Merge branch 'release/v18.7.6'

rowers/alerts.py

@@ -3,7 +3,7 @@ from rowers.teams import coach_getcoachees
 from rowers.dataprep import getsmallrowdata_db, getrowdata_db
 import datetime
 import numpy as np
-
+import math
 
 def create_alert(manager, rower, measured, period=7, emailalert=True,
                  reststrokes=False, workouttype='water', boattype='1x',
@@ -195,7 +195,7 @@ def alert_get_stats(alert, nperiod=0):  # pragma: no cover
     median = df[alert.measured.metric].median()
     std = df[alert.measured.metric].std()
 
-    return {
+    data = {
         'workouts': workouts.count(),
         'startdate': startdate,
         'enddate': enddate,
@@ -208,6 +208,18 @@ def alert_get_stats(alert, nperiod=0):  # pragma: no cover
         'standard_dev': std,
     }
 
+    data_clean = {}
+
+    for k in data:
+        data_clean[k] = data[k]
+        try:
+            if math.isnan(data[k]):
+                data_clean[k] = 0
+        except TypeError:
+            pass
+
+    return data_clean
+
 # run alert report
 
 # check alert permission

rowers/interactiveplots.py

@@ -4082,6 +4082,9 @@ def instroke_multi_interactive_chart(selected):
     df_plot = pd.DataFrame()
     ids = [analysis.id for analysis in selected]
     metrics = list(set([analysis.metric for analysis in selected]))
+    maximum_values = {}
+    for metric in metrics:
+        maximum_values[metric] = 0
     for analysis in selected:
         #start_second, end_second, spm_min, spm_max, name
         activeminutesmin = int(analysis.start_second/60.)
@@ -4095,14 +4098,22 @@ def instroke_multi_interactive_chart(selected):
             activeminutesmax=activeminutesmax,
         )
         mean_vals = data.mean()
-        if len(metrics)>1:
-            mean_vals = mean_vals/mean_vals.max()
+        if analysis.metric == 'boat accelerator curve':
+            mean_vals[0] = (mean_vals[1]+ mean_vals[len(mean_vals)-1])/2.
+        if len(metrics) > 1:
+            if mean_vals.max() > maximum_values[analysis.metric]:
+                maximum_values[analysis.metric] = mean_vals.max()
         xvals = np.arange(len(mean_vals))
         xname = 'x_'+str(analysis.id)
         yname = 'y_'+str(analysis.id)
         df_plot[xname] = xvals
         df_plot[yname] = mean_vals
 
+    if len(metrics) > 1:
+        for analysis in selected:
+            yname = 'y_'+str(analysis.id)
+            df_plot[yname] = df_plot[yname] / maximum_values[analysis.metric]
+
     source = ColumnDataSource(
         df_plot
     )

rowers/management/commands/processalerts.py

@@ -9,7 +9,7 @@ from rowers.tasks import handle_send_email_alert
 
 from rowers import alerts
 
-from rowers.utils import myqueue
+from rowers.utils import myqueue, dologging
 
 import datetime
 
@@ -57,6 +57,11 @@ class Command(BaseCommand):
                         stats, debug=True,
                         othertexts=othertexts)
 
+            dologging('alerts.log', 'Sent alert {id} to {email}'.format(
+                id = alert.id,
+                email = alert.manager.email,
+            ))
+
             # advance next_run
             if not testing:
                 alert.next_run = datetime.date.today() + datetime.timedelta(days=alert.period-1)

rowers/models.py

@@ -1444,7 +1444,7 @@ class Alert(models.Model):
         default=False, null=True, verbose_name='Include Rest Strokes')
     period = models.IntegerField(
         default=7, verbose_name='Reporting Period (days)')
-    next_run = models.DateField(default=timezone.now)
+    next_run = models.DateField(default=current_day)
     emailalert = models.BooleanField(
         default=True, verbose_name='Send email alerts')
     workouttype = models.CharField(choices=rowchoices, max_length=50,
@@ -1452,6 +1452,12 @@ class Alert(models.Model):
     boattype = models.CharField(choices=mytypes.boattypes, max_length=50,
                                 verbose_name='Boat Type', default='1x')
 
+    def save(self, *args, **kwargs):
+        if self.next_run > (timezone.now()+datetime.timedelta(days=self.period)).date():
+            self.next_run = (timezone.now()+datetime.timedelta(days=self.period)).date()
+        super(Alert, self).save(*args, **kwargs)
+
+
     def __str__(self):
         metricdict = {key: value for (key, value) in parchoicesy1}
         stri = u'Alert {name} on {metric} for {workouttype} - running on {first_name} every {period} days'.format(

rowers/tasks.py

@@ -408,7 +408,11 @@ def handle_c2_sync(workoutid, url, headers, data, debug=False, **kwargs):
     s = response.json()
     c2id = s['data']['id']
 
-    workout = Workout.objects.get(id=workoutid)
+    try:
+        workout = Workout.objects.get(id=workoutid)
+    except Workout.DoesNotExist:
+        return 0
+
     workout.uploadedtoc2 = c2id
     workout.save()
 
@@ -492,7 +496,11 @@ def handle_strava_sync(stravatoken, workoutid, filename, name, activity_type, de
         failed = True
 
     if not failed:
-        workout = Workout.objects.get(id=workoutid)
+        try:
+            workout = Workout.objects.get(id=workoutid)
+        except Workout.DoesNotExist:
+            return 0
+        
         workout.uploadedtostrava = res.id
         workout.save()
         try:

rowers/views/importviews.py

@@ -1289,6 +1289,9 @@ def workout_stravaimport_view(request, message="", userid=0):
 
         checknew = request.GET.get('selectallnew', False)
 
+        # 2022-10-24 sorting the results
+        workouts = sorted(workouts, key = lambda d:d['starttime'], reverse=True)
+
         return render(request, 'strava_list_import.html',
                       {'workouts': workouts,
                        'rower': rower,

rowers/views/statements.py

@@ -380,7 +380,11 @@ def createShareModel(request, model_id):  # pragma: no cover
 
 class JSONResponse(HttpResponse):
     def __init__(self, data, **kwargs):
-        content = JSONRenderer().render(data)
+        try:
+            content = JSONRenderer().render(data)
+        except ValueError:
+            content = ''
+
         kwargs['content_type'] = 'application/json'
         super(JSONResponse, self).__init__(content, **kwargs)