def fit_func(t, T0_offset):
return rv.radial_velocity(t, orbital_parameters[0],
orbital_parameters[1],
orbital_parameters[2],
orbital_parameters[3],
orbital_parameters[4] + T0_offset)
p0=[0])
print("T0_offset Serval = ", popt_s)
T0_offset_s = popt_s[0]
sigma_ser = np.nanstd(
sigma_clip(ser_rvc[:, 1] - np.nanmean(ser_rvc[:, 1]) -
fit_func(ser_rvc[:, 0], T0_offset_s),
sigma=5))
xlst = np.linspace(w_dates[0],
w_dates[0] + orbital_parameters[1] * 0.99999,
num=100)
ylst = [
rv.radial_velocity(t, orbital_parameters[0],
orbital_parameters[1],
orbital_parameters[2],
orbital_parameters[3],
orbital_parameters[4] + T0_offset)
for t in xlst
]
#sort by xlst
pltlst = [[xlst[j], ylst[j]] for j in range(len(xlst))]
def mod_sort(elem):
return elem[0] % orbital_parameters[1]
pltlst = sorted(pltlst, key=mod_sort)
pltlst = np.asarray(pltlst)
pltlst = [pltlst[:, 0], pltlst[:, 1]]
#to_print = pltlst[0] % i[3]
#print(to_print)
def compare_results(file_list, parameter_change_list, bary_starname,
orbital_parameters, objects, servaldir):
"""
Compares result files to find the best rv scatter around literature fit returns change in parameter that yielded best results
file_list: list of `str`
list containing the file paths of the files to be compared
parameter_change_list: list of `int`
list containing the parameter exponent shifts used to create the files in file_list
orbital_parameters : list of `float`
orbital_parameters = [K, P, e, omega, T0]
parameters of the keplerian fit to be used as "true" baseline
"""
sigma_list = np.zeros(len(file_list)) + 100 # 100 is a fudge factor
plots = True
if plots:
rec_loop_directory, key_name = os.path.split(
os.path.split(file_list[0])
[0]) # HACK go up 2 directories to loop directory
plot_directory = rec_loop_directory + "/compare_plots"
os.makedirs(plot_directory, exist_ok=True)
pp = PdfPages(plot_directory + "/" + key_name + ".pdf")
fig = plt.figure(figsize=(15, 9), dpi=200)
mpl.rc('font', size=16)
plt.clf()
fig.clf()
ax1 = plt.gca()
for f, fil in enumerate(file_list):
#assumes order of file_listand parameter_change_list are matched. (maybe extract from file name?)
wobble_res = h5py.File(fil, 'r')
w_dates = wobble_res['dates'][()]
w_dates_utc = wobble_res['dates_utc'][()]
w_RVs = wobble_res['star_time_rvs'][()]
w_RVs_original = w_RVs
w_RVs_er = wobble_res['star_time_sigmas'][()]
#barycorr for wobble_orig
from scipy.constants import codata
lightvel = codata.value('speed of light in vacuum') #for barycorr
# CAHA Coordinates for barycorr
_lat = 37.2236
_lon = -2.54625
_elevation = 2168.
w_RVs_original_barycorr = np.zeros(len(w_dates))
for n in tqdm(range(len(w_RVs_original_barycorr))):
w_RVs_original_barycorr[n] = bary.get_BC_vel(
w_dates_utc[n],
starname=bary_starname,
lat=_lat,
longi=_lon,
alt=_elevation,
zmeas=w_RVs_original[n] / lightvel)[0]
#Serval Correction
#read in SERVAL
ser_rvc = np.loadtxt(servaldir + objects[1] + "/" + objects[1] +
".rvc.dat")
# remove entries with nan in drift
ind_finitedrift = np.isfinite(ser_rvc[:, 3])
ser_rvc = ser_rvc[ind_finitedrift]
ser_corr = -ser_rvc[:, 8] - ser_rvc[:, 3]
#match wobble and serval
indices_serval = []
indices_wobble = []
for n in range(len(w_dates)):
ind_jd = np.where(
np.abs(ser_rvc[:, 0] - w_dates[n]) == np.nanmin(
np.abs(ser_rvc[:, 0] - w_dates[n])))[0][0]
if (ser_rvc[ind_jd, 0] - w_dates[n]
) * 24 * 60 < 20.: #only takes matches closer than 20 minutes
indices_serval.append(ind_jd)
indices_wobble.append(n)
print("#serval_ind:" + str(len(indices_serval)),
"#wobble_ind:" + str(len(indices_wobble)))
#now set up all the data according to the indices
ser_rvc = ser_rvc[indices_serval]
ser_corr = ser_corr[indices_serval]
w_dates = w_dates[indices_wobble]
w_dates_utc = w_dates_utc[indices_wobble]
w_RVs_original_barycorr = w_RVs_original_barycorr[
indices_wobble] + ser_corr
w_RVs_er = w_RVs_er[indices_wobble]
def fit_func(t, T0_offset):
return rv.radial_velocity(t, orbital_parameters[0],
orbital_parameters[1],
orbital_parameters[2],
orbital_parameters[3],
orbital_parameters[4] + T0_offset)
#fit to Wobble
xdata = w_dates
ydata = w_RVs_original_barycorr - np.nanmean(w_RVs_original_barycorr)
popt, pcov = sp.optimize.curve_fit(fit_func,
xdata,
ydata,
sigma=w_RVs_er,
absolute_sigma=True)
print("T0_offset Wobble = ", popt)
T0_offset = popt[0]
#make these weighted (maybe: thsi may not be a good idea if residuals are not strongly correlated to error (as with wobble results))
sigma_wob = np.nanstd(
sigma_clip(w_RVs_original_barycorr -
np.nanmean(w_RVs_original_barycorr) -
fit_func(w_dates, T0_offset),
sigma=5))
sigma_list[f] = sigma_wob
sigma_wob_noclip = np.nanstd(w_RVs_original_barycorr -
np.nanmean(w_RVs_original_barycorr) -
fit_func(w_dates, T0_offset))
if plots:
#fit to serval:
xdata = ser_rvc[:, 0]
ydata = ser_rvc[:, 1] - np.nanmean(ser_rvc[:, 1])
popt_s, pcov_s = sp.optimize.curve_fit(fit_func,
xdata,
ydata,
sigma=ser_rvc[:, 2],
absolute_sigma=True)
print("T0_offset Serval = ", popt_s)
T0_offset_s = popt_s[0]
sigma_ser = np.nanstd(
sigma_clip(ser_rvc[:, 1] - np.nanmean(ser_rvc[:, 1]) -
fit_func(ser_rvc[:, 0], T0_offset_s),
sigma=5))
sigma_ser_noclip = np.nanstd(ser_rvc[:, 1] -
np.nanmean(ser_rvc[:, 1]) -
fit_func(ser_rvc[:, 0], T0_offset_s))
xlst = np.linspace(w_dates[0],
w_dates[0] + orbital_parameters[1] * 0.99999,
num=100)
ylst = [
rv.radial_velocity(t, orbital_parameters[0],
orbital_parameters[1],
orbital_parameters[2],
orbital_parameters[3],
orbital_parameters[4] + T0_offset)
for t in xlst
]
#sort by xlst
pltlst = [[xlst[j], ylst[j]] for j in range(len(xlst))]
def mod_sort(elem):
return elem[0] % orbital_parameters[1]
pltlst = sorted(pltlst, key=mod_sort)
pltlst = np.asarray(pltlst)
pltlst = [pltlst[:, 0], pltlst[:, 1]]
ax1.plot(pltlst[0] % orbital_parameters[1],
pltlst[1],
"r-",
label="literature orbit (Wobble T0_offset)")
ax1.errorbar(
(w_dates) % orbital_parameters[1],
(w_RVs_original_barycorr -
np.nanmean(w_RVs_original_barycorr)),
yerr=w_RVs_er,
fmt="x",
label="Wobble_Corr, clipped_sigma = {0:.3f}, noclip = {1:.3f} "
.format(sigma_wob, sigma_wob_noclip))
ax1.errorbar(
(ser_rvc[:, 0]) % orbital_parameters[1],
ser_rvc[:, 1] - np.nanmean(ser_rvc[:, 1]),
yerr=ser_rvc[:, 2],
fmt="x",
label="SERVAL_Corr, clipped_sigma = {0:.3f}, noclip = {1:.3f}".
format(sigma_ser, sigma_ser_noclip),
color="C2")
ax1.set_ylabel("RVs [m/s]")
ax1.set_xlabel('jd')
# add the parameter change to the title
title_pre = os.path.split(os.path.split(fil)[0])[1]
plt.title(title_pre + ", Phased (" + str(orbital_parameters[1]) +
"d) RVs for " + objects[0] + " (" + objects[2] + ") " +
" - " + objects[1] + ";")
plt.grid(True)
plt.tight_layout()
plt.legend(shadow=True)
plt.savefig(pp, format='pdf')
plt.clf()
fig.clf()
ax1 = plt.gca()
if plots: # include some nice progress plots. TODO make it not crudely placed inside this function?
plt.close(fig)
pp.close()
best_index = np.argmin(sigma_list)
return parameter_change_list[best_index]
ser_residuals = np.absolute(ser_residuals_sign)
sigma_ser = np.nanstd(sigma_clip(
ser_residuals_sign
,sigma = 5) )
sigma_ser_no_clip = np.nanstd(
ser_residuals_sign)
#residual vs error clipping
ind_bad_ser = np.where(ser_residuals > error_clipping_kappa * ser_rvc[:,2])[0]
ind_good_ser = np.where(ser_residuals <= error_clipping_kappa * ser_rvc[:,2])[0]
sigma_ser_error_clipped = np.nanstd(ser_residuals_sign[ind_good_ser])
xlst = np.linspace(w_dates[0], w_dates[0] + orbital_parameters[1]*0.99999, num=100)
ylst = [rv.radial_velocity(t , orbital_parameters[0], orbital_parameters[1], orbital_parameters[2],orbital_parameters[3], orbital_parameters[4] + T0_offset) for t in xlst]
#sort by xlst
pltlst = [[xlst[j],ylst[j]] for j in range(len(xlst))]
def mod_sort(elem):
return elem[0] % orbital_parameters[1]
pltlst = sorted(pltlst, key = mod_sort)
pltlst = np.asarray(pltlst)
pltlst = [pltlst[:,0],pltlst[:,1]]
#to_print = pltlst[0] % i[3]
#print(to_print)
ax1.plot(pltlst[0] % i[3], pltlst[1], "r-", label = "literature orbit (Wobble T0_offset)")
#ax1.plot(w_dates % i[3], (w_RVs_original+w_bervs-np.nanmean(w_RVs_original+w_bervs)),"x", label="Wobble_orig")
ax1.errorbar((w_dates) % i[3], (w_RVs_original_barycorr-np.nanmean(w_RVs_original_barycorr)), yerr = w_RVs_er,fmt = "x", label="Wobble_Corr, no_clip ={0:.3f},clipped_sigma = {1:.3f}, err_clipped = {2:.3f} ".format(sigma_wob_no_clip, sigma_wob, sigma_wob_error_clipped))
ax1.plot((ser_rvc[:,0]) % i[3], ser_rvc[:,5] - np.nanmean(ser_rvc[:,5]), "x", label= "SERVAL")
ax1.errorbar((ser_rvc[:,0]) % i[3], ser_rvc[:,1] - np.nanmean(ser_rvc[:,1]),yerr = ser_rvc[:,2] ,fmt = "x", label= "SERVAL_Corr, no_clip ={0:.3f}, clipped_sigma = {1:.3f}, err_clipped = {2:.3f}".format(sigma_ser_no_clip, sigma_ser, sigma_ser_error_clipped))
