Python package that uses the results from He et al. 2018 in order to fit light curves to SNe Ia. Please cite this paper, as well as the DOI for this software, 10.5281/zenodo.6450266, when using this code.
Package by Lauren Aldoroty, Jiawen Yang, Shiyuan He 2022.
Package currently incomplete.
git clone https://github.com/laldoroty/snlcpy
Then, cd into the snlcpy directory.
python setup.py install
snlcpy accepts three lists or three numpy arrays as data input. Magnitude error is an optional input. If you include magnitude error, the FPCA fit will be weighted by these errors.
snlcpy only fits one photometric bandpass at a time. Available templates are Johnson B, V, R, I filters and one 'vague' template, which may be used for any bandpass. If a Johnson filter is not specified in fit_params(), the program will default to 'vague'.
sn = pd.read_csv('SN2011fe.csv')
B_data = sn[sn['filt'] == 'B']
date = np.array(B_data['JD'])
mag = np.array(B_data['mag'])
emag = np.array(B_data['mag_err'])
res = snlcpy.fit_pcparams(date,mag,emag,'B')
lc = snlcpy.make_fittedlc('B', res)
snlcpy.plot_lc(date,mag,res,fpca_f='B',emag=emag,input_func=True)
Note that if you do not provide emag and you get a fitting error about data type, it may be because you need to specify the arguments for the inputs.
Here, lc is a continuous function produced by scipy.interpolate.interp1d. In order to output an array instead, you should use input_func=False in plot_lc(), return_func=False in make_fittedlc().
The fit result (i.e., PC coefficients) can be accessed with:
print(res['params']))