Source code for astropy.visualization.mpl_normalize

"""
Normalization class for Matplotlib that can be used to produce
colorbars.
"""

import inspect

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)

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']}


[docs]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=True): # this super call checks for matplotlib super().__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 if stretch is not None and 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
[docs] 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)
[docs] 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
[docs]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, log_a=1000): """ 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. log : float, optional The log index for ``stretch='log'``. The default is 1000. 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(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)
# used in imshow_norm _norm_sig = inspect.signature(ImageNormalize)
[docs]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 - see `~matplotlib.pyplot.imshow` The data to show. Can be whatever `~matplotlib.pyplot.imshow` and `ImageNormalize` both accept. ax : None or `~matplotlib.axes.Axes` If None, use pyplot's imshow. Otherwise, calls ``imshow`` method of the supplied axes. imshow_only_kwargs : dict 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. All other keyword arguments are parsed first by the `ImageNormalize` initializer, then to`~matplotlib.pyplot.imshow`. Notes ----- The ``norm`` matplotlib keyword is not supported. """ 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('Provided a keyword to imshow_only_kwargs ({}) ' 'that is not a keyword for ImageNormalize. This is' ' not supported, you should pass the keyword' 'directly into imshow_norm instead'.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']