<p align="center"><img src="logo.png" align="left" alt="MOSFiT" width="300"/></p>
[](https://travis-ci.org/guillochon/MOSFiT)
[](https://coveralls.io/github/guillochon/MOSFiT?branch=master)
[](https://www.python.org)
[](https://badge.fury.io/py/mosfit)
`MOSFiT` (**M**odular **O**pen-**S**ource **Fi**tter for **T**ransients) is a Python 2.7/3.x package that performs maximum likelihood analysis to fit semi-analytical model predictions to observed transient data. Data can be provided by the user, or can be pulled automatically from the [Open Supernova Catalog](https://sne.space) by its name, and thus the code can be used to fit *any* supernova within that database, or any database that shares the format described in the [OSC schema](https://github.com/astrocatalogs/supernovae/blob/master/SCHEMA.md) (such as the [Open TDE Catalog](https://tde.space) or the [Open Nova Catalog](https://opennova.space)).<br clear="all">
##Getting Started
To install `MOSFiT` into your Python environment, clone the package and then run the `setup.py` file:
```bash
git clone https://github.com/guillochon/MOSFiT.git
cd MOSFiT
python setup.py install
```
Once installed, MOSFiT can be run from any directory, and it's typically convenient to make a new directory for your project.
```bash
mkdir mosfit_runs
cd mosfit_runs
```
Then, to run `MOSFiT`, pass an event name to the program via the `-e` flag (the default model is a simple Nickel-Cobalt decay with diffusion):
```bash
python -m mosfit -e LSQ12dlf
```
Multiple events can be fit in succession by passing a list of names separated by spaces (names containing spaces can be specified using quotation marks):
```bash
python -m mosfit -e LSQ12dlf SN2015bn "SDSS-II SN 5751"
```
MOSFiT is parallelized and can be run in parallel by prepending `mpirun -np #`, where `#` is the number of processors in your machine +1 for the master process. So, if you computer has 4 processors, the above command would be:
```bash
mpirun -np 5 python -m mosfit -e LSQ12dlf
```
MOSFiT can also be run without specifying an event, which will yield a collection of light curves for the specified model described by the priors on the possible combinations of input parameters specified in the `parameters.json` file. This is useful for determining the range of possible outcomes for a given theoretical model:
```bash
mpirun -np 5 python -m mosfit -i 0 -m magnetar
```
The code outputs JSON files for each event/model combination that each contain a set of walkers that have been relaxed into an equilibrium about the combinations of parameters with the maximum likelihood. This output is visualized via an example Jupyter notebook (`mosfit.ipynb`) included with the software in the main directory, which by default shows output from the last `MOSFiT` run.
[](https://travis-ci.org/guillochon/MOSFiT)
[](https://coveralls.io/github/guillochon/MOSFiT?branch=master)
[](https://www.python.org)
[](https://badge.fury.io/py/mosfit)
`MOSFiT` (**M**odular **O**pen-**S**ource **Fi**tter for **T**ransients) is a Python 2.7/3.x package that performs maximum likelihood analysis to fit semi-analytical model predictions to observed transient data. Data can be provided by the user, or can be pulled automatically from the [Open Supernova Catalog](https://sne.space) by its name, and thus the code can be used to fit *any* supernova within that database, or any database that shares the format described in the [OSC schema](https://github.com/astrocatalogs/supernovae/blob/master/SCHEMA.md) (such as the [Open TDE Catalog](https://tde.space) or the [Open Nova Catalog](https://opennova.space)).<br clear="all">
##Getting Started
To install `MOSFiT` into your Python environment, clone the package and then run the `setup.py` file:
```bash
git clone https://github.com/guillochon/MOSFiT.git
cd MOSFiT
python setup.py install
```
Once installed, MOSFiT can be run from any directory, and it's typically convenient to make a new directory for your project.
```bash
mkdir mosfit_runs
cd mosfit_runs
```
Then, to run `MOSFiT`, pass an event name to the program via the `-e` flag (the default model is a simple Nickel-Cobalt decay with diffusion):
```bash
python -m mosfit -e LSQ12dlf
```
Multiple events can be fit in succession by passing a list of names separated by spaces (names containing spaces can be specified using quotation marks):
```bash
python -m mosfit -e LSQ12dlf SN2015bn "SDSS-II SN 5751"
```
MOSFiT is parallelized and can be run in parallel by prepending `mpirun -np #`, where `#` is the number of processors in your machine +1 for the master process. So, if you computer has 4 processors, the above command would be:
```bash
mpirun -np 5 python -m mosfit -e LSQ12dlf
```
MOSFiT can also be run without specifying an event, which will yield a collection of light curves for the specified model described by the priors on the possible combinations of input parameters specified in the `parameters.json` file. This is useful for determining the range of possible outcomes for a given theoretical model:
```bash
mpirun -np 5 python -m mosfit -i 0 -m magnetar
```
The code outputs JSON files for each event/model combination that each contain a set of walkers that have been relaxed into an equilibrium about the combinations of parameters with the maximum likelihood. This output is visualized via an example Jupyter notebook (`mosfit.ipynb`) included with the software in the main directory, which by default shows output from the last `MOSFiT` run.
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Release files for mosfit 0.2.3
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| File | Size | Uploaded | |
|---|---|---|---|
| mosfit-0.2.3.tar.gz | 1.8 MB | Details |
Release files / mosfit-0.2.3.tar.gz
| Download URL | mosfit-0.2.3.tar.gz |
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