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)