This is development documentation. Some features are not available in the stable release. View stable documentation.

Processing

• Slicing NDDatasets
  • What is slicing?
  • Slicing of NDDatasets
    • Slicing with indexes
    • Slicing with coordinates
• Basic transformations
  • Masking data
  • Transposition
  • Changing units
• Chemometric preprocessing
  • Normalization
  • Mean-centering and autoscaling
  • Standard Normal Variate (SNV)
  • Multiplicative Scatter Correction (MSC)
  • Using transformers for machine-learning workflows
• Mathematical operations
  • Ufuncs (Universal Numpy’s functions)
  • List of UFuncs working on NDDataset:
    • Functions affecting magnitudes of the number but keeping units
    • Functions affecting magnitudes of the number but also units
    • Functions that require no units or dimensionless units for inputs. Returns dimensionless objects.
    • Functions that return numpy arrays (Work only for NDDataset)
    • Trigonometric functions. Require unitless data or radian units.
    • Hyperbolic functions
    • Unit conversions
    • Binary Ufuncs
  • Usage
    • Unary functions
    • Binary functions
  • Complex NDDatasets
• Filtering, Smoothing and Denoising
  • The Filter processor
    • Moving average
    • Convolution with window filters
    • Savitzky-Golay filter
    • Savitzky-Golay derivatives
    • Whittaker-Eilers filter
  • Filtering using API or NDDataset methods.
    • The smooth method
    • Convolution with windows
    • Savitzky-Golay filter:savgol
    • Whittaker-eilers filter : whittaker
• Baseline corrections
  • The Baseline processor
    • How it works?
    • Example
    • Overview of the other models
    • Multivariate approach
  • Baseline correction using NDDataset or API methods
    • Detrending
    • basc
    • Rubberband
    • Code snippet for ‘advanced’ baseline correction
• Denoising
  • Denoising 2D spectra
  • Removing cosmic rays spike from Raman spectra
    • Median filter
    • despike method
• Apodization
  • Introduction
    • Plot of the Real and Imaginary original data
    • Exponential multiplication
    • Other apodization functions
• Time domain baseline correction (NMR)
• One-dimensional (1D) Fourier transformation
  • FFT of 1D NMR spectra
  • Preprocessing
    • Line broadening
    • Zero-filling
    • Time domain baseline correction
    • Magnitude calculation
    • Power spectrum
• Real Fourier transform
• FTIR interferogram processing
  • Comparison with the OMNIC processing.
• Alignment of datasets
  • Example
  • Methods for alignments
    • inner method
    • first method
    • last method
  • Alignment along several dimensions