Disable mad flagging on off-diagonals by default (#300)

* Fix version drift.

* Bump to 0.2.0

* Disable flagging based on off-diagonal correlations in the mad flagger by default. This should make the mad flagger less agressive on data with unmodelled polarised emission.

quartical/config/argument_schema.yaml

@@ -310,6 +310,15 @@ mad_flags:
       X_bl > max_deviation*X will be flagged. Set to zero to disable flagging
       on this statistic.
 
+  use_off_diagonals:
+    dtype: bool
+    default: False
+    info:
+      Controls whether or not the mad flagger will be run on the off-diagonal
+      elements of the residual. This is disabled by default as the residual
+      will tend to contain structure in the absence of a polarised model and
+      adequate leakage calibration.
+
 solver:
   terms:
     dtype: List[str]

quartical/flagging/flagging.py

@@ -123,6 +123,8 @@ def valid_median(arr):
 
 def add_mad_graph(data_xds_list, mad_opts):
 
+    diag_corrs = ['RR', 'LL', 'XX', 'YY']
+
     bl_thresh = mad_opts.threshold_bl
     gbl_thresh = mad_opts.threshold_global
     max_deviation = mad_opts.max_deviation
@@ -147,6 +149,13 @@ def add_mad_graph(data_xds_list, mad_opts):
         n_bl_w_autos = (n_ant * (n_ant - 1))/2 + n_ant
         n_t_chunk, n_f_chunk, _ = residuals.numblocks
 
+        if mad_opts.use_off_diagonals:
+            corr_sel = tuple(np.arange(residuals.shape[-1]))
+        else:
+            corr_sel = tuple(
+                [i for i, c in enumerate(xds.corr.values) if c in diag_corrs]
+            )
+
         wres = da.blockwise(
             compute_whitened_residual, ("rowlike", "chan", "corr"),
             residuals, ("rowlike", "chan", "corr"),
@@ -228,6 +237,7 @@ def add_mad_graph(data_xds_list, mad_opts):
             gbl_thresh, None,
             bl_thresh, None,
             max_deviation, None,
+            corr_sel, None,
             n_ant, None,
             dtype=np.int8,
             align_arrays=False,

quartical/flagging/flagging_kernels.py

@@ -99,6 +99,7 @@ def compute_mad_flags(
     gbl_threshold,
     bl_threshold,
     max_deviation,
+    corr_sel,
     n_ant
 ):
 
@@ -114,7 +115,7 @@ def compute_mad_flags(
     gbl_threshold2 = gbl_threshold ** 2 or np.inf
     max_deviation2 = max_deviation ** 2 or np.inf
 
-    for corr in range(n_corr):
+    for corr in corr_sel:
 
         gbl_mad_real = gbl_mad_and_med_real[0, 0, corr, 0] * scale_factor
         gbl_med_real = gbl_mad_and_med_real[0, 0, corr, 1]