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a/volume_render_example.py b/volume_render_example.py deleted file mode 100644 index d1545031..00000000 --- a/volume_render_example.py +++ /dev/null @@ -1,84 +0,0 @@ -# %% -import matplotlib.pyplot as plt -import numpy as np -from matplotlib.colors import LogNorm - -from swiftsimio import load -from swiftsimio.visualisation import volume_render - -# Load the data -data = load("test_data/eagle_6.hdf5") - -# Rough location of an interesting galaxy in the volume. -region = [ - 0.225 * data.metadata.boxsize[0], - 0.275 * data.metadata.boxsize[0], - 0.12 * data.metadata.boxsize[1], - 0.17 * data.metadata.boxsize[1], - 0.45 * data.metadata.boxsize[2], - 0.5 * data.metadata.boxsize[2], -] - -# Render the volume (note 1024 is reasonably high resolution so this won't complete -# immediately; you should consider using 256, etc. for testing). -rendered = volume_render.render_gas(data, resolution=1024, region=region, parallel=True) - -# Quick view! By projecting along the final axis you can get -# the projected density from the rendered image. -plt.imsave("volume_render_quick_view.png", LogNorm()(rendered.sum(-1))) - -# Now we will move onto the real volume rendering. Let's use the log of the density; -# using the real density leads to low contrast images. -log_rendered = np.log10(rendered) - -# The volume rendering function expects centers of 'bins' and widths. These -# bins actually represent gaussian functions around a specific density (or other -# visualization quantity). The brightest pixel value is at center. We will -# visualise this later! -width = 0.1 -std = np.std(log_rendered) -mean = np.mean(log_rendered) - -# It's helpful to choose the centers relative to the data you have. When making -# a movie, you will obviously want to choose the centers to be the same for each -# frame. -centers = [mean + x * std for x in [1.0, 3.0, 5.0, 7.0]] - -# This will visualize your render options. The centers are shown as gaussians and -# vertical lines. -fig, ax = volume_render.visualise_render_options( - centers=centers, widths=width, cmap="viridis" -) - -histogram, edges = np.histogram( - log_rendered.flat, - bins=128, - range=(min(centers) - 5.0 * width, max(centers) + 5.0 * width), -) -bc = (edges[:-1] + edges[1:]) / 2.0 - -# The normalization here is the height of a gaussian! -ax.plot(bc, histogram / (np.max(histogram) * np.sqrt(2.0 * np.pi) * width)) -ax.semilogy() -ax.set_xlabel("$\\log_{10}(\\rho)$") - -plt.savefig("volume_render_options.png") - -# Now we can really visualize the rendering. -img, norms = volume_render.visualise_render( - log_rendered, - centers, - widths=width, - cmap="viridis", -) - -# Sometimes, these images can be a bit dark. 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