pyLIMA.microloutputs module¶
Created on Mon Nov 9 16:38:14 2015
@author: ebachelet
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pyLIMA.microloutputs.
LM_fit_errors
(fit)[source]¶ Estimate the parameters errors from the fit.fit_covariance matrix.
Parameters: fit – a fit object. See the microlfits for more details. Returns: a namedtuple object containing the square roots of parameters variance. Return type: object
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pyLIMA.microloutputs.
LM_outputs
(fit)[source]¶ Standard ‘LM’ and ‘DE’ outputs.
Parameters: fit (object) – a fit object. See the microlfits for more details. Returns: a namedtuple containing the following attributes : fit_parameters : an namedtuple object containing all the fitted parameters
fit_errors : an namedtuple object containing all the fitted parameters errors
fit_correlation_matrix : a numpy array representing the fitted parameters correlation matrix
figure_lightcurve : a two matplotlib figure showing the data and model and the correspoding residuals
Return type: object
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pyLIMA.microloutputs.
LM_parameters_result
(fit)[source]¶ Produce a namedtuple object containing the fitted parameters in the fit.fit_results.
Parameters: - fit – a fit object. See the microlfits for more details.
- fit_parameters – a namedtuple object containing the fitted parameters.
Return type:
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pyLIMA.microloutputs.
LM_plot_align_data
(fit, figure_axe)[source]¶ Plot the aligned data.
Parameters: - fit (object) – a fit object. See the microlfits for more details.
- figure_axe (matplotlib_axes) – a matplotlib axes correpsonding to the figure.
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pyLIMA.microloutputs.
LM_plot_lightcurves
(fit)[source]¶ Plot the aligned datasets and the best fit on the first subplot figure_axes[0] and residuals on the second subplot figure_axes[1].
Parameters: fit (object) – a fit object. See the microlfits for more details. Returns: a figure representing data+model and residuals. Return type: matplotlib_figure
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pyLIMA.microloutputs.
LM_plot_model
(fit, figure_axe)[source]¶ Plot the microlensing model from the fit.
Parameters: - fit (object) – a fit object. See the microlfits for more details.
- figure_axe (matplotlib_axes) – a matplotlib axes correpsonding to the figure.
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pyLIMA.microloutputs.
LM_plot_residuals
(fit, figure_axe)[source]¶ Plot the residuals from the fit.
Parameters: - fit (object) – a fit object. See the microlfits for more details.
- figure_axe (matplotlib_axes) – a matplotlib axes correpsonding to the figure.
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pyLIMA.microloutputs.
MCMC_compute_fs_g
(fit, mcmc_chains)[source]¶ Compute the corresponding source flux fs and blending factor g corresponding to each mcmc chain.
Parameters: - fit – a fit object. See the microlfits for more details.
- mcmc_chains – a numpy array representing the mcmc chains.
Returns: a numpy array containing the corresponding fluxes parameters
Return type: array_type
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pyLIMA.microloutputs.
MCMC_covariance
(mcmc_chains)[source]¶ Estimate the covariance matrix from the mcmc_chains
Parameters: mcmc_chains – a numpy array representing the mcmc chains. :return : a numpy array representing the covariance matrix of your MCMC sampling. :rtype: array_like
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pyLIMA.microloutputs.
MCMC_outputs
(fit)[source]¶ Standard ‘LM’ and ‘DE’ outputs.
Parameters: fit (object) – a fit object. See the microlfits for more details. Returns: a namedtuple containing the following attributes : MCMC_chains : a numpy array containing all the parameters chains + the corresponding objective function.
MCMC_correlations : a numpy array representing the fitted parameters correlation matrix from the MCMC chains
figure_lightcurve : a two matplotlib subplot showing the data and 35 models and the residuals corresponding to the best model.
figure_distributions : a multiple matplotlib subplot representing the parameters distributions (2D slice + histogram)
Return type: object
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pyLIMA.microloutputs.
MCMC_plot_align_data
(fit, parameters, plot_axe)[source]¶ Plot the data on the figure. Telescopes are aligned to the survey telescope (i.e number 0).
Parameters: - fit – a fit object. See the microlfits for more details.
- parameters – the parameters [list] of the model you want to plot.
- plot_axe – the matplotlib axes where you plot the data
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pyLIMA.microloutputs.
MCMC_plot_lightcurves
(fit, mcmc_best)[source]¶ Plot 35 models from the mcmc_best sample. This is made to have 35 models equally spaced in term of objective funtion (~chichi)
Parameters: - fit – a fit object. See the microlfits for more details.
- mcmc_best – a numpy array representing the best (<= 6 sigma) mcmc chains.
Returns: a two matplotlib subplot showing the data and 35 models and the residuals
corresponding to the best model. :rtype: matplotlib_figure
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pyLIMA.microloutputs.
MCMC_plot_model
(fit, reference_telescope, parameters, couleurs, figure_axes, scalar_couleur_map)[source]¶ Plot a model to a given figure, with the color corresponding to the objective function of the model.
Parameters: - fit – a fit object. See the microlfits for more details.
- parameters – the parameters [list] of the model you want to plot.
- 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)
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pyLIMA.microloutputs.
MCMC_plot_parameters_distribution
(fit, mcmc_best)[source]¶ Plot the fit parameters distributions. Only plot the best mcmc_chains are plotted. :param fit: a fit object. See the microlfits for more details. :param mcmc_best: a numpy array representing the best (<= 6 sigma) mcmc chains. :return: a multiple matplotlib subplot representing the parameters distributions (2D slice + histogram) :rtype: matplotlib_figure
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pyLIMA.microloutputs.
MCMC_plot_residuals
(fit, parameters, ax)[source]¶ Plot the data residual on the appropriate figure.
Parameters: - fit – a fit object. See the microlfits for more details.
- parameters – the parameters [list] of the model you want to plot.
- ax – the matplotlib axes where you plot the data
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pyLIMA.microloutputs.
align_telescope_lightcurve
(lightcurve_telescope_flux, model_ghost, model_telescope)[source]¶ Align data to the survey telescope (i.e telescope 0).
Parameters: 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)
Returns: the aligned to survey lightcurve in magnitude Return type: array_like
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pyLIMA.microloutputs.
cov2corr
(covariance_matrix)[source]¶ Covariance matrix to correlation matrix.
Parameters: covariance_matrix (array_like) – a (square) numpy array representing the covariance matrix Returns: a (square) numpy array representing the correlation matrix Return type: array_like
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pyLIMA.microloutputs.
initialize_plot_lightcurve
(fit)[source]¶ Initialize the lightcurve plot.
Parameters: fit (object) – a fit object. See the microlfits for more details. Returns: a matplotlib figure and the corresponding matplotlib axes Return type: matplotlib_figure,matplotlib_axes
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pyLIMA.microloutputs.
initialize_plot_parameters
(fit)[source]¶ Initialize the parameters plot.
Parameters: fit (object) – a fit object. See the microlfits for more details. Returns: a matplotlib figure and the corresponding matplotlib axes. Return type: matplotlib_figure,matplotlib_axes
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pyLIMA.microloutputs.
plot_LM_ML_geometry
(fit)[source]¶ Plot the lensing geometry (i.e source trajectory) and the table of best parameters. :param object fit: a fit object. See the microlfits for more details. :param list best_parameters: a list containing the model you want to plot the trajectory
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pyLIMA.microloutputs.
plot_MCMC_ML_geometry
(fit, best_chains)[source]¶ Plot the lensing geometry (i.e source trajectory) and the table of best parameters. :param object fit: a fit object. See the microlfits for more details. :param list best_parameters: a list containing the model you want to plot the trajectory
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pyLIMA.microloutputs.
statistical_outputs
(fit)[source]¶ Compute statistics to estimate the fit quality
Parameters: fit (object) – a fit object. See the microlfits for more details. Returns: a namedtuple containing the following attributes : fit_parameters : an namedtuple object containing all the fitted parameters
fit_errors : an namedtuple object containing all the fitted parameters errors
fit_correlation_matrix : a numpy array representing the fitted parameters correlation matrix
figure_lightcurve : a two matplotlib figure showing the data and model and the correspoding residuals
Return type: object