Remove unused irregular flag and clean up redundant imports

Author: BitForge95

experanto/experiment.py

@@ -66,11 +66,6 @@ def __init__(
         modality_config: dict = DEFAULT_MODALITY_CONFIG,
         cache_data: bool = False,
     ) -> None:
-        """
-        root_folder: path to the data folder
-        modality_config: dict for configuring interpolators
-        cache_data: if True, loads and keeps all trial data in memory
-        """
         self.root_folder = Path(root_folder)
         self.devices = dict()
         self.start_time = np.inf
@@ -112,16 +107,6 @@ def _load_devices(self) -> None:
                     cache_data=self.cache_data,
                     **{str(k): v for k, v in dict(interp_conf).items()},
                 )
-                # Default back to original logic
-                warnings.warn(
-                    "Falling back to original Interpolator creation logic.",
-                    UserWarning,
-                )
-                dev = Interpolator.create(
-                    d,
-                    cache_data=self.cache_data,
-                    **interp_conf,  # type: ignore[arg-type]
-                )
 
             self.devices[d.name] = dev
             if dev.start_time is not None:

tests/create_experiment.py

@@ -1,7 +1,6 @@
 import copy
 import shutil
 from contextlib import contextmanager
-from pathlib import Path
 
 from .create_sequence_data import _generate_sequence_data
 

tests/create_sequence_data.py

@@ -17,10 +17,8 @@ def _generate_sequence_data(
     t_end=10.0,
     sampling_rate=10.0,
     contain_nans=False,
-    irregular=False,
 ):
     """Generates synthetic sequence data folders for testing interpolator logic."""
-    from pathlib import Path
 
     sequence_root = Path(sequence_root)
     sequence_root.mkdir(parents=True, exist_ok=True)
@@ -40,14 +38,7 @@ def _generate_sequence_data(
     duration = meta["end_time"] - meta["start_time"]
     n_samples = int(round(duration * meta["sampling_rate"])) + 1
 
-    if not irregular:
-        timestamps = np.linspace(meta["start_time"], meta["end_time"], n_samples)
-    else:
-        steps = np.random.uniform(0.5, 1.5, size=n_samples - 1) / sampling_rate
-        timestamps = np.zeros(n_samples)
-        timestamps[0] = meta["start_time"]
-        timestamps[1:] = meta["start_time"] + np.cumsum(steps)
-        meta["end_time"] = float(timestamps[-1])
+    timestamps = np.linspace(meta["start_time"], meta["end_time"], n_samples)
 
     data = np.random.rand(len(timestamps), n_signals)