smx.zones.build#

Build spectral zones directly from a single spectrum.

Functions#

building_spectral_zones(→ List[Tuple[str, Union[int, ...)

Detect local minima/maxima and build spectral cuts from a single spectrum.

smx.zones.build.building_spectral_zones(spectrum: numpy.ndarray | pandas.Series | pandas.DataFrame, min_window_length: int = 7, prominence: float = 0.3, svg_smooth: bool = False, svg_window_length: int = 7, svg_polyorder: int = 3, svg_deriv: int = 1, ploting: bool = True, plotting: bool | None = None, theme: smx.plotting.theme.SMXTheme | None = None, title: str | None = None, output_path: str | Path | None = None, _show_minima: bool = False) → List[Tuple[str, int | float | str, int | float | str]][source]#

Detect local minima/maxima and build spectral cuts from a single spectrum.

spectrumnumpy.ndarray, pandas.Series, or pandas.DataFrame: Spectrum values. If a DataFrame or a 2-D numpy array is provided, the mean spectrum (averaged over rows) is used before zone detection. If a Series or 1-D array is provided, it is used directly.
min_window_lengthint, default 7: Window length used by argrelmin to detect local minima.
prominencefloat, default 0.3: Minimum prominence for local maxima detection.
svg_smoothbool, default False: If True, apply Savitzky-Golay smoothing before peak detection.
svg_window_lengthint, default 7: Savitzky-Golay window length.
svg_polyorderint, default 3: Savitzky-Golay polynomial order.
svg_derivint, default 1: Derivative order for Savitzky-Golay filtering. Use 0 for no derivative (smoothing only), 1 for first derivative, 2 for second derivative, etc. Only used when svg_smooth=True.
plotingbool, default True: When True, plot the spectrum with zone backgrounds and identified peaks.
plottingbool, optional: Backward-compatible alias for ploting.
themeSMXTheme, optional: Optional visual theme for plotting.
titlestr, optional: Optional plot title when ploting=True.
output_pathstr or Path, optional: Optional output path for saving the plot (HTML or static image).