def loadtest(request):
# The multimech results api is kinda all over the place.
# the docs are here: http://testutils.org/multi-mechanize/datastore.html
scripts = ['submit_form.py', 'ota_restore.py']
tests = []
# datetime info seems to be buried in GlobalConfig.results[0].run_id,
# which makes ORM-level sorting problematic
for gc in Session.query(GlobalConfig).all()[::-1]:
gc.scripts = dict((uc.script, uc) for uc in gc.user_group_configs)
if gc.results:
for script, uc in gc.scripts.items():
uc.results = filter(
lambda res: res.user_group_name == uc.user_group,
gc.results
)
test = {
'datetime': gc.results[0].run_id,
'run_time': gc.run_time,
'results': gc.results,
}
for script in scripts:
test[script.split('.')[0]] = gc.scripts.get(script)
tests.append(test)
context = get_hqadmin_base_context(request)
context.update({
"tests": tests,
"hide_filters": True,
})
date_axis = Axis(label="Date", dateFormat="%m/%d/%Y")
tests_axis = Axis(label="Number of Tests in 30s")
chart = LineChart("HQ Load Test Performance", date_axis, tests_axis)
submit_data = []
ota_data = []
total_data = []
max_val = 0
max_date = None
min_date = None
for test in tests:
date = test['datetime']
total = len(test['results'])
max_val = total if total > max_val else max_val
max_date = date if not max_date or date > max_date else max_date
min_date = date if not min_date or date < min_date else min_date
submit_data.append({'x': date, 'y': len(test['submit_form'].results)})
ota_data.append({'x': date, 'y': len(test['ota_restore'].results)})
total_data.append({'x': date, 'y': total})
deployments = [row['key'][1] for row in HqDeploy.get_list(settings.SERVER_ENVIRONMENT, min_date, max_date)]
deploy_data = [{'x': min_date, 'y': 0}]
for date in deployments:
deploy_data.extend([{'x': date, 'y': 0}, {'x': date, 'y': max_val}, {'x': date, 'y': 0}])
deploy_data.append({'x': max_date, 'y': 0})
chart.add_dataset("Deployments", deploy_data)
chart.add_dataset("Form Submission Count", submit_data)
chart.add_dataset("OTA Restore Count", ota_data)
chart.add_dataset("Total Count", total_data)
context['charts'] = [chart]
template = "hqadmin/loadtest.html"
return render(request, template, context)
def charts(self):
product_availability = self.rows
if product_availability:
def convert_product_data_to_stack_chart(rows, chart_config):
ret_data = []
for k in ['Stocked out', 'Not Stocked out', 'No Stock Data']:
def calculate_percent(x, y):
return float(x) / float((y or 1))
datalist = []
for row in rows:
total = row['total']
if k == 'No Stock Data':
datalist.append([
row['product_code'],
calculate_percent(row['without_data'], total),
row['product_name']
])
elif k == 'Stocked out':
datalist.append([
row['product_code'],
calculate_percent(row['without_stock'], total),
row['product_name']
])
elif k == 'Not Stocked out':
datalist.append([
row['product_code'],
calculate_percent(row['with_stock'], total),
row['product_name']
])
ret_data.append({
'color': chart_config['label_color'][k],
'label': k,
'data': datalist
})
return ret_data
chart = EWSMultiBarChart('',
x_axis=Axis('Products'),
y_axis=Axis('', '%'))
chart.rotateLabels = -45
chart.marginBottom = 120
chart.stacked = False
chart.tooltipFormat = " on "
chart.forceY = [0, 1]
chart.product_code_map = {
sql_product.code: sql_product.name
for sql_product in SQLProduct.objects.filter(
domain=self.domain)
}
chart.is_rendered_as_email = self.config.get(
'is_rendered_as_email', False)
for row in convert_product_data_to_stack_chart(
product_availability, self.chart_config):
chart.add_dataset(row['label'], [{
'x': r[0],
'y': r[1],
'name': r[2]
} for r in sorted(row['data'], key=lambda x: x[0])],
color=row['color'])
return [chart]
return []
def charts(self):
chart = MultiBarChart(None, Axis(_('Sites')), Axis(''))
chart.height = 400
chart.marginBottom = 100
chart.data = self.get_data_for_graph()
return [chart]
def charts(self):
case_finding_sql_data = self.case_finding_sql_data[0]
sputum_conversion_report = ReportFactory.from_spec(
StaticReportConfiguration.by_id('static-%s-sputum_conversion' %
self.domain),
include_prefilters=True)
filter_values = {'date': self.datespan}
locations_id = [
Choice(value=location_id, display='')
for location_id in self.report_config.locations_id if location_id
]
if locations_id:
filter_values['village'] = locations_id
if self.report_config.is_migrated is not None:
filter_values['is_migrated'] = Choice(
value=self.report_config.is_migrated, display='')
sputum_conversion_report.set_filter_values(filter_values)
sputum_conversion_data = sputum_conversion_report.get_data()[0]
charts_sql_data = self.charts_sql_data[0]
treatment_outcome_sql_data = self.treatment_outcome_sql_data[0]
default_value = {'sort_key': 0}
chart = PieChart(title=_('Cases by Gender'), key='gender', values=[])
chart.data = [{
'label':
_('Male'),
'value':
case_finding_sql_data.get('male_total', default_value)['sort_key']
}, {
'label':
_('Female'),
'value':
case_finding_sql_data.get('female_total',
default_value)['sort_key']
}, {
'label':
_('Transgender'),
'value':
case_finding_sql_data.get('transgender_total',
default_value)['sort_key']
}]
chart2 = MultiBarChart(_('Cases By Type'),
x_axis=Axis(''),
y_axis=Axis(''))
chart2.stacked = False
chart2.showLegend = False
positive_smear = case_finding_sql_data.get('new_positive_tb_pulmonary',
default_value)['sort_key']
negative_smear = case_finding_sql_data.get('new_negative_tb_pulmonary',
default_value)['sort_key']
positive_extra_pulmonary = case_finding_sql_data.get(
'new_positive_tb_extrapulmonary', default_value)['sort_key']
relapse_cases = case_finding_sql_data.get('recurrent_positive_tb',
default_value)['sort_key']
failure_cases = case_finding_sql_data.get('failure_positive_tb',
default_value)['sort_key']
lfu_cases = case_finding_sql_data.get('lfu_positive_tb',
default_value)['sort_key']
others_cases = case_finding_sql_data.get('others_positive_tb',
default_value)['sort_key']
chart2.add_dataset(_('New'), [{
'x': 'Smear +ve',
'y': positive_smear
}, {
'x': 'Smear -ve',
'y': negative_smear
}, {
'x': 'EP',
'y': positive_extra_pulmonary
}])
chart2.add_dataset(_('Retreatment'), [{
'x': 'Relapse',
'y': relapse_cases
}, {
'x': 'Failure',
'y': failure_cases
}, {
'x': 'Treatment After Default',
'y': lfu_cases
}, {
'x': 'Others',
'y': others_cases
}])
chart3 = MultiBarChart('Sputum Conversion By Patient Type', Axis(''),
Axis(''))
chart3.stacked = True
chart3.add_dataset('Positive', [
{
'x':
_('New Sputum +ve (2 month IP)'),
'y':
sputum_conversion_data.get(
'new_sputum_positive_patient_2months_ip', 0)
},
{
'x':
_('New Sputum +ve (3 month IP)'),
'y':
sputum_conversion_data.get(
'new_sputum_positive_patient_3months_ip', 0)
},
{
'x':
_('Cat II (3 month IP)'),
'y':
sputum_conversion_data.get('positive_endofip_patients_cat2', 0)
},
])
chart3.add_dataset(_('Negative'), [
{
'x':
_('New Sputum +ve (2 month IP)'),
'y':
sputum_conversion_data.get(
'new_sputum_negative_patient_2months_ip', 0)
},
{
'x':
_('New Sputum +ve (3 month IP)'),
'y':
sputum_conversion_data.get(
'new_sputum_negative_patient_3months_ip', 0)
},
{
'x':
_('Cat II (3 month IP)'),
'y':
sputum_conversion_data.get('negative_endofip_patients_cat2', 0)
},
])
chart3.add_dataset('NA', [
{
'x':
_('New Sputum +ve (2 month IP)'),
'y':
sputum_conversion_data.get('new_sputum_na_patient_2months_ip',
0)
},
{
'x':
_('New Sputum +ve (3 month IP)'),
'y':
sputum_conversion_data.get('new_sputum_na_patient_3months_ip',
0)
},
{
'x': _('Cat II (3 month IP)'),
'y': sputum_conversion_data.get('na_endofip_patients_cat2', 0)
},
])
chart4 = PieChart(title=_('Total number of patients by category'),
key='',
values=[])
chart4.data = [{
'label':
_('Cat1'),
'value':
charts_sql_data.get('cat1_patients', default_value)['sort_key']
}, {
'label':
_('Cat2'),
'value':
charts_sql_data.get('cat2_patients', default_value)['sort_key']
}]
chart5 = MultiBarChart('Outcome By Type', Axis(''), Axis(''))
chart5.stacked = True
chart5.add_dataset(_('Cured'), [{
'x':
_('New'),
'y':
treatment_outcome_sql_data.get('new_patients_cured',
default_value)['sort_key']
}, {
'x':
_('Retreatment'),
'y':
treatment_outcome_sql_data.get('recurrent_patients_cured',
default_value)['sort_key']
}])
chart5.add_dataset('Treatment Complete', [{
'x':
_('New'),
'y':
treatment_outcome_sql_data.get('new_patients_treatment_complete',
default_value)['sort_key']
}, {
'x':
_('Retreatment'),
'y':
treatment_outcome_sql_data.get(
'recurrent_patients_treatment_complete',
default_value)['sort_key']
}])
chart5.add_dataset('Died', [{
'x':
_('New'),
'y':
treatment_outcome_sql_data.get('new_patients_died',
default_value)['sort_key']
}, {
'x':
_('Retreatment'),
'y':
treatment_outcome_sql_data.get('recurrent_patients_died',
default_value)['sort_key']
}])
chart5.add_dataset(_('Failure'), [{
'x':
_('New'),
'y':
treatment_outcome_sql_data.get('new_patients_treatment_failure',
default_value)['sort_key']
}, {
'x':
_('Retreatment'),
'y':
treatment_outcome_sql_data.get(
'recurrent_patients_treatment_failure',
default_value)['sort_key']
}])
chart5.add_dataset(_('Loss to Follow-up'), [{
'x':
_('New'),
'y':
treatment_outcome_sql_data.get('new_patients_loss_to_follow_up',
default_value)['sort_key']
}, {
'x':
_('Retreatment'),
'y':
treatment_outcome_sql_data.get(
'recurrent_patients_loss_to_follow_up',
default_value)['sort_key']
}])
chart5.add_dataset(_('Regimen Changed'), [{
'x':
_('New'),
'y':
treatment_outcome_sql_data.get('new_patients_regimen_changed',
default_value)['sort_key']
}, {
'x':
_('Retreatment'),
'y':
treatment_outcome_sql_data.get(
'recurrent_patients_regimen_changed',
default_value)['sort_key']
}])
chart5.add_dataset('Not Evaluated', [{
'x':
_('New'),
'y':
treatment_outcome_sql_data.get('new_patients_not_evaluated',
default_value)['sort_key']
}, {
'x':
_('Retreatment'),
'y':
treatment_outcome_sql_data.get('recurrent_patients_not_evaluated',
default_value)['sort_key']
}])
return [chart, chart2, chart3, chart4, chart5]
def charts(self):
if 'location_id' in self.request.GET: # hack: only get data if we're loading an actual report
chart = MultiBarChart(None, Axis(_('Products')),
Axis(_('% of Facilities'), ',.1d'))
chart.data = self.get_data_for_graph()
return [chart]
def charts(self):
chart = PNAMultiBarChart(None, Axis('Location'),
Axis('Amount', format='i'))
chart.height = 550
chart.marginBottom = 150
chart.rotateLabels = -45
chart.showControls = False
def data_to_chart(quantities_list):
quantities_to_return = []
locations_data = {}
added_locations = []
quantities_list = sorted(quantities_list,
key=lambda x: x['location_name'])
for quantity in quantities_list:
location_name = quantity['location_name']
location_id = quantity['location_id']
for product in quantity['products']:
final_pna_stock_valuation = product[
'final_pna_stock_valuation']
if location_id not in added_locations:
added_locations.append(location_id)
locations_data[location_id] = {
'location_name': location_name,
'final_pna_stock_valuation':
final_pna_stock_valuation,
}
else:
locations_data[location_id][
'final_pna_stock_valuation'] += final_pna_stock_valuation
sorted_locations_data_values = sorted(
locations_data.values(), key=lambda x: x['location_name'])
for location_info in sorted_locations_data_values:
location_name = location_info['location_name']
final_pna_stock_valuation = location_info[
'final_pna_stock_valuation']
pna = final_pna_stock_valuation if final_pna_stock_valuation != 0 else 0
quantities_to_return.append([
location_name, {
'html': '{:.2f} %'.format(pna),
'sort_key': pna
}
])
return quantities_to_return
def get_data_for_graph():
com = []
rows = data_to_chart(self.clean_rows)
for row in rows:
com.append({"x": row[0], "y": row[1]['sort_key']})
return [
{
"key": 'Valeur des stocks PNA disponible',
'values': com
},
]
chart.data = get_data_for_graph()
return [chart]
def get_chart(self, rows, x_label, y_label, data_provider):
def _get_label_with_percentage(row):
return "%s [%d: %s%%]" % (row[0]['html'], int(
row[-2]['html']), str(int(row[-1]['html'][:-1])))
if isinstance(data_provider, NutritionBirthWeightDetails):
chart = PieChart(data_provider.chart_title, '',
[{
'label': _get_label_with_percentage(row),
'value': int(row[-1]['html'][:-1])
} for row in rows[2:]], ['red', 'green'])
chart.showLabels = False
chart.marginLeft = 20
chart.marginRight = 0
chart.marginBottom = 20
elif isinstance(data_provider, DeliveryPlaceDetailsExtended):
chart = PieChart(data_provider.chart_title, '',
[{
'label': _get_label_with_percentage(row),
'value': int(row[-1]['html'][:-1])
} for row in rows[1:]])
chart.showLabels = False
elif isinstance(data_provider,
(PostnatalCareOverview, ImmunizationOverview)):
chart = MultiBarChart(data_provider.chart_title,
x_axis=Axis(x_label),
y_axis=Axis(y_label, '.2%'))
chart.rotateLabels = -45
chart.marginBottom = 150
chart.marginLeft = 45
chart.marginRight = 0
if isinstance(data_provider, ImmunizationOverview):
chart.stacked = True
chart.add_dataset('Percentage',
[{
'x': row[0]['html'],
'y': int(row[3]['html'][:-1]) / 100.0
} for row in rows],
color='green')
chart.add_dataset('Dropout Percentage',
[{
'x': row[0]['html'],
'y': int(row[-1]['html'][:-1]) / 100.0
} for row in rows],
color='red')
else:
chart.add_dataset('Percentage',
[{
'x': row[0]['html'],
'y': int(row[-1]['html'][:-1]) / 100.0
} for row in rows])
elif isinstance(data_provider, AnteNatalCareServiceOverviewExtended):
chart1 = MultiBarChart('ANC Visits',
x_axis=Axis(x_label),
y_axis=Axis(y_label, '.2%'))
chart2 = MultiBarChart('Maternal TT & IFA',
x_axis=Axis(x_label),
y_axis=Axis(y_label, '.2%'))
chart1.rotateLabels = -45
chart2.rotateLabels = -45
chart1.marginBottom = 150
chart2.marginBottom = 150
chart1.marginLeft = 20
chart2.marginLeft = 45
chart1.marginRight = 0
chart2.marginRight = 0
chart1.add_dataset('Percentage',
[{
'x': row[0]['html'],
'y': int(row[-1]['html'][:-1]) / 100.0
} for row in rows[1:6]])
chart2.add_dataset('Percentage',
[{
'x': row[0]['html'],
'y': int(row[-1]['html'][:-1]) / 100.0
} for row in rows[6:12]])
return [chart1, chart2]
elif isinstance(data_provider, ChildrenDeathsByMonth):
chart = MultiBarChart(data_provider.chart_title,
x_axis=Axis(x_label, dateFormat="%B"),
y_axis=Axis(y_label, '.2%'))
chart.rotateLabels = -45
chart.marginBottom = 50
chart.marginLeft = 20
chart.add_dataset('Percentage',
[{
'x': row[0],
'y': int(row[-1]['html'][:-1]) / 100.0
} for row in rows])
else:
chart = PieChart(data_provider.chart_title, '',
[{
'label': _get_label_with_percentage(row),
'value': int(row[-1]['html'][:-1])
} for row in rows])
chart.showLabels = False
chart.marginLeft = 20
chart.marginRight = 0
chart.marginBottom = 0
return [chart]
def charts(self):
chart = PNAMultiBarChart(None, Axis('Location'),
Axis('Percent', format='.2f'))
chart.height = 550
chart.marginBottom = 150
chart.rotateLabels = -45
chart.showControls = False
chart.forceY = [0, 100]
def data_to_chart(quantities_list):
quantities_to_return = []
locations_data = {}
added_locations = []
for quantity in quantities_list:
location_name = quantity['location_name']
location_id = quantity['location_id']
for product in quantity['products']:
amt_delivered_convenience = product[
'amt_delivered_convenience']
ideal_topup = product['ideal_topup']
if location_id not in added_locations:
added_locations.append(location_id)
locations_data[location_id] = {
'location_name': location_name,
'amt_delivered_convenience':
amt_delivered_convenience,
'ideal_topup': ideal_topup,
}
else:
locations_data[location_id][
'amt_delivered_convenience'] += amt_delivered_convenience
locations_data[location_id][
'ideal_topup'] += ideal_topup
sorted_locations_data_values = sorted(
locations_data.values(), key=lambda x: x['location_name'])
for location_info in sorted_locations_data_values:
location_name = location_info['location_name']
amt_delivered_convenience = location_info[
'amt_delivered_convenience']
ideal_topup = location_info['ideal_topup']
percent = (amt_delivered_convenience / float(ideal_topup) * 100) \
if ideal_topup != 0 else 0
quantities_to_return.append([
location_name, {
'html': '{:.2f} %'.format(percent),
'sort_key': percent
}
])
return quantities_to_return
def get_data_for_graph():
com = []
rows = data_to_chart(self.clean_rows)
for row in rows:
com.append({"x": row[0], "y": row[1]['sort_key']})
return [
{
"key": 'Taux de Satisfaction des produits',
'values': com
},
]
chart.data = get_data_for_graph()
return [chart]
def charts(self):
x_axis = 'Product'
chart = PNAMultiBarChart(None, Axis(x_axis), Axis('Percent', format='.2f'))
chart.height = 550
chart.marginBottom = 150
chart.forceY = [0, 100]
chart.rotateLabels = -45
chart.showControls = False
def data_to_chart(stocks_list):
stocks_to_return = []
products_data = []
added_products = []
for stock in stocks_list:
location_id = stock['location_id']
location_name = stock['location_name']
for product in stock['products']:
product_id = product['product_id']
product_name = product['product_name']
in_ppses = product['in_ppses']
all_ppses = product['all_ppses']
if product_id not in added_products:
added_products.append(product_id)
product_dict = {
'product_id': product_id,
'product_name': product_name,
'location_id': location_id,
'location_name': location_name,
'in_ppses': in_ppses,
'all_ppses': all_ppses,
}
products_data.append(product_dict)
else:
for product_data in products_data:
if product_data['product_id'] == product_id:
product_data['in_ppses'] += in_ppses
product_data['all_ppses'] += all_ppses
products = sorted(products_data, key=lambda x: x['product_name'])
for product in products:
product_name = product['product_name']
in_ppses = product['in_ppses']
all_ppses = product['all_ppses']
percent = (in_ppses / float(all_ppses)) * 100 if all_ppses != 0 else 0
stocks_to_return.append([
product_name,
{
'html': '{}'.format(percent),
'sort_key': percent
}
])
return stocks_to_return
def get_data_for_graph():
com = []
rows = data_to_chart(self.clean_rows)
for row in rows:
com.append({"x": row[0], "y": row[1]['sort_key']})
return [
{
"key": 'Taux de disponibilité de la Gamme des produits',
'values': com
},
]
chart.data = get_data_for_graph()
return [chart]
def get_chart(self, rows, x_label, y_label, data_provider):
def _get_label_with_percentage(row):
return "%s [%s%%]" % (row[0], str(float(row[-1]['html'][:-1])))
if isinstance(data_provider,
(ClosedMotherCasesBreakdown, ClosedChildCasesBreakdown)):
chart = PieChart('', '', [{
'label': _get_label_with_percentage(row),
'value': float(row[-1]['html'][:-1])
} for row in rows])
elif isinstance(data_provider, NutritionBirthWeightDetails):
chart = PieChart('BirthWeight', '', [{
'label':
"%s [%s%%]" %
(row[0]['html'], str(float(row[-1]['html'][:-1]))),
'value':
float(row[-1]['html'][:-1])
} for row in rows[1:]], ['red', 'green'])
elif isinstance(data_provider, DeliveryPlaceDetailsExtended):
chart = PieChart('', '', [{
'label': _get_label_with_percentage(row),
'value': float(row[-1]['html'][:-1])
} for row in rows[1:]])
elif isinstance(data_provider,
(PostnatalCareOverview, ImmunizationOverview)):
chart = MultiBarChart('',
x_axis=Axis(x_label),
y_axis=Axis(y_label, '.2%'))
chart.rotateLabels = -45
chart.marginBottom = 120
if isinstance(data_provider, ImmunizationOverview):
chart.stacked = True
chart.add_dataset('Percentage',
[{
'x': row[0]['html'],
'y': float(row[3]['html'][:-1]) / 100
} for row in rows],
color='green')
chart.add_dataset('Dropout Percentage',
[{
'x': row[0]['html'],
'y': float(row[-1]['html'][:-1]) / 100
} for row in rows],
color='red')
else:
chart.add_dataset('Percentage',
[{
'x': row[0]['html'],
'y': float(row[-1]['html'][:-1]) / 100
} for row in rows])
elif isinstance(data_provider, AnteNatalCareServiceOverviewExtended):
chart1 = MultiBarChart('',
x_axis=Axis(x_label),
y_axis=Axis(y_label, '.2%'))
chart2 = MultiBarChart('',
x_axis=Axis(x_label),
y_axis=Axis(y_label, '.2%'))
chart1.rotateLabels = -45
chart2.rotateLabels = -45
chart1.marginBottom = 120
chart2.marginBottom = 120
chart1.add_dataset('Percentage',
[{
'x': row[0]['html'],
'y': float(row[-1]['html'][:-1]) / 100
} for row in rows[1:6]])
chart2.add_dataset('Percentage',
[{
'x': row[0]['html'],
'y': float(row[-1]['html'][:-1]) / 100
} for row in rows[6:12]])
return [chart1, chart2]
elif isinstance(data_provider, ChildrenDeathsByMonth):
chart = LineChart('Seasonal Variation of Child Deaths',
x_axis=Axis(x_label, dateFormat="%B"),
y_axis=Axis(y_label, '.2%'))
chart.rotateLabels = -45
chart.marginBottom = 120
months_mapping = dict(
(v, k) for k, v in enumerate(calendar.month_abbr))
chart.add_dataset(
'Percentage',
[{
'x': datetime.date(1, months_mapping[row[0][:3]], 1),
'y': float(row[-1]['html'][:-1]) / 100
} for row in rows])
else:
chart = PieChart('', '', [{
'label':
"%s [%s%%]" %
(row[0]['html'], str(float(row[-1]['html'][:-1]))),
'value':
float(row[-1]['html'][:-1])
} for row in rows])
return [chart]
def charts(self):
chart = PNAMultiBarChart(None, Axis('Location'),
Axis('Amount', format='i'))
chart.height = 550
chart.marginBottom = 150
chart.rotateLabels = -45
chart.showControls = False
def data_to_chart(stocks_list):
stocks_to_return = []
products_data = []
added_products = []
for stock in stocks_list:
sorted_stock = sorted(stock['products'],
key=lambda x: x['product_name'])
for product in sorted_stock:
product_id = product['product_id']
product_name = product['product_name']
actual_consumption = product['actual_consumption']
if product_id not in added_products:
added_products.append(product_id)
product_dict = {
'product_id': product_id,
'product_name': product_name,
'actual_consumption': actual_consumption,
}
products_data.append(product_dict)
else:
for product_data in products_data:
if product_data['product_id'] == product_id:
product_data[
'actual_consumption'] += actual_consumption
products = sorted(products_data, key=lambda x: x['product_name'])
for product in products:
product_name = product['product_name']
actual_consumption = product['actual_consumption']
consumption = actual_consumption if actual_consumption is not 0 else 0
stocks_to_return.append([
product_name, {
'html': '{}'.format(consumption),
'sort_key': consumption
}
])
return stocks_to_return
def get_data_for_graph():
com = []
rows = data_to_chart(self.clean_rows)
for row in rows:
com.append({"x": row[0], "y": row[1]['sort_key']})
return [
{
"key": 'Consommation par produit au niveau national',
'values': com
},
]
chart.data = get_data_for_graph()
return [chart]
