update init

pycrostates/cluster/array.py

@@ -1,5 +1,6 @@
 from pathlib import Path
 from typing import TYPE_CHECKING
+from warnings import warn
 
 import numpy as np
 from mne import Info
@@ -54,7 +55,7 @@ def __init__(
         self._n_clusters = data.shape[0]
         self._info = ChInfo(info=info)  # no-op if a ChInfo is priovided
         self._cluster_centers_ = data
-        # validate optional inputs
+        # validate cluster names
         if cluster_names is not None:
             _check_type(cluster_names, (list, tuple), "cluster_names")
             if len(cluster_names) != self._n_clusters:
@@ -65,9 +66,57 @@ def __init__(
             self._cluster_names = cluster_names
         else:
             self._cluster_names = [str(k) for k in range(self._n_clusters)]
-        # validate fitted_data, which is either from a Raw ()
+        # validate fitted_data, which is either from a Raw (n_channels, n_times) or
+        # Epochs (n_epochs, n_channels, n_times).
+        _check_type(fitted_data, (np.ndarray, None), "fitted_data")
+        if fitted_data is not None and fitted_data.ndim == 2:
+            if fitted_data.shape[0] != len(info["ch_names"]):
+                raise ValueError(
+                    f"The number of channels in 'fitted_data' ({fitted_data.shape[0]}) "
+                    "must match the number of channels in 'info' "
+                    f"({len(info['ch_names'])})."
+                )
+        elif fitted_data is not None and fitted_data.ndim == 3:
+            # either with the (n_channels,) as first or second dimension
+            if fitted_data.shape[1] != len(info["ch_names"]):
+                raise ValueError(
+                    "The number of channels in 'fitted_data' "
+                    f"({fitted_data.shape[1]}) must match the number of channels "
+                    f"in 'info' ({len(info['ch_names'])}). Please provide "
+                    "'fitted_data' as (n_epochs, n_channels, n_times) for Epochs."
+                )
+            fitted_data = np.swapaxes(fitted_data, 0, 1)
+            fitted_data = fitted_data.reshape(fitted_data.shape[0], -1)
+        else:
+            raise ValueError(
+                "'fitted_data' must be a 2D (raw, ChData) or 3D (epochs) array. The "
+                f"provided {fitted_data.ndim}D array is invalid."
+            )
+        self._fitted_data = fitted_data
+        # validate labels
+        _check_type(labels, (np.ndarray, None), "labels")
+        if labels is not None:
+            if labels.ndim != 1:
+                raise ValueError(
+                    f"'labels' must be a 1D array. The provided {labels.ndim}D array "
+                    "is invalid."
+                )
+            if self._fitted_data is None:
+                warn(
+                    RuntimeWarning,
+                    "'labels' were provided without 'fitted_data' to which "
+                    "they apply.",
+                    stacklevel=2,
+                )
+            else:
+                if labels.size != self._fitted_data.shape[1]:
+                    raise ValueError(
+                        f"The number of samples in 'labels' ({labels.size}) must match "
+                        "the number of samples in 'fitted_data' "
+                        f"({self._fitted_data.shape[1]})."
+                    )
+        self._labels_ = labels
 
-    # left for empty
     @copy_doc(_BaseCluster.save)
     def save(self, fname: Union[str, Path]):
         super().save(fname)