iris.palette#
Color map pallettes management.
Load, configure and register color map palettes and initialise color map meta-data mappings.
- class iris.palette.SymmetricNormalize(pivot, *args, **kwargs)[source]#
Bases:
Normalize
Provides a symmetric normalization class around a given pivot point.
- Parameters:
vmin (float or 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)
callsautoscale_None(A)
.vmax (float or 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)
callsautoscale_None(A)
.clip (bool, 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 linearly, resulting in values outside[0, 1]
. For a standard use with colormaps, this behavior is desired because colormaps mark these outside values with specific colors for over or under.If
True
values falling outside the range[vmin, vmax]
, are mapped to 0 or 1, whichever is closer. This makes these values indistinguishable from regular boundary values and can lead to misinterpretation of the data.
Notes
Returns 0 if
vmin == vmax
.- __call__(value, clip=None)#
Normalize value data in the
[vmin, vmax]
interval into the[0.0, 1.0]
interval and return it.- Parameters:
value – Data to normalize.
clip (bool, optional) –
See the description of the parameter clip in .Normalize.
If
None
, defaults toself.clip
(which defaults toFalse
).
Notes
If not already initialized,
self.vmin
andself.vmax
are initialized usingself.autoscale_None(value)
.
- autoscale(A)#
Set vmin, vmax to min, max of A.
- autoscale_None(A)#
If vmin or vmax are not set, use the min/max of A to set them.
- property clip#
- inverse(value)#
- static process_value(value)#
Homogenize the input value for easy and efficient normalization.
value can be a scalar or sequence.
- Returns:
result (masked array) – Masked array with the same shape as value.
is_scalar (bool) – 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 set.
- property vmax#
- property vmin#
- iris.palette.auto_palette(func)[source]#
Auto palette decorator wrapper function to control the default behaviour.
Decorator wrapper function to control the default behaviour of the matplotlib cmap and norm keyword arguments.
- Parameters:
func (callable) – Callable function to be wrapped by the decorator.
- Return type:
Closure wrapper function.
- iris.palette.cmap_norm(cube)[source]#
Determine the default.
Determine the default
matplotlib.colors.LinearSegmentedColormap
andiris.palette.SymmetricNormalize
instances associated with the cube.- Parameters:
cube (
iris.cube.Cube
) – Source cube to generate default palette from.- Returns:
Tuple of
matplotlib.colors.LinearSegmentedColormap
andiris.palette.SymmetricNormalize
.- Return type:
Notes
This function maintains laziness when called; it does not realise data. See more at Real and Lazy Data.