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jaxley/modules/cell.py

@@ -179,7 +179,7 @@ def _init_morph_jaxley_spsolve(self):
         )
         self.root_inds = jnp.asarray([0])
 
-        # Generate mapping to dealing with the masking which allows using the custom
+        # Generate mapping to deal with the masking which allows using the custom
         # sparse solver to deal with different nseg per branch.
         self._remapped_node_indices = remap_index_to_masked(
             self._internal_node_inds,

jaxley/modules/network.py

@@ -202,7 +202,7 @@ def _init_morph_jax_spsolve(self):
             self._cumsum_nseg_per_cell, self.cumsum_nbranchpoints_per_cell, self.cells
         ):
             offset_within_cell = cell.cumsum_nseg[-1]
-            condition = np.isin(cell._comp_edges["type"].to_numpy(), [1, 2])
+            condition = cell._comp_edges["type"].isin([1, 2])
             rows = cell._comp_edges[condition]
             self._comp_edges = pd.concat(
                 [
@@ -222,7 +222,7 @@ def _init_morph_jax_spsolve(self):
             self._cumsum_nseg_per_cell, self.cumsum_nbranchpoints_per_cell, self.cells
         ):
             offset_within_cell = cell.cumsum_nseg[-1]
-            condition = np.isin(cell._comp_edges["type"].to_numpy(), [3, 4])
+            condition = cell._comp_edges["type"].isin([3, 4])
             rows = cell._comp_edges[condition]
             self._comp_edges = pd.concat(
                 [

jaxley/utils/cell_utils.py

@@ -438,7 +438,7 @@ def compute_axial_conductances(
         conds_c2c = jnp.asarray([])
 
     # `branchpoint-to-compartment` (bp2c) axial coupling conductances.
-    condition = np.isin(comp_edges["type"].to_numpy(), [1, 2])
+    condition = comp_edges["type"].isin([1, 2])
     sink_comp_inds = np.asarray(comp_edges[condition]["sink"].to_list())
 
     if len(sink_comp_inds) > 0:
@@ -454,7 +454,7 @@ def compute_axial_conductances(
         conds_bp2c = jnp.asarray([])
 
     # `compartment-to-branchpoint` (c2bp) axial coupling conductances.
-    condition = np.isin(comp_edges["type"].to_numpy(), [3, 4])
+    condition = comp_edges["type"].isin([3, 4])
     source_comp_inds = np.asarray(comp_edges[condition]["source"].to_list())
 
     if len(source_comp_inds) > 0:

jaxley/utils/solver_utils.py

@@ -14,7 +14,10 @@ def remap_index_to_masked(
     """Convert actual index of the compartment to the index in the masked system.
 
     E.g. if `nsegs = [2, 4]`, then the index `3` would be mapped to `5` because the
-    masked `nsegs` are `[4, 4]`.
+    masked `nsegs` are `[4, 4]`. I.e.:
+
+    original: [0, 1,           2, 3, 4, 5]
+    masked:   [0, 1, (2) ,(3) ,4, 5, 6, 7]
     """
     cumsum_nseg_per_branch = jnp.concatenate(
         [