accPlot.py

"""Script to plot accelerometer traces."""

import matplotlib
matplotlib.use('Agg')
from accelerometer import accUtils
import argparse
from datetime import datetime, timedelta, time
import matplotlib.pyplot as plt
import matplotlib.lines  as mlines 
import matplotlib.patches as mpatches
import numpy as np
import pandas as pd
import sys

DOHERTY_NatComms_COLOURS = {'sleep':'blue', 'sedentary':'red',
    'tasks-light':'darkorange', 'walking':'lightgreen', 'moderate':'green'}

WILLETS_SciReports_COLOURS = {'sleep':'blue', 'sit.stand':'red',
    'vehicle':'darkorange', 'walking':'lightgreen', 'mixed':'green',
    'bicycling':'purple'}



def main():
    """
    Application entry point responsible for parsing command line requests
    """

    parser = argparse.ArgumentParser(
        description="A script to plot acc time series data.", add_help=True)
    # required
    parser.add_argument('timeSeriesFile', metavar='input file', type=str,
                            help="input .csv.gz time series file to plot")
    parser.add_argument('plotFile', metavar='output file', type=str,
                            help="output .png file to plot to")
    parser.add_argument('--activityModel', type=str,
                            default="activityModels/doherty2018.tar",
                            help="""trained activity model .tar file""")
    
    # check input is ok
    if len(sys.argv) < 3:
            msg = "\nInvalid input, please enter at least 2 parameters, e.g."
            msg += "\npython accPlot.py timeSeries.csv.gz plot.png \n"
            accUtils.toScreen(msg)
            parser.print_help()
            sys.exit(-1)
    args = parser.parse_args()
    
    # and then call plot function
    plotTimeSeries(args.timeSeriesFile, args.plotFile, 
        activityModel = args.activityModel)



def plotTimeSeries(tsFile, plotFile, 
    activityModel = "activityModels/doherty2018.tar"):
    """Plot overall activity and classified activity types

    :param str tsFile: Input filename with .csv.gz time series data
    :param str tsFile: Output filename for .png image
    :param str activityModel: Input tar model file used for activity classification
    
    :return: Writes plot to <plotFile>
    :rtype: void

    :Example:
    >>> import accPlot
    >>> accPlot.plotTimeSeries("sample-timeSeries.csv.gz", "sample-plot.png")
    <plot file written to sample-plot.png>
    """

    # read time series file to pandas DataFrame
    d = accUtils.loadTimeSeriesCSV(tsFile)
    d['acc'] = d['acc'].rolling(window=12, min_periods=1).mean() # smoothing
    d['time'] = d.index.time
    ymin = d['acc'].min()
    ymax = d['acc'].max()

    # infer labels
    labels = []
    for col in d.columns.tolist():
        if col not in [accUtils.TIME_SERIES_COL, 'imputed', 'acc', 'MET']:
            labels += [col]
    print(labels)
    if activityModel.endswith("doherty2018.tar"):
        labels_as_col = DOHERTY_NatComms_COLOURS
    elif activityModel.endswith("willetts2018.tar"):
        labels_as_col = WILLETS_SciReports_COLOURS
    # add imputation label colour
    labels_as_col['imputed'] = '#fafc6f'

    convert_date = np.vectorize(lambda day, x: matplotlib.dates.date2num(datetime.combine(day, x)))

    # number of rows to display in figure (all days + legend)
    d['date'] = d.index.date
    groupedDays = d[['acc','time','imputed'] + labels].groupby(by=d['date'])
    nrows = len(groupedDays) + 1

    # create overall figure
    fig = plt.figure(1, figsize=(10,nrows), dpi=100)
    fig.canvas.set_window_title(tsFile)

    # create individual plot for each day
    i = 0
    ax_list = []
    for day, group in groupedDays:
        # set start and end to zero to avoid diagonal fill boxes
        group['imputed'].values[0] = 0
        group['imputed'].values[-1] = 0
        for label in labels:
            group[label].values[0] = 0
            group[label].values[-1] = 0
        
        # retrieve time series data for this day
        timeSeries = convert_date( day, group['time'] ) 
        # and then plot time series data for this day
        plt.subplot(nrows, 1, i+1)
        plt.plot(timeSeries, group['acc'], c='k')
        plt.fill(timeSeries, np.multiply(group['imputed'], ymax), 
            labels_as_col['imputed'], alpha=0.5)
        
        # change display properties of this subplot
        ax = plt.gca()
        if len(labels)>0:
            ax.stackplot(timeSeries, [np.multiply(group[l], ymax) for l in labels], 
                colors=[labels_as_col[l] for l in labels], alpha=0.5, edgecolor="none")
        # add date label to left hand side of each day's activity plot
        plt.title(
            day.strftime("%A,\n%d %B"), weight='bold',
            x=-.2, y=0.5, 
            horizontalalignment='left',
            verticalalignment='center',
            rotation='horizontal',
            transform=ax.transAxes,
            fontsize='medium',
            color='k'
            ) 
        # run gridlines for each hour bar
        ax.get_xaxis().grid(True, which='major', color='grey', alpha=0.5)
        ax.get_xaxis().grid(True, which='minor', color='grey', alpha=0.25)
        # set x and y-axes
        ax.set_xlim((datetime.combine(day,time(0, 0, 0, 0)), 
            datetime.combine(day + timedelta(days=1), time(0, 0, 0, 0))))
        ax.set_xticks(pd.date_range(start=datetime.combine(day,time(0, 0, 0, 0)), 
            end=datetime.combine(day + timedelta(days=1), time(0, 0, 0, 0)), 
            freq='4H'))
        ax.set_xticks(pd.date_range(start=datetime.combine(day,time(0, 0, 0, 0)), 
            end=datetime.combine(day + timedelta(days=1), time(0, 0, 0, 0)), 
            freq='1H'), minor=True)
        ax.set_ylim((ymin, ymax))
        ax.get_yaxis().set_ticks([]) # hide y-axis lables
        # make border less harsh between subplots
        ax.spines['top'].set_color('#d3d3d3') # lightgray
        ax.spines['right'].set_visible(False)
        ax.spines['left'].set_visible(False)
        # set background colour to lightgray
        ax.set_facecolor('#d3d3d3')
        
        # append to list and incrament list counter
        ax_list.append(ax)
        i += 1
        
    # create new subplot to display legend
    plt.subplot(nrows, 1, i+1)
    ax = plt.gca()
    ax.axis('off') # don't display axis information
    # create a 'patch' for each legend entry
    legend_patches = [mpatches.Patch(color= labels_as_col['imputed'], 
                                     label='imputed', alpha=0.5), 
                      mlines.Line2D([],[],color='k',label='acceleration')]
    # create lengend entry for each label
    for label in labels:
        col = labels_as_col[label]
        legend_patches.append(mpatches.Patch(color=col, label=label, alpha=0.5))
    # create overall legend 
    plt.legend(handles=legend_patches, bbox_to_anchor=(0., 0., 1., 1.), 
        loc='center', ncol=min(4,len(legend_patches)), mode="best", 
        borderaxespad=0, framealpha=0.6, frameon=True, fancybox=True)

    # remove legend border
    ax.spines['left'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax_list.append(ax)

    # format x-axis to show hours
    plt.gcf().autofmt_xdate()
    # add hour labels to top of plot
    hours2Display = range(0, 24, 4)
    hrLabels = [(str(hr) + 'am') if hr<=12 else (str(hr-12) + 'pm') for hr in hours2Display]  
    ax_list[0].set_xticklabels(hrLabels)
    ax_list[0].tick_params(labelbottom=False, labeltop=True, labelleft=False)

    plt.savefig(plotFile, dpi=200, bbox_inches='tight')
    print('Plot file written to:', plotFile)



if __name__ == '__main__':
    main()  # Standard boilerplate to call the main() function to begin the program.