Added AsinhNorm for colorscale support

Author: rwpenney

examples/images_contours_and_fields/colormap_normalizations_symlognorm.py

@@ -25,7 +25,7 @@
 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
 Z1 = np.exp(-X**2 - Y**2)
 Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
-Z = (Z1 - Z2) * 2
+Z = (5 * Z1 - Z2) * 2
 
 fig, ax = plt.subplots(2, 1)
 

lib/matplotlib/colors.py

@@ -1652,6 +1652,27 @@ def linthresh(self, value):
         self._scale.linthresh = value
 
 
+@make_norm_from_scale(
+    scale.AsinhScale,
+    init=lambda linear_width=1, vmin=None, vmax=None, clip=False: None)
+class AsinhNorm(Normalize):
+    """
+    The inverse hyperbolic sine scale is approximately linear near
+    the origin, but becomes logarithmic for larger positive
+    or negative values. Unlike the `SymLogNorm`, the transition between
+    these linear and logarithmic regions is smooth, which may reduce
+    the risk of visual artifacts.
+    """
+
+    @property
+    def linear_width(self):
+        return self._scale.linear_width
+
+    @linear_width.setter
+    def linear_width(self, value):
+        self._scale.linear_width = value
+
+
 class PowerNorm(Normalize):
     """
     Linearly map a given value to the 0-1 range and then apply

lib/matplotlib/scale.py

@@ -519,7 +519,8 @@ class AsinhScale(ScaleBase):
         10: (2, 5),
         16: (2, 4, 8),
         64: (4, 16),
-        1024: (256, 512) }
+        1024: (256, 512)
+    }
 
     def __init__(self, axis, *, linear_width=1.0,
                  base=10, subs='auto', **kwargs):

lib/matplotlib/tests/test_colors.py

@@ -745,6 +745,28 @@ def test_SymLogNorm_single_zero():
     plt.close(fig)
 
 
+class TestAsinhNorm:
+    """
+    Tests for `~.colors.AsinhNorm`
+    """
+
+    def test_init(self):
+        norm0 = mcolors.AsinhNorm()
+        assert norm0.linear_width == 1
+
+        norm5 = mcolors.AsinhNorm(linear_width=5)
+        assert norm5.linear_width == 5
+
+    def test_norm(self):
+        norm = mcolors.AsinhNorm(2, vmin=-4, vmax=4)
+        vals = np.arange(-3.5, 3.5, 10)
+        normed_vals = norm(vals)
+        asinh2 = np.arcsinh(2)
+
+        expected = (2 * np.arcsinh(vals / 2) + 2 * asinh2) / (4 * asinh2)
+        assert_array_almost_equal(normed_vals, expected)
+
+
 def _inverse_tester(norm_instance, vals):
     """
     Checks if the inverse of the given normalization is working.

lib/matplotlib/tests/test_ticker.py

@@ -549,7 +549,6 @@ def test_base_rounding(self):
                              0.2, 1, 5, 25, 125, 625])
 
 
-
 class TestScalarFormatter:
     offset_data = [
         (123, 189, 0),

lib/matplotlib/ticker.py

@@ -2664,7 +2664,7 @@ def tick_values(self, vmin, vmax):
         if self.base > 1:
             log_base = math.log(self.base)
             powers = (
-                np.where(zero_xs, 0, np.where(xs >=0, 1, -1)) *
+                np.where(zero_xs, 0, np.where(xs >= 0, 1, -1)) *
                 np.power(self.base,
                          np.where(zero_xs, 0.0,
                                   np.floor(np.log(np.abs(xs) + zero_xs*1e-6)
@@ -2684,7 +2684,7 @@ def tick_values(self, vmin, vmax):
             qs = powers * np.round(xs / powers)
         ticks = np.array(sorted(set(qs)))
 
-        if len(ticks) >=  2:
+        if len(ticks) >= 2:
             return ticks
         else:
             return np.linspace(vmin, vmax, self.numticks)