Authors : Etienne Bachelet, firstname.lastname@example.org Rachel Street, email@example.com Valerio Bozza, firstname.lastname@example.org Martin Norbury, email@example.com and friends!
pyLIMA is an open source for modeling microlensing events. It should be flexible enough to handle your data and fit it. You can also practice by simulating events.
Documentation and Installation
You need pip or you can install manually the required libraries Documentation
pyLIMA should now run both on python3 (python2 is not supported anymore, time for update!).
Installation and use
Lot of efforts have been made to have pyLIMA (and VBBinaryLensing) install through pip:
>>> pip install pyLIMA
This new procedure which should avoid the previous installations headaches! Successfully test on various UNIX, MAC and Windows! If you encounter any problems, please contact firstname.lastname@example.org.
You should be able to load pyLIMA as general module :
from pyLIMA import microlmagnification
Examples can be found in your pyLIMA directory. Look on the documentation to learn how to run it. There is two version for each examples, one using Jupyter notebook (.ipynb) or classic Python file (.py).
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?
What can you do?
pyLIMA is now in beta!! Here is the status of implemented microlensing models:
pyLIMA can also treat Second Order effects :
|Second-Order Effects||Implemented||Examples||Fit Method Advice|
|Annual parallax||No||Levenberg-Marquardt (LM)|
|Terrestrial parallax||No||Levenberg-Marquardt (LM)|
|Space parallax||No||Levenberg-Marquardt (LM)|
How to contribute?
Want to contribute? Bug detections? Comments? Please email us : email@example.com, firstname.lastname@example.org, email@example.com