Updates for TREC pipelines

Changes supporting use of pipelines in trec-adaptive-feedback project:

• generally, accessors' and models' IDs can be set with respective argument
• test client methods now handle response success check and variable parsing
• implemented functional threshold segmentation and (in base.roiset) classification models; threshold is relative to dataset full range (0.0 to 1.0)
• consequently, more tests cover base package without dependency on ilastik
• RoiSet returned by pipelines get IDs and are registered in server session, accesible at respective endpoints
• Pydantic models only used by FastAPI now; underlying model, accessor, and pipeline functions should interact with python objects and parameter dictionaries only
• RoiSet filters also include minimal and diagonal height, width dimension; are applied before time-intensity pixel array operations to increase performance
• performance improvement when passing patch stacks to ilastik pixel classifier
• RoiSet.classify_by can optionally filter by additional classification columns, e.g. to implement intensity gating

model_server/base/accessors.py

@@ -3,6 +3,7 @@ import os
 from pathlib import Path
 
 import numpy as np
+import pandas as pd
 from skimage.io import imread, imsave
 
 import czifile
@@ -491,6 +492,18 @@ class PatchStack(InMemoryDataAccessor):
     def shape_dict(self):
         return dict(zip(('P', 'Y', 'X', 'C', 'Z'), self.data.shape))
 
+    @property
+    def df(self):
+        df = pd.DataFrame([
+            {
+                'label': i,
+                'area': (self.iat(i).data > 0).sum(),
+                'sum': self.iat(i).data.sum()
+            } for i in range(0, self.count)
+        ])
+        df['intensity_mean'] = df['sum'] / df['area']
+        return df
+
 
 def make_patch_stack_from_file(fpath):  # interpret t-dimension as patch position
     if not Path(fpath).exists():