# MulensModel

- MulensModel is package for modeling microlensing (or μ-lensing)
events.

Latest release: 1.5.3 and we're working on further developing the code.

MulensModel can generate a microlensing light curve for a given set of microlensing parameters, fit that light curve to some data, and return a chi2 value. That chi2 can then be input into an arbitrary likelihood function to find the best fit parameters.

Are you interested in Microlensing Data Analysis Challenge? For more details on using MulensModel in data challenge, see this document.

A few useful resources:

- Basic usage tutorial,
- Fitting tutorial,
- Microlensing parallax fitting tutorial,
- Examples on how to use the code:
- Example 01 -- plot simple point-source/point-lens (PSPL) model and model with planetary lens,
- Example 02 -- fit PSPL model to the data using scipy.optimize.minimize(),
- Example 03 -- define PSPL model using physical properties and plot the resulting magnification curve,
- Example 04 -- calculate the Einstein ring size for a grid of lens masses and distances,
- Example 05 -- plot multiple datasets for a single model, plot the residuals, and do this both in magnitude and magnification spaces,
- Example 06 -- fit parallax model using EMCEE,
- Example 07 -- fit parallax model using MultiNest,
- Example 08 -- shows how to fit simulated WFIRST light curve with planetary model,
- Example 09 -- fit point lens model using chi^2 gradient,
- Example 10 -- fit model and extract posterior fluxes.

- Instructions on getting satellite positions

Documentation includes description of input and output of every function.

If you want to learn more about microlensing, please visit Microlensing Source website.

Currently, MulensModel supports:

- Lens Systems: point lens or binary lens.
- Source Stars: single source or binary source.
- Effects: finite source (1-parameter), parallax (satellite & annual), binary lens orbital motion, different parametrizations of microlensing models.

Need more? Open an issue or send us an e-mail.

Are you using MulensModel for scientific research? Please give us credit by citing the paper and ASCL reference.

## How to install?

Download the source code and run:

```
python setup.py install
```

MulensModel requires some standard packages plus astropy package. To make sure that you have everything that's needed, just run:

```
pip install -r requirements.txt
```

If you have any problems, please contact the authors and we will try to help.

file revised Sep 2018

## Github

link |

Stars: 6 |

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## Dependencies

## Used By

Total: 0

## Releases

## v1.5.3 - Sep 8, 2018

Binary sources - basic functions work properly. Also pi_E added in mulenssystem.py.

## v1.4.0 - Jul 14, 2018

Event.sum_function added

## v1.3.0 - Jun 21, 2018

chi2 calculated in flux space as default, tabulated coefficients for FSPL models, Skowron and Gould 2012 as default method for binary lens polynomial root solving

## v1.2.0 - May 2, 2018

gradient of chi^2 for PSPL events

## v1.1.0 - Mar 31, 2018

orbital motion, additional input file format in Horizons, improved BLPS calculations