PR #1134 with freshly made branch

benchmarks/test_pkresponse.py

@@ -0,0 +1,181 @@
+import numpy as np
+from pyccl.pkresponse import Pmm_resp, darkemu_Pgm_resp, darkemu_Pgg_resp
+import pyccl as ccl
+
+# Set cosmology
+Om = 0.3156
+ob = 0.02225
+oc = 0.1198
+OL = 0.6844
+As = 2.2065e-9
+ns = 0.9645
+h = 0.6727
+
+cosmo = ccl.Cosmology(
+    Omega_c=oc / (h**2),
+    Omega_b=ob / (h**2),
+    h=h,
+    n_s=ns,
+    A_s=As,
+    m_nu=0.06,
+    transfer_function="boltzmann_camb",
+    extra_parameters={"camb": {"halofit_version": "takahashi"}},
+)
+
+
+# Load data
+pkresponse_data = np.load("benchmarks/data/pkresponse.npz")
+k_data = pkresponse_data["k_h"]
+k_data_mm = pkresponse_data["k_h_mm"]
+Pmm_resp_data = pkresponse_data["Pmm_resp"] / h**3
+Pgm_resp_data = pkresponse_data["Pgm_resp"] / h**3
+Pgg_resp_data = pkresponse_data["Pgg_resp"] / h**3
+
+
+# HOD parameters
+logMhmin = 13.94
+logMh1 = 14.46
+alpha = 1.192
+kappa = 0.60
+sigma_logM = 0.5
+sigma_lM = sigma_logM * np.log(10)
+logMh0 = logMhmin + np.log10(kappa)
+
+logMmin = np.log10(10**logMhmin / h)
+logM0 = np.log10(10**logMh0 / h)
+logM1 = np.log10(10**logMh1 / h)
+
+# Construct HOD
+mass_def = ccl.halos.MassDef(200, "matter")
+cm = ccl.halos.ConcentrationDuffy08(mass_def=mass_def)
+prof_hod = ccl.halos.HaloProfileHOD(
+    mass_def=mass_def,
+    concentration=cm,
+    log10Mmin_0=logMmin,
+    siglnM_0=sigma_lM,
+    log10M0_0=logM0,
+    log10M1_0=logM1,
+    alpha_0=alpha,
+)
+
+# Define input parameters for pkresponse functions
+log10Mh_min = np.log10(2.6e12)
+log10Mh_max = 15.9
+a_arr = np.array([1.0, 0.64516129, 0.49382716, 0.40387722])
+indx = (k_data > 1e-2) & (k_data < 3)
+lk_arr = np.log(k_data[indx] * h)  # Using loaded k_data
+indx_mm = (k_data_mm > 1e-2) & (k_data_mm < 3)
+lk_arr_mm = np.log(k_data_mm[indx_mm] * h)  # Using loaded k_data
+
+# Generate power spectrum responses using pkresponse.py functions
+use_log = False
+
+generated_Pmm_resp = Pmm_resp(
+    cosmo, deltah=0.02, lk_arr=lk_arr_mm, a_arr=a_arr, use_log=use_log
+)
+
+generated_Pgm_resp = darkemu_Pgm_resp(
+    cosmo,
+    prof_hod,
+    deltah=0.02,
+    log10Mh_min=log10Mh_min,
+    log10Mh_max=log10Mh_max,
+    lk_arr=lk_arr,
+    a_arr=a_arr,
+    use_log=use_log,
+)
+
+generated_Pgg_resp = darkemu_Pgg_resp(
+    cosmo,
+    prof_hod,
+    deltalnAs=0.03,
+    log10Mh_min=log10Mh_min,
+    log10Mh_max=log10Mh_max,
+    lk_arr=lk_arr,
+    a_arr=a_arr,
+    use_log=use_log,
+)
+
+
+# Compare the generated responses with simulation data.
+# The error bars from the simulations are too strict,
+# probably because we use the pair and fixed initial density method.
+# Hence instead of "atol" (absolute difference compared to the error bars),
+# we use "rtol" (relative difference compared to the simulated response).
+# The difference between the model and the simulations are
+# typically few ten percents.
+# Higher redshifts of Pgm, Pgg (especially Pgg) have worse accuracy due to the
+# accuracy of darkemulator itself,
+# but they are still a factor of difference at most.
+
+
+def test_pmm_resp():
+    assert np.allclose(
+        generated_Pmm_resp,
+        Pmm_resp_data[:, indx_mm],
+        rtol=0.15,
+    )
+
+
+def test_pgm_resp():
+    assert np.allclose(
+        generated_Pgm_resp[0],
+        Pgm_resp_data[0, indx],
+        rtol=0.3,
+    )
+
+
+def test_pgm_resp1():
+    assert np.allclose(
+        generated_Pgm_resp[1],
+        Pgm_resp_data[1, indx],
+        rtol=0.36,
+    )
+
+
+def test_pgm_resp2():
+    assert np.allclose(
+        generated_Pgm_resp[2],
+        Pgm_resp_data[2, indx],
+        rtol=0.78,
+    )
+
+
+def test_pgm_resp3():
+    assert np.allclose(
+        generated_Pgm_resp[3],
+        Pgm_resp_data[3, indx],
+        rtol=0.85,
+    )
+
+
+def test_pgg_resp():
+    assert np.allclose(
+        generated_Pgg_resp[0],
+        Pgg_resp_data[0, indx],
+        rtol=0.65,
+    )
+
+
+def test_pgg_resp1():
+    assert np.allclose(
+        generated_Pgg_resp[1],
+        Pgg_resp_data[1, indx],
+        rtol=1.7,
+    )
+
+
+def test_pgg_resp2():
+    assert np.allclose(
+        generated_Pgg_resp[2],
+        Pgg_resp_data[2, indx],
+        rtol=3.1,
+    )
+
+
+def test_pgg_resp3():
+    assert np.allclose(
+        generated_Pgg_resp[3],
+        Pgg_resp_data[3, indx],
+        rtol=2.6,
+    )

pyccl/__init__.py

@@ -45,3 +45,5 @@
 from . import nl_pt
 
 from .cosmology import *
+
+from .pkresponse import *