read frame_interval from pretrained encoder config file

src/maggotuba/data/make_dataset.py

 import glob
 import pathlib
 
-def make_dataset(backend, labels_expected=False, trxmat_only=False, balancing_strategy='maggotuba', **kwargs):
+def make_dataset(backend, labels_expected=False, trxmat_only=False,
+                 balancing_strategy='maggotuba',
+                 pretrained_model_instance='default', **kwargs):
     if labels_expected:
         larva_dataset_file = glob.glob(str(backend.raw_data_dir() / "larva_dataset_*.hdf5"))
         if larva_dataset_file:
@@ -11,7 +13,19 @@ def make_dataset(backend, labels_expected=False, trxmat_only=False, balancing_st
             # make the file available in data/interim/{instance}/
             print(f"moving file to interim: {larva_dataset_file}")
             backend.move_to_interim(larva_dataset_file, copy=False)
+
+        else:
+            if 'frame_interval' not in kwargs:
+                autoencoder_config = glob.glob(str(backend.project_dir / "pretrained_models" / pretrained_model_instance / "*config.json"))
+                with open(autoencoder_config, "r") as f:
+                    config = json.load(f)
+                try:
+                    frame_interval = config['frame_interval']
+                except KeyError:
+                    pass
                 else:
+                    kwargs['frame_interval'] = frame_interval
+
             print("generating a larva_dataset file...")
             # generate a larva_dataset_*.hdf5 file in data/interim/{instance}/
             if False:#trxmat_only: