8000 Cleanup scales by anntzer · Pull Request #7144 · matplotlib/matplotlib · GitHub
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Cleanup scales #7144

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Merged
merged 2 commits into from
Nov 13, 2016
Merged

Cleanup scales #7144

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f8b1a9a
Cleanup of style.<>Transforms; catch nan warning.
anntzer Sep 20, 2016
7e0811d
Fix slightly imprecise test image.
anntzer Nov 5, 2016
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134 changes: 30 additions & 104 deletions lib/matplotlib/scale.py
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8000
Original file line number Diff line number Diff line change
Expand Up @@ -84,24 +84,6 @@ def get_transform(self):
return IdentityTransform()


def _mask_non_positives(a):
"""
Return a Numpy array where all non-positive values are
replaced with NaNs. If there are no non-positive values, the
original array is returned.
"""
mask = a <= 0.0
if mask.any():
return np.where(mask, np.nan, a)
return a


def _clip_non_positives(a):
a = np.array(a, float)
a[a <= 0.0] = 1e-300
return a


class LogTransformBase(Transform):
input_dims = 1
output_dims = 1
Expand All @@ -111,31 +93,35 @@ class LogTransformBase(Transform):
def __init__(self, nonpos):
Transform.__init__(self)
if nonpos == 'mask':
self._handle_nonpos = _mask_non_positives
self._fill_value = np.nan
else:
self._handle_nonpos = _clip_non_positives


class Log10Transform(LogTransformBase):
base = 10.0
self._fill_value = 1e-300

def transform_non_affine(self, a):
a = self._handle_nonpos(a * 10.0)
return np.log10(a)

def inverted(self):
return InvertedLog10Transform()
with np.errstate(invalid="ignore"):
a = np.where(a <= 0, self._fill_value, a)
return np.divide(np.log(a, out=a), np.log(self.base), out=a)


class InvertedLog10Transform(Transform):
class InvertedLogTransformBase(Transform):
input_dims = 1
output_dims = 1
is_separable = True
has_inverse = True
base = 10.0

def transform_non_affine(self, a):
return ma.power(10.0, a) / 10.0
return ma.power(self.base, a)


class Log10Transform(LogTransformBase):
base = 10.0

def inverted(self):
return InvertedLog10Transform()
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You could rewrite this like:

class Log10Transform(LogTransformBase):
    base = 10.0
    inverted = InvertedLog10Transform

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No, because InvertedLog10Transform is not defined yet at that point. (InvertedLog10Transform could define inverted as a reference to the Log10Transform class but I decided to keep definitions symmetric.)

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Ah, yes. You are right.



class InvertedLog10Transform(InvertedLogTransformBase):
base = 10.0

def inverted(self):
return Log10Transform()
Expand All @@ -144,88 +130,45 @@ def inverted(self):
class Log2Transform(LogTransformBase):
base = 2.0

def transform_non_affine(self, a):
a = self._handle_nonpos(a * 2.0)
return np.log2(a)

def inverted(self):
return InvertedLog2Transform()


class InvertedLog2Transform(Transform):
input_dims = 1
output_dims = 1
is_separable = True
has_inverse = True
class InvertedLog2Transform(InvertedLogTransformBase):
base = 2.0

def transform_non_affine(self, a):
return ma.power(2.0, a) / 2.0

def inverted(self):
return Log2Transform()


class NaturalLogTransform(LogTransformBase):
base = np.e

def transform_non_affine(self, a):
a = self._handle_nonpos(a * np.e)
return np.log(a)

def inverted(self):
return InvertedNaturalLogTransform()


class InvertedNaturalLogTransform(Transform):
input_dims = 1
output_dims = 1
is_separable = True
has_inverse = True
class InvertedNaturalLogTransform(InvertedLogTransformBase):
base = np.e

def transform_non_affine(self, a):
return ma.power(np.e, a) / np.e

def inverted(self):
return NaturalLogTransform()


class LogTransform(Transform):
input_dims = 1
output_dims = 1
is_separable = True
has_inverse = True

class LogTransform(LogTransformBase):
def __init__(self, base, nonpos):
Transform.__init__(self)
LogTransformBase.__init__(self, nonpos)
self.base = base
if nonpos == 'mask':
self._handle_nonpos = _mask_non_positives
else:
self._handle_nonpos = _clip_non_positives

def transform_non_affine(self, a):
a = self._handle_nonpos(a * self.base)
return np.log(a) / np.log(self.base)

def inverted(self):
return InvertedLogTransform(self.base)


class InvertedLogTransform(Transform):
input_dims = 1
output_dims = 1
is_separable = True
has_inverse = True

class InvertedLogTransform(InvertedLogTransformBase):
def __init__(self, base):
Transform.__init__(self)
InvertedLogTransformBase.__init__(self)
self.base = base

def transform_non_affine(self, a):
return ma.power(self.base, a) / self.base

def inverted(self):
return LogTransform(self.base)

Expand Down Expand Up @@ -474,25 +417,6 @@ def get_transform(self):
return self._transform


def _mask_non_logit(a):
"""
Return a Numpy array where all values outside ]0, 1[ are
replaced with NaNs. If all values are inside ]0, 1[, the original
array is returned.
"""
mask = (a <= 0.0) | (a >= 1.0)
if mask.any():
return np.where(mask, np.nan, a)
return a


def _clip_non_logit(a):
a = np.array(a, float)
a[a <= 0.0] = 1e-300
a[a >= 1.0] = 1 - 1e-300
return a


class LogitTransform(Transform):
input_dims = 1
output_dims = 1
Expand All @@ -502,15 +426,17 @@ class LogitTransform(Transform):
def __init__(self, nonpos):
Transform.__init__(self)
if nonpos == 'mask':
self._handle_nonpos = _mask_non_logit
self._fill_value = np.nan
else:
self._handle_nonpos = _clip_non_logit
self._fill_value = 1e-300
self._nonpos = nonpos

def transform_non_affine(self, a):
"""logit transform (base 10), masked or clipped"""
a = self._handle_nonpos(a)
return np.log10(1.0 * a / (1.0 - a))
with np.errstate(invalid="ignore"):
a = np.select(
[a <= 0, a >= 1], [self._fill_value, 1 - self._fill_value], a)
return np.log10(a / (1 - a))

def inverted(self):
return LogisticTransform(self._nonpos)
Expand Down
Binary file modified lib/matplotlib/tests/baseline_images/test_axes/loglog.png
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