def generate_model_lightcurve(e):
"""Function to produce a model lightcurve based on a parameter set
fitted by pyLIMA
Inputs:
e Event object
"""
lc = e.telescopes[0].lightcurve_magnitude
fit_params = e.fits[-1].model.compute_pyLIMA_parameters(e.fits[-1].fit_results)
ts = np.linspace(lc[:,0].min(), lc[:,0].max(), len(lc[:,0]))
reference_telescope = copy.copy(e.fits[-1].event.telescopes[0])
reference_telescope.lightcurve_magnitude = np.array([ts, [0] * len(ts), [0] * len(ts)]).T
reference_telescope.lightcurve_flux = reference_telescope.lightcurve_in_flux()
if e.fits[-1].model.parallax_model[0] != 'None':
reference_telescope.compute_parallax(e.fits[-1].event, e.fits[-1].model.parallax_model)
print_fit_params(fit_params)
flux_model = e.fits[-1].model.compute_the_microlensing_model(reference_telescope, fit_params)[0]
mag_model = microltoolbox.flux_to_magnitude(flux_model)
return mag_model
def lightcurve_in_magnitude(self):
"""
Transform flux to magnitude using m = 27.4-2.5*log10(flux) convention. Transform error bar
accordingly. More details in microltoolbox module.
:return: the lightcurve in magnitude, lightcurve_magnitude.
:rtype: array_like
"""
lightcurve = self.lightcurve_flux
time = lightcurve[:, 0]
flux = lightcurve[:, 1]
error_flux = lightcurve[:, 2]
magnitude = microltoolbox.flux_to_magnitude(flux)
error_magnitude = microltoolbox.error_flux_to_error_magnitude(
error_flux, flux)
ligthcurve_magnitude = np.array([time, magnitude, error_magnitude]).T
return ligthcurve_magnitude
# def hidden(self):
#import webbrowser
#controller = webbrowser.get()
#if self.name =='Mexicola':
#controller.open("https://www.youtube.com/watch?v=GcQdU2qA7D4&t=1684s")
def test_flux_to_magnitude():
flux = np.array([5000, 7000, 15000])
magnitude = microltoolbox.flux_to_magnitude(flux)
assert len(magnitude) == len(flux)
assert magnitude[0] > magnitude[1]
assert magnitude[1] > magnitude[2]
def align_telescope_lightcurve(lightcurve_telescope_flux, model_ghost,
model_telescope):
"""Align data to the survey telescope (i.e telescope 0).
:param array_like lightcurve_telescope_mag: the survey telescope in magnitude
:param float fs_reference: thce survey telescope reference source flux (i.e the fitted value)
:param float g_reference: the survey telescope reference blending parameter (i.e the fitted
value)
:param float fs_telescope: the telescope source flux (i.e the fitted value)
:param float g_reference: the telescope blending parameter (i.e the fitted value)
:return: the aligned to survey lightcurve in magnitude
:rtype: array_like
"""
time = lightcurve_telescope_flux[:, 0]
flux = lightcurve_telescope_flux[:, 1]
error_flux = lightcurve_telescope_flux[:, 2]
err_mag = microltoolbox.error_flux_to_error_magnitude(error_flux, flux)
residuals = 2.5 * np.log10(model_telescope / flux)
magnitude_normalised = microltoolbox.flux_to_magnitude(
model_ghost) + residuals
lightcurve_normalised = [time, magnitude_normalised, err_mag]
lightcurve_mag_normalised = np.array(lightcurve_normalised).T
return lightcurve_mag_normalised
def LM_plot_model(fit, figure_axe):
"""Plot the microlensing model from the fit.
:param object fit: a fit object. See the microlfits for more details.
:param matplotlib_axes figure_axe: a matplotlib axes correpsonding to the figure.
"""
pyLIMA_parameters = fit.model.compute_pyLIMA_parameters(fit.fit_results)
min_time = min(
[min(i.lightcurve_magnitude[:, 0]) for i in fit.event.telescopes])
max_time = max(
[max(i.lightcurve_magnitude[:, 0]) for i in fit.event.telescopes])
time = np.linspace(min_time, max_time + 100, 30000)
try:
extra_time = np.linspace(
pyLIMA_parameters.to - 2 * np.abs(pyLIMA_parameters.tE),
pyLIMA_parameters.to + 2 * np.abs(pyLIMA_parameters.tE), 30000)
except:
extra_time = np.linspace(
pyLIMA_parameters.tc - 2 * np.abs(pyLIMA_parameters.tE),
pyLIMA_parameters.tc + 2 * np.abs(pyLIMA_parameters.tE), 30000)
time = np.sort(np.append(time, extra_time))
reference_telescope = copy.copy(fit.event.telescopes[0])
reference_telescope.lightcurve_magnitude = np.array(
[time, [0] * len(time), [0] * len(time)]).T
reference_telescope.lightcurve_flux = reference_telescope.lightcurve_in_flux(
)
if fit.model.parallax_model[0] != 'None':
reference_telescope.compute_parallax(fit.event,
fit.model.parallax_model)
flux_model = fit.model.compute_the_microlensing_model(
reference_telescope, pyLIMA_parameters)[0]
magnitude = microltoolbox.flux_to_magnitude(flux_model)
figure_axe.plot(time, magnitude, '--k', label=fit.model.model_type, lw=2)
figure_axe.set_ylim([
min(magnitude) - plot_lightcurve_windows,
max(magnitude) + plot_lightcurve_windows
])
try:
figure_axe.set_xlim([
pyLIMA_parameters.to - 3 * np.abs(pyLIMA_parameters.tE),
pyLIMA_parameters.to + 3 * np.abs(pyLIMA_parameters.tE) + 100
])
except:
figure_axe.set_xlim([
pyLIMA_parameters.tc - 3 * np.abs(pyLIMA_parameters.tE),
pyLIMA_parameters.tc + 3 * np.abs(pyLIMA_parameters.tE) + 100
])
figure_axe.invert_yaxis()
figure_axe.text(0.01,
0.96,
'provided by pyLIMA',
style='italic',
fontsize=10,
transform=figure_axe.transAxes)
def generate_model_lightcurve(e, ts=None, diagnostics=False):
"""Function to produce a model lightcurve based on a parameter set
fitted by pyLIMA
Inputs:
e Event object, with attributed lightcurve data and model fit(s)
"""
lc = e.telescopes[0].lightcurve_magnitude
fit_params = e.fits[-1].model.compute_pyLIMA_parameters(
e.fits[-1].fit_results)
if type(ts) != type(np.zeros(1)):
ts = np.linspace(lc[:, 0].min(), lc[:, 0].max(), len(lc[:, 0]))
reference_telescope = copy.copy(e.fits[-1].event.telescopes[0])
reference_telescope.lightcurve_magnitude = np.array(
[ts, [0] * len(ts), [0] * len(ts)]).T
reference_telescope.lightcurve_flux = reference_telescope.lightcurve_in_flux(
)
if e.fits[-1].model.parallax_model[0] != 'None':
reference_telescope.compute_parallax(e.fits[-1].event,
e.fits[-1].model.parallax_model)
flux_model = e.fits[-1].model.compute_the_microlensing_model(
reference_telescope, fit_params)[0]
mag_model = microltoolbox.flux_to_magnitude(flux_model)
if diagnostics:
fig = plt.figure(1, (10, 10))
plt.plot(ts, mag_model, 'r-')
plt.xlabel('HJD')
plt.ylabel('Magnitude')
rev_yaxis = True
if rev_yaxis:
[xmin, xmax, ymin, ymax] = plt.axis()
plt.axis([xmin, xmax, ymax, ymin])
plt.grid()
plt.savefig('lc_model_test.png')
plt.close(1)
return mag_model
def lightcurve_in_magnitude(self):
"""
Transform flux to magnitude using m = 27.4-2.5*log10(flux) convention. Transform error bar
accordingly. More details in microltoolbox module.
:return: the lightcurve in magnitude, lightcurve_magnitude.
:rtype: array_like
"""
lightcurve = self.lightcurve_flux
time = lightcurve[:, 0]
flux = lightcurve[:, 1]
error_flux = lightcurve[:, 2]
magnitude = microltoolbox.flux_to_magnitude(flux)
error_magnitude = microltoolbox.error_flux_to_error_magnitude(error_flux, flux)
ligthcurve_magnitude = np.array([time, magnitude, error_magnitude]).T
return ligthcurve_magnitude
def MCMC_plot_model(fit, reference_telescope, parameters, couleurs,
figure_axes, scalar_couleur_map):
""" Plot a model to a given figure, with the color corresponding to the objective function
of the model.
:param fit: a fit object. See the microlfits for more details.
:param parameters: the parameters [list] of the model you want to plot.
:param couleurs: the values of the objective function for the model that match the color
table scalar_couleur_map
:param figure_axes: the axes where the plot are draw
:param scalar_couleur_map: a matplotlib table that return a color given a scalar value (the
objective function here)
"""
pyLIMA_parameters = fit.model.compute_pyLIMA_parameters(parameters)
flux_model = fit.model.compute_the_microlensing_model(
reference_telescope, pyLIMA_parameters)[0]
magnitude_model = microltoolbox.flux_to_magnitude(flux_model)
figure_axes.plot(reference_telescope.lightcurve_magnitude[:, 0],
magnitude_model,
color=scalar_couleur_map.to_rgba(couleurs),
alpha=0.5)
def initial_guess_PSPL(event):
"""Function to find initial PSPL guess for Levenberg-Marquardt solver (method=='LM').
This assumes no blending.
:param object event: the event object on which you perform the fit on. More details on the
event module.
:return: the PSPL guess for this event. A list of parameters associated to the PSPL model + the source flux of
:return: the PSPL guess for this event. A list of parameters associated to the PSPL model + the source flux of
the survey telescope.
:rtype: list,float
"""
# to estimation
to_estimations = []
maximum_flux_estimations = []
errors_magnitude = []
for telescope in event.telescopes:
# Lot of process here, if one fails, just skip
lightcurve_magnitude = telescope.lightcurve_magnitude
mean_error_magnitude = np.mean(lightcurve_magnitude[:, 2])
try:
# only the best photometry
good_photometry_indexes = np.where((lightcurve_magnitude[:, 2] <
max(0.1, mean_error_magnitude)))[0]
lightcurve_bis = lightcurve_magnitude[good_photometry_indexes]
lightcurve_bis = lightcurve_bis[lightcurve_bis[:, 0].argsort(), :]
mag = lightcurve_bis[:, 1]
flux = microltoolbox.magnitude_to_flux(mag)
# clean the lightcurve using Savitzky-Golay filter on 3 points, degree 1.
mag_clean = ss.savgol_filter(mag, 3, 1)
time = lightcurve_bis[:, 0]
flux_clean = microltoolbox.flux_to_magnitude(mag_clean)
errmag = lightcurve_bis[:, 2]
flux_source = min(flux_clean)
good_points = np.where(flux_clean > flux_source)[0]
while (np.std(time[good_points]) > 5) | (len(good_points) > 100):
indexes = \
np.where((flux_clean[good_points] > np.median(flux_clean[good_points])) & (
errmag[good_points] <= max(0.1, 2.0 * np.mean(errmag[good_points]))))[0]
if len(indexes) < 1:
break
else:
good_points = good_points[indexes]
# gravity = (
# np.median(time[good_points]), np.median(flux_clean[good_points]),
# np.mean(errmag[good_points]))
# distances = np.sqrt((time[good_points] - gravity[0]) ** 2 / gravity[0] ** 2)
to = np.median(time[good_points])
max_flux = max(flux[good_points])
to_estimations.append(to)
maximum_flux_estimations.append(max_flux)
errors_magnitude.append(np.mean(lightcurve_bis[good_points, 2]))
except:
time = lightcurve_magnitude[:, 0]
flux = microltoolbox.magnitude_to_flux(lightcurve_magnitude[:, 1])
to = np.median(time)
max_flux = max(flux)
to_estimations.append(to)
maximum_flux_estimations.append(max_flux)
errors_magnitude.append(mean_error_magnitude)
to_guess = sum(np.array(to_estimations) / np.array(errors_magnitude) ** 2) / sum(
1 / np.array(errors_magnitude) ** 2)
survey = event.telescopes[0]
lightcurve = survey.lightcurve_magnitude
lightcurve = lightcurve[lightcurve[:, 0].argsort(), :]
## fs, uo, tE estimations only one the survey telescope
time = lightcurve[:, 0]
flux = microltoolbox.magnitude_to_flux(lightcurve[:, 1])
errflux = microltoolbox.error_magnitude_to_error_flux(lightcurve[:, 2], flux)
# fs estimation, no blend
baseline_flux_0 = np.min(flux)
baseline_flux = np.median(flux)
while np.abs(baseline_flux_0 - baseline_flux) > 0.01 * baseline_flux:
baseline_flux_0 = baseline_flux
indexes = np.where((flux < baseline_flux))[0].tolist() + np.where(
np.abs(flux - baseline_flux) < np.abs(errflux))[0].tolist()
baseline_flux = np.median(flux[indexes])
if len(indexes) < 100:
baseline_flux = np.median(flux[flux.argsort()[:100]])
break
fs_guess = baseline_flux
# uo estimation
max_flux = maximum_flux_estimations[0]
Amax = max_flux / fs_guess
if (Amax < 1.0) | np.isnan(Amax):
Amax = 1.1
uo_guess = np.sqrt(-2 + 2 * np.sqrt(1 - 1 / (1 - Amax ** 2)))
# tE estimations
tE_guesses = []
# Method 1 : flux(t_demi_amplification) = 0.5 * fs_guess * (Amax + 1)
half_magnification = 0.5 * (Amax + 1)
flux_demi_amplification = fs_guess * half_magnification
index_plus = np.where((time > to_guess) & (flux < flux_demi_amplification))[0]
index_moins = np.where((time < to_guess) & (flux < flux_demi_amplification))[0]
if len(index_plus) != 0:
if len(index_moins) != 0:
t_demi_amplification = (time[index_plus[0]] - time[index_moins[-1]])
tE_demi_amplification = t_demi_amplification / (
2 * np.sqrt(-2 + 2 * np.sqrt(1 + 1 / (half_magnification ** 2 - 1)) - uo_guess ** 2))
tE_guesses.append(tE_demi_amplification)
else:
t_demi_amplification = time[index_plus[0]] - to_guess
tE_demi_amplification = t_demi_amplification / np.sqrt(
-2 + 2 * np.sqrt(1 + 1 / (half_magnification ** 2 - 1)) - uo_guess ** 2)
tE_guesses.append(tE_demi_amplification)
else:
if len(index_moins) != 0:
t_demi_amplification = to_guess - time[index_moins[-1]]
tE_demi_amplification = t_demi_amplification / np.sqrt(
-2 + 2 * np.sqrt(1 + 1 / (half_magnification ** 2 - 1)) - uo_guess ** 2)
tE_guesses.append(tE_demi_amplification)
# Method 2 : flux(t_E) = fs_guess * (uo^+3)/[(uo^2+1)^0.5*(uo^2+5)^0.5]
amplification_tE = (uo_guess ** 2 + 3) / ((uo_guess ** 2 + 1) ** 0.5 * np.sqrt(uo_guess ** 2 + 5))
flux_tE = fs_guess * amplification_tE
index_tE_plus = np.where((flux < flux_tE) & (time > to))[0]
index_tE_moins = np.where((flux < flux_tE) & (time < to))[0]
if len(index_tE_moins) != 0:
index_tE_moins = index_tE_moins[-1]
tE_moins = to_guess - time[index_tE_moins]
tE_guesses.append(tE_moins)
if len(index_tE_plus) != 0:
index_tE_plus = index_tE_plus[0]
tE_plus = time[index_tE_plus] - to_guess
tE_guesses.append(tE_plus)
# Method 3 : the first points before/after to_guess that reach the baseline. Very rough
# approximation ot tE.
index_tE_baseline_plus = np.where((time > to) & (np.abs(flux - fs_guess) < np.abs(errflux)))[0]
index_tE_baseline_moins = np.where((time < to) & (np.abs(flux - fs_guess) < np.abs(errflux)))[0]
if len(index_tE_baseline_plus) != 0:
tEPlus = time[index_tE_baseline_plus[0]] - to_guess
tE_guesses.append(tEPlus)
if len(index_tE_baseline_moins) != 0:
tEMoins = to_guess - time[index_tE_baseline_moins[-1]]
tE_guesses.append(tEMoins)
tE_guess = np.median(tE_guesses)
# safety reason, unlikely
if (tE_guess < 0.1) | np.isnan(tE_guess):
tE_guess = 20.0
# [to,uo,tE], fsource
return [to_guess, uo_guess, tE_guess], fs_guess
def fit_PSPL_parallax(ra, dec, photometry, emag_limit=None, cores=None):
# Filter orders
filters_order = ['I', 'ip', 'i_ZTF', 'r_ZTF', 'R', 'g_ZTF', 'gp', 'G']
filters = np.unique(photometry[:, -1])
order = []
for fil in filters_order:
mask = np.where(filters == fil)[0]
if len(mask) != 0:
order += mask.tolist()
for fil in filters:
if fil not in filters_order:
mask = np.where(filters == fil)[0]
if len(mask) != 0:
order += mask.tolist()
filters = filters[order]
t0_fit, u0_fit, tE_fit, mag_source_fit, mag_blend_fit, mag_baseline_fit, chi2_fit = fit_PSPL(
photometry, emag_limit=None, cores=cores)
current_event = event.Event()
current_event.name = 'MOP_to_fit'
current_event.ra = ra
current_event.dec = dec
for ind, filt in enumerate(filters):
if emag_limit:
mask = (photometry[:, -1] == filt) & (np.abs(
photometry[:, -2].astype(float)) < emag_limit)
else:
mask = (photometry[:, -1] == filt)
lightcurve = photometry[mask, :-1].astype(float)
telescope = telescopes.Telescope(name='Tel_' + str(ind),
camera_filter=filt,
light_curve_magnitude=lightcurve,
light_curve_magnitude_dictionnary={
'time': 0,
'mag': 1,
'err_mag': 2
},
clean_the_lightcurve='Yes')
if len(lightcurve) > 5:
current_event.telescopes.append(telescope)
t0_par = t0_fit
Model_parallax = microlmodels.create_model('PSPL',
current_event,
parallax=['Full', t0_par])
Model_parallax.parameters_boundaries[0] = [t0_fit - 100, t0_fit + 100]
Model_parallax.parameters_boundaries[1] = [-2, 2]
Model_parallax.parameters_boundaries[2] = [0.1, 500]
#Model_parallax.parameters_boundaries[3] = [-1,1]
#Model_parallax.parameters_boundaries[4] = [-1,1]
Model_parallax.parameters_guess = [t0_fit, u0_fit, tE_fit, 0, 0]
if cores != 0:
import multiprocessing
with multiprocessing.Pool(processes=cores) as pool:
current_event.fit(Model_parallax,
'DE',
DE_population_size=10,
flux_estimation_MCMC='polyfit',
computational_pool=pool)
else:
current_event.fit(Model_parallax,
'DE',
DE_population_size=10,
flux_estimation_MCMC='polyfit')
#if (chi2_fit-current_event.fits[-1].fit_results[-1])/current_event.fits[-1].fit_results[-1]<0.1:
#return [t0_fit,u0_fit,tE_fit,None,None,mag_source_fit,mag_blend_fit,mag_baseline_fit]
t0_fit = current_event.fits[-1].fit_results[0]
u0_fit = current_event.fits[-1].fit_results[1]
tE_fit = current_event.fits[-1].fit_results[2]
piEN_fit = current_event.fits[-1].fit_results[3]
piEE_fit = current_event.fits[-1].fit_results[4]
mag_source_fit = flux_to_mag(current_event.fits[-1].fit_results[5])
mag_blend_fit = flux_to_mag(current_event.fits[-1].fit_results[5] *
current_event.fits[-1].fit_results[6])
mag_baseline_fit = flux_to_mag(current_event.fits[-1].fit_results[5] *
(1 + current_event.fits[-1].fit_results[6]))
if np.isnan(mag_blend_fit):
mag_blend_fit = "null"
microloutputs.create_the_fake_telescopes(
current_event.fits[0], current_event.fits[0].fit_results[:-1])
model_telescope = current_event.fits[0].event.fake_telescopes[0]
pyLIMA_parameters = Model_parallax.compute_pyLIMA_parameters(
current_event.fits[0].fit_results[:-1])
flux_model = current_event.fits[0].model.compute_the_microlensing_model(
model_telescope, pyLIMA_parameters)[0]
magnitude = microltoolbox.flux_to_magnitude(flux_model)
model_telescope.lightcurve_magnitude[:, 1] = magnitude
mask = ~np.isnan(magnitude)
model_telescope.lightcurve_magnitude = model_telescope.lightcurve_magnitude[
mask]
try:
to_return = [
np.around(t0_fit, 3),
np.around(u0_fit, 5),
np.around(tE_fit, 3),
np.around(piEN_fit, 5),
np.around(piEE_fit, 5),
np.around(mag_source_fit, 3),
np.around(mag_blend_fit, 3),
np.around(mag_baseline_fit, 3),
current_event.fits[-1].fit_covariance, model_telescope
]
except:
to_return = [
np.around(t0_fit, 3),
np.around(u0_fit, 5),
np.around(tE_fit, 3),
np.around(piEN_fit, 5),
np.around(piEE_fit, 5),
np.around(mag_source_fit, 3), mag_blend_fit,
np.around(mag_baseline_fit, 3),
current_event.fits[-1].fit_covariance, model_telescope
]
return to_return
def plot_model(figure_axe,
fit,
parameters=None,
telescope_index=0,
model_color='b',
model_alpha=1.0,
label=True,
bokeh_plot=None):
"""Plot the microlensing model corresponding to parameters, time and with the same properties as telescope, the best fit and first telescope.
:param object fit: a fit object. See the microlfits for more details.
:param matplotlib_axes figure_axe: a matplotlib axes correpsonding to the figure.
:param list parameters : a list of model parameters.
:param np.array time : the time stamps for the model.
:param int telescope_index : which telescope you want a model (depends on filter, location etc...)
:param str model_color : the model color
:param float model_alpha : the intensity of the model line
"""
if parameters is not None:
pyLIMA_parameters = fit.model.compute_pyLIMA_parameters(parameters)
else:
pyLIMA_parameters = fit.model.compute_pyLIMA_parameters(
fit.fit_results)
telescope = fit.event.fake_telescopes[telescope_index]
time = telescope.lightcurve_flux[:, 0]
flux_model = fit.model.compute_the_microlensing_model(
telescope, pyLIMA_parameters)[0]
magnitude = microltoolbox.flux_to_magnitude(flux_model)
if label == True:
figure_axe.plot(time,
magnitude,
c=model_color,
label=fit.model.model_type,
lw=3,
alpha=model_alpha)
else:
figure_axe.plot(time,
magnitude,
c=model_color,
lw=3,
alpha=model_alpha)
if telescope_index == 0:
figure_axe.set_ylim([
min(magnitude) - plot_lightcurve_windows,
max(magnitude) + plot_lightcurve_windows
])
figure_axe.set_xlim([
pyLIMA_parameters.to - 2 * np.abs(pyLIMA_parameters.tE),
pyLIMA_parameters.to + 2 * np.abs(pyLIMA_parameters.tE)
])
figure_axe.invert_yaxis()
if bokeh_plot is not None:
if label == True:
bokeh_plot.line(time,
magnitude,
color=model_color,
alpha=model_alpha,
legend=fit.model.model_type)
else:
bokeh_plot.line(time,
magnitude,
color=model_color,
alpha=model_alpha)
def plot_mulens(n_clicks, object_data):
""" Fit for microlensing event
TODO: implement a fit using pyLIMA
"""
if n_clicks is not None:
pdf_ = pd.read_json(object_data)
cols = [
'i:jd', 'i:magpsf', 'i:sigmapsf', 'i:fid', 'i:ra', 'i:dec',
'i:magnr', 'i:sigmagnr', 'i:magzpsci', 'i:isdiffpos', 'i:objectId'
]
pdf = pdf_.loc[:, cols]
pdf['i:fid'] = pdf['i:fid'].astype(str)
pdf = pdf.sort_values('i:jd', ascending=False)
mag_dc, err_dc = np.transpose([
dc_mag(*args) for args in zip(
pdf['i:fid'].astype(int).values, pdf['i:magpsf'].astype(
float).values, pdf['i:sigmapsf'].astype(float).values,
pdf['i:magnr'].astype(float).values, pdf['i:sigmagnr'].astype(
float).values, pdf['i:magzpsci'].astype(
float).values, pdf['i:isdiffpos'].values)
])
current_event = event.Event()
current_event.name = pdf['i:objectId'].values[0]
current_event.ra = pdf['i:ra'].values[0]
current_event.dec = pdf['i:dec'].values[0]
filts = {'1': 'g', '2': 'r'}
for fid in np.unique(pdf['i:fid'].values):
mask = pdf['i:fid'].values == fid
telescope = telescopes.Telescope(
name='ztf_{}'.format(filts[fid]),
camera_filter=format(filts[fid]),
light_curve_magnitude=np.transpose([
pdf['i:jd'].values[mask], pdf['i:magpsf'].values[mask],
pdf['i:sigmapsf'].values[mask]
]),
light_curve_magnitude_dictionnary={
'time': 0,
'mag': 1,
'err_mag': 2
})
current_event.telescopes.append(telescope)
# Le modele le plus simple
mulens_model = microlmodels.create_model('PSPL', current_event)
current_event.fit(mulens_model, 'DE')
# 4 parameters
dof = len(pdf) - 4 - 1
results = current_event.fits[0]
normalised_lightcurves = microltoolbox.align_the_data_to_the_reference_telescope(
results, 0, results.fit_results)
# Model
create_the_fake_telescopes(results, results.fit_results)
telescope_ = results.event.fake_telescopes[0]
flux_model = mulens_model.compute_the_microlensing_model(
telescope_,
results.model.compute_pyLIMA_parameters(results.fit_results))[0]
time = telescope_.lightcurve_flux[:, 0]
magnitude = microltoolbox.flux_to_magnitude(flux_model)
if '1' in np.unique(pdf['i:fid'].values):
plot_filt1 = {
'x': [
convert_jd(t, to='iso')
for t in normalised_lightcurves[0][:, 0]
],
'y':
normalised_lightcurves[0][:, 1],
'error_y': {
'type': 'data',
'array': normalised_lightcurves[0][:, 2],
'visible': True,
'color': '#1f77b4'
},
'mode':
'markers',
'name':
'g band',
'text': [
convert_jd(t, to='iso')
for t in normalised_lightcurves[0][:, 0]
],
'marker': {
'size': 12,
'color': '#1f77b4',
'symbol': 'o'
}
}
else:
plot_filt1 = {}
if '2' in np.unique(pdf['i:fid'].values):
plot_filt2 = {
'x': [
convert_jd(t, to='iso')
for t in normalised_lightcurves[1][:, 0]
],
'y':
normalised_lightcurves[1][:, 1],
'error_y': {
'type': 'data',
'array': normalised_lightcurves[1][:, 2],
'visible': True,
'color': '#ff7f0e'
},
'mode':
'markers',
'name':
'r band',
'text': [
convert_jd(t, to='iso')
for t in normalised_lightcurves[1][:, 0]
],
'marker': {
'size': 12,
'color': '#ff7f0e',
'symbol': 'o'
}
}
else:
plot_filt2 = {}
fit_filt = {
'x': [convert_jd(float(t), to='iso') for t in time],
'y': magnitude,
'mode': 'lines',
'name': 'fit',
'showlegend': False,
'line': {
'color': '#7f7f7f',
}
}
figure = {
'data': [fit_filt, plot_filt1, plot_filt2],
"layout": layout_mulens
}
# fitted parameters
names = results.model.model_dictionnary
params = results.fit_results
err = np.diag(np.sqrt(results.fit_covariance))
mulens_params = """
```python
# Fitted parameters
t0: {} +/- {} (jd)
tE: {} +/- {} (days)
u0: {} +/- {}
chi2/dof: {}
```
---
""".format(params[names['to']], err[names['to']], params[names['tE']],
err[names['tE']], params[names['uo']], err[names['uo']],
params[-1] / dof)
return figure, mulens_params
mulens_params = """
```python
# Fitted parameters
t0: None
tE: None
u0: None
chi2: None
```
---
"""
return {'data': [], "layout": layout_mulens}, mulens_params
