ran linter

Author: eodole

bayesflow/adapters/transforms/as_set.py

@@ -5,18 +5,18 @@
 
 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 is useful, for example, in a linear regression context where we can index
-        the observations in arbitrary order and always get the same regression line.
-        
-        Useage:
-
-        adapter = (
-            bf.Adapter()
-            .as_set(["x", "y"])
-            )
+    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 is useful, for example, in a linear regression context where we can index
+    the observations in arbitrary order and always get the same regression line.
+
+    Useage:
+
+    adapter = (
+        bf.Adapter()
+        .as_set(["x", "y"])
+        )
     """
 
     def forward(self, data: np.ndarray, **kwargs) -> np.ndarray:

bayesflow/adapters/transforms/constrain.py

@@ -41,7 +41,7 @@ class Constrain(ElementwiseTransform):
             .constrain("sigma", lower=0)
             )
 
-        2 ) Suppose p is the parameter for a binomial distribution where p must be in 
+        2 ) 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:

bayesflow/adapters/transforms/keep.py

@@ -12,33 +12,33 @@
 @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;
+        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;
 
-            adapter = (
-                bf.adapters.Adapter()
-                    # drop data from unneeded priors alpha, and r
-                    # only keep theta and x
-                    .keep(("theta", "x"))
-                )
+        adapter = (
+            bf.adapters.Adapter()
+                # drop data from unneeded priors alpha, and r
+                # only keep theta and x
+                .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"))
-        >>> keeper.forward(dat)
-        {'b': [[1, 2], [3, 4]], 'c': [[5, 6, 7, 8]]}
+    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]]}
 
     """