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Everest pipeline for K2 data #15
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I would recommend starting with the documentation. There is an example given on how to mask the transits, which is necessary for detrending (since you don't want the transits to affect the pixel-level decorelation). An alternative to masking is to do an initial fit to the transits, and then subtract the fit, and carry out the PLD on the residuals. |
I haven't used EVEREST on Windows, so I can't help. @rodluger ? |
RESTART: C:/Users/User/Desktop/everesr.py ================= |
Hi @sakhawat18 the problem is in installing the |
Hello @rodluger I followed your way but when I tried your mcmc code following import matplotlib.pyplot as pl def lnprior(x): def lnlike(x, star): Initialize the everest modelstar = Everest(201635569) Set up the MCMC samplerparams = ['Period (days)', r't$_0$ (BJD - 2456811)', 'Impact parameter'] Run!for i in tqdm(sampler.sample(x0, iterations=nsteps, blobs0=blobs0), Add the blobs to the chain for plottingchain = np.concatenate((sampler.chain, Re-scale the transit time for prettier axes labelschain[:, :, 1] -= 1978. Take the absolute value of the impact parameter for plottingchain[:, :, 2] = np.abs(chain[:, :, 2]) Re-scale the transit depth as a percentagechain[:, :, 3] *= 100. Plot the chainsfig1, ax = pl.subplots(ndim + nblobs, figsize=(6, 7)) Plot the posterior distributionssamples = chain[:, nburn:, :].reshape(-1, ndim + nblobs) 0%| | 0/1000 [00:00<?, ?it/s]emcee: Exception while calling your likelihood function: |
That depends on which system you are studying - this is only useful if you have a known transiting exoplanet; if the planet is not known, then the transits cannot be masked. |
Well, for each candidate you will have to look up the period and time of first transit. |
How can I use this package to compute new k2 planet candidate? @rodluger
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