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"""
Normalization class for Matplotlib that can be used to produce
colorbars.
"""
from __future__ import division, print_function
import numpy as np
from numpy import ma
from .interval import (PercentileInterval, AsymmetricPercentileInterval,
ManualInterval, MinMaxInterval)
from .stretch import (LinearStretch, SqrtStretch, PowerStretch, LogStretch,
AsinhStretch)
try:
import matplotlib # pylint: disable=W0611
from matplotlib.colors import Normalize
# On older versions of matplotlib Normalize is an old-style class
if not isinstance(Normalize, type):
class Normalize(Normalize, object):
pass
except ImportError:
class Normalize(object):
def __init__(self, *args, **kwargs):
raise ImportError('matplotlib is required in order to use this '
'class.')
__all__ = ['ImageNormalize', 'simple_norm']
__doctest_requires__ = {'*': ['matplotlib']}
class ImageNormalize(Normalize):
"""
Normalization class to be used with Matplotlib.
Parameters
----------
data : `~numpy.ndarray`, optional
The image array. This input is used only if ``interval`` is
also input. ``data`` and ``interval`` are used to compute the
vmin and/or vmax values only if ``vmin`` or ``vmax`` are not
input.
interval : `~astropy.visualization.BaseInterval` subclass instance, optional
The interval object to apply to the input ``data`` to determine
the ``vmin`` and ``vmax`` values. This input is used only if
``data`` is also input. ``data`` and ``interval`` are used to
compute the vmin and/or vmax values only if ``vmin`` or ``vmax``
are not input.
vmin, vmax : float
The minimum and maximum levels to show for the data. The
``vmin`` and ``vmax`` inputs override any calculated values from
the ``interval`` and ``data`` inputs.
stretch : `~astropy.visualization.BaseStretch` subclass instance, optional
The stretch object to apply to the data. The default is
`~astropy.visualization.LinearStretch`.
clip : bool, optional
If `True` (default), data values outside the [0:1] range are
clipped to the [0:1] range.
"""
def __init__(self, data=None, interval=None, vmin=None, vmax=None,
stretch=LinearStretch(), clip=False):
# this super call checks for matplotlib
super(ImageNormalize, self).__init__(vmin=vmin, vmax=vmax, clip=clip)
self.vmin = vmin
self.vmax = vmax
if data is not None and interval is not None:
_vmin, _vmax = interval.get_limits(data)
if self.vmin is None:
self.vmin = _vmin
if self.vmax is None:
self.vmax = _vmax
self.stretch = stretch
self.interval = interval
self.inverse_stretch = stretch.inverse
self.clip = clip
def __call__(self, values, clip=None):
if clip is None:
clip = self.clip
if isinstance(values, ma.MaskedArray):
if clip:
mask = False
else:
mask = values.mask
values = values.filled(self.vmax)
else:
mask = False
# Make sure scalars get broadcast to 1-d
if np.isscalar(values):
values = np.array([values], dtype=float)
else:
# copy because of in-place operations after
values = np.array(values, copy=True, dtype=float)
# Set default values for vmin and vmax if not specified
self.autoscale_None(values)
# Normalize based on vmin and vmax
np.subtract(values, self.vmin, out=values)
np.true_divide(values, self.vmax - self.vmin, out=values)
# Clip to the 0 to 1 range
if self.clip:
values = np.clip(values, 0., 1., out=values)
# Stretch values
values = self.stretch(values, out=values, clip=False)
# Convert to masked array for matplotlib
return ma.array(values, mask=mask)
def inverse(self, values):
# Find unstretched values in range 0 to 1
values_norm = self.inverse_stretch(values, clip=False)
# Scale to original range
return values_norm * (self.vmax - self.vmin) + self.vmin
def simple_norm(data, stretch='linear', power=1.0, asinh_a=0.1, min_cut=None,
max_cut=None, min_percent=None, max_percent=None,
percent=None, clip=True):
"""
Return a Normalization class that can be used for displaying images
with Matplotlib.
This function enables only a subset of image stretching functions
available in `~astropy.visualization.mpl_normalize.ImageNormalize`.
This function is used by the
``astropy.visualization.scripts.fits2bitmap`` script.
Parameters
----------
data : `~numpy.ndarray`
The image array.
stretch : {'linear', 'sqrt', 'power', log', 'asinh'}, optional
The stretch function to apply to the image. The default is
'linear'.
power : float, optional
The power index for ``stretch='power'``. The default is 1.0.
asinh_a : float, optional
For ``stretch='asinh'``, the value 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. The default is 0.1.
min_cut : float, optional
The pixel value of the minimum cut level. Data values less than
``min_cut`` will set to ``min_cut`` before stretching the image.
The default is the image minimum. ``min_cut`` overrides
``min_percent``.
max_cut : float, optional
The pixel value of the maximum cut level. Data values greater
than ``min_cut`` will set to ``min_cut`` before stretching the
image. The default is the image maximum. ``max_cut`` overrides
``max_percent``.
min_percent : float, optional
The percentile value used to determine the pixel value of
minimum cut level. The default is 0.0. ``min_percent``
overrides ``percent``.
max_percent : float, optional
The percentile value used to determine the pixel value of
maximum cut level. The default is 100.0. ``max_percent``
overrides ``percent``.
percent : float, optional
The percentage of the image values used to determine the pixel
values of the minimum and maximum cut levels. The lower cut
level will set at the ``(100 - percent) / 2`` percentile, while
the upper cut level will be set at the ``(100 + percent) / 2``
percentile. The default is 100.0. ``percent`` is ignored if
either ``min_percent`` or ``max_percent`` is input.
clip : bool, optional
If `True` (default), data values outside the [0:1] range are
clipped to the [0:1] range.
Returns
-------
result : `ImageNormalize` instance
An `ImageNormalize` instance that can be used for displaying
images with Matplotlib.
"""
if percent is not None:
interval = PercentileInterval(percent)
elif min_percent is not None or max_percent is not None:
interval = AsymmetricPercentileInterval(min_percent or 0.,
max_percent or 100.)
elif min_cut is not None or max_cut is not None:
interval = ManualInterval(min_cut, max_cut)
else:
interval = MinMaxInterval()
if stretch == 'linear':
stretch = LinearStretch()
elif stretch == 'sqrt':
stretch = SqrtStretch()
elif stretch == 'power':
stretch = PowerStretch(power)
elif stretch == 'log':
stretch = LogStretch()
elif stretch == 'asinh':
stretch = AsinhStretch(asinh_a)
else:
raise ValueError('Unknown stretch: {0}.'.format(stretch))
vmin, vmax = interval.get_limits(data)
return ImageNormalize(vmin=vmin, vmax=vmax, stretch=stretch, clip=clip)
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