pint.templates.lceprimitives.LCELorentzian

class pint.templates.lceprimitives.LCELorentzian(**kwargs)[source]

Bases: LCEWrappedFunction, LCLorentzian

Represent a (wrapped) Lorentzian peak.

Parameters:

Width – the width paramater of the wrapped Cauchy distribution, namely HWHM*2PI for narrow distributions
Location – the center of the peak in phase

Generally, class-specific setup work is performed in init. Here, init is called and certain guaranteed default members are established.

Methods

`approx_derivative`(phases[, log10_ens, ...])
`approx_gradient`(phases[, log10_ens, eps])
`approx_hessian`(phases[, log10_ens, eps])
`base_derivative`(phases[, log10_ens, index, ...])
`base_func`(phases[, log10_ens, index])
`base_grad`(phases[, log10_ens, index])
`base_grad_deriv`(phases[, log10_ens, index])
`base_hess`(phases[, log10_ens, index])
`base_int`(phases[, log10_ens, index])
`cdf`(x[, log10_ens])
`center_gauss_prior`([enable])	[Convenience] Set gauss mode to current params.
`check_bounds`([p])
`check_gradient`([atol, rtol, quiet])
`closest_to_peak`(phases)	Return the minimum distance between a member of the array of phases and the position of the mode of the primitive.
`copy`()
`derivative`(phases[, log10_ens, order])	Return the phase gradient (dprim/dphi) at a vector of phases.
`dict_string`()	Return a string to express the object as a dictionary that can be easily instantiated using its keys.
`enable_gauss_prior`([enable])	[Convenience] Turn on gaussian prior.
`eval_string`()	Return a string that can be evaluated to instantiate a nearly- identical object.
`fwhm`()	Return the full-width at half-maximum of the light curve model.
`get_bounds`([free])
`get_errors`([free])
`get_fixed_energy_version`([log10_en])	Return the version of this primitive with parameters appropriate for the given energy.
`get_free_mask`()	Return a mask with True if parameters are free, else False.
`get_gauss_prior_parameters`()
`get_location`([error])
`get_norm`([error])
`get_parameter_names`([free])
`get_parameters`([free])
`get_width`([error, hwhm, right])	Return the width of the distribution.
`gradient`(phases[, log10_ens, free])	Return the gradient of the primitives wrt the parameters.
`gradient_derivative`(phases[, log10_ens, free])	Return the gradient evaluated at a vector of phases.
`hessian`(phases[, log10_ens, free])	Return the hessian.
`hwhm`([right])	Return the half-width at half-maximum of the light curve model.
`init`()
`integrate`(x1, x2[, log10_ens])	Base implemention with scipy quad.
`is_energy_dependent`()
`is_two_sided`()	True if primitive is asymmetric.
`num_parameters`([free])
`parse_kwargs`(kwargs)
`random`(n)	Default is accept/reject.
`sanity_checks`([eps])	A few checks on normalization, integration, etc.
`set_errors`(errs)
`set_location`(loc)
`set_parameters`(p[, free])

__call__(phases, log10_ens=3): Return wrapped template + DC component corresponding to truncation.

center_gauss_prior(enable=False): [Convenience] Set gauss mode to current params.

closest_to_peak(phases): Return the minimum distance between a member of the array of phases and the position of the mode of the primitive.

derivative(phases, log10_ens=3, order=1)

Return the phase gradient (dprim/dphi) at a vector of phases.

order: order of derivative (1=1st derivative, etc.)

output : a len(phases) ndarray, dprim/dphi

NB this will generally be opposite in sign to the gradient of the location parameter.

dict_string(): Return a string to express the object as a dictionary that can be easily instantiated using its keys.

enable_gauss_prior(enable=True): [Convenience] Turn on gaussian prior.

eval_string(): Return a string that can be evaluated to instantiate a nearly- identical object.

fwhm(): Return the full-width at half-maximum of the light curve model.

get_fixed_energy_version(log10_en=3): Return the version of this primitive with parameters appropriate for the given energy. I think this will be unnecessary once everything is signature compatible.

get_free_mask(): Return a mask with True if parameters are free, else False.

get_width(error=False, hwhm=False, right=False)

Return the width of the distribution.

Parameters:

error (bool) – if True, return tuple with value and error
hwhm (bool) – if True, scale width to be HWHM
right (bool) – if True, return “right” component, else “left”. There is no distinction for symmetric dists.

gradient(phases, log10_ens=3, free=False): Return the gradient of the primitives wrt the parameters.

gradient_derivative(phases, log10_ens=3, free=False)

Return the gradient evaluated at a vector of phases.

outputa num_parameter x len(phases) ndarray,: the num_parameter-dim gradient at each phase

hessian(phases, log10_ens=3, free=False)

Return the hessian.

For the linear model, this is a block matrix where the upper-left corner is the original hessian, the upper-right/lower-left corners are hessians weighted by the energy, and the lower-right by e^2.

hwhm(right=False): Return the half-width at half-maximum of the light curve model.

integrate(x1, x2, log10_ens=3): Base implemention with scipy quad.

is_two_sided(): True if primitive is asymmetric. Default is False, two-sided child classes should override.

random(n): Default is accept/reject.

sanity_checks(eps=1e-06): A few checks on normalization, integration, etc.