mangadap.util.lineprofiles module

Implements a set of line profile parameterizations.

class mangadap.util.lineprofiles.FFTGaussianLSF(p=None, dx=None, pixel=True)[source]

Bases: GaussianLSF

Define a Gaussian line profile by first constructing the analytic FFT of the profile and then returning the inverse real FFT. See ppxf_util.emline by M. Cappellari. The sampling must be uniform in \(x\).

Parameters:

p (array-like) – (Optional) Input parameters ordered as the total integral of the profile, the profile center, and the profile standard deviation. Assumed to be (1.0, 0.0, 1.0) by default.
dx (float) – (Optional) Sampling width. Default is 1.
pixel (bool) – (Optional) Flag to produce profile integrated over the sampling width.

p

Most recently used parameters

Type:: numpy.ndarray

dx

Assumed sampling.

Type:: float

pixel

Flag to produce profile integrated over the sampling width.

Type:: bool

Raises:: ValueError – Raised if the provided parameter vector is not 3 elements long.

sample(x)[source]

Sample the profile.

Warning

Does not check if the provided \(x\) values are sampled at dx.

Parameters:: x (array-like) – Independent variable.

class mangadap.util.lineprofiles.GaussianLSF(p=None)[source]

Bases: object

Define a Gaussian line profile, sampled over the width of the sampling step, parameterized by its integral (\(F\)), center (\(\mu\)), and standard deviation (\(\sigma\)). I.e:

\[\mathcal{N}(x|f,\mu,\sigma) = \frac{f}{\sqrt{2\pi}\sigma} \exp\left(\frac{-\Delta^2}{2\sigma^2}\right)\]

where \(\Delta = x-\mu\). The coordinate vector \(x\) does not need to be uniformly sampled.

Parameters:: p (array-like) – (Optional) Input parameters ordered as the total integral of the profile, the profile center, and the profile standard deviation. Assumed to be (1.0, 0.0, 1.0) by default.

p

Most recently used parameters

Type:: numpy.ndarray

Raises:: ValueError – Raised if the provided parameter vector is not 3 elements long.

static npar()[source]

parameters_from_moments(mom0, mom1, mom2)[source]: Provided the 0th, 1st, and 2nd moments, produce a set of parameters for the profile.

sample(x)[source]

Sample the profile.

Parameters:: x (array-like) – Independent variable.

set_par(p)[source]

Set the internal parameters to the provided set.

Parameters:: p (array-like) – LSF parameters.
Raises:: ValueError – Raised if the provided parameter vector is not 3 elements long.

class mangadap.util.lineprofiles.GaussianLineProfile(zmom=1.0, mean=0.0, sigma=1.0, **kwargs)[source]

Bases: FittableModel

Define a Gaussian line profile as parameterized by its zeroth moment, mean, and standard deviation:

\[\mathcal{N}(x|f,\mu,\sigma) = \frac{f}{\sqrt{2\pi}\sigma} \exp\left(\frac{-(x-\mu)^2}{2\sigma^2}\right)\]

The base class is astropy.modeling.FittableModel, which facilitates its use in combining multiple components and other models in the astropy.modeling suite.

_abc_impl = <_abc._abc_data object>

_fix_inputs(right)

_is_dynamic = False

_parameters_ = {'mean': Parameter('mean', value=0.0), 'sigma': Parameter('sigma', value=1.0), 'zmom': Parameter('zmom', value=1.0)}

static evaluate(x, zmom, mean, sigma)[source]: Evaluate the model on some input variables.

static fit_deriv(x, zmom, mean, sigma)[source]

static fix_flux()[source]

static fix_mean()[source]

static fix_stddev()[source]

static flux(zmom, mean, sigma)[source]

static flux_err(zmom, mean, sigma, zmome, meane, sigmae)[source]

inputs = ('x',)

static integral(zmom, mean, sigma)[source]

mean = Parameter('mean', value=0.0)

static mean_indx()[source]

static moment(order, zmom, mean, sigma)[source]

static moment_err(order, zmom, mean, sigma, zmome, meane, sigmae)[source]

outputs = ('y',)

param_names = ('zmom', 'mean', 'sigma')

Names of the parameters that describe models of this type.

The parameters in this tuple are in the same order they should be passed in when initializing a model of a specific type. Some types of models, such as polynomial models, have a different number of parameters depending on some other property of the model, such as the degree.

When defining a custom model class the value of this attribute is automatically set by the ~astropy.modeling.Parameter attributes defined in the class body.

static scale_flux(zmom, mean, sigma, fac)[source]

static scale_stddev(zmom, mean, sigma, fac)[source]

static shift_mean(zmom, mean, sigma, shift)[source]

sigma = Parameter('sigma', value=1.0)

zmom = Parameter('zmom', value=1.0)

class mangadap.util.lineprofiles.IntegratedGaussianLSF(p=None, dx=None)[source]

Bases: GaussianLSF

Define a Gaussian line profile, integrated over the width of the sampling step, parameterized by its integral (\(F\)), center (\(\mu\)), and standard deviation (\(\sigma\)). I.e:

\[\mathcal{N}(x|F,\mu,\sigma) = \frac{F}{2} \left[ {\rm erf}\left(\frac{\Delta+\delta_x/2}{\sqrt{2}\sigma}\right) - {\rm erf}\left(\frac{\Delta-\delta_x/2}{\sqrt{2}\sigma}\right)\right]\]

where \({\rm erf}(x)\) is the error function, \(\Delta = x-\mu\), and \(\delta_x\) is the sampling step. The sampling must be uniform in \(x\).

Parameters:

p (array-like) – (Optional) Input parameters ordered as the total integral of the profile, the profile center, and the profile standard deviation. Assumed to be (1.0, 0.0, 1.0) by default.
dx (float) – (Optional) Sampling width. Default is 1.

p

Most recently used parameters

Type:: numpy.ndarray

dx

Assumed sampling.

Type:: float

Raises:: ValueError – Raised if the provided parameter vector is not 3 elements long.

sample(x)[source]

Sample the profile.

Warning

Does not check if the provided \(x\) values are sampled at dx.

Parameters:: x (array-like) – Independent variable.

class mangadap.util.lineprofiles.NCompLineProfile(ncomp, par=None, err=None, profile=<class 'mangadap.util.lineprofiles.GaussianLineProfile'> Name: GaussianLineProfile N_inputs: <property object> N_outputs: <property object> Fittable parameters: ('zmom', 'mean', 'sigma'))[source]

Bases: object

Construct a single line profile from many components with the same profile parameterization.

_mom1_integrand(x)[source]

_mom2_integrand(x)[source]

_quick_sample(x)[source]: Sample without providing/checking new input parameters.

assign_err(err)[source]

assign_par(par)[source]

fix_flux()[source]

fix_mean()[source]

fix_stddev()[source]

flux(par=None)[source]

flux_err(par=None, err=None)[source]

mean_indx()[source]

moment(order=0, par=None)[source]: Todo

impose some reasonable limits for the integrals

moment_err(order=0, par=None, err=None)[source]: Todo

impose some reasonable limits for the integrals

sample(x, par=None)[source]

scale_flux(fac)[source]

scale_stddev(fac)[source]

set_flux(flx)[source]

set_mean(mean)[source]

set_stddev(stddev)[source]

shift_mean(shift)[source]