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Rank_Master.11.py
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"""
@author: Charles Fortenbach
Code associated with manuscript submitted to PASP (TBD)
Title: A FRAMEWORK FOR OPTIMIZING EXOPLANET TARGET SELECTION FOR
THE JAMES WEBB SPACE TELESCOPE
Subprogram: Rank_Master.py
Version: 1.2
This project is licensed under the GNU GPLv3 License.
"""
import numpy as np
import textwrap
import datetime
import time
import os
###############################################################################
# Read in JET input file
###############################################################################
JET_params = np.genfromtxt('JET_Input.txt', dtype=str, delimiter=', ',
skip_header=6)
# Target catalog starting and ending rows:
Nrow_start = int(JET_params[0, 1])
Nrow_end = int(JET_params[1, 1])
# JWST instrument/mode
Instrument = JET_params[2, 1]
# Wevelength range for this instrument (microns):
wavelimlo = float(JET_params[3, 1])
wavelimhi = float(JET_params[4, 1])
# Brightness limit (Jmag) at 100% FW as f(Teff):
Jmag_10000K_100FW = float(JET_params[5, 1])
Jmag_5000K_100FW = float(JET_params[6, 1])
Jmag_2500K_100FW = float(JET_params[7, 1])
# Detector linearity limit as fraction of FW (%):
percentFW = float(JET_params[8, 1])
# Instrument multi-transit residual noise floor (ppm):
nfloor = float(JET_params[9, 1])
# R value of simulation:
Res = int(JET_params[10, 1])
# Number of samples for each dBIC vs ntr g pt. (Npdxo):
Npdxo = int(JET_params[11, 1])
# Detection threshold (dBIC):
dBIC_thresh = int(JET_params[12, 1])
# Free model spectrum BIC parameters:
n_params = int(JET_params[13, 1])
# Eq. of State (lo metal atm):
EOS_lo = JET_params[14, 1]
# Cloud_lo (Pa):
Cloud_lo = float(JET_params[15, 1])
# Eq. of State (hi metal atm):
EOS_hi = JET_params[16, 1]
# Cloud_hi (Pa):
Cloud_hi = float(JET_params[17, 1])
# Observing time elements incl. overheads:
# Out of transit factor (% tdur):
Out_trans_factor = float(JET_params[18, 1])
# + Out of transit "timing tax"(sec):
Timing_tax = float(JET_params[19, 1])
# Slew duration avg. (sec):
Slew_time = float(JET_params[20, 1])
# SAMs: small angle maneuvers (sec):
SAMs = float(JET_params[21, 1])
# GS Acq: guide star acquisition(s)(sec):
GS_acq = float(JET_params[22, 1])
# Targ Acq: target acquisition if any (sec):
Targ_acq = float(JET_params[23, 1])
# Exposure Ovhd: factor 1:
Exp_Ovhd_factor1 = float(JET_params[24, 1])
# Exposure Ovhd: factor 2 (sec):
Exp_Ovhd_factor2 = float(JET_params[25, 1])
# Mech: mechanism movements (sec):
Mech = float(JET_params[26, 1])
# OSS: Onboard Script System compilation (sec):
OSS = float(JET_params[27, 1])
# MSA: NIRSpec MSA configuration (sec):
MSA = float(JET_params[28, 1])
# IRS2: NIRSpec IRS2 Detector Mode setup (sec):
IRS2 = float(JET_params[29, 1])
# Visit Ovhd: visit cleanup activities (sec):
Visit_Ovhd = float(JET_params[30, 1])
# Obs Ovhd factor (%):
Obs_Ovhd_factor = float(JET_params[31, 1])
# DS Ovhd (sec):
DS_Ovhd = float(JET_params[32, 1])
# Set Run flags
RunExoT = JET_params[33, 1]
RunPdxo = JET_params[34, 1]
RunRank = JET_params[35, 1]
###############################################################################
# Check Run flag
###############################################################################
if RunRank == 'N':
raise SystemExit
###############################################################################
# Print notification of running Pdxo_Master
###############################################################################
print('\n')
print(" Running Rank_Master:")
###############################################################################
# Read in survey data file
###############################################################################
summary_data = np.loadtxt('Pdxo_Output.txt', ndmin=2, skiprows=0)
# Reset target indices
Nrow_start = int(summary_data[0, 2])
targets = np.max(summary_data, axis=0)
Nrow_end = int(targets[2])
###############################################################################
# Initialize arrays
###############################################################################
start = np.zeros(7, dtype=int)
end = np.zeros(7, dtype=int)
###############################################################################
# Write summary table for un-ranked targets to txt file in working directory
###############################################################################
now = datetime.datetime.now()
timestr = time.strftime("%Y%m%d-%H%M%S")
fout = open(os.getcwd() + '/JET_Smry_tables/' +'JET_Smry_'
+ timestr + '_un-ranked.txt', "w", newline='')
fout.write('JET run ID: ' + str(now) + '\n' + '\n')
fout.write('Input Summary: ' + '\n' + '\n')
fout.write(' Parameter '
+ 'Input Value' + '\n')
fout.write(' --------- '
+ '-----------' + '\n')
fout.write(' Starting target for this sequence of runs: '
+ str(Nrow_start) + '\n')
fout.write(' Ending target from catalog: '
+ str(Nrow_end) + '\n' + '\n')
fout.write(' JWST instrument: '
+ Instrument + '\n')
fout.write(' Wavelength short limit (microns): '
+ str(wavelimlo) + '\n')
fout.write(' Wavelength long limit (microns): '
+ str(wavelimhi) + '\n')
fout.write(' Jmag limit (Teff = 10000K): '
+ str(Jmag_10000K_100FW) + '\n')
fout.write(' Jmag limit (Teff = 5000K): '
+ str(Jmag_5000K_100FW) + '\n')
fout.write(' Jmag limit (Teff = 2500K): '
+ str(Jmag_2500K_100FW) + '\n')
fout.write(' Detector linear response limit (% FW): '
+ str(percentFW) + '\n')
fout.write(' Noise floor (nfloor) (ppm): '
+ str(nfloor) + '\n')
fout.write(' R value of sim (Res): '
+ str(Res) + '\n')
fout.write(' Number of samples for dBIC vs ntr grid pts.: '
+ str(Npdxo) + '\n')
fout.write(' Detection threshold (dBIC): '
+ str(dBIC_thresh) + '\n')
fout.write(' Free model spectrum BIC parameters: '
+ str(n_params) + '\n' + '\n')
fout.write(' Eq. of State (lo metal atm): '
+ EOS_lo + '\n')
fout.write(' Cloud_lo (Pa): '
+ str(Cloud_lo) + '\n')
fout.write(' Eq. of State (hi metal atm): '
+ EOS_hi + '\n')
fout.write(' Cloud_hi (Pa): '
+ str(Cloud_hi) + '\n' + '\n')
fout.write(' Out of transit factor (% tdur): '
+ str(Out_trans_factor) + '\n')
fout.write(' + Out of transit "timing tax" (sec): '
+ str(Timing_tax) + '\n')
fout.write(' Slew duration avg. (sec): '
+ str(Slew_time) + '\n')
fout.write(' SAMs: small angle maneuvers (sec): '
+ str(SAMs) + '\n')
fout.write(' GS Acq: guide star acquisition(s)(sec): '
+ str(GS_acq) + '\n')
fout.write(' Targ Acq: target acquisition if any (sec): '
+ str(Targ_acq) + '\n')
fout.write(' Exposure Ovhd: factor 1: '
+ str(Exp_Ovhd_factor1) + '\n')
fout.write(' Exposure Ovhd: factor 2 (sec): '
+ str(Exp_Ovhd_factor2) + '\n')
fout.write(' Mech: mechanism movements (sec): '
+ str(Mech) + '\n')
fout.write(' OSS: Onboard Script System compilation (sec): '
+ str(OSS) + '\n')
fout.write(' MSA: NIRSpec MSA configuration (sec): '
+ str(MSA) + '\n')
fout.write(' IRS2: NIRSpec IRS2 Detector Mode setup (sec): '
+ str(IRS2) + '\n')
fout.write(' Visit Ovhd: visit cleanup activities (sec): '
+ str(Visit_Ovhd) + '\n')
fout.write(' Obs Ovhd factor (%): '
+ str(Obs_Ovhd_factor) + '\n')
fout.write(' DS_Ovhd (sec): '
+ str(DS_Ovhd) + '\n' + '\n')
fout.write(textwrap.dedent("""\
Output Summary (un-ranked):
Col Label Explanations
--------------------------------------------------------------------------
--- ranking:
0 Cat Planet demographic category (1 - 7)
--- survey data:
1 Targ Survey table index (starting with 1)
2 RAdeg Right Ascension in decimal degrees (J2000)
3 DEdeg Declination in decimal degrees (J2000)
4 Rp Planetary radius (mean) in Earth units (R/Rearth)
5 Per Period (days)
6 S Planetary insolation in Earth units (S/Searth)
7 R* Stellar radius (Solar radii)
8 Teff Stellar effective temperature (K)
9 Jmag Apparent J band magnitude (mag)
--- computed values:
10 a Semi-major axis of planet orbit (Solar radii)
11 Teq Planet equilibrium temperature (K)
12 gs Planet surface gravity (m/s^2)
13 Mmed Planet mass (median) in Earth units (M/Mearth)
14 tdur Transit duration (hrs)
15 nt10yr Number of transits observable in 10yr mission
16 nt_lo Number of transits needed for detection; lo-metal atm
17 nt_hi Number of transits needed for detection; hi-metal atm
18 tT_lo Total observ. cycle (in/out of transit); lo-metal atm (hrs)
19 tT_hi Total observ. cycle (in/out of transit); hi-metal atm (hrs)
21 tT_avg Total observ. cycle for 'average' atm detection (hrs)
"""))
fout.write('\n')
fout.write('\n')
# Define headers and formats for output
head1 = (" |----------------------------------survey data-------"
"-------------------------|"
"---------------------------------------------computed values--------"
"-----------------------------|\n")
head2 = (" Cat Targ RAdeg DEdeg Rp Per "
"S R* Teff Jmag a Teq gs "
"Mmed tdur nt10yr nt_lo nt_hi "
"tT_lo tT_hi tT_avg\n")
fmt1 = (" {Cat:4.0f} {n:15.0f} {RAdeg:8.4f} {DEdeg:9.4f} {Rp:7.4f} {Per:8.4f}"
" {S:10.3f} {Rstar:7.4f} {Teff:6.0f} {Jmag:8.3f} {a:10.4f} {Teq:6.0f}"
" {gs:9.4f} {Mmed:9.4f} {tdur:8.4f} {nt10yr:7.0f} {nt_lo:6.0f}"
" {nt_hi:6.0f} {tT_lo:9.1f} {tT_hi:9.1f} {tT_avg:9.1f}\n")
fout.write(head1)
fout.write(head2)
for i in range(0, Nrow_end - Nrow_start + 1):
fout.write(fmt1.format(
Cat = summary_data[i, 0],
Rank = summary_data[i, 1],
n = summary_data[i, 2],
RAdeg = summary_data[i, 3],
DEdeg = summary_data[i, 4],
Rp = summary_data[i, 5],
Per = summary_data[i, 6],
S = summary_data[i, 7],
K = summary_data[i, 8],
Rstar = summary_data[i, 9],
Teff = summary_data[i, 10],
Jmag = summary_data[i, 11],
a = summary_data[i, 12],
Teq = summary_data[i, 13],
gs = summary_data[i, 14],
Mmed = summary_data[i, 15],
Rhomed = summary_data[i, 18],
tdur = summary_data[i, 19],
nt10yr = summary_data[i, 20],
nt_lo = summary_data[i, 21],
nt_hi = summary_data[i, 22],
tT_lo = summary_data[i, 23],
tT_hi = summary_data[i, 24],
tT_avg = summary_data[i, 25]))
fout.write(textwrap.dedent("""\
Notes:
for nt_ and tT_ columns:
"996 and 9960.0" Rp > 10: Mmed estimates are unreliable
"997 and 9970.0" number of transits needed for detection exceeds"""
+ """ those observable in 10 yr mission
"998 and 9980.0" target Jmag is below (brighter than) the saturation"""
+ """ threshold of this instrument/mode
"999 and 9990.0" dBIC is < dBIC threshold for this target: """
+ """no detection for this atm assumption
"4990.0 to 5200.0" tT_avg undetermined since hi_metal atm not detected
"""))
fout.close()
###############################################################################
# Sort summary data by category and then total observation time
###############################################################################
print('\n')
print(" Sorting and ranking targets . . .\n\n" )
summary_data = sorted(summary_data, key=lambda x: (x[0], x[25]))
summary_data = np.asarray(summary_data)
for i in range(0, Nrow_end - Nrow_start):
if summary_data[i+1, 0] == summary_data[i, 0]: # if Cat is the same
summary_data[i+1, 1] = summary_data[i, 1] + 1 # then increment rank
else:
summary_data[i+1, 1] = 1 # otherwise reset the rank to one, and loop
###############################################################################
# Write summary table to txt file in working directory
###############################################################################
fout = open(os.getcwd() + '/JET_Smry_tables/' +'JET_Smry_'
+ timestr + '.txt', "w", newline='')
fout.write('JET run ID: ' + str(now) + '\n' + '\n')
fout.write('Input Summary: ' + '\n' + '\n')
fout.write(' Parameter '
+ 'Input Value' + '\n')
fout.write(' --------- '
+ '-----------' + '\n')
fout.write(' Starting target for this sequence of runs: '
+ str(Nrow_start) + '\n')
fout.write(' Ending target from catalog: '
+ str(Nrow_end) + '\n' + '\n')
fout.write(' JWST instrument: '
+ Instrument + '\n')
fout.write(' Wavelength short limit (microns): '
+ str(wavelimlo) + '\n')
fout.write(' Wavelength long limit (microns): '
+ str(wavelimhi) + '\n')
fout.write(' Jmag limit (Teff = 10000K): '
+ str(Jmag_10000K_100FW) + '\n')
fout.write(' Jmag limit (Teff = 5000K): '
+ str(Jmag_5000K_100FW) + '\n')
fout.write(' Jmag limit (Teff = 2500K): '
+ str(Jmag_2500K_100FW) + '\n')
fout.write(' Detector linear response limit (% FW): '
+ str(percentFW) + '\n')
fout.write(' Noise floor (nfloor) (ppm): '
+ str(nfloor) + '\n')
fout.write(' R value of sim (Res): '
+ str(Res) + '\n')
fout.write(' Number of samples for dBIC vs ntr grid pts.: '
+ str(Npdxo) + '\n')
fout.write(' Detection threshold (dBIC): '
+ str(dBIC_thresh) + '\n')
fout.write(' Free model spectrum BIC parameters: '
+ str(n_params) + '\n' + '\n')
fout.write(' Eq. of State (lo metal atm): '
+ EOS_lo + '\n')
fout.write(' Cloud_lo (Pa): '
+ str(Cloud_lo) + '\n')
fout.write(' Eq. of State (hi metal atm): '
+ EOS_hi + '\n')
fout.write(' Cloud_hi (Pa): '
+ str(Cloud_hi) + '\n' + '\n')
fout.write(' Out of transit factor (% tdur): '
+ str(Out_trans_factor) + '\n')
fout.write(' + Out of transit "timing tax" (sec): '
+ str(Timing_tax) + '\n')
fout.write(' Slew duration avg. (sec): '
+ str(Slew_time) + '\n')
fout.write(' SAMs: small angle maneuvers (sec): '
+ str(SAMs) + '\n')
fout.write(' GS Acq: guide star acquisition(s)(sec): '
+ str(GS_acq) + '\n')
fout.write(' Targ Acq: target acquisition if any (sec): '
+ str(Targ_acq) + '\n')
fout.write(' Exposure Ovhd: factor 1: '
+ str(Exp_Ovhd_factor1) + '\n')
fout.write(' Exposure Ovhd: factor 2 (sec): '
+ str(Exp_Ovhd_factor2) + '\n')
fout.write(' Mech: mechanism movements (sec): '
+ str(Mech) + '\n')
fout.write(' OSS: Onboard Script System compilation (sec): '
+ str(OSS) + '\n')
fout.write(' MSA: NIRSpec MSA configuration (sec): '
+ str(MSA) + '\n')
fout.write(' IRS2: NIRSpec IRS2 Detector Mode setup (sec): '
+ str(IRS2) + '\n')
fout.write(' Visit Ovhd: visit cleanup activities (sec): '
+ str(Visit_Ovhd) + '\n')
fout.write(' Obs Ovhd factor (%): '
+ str(Obs_Ovhd_factor) + '\n')
fout.write(' DS_Ovhd (sec): '
+ str(DS_Ovhd) + '\n' + '\n')
fout.write(textwrap.dedent("""\
Output Summary (fully sorted and ranked):
Col Label Explanations
--------------------------------------------------------------------------
Category Planet demographic category (1 - 7)
--- ranking:
0 Rank Rank within Cat based on total observ. cycle for detection
--- survey data:
1 Targ Survey table index (starting with 1)
2 RAdeg Right Ascension in decimal degrees (J2000)
3 DEdeg Declination in decimal degrees (J2000)
4 Rp Planetary radius (mean) in Earth units (R/Rearth)
5 Per Period (days)
6 S Planetary insolation in Earth units (S/Searth)
7 R* Stellar radius (Solar radii)
8 Teff Stellar effective temperature (K)
9 Jmag Apparent J band magnitude (mag)
--- computed values:
10 a Semi-major axis of planet orbit (Solar radii)
11 Teq Planet equilibrium temperature (K)
12 gs Planet surface gravity (m/s^2)
13 Mmed Planet mass (median) in Earth units (M/Mearth)
14 tdur Transit duration (hrs)
15 nt10yr Number of transits observable in 10yr mission
16 nt_lo Number of transits needed for detection; lo-metal atm
17 nt_hi Number of transits needed for detection; hi-metal atm
18 tT_lo Total observ. cycle (in/out of transit); lo-metal atm (hrs)
19 tT_hi Total observ. cycle (in/out of transit); hi-metal atm (hrs)
20 tT_avg Total observ. cycle for 'average' atm detection (hrs)
"""))
# Define headers and formats for output
head1 = (" |----------------------------------survey data----------------"
"----------------|"
"--------------------------------------------computed values---------"
"-----------------------------|\n")
head2 = (" Rank Targ RAdeg DEdeg Rp Per S R* "
" Teff Jmag a Teq gs "
"Mmed tdur nt10yr nt_lo nt_hi "
"tT_lo tT_hi tT_avg\n")
fmt1 = (" {Rank:4.0f} {n:6.0f} {RAdeg:8.4f} {DEdeg:9.4f} {Rp:7.4f} {Per:8.4f}"
" {S:10.3f} {Rstar:7.4f} {Teff:6.0f} {Jmag:8.3f} {a:10.4f} {Teq:6.0f}"
" {gs:9.4f} {Mmed:9.4f} {tdur:8.4f} {nt10yr:7.0f} {nt_lo:6.0f}"
" {nt_hi:6.0f} {tT_lo:9.1f} {tT_hi:9.1f} {tT_avg:9.1f}\n")
head3 = (" Rank Targ RAdeg DEdeg Rp Per S R* "
" Teff Jmag a Teq gs "
"Mmed tdur nt10yr\n")
fmt2 = (" Min: {Rp:7.4f} {Per:8.4f}"
" {S:10.3f} {Rstar:7.4f} {Teff:6.0f} {Jmag:8.3f} {a:10.4f} {Teq:6.0f}"
" {gs:9.4f} {Mmed:9.4f} {tdur:8.4f} {nt10yr:7.0f}\n")
fmt3 = (" Med: {Rp:7.4f} {Per:8.4f}"
" {S:10.3f} {Rstar:7.4f} {Teff:6.0f} {Jmag:8.3f} {a:10.4f} {Teq:6.0f}"
" {gs:9.4f} {Mmed:9.4f} {tdur:8.4f} {nt10yr:7.0f}\n")
fmt4 = ("Mean: {Rp:7.4f} {Per:8.4f}"
" {S:10.3f} {Rstar:7.4f} {Teff:6.0f} {Jmag:8.3f} {a:10.4f} {Teq:6.0f}"
" {gs:9.4f} {Mmed:9.4f} {tdur:8.4f} {nt10yr:7.0f}\n")
fmt5 = (" Max: {Rp:7.4f} {Per:8.4f}"
" {S:10.3f} {Rstar:7.4f} {Teff:6.0f} {Jmag:8.3f} {a:10.4f} {Teq:6.0f}"
" {gs:9.4f} {Mmed:9.4f} {tdur:8.4f} {nt10yr:7.0f}\n")
head5 = "Full Target Set:\n"
head4 = (" Targ RAdeg DEdeg Rp Per S R* "
" Teff Jmag a Teq gs "
"Mmed tdur nt10yr\n")
fmt6 = (" Min: {n:6.0f} {RAdeg:8.4f} {DEdeg:9.4f} {Rp:7.4f} {Per:8.4f}"
" {S:10.3f} {Rstar:7.4f} {Teff:6.0f} {Jmag:8.3f} {a:10.4f} {Teq:6.0f}"
" {gs:9.4f} {Mmed:9.4f} {tdur:8.4f} {nt10yr:7.0f}\n")
fmt7 = (" Med: {Rp:7.4f} {Per:8.4f}"
" {S:10.3f} {Rstar:7.4f} {Teff:6.0f} {Jmag:8.3f} {a:10.4f} {Teq:6.0f}"
" {gs:9.4f} {Mmed:9.4f} {tdur:8.4f} {nt10yr:7.0f}\n")
fmt8 = ("Mean: {Rp:7.4f} {Per:8.4f}"
" {S:10.3f} {Rstar:7.4f} {Teff:6.0f} {Jmag:8.3f} {a:10.4f} {Teq:6.0f}"
" {gs:9.4f} {Mmed:9.4f} {tdur:8.4f} {nt10yr:7.0f}\n")
fmt9 = (" Max: {n:6.0f} {RAdeg:8.4f} {DEdeg:9.4f} {Rp:7.4f} {Per:8.4f}"
" {S:10.3f} {Rstar:7.4f} {Teff:6.0f} {Jmag:8.3f} {a:10.4f} {Teq:6.0f}"
" {gs:9.4f} {Mmed:9.4f} {tdur:8.4f} {nt10yr:7.0f}\n")
# Set start and end rows for each category
for j in range(0, 7):
for i in range(0, Nrow_end - Nrow_start + 1):
if np.int(summary_data[i, 0]) == j + 1:
if np.int(summary_data[i, 1]) == 1:
start[j] = i
for j in range(0, 7):
for i in range(0, Nrow_end - Nrow_start):
if np.int(summary_data[i, 0]) == j + 1:
if np.int(summary_data[i+1, 1]) <= np.int(summary_data[i, 1]):
end[j] = i
for j in range(0, 7):
for i in range(0, Nrow_end - Nrow_start + 1):
if j == np.max(summary_data[:, 0])-1 and i == (Nrow_end - Nrow_start):
end[j] = i
# Set flag for targets with tT_avg undetermined since hi_metal atm not detected
for i in range(0, Nrow_end - Nrow_start + 1):
if summary_data[i, 25] > 4000 and summary_data[i, 25] < 6000:
summary_data[i, 25] = 9950
# Write summary table
for j in range(0, 7):
fout.write('\n')
fout.write('\n')
fout.write('Category: ' + str(j+1))
fout.write('\n')
fout.write(head1)
fout.write(head2)
for i in range(0, Nrow_end - Nrow_start + 1):
if np.int(summary_data[i, 0]) == j + 1:
fout.write(fmt1.format(
Cat = summary_data[i, 0],
Rank = summary_data[i, 1],
n = summary_data[i, 2],
RAdeg = summary_data[i, 3],
DEdeg = summary_data[i, 4],
Rp = summary_data[i, 5],
Per = summary_data[i, 6],
S = summary_data[i, 7],
K = summary_data[i, 8],
Rstar = summary_data[i, 9],
Teff = summary_data[i, 10],
Jmag = summary_data[i, 11],
a = summary_data[i, 12],
Teq = summary_data[i, 13],
gs = summary_data[i, 14],
Mmed = summary_data[i, 15],
Rhomed = summary_data[i, 18],
tdur = summary_data[i, 19],
nt10yr = summary_data[i, 20],
nt_lo = summary_data[i, 21],
nt_hi = summary_data[i, 22],
tT_lo = summary_data[i, 23],
tT_hi = summary_data[i, 24],
tT_avg = summary_data[i, 25]))
for i in range(0, Nrow_end - Nrow_start + 1):
if np.int(summary_data[i, 0]) == j + 1:
# Compute stats for each Category
fout.write('\n')
fout.write(fmt2.format(
Cat = np.min(summary_data[start[j]:end[j]+1, 0]),
Rank = np.min(summary_data[start[j]:end[j]+1, 1]),
n = np.min(summary_data[start[j]:end[j]+1, 2]),
RAdeg = np.min(summary_data[start[j]:end[j]+1, 3]),
DEdeg = np.min(summary_data[start[j]:end[j]+1, 4]),
Rp = np.min(summary_data[start[j]:end[j]+1, 5]),
Per = np.min(summary_data[start[j]:end[j]+1, 6]),
S = np.min(summary_data[start[j]:end[j]+1, 7]),
K = np.min(summary_data[start[j]:end[j]+1, 8]),
Rstar = np.min(summary_data[start[j]:end[j]+1, 9]),
Teff = np.min(summary_data[start[j]:end[j]+1, 10]),
Jmag = np.min(summary_data[start[j]:end[j]+1, 11]),
a = np.min(summary_data[start[j]:end[j]+1, 12]),
Teq = np.min(summary_data[start[j]:end[j]+1, 13]),
gs = np.min(summary_data[start[j]:end[j]+1, 14]),
Mmed = np.min(summary_data[start[j]:end[j]+1, 15]),
Rhomed = np.min(summary_data[start[j]:end[j]+1, 18]),
tdur = np.min(summary_data[start[j]:end[j]+1, 19]),
nt10yr = np.min(summary_data[start[j]:end[j]+1, 20]),
nt_lo = np.min(summary_data[start[j]:end[j]+1, 21]),
nt_hi = np.min(summary_data[start[j]:end[j]+1, 22]),
tT_lo = np.min(summary_data[start[j]:end[j]+1, 23]),
tT_hi = np.min(summary_data[start[j]:end[j]+1, 24]),
tT_avg = np.min(summary_data[start[j]:end[j]+1, 25])))
fout.write(fmt3.format(
Cat = np.median(summary_data[start[j]:end[j]+1, 0]),
Rank = np.median(summary_data[start[j]:end[j]+1, 1]),
n = np.median(summary_data[start[j]:end[j]+1, 2]),
RAdeg = np.median(summary_data[start[j]:end[j]+1, 3]),
DEdeg = np.median(summary_data[start[j]:end[j]+1, 4]),
Rp = np.median(summary_data[start[j]:end[j]+1, 5]),
Per = np.median(summary_data[start[j]:end[j]+1, 6]),
S = np.median(summary_data[start[j]:end[j]+1, 7]),
K = np.median(summary_data[start[j]:end[j]+1, 8]),
Rstar = np.median(summary_data[start[j]:end[j]+1, 9]),
Teff = np.median(summary_data[start[j]:end[j]+1, 10]),
Jmag = np.median(summary_data[start[j]:end[j]+1, 11]),
a = np.median(summary_data[start[j]:end[j]+1, 12]),
Teq = np.median(summary_data[start[j]:end[j]+1, 13]),
gs = np.median(summary_data[start[j]:end[j]+1, 14]),
Mmed = np.median(summary_data[start[j]:end[j]+1, 15]),
Rhomed = np.median(summary_data[start[j]:end[j]+1, 18]),
tdur = np.median(summary_data[start[j]:end[j]+1, 19]),
nt10yr = np.median(summary_data[start[j]:end[j]+1, 20]),
nt_lo = np.median(summary_data[start[j]:end[j]+1, 21]),
nt_hi = np.median(summary_data[start[j]:end[j]+1, 22]),
tT_lo = np.median(summary_data[start[j]:end[j]+1, 23]),
tT_hi = np.median(summary_data[start[j]:end[j]+1, 24]),
tT_avg = np.median(summary_data[start[j]:end[j]+1, 25])))
fout.write(fmt4.format(
Cat = np.mean(summary_data[start[j]:end[j]+1, 0]),
Rank = np.mean(summary_data[start[j]:end[j]+1, 1]),
n = np.mean(summary_data[start[j]:end[j]+1, 2]),
RAdeg = np.mean(summary_data[start[j]:end[j]+1, 3]),
DEdeg = np.mean(summary_data[start[j]:end[j]+1, 4]),
Rp = np.mean(summary_data[start[j]:end[j]+1, 5]),
Per = np.mean(summary_data[start[j]:end[j]+1, 6]),
S = np.mean(summary_data[start[j]:end[j]+1, 7]),
K = np.mean(summary_data[start[j]:end[j]+1, 8]),
Rstar = np.mean(summary_data[start[j]:end[j]+1, 9]),
Teff = np.mean(summary_data[start[j]:end[j]+1, 10]),
Jmag = np.mean(summary_data[start[j]:end[j]+1, 11]),
a = np.mean(summary_data[start[j]:end[j]+1, 12]),
Teq = np.mean(summary_data[start[j]:end[j]+1, 13]),
gs = np.mean(summary_data[start[j]:end[j]+1, 14]),
Mmed = np.mean(summary_data[start[j]:end[j]+1, 15]),
Rhomed = np.mean(summary_data[start[j]:end[j]+1, 18]),
tdur = np.mean(summary_data[start[j]:end[j]+1, 19]),
nt10yr = np.mean(summary_data[start[j]:end[j]+1, 20]),
nt_lo = np.mean(summary_data[start[j]:end[j]+1, 21]),
nt_hi = np.mean(summary_data[start[j]:end[j]+1, 22]),
tT_lo = np.mean(summary_data[start[j]:end[j]+1, 23]),
tT_hi = np.mean(summary_data[start[j]:end[j]+1, 24]),
tT_avg = np.mean(summary_data[start[j]:end[j]+1, 25])))
fout.write(fmt5.format(
Cat = np.max(summary_data[start[j]:end[j]+1, 0]),
Rank = np.max(summary_data[start[j]:end[j]+1, 1]),
n = np.max(summary_data[start[j]:end[j]+1, 2]),
RAdeg = np.max(summary_data[start[j]:end[j]+1, 3]),
DEdeg = np.max(summary_data[start[j]:end[j]+1, 4]),
Rp = np.max(summary_data[start[j]:end[j]+1, 5]),
Per = np.max(summary_data[start[j]:end[j]+1, 6]),
S = np.max(summary_data[start[j]:end[j]+1, 7]),
K = np.max(summary_data[start[j]:end[j]+1, 8]),
Rstar = np.max(summary_data[start[j]:end[j]+1, 9]),
Teff = np.max(summary_data[start[j]:end[j]+1, 10]),
Jmag = np.max(summary_data[start[j]:end[j]+1, 11]),
a = np.max(summary_data[start[j]:end[j]+1, 12]),
Teq = np.max(summary_data[start[j]:end[j]+1, 13]),
gs = np.max(summary_data[start[j]:end[j]+1, 14]),
Mmed = np.max(summary_data[start[j]:end[j]+1, 15]),
Rhomed = np.max(summary_data[start[j]:end[j]+1, 18]),
tdur = np.max(summary_data[start[j]:end[j]+1, 19]),
nt10yr = np.max(summary_data[start[j]:end[j]+1, 20]),
nt_lo = np.max(summary_data[start[j]:end[j]+1, 21]),
nt_hi = np.max(summary_data[start[j]:end[j]+1, 22]),
tT_lo = np.max(summary_data[start[j]:end[j]+1, 23]),
tT_hi = np.max(summary_data[start[j]:end[j]+1, 24]),
tT_avg = np.max(summary_data[start[j]:end[j]+1, 25])))
break
# Compute stats for full target set
fout.write('\n')
fout.write('\n')
fout.write('\n')
fout.write('\n')
fout.write(head5)
fout.write(head1)
fout.write(head4)
fout.write(fmt6.format(
Cat = np.min(summary_data[:, 0]),
Rank = np.min(summary_data[:, 1]),
n = np.min(summary_data[:, 2]),
RAdeg = np.min(summary_data[:, 3]),
DEdeg = np.min(summary_data[:, 4]),
Rp = np.min(summary_data[:, 5]),
Per = np.min(summary_data[:, 6]),
S = np.min(summary_data[:, 7]),
K = np.min(summary_data[:, 8]),
Rstar = np.min(summary_data[:, 9]),
Teff = np.min(summary_data[:, 10]),
Jmag = np.min(summary_data[:, 11]),
a = np.min(summary_data[:, 12]),
Teq = np.min(summary_data[:, 13]),
gs = np.min(summary_data[:, 14]),
Mmed = np.min(summary_data[:, 15]),
Rhomed = np.min(summary_data[:, 18]),
tdur = np.min(summary_data[:, 19]),
nt10yr = np.min(summary_data[:, 20]),
nt_lo = np.min(summary_data[:, 21]),
nt_hi = np.min(summary_data[:, 22]),
tT_lo = np.min(summary_data[:, 23]),
tT_hi = np.min(summary_data[:, 24]),
tT_avg = np.min(summary_data[:, 25])))
fout.write(fmt7.format(
Cat = np.median(summary_data[:, 0]),
Rank = np.median(summary_data[:, 1]),
n = np.median(summary_data[:, 2]),
RAdeg = np.median(summary_data[:, 3]),
DEdeg = np.median(summary_data[:, 4]),
Rp = np.median(summary_data[:, 5]),
Per = np.median(summary_data[:, 6]),
S = np.median(summary_data[:, 7]),
K = np.median(summary_data[:, 8]),
Rstar = np.median(summary_data[:, 9]),
Teff = np.median(summary_data[:, 10]),
Jmag = np.median(summary_data[:, 11]),
a = np.median(summary_data[:, 12]),
Teq = np.median(summary_data[:, 13]),
gs = np.median(summary_data[:, 14]),
Mmed = np.median(summary_data[:, 15]),
Rhomed = np.median(summary_data[:, 18]),
tdur = np.median(summary_data[:, 19]),
nt10yr = np.median(summary_data[:, 20]),
nt_lo = np.median(summary_data[:, 21]),
nt_hi = np.median(summary_data[:, 22]),
tT_lo = np.median(summary_data[:, 23]),
tT_hi = np.median(summary_data[:, 24]),
tT_avg = np.median(summary_data[:, 25])))
fout.write(fmt8.format(
Cat = np.mean(summary_data[:, 0]),
Rank = np.mean(summary_data[:, 1]),
n = np.mean(summary_data[:, 2]),
RAdeg = np.mean(summary_data[:, 3]),
DEdeg = np.mean(summary_data[:, 4]),
Rp = np.mean(summary_data[:, 5]),
Per = np.mean(summary_data[:, 6]),
S = np.mean(summary_data[:, 7]),
K = np.mean(summary_data[:, 8]),
Rstar = np.mean(summary_data[:, 9]),
Teff = np.mean(summary_data[:, 10]),
Jmag = np.mean(summary_data[:, 11]),
a = np.mean(summary_data[:, 12]),
Teq = np.mean(summary_data[:, 13]),
gs = np.mean(summary_data[:, 14]),
Mmed = np.mean(summary_data[:, 15]),
Rhomed = np.mean(summary_data[:, 18]),
tdur = np.mean(summary_data[:, 19]),
nt10yr = np.mean(summary_data[:, 20]),
nt_lo = np.mean(summary_data[:, 21]),
nt_hi = np.mean(summary_data[:, 22]),
tT_lo = np.mean(summary_data[:, 23]),
tT_hi = np.mean(summary_data[:, 24]),
tT_avg = np.mean(summary_data[:, 25])))
fout.write(fmt9.format(
Cat = np.max(summary_data[:, 0]),
Rank = np.max(summary_data[:, 1]),
n = np.max(summary_data[:, 2]),
RAdeg = np.max(summary_data[:, 3]),
DEdeg = np.max(summary_data[:, 4]),
Rp = np.max(summary_data[:, 5]),
Per = np.max(summary_data[:, 6]),
S = np.max(summary_data[:, 7]),
K = np.max(summary_data[:, 8]),
Rstar = np.max(summary_data[:, 9]),
Teff = np.max(summary_data[:, 10]),
Jmag = np.max(summary_data[:, 11]),
a = np.max(summary_data[:, 12]),
Teq = np.max(summary_data[:, 13]),
gs = np.max(summary_data[:, 14]),
Mmed = np.max(summary_data[:, 15]),
Rhomed = np.max(summary_data[:, 18]),
tdur = np.max(summary_data[:, 19]),
nt10yr = np.max(summary_data[:, 20]),
nt_lo = np.max(summary_data[:, 21]),
nt_hi = np.max(summary_data[:, 22]),
tT_lo = np.max(summary_data[:, 23]),
tT_hi = np.max(summary_data[:, 24]),
tT_avg = np.max(summary_data[:, 25])))
fout.write(textwrap.dedent("""\
Notes:
for nt_ and tT_ columns:
"996 and 9960.0" Rp > 10: Mmed estimates are unreliable
"997 and 9970.0" number of transits needed for detection exceeds"""
+ """ those observable in 10 yr mission
"998 and 9980.0" target Jmag is below (brighter than) the saturation"""
+ """ threshold of this instrument/mode
"999 and 9990.0" dBIC is < dBIC threshold for this target: """
+ """no detection for this atm assumption
"4990.0 to 5200.0" tT_avg undetermined since hi_metal atm not detected
"""))
fout.close()
print(' JET run complete!')
raise SystemExit