Added a new best-fit plotting function #18
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pscicluna
reviewed
Oct 19, 2022
pgmuvi/lightcurve.py
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| y_raw = ydata() | ||
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| # creating array of 10000 test points across the range of the data | ||
| x_fine = torch.linspace(x_raw.min(), x_raw.max(), 10000) |
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I think x_fine should be defined in terms of self._xdata_transformed instead of self.xdata_raw. This would also have some knock-on effects further down
Suggested change
| x_fine = torch.linspace(x_raw.min(), x_raw.max(), 10000) | |
| x_fine = torch.linspace(self._xdata_transformed.min(), self._xdata_transformed.max(), 10000) |
pgmuvi/lightcurve.py
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| ax.plot(x_raw.numpy(), y_raw.numpy(), 'k*') | ||
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| # Plot predictive GP mean as blue line | ||
| ax.plot(x_fine.numpy(), observed_pred.mean.numpy(), 'b') |
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This is the next place where the transformation matters, as x_fine is now defined in the transformed system. I think this should look something like:
Suggested change
| ax.plot(x_fine.numpy(), observed_pred.mean.numpy(), 'b') | |
| ax.plot(self.x_transform.inverse(x_fine).numpy(), observed_pred.mean.numpy(), 'b') |
pgmuvi/lightcurve.py
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| ax.plot(x_fine.numpy(), observed_pred.mean.numpy(), 'b') | ||
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| # Shade between the lower and upper confidence bounds | ||
| ax.fill_between(x_fine.numpy(), lower.numpy(), upper.numpy(), alpha=0.5) |
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This line would also need an inverse transform for x_fine, just like line 259.
pscicluna
reviewed
Oct 19, 2022
pscicluna
reviewed
Oct 19, 2022
Co-authored-by: Peter Scicluna <pscicluna@users.noreply.github.com>
Co-authored-by: Peter Scicluna <pscicluna@users.noreply.github.com>
… gp can predict on it
…into edit_branch
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Plotting function that plots the best-fit GP model, along with the confidence region and the raw data. I've tried to adjust it to the new naming scheme so see what you think :)