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multiples_analyze_functions.py
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multiples_analyze_functions.py
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import matplotlib
matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
import joblib
from lmfit.models import SkewedGaussianModel
import emcee, corner, imp
import thecannon as tc
from astropy.table import Table, join
from time import time
from copy import deepcopy
from os import path
from socket import gethostname
np.warnings.filterwarnings('ignore')
# PC hostname
pc_name = gethostname()
USE_IDR3 = True
dr53_dir = '/shared/ebla/cotar/dr5.3/'
data_dir = '/shared/ebla/cotar/'
out_dir_root = '/shared/data-camelot/cotar/_Multiples_binaries_results_iDR3/'
imp.load_source('helper_functions', '../Carbon-Spectra/helper_functions.py')
from helper_functions import *
# read galah, gaia, cannon, and photometric data for all galah objects
if USE_IDR3:
galah_gaia_data = Table.read(data_dir + 'galah_cannon_DR3_gaia_photometry_20181221_ebv-corr.fits')
else:
galah_gaia_data = Table.read(data_dir + 'galah_cannon_gaia_photometry_20180327_ebv-corr.fits')
# remove unused columns
galah_gaia_data.remove_columns(['gmag', 'e_gmag', 'rmag', 'e_rmag', 'imag', 'e_imag', 'zmag', 'e_zmag', 'ymag', 'e_ymag'])
# read precomputed photometry table
if USE_IDR3:
median_photometry_data = Table.read(data_dir + 'median_photometry_cannon0_DR3_ruwe_80_005_01_all_ebv.fits') # with or without parallax filtering
else:
median_photometry_data = Table.read(data_dir + 'median_photometry_cannon0_ruwe_80_005_01_all_ebv.fits') # with or without parallax filtering
# median_photometry_data.remove_columns(['gmag', 'rmag', 'imag', 'zmag', 'ymag'])
# read t-SNE classes determined by Gregor
galah_tsne = Table.read(data_dir + 'tsne_class_1_0.csv')
bin_sid = galah_tsne[galah_tsne['published_reduced_class_proj1'] == 'binary']['sobject_id']
# remove SB2 binaries from the galah set
idx_bin = np.in1d(galah_gaia_data['sobject_id'], bin_sid)
idx_ok = np.logical_not(idx_bin)
idx_parallax_ok = galah_gaia_data['ruwe'] <= 1.4
idx_ok = np.logical_and(idx_ok, idx_parallax_ok)
galah_tsne = None
# Canonn linelist
galah_linelist = Table.read(data_dir + 'GALAH_Cannon_linelist_newer.csv')
# preload and prepare everything - drugace se pojavlja nek cuden error ce to delam znotraj sample procedure
# load cannon spectral model created by Gregor
if USE_IDR3:
cannon_model = tc.CannonModel.read(data_dir + 'model_cannon181221_DR3_ccd1234_noflat_red0_cannon0_oksnr_vsiniparam_dwarfs.dat')
else:
cannon_model = tc.CannonModel.read(data_dir + 'model_cannon180325_ccd1234_noflat_red0_cannon0_oksnr_vsiniparam.dat')
thetas = cannon_model.theta
vectorizer = cannon_model.vectorizer
fid = cannon_model._fiducials
sca = cannon_model._scales
print cannon_model.dispersion
# # load cannon photometric model created by me
# cannon_model_p = tc.CannonModel.read('model_cannon180325_photometry_cannon0_parallaxok.dat')
# cannon_txt = open('model_cannon180325_photometry_cannon0_parallaxok_cols.txt', 'r')
# cannon_model_p_cols = cannon_txt.read().split(',')
# cannon_txt.close()
# thetas_p = cannon_model_p.theta
# vectorizer_p = cannon_model_p.vectorizer
# fid_p = cannon_model_p._fiducials
# sca_p = cannon_model_p._scales
p_cols = ['Bmag','Vmag','gpmag','rpmag','ipmag','phot_g_mean_mag','phot_bp_mean_mag','phot_rp_mean_mag','Jmag','Hmag','Kmag','W1mag','W2mag']#, 'gmag', 'rmag', 'imag', 'zmag', 'ymag']#,'W3mag','W4mag']
p_cols_galah = ['Bmag','Vmag','rpmag','ipmag']
# p_cols = ['Bmag','Vmag','gpmag','rpmag','ipmag','Jmag','Hmag','Kmag','W1mag','W2mag']#,'W3mag','W4mag']
p_cols_sigma = ['e_'+c for c in p_cols]
def get_sobjects_from_final_selection(full_path):
sel_txt = open(full_path, 'r')
solar_like_sobjects = sel_txt.read()
sel_txt.close()
solar_like_sobjects = [np.int64(sid) for sid in solar_like_sobjects.split(',')]
return np.array(solar_like_sobjects)
def get_spectra_complete(s_id, get_bands=[1,2,3,4]):
flx, wvl, sig = get_spectra_dr52(str(s_id), bands=get_bands, root=dr53_dir, extension=4, read_sigma=True)
# TODO: if needed read extension 0 and normalize spectrum with my procedure, same as in solar twin search,
# procedure is not consistent with used Cannon model
return np.hstack(flx), np.hstack(sig)*np.hstack(flx), np.hstack(wvl)
def get_linelist_mask(wvl_values, d_wvl=0., element=None):
idx_lines_mask = wvl_values < 0.
if element is None:
galah_linelist_use = deepcopy(galah_linelist)
else:
galah_linelist_use = galah_linelist[galah_linelist['Element'] == element]
for line in galah_linelist_use:
idx_lines_mask[np.logical_and(wvl_values >= line['line_start'] - d_wvl, wvl_values <= line['line_end'] + d_wvl)] = True
return idx_lines_mask
def plot_lnprob(chain_vals, fit_sel_vals, path, write_out=True):
c_fig = corner.corner(chain_vals, truths=fit_sel_vals,
quantiles=[0.16, 0.5, 0.84],
show_titles=True, range=[(5600, 6100), (5100, 5900), (4750, 5650)], title_fmt='.0f',
labels=[u'T$_{eff1}$ [K]', u'T$_{eff2}$ [K]', u'T$_{eff3}$ [K]'], bins=60, plot_contours=False)
if write_out:
# c_fig.tight_layout()
c_fig.subplots_adjust(left=0.105, bottom=0.105)
c_fig.savefig(path, dpi=250)
plt.close(c_fig)
def plot_walkers(walkers_prob, path, write_out=True, miny_perc=18., sigma_lnprob=None):
plt.rcParams['font.size'] = 15
plt.figure(figsize=(7, 5.))
for i_w in range(walkers_prob.shape[0]):
plt.plot(walkers_prob[i_w, :], lw=0.2)
walkers_last = walkers_prob[:, 20].flatten()
walkers_prob = walkers_prob.flatten() # without this correction numpy
walkers_prob = walkers_prob[np.isfinite(walkers_prob)] # percentile may return incorrect -inf value
if len(walkers_prob) < 5:
return
if sigma_lnprob is None:
min_prob = np.nanpercentile(walkers_prob, 50.)
sigma_lnprob = np.nanstd(walkers_last[walkers_last > min_prob])
# plt.title('Lnprob best 1%: {:.1f}, sigma selection: {:.1f}'.format(np.nanpercentile(walkers_prob, 99), sigma_lnprob))
# plt.ylim((np.nanpercentile(walkers_prob, miny_perc), np.nanpercentile(walkers_prob, 99.9)))
plt.ylim((350, np.nanpercentile(walkers_prob, 100.)+5))
# plt.axhline(np.nanpercentile(walkers_prob, 25.), color='black', ls='--', lw=1)
# plt.axhline(np.nanpercentile(walkers_prob, 50.), color='black', ls='--', lw=1)
# plt.axhline(np.nanpercentile(walkers_prob, 75.), color='black', ls='--', lw=1)
plt.xlim(-3, 203)
plt.grid(ls='--', alpha=0.2, color='black')
plt.ylabel('Log-probability value')
plt.xlabel('Sequential number of the walkers step')
if write_out:
plt.tight_layout()
plt.savefig(path, dpi=300)
plt.close()
plt.rcParams['font.size'] = 12
def get_distribution_peaks(chain_vals, plot_ref='', plot=False, write_out=True):
n_par = chain_vals.shape[1]
peak_center = list([])
for i_p in range(n_par):
data = chain_vals[:, i_p]
hist, bins = np.histogram(data, range=(np.percentile(data, 2.), np.percentile(data, 98.)), bins=150)
d_bin = bins[1] - bins[0]
bins = bins[:-1] + d_bin / 2.
model = SkewedGaussianModel()
params = model.make_params(amplitude=np.max(hist), center=np.nanmedian(data), sigma=1, gamma=0)
result = model.fit(hist, params, x=bins)
# print result.fit_report()
hist_peak = bins[np.argmax(result.best_fit)]
peak_center.append(hist_peak)
if plot:
# output results
plt.plot(bins, hist, c='black')
plt.axvline(x=hist_peak)
plt.plot(bins, result.best_fit, c='red')
if write_out:
plt.savefig(plot_ref + '_chainvals_' + str(i_p) + '.png', dpi=250)
plt.close()
return peak_center
def _prepare_hist_data(d, bins, range, norm=True):
heights, edges = np.histogram(d, bins=bins, range=range)
width = np.abs(edges[0] - edges[1])
if norm:
heights = 1.*heights / np.nanmax(heights)
return edges[:-1], heights, width
def fit_skewed(edges, hist):
bins = edges + (edges[1]-edges[0])/2.
model = SkewedGaussianModel()
params = model.make_params(amplitude=np.max(hist), center=bins[np.argmax(hist)], sigma=1., gamma=0.)
result = model.fit(hist, params, x=bins)
hist_peak = bins[np.argmax(result.best_fit)]
return hist_peak, bins, result.best_fit
def median_photometry(teff, logg, feh, p_cols,
d_teff=50, d_logg=0.25, d_feh=0.1, plot_res=False, path=None, min_data=20, idx_init=None):
idx_use = galah_gaia_data['flag_cannon'] == 0
# idx_use = np.logical_and(data['flag_cannon'] == 0, data['red_flag'] == 0)
if idx_init is not None:
idx_use = np.logical_and(idx_use, idx_init)
# idx_use = np.logical_and(idx_use, np.isfinite(galah_gaia_data['parallax']))
idx_use = np.logical_and(idx_use, np.isfinite(galah_gaia_data['r_est']))
idx_use = np.logical_and(idx_use, np.logical_and(galah_gaia_data['Teff_cannon'] >= teff-d_teff/2., galah_gaia_data['Teff_cannon'] <= teff+d_teff/2.))
idx_use = np.logical_and(idx_use, np.logical_and(galah_gaia_data['Fe_H_cannon'] >= feh-d_feh/2., galah_gaia_data['Fe_H_cannon'] <= feh+d_feh/2.))
idx_use = np.logical_and(idx_use, np.logical_and(galah_gaia_data['Logg_cannon'] >= logg-d_logg/2., galah_gaia_data['Logg_cannon'] <= logg+d_logg/2.))
# get data
data_use = galah_gaia_data[idx_use][p_cols]
# print ','.join([str(sid) for sid in galah_gaia_data['sobject_id'][idx_use]])
if len(data_use) < min_data:
return np.full(len(p_cols), np.nan)
# print ' Data points:', len(data_use), 'for', teff, logg, feh
# print galah_gaia_data[idx_use]['Teff_cannon','Fe_H_cannon','Logg_cannon','e_Logg_cannon']
# data statistics
# if len(p_cols) > 1:
# abs_mag_vals = data_use.to_pandas().values - 2.5*np.log10(((1e3/galah_gaia_data[idx_use]['parallax'])/10.)**2).reshape(-1, 1)
abs_mag_vals = data_use.to_pandas().values - 2.5*np.log10(((galah_gaia_data[idx_use]['r_est'])/10.)**2).reshape(-1, 1)
photo_med = np.nanmedian(abs_mag_vals, axis=0)
# else:
# abs_mag_vals = data_use.data - 2.5*np.log10(((1e3/galah_gaia_data[idx_use]['parallax'])/10.)**2).reshape(-1, 1)
# photo_med = np.nanmedian(abs_mag_vals)
if plot_res:
n_x = 4
n_y = 4
fig, ax = plt.subplots(n_y, n_x, figsize=(8, 8))
fig.subplots_adjust(hspace=0.2, wspace=0.3, left=0.025, right=0.975, top=0.975, bottom=0.025)
for i_p in range(len(p_cols)):
x_p = i_p % n_x
y_p = int(i_p / n_x)
# plot_vals = data_use[p_cols[i_p]] - 2.5*np.log10(((1e3/galah_gaia_data[idx_use]['parallax'])/10.)**2)
plot_vals = data_use[p_cols[i_p]] - 2.5*np.log10(((galah_gaia_data[idx_use]['r_est'])/10.)**2)
plot_vals_median = np.nanmedian(plot_vals)
plot_vals_std = 2. * np.nanstd(plot_vals)
h_edg, h_hei, h_wid = _prepare_hist_data(plot_vals, 75, (np.nanpercentile(plot_vals, .25), np.nanpercentile(plot_vals, 99.75)))
#
plt_fit_val, x_fit, y_fit = fit_skewed(h_edg, h_hei)
# plots
ax[y_p, x_p].bar(h_edg, h_hei, width=h_wid, color='black', alpha=0.4)
ax[y_p, x_p].axvline(x=plot_vals_median, color='red', ls='--', lw=1.5, alpha=0.75)
ax[y_p, x_p].axvline(x=plot_vals_median - plot_vals_std, color='red', ls='--', lw=1.5, alpha=0.25)
ax[y_p, x_p].axvline(x=plot_vals_median + plot_vals_std, color='red', ls='--', lw=1.5, alpha=0.25)
ax[y_p, x_p].axvline(x=plt_fit_val, color='blue', ls='--', lw=1.5, alpha=0.75)
ax[y_p, x_p].plot(x_fit, y_fit, color='blue', lw=1.5, alpha=0.75)
ax[y_p, x_p].grid(ls='--', alpha=0.15, color='black')
# write out labels to the plot
ax[y_p, x_p].set(title=p_cols[i_p])
if path is None:
plt.show()
else:
plt.savefig(path, dpi=300)
plt.close()
return photo_med
def median_photometry_precomputed_intepol(teff, logg, feh, p_cols,
d_teff=50, d_logg=0.25, d_feh=0.1):
# select upper and lower bin for given values
u_teff = np.unique(median_photometry_data['teff'])
teff_sel = np.sort(u_teff[np.argsort(np.abs(u_teff - teff))[:2]])
# teff_best = u_teff[np.argmin(np.abs(u_teff - teff))]
u_logg = np.unique(median_photometry_data['logg'])
logg_sel = np.sort(u_logg[np.argsort(np.abs(u_logg - logg))[:2]])
u_feh = np.unique(median_photometry_data['feh'])
# feh_sel = np.sort(u_feh[np.argsort(np.abs(u_feh - feh))[:2]])
feh_best = u_feh[np.argmin(np.abs(u_feh - feh))]
try:
# get photometry values for both border values
idx_feh_sel = median_photometry_data['feh'] == feh_best
idx_00 = np.where(np.logical_and(idx_feh_sel,
np.logical_and(median_photometry_data['teff'] == teff_sel[0],
median_photometry_data['logg'] == logg_sel[0])))[0]
idx_01 = np.where(np.logical_and(idx_feh_sel,
np.logical_and(median_photometry_data['teff'] == teff_sel[0],
median_photometry_data['logg'] == logg_sel[1])))[0]
idx_10 = np.where(np.logical_and(idx_feh_sel,
np.logical_and(median_photometry_data['teff'] == teff_sel[1],
median_photometry_data['logg'] == logg_sel[0])))[0]
idx_11 = np.where(np.logical_and(idx_feh_sel,
np.logical_and(median_photometry_data['teff'] == teff_sel[1],
median_photometry_data['logg'] == logg_sel[1])))[0]
vals_00 = median_photometry_data[idx_00][p_cols].to_pandas().values
vals_01 = median_photometry_data[idx_01][p_cols].to_pandas().values
vals_10 = median_photometry_data[idx_10][p_cols].to_pandas().values
vals_11 = median_photometry_data[idx_11][p_cols].to_pandas().values
vals_low = vals_00 - (vals_00 - vals_10) * (teff_sel[0] - teff) / (teff_sel[0] - teff_sel[1])
vals_hig = vals_01 - (vals_01 - vals_11) * (teff_sel[0] - teff) / (teff_sel[0] - teff_sel[1])
final_vals = vals_low - (vals_low - vals_hig) * (logg_sel[0]-logg)/(logg_sel[0]-logg_sel[1])
return Table(final_vals)
except:
return []
def median_photometry_precomputed(teff, logg, feh, p_cols,
d_teff=50, d_logg=0.25, d_feh=0.1, get_bin_vals=False):
# select best match based on given teff
u_teff = np.unique(median_photometry_data['teff'])
teff_sel = u_teff[np.argmin(np.abs(u_teff - teff))]
if np.abs(teff_sel - teff) < d_teff / 2.:
logg_vals = median_photometry_data['logg'][median_photometry_data['teff'] == teff_sel]
u_logg = np.unique(logg_vals)
logg_sel = u_logg[np.argmin(np.abs(u_logg - logg))]
if np.abs(logg_sel - logg) < d_logg / 2.:
feh_vals = median_photometry_data['feh'][np.logical_and(median_photometry_data['teff'] == teff_sel,
median_photometry_data['logg'] == logg_sel)]
u_feh = np.unique(feh_vals)
feh_sel = u_feh[np.argmin(np.abs(u_feh - feh))]
if np.abs(feh_sel - feh) < d_feh / 2.:
idx_row = np.where(np.logical_and(median_photometry_data['feh'] == feh_sel,
np.logical_and(median_photometry_data['teff'] == teff_sel,
median_photometry_data['logg'] == logg_sel)))[0]
# print 'Precomputed:', teff, logg, feh, ' ---> ', median_photometry_data[idx_row]['teff', 'logg', 'feh'].to_pandas().values[0]
if get_bin_vals:
return median_photometry_data[idx_row][p_cols], median_photometry_data[idx_row]['teff', 'logg', 'feh']
else:
return median_photometry_data[idx_row][p_cols]
if get_bin_vals:
return [], []
else:
return []
# def median_photometry_cannon(teff, logg, feh, p_cols):
# sint = thetas_p[:, 0] * 0.0
# labs = (np.array([teff, logg, feh]) - fid_p) / sca_p
# vec = vectorizer_p(labs)
# for i, j in enumerate(vec):
# sint += thetas_p[:, i] * j
# return Table(sint, names=cannon_model_p_cols)[p_cols]
def eval_params(param, teff_range):
if len(param) == 1:
t1 = param
t2 = teff_range[0] + 2
t3 = teff_range[0] + 1
elif len(param) == 2:
t1, t2 = param
t3 = teff_range[0] + 1
else:
t1, t2, t3 = param
if not t1 >= t2:
# print t1,t2,t3
# print 'T1'
return False
if not t2 >= t3:
# print 'T2'
return False
if not teff_range[0] < t1 < teff_range[1]:
# print 'R1'
return False
if not teff_range[0] < t2 < teff_range[1]:
# print 'R2'
return False
if not teff_range[0] < t3 < teff_range[1]:
# print 'R3'
return False
return True
def _list_mag_photometry(teff_params, logg, feh, get_col):
if len(logg) == 1:
logg = np.repeat(logg, len(teff_params))
# compute median object photometry
phot_return = list([])
for i_t in range(len(teff_params)):
try:
# narrower ranges + filtering
phot_obj = median_photometry_precomputed_intepol(teff_params[i_t], logg[i_t], feh, get_col).to_pandas().values[0]
# phot_obj = median_photometry(teff_params[i_t], logg[i_t], feh, get_col, d_teff=80, d_logg=0.05, d_feh=0.1, idx_init=idx_ok)
except:
# broader ranges + no filtering
# print ' Using broader ranges - _list_mag_photometry'
# print ' - for inputs', teff_params[i_t], logg[i_t], feh
phot_obj = median_photometry(teff_params[i_t], logg[i_t], feh, get_col, d_teff=120, d_logg=0.2, d_feh=0.2, idx_init=idx_ok)
phot_return.append(phot_obj)
return phot_return
def _comb_mag_photometry(teff_params, logg, feh, return_flux=False):
if len(logg) == 1:
logg = np.repeat(logg, len(teff_params))
# compute median object photometry
j_comb = 0
for i_t in range(len(teff_params)):
try:
# narrower ranges + filtering
phot = median_photometry(teff_params[i_t], logg[i_t], feh, p_cols, d_teff=80, d_logg=0.05, d_feh=0.1, idx_init=idx_ok)
except:
# try broader ranges and no filtering
# print ' Using broader ranges - _comb_mag_photometry'
phot = median_photometry(teff_params[i_t], logg[i_t], feh, p_cols, d_teff=100, d_logg=0.2, d_feh=0.2, idx_init=idx_ok)
if len(phot) == 0 or not np.isfinite(phot).all():
return []
j_comb += 10 ** (-0.4 * phot)
if return_flux:
return j_comb
else:
return -2.5 * np.log10(j_comb)
def _comb_mag_photometry_precomputed(teff_params, logg, feh, return_flux=False):
if len(logg) == 1:
logg = np.repeat(logg, len(teff_params))
# compute median object photometry
j_comb = 0
for i_t in range(len(teff_params)):
phot = median_photometry_precomputed_intepol(teff_params[i_t], logg[i_t], feh, p_cols)
if len(phot) == 0:
return []
j_comb += 10 ** (-0.4 * phot.to_pandas().values[0])
if return_flux:
return j_comb
else:
return -2.5 * np.log10(j_comb)
def _get_logg_MS(teff_params):
# # compute logg along main sequence for observed Galah stars
# k_teff = -4.3e-4 # computed from two points on the Galah main sequence
# n_logg = logg - k_teff * teff
# y_logg = k_teff*teff_params + n_logg
# quadratic model for the MS model, same for all objects
if USE_IDR3:
# iDR3 SME Results on complete dataset
# y_logg = 3.616042 + 0.0003813298 * teff_params - 7.922988e-9 * teff_params ** 2 - 6.091324e-12 * teff_params ** 3
y_logg = 2.575989 + 0.0009476907 * teff_params - 1.100047e-7 * teff_params ** 2
else:
# DR2 Cannon
y_logg = -4.062806 + 0.003456557 * teff_params - 3.470085e-7 * teff_params ** 2
return y_logg
def lnprob_mag_fit(params, feh, photo_obj, photo_obj_std, teff_range):
if eval_params(params, teff_range):
# compute logg along main sequence for observed Galah stars
y_logg = _get_logg_MS(params)
# get combined mag and compare it
phot_comb = _comb_mag_photometry_precomputed(params, y_logg, feh, return_flux=False)
# phot_comb = _comb_mag_photometry(params, y_logg, feh, return_flux=False)
if len(phot_comb) == 0:
return -np.inf
# determine chi2
# V1 - compute difference directly on magnitude values
phot_diff = (photo_obj - phot_comb)**2
lnprob_val = -10 * (np.nansum(phot_diff / photo_obj_std ** 2 + np.log(2. * np.pi * photo_obj_std ** 2)))
# V2 - compute difference on flux
# phot_diff = (2.5**(np.abs(photo_obj - phot_comb))) ** 2
# lnprob_val = -100.*(np.nansum(phot_diff/(2.5**photo_obj_std)**2 + np.log(2.*np.pi*(2.5**photo_obj_std)**2)))
# print lnprob_val, params, y_logg, feh
# print phot_comb
if np.isfinite(lnprob_val):
return lnprob_val
else:
return -np.inf
else:
return -np.inf
def plot_corner_values_only(flatchain, write_out=True, path='figure.png'):
plt.rcParams['font.size'] = 15
fig, ax = plt.subplots(2, 2, sharex=False, sharey=False, figsize=(7, 5.5))
ax[0, 0].plot([4000, 7000], [4000, 7000], c='black')
ax[0, 0].scatter(flatchain[:, 0], flatchain[:, 1], c='black', lw=0, s=2.5, alpha=0.6)
ax[0, 0].set(xlabel=u'T$_{eff1}$ [K]', ylabel=u'T$_{eff2}$ [K]', ylim=(5100, 6300), xlim=(5380, 6570),
xticks=[5400, 5600, 5800, 6000, 6200, 6400], xticklabels=[],
yticks=[5200, 5400, 5600, 5800, 6000, 6200], yticklabels=['5200', '', '5600', '', '6000', ''])
ax[0, 0].grid(ls='--', alpha=0.2, color='black')
ax[1, 0].plot([4000, 7000], [4000, 7000], c='black')
ax[1, 0].scatter(flatchain[:, 0], flatchain[:, 2], c='black', lw=0, s=2.5, alpha=0.6)
ax[1, 0].set(xlabel=u'T$_{eff1}$ [K]', ylabel=u'T$_{eff3}$ [K]', ylim=(4650, 5910), xlim=(5380, 6570),
xticks=[5400, 5600, 5800, 6000, 6200, 6400], xticklabels=['5400', '', '5800', '', '6200', ''],
yticks=[4800, 5000, 5200, 5400, 5600, 5800], yticklabels=['4800', '', '5200', '', '5600', ''])
ax[1, 0].grid(ls='--', alpha=0.2, color='black')
ax[1, 1].plot([4000, 7000], [4000, 7000], c='black')
ax[1, 1].scatter(flatchain[:, 1], flatchain[:, 2], c='black', lw=0, s=2.5, alpha=0.6)
ax[1, 1].set(xlabel=u'T$_{eff2}$ [K]', ylabel='', xlim=(5100, 6300), ylim=(4650, 5910),
xticks=[5200, 5400, 5600, 5800, 6000, 6200], xticklabels=['5200', '', '5600', '', '6000', ''],
yticks=[4800, 5000, 5200, 5400, 5600, 5800], yticklabels=[])
ax[1, 1].grid(ls='--', alpha=0.2, color='black')
# remove unused plot axis
ax[0, 1].set_visible(False)
if write_out:
plt.tight_layout()
plt.subplots_adjust(hspace=0, wspace=0)
plt.savefig(path, dpi=250)
plt.close(fig)
plt.rcParams['font.size'] = 12
def plot_corner_with_lnprob(flatchain, flatlnprob, path='', low_perc=90., write_out=True):
vmin_lnp = np.nanpercentile(flatlnprob, low_perc)
vmax_lnp = np.nanpercentile(flatlnprob, 100)+10
flatlnprob_best = flatlnprob[flatlnprob >= vmin_lnp]
flatchain_best = flatchain[np.where(flatlnprob >= vmin_lnp)[0], :]
median_val = np.median(flatchain_best, axis=0)
if flatchain.shape[1] == 1:
fig, ax = plt.subplots(1, 1)
x_range = np.percentile(flatchain[:, 0], [0.5, 99.5])
ax.hist(flatchain[:, 0], range=x_range, bins=100, alpha=1.)
ax.hist(flatchain_best[:, 0], range=x_range, bins=100, alpha=1.)
ax.axvline(median_val[0], ls='--', color='red')
ax.set(xlabel='Teff 1', ylabel='Distribution of values in flatchain')
elif flatchain.shape[1] == 2:
fig, ax = plt.subplots(1, 1)
ax.scatter(median_val[0], median_val[1], lw=0, s=75, c='red', marker='X')
ax.scatter(flatchain[:, 0], flatchain[:, 1], c='black', lw=0, s=0.5, alpha=0.2)
color_ax = ax.scatter(flatchain_best[:, 0], flatchain_best[:, 1], c=flatlnprob_best, lw=0, s=1, vmin=vmin_lnp, vmax=vmax_lnp)
plt.colorbar(color_ax, ax=ax)
ax.set(xlabel='Teff 1', ylabel='Teff 2')
else:
fig, ax = plt.subplots(2, 2)
ax[0, 0].scatter(median_val[0], median_val[1], s=75, c='red', marker='X', lw=0)
ax[0, 0].scatter(flatchain[:, 0], flatchain[:, 1], c='black', lw=0, s=0.5, alpha=0.2)
color_ax = ax[0, 0].scatter(flatchain_best[:, 0], flatchain_best[:, 1], c=flatlnprob_best, lw=0, s=1, vmin=vmin_lnp, vmax=vmax_lnp)
ax[0, 0].set(xlabel='Teff 1', ylabel='Teff 2')
ax[1, 0].scatter(median_val[0], median_val[2], s=75, c='red', marker='X', lw=0)
ax[1, 0].scatter(flatchain[:, 0], flatchain[:, 2], c='black', lw=0, s=0.5, alpha=0.2)
ax[1, 0].scatter(flatchain_best[:, 0], flatchain_best[:, 2], c=flatlnprob_best, lw=0, s=1, vmin=vmin_lnp, vmax=vmax_lnp)
ax[1, 0].set(xlabel='Teff 1', ylabel='Teff 3')
ax[1, 1].scatter(median_val[1], median_val[2], s=75, c='red', marker='X', lw=0)
ax[1, 1].scatter(flatchain[:, 1], flatchain[:, 2], c='black', lw=0, s=0.5, alpha=0.2)
ax[1, 1].scatter(flatchain_best[:, 1], flatchain_best[:, 2], c=flatlnprob_best, lw=0, s=1, vmin=vmin_lnp, vmax=vmax_lnp)
ax[1, 1].set(xlabel='Teff 2', ylabel='Teff 3')
plt.colorbar(color_ax, ax=ax[0, 1])
plt.tight_layout()
if write_out:
plt.savefig(path, dpi=400)
plt.close(fig)
def plt_magnitudes(mag_1, mag_2, label_1, label_2, x_labels, path, mag_std_1=None, mag_std_2=None, write_out=True):
x_p_pos = np.arange(len(x_labels))
plt.errorbar(x_p_pos, mag_1, ms=6, label=label_1, yerr=mag_std_1, capsize=0, ls='None', fmt='.', mew=0, elinewidth=1)
plt.errorbar(x_p_pos, mag_2, ms=6, label=label_2, yerr=mag_std_2, capsize=0, ls='None', fmt='.', mew=0, elinewidth=1)
plt.xticks(x_p_pos, x_labels, rotation=90)
plt.title('Median diff: {:.2f} Chi2: {:.2f}'.format(np.nanmedian(mag_2 - mag_1), np.nansum((mag_1 - mag_2)**2/mag_std_1**2)))
plt.gca().invert_yaxis()
plt.legend()
plt.tight_layout()
if write_out:
plt.savefig(path, dpi=200)
plt.close()
def get_cannon(teff, logg, feh, vsini=None):
sint = thetas[:, 0] * 0.0
if vsini is None:
labs = (np.array([teff, logg, feh]) - fid) / sca
else:
labs = (np.array([teff, logg, feh, vsini]) - fid) / sca
vec = vectorizer(labs)
for i, j in enumerate(vec):
sint += thetas[:, i] * j
return sint
def synthetic_spectra_combine(teff_vals, logg_vals, feh, mag_values):
# define wavelength ranges of HERMES arms
min_wvl = np.array([4705, 5640, 6470, 7680])
max_wvl = np.array([4915, 5885, 6750, 7900])
feh_use = feh[0]
# determine vsini values for objects in the parameter vicinity of the requested
d_teff = 80.
d_logg = 0.05
d_feh = 0.1
idx_use = galah_gaia_data['flag_cannon'] == 0
if idx_ok is not None:
idx_use = np.logical_and(idx_use, idx_ok)
vsini_vals = list([])
for i_t in range(len(teff_vals)):
# compute and add vsini
idx_use_vsini = np.logical_and(idx_use, np.abs(galah_gaia_data['Teff_cannon'] - teff_vals[i_t]) < d_teff)
idx_use_vsini = np.logical_and(idx_use_vsini, np.abs(galah_gaia_data['Logg_cannon'] - logg_vals[i_t]) < d_logg)
idx_use_vsini = np.logical_and(idx_use_vsini, np.abs(galah_gaia_data['Fe_H_cannon'] - feh_use) < d_feh)
if np.sum(idx_use_vsini) < 10:
# use a bit wider parameter space to determine median vsini of the stars
d_teff = 100.
d_logg = 0.1
idx_use_vsini = np.logical_and(idx_use, np.abs(galah_gaia_data['Teff_cannon'] - teff_vals[i_t]) < d_teff)
idx_use_vsini = np.logical_and(idx_use_vsini, np.abs(galah_gaia_data['Logg_cannon'] - logg_vals[i_t]) < d_logg)
idx_use_vsini = np.logical_and(idx_use_vsini, np.abs(galah_gaia_data['Fe_H_cannon'] - feh_use) < d_feh)
vsini_vals.append(np.nanmedian(galah_gaia_data['Vsini_cannon'][idx_use_vsini]))
flx_complete = list([])
for ib in range(len(min_wvl)):
idx_wvl_mask = np.logical_and(cannon_model.dispersion >= min_wvl[ib], cannon_model.dispersion <= max_wvl[ib])
# flx_1 = get_cannon(teff_vals[0], logg_vals[0], feh_use)[idx_wvl_mask]
flx_1 = get_cannon(teff_vals[0], logg_vals[0], feh_use, vsini=vsini_vals[0])[idx_wvl_mask]
if len(teff_vals) == 1:
flx_model = flx_1
elif len(teff_vals) == 2:
j_1 = 10 ** (-0.4 * mag_values[1][ib]) / 10 ** (-0.4 * mag_values[0][ib])
# flx_2 = get_cannon(teff_vals[1], logg_vals[1], feh_use)[idx_wvl_mask]
flx_2 = get_cannon(teff_vals[1], logg_vals[1], feh_use, vsini=vsini_vals[1])[idx_wvl_mask]
flx_model = 1. / (1. + j_1) * flx_1 + 1. / (1. + (1. / j_1)) * flx_2
elif len(teff_vals) == 3:
j_1 = 10 ** (-0.4 * mag_values[1][ib]) / 10 ** (-0.4 * mag_values[0][ib])
j_2 = 10 ** (-0.4 * mag_values[2][ib]) / 10 ** (-0.4 * mag_values[0][ib])
# flx_2 = get_cannon(teff_vals[1], logg_vals[1], feh_use)[idx_wvl_mask]
# flx_3 = get_cannon(teff_vals[2], logg_vals[2], feh_use)[idx_wvl_mask]
flx_2 = get_cannon(teff_vals[1], logg_vals[1], feh_use, vsini=vsini_vals[1])[idx_wvl_mask]
flx_3 = get_cannon(teff_vals[1], logg_vals[1], feh_use, vsini=vsini_vals[2])[idx_wvl_mask]
flx_model = 1. / (1. + j_1 + j_2) * flx_1 + j_1 / (1. + j_1 + j_2) * flx_2 + j_2 / (1. + j_1 + j_2) * flx_3
flx_complete.append(flx_model)
return np.hstack(flx_complete)
def lnprob_flx_fit(feh, teff_model, logg_model, flx, flx_s, wvl_mask):
if -0.5 < feh < 0.4:
try:
mag_values = _list_mag_photometry(teff_model, logg_model, feh, p_cols_galah)
flx_star_model = synthetic_spectra_combine(teff_model, logg_model, feh, mag_values)[wvl_mask]
except:
return -np.inf
lnprob_flux = -0.5 * (np.nansum((flx - flx_star_model) ** 2 / flx_s**2 + np.log(2*np.pi*flx_s**2)))
if np.isfinite(lnprob_flux):
return lnprob_flux
else:
return -np.inf
else:
return -np.inf
def teff_final_from_best_lnprob(flatlnprob, flatchain, percentile=80.):
min_lnprob = np.nanpercentile(flatlnprob, percentile)
idx_use_chainvla = np.where(flatlnprob >= min_lnprob)[0]
if len(idx_use_chainvla) <= 0:
idx_use_chainvla = np.where(flatlnprob >= np.nanpercentile(flatlnprob, percentile - 10.))[0]
print ' - points used to eval teff:', len(idx_use_chainvla)
return np.median(flatchain[idx_use_chainvla, :], axis=0)
def plt_feh_distribution(data, path, orig_feh=None, orig_feh_flag=None, write_out=True):
plt.hist(data, bins=100, range=(-0.5, 0.5))
median_feh = np.median(data)
plt.axvline(median_feh, color='black', ls='--')
if orig_feh is not None:
plt.axvline(orig_feh, color='red', ls='--')
title_add = ', change: {:.2f}'.format(median_feh - orig_feh)
else:
title_add = ''
if orig_feh_flag is not None:
if orig_feh_flag > 0:
title_add += ', Cannon flag: {:.0f}'.format(orig_feh_flag)
plt.title('[Fe/H] distribution for teff combination'+title_add)
if write_out:
plt.savefig(path, dpi=200)
plt.close()
def fit_photometry_to_object(data_obj, flx, flx_s, wvl,
fit_single=False, fit_double=True, fit_tripple=True,
nwalkers=20, n_steps_1=50, n_steps_2=200, n_steps_feh=40, n_threds=10,
suffix='', save_pkl=True, write_out=True,
complete_wvl_range=False, fe_wvl_range_only=False):
print ' Threads to be used:', n_threds
if not write_out:
print ' Omitting all outputs'
save_pkl = False
str_s_id = str(data_obj['sobject_id'][0])
# obj_photo = (data_obj[p_cols].to_pandas().values - 2.5*np.log10(((1e3/data_obj['parallax'][0])/10.)**2))[0]
obj_photo = (data_obj[p_cols].to_pandas().values - 2.5*np.log10(((data_obj['r_est'][0])/10.)**2))[0]
# get photometry std and check valid std values
obj_photo_std = np.full(len(p_cols_sigma), 0.)
for i_s_c in range(len(p_cols_sigma)):
if p_cols_sigma[i_s_c] in data_obj.colnames:
obj_photo_std[i_s_c] = data_obj[p_cols_sigma[i_s_c]][0]
obj_photo_std[obj_photo_std <= 0.] = np.median(obj_photo_std[obj_photo_std > 0.])
n_photo_finite = np.sum(np.isfinite(obj_photo))
print ' Ok phot:', n_photo_finite, 'out of', len(obj_photo)
if n_photo_finite < 3:
print ' TERMINATED: not enough photometric data to compute anything'
return np.full(24, np.nan)
# med_photo_cannonparams = median_photometry_precomputed_intepol(data_obj['Teff_cannon'], data_obj['Logg_cannon'], data_obj['Fe_H_cannon_orig'], p_cols).to_pandas().values[0]
med_photo_cannonparams = median_photometry(data_obj['Teff_cannon'], data_obj['Logg_cannon'], data_obj['Fe_H_cannon_orig'], p_cols, d_teff=80, d_logg=0.05, d_feh=0.1, idx_init=idx_ok, min_data=10)
# output plot of magnitudes
if write_out:
plt_magnitudes(obj_photo, med_photo_cannonparams, 'Observed', 'Median photo Cannon', p_cols, str_s_id + '_aphot' + suffix + '_1.png',
mag_std_1=obj_photo_std, write_out=write_out)
# prepare original spectra
# define used subset of the wvl data
if complete_wvl_range:
idx_cannon_wvl_mask = cannon_model.dispersion > 0
else:
if fe_wvl_range_only:
idx_cannon_wvl_mask = get_linelist_mask(cannon_model.dispersion, d_wvl=0., element='Fe')
else:
idx_cannon_wvl_mask = get_linelist_mask(cannon_model.dispersion, d_wvl=0.)
idx_cannon_wvl_feh = get_linelist_mask(cannon_model.dispersion, d_wvl=0., element='Fe')
# resample read spectra to the same wvl pixels as cannon mask
wvl_new = cannon_model.dispersion[idx_cannon_wvl_mask]
flx_new = spectra_resample(flx, wvl, wvl_new, k=1)
flx_s_new = spectra_resample(flx_s, wvl, wvl_new, k=1)
# resample read spectra to the same wvl pixels as feh mask
wvl_new_feh = cannon_model.dispersion[idx_cannon_wvl_feh]
flx_new_feh = spectra_resample(flx, wvl, wvl_new_feh, k=1)
flx_s_new_feh = spectra_resample(flx_s, wvl, wvl_new_feh, k=1)
def _p0_generate(teff_init_range, mean_val, n_walkers, n_instances=3):
p0 = list([])
for i_w in range(n_walkers):
t_vals = mean_val - teff_init_range / 2. + np.random.rand(n_instances) * teff_init_range
p0_new = np.sort(t_vals)[::-1]
p0.append(p0_new)
return p0
def _p0_perturbe(p0_vals, perc=2.):
p0_shape = p0_vals.shape
p0_pertrubed = p0_vals + p0_vals*(np.random.uniform(-perc, perc, size=p0_shape)/100.)
return p0_pertrubed
def run_teff_mcmc(p0, feh_use, n_s, n_t):
t_1 = time()
sampler = emcee.EnsembleSampler(len(p0), len(p0[0]), lnprob_mag_fit, threads=n_t,
args=(feh_use, obj_photo, obj_photo_std, [4700, 6400]))
try:
p0, lnp, _ = sampler.run_mcmc(p0, n_s)
sampler.pool.close()
sampler.pool = None
print ' - took {:.2f} min'.format((time() - t_1) / 60.)
return sampler
except ValueError:
sampler.pool.close()
sampler.pool = None
sampler.reset()
return None
def run_flux_mcmc(p0, teff_list, logg_list, n_s, n_t):
t_1 = time()
sampler = emcee.EnsembleSampler(len(p0), 1, lnprob_flx_fit, threads=n_t,
args=(teff_list, logg_list, flx_new_feh, flx_s_new_feh, idx_cannon_wvl_feh))
try:
p0, lnp, _ = sampler.run_mcmc(p0, n_s)
sampler.pool.close()
sampler.pool = None
print ' - took {:.2f} min'.format((time() - t_1) / 60.)
return sampler
except ValueError:
sampler.pool.close()
sampler.pool = None
sampler.reset()
return None
def run_complete_fit_procedure(n_stars, write_out=True):
data_obj_stars = deepcopy(data_obj)
# print ' Initial [Fe/H]:'
# print '', data_obj_stars['Fe_H_cannon', 'Fe_H_cannon_orig']
# init samples for 2 star fit
out_file_pkl = str_s_id + suffix + '_s'+str(n_stars)+'_0.pkl'
if not save_pkl:
run_mcmc = True
else:
run_mcmc = not path.isfile(out_file_pkl)
if run_mcmc:
if n_stars == 1:
n_steps_init = 150
w_m_b = 5
p0_1 = _p0_generate(1000, np.array([data_obj_stars['Teff_cannon'][0]]), w_m_b*nwalkers, n_instances=n_stars)
elif n_stars == 2:
n_steps_init = 175
w_m_b = 7
p0_1 = _p0_generate(1100, np.array([data_obj_stars['Teff_cannon'][0] + 300,
data_obj_stars['Teff_cannon'][0] - 300.]), w_m_b*nwalkers, n_instances=n_stars)
elif n_stars == 3:
n_steps_init = 200
w_m_b = 8
p0_1 = _p0_generate(1100, np.array([data_obj_stars['Teff_cannon'][0] + 300,
data_obj_stars['Teff_cannon'][0],
data_obj_stars['Teff_cannon'][0] - 400.]), w_m_b*nwalkers, n_instances=n_stars)
# plot initial walker values
plot_corner_values_only(np.array(p0_1), write_out=True,
path=str_s_id + suffix + '_init' + '_' + str(n_stars) + '.png')
print ' Initial MCMC burn ('+str(n_stars)+' stars) - {:.0f} steps'.format(n_steps_init)
sampler = run_teff_mcmc(p0_1, data_obj_stars['Fe_H_cannon'][0], n_steps_init, n_threds)
if save_pkl and write_out:
joblib.dump(sampler, out_file_pkl)
else:
# save chain and lnprob for later use
sampler = joblib.load(out_file_pkl)
if write_out:
# other plots
teff_s0_stars = teff_final_from_best_lnprob(sampler.flatlnprobability, sampler.flatchain, percentile=85.)
plot_lnprob(sampler.flatchain, teff_s0_stars, str_s_id + suffix + '_corner' + '_'+str(n_stars)+'star_00.png', write_out=write_out)
plot_corner_with_lnprob(sampler.flatchain, sampler.flatlnprobability,
path=str_s_id + suffix + '_corner' + '_' + str(n_stars) + 'star_0.png', write_out=write_out)
plot_walkers(sampler.lnprobability, str_s_id + suffix + '_lnprob' + '_'+str(n_stars)+'star_0.png',
write_out=write_out)
# select and pertrubate the best from initial burn
out_file_pkl = str_s_id + suffix + '_s' + str(n_stars) + '_1.pkl'
if not save_pkl:
run_mcmc = True
else:
run_mcmc = not path.isfile(out_file_pkl)
if run_mcmc:
# select walkers from the last run
ln = sampler.lnprobability[:, -1]
# use only the best ones
idx_ln_best = np.argsort(ln)[::-1][:nwalkers]
p0_1 = sampler.chain[idx_ln_best, -1, :]
print ' First MCMC run (' + str(n_stars) + ' stars) - {:.0f} steps'.format(n_steps_1)
# run with original walkers
# sampler = run_teff_mcmc(p0_1, data_obj_stars['Fe_H_cannon'][0], n_steps_1, n_threds)
# run with pertrubed walkers
print ' Running with pertrubed walkers.'
sampler = run_teff_mcmc(_p0_perturbe(p0_1), data_obj_stars['Fe_H_cannon'][0], n_steps_1, n_threds)
if save_pkl and write_out:
joblib.dump(sampler, out_file_pkl)
else:
# save chain and lnprob for later use
sampler = joblib.load(out_file_pkl)
# evaluate results from the initial burn, compute new priors accordingly based on the best lnprobs in the last few steps
teff_s1_stars = teff_final_from_best_lnprob(sampler.flatlnprobability, sampler.flatchain, percentile=90.)
print ' Intermediate teff:', teff_s1_stars
# plot lnprobs and walkers
if write_out:
plot_lnprob(sampler.flatchain, teff_s1_stars, str_s_id + suffix + '_corner' + '_'+str(n_stars)+'star_2.png', write_out=write_out)
plot_walkers(sampler.lnprobability, str_s_id + suffix + '_lnprob' + '_'+str(n_stars)+'star_2.png')
plot_corner_with_lnprob(sampler.flatchain, sampler.flatlnprobability,
path=str_s_id + suffix + '_corner' + '_'+str(n_stars)+'star_1.png', write_out=write_out)
y_logg_stars = _get_logg_MS(teff_s1_stars)
sampler.reset()
step1_mag_stars = _comb_mag_photometry_precomputed(teff_s1_stars, y_logg_stars, data_obj_stars['Fe_H_cannon'])
# step1_mag_stars = _comb_mag_photometry(teff_s1_stars, y_logg_stars, data_obj_stars['Fe_H_cannon'])
if write_out:
plt_magnitudes(obj_photo, step1_mag_stars, 'Observed', 'Fitted', p_cols,
str_s_id + suffix + '_aphot' + '_'+str(n_stars)+'star_1-nofeh.png',
mag_std_1=obj_photo_std, write_out=write_out)
# determine best matching metalicity for the selected teff combination
p0_feh = _p0_generate(0.4, np.array([data_obj_stars['Fe_H_cannon'][0]]), nwalkers, n_instances=1)
print ' Fe/H MCMC run ('+str(n_stars)+' stars) - {:.0f} steps'.format(n_steps_feh)
sampler = run_flux_mcmc(p0_feh, teff_s1_stars, y_logg_stars, n_steps_feh, n_threds)
if sampler is None:
feh_stars = np.nan
model_ok_stars = False
else:
feh_stars = np.nanmedian(sampler.chain)
print ' '+str(n_stars)+' star Feh:', feh_stars
if write_out:
plt_feh_distribution(sampler.flatchain, str_s_id + suffix + '_feh' + '_'+str(n_stars)+'star.png',
orig_feh=data_obj_stars['Fe_H_cannon'][0], orig_feh_flag=data_obj['flag_cannon'][0], write_out=write_out)
sampler.reset()
# set feh of the objects
data_obj_stars['Fe_H_cannon'] = feh_stars
if np.isfinite(feh_stars):
# final fit for for star fit
p0_1 = _p0_generate(100, np.array(teff_s1_stars), nwalkers, n_instances=n_stars)
print ' Second and final MCMC run ('+str(n_stars)+' stars) - {:.0f} steps'.format(n_steps_2)
sampler = run_teff_mcmc(p0_1, data_obj_stars['Fe_H_cannon'][0], n_steps_2, n_threds)
# evaluate results from the last burn
teff_s1_stars = teff_final_from_best_lnprob(sampler.flatlnprobability, sampler.flatchain, percentile=70.)
# plot lnprobs and walkers
if write_out:
plot_lnprob(sampler.flatchain, teff_s1_stars, str_s_id + suffix + '_corner' + '_'+str(n_stars)+'star_final.png', write_out=write_out)
plot_walkers(sampler.lnprobability, str_s_id + suffix + '_lnprob' + '_'+str(n_stars)+'star_final.png')
y_logg_stars = _get_logg_MS(teff_s1_stars)
sampler.reset()
# check if a star model is even possible within the Cannon limitation
model_ok_stars = True
try:
final_mag_stars = _comb_mag_photometry_precomputed(teff_s1_stars, y_logg_stars, data_obj_stars['Fe_H_cannon'])
# final_mag_stars = _comb_mag_photometry(teff_s1_stars, y_logg_stars, data_obj_stars['Fe_H_cannon'])
if len(final_mag_stars) == 0:
model_ok_stars = False
if write_out:
plt_magnitudes(obj_photo, final_mag_stars, 'Observed', 'Fitted', p_cols,
str_s_id + suffix + '_aphot' + '_'+str(n_stars)+'star_2.png',
mag_std_1=obj_photo_std, write_out=write_out)
except:
final_mag_stars = np.full_like(step1_mag_stars, np.nan) # np.full(len(p_cols), np.nan)
model_ok_stars = False
plt.close()
flx_onestar = synthetic_spectra_combine(data_obj_stars['Teff_cannon'], data_obj_stars['Logg_cannon'], data_obj_stars['Fe_H_cannon_orig'], None)[idx_cannon_wvl_mask]
sim_f_onestar = np.nansum((flx_new - flx_onestar) ** 2 / flx_s_new ** 2)
sim_p0_chi = np.nansum((obj_photo - med_photo_cannonparams) ** 2 / obj_photo_std ** 2)
sim_p0_exc = np.nanmedian(med_photo_cannonparams - obj_photo)
# compute and return similarity values
if model_ok_stars:
mag_values_stars = _list_mag_photometry(teff_s1_stars, y_logg_stars, data_obj_stars['Fe_H_cannon'], p_cols_galah)
flx_stars = synthetic_spectra_combine(teff_s1_stars, y_logg_stars, data_obj_stars['Fe_H_cannon'], mag_values_stars)[idx_cannon_wvl_mask]
# similarity computation - flux
sim_f = np.nansum((flx_new - flx_stars) ** 2 / flx_s_new ** 2)
# similarity computation - photometry
sim_p2 = np.nansum((obj_photo - final_mag_stars) ** 2 / obj_photo_std ** 2)
else:
flx_stars = np.full_like(flx_new, np.nan)
sim_f = np.nan
sim_p2 = np.nan
# multiple similarities, just for check
idx_1 = get_linelist_mask(wvl_new, d_wvl=0.)
idx_2 = get_linelist_mask(wvl_new, d_wvl=0., element='Fe')
print ' Sim fe, abs, all, onestar: ', np.nansum((flx_new[idx_2] - flx_stars[idx_2])**2/flx_s_new[idx_2]**2), np.nansum((flx_new[idx_1] - flx_stars[idx_1])**2/flx_s_new[idx_1]**2), sim_f, sim_f_onestar
return flx_stars, np.hstack((teff_s1_stars, feh_stars, sim_p0_exc, sim_p0_chi, sim_p2, sim_f_onestar, sim_f))
# --------------------------------
# ------- Run fits for selected number of stars in the configuration
# --------------------------------
# single star fit
if fit_single:
s1_flx, s1_fit_final = run_complete_fit_procedure(1, write_out=write_out)
else:
s1_fit_final = np.full(7, np.nan)
s1_flx = np.full_like(flx_new, np.nan)
# double star fit
if fit_double:
s2_flx, s2_fit_final = run_complete_fit_procedure(2, write_out=write_out)
else:
s2_fit_final = np.full(8, np.nan)
s2_flx = np.full_like(flx_new, np.nan)
# triple star fit
if fit_tripple:
s3_flx, s3_fit_final = run_complete_fit_procedure(3, write_out=write_out)
else:
s3_fit_final = np.full(9, np.nan)
s3_flx = np.full_like(flx_new, np.nan)
# --------------------------------
# ------- Outputs, table, plots --
# --------------------------------
if write_out:
flx_onestar = synthetic_spectra_combine(data_obj['Teff_cannon'], data_obj['Logg_cannon'], data_obj['Fe_H_cannon'], None)[idx_cannon_wvl_mask]
if complete_wvl_range:
plt.figure(figsize=(35, 5))
else:
plt.figure(figsize=(17, 5))
plt.plot(flx_new, label='Observed', lw=0.5, c='black')
plt.plot(s1_flx, label='1 star model', lw=0.5, c='C0')
plt.plot(s2_flx, label='2 star model', lw=0.5, c='C1')
plt.plot(s3_flx, label='3 star model', lw=0.5, c='C2')
plt.plot(flx_onestar, label='Params star model', lw=0.5, c='C3')
plt.title('Cannon paramas: {:.0f} {:.2f} {:.2f}, similarity 1 star: {:.2f} 2 stars: {:.2f} 3 stars: {:.2f} params: {:.2f}'.format(data_obj['Teff_cannon'][0], data_obj['Logg_cannon'][0], data_obj['Fe_H_cannon_orig'][0], s1_fit_final[-1], s2_fit_final[-1], s3_fit_final[-1], s1_fit_final[-2]))
plt.tight_layout()
plt.xlim(0, len(flx_new))
plt.ylim(0.3, 1.02)
plt.legend()
plt.savefig(str_s_id + suffix + '_spectra' + '_all.png', dpi=300)
plt.close()
_, n_binary_photo2, n_binary_spectra = determine_number_of_star(s1_fit_final, s2_fit_final, s3_fit_final)
return np.hstack((s1_fit_final, s2_fit_final, s3_fit_final,
n_binary_photo2, n_binary_spectra))
def determine_number_of_star(s1_fit_final, s2_fit_final, s3_fit_final):
# --------------------------------
# ------- Determine the best combination for the photometry and spectroscopy
# --------------------------------
# determine number of stars
if np.sum(np.isfinite([s1_fit_final[-3], s2_fit_final[-3], s3_fit_final[-3]])) == 0:
n_binary_photo2 = np.nan
else:
n_binary_photo2 = np.nanargmin([s1_fit_final[-3], s2_fit_final[-3], s3_fit_final[-3]]) + 1
if np.sum(np.isfinite([s1_fit_final[-1], s2_fit_final[-1], s3_fit_final[-1]])) == 0: