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Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/msi_segmentation commit ed7d3e6f1a09c78c8f71cc1bdc1a20249767f646 |
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modified:
segmentation_tool.xml test-data/centroids_imzml.pdf test-data/classes_ssc.tabular test-data/cluster_skm.tabular test-data/kmeans_imzml.pdf test-data/loadings_pca.tabular test-data/pca_imzml.pdf test-data/scores_pca.tabular test-data/toplabels_skm.tabular test-data/toplabels_ssc.tabular |
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| diff -r d4158c9955ea -r f66c5789deac segmentation_tool.xml --- a/segmentation_tool.xml Thu Mar 01 08:26:19 2018 -0500 +++ b/segmentation_tool.xml Sun Mar 11 10:39:01 2018 -0400 |
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| b'@@ -1,4 +1,4 @@\n-<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.7.0.1">\n+<tool id="mass_spectrometry_imaging_segmentations" name="MSI segmentation" version="1.7.0.2">\n <description>tool for spatial clustering</description>\n <requirements>\n <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement>\n@@ -180,7 +180,7 @@\n pca = PCA(msidata, ncomp=$segm_cond.pca_ncomp, column = component_vector, superpose = FALSE, \n method = "$segm_cond.pca_method", scale = $segm_cond.pca_scale, layout = c(ncomp, 1))\n \n- print(image(pca, main="PCA image", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.pca_imagecontrast", smooth.image = "$segm_cond.pca_imagesmoothing", col=colourvector, ylim=c(maximumy+2, 0)))\n+ print(image(pca, main="PCA image", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col=colourvector, ylim=c(maximumy+2, 0)))\n print(plot(pca, main="PCA plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))))\n \n \n@@ -194,13 +194,19 @@\n print(\'kmeans\')\n ##k-means\n \n- skm = spatialKMeans(msidata, r=$segm_cond.kmeans_r, k=$segm_cond.kmeans_k, method="$segm_cond.kmeans_method")\n- print(image(skm, key=TRUE, main="K-means clustering", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.kmeans_imagecontrast", col= colourvector, smooth.image = "$segm_cond.kmeans_imagesmoothing", ylim=c(maximumy+2, 0)))\n- print(plot(skm, main="K-means plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))))\n+ skm = spatialKMeans(msidata, r=c($segm_cond.kmeans_r), k=c($segm_cond.kmeans_k), method="$segm_cond.kmeans_method")\n+ print(image(skm, key=TRUE, main="K-means clustering", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), col= colourvector, ylim=c(maximumy+2, 0), layout=c(1,1)))\n+\n+ print(plot(skm, main="K-means plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), layout=c($segm_cond.kmeans_layout)))\n \n \n- skm_clusters = (skm@resultData\\$r\\$cluster)\n- skm_toplabels = topLabels(skm, n=500)\n+ skm_clusters = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))\n+ for (iteration in 1:length(skm@resultData)){\n+ skm_cluster = ((skm@resultData)[[iteration]]\\$cluster)\n+ skm_clusters = cbind(skm_clusters, skm_cluster) }\n+ colnames(skm_clusters) = names((skm@resultData)) \n+\n+ skm_toplabels = topLabels(skm, n=$segm_cond.kmeans_toplabels)\n \n write.table(skm_toplabels, file="$mzfeatures", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\\t")\n write.table(skm_clusters, file="$pixeloutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\\t")\n@@ -210,12 +216,17 @@\n print(\'centroids\')\n ##centroids\n \n- ssc = spatialShrunkenCentroids(msidata, r=$segm_cond.centroids_r, k=$segm_cond.centroids_k, s=$segm_cond.centroids_s, method="$segm_cond.centroids_method")\n- print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=TRUE, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9)), contrast.enhance = "$segm_cond.centroids_imagecontrast", col= colourvector, smooth.image = "$segm_cond.centroids_imagesmoothing", ylim=c(maximumy+2, 0)))\n- print(plot(ssc, main="Spatial shrunken centroids plot", lattice=TRUE, col= colourvector, strip = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))))\n+ '..b'r (r)." help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>\n <param name="centroids_k" type="text" value="5"\n- label="The initial number of clusters (k)."/>\n- <param name="centroids_s" type="integer" value="2"\n+ label="The initial number of clusters (k)." help="Multiple values are allowed (e.g. 1,2,3 or 2:5)"/>\n+ <param name="centroids_s" type="text" value="2"\n label="The sparsity thresholding parameter by which to shrink the t-statistics (s)."\n- help="As s increases, fewer mass features (m/z values) will be used in the spatial segmentation, and only the informative mass features will be retained."/>\n+ help="As s increases, fewer mass features (m/z values) will be used in the spatial segmentation, and only the informative mass features will be retained. Multiple values are allowed (e.g. 1,2,3 or 2:5)."/>\n <param name="centroids_method" type="select" display="radio" label = "The method to use to calculate the spatial smoothing kernels for the embedding. The \'gaussian\' method refers to spatially-aware (SA) weights, and \'adaptive\' refers to spatially-aware structurally-adaptive (SASA) weights.">\n <option value="gaussian" selected="True">gaussian</option>\n <option value="adaptive">adaptive</option>\n </param>\n- <param name="centroids_imagecontrast" type="select" label="Select a contrast enhancement function." help="The \'histogram\' equalization method flatterns the distribution of intensities. The hotspot \'suppression\' method uses thresholding to reduce the intensities of hotspots">\n- <option value="none" selected="True">none</option>\n- <option value="suppression">suppression</option>\n- <option value="histogram">histogram</option>\n- </param>\n- <param name="centroids_imagesmoothing" type="select" label="Select an image smoothing function." help="The \'gaussian\' smoothing method smooths images with a simple gaussian kernel. The \'adaptive\' method uses bilateral filtering to preserve edges.">\n- <option value="none" selected="True">none</option>\n- <option value="gaussian">gaussian</option>\n- <option value="adaptive">adaptive</option>\n- </param>\n+ <param name="centroids_toplabels" type="integer" value="500"\n+ label="Number of toplabels (masses) which should be written in tabular output"/>\n+ <param name="centroids_layout" type="text" value="1,1"\n+ label="Number of rows and columns to plot pictures in pdf output" help="e.g. 1,1 means 1 plot per page; 2,3 means 2 rows with 3 plots each = 6 plots per page"/>\n </when>\n </conditional>\n <repeat name="colours" title="Colours for the plots" min="1" max="50">\n@@ -352,6 +341,8 @@\n <composite_data value="Analyze75.t2m" />\n </param>\n <param name="segmentationtool" value="kmeans"/>\n+ <param name="kmeans_r" value="1:3"/>\n+ <param name="kmeans_k" value="2,3"/>\n <repeat name="colours">\n <param name="feature_color" value="#ff00ff"/>\n </repeat>\n@@ -368,6 +359,9 @@\n <test>\n <param name="infile" value="preprocessing_results1.RData" ftype="rdata"/>\n <param name="segmentationtool" value="centroids"/>\n+ <param name="centroids_r" ftype="text" value="1,2"/>\n+ <param name="centroids_k" ftype="text" value="5"/>\n+ <param name="centroids_toplabels" ftype="integer" value="100"/>\n <repeat name="colours">\n <param name="feature_color" value="#0000FF"/>\n </repeat>\n' |
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| diff -r d4158c9955ea -r f66c5789deac test-data/centroids_imzml.pdf |
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| Binary file test-data/centroids_imzml.pdf has changed |
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| diff -r d4158c9955ea -r f66c5789deac test-data/classes_ssc.tabular --- a/test-data/classes_ssc.tabular Thu Mar 01 08:26:19 2018 -0500 +++ b/test-data/classes_ssc.tabular Sun Mar 11 10:39:01 2018 -0400 |
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| @@ -1,10 +1,10 @@ - x -x = 1, y = 1 1 -x = 2, y = 1 1 -x = 3, y = 1 1 -x = 1, y = 2 1 -x = 2, y = 2 1 -x = 3, y = 2 1 -x = 1, y = 3 1 -x = 2, y = 3 1 -x = 3, y = 3 1 + r = 1, k = 5, s = 2 r = 2, k = 5, s = 2 +x = 1, y = 1 1 1 +x = 2, y = 1 1 1 +x = 3, y = 1 1 1 +x = 1, y = 2 1 1 +x = 2, y = 2 1 1 +x = 3, y = 2 1 1 +x = 1, y = 3 1 1 +x = 2, y = 3 1 1 +x = 3, y = 3 1 1 |
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| diff -r d4158c9955ea -r f66c5789deac test-data/cluster_skm.tabular --- a/test-data/cluster_skm.tabular Thu Mar 01 08:26:19 2018 -0500 +++ b/test-data/cluster_skm.tabular Sun Mar 11 10:39:01 2018 -0400 |
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| @@ -1,10 +1,10 @@ - x -x = 1, y = 1 3 -x = 2, y = 1 1 -x = 3, y = 1 1 -x = 1, y = 2 1 -x = 2, y = 2 1 -x = 3, y = 2 1 -x = 1, y = 3 2 -x = 2, y = 3 1 -x = 3, y = 3 1 + r = 1, k = 2 r = 1, k = 3 r = 2, k = 2 r = 2, k = 3 r = 3, k = 2 r = 3, k = 3 +x = 1, y = 1 2 3 2 3 2 3 +x = 2, y = 1 2 2 2 2 2 2 +x = 3, y = 1 2 2 2 2 2 2 +x = 1, y = 2 2 2 2 2 2 2 +x = 2, y = 2 2 2 2 2 2 2 +x = 3, y = 2 2 2 2 2 2 2 +x = 1, y = 3 1 1 1 1 1 1 +x = 2, y = 3 2 2 2 2 2 2 +x = 3, y = 3 2 2 2 2 2 2 |
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| diff -r d4158c9955ea -r f66c5789deac test-data/kmeans_imzml.pdf |
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| Binary file test-data/kmeans_imzml.pdf has changed |
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| diff -r d4158c9955ea -r f66c5789deac test-data/loadings_pca.tabular --- a/test-data/loadings_pca.tabular Thu Mar 01 08:26:19 2018 -0500 +++ b/test-data/loadings_pca.tabular Sun Mar 11 10:39:01 2018 -0400 |
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| b'@@ -1,8400 +1,8400 @@\n \tPC1\tPC2\n-m/z = 100.08\t-5.85811444155841e-19\t4.67563645911017e-19\n-m/z = 100.17\t-1.59215272704497e-18\t1.29545556462352e-18\n-m/z = 100.25\t1.63451392689733e-18\t-1.31474090407241e-18\n-m/z = 100.33\t2.48270941597418e-19\t-2.14471732080631e-19\n-m/z = 100.42\t2.34894314620814e-18\t-1.90016179215182e-18\n-m/z = 100.5\t-7.45181009466368e-19\t6.10956625723651e-19\n-m/z = 100.58\t-0.000104656106492355\t-0.000353453375099747\n-m/z = 100.67\t-0.00191806412960809\t-0.00647784695026132\n-m/z = 100.75\t-0.00180844223665621\t-0.00610762270489928\n-m/z = 100.83\t0.000360645067163724\t-0.00196796460781247\n-m/z = 100.92\t0.0149012861337974\t-0.00227595239712745\n-m/z = 101\t0.0175435591487301\t0.0143023652391708\n-m/z = 101.08\t0.031002747135816\t-0.0122614203884686\n-m/z = 101.17\t0.0338611449231095\t-0.0223356162474472\n-m/z = 101.25\t0.00673689843629035\t-0.00469328645894402\n-m/z = 101.33\t-6.07248591968161e-06\t7.65206415618374e-07\n-m/z = 101.42\t-8.7285605006811e-09\t-2.94788261457361e-08\n-m/z = 101.5\t3.45834782551742e-07\t1.20261345070282e-06\n-m/z = 101.58\t3.23622451834329e-07\t1.12537180515484e-06\n-m/z = 101.67\t3.86117478477127e-06\t-2.37941054624113e-06\n-m/z = 101.75\t1.16030815032369e-06\t-8.04388710715516e-07\n-m/z = 101.83\t1.00752913826428e-07\t-7.12295619838279e-08\n-m/z = 101.92\t-1.83947514156733e-12\t-6.21243790795034e-12\n-m/z = 102\t-4.17262048378754e-14\t-1.40918758230853e-13\n-m/z = 102.08\t3.30266968225588e-11\t1.1484777208885e-10\n-m/z = 102.17\t2.71286071777003e-10\t-1.38524373101001e-10\n-m/z = 102.25\t1.61761088728649e-10\t-1.07254855324628e-10\n-m/z = 102.33\t2.76943739598385e-11\t-1.9315695760279e-11\n-m/z = 102.42\t-2.89670759294901e-16\t-9.89919504461857e-16\n-m/z = 102.5\t-3.57673851057773e-17\t-1.13137838709792e-16\n-m/z = 102.58\t2.02847312745712e-15\t7.05305368422956e-15\n-m/z = 102.67\t1.45995928099974e-15\t5.08080235002388e-15\n-m/z = 102.75\t1.81355645931963e-14\t-1.13407706336188e-14\n-m/z = 102.83\t4.99451434889525e-15\t-3.46493805095179e-15\n-m/z = 102.92\t3.5590609930103e-16\t-2.53221869805701e-16\n-m/z = 103\t-6.65271632395851e-19\t5.01880339138763e-19\n-m/z = 103.08\t-5.29182193398412e-20\t4.24219607913396e-20\n-m/z = 103.17\t-1.08308254372137e-18\t1.53090778014904e-18\n-m/z = 103.25\t2.0140172965681e-18\t-1.23428494228165e-18\n-m/z = 103.33\t6.5948747628826e-19\t-4.31502445015621e-19\n-m/z = 103.42\t1.15829285209295e-18\t-9.18578323874381e-19\n-m/z = 103.5\t7.64996557530185e-20\t-6.11499914645595e-20\n-m/z = 103.58\t-3.96376219386837e-19\t3.22209858475563e-19\n-m/z = 103.67\t-6.18158027538271e-19\t5.21850678040856e-19\n-m/z = 103.75\t2.51212447788151e-19\t-2.04539641072483e-19\n-m/z = 103.83\t-1.13118208565757e-18\t9.23065173685343e-19\n-m/z = 103.92\t8.59022328826729e-19\t-7.03173001569609e-19\n-m/z = 104\t6.42791081949006e-20\t-5.09471219107003e-20\n-m/z = 104.08\t-7.73702193533547e-19\t6.35560525449952e-19\n-m/z = 104.17\t4.17934916777221e-19\t-3.36591203136874e-19\n-m/z = 104.25\t-8.01082847078073e-19\t6.33870455154861e-19\n-m/z = 104.33\t1.31679769529789e-18\t-1.09457644857517e-18\n-m/z = 104.42\t8.53652984445634e-19\t-7.09046174103633e-19\n-m/z = 104.5\t-1.32729897350366e-18\t1.06101657501984e-18\n-m/z = 104.58\t-1.10949121723327e-18\t8.86382197332373e-19\n-m/z = 104.67\t-1.26009825490333e-18\t1.01767561272778e-18\n-m/z = 104.75\t1.00700454926584e-18\t-7.99676621006995e-19\n-m/z = 104.83\t2.63226402912176e-19\t-2.0886467463537e-19\n-m/z = 104.92\t7.28099510855941e-19\t-5.89019122279884e-19\n-m/z = 105\t-1.16136322796224e-18\t9.33375216908331e-19\n-m/z = 105.08\t1.4329085782091e-19\t-1.11920860620786e-19\n-m/z = 105.17\t-6.04272845469955e-19\t4.88221747854317e-19\n-m/z = 105.25\t-1.55764714886487e-18\t1.25482364948323e-18\n-m/z = 105.33\t2.28470713117219e-18\t-1.8431367473114e-18\n-m/z = 105.42\t4.24073781198572e-19\t-3.43761034406124e-19\n-m/z = 105.5\t3.14983624201331e-19\t-2.53533350409124e-19\n-m/z = 105.58\t-6.52620969795775e-19\t5.32146736817344e-19\n-m/z = 105.67\t4.2548701251123e-19\t-3.45126802720901e-19\n-m/z = 105.75\t1.03961531671925e-18\t-8.37212825377058e-19\n-m/z = 105.83\t5.66167234663928e-19\t-4.43164714765139e-19\n-m/z = 105.92\t-1.008908644046'..b'65761e-07\n+m/z = 794.08\t1.02067038585652e-07\t-1.45258975023365e-07\n+m/z = 794.17\t1.12379272158978e-08\t-1.5993505949875e-08\n+m/z = 794.25\t3.25812731867457e-19\t1.6261766047749e-19\n+m/z = 794.33\t5.35425380748341e-19\t2.58574782171827e-19\n+m/z = 794.42\t6.29525885946667e-19\t2.56199158226666e-19\n+m/z = 794.5\t-7.4313801194953e-20\t-3.34143002183519e-20\n+m/z = 794.58\t0.00477541680078603\t-0.00754050838457745\n+m/z = 794.67\t-0.00457278398695877\t-0.0426132474217616\n+m/z = 794.75\t-0.00833966396316628\t-0.0346155215563304\n+m/z = 794.83\t-0.000966644754810593\t-0.00353481494848324\n+m/z = 794.92\t0.00195096214440752\t0.00658284942421866\n+m/z = 795\t0.000640470913415025\t0.00216305205292817\n+m/z = 795.08\t5.99410062227021e-05\t0.000202437790458378\n+m/z = 795.17\t5.40140143890875e-07\t-7.687124536724e-07\n+m/z = 795.25\t-7.01767360338712e-07\t-3.35591597300985e-06\n+m/z = 795.33\t-3.18791439545305e-07\t-1.20612425912571e-06\n+m/z = 795.42\t8.91159484680883e-08\t2.96886191630594e-07\n+m/z = 795.5\t8.73188753129299e-08\t2.94900624757485e-07\n+m/z = 795.58\t1.56512048733412e-08\t5.28585609802919e-08\n+m/z = 795.67\t3.01232744247483e-11\t-4.28706072043245e-11\n+m/z = 795.75\t-1.48510033919398e-11\t-1.72677972498098e-10\n+m/z = 795.83\t-3.8506092248427e-11\t-1.57275898001786e-10\n+m/z = 795.92\t-9.04270379006492e-12\t-3.14452966353805e-11\n+m/z = 796\t9.44016200568508e-12\t3.18744875921646e-11\n+m/z = 796.08\t2.76488420740076e-12\t9.33779850436432e-12\n+m/z = 796.17\t2.15724230034952e-13\t7.2856266096936e-13\n+m/z = 796.25\t2.58184007910726e-15\t-3.67510829197051e-15\n+m/z = 796.33\t-3.49410120861527e-15\t-1.61265568105337e-14\n+m/z = 796.42\t-1.40388561254596e-15\t-5.24732395729428e-15\n+m/z = 796.5\t5.43523012384417e-16\t1.81818107314495e-15\n+m/z = 796.58\t3.90988783626378e-16\t1.31962921392625e-15\n+m/z = 796.67\t0.0111966558774574\t-0.00184642752951289\n+m/z = 796.75\t0.00616492950080405\t-0.00101665137094283\n+m/z = 796.83\t0.00094732255957378\t-0.000156221864141374\n+m/z = 796.92\t1.33798566132764e-19\t-5.7871504335021e-19\n+m/z = 797\t1.42162270674517e-19\t-5.27679788010646e-20\n+m/z = 797.08\t-9.28249269025108e-20\t8.85230209237438e-20\n+m/z = 797.17\t6.18529780122341e-08\t-1.02001028374736e-08\n+m/z = 797.25\t7.22222614326619e-07\t-1.19100893349313e-07\n+m/z = 797.33\t1.81894222730583e-07\t-2.99959652223831e-08\n+m/z = 797.42\t9.21039873740566e-09\t-1.51887616885188e-09\n+m/z = 797.5\t-1.15785643540274e-19\t-5.15910338733918e-20\n+m/z = 797.58\t-1.73756988718108e-19\t-7.36943752159947e-20\n+m/z = 797.67\t-3.19437877280519e-19\t-1.35634162941306e-19\n+m/z = 797.75\t6.19645964447919e-11\t-1.02185098227732e-11\n+m/z = 797.83\t2.72928146751876e-11\t-4.50082644497942e-12\n+m/z = 797.92\t0.00389097733292418\t-0.00553753088718732\n+m/z = 798\t0.00407514724498003\t-0.00579963639658735\n+m/z = 798.08\t0.000963732393772138\t-0.00137155718100157\n+m/z = 798.17\t3.59243592475198e-05\t-5.11265505001434e-05\n+m/z = 798.25\t1.58464294301486e-15\t-2.61336252801625e-16\n+m/z = 798.33\t3.32775773826793e-15\t-5.4879277748818e-16\n+m/z = 798.42\t7.7058874379474e-16\t-1.27217824944827e-16\n+m/z = 798.5\t3.69626374554758e-07\t-5.26041992106029e-07\n+m/z = 798.58\t-5.18525840962872e-05\t-0.000407429722098976\n+m/z = 798.67\t-0.00374645335939354\t-0.0293383545235392\n+m/z = 798.75\t-0.00291920523455583\t-0.022860064548501\n+m/z = 798.83\t-0.000533036946002739\t-0.00417417002686778\n+m/z = 798.92\t-4.60535879673083e-20\t-9.84469151432828e-20\n+m/z = 799\t1.3950420874914e-11\t-1.98538513123533e-11\n+m/z = 799.08\t1.86620413371336e-11\t-2.65593003406967e-11\n+m/z = 799.17\t4.06033648088994e-12\t-5.77855752803845e-12\n+m/z = 799.25\t-3.15016300887882e-07\t-2.46686834032189e-06\n+m/z = 799.33\t-9.32606479026281e-08\t-7.30316733349827e-07\n+m/z = 799.42\t-7.5857517202454e-09\t-5.94034197796886e-08\n+m/z = 799.5\t-1.14469887468129e-19\t-5.14614581306986e-20\n+m/z = 799.58\t-0.00015220686092269\t-0.00119191985006001\n+m/z = 799.67\t-0.00174853153576808\t-0.0136926117080108\n+m/z = 799.75\t-0.00121053975241958\t-0.00947964074305674\n+m/z = 799.83\t-0.000239952374183483\t-0.00187904800165073\n+m/z = 799.92\t-1.24635044100415e-18\t-5.24231857283351e-19\n' |
| b |
| diff -r d4158c9955ea -r f66c5789deac test-data/pca_imzml.pdf |
| b |
| Binary file test-data/pca_imzml.pdf has changed |
| b |
| diff -r d4158c9955ea -r f66c5789deac test-data/scores_pca.tabular --- a/test-data/scores_pca.tabular Thu Mar 01 08:26:19 2018 -0500 +++ b/test-data/scores_pca.tabular Sun Mar 11 10:39:01 2018 -0400 |
| b |
| @@ -1,10 +1,10 @@ PC1 PC2 -x = 1, y = 1 -3.10354953946921 -0.882256622295146 -x = 2, y = 1 0.896503112130722 2.98575442188366 -x = 3, y = 1 -1.57899570254884 -2.26797345312228 -x = 1, y = 2 4.349387430717 6.43242810521547 -x = 2, y = 2 -4.25927282262867 0.298723132409784 -x = 3, y = 2 -3.62418135901872 -0.62786843932794 -x = 1, y = 3 -3.27840077885159 1.98430745878087 -x = 2, y = 3 -1.38097137864787 -4.74863282848963 -x = 3, y = 3 11.9794810383172 -3.17448177505483 +x = 1, y = 1 3.10354953946922 0.882256622295189 +x = 2, y = 1 -0.896503112130735 -2.9857544218837 +x = 3, y = 1 1.57899570254884 2.26797345312228 +x = 1, y = 2 -4.349387430717 -6.43242810521545 +x = 2, y = 2 4.25927282262866 -0.298723132409806 +x = 3, y = 2 3.62418135901873 0.627868439327952 +x = 1, y = 3 3.2784007788516 -1.9843074587809 +x = 2, y = 3 1.38097137864786 4.74863282848964 +x = 3, y = 3 -11.9794810383172 3.17448177505481 |
| b |
| diff -r d4158c9955ea -r f66c5789deac test-data/toplabels_skm.tabular --- a/test-data/toplabels_skm.tabular Thu Mar 01 08:26:19 2018 -0500 +++ b/test-data/toplabels_skm.tabular Sun Mar 11 10:39:01 2018 -0400 |
| b |
| b'@@ -1,501 +1,501 @@\n 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|
| b |
| diff -r d4158c9955ea -r f66c5789deac test-data/toplabels_ssc.tabular --- a/test-data/toplabels_ssc.tabular Thu Mar 01 08:26:19 2018 -0500 +++ b/test-data/toplabels_ssc.tabular Sun Mar 11 10:39:01 2018 -0400 |
| b |
| b'@@ -1,501 +1,101 @@\n 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|