def series_definitions(self, base_measure, base_series_definition):
dd = super().series_definitions(base_measure, base_series_definition)
dd['atl'] = SeriesDefinition(measure='atl',
underlying_measure=base_measure)
dd['ctl'] = SeriesDefinition(measure='ctl',
underlying_measure=base_measure)
dd['tsb'] = SeriesDefinition(measure='tsb',
underlying_measure=base_measure)
return dd
def __init__(self,
data_definition,
series_definition=SeriesDefinition(),
processor=NoOpProcessor(),
x_axis_number=1):
self.data_definition = data_definition
self.series_definition = series_definition
self.processor = processor
self.x_axis_number = x_axis_number
def call_resource(self, request):
period_year = Period(pandas_period=PandasPeriod.Y_DEC,
aggregation=Aggregation.MEAN,
to_date=False,
incl_zeroes=False)
series_definition = SeriesDefinition(period=period_year, rolling_definition=NoOpRoller())
time_series_sets = list()
for reading in ReadingEnum:
dd = DataDefinition(measure=reading.value, day_aggregation_method=DayAggregation.MEAN)
time_series_sets.append(TimeSeriesManager.TimeSeriesSet(data_definition=dd,
series_definition=series_definition,
processor=TimeSeriesProcessor.get_processor("No-op")))
diary_time_period = TrainingDataManager().diary_time_period()
# year summaries do need time period to be to year end
diary_time_period.end = date(diary_time_period.end.year, 12, 31)
tsl, errors = TimeSeriesManager().time_series_list(requested_time_period=diary_time_period, time_series_list=time_series_sets)
if len(tsl) > 0:
total_series = {'date': "Total"}
for k in tsl[0].keys():
if k != 'date':
entries = [d[k] for d in tsl]
year_count = sum([1 for e in entries if e > 0])
total_series[k] = 0 if year_count == 0 else sum(entries) / year_count
tsl.append(total_series)
for dd in tsl:
dd['name'] = dd['date']
response = TrainingDiaryResponse()
[response.add_message(response.MSG_ERROR, e) for e in errors]
response.add_data('time_series', tsl)
return JsonResponse(data=response.as_dict())
def call_resource(self, request):
response = TrainingDiaryResponse()
year = int(request.POST['year'])
tp = TimePeriod(date(year,1,1), date(year, 12,31))
swim_km = DataDefinition(activity='Swim', activity_type='All', equipment='All', measure='km', day_aggregation_method=DayAggregation.SUM)
bike_km = DataDefinition(activity='Bike', activity_type='All', equipment='All', measure='km', day_aggregation_method=DayAggregation.SUM)
run_km = DataDefinition(activity='Run', activity_type='All', equipment='All', measure='km', day_aggregation_method=DayAggregation.SUM)
hours = DataDefinition(activity='All', activity_type='All', equipment='All', measure='hours', day_aggregation_method=DayAggregation.SUM)
reps = DataDefinition(activity='Gym', activity_type='PressUp', equipment='All', measure='reps', day_aggregation_method=DayAggregation.SUM)
series_defn = SeriesDefinition(period=Period(PandasPeriod(request.POST['period'])))
summary = list()
summary.append(TimeSeriesManager.TimeSeriesSet(data_definition=swim_km, series_definition=series_defn))
summary.append(TimeSeriesManager.TimeSeriesSet(data_definition=bike_km, series_definition=series_defn))
summary.append(TimeSeriesManager.TimeSeriesSet(data_definition=run_km, series_definition=series_defn))
summary.append(TimeSeriesManager.TimeSeriesSet(data_definition=hours, series_definition=series_defn))
summary.append(TimeSeriesManager.TimeSeriesSet(data_definition=reps, series_definition=series_defn))
dd, errors = TimeSeriesManager().time_series_list(tp, summary)
response.add_data('time_series', sorted(dd, key=lambda x: x['date']))
[response.add_message(response.MSG_ERROR, error) for error in errors]
return JsonResponse(data=response.as_dict())
def series_definitions(self, base_measure, base_series_definition):
dd = super().series_definitions(base_measure, base_series_definition)
dd['annual_ed_num'] = SeriesDefinition(measure='annual_ed_num', underlying_measure=base_measure)
dd['annual_plus_one'] = SeriesDefinition(measure='annual_plus_one', underlying_measure=base_measure)
return dd
def series_definitions(self, base_measure, base_series_definition):
dd = super().series_definitions(base_measure, base_series_definition)
dd['ed_num'] = SeriesDefinition(measure='ed_num', underlying_measure=base_measure)
dd['plus_one'] = SeriesDefinition(measure='plus_one', underlying_measure=base_measure)
dd['contributor'] = SeriesDefinition(measure='contributor', underlying_measure=base_measure)
return dd
def series_definitions(self, base_measure, base_series_definition):
dd = super().series_definitions(base_measure, base_series_definition)
dd[self.ED_NUM] = SeriesDefinition(measure=self.ED_NUM, underlying_measure=base_measure)
dd[self.PLUS_ONE] = SeriesDefinition(measure=self.PLUS_ONE, underlying_measure=base_measure)
return dd
def call_resource(self, request):
response = TrainingDiaryResponse()
tms = TimeSeriesManager()
graph = request.POST['graph']
activity = request.POST['activity']
year_str = self._normalise_year_str(request.POST['year'])
yr_title = year_str if year_str != 'Total' else "All Time"
if year_str == 'Total':
tdm = TrainingDataManager()
tp = tdm.diary_time_period()
else:
year = int(year_str)
tp = TimePeriod(date(year,1,1), date(year,12,31))
tss_list = list()
if graph == 'tss':
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=DataDefinition(activity='All' if activity == "Total" else activity,
activity_type='All',
equipment='All',
measure='tss',
day_aggregation_method=DayAggregation.SUM),
processor=TSBProcessor(7, 7, 42, 42)))
elif graph == 'duration':
duration_defn = DataDefinition(activity='All' if activity == "Total" else activity,
activity_type='All',
equipment='All',
measure='hours',
day_aggregation_method=DayAggregation.SUM)
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=duration_defn))
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=duration_defn, series_definition=SeriesDefinition(Period(), RollingDefinition(7, Aggregation.SUM))))
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=duration_defn, series_definition=SeriesDefinition(Period(PandasPeriod.Y_DEC, to_date=True))))
elif graph == 'km':
km_defn = DataDefinition(activity='All' if activity == "Total" else activity,
activity_type='All',
equipment='All',
measure='km',
day_aggregation_method=DayAggregation.SUM)
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=km_defn))
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=km_defn, series_definition=SeriesDefinition(Period(), RollingDefinition(7, Aggregation.SUM))))
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=km_defn, series_definition=SeriesDefinition(Period(PandasPeriod.Y_DEC, to_date=True))))
elif graph == 'reading':
reading_defn = DataDefinition(activity='All',
activity_type='All',
equipment='All',
measure=activity,
day_aggregation_method=DayAggregation.MEAN)
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=reading_defn))
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=reading_defn, series_definition=SeriesDefinition(Period(), RollingDefinition(7, Aggregation.MEAN))))
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=reading_defn, series_definition=SeriesDefinition(Period(), RollingDefinition(31, Aggregation.MEAN))))
elif graph == 'bike':
bike_defn = DataDefinition(activity='Bike',
activity_type='All',
equipment='All' if activity == 'Total' else activity,
measure='km',
day_aggregation_method=DayAggregation.SUM)
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=bike_defn))
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=bike_defn,
series_definition=SeriesDefinition(Period(), RollingDefinition(7, Aggregation.SUM))))
tss_list.append(TimeSeriesManager.TimeSeriesSet(data_definition=bike_defn,
series_definition=SeriesDefinition(Period(PandasPeriod.Y_DEC, Aggregation.SUM, to_date=True))))
if len(tss_list) > 0:
values = tms.time_series_graph(requested_time_period=tp, time_series_list=tss_list)
else:
values = {'title': "No Data"}
response.add_data('chart_title', f"{yr_title} {values['title']}")
response.add_data('time_series', values)
return JsonResponse(data=response.as_dict())
def call_resource(self, request):
dd, errors = self._process_data(request.POST['json'])
dd_keys = set([d for d in dd.keys()])
data_definition = DataDefinition(activity=dd['activity'],
activity_type=dd['activity_type'],
equipment=dd['equipment'],
measure=dd['measure'],
day_aggregation_method=DayAggregation(dd['day_aggregation']),
day_type=dd['day_type'],
day_of_week=dd['day_of_week'],
month=dd['month'],
interpolation=dd['interpolation'])
dd_keys.remove('activity')
dd_keys.remove('activity_type')
dd_keys.remove('equipment')
dd_keys.remove('measure')
dd_keys.remove('day_aggregation')
dd_keys.remove('day_type')
dd_keys.remove('day_of_week')
dd_keys.remove('month')
dd_keys.remove('interpolation')
period = Period(pandas_period=PandasPeriod(dd['period']), aggregation=Aggregation(dd['period_aggregation']),
to_date=dd['to_date'] == 'yes',
incl_zeroes=dd['period_include_zeroes'] == 'yes')
dd_keys.remove('period')
dd_keys.remove('period_aggregation')
dd_keys.remove('to_date')
dd_keys.remove('period_include_zeroes')
if dd['rolling'] == 'yes':
rolling_definition = RollingDefinition(periods=int(dd['number_of_rolling_periods']), aggregation=Aggregation(dd['rolling_aggregation']),
incl_zeros=dd['rolling_include_zeroes'] == 'yes')
else:
rolling_definition = NoOpRoller()
dd_keys.remove('number_of_rolling_periods')
dd_keys.remove('rolling_aggregation')
dd_keys.remove('rolling_include_zeroes')
dd_keys.remove('rolling')
series_definition = SeriesDefinition(period=period, rolling_definition=rolling_definition)
processor = self.get_processor(dd)
dd_keys.remove('processor_type')
response = TrainingDiaryResponse()
[response.add_message(response.MSG_ERROR, e) for e in errors]
diary_time_period = TrainingDataManager().diary_time_period()
data_tp = TimePeriod(diary_time_period.start if dd['series_start'] is None else dd['series_start'],
diary_time_period.end if dd['series_end'] is None else dd['series_end'])
dd_keys.remove('series_start')
dd_keys.remove('series_end')
x_axis_number = 1
if 'x_axis_number' in dd:
x_axis_number = dd['x_axis_number']
dd_keys.remove('x_axis_number')
tss = TimeSeriesManager.TimeSeriesSet(data_definition, series_definition=series_definition, processor=processor, x_axis_number=x_axis_number)
ts = TimeSeriesManager().time_series_graph(data_tp, [tss])
response.add_data('time_series', ts)
if len(dd_keys) > 0:
response.add_message(response.MSG_WARNING, f"The following data was not used: {' ,'.join(dd_keys)}")
return JsonResponse(data=response.as_dict())