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Function: _check_task_dependency(self, level)
Docstring:
Fill the level list with all the needed modules
.. warning::
the ordering of modules is obviously dependent on CLASS module order
in the main.c file. This has to be updated in case of a change to
this file.
Parameters
----------
level : list
list of strings, containing initially only the last module required.
For instance, to recover all the modules, the input should be
['lensing']
---------------------------------
Function: compute(self, level=["distortions"])
Docstring:
compute(level=["distortions"])
Main function, execute all the _init methods for all desired modules.
This is called in MontePython, and this ensures that the Class instance
of this class contains all the relevant quantities. Then, one can deduce
Pk, Cl, etc...
Parameters
----------
level : list
list of the last module desired. The internal function
_check_task_dependency will then add to this list all the
necessary modules to compute in order to initialize this last
one. The default last module is "lensing".
.. warning::
level default value should be left as an array (it was creating
problem when casting as a set later on, in _check_task_dependency)
---------------------------------
Function: density_factor(self)
Docstring:
The density factor required to convert from the class-units of density to kg/m^3 (SI units)
---------------------------------
Function: kgm3_to_eVMpc3(self)
Docstring:
Convert from kg/m^3 to eV/Mpc^3
---------------------------------
Function: kgm3_to_MsolMpc3(self)
Docstring:
Convert from kg/m^3 to Msol/Mpc^3
---------------------------------
Function: raw_cl(self, lmax=-1, nofail=False)
Docstring:
raw_cl(lmax=-1, nofail=False)
Return a dictionary of the primary C_l
Parameters
----------
lmax : int, optional
Define the maximum l for which the C_l will be returned
(inclusively). This number will be checked against the maximum l
at which they were actually computed by CLASS, and an error will
be raised if the desired lmax is bigger than what CLASS can
give.
nofail: bool, optional
Check and enforce the computation of the harmonic module
beforehand, with the desired lmax.
Returns
-------
cl : dict
Dictionary that contains the power spectrum for 'tt', 'te', etc... The
index associated with each is defined wrt. Class convention, and are non
important from the python point of view. It also returns now the
ell array.
---------------------------------
Function: lensed_cl(self, lmax=-1,nofail=False)
Docstring:
lensed_cl(lmax=-1, nofail=False)
Return a dictionary of the lensed C_l, computed by CLASS, without the
density C_ls. They must be asked separately with the function aptly
named density_cl
Parameters
----------
lmax : int, optional
Define the maximum l for which the C_l will be returned (inclusively)
nofail: bool, optional
Check and enforce the computation of the lensing module beforehand
Returns
-------
cl : dict
Dictionary that contains the power spectrum for 'tt', 'te', etc... The
index associated with each is defined wrt. Class convention, and are non
important from the python point of view.
---------------------------------
Function: density_cl(self, lmax=-1, nofail=False)
Docstring:
density_cl(lmax=-1, nofail=False)
Return a dictionary of the primary C_l for the matter
Parameters
----------
lmax : int, optional
Define the maximum l for which the C_l will be returned (inclusively)
nofail: bool, optional
Check and enforce the computation of the lensing module beforehand
Returns
-------
cl : numpy array of numpy.ndarrays
Array that contains the list (in this order) of self correlation of
1st bin, then successive correlations (set by non_diagonal) to the
following bins, then self correlation of 2nd bin, etc. The array
starts at index_ct_dd.
---------------------------------
Function: luminosity_distance(self, z)
Docstring:
luminosity_distance(z)
---------------------------------
Function: pk(self,double k,double z)
Docstring:
Gives the total matter pk (in Mpc**3) for a given k (in 1/Mpc) and z (will be non linear if requested to Class, linear otherwise)
.. note::
there is an additional check that output contains `mPk`,
because otherwise a segfault will occur
---------------------------------
Function: pk_cb(self,double k,double z)
Docstring:
Gives the cdm+b pk (in Mpc**3) for a given k (in 1/Mpc) and z (will be non linear if requested to Class, linear otherwise)
.. note::
there is an additional check that output contains `mPk`,
because otherwise a segfault will occur
---------------------------------
Function: pk_lin(self,double k,double z)
Docstring:
Gives the linear total matter pk (in Mpc**3) for a given k (in 1/Mpc) and z
.. note::
there is an additional check that output contains `mPk`,
because otherwise a segfault will occur
---------------------------------
Function: pk_cb_lin(self,double k,double z)
Docstring:
Gives the linear cdm+b pk (in Mpc**3) for a given k (in 1/Mpc) and z
.. note::
there is an additional check that output contains `mPk`,
because otherwise a segfault will occur
---------------------------------
Function: pk_numerical_nw(self,double k,double z)
Docstring:
Gives the nowiggle (smoothed) linear total matter pk (in Mpc**3) for a given k (in 1/Mpc) and z
.. note::
there is an additional check that `numerical_nowiggle` was set to `yes`,
because otherwise a segfault will occur
---------------------------------
Function: pk_analytic_nw(self,double k)
Docstring:
Gives the linear total matter pk (in Mpc**3) for a given k (in 1/Mpc) and z
.. note::
there is an additional check that `analytic_nowiggle` was set to `yes`,
because otherwise a segfault will occur
---------------------------------
Function: get_pk(self, np.ndarray[DTYPE_t,ndim=3] k, np.ndarray[DTYPE_t,ndim=1] z, int k_size, int z_size, int mu_size)
Docstring:
Fast function to get the power spectrum on a k and z array
---------------------------------
Function: get_pk_cb(self, np.ndarray[DTYPE_t,ndim=3] k, np.ndarray[DTYPE_t,ndim=1] z, int k_size, int z_size, int mu_size)
Docstring:
Fast function to get the power spectrum on a k and z array
---------------------------------
Function: get_pk_lin(self, np.ndarray[DTYPE_t,ndim=3] k, np.ndarray[DTYPE_t,ndim=1] z, int k_size, int z_size, int mu_size)
Docstring:
Fast function to get the linear power spectrum on a k and z array
---------------------------------
Function: get_pk_cb_lin(self, np.ndarray[DTYPE_t,ndim=3] k, np.ndarray[DTYPE_t,ndim=1] z, int k_size, int z_size, int mu_size)
Docstring:
Fast function to get the linear power spectrum on a k and z array
---------------------------------
Function: get_pk_all(self, k, z, nonlinear = True, cdmbar = False, z_axis_in_k_arr = 0, interpolation_kind='cubic')
Docstring:
General function to get the P(k,z) for ARBITRARY shapes of k,z
Additionally, it includes the functionality of selecting wether to use the non-linear parts or not,
and wether to use the cdm baryon power spectrum only
For Multi-Dimensional k-arrays, it assumes that one of the dimensions is the z-axis
This is handled by the z_axis_in_k_arr integer, as described in the source code
---------------------------------
Function: get_pk_and_k_and_z(self, nonlinear=True, only_clustering_species = False, h_units=False)
Docstring:
Returns a grid of matter power spectrum values and the z and k
at which it has been fully computed. Useful for creating interpolators.
Parameters
----------
nonlinear : bool
Whether the returned power spectrum values are linear or non-linear (default)
only_clustering_species : bool
Whether the returned power spectrum is for galaxy clustering and excludes massive neutrinos, or always includes everything (default)
h_units : bool
Whether the units of k in output are h/Mpc or 1/Mpc (default)
Returns
-------
pk : grid of power spectrum values, pk[index_k,index_z]
k : vector of k values, k[index_k] (in units of 1/Mpc by default, or h/Mpc when setting h_units to True)
z : vector of z values, z[index_z]
---------------------------------
Function: get_transfer_and_k_and_z(self, output_format='class', h_units=False)
Docstring:
Returns a dictionary of grids of density and/or velocity transfer function values and the z and k at which it has been fully computed.
Useful for creating interpolators.
When setting CLASS input parameters, include at least one of 'dTk' (for density transfer functions) or 'vTk' (for velocity transfer functions).
Following the default output_format='class', all transfer functions will be normalised to 'curvature R=1' at initial time
(and not 'curvature R = -1/k^2' like in CAMB).
You may switch to output_format='camb' for the CAMB definition and normalisation of transfer functions.
(Then, 'dTk' must be in the input: the CAMB format only outputs density transfer functions).
When sticking to output_format='class', you also get the newtonian metric fluctuations phi and psi.
If you set the CLASS input parameter 'extra_metric_transfer_functions' to 'yes',
you get additional metric fluctuations in the synchronous and N-body gauges.
Parameters
----------
output_format : ('class' or 'camb')
Format transfer functions according to CLASS (default) or CAMB
h_units : bool
Whether the units of k in output are h/Mpc or 1/Mpc (default)
Returns
-------
tk : dictionary containing all transfer functions.
For instance, the grid of values of 'd_c' (= delta_cdm) is available in tk['d_c']
All these grids have indices [index_k,index,z], for instance tk['d_c'][index_k,index,z]
k : vector of k values (in units of 1/Mpc by default, or h/Mpc when setting h_units to True)
z : vector of z values
---------------------------------
Function: get_Weyl_pk_and_k_and_z(self, nonlinear=False, h_units=False)
Docstring:
Returns a grid of Weyl potential (phi+psi) power spectrum values and the z and k
at which it has been fully computed. Useful for creating interpolators.
Note that this function just calls get_pk_and_k_and_z and corrects the output
by the ratio of transfer functions [(phi+psi)/d_m]^2.
Parameters
----------
nonlinear : bool
Whether the returned power spectrum values are linear or non-linear (default)
h_units : bool
Whether the units of k in output are h/Mpc or 1/Mpc (default)
Returns
-------
Weyl_pk : grid of Weyl potential (phi+psi) spectrum values, Weyl_pk[index_k,index_z]
k : vector of k values, k[index_k] (in units of 1/Mpc by default, or h/Mpc when setting h_units to True)
z : vector of z values, z[index_z]
---------------------------------
Function: sigma(self,R,z, h_units = False)
Docstring:
Gives sigma (total matter) for a given R and z
(R is the radius in units of Mpc, so if R=8/h this will be the usual sigma8(z).
This is unless h_units is set to true, in which case R is the radius in units of Mpc/h,
and R=8 corresponds to sigma8(z))
.. note::
there is an additional check to verify whether output contains `mPk`,
and whether k_max > ...
because otherwise a segfault will occur
---------------------------------
Function: sigma_cb(self,double R,double z, h_units = False)
Docstring:
Gives sigma (cdm+b) for a given R and z
(R is the radius in units of Mpc, so if R=8/h this will be the usual sigma8(z)
This is unless h_units is set to true, in which case R is the radius in units of Mpc/h,
and R=8 corresponds to sigma8(z))
.. note::
there is an additional check to verify whether output contains `mPk`,
and whether k_max > ...
because otherwise a segfault will occur
---------------------------------
Function: pk_tilt(self,double k,double z)
Docstring:
Gives effective logarithmic slope of P_L(k,z) (total matter) for a given k and z
(k is the wavenumber in units of 1/Mpc, z is the redshift, the output is dimensionless)
.. note::
there is an additional check to verify whether output contains `mPk` and whether k is in the right range
---------------------------------
Function: angular_distance(self, z)
Docstring:
angular_distance(z)
Return the angular diameter distance (exactly, the quantity defined by Class
as index_bg_ang_distance in the background module)
Parameters
----------
z : float
Desired redshift
---------------------------------
Function: angular_distance_from_to(self, z1, z2)
Docstring:
angular_distance_from_to(z)
Return the angular diameter distance of object at z2 as seen by observer at z1,
that is, sin_K((chi2-chi1)*np.sqrt(|k|))/np.sqrt(|k|)/(1+z2).
If z1>z2 returns zero.
Parameters
----------
z1 : float
Observer redshift
z2 : float
Source redshift
Returns
-------
d_A(z1,z2) in Mpc
---------------------------------
Function: comoving_distance(self, z)
Docstring:
comoving_distance(z)
Return the comoving distance
Parameters
----------
z : float
Desired redshift
---------------------------------
Function: scale_independent_growth_factor(self, z)
Docstring:
scale_independent_growth_factor(z)
Return the scale invariant growth factor D(a) for CDM perturbations
(exactly, the quantity defined by Class as index_bg_D in the background module)
Parameters
----------
z : float
Desired redshift
---------------------------------
Function: scale_independent_growth_factor_f(self, z)
Docstring:
scale_independent_growth_factor_f(z)
Return the scale independent growth factor f(z)=d ln D / d ln a for CDM perturbations
(exactly, the quantity defined by Class as index_bg_f in the background module)
Parameters
----------
z : float
Desired redshift
---------------------------------
Function: scale_dependent_growth_factor_f(self, k, z, h_units=False, nonlinear=False, Nz=20)
Docstring:
scale_dependent_growth_factor_f(k,z)
Return the scale dependent growth factor
f(z)= 1/2 * [d ln P(k,a) / d ln a]
= - 0.5 * (1+z) * [d ln P(k,z) / d z]
where P(k,z) is the total matter power spectrum
Parameters
----------
z : float
Desired redshift
k : float
Desired wavenumber in 1/Mpc (if h_units=False) or h/Mpc (if h_units=True)
---------------------------------
Function: scale_dependent_growth_factor_f_cb(self, k, z, h_units=False, nonlinear=False, Nz=20)
Docstring:
scale_dependent_growth_factor_f_cb(k,z)
Return the scale dependent growth factor calculated from CDM+baryon power spectrum P_cb(k,z)
f(z)= 1/2 * [d ln P_cb(k,a) / d ln a]
= - 0.5 * (1+z) * [d ln P_cb(k,z) / d z]
Parameters
----------
z : float
Desired redshift
k : float
Desired wavenumber in 1/Mpc (if h_units=False) or h/Mpc (if h_units=True)
---------------------------------
Function: scale_independent_f_sigma8(self, z)
Docstring:
scale_independent_f_sigma8(z)
Return the scale independent growth factor f(z) multiplied by sigma8(z)
Parameters
----------
z : float
Desired redshift
Returns
-------
f(z)*sigma8(z) (dimensionless)
---------------------------------
Function: effective_f_sigma8(self, z, z_step=0.1)
Docstring:
effective_f_sigma8(z)
Returns the time derivative of sigma8(z) computed as (d sigma8/d ln a)
Parameters
----------
z : float
Desired redshift
z_step : float
Default step used for the numerical two-sided derivative. For z < z_step the step is reduced progressively down to z_step/10 while sticking to a double-sided derivative. For z< z_step/10 a single-sided derivative is used instead.
Returns
-------
(d ln sigma8/d ln a)(z) (dimensionless)
---------------------------------
Function: effective_f_sigma8_spline(self, z, Nz=20)
Docstring:
effective_f_sigma8_spline(z)
Returns the time derivative of sigma8(z) computed as (d sigma8/d ln a)
Parameters
----------
z : float
Desired redshift
Nz : integer
Number of values used to spline sigma8(z) in the range [z-0.1,z+0.1]
Returns
-------
(d ln sigma8/d ln a)(z) (dimensionless)
---------------------------------
Function: z_of_tau(self, tau)
Docstring:
Redshift corresponding to a given conformal time.
Parameters
----------
tau : float
Conformal time
---------------------------------
Function: Hubble(self, z)
Docstring:
Hubble(z)
Return the Hubble rate (exactly, the quantity defined by Class as index_bg_H
in the background module)
Parameters
----------
z : float
Desired redshift
---------------------------------
Function: Om_m(self, z)
Docstring:
Omega_m(z)
Return the matter density fraction (exactly, the quantity defined by Class as index_bg_Omega_m
in the background module)
Parameters
----------
z : float
Desired redshift
---------------------------------
Function: Om_b(self, z)
Docstring:
Omega_b(z)
Return the baryon density fraction (exactly, the ratio of quantities defined by Class as
index_bg_rho_b and index_bg_rho_crit in the background module)
Parameters
----------
z : float
Desired redshift
---------------------------------
Function: Om_cdm(self, z)
Docstring:
Omega_cdm(z)
Return the cdm density fraction (exactly, the ratio of quantities defined by Class as
index_bg_rho_cdm and index_bg_rho_crit in the background module)
Parameters
----------
z : float
Desired redshift
---------------------------------
Function: Om_ncdm(self, z)
Docstring:
Omega_ncdm(z)
Return the ncdm density fraction (exactly, the ratio of quantities defined by Class as
Sum_m [ index_bg_rho_ncdm1 + n ], with n=0...N_ncdm-1, and index_bg_rho_crit in the background module)
Parameters
----------
z : float
Desired redshift
---------------------------------
Function: ionization_fraction(self, z)
Docstring:
ionization_fraction(z)
Return the ionization fraction for a given redshift z
Parameters
----------
z : float
Desired redshift
---------------------------------
Function: baryon_temperature(self, z)
Docstring:
baryon_temperature(z)
Give the baryon temperature for a given redshift z
Parameters
----------
z : float
Desired redshift
---------------------------------
Function: T_cmb(self)
Docstring:
Return the CMB temperature
---------------------------------
Function: Omega0_m(self)
Docstring:
Return the sum of Omega0 for all non-relativistic components
---------------------------------
Function: get_background(self)
Docstring:
Return an array of the background quantities at all times.
Parameters
----------
Returns
-------
background : dictionary containing background.
---------------------------------
Function: get_thermodynamics(self)
Docstring:
Return the thermodynamics quantities.
Returns
-------
thermodynamics : dictionary containing thermodynamics.
---------------------------------
Function: get_primordial(self)
Docstring:
Return the primordial scalar and/or tensor spectrum depending on 'modes'.
'output' must be set to something, e.g. 'tCl'.
Returns
-------
primordial : dictionary containing k-vector and primordial scalar and tensor P(k).
---------------------------------
Function: get_perturbations(self, return_copy=True)
Docstring:
Return scalar, vector and/or tensor perturbations as arrays for requested
k-values.
.. note::
you need to specify both 'k_output_values', and have some
perturbations computed, for instance by setting 'output' to 'tCl'.
Do not enable 'return_copy=False' unless you know exactly what you are doing.
This will mean that you get access to the direct C pointers inside CLASS.
That also means that if class is deallocated,
your perturbations array will become invalid. Beware!
Returns
-------
perturbations : dict of array of dicts
perturbations['scalar'] is an array of length 'k_output_values' of
dictionary containing scalar perturbations.
Similar for perturbations['vector'] and perturbations['tensor'].
---------------------------------
Function: get_transfer(self, z=0., output_format='class')
Docstring:
Return the density and/or velocity transfer functions for all initial
conditions today. You must include 'mTk' and/or 'vCTk' in the list of
'output'. The transfer functions can also be computed at higher redshift z
provided that 'z_pk' has been set and that 0<z<z_pk.
Parameters
----------
z : redshift (default = 0)
output_format : ('class' or 'camb') Format transfer functions according to
CLASS convention (default) or CAMB convention.
Returns
-------
tk : dictionary containing transfer functions.
---------------------------------
Function: get_current_derived_parameters(self, names)
Docstring:
get_current_derived_parameters(names)
Return a dictionary containing an entry for all the names defined in the
input list.
Parameters
----------
names : list
Derived parameters that can be asked from Monte Python, or
elsewhere.
Returns
-------
derived : dict
.. warning::
This method used to take as an argument directly the data class from
Monte Python. To maintain compatibility with this old feature, a
check is performed to verify that names is indeed a list. If not, it
returns a TypeError. The old version of this function, when asked
with the new argument, will raise an AttributeError.
---------------------------------
Function: nonlinear_scale(self, np.ndarray[DTYPE_t,ndim=1] z, int z_size)
Docstring:
nonlinear_scale(z, z_size)
Return the nonlinear scale for all the redshift specified in z, of size
z_size
Parameters
----------
z : numpy array
Array of requested redshifts
z_size : int
Size of the redshift array
---------------------------------
Function: nonlinear_scale_cb(self, np.ndarray[DTYPE_t,ndim=1] z, int z_size)
Docstring:
make nonlinear_scale_cb(z, z_size)
Return the nonlinear scale for all the redshift specified in z, of size
z_size
Parameters
----------
z : numpy array
Array of requested redshifts
z_size : int
Size of the redshift array
---------------------------------
Function: __call__(self, ctx)
Docstring:
Function to interface with CosmoHammer
Parameters
----------
ctx : context
Contains several dictionaries storing data and cosmological
information
---------------------------------
Function: get_pk_array(self, np.ndarray[DTYPE_t,ndim=1] k, np.ndarray[DTYPE_t,ndim=1] z, int k_size, int z_size, nonlinear)
Docstring:
Fast function to get the power spectrum on a k and z array
---------------------------------
Function: get_pk_cb_array(self, np.ndarray[DTYPE_t,ndim=1] k, np.ndarray[DTYPE_t,ndim=1] z, int k_size, int z_size, nonlinear)
Docstring:
Fast function to get the power spectrum on a k and z array
---------------------------------
Function: Omega0_k(self)
Docstring:
Curvature contribution
---------------------------------
Function: get_sources(self)
Docstring:
Return the source functions for all k, tau in the grid.
Returns
-------
sources : dictionary containing source functions.
k_array : numpy array containing k values.
tau_array: numpy array containing tau values.
---------------------------------
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