import pandas as pd
import matplotlib.pyplot as plt
%pylab inline
def time_ticks(x, pos):
    d = datetime.timedelta(seconds=x)
    return str(d)

def timestring_to_seconds(time_string):
 #   if type(time_string) != str:
 #       return numpy.nan
    parts = time_string.split(':')
    return int(parts[0])*3600 + int(parts[1])*60 + int(parts[2])

# Convert an int seconds into a str hh:mm:ss
def seconds_to_timestring(time_seconds):
    if math.isnan(time_seconds):
        return nan
    
    # If the number is negative, work with the absolute value to avoid modulo with negative numbers.
    time = abs(time_seconds)
    
    hours = int(time/3600)
    minutes = int((time % 3600)/60)
    seconds = time % 60
    output = "{0}:{1:02.0f}:{2:04.1f}".format(hours, minutes, seconds)
    if time_seconds < 0:
        output = '-' + output
    return output

def finish_time_histogram(data, color=None):
    fig, ax = pyplot.subplots(figsize=(12,8))
    ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(time_ticks))
    ax.xaxis.set_major_locator(MultipleLocator(300))
    ax.set_title('Taipei Marathon Finishers in 2013~2017', color=('b'), size=30)
    ax.yaxis.set_label_text('Number of finishers')
    pylab.xticks(rotation='vertical')
    return pyplot.hist(data.finish_seconds, bins=bins, color=color)

data = pd.read_csv('allTPE_Runner2013_2017.csv')
mData=data[data.Full_half=='MA']

mData['finish_seconds'] = mData['Net_Time'].apply(timestring_to_seconds)

male_female = data.groupby(['Year','Full_half']).Gender.value_counts().unstack()
male_female_normed = male_female.div(male_female.sum(1), axis=0)
ax = male_female_normed.plot(kind='barh', stacked=True, fontsize=16, title='Male/Female Ratio')
ax.xaxis.set_major_locator(plt.MultipleLocator(0.1))
ax.invert_yaxis()
# Show raw data
male_female_normed.applymap(lambda x: '{:.2%}'.format(x))

bins=[7200 + 60*i for i in range (0, 4*60 + 1)]

plot = finish_time_histogram(mData) 
plot[2][119].set_facecolor('r')
plot[2][149].set_facecolor('r')
plot[2][179].set_facecolor('r')
plot[2][209].set_facecolor('y')