Diemer & Kravtsov 2014 profile¶
This module implements the Diemer & Kravtsov 2014 form of the density profile. Please see Halo Density Profiles for a general introduction to the Colossus density profile module.
Basics¶
The DK14 profile (Diemer & Kravtsov 2014) is defined by the following density form:
\[ \begin{align}\begin{aligned}\rho(r) &= \rho_{\rm inner} \times f_{\rm trans} + \rho_{\rm outer}\\\rho_{\rm inner} &= \rho_{\rm Einasto} = \rho_{\rm s} \exp \left( \frac{2}{\alpha} \left[ \left( \frac{r}{r_{\rm s}} \right)^\alpha 1 \right] \right)\\f_{\rm trans} &= \left[ 1 + \left( \frac{r}{r_{\rm t}} \right)^\beta \right]^{\frac{\gamma}{\beta}}\end{aligned}\end{align} \]
This profile corresponds to an Einasto profile at small radii, and steepens around the virial radius. The profile formula has 6 free parameters, but most of those can be fixed to particular values that depend on the mass and mass accretion rate of a halo. The parameters have the following meaning:
Param.  Symbol  Explanation 

rhos  \(\rho_s\)  The central scale density, in physical \(M_{\odot} h^2 / {\rm kpc}^3\) 
rs  \(r_{\rm s}\)  The scale radius in physical kpc/h 
alpha  \(\alpha\)  Determines how quickly the slope of the inner Einasto profile steepens 
rt  \(r_{\rm t}\)  The radius where the profile steepens beyond the Einasto profile, in physical kpc/h 
beta  \(\beta\)  Sharpness of the steepening 
gamma  \(\gamma\)  Asymptotic negative slope of the steepening term 
There are two ways to initialize a DK14 profile. First, the user can pass the fundamental parameters of the profile listed above. Second, the user can pass a spherical overdensity mass and concentration, the conversion to the native parameters then relies on the calibrations in DK14. In this case, the user can give additional information about the profile that can be used in setting the fundamental parameters.
In particular, the fitting function was calibrated for the median and mean profiles of two
types of halo samples, namely samples selected by mass, and samples selected by both mass and
mass accretion rate. The user can choose between those by setting selected_by = 'M'
or
selected_by = 'Gamma'
. The latter option results in a more accurate representation
of the density profile, but the mass accretion rate must be known.
If the profile is chosen to model halo samples selected by mass, we set
\((\beta, \gamma) = (4, 8)\). If the sample is selected by both mass and mass
accretion rate, we set \((\beta, \gamma) = (6, 4)\). Those choices result in a different
calibration of the turnover radius \(r_{\rm t}\). In the latter case, both z
and Gamma
must not be None
. See the deriveParameters()
function
for more details.
Note
The DK14 profile makes sense only if some description of the outer profile is added.
Adding outer terms is easy using the wrapper function getDK14ProfileWithOuterTerms()
:
from colossus.cosmology import cosmology
from colossus.halo import profile_dk14
cosmology.setCosmology('planck15')
p = profile_dk14.getDK14ProfileWithOuterTerms(M = 1E12, c = 10.0, z = 0.0, mdef = 'vir')
This line will return a DK14 profile object with a powerlaw outer profile and the mean density of
the universe added by default. Alternatively, the user can pass a list of OuterTerm objects
(see documentation of the HaloDensityProfile
parent class). The user
can pass additional parameters to the outer profile terms:
p = profile_dk14.getDK14ProfileWithOuterTerms(M = 1E12, c = 10.0, z = 0.0, mdef = 'vir',
power_law_slope = 1.2)
or change the outer terms altogether:
p = profile_dk14.getDK14ProfileWithOuterTerms(M = 1E12, c = 10.0, z = 0.0, mdef = 'vir',
outer_term_names = ['mean', 'cf'], derive_bias_from = None, bias = 1.2)
Some of the outer term parameterizations (namely the 2halo term) rely, in turn, on properties of the total profile such as the mass. In those cases, the constructor determines the mass iteratively, taking the changing contribution of the outer term into account. This procedure can make the constructor slow. Thus, it is generally preferred to initialize the outer terms with fixed parameters (e.g., pivot radius or bias). Please see the Tutorials for more code examples.
Module reference¶

class
halo.profile_dk14.
DK14Profile
(rhos=None, rs=None, rt=None, alpha=None, beta=None, gamma=None, M=None, c=None, mdef=None, z=None, selected_by='M', Gamma=None, outer_terms=[], acc_warn=0.01, acc_err=0.05)¶ The Diemer & Kravtsov 2014 density profile.
The redshift must always be passed to this constructor, regardless of whether the fundamental parameters or a mass and concentration are given.
Parameters:  rhos: float
The central scale density, in physical \(M_{\odot} h^2 / {\rm kpc}^3\).
 rs: float
The scale radius in physical kpc/h.
 rt: float
The radius where the profile steepens, in physical kpc/h.
 alpha: float
Determines how quickly the slope of the inner Einasto profile steepens.
 beta: float
Sharpness of the steepening.
 gamma: float
Asymptotic negative slope of the steepening term.
 M: float
Halo mass in \(M_{\odot}/h\).
 c: float
Concentration in the same mass definition as
M
. mdef: str
The mass definition to which
M
corresponds. See Halo Mass Definitions for details. z: float
Redshift
 selected_by: str
The halo sample to which this profile refers can be selected mass
M
or by accretion rateGamma
. This parameter influences how some of the fixed parameters in the profile are set, in particular those that describe the steepening term. Gamma: float
The mass accretion rate as defined in DK14. This parameter only needs to be passed if
selected_by == 'Gamma'
. acc_warn: float
If the function achieves a relative accuracy in matching
M
less than this value, a warning is printed. acc_err: float
If the function achieves a relative accuracy in matching
M
less than this value, an exception is raised.
Methods
M4rs
()The mass within 4 scale radii, \(M_{<4rs}\). MDelta
(z, mdef)The spherical overdensity mass of a given mass definition. Msp
([search_range])The mass enclosed within \(R_{\rm sp}\), \(M_{\rm sp}\). 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. RMsp
([search_range])The splashback radius and mass within, \(R_{\rm sp}\) and \(M_{\rm sp}\). Rsp
([search_range])The splashback radius, \(R_{\rm sp}\). 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. deriveParameters
(selected_by[, nu200m, z, Gamma])Calibration of the parameters \(\alpha\), \(\beta\), \(\gamma\), and \(r_{\rm t}\). 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, or surface density profile to a given set of data points. getParameterArray
([mask])Returns an array of the profile parameters. 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 options after a parameter change. 
static
deriveParameters
(selected_by, nu200m=None, z=None, Gamma=None)¶ Calibration of the parameters \(\alpha\), \(\beta\), \(\gamma\), and \(r_{\rm t}\).
This function determines the values of those parameters in the DK14 profile that can be calibrated based on mass, and potentially mass accretion rate. If the profile is chosen to model halo samples selected by mass (
selected_by = 'M'
), we set \((\beta, \gamma) = (4, 8)\). If the sample is selected by both mass and mass accretion rate (selected_by = 'Gamma'
), we set \((\beta, \gamma) = (6, 4)\).Those choices result in a different calibration of the turnover radius \(r_{\rm t}\). If
selected_by = 'M'
, we use Equation 6 in DK14. Though this relation was originally calibrated for \(\nu = \nu_{\rm vir}\), but the difference is small. Ifselected_by = 'Gamma'
, \(r_{\rm t}\) is calibrated fromGamma
andz
.Finally, the parameter that determines how quickly the Einasto profile steepens with radius, \(\alpha\), is calibrated according to the Gao et al. 2008 relation. This function was also originally calibrated for \(\nu = \nu_{\rm vir}\), but the difference is small.
Parameters:  selected_by: str
The halo sample to which this profile refers can be selected mass
M
or by accretion rateGamma
. nu200m: float
The peak height of the halo for which the parameters are to be calibrated. This parameter only needs to be passed if
selected_by == 'M'
. z: float
Redshift
 Gamma: float
The mass accretion rate as defined in DK14. This parameter only needs to be passed if
selected_by == 'Gamma'
.

update
()¶ Update the profile options after a parameter change.
The DK14 profile has one internal option,
opt['R200m']
, 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 \(R_{\rm 200m}\).

densityInner
(r)¶ Density of the inner profile 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
.

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

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.

M4rs
()¶ The mass within 4 scale radii, \(M_{<4rs}\).
This mass definition was suggested by More et al. 2015, see the Advanced mass definition functions section for details.
Returns:  M4rs: float
The mass within 4 scale radii, \(M_{<4rs}\), in \(M_{\odot} / h\).

Rsp
(search_range=5.0)¶ The splashback radius, \(R_{\rm sp}\).
See the Splashback radius section for a detailed description of the splashback radius. Here, we define \(R_{\rm sp}\) as the radius where the profile reaches its steepest logarithmic slope.
Parameters:  search_range: float
When searching for the radius of steepest slope, search within this factor of \(R_{\rm 200m}\) (optional).
Returns:  Rsp: float
The splashback radius, \(R_{\rm sp}\), in physical kpc/h.

RMsp
(search_range=5.0)¶ The splashback radius and mass within, \(R_{\rm sp}\) and \(M_{\rm sp}\).
See the Splashback radius section for a detailed description of the splashback radius. Here, we define \(R_{\rm sp}\) as the radius where the profile reaches its steepest logarithmic slope.
Parameters:  search_range: float
When searching for the radius of steepest slope, search within this factor of \(R_{\rm 200m}\) (optional).
Returns:  Rsp: float
The splashback radius, \(R_{\rm sp}\), in physical kpc/h.
 Msp: float
The mass enclosed within the splashback radius, \(M_{\rm sp}\), in \(M_{\odot} / h\).

Msp
(search_range=5.0)¶ The mass enclosed within \(R_{\rm sp}\), \(M_{\rm sp}\).
See the Splashback radius section for a detailed description of the splashback radius. Here, we define \(R_{\rm sp}\) as the radius where the profile reaches its steepest logarithmic slope.
Parameters:  search_range: float
When searching for the radius of steepest slope, search within this factor of \(R_{\rm 200m}\) (optional).
Returns:  Msp: float
The mass enclosed within the splashback radius, \(M_{\rm sp}\), in \(M_{\odot} / h\).

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 \(M_{\odot} /h\).

RMDelta
(z, mdef)¶ The spherical overdensity radius and mass of a given mass definition.
This is a wrapper for the
RDelta()
andMDelta()
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 \(M_{\odot} /h\).

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

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.

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 cutoff radius. See Halo Mass Definitions for details.
Returns:  pdf: array_like
The probability for mass to lie within radius
r
; has the same dimensions asr
.

deltaSigma
(r, interpolate=True, interpolate_surface_density=True, accuracy=0.0001, min_r_interpolate=1e06, 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 asr
.

deltaSigmaInner
(r, interpolate=True, interpolate_surface_density=True, accuracy=0.0001, min_r_interpolate=1e06, 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 asr
.

deltaSigmaOuter
(r, interpolate=True, interpolate_surface_density=True, accuracy=0.0001, min_r_interpolate=1e06, 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 asr
.

density
(r)¶ Density 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
.

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
.

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
.

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
.

enclosedMass
(r, accuracy=1e06)¶ 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 asr
.

enclosedMassInner
(r, accuracy=1e06)¶ The mass enclosed within radius r due to the inner 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 asr
.

enclosedMassOuter
(r, accuracy=1e06)¶ 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 asr
.

fit
(r, q, quantity, q_err=None, q_cov=None, method='leastsq', mask=None, verbose=True, tolerance=1e05, maxfev=0, initial_step=0.1, nwalkers=100, random_seed=None, convergence_step=100, converged_GR=0.01, best_fit='median', output_every_n=100)¶ Fit the density, mass, or surface density profile to a given set of data points.
This function represents a general interface for finding the bestfit 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
, orDeltaSigma
).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
andq_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.There are two fundamental methods for performing the fit, a leastsquares minimization (
method = 'leastsq'
) and a MarkovChain Monte Carlo (method = 'mcmc'
). The MCMC method has some specific options (see below). In either case, the current parameters of the profile instance serve as an initial guess. Finally, the user can choose to vary only a subset of the profile parameters through themask
parameter.The function returns a dictionary with outputs that depend on which method is chosen. After this function has completed, the profile instance represents the bestfit profile to the data points (i.e., its parameters are the bestfit 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 berho
,M
,Sigma
, orDeltaSigma
. q_err: array_like
Optional; the uncertainty on the values in
q
in the same units. Ifmethod == 'mcmc'
, eitherq_err
orq_cov
must be passed. Ifmethod == 'leastsq'
and neitherq_err
norq_cov
are passed, the absolute different between data points and fit is minimized. In this case, the returnedchi2
is in units of absolute difference, meaning its value will depend on the units ofq
. q_cov: array_like
Optional; the covariance matrix of the elements in
q
, as a 2dimensional numpy array. This array must have dimensions of q**2 and be in units of the square of the units ofq
. Ifq_cov
is passed,q_err
is ignored since the diagonal elements ofq_cov
correspond to q_err**2. method: str
The fitting method; can be
leastsq
for a leastsquares minimization ofmcmc
for a MarkovChain 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 bestfit parameters are found. maxfev: int
Only active when
method == 'leastsq'
. The maximum number of function evaluations before the fit is aborted. If zero, the default value of the scipy leastsq function is used. 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 lengthN_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 ifverbose == True
). converged_GR: float
Only active when
method == 'mcmc'
. The maximum difference between different chains, according to the GelmanRubin 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 themean
ormedian
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 ifverbose == 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_likeThe bestfit result vector. If mask is passed, this vector only contains those variables that were varied in the fit.
q_fit
: array_likeThe fitted profile at the radii of the data points; has the same units as
q
and the same dimensions asr
.chi2
: floatThe chi^2 of the bestfit 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 ofq
.ndof
: intThe number of degrees of freedom, i.e. the number of fitted data points minus the number of free parameters.
chi2_ndof
: floatThe 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
: intThe number of function calls used in the fit.
x_err
: array_likeAn 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 leastsquares. In some cases, the fitter fails to return a covariance matrix, in which casex_err
isNone
.
If
method == 'mcmc'
, the dictionary contains the following entries:x_initial
: array_likeThe initial positions of the walkers, in an array of dimensions
[nwalkers, nparams]
.chain_full
: array_likeA 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 autocorrelation time, meaning that the samples in this chain are truly independent.chain_thin
: array_likeLike 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_likeA numpy array containing the GR indicator at each step when it was saved.
x_mean
: array_likeThe mean of the chain for each parameter; has length
nparams
.x_median
: array_likeThe median of the chain for each parameter; has length
nparams
.x_stddev
: array_likeThe standard deviation of the chain for each parameter; has length
nparams
.x_percentiles
: array_likeThe 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.

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 simply 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).

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

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 asr
.

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 asr
.

halo.profile_dk14.
getDK14ProfileWithOuterTerms
(outer_term_names=['mean', 'pl'], power_law_norm=1.0, power_law_slope=1.5, power_law_max=1000.0, derive_bias_from='R200m', bias=1.0, **kwargs)¶ A wrapper function to create a DK14 profile with one or many outer profile terms.
The DK14 profile only makes sense if some description of the outer profile is added. This function provides a convenient way to construct such profiles without having to set the properties of the outer terms manually. Valid keys for outer terms include the following.
mean
: The mean density of the universe at redshiftz
(see the documentation ofOuterTermMeanDensity
).pl
: A powerlaw profile in radius (see the documentation ofOuterTermPowerLaw
). For the DK14 profile, the chosen pivot radius is \(5 R_{\rm 200m}\). Note that \(R_{\rm 200m}\) is set as a profile option in the constructor once, but not adjusted thereafter unless theupdate()
function is called. Thus, in a fit, the fitted norm and slope refer to a pivot of the original \(R_{\rm 200m}\) until update() is called which adjusts these parameters. Furthermore, the parameters for the powerlaw outer profile (norm and slope, called \(b_{\rm e}\) and \(s_{\rm e}\) in the DK14 paper) exhibit a complicated dependence on halo mass, redshift and cosmology. At low redshift, and for the cosmology considered in DK14,power_law_norm = 1.0
andpower_law_slope = 1.5
are reasonable values over a wide range of masses (see Figure 18 in DK14), but these values are by no means universal or accurate.cf
: The mattermatter correlation function times halo bias (see the documentation ofOuterTermCorrelationFunction
). Here, the user has a choice regarding halo bias: it can enter the profile as a parameter (ifderive_bias_from == None
or it can be derived according to the default model of halo bias based on \(M_{\rm 200m}\) (in which casederive_bias_from = 'R200m'
and the bias parameter is ignored). The latter option can make the constructor slow because of the iterative evaluation of bias and \(M_{\rm 200m}\).
Parameters:  outer_term_names: array_like
A list of outer profile term identifiers, can be
mean
,pl
, orcf
. power_law_norm: float
The normalization of a powerlaw term (called \(b_{\rm e}\) in DK14).
 power_law_slope: float
The negative slope of a powerlaw term (called \(s_{\rm e}\) in DK14).
 power_law_max: float
The maximum density contributed by a powerlaw term.
 derive_bias_from: str
See
cf
section above. bias: float
See
cf
section above. kwargs: kwargs
The arguments passed to the DK14 profile constructor (i.e., the fundamental parameters or
M
,c
etc).