def get_data(self):
s = self.running_table.join(self.file_table).\
select().where(self.file_table.c.file_hash==self._metadata.file_hash)
cadence = TimeSeriesData(name="cadence" ,labels=[],data=[],unit='rpm')
distance = TimeSeriesData(name="distance" ,labels=[],data=[],unit='m')
heart_rate = TimeSeriesData(name="heart_rate",labels=[],data=[],unit="bpm")
speed = TimeSeriesData(name="speed" ,labels=[],data=[],unit="m/s")
rows = 0
abs_len = 0
last_ts = 0
row = None
for row in self._alchemy_logbook.execute(s):
if row.cadence and row.distance and row.enhanced_speed and row.heart_rate:
rows = rows + 1
if last_ts == 0:
last_ts = row.timestamp
ts = ((row.timestamp-last_ts).seconds/60)
cadence.data.append(row.cadence)
cadence.labels.append(ts)
distance.data.append(row.distance-abs_len)
abs_len = row.distance
distance.labels.append(ts)
heart_rate.data.append(row.heart_rate)
heart_rate.labels.append(ts)
speed.data.append(row.enhanced_speed)
speed.labels.append(ts)
if row:
self._data = [cadence,distance,heart_rate,speed]
self._formdata = []
self._formdata.append(TimeSeriesMetaData("Total Length",row.distance,"m"))
self._formdata.append(TimeSeriesMetaData("Time per 100m","%.1f" %1,"s"))
self._formdata.append(TimeSeriesMetaData("average speed","%.1f" %(1/1),"m/s"))
self._formdata.append(TimeSeriesMetaData("Total calories",1,"kcal"))
self._formdata.append(TimeSeriesMetaData("Event duration","%.1f" %(1),"min"))
def get_data(self):
s = self.swim_table.join(self.file_table).\
select().where(self.file_table.c.file_hash==self._metadata.file_hash)
strokes_data = TimeSeriesData(name="strokes",
labels=[],
data=[],
unit=None)
avg_strokes = TimeSeriesData(name="avg strokes",
labels=[],
data=[],
unit="Strokes/lap")
calories_data = TimeSeriesData(name="calories",
labels=[],
data=[],
unit=None)
speed_data = TimeSeriesData(name="speed",
labels=[],
data=[],
unit="min/100m")
rows = 0
total_calories = 0
event_duration = 0
strokes_data.data.append(0)
strokes_data.labels.append(0)
avg_strokes.data.append(0)
avg_strokes.labels.append(0)
calories_data.data.append(0)
calories_data.labels.append(0)
speed_data.data.append(0)
speed_data.labels.append(0)
stro = 0
row = None
for row in self._alchemy_logbook.execute(s):
if row.total_strokes and row.distance and row.total_calories and row.total_elapsed_time:
rows = rows + 1
strokes_data.data.append(row.total_strokes)
strokes_data.labels.append(row.distance)
stro = stro + row.total_strokes
avg_strokes.data.append((stro / row.distance) * 50)
avg_strokes.labels.append(row.distance)
calories_data.data.append(row.total_calories)
calories_data.labels.append(row.distance)
speed_data.data.append(
((row.total_elapsed_time / 50) * 100) / 60) #FIXME
speed_data.labels.append(row.distance)
total_calories = total_calories + row.total_calories
event_duration = event_duration + row.total_elapsed_time
if row:
lap_distance = int(row.distance / rows)
total_length = row.distance
total_time = row.start_time
self._data = [strokes_data, calories_data, speed_data, avg_strokes]
time_per_hundred = (100 / lap_distance) * (event_duration /
lap_distance)
self._formdata = []
self._formdata.append(
TimeSeriesMetaData("Lap length", lap_distance, "m"))
self._formdata.append(
TimeSeriesMetaData("Total Length", total_length, "m"))
self._formdata.append(
TimeSeriesMetaData("Time per 100m", "%.1f" % time_per_hundred,
"s"))
self._formdata.append(
TimeSeriesMetaData("average speed",
"%.1f" % (total_length / event_duration),
"m/s"))
# self._formdata.append(TimeSeriesMetaData("Total time",total_time,"s"))
self._formdata.append(
TimeSeriesMetaData("Total calories", total_calories, "kcal"))
self._formdata.append(
TimeSeriesMetaData("Event duration",
"%.1f" % (event_duration / 60), "min"))
def get_data(self,event):
self._data = [TimeSeriesData(name="dummy" ,labels=[],data=[],unit=None)]
def get_data(self, filehash):
s = self.cycling_table.join(self.file_table).\
select().where(self.file_table.c.file_hash==filehash)
distance = TimeSeriesData(name="distance",
labels=[],
data=[],
unit='m',
xlabel="duration(min)")
enhanced_altitude = TimeSeriesData(name="enhanced_altitude",
labels=[],
data=[],
unit='m',
xlabel="duration(min)")
heart_rate = TimeSeriesData(name="heart_rate",
labels=[],
data=[],
unit="bpm",
xlabel="duration(min)")
# speed = TimeSeriesData(name="speed" ,labels=[],data=[],unit="m/s",xlabel="duration(min)")
rows = 0
abs_len = 0
last_ts = 0
row = None
for row in self._alchemy_logbook.execute(s):
rows = rows + 1
if last_ts == 0:
last_ts = row.timestamp
ts = ((row.timestamp - last_ts).seconds / 60)
enhanced_altitude.data.append(row.enhanced_altitude)
enhanced_altitude.labels.append(ts)
distance.data.append(row.distance - abs_len)
abs_len = row.distance
distance.labels.append(ts)
heart_rate.data.append(row.heart_rate)
heart_rate.labels.append(ts)
if row:
data = [enhanced_altitude, distance, heart_rate]
formdata = []
formdata.append(
TimeSeriesMetaData("Total Length", row.distance, "m"))
formdata.append(
TimeSeriesMetaData("Time per 100m", "%.1f" % 1, "s"))
formdata.append(
TimeSeriesMetaData("average speed", "%.1f" % (1 / 1), "m/s"))
formdata.append(TimeSeriesMetaData("Total calories", 1, "kcal"))
formdata.append(
TimeSeriesMetaData("Event duration", "%.1f" % (1), "min"))
return TimeSeries(data=data, metadata=formdata)