Line collections quantitative cmap

Example showing a line collection with a quantitative cmap

/home/runner/work/fastplotlib/fastplotlib/fastplotlib/graphics/_features/_base.py:18: UserWarning: casting float64 array to float32
  warn(f"casting {array.dtype} array to float32")

# test_example = true

from itertools import product
import numpy as np
import fastplotlib as fpl

def make_circle(center, radius: float, n_points: int = 75) -> np.ndarray:
    theta = np.linspace(0, 2 * np.pi, n_points)
    xs = radius * np.sin(theta)
    ys = radius * np.cos(theta)

    return np.column_stack([xs, ys]) + center


spatial_dims = (50, 50)

circles = list()
for center in product(range(0, spatial_dims[0], 15), range(0, spatial_dims[1], 15)):
    circles.append(make_circle(center, 5, n_points=75))

pos_xy = np.vstack(circles)

# this makes 16 circles, so we can create 16 cmap values, so it will use these values to set the
# color of the line based by using the cmap as a LUT with the corresponding cmap_value

# highest values, lowest values, mid-high values, mid values
cmap_values = [10] * 4 + [0] * 4 + [7] * 4 + [5] * 4

figure = fpl.Figure(size=(700, 560))

figure[0, 0].add_line_collection(
    circles, cmap="bwr", cmap_transform=cmap_values, thickness=10
)

# remove clutter
figure[0, 0].axes.visible = False

figure.show()


# NOTE: `if __name__ == "__main__"` is NOT how to use fastplotlib interactively
# please see our docs for using fastplotlib interactively in ipython and jupyter
if __name__ == "__main__":
    print(__doc__)
    fpl.run()