Refactor probegroup to be array-based

src/probeinterface/io.py

@@ -202,10 +202,7 @@ def read_BIDS_probe(folder: str | Path, prefix: str | None = None) -> ProbeGroup
 
         # create probe object and register with probegroup
         probe = Probe.from_dataframe(df=df_probe)
-        probe.annotate(probe_id=probe_id)
-
         probes[str(probe_id)] = probe
-        probegroup.add_probe(probe)
 
         ignore_annotations = [
             "probe_ids",
@@ -294,6 +291,10 @@ def read_BIDS_probe(folder: str | Path, prefix: str | None = None) -> ProbeGroup
 
                 probe.annotate(**{contact_param: value_list})
 
+    # Step 5: add probes to probegroup
+    for probe in probes.values():
+        probegroup.add_probe(probe)
+
     return probegroup
 
 
@@ -337,10 +338,6 @@ def write_BIDS_probe(folder: str | Path, probe_or_probegroup: Probe | ProbeGroup
 
     # Step 1: GENERATION OF PROBE.TSV
     # ensure required keys (probe_id, probe_type) are present
-
-    if any("probe_id" not in p.annotations for p in probes):
-        probegroup.auto_generate_probe_ids()
-
     for probe in probes:
         if "probe_id" not in probe.annotations:
             raise ValueError(

src/probeinterface/probe.py

@@ -99,6 +99,9 @@ def __init__(
         # vertices for the shape of the probe
         self.probe_planar_contour = None
 
+        # the Probe can belong to a ProbeGroup
+        self._probe_group = None
+
         # This handles the shank id per contact
         # If None then one shank only
         self._shank_ids = None
@@ -129,9 +132,6 @@ def __init__(
         # same idea but handle in vector way for contacts
         self.contact_annotations = dict()
 
-        # the Probe can belong to a ProbeGroup
-        self._probe_group = None
-
     @property
     def contact_positions(self):
         """The position of the center for each contact"""
@@ -260,6 +260,11 @@ def annotate(self, **kwargs):
         ----------
         **kwargs : list of keyword arguments to add to the annotations (e.g., brain_area="CA1")
         """
+        if self._probe_group is not None:
+            raise ValueError(
+                "You cannot annotate a probe that belongs to a ProbeGroup. "
+                "Annotate the probe before adding it to the ProbeGroup or use the `ProbeGroup.annotate_probe` method."
+            )
         self.annotations.update(kwargs)
         self.check_annotations()
 
@@ -271,6 +276,11 @@ def annotate_contacts(self, **kwargs):
         ----------
         **kwargs : list of keyword arguments to add to the annotations (e.g., quality=["good", "bad", ...])
         """
+        if self._probe_group is not None:
+            raise ValueError(
+                "You cannot annotate contacts of a probe that belongs to a ProbeGroup. "
+                "Annotate the probe before adding it to the ProbeGroup instead."
+            )
         n = self.get_contact_count()
         for k, values in kwargs.items():
             assert len(values) == n, (
@@ -977,7 +987,7 @@ def from_dict(d: dict) -> "Probe":
 
         return probe
 
-    def to_numpy(self, complete: bool = False) -> np.ndarray:
+    def to_numpy(self, complete: bool = False, probe_index: int | None = None) -> np.ndarray:
         """
         Export the probe to a numpy structured array.
         This array handles all contact attributes.
@@ -1035,7 +1045,10 @@ def to_numpy(self, complete: bool = False) -> np.ndarray:
         """
 
         # First define the dtype
-        dtype = [("x", "float64"), ("y", "float64")]
+        dtype = []
+        if probe_index is not None:
+            dtype = [("probe_index", "int64")]
+        dtype += [("x", "float64"), ("y", "float64")]
         if self.ndim == 3:
             dtype += [("z", "float64")]
 
@@ -1070,6 +1083,8 @@ def to_numpy(self, complete: bool = False) -> np.ndarray:
 
         # Then add the data to the structured array
         arr = np.zeros(self.get_contact_count(), dtype=dtype)
+        if probe_index is not None:
+            arr["probe_index"] = probe_index
         arr["x"] = self.contact_positions[:, 0]
         arr["y"] = self.contact_positions[:, 1]
         if self.ndim == 3: