NoisySignalIntegration.jl

NoisySignalIntegration.jl – A tool to determine uncertainty in numeric integrals of noisy x-y data.

NoisySignalIntegration implements a method to determine the uncertainty in numeric integrals of noisy x-y data on the basis of a Monte-Carlo process. It can include uncertainty due to noise, baseline subtraction, and placement in integration bounds. To do this, the integration is repeated many times while the noise of the data, baseline, and integration bounds are varied based on a noise model and user supplied probability distributions.

A predecessor of this package was originally intended to estimate uncertainties of band signals in FTIR spectra (see G. Karir et al., 2019), which is reflected in the example given in the Usage Guide.

Table of Contents

• Package Overview
• • Workflow
  • Data requirements
• Usage Guide
• • Cropping the spectrum
  • Noise analysis
  • Preparing the spectrum for integration
  • Integration bounds
  • • Defining an UncertainBound using a start and end point
    • Defining an UncertainBound using a position, width, and UncertainCurve (symmetric bands)
    • Defining an UncertainBound using several positions, a width, and UncertainCurve (several symmetric bands with same width)
  • Plotting Monte-Carlo draws
  • Running the integration algorithm
• Case studies
• • Raman spectra
  • Mexican hat wavelet
  • Error propagation
• Baseline Handling
• • Build-in
  • Preprocessing
  • • Simple baseline subtraction
    • Uncertain baseline
• API Reference
• • Types
  • Functions
  • • Manipulation of Curves
    • Noise analysis
    • Statistics
    • Integration
    • Band Centers
  • Macros
  • Misc
• Internals