Tweaked indendation etc. following review by @QuLogic

Author: rwpenney

doc/users/next_whats_new/asinh_scale.rst

@@ -10,10 +10,10 @@ of magnitude.
 .. plot::
 
     import matplotlib.pyplot as plt
-    import numpy
+    import numpy as np
 
     fig, (ax0, ax1) = plt.subplots(1, 2, sharex=True)
-    x = numpy.linspace(-3, 6, 100)
+    x = np.linspace(-3, 6, 100)
 
     ax0.plot(x, x)
     ax0.set_yscale('symlog')
@@ -26,7 +26,7 @@ of magnitude.
     ax1.set_title(r'$sinh^{-1}$')
 
     for p in (-2, 2):
-      for ax in (ax0, ax1):
-        c = plt.Circle((p, p), radius=0.5, fill=False,
-                       color='red', alpha=0.8, lw=3)
-        ax.add_patch(c)
+        for ax in (ax0, ax1):
+            c = plt.Circle((p, p), radius=0.5, fill=False,
+                           color='red', alpha=0.8, lw=3)
+            ax.add_patch(c)

examples/images_contours_and_fields/colormap_normalizations_symlognorm.py

@@ -8,7 +8,7 @@
 .. redirect-from:: /gallery/userdemo/colormap_normalization_symlognorm
 """
 
-########################################
+###############################################################################
 # Synthetic dataset consisting of two humps, one negative and one positive,
 # the positive with 8-times the amplitude.
 # Linearly, the negative hump is almost invisible,
@@ -52,7 +52,7 @@ def rbf(x, y):
 ax[1].text(-2.5, 1.5, 'linear')
 
 
-########################################
+###############################################################################
 # 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