AFM (version 2.0)

AFM: Atomic Force Microscopy images tools

Description

The AFM package provides statistics analysis tools for Atomic Force Microscopy image analysis. Licence: Affero GPL v3

Details

A graphical user interface is available by using runAFMApp command.

Several high level functions are :

• create your AFM image from a list of measured heights (see example section of AFMImage)

• import your image from Nanoscope Analysis (TM) tool (importFromNanoscope)

• check if your sample is normally distributed and isotropic and get a pdf report (generateCheckReport)

• calculate the Gaussian mixes of the heights (performGaussianMixCalculation)

• perform variance (variogram), roughness against lengthscale, fractal analysis and get a pdf report (generateReport)

• identify 2D networks (getNetworkParameters)

Other functions are :

• check sample: for normality (checkNormality) and for isotropy (checkIsotropy)

• calculate total RMS roughness: quick calculation of total root mean square roughness(totalRMSRoughness)

• calculate omnidirectional variogram: calculate estimated variogram (calculateOmnidirectionalVariogram)

• calculate roughness against lenghscale and Power Spectrum Density (PSD): calculate roughness against length scale (RoughnessByLengthScale), PSD 1D (PSD1DAgainstFrequency) or PSD 2D (PSD2DAgainstFrequency) against frequencies

• calculate fractal dimension and scale: use (getFractalDimensions) function

• print in 3D (3D print) (exportToSTL) your AFM image

An EC2 instance is available for basic testing at the following address: http://www.afmist.org

Note: To use with a Brucker(TM) Atomic Force Microscope, use nanoscope analysis(TM) software and

• Use the "Flatten" function.

• Save the flattened image.

• Use the "Browse Data Files" windows, right click on image name and then Export the AFM image with the headers and the "Export> ASCII" contextual menu option.

References

Gneiting2012, Tilmann Gneiting, Hana Sevcikova and Donald B. Percival 'Estimators of Fractal Dimension: Assessing the Roughness of Time Series and Spatial Data - Statistics in statistical Science, 2012, Vol. 27, No. 2, 247-277' Olea2006, Ricardo A. Olea "A six-step practical approach to semivariogram modeling", 2006, "Stochastic Environmental Research and Risk Assessment, Volume 20, Issue 5 , pp 307-318" Sidick2009, Erkin Sidick "Power Spectral Density Specification and Analysis of Large Optical Surfaces", 2009, "Modeling Aspects in Optical Metrology II, Proc. of SPIE Vol. 7390 73900L-1"

See Also

gstat, fractaldim, rgl

Examples

# NOT RUN {
  library(AFM)
# Analyse the AFMImageOfRegularPeaks AFM Image from this package
  data("AFMImageOfRegularPeaks")
  AFMImage<-AFMImageOfRegularPeaks
# exportDirectory="C:/Users/my_windows_login" or exportDirectory="/home/ubuntu"
  exportDirectory=tempdir()
  AFMImage@fullfilename<-paste(exportDirectory,"AFMImageOfRegularPeaks.txt",sep="/")
  
# Start to check if your sample is normaly distributed and isotropic.
  generateCheckReport(AFMImage)
  
# If the sample is normaly distributed and isotropic, generate a full report
  generateReport(AFMImage)
  
# }