Apply suggestions from code review by greglucas

Co-authored-by: Greg Lucas <[email protected]>

Author: rwpenney

doc/users/next_whats_new/asinh_scale.rst

@@ -4,13 +4,13 @@ New axis scale ``asinh``
 The new ``asinh`` axis scale offers an alternative to ``symlog`` that
 smoothly transitions between the quasi-linear and asymptotically logarithmic
 regions of the scale. This is based on an arcsinh transformation that
-allows plotting both positive and negative values than span many orders
+allows plotting both positive and negative values that span many orders
 of magnitude. A scale parameter is provided to allow the user
 to tune the width of the linear region of the scale.
 
 .. plot::
 
-    from matplotlib import pyplot as plt
+    import matplotlib.pyplot as plt
     import numpy
 
     fig, (ax0, ax1) = plt.subplots(1, 2, sharex=True)

examples/scales/asinh_demo.py

@@ -31,7 +31,7 @@
 from *separate* linear and logarithmic transformations.
 The ``asinh`` scaling uses a transformation that is smooth
 for all (finite) values, which is both mathematically cleaner
-and should reduce visual artifacts associated with an abrupt
+and reduces visual artifacts associated with an abrupt
 transition between linear and logarithmic regions of the plot.
 
 See `~.scale.AsinhScale`, `~.scale.SymmetricalLogScale`.
@@ -45,7 +45,7 @@
 
 ########################################
 # Compare "symlog" and "asinh" behaviour on sample y=x graph,
-# where the discontinuous gradient in "symlog" near y=2 is obvious:
+# where there is a discontinuous gradient in "symlog" near y=2:
 fig1 = plt.figure()
 ax0, ax1 = fig1.subplots(1, 2, sharex=True)
 

lib/matplotlib/scale.py

@@ -465,7 +465,7 @@ class AsinhTransform(Transform):
     def __init__(self, linear_width):
         super().__init__()
         if linear_width <= 0.0:
-            raise ValueError("Scale parameter 'a0' must be strictly positive")
+            raise ValueError("Scale parameter 'linear_width' must be strictly positive")
         self.linear_width = linear_width
 
     def transform_non_affine(self, a):