oilftir¶

Submodules¶

oilftir.astm_utils

Attributes¶

`__version__`
`__author__`

Functions¶

`load_spectrum`(→ tuple[pandas.DataFrame, dict])	Load an FTIR spectrum from a file.
`remove_baseline`(→ pandas.DataFrame)	Subtract a polynomial baseline from the spectrum using the modified
`scale_reference`(→ pandas.DataFrame)	Scale a reference spectrum to best match a sample spectrum using
`subtract_reference`(→ tuple[numpy.ndarray, float, ...)	Scale and subtract a reference spectrum from a sample spectrum over a
`astm_areas`(→ dict)	Compute all standard ASTM E2412 band areas in a single call.
`area_oxidation`(→ float)	Oxidation band area (1800–1670 cm⁻¹) – ASTM E2412.
`area_nitration`(→ float)	Nitration band area (1600–1650 cm⁻¹) – ASTM E2412.
`area_sulfation`(→ float)	Sulfation band area (1120–1180 cm⁻¹) – ASTM E2412.
`area_water`(→ float)	Water contamination band area (3150–3500 cm⁻¹) – ASTM E2412.
`area_glycol`(→ float)	Ethylene glycol (coolant) band area (1030–1100 cm⁻¹) – ASTM E2412.
`area_fuel_petrol`(→ float)	Petrol (gasoline) fuel dilution band area (745–755 cm⁻¹) – ASTM E2412.
`area_fuel_diesel`(→ float)	Diesel fuel dilution band area (805–815 cm⁻¹) – ASTM E2412.
`area_antiwear_zddp`(→ float)	ZDDP antiwear additive band area (960–1025 cm⁻¹) – ASTM E2412.
`soot_load`(→ float)	Soot (carbon black) load index – ASTM E2412.
`area_to_concentration`(→ float)	Convert a band area to a concentration using a polynomial calibration curve.
`hqi`(→ float)	Compute the Hit Quality Index (HQI) between two spectra.
`library_search`(→ list[dict])	Parallel brute-force HQI library search.
`plot_spectra`(, ylim_A, ylim_T, save_path)	Plot one or more FTIR spectra (absorbance and transmittance panels).

Package Contents¶

oilftir.load_spectrum(path: str, magnitude: str = 'A') → tuple[pandas.DataFrame, dict][source]¶

Load an FTIR spectrum from a file.

Supported formats¶

CSV / TXT – two-column file (wavenumber, amplitude); delimiter is
auto-detected (comma or whitespace).
.sp – PerkinElmer binary format (requires the specio package).

param path:

Path to the spectrum file.

type path:

str

param magnitude:

Indicates whether amplitudes in the file are Absorbance (‘A’) or Transmittance (‘T’, expressed as a fraction 0–1 or percentage 0–100).

type magnitude:

{'A', 'T'}

returns:

df (pd.DataFrame) – Columns: cm-1 (wavenumber), A (absorbance), T (transmittance 0–1).
meta (dict) – Metadata extracted from the file (empty dict for plain text files).

raises ValueError:

If magnitude is not ‘A’ or ‘T’.

raises ImportError:

If a .sp file is requested but specio is not installed.

oilftir.remove_baseline(df: pandas.DataFrame, units: str = 'A', poly_order: int = 5) → pandas.DataFrame[source]¶

Subtract a polynomial baseline from the spectrum using the modified polynomial method (ModPoly).

Parameters:

df (pd.DataFrame) – Spectrum DataFrame with columns cm-1 and units.
units (str) – Column to correct (‘A’ or ‘T’).
poly_order (int) – Degree of the fitting polynomial (default 5).

Returns:

Copy of df with the baseline subtracted in-place.

Return type:

pd.DataFrame

oilftir.scale_reference(df_sample: pandas.DataFrame, df_ref: pandas.DataFrame, units: str = 'A') → pandas.DataFrame[source]¶

Scale a reference spectrum to best match a sample spectrum using least-squares projection (scalar factor k).

The scaling factor is computed as:

k = (sample · reference) / (reference · reference)

Parameters:

df_sample (pd.DataFrame) – Sample spectrum.
df_ref (pd.DataFrame) – Reference spectrum to be scaled.
units (str) – Column used for comparison.

Returns:

Scaled copy of df_ref.

Return type:

pd.DataFrame

oilftir.subtract_reference(df_sample: pandas.DataFrame, df_ref: pandas.DataFrame, wavenumber_range: tuple[float, float], units: str = 'A') → tuple[numpy.ndarray, float, numpy.ndarray][source]¶

Scale and subtract a reference spectrum from a sample spectrum over a specified wavenumber range.

The reference is first interpolated onto the sample’s wavenumber axis, then scaled using the least-squares projection within wavenumber_range.

Parameters:

df_sample (pd.DataFrame) – Sample spectrum.
df_ref (pd.DataFrame) – Reference spectrum (may have a different wavenumber axis).
wavenumber_range ((float, float)) – (k_min, k_max) – region used to compute the scaling factor.
units (str) – Column to use (‘A’ or ‘T’).

Returns:

y_diff (np.ndarray) – Difference spectrum (sample − scaled reference).
k (float) – Scaling factor applied to the reference.
y_ref_scaled (np.ndarray) – Scaled reference interpolated onto the sample axis.

oilftir.astm_areas(df: pandas.DataFrame) → dict[source]¶

Compute all standard ASTM E2412 band areas in a single call.

Parameters:: df (pd.DataFrame) – Spectrum with columns cm-1 and A.
Returns:: Keys: oxidation, nitration, sulfation, water, glycol, fuel_petrol, fuel_diesel, antiwear_zddp, soot.
Return type:: dict

oilftir.area_oxidation(df: pandas.DataFrame) → float[source]¶

Oxidation band area (1800–1670 cm⁻¹) – ASTM E2412.

Baseline anchored at the minima in 550–650 cm⁻¹ and 1900–2200 cm⁻¹.

Parameters:: df (pd.DataFrame) – Spectrum with columns cm-1 and A.
Returns:: Net integrated area (absorbance·cm⁻¹).
Return type:: float

oilftir.area_nitration(df: pandas.DataFrame) → float[source]¶

Nitration band area (1600–1650 cm⁻¹) – ASTM E2412.

Baseline anchored at the minima in 550–650 cm⁻¹ and 1900–2200 cm⁻¹.

Parameters:: df (pd.DataFrame) – Spectrum with columns cm-1 and A.
Returns:: Net integrated area (absorbance·cm⁻¹).
Return type:: float

oilftir.area_sulfation(df: pandas.DataFrame) → float[source]¶

Sulfation band area (1120–1180 cm⁻¹) – ASTM E2412.

Baseline anchored at the minima in 550–650 cm⁻¹ and 1900–2200 cm⁻¹.

Parameters:: df (pd.DataFrame) – Spectrum with columns cm-1 and A.
Returns:: Net integrated area (absorbance·cm⁻¹).
Return type:: float

oilftir.area_water(df: pandas.DataFrame) → float[source]¶

Water contamination band area (3150–3500 cm⁻¹) – ASTM E2412.

Baseline anchored at the minima in 1900–2200 cm⁻¹ and 3680–4000 cm⁻¹.

Parameters:: df (pd.DataFrame) – Spectrum with columns cm-1 and A.
Returns:: Net integrated area (absorbance·cm⁻¹).
Return type:: float

oilftir.area_glycol(df: pandas.DataFrame) → float[source]¶

Ethylene glycol (coolant) band area (1030–1100 cm⁻¹) – ASTM E2412.

Baseline anchored at the minima in 1010–1030 cm⁻¹ and 1100–1130 cm⁻¹.

Parameters:: df (pd.DataFrame) – Spectrum with columns cm-1 and A.
Returns:: Net integrated area (absorbance·cm⁻¹).
Return type:: float

oilftir.area_fuel_petrol(df: pandas.DataFrame) → float[source]¶

Petrol (gasoline) fuel dilution band area (745–755 cm⁻¹) – ASTM E2412.

Baseline anchored at the minima in 730–750 cm⁻¹ and 760–780 cm⁻¹.

Parameters:: df (pd.DataFrame) – Spectrum with columns cm-1 and A.
Returns:: Net integrated area (absorbance·cm⁻¹).
Return type:: float

oilftir.area_fuel_diesel(df: pandas.DataFrame) → float[source]¶

Diesel fuel dilution band area (805–815 cm⁻¹) – ASTM E2412.

Baseline anchored at the minima in 795–805 cm⁻¹ and 825–835 cm⁻¹.

Parameters:: df (pd.DataFrame) – Spectrum with columns cm-1 and A.
Returns:: Net integrated area (absorbance·cm⁻¹).
Return type:: float

Notes

The raw integrated area is returned. Convert to a concentration using a laboratory-specific calibration curve.

oilftir.area_antiwear_zddp(df: pandas.DataFrame) → float[source]¶

ZDDP antiwear additive band area (960–1025 cm⁻¹) – ASTM E2412.

Baseline anchored at the minima in 550–650 cm⁻¹ and 1900–2200 cm⁻¹.

Parameters:: df (pd.DataFrame) – Spectrum with columns cm-1 and A.
Returns:: Net integrated area (absorbance·cm⁻¹).
Return type:: float

oilftir.soot_load(df: pandas.DataFrame) → float[source]¶

Soot (carbon black) load index – ASTM E2412.

The absorbance at the carbon-black scattering reference point (2000 cm⁻¹) is multiplied by 100 to yield a dimensionless index.

Parameters:: df (pd.DataFrame) – Spectrum with columns cm-1 and A.
Returns:: Soot index (dimensionless, ≥ 0).
Return type:: float

oilftir.area_to_concentration(area: float, calibration_areas: numpy.ndarray, calibration_concentrations: numpy.ndarray, poly_order: int = 3) → float[source]¶

Convert a band area to a concentration using a polynomial calibration curve.

Build and apply your own calibration curve by providing known (area, concentration) pairs measured on your instrument.

Parameters:

area (float) – Measured band area to convert.
calibration_areas (array-like) – Band areas of the calibration standards.
calibration_concentrations (array-like) – Corresponding concentrations (same units as desired output).
poly_order (int) – Degree of the fitting polynomial (default 3).

Returns:

Estimated concentration (≥ 0).

Return type:

float

Example

>>> # Example: water content calibration
>>> cal_areas = np.array([0.0, 70.0, 87.0, 120.0, 144.0])
>>> cal_conc  = np.array([0.0,  2.0,  4.0,   5.0,  10.0])  # % v/v
>>> conc = area_to_concentration(measured_area, cal_areas, cal_conc)

oilftir.hqi(x_query: numpy.ndarray, y_query: numpy.ndarray, x_ref: numpy.ndarray, y_ref: numpy.ndarray) → float[source]¶

Compute the Hit Quality Index (HQI) between two spectra.

HQI is defined as the squared cosine similarity (0–100 %):

HQI = 100 × (A·B)² / (‖A‖² · ‖B‖²)

The reference is interpolated onto the query’s wavenumber axis using the overlapping region only.

Parameters:

x_query (np.ndarray) – Query spectrum (wavenumber, absorbance).
y_query (np.ndarray) – Query spectrum (wavenumber, absorbance).
x_ref (np.ndarray) – Reference spectrum.
y_ref (np.ndarray) – Reference spectrum.

Returns:

HQI score in percent (0–100).

Return type:

float

oilftir.library_search(query_path: str, library_dir: str, top_n: int = 5, extension: str = '*.sp') → list[dict][source]¶

Parallel brute-force HQI library search.

Loads the query spectrum, then compares it against every file matching extension in library_dir (and subdirectories) using the Hit Quality Index.

Parameters:

query_path (str) – Path to the query spectrum file.
library_dir (str) – Root directory of the reference library.
top_n (int) – Number of top matches to display.
extension (str) – Glob pattern for candidate files (e.g. '*.sp', '*.txt').

Returns:

All valid results sorted by HQI (descending), each with keys 'file' and 'hqi'.

Return type:

list[dict]

oilftir.plot_spectra(data_dict: dict, title: str = 'FTIR Spectroscopy', xlim: tuple = (4000, 450), ylim_A: tuple = (0, 0.4), ylim_T: tuple = (0, 1), save_path: str | None = None) → tuple[source]¶

Plot one or more FTIR spectra (absorbance and transmittance panels).

Parameters:

data_dict (dict) – {'Label': {'df': df, 'color': 'black', 'alpha': 1.0, 'linewidth': 2}} Only 'df' is required per entry.
title (str) – Figure title.
xlim ((float, float)) – Wavenumber axis limits (default: 4000–450 cm⁻¹).
ylim_A ((float, float)) – Absorbance axis limits.
ylim_T ((float, float)) – Transmittance axis limits.
save_path (str or None) – If given, the figure is saved to this path (PNG/PDF/SVG).

Returns:

fig, axes

Return type:

matplotlib Figure and Axes array

oilftir.__version__ = '0.1.0'[source]¶

oilftir.__author__ = 'Nahuel Mendez'[source]¶