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plotting.py
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plotting.py
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import matplotlib.pyplot as pp
import matplotlib.dates
import matplotlib as mpl
import numpy as np
from scipy import stats as st
import datetime
import stats
COLORS = ['#08C43A', '#6B2747', 'b']
FONT_SIZE = 18
def MinuteFormatter(x, p):
return "%02.d:%02.d" % (int(x / 60), x % 60)
def HourMinFormatter(x, p):
return "%d:%02.d" % (int(x / 3600), int((x % 3600) / 60))
def PlotAlt(workout):
pp.figure()
alt = workout.GetAltArray()
dist = workout.GetDistArray()
#print alt
pp.plot(dist, alt)
pp.show()
def PlotPace(workouts):
pp.figure()
ax = pp.subplot(111)
for i,workout in enumerate(workouts):
pace = workout.GetPace()
lap_starts = workout.GetLapEnds()
pp.plot(lap_starts, pace, "o--", markersize=12,
mfc=COLORS[i], label=workout.name,
color='k')
pp.ylabel("Run pace (min / km)", fontsize=FONT_SIZE)
pp.xlabel("Distance (km)", fontsize=FONT_SIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(FONT_SIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(FONT_SIZE)
ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(MinuteFormatter))
pp.grid(color='b')
#pp.ylim([200, 450])
pp.legend(loc='lower left', fancybox=True, fontsize=FONT_SIZE)
pp.tight_layout()
pp.show()
def PlotPaceVsDistance(workouts, fname=None):
nitems = len(workouts)
dist = np.zeros(nitems)
pace = np.zeros(nitems)
recent_dist = []
recent_pace = []
# Grab dist and pace data for all and lates runs
for i, workout in enumerate(workouts):
dist[i] = workout.GetTotalDist() / 1000.0 # in km
pace[i] = workout.GetTotalTime() / dist[i]
if datetime.datetime.now() - workout.GetStartTime() < datetime.timedelta(days=60):
recent_dist.append(dist[i])
recent_pace.append(pace[i])
# Finally, do the scatters
f = pp.figure()
ax = pp.subplot(111)
pp.plot(dist, pace, "o", markersize=6)
pp.plot(recent_dist, recent_pace, "o", mfc=COLORS[0], markersize=6)
pp.ylabel("Run pace (min / km)", fontsize=FONT_SIZE)
pp.xlabel("Run length (km)", fontsize=FONT_SIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(FONT_SIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(FONT_SIZE)
pp.xlim([0, 47.0])
pp.ylim([200, 370])
ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(MinuteFormatter))
# Fit for all runs
slope, intercept, r_value, p_value, std_err = st.linregress(dist, pace)
regr_x = np.array([5, 42])
regr_y = intercept + regr_x * slope
pp.plot(regr_x, regr_y, '--', color='k')
pp.annotate("%.2f s/km^2" % slope, (regr_x[1] - 10.0, regr_y[1] - 10), fontsize=FONT_SIZE-2)
# Fit for recent runs
slope, intercept, r_value, p_value, std_err = st.linregress(recent_dist, recent_pace)
regr_x = np.array([5, 42])
regr_y = intercept + regr_x * slope
pp.plot(regr_x, regr_y, '--', color=COLORS[0])
pp.annotate("%.2f s/km^2" % slope, (regr_x[1] - 10.0, regr_y[1] - 10), color=COLORS[0], fontsize=FONT_SIZE-2)
ax.yaxis.grid(color='b')
pp.legend(["Total", "Last 60 days"], loc='lower right', fancybox=True, fontsize=FONT_SIZE)
pp.tight_layout()
if fname:
pp.savefig(fname)
pp.show()
def PlotDistanceAtPace(workouts, fname=None):
paces = []
dists = []
recent_dist = []
recent_pace = []
for workout in workouts:
for lap in workout.laps:
if lap.dist == 0.0:
continue
pace = lap.time / (lap.dist / 1000.0)
paces.append(pace)
dists.append(lap.dist / 1000.0)
if datetime.datetime.now() - workout.GetStartTime() < datetime.timedelta(days=60):
recent_dist.append(lap.dist / 1000.0)
recent_pace.append(pace)
pp.figure()
ax = pp.subplot(111)
pp.hist(paces, weights=dists, bins=30, range=[180, 480], color='#52730B')
pp.hist(recent_pace, weights=recent_dist, bins=30, range=[180, 480], color='r')
ax.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(MinuteFormatter))
pp.xlabel("Pace (min / km)", fontsize=FONT_SIZE)
pp.ylabel("Total distance at pace (km)", fontsize=FONT_SIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(FONT_SIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(FONT_SIZE)
pp.legend(["Total", "Last 60 days"], loc='upper right', fancybox=True, fontsize=FONT_SIZE)
pp.grid(color='b')
pp.tight_layout()
if fname:
pp.savefig(fname)
pp.show()
def PlotMonthlyDist(workouts, fname=None):
monthly = {}
for workout in workouts:
month = workout.GetStartTime().month
year = workout.GetStartTime().year
month = workout.GetStartTime().replace(day=1, hour=0, minute=0, second=0, microsecond=0)
if not month in monthly:
monthly[month] = 0.0
monthly[month] += workout.GetTotalDist() / 1000.0
pp.figure()
ax = pp.subplot(111, aspect=0.4)
n_items = len(monthly.keys())
sort_ind = sorted(range(n_items), key=lambda i: monthly.keys()[i])
months = [monthly.keys()[ind] for ind in sort_ind]
totals = [monthly.values()[ind] for ind in sort_ind]
AV_MONTHS = 3
totals_av = []
# get moving average
for i in range(AV_MONTHS-1, n_items):
total = 0.0
for j in range(AV_MONTHS):
total += totals[i-j]
totals_av.append(total / AV_MONTHS)
pp.plot(months, totals, 'o--', markersize=14, color='k', mfc='b')
pp.plot(months[AV_MONTHS-1:], totals_av, 'o--', color='k', markersize=14, mfc='g')
pp.legend(["Monthly", "3-month average"], loc='upper left', fancybox=True, fontsize=FONT_SIZE)
pp.xlim([months[0] - datetime.timedelta(days=14), months[-1] + datetime.timedelta(days=14)])
ax.yaxis.grid(color='b')
pp.ylabel("Total distance (km)", fontsize=FONT_SIZE)
locator = mpl.dates.MonthLocator()
formatter = mpl.dates.AutoDateFormatter(locator)
formatter.scaled[30.0] = '%b' # only show month
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(FONT_SIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(FONT_SIZE)
pp.tight_layout()
if fname:
pp.savefig(fname)
pp.show()
def PlotRaces(workouts, fname):
pp.figure()
ax = pp.subplot(111)
race_times = []
dates = []
for workout in workouts:
if workout.GetTotalDist() < 41.0 * 1000:
continue
race_times.append(workout.GetTotalTime())
dates.append(workout.GetStartTime())
pp.plot(dates, race_times, "o", markersize=12,
# mfc=COLORS[i], label=workout.name,
color='k')
# calculate regression
days_diff = np.arange(len(dates))
for i,d in enumerate(dates):
days_diff[i] = (d - min(dates)).days
slope, intercept, r_value, p_value, std_err = st.linregress(days_diff, race_times)
regr_y = intercept + days_diff * slope
# plot regression
pp.plot(dates, regr_y, "--", color='#555555', lw=1.5, label=None)
month_slope = 30 * slope
pp.text(0.35, 0.30,
r"Trend: %d:%02.d / month" % (int(month_slope / 60),
int(abs(month_slope) % 60)),
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
pp.ylabel("Marathon time", fontsize=FONT_SIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(FONT_SIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(FONT_SIZE)
pp.xticks(rotation=75)
xmin = min(dates) - datetime.timedelta(weeks=4)
xmax = max(dates) + datetime.timedelta(weeks=4)
pp.xlim([xmin, xmax])
ymin = 2 * 3600
ymax = 4 * 3600 + 20 * 60
pp.ylim([ymin, ymax])
lim = (ax.get_xlim()[1] - ax.get_xlim()[0]) /\
(ax.get_ylim()[1] - ax.get_ylim()[0])
ax.set_aspect(0.3 * lim)
ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(HourMinFormatter))
ax.set_axisbelow(True)
pp.grid(ls="-", color="#cccccc")
pp.tight_layout()
if fname:
pp.savefig(fname)
pp.show()
def PlotYearlyCumulative(workouts, fname=None):
workouts_ = sorted(workouts, key=lambda x: x.GetStartTime())
years = []
for w in workouts_:
year = w.GetStartTime().year
if year not in years:
years.append(year)
pp.figure()
ax = pp.subplot(111)
for year in years:
year_start = datetime.date(year=year, month=1, day=1)
cum_dist = 0.0
days = []
for i in range(366):
days.append(year_start + datetime.timedelta(days=i))
daily_cum_dist = np.zeros(366) * float("NaN")
daily_cum_dist[0] = 0.0
for w in workouts_:
if w.GetStartTime().year != year:
continue
day_ind = (w.GetStartTime().date() - year_start).days
cum_dist += w.GetTotalDist() / 1000.0
daily_cum_dist[day_ind] = cum_dist
# fixup days that didn't contribute
for i,d in enumerate(days):
if np.isnan(daily_cum_dist[i]) and d < datetime.date.today():
daily_cum_dist[i] = daily_cum_dist[i-1]
pp.plot(daily_cum_dist, label=str(year), lw=2)
# sort both labels and handles by labels
handles, labels = ax.get_legend_handles_labels()
labels, handles = zip(*sorted(zip(labels, handles), reverse=True))
ax.legend(handles, labels,
loc='upper left', fancybox=True, fontsize=FONT_SIZE)
pp.xlim([0, 365])
lim = (ax.get_xlim()[1] - ax.get_xlim()[0]) /\
(ax.get_ylim()[1] - ax.get_ylim()[0])
ax.set_aspect(0.3 * lim)
ax.yaxis.grid(ls='-', color='#cccccc')
pp.ylabel("Cumulative distance (km)", fontsize=FONT_SIZE)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(FONT_SIZE)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(FONT_SIZE)
pp.tight_layout()
if fname:
pp.savefig(fname)
pp.show()