pint.templates.lcprimitives.LCGaussian2

class pint.templates.lcprimitives.LCGaussian2(**kwargs)

Bases: LCWrappedFunction

Represent a (wrapped) two-sided Gaussian peak.

Parameters Width1 the standard deviation parameter of the norm dist. Width2 the standard deviation parameter of the norm dist. Location the mode of the distribution

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(x1, x2[, 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])
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 evaluated at a vector of phases.
gradient_derivative(phases[, log10_ens, free])	Return the gradient evaluated at a vector of phases.
hessian(phases[, log10_ens, free])	Return the hessian evaluated at a vector of phases.
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)	Use multinomial technique to return random photons from both components.
sanity_checks([eps])	A few checks on normalization, integration, etc.
set_errors(errs)
set_location(loc)
set_parameters(p[, free])

is_two_sided()

True if primitive is asymmetric. Default is False, two-sided child classes should override.

hwhm(right=False)

Return the half-width at half-maximum of the light curve model.

random(n)

Use multinomial technique to return random photons from both components.

__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_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 evaluated at a vector of phases.

outputa num_parameter x len(phases) ndarray,

the num_parameter-dim gradient at each phase

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 evaluated at a vector of phases.

NB that this is restricted to the sub-space for this primitive.

outputa num_parameter x num_parameter x len(phases) ndarray,

the num_parameter-dim^2 hessian at each phase

integrate(x1, x2, log10_ens=3)

Base implemention with scipy quad.

sanity_checks(eps=1e-06)

A few checks on normalization, integration, etc.