fix: only add Trx bias to autocorr noise

CHANGELOG.rst

@@ -2,6 +2,24 @@
 Changelog
 =========
 
+v2.3.2 [2022.02.18]
+===================
+
+Added
+-----
+- ``_extract_kwargs`` attribute added to the ``SimulationComponent`` class. This
+  attribute is used by the ``Simulator`` to determine which optional parameters
+  should actually be extracted from the data.
+- ``antpair`` optional parameter added to the ``ThermalNoise`` class. This is
+  used to determine whether to simulate noise via the radiometer equation (as is
+  appropriate for a cross-correlation) or to just add a bias from the receiver
+  temperature (which is our proxy for what should happen to an auto-correlation).
+
+Fixed
+-----
+- The ``Simulator`` class now correctly uses the auto-correlations to simulate
+  noise for the cross-correlations.
+
 v2.3.1 [2022.01.19]
 ===================
 

hera_sim/components.py

@@ -39,6 +39,9 @@ class determine how to apply the effect simulated by
 
     _alias: Tuple[str] = tuple()
 
+    # Keyword arguments for the Simulator to extract from the data
+    _extract_kwargs = set()
+
     def __init_subclass__(cls, is_abstract: bool = False):
         """Provide some useful augmentations to subclasses.
 

hera_sim/noise.py

@@ -44,9 +44,21 @@ class ThermalNoise(Noise):
         Receiver temperature in K
     autovis : float, optional
         Autocorrelation visibility amplitude. Used if provided instead of ``Tsky_mdl``.
+    antpair : tuple of int, optional
+        Antenna numbers for the baseline that noise is being simulated for. This is
+        just used to determine whether to simulate noise via the radiometer equation
+        or to just add a bias from the receiver temperature.
+
+    Notes
+    -----
+    At the time of writing, we're unsure of the correct prescription for
+    autocorrelations, so we only add a receiver temperature bias to baselines that
+    are interpreted as autocorrelations (i.e. where the ``bl_vec`` parameter provided
+    on calling an instance of this class is nearly zero length).
     """
 
     _alias = ("thermal_noise",)
+    _extract_kwargs = {"autovis", "antpair"}
 
     def __init__(
         self,
@@ -56,6 +68,7 @@ def __init__(
         channel_width=None,
         Trx=0,
         autovis=None,
+        antpair=None,
     ):
         super().__init__(
             Tsky_mdl=Tsky_mdl,
@@ -64,6 +77,7 @@ def __init__(
             channel_width=channel_width,
             Trx=Trx,
             autovis=autovis,
+            antpair=antpair,
         )
 
     def __call__(self, lsts: np.ndarray, freqs: np.ndarray, **kwargs):
@@ -79,7 +93,9 @@ def __call__(self, lsts: np.ndarray, freqs: np.ndarray, **kwargs):
         Returns
         -------
         array
-            A 2D array shaped ``(lsts, freqs)`` with the thermal noise.
+            A 2D array shaped ``(lsts, freqs)`` with the thermal noise. If the
+            provided ``antpair`` is for an autocorrelation, then only a receiver
+            temperature bias is returned.
         """
         # validate the kwargs
         self._check_kwargs(**kwargs)
@@ -92,6 +108,7 @@ def __call__(self, lsts: np.ndarray, freqs: np.ndarray, **kwargs):
             channel_width,
             Trx,
             autovis,
+            antpair,
         ) = self._extract_kwarg_values(**kwargs)
 
         # get the channel width in Hz if not specified
@@ -113,6 +130,11 @@ def __call__(self, lsts: np.ndarray, freqs: np.ndarray, **kwargs):
         if callable(omega_p):
             omega_p = omega_p(freqs)
 
+        # If this is an autocorrelation, only add receiver temperature bias
+        if antpair is not None:
+            if antpair[0] == antpair[1]:
+                return Trx / utils.jansky_to_kelvin(freqs, omega_p).reshape(1, -1)
+
         # get the sky temperature; use an autocorrelation if provided
         if autovis is not None and not np.all(np.isclose(autovis, 0)):
             Tsky = autovis * utils.jansky_to_kelvin(freqs, omega_p).reshape(1, -1)

hera_sim/simulate.py

@@ -885,7 +885,11 @@ def func(freqs, ants, other=None):
         has a default value, it will not be in the returned dictionary.
         """
         model_params = self._get_model_parameters(model)
-        model_params = {k: v for k, v in model_params.items() if v is inspect._empty}
+        model_params = {
+            k: v
+            for k, v in model_params.items()
+            if v is inspect._empty or k in model._extract_kwargs
+        }
 
         # Pull the LST and frequency arrays if they are required.
         # TODO: update this to allow more flexibility in which arguments are

hera_sim/tests/test_beams.py

@@ -152,7 +152,7 @@ def run_sim(
         use_pixel_beams=use_pixel_beams,
         use_gpu=use_gpu,
         mpi_comm=DummyMPIComm() if use_mpi else None,
-        bm_pix=200,
+        bm_pix=201,
         precision=2,
     )
 

hera_sim/tests/test_simulator.py

@@ -212,15 +212,16 @@ def test_get_with_one_seed(base_sim, pol, conj):
 # TODO: this will need to be updated when full polarization support is added
 @pytest.mark.parametrize("pol", [None, "xx"])
 @pytest.mark.parametrize("conj", [True, False])
-def test_get_with_initial_seed(base_sim, pol, conj):
+def test_get_with_initial_seed(pol, conj):
     # Simulate an effect where we would actually use this setting.
-    base_sim.add("thermal_noise", seed="initial")
+    sim = create_sim(autos=True)
+    sim.add("thermal_noise", seed="initial")
     ant1, ant2 = (0, 1) if conj else (1, 0)
-    vis = base_sim.get("thermal_noise", key=(ant1, ant2, pol))
+    vis = sim.get("thermal_noise", key=(ant1, ant2, pol))
     if pol:
-        assert np.allclose(base_sim.data.get_data(ant1, ant2, pol), vis)
+        assert np.allclose(sim.data.get_data(ant1, ant2, pol), vis)
     else:
-        assert np.allclose(base_sim.data.get_data(ant1, ant2), vis[..., 0])
+        assert np.allclose(sim.data.get_data(ant1, ant2), vis[..., 0])
 
 
 def test_get_nonexistent_component(ref_sim):

hera_sim/visibilities/vis_cpu.py

@@ -74,7 +74,7 @@ class VisCPU(VisibilitySimulator):
 
     def __init__(
         self,
-        bm_pix: int = 100,
+        bm_pix: int = 101,
         use_pixel_beams: bool = True,
         precision: int = 1,
         use_gpu: bool = False,