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

Author: rwpenney

examples/images_contours_and_fields/colormap_normalizations_symlognorm.py

@@ -53,13 +53,12 @@ def rbf(x, y):
 
 
 ########################################
-# Clearly, tt may be necessary to experiment with multiple different
-# colorscales in order to find the best visualization for
-# any particular dataset.
+# In order to find the best visualization for any particular dataset,
+# it may be necessary to experiment with multiple different color scales.
 # As well as the `~.colors.SymLogNorm` scaling, there is also
 # the option of using the `~.colors.AsinhNorm`, which has a smoother
 # transition between the linear and logarithmic regions of the transformation
-# applied to the "z" axis.
+# applied to the data values, "Z".
 # In the plots below, it may be possible to see contour-like artifacts
 # around each hump despite there being no sharp features
 # in the dataset itself. The ``asinh`` scaling shows a smoother shading

lib/matplotlib/scale.py

@@ -477,7 +477,7 @@ def inverted(self):
 
 
 class InvertedAsinhTransform(Transform):
-    """Hyperbolic-sine transformation used by `.AsinhScale`"""
+    """Hyperbolic sine transformation used by `.AsinhScale`"""
     input_dims = output_dims = 1
 
     def __init__(self, linear_width):
@@ -496,7 +496,7 @@ class AsinhScale(ScaleBase):
     A quasi-logarithmic scale based on the inverse hyperbolic sine (asinh)
 
     For values close to zero, this is essentially a linear scale,
-    but for larger values (either positive or negative) it is asymptotically
+    but for large magnitude values (either positive or negative) it is asymptotically
     logarithmic. The transition between these linear and logarithmic regimes
     is smooth, and has no discontinuities in the function gradient
     in contrast to the `.SymmetricalLogScale` ("symlog") scale.