simple_norm

astropy.visualization.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 ImageNormalize.

This function is used by the astropy.visualization.scripts.fits2bitmap script.

Parameters

datandarray

The image array.

stretch{‘linear’, ‘sqrt’, ‘power’, log’, ‘asinh’}, optional

The stretch function to apply to the image. The default is ‘linear’.

powerfloat, optional

The power index for stretch='power'. The default is 1.0.

asinh_afloat, 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_cutfloat, 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_cutfloat, 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_percentfloat, optional

The percentile value used to determine the pixel value of minimum cut level. The default is 0.0. min_percent overrides percent.

max_percentfloat, optional

The percentile value used to determine the pixel value of maximum cut level. The default is 100.0. max_percent overrides percent.

percentfloat, 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.

clipbool, optional

If True, data values outside the [0:1] range are clipped to the [0:1] range.

log_afloat, optional

The log index for stretch='log'. The default is 1000.

invalidNone 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

resultImageNormalize instance

An ImageNormalize instance that can be used for displaying images with Matplotlib.