Age/Metallicity Recovery Grids¶

Here we show recovered mass in the correct age bin and difference between input and recovered [Fe/H] as a function of input age, metallicity, filter, and distance.

import pandas as pd
import holoviews as hv
hv.notebook_extension('bokeh')

df = pd.read_csv('data/MIST_good_agebin.csv', usecols=list(range(5)) + [7])
# get rid of 'WFIRST_' from filter column
df['filt'] = df['filt'].str.split('_').str[-1]
df['feh_diff'] = df.feh_meas_mean - df.feh
df.head()

# dimensions & labels
kdims = [('filt', 'Filter'), ('dist', 'Distance (Mpc)'),
         ('age', 'Input age (log yr)'),
         ('feh', 'Input [Fe/H]')]
vdims = [('feh_meas_mean', 'Measured [Fe/H]'),
         ('massdiff_mean', 'Fraction of mass in age bin'),
         ('feh_diff', 'Measured - input [Fe/H]')]

massfrac = hv.Table(df, kdims=kdims, vdims=vdims)

%opts Layout [show_title=False] \
HeatMap [width=300 height=450 invert_yaxis=False title_format='{label}' \
               colorbar=True colorbar_position='bottom' normalize=True] \
HeatMap.mass [colorbar_opts={'title':'Mass fraction at correct age'}] (cmap='inferno') \
HeatMap.feh [colorbar_opts={'title':'Measured - input [Fe/H]'}] (cmap='Spectral_r')

hm1 = massfrac.to.heatmap(kdims=['age','feh'], vdims=['massdiff_mean'],
                          group='mass', label='Mass at correct age')
hm2 = massfrac.to.heatmap(kdims=['age','feh'], vdims=['feh_diff'],
                          group='feh', label='Measured - input [Fe/H]')
hm1+hm2

	filt	dist	age	feh	feh_meas_mean	massdiff_mean	feh_diff
0	X606	4.0	9.3	-2.2	-2.1987	0.96969	0.0013
1	X606	4.0	9.3	-1.8	-1.7987	0.98503	0.0013
2	X606	4.0	9.3	-1.3	-1.2970	0.96305	0.0030
3	X606	4.0	9.3	-0.8	-0.7982	0.93865	0.0018
4	X606	4.0	9.3	-0.5	-0.4988	0.91863	0.0012