feat(delay): provide an initial guess for the gibbs sampler

Provide a file path to delay power spectrum estimator when using
a Gibbs sampling method.

draco/analysis/delay.py

@@ -607,11 +607,18 @@ class DelayGibbsSamplerBase(DelayTransformBase, random.RandomTask):
     initial_amplitude : float, optional
         The Gibbs sampler will be initialized with a flat power spectrum with
         this amplitude. Default: 10.
+    save_samples : bool, optional.
+        The entire chain of samples will be saved rather than just the final
+        result. Default: False
+    initial_sample_path : str, optional
+        File path to load an initial power spectrum sample. If no file is given,
+        start with a flat power spectrum. Default: None
     """
 
     nsamp = config.Property(proptype=int, default=20)
     initial_amplitude = config.Property(proptype=float, default=10.0)
     save_samples = config.Property(proptype=bool, default=False)
+    initial_sample_path = config.Property(proptype=str, default=None)
 
     def _create_output(
         self,
@@ -662,6 +669,32 @@ def _create_output(
 
         return delay_spec
 
+    def _get_initial_S(self, nbase, ndelay, dtype):
+        """Load the initial spectrum estimate if a file path exists.
+
+        Parameters
+        ----------
+        nbase : int
+            Number of baselines
+        ndelay : int
+            Number of delay samples
+        dtype : type | np.dtype | str
+            Datatype for the sample if no path is given
+        """
+        if self.initial_sample_path is not None:
+            cont = ContainerBase.from_file(self.initial_sample_path, distributed=True)
+
+            cont.redistribute("baseline")
+
+            # Extract the spectrum and ove the baseline axis to the front
+            initial_S = cont.spectrum[:].local_array
+            bl_ax = cont.spectrum.attrs["axis"].tolist().index("baseline")
+            initial_S = np.moveaxis(initial_S, bl_ax, 0)
+        else:
+            initial_S = np.ones((nbase, ndelay), dtype=dtype) * self.initial_amplitude
+
+        return initial_S
+
     def _evaluate(self, data_view, weight_view, out_cont, delays, channel_ind):
         """Estimate the delay spectrum or power spectrum.
 
@@ -687,7 +720,7 @@ def _evaluate(self, data_view, weight_view, out_cont, delays, channel_ind):
         ndelay = len(delays)
 
         # Set initial conditions for delay power spectrum
-        initial_S = np.ones_like(delays) * self.initial_amplitude
+        initial_S = self._get_initial_S(nbase, ndelay, delays.dtype)
 
         # Initialize the random number generator we'll use
         rng = self.rng
@@ -710,7 +743,7 @@ def _evaluate(self, data_view, weight_view, out_cont, delays, channel_ind):
                 data,
                 ndelay,
                 weight,
-                initial_S,
+                initial_S[lbi],
                 window=self.window if self.apply_window else None,
                 fsel=non_zero_channel,
                 niter=self.nsamp,
@@ -1053,16 +1086,24 @@ def _create_output(
     def _evaluate(self, data_view, weight_view, out_cont, delays, channel_ind):
         ndata = len(data_view)
         ndelay = len(delays)
+        nbase = out_cont.spectrum.shape[-2]
 
-        initial_S = (
-            np.identity(ndata)[:, :, np.newaxis]
-            * np.ones_like(delays)
-            * self.initial_amplitude
-        )
+        initial_S = self._get_initial_S(nbase, ndelay, delays.dtype)
+
+        if initial_S.ndim == 2:
+            # Expand the sample shape to match the number of datasets
+            initial_S = (
+                np.identity(ndata)[np.newaxis, ..., np.newaxis]
+                * initial_S[:, np.newaxis, np.newaxis]
+            )
+        elif (initial_S.ndim != 4) or (initial_S.shape[1] != ndata):
+            raise ValueError(
+                f"Expected an initial sample with dimension 4 and {ndata} datasets. "
+                f"Got sample with dimension {initial_S.ndim} and shape {initial_S.shape}."
+            )
 
         # Initialize the random number generator we'll use
         rng = self.rng
-        nbase = out_cont.spectrum.shape[-2]
 
         # Iterate over all baselines and use the Gibbs sampler to estimate the spectrum
         for lbi, bi in out_cont.spectrum[:].enumerate(axis=-2):
@@ -1082,7 +1123,7 @@ def _evaluate(self, data_view, weight_view, out_cont, delays, channel_ind):
                 data,
                 ndelay,
                 weight,
-                initial_S,
+                initial_S[lbi],
                 window=self.window if self.apply_window else None,
                 fsel=non_zero_channel,
                 niter=self.nsamp,