CCF_Systematics.py 文件源码

def get_ccf_data(basedir, primary_name=None, secondary_name=None, vel_arr=np.arange(-900.0, 900.0, 0.1), type='bright'):
    """
    Searches the given directory for CCF files, and classifies
    by star, temperature, metallicity, and vsini
    :param basedir: The directory to search for CCF files
    :keyword primary_name: Optional keyword. If given, it will only get the requested primary star data
    :keyword secondary_name: Same as primary_name, but only reads ccfs for the given secondary
    :keyword vel_arr: The velocities to interpolate each ccf at
    :return: pandas DataFrame
    """
    if not basedir.endswith('/'):
        basedir += '/'
    all_files = ['{}{}'.format(basedir, f) for f in os.listdir(basedir) if type in f.lower()]
    primary = []
    secondary = []
    vsini_values = []
    temperature = []
    gravity = []
    metallicity = []
    ccf = []
    for fname in all_files:
        star1, star2, vsini, temp, logg, metal = classify_filename(fname, type=type)
        if primary_name is not None and star1.lower() != primary_name.lower():
            continue
        if secondary_name is not None and star2.lower() != secondary_name.lower():
            continue
        vel, corr = np.loadtxt(fname, unpack=True)
        fcn = spline(vel, corr)
        ccf.append(fcn(vel_arr))
        primary.append(star1)
        secondary.append(star2)
        vsini_values.append(vsini)
        temperature.append(temp)
        gravity.append(logg)
        metallicity.append(metal)

    # Make a pandas dataframe with all this data
    df = pd.DataFrame(data={'Primary': primary, 'Secondary': secondary, 'Temperature': temperature,
                                'vsini': vsini_values, 'logg': gravity, '[Fe/H]': metallicity, 'CCF': ccf})
    return df