Remove duplicate get_neighboring_atoms (#20)

cookbook/00_Template.ipynb

@@ -123,29 +123,6 @@
     "    for targ_atom in mol.atoms:\n",
     "        if targ_atom.position.unpack() in near_point_set:\n",
     "            neighbor_atoms.append(targ_atom)\n",
-    "    return neighbor_atoms\n",
-    "\n",
-    "\n",
-    "def get_neighboring_atoms(target_reference: structure.Complex, selected_atoms: list, radius=5):\n",
-    "    \"\"\"Use KDTree to find target atoms within site_size radius of selected atoms.\n",
-    "    \n",
-    "    returns a list of atoms within `site_size` angstroms of the ligand.\n",
-    "    \"\"\"\n",
-    "    mol = next(\n",
-    "        mol for i, mol in enumerate(target_reference.molecules)\n",
-    "        if i == target_reference.current_frame)\n",
-    "    ligand_positions = [atom.position.unpack() for atom in selected_atoms]\n",
-    "    target_atoms = itertools.chain(*[ch.atoms for ch in mol.chains if not ch.name.startswith(\"H\")])\n",
-    "    target_tree = KDTree([atom.position.unpack() for atom in target_atoms])\n",
-    "    target_point_indices = target_tree.query_ball_point(ligand_positions, radius)\n",
-    "    near_point_set = set()\n",
-    "    for point_indices in target_point_indices:\n",
-    "        for point_index in point_indices:\n",
-    "            near_point_set.add(tuple(target_tree.data[point_index]))\n",
-    "    neighbor_atoms = []\n",
-    "    for targ_atom in mol.atoms:\n",
-    "        if targ_atom.position.unpack() in near_point_set:\n",
-    "            neighbor_atoms.append(targ_atom)\n",
     "    return neighbor_atoms"
    ]
   },