v3 preparation (#1087)

* Removed v2 vestiges. Now in v3 mode.

benchmarks/test_cib.py

@@ -13,8 +13,8 @@ def test_cibcl():
                           h=0.7,
                           n_s=0.9645,
                           A_s=2.02E-9,
-                          Neff=3.046)
-    mdef = ccl.halos.MassDef200m()
+                          Neff=3.046, T_CMB=2.725)
+    mdef = ccl.halos.MassDef200m
     cM = ccl.halos.ConcentrationDuffy08(mass_def=mdef)
     nM = ccl.halos.MassFuncTinker10(mass_def=mdef, norm_all_z=True)
     bM = ccl.halos.HaloBiasTinker10(mass_def=mdef)

benchmarks/test_correlation_3d.py

@@ -25,7 +25,7 @@ def test_correlation_3d(model):
         h=0.7,
         sigma8=0.8,
         n_s=0.96,
-        Neff=3.046,
+        Neff=3.046, T_CMB=2.725,
         mass_split='normal',
         Omega_g=0,
         Omega_k=1.0 - 0.25 - 0.05 - Omega_v[model],

benchmarks/test_covariances.py

@@ -1,6 +1,5 @@
 import os
 import numpy as np
-import pytest
 import pyccl as ccl
 
 
@@ -13,11 +12,12 @@ def test_ssc_WL():
     cosmo = ccl.Cosmology(Omega_c=0.25, Omega_b=0.05, h=h, n_s=0.97,
                           sigma8=0.8, m_nu=0.0)
 
-    mass_def = ccl.halos.MassDef200m()
+    mass_def = ccl.halos.MassDef200m
     hmf = ccl.halos.MassFuncTinker10(mass_def=mass_def)
     hbf = ccl.halos.HaloBiasTinker10(mass_def=mass_def)
     con = ccl.halos.ConcentrationDuffy08(mass_def=mass_def)
-    nfw = ccl.halos.HaloProfileNFW(concentration=con, fourier_analytic=True)
+    nfw = ccl.halos.HaloProfileNFW(mass_def=mass_def, concentration=con,
+                                   fourier_analytic=True)
     hmc = ccl.halos.HMCalculator(mass_function=hmf, halo_bias=hbf,
                                  mass_def=mass_def)
 
@@ -30,12 +30,10 @@ def test_ssc_WL():
     a = np.linspace(1/(1+6), 1, n_z)
     k = np.geomspace(k_min, k_max, n_k)
 
-    with pytest.warns(ccl.CCLDeprecationWarning):  # TODO: remove normprof v3
-        tk3D = ccl.halos.halomod_Tk3D_SSC(cosmo=cosmo, hmc=hmc,
-                                          prof=nfw, prof2=nfw, prof12_2pt=None,
-                                          lk_arr=np.log(k), a_arr=a,
-                                          use_log=True,
-                                          normprof1=True, normprof2=True)
+    tk3D = ccl.halos.halomod_Tk3D_SSC(cosmo=cosmo, hmc=hmc,
+                                      prof=nfw, prof2=nfw, prof12_2pt=None,
+                                      lk_arr=np.log(k), a_arr=a,
+                                      use_log=True)
 
     z, nofz = np.loadtxt(os.path.join(data_dir, "ssc_WL_nofz.txt"),
                          unpack=True)

benchmarks/test_eh.py

@@ -17,7 +17,7 @@ def test_eh(transfer, fname):
         h=0.7,
         sigma8=0.8,
         n_s=0.96,
-        Neff=0,
+        Neff=0, T_CMB=2.725,
         m_nu=0.0,
         w0=-1.0,
         wa=0.0,

benchmarks/test_growth.py

@@ -63,7 +63,6 @@ def read_growth_allz_benchmark_file():
 
     # Split into redshift column and growth(z) columns
     z = dat[0]
-    # gfac = np.column_stack((dat[1:], dat_MG[1:]))
     gfac = dat[1:]
 
     return z, gfac
@@ -120,45 +119,3 @@ def test_growth_highz_model(i):
 def test_growth_allz_model(i):
     compare_growth(z_allz, gfac_allz[i], Omega_v_vals[i], w0_vals[i],
                    wa_vals[i], mu0_vals[i], Sig0_vals[i])
-
-
-def test_growth_mg():
-    """
-    Compare the modified growth function computed by CCL against the exact
-    result for a particular modification of the growth rate.
-    """
-    # Define differential growth rate arrays
-    nz_mg = 128
-    z_mg = np.zeros(nz_mg)
-    df_mg = np.zeros(nz_mg)
-    for i in range(0, nz_mg):
-        z_mg[i] = 4. * (i + 0.0) / (nz_mg - 1.)
-        df_mg[i] = 0.1 / (1. + z_mg[i])
-
-    # Define two test cosmologies, without and with modified growth
-    # respectively
-    cosmo1 = ccl.Cosmology(
-        Omega_c=0.25, Omega_b=0.05, Omega_k=0., Neff=0., m_nu=0.,
-        w0=-1., wa=0., h=0.7, A_s=2.1e-9, n_s=0.96)
-    cosmo2 = ccl.Cosmology(
-        Omega_c=0.25, Omega_b=0.05, Omega_k=0., Neff=0., m_nu=0.,
-        w0=-1., wa=0., h=0.7, A_s=2.1e-9, n_s=0.96,
-        z_mg=z_mg, df_mg=df_mg)
-
-    # We have included a growth modification \delta f = K*a, with K==0.1
-    # (arbitrarily). This case has an analytic solution, given by
-    # D(a) = D_0(a)*exp(K*(a-1)). Here we compare the growth computed by CCL
-    # with the analytic solution.
-    a = 1. / (1. + z_mg)
-
-    d1 = ccl.growth_factor(cosmo1, a)
-    d2 = ccl.growth_factor(cosmo2, a)
-    f1 = ccl.growth_rate(cosmo1, a)
-    f2 = ccl.growth_rate(cosmo2, a)
-
-    f2r = f1 + 0.1*a
-    d2r = d1 * np.exp(0.1*(a-1.))
-
-    # Check that ratio of calculated and analytic results is within tolerance
-    assert np.allclose(d2r, d2, atol=0, rtol=GROWTH_TOLERANCE)
-    assert np.allclose(f2r, f2, atol=0, rtol=GROWTH_TOLERANCE)

benchmarks/test_halomod.py

@@ -36,26 +36,22 @@ def test_halomod(model):
     hbf = ccl.halos.HaloBiasSheth99(mass_def=mass_def,
                                     mass_def_strict=False)
     cM = ccl.halos.ConcentrationDuffy08(mass_def=mass_def)
-    prf = ccl.halos.HaloProfileNFW(concentration=cM)
+    prf = ccl.halos.HaloProfileNFW(mass_def=mass_def, concentration=cM)
     hmc = ccl.halos.HMCalculator(mass_function=hmf, halo_bias=hbf,
                                  mass_def=mass_def)
 
     z = 0.
     k = data_z0[:, 0] * cosmo['h']
     pk = data_z0[:, -1] / (cosmo['h']**3)
-    with pytest.warns(ccl.CCLDeprecationWarning):  # TODO: remove normprof v3
-        pk_ccl = ccl.halos.halomod_power_spectrum(cosmo, hmc, k, 1./(1+z), prf,
-                                                  normprof1=True)
+    pk_ccl = ccl.halos.halomod_power_spectrum(cosmo, hmc, k, 1./(1+z), prf)
     tol = pk * HALOMOD_TOLERANCE
     err = np.abs(pk_ccl - pk)
     assert np.all(err <= tol)
 
     z = 1.
     k = data_z1[:, 0] * cosmo['h']
     pk = data_z1[:, -1] / (cosmo['h']**3)
-    with pytest.warns(ccl.CCLDeprecationWarning):  # TODO: remove normprof v3
-        pk_ccl = ccl.halos.halomod_power_spectrum(cosmo, hmc, k, 1./(1+z), prf,
-                                                  normprof1=True)
+    pk_ccl = ccl.halos.halomod_power_spectrum(cosmo, hmc, k, 1./(1+z), prf)
     tol = pk * HALOMOD_TOLERANCE
     err = np.abs(pk_ccl - pk)
     assert np.all(err <= tol)

benchmarks/test_haloprofile.py

@@ -14,10 +14,9 @@
     wa=0,
     m_nu=0,
     mass_split='normal',
-    Neff=3.046,
+    Neff=3.046, T_CMB=2.725,
     Omega_k=0,
-    transfer_function='eisenstein_hu',
-    mass_function='shethtormen')
+    transfer_function='eisenstein_hu')
 
 
 def test_profile_Hernquist():
@@ -34,7 +33,8 @@ def test_profile_Hernquist():
 
     mdef = ccl.halos.MassDef(mDelta, 'matter')
     c = ccl.halos.ConcentrationConstant(c=concentration, mass_def=mdef)
-    p = ccl.halos.HaloProfileHernquist(concentration=c, truncated=False)
+    p = ccl.halos.HaloProfileHernquist(mass_def=mdef, concentration=c,
+                                       truncated=False)
 
     prof = p.real(COSMO, r, halomass, a)
 
@@ -57,7 +57,8 @@ def test_profile_Einasto():
 
     mdef = ccl.halos.MassDef(mDelta, 'matter')
     c = ccl.halos.ConcentrationConstant(c=concentration, mass_def=mdef)
-    p = ccl.halos.HaloProfileEinasto(concentration=c, truncated=False)
+    p = ccl.halos.HaloProfileEinasto(mass_def=mdef, concentration=c,
+                                     truncated=False)
 
     prof = p.real(COSMO, r, halomass, a)
 
@@ -80,7 +81,8 @@ def test_profile_NFW():
 
     mdef = ccl.halos.MassDef(mDelta, 'matter')
     c = ccl.halos.ConcentrationConstant(c=concentration, mass_def=mdef)
-    p = ccl.halos.HaloProfileNFW(concentration=c, truncated=False)
+    p = ccl.halos.HaloProfileNFW(mass_def=mdef, concentration=c,
+                                 truncated=False)
 
     prof = p.real(COSMO, r, halomass, a)
 
@@ -108,18 +110,21 @@ def test_haloprofile(model):
     c = ccl.halos.ConcentrationConstant(c=concentration, mass_def=mdef)
 
     if model == 'nfw':
-        p = ccl.halos.HaloProfileNFW(concentration=c, truncated=False)
+        p = ccl.halos.HaloProfileNFW(mass_def=mdef, concentration=c,
+                                     truncated=False)
         prof = p.real(COSMO, r, halomass, a)
     elif model == 'projected_nfw':
-        p = ccl.halos.HaloProfileNFW(concentration=c, truncated=False,
-                                     projected_analytic=True)
+        p = ccl.halos.HaloProfileNFW(mass_def=mdef, concentration=c,
+                                     truncated=False, projected_analytic=True)
         prof = p.projected(COSMO, r, halomass, a)
     elif model == 'einasto':
         mdef = ccl.halos.MassDef(halomassdef, 'matter')
-        p = ccl.halos.HaloProfileEinasto(concentration=c, truncated=False)
+        p = ccl.halos.HaloProfileEinasto(mass_def=mdef, concentration=c,
+                                         truncated=False)
         prof = p.real(COSMO, r, halomass, a)
     elif model == 'hernquist':
-        p = ccl.halos.HaloProfileHernquist(concentration=c, truncated=False)
+        p = ccl.halos.HaloProfileHernquist(mass_def=mdef, concentration=c,
+                                           truncated=False)
         prof = p.real(COSMO, r, halomass, a)
 
     tol = np.clip(np.abs(HALOPROFILE_TOLERANCE * data[:, 1]), 1e-12, np.inf)
@@ -148,10 +153,9 @@ def test_weak_lensing_functions():
 
     mdef = ccl.halos.MassDef(mDelta, 'matter')
     c = ccl.halos.ConcentrationConstant(c=concentration, mass_def=mdef)
-    p = ccl.halos.HaloProfileNFW(
-        concentration=c,
-        truncated=False, projected_analytic=True, cumul2d_analytic=True
-    )
+    p = ccl.halos.HaloProfileNFW(mass_def=mdef, concentration=c,
+                                 truncated=False, projected_analytic=True,
+                                 cumul2d_analytic=True)
 
     kappa = p.convergence(COSMO, r_al, halomass,
                           a_lens=a_lens, a_source=a_source)

benchmarks/test_hmf.py

@@ -27,8 +27,7 @@ def test_hmf_despali16():
 
 
 def test_hmf_bocquet16():
-    hmd = ccl.halos.MassDef200c()
-    mf = ccl.halos.MassFuncBocquet16(mass_def=hmd)
+    mf = ccl.halos.MassFuncBocquet16(mass_def="200c")
     d_hmf = np.loadtxt(os.path.join(dirdat, 'hmf_bocquet16.txt'),
                        unpack=True)
     m = d_hmf[0]

benchmarks/test_hod.py

@@ -1,5 +1,4 @@
 import numpy as np
-import pytest
 import pyccl as ccl
 
 
@@ -56,6 +55,7 @@ def _nz_2mrs(z):
     hmc = ccl.halos.HMCalculator(mass_function=hmf, halo_bias=hbf,
                                  mass_def=mass_def)
     prf = ccl.halos.HaloProfileHOD(
+        mass_def=mass_def,
         concentration=cm,
         log10Mmin_0=np.log10(10.**log10Mcut/cosmo['h']),
         siglnM_0=sigma_Ncen,
@@ -67,10 +67,8 @@ def _nz_2mrs(z):
     prf2pt = ccl.halos.Profile2ptHOD()
     # P(k)
     a_arr, lk_arr, _ = cosmo.get_linear_power().get_spline_arrays()
-    with pytest.warns(ccl.CCLDeprecationWarning):  # TODO: remove normprof v3
-        pk_hod = ccl.halos.halomod_Pk2D(cosmo, hmc, prf, prof_2pt=prf2pt,
-                                        lk_arr=lk_arr, a_arr=a_arr,
-                                        normprof1=True)
+    pk_hod = ccl.halos.halomod_Pk2D(cosmo, hmc, prf, prof_2pt=prf2pt,
+                                    lk_arr=lk_arr, a_arr=a_arr)
     # C_ell
     tr = ccl.NumberCountsTracer(cosmo, has_rsd=False, dndz=(z_arr, dndz),
                                 bias=(z_arr, np.ones(len(dndz))))

benchmarks/test_ia_profile.py

@@ -1,5 +1,4 @@
 import numpy as np
-import pytest
 import pyccl as ccl
 
 
@@ -48,21 +47,15 @@ def test_satellite_shear_profile():
     NFW = ccl.halos.HaloProfileNFW(concentration=cM, truncated=True,
                                    fourier_analytic=True, mass_def='200m')
 
-    with pytest.warns(ccl.CCLDeprecationWarning):
-        pk_GI_1h = ccl.halos.halomod_Pk2D(cosmo, hmc, NFW,
-                                          normprof1=True,
-                                          normprof2=True,
-                                          prof2=sat_gamma_HOD,
-                                          get_2h=False,
-                                          lk_arr=np.log(k_arr),
-                                          a_arr=a_arr)
-    with pytest.warns(ccl.CCLDeprecationWarning):
-        pk_II_1h = ccl.halos.halomod_Pk2D(cosmo, hmc, sat_gamma_HOD,
-                                          normprof1=True,
-                                          normprof2=True,
-                                          get_2h=False,
-                                          lk_arr=np.log(k_arr),
-                                          a_arr=a_arr)
+    pk_GI_1h = ccl.halos.halomod_Pk2D(cosmo, hmc, NFW,
+                                      prof2=sat_gamma_HOD,
+                                      get_2h=False,
+                                      lk_arr=np.log(k_arr),
+                                      a_arr=a_arr)
+    pk_II_1h = ccl.halos.halomod_Pk2D(cosmo, hmc, sat_gamma_HOD,
+                                      get_2h=False,
+                                      lk_arr=np.log(k_arr),
+                                      a_arr=a_arr)
 
     z_arr = np.linspace(0., 3., 256)
     z0 = 0.1
@@ -90,5 +83,5 @@ def test_satellite_shear_profile():
                           sigma8=0.81, n_s=0.96, h=1.)
     cosmo.compute_sigma()
     Ns_M_mean = sat_gamma_HOD._Ns(mass_benchmark, 1.)
-    norm = Ns_M_mean / sat_gamma_HOD.get_normalization(cosmo, 1, hmc)
+    norm = Ns_M_mean / sat_gamma_HOD.get_normalization(cosmo, 1, hmc=hmc)
     assert np.all(np.fabs(norm / norm_benchmark - 1) < 0.005)

benchmarks/test_massdef.py

@@ -21,10 +21,10 @@
 cs_200c_p = dc[7]
 cs_200c_di = dc[8]
 
-hmd_vir = ccl.halos.MassDefVir()
-hmd_200m = ccl.halos.MassDef200m()
-hmd_200c = ccl.halos.MassDef200c()
-hmd_500c = ccl.halos.MassDef500c()
+hmd_vir = ccl.halos.MassDefVir
+hmd_200m = ccl.halos.MassDef200m
+hmd_200c = ccl.halos.MassDef200c
+hmd_500c = ccl.halos.MassDef500c
 
 
 def test_mdef_get_radius():

benchmarks/test_power_nu.py

@@ -24,7 +24,7 @@ def test_power_nu(model):
         h=0.7,
         A_s=2.1e-9,
         n_s=0.96,
-        Neff=3.046,
+        Neff=3.046, T_CMB=2.725,
         Omega_k=0,
         w0=w_0[model],
         wa=w_a[model],

benchmarks/test_ptpk.py

@@ -8,6 +8,7 @@
 # Set cosmology
 COSMO = ccl.Cosmology(Omega_c=0.25, Omega_b=0.05,
                       h=0.7, sigma8=0.8, n_s=0.96,
+                      T_CMB=2.725,
                       transfer_function='bbks')
 
 # Redshifts

benchmarks/test_sz.py

@@ -10,7 +10,7 @@ def test_szcl():
         h=0.7,
         n_s=0.9645,
         A_s=2.02E-9,
-        Neff=3.046,
+        Neff=3.046, T_CMB=2.725,
         transfer_function='boltzmann_class')
     bm = np.loadtxt("benchmarks/data/sz_cl_P13_szpowerspectrum.txt",
                     unpack=True)

pyccl/__init__.py

@@ -42,21 +42,4 @@
 from . import halos
 from . import nl_pt
 
-from .bcm import *           # deprecated
-from .halomodel import *     # deprecated
-from .massfunction import *  # deprecated
-from .haloprofile import *   # deprecated
-
 from .cosmology import *
-
-
-def __getattr__(name):
-    rename = {"core": "cosmology", "cls": "cells"}
-    if name in rename:
-        from .errors import CCLDeprecationWarning
-        import warnings
-        warnings.warn(f"Module {name} has been renamed to {rename[name]}.",
-                      CCLDeprecationWarning)
-        name = rename[name]
-        return eval(name)
-    raise AttributeError(f"No module named {name}.")