Numerical Cosmology - NumCosmo

• Documentation
  • GtkDoc generated NumCosmo manual
  • GObject doctool generated NumCosmoMath manual (C)
  • GObject doctool generated NumCosmo manual (C)
  • GObject doctool generated NumCosmoMath manual (Python)
  • GObject doctool generated NumCosmo manual (Python)
• NumCosmo project
• Latest News
• Download and Install
• Browse the sources repository
• Examples
  • Simple example in C
  • Simple example in Python
  • Recombination example in Python (with figures)
  • Supernova Ia model fitting example in Python
  • Supernova Ia and BAO model fitting and confidence region example in Python
  • Cluster abundance example in C
  • Cluster abundance example in Python

The NumCosmo is a free software C library whose main purposes are to test cosmological models using observational data and to provide a set of tools to perform cosmological calculations. Particularly, the current version has implemented three different probes: cosmic microwave background (CMB),supernovae type Ia (SNeIa) and large scale structure (LSS) information, such as baryonic acoustic oscillations (BAO) and galaxy cluster abundance. The code supports a joint analysis of these data and the parameter space can include cosmological and phenomenological parameters. It is worth emphasizing that NumCosmo matter power spectrum and CMB codes were written independently of other implementations such as CMBFAST, CAMB, etc.

The library is structured in such way to simplify the inclusion of non-standard cosmological models. Besides the functions related to cosmological quantities, this library also implements mathematical and statistical tools. The former was developed to enable the inclusion of other probes and/or theoretical models and to optimize the codes. The statistical framework comprises algorithms which define likelihood functions, minimization, Monte Carlo, Fisher Matrix and profile likelihood methods.

Download and installation

Version 0.9.0 of NumCosmo is the latest version available from our web site:

• http://download.savannah.gnu.org/releases/numcosmo/

NumCosmo uses autotools for configuration and installation. The standard procedure is

./configure && make && sudo make install

More about compilation and installation here.

There are also some pre-compiled packages in openSUSE Build Service.These are not tested yet.

Mailing Lists

Subscribe to the numcosmo-help mailing list for discussion of questions and problems about using NumCosmo.

Examples

A simple example in C. Download the source.

To compile the example use:

gcc -Wall example_simple.c -o example_simple `pkg-config numcosmo --libs --cflags`

  1 #include <glib.h>
  2 #include <numcosmo/numcosmo.h>
  3 
  4 gint
  5 main (gint argc, gchar *argv[])
  6 {
  7   NcHICosmo *cosmo;
  8   NcDistance *dist;
  9   gint i;
 10 
 11   /**************************************************************************** 
 12    * Initializing the library objects, this must be called before 
 13    * any other library function.
 14    ****************************************************************************/  
 15   ncm_cfg_init ();
 16   
 17   /**************************************************************************** 
 18    * New homogeneous and isotropic cosmological model NcHICosmoDEXcdm.
 19    ****************************************************************************/  
 20   cosmo = nc_hicosmo_new_from_name (NC_TYPE_HICOSMO, "NcHICosmoDEXcdm");
 21 
 22   /**************************************************************************** 
 23    * New cosmological distance objects optimizied to perform calculations
 24    * up to redshift 2.0.
 25    ****************************************************************************/  
 26   dist = nc_distance_new (2.0);
 27  
 28   /**************************************************************************** 
 29    * Setting values for the cosmological model, those not set stay in the
 30    * default values. Remeber to use the _orig_ version to set the original
 31    * parameters in case when a reparametrization is used.
 32    ****************************************************************************/ 
 33   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_H0, 70.0);
 34   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_OMEGA_C, 0.25);
 35   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_OMEGA_X, 0.7);
 36   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_T_GAMMA0, 1.0);
 37   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_OMEGA_B, 0.05);
 38   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_SPECINDEX, 1.0);
 39   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_SIGMA8, 0.9);
 40   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_XCDM_W, -1.1);
 41 
 42   /**************************************************************************** 
 43    * Printing the parameters used.
 44    ****************************************************************************/
 45   printf ("# Model parameters:\n"); 
 46   ncm_model_params_log_all (NCM_MODEL (cosmo));
 47 
 48   /**************************************************************************** 
 49    * Printing some distances up to redshift 1.0.
 50    ****************************************************************************/ 
 51   for (i = 0; i < 10; i++)
 52   {
 53     gdouble z = 1.0 / 9.0 * i;
 54     gdouble cd = ncm_c_hubble_radius () * nc_distance_comoving (dist, cosmo, z);
 55     printf ("% 10.8f % 20.15g\n", z, cd);
 56   }
 57 
 58   /**************************************************************************** 
 59    * Freeing objects.
 60    ****************************************************************************/ 
 61   nc_distance_free (dist);
 62   ncm_model_free (NCM_MODEL (cosmo));
 63 
 64   return 0;
 65 }

This example has the following output:

# Model parameters:
                    70                  0.25                   0.7                     1                  0.05                     1                   0.9                  -1.1
 0.00000000                    0
 0.11111111     326.291354567573
 0.22222222      637.56332484321
 0.33333333     932.394947130311
 0.44444444     1210.16934019712
 0.55555556     1470.91014294491
 0.66666667     1715.10326170554
 0.77777778     1943.53573595738
 0.88888889     2157.16638285178
 1.00000000     2357.03066379095

The same example in Python. Download the source.

To try this example you must have PyGObject installed, and numcosmo built with --enable-introspection option.

  1 #!/usr/bin/python2
  2 
  3 from gi.repository import NumCosmo as Nc
  4 from gi.repository import NumCosmoMath as Ncm
  5 
  6 #
  7 #  Initializing the library objects, this must be called before 
  8 #  any other library function.
  9 #
 10 Ncm.cfg_init ()
 11 
 12 #
 13 #  New homogeneous and isotropic cosmological model NcHICosmoDEXcdm 
 14 #
 15 cosmo = Nc.HICosmo.new_from_name (Nc.HICosmo, "NcHICosmoDEXcdm")
 16 
 17 #
 18 #  New cosmological distance objects optimizied to perform calculations
 19 #  up to redshift 2.0.
 20 #
 21 dist = Nc.Distance.new (2.0)
 22 
 23 #
 24 #  Setting values for the cosmological model, those not set stay in the
 25 #  default values. Remember to use the _orig_ version to set the original
 26 #  parameters when a reparametrization is used.
 27 #
 28 
 29 #
 30 # C-like
 31 #
 32 cosmo.orig_param_set (Nc.HICosmoDEParams.H0,        70.0)
 33 cosmo.orig_param_set (Nc.HICosmoDEParams.OMEGA_C,   0.25)
 34 cosmo.orig_param_set (Nc.HICosmoDEParams.OMEGA_X,   0.70)
 35 cosmo.orig_param_set (Nc.HICosmoDEParams.T_GAMMA0,  2.72)
 36 cosmo.orig_param_set (Nc.HICosmoDEParams.OMEGA_B,   0.05)
 37 cosmo.orig_param_set (Nc.HICosmoDEParams.SPECINDEX, 1.00)
 38 cosmo.orig_param_set (Nc.HICosmoDEParams.SIGMA8,    0.90)
 39 cosmo.orig_param_set (Nc.HICosmoDEXCDMParams.W,    -1.00)
 40 
 41 #
 42 # OO-like
 43 #
 44 cosmo.props.H0      = 70.0
 45 cosmo.props.Omegab  = 0.05
 46 cosmo.props.Omegac  = 0.25
 47 cosmo.props.Omegax  = 0.70
 48 cosmo.props.Tgamma0 = 2.72
 49 cosmo.props.ns      = 1.0
 50 cosmo.props.sigma8  = 0.9
 51 cosmo.props.w       = -1.0
 52 
 53 #
 54 #  Printing the parameters used.
 55 #
 56 print "# Model parameters: ", 
 57 cosmo.params_log_all ()
 58 
 59 #
 60 #  Printing some distances up to redshift 1.0.
 61 #
 62 for i in range (0, 10):
 63   z = 1.0 / 9.0 * i
 64   cd = Ncm.C.hubble_radius () * dist.comoving (cosmo, z)
 65   print "% 10.8f % 20.15g" % (z, cd)
 66 

An example of the recombination object in Python. Download the source.

To try this example you must have PyGObject installed, matplotlib matplotlib and numcosmo built with --enable-introspection option.

  1 #!/usr/bin/python2
  2 
  3 from math import *
  4 from gi.repository import GObject
  5 import matplotlib.pyplot as plt
  6 from gi.repository import NumCosmo as Nc
  7 from gi.repository import NumCosmoMath as Ncm
  8 
  9 #
 10 #  Initializing the library objects, this must be called before 
 11 #  any other library function.
 12 #
 13 Ncm.cfg_init ()
 14 
 15 #
 16 #  New homogeneous and isotropic cosmological model NcHICosmoDEXcdm 
 17 #
 18 cosmo = Nc.HICosmo.new_from_name (Nc.HICosmo, "NcHICosmoDEXcdm")
 19 
 20 #
 21 #  New recombination object configured to calculate up to redshift 
 22 #  10^9 and precision 10^-7.
 23 #
 24 recomb = Nc.Recomb.new_from_name ("NcRecombSeager{'prec':<1.0e-7>, 'zi':<1e9>}")
 25 
 26 #
 27 #  Setting values for the cosmological model, those not set stay in the
 28 #  default values. Remeber to use the _orig_ version to set the original
 29 #  parameters in case when a reparametrization is used.
 30 #
 31 
 32 #
 33 # C-like
 34 #
 35 cosmo.orig_param_set (Nc.HICosmoDEParams.H0,        70.0)
 36 cosmo.orig_param_set (Nc.HICosmoDEParams.OMEGA_C,   0.25)
 37 cosmo.orig_param_set (Nc.HICosmoDEParams.OMEGA_X,   0.70)
 38 cosmo.orig_param_set (Nc.HICosmoDEParams.T_GAMMA0,  2.72)
 39 cosmo.orig_param_set (Nc.HICosmoDEParams.OMEGA_B,   0.05)
 40 cosmo.orig_param_set (Nc.HICosmoDEParams.SPECINDEX, 1.00)
 41 cosmo.orig_param_set (Nc.HICosmoDEParams.SIGMA8,    0.90)
 42 cosmo.orig_param_set (Nc.HICosmoDEXCDMParams.W,    -1.00)
 43 
 44 #
 45 # OO-like
 46 #
 47 cosmo.props.H0      = 70.0
 48 cosmo.props.Omegab  = 0.05
 49 cosmo.props.Omegac  = 0.25
 50 cosmo.props.Omegax  = 0.70
 51 cosmo.props.Tgamma0 = 2.72
 52 cosmo.props.ns      = 1.0
 53 cosmo.props.sigma8  = 0.9
 54 cosmo.props.w       = -1.0
 55 
 56 #
 57 #  Preparing recomb with cosmo.
 58 #
 59 recomb.prepare (cosmo)
 60 
 61 #
 62 #  Calculating Xe, equilibrium Xe, v_tau and its derivatives.
 63 #
 64 x_a = []
 65 Xe_a = []
 66 Xefi_a = []
 67 v_tau_a = []
 68 dv_tau_dlambda_a = []
 69 d2v_tau_dlambda2_a = []
 70 
 71 for i in range (10000):
 72   alpha = -log (10000.0) + (log (10000.0) - log (100.0)) / 9999.0 * i
 73   Xe = recomb.Xe (cosmo, alpha)
 74   x = exp (-alpha)
 75   Xefi = recomb.equilibrium_Xe (cosmo, x)
 76   v_tau = recomb.v_tau (cosmo, alpha)
 77   dv_tau_dlambda = recomb.dv_tau_dlambda (cosmo, alpha)
 78   d2v_tau_dlambda2 = recomb.d2v_tau_dlambda2 (cosmo, alpha)
 79   
 80   x_a.append (x)
 81   Xe_a.append (Xe)
 82   Xefi_a.append (Xefi)
 83   v_tau_a.append (-v_tau)
 84   dv_tau_dlambda_a.append (-dv_tau_dlambda / 10.0)
 85   d2v_tau_dlambda2_a.append (-d2v_tau_dlambda2 / 200.0)
 86 
 87 #
 88 #  Ploting ionization history.
 89 #
 90 
 91 plt.title ("Ionization History")
 92 plt.xscale('log')
 93 plt.plot (x_a, Xe_a, 'r', label="Recombination")
 94 plt.plot (x_a, Xefi_a, 'b--', label="Equilibrium")
 95 plt.xlabel('$x$')
 96 plt.ylabel('$X_{e^-}$')
 97 plt.legend(loc=2)
 98 
 99 plt.savefig ("recomb_Xe.png")
100 
101 plt.clf ()
102 
103 (lambdam, lambdal, lambdau) = recomb.v_tau_lambda_features (cosmo, 2.0 * log (10.0))
104 
105 #
106 #  Ploting visibility function and derivatives.
107 #
108 
109 plt.title ("Visibility Function and Derivatives")
110 plt.xscale('log')
111 plt.plot (x_a, v_tau_a, 'r', label=r'$v_\tau$')
112 plt.plot (x_a, dv_tau_dlambda_a, 'b-', label=r'$\frac{1}{10}\frac{dv_\tau}{d\lambda}$')
113 plt.plot (x_a, d2v_tau_dlambda2_a, 'g--', label=r'$\frac{1}{200}\frac{d^2v_\tau}{d\lambda^2}$')
114 plt.legend()
115 plt.legend(loc=3)
116 
117 #
118 #  Annotating max and width.
119 #
120 
121 v_tau_max = -recomb.v_tau (cosmo, lambdam)
122 
123 plt.annotate (r'$v_\tau^{max}$, $z=%5.2f$' % (exp(-lambdam)-1), xy=(exp(-lambdam), v_tau_max),  xycoords='data',
124               xytext=(0.1, 0.95), textcoords='axes fraction',
125               arrowprops=dict(facecolor='black', shrink=0.05))
126 
127 v_tau_u = -recomb.v_tau (cosmo, lambdau)
128 
129 plt.annotate (r'$v_\tau=10^{-2}v_\tau^{max}$, $z=%5.2f$' % (exp(-lambdau)-1), xy=(exp(-lambdau), v_tau_u),  xycoords='data',
130               xytext=(0.02, 0.75), textcoords='axes fraction',
131               arrowprops=dict(facecolor='black', shrink=0.05))
132 
133 v_tau_l = -recomb.v_tau (cosmo, lambdal)
134 
135 plt.annotate (r'$v_\tau=10^{-2}v_\tau^{max}$, $z=%5.2f$' % (exp(-lambdal)-1), xy=(exp(-lambdal), v_tau_l),  xycoords='data',
136               xytext=(0.65, 0.25), textcoords='axes fraction',
137               arrowprops=dict(facecolor='black', shrink=0.05))
138 
139 #
140 #  Annotating value of zstar.
141 #
142 
143 lambdastar = recomb.tau_zstar (cosmo)
144 
145 plt.annotate (r'$z^\star=%5.2f$' % (exp(-lambdastar)-1), 
146               xy=(0.65, 0.95), xycoords='axes fraction')
147 
148 plt.savefig ("recomb_v_tau.png")
149 

This example produces:

An example of Supernova Ia model fitting in Python. Download the source.

To try this example you must have PyGObject installed and numcosmo built with --enable-introspection option.

  1 #!/usr/bin/python2
  2 
  3 from math import *
  4 from gi.repository import GObject
  5 import matplotlib.pyplot as plt
  6 from gi.repository import NumCosmo as Nc
  7 from gi.repository import NumCosmoMath as Ncm
  8 
  9 #
 10 #  Initializing the library objects, this must be called before 
 11 #  any other library function.
 12 #
 13 Ncm.cfg_init ()
 14 
 15 #
 16 #  New homogeneous and isotropic cosmological model NcHICosmoDEXcdm 
 17 #
 18 cosmo = Nc.HICosmo.new_from_name (Nc.HICosmo, "NcHICosmoDEXcdm")
 19 
 20 #
 21 #  Setting values for the cosmological model, those not set stay in the
 22 #  default values. Remeber to use the _orig_ version to set the original
 23 #  parameters in case when a reparametrization is used.
 24 #
 25 
 26 #
 27 # OO-like
 28 #
 29 cosmo.props.H0      = 70.0
 30 cosmo.props.Omegab  = 0.05
 31 cosmo.props.Omegac  = 0.25
 32 cosmo.props.Omegax  = 0.70
 33 cosmo.props.Tgamma0 = 2.72
 34 cosmo.props.ns      = 1.0
 35 cosmo.props.sigma8  = 0.9
 36 cosmo.props.w       = -1.0
 37 
 38 #
 39 #  Creating a new Modelset and set cosmo as the HICosmo model to be used.
 40 #
 41 mset = Ncm.MSet ()
 42 mset.set (cosmo)
 43 
 44 #
 45 #  Setting parameters Omega_c and w to be fitted and change parameter
 46 #  Omega_x -> Omega_k.
 47 #
 48 cosmo.de_omega_x2omega_k ()
 49 cosmo.props.Omegac_fit = True
 50 cosmo.props.w_fit = True
 51 
 52 #
 53 #  Creating a new Distance object optimized to redshift 2.
 54 #
 55 dist = Nc.Distance (zf = 2.0)
 56 
 57 #
 58 #  Creating a new Data object from distance modulus catalogs.
 59 #
 60 snia = Nc.DataDistMu.new (dist, Nc.DataSNIAId.SIMPLE_UNION2_1)
 61 
 62 #
 63 #  Creating a new Dataset and add snia to it.
 64 #
 65 dset = Ncm.Dataset ()
 66 dset.append_data (snia)
 67 
 68 #
 69 #  Creating a Likelihood from the Dataset.
 70 #
 71 lh = Ncm.Likelihood (dataset = dset)
 72 
 73 #
 74 #  Creating a Fit object of type NLOPT using the fitting algorithm ln-neldermead to
 75 #  fit the Modelset mset using the Likelihood lh and using a numerical differentiation
 76 #  algorithm (NUMDIFF_FORWARD) to obtain the gradient (if needed).
 77 #
 78 fit = Ncm.Fit.new (Ncm.FitType.NLOPT, "ln-neldermead", lh, mset, Ncm.FitGradType.NUMDIFF_FORWARD)
 79 
 80 #
 81 #  Running the fitter printing messages.
 82 #
 83 fit.run (Ncm.FitRunMsgs.SIMPLE)
 84 
 85 #
 86 #  Printing fitting informations.
 87 #
 88 fit.log_info ()
 89 
 90 #
 91 #  Calculating the parameters covariance using numerical differentiation.
 92 #
 93 fit.numdiff_m2lnL_covar ()
 94 
 95 #
 96 #  Printing the covariance matrix.
 97 # 
 98 fit.log_covar ()

This example produces:

#----------------------------------------------------------------------------------
# Model fitting. Interating using:
#  - solver:            NLOpt:ln-neldermead
#  - differentiation:   Numerical differentiantion (forward)
#.............
#  Minimum found with precision:  1.00000e-08 (|df|/f or |dx|)
#  Elapsed time: 00 days, 00:00:00.0360950
#  iteration            [000043]
#  function evaluations [000043]
#  gradient evaluations [000000]
#  degrees of freedom   [000577]
#  m2lnL     =     562.224308438599
#  Fit parameters:
#    [ 0.230882725715637  ] [-1.00940240383148   ] 
#----------------------------------------------------------------------------------
# Data used:
#   - Union2.1 sample
#----------------------------------------------------------------------------------
# Model[00]:
#   - XCDM - Constant EOS
#----------------------------------------------------------------------------------
# Model parameters
#   -      H0[00]:  70                  [FIXED]
#   -  Omegac[01]:  0.230882725715637   [FREE]
#   -  Omegak[02]:  0                   [FIXED]
#   - Tgamma0[03]:  2.72                [FIXED]
#   -  Omegab[04]:  0.05                [FIXED]
#   -      ns[05]:  1                   [FIXED]
#   -  sigma8[06]:  0.9                 [FIXED]
#   -       w[07]: -1.00940240383148    [FREE]
#----------------------------------------------------------------------------------
# Fitted parameters covariance matrix
#                                              -------------------------------
# Omegac[0001] =  0.2309      +/-  0.07425     |  1           | -0.9653      |
#      w[0007] = -1.009       +/-  0.2014      | -0.9653      |  1           |
#                                              -------------------------------

An example of Supernova Ia and BAO model fitting and confidence region calculation in Python. Download the source.

To try this example you must have PyGObject installed, matplotlib matplotlib and numcosmo built with --enable-introspection option.

  1 #!/usr/bin/python2
  2 
  3 from math import *
  4 from gi.repository import GObject
  5 import matplotlib.pyplot as plt
  6 from gi.repository import NumCosmo as Nc
  7 from gi.repository import NumCosmoMath as Ncm
  8 
  9 #
 10 #  Initializing the library objects, this must be called before 
 11 #  any other library function.
 12 #
 13 Ncm.cfg_init ()
 14 
 15 #
 16 #  New homogeneous and isotropic cosmological model NcHICosmoDEXcdm 
 17 #
 18 cosmo = Nc.HICosmo.new_from_name (Nc.HICosmo, "NcHICosmoDEXcdm")
 19 
 20 #
 21 #  Setting values for the cosmological model, those not set stay in the
 22 #  default values. Remeber to use the _orig_ version to set the original
 23 #  parameters in case when a reparametrization is used.
 24 #
 25 
 26 #
 27 # OO-like
 28 #
 29 cosmo.props.H0      = 70.0
 30 cosmo.props.Omegab  = 0.05
 31 cosmo.props.Omegac  = 0.25
 32 cosmo.props.Omegax  = 0.70
 33 cosmo.props.Tgamma0 = 2.72
 34 cosmo.props.ns      = 1.0
 35 cosmo.props.sigma8  = 0.9
 36 cosmo.props.w       = -1.0
 37 
 38 #
 39 #  A new Modelset with cosmo as the HICosmo model to be used.
 40 #
 41 mset = Ncm.MSet ()
 42 mset.set (cosmo)
 43 
 44 #
 45 #  Setting parameters Omega_c, Omega_x and w to be fitted (and change 
 46 #  parameter Omega_x -> Omega_k).
 47 #
 48 #cosmo.de_omega_x2omega_k ()
 49 cosmo.props.Omegac_fit = True
 50 cosmo.props.Omegax_fit = True
 51 cosmo.props.w_fit = True
 52 
 53 #
 54 #  A new Distance object optimized to redshift 2.
 55 #
 56 dist = Nc.Distance (zf = 2.0)
 57 
 58 #
 59 #  A new Data object from distance modulus catalogs.
 60 #  A new Data object from BAO catalogs.
 61 #
 62 snia = Nc.DataDistMu.new (dist, Nc.DataSNIAId.SIMPLE_UNION2_1)
 63 bao = Nc.data_bao_create (dist, Nc.DataBaoId.A_EISENSTEIN2005)
 64 
 65 #
 66 #  A new Dataset with snia and bao set.
 67 #
 68 dset = Ncm.Dataset ()
 69 dset.append_data (snia)
 70 dset.append_data (bao)
 71 
 72 #
 73 #  Creating a Likelihood from the Dataset.
 74 #
 75 lh = Ncm.Likelihood (dataset = dset)
 76 
 77 #
 78 #  Creating a Fit object of type NLOPT using the fitting algorithm ln-neldermead to
 79 #  fit the Modelset mset using the Likelihood lh and using a numerical differentiation
 80 #  algorithm (NUMDIFF_FORWARD) to obtain the gradient (if needed).
 81 #
 82 fit = Ncm.Fit.new (Ncm.FitType.NLOPT, "ln-neldermead", lh, mset, Ncm.FitGradType.NUMDIFF_FORWARD)
 83 
 84 #
 85 #  Running the fitter printing messages.
 86 #
 87 fit.run (Ncm.FitRunMsgs.SIMPLE)
 88 
 89 #
 90 #  Printing fitting informations.
 91 #
 92 fit.log_info ()
 93 
 94 #
 95 #  Calculating the parameters covariance using numerical differentiation.
 96 #
 97 fit.numdiff_m2lnL_covar ()
 98 
 99 #
100 #  Printing the covariance matrix.
101 # 
102 fit.log_covar ()
103 
104 #
105 #  Creating a new Likelihood ratio test object.
106 #  First we create two PIndex indicating which parameter
107 #    we are going to study.
108 # 
109 p1 = Ncm.MSetPIndex.new (cosmo.id (), Nc.HICosmoDEParams.OMEGA_C)
110 p2 = Ncm.MSetPIndex.new (cosmo.id (), Nc.HICosmoDEXCDMParams.W)
111 
112 lhr2d = Ncm.LHRatio2d.new (fit, p1, p2)
113 
114 #
115 #  Calculating the confidence region using the Likelihood ratio test.
116 #  Also calculate using the Fisher matrix approach.
117 #
118 cr_rg = lhr2d.conf_region (0.6826, 300.0, Ncm.FitRunMsgs.SIMPLE)
119 fisher_rg = lhr2d.fisher_border (0.6826, 300.0, Ncm.FitRunMsgs.SIMPLE)
120 
121 cr_p1array = cr_rg.p1.dup_array ()
122 cr_p2array = cr_rg.p2.dup_array ()
123 
124 fisher_p1array = fisher_rg.p1.dup_array ()
125 fisher_p2array = fisher_rg.p2.dup_array ()
126 
127 #
128 #  Ploting the confidence regions obtained from both methods.
129 #
130 
131 plt.title ("Confidence regions (%f)" % (cr_rg.clevel * 100.0))
132 plt.plot (cr_p1array, cr_p2array, 'r', label="Likelihood Ratio")
133 plt.plot (fisher_p1array, fisher_p2array, 'b-', label="Fisher Matrix")
134 
135 plt.xlabel(r'$\Omega_c$')
136 plt.ylabel(r'$w$')
137 
138 plt.legend(loc=4)
139 
140 plt.savefig ("snia_bao_rg_omegac_w.png")

This example has the following output and figure:

#----------------------------------------------------------------------------------
# Model fitting. Interating using:
#  - solver:            NLOpt:ln-neldermead
#  - differentiation:   Numerical differentiantion (forward)
#......................
#  Minimum found with precision:  1.00000e-08 (|df|/f or |dx|)
#  Elapsed time: 00 days, 00:00:00.1035530
#  iteration            [000137]
#  function evaluations [000137]
#  gradient evaluations [000000]
#  degrees of freedom   [000577]
#  m2lnL     =     562.202808924736
#  Fit parameters:
#    [ 0.225227921129909  ] [ 0.584140145983326  ] [-1.18852980893615   ] 
#----------------------------------------------------------------------------------
# Data used:
#   - Union2.1 sample
#   - Eisenstein 2005, BAO Sample A
#----------------------------------------------------------------------------------
# Model[00]:
#   - XCDM - Constant EOS
#----------------------------------------------------------------------------------
# Model parameters
#   -      H0[00]:  70                  [FIXED]
#   -  Omegac[01]:  0.225227921129909   [FREE]
#   -  Omegax[02]:  0.584140145983326   [FREE]
#   - Tgamma0[03]:  2.72                [FIXED]
#   -  Omegab[04]:  0.05                [FIXED]
#   -      ns[05]:  1                   [FIXED]
#   -  sigma8[06]:  0.9                 [FIXED]
#   -       w[07]: -1.18852980893615    [FREE]
#----------------------------------------------------------------------------------
# Fitted parameters covariance matrix
#                                              ----------------------------------------------
# Omegac[0001] =  0.2252      +/-  0.01881     |  1           |  0.1347      |  0.1098      |
# Omegax[0002] =  0.5841      +/-  0.4915      |  0.1347      |  1           |  0.9963      |
#      w[0007] = -1.189       +/-  0.9275      |  0.1098      |  0.9963      |  1           |
#                                              ----------------------------------------------
#----------------------------------------------------------------------------------
# Likelihood ratio confidence region at 68.260%, bestfit [  0.22522792   -1.1885298]:
#
#  looking root in interval [           0    1.5149868]:
#............
#  root found at   0.51764498 with precision 1.00000000e-05.
#  border found at   0.51764498.
#.
#  looking root in interval [   7.0669608    8.6377572]:
#.......
#  root found at    7.9006802 with precision 1.00000000e-05.
#  looking root in interval [   7.1152821    8.6860784]:
#......
.
.
. Several messages like the above.
.
.
#  root found at    14.179015 with precision 1.00000000e-05.
#  looking root in interval [   13.393617    14.964413]:
#.....
#  root found at    14.180544 with precision 1.00000000e-05.
#  looking root in interval [   13.395146    14.965943]:
#.......
#  root found at    14.182127 with precision 1.00000000e-05.
#  Start found at [270], ending...

Cluster abundance example in C. Download the source.

To compile the example use:

gcc -Wall example_ca.c -o example_ca `pkg-config numcosmo --libs --cflags`

  1 #include <glib.h>
  2 #include <numcosmo/numcosmo.h>
  3 
  4 gint
  5 main (gint argc, gchar *argv[])
  6 {
  7   NcHICosmo *cosmo;
  8   NcDistance *dist;
  9   NcWindow *wp;
 10   NcTransferFunc *tf;
 11   NcMatterVar *vp;
 12   NcGrowthFunc *gf;
 13   NcMultiplicityFunc *mulf;
 14   NcMassFunction *mf;
 15   guint np = 50;
 16   gint i;
 17 
 18   /**************************************************************************** 
 19    * Initializing the library objects, this must be called before 
 20    * any other library function.
 21    ****************************************************************************/  
 22   ncm_cfg_init ();
 23   
 24   /**************************************************************************** 
 25    * New homogeneous and isotropic cosmological model NcHICosmoDEXcdm.
 26    ****************************************************************************/  
 27   cosmo = nc_hicosmo_new_from_name (NC_TYPE_HICOSMO, "NcHICosmoDEXcdm");
 28 
 29   /**************************************************************************** 
 30    * New cosmological distance objects optimizied to perform calculations
 31    * up to redshift 2.0.
 32    ****************************************************************************/  
 33   dist = nc_distance_new (2.0);
 34  
 35   /**************************************************************************** 
 36    * New windown function 'NcWindowTophat'
 37    ****************************************************************************/  
 38   wp = nc_window_new_from_name ("NcWindowTophat");
 39 
 40   /**************************************************************************** 
 41    * New transfer function 'NcTransferFuncEH' using the Einsenstein, Hu
 42    * fitting formula.
 43    ****************************************************************************/  
 44   tf = nc_transfer_func_new_from_name ("NcTransferFuncEH");
 45 
 46   /**************************************************************************** 
 47    * New matter variance object using FFT method for internal calculations and
 48    * the window and transfer functions defined above.
 49    ****************************************************************************/  
 50   vp = nc_matter_var_new (NC_MATTER_VAR_FFT, wp, tf);
 51 
 52   /**************************************************************************** 
 53    * New growth function
 54    ****************************************************************************/  
 55   gf = nc_growth_func_new ();
 56 
 57   /**************************************************************************** 
 58    * New multiplicity function 'NcMultiplicityFuncTinkerMean'
 59    ****************************************************************************/  
 60   mulf = nc_multiplicity_func_new_from_name ("NcMultiplicityFuncTinkerMean");
 61 
 62   /**************************************************************************** 
 63    * New mass function object using the objects defined above.
 64    ****************************************************************************/  
 65   mf = nc_mass_function_new (dist, vp, gf, mulf);
 66 
 67   /**************************************************************************** 
 68    * Setting values for the cosmological model, those not set stay in the
 69    * default values. Remeber to use the _orig_ version to set the original
 70    * parameters in case when a reparametrization is used.
 71    ****************************************************************************/ 
 72   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_H0, 70.0);
 73   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_OMEGA_C, 0.25);
 74   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_OMEGA_X, 0.7);
 75   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_T_GAMMA0, 1.0);
 76   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_OMEGA_B, 0.05);
 77   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_SPECINDEX, 1.0);
 78   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_SIGMA8, 0.9);
 79   ncm_model_orig_param_set (NCM_MODEL (cosmo), NC_HICOSMO_DE_XCDM_W, -1.1);
 80 
 81   /**************************************************************************** 
 82    * Printing the parameters used.
 83    ****************************************************************************/
 84   printf ("# Model parameters:\n#"); 
 85   ncm_model_params_log_all (NCM_MODEL (cosmo));
 86 
 87   /**************************************************************************** 
 88    * Printing the growth function and its derivative with respect to z 
 89    * up to redshift 1.
 90    ****************************************************************************/ 
 91   nc_growth_func_prepare (gf, cosmo);
 92 
 93   for (i = 0; i < np; i++)
 94   {
 95     gdouble z = 1.0 / (np - 1.0) * i;
 96     gdouble gfz = nc_growth_func_eval (gf, cosmo, z);
 97     gdouble dgfz = nc_growth_func_eval_deriv (gf, cosmo, z);
 98     printf ("% 10.8f % 20.15g % 20.15g\n", z, gfz, dgfz);
 99   }
100   printf ("\n\n");
101 
102   /**************************************************************************** 
103    * Printing the transfer function and the matter power spectrum in the 
104    * kh (in unities of h/Mpc) interval [1e-3, 1e3] 
105    ****************************************************************************/
106   nc_transfer_func_prepare (tf, cosmo);
107 
108   for (i = 0; i < np; i++)
109   {
110     gdouble lnkh = log (1e-3) + log (1e6) / (np - 1.0) * i;
111     gdouble tfkh = nc_transfer_func_eval (tf, cosmo, exp (lnkh));
112     gdouble Pmkh = nc_transfer_func_matter_powerspectrum (tf, cosmo, exp (lnkh));
113     printf ("% 10.8f % 20.15g % 20.15g\n", exp (lnkh), tfkh, Pmkh);
114   }
115   printf ("\n\n");
116 
117   /**************************************************************************** 
118    * Printing the variance filtered with the tophat windown function using
119    * scales R in the interval [5, 50] at redshift 0.3.
120    * First calculates the growth function at z = 0.3 and then the spectrum
121    * amplitude from the sigma8 parameter.
122    ****************************************************************************/
123   nc_matter_var_prepare (vp, cosmo);
124   {
125     gdouble Dz = nc_growth_func_eval (gf, cosmo, 0.3);
126     gdouble A = nc_matter_var_sigma8_sqrtvar0 (vp, cosmo);
127     gdouble prefac = Dz * Dz * A * A;
128     
129     for (i = 0; i < np; i++)
130     {
131       gdouble lnR = log (5.0) + log (10.0) / (np - 1.0) * i;
132       gdouble sigma2 = prefac * nc_matter_var_var0 (vp, cosmo, lnR);
133       gdouble dsigma2_dlnR = nc_matter_var_dlnvar0_dlnR (vp, cosmo, lnR);
134       printf ("% 10.8f % 20.15g % 20.15g\n", exp (lnR), sigma2, dsigma2_dlnR);
135     }
136     printf ("\n\n");
137   }
138 
139   /**************************************************************************** 
140    * Printing the mass function integrated in the mass interval [1e14, 1e16]
141    * for the redhshifts in the interval [0, 2.0] and area 200 squared degree.
142    ****************************************************************************/
143   nc_mass_function_set_area_sd (mf, 200.0);
144   nc_mass_function_set_eval_limits (mf, cosmo, log(1e14), log(1e16), 0.0, 2.0);
145   nc_mass_function_prepare (mf, cosmo);
146 
147   for (i = 0; i < np; i++)
148   {
149     gdouble z = 2.0 / (np - 1.0) * i;
150     gdouble dndz = nc_mass_function_dn_dz (mf, cosmo, log(1e14), log(1e16), z, FALSE);
151     printf ("% 10.8f % 20.15g\n", z, dndz);
152   }
153   printf ("\n\n");
154 
155 
156   /**************************************************************************** 
157    * Freeing objects.
158    ****************************************************************************/ 
159   nc_distance_free (dist);
160   ncm_model_free (NCM_MODEL (cosmo));
161   nc_window_free (wp);
162   nc_transfer_func_free (tf);
163   nc_matter_var_free (vp);
164   nc_growth_func_free (gf);
165   nc_multiplicity_func_free (mulf);
166   nc_mass_function_free (mf);
167 
168   return 0;
169 }

This example has the following output:

# Model parameters:
#                    70                  0.25                   0.7                     1                  0.05                     1                   0.9                  -1.1
 0.00000000                    1   -0.514645182582311
 0.02040816    0.989521827230155   -0.512155427490298
 0.04081633    0.979098119618428   -0.509310730457052
 0.06122449    0.968735891971304   -0.506133999217271
 0.08163265    0.958441692552852   -0.502647779408878
 0.10204082    0.948221611387305   -0.498874222357099
 0.12244898    0.938081287298275   -0.494835138070745
 0.14285714    0.928025915309424   -0.490552352178394
 0.16326531    0.918060256550019   -0.486046757532692
 0.18367347    0.908188651680731   -0.481338496480979
 0.20408163    0.898415033975097   -0.476447152385309
 0.22448980    0.888742942158506   -0.471391975844945
 0.24489796    0.879175535674136   -0.466191041167092
 0.26530612    0.869715611611056    -0.46086158700888
 0.28571429     0.86036562079852   -0.455420201103897
 0.30612245    0.851127683238634   -0.449882877489654
 0.32653061     0.84200360568588   -0.444264420161198
 0.34693878    0.832994899324174   -0.438578802751155
 0.36734694    0.824102796392762   -0.432839361153584
 0.38775510    0.815328266029958   -0.427058679848391
 0.40816327     0.80667203166222   -0.421248292090362
 0.42857143    0.798134587624337   -0.415418985803334
 0.44897959    0.789716214481579   -0.409580948260127
 0.46938776    0.781416993769452   -0.403743512810795
 0.48979592    0.773236824337708   -0.397915320338556
 0.51020408    0.765175435067201   -0.392104312982734
 0.53061224     0.75723239998386   -0.386317786156192
 0.55102041    0.749407150113895   -0.380562410752784
 0.57142857    0.741698987103028   -0.374844249115267
 0.59183673    0.734107094038444   -0.369168818070038
 0.61224490    0.726630547606273    -0.36354107404639
 0.63265306    0.719268327846196   -0.357965499439271
 0.65306122    0.712019328866866   -0.352446072862367
 0.67346939    0.704882367667553   -0.346986368541785
 0.69387755    0.697856193074738   -0.341589471243274
 0.71428571    0.690939494223693   -0.336258152541685
 0.73469388    0.684130907771432   -0.330994814459831
 0.75510204    0.677429025204744   -0.325801494258627
 0.77551020    0.670832399464354   -0.320679920856448
 0.79591837     0.66433955103003   -0.315631539855434
 0.81632653    0.657948973544065   -0.310657537600845
 0.83673469    0.651659138971016   -0.305758866530375
 0.85714286    0.645468502441459   -0.300936233372512
 0.87755102    0.639375506514683   -0.296190151348566
 0.89795918    0.633378585424913   -0.291520942240238
 0.91836735    0.627476168505608   -0.286928757313555
 0.93877551    0.621666683639976   -0.282413591560572
 0.95918367    0.615948560255777   -0.277975299165827
 0.97959184    0.610320232074484   -0.273613606522902
 1.00000000    0.604780139583612   -0.269328125088067


 0.00100000    0.999805328836874  0.00099961069557061
 0.00132571     0.99966036548697   0.0013248110038449
 0.00175751    0.999408916610617  0.00175543356821907
 0.00232995    0.998974629521377  0.00232517613258463
 0.00308884    0.998228668319402  0.00307791055504462
 0.00409492    0.996956115835977  0.00407002410849081
 0.00542868    0.994802993737763  0.00537239634122765
 0.00719686    0.991193154320994  0.00707065170884944
 0.00954095    0.985194564691879  0.00926053017245344
 0.01264855    0.975287700876948   0.0120311270012985
 0.01676833    0.958925135476369    0.015419106248539
 0.02222996    0.931708734586102   0.0192974137879651
 0.02947052    0.886491810088735   0.0231599282977442
 0.03906940    0.815930668834037   0.0260101735325903
 0.05179475    0.726956269760714   0.0273717325212831
 0.06866488    0.652751656188497   0.0292570584059584
 0.09102982    0.608453134180805    0.033700623703963
 0.12067926    0.553582842935875   0.0369826388441461
 0.15998587    0.477966307235999   0.0365490589605499
 0.21209509    0.407543542501762   0.0352272421382249
 0.28117687    0.334015235521443   0.0313698285817094
 0.37275937    0.264888077550762   0.0261549198951282
 0.49417134    0.202854253187564   0.0203350753857352
 0.65512856    0.150128256891569   0.0147656107317776
 0.86851137    0.107719872554174   0.0100778333402021
 1.15139540   0.0752208251004834  0.00651479381825275
 1.52641797   0.0513422137708357  0.00402367273917402
 2.02358965   0.0343913610414364  0.00239343245510617
 2.68269580   0.0226840078767555  0.00138041925156272
 3.55648031   0.0147706213896594  0.000775921776189659
 4.71486636  0.00951269449406509  0.000426654653121894
 6.25055193  0.00606782572434713  0.000230136002447234
 8.28642773  0.00383743951101831  0.000122025454123775
 10.98541142  0.00240823254484467  6.3710816194812e-05
 14.56348478  0.00150080180911841  3.28028815112764e-05
 19.30697729  0.000929403783999379  1.66772008206936e-05
 25.59547923  0.000572276426899725   8.382527350344e-06
 33.93221772  0.000350566272802228  4.17015797585198e-06
 44.98432669  0.000213756088939872  2.0554088104965e-06
 59.63623317  0.000129792925234918  1.00464411637065e-06
 79.06043211  7.85140104015962e-05   4.873640672225e-07
 104.81131342  4.73333022366793e-05  2.34823616311431e-07
 138.94954944  2.84479990994849e-05  1.12450293666329e-07
 184.20699693  1.70501130001047e-05  5.35501443336509e-08
 244.20530945  1.01930476529716e-05  2.5372497078206e-08
 323.74575428  6.07966647435448e-06  1.19664020805526e-08
 429.19342601  3.61860031217541e-06  5.61997383816258e-09
 568.98660290  2.14962619374807e-06  2.62922608119628e-09
 754.31200634  1.27471880566688e-06  1.22568763887568e-09
 1000.00000000  7.54666527284974e-07  5.69521567404362e-10


 5.00000000      1.8274174978287    -2.24929762765026
 5.24056567     1.64293188219668    -2.28009947787663
 5.49270571     1.47493241367498    -2.31095510706532
 5.75697700      1.3221925792073    -2.34183286681469
 6.03396320     1.18355153549511    -2.37270076477371
 6.32427608     1.05791325775041    -2.40352736917292
 6.62855683    0.944244617464068    -2.43428104986324
 6.94747747    0.841574098075207    -2.46492970566601
 7.28174239    0.748990090162159    -2.49544217653731
 7.63208984    0.665638924715768    -2.52578787846441
 7.99929360    0.590723205987652    -2.55593595388316
 8.38416468    0.523499595669407    -2.58585606552141
 8.78755312    0.463276879910984    -2.61551870146876
 9.21034985    0.409413777456172    -2.64489339217275
 9.65348864    0.361316639593754     -2.6739499764721
 10.11794824    0.318437327146138    -2.70266205737228
 10.60475444     0.28027072841772    -2.73100549380316
 11.11498241    0.246352524257818    -2.75895817056931
 11.64975905    0.216256917442029    -2.78649830826439
 12.21026547    0.189594331575874    -2.81360304121439
 12.79773961    0.166009225421682    -2.84024511306228
 13.41347898    0.145177827321541    -2.86639976168026
 14.05884349    0.126805984633142    -2.89204759424341
 14.73525851    0.110626993051815    -2.91717223761638
 15.44421798   0.0963995862730792    -2.94175681427814
 16.18728771   0.0839060392870728    -2.96577688699737
 16.96610886     0.07295030478778    -2.98920061142062
 17.78240153   0.0633562382313602    -3.01199019960636
 18.63796860   0.0549658740495639     -3.0341280685365
 19.53469969   0.0476378264992342    -3.05559623589061
 20.47457531   0.0412458222156237    -3.07633382073925
 21.45967130   0.0356773551634426     -3.0962771409813
 22.49216334   0.0308323789669883    -3.11537003140222
 23.57433182   0.0266220242345668    -3.13354300769251
 24.70856681   0.0229675229228567    -3.15069896513196
 25.89737340   0.0197992636572314    -3.16667341306591
 27.14337720   0.0170557865368846    -3.18126467662515
 28.44933015   0.0146829227444275    -3.19422505388485
 29.81811658   0.0126330691958588    -3.20516121869808
 31.25275963   0.0108644371224447    -3.21348908975707
 32.75642784  0.00934051005036877    -3.21826737268193
 34.33244225  0.00802928574485255    -3.21889859337347
 35.98428365  0.00690264753039734    -3.21549443391645
 37.71560032  0.00593537466549436    -3.20976091658663
 39.53021605  0.00510509654368734    -3.20401614384521
 41.43213864    0.004391992685419    -3.19993910489024
 43.42556869  0.00377897660332001    -3.19875396917298
 45.51490890    0.003251432871344    -3.20121466693825
 47.70477382  0.00279696196220972    -3.20752079294885
 50.00000000   0.0024050850534294    -3.21755054030217


 0.00000000                    0
 0.04081633     166.593553876111
 0.08163265     615.595498675364
 0.12244898     1274.46682676192
 0.16326531     2076.70067755906
 0.20408163       2962.968012134
 0.24489796     3881.84589051384
 0.28571429     4790.14715032561
 0.32653061     5652.89636477869
 0.36734694     6443.01005196235
 0.40816327     7140.74456233729
 0.44897959     7732.97778077429
 0.48979592     8212.38825681368
 0.53061224     8576.58574685379
 0.57142857      8827.2385356839
 0.61224490     8969.23397350714
 0.65306122      9009.8988875736
 0.69387755     8958.29720342208
 0.73469388      8824.6142916826
 0.77551020      8619.6326663454
 0.81632653     8354.29763821171
 0.85714286     8039.36852319812
 0.89795918     7685.14854877498
 0.93877551     7301.28508364848
 0.97959184     6896.63120166262
 1.02040816     6479.15942753275
 1.06122449     6055.91877435476
 1.10204082     5633.02681460608
 1.14285714     5215.68926906412
 1.18367347     4808.24043655454
 1.22448980     4414.19865817605
 1.26530612     4036.33196753622
 1.30612245     3676.72980504035
 1.34693878     3336.87749760777
 1.38775510     3017.73082311451
 1.42857143     2719.78870837676
 1.46938776     2443.16238331635
 1.51020408     2187.64026355105
 1.55102041     1952.74734603486
 1.59183673     1737.79916708241
 1.63265306     1541.94995139757
 1.67346939     1364.23501441291
 1.71428571     1203.60759346322
 1.75510204     1058.97038812295
 1.79591837     929.202162688091
 1.83673469      813.17981601913
 1.87755102     709.796336489963
 1.91836735      617.97510505145
 1.95918367     536.680892784187
 2.00000000     464.928124731828

An example of the cluster abundance object in Python. Download the source.

To try this example you must have PyGObject installed, matplotlib matplotlib and numcosmo built with --enable-introspection option.

  1 #!/usr/bin/python2
  2 
  3 from math import *
  4 from gi.repository import GObject
  5 import matplotlib.pyplot as plt
  6 from gi.repository import NumCosmo as Nc
  7 from gi.repository import NumCosmoMath as Ncm
  8 
  9 #
 10 #  Initializing the library objects, this must be called before 
 11 #  any other library function.
 12 #
 13 Ncm.cfg_init ()
 14 
 15 #
 16 #  New homogeneous and isotropic cosmological model NcHICosmoDEXcdm 
 17 #
 18 cosmo = Nc.HICosmo.new_from_name (Nc.HICosmo, "NcHICosmoDEXcdm")
 19 
 20 #
 21 #  New cosmological distance objects optimizied to perform calculations
 22 #  up to redshift 2.0.
 23 #
 24 dist = Nc.Distance.new (2.0)
 25 
 26 #
 27 # New windown function 'NcWindowTophat'
 28 #
 29 wp =  Nc.Window.new_from_name ("NcWindowTophat")
 30 
 31 #
 32 # New transfer function 'NcTransferFuncEH' using the Einsenstein, Hu
 33 # fitting formula.
 34 #
 35 tf = Nc.TransferFunc.new_from_name ("NcTransferFuncEH")
 36 
 37 #
 38 # New matter variance object using FFT method for internal calculations and
 39 # the window and transfer functions defined above.
 40 #
 41 vp = Nc.MatterVar.new (Nc.MatterVarStrategy.FFT, wp, tf)
 42 
 43 #
 44 # New growth function
 45 #
 46 gf = Nc.GrowthFunc.new ()
 47 
 48 #
 49 # New multiplicity function 'NcMultiplicityFuncTinkerMean'
 50 #
 51 mulf = Nc.MultiplicityFunc.new_from_name ("NcMultiplicityFuncTinkerMean")
 52 
 53 #
 54 # New mass function object using the objects defined above.
 55 #
 56 mf = Nc.MassFunction.new (dist, vp, gf, mulf)
 57 
 58 #
 59 #  Setting values for the cosmological model, those not set stay in the
 60 #  default values. Remember to use the _orig_ version to set the original
 61 #  parameters when a reparametrization is used.
 62 #
 63 cosmo.props.H0      = 70.0
 64 cosmo.props.Omegab  = 0.05
 65 cosmo.props.Omegac  = 0.25
 66 cosmo.props.Omegax  = 0.70
 67 cosmo.props.Tgamma0 = 2.72
 68 cosmo.props.ns      = 1.0
 69 cosmo.props.sigma8  = 0.9
 70 cosmo.props.w       = -1.0
 71 
 72 #
 73 #  Printing the parameters used.
 74 #
 75 print "# Model parameters: ", 
 76 cosmo.params_log_all ()
 77 
 78 #
 79 # Number of points to build the plots
 80 #
 81 np = 2000
 82 divfac = 1.0 / (np - 1.0)
 83 
 84 #
 85 #  Calculating growth and its derivative in the [0, 2] redshift
 86 #  range.
 87 #
 88 za = []
 89 Da = []
 90 dDa = []
 91 
 92 gf.prepare (cosmo)
 93 
 94 for i in range (0, np):
 95   z = 2.0 * divfac * i
 96   D = gf.eval (cosmo, z)
 97   dD = gf.eval_deriv (cosmo, z)
 98   za.append (z)
 99   Da.append (D)
100   dDa.append (dD)
101 
102 #
103 #  Ploting growth function.
104 #
105 
106 plt.title ("Growth Function")
107 plt.plot (za, Da, 'r', label="D")
108 plt.plot (za, dDa, 'b--', label="dD/dz")
109 plt.xlabel('$z$')
110 plt.legend(loc=2)
111 
112 plt.savefig ("growth_func.png")
113 plt.clf ()
114 
115 #
116 # Calculating the transfer function and the matter power spectrum in the
117 # kh (in unities of h/Mpc) interval [1e-3, 1e3]
118 #
119 
120 kha = []
121 Ta = []
122 Pma = []
123 
124 tf.prepare (cosmo)
125 
126 for i in range (0, np):
127   lnkh = log (1e-4) +  log (1e7) * divfac * i
128   kh = exp (lnkh)
129   T = tf.eval (cosmo, kh)
130   Pm = 1.0e3 / 7.0 * tf.matter_powerspectrum (cosmo, kh)
131   kha.append (kh)
132   Ta.append (T)
133   Pma.append (Pm)
134 
135 #
136 #  Ploting transfer and matter power spectrum
137 #
138 
139 plt.title ("Transfer Function and Matter Power Spectrum")
140 plt.xscale('log')
141 plt.plot (kha, Ta, 'r', label="T(kh)")
142 plt.plot (kha, Pma, 'b--', label="P_m(kh)")
143 plt.xlabel('$k$')
144 plt.legend(loc=1)
145 
146 plt.savefig ("transfer_func.png")
147 plt.clf ()
148 
149 #
150 # Calculating the variance filtered with the tophat windown function using
151 # scales R in the interval [5, 50] at redshift 0.3.
152 # First calculates the growth function at z = 0.3 and then the spectrum
153 # amplitude from the sigma8 parameter.
154 #
155 
156 vp.prepare (cosmo)
157 
158 Dz = gf.eval (cosmo, 0.3)
159 A  = vp.sigma8_sqrtvar0 (cosmo)
160 prefact = A * A * Dz * Dz
161 
162 Ra = []
163 sigma2a = []
164 dlnsigma2a = []
165 
166 for i in range (0, np):
167   lnR = log (5.0) +  log (10.0) * divfac * i
168   R = exp (lnR)
169   sigma2 = prefact * vp.var0 (cosmo, lnR)
170   dlnsigma2 = vp.dlnvar0_dlnR (cosmo, lnR) 
171   Ra.append (R)
172   sigma2a.append (sigma2)
173   dlnsigma2a.append (dlnsigma2)
174 
175 #
176 #  Ploting filtered matter variance
177 #
178 
179 plt.title ("Variance and Variance Derivative")
180 plt.plot (Ra, sigma2a, 'r', label='$\sigma^2(\ln(R))$')
181 plt.plot (Ra, dlnsigma2a, 'b--', label='$d\ln(\sigma^2)/d\ln(R)$')
182 plt.xlabel('$R$')
183 plt.legend(loc=1)
184 
185 plt.savefig ("matter_var.png")
186 plt.clf ()
187 
188 #
189 # Calculating the mass function integrated in the mass interval [1e14, 1e16]
190 # for the redhshifts in the interval [0, 2.0] and area 200 squared degree.
191 #
192 
193 mf.set_area_sd (200.0)
194 mf.set_eval_limits (cosmo, log (1e14), log(1e16), 0.0, 2.0)
195 
196 dndza = []
197 
198 for i in range (0, np):
199   dndz = mf.dn_dz (cosmo, log(1e14), log(1e16), za[i], True)
200   dndza.append (dndz)
201 
202 #
203 #  Ploting the mass function
204 #
205 
206 plt.title ("Mass Function")
207 plt.plot (za, dndza, 'r', label='$dn/dz$')
208 plt.xlabel('$z$')
209 plt.legend(loc=1)
210 
211 plt.savefig ("mass_function.png")
212 plt.clf ()

This example produces:

Contact

Please report bugs, ask questions and send comments/suggestions at numcosmo-help@nongnu.org