/gist:bf417150abe1f7dca2c3 Secret

## gistfile1.txt
{
 "metadata": {
  "name": "",
  "signature": "sha256:e429e37ed4ed850b772f67a17326ae88b567d0651fee6f7d72632c2b785d26c1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![MET logo](http://met.no/filestore/logo_2013_no.png \"MET logo\")"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Kodeklubben"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Innhold"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "1. IPython Notebook\n",
      "2. [api.met.no](http://api.met.no) - kodeeksempler"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1. IPython Notebook"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython Notebook er en slags digital loggbok, som kan inneholde alt fra ren tekst til kj\u00f8rbar Python kode, med resultater vist direkte i nettleseren.\n",
      "\n",
      "Jeg kommer til \u00e5 legge ut denne notatboken p\u00e5 [it.met.no](http://it.met.no) i l\u00f8pet av morgendagen, sammen med instruksjoner til hvordan dere kan installere IPython Notebook, slik at dere kan videreutvikle programsnuttene vi lager etterp\u00e5.\n",
      "\n",
      "F\u00f8rst sier vi at plot skal vises direkte i notatboken:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Litt eksperimentering med plotting:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = linspace(0, 10, 50)\n",
      "\n",
      "y = 0.2*x\n",
      "y2 = cos(x)\n",
      "\n",
      "plot(x,y)\n",
      "plot(x,y2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7fae9ad74a10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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c9M5ZHqRZ4WZsPr2Zfb/vsx1FRZmzZw6FshbSgu9FMqfJTJNnmjD257G2o/gE\nLfou9FCKh3jr2bcYuWGk7SgKMMb8dTGW8i5dynRh0tZJXL9z3XYUr6dF38XaP9uehQcWcubqGdtR\n/N7yw8sJCgiiep7qtqOoJMr3n3xUeLIC03dMtx3F62nRd7HMaTLTtFBTPvn5E9tR/N6IjSPoXk5H\nLnir7mW78/FPH3M/4r7tKF5Ni74bdC3blclbJ3P19lXbUfzWtjPbOHjhII0KNrIdRSVT+SfLky1d\nNhbuX2g7ilfTou8GeTLmoWruqkzZPsV2FL81cuNIOpXupFdIe7me5XoybP0wvf7FAVr03aRHuR6M\n+mkUd+/ftR3F7/x25TeWH15O6+KtbUdRDnox/4tcu3ON8OPhtqN4LS36bvLsE8+SO0Nu5u+bbzuK\n3xn902haFG3BI6kfsR1FOShAAuherjvDNwy3HcVradF3ox7levDRho/0rakbXb51mek7ptOptI5c\n8BX/Lfxfdp7dya5zu2xH8Upa9N2oZr6a3Ll/h++PfW87it+YvHUytZ+qTY5HctiOopwkdVBq3i79\nNiM2jLAdxStp0XejAAmge9nufLRBJzu6w537dxizaQzdynazHUU5WduSbVl6cCm/XfnNdhSvo0Xf\nzV4t9Cp7zu9h59mdtqP4vDm75xCcJZii2YrajqKcLEPqDLQs1pLRP422HcXraNF3s1RBqXi71NuM\n2KhvTV0pwkQwbP0wepbraTuKcpHOZTozfcd0Lt28ZDuKV9Gib8GbJd9k2aFlnLhyIuGNVbIsPrCY\ndCnT6cgFH5Y9fXbq5K/DhC0TbEfxKlr0LciQOgOvF3ld35q6iDGGD9Z9QJ+KfXTkgo/rXq47YzaN\n4da9W7ajeA0t+pZ0KduF6Tunc+HGBdtRfM53R7/jxt0b1Mlfx3YU5WLPPPoMJR8vyYydM2xH8Rpa\n9C3Jnj47DYIbELYpzHYUn/PBug/oVb4XAaL/vP1Bz/I9GbFhhA5iSyT9rbCoV4VejN88niu3rtiO\n4jM2ntjI8cvHafxMY9tRlJtUfLIimR7KxOJfFtuO4hW06FuUJ2MeauarybjN42xH8RlD1w2lZ7me\nOljNj4gI75R/h/fXvq9XuyeCFn3LelfoTdimML0jkBPsOreLzac306JYC9tRlJvVfboud+/fZdmh\nZbajeDwt+pYVyFKASjkrMXnrZNtRvN6H6z6kS5kupA5KbTuKcrMACaB/pf4M+XGIHu0nQIu+B+hb\nsS8jNo6KtNayAAAQH0lEQVTQZWcOOHLxCKuOrqJtyba2oyhLGhRowNU7V1l1dJXtKB5Ni74HKJqt\nKMWyFWPa9mm2o3it4euH065kO9KnSm87irIkQALoV7Ef7655V4/2H0CLvofoW7Evw9YP05usJMOp\nP08xf9983i79tu0oyrJXCr7CHzf+YPXx1bajeCwt+h6ibI6y5M2Ul1m7Z9mO4nU+3vgxrxV5jcxp\nMtuOoiwLDAikX8V+DF4z2HYUj6VF34P0rdiXD9Z+oBeZJMGFGxeYtmMa3crp+GQVqUmhJpz88yRr\njq+xHcUjadH3ICG5QsiSNoveUjEJRmwYwcsFXiZ7+uy2oygPERQQRJ+KfRjy4xDbUTySFn0PIiL0\nq9iP99e+T4SJsB3H4529dpbJ2ybTv1J/21GUh2lWuBmHLx5m/W/rbUfxOMku+iKSSURWichBEVkp\nIhni2e64iOwSke0i8nPyo/qH0LyhpAxMyZJfltiO4vE+WPsBzQs311shqn9JEZhCj/bj4ciRfi9g\nlTHmKeD7qMdxMUCIMaaYMaaUA/vzCyLCgEoDGLB6gPb2H+DXy78ya/cselfsbTuK8lCvFXmNfb/v\nY9PJTbajeBRHin4d4POozz8H6j1gWx1qngR18tchXcp0zN4923YUjzV4zWDalWzHo2kftR1FeahU\nQanoVaGXHu3H4kjRz2qMORf1+TkgazzbGeA7EdkiIq0d2J/fEBE+rP4hA8IHcPvebdtxPM4vf/zC\nkoNL6F6uu+0oysO1LNaSHWd3sPX0VttRPMYDi35Uz353HB//uDuFibz8Lb5L4MobY4oBNYG3RKSi\nc6L7tko5K1EwS0EmbploO4rHGRA+gG5lu5EhdZynkZT6S+qg1PSt2Jd3vntHr9KNEvSgbxpjnovv\neyJyTkSyGWPOishjwPl4XuNM1P/+LiJfAaWAtXFtO2jQoL8+DwkJISQkJKH8Pm1otaFU/6I6LYq1\n0PECUXac3cGPv/7I1DpTbUdRXuKN4m8QtimM5YeXUzNfTdtxHBYeHk54eHiyny/J/esnIsOBC8aY\nYSLSC8hgjOkVa5s0QKAx5qqIpAVWAu8aY1bG8XpG/xL/W/OvmpMrQy4GV9ErDAFemP0CNf6vBh1L\nd7QdRXmRxQcW0/eHvuxou4OggAce63odEcEYk+jzpo709D8EnhORg0DVqMeIyOMi8k3UNtmAtSKy\nA9gELI2r4Kv4Da4ymHGbx3Hu2rmEN/ZxG05sYPf53bQp0cZ2FOVl6uSvw3/S/IfpO6bbjmJdso/0\nnU2P9OPXZXkX7kbcZWytsbajWGOMocrnVWhepDkti7W0HUd5oc2nNlPvf/X4pcMvpEuZznYcp3Hn\nkb5yk76V+jJ3z1wOXzxsO4o13x39jjPXztC8SHPbUZSXevaJZ6mUsxIjN4y0HcUqLfpeIHOazHQq\n3Yn+q/1z3IAxhj4/9GFIlSE+149V7vVB1Q8Y8/MYzl47azuKNVr0vUSXsl0IPx7OtjPbbEdxu9m7\nZxNhIni5wMu2oygvlztjbloUbcHA1QNtR7FGe/peZNzP41hycAkr/rvCdhS3uXLrCsHjglnYaCFl\nspexHUf5gEs3L5F/bH7CXw+nQJYCtuM4THv6Pqx1idaR94I94j/3AB2wegC189XWgq+cJuNDGeld\noTc9V/W0HcUKLfpeJGVgSj6u8THtl7Xn5t2btuO43PYz25m7dy5Dqw+1HUX5mPbPtmff7/tYfcz/\nbquoRd/L1MlfhyJZi/D+2vdtR3GpCBNB+2Xtea/Ke3obROV0qYJSMbTaULqv6u53967Qou+FxtQc\nw6Stk9hzfo/tKC4zbfs0jDG0Kt7KdhTlo14p+AqpAlMxeetk21HcSk/keqmJWyYyY+cM1rVcR4D4\n1t/uCzcuUGB8AZY3XU6xx4rZjqN82L7f91F5emW2tN5Czgw5bcdJFj2R6yfalGiDiDBpyyTbUZyu\n9/e9eaXAK1rwlcsVyFKArmW60vrr1n4zhVOLvpcKkAAmvzCZAeEDOH31tO04TrPp5CaWHlzKkKp6\n4wvlHj3K9+DizYtM3e4fk1u16Huxgo8WpG2Jtrz97du2ozjF/Yj7tPumHcOqD9NZ+cptggKCmFZ3\nGr2+78XJP0/ajuNyWvS9XN9Kfdl1bpdP3Eh9wpYJPJzqYf5b+L+2oyg/UyhrITqW6kibr9v4fJtH\ni76XSx2UmskvTqbDsg5cvX3VdpxkO3bpGO+ueZfxtcYjordUVu7Xu0JvTl89zYydM2xHcSkt+j4g\nJFcI1fNUp98P/WxHSZZb927RcH5D+lbsS8FHC9qOo/xUisAUTKs7jR6revjUebLYdMmmj7hw4wLP\nTHiGuQ3mUjlXZdtxkqT9N+05f/088xvO16N8ZV3/H/qz6/wuFjVa5BX/HnXJpp/6T5r/MKPeDBov\naMyvl3+1HSfRZu+ezaqjq5hSZ4pX/IIp39evUj+OXDzCnD1zbEdxCS36PuS5/3uOHuV68NL/XuLG\n3Ru24yRo/+/76bS8E/MbzueR1I/YjqMUEDmiYXq96XRZ0YWjl47ajuN0WvR9TJcyXSj4aEFaLWnl\n0asQrt+5TsP5DRlabShFsxW1HUepfyj5eEkGVBpA7dm1uXTzku04TqU9fR908+5NKk2vRMMCDelZ\n3vPGxxpjeH3x6xhj+Lze59rWUR6r24pubDu7jeVNl5MqKJXtOHHSnr7ioRQP8VWjrwjbFMbyw8tt\nx/mXKdunsPX0VibUnqAFX3m0j57/iIypM/LG12943DvnSzcv0fyrpN8zWou+j8qePjvzXp7Ha4te\n4+CFg7bj/GXH2R30/r438xvOJ23KtLbjKPVAARLAzPozOXjhIIPCB9mO85fLty7z3BfPkSVNliQ/\nV4u+Dyv/ZHmGVBlCvbn1+PP2n7bjcOjCIerNrceY0DEEZwm2HUepREmTIg1LGi/hi11fMH3HdNtx\nuHLrCs9/8TwVnqzAiOdHJPn52tP3A+2WtuPU1VMseGUBKQJTWMmw4+wOas2qxbsh79K6RGsrGZRy\nxIE/DlB5emVm159NtTzVrGT48/afPP/F85R6ohRhoWGIiPb01b+F1QzDYKg5qyYXb150+/7X/rqW\n5794njE1x2jBV17r6cxPM+/leTRZ0MTKDYyu3r5K6MxQSjxW4q+Cnxxa9P1AysCULGq0iCJZi1Dm\nszJu7fEvPbiU+vPqM7vBbF4u8LLb9quUK1TOVZmPa3xMjZk1WPvrWrft9+rtq9ScVZPCWQvzSa1P\nHFoAoe0dP/PZts/o+0Nft7xFnblrJt1Xdmdx48WUzl7apftSyp2WHVpGy8Utebv02/Sq0Muld6+7\nducatWbV4unMTzPxhYn/2ldS2zta9P1Q+PFwGn/ZmEEhg2hbsq1L9jFm0xg+2vARy5su1yFqyied\n/PMkTRY0IU2KNHzx0hc8mvZRp+9j7/m9vPH1GxTMUpDJL06O84+L23r6ItJQRPaKyH0RKf6A7UJF\n5ICIHBKRd5K7P+U8IblCWNdyHWGbwui4rCP3Iu457bXPXz9Px2UdGfvzWNa2WKsFX/ms7Omzs/q1\n1ZR4rATFJxUn/Hi401778q3LdF7emSqfV6FpoabxFvzkcORVdgMvAT/Gt4GIBAJjgVCgANBERHSt\nXgLCw8Ndvo+8mfKysdVGDl48SLUZ1VhxeAURJiLZr3fu2jm6r+xO8LhgDIb1LdeTK0Muh3O642fh\nLfRn8TdP+VkEBQTxQbUPmFJnCk0WNGHwmsHcj7if7NeLMBFM3T6V4HHB3Lh7g73t99KhVAento+S\n/UrGmAPGmITOCJYCDhtjjhtj7gJzgbrJ3ae/cNc/6AypM/DNq9/QtFBT+vzQh7xj8jJ07VDOXjub\n6Nc4e+0sXVd0JXhcMLfv3WZn252MrTWWLGmTftFIXDzll9sT6M/ib572s6iRtwZb22zlh2M/UPLT\nkgxdO5R9v+9L0lW8P5/6mbJTyvLptk/5usnXTH5xstN+j2IKcvor/tMTwIkYj08CekbPgwQFBNGm\nRBvalGjDltNbmLRlEsHjgqmepzpvlniTqrmrApEnk2J+XL19lUUHFvH5zs9pVrgZe9rv4fGHH7f8\nX6OUPY8//DjfN/+e1cdXs/jAYkJnhpIyMCV189el7tN1KZejHEEBQUSYCE7+eZJDFw5x6OIhDl88\nzN7f97Lz7E6GVhtKsyLNXHpi+IFFX0RWAdni+FYfY8zXiXh9PTPrRUo+XpKSdUoyssZIZu2aRfeV\n3dn/x37u3r9L2pRpSZcyHQ+nfJh0KdORLmU6Sj5eUou9UjEEBgRSPU91quepzpiaY9hxdgdLfllC\n5+Wd+e3Kb2RLl42jl46S8aGM5MuUj3yZ8pE3U15aF29N9TzVSZ8qvcszOrx6R0RWA92MMdvi+F4Z\nYJAxJjTqcW8gwhgzLI5t9Q+EUkolQ1JW7zirvRPfDrcA+UQkF3AaaAQ0iWvDpIRWSimVPI4s2XxJ\nRE4AZYBvROTbqK8/LiLfABhj7gEdgBXAPuB/xpj9jsdWSimVHB5zcZZSSinXsz57Ry/eiiQiOURk\nddQFb3tE5G3bmWwTkUAR2S4iiVk04LNEJIOIfCki+0VkX9S5Mr8kIr2jfkd2i8hsEfHM21m5gIhM\nFZFzIrI7xtcyicgqETkoIitFJENCr2O16OvFW/9wF+hijClIZMvsLT/+WUTrRGRb0N/fjoYBy4wx\nwUBhwC9bpFHnBlsDxY0xhYBAoLHNTG42jchaGVMvYJUx5ing+6jHD2T7SF8v3opijDlrjNkR9fk1\nIn+x/XYtpIhkB2oBnxH/QgGfJyKPABWNMVMh8jyZMeaK5Vi2/EnkwVEaEQkC0gCn7EZyH2PMWiD2\nXdrrAJ9Hff45UC+h17Fd9OO6eOsJS1k8RtQRTTFgk90kVo0CegDJnw3hG3IDv4vINBHZJiKfikga\n26FsMMZcBEYCvxG5GvCyMeY7u6msy2qMORf1+Tkga0JPsF30/f1t+7+ISDrgS6BT1BG/3xGRF4Dz\nxpjt+PFRfpQgoDgw3hhTHLhOIt7C+yIR+T+gM5CLyHfB6USkqdVQHiRqTHGCNdV20T8F5IjxOAeR\nR/t+SURSAAuAmcaYRbbzWFQOqCMix4A5QFURmWE5ky0ngZPGmM1Rj78k8o+APyoJbDDGXIhaDr6Q\nyH8r/uyciGQDEJHHgPMJPcF20f/r4i0RSUnkxVtLLGeyQiJvhTMF2GeMGW07j03GmD7GmBzGmNxE\nnqj7wRjT3HYuG4wxZ4ETIvJU1JeqA3stRrLpAFBGRB6K+n2pTuSJfn+2BHgt6vPXgAQPFl09cO2B\njDH3RCT64q1AYIofX7xVHvgvsEtEtkd9rbcxZrnFTJ7C39uAHYFZUQdGR4AWlvNYYYzZGfWObwuR\n53q2AZPtpnIfEZkDVAYyR10YOwD4EJgnIq2A48ArCb6OXpyllFL+w3Z7RymllBtp0VdKKT+iRV8p\npfyIFn2llPIjWvSVUsqPaNFXSik/okVfKaX8iBZ9pZTyI/8PepRmSDZ7Y2cAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fae9ad74a50>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Variable er globale for hele notatboken, her skriver vi ut 'x':"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([  0.        ,   0.20408163,   0.40816327,   0.6122449 ,\n",
        "         0.81632653,   1.02040816,   1.2244898 ,   1.42857143,\n",
        "         1.63265306,   1.83673469,   2.04081633,   2.24489796,\n",
        "         2.44897959,   2.65306122,   2.85714286,   3.06122449,\n",
        "         3.26530612,   3.46938776,   3.67346939,   3.87755102,\n",
        "         4.08163265,   4.28571429,   4.48979592,   4.69387755,\n",
        "         4.89795918,   5.10204082,   5.30612245,   5.51020408,\n",
        "         5.71428571,   5.91836735,   6.12244898,   6.32653061,\n",
        "         6.53061224,   6.73469388,   6.93877551,   7.14285714,\n",
        "         7.34693878,   7.55102041,   7.75510204,   7.95918367,\n",
        "         8.16326531,   8.36734694,   8.57142857,   8.7755102 ,\n",
        "         8.97959184,   9.18367347,   9.3877551 ,   9.59183673,\n",
        "         9.79591837,  10.        ])"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Litt mer plotting:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.xkcd()\n",
      "plt.title('Cosine!')\n",
      "plot(x, y2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7fae9ac47410>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cUlISN2vWLOHcnDlzOL1ezxUXF3Mcx3Eej4f7+uuvuTNnzgjXWCyWcvfZvHkz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fae9ac473d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Noen flere eksempler p\u00e5 hva vi kan gj\u00f8re i en notatbok:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![MET logo](http://met.no/filestore/logo_2013_no.png \"MET logo\")"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo\n",
      "\n",
      "YouTubeVideo('t_TzRaK9kpU')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"https://www.youtube.com/embed/t_TzRaK9kpU\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x7ff3d0d0d090>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<IPython.core.display.HTML at 0x7ff3d0d0d050>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "En notatbok kan ogs\u00e5 lett deles p\u00e5 nett, f.eks. p\u00e5 [nbviewer.ipython.org](http://nbviewer.ipython.org). F\u00f8lg med p\u00e5 [it.met.no](http://it.met.no), s\u00e5 f\u00e5r dere vite hvordan."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2. api.met.no"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Her f\u00f8lger tre kodeeksempler (det f\u00f8rste eksempelet henter kun et bilde via en url) som benytter api.met.no:"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Hente satellittbilde"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![Satellittbilde](http://api.met.no/weatherapi/geosatellite/1.3/?area=global;width=1200;height=600 'Satellittbilde av Europa')"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Sol opp/ned"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from lxml import etree\n",
      "import dateutil.parser as dparser\n",
      "\n",
      "# les XML-dokument fra url og hent ut rot-noden (se http://api.met.no/weatherapi/sunrise/1.0/documentation)\n",
      "tree = etree.parse('http://api.met.no/weatherapi/sunrise/1.0/?lat=59.9132694;lon=10.7391112;date=2015-02-19')\n",
      "root = tree.getroot()\n",
      "\n",
      "# skriv ut hele XML-dokumentet\n",
      "#print(etree.tostring(root, pretty_print=True))\n",
      "\n",
      "# 1. finn den f\u00f8rste tag-en ved navn \"sun\", og les attributtene \"rise\" og \"set\"\n",
      "# 2. les disse attributtene inn i et dato-og-tid objekt (ved hjelp av dparser.parse())\n",
      "sunrise = dparser.parse(next(tree.iter('sun')).attrib['rise'])\n",
      "sunset = dparser.parse(next(tree.iter('sun')).attrib['set'])\n",
      "\n",
      "# hent ut tid-delen, gj\u00f8r denne om til en tekststreng og skriv ut\n",
      "print \"Rise (UTC tid): \" + str(sunrise.time())\n",
      "print \"Set (UTC tid): \" + str(sunset.time())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Rise (UTC tid): 06:44:57\n",
        "Set (UTC tid): 16:17:51\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Hent aktuelt v\u00e6rsymbol"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from lxml import etree\n",
      "import dateutil.parser as dparser\n",
      "import datetime as dt\n",
      "from IPython.display import Image\n",
      "\n",
      "# les XML-dokument fra url og hent ut rot-noden (se http://api.met.no/weatherapi/locationforecast/1.9/documentation)\n",
      "tree = etree.parse('http://api.met.no/weatherapi/locationforecast/1.9/?lat=59.9132694;lon=10.7391112')\n",
      "root = tree.getroot()\n",
      "\n",
      "# skriv ut hele XML-dokumentet\n",
      "#print(etree.tostring(root, pretty_print=True))\n",
      "\n",
      "# finn modellen som heter \"LOCAL\" og les ut tidsperioden varselet gjelder for\n",
      "for element in tree.iter('model'):\n",
      "    if element.attrib['name'] == 'LOCAL':\n",
      "        from_time = dparser.parse(element.attrib['from'])\n",
      "        to_time = dparser.parse(element.attrib['to'])\n",
      "\n",
      "print \"Leser fra varsel for perioden \" + str(from_time) + \" til \" + str(to_time) + \" (UTC tid)\"\n",
      "\n",
      "\n",
      "# 1. g\u00e5 igjennom alle time-tag-er, finn tag-en som dekker n\u00e5tid til +1 time\n",
      "# 2. finn s\u00e5 v\u00e6rsymbolet gitt i tag-en \"symbol\", som er en barne-tag av time-tag-en\n",
      "for element in tree.iter('time'):\n",
      "    current_from = dparser.parse(element.attrib['from'])\n",
      "    current_to = dparser.parse(element.attrib['to'])\n",
      "    if current_from == from_time and (from_time + dt.timedelta(hours=1)) == current_to:\n",
      "        weather_symbol_text = next(element.iterdescendants('symbol')).attrib['id']\n",
      "        weather_symbol_number = next(element.iterdescendants('symbol')).attrib['number']\n",
      "     \n",
      "print \"\\nAktuelt v\u00e6rsymbol:\"\n",
      "\n",
      "# konstruer url for \u00e5 hente riktig v\u00e6rsymbol fra api.met.no\n",
      "weather_symbol_url = \"http://api.met.no/weatherapi/weathericon/1.1/?symbol=\" + weather_symbol_number +\";is_night=0;content_type=image/png\"\n",
      "#print weather_symbol_url\n",
      "\n",
      "# hent v\u00e6rsymbol-bildet og vis det\n",
      "weather_symbol_image = Image(url=weather_symbol_url)\n",
      "display(weather_symbol_image)\n",
      "\n",
      "print weather_symbol_text"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Leser fra varsel for perioden 2015-02-19 12:00:00+00:00 til 2015-02-22 00:00:00+00:00 (UTC tid)\n",
        "\n",
        "Aktuelt v\u00e6rsymbol:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "html": [
        "<img src=\"http://api.met.no/weatherapi/weathericon/1.1/?symbol=4;is_night=0;content_type=image/png\"/>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Image at 0x7fae99480510>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cloud\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}