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spirou_blong_timeseries.py
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spirou_blong_timeseries.py
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# -*- coding: iso-8859-1 -*-
"""
Created on June 6 2020
Description: Calculate longitudinal magnetic field (B-long) time series analysis for an input list of SPIRou LSD files
@author: Eder Martioli <[email protected]>
Institut d'Astrophysique de Paris, France.
Simple usage examples:
python ~/spirou-tools/spirou-polarimetry/spirou_blong_timeseries.py --input=*_lsd.fits -pv -m
python /Volumes/Samsung_T5/spirou-tools/spirou-polarimetry/spirou_blong_timeseries.py --input=2*_lsd.fits -pv -m
"""
__version__ = "1.0"
__copyright__ = """
Copyright (c) ... All rights reserved.
"""
from optparse import OptionParser
import os,sys
import numpy as np
import glob
import matplotlib.pyplot as plt
import matplotlib
import astropy.io.fits as fits
from scipy.interpolate import interp1d
from scipy import ndimage, misc
import spirouPolarUtils as spu
import spirouLSD
def measure_lsd_noise(inputdata, rv, fwhm, nsigclip=3, nfwhm=2.5, plot=False) :
median_flux, median_pol, median_null = [], [], []
sig_flux, sig_pol, sig_null = [], [], []
bjd = []
for i in range(len(inputdata)) :
hdu = fits.open(inputdata[i])
hdr = hdu[0].header + hdu[1].header
if i == 0 :
vels = hdu['VELOCITY'].data
mask = vels < rv - nfwhm*fwhm
mask ^= vels > rv + nfwhm*fwhm
median_flux.append(np.nanmedian(hdu['STOKESI'].data[mask]))
median_pol.append(np.nanmedian(hdu['STOKESVQU'].data[mask]))
median_null.append(np.nanmedian(hdu['NULL'].data[mask]))
sig_flux.append(np.nanstd(hdu['STOKESI'].data[mask]))
sig_pol.append(np.nanstd(hdu['STOKESVQU'].data[mask]))
sig_null.append(np.nanstd(hdu['NULL'].data[mask]))
try :
bjd.append(hdr["BJD"])
except :
bjd.append(hdr["BJDCEN"])
loc = {}
loc["median_flux"] = np.array(median_flux)
loc["sig_flux"] = np.array(sig_flux)
loc["median_pol"] = np.array(median_pol)
loc["sig_pol"] = np.array(sig_pol)
loc["median_null"] = np.array(median_null)
loc["sig_null"] = np.array(sig_null)
loc["BJD"] = np.array(bjd)
median_sigpol = np.nanmedian(loc["sig_pol"])
mad_sigpol = np.nanmedian(np.abs(loc["sig_pol"] - median_sigpol)) / 0.67449
good = loc["sig_pol"] > median_sigpol - nsigclip*mad_sigpol
good &= loc["sig_pol"] < median_sigpol + nsigclip*mad_sigpol
loc["good"] = good
if plot :
minbjd, maxbjd = np.min(loc["BJD"]), np.max(loc["BJD"])
plt.xlim(minbjd, maxbjd)
plt.plot(median_sigpol)
plt.plot(loc["BJD"][good], loc["sig_pol"][good], 'o', color='darkgreen')
plt.plot(loc["BJD"][~good], loc["sig_pol"][~good], 'o', color='red', alpha=0.6)
plt.hlines((median_sigpol),minbjd, maxbjd,ls="-",color="darkblue")
plt.hlines((median_sigpol-nsigclip*mad_sigpol,median_sigpol+nsigclip*mad_sigpol),minbjd, maxbjd,ls="--",color="darkblue")
plt.xlabel("BJD")
plt.ylabel("polarimetric noise")
plt.show()
return loc
def measure_rvs_and_fwhm(inputdata, nsigclip=5, set_all_rvs_to_systemic=True, sysrv_type=1, vel_min=-1e50, vel_max=+1e50, plot=False, verbose=False) :
lsd_flux = []
rv, fwhm = np.array([]), np.array([])
for i in range(len(inputdata)) :
hdu = fits.open(inputdata[i])
hdr = hdu[0].header + hdu[1].header
if i == 0 :
vels = hdu['VELOCITY'].data
lsd_flux.append(hdu['STOKESI'].data)
try :
stokesI_fit = spu.fit_lsd_flux_profile(hdu['VELOCITY'].data, hdu['STOKESI'].data, hdu['STOKESI_ERR'].data, guess=None, func_type="gaussian", plot=False)
rv = np.append(rv, stokesI_fit["VSHIFT"])
fwhm = np.append(fwhm, 2.355 * stokesI_fit["SIG"])
except :
rv = np.append(rv, np.nan)
fwhm = np.append(fwhm, np.nan)
continue
if verbose :
print("FWHM={:.2f}+-{:.2f} km/s".format(np.nanmean(fmhm),np.nanstd(fmhm)))
#plt.plot(fmhm)
#plt.show()
systemic_rv1 = np.nanmedian(rv)
lsd_flux = np.array(lsd_flux, dtype=float)
lsd_template = spu.subtract_median(lsd_flux, vels=vels, fit=True, verbose=False, median=True, subtract=True)
min = np.argmin(lsd_template['ccf_med'])
rv_min = vels[min]
fitrange = vels - rv_min > vel_min
fitrange &= vels - rv_min < vel_max
try :
median_stokesI_fit = spu.fit_lsd_flux_profile(vels[fitrange], lsd_template['ccf_med'][fitrange], lsd_template['ccf_sig'][fitrange], guess=None, func_type="gaussian", plot=plot)
systemic_rv2 = median_stokesI_fit["VSHIFT"]
except :
systemic_rv2 = systemic_rv1
sigma = np.nanmean(lsd_template["ccf_sig"])
for i in range(len(inputdata)) :
loc_sigma = np.nanstd(lsd_template["residuals"][i])
if verbose :
print("Exposure {0}/{1} SysRV={2:.3f} km/s RV={3:.3f} km/s rms={4:.1f} x sigma".format(i, len(inputdata), systemic_rv1, rv[i], loc_sigma/sigma))
if loc_sigma/sigma < nsigclip :
try :
stokesI_fit = spu.fit_lsd_flux_profile(vels[fitrange], lsd_template['ccf'][i][fitrange], lsd_template['ccf_sig'][fitrange], guess=None, func_type="gaussian", plot=False)
rv[i] = stokesI_fit["VSHIFT"]
except :
rv[i] = systemic_rv2
else :
rv[i] = np.nan
#print(inputdata[i], i, rv[i])
systemic_rv1 = np.nanmedian(rv)
if set_all_rvs_to_systemic and sysrv_type == 1 :
rv = np.full_like(rv,systemic_rv1)
elif set_all_rvs_to_systemic and sysrv_type == 2 :
fitrange = vels - systemic_rv1 > vel_min
fitrange &= vels - systemic_rv1 < vel_max
try :
median_stokesI_fit = spu.fit_lsd_flux_profile(vels[fitrange], lsd_template['ccf_med'][fitrange], lsd_template['ccf_sig'][fitrange], guess=None, func_type="gaussian", plot=plot)
systemic_rv2 = median_stokesI_fit["VSHIFT"]
except :
systemic_rv2 = systemic_rv1
rv = np.full_like(rv,systemic_rv2)
return rv, fwhm
def load_lsd_time_series(inputdata, constant_rv=False, nsigclip=5, fit_profile=False, vel_min=-1e50, vel_max=+1e50, auto_vel_range=True, auto_vel_nfwhm=3.5, verbose=False, plot=False) :
loc = {}
lsd_rv, lsd_fwhm = measure_rvs_and_fwhm(inputdata, nsigclip=nsigclip, set_all_rvs_to_systemic=constant_rv, vel_min=-20, vel_max=20)
if auto_vel_range :
fwhm, efwhm = np.nanmedian(lsd_fwhm), np.nanstd(lsd_fwhm)
fullrange = np.nanmedian(lsd_fwhm) * auto_vel_nfwhm
vel_min = - fullrange/2
vel_max = + fullrange/2
if verbose:
print("Automatic velocity range results: FWHM={:.2f}+-{:.2f} km/s n={}x vel_min={:.2f} km/s vel_max={:.2f} km/s".format(fwhm, efwhm, auto_vel_nfwhm, vel_min, vel_max))
lsd_rv, lsd_fwhm = measure_rvs_and_fwhm(inputdata, nsigclip=nsigclip, set_all_rvs_to_systemic=constant_rv, vel_min=vel_min, vel_max=vel_max)
maxrv, minrv = np.nanmax(lsd_rv), np.nanmin(lsd_rv)
lsd_noise = measure_lsd_noise(inputdata, np.nanmedian(lsd_rv), np.nanmedian(lsd_fwhm), plot=plot)
bjd = []
airmass, snr = [], []
waveavg, landeavg = [], []
lsd_pol, lsd_null, lsd_flux = [], [], []
lsd_pol_err, lsd_flux_err = [], []
lsd_pol_corr, lsd_flux_corr = [], []
bfield = np.full(len(inputdata), np.nan)
bfield_err = np.full(len(inputdata), np.nan)
lsd_pol_gaussmodel = np.full(len(inputdata), None)
lsd_pol_voigtmodel = np.full(len(inputdata), None)
pol_rv = np.array(lsd_rv)
zeeman_split = np.full(len(inputdata), np.nan)
pol_line_depth = np.full(len(inputdata), np.nan)
pol_fwhm = np.full(len(inputdata), np.nan)
# Get velocity range from base exposure
basehdu = fits.open(inputdata[0])
vels_sup_lim = np.nanmax(basehdu['VELOCITY'].data)
vels_inf_lim = np.nanmin(basehdu['VELOCITY'].data)
if (vel_min + minrv) < vels_inf_lim :
print("WARNING: requested RVs outside range, reseting vel_min to {:.1f} km/s".format(vels_inf_lim - minrv))
vel_min = vels_inf_lim
if (vel_max + maxrv) > vels_sup_lim :
print("WARNING: requested RVs outside range, reseting vel_max to {:.1f} km/s".format(vels_sup_lim - maxrv))
vel_max = vels_sup_lim
mask = basehdu['VELOCITY'].data > vel_min
mask &= basehdu['VELOCITY'].data < vel_max
vels = basehdu['VELOCITY'].data[mask]
for i in range(len(inputdata)) :
if np.isnan(lsd_rv[i]) :
if verbose:
print("Rejecting LSD profile in file {0}/{1}: {2}".format(i, len(inputdata), inputdata[i]))
continue
if verbose:
print("Loading LSD profile {0}/{1}: {2} ".format(i, len(inputdata), inputdata[i]))
hdu = fits.open(inputdata[i])
hdr = hdu[0].header + hdu[1].header
if "MEANBJD" in hdr.keys() :
bjd.append(float(hdr["MEANBJD"]))
elif "BJD" in hdr.keys() :
bjd.append(float(hdr["BJD"]))
else :
print("Could not read BJD from header, exit ...")
exit()
if "SNR33" in hdr.keys() :
snr.append(float(hdr["SNR33"]))
else :
snr.append(1.0)
airmass.append(float(hdr["AIRMASS"]))
lsd_pol.append(hdu['STOKESVQU'].data[mask])
lsd_null.append(hdu['NULL'].data[mask])
lsd_flux.append(hdu['STOKESI'].data[mask])
lsd_pol_err.append(hdu['STOKESVQU_ERR'].data[mask])
lsd_flux_err.append(hdu['STOKESI_ERR'].data[mask])
if fit_profile :
try :
# fit gaussian to the measured Stokes VQU LSD profile
zeeman_gauss = spu.fit_zeeman_split(hdu['VELOCITY'].data[mask], hdu['STOKESVQU'].data[mask], pol_err=hdu['STOKESVQU_ERR'].data[mask], func_type="gaussian", plot=False)
lsd_pol_gaussmodel[i] = zeeman_gauss["MODEL"]
try :
amplitude = zeeman_gauss["AMP"]
cont = zeeman_gauss["CONT"]
vel1 = zeeman_gauss["V1"]
vel2 = zeeman_gauss["V2"]
sigma = zeeman_gauss["SIG"]
guess = [amplitude, vel1, vel2, sigma, sigma, cont]
zeeman_voigt = spu.fit_zeeman_split(hdu['VELOCITY'].data[mask], hdu['STOKESVQU'].data[mask], pol_err=hdu['STOKESVQU_ERR'].data[mask], guess=guess, func_type="voigt", plot=False)
lsd_pol_voigtmodel[i] = zeeman_voigt["MODEL"]
pol_rv[i] = zeeman_voigt["VSHIFT"]
zeeman_split[i] = zeeman_voigt["DELTAV"]
pol_line_depth[i] = zeeman_voigt["AMP"]
pol_fwhm[i] = zeeman_voigt["SIG"]
except :
try :
zeeman_voigt = spu.fit_zeeman_split(hdu['VELOCITY'].data[mask], hdu['STOKESVQU'].data[mask], pol_err=hdu['STOKESVQU_ERR'].data[mask], func_type="voigt", plot=False)
lsd_pol_voigtmodel[i] = zeeman_voigt["MODEL"]
pol_rv[i] = zeeman_voigt["VSHIFT"]
zeeman_split[i] = zeeman_voigt["DELTAV"]
pol_line_depth[i] = zeeman_voigt["AMP"]
pol_fwhm[i] = zeeman_voigt["SIG"]
except :
pol_rv[i] = zeeman_gauss["VSHIFT"]
pol_line_depth[i] = zeeman_gauss["AMP"]
pol_fwhm[i] = zeeman_gauss["SIG"]
print("WARNING: could not fit Voigt function")
except :
print("WARNING: could not fit Gauss function")
vels_corr = hdu['VELOCITY'].data - lsd_rv[i]
pol_fit = interp1d(vels_corr, hdu['STOKESVQU'].data, kind='cubic')
lsd_pol_corr.append(pol_fit(hdu['VELOCITY'].data[mask]))
flux_fit = interp1d(vels_corr, hdu['STOKESI'].data, kind='cubic')
lsd_flux_corr.append(flux_fit(hdu['VELOCITY'].data[mask]))
b, berr = spu.longitudinal_b_field(hdu['VELOCITY'].data[mask], hdu['STOKESVQU'].data[mask], hdu['STOKESI'].data[mask], hdr['WAVEAVG'], hdr['LANDEAVG'], pol_err=hdu['STOKESVQU_ERR'].data[mask], flux_err=hdu['STOKESI_ERR'].data[mask])
landeavg.append(hdr['LANDEAVG'])
waveavg.append(hdr['WAVEAVG'])
bfield[i] = b
bfield_err[i] = berr
hdu.close()
bjd = np.array(bjd)
airmass, snr = np.array(airmass), np.array(snr)
landeavg, waveavg = np.array(landeavg), np.array(waveavg)
bfield, bfield_err = np.array(bfield), np.array(bfield_err)
pol_rv, zeeman_split = np.array(pol_rv), np.array(zeeman_split)
pol_line_depth, pol_fwhm = np.array(pol_line_depth), np.array(pol_fwhm)
lsd_pol = np.array(lsd_pol, dtype=float)
lsd_pol_err = np.array(lsd_pol_err, dtype=float)
lsd_pol_corr = np.array(lsd_pol_corr, dtype=float)
lsd_flux_corr = np.array(lsd_flux_corr, dtype=float)
lsd_flux = np.array(lsd_flux, dtype=float)
lsd_flux_err = np.array(lsd_flux_err, dtype=float)
lsd_null = np.array(lsd_null, dtype=float)
loc["SOURCE_RV"] = np.nanmedian(lsd_rv)
loc["VELS"] = vels
loc["BJD"] = bjd
loc["AIRMASS"] = airmass
loc["SNR"] = snr
loc["WAVEAVG"] = waveavg
loc["LANDEAVG"] = landeavg
loc["LSD_FWHM"] = lsd_fwhm
loc["LSD_RV"] = lsd_rv
loc["POL_RV"] = pol_rv
loc["ZEEMAN_SPLIT"] = zeeman_split
loc["POL_LINE_DEPTH"] = pol_line_depth
loc["POL_FWHM"] = pol_fwhm
loc["BLONG"], loc["BLONG_ERR"] = bfield, bfield_err
loc["LSD_POL"] = lsd_pol
loc["LSD_FLUX"] = lsd_flux
loc["LSD_FLUX_CORR"] = lsd_flux_corr
loc["LSD_POL_ERR"] = lsd_pol_err
loc["LSD_FLUX_ERR"] = lsd_flux_err
loc["LSD_NULL"] = lsd_null
loc["LSD_POL_CORR"] = lsd_pol_corr
loc["LSD_POL_GAUSSMODEL"] = lsd_pol_gaussmodel
loc["LSD_POL_VOIGTMODEL"] = lsd_pol_voigtmodel
loc["LSD_FLUX_MEDIAN"] = lsd_noise["median_flux"]
loc["LSD_FLUX_RMS"] = lsd_noise["sig_flux"]
loc["LSD_POL_MEDIAN"] = lsd_noise["median_pol"]
loc["LSD_POL_RMS"] = lsd_noise["sig_pol"]
loc["LSD_NULL_MEDIAN"] = lsd_noise["median_null"]
loc["LSD_NULL_RMS"] = lsd_noise["sig_null"]
return loc
def calculate_blong_timeseries(lsddata, norm_errs=True, norm_errs_from_time_series=False, use_mc=False, use_corr_data=True, plot=False, debug=False) :
bjd = lsddata["BJD"]
vels = lsddata["VELS"]
waveavg = lsddata["WAVEAVG"]
landeavg = lsddata["LANDEAVG"]
blong, blong_err = [], []
## First calculate possible residual continuum from the median profile
# and remove it from the data.
pol_cont, pol_cont_err = continuum_lsd_I(lsddata["VELS"], lsddata["LSD_POL_MED"], lsddata["LSD_POL_MED_ERR"],fit_continuum=False, npcont=7, plot=False)
flux_cont, flux_cont_err = continuum_lsd_I(lsddata["VELS"], lsddata["LSD_FLUX_MED"], lsddata["LSD_FLUX_MED_ERR"],fit_continuum=False, npcont=7, plot=False)
if use_corr_data :
lsd_pol = lsddata["LSD_POL_CORR"] - pol_cont
lsd_flux = lsddata["LSD_FLUX_CORR"] / flux_cont
else :
lsd_pol = lsddata["LSD_POL"] - pol_cont
lsd_flux = lsddata["LSD_FLUX"] / flux_cont
if debug :
for i in range(len(bjd)) :
plt.plot(lsddata["VELS"], lsd_flux[i], '.', alpha=0.3)
plt.plot(lsddata["VELS"], lsddata["LSD_FLUX_MED"]/flux_cont, '-', lw=2)
plt.plot(lsddata["VELS"], np.full_like(lsddata["VELS"], 1.), '-', lw=2)
plt.show()
for i in range(len(bjd)) :
plt.plot(lsddata["VELS"], lsd_pol[i], '.', alpha=0.3)
plt.plot(lsddata["VELS"], lsddata["LSD_POL_MED"]-pol_cont, '-', lw=2)
plt.plot(lsddata["VELS"], np.full_like(lsddata["VELS"], 0.), '-', lw=2)
plt.show()
##-------------------
for i in range(len(bjd)) :
if norm_errs :
median_polerr = np.nanmedian(lsddata["LSD_POL_ERR"][i])
median_fluxerr = np.nanmedian(lsddata["LSD_FLUX_ERR"][i])
if norm_errs_from_time_series :
# adopt errors from the dispersion in the time series of all LSD data
pol_err = (lsddata["LSD_POL_ERR"][i] / median_polerr) * lsddata["LSD_POL_MED_ERR"]
flux_err = (lsddata["LSD_FLUX_ERR"][i] / median_fluxerr) * lsddata["LSD_FLUX_MED_ERR"] / flux_cont
else :
# adopt errors from the dispersion in the time series of all LSD data
pol_err = (lsddata["LSD_POL_ERR"][i] / median_polerr) * lsddata["LSD_POL_RMS"][i]
flux_err = (lsddata["LSD_FLUX_ERR"][i] / median_fluxerr) * lsddata["LSD_FLUX_RMS"][i] / flux_cont
else :
# adopt errors from LSD analysis
pol_err = lsddata["LSD_POL_ERR"][i]
flux_err = lsddata["LSD_FLUX_ERR"][i] / flux_cont
if use_mc :
# Below it calculates B-long using monte carlo, which gives a posterior distribution that's conditioned on the input data and uncertainties.
b, berr = spu.longitudinal_b_field_montecarlo(vels, lsd_pol[i], pol_err, lsd_flux[i], flux_err, waveavg[i], landeavg[i], nsamples = 10000, plot=False, verbose=True)
else :
# Below it calculates B-long using the errors measured from the dispersion in the time-series
b, berr = spu.longitudinal_b_field(vels, lsd_pol[i], lsd_flux[i], waveavg[i], landeavg[i], pol_err=pol_err, flux_err=flux_err)
blong.append(b)
blong_err.append(berr)
blong = np.array(blong)
blong_err = np.array(blong_err)
lsddata["BLONG"], lsddata["BLONG_ERR"] = blong, blong_err
bmed, bmederr = spu.longitudinal_b_field(vels, lsddata["LSD_POL_MED"] - pol_cont, lsddata["LSD_FLUX_MED"]/flux_cont, np.mean(waveavg), np.mean(landeavg), pol_err=lsddata["LSD_POL_MED_ERR"], flux_err=lsddata["LSD_FLUX_MED_ERR"]/flux_cont)
if plot :
font = {'size': 16}
matplotlib.rc('font', **font)
plt.errorbar(lsddata["BJD"], lsddata["BLONG"], yerr=lsddata["BLONG_ERR"], fmt='.', color="olive", label=r"B$_l$")
plt.axhline(y=bmed-bmederr, ls='--', lw=1, color="orange")
plt.axhline(y=bmed, ls='-', lw=2, color="blue", label=r"Mean B$_l$={0:.1f}+-{1:.1f} G".format(bmed, bmederr))
plt.axhline(y=bmed+bmederr, ls='--', lw=1, color="orange")
plt.plot(lsddata["BJD"], lsddata["BLONG"], '-', lw=0.7, color="olive")
plt.ylabel("Longitudinal magnetic field [G]")
plt.xlabel("BJD")
plt.legend()
plt.show()
return lsddata
def save_blong_time_series(output, bjd, blong, blongerr, time_in_rjd=False) :
sorted = np.argsort(bjd)
outfile = open(output,"w+")
for i in range(len(bjd[sorted])) :
if time_in_rjd :
time = bjd[sorted][i] - 2400000.
else :
time = bjd[sorted][i]
outfile.write("{0:.10f} {1:.5f} {2:.5f}\n".format(time, blong[sorted][i], blongerr[sorted][i]))
outfile.close()
def chi_square(lsddata, verbose=False) :
meanblong = np.nanmean(lsddata["BLONG"])
mblong = np.nanmedian(lsddata["BLONG"])
blong_sig = np.nanstd(lsddata["BLONG"])
blong_mad = np.nanmedian(np.abs(lsddata["BLONG"] - mblong)) / 0.67449
dof = len(lsddata["BLONG"][np.isfinite(lsddata["BLONG"])])
chisqr = np.nansum( ( (lsddata["BLONG"] - mblong)/lsddata["BLONG_ERR"] )**2)
redchisqr = chisqr / dof
if verbose :
print("Chi-square={:.3f}; DOF={} ; Reduced chi-square={:.3f}; Constant model: Bl_median={:.2f}+/-{:.2f} G Bl_mean={:.2f}+/-{:.2f} G".format(chisqr, dof, redchisqr, mblong, blong_mad, meanblong, blong_sig))
return chisqr, dof, redchisqr
def reduce_lsddata(lsddata, niter=3, apply_median_filter=True, median_filter_size=3, use_residuals=False, plot=False) :
bjd = lsddata["BJD"]
vels = lsddata["VELS"]
if plot :
x_lab = r"$Velocity$ [km/s]" #Wavelength axis
y_lab = r"Time [BJD]" #Time axis
z_lab_pol = r"Degree of polarization (Stokes V)" #Intensity (exposures)
z_lab_null = r"Null polarization (Stokes V)" #Intensity (exposures)
z_lab_flux = r"Intensity (Stokes I)" #Intensity (exposures)
LAB_pol = [x_lab,y_lab,z_lab_pol]
LAB_null = [x_lab,y_lab,z_lab_null]
LAB_flux = [x_lab,y_lab,z_lab_flux]
lsd_pol_corr = spu.subtract_median(lsddata["LSD_POL_CORR"], vels=vels, fit=True, verbose=False, median=True, subtract=True)
lsd_flux_corr = spu.subtract_median(lsddata["LSD_FLUX_CORR"], vels=vels, fit=True, verbose=False, median=True, subtract=True)
lsd_pol = spu.subtract_median(lsddata["LSD_POL"], vels=vels, fit=True, verbose=False, median=True, subtract=True)
lsd_flux = spu.subtract_median(lsddata["LSD_FLUX"], vels=vels, fit=True, verbose=False, median=True, subtract=True)
lsd_null = spu.subtract_median(lsddata["LSD_NULL"] - np.median(lsddata["LSD_NULL"]), vels=vels, fit=True, verbose=False, median=True, subtract=True)
# Polarimetry LSD Stokes V profiles:
for iter in range(niter) :
lsd_pol = spu.subtract_median(lsd_pol['ccf'], vels=vels, fit=True, verbose=False, median=True, subtract=True)
lsd_flux = spu.subtract_median(lsd_flux['ccf'], vels=vels, fit=True, verbose=False, median=True, subtract=True)
lsd_pol_corr = spu.subtract_median(lsd_pol_corr['ccf'], vels=vels, fit=True, verbose=False, median=True, subtract=True)
lsd_flux_corr = spu.subtract_median(lsd_flux_corr['ccf'], vels=vels, fit=True, verbose=False, median=True, subtract=True)
lsd_null = spu.subtract_median(lsd_null['ccf'], vels=vels, fit=True, verbose=False, median=True, subtract=True)
if use_residuals :
lsd_pol = spu.subtract_median(lsd_pol['residuals'], vels=vels, fit=True, verbose=False, median=True, subtract=True)
lsd_pol_corr = spu.subtract_median(lsd_pol_corr['residuals'], vels=vels, fit=True, verbose=False, median=True, subtract=True)
if plot :
spu.plot_2d(lsd_pol_corr['vels'], bjd, lsd_pol_corr['ccf'], LAB=LAB_pol, title="LSD Stokes V profiles", cmap="seismic")
spu.plot_2d(lsd_flux_corr['vels'], bjd, lsd_flux_corr['ccf'], LAB=LAB_flux, title="LSD Stokes I profiles", cmap="seismic")
if apply_median_filter :
lsd_pol_medfilt = ndimage.median_filter(lsd_pol['ccf'], size=median_filter_size)
lsd_flux_medfilt = ndimage.median_filter(lsd_flux['ccf'], size=median_filter_size)
lsd_pol_corr_medfilt = ndimage.median_filter(lsd_pol_corr['ccf'], size=median_filter_size)
lsd_flux_corr_medfilt = ndimage.median_filter(lsd_flux_corr['ccf'], size=median_filter_size)
lsd_null_medfilt = ndimage.median_filter(lsd_null['ccf'], size=median_filter_size)
if plot :
spu.plot_2d(lsd_pol_corr['vels'], bjd, lsd_pol_corr_medfilt, LAB=LAB_pol, title="Median-filtered LSD Stokes V profiles", cmap="seismic")
spu.plot_2d(lsd_flux_corr['vels'], bjd, lsd_flux_corr_medfilt, LAB=LAB_flux, title="Median-filtered LSD Stokes I profiles", cmap="seismic")
lsddata["LSD_POL"] = lsd_pol_medfilt
lsddata["LSD_FLUX"] = lsd_flux_medfilt
lsddata["LSD_POL_CORR"] = lsd_pol_corr_medfilt
lsddata["LSD_FLUX_CORR"] = lsd_flux_corr_medfilt
lsddata["LSD_NULL"] = lsd_null_medfilt
else :
lsddata["LSD_POL"] = lsd_pol['ccf']
lsddata["LSD_FLUX"] = lsd_flux['ccf']
lsddata["LSD_POL_CORR"] = lsd_pol_corr['ccf']
lsddata["LSD_FLUX_CORR"] = lsd_flux_corr['ccf']
lsddata["LSD_NULL"] = lsd_null['ccf']
lsddata["LSD_POL_MED"] = lsd_pol_corr['ccf_med']
lsddata["LSD_POL_MED_ERR"] = lsd_pol_corr['ccf_sig']
lsddata["LSD_FLUX_MED"] = lsd_flux_corr['ccf_med']
lsddata["LSD_FLUX_MED_ERR"] = lsd_flux_corr['ccf_sig']
return lsddata
def continuum_lsd_I(vels, flux, fluxerr, fit_continuum=True, npcont=10, plot=False) :
cont_sample = np.append(flux[:npcont],flux[-npcont:])
cont_err_sample = np.append(fluxerr[:npcont],fluxerr[-npcont:])
cont_vels = np.append(vels[:npcont],vels[-npcont:])
if fit_continuum :
c = np.polyfit(cont_vels, cont_sample, 1)
p = np.poly1d(c)
cont = p(vels)
else :
c = np.nanmedian(cont_sample)
cont = np.full_like(flux, c)
err = np.full_like(fluxerr,np.nanmedian(cont_err_sample))
if plot :
# plot flux profile to check continuum
plt.errorbar(vels, flux, fluxerr, fmt='.')
plt.plot(cont_vels, cont_sample, 'o')
plt.plot(vels, cont, '--')
plt.show()
return cont, err
parser = OptionParser()
parser.add_option("-i", "--input", dest="input", help="Input LSD data pattern",type='string',default="*_lsd.fits")
parser.add_option("-o", "--output", dest="output", help="Output B-long time series file",type='string',default="")
parser.add_option("-1", "--min_vel", dest="min_vel", help="Minimum velocity [km/s]",type='float',default=0.)
parser.add_option("-2", "--max_vel", dest="max_vel", help="Maximum velocity [km/s]",type='float',default=0.)
parser.add_option("-s", "--nsigclip", dest="nsigclip", help="Threshold in number of sigmas to keep LSD",type='float',default=5.)
parser.add_option("-n", "--nfwhm", dest="nfwhm", help="LSD velocity range in number of FWHMs to integrate B-long",type='float',default=7.)
parser.add_option("-e", action="store_true", dest="use_residuals", help="use residual Stokes VQU profiles", default=False)
parser.add_option("-m", action="store_true", dest="use_mc", help="Use Monte Carlo sampling to calculate final values of B-long", default=False)
parser.add_option("-c", action="store_true", dest="constant_rv", help="Set all profiles with a constant velocity", default=False)
parser.add_option("-f", action="store_true", dest="median_filter", help="Apply median filter to polar time series", default=False)
parser.add_option("-p", action="store_true", dest="plot", help="plot", default=False)
parser.add_option("-v", action="store_true", dest="verbose", help="verbose", default=False)
try:
options,args = parser.parse_args(sys.argv[1:])
except:
print("Error: check usage with spirou_blong_timeseries.py -h ")
sys.exit(1)
if options.verbose:
print('Input LSD data pattern: ', options.input)
print('Output Blong time series file: ', options.output)
if options.min_vel :
print('Minimum velocity = {0:.2f} km/s: '.format(options.min_vel))
if options.min_vel :
print('Maximum velocity = {0:.2f} km/s: '.format(options.min_vel))
print('Threshold in number of sigmas to keep LSD: {0:.0f}'.format(options.nsigclip))
print('LSD velocity range in number of FWHMs to integrate B-long: {0:.0f}'.format(options.nfwhm))
# make list of data files
if options.verbose:
print("Creating list of lsd files...")
inputdata = sorted(glob.glob(options.input))
#---
if options.min_vel != 0 and options.max_vel != 0 :
auto_vel_range = False
else :
auto_vel_range = True
if options.min_vel == 0:
options.min_vel = -35.
if options.max_vel == 0:
options.max_vel = +35.
norm_errs = True
lsddata = load_lsd_time_series(inputdata, constant_rv=options.constant_rv, nsigclip=options.nsigclip, fit_profile=False, vel_min=options.min_vel, vel_max=options.max_vel, auto_vel_range=auto_vel_range, auto_vel_nfwhm=options.nfwhm, verbose=options.verbose, plot=options.plot)
lsddata = reduce_lsddata(lsddata, apply_median_filter=options.median_filter, median_filter_size=(5,2), use_residuals=options.use_residuals, plot=False)
lsddata = calculate_blong_timeseries(lsddata, norm_errs=norm_errs, norm_errs_from_time_series=False, use_mc=options.use_mc, use_corr_data=True, plot=options.plot)
chisqr, dof, redchisqr = chi_square(lsddata, verbose=options.verbose)
if options.output != "" :
save_blong_time_series(options.output, lsddata["BJD"], lsddata["BLONG"], lsddata["BLONG_ERR"])