From 1104b6b1de37b8331526ebe0582bdb9b20c3d117 Mon Sep 17 00:00:00 2001
From: Mario Raciti <mario.raciti@inaf.it>
Date: Mon, 7 Jun 2021 16:22:36 +0200
Subject: [PATCH] TMSS-610: Update data_ingested_per_site_and_type info
---
.../src/tmss/tmssapp/adapters/reports.py | 44 ++++++++++---------
1 file changed, 24 insertions(+), 20 deletions(-)
diff --git a/SAS/TMSS/backend/src/tmss/tmssapp/adapters/reports.py b/SAS/TMSS/backend/src/tmss/tmssapp/adapters/reports.py
index 21e1e0aa483..3f0b97b4b32 100644
--- a/SAS/TMSS/backend/src/tmss/tmssapp/adapters/reports.py
+++ b/SAS/TMSS/backend/src/tmss/tmssapp/adapters/reports.py
@@ -156,26 +156,30 @@ def _get_data_ingested_per_site_and_type(request: Request, cycle: models.Cycle)
"""
result = []
- # Get DataProducts related to the cycle with an ArchiveInfo
- archive_info = models.DataproductArchiveInfo.objects.filter(dataproduct__producer__subtask__task_blueprints__draft__scheduling_unit_draft__scheduling_set__project__cycles=cycle.pk)
- dataproducts = [ai.dataproduct for ai in archive_info]
- # Filter DataProducts from Subtasks of type 'observation'
- dp_from_observations = dataproducts.filter(producer__subtask__specifications_template__type='observation')
- # Filter DataProducts from Subtasks of type 'pipeline'
- dp_from_pipelines = dataproducts.filter(producer__subtask__specifications_template__type='pipeline')
-
- # TODO: Filter categories basing on DataType, TaskType, DataFormat.
- # Filter DataProducts of type 'visibilities' and 'time series' from observations and pipelines
- dp_visibilities = dp_from_observations.filter(datatype='visibilities') | dp_from_pipelines.filter(datatype='visibilities')
- dp_time_series = dp_from_observations.filter(datatype='time series') | dp_from_pipelines.filter(datatype='time series')
-
- # TODO: Group dataproducts also per site.
- # Iterate over categories
- # categories = ('', '', '', '')
- # for dt in datatypes:
- # dataproducts_per_type = dataproducts.filter(datatype=dt.value)
- # dataproducts_per_type_data = [serializers.DataproductSerializer(dp, context={'request': request}).data for dp in dataproducts_per_type]
- # result.append({'type': dt.value, 'dataproducts': dataproducts_per_type_data})
+ # TODO: Currently there is no way to fitler per LTA site.
+ # # Get DataProducts related to the cycle with an ArchiveInfo
+ # archive_info = models.DataproductArchiveInfo.objects.filter(dataproduct__producer__subtask__task_blueprints__draft__scheduling_unit_draft__scheduling_set__project__cycles=cycle.pk)
+ # dataproducts = [ai.dataproduct for ai in archive_info]
+
+ # Get DataProducts related to the cycle
+ dataproducts = models.Dataproduct.objects.filter(producer__subtask__task_blueprints__draft__scheduling_unit_draft__scheduling_set__project__cycles=cycle.pk)
+
+ # Combine and filter DataPrducts accordingly
+ dps_interferometric_obs = dataproducts.filter(producer__subtask__specifications_template__type='observation', dataformat='MeasurementSet', datatype='visibilities')
+ dps_interferometric_obs_data = [serializers.DataproductSerializer(dp, context={'request': request}).data for dp in dps_interferometric_obs]
+ result.append({'category': 'Interferometric Observation', 'dataproducts': dps_interferometric_obs_data})
+
+ dps_beamformed_obs = dataproducts.filter(producer__subtask__specifications_template__type='observation', dataformat='Beamformed', datatype='time series')
+ dps_beamformed_obs_data = [serializers.DataproductSerializer(dp, context={'request': request}).data for dp in dps_beamformed_obs]
+ result.append({'category': 'Beamformed Observation', 'dataproducts': dps_beamformed_obs_data})
+
+ dp_averaging_pip = dataproducts.filter(producer__subtask__specifications_template__type='pipeline', dataformat='MeasurementSet', datatype='visibilities')
+ dp_averaging_pip_data = [serializers.DataproductSerializer(dp, context={'request': request}).data for dp in dp_averaging_pip]
+ result.append({'category': 'Averaging Pipeline', 'dataproducts': dp_averaging_pip_data})
+
+ dp_pulsar_pip = dataproducts.filter(producer__subtask__specifications_template__type='pipeline', dataformat__in=['pulp summary', 'pulp analysis'], datatype__in=['visibilities', 'pulsar profile'])
+ dp_pulsar_pip_data = [serializers.DataproductSerializer(dp, context={'request': request}).data for dp in dp_pulsar_pip]
+ result.append({'category': 'Pulsar Pipeline', 'dataproducts': dp_pulsar_pip_data})
return result
--
GitLab