Fix: Correct GMM fitting visualization in amrex_data.ipynb#165
Fix: Correct GMM fitting visualization in amrex_data.ipynb#165henry2004y wants to merge 2 commits into
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- Switched from `get_gmm_temperatures` to `get_gmm_parameters` to use thermal velocities instead of temperatures in the GMM fitting examples. - Updated the plotting to use contours of the reconstructed Gaussian distributions instead of ellipses for a more accurate visualization.
Summary of ChangesHello @henry2004y, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request refines the Gaussian Mixture Model (GMM) fitting visualization within the Highlights
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Code Review
This pull request correctly switches from get_gmm_temperatures to get_gmm_parameters and updates the GMM fitting visualization to use contours, which is a great improvement for accuracy. However, the implementation of the contour plotting doesn't produce the 1, 2, and 3-sigma contours as described in the notebook. I've provided suggestions to correct the contour level calculations in both plotting sections. The other changes are well-implemented and consistent.
| " rv = multivariate_normal(mean, cov)\n", | ||
| " ax.contour(X, Y, rv.pdf(pos), levels=3, colors='red', linestyles='--')\n", |
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The use of levels=3 in ax.contour does not plot 1, 2, and 3-sigma contours as stated in the markdown description. It will instead draw 4 automatically-chosen contour levels. To accurately plot the sigma contours, you should calculate the PDF values corresponding to 1, 2, and 3 standard deviations.
rv = multivariate_normal(mean, cov)
levels = rv.pdf(mean) * np.exp(-0.5 * np.array([1.0, 2.0, 3.0])**2)
ax.contour(X, Y, rv.pdf(pos), levels=np.sort(levels), colors='red', linestyles='--')
| " rv = multivariate_normal(mean, cov)\n", | ||
| " ax.contour(X, Y, rv.pdf(pos), levels=3, colors='red', linestyles='--')\n", |
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Similar to the isotropic case, using levels=3 here will not produce the desired 1, 2, and 3-sigma contours. It will draw 4 automatically-chosen levels. To correctly visualize the sigma contours, the levels must be calculated based on the probability density function.
rv = multivariate_normal(mean, cov)
levels = rv.pdf(mean) * np.exp(-0.5 * np.array([1.0, 2.0, 3.0])**2)
ax.contour(X, Y, rv.pdf(pos), levels=np.sort(levels), colors='red', linestyles='--')
- Switched from `get_gmm_temperatures` to `get_gmm_parameters` to use thermal velocities instead of temperatures in the GMM fitting examples. - Updated the plotting to use contours of the reconstructed Gaussian distributions instead of ellipses for a more accurate visualization. - Correctly calculate and plot 1, 2, and 3-sigma contours. - Updated the markdown description to reflect the contour plot changes.
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get_gmm_temperaturestoget_gmm_parametersto use thermal velocities instead of temperatures in the GMM fitting examples.