subsample.py

"""
====================
Dynamic Downsampling
====================

Generates a large image with three levels of detail.

When zoomed out, appears as a difference of 2D Gaussians.
At medium zoom, a diagonal sinusoidal pattern is apparent.
When zoomed in close, noise is visible.

The image is dynamically subsampled using a local mean which hides the finer details.
"""

fromtypingimportTuple, Dict, Any, Union

importmatplotlibasmpl
importmatplotlib.pyplotasplt
frommatplotlib.colorsimportNormalize

importnumpyasnp

fromdata_prototype.descriptionimportDesc, desc_like
fromdata_prototype.artistimportCompatibilityArtistasCA
fromdata_prototype.imageimportImage
fromdata_prototype.containersimportArrayContainer

fromskimage.transformimportdownscale_local_mean


x=y=np.linspace(-3, 3, 3000)
X, Y=np.meshgrid(x, y)
Z1=np.exp(-(X**2) -Y**2) +0.08*np.sin(50* (X+Y))
Z2=np.exp(-((X-1) **2) - (Y-1) **2)
Z= (Z1-Z2) *2

Z+=np.random.random(Z.shape) -0.5


classSubsample:
defdescribe(self):
return {
"x": Desc((2,)),
"y": Desc((2,)),
"image": Desc(("M", "N")),
 }

defquery(
self,
graph,
parent_coordinates="axes",
 ) ->Tuple[Dict[str, Any], Union[str, int]]:
desc=Desc(("N",), coordinates="data")
xy= {"x": desc, "y": desc}
data_lim=graph.evaluator(xy, desc_like(xy, coordinates="axes")).inverse

pts=data_lim.evaluate({"x": (0, 1), "y": (0, 1)})
x1, x2=pts["x"]
y1, y2=pts["y"]

xi1=np.argmin(np.abs(x-x1))
yi1=np.argmin(np.abs(y-y1))
xi2=np.argmin(np.abs(x-x2))
yi2=np.argmin(np.abs(y-y2))

xscale=int(np.ceil((xi2-xi1) /50))
yscale=int(np.ceil((yi2-yi1) /50))

return {
"x": [x1, x2],
"y": [y1, y2],
"image": downscale_local_mean(Z[xi1:xi2, yi1:yi2], (xscale, yscale)),
 }, hash((x1, x2, y1, y2))


non_sub=ArrayContainer(**{"image": Z, "x": np.array([0, 1]), "y": np.array([0, 10])})

sub=Subsample()
cmap=mpl.colormaps["coolwarm"]
norm=Normalize(-2.2, 2.2)
im=Image(sub, cmap=cmap, norm=norm)

fig, ax=plt.subplots()
ax.add_artist(CA(im))
ax.set_xlim(-3, 3)
ax.set_ylim(-3, 3)
plt.show()