AFM {AFM} | R Documentation |

The AFM package provides statistics analysis tools for Atomic Force Microscopy image analysis.

Licence: Affero GPL v3

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 frequenciescalculate fractal dimension and scale: use (

`getFractalDimensions`

) functionprint 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.

M.Beauvais, J.Landoulsi, I.Liascukiene

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"

## 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) ## End(Not run)

[Package *AFM* version 2.0 Index]