ibl_dataframe format validation added, uneven number of frames check …

…added, invalid channel check added

src/iblphotometry/io.py

@@ -102,6 +102,7 @@ def from_ibl_dataframe(
     channel_column: str = 'name',
     channel_names: list[str] | None = None,
     rename: dict | None = None,
+    validate: bool = True,
 ) -> dict:
     """main function to convert to analysis ready format
 
@@ -134,8 +135,8 @@ def from_ibl_dataframe(
         channel_names = ibl_df[channel_column].unique()
 
     # drop empty acquisition channels
-    to_drop = ['None', '']
-    channel_names = [ch for ch in channel_names if ch not in to_drop]
+    if validate:
+        ibl_df = validate_ibl_dataframe(ibl_df)
 
     dfs = {}
     for channel in channel_names:
@@ -222,7 +223,42 @@ def from_raw_neurophotometrics_file(
 """
 
 
-def validate_ibl_dataframe(df: pd.DataFrame) -> pd.DataFrame: ...
+def validate_ibl_dataframe(ibl_df: pd.DataFrame) -> pd.DataFrame:
+    # for now, check if number of frames are equal and drop the longer one
+    # to be expanded into a full panderas check
+
+    # 1) drop first frame if invalid
+    first_frame_name = ibl_df.iloc[0]['name']
+    if '+' in first_frame_name or first_frame_name == '':
+        ibl_df = ibl_df.drop(index=0)
+
+    # 2) if unequal number of frames per acquisition channel, drop excess frames
+    frame_counts = ibl_df.groupby('name')['times'].count()
+    if not np.all(frame_counts.values == frame_counts.values[0]):
+        # find shortest
+        dfs = []
+        min_frames = frame_counts.iloc[np.argmin(frame_counts)]
+        for name, group in ibl_df.groupby('name'):
+            dfs.append(group.iloc[:min_frames])
+            n_dropped = group.shape[0] - min_frames
+            warnings.warn(f'dropping {n_dropped} frames for channel {name}')
+
+        ibl_df = pd.concat(dfs).sort_index()
+
+    # 3) panderas validation
+    data_columns = infer_data_columns(ibl_df)
+    schema_ibl_df = pandera.DataFrameSchema(
+        columns=dict(
+            times=pandera.Column(pandera.Float64),
+            # valid=pandera.Column(pandera.Bool), # NOTE as of now, it seems like valid is an optional column found in alejandro but not in carolina
+            wavelength=pandera.Column(pandera.Float64),
+            name=pandera.Column(pandera.String),
+            color=pandera.Column(pandera.String),
+            **{k: pandera.Column(pandera.Float64) for k in data_columns},
+        )
+    )
+    ibl_df = schema_ibl_df.validate(ibl_df)
+    return ibl_df
 
 
 def _validate_neurophotometrics_df(