sampling_resolution

sampling_resolution ¤

SamplingResolutionEvents ¤

SamplingResolutionEvents(
    dataframe: DataFrame,
    signal_uuid: str,
    *,
    value_column: str = "value_double",
    event_uuid: str = "quality:sampling_resolution",
    time_column: str = "systime"
)

Bases: Base

Quality: Sampling & Resolution Analysis

Analyze digital measurement system limitations: quantization effects, effective resolution, aliasing detection, and sampling synchronization/jitter.

Methods: - detect_quantization: Detect A/D quantization in measurements. - effective_resolution: Estimate effective vs theoretical resolution. - detect_aliasing: Check for Nyquist violations via FFT. - sampling_jitter: Measure timing regularity of sampling.

detect_quantization ¤

detect_quantization(
    window: str = "1h", threshold_ratio: float = 0.3
) -> pd.DataFrame

Detect A/D quantization effects in measurements.

Identifies if measurements are clustered at discrete levels rather than continuously distributed.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window size.	`'1h'`
`threshold_ratio`	`float`	Ratio of unique values to expected unique below which signal is flagged as quantized.	`0.3`

Returns:

Type	Description
`DataFrame`	Summary DataFrame with columns: start, end, duration_seconds
`DataFrame`	(the window bounds), window_start (the resample label; a
`DataFrame`	deprecated twin of `start`), n_unique_values,
`DataFrame`	expected_unique, quantization_step, quantization_ratio,
`DataFrame`	quantized.

effective_resolution ¤

effective_resolution(window: str = '1h') -> pd.DataFrame

Estimate effective measurement resolution from data.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window size.	`'1h'`

Returns:

Type	Description
`DataFrame`	Summary DataFrame with columns: start, end, duration_seconds
`DataFrame`	(the window bounds), window_start (the resample label; a
`DataFrame`	deprecated twin of `start`), theoretical_resolution,
`DataFrame`	effective_resolution, resolution_ratio, assessment.

detect_aliasing ¤

detect_aliasing(
    window: str = "1h",
    expected_frequency_hz: float | None = None,
) -> pd.DataFrame

Check for Nyquist violations using FFT.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window size.	`'1h'`
`expected_frequency_hz`	`float \| None`	Known process frequency in Hz. If None, dominant frequency is estimated from FFT.	`None`

Returns:

Type	Description
`DataFrame`	Summary DataFrame with columns: start, end, duration_seconds
`DataFrame`	(the window bounds), window_start (the resample label; a
`DataFrame`	deprecated twin of `start`), sampling_rate_hz,
`DataFrame`	nyquist_hz, dominant_frequency, aliasing_risk.

sampling_jitter ¤

sampling_jitter(window: str = '1h') -> pd.DataFrame

Measure timing jitter (variation in sampling interval).

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window size.	`'1h'`

Returns:

Type	Description
`DataFrame`	Summary DataFrame with columns: start, end, duration_seconds
`DataFrame`	(the window bounds), window_start (the resample label; a
`DataFrame`	deprecated twin of `start`), mean_interval_ms,
`DataFrame`	mean_interval_seconds, std_interval_ms, std_interval_seconds,
`DataFrame`	jitter_pct, max_gap_ms, max_gap_seconds, regularity_score. Each
`DataFrame`	`_seconds` column follows its legacy `_ms` twin (the same
`DataFrame`	value in seconds); the `*_ms` columns are deprecated.