def calculate_weightAvg_async(user):
tracks = Track.objects.order_by('timestamp')
times = []
weights = []
postRef = []
#Loop through all tracks and create period object for each day
for track in tracks:
times.append(Period(track.timestamp, freq='D'))
weights.append(track.weight)
postRef.append(track.to_dbref())
periods = PeriodIndex(times)
ts = Series(weights, index=periods)
mean = rolling_mean(ts, 10)
print mean
new_Analysis = Analysis(
author = user
)
for i, entry in enumerate(mean):
my_data = DailyAnalysis (
weightAvg = entry,
date = times[i].to_timestamp(),
postRef = postRef[i]
)
new_Analysis.dailyAnalysis.append(my_data)
new_Analysis.save()
def analyze(request, project_id):
project = get_object_or_404(Project, pk=project_id)
result_analysis = code_analysis(project)
analysis = Analysis()
analysis.project = project
analysis.pep8 = result_analysis['pep8']['percentage_errors']
analysis.pyflakes = result_analysis['pyflakes']['percentage_errors']
analysis.clonedigger = result_analysis['clonedigger']['percentage_errors']
analysis.jshint = result_analysis['jshint']['percentage_errors']
analysis.csslint = result_analysis['csslint']['percentage_errors']
analysis.result = result_analysis
analysis.save()
return HttpResponse('done')
def create(self, request):
analysis = Analysis(**request.data)
analysis.save()
return HttpResponse(analysis.to_json())
