Contents Menu Expand Light mode Dark mode Auto light/dark mode
RamanSPy
Logo

Quick start:

  • Overview
  • Installation

Documentation:

  • Data containers
  • Data loading
  • Datasets
  • Metrics
  • Data visualisation
  • Preprocessing
    • ramanspy.preprocessing.misc.Cropper
    • ramanspy.preprocessing.misc.BackgroundSubtractor
    • ramanspy.preprocessing.despike.WhitakerHayes
    • ramanspy.preprocessing.denoise.SavGol
    • ramanspy.preprocessing.denoise.Whittaker
    • ramanspy.preprocessing.denoise.Kernel
    • ramanspy.preprocessing.denoise.Gaussian
    • ramanspy.preprocessing.baseline.ASLS
    • ramanspy.preprocessing.baseline.IASLS
    • ramanspy.preprocessing.baseline.AIRPLS
    • ramanspy.preprocessing.baseline.ARPLS
    • ramanspy.preprocessing.baseline.DRPLS
    • ramanspy.preprocessing.baseline.IARPLS
    • ramanspy.preprocessing.baseline.ASPLS
    • ramanspy.preprocessing.baseline.Poly
    • ramanspy.preprocessing.baseline.ModPoly
    • ramanspy.preprocessing.baseline.PenalisedPoly
    • ramanspy.preprocessing.baseline.IModPoly
    • ramanspy.preprocessing.baseline.Goldindec
    • ramanspy.preprocessing.baseline.IRSQR
    • ramanspy.preprocessing.baseline.CornerCutting
    • ramanspy.preprocessing.baseline.FABC
    • ramanspy.preprocessing.normalise.Vector
    • ramanspy.preprocessing.normalise.MinMax
    • ramanspy.preprocessing.normalise.MaxIntensity
    • ramanspy.preprocessing.normalise.AUC
    • ramanspy.preprocessing.protocols.georgiev2023_P1
    • ramanspy.preprocessing.protocols.georgiev2023_P3
    • ramanspy.preprocessing.protocols.georgiev2023_P3
    • ramanspy.preprocessing.protocols.bergholt2016
  • Analysis
    • ramanspy.analysis.decompose.PCA
    • ramanspy.analysis.decompose.NMF
    • ramanspy.analysis.decompose.ICA
    • ramanspy.analysis.cluster.KMeans
    • ramanspy.analysis.unmix.PPI
    • ramanspy.analysis.unmix.FIPPI
    • ramanspy.analysis.unmix.NFINDR
    • ramanspy.analysis.unmix.VCA
  • Synthetic data

Gallery:

  • Tutorials
    • Storing generic data
    • Storing spectra
    • Storing imaging data
    • Storing volumetric data
    • Loading WITec data
    • Loading Renishaw data
    • Loading Ocean Insight data
    • Loading other data
    • Loading the RRUFF dataset
    • Loading the Bacteria dataset
    • Using built-in metrics
    • Visualising spectra
    • Visualising peaks
    • Visualising spectral distributions
    • Visualising imaging data
    • Visualising volumetric data
    • Visualising peak distributions
    • Customising plots
    • Built-in methods
    • Custom methods
    • Custom pipelines
    • Built-in protocols
    • Built-in decomposition methods
    • Built-in clustering methods
    • Built-in unmixing methods
    • Integrative analysis: Support Vector Machine (SVM) classification
    • Integrative analysis: Neural Network (NN) classification
    • Generate synthetic spectra
  • Examples
    • Cell phenotyping
    • Preprocessing pipelines
    • AI-based denoising
    • Bacteria classification

Links

  • GitHub repository
  • Paper
Back to top

Note

Go to the end to download the full example code

Generate synthetic spectra

In this example, we will use RamanSPy to generate synthetic spectra.

Raman spectra
import ramanspy as rp

# Generate synthetic spectra
spectra = rp.synth.generate_spectra(5, 1000, realistic=True)

rp.plot.spectra(spectra, plot_type='single stacked')
rp.plot.show()

Total running time of the script: (0 minutes 0.115 seconds)

Download Python source code: plot_i_endmembers.py

Download Jupyter notebook: plot_i_endmembers.ipynb

Next
Examples
Previous
Integrative analysis: Neural Network (NN) classification
Copyright © 2024, Dimitar Georgiev
Made with Furo