
The purpose of CLASS is to simulate the evolution of linear perturbations in the universe and to compute CMB and large scale structure observables. Its name also comes from the fact that it is written in objectoriented style mimicking the notion of class. Classes are a wonderfull programming feature available e.g. in C++ and python, but these languages are known to be less vectorizable/parallelizable than plain C (or Fortran), and hence potentially slower. For CLASS we choose to use plain C for high performances, while organizing the code in a few modules that reproduce the architecture and philosophy of C++ classes, for optimal readability and modularity.
The use of CLASS is free provided that if you use it in a publication, you will cite at least the paper CLASS II: Approximation schemes (reference below). You are welcome to cite any other CLASS paper if relevant!
There are two ways to download CLASS. The simplest thing is to download a tar.gz archive of the latest released (master branch) version, v2.7.2, by cliking class_public2.7.2.tar.gz. But if you are familiar with git repositories, and you wish to do modifications to the code, or develop a new branch of the code, or see all public branches and/or old versions, you will prefer to clone it from the class_public git repository.
After downloading the code, unpack the archive (tar zxvf
class_publicvx.y.z.tar.gz), go to the class directory (cd
class_publicvx.y.z/) and compile (make clean; make class; maybe with option j to speed it up). If the
first compilation attempt fails, you may need to open the Makefile and
adapt the name of the compiler (default: gcc), of the
optization flag (default: O4 ffastmath) and of the OpenMP flag
(default: fopenmp; this flag is facultative, you are free to
compile without OpenMP if you don't want parallel execution; note that
you need the version 4.2 or higher of gcc to be able to
compile with fopenmp). Several details on the CLASS
compilation are given on the installation wiki page (in particular, for compiling
on Mac OS >= 10.9 despite potential incompatibilities between clang, OpenMP, and versions of python compiled on your computer with gcc).
To check that the code runs, type: ./class explanatory.ini
The explanatory.ini file is a reference input file,
containing and explaning the use of all possible input parameters. We
recommend to read it, to keep it unchanged (for future reference), and
to create for your own purposes some shorter input files, containing
only the input lines which are useful for you. Input files must have a
*.ini extension.
If you want to play with the precision/speed of the code, you can use
one of the provided precision files (e.g. cl_permille.pre) or
modify one of them, and run with two input files (e.g. ./class
test.ini cl_permille.pre).
For more details, view
our online
html documentation generated by doxygen (credits Deanna
Hooper). Other CLASS papers dedicated to various aspects of the code
are listed below. You can also look
at slides from CLASSdedicated courses.
To use CLASS from python, or ipython notebooks, or from the Monte
Python parameter extraction code, you need to compile not only the
code, but also its python wrapper. This can be done by typing just
make instead of make class. More details on the
wrapper and its compilation are found on the wiki
page.
Since version 2.3, the package includes an improved plotting script
called CPU.py (Class Plotting Utility), written by Benjamin
Audren and Jesus Torrado. It can plot the Cl's, the P(k) or any other
CLASS puput, for one or several models, as well as their ratio or
percentage difference. The syntax and list of available options is
obtained by typing pyhton CPU.py help. There is a similar
script for MATLAB, written by Thomas Tram. To use it, once in MATLAB,
type help plot_CLASS_output.m
If you want to develop the code, we
suggest that you download it from the github
webpage rather than from classcode.net. Then you will enjoy all
the feature of git repositories. You can even develop your own branch
and get it merged to the public distribution, following the public contributing instructions.
 CLASS I:
Overview, by J. Lesgourgues, arXiv:1104.2932
[astroph.IM]
 CLASS II: Approximation schemes, by D. Blas,
J. Lesgourgues, T. Tram, arXiv:1104.2933
[astroph.CO], JCAP 1107 (2011) 034
 CLASS III: Comparision
with CAMB for LambdaCDM, by J. Lesgourgues, arXiv:1104.2934
[astroph.CO]
 CLASS IV: Efficient implementation of noncold
relics, by J. Lesgourgues, T. Tram, arXiv:1104.2935
[astroph.CO], JCAP 1109 (2011) 032
 Efficient computation of
the nonlinear matter power spectrum of LambdaCDM, by B. Audren,
J. Lesgourgues, arXiv:1106.2607
[astroph.CO], JCAP 1110 (2011) 037
 Optimal polarisation
equations in FLRW universes, by Thomas Tram, Julien Lesgourgues, JCAP 1310 (2013) 002
 Fast and accurate CMB computations in nonflat FLRW universes, by
Julien Lesgourgues, Thomas Tram, JCAP
09 (2014) 032
 The CLASSgal code for Relativistic Cosmological
Large Scale Structure, by E. Di Dio, F. Montanari, J. Lesgourgues and
R. Durrer, JCAP 1311
(2013) 044
The developement of CLASS benefits from various
essential contributors credited below. In absence of specific credits,
developements are written by the main CLASS authors, Julien
Lesgourgues and Thomas Tram.
In case you are interested in downloading an old version, go to the
class_public page. There is a horizontal bar with commits, branches, releases, contributors. Click releases and you'll get zip or tar.gz archives of all previous versions.
v2.7 (10.09.2018) 
 includes a new graphical interface showing the evolution of linear perturbations in real space, useful for pedagogical purposes. To run it on a browser, read instructions in RealSpaceInterface/README (credits: Max Beutelspacher, Georgios Samaras)
 when running with ncdm (non cold dark matter) while asking for the matter power spectrum mPk, you will automatically get both the total nonrelativistic matter spectrum Pm(k,z) and the baryonspluscdmonly (cb) spectrum Pcb(k,z). The latter is useful e.g. for computing the power spectrum of galaxies, which traces bc instead of total matter (see e.g. 1311.0866, 1807.04672). From the classy wrapper you get the cb quantities through several new functions like pk_cb(), get_pk_cb(), sigma8_cb(), etc. (Credits: M. Archidiacono, Samuel Brieden, Nils Schöneberg)
 implementation of the P(k) equal method (see 0810.0190, 1601.07230) when using Halofit in combination with a fluid with arbitrary (w0_fld, wa_fld) parameters. The simultaneous use of Halofit, of the fluid, and of a nonzero wa_fld switches up this method automatically, up to a redshift pk_eq_z_max set by default to 5 (Credits: M. Archidiacono, S. Clesse)
 includes eleven new illustrative notebooks in notebooks/, and their plain python script counterparts in scripts/ (content documented in the Cambridge or eventually Darmouth CLASS lectures slides)
 few bug and memory leak fixes
 22.10.2018 (v2.7.1): removed useless .ini and .pre files; added input files matching Planck baseline model with best fit values (base_2015_plikHM_TT_lowTEB_lensing.ini, base_2018_plikHM_TTTEEE_lowl_lowE_lensing.ini); in python wrapper, reinitialisation with struct_cleanup() now done automatically; several minor bug fixes (credits D.C. Hooper, J. Torrado, N. Schöneberg)
 11.04.2019 (v2.7.2): added couple of functions in wrapper (credits J. Torrado, N. Schöneberg) for compatibility with LSS likelihoods in Cobaya v1.1

v2.6 (25.03.2017) 
 by default, the perturbations of the fluid sector _fld (usually employed to play the role of Dark Energy) now follow the PPF approximation instead of real fluid equations, which allows to cross the phantom divide (but user can still switch to true fluid equations, see use_ppf parameter in explanatory.ini). Our PPF equations afe implemented in both newtonian and synchronous gauge as explained in these notes. Also, it is now simpler to customise the function w_fld(a) because it appears in only one place in the function background_w_fld().
 compilation and installation scripts made more robust, classy wrapper compatible with python 3 if you compile with the option PYTHON=python3 make all (credits M. Millea); you can use it in your python sessions, scripts, notebooks, etc.; note however that MontePython is still only compatible with python 2.7 for the moment, and wants a classy version compiled with python 2.x.
 more accurate calculation of the scale independent CDM growth factor D(a)  and f(a)  taking into account radiation and dark energy.
 option extra metric transfer functions to output the transfer functions of all metric perturbations (credits W. Valkenburg).
 among the reionisation parametrisation options reio_parametrisation, added a new scheme reio_inter for linear interpolation between discrete values of xe(z). Other schemes (e.g. tanh() steps) are still there.
 2.07.2017 (v2.6.1): few bug fixes (e.g. for P_NL(k) with isocurvature modes); solved memory leak issues in the wrapper.
 23.10.2017 (v2.6.2): minor improvements; the most important is a boost of the Halofit precision: previously, purely numerical random errors in Halofit could spoil your future LSS experiment forecasts; also updated the BBN interpolation table (credits: Ophelia Pisanti), added sigma8 as a possible input parameter instead of A_s, and gave the user the possibility to control manually the quadrature scheme used to discretise the momentum space of NCDM (only relevant for NCDM experts willing to achieve high precision, e.g., with WDM at large k); also, improved compilation script, gave possibility to run with tensors only (e.g. modes=t, A_s=1, r=1 would mean: tensors only with A_t=1), and improved formula for the diffusion damping scale.
 24.10.2017 (v2.6.3): restored compatibility with python 3 (broken in 2.6.2 by small modification of setup.py)

v2.5 (29.03.2016) 
 automatic documentation generated by doxygen (credits Deanna Hooper). You can browse the html documentation online, or view the PDF located in your CLASS folder doc/manual/.
 number density count (known as CLASSgal features) generalised to curved space, as described in paper in preparation. Also corrected evolution bias and improved Limber approximation scheme for number counts (credits: Francesco Montanari, Enea Di Dio)
 a few new parameters, like the effective sound/viscosity speed of ultrarelativistic species ceff2_ur, cvis2_ur
 for manystep reionisation, coded the scheme reio_many_tanh, a more sensible scheme than reio_bins_tanh (see section 3.c of explanatory.ini for details)
 if you want to plot transfer functions at very high redshift (even before recombination), you can now increase z_pk or z_max_pk up to arbitrary high values, provided that the output contains only nonCMB (nontCl,pCl,lCl) entries.
 new options in inflationary module, like N_star or inflation_behavior = analytical (see explanatory.ini for details)
 several minor bug fixes (credits Sandro Pinto, Francesco Montanari, Guido Pettinari, Aurelien BenoitLevy and others)
 10.03.2017 (v2.5.1): compatible with python3 (credits M. Millea), fixes for a more robust wrapper installation, new reionisation scheme 'reio_inter' (see explanatory.ini for details)

v2.4 (29.09.2014)   extended
inflation simulator embedded in the primordial module. Option
'inflation_V' (input = V(phi) function in observable range)
still exists, but more efficient; two new options
'inflation_H' (input = H(phi) function in observable range)
and 'inflation_V_end' (input = V(phi) trusted until the end
of inflation, and parameters specifying the amount of inflation
between the Hubble crossing for the pivot scale and the end of
inflation). See explanatory.ini for details on new input
parameters.
 improved accuracy of Limber approximation in curved
space, as explained in Appendix E of JCAP 09 (2014) 032
 the python code for plotting
output files, CPU.py, has been significantly improved (type
python CPU.py help to check options and input format)
(credits: Benjamin Audren, Jesus Torrado).
 the classy
wrapper can now output many more quantities than just the CMB Cl's and
P(k): density Cl's (this can be used for Euclid forecast with
detailled modelling of the power spectrum, credits Benjamin Audren),
evolution of background, thermodynamical, perturbation quantities,
density and velocity transfer functions.
 expanded automatic
tests, with a detailled explanation in the header of
python/test_class.py. It is now easy to combine models, add
new scenarios to test, and systematically compare the implementation
in synchronous and Newtonian gauge.
 improvements in the Decay
Dark Matter (ddm) sector: choice between more input formats,
fixed error in Newtonian gauge.
 increased precision settings for
CMB and P(k) with massive neutrinos or WDM (ncdm) in the
Newtonian gauge (thanks to Mathias Garny).
 various small bug
fixes (credits Guido Pettinari) and memory leak fixes.
 12.10.2014 (v2.4.1): default parameter values updated close
to Planck 2013 best fit
 06.01.2015 (v2.4.2): minor improvements, mainly related to the plotting unit CPU.py, to the Makefile and to the use of the functionality k_output_values=... (perturbations are now stored in files for exactly those values, not values close to them)
 10.03.2015 (v2.4.3): minor mistakes corrected (lensing potential in curved space, details of nCl and DM annihilation calculation, densitylensing correlations between bins), as well as a longstanding rare bug in the integrator
 21.10.2015 (v2.4.4): few bugs corrected in thermodynamics and transfer module. One of them, introduced in 2.4.3, could cause the code to remained stalled (only on some architectures). Another one was problematic only when using HyRec with annihilating Dark Matter. Small revision of the expression and sampling of the jumps in the reio_bins_tanh scheme, to make it more robust against interpolation errors
 22.02.2016 (v2.4.5): Corrected a few bugs affecting Halofit precision, mainly with with massive nu or decaying DM (Thanks to Carmelita Carbone, Matteo Zennaro, Vivian Poulin)
 v2.3 (09.05.2014) 
 implemented a new species: decaying cold dark matter
(dcdm) and its relativistic decay product (dr);
also, background module improved, now ready for adding easily other
cases where one needs to integrate more background quantitites over
time than just the scale factor a(tau).
 implemented a new input
parameter: '100*theta_s', the angle of the sound horizon at
decoupling (thanks to S. Plaszczynski); also, input module improved,
now ready for adding easily several input parameters which require a
shooting method (like '100*theta_s' or the decaying dark
matter density today 'Omega_dcdmdr', another example not
written yet would be 'sigma_8')
 increased precision
settings, consequences mainly for P(k) at large k >> 0.1 h/Mpc (thanks
to ChiTing Chiang, Mathias Garny, Aurel Schneider)
 tensor
calculation sped up, thanks to independent sampling of wavenumber
space for scalars and tensors
 the 'root' name preceeding
all output file names is now set by default to the input file name
plus the first available integer number
(e.g. output/myname00_ the first time,
output/myname01_ in the next run...); also, improved output
file headers with automatic column numbering
 classy wrapper
improved (installation compatible with both Python2.6 and 2.7, handles
tensor and isocurvature modes, improved automatic testing with
nosetests python/test_class.py, changed the function
get_current_derived_parameters() to accept as argument a list
of names) (credits: Benjamin Audren)
 new MATLAB plotting routine
plot_CLASS_output.m, type help plot_CLASS_output.m
in MATLAB for usage
 the other plotting routine, CPU, is
in the process of being rewritten. A new file, CPU.py is
using matplotlib, and works exactly like the MATLAB routine  but is
so far incomplete (credits: Benjamin Audren)
 26.05.2014
(v2.3.2): fixed minor bugs and extended functionalities of
CPU.py
 v2.2 (28.03.2014) 
 incorporated massless and massive neutrinos (in the language
of the code, the ultrarelativistic ur and noncold dark
matter ncdm species) in the calculation of tensor modes (for
temperature and polarisation)
 for speed up, included two
approximation schemes for tensors, described in the field tensor
method of explanatory.ini. By default, when evolving
tensor perturbations, the code will replace massive neutrinos by an
appropriate amount of massless ones. This works very well, especially
for masses below 0.6eV, and even above. The exact calculation
involving the ncdm momentum space can always be restored.
 v2.1 (07.03.2014) 
 the ClassGAL features developped in JCAP 1311 (2013) 044
(by E. Di Dio, F. Montanari, J. Lesgourgues and R. Durrer) have been
imported into the main distribution of the code. The naive "density
Cl's" are replaced by the "number count Cls", calculated if the
'output' entry includes 'nCl'. The user can choose
to include all relativistic corrections by passing 'number count
contributions = density, rsd, lensing, gr', or only the dominant
terms. One can also tune the selection function, linear bias,
magnification bias and evolution effects (see the description of the
fields 'dNdz_selection', 'bias', 's_bias', 'dNdz_evolution'
in explanatory.ini and in the appendix of JCAP 1311 (2013) 044).
 the nonlinear module has been completely rewritten. In the main
function, the nonlinear module is now called before the transfer
module, to allow for the calculation of lensing Cl's, density Cl's and
lensed CMB Cl's including nonlinear corrections. The module features
HALOFIT recalibrated by Takahashi et al., extended to massive
neutrinos following the same method as in arXiv:1109.4416 (input
command 'non linear = halofit' (vredits Simeon Bird). The
oneloop and time renormalisation algorithms of previous versions have
been dropped temporarily, but new methods are now easier to
incorporate, and will be implemented in the future.
 new features
for passing or plotting the primordial power spectrum (credits Jesus
Torrado). They are active when the input is set to 'P_k_ini type =
external_Pk' (instead of the other options 'analytic_Pk',
'inflation_V', 'two scales'). The user can pass the primordial
spectrum as an input file, or even compute it onthefly with an
external code (in whatever language). The input is then the command
line for running that code. This is described briefly in
explanatory.ini and extensively in the
external_Pk/README.md file delivered with the code. Finally,
for whatever way of computing the primordial spectrum, the user can
output it in a file with the input command 'write primordial'
described in explanatory.ini.
 implementation of
perturbed recombination (credits Luc Voruz), based on arXiv:0707.2727 and astroph/0503196. A
detailed description of related equations is provided in these notes. Perturbed recombination can be switched on by
passing 'perturbed recombination = yes', as explained in
explanatory.ini.
 facility for printing perturbations as
a function of time (credits Thomas Tram). In order to physically
understand what you are doing, or produce nice plots in a paper, it is
useful to output the evolution of each perturbation (of matter
components, of the metric, etc.) as a function of time. This feature
is now directly accessible from the input file, by passing a list of
wavenumbers in the field 'k_output_values' as described in
explanatory.ini. Then the corresponding output files will be
written.
 compatible with version 2.0 of Monte Python (credits Benjamin
Audren)
 improved Python wrapper classy.pyx (credits
Benjamin Audren), mainly a change of function names and error
classes. (For developers: a test suite has been written, in
python/test_class.py, which necessitates the python "nose"
package. It calls CLASS sequentially on many combinations of models,
and is useful to check that CLASS is still working after having made
modifications.) For details see the new wiki on the python wrapper and on the python test
function.
 22.03.2013 (v2.1.2): corrected a few bugs
introduced in v2.1.0 (most important related to calculation of baryon
drag redshift, credits Francesco Montesano), and updated Halofit with
the revision of Takahashi et al. 2012 extended to massive neutrinos
(credits Simeon Bird)
 v2.0 (01.10.2013) 
 implementation of spatial curvature (credits Thomas
Tram). Curvature is set using the 'Omega_k' input parameter
(positive for negative curvature, following the usual convention).
 the perturbation module relies on the optimal Boltzmann hierarchy
described in arXiv:1305.3261
[astroph.CO].
 the transfer module has exprienced major
changes. For the computation of hyperspherical Bessel functions and
transfer functions, some explanations on the methodology, the
precision, the comparison with other codes are given in arXiv:1312.2697. In brief,
with default precision settings, the scalar temperature Cls are
precise at the 0.1% level, and the scalar polarisation Cls at the 0.2%
level. The code remains very fast for small curvature, but the running
time increases with the absolute value of Omega_k.
 more
accurate for tensor modes, and ready for working with vector modes
(they are already implemented in the transfer module).
 spherical
Bessel functions cannot be stored/read from a file anymore, because
the gain in time was too marginal. All Bessel functions are now
computed onthefly by a set of new efficient functions, called
directly from the transfer module. Hence, the bessels module
and structure have been eliminated.
 09.10.2013
(v2.0.2): added the possibility to output the correlation Cls
between density and galaxy lensing; computation of hyperspherical
bessel functions made faster thanks to a vectorisation scheme; fixed a
few bugs; restored the same sign convention for Emodes as CAMB and
CMBFAST, while v2.0.1 had the opposite convention, taken from
Hu & White.
 31.10.2013 (v2.0.3): several little bug
fixes, accuracy of ClTphi and ClEphi restored, thanks to
S. Plaszczynski and G. Pettinari
 09.12.2013 (v2.0.4):
wavenumber sampling and few approximations improved, to match arXiv:1312.2697. New input
field temperature contributions, allows to compute separately
the SW, late and early ISW, Doppler and polarisation contributions.
 v1.7 (21.03.2013) 
 version ready for use with Planck. Three precision
parameters increased to match Planck sensitivity. With new default
settings, Planck chi2 accurate up to approximately 0.5. This has been
proved to be sufficient for parameter extraction from Planck data when
using MCMC algorithms (checked through intensive testing within Planck
collaboration, comparing different codes and various accuracy
settings). Still possible to use higher precision settings when
needed, e.g. for a very precise minimization of Planck chi2, or to
derive frequentist bounds.
 compatible with version 1.2 of Monte Python (credits Benjamin
Audren). This version of the parameter extraction code is interfaced
with the Planck and WMAP9 likelihoods, and features fast nuisance
parameters (similar to the new CosmoMC implementation but simpler:
does the Cholesky parameter rotation, but not the fastparameter
dragging scheme; this is sufficient for a considerable speed up)
(credits A. Lewis)
 implements an alternative parameter set for
the primordial spectrum, consisting in amplitudes at two scales
instead of one amplitude and one tilt, identical to the parameters in
the isocurvature section of the Planck inflation paper. See
explanatory.ini for all possibilities.
 plethora of
derived parameters defined in the python wrapper
python/classy.pyx, ready to be used in Monte Python: slowroll parameters,
isocurvature fraction defined in different manners, tilt and running
computed numerically at the end of the inflationary module.
 for
DM annihilation, new flag to switch on/off the onthespot
approximation.
 28.03.2013: restored computation of lensed BB
spectrum, set accidentally to zero in versions 1.6.x and 1.7.0 (but OK
in earlier versions; thanks to Giulio Fabbian for pointing it out)
 04.04.2013: fixed bug introduced in 1.7.0, leading to
segmentation fault in newtonian gauge (thanks to Ivan Duran Sancho for
pointing it out)
 v1.6 (15.01.2013) 
 computation of fullsky galaxy lensing potential Cl's,
useful for cosmic shear experiments (if you add sCl to the
field output=...)
 for both lensing Cl's (sCl)
and density Cl's (dCl), source galaxies can be distributed in
redshift bins with either gaussian, tophat or
diraclike selection functions (see field selection
in explanatory.ini). Also the dCl calculation has
been speeded up and redesigned for more accurate results, even when
the bins are many, and/or thin, and/or at small redhsift.
 the
python wrapper python/classy.pyx includes more
functionalities, useful for running with the updated version 1.1 of Monte Python (credits Benjamin
Audren)
 several bugs fixed (many thanks to Stephane Plaszczynski,
Sophie HenrotVersille, Alberto Vallinotto, Katarina Markovic, Guido
Pettinari, Matthieu Roman). The major one affected the CMB lensing
potential Cl's, but only occasionally and when running with high
precision settings (with a value of the precision parameter
perturb_sampling_stepsize smaller than the default
one). Another bug could occasionally affect the result of the Halofit
routine.
 v1.5 (25.10.2012) 
 changed three defaut values of RECFAST parameters to
match RECFAST v1.5.2
 module primordial includes an
inflation simulator computing the primordial spectrum fully
numerically, for a given expression of the scalar potential valid
within the observable inflation range (as in Lesgourgues and
Walkenburg (2007)), see explanatory.ini for details on
possible input parameters
 includes several parameters to model
CDM annihilation (in the smooth background and in halos), as in Gisen et al. (2012)
 code can output not only individual density transfer functions,
but also individual velocity transfer functions (if you add
vTk to the field output=...)
 python wrapper
python/classy.pyx expanded for compatibility with parameter
inference code Monte Python v1.0
 corrected bug that showed up when trying to get finely sampled
transfer functions. Sampling can now be arbitrarily fine.

internal reordering of operations in transfer modules, that
speeds up the calculation of matter density Cl's. Also, the user can
compute correlation between all shells or only between neighbouring
shells.
 v1.4 (13.02.2012) 
 recombination can be computed either with RECFAST v1.5,
or with the HyRec code
by Y. AliHaimoud & C. Hirata. Just set the input parameter
recombination to either RECFAST or
HyRec. HyRec is slower, but more precise and flexible. Both
codes agree at the level of precision of Planck and for standard
cosmology. The November 2011 version of HyRec is distributed together
with v1.4. Class can be compiled without HyRec by leaving HYREC =
blank in the Makefile, instead of putting there the path to
HyRec (default: compiled with the distributed version, located in
class/hyrec/) (credits Y. AliHaimoud and C. Hirata)

user can choose between the same reionization history as in CAMB
(featuring a single step for hydrogen reionization), or a binned
reionization history, with arbitrary bin number, position and width
(similar to implementation in Lewis, Weller and Battye
(2006)). Set input parameter reionization to either
reio_camb or reio_bins_tanh, and see
explanatory.ini for more details on input parameters
(binned_reio_z, binned_reio_z, etc.)
 user can
choose to pass a numerical value for the primordial helium fraction
YHe, or let it be fixed as a function of omega_b and N_eff by
standard BBN calculations, by setting YHe to BBN (as
in Hamann, Lesgourgues
and Mangano (2008))
 includes latest version of HALOFIT,
working even in presence of massive neutrinos (from Bird, Viel and Haehnelt
(2011)). Set non linear to HALOFIT to get the
nonlinear P(k) (credits B. Audren and S. Bird)
 improved
newtonian gauge: phi is integrated over time rather than
infered from constraint equation; in that way newtonian gauge
calculations are more accurate than in v1.3, and remain stable even in
presence of isocurvature modes or massive neutrinos (credits Guido
W. Pettinari)
 user can choose to pass some numerical values for
the tensor tilt and running (n_t, alpha_t), or let
them be fixed by the two selfconsistency
conditions of single field slowroll inflation, by setting these
entries to scc (see explanatory.ini for details)
 if output includes dCl, the code will compute
the Cl spectra of matter density perturbations in an arbitrary number
of redshift bins (defined by gaussian window functions, with arbitrary
centers and widths, fixed through input variables
selection_mean and selection_width). The output
..._cl.dat file will then contain CMB (if requested) plus
density spectra, including all possible crosscorrelations. See
explanatory.ini for details on input parameters.

corrected a few bugs previously affecting temperature/polarization
tensor spectra.
 python wrapper for class, available in the
python/ directory (see python/README). Used by the
cosmological parameter extraction code that we will release soon
(credits K. Benabed and B. Audren)
 c++ wrapper for class,
available in the cpp/ directory (see cpp/README)
(credits S. Plaszczynski)
 v1.3 (08.11.2011) 
 implementation of the Newtonian / longitudinal gauge
equations on top of the synchronous ones. The user can choose the
gauge by writing e.g. gauge=newtonian in the input file (by
default, set to synchronous gauge). The Newtonian gauge calculations
turn out to be essentially as stable, fast and accurate as the
synchronous ones for LambdaCDM. Instabilities may occur when
ncdm (= noncold dark matter, e.g. massive neutrinos) and/or
isocurvature modes are turned on: in those two cases, the code outputs
roughly the right answer (provided that the tol_ncdm
precision parameter is decreased by a factor 100), but the convergence
of the spectra in the limit of increased precision parameters is not
obvious.
 by default, the component called fluid can
have not only an arbitrary sound speed, but also a timevarying
equation of state of the form w=w0+wa*(1a/a0); a couple of
bugs in this fluid sector have been fixed.
 CPU (Class
Plotting Utility) upgraded to version 1.3, with an extra option
colnum N useful for plotting polarisation and lensing
potential spectra. More details on CPU in the README file.
 issue of initial conditions in the power spectrum renormalisation
module trg.c fixed according to discussion in Audren & Lesgourgues 2011.
 v1.2 (13.06.2011) 
 new trg.c module allowing to compute the
nonlinear density, velocity and cross densityvelocity power spectra
on mildly nonlinear scales, using either the dynamical 1loop method
(fast) or the Time Renormalization Group method (1 hour/number of
CPU); see Audren &
Lesgourgues 2011. In the input file, you just need to set non
linear=oneloop or non linear=trg. So far, this module
works only for LambdaCDM with no noncold relics or dark energy.
 fast mode for CMB lensed spectra calculation. Speeds up the code
significantly. For very accurate calculations, you can still tune the
precision parameter accurate_lensing and use the previous
method. Credits: Simon Prunet.
 in input file, user can choose the
format of output files: format=class for CLASS format (as in
previous versions), format=camb for camblike format
(compatible with Healpix, etc.). Also, all output file headers can be
omitted with headers=no.
 convenient python module named
CPU for plotting CLASS output spectra. Can plot one or
several spectra, or the relative difference between two spectra (even
when they are sampled with different values, as it is usually the case
for P(k)'s). Works for C_l^TT and P(k). Detects the type of output
file and infers axes and labels. See the various options by typing
python CPU help or python CPU h. Typical usage:
python CPU output/myspectrum.dat for a single spectrum. For
using CPU you need to have on your machine a recent version of python
and scientific python (scipy), as well as gnuplot.
 new precision
file chi2pl10.pre for even faster (but less accurate) CMB
calculation than with default settings.
 bug in dark energy fluid
equations fixed on 16.06.11, thanks to Vid Irsic for pointing it out.
 v1.1 (16.05.2011) 
 slightly modified definition of three trigger parameters
controlling UFA and RSA approximations (refer to tau instead of
tau_h). Changed precision files accordingly.
 corrected bug
previously leading to segmentation fault when computing lensed
temperature but not polarisation
 v1.0 (16.04.2011) 
 Please report
bugs, ask questions and send comments/suggestions through the GitHub
website class_public. Go to that page, click
issue and then New issue. Then write your text and
press Submit new issue. Your text will automatically be sent
by email to the class developpers, and they will answer you as soon
as possible. If your question is related to the MontePython
code, you should do the same but on the montepython_public page. 
