matplotlib.colors.Normalize — Matplotlib 3.10.1 documentation (original) (raw)
class matplotlib.colors.Normalize(vmin=None, vmax=None, clip=False)[source]#
Bases: object
A class which, when called, maps values within the interval[vmin, vmax] linearly to the interval [0.0, 1.0]. The mapping of values outside [vmin, vmax] depends on clip.
Examples
x = [-2, -1, 0, 1, 2]
norm = mpl.colors.Normalize(vmin=-1, vmax=1, clip=False) norm(x) # [-0.5, 0., 0.5, 1., 1.5] norm = mpl.colors.Normalize(vmin=-1, vmax=1, clip=True) norm(x) # [0., 0., 0.5, 1., 1.]
Parameters:
vmin, vmaxfloat or 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: 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.
__call__(value, clip=None)[source]#
Normalize the data and return the normalized data.
Parameters:
value
Data to normalize.
clipbool, optional
See the description of the parameter clip in Normalize.
If None, defaults to self.clip (which defaults toFalse).
Notes
If not already initialized, self.vmin and self.vmax are initialized using self.autoscale_None(value).
Set vmin, vmax to min, max of A.
If vmin or vmax are not set, use the min/max of A to set them.
property clip#
Maps the normalized value (i.e., index in the colormap) back to image data value.
Parameters:
value
Normalized value.
static process_value(value)[source]#
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.
Return whether vmin and vmax are both set.
property vmax#
property vmin#