def main(filename):
#Set A4 figure size
plt.rc('figure', figsize=(11.69, 8.27), dpi=100)
basename = filename[:-4]
df = parse_tcx.get_activity_data(filename)
with PdfPages(basename + '-ActivitySummary.pdf') as pdf:
print('Maps')
mapfig = activitymap.activity_map_figure(df)
plt.tight_layout()
pdf.savefig(mapfig)
plt.close(mapfig)
print('Minimal report')
mr_fig = minimalreport.minimal_report_figure(df)
plt.tight_layout()
pdf.savefig(mr_fig)
plt.close(mr_fig)
info = pdf.infodict()
info['CreationDate'] = datetime.datetime.today()
for i, (unit, col) in enumerate(zip(units, columns)):
ax = fig.add_subplot(1, 2, i+1, projection=tiler.crs)
ax.set_extent((lon.min()-dlon, lon.max()+dlon,
lat.min()-dlat, lat.max()+dlat))
ax.add_image(tiler, 15)
#Plot activity positions on map, color by speed
im = ax.scatter(df['Longitude'], df['Latitude'], c=df[col],
transform=ccrs.Geodetic(), s=40, edgecolor='none',
cmap=plt.cm.hot_r, alpha=0.5)
ax.set_title('Mean = {0:.1f} [{1}]'.format(df[col].mean(), unit))
cbar = plt.colorbar(im, shrink=.5, drawedges=False)
cbar.set_label('{0} [{1}]'.format(col, unit))
cbar.solids.set_rasterized(True)
cbar.solids.set_edgecolor('face')
#plt.suptitle('{0:.2f} km, {1:.1f} min'.format(
# df['DistanceMeters'].max()/1000,
# df['SecondsElapsed'].max()/60))
return fig
if __name__ == '__main__':
pd.options.display.mpl_style = 'default'
filename = sys.argv[1]
basename = filename[:-4]
df = parse_tcx.get_activity_data(filename)
print('Plotting maps')
fig = activity_map_figure(df)
plt.tight_layout()
fig.savefig(basename + '-mapspeed.png', dpi=200)
plt.close(fig)
'Warmup': hrmin + 0.6 * hrr,
'Recover': hrmin + 0.7 * hrr,
'Aerobic': hrmin + 0.8 * hrr,
'Anaerobic': hrmin + 0.9 * hrr,
'Redline': hrmin + hrr
}
zones = pd.Series(zones_dict)
zones.sort(inplace=True)
return zones
if __name__ == '__main__':
hrmin = float(sys.argv[2])
hrmax = float(sys.argv[3])
lt = float(sys.argv[4])
df = parse_tcx.get_activity_data(sys.argv[1])
times = df.SecondsElapsed
hrseries = pd.Series(index=times, data=df['HeartRateBpm'].values)
zones = get_zones_lactate_thresh(lt, hrmin, hrmax)
zones_kavonen = get_zones_kavonen_five(hrmin, hrmax)
tiz = get_time_in_zones(hrseries, zones)
tizkav = get_time_in_zones(hrseries, zones_kavonen)
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
tiz['TimeInZone'].plot(ax=ax[0], kind='barh')
tizkav['TimeInZone'].plot(ax=ax[1], kind='barh')
df['HeartRateBpm'].plot(ax=ax[2], kind='kde')
df['HeartRateBpm'].plot(ax=ax[2],
kind='hist',
normed=True,
bins=np.linspace(hrmin, hrmax,
(hrmax - hrmin) // 2),
hrr = hrmax - hrmin
zones_dict = {'Warmup': hrmin + 0.6*hrr,
'Recover': hrmin + 0.7*hrr,
'Aerobic': hrmin + 0.8*hrr,
'Anaerobic': hrmin + 0.9*hrr,
'Redline': hrmin + hrr}
zones = pd.Series(zones_dict)
zones.sort(inplace=True)
return zones
if __name__ == '__main__':
hrmin = float(sys.argv[2])
hrmax = float(sys.argv[3])
lt = float(sys.argv[4])
df = parse_tcx.get_activity_data(sys.argv[1])
times = df.SecondsElapsed
hrseries = pd.Series(index=times, data=df['HeartRateBpm'].values)
zones = get_zones_lactate_thresh(lt, hrmin, hrmax)
zones_kavonen = get_zones_kavonen_five(hrmin, hrmax)
tiz = get_time_in_zones(hrseries, zones)
tizkav = get_time_in_zones(hrseries, zones_kavonen)
fig, ax = plt.subplots(1, 3, figsize=(15,5))
tiz['TimeInZone'].plot(ax=ax[0], kind='barh')
tizkav['TimeInZone'].plot(ax=ax[1], kind='barh')
df['HeartRateBpm'].plot(ax=ax[2], kind='kde')
df['HeartRateBpm'].plot(ax=ax[2], kind='hist', normed=True,
bins=np.linspace(hrmin, hrmax, (hrmax-hrmin)//2), alpha=0.3)
print(tiz)
print(tizkav)
plt.tight_layout()
