def main(posargs):

    # Check then handle the positional arguements
    # brough in from the command line
    if len(posargs) < 3:
        raise ValueError("At least three arguments are required.\n")

    name = posargs.pop()
    stat = posargs[0]
    tocompare = posargs[1:]
    
    # Create a csv file to tabulate into
    f = open('{0}_{1}.csv'.format(name, stat), 'w')
    csvw = csv.writer(f)
    csvw.writerow(["mean", "sd", "D", "dataset", "model", "boldmeta",
            "dmmeta", "cond"])
    
    models = get_model_names(tocompare[0])  ## Assume models are identical for 
                                            ## each hdf to compare
    for model in models:
        for ii, comp in enumerate(tocompare):
            meta = get_model_meta(comp, model)
            boldmeta = "_".join([str(b) for b in meta["bold"]])
            dmmeta = "_".join([str(d) for d in meta["dm"]])
            
            # Generate the data to add to the table.
            hist_list = create_hist_list(comp, model, stat)
            means = [hist.mean() for hist in hist_list]
            stdevs = [hist.stdev() for hist in hist_list]
            names = [hist.name for hist in hist_list]
            
            # Calculate effect sizes
            ns = [hist.n() for hist in hist_list]
            k = len(hist_list) + 1      ## Number of predcitors 
                                        ## +1 for the dummy
            
            cohen_ds = [(2.0 * mean) / np.sqrt(n - k - 1.0) for 
                    mean, n in zip(means, ns)]
                        ## d = 2*t / sqrt(DF)
                        ## DF = n - k - 1
                        ##  n = sample number
                        ##  k = predictor number
            
            # And add it.
            databasename = os.path.splitext(os.path.basename(comp))[0]
            for mean, sd, d, name in zip(means, stdevs, cohen_ds, names):
                row = [mean, sd, d, databasename, model, boldmeta, dmmeta, name]
                csvw.writerow(row)

    f.close()
def main(posargs):

    # Check then handle the positional arguements
    # brough in from the command line
    if len(posargs) != 4:
        raise ValueError("Four arguments are required.\n")

    hdf = posargs[0]
    stat = posargs[1]
    criterion = float(posargs[2])
    name = posargs[3]

    # Create csv writer named name
    # And give it a header
    f = open("{0}_{1}{2}.csv".format(name, stat, criterion), "w")
    csvw = csv.writer(f)
    csvw.writerow(["area", "model", "boldmeta", "dmmeta", "cond"])

    models = get_model_names(hdf)
    for model in models:
        hist_list = create_hist_list(hdf, model, stat)

        # Loop over the hist_list adding
        # the results from each hist.above(criterion)
        # to a bar plot.
        areas = [hist.above(criterion) for hist in hist_list]
        names = [hist.name for hist in hist_list]

        # Pretty things up then save
        # this barplot to the pdf
        # and move onto the next model
        meta = get_model_meta(hdf, model)
        boldmeta = "_".join([str(b) for b in meta["bold"]])
        dmmeta = "_".join([str(d) for d in meta["dm"]])

        for area, name in zip(areas, names):
            row = [area, model, boldmeta, dmmeta, name]
            csvw.writerow(row)

    f.close()