Example #1
0
def calculate_L_Lbol(Halpha_EW, Halpha_EW_err, spt):

    chi_interp = interp1d(kastclassify.defs.chi_factor_types,
                          kastclassify.defs.chi_factor_values,
                          bounds_error=False,
                          fill_value=np.nan)

    chi_err_interp = interp1d(kastclssify.defs.chi_factor_types,
                              kastclassify.defs.chi_factor_values_err,
                              bounds_error=False,
                              fill_value=np.nan)

    stype = typeToNum(spt)

    chi = chi_interp(stype)

    chi_err = chi_err_interp(stype)

    Lratio = chi * Halpha_EW
    Lratio_err = chi_err * Halpha_EW_err

    return Lratio, Lratio_err
Example #2
0
def calc_spec_info(filepath,
                   plot=False,
                   no_index_trials=100,
                   no_EW_trials=30,
                   diag_path=None):

    t1 = datetime.datetime.now()

    #collecting header info
    print('Reading in {}'.format(filepath))

    spec = kastredux.readSpectrum(filepath)
    spec.name = fits.getheader(filepath)['OBJECT']

    ra = fits.getheader(filepath)['RA']

    dec = fits.getheader(filepath)['DEC']

    date = fits.getheader(filepath)['DATE']

    filename = os.path.basename(filepath)

    if plot:
        spec.plot()
        plt.show()
    if diag_path is not None:

        spec.plot()
        plt.savefig(diag_path + spec.name + '_spec.png')

    print('Classifying by standard comparison...')
    spt, stand_name, spt_chi2, spt_scale = classify_by_standard(
        spec, ref='all', plot=plot, diag_path=diag_path)

    print('Done!')

    print('Measuring indices...')
    indices = {}

    for ref in list(kastclassify.defs.index_dict):

        print('Mesuring index set {}...'.format(ref))

        indices[ref] = measure_index_set(spec,
                                         ref=ref,
                                         no_trials=no_index_trials)

        print('Done!')

    print('Done!')

    print('Measuring Equivalent Widths...')
    EWs = {}

    for ref in list(kastclassify_EW_dict):

        print('Measuring EW for {}...'.format(ref))

        EWs[ref] = measure_EW_element(spec,
                                      ref=ref,
                                      plot=plot,
                                      no_trials=no_EW_trials,
                                      diag_path=diag_path)

        print('Done!')

    print('Done!')
    #calculating with the measurements

    print('Calculating spectral types by index...')
    index_spt, zeta_info, metallicity_class = classify_by_index(
        spec, index_dict=indices)

    print('Done!')

    print('Calculating L_Halpha/L_bol...')

    Lratio, Lratio_err = calculate_L_Lbol(EWs['Halpha']['EW'],
                                          EWs['Halpha']['EW_err'], spt)

    print('Done!')

    plt.close('all')
    #unpacking results

    print('Packaging results...')

    results = {}

    results['name'] = spec.name
    results['filename'] = filename
    results['RA'] = ra
    results['DEC'] = dec
    results['OBSDATE'] = date

    results['spt'] = spt
    results['subtype'] = typeToNum(spt) - 10
    results['spt_chi2'] = spt_chi2
    results['spt_scale'] = spt_scale

    for ref in list(indices):

        for name in list(indices[ref]):

            results[ref + '_' + name] = indices[ref][name][0]
            results[ref + '_' + name + '_err'] = indices[ref][name][1]

    for ref in list(EWs):

        for name in list(EWs[ref]):

            results[ref + '_' + name + '_(Angstrom)'] = EWs[ref][name]

    for ref in list(index_spt):

        results[ref] = index_spt[ref]

    results['zeta'] = zeta_info[0]

    results['zeta_err'] = zeta_info[1]

    results['metallicity_class'] = metallicity_class

    results['L_Halpha/L_bol'] = Lratio

    results['L_Halpha/L_bol_err'] = Lratio_err

    print('Done!')

    t2 = datetime.datetime.now()
    dt = t2 - t1

    print('All done in {}! All done at {}'.format(dt, t2))

    return results