ImageNormalize

class astropy.visualization.mpl_normalize.ImageNormalize(data=None, interval=None, vmin=None, vmax=None, stretch=<astropy.visualization.stretch.LinearStretch object>, clip=False, invalid=-1.0)

Bases: Normalize

Normalization class to be used with Matplotlib.

Parameters:

datandarray, 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.

intervalBaseInterval 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, vmaxpython: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.

stretchBaseStretch 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.

invalidpython:None or python: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.

Parameters:

vmin, vmaxpython:float or python:None

If vmin and/or vmax is not given, they are initialized from the minimum and maximum value, respectively, of the first input processed; i.e., __call__(A) calls autoscale_None(A).

clipbool, default: python:False

If True values falling outside the range [vmin, vmax], are mapped to 0 or 1, whichever is closer, and masked values are set to 1. If False masked values remain masked.

Clipping silently defeats the purpose of setting the over, under, and masked colors in a colormap, so it is likely to lead to surprises; therefore the default is clip=False.

Notes

Returns 0 if vmin == vmax.

Methods Summary

__call__(values[, clip, invalid])	Transform values using this normalization.
inverse(values[, invalid])

Methods Documentation

__call__(values, clip=None, invalid=None)

Transform values using this normalization.

Parameters:

valuesnumpy:array_like

The input values.

clipbool, 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).

invalidpython:None or python: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.

inverse(values, invalid=None)