File size: 11,155 Bytes
7ffff7d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 | """
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']}
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
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, 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('Unknown stretch: {0}.'.format(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)
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']
|