Using `isoc` for isochrone retrieval#

isoc queries and processes stellar isochrone data from the Padova/PARSEC and the MIST/MESA databases. In effect it fills out the relevant webform for you and returns the result not as a text file but as an Isochrone object that stores all data in astropy tables with physical units. Isochrone has two main properties - data and photometry - as well as secondary properties for quick access to data columns such as log_age, mass and metallicity.

This tutorial is divided into sections:

Querying — getting an Isochrone
Data — data tables and useful shorthands in Isochrone
Photometry — accessing photometric systems in Isochrone
- 3.1: Computing colors
- 3.2: Adding multiple photometric systems to a dataset
Plotting — a few basic examples, including:
Parameter help — for Isochrone.from_mist and Isochrone.from_padova

1. Querying#

[1]:

# avoid need to add `print` to all but the last call in a cell
from IPython.core.interactiveshell import InteractiveShell

InteractiveShell.ast_node_interactivity = "all"

Query the MIST/MESA and Padova/PARSEC databases with Isochrone.from_mist and Isochrone.from_padova, like this:

[2]:

from isoc.isochrone import Isochrone

[3]:

im = Isochrone.from_mist(
    age=1e9,
    metallicity=0.0,
    photometry="JWST NIRCAM",
    )

Inferred linear age scale based on age values 1000000000.0.
Querying https://mist.science/iso_form.php
Query parameters sent to the MIST server:
        version=MIST2
        v_div_vcrit=vvcrit0.4
        FeH_value=0.0
        alpha_value=p0
        age_type=single
        age_scale=linear
        age_value=1000000000.0
        age_range_low=5
        age_range_high=10.3
        age_range_delta=0.05
        output_option=photometry
        output=JWST
        Av_value=0
Downloading data from https://mist.science/output/MIST_iso_69b35d1f4b060.zip
Zip archive contains: ['MIST_iso_69b35d1f4b060.iso', 'MIST_iso_69b35d1f4b060.iso.JWST']
Parsing theory file: MIST_iso_69b35d1f4b060.iso
Parsing photometry file: MIST_iso_69b35d1f4b060.iso.JWST
  Moving theory column '[Fe/H]_init' from photometry file to theory table.
  Moving theory column '[Fe/H]' from photometry file to theory table.
Theory file has 172 columns, photometry file has 40 columns, identified 29 photometry-only columns: ['NIRCAM_F070W', 'NIRCAM_F090W', 'NIRCAM_F115W', 'NIRCAM_F140W', 'NIRCAM_F150W', 'NIRCAM_F150W2', 'NIRCAM_F162M', 'NIRCAM_F164N', 'NIRCAM_F182M', 'NIRCAM_F187N', 'NIRCAM_F200W', 'NIRCAM_F210M', 'NIRCAM_F212N', 'NIRCAM_F250M', 'NIRCAM_F277W', 'NIRCAM_F300M', 'NIRCAM_F322W2', 'NIRCAM_F323N', 'NIRCAM_F335M', 'NIRCAM_F356W', 'NIRCAM_F360M', 'NIRCAM_F405N', 'NIRCAM_F410M', 'NIRCAM_F430M', 'NIRCAM_F444W', 'NIRCAM_F460M', 'NIRCAM_F466N', 'NIRCAM_F470W', 'NIRCAM_F480W']

Or for Padova/PARSEC:

[4]:

# age=9 is interpreted as log10(age/yr) = 9
# Z=0.0207 is solar metallicity
ip = Isochrone.from_padova(
    age=9,
    metallicity=0.0207,
    metallicity_type="Z",
    photometry="Euclid VIS+NISP (ABmags)",
    )

Inferred log10 age scale based on age values (9.0, 9.0, 0.0).
Downloading parsec.json from https://raw.githubusercontent.com/mfouesneau/ezpadova/master/src/ezpadova/parsec.json


Querying http://stev.oapd.inaf.it/cgi-bin/cmd...
Retrieving data...
Downloading data...http://stev.oapd.inaf.it/tmp/output997402856898.dat
User-supplied query parameters (as seen by ezpadova):
        {'age_yr': None, 'logage': (9.0, 9.0, 0.0), 'Z': (0.0207, 0.0207, 0.0), 'MH': None, 'photsys_file': 'euclid_nisp'}
All query parameters (including defaults for those not specified):
        {'age_yr': None, 'logage': (9.0, 9.0, 0.0), 'Z': (0.0207, 0.0207, 0.0), 'MH': None, 'photsys_file': 'euclid_nisp', 'cmd_version': '3.8', 'track_omegai': '0.00', 'track_parsec': 'parsec_CAF09_v1.2S', 'track_colibri': 'parsec_CAF09_v1.2S_S_LMC_08_web', 'track_postagb': 'no', 'n_inTPC': '10', 'eta_reimers': '0.2', 'kind_interp': '1', 'kind_postagb': '-1', 'photsys_version': 'YBCnewVega', 'dust_sourceM': 'dpmod60alox40', 'dust_sourceC': 'AMCSIC15', 'kind_mag': '2', 'kind_dust': '0', 'extinction_av': '0.0', 'extinction_coeff': 'constant', 'extinction_curve': 'cardelli', 'kind_LPV': '3', 'imf_file': 'tab_imf/imf_kroupa_orig.dat', 'isoc_isagelog': '0', 'isoc_agelow': '1.0e9', 'isoc_ageupp': '1.0e10', 'isoc_dage': '0.0', 'isoc_lagelow': '6.6', 'isoc_lageupp': '10.13', 'isoc_dlage': '0.0', 'isoc_ismetlog': '0', 'isoc_zlow': '0.0152', 'isoc_zupp': '0.03', 'isoc_dz': '0.0', 'isoc_metlow': '-2', 'isoc_metupp': '0.3', 'isoc_dmet': '0.0', 'output_kind': '0', 'output_evstage': '1', 'lf_maginf': '-15', 'lf_magsup': '20', 'lf_deltamag': '0.5', 'sim_mtot': '1.0e4', 'submit_form': 'Submit'}

If you prefer the ezpadova.get_isochrones argument style, that works too:

[5]:

# equivalent to the above call with `from_padova`
ip_ez = Isochrone.query_ezpadova(
    logage=(9,9,0),
    Z=(0.0207,0.0207,0),
    photsys_file="euclid_nisp",
    )

Querying http://stev.oapd.inaf.it/cgi-bin/cmd...
Retrieving data...
Downloading data...http://stev.oapd.inaf.it/tmp/output505909469517.dat
User-supplied query parameters (as seen by ezpadova):
        {'age_yr': None, 'logage': (9.0, 9.0, 0.0), 'Z': (0.0207, 0.0207, 0.0), 'MH': None, 'photsys_file': 'euclid_nisp'}
All query parameters (including defaults for those not specified by user):
        {'age_yr': None, 'logage': (9.0, 9.0, 0.0), 'Z': (0.0207, 0.0207, 0.0), 'MH': None, 'photsys_file': 'euclid_nisp', 'cmd_version': '3.8', 'track_omegai': '0.00', 'track_parsec': 'parsec_CAF09_v1.2S', 'track_colibri': 'parsec_CAF09_v1.2S_S_LMC_08_web', 'track_postagb': 'no', 'n_inTPC': '10', 'eta_reimers': '0.2', 'kind_interp': '1', 'kind_postagb': '-1', 'photsys_version': 'YBCnewVega', 'dust_sourceM': 'dpmod60alox40', 'dust_sourceC': 'AMCSIC15', 'kind_mag': '2', 'kind_dust': '0', 'extinction_av': '0.0', 'extinction_coeff': 'constant', 'extinction_curve': 'cardelli', 'kind_LPV': '3', 'imf_file': 'tab_imf/imf_kroupa_orig.dat', 'isoc_isagelog': '0', 'isoc_agelow': '1.0e9', 'isoc_ageupp': '1.0e10', 'isoc_dage': '0.0', 'isoc_lagelow': '6.6', 'isoc_lageupp': '10.13', 'isoc_dlage': '0.0', 'isoc_ismetlog': '0', 'isoc_zlow': '0.0152', 'isoc_zupp': '0.03', 'isoc_dz': '0.0', 'isoc_metlow': '-2', 'isoc_metupp': '0.3', 'isoc_dmet': '0.0', 'output_kind': '0', 'output_evstage': '1', 'lf_maginf': '-15', 'lf_magsup': '20', 'lf_deltamag': '0.5', 'sim_mtot': '1.0e4', 'submit_form': 'Submit'}

Or instead of querying, you can load a local isochrone file:

[6]:

# load a mist file from a query over a grid of ages, including photometry
im_local = Isochrone.from_mist(from_file="data_files/mist_multiple.zip")

print("\n")

# load a padova file for a single isochrone, including photometry
ip_local = Isochrone.from_padova(from_file="data_files/padova_single.dat")

'from_file' is not None. Loading isochrone data from file: data_files/mist_multiple.zip rather than performing a query.
Zip archive contains: ['MIST_iso_69b030f63c54a.iso', 'MIST_iso_69b030f63c54a.iso.SDSSugriz']
Parsing theory file: MIST_iso_69b030f63c54a.iso
Parsing photometry file: MIST_iso_69b030f63c54a.iso.SDSSugriz
  Moving theory column '[Fe/H]_init' from photometry file to theory table.
  Moving theory column '[Fe/H]' from photometry file to theory table.
Theory file has 172 columns, photometry file has 16 columns, identified 5 photometry-only columns: ['SDSS_u', 'SDSS_g', 'SDSS_r', 'SDSS_i', 'SDSS_z']


'from_file' is not None. Loading isochrone data from file: data_files/padova_single.dat rather than performing a query.

2. Data#

The data in Isochrone is stored in astropy tables with physical units. All non-photometric data is in Isochrone.data. For the Padova/PARSEC isochrone queried above, we can get an overview of the data at multiple levels:

[7]:

ip

[7]:

Isochrone(database=padova): 533 rows x 27 columns

[8]:

ip.columns

[8]:

<TableColumns names=('Zini','MH','logAge','Mini','int_IMF','Mass','logL','logTe','logg','label','McoreTP','C_O','period0','period1','period2','period3','period4','pmode','Mloss','tau1m','X','Y','Xc','Xn','Xo','Cexcess','Z')>

[9]:

ip.data

[9]:

QTable length=533

Zini	MH	logAge	Mini	int_IMF	Mass	logL	logTe	logg	label	McoreTP	C_O	period0	period1	period2	period3	period4	pmode	Mloss	tau1m	X	Y	Xc	Xn	Xo	Cexcess	Z
	dex	dex(yr)	solMass		solMass	dex(solLum)	dex(K)	dex(cm / s2)		solMass		d	d	d	d	d		solMass / yr
float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64
0.0207	0.15867	9.0	0.0908484459	1.0897678296	0.091	-3.376	3.3592	5.161	0.0	0.0	0.545	0.0	0.0	0.0	0.0	0.0	-1.0	-4.66e-17	0.0	0.6935	0.2856	0.003749	0.001011	0.009172	-1.0	0.02085
0.0207	0.15867	9.0	0.1008355916	1.1780487038	0.101	-3.231	3.3795	5.143	0.0	0.0	0.545	0.0	0.0	0.0	0.0	0.0	-1.0	-6.29e-17	0.0	0.6935	0.2857	0.003749	0.001011	0.009172	-1.0	0.02085
0.0207	0.15867	9.0	0.1086777225	1.2397594949	0.109	-3.146	3.3911	5.136	0.0	0.0	0.545	0.0	0.0	0.0	0.0	0.0	-1.0	-7.42e-17	0.0	0.6935	0.2857	0.003749	0.001011	0.009172	-1.0	0.02085
0.0207	0.15867	9.0	0.113472119	1.2747051904	0.113	-3.092	3.3982	5.129	0.0	0.0	0.545	0.0	0.0	0.0	0.0	0.0	-1.0	-8.27e-17	0.0	0.6935	0.2857	0.003749	0.001011	0.009172	-1.0	0.02085
0.0207	0.15867	9.0	0.1183084249	1.3080637026	0.118	-3.036	3.4053	5.119	0.0	0.0	0.545	0.0	0.0	0.0	0.0	0.0	-1.0	-9.31e-17	0.0	0.6935	0.2857	0.003749	0.001011	0.009172	-1.0	0.02085
0.0207	0.15867	9.0	0.1208098754	1.3246297029	0.121	-3.008	3.4088	5.115	0.0	0.0	0.545	0.0	0.0	0.0	0.0	0.0	-1.0	-9.86e-17	0.0	0.6934	0.2857	0.003749	0.001011	0.009172	-1.0	0.02085
0.0207	0.15867	9.0	0.1407841742	1.4426669988	0.141	-2.826	3.43	5.084	0.0	0.0	0.545	0.0	0.0	0.0	0.0	0.0	-1.0	-1.44e-16	0.0	0.6934	0.2858	0.003749	0.001011	0.009172	-1.0	0.02085
0.0207	0.15867	9.0	0.1423892677	1.4511987412	0.142	-2.814	3.4312	5.082	0.0	0.0	0.545	0.0	0.0	0.0	0.0	0.0	-1.0	-1.48e-16	0.0	0.6934	0.2858	0.003749	0.001011	0.009172	-1.0	0.02085
0.0207	0.15867	9.0	0.1460757852	1.4703293683	0.146	-2.787	3.434	5.077	0.0	0.0	0.545	0.0	0.0	0.0	0.0	0.0	-1.0	-1.56e-16	0.0	0.6934	0.2858	0.003749	0.001011	0.009172	-1.0	0.02085
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
0.0207	0.15867	9.0	2.3635585308	2.5813058508	0.78	3.664	3.5044	-0.364	8.0	0.655	0.765	247.7	-1.0	-1.0	-1.0	-1.0	0.0	-1.52e-06	0.05	0.6547	0.3218	0.004842	0.003165	0.00844	-1.0	0.02351
0.0207	0.15867	9.0	2.3635587692	2.5813058577	0.775	3.708	3.5104	-0.387	8.0	0.655	0.765	244.54	-1.0	-1.0	-1.0	-1.0	0.0	-2.32e-06	0.09	0.6547	0.3218	0.004844	0.003165	0.00844	-1.0	0.02351
0.0207	0.15867	9.0	2.3635594845	2.5813058784	0.767	3.781	3.5209	-0.423	8.0	0.655	0.766	240.45	-1.0	-1.0	-1.0	-1.0	0.0	-4.62e-06	0.34	0.6547	0.3218	0.004846	0.003165	0.00844	-1.0	0.02351
0.0207	0.15867	9.0	2.3635601997	2.5813058991	0.759	3.846	3.5321	-0.447	8.0	0.655	0.766	235.45	-1.0	-1.0	-1.0	-1.0	0.0	-8.49e-06	0.62	0.6547	0.3218	0.004849	0.003165	0.00844	-1.0	0.02352
0.0207	0.15867	9.0	2.363560915	2.5813059198	0.748	3.886	3.5419	-0.454	8.0	0.655	0.767	229.51	-1.0	-1.0	-1.0	-1.0	0.0	-1.25e-05	0.89	0.6546	0.3218	0.004852	0.003165	0.008439	-1.0	0.02352
0.0207	0.15867	9.0	2.3635621071	2.5813059543	0.736	3.902	3.5501	-0.445	8.0	0.655	0.767	222.69	-1.0	-1.0	-1.0	-1.0	0.0	-1.48e-05	1.07	0.6546	0.3218	0.004856	0.003165	0.008439	-1.0	0.02353
0.0207	0.15867	9.0	2.3635630608	2.5813059819	0.721	3.904	3.5574	-0.426	8.0	0.655	0.768	215.02	-1.0	-1.0	-1.0	-1.0	0.0	-1.56e-05	1.1	0.6546	0.3219	0.004861	0.003165	0.008439	-1.0	0.02353
0.0207	0.15867	9.0	2.3635644913	2.5813060233	0.706	3.9	3.5648	-0.402	8.0	0.655	0.769	206.57	-1.0	-1.0	-1.0	-1.0	0.0	-1.56e-05	1.07	0.6546	0.3219	0.004867	0.003165	0.008439	-1.0	0.02354
0.0207	0.15867	9.0	2.363568306	2.5813061338	0.655	-9.999	4.2037	2.116	9.0	0.656	0.0	0.0	0.0	0.0	0.0	0.0	-1.0	-9.999999999999999e-31	0.0	0.6546	0.3219	0.004882	0.003165	0.008439	-1.0	0.02355

Or for the MIST Isochrone:

[10]:

im.data

[10]:

QTable length=1401

EEP	isochrone_age_yr	initial_mass	star_mass	star_mdot	he_core_mass	c_core_mass	o_core_mass	log_L	log_L_div_Ledd	log_LH	log_LHe	log_LZ	log_Teff	log_abs_Lgrav	log_R	log_g	log_surf_cell_z	surf_avg_omega	surf_avg_v_rot	surf_num_c12_div_num_o16	surf_avg_omega_crit	surf_avg_omega_div_omega_crit	surf_avg_v_crit	surf_avg_v_div_v_crit	surf_avg_Lrad_div_Ledd	v_div_csound_surf	surf_r_equatorial_div_r	surf_r_polar_div_r	total_angular_momentum	grav_dark_L_polar	grav_dark_Teff_polar	grav_dark_L_equatorial	grav_dark_Teff_equatorial	surface_h1	surface_h2	surface_he3	surface_he4	surface_li7	surface_be7	surface_be9	surface_be10	surface_b8	surface_c12	surface_c13	surface_n13	surface_n14	surface_n15	surface_o14	surface_o15	surface_o16	surface_o17	surface_o18	surface_f17	surface_f18	surface_f19	surface_ne18	surface_ne19	surface_ne20	surface_ne21	surface_ne22	surface_na21	surface_na22	surface_na23	surface_na24	surface_mg23	surface_mg24	surface_mg25	surface_mg26	surface_al25	surface_al26	surface_al27	surface_si27	surface_si28	surface_si29	surface_si30	surface_p30	surface_p31	surface_s31	surface_s32	surface_s33	surface_s34	surface_ca40	surface_ti48	surface_fe56	log_center_T	log_center_Rho	center_degeneracy	center_omega	center_gamma	mass_conv_core	center_h1	center_h2	center_he3	center_he4	center_li7	center_be7	center_be9	center_be10	center_b8	center_c12	center_c13	center_n13	center_n14	center_n15	center_o14	center_o15	center_o16	center_o17	center_o18	center_f17	center_f18	center_f19	center_ne18	center_ne19	center_ne20	center_ne21	center_ne22	center_na21	center_na22	center_na23	center_na24	center_mg23	center_mg24	center_mg25	center_mg26	center_al25	center_al26	center_al27	center_si27	center_si28	center_si29	center_si30	center_p30	center_p31	center_s31	center_s32	center_s33	center_s34	center_ca40	center_ti48	center_fe56	pp	cno	tri_alfa	burn_c	burn_n	burn_o	c12_c12	apsidal_constant_k2	delta_nu	delta_Pg	nu_max	acoustic_cutoff	max_conv_vel_div_csound	max_gradT_div_grada	gradT_excess_alpha	min_Pgas_div_P	max_L_rad_div_Ledd	e_thermal	conv_env_top_mass	conv_env_bot_mass	conv_env_top_radius	conv_env_bot_radius	conv_env_turnover_time_l_t	conv_env_turnover_time_l_b	conv_env_turnover_time_g	envelope_binding_energy	total_moment_of_inertia	phase	[Fe/H]_init	[Fe/H]
		solMass	solMass	solMass / yr	solMass	solMass	solMass	dex(solLum)	dex	dex(solLum)	dex(solLum)	dex(solLum)	dex(K)	dex(solLum)	dex(solRad)	dex(cm / s2)	dex	rad / s	km / s		rad / s		km / s						cm2 g / s	solLum	K	solLum	K																																																				dex(K)	dex(g / cm3)		rad / s		solMass																																																				dex(solLum)	dex(solLum)	dex(solLum)	dex(solLum)	dex(solLum)	dex(solLum)	dex(solLum)		uHz	s	uHz	uHz						erg	solMass	solMass	solRad	solRad	s	s	s	erg	cm2 g
float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64
189.0	9.0	0.1	0.099999779678962	-4.294061613144032e-17	0.0	0.0	0.0	-3.0639057420333473	-6.93398619083983	-3.0687227116564464	-74.96377136037967	-98.89141074486867	3.44867528806306	-5.023939721593992	-0.9060446106901656	5.249977617950291	-1.7862787350383975	0.0	0.0	0.48595659010177983	0.0	0.0	0.0	0.0	0.0	1.3349484906378118e-15	0.0	0.0	0.0	0.0008631659575125392	2809.799225771435	0.0008631659575125392	2809.799225771435	0.7131899933251503	2.5291878196135213e-17	0.0002322015397185021	0.2702201418212206	1.2089833494288701e-18	5.588612478507706e-28	1.6776670312978908e-10	1.0124146946006672e-99	7.245534304465875e-99	0.0027836552348062932	3.118277673410103e-05	1.0039640024905058e-99	0.0008215363643255157	3.0203512429093096e-06	1.0201734066499735e-99	1.0203501357865112e-99	0.007637596420490365	2.8479411848968505e-06	1.5314574048978416e-05	1.020156684883626e-99	1.938058142874551e-96	4.060707890594906e-07	1.008519850865645e-99	1.0173005592551143e-99	0.0015959394599979854	3.825731137495182e-06	0.00011734932300985198	1.0216031199605605e-99	1.0083789314590178e-99	3.39953430469572e-05	1.000594015753046e-99	1.0246827571450015e-99	0.0005166105714193496	6.541998614991763e-05	7.19731028269094e-05	1.0158169295072354e-99	1.0096909029646111e-99	5.90134633069256e-05	1.0117159597168575e-99	0.0006656369691291614	3.3799146091957446e-05	2.2280548144971945e-05	1.0180077131218347e-99	7.95936673408997e-06	1.0155940799717452e-99	0.00034181837454956955	2.698054904496601e-06	1.5163068562980892e-05	6.350246378492004e-05	2.9506717358962757e-06	0.0012498817453984246	6.660777418912854	2.610154707406735	2.9569976153462254	0.0	0.407305311455412	0.099999779678962	0.7131899933246856	1.2149669841594712e-17	0.00023220154018308793	0.27022014182122067	1.2086592814574192e-18	6.931392163919109e-22	1.6776670312978903e-10	1.0124146946006676e-99	1.0284493028427043e-38	0.0027836552348062932	3.118277673410103e-05	4.156014488333652e-24	0.0008215363643255157	3.0203512429093096e-06	1.953964531437869e-49	6.642817155797774e-29	0.007637596420490365	2.8479411848968514e-06	1.5314574048978416e-05	2.0620182397554648e-31	6.1412539040498955e-33	4.060707890594906e-07	1.0007290992585647e-99	1.0000051648491528e-99	0.0015959394599979854	3.82573113749518e-06	0.000117349323009852	1.0000118108154214e-99	1.0083789314590176e-99	3.39953430469572e-05	1.0005940500867903e-99	1.000000268288428e-99	0.0005166105714193494	6.541998614991763e-05	7.197310282690939e-05	1.0000000176621929e-99	1.0096909029646115e-99	5.9013463306925606e-05	1.0000000026966154e-99	0.0006656369691291615	3.379914609195744e-05	2.2280548144971948e-05	1.000119772786094e-99	7.959366734089968e-06	1.0038635580162552e-99	0.00034181837454956955	2.698054904496602e-06	1.5163068562980883e-05	6.350246378492004e-05	2.9506717358962752e-06	0.0012498817453984244	-3.068722712203384	-11.968569984978997	-74.96377136037967	-56.90297711117493	-67.20865991385845	-77.37542303768979	-98.99999999999999	0.16044380101365566	1069.736624300975	0.0	28837.892464640056	239707.25174037172	0.02397736032033315	1.0060960520833893	0.0	0.9999966298341778	5.262280936676797e-06	1.1749383002259027e+47	0.099999779678962	0.0	0.12415247839448117	0.0	-1e+99	43547755.670947805	31272169.535834383	-1.345782396473872e+47	3.1992338721427923e+51	-1.0	0.0	-4e-06
190.0	9.0	0.10010840125198968	0.10010818063871796	-4.311605973965251e-17	0.0	0.0	0.0	-3.0628538166255876	-6.9326315046404074	-3.0676222065509964	-74.9497131165856	-98.99999999999999	3.448780238044928	-5.026834369291651	-0.9057285479500221	5.249727262491179	-1.7862787350379934	0.0	0.0	0.4859565900706731	0.0	0.0	0.0	0.0	0.0	1.3995444200116036e-15	0.0	0.0	0.0	0.000867080042465856	2810.518712444916	0.000867080042465856	2810.518712444916	0.7131896600329788	2.5286202601679413e-17	0.00023253482788480472	0.27022014182521054	1.225766002816097e-18	3.148226605758009e-26	1.6776670312978908e-10	1.0123901189094702e-99	6.358710473604288e-98	0.0027836552346281085	3.118277692068945e-05	1.0039618908354129e-99	0.00082153636433406	3.0203512411912494e-06	1.0201716226869155e-99	1.0203460923486108e-99	0.0076375964204903665	2.847941184897063e-06	1.531457404897616e-05	1.020152911840124e-99	2.540275293437566e-91	4.0607078905949267e-07	1.0085270205522833e-99	1.0173110341362395e-99	0.0015959394599979852	3.825731137495182e-06	0.00011734932300985198	1.021606595669723e-99	1.0083629799213138e-99	3.39953430469572e-05	1.0006229613115982e-99	1.0246755260595463e-99	0.0005166105714193496	6.541998614991763e-05	7.19731028269094e-05	1.0158219234721594e-99	1.0096744671364398e-99	5.90134633069256e-05	1.0117271282031946e-99	0.0006656369691291614	3.3799146091957446e-05	2.2280548144971945e-05	1.0179975571807197e-99	7.95936673408997e-06	1.0155928393616359e-99	0.00034181837454956955	2.698054904496601e-06	1.5163068562980892e-05	6.350246378492004e-05	2.9506717358962757e-06	0.0012498817453984246	6.661001706180254	2.6096107721216133	2.9530925139416095	0.0	0.4069972412816982	0.10010818063871796	0.713189660032513	1.214539803476738e-17	0.00023253482835044353	0.2702201418252106	1.2252783468586205e-18	9.485970394206437e-22	1.6776670312978903e-10	1.0123901189094706e-99	1.3573317039359626e-37	0.0027836552346281085	3.118277692068946e-05	5.367334874311931e-24	0.00082153636433406	3.0203512411912494e-06	1.4469868362081216e-47	1.0231751061829458e-28	0.0076375964204903665	2.847941184897064e-06	1.531457404897616e-05	4.020017275904955e-31	1.2230379646758328e-32	4.0607078905949267e-07	1.0007280425095472e-99	1.0000054037820808e-99	0.0015959394599979852	3.82573113749518e-06	0.000117349323009852	1.0000117950687482e-99	1.0083629799213136e-99	3.39953430469572e-05	1.0006229960987726e-99	1.0000002685246702e-99	0.0005166105714193494	6.541998614991763e-05	7.197310282690939e-05	1.0000000177060294e-99	1.0096744671364402e-99	5.9013463306925606e-05	1.0000106174929415e-99	0.0006656369691291615	3.379914609195744e-05	2.2280548144971948e-05	1.0001200425813163e-99	7.959366734089968e-06	1.0038727021013184e-99	0.00034181837454956955	2.698054904496602e-06	1.5163068562980883e-05	6.350246378492004e-05	2.9506717358962752e-06	0.0012498817453984244	-3.067622207181453	-11.963402736906144	-74.9497131165856	-56.890569665629314	-67.19439885276974	-77.35967164907183	-98.99999999999999	0.1604293972452102	1069.1111353726694	0.0	28820.891621750274	239547.82862143673	0.023999514219998137	1.0061056799277035	0.0	0.9999966057016325	5.300401472657831e-06	1.1782126727536186e+47	0.10010818063871796	0.0	0.12426040558485175	0.0	-1e+99	43545930.39407836	31271561.80473397	-1.3475018364905235e+47	3.21244657147979e+51	-1.0	0.0	-3e-06
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1602.0	9.0	5.343259087189497	0.9233208411754849	0.0	0.9233204107161559	0.920883162381092	0.0	-2.820520179922964	-2.1120817764516064	-8.266994181930318	-98.99999999999997	-82.44234918256595	4.089567350941473	-2.845926664122101	-2.066741630979931	8.536896062387552	-14.21902353404942	0.0001629679052423274	0.9718936998879268	1.402166076559102	0.7597127318196188	0.0002143845715212653	4532.091402937664	0.0002143845715212653	2.966177226886429e-07	0.0	1.000000007698323	0.9999999846033533	1.7180427594120896e+46	0.0015132169165644194	12291.697595552872	0.001513216871735064	12291.697550293076	0.9999999988729733	0.0	7.326670179176646e-10	3.943520114745179e-10	0.0	0.0	0.0	0.0	0.0	8.751956643918285e-16	8.456531537319719e-16	2.4672392920531975e-55	8.792700659630457e-16	1.4144757544255886e-20	5.974024697959228e-99	1.8486131682334554e-61	8.322409579982936e-16	8.292097339018088e-18	1.0912091998291306e-21	1.1484705056992588e-66	1.744990546561365e-99	1.4467680056309735e-16	1.744523383494966e-99	1.744523383494966e-99	6.909831325901844e-16	0.0	7.985293718273036e-16	0.0	0.0	0.0	0.0	0.0	8.220649294951009e-16	0.0	8.512823790640457e-16	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	7.570039167850648e-16	6.871541348866711	7.35116594879482	1170.8841225702367	0.00015681860770306577	182.19992905734267	0.0	2.9650115600908263e-46	0.0	2.3205688525882726e-25	8.197352170294999e-26	0.0	0.0	0.0	0.0	0.0	0.39385087856606904	1.0957270655087987e-09	1.1660921721267065e-44	3.490773036922101e-14	2.050058460084398e-17	1.7797063616806047e-90	4.793047951511427e-53	0.5833064286423062	6.007179005767188e-06	8.410239322744205e-13	6.386268493682786e-41	0.0	0.0004294619274284985	0.0	0.0	0.0016977347825975213	0.0	0.017423176593731868	0.0	0.0	0.0	0.0	0.0	0.0005820370531705742	0.0	0.00017627599472813855	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0025279981643591687	-8.267710239188418	-11.09421432747371	-98.99999999999997	-98.99999999999997	-98.99999999999997	-49.29831400913655	-98.99999999999997	0.0873943857541383	156772.1055398742	0.0	26485408.65116809	21645659.061452266	0.009246022790063773	1.0050525643325376	0.0	0.9999899000439885	1.3695877877677948e-05	2.3929801495356643e+47	0.9233208411754849	0.9233208405375569	0.008575662460745183	0.008563346209963008	112.51209255881125	147.18833738346518	321.7875658324076	-1.780383691356764e+44	1.0862051809614018e+50	6.0	0.0	-12.579543
1603.0	9.0	5.419733983305413	0.928289774594923	0.0	0.9282893708444949	0.9260060954075581	0.0	-2.8182305540921666	-2.147643563976592	-8.323879099910615	-98.99999999999999	-22.846131283761142	4.091516204644288	-2.844262396038127	-2.0694945254701604	8.544734635077262	-14.247606533025158	0.0001735713533569202	1.0284848375828703	1.4321784093542769	0.769020952881924	0.00022552238585203291	4558.685319014015	0.00022552238585203291	1.9272019361022684e-06	0.0	1.0000000085177143	0.9999999829645718	1.8135754834309093e+46	0.001527506564310845	12350.335218630518	0.0015275065117201308	12350.335166419123	0.9999999979564153	0.0	9.550878278054172e-10	1.0884891686169872e-09	0.0	0.0	0.0	0.0	0.0	8.92217369210547e-16	8.426090508075291e-16	4.965515071239883e-55	8.749180627939159e-16	1.4611049464608202e-20	8.508096645259958e-99	4.200647313372958e-61	8.308152272619187e-16	8.388617335458337e-18	1.0460185995095162e-21	3.102846472123961e-66	1.7991049287551186e-99	1.441016694658358e-16	1.7991049287551186e-99	1.7991049287551186e-99	6.873365794346124e-16	0.0	7.822411165238568e-16	0.0	0.0	0.0	0.0	0.0	8.313309170734228e-16	0.0	8.518375807422195e-16	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	7.562222580393134e-16	6.8725465803541015	7.363949269383104	1184.9277606692108	0.00016709469558053166	183.12821896004013	0.0	7.056483230751865e-47	0.0	1.9868212111686527e-25	5.1067035779518574e-26	0.0	0.0	0.0	0.0	0.0	0.3995838366804235	1.4731448008684776e-09	3.593650593370555e-45	6.194325325176997e-14	2.1705957397822298e-17	9.994584535436267e-91	4.207059397244435e-52	0.577553357111632	6.224915268470768e-06	9.042582216663665e-13	6.350863766677531e-41	0.0	0.00042963488191764387	0.0	0.0	0.0017015319730620151	0.0	0.01743277320440526	0.0	0.0	0.0	0.0	0.0	0.0005837443460139356	0.0	0.00017877513311186568	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.002530120280054184	-8.32646891028344	-11.050792425852997	-98.99999999999999	-98.99999999999999	-98.99999999999999	-49.10598049396322	-98.99999999999999	0.0870723727734829	158552.48143881877	0.0	26905601.29174059	21312874.472431615	0.009093390824326284	1.0048995030925696	0.0	0.9999865023696493	1.846261308813379e-05	2.4138146215756804e+47	0.835282025409214	0.8352820247692454	0.007695052496019925	0.007683715783665099	107.65118739350636	141.0301081845554	312.24971791936514	-1.6904026800979611e+44	1.0765120126126963e+50	6.0	0.0	-12.608126
1604.0	9.0	5.456161073112079	0.9311845432817789	0.0	0.9311841722752616	0.9290554171330992	0.0	-2.816580889312843	-2.0872817762913805	-8.417209884203166	-98.99999999999999	-25.792322529953147	4.092757895979074	-2.8429572346230616	-2.0711530757500727	8.549396824954082	-14.227993931012868	0.0001821478450483287	1.0746075645189055	1.4299367474622051	0.7747015789101656	0.00023471228309580175	4574.566732795323	0.00023471228309580175	2.114434183832404e-06	0.0	1.0000000092642467	0.9999999814715071	1.889749123504372e+46	0.0015422454494274187	12391.320422154711	0.0015422453892062575	12391.320363497342	0.999999996175118	0.0	1.3506271354861838e-09	2.4742472428867147e-09	0.0	0.0	0.0	0.0	0.0	8.995876726650446e-16	8.412894335884085e-16	7.65327465895054e-55	8.743943149731689e-16	1.507826435659486e-20	1.1006190185660296e-98	6.675497775158833e-61	8.391710538388597e-16	8.532876644446823e-18	1.0389030089226732e-21	5.141047947331728e-66	1.9233209618192084e-99	1.4524880647279667e-16	1.9233209618192084e-99	1.9233209618192084e-99	6.922114042348461e-16	0.0	7.913809217037196e-16	0.0	0.0	0.0	0.0	0.0	8.416907138930715e-16	0.0	8.474147646558417e-16	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	7.659612203339958e-16	6.873950783230963	7.371591231967924	1191.518441944134	0.0001751954921534088	184.2288702270305	0.0	1.6181872436542519e-46	0.0	2.3081708571468724e-25	4.079850340639007e-26	0.0	0.0	0.0	0.0	0.0	0.39379888949757735	1.866729378070572e-09	9.305292861106925e-45	9.793622420780815e-14	2.3156126410573513e-17	6.098097375650482e-90	6.288491130811806e-52	0.5833192602996139	6.49527587245643e-06	9.64715771660539e-13	2.3912526024899205e-40	0.0	0.0004295270076128081	0.0	0.0	0.0017119799016504518	0.0	0.017432339711352753	0.0	0.0	0.0	0.0	0.0	0.000587292932112212	0.0	0.00018328722054723151	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0025309262858687555	-8.421400504967659	-11.045402715574216	-98.99999999999999	-98.99999999999999	-98.99999999999999	-48.925026378501215	-98.99999999999999	0.08688539869278146	159454.84215523704	0.0	27157320.64844084	20454172.49433279	0.009026256221604073	1.0046438799388404	0.0	0.9999833987495352	2.292776815711604e-05	2.4292478113354764e+47	0.6664931770650162	0.6664931764232098	0.0061381962013546554	0.006128759747492772	97.19889647735248	126.20193584899916	275.82807400590025	-1.5704553534569399e+44	1.0706387390334607e+50	6.0	0.0	-12.588513

Access columns in the data with their titles:

[11]:

ip.data["Mini"]

[11]:

$[0.090848446,~0.10083559,~0.10867772,~\dots,~2.3635631,~2.3635645,~2.3635683] \; \mathrm{M_{\odot}}$

Or use shorthands for the most commonly used columns:

[12]:

# print first few values of properties
ip.log_age[:5]
ip.log_Teff[:5]
ip.initial_mass[:5]
ip.mass[:5]
ip.initial_metallicity[:5]
ip.metallicity[:5]
ip.log_g[:5]
ip.log_L[:5]

[12]:

$[9,~9,~9,~9,~9] \; \mathrm{dex\left(yr\right)}$

[12]:

$[3.3592,~3.3795,~3.3911,~3.3982,~3.4053] \; \mathrm{dex\left(K\right)}$

[12]:

$[0.090848446,~0.10083559,~0.10867772,~0.11347212,~0.11830842] \; \mathrm{M_{\odot}}$

[12]:

$[0.091,~0.101,~0.109,~0.113,~0.118] \; \mathrm{M_{\odot}}$

[12]:

$[0.0207,~0.0207,~0.0207,~0.0207,~0.0207] \; \mathrm{}$

[12]:

$[0.02085,~0.02085,~0.02085,~0.02085,~0.02085] \; \mathrm{}$

[12]:

$[5.161,~5.143,~5.136,~5.129,~5.119] \; \mathrm{dex\left(\frac{cm}{s^{2}}\right)}$

[12]:

$[-3.376,~-3.231,~-3.146,~-3.092,~-3.036] \; \mathrm{dex\left(L_{\odot}\right)}$

If the dataset has multiple isochrones (we queried over an age or metallicity grid), they’re all stored in Isochrone.data, but we can also access them individually with Isochrone.isochrone:

[13]:

print(f"Number of isochrones in MIST data: {im_local.n_isochrones}")
print(f"As individual objects: {im_local.isochrone}\n")

# get an `Isochrone` with the data only for the isochrone of log_age=9.5
im_local.isochrone[1]
# same as above
im_local.isochrone['log_age=9.5']

Number of isochrones in MIST data: 3
As individual objects: <IsochroneIndex: 3 isochrone(s): ['log_age=9', 'log_age=9.5', 'log_age=10']>

[13]:

Isochrone(database=mist): 1398 rows x 172 columns

[13]:

Isochrone(database=mist): 1398 rows x 172 columns

A few other Isochrone properties may be helpful:

[14]:

# values used to perform the MIST query, as seen by the server
print(f"MIST query parameters\n{im.query_parameters}\n")

print(f"PARSEC query parameters\n{ip.query_parameters}\n")

# header of file downloaded from MIST query
print(f"MIST header:\n{im.header}\n")

print(f"PARSEC header:\n{ip.header}\n")

MIST query parameters
{'age': 1000000000.0, 'FeH': 0.0, 'alphaFe': None, 'v_div_vcrit': None, 'output_option': 'photometry', 'output': 'JWST', 'extinction_Av': None, 'version': None}

PARSEC query parameters
{'age_yr': None, 'logage': (9.0, 9.0, 0.0), 'Z': (0.0207, 0.0207, 0.0), 'MH': None, 'photsys_file': 'euclid_nisp', 'cmd_version': '3.8', 'track_omegai': '0.00', 'track_parsec': 'parsec_CAF09_v1.2S', 'track_colibri': 'parsec_CAF09_v1.2S_S_LMC_08_web', 'track_postagb': 'no', 'n_inTPC': '10', 'eta_reimers': '0.2', 'kind_interp': '1', 'kind_postagb': '-1', 'photsys_version': 'YBCnewVega', 'dust_sourceM': 'dpmod60alox40', 'dust_sourceC': 'AMCSIC15', 'kind_mag': '2', 'kind_dust': '0', 'extinction_av': '0.0', 'extinction_coeff': 'constant', 'extinction_curve': 'cardelli', 'kind_LPV': '3', 'imf_file': 'tab_imf/imf_kroupa_orig.dat', 'isoc_isagelog': '0', 'isoc_agelow': '1.0e9', 'isoc_ageupp': '1.0e10', 'isoc_dage': '0.0', 'isoc_lagelow': '6.6', 'isoc_lageupp': '10.13', 'isoc_dlage': '0.0', 'isoc_ismetlog': '0', 'isoc_zlow': '0.0152', 'isoc_zupp': '0.03', 'isoc_dz': '0.0', 'isoc_metlow': '-2', 'isoc_metupp': '0.3', 'isoc_dmet': '0.0', 'output_kind': '0', 'output_evstage': '1', 'lf_maginf': '-15', 'lf_magsup': '20', 'lf_deltamag': '0.5', 'sim_mtot': '1.0e4', 'submit_form': 'Submit'}

MIST header:
MIST version number  = 2.5
MESA revision number =    11701
--------------------------------------------------------------------------------------
Yinit        Zinit   [Fe/H]   [a/Fe]  v/vcrit
0.2722  1.78798E-02     0.00     0.00     0.40
--------------------------------------------------------------------------------------
number of isochrones =     1
--------------------------------------------------------------------------------------
number of EEPs, cols =  1401  170

PARSEC header:
File generated by CMD 3.9 (http://stev.oapd.inaf.it/cmd) on Fri Mar 13 00:41:33 UTC 2026
isochrones based on PARSEC release v1.2S +  COLIBRI S_37 + S_35 + PR16
Basic references: Bressan et al. (2012), MNRAS, 427, 127 + Chen et al. (2014, 2015), MNRAS, 444, 2525 + MNRAS, 452, 1068 + Tang et al. (2014), MNRAS, 445, 4287 + Marigo et al. (2017), ApJ, 835, 77 + Pastorelli al. (2019), MNRAS, 485, 5666 + Pastorelli al. (2020), MNRAS, in press
Thermal pulse cycles included
On RGB, assumed Reimers mass loss with efficiency eta=0.2
LPV periods and growth rates added cf. Trabucchi et al. (2019)
but fundamental-mode LPV periods are from Trabucchi et al. (2021)
Photometric system: Euclid VIS+NISP (ABmags)
Using YBC version of bolometric corrections as in Chen et al. (2019)
O-rich circumstellar dpmod60alox40 dust from Groenewegen (2006)
C-rich circumstellar AMCSIC15 dust from Groenewegen (2006)
IMF: Kroupa (2001, 2002) + Kroupa et al. (2013) canonical two-part-power law IMF corrected for unresolved binaries
Kind of output: isochrone tables

3. Photometry#

Photometry data is kept separate from Isochrone.data and is in Isochrone.photometry.data. Its attributes largely mirror those in Isochrone.data. For example:

[15]:

ip.photometry

[15]:

<Photometry: 1 system(s): Euclid_VIS+NISP_(ABmags) (7 bands)>

(The ‘system’ here is the photometric system, composed of multiple bands. Data from each photometric system is stored in its own table in Isochrone.photometry. We’ll add more photometric systems to a dataset below.)

[16]:

ip.photometry.columns

[16]:

{'Euclid_VIS+NISP_(ABmags)': ['mbolmag',
  'VISmag',
  'Ymag',
  'Bluemag',
  'Jmag',
  'Redmag',
  'Hmag']}

[17]:

ip.photometry.data

[17]:

{'Euclid_VIS+NISP_(ABmags)': <QTable length=533>
 mbolmag  VISmag   Ymag  Bluemag   Jmag   Redmag   Hmag
   mag     mag     mag     mag     mag     mag     mag
 float64 float64 float64 float64 float64 float64 float64
 ------- ------- ------- ------- ------- ------- -------
   13.21  14.824  12.333  12.258  12.048  11.982  11.984
  12.848  14.462  11.971  11.897  11.687  11.622  11.623
  12.635  14.249  11.758  11.683  11.474  11.409   11.41
    12.5  14.113  11.622  11.548  11.339  11.274  11.275
   12.36  13.973  11.482  11.408    11.2  11.135  11.135
  12.291  13.904  11.413  11.339  11.131  11.066  11.066
  11.835  13.267  10.959  10.893  10.689  10.628  10.631
  11.805   13.22  10.929  10.863   10.66    10.6  10.602
  11.737  13.117  10.862  10.797  10.595  10.536  10.539
     ...     ...     ...     ...     ...     ...     ...
  -4.389  -2.434  -4.995  -5.073  -5.574  -5.718  -5.859
    -4.5  -2.688  -5.087  -5.157  -5.638  -5.785  -5.928
  -4.683  -2.859  -5.077  -5.144  -5.647  -5.821  -5.991
  -4.845  -2.841   -4.98  -5.054  -5.608  -5.817  -6.021
  -4.944  -2.694  -4.829   -4.91  -5.524  -5.766  -6.001
  -4.984  -2.565  -4.694   -4.78  -5.433  -5.696  -5.951
   -4.99  -2.617  -4.667  -4.752  -5.405  -5.667  -5.921
   -4.98  -2.739   -4.68  -4.763  -5.397  -5.652  -5.897
  29.767  31.402   32.07   32.11  32.494  32.689  32.976}

[18]:

ip.photometry.data["Ymag"]

[18]:

$[12.333,~11.971,~11.758,~\dots,~-4.667,~-4.68,~32.07] \; \mathrm{mag}$

[19]:

print(f"MIST photometry header:\n{im.photometry.header}\n")

print(f"PARSEC photometry header:\n{ip.photometry.header}\n")

MIST photometry header:
PHOTOMETRY.JWST
MIST version number  = 2.5
MESA revision number =    11701
photometric system   = JWST NIRCAM (Vega)
--------------------------------------------------------------------------------------
Yinit        Zinit   [Fe/H]   [a/Fe]  v/vcrit
0.2722  1.78798E-02     0.00     0.00     0.40
--------------------------------------------------------------------------------------
number of isochrones =     1
extinction:   Av, Rv =  0.000 3.100
--------------------------------------------------------------------------------------
number of EEPs, cols =  1401   40

PARSEC photometry header:
PHOTOMETRY.euclid_nisp
File generated by CMD 3.9 (http://stev.oapd.inaf.it/cmd) on Fri Mar 13 00:41:33 UTC 2026
isochrones based on PARSEC release v1.2S +  COLIBRI S_37 + S_35 + PR16
Basic references: Bressan et al. (2012), MNRAS, 427, 127 + Chen et al. (2014, 2015), MNRAS, 444, 2525 + MNRAS, 452, 1068 + Tang et al. (2014), MNRAS, 445, 4287 + Marigo et al. (2017), ApJ, 835, 77 + Pastorelli al. (2019), MNRAS, 485, 5666 + Pastorelli al. (2020), MNRAS, in press
Thermal pulse cycles included
On RGB, assumed Reimers mass loss with efficiency eta=0.2
LPV periods and growth rates added cf. Trabucchi et al. (2019)
but fundamental-mode LPV periods are from Trabucchi et al. (2021)
Photometric system: Euclid VIS+NISP (ABmags)
Using YBC version of bolometric corrections as in Chen et al. (2019)
O-rich circumstellar dpmod60alox40 dust from Groenewegen (2006)
C-rich circumstellar AMCSIC15 dust from Groenewegen (2006)
IMF: Kroupa (2001, 2002) + Kroupa et al. (2013) canonical two-part-power law IMF corrected for unresolved binaries
Kind of output: isochrone tables

3.1 Computing colors#

Select two magnitude columns in a photometric dataset to compute a color, which will be added as a new column to the dataset:

[20]:

# B-V color
ip_local.photometry.get_color("Bmag", "Vmag")

print(f"Updated photometry data:\n{ip_local.photometry.data}\n")

Bmag-Vmag column added to photometry table.

[20]:

$[1.83,~1.823,~1.795,~\dots,~2.735,~2.715,~-0.187] \; \mathrm{mag}$

Updated photometry data:
{'YBC_tab_mag_odfnew/tab_mag_ubvrijhk.dat': <QTable length=638>
mbolmag   Umag    Bmag    Vmag  ...   Jmag    Hmag    Kmag       Bmag-Vmag
  mag     mag     mag     mag   ...   mag     mag     mag           mag
float64 float64 float64 float64 ... float64 float64 float64       float64
------- ------- ------- ------- ... ------- ------- ------- --------------------
  13.23  22.457  19.862  18.032 ...   11.14  10.526  10.234   1.8299999999999983
 12.826  22.013   19.44  17.617 ...  10.737  10.124   9.829   1.8230000000000004
 12.208  21.023  18.583  16.788 ...  10.133    9.52    9.22   1.7949999999999982
  12.14  20.769  18.389  16.607 ...  10.073   9.459   9.159                1.782
 11.755  19.486  17.374  15.667 ...   9.727   9.112   8.812    1.706999999999999
 11.585  18.996  16.976  15.296 ...   9.572   8.956   8.657   1.6799999999999997
 11.403  18.485  16.559    14.9 ...   9.409   8.791   8.494   1.6590000000000007
 10.865  17.155  15.445  13.843 ...   8.915   8.296   8.008   1.6020000000000003
  10.36  16.059  14.495  12.927 ...   8.452    7.83   7.552   1.5679999999999996
    ...     ...     ...     ... ...     ...     ...     ...                  ...
 -4.734   3.607   0.509  -1.879 ...   -6.42   -7.49  -7.951                2.388
 -4.845   4.024   0.756  -1.732 ...   -6.53  -7.609  -8.105                2.488
 -5.016   4.846   1.234  -1.416 ...  -6.647   -7.74  -8.324                 2.65
 -5.163   5.181   1.416  -1.303 ...  -6.728  -7.844  -8.484                2.719
 -5.255   5.291   1.474  -1.272 ...  -6.768  -7.905  -8.575                2.746
 -5.297   5.257   1.438  -1.309 ...  -6.808  -7.946  -8.617                2.747
 -5.311   5.158    1.36  -1.375 ...  -6.844  -7.973  -8.632   2.7350000000000003
 -5.314   4.995   1.239  -1.476 ...  -6.888    -8.0  -8.634                2.715
 29.767  30.491  31.398  31.585 ...  32.048  32.128  32.199 -0.18700000000000117}

3.2 Adding multiple photometric systems to data#

To add more photometry to an existing Isochrone, you can first see which systems are currently in your data:

[21]:

ip.photometry.systems

[21]:

{'euclid_nisp': 'Euclid_VIS+NISP_(ABmags)'}

The key in this dictionary is the shorthand name for the system used by the web server, and the value is the name in the dropdown menu of the webform (with spaces replaced with ‘_’).

We can see which other systems are available from the server:

[22]:

ip.photometry.available_systems

[22]:

{'2mass_spitzer': '2MASS + Spitzer (IRAC+MIPS)',
 '2mass_spitzer_wise': '2MASS + Spitzer (IRAC+MIPS) + WISE',
 '2mass': '2MASS JHKs',
 'ogle_2mass_spitzer': 'OGLE + 2MASS + Spitzer (IRAC+MIPS)',
 'ubvrijhk': 'UBVRIJHK (cf. Maiz-Apellaniz 2006 + Bessell 1990)',
 'bessell': 'UBVRIJHKLMN (cf. Bessell 1990 + Bessell & Brett 1988)',
 'akari': 'AKARI',
 'batc': 'BATC',
 'megacam_wircam': 'CFHT Megacam + Wircam (all ABmags)',
 'wircam': 'CFHT Wircam',
 'megacam_post2014': "CFHT/Megacam post-2014 u*g'r'i'z'",
 'megacam': "CFHT/Megacam pre-2014 u*g'r'i'z'",
 'ciber': 'CIBER',
 'clue_galex': 'CLUE + GALEX (Vegamags)',
 'CSST': 'CSST (ABmags)',
 'decam': 'DECAM (ABmags)',
 'denis': 'DENIS',
 'dmc14': 'DMC 14 filters',
 'dmc15': 'DMC 15 filters',
 'eis': 'ESO/EIS (WFI UBVRIZ + SOFI JHK)',
 'wfi': 'ESO/WFI',
 'wfi2': 'ESO/WFI2',
 'euclid_nisp': 'Euclid VIS+NISP (ABmags)',
 'galex_sloan': 'GALEX FUV+NUV (Vegamag) + SDSS ugriz (ABmags)',
 'galex': "GALEX FUV+NUV + Johnson's UBV (Maiz-Apellaniz version), all Vegamags",
 'gaia_tycho2_2mass': 'Gaia DR1 + Tycho2 + 2MASS (all Vegamags)',
 'gaiaDR2_tycho2_2mass': 'Gaia DR2 + Tycho2 + 2MASS (all Vegamags, Gaia passbands from Evans et al. 2018)',
 'gaiaDR2weiler_tycho2_2mass': 'Gaia DR2 + Tycho2 + 2MASS (all Vegamags, Gaia passbands from Weiler 2018)',
 'gaiaEDR3': 'Gaia EDR3 (all Vegamags, Gaia passbands from ESA/Gaia website)',
 'gaia': "Gaia's DR1 G, G_BP and G_RP (Vegamags)",
 'gaiaDR2': "Gaia's DR2 G, G_BP and G_RP (Vegamags, Gaia passbands from Evans et al. 2018)",
 'gaiaDR2maiz': "Gaia's DR2 G, G_BP and G_RP (Vegamags, Gaia passbands from Maiz-Apellaniz and Weiler 2018)",
 'gaiaDR2weiler': "Gaia's DR2 G, G_BP and G_RP (Vegamags, Gaia passbands from Weiler 2018)",
 'UVbright': 'HST+GALEX+Swift/UVOT UV filters',
 'acs_hrc': 'HST/ACS HRC',
 'acs_wfc_pos04jul06': 'HST/ACS WFC (c.f. 2007 revision, pos-04jul06)',
 'acs_wfc_202101': 'HST/ACS WFC - updated filters and zeropoints, 2021',
 'nicmosab': 'HST/NICMOS AB',
 'nicmosvega': 'HST/NICMOS Vega',
 'stis': 'HST/STIS imaging mode, Vegamag',
 'wfc3_wideverywide': 'HST/WFC3 all W+LP+X filters (UVIS1+IR, final throughputs)',
 'wfc3_202101_verywide': 'HST/WFC3 long-pass and extremely wide filters (UVIS) - updated filters and zeropoints, 2021',
 'wfc3_202101_medium': 'HST/WFC3 medium filters (UVIS+IR) - updated filters and zeropoints, 2021',
 'wfc3_202101_wide': 'HST/WFC3 wide filters (UVIS+IR) - updated filters and zeropoints, 2021',
 'wfpc2': 'HST/WFPC2 (Vegamag, cf. Holtzman et al. 1995)',
 'hipparcos': 'Hipparcos+Tycho+Gaia DR1 (Vegamags)',
 'int_wfc': 'INT/WFC (Vegamag)',
 'iphas': 'IPHAS',
 'jwst_miri_wide': 'JWST MIRI wide filters, Vegamags',
 'jwst_nircam_wide': 'JWST NIRCam wide+verywide filters, Vegamags',
 'jwst_nircam_widemedium_nov22': 'JWST NIRCam wide+verywide+medium filters (Nov 2022), Vegamags',
 'jwst_nircam_widemedium': 'JWST NIRCam wide+verywide+medium filters, Vegamags',
 'jwst_niriss_nov22': 'JWST NIRISS filters (Nov 2022), Vegamags',
 'jwst_nirspec': 'JWST Nirspec filters, Vegamags',
 'jwst_fnl': 'JWST custom, Vegamags',
 'kepler': 'Kepler + SDSS griz + DDO51 (in ABmags)',
 'kepler_2mass': 'Kepler + SDSS griz + DDO51 (in ABmags) + 2MASS (~Vegamag)',
 'vst_vista': 'KiDS/VIKING (VST/OMEGAM + VISTA/VIRCAM, all ABmags)',
 'lbt_lbc': 'LBT/LBC (Vegamag)',
 'lsst_wfirst_proposed2017': 'LSST (ABmags) + WFIRST proposed filters (Vegamags)',
 'lsst': 'LSST ugrizY, March 2012 total filter throughputs (all ABmags)',
 'lsstDP0': 'LSST ugrizy, Oct 2017 total filter throughputs for DP0 (all ABmags)',
 'lsstR1.9': 'LSST ugrizy, Sept 2023, total filter throughputs R1.9 (all ABmags)',
 'noao_ctio_mosaic2': 'NOAO/CTIO/MOSAIC2 (Vegamag)',
 'ogle': 'OGLE-II',
 'panstarrs1': 'Pan-STARRS1',
 'gulli': 'Pan-STARRS1 + narrow Omegacam filters (ABmags)',
 'Roman2021': 'Roman (ex-WFIRST) 2021 filters, Vegamags',
 'splus': 'S-PLUS (Vegamags), revised on Nov. 2017',
 'sloan': 'SDSS ugriz',
 'sloan_2mass': 'SDSS ugriz + 2MASS JHKs',
 'sloan_ukidss': 'SDSS ugriz + UKIDSS ZYJHK',
 'swift_uvot': 'SWIFT/UVOT UVW2, UVM2, UVW1,u (Vegamag)',
 'skymapper': 'SkyMapper (ABmags)',
 'spitzer': 'Spitzer IRAC+MIPS',
 'stroemgren': 'Stroemgren-Crawford',
 'hsc': 'Subaru/Hyper Suprime-Cam (ABmags)',
 'suprimecam': 'Subaru/Suprime-Cam (ABmags)',
 'SuperBIT': 'SuperBIT, all ABmags',
 'TESS_2mass': 'TESS + 2MASS (Vegamags)',
 'TESS_2mass_kepler': 'TESS + 2MASS (Vegamags) + Kepler + SDSS griz + DDO51 (in ABmags)',
 'ukidss': 'UKIDSS ZYJHK (Vegamag)',
 'uvit': 'UVIT (all ABmags)',
 'visir': 'VISIR',
 'vista': 'VISTA ZYJHKs (Vegamag)',
 'vphas': 'VPHAS+ (ABmags)',
 'vst_omegacam': 'VST/OMEGACAM (ABmag)',
 'vilnius': 'Vilnius',
 'wfc3_uvisCaHK': 'WFC3/UVIS around CaHK',
 'washington_ddo51': 'Washington CMT1T2 + DDO51',
 'ztf': 'ZTF (ABmags)',
 'deltaa': 'deltaa (Paunzen) + UBV (Maiz-Apellaniz), in Vegamags',
 'photsys_version': "['YBC', 'YBC']"}

And we can add one or more to the existing Isochrone by passing either the shorthand or full names:

[23]:

ip.photometry.add_photometry(["galex_sloan", "UBVRIJHKLMN (cf. Bessell 1990 + Bessell & Brett 1988)"])

Querying padova for photometric system 'galex_sloan'...
Querying http://stev.oapd.inaf.it/cgi-bin/cmd...
Retrieving data...
Downloading data...http://stev.oapd.inaf.it/tmp/output498372857299.dat
Added system 'GALEX_FUV+NUV_(Vegamag)_+_SDSS_ugriz_(ABmags)' with 8 columns: ['mbolmag', 'FUVmag', 'NUVmag', 'umag', 'gmag', 'rmag', 'imag', 'zmag']
Querying padova for photometric system 'bessell'...
Querying http://stev.oapd.inaf.it/cgi-bin/cmd...
Retrieving data...
Downloading data...http://stev.oapd.inaf.it/tmp/output176902455817.dat
Added system 'UBVRIJHKLMN_(cf._Bessell_1990_+_Bessell_&_Brett_1988)' with 13 columns: ['mbolmag', 'UXmag', 'BXmag', 'Bmag', 'Vmag', 'Rmag', 'Imag', 'Jmag', 'Hmag', 'Kmag', 'Lmag', "L'mag", 'Mmag']

This Isochrone now has data from 3 photometric systems…

[24]:

ip.photometry

[24]:

<Photometry: 3 system(s): Euclid_VIS+NISP_(ABmags) (7 bands), GALEX_FUV+NUV_(Vegamag)_+_SDSS_ugriz_(ABmags) (8 bands), UBVRIJHKLMN_(cf._Bessell_1990_+_Bessell_&_Brett_1988) (13 bands)>

…each with their own table:

[25]:

ip.photometry.data

[25]:

{'Euclid_VIS+NISP_(ABmags)': <QTable length=533>
 mbolmag  VISmag   Ymag  Bluemag   Jmag   Redmag   Hmag
   mag     mag     mag     mag     mag     mag     mag
 float64 float64 float64 float64 float64 float64 float64
 ------- ------- ------- ------- ------- ------- -------
   13.21  14.824  12.333  12.258  12.048  11.982  11.984
  12.848  14.462  11.971  11.897  11.687  11.622  11.623
  12.635  14.249  11.758  11.683  11.474  11.409   11.41
    12.5  14.113  11.622  11.548  11.339  11.274  11.275
   12.36  13.973  11.482  11.408    11.2  11.135  11.135
  12.291  13.904  11.413  11.339  11.131  11.066  11.066
  11.835  13.267  10.959  10.893  10.689  10.628  10.631
  11.805   13.22  10.929  10.863   10.66    10.6  10.602
  11.737  13.117  10.862  10.797  10.595  10.536  10.539
     ...     ...     ...     ...     ...     ...     ...
  -4.389  -2.434  -4.995  -5.073  -5.574  -5.718  -5.859
    -4.5  -2.688  -5.087  -5.157  -5.638  -5.785  -5.928
  -4.683  -2.859  -5.077  -5.144  -5.647  -5.821  -5.991
  -4.845  -2.841   -4.98  -5.054  -5.608  -5.817  -6.021
  -4.944  -2.694  -4.829   -4.91  -5.524  -5.766  -6.001
  -4.984  -2.565  -4.694   -4.78  -5.433  -5.696  -5.951
   -4.99  -2.617  -4.667  -4.752  -5.405  -5.667  -5.921
   -4.98  -2.739   -4.68  -4.763  -5.397  -5.652  -5.897
  29.767  31.402   32.07   32.11  32.494  32.689  32.976,
 'GALEX_FUV+NUV_(Vegamag)_+_SDSS_ugriz_(ABmags)': <QTable length=533>
 mbolmag  FUVmag  NUVmag   umag    gmag    rmag    imag    zmag
   mag     mag     mag     mag     mag     mag     mag     mag
 float64 float64 float64 float64 float64 float64 float64 float64
 ------- ------- ------- ------- ------- ------- ------- -------
   13.21  52.842  32.471  23.155  18.879  17.209  14.793  13.265
  12.848  52.545  32.084  22.758  18.506  16.842   14.43  12.904
  12.635  52.354  31.861  22.532  18.288  16.627  14.216   12.69
    12.5  52.244  31.716  22.383  18.149   16.49   14.08  12.556
   12.36  52.138  31.562  22.224  18.002  16.347  13.939  12.416
  12.291  52.085  31.487  22.147  17.931  16.277   13.87  12.347
  11.835  51.208  29.816  20.914  17.009  15.413   13.17  11.801
  11.805   51.09  29.676  20.819  16.938  15.348  13.119  11.763
  11.737  50.819  29.362  20.608  16.779  15.203  13.004  11.677
     ...     ...     ...     ...     ...     ...     ...     ...
  -4.389  28.531   9.472   3.648   1.528  -0.073   -2.67  -3.915
    -4.5  26.751   9.458   3.739   1.181  -0.439  -2.945   -4.12
  -4.683  24.398  10.165   4.743   1.025   -0.84  -3.151  -4.256
  -4.845  23.256  11.098   5.963   1.114   -1.03  -3.168  -4.231
  -4.944  22.938  12.047   7.146   1.374  -1.069  -3.042  -4.087
  -4.984   22.83  12.699   7.951   1.555  -1.097  -2.922  -3.948
   -4.99  22.511  12.688   7.997     1.4  -1.309  -2.968  -3.941
   -4.98  22.096  12.426   7.748   1.111  -1.589  -3.079  -3.985
  29.767  29.128   29.32  30.922  30.862  31.237  31.532  31.791,
 'UBVRIJHKLMN_(cf._Bessell_1990_+_Bessell_&_Brett_1988)': <QTable length=533>
 mbolmag  UXmag   BXmag    Bmag    Vmag  ...   Kmag    Lmag   L'mag    Mmag
   mag     mag     mag     mag     mag   ...   mag     mag     mag     mag
 float64 float64 float64 float64 float64 ... float64 float64 float64 float64
 ------- ------- ------- ------- ------- ... ------- ------- ------- -------
   13.21   22.15  19.696  19.747  17.966 ...  10.204   9.819    9.59   9.777
  12.848  21.755   19.32  19.371  17.596 ...   9.842   9.458   9.229   9.413
  12.635  21.529  19.102  19.153  17.379 ...   9.628   9.244   9.015   9.198
    12.5  21.381  18.961  19.012  17.241 ...   9.493   9.109    8.88   9.063
   12.36  21.222  18.813  18.864  17.096 ...   9.353   8.969    8.74   8.921
  12.291  21.145  18.741  18.792  17.025 ...   9.284     8.9   8.672   8.852
  11.835  19.932  17.778  17.825  16.114 ...   8.858   8.493   8.279   8.426
  11.805  19.839  17.704   17.75  16.045 ...    8.83   8.467   8.255   8.399
  11.737  19.632  17.537  17.583  15.892 ...    8.77   8.411   8.201   8.341
     ...     ...     ...     ...     ... ...     ...     ...     ...     ...
  -4.389    2.76   2.005   2.013   0.672 ...  -7.569  -7.829  -7.856  -7.672
    -4.5   2.841   1.744    1.76   0.292 ...  -7.649   -7.95  -7.987  -7.834
  -4.683    3.82   1.782   1.815  -0.044 ...   -7.79  -8.282   -8.36  -8.304
  -4.845   5.018   2.023    2.07  -0.151 ...  -7.916  -8.605   -8.72  -8.746
  -4.944   6.181   2.389   2.445  -0.095 ...  -7.988  -8.837  -8.977  -9.057
  -4.984   6.972   2.634   2.696  -0.061 ...  -7.996  -8.933  -9.085  -9.188
   -4.99   7.015   2.492   2.556  -0.268 ...  -7.981  -8.946  -9.102  -9.209
   -4.98    6.77   2.197   2.262  -0.566 ...  -7.954  -8.926  -9.083  -9.186
  29.767  30.136  30.937  30.931  31.089 ...  31.562  31.605  31.609  31.622}

4. Plotting#

There are simple plotting routines in isoc.plot to make an H-R diagram or a color-magnitude diagram.

4.1: Comparing Padova/PARSEC and MIST/MESA isochrones on an H-R diagram#

[26]:

from isoc.plot import plot_isochrone

fig, ax = plot_isochrone(im, x="log_Teff", y="log_L",
                         color='#EE4B2B', marker='x', ls='none', markersize=5, label="MIST")
plot_isochrone(ip, x="log_Teff", y="log_L", ax=ax,
               color='#0BDA51', marker='+', ls='none', label="Padova")

ax.set_xlim(4.0, 3.3)
ax.set_ylim(-4.5, 4.5)
ax.legend(loc='lower left')
ax.set_title("H-R diagram: 1 Gyr, solar metallicity");

4.2: Plotting over a grid of ages#

(We could also plot isochrones across a metallicity grid for Padova/PARSEC.)

[27]:

ip_grid = Isochrone.from_padova(
    age=(7.0, 10.0, 0.5),
    metallicity=0.0207,
    metallicity_type="Z",
)

ip_grid.isochrone

Inferred log10 age scale based on age values (7.0, 10.0, 0.5).
'photometry' is None. Using default ezpadova photometric system.


Querying http://stev.oapd.inaf.it/cgi-bin/cmd...
Retrieving data...
Downloading data...http://stev.oapd.inaf.it/tmp/output754520679199.dat
User-supplied query parameters (as seen by ezpadova):
        {'age_yr': None, 'logage': (7.0, 10.0, 0.5), 'Z': (0.0207, 0.0207, 0.0), 'MH': None, 'photsys_file': None}
All query parameters (including defaults for those not specified):
        {'age_yr': None, 'logage': (7.0, 10.0, 0.5), 'Z': (0.0207, 0.0207, 0.0), 'MH': None, 'photsys_file': None, 'cmd_version': '3.8', 'track_omegai': '0.00', 'track_parsec': 'parsec_CAF09_v1.2S', 'track_colibri': 'parsec_CAF09_v1.2S_S_LMC_08_web', 'track_postagb': 'no', 'n_inTPC': '10', 'eta_reimers': '0.2', 'kind_interp': '1', 'kind_postagb': '-1', 'photsys_version': 'YBCnewVega', 'dust_sourceM': 'dpmod60alox40', 'dust_sourceC': 'AMCSIC15', 'kind_mag': '2', 'kind_dust': '0', 'extinction_av': '0.0', 'extinction_coeff': 'constant', 'extinction_curve': 'cardelli', 'kind_LPV': '3', 'imf_file': 'tab_imf/imf_kroupa_orig.dat', 'isoc_isagelog': '0', 'isoc_agelow': '1.0e9', 'isoc_ageupp': '1.0e10', 'isoc_dage': '0.0', 'isoc_lagelow': '6.6', 'isoc_lageupp': '10.13', 'isoc_dlage': '0.0', 'isoc_ismetlog': '0', 'isoc_zlow': '0.0152', 'isoc_zupp': '0.03', 'isoc_dz': '0.0', 'isoc_metlow': '-2', 'isoc_metupp': '0.3', 'isoc_dmet': '0.0', 'output_kind': '0', 'output_evstage': '1', 'lf_maginf': '-15', 'lf_magsup': '20', 'lf_deltamag': '0.5', 'sim_mtot': '1.0e4', 'submit_form': 'Submit'}

[27]:

<IsochroneIndex: 7 isochrone(s): ['log_age=7', 'log_age=7.5', 'log_age=8', 'log_age=8.5', 'log_age=9', 'log_age=9.5', 'log_age=10']>

[28]:

fig, ax = plot_isochrone(
    ip_grid.isochrone,
    iso_labels=ip_grid.isochrone.labels,
    marker='.',
    linestyle='none',
    x="log_Teff",
    y="log_L",
    cmap="plasma",
    )

ax.set_xlim(4.5, 3.3)
ax.set_ylim(-4.5, 5.5)
ax.legend(loc='lower left')
ax.set_title("Padova — age grid");

4.3: Color-magnitude diagram#

[29]:

from isoc.plot import plot_color_magnitude

fig, ax = plot_color_magnitude(ip, band1="Bmag", band2="Vmag", mag="Bmag", marker='.', c='k', ls='none')

Bmag-Vmag column added to photometry table.

5. Parameter help#

Checking the arguments for from_mist shows that you can provide an age as a float, a list or a tuple for a range (with min, max, step), and you can supply any of these in linear or log space. A few other arguments have generalized names whose corresponding values on the MIST webform are given in the docstring, e.g., metallicity for “[Fe/H]”, abundance for “[alpha/Fe]”, and extinction for “Extinction Av”.

[30]:

Isochrone.from_mist?

Signature:
Isochrone.from_mist(
    age: tuple[float, float, float] | list | float | None = None,
    metallicity: float | None = None,
    abundance: float | None = None,
    v_div_vcrit: float | None = None,
    photometry: str | None = None,
    extinction: float | None = None,
    version: Literal['MIST1', 'MIST2', None] = None,
    from_file: str | None = None,
    **kwargs,
) -> 'Isochrone'
Docstring:
Query the MIST/MESA service and return an :class:`Isochrone`.

Any parameters passed as ``None`` will use the MIST defaults
(see `Isochrone.default_values`).

Parameters
----------
age : float, tuple of (low, high, step), list or None
    Age in years or log10(years). A single float queries one isochrone; a tuple of
    length 3 sets a min, max and step size to query over the corresponding age grid,
    and a list queries over an age grid.
metallicity : float or None
     [Fe/H] metallicity value.
abundance : float or None
    [alpha/Fe] abundance ratio.
v_div_vcrit : float or None
    Initial rotation velocity v/v_crit.
photometry : str or None
    The name of a photometric system to retrieve synthetic photometry for.
    If None, MIST output is set to "theory".
extinction : float or None
    Extinction A_V to apply to photometry.  Has no effect if *photometry* is
    ``None``.
version : str or None
    MIST version to query.
from_file : str or None
    If provided, load isochrone data from a file instead of querying the MIST service.
**kwargs
    Additional keyword arguments forwarded to the MIST query.

Returns
-------
Isochrone
    An Isochrone object containing the queried data.
File:      ~/Desktop/isoc/isoc/isochrone.py
Type:      method

Checking the arguments for from_padova, age and metallicity generalize and handle multiple input types just as they do in from_mist - though for from_padova you also have to specify metallicity_type as Z or MH. The **kwargs take any arguments named in ezpadova (which from_padova uses as a backend to perform the query).

[31]:

Isochrone.from_padova?

Signature:
Isochrone.from_padova(
    age: tuple[float, float, float] | float | None = None,
    metallicity: tuple[float, float, float] | float | None = None,
    metallicity_type: Literal['Z', 'MH', None] = None,
    photometry: str | None = None,
    from_file: str | None = None,
    **kwargs,
) -> 'Isochrone'
Docstring:
Query the Padova/PARSEC service and return an :class:`Isochrone`.

Any parameters passed as None will use the Padova/PARSEC defaults
(see `Isochrone.default_values`).

Parameters
----------
age : tuple of (low, high, step) or float or None
    Age in years or log10(years). A single float queries one isochrone; a tuple of
    length 3 sets a min, max and step size to query over the corresponding age grid,
    and a list queries over an age grid.
metallicity : tuple of (low, high, step) or float or None
    Metallicity value. Interpreted as Z or [M/H] depending on metallicity_type.
metallicity_type : str or None
    Whether the metallicity parameter is interpreted as "Z" or "MH" ([M/H]).
photometry : str or None
    The name of a photometric system to retrieve synthetic photometry for.
from_file : str or None
    If provided, load isochrone data from a file instead of querying the Padova service.
**kwargs
    Additional keyword arguments forwarded to the Padova query.

Returns
-------
Isochrone
    An Isochrone object containing the queried data.
File:      ~/Desktop/isoc/isoc/isochrone.py
Type:      method

The arguments to query_ezpadova follow the ezpadova names and expected types, if you prefer this over using from_padova:

[32]:

Isochrone.query_ezpadova?

Signature:
Isochrone.query_ezpadova(
    age_yr: tuple[float, float, float] | None = None,
    Z: tuple[float, float, float] | None = None,
    logage: tuple[float, float, float] | None = None,
    MH: tuple[float, float, float] | None = None,
    default_ranges: bool = False,
    **kwargs,
) -> 'Isochrone'
Docstring: Query the Padova/PARSEC service and return an :class:`Isochrone`.
File:      ~/Desktop/isoc/isoc/isochrone.py
Type:      method

To understand all available parameters for Padova/PARSEC or MIST/MESA queries and see their defaults (note that these are the parameter names and types expected by the server, not those in from_mist or from_padova):

[33]:

print("MIST/MESA:")
print("\nParameter descriptions:")
Isochrone.parameter_descriptions(database="mist")
print("\nDefaults:")
Isochrone.default_values(database="mist")

print("Padova/PARSEC:")
print("\nParameter descriptions:")
Isochrone.parameter_descriptions(database="padova")
print("\nDefaults:")
Isochrone.default_values(database="padova")

MIST/MESA:

Parameter descriptions:
See https://mist.science/interp_isos.html and https://www.mist.science/resources.html

Defaults:

[33]:

{'v_div_vcrit': 'vvcrit0.4',
 'FeH_value': '0',
 'alpha_value': 'p0',
 'age_value': '',
 'age_range_low': '5',
 'age_range_high': '10.3',
 'age_range_delta': '0.05',
 'Av_value': '0',
 'output_option': 'theory',
 'output': 'UBVRIplus',
 'version': 'MIST2',
 'age_type': 'standard',
 'age_scale': 'log10',
 '_aliases': {'FeH_value': 'metallicity',
  'afe': 'abundance',
  'output': 'photometry',
  'Av_value': 'extinction'}}

Padova/PARSEC:

Parameter descriptions:
See https://stev.oapd.inaf.it/cgi-bin/cmd, https://github.com/mfouesneau/ezpadova/blob/master/src/ezpadova/parsec.md and https://stev.oapd.inaf.it/cmd_3.9/help.html

Defaults:

[33]:

{'cmd_version': '3.8',
 'track_omegai': '0.00',
 'track_parsec': 'parsec_CAF09_v1.2S',
 'track_colibri': 'parsec_CAF09_v1.2S_S_LMC_08_web',
 'track_postagb': 'no',
 'n_inTPC': '10',
 'eta_reimers': '0.2',
 'kind_interp': '1',
 'kind_postagb': '-1',
 'photsys_file': 'YBC_tab_mag_odfnew/tab_mag_ubvrijhk.dat',
 'photsys_version': 'YBCnewVega',
 'dust_sourceM': 'dpmod60alox40',
 'dust_sourceC': 'AMCSIC15',
 'kind_mag': '2',
 'kind_dust': '0',
 'extinction_av': '0.0',
 'extinction_coeff': 'constant',
 'extinction_curve': 'cardelli',
 'kind_LPV': '3',
 'imf_file': 'tab_imf/imf_kroupa_orig.dat',
 'isoc_isagelog': '0',
 'isoc_agelow': '1.0e9',
 'isoc_ageupp': '1.0e10',
 'isoc_dage': '0.0',
 'isoc_lagelow': '6.6',
 'isoc_lageupp': '10.13',
 'isoc_dlage': '0.0',
 'isoc_ismetlog': '0',
 'isoc_zlow': '0.0152',
 'isoc_zupp': '0.03',
 'isoc_dz': '0.0',
 'isoc_metlow': '-2',
 'isoc_metupp': '0.3',
 'isoc_dmet': '0.0',
 'output_kind': '0',
 'output_evstage': '1',
 'lf_maginf': '-15',
 'lf_magsup': '20',
 'lf_deltamag': '0.5',
 'sim_mtot': '1.0e4',
 'submit_form': 'Submit',
 '_aliases': {'age_yr': 'age',
  'logage': 'age',
  'Z': 'metallicity_type',
  'MH': 'metallicity_type'}}

Using isoc for isochrone retrieval#