Control over Malmquist

csiborgtools/flow/flow_model.py

@@ -356,7 +356,8 @@ def e2_distmod_TFR(e2_mag, e2_eta, eta, b, c, e_mu_intrinsic):
 
 def sample_TFR(e_mu_min, e_mu_max, a_mean, a_std, b_mean, b_std,
                c_mean, c_std, alpha_min, alpha_max, sample_alpha,
-               a_dipole_mean, a_dipole_std, sample_a_dipole, name):
+               a_dipole_mean, a_dipole_std, sample_a_dipole,
+               sample_curvature, name):
     """Sample Tully-Fisher calibration parameters."""
     e_mu = sample(f"e_mu_{name}", Uniform(e_mu_min, e_mu_max))
     a = sample(f"a_{name}", Normal(a_mean, a_std))
@@ -367,7 +368,11 @@ def sample_TFR(e_mu_min, e_mu_max, a_mean, a_std, b_mean, b_std,
         ax, ay, az = 0.0, 0.0, 0.0
 
     b = sample(f"b_{name}", Normal(b_mean, b_std))
-    c = sample(f"c_{name}", Normal(c_mean, c_std))
+
+    if sample_curvature:
+        c = sample(f"c_{name}", Normal(c_mean, c_std))
+    else:
+        c = 0.
 
     alpha = sample_alpha_bias(name, alpha_min, alpha_max, sample_alpha)
 
@@ -513,12 +518,18 @@ class PV_LogLikelihood(BaseFlowValidationModel):
         Whether to directly sample the distance without numerical
         marginalisation. in which case the tracers can be coupled by a
         covariance matrix. By default `False`.
+    with_homogeneous_malmquist : bool, optional
+        Whether to include the homogeneous Malmquist bias. By default `True`.
+    with_inhomogeneous_malmquist : bool, optional
+        Whether to include the inhomogeneous Malmquist bias. By default `True`.
     """
 
     def __init__(self, los_density, los_velocity, RA, dec, z_obs, e_zobs,
                  calibration_params, abs_calibration_params, mag_selection,
                  r_xrange, Omega_m, kind, name, void_kwargs=None,
-                 wo_num_dist_marginalisation=False):
+                 wo_num_dist_marginalisation=False,
+                 with_homogeneous_malmquist=True,
+                 with_inhomogeneous_malmquist=True):
         if e_zobs is not None:
             e2_cz_obs = jnp.asarray((SPEED_OF_LIGHT * e_zobs)**2)
         else:
@@ -557,6 +568,8 @@ class PV_LogLikelihood(BaseFlowValidationModel):
         self.name = name
         self.Omega_m = Omega_m
         self.wo_num_dist_marginalisation = wo_num_dist_marginalisation
+        self.with_homogeneous_malmquist = with_homogeneous_malmquist
+        self.with_inhomogeneous_malmquist = with_inhomogeneous_malmquist
         self.norm = - self.ndata * jnp.log(self.num_sims)
 
         if mag_selection is not None:
@@ -771,9 +784,14 @@ class PV_LogLikelihood(BaseFlowValidationModel):
                     "marginalising the true distance.")
 
             # Calculate p(r) (Malmquist bias). Shape is (ndata, nxrange)
-            log_ptilde = log_ptilde_wo_bias(
-                self.mu_xrange[None, :], mu[:, None], e2_mu[:, None],
-                self.log_r2_xrange[None, :])
+            if self.with_homogeneous_malmquist:
+                log_ptilde = log_ptilde_wo_bias(
+                    self.mu_xrange[None, :], mu[:, None], e2_mu[:, None],
+                    self.log_r2_xrange[None, :])
+            else:
+                log_ptilde = log_ptilde_wo_bias(
+                    self.mu_xrange[None, :], mu[:, None], e2_mu[:, None],
+                    0.)
 
             if self.is_void_data:
                 rLG = field_calibration_params["rLG"]
@@ -785,7 +803,9 @@ class PV_LogLikelihood(BaseFlowValidationModel):
 
             # Inhomogeneous Malmquist bias. Shape: (nsims, ndata, nxrange)
             alpha = distmod_params["alpha"]
-            log_ptilde = log_ptilde[None, ...] + alpha * log_los_density
+            log_ptilde = log_ptilde[None, ...]
+            if self.with_inhomogeneous_malmquist:
+                log_ptilde += alpha * log_los_density
 
             ptilde = jnp.exp(log_ptilde)
             # Normalization of p(r). Shape: (nsims, ndata)
@@ -840,11 +860,13 @@ class PV_LogLikelihood(BaseFlowValidationModel):
             alpha = distmod_params["alpha"]
 
             # Normalisation of p(mu), shape is `(n_sims, n_data, n_rad)`
-            pnorm = (
-                + self.log_r2_xrange[None, None, :]
-                + alpha * log_los_density_grid
-                + normal_logpdf(
-                    self.mu_xrange[None, :], mu[:, None], e_mu[:, None])[None, ...])  # noqa
+            pnorm = normal_logpdf(
+                self.mu_xrange[None, :], mu[:, None], e_mu[:, None])[None, ...]
+            if self.with_homogeneous_malmquist:
+                pnorm += self.log_r2_xrange[None, None, :]
+            if self.with_inhomogeneous_malmquist:
+                pnorm += alpha * log_los_density_grid
+
             pnorm = jnp.exp(pnorm)
             # Now integrate over the radial steps. Shape is `(nsims, ndata)`.
             # No Jacobian here because I integrate over distance, not the
@@ -855,11 +877,12 @@ class PV_LogLikelihood(BaseFlowValidationModel):
             jac = jnp.abs(distmod2dist_gradient(mu_true, self.Omega_m))
 
             # Calculate unnormalized log p(mu). Shape is (nsims, ndata)
-            ll = (
-                + jnp.log(jac)[None, :]
-                + (2 * jnp.log(r_true) - self.log_r2_xrange_mean)[None, :]
-                + alpha * log_density
-                )
+            ll = 0.
+            if self.with_homogeneous_malmquist:
+                ll = (+ jnp.log(jac)
+                      + (2 * jnp.log(r_true) - self.log_r2_xrange_mean))
+            if self.with_inhomogeneous_malmquist:
+                ll += alpha * log_density
 
             # Subtract the normalization. Shape remains (nsims, ndata)
             ll -= jnp.log(pnorm)