<jupyter_start><jupyter_text>Testing<jupyter_code>gaia_source_id, id_2MASS = 	2430036837596487424, np.nan #get_names('HIP101345')
#gaia_row = gaia_values.loc[gaia_values['source_id'] == int(gaia_source_id)] # star has been run so should be here
gaia_row = query_gaia_data(gaia_source_id)
#gaia_row = gaia_row.median(axis = 0, skipna = True)
gaia_row = gaia_row.to_pandas()
np.nanmedian(gaia_row['teff_val'])
gaia_source_id = 2430036837596487424
gaia_values = pd.read_csv('gaia_values.csv')
if gaia_values['source_id'].isin([gaia_source_id]).any(): # if this star already exists in the gaia csv file
    gaia_data = gaia_values.loc[gaia_values['source_id'] == int(gaia_source_id)]
gaia_data
gaia_Teff = np.nanmedian(gaia_data['teff_val'])
gaia_Teff
int((gaia_Teff < 3056) or (gaia_Teff > 6738))
plt.figure(figsize = [6,6])
bins = np.linspace(0,17,40)
values, bins = np.histogram(gaia_data['radius_val'].to_numpy(), bins = bins)
bincenters= [(bins[i]+bins[i+1])/2. for i in range(len(bins)-1)]
plt.step(bincenters,values/len(gaia_data['radius_val'].to_numpy()),where='mid', label = 'Gaia', alpha = 0.9)
values, bin_edges = np.histogram(yee_data['R* [Rsun]'], bins = bins)
bincenters= [(bins[i]+bins[i+1])/2. for i in range(len(bins)-1)]
plt.step(bincenters,values/len(yee_data['R* [Rsun]']),where='mid', label = 'SM library', alpha = 0.9)
plt.vlines([1], ymin = 0.01, ymax = 0.54, label = 'R = 1.0Ro', lw = 1, alpha = 0.75, ls = 'dashed')
plt.vlines([2.5], ymin = 0.01, ymax = 0.54, label = 'R = 2.5Ro', lw = 1, alpha = 0.75, ls = 'dashed')
plt.vlines([6], ymin = 0.01, ymax = 0.54, label = 'R = 6.0Ro', lw = 1, alpha = 0.75, ls = 'dashed')
plt.ylabel('Normalized Counts')
plt.xlabel('Radius [Ro]')
plt.legend()
plt.ylim([0,0.55])

plt.hist(yee_data['R* [Rsun]'], bins = 40)<jupyter_output><empty_output>
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