Analysis

Here you’ll find some tutorials on the analysis methods provided by SpectroChemPy.

The analysis category includes all methods and classes designed to extract properties and features from one or more NDDatasets. The information given on these tutorial pages can be effectively supplemented by a review of the examples in the gallery (Example’s gallery).

Note

This part is under work and for now very limited. We are working to improve this.

• Peak Maxima Finding
  • Loading an experimental dataset
  • Find maxima by manual inspection of the plot
  • Find maxima with an automated method: find_peaks()
• Peak Analysis Workflow
  • Load and prepare a spectrum
  • Detect peaks and inspect the structured result
  • Export the peak table
  • Select starting candidates for fitting
  • Validate the script before fitting
  • Fit the spectrum and inspect the result
  • Summary
• Peak Integration
• Fitting
  • Solving a linear equation using the least square method (LSTSQ)
  • Least square with non-negativity constraint (NNLS)
  • NDDataset modelling using non-linear optimisation method
• Principal Component Analysis
  • Introduction
  • Loading of the dataset
  • Running a PCA
• Partial Least Squares Regression (PLSRegression)
  • Introduction
  • Loading of the dataset
  • Running PLSRegression
• MCR-ALS: Multivariate Curve Resolution with Alternating Least Squares
  • Part I — Classical MCR-ALS
  • Part II — Constraints
  • Part III — Augmented datasets
  • Summary — choosing the right workflow