def call_resource(self, request):
readings = TrainingDataManager().reading_types_unused_for_date(
request.POST['date'])
response = TrainingDiaryResponse()
response.add_data('readings', readings)
return JsonResponse(data=response.as_dict())
def call_resource(self, request):
response = TrainingDiaryResponse()
last_date = TrainingDataManager().latest_date()
d = parser.parse(last_date).date()
next_date = d + timedelta(1)
response.add_data('next_date', next_date)
return JsonResponse(data=response.as_dict())
def call_resource(self, request):
response = TrainingDiaryResponse()
try:
tdm = TrainingDataManager()
tdm.delete_reading_for_primary_key(request.POST['primary_key'])
response.set_status(response.SUCCESS)
response.add_data('primary_key', request.POST['primary_key'])
response.add_message(
response.MSG_INFO,
f"Reading Deleted: {request.POST['primary_key']}")
except Exception as e:
response.set_status(response.ERROR)
response.add_message(response.MSG_ERROR,
f"Delete failed: {str(e)}")
return JsonResponse(data=response.as_dict())
def post(self, request, *args, **kwargs):
r = super().post(request, args, kwargs)
user = authenticate(request, username=request.POST['username'], password=request.POST['password'])
if user is None:
return JsonResponse(data=TrainingDiaryResponse.error_response_dict('Invalid username and password. Please try again'))
if r.status_code == 302:
login(self.request, user)
response = TrainingDiaryResponse()
response.set_status(response.SUCCESS)
response.add_data('user', str(user))
response.add_data('logged_in', True)
return JsonResponse(data=response.as_dict())
else:
return JsonResponse(data=TrainingDiaryResponse.error_response_dict('Unable to login. No idea why'))
def call_resource(self, request):
response = TrainingDiaryResponse()
tdm = TrainingDataManager()
instances = []
if request.POST['data_type'] == 'Day':
instances = tdm.days_between(request.POST['from_date'],
request.POST['to_date'])
elif request.POST['data_type'] == 'Reading':
instances = tdm.readings_between(request.POST['from_date'],
request.POST['to_date'])
elif request.POST['data_type'] == 'Workout':
instances = tdm.workouts_between(request.POST['from_date'],
request.POST['to_date'])
response.add_data('instances',
[i.data_dictionary() for i in instances])
return JsonResponse(data=response.as_dict())
def call_resource(self, request):
date = request.POST['date']
readings = list()
tdm = TrainingDataManager()
for reading in json.loads(request.POST['json']):
tdm.save_reading(date, reading['reading'], reading['value'])
readings.append(reading['reading'])
readings_saved = list()
for r in readings:
readings_saved.append(
tdm.reading_for_date_and_type(date, r)[0].data_dictionary())
response = TrainingDiaryResponse()
response.add_message(response.MSG_INFO,
f"{len(readings)} saved: {', '.join(readings)}")
response.add_data('readings', readings_saved)
return JsonResponse(data=response.as_dict())
def call_resource(self, request):
response = TrainingDiaryResponse()
tdm = TrainingDataManager()
summary = tdm.training_annual_summary()
totals = {"name": 'Total'}
years = list()
for yr, v in summary.items():
for activity, dd in v.items():
a_dd = totals.get(activity, {'km': 0, 'seconds': 0, 'tss': 0})
a_dd['km'] += dd['km']
a_dd['seconds'] += dd['seconds']
a_dd['tss'] += dd['tss']
totals[activity] = a_dd
v['name'] = yr
years.append(v)
years.append(totals)
# response.add_data('summary', summary)
response.add_data('years', years)
return JsonResponse(data=response.as_dict())
def call_resource(self, request):
response = TrainingDiaryResponse()
dd, errors = self._process_data(request.POST['json'])
[response.add_message(response.MSG_ERROR, e) for e in errors]
tdm = TrainingDataManager()
primary_key = tdm.workout_primary_key(dd['date'], dd['workout_number'])
try:
if 'primary_key' not in dd or dd['primary_key'] == "":
# new workout
lastrowid = tdm.save_workout(
**self._filtered_dict(dd, self.NEW_FIELDS))
response.add_message(response.MSG_INFO, f"New workout saved")
response.add_data(
'workout',
tdm.workout_for_rowid(lastrowid)[0].data_dictionary())
elif primary_key == dd['primary_key']:
# update workout
tdm.update_workout(
**self._filtered_dict(dd, self.UPDATE_FIELDS))
response.add_message(response.MSG_INFO, f"Workout updated")
response.add_data(
'workout',
tdm.workout_for_primary_key(primary_key)
[0].data_dictionary())
else:
# changed the date. Need a new workout to get primary_keys right
old_key = dd['primary_key']
tdm.delete_workout_for_primary_key(old_key)
response.add_data('removed_primary_key', old_key)
lastrowid = tdm.save_workout(
**self._filtered_dict(dd, self.NEW_FIELDS))
workout = tdm.workout_for_rowid(lastrowid)[0]
response.add_message(
response.MSG_INFO,
f"Workout date changed so old workout deleted and new one added. Remove {old_key} and added {workout.primary_key}"
)
response.add_data('workout', workout.data_dictionary())
except TypeError as e:
response.set_status(response.ERROR)
response.add_message(response.MSG_ERROR, str(e))
return JsonResponse(data=response.as_dict())
def call_resource(self, request):
target_date = request.POST['date']
response = TrainingDiaryResponse()
tdm = TrainingDataManager()
if tdm.day_exists(target_date):
tdm.update_day(target_date, request.POST['day_type'],
request.POST['comments'])
response.add_message(response.MSG_INFO,
f"Day updated for {target_date}")
else:
tdm.save_day(target_date, request.POST['day_type'],
request.POST['comments'])
response.add_message(response.MSG_INFO,
f"New day added for {target_date}")
response.add_data('day',
tdm.day_for_date(target_date).data_dictionary())
return JsonResponse(data=response.as_dict())
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()
tdm = TrainingDataManager()
summary = tdm.bike_summary()
bikes = list()
totals = {'name': 'Total'}
for bike, years in summary.items():
for year, value in years.items():
y = totals.get(year, 0)
y += value
totals[year] = y
years['name'] = bike
bikes.append(years)
bikes.append(totals)
response.add_data('bikes', bikes)
years = list()
for k, v in summary.items():
years = [y for y in sorted(v.keys(), reverse=True)]
break
response.add_data('years', years)
return JsonResponse(data=response.as_dict())
def call_resource(self, request):
type = request.POST['type']
tdm = TrainingDataManager()
choices = list()
if type == 'activity':
choices = tdm.activities()
elif type == 'activityType':
choices = tdm.activity_types()
elif type == 'equipment':
choices = tdm.equipment_types()
elif type == 'tssMethod':
choices = tdm.tss_methods()
elif type == 'dayType':
choices = tdm.day_types()
elif type == 'measure':
choices = [m.value for m in WorkoutFloatMeasureEnum] + [m.value for m in ReadingEnum]
elif type == 'generated_measure':
choices = [m for m in TimeSeriesProcessor.generated_measures()]
elif type == 'period':
choices = [p.value for p in PandasPeriod]
elif type == 'aggregation':
choices = [a.value for a in Aggregation]
elif type == 'day_aggregation':
choices = [a.value for a in DayAggregation]
elif type == 'years':
choices = range(dateutil.parser.parse(tdm.earliest_date()).year, dateutil.parser.parse(tdm.latest_date()).year + 1)
elif type == 'processor':
choices = TimeSeriesProcessor.TYPES
elif type == 'interpolation':
choices = [i.value for i in PandasInterpolation]
if request.POST['include_all'] == 'true':
choices.append('All')
response = TrainingDiaryResponse()
response.add_data('choices', sorted([{'text': c, 'id': c} for c in choices], key=lambda c: c["text"]))
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 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):
logout(request)
response = TrainingDiaryResponse()
response.set_status(response.SUCCESS)
response.add_data('logged_in', False)
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())