The latest version of pyLIMA applies the multiprocessing library to parallelize aspects of its model fitting processes in order to optimize for speed. This has been tested and works under Ubuntu Linux with Python 3.11. Users should be aware that the multiprocessing library uses a different method ('spawn') to start threads on the Mac and Windows platforms compared to the method used on Linux ('fork'), as the fork method is considered to be unsafe on these platforms. Unfortunately, this has meant that pyLIMA crashes if run under the latest version of Python (3.11) under a Mac (and likely under Windows), due to the outstanding issue with the multiprocessing library. We are currently investigating a fix for this issue. In the interim we recommend using an earlier version of Python with the latest pyLIMA.
Authors : Etienne Bachelet (etibachelet@gmail.com), Rachel Street (rstreet@lcogt.net), Valerio Bozza (valboz@sa.infn.it), Yiannis Tsapras (ytsapras@ari.uni-heidelberg.de) and friends!
pyLIMA is the first open source software for modeling microlensing events. It should be flexible enough to handle your data and fit it. You can also practice by simulating events.
You need pip and python, that's it!
>>> pip install pyLIMA
You should be able to load pyLIMA as general module :
import pyLIMA
print(pyLIMA.__version__)Examples can be found in the pyLIMA directory after cloning this repository. More details can be found in the Documentation There is two version for each examples, one using Jupyter notebook or classic Python file.
Example_1 : HOW TO FIT MY DATA?
Example_2 : HOW TO USE YOUR PREFERED PARAMETERS?
Example_3 : HOW TO SIMULATE EVENST?
Example_4 : HOW TO USE YOUR OWN FITTING ROUTINES?
Example_5 : HOW TO FIT PARALLAX?
Want to contribute? Bug detections? Comments? Please email us (etibachelet@gmail.com) or raise an issue (recommended).