Color maps

from ipyscales import (
    LinearColorScale, LogColorScale, NamedSequentialColorMap, NamedDivergingColorMap,
    NamedOrdinalColorMap, ColorBar
)

from ipyscales._example_helper import use_example_model_ids
use_example_model_ids()

from IPython.display import display

def visualize(cm):
    display(ColorBar(
        colormap=cm,
        length=500,
        orientation='horizontal'
    ))

visualize(
    LinearColorScale(range=('red', 'blue'))
)

visualize(
    LogColorScale(range=('green', 'orange'))
)

visualize(
    NamedSequentialColorMap('Viridis')
)

visualize(
    NamedDivergingColorMap('RdBu', domain=(-1, 0, 1))
)

visualize(
    NamedOrdinalColorMap('Accent', domain=tuple(range(10)))
)

import numpy as np
from ipyscales.colorarray import ArrayColorScale
visualize(
    ArrayColorScale(np.array([[1, 0, 0], [0.5, 0.5, 0.5], [0, 1, 1]], dtype='float32'))
)

visualize(
    ArrayColorScale(
        np.array([[0.07, 1, 0.5, 0.3], [0.16, 1, 0.5, 1.0], [0.30, 0.8, 0.4, 0.3]], dtype='float32'),
    )
)

visualize(
    ArrayColorScale(
        np.array([[0.07, 1, 0.5, 0.3], [0.16, 1, 0.5, 1.0], [0.30, 0.8, 0.4, 1.0]], dtype='float32'),
        space='hsl',
    )
)

Example use with image

Generate some 2D data to use as image

import numpy as np

delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2

Set up the scale to use, the color transform, and the image:

from ipyscales.datawidgets import ScaledArray
from ipydatawidgets import DataImage

scale = NamedSequentialColorMap(domain=(np.min(Z), np.max(Z)))
data = ScaledArray(data=Z, scale=scale, output_dtype='uint8')
img = DataImage(data=data)

Finally, display the image side-by-side with a color bar, and add a color map selector box underneath:

from ipywidgets import HBox, VBox

VBox([
    HBox([img, ColorBar(colormap=data.scale, length=len(x))]),
    scale.edit()
])