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Re-write sym-log-norm#16391

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dstansby wants to merge8 commits intomatplotlib:masterfromdstansby:symlog-overhaul
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12 changes: 11 additions & 1 deletiondoc/api/prev_api_changes/api_changes_3.2.0/behavior.rst
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Original file line numberDiff line numberDiff line change
Expand Up@@ -2,9 +2,19 @@
Behavior changes
----------------

Fixed calculations in `.SymLogNorm`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The symmetrical log normalizer was previously returning erroneous values, so has
be re-written to give the correct behaviour. Aside from returning different
values:

- The base in which the number of decades in the log range is calculated can
now be specified by the ``base`` keyword argument, which defaults to 10.
- The behaviour when passing both ``vmin`` and ``vmax`` which were not negative
of each other is ill-defined, so this has been disallowed.

Reduced default value of :rc:`axes.formatter.limits`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Changed the default value of :rc:`axes.formatter.limits` from -7, 7 to
-5, 6 for better readability.

Expand Down
92 changes: 55 additions & 37 deletionslib/matplotlib/colors.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -1214,8 +1214,8 @@ class SymLogNorm(Normalize):
(-*linthresh*, *linthresh*).
"""
def __init__(self, linthresh, linscale=1.0,
vmin=None, vmax=None, clip=False):
"""
vmin=None, vmax=None, clip=False, base=10):
r"""
Parameters
----------
linthresh : float
Expand All@@ -1228,12 +1228,34 @@ def __init__(self, linthresh, linscale=1.0,
example, when *linscale* == 1.0 (the default), the space used for
the positive and negative halves of the linear range will be equal
to one decade in the logarithmic range.
base : float, default: 10
The base in which the number of decades in the log range is
calculated. For example, if ``base=2``, ``linthresh=2`` and
``vmax=8``, the number of decades is :math:`\log_{2} (8 / 2) = 2`.

Notes
-----
Currently SymLogNorm only works with ``vmin == -vmax``, such that an
input of 0 always maps to half-way in the scale.
"""
if vmin is not None and vmax is not None and vmin != -vmax:
raise ValueError('SymLogNorm only works for vmin = -vmax '
f'(got vmin={vmin}, vmax={vmax})')
Normalize.__init__(self, vmin, vmax, clip)
self.linthresh = float(linthresh)
self._linscale_adj = (linscale / (1.0 - np.e ** -1))
if vmin is not None and vmax is not None:
self._transform_vmin_vmax()
self.base = float(base)
self.linscale = float(linscale)

@property
def _linear_size(self):
'Return the size of the linear portion in transformed space'
# Number of decades in the logarithmic range
ndec = self._logbase(self.vmax / self.linthresh)
return 1 / (1 + self.linscale / ndec)

def _logbase(self, val):
'Take the log of val in the base `self.base`'
return np.log(val) / np.log(self.base)

def __call__(self, value, clip=None):
if clip is None:
Expand All@@ -1254,57 +1276,53 @@ def __call__(self, value, clip=None):
mask=mask)
# in-place equivalent of above can be much faster
resdat = self._transform(result.data)
resdat -= self._lower
resdat /= (self._upper - self._lower)

if is_scalar:
result = result[0]
return result

def _transform(self, a):
"""Inplace transformation."""
"""In-place mapping from *a* to [0, 1]"""
with np.errstate(invalid="ignore"):
masked = np.abs(a) > self.linthresh
sign = np.sign(a[masked])
log = (self._linscale_adj + np.log(np.abs(a[masked]) / self.linthresh))
log *= sign * self.linthresh
a[masked] = log
a[~masked] *= self._linscale_adj
logregion = np.abs(a) > self.linthresh

# Transform log value
sign = np.sign(a[logregion])
log = ((1 - self._linear_size) * self._logbase(np.abs(a[logregion])) +
self._linear_size)
a[logregion] = log * sign

# Transform linear values
a[~logregion] *= self._linear_size / self.linthresh

# Transform from [-1, 1] to [0, 1]
a += 1
a /= 2
return a

def _inv_transform(self, a):
"""Inverse inplace Transformation."""
masked = np.abs(a) > (self.linthresh * self._linscale_adj)
sign = np.sign(a[masked])
exp = np.exp(sign * a[masked] / self.linthresh - self._linscale_adj)
exp *= sign * self.linthresh
a[masked] = exp
a[~masked] /= self._linscale_adj
# Transform from [0, 1] to [-1, 1]
a *= 2
a -= 1

# Transform back log values
logregion = np.abs(a) > self._linear_size
sign = np.sign(a[logregion])
exp = self.base**((np.abs(a[logregion]) - self._linear_size) /
(1 - self._linear_size))
a[logregion] = exp * sign

# Transform back linear values
a[~logregion] /= self._linear_size / self.linthresh
return a

def _transform_vmin_vmax(self):
"""Calculates vmin and vmax in the transformed system."""
vmin, vmax = self.vmin, self.vmax
arr = np.array([vmax, vmin]).astype(float)
self._upper, self._lower = self._transform(arr)

def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
val = np.ma.asarray(value)
val = val * (self._upper - self._lower) + self._lower
return self._inv_transform(val)

def autoscale(self, A):
# docstring inherited.
super().autoscale(A)
self._transform_vmin_vmax()

def autoscale_None(self, A):
# docstring inherited.
super().autoscale_None(A)
self._transform_vmin_vmax()


class PowerNorm(Normalize):
"""
Expand Down
44 changes: 35 additions & 9 deletionslib/matplotlib/tests/test_colors.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -395,22 +395,48 @@ def test_TwoSlopeNorm_premature_scaling():


def test_SymLogNorm():
"""
Test SymLogNorm behavior
"""
norm = mcolors.SymLogNorm(3, vmax=5, linscale=1.2)
# Test SymLogNorm behavior
norm = mcolors.SymLogNorm(linthresh=3, vmax=5, linscale=1.2, base=np.e)
vals = np.array([-30, -1, 2, 6], dtype=float)
normed_vals = norm(vals)
expected = [0., 0.53980074, 0.826991, 1.02758204]
expected = [-0.842119, 0.450236, 0.599528, 1.277676]
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I think these values were just plain wrong before...

  • The first value (-30) is less thanvmax (5), so should come out as less than zero
  • The second value (-1) is less than 0, so should come out as less than 0.5

assert_array_almost_equal(normed_vals, expected)
_inverse_tester(norm, vals)
_scalar_tester(norm, vals)
_mask_tester(norm, vals)

# Ensure that specifying vmin returns the same result as above
norm = mcolors.SymLogNorm(3, vmin=-30, vmax=5, linscale=1.2)

def test_symlognorm_vals():
vals = [-10, -1, 0, 1, 10]

norm = mcolors.SymLogNorm(linthresh=1, vmin=-10, vmax=10, linscale=1)
normed_vals = norm(vals)
assert_array_almost_equal(normed_vals, expected)
expected = [0, 0.25, 0.5, 0.75, 1]
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I don't think this is consistent withSymmetricLogTransform yet. I'm OK if we want to change that as well to be consistent with this code, but they can't be inconsistent!

importmatplotlib.scaleasmscaletrans=mscale.SymmetricalLogTransform(10,1,1)new=trans.transform([-10,-1,0,1,10])new= (new-new[0])/ (new[-1]-new[0])print(new)
[0.0 0.23684210526315788 0.5 0.7631578947368421 1.0]

I'm fine if this implementation is desired, but then we need to changeSymmetricLogTransform.

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assert_array_almost_equal(norm(vals), normed_vals)
assert_array_almost_equal(norm.inverse(norm(vals)), vals)

# If we increase linscale to 2, the space for the linear range [0, 1]
# should be twice as large as the space for the logarithmic range [1, 10]
norm = mcolors.SymLogNorm(linthresh=1, vmin=-10, vmax=10, linscale=2)
normed_vals = norm(vals)
expected = [0, 1/6, 0.5, 5/6, 1]
assert_array_almost_equal(norm(vals), normed_vals)
assert_array_almost_equal(norm.inverse(norm(vals)), vals)

# Similarly, going the other way means the linear range should shrink
norm = mcolors.SymLogNorm(linthresh=1, vmin=-10, vmax=10, linscale=0.5)
normed_vals = norm(vals)
expected = [0, 2/6, 0.5, 4/6, 1]
assert_array_almost_equal(norm(vals), normed_vals)
assert_array_almost_equal(norm.inverse(norm(vals)), vals)

# Now check a different base to base 10
vals = [-8, 4, -2, 0, 2, 4, 8]
norm = mcolors.SymLogNorm(linthresh=2, vmax=8, linscale=1, base=2)
normed_vals = norm(vals)
expected = [0, 1/8, 2/8, 3/8, 0.5, 5/8, 6/8, 7/8, 1]
assert_array_almost_equal(norm(vals), normed_vals)
assert_array_almost_equal(norm.inverse(norm(vals)), vals)


def test_SymLogNorm_colorbar():
Expand DownExpand Up@@ -907,7 +933,7 @@ def __add__(self, other):
for norm in [mcolors.Normalize(), mcolors.LogNorm(),
mcolors.SymLogNorm(3, vmax=5, linscale=1),
mcolors.Normalize(vmin=mydata.min(), vmax=mydata.max()),
mcolors.SymLogNorm(3, vmin=mydata.min(), vmax=mydata.max()),
mcolors.SymLogNorm(3, vmin=-10, vmax=10),
mcolors.PowerNorm(1)]:
assert_array_equal(norm(mydata), norm(data))
fig, ax = plt.subplots()
Expand Down
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