def get_context_data(self, **kwargs):
context = super(ListView, self).get_context_data(**kwargs)
activity = self.kwargs['activity']
stat = self.kwargs['stat']
activity_string = activities[activities_short.index(activity)]
stat_string = stat_strings[stat]
# Iterate over all dataflows and construct the relevant dataseries list
f1 = Q(agg_level='Country')
f2 = Q(activity=activity)
series_list = DataSeries.objects.filter(f1 & f2)
# series_list = DataSeries.objects.all()
values = []
for series in series_list:
# if series.region == '' and series.activity == activity:
value = json.loads(series.metrics)[stat]
values.append(value)
print(stat)
context.update({'stat': stat_string})
context.update({'activity': activity_string})
context.update({'values': values})
return context
def get_context_data(self, **kwargs):
context = super(ListView, self).get_context_data(**kwargs)
activity = self.kwargs['activity']
# Iterate over all dataflows and construct the relevant dataseries list
f1 = Q(agg_level='Country')
f2 = Q(activity=activity)
series_list = DataSeries.objects.filter(f1 & f2)
# series_list = DataSeries.objects.all()
stats_list = []
for series in series_list:
# if series.region == '' and series.activity == activity:
stats = {}
stats['name'] = country_dict[series.df_name]
stats['Max'] = json.loads(series.metrics)['Max']
stats['Min'] = json.loads(series.metrics)['Min']
stats['Average'] = json.loads(series.metrics)['Mean']
stats['Latest'] = json.loads(series.metrics)['T']
stats['Vol'] = json.loads(series.metrics)['Vol']
stats_list.append(stats)
activity_string = activities[activities_short.index(activity)]
context.update({'activity': activity_string})
context.update({'stats_list': stats_list})
print(stats_list)
return context
def get_context_data(self, **kwargs):
context = super(DetailView, self).get_context_data(**kwargs)
# get the geoslice
geoslice = super(GSMapView, self).get_object()
ac = geoslice.identifier.split('.')[1]
# get the list of dataseries that are part of the geoslice
dataseries = geoslice.dataset_id
# print(dataseries)
# construct the dataset to send to the template
# the dataset for the mobility type for each country
dataset = {}
query = Q()
for ds in dataseries:
query |= Q(identifier=ds)
entries = DataSeries.objects.filter(query)
for full_entry in entries:
print(full_entry)
entry = {}
# the redirect url in case we want to drill down
entry['url'] = 'plot/' + full_entry.identifier
# # we fetch the actual dataseries
# full_entry = DataSeries.objects.filter(identifier=ds)
# TBD
dataid = full_entry.identifier.split('.')[0]
# DS title
entry['title'] = full_entry.title
# DS long title
entry['title_long'] = full_entry.title_long
# DS observation dates
entry['dates'] = full_entry.dates
# DS observation values
# values (% change)
entry['values'] = full_entry.values
# values (demeaned)
dataset[dataid] = entry
activity_dict = {}
for i in activities_short:
activity_dict[i] = activities[activities_short.index(i)]
activity_string = activities[activities_short.index(ac)]
context.update({'activity': activity_string})
context.update({'activity_dict': activity_dict})
context.update({'dataset': json.dumps(dataset)})
return context
def get_context_data(self, **kwargs):
# Retrieve all dataflow objects and construct content description text for help display
# object_list = DataFlow.objects.all()
# statistics = {}
# total_datasets = 0
# tracked_datasets = 0
# live_datasets = 0
# for df in object_list:
# total_datasets += df.google_n
# if df.tracked:
# tracked_datasets += df.dashboard_n
# live_datasets += len(df.dataset_id)
#
# statistics['total_datasets'] = total_datasets
# statistics['tracked_datasets'] = tracked_datasets
# statistics['live_datasets'] = live_datasets
activity_dict = {}
for i in activities_short:
activity_dict[i] = activities[activities_short.index(i)]
context = super(ListView, self).get_context_data(**kwargs)
context.update({'activity_dict': activity_dict})
context.update({'activity': activities_short})
return context
def get_context_data(self, **kwargs):
metadata = DashBoardParams.objects.all()[0].country_metadata
print(metadata)
context = super(ListView, self).get_context_data(**kwargs)
activity1 = self.kwargs['activity1']
activity2 = self.kwargs['activity2']
activity_string1 = activities[activities_short.index(activity1)]
activity_string2 = activities[activities_short.index(activity2)]
# Filter the relevant dataseries list
f1 = Q(agg_level='Country')
f2 = Q(activity=activity1)
f3 = Q(activity=activity2)
series_list1 = DataSeries.objects.filter(f1 & f2)
series_list2 = DataSeries.objects.filter(f1 & f3)
N1 = len(series_list1)
N2 = len(series_list2)
dictionary_data = {
'A1': 'Minimum Observed ' + activity_string1 + ' Mobility',
'A2': 'Maximum Observed ' + activity_string1 + ' Mobility',
'A3': 'Average Observed Mobility for ' + activity_string1,
'A4': 'Latest Mobility Observation for ' + activity_string1,
}
stat_dict = {''}
# All country dict
policy1 = {}
policy2 = {}
i = 1
for series1 in series_list1:
for series2 in series_list2:
if series2.df_name == series1.df_name:
# country data dict
country_name = country_dict[series1.df_name]
country1 = {}
country1['A1'] = json.loads(series1.metrics)['Min']
country1['A2'] = json.loads(series1.metrics)['Max']
country1['A3'] = json.loads(series1.metrics)['Mean']
country1['A4'] = json.loads(series1.metrics)['T']
country1['M0'] = country_name
country1['M1'] = metadata[
series1.df_name]['population_count']
try:
country1['M2'] = metadata[
series1.df_name]['stress_level']
except:
country1['M2'] = 0
country2 = {}
country2['A1'] = json.loads(series2.metrics)['Min']
country2['A2'] = json.loads(series2.metrics)['Max']
country2['A3'] = json.loads(series2.metrics)['Mean']
country2['A4'] = json.loads(series2.metrics)['T']
country2['M0'] = country_name
country1['M1'] = metadata[
series1.df_name]['population_count']
# country1['M2'] = metadata[series1.df_name]['stress_level']
# indexed by integer value
policy1[str(i)] = country1
policy2[str(i)] = country2
i += 1
context.update({'policy1': policy1})
context.update({'policy2': policy2})
context.update({'dict_data': dictionary_data})
context.update({'activity1': activity_string1 + ' Mobility'})
context.update({'activity2': activity_string2 + ' Mobility'})
return context