"""
Normalization class for Matplotlib that can be used to produce
colorbars.
"""
import inspect
import warnings
import numpy as np
from numpy import ma
from .interval import (PercentileInterval, AsymmetricPercentileInterval,
ManualInterval, MinMaxInterval, BaseInterval)
from .stretch import (LinearStretch, SqrtStretch, PowerStretch, LogStretch,
AsinhStretch, BaseStretch)
from ..utils.exceptions import AstropyDeprecationWarning
try:
import matplotlib # pylint: disable=W0611
from matplotlib.colors import Normalize
from matplotlib import pyplot as plt
except ImportError:
class Normalize:
def __init__(self, *args, **kwargs):
raise ImportError('matplotlib is required in order to use this '
'class.')
__all__ = ['ImageNormalize', 'simple_norm', 'imshow_norm']
__doctest_requires__ = {'*': ['matplotlib']}
[ドキュメント]class ImageNormalize(Normalize):
"""
Normalization class to be used with Matplotlib.
Parameters
----------
data : 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, optional
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
The stretch object to apply to the data. The default is
`~astropy.visualization.LinearStretch`.
clip : bool, optional
If `True`, data values outside the [0:1] range are clipped to
the [0:1] range.
invalid : None or float, optional
Value to assign NaN values generated by this class. NaNs in the
input ``data`` array are not changed. For matplotlib
normalization, the ``invalid`` value should map to the
matplotlib colormap "under" value (i.e., any finite value < 0).
If `None`, then NaN values are not replaced. This keyword has
no effect if ``clip=True``.
"""
def __init__(self, data=None, interval=None, vmin=None, vmax=None,
stretch=LinearStretch(), clip=False, invalid=-1.0):
# this super call checks for matplotlib
super().__init__(vmin=vmin, vmax=vmax, clip=clip)
self.vmin = vmin
self.vmax = vmax
if stretch is None:
raise ValueError('stretch must be input')
if not isinstance(stretch, BaseStretch):
raise TypeError('stretch must be an instance of a BaseStretch '
'subclass')
self.stretch = stretch
if interval is not None and not isinstance(interval, BaseInterval):
raise TypeError('interval must be an instance of a BaseInterval '
'subclass')
self.interval = interval
self.inverse_stretch = stretch.inverse
self.clip = clip
self.invalid = invalid
# Define vmin and vmax if not None and data was input
if data is not None:
self._set_limits(data)
def _set_limits(self, data):
if self.vmin is not None and self.vmax is not None:
return
# Define vmin and vmax from the interval class if not None
if self.interval is None:
if self.vmin is None:
self.vmin = np.min(data[np.isfinite(data)])
if self.vmax is None:
self.vmax = np.max(data[np.isfinite(data)])
else:
_vmin, _vmax = self.interval.get_limits(data)
if self.vmin is None:
self.vmin = _vmin
if self.vmax is None:
self.vmax = _vmax
[ドキュメント] def __call__(self, values, clip=None, invalid=None):
"""
Transform values using this normalization.
Parameters
----------
values : array-like
The input values.
clip : bool, optional
If `True`, values outside the [0:1] range are clipped to the
[0:1] range. If `None` then the ``clip`` value from the
`ImageNormalize` instance is used (the default of which is
`False`).
invalid : None or float, optional
Value to assign NaN values generated by this class. NaNs in
the input ``data`` array are not changed. For matplotlib
normalization, the ``invalid`` value should map to the
matplotlib colormap "under" value (i.e., any finite value <
0). If `None`, then the `ImageNormalize` instance value is
used. This keyword has no effect if ``clip=True``.
"""
if clip is None:
clip = self.clip
if invalid is None:
invalid = self.invalid
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)
# Define vmin and vmax if not None
self._set_limits(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 clip:
values = np.clip(values, 0., 1., out=values)
# Stretch values
if self.stretch._supports_invalid_kw:
values = self.stretch(values, out=values, clip=False,
invalid=invalid)
else:
values = self.stretch(values, out=values, clip=False)
# Convert to masked array for matplotlib
return ma.array(values, mask=mask)
[ドキュメント] def inverse(self, values, invalid=None):
# Find unstretched values in range 0 to 1
if self.inverse_stretch._supports_invalid_kw:
values_norm = self.inverse_stretch(values, clip=False,
invalid=invalid)
else:
values_norm = self.inverse_stretch(values, clip=False)
# Scale to original range
return values_norm * (self.vmax - self.vmin) + self.vmin
[ドキュメント]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=False, log_a=1000, invalid=-1.0):
"""
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 : 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`, data values outside the [0:1] range are clipped to
the [0:1] range.
log_a : float, optional
The log index for ``stretch='log'``. The default is 1000.
invalid : None or float, optional
Value to assign NaN values generated by the normalization. NaNs
in the input ``data`` array are not changed. For matplotlib
normalization, the ``invalid`` value should map to the
matplotlib colormap "under" value (i.e., any finite value < 0).
If `None`, then NaN values are not replaced. This keyword has
no effect if ``clip=True``.
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(log_a)
elif stretch == 'asinh':
stretch = AsinhStretch(asinh_a)
else:
raise ValueError(f'Unknown stretch: {stretch}.')
vmin, vmax = interval.get_limits(data)
return ImageNormalize(vmin=vmin, vmax=vmax, stretch=stretch, clip=clip,
invalid=invalid)
# used in imshow_norm
_norm_sig = inspect.signature(ImageNormalize)
[ドキュメント]def imshow_norm(data, ax=None, imshow_only_kwargs={}, **kwargs):
""" A convenience function to call matplotlib's `matplotlib.pyplot.imshow`
function, using an `ImageNormalize` object as the normalization.
Parameters
----------
data : 2D or 3D array-like
The data to show. Can be whatever `~matplotlib.pyplot.imshow` and
`ImageNormalize` both accept. See `~matplotlib.pyplot.imshow`.
ax : None or `~matplotlib.axes.Axes`, optional
If None, use pyplot's imshow. Otherwise, calls ``imshow`` method of
the supplied axes.
imshow_only_kwargs : dict, optional
Deprecated since Astropy v4.1. Note that settting both ``norm``
and ``vmin/vmax`` is deprecated in ``matplotlib >= 3.3``.
Arguments to be passed directly to `~matplotlib.pyplot.imshow` without
first trying `ImageNormalize`. This is only for keywords that have the
same name in both `ImageNormalize` and `~matplotlib.pyplot.imshow` - if
you want to set the `~matplotlib.pyplot.imshow` keywords only, supply
them in this dictionary.
kwargs : dict, optional
All other keyword arguments are parsed first by the
`ImageNormalize` initializer, then to
`~matplotlib.pyplot.imshow`.
Returns
-------
result : tuple
A tuple containing the `~matplotlib.image.AxesImage` generated
by `~matplotlib.pyplot.imshow` as well as the `ImageNormalize`
instance.
Notes
-----
The ``norm`` matplotlib keyword is not supported.
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.visualization import (imshow_norm, MinMaxInterval,
SqrtStretch)
# Generate and display a test image
image = np.arange(65536).reshape((256, 256))
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
im, norm = imshow_norm(image, ax, origin='lower',
interval=MinMaxInterval(),
stretch=SqrtStretch())
fig.colorbar(im)
"""
if imshow_only_kwargs:
warnings.warn('imshow_only_kwargs is deprecated since v4.1 and will '
'be removed in a future version.',
AstropyDeprecationWarning)
if 'X' in kwargs:
raise ValueError('Cannot give both ``X`` and ``data``')
if 'norm' in kwargs:
raise ValueError('There is no point in using imshow_norm if you give '
'the ``norm`` keyword - use imshow directly if you '
'want that.')
imshow_kwargs = dict(kwargs)
norm_kwargs = {'data': data}
for pname in _norm_sig.parameters:
if pname in kwargs:
norm_kwargs[pname] = imshow_kwargs.pop(pname)
for k, v in imshow_only_kwargs.items():
if k not in _norm_sig.parameters:
# the below is not strictly "has to be true", but is here so that
# users don't start using both imshow_only_kwargs *and* keyword
# arguments to this function, as that makes for more confusing
# user code
raise ValueError('You provided a keyword to imshow_only_kwargs '
'({}) that is not a keyword for ImageNormalize. '
'This is not supported. Instead you should '
'pass the keyword directly into imshow_norm'
.format(k))
imshow_kwargs[k] = v
imshow_kwargs['norm'] = ImageNormalize(**norm_kwargs)
if ax is None:
imshow_result = plt.imshow(data, **imshow_kwargs)
else:
imshow_result = ax.imshow(data, **imshow_kwargs)
return imshow_result, imshow_kwargs['norm']