本文整理匯總了Python中corehq.apps.reports.graph_models.MultiBarChart.showLegend方法的典型用法代碼示例。如果您正苦於以下問題：Python MultiBarChart.showLegend方法的具體用法？Python MultiBarChart.showLegend怎麽用？Python MultiBarChart.showLegend使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類corehq.apps.reports.graph_models.MultiBarChart的用法示例。
在下文中一共展示了MultiBarChart.showLegend方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: charts
# 需要導入模塊: from corehq.apps.reports.graph_models import MultiBarChart [as 別名]
# 或者: from corehq.apps.reports.graph_models.MultiBarChart import showLegend [as 別名]
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': QuarterFilter.get_value(self.request, self.domain)}
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
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)
},
#.........這裏部分代碼省略.........