def get_data(self, request, scenario_id):
scenario = get_object_or_404(Scenario, pk=scenario_id)
data = analysis.monthly_volume_deficit_pct(
scenario.get_data(),
scenario.get_gap_attribute_name(),
scenario.get_target_attribute_name(),
analysis.CFS_TO_AFD
)
return data.reset_index()
def project_deficit_stats_pct_plot(request, project_id):
return __dataframe_barplot_helper(
request,
project_id,
"Average monthly volume deficit relative to target",
lambda d, g, t: analysis.monthly_volume_deficit_pct(
d, g, t, CFS_TO_AFD).mean().abs(),
"%",
formatter=FuncFormatter(to_percent),
ylim=[0.0, 1.0])
def project_deficit_stats_pct_csv(request, project_id):
project = get_object_or_404(Project, pk=project_id)
monthly_result = __get_deficit_stats_comparison(
project, lambda d, g, t: analysis.monthly_volume_deficit_pct(
d, g, t, CFS_TO_AFD).mean().abs(), "%")
annual_result = __get_deficit_stats_comparison(
project, lambda d, g, t: analysis.annual_volume_deficit_pct(
d, g, t, CFS_TO_AFD), "%").mean().abs()
annual_result.name = "Annual Average"
response = HttpResponse(content_type="text/csv")
result = pd.concat(
[monthly_result, annual_result.to_frame().transpose()], axis=0)
result.to_csv(response)
return response
def project_deficit_stats_pct_csv(request, project_id):
project = get_object_or_404(Project, pk=project_id)
monthly_result = __get_deficit_stats_comparison(
project,
lambda d, g, t: analysis.monthly_volume_deficit_pct(d, g, t, CFS_TO_AFD).mean().abs(),
"%")
annual_result = __get_deficit_stats_comparison(
project,
lambda d, g, t: analysis.annual_volume_deficit_pct(d, g, t, CFS_TO_AFD),
"%").mean().abs()
annual_result.name = "Annual Average"
response = HttpResponse(content_type="text/csv")
result = pd.concat([monthly_result, annual_result.to_frame().transpose()], axis=0)
result.to_csv(response)
return response
def project_deficit_stats_pct_plot(request, project_id):
return __dataframe_barplot_helper(request, project_id,
"Average monthly volume deficit relative to target",
lambda d, g, t: analysis.monthly_volume_deficit_pct(d, g, t, CFS_TO_AFD).mean().abs(),
"%", formatter=FuncFormatter(to_percent), ylim=[0.0, 1.0])