Add radial selection

tests/test_likelihood.py

@@ -7,11 +7,12 @@ import borg_velocity.likelihood as likelihood
 import borg_velocity.forwards as forwards
 import borg_velocity.utils as utils
 
-ini_file = '../conf/basic_ini.ini'
+ini_file = '../conf/supranta_ini.ini'
 test_scaling = True
 test_sigma8 = True
 test_omegam = True
 test_alpha = True
+test_lam = True
 test_muA = True
 
 # Input box
@@ -34,6 +35,7 @@ mylike = likelihood.VelocityBORGLikelihood(model, fwd_param, fwd_vel, ini_file)
 state = borg.likelihood.MarkovState()
 mylike.initializeLikelihood(state)
 mylike.updateCosmology(cosmo)
+np.random.seed(2)
 s_hat = np.fft.rfftn(np.random.randn(*box_in.N)) / box_in.Ntot ** (0.5)
 mylike.generateMockData(s_hat, state)
 
@@ -84,8 +86,7 @@ if test_sigma8:
     fig.clf()
     plt.close(fig)
     
-    
-# Test sigma8
+# Test omegam
 if test_omegam:
     all_omegam = np.linspace(0.1, 0.6, 100)
     all_lkl = np.empty(all_omegam.shape)
@@ -134,6 +135,29 @@ if test_alpha:
     fig.clf()
     plt.close(fig)
     
+# Test radial sampling
+if test_lam:
+    all_lam = np.linspace(0.0, 10.0, 100)
+    all_lkl = np.empty(all_lam.shape)
+    for i, lam in enumerate(all_lam):
+        mylike.fwd_param.setModelParams({'lam0':lam})
+        all_lkl[i] = mylike.logLikelihoodComplex(s_hat, None)
+    mylike.fwd_param.setModelParams({'lam0':mylike.lam[0]})
+    fid_lkl = mylike.logLikelihoodComplex(s_hat, None)
+    all_lkl -= fid_lkl
+    all_lkl = np.exp(-all_lkl)
+
+    fig, ax = plt.subplots(1, 1, figsize=(5,5))
+    ax.plot(all_lam, all_lkl)
+    ax.axhline(y=0, color='k')
+    ax.axvline(x=mylike.lam[0], color='k')
+    ax.set_xlabel(r'$\lambda_0$')
+    ax.set_ylabel(r'$\mathcal{L}$')
+    fig.tight_layout()
+    fig.savefig('../figs/lam_test.png')
+    fig.clf()
+    plt.close(fig)
+    
     
 # Test bias model 
 if test_muA:
@@ -157,3 +181,4 @@ if test_muA:
     fig.savefig('../figs/muA_test.png')
     fig.clf()
     plt.close(fig)
+