def deltac_vs_time(u0, tE, dt_max, t0, t_obs, t_obs_prop=None, title=''):
"""
u0 : dim'less impact parameter
tE : in years
t0 : str, in YYYY-MM-DD format
t_obs : list of dates in str YYYY-MM-DD format.
dt_max : maximum time after photometric peak
"""
t0_strp = dt.strptime(t0, '%Y-%m-%d')
t0_dec = dtUtil.toYearFraction(t0_strp)
t_arr = np.linspace(t0_dec - 0.1, t0_dec + dt_max, 200)
if title=='MB19284':
t_arr = np.linspace(t0_dec - 1, t0_dec + dt_max, 200)
# Turn the epoch YYYY-MM-DD into a decimal.
t_obs_dec = np.zeros(len(t_obs))
for idx, tt in enumerate(t_obs):
t_strp = dt.strptime(tt, '%Y-%m-%d')
t_dec = dtUtil.toYearFraction(t_strp)
t_obs_dec[idx] = t_dec
if t_obs_prop is not None:
# Turn the epoch YYYY-MM-DD into a decimal.
t_obs_prop_dec = np.zeros(len(t_obs_prop))
for idx, tt in enumerate(t_obs_prop):
t_strp = dt.strptime(tt, '%Y-%m-%d')
t_dec = dtUtil.toYearFraction(t_strp)
t_obs_prop_dec[idx] = t_dec
u = np.sqrt(u0**2 + ((t_arr - t0_dec)/tE)**2)
deltac = u/(u**2 + 2)
fig, ax = plt.subplots()
ax.plot(t_arr, deltac)
ax.set_xlabel('Time (Year)')
ax.set_ylabel(r'$\delta_c$/$\theta_E$')
for obs in t_obs_dec:
ax.axvline(x = obs, color='black')
ax.axvline(x = 0, color='black', label='Completed')
if t_obs_prop is not None:
for obs in t_obs_prop_dec:
ax.axvline(x = obs, color='red', ls=':')
ax.axvline(x = 0, color='red', ls=':', label='Proposed')
ax.set_xlim(t_arr[0], t_arr[-1])
ax.legend()
ax.set_ylim(bottom=0)
ax.set_title(title)
plt.savefig(title + '_deltac_vs_time.png')
plt.show()
return
def calc_mean_year(fitsFiles, verbose=False):
"""
Calculate the average decimal year for a list of HST fits files.
An example call is:
year = calc_mean_year(glob.glob('*_flt.fits')
You can print out the individual years in verbose mode:
year = calc_mean_year(glob.glob('*_flt.fits', verbose=True)
"""
numObs = 0
meanYear = 0.0
nfiles = len(fitsFiles)
for ii in range(len(fitsFiles)):
hdr = pyfits.getheader(fitsFiles[ii])
date = hdr['DATE-OBS']
time = hdr['TIME-OBS']
dateObj = dt.strptime(date + ' ' + time, '%Y-%m-%d %H:%M:%S')
year = dtUtil.toYearFraction(dateObj)
meanYear += year
if verbose:
print('{0:12s} {1:12s} {2:8.3f} {3}'.format(
date, time, year, fitsFiles[ii]))
meanYear /= nfiles
if verbose:
print('*** AVERAGE YEAR = {0:8.3f} ***'.format(meanYear))
return meanYear
def calc_mean_year(fitsFiles, verbose=False):
"""
Calculate the average decimal year for a list of HST fits files.
An example call is:
year = calc_mean_year(glob.glob('*_flt.fits')
You can print out the individual years in verbose mode:
year = calc_mean_year(glob.glob('*_flt.fits', verbose=True)
"""
numObs = 0
meanYear = 0.0
nfiles = len(fitsFiles)
for ii in range(len(fitsFiles)):
hdr = pyfits.getheader(fitsFiles[ii])
date = hdr['DATE-OBS']
time = hdr['TIME-OBS']
dateObj = dt.strptime(date + ' ' + time, '%Y-%m-%d %H:%M:%S')
year = dtUtil.toYearFraction(dateObj)
meanYear += year
if verbose:
print('{0:12s} {1:12s} {2:8.3f} {3}'.format(date, time, year,
fitsFiles[ii]))
meanYear /= nfiles
if verbose:
print('*** AVERAGE YEAR = {0:8.3f} ***'.format(meanYear))
return meanYear
def deltac_vs_time_BH15_targets(target, t_obs_prop=None, dtmax=None):
"""
target : 'ob120169', 'ob140613', 'ob150029', 'ob150211'
"""
target_name = copy.deepcopy(target)
target_name = target_name.replace('OB', 'ob')
print(target_name)
mod_fit, data = lu_2019_lens.get_data_and_fitter(tdir[target_name])
mod_all = mod_fit.get_best_fit_modes_model(def_best = 'maxL')
mod = mod_all[0]
# Sample time
tmax = np.max(np.append(data['t_phot1'], data['t_phot2'])) + 90.0
if dtmax is not None:
tmax += dtmax
t_mod_ast = np.arange(data['t_ast'].min() - 180.0, tmax, 2)
t_mod_pho = np.arange(data['t_phot1'].min(), tmax, 2)
# Get the linear motion curves for the source (includes parallax)
p_unlens_mod = mod.get_astrometry_unlensed(t_mod_ast)
p_unlens_mod_at_ast = mod.get_astrometry_unlensed(data['t_ast'])
# Get the lensed motion curves for the source
p_lens_mod = mod.get_astrometry(t_mod_ast)
p_lens_mod_at_ast = mod.get_astrometry(data['t_ast'])
x = (data['xpos'] - p_unlens_mod_at_ast[:,0]) * -1e3
xe = data['xpos_err'] * 1e3
y = (data['ypos'] - p_unlens_mod_at_ast[:, 1]) * 1e3
ye = data['ypos_err'] * 1e3
r2 = x**2 + y**2
r = np.sqrt(r2)
xterm = (x * xe)**2/r2
yterm = (y * ye)**2/r2
re = np.sqrt(xterm + yterm)
xmod = (p_lens_mod[:, 0] - p_unlens_mod[:, 0])*-1e3
ymod = (p_lens_mod[:, 1] - p_unlens_mod[:, 1])*1e3
# Convert to decimal dates
t_ast_dec = Time(data['t_ast'], format='mjd', scale='utc')
t_mod_ast_dec = Time(t_mod_ast, format='mjd', scale='utc')
t_ast_dec.format='decimalyear'
t_mod_ast_dec.format='decimalyear'
t_ast_dec = t_ast_dec.value
t_mod_ast_dec = t_mod_ast_dec.value
if t_obs_prop is not None:
# Turn the epoch YYYY-MM-DD into a decimal.
t_obs_prop_dec = np.zeros(len(t_obs_prop))
for idx, tt in enumerate(t_obs_prop):
t_strp = dt.strptime(tt, '%Y-%m-%d')
t_dec = dtUtil.toYearFraction(t_strp)
t_obs_prop_dec[idx] = t_dec
plt.figure(1)
plt.clf()
plt.errorbar(t_ast_dec, x, yerr=xe, fmt='k.', alpha=1, zorder = 1000, label='Data')
plt.scatter(t_mod_ast_dec, xmod, s = 1)
plt.title(target)
plt.xlabel('Time (Year)')
plt.ylabel('$\delta$RA (mas)')
if t_obs_prop is not None:
for obs in t_obs_prop_dec:
plt.axvline(x = obs, color='red', ls=':')
plt.axvline(x = 0, color='red', ls=':', label='Proposed')
plt.xlim(t_mod_ast_dec.min() - 0.1, t_mod_ast_dec.max() + 0.1)
plt.axhline(y=0, color='black', alpha=0.5)
plt.legend(loc=1)
plt.title(target)
plt.savefig(target + '_deltac_RA_vs_time.png')
plt.show()
plt.figure(2)
plt.clf()
plt.errorbar(t_ast_dec, y, yerr=ye, fmt='k.', alpha=1, zorder = 1000, label='Data')
plt.scatter(t_mod_ast_dec, ymod, s = 1)
plt.title(target)
plt.xlabel('Time (Year)')
plt.ylabel('$\delta$Dec (mas)')
if t_obs_prop is not None:
for obs in t_obs_prop_dec:
plt.axvline(x = obs, color='red', ls=':')
plt.axvline(x = 0, color='red', ls=':', label='Proposed')
plt.xlim(t_mod_ast_dec.min() - 0.1, t_mod_ast_dec.max() + 0.1)
plt.axhline(y=0, color='black', alpha=0.5)
plt.legend(loc=1)
plt.title(target)
plt.savefig(target + '_deltac_Dec_vs_time.png')
plt.show()
plt.figure(3)
plt.clf()
plt.errorbar(t_ast_dec, r, yerr=re, fmt='k.', alpha=1, zorder = 1000, label='Data')
plt.scatter(t_mod_ast_dec, np.sqrt(xmod**2 + ymod**2), s = 1)
plt.xlabel('Time (Year)')
plt.ylabel('$\delta_c$ (mas)')
if t_obs_prop is not None:
for obs in t_obs_prop_dec:
plt.axvline(x = obs, color='red', ls=':')
plt.axvline(x = 0, color='red', ls=':', label='Proposed')
plt.xlim(t_mod_ast_dec.min() - 0.1, t_mod_ast_dec.max() + 0.1)
plt.gca().set_ylim(bottom=0)
plt.axhline(y=0.15, color='gray', ls='--')
plt.legend(loc=1)
plt.title(target)
plt.savefig(target + '_deltac_vs_time.png')
plt.show()
def ob110462_ast_v_time():
target = 'OB110462'
target_name = 'ob110462'
t_obs_prop = ['2021-04-01']
# Get the model
mod_fit, data = lu_2019_lens.get_data_and_fitter(tdir[target_name])
mod_all = mod_fit.get_best_fit_modes_model(def_best='maxL')
mod = mod_all[0]
# Sample N random draws from the posterior
ndraw = 100
res = mod_fit.load_mnest_results()
# Indices of the posterior we want
pdx = np.random.choice(len(res), ndraw, replace=True, p=res['weights'])
# Sample time
tmax = np.max(np.append(data['t_phot1'], data['t_phot2'])) + 90.0
tmax += 2500
t_mod_ast = np.arange(data['t_ast'].min() - 180.0, tmax, 0.1)
t_mod_pho = np.arange(data['t_phot1'].min(), tmax, 5)
days_per_year = 365.25
dt_mod_ast = (t_mod_ast - mod.t0) / days_per_year
dt_data_ast = (data['t_ast'] - mod.t0) / days_per_year
# Get the linear motion curves for the source (NO parallax)
p_unlens_mod = mod.xS0 + np.outer(dt_mod_ast, mod.muS) * 1e-3
p_unlens_mod_at_ast = mod.xS0 + np.outer(dt_data_ast, mod.muS) * 1e-3
# Get the lensed motion curves for the source
p_lens_mod = mod.get_astrometry(t_mod_ast)
p_lens_mod_at_ast = mod.get_astrometry(data['t_ast'])
x = (data['xpos'] - p_unlens_mod_at_ast[:, 0]) * -1e3
xe = data['xpos_err'] * 1e3
y = (data['ypos'] - p_unlens_mod_at_ast[:, 1]) * 1e3
ye = data['ypos_err'] * 1e3
xmod = (p_lens_mod[:, 0] - p_unlens_mod[:, 0]) * -1e3
ymod = (p_lens_mod[:, 1] - p_unlens_mod[:, 1]) * 1e3
# Convert to decimal dates
t_ast_dec = Time(data['t_ast'], format='mjd', scale='utc')
t_mod_ast_dec = Time(t_mod_ast, format='mjd', scale='utc')
t_ast_dec.format = 'decimalyear'
t_mod_ast_dec.format = 'decimalyear'
t_ast_dec = t_ast_dec.value
t_mod_ast_dec = t_mod_ast_dec.value
if t_obs_prop is not None:
# Turn the epoch YYYY-MM-DD into a decimal.
t_obs_prop_dec = np.zeros(len(t_obs_prop))
for idx, tt in enumerate(t_obs_prop):
t_strp = dt.strptime(tt, '%Y-%m-%d')
t_dec = dtUtil.toYearFraction(t_strp)
t_obs_prop_dec[idx] = t_dec
###
# X VS TIME
###
plt.figure(1)
plt.clf()
plt.errorbar(t_ast_dec,
x,
yerr=xe,
fmt='r.',
alpha=1,
zorder=1000,
label='Data')
plt.scatter(t_mod_ast_dec, xmod, s=0.1, color='black')
plt.title(target)
plt.xlabel('Time (Year)')
plt.ylabel(r'$\Delta \alpha^*$ (mas)')
for ii in range(len(pdx)):
# Plot the posterior draws
pdraw = mod_fit.get_model(res[ii])
# Get the lensed motion curves for the source
p_lens_pdraw = pdraw.get_astrometry(t_mod_ast)
p_lens_pdraw_at_ast = pdraw.get_astrometry(data['t_ast'])
xpdraw = (p_lens_pdraw[:, 0] - p_unlens_mod[:, 0]) * -1e3
ypdraw = (p_lens_pdraw[:, 1] - p_unlens_mod[:, 1]) * 1e3
plt.scatter(t_mod_ast_dec,
xpdraw,
s=0.01,
alpha=0.02,
color='lightgray')
plt.xticks([2010, 2013, 2016, 2019, 2022])
plt.axvline(x=t_obs_prop_dec, color='red', ls=':')
plt.legend(loc=1)
plt.title(target)
plt.savefig(target + '_deltac_RA_vs_time.png')
plt.show()
###
# Y VS TIME
###
plt.figure(2)
plt.clf()
plt.errorbar(t_ast_dec,
y,
yerr=ye,
fmt='r.',
alpha=1,
zorder=1000,
label='Data')
plt.scatter(t_mod_ast_dec, ymod, s=0.1, color='black')
plt.title(target)
plt.xlabel('Time (Year)')
plt.ylabel('$\Delta \delta$ (mas)')
for ii in range(len(pdx)):
# Plot the posterior draws
pdraw = mod_fit.get_model(res[ii])
# Get the lensed motion curves for the source
p_lens_pdraw = pdraw.get_astrometry(t_mod_ast)
p_lens_pdraw_at_ast = pdraw.get_astrometry(data['t_ast'])
xpdraw = (p_lens_pdraw[:, 0] - p_unlens_mod[:, 0]) * -1e3
ypdraw = (p_lens_pdraw[:, 1] - p_unlens_mod[:, 1]) * 1e3
plt.scatter(t_mod_ast_dec,
ypdraw,
s=0.01,
alpha=0.02,
color='lightgray')
plt.xticks([2010, 2013, 2016, 2019, 2022])
plt.axvline(x=t_obs_prop_dec, color='red', ls=':')
plt.legend(loc=1)
plt.title(target)
plt.savefig(target + '_deltac_Dec_vs_time.png')
plt.show()