WDmodel.viz module¶

Routines to visualize the DA White Dwarf model atmosphere fit

WDmodel.viz.plot_mcmc_line_fit(spec, linedata, model, cont_model, draws, balmer=None)[source]¶

Plot a comparison of the normalized hydrogen Balmer lines of the spectrum and model

Note that we fit the full spectrum, not just the lines. The lines are extracted using a coarse continuum fit in WDmodel.fit.pre_process_spectrum(). This fit is purely cosmetic and in no way contributes to the likelihood. It’s particularly useful to detect small velocity offsets or wavelength calibration errors.

Parameters:

spec (numpy.recarray) – The spectrum. Must have dtype=[('wave', '<f8'), ('flux', '<f8'), ('flux_err', '<f8')]
linedata (numpy.recarray) – The observations of the spectrum corresponding to the hydrogen Balmer lines. Must have dtype=[('wave', '<f8'), ('flux', '<f8'), ('flux_err', '<f8'), ('line_mask', 'i4'), ('line_ind', 'i4')]
model (WDmodel.WDmodel.WDmodel instance) – The DA White Dwarf SED model generator
cont_model (numpy.recarray) – The continuuum model. Must have the same structure as spec Produced by WDmodel.fit.pre_process_spectrum()
draws (array-like) – produced by plot_mcmc_spectrum_fit() - see notes for content.
balmer (array-like, optional) – list of Balmer lines to plot - elements must be in range [1, 6] These correspond to the lines defined in WDmodel.WDmodel.WDmodel._lines. Default is range(1, 7)

Returns:

fig (matplotlib.figure.Figure instance) – The output figure containing the line profile plot
fig2 (matplotlib.figure.Figure instance) – The output figure containing histograms of the line residuals

See also

WDmodel.viz.plot_mcmc_spectrum_fit()

WDmodel.viz.plot_mcmc_spectrum_fit(spec, objname, specfile, scale_factor, model, covmodel, result, param_names, samples, ndraws=21, everyn=1)[source]¶

Plot the spectrum of the DA White Dwarf and the “best fit” model

The full fit parametrizes the covariance model using a stationary Gaussian process as defined by WDmodel.covariance.WDmodel_CovModel. The posterior function constructed in WDmodel.likelihood.WDmodel_Posterior is evaluated by the sampler in the WDmodel.fit.fit_model() method. The median value is reported as the best-fit value for each of the fit parameters in WDmodel.likelihood.WDmodel_Likelihood.parameter_names.

Parameters:

spec (numpy.recarray) – The spectrum. Must have dtype=[('wave', '<f8'), ('flux', '<f8'), ('flux_err', '<f8')]
objname (str) – object name - used to title plots
outdir (str) – controls where the plot is written out if save=True
specfile (str) – Used in the title, and to set the name of the outfile if save=True
scale_factor (float) – factor by which the flux was scaled for y-axis label
model (WDmodel.WDmodel.WDmodel instance) – The DA White Dwarf SED model generator
covmodel (WDmodel.covariance.WDmodel_CovModel instance) – The parametrized model for the covariance of the spectrum spec
result (dict) – dictionary of parameters with keywords value, fixed, scale, bounds for each. Same format as returned from WDmodel.io.read_params()
param_names (array-like) – Ordered list of free parameter names
samples (array-like) – Samples from the flattened Markov Chain with shape (N, len(param_names))
ndraws (int, optional) – Number of draws to make from the Markov Chain to overplot. Higher numbers provide a better sense of the uncertainty in the model at the cost of speed and a larger, slower to render output plot.
everyn (int, optional) – If the posterior function was evaluated using only every nth observation from the data, this should be specified to visually indicate the observations used.

Returns:

fig (matplotlib.figure.Figure instance) – The output figure
draws (array-like) – The actual draws from the Markov Chain used in fig

Notes

It’s faster to draw samples from the posterior in one location, and pass along the same samples to all the methods in WDmodel.viz.

Consequently, most require draws as an input. This makes all the plots connected, and none will return if an error is thrown here, but this is the correct behavior as all of them are visualizing one aspect of the same fit.

Each element of draws contains

smoothedmod - the model spectrum
wres - the prediction from the Gaussian process
wres_err - the diagonal of the covariance matrix for the prediction from the Gaussian process
full_mod - the full model SED, in order to compute the synthetic photometry
out_draw - the dictionary of model parameters from this draw. Same format as result.

WDmodel.viz.plot_mcmc_spectrum_nogp_fit(spec, objname, specfile, scale_factor, cont_model, draws, covtype=u'Matern32', everyn=1)[source]¶

Plot the spectrum of the DA White Dwarf and the “best fit” model without the Gaussian process

Unlike plot_mcmc_spectrum_fit() this version does not apply the prediction from the Gaussian process to the spectrum model to match the observed spectrum. This visualization is useful to indicate if the Gaussian process - i.e. the kernel choice covtype used to parametrize the covariance is - is appropriate.

Parameters:	spec (`numpy.recarray`) – The spectrum. Must have `dtype=[('wave', '<f8'), ('flux', '<f8'), ('flux_err', '<f8')]` objname (str) – object name - used to title plots outdir (str) – controls where the plot is written out if `save=True` specfile (str) – Used in the title, and to set the name of the outfile if `save=True` scale_factor (float) – factor by which the flux was scaled for y-axis label cont_model (`numpy.recarray`) – The continuuum model. Must have the same structure as `spec` Produced by `WDmodel.fit.pre_process_spectrum()` draws (array-like) – produced by `plot_mcmc_spectrum_fit()` - see notes for content. covtype (`{'Matern32', 'SHO', 'Exp', 'White'}`) – stationary kernel type used to parametrize the covariance in `WDmodel.covariance.WDmodel_CovModel` everyn (int, optional) – If the posterior function was evaluated using only every nth observation from the data, this should be specified to visually indicate the observations used.
Returns:	fig – The output figure
Return type:	`matplotlib.figure.Figure` instance