def make_mouse_boxplot(self, request, user, pk=None): # pylint: disable=unused-argument
try:
try:
meta = AverageFeaturesMeta.objects.get(
average_frames_value=Defaults.AverageFramesCount.value)
except Exception:
return create_custom_response(
status.HTTP_400_BAD_REQUEST, {
'reason':
'no meta for average features (create average features first)'
})
features = models_to_list(
AveragedMiceFeatures.objects.filter(
mouse=pk,
meta=meta.id,
).order_by('date'))
# TODO: boxplots
boxplot = BoxPlot.create(features)
db_boxplot = [
MiceBoxPlot(**{
**boxplot[index],
'mouse_id': pk,
}) for index in range(len(boxplot))
]
MiceBoxPlot.objects.filter(mouse=pk).delete()
MiceBoxPlot.objects.bulk_create(db_boxplot)
return create_custom_response(status.HTTP_200_OK, )
except Exception:
return create_custom_response(
status.HTTP_500_INTERNAL_SERVER_ERROR)
def make_mouse_averaged_normalized_features(self, request, user, pk=None): # pylint: disable=unused-argument
try:
request_data = json.loads(request.body)
average_frames_count = int(
request_data.get(
'average_frames_count',
Defaults.AverageFramesCount.value,
))
normalization_method = request_data.get(
'method', Defaults.NormalizationMethod.value)
meta, _ = AverageFeaturesMeta.objects.get_or_create(
average_frames_value=average_frames_count)
# find latest date added before
try:
latest_added_date = AveragedNormalizedMiceFeatures.objects\
.filter(
mouse=pk,
normalization_method=normalization_method,
meta=meta.id,
)\
.latest(FeatureNames.Date.value).date
except Exception:
latest_added_date = datetime.min
averaged_features = models_to_list(
AveragedMiceFeatures.objects.filter(
mouse=pk,
meta=meta.id,
date__gt=latest_added_date,
).order_by('date'))
averaged_normalized_features = Preprocessing.normalize_features(
averaged_features, normalization_method)
db_averaged_normalized_features = [
AveragedNormalizedMiceFeatures(
**{
**averaged_normalized_features[features_index],
'mouse_id':
pk,
'normalization_method':
normalization_method,
'meta':
meta,
})
for features_index in range(len(averaged_normalized_features))
]
AveragedNormalizedMiceFeatures.objects.bulk_create(
db_averaged_normalized_features)
return create_custom_response(status.HTTP_200_OK, )
except Exception:
return create_custom_response(
status.HTTP_500_INTERNAL_SERVER_ERROR)
def list(self, request, user): # pylint: disable=unused-argument
try:
user_id = user['user_info']['id']
cameras = list(Cameras.objects.filter(user_id=user_id))
return create_custom_response(status.HTTP_200_OK,
{'cameras': models_to_list(cameras)})
except Exception:
return create_custom_response(
status.HTTP_500_INTERNAL_SERVER_ERROR)
def make_mouse_normalized_average_healthy(self, request, user, pk=None): # pylint: disable=unused-argument
try:
request_data = json.loads(request.body)
average_frames_count = int(
request_data.get(
'average_frames_count',
Defaults.AverageFramesCount.value,
))
normalization_method = request_data.get(
'method', Defaults.NormalizationMethod.value)
meta, _ = AverageFeaturesMeta.objects.get_or_create(
average_frames_value=average_frames_count)
averaged_normalized_features = models_to_list(
AveragedNormalizedMiceFeatures.objects.filter(
mouse=pk,
meta=meta.id,
normalization_method=normalization_method,
).order_by('date'))
normalized_average_healthy = Preprocessing.create_average_healthy(
averaged_normalized_features, )
db_normalized_average_healthy = [
NormalizedAverageHealthyMice(
**{
**normalized_average_healthy[features_index],
'mouse_id': pk,
'normalization_method': normalization_method,
'meta': meta,
})
for features_index in range(len(normalized_average_healthy))
]
NormalizedAverageHealthyMice.objects\
.filter(
mouse=pk,
normalization_method=normalization_method,
meta=meta.id,
)\
.delete()
NormalizedAverageHealthyMice.objects.bulk_create(
db_normalized_average_healthy)
return create_custom_response(status.HTTP_200_OK, )
except Exception:
return create_custom_response(
status.HTTP_500_INTERNAL_SERVER_ERROR)
def make_experiment_pca(self, request, user, pk=None): # pylint: disable=unused-argument
try:
try:
meta = AverageFeaturesMeta.objects.get(
average_frames_value=Defaults.AverageFramesCount.value)
except Exception:
return create_custom_response(
status.HTTP_400_BAD_REQUEST, {
'reason':
'no meta for average features (create average features first)'
})
is_days = json.loads(request.body)['is_days']
experiment_mice = Mice.objects \
.filter(experiment=pk). \
values_list('id', flat=True)
mice_features = {
mouse_id: models_to_list(
AveragedMiceFeatures.objects.filter(
mouse=mouse_id,
meta=meta.id,
).order_by('date'))
for mouse_id in experiment_mice
}
experiment_pca = PCA.create(mice_features, is_days=is_days)
ExperimentsPCA.objects.filter(
experiment=pk,
is_days=is_days,
).delete()
db_experiment_pca = [
ExperimentsPCA(
**{
**experiment_pca[index],
'experiment_id': pk,
'meta': meta,
'is_days': is_days,
}) for index in range(len(experiment_pca))
]
ExperimentsPCA.objects.bulk_create(db_experiment_pca)
return create_custom_response(status.HTTP_200_OK, )
except Exception:
return create_custom_response(
status.HTTP_500_INTERNAL_SERVER_ERROR)
def list(self, request, user): # pylint: disable=unused-argument
try:
user_id = user['user_info']['id']
favorite_mice_ids = FavoriteMice.objects\
.filter(user_id=user_id)\
.values_list('mouse', flat=True)
mice = models_to_list(Mice.objects.filter(id__in=favorite_mice_ids))
return create_custom_response(
status.HTTP_200_OK,
{'favorite': mice}
)
except Exception:
return create_custom_response(status.HTTP_500_INTERNAL_SERVER_ERROR)
def retrieve(self, request, user, pk=None): # pylint: disable=unused-argument
try:
user_id = user['user_info']['id']
project = model_to_dict(
AnalysisProjects.objects.get(user_id=user_id, id=pk))
project_mice = AnalysisMouseRelation.objects.filter(
analysis_project=pk)
project['mice'] = models_to_list(project_mice)
return create_custom_response(status.HTTP_200_OK,
{'project': project})
except Exception:
return create_custom_response(
status.HTTP_500_INTERNAL_SERVER_ERROR)
def make_mouse_normalized_features(self, request, user, pk=None): # pylint: disable=unused-argument
try:
request_data = json.loads(request.body)
normalization_method = request_data.get(
'method',
Defaults.NormalizationMethod.value,
)
# find latest date added before
try:
latest_added_date = NormalizedMiceFeatures.objects\
.filter(
mouse=pk,
normalization_method=normalization_method,
)\
.latest(FeatureNames.Date.value).date
except Exception:
latest_added_date = datetime.min
features = models_to_list(
MiceFeatures.objects.filter(
mouse=pk,
date__gt=latest_added_date,
).order_by('date'))
normalized_features = Preprocessing.normalize_features(
features, normalization_method)
db_normalized_features = [
NormalizedMiceFeatures(
**{
**normalized_features[features_index],
'mouse_id': pk,
'normalization_method': normalization_method,
}) for features_index in range(len(normalized_features))
]
NormalizedMiceFeatures.objects.bulk_create(db_normalized_features)
return create_custom_response(status.HTTP_200_OK, )
except Exception:
return create_custom_response(
status.HTTP_500_INTERNAL_SERVER_ERROR)
def make_mouse_heatmap(self, request, user, pk=None): # pylint: disable=unused-argument
try:
features = models_to_list(
AveragedMiceFeatures.objects.filter(mouse=pk).order_by('date'))
heatmap = HeatMap.create(features)
db_heatmap = [
MiceHeatMap(**{
**heatmap[index],
'mouse_id': pk,
}) for index in range(len(heatmap))
]
MiceHeatMap.objects.filter(mouse=pk).delete()
MiceHeatMap.objects.bulk_create(db_heatmap)
return create_custom_response(status.HTTP_200_OK, )
except Exception:
return create_custom_response(
status.HTTP_500_INTERNAL_SERVER_ERROR)
def make_mouse_seasonal_decomposition(self, request, user, pk=None): # pylint: disable=unused-argument
try:
try:
meta = AverageFeaturesMeta.objects.get(
average_frames_value=Defaults.AverageFramesCount.value)
except Exception:
return create_custom_response(
status.HTTP_400_BAD_REQUEST, {
'reason':
'no meta for average features (create average features first)'
})
features = models_to_list(
AveragedMiceFeatures.objects.filter(
mouse=pk,
meta=meta.id,
).order_by('date'))
seasonal_decomposition = SeasonalDecomposition.create(features)
MiceSeasonalDecomposition.objects.filter(mouse=pk).delete()
db_seasonal_decomposition = [
MiceSeasonalDecomposition(
**{
**seasonal_decomposition[component][index],
'mouse_id': pk,
'component': component,
}) for component in SEASONAL_DECOMPOSITION_COMPONENTS
for index in range(len(seasonal_decomposition[component]))
]
MiceSeasonalDecomposition.objects.bulk_create(
db_seasonal_decomposition)
return create_custom_response(status.HTTP_200_OK, )
except Exception:
return create_custom_response(
status.HTTP_500_INTERNAL_SERVER_ERROR)
def filter_experiments(experiments, filters):
# TODO: сделать фильтрацию экспериментов https://tracker.yandex.ru/VPAGROUPDEV-601
return models_to_list(experiments)
import json
