Modifying the indices table

In this tutorial we will modify the indices table to include an array of indices with different bandpasses.

We will include in the table a modified version of the H\(\alpha\) index using central bandpasses between 1.6 and 2.0 angtroms but the same reference lines as the original.

import numpy as np

from actin2 import ACTIN
actin = ACTIN()

The pandas DataFrame table with the activity indices line parameters is in the method table inside the class actin.Indtable:

indtable = actin.IndTable()
table = indtable.table

table

	ind_id	ind_var	ln_id	ln_c	ln_ctr	ln_win	bandtype
0	I_CaII	L1	CaIIK	1.0	3933.664	1.09	tri
1	I_CaII	L2	CaIIH	1.0	3968.470	1.09	tri
2	I_CaII	R1	CaIIR1	1.0	3901.070	20.00	sq
3	I_CaII	R2	CaIIR2	1.0	4001.070	20.00	sq
4	I_NaI	L1	NaID1	1.0	5895.920	0.50	sq
5	I_NaI	L2	NaID2	1.0	5889.950	0.50	sq
6	I_NaI	R1	NaIR1	1.0	5805.000	10.00	sq
7	I_NaI	R2	NaIR2	1.0	6097.000	20.00	sq
8	I_Ha16	L1	Ha16	1.0	6562.808	1.60	sq
9	I_Ha16	R1	HaR1	1.0	6550.870	10.75	sq
10	I_Ha16	R2	HaR2	1.0	6580.310	8.75	sq
11	I_Ha06	L1	Ha06	1.0	6562.808	0.60	sq
12	I_Ha06	R1	HaR1	1.0	6550.870	10.75	sq
13	I_Ha06	R2	HaR2	1.0	6580.310	8.75	sq
14	I_HeI	L1	HeI	1.0	5875.620	0.40	sq
15	I_HeI	R1	HeIR1	1.0	5869.000	5.00	sq
16	I_HeI	R2	HeIR2	1.0	5881.000	5.00	sq
17	I_CaI	L1	CaI	1.0	6572.795	0.34	tri
18	I_CaI	R1	HaR1	1.0	6550.870	10.75	sq
19	I_CaI	R2	HaR2	1.0	6580.310	8.75	sq

The columns of the table are as follows: - ind_id: The index identification. For the spectral lines to be assigned to an index, their ind_id entries must have the same index ID. - ind_var: The identification of the line in the index equation: L1, L2, etc, for activity lines (numerator); R1, R2, etc, for reference lines (denominator) - ln_id: The spectral line identification. - ln_c: Constant to be multiplied to the flux in each line. - ln_ctr: Line centre (in angstroms). - ln_win: Bandwidth (in angstroms). - bandtype: Function used to integrate the flux in the bandpass: ‘sq’ for square; ‘tri’ for triangular. If using the triangular bandpass, ‘ln_win’ will be full-width-half-maximum of the bandpass.

So, we will need to give a new index ID to the modified version of the central H\(\alpha\) line and add the H\(\alpha\) reference lines to that index ID.

We will add new lines using the IndTable function add_line which have as arguments the keys in the IndTable.table, i.e., ind_id, ind_var, ln_id, etc.

We can get the reference bands parameters using

mask = (table.ln_id=='HaR1') & (table.ind_id=='I_Ha16')
r1_params = table[mask].to_dict('records')[0]
del r1_params['ind_id']

mask = (table.ln_id=='HaR2') & (table.ind_id=='I_Ha16')
r2_params = table[mask].to_dict('records')[0]
del r2_params['ind_id']

We deleted the entries with the ind_id because we will give them a new name.

The H\(\alpha\) line centre is:

ln_ctr = table[table.ln_id=='Ha16'].ln_ctr.values[0]

Now, we create grid of values for the central bandpass

pass_grid = np.arange(1.6, 2.1, 0.1)

And finally loop over the values to create the index IDs and lines

for i, ln_win in enumerate(pass_grid[:]):

    ind_id = f"I_HaMod{ln_win:.1f}"
    indtable.add_line(ind_id, "L1", f"Ha{ln_win:.1f}", 1.0, ln_ctr, ln_win, "sq")
    indtable.add_line(ind_id, **r1_params)
    indtable.add_line(ind_id, **r2_params)

The table now includes the new I_HaMod indices with the different bandpasses:

indtable.table

	ind_id	ind_var	ln_id	ln_c	ln_ctr	ln_win	bandtype
0	I_CaII	L1	CaIIK	1.0	3933.664	1.09	tri
1	I_CaII	L2	CaIIH	1.0	3968.470	1.09	tri
2	I_CaII	R1	CaIIR1	1.0	3901.070	20.00	sq
3	I_CaII	R2	CaIIR2	1.0	4001.070	20.00	sq
4	I_NaI	L1	NaID1	1.0	5895.920	0.50	sq
5	I_NaI	L2	NaID2	1.0	5889.950	0.50	sq
6	I_NaI	R1	NaIR1	1.0	5805.000	10.00	sq
7	I_NaI	R2	NaIR2	1.0	6097.000	20.00	sq
8	I_Ha16	L1	Ha16	1.0	6562.808	1.60	sq
9	I_Ha16	R1	HaR1	1.0	6550.870	10.75	sq
10	I_Ha16	R2	HaR2	1.0	6580.310	8.75	sq
11	I_Ha06	L1	Ha06	1.0	6562.808	0.60	sq
12	I_Ha06	R1	HaR1	1.0	6550.870	10.75	sq
13	I_Ha06	R2	HaR2	1.0	6580.310	8.75	sq
14	I_HeI	L1	HeI	1.0	5875.620	0.40	sq
15	I_HeI	R1	HeIR1	1.0	5869.000	5.00	sq
16	I_HeI	R2	HeIR2	1.0	5881.000	5.00	sq
17	I_CaI	L1	CaI	1.0	6572.795	0.34	tri
18	I_CaI	R1	HaR1	1.0	6550.870	10.75	sq
19	I_CaI	R2	HaR2	1.0	6580.310	8.75	sq
20	I_HaMod1.6	L1	Ha1.6	1.0	6562.808	1.60	sq
21	I_HaMod1.6	R1	HaR1	1.0	6550.870	10.75	sq
22	I_HaMod1.6	R2	HaR2	1.0	6580.310	8.75	sq
23	I_HaMod1.7	L1	Ha1.7	1.0	6562.808	1.70	sq
24	I_HaMod1.7	R1	HaR1	1.0	6550.870	10.75	sq
25	I_HaMod1.7	R2	HaR2	1.0	6580.310	8.75	sq
26	I_HaMod1.8	L1	Ha1.8	1.0	6562.808	1.80	sq
27	I_HaMod1.8	R1	HaR1	1.0	6550.870	10.75	sq
28	I_HaMod1.8	R2	HaR2	1.0	6580.310	8.75	sq
29	I_HaMod1.9	L1	Ha1.9	1.0	6562.808	1.90	sq
30	I_HaMod1.9	R1	HaR1	1.0	6550.870	10.75	sq
31	I_HaMod1.9	R2	HaR2	1.0	6580.310	8.75	sq
32	I_HaMod2.0	L1	Ha2.0	1.0	6562.808	2.00	sq
33	I_HaMod2.0	R1	HaR1	1.0	6550.870	10.75	sq
34	I_HaMod2.0	R2	HaR2	1.0	6580.310	8.75	sq

This table can be used as input in ACTIN using the option table_df.

To save the new table as .csv file to the same folder as this file uncomment the lines below:

#file_path = os.path.join(os.path.dirname(__file__), "new_indtable.csv")
#indtable.save_table(file_path)