Improvements to AzimuthIntervals

src/toast/intervals.py

@@ -69,15 +69,18 @@ def __init__(self, timestamps, intervals=None, timespans=None, samplespans=None)
                 raise RuntimeError(
                     "If constructing from intervals, other spans should be None"
                 )
-            timespans = [(x.start, x.stop) for x in intervals]
-            indices = self._find_indices(timespans)
-            self.data = np.array(
-                [
-                    (self.timestamps[x[0]], self.timestamps[x[1]], x[0], x[1])
-                    for x in indices
-                ],
-                dtype=interval_dtype,
-            ).view(np.recarray)
+            if len(intervals) == 0:
+                self.data = np.zeros(0, dtype=interval_dtype).view(np.recarray)
+            else:
+                timespans = [(x.start, x.stop) for x in intervals]
+                indices = self._find_indices(timespans)
+                self.data = np.array(
+                    [
+                        (self.timestamps[x[0]], self.timestamps[x[1]], x[0], x[1])
+                        for x in indices
+                    ],
+                    dtype=interval_dtype,
+                ).view(np.recarray)
         elif timespans is not None:
             if samplespans is not None:
                 raise RuntimeError("Cannot construct from both time and sample spans")

src/toast/ops/CMakeLists.txt

@@ -8,6 +8,7 @@ install(FILES
     delete.py
     copy.py
     reset.py
+    fill_gaps.py
     arithmetic.py
     memory_counter.py
     simple_deglitch.py

src/toast/ops/__init__.py

@@ -15,6 +15,7 @@
 from .delete import Delete
 from .demodulation import Demodulate, StokesWeightsDemod
 from .elevation_noise import ElevationNoise
+from .fill_gaps import FillGaps
 from .filterbin import FilterBin, combine_observation_matrix
 from .flag_intervals import FlagIntervals
 from .flag_sso import FlagSSO

src/toast/ops/demodulation.py

@@ -309,18 +309,13 @@ def _exec(self, data, detectors=None, **kwargs):
             self.demod_data.obs.append(demod_obs)
 
             if self.purge:
-                if self.shared_flags is not None:
-                    del obs.shared[self.shared_flags]
-                for det_data in self.det_data.split(";"):
-                    del obs.detdata[det_data]
-                if self.det_flags is not None:
-                    del obs.detdata[self.det_flags]
-                if self.noise_model is not None:
-                    del obs[self.noise_model]
+                obs.clear()
 
             log.debug_rank(
                 "Demodulated observation in", comm=data.comm.comm_group, timer=timer
             )
+        if self.purge:
+            data.clear()
 
         return
 

src/toast/ops/fill_gaps.py

@@ -0,0 +1,183 @@
+# Copyright (c) 2024-2024 by the parties listed in the AUTHORS file.
+# All rights reserved.  Use of this source code is governed by
+# a BSD-style license that can be found in the LICENSE file.
+import os
+
+import numpy as np
+import traitlets
+from astropy import units as u
+
+from ..observation import default_values as defaults
+from ..timing import Timer, function_timer
+from ..traits import Int, Quantity, Unicode, trait_docs
+from ..utils import Environment, Logger, flagged_noise_fill
+from .operator import Operator
+
+
+@trait_docs
+class FillGaps(Operator):
+    """Operator that fills flagged samples with noise.
+
+    Currently this operator just fills flagged samples with a simple polynomial
+    plus white noise.  It is mostly used for visualization.  No attempt is made
+    yet to fill the gaps with a constrained noise realization.
+
+    """
+
+    # Class traits
+
+    API = Int(0, help="Internal interface version for this operator")
+
+    times = Unicode(defaults.times, help="Observation shared key for timestamps")
+
+    det_data = Unicode(
+        defaults.det_data,
+        help="Observation detdata key",
+    )
+
+    det_mask = Int(
+        defaults.det_mask_invalid,
+        help="Bit mask value for per-detector flagging",
+    )
+
+    shared_flags = Unicode(
+        defaults.shared_flags,
+        allow_none=True,
+        help="Observation shared key for telescope flags to use",
+    )
+
+    shared_flag_mask = Int(
+        defaults.shared_mask_invalid,
+        help="Bit mask value for optional shared flagging",
+    )
+
+    det_flags = Unicode(
+        defaults.det_flags,
+        allow_none=True,
+        help="Observation detdata key for flags to use",
+    )
+
+    det_flag_mask = Int(
+        defaults.det_mask_invalid,
+        help="Bit mask value for detector sample flagging",
+    )
+
+    buffer = Quantity(
+        1.0 * u.s,
+        help="Buffer of time on either side of each gap",
+    )
+
+    poly_order = Int(
+        1,
+        help="Order of the polynomial to fit across each gap",
+    )
+
+    @traitlets.validate("poly_order")
+    def _check_poly_order(self, proposal):
+        check = proposal["value"]
+        if check <= 0:
+            raise traitlets.TraitError("poly_order should be >= 1")
+        return check
+
+    @traitlets.validate("det_mask")
+    def _check_det_mask(self, proposal):
+        check = proposal["value"]
+        if check < 0:
+            raise traitlets.TraitError("Det mask should be a positive integer")
+        return check
+
+    @traitlets.validate("det_flag_mask")
+    def _check_det_flag_mask(self, proposal):
+        check = proposal["value"]
+        if check < 0:
+            raise traitlets.TraitError("Flag mask should be a positive integer")
+        return check
+
+    @traitlets.validate("shared_flag_mask")
+    def _check_shared_flag_mask(self, proposal):
+        check = proposal["value"]
+        if check < 0:
+            raise traitlets.TraitError("Flag mask should be a positive integer")
+        return check
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    @function_timer
+    def _exec(self, data, detectors=None, **kwargs):
+        env = Environment.get()
+        log = Logger.get()
+
+        for ob in data.obs:
+            timer = Timer()
+            timer.start()
+
+            # Sample rate for this observation
+            rate = ob.telescope.focalplane.sample_rate.to_value(u.Hz)
+
+            # The buffer size in samples
+            buf_samp = int(self.buffer.to_value(u.second) * rate)
+
+            # Check that parameters make sense
+            if self.poly_order > buf_samp + 1:
+                msg = f"Cannot fit an order {self.poly_order} polynomial "
+                msg += f"to {buf_samp} samples"
+                raise RuntimeError(msg)
+
+            if buf_samp > ob.n_local_samples // 4:
+                msg = f"Using {buf_samp} samples of buffer around gaps is"
+                msg += f" not reasonable for an observation with {ob.n_local_samples}"
+                msg += " local samples"
+                raise RuntimeError(msg)
+
+            # Local detectors we are considering
+            local_dets = ob.select_local_detectors(flagmask=self.det_mask)
+            n_dets = len(local_dets)
+
+            # The shared flags
+            if self.shared_flags is None:
+                shared_flags = np.zeros(ob.n_local_samples, dtype=bool)
+            else:
+                shared_flags = (
+                    ob.shared[self.shared_flags].data & self.shared_flag_mask
+                ) != 0
+
+            for idet, det in enumerate(local_dets):
+                if self.det_flags is None:
+                    flags = shared_flags
+                else:
+                    flags = np.logical_or(
+                        shared_flags,
+                        (ob.detdata[self.det_flags][det, :] & self.det_flag_mask) != 0,
+                    )
+                flagged_noise_fill(
+                    ob.detdata[self.det_data][det],
+                    flags,
+                    buf_samp,
+                    poly_order=self.poly_order,
+                )
+            msg = f"FillGaps {ob.name}: completed in"
+            log.debug_rank(msg, comm=data.comm.comm_group, timer=timer)
+
+    def _finalize(self, data, **kwargs):
+        return
+
+    def _requires(self):
+        # Note that the hwp_angle is not strictly required- this
+        # is just a no-op.
+        req = {
+            "shared": [self.times],
+            "detdata": [self.det_data],
+        }
+        if self.shared_flags is not None:
+            req["shared"].append(self.shared_flags)
+        if self.det_flags is not None:
+            req["detdata"].append(self.det_flags)
+        return req
+
+    def _provides(self):
+        prov = {
+            "meta": [],
+            "detdata": [self.det_data],
+        }
+        return prov

src/toast/ops/flag_intervals.py

@@ -105,10 +105,15 @@ def _exec(self, data, detectors=None, **kwargs):
             if ob.comm_col_rank == 0:
                 new_flags = np.array(ob.shared[self.shared_flags])
                 for vname, vmask in self.view_mask:
-                    views = ob.view[vname]
-                    for vw in views:
-                        # Note that a View acts like a slice
-                        new_flags[vw] |= vmask
+                    if vname in ob.intervals:
+                        views = ob.view[vname]
+                        for vw in views:
+                            # Note that a View acts like a slice
+                            new_flags[vw] |= vmask
+                    else:
+                        msg = f"Intervals '{vname}' does not exist in {ob.name}"
+                        msg += " skipping flagging"
+                        log.warning(msg)
             ob.shared[self.shared_flags].set(new_flags, offset=(0,), fromrank=0)
 
     def _finalize(self, data, **kwargs):

src/toast/ops/hwpss_model.py

@@ -19,7 +19,7 @@
 from ..observation import default_values as defaults
 from ..timing import Timer, function_timer
 from ..traits import Bool, Int, Quantity, Unicode, Float, trait_docs
-from ..utils import Environment, Logger
+from ..utils import Environment, Logger, flagged_noise_fill
 from .operator import Operator
 
 
@@ -126,7 +126,7 @@ class HWPSynchronousModel(Operator):
 
     time_drift = Bool(False, help="If True, include time drift terms in the model")
 
-    fill_gaps = Bool(False, help="If True, fit a simple line across gaps")
+    fill_gaps = Bool(False, help="If True, fill gaps with a simple noise model")
 
     debug = Unicode(
         None,
@@ -334,7 +334,15 @@ def _exec(self, data, detectors=None, **kwargs):
                     dc = np.mean(ob.detdata[self.det_data][det][good])
                     ob.detdata[self.det_data][det][good] -= dc
                 if self.fill_gaps:
-                    self._fill_gaps(ob, det, det_flags[det])
+                    rate = ob.telescope.focalplane.sample_rate.to_value(u.Hz)
+                    # 1 second buffer
+                    buffer = int(rate)
+                    flagged_noise_fill(
+                        ob.detdata[self.det_data][det],
+                        det_flags[det],
+                        buffer,
+                        poly_order=1,
+                    )
                 if self.relcal_continuous is not None:
                     ob.detdata[self.relcal_continuous][det, :] = cal_center / det_mag
 
@@ -925,33 +933,6 @@ def _stopped_flags(self, obs):
         stopped = np.array(unstable, dtype=np.uint8)
         return stopped
 
-    def _fill_gaps(self, obs, det, flags):
-        # Fill gaps with a line, just to kill large artifacts in flagged
-        # regions after removal of the HWPSS.  This is mostly just for visualization.
-        # Downstream codes should ignore these flagged samples anyway.
-        sig = obs.detdata[self.det_data][det]
-        flag_indx = np.arange(len(flags), dtype=np.int64)[np.nonzero(flags)]
-        flag_groups = np.split(flag_indx, np.where(np.diff(flag_indx) != 1)[0] + 1)
-        for grp in flag_groups:
-            if len(grp) == 0:
-                continue
-            bad_first = grp[0]
-            bad_last = grp[-1]
-            if bad_first == 0:
-                # Starting bad samples
-                sig[: bad_last + 1] = sig[bad_last + 1]
-            elif bad_last == len(flags) - 1:
-                # Ending bad samples
-                sig[bad_first:] = sig[bad_first - 1]
-            else:
-                int_first = bad_first - 1
-                int_last = bad_last + 1
-                sig[bad_first : bad_last + 1] = np.interp(
-                    np.arange(bad_first, bad_last + 1, 1, dtype=np.int32),
-                    [int_first, int_last],
-                    [sig[int_first], sig[int_last]],
-                )
-
     def _finalize(self, data, **kwargs):
         return
 

src/toast/ops/simple_deglitch.py

@@ -17,7 +17,7 @@
 from ..observation import default_values as defaults
 from ..timing import Timer, function_timer
 from ..traits import Bool, Float, Instance, Int, Quantity, Unicode, trait_docs
-from ..utils import Environment, Logger, name_UID
+from ..utils import Environment, Logger, name_UID, flagged_noise_fill
 from .operator import Operator
 
 
@@ -48,7 +48,7 @@ class SimpleDeglitch(Operator):
     )
 
     reset_det_flags = Bool(
-        True,
+        False,
         help="Replace existing detector flags",
     )
 
@@ -204,17 +204,15 @@ def _exec(self, data, detectors=None, **kwargs):
                         continue
                     bad_view = np.isnan(sig_view)
                     det_flags[ind][bad_view] |= self.glitch_mask
-                    if self.fill_gaps:
-                        nbad = np.sum(bad_view)
-                        corrected_signal = sig[ind].copy()
-                        corrected_signal[bad_view] = trend[bad_view]
-                        corrected_signal[bad_view] += np.random.randn(nbad) * rms
-                        # DEBUG begin
-                        # import pdb
-                        # import matplotlib.pyplot as plt
-                        # pdb.set_trace()
-                        # DEBUG end
-                        sig[ind] = corrected_signal
+                if self.fill_gaps:
+                    # 1 second buffer
+                    buffer = int(focalplane.sample_rate.to_value(u.Hz))
+                    flagged_noise_fill(
+                        sig,
+                        det_flags,
+                        buffer,
+                        poly_order=1,
+                    )
 
         return