Spectroscopy

Code for spectroscopic data processing in the PAM group.

This repository is organized as a reusable Python package with one example notebook that demonstrates end-to-end usage.

Repository Layout

1. spectroscopy/: reusable processing and plotting modules.
2. Spectroscopy-Examples.ipynb: worked examples for all major workflows.
3. archive/notebooks/: archived legacy notebooks.
4. environment.yml: environment definition for Conda.

Create the Environment

This project uses an environment file named environment.yml.

conda env create -f environment.yml
conda activate spectroscopy

To update the environment after dependency changes:

conda env update -f environment.yml --prune

To remove the environment:

conda env remove -n spectroscopy

Note: .env files are typically for runtime environment variables, not Conda package environments. For this project, use environment.yml to create the Python environment.

Quick Start

from pathlib import Path
from spectroscopy import load_perkinelmer_results, calibrate_reflectance

sample_folder = Path(r"C:/path/to/perkin_folder")
sample_names, perkin_data = load_perkinelmer_results(sample_folder)
calibrated, calibration = calibrate_reflectance(
	perkin_data,
	dark_standard_column="2p5R_standard",
	white_standard_column="99R_standard",
	dark_reference_csv=Path(r"C:/path/to/dark_reference.xlsx"),
	white_reference_csv=Path(r"C:/path/to/white_reference.xlsx"),
)

Open Spectroscopy-Examples.ipynb to run complete examples and update file paths to your local data.

Full Functionality

Import everything from the public API:

from spectroscopy import *

Raman

• read_raman_spectrum(file_path): read single Raman spectrum text file.
• preprocess_raman_spectrum(spectrum, wav_lower_limit, wav_upper_limit, ...): baseline-correct and filter spectrum.
• extract_mapping_line(file_path, y_value, x_min, wav_lower_limit, ...): extract one mapping line from mapping export.
• preprocess_raman_mapping_line(raw_data, x_points, y_value, wav_points, ...): process extracted mapping line.
• find_nearest_wavenumber(wavenumbers, target): find nearest available wavenumber.

PerkinElmer Reflectance

• load_perkinelmer_results(sample_folder): load PerkinElmer result table and sample raw files.
• load_reflectance_reference(reference_path): load dark/white reference from .csv, .xlsx, .xls, or .xlsm.
• calibrate_reflectance(data, dark_standard_column, white_standard_column, dark_reference_csv, white_reference_csv): apply calibration to measured spectra.
• export_and_plot_calibrated_reflectance(calibrated, output_csv_path, ...): export calibrated data to CSV and plot calibrated %R.

FTIR

• load_ftir_spectra(folder, file_stems): load FTIR spectra files into a DataFrame.
• normalize_and_filter_ftir(data, ...): normalize and smooth FTIR spectra.
• find_peak_wavenumbers(series, prominence=0.01): identify prominent FTIR peak positions.

Fluorolog

• load_fluorolog_dat(file_path, ...): load one Fluorolog .dat file.
• load_fluorolog_folder(folder, ...): load all Fluorolog .dat files in a folder.
• fit_fluorolog_peaks(x, y, ...): fit Gaussian-like peaks to a Fluorolog trace.

Power / Lux Conversion

• load_light_spectrum(light_file): load spectral irradiance data.
• load_luminous_efficacy(csv_file): load photopic luminous efficacy table.
• apply_luminous_efficacy(spectrum, efficacy): map spectral power to lumen contribution.
• compute_irradiance_and_lux(with_lumen): compute integrated irradiance and lux.

Camera Image Sequences

• discover_image_sequence(sample_folder, prefix): count images in a sequence.
• load_image_sequence(sample_folder, prefix, count, ...): load image series.
• build_grayscale_histograms(images): create grayscale histograms.
• fft_image_sequence(images): compute FFT for each image.

Plotting Utilities

• create_figure(...): create figure and axes with shared defaults.
• plot_series(ax, x, y, ...): plot one trace on an axis.
• plot_dataframe_columns(ax, data, ...): plot selected DataFrame columns.
• finalize_axis(ax, ...): apply labels, limits, title, legend.
• add_vertical_markers(ax, x_positions, ...): draw vertical guides.
• plot_perkin_data_overview(perkin_data, ...): quick PerkinElmer overview plot.
• plot_fluorolog_overview(fluorolog_data, ...): quick Fluorolog overview plot.
• save_figure(fig, file_path, dpi=300): save figure with tight layout.

Common Usage Examples

Export and Plot Calibrated Reflectance

from pathlib import Path
from spectroscopy import (
	calibrate_reflectance,
	export_and_plot_calibrated_reflectance,
	load_perkinelmer_results,
)

sample_folder = Path(r"C:/path/to/perkin_folder")
_, perkin_data = load_perkinelmer_results(sample_folder)

calibrated, calibration = calibrate_reflectance(
	perkin_data,
	dark_standard_column="2p5R_standard",
	white_standard_column="99R_standard",
	dark_reference_csv=Path(r"C:/path/to/dark_reference.xlsx"),
	white_reference_csv=Path(r"C:/path/to/white_reference.xlsx"),
)

output_csv, fig, ax = export_and_plot_calibrated_reflectance(
	calibrated,
	sample_folder / "calibrated_reflectance.csv",
	columns=[c for c in calibrated.columns if "standard" not in c.lower()],
)

Fluorolog Folder Overview

from pathlib import Path
from spectroscopy import load_fluorolog_folder, plot_fluorolog_overview

folder = Path(r"C:/path/to/fluorolog_folder")
file_table, fluorolog_data = load_fluorolog_folder(folder)
fig, ax, selected = plot_fluorolog_overview(
	fluorolog_data,
	normalize=True,
	peak_prominence=0.05,
	peak_fit_window_points=11,
)