Comparing MIST predictions to Willmer 2019

In [1]:

    

%%file sun_observed.txt

Filter	Abs_Vega	Abs_AB	Abs_ST	App_Vega	App_AB	App_ST	Vega_AB	Vega_ST	lambda_pivot	Source			
Johnson_U	5.61	6.33	5.42	-25.97	-25.25	-26.15	0.721	-0.183	0.3611	1	
Johnson_B	5.44	5.31	4.84	-26.13	-26.26	-26.74	-0.128	-0.605	0.4396	1	
Johnson_V	4.81	4.80	4.81	-26.76	-26.77	-26.76	-0.013	0.001	0.5511	1	
Cousins_R	4.43	4.60	5.00	-27.15	-26.97	-26.57	0.178	0.578	0.6582	1	
Cousins_I	4.10	4.51	5.35	-27.47	-27.06	-26.22	0.414	1.247	0.8034	1	
Tycho_Bt	5.58	5.48	4.91	-25.99	-26.09	-26.66	-0.097	-0.667	0.4212	1	
Tycho_Vt	4.88	4.85	4.79	-26.69	-26.72	-26.78	-0.035	-0.091	0.5335	1	
Hipparcos_Hp	4.87	4.87	4.88	-26.70	-26.70	-26.69	-0.002	0.011	0.5508	1	
2MASS_J	3.67	4.54	6.31	-27.90	-27.03	-25.26	0.870	2.644	1.2393	2	
2MASS_H	3.32	4.66	7.06	-28.25	-26.91	-24.51	1.344	3.739	1.6495	2	
2MASS_Ks	3.27	5.08	8.07	-28.30	-26.49	-23.50	1.814	4.798	2.1638	2	
SDSS_u	5.49	6.39	5.45	-26.08	-25.18	-26.12	0.900	-0.037	0.3556	3	
SDSS_g	5.23	5.11	4.78	-26.34	-26.47	-26.80	-0.125	-0.456	0.4702	3	
SDSS_r	4.53	4.65	4.91	-27.04	-26.93	-26.66	0.119	0.380	0.6176	3	
SDSS_i	4.19	4.53	5.21	-27.38	-27.05	-26.37	0.332	1.012	0.7490	3	
SDSS_z	4.01	4.50	5.57	-27.56	-27.07	-26.00	0.494	1.560	0.8947	3	
DES_u	5.83	6.14	5.38	-25.74	-25.44	-26.20	0.307	-0.452	0.3859	4	
DES_g	5.17	5.05	4.78	-26.41	-26.52	-26.80	-0.114	-0.391	0.4820	4	
DES_r	4.45	4.61	4.96	-27.12	-26.96	-26.61	0.159	0.505	0.6423	4	
DES_i	4.14	4.52	5.29	-27.43	-27.05	-26.28	0.382	1.152	0.7807	4	
DES_z	4.01	4.50	5.62	-27.56	-27.07	-25.95	0.493	1.610	0.9158	4	
DES_Y	3.96	4.50	5.78	-27.61	-27.07	-25.79	0.540	1.819	0.9866	4	
PS1_g	5.14	5.03	4.77	-26.43	-26.54	-26.80	-0.112	-0.376	0.4849	5	
PS1_r	4.53	4.64	4.92	-27.05	-26.93	-26.66	0.120	0.390	0.6201	5	
PS1_i	4.18	4.52	5.22	-27.39	-27.05	-26.35	0.339	1.033	0.7535	5	
PS1_z	4.02	4.51	5.50	-27.55	-27.07	-26.07	0.483	1.482	0.8674	5	
PS1_Y	3.99	4.50	5.73	-27.59	-27.07	-25.85	0.515	1.741	0.9628	5	
cfhtls_u	5.70	6.04	5.25	-25.87	-25.53	-26.33	0.336	-0.455	0.3803	6	
cfhtls_g	5.15	5.03	4.77	-26.42	-26.54	-26.80	-0.116	-0.382	0.4844	6	
cfhtls_r	4.50	4.64	4.92	-27.07	-26.94	-26.65	0.131	0.417	0.6248	6	
cfhtls_i	4.16	4.52	5.26	-27.41	-27.05	-26.32	0.362	1.096	0.7678	6	
cfhtls_z	4.02	4.51	5.55	-27.56	-27.07	-26.02	0.490	1.535	0.8859	6	
CFHT_12kx8k_B	5.43	5.28	4.80	-26.14	-26.30	-26.77	-0.157	-0.632	0.4399	7	
CFHT_12kx8k_R	4.39	4.59	5.00	-27.18	-26.98	-26.57	0.196	0.605	0.6610	7	
CFHT_12kx8k_I	4.10	4.51	5.38	-27.47	-27.06	-26.19	0.415	1.282	0.8159	7	
UKIRT_z	4.02	4.51	5.54	-27.56	-27.07	-26.03	0.489	1.526	0.8826	8	
UKIRT_Y	3.92	4.51	5.88	-27.66	-27.07	-25.69	0.591	1.966	1.0315	8	
UKIRT_J	3.65	4.54	6.33	-27.92	-27.03	-25.24	0.891	2.684	1.2502	8	
UKIRT_H	3.33	4.66	7.03	-28.25	-26.92	-24.54	1.329	3.705	1.6360	8	
UKIRT_K	3.27	5.12	8.14	-28.30	-26.45	-23.43	1.848	4.874	2.2060	8	
LSST_u	5.65	6.27	5.40	-25.93	-25.30	-26.17	0.627	-0.244	0.3665	9	
LSST_g	5.17	5.06	4.77	-26.40	-26.52	-26.80	-0.116	-0.399	0.4808	9	
LSST_r	4.52	4.64	4.92	-27.05	-26.93	-26.66	0.121	0.395	0.6210	9	
LSST_i	4.18	4.52	5.22	-27.39	-27.05	-26.35	0.340	1.034	0.7537	9	
LSST_z	4.02	4.51	5.51	-27.55	-27.07	-26.06	0.484	1.486	0.8686	9	
LSST_y	3.98	4.50	5.74	-27.59	-27.07	-25.83	0.520	1.763	0.9705	9	
Bessell_Murphy_U	5.57	6.34	5.43	-26.00	-25.23	-26.14	0.768	-0.144	0.3597	10	
Bessell_Murphy_B	5.46	5.33	4.84	-26.11	-26.24	-26.73	-0.134	-0.620	0.4378	10	
Bessell_Murphy_V	4.82	4.81	4.81	-26.75	-26.77	-26.76	-0.017	-0.012	0.5489	10	
Bessell_Murphy_R	4.44	4.61	4.99	-27.13	-26.96	-26.58	0.168	0.548	0.6524	10	
Bessell_Murphy_I	4.11	4.52	5.33	-27.46	-27.06	-26.24	0.408	1.227	0.7984	10	
Bessell_Murphy_Bt	5.60	5.51	4.93	-25.98	-26.06	-26.65	-0.088	-0.669	0.4190	10	
Bessell_Murphy_Vt	4.89	4.86	4.79	-26.68	-26.71	-26.79	-0.038	-0.108	0.5300	10	
Bessell_Murphy_Hp	4.93	4.92	4.86	-26.64	-26.66	-26.71	-0.018	-0.068	0.5349	10	
Bessell_88_J	3.67	4.54	6.30	-27.90	-27.03	-25.27	0.866	2.632	1.2347	11	
Bessell_88_H	3.32	4.66	7.05	-28.25	-26.91	-24.52	1.337	3.726	1.6450	11	
Bessell_88_K	3.27	5.09	8.07	-28.30	-26.49	-23.50	1.815	4.802	2.1663	11	
Bessell_88_L	3.26	5.98	10.00	-28.31	-25.59	-21.58	2.721	6.737	3.4797	11	
Bessell_88_Lprime	3.26	6.17	10.39	-28.31	-25.40	-21.18	2.914	7.135	3.8247	11	
Bessell_88_M	3.29	6.64	11.33	-28.28	-24.93	-20.25	3.349	8.034	4.7347	11	
GALEX_FUV	15.22	17.30	14.54	-16.36	-14.27	-17.03	2.085	-0.676	0.1535	12	
GALEX_NUV	8.53	10.16	8.28	-23.04	-21.41	-23.30	1.629	-0.253	0.2301	12	
WISE_1	3.26	5.91	9.87	-28.31	-25.66	-21.70	2.655	6.614	3.3897	13	
WISE_2	3.28	6.57	11.22	-28.29	-25.00	-20.36	3.291	7.932	4.6406	13	
WISE_3	3.26	8.48	15.28	-28.31	-23.09	-16.29	5.215	12.019	12.5705	13	
WISE_4	3.27	9.88	17.93	-28.30	-21.70	-13.65	6.602	14.652	22.3142	13	
IRAS12	3.26	8.30	14.89	-28.31	-23.27	-16.69	5.037	11.621	11.3562	13	
IRAS25	3.27	9.92	18.09	-28.30	-21.65	-13.48	6.646	14.819	23.6079	13	
IRAS60	3.28	11.90	22.12	-28.29	-19.67	-9.46	8.621	18.833	60.3699	13	
IRAS100	3.29	13.14	24.47	-28.28	-18.43	-7.10	9.854	21.186	101.1267	13	
IRAC_3.6	3.26	6.02	10.08	-28.31	-25.56	-21.50	2.758	6.817	3.5508	13	
IRAC_4.5	3.28	6.51	11.08	-28.29	-25.06	-20.49	3.232	7.804	4.4960	13	
IRAC_5.8	3.28	7.00	12.09	-28.30	-24.58	-19.48	3.720	8.816	5.7245	13	
IRAC_8.0	3.26	7.62	13.41	-28.31	-23.95	-18.16	4.360	10.152	7.8842	13	
IRS_16	3.27	9.11	16.42	-28.31	-22.47	-15.15	5.839	13.157	15.9222	13	
IRS_22	3.27	9.86	17.92	-28.30	-21.72	-13.65	6.584	14.650	22.4704	14	
MIPS_24	3.27	10.01	18.19	-28.30	-21.57	-13.38	6.731	14.918	23.7592	14	
MIPS_70	3.29	12.40	23.00	-28.28	-19.17	-8.58	9.114	19.708	71.9861	14	
MIPS_160	3.29	14.15	26.42	-28.28	-17.43	-5.15	10.857	23.137	156.4274	14	
ACS_F330W	5.34	6.43	5.47	-26.24	-25.14	-26.10	1.097	0.139	0.3521	14	
ACS_F410W	5.70	5.67	5.02	-25.87	-25.90	-26.55	-0.033	-0.680	0.4064	14	
ACS_F435W	5.48	5.35	4.84	-26.09	-26.22	-26.73	-0.129	-0.639	0.4328	14	
ACS_F475W	5.21	5.09	4.78	-26.36	-26.49	-26.80	-0.122	-0.432	0.4747	14	
ACS_F555W	4.87	4.84	4.79	-26.71	-26.74	-26.78	-0.030	-0.076	0.5361	14	
ACS_F606W	4.66	4.72	4.89	-26.92	-26.85	-26.68	0.063	0.233	0.5922	14	
ACS_F625W	4.49	4.63	4.94	-27.08	-26.94	-26.64	0.140	0.448	0.6312	14	
ACS_F775W	4.16	4.52	5.26	-27.42	-27.05	-26.31	0.364	1.103	0.7694	14	
ACS_F814W	4.12	4.52	5.36	-27.46	-27.06	-26.22	0.400	1.239	0.8059	14	
ACS_F850LP	4.01	4.50	5.59	-27.56	-27.07	-25.98	0.494	1.577	0.9016	14	
WFC3_F218W	9.09	10.74	8.79	-22.48	-20.83	-22.78	1.654	-0.298	0.2229	14	
WFC3_F225W	8.51	10.13	8.32	-23.06	-21.44	-23.25	1.625	-0.191	0.2372	14	
WFC3_F336W	5.49	6.64	5.58	-26.09	-24.93	-25.99	1.158	0.094	0.3355	14	
WFC3_F390W	5.66	5.85	5.12	-25.91	-25.73	-26.45	0.187	-0.536	0.3924	14	
WFC3_F438W	5.50	5.32	4.81	-26.07	-26.25	-26.76	-0.178	-0.690	0.4326	14	
WFC3_F475W	5.19	5.07	4.77	-26.38	-26.50	-26.80	-0.122	-0.419	0.4774	14	
WFC3_F555W	4.91	4.86	4.79	-26.67	-26.72	-26.78	-0.048	-0.116	0.5308	14	
WFC3_F606W	4.67	4.73	4.88	-26.91	-26.85	-26.69	0.059	0.217	0.5887	14	
WFC3_F625W	4.52	4.64	4.92	-27.06	-26.93	-26.65	0.124	0.409	0.6241	14	
WFC3_F775W	4.16	4.52	5.25	-27.41	-27.05	-26.33	0.357	1.083	0.7648	14	
WFC3_F814W	4.12	4.52	5.35	-27.45	-27.06	-26.22	0.395	1.226	0.8030	14	
WFC3_F098m	3.96	4.50	5.78	-27.61	-27.07	-25.79	0.538	1.816	0.9864	14	
WFC3_F105W	3.89	4.51	5.94	-27.68	-27.06	-25.63	0.622	2.046	1.0551	14	
WFC3_F125W	3.66	4.54	6.33	-27.91	-27.03	-25.24	0.877	2.667	1.2486	14	
WFC3_F140W	3.51	4.56	6.59	-28.06	-27.01	-24.98	1.052	3.079	1.3922	14	
WFC3_F160W	3.37	4.60	6.84	-28.20	-26.97	-24.73	1.228	3.469	1.5370	14	
WFPC2_F218W	9.17	10.83	8.86	-22.40	-20.74	-22.72	1.657	-0.316	0.2207	15	
WFPC2_F300W	6.10	7.40	6.09	-25.48	-24.17	-25.48	1.307	-0.005	0.2992	15	
WFPC2_F450W	5.31	5.20	4.80	-26.26	-26.37	-26.77	-0.110	-0.509	0.4556	15	
WFPC2_F555W	4.84	4.82	4.81	-26.73	-26.75	-26.77	-0.025	-0.038	0.5442	15	
WFPC2_F606W	4.62	4.70	4.90	-26.95	-26.87	-26.67	0.077	0.276	0.6001	15	
WFPC2_F702W	4.33	4.57	5.08	-27.24	-27.00	-26.49	0.240	0.748	0.6919	15	
WFPC2_F814W	4.12	4.52	5.34	-27.45	-27.05	-26.23	0.392	1.216	0.8002	15	
NIC2_F110W	3.82	4.52	6.08	-27.75	-27.05	-25.49	0.704	2.265	1.1235	15	
NIC2_F160W	3.35	4.64	6.97	-28.22	-26.93	-24.60	1.286	3.618	1.6030	15	
NIC3_F110W	3.82	4.52	6.08	-27.75	-27.05	-25.50	0.701	2.255	1.1200	15	
NIC3_F160W	3.35	4.64	6.97	-28.22	-26.93	-24.60	1.287	3.621	1.6042	15	
NIRCAM_F070W	4.29	4.56	5.10	-27.28	-27.02	-26.47	0.264	0.811	0.7046	15	
NIRCAM_F090W	4.02	4.50	5.59	-27.56	-27.07	-25.98	0.488	1.573	0.9025	15	
NIRCAM_F115W	3.77	4.53	6.15	-27.80	-27.05	-25.43	0.753	2.373	1.1543	15	
NIRCAM_F140M	3.48	4.56	6.60	-28.09	-27.02	-24.97	1.079	3.126	1.4053	15	
NIRCAM_F150W	3.41	4.59	6.78	-28.16	-26.98	-24.79	1.182	3.371	1.5007	15	
NIRCAM_F150W2	3.50	4.70	7.11	-28.07	-26.87	-24.46	1.203	3.610	1.6588	15	
NIRCAM_F162M	3.32	4.65	7.01	-28.25	-26.93	-24.56	1.328	3.693	1.6272	15	
NIRCAM_F164N	3.29	4.66	7.05	-28.28	-26.91	-24.53	1.368	3.756	1.6445	15	
NIRCAM_F182M	3.28	4.81	7.45	-28.29	-26.76	-24.12	1.534	4.172	1.8452	15	
NIRCAM_F187N	3.25	4.85	7.52	-28.33	-26.72	-24.05	1.600	4.272	1.8739	15	
NIRCAM_F200W	3.28	4.93	7.73	-28.30	-26.64	-23.84	1.652	4.453	1.9886	15	
NIRCAM_F200W	3.28	4.93	7.73	-28.30	-26.64	-23.84	1.652	4.453	1.9886	15	
NIRCAM_F210M	3.27	5.03	7.94	-28.30	-26.54	-23.63	1.757	4.671	2.0955	15	
NIRCAM_F250M	3.27	5.37	8.67	-28.30	-26.21	-22.91	2.093	5.393	2.5032	15	
NIRCAM_F277W	3.26	5.53	9.04	-28.31	-26.04	-22.53	2.265	5.779	2.7618	15	
NIRCAM_F300M	3.26	5.69	9.37	-28.31	-25.88	-22.20	2.429	6.115	2.9892	15	
NIRCAM_F322W2	3.26	5.77	9.63	-28.31	-25.80	-21.95	2.509	6.365	3.2320	15	
NIRCAM_F323N	3.26	5.84	9.70	-28.31	-25.73	-21.87	2.583	6.441	3.2369	15	
NIRCAM_F335M	3.26	5.92	9.86	-28.31	-25.66	-21.71	2.658	6.599	3.3621	15	
NIRCAM_F356W	3.26	6.02	10.09	-28.31	-25.55	-21.48	2.763	6.833	3.5684	15	
NIRCAM_F405N	3.24	6.30	10.65	-28.33	-25.27	-20.93	3.058	7.404	4.0517	15	
NIRCAM_F410M	3.26	6.31	10.67	-28.32	-25.27	-20.90	3.049	7.411	4.0822	15	
NIRCAM_F430M	3.27	6.41	10.88	-28.31	-25.16	-20.69	3.147	7.613	4.2813	15	
NIRCAM_F444W	3.27	6.46	10.99	-28.30	-25.11	-20.59	3.185	7.712	4.4040	15	
NIRCAM_F460M	3.29	6.60	11.23	-28.28	-24.97	-20.34	3.308	7.943	4.6285	15	
NIRCAM_F466N	3.26	6.62	11.26	-28.31	-24.96	-20.31	3.352	8.000	4.6544	15	
NIRCAM_F470N	3.29	6.63	11.30	-28.28	-24.94	-20.27	3.341	8.013	4.7078	15	
NIRCAM_F480M	3.29	6.67	11.39	-28.28	-24.90	-20.18	3.383	8.104	4.8167	15	
MIRI_F560W	3.28	6.97	12.03	-28.29	-24.60	-19.54	3.693	8.756	5.6362	16	
MIRI_F770W	3.26	7.58	13.30	-28.31	-24.00	-18.27	4.314	10.039	7.6428	16	
MIRI_F1000W	3.26	8.15	14.45	-28.31	-23.42	-17.13	4.883	11.181	9.9544	16	
MIRI_F1130W	3.26	8.43	15.00	-28.31	-23.14	-16.57	5.166	11.741	11.3087	16	
MIRI_F1500W	3.27	9.03	16.23	-28.31	-22.54	-15.35	5.763	12.961	15.0651	16	
MIRI_F1800W	3.27	9.42	17.00	-28.30	-22.15	-14.57	6.149	13.732	17.9865	16	
MIRI_F2100W	3.27	9.72	17.62	-28.30	-21.85	-13.95	6.453	14.351	20.7950	16	
MIRI_F2550W	3.28	10.16	18.49	-28.30	-21.41	-13.08	6.887	15.216	25.3639	16	
NIRISS_F090W	4.02	4.50	5.59	-27.56	-27.07	-25.98	0.488	1.575	0.9031	17	
NIRISS_F115W	3.78	4.53	6.14	-27.79	-27.05	-25.43	0.747	2.358	1.1499	17	
NIRISS_F140M	3.48	4.56	6.60	-28.09	-27.02	-24.97	1.078	3.123	1.4044	17	
NIRISS_F150W	3.41	4.59	6.77	-28.16	-26.98	-24.81	1.173	3.352	1.4936	17	
NIRISS_F158M	3.35	4.62	6.93	-28.23	-26.95	-24.64	1.277	3.582	1.5825	17	
NIRISS_F200W	3.28	4.93	7.74	-28.30	-26.64	-23.83	1.656	4.461	1.9930	17	
NIRISS_F277W	3.27	5.53	9.04	-28.30	-26.05	-22.53	2.258	5.774	2.7641	17	
NIRISS_F356W	3.26	6.03	10.11	-28.31	-25.54	-21.46	2.769	6.854	3.5926	17	
NIRISS_F380M	3.26	6.17	10.39	-28.31	-25.40	-21.18	2.908	7.128	3.8229	17	
NIRISS_F430M	3.27	6.40	10.87	-28.30	-25.17	-20.70	3.130	7.595	4.2792	17	
NIRISS_F444W	3.27	6.47	11.00	-28.30	-25.11	-20.57	3.191	7.729	4.4270	17	
NIRISS_F480M	3.29	6.66	11.38	-28.28	-24.91	-20.19	3.366	8.086	4.8113	17	
OMEGACAM_u	5.46	6.34	5.43	-26.11	-25.23	-26.15	0.881	-0.035	0.3590	18	
OMEGACAM_g	5.21	5.09	4.77	-26.36	-26.48	-26.80	-0.126	-0.442	0.4735	18	
OMEGACAM_r	4.50	4.63	4.93	-27.07	-26.94	-26.64	0.133	0.429	0.6276	18	
OMEGACAM_i	4.20	4.53	5.21	-27.38	-27.05	-26.36	0.331	1.013	0.7495	18	
OMEGACAM_z	4.01	4.51	5.55	-27.56	-27.07	-26.03	0.493	1.534	0.8842	18	
VIRCAM_Z	4.02	4.51	5.56	-27.56	-27.07	-26.01	0.491	1.546	0.8899	19	
VIRCAM_Y	3.93	4.51	5.87	-27.64	-27.07	-25.70	0.577	1.940	1.0253	19	
VIRCAM_H	3.65	4.54	6.34	-27.93	-27.03	-25.23	0.892	2.691	1.2535	19	
VIRCAM_J	3.32	4.66	7.05	-28.25	-26.91	-24.53	1.335	3.721	1.6430	19	
VIRCAM_Ks	3.27	5.07	8.04	-28.30	-26.50	-23.53	1.797	4.767	2.1494	19	
SkyMapper_u	5.33	6.32	5.40	-26.24	-25.25	-26.17	0.989	0.073	0.3590	20	
SkyMapper_v	5.81	6.09	5.31	-25.77	-25.49	-26.26	0.280	-0.493	0.3836	20	
SkyMapper_g	5.03	4.94	4.78	-26.55	-26.63	-26.79	-0.082	-0.247	0.5075	20	
SkyMapper_r	4.56	4.66	4.91	-27.02	-26.91	-26.66	0.104	0.352	0.6138	20	
SkyMapper_i	4.14	4.52	5.28	-27.43	-27.05	-26.29	0.377	1.137	0.7768	20	
SkyMapper_z	4.00	4.50	5.62	-27.57	-27.07	-25.95	0.502	1.615	0.9143	20


    

    




Overwriting sun_observed.txt

In [2]:

    

import pandas as pd

df = pd.read_csv('sun_observed.txt', delim_whitespace=True, index_col=0)
df.head()


    

    
Out[2]:







  

    

      

      
Abs_Vega
      
Abs_AB
      
Abs_ST
      
App_Vega
      
App_AB
      
App_ST
      
Vega_AB
      
Vega_ST
      
lambda_pivot
      
Source
    
    

      
Filter
      

      

      

      

      

      

      

      

      

      

    
  
  

    

      
Johnson_U
      
5.61
      
6.33
      
5.42
      
-25.97
      
-25.25
      
-26.15
      
0.721
      
-0.183
      
0.3611
      
1
    
    

      
Johnson_B
      
5.44
      
5.31
      
4.84
      
-26.13
      
-26.26
      
-26.74
      
-0.128
      
-0.605
      
0.4396
      
1
    
    

      
Johnson_V
      
4.81
      
4.80
      
4.81
      
-26.76
      
-26.77
      
-26.76
      
-0.013
      
0.001
      
0.5511
      
1
    
    

      
Cousins_R
      
4.43
      
4.60
      
5.00
      
-27.15
      
-26.97
      
-26.57
      
0.178
      
0.578
      
0.6582
      
1
    
    

      
Cousins_I
      
4.10
      
4.51
      
5.35
      
-27.47
      
-27.06
      
-26.22
      
0.414
      
1.247
      
0.8034
      
1
    
  

In [3]:

    

from isochrones import get_ichrone
import numpy as np

bands = ['J', 'H', 'K', 'W1', 'W2', 'W3', 'W4', 'B', 'V']
mist = get_ichrone('mist', bands=bands)

mass = 1.0
age = np.log10(4.54e9)
feh = 0.0

theory = mist.generate(mass, age, feh, accurate=True)


    

    




Emcee3 not imported; be advised.
/Users/tmorton/miniconda3/envs/isochrones-test/lib/python3.7/site-packages/isochrones-2.0.1-py3.7.egg/isochrones/mags.py:32: NumbaPendingDeprecationWarning: 
Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'icols' of function 'interp_value_4d'.

For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types

File "../../../miniconda3/envs/isochrones-test/lib/python3.7/site-packages/isochrones-2.0.1-py3.7.egg/isochrones/interp.py", line 291:
@nb.jit(nopython=True)
def interp_value_4d(x0, x1, x2, x3,
^

  bc_ii0, bc_ii1, bc_ii2, bc_ii3)
/Users/tmorton/miniconda3/envs/isochrones-test/lib/python3.7/site-packages/numba/ir_utils.py:1969: NumbaPendingDeprecationWarning: 
Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'bc_cols' of function 'interp_mag'.

For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types

File "../../../miniconda3/envs/isochrones-test/lib/python3.7/site-packages/isochrones-2.0.1-py3.7.egg/isochrones/mags.py", line 9:
@nb.jit(nopython=True)
def interp_mag(pars, index_order, model_grid,
^

  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))
/Users/tmorton/miniconda3/envs/isochrones-test/lib/python3.7/site-packages/numba/ir_utils.py:1969: NumbaPendingDeprecationWarning: 
Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'index_order' of function 'interp_mag'.

For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types

File "../../../miniconda3/envs/isochrones-test/lib/python3.7/site-packages/isochrones-2.0.1-py3.7.egg/isochrones/mags.py", line 9:
@nb.jit(nopython=True)
def interp_mag(pars, index_order, model_grid,
^

  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))

In [4]:

    

band_map = {'J': '2MASS_J', 'H': '2MASS_H', 'K': '2MASS_Ks', 
            'W1': 'WISE_1', 'W2': 'WISE_2', 'W3': 'WISE_3', 'W4': 'WISE_4',
            'B': 'Bessell_Murphy_B', 'V': 'Bessell_Murphy_V'}

ZP = 'Vega'
for b, bb in band_map.items():
    print('{0}: {1} ({2})'.format(b, float(theory[f'{b}_mag']), df.loc[bb, f'Abs_{ZP}']))


    

    




J: 3.577017313836566 (3.67)
H: 3.2564594405901834 (3.32)
K: 3.2214882526616657 (3.27)
W1: 3.2106210145363736 (3.26)
W2: 3.228280674015413 (3.28)
W3: 3.200198325232515 (3.26)
W4: 3.19964003290378 (3.27)
B: 5.268001106910643 (5.46)
V: 4.656077520470088 (4.82)

In [5]:

    

mags = {b: (df.loc[band_map[b], 'Abs_Vega'], 0.01) for b in bands}


    

In [22]:

    

from isochrones import SingleStarModel
from isochrones.priors import FlatPrior

mod = SingleStarModel(mist, **mags, parallax=(100, 0.1), name='sun_test')
mod.set_prior(AV=FlatPrior((0, 0.001)))


    

In [23]:

    

mod.fit(refit=True)


    

In [24]:

    

mod.corner_physical();


    

In [25]:

    

mod.corner_observed();


    

In [30]:

    

mod2 = SingleStarModel(mist, **mags, parallax=(100, 0.1), feh=(0.0, 0.08), AV=(0, 0.001), name='sun_test_feh')
mod2.set_prior(AV=FlatPrior((0, 0.001)))
mod2.fit(refit=True)


    

In [31]:

    

mod2.corner_physical();


    

In [ ]: