Add HMC and BlackJax parameter samplers

tests/test_likelihood.py

@@ -14,6 +14,7 @@ test_omegam = True
 test_alpha = True
 test_lam = True
 test_muA = True
+test_bulk = True
 
 # Input box
 box_in = borg.forward.BoxModel()
@@ -181,4 +182,36 @@ if test_muA:
     fig.savefig('../figs/muA_test.png')
     fig.clf()
     plt.close(fig)
+    
+    
+# Test bulk flow 
+if test_bulk:
+    all_vx = np.linspace(-100, 100, 50)
+    all_lkl = np.empty(all_vx.shape)
+    
+    for i, vx in enumerate(all_vx):
+        mylike.fwd_param.setModelParams({'bulk_flow_x':vx})
+        all_lkl[i] = mylike.logLikelihoodComplex(s_hat, None)
+    mylike.fwd_param.setModelParams({'bulk_flow_x':mylike.bulk_flow[0]})
+    # fid_lkl = mylike.logLikelihoodComplex(s_hat, None)
+    fid_lkl = np.amin(all_lkl)
+    all_lkl -= fid_lkl
+    all_lkl = np.exp(-all_lkl)
+    
+    sigma_bulk = utils.get_sigma_bulk(mylike.L[0], mylike.fwd.getCosmoParams())
+    ref_lkl = np.exp(-all_vx ** 2 / 2 / sigma_bulk ** 2)
+    ref_lkl *= np.amax(all_lkl) / np.amax(ref_lkl)
+
+    fig, ax = plt.subplots(1, 1, figsize=(5,5))
+    ax.plot(all_vx, all_lkl, label='Posterior')
+    ax.plot(all_vx, ref_lkl, ls='--', label=r'$\Lambda$CDM prior')
+    ax.axhline(y=0, color='k')
+    ax.axvline(x=mylike.muA[0], color='k')
+    ax.legend()
+    ax.set_xlabel(r'$v_{{\rm bulk}, x}$')
+    ax.set_ylabel(r'$\mathcal{L}$')
+    fig.tight_layout()
+    fig.savefig('../figs/bulk_test.png')
+    fig.clf()
+    plt.close(fig)