graphics.py 文件源码

def plot_doy_heatmap(data, cmap='nipy_spectral', vmin=None, vmax=None, 
                     overlay=None, title=None, figsize=(7.0, 3.0)):
    """
    Create a day-of-year (X-axis) vs. time-of-day (Y-axis) heatmap.

    Parameters
    ----------
    data : pandas DataFrame or pandas Series
        Data (single column), indexed by time

    cmap : string (optional)
        Colomap, default = nipy_spectral

    vmin : float (optional)
        Colomap minimum, default = None (autoscale)

    vmax : float (optional)
        Colomap maximum, default = None (autoscale)

    overlay : pandas DataFrame (optional)
        Data to overlay on the heatmap.  
        Time index should be in day-of-year (X-axis) 
        Values should be in time-of-day in minutes (Y-axis)

    title : string (optional)
        Title, default = None

    figsize : tuple (optional)
        Figure size, default = (7.0, 3.0)
    """

    if type(data) is pd.core.series.Series:
        data = data.to_frame()

    # Convert data to a pivot table
    col_name = data.columns[0]
    data['X'] = data.index.dayofyear
    data['Y'] = data.index.hour*60 + \
                data.index.minute + \
                data.index.second/60 + \
                data.index.microsecond/(60*1000000.0)
    piv = pd.pivot_table(data,values=col_name,index=['Y'],columns=['X'],fill_value=np.NaN)

    # Create the heatmap
    plt.figure(figsize = figsize)
    fig, ax = plt.subplots(figsize=figsize)
    im = ax.imshow(piv, cmap=cmap, aspect='auto', vmin=vmin, vmax=vmax,
                   extent=[data['X'].min()-0.5,data['X'].max()+0.5, 
                           data['Y'].max()-0.5,data['Y'].min()+0.5])
    fig.colorbar(im, ax=ax)

    # Add overlay
    if type(overlay) is pd.core.frame.DataFrame:
        overlay.plot(ax=ax)

    # Add title and labels
    if title:
        ax.set_title(title)
    ax.set_xlabel("Day of the year")
    ax.set_ylabel("Time of day (minutes)")
    plt.tight_layout()