ruff fixes hopefully passes the linter, but weird stuff going on in t…

…he broadcast documentation

bayesflow/adapters/transforms/as_set.py

@@ -5,12 +5,13 @@
 
 class AsSet(ElementwiseTransform):
     """
-    The `.as_set(["x", "y"])` transform indicates that both `x` and `y` are treated as sets. 
-    That is, their values will be treated as *exchangable* such that they will imply the same inference regardless of the values' order. 
-    This would be useful in a linear regression context where we can index the observations in arbitrary order and always get the same regression line.
+    The `.as_set(["x", "y"])` transform indicates that both `x` and `y` are treated as sets.
+    That is, their values will be treated as *exchangable* such that they will imply the same inference regardless of 
+    the values' order. This would be useful in a linear regression context where we can index the observations in 
+    arbitrary order and always get the same regression line.
+
+    Useage:
 
-    Useage: 
-    
     adapter = (
         bf.Adapter()
         .as_set(["x", "y"])

bayesflow/adapters/transforms/concatenate.py

@@ -12,17 +12,17 @@
 
 @serializable(package="bayesflow.adapters")
 class Concatenate(Transform):
-    """Concatenate multiple arrays into a new key. Used to specify how data variables should be treated by the network. 
+    """Concatenate multiple arrays into a new key. Used to specify how data variables should be treated by the network.
 
-    Parameters: 
+    Parameters:
         keys: Input a list of strings, where the strings are the names of data variables.
-        into: A string telling network how to use the variables named in keys. 
+        into: A string telling network how to use the variables named in keys.
             -options: "inference_variables", "summary_variables", "inference_conditions"
-        axis: integer specifing along which axis to concatonate the keys. The last axis is used by default. 
+        axis: integer specifing along which axis to concatonate the keys. The last axis is used by default.
 
-    Example: 
-    Suppose you have a simulator that generates variables beta, sigma from priors  and then observation variables "x" and "y".
-    We can then use concatonate in the following way
+    Example:
+    Suppose you have a simulator that generates variables beta, sigma from priors  and then observation 
+    variables "x" and "y". We can then use concatonate in the following way
 
     adapter = (
         bf.Adapter()

bayesflow/adapters/transforms/constrain.py

@@ -16,33 +16,34 @@
 @serializable(package="bayesflow.adapters")
 class Constrain(ElementwiseTransform):
     """
-    Constrains neural network predictions of a data variable to specificied bounds. 
-    
-    Parameters: 
-        String containing the name of the data variable to be transformed e.g. "sigma". See examples below. 
+    Constrains neural network predictions of a data variable to specificied bounds.
 
-    Named Parameters: 
+    Parameters:
+        String containing the name of the data variable to be transformed e.g. "sigma". See examples below.
+
+    Named Parameters:
         lower: Lower bound for named data variable.
         upper: Upper bound for named data variable.
-        method: Method by which to shrink the network predictions space to specified bounds. Choose from 
+        method: Method by which to shrink the network predictions space to specified bounds. Choose from
             - Double bounded methods: sigmoid, expit, (default = sigmoid)
             - Lower bound only methods: softplus, exp, (default = softplus)
             - Upper bound only methods: softplus, exp, (default = softplus)
-        
 
 
-    Examples: 
-        Let sigma be the standard deviation of a normal distribution, then sigma should always be greater than zero. 
 
-        Useage: 
+    Examples:
+        Let sigma be the standard deviation of a normal distribution, then sigma should always be greater than zero.
+
+        Useage:
         adapter = (
             bf.Adapter()
             .constrain("sigma", lower=0)
             )
 
-        Suppose p is the parameter for a binomial distribution where p must be in [0,1] then we would constrain the neural network to estimate p in the following way
+        Suppose p is the parameter for a binomial distribution where p must be in [0,1] then we would constrain the 
+        neural network to estimate p in the following way
 
-        Usage: 
+        Usage:
         adapter = (
             bf.Adapter()
             .constrain("p", lower=0, upper=1, method = "sigmoid")

bayesflow/adapters/transforms/convert_dtype.py

@@ -11,8 +11,9 @@
 @serializable(package="bayesflow.adapters")
 class ConvertDType(ElementwiseTransform):
     """
-    Default transform used to convert all floats from float64 to float32 to be in line with keras framework. 
+    Default transform used to convert all floats from float64 to float32 to be in line with keras framework.
     """
+
     def __init__(self, from_dtype: str, to_dtype: str):
         super().__init__()
 

bayesflow/adapters/transforms/drop.py

@@ -11,22 +11,22 @@
 
 @serializable(package="bayesflow.adapters")
 class Drop(Transform):
-    """ 
-    Transform to drop variables from further calculation. 
+    """
+    Transform to drop variables from further calculation.
+
+    Parameters:
+        keys: list of strings, containing names of data variables that should be dropped
 
-    Parameters: 
-        keys: list of strings, containing names of data variables that should be dropped 
-    
-    Example: 
+    Example:
 
     >>> import bayesflow as bf
-    >>> a = [1,2,3,4]
-    >>> b = [[1,2],[3,4]]
-    >>> c = [[5,6,7,8]]
-    >>> dat = dict(a=a,b=b,c=c)
+    >>> a = [1, 2, 3, 4]
+    >>> b = [[1, 2], [3, 4]]
+    >>> c = [[5, 6, 7, 8]]
+    >>> dat = dict(a=a, b=b, c=c)
     >>> dat
         {'a': [1, 2, 3, 4], 'b': [[1, 2], [3, 4]], 'c': [[5, 6, 7, 8]]}
-    >>> drop = bf.adapters.transforms.Drop(("b","c"))
+    >>> drop = bf.adapters.transforms.Drop(("b", "c"))
     >>> drop.forward(dat)
         {'a': [1, 2, 3, 4]}
     """

bayesflow/adapters/transforms/elementwise_transform.py

@@ -4,7 +4,6 @@
 
 @serializable(package="bayesflow.adapters")
 class ElementwiseTransform:
-
     """Base class on which other transforms are based"""
 
     def __call__(self, data: np.ndarray, inverse: bool = False, **kwargs) -> np.ndarray:

bayesflow/adapters/transforms/keep.py

@@ -12,35 +12,36 @@
 @serializable(package="bayesflow.adapters")
 class Keep(Transform):
     """
-    Name the data parameters that should be kept for futher calculation. 
+    Name the data parameters that should be kept for futher calculation.
 
-    Parameters: 
+    Parameters:
 
-        cls: tuple containing the names of kept data variables as strings. 
+        cls: tuple containing the names of kept data variables as strings.
 
-    Useage: 
+    Useage:
 
         Two moons simulator generates data for priors alpha, r and theta as well as observation data x.
-        We are interested only in theta and x, to keep only theta and x we should use the following; 
+        We are interested only in theta and x, to keep only theta and x we should use the following;
 
         adapter = (
             bf.adapters.Adapter()
 
-            # drop data from unneeded priors alpha, and r 
+            # drop data from unneeded priors alpha, and r
             .keep(("theta", "x"))
             )
 
-    Example: 
-    >>> a = [1,2,3,4]
-    >>> b = [[1,2],[3,4]]
-    >>> c = [[5,6,7,8]]
-    >>> dat = dict(a=a,b=b,c =c)
-    # Here we want to only keep elements b and c 
-    >>> keeper = bf.adapters.transforms.Keep(("b","c"))
+    Example:
+    >>> a = [1, 2, 3, 4]
+    >>> b = [[1, 2], [3, 4]]
+    >>> c = [[5, 6, 7, 8]]
+    >>> dat = dict(a=a, b=b, c=c)
+    # Here we want to only keep elements b and c
+    >>> keeper = bf.adapters.transforms.Keep(("b", "c"))
     >>> keeper.forward(dat)
     {'b': [[1, 2], [3, 4]], 'c': [[5, 6, 7, 8]]}
 
     """
+
     def __init__(self, keys: Sequence[str]):
         self.keys = keys
 

bayesflow/adapters/transforms/to_array.py

@@ -13,20 +13,19 @@
 class ToArray(ElementwiseTransform):
     """
     Checks provided data for any non-arrays and converts them to numpy arrays.
-    This ensures all data is in a format suitable for training. 
+    This ensures all data is in a format suitable for training.
 
-    Example: 
+    Example:
     >>> ta = bf.adapters.transforms.ToArray()
-    >>> a = [1,2,3,4]
+    >>> a = [1, 2, 3, 4]
     >>> ta.forward(a)
         array([1, 2, 3, 4])
-    >>> b = [[1,2],[3,4]]
+    >>> b = [[1, 2], [3, 4]]
     >>> ta.forward(b)
         array([[1, 2],
             [3, 4]])
     """
 
-
     def __init__(self):
         super().__init__()
         self.original_type = None