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zhengyangchoong/runjumpfly.py

Created November 20, 2016 11:50
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  1. Choong Zheng Yang created this gist Nov 20, 2016.
    79 changes: 79 additions & 0 deletions runjumpfly.py
    View file
    Original file line number Diff line number Diff line change
    @@ -0,0 +1,79 @@
    """
    categories: pro, premier, open.
    cly --> strong men
    ath --> strong women
    swim, t1, bike, t2, run, total
    where t1 and t2 refer to the times needed for the transition between the modes of exercise.
    """

    import matplotlib.pyplot as plt
    import numpy as np
    import datetime

    f = open('HiMCM_TriDataSet.csv', 'r')

    people = []

    class person():
    def __init__(self):
    self.id = 0
    self.age = 0
    self.gender = ""
    self.cat = ""
    self.timings = []
    self.speeds = []
    def processTimings(self):
    newtimings = []
    speeds = []
    for i in self.timings:
    #print int(i.split(':')[1]) * 60
    t = int(i.split(':')[0]) * 3600 + int(i.split(':')[1]) * 60 + int(i.split(':')[2].strip())
    newtimings.append(t)
    self.speeds.append(1.0/t)
    self.timings = newtimings

    for line in f:
    if line[:1] == "#":
    continue
    if len(line) == 0:
    continue
    a = person()
    data = line.strip().split(",")
    a.id = int(data[0])
    a.age = int(data[1])
    a.gender = data[2]
    a.cat = data[3]
    a.timings = data[4:]
    a.processTimings()
    people.append(a)

    male_open_totaltiming = []
    female_open_totaltiming = []

    def genHistPlot(cats, timingindex, filename, fn = None): # category, timings index
    _data = []
    if fn == None:
    for i in cats:
    _data.append([person.timings[timingindex] for person in people if person.cat == i])
    else:
    for i in cats:
    _data.append([fn(person.timings[timingindex]) for person in people if person.cat == i])

    with plt.style.context('fivethirtyeight'):

    plt.gcf().subplots_adjust(bottom=0.15)
    plt.gcf().subplots_adjust(left=0.17)
    plt.grid('off')
    for i in _data:
    plt.hist(i, normed=1,alpha = 0.5)
    plt.xlabel("Total time [s]", fontsize=16)
    plt.ylabel("Normalised frequency [#]", fontsize=16)
    plt.savefig(filename+'.pdf')
    plt.clf()
    plt.cla()
    plt.close()

    genHistPlot(["M OPEN", "F OPEN"], 5, "open_gender_timing_distribution")
    genHistPlot(["M OPEN", "F OPEN"], 5, "open_gender_speed_distribution", fn = lambda x: 1.0/x)
    genHistPlot(["M PREMIER", "M PRO", "CLY"], 1, "male_non-open_swimtime_distribution")