Format Python code with psf/black push (#161)

There appear to be some python formatting errors in
5d604cf. This pull request
uses the [psf/black](https://github.com/psf/black) formatter to fix
these issues.

dacapo/compute_context/local_torch.py

@@ -4,15 +4,16 @@
 
 from typing import Optional
 
+
 @attr.s
 class LocalTorch(ComputeContext):
     """
-    The LocalTorch class is a subclass of the ComputeContext class. 
+    The LocalTorch class is a subclass of the ComputeContext class.
     It is used to specify the context in which computations are to be done.
     LocalTorch is used to specify that computations are to be done on the local machine using PyTorch.
-    
+
     Attributes:
-        _device (Optional[str]): This stores the type of device on which torch computations are to be done. It can 
+        _device (Optional[str]): This stores the type of device on which torch computations are to be done. It can
         take "cuda" for GPU or "cpu" for CPU. None value results in automatic detection of device type.
     """
 
@@ -27,7 +28,7 @@ class LocalTorch(ComputeContext):
     @property
     def device(self):
         """
-        A property method that returns the torch device object. It automatically detects and uses "cuda" (GPU) if 
+        A property method that returns the torch device object. It automatically detects and uses "cuda" (GPU) if
         available, else it falls back on using "cpu".
         """
         if self._device is None:

dacapo/experiments/architectures/architecture.py

@@ -7,11 +7,12 @@
 
 class Architecture(torch.nn.Module, ABC):
     """
-    An abstract base class for defining the architecture of a neural network model. 
+    An abstract base class for defining the architecture of a neural network model.
     It is inherited from PyTorch's Module and built-in class `ABC` (Abstract Base Classes).
     Other classes can inherit this class to define their own specific variations of architecture.
     It requires to implement several property methods, and also includes additional methods related to the architecture design.
     """
+
     @property
     @abstractmethod
     def input_shape(self) -> Coordinate:
@@ -60,9 +61,9 @@ def num_out_channels(self) -> int:
     def dims(self) -> int:
         """
         Returns the number of dimensions of the input shape.
-        
+
         Returns:
-            int: The number of dimensions. 
+            int: The number of dimensions.
         """
         return self.input_shape.dims
 
@@ -76,4 +77,4 @@ def scale(self, input_voxel_size: Coordinate) -> Coordinate:
         Returns:
             Coordinate: The scaled voxel size.
         """
-        return input_voxel_size
+        return input_voxel_size

dacapo/experiments/architectures/architecture_config.py

@@ -18,6 +18,7 @@ class ArchitectureConfig:
         validates the given architecture.
 
     """
+
     name: str = attr.ib(
         metadata={
             "help_text": "A unique name for this architecture. This will be saved so "
@@ -37,4 +38,4 @@ def verify(self) -> Tuple[bool, str]:
         str
             A description of the architecture.
         """
-        return True, "No validation for this Architecture"
+        return True, "No validation for this Architecture"

dacapo/experiments/architectures/dummy_architecture.py

@@ -5,14 +5,14 @@
 
 class DummyArchitecture(Architecture):
     """
-    A class used to represent a dummy architecture layer for a 3D CNN. 
-    
+    A class used to represent a dummy architecture layer for a 3D CNN.
+
     Attributes:
         channels_in: An integer representing the number of input channels.
         channels_out: An integer representing the number of output channels.
         conv: A 3D convolution object.
         input_shape: A coordinate object representing the shape of the input.
-        
+
     Methods:
         forward(x): Performs the forward pass of the network.
     """
@@ -33,7 +33,7 @@ def __init__(self, architecture_config):
     def input_shape(self):
         """
         Returns the input shape for this architecture.
-        
+
         Returns:
             Coordinate: Input shape of the architecture.
         """
@@ -43,7 +43,7 @@ def input_shape(self):
     def num_in_channels(self):
         """
         Returns the number of input channels for this architecture.
-        
+
         Returns:
             int: Number of input channels.
         """
@@ -53,7 +53,7 @@ def num_in_channels(self):
     def num_out_channels(self):
         """
         Returns the number of output channels for this architecture.
-        
+
         Returns:
             int: Number of output channels.
         """
@@ -62,11 +62,11 @@ def num_out_channels(self):
     def forward(self, x):
         """
         Perform the forward pass of the network.
-        
+
         Args:
             x: Input tensor.
-            
+
         Returns:
             Tensor: Output tensor after the forward pass.
         """
-        return self.conv(x)
+        return self.conv(x)

dacapo/experiments/architectures/dummy_architecture_config.py

@@ -9,12 +9,12 @@
 @attr.s
 class DummyArchitectureConfig(ArchitectureConfig):
     """A dummy architecture configuration class used for testing purposes.
-    
+
     It extends the base class "ArchitectureConfig". This class contains dummy attributes and always
     returns that the configuration is invalid when verified.
 
     Attributes:
-        architecture_type (:obj:`DummyArchitecture`): A class attribute assigning 
+        architecture_type (:obj:`DummyArchitecture`): A class attribute assigning
             the DummyArchitecture class to this configuration.
         num_in_channels (int): The number of input channels. This is a dummy attribute and has no real
             functionality or meaning.
@@ -37,5 +37,5 @@ def verify(self) -> Tuple[bool, str]:
         Returns:
             tuple: A tuple containing a boolean validity flag and a reason message string.
         """
-  
-        return False, "This is a DummyArchitectureConfig and is never valid"
+
+        return False, "This is a DummyArchitectureConfig and is never valid"

dacapo/experiments/arraytypes/arraytype.py

@@ -15,10 +15,10 @@ class ArrayType(ABC):
     @abstractmethod
     def interpolatable(self) -> bool:
         """
-        This is an abstract method which should be overridden in each of the subclasses 
+        This is an abstract method which should be overridden in each of the subclasses
         to determine if an array is interpolatable or not.
 
         Returns:
             bool: True if the array is interpolatable, False otherwise.
         """
-        pass
+        pass

dacapo/experiments/arraytypes/binary.py

@@ -31,8 +31,8 @@ class BinaryArray(ArrayType):
     def interpolatable(self) -> bool:
         """
         This function returns the interpolatable property value of the binary array.
-        
+
         Returns:
             bool: Always returns False because interpolation is not possible.
         """
-        return False
+        return False

dacapo/experiments/arraytypes/mask.py

@@ -2,16 +2,18 @@
 
 import attr
 
+
 @attr.s
 class Mask(ArrayType):
     """
     A class that inherits the ArrayType class. This is a representation of a Mask in the system.
-    
+
     Methods
     -------
     interpolatable():
         It is a method that returns False.
     """
+
     @property
     def interpolatable(self) -> bool:
         """
@@ -22,4 +24,4 @@ def interpolatable(self) -> bool:
         bool
             Returns a boolean value of False representing that the values are not interpolatable.
         """
-        return False
+        return False

dacapo/experiments/arraytypes/probabilities.py

@@ -6,11 +6,11 @@
 @attr.s
 class ProbabilityArray(ArrayType):
     """
-    Class to represent an array containing probability distributions for each voxel pointed by its coordinate. 
+    Class to represent an array containing probability distributions for each voxel pointed by its coordinate.
 
-    The class defines a ProbabilityArray object with each voxel having a vector of length `c`, where `c` is the 
+    The class defines a ProbabilityArray object with each voxel having a vector of length `c`, where `c` is the
     number of classes. The l1 norm of this vector should always be 1. The class of each voxel can be
-    determined by simply taking the argmax. 
+    determined by simply taking the argmax.
 
     Attributes:
         classes (List[str]): A mapping from channel to class on which distances were calculated.
@@ -30,4 +30,4 @@ def interpolatable(self) -> bool:
         Returns:
             bool: True indicating that the data can be interpolated.
         """
-        return True
+        return True

dacapo/experiments/datasplits/datasets/dataset.py

@@ -3,6 +3,7 @@
 from abc import ABC
 from typing import Optional, Any, List
 
+
 class Dataset(ABC):
     """
     A class to represent a dataset.
@@ -15,7 +16,7 @@ class Dataset(ABC):
         weight (int, optional): The weight of the dataset.
         sample_points (list[Coordinate], optional): The list of sample points in the dataset.
     """
-    
+
     name: str
     raw: Array
     gt: Optional[Array]
@@ -49,7 +50,7 @@ def __repr__(self) -> str:
         Returns the official string representation of the dataset object.
 
         Returns:
-            str: String representation of the dataset. 
+            str: String representation of the dataset.
         """
         return f"Dataset({self.name})"
 
@@ -58,7 +59,7 @@ def __str__(self) -> str:
         Returns the string representation of the dataset object.
 
         Returns:
-            str: String representation of the dataset. 
+            str: String representation of the dataset.
         """
         return f"Dataset({self.name})"
 
@@ -93,4 +94,4 @@ def _neuroglancer_layers(self, prefix="", exclude_layers=None):
             and self.mask._source_name() not in exclude_layers
         ):
             layers[self.mask._source_name()] = self.mask._neuroglancer_layer()
-        return layers
+        return layers

dacapo/experiments/datasplits/datasets/dataset_config.py

@@ -5,26 +5,26 @@
 
 @attr.s
 class DatasetConfig:
-    """A class used to define configuration for datasets. This provides the 
+    """A class used to define configuration for datasets. This provides the
     framework to create a Dataset instance.
 
     Attributes:
         name: str (eg: "sample_dataset").
-            A unique identifier to name the dataset. 
-            It aids in easy identification and reusability of this dataset. 
+            A unique identifier to name the dataset.
+            It aids in easy identification and reusability of this dataset.
             Advised to keep it short and refrain from using special characters.
-        
+
         weight: int (default=1).
-            A numeric value that indicates how frequently this dataset should be 
-            sampled in comparison to others. Higher the weight, more frequently it 
+            A numeric value that indicates how frequently this dataset should be
+            sampled in comparison to others. Higher the weight, more frequently it
             gets sampled.
 
     Methods:
-        verify: 
-            Checks and validates the dataset configuration. The specific rules for 
+        verify:
+            Checks and validates the dataset configuration. The specific rules for
             validation need to be defined by the user.
     """
-    
+
     name: str = attr.ib(
         metadata={
             "help_text": "A unique name for this dataset. This will be saved so you "
@@ -44,12 +44,12 @@ def verify(self) -> Tuple[bool, str]:
         """
         Method to verify the dataset configuration.
 
-        Since there is no specific validation logic defined for this DataSet, this 
-        method will always return True as default reaction and a message stating 
+        Since there is no specific validation logic defined for this DataSet, this
+        method will always return True as default reaction and a message stating
         the lack of validation.
 
         Returns:
-            tuple: A tuple of boolean value indicating the check (True or False) and 
+            tuple: A tuple of boolean value indicating the check (True or False) and
             message specifying result of validation.
         """
-        return True, "No validation for this DataSet"
+        return True, "No validation for this DataSet"

dacapo/experiments/datasplits/datasets/dummy_dataset.py

@@ -1,13 +1,14 @@
 from .dataset import Dataset
 from .arrays import Array
 
+
 class DummyDataset(Dataset):
     """DummyDataset is a child class of the Dataset. This class has property 'raw' of Array type and a name.
 
     Args:
         dataset_config (object): an instance of a configuration class.
     """
-    
+
     raw: Array
 
     def __init__(self, dataset_config):
@@ -19,4 +20,4 @@ def __init__(self, dataset_config):
         """
         super().__init__()
         self.name = dataset_config.name
-        self.raw = dataset_config.raw_config.array_type(dataset_config.raw_config)
+        self.raw = dataset_config.raw_config.array_type(dataset_config.raw_config)

dacapo/experiments/datasplits/datasets/dummy_dataset_config.py

@@ -30,5 +30,5 @@ def verify(self) -> Tuple[bool, str]:
         Returns:
             A tuple of False and a message indicating the invalidity.
         """
-        
+
         return False, "This is a DummyDatasetConfig and is never valid"

dacapo/experiments/datasplits/datasets/raw_gt_dataset_config.py

@@ -20,14 +20,15 @@ class RawGTDatasetConfig(DatasetConfig):
     will be extracted.
 
     Attributes:
-        dataset_type (class): The type of dataset that is being configured. 
-        raw_config (Optional[ArrayConfig]): Configuration for the raw data associated with this dataset. 
-        gt_config (Optional[ArrayConfig]): Configuration for the ground truth data associated with this dataset. 
-        mask_config (Optional[ArrayConfig]): An optional mask configuration that sets the loss 
+        dataset_type (class): The type of dataset that is being configured.
+        raw_config (Optional[ArrayConfig]): Configuration for the raw data associated with this dataset.
+        gt_config (Optional[ArrayConfig]): Configuration for the ground truth data associated with this dataset.
+        mask_config (Optional[ArrayConfig]): An optional mask configuration that sets the loss
                                              equal to zero on voxels where the mask is 1.
-        sample_points (Optional[List[Coordinate]]): An optional list of points around which 
+        sample_points (Optional[List[Coordinate]]): An optional list of points around which
                                                     training samples will be extracted.
     """
+
     dataset_type = RawGTDataset
 
     raw_config: Optional[ArrayConfig] = attr.ib(
@@ -53,4 +54,4 @@ class RawGTDatasetConfig(DatasetConfig):
             "help_text": "An optional list of points around which training samples will be "
             "extracted."
         },
-    )
+    )