ImageNormalize#
- class astropy.visualization.ImageNormalize(data=None, interval=None, vmin=None, vmax=None, stretch=<astropy.visualization.stretch.LinearStretch object>, clip=False, invalid=-1.0)[source]#
Bases:
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
andinterval
are used to compute the vmin and/or vmax values only ifvmin
orvmax
are not input.- interval
BaseInterval
subclass instance, optional The interval object to apply to the input
data
to determine thevmin
andvmax
values. This input is used only ifdata
is also input.data
andinterval
are used to compute the vmin and/or vmax values only ifvmin
orvmax
are not input.- vmin, vmax
float
, optional The minimum and maximum levels to show for the data. The
vmin
andvmax
inputs override any calculated values from theinterval
anddata
inputs.- stretch
BaseStretch
subclass instance The stretch object to apply to the data. The default is
LinearStretch
.- clipbool, optional
If
True
, data values outside the [0:1] range are clipped to the [0:1] range.- invalid
None
orfloat
, optional Value to assign NaN values generated by this class. NaNs in the input
data
array are not changed. For matplotlib normalization, theinvalid
value should map to the matplotlib colormap “under” value (i.e., any finite value < 0). IfNone
, then NaN values are not replaced. This keyword has no effect ifclip=True
.
- data
Notes
If
vmin == vmax
, the input data will be mapped to 0.- Parameters:
- vmin, vmax
float
orNone
Values within the range
[vmin, vmax]
from the input data will be linearly mapped to[0, 1]
. If either vmin or vmax is not provided, they default to the minimum and maximum values of the input, respectively.- clipbool, default:
False
Determines the behavior for mapping values outside the range
[vmin, vmax]
.If clipping is off, values outside the range
[vmin, vmax]
are also transformed, resulting in values outside[0, 1]
. This behavior is usually desirable, as colormaps can mark these under and over values with specific colors.If clipping is on, values below vmin are mapped to 0 and values above vmax are mapped to 1. Such values become indistinguishable from regular boundary values, which may cause misinterpretation of the data.
- vmin, vmax
Notes
If
vmin == vmax
, input data will be mapped to 0.Methods Summary
__call__
(values[, clip, invalid])Transform values using this normalization.
inverse
(values[, invalid])Maps the normalized value (i.e., index in the colormap) back to image data value.
Methods Documentation
- __call__(values, clip=None, invalid=None)[source]#
Transform values using this normalization.
- Parameters:
- valuesarray_like
The input values.
- clipbool, optional
If
True
, values outside the [0:1] range are clipped to the [0:1] range. IfNone
then theclip
value from theImageNormalize
instance is used (the default of which isFalse
).- invalid
None
orfloat
, optional Value to assign NaN values generated by this class. NaNs in the input
data
array are not changed. For matplotlib normalization, theinvalid
value should map to the matplotlib colormap “under” value (i.e., any finite value < 0). IfNone
, then theImageNormalize
instance value is used. This keyword has no effect ifclip=True
.