Iris Scatter Plot Data Slicing

Example showing data slice for scatter plot.

/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

import fastplotlib as fpl
import numpy as np
from sklearn import datasets


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

data = datasets.load_iris()["data"]

n_points = 50
colors = ["yellow"] * n_points + ["cyan"] * n_points + ["magenta"] * n_points

scatter_graphic = figure[0, 0].add_scatter(data=data[:, :-1], sizes=6, alpha=0.7, colors=colors)

figure.show()

scatter_graphic.data[0] = np.array([[5, 3, 1.5]])
scatter_graphic.data[1] = np.array([[4.3, 3.2, 1.3]])
scatter_graphic.data[2] = np.array([[5.2, 2.7, 1.7]])

scatter_graphic.data[10:15] = scatter_graphic.data[0:5] + np.array([1, 1, 1])
scatter_graphic.data[50:100:2] = scatter_graphic.data[100:150:2] + np.array([1, 1, 0])


if __name__ == "__main__":
    print(__doc__)
    fpl.loop.run()