Fix shape convention to be consistent with HDF5 pattern

When time_axis=1, shape should be (channels, time) not (time, channels).
This makes raw binary handling consistent with HDF5:

- time_axis=0: shape is (time, channels) - MULTIPLEXED
- time_axis=1: shape is (channels, time) - VECTORIZED

Changes:
- BrainVisionRawIO: Set shape as (channels, time) when VECTORIZED
- BaseRawIO: Use shape[time_axis] consistently for i_stop default
- Removed duplicate time_axis retrieval

All tests pass with identical MNE-Python validation.

Author: Kkuntal990

neo/rawio/baserawio.py

@@ -1577,14 +1577,11 @@ def __init__(self, *arg, **kwargs):
     def _get_signal_size(self, block_index, seg_index, stream_index):
         buffer_id = self.header["signal_streams"][stream_index]["buffer_id"]
         buffer_desc = self.get_analogsignal_buffer_description(block_index, seg_index, buffer_id)
-        # For "raw" type, shape is always (time, channels) regardless of file layout
-        # For "hdf5" type, shape can be (time, channels) or (channels, time) based on time_axis
-        if buffer_desc["type"] == "raw":
-            return buffer_desc["shape"][0]
-        else:
-            # some hdf5 revert the buffer
-            time_axis = buffer_desc.get("time_axis", 0)
-            return buffer_desc["shape"][time_axis]
+        # time_axis indicates which dimension is time:
+        # time_axis=0: shape is (time, channels)
+        # time_axis=1: shape is (channels, time)
+        time_axis = buffer_desc.get("time_axis", 0)
+        return buffer_desc["shape"][time_axis]
 
     def _get_analogsignal_chunk(
         self,
@@ -1603,13 +1600,14 @@ def _get_analogsignal_chunk(
 
         buffer_desc = self.get_analogsignal_buffer_description(block_index, seg_index, buffer_id)
 
+        # Get time_axis to determine which dimension is time
+        time_axis = buffer_desc.get("time_axis", 0)
+
         i_start = i_start or 0
-        i_stop = i_stop or buffer_desc["shape"][0]
+        i_stop = i_stop or buffer_desc["shape"][time_axis]
 
         if buffer_desc["type"] == "raw":
 
-            time_axis = buffer_desc.get("time_axis", 0)
-
             if time_axis == 0:
                 # MULTIPLEXED: time_axis=0 means (time, channels) layout
                 # open files on demand and keep reference to opened file
@@ -1637,12 +1635,12 @@ def _get_analogsignal_chunk(
                 )
 
             elif time_axis == 1:
-                # VECTORIZED: time_axis=1 means (channels, time) layout
+                # VECTORIZED: time_axis=1 means shape is (channels, time)
                 # Data is stored as [all_samples_ch1, all_samples_ch2, ...]
                 dtype = np.dtype(buffer_desc["dtype"])
-                num_channels = buffer_desc["shape"][1]
+                num_channels = buffer_desc["shape"][0]  # shape is (channels, time)
                 num_samples = i_stop - i_start
-                total_samples_per_channel = buffer_desc["shape"][0]
+                total_samples_per_channel = buffer_desc["shape"][1]  # shape is (channels, time)
 
                 # Determine which channels to read
                 if channel_indexes is None:

neo/rawio/brainvisionrawio.py

@@ -91,11 +91,18 @@ def _parse_header(self):
         self._buffer_descriptions = {0: {0: {}}}
         self._stream_buffer_slice = {}
 
-        shape = get_memmap_shape(binary_filename, sig_dtype, num_channels=nb_channel, offset=0)
-
         # time_axis indicates data layout: 0 for MULTIPLEXED (time, channels), 1 for VECTORIZED (channels, time)
         time_axis = 0 if self._data_orientation == "MULTIPLEXED" else 1
 
+        # Get shape - always returns (num_samples, num_channels)
+        temp_shape = get_memmap_shape(binary_filename, sig_dtype, num_channels=nb_channel, offset=0)
+
+        # For consistency with HDF5 pattern: when time_axis=1, shape should be (channels, time)
+        if time_axis == 1:
+            shape = (temp_shape[1], temp_shape[0])  # (num_channels, num_samples)
+        else:
+            shape = temp_shape  # (num_samples, num_channels)
+
         self._buffer_descriptions[0][0][buffer_id] = {
             "type": "raw",
             "file_path": binary_filename,