Hares.ipynb

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   "source": [
    "## Exercise for the course [Python for MATLAB users](http://sese.nu/python-for-matlab-users-ht15/), by Olivier Verdier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This exercise comes from the [scipy lecture notes](http://www.scipy-lectures.org)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The population of Hares, Lynxes and carrots in Canada is in the file [population.txt](http://www.scipy-lectures.org/_downloads/populations.txt). Download that file and place it in this directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once this is done, execute the code below to load the data in the variable `data`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Important remark: **you should not use any `for` loop in this exercise!** :-)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "data = np.loadtxt('populations.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "year, hares, lynxes, carrots = data.T  # trick: columns to variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "year, hares, lynxes, carrots = data.T\n",
    "populations = data[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1900.,  30000.,   4000.,  48300.],\n",
       "       [  1901.,  47200.,   6100.,  48200.],\n",
       "       [  1902.,  70200.,   9800.,  41500.],\n",
       "       [  1903.,  77400.,  35200.,  38200.],\n",
       "       [  1904.,  36300.,  59400.,  40600.],\n",
       "       [  1905.,  20600.,  41700.,  39800.],\n",
       "       [  1906.,  18100.,  19000.,  38600.],\n",
       "       [  1907.,  21400.,  13000.,  42300.],\n",
       "       [  1908.,  22000.,   8300.,  44500.],\n",
       "       [  1909.,  25400.,   9100.,  42100.],\n",
       "       [  1910.,  27100.,   7400.,  46000.],\n",
       "       [  1911.,  40300.,   8000.,  46800.],\n",
       "       [  1912.,  57000.,  12300.,  43800.],\n",
       "       [  1913.,  76600.,  19500.,  40900.],\n",
       "       [  1914.,  52300.,  45700.,  39400.],\n",
       "       [  1915.,  19500.,  51100.,  39000.],\n",
       "       [  1916.,  11200.,  29700.,  36700.],\n",
       "       [  1917.,   7600.,  15800.,  41800.],\n",
       "       [  1918.,  14600.,   9700.,  43300.],\n",
       "       [  1919.,  16200.,  10100.,  41300.],\n",
       "       [  1920.,  24700.,   8600.,  47300.]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data (year vs each population item), using `label` and `legend`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the mean (`np.mean?`) and standard deviation (`np.std?`) of each piece of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which year each species had the largest population? (`np.argmax?` and use fancy indexing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
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   "source": [
    "Which year any of the population is over 50000?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
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   "source": [
    "The top two years for each species when they had the lowest population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may use `argsort`, which gives the indices for which the array value would be sorted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.argsort?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare (plot) the change in hare population (see `np.gradient?`) and the number of lynxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "source": []
  }
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