autoformat all docstrings using docformatter

.pre-commit-config.yaml

@@ -10,3 +10,9 @@ repos:
     hooks:
       - id: isort
         args: ["--profile", "black", "--filter-files"]
+
+  - repo: https://github.com/myint/docformatter
+    rev: v1.4
+    hooks:
+      - id: docformatter
+        args: [--in-place, --wrap-summaries=99, --wrap-descriptions=99]

sinabs/activation/quantize.py

@@ -2,9 +2,8 @@
 
 
 class Quantize(torch.autograd.Function):
-    """PyTorch-compatible function that applies a floor() operation on the input,
-    while providing a surrogate gradient (equivalent to that of a linear
-    function) in the backward pass."""
+    """PyTorch-compatible function that applies a floor() operation on the input, while providing a
+    surrogate gradient (equivalent to that of a linear function) in the backward pass."""
 
     @staticmethod
     def forward(ctx, inp):
@@ -19,10 +18,12 @@ def backward(ctx, grad_output):
 
 
 class StochasticRounding(torch.autograd.Function):
-    """PyTorch-compatible function that applies stochastic rounding. The input x
+    """PyTorch-compatible function that applies stochastic rounding. The input x.
+
     is quantized to ceil(x) with probability (x - floor(x)), and to floor(x)
     otherwise. The backward pass is provided as a surrogate gradient
-    (equivalent to that of a linear function)."""
+    (equivalent to that of a linear function).
+    """
 
     @staticmethod
     def forward(ctx, inp):

sinabs/activation/reset_mechanism.py

@@ -4,9 +4,7 @@
 
 @dataclass
 class MembraneReset:
-    """
-    Reset the membrane potential v_mem to a given value
-    after it spiked.
+    """Reset the membrane potential v_mem to a given value after it spiked.
 
     Parameters:
         reset_value: fixed value that a neuron should be reset to. Defaults to zero.
@@ -27,9 +25,7 @@ def __call__(self, spikes, state, spike_threshold):
 
 @dataclass
 class MembraneSubtract:
-    """
-    Subtract the spiking threshold from the membrane potential
-    for every neuron that spiked.
+    """Subtract the spiking threshold from the membrane potential for every neuron that spiked.
 
     Parameters:
         subtract_value: optional value that will be subtraced from

sinabs/activation/spike_generation.py

@@ -15,10 +15,9 @@ def backward(ctx, grad_output: torch.tensor):
 
 
 class MultiSpike(BackwardClass, torch.autograd.Function):
-    """
-    Autograd function that returns membrane potential integer-divided by spike threshold.
-    Do not instantiate this class when passing as spike_fn (see example).
-    Can be combined with different surrogate gradient functions.
+    """Autograd function that returns membrane potential integer-divided by spike threshold. Do not
+    instantiate this class when passing as spike_fn (see example). Can be combined with different
+    surrogate gradient functions.
 
     Example:
         >>> layer = sinabs.layers.LIF(spike_fn=MultiSpike, ...)
@@ -44,9 +43,9 @@ def forward(
 
 
 class MaxSpikeInner(BackwardClass, torch.autograd.Function):
-    """
-    Autograd function that returns membrane potential divided by
-    spike threshold for a maximum number of spikes per time step.
+    """Autograd function that returns membrane potential divided by spike threshold for a maximum
+    number of spikes per time step.
+
     Can be combined with different surrogate gradient functions.
     """
 
@@ -74,10 +73,8 @@ def forward(
 
 @dataclass
 class MaxSpike:
-    """
-    Wrapper for MaxSpikeInner autograd function. This class needs to
-    be instantiated when used as spike_fn. Notice the difference in example
-    to Single/MultiSpike.
+    """Wrapper for MaxSpikeInner autograd function. This class needs to be instantiated when used
+    as spike_fn. Notice the difference in example to Single/MultiSpike.
 
     Example:
         >>> layer = sinabs.layers.LIF(spike_fn=MaxSpike(max_num_spikes_per_bin=10), ...)
@@ -101,11 +98,9 @@ def required_states(self):
 
 
 class SingleSpike(BackwardClass, torch.autograd.Function):
-    """
-    Autograd function that returns membrane potential divided by
-    spike threshold for a maximum of one spike per time step.
-    Do not instantiate this class when passing as spike_fn (see example).
-    Can be combined with different surrogate gradient functions.
+    """Autograd function that returns membrane potential divided by spike threshold for a maximum
+    of one spike per time step. Do not instantiate this class when passing as spike_fn (see
+    example). Can be combined with different surrogate gradient functions.
 
     Example:
         >>> layer = sinabs.layers.LIF(spike_fn=SingleSpike, ...)

sinabs/activation/surrogate_gradient_fn.py

@@ -6,8 +6,7 @@
 
 @dataclass
 class Heaviside:
-    """
-    Heaviside surrogate gradient with optional shift.
+    """Heaviside surrogate gradient with optional shift.
 
     Parameters:
         window: Distance between step of Heaviside surrogate gradient and
@@ -28,8 +27,7 @@ def gaussian(x: torch.Tensor, mu: float, sigma: float):
 
 @dataclass
 class Gaussian:
-    """
-    Gaussian surrogate gradient function.
+    """Gaussian surrogate gradient function.
 
     Parameters
         mu: The mean of the Gaussian.
@@ -50,8 +48,7 @@ def __call__(self, v_mem, spike_threshold):
 
 @dataclass
 class MultiGaussian:
-    """
-    Surrogate gradient as defined in Yin et al., 2021.
+    """Surrogate gradient as defined in Yin et al., 2021.
 
     https://www.biorxiv.org/content/10.1101/2021.03.22.436372v2
 
@@ -86,8 +83,7 @@ def __call__(self, v_mem, spike_threshold):
 
 @dataclass
 class SingleExponential:
-    """
-    Surrogate gradient as defined in Shrestha and Orchard, 2018.
+    """Surrogate gradient as defined in Shrestha and Orchard, 2018.
 
     https://papers.nips.cc/paper/2018/hash/82f2b308c3b01637c607ce05f52a2fed-Abstract.html
     """
@@ -106,8 +102,7 @@ def __call__(self, v_mem, spike_threshold):
 
 @dataclass
 class PeriodicExponential:
-    """
-    Surrogate gradient as defined in Weidel and Sheik, 2021.
+    """Surrogate gradient as defined in Weidel and Sheik, 2021.
 
     https://arxiv.org/abs/2111.01456
     """

sinabs/cnnutils.py

@@ -2,15 +2,13 @@
 
 
 def conv_output_size(image_length: int, kernel_length: int, stride: int) -> int:
-    """
-    Computes output dimension given input dimension, kernel size and stride,
-    assumign no padding, *per* dimension given
+    """Computes output dimension given input dimension, kernel size and stride, assumign no
+    padding, *per* dimension given.
 
     :param image_length: int image size on one dimension
     :param kernel_length: int kernel_length size on one dimension
     :param stride: int Stride size on one dimension
     :return: int -- convolved output image size on one dimension
-
     """
     try:
         assert image_length >= kernel_length
@@ -24,8 +22,7 @@ def conv_output_size(image_length: int, kernel_length: int, stride: int) -> int:
 
 
 def compute_same_padding_size(kernel_length: int) -> (int, int):
-    """
-    Computes padding for 'same' padding *per* dimension given
+    """Computes padding for 'same' padding *per* dimension given.
 
     :param kernel_length: int Kernel size
     :returns: Tuple -- (padStart, padStop) , padding on left/right or top/bottom
@@ -40,8 +37,7 @@ def compute_same_padding_size(kernel_length: int) -> (int, int):
 def compute_padding(
     kernel_shape: tuple, input_shape: tuple, mode="valid"
 ) -> (int, int, int, int):
-    """
-    Computes padding for 'same' or 'valid' padding
+    """Computes padding for 'same' or 'valid' padding.
 
     :param kernel_shape: Kernel shape (height, width)
     :param input_shape: Input shape (channels, height, width)
@@ -61,8 +57,7 @@ def compute_padding(
 
 
 def infer_output_shape(torch_layer, input_shape: Tuple) -> Tuple:
-    """
-    Compute the output dimensions given input dimensions
+    """Compute the output dimensions given input dimensions.
 
     :param torch_layer: a Torch layer
     :param input_shape: the shape of the input tensor

sinabs/conversion.py

@@ -6,8 +6,7 @@
 
 
 def replace_module(model: nn.Module, source_class: type, mapper_fn: Callable):
-    """
-    A utility function that returns a copy of the model, where specific layers are replaced with
+    """A utility function that returns a copy of the model, where specific layers are replaced with
     another type depending on the mapper function.
 
     Parameters:
@@ -18,7 +17,7 @@ def replace_module(model: nn.Module, source_class: type, mapper_fn: Callable):
     Returns:
         A model copy with replaced modules according to mapper_fn.
     """
-    
+
     # Handle case where `model` is of type `source_class`
     if type(model) == source_class:
         return mapper_fn(model)
@@ -29,8 +28,7 @@ def replace_module(model: nn.Module, source_class: type, mapper_fn: Callable):
 
 
 def replace_module_(model: nn.Sequential, source_class: type, mapper_fn: Callable):
-    """
-    In-place version of replace_module that will step through modules that have children and
+    """In-place version of replace_module that will step through modules that have children and
     apply the mapper_fn.
 
     Parameters:
@@ -56,4 +54,3 @@ def replace_module_(model: nn.Sequential, source_class: type, mapper_fn: Callabl
 
         if type(module) == source_class:
             setattr(model, name, mapper_fn(module))
-

sinabs/from_torch.py

@@ -27,10 +27,9 @@ def from_model(
     backend=None,
     kwargs_backend: dict = dict(),
 ):
-    """
-    Converts a Torch model and returns a Sinabs network object.
-    The modules in the model are analyzed, and a copy is returned, with all
-    ReLUs and NeuromorphicReLUs turned into SpikingLayers.
+    """Converts a Torch model and returns a Sinabs network object. The modules in the model are
+    analyzed, and a copy is returned, with all ReLUs and NeuromorphicReLUs turned into
+    SpikingLayers.
 
     Parameters:
         model: Torch model

sinabs/layers/alif.py

@@ -11,8 +11,8 @@
 
 
 class ALIF(StatefulLayer):
-    """
-    Adaptive Leaky Integrate and Fire neuron layer that inherits from :class:`~sinabs.layers.StatefulLayer`.
+    """Adaptive Leaky Integrate and Fire neuron layer that inherits from
+    :class:`~sinabs.layers.StatefulLayer`.
 
     Pytorch implementation of a Long Short Term Memory SNN (LSNN) by Bellec et al., 2018:
     https://papers.neurips.cc/paper/2018/hash/c203d8a151612acf12457e4d67635a95-Abstract.html
@@ -210,8 +210,8 @@ def _param_dict(self) -> dict:
 
 
 class ALIFRecurrent(ALIF):
-    """
-    Adaptive Leaky Integrate and Fire neuron layer with recurrent connections which inherits from :class:`~sinabs.layers.ALIF`.
+    """Adaptive Leaky Integrate and Fire neuron layer with recurrent connections which inherits
+    from :class:`~sinabs.layers.ALIF`.
 
     Pytorch implementation of a Long Short Term Memory SNN (LSNN) by Bellec et al., 2018:
     https://papers.neurips.cc/paper/2018/hash/c203d8a151612acf12457e4d67635a95-Abstract.html
@@ -347,8 +347,7 @@ def forward(self, input_data: torch.Tensor):
 
 
 class ALIFSqueeze(ALIF, SqueezeMixin):
-    """
-    ALIF layer with 4-dimensional input (Batch*Time, Channel, Height, Width).
+    """ALIF layer with 4-dimensional input (Batch*Time, Channel, Height, Width).
 
     Same as parent ALIF class, only takes in squeezed 4D input (Batch*Time, Channel, Height, Width)
     instead of 5D input (Batch, Time, Channel, Height, Width) in order to be compatible with
@@ -375,10 +374,8 @@ def __init__(
         self.squeeze_init(batch_size, num_timesteps)
 
     def forward(self, input_data: torch.Tensor) -> torch.Tensor:
-        """
-        Forward call wrapper that will flatten the input to and
-        unflatten the output from the super class forward call.
-        """
+        """Forward call wrapper that will flatten the input to and unflatten the output from the
+        super class forward call."""
         return self.squeeze_forward(input_data, super().forward)
 
     @property

sinabs/layers/crop2d.py

@@ -7,8 +7,7 @@
 
 
 class Cropping2dLayer(nn.Module):
-    """
-    Crop input image by
+    """Crop input image by.
 
     Parameters:
         cropping: ((top, bottom), (left, right))
@@ -37,8 +36,7 @@ def forward(self, binary_input):
         return crop_out
 
     def get_output_shape(self, input_shape: Tuple) -> Tuple:
-        """
-        Retuns the output dimensions
+        """Retuns the output dimensions.
 
         Parameters:
             input_shape: (channels, height, width)

sinabs/layers/exp_leak.py

@@ -7,8 +7,8 @@
 
 
 class ExpLeak(LIF):
-    """
-    Leaky Integrator layer which is a special case of :class:`~sinabs.layers.LIF` without activation function.
+    """Leaky Integrator layer which is a special case of :class:`~sinabs.layers.LIF` without
+    activation function.
 
     Neuron dynamics in discrete time:
 
@@ -61,8 +61,7 @@ def _param_dict(self) -> dict:
 
 
 class ExpLeakSqueeze(ExpLeak, SqueezeMixin):
-    """
-    ExpLeak layer with 4-dimensional input (Batch*Time, Channel, Height, Width).
+    """ExpLeak layer with 4-dimensional input (Batch*Time, Channel, Height, Width).
 
     Same as parent ExpLeak class, only takes in squeezed 4D input (Batch*Time, Channel, Height, Width)
     instead of 5D input (Batch, Time, Channel, Height, Width) in order to be compatible with
@@ -82,10 +81,8 @@ def __init__(self, batch_size=None, num_timesteps=None, **kwargs):
         self.squeeze_init(batch_size, num_timesteps)
 
     def forward(self, input_data: torch.Tensor) -> torch.Tensor:
-        """
-        Forward call wrapper that will flatten the input to and
-        unflatten the output from the super class forward call.
-        """
+        """Forward call wrapper that will flatten the input to and unflatten the output from the
+        super class forward call."""
         return self.squeeze_forward(input_data, super().forward)
 
     @property