master.py 文件源码

def get_training_image():
    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
    from matplotlib.figure import Figure
    import seaborn as sns
    import StringIO

    fig = Figure()
    df = pd.DataFrame.from_dict(get_flattened_training_data())
    features = [f for f in df.columns if f not in ['mac', 'location']]
    df = df.rename(columns=dict(zip(features, [POWER_SLAVE_PREFIX + f for f in features])))

    sns_plot = sns.pairplot(df, hue="location", vars=[POWER_SLAVE_PREFIX + f for f in features])
    png_output = StringIO.StringIO()
    sns_plot.savefig(png_output, format='png')

    canvas = FigureCanvas(fig)
    canvas.print_png(png_output)
    print png_output.getvalue()
    return