distantgiants/kpf_requester.py at master · California-Planet-Search/distantgiants · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
import numpy as np
import pandas as pd

import tks_target_list_gen.sim.hires.exposure as exp


def df_maker():

    overview_df = pd.read_csv('csv/overview_df.csv')

    ## Name
    star_name = overview_df.star_id

    ## RA columns
    RA_deg = np.array(overview_df.ra_deg)

    if RA_deg.max() < 25:
        print('WARNING: RA is probably provided in Hr instead of deg')

    ra_hr = (RA_deg/15).astype(int)
    ra_min = ((RA_deg/15%1)*60).astype(int)
    ra_sec = np.round((((RA_deg/15%1)*60)%1)*60, 1)


    ## Dec columns
    DEC_deg = np.array(overview_df.dec_deg)

    dec_deg = DEC_deg.astype(int)
    dec_min = (( abs(DEC_deg) - (abs(DEC_deg)).astype(int) )*60).astype(int)
    dec_sec = (np.round((((abs(DEC_deg) - (abs(DEC_deg)).astype(int) )*60)%1)*60, 1))

    ## Epoch
    epoch = [2000 for i in range(len(overview_df))]

    ## vmag= dummy column
    vmag_str = ['vmag=' for i in range(len(overview_df))]

    ## Vmag
    vmag = overview_df.vmag

    ## Counts (in thousands)
    counts = np.array([60 for i in range(len(overview_df))]) # 60k for all DG targets

    ## exp_time
    t_exp = (np.round(exp.exposure_time(vmag, counts, iod = True))).astype(int)

    ## max_exp_time
    t_max_list = np.array([500, 600, 900, 1200, 1500, 1800, 2700, 3600, 5400, 6000])
    t_max = [(2*t_exp[j] + min([i for i in t_max_list - 2*t_exp[j] if i>0])) for j in range(len(t_exp))]

    ## Decker
    decker = ['B5' if vmag[i]<= 10 else 'C2' for i in range(len(overview_df))]

    ## Desired obs
    cadence = 40

    # Choose the maximum of the following 2 options:
    #######################
    # Baseline-limited
    N_baseline = np.ceil((3*365 - overview_df.baseline_hires)/cadence) # Specified cadence until 3-year baseline reached
    # Observation-limited
    N_iod = 30 - overview_df.tot_iodine_hires
    #######################

    N_needed = np.amax([N_baseline, N_iod], axis=0)
    N_needed = np.maximum(N_needed, 0) # Cooked targets give negative obs_needed. Take 0 instead


    # Number of possible obs per semester depends on cadence
    N_sem = int(180/cadence)
    print('With the given cadence, we get {} obs per semester'.format(N_sem))
    N_obs = np.minimum(N_needed, N_sem) # Only ask for at most 7 obs per semester

    ## Number of observations acquired
    N_acq = [1 for i in range(len(overview_df))]


    ## Iodine
    iodine = ['in' for i in range(len(overview_df))]

    ## Priority
    prio = ['p1' for i in range(len(overview_df))]

    ## Program code
    prog_code = ['DG' for i in range(len(overview_df))]

    ## Telescope proposal code
    tel_code = ['U062' for i in range(len(overview_df))]


    # print(star_name, ra_hr, ra_min, ra_sec)
    # sfd

    kpf_df = pd.DataFrame({'N_obs':N_obs,
                          'Starname':star_name,
                          'RAH':ra_hr,
                          'RAM':ra_min,
                          'RAS':ra_sec,
                          'DECD':dec_deg,
                          'DECM':dec_min,
                          'DECS':dec_sec,
                          'epoch':epoch,
                          'vmag_str':vmag_str,
                          'vmag':vmag,
                          'T_exp':t_exp,
                          'T_max':t_max,
                          'counts':counts,
                          'decker':decker,
                          'N_acq':N_acq,
                          'iodine':iodine,
                          'prio':prio,
                          'prog_code':prog_code,
                          'tel_code':tel_code})
    # print(kpf_df.head(5))

    kpf_df.to_csv('kpf_df.csv')


if __name__ == "__main__":
    df_maker()