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.

Notes

If vmin == vmax, the input data will be mapped to 0.

Parameters:

vmin, vmaxpython:float or python:None

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: python: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.

Notes

If vmin == vmax, input data will be mapped to 0.

Attributes Summary

clip
vmax
vmin

Methods Summary

__call__(values[, clip, invalid])	Transform values using this normalization.
autoscale(A)	Set vmin, vmax to min, max of A.
autoscale_None(A)	If vmin or vmax are not set, set them according to the interval.
inverse(values[, invalid])	Maps the normalized value (i.e., index in the colormap) back to image data value.
process_value(value)	Homogenize the input value for easy and efficient normalization.
scaled()	Return whether vmin and vmax are both set.

Attributes Documentation

clip

vmax

vmin

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.

autoscale(A)

Set vmin, vmax to min, max of A.

autoscale_None(A)

If vmin or vmax are not set, set them according to the interval. If no interval is set, set them to the min/max of the data array.

inverse(values, invalid=None)

Maps the normalized value (i.e., index in the colormap) back to image data value.

Parameters:

value

Normalized value.

static process_value(value)

Homogenize the input value for easy and efficient normalization.

value can be a scalar or sequence.

Parameters:

value

Data to normalize.

Returns:

resultmasked array

Masked array with the same shape as value.

is_scalarbool

Whether value is a scalar.

Notes

Float dtypes are preserved; integer types with two bytes or smaller are converted to np.float32, and larger types are converted to np.float64. Preserving float32 when possible, and using in-place operations, greatly improves speed for large arrays.

scaled()

Return whether vmin and vmax are both set.