def get_ccf(self, params, df=None):
"""
Get the ccf with the given parameters.
Parameters:
===========
- params: dictionary:
All the parameters necessary to define a single ccf. This should be
a python dictionary with the keys:
- 'starname': The name of the star. Try self.list_stars() for the options.
- 'date': The UT date of the observations. Try self.list_dates() for the options.
- 'T': temperature of the model
- 'logg': the log(g) of the model
- 'vsini': the vsini by which the model was broadened before correlation
- '[Fe/H]': the metallicity of the model
- 'addmode': The way the order CCFs were added to make a total one. Can be:
- 'simple'
- 'ml'
- 'weighted'
- 'dc'
- df: a pandas DataFrame such as outputted by _compile_data
Returns:
========
-ccf: pandas DataFrame
Holds columns of velocity and CCF power
"""
if df is None:
try:
df = self._compile_data(params['starname'], params['date'])
except KeyError:
raise KeyError('Must give get_ccf params with starname and date keywords, if df is not given!')
Tvals = df['T'].unique()
T = Tvals[np.argmin(abs(Tvals - params['T']))]
good = df.loc[(df['T'] == T) & (df.logg == params['logg']) & (df.vsini == params['vsini']) \
& (df['[Fe/H]'] == params['[Fe/H]']) & (df.addmode == params['addmode'])]
return pd.DataFrame(data={'velocity': self.velocities, 'CCF': good['ccf'].item()})
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