--- a/segmentation.xml	Wed Mar 25 05:43:05 2020 -0400
+++ b/segmentation.xml	Thu Apr 23 08:09:32 2020 -0400
@@ -1,12 +1,11 @@
-<tool id="cardinal_segmentations" name="MSI segmentation" version="@VERSION@.3">
+<tool id="cardinal_segmentations" name="MSI segmentation" version="@VERSION@.0">
     <description>mass spectrometry imaging spatial clustering</description>
     <macros>
         <import>macros.xml</import>
     </macros>
     <expand macro="requirements">
         <requirement type="package" version="2.3">r-gridextra</requirement>
-        <requirement type="package" version="0.20_35">r-lattice</requirement>
-    </expand>
+      </expand>
     <command detect_errors="exit_code">
     <![CDATA[
 
@@ -19,17 +18,14 @@
     <configfiles>
         <configfile name="MSI_segmentation"><![CDATA[
 
-
 ################################# load libraries and read file #################
 
 library(Cardinal)
 library(gridExtra)
-library(lattice)
-
 
+@READING_MSIDATA@
 
-@READING_MSIDATA_INRAM@
-
+       msidata = as(msidata, "MSImageSet") ##coercion to MSImageSet
 
 ## remove duplicated coordinates
 msidata <- msidata[,!duplicated(coord(msidata))]
@@ -61,21 +57,6 @@
         #set $color_string = ','.join(['"%s"' % $color.feature_color for $color in $colours])
         colourvector = c($color_string)
 
-        ### preparation for images and plots:
-        #if str($image_type) == "standard_image":
-            print("standard image")
-
-            strip_input = FALSE
-            lattice_input = FALSE
-
-        #elif str($image_type) == "lattice_image":
-            print("lattice image")
-
-            strip_input = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))
-            lattice_input = TRUE
-
-        #end if
-
         ## set seed to make analysis reproducible
         set.seed($setseed)
 
@@ -105,11 +86,11 @@
             colnames(sd_table)[1] = "Principal components"
             grid.table(sd_table, rows=NULL)
             ### images in pdf file
-            print(image(pca_result, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector, ylim=c(maximumy+2, minimumy-2)))
+            print(image(pca_result, main="PCA image", strip = FALSE, col=colourvector, ylim=c(maximumy+2, minimumy-2)))
             for (PCs in 1:$segm_cond.pca_ncomp){
-                print(image(pca_result, column = c(paste0("PC",PCs)), lattice=lattice_input,strip = strip_input, superpose = FALSE, main=paste0("PC", PCs), col.regions = risk.colors(100), ylim=c(maximumy+2, minimumy-2)))}
+                print(image(pca_result, column = c(paste0("PC",PCs)),strip = FALSE, superpose = FALSE, main=paste0("PC", PCs), col.regions = risk.colors(100), ylim=c(maximumy+2, minimumy-2)))}
             ### plots in pdf file
-            print(plot(pca_result, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input))
+            print(plot(pca_result, main="PCA plot", col= colourvector, strip = FALSE))
             for (PCs in 1:$segm_cond.pca_ncomp){
                 print(plot(pca_result, column = c(paste0("PC",PCs)),main=paste0("PC", PCs),strip = FALSE,superpose = FALSE))}
 
@@ -149,8 +130,8 @@
             rm(msidata)
             gc()
 
-            print(image(skm, key=TRUE, main="K-means clustering", lattice=lattice_input, strip=strip_input, col= colourvector, layout=c(1,1), ylim=c(maximumy+2, minimumy-2)))
-            print(plot(skm, main="K-means plot", lattice=lattice_input, col= colourvector, strip=strip_input, layout=c(1,1)))
+            print(image(skm, key=TRUE, main="K-means clustering", strip=FALSE, col= colourvector, layout=c(1,1), ylim=c(maximumy+2, minimumy-2)))
+            print(plot(skm, main="K-means plot", col= colourvector, strip=FALSE, layout=c(1,1)))
 
             skm_clusters = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))
             for (iteration in 1:length(skm@resultData)){
@@ -167,7 +148,7 @@
             skm_clusters2 = data.frame(pixel_names, x_coordinates, y_coordinates, skm_clusters)
             colnames(skm_clusters2) = c("pixel names", "x", "y",names(skm@resultData))
 
-            skm_toplabels = topLabels(skm, n=$segm_cond.kmeans_toplabels)
+            skm_toplabels = topFeatures(skm, n=$segm_cond.kmeans_toplabels)
 
             write.table(skm_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
             write.table(skm_clusters2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
@@ -188,9 +169,9 @@
             ## remove msidata to clean up RAM space
             rm(msidata)
             gc()
-            print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=lattice_input, strip = TRUE, col= colourvector,layout=c(1,1), ylim=c(maximumy+2, minimumy-2)))
-            print(plot(ssc, main="Spatial shrunken centroids plot", lattice=lattice_input, col= colourvector, strip = TRUE,layout=c(1,1)))
-            print(plot(ssc, mode = "tstatistics",key = TRUE, lattice=lattice_input, layout = c(1,1), main="t-statistics", col=colourvector))
+            print(image(ssc, key=TRUE, main="Spatial shrunken centroids", strip = TRUE, col= colourvector,layout=c(1,1), ylim=c(maximumy+2, minimumy-2)))
+            print(plot(ssc, main="Spatial shrunken centroids plot", col= colourvector, strip = TRUE,layout=c(1,1)))
+            print(plot(ssc, mode = "tstatistics",key = TRUE, layout = c(1,1), main="t-statistics", col=colourvector))
 
             plot(summary(ssc), main = "Number of segments")
 
@@ -209,7 +190,7 @@
             ssc_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, ssc_classes)
             colnames(ssc_classes2) = c("pixel names", "x", "y", names(ssc@resultData))
 
-            ssc_toplabels =  topLabels(ssc, n=$segm_cond.centroids_toplabels)
+            ssc_toplabels =  topFeatures(ssc, n=$segm_cond.centroids_toplabels)
 
             write.table(ssc_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
             write.table(ssc_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
@@ -313,8 +294,6 @@
                        label="Number of toplabels (m/z) which should be written in tabular output"/>
                 </when>
             </conditional>
-            <param name="image_type" type="boolean" checked="True" truevalue="standard_image" falsevalue="lattice_image" 
-            label="Standard image" help="No: lattice function is used to display image"/>
             <param name="svg_pixelimage" type="boolean" label="Export first segmentation image as svg"/>
             <repeat name="colours" title="Colours for the plots" min="1" max="50">
                 <param name="feature_color" type="color" label="Colours" value="#ff00ff" help="Numbers of colours should be the same as number of components">
@@ -343,7 +322,6 @@
         <test>
             <expand macro="infile_imzml"/>
             <param name="segmentationtool" value="pca"/>
-            <param name="image_type" value="lattice_image"/>
             <repeat name="colours">
                 <param name="feature_color" value="#ff00ff"/>
             </repeat>
@@ -353,7 +331,8 @@
             <output name="segmentationimages" file="pca_imzml.pdf" compare="sim_size"/>
             <output name="mzfeatures">
                 <assert_contents>
-                    <has_text text="300.17" />
+                    <has_text text="300.1667" />
+                    <has_text text="300.25" />
                     <has_text text="-4.234458e-04" />
                     <has_text text="3.878545e-10" />
                     <has_n_columns n="3" />
@@ -362,7 +341,7 @@
             <output name="pixeloutput" file="scores_pca.tabular"/>
         </test>
         <test>
-            <expand macro="infile_analyze75"/>
+            <expand macro="infile_imzml"/>
             <param name="segmentationtool" value="kmeans"/>
             <param name="kmeans_r" value="1:3"/>
             <param name="kmeans_k" value="2,3"/>
@@ -397,16 +376,38 @@
             <repeat name="colours">
                 <param name="feature_color" value="#B0171F"/>
             </repeat>
-            <repeat name="colours">
-                <param name="feature_color" value="#FFD700"/>
-            </repeat>
-            <repeat name="colours">
-                <param name="feature_color" value="#848484"/>
-            </repeat>
             <output name="segmentationimages" file="centroids_rdata.pdf" compare="sim_size"/>
             <output name="mzfeatures" file="toplabels_ssc.tabular"/>
             <output name="pixeloutput" file="classes_ssc.tabular"/>
         </test>
+        <test>
+            <param name="infile" value="" ftype="imzml">
+                <composite_data value="preprocessing_results3.imzml"/>
+                <composite_data value="preprocessing_results3.ibd"/>
+            </param>
+            <conditional name="processed_cond">
+                <param name="processed_file" value="processed"/>
+                <param name="accuracy" value="1"/>
+                <param name="units" value="mz"/>
+            </conditional>
+            <param name="segmentationtool" value="centroids"/>
+            <param name="centroids_r" value="1"/>
+            <param name="centroids_k" value="2,3"/>
+            <param name="centroids_s" value="0,3"/>
+            <param name="centroids_toplabels" value="100"/>
+            <repeat name="colours">
+                <param name="feature_color" value="#0000FF"/>
+            </repeat>
+            <repeat name="colours">
+                <param name="feature_color" value="#00C957"/>
+            </repeat>
+            <repeat name="colours">
+                <param name="feature_color" value="#B0171F"/>
+            </repeat>
+            <output name="segmentationimages" file="centroids_proc.pdf" compare="sim_size"/>
+            <output name="mzfeatures" file="toplabels_proc.tabular"/>
+            <output name="pixeloutput" file="classes_proc.tabular"/>
+        </test>
     </tests>
     <help>
         <![CDATA[