207 lines
6.3 KiB
Python
207 lines
6.3 KiB
Python
from rowers.models import Alert, Condition, User, Rower, Workout
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from rowers.teams import coach_getcoachees
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from rowers.dataprep import getsmallrowdata_db,getrowdata_db
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import datetime
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## BASIC operations
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# create alert
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def create_alert(manager, rower, measured,period=7, emailalert=True,
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reststrokes=False, workouttype='water',boattype='1x',
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name='',**kwargs):
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# check if manager is coach of rower. If not return 0
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if manager.rower != rower: # pragma: no cover
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if rower not in coach_getcoachees(manager.rower):
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return 0,'You are not allowed to create this alert'
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m = Condition(
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metric = measured['metric'],
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value1 = measured['value1'],
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value2 = measured['value2'],
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condition=measured['condition']
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)
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m.save()
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alert = Alert(name=name,
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manager=manager,
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rower=rower,
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measured=m,
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reststrokes=reststrokes,
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period=period,
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emailalert=emailalert,
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workouttype=workouttype,
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boattype=boattype,
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)
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alert.save()
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if 'filter' in kwargs:
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filters = kwargs['filter']
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for f in filters:
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if f['metric'] and f['condition']:
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m = Condition(
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metric = f['metric'],
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value1 = f['value1'],
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value2 = f['value2'],
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condition = f['condition']
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)
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m.save()
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alert.filter.add(m)
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return alert.id,'Your alert was created'
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# update alert
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def alert_add_filters(alert,filters):
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for f in alert.filter.all():
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alert.filter.remove(f)
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f.delete()
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for f in filters:
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metric = f['metric']
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value1 = f['value1']
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value2 = f['value2']
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condition = f['condition']
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if condition and metric and value1:
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m = Condition(
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metric = f['metric'],
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value1 = f['value1'],
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value2 = f['value2'],
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condition = f['condition']
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)
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m.save()
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alert.filter.add(m)
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return 1
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# get alert stats
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# nperiod = 0: current period, i.e. next_run - n days to today
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# nperiod = 1: 1 period ago , i.e. next_run -2n days to next_run -n days
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def alert_get_stats(alert,nperiod=0): # pragma: no cover
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# get strokes
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workstrokesonly = not alert.reststrokes
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startdate = (alert.next_run - datetime.timedelta(days=(nperiod+1)*alert.period-1))
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enddate = alert.next_run - datetime.timedelta(days=(nperiod)*alert.period)
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columns = [alert.measured.metric]
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for condition in alert.filter.all():
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columns += condition.metric
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workouts = Workout.objects.filter(date__gte=startdate,date__lte=enddate,user=alert.rower,
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workouttype=alert.workouttype,duplicate=False,
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boattype=alert.boattype)
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ids = [w.id for w in workouts]
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df = getsmallrowdata_db(columns,ids=ids,doclean=True,workstrokesonly=workstrokesonly)
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if df.empty:
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return {
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'workouts':workouts.count(),
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'startdate':startdate,
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'enddate':enddate,
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'nr_strokes':0,
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'nr_strokes_qualifying':0,
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'percentage':0,
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'nperiod':nperiod,
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'median': 0,
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'median_q': 0,
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'standard_dev': 0,
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}
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# check if filters are in columns list
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pdcolumns = set(df.columns) # pragma: no cover
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# drop strokes through filter
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if set(columns) <= pdcolumns: # pragma: no cover
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for condition in alert.filter.all():
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if condition.condition == '>':
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mask = df[condition.metric] > condition.value1
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df.loc[mask,alert.measured.metric] = np.nan
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elif condition.condition == '<':
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mask = df[condition.metric] < condition.value1
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df.loc[mask,alert.measured.metric] = np.nan
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elif condition.condition == 'between':
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mask = df[condition.metric] > condition.value1
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mask2 = df[condition.metric] < condition.value2
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df.loc[mask & mask2,alert.measured.metric] = np.nan
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elif condition.condition == '=':
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mask = df[condition.metric] == condition.value1
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df.loc[mask,alert.measured.metric] = np.nan
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df.dropna(inplace=True,axis=0)
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else: # pragma: no cover
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return {
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'workouts':workouts.count(),
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'startdate':startdate,
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'enddate':enddate,
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'nr_strokes':0,
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'nr_strokes_qualifying':0,
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'percentage':0,
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'nperiod':nperiod,
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'median': 0,
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'median_q': 0,
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'standard_dev': 0,
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}
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# count strokes
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nr_strokes = len(df)
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# count qualifying
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if alert.measured.condition == '>':
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mask = df[alert.measured.metric] > alert.measured.value1
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df2 = df[mask].copy()
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elif alert.measured.condition == '<':
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mask = df[alert.measured.metric] < alert.measured.value1
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df2 = df[mask].copy()
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elif alert.measured.condition == 'between':
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mask = df[alert.measured.metric] > alert.measured.value1
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mask2 = df[alert.measured.metric] < alert.measured.value2
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df2 = df[mask & mask2].copy()
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else:
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mask = df[alert.measured.metric] == alert.measured.value1
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df2 = df[mask].copy()
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nr_strokes_qualifying = len(df2)
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if nr_strokes > 0:
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percentage = int(100.*nr_strokes_qualifying/nr_strokes)
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else:
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percentage = 0
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median_q = df2[alert.measured.metric].median()
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median = df[alert.measured.metric].median()
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std = df[alert.measured.metric].std()
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return {
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'workouts':workouts.count(),
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'startdate':startdate,
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'enddate':enddate,
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'nr_strokes':nr_strokes,
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'nr_strokes_qualifying':nr_strokes_qualifying,
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'percentage': percentage,
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'nperiod':nperiod,
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'median':median,
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'median_q':median_q,
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'standard_dev':std,
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}
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# run alert report
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# check alert permission
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def checkalertowner(alert,user):
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if alert.manager == user:
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return True
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if alert.rower.user == user: # pragma: no cover
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return True
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return False # pragma: no cover
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