def save(self, *args, **kwargs):
self.kesto_h = coalesce(self.kesto_h, 0)
self.kesto_min = coalesce(self.kesto_min, 0)
self.kesto = duration_to_decimal(self.kesto_h, self.kesto_min)
self.vauhti_min_km = speed_min_per_km(self.vauhti_min, self.vauhti_s)
if self.vauhti_min_km is not None and self.vauhti_min_km != 0:
self.vauhti_km_h = 60 / self.vauhti_min_km
super(Teho, self).save(*args, **kwargs)
def save(self, *args, **kwargs):
self.aika_id = self.pvm.strftime('%Y%m%d')
self.kesto_h = coalesce(self.kesto_h, 0)
self.kesto_min = coalesce(self.kesto_min, 0)
self.kesto = duration_to_decimal(self.kesto_h, self.kesto_min)
self.vauhti_min_km = speed_min_per_km(self.vauhti_min, self.vauhti_s)
if self.vauhti_min_km is None and self.vauhti_km_h is not None and self.vauhti_km_h != 0:
self.vauhti_min_km = 60 / self.vauhti_km_h
self.vauhti_min = int(self.vauhti_min_km)
self.vauhti_s = round((self.vauhti_min_km * 60) % 60, 0)
elif self.vauhti_min_km is not None and self.vauhti_min_km != 0 and self.vauhti_km_h is None:
self.vauhti_km_h = 60 / self.vauhti_min_km
super(Harjoitus, self).save(*args, **kwargs)
def hours_year_to_date(user_id):
current_day = datetime.now().date()
hours = Harjoitus.objects.filter(user=user_id,
aika__vuosi=current_day.year,
aika__pvm__lte=current_day).aggregate(
Sum('kesto'))['kesto__sum']
hours = float(utils.coalesce(hours, 0))
return hours
def avg_feeling_per_period(user_id, start_date, end_date):
feeling = Harjoitus.objects.filter(
user=user_id,
aika__pvm__gte=start_date,
aika__pvm__lte=end_date,
).aggregate(Avg('tuntuma'))['tuntuma__avg']
feeling = float(utils.coalesce(feeling, 0))
return feeling
def hours_past_year_to_date(user_id):
current_day = datetime.now().date()
current_day_minus_365 = pd.Timestamp(current_day - timedelta(days=365))
hours = Harjoitus.objects.filter(
user=user_id,
aika__vuosi=current_day.year - 1,
aika__pvm__lte=current_day_minus_365).aggregate(
Sum('kesto'))['kesto__sum']
hours = float(utils.coalesce(hours, 0))
return hours
def trainings_to_df(user_id,
columns,
startdate=None,
enddate=None,
sport='Kaikki',
restdays=True,
duration_format='str',
date_format='%Y-%m-%d'):
"""
Fetch training data from database and return cleaned pandas dataframe of it
"""
if not Harjoitus.objects.filter(user=user_id):
return None
startdate = int(
coalesce(startdate,
first_training_date(user_id).strftime('%Y%m%d')))
enddate = int(coalesce(enddate, datetime.now().date().strftime('%Y%m%d')))
join_type = 'left' if restdays is True else 'inner'
days = Aika.objects.filter(vvvvkkpp__gte=startdate,
vvvvkkpp__lte=enddate).values_list(
'pvm', 'vvvvkkpp', 'viikonpaiva_lyh', 'vko')
days_df = pd.DataFrame(days,
columns=['Pvm', 'vvvvkkpp', 'Viikonpäivä', 'Vko'])
trainings = Harjoitus.objects.filter(user=user_id).values_list(
'id', 'id', 'aika_id', 'kesto', 'kesto_h', 'kesto_min',
'laji__laji_nimi', 'matka', 'vauhti_km_h', 'keskisyke', 'tuntuma',
'kommentti')
trainings_df = pd.DataFrame(trainings,
columns=[
'edit', 'delete', 'vvvvkkpp', 'Kesto', 'h',
'min', 'Laji', 'Matka (km)',
'Vauhti (km/h)', 'Keskisyke', 'Tuntuma',
'Kommentti'
])
trainings_df['details'] = np.nan
trainings_df = days_df.merge(trainings_df,
how=join_type,
left_on='vvvvkkpp',
right_on='vvvvkkpp')
# cleaning data
trainings_df['Laji'].fillna('Lepo', inplace=True)
trainings_df['Pvm'] = pd.to_datetime(
trainings_df['Pvm']).dt.strftime(date_format)
trainings_df[['Matka (km)', 'Vauhti (km/h)'
]] = trainings_df[['Matka (km)',
'Vauhti (km/h)']].astype(float).round(1)
trainings_df[['delete', 'edit', 'Keskisyke', 'Tuntuma']] = trainings_df[[
'delete', 'edit', 'Keskisyke', 'Tuntuma'
]].fillna(-1).astype(int).astype(str).replace('-1', np.nan)
trainings_df['Päivä'] = trainings_df[['Pvm', 'Viikonpäivä'
]].apply(lambda x: ' '.join(x),
axis=1)
if duration_format == 'str':
trainings_df['Kesto'] = trainings_df.apply(
lambda row: duration_to_string(row['h'], row['min']), axis=1)
# calculate duration per zone
zones = Teho.objects.filter(harjoitus_id__user=user_id).values_list(
'harjoitus_id', 'tehoalue_id__tehoalue', 'kesto', 'kesto_h',
'kesto_min')
if zones:
zones_df = pd.DataFrame(zones,
columns=['id', 'teho', 'kesto', 'h',
'min']).fillna(np.nan)
zones_df['kesto'] = zones_df['kesto'].astype(float)
zones_df = zones_df.groupby(['id', 'teho']).sum().reset_index()
if duration_format == 'str':
zones_df['kesto'] = zones_df.apply(
lambda row: duration_to_string(row['h'], row['min']), axis=1)
zones_df = zones_df.pivot(index='id', columns='teho', values='kesto')
zones_df.index = zones_df.index.map(str)
trainings_df = trainings_df.merge(zones_df,
how='left',
left_on='edit',
right_index=True)
# filter by sport
if sport != 'Kaikki':
user_sports = sports_to_dict(user_id)
if sport in user_sports.keys():
trainings_df = trainings_df[trainings_df['Laji'].isin(
user_sports[sport])]
else:
trainings_df = trainings_df[trainings_df['Laji'] == sport]
trainings_df.sort_values(by='vvvvkkpp', ascending=False, inplace=True)
return trainings_df[columns]
def test_not_none(self):
x = 'koira'
val = 'kissa'
self.assertEqual(coalesce(x, val), 'koira')
def test_nan(self):
x = np.nan
val = 'kissa'
self.assertEqual(coalesce(x, val), 'kissa')
def total_hours_per_year(user_id, year):
hours = Harjoitus.objects.filter(user=user_id, aika__vuosi=year).aggregate(
Sum('kesto'))['kesto__sum']
hours = float(utils.coalesce(hours, 0))
return hours
def trainings_view(request):
"""
List of trainings
"""
user_id = request.user.id
current_day = datetime.now().date()
startdate = utils.coalesce(cl.first_training_date(user_id),
current_day).strftime('%d.%m.%Y')
enddate = current_day.strftime('%d.%m.%Y')
sports = tr.sports_to_dict(user_id)
sport = 'Kaikki'
zones = tr.zone_areas_to_list(user_id)
table_headers = [
'details', 'Vko', 'Päivä', 'Laji', 'Kesto', 'Keskisyke', 'Matka (km)',
'Vauhti (km/h)', 'Tuntuma'
] + zones + ['Kommentti', 'edit', 'delete']
if request.method == "POST":
if 'polar' in request.POST:
return redirect('accesslink_trainings')
sport = request.POST['sport']
startdate = request.POST['startdate']
enddate = request.POST['enddate']
if 'lepo' in request.POST:
restdays = True
else:
restdays = False
export_columns = [
'Vko', 'Pvm', 'Viikonpäivä', 'Kesto', 'Laji', 'Matka (km)',
'Vauhti (km/h)', 'Keskisyke', 'Tuntuma', 'Kommentti'
] + zones
trainings_df = tr.trainings_to_df(
user_id=user_id,
columns=export_columns,
startdate=datetime.strptime(startdate,
'%d.%m.%Y').strftime('%Y%m%d'),
enddate=datetime.strptime(enddate, '%d.%m.%Y').strftime('%Y%m%d'),
sport=sport,
restdays=restdays,
duration_format='decimal',
date_format='%d.%m.%Y')
if trainings_df is None or trainings_df.empty:
messages.add_message(request, messages.ERROR, 'Ei harjoituksia')
else:
try:
if 'export_csv' in request.POST:
return utils.dataframe_to_csv(trainings_df)
if 'export_xls' in request.POST:
return utils.dataframe_to_excel(trainings_df)
except Exception as e:
messages.add_message(request, messages.ERROR,
'Lataus epäonnistui: {}'.format(str(e)))
return render(request,
'trainings.html',
context={
'sport': sport,
'sports': sports,
'startdate': startdate,
'enddate': enddate,
'table_headers': table_headers
})