SpecFit

SpecFit is designed to be a versatile spectrum-fitting tool. Utilizing the Python package lmfit, this tool performs non-linear least-squares minimization to accurately model astrophysical spectra. SpecFit streamlines the process of deriving physical parameters such as blackbody temperature by integrating an internal preprocessing pipeline. With the support of pybind11 and C++, SpecFit allows for the iteration of complex (non-vectorizable) models within lmfit, combining Python's simplicity to perform data analysis with the computational efficiency of C++.

Installation

Model Data

Prior to installing SpecFit, it is important to note that some data is necessary for some models:

• Free-free emission

To retrieve this data, after cloning this repository, run the './retrieve_data.py' script. Currently, the script downloads the following:

• Gaunt factor table from https://data.nublado.org/gauntff

The data will be downloaded and subsequently copied into the Python site-packages SpecFit directory upon installation (the following step).

Building

The Python bindings of the C++ modeling code must be built first before using this package. The 'setup.py' and 'pyproject.toml' handle this automatically; to install SpecFit, simply do so using:

python -m pip install path/to/SpecFit

Performing this step will automatically install its other Python dependencies, which are:

• NumPy
• SciPy
• lmfit
• SpectrumCore (My core repository for spectrum operations)

Operation

See the examples in the 'SpecFit/examples' directory.

Using SpecFit is intended to be simple and uses the following general approach:

1. Instantiate the SpecFit object by providing the file name (for simple data with wave/flux/error as the first three columns) or just a NumPy array of the with wave/flux/error(optional) in the first 3 columns:

   from SpecFit import SpecFit
   
   fn = 'my_spectrum.dat'
   optional_read_params = {
       # ...
   }
   spec = SpecFit(fn, **optional_read_params)

2. Add submodels to the composite model to be processed by lmfit. Custom functions may be passed, or strings corresponding to the available premade models in C++:

   # Adds a "blackbody" (Planck function) model
   spec.add_model('bb')
   
   # Adds a "free-free emission" model
   spec.add_model('free_free')

   Default parameters are determined during this step for premade models; however, one may optionally provide initial parameters to add_model in the same manner as for lmfit. See examples for details. If providing a custom callable function to add_model, providing initial guesses for parameters will be necessary.

3. Fit the parameters of the provided model.

   spec.fit()

   This method essentially calls the lmfit.Model.fit() method with appropriate arguments provided to it. However, SpecFit.fit() passes any additional keyword-arguments given to it directly to the lmfit.Model.fit() method (see examples).

4. After fitting, data analysis can occur. Best-fit parameters can be viewed with SpecFit.fit_report(), or a plot may be retrieved with SpecFit.plot(). See examples for more details.