Reformatted with Black

Author: C-Earl

bindsnet/datasets/preprocess.py

@@ -151,7 +151,7 @@ def crop_sample(sample):
     opts = {}
     image, bb = sample["image"], sample["bb"]
     orig_bbox = BoundingBox(bb[0], bb[1], bb[2], bb[3])
-    (output_image, pad_image_location, edge_spacing_x, edge_spacing_y) = cropPadImage(
+    output_image, pad_image_location, edge_spacing_x, edge_spacing_y = cropPadImage(
         orig_bbox, image
     )
     new_bbox = BoundingBox(0, 0, 0, 0)

bindsnet/datasets/torchvision_wrapper.py

@@ -16,18 +16,13 @@ def create_torchvision_dataset_wrapper(ds_type):
         ds_type = getattr(torchDB, ds_type)
 
     class TorchvisionDatasetWrapper(ds_type):
-        __doc__ = (
-            """BindsNET torchvision dataset wrapper for:
+        __doc__ = """BindsNET torchvision dataset wrapper for:
 
         The core difference is the output of __getitem__ is no longer
         (image, label) rather a dictionary containing the image, label,
         and their encoded versions if encoders were provided.
 
-            \n\n"""
-            + str(ds_type)
-            if ds_type.__doc__ is None
-            else ds_type.__doc__
-        )
+            \n\n""" + str(ds_type) if ds_type.__doc__ is None else ds_type.__doc__
 
         def __init__(
             self,

bindsnet/learning/learning.py

@@ -877,7 +877,7 @@ def _conv1d_connection_update(self, **kwargs) -> None:
         ``AbstractConnection`` class.
         """
         # Get convolutional layer parameters.
-        (out_channels, in_channels, kernel_size) = self.connection.w.size()
+        out_channels, in_channels, kernel_size = self.connection.w.size()
         padding, stride = self.connection.padding, self.connection.stride
         batch_size = self.source.batch_size
 

bindsnet/network/topology_features.py

@@ -508,7 +508,7 @@ def __init__(
         super().__init__(
             name=name,
             value=value,
-            range=[0, 1],       # note: Value isn't used, not 'None' to avoid errors
+            range=[0, 1],  # note: Value isn't used, not 'None' to avoid errors
             norm=norm,
             learning_rule=learning_rule,
             nu=nu,
@@ -580,9 +580,9 @@ def compute(self, conn_spikes) -> Union[torch.Tensor, float, int]:
         # Decay
         if self.delay_decay:
             self.delay_buffer = self.delay_buffer - self.delay_decay.to("cuda")
-            self.delay_buffer[
-                self.delay_decay < 0
-            ] = 0  # TODO: Determine if this is faster than clamp(min=0)
+            self.delay_buffer[self.delay_decay < 0] = (
+                0  # TODO: Determine if this is faster than clamp(min=0)
+            )
 
         return out_signal
 

examples/benchmark/sparse_vs_dense_tensors.py

@@ -4,7 +4,6 @@
 
 from bindsnet.evaluation import all_activity, assign_labels, proportion_weighting
 
-
 parser = argparse.ArgumentParser()
 parser.add_argument("--benchmark_type", choices=["memory", "runtime"], default="memory")
 args = parser.parse_args()

examples/mnist/MCC_reservoir.py

@@ -26,7 +26,6 @@
 from bindsnet.network.topology import MulticompartmentConnection
 from bindsnet.utils import get_square_weights
 
-
 parser = argparse.ArgumentParser()
 parser.add_argument("--seed", type=int, default=0)
 parser.add_argument("--n_neurons", type=int, default=500)

examples/mnist/reservoir_delays.py

@@ -28,7 +28,6 @@
 from bindsnet.network.topology_features import Delay, Mask, Probability, Weight
 from bindsnet.learning.MCC_learning import PostPre, MSTDP, NoOp
 
-
 parser = argparse.ArgumentParser()
 parser.add_argument("--seed", type=int, default=0)
 parser.add_argument("--n_neurons", type=int, default=500)