def plot_segment_performance_to_investment(config, segment_id):
client = config.client
activities = client.get_activities()
ridden_segs = read_data(DATAFILE)
try:
efforts = ridden_segs[segment_id].efforts
except KeyError:
print('Segment with given ID not ridden at least once')
print('Possibly update the your ridden segments or check the ID')
return
datetimes = [] # list of datetime.datetime objects
distances = [] # list of distances in meters (floats)
durations = [] # list of moving times, TimeIntervallAttribute (timedelta (seconds) probably)
for a in activities:
if a.type != unicode('Ride'):
continue
datetimes.append(a.start_date)
distances.append(a.distance / 1000) # in km pls
durations.append(a.moving_time)
# TODO: Smooth curves a bit
durations = [datetime.timedelta.total_seconds(x) / 60 for x in durations]
plt.figure()
effort_dates = [e.date for e in efforts]
effort_times = [datetime.timedelta.total_seconds(e.elapsed_time) for e in efforts]
plt.plot(datetimes, distances)
plt.plot(datetimes, durations)
plt.plot(effort_dates, effort_times)
plt.show()
def plot_times(config, segment_id, distribution):
"""
Generates a plot to visualize the performance of the current athlete at a specific segment
in comparison to other athletes.
:param config: Config object providing API access via security token
:param segment_id: ID of the strava segment in question
:param distribution: Whether to plot the time distribution over efforts instead of a boxplot
:return:
"""
client = config.client
ridden_segs = read_data(DATAFILE)
all_efforts = client.get_segment_efforts(segment_id)
X = [e.elapsed_time for e in all_efforts]
X = np.array([datetime.timedelta.total_seconds(x) for x in X])
Y = np.array([x for x in ridden_segs[segment_id].times])
if distribution:
plt.xlabel('Time in seconds')
sns.distplot(X, hist=False, rug=True)
sns.distplot(Y, hist=False, rug=True)
plt.show()
else:
plt.ylabel('Time in seconds')
data = np.array([X, Y])
sns.boxplot(data=data, orient='v')
plt.show()
def plot_segment_performance_to_investment(config, segment_id):
client = config.client
activities = client.get_activities()
ridden_segs = read_data(DATAFILE)
try:
efforts = ridden_segs[segment_id].efforts
except KeyError:
print('Segment with given ID not ridden at least once')
print('Possibly update the your ridden segments or check the ID')
return
datetimes = [] # list of datetime.datetime objects
distances = [] # list of distances in meters (floats)
durations = [
] # list of moving times, TimeIntervallAttribute (timedelta (seconds) probably)
for a in activities:
if a.type != unicode('Ride'):
continue
datetimes.append(a.start_date)
distances.append(a.distance / 1000) # in km pls
durations.append(a.moving_time)
# TODO: Smooth curves a bit
durations = [datetime.timedelta.total_seconds(x) / 60 for x in durations]
plt.figure()
effort_dates = [e.date for e in efforts]
effort_times = [
datetime.timedelta.total_seconds(e.elapsed_time) for e in efforts
]
plt.plot(datetimes, distances)
plt.plot(datetimes, durations)
plt.plot(effort_dates, effort_times)
plt.show()
def plot_times(config, segment_id, distribution):
"""
Generates a plot to visualize the performance of the current athlete at a specific segment
in comparison to other athletes.
:param config: Config object providing API access via security token
:param segment_id: ID of the strava segment in question
:param distribution: Whether to plot the time distribution over efforts instead of a boxplot
:return:
"""
client = config.client
ridden_segs = read_data(DATAFILE)
all_efforts = client.get_segment_efforts(segment_id)
X = [e.elapsed_time for e in all_efforts]
X = np.array([datetime.timedelta.total_seconds(x) for x in X])
Y = np.array([x for x in ridden_segs[segment_id].times])
if distribution:
plt.xlabel('Time in seconds')
sns.distplot(X, hist=False, rug=True)
sns.distplot(Y, hist=False, rug=True)
plt.show()
else:
plt.ylabel('Time in seconds')
data = np.array([X, Y])
sns.boxplot(data=data, orient='v')
plt.show()
def segment_ranking(config, order, segment):
client = config.client
ridden_segs = read_data(DATAFILE)
# Restrict to given segment if -s option is specified
filter_segments(ridden_segs, segment)
# Crawl leaderboards and times for every segment
athlete_id = client.get_athlete().id
leaderboards = collections.defaultdict()
for k in ridden_segs.keys():
try:
leaderboards[k] = client.get_segment_leaderboard(k)
except:
continue
ranks = {
k: rank(leaderboards.get(k), athlete_id)
for k in ridden_segs.keys()
}
rel_times = {
k: relative_time(leaderboards.get(k), ridden_segs[k])
for k in ridden_segs.keys()
}
orderings = {
'tries': lambda x: len(x.efforts),
'elevation': lambda x: x.total_elevation_gain,
'rank': lambda x: ranks[x.id][0],
'time': lambda x: rel_times[x.id]
}
reverse = {'tries': True, 'elevation': True, 'rank': False, 'time': False}
for v in sorted(ridden_segs.values(),
key=orderings[order],
reverse=reverse[order]):
rank_string = '%3d/%4d' % (ranks[v.id][0], ranks[v.id][1])
print(
u'Position: %s - Tries: %3d - Elevation gain: %4d - Average time: %4d sec. - '
'Std. variation in time: %3d sec. - Relative to KOM: %3.1f%% '
'- Segment: %.30s - ID: %7s' %
(rank_string, len(v.efforts), v.total_elevation_gain, v.avg_time,
v.std_time, rel_times[v.id], v.name, v.id))
return
def segment_ranking(config, order, segment):
client = config.client
ridden_segs = read_data(DATAFILE)
# Restrict to given segment if -s option is specified
filter_segments(ridden_segs, segment)
# Crawl leaderboards and times for every segment
athlete_id = client.get_athlete().id
leaderboards = collections.defaultdict()
for k in ridden_segs.keys():
try:
leaderboards[k] = client.get_segment_leaderboard(k)
except:
continue
ranks = {k: rank(leaderboards.get(k), athlete_id) for k in ridden_segs.keys()}
rel_times = {k: relative_time(leaderboards.get(k), ridden_segs[k]) for k in ridden_segs.keys()}
orderings = {
'tries' : lambda x: len(x.efforts),
'elevation': lambda x: x.total_elevation_gain,
'rank': lambda x: ranks[x.id][0],
'time': lambda x: rel_times[x.id]
}
reverse = {
'tries': True,
'elevation': True,
'rank': False,
'time': False
}
for v in sorted(ridden_segs.values(), key=orderings[order], reverse=reverse[order]):
rank_string = '%3d/%4d' % (ranks[v.id][0], ranks[v.id][1])
print (u'Position: %s - Tries: %3d - Elevation gain: %4d - Average time: %4d sec. - '
'Std. variation in time: %3d sec. - Relative to KOM: %3.1f%% '
'- Segment: %.30s - ID: %7s' % (rank_string, len(v.efforts), v.total_elevation_gain,
v.avg_time, v.std_time, rel_times[v.id],
v.name, v.id))
return
def graph():
graph_date = request.args.get('date')
graph_type = request.args.get('type')
if graph_date is None:
graph_date = datetime.now().strftime('%Y%m%d')
if graph_type not in ROW_TITLES:
graph_type = 'Temperature'
date_range = [datetime.strptime(graph_date, '%Y%m%d')] * 2
date_range[0] -= timedelta(0)
date_range[1] += timedelta(1)
date_range = [datetime.strftime(dt, '%Y-%m-%d') for dt in date_range]
data = cache.get('data')
if data is None:
data = read_data()
cache.set('data', data, timeout=10 * 60)
data = data[data.Datetime.dt.strftime('%Y%m%d') == graph_date]
# Create a trace
traces = []
titles = []
for g, d in data.groupby('Name'):
trace = go.Scatter(
name=g,
x=d.Datetime,
y=d[graph_type],
marker={'color': '57A6C4'},
mode='lines',
)
if not d[graph_type].isnull().values.all():
traces.append(trace)
titles.append(g)
# layout = go.Layout(
# title='Sensor Measurements',
# yaxis=dict(
# title=graph_type,
# # range=[0, 40],
# ),
# xaxis=dict(
# range=date_range,
# ),
# showlegend=True)
fig = tools.make_subplots(rows=len(traces),
cols=1,
shared_xaxes=True,
specs=[[{}]] * len(traces),
subplot_titles=titles)
for i, trace in enumerate(traces):
fig.append_trace(trace, i + 1, 1)
fig['layout'].update({
'height': 800,
'title': 'Sensor Measurements',
'showlegend': False,
})
# graph = go.Figure(data=traces, layout=layout)
graphJSON = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return render_template('graph.html',
graphJSON=graphJSON,
sensors=ROW_TITLES[3:])
