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multigraphic_gridplot.py
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118 lines (87 loc) · 3.24 KB
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"""
Multi-Graphic GridPlot
======================
Example showing a Figure with multiple subplots and multiple graphic types.
"""
# test_example = false
# sphinx_gallery_pygfx_docs = 'screenshot'
import fastplotlib as fpl
import numpy as np
import imageio.v3 as iio
from itertools import product
# define figure
figure = fpl.Figure(
shape=(2, 2),
names=[["image-overlay", "circles"], ["line-stack", "scatter"]],
size=(700, 560)
)
img = iio.imread("imageio:coffee.png")
# add image to subplot
figure["image-overlay"].add_image(data=img)
# generate overlay
# empty array for overlay, shape is [nrows, ncols, RGBA]
overlay = np.zeros(shape=(*img.shape[:2], 4), dtype=np.float32)
# set the blue values of some pixels with an alpha > 1
overlay[img[:, :, -1] > 200] = np.array([0.0, 0.0, 1.0, 0.6]).astype(np.float32)
# add overlay to image
figure["image-overlay"].add_image(data=overlay)
# generate some circles
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)
# 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_transform
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))
# things like class labels, cluster labels, etc.
cmap_transform = [
0, 1, 1, 2,
0, 0, 1, 1,
2, 2, 8, 3,
1, 9, 1, 5
]
# add an image to overlay the circles on
img2 = iio.imread("imageio:coins.png")[10::5, 5::5]
figure["circles"].add_image(data=img2, cmap="gray")
# add the circles to the figure
figure["circles"].add_line_collection(
circles,
cmap="tab10",
cmap_transform=cmap_transform,
thickness=3,
alpha=0.5,
name="circles-graphic"
)
# move the circles graphic so that it is centered over the image
figure["circles"]["circles-graphic"].offset = np.array([7, 7, 2])
# generate some sine data
# linspace, create 100 evenly spaced x values from -10 to 10
xs = np.linspace(-10, 10, 100)
# sine wave
ys = np.sin(xs)
sine = np.dstack([xs, ys])[0]
# make 10 identical waves
sine_waves = 10 * [sine]
# add the line stack to the figure
figure["line-stack"].add_line_stack(data=sine_waves, cmap="Wistia", separation=1)
figure["line-stack"].auto_scale(maintain_aspect=True)
# generate some scatter data
# create a gaussian cloud of 500 points
n_points = 500
mean = [0, 0] # mean of the Gaussian distribution
covariance = [[1, 0], [0, 1]] # covariance matrix
gaussian_cloud = np.random.multivariate_normal(mean, covariance, n_points)
gaussian_cloud2 = np.random.multivariate_normal(mean, covariance, n_points)
# add the scatter graphics to the figure
figure["scatter"].add_scatter(data=gaussian_cloud, sizes=2, cmap="jet")
figure["scatter"].add_scatter(data=gaussian_cloud2, colors="r", sizes=2)
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.loop.run()