binned test

class-sz/test/test_classy_sz_cluster_counts.py

@@ -62,6 +62,75 @@
     'z_min': 0.008
 }
 
+pdict_binned = {'A_ym': 4.949940743969354e-05,
+ 'B': 1.25,
+ 'B_ym': 0.12329999999999997,
+ 'C_ym': 0.0,
+ 'HMF_prescription_NCDM': 1,
+ 'M_max': 8400000000000000.0,
+ 'M_min': 5600000000000.0,
+ 'N_ncdm': 1,
+ 'N_samp_fftw': 6400,
+ 'N_ur': 2.0328,
+ 'T_ncdm': 0.71611,
+ 'bin_dlog10_snr': 0.25,
+ 'bin_dz_cluster_counts': 0.1,
+ 'bin_z_max_cluster_counts': 2.01,
+ 'bin_z_min_cluster_counts': 0.01,
+ 'cluster_count_completeness_grid_z_cutoff_low': 0.4,
+ 'cluster_count_completeness_grid_z_cutoff_mid': 1.0,
+ 'concentration parameter': 'B13',
+ 'cosmo_model': 0,
+ 'dlnM_cluster_count_completeness_grid': 0.01,
+ 'dlny': 0.025,
+ 'dz_cluster_count_completeness_grid_high_z': 0.01,
+ 'dz_cluster_count_completeness_grid_low_z': 0.001,
+ 'dz_cluster_count_completeness_grid_mid_z': 0.001,
+ 'experiment': 1,
+ 'h': 0.7,
+ 'has_selection_function': 1,
+ 'lnymax': 10.0,
+ 'lnymin': -20.0,
+ 'log10_snr_max': 2.0,
+ 'log10_snr_min': 0.6,
+ 'm_ncdm': 0.06,
+ 'm_pivot_ym_[Msun]': 300000000000000.0,
+ 'mass function': 'T08M500c',
+ 'mass_epsabs': 1e-100,
+ 'mass_epsrel': 1e-06,
+ 'n_m_dndlnM': 8000,
+ 'n_s': 0.96,
+ 'ndim_masses': 100,
+ 'ndim_redshifts': 5000,
+ 'no_spline_in_tinker': 1,
+ 'ntab_dlnm_dlnq': 1000,
+ 'omega_b': 0.023995299999999997,
+ 'omega_cdm': 0.1303547,
+ 'output': 'sz_cluster_counts',
+ 'redshift_epsabs': 1e-100,
+ 'redshift_epsrel': 1e-06,
+ 'sigma8': 0.811,
+ 'sigmaM_ym': 0.173,
+ 'sigma_derivative': 0,
+ 'signal-to-noise_cut-off_for_survey_cluster_completeness': 5.0,
+ 'sz_selection_function_skyfracs_file': os.environ['PATH_TO_CLASS_SZ_DATA'] + '/class_sz/class-sz/class_sz_auxiliary_files/includes/so_sim_sz_mf_noise_skyfracs.txt',
+ 'sz_selection_function_thetas_file': os.environ['PATH_TO_CLASS_SZ_DATA'] + '/class_sz/class-sz/class_sz_auxiliary_files/includes/so_sim_sz_mf_noise_thetas.txt',
+ 'sz_selection_function_ylims_file': os.environ['PATH_TO_CLASS_SZ_DATA'] + '/class_sz/class-sz/class_sz_auxiliary_files/includes/so_sim_sz_mf_noise_sigmas.txt',
+ 'szcc_dof': 0.0,
+ 'szcc_qtrunc': -1.0,
+ 'szcounts_fft_nz': 550,
+ 'szcounts_fft_z_max': 3.5999999999999996,
+ 'szcounts_fft_z_min': 0.008,
+ 'szcounts_qmax_fft_padded': 500.0,
+ 'tau_reio': 0.06,
+ 'tol_dlnm_dlnq': 0.001,
+ 'use_m200c_in_ym_relation': 0,
+ 'use_m500c_in_ym_relation': 1,
+ 'use_skyaveraged_noise': 0,
+ 'y_m_relation': 1,
+ 'z_max': 3.5999999999999996,
+ 'z_min': 0.008}
+
 
 
 def get_Ntot(classy_sz):
@@ -98,6 +167,28 @@ def get_Ntot(classy_sz):
     return Ntot
 
 
+def get_Ntot_binned(classy_sz):
+    dNdzdy_theoretical = classy_sz.dndzdy_theoretical()['dndzdy']
+    z_center = classy_sz.dndzdy_theoretical()['z_center']
+    z_edges = classy_sz.dndzdy_theoretical()['z_edges']
+    log10y_center = classy_sz.dndzdy_theoretical()['log10y_center']
+    log10y_edges = classy_sz.dndzdy_theoretical()['log10y_edges']
+
+    N_z,N_y = np.shape(dNdzdy_theoretical)
+    N_clusters_z_theory = []
+    for iz in range(N_z):
+        N_clusters_z_theory.append(np.sum(dNdzdy_theoretical[iz][0:]))
+    N_clusters_y_theory = []
+
+    for iy in range(N_y):
+        N_clusters_y_theory.append(np.sum(np.asarray(dNdzdy_theoretical)[:,iy]))
+
+    # print total number of clusters: 
+    Ntot = np.sum(N_clusters_z_theory)
+
+    print("get Ntot binned:", Ntot)
+    return Ntot
+
 
 # Function to run the test
 def test_sz_cluster_counts_cosmo_model_1():
@@ -148,6 +239,32 @@ def test_sz_cluster_counts_cosmo_model_0():
     # Print message if the test passes
     print(f"Test passed: Ntot is {Ntot}, close to 15003")
 
+    # End timing
+    end_time = time.time()
+    elapsed_time = end_time - start_time
+    print(f"Test executed in {elapsed_time:.2f} seconds")
+
+
+
+# Function to run the test
+def test_sz_cluster_counts_binned():
+    start_time = time.time()
+    # Initialize the Class object
+    classy_sz = Class_sz()
+
+    # Set the parameters in the Class object
+    classy_sz.set(pdict_binned)
+
+    # Compute the results
+    classy_sz.compute_class_szfast()
+
+    Ntot = get_Ntot_binned(classy_sz)
+
+    # Check the result is close to the expected value (15017)
+    assert Ntot == pytest.approx(14983.433909, rel=0.01), f"Ntot is {Ntot}, expected close to 14983.433909"
+    # Print message if the test passes
+    print(f"Test passed for binned: Ntot is {Ntot}, close to 14983.433909")
+
     # End timing
     end_time = time.time()
     elapsed_time = end_time - start_time