diff --git a/doc_template/source/conf.py b/doc_template/source/conf.py
index b38833ac..6e55fb07 100644
--- a/doc_template/source/conf.py
+++ b/doc_template/source/conf.py
@@ -1,4 +1,5 @@
 """Configuration file for the Sphinx documentation builder."""
+
 #
 # For the full list of built-in configuration values, see the documentation:
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
diff --git a/src/neuron_proofreader/__init__.py b/src/neuron_proofreader/__init__.py
index d0a85479..024d51f2 100644
--- a/src/neuron_proofreader/__init__.py
+++ b/src/neuron_proofreader/__init__.py
@@ -1,2 +1,3 @@
 """Init package"""
+
 __version__ = "0.0.0"
diff --git a/src/neuron_proofreader/machine_learning/augmentation.py b/src/neuron_proofreader/machine_learning/augmentation.py
index 91951eba..8d7b1722 100644
--- a/src/neuron_proofreader/machine_learning/augmentation.py
+++ b/src/neuron_proofreader/machine_learning/augmentation.py
@@ -30,7 +30,7 @@ def __init__(self):
             RandomFlip3D(),
             RandomRotation3D(),
             RandomNoise3D(),
-            RandomContrast3D()
+            RandomContrast3D(),
         ]
 
     def __call__(self, patches):
diff --git a/src/neuron_proofreader/machine_learning/exaspim_dataloader.py b/src/neuron_proofreader/machine_learning/exaspim_dataloader.py
index 997a7cca..7724dc86 100644
--- a/src/neuron_proofreader/machine_learning/exaspim_dataloader.py
+++ b/src/neuron_proofreader/machine_learning/exaspim_dataloader.py
@@ -328,9 +328,7 @@ def sample_bright_voxel(self, brain_id):
                 pending = dict()
                 for _ in range(self.prefetch_foreground_sampling):
                     voxel = self.sample_interior_voxel(brain_id)
-                    thread = executor.submit(
-                        self.read_image, brain_id, voxel
-                    )
+                    thread = executor.submit(self.read_image, brain_id, voxel)
                     pending[thread] = voxel
 
                 # Check if image patch is bright enough
@@ -489,8 +487,20 @@ def _load_batch(self, start_idx):
                 )
 
             # Process results
-            img_patches = np.zeros((batch_size, 1,) + self.patch_shape)
-            mask_patches = np.zeros((batch_size, 1,) + self.patch_shape)
+            img_patches = np.zeros(
+                (
+                    batch_size,
+                    1,
+                )
+                + self.patch_shape
+            )
+            mask_patches = np.zeros(
+                (
+                    batch_size,
+                    1,
+                )
+                + self.patch_shape
+            )
             for i, process in enumerate(as_completed(processes)):
                 img, mask = process.result()
                 img_patches[i, 0, ...] = img
diff --git a/src/neuron_proofreader/machine_learning/geometric_gnn_models.py b/src/neuron_proofreader/machine_learning/geometric_gnn_models.py
index 4f65c90d..0c68d7f1 100644
--- a/src/neuron_proofreader/machine_learning/geometric_gnn_models.py
+++ b/src/neuron_proofreader/machine_learning/geometric_gnn_models.py
@@ -75,9 +75,7 @@ def __init__(self, ggnn_name, output_dim=64):
         # Set geometric gnn
         if ggnn_name == "egnn":
             self.geometric_gnn = EGNN(
-                in_node_dim=1,
-                hidden_dim=32,
-                out_node_dim=output_dim
+                in_node_dim=1, hidden_dim=32, out_node_dim=output_dim
             )
 
     # --- Core Routines ---
@@ -97,7 +95,9 @@ def forward(self, h, x, edge_index, batch):
             )
 
             # Pool node embeddings
-            h_g, x_g, edge_index_g = self.pool_nonbranching_paths(h_g, x_g, edge_index_g)
+            h_g, x_g, edge_index_g = self.pool_nonbranching_paths(
+                h_g, x_g, edge_index_g
+            )
 
             # Encode pooled graph
             h_g = self.encode_pooled_graph(h_g, x_g, edge_index_g)
@@ -158,7 +158,9 @@ def pool_nonbranching_paths(self, h, x, edge_index):
         # Finish
         h_pooled = torch.stack(h_pooled, dim=0)
         x_pooled = torch.stack(x_pooled, dim=0)
-        edge_index_pooled = self.get_edge_index_pooled(edge_index, node_to_path)
+        edge_index_pooled = self.get_edge_index_pooled(
+            edge_index, node_to_path
+        )
         return h_pooled, x_pooled, edge_index_pooled
 
     def get_adj_and_deg(self, edge_index, num_nodes):
@@ -201,10 +203,13 @@ def extract_subgraph(self, h, x, edge_index, node_mask):
         id_map = {int(n): i for i, n in enumerate(node_ids.tolist())}
         edge_mask = node_mask[edge_index[0]] & node_mask[edge_index[1]]
         edge_index_g = edge_index[:, edge_mask]
-        edge_index_g = torch.stack([
-            torch.tensor([id_map[int(u)] for u in edge_index_g[0]]),
-            torch.tensor([id_map[int(v)] for v in edge_index_g[1]])
-        ], dim=0)
+        edge_index_g = torch.stack(
+            [
+                torch.tensor([id_map[int(u)] for u in edge_index_g[0]]),
+                torch.tensor([id_map[int(v)] for v in edge_index_g[1]]),
+            ],
+            dim=0,
+        )
         return h_g, x_g, edge_index_g
 
     @staticmethod
diff --git a/src/neuron_proofreader/machine_learning/gnn_models.py b/src/neuron_proofreader/machine_learning/gnn_models.py
index c8954ec3..b5add009 100644
--- a/src/neuron_proofreader/machine_learning/gnn_models.py
+++ b/src/neuron_proofreader/machine_learning/gnn_models.py
@@ -26,6 +26,7 @@ class VisionHGAT(torch.nn.Module):
     Heterogeneous graph attention network that processes multimodal features
     such as image patches and feature vectors.
     """
+
     # Class attributes
     relations = [
         str(("branch", "to", "branch")),
@@ -47,7 +48,9 @@ def __init__(
 
         # Initial embeddings
         self.node_embedding = init_node_embedding(hidden_dim)
-        self.patch_embedding = init_patch_embedding(patch_shape, hidden_dim // 2)
+        self.patch_embedding = init_patch_embedding(
+            patch_shape, hidden_dim // 2
+        )
 
         # Message passing layers
         self.disable_msg_passing = disable_msg_passing
@@ -58,7 +61,7 @@ def __init__(
         else:
             self.gat1 = self.init_gat(hidden_dim, hidden_dim, heads)
             self.gat2 = self.init_gat(hidden_dim * heads, hidden_dim, heads)
-            self.output = nn.Linear(hidden_dim * heads ** 2, 1)
+            self.output = nn.Linear(hidden_dim * heads**2, 1)
 
         # Initialize weights
         self.init_weights()
@@ -81,9 +84,7 @@ def init_mlp_layers(self, hidden_dim, n_layers=2):
         for _ in range(n_layers):
             layers.append(
                 nn_geometric.HeteroDictLinear(
-                    hidden_dim,
-                    hidden_dim,
-                    types=("branch", "proposal")
+                    hidden_dim, hidden_dim, types=("branch", "proposal")
                 )
             )
         return layers
@@ -160,10 +161,12 @@ def init_node_embedding(output_dim):
     dim_p = node_input_dims["proposal"]
 
     # Set node embedding layer
-    node_embedding = nn.ModuleDict({
-        "branch": FeedForwardNet(dim_b, output_dim, 3),
-        "proposal": FeedForwardNet(dim_p, output_dim // 2, 3),
-    })
+    node_embedding = nn.ModuleDict(
+        {
+            "branch": FeedForwardNet(dim_b, output_dim, 3),
+            "proposal": FeedForwardNet(dim_p, output_dim // 2, 3),
+        }
+    )
     return node_embedding
 
 
diff --git a/src/neuron_proofreader/machine_learning/train.py b/src/neuron_proofreader/machine_learning/train.py
index bce9b6e3..20edafb4 100644
--- a/src/neuron_proofreader/machine_learning/train.py
+++ b/src/neuron_proofreader/machine_learning/train.py
@@ -68,7 +68,7 @@ def __init__(
         lr=1e-3,
         max_epochs=200,
         min_recall=0,
-        save_mistake_mips=False
+        save_mistake_mips=False,
     ):
         """
         Instantiates a Trainer object.
@@ -292,7 +292,7 @@ def compute_stats(y, hat_y):
             "f1": avg_f1,
             "precision": avg_prec,
             "recall": avg_recall,
-            "accuracy": avg_acc
+            "accuracy": avg_acc,
         }
         return stats
 
@@ -426,7 +426,7 @@ def __init__(
         device="cuda",
         lr=1e-3,
         max_epochs=200,
-        save_mistake_mips=False
+        save_mistake_mips=False,
     ):
         """
         Instantiates a DistributedTrainer object.
@@ -452,7 +452,7 @@ def __init__(
             device=device,
             lr=lr,
             max_epochs=max_epochs,
-            save_mistake_mips=save_mistake_mips
+            save_mistake_mips=save_mistake_mips,
         )
 
         # Check that multiple GPUs are available
diff --git a/src/neuron_proofreader/machine_learning/vision_models.py b/src/neuron_proofreader/machine_learning/vision_models.py
index bf99fe65..21b6760d 100644
--- a/src/neuron_proofreader/machine_learning/vision_models.py
+++ b/src/neuron_proofreader/machine_learning/vision_models.py
@@ -142,7 +142,7 @@ def __init__(self, checkpoint_path, model_config):
             checkpoint_path=checkpoint_path,
             model_config=model_config,
             task_head_config="binary_classifier",
-            freeze_encoder=True
+            freeze_encoder=True,
         )
 
         # Instance attributes
diff --git a/src/neuron_proofreader/merge_proofreading/merge_dataloading.py b/src/neuron_proofreader/merge_proofreading/merge_dataloading.py
index 0bf8656d..d9c6fd50 100644
--- a/src/neuron_proofreader/merge_proofreading/merge_dataloading.py
+++ b/src/neuron_proofreader/merge_proofreading/merge_dataloading.py
@@ -43,7 +43,9 @@ def load_fragments(dataset, is_test=False):
         sub_df = merge_sites_df.loc[merge_sites_df["brain_id"] == brain_id]
         for segmentation_id in sub_df["segmentation_id"].unique():
             if (brain_id, segmentation_id) in target_pairs:
-                swc_pointer = f"{root}/{brain_id}/{segmentation_id}/merged_fragments.zip"
+                swc_pointer = (
+                    f"{root}/{brain_id}/{segmentation_id}/merged_fragments.zip"
+                )
                 dataset.load_fragment_graphs(
                     brain_id, swc_pointer, use_anisotropy=False
                 )
diff --git a/src/neuron_proofreader/merge_proofreading/merge_datasets.py b/src/neuron_proofreader/merge_proofreading/merge_datasets.py
index fe9f4420..63a838e0 100644
--- a/src/neuron_proofreader/merge_proofreading/merge_datasets.py
+++ b/src/neuron_proofreader/merge_proofreading/merge_datasets.py
@@ -29,7 +29,7 @@
     subgraph_to_point_cloud,
 )
 from neuron_proofreader.merge_proofreading.merge_dataloading import (
-    get_brain_merge_sites
+    get_brain_merge_sites,
 )
 from neuron_proofreader.skeleton_graph import SkeletonGraph
 from neuron_proofreader.utils import (
@@ -71,6 +71,7 @@ class MergeSiteDataset(Dataset):
     patch_shape : Tuple[int], optional
         Shape of the 3D image patches to extract.
     """
+
     random_negative_example_prob = 0.8
 
     def __init__(
@@ -121,9 +122,7 @@ def __init__(
         self.merge_site_kdtrees = dict()
 
     # --- Load Data ---
-    def load_fragment_graphs(
-        self, brain_id, swc_pointer, use_anisotropy=True
-    ):
+    def load_fragment_graphs(self, brain_id, swc_pointer, use_anisotropy=True):
         """
         Loads fragments containing merge mistakes for a whole-brain dataset,
         then stores them in the "graphs" attribute.
@@ -139,7 +138,7 @@ def load_fragment_graphs(
         graph = SkeletonGraph(
             anisotropy=self.anisotropy,
             node_spacing=self.node_spacing,
-            use_anisotropy=use_anisotropy
+            use_anisotropy=use_anisotropy,
         )
         graph.load(swc_pointer)
 
@@ -771,6 +770,7 @@ def generate_negative_examples(self):
         negative_examples : List[dict]
             List of negative examples collected across all graphs.
         """
+
         # Subroutines
         def add_examples():
             """
@@ -924,7 +924,7 @@ def __init__(
         is_multimodal=False,
         modality=None,
         sampler=None,
-        use_shuffle=True
+        use_shuffle=True,
     ):
         """
         Instantiates a MergeSiteDataLoader object.
@@ -970,11 +970,11 @@ def __iter__(self):
         for start in range(0, len(idxs), self.batch_size):
             end = min(start + self.batch_size, len(idxs))
             if self.is_multimodal and self.modality == "graph":
-                yield self._load_image_graph_batch(idxs[start: end])
+                yield self._load_image_graph_batch(idxs[start:end])
             elif self.is_multimodal and self.modality == "pointcloud":
-                yield self._load_image_pc_batch(idxs[start: end])
+                yield self._load_image_pc_batch(idxs[start:end])
             else:
-                yield self._load_image_batch(idxs[start: end])
+                yield self._load_image_batch(idxs[start:end])
 
     def _load_image_batch(self, batch_idxs):
         """
@@ -1084,9 +1084,7 @@ def _load_image_graph_batch(self, idxs):
                 h.append(h_i)
                 x.append(x_i)
                 edge_index.append(edge_index_i)
-                batches.append(
-                    torch.full((n_i,), i, dtype=torch.long)
-                )
+                batches.append(torch.full((n_i,), i, dtype=torch.long))
 
                 node_offset += n_i
 
@@ -1100,7 +1098,7 @@ def _load_image_graph_batch(self, idxs):
         batch = ml_util.TensorDict(
             {
                 "img": ml_util.to_tensor(patches),
-                "graph": (h, x, edge_index, batches)
+                "graph": (h, x, edge_index, batches),
             }
         )
         return batch, ml_util.to_tensor(targets)
diff --git a/src/neuron_proofreader/merge_proofreading/merge_inference.py b/src/neuron_proofreader/merge_proofreading/merge_inference.py
index 73bb319d..474ea3a9 100644
--- a/src/neuron_proofreader/merge_proofreading/merge_inference.py
+++ b/src/neuron_proofreader/merge_proofreading/merge_inference.py
@@ -250,7 +250,7 @@ def __init__(
         prefetch=64,
         segmentation_path=None,
         subgraph_radius=100,
-        use_new_mask=False
+        use_new_mask=False,
     ):
         # Call parent class
         super().__init__()
@@ -317,7 +317,9 @@ def find_fragments_to_search(self):
         for nodes in nx.connected_components(self.graph):
             # Compute path length
             node = util.sample_once(list(nodes))
-            length = self.graph.cable_length(max_depth=self.min_size, root=node)
+            length = self.graph.cable_length(
+                max_depth=self.min_size, root=node
+            )
 
             # Check if path length satisfies threshold
             if length > self.min_size:
@@ -431,7 +433,7 @@ def __init__(
         segmentation_path=None,
         step_size=10,
         subgraph_radius=100,
-        use_new_mask=False
+        use_new_mask=False,
     ):
         # Call parent class
         super().__init__(
@@ -445,7 +447,7 @@ def __init__(
             prefetch=prefetch,
             segmentation_path=segmentation_path,
             subgraph_radius=subgraph_radius,
-            use_new_mask=use_new_mask
+            use_new_mask=use_new_mask,
         )
 
         # Instance attributes
@@ -537,7 +539,13 @@ def _get_batch(self, nodes, img, offset):
         patch_centers = self.get_patch_centers(nodes) - offset
 
         # Populate batch array
-        batch = np.empty((len(nodes), 2,) + self.patch_shape)
+        batch = np.empty(
+            (
+                len(nodes),
+                2,
+            )
+            + self.patch_shape
+        )
         for i, center in enumerate(patch_centers):
             s = img_util.get_slices(center, self.patch_shape)
             batch[i, 0, ...] = img_util.normalize(img[s])
@@ -550,7 +558,13 @@ def _get_multimodal_batch(self, nodes, img, offset):
         patch_centers = self.get_patch_centers(nodes) - offset
 
         # Populate batch array
-        patches = np.empty((len(nodes), 2,) + self.patch_shape)
+        patches = np.empty(
+            (
+                len(nodes),
+                2,
+            )
+            + self.patch_shape
+        )
         point_clouds = np.empty((len(nodes), 3, 3600), dtype=np.float32)
         for i, (node, center) in enumerate(zip(nodes, patch_centers)):
             s = img_util.get_slices(center, self.patch_shape)
@@ -610,7 +624,7 @@ def __init__(
         prefetch=128,
         segmentation_path=None,
         subgraph_radius=100,
-        use_new_mask=False
+        use_new_mask=False,
     ):
         # Call parent class
         super().__init__(
@@ -623,7 +637,7 @@ def __init__(
             prefetch=prefetch,
             segmentation_path=segmentation_path,
             subgraph_radius=subgraph_radius,
-            use_new_mask=use_new_mask
+            use_new_mask=use_new_mask,
         )
 
         # Instance attributes
diff --git a/src/neuron_proofreader/proposal_graph.py b/src/neuron_proofreader/proposal_graph.py
index 92b624fd..37382210 100644
--- a/src/neuron_proofreader/proposal_graph.py
+++ b/src/neuron_proofreader/proposal_graph.py
@@ -85,7 +85,8 @@ def __init__(
 
         self.reset_proposals()
         self.proposal_generator = ProposalGenerator(
-            self, max_proposals_per_leaf=max_proposals_per_leaf,
+            self,
+            max_proposals_per_leaf=max_proposals_per_leaf,
         )
 
         # Graph Loader
@@ -138,7 +139,7 @@ def generate_proposals(
         self,
         search_radius,
         allow_nonleaf_proposals=False,
-        min_size_with_proposals=0
+        min_size_with_proposals=0,
     ):
         """
         Generates proposals from leaf nodes.
diff --git a/src/neuron_proofreader/skeleton_graph.py b/src/neuron_proofreader/skeleton_graph.py
index 32fc7b4d..eb75f46d 100644
--- a/src/neuron_proofreader/skeleton_graph.py
+++ b/src/neuron_proofreader/skeleton_graph.py
@@ -330,7 +330,7 @@ def remove_soma_merges(self):
         self.relabel_nodes()
         results = [
             f"# Soma Fragments: {len(self.soma_centroids)}",
-            f"# Soma Merges: {n_soma_merges}"
+            f"# Soma Merges: {n_soma_merges}",
         ]
         return "\n".join(results)
 
diff --git a/src/neuron_proofreader/split_proofreading/proposal_generation.py b/src/neuron_proofreader/split_proofreading/proposal_generation.py
index 4f406673..729597cb 100644
--- a/src/neuron_proofreader/split_proofreading/proposal_generation.py
+++ b/src/neuron_proofreader/split_proofreading/proposal_generation.py
@@ -56,7 +56,7 @@ def __call__(
         self,
         initial_radius,
         allow_nonleaf_proposals=False,
-        min_size_with_proposals=0
+        min_size_with_proposals=0,
     ):
         """
         Generates edge proposals between fragments within the given search
diff --git a/src/neuron_proofreader/split_proofreading/split_feature_extraction.py b/src/neuron_proofreader/split_proofreading/split_feature_extraction.py
index cef95a29..63bab677 100644
--- a/src/neuron_proofreader/split_proofreading/split_feature_extraction.py
+++ b/src/neuron_proofreader/split_proofreading/split_feature_extraction.py
@@ -62,7 +62,7 @@ def __init__(
                 brightness_clip=brightness_clip,
                 patch_shape=patch_shape,
                 padding=padding,
-           ),
+            ),
         ]
 
     def __call__(self, subgraph):
@@ -329,7 +329,6 @@ def create_segment_mask(self, proposal, shape, offset):
                     img_util.annotate_voxels(mask, voxels, val=0.25)
                     visited.add(frozenset({i, j}))
         return mask
-        
 
     def read_image(self, center, shape):
         """
diff --git a/src/neuron_proofreader/split_proofreading/split_inference.py b/src/neuron_proofreader/split_proofreading/split_inference.py
index b3bfcc7a..ed84f02b 100644
--- a/src/neuron_proofreader/split_proofreading/split_inference.py
+++ b/src/neuron_proofreader/split_proofreading/split_inference.py
@@ -175,8 +175,12 @@ def __call__(
             while self.dataset.proposals:
                 # Generate predictons
                 cnt += 1
-                self.log(f"\nThreshold={new_threshold} w/ only_leaf2leaf={only_leaf2leaf}")
-                preds = self.predict_proposals(suffix=f"{name}_round={cnt}_threshold={new_threshold}")
+                self.log(
+                    f"\nThreshold={new_threshold} w/ only_leaf2leaf={only_leaf2leaf}"
+                )
+                preds = self.predict_proposals(
+                    suffix=f"{name}_round={cnt}_threshold={new_threshold}"
+                )
 
                 # Merge accetped proposals
                 cur_threshold = new_threshold
diff --git a/src/neuron_proofreader/utils/graph_util.py b/src/neuron_proofreader/utils/graph_util.py
index f01c0d50..fb77eda2 100644
--- a/src/neuron_proofreader/utils/graph_util.py
+++ b/src/neuron_proofreader/utils/graph_util.py
@@ -80,7 +80,9 @@ def __init__(
         self.node_spacing = node_spacing
         self.prefetch = prefetch
         self.prune_depth = prune_depth
-        self.swc_reader = swc_util.Reader(anisotropy, min_cable_length, verbose)
+        self.swc_reader = swc_util.Reader(
+            anisotropy, min_cable_length, verbose
+        )
         self.verbose = verbose
 
     def __call__(self, swc_pointer):
@@ -146,7 +148,7 @@ def load(self, swc_dict):
             subgraph.
         """
         # Build graph
-        graph = swc_util.to_graph(swc_dict, set_attrs=True)
+        graph = swc_util.to_graph(swc_dict)
         prune_branches(graph, self.prune_depth)
 
         # Extract irreducible components (if applicable)
diff --git a/src/neuron_proofreader/utils/swc_util.py b/src/neuron_proofreader/utils/swc_util.py
index 419ecaab..903c784c 100644
--- a/src/neuron_proofreader/utils/swc_util.py
+++ b/src/neuron_proofreader/utils/swc_util.py
@@ -1,5 +1,5 @@
 """
-Created on Wed June 5 16:00:00 2023
+Created on Thu May 21 12:00:00 2026
 
 @author: Anna Grim
 @email: anna.grim@alleninstitute.org
@@ -14,15 +14,17 @@
     "z" (float): z coordinate
     "pid" (int): node ID of parent
 
-Note: Each uncommented line in an SWC file corresponds to a node and contains
-      these attributes in the same order.
+Note: Each line in an SWC file corresponds to a node and contains these
+      attributes in the same order.
 """
 
+from botocore import UNSIGNED
+from botocore.client import Config
 from collections import deque
 from concurrent.futures import (
+    as_completed,
     ProcessPoolExecutor,
     ThreadPoolExecutor,
-    as_completed,
 )
 from google.auth.exceptions import RefreshError, TransportError
 from google.cloud import storage
@@ -31,6 +33,7 @@
 from zipfile import ZipFile
 
 import ast
+import boto3
 import networkx as nx
 import numpy as np
 import os
@@ -41,10 +44,10 @@
 class Reader:
     """
     Class that reads SWC files stored in a (1) local directory, (2) local ZIP
-    archive, or (3) GCS directory, (4) GCS directory of ZIP archives.
+    archive, and (3) local directory of ZIP archives.
     """
 
-    def __init__(self, anisotropy=(1.0, 1.0, 1.0), min_size=0, verbose=True):
+    def __init__(self, anisotropy=(1.0, 1.0, 1.0), verbose=True):
         """
         Initializes a Reader object that reads SWC files.
 
@@ -52,149 +55,138 @@ def __init__(self, anisotropy=(1.0, 1.0, 1.0), min_size=0, verbose=True):
         ----------
         anisotropy : Tuple[float], optional
             Image to physical coordinates scaling factors to account for the
-            anisotropy of the microscope. Default is [1.0, 1.0, 1.0].
-        min_size : int, optional
-            Threshold on the number nodes in SWC files that are parsed and
-            returned. Default is 0.
-        verbose : bool
-            Indication of whether to display a progress bar during loading.
-            Default is True.
+            anisotropy of the microscope. Default is (1.0, 1.0, 1.0).
+        verbose : bool, optional
+            Indication of whether to display a progress bar. Default is True.
         """
         self.anisotropy = anisotropy
-        self.min_size = min_size
         self.verbose = verbose
 
+    # --- Read Data ---
     def __call__(self, swc_pointer):
         """
-        Reads SWC files located at the path specified by "swc_pointer".
+        Loads SWC files based on the type pointer provided.
 
         Parameters
         ----------
-        swc_pointer : str or List[str]
+        swc_pointer : str
             Object that points to SWC files to be read, must be one of:
                 - file_path: Path to single SWC file
                 - dir_path: Path to local directory with SWC files
                 - zip_path: Path to local ZIP with SWC files
                 - zip_dir_path: Path to local directory of ZIPs with SWC files
+                - s3_dir_path: Path to S3 prefix with SWC files
                 - gcs_dir_path: Path to GCS prefix with SWC files
                 - gcs_zip_dir_path: Path to GCS prefix with ZIPs of SWC files
-                - path_list: List of paths to local SWC files
 
         Returns
         -------
         Deque[dict]
-            List of dictionaries whose keys and values are the attribute names
-            and values from the SWC files. Each dictionary contains the
-            following items:
+            Dictionaries whose keys and values are the attribute names and
+            values from the SWC files. Each dictionary contains the following:
+            items:
                 - "id": unique identifier of each node in an SWC file.
                 - "pid": parent ID of each node.
                 - "radius": radius value corresponding to each node.
                 - "xyz": coordinate corresponding to each node.
-                - "soma_nodes": nodes with soma type.
-                - "swc_name": name of SWC file, minus the ".swc".
+                - "filename": filename of SWC file
+                - "swc_id": name of SWC file, minus the ".swc".
         """
-        # List of paths to SWC files
-        if isinstance(swc_pointer, list):
-            return self.read_from_paths(swc_pointer)
-
         # Directory containing...
         if os.path.isdir(swc_pointer):
-            # ZIP archives with SWC files
+            # Local ZIP archives with SWC files
             paths = util.list_paths(swc_pointer, extension=".zip")
             if len(paths) > 0:
-                return self.read_from_zips(swc_pointer)
+                return self.read_zips(swc_pointer, self.read_zip)
 
-            # SWC files
-            paths = util.list_paths(swc_pointer, extension=".swc")
+            # Local SWC files
+            paths = util.read_paths(swc_pointer, extension=".swc")
             if len(paths) > 0:
-                return self.read_from_paths(paths)
+                return self.read_swcs(paths, self.read_swc)
 
-            raise Exception(f"Directory is invalid - {swc_pointer}")
+            raise Exception("Directory is Invalid!")
 
         # Path to...
         if isinstance(swc_pointer, str):
-            # Single SWC file in GCS
-            if util.is_gcs_path(swc_pointer) and swc_pointer.endswith(".swc"):
-                bucket_name, path = util.parse_cloud_path(swc_pointer)
-                return [self.read_from_gcs_swc(bucket_name, path)]
-
-            # GCS directory
-            if util.is_gcs_path(swc_pointer):
-                return self.read_from_gcs(swc_pointer)
+            # Cloud GCS/S3 storage
+            if util.is_gcs_path(swc_pointer) or util.is_s3_path(swc_pointer):
+                return self.read_from_cloud(swc_pointer)
 
-            # ZIP archive with SWC files
+            # Local ZIP archive with SWC files
             if swc_pointer.endswith(".zip"):
-                return self.read_from_zip(swc_pointer)
+                return self.read_zip(swc_pointer)
 
-            # Single SWC file
+            # Local path to single SWC file
             if swc_pointer.endswith(".swc"):
-                return self.read_from_path(swc_pointer)
+                return self.read_swc(swc_pointer)
 
-            raise Exception(f"Path is invalid {swc_pointer}")
+            raise Exception("Path is Invalid!")
 
-        raise Exception(f"SWC Pointer is invalid {swc_pointer}")
+        raise Exception("SWC Pointer is Invalid!")
 
-    # --- Read subroutines ---
-    def read_from_paths(self, swc_paths):
+    def read_swc(self, path):
         """
-        Reads a list of SWC files stored on the local machine.
+        Reads a single SWC file.
 
         Paramters
         ---------
-        swc_paths : List[str]
-            Paths to SWC files stored on the local machine.
+        path : str
+            Path to SWC file.
 
         Returns
         -------
-        swc_dicts : Dequeue[dict]
-            List of dictionaries whose keys and values are the attribute
-            names and values from an SWC file.
+        dict
+            Dictionary whose keys and values are the attribute names and
+            values from an SWC file.
         """
-        with ProcessPoolExecutor() as executor:
-            # Assign processes
-            processes = list()
-            for path in swc_paths:
-                processes.append(executor.submit(self.read_from_path, path))
+        content = util.read_txt(path).splitlines()
+        filename = os.path.basename(path)
+        return self.parse(content, filename)
 
-            # Store results
-            swc_dicts = deque()
-            for process in as_completed(processes):
-                result = process.result()
-                if result:
-                    swc_dicts.append(result)
-        return swc_dicts
-
-    def read_from_path(self, path):
+    def read_swcs(self, swc_paths, read_fn):
         """
-        Reads a single SWC file stored on the local machine.
+        Reads SWC files stored in a GCS or S3 bucket.
 
-        Paramters
-        ---------
-        path : str
-            Path to SWC file stored on the local machine.
+        Parameters
+        ----------
+        bucket_name : str
+            Name of bucket containing SWC files.
+        swc_paths : List[str]
+            List of paths to SWC files to be read.
 
         Returns
         -------
-        swc_dict : dict
+        swc_dicts : Deque[dict]
             Dictionaries whose keys and values are the attribute names and
             values from an SWC file.
         """
-        content = util.read_txt(path)
-        if len(content) > self.min_size - 10:
-            swc_dict = self.parse(content)
-            swc_dict["swc_name"] = get_swc_name(path)
-            return swc_dict
-        else:
-            return False
+        with ThreadPoolExecutor() as executor:
+            # Assign threads
+            threads = set()
+            for path in swc_paths:
+                threads.add(executor.submit(read_fn, path))
 
-    def read_from_zips(self, zip_dir):
+            # Store results
+            swc_dicts = deque()
+            pbar = self.manual_progress_bar(len(threads))
+            for thread in as_completed(threads):
+                result = thread.result()
+                if result:
+                    swc_dicts.append(result)
+                if self.verbose:
+                    pbar.update(1)
+        return swc_dicts
+
+    def read_zips(self, zip_paths, read_fn):
         """
-        Reads a directory containing ZIP archives with SWC files.
+        Reads SWC files stored in ZIP archives.
 
         Parameters
         ----------
-        zip_dir : str
-            Path to directory containing ZIP archives with SWC files.
+        bucket_name : str
+            Name of bucket containing SWC files.
+        zip_paths : List[str]
+            Paths to ZIP archives containing SWC files to be read.
 
         Returns
         -------
@@ -202,273 +194,251 @@ def read_from_zips(self, zip_dir):
             Dictionaries whose keys and values are the attribute names and
             values from an SWC file.
         """
-        # Initializations
-        zip_names = [f for f in os.listdir(zip_dir) if f.endswith(".zip")]
-        iterator = zip_names
-        if self.verbose:
-            pbar = tqdm(iterator, desc="Read SWCs")
-
-        # Main
+        pbar = self.manual_progress_bar(len(zip_paths))
         with ProcessPoolExecutor() as executor:
-            # Assign threads
-            processes = list()
-            for f in iterator:
-                zip_path = os.path.join(zip_dir, f)
-                processes.append(executor.submit(self.read_from_zip, zip_path))
+            # Assign processes
+            futures = {executor.submit(read_fn, path) for path in zip_paths}
 
             # Store results
             swc_dicts = deque()
-            for process in as_completed(processes):
-                swc_dicts.extend(process.result())
+            for process in as_completed(futures):
+                try:
+                    swc_dicts.extend(process.result())
+                except RefreshError:
+                    pass
+
                 if self.verbose:
                     pbar.update(1)
         return swc_dicts
 
-    def read_from_zip(self, zip_path):
+    def read_zip(self, zip_path):
         """
-        Reads SWC files from a ZIP archive stored on the local machine.
+        Reads SWC files from a ZIP archive.
 
         Paramters
         ---------
-        str : str
-            Path to a ZIP archive on the local machine.
+        zip_path : str
+            Path to ZIP archive.
 
         Returns
         -------
-        swc_dicts : Dequeue[dict]
-            List of dictionaries whose keys and values are the attribute
-            names and values from an SWC file.
+        swc_dicts : Deque[dict]
+            Dictionaries whose keys and values are the attribute names and
+            values from an SWC file.
         """
         with ThreadPoolExecutor() as executor:
-            with ZipFile(zip_path, "r") as zf:
-                # Submit threads
-                threads = list()
-                for f in [f for f in zf.namelist() if f.endswith(".swc")]:
-                    threads.append(
-                        executor.submit(self.read_from_zipped_file, zf, f)
-                    )
+            # Assign threads
+            threads = set()
+            zf = ZipFile(zip_path, "r")
+            for name in [f for f in zf.namelist() if f.endswith(".swc")]:
+                threads.add(executor.submit(self.read_zipped_swc, zf, name))
 
-                # Store results
-                swc_dicts = deque()
-                for thread in as_completed(threads):
-                    swc_dict = thread.result()
-                    if swc_dict:
-                        swc_dicts.append(swc_dict)
+            # Store results
+            swc_dicts = deque()
+            for thread in as_completed(threads):
+                result = thread.result()
+                if result:
+                    swc_dicts.append(result)
         return swc_dicts
 
-    def read_from_zipped_file(self, zip_file, path):
+    def read_zipped_swc(self, zipfile, path):
         """
-        Reads SWC file stored in a ZIP archive.
+        Reads an SWC file stored in a ZIP archive.
 
         Parameters
         ----------
-        zip_file : ZipFile
-            ZIP archive containing SWC file to be read.
+        zipfile : ZipFile
+            ZIP archive containing SWC files.
         path : str
-            Path to SWC file to be read.
+            Path to SWC file.
 
         Returns
         -------
-        swc_dict : dict
-            Dictionaries whose keys and values are the attribute names and
+        dict
+            Dictionary whose keys and values are the attribute names and
             values from an SWC file.
         """
-        content = util.read_zip(zip_file, path).splitlines()
-        if len(content) > self.min_size - 10:
-            swc_dict = self.parse(content)
-            swc_dict["swc_name"] = get_swc_name(path)
-            return swc_dict
-        else:
-            return False
+        content = util.read_zip(zipfile, path).splitlines()
+        filename = os.path.basename(path)
+        return self.parse(content, filename)
 
-    def read_from_gcs(self, gcs_path):
+    def read_from_cloud(self, path):
         """
-        Reads SWC files stored in a GCS bucket.
+        Reads SWC files stored in a GCS or S3 bucket.
 
         Parameters
         ----------
-        gcs_path : str
-            Path to SWC files located in a GCS bucket.
+        path : str
+            Path to location in a GCS or S3 bucket containing SWC files,
+            must be in the format "{scheme}://{bucket_name}/{prefix}".
 
         Returns
         -------
-        Dequeue[dict]
-            List of dictionaries whose keys and values are the attribute
-            names and values from an SWC file.
+        Deque[dict]
+            Dictionaries whose keys and values are the attribute names and
+            values from an SWC file.
         """
-        # List filenames
-        bucket_name, prefix = util.parse_cloud_path(gcs_path)
-        swc_paths = util.list_gcs_filenames(bucket_name, prefix, ".swc")
-        zip_paths = util.list_gcs_filenames(bucket_name, prefix, ".zip")
+        # Extact info
+        assert util.is_s3_path(path) or util.is_gcs_path(path)
+        use_s3 = util.is_s3_path(path)
+
+        # List paths
+        swc_paths = util.list_cloud_paths(path, ".swc")
+        zip_paths = util.list_cloud_paths(path, ".zip")
 
         # Call reader
-        if len(swc_paths) > 0:
-            return self.read_from_gcs_swcs(bucket_name, swc_paths)
-        if len(zip_paths) > 0:
-            return self.read_from_gcs_zips(bucket_name, zip_paths)
+        if swc_paths:
+            return self.read_swcs(swc_paths, self.read_swc)
+        elif zip_paths:
+            read_fn = self.read_s3_zip if use_s3 else self.read_gcs_zip
+            return self.read_zips(zip_paths, read_fn)
 
-        # Error
-        raise Exception(f"GCS Pointer is invalid {gcs_path}")
+        raise Exception(f"SWC Pointer is invalid {path}")
 
-    def read_from_gcs_swcs(self, bucket_name, swc_paths):
+    def read_gcs_swc(self, path):
         """
-        Reads SWC files stored in a GCS bucket.
+        Reads a single SWC file stored in a GCS bucket.
 
         Parameters
         ----------
-        bucket_name : str
-            Name of GCS bucket containing SWC files.
-        swc_paths : List[str]
-            Paths to SWC files.
+        path : List[str]
+            Path to SWC file to be read.
 
         Returns
         -------
-        swc_dicts : Dequeue[dict]
-            List of dictionaries whose keys and values are the attribute
-            names and values from an SWC file.
+        Deque[dict]
+            Dictionaries whose keys and values are the attribute names and
+            values from an SWC file.
         """
-        if self.verbose:
-            pbar = tqdm(total=len(swc_paths), desc="Read SWCs")
-
-        with ThreadPoolExecutor() as executor:
-            # Assign threads
-            threads = list()
-            for path in swc_paths:
-                threads.append(
-                    executor.submit(self.read_from_gcs_swc, bucket_name, path)
-                )
+        # Initialize cloud reader
+        bucket_name, subpath = util.parse_cloud_path(path)
+        bucket = storage.Client().bucket(bucket_name)
+        blob = bucket.blob(subpath)
 
-            # Store results
-            swc_dicts = deque()
-            for thread in as_completed(threads):
-                result = thread.result()
-                if result:
-                    swc_dicts.append(result)
-                if self.verbose:
-                    pbar.update(1)
-        return swc_dicts
+        # Parse swc contents
+        content = blob.download_as_text().splitlines()
+        filename = os.path.basename(subpath)
+        return self.parse(content, filename)
 
-    def read_from_gcs_swc(self, bucket_name, path):
+    def read_gcs_zip(self, path):
         """
-        Reads a single SWC file stored in a GCS bucket.
+        Reads SWC files stored in a ZIP archive downloaded from a GCS
+        bucket.
 
         Parameters
         ----------
-        bucket_name : str
-            Name of GCS bucket containing SWC files.
-        swc_path : str
-            Path to SWC file to be read.
+        path : str
+            Path to ZIP archive containing SWC files to be read.
 
         Returns
         -------
-        swc_dict : dict
+        swc_dicts : Deque[dict]
             Dictionaries whose keys and values are the attribute names and
             values from an SWC file.
         """
-        # Initialize cloud reader
-        client = storage.Client()
-        bucket = client.bucket(bucket_name)
-        blob = bucket.blob(path)
+        # Download ZIP
+        bucket_name, path = util.parse_cloud_path(path)
+        bucket = storage.Client().bucket(bucket_name)
+        try:
+            zip_content = bucket.blob(path).download_as_bytes()
+        except TransportError:
+            print(f"Failed to read {path}!")
+            return deque()
 
-        # Parse swc contents
-        content = blob.download_as_text().splitlines()
-        if len(content) > self.min_size - 10:
-            swc_dict = self.parse(content)
-            swc_dict["swc_name"] = get_swc_name(path)
-            return swc_dict
-        else:
-            return False
+        # Parse ZIP contents
+        swc_dicts = deque()
+        with ZipFile(BytesIO(zip_content), "r") as zf:
+            with ThreadPoolExecutor() as executor:
+                # Assign threads
+                threads = set()
+                for name in zf.namelist():
+                    if self.confirm_read(name):
+                        threads.add(
+                            executor.submit(self.read_zipped_swc, zf, name)
+                        )
+
+                # Process results
+                for thread in as_completed(threads):
+                    result = thread.result()
+                    if result:
+                        swc_dicts.append(result)
+        return swc_dicts
 
-    def read_from_gcs_zips(self, bucket_name, zip_paths):
+    def read_s3_zip(self, path):
         """
-        Reads SWC files from ZIP archives stored in a GCS bucket.
+        Reads SWC files stored in a ZIP archive downloaded from an S3
+        bucket.
 
         Parameters
         ----------
-        bucket_name : str
-            Name of GCS bucket containing SWC files.
-        zip_paths : List[str]
-            Paths to ZIP archives in a GCS bucket.
+        path : str
+            Path to ZIP archive containing SWC files to be read.
 
         Returns
         -------
-        swc_dicts : Dequeue[dict]
-            List of dictionaries whose keys and values are the attribute
-            names and values from an SWC file.
+        swc_dicts : Deque[dict]
+            Dictionaries whose keys and values are the attribute names and
+            values from an SWC file.
         """
-        # Initializations
-        batch_size = 1000
-        if self.verbose:
-            pbar = tqdm(total=len(zip_paths), desc="Read SWCs")
-
-        # Main
-        swc_dicts = deque()
-        with ProcessPoolExecutor() as executor:
-            for i in range(0, len(zip_paths), batch_size):
-                # Assign processes
-                processes = list()
-                for zip_path in zip_paths[i: i + batch_size]:
-                    processes.append(
-                        executor.submit(
-                            self.read_from_gcs_zip, bucket_name, zip_path
-                        )
+        # Initialize cloud reader
+        bucket, key = util.parse_cloud_path(path)
+        s3 = boto3.client("s3", config=Config(signature_version=UNSIGNED))
+        zip_content = s3.get_object(Bucket=bucket, Key=key)["Body"].read()
+
+        # Parse ZIP
+        with ZipFile(BytesIO(zip_content), "r") as zf:
+            with ThreadPoolExecutor() as executor:
+                # Assign threads
+                threads = set()
+                for name in zf.namelist():
+                    threads.add(
+                        executor.submit(self.read_zipped_swc, zf, name)
                     )
 
                 # Store results
-                for process in as_completed(processes):
-                    try:
-                        swc_dicts.extend(process.result())
-                    except RefreshError:
-                        pass
-                    if self.verbose:
-                        pbar.update(1)
+                swc_dicts = deque()
+                for thread in as_completed(threads):
+                    result = thread.result()
+                    if result:
+                        swc_dicts.append(result)
         return swc_dicts
 
-    def read_from_gcs_zip(self, bucket_name, zip_path, filenames=None):
+    # -- Process Text ---
+    def iterator(self, iterator):
         """
-        Reads SWC files stored in a ZIP archive downloaded from a cloud
-        bucket.
+        Gets an iterator that optionally displays a progress bar.
 
         Parameters
         ----------
-        bucket_name : str
-            Name of GCS bucket containing SWC files.
-        zip_path : str
-            Path to ZIP archive to be read.
-        filenames : None or List[str], optional
-            Filenames to be read if provided. Default is None.
+        iterator : iterable
+            Object to be iterated over.
 
         Returns
         -------
-        swc_dicts : Dequeue[dict]
-            List of dictionaries whose keys and values are the attribute
-            names and values from an SWC file.
+        tqdm.tqdm
+            Iterator that is optionally wrapped in a progress bar.
         """
-        try:
-            # Download zip
-            client = storage.Client()
-            bucket = client.bucket(bucket_name)
-            zip_content = bucket.blob(zip_path).download_as_bytes()
-        except TransportError:
-            print(f"Failed to read {zip_path}!")
-            return deque()
+        return tqdm(iterator, desc="Read SWCs") if self.verbose else iterator
 
-        # Process files
-        swc_dicts = deque()
-        with ZipFile(BytesIO(zip_content), "r") as zip_file:
-            filenames = zip_file.namelist() if filenames is None else filenames
-            for filename in filenames:
-                result = self.read_from_zipped_file(zip_file, filename)
-                if result:
-                    swc_dicts.append(result)
-        return swc_dicts
+    def manual_progress_bar(self, total):
+        """
+        Gets progress bar that needs to be updated manually.
+
+        Parameters
+        ----------
+        total : int
+            Size of progress bar.
+
+        Returns
+        -------
+        tqdm.tqdm
+            Iterator that is optionally wrapped in a progress bar.
+        """
+        return tqdm(total=total, desc="Read SWCs") if self.verbose else None
 
-    # --- Process content ---
-    def parse(self, content):
+    def parse(self, content, filename):
         """
         Parses an SWC file to extract the content which is stored in a dict.
-        Note that node_ids from SWC are reindex from 0 to n-1 where n is the
-        number of nodes in the SWC file.
 
         Parameters
         ----------
@@ -477,34 +447,36 @@ def parse(self, content):
 
         Returns
         -------
-        swc_dict : dict
-            Dictionaries whose keys and values are the attribute names
-            and values from an SWC file.
+        dict
+            Dictionary whose keys and values are the attribute names and
+            values from an SWC file.
         """
         # Initializations
+        swc_name, _ = os.path.splitext(filename)
         content, offset = self.process_content(content)
-        swc_dict = {
-            "id": np.zeros((len(content)), dtype=int),
-            "radius": np.zeros((len(content)), dtype=np.float16),
-            "pid": np.zeros((len(content)), dtype=int),
-            "xyz": np.zeros((len(content), 3), dtype=np.float32),
-            "soma_nodes": set(),
-        }
-
-        # Parse content
-        for i, line in enumerate(content):
-            parts = line.split()
-            swc_dict["id"][i] = parts[0]
-            swc_dict["radius"][i] = float(parts[-2])
-            swc_dict["pid"][i] = parts[-1]
-            swc_dict["xyz"][i] = self.read_xyz(parts[2:5], offset)
-            if int(parts[1]) == 1:
-                swc_dict["soma_nodes"].add(parts[0])
-
-        # Convert radius from nanometers to microns
-        if swc_dict["radius"][0] > 100:
-            swc_dict["radius"] /= 1000
-        return swc_dict
+        if len(content) > 0:
+            swc_dict = {
+                "id": np.zeros((len(content)), dtype=int),
+                "pid": np.zeros((len(content)), dtype=int),
+                "radius": np.zeros((len(content)), dtype=float),
+                "xyz": np.zeros((len(content), 3), dtype=np.int32),
+                "swc_name": swc_name,
+            }
+
+            # Parse content
+            for i, line in enumerate(content):
+                parts = line.split()
+                swc_dict["id"][i] = parts[0]
+                swc_dict["pid"][i] = parts[-1]
+                swc_dict["radius"][i] = float(parts[-2])
+                swc_dict["xyz"][i] = self.read_coordinate(parts[2:5], offset)
+
+            # Convert radius from nanometers to microns
+            if swc_dict["radius"][0] > 100:
+                swc_dict["radius"] /= 1000
+            return swc_dict
+        else:
+            return None
 
     def process_content(self, content):
         """
@@ -520,33 +492,33 @@ def process_content(self, content):
         Returns
         -------
         content : List[str]
-            List of strings representing the lines of text starting from the
-            line immediately after the last commented line.
-        offset : List[float]
-            Offset used to shift coordinates.
+            Lines from an SWC file after comments.
+        offset : Tuple[int]
+            Offset used to shift coordinate.
         """
         offset = (0, 0, 0)
         for i, line in enumerate(content):
             if line.startswith("# OFFSET"):
-                offset = self.read_xyz(line.split()[2:5])
-            if not line.startswith("#") and len(line) > 0:
+                parts = line.split()
+                offset = self.read_coordinate(parts[2:5])
+            if not line.startswith("#") and len(line.strip()) > 0:
                 return content[i:], offset
 
-    def read_xyz(self, xyz_str, offset=(0, 0, 0)):
+    def read_coordinate(self, xyz_str, offset=(0, 0, 0)):
         """
-        Reads a 3D coordinate from a string and transforms it.
+        Reads a coordinate from a string and converts it to voxel coordinates.
 
         Parameters
         ----------
         xyz_str : str
-            Coordinate stored as a str.
-        offset : List[float], optional
-            Shift applied to coordinate. Default is (0, 0, 0).
+            Coordinate stored as a string.
+        offset : Tuple[int]
+            Offset of coordinates in SWC file. Default is (0, 0, 0).
 
         Returns
         -------
-        List[float]
-            Coordinate of node from an SWC file.
+        Tuple[int]
+            xyz coordinates of an entry from an SWC file.
         """
         iterator = zip(self.anisotropy, xyz_str, offset)
         return [a * (float(s) + o) for a, s, o in iterator]
@@ -575,10 +547,10 @@ def write_points(
     radius : float, optional
         Radius to be used in SWC file. Default is 10.
     """
-    zip_writer = ZipFile(zip_path, write_mode)
+    zf = ZipFile(zip_path, write_mode)
     for i, xyz in enumerate(points):
         filename = prefix + str(i + 1) + ".swc"
-        to_zipped_point(zip_writer, filename, xyz, color=color, radius=radius)
+        to_zipped_point(zf, filename, xyz, color=color, radius=radius)
 
 
 def to_zipped_point(zip_writer, filename, xyz, color=None, radius=5):
@@ -654,17 +626,14 @@ def get_swc_name(path):
     return name
 
 
-def to_graph(swc_dict, set_attrs=False):
+def to_graph(swc_dict):
     """
-    Converts an SWC dict to a NetworkX graph with reindexed nodes.
+    Converts an SWC dictionary to a NetworkX graph with reindexed nodes.
 
     Parameters
     ----------
     swc_dict : dict
         Contents of an SWC file.
-    set_attrs : bool, optional
-        Indication of whether to set "xyz" and "radius" as graph-level
-        attributes. Default is False.
 
     Returns
     -------
@@ -679,9 +648,10 @@ def to_graph(swc_dict, set_attrs=False):
     ]
 
     # Build graph with reindexed edges
-    graph = nx.Graph(swc_name=swc_dict["swc_name"])
+    graph = nx.Graph(
+        swc_name=swc_dict["swc_name"],
+        radius=swc_dict["radius"],
+        xyz=swc_dict["xyz"],
+    )
     graph.add_edges_from(edges)
-    if set_attrs:
-        graph.graph["xyz"] = swc_dict["xyz"]
-        graph.graph["radius"] = swc_dict["radius"]
     return graph
diff --git a/src/neuron_proofreader/utils/util.py b/src/neuron_proofreader/utils/util.py
index d0abe0ce..180cac62 100644
--- a/src/neuron_proofreader/utils/util.py
+++ b/src/neuron_proofreader/utils/util.py
@@ -223,20 +223,25 @@ def read_json(path):
 
 def read_txt(path):
     """
-    Reads txt file located at the given path.
+    Reads txt file at the given path.
 
     Parameters
     ----------
     path : str
-        Path to txt file to be read.
+        Path to txt file.
 
     Returns
     -------
     str
-        Contents of txt file.
+        Text from the txt file.
     """
-    with open(path, "r") as f:
-        return f.read().splitlines()
+    if is_s3_path(path):
+        return read_txt_from_s3(path)
+    elif is_gcs_path(path):
+        return read_txt_from_gcs(path)
+    else:
+        with open(path, "r") as f:
+            return f.read()
 
 
 def read_zip(zip_file, path):
@@ -328,6 +333,58 @@ def write_txt(path, contents):
     f.close()
 
 
+# --- Cloud Utils ---
+def list_cloud_paths(path, extension=""):
+    """
+    Lists all files in a GCS/S3 bucket with the given extension.
+
+    Parameters
+    ----------
+    path : str
+        Path to cloud prefix to be searched, must be in the format:
+        f"{scheme}://{bucket_name}/{prefix}".
+    extension : str, optional
+        File extension of filenames to be listed. Default is an empty string.
+
+    Returns
+    -------
+    List[str]
+        Filenames stored at the GCS path with the given extension.
+    """
+    assert is_gcs_path(path) or is_s3_path(path)
+    bucket_name, prefix = parse_cloud_path(path)
+    list_fn = list_gcs_paths if is_gcs_path(path) else list_s3_paths
+    return list_fn(bucket_name, prefix, extension=extension)
+
+
+def parse_cloud_path(path):
+    """
+    Parses a cloud storage path into its bucket name and key/prefix. Supports
+    paths of the form: "{scheme}://bucket_name/prefix" or without a scheme.
+
+    Parameters
+    ----------
+    path : str
+        Path to be parsed.
+
+    Returns
+    -------
+    bucket_name : str
+        Name of the bucket.
+    prefix : str
+        Cloud prefix.
+    """
+    # Remove s3:// or gs:// if present
+    if path.startswith("s3://") or path.startswith("gs://"):
+        path = path[len("s3://"):]
+
+    # Split path
+    parts = path.split("/", 1)
+    bucket_name = parts[0]
+    prefix = parts[1] if len(parts) > 1 else ""
+    return bucket_name, prefix
+
+
 # --- GCS utils ---
 def check_gcs_file_exists(bucket_name, path):
     """
@@ -368,14 +425,14 @@ def is_gcs_path(path):
     return path.startswith("gs://")
 
 
-def list_gcs_filenames(bucket_name, prefix, extension=""):
+def list_gcs_paths(bucket_name, prefix, extension=""):
     """
-    Lists all files in a GCS bucket with the given extension.
+    Lists paths at a GCS prefix with the given extension.
 
     Parameters
     ----------
     bucket_name : str
-        Name of bucket to be searched.
+        Name of bucket containing prefix.
     prefix : str
         Path to location within bucket to be searched.
     extension : str, optional
@@ -384,11 +441,14 @@ def list_gcs_filenames(bucket_name, prefix, extension=""):
     Returns
     -------
     List[str]
-        Filenames stored at the GCS path with the given extension.
+        Paths under the GCS prefix with the given extension.
     """
     bucket = storage.Client().bucket(bucket_name)
-    blobs = bucket.list_blobs(prefix=prefix)
-    return [blob.name for blob in blobs if extension in blob.name]
+    paths = list()
+    for name in [b.name for b in bucket.list_blobs(prefix=prefix)]:
+        if extension in name:
+            paths.append(os.path.join(f"gs://{bucket_name}", name))
+    return paths
 
 
 def list_gcs_subdirectories(bucket_name, prefix):
@@ -447,6 +507,25 @@ def read_json_from_gcs(bucket_name, blob_path):
     return json.loads(blob.download_as_text())
 
 
+def read_txt_from_gcs(path):
+    """
+    Reads a txt file stored in a GCS bucket.
+
+    Parameters
+    ----------
+    path : str
+        Path to txt file to be read.
+
+    Returns
+    -------
+    str
+        Contents of txt file.
+    """
+    bucket_name, subpath = parse_cloud_path(path)
+    bucket = storage.Client().bucket(bucket_name)
+    return bucket.blob(subpath).download_as_text()
+
+
 # --- S3 utils ---
 def is_s3_path(path):
     """
@@ -465,35 +544,58 @@ def is_s3_path(path):
     return path.startswith("s3://")
 
 
-def list_s3_prefixes(bucket_name, prefix):
+def list_s3_paths(bucket_name, prefix, extension=""):
     """
-    Lists all immediate subdirectories of a given S3 path (prefix).
+    Lists all object keys in a public S3 bucket under a given prefix,
+    optionally filters by file extension.
 
     Parameters
-    -----------
+    ----------
     bucket_name : str
-        Name of the S3 bucket to search.
+        Name of the S3 bucket.
     prefix : str
-        S3 prefix to search within.
+        Prefix to search under.
+    extension : str, optional
+        File extension to filter by. Default is an empty string.
 
-    Returns:
-    --------
-    List[str]
-        Immediate subdirectories under the specified prefix.
+    Returns
+    -------
+    paths : List[str]
+        S3 object keys that match the prefix and extension filter.
     """
-    # Check prefix is valid
-    if not prefix.endswith("/"):
-        prefix += "/"
+    # Create an anonymous client for public buckets
+    s3 = boto3.client("s3", config=Config(signature_version=UNSIGNED))
+    response = s3.list_objects_v2(Bucket=bucket_name, Prefix=prefix)
+
+    # List all objects under the prefix
+    paths = list()
+    if "Contents" in response:
+        for obj in response["Contents"]:
+            filename = obj["Key"]
+            if filename.endswith(extension):
+                path = os.path.join(f"s3://{bucket_name}", filename)
+                paths.append(path)
+    return paths
+
 
-    # Call the list_objects_v2 API
+def read_txt_from_s3(path):
+    """
+    Reads a txt file stored in an S3 bucket.
+
+    Parameters
+    ----------
+    path : str
+        Path to txt file to be read.
+
+    Returns
+    -------
+    str
+        Contents of txt file.
+    """
+    bucket_name, subpath = parse_cloud_path(path)
     s3 = boto3.client("s3", config=Config(signature_version=UNSIGNED))
-    response = s3.list_objects_v2(
-        Bucket=bucket_name, Prefix=prefix, Delimiter="/"
-    )
-    if "CommonPrefixes" in response:
-        return [cp["Prefix"] for cp in response["CommonPrefixes"]]
-    else:
-        return list()
+    obj = s3.get_object(Bucket=bucket_name, Key=subpath)
+    return obj["Body"].read().decode("utf-8")
 
 
 def upload_dir_to_s3(dir_path, bucket_name, prefix):
@@ -618,34 +720,6 @@ def numpy_to_hashable(arr):
     return [tuple(item) for item in arr.tolist()]
 
 
-def parse_cloud_path(path):
-    """
-    Parses a cloud storage path into its bucket name and key/prefix. Supports
-    paths of the form: "{scheme}://bucket_name/prefix" or without a scheme.
-
-    Parameters
-    ----------
-    path : str
-        Path to be parsed.
-
-    Returns
-    -------
-    bucket_name : str
-        Name of the bucket.
-    prefix : str
-        Cloud prefix.
-    """
-    # Remove s3:// or gs:// if present
-    if path.startswith("s3://") or path.startswith("gs://"):
-        path = path[len("s3://"):]
-
-    # Split path
-    parts = path.split("/", 1)
-    bucket_name = parts[0]
-    prefix = parts[1] if len(parts) > 1 else ""
-    return bucket_name, prefix
-
-
 def sample_once(my_container):
     """
     Samples a single element from the given container.
diff --git a/tests/__init__.py b/tests/__init__.py
index d0a85479..024d51f2 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -1,2 +1,3 @@
 """Init package"""
+
 __version__ = "0.0.0"