plot_training_week_summary_bars.py

def plot_training_week_summary_bars(headline, header, chart, footer, week, fontsize=15):
    """ Plot a summary of the training week.
    
    Args:
        headline - axis for the chart headline/title.
        header - axis for the chart header info (weekly mins, RE, % easy).
        chart - axis for the main chart (horizontal bar).
        footer - axis for the weekly kms and mean pace.
        
        week - a dataframe with data for the week to be summarised.
        fontsize - fontsize to be used for all text annotations.
    """
        
    # ------------------------------------------------------------------------ #
    # The start/end of the current training week.
    
    # Calculate the number of week before the race.
    num_weeks_before_race = week['days_before_race'].iloc[0]//7
    
    # The date of the first day of the week.
    first_session_day_num = int(week.index[0])
    first_session_date = week.iloc[0]['date']
    
    week_start_date = (first_session_date-timedelta(days=first_session_day_num))
    week_end_date = week_start_date + timedelta(days=6)

    # ------------------------------------------------------------------------ #
    # Get the session data needed ...
    
    # Sum across the week's sessions.
    w = week.select_dtypes(include='number').sum()
    
    # Total kms and mins for the week.
    total_kms = w['distance']/1000
    total_mins = w['moving_time']/60
    
    # Mean pace for the week.
    mean_pace = convert_to_pace(total_mins/total_kms)
    
    # The number of activities and total relative effort
    num_activities = len(week)
    total_suffer_score = w['suffer_score']
    
    # The % of easy (Z1/Z2) running.
    pct_easy = (
        100*(week[['z1_moving_time', 'z2_moving_time']]
             .sum(axis=1)
             .sum()/w['moving_time'])
    )
    
    # The kms in each HR zone.
    kms_in_zone = w.filter(regex='z\d_distance')/1000
    mins_in_zone = w.filter(regex='z\d_moving_time')/60
    
    # ------------------------------------------------------------------------ #
    # Creating the weekly chart
    
    # Remove the axis decorations.
    headline.set_axis_off()
    header.set_axis_off()
    footer.set_axis_off()
    
    # The colours for the 6 HR zones.
    use_cmap = mpl.colormaps['coolwarm']
    use_colours = use_cmap(np.linspace(.25, 1, 6))
    
    # Plot the bar chart.
    chart.barh(range(len(mins_in_zone)), mins_in_zone, color=use_colours)

    chart.set_xlim(0, 450)
    chart.set_yticks(range(len(kms_in_zone)))
    chart.set_yticklabels(['Z1', 'Z2', 'Z3', 'Z4', 'Z5', 'Z6'], fontsize=fontsize)
    
    # Remove the spines/ticks for the right/top/bottom axes.
    chart.spines[['right', 'top', 'bottom']].set_visible(False)
    chart.xaxis.set_tick_params(bottom=False, labelbottom=False)

    # Add the labels for each bar.
    for z, m in enumerate(mins_in_zone):
        if int(m)>0: chart.text(m, z, ' {:.0f} mins'.format(m), fontsize=fontsize, ha='left', va='center', color='grey')
    
    # -------------------------------------------------------------------------#
    # Add the headline, header, and footer annotations
    
    headline.text(
        0, np.mean(footer.get_ylim()),
        'Wk {}. {} - {} (x{:.0f})'.format(num_weeks_before_race, week_start_date.strftime('%b %d'), week_end_date.strftime('%b %d'), num_activities),
        fontsize=fontsize, fontweight='bold',
        ha='left', va='center'
    )
    
    header.text(
        0, np.mean(header.get_ylim()),
        '{:.0f} mins, {:.0f} RE, {:.0f}% Easy'.format(total_mins, total_suffer_score, pct_easy), 
        fontsize=fontsize, color='grey', 
        ha='left', va='center'
    )

    footer.text(
        0, np.mean(footer.get_ylim()),
        '{:.2f} kms @ {} mins/km'.format(total_kms, mean_pace),
        fontsize=fontsize, ha='left', va='center', color='grey'
    )