histogram aggregation + parser extension for BW interval fields
Three layered changes that together make histogram charts visually
match BW's printout (one bar per interval, not per codec block):
1. bw_ascii_report parser captures histogram fields it previously
dropped:
- Histogram Start/Stop Time + Date → datetime
- Number of Intervals + Interval Size (string + parsed seconds)
- <Channel> Peak Time + Peak Date → datetime (per-channel)
- Peak Vector Sum Date (combined with PVS Time → datetime;
clears the bogus seconds parse that interpreted "22:33:52"
as 22.0)
New _parse_iso_date() handles BW's ISO format for histograms
(waveforms use "May 8, 2026" long form). New _parse_interval_size()
handles "1 minute" / "5 minutes" / "15 seconds" etc.
2. _bw_report_to_dict() projects the new fields into a new
bw_report.histogram block in the sidecar.
3. /db/events/{id}/waveform.json wraps the existing path 1 (HDF5)
output with _maybe_aggregate_histogram(): when the event is a
histogram AND the sidecar has bw_report.histogram.n_intervals,
group the codec's per-block samples into N intervals via
max-per-group and return the aggregated array. time_axis gains
histogram_aggregated / n_intervals / interval_size_s / interval_times
fields.
Frontend (both modal chart in sfm_webapp.html + standalone event
browser) uses interval_times as x-axis labels when provided (BW-style
HH:MM:SS), falls back to interval index.
Defensive: aggregation is no-op when the sidecar lacks the histogram
block (events ingested before this change). Activates automatically
on prod once a watcher re-forward populates new sidecars.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
@@ -144,6 +144,23 @@ class BwAsciiReport:
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# ── Vector sum ──────────────────────────────────────────────────────────
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peak_vector_sum_ips: Optional[float] = None
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peak_vector_sum_time_s: Optional[float] = None
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# Histograms additionally have an absolute date+time for the PVS
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# (it occurred at a specific interval). Waveform reports show
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# only the relative-time value above.
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peak_vector_sum_when: Optional[datetime.datetime] = None
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# ── Histogram-specific fields (populated only when Event Type starts
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# with 'Histogram' / 'Full Histogram' / 'Histogram + Continuous') ──
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histogram_start: Optional[datetime.datetime] = None
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histogram_stop: Optional[datetime.datetime] = None
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histogram_n_intervals: Optional[int] = None # e.g. 4, 1436
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histogram_interval_size_str: Optional[str] = None # "1 minute" / "5 minutes" / "15 seconds"
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histogram_interval_size_s: Optional[float] = None # parsed to seconds
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# Per-channel absolute peak time+date (histogram-specific). For
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# waveform events these are None — those reports use the channel's
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# time_of_peak_s (relative to trigger) instead. Keyed by channel
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# name ("Tran", "Vert", "Long", "MicL").
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channel_peak_when: Dict[str, datetime.datetime] = field(default_factory=dict)
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# ── Sensor self-check (per channel) ─────────────────────────────────────
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sensor_check: Dict[str, SensorCheck] = field(default_factory=dict)
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@@ -223,6 +240,46 @@ def _parse_event_date(s: str) -> Optional[datetime.date]:
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return None
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def _parse_iso_date(s: str) -> Optional[datetime.date]:
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"""Parse "2026-05-16" → date. Histograms use ISO format for their
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Start Date / Stop Date / Peak Date fields; waveforms use the
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"May 8, 2026" long form which `_parse_event_date` handles."""
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s = s.strip()
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try:
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return datetime.date.fromisoformat(s)
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except ValueError:
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return None
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_INTERVAL_UNIT_SECONDS = {
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"second": 1, "seconds": 1, "sec": 1, "secs": 1,
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"minute": 60, "minutes": 60, "min": 60, "mins": 60,
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"hour": 3600, "hours": 3600, "hr": 3600, "hrs": 3600,
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}
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def _parse_interval_size(s: str) -> Optional[float]:
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"""Parse "1 minute" / "5 minutes" / "15 seconds" / "2 seconds" → seconds.
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Handles the BW Compliance Setup → Histogram Interval values verbatim
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("2 seconds", "5 seconds", "15 seconds", "1 minute", "5 minutes",
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"15 minutes") plus a few defensive variants.
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"""
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if not s:
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return None
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parts = s.strip().split()
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if len(parts) < 2:
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return None
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try:
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n = float(parts[0])
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except ValueError:
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return None
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unit_per_s = _INTERVAL_UNIT_SECONDS.get(parts[1].lower())
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if unit_per_s is None:
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return None
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return n * unit_per_s
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def _parse_event_time(s: str) -> Optional[datetime.time]:
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"""Parse "15:56:35" → time."""
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s = s.strip()
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@@ -336,6 +393,15 @@ def parse_report(text: Union[str, bytes], *, parse_samples: bool = False) -> BwA
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in_user_notes_block = False
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user_note_position = 0
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# Histogram-field staging — BW writes <Channel> Peak Time and
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# <Channel> Peak Date on separate lines (and similarly Histogram
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# Start Time / Date). We stash the partial value when the time
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# line arrives and combine it when the matching date line arrives.
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_hist_start_time: Optional[datetime.time] = None
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_hist_stop_time: Optional[datetime.time] = None
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_pending_peak_time: Dict[str, Optional[datetime.time]] = {}
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_pvs_time_raw: Optional[str] = None # last Peak Vector Sum Time value, raw
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while i < n:
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raw_line = lines[i]
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i += 1
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@@ -427,11 +493,66 @@ def parse_report(text: Union[str, bytes], *, parse_samples: bool = False) -> BwA
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elif stat == "Peak Acceleration": cs.peak_accel_g = num
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elif stat == "Peak Displacement": cs.peak_disp_in = num
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# ── Histogram-specific fields ────────────────────────────────────────
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# Histograms have Start/Stop time+date pairs + an interval count
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# and size, plus per-channel absolute Peak Time/Date instead of
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# the waveform's relative Time of Peak.
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elif key == "Histogram Start Time":
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_hist_start_time = _parse_event_time(value)
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elif key == "Histogram Start Date":
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_d = _parse_iso_date(value)
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if _d and _hist_start_time:
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report.histogram_start = datetime.datetime.combine(_d, _hist_start_time)
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elif key == "Histogram Stop Time":
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_hist_stop_time = _parse_event_time(value)
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elif key == "Histogram Stop Date":
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_d = _parse_iso_date(value)
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if _d and _hist_stop_time:
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report.histogram_stop = datetime.datetime.combine(_d, _hist_stop_time)
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elif key == "Number of Intervals":
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try:
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report.histogram_n_intervals = int(float(value.strip()))
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except ValueError:
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pass
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elif key == "Interval Size":
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report.histogram_interval_size_str = value.strip()
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report.histogram_interval_size_s = _parse_interval_size(value)
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# ── Per-channel histogram Peak Date / Peak Time ──
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# Lines like "Tran Peak Time : 22:31:38" + "Tran Peak Date : 2026-05-16"
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elif key in ("Tran Peak Time", "Vert Peak Time", "Long Peak Time", "MicL Time"):
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ch_name = "MicL" if key == "MicL Time" else key.split(" ", 1)[0]
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_pending_peak_time[ch_name] = _parse_event_time(value)
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elif key in ("Tran Peak Date", "Vert Peak Date", "Long Peak Date", "MicL Date"):
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ch_name = "MicL" if key == "MicL Date" else key.split(" ", 1)[0]
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_d = _parse_iso_date(value)
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_t = _pending_peak_time.get(ch_name)
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if _d and _t:
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report.channel_peak_when[ch_name] = datetime.datetime.combine(_d, _t)
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# ── Vector Sum ───────────────────────────────────────────────────────
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elif key == "Peak Vector Sum":
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report.peak_vector_sum_ips = _parse_number(value)
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elif key == "Peak Vector Sum Time":
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report.peak_vector_sum_time_s = _parse_number(value)
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_pvs_time_raw = value
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elif key == "Peak Vector Sum Date":
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# Histogram-mode PVS gets paired with a date. We may have
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# captured 'Peak Vector Sum Time' as either a relative
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# seconds float (waveform) or an HH:MM:SS string we
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# interpreted as a number. For histograms, BW writes
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# "Peak Vector Sum Time : 22:33:52" which _parse_number
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# parses as 22.0 (loses information). When Peak Vector Sum
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# Date arrives, re-parse the previous PVS time line as a
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# clock time and combine into an absolute datetime.
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_d = _parse_iso_date(value)
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if _d and _pvs_time_raw is not None:
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_t = _parse_event_time(_pvs_time_raw)
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if _t:
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report.peak_vector_sum_when = datetime.datetime.combine(_d, _t)
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# The earlier seconds parse was bogus for histograms;
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# clear it so consumers don't think it's a real offset.
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report.peak_vector_sum_time_s = None
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# ── Microphone block ────────────────────────────────────────────────
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elif key == "Microphone":
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