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# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Classes that deal with stretching, i.e. mapping a range of [0:1] values onto
another set of [0:1] values with a transformation
"""
from __future__ import division, print_function
import numpy as np
from ..extern import six
from ..utils.misc import InheritDocstrings
from .transform import BaseTransform
__all__ = ["BaseStretch", "LinearStretch", "SqrtStretch", "PowerStretch",
"PowerDistStretch", "SquaredStretch", "LogStretch", "AsinhStretch",
"SinhStretch", "HistEqStretch", "ContrastBiasStretch"]
def _logn(n, x, out=None):
"""Calculate the log base n of x."""
# We define this because numpy.lib.scimath.logn doesn't support out=
if out is None:
return np.log(x) / np.log(n)
else:
np.log(x, out=out)
np.true_divide(out, np.log(n), out=out)
return out
def _prepare(values, clip=True, out=None):
"""
Prepare the data by optionally clipping and copying, and return the
array that should be subsequently used for in-place calculations.
"""
if clip:
return np.clip(values, 0., 1., out=out)
else:
if out is None:
return np.array(values, copy=True)
else:
out[:] = np.asarray(values)
return out
@six.add_metaclass(InheritDocstrings)
class BaseStretch(BaseTransform):
"""
Base class for the stretch classes, which, when called with an array
of values in the range [0:1], return an transformed array of values,
also in the range [0:1].
"""
def __call__(self, values, clip=True, out=None):
"""
Transform values using this stretch.
Parameters
----------
values : array-like
The input values, which should already be normalized to the
[0:1] range.
clip : bool, optional
If `True` (default), values outside the [0:1] range are
clipped to the [0:1] range.
out : `~numpy.ndarray`, optional
If specified, the output values will be placed in this array
(typically used for in-place calculations).
Returns
-------
result : `~numpy.ndarray`
The transformed values.
"""
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
class LinearStretch(BaseStretch):
"""
A linear stretch.
The stretch is given by:
.. math::
y = x
"""
def __call__(self, values, clip=True, out=None):
return _prepare(values, clip=clip, out=out)
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return LinearStretch()
class SqrtStretch(BaseStretch):
r"""
A square root stretch.
The stretch is given by:
.. math::
y = \sqrt{x}
"""
def __call__(self, values, clip=True, out=None):
values = _prepare(values, clip=clip, out=out)
np.sqrt(values, out=values)
return values
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return PowerStretch(2)
class PowerStretch(BaseStretch):
r"""
A power stretch.
The stretch is given by:
.. math::
y = x^a
Parameters
----------
a : float
The power index (see the above formula).
"""
def __init__(self, a):
super(PowerStretch, self).__init__()
self.power = a
def __call__(self, values, clip=True, out=None):
values = _prepare(values, clip=clip, out=out)
np.power(values, self.power, out=values)
return values
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return PowerStretch(1. / self.power)
class PowerDistStretch(BaseStretch):
r"""
An alternative power stretch.
The stretch is given by:
.. math::
y = \frac{a^x - 1}{a - 1}
Parameters
----------
a : float, optional
The ``a`` parameter used in the above formula. Default is 1000.
``a`` cannot be set to 1.
"""
def __init__(self, a=1000.0):
if a == 1: # singularity
raise ValueError("a cannot be set to 1")
super(PowerDistStretch, self).__init__()
self.exp = a
def __call__(self, values, clip=True, out=None):
values = _prepare(values, clip=clip, out=out)
np.power(self.exp, values, out=values)
np.subtract(values, 1, out=values)
np.true_divide(values, self.exp - 1.0, out=values)
return values
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return InvertedPowerDistStretch(a=self.exp)
class InvertedPowerDistStretch(BaseStretch):
r"""
Inverse transformation for
`~astropy.image.scaling.PowerDistStretch`.
The stretch is given by:
.. math::
y = \frac{\log(y (a-1) + 1)}{\log a}
Parameters
----------
a : float, optional
The ``a`` parameter used in the above formula. Default is 1000.
``a`` cannot be set to 1.
"""
def __init__(self, a=1000.0):
if a == 1: # singularity
raise ValueError("a cannot be set to 1")
super(InvertedPowerDistStretch, self).__init__()
self.exp = a
def __call__(self, values, clip=True, out=None):
values = _prepare(values, clip=clip, out=out)
np.multiply(values, self.exp - 1.0, out=values)
np.add(values, 1, out=values)
_logn(self.exp, values, out=values)
return values
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return PowerDistStretch(a=self.exp)
class SquaredStretch(PowerStretch):
r"""
A convenience class for a power stretch of 2.
The stretch is given by:
.. math::
y = x^2
"""
def __init__(self):
super(SquaredStretch, self).__init__(2)
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return SqrtStretch()
class LogStretch(BaseStretch):
r"""
A log stretch.
The stretch is given by:
.. math::
y = \frac{\log{(a x + 1)}}{\log{(a + 1)}}.
Parameters
----------
a : float
The ``a`` parameter used in the above formula. Default is 1000.
"""
def __init__(self, a=1000.0):
super(LogStretch, self).__init__()
self.exp = a
def __call__(self, values, clip=True, out=None):
values = _prepare(values, clip=clip, out=out)
np.multiply(values, self.exp, out=values)
np.add(values, 1., out=values)
np.log(values, out=values)
np.true_divide(values, np.log(self.exp + 1.), out=values)
return values
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return InvertedLogStretch(self.exp)
class InvertedLogStretch(BaseStretch):
r"""
Inverse transformation for `~astropy.image.scaling.LogStretch`.
The stretch is given by:
.. math::
y = \frac{e^{y} (a + 1) -1}{a}
Parameters
----------
a : float, optional
The ``a`` parameter used in the above formula. Default is 1000.
"""
def __init__(self, a):
super(InvertedLogStretch, self).__init__()
self.exp = a
def __call__(self, values, clip=True, out=None):
values = _prepare(values, clip=clip, out=out)
np.multiply(values, np.log(self.exp + 1.), out=values)
np.exp(values, out=values)
np.subtract(values, 1., out=values)
np.true_divide(values, self.exp, out=values)
return values
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return LogStretch(self.exp)
class AsinhStretch(BaseStretch):
r"""
An asinh stretch.
The stretch is given by:
.. math::
y = \frac{{\rm asinh}(x / a)}{{\rm asinh}(1 / a)}.
Parameters
----------
a : float, optional
The ``a`` parameter used in the above formula. The value of
this parameter is where the asinh curve transitions from linear
to logarithmic behavior, expressed as a fraction of the
normalized image. Must be in the range between 0 and 1.
Default is 0.1
"""
def __init__(self, a=0.1):
super(AsinhStretch, self).__init__()
self.a = a
def __call__(self, values, clip=True, out=None):
values = _prepare(values, clip=clip, out=out)
np.true_divide(values, self.a, out=values)
np.arcsinh(values, out=values)
np.true_divide(values, np.arcsinh(1. / self.a), out=values)
return values
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return SinhStretch(a=1. / np.arcsinh(1. / self.a))
class SinhStretch(BaseStretch):
r"""
A sinh stretch.
The stretch is given by:
.. math::
y = \frac{{\rm sinh}(x / a)}{{\rm sinh}(1 / a)}
Parameters
----------
a : float, optional
The ``a`` parameter used in the above formula. Default is 1/3.
"""
def __init__(self, a=1./3.):
super(SinhStretch, self).__init__()
self.a = a
def __call__(self, values, clip=True, out=None):
values = _prepare(values, clip=clip, out=out)
np.true_divide(values, self.a, out=values)
np.sinh(values, out=values)
np.true_divide(values, np.sinh(1. / self.a), out=values)
return values
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return AsinhStretch(a=1. / np.sinh(1. / self.a))
class HistEqStretch(BaseStretch):
"""
A histogram equalization stretch.
Parameters
----------
data : array-like
The data defining the equalization.
values : array-like, optional
The input image values, which should already be normalized to
the [0:1] range.
"""
def __init__(self, data, values=None):
# Assume data is not necessarily normalized at this point
self.data = np.sort(data.ravel())
vmin = self.data.min()
vmax = self.data.max()
self.data = (self.data - vmin) / (vmax - vmin)
# Compute relative position of each pixel
if values is None:
self.values = np.linspace(0., 1., len(self.data))
else:
self.values = values
def __call__(self, values, clip=True, out=None):
values = _prepare(values, clip=clip, out=out)
values[:] = np.interp(values, self.data, self.values)
return values
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return InvertedHistEqStretch(self.data, values=self.values)
class InvertedHistEqStretch(BaseStretch):
"""
Inverse transformation for `~astropy.image.scaling.HistEqStretch`.
Parameters
----------
data : array-like
The data defining the equalization.
values : array-like, optional
The input image values, which should already be normalized to
the [0:1] range.
"""
def __init__(self, data, values=None):
self.data = data
if values is None:
self.values = np.linspace(0., 1., len(self.data))
else:
self.values = values
def __call__(self, values, clip=True, out=None):
values = _prepare(values, clip=clip, out=out)
values[:] = np.interp(values, self.values, self.data)
return values
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return HistEqStretch(self.data, values=self.values)
class ContrastBiasStretch(BaseStretch):
r"""
A stretch that takes into account contrast and bias.
The stretch is given by:
.. math::
y = (x - {\rm bias}) * {\rm contrast} + 0.5
and the output values are clipped to the [0:1] range.
Parameters
----------
contrast : float
The contrast parameter (see the above formula).
bias : float
The bias parameter (see the above formula).
"""
def __init__(self, contrast, bias):
super(ContrastBiasStretch, self).__init__()
self.contrast = contrast
self.bias = bias
def __call__(self, values, clip=True, out=None):
# As a special case here, we only clip *after* the
# transformation since it does not map [0:1] to [0:1]
values = _prepare(values, clip=False, out=out)
np.subtract(values, self.bias, out=values)
np.multiply(values, self.contrast, out=values)
np.add(values, 0.5, out=values)
if clip:
np.clip(values, 0, 1, out=values)
return values
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return InvertedContrastBiasStretch(self.contrast, self.bias)
class InvertedContrastBiasStretch(BaseStretch):
"""
Inverse transformation for ContrastBiasStretch.
Parameters
----------
contrast : float
The contrast parameter (see
`~astropy.visualization.ConstrastBiasStretch).
bias : float
The bias parameter (see
`~astropy.visualization.ConstrastBiasStretch).
"""
def __init__(self, contrast, bias):
super(InvertedContrastBiasStretch, self).__init__()
self.contrast = contrast
self.bias = bias
def __call__(self, values, clip=True, out=None):
# As a special case here, we only clip *after* the
# transformation since it does not map [0:1] to [0:1]
values = _prepare(values, clip=False, out=out)
np.subtract(values, 0.5, out=values)
np.true_divide(values, self.contrast, out=values)
np.add(values, self.bias, out=values)
if clip:
np.clip(values, 0, 1, out=values)
return values
@property
def inverse(self):
"""A stretch object that performs the inverse operation."""
return ContrastBiasStretch(self.contrast, self.bias)
|
