Skip to content
This repository has been archived by the owner on Jul 18, 2023. It is now read-only.

Commit

Permalink
Implement Quality Control Report as new endpoint
Browse files Browse the repository at this point in the history
  • Loading branch information
@aaronstephenson
aaronstephenson committed May 15, 2019
1 parent 5e09983 commit aa47c3a
Showing 1 changed file with 74 additions and 19 deletions.
93 changes: 74 additions & 19 deletions lideservices/views.py
View file
Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
from collections import Counter
from django.http import JsonResponse
from django.db.models import F, Q, Count, Sum, Max, Min, Avg, FloatField, IntegerField
from django.db.models import F, Q, Count, Sum, Min, Max, Avg, FloatField
from django.db.models.functions import Cast
from rest_framework import views, viewsets, permissions, authentication, status
from rest_framework.decorators import action
Expand Down Expand Up @@ -1533,6 +1534,7 @@ class QualityControlReportView(views.APIView):
def post(self, request):
queryset = Sample.objects.all()
request_data = JSONParser().parse(request)
resp = {}

samples = request_data.get('samples', None)
if samples is not None:
Expand All @@ -1551,63 +1553,116 @@ def post(self, request):
final_concentrated_sample_volume_max = queryset.aggregate(
max=Max('finalconcentratedsamplevolume__final_concentrated_sample_volume'))

resp = []
# Sample-level QC summary stats
sample_stats = []
for matrix in matrix_counts:
resp.append({
sample_stats.append({
"metric": "sample_matrix",
"value": matrix['matrix__name'],
"count": matrix['count'],
"min": None,
"max": None
})
for sample_type in sample_type_counts:
resp.append({
sample_stats.append({
"metric": "sample_type",
"value": sample_type['sample_type__name'],
"count": sample_type['count'],
"min": None,
"max": None
})
for unit in total_volume_sampled_unit_initial_counts:
resp.append({
sample_stats.append({
"metric": "total_volume_sampled_unit_initial",
"value": unit['total_volume_sampled_unit_initial__name'],
"count": unit['count'],
"min": None,
"max": None
})
resp.append({
sample_stats.append({
"metric": "post_dilution_volume",
"value": None,
"count": None,
"min": post_dilution_volume_min['min'],
"max": post_dilution_volume_max['max']
})
resp.append({
sample_stats.append({
"metric": "total_volume_or_mass_sampled",
"value": None,
"count": None,
"min": total_volume_or_mass_sampled_min['min'],
"max": total_volume_or_mass_sampled_max['max']
})
resp.append({
sample_stats.append({
"metric": "final_concentrated_sample_volume",
"value": None,
"count": None,
"min": final_concentrated_sample_volume_min['min'],
"max": final_concentrated_sample_volume_max['max']
})
resp['sample_quality_control'] = sample_stats

# resp = {
# "matrix_counts": matrix_counts,
# "sample_type_counts": sample_type_counts,
# "total_volume_sampled_unit_initial_counts": total_volume_sampled_unit_initial_counts,
# "post_dilution_volume_min": post_dilution_volume_min,
# "post_dilution_volume_max": post_dilution_volume_max,
# "total_volume_or_mass_sampled_min": total_volume_or_mass_sampled_min,
# "total_volume_or_mass_sampled_max": total_volume_or_mass_sampled_max,
# "final_concentrated_sample_volume_min": final_concentrated_sample_volume_min,
# "final_concentrated_sample_volume_max": final_concentrated_sample_volume_max,
# }
# ExtractionBatch-level raw values
if samples is not None:
eb_raw_data = ExtractionBatch.objects.filter(analysis_batch__samples__in=samples)
else:
eb_raw_data = ExtractionBatch.objects.all()

eb_raw_data = eb_raw_data.annotate(rt_template_volume=Max(
'reversetranscriptions__template_volume', filter=Q(reversetranscriptions__re_rt__isnull=True)))
eb_raw_data = eb_raw_data.annotate(rt_reaction_volume=Max(
'reversetranscriptions__reaction_volume', filter=Q(reversetranscriptions__re_rt__isnull=True)))

eb_raw_data = eb_raw_data.values('analysis_batch', 'extraction_number', 'extraction_volume', 'elution_volume',
'rt_template_volume', 'rt_reaction_volume', 'qpcr_template_volume',
'qpcr_reaction_volume')
resp['extraction_raw_data'] = list(eb_raw_data)

# ExtractionBatch-level QC summary stats
extraction_volumes = eb_raw_data.values('extraction_volume').annotate(count=Count('extraction_volume'))
elution_volumes = eb_raw_data.values('elution_volume').annotate(count=Count('elution_volume'))
rt_template_volumes = Counter(list(eb_raw_data.values_list('rt_template_volume', flat=True)))
rt_reaction_volumes = Counter(list(eb_raw_data.values_list('rt_reaction_volume', flat=True)))
qpcr_template_volumes = eb_raw_data.values('qpcr_template_volume').annotate(count=Count('qpcr_template_volume'))
qpcr_reaction_volumes = eb_raw_data.values('qpcr_reaction_volume').annotate(count=Count('qpcr_reaction_volume'))

extraction_stats = []
for extraction_volume in extraction_volumes:
extraction_stats.append({
"metric": "extraction_volume",
"value": extraction_volume['extraction_volume'],
"count": extraction_volume['count']
})
for elution_volume in elution_volumes:
extraction_stats.append({
"metric": "elution_volume",
"value": elution_volume['elution_volume'],
"count": elution_volume['count']
})
for key, value in rt_template_volumes.items():
extraction_stats.append({
"metric": "rt_template_volume",
"value": key,
"count": value
})
for key, value in rt_reaction_volumes.items():
extraction_stats.append({
"metric": "rt_reaction_volume",
"value": key,
"count": value
})
for qpcr_template_volume in qpcr_template_volumes:
extraction_stats.append({
"metric": "qpcr_template_volumes",
"value": qpcr_template_volume['qpcr_template_volume'],
"count": qpcr_template_volume['count']
})
for qpcr_reaction_volume in qpcr_reaction_volumes:
extraction_stats.append({
"metric": "qpcr_reaction_volume",
"value": qpcr_reaction_volume['qpcr_reaction_volume'],
"count": qpcr_reaction_volume['count']
})
resp['extraction_quality_control'] = extraction_stats

return Response(resp)

0 comments on commit aa47c3a

Please sign in to comment.