plot_session_pie.py

def plot_session_pie(headline, header, chart, footer, session, fontsize=15, min_frac=0.05):
    """ Plot a pie chart based on the minutes in each HR zone for the session.
     
    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.
         min_frac - only plot wedges above this fraction.
    """
        
    # -------------------------------------------------------------------------#
    # Extract the data needed from the session dataframe.
    
    # The title is based on the workout name, truncated to fit.
    max_title_len = 18
    title = session['workout_name']
    title = title[:max_title_len]+'...' if len(title)>max_title_len else title
    
    # Session distance and time
    total_kms = session['distance']/1000
    rel_kms = session['rel_distance']

    total_mins = session['moving_time']/60
    rel_mins = session['rel_moving_time']
    
    # Calculate mean session pace and convert to mins:sec string.
    mean_pace = convert_to_pace(total_mins/total_kms)

    # Session relative effort/suffer score
    suffer_score = session['suffer_score']
    rel_suffer_score = session['rel_suffer_score']
    
    # The distance in each HR zone
    kms_in_zone = session.filter(regex='z\d_distance')/1000
    mins_in_zone = session.filter(regex='z\d_moving_time')/60
    fracs_in_zone = mins_in_zone/mins_in_zone.sum()
    
    # The time elapsed since the previous session.
    rest_hours = session['rest_hours']
    
    # -------------------------------------------------------------------------#
    # Creating the session pie chart.
    
    # Remove all axis decoration.
    headline.set_axis_off()
    header.set_axis_off()
    chart.set_axis_off()
    footer.set_axis_off()

    # A 6-step colourmap for the 6 HR zones.
    use_cmap = mpl.colormaps['coolwarm']
    use_colours = use_cmap(np.linspace(.25, 1, 6))
    
    # The HR zone labels for the pie chart.
    labels = ['Z1', 'Z2', 'Z3', 'Z4', 'Z5', 'Z6']
    labeldistance = 1.25 if len(fracs_in_zone[fracs_in_zone>=min_frac])>1 else 1.3
    
    # The size of the pie chart is based on the relative session duration.
    base_radius = 0.3
    radius = (base_radius+(rel_mins*(1-base_radius)))**.5
    
    # Draw the pie chart.
    patches, labels = chart.pie(
        mins_in_zone, 
        radius=radius, 
        
        # Adjust positioning of wedges to visually separate Z1/Z2, Z3/Z4, Z5/Z6.
        explode=[0, 0, .075, .075, .15, .15],
        
        # The wedge labels.
        labels=labels, labeldistance=labeldistance, textprops=dict(fontsize=fontsize),
        
        # The segments start at the 12 o'clock and proceed clockwise.
        startangle=90, counterclock=False, 
        
        # The wedges colours.
        colors=use_colours, wedgeprops=dict(ec='w', lw=1),
    )
    
    # Remove patches that are too small; avoids display problems.
    for frac, patch, label in zip(fracs_in_zone, patches, labels):
        if frac<min_frac: 
            patch.set_visible(False)
            label.set_visible(False)
    
    # Add the relative effort/suffer_score in the center of the pie and
    # colour code it based on the relative suffer score.
    chart.text(
        0, 0, '{:.0f}'.format(suffer_score), 
        color='k' if 0.2<rel_suffer_score<0.75 else 'w', fontsize=fontsize, 
        ha='center', va='center',
        bbox=dict(
            boxstyle='circle', 
            facecolor=use_cmap(rel_suffer_score), 
            lw=.5, alpha=.75, pad=0.5
        )
    )
    
    # -------------------------------------------------------------------------#
    # Additional annotations.
    
    # The session title is used as the headline.
    headline.text(
        np.mean(headline.get_xlim()), np.mean(headline.get_ylim()),
        title, 
        fontsize=fontsize, fontweight='bold', 
        ha='center', va='center'
    )
    
    # Add session time and hours since previous session.
    if rest_hours>0: 
        header.text(
            np.mean(footer.get_xlim()), np.mean(footer.get_ylim()),
            '{:.0f} mins ({:.0f} hrs rest)'.format(total_mins, rest_hours), 
            fontsize=fontsize, color='grey',
            ha='center', va='center'
        )
    
    # Session distance and pace.
    footer.text(
        np.mean(footer.get_xlim()), np.mean(footer.get_xlim()),
        '{:.1f} km @ {} mins/km'.format(total_kms, mean_pace), 
        fontsize=fontsize, color='grey',
        ha='center', va='center'
    )