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Protect Results Summary Report percent_positive against divide-by-zero
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@aaronstephenson
aaronstephenson committed Jun 12, 2019
1 parent 81ff034 commit 1776a0f
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Showing 2 changed files with 24 additions and 4 deletions.
2 changes: 1 addition & 1 deletion code.json
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Expand Up @@ -3,7 +3,7 @@
"name": "lide",
"organization": "U.S. Geological Survey",
"description": "Web services for LILI (LIDE (Laboratory for Infectious Disease and the Environment) Information Management System)",
"version": "v0.104.0",
"version": "v0.104.1",
"status": "Release Candidate",

"permissions": {
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26 changes: 23 additions & 3 deletions lideservices/views.py
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Expand Up @@ -499,51 +499,71 @@ def summary_statistics(self, request):
# calculate the requested statistics per object
if ('sample_count' in statistic_list
or ('percent_positive' in statistic_list and 'sample_count' not in statistic_list)):
# include the sample_count by target
queryset = queryset.annotate(sample_count=Count('id'))
# include the sample_count for all targets
totals['sample_count'] = totals_queryset.aggregate(Count('sample_id', distinct=True))['sample_id__count']
if ('positive_count' in statistic_list
or ('percent_positive' in statistic_list and 'positive_count' not in statistic_list)):
# include the positive_count by target
queryset = queryset.annotate(positive_count=Count('id', filter=Q(final_sample_mean_concentration__gt=0)))
# include the positive_count for all targets
totals['positive_count'] = queryset.aggregate(Count('positive_count'))['positive_count__count']
if 'percent_positive' in statistic_list:
# include the percent_positive by target
queryset = queryset.annotate(
percent_positive=(Cast('positive_count', FloatField()) / Cast('sample_count', FloatField()) * 100))
totals['percent_positive'] = (queryset.aggregate(
Count('positive_count'))['positive_count__count'] / totals_queryset.aggregate(
Count('sample_id', distinct=True))['sample_id__count']) * 100
# include the percent_positive for all targets
pos_count = queryset.aggregate(Count('positive_count'))['positive_count__count']
samp_count = totals_queryset.aggregate(Count('sample_id', distinct=True))['sample_id__count']
totals['percent_positive'] = (pos_count / samp_count) * 100 if pos_count else 0
if 'max_concentration' in statistic_list:
# include the max_concentration by target
queryset = queryset.annotate(max_concentration=Max('final_sample_mean_concentration'))
# include the max_concentration for all targets
totals['max_concentration'] = totals_queryset.aggregate(
Max('final_sample_mean_concentration'))['final_sample_mean_concentration__max']
if 'min_concentration' in statistic_list:
# include the min_concentration by target
queryset = queryset.annotate(min_concentration=Min('final_sample_mean_concentration'))
# include the min_concentration for all targets
totals['min_concentration'] = totals_queryset.aggregate(
Min('final_sample_mean_concentration'))['final_sample_mean_concentration__min']
if 'median_concentration' in statistic_list:
# include the median_concentration by target
queryset = queryset.annotate(median_concentration=Median('final_sample_mean_concentration'))
# include the median_concentration for all targets
totals['median_concentration'] = totals_queryset.aggregate(
Median('final_sample_mean_concentration'))['final_sample_mean_concentration__median']
if 'average_concentration' in statistic_list:
# include the average_concentration by target
queryset = queryset.annotate(average_concentration=Avg('final_sample_mean_concentration'))
# include the average_concentration for all targets
totals['average_concentration'] = totals_queryset.aggregate(
Avg('final_sample_mean_concentration'))['final_sample_mean_concentration__avg']
if 'min_concentration_positive' in statistic_list:
# include the min_concentration_positive by target
queryset = queryset.annotate(
min_concentration_positive=Min('final_sample_mean_concentration',
filter=Q(final_sample_mean_concentration__gt=0)))
# include the min_concentration_positive for all targets
totals['min_concentration_positive'] = totals_queryset.aggregate(
final_sample_mean_concentration__min=Min('final_sample_mean_concentration', filter=Q(
final_sample_mean_concentration__gt=0)))['final_sample_mean_concentration__min']
if 'median_concentration_positive' in statistic_list:
# include the median_concentration_positive by target
queryset = queryset.annotate(median_concentration_positive=Median(
'final_sample_mean_concentration', filter=Q(final_sample_mean_concentration__gt=0)))
# include the median_concentration_positive for all targets
totals['median_concentration_positive'] = totals_queryset.aggregate(
final_sample_mean_concentration__median=Median('final_sample_mean_concentration', filter=Q(
final_sample_mean_concentration__gt=0)))['final_sample_mean_concentration__median']
if 'average_concentration_positive' in statistic_list:
# include the average_concentration_positive by target
queryset = queryset.annotate(
average_concentration_positive=Avg('final_sample_mean_concentration',
filter=Q(final_sample_mean_concentration__gt=0)))
# include the average_concentration_positive for all targets
totals['average_concentration_positive'] = totals_queryset.aggregate(
final_sample_mean_concentration__avg=Avg('final_sample_mean_concentration', filter=Q(
final_sample_mean_concentration__gt=0)))['final_sample_mean_concentration__avg']
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