ran linter

Author: eodole

bayesflow/adapters/transforms/standardize.py

@@ -16,7 +16,8 @@ class Standardize(ElementwiseTransform):
 
     z = (x - mean(x))/std(x)
 
-    Important to note that when specifying a mean and sd user should be careful to specify which variables should be standardized. Please see examples. 
+    Important to note that when specifying a mean and sd user should be careful to specify which variables should
+    be standardized. Please see examples.
 
     Parameters:
     mean: integer or float used to specify a mean if known but will be estimated from data when not provided
@@ -25,31 +26,35 @@ class Standardize(ElementwiseTransform):
         standardization happens individually for each dimension
     momentum: float in (0,1) specifying the momentum during training
 
-    Examples: 
+    Examples:
+
+    1) Standardize all variables using estimated mean and standard deviation
 
-    1) Standardize all variables using estimated mean and standard deviation 
- 
         adapter = (
                 bf.adapters.Adapter()
                     .standardize()
             )
- 
 
-    2) Standardize all with same known mean and standard deviation. In this example all data is drawn from a standard normal
+
+    2) Standardize all with same known mean and standard deviation. In this example all data is drawn from a
+    standard normal
 
         adapter = (
                 bf.adapters.Adapter()
                     .standardize(mean = 1, sd = 0)
             )
- 
 
-    3) Mix of specified and auto-computed means/sds. Suppose we have priors for "beta" and "sigma" where we know the mean and standard deviations. However for our simulated data "x" and "y" the mean and standard deviations are unknown. Then standardize should be used in several stages specifying which variables to include or exclude. 
- 
+
+    3) Mix of specified and auto-computed means/sds. Suppose we have priors for "beta" and "sigma" where we
+    know the mean and standard deviations. However for our simulated data "x" and "y" the mean and standard
+    deviations are unknown. Then standardize should be used in several stages specifying which variables to
+    include or exclude.
+
         adapter = (
                 bf.adapters.Adapter()
                     .standardize(include = "beta", mean = 1)   # specify only mean/sd
-                    .standardize(include = "sigma", mean = 0.6, sd = 1) # specify both mean and sd 
-                    .standardize(exclude = ["beta", "sigma"])  # specify neither mean nor sd 
+                    .standardize(include = "sigma", mean = 0.6, sd = 1) # specify both mean and sd
+                    .standardize(exclude = ["beta", "sigma"])  # specify neither mean nor sd
             )
 
     """