Improve automatic local referencing

naplib/preprocessing/rereference.py

@@ -116,7 +116,7 @@ def _rereference(data_arr, method='avg', return_ref=False):
     return data_rereferenced
 
 
-def make_contact_rereference_arr(channelnames, extent=None):
+def make_contact_rereference_arr(channelnames, extent=None, grid_sizes={}):
     """
     Create grid which defines re-referencing scheme based on electrodes being on the same contact as
     each other.
@@ -128,13 +128,16 @@ def make_contact_rereference_arr(channelnames, extent=None):
         be alphanumeric, with any numbers only being on the right. 2) The numeric portion specifies a
         different electrode number, while the character portion in the left of the channelname specifies the
         contact name. E.g. ['RT1','RT2','RT3','Ls1','Ls2'] indicates two contacts, the first with 3 electrodes
-        and the second with 2 electrodes. 3) Electrodes from the same contact must be contiguous.
+        and the second with 2 electrodes.
     extent : int, optional, default=None
         If provided, then only contacts from the same group which are within ``extent`` electrodes away
-        from each other (inclusive) are still grouped together. Only used if ``method='contact'``. For
-        example, if ``extent=1``, only the nearest electrode on either side of a given electrode on the
-        same contact is still grouped with it. For example, extent=1 produces the traditional local
-        average reference scheme.
+        from each other (inclusive) are still grouped together. For example, if ``extent=1``, only the
+        nearest electrode on either side of a given electrode on the same contact is still grouped with it.
+        This ``extent=1`` produces the traditional local average reference scheme.
+    grid_sizes : dict, optional, default={}
+        If provided, contains {'contact_name': (nrow, ncol)} values for any known ECoG grid sizes.
+        E.g. {'GridA': (8, 16)} indicates that electrodes on contact 'GridA' are arranged in an 8 x 16 grid, 
+        which is needed to determine adjacent electrodes for local average referencing with ``extent >= 1``.
 
     Returns
     -------
@@ -145,18 +148,79 @@ def make_contact_rereference_arr(channelnames, extent=None):
     --------
     rereference
     """
-    contact_arrays = pd.Series([x.rstrip('0123456789') for x in channelnames])
-    connections = np.zeros((len(contact_arrays),) * 2, dtype=float)
-    for _, inds in contact_arrays.groupby(contact_arrays):
-        for i in inds.index:
-            connections[i, inds.index] = 1.0
+    def _find_adjacent_numbers(a, b, number, extent):
+        '''
+        Used to determine electrodes for local averaging ECoG grid"
+        '''
+        # Validate if the number is within the valid range
+        if number < 1 or number > a * b:
+            raise ValueError("The number is outside the range of the grid.")
 
-    # remove longer than extent if desired
-    if extent is not None:
-        if extent < 1:
-            raise ValueError(f'Invalid extent. Must be no less than 1 but got extent={extent}')
-        connections *= np.tri(*connections.shape, k=extent)
-        connections *= np.fliplr(np.flipud(np.tri(*connections.shape, k=extent)))
-        connections = connections
-
+        # Calculate the row and column of the given number
+        row = (number - 1) // b
+        col = (number - 1) % b
+
+        # Find all adjacent numbers within the extent
+        adjacent_numbers = []
+        for dr in range(-extent, extent + 1):  # Rows within the extent
+            for dc in range(-extent, extent + 1):  # Columns within the extent
+                if dr == 0 and dc == 0:
+                    continue  # Skip the number itself
+                new_row, new_col = row + dr, col + dc
+                if 0 <= new_row < a and 0 <= new_col < b:
+                    adjacent_num = new_row * b + new_col + 1
+                    adjacent_numbers.append(adjacent_num)
+
+        return np.array(adjacent_numbers, dtype=int)
+    connections = np.zeros((len(channelnames),) * 2, dtype=float)
+    channelnames = np.array(channelnames)
+    contact_arrays = np.array([x.rstrip('0123456789') for x in channelnames])
+    contacts, ch_per_contact = np.unique([x.rstrip('0123456789') for x in channelnames], return_counts=True)
+    if extent is None:
+        # Common average referencing per electrode array (ECoG grid or sEEG shank)
+        # CAR will end up subtracting parts of channel ch from itself
+        for contact, num_ch in zip(contacts, ch_per_contact):
+            for ch in range(1,num_ch+1):
+                curr = np.where(channelnames==f'{contact}{ch}')[0]
+                inds = np.where(contact_arrays==contact)[0]
+                connections[curr,inds] = 1
+    elif extent < 1:
+        raise ValueError(f'Invalid extent. Must be no less than 1 but got extent={extent}')
+    else:
+        # Local average referencing within each electrode array
+        # LAR will NOT subtract parts of channel ch from itself
+        for contact, num_ch in zip(contacts, ch_per_contact):
+            for ch in range(1,num_ch+1):
+                # Local referencing for ECoG grids
+                if 'grid' in contact.lower():
+                    side = np.sqrt(num_ch)
+                    half_side = np.sqrt(num_ch/2)
+                    # Check grid_sizes dict
+                    if contact in grid_sizes:
+                        nrows, ncols = grid_sizes[contact]
+                    # Assume a square
+                    elif np.isclose(side, int(side)):
+                        nrows, ncols = side, side
+                    # Assume a 1 x 2 rectangle
+                    elif np.isclose(half_side, int(half_side)):
+                        nrows, ncols = half_side, half_side*2
+                    else:
+                        raise Exception(f'Cannot determine {contact} layout. Please include layout in `grid_sizes`')
+                    adjacent = _find_adjacent_numbers(nrows, ncols, ch, extent)
+                    curr = np.where(channelnames==f'{contact}{ch}')[0]
+                    inds = []
+                    for adj in adjacent:
+                        inds.append(np.where(channelnames==f'{contact}{adj}')[0])
+
+                # Local referencing for sEEG shanks and strips
+                else:
+                    curr = np.where(channelnames==f'{contact}{ch}')[0]
+                    inds = []
+                    for cc in range(ch-extent, ch+extent+1):
+                        if cc != ch:
+                            inds.append(np.where(channelnames==f'{contact}{cc}')[0])
+
+                inds = np.concatenate(inds)
+                connections[curr,inds] = 1
+
     return connections

tests/preprocessing/test_rereference.py

@@ -4,10 +4,29 @@
 
 
 def test_create_contact_rereference_arr():
-    expected = np.array([[1,1,0,0],[1,1,0,0],[0,0,1,1],[0,0,1,1]])
-    g = ['LT1','LT2','RT1','RT2']
-    arr = make_contact_rereference_arr(g)
+    expected = np.array([[0,1,0,0,0,0,0,0],
+                         [1,0,0,0,0,0,0,0],
+                         [0,0,0,1,0,0,0,0],
+                         [0,0,1,0,0,0,0,0],
+                         [0,0,0,0,0,1,1,1],
+                         [0,0,0,0,1,0,1,1],
+                         [0,0,0,0,1,1,0,1],
+                         [0,0,0,0,1,1,1,0],
+                        ])
+    expected1 = np.array([[1,1,0,0,0,0,0,0],
+                          [1,1,0,0,0,0,0,0],
+                          [0,0,1,1,0,0,0,0],
+                          [0,0,1,1,0,0,0,0],
+                          [0,0,0,0,1,1,1,1],
+                          [0,0,0,0,1,1,1,1],
+                          [0,0,0,0,1,1,1,1],
+                          [0,0,0,0,1,1,1,1],
+                         ])
+    g = ['LT1','LT2','GridA1','GridA2'] + [f'GridB{n}' for n in range(1,5)]
+    arr = make_contact_rereference_arr(g, extent=1, grid_sizes={'GridA':(1,2)})
+    arr1 = make_contact_rereference_arr(g)
     assert np.allclose(expected, arr)
+    assert np.allclose(expected1, arr1)
 
 def test_rereference_avg():
     arr = np.array([[1,1,0,0],[1,1,0,0],[0,0,1,1],[0,0,1,1]])