Class
NumCosmoClusterMassRichness
Description [src]
abstract class NumCosmo.ClusterMassRichness : NumCosmo.ClusterMass
{
/* No available fields */
}Abstract class for cluster mass-richness observable relations.
This abstract class implements the truncated lognormal distribution for
cluster mass-richness relations. Subclasses must implement the virtual
functions mu() and sigma() that define the mean and standard deviation
of the log-richness as a function of mass and redshift.
The probability distribution is given by: $$ P(\ln \lambda | M, z) = \frac{1}{\sqrt{2\pi}\sigma} \exp\left[-\frac{(\ln \lambda - \mu)^2}{2\sigma^2}\right] $$ where $\mu = \mu(M, z)$ and $\sigma = \sigma(M, z)$ are model-specific functions.
Instance methods
nc_cluster_mass_richness_compute_truncated_mean
Computes the mean of the truncated log-richness distribution given the untruncated mean and standard deviation. The distribution is truncated at the cut threshold.
nc_cluster_mass_richness_compute_truncated_std
Computes the standard deviation of the truncated log-richness distribution given the untruncated mean and standard deviation. The distribution is truncated at the cut threshold.
nc_cluster_mass_richness_get_std
Computes the standard deviation of the truncated richness distribution.
nc_cluster_mass_richness_mu
Computes the mean of the log-richness distribution as a function of mass and redshift.
nc_cluster_mass_richness_mu_sigma
Computes both the mean and standard deviation of the log-richness distribution
simultaneously. This can be more efficient than calling mu() and sigma()
separately when subclasses share intermediate computations.
nc_cluster_mass_richness_sigma
Computes the standard deviation of the log-richness distribution as a function of mass and redshift.
Methods inherited from NcClusterMass (15)
nc_cluster_mass_free
Atomically decrements the reference count of clusterm by one. If the reference count drops to 0,
all memory allocated by clusterm is released.
nc_cluster_mass_intp
It computes the clusterm probability distribution of lnM lying
in the range $[]$, namely,
$$ intp = \int_{\ln M^{obs}{min}}^{\ln M^{obs}{max}} p \, d\ln M^{obs},$$
where $p$ is [nc_cluster_mass_p()].
nc_cluster_mass_intp_bin
Computes the integrated probability over the observed mass bin.
nc_cluster_mass_n_limits
Computes the mass limits for the cluster abundance calculation.
The function which will call this one is responsible to allocate memory for lnM_lower and lnM_upper.
nc_cluster_mass_p
Computes the probability distribution $P(\ln M_{\mathrm{obs}}|\ln M, z)$.
nc_cluster_mass_p_bin_limits
Computes the integration limits for the true mass given the observed mass bin boundaries.
nc_cluster_mass_p_limits
Computes the integration limits for the true mass given the observed mass and its parameters.
nc_cluster_mass_p_vec_z_lnMobs
This method computes the probability distribution of lnM for each redshift in z
given the true mass lnM and the observed mass proxies lnM_obs and their parameters lnM_obs_params.
nc_cluster_mass_plcl_Msz_Ml_p_ndetone
This function computes the i-th term of the posterior given flat priors for the selection function and mass function. See function nc_cluster_pseudo_counts_posterior_ndetone().
nc_cluster_mass_plcl_pdf
Compute the joint probability density used internally by the PL-CL mass model. Integrals in $M_{sz}$ and $M_l$ are performed in the dimensionless quantities $\ln (M_{sz} / M_0)$ and $\ln (M_l / M_0)$, respectively. The Gaussian distributions between $M_{Pl}$ and $M_{CL}$ are written in terms of the dimensionless quantities $M_{Pl}/M_0$, $M_{CL}/M_0$, $\sigma_{Pl}/M_0$ and $\sigma_{CL}/M_0$.
nc_cluster_mass_plcl_pdf_only_lognormal
nc_cluster_mass_ref
Increases the reference count of clusterm by one.
nc_cluster_mass_resample
Generates a random sample of the observed mass proxies given the true mass and redshift.
nc_cluster_mass_resample_vec
Generates a random sample of the observed mass proxies given the true mass and redshift.
This is a convenience wrapper around nc_cluster_mass_resample() that uses NcmVector
for proper Python bindings support.
nc_cluster_mass_volume
Computes the effective volume in the observable mass space.
Properties
NumCosmo.ClusterMassRichness:lnRichness-max
Maximum logarithm (base e) of richness for cluster selection.
NumCosmo.ClusterMassRichness:lnRichness-min
Minimum logarithm (base e) of richness for cluster selection.
Properties inherited from NcmModel (9)
NumCosmoMath.Model:implementation
NumCosmoMath.Model:name
NumCosmoMath.Model:nick
NumCosmoMath.Model:params-types
NumCosmoMath.Model:reparam
NumCosmoMath.Model:scalar-params-len
NumCosmoMath.Model:sparam-array
NumCosmoMath.Model:submodel-array
NumCosmoMath.Model:vector-params-len
Signals
Signals inherited from GObject (1)
GObject::notify
The notify signal is emitted on an object when one of its properties has its value set through g_object_set_property(), g_object_set(), et al.
Class structure
struct NumCosmoClusterMassRichnessClass {
gdouble (* mu) (
NcClusterMassRichness* mr,
gdouble lnM,
gdouble z
);
gdouble (* sigma) (
NcClusterMassRichness* mr,
gdouble lnM,
gdouble z
);
void (* mu_sigma) (
NcClusterMassRichness* mr,
gdouble lnM,
gdouble z,
gdouble* mu,
gdouble* sigma
);
}No description available.
Class members
mu: gdouble (* mu) ( NcClusterMassRichness* mr, gdouble lnM, gdouble z )No description available.
sigma: gdouble (* sigma) ( NcClusterMassRichness* mr, gdouble lnM, gdouble z )No description available.
mu_sigma: void (* mu_sigma) ( NcClusterMassRichness* mr, gdouble lnM, gdouble z, gdouble* mu, gdouble* sigma )No description available.
Virtual methods
NumCosmo.ClusterMassRichnessClass.mu
Computes the mean of the log-richness distribution as a function of mass and redshift.
NumCosmo.ClusterMassRichnessClass.mu_sigma
Computes both the mean and standard deviation of the log-richness distribution
simultaneously. This can be more efficient than calling mu() and sigma()
separately when subclasses share intermediate computations.
NumCosmo.ClusterMassRichnessClass.sigma
Computes the standard deviation of the log-richness distribution as a function of mass and redshift.