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def get_cache_info(*args, **kwargs):
    import inspect
    from pathlib import Path

    # Wait until the DAG has been built
    snakemake.workflow.checkpoints.syw__dag.get()

    # Get all jobs from the DAG
    dag = None
    levels = inspect.stack()

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import pymc3 as pm
import pymc3_ext as pmx
import numpy as np
import starry
import theano.tensor as tt
from astropy import constants, units
import matplotlib.pyplot as plt


def get_stellar_inclination_prior(incstar):

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# Hack to allow importing `pymc3` without an internet connection
# This was fixed in https://github.com/dask/distributed/pull/3991
try:
    import distributed
except Exception as e:
    try:
        import socket

        getaddrinfo = socket.getaddrinfo
        socket.getaddrinfo = lambda *args: [

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"""
A very simple / slow / inefficient / unstable implementation of a Gaussian
process for stellar light curves. We sample lots of surface maps from a 
given distribution of spot sizes / locations / contrasts and compute the
empirical mean and covariance of the resulting distribution. In the limit
that our number of samples is infinite, this yields the meand and covariance
of the desired GP!
"""
import starry
import numpy as np

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import starry
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm

# Settings
starry.config.lazy = False
np.random.seed(12)

# Load a map of the earth

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Working with a public repo and a private repo

We currently have a public vplanet repo and a private vplanet-private repo. Here's what we decided our workflow should look like:

All development is done on the (non-master) branches of vplanet-private and synced to master via pull requests. When a change is ready to be incorporated into the current released version of the code, we go to our local clone of the public vplanet repository and pull from master on vplanet-private to local dev. Then, on the GitHub UI, we issue a pull request for master on vplanet.

Setting up tracking on your local machine