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Installing Moose

Moose can be installed with pip to get the latest release, note that on PyPI it is known as moose-spectra:

pip install moose-spectra

You can also install the lastest development version of Moose, which may not be released to PyPI yet, using this git repository directly:

pip install git+https://github.com/AntoineTUE/Moose.git

You can also install several optional dependencies alongside Moose for fitting spectra, interactive plotting, or locally updating these documentation pages.

To do that run one of the following commands:

# installs lmfit dependency
pip install moose-spectra[fit]

# install doc page dependencies
pip install moose-spectra[docs]

# install dependencies for running the examples
pip install moose-spectra[examples]

# install the all the above dependencies
pip install moose-spectra[all]

How to use Moose

A basic example demonstrating how to simulate and fit a spectrum is as follows.

  1. Query a database for the emission system of interest, which is done with query_DB. This function will look for databases in the location where Moose has been installed, but it can also be provided with a path to your own compatible database. See the code reference for more information.

  2. Create a sample spectrum and add noise to it. To do this, first call the model_for_fit function, which is a high level function to calculate a spectrum, given line positions, temperatures, broadening parameters, and so forth.

  3. Next prepare to fit this simulated spectrum. Create an lmfit.Model, by wrapping the model_for_fit function and passing in the database as well to the wrapper.

  4. As initial guess and bounds for the fit parameters, create an lmfit.Parameters object. For simplicity you can set these with some default values using Moose.default_params.

import Moose
import numpy as np
import lmfit

rng = np.random.default_rng(100)

db = Moose.query_DB('N2CB', wl=(320,390)) # Restrict to wavelength between 320 and 390 nm

x = np.linspace(320,390,2000)

simulated = Moose.model_for_fit(x,sigma=0.01,gamma=0.01,mu=0.5,T_rot=1000, T_vib=5000, sim_db=db)

y = rng.normal(simulated, 0.01)# add noise

model = lmfit.Model(Moose.model_for_fit, sim_db=db, independent_vars=["x"])
params = lmfit.create_params(**Moose.default_params)

result = model.fit(y, x=x, params=params)

result.plot(title='N2CB fit', datafmt='x')

For more examples see the examples section