Getting started

In this tutorial we will read HARPS spectra, extract and plot three activity indicators and radial velocity for the star HD41248.

Start by importing and initializing ACTIN

[1]:

%matplotlib inline

from actin2 import ACTIN
actin = ACTIN()

Get the HARPS 1D fits files of the star HD41248 from the test folder

[2]:

import glob, os
files = glob.glob(os.path.join(os.pardir, "actin2/test/HARPS/HD41248", "*_s1d_A.fits"))
files

[2]:

['../actin2/test/HARPS/HD41248/HARPS.2014-01-24T01:18:06.472_s1d_A.fits',
 '../actin2/test/HARPS/HD41248/HARPS.2014-01-16T06:24:23.418_s1d_A.fits',
 '../actin2/test/HARPS/HD41248/HARPS.2014-01-24T04:17:29.213_s1d_A.fits',
 '../actin2/test/HARPS/HD41248/HARPS.2014-01-21T05:33:32.740_s1d_A.fits',
 '../actin2/test/HARPS/HD41248/HARPS.2014-01-21T03:16:16.891_s1d_A.fits',
 '../actin2/test/HARPS/HD41248/HARPS.2014-01-16T05:37:46.157_s1d_A.fits']

Check which indices come pre-installed. We start by looking at the full indices table:

[3]:

actin.IndTable().table

[3]:
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 available indices are:

[4]:

actin.IndTable().indices

[4]:

['I_CaI', 'I_CaII', 'I_Ha06', 'I_Ha16', 'I_HeI', 'I_NaI']

We are going to calculate the indices based on the CaII H&K, H\(\alpha\) (using 0.6 ang central band) and NaI D2 lines using the respective index ID as in ind_id:

[5]:

indices = ['I_CaII', 'I_Ha06', 'I_NaI']

Now calculate the indices for the loaded files. The results will be stored in a pandas DataFrame.

[6]:

df = actin.run(files, indices)

100%|██████████| 6/6 [00:02<00:00,  2.94it/s]

See the results headers

[7]:

df.keys()

[7]:

Index(['obj', 'instr', 'date_obs', 'bjd', 'drs', 'exptime', 'ra', 'dec',
       'snr7', 'snr50', 'prog_id', 'pi_coi', 'cal_th_err', 'berv', 'spec_rv',
       'snr_med', 'ftype', 'rv_flg', 'rv', 'dvrms', 'ccf_noise', 'fwhm',
       'cont', 'ccf_mask', 'drift_noise', 'drift_rv', 'rv_wave_corr', 'rv_err',
       'spec_flg', 'file', 'I_CaII', 'I_CaII_err', 'I_CaII_Rneg', 'I_Ha06',
       'I_Ha06_err', 'I_Ha06_Rneg', 'I_NaI', 'I_NaI_err', 'I_NaI_Rneg',
       'actin_ver'],
      dtype='object')

and the full table

[8]:

df

[8]:
obj instr date_obs bjd drs exptime ra dec snr7 snr50 ... I_CaII I_CaII_err I_CaII_Rneg I_Ha06 I_Ha06_err I_Ha06_Rneg I_NaI I_NaI_err I_NaI_Rneg actin_ver
0 HD41248 HARPS 2014-01-16T05:37:46.156 2.456674e+06 HARPS_3.7 600.0005 90.136003 -56.16330 12.0 55.6 ... 0.126963 0.001206 0.002865 0.107759 0.000652 0.0 0.358022 0.001249 0.0 2.0 beta 8
1 HD41248 HARPS 2014-01-16T06:24:23.418 2.456674e+06 HARPS_3.7 600.0007 90.139297 -56.16415 12.4 55.6 ... 0.138301 0.001236 0.001154 0.105833 0.000653 0.0 0.356299 0.001252 0.0 2.0 beta 8
2 HD41248 HARPS 2014-01-21T03:16:16.890 2.456679e+06 HARPS_3.7 900.0010 90.138288 -56.16339 26.5 101.3 ... 0.145480 0.000697 0.000000 0.105242 0.000374 0.0 0.357987 0.000708 0.0 2.0 beta 8
3 HD41248 HARPS 2014-01-21T05:33:32.739 2.456679e+06 HARPS_3.7 900.0005 90.135625 -56.16338 24.6 98.0 ... 0.141643 0.000732 0.000000 0.105055 0.000386 0.0 0.357836 0.000731 0.0 2.0 beta 8
4 HD41248 HARPS 2014-01-24T01:18:06.471 2.456682e+06 HARPS_3.7 600.0018 90.136782 -56.16351 15.7 67.6 ... 0.139618 0.001047 0.000194 0.106154 0.000542 0.0 0.356930 0.001037 0.0 2.0 beta 8
5 HD41248 HARPS 2014-01-24T04:17:29.213 2.456682e+06 HARPS_3.7 600.0014 90.138346 -56.16322 16.4 68.2 ... 0.137774 0.001003 0.000208 0.107019 0.000551 0.0 0.357135 0.001038 0.0 2.0 beta 8

6 rows × 40 columns

and plot the results

[9]:

import matplotlib.pylab as plt

plt.figure(figsize=(6, (len(indices)+1)*2))

plt.subplot(len(indices)+1, 1, 1)
plt.ylabel("RV [m/s]")
plt.errorbar(df.bjd - 2450000, df.rv, df.rv_err, fmt='k.')

for i, index in enumerate(indices):
    plt.subplot(len(indices)+1, 1, i+2)
    plt.ylabel(index)
    plt.errorbar(df.bjd - 2450000, df[index], df[index + "_err"], fmt='k.')
plt.xlabel("BJD $-$ 2450000 [days]")
plt.tight_layout()
_images/getstarted_17_0.png