Background Calculations

.flake8

@@ -1,6 +1,8 @@
 [flake8]
 ignore = E741,E226,W503,W504
-exclude = 
+per-file-ignores =
+    */__init__.py:F401,F403,F405
+exclude =
     build/,
     .eggs/,
     benchmarks/data,

CMakeLists.txt

@@ -19,7 +19,7 @@ option(FORCE_OPENMP "Forcibly use OpenMP " NO)
 set(CCL_SRC
     src/ccl_background.c src/ccl_core.c src/ccl_error.c
     src/ccl_bbks.c src/ccl_f1d.c src/ccl_f2d.c src/ccl_f3d.c
-    src/ccl_power.c src/ccl_bcm.c
+    src/ccl_power.c
     src/ccl_eh.c src/ccl_musigma.c
     src/ccl_utils.c src/ccl_cls.c src/ccl_massfunc.c
     src/ccl_neutrinos.c

README.md

@@ -11,7 +11,10 @@ STYLE CONVENTION USED
         **`type`** or **`structure`**
 -->
 # CCL
-[![Build Status](https://travis-ci.org/LSSTDESC/CCL.svg?branch=master)](https://travis-ci.org/LSSTDESC/CCL) [![Coverage Status](https://coveralls.io/repos/github/LSSTDESC/CCL/badge.svg?branch=master)](https://coveralls.io/github/LSSTDESC/CCL?branch=master) [![Documentation Status](https://readthedocs.org/projects/ccl/badge/?version=latest)](https://ccl.readthedocs.io/en/latest/?badge=latest)
+![Build](https://github.com/LSSTDESC/CCL/workflows/continuous-integration/badge.svg?branch=master) 
+[![Coverage](https://coveralls.io/repos/github/LSSTDESC/CCL/badge.svg?branch=master)](https://coveralls.io/github/LSSTDESC/CCL?branch=master) 
+[![Documentation](https://readthedocs.org/projects/ccl/badge/?version=latest)](https://ccl.readthedocs.io/en/latest/) 
+[![DOI](https://img.shields.io/badge/DOI-10.3847%2F1538--4365%2Fab1658-B31B1B.svg)](https://iopscience.iop.org/article/10.3847/1538-4365/ab1658)
 
 The Core Cosmology Library (CCL) is a standardized library of routines to calculate
 basic observables used in cosmology. It will be the standard analysis package used by the
@@ -72,7 +75,7 @@ cosmo = ccl.Cosmology(
     transfer_function='bbks')
 
 # Define a simple binned galaxy number density curve as a function of redshift
-z_n = np.linspace(0., 1., 200)
+z_n = np.linspace(0., 1., 500)
 n = np.ones(z_n.shape)
 
 # Create objects to represent tracers of the weak lensing signal with this

benchmarks/ccl_test_angpow.c

@@ -4,11 +4,11 @@
 #include <math.h>
 
 #define NZ 1024
-#define Z0_GC 1.0 
+#define Z0_GC 1.0
 #define SZ_GC 0.02
 #define NL 499
 
-#define CLS_PRECISION 3E-3 
+#define CLS_PRECISION 3E-3
 
 CTEST_DATA(angpow) {
   double Omega_c;
@@ -30,7 +30,7 @@ CTEST_DATA(angpow) {
 
 
 
-// Set up the cosmological parameters to be used 
+// Set up the cosmological parameters to be used
 CTEST_SETUP(angpow){
   data->Omega_c = 0.25;
   data->Omega_b = 0.05;
@@ -55,7 +55,7 @@ static void test_angpow_precision(struct angpow_data * data)
 {
   // Status flag
   int status =0;
-  
+
   // Initialize cosmological parameters
   ccl_configuration ccl_config=default_config;
   ccl_config.transfer_function_method=ccl_boltzmann_class;
@@ -78,7 +78,7 @@ static void test_angpow_precision(struct angpow_data * data)
       nz_arr_gc[i]=exp(-0.5*pow((z_arr_gc[i]-Z0_GC)/SZ_GC,2));
       bz_arr[i]=1;
     }
-  
+
   // Galaxy clustering tracer
   CCL_ClTracer *ct_gc_A=ccl_cl_tracer_number_counts(ccl_cosmo,1,1,0,
 						    NZ,z_arr_gc,nz_arr_gc,
@@ -88,7 +88,7 @@ static void test_angpow_precision(struct angpow_data * data)
 						    NZ,z_arr_gc,nz_arr_gc,
 						    NZ,z_arr_gc,bz_arr,
 						    -1,NULL,NULL, &status);
-  
+
   int *ells=malloc(NL*sizeof(int));
   double *cells_gg_angpow=malloc(NL*sizeof(double));
   for(int ii=0;ii<NL;ii++)
@@ -98,7 +98,7 @@ static void test_angpow_precision(struct angpow_data * data)
   // Workspaces
   double linstep = 40;
   double logstep = 1.15;
-  
+
   // Compute C_ell
   ccl_angular_cls_nonlimber(ccl_cosmo,logstep,linstep,
 			    ct_gc_A,ct_gc_A,NULL,
@@ -117,13 +117,13 @@ static void test_angpow_precision(struct angpow_data * data)
   fclose(f);
   rel_precision /= NL;
   ASSERT_TRUE(rel_precision < CLS_PRECISION);
-  
+
   //Free up tracers
   ccl_cl_tracer_free(ct_gc_A);
   ccl_cl_tracer_free(ct_gc_B);
   free(ells);
   free(cells_gg_angpow);
-  ccl_cosmology_free(ccl_cosmo);  
+  ccl_cosmology_free(ccl_cosmo);
 }
 
 CTEST2(angpow,precision) {

benchmarks/ccl_test_f2d.c

@@ -289,7 +289,7 @@ CTEST2(f2d,pk) {
   config.matter_power_spectrum_method = ccl_linear;
   ccl_parameters params = ccl_parameters_create(data->Omega_c,data->Omega_b, 0.0, 3.046, &mnu,
                                                 0, -1.0, 0.0, data->h, data->A_s,data->n_s,
-                                                -1, -1, -1, 0., 0., 1.0, 1.0, 0.0, 
+                                                -1, -1, -1, 0., 0., 1.0, 1.0, 0.0,
 						-1, NULL, NULL, &status);
   params.T_CMB=2.7;
   params.Omega_k=0;

benchmarks/test_bcm.py

@@ -6,22 +6,6 @@
 
 def test_bcm():
     cosmo = ccl.Cosmology(
-        Omega_c=0.25,
-        Omega_b=0.05,
-        h=0.7,
-        A_s=2.2e-9,
-        n_s=0.96,
-        Neff=3.046,
-        m_nu_type='normal',
-        m_nu=0.0,
-        Omega_g=0,
-        Omega_k=0,
-        w0=-1,
-        wa=0,
-        bcm_log10Mc=14.0,
-        baryons_power_spectrum='bcm')
-
-    cosmo_nobar = ccl.Cosmology(
         Omega_c=0.25,
         Omega_b=0.05,
         h=0.7,
@@ -41,12 +25,14 @@ def test_bcm():
     k = data[:, 0] * cosmo['h']
     a = 1
 
-    fbcm = ccl.bcm_model_fka(cosmo, k, a)
+    bar = ccl.BaryonsSchneider15(log10Mc=14.)
+    fbcm = bar.boost_factor(cosmo, k, a)
     err = np.abs(data[:, 1]/data_nobar[:, 1]/fbcm - 1)
     assert np.allclose(err, 0, atol=BCM_TOLERANCE, rtol=0)
 
-    ratio = (
-        ccl.nonlin_matter_power(cosmo, k, a) /
-        ccl.nonlin_matter_power(cosmo_nobar, k, a))
+    cosmo.compute_nonlin_power()
+    pk_nobar = cosmo.get_nonlin_power()
+    pk_wbar = bar.include_baryonic_effects(cosmo, pk_nobar)
+    ratio = pk_wbar.eval(k, a)/pk_nobar.eval(k, a)
     err = np.abs(data[:, 1]/data_nobar[:, 1]/ratio - 1)
     assert np.allclose(err, 0, atol=BCM_TOLERANCE, rtol=0)

benchmarks/test_cls.py → benchmarks/test_cells.py

@@ -68,21 +68,21 @@ def set_up(request):
 
     # Initialize tracers
     trc = {}
-    trc['g1'] = ccl.NumberCountsTracer(cosmo, False,
-                                       (z1, pz1),
-                                       (z2, bz))
-    trc['g2'] = ccl.NumberCountsTracer(cosmo, False,
-                                       (z2, pz2),
-                                       (z2, bz))
-    trc['l1'] = ccl.WeakLensingTracer(cosmo, (z1, pz1))
-    trc['l2'] = ccl.WeakLensingTracer(cosmo, (z2, pz2))
-    trc['i1'] = ccl.WeakLensingTracer(cosmo, (z1, pz1),
+    trc['g1'] = ccl.NumberCountsTracer(cosmo, has_rsd=False,
+                                       dndz=(z1, pz1),
+                                       bias=(z2, bz))
+    trc['g2'] = ccl.NumberCountsTracer(cosmo, has_rsd=False,
+                                       dndz=(z2, pz2),
+                                       bias=(z2, bz))
+    trc['l1'] = ccl.WeakLensingTracer(cosmo, dndz=(z1, pz1))
+    trc['l2'] = ccl.WeakLensingTracer(cosmo, dndz=(z2, pz2))
+    trc['i1'] = ccl.WeakLensingTracer(cosmo, dndz=(z1, pz1),
                                       has_shear=False,
                                       ia_bias=(z1, a1))
-    trc['i2'] = ccl.WeakLensingTracer(cosmo, (z2, pz2),
+    trc['i2'] = ccl.WeakLensingTracer(cosmo, dndz=(z2, pz2),
                                       has_shear=False,
                                       ia_bias=(z2, a2))
-    trc['ct'] = ccl.CMBLensingTracer(cosmo, 1100.)
+    trc['ct'] = ccl.CMBLensingTracer(cosmo, z_source=1100.)
 
     # Read benchmarks
     def read_bm(fname):
@@ -193,8 +193,8 @@ def read_bm(fname):
                           ('i2', 'i2', 'ii_22',   # IA2-IA2
                            'll_22', 'll_22', 'll_22', 'll_22',
                            'fl_ll')])
-def test_cls(set_up, t1, t2, bm,
-             a1b1, a1b2, a2b1, a2b2, fl):
+def test_cells(set_up, t1, t2, bm,
+               a1b1, a1b2, a2b1, a2b2, fl):
     cosmo, trcs, lfc, bmk = set_up
     cl = ccl.angular_cl(cosmo, trcs[t1], trcs[t2], lfc['ells'],
                         limber_integration_method='qag_quad') * lfc[fl]
@@ -213,8 +213,8 @@ def test_cls(set_up, t1, t2, bm,
                           ('l2', 'l2', 'll_22',   # WL2-WL2
                            'll_22', 'll_22', 'll_22', 'll_22',
                            'fl_ll')])
-def test_cls_spline(set_up, t1, t2, bm,
-                    a1b1, a1b2, a2b1, a2b2, fl):
+def test_cells_spline(set_up, t1, t2, bm,
+                      a1b1, a1b2, a2b1, a2b2, fl):
     cosmo, trcs, lfc, bmk = set_up
     cl = ccl.angular_cl(cosmo, trcs[t1], trcs[t2], lfc['ells'],
                         limber_integration_method='spline') * lfc[fl]

benchmarks/test_cib.py

@@ -45,4 +45,4 @@ def test_cibcl():
     dl = cl*ls*(ls+1)/(2*np.pi)
 
     # Compare
-    assert np.all(np.fabs(dl/(bm[31]+bm[32])-1) < 1E-2)
+    assert np.all(np.fabs(dl/(bm[31]+bm[32])-1) < 1.2E-2)

benchmarks/test_concentration_Ishiyama21.py

@@ -58,5 +58,5 @@ def test_concentration_Ishiyama21(pars):
 
     for i, zz in enumerate(Z):
         dat = data[:, i+1]
-        mod = cm.get_concentration(COSMO, M_use/H100, 1/(1+zz))
+        mod = cm(COSMO, M_use/H100, 1/(1+zz))
         assert np.allclose(mod, dat, rtol=UCHUU_DATA_SCATTER)

benchmarks/test_correlation.py

@@ -71,21 +71,21 @@ def set_up(request):
 
     # Initialize tracers
     trc = {}
-    trc['g1'] = ccl.NumberCountsTracer(cosmo, False,
-                                       (z1, pz1),
-                                       (z2, bz))
-    trc['g2'] = ccl.NumberCountsTracer(cosmo, False,
-                                       (z2, pz2),
-                                       (z2, bz))
-    trc['l1'] = ccl.WeakLensingTracer(cosmo, (z1, pz1))
-    trc['l2'] = ccl.WeakLensingTracer(cosmo, (z2, pz2))
-    trc['i1'] = ccl.WeakLensingTracer(cosmo, (z1, pz1),
+    trc['g1'] = ccl.NumberCountsTracer(cosmo, has_rsd=False,
+                                       dndz=(z1, pz1),
+                                       bias=(z2, bz))
+    trc['g2'] = ccl.NumberCountsTracer(cosmo, has_rsd=False,
+                                       dndz=(z2, pz2),
+                                       bias=(z2, bz))
+    trc['l1'] = ccl.WeakLensingTracer(cosmo, dndz=(z1, pz1))
+    trc['l2'] = ccl.WeakLensingTracer(cosmo, dndz=(z2, pz2))
+    trc['i1'] = ccl.WeakLensingTracer(cosmo, dndz=(z1, pz1),
                                       has_shear=False,
                                       ia_bias=(z1, a1))
-    trc['i2'] = ccl.WeakLensingTracer(cosmo, (z2, pz2),
+    trc['i2'] = ccl.WeakLensingTracer(cosmo, dndz=(z2, pz2),
                                       has_shear=False,
                                       ia_bias=(z2, a2))
-    trc['ct'] = ccl.CMBLensingTracer(cosmo, 1100.)
+    trc['ct'] = ccl.CMBLensingTracer(cosmo, z_source=1100.)
 
     # Read benchmarks
     def read_bm(fname):

benchmarks/test_correlation_MG.py

@@ -48,10 +48,12 @@ def set_up(request):
 
     # Initialize tracers
     trc = {}
-    trc['g1'] = ccl.NumberCountsTracer(cosmo, False, (z1, pz1), (z1, bz1))
-    trc['g2'] = ccl.NumberCountsTracer(cosmo, False, (z2, pz2), (z2, bz2))
-    trc['l1'] = ccl.WeakLensingTracer(cosmo, (z1, pz1))
-    trc['l2'] = ccl.WeakLensingTracer(cosmo, (z2, pz2))
+    trc['g1'] = ccl.NumberCountsTracer(cosmo, has_rsd=False,
+                                       dndz=(z1, pz1), bias=(z1, bz1))
+    trc['g2'] = ccl.NumberCountsTracer(cosmo, has_rsd=False,
+                                       dndz=(z2, pz2), bias=(z2, bz2))
+    trc['l1'] = ccl.WeakLensingTracer(cosmo, dndz=(z1, pz1))
+    trc['l2'] = ccl.WeakLensingTracer(cosmo, dndz=(z2, pz2))
 
     # Read benchmarks
     bms = {}

benchmarks/test_correlation_MG2.py

@@ -48,10 +48,12 @@ def set_up(request):
 
     # Initialize tracers
     trc = {}
-    trc['g1'] = ccl.NumberCountsTracer(cosmo, False, (z1, pz1), (z1, bz1))
-    trc['g2'] = ccl.NumberCountsTracer(cosmo, False, (z2, pz2), (z2, bz2))
-    trc['l1'] = ccl.WeakLensingTracer(cosmo, (z1, pz1))
-    trc['l2'] = ccl.WeakLensingTracer(cosmo, (z2, pz2))
+    trc['g1'] = ccl.NumberCountsTracer(cosmo, has_rsd=False,
+                                       dndz=(z1, pz1), bias=(z1, bz1))
+    trc['g2'] = ccl.NumberCountsTracer(cosmo, has_rsd=False,
+                                       dndz=(z2, pz2), bias=(z2, bz2))
+    trc['l1'] = ccl.WeakLensingTracer(cosmo, dndz=(z1, pz1))
+    trc['l2'] = ccl.WeakLensingTracer(cosmo, dndz=(z2, pz2))
 
     # Read benchmarks
     bms = {}

benchmarks/test_correlation_MG3_SD.py

@@ -50,10 +50,12 @@ def set_up(request):
 
     # Initialize tracers
     trc = {}
-    trc['g1'] = ccl.NumberCountsTracer(cosmo, False, (z1, pz1), (z1, bz1))
-    trc['g2'] = ccl.NumberCountsTracer(cosmo, False, (z2, pz2), (z2, bz2))
-    trc['l1'] = ccl.WeakLensingTracer(cosmo, (z1, pz1))
-    trc['l2'] = ccl.WeakLensingTracer(cosmo, (z2, pz2))
+    trc['g1'] = ccl.NumberCountsTracer(cosmo, has_rsd=False,
+                                       dndz=(z1, pz1), bias=(z1, bz1))
+    trc['g2'] = ccl.NumberCountsTracer(cosmo, has_rsd=False,
+                                       dndz=(z2, pz2), bias=(z2, bz2))
+    trc['l1'] = ccl.WeakLensingTracer(cosmo, dndz=(z1, pz1))
+    trc['l2'] = ccl.WeakLensingTracer(cosmo, dndz=(z2, pz2))
 
     # Read benchmarks
     bms = {}

benchmarks/test_covariances.py

@@ -42,17 +42,17 @@ def test_ssc_WL():
 
     z, nofz = np.loadtxt(os.path.join(data_dir, "ssc_WL_nofz.txt"),
                          unpack=True)
-    WL_tracer = ccl.WeakLensingTracer(cosmo, (z, nofz))
+    WL_tracer = ccl.WeakLensingTracer(cosmo, dndz=(z, nofz))
 
     ell = np.loadtxt(os.path.join(data_dir, "ssc_WL_ell.txt"))
 
     fsky = 0.05
 
-    sigma2_B = ccl.sigma2_B_disc(cosmo, a=a, fsky=fsky)
+    sigma2_B = ccl.sigma2_B_disc(cosmo, a_arr=a, fsky=fsky)
     cov_ssc = ccl.covariances.angular_cl_cov_SSC(cosmo,
-                                                 cltracer1=WL_tracer,
-                                                 cltracer2=WL_tracer,
-                                                 ell=ell, tkka=tk3D,
+                                                 tracer1=WL_tracer,
+                                                 tracer2=WL_tracer,
+                                                 ell=ell, t_of_kk_a=tk3D,
                                                  sigma2_B=(a, sigma2_B),
                                                  fsky=None)
     var_ssc_ccl = np.diag(cov_ssc)

benchmarks/test_distances.py

@@ -46,7 +46,7 @@
 def Neff_from_N_ur_N_ncdm(N_ur, N_ncdm):
     """Calculate N_eff from the number of relativistic
     and massive neutrinos."""
-    Neff = N_ur + N_ncdm * ccl.physical_constants.TNCDM**4 / (4./11.)**(4./3.)
+    Neff = N_ur + N_ncdm * ccl.core._Defaults.T_ncdm**4 / (4./11.)**(4./3.)
     return Neff
 
 

benchmarks/test_emu.py

@@ -14,8 +14,8 @@ def test_emu_nu(model):
 
     cosmos = np.loadtxt("./benchmarks/data/emu_nu_cosmologies.txt")
 
-    mnu = ccl.nu_masses(
-        cosmos[model, 7] * cosmos[model, 2]**2, 'equal', T_CMB=2.725)
+    mnu = ccl.nu_masses(Omega_nu_h2=cosmos[model, 7]*cosmos[model, 2]**2,
+                        mass_split='equal')
 
     cosmo = ccl.Cosmology(
         Omega_c=cosmos[model, 0],
@@ -79,8 +79,8 @@ def test_emu(model):
 def test_emu_lin(model):
     cosmos = np.loadtxt("./benchmarks/data/emu_input_cosmologies.txt")
 
-    mnu = ccl.nu_masses(
-        cosmos[model, 7] * cosmos[model, 2]**2, 'equal', T_CMB=2.725)
+    mnu = ccl.nu_masses(Omega_nu_h2=cosmos[model, 7]*cosmos[model, 2]**2,
+                        mass_split='equal')
 
     cosmo = ccl.Cosmology(
         Omega_c=cosmos[model, 0],

benchmarks/test_haloprofile.py

@@ -58,8 +58,7 @@ def test_profile_Einasto():
 
     mdef = ccl.halos.MassDef(mDelta, 'matter')
     c = ccl.halos.ConcentrationConstant(c=concentration, mdef=mdef)
-    mdef = ccl.halos.MassDef(mDelta, 'matter',
-                             c_m_relation=c)
+    mdef = ccl.halos.MassDef(mDelta, 'matter', concentration=c)
     p = ccl.halos.HaloProfileEinasto(c, truncated=False)
 
     prof = p.real(COSMO, r, halomass, a, mdef)
@@ -118,7 +117,7 @@ def test_haloprofile(model):
                                      projected_analytic=True)
         prof = p.projected(COSMO, r, halomass, a, mdef)
     elif model == 'einasto':
-        mdef = ccl.halos.MassDef(halomassdef, 'matter', c_m_relation=c)
+        mdef = ccl.halos.MassDef(halomassdef, 'matter', concentration=c)
         p = ccl.halos.HaloProfileEinasto(c, truncated=False)
         prof = p.real(COSMO, r, halomass, a, mdef)
     elif model == 'hernquist':