Package 'AFM'

Description

add a node to an AFMImage

Usage

addNode(circleAFMImage, nodeDT, filterIndex)

Arguments

Value

an AFMImage

Author(s)

M.Beauvais

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.

Author(s)

M.Beauvais, J.Landoulsi, I.Liascukiene

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)
  
## End(Not run)

Description

A S4 class to store and manipulate images from Atomic Force Microscopes.

Usage

AFMImage(
  data,
  samplesperline,
  lines,
  hscansize,
  vscansize,
  scansize,
  fullfilename
)

## S4 method for signature 'AFMImage'
initialize(
  .Object,
  data,
  samplesperline,
  lines,
  hscansize,
  vscansize,
  scansize,
  fullfilename
)

AFMImage(
  data,
  samplesperline,
  lines,
  hscansize,
  vscansize,
  scansize,
  fullfilename
)

Arguments

Slots

data

($x,$y,$h): a data.table storing the coordinates of the sample and the measured heights

samplesperline

number of samples per line (e.g.: 512)

lines

number of line (e.g.: 512)

hscansize

horizontal size of scan usualy in nanometer (e.g.: hscansize=1000 for a scan size of 1000 nm)

vscansize

vertical size of scan usualy in nanometer (e.g.: vscansize=1000 for a scan size of 1000 nm)

scansize

if hscansize equals vscansize, scansize is the size of scan usualy in nanometer (e.g.: scansize=1000 for a scan size of 1000 nm)

fullfilename

directory and filename on the disk (e.g.: /users/ubuntu/flatten-image.txt)

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
library(data.table)

# create a 128 pixels by 128 pixels AFM image
Lines=128
Samplesperline=128
fullfilename="RandomFakeAFMImage"
# the size of scan is 128 nm
ScanSize=128
# the heights is a normal distribution in nanometers
nm<-c(rnorm(128*128, mean=0, sd=1 )) 

scanby<-ScanSize/Samplesperline
endScan<-ScanSize*(1-1/Samplesperline)
RandomFakeAFMImage<-AFMImage(
     data = data.table(x = rep(seq(0,endScan, by= scanby), times = Lines),
                       y = rep(seq(0,endScan, by= scanby), each = Samplesperline), 
                       h = nm),
     samplesperline = Samplesperline, lines = Lines, 
     vscansize = ScanSize, hscansize = ScanSize, scansize = ScanSize, 
     fullfilename = fullfilename )

## End(Not run)

Description

AFMImage3DModelAnalysis

Slots

f1

a face of the 3D model

f2

a face of the 3D model

f3

a face of the 3D model

f4

a face of the 3D model

Author(s)

M.Beauvais

Description

A S4 class to handle the analysis of one AFM Image.

Usage

AFMImageAnalyser(AFMImage)

AFMImageAnalyser(AFMImage)

Arguments

Slots

AFMImage

AFMImage to be analysed

variogramAnalysis

AFMImageVariogramAnalysis

psdAnalysis

AFMImagePSDAnalysis

fdAnalysis

AFMImageFractalDimensionsAnalysis

gaussianMixAnalysis

AFMImageGaussianMixAnalysis

networksAnalysis

AFMImageNetworksAnalysis

mean

the mean of heights of the AFMImage

variance

the variance of heights of the AFMImage

TotalRrms

the total Root Mean Square Roughness of the AFMImage calculated from variance

Ra

mean roughness or mean of absolute values of heights

fullfilename

to be removed ?

updateProgress

a function to update a graphical user interface

Author(s)

M.Beauvais

Description

A real dataset containing an AFMImage of a collagen network. The image is made of 192*192 samples of a 1500 nm * 1500 nm surface. samplesperline=192 lines=192 hscansize=1500 vscansize=1500

Description

AFMImageFractalDimensionMethod stores calculation from one fractal dimension method

Usage

AFMImageFractalDimensionMethod(fd_method, fd, fd_scale)

## S4 method for signature 'AFMImageFractalDimensionMethod'
initialize(.Object, fd_method, fd, fd_scale)

AFMImageFractalDimensionMethod(fd_method, fd, fd_scale)

Arguments

Slots

fd_method

Two dimensional function names used to evaluate the fractal dimension and fractal scale

fd

the value of the fractal dimension

fd_scale

the value of the fractal scale

Author(s)

M.Beauvais

See Also

fractaldim

Description

A S4 class to handle the fractal dimension calculation with several fractal dimension methods

Usage

AFMImageFractalDimensionsAnalysis()

## S4 method for signature 'AFMImageFractalDimensionsAnalysis'
initialize(.Object, fractalDimensionMethods, csvFullfilename)

AFMImageFractalDimensionsAnalysis()

fractalDimensionMethods(object)

## S4 method for signature 'AFMImageFractalDimensionsAnalysis'
fractalDimensionMethods(object)

Arguments

Slots

fractalDimensionMethods

a list of AFMImageFractalDimensionMethod

csvFullfilename

To be removed ?

updateProgress

a function to update a graphical user interface

Author(s)

M.Beauvais

Description

AFMImageGaussianMixAnalysis handles an AFMImage Gaussian mix of heights analysis

Usage

AFMImageGaussianMixAnalysis()

## S4 method for signature 'AFMImageGaussianMixAnalysis'
initialize(.Object)

AFMImageGaussianMixAnalysis()

summaryMixture(object)

## S4 method for signature 'AFMImageGaussianMixAnalysis'
summaryMixture(object)

eachComponentsCounts(object)

## S4 method for signature 'AFMImageGaussianMixAnalysis'
eachComponentsCounts(object)

tcdfsEcdfsCheck(object)

densityCurvesAllHeights(object)

## S4 method for signature 'AFMImageGaussianMixAnalysis'
densityCurvesAllHeights(object)

tcdfsEcdfsCheck(object)

## S4 method for signature 'AFMImageGaussianMixAnalysis'
tcdfsEcdfsCheck(object)

gaussianMix(object)

## S4 method for signature 'AFMImageGaussianMixAnalysis'
gaussianMix(object)

minGaussianMix(object)

## S4 method for signature 'AFMImageGaussianMixAnalysis'
minGaussianMix(object)

maxGaussianMix(object)

## S4 method for signature 'AFMImageGaussianMixAnalysis'
maxGaussianMix(object)

epsilonGaussianMix(object)

## S4 method for signature 'AFMImageGaussianMixAnalysis'
epsilonGaussianMix(object)

Arguments

Slots

minGaussianMix

the minimum number of components to calculate

maxGaussianMix

the maximum number of components to calculate

epsilonGaussianMix

the convergence criterion

gaussianMix

a data.table to store the calculated Gaussian mixes

summaryMixture

a data.table to summaryse the mixtures

tcdfsEcdfsCheck

an array to store the points to draw tcdfs ecdfs check

densityCurvesAllHeights

an array to store the points to draw the density curves

eachComponentsCounts

an array to store the points to draw counts of each components

updateProgress

a function to update a graphical user interface

Author(s)

M.Beauvais

Description

A S4 class to handle the networks calculation

Usage

AFMImageNetworksAnalysis()

## S4 method for signature 'AFMImageNetworksAnalysis'
initialize(
  .Object,
  vertexHashsize,
  binaryAFMImage,
  binaryAFMImageWithCircles,
  circlesTable,
  edgesTable,
  fusionedNodesCorrespondance,
  fusionedNodesEdgesTable,
  isolatedNodesList,
  heightNetworksslider,
  filterNetworkssliderMin,
  filterNetworkssliderMax,
  smallBranchesTreatment,
  originalGraph,
  skeletonGraph,
  shortestPaths,
  networksCharacteristics,
  holes,
  holesCharacteristics,
  graphEvcent,
  graphBetweenness,
  libVersion
)

AFMImageNetworksAnalysis()

Arguments

Slots

vertexHashsize

hash to transform coordinates to vertexId

binaryAFMImage

the AFMImage after transformation before analysis

binaryAFMImageWithCircles

the AFMImage after transformation with the spotted circles

circlesTable

a data.table of identified circles

edgesTable

a data.table of edges

fusionedNodesCorrespondance

a data.table of corresponsdance between intial node and fusioned node

fusionedNodesEdgesTable

a data.table of nodes fusioned because of intersecting

isolatedNodesTable

a data.table of isolated nodes

heightNetworksslider

used multiplier of heights to facilitate analysis

filterNetworkssliderMin

used filter minimum value to facilitate analysis

filterNetworkssliderMax

used filter maximum value to facilitate analysis

smallBranchesTreatment

boolean - smallest circle used or not

originalGraph

a list of igraph

skeletonGraph

a list of igraph

shortestPaths

a data.table of shortest paths

networksCharacteristics

a data.table to store the skeleton graph characteristics

graphEvcent

an array to store Evcent

graphBetweenness

an array to store the graph betweenness

libVersion

version of the AFM library used to perform the analysis

updateProgress

a function to update a graphical user interface

Author(s)

M.Beauvais

Description

A real dataset containing an AFMImage of an Aluminium interface. The image is made of 512*512 samples of a 1000 nm * 1000 nm surface. samplesperline=512 lines=512 hscansize=1000 vscansize=1000

Author(s)

J.Landoulsi, I.Liascukiene

Description

A fake dataset containing a manually generated AFMImage (a normal distribution of heights). The image is made of 128*128 samples of a 128 nm * 128 nm surface. samplesperline= 128 lines= 128 hscansize= 128 vscansize= 128

Description

A fake dataset containing a manually generated AFMImage (one peak positioned on the surface). The image is made of 128*128 samples of a 128 nm * 128 nm surface. samplesperline= 128 lines= 128 hscansize= 128 vscansize= 128

Description

A fake dataset containing a manually generated AFMImage (peaks regularly positioned on the surface). The image is made of 128*128 samples of a 128 nm * 128 nm surface. samplesperline= 128 lines= 128 hscansize= 128 vscansize= 128

Description

AFMImagePSDAnalysis handles an AFMImage roughness against lenghscale analysis

Usage

AFMImagePSDAnalysis()

## S4 method for signature 'AFMImagePSDAnalysis'
initialize(.Object)

AFMImagePSDAnalysis()

psd1d_breaks(object)

## S4 method for signature 'AFMImagePSDAnalysis'
psd1d_breaks(object)

psd2d_maxHighLengthScale(object)

## S4 method for signature 'AFMImagePSDAnalysis'
psd2d_maxHighLengthScale(object)

psd2d_truncHighLengthScale(object)

## S4 method for signature 'AFMImagePSDAnalysis'
psd2d_truncHighLengthScale(object)

psd1d(object)

## S4 method for signature 'AFMImagePSDAnalysis'
psd1d(object)

psd2d(object)

## S4 method for signature 'AFMImagePSDAnalysis'
psd2d(object)

roughnessAgainstLengthscale(object)

## S4 method for signature 'AFMImagePSDAnalysis'
roughnessAgainstLengthscale(object)

intersections(object)

## S4 method for signature 'AFMImagePSDAnalysis'
intersections(object)

Arguments

Slots

roughnessAgainstLengthscale

a data.table to store the roughness against lengthscale data

intersections

a list to store the lengthscales values as the intersections between slopes and the sill in roughness against lenghscale graph

updateProgress

a function to update a graphical user interface

Author(s)

M.Beauvais

Description

AFMImagePSDSlopesAnalysis stores the analysis of the second slope in roughness against lenghtscale

Usage

AFMImagePSDSlopesAnalysis()

## S4 method for signature 'AFMImagePSDSlopesAnalysis'
initialize(.Object)

AFMImagePSDSlopesAnalysis()

Arguments

Slots

lc

to be removed ?

wsat

to be removed ?

slope

to be removed ?

yintersept

to be removed ?

Author(s)

M.Beauvais

Description

AFMImageVariogramAnalysis manages the variogram analysis of an AFMImage

Usage

AFMImageVariogramAnalysis(sampleFitPercentage)

## S4 method for signature 'AFMImageVariogramAnalysis'
initialize(.Object, sampleFitPercentage, updateProgress)

AFMImageVariogramAnalysis(sampleFitPercentage)

variogramModels(object)

## S4 method for signature 'AFMImageVariogramAnalysis'
variogramModels(object)

omnidirectionalVariogram(object)

## S4 method for signature 'AFMImageVariogramAnalysis'
omnidirectionalVariogram(object)

directionalVariograms(object)

## S4 method for signature 'AFMImageVariogramAnalysis'
directionalVariograms(object)

variogramSlopeAnalysis(object)

## S4 method for signature 'AFMImageVariogramAnalysis'
variogramSlopeAnalysis(object)

Arguments

Slots

width

(optional) a distance step for the calculation of the variograms (e.g.: width= integer of (scan Size divided by number of lines)= ceil(1000 / 512) for AFMImageOfAluminiumInterface

omnidirectionalVariogram

a data.table to store the omnidirectional variogram

variogramSlopeAnalysis

a AFMImageVariogramAnalysis to analyse slope in log log omnidirectional semivariogram

directionalVariograms

a data.table to store the directional variograms

sampleFitPercentage

a sample size as a percentage of random points in the AFMImage. These points will be used to fit the variogram models.

chosenFitSample

the chosen random points of the AFMImage to perform the fitting of the variogram models.

cuts

the cuts for spplot of the AFMImage. The same cuts will be used for the predicted AFMImage

variogramModels

A list of AFMImageVariogramModel containing the various evaluated variogram models.

fullfilename

to be removed ?

updateProgress

a function to update a graphical user interface

Author(s)

M.Beauvais

Description

AFMImageVariogramModelstores the evaluation of one experimental variogram model

Usage

AFMImageVariogramModel()

## S4 method for signature 'AFMImageVariogramModel'
initialize(
  .Object,
  model,
  fit.v = data.table(),
  mykrige,
  res = data.table(),
  cor,
  press,
  sill,
  imageFullfilename
)

AFMImageVariogramModel()

Arguments

Slots

model

the variogram model name

fit.v

the values from the fit.variogram function in the gstat package

mykrige

the values from the krige function in the gstat library

res

a data.table to store: (cor) the correlation between the predicted sample and the real sample (press) the sum of the square of the differences between real and predicted values for each point of the sample

cor

to be removed ?

press

to be removed ?

sill

to be removed ?

imageFullfilename

to be removed ?

Author(s)

M.Beauvais

Description

A function to wrap all the analysis of an AFMImage

• variogram analysis including evaluation of basic variogram models with sill and range calculation

• power spectrum density analysis including roughness against lengthscale calculation

• fractal dimension analysis including fractal dimensions calculation

• basic roughness parameters analysis such as mean, variance, Rrms, Ra

Usage

analyse(AFMImageAnalyser)

Arguments

Value

an AFMImageAnalyser containing all the analysis

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

data(AFMImageOfAluminiumInterface)
AFMImage<-extractAFMImage(AFMImageOfAluminiumInterface, 0, 0, 32)
AFMImageAnalyser<-new("AFMImageAnalyser", AFMImage= AFMImage, fullfilename = AFMImage@fullfilename)
AFMImageAnalyser<-analyse(AFMImageAnalyser)
print(AFMImageAnalyser@fdAnalysis)

## End(Not run)

Description

check if nodes represented by circles are connected. The function defines all the possible segments between the circles and check if at least one segment exists.

Usage

AreNodesConnected(binaryAFMImage, center1, radius1, center2, radius2)

Arguments

Value

TRUE if the nodes are connected

Author(s)

M.Beauvais

Description

calculate3DModel update AFMImage3DModelAnalysis

Usage

calculate3DModel(AFMImage3DModelAnalysis, AFMImage)

## S4 method for signature 'AFMImage3DModelAnalysis'
calculate3DModel(AFMImage3DModelAnalysis, AFMImage)

Arguments

Author(s)

M.Beauvais

Description

calculate four experimental directional variograms of an AFMImage with the variogram function of the gstat package. The directional semi-variogram can be used to check the isotropy of the sample. Note: The sample will be isotropic if the slopes of the four variograms are similar.

Usage

calculateDirectionalVariograms(AFMImageVariogramAnalysis, AFMImage)

Arguments

Details

calculateDirectionalVariograms returns the directional variograms

Value

Four directional variograms

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
library(ggplot2)

data(AFMImageOfRegularPeaks)
variogramAnalysis<-AFMImageVariogramAnalysis(sampleFitPercentage=3.43/100)
varios<-AFM::calculateDirectionalVariograms(AFMImage= AFMImageOfRegularPeaks, 
                                            AFMImageVariogramAnalysis= variogramAnalysis)
dist<-gamma<-NULL
p <- ggplot(varios, aes(x=dist, y=gamma,  
                        color= as.factor(dir.hor), 
                        shape=as.factor(dir.hor)))
p <- p + expand_limits(y = 0)
p <- p + geom_point()
p <- p + geom_line()
p <- p + ylab("semivariance (nm^2)")
p <- p + xlab("distance (nm)")
p <- p + ggtitle("Directional")
p

## End(Not run)

Description

calculateGaussianMixture return a data.table containing the result of the Gaussian Mixture and result of the test

Usage

calculateGaussianMixture(AFMImage)

Arguments

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
data(AFMImageOfNetworks)
mixtureCharacteristics<-calculateGaussianMixture(AFMImageOfNetworks)
print(mixtureCharacteristics)

## End(Not run)

Description

Calculate the holes characteristics

Usage

calculateHolesCharacteristics(AFMImageNetworksAnalysis)

Arguments

Value

a data.table with all the parameters

Author(s)

M.Beauvais

Description

calculateIgraph return

Usage

calculateIgraph(AFMImage, AFMImageNetworksAnalysis)

Arguments

Author(s)

M.Beauvais

Description

Calculate and return the networks parameters

Usage

calculateNetworkParameters(AFMImageNetworksAnalysis, AFMImage)

Arguments

Value

a data.table with all the parameters

Author(s)

M.Beauvais

Description

calculateNetworks update AFMImageNetworksAnalysis

Usage

calculateNetworks(AFMImageNetworksAnalysis, AFMImage)

## S4 method for signature 'AFMImageNetworksAnalysis'
calculateNetworks(AFMImageNetworksAnalysis, AFMImage)

Arguments

Author(s)

M.Beauvais

Description

calculateNetworkSkeleton return

Usage

calculateNetworkSkeleton(AFMImage, AFMImageNetworksAnalysis)

Arguments

Author(s)

M.Beauvais

Description

calculateOmnidirectionalVariogram returns the semivariance calculated for all the directions calculate the experimental omnidirectional variogram of an AFMImage with the variogram function of the gstat package. The experimental semi-variogram is used to fit (find the best sill and range) the theoretical variogram models. With 512*512 images, it takes several minutes to calculate.

Usage

calculateOmnidirectionalVariogram(AFMImageVariogramAnalysis, AFMImage)

Arguments

Value

the semivariance calculated in all the directions

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
library(ggplot2)

data(AFMImageOfRegularPeaks)
variogramAnalysis<-AFMImageVariogramAnalysis(sampleFitPercentage=3.43/100)
avario<-AFM::calculateOmnidirectionalVariogram(AFMImageVariogramAnalysis= variogramAnalysis, 
                                               AFMImage= AFMImageOfRegularPeaks)
dist<-gamma<-NULL
p <- ggplot(avario, aes(x=dist, y=gamma))
p <- p + geom_point()
p <- p + geom_line()
p <- p + ylab("semivariance")
p <- p + xlab("distance (nm)")
p <- p + ggtitle("Experimental semivariogram")
p

## End(Not run)

Description

calculate the physical distances between nodes

Usage

calculatePhysicalDistanceFromPath(pathVidVector, hscale, vscale)

Arguments

Value

the physical distance the extrmities of the path

Author(s)

M.Beauvais

Description

Calculate the shortest path between all nodes of degree different to 2 that are connected with nodes of degree equal to 2 Calculate the distance between the above nodes.

Usage

calculateShortestPaths(..., AFMImageNetworksAnalysis)

Arguments

Author(s)

M.Beauvais

Description

canBeRemoved return

Usage

canBeRemoved(vertexId, g, allVertices, DEGREE_LIMIT_FOR_CANDIDATE_VERTICE)

Arguments

Author(s)

M.Beauvais

Description

checkIsotropy is used to check the isotropy of an AFMImage. A directional variogram is calculated for various directions. If the variogram is very similar for all the directions then the sample is isotropic.

Usage

checkIsotropy(AFMImage, AFMImageAnalyser)

Arguments

Value

an AFMImageAnalyser containing the directional variograms

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
library(ggplot2)

data(AFMImageOfAluminiumInterface)
AFMImage<-extractAFMImage(AFMImageOfAluminiumInterface, 0, 0, 32)
AFMImageAnalyser<-new("AFMImageAnalyser", AFMImage= AFMImage, fullfilename = AFMImage@fullfilename)
AFMImageAnalyser<-checkIsotropy(AFMImage,AFMImageAnalyser)
varios<-AFMImageAnalyser@variogramAnalysis@directionalVariograms
p2 <- ggplot(varios, aes(x=dist, y=gamma,  
                         color= as.factor(dir.hor), shape=as.factor(dir.hor)))
p2 <- p2 + expand_limits(y = 0)
p2 <- p2 + geom_point()
p2 <- p2 + geom_line()
p2 <- p2 + ylab("semivariance (nm^2)")
p2 <- p2 + xlab("distance (nm)")
p2 <- p2 + ggtitle("Directional")
p2

## End(Not run)

Description

checkNormality performs a visual check to know if the distribution of heights of an AFMImage follows a normal distribution. The function displays Quantile/Quantile and distribution plots.

Usage

checkNormality(..., AFMImage)

Arguments

Author(s)

M.Beauvais

References

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"

Examples

## Not run: 
library(AFM)

# display Quantile/Quantile and distribution plots.
  data(AFMImageOfNormallyDistributedHeights)
  checkNormality(AFMImage= AFMImageOfNormallyDistributedHeights)

# display and save on disk Quantile/Quantile and distribution plots.
  data(AFMImageOfNormallyDistributedHeights)
  checkNormality(AFMImage= AFMImageOfNormallyDistributedHeights, 
                 pngfullfilename=paste(tempdir(), "checkNormality.png", sep="/"))

## End(Not run)

Description

create the igraph weighted graph from the nodes and edges

Usage

createGraph(AFMImageNetworksAnalysis)

Arguments

Author(s)

M.Beauvais

Description

display network

Usage

displayColoredNetworkWithVerticesSize(AFMImageNetworksAnalysis, fullfilename)

Arguments

Author(s)

M.Beauvais

Description

display the network of nodes and edges

Usage

displaygridIgraphPlot(AFMImageNetworksAnalysis)

Arguments

Author(s)

M.Beauvais

Description

display the network of nodes and edges

Usage

displaygridIgraphPlotFromEdges(AFMImage, edges, isolates)

Arguments

Author(s)

M.Beauvais

Description

Display a 3D image of the holes in an AFMImage and store it on disk if fullfilename variable is set. It uses the rgl package.

Usage

displayHolesIn3D(AFMImage, width, fullfilename, changeViewpoint, noLight)

Arguments

Author(s)

M.Beauvais

Description

Display a 3D image of an AFMImage and store it on disk if fullfilename variable is set. It uses the rgl package.

Usage

displayIn3D(AFMImage, width, fullfilename, changeViewpoint, noLight)

Arguments

Author(s)

M.Beauvais

Description

dnormalmix density of a mixture of normals

Usage

dnormalmix(x, mixture, log = FALSE)

Arguments

Description

evaluateVariogramModels method to evaluate the basic variogram models available in the gstat package A AFMImageVariogramAnalysis method to handle the variogram analysis of an AFMImage. The variogram models used can be seen with the show.vgms() function from the gstat package.

Usage

evaluateVariogramModels(AFMImageVariogramAnalysis, AFMImage)

## S4 method for signature 'AFMImageVariogramAnalysis'
evaluateVariogramModels(AFMImageVariogramAnalysis, AFMImage)

Arguments

Examples

## Not run: 
library(AFM)

data("AFMImageOfRegularPeaks")
# take an extract of the image to fasten the calculation      
AFMImage<-extractAFMImage(AFMImageOfRegularPeaks, 40, 40, 32)
# e.g. AFMImage@fullfilename<-"/users/ubuntu/AFMImageOfRegularPeaks-extract.txt"
AFMImage@fullfilename<-paste(tempdir(), "AFMImageOfRegularPeaks-extract.txt", sep="/")
 
AFMImageAnalyser<-AFMImageAnalyser(AFMImage)
 
 # Variogram analysis 
sampleFitPercentage<-3.43/100
variogramAnalysis<-AFMImageVariogramAnalysis(sampleFitPercentage)
variogramAnalysis@omnidirectionalVariogram<- 
              AFM::calculateOmnidirectionalVariogram(AFMImage=AFMImage,
                                                     AFMImageVariogramAnalysis=variogramAnalysis)
variogramAnalysis@directionalVariograms<- 
              AFM::calculateDirectionalVariograms(AFMImage=AFMImage,
                                                 AFMImageVariogramAnalysis=variogramAnalysis)
 
# manage model evaluations
AFMImageVariogram<-variogramAnalysis@omnidirectionalVariogram
class(AFMImageVariogram)=c("gstatVariogram","data.frame")
variogramAnalysis<-evaluateVariogramModels(variogramAnalysis, AFMImage)

mergedDT<-getDTModelEvaluation(variogramAnalysis)
mergedDT
sillRangeDT<-getDTModelSillRange(variogramAnalysis)
sillRangeDT

## End(Not run)

Description

existsEdge return TRUE if an edge exists for this vertex id

Usage

existsEdge(AFMImage, vertexId)

Arguments

Author(s)

M.Beauvais

Description

existsSegment return a boolean

Usage

existsSegment(AFMImage, segment)

Arguments

Value

TRUE if all the heights of the segment are different from zero

Author(s)

M.Beauvais

Description

Export an AFMImage as a STL format file thanks to the rgl package. The STL file can be used as an input for a 3D printing software tool.

exportToSTL is compatible with slicr (http://slic3r.org) version 1.2.9 (GPL v3 licence).
In order to 3D print the AFM Image with slic3r, do as following:

• Use "File> Repair STL file..." menu option to create a file with the obj extension.

• Use "Add" button below the menu to display your AFM Image on the print board

• Right click on your AFM image. Use "Scale> uniformely" option, Set "15

Usage

exportToSTL(AFMImage3DModelAnalysis, AFMImage, stlfullfilename)

Arguments

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
data("AFMImageOfRegularPeaks")
AFMImage<-AFMImageOfRegularPeaks
# calculate the 3D model : surface and the faces
AFMImage3DModelAnalysis<-new ("AFMImage3DModelAnalysis")
AFMImage3DModelAnalysis<-calculate3DModel(AFMImage3DModelAnalysis= AFMImage3DModelAnalysis,
                                          AFMImage= AFMImage)
# export the 3D model to file
exportDirectory=tempdir()
print(paste("saving model in ", exportDirectory))
exportToSTL(AFMImage3DModelAnalysis=AFMImage3DModelAnalysis,
            AFMImage=AFMImage, 
            stlfullfilename=paste(exportDirectory, "myFile.stl", sep="/"))

## End(Not run)

Description

The extract will be a square of the specified size. If the size is too large for the original AFMImage, only the biggest valid size will be kept.

Usage

extractAFMImage(AFMImage, cornerX, cornerY, size)

Arguments

Details

extractAFMImage returns an extract of the AFMImage

Value

a new AFMImage sample

Author(s)

M.Beauvais

Examples

## Not run: 
  data(AFMImageOfAluminiumInterface)
  anAFMImageExtract<-extractAFMImage(AFMImageOfAluminiumInterface,15,15,256)

## End(Not run)

Description

filterAFMImage returns a filtered AFMImage

Usage

filterAFMImage(AFMImage, Min, Max)

Arguments

Value

an AFMImage

Author(s)

M.Beauvais

Description

manage the fusion of nodes which circles instersect keep all the circles, manage a fusion table node id / fusion id

Usage

fusionCloseNodes(AFMImageNetworksAnalysis)

Arguments

Value

a list of edges with fusioned nodes

Author(s)

M.Beauvais

Description

generateAFMImageReport generates a report from an AFMImageAnalyser object

Usage

generateAFMImageReport(AFMImageAnalyser, reportFullfilename, isCheckReport)

Arguments

Author(s)

M.Beauvais

Description

Generate a check report in pdf format in order to analyse the distribution and the isotropy of heights of the AFMImage.

Usage

generateCheckReport(AFMImage)

Arguments

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

# Analyse the AFMImageOfRegularPeaks AFMImage sample 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)

# Analyse your own AFM image from nanoscope analysis (TM) software tool
   anotherAFMImage<-importFromNanoscope("c:/users/me/myimage.txt")
# Start to check if your sample is normaly distributed and isotropic.
   generateCheckReport(anotherAFMImage)
# If your sample is normaly distributed and isotropic, generate a full report
   generateReport(anotherAFMImage)

## End(Not run)

Description

generate a convex polygon from circles

Usage

generatePolygonEnvelope(AFMImageNetworksAnalysis, centers, radius)

Arguments

Value

a polygon

Author(s)

M.Beauvais

Description

A function to analyse an AFMImage and save on disk the analysis. The analysis are saved in outputs directory located in the image directory. All the rdata and image files in the reportDirectory directory are loaded to generate one report for one AFMImage.

Usage

generateReport(AFMImage)

Arguments

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

# Analyse the AFMImageOfRegularPeaks AFMImage sample 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)


# Analyse your own AFM image from nanoscope analysis (TM) software tool
   anotherAFMImage<-importFromNanoscope("c:/users/my_windows_login/myimage.txt")
# Start to check if your sample is normaly distributed and isotropic.
   generateCheckReport(anotherAFMImage)
# If your sample is normaly distributed and isotropic, generate a full report
   generateReport(anotherAFMImage)

## End(Not run)

Description

A function to generate a pdf report for each AFMImage in a directory. Images should be in export Nanoscope format as the importFromNanoscope function will be used.

Usage

generateReportFromNanoscopeImageDirectory(imageDirectory, imageNumber)

Arguments

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
# A report will be generated for all the images in imageDirectory directory
# imageDirectory="c:/images"
imageDirectory=tempdir()
exit<-generateReportFromNanoscopeImageDirectory(imageDirectory)

# A report will be generated for the fifth image in the imageDirectory directory
exit<-generateReportFromNanoscopeImageDirectory(imageDirectory,5)

## End(Not run)

Description

get 3D image full filename

Usage

get3DImageFullfilename(exportDirectory, imageName)

Arguments

Author(s)

M.Beauvais

Description

get the points inside envelope

Usage

getAllPointsToRemove(AFMImageNetworksAnalysis, envelope)

Arguments

Value

a data.table of points

Author(s)

M.Beauvais

Description

calculate the angle between two vectors

Usage

getAngle(x, y)

Arguments

Value

the angle between the vectors

Author(s)

M.Beauvais

Description

Calculate slopes and intersections in variogram getAutoIntersectionForOmnidirectionalVariogram returns the slope in the omnidirectional variograms

Usage

getAutoIntersectionForOmnidirectionalVariogram(AFMImageAnalyser)

Arguments

Value

an omniVariogramSlopeAnalysis

Author(s)

M.Beauvais

Description

getAutoIntersectionForRoughnessAgainstLengthscale get the intersection between tangente and plateau

Usage

getAutoIntersectionForRoughnessAgainstLengthscale(
  AFMImageAnalyser,
  second_slope = FALSE
)

Arguments

Value

a AFMImagePSDSlopesAnalysis

Author(s)

M.Beauvais

Description

calculate a width to be used for experimental variogram calculation in order to generate a line instead of a cloud of points. If the chosen width is too small, the experimental variogram will be a cloud of points instead of a line.

Usage

getAutomaticWidthForVariogramCalculation(AFMImage)

Arguments

Details

getAutomaticWidthForVariogramCalculation returns the width to be used for variogram calculation

Value

the smallest width to be used for variogram calculation

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

data(AFMImageOfAluminiumInterface)
print(getAutomaticWidthForVariogramCalculation(AFMImageOfAluminiumInterface))

## End(Not run)

Description

getBresenham2DSegment return the Bresenham segment in 2D from extremities coordinates

Usage

getBresenham2DSegment(x1, y1, x2, y2)

Arguments

Value

a data.table of points - data.table(x, y)

Author(s)

M.Beauvais

Description

get the spatial points on the circle including the center of the circle

Usage

getCircleSpatialPoints(binaryAFMImage, center, circleRadius)

Arguments

Value

a SpatialPoints object of all the points of the circle including the center of the circle

Author(s)

M.Beauvais

Description

getCoordinatesFromVertexId return a list x,y coordinates

Usage

getCoordinatesFromVertexId(vId)

Arguments

Author(s)

M.Beauvais

Description

getDTModelEvaluation method

Usage

getDTModelEvaluation(AFMImageVariogramAnalysis)

## S4 method for signature 'AFMImageVariogramAnalysis'
getDTModelEvaluation(AFMImageVariogramAnalysis)

Arguments

Description

getDTModelSillRange method

Usage

getDTModelSillRange(AFMImageVariogramAnalysis)

## S4 method for signature 'AFMImageVariogramAnalysis'
getDTModelSillRange(AFMImageVariogramAnalysis)

Arguments

Description

getFractalDimensions calculates fractal dimensions and scales of an AFMImage with the fd.estim.method from the fractaldim package.

Usage

getFractalDimensions(AFMImage, AFMImageFractalDimensionsAnalysis)

Arguments

Value

a list of AFMImageFractalDimensionMethod objects with the calculated fractal dimensions and scales

Author(s)

M.Beauvais

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'

See Also

fractaldim

Examples

## Not run: 
library(AFM)
data(AFMImageOfAluminiumInterface)
print(getFractalDimensions(AFMImageOfAluminiumInterface))

## End(Not run)

Description

getHolesStatistics returns a binary AFMImage

Usage

getHolesStatistics(AFMImage)

Arguments

Value

an AFMImage

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

data(AFMImageOfAluminiumInterface)
newAFMImage<-copy(AFMImageOfAluminiumInterface)
displayIn3D(newAFMImage,noLight=TRUE)
newAFMImage<-multiplyHeightsAFMImage(newAFMImage, multiplier=2)
displayIn3D(newAFMImage,noLight=TRUE)
newAFMImage<-filterAFMImage(newAFMImage,  Min=140, Max=300)
displayIn3D(newAFMImage,noLight=TRUE)
newAFMImage<-makeBinaryAFMImage(newAFMImage)
displayIn3D(newAFMImage,noLight=TRUE)

holesStats<-getHolesStatistics(newAFMImage)
print(holesStats)

## End(Not run)

Description

getIntersectionForRoughnessAgainstLengthscale get the intersection between tangente and plateau

Usage

getIntersectionForRoughnessAgainstLengthscale(
  AFMImageAnalyser,
  minValue,
  maxValue,
  second_slope = FALSE
)

Arguments

Value

a AFMImagePSDSlopesAnalysis

Author(s)

M.Beauvais

Description

getIntersectionPointWithBorder return a data.table

Usage

getIntersectionPointWithBorder(AFMImage, center, r, deg)

Arguments

Author(s)

M.Beauvais

Description

getListOfDiameters return

Usage

getListOfDiameters(g)

Arguments

Author(s)

M.Beauvais

Description

Get the graph of the Log Log omnidiretction variogram getLogLogOmnidirectionalSlopeGraph returns Get the graph of the Log Log omnidirectional variogram

Usage

getLogLogOmnidirectionalSlopeGraph(AFMImageAnalyser, withFratcalSlope = FALSE)

Arguments

Value

a ggplot2 graph

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
library(ggplot2)

data(AFMImageOfRegularPeaks)

AFMImageAnalyser = new("AFMImageAnalyser",
         fullfilename="/home/ubuntu/AFMImageOfRegularPeaks-Analyser.txt")
variogramAnalysis<-AFMImageVariogramAnalysis(sampleFitPercentage=3.43/100)
AFMImageAnalyser@variogramAnalysis<-variogramAnalysis
AFMImageAnalyser@variogramAnalysis@omnidirectionalVariogram<-
     calculateOmnidirectionalVariogram(AFMImage= AFMImageOfRegularPeaks, 
                                       AFMImageVariogramAnalysis= variogramAnalysis)
p<-getLogLogOmnidirectionalSlopeGraph(AFMImageAnalyser,  withFratcalSlope=TRUE)
p

## End(Not run)

Description

for each pixel of the image, if the pixel is not empty try to place one circle start with biggets circle as soon as a circle is found the circle, the pixel is associated with with the circle raidus

Usage

getMaxCircleMatrix(..., newCircleAFMImage, CIRCLE_RADIUS_INIT)

Arguments

Value

res a matrix

Author(s)

M.Beauvais

Description

getNetworkGridLayout return a list x,y coordinates

Usage

getNetworkGridLayout(AFMImage, vId)

Arguments

Author(s)

M.Beauvais

Description

Get basic network parameters : Total root mean square Roughness or Total Rrms or totalRMSRoughness_TotalRrms
Mean roughness or Ra or MeanRoughness_Ra

Usage

getNetworkParameters(AFMImageNetworksAnalysis, AFMImage)

## S4 method for signature 'AFMImageNetworksAnalysis'
getNetworkParameters(AFMImageNetworksAnalysis, AFMImage)

Arguments

Details

getNetworkParameters returns a data.table of network parameters

Value

a data.table of network parameters:

• totalNumberOfNodes the total number of nodes with degree different of 2

• totalNumberOfNodesWithDegreeTwoOrMore the total number of nodes with degree 2 or more

• totalNumberOfNodesWithDegreeOne the total number of nodes with degree one

• numberOfNodesPerArea the total number of nodes with degree diffrent of 2 per area

• numberOfNodesPerSurfaceArea the total number of nodes with degree diffrent of 2 per surface area

• MeanPhysicalDistanceBetweenNodes the mean physical distance between nodes of degree different of two

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
library(parallel)

data(AFMImageCollagenNetwork)
AFMImage<-AFMImageCollagenNetwork
AFMIA = new("AFMImageNetworksAnalysis")
AFMIA@heightNetworksslider=10
AFMIA@filterNetworkssliderMin=150
AFMIA@filterNetworkssliderMax=300
AFMIA@smallBranchesTreatment=TRUE
clExist<-TRUE
cl <- makeCluster(2,outfile="")
AFMIA<-transformAFMImageForNetworkAnalysis(AFMImageNetworksAnalysis=AFMIA,AFMImage= AFMImage)
AFMIA<-identifyNodesAndEdges(cl=cl,AFMImageNetworksAnalysis= AFMIA,maxHeight= 300)
AFMIA<-identifyEdgesFromCircles(cl=cl,AFMImageNetworksAnalysis= AFMIA, MAX_DISTANCE = 75)
AFMIA<-identifyIsolatedNodes(AFMIA)
AFMIA<-createGraph(AFMIA)
AFMIA<-calculateShortestPaths(cl=cl, AFMImageNetworksAnalysis=AFMIA)
AFMIA<-calculateNetworkParameters(AFMImageNetworksAnalysis=AFMIA, AFMImage=AFMImage)
AFMIA<-calculateHolesCharacteristics(AFMImageNetworksAnalysis=AFMIA)
stopCluster(cl)

## End(Not run)

Description

Get the Nyquist spatial frequency of an AFMImage calculated as following:
0.5 multiplied by the minimum between the horizontal scansize divided by the number of samples per line and the vertical scansize divided by the number of lines

Usage

getNyquistSpatialFrequency(AFMImage)

## S4 method for signature 'AFMImage'
getNyquistSpatialFrequency(AFMImage)

Arguments

Details

getNyquistSpatialFrequency returns the Nyquist spatial frequency as a numeric

Value

the Nyquist spatial frequency of the AFMImage

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

data(AFMImageOfNormallyDistributedHeights)
NyquistSpatialFrequency<-getNyquistSpatialFrequency(AFMImageOfNormallyDistributedHeights)
print(NyquistSpatialFrequency)

## End(Not run)

Description

Get a zero padded AFMImage useful in Power Spectral Density analysis. The original AFMImage is padded with zero in order to get a larger square AFMImage which size is a power of 2.

Usage

getPaddedAFMImage(AFMImage)

Arguments

Value

a zero-padded AFMImage with a fullfilename equals to the original fullfilename pasted with padded-to-"ScanSize".txt

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

data(AFMImageOfNormallyDistributedHeights)
paddedAFMImage<-getPaddedAFMImage(AFMImageOfNormallyDistributedHeights)
displayIn3D(AFMImage= paddedAFMImage, width= 1024,noLight=TRUE)

## End(Not run)

Description

Get basic roughness parameters as amplitude parameters: Total root mean square Roughness or Total Rrms or totalRMSRoughness_TotalRrms
Mean roughness or Ra or MeanRoughness_Ra

Usage

getRoughnessParameters(AFMImage)

## S4 method for signature 'AFMImage'
getRoughnessParameters(AFMImage)

Arguments

Details

getRoughnessParameters returns a data.table of roughness parameters

Value

a data.table of roughness parameters:

• totalRMSRoughness_TotalRrms the total RMS Roughness as the square root of the variance of heights

• MeanRoughness_Ra the average roughness as the mean of absolute value of heights

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

data(AFMImageOfAluminiumInterface)
roughnessParameters<-getRoughnessParameters(AFMImageOfAluminiumInterface)
print(roughnessParameters)

## End(Not run)

Description

get a Lattice (trellis) plot of an AFMImage using the spplot method of the sp package. This function is used to evaluate visually the quality of the predicted surface when a variogram model is used.

Usage

getSpplotFromAFMImage(AFMImage, expectedWidth, expectHeight, withoutLegend)

Arguments

Details

getSpplotFromAFMImage get a Lattice (trellis) plot of an AFMImage on disk

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

data(AFMImageOfAluminiumInterface)
p<-getSpplotFromAFMImage(AFMImageOfAluminiumInterface, 800,800, TRUE)
print(p)

## End(Not run)

Description

getSurroundingVertexesList return the vertexId

Usage

getSurroundingVertexesList(AFMImage, x, y)

Arguments

Author(s)

M.Beauvais

Description

getTopologyAFMImage return the global topological distance

Usage

getTopologyAFMImage(BinaryAFMImage, AFMImageNetworksAnalysis)

Arguments

Author(s)

M.Beauvais

Description

getTriangle return a data.table points of a triangle

Usage

getTriangle(AFMImage, center, r, deg1, deg2)

Arguments

Author(s)

M.Beauvais

Description

getVertexId return the vertexId

Usage

getVertexId(AFMImage, x, y)

Arguments

Author(s)

M.Beauvais

Description

gridIgraphPlot return TRUE if vertex is adjacent to a better vertex

Usage

gridIgraphPlot(AFMImage, g)

Arguments

Author(s)

M.Beauvais

Description

display the network of nodes and edges

Usage

identifyEdgesFromCircles(..., AFMImageNetworksAnalysis, MAX_DISTANCE = 40)

Arguments

Author(s)

M.Beauvais

Description

identify isolated nodes comparing the list of edges and the list of nodes

Usage

identifyIsolatedNodes(AFMImageNetworksAnalysis)

Arguments

Value

the updated instance of AFMImageNetworksAnalysis

Author(s)

M.Beauvais

Description

identifyMaxCircleRadius

Usage

identifyMaxCircleRadius(
  i,
  allXY,
  newCircleAFMImage,
  binaryAFMImageMatrix,
  maxCircleRadiusMatrix,
  circleRadius,
  circlenm
)

Arguments

Value

a data table with x,y,radius columns

Author(s)

M.Beauvais

Description

find nodes and edges

Usage

identifyNodesAndEdges(..., AFMImageNetworksAnalysis, maxHeight)

Arguments

Value

AFMImageNetworksAnalysis a AFMImageNetworksAnalysis

Author(s)

M.Beauvais

Description

identifyNodesWithCircles return TRUE if vertex is adjacent to a better vertex

Usage

identifyNodesWithCircles(..., AFMImageNetworksAnalysis)

Arguments

Value

AFMImageNetworksAnalysis the AFMImageNetworksAnalysis instance

Author(s)

M.Beauvais

Description

The imported file should contain a header and list of heights The header should contain the following fields:

• Lines: number of scanned lines (e.g. 512)

• Sampsline: number of scan per line (e.g. 512)

• ScanSize: the sample size (e.g. 1000nm) the extension nm is mandatory and will be removed

Usage

importFromNanoscope(fullfilename)

Arguments

Details

importFromNanoscope returns an AFMImage

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

fullfilename<-"/user/ubuntu/NanoscopeFlattenExportedFile.txt"
myAFMimage<-importFromNanoscope(fullfilename)
displayIn3D(myAFMimage, width=1024, noLight=TRUE))

## End(Not run)

Description

Constructor method of AFMImageAnalyser Class.

Usage

## S4 method for signature 'AFMImageAnalyser'
initialize(
  .Object,
  AFMImage,
  variogramAnalysis,
  psdAnalysis,
  fdAnalysis,
  gaussianMixAnalysis,
  networksAnalysis,
  threeDimensionAnalysis,
  mean,
  variance,
  TotalRrms,
  Ra,
  fullfilename
)

Arguments

Description

invertBinaryAFMImage returns a binary AFMImage

Usage

invertBinaryAFMImage(AFMImage)

Arguments

Value

an AFMImage

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
data(AFMImageOfAluminiumInterface)
newAFMImage<-copy(AFMImageOfAluminiumInterface)
displayIn3D(newAFMImage,noLight=TRUE)
newAFMImage<-multiplyHeightsAFMImage(newAFMImage, multiplier=2)
displayIn3D(newAFMImage,noLight=TRUE)
newAFMImage<-filterAFMImage(newAFMImage,  Min=140, Max=300)
displayIn3D(newAFMImage,noLight=TRUE)
newAFMImage<-makeBinaryAFMImage(newAFMImage)
displayIn3D(newAFMImage,noLight=TRUE)
newAFMImage<-invertBinaryAFMImage(newAFMImage)
displayIn3D(newAFMImage,noLight=TRUE)

## End(Not run)

Description

isAdjacentToBetterVertex return TRUE if vertex is adjacent to a better vertex

Usage

isAdjacentToBetterVertex(AFMImage, x, y)

Arguments

Author(s)

M.Beauvais

Description

check if all the angles between one edge and a list of edges is superior to a specified value.

Usage

isAngleBetweenEdgesAlwaysSuperiorToMinAngle(
  binaryAFMImage,
  edge1,
  edges2,
  minAngle
)

Arguments

Value

TRUE if all the angle are superior to the specified value

Author(s)

M.Beauvais

Description

isBinary returns TRUE is the heights of the AFMImage is 0 or 1

Usage

isBinary(AFMImage)

Arguments

Value

a boolean

Author(s)

M.Beauvais

Description

loglike sum of density of a mixture of normals

Usage

loglike.normalmix(x, mixture)

Arguments

Description

makeBinaryAFMImage returns a binary AFMImage

Usage

makeBinaryAFMImage(AFMImage)

Arguments

Value

an AFMImage

Author(s)

M.Beauvais

Description

multiplyHeightsAFMImage returns a simplified AFMImage

Usage

multiplyHeightsAFMImage(AFMImage, multiplier)

Arguments

Value

an AFMImage

Author(s)

M.Beauvais

Examples

## Not run: 
data(AFMImageOfAluminiumInterface)
newAFMImage<-multiplyHeightsAFMImage(AFMImageOfAluminiumInterface,10)
displayIn3D(newAFMImage,noLight=TRUE)

## End(Not run)

Description

omniVariogramSlopeAnalysis stores the analysis of the second slope in roughness against lenghtscale

Usage

omniVariogramSlopeAnalysis()

## S4 method for signature 'omniVariogramSlopeAnalysis'
initialize(.Object)

omniVariogramSlopeAnalysis()

Arguments

Slots

intersection_sill

to be removed ?

sill

to be removed ?

slope

to be removed ?

yintersept

to be removed ?

Author(s)

M.Beauvais

Description

performAllPSDCalculation perform all the calculation for PSD exploitation

Usage

performAllPSDCalculation(AFMImagePSDAnalysis, AFMImage)

Arguments

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

data(AFMImageOfNormallyDistributedHeights)

newAFMImage<-AFMImageOfNormallyDistributedHeights
newAFMImage@fullfilename<-"C:/Users/one/AFMImageOfNormallyDistributedHeights.txt"
psdAnalysis<-AFMImagePSDAnalysis()
# Create a closure to update progress
psdAnalysis@updateProgress<- function(value = NULL, detail = NULL, message = NULL) {
  if (exists("progressPSD")){
    if (!is.null(message)) {
      progressPSD$set(message = message, value = 0)
    }else{
      progressPSD$set(value = value, detail = detail)
    }
  }
}
psdAnalysis@psd1d_breaks<-2^3
psdAnalysis@psd2d_truncHighLengthScale<-TRUE
psdAnalysis<-performAllPSDCalculation(AFMImagePSDAnalysis= psdAnalysis, AFMImage= newAFMImage)
print("done psdAnalysis")

## End(Not run)

Description

performGaussianMixCalculation perform all the calculation for PSD exploitation

Usage

performGaussianMixCalculation(AFMImageGaussianMixAnalysis, AFMImage)

Arguments

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

data(AFMImageCollagenNetwork)

AFMImage<-AFMImageCollagenNetwork
AFMImage@fullfilename<-"/Users/one/AFMImageCollagenNetwork.txt"
gMixAnalysis<-AFMImageGaussianMixAnalysis()
# from two components
gMixAnalysis@minGaussianMix<-2
# to four components
gMixAnalysis@maxGaussianMix<-4
# convergence criteria
gMixAnalysis@epsilonGaussianMix<-1e-4
# Create a closure to update progress
gMixAnalysis@updateProgress<- function(value = NULL, detail = NULL, message = NULL) {
  if (exists("progressGaussianMix")){
    if (!is.null(message)) {
      progressGaussianMix$set(message = message, value = 0)
    }else{
      progressGaussianMix$set(value = value, detail = detail)
    }
  }
}
gMixAnalysis<-performGaussianMixCalculation(AFMImageGaussianMixAnalysis= gMixAnalysis, AFMImage)
print("done performGaussianMixCalculation")

## End(Not run)

Description

pnormmix distribution of a mixture of normals

Usage

pnormmix(q, mixture)

Arguments

Description

printVariogramModelEvaluations generates a graphic element containing the evaluation of all variogram models

Usage

printVariogramModelEvaluations(
  AFMImageAnalyser,
  sampleDT,
  numberOfModelsPerPage
)

Arguments

Author(s)

M.Beauvais

Calculate the 1D Power Spectral Density; returns a data table of PSD 1D and PSD 2D values against spatial frequencies.
As mentionned in Sidick2009, this function calculates the PSD against spatial frequencies in 1D from PSD2DAgainstFrequency by using breaks in the log space to sum PSD 2D and frequency values.

Description

Calculate the 1D Power Spectral Density; returns a data table of PSD 1D and PSD 2D values against spatial frequencies.
As mentionned in Sidick2009, this function calculates the PSD against spatial frequencies in 1D from PSD2DAgainstFrequency by using breaks in the log space to sum PSD 2D and frequency values.

Usage

PSD1DAgainstFrequency(AFMImage, AFMImagePSDAnalysis)

## S4 method for signature 'AFMImage'
PSD1DAgainstFrequency(AFMImage, AFMImagePSDAnalysis)

Arguments

Value

PSD1DAgainstFrequency returns a data table of frequencies and PSD values

• freq: the considered frequency

• PSD: the considered PSD value

• type: PSD-1D

• fullfilename: directory and filename on the disk

Examples

## Not run: 
library(AFM)
library(ggplot2)
library(plyr)
library(scales)
data("AFMImageOfNormallyDistributedHeights")
 newAFMImage<-AFMImageOfNormallyDistributedHeights
newAFMImage@fullfilename<-"C:/Users/one/AFMImageOfNormallyDistributedHeights.txt"
psdAnalysis<-AFMImagePSDAnalysis()
# Create a closure to update progress
psdAnalysis@updateProgress<- function(value = NULL, detail = NULL, message = NULL) {
  if (exists("progressPSD")){
   if (!is.null(message)) {
     progressPSD$set(message = message, value = 0)
   }else{
     progressPSD$set(value = value, detail = detail)
   }
  }
}
psdAnalysis@psd1d_breaks<-2^3
psdAnalysis@psd2d_truncHighLengthScale<-TRUE
psdAnalysis<-performAllPSDCalculation(AFMImagePSDAnalysis= psdAnalysis, AFMImage= newAFMImage)
datap<-psdAnalysis@psd1d
p <- ggplot(data=datap)
p <- p + geom_point(aes(freq, PSD, color=type),data=datap[datap$type %in% c("PSD-2D")])
p <- p + geom_line(aes(freq, PSD, color=type),data=datap[datap$type %in% c("PSD-1D")],size=1.1)
p <- p + scale_x_log10()
p <- p + scale_y_log10()
p <- p + ylab("PSD (nm^4)")
p <- p + xlab("Frequency (nm^-1)")
p

## End(Not run)

Description

PSD2DAgainstFrequency returns a data table of PSD 2D values against spatial frequencies

Usage

PSD2DAgainstFrequency(AFMImage, AFMImagePSDAnalysis)

## S4 method for signature 'AFMImage,AFMImagePSDAnalysis'
PSD2DAgainstFrequency(AFMImage, AFMImagePSDAnalysis)

Arguments

Value

PSD2DAgainstFrequency returns a data table of frequencies and PSD values

• freq: the considered frequency

• PSD: the considered PSD value

• type: PSD-2D

• fullfilename: directory and filename on the disk

References

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"

Examples

## Not run: 
library(AFM)
library(ggplot2)
library(plyr)

# Calculate Power Spectrum Density in 2D against frequency
data("AFMImageOfNormallyDistributedHeights")
oneAFMImage<-AFMImageOfNormallyDistributedHeights
psd2d<-PSD2DAgainstFrequency(oneAFMImage)
p <- ggplot(data=psd2d)
p <- p + geom_point(aes(freq, PSD, color=type),subset = .(type %in% c("PSD-2D")))
p <- p + geom_line(aes(freq, PSD, color=type),subset = .(type %in% c("PSD-1D")),size=1.1)
p <- p + scale_x_log10()
p <- p + scale_y_log10()
p <- p + ylab("PSD (nm^4)")
p <- p + xlab("Frequency (nm^-1)")
p <- p + ggtitle(basename(oneAFMImage@fullfilename))
p

## End(Not run)

Description

A function to export to all the data from all analysis of an AFMImage and put them on disk as rdata file

Usage

putAnalysisOnDisk(AFMImageAnalyser, AFMImage)

## S4 method for signature 'AFMImageAnalyser'
putAnalysisOnDisk(AFMImageAnalyser, AFMImage)

Arguments

Author(s)

M.Beauvais

Description

A function to put on disk all the images from variogram, PSD Analysis of an AFMImage An AFM Image 3D representation is saved on disk thanks to the rgl package. On Unix system, it is necessary to have a X server connection to be able to use the rgl package.

Usage

putImagesFromAnalysisOnDisk(AFMImageAnalyser, AFMImage, exportDirectory)

Arguments

Author(s)

M.Beauvais

Description

Find and remove the longuest edge if it is unique

Usage

removeLonguestEdge(k, res, sides, myRes, vertex1)

Arguments

Value

a data.table with from, to

Author(s)

M.Beauvais

Description

remove a node from an AFMImage

Usage

removeNode(circleAFMImage, nodeDT)

Arguments

Value

an AFMImage

Author(s)

M.Beauvais

Description

The calculation of the roughness against lengthscale is performed throught a FFT 2D calculation, PSD 2D calculation and a meshgrid of frequencies. RoughnessByLengthScale returns a data.table of roughnesses against length scales

Usage

RoughnessByLengthScale(AFMImage, AFMImagePSDAnalysis)

## S4 method for signature 'AFMImage'
RoughnessByLengthScale(AFMImage, AFMImagePSDAnalysis)

Arguments

Value

a data table of lenght scale (r) and roughness values (roughness)

• roughness: roughnesses

• r: length scales

• filename: fullfilename slot of the AFMImage

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
library(ggplot2)

data("AFMImageOfNormallyDistributedHeights")
oneAFMImage<-AFMImageOfNormallyDistributedHeights
AFMImagePSDAnalysis<-AFMImagePSDAnalysis()
data<-RoughnessByLengthScale(oneAFMImage, AFMImagePSDAnalysis)
r<-roughness<-filename<-NULL
p1 <- ggplot(data, aes(x=r, y=roughness, colour= basename(filename)))
p1 <- p1 + geom_point()
p1 <- p1 + geom_line()
p1 <- p1 + ylab("roughness (nm)")
p1 <- p1 + xlab("lengthscale (nm)")
p1

## End(Not run)

Description

Launch the AFM shiny graphical interface to access most of the fonctionalities of the AFM library

Usage

runAFMApp()

Author(s)

M.Beauvais

Examples

## Not run: 
install.packages("AFM")
AFM::runAFMApp()

## End(Not run)

Description

Random selection of heights to keep in an AFMImage. This function can be used to calculate quickly an approximated variogram of a large image.

Usage

sampleAFMImage(AFMImage, percentage)

Arguments

Details

sampleAFMImage returns a sample of the AFMImage to boost calculation time of variogram

Value

a sample of an AFMImage

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
library(ggplot2)

data(AFMImageOfAluminiumInterface)
anAFMImageSample<-sampleAFMImage(AFMImageOfAluminiumInterface,15)
variogramAnalysis<-AFMImageVariogramAnalysis(sampleFitPercentage=3.43)
avario<-AFM::calculateOmnidirectionalVariogram(AFMImage= anAFMImageSample, 
                                               AFMImageVariogramAnalysis= variogramAnalysis)
dist<-gamma<-NULL
p1 <- ggplot(avario, aes(x=dist, y=gamma))
p1 <- p1 + geom_point()
p1 <- p1 + geom_line()
p1 <- p1 + ylab("semivariance")
p1 <- p1 + xlab("distance (nm)")
p1 <- p1 + ggtitle("Approximation of variogram thanks to sampling")
p1

## End(Not run)

Description

The function saves the an AFMImage as a rdata file. It uses the fullfilename param of the AFMImage and add "AFMImage.rda" extension to save the rdata file on disk.

Usage

saveOnDisk(AFMImage, exportDirectory)

Arguments

Details

saveOnDisk save on disk an AFMImage as rdata file

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

data(AFMImageOfAluminiumInterface)
# save the rdata file of the AFMImage in the tempdir() directory;
# select another directory to save it permanently on your hard drive
saveOnDisk(AFMImageOfAluminiumInterface, tempdir())

## End(Not run)

Description

saveOnDiskIntersectionForRoughnessAgainstLengthscale save an image of the roughness against lenghtscale calculations

Usage

saveOnDiskIntersectionForRoughnessAgainstLengthscale(
  AFMImageAnalyser,
  exportDirectory
)

Arguments

Author(s)

M.Beauvais

Description

save a Lattice (trellis) plot of an AFMImage using the spplot method of the sp package. This function is used to evaluate visually the quality of the predicted surface when a variogram model is used.

Usage

saveSpplotFromAFMImage(
  AFMImage,
  fullfilename,
  expectedWidth,
  expectHeight,
  withoutLegend
)

Arguments

Details

saveSpplotFromAFMImage save a a Lattice (trellis) plot of an AFMImage on disk

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

data(AFMImageOfAluminiumInterface)
saveSpplotFromAFMImage(AFMImageOfAluminiumInterface,
                       paste(tempdir(), "myFileWithoutLegend.png", sep="/"), 800,800, TRUE)
saveSpplotFromAFMImage(AFMImageOfAluminiumInterface, 
                       paste(tempdir(), "myFileWithLegend.png", sep="/"), 800,800, FALSE)

## End(Not run)

Description

shiftedPSDuv returns the Power Spectral Density matrix in the frequency space from shifted FFT 2D

Usage

shiftedPSDuv(AFMImage)

Arguments

Value

(1/NM^2) * abs(shiftedFFT2Ddata)^2) with N the number of lines of the sample and M the number of samples per line of the sample

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
library(ggplot2)

data(AFMImageOfRegularPeaks)
AFMImage<-AFMImageOfRegularPeaks
nMheightsData= matrix(AFMImage@data$h, nrow=AFMImage@samplesperline)
shiftedPSDuv<-shiftedPSDuv(AFMImage)
a=AFMImage@scansize
b=AFMImage@scansize

M=AFMImage@sampsline
N=AFMImage@lines
NM=N*M # pixels^2
MN = M*N 
A=a*b
ab=a*b

dx=a/M
dy=b/N

um = seq( (1-(M+1)/2)/(M*dx), (M-(M+1)/2)/(M*dx), by=1/(M*dx))
vn = seq( (1-(N+1)/2)/(N*dy), (N-(N+1)/2)/(N*dy), by=1/(N*dy))
x = rep(um, times = AFMImage@lines)
y = rep(vn, each = AFMImage@sampsline)
z = as.vector(shiftedPSDuv)

data<-data.frame(x=x, y=y, z=z)

p5 <- qplot(x, y, data=data, colour=log10(z))
p5 <- p5 + scale_colour_gradientn(colours = rainbow(7))
p5 <- p5 + ylab("v")
p5 <- p5 + xlab("u")
title<-paste("shifted PSD of", basename(AFMImage@fullfilename))
p5 <- p5 + ggtitle(title)
# Hide all the horizontal gridlines
p5 <- p5 + theme(panel.grid.minor.x=element_blank(), panel.grid.major.x=element_blank())
# Hide all the vertical gridlines
p5 <- p5 + theme(panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank())
p5 <- p5 + theme(panel.background = element_rect(fill = 'white', colour = 'black'))
p5

## End(Not run)

Description

shiftFFT2D returns the FFT 2D matrix shifted to put zero frequencies in the middle.

Usage

shiftFFT2D(fft2data)

Arguments

Value

The shifted matrix

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)
library(fftwtools)

data(AFMImageOfNormallyDistributedHeights)
AFMImage<-AFMImageOfNormallyDistributedHeights
nMheightsData= matrix(AFMImage@data$h, nrow=AFMImage@samplesperline)
shiftedFFT2D<-shiftFFT2D(fftwtools::fftw2d(nMheightsData))

## End(Not run)

Description

The simplification is taking a very simple gridded sample of the image. It can be useful to speed up display.

Usage

simplifyAFMImage(AFMImage, newSamplesperline, newLines)

Arguments

Details

simplifyAFMImage returns a simplified AFMImage

Value

a new simplified AFMImage

Author(s)

M.Beauvais

Examples

## Not run: 
  data(AFMImageOfAluminiumInterface)
  anAFMImageExtract<-simplifyAFMImage(AFMImageOfAluminiumInterface,16,16)

## End(Not run)

Description

simplify the network keeping only the important edges

Usage

simplifyNetwork(..., allVertices, allEdges)

Arguments

Value

a data.table of edges

Author(s)

M.Beauvais

Description

thin an Image in matrix format

Usage

thinImage(imageMatrix)

Arguments

Author(s)

M.Beauvais

Description

totalRMSRoughness returns the total RMS roughness calculated from the variance of heights

Usage

totalRMSRoughness(AFMImage)

Arguments

Value

a numeric as the square root of the variance of heights

Author(s)

M.Beauvais

Examples

## Not run: 
library(AFM)

data(AFMImageOfAluminiumInterface)
totalRMSRoughness<-totalRMSRoughness(AFMImageOfAluminiumInterface)
print(totalRMSRoughness)

## End(Not run)

Description

transformAFMImageForNetworkAnalysis update AFMImageNetworksAnalysis making a binary AFMImage

Usage

transformAFMImageForNetworkAnalysis(AFMImageNetworksAnalysis, AFMImage)

## S4 method for signature 'AFMImageNetworksAnalysis'
transformAFMImageForNetworkAnalysis(AFMImageNetworksAnalysis, AFMImage)

Arguments

Author(s)

M.Beauvais

Description

is a function used by a GUI such as shiny GUI

Usage

updateProgress(AFMImageVariogramAnalysis, value, detail, message)

Arguments