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    "from scipy import sparse as sp\n",
    "import numpy as np\n",
    "\n",
    "def spcategorical(n_cat_ids):\n",
    "    '''\n",
    "    Returns a dummy matrix given an array of categorical variables.\n",
    "    Parameters\n",
    "    ----------\n",
    "    n_cat_ids    : array\n",
    "                   A 1d vector of the categorical labels for n observations.\n",
    "\n",
    "    Returns\n",
    "    --------\n",
    "    dummy        : array\n",
    "                   A sparse matrix of dummy (indicator/binary) variables for the\n",
    "                   categorical data.  \n",
    "\n",
    "    '''\n",
    "    if np.squeeze(n_cat_ids).ndim == 1:\n",
    "        cat_set = np.unique(n_cat_ids)\n",
    "        n = len(n_cat_ids)\n",
    "        index = [np.where(cat_set == id)[0].tolist()[0] for id in n_cat_ids] #This list comprehension is likely \n",
    "        print index                                                                    #the most intense part of the algorithm\n",
    "        indptr = np.arange(n+1, dtype=int) \n",
    "        return sp.csr_matrix((np.ones(n), index, indptr))\n",
    "    else:\n",
    "        raise IndexError(\"The index %s is not understood\" % col)\n",
    "\n",
    "#If the variable, n_cat_ids, is already composed of integers and the integers are the n x 1 vector of\n",
    "#origins or destinations in OD pairs for which w ewant to build fixed effects then there is no need to \n",
    "#create the index variable, which probably takes the most time within this function. Instead n_cat_ids can\n",
    "#passed directly to the csr matrix constructor and some speed-ups can be achieved. In the case where the\n",
    "#origin/destination ids are not integers but are strings a speed-up may be possible by alterign the algorithm\n",
    "#so that the index is build in chunks (say each origin/destination) rather than for each row of of the n x 1\n",
    "#n_cat_ids array as is done in creating the index variable."
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