plt.show()
np.savetxt('plot_t.txt', t)
np.savetxt('plot_RV_HARPS.txt', XX[idx])
np.savetxt('plot_y.txt', y)
np.savetxt('plot_yerr.txt', yerr)
np.savetxt('plot_x.txt', x)
np.savetxt('plot_mu.txt', mu)
np.savetxt('plot_std.txt', std)
#==============================================================================
# Smoothing
#==============================================================================
sl = 0.5 # smoothing length
# xx = gaussian_smoothing(t, XX, t, sl)
xy = gaussian_smoothing(t, y, t, sl, 1 / yerr**2)
# zx = gaussian_smoothing(t, ZX, t, sl)
np.savetxt('../../data/' + star + '/xy.txt', xy)
plt.figure()
# plt.errorbar(t, xx, yerr=yerr, fmt=".k", capsize=0, label='$RV_{HARPS}$')
plt.errorbar(t,
xy,
yerr=yerr,
fmt=".r",
capsize=0,
label='$RV_{HARPS} - RV_{FT,L}$')
plt.ylabel("RV [m/s]")
plt.xlabel("JD - 2,400,000")
plt.legend()
plt.savefig('s1-RV1.png')
# #==============================================================================
# # GP smoothing
# #==============================================================================
# import pymc3 as pm
# from theano import shared
# from pymc3.distributions.timeseries import GaussianRandomWalk
# from scipy import optimize
# import scipy.stats as stats
#==============================================================================
# Test smoothing
#==============================================================================
if 0:
sl = 3 # smoothing length
YYs = gaussian_smoothing(t, YY, t, sl)
ZZs = gaussian_smoothing(t, ZZ, t, sl)
plt.plot(t, YYs, 'bo', label=r'$RV_{FT,L}$')
plt.plot(t, ZZs, 'rs', label=r'$RV_{FT,H}$')
plt.plot(t, GG, '-')
plt.show()
# plt.plot(RV_jitter, GG-YY, 's')
plt.plot(RV_jitter, GG - YY, 'o')
plt.plot(RV_jitter, GG - YYs, '*')
# # plt.plot(RV_jitter, ZZ-GG, 's')
# plt.plot(RV_jitter, ZZs-GG, 'o')
plt.show()
#==============================================================================
# Periodogram
#==============================================================================
plt.ylabel("$RV_{FT,H} - RV_{HARPS}$ [m/s]")
plt.savefig('2-correlation_ZX.png')
# plt.show()
plt.figure()
plt.errorbar(XY, ZX, yerr=yerr, fmt=".r")
plt.xlabel("$RV_{HARPS} - RV_{FT,L}$ [m/s]")
plt.ylabel("$RV_{FT,H} - RV_{HARPS}$ [m/s]")
plt.savefig('2-correlation_XYZ.png')
# plt.show()
#==============================================================================
# Smoothing
#==============================================================================
sl = 1 # smoothing length
xx = gaussian_smoothing(t, XX, t, sl)
xy = gaussian_smoothing(t, XY, t, sl)
zx = gaussian_smoothing(t, ZX, t, sl)
plt.figure()
plt.errorbar(t, xx, yerr=yerr, fmt=".k", capsize=0, label='$RV_{HARPS}$')
plt.errorbar(t, xy, yerr=yerr, fmt=".r", capsize=0, label='$RV_{HARPS} - RV_{FT,L}$')
plt.ylabel("RV [m/s]")
plt.xlabel("JD - 2,400,000")
plt.legend()
plt.savefig('s1-RV1.png')
# plt.show()
plt.figure()
plt.errorbar(t, xx, yerr=yerr, fmt=".k", capsize=0, label='$RV_{HARPS}$')
plt.errorbar(t, zx, yerr=yerr, fmt=".r", capsize=0, label='$RV_{FT,H} - RV_{HARPS}$')
#==============================================================================
# Read data
#==============================================================================
BJD = np.loadtxt('MJD_2012.txt')
Jitter = np.loadtxt('Jitter_model_2012.txt')
RV_noise = np.loadtxt('RV_noise_2012.txt')
weight = 1 / RV_noise**2
RV_additional = np.loadtxt('RV_additional')
RV_conv = np.loadtxt('RV_conv')
from functions import gaussian_smoothing
t_resample = np.linspace(min(BJD), max(BJD), 200)
Jitter2 = gaussian_smoothing(BJD, Jitter, t_resample, 1., weight)
jitter_smooth = gaussian_smoothing(BJD, Jitter, BJD, 1., weight)
np.savetxt(out_dir + 'jitter_smooth_2012.txt', jitter_smooth)
plt.errorbar(BJD,
Jitter,
yerr=RV_noise,
fmt=".k",
capsize=0,
label='noisy jitter')
plt.plot(t_resample, Jitter2, '--', label='filtered_jitter')
plt.show()
# bin the data of the same night - use a moving average #
n_bin = int(max(BJD) - min(BJD) + 1)
x = np.loadtxt(directory + 'GG.txt')
y = np.loadtxt(directory + 'YY.txt')
z = np.loadtxt(directory + 'ZZ.txt')
bi = np.loadtxt(directory + 'BI.txt')
# xy = 0 - y
# zx = z
xy = x - y
zx = z - x
DIR = '/Volumes/DataSSD/OneDrive - UNSW/Hermite_Decomposition/ESO_HARPS/' + star
RV_noise = np.loadtxt(DIR + '/RV_noise.dat')
MJD = np.loadtxt(DIR + '/MJD.dat')
from functions import gaussian_smoothing
plt.errorbar(MJD, xy, yerr=RV_noise * 3**0.5, fmt="r.", capsize=0, alpha=0.2)
jitter_s = gaussian_smoothing(MJD, xy, MJD, 0.5, 1 / RV_noise**2)
plt.errorbar(MJD,
jitter_s,
yerr=RV_noise * 3**0.5,
fmt="b.",
capsize=0,
alpha=0.3)
plt.show()
plt.errorbar(MJD, x, yerr=RV_noise, fmt="k.", capsize=0, alpha=0.5)
# plt.errorbar(MJD, xy, yerr=RV_noise*3**0.5, fmt="r.", capsize=0, alpha=0.2)
plt.errorbar(MJD, zx, yerr=RV_noise * 3**0.5, fmt="r.", capsize=0, alpha=0.2)
plt.show()
# smoothing
# x = gaussian_smoothing(MJD, x, MJD, 1, 1/RV_noise**2)
#==============================================================================
# Align the data using a moving average
#==============================================================================
sl = 0.1
if YEAR == 2009:
idx = idx1
iddx = iddx1
if YEAR == 2010:
idx = idx2
iddx = iddx2
if YEAR == 2011:
idx = idx3
iddx = iddx3
fe4376_s = gaussian_smoothing(JD_wise[idx], fe4376[idx], t, sl,
np.ones(sum(idx)))
fe5250_s = gaussian_smoothing(JD_wise[idx], fe5250[idx], t, sl,
np.ones(sum(idx)))
log_RHK_s = gaussian_smoothing(BJD[iddx], log_RHK[iddx], t, sl,
np.ones(sum(iddx)))
BIS_s = gaussian_smoothing(BJD[iddx], BIS[iddx], t, sl, np.ones(sum(iddx)))
FWHM_s = gaussian_smoothing(BJD[iddx], FWHM[iddx], t, sl, np.ones(sum(iddx)))
FTL_s = gaussian_smoothing(t, y, t, sl, yerr)
#==============================================================================
# Correlogram
#==============================================================================
left = 0.1 # the left side of the subplots of the figure
right = 0.97 # the right side of the subplots of the figure
bottom = 0.07 # the bottom of the subplots of the figure
rv2 = np.loadtxt('HD85390_HARPSbinjit2.vels')
all_rvs = np.vstack((rv1, rv2))
# all_rvs = np.loadtxt('HD85390_quad.vels')
# RV_HARPS = np.loadtxt('RV_HARPS.dat')
t = all_rvs[:, 0]
x = all_rvs[:, 0]
y = all_rvs[:, 1]
yerr = all_rvs[:, 2]
jitter_raw = np.loadtxt('./data/jitter_raw.txt')
fwhm = np.loadtxt('./data/hd85390_fwhm.dat')
from functions import gaussian_smoothing
t_resample = np.linspace(min(t), max(t), 1000)
jitter_smooth = gaussian_smoothing(t[:96], jitter_raw, t[:96], 200.,
np.ones(96))
fwhm_smooth = gaussian_smoothing(fwhm[:, 0], fwhm[:, 1], t[:96], 200.,
np.ones(114))
plt.figure()
# plt.plot(jitter_smooth, fwhm[:,1])
plt.plot(fwhm[:, 0], fwhm[:, 1], '.', label='FWHM')
plt.plot(t[:96], fwhm_smooth, label='smoothed FWHM')
plt.xlabel("BJD - 2400000")
plt.ylabel('FWHM [km/s]')
plt.legend()
# plt.savefig('FWHM.png')
plt.show()
plt.figure()
plt.plot(fwhm_smooth[0:96], jitter_smooth, '.')
new_dir = str(n) + '/'
os.makedirs(new_dir)
################
# Present data #
################
# re-sample
x = gran_gen(n_group, n_obs)
print('Observation samples:')
x = np.sort(random.sample(range(200), 40))
print(x)
RV_IN = np.array([RV_IN0[i] for i in x])
RV_FT = np.array([RV_FT0[i] for i in x])
RV_diff = RV_IN - RV_FT
RV_diff = gaussian_smoothing(x, RV_diff, x, 2)
# Gaussian smoothing
# RV_diff2 = gaussian_smoothing(x, RV_diff, x, 6)
# plt.plot(t, Jitter_in, x, RV_diff*5, '.', x, RV_diff2*5, '*'); plt.show()
if 1: # sub-sampling
fig = plt.figure()
plt.plot(t, RV_IN0, 'o', label='full samples')
plt.plot(x, RV_IN, 'r.', label='sub-sampling')
plt.title('Sampling = %i' % n_obs)
plt.xlabel(r"$t$")
plt.ylabel('Measured RV [m/s]')
plt.legend()
plt.savefig(new_dir + '0-RV_sampling.png')
if 1: # Time series
import numpy as np
from functions import gaussian_smoothing
BJD = np.loadtxt('MJD.dat')
BJD = BJD - min(BJD)
# BJD = BJD[:-1]
RV_HARPS = np.loadtxt('RV_HARPS.dat')
RV_HARPS = (RV_HARPS - np.mean(RV_HARPS)) * 1000
# RV_HARPS = RV_HARPS[:-1]
Jitter = np.loadtxt('RV_jitter.txt')
# Jitter = Jitter[:-1]
RV_noise = np.loadtxt('RV_noise.dat')
# RV_noise = RV_noise[:-1]
weight = 1 / RV_noise**2
t_resample = np.linspace(min(BJD), max(BJD), 10000)
Jitter2 = gaussian_smoothing(BJD, Jitter, BJD, 1.5, weight)
# plt.errorbar(BJD, RV_HARPS, yerr=RV_noise, fmt=".", capsize=0)
plt.plot(BJD, Jitter, '.')
plt.plot(BJD, Jitter2, '.')
plt.show()
#==============================================================================
# Periodogram
#==============================================================================
from astropy.stats import LombScargle
min_f = 0.001
max_f = 5
spp = 10
plt.errorbar(x, y - YY, yerr=yerr, fmt=".r", capsize=0)
plt.ylabel("RV [m/s]")
plt.xlabel("JD - 2,400,000")
# plt.savefig('HD10700-1-RV.png')
plt.show()
# 54099
# 54422: 4 day period?
# XX = np.loadtxt('XX.txt')
YY = np.loadtxt('YY.txt')
ZZ = np.loadtxt('ZZ.txt')
sl = 0.5 # smoothing length
XY = gaussian_smoothing(x, y - YY, x, sl)
XX = gaussian_smoothing(x, y, x, sl)
plt.figure()
# plt.errorbar(x, XX, yerr=yerr, fmt=".k", capsize=0)
# plt.errorbar(x, XY, yerr=yerr, fmt=".r", capsize=0)
plt.plot(x, XX, '.k')
plt.plot(x, XY, '.r')
plt.ylabel("RV [m/s]")
plt.xlabel("JD - 2,400,000")
# plt.savefig('HD10700-1-RV.png')
plt.show()
# idx = (x > 53500) & (x < 53800)
idx = (x > 54250) & (x < 54500)
x = x[idx]
# #==============================================================================
# # GP smoothing
# #==============================================================================
# import pymc3 as pm
# from theano import shared
# from pymc3.distributions.timeseries import GaussianRandomWalk
# from scipy import optimize
# import scipy.stats as stats
#==============================================================================
# Test smoothing
#==============================================================================
if 0:
sl = 8 # smoothing length
YYs = gaussian_smoothing(t, YY, t, sl)
ZZs = gaussian_smoothing(t, ZZ, t, sl)
plt.plot(t, YYs, 'bo', label=r'$RV_{FT,L}$')
plt.plot(t, ZZs, 'rs', label=r'$RV_{FT,H}$')
plt.plot(t, GG, '-')
# plt.show()
# plt.plot(RV_jitter, GG-YY, 's')
# plt.plot(RV_jitter, GG-YYs, 'o')
# # plt.plot(RV_jitter, ZZ-GG, 's')
# plt.plot(RV_jitter, ZZs-GG, 'o')
plt.show()
#==============================================================================
# Periodogram
#==============================================================================
from astropy.stats import LombScargle
import numpy as np
from functions import gaussian_smoothing
BJD = np.loadtxt('MJD.dat')
BJD = BJD - min(BJD)
BJD = BJD[:-1]
RV_HARPS = np.loadtxt('RV_HARPS.dat')
RV_HARPS = (RV_HARPS - np.mean(RV_HARPS))*1000
RV_HARPS = RV_HARPS[:-1]
Jitter = np.loadtxt('RV_jitter.txt')
Jitter = Jitter[:-1]
RV_noise = np.loadtxt('RV_noise.dat')
RV_noise = RV_noise[:-1]
weight = 1 / RV_noise**2
t_resample = np.linspace(min(BJD), max(BJD), 10000)
Jitter2 = gaussian_smoothing(BJD, Jitter, t_resample, 1.5, weight)
plt.errorbar(BJD, RV_HARPS, yerr=RV_noise, fmt=".", capsize=0)
# plt.plot(BJD, Jitter, '.')
# plt.plot(t_resample, Jitter2, '-')
plt.show()
#==============================================================================
# Periodogram
#==============================================================================
from astropy.stats import LombScargle
min_f = 0.01
