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 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 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 LM_plot_residuals(fit, figure_axe):
"""Plot the residuals 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.
"""
MARKER_SYMBOLS.reset()
for telescope in fit.event.telescopes:
time = telescope.lightcurve_flux[:, 0]
flux = telescope.lightcurve_flux[:, 1]
error_flux = telescope.lightcurve_flux[:, 2]
err_mag = microltoolbox.error_flux_to_error_magnitude(error_flux, flux)
pyLIMA_parameters = fit.model.compute_pyLIMA_parameters(fit.fit_results)
flux_model = fit.model.compute_the_microlensing_model(telescope, pyLIMA_parameters)[0]
residuals = 2.5 * np.log10(flux_model / flux)
figure_axe.errorbar(time, residuals, yerr=err_mag, ls='None', markersize=7.5,
marker=str(MARKER_SYMBOLS.next()),capsize=0.0)
figure_axe.set_ylim([-plot_residuals_windows, plot_residuals_windows])
figure_axe.invert_yaxis()
def LM_plot_residuals(fit, figure_axe):
"""Plot the residuals 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.
"""
MARKER_SYMBOLS.reset()
for telescope in fit.event.telescopes:
time = telescope.lightcurve_flux[:, 0]
flux = telescope.lightcurve_flux[:, 1]
error_flux = telescope.lightcurve_flux[:, 2]
err_mag = microltoolbox.error_flux_to_error_magnitude(error_flux, flux)
pyLIMA_parameters = fit.model.compute_pyLIMA_parameters(fit.fit_results)
flux_model = fit.model.compute_the_microlensing_model(telescope, pyLIMA_parameters)[0]
residuals = 2.5 * np.log10(flux_model / flux)
figure_axe.errorbar(time, residuals, yerr=err_mag, ls='None', markersize=7.5,
marker=str(MARKER_SYMBOLS.next()),capsize=0.0)
figure_axe.set_ylim([-plot_residuals_windows, plot_residuals_windows])
figure_axe.invert_yaxis()
def MCMC_plot_residuals(fit, parameters, ax):
"""Plot the data residual on the appropriate figure.
:param fit: a fit object. See the microlfits for more details.
:param parameters: the parameters [list] of the model you want to plot.
:param ax: the matplotlib axes where you plot the data
"""
MARKER_SYMBOLS.reset()
for telescope in fit.event.telescopes:
time = telescope.lightcurve_flux[:, 0]
flux = telescope.lightcurve_flux[:, 1]
error_flux = telescope.lightcurve_flux[:, 2]
err_mag = microltoolbox.error_flux_to_error_magnitude(error_flux, flux)
pyLIMA_parameters = fit.model.compute_pyLIMA_parameters(parameters)
flux_model = fit.model.compute_the_microlensing_model(telescope, pyLIMA_parameters)[0]
residuals = 2.5 * np.log10(flux_model / flux)
ax.errorbar(time, residuals, yerr=err_mag, ls='None', marker=str(MARKER_SYMBOLS.next()))
ax.set_ylim([-plot_residuals_windows, plot_residuals_windows])
ax.invert_yaxis()
ax.xaxis.get_major_ticks()[0].draw = lambda *args: None
ax.ticklabel_format(useOffset=False, style='plain')
def MCMC_plot_residuals(fit, parameters, ax):
"""Plot the data residual on the appropriate figure.
:param fit: a fit object. See the microlfits for more details.
:param parameters: the parameters [list] of the model you want to plot.
:param ax: the matplotlib axes where you plot the data
"""
MARKER_SYMBOLS.reset()
for telescope in fit.event.telescopes:
time = telescope.lightcurve_flux[:, 0]
flux = telescope.lightcurve_flux[:, 1]
error_flux = telescope.lightcurve_flux[:, 2]
err_mag = microltoolbox.error_flux_to_error_magnitude(error_flux, flux)
pyLIMA_parameters = fit.model.compute_pyLIMA_parameters(parameters)
flux_model = fit.model.compute_the_microlensing_model(telescope, pyLIMA_parameters)[0]
residuals = 2.5 * np.log10(flux_model / flux)
ax.errorbar(time, residuals, yerr=err_mag, ls='None', marker=str(MARKER_SYMBOLS.next()))
ax.set_ylim([-plot_residuals_windows, plot_residuals_windows])
ax.invert_yaxis()
ax.xaxis.get_major_ticks()[0].draw = lambda *args: None
ax.ticklabel_format(useOffset=False, style='plain')
