The HaloDensityProfile base class

The majority of all profile functions are implemented in this base class. Please see Halo Density Profiles for a general introduction and Tutorials for coding examples.

class halo.profile_base.HaloDensityProfile(allowed_mdefs=None, ignore_params=False, outer_terms=[], **kwargs)

Abstract base class for a halo density profile.

A particular functional form for the density profile can be implemented by inheriting this class. These child classes must set their parameter and option names before calling this constructor, and they must overwrite the density() and setNativeParameters() methods. In practice, a number of other functions should also be overwritten for speed and convenience.

This base class provides a general implementation of outer profile terms, i.e., descriptions of the infalling and 2-halo profile beyond the virial radius. These terms can be added to any derived density profile class without adding new code.

Parameters

allowed_mdefs: list

A list of mass definitions that the setNativeParameters() routine of a derived class can accept. If None, it is assumed that any definition is acceptable. If the user passes a mass definition different from the allowed one(s), the constructor automatically iterates to find the correct parameters.

ignore_params: bool

If True, the constructor does not attempt to set the profile parameters from a given mass and concentration. Instead, the profile is accepted as is. This option should only be set for parameter-free child classes such as spline profiles.

outer_terms: list

A list of OuterTerm objects to be added to the density profile.

Methods

MDelta(z, mdef)	The spherical overdensity mass of a given mass definition.
RDelta(z, mdef)	The spherical overdensity radius of a given mass definition.
RMDelta(z, mdef)	The spherical overdensity radius and mass of a given mass definition.
Rsteepest([search_range])	The radius where the logarithmic slope of the density profile is steepest.
Vmax()	The maximum circular velocity, and the radius where it occurs.
circularVelocity(r)	The circular velocity, \(v_c \equiv \sqrt{GM(<r)/r}\).
cumulativePdf(r[, Rmax, z, mdef])	The cumulative distribution function of the profile.
deltaSigma(r[, interpolate, ...])	The excess surface density at radius r.
deltaSigmaInner(r[, interpolate, ...])	The excess surface density at radius r due to the inner profile.
deltaSigmaOuter(r[, interpolate, ...])	The excess surface density at radius r due to the outer profile.
density(r)	Density as a function of radius.
densityDerivativeLin(r)	The linear derivative of density, \(d \rho / dr\).
densityDerivativeLinInner(r)	The linear derivative of the inner density, \(d \rho_{\rm inner} / dr\).
densityDerivativeLinOuter(r)	The linear derivative of the outer density, \(d \rho_{\rm outer} / dr\).
densityDerivativeLog(r)	The logarithmic derivative of density, \(d \log(\rho) / d \log(r)\).
densityDerivativeLogInner(r)	The logarithmic derivative of the inner density, \(d \log(\rho_{\rm inner}) / d \log(r)\).
densityDerivativeLogOuter(r)	The logarithmic derivative of the outer density, \(d \log(\rho_{\rm outer}) / d \log(r)\).
densityInner(r)	Density of the inner profile as a function of radius.
densityOuter(r)	Density of the outer profile as a function of radius.
enclosedMass(r[, accuracy])	The mass enclosed within radius r.
enclosedMassInner(r[, accuracy])	The mass enclosed within radius r due to the inner profile term.
enclosedMassOuter(r[, accuracy])	The mass enclosed within radius r due to the outer profile term.
fit(r, q, quantity[, q_err, q_cov, method, ...])	Fit the density, mass, surface density, or DeltaSigma profile to a given set of data points.
getParameterArray([mask])	Returns an array of the profile parameters.
setNativeParameters(M, c, z, mdef, **kwargs)	Determine the native profile parameters from mass and concentration.
setParameterArray(pars[, mask])	Set the profile parameters from an array.
surfaceDensity(r[, interpolate, accuracy, ...])	The projected surface density at radius r.
surfaceDensityInner(r[, interpolate, ...])	The projected surface density at radius r due to the inner profile.
surfaceDensityOuter(r[, interpolate, ...])	The projected surface density at radius r due to the outer profile.
update()	Update the profile object after a change in parameters or cosmology.
updateR200m()	Update the internally stored R200m after a parameter change.

abstract setNativeParameters(M, c, z, mdef, **kwargs)

Determine the native profile parameters from mass and concentration.

Abstract function which must be overwritten by child classes.

Parameters

M: float

Spherical overdensity mass in \(M_{\odot}/h\).

c: float

The concentration, \(c = R / r_{\rm s}\), corresponding to the given halo mass and mass definition.

z: float

Redshift

mdef: str

The mass definition in which M and c are given. See Halo Mass Definitions for details.

kwargs: kwargs

Parameters passed to the constructor of the child class.

getParameterArray(mask=None)

Returns an array of the profile parameters.

The profile parameters are internally stored in an ordered dictionary. For some applications (e.g., fitting), a simple array is more appropriate.

Parameters

mask: array_like

Optional; must be a numpy array (not a list) of booleans, with the same length as the parameter vector of the profile class (profile.N_par). Only those parameters that correspond to True values are returned.

Returns

par: array_like

A numpy array with the profile’s parameter values.

setParameterArray(pars, mask=None)

Set the profile parameters from an array.

The profile parameters are internally stored in an ordered dictionary. For some applications (e.g., fitting), setting them directly from an array might be necessary. If the profile contains values that depend on the parameters, the profile class must overwrite this function and update according to the new parameters.

Parameters

pars: array_like

The new parameter array.

mask: array_like

Optional; must be a numpy array (not a list) of booleans, with the same length as the parameter vector of the profile class (profile.N_par). If passed, only those parameters that correspond to True values are set (meaning the pars parameter must be shorter than profile.N_par).

update()

Update the profile object after a change in parameters or cosmology.

If the parameters dictionary has been changed (e.g. by the user or during fitting), this function must be called to ensure consistency within the profile object. This involves deleting any pre-computed quantities (e.g., tabulated enclosed masses) and re-computing profile properties that depend on the parameters. The corresponding functions for outer terms are automatically called as well.

updateR200m()

Update the internally stored R200m after a parameter change.

If the profile has the internal option opt['R200m'] option, that does not stay in sync with the other profile parameters if they are changed (either inside or outside the constructor). This function adjusts \(R_{\rm 200m}\), in addition to whatever action is taken in the update function of the super class. Note that this adjustment needs to be done iteratively if any outer profiles rely on \(R_{\rm 200m}\).

density(r)

Density as a function of radius.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

Returns

density: array_like

Density in physical \(M_{\odot} h^2 / {\rm kpc}^3\); has the same dimensions as r.

abstract densityInner(r)

Density of the inner profile as a function of radius.

Abstract function which must be overwritten by child classes.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

Returns

density: array_like

Density in physical \(M_{\odot} h^2 / {\rm kpc}^3\); has the same dimensions as r.

densityOuter(r)

Density of the outer profile as a function of radius.

This function should generally not be overwritten by child classes since it handles the general case of adding up the contributions from all outer profile terms.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

Returns

density: array_like

Density in physical \(M_{\odot} h^2 / {\rm kpc}^3\); has the same dimensions as r.

densityDerivativeLin(r)

The linear derivative of density, \(d \rho / dr\).

This function should generally not be overwritten by child classes since it handles the general case of adding up the contributions from the inner and outer terms.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

Returns

derivative: array_like

The linear derivative in physical \(M_{\odot} h / {\rm kpc}^2\); has the same dimensions as r.

densityDerivativeLinInner(r)

The linear derivative of the inner density, \(d \rho_{\rm inner} / dr\).

This function provides a numerical approximation to the derivative of the inner term, and should be overwritten by child classes if the derivative can be expressed analytically.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

Returns

derivative: array_like

The linear derivative in physical \(M_{\odot} h / {\rm kpc}^2\); has the same dimensions as r.

densityDerivativeLinOuter(r)

The linear derivative of the outer density, \(d \rho_{\rm outer} / dr\).

This function should generally not be overwritten by child classes since it handles the general case of adding up the contributions from all outer profile terms.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

Returns

derivative: array_like

The linear derivative in physical \(M_{\odot} h / {\rm kpc}^2\); has the same dimensions as r.

densityDerivativeLog(r)

The logarithmic derivative of density, \(d \log(\rho) / d \log(r)\).

This function should generally not be overwritten by child classes since it handles the general case of adding up the contributions from the inner and outer profile terms.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

Returns

derivative: array_like

The dimensionless logarithmic derivative; has the same dimensions as r.

densityDerivativeLogInner(r)

The logarithmic derivative of the inner density, \(d \log(\rho_{\rm inner}) / d \log(r)\).

This function evaluates the logarithmic derivative based on the linear derivative. If there is an analytic expression for the logarithmic derivative, child classes should overwrite this function.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

Returns

derivative: array_like

The dimensionless logarithmic derivative; has the same dimensions as r.

densityDerivativeLogOuter(r)

The logarithmic derivative of the outer density, \(d \log(\rho_{\rm outer}) / d \log(r)\).

This function should generally not be overwritten by child classes since it handles the general case of adding up the contributions from outer profile terms.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

Returns

derivative: array_like

The dimensionless logarithmic derivative; has the same dimensions as r.

enclosedMass(r, accuracy=1e-06)

The mass enclosed within radius r.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

accuracy: float

The minimum accuracy of the integration.

Returns

M: array_like

The mass enclosed within radius r, in \(M_{\odot}/h\); has the same dimensions as r.

enclosedMassInner(r, accuracy=1e-06)

The mass enclosed within radius r due to the inner profile term.

This function should be overwritten by child classes if an analytical expression exists.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

accuracy: float

The minimum accuracy of the integration.

Returns

M: array_like

The mass enclosed within radius r, in \(M_{\odot}/h\); has the same dimensions as r.

enclosedMassOuter(r, accuracy=1e-06)

The mass enclosed within radius r due to the outer profile term.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

accuracy: float

The minimum accuracy of the integration.

Returns

M: array_like

The mass enclosed within radius r, in \(M_{\odot}/h\); has the same dimensions as r.

cumulativePdf(r, Rmax=None, z=None, mdef=None)

The cumulative distribution function of the profile.

Some density profiles do not converge to a finite mass at large radius, and the distribution thus needs to be cut off. The user can specify either a radius (in physical kpc/h) where the profile is cut off, or a mass definition and redshift to compute this radius (e.g., the virial radius \(R_{vir}\) at z = 0).

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

Rmax: float

The radius where to cut off the profile in physical kpc/h.

z: float

Redshift

mdef: str

The radius definition for the cut-off radius. See Halo Mass Definitions for details.

Returns

pdf: array_like

The probability for mass to lie within radius r; has the same dimensions as r.

surfaceDensity(r, interpolate=True, accuracy=0.0001, max_r_interpolate=100000000.0, max_r_integrate=1e+20)

The projected surface density at radius r.

The surface density is computed by projecting the 3D density along the line of sight,

\[\Sigma(R) = 2 \int_R^{\infty} \frac{r \rho(r)}{\sqrt{r^2-R^2}} dr\]

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

interpolate: bool

Use an interpolation table for density during the integration. This should make the evaluation somewhat faster, depending on how large the radius array is.

accuracy: float

The minimum accuracy of the integration.

max_r_interpolate: float

The maximum radius in physical kpc/h to which the density profile is integrated when using interpolating density.

max_r_integrate: float

The maximum radius in physical kpc/h to which the density profile is integrated when using exact densities.

Returns

Sigma: array_like

The surface density at radius r, in physical \(M_{\odot} h/{\rm kpc}^2\); has the same dimensions as r.

surfaceDensityInner(r, interpolate=True, accuracy=0.0001, max_r_interpolate=100000000.0, max_r_integrate=1e+20)

The projected surface density at radius r due to the inner profile.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

interpolate: bool

Use an interpolation table for density during the integration. This should make the evaluation somewhat faster, depending on how large the radius array is.

accuracy: float

The minimum accuracy of the integration.

max_r_interpolate: float

The maximum radius in physical kpc/h to which the density profile is integrated when using interpolating density.

max_r_integrate: float

The maximum radius in physical kpc/h to which the density profile is integrated when using exact densities.

Returns

Sigma: array_like

The surface density at radius r, in physical \(M_{\odot} h/{\rm kpc}^2\); has the same dimensions as r.

surfaceDensityOuter(r, interpolate=True, accuracy=0.0001, max_r_interpolate=100000000.0, max_r_integrate=1e+20)

The projected surface density at radius r due to the outer profile.

This function checks whether there are explicit expressions for the surface density of the outer profile terms available, and uses them if possible. Note that there are some outer terms whose surface density integrates to infinity, such as the mean density of the universe which is constant to infinitely large radii.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

interpolate: bool

Use an interpolation table for density during the integration. This should make the evaluation somewhat faster, depending on how large the radius array is.

accuracy: float

The minimum accuracy of the integration.

max_r_interpolate: float

The maximum radius in physical kpc/h to which the density profile is integrated when using interpolating density.

max_r_integrate: float

The maximum radius in physical kpc/h to which the density profile is integrated when using exact densities.

Returns

Sigma: array_like

The surface density at radius r, in physical \(M_{\odot} h/{\rm kpc}^2\); has the same dimensions as r.

deltaSigma(r, interpolate=True, interpolate_surface_density=True, accuracy=0.0001, min_r_interpolate=1e-06, max_r_interpolate=100000000.0, max_r_integrate=1e+20)

The excess surface density at radius r.

This quantity is useful in weak lensing studies, and is defined as \(\Delta\Sigma(R) = \Sigma(<R)-\Sigma(R)\) where \(\Sigma(<R)\) is the averaged surface density within R weighted by area,

\[\Delta\Sigma(R) = \frac{1}{\pi R^2} \int_0^{R} 2 \pi r \Sigma(r) dr - \Sigma(R)\]

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

interpolate: bool

Use an interpolation table for the surface density during the integration. This can speed up the evaluation significantly, as the surface density can be expensive to evaluate.

interpolate_surface_density: bool

Use an interpolation table for density during the computation of the surface density. This should make the evaluation somewhat faster, but can fail for some density terms which are negative at particular radii.

accuracy: float

The minimum accuracy of the integration (used both to compute the surface density and average it to get DeltaSigma).

min_r_interpolate: float

The minimum radius in physical kpc/h from which the surface density profile is averaged.

max_r_interpolate: float

The maximum radius in physical kpc/h to which the density profile is integrated when using interpolating density.

max_r_integrate: float

The maximum radius in physical kpc/h to which the density profile is integrated when using exact densities.

Returns

DeltaSigma: array_like

The excess surface density at radius r, in physical \(M_{\odot} h/{\rm kpc}^2\); has the same dimensions as r.

deltaSigmaInner(r, interpolate=True, interpolate_surface_density=True, accuracy=0.0001, min_r_interpolate=1e-06, max_r_interpolate=100000000.0, max_r_integrate=1e+20)

The excess surface density at radius r due to the inner profile.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

interpolate: bool

Use an interpolation table for the surface density during the integration. This can speed up the evaluation significantly, as the surface density can be expensive to evaluate.

interpolate_surface_density: bool

Use an interpolation table for density during the computation of the surface density. This should make the evaluation somewhat faster, but can fail for some density terms which are negative at particular radii.

accuracy: float

The minimum accuracy of the integration (used both to compute the surface density and average it to get DeltaSigma).

min_r_interpolate: float

The minimum radius in physical kpc/h from which the surface density profile is averaged.

max_r_interpolate: float

The maximum radius in physical kpc/h to which the density profile is integrated when using interpolating density.

max_r_integrate: float

The maximum radius in physical kpc/h to which the density profile is integrated when using exact densities.

Returns

DeltaSigma: array_like

The excess surface density at radius r, in physical \(M_{\odot} h/{\rm kpc}^2\); has the same dimensions as r.

deltaSigmaOuter(r, interpolate=True, interpolate_surface_density=True, accuracy=0.0001, min_r_interpolate=1e-06, max_r_interpolate=100000000.0, max_r_integrate=1e+20)

The excess surface density at radius r due to the outer profile.

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

interpolate: bool

Use an interpolation table for the surface density during the integration. This can speed up the evaluation significantly, as the surface density can be expensive to evaluate.

interpolate_surface_density: bool

Use an interpolation table for density during the computation of the surface density. This should make the evaluation somewhat faster, but can fail for some density terms which are negative at particular radii.

accuracy: float

The minimum accuracy of the integration (used both to compute the surface density and average it to get DeltaSigma).

min_r_interpolate: float

The minimum radius in physical kpc/h from which the surface density profile is averaged.

max_r_interpolate: float

The maximum radius in physical kpc/h to which the density profile is integrated when using interpolating density.

max_r_integrate: float

The maximum radius in physical kpc/h to which the density profile is integrated when using exact densities.

Returns

DeltaSigma: array_like

The excess surface density at radius r, in physical \(M_{\odot} h/{\rm kpc}^2\); has the same dimensions as r.

circularVelocity(r)

The circular velocity, \(v_c \equiv \sqrt{GM(<r)/r}\).

Parameters

r: array_like

Radius in physical kpc/h; can be a number or a numpy array.

Returns

vc: float

The circular velocity in km / s; has the same dimensions as r.

See also

Vmax

The maximum circular velocity, and the radius where it occurs.

Vmax()

The maximum circular velocity, and the radius where it occurs.

Returns

vmax: float

The maximum circular velocity in km / s.

rmax: float

The radius where fmax occurs, in physical kpc/h.

See also

circularVelocity

The circular velocity, \(v_c \equiv \sqrt{GM(<r)/r}\).

RDelta(z, mdef)

The spherical overdensity radius of a given mass definition.

Parameters

z: float

Redshift

mdef: str

The mass definition for which the spherical overdensity radius is computed. See Halo Mass Definitions for details.

Returns

R: float

Spherical overdensity radius in physical kpc/h.

See also

MDelta

The spherical overdensity mass of a given mass definition.

RMDelta

The spherical overdensity radius and mass of a given mass definition.

RMDelta(z, mdef)

The spherical overdensity radius and mass of a given mass definition.

This is a wrapper for the RDelta() and MDelta() functions which returns both radius and mass.

Parameters

z: float

Redshift

mdef: str

The mass definition for which the spherical overdensity mass is computed. See Halo Mass Definitions for details.

Returns

R: float

Spherical overdensity radius in physical kpc/h.

M: float

Spherical overdensity mass in \(M_{\odot} /h\).

See also

RDelta

The spherical overdensity radius of a given mass definition.

MDelta

The spherical overdensity mass of a given mass definition.

MDelta(z, mdef)

The spherical overdensity mass of a given mass definition.

Parameters

z: float

Redshift

mdef: str

The mass definition for which the spherical overdensity mass is computed. See Halo Mass Definitions for details.

Returns

M: float

Spherical overdensity mass in \(M_{\odot} /h\).

See also

RDelta

The spherical overdensity radius of a given mass definition.

RMDelta

The spherical overdensity radius and mass of a given mass definition.

Rsteepest(search_range=10.0)

The radius where the logarithmic slope of the density profile is steepest.

This function finds the radius where the logarithmic slope of the profile is minimal, within some very generous bounds. The function makes sense only if at least one outer term has been added because the inner profile steepens with radius without ever become shallower again (for any reasonable functional form).

The radius of steepest slope is often taken as a proxy for the splashback radius, \(R_{\rm sp}\), but this correspondence is only approximate because the \(R_{\rm steep}\) is the result of a tradeoff between the orbiting and infalling profiles, whereas the splashback radius is determined by the dynamics of orbiting particles. See the Splashback radius section for a detailed description of the splashback radius.

Parameters

search_range: float

When searching for the radius of steepest slope, search within this factor of \(R_{\rm 200m}\), \(r_{\rm s}\), or another initial guess, which is determined automatically.

Returns

Rsteep: float

The radius where the slope is steepest, in physical kpc/h.

fit(r, q, quantity, q_err=None, q_cov=None, method='leastsq', mask=None, verbose=True, tolerance=1e-05, maxfev=None, leastsq_algorithm='trf', use_legacy_leastsq=False, bounds=None, initial_step=0.01, nwalkers=100, random_seed=None, convergence_step=100, converged_GR=0.01, best_fit='median', output_every_n=100)

Fit the density, mass, surface density, or DeltaSigma profile to a given set of data points.

This function represents a general interface for finding the best-fit parameters of a halo density profile given a set of data points. These points can represent a number of different physical quantities: quantity can either be density, enclosed mass, surface density, or Delta Sigma (rho, M, Sigma, or DeltaSigma).

The data points q at radii r can optionally have error bars, and the user can pass a full covariance matrix. Please note that not passing any estimate of the uncertainty, i.e. q_err = None and q_cov = None, can lead to very poor fit results: the fitter will minimize the absolute difference between points, strongly favoring the high densities at the center.

The user can choose to vary only a sub-set of the profile parameters through the mask parameter. The current parameters of the profile instance serve as an initial guess.

By default, the parameters are transformed into log space during fitting to ensure positivity. Child classes can change this behavior by overwriting the _fitConvertParams() and _fitConvertParamsBack() functions.

There are two fundamental methods for performing the fit, a least-squares minimization (method = 'leastsq') and a Markov-Chain Monte Carlo (method = 'mcmc'). Both variants obey a few specific options (see below). The function returns a dictionary with outputs that somewhat depend on which method is chosen. After the function has completed, the profile instance represents the best-fit profile to the data points (i.e., its parameters are the best-fit parameters). Note that all output parameters are bundled into one dictionary. The explanations below refer to the entries in this dictionary.

Parameters

r: array_like

The radii of the data points, in physical kpc/h.

q: array_like

The data to fit; can either be density in physical \(M_{\odot} h^2 / {\rm kpc}^3\), enclosed mass in \(M_{\odot} /h\), or surface density in physical \(M_{\odot} h/{\rm kpc}^2\). Must have the same dimensions as r.

quantity: str

Indicates which quantity is given in as input in q, can be rho, M, Sigma, or DeltaSigma.

q_err: array_like

Optional; the uncertainty on the values in q in the same units. If method == 'mcmc', either q_err or q_cov must be passed. If method == 'leastsq' and neither q_err nor q_cov are passed, the absolute different between data points and fit is minimized. In this case, the returned chi2 is in units of absolute difference, meaning its value will depend on the units of q.

q_cov: array_like

Optional; the covariance matrix of the elements in q, as a 2-dimensional numpy array. This array must have dimensions of q**2 and be in units of the square of the units of q. If q_cov is passed, q_err is ignored since the diagonal elements of q_cov correspond to q_err**2.

method: str

The fitting method; can be leastsq for a least-squares minimization of mcmc for a Markov-Chain Monte Carlo.

mask: array_like

Optional; a numpy array of booleans that has the same length as the variables vector of the density profile class. Only variables where mask == True are varied in the fit, all others are kept constant. Important: this has to be a numpy array rather than a list.

verbose: bool / int

If true, output information about the fitting process. The flag can also be set as a number, where 1 has the same effect as True, and 2 outputs large amounts of information such as the fit parameters at each iteration.

tolerance: float

Only active when method == 'leastsq'. The accuracy to which the best-fit parameters are found.

maxfev: int

Only active when method == 'leastsq'. The maximum number of function evaluations before the fit is aborted. If None, the default value of the scipy least_squares function is used.

leastsq_algorithm: str

Only active when method == 'leastsq'. Can be any of the methods accepted by the scipy least_squares() function. Default is trf (trust region reflective), which works well for profile fits.

use_legacy_leastsq: bool

Only active when method == 'leastsq'. If True, this setting falls back to the old leastsq() in scipy rather than using the newer least_squares(). Should only be used for backward compatibility.

bounds: array_like

Only active when method == 'leastsq' and use_legacy_leastsq == False. If not None, this parameter must be an array of dimensions [2, N_par], giving two sets (lower and upper limits) of the fitted parameters (not the entire parameter array, if a mask is imposed). The limits must be given in linear space. If the parameters are fitted in log space (or some other transformation), that transformation is automatically applied to the bounds. For example, when fitting in log space (the default), the lower bounds must be positive, but the upper bounds can be np.inf.

initial_step: array_like

Only active when method == 'mcmc'. The MCMC samples (“walkers”) are initially distributed in a Gaussian around the initial guess. The width of the Gaussian is given by initial_step, either as an array of length N_par (giving the width of each Gaussian) or as a float number, in which case the width is set to initial_step times the initial value of the parameter.

nwalkers: int

Only active when method == 'mcmc'. The number of MCMC samplers that are run in parallel.

random_seed: int

Only active when method == 'mcmc'. If random_seed is not None, it is used to initialize the random number generator. This can be useful for reproducing results.

convergence_step: int

Only active when method == 'mcmc'. The convergence criteria are computed every convergence_step steps (and output is printed if verbose == True).

converged_GR: float

Only active when method == 'mcmc'. The maximum difference between different chains, according to the Gelman-Rubin criterion. Once the GR indicator is lower than this number in all parameters, the chain is ended. Setting this number too low leads to very long runtimes, but setting it too high can lead to inaccurate results.

best_fit: str

Only active when method == 'mcmc'. This parameter determines whether the mean or median value of the likelihood distribution is used as the output parameter set.

output_every_n: int

Only active when method == 'mcmc'. This parameter determines how frequently the MCMC chain outputs information. Only effective if verbose == True.

Returns

results: dict

A dictionary bundling the various fit results. Regardless of the fitting method, the dictionary always contains the following entries:

x: array_like

The best-fit result vector. If mask is passed, this vector only contains those variables that were varied in the fit.

q_fit: array_like

The fitted profile at the radii of the data points; has the same units as q and the same dimensions as r.

chi2: float

The chi^2 of the best-fit profile. If a covariance matrix was passed, the covariances are taken into account. If no uncertainty was passed at all, chi2 is in units of absolute difference, meaning its value will depend on the units of q.

ndof: int

The number of degrees of freedom, i.e. the number of fitted data points minus the number of free parameters.

chi2_ndof: float

The chi^2 per degree of freedom.

If method == 'leastsq', the dictionary additionally contains the entries returned by scipy.optimize.leastsq as well as the following:

nfev: int

The number of function calls used in the fit.

x_err: array_like

An array of dimensions [2, nparams] which contains an estimate of the lower and upper uncertainties on the fitted parameters. These uncertainties are computed from the covariance matrix estimated by the fitter. Please note that this estimate does not exactly correspond to a 68% likelihood. In order to get more statistically meaningful uncertainties, please use the MCMC samples instead of least-squares. In some cases, the fitter fails to return a covariance matrix, in which case x_err is None.

If method == 'mcmc', the dictionary contains the following entries:

x_initial: array_like

The initial positions of the walkers, in an array of dimensions [nwalkers, nparams].

chain_full: array_like

A numpy array of dimensions [n_independent_samples, nparams] with the parameters at each step in the chain. In this thin chain, only every nth step is output, where n is the auto-correlation time, meaning that the samples in this chain are truly independent.

chain_thin: array_like

Like the thin chain, but including all steps. Thus, the samples in this chain are not indepedent from each other. However, the full chain often gives better plotting results.

R: array_like

A numpy array containing the GR indicator at each step when it was saved.

x_mean: array_like

The mean of the chain for each parameter; has length nparams.

x_median: array_like

The median of the chain for each parameter; has length nparams.

x_stddev: array_like

The standard deviation of the chain for each parameter; has length nparams.

x_percentiles: array_like

The lower and upper values of each parameter that contain a certain percentile of the probability; has dimensions [n_percentages, 2, nparams] where the second dimension contains the lower/upper values.