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Easy command line tools for Kepler, K2 & TESS data analysis.

Results 38 pyke issues
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Hi, when I installed pyke, I can not import it. I used both the two methods in pyke.keplerscience.org/install.html. What's the problem? Thanks for your help! from pyke import kepextract ---------------------------------------------------------------------------...

I am using kepextract and keep having issues with null values kepextract itself runs fine, but the fits file it creates has value of zero instead of NULL which causes...

As a convenient "container" for the various priors and likelihoods that we may want to use when carrying out PSF photometry, we should think about adding a `KeplerSceneModel` class. In...

feature-request
psf

This commit is a first pass to resolve #88, converting pyke to more Pythonic logging. I have diffs ready to migrate the other files to use keplog.py, but the diffs...

Now that PyKE v3.1.0 is released, let's work towards a v.3.2.0 release in which the new API (i.e. the `LightCurve`, `KeplerTargetPixelFile`, `SFFCorrector`, .... classes) is removed in favor of a...

refactoring

Would be helpful for planet vetting. Same as keppixseries. For K2 this should be detrendable for every pixel.

PyKE could offer to inject a planet signal using a very simple transit model. For more advanced transit models, we'd want to show in the documentation how the user can...

Sir @barentsen mentioned today that one could jointly fit for the CBV weights and an astrophysical variability (so that the astrophysical signal wouldn't be taken out by the CBVs) by...

enhancement

We should be able to come up with something that would smartly choose the number of CBVs. A tradeoff between CDPP and overfitting... In the example below, I used the...

enhancement
low priority

Right now, in `SFFCorrector`, we fit the motion polynomials as follows: ``` # Next, we fit the motion polynomial after removing outliers self.outlier_cent = sigma_clip(data=self.rot_col, sigma=sigma_2).mask coeffs = np.polyfit(self.rot_row[~self.outlier_cent], self.rot_col[~self.outlier_cent],...

enhancement
high priority