 metov kocke');\n",
      "ylabel(r'Verjetnostna gostota');\n",
      "grid();\n",
      "axvline(330, color='r', linewidth=2.0, ls='--')\n",
      "annotate(r'(b)', xy=(0.9,0.9), xycoords='axes fraction')\n",
      "tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7dvlSrVaxsLBQd9UkyzKi0Sg4jmv5u8FgEJIkAVi5DXh4eBjASt2G4zjD5bXM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\nanAJIcQi1OASQohFqMElhBCL/H+DLtFIxa8LYAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1049db390>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Naloge"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<OL>\n",
      "<LI>V datoteki <a href=\"http://burana.ijs.si/rovf14/podatki/Maribor.zip\">Maribor.zip</a> so zbrani vremenski podatki z merilne postaje Maribor-letali\u0161\u010de za obdobje od 1. 1. 1977 do 31. 1. 2015. Nari\u0161i histogram povpre\u010dne dnevne temperature (datoteka TG_STAID003331.txt) v tem obdobju.\n",
      "\n",
      "<LI>V enakomernih korakih tabeliraj funkcijo $y=(1/\\tau)\\exp(-t/\\tau)$, kjer je $\\tau=2{,}2$ sekunde. Funkcija podaja verjetnostno gostoto radioaktivnih razpadov atoma z \u017eivljenskim \u010dasom $\\tau$. Nari\u0161i kumulativno verjetnost kot funkcijo \u010dasa. S pomo\u010djo histograma oceni, v kak\u0161nem \u010dasu razpade polovica vseh atomov.\n",
      "\n",
      "<LI>Nari\u0161i histogram porazdelitve podatkov (logaritem razmerja vpadnega in prepu\u0161\u010denega toka, drugi stolpec) iz datoteke <a href=\"http://burana.ijs.si/rovfx/podatki/Fe_Co.dat\">Fe_Co.dat</a>, ki vsebuje nekoliko predelan absorpcijski spekter EXAFS (Extended X-ray Absorption Fine Structure = drobna struktura rentgenskih absorpcijskih robov) me\u0161anega \u017eelezovo-kobaltovega oksida. \u010ceprav je neodvisna spremenljivka (energija fotona v eV, prvi stolpec) netrivialna in pomembna fizikalna koli\u010dina, je zanimivo pogledati podatke tudi neodvisno od energije. Izberi primerno \u0161tevilo predal\u010dkov. Porazdelitev ima vrhove pri skoraj konstantnih vrednostih absorpcije med robovi. &mdash; Zanimiva je \u0161e ena modifikacija: koraki v energiji, pri katerih smo merili absorpcijo, niso enako razmaknjeni (ekvidistantni). Zato v predal\u010denju vse to\u010dke niso enako pravi\u010dno obravnavane. Predstavljajmo si, da opravimo meritev \u0161e enkrat z zelo drobnim ekvidistantnim energijskim korakom. Vidimo, da je pravi\u010dna te\u017ea vsake to\u010dke velikost koraka, to\u010dneje interval, ki obsega pol desnega in pol levega energijskega koraka. S tem smo dolo\u010dili matemati\u010dno mero vsake to\u010dke. Primerjaj porazdelitev po prej\u0161njem in novem na\u010delu. \u010ce je prva normirana s \u0161tevilom vseh to\u010dk, je druga normirana z dol\u017eino energijskega intervala celega spektra. \n",
      "</OL>"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}