Merge pull request #97 from LSSTDESC/cclv3

Update to CCLv3

environment.yml

@@ -13,7 +13,7 @@ dependencies:
   - numpy
   - scipy
   - pyyaml
-  - pyccl>=2.5.0,<2.8.0
+  - pyccl>=3.0.0
   - sacc>=0.12
   - namaster
   - camb

examples/clusters/conf_covariance_clusters.yaml

@@ -19,4 +19,3 @@ parameters:
   w0: -1
   wa: 0
   transfer_function: 'boltzmann_camb'
-  baryons_power_spectrum: 'nobaryons'

pyproject.toml

@@ -27,7 +27,7 @@ dependencies = [
     "numpy",
     "Jinja2",
     "pyyaml",
-    "pyccl>=2.5.0,<2.8.0",
+    "pyccl>=3.0.0",
     "sacc>=0.12",
     "camb",
     "healpy",

tests/benchmarks/32_DES_tjpcov_bm/desy1_tjpcov_bm.yml

@@ -53,7 +53,6 @@ cov:
       w0: -1
       wa: 0
       transfer_function: 'boltzmann_camb'
-      baryons_power_spectrum: 'nobaryons'
 
 
     #### Biases to apply to the tracers ####

tests/data/conf_covariance_builder_minimal.yaml

@@ -43,4 +43,3 @@ parameters:
   w0: -1
   wa: 0
   transfer_function: 'boltzmann_camb'
-  baryons_power_spectrum: 'nobaryons'

tests/data/conf_covariance_clusters.yaml

@@ -19,4 +19,3 @@ parameters:
   w0: -1
   wa: 0
   transfer_function: 'boltzmann_camb'
-  baryons_power_spectrum: 'nobaryons'

tests/data/conf_covariance_gaussian_fourier_nmt.yaml

@@ -64,4 +64,3 @@ parameters:
   w0: -1
   wa: 0
   transfer_function: 'boltzmann_camb'
-  baryons_power_spectrum: 'nobaryons'

tests/data/conf_covariance_gaussian_fourier_nmt_no_nmtconf.yaml

@@ -46,4 +46,3 @@ parameters:
   w0: -1
   wa: 0
   transfer_function: 'boltzmann_camb'
-  baryons_power_spectrum: 'nobaryons'

tests/data/conf_covariance_gaussian_fourier_nmt_txpipe.yaml

@@ -50,7 +50,6 @@ parameters:
   w0: -1
   wa: 0
   transfer_function: 'boltzmann_camb'
-  baryons_power_spectrum: 'nobaryons'
 
 cache:
   'workspaces':

tests/data/conf_covariance_gaussian_fourier_nmt_txpipe_mpi.yaml

@@ -50,7 +50,6 @@ parameters:
   w0: -1
   wa: 0
   transfer_function: 'boltzmann_camb'
-  baryons_power_spectrum: 'nobaryons'
 
 cache:
   'workspaces':

tests/data/conf_covariance_ssc.yaml

@@ -43,4 +43,3 @@ parameters:
   w0: -1
   wa: 0
   transfer_function: 'boltzmann_camb'
-  baryons_power_spectrum: 'nobaryons'

tests/data/cosmo_desy1.yaml

@@ -1,29 +1,29 @@
 Omega_c: 2.545000e-01
 Omega_b: 4.850000e-02
-Omega_m: 3.030000e-01
+# Omega_m: 3.030000e-01
 Omega_k: 0.000000e+00
-k_sign: 0
+# k_sign: 0
 w0: -1.000000e+00
 wa: 0.000000e+00
-H0: 6.820000e+01
+# H0: 6.820000e+01
 h: 6.820000e-01
 Neff: 3.046000e+00
-N_nu_mass: 0
-N_nu_rel: 3.046000e+00
-sum_nu_masses: 0.000000e+00
-Omega_nu_mass: 0.000000e+00
-Omega_nu_rel: 3.675179e-05
-A_s: nan
+# N_nu_mass: 0
+# N_nu_rel: 3.046000e+00
+# sum_nu_masses: 0.000000e+00
+# Omega_nu_mass: 0.000000e+00
+# Omega_nu_rel: 3.675179e-05
+# A_s: nan
 n_s: 9.710000e-01
 Omega_g: 5.312726e-05
 T_CMB: 2.725000e+00
-bcm_log10Mc: 1.407918e+01
-bcm_etab: 5.000000e-01
-bcm_ks: 5.500000e+01
-mu_0: 0.000000e+00
-sigma_0: 0.000000e+00
+# bcm_log10Mc: 1.407918e+01
+# bcm_etab: 5.000000e-01
+# bcm_ks: 5.500000e+01
+# mu_0: 0.000000e+00
+# sigma_0: 0.000000e+00
 sigma8: 8.010000e-01
-Omega_l: 6.969101e-01
-z_star: nan
-has_mgrowth: 0
-nz_mgrowth: 0
+# Omega_l: 6.969101e-01
+# z_star: nan
+# has_mgrowth: 0
+# nz_mgrowth: 0

tests/test_covariance_clusters.py

@@ -19,6 +19,8 @@
 N_z_bins = 18
 N_lambda_bins = 3
 
+MASSDEF = ccl.halos.MassDef200m
+
 
 def teardown_module():
     shutil.rmtree(OUTDIR)
@@ -108,7 +110,7 @@ def mock_covariance_gauss(mock_cosmo):
     cc_cov.fft_helper = FFTHelper(
         mock_cosmo, cc_cov.z_lower_limit, cc_cov.z_upper_limit
     )
-    cc_cov.mass_func = hmf.MassFuncTinker10(mock_cosmo)
+    cc_cov.mass_func = hmf.MassFuncTinker10(mass_def=MASSDEF)
     cc_cov.h0 = 0.67
     return cc_cov
 
@@ -120,7 +122,7 @@ def mock_covariance_ssc(mock_cosmo):
     cc_cov.fft_helper = FFTHelper(
         mock_cosmo, cc_cov.z_lower_limit, cc_cov.z_upper_limit
     )
-    cc_cov.mass_func = hmf.MassFuncTinker10(mock_cosmo)
+    cc_cov.mass_func = hmf.MassFuncTinker10(mass_def=MASSDEF)
     cc_cov.h0 = 0.67
     return cc_cov
 
@@ -163,21 +165,21 @@ def test_integral_mass_no_bias(
     mock_covariance_gauss: CovarianceClusterCounts, z, ref_val
 ):
     test = mock_covariance_gauss.mass_richness_integral(z, 0, remove_bias=True)
-    assert test == pytest.approx(ref_val, rel=1e-4)
+    assert test == pytest.approx(ref_val, rel=1e-3)
 
 
 def test_double_bessel_integral(
     mock_covariance_gauss: CovarianceClusterCounts,
 ):
     ref = 8.427201745032292e-05
     test = mock_covariance_gauss.double_bessel_integral(0.3, 0.3)
-    assert test == pytest.approx(ref, rel=1e-4)
+    assert test == pytest.approx(ref, rel=1e-3)
 
 
 def test_shot_noise(mock_covariance_gauss: ClusterCountsGaussian):
     ref = 63973.635143644424
     test = mock_covariance_gauss.shot_noise(0, 0)
-    assert test == pytest.approx(ref, 1e-4)
+    assert test == pytest.approx(ref, rel=1e-3)
 
 
 @pytest.mark.parametrize(
@@ -191,7 +193,7 @@ def test_integral_mass(
     mock_covariance_gauss: CovarianceClusterCounts, z, reference_val
 ):
     test = mock_covariance_gauss.mass_richness_integral(z, 0)
-    assert test == pytest.approx(reference_val, rel=1e-4)
+    assert test == pytest.approx(reference_val, rel=1e-3)
 
 
 @pytest.mark.parametrize(
@@ -278,7 +280,7 @@ def test_cov_nxn(
         ("mock_survey", "bin_z_0", "bin_rich_0"),
         ("mock_survey", "bin_z_0", "bin_rich_0"),
     )
-    assert cov_00_gauss + cov_00_ssc == pytest.approx(ref_sum, rel=1e-4)
+    assert cov_00_gauss + cov_00_ssc == pytest.approx(ref_sum, rel=1e-3)
 
 
 def test_cluster_count_tracer_missing_throws():

tests/test_covariance_fourier_base.py

@@ -1,11 +1,11 @@
 import os
 
 import numpy as np
-import pyccl as ccl
 import pymaster as nmt
 import pytest
 import sacc
 import shutil
+import pyccl as ccl
 
 from tjpcov.covariance_builder import CovarianceFourier
 
@@ -235,14 +235,31 @@ def test_get_tracer_info(mock_cov_fourier):
         else:
             assert np.abs(nl / (sigma_e**2 / Ngal) - 1) < 1e-5
 
-    # TODO: We should check the CCL tracers are the same
+    sacc = mock_cov_fourier.io.sacc_file
+    cosmo = mock_cov_fourier.get_cosmology()
     for tr, ccltr in ccl_tracers.items():
+        trsacc = sacc.tracers[tr]
         if "gc" in tr:
-            assert isinstance(ccltr, ccl.NumberCountsTracer)
+            z, nz = (trsacc.z, trsacc.nz)
+            assert nz == pytest.approx(ccltr.get_dndz(z), rel=1e-4)
+            chi = ccl.comoving_radial_distance(cosmo, 1 / (1 + z))
+            kz = ccltr.get_kernel(chi)[0]
+            z_max_nz = z[np.argmax(nz)]
+            z_max_kz = z[np.argmax(kz)]
+            assert z_max_nz == pytest.approx(z_max_kz, rel=1e-2)
         elif "wl" in tr:
-            assert isinstance(ccltr, ccl.WeakLensingTracer)
+            z, nz = (trsacc.z, trsacc.nz)
+            assert nz == pytest.approx(ccltr.get_dndz(z), rel=1e-4)
+            chi = ccl.comoving_radial_distance(cosmo, 1 / (1 + z))
+            kz = ccltr.get_kernel(chi)[0]
+            z_max_nz = z[np.argmax(nz)]
+            z_max_kz = z[np.argmax(kz)]
+            assert z_max_kz < z_max_nz
         elif "cv" in tr:
-            assert isinstance(ccltr, ccl.CMBLensingTracer)
+            assert not isinstance(ccltr, ccl.tracers.NzTracer)
+            chi_max = ccltr.chi_max
+            chi_max2 = ccl.comoving_radial_distance(cosmo, 1 / (1 + 1100))
+            assert chi_max == pytest.approx(chi_max2, rel=1e-4)
 
     # Check tracer_noise_coupled. Modify the sacc file to add metadata
     # information for the tracer noise

tests/test_covariance_fourier_gaussian_nmt.py

@@ -553,7 +553,7 @@ def test_get_covariance_block(tracer_comb1, tracer_comb2):
     # Check chi2, which is what we actually care about
     if tracer_comb1 == tracer_comb2:
         s = cnmt.io.get_sacc_file()
-        assert_chi2(s, tracer_comb1, tracer_comb2, cov, cov_bm, 1e-4)
+        assert_chi2(s, tracer_comb1, tracer_comb2, cov, cov_bm, 1e-3)
 
     # Check that it runs if one of the masks does not overlap with the others
     if tracer_comb1 != tracer_comb2:
@@ -1153,7 +1153,7 @@ def test_full_covariance_benchmark():
     delta = clf - cl
     chi2 = delta.dot(np.linalg.inv(cov)).dot(delta)
     chi2_bm = delta.dot(np.linalg.inv(cov_bm)).dot(delta)
-    assert np.abs(chi2 / chi2_bm - 1) < 1e-4
+    assert np.abs(chi2 / chi2_bm - 1) < 1e-3
 
     # Clean after the test
     clean_outdir()
@@ -1224,4 +1224,4 @@ def test_txpipe_like_input():
     delta = clf - cl
     chi2 = delta.dot(np.linalg.inv(cov)).dot(delta)
     chi2_bm = delta.dot(np.linalg.inv(cov_bm)).dot(delta)
-    assert np.abs(chi2 / chi2_bm - 1) < 1e-4
+    assert np.abs(chi2 / chi2_bm - 1) < 1e-3

tests/test_covariance_fourier_ssc.py

@@ -51,18 +51,18 @@ def get_config():
 
 
 def get_halo_model(cosmo):
-    md = ccl.halos.MassDef200m()
-    mf = ccl.halos.MassFuncTinker08(cosmo, mass_def=md)
-    hb = ccl.halos.HaloBiasTinker10(cosmo, mass_def=md)
-    hmc = ccl.halos.HMCalculator(cosmo, mf, hb, md)
+    md = ccl.halos.MassDef200m
+    mf = ccl.halos.MassFuncTinker08(mass_def=md)
+    hb = ccl.halos.HaloBiasTinker10(mass_def=md)
+    hmc = ccl.halos.HMCalculator(mass_function=mf, halo_bias=hb, mass_def=md)
 
     return hmc
 
 
 def get_NFW_profile():
-    md = ccl.halos.MassDef200m()
-    cm = ccl.halos.ConcentrationDuffy08(mdef=md)
-    pNFW = ccl.halos.HaloProfileNFW(cm)
+    md = ccl.halos.MassDef200m
+    cm = ccl.halos.ConcentrationDuffy08(mass_def=md)
+    pNFW = ccl.halos.HaloProfileNFW(mass_def=md, concentration=cm)
 
     return pNFW
 
@@ -158,7 +158,7 @@ def test_get_covariance_block(cov_fssc, tracer_comb1, tracer_comb2):
     tkk_ssc = ccl.halos.halomod_Tk3D_SSC_linear_bias(
         cosmo,
         hmc,
-        nfw_profile,
+        prof=nfw_profile,
         bias1=bias1,
         bias2=bias2,
         bias3=bias3,
@@ -170,7 +170,7 @@ def test_get_covariance_block(cov_fssc, tracer_comb1, tracer_comb2):
     )
 
     cl_mask = get_cl_footprint(*tracer_comb1, *tracer_comb2)
-    sigma2_B = ccl.sigma2_B_from_mask(cosmo, a=a_arr, mask_wl=cl_mask)
+    sigma2_B = ccl.sigma2_B_from_mask(cosmo, a_arr=a_arr, mask_wl=cl_mask)
 
     ccl_tracers, _ = cov_fssc.get_tracer_info()
     tr1 = ccl_tracers[tracer_comb1[0]]
@@ -179,13 +179,13 @@ def test_get_covariance_block(cov_fssc, tracer_comb1, tracer_comb2):
     tr4 = ccl_tracers[tracer_comb2[1]]
     cov_ccl = ccl.angular_cl_cov_SSC(
         cosmo,
-        cltracer1=tr1,
-        cltracer2=tr2,
+        tracer1=tr1,
+        tracer2=tr2,
         ell=ell,
-        tkka=tkk_ssc,
+        t_of_kk_a=tkk_ssc,
         sigma2_B=(a_arr, sigma2_B),
-        cltracer3=tr3,
-        cltracer4=tr4,
+        tracer3=tr3,
+        tracer4=tr4,
     )
 
     assert np.max(np.fabs(np.diag(cov_ssc / cov_ccl - 1))) < 1e-5
@@ -266,7 +266,7 @@ def test_get_covariance_block_WL_benchmark(cov_fssc):
     cosmo = ccl.Cosmology(
         Omega_c=0.25, Omega_b=0.05, h=h, n_s=0.97, sigma8=0.8, m_nu=0.0
     )
-    WL_tracer = ccl.WeakLensingTracer(cosmo, (z, nofz))
+    WL_tracer = ccl.WeakLensingTracer(cosmo, dndz=(z, nofz))
 
     # Trick TJPCov to use the ells we want instead the ones in the file
     s = sacc.Sacc()

tests/test_mpi.py

@@ -145,7 +145,7 @@ def test_get_covariance(fg_nmt_cov):
     cov = fg_nmt_cov.get_covariance() + 1e-100
     cov_bm = s.covariance.covmat + 1e-100
     assert np.max(np.abs(np.diag(cov) / np.diag(cov_bm) - 1)) < 1e-3
-    assert np.max(np.abs(cov / cov_bm - 1)) < 1
+    assert np.max(np.abs(np.diag(cov, 1) / np.diag(cov_bm, 1) - 1)) < 1e-2
 
     # Check chi2
     clf = np.array([])
@@ -157,7 +157,7 @@ def test_get_covariance(fg_nmt_cov):
     delta = clf - cl
     chi2 = delta.dot(np.linalg.inv(cov)).dot(delta)
     chi2_bm = delta.dot(np.linalg.inv(cov_bm)).dot(delta)
-    assert np.abs(chi2 / chi2_bm - 1) < 1e-4
+    assert np.abs(chi2 / chi2_bm - 1) < 1e-3
 
 
 def test_covariance_calculator(cov_calc, cov_calc_mpi):

tjpcov/covariance_builder.py

@@ -241,7 +241,7 @@ def get_cosmology(self):
 
             if cosmo is None or cosmo == "set":
                 self.cosmo = ccl.Cosmology(**self.config["parameters"])
-            elif isinstance(cosmo, ccl.core.Cosmology):
+            elif isinstance(cosmo, ccl.Cosmology):
                 self.cosmo = cosmo
             elif isinstance(cosmo, str):
                 ext = cosmo.split(".")[-1]
@@ -258,7 +258,7 @@ def get_cosmology(self):
             else:
                 raise ValueError(
                     "cosmo entry looks wrong. It has to be one"
-                    "of ['set', ccl.core.Cosmology instance, "
+                    "of ['set', ccl.Cosmology instance, "
                     "a yaml file or a pickle"
                 )