Release 0.9.4

promort/VERSION

@@ -1 +1 @@
-0.9.3
+0.9.4

promort/predictions_manager/management/commands/tissue_to_rois.py

@@ -27,7 +27,7 @@
 import requests
 from django.contrib.auth.models import User
 from django.core.management.base import BaseCommand, CommandError
-from predictions_manager.models import TissueFragment
+from predictions_manager.models import TissueFragment, Prediction
 from reviews_manager.models import ROIsAnnotationStep
 from rois_manager.models import Core, Slice
 from shapely.geometry import Polygon
@@ -64,51 +64,58 @@ def handle(self, *args, **opts):
 
             for step in annotation_steps:
                 logger.info("Processing ROIs annotation step %s", step.label)
-                fragments = TissueFragment.objects.filter(
-                    collection__prediction__slide=step.slide
-                )
-                if not fragments:
-                    logger.info(
-                        "Skipping step %s, no tissue fragment found",
-                        step,
-                    )
-                    continue
+                latest_prediction = Prediction.objects.filter(
+                    slide=step.slide, type='TISSUE'
+                ).order_by('-creation_date').first()
+
+                fragments_collection = latest_prediction.fragments_collection.order_by('-creation_date').first()
+
+                if fragments_collection and fragments_collection.fragments.count() > 0:
+                    fragments = fragments_collection.fragments.all()
 
-                slide_bounds = self._get_slide_bounds(step.slide)
+                    slide_bounds = self._get_slide_bounds(step.slide)
 
-                slide_mpp = step.slide.image_microns_per_pixel
-                all_shapes = [json.loads(fragment.shape_json) for fragment in fragments]
-                grouped_shapes = self._group_nearest_cores(all_shapes)
-                for idx, shapes in enumerate(grouped_shapes):
-                    slice_label = idx + 1
-                    shapes_coords = self._get_slice_coordinates(shapes)
+                    slide_mpp = step.slide.image_microns_per_pixel
+                    all_shapes = [json.loads(fragment.shape_json) for fragment in fragments]
+                    grouped_shapes = self._group_nearest_cores(all_shapes)
+                    for idx, shapes in enumerate(grouped_shapes):
+                        slice_label = idx + 1
+                        shapes_coords = self._get_slice_coordinates(shapes)
 
-                    slice_obj = self._create_slice(
-                        shapes_coords,
-                        slice_label,
-                        len(shapes),
-                        step,
-                        user,
-                        slide_bounds,
-                    )
-                    logger.info("Slice saved with ID %d", slice_obj.id)
-                    for core_index, core in enumerate(shapes):
-                        logger.info(
-                            "Loading core %d of %d",
-                            core_index + 1,
-                            len(shapes),
-                        )
-                        core_obj = self._create_core(
-                            core["coordinates"],
-                            core["length"] * slide_mpp,
-                            core["area"] * math.pow(slide_mpp, 2),
-                            slice_obj,
+                        slice_obj = self._create_slice(
+                            shapes_coords,
                             slice_label,
-                            core_index + 1,
+                            len(shapes),
+                            step,
                             user,
                             slide_bounds,
+                            fragments_collection
                         )
-                        logger.info("Core saved with ID %d", core_obj.id)
+                        logger.info("Slice saved with ID %d", slice_obj.id)
+                        for core_index, core in enumerate(shapes):
+                            logger.info(
+                                "Loading core %d of %d",
+                                core_index + 1,
+                                len(shapes),
+                            )
+                            core_obj = self._create_core(
+                                core["coordinates"],
+                                core["length"] * slide_mpp,
+                                core["area"] * math.pow(slide_mpp, 2),
+                                slice_obj,
+                                slice_label,
+                                core_index + 1,
+                                user,
+                                slide_bounds,
+                                fragments_collection
+                            )
+                            logger.info("Core saved with ID %d", core_obj.id)
+                else:
+                    logger.info(
+                        "Skipping prediction %s for step %s, no tissue fragment found",
+                        latest_prediction.label, step.label,
+                    )
+                    continue
         else:
             logger.info("== There are no suitable ROIs annotation steps")
         logger.info("== Job completed ==")
@@ -167,6 +174,7 @@ def _create_slice(
         annotation_step,
         user,
         slide_bounds,
+        collection
     ):
         slice_coordinates = self._adjust_roi_coordinates(
             slice_coordinates, slide_bounds
@@ -181,6 +189,7 @@ def _create_slice(
             author=user,
             roi_json=json.dumps(roi_json),
             total_cores=cores_count,
+            source_collection=collection
         )
         slice_.save()
         return slice_
@@ -195,6 +204,7 @@ def _create_core(
         core_id,
         user,
         slide_bounds,
+        collection
     ):
         core_coordinates = self._adjust_roi_coordinates(core_coordinates, slide_bounds)
         roi_json = self._create_roi_json(
@@ -207,6 +217,7 @@ def _create_core(
             roi_json=json.dumps(roi_json),
             length=core_length,
             area=core_area,
+            source_collection=collection
         )
         core.save()
         return core

promort/predictions_manager/migrations/0004_auto_20220303_1539.py

@@ -0,0 +1,19 @@
+# Generated by Django 3.1.13 on 2022-03-03 15:39
+
+from django.db import migrations, models
+import django.db.models.deletion
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('predictions_manager', '0003_prediction_review_required'),
+    ]
+
+    operations = [
+        migrations.AlterField(
+            model_name='tissuefragmentscollection',
+            name='prediction',
+            field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='fragments_collection', to='predictions_manager.prediction'),
+        ),
+    ]

promort/predictions_manager/migrations/0005_auto_20220318_1531.py

@@ -0,0 +1,29 @@
+# Generated by Django 3.1.9 on 2022-03-18 15:31
+
+from django.db import migrations, models
+import django.db.models.deletion
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('predictions_manager', '0004_auto_20220303_1539'),
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='Provenance',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('model', models.TextField()),
+                ('start_date', models.DateTimeField()),
+                ('end_date', models.DateTimeField()),
+                ('params', models.JSONField()),
+            ],
+        ),
+        migrations.AlterField(
+            model_name='prediction',
+            name='provenance',
+            field=models.OneToOneField(blank=True, default=None, null=True, on_delete=django.db.models.deletion.CASCADE, to='predictions_manager.provenance'),
+        ),
+    ]

promort/predictions_manager/migrations/0006_auto_20220324_0910.py

@@ -0,0 +1,23 @@
+# Generated by Django 3.1.13 on 2022-03-24 09:10
+
+from django.db import migrations, models
+import django.db.models.deletion
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('predictions_manager', '0005_auto_20220318_1531'),
+    ]
+
+    operations = [
+        migrations.RemoveField(
+            model_name='prediction',
+            name='provenance',
+        ),
+        migrations.AddField(
+            model_name='provenance',
+            name='prediction',
+            field=models.OneToOneField(blank=True, default=None, null=True, on_delete=django.db.models.deletion.PROTECT, to='predictions_manager.prediction'),
+        ),
+    ]

promort/predictions_manager/models.py

@@ -32,29 +32,45 @@ class Prediction(models.Model):
 
     label = models.CharField(max_length=100, unique=True)
     creation_date = models.DateTimeField(auto_now_add=True)
-    slide = models.ForeignKey(Slide, on_delete=models.PROTECT, blank=False,
-                              related_name='predictions')
-    type = models.CharField(max_length=7, choices=PREDICTION_TYPES, blank=False, null=False)
+    slide = models.ForeignKey(
+        Slide, on_delete=models.PROTECT, blank=False, related_name="predictions"
+    )
+    type = models.CharField(
+        max_length=7, choices=PREDICTION_TYPES, blank=False, null=False
+    )
     omero_id = models.IntegerField(blank=True, null=True, default=None)
-    provenance = models.TextField(blank=True, null=True)
     review_required = models.BooleanField(blank=False, null=False, default=False)
-    
+
     def require_review(self):
         self.review_required = True
         self.save()
 
 
 class TissueFragmentsCollection(models.Model):
     prediction = models.ForeignKey(Prediction, on_delete=models.PROTECT, blank=False,
-                                   related_name='tissue_fragments')
+                                   related_name='fragments_collection')
     creation_date = models.DateTimeField(auto_now_add=True)
 
     def get_slide(self):
         return self.prediction.slide
 
 
 class TissueFragment(models.Model):
-    collection = models.ForeignKey(TissueFragmentsCollection, on_delete=models.PROTECT, blank=False,
-                                   related_name='fragments')
+    collection = models.ForeignKey(
+        TissueFragmentsCollection,
+        on_delete=models.PROTECT,
+        blank=False,
+        related_name="fragments",
+    )
     shape_json = models.TextField(blank=False)
     creation_date = models.DateTimeField(auto_now_add=True)
+
+
+class Provenance(models.Model):
+    prediction = models.OneToOneField(
+        Prediction, on_delete=models.PROTECT, blank=True, null=True, default=None
+    )
+    model = models.TextField(blank=False, null=False)
+    start_date = models.DateTimeField(blank=False, null=False)
+    end_date = models.DateTimeField(blank=False, null=False)
+    params = models.JSONField(blank=False, null=False)

promort/predictions_manager/serializers.py

@@ -22,26 +22,38 @@
 except ImportError:
     import json
 
+from predictions_manager.models import (Prediction, Provenance, TissueFragment,
+                                        TissueFragmentsCollection)
 from rest_framework import serializers
-
-from predictions_manager.models import Prediction, TissueFragmentsCollection, TissueFragment
 from slides_manager.serializers import SlideSerializer
 
+class ProvenanceSerializer(serializers.ModelSerializer):
+    class Meta:
+        model = Provenance
+        fields = ('id', 'model', 'start_date', 'end_date','params')
+
 
 class PredictionSerializer(serializers.ModelSerializer):
+    provenance = ProvenanceSerializer(required=False)
 
     class Meta:
         model = Prediction
         fields = ('id', 'label', 'creation_date', 'slide', 'type', 'omero_id', 'provenance',
                   'review_required')
         read_only_fields = ('id', 'creation_date')
 
-    def validate_provenance(self, value):
+    def create(self, validated_data):
         try:
-            json.loads(value)
-            return value
-        except ValueError:
-            raise serializers.ValidationError('Not a valid JSON in \'provenance\' field')
+            provenance_kwargs = validated_data.pop('provenance')
+        except KeyError:
+            provenance_kwargs = {}
+        prediction = Prediction.objects.create(**validated_data)
+        if provenance_kwargs:
+            provenance_kwargs['prediction'] = prediction
+            prov = Provenance.objects.create(**provenance_kwargs)
+
+        return prediction
+
 
 
 class PredictionDetailsSerializer(serializers.ModelSerializer):

promort/predictions_manager/tests/conftest.py

@@ -17,6 +17,7 @@
 #  IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
 #  CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
+import datetime as dt
 import json
 
 import factory
@@ -25,6 +26,7 @@
 from pytest_factoryboy import register
 from shapely.affinity import translate
 from shapely.geometry import box
+from slides_manager.models import Case, Slide
 
 
 @register
@@ -159,3 +161,21 @@ def _create(n_steps, with_fragments):
         return steps
 
     return _create
+
+
+@fixture
+def prediction_data(provenance_data: bool, depends_on: bool):
+
+    case = Case.objects.create(id="case")
+    slide = Slide.objects.create(id="slide", case=case)
+    now = dt.datetime.now()
+    data = {"label": "label", "slide": slide.id, "type": "TUMOR"}
+
+    if provenance_data:
+        data["provenance"] = {
+            "model": "model",
+            "start_date": now,
+            "end_date": now,
+            "params": "{}",
+        }
+    return data