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ts_shape.events.engineering.startup_events ¤

Classes:

StartupDetectionEvents ¤

StartupDetectionEvents(dataframe: DataFrame, target_uuid: str, *, event_uuid: str = 'startup_event', value_column: str = 'value_double', time_column: str = 'systime')

Bases: Base

Detect equipment startup intervals based on threshold crossings or sustained positive slope in a numeric metric (speed, temperature, etc.).

Schema assumptions (columns): - uuid, sequence_number, systime, plctime, is_delta - value_integer, value_string, value_double, value_bool, value_bytes

Methods:

Source code in src/ts_shape/events/engineering/startup_events.py 
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def __init__(
    self,
    dataframe: pd.DataFrame,
    target_uuid: str,
    *,
    event_uuid: str = "startup_event",
    value_column: str = "value_double",
    time_column: str = "systime",
) -> None:
    super().__init__(dataframe, column_name=time_column)
    self.target_uuid = target_uuid
    self.event_uuid = event_uuid
    self.value_column = value_column
    self.time_column = time_column

    self.series = (
        self.dataframe[self.dataframe["uuid"] == self.target_uuid]
        .copy()
        .sort_values(self.time_column)
    )
    self.series[self.time_column] = pd.to_datetime(self.series[self.time_column])

detect_startup_by_slope ¤

detect_startup_by_slope(*, min_slope: float, slope_window: str = '0s', min_duration: str = '0s') -> DataFrame

Startup intervals where per-second slope >= min_slope for at least min_duration. slope_window is accepted for API completeness but the current implementation uses instantaneous slope between samples.

Returns:

  • DataFrame –

    DataFrame with columns: start, end, uuid, is_delta, method, min_slope, avg_slope.

Source code in src/ts_shape/events/engineering/startup_events.py 
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def detect_startup_by_slope(
    self,
    *,
    min_slope: float,
    slope_window: str = "0s",
    min_duration: str = "0s",
) -> pd.DataFrame:
    """
    Startup intervals where per-second slope >= `min_slope` for at least
    `min_duration`. `slope_window` is accepted for API completeness but the
    current implementation uses instantaneous slope between samples.

    Returns:
        DataFrame with columns: start, end, uuid, is_delta, method, min_slope, avg_slope.
    """
    if self.series.empty:
        return pd.DataFrame(columns=["start", "end", "uuid", "is_delta", "method", "min_slope", "avg_slope"])

    s = self.series[[self.time_column, self.value_column]].copy()
    s["dt_s"] = s[self.time_column].diff().dt.total_seconds()
    s["dv"] = s[self.value_column].diff()
    s["slope"] = s["dv"] / s["dt_s"]
    mask = s["slope"] >= float(min_slope)

    gid = (mask != mask.shift()).cumsum()
    min_d = pd.to_timedelta(min_duration)
    events: List[Dict[str, Any]] = []
    for _, seg in s.groupby(gid):
        seg_mask = mask.loc[seg.index]
        if not seg_mask.any():
            continue
        start_t = seg.loc[seg_mask, self.time_column].iloc[0]
        end_t = seg.loc[seg_mask, self.time_column].iloc[-1]
        if (end_t - start_t) < min_d:
            continue
        avg_slope = seg.loc[seg_mask, "slope"].mean()
        events.append(
            {
                "start": start_t,
                "end": end_t,
                "uuid": self.event_uuid,
                "is_delta": True,
                "method": "slope",
                "min_slope": float(min_slope),
                "avg_slope": float(avg_slope) if pd.notna(avg_slope) else None,
            }
        )

    return pd.DataFrame(events)

detect_startup_by_threshold ¤

detect_startup_by_threshold(*, threshold: float, hysteresis: tuple[float, float] | None = None, min_above: str = '0s') -> DataFrame

Startup begins at first crossing above threshold (or hysteresis enter) and is valid only if the metric stays above the (exit) threshold for at least min_above.

Returns:

  • DataFrame –

    DataFrame with columns: start, end, uuid, is_delta, method, threshold.

Source code in src/ts_shape/events/engineering/startup_events.py 
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def detect_startup_by_threshold(
    self,
    *,
    threshold: float,
    hysteresis: tuple[float, float] | None = None,
    min_above: str = "0s",
) -> pd.DataFrame:
    """
    Startup begins at first crossing above `threshold` (or hysteresis enter)
    and is valid only if the metric stays above the (exit) threshold for at
    least `min_above`.

    Returns:
        DataFrame with columns: start, end, uuid, is_delta, method, threshold.
    """
    if self.series.empty:
        return pd.DataFrame(columns=["start", "end", "uuid", "is_delta", "method", "threshold"])

    enter_thr = threshold if hysteresis is None else hysteresis[0]
    exit_thr = threshold if hysteresis is None else hysteresis[1]
    min_above_td = pd.to_timedelta(min_above)

    s = self.series[[self.time_column, self.value_column]].copy()
    above_enter = s[self.value_column] >= enter_thr
    rising = (~above_enter.shift(fill_value=False)) & above_enter
    rise_times = s.loc[rising, self.time_column]

    events: List[Dict[str, Any]] = []
    for t0 in rise_times:
        # ensure dwell above exit threshold for min_above
        win = s[(s[self.time_column] >= t0) & (s[self.time_column] <= t0 + min_above_td)]
        if win.empty:
            continue
        if (win[self.value_column] >= exit_thr).all():
            events.append(
                {
                    "start": t0,
                    "end": t0 + min_above_td,
                    "uuid": self.event_uuid,
                    "is_delta": True,
                    "method": "threshold",
                    "threshold": float(threshold),
                }
            )

    return pd.DataFrame(events)

get_dataframe ¤

get_dataframe() -> DataFrame

Returns the processed DataFrame.

Source code in src/ts_shape/utils/base.py 
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def get_dataframe(self) -> pd.DataFrame:
    """Returns the processed DataFrame."""
    return self.dataframe