feat(dayenu): optionally apply dayenu filter in loop from single or m…

…ultiple datasets

draco/analysis/dayenu.py

@@ -570,55 +570,47 @@ class ApplyDelayFilterHybridVis(task.SingleTask):
 
     atten_threshold = config.Property(proptype=float, default=0.0)
 
-    def setup(self, ss):
-        """Set the DAYENU filter to be applied to hybrid beamformed data.
-
-        Parameters
-        ----------
-        ss: containers.HybridVisStream
-          The filter of HybridVisStream to be applied.
-        """
-        self.sstream = ss
-
-    def process(self, hv):
+    def process(self, hv, source):
         """Apply the DAYENU filter to a HybridVisStream.
 
         Parameters
         ----------
         hv: containers.HybridVisStream
-         The data the filter will be applied to.
+            The data the filter will be applied to.
+        source: containers.HybridVisStream
+            The filter of HybridVisStream to be applied.
 
         Returns
         -------
         hv_filt: containers.HybridVisStream
-          The filtered dataset.
+            The filtered dataset.
         """
         # Distribute over products
         hv.redistribute(["ra", "time"])
-        self.sstream.redistribute(["ra", "time"])
+        source.redistribute(["ra", "time"])
 
         # Validate that both hybrid beamformed visibilites match
-        if not np.array_equal(self.sstream.freq, hv.freq):
+        if not np.array_equal(source.freq, hv.freq):
             raise ValueError("Frequencies do not match for hybrid visibilities.")
 
-        if not np.array_equal(self.sstream.index_map["el"], hv.index_map["el"]):
+        if not np.array_equal(source.index_map["el"], hv.index_map["el"]):
             raise ValueError("Elevations do not match for hybrid visibilities.")
 
-        if not np.array_equal(self.sstream.index_map["ew"], hv.index_map["ew"]):
+        if not np.array_equal(source.index_map["ew"], hv.index_map["ew"]):
             raise ValueError("EW baselines do not match for hybrid visibilities.")
 
-        if not np.array_equal(self.sstream.index_map["pol"], hv.index_map["pol"]):
+        if not np.array_equal(source.index_map["pol"], hv.index_map["pol"]):
             raise ValueError("Polarisations do not match for hybrid visibilities.")
 
-        if not np.array_equal(self.sstream.ra, hv.ra):
+        if not np.array_equal(source.ra, hv.ra):
             raise ValueError("Right Ascension do not match for hybrid visibilities.")
 
         npol, nfreq, new, nel, ntime = hv.vis.local_shape
 
         # Dereference the required datasets
         vis = hv.vis[:].local_array
         weight = hv.weight[:].local_array
-        filt = self.sstream.filter[:].local_array
+        filt = source.filter[:].local_array
 
         # loop over products
         for tt in range(ntime):
@@ -655,6 +647,40 @@ def process(self, hv):
         return hv
 
 
+class ApplyDelayFilterHybridVisSingleSource(ApplyDelayFilterHybridVis):
+    """Apply a previously saved filter to the hybrid beamformed visibilities.
+
+    This task differs from `ApplyDelayFilterHybridVis` in that it applies
+    a _single_ filter to multiple possible datasets.
+    """
+
+    def setup(self, source):
+        """Set the DAYENU filter to be applied to hybrid beamformed data.
+
+        Parameters
+        ----------
+        source: containers.HybridVisStream
+          The filter of HybridVisStream to be applied.
+        """
+        self.source = source
+
+    def process(self, hv):
+        """Apply the DAYENU filter to a HybridVisStream.
+
+        Parameters
+        ----------
+        hv: containers.HybridVisStream
+            The data the filter will be applied to.
+
+        Returns
+        -------
+        hv_filt: containers.HybridVisStream
+            The filtered dataset.
+        """
+        # Apply the previously saved filter to this dataset
+        return super().process(hv, self.source)
+
+
 class DayenuDelayFilterMap(task.SingleTask):
     """Apply a DAYENU high-pass delay filter to ringmap data.