# cmap_values from an array, so the colors on the sine line will be based on the sine y-values
sine_graphic = plot.add_line(
    data=sine, 
    thickness=10, 
    cmap="plasma", 
    cmap_values=sine[:, 1]
)

# qualitative colormaps, useful for cluster labels for example
cmap_values = [0] * 25 + [5] * 25 + [1] * 25 + [2] * 25
cosine_graphic = plot.add_line(
    data=cosine, 
    thickness=10, 
    cmap="tab10", 
    cmap_values=cmap_values
)

# 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

plot.add_line_collection(
    circles, 
    cmap="bwr", 
    cmap_values=cmap_values, 
    thickness=10
)

# 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

# qualitative colormap used for mapping 16 cmap values for each line
# for example, these could be cluster labels
cmap_values = [
    0, 1, 1, 2,
    0, 0, 1, 1,
    2, 2, 3, 3,
    1, 1, 1, 5
]

plot = fpl.Plot()

plot.add_line_collection(
    circles, 
    cmap="tab10", 
    cmap_values=cmap_values, 
    thickness=10
)

plot.show()

plot.canvas.set_logical_size(800, 800)

from fastplotlib import Plot, run
import numpy as np
from pathlib import Path
from sklearn.cluster import AgglomerativeClustering


plot = Plot()

data_path = Path(__file__).parent.parent.joinpath("data", "iris.npy")
data = np.load(data_path)


agg = AgglomerativeClustering(n_clusters=3)

agg.fit_predict(data)


scatter_graphic = plot.add_scatter(
    data=data[:, :-1],
    sizes=15,
    alpha=0.7,
    cmap="Set1",
    cmap_values=agg.labels_
)

plot.show()

plot.canvas.set_logical_size(800, 800)

plot.auto_scale()

scatter_graphic.cmap = "tab10"

if __name__ == "__main__":
    run()