Add option to collapse the Jones chain inside the solver

quartical/calibration/solver.py

@@ -4,6 +4,7 @@
 from numba import set_num_threads
 from collections import namedtuple
 from itertools import cycle
+from quartical.gains.general.generics import combine_gains, combine_flags
 from quartical.weights.robust import robust_reweighting
 from quartical.statistics.stat_kernels import compute_mean_postsolve_chisq
 from quartical.statistics.logging import log_chisq
@@ -207,9 +208,26 @@ def solver_wrapper(
             **{k: chain_kwargs.get(k, None) for k in chain_fields}
         )
 
+        # NOTE: This is newer code which is difficult to test in all cases.
+        # If users run in to issues, particularly with direction-dependent
+        # terms, this may well be the culprit.
+        if solver_opts.collapse_chain:
+            mapping_inputs, chain_inputs, collapsed_term = get_collapsed_inputs(
+                ms_kwargs,
+                mapping_kwargs,
+                chain_kwargs,
+                term_spec_list,
+                chain,
+                active_term
+            )
+        else:
+            collapsed_term = active_term
+
+        # NOTE: Collapsed term is either the active term or its equivalent in
+        # the collapsed chain. See get_collapsed_inputs.
         meta_inputs = meta_args_nt(
             iters,
-            active_term,
+            collapsed_term,
             solver_opts.convergence_fraction,
             solver_opts.convergence_criteria,
             solver_opts.threads,
@@ -283,3 +301,151 @@ def solver_wrapper(
     gc.collect()
 
     return results_dict
+
+
+def get_collapsed_inputs(
+    ms_kwargs,
+    mapping_kwargs,
+    chain_kwargs,
+    term_spec_list,
+    chain,
+    active_term
+):
+
+    term = chain[active_term]
+
+    _, net_t_map = np.unique(ms_kwargs["TIME"], return_inverse=True)
+    net_t_map = net_t_map.astype(np.int32)
+    n_t = mapping_kwargs["time_bins"][active_term].size
+    n_f = mapping_kwargs["freq_maps"][active_term].size
+    net_t_bins = np.arange(n_t, dtype=np.int32)
+    net_f_map = np.arange(n_f, dtype=np.int32)
+
+    n_a = max([s.shape[2] for s in term_spec_list])
+    n_d = max([s.shape[3] for s in term_spec_list])
+    n_c = max([s.shape[4] for s in term_spec_list])
+
+    l_terms = term_spec_list[:active_term] or None
+    r_terms = term_spec_list[active_term + 1:] or None
+
+    # Determine how to slice collapsed inputs to produce the shortest chain
+    # possible. Special cases for length one chains, and the first and last
+    # element in a chain.
+    if len(chain) == 1:
+        sel = slice(1, 2)
+        collapsed_term = 0
+    elif active_term == 0:
+        sel = slice(1, None)
+        collapsed_term = 0
+    elif active_term == len(chain) - 1:
+        sel = slice(0, 2)
+        collapsed_term = 1
+    else:
+        sel = slice(0, None)
+        collapsed_term = 1
+
+    if l_terms:
+        n_l_d = max([s.shape[3] for s in l_terms])
+        dir_map_func = np.arange if n_l_d > 1 else np.zeros
+        l_dir_map = dir_map_func(n_d, dtype=np.int32)
+
+        # TODO: Cache array to avoid allocation?
+        l_gain = combine_gains(
+            chain_kwargs["gains"][:active_term],
+            mapping_kwargs["time_bins"][:active_term],
+            mapping_kwargs["freq_maps"][:active_term],
+            mapping_kwargs["dir_maps"][:active_term],
+            (n_t, n_f, n_a, n_l_d, n_c),
+            n_c
+        )
+
+        # TODO: Add dtype option/alternative.
+        l_flag = combine_flags(
+            chain_kwargs["gain_flags"][:active_term],
+            mapping_kwargs["time_bins"][:active_term],
+            mapping_kwargs["freq_maps"][:active_term],
+            mapping_kwargs["dir_maps"][:active_term],
+            (n_t, n_f, n_a, n_l_d, n_c),
+        ).astype(np.int8)
+    else:
+        l_dir_map, l_gain, l_flag = (None,) * 3
+
+    if r_terms:
+        n_r_d = max([s.shape[3] for s in r_terms])
+        dir_map_func = np.arange if n_r_d > 1 else np.zeros
+        r_dir_map = dir_map_func(n_d, dtype=np.int32)
+
+        r_gain = combine_gains(
+            chain_kwargs["gains"][active_term + 1:],
+            mapping_kwargs["time_bins"][active_term + 1:],
+            mapping_kwargs["freq_maps"][active_term + 1:],
+            mapping_kwargs["dir_maps"][active_term + 1:],
+            (n_t, n_f, n_a, n_r_d, n_c),
+            n_c
+        )
+
+        # TODO: Add dtype option/alternative.
+        r_flag = combine_flags(
+            chain_kwargs["gain_flags"][active_term + 1:],
+            mapping_kwargs["time_bins"][active_term + 1:],
+            mapping_kwargs["freq_maps"][active_term + 1:],
+            mapping_kwargs["dir_maps"][active_term + 1:],
+            (n_t, n_f, n_a, n_r_d, n_c)
+        ).astype(np.int8)
+    else:
+        r_dir_map, r_gain, r_flag = (None,) * 3
+
+    mapping_kwargs = {
+        "time_bins": (
+            net_t_bins, mapping_kwargs["time_bins"][active_term], net_t_bins
+        ),
+        "time_maps": (
+            net_t_map, mapping_kwargs["time_maps"][active_term], net_t_map
+        ),
+        "freq_maps": (
+            net_f_map, mapping_kwargs["freq_maps"][active_term], net_f_map
+        ),
+        "dir_maps": (
+            l_dir_map, mapping_kwargs["dir_maps"][active_term], r_dir_map
+        ),
+        "param_time_bins": (
+            net_t_bins,
+            mapping_kwargs["param_time_bins"][active_term],
+            net_t_bins
+        ),
+        "param_time_maps": (
+            net_t_map,
+            mapping_kwargs["param_time_maps"][active_term],
+            net_t_map
+        ),
+        "param_freq_maps": (
+            net_f_map,
+            mapping_kwargs["param_freq_maps"][active_term],
+            net_f_map
+        ),
+    }
+
+    mapping_kwargs = {k: v[sel] for k, v in mapping_kwargs.items()}
+
+    mapping_fields = term.mapping_inputs._fields
+    mapping_inputs = term.mapping_inputs(
+        **{k: mapping_kwargs.get(k, None) for k in mapping_fields}
+    )
+
+    chain_kwargs = {
+        "gains": (l_gain, chain_kwargs["gains"][active_term], r_gain),
+        "gain_flags": (l_flag, chain_kwargs["gain_flags"][active_term], r_flag),
+        "params": (chain_kwargs["params"][active_term],) * 3,
+        "param_flags": (
+            l_flag, chain_kwargs["param_flags"][active_term], r_flag
+        )
+    }
+
+    chain_kwargs = {k: v[sel] for k, v in chain_kwargs.items()}
+
+    chain_fields = term.chain_inputs._fields
+    chain_inputs = term.chain_inputs(
+        **{k: chain_kwargs.get(k, None) for k in chain_fields}
+    )
+
+    return mapping_inputs, chain_inputs, collapsed_term

quartical/config/argument_schema.yaml

@@ -357,6 +357,16 @@ solver:
       almost always be enabled so that data associated with diverging gains
       is properly flagged.
 
+  collapse_chain:
+    dtype: bool
+    default: True
+    info:
+      Determines whether the chain is collapsed into the minimum number of
+      terms inside the solver. This will typically increase memory footprint,
+      but may speed up calibration when utilising many gain terms. Set to false
+      to apply every term on-the-fly inside the solver (behaviour prior to
+      v0.2.6).
+
   robust:
     dtype: bool
     default: False

quartical/config/helper.py

@@ -88,7 +88,7 @@ def help():
         print_help(HelpConfig)
     else:
         selection = help_arg.split("=")[-1]
-        selection = re.sub('[\[\] ]', "", selection)  # noqa
+        selection = re.sub(r'[\[\] ]', "", selection)
         selection = selection.split(",")
         print_help(HelpConfig, section_names=selection)
 

quartical/gains/gain.py

@@ -39,7 +39,8 @@
         "WEIGHT",
         "FLAG",
         "ROW_MAP",
-        "ROW_WEIGHTS"
+        "ROW_WEIGHTS",
+        "TIME"
     )
 )
 

quartical/gains/parallactic_angle/__init__.py

@@ -16,7 +16,7 @@
 ms_inputs = namedtuple(
     'ms_inputs', 
     ParameterizedGain.ms_inputs._fields + \
-        ('RECEPTOR_ANGLE', 'POSITION', 'TIME')
+        ('RECEPTOR_ANGLE', 'POSITION')
 )
 
 class ParallacticAngle(ParameterizedGain):
@@ -38,6 +38,10 @@ def __init__(self, term_name, term_opts):
 
         super().__init__(term_name, term_opts)
 
+        # NOTE: Ignore user-specified values on this term.
+        self.time_interval = 1
+        self.freq_interval = 0
+
     @classmethod
     def _make_freq_map(cls, chan_freqs, chan_widths, freq_interval):
         # Overload gain mapping construction - we evaluate it in every channel.

quartical/gains/parameterized_gain.py

@@ -14,7 +14,8 @@
         "WEIGHT",
         "FLAG",
         "ROW_MAP",
-        "ROW_WEIGHTS"
+        "ROW_WEIGHTS",
+        "TIME"
     )
 )