diff --git a/scripts/Unsupervised_analysis.Rmd b/scripts/Unsupervised_analysis.Rmd
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--- /dev/null
+++ b/scripts/Unsupervised_analysis.Rmd
@@ -0,0 +1,185 @@
+---
+title: "Philologie computationnelle - TP Chrétien"
+author: "JBC & FC"
+date: "2023-2024"
+output:
+  pdf_document: default
+  word_document: default
+  html_document: default
+---
+
+
+```{r}
+source("functions.R")
+```
+
+
+# Data 
+
+## Load texts
+
+```{r}
+data = t(as.matrix(read.csv("chrestien_fw_abs.csv", row.names = 1, header = TRUE)[, c(-1,-2)]))
+auts = read.csv("chrestien_fw_abs.csv", row.names = 1, header = TRUE)[, 1]
+data = data[rowSums(data) > 0,]
+colnames(data) = gsub(pattern = "000", replacement = "k", colnames(data))
+data_abs_save = data
+```
+
+
+## Features extraction, culling and selection
+
+Now, we will use survey method to keep only reliable features,
+
+```{r}
+# select = selection(data, z = 1.645)
+# select = select[,4]
+# # Get relative frequencies
+# data_abs_save = data
+# data = relativeFreqs(data)
+# # keep only reliable features
+# data = data[select,]
+```
+
+
+# Dimensionality reduction
+
+## Principal component analysis (PCA)
+
+Let's use `FactoMineR`.
+
+```{r, fig.width=10, fig.height=10, dpi=45}
+# Load FactoMineR and factoextra for better visuals
+library('FactoMineR')
+library('factoextra')
+# And compute ACP. As the function expects individuals as lines and variables as columns, I'm transposing it
+myCA = CA(t(data)[1:79,], graph = FALSE)
+```
+
+I can now look at the significance of my axes.
+
+```{r}
+myCA$eig
+```
+or plot it
+```{r}
+barplot(myCA$eig[,2], main="%age of var", names.arg=1:nrow(myCA$eig))
+```
+Contribution:
+```{r}
+sort(myCA$col$contrib[,1], decreasing = TRUE)
+```
+
+```{r, fig.width=10, fig.height=10, dpi=45}
+#fviz_ca_row(myCA, geom=c("text"), labelsize=2)
+fviz_ca_row(myCA, col.row = sub("\\_.*", "", rownames(myCA$row$coord)), title="", geom=c("point"), labelsize=2)
+#fviz_ca_col(myCA, col.col = sub("\\_.*", "", rownames(myCA$col$coord)), title="", geom=c("point", "text"), labelsize=2)
+```
+
+
+# Clustering
+
+## Selecting features
+
+## Weighting
+
+In oppositions to analyses like Correspondance analysis, that perform their own kind of 'centering' of the variables, here, we will do two types of normalisations:
+
+- variables (rows) with z-scores;
+- individuals (columns) with vector length normalisation (Euclidean norm).
+
+```{r}
+data = normalisations(data)
+```
+
+And let's go.
+
+## Clustering methods: hierarchical clustering
+
+
+```{r, fig.width=20, fig.height=10, dpi=45}
+library(cluster)
+maCAH = agnes(t(data), metric = "manhattan", method="ward")
+#plot(maCAH, which.plots = 2)
+# Et une fonction un peu plus esthétique
+maCAH_save = maCAH
+cahPlotCol(maCAH, k = 3)
+```
+
+
+# Class description
+
+
+### Creating groups: cutting an AHC
+
+If we want to describe a classification, in terms of the specificities of each group, several solutions are possible. We can start from supervised clustering from K-means or K-medoids, or from the cut of a CAH.
+
+The number of classes should be chosen according to the dendrogram, and a height graph can be used (corresponding to the heights at which the branches of the dendrogram are separated).
+
+
+```{r}
+maCAH = maCAH_save
+maCAH2 = as.hclust(maCAH)
+plot(maCAH2$height, type="h", ylab="heights")
+#cahPlotCol(maCAH, k = 4)
+```
+
+
+And let's cut
+
+```{r}
+#Je découpe en classes
+classes = cutree(maCAH, k = "4") # ="4"
+#J'ajoute les classes à mon tableau
+myClassif = t(data)
+myClassif = cbind(as.data.frame(myClassif), as.factor(classes))
+colnames(myClassif[length(myClassif)])[] = "Classes"
+```
+
+
+```{r}
+myClassif[length(myClassif)]
+```
+
+### Describing classes
+
+Let's try to understand now what put _Istorietta_ and _Novella_ in the same class.
+
+
+Let's do a v-test,
+
+```{r, fig.width=20, fig.height=10, dpi=45}
+#Je charge FactoMineR
+library(FactoMineR)
+#Et je décrit les classes
+mesClasses = catdes(myClassif, num.var = length(myClassif))
+# $\eta^2$
+mesClasses$quanti.var[1:20,]
+# v-test pour la classe 1
+mesClasses$quanti$`2`
+plot(mesClasses, level = 0.01, barplot = TRUE)
+```
+
+```{r}
+classesDesc(maCAH, data, k = 4)
+```
+
+It is also possible to use other computations of specificities, like Lafon (1984), that needs absolute frequencies.
+
+```{r}
+# install.packages("textometry")
+freq_abs = data_abs_save
+# On splitte le corpus selon les classes
+freq_abs_class = matrix(nrow = nrow(freq_abs), ncol = length(unique(classes)), dimnames = list(rownames(freq_abs), unique(classes)))
+for(i in 1:length(unique(classes))){
+  # sum the values for each member of the class
+  freq_abs_class[, i] = rowSums(freq_abs[, classes == i])
+}
+specs = textometry::specificities(freq_abs_class)
+# and now we can look at the specificities for class 1 
+# positive or negative
+head(sort(specs[, 2], decreasing = TRUE))
+head(sort(specs[, 2]))
+```
+
+And one can use other varieties of non-parametric tests, such as the Mann-Whitney test, for example, to compare two populations...
diff --git a/scripts/Unsupervised_analysis.pdf b/scripts/Unsupervised_analysis.pdf
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diff --git a/scripts/chrestien_fw_abs.csv b/scripts/chrestien_fw_abs.csv
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--- /dev/null
+++ b/scripts/chrestien_fw_abs.csv
@@ -0,0 +1,135 @@
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+ChrTr_Cliges_21000-24000,ChrTr,NA,218,196,155,78,94,110,58,77,35,11,42,48,5,27,29,48,30,15,26,25,24,17,30,18,13,6,18,7,8,3,1,17,11,1,8,8,5,2,13,9,4,7,7,2,2,1,4,0,7,14,1,4,7,8,3,1,4,2,0,3,2,5,0,0,3,0,1,7,7,1,3,1,0,1,2,1,1,0,0,2,4,9,3,1,2,0,1,1,1,1,0,0,0,1,0,0,7,0,0,1,1,0,0,2,2,1,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,2,2,2,0,0,0,0,0,1,0,2,0,2,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,66,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0
+ChrTr_Cliges_24000-27000,ChrTr,NA,128,182,131,74,117,149,76,79,22,72,25,45,28,28,34,29,17,20,11,26,31,21,28,28,17,17,22,15,3,12,5,9,13,0,11,17,11,5,6,7,4,5,6,2,0,1,1,6,6,9,2,0,4,3,5,1,5,2,2,0,1,6,7,0,4,0,2,5,3,1,1,1,6,3,4,0,3,0,2,6,0,4,1,3,2,0,2,1,2,1,0,3,2,1,4,1,0,0,0,0,4,3,1,0,5,2,1,2,2,0,0,0,0,0,0,0,2,0,0,0,2,0,1,0,1,1,0,1,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,1,0,2,0,0,0,78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Cliges_27000-30000,ChrTr,NA,204,158,136,99,90,117,69,58,44,24,49,39,5,16,24,45,22,16,30,21,17,12,35,21,12,10,15,13,6,0,11,12,22,7,18,8,10,1,5,8,5,7,5,8,0,3,2,0,13,9,6,1,5,2,5,2,9,8,3,0,3,3,3,2,6,0,1,4,6,3,1,1,0,9,3,0,6,1,3,4,1,11,1,4,2,0,1,0,1,3,1,0,0,1,1,0,3,0,0,2,3,9,2,0,2,0,1,4,0,2,0,0,1,0,0,0,0,2,1,3,0,1,0,0,3,0,0,0,0,0,6,0,0,0,1,1,2,1,0,1,2,3,1,1,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,0,0,0,3,0,1,0,93,0,1,0,0,5,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Cliges_30000-33000,ChrTr,NA,117,178,122,71,95,160,60,76,39,90,27,23,53,18,36,35,11,18,15,14,33,18,32,27,20,13,27,9,8,0,32,8,17,8,16,24,3,3,6,9,5,4,4,4,4,3,7,17,0,9,7,5,6,2,7,0,3,2,4,0,0,5,7,2,4,2,9,1,4,1,1,1,5,1,4,0,1,0,0,5,1,2,2,3,3,3,0,1,3,2,0,0,1,1,7,1,3,0,0,3,2,3,0,2,1,0,1,4,1,0,0,0,0,3,0,0,2,0,0,0,0,1,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,1,2,2,0,0,0,2,1,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,0,104,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Cliges_33000-36000,ChrTr,NA,151,188,134,59,110,140,56,60,40,44,35,25,27,21,40,37,17,21,8,14,25,18,26,30,9,15,17,17,7,19,19,8,11,14,16,21,17,3,2,10,5,10,1,4,0,4,9,7,4,13,2,2,9,1,8,0,4,3,4,5,1,8,3,1,2,2,6,1,2,1,0,2,8,1,2,2,2,1,2,3,0,2,3,4,6,0,1,0,1,4,1,0,2,1,1,1,0,0,1,0,2,4,0,1,1,3,0,2,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,5,0,0,0,0,2,0,0,1,0,0,1,1,0,1,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,79,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,4,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Cliges_36000-39000,ChrTr,NA,194,174,155,71,115,133,58,59,39,31,27,10,21,15,32,38,23,15,10,18,21,24,17,35,16,5,21,15,11,3,11,10,14,6,18,11,3,11,4,12,0,5,2,11,2,5,8,9,4,3,7,2,5,1,5,0,5,4,1,1,0,15,0,6,1,0,10,1,2,0,2,0,2,3,1,3,3,4,3,0,0,0,3,2,1,4,1,0,0,6,1,3,0,1,0,2,0,1,0,3,3,2,0,3,0,3,0,4,0,0,0,2,2,0,0,2,0,0,0,0,2,0,0,0,1,0,1,1,2,2,0,1,1,1,2,1,1,0,0,1,0,1,0,0,1,0,0,0,0,0,1,0,0,1,1,3,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,2,0,0,0,87,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0
+ChrTr_Erec_0-3000,ChrTr,NA,245,141,110,89,85,75,69,64,25,47,37,40,14,18,27,28,26,17,15,18,11,32,13,31,14,8,8,16,17,0,7,10,14,2,10,6,10,4,8,4,11,4,6,7,0,2,2,4,13,2,1,4,1,6,4,0,1,1,1,0,3,3,4,1,0,3,3,3,3,0,1,3,2,6,3,1,0,0,3,0,2,1,1,1,3,0,0,0,0,1,5,1,1,0,1,0,3,1,2,1,2,1,0,4,1,1,0,1,0,1,0,0,0,5,1,1,1,1,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,1,1,1,0,0,0,1,1,1,0,0,2,0,0,0,0,0,0,0,0,1,0,2,0,0,0,99,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0
+ChrTr_Erec_3000-6000,ChrTr,NA,223,167,105,91,72,94,58,54,20,53,31,30,29,21,33,37,26,16,23,17,21,29,20,19,10,3,9,16,8,15,19,10,9,3,6,10,16,1,6,9,6,4,3,8,2,1,5,10,5,7,0,4,4,4,6,0,2,0,2,1,2,10,6,5,2,7,0,2,4,1,1,5,4,2,1,0,3,0,3,3,2,1,1,4,1,0,2,1,1,2,2,2,3,0,0,2,0,0,0,2,0,2,1,3,1,1,0,3,0,0,1,2,3,1,1,0,0,2,1,1,0,0,2,0,2,0,1,0,1,0,1,0,1,0,0,0,1,3,1,2,1,0,0,3,1,0,0,4,0,0,0,0,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,7,0,0,0,96,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Erec_6000-9000,ChrTr,NA,185,130,130,81,61,73,58,76,21,56,34,46,49,18,22,33,27,12,14,19,22,31,13,28,17,2,7,6,13,10,31,9,14,4,13,3,6,3,3,12,3,15,11,3,1,6,12,15,8,5,6,5,5,4,6,0,2,0,5,4,3,5,2,3,2,0,1,1,0,1,1,2,3,1,10,2,3,0,2,1,0,2,4,1,0,1,0,0,0,5,0,1,2,2,1,1,1,0,1,2,1,1,1,0,0,0,1,1,1,1,1,1,0,0,2,0,1,2,0,1,1,0,0,0,5,0,1,2,1,0,0,0,0,0,0,2,2,1,1,0,0,1,1,4,0,0,0,5,0,0,1,1,0,1,3,0,1,0,0,0,0,0,0,0,0,2,6,1,0,0,0,0,0,0,0,0,0,0,0,2,92,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Erec_9000-12000,ChrTr,NA,255,130,148,143,73,96,58,66,19,26,41,50,21,20,27,15,23,15,14,9,6,28,7,33,16,11,6,8,9,0,11,14,19,2,17,1,9,3,6,21,3,3,3,8,0,0,1,1,4,5,0,3,3,8,6,0,1,1,1,0,1,6,1,3,1,0,0,1,3,0,0,2,1,1,3,0,0,1,5,1,0,3,3,2,2,0,1,0,3,4,1,0,1,0,1,0,4,0,1,1,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,1,0,0,1,0,0,0,0,0,2,1,2,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,91,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,6,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Erec_12000-15000,ChrTr,NA,198,138,141,161,69,69,66,64,23,13,29,45,7,17,32,42,24,19,23,28,5,28,14,30,11,11,6,33,1,0,3,11,25,3,17,1,1,6,3,8,5,13,4,5,1,6,3,0,13,0,9,7,3,6,6,0,4,2,0,0,1,4,1,1,3,2,2,1,4,5,0,0,2,2,1,2,1,2,3,3,3,0,2,4,3,1,0,0,8,2,1,0,2,3,2,2,2,0,0,0,3,0,1,0,2,1,1,3,0,0,0,0,1,1,2,0,1,0,1,0,2,0,5,0,2,0,0,0,1,0,2,0,1,0,0,0,1,0,3,1,0,0,0,0,1,0,0,1,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,6,0,0,0,73,0,0,0,1,1,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Erec_15000-18000,ChrTr,NA,141,189,102,78,108,116,58,77,38,87,20,24,59,20,26,43,25,5,19,18,23,24,23,15,15,5,6,12,8,9,24,21,12,1,13,13,5,1,8,15,7,4,3,4,0,3,6,11,9,3,2,5,1,4,4,0,0,2,6,5,0,7,1,5,6,3,1,5,2,1,2,0,11,3,1,1,2,1,2,2,3,2,1,3,2,0,1,1,1,2,3,2,1,1,0,1,1,0,1,0,1,0,0,1,1,2,0,2,0,0,0,0,3,2,0,0,1,1,1,1,1,0,0,0,1,1,1,0,0,0,0,1,0,0,2,2,2,1,2,0,0,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,73,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0
+ChrTr_Erec_18000-21000,ChrTr,NA,163,185,106,80,87,105,61,69,34,52,20,40,61,14,26,38,12,13,16,13,21,28,20,25,8,9,7,11,11,2,23,7,9,0,8,9,6,3,8,8,9,8,7,3,2,0,5,13,10,8,1,4,3,1,6,0,4,4,4,0,0,8,2,1,0,2,0,4,1,1,1,5,6,4,3,0,1,0,4,2,1,4,1,2,6,3,2,0,0,0,2,5,3,1,0,4,2,0,0,1,1,0,1,2,2,2,0,2,0,0,1,0,2,1,1,0,0,4,0,0,1,0,0,0,2,0,0,0,0,3,1,1,1,0,0,2,0,1,1,0,3,3,1,1,0,0,1,2,0,0,0,1,1,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,1,0,0,0,2,0,0,0,89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0
+ChrTr_Erec_21000-24000,ChrTr,NA,179,138,119,103,99,97,40,64,26,74,32,33,44,9,25,49,27,18,22,17,18,28,17,24,10,12,14,19,9,5,23,10,17,1,10,8,7,4,13,4,8,11,5,1,1,3,4,8,13,4,3,4,2,3,6,0,4,1,6,0,2,6,2,3,1,3,2,2,1,2,0,4,4,6,2,0,2,2,5,2,3,0,0,1,0,0,1,0,1,2,5,4,4,2,2,1,1,0,2,4,1,2,0,5,0,1,0,1,1,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,1,0,1,4,4,0,0,1,2,1,0,0,1,1,0,0,1,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,4,0,0,0,79,0,0,0,0,0,0,0,0,1,0,0,2,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0
+ChrTr_Erec_24000-27000,ChrTr,NA,160,175,127,66,79,71,57,80,36,69,20,36,56,12,25,26,17,25,11,22,22,30,15,15,15,7,7,14,5,9,26,6,7,4,8,12,6,4,7,8,2,6,8,1,0,1,5,21,9,6,5,6,1,8,3,0,5,1,12,1,1,5,5,4,2,3,0,3,0,2,0,5,3,4,9,1,2,2,3,2,0,3,1,1,0,1,1,0,1,2,1,1,2,2,1,2,0,0,3,2,2,3,0,4,0,1,0,0,2,1,0,0,0,1,3,1,0,0,2,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,1,1,3,1,1,1,0,1,0,1,0,0,1,1,0,0,1,0,0,0,1,1,0,0,0,0,0,0,1,0,0,1,2,0,0,0,0,1,0,1,0,0,5,0,0,0,71,0,0,1,1,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,1,0,1,0,0,0,0,6,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0
+ChrTr_Erec_27000-30000,ChrTr,NA,157,156,115,73,80,112,54,68,33,53,35,46,48,16,27,39,23,12,16,18,25,16,20,16,17,10,13,14,13,14,16,6,7,8,15,11,10,1,8,14,5,3,2,1,0,5,6,6,8,9,4,5,2,3,5,1,2,3,4,5,2,9,5,2,6,1,2,6,2,5,1,2,4,7,1,2,0,1,3,1,1,2,1,1,1,0,3,0,2,3,2,2,0,2,2,1,3,0,2,2,2,1,0,2,1,0,0,1,0,0,0,1,2,1,0,1,1,1,0,0,1,0,0,0,2,1,0,0,0,0,0,1,0,0,2,1,0,3,1,0,1,0,2,0,0,0,0,1,0,0,0,1,0,2,0,1,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,90,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0
+ChrTr_Erec_30000-33000,ChrTr,NA,154,152,142,89,86,86,65,66,23,50,40,34,36,26,30,19,24,16,9,23,19,29,19,33,6,8,20,16,11,3,4,13,15,5,15,3,7,9,11,15,3,2,4,3,6,2,1,8,2,7,2,5,3,4,2,0,3,1,6,2,1,8,0,1,3,0,0,2,2,1,12,3,1,2,2,1,0,1,3,5,0,2,3,5,0,0,3,0,6,2,0,2,4,0,1,2,2,0,5,2,3,1,1,2,0,1,1,4,1,1,0,2,1,0,1,1,2,0,0,0,3,0,0,0,2,0,0,0,0,0,0,0,3,1,1,0,0,1,0,1,0,0,0,4,0,1,1,0,0,0,2,0,0,1,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,2,0,0,0,92,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Erec_33000-36000,ChrTr,NA,155,124,132,81,113,113,60,69,34,68,47,20,52,24,38,38,23,18,13,14,31,21,32,24,5,9,17,23,2,9,18,9,25,1,16,9,11,3,7,9,2,13,5,5,0,3,2,12,9,5,3,2,3,2,8,0,9,1,6,5,1,8,1,4,6,4,4,5,5,0,2,2,4,5,3,2,2,2,2,2,0,1,1,4,3,2,0,0,4,1,2,1,6,2,2,1,2,0,2,2,4,1,1,2,6,4,1,3,1,1,0,3,0,0,2,2,0,0,0,1,1,0,0,0,1,0,4,1,2,1,1,0,0,0,1,0,1,1,1,0,0,0,1,1,1,0,1,2,0,0,0,0,0,1,0,1,1,1,0,0,2,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,2,0,0,0,86,0,0,1,0,1,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Erec_36000-39000,ChrTr,NA,167,168,156,99,99,105,75,71,38,53,37,42,28,19,28,35,18,14,19,15,8,26,25,21,9,16,8,15,4,0,18,11,18,5,19,4,10,6,10,7,4,6,5,5,3,4,5,4,3,5,4,5,2,7,7,0,5,5,1,0,2,7,3,3,0,0,0,2,6,4,3,3,4,4,6,1,0,0,3,5,2,1,5,7,1,0,0,0,4,4,0,0,1,1,3,2,1,0,2,4,1,1,0,0,0,1,0,1,1,0,0,0,0,2,0,0,0,0,1,1,0,0,0,0,3,0,1,0,0,2,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,5,0,0,1,91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+GerbM_4eContv1_0-3000,GerbM,NA,169,109,113,109,80,83,67,78,39,40,41,23,33,18,29,28,46,39,8,15,16,27,28,13,13,7,10,34,3,18,8,6,16,3,11,5,8,2,7,11,7,2,5,4,3,4,4,6,1,8,4,4,7,1,5,0,4,1,2,2,2,4,3,2,8,1,8,1,6,0,1,0,2,5,3,1,0,4,3,2,0,0,0,2,8,0,0,0,5,1,2,5,0,0,3,1,1,2,0,1,1,1,2,0,3,2,0,0,1,1,0,4,2,4,1,0,0,0,4,2,0,0,2,0,2,1,2,1,1,0,0,1,2,0,0,0,0,2,0,1,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,97,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,3,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
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+JnRen_Rose_27000-30000,JnRen,NA,180,175,86,105,105,87,72,64,55,48,36,61,21,26,26,26,38,11,27,26,19,25,14,14,8,10,2,4,19,5,21,9,19,5,7,6,14,3,8,15,5,17,2,5,0,7,4,7,4,6,4,5,5,3,7,1,8,1,4,0,0,1,4,5,2,0,2,6,1,1,4,1,1,2,1,2,0,0,1,2,0,2,1,0,3,2,2,0,4,1,3,5,1,1,1,0,1,2,3,1,1,1,0,0,0,0,0,0,0,1,1,0,1,2,0,1,1,1,0,1,0,0,0,0,11,0,0,0,2,0,0,0,3,0,0,1,0,0,0,1,0,1,1,0,0,1,1,1,0,4,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1,1,0,0,0,0,0,1,0,0,0,90,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,3,0,0,1,0,1,0,1,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0
+JnRen_Rose_30000-33000,JnRen,NA,178,163,82,97,99,92,63,82,34,65,39,48,27,36,31,24,18,27,25,37,16,19,11,31,15,13,2,12,5,1,34,6,8,7,7,7,21,10,11,16,4,9,2,5,0,3,9,7,5,5,2,4,10,0,11,1,4,2,2,0,3,3,1,3,2,0,4,2,5,1,1,0,1,2,0,6,1,0,2,3,1,1,0,2,1,1,2,0,1,0,3,3,0,0,1,1,0,0,0,0,1,0,0,0,2,0,0,1,0,2,0,2,5,0,0,0,0,0,0,4,1,0,3,0,1,2,0,0,1,1,1,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,98,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,1,0,0,0,0,9,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ANO_Philom_0-3000,ANO,NA,125,171,138,94,121,116,63,62,22,63,30,60,45,28,29,23,23,13,22,22,36,21,23,16,15,7,19,15,8,0,24,6,23,1,11,9,8,5,8,9,0,7,5,3,0,0,3,9,2,7,2,6,13,0,3,0,4,0,0,0,1,6,6,1,4,0,2,2,3,4,2,0,2,3,5,4,1,1,4,1,1,0,3,3,3,2,2,1,1,2,0,1,5,2,1,1,3,0,0,0,3,2,0,0,0,0,1,0,1,0,3,0,1,1,0,1,2,0,1,2,1,0,1,0,5,1,2,1,1,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,85,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,0,2,0,1,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ANO_Philom_3000-6000,ANO,NA,106,166,126,100,117,115,59,66,37,63,35,44,43,39,28,36,15,22,19,44,19,23,27,6,25,15,20,19,13,15,29,5,15,1,13,20,11,2,3,9,2,5,6,4,0,5,4,7,2,7,3,2,7,0,3,0,3,4,1,1,0,12,4,2,11,1,3,2,3,2,5,3,8,9,4,7,0,0,3,2,1,2,1,1,3,1,0,0,0,1,1,3,1,2,0,0,1,0,0,0,2,0,1,0,1,0,0,0,0,0,1,2,3,2,0,1,0,0,0,0,2,0,0,0,10,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,69,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0
+ANO_Philom_6000-9000,ANO,NA,150,211,122,88,120,127,59,70,45,23,30,71,9,18,37,32,23,14,23,18,15,21,23,23,25,17,21,10,8,14,5,4,17,3,9,11,3,3,6,10,5,13,6,5,1,8,5,1,1,2,5,10,2,1,9,0,6,5,5,3,0,8,4,1,4,0,7,1,2,3,6,5,7,4,4,1,0,2,0,2,0,0,0,2,1,0,0,0,1,1,2,2,1,2,1,2,1,3,0,0,3,0,2,0,0,0,0,0,0,0,0,4,1,0,1,0,2,0,0,0,0,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,2,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,68,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0
+ChrTr_Lancel_0-3000,ChrTr,NA,183,171,132,64,107,91,72,90,49,58,36,20,27,30,25,32,19,18,23,18,14,23,27,31,12,21,8,13,9,27,9,8,7,3,4,8,9,1,13,13,5,6,6,3,0,7,4,7,9,8,5,7,6,11,0,0,11,4,2,3,1,8,7,2,3,3,2,3,4,3,1,3,1,3,9,0,1,1,2,3,0,1,2,3,2,2,1,1,2,3,1,0,3,2,1,1,0,0,0,3,3,1,1,1,1,0,1,1,2,0,1,0,1,1,0,1,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,2,3,2,0,1,0,2,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,71,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Lancel_3000-6000,ChrTr,NA,214,202,150,64,98,87,47,84,62,74,24,21,37,11,21,32,28,30,49,14,22,27,20,20,20,16,4,15,8,21,7,13,8,3,4,7,3,1,11,5,3,5,7,3,0,5,12,3,9,4,5,8,5,1,6,0,5,1,2,7,1,4,2,1,2,2,3,6,3,1,0,3,1,2,1,2,3,2,3,4,0,1,0,2,4,0,3,1,0,1,2,1,5,3,2,0,1,1,0,2,2,0,1,1,0,5,0,2,2,0,1,0,0,1,0,0,4,0,2,0,0,0,0,0,4,0,0,0,2,0,1,1,0,0,4,1,0,2,3,1,0,1,0,0,1,0,1,2,0,0,0,4,0,0,0,0,1,0,0,0,1,0,0,0,1,1,1,0,0,0,0,1,0,0,0,0,2,0,1,0,64,0,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0
+ChrTr_Lancel_6000-9000,ChrTr,NA,169,195,142,76,107,109,52,77,67,68,24,31,40,13,23,51,35,17,46,18,27,22,16,12,22,10,8,17,4,6,9,12,10,2,7,8,11,2,8,9,2,5,15,3,0,3,8,2,6,9,4,1,3,3,7,1,9,0,7,1,2,8,4,1,3,1,11,0,2,4,0,4,1,4,3,2,4,0,5,4,0,2,1,2,2,2,2,1,0,3,2,1,4,2,1,4,2,0,0,2,1,0,0,4,0,0,2,3,2,1,0,3,0,1,0,2,2,0,0,0,2,0,1,0,2,1,0,0,0,1,1,1,0,3,4,0,0,2,0,1,0,2,0,0,0,0,0,2,0,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,2,0,65,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0
+ChrTr_Lancel_9000-12000,ChrTr,NA,137,178,138,70,116,116,59,79,37,88,33,29,43,29,27,25,21,18,32,26,24,20,25,30,16,10,6,16,2,25,17,10,20,7,12,13,10,5,7,21,3,5,11,8,0,3,8,7,9,6,2,4,5,7,4,1,4,2,7,4,1,11,4,4,1,0,2,4,2,5,0,7,3,5,2,1,5,4,1,3,1,4,1,2,7,4,0,0,2,4,2,3,1,0,0,1,3,0,0,3,3,2,2,1,3,1,0,2,3,5,0,0,0,0,1,0,2,0,2,1,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,4,0,0,0,1,0,0,1,1,1,0,0,4,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,2,0,0,0,2,0,0,0,2,0,2,0,61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0
+ChrTr_Lancel_12000-15000,ChrTr,NA,184,193,151,76,89,80,75,69,41,39,45,30,36,14,19,37,32,10,34,13,18,26,17,36,15,9,11,18,3,9,8,10,6,11,4,10,18,4,8,9,4,8,3,3,5,7,6,5,9,8,1,5,5,13,7,0,7,1,6,0,3,4,4,2,0,4,4,4,2,1,1,0,0,8,5,0,5,2,2,4,0,1,2,8,2,1,1,1,3,6,1,0,1,7,3,2,2,1,0,4,4,3,2,1,0,2,0,2,4,1,0,2,0,5,0,2,1,0,1,2,1,0,1,0,3,1,0,1,0,0,0,1,0,0,2,2,2,0,1,0,0,1,1,1,1,0,0,2,0,0,0,2,0,1,1,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,90,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Lancel_15000-18000,ChrTr,NA,186,199,135,81,107,90,68,62,43,50,33,29,24,30,29,32,29,12,41,25,29,33,22,16,8,18,3,21,5,47,6,19,13,2,9,12,12,2,10,3,3,9,8,4,0,3,4,3,8,2,1,2,2,2,6,0,8,5,1,4,1,6,5,6,3,0,5,1,11,2,1,4,1,7,3,3,5,0,1,1,2,1,2,5,4,1,0,1,1,1,0,2,6,0,2,3,0,0,0,3,4,2,1,1,3,1,1,1,2,0,0,1,3,3,0,1,0,0,1,1,1,0,0,0,4,0,0,2,0,0,0,0,1,0,0,2,0,0,0,1,0,1,2,0,2,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,1,80,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0
+ChrTr_Lancel_18000-21000,ChrTr,NA,148,153,134,89,116,130,63,67,45,59,35,27,56,29,30,24,10,13,20,20,29,25,27,24,13,10,9,15,2,32,14,13,15,6,18,15,14,3,5,10,2,14,8,5,2,2,5,9,8,5,2,3,2,1,7,0,12,2,2,4,1,15,1,0,0,0,5,10,1,1,2,1,1,3,6,1,1,1,3,3,1,2,1,2,5,1,0,2,0,2,0,2,2,1,2,2,1,0,0,0,3,2,0,0,0,0,2,4,1,0,0,0,1,5,0,3,3,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,1,0,1,1,1,2,1,2,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,87,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0
+ChrTr_Lancel_21000-24000,ChrTr,NA,179,195,139,88,110,123,69,57,48,53,35,29,27,21,38,47,9,8,20,32,31,41,18,18,5,17,10,14,9,28,5,9,12,4,7,15,8,2,11,15,4,6,6,4,1,7,12,8,2,5,5,6,1,2,5,0,9,3,0,6,0,8,1,2,1,3,0,7,0,1,1,3,5,5,1,2,1,0,3,3,1,1,1,4,2,1,0,2,2,2,6,0,2,0,1,7,1,0,0,1,1,4,0,1,2,0,0,1,0,1,0,0,1,2,1,0,3,1,0,0,3,0,2,0,1,0,0,0,0,0,0,0,1,0,3,2,1,1,1,1,1,1,0,3,0,0,0,0,0,0,1,0,0,2,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,65,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0
+ChrTr_Lancel_24000-27000,ChrTr,NA,140,195,109,65,137,111,62,71,34,94,42,36,29,27,33,39,11,10,22,26,30,31,29,13,16,10,2,13,7,5,17,6,11,5,14,7,11,3,10,11,8,10,5,7,0,3,3,1,2,6,4,4,5,4,9,0,11,10,3,0,1,3,8,2,4,0,4,3,1,2,5,3,4,3,7,2,4,0,4,1,1,6,4,1,5,6,0,0,1,4,1,5,4,2,0,3,1,0,0,1,3,2,0,0,0,1,0,0,3,0,0,0,5,0,0,1,0,0,0,1,3,0,0,0,9,0,0,1,1,0,0,0,4,0,0,1,0,0,0,1,0,0,1,0,1,0,2,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,95,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Lancel_27000-30000,ChrTr,NA,147,197,119,91,112,134,52,72,34,75,33,45,48,25,19,50,12,9,13,25,27,37,31,25,17,5,7,23,5,0,12,10,8,2,13,10,8,4,11,9,4,5,11,4,0,4,7,6,2,5,1,4,6,0,9,0,9,6,3,0,0,10,3,2,3,1,5,5,0,2,5,2,3,4,2,5,3,1,1,0,0,6,1,3,1,1,1,0,1,2,1,1,0,2,0,4,1,1,0,3,1,2,0,0,1,2,0,6,1,1,0,1,0,0,1,1,0,1,0,0,0,0,0,0,4,0,1,0,0,3,0,0,0,1,0,1,0,0,1,1,1,1,1,1,1,0,0,4,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,2,1,0,0,0,2,0,0,0,79,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,3,0,2,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Lancel_30000-33000,ChrTr,NA,175,189,171,84,107,95,58,82,41,57,40,35,19,25,32,44,20,8,9,19,25,19,16,23,12,9,4,14,3,13,11,12,8,9,12,16,15,4,3,10,2,13,6,1,1,6,0,6,6,7,6,7,3,5,6,0,3,2,4,1,1,6,2,2,0,2,5,9,2,1,1,2,3,1,5,0,1,0,1,5,4,2,2,6,2,5,0,0,2,7,0,0,3,1,2,0,3,1,0,4,3,0,0,1,2,0,1,1,4,2,0,2,1,0,0,1,0,0,1,3,0,0,0,0,5,0,2,1,4,0,0,0,0,0,0,4,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,1,0,0,4,0,1,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,1,0,0,0,2,0,0,0,59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Lancel_33000-36000,ChrTr,NA,200,189,152,70,116,85,65,74,52,38,50,25,27,28,31,37,19,18,36,23,15,25,22,35,9,11,4,27,9,1,8,6,10,6,10,9,5,6,6,13,4,5,0,2,2,9,7,7,3,5,6,6,6,5,2,0,5,4,1,0,1,9,3,0,1,0,2,5,2,2,4,1,1,2,3,2,1,2,5,2,2,2,0,7,3,1,0,1,2,3,0,1,1,2,0,2,1,0,0,5,2,1,3,2,3,0,2,2,4,0,0,0,1,6,0,0,0,0,0,2,1,0,0,0,5,0,2,0,0,0,1,0,1,1,0,1,0,0,0,1,0,1,1,0,2,0,1,0,0,0,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,2,91,0,1,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Lancel_36000-39000,ChrTr,NA,182,212,136,85,119,88,74,90,52,37,39,38,26,27,27,44,20,13,33,17,26,20,19,22,13,6,4,13,4,1,6,9,9,6,13,8,8,7,2,13,5,6,6,7,1,7,6,4,6,8,7,2,6,1,4,0,4,7,2,1,1,6,3,2,2,2,4,3,1,1,1,0,1,3,3,1,0,1,4,4,1,3,2,6,3,1,0,0,3,6,2,0,3,1,0,2,4,0,0,2,4,0,3,1,0,1,0,4,3,3,0,1,0,1,0,0,1,0,0,3,0,0,0,0,6,1,3,0,0,4,0,0,0,0,1,2,1,3,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,1,0,0,0,0,0,0,0,0,4,0,0,0,78,0,0,0,0,2,1,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Lancel_39000-42000,ChrTr,NA,91,155,128,61,114,114,53,70,44,88,41,18,44,50,22,36,20,18,20,31,35,13,48,20,19,14,7,16,11,15,14,0,15,0,9,13,7,0,9,22,4,6,4,11,0,2,9,9,2,14,3,7,4,0,5,2,6,2,5,6,5,5,2,1,6,0,13,1,0,0,1,3,7,5,0,1,3,3,5,3,1,2,0,0,0,7,4,0,3,1,1,2,2,2,2,2,1,1,0,1,3,0,0,0,5,0,0,7,2,1,0,0,3,0,3,0,4,0,2,2,2,0,0,0,8,0,1,0,0,2,0,0,3,0,0,1,0,0,1,1,0,2,1,0,0,1,1,1,0,0,3,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,5,1,0,0,0,0,0,0,0,117,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0
+ChrTr_Percev_0-3000,ChrTr,NA,195,152,126,85,124,90,54,66,41,88,55,24,27,30,27,24,8,13,15,14,22,15,16,12,6,12,11,9,3,33,13,11,19,2,11,11,11,9,11,12,0,3,4,7,1,7,2,7,1,3,4,3,4,2,5,0,7,9,7,4,3,11,1,5,3,1,3,4,0,1,3,1,1,0,3,4,7,0,1,3,2,0,1,2,4,1,0,1,0,2,3,2,1,1,2,2,1,0,0,1,1,3,1,2,0,0,1,4,1,3,3,0,2,0,0,0,0,1,1,0,1,1,0,0,7,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,3,0,0,0,1,0,1,0,1,0,1,0,0,0,0,1,0,0,0,3,0,0,0,97,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Percev_3000-6000,ChrTr,NA,195,184,127,77,103,86,59,81,42,78,37,34,46,23,20,16,21,16,21,16,24,18,14,35,7,10,3,21,8,16,16,5,13,0,4,14,4,1,6,12,7,1,3,9,1,9,3,10,5,2,5,3,4,0,3,1,5,5,7,2,2,4,4,2,2,2,3,5,1,0,4,2,1,2,1,3,3,0,2,3,1,3,1,1,1,1,0,0,1,1,1,1,2,3,0,3,0,0,0,3,2,2,0,3,0,1,2,3,1,1,1,0,0,1,0,0,0,0,0,2,2,1,1,0,4,0,0,0,1,0,0,1,1,0,1,1,2,0,0,0,0,0,1,0,0,0,1,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,69,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,6,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0
+ChrTr_Percev_6000-9000,ChrTr,NA,240,184,147,87,116,74,54,61,46,65,43,34,25,22,14,23,20,25,17,21,16,24,15,23,8,7,6,11,4,8,16,8,11,0,14,2,4,0,5,15,7,3,2,3,0,3,3,14,10,6,2,3,4,3,2,1,7,4,2,0,1,9,2,2,3,2,3,7,0,1,6,2,1,7,0,1,3,2,2,2,0,1,1,5,2,0,1,1,2,0,0,2,2,1,0,5,0,0,0,0,2,0,0,3,0,1,3,1,1,5,2,0,2,3,0,0,0,1,0,1,1,0,1,0,2,0,0,1,0,0,0,0,1,0,2,2,0,1,1,1,1,2,0,0,2,1,0,2,0,0,1,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,58,0,1,0,0,0,0,1,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Percev_9000-12000,ChrTr,NA,182,165,155,77,110,105,62,61,36,39,39,20,57,19,24,31,23,17,19,11,36,23,21,33,10,12,1,12,13,0,10,11,12,2,6,10,3,5,5,7,7,7,8,13,2,4,4,10,6,3,4,4,4,4,7,1,4,3,2,0,0,8,1,1,0,1,3,4,3,2,0,4,8,0,3,2,3,1,7,0,0,0,2,2,4,2,1,0,0,1,1,1,2,1,4,2,2,1,0,1,1,3,0,1,1,0,1,1,2,3,0,4,1,0,0,0,0,0,1,0,2,0,0,0,1,0,1,0,0,3,0,0,0,1,0,1,0,4,1,0,0,0,0,2,2,0,0,1,0,0,1,0,0,0,4,0,0,0,0,0,0,1,0,0,1,1,0,2,0,0,0,0,0,0,0,0,5,0,0,2,79,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0
+ChrTr_Percev_12000-15000,ChrTr,NA,142,164,157,78,129,93,56,73,52,86,45,36,44,22,25,33,17,18,31,26,23,16,17,22,7,9,3,19,7,27,11,7,13,7,13,10,14,3,8,5,6,7,3,5,2,4,5,9,6,9,2,6,4,0,6,1,9,4,4,4,0,4,1,0,3,3,7,4,2,1,4,3,3,2,1,1,3,2,1,0,0,0,0,2,4,1,1,0,0,2,1,3,2,1,2,0,2,0,0,1,1,3,0,2,3,0,0,3,2,6,0,0,1,3,0,0,1,0,0,2,2,1,3,0,1,0,2,0,1,5,0,0,1,0,2,0,0,0,0,0,0,1,0,3,1,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,1,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,0,64,0,0,2,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0
+ChrTr_Percev_15000-18000,ChrTr,NA,195,202,146,70,105,73,69,89,59,29,47,49,20,23,47,41,17,12,27,19,19,18,27,17,10,14,3,10,3,1,4,12,8,0,9,9,5,8,5,9,6,3,7,2,0,8,2,5,4,4,2,3,6,4,6,0,4,8,2,0,2,5,1,1,1,1,4,3,4,3,3,4,2,5,2,1,0,10,3,10,1,2,1,6,0,1,1,2,3,1,0,0,3,1,0,0,3,0,0,2,2,0,0,2,2,1,1,0,0,1,0,3,1,3,1,0,0,0,1,0,1,0,1,0,0,1,0,1,0,2,0,0,0,2,0,0,1,2,0,1,0,1,1,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,74,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Percev_18000-21000,ChrTr,NA,217,194,143,111,88,83,48,72,52,38,57,21,31,19,21,39,34,14,30,14,15,10,11,39,16,15,1,8,4,8,5,9,5,4,12,3,6,3,3,7,18,1,4,18,2,8,6,3,9,3,3,2,4,6,2,0,6,10,1,0,0,7,4,1,2,3,3,3,4,3,1,7,6,1,6,1,0,1,3,4,1,1,2,1,2,0,2,1,1,2,2,1,1,3,2,0,1,0,1,2,1,2,0,3,1,0,0,1,0,4,1,9,1,5,0,0,2,1,0,1,2,0,2,0,1,0,0,0,1,0,0,0,2,0,2,0,0,1,0,0,1,0,0,0,1,0,0,3,0,0,6,1,0,2,2,1,0,1,0,2,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,3,0,1,0,75,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,4,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Percev_21000-24000,ChrTr,NA,115,190,131,71,113,107,39,60,52,121,49,31,47,32,13,21,26,24,14,25,37,7,26,27,17,17,1,16,9,24,16,10,10,3,14,10,6,2,4,12,8,10,8,6,0,4,5,2,4,10,2,3,3,5,6,1,3,10,7,6,1,9,7,1,4,2,3,2,1,0,3,3,4,2,2,6,3,0,3,0,0,2,3,1,4,8,1,0,1,0,0,1,1,0,5,0,2,1,2,1,0,3,0,1,0,0,1,1,0,1,0,0,4,2,0,0,1,0,0,0,0,0,3,0,7,0,1,5,0,0,0,0,1,0,0,1,1,2,1,0,0,1,0,0,0,0,0,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,3,0,1,0,0,0,0,0,0,3,0,0,2,68,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,4,0,1,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Percev_24000-27000,ChrTr,NA,181,229,160,69,117,74,50,58,57,49,47,42,47,14,35,34,18,17,21,23,15,13,14,29,9,16,1,23,6,10,8,6,6,0,4,18,7,1,7,6,4,5,7,9,0,3,6,9,10,7,1,3,3,5,3,1,3,3,4,2,2,6,1,6,2,0,0,4,1,2,1,2,1,2,1,2,1,0,1,2,0,2,0,2,1,1,0,0,1,3,0,0,1,1,2,3,4,0,4,2,4,1,0,1,0,0,0,2,2,0,0,0,3,3,0,0,0,0,1,2,0,1,4,1,2,0,1,1,1,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,68,0,0,0,0,0,0,2,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Percev_27000-30000,ChrTr,NA,154,151,158,91,99,79,56,75,53,61,60,33,44,21,27,17,22,12,19,13,25,19,8,34,8,17,0,14,6,21,18,10,8,3,9,9,5,4,2,11,8,3,7,6,2,6,4,6,3,3,5,2,6,5,4,0,5,5,4,2,1,7,1,2,1,0,2,2,0,0,3,5,2,0,2,3,2,2,0,2,2,2,1,3,2,2,2,0,5,5,1,0,0,1,4,4,3,0,2,0,1,2,2,1,1,1,2,0,2,0,0,0,2,0,0,0,2,1,1,1,0,0,1,0,2,0,0,2,2,0,1,0,1,0,1,3,0,0,0,2,1,0,1,0,0,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,2,0,0,1,79,0,0,0,0,2,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0
+ChrTr_Percev_30000-33000,ChrTr,NA,158,209,160,70,106,73,54,86,63,46,52,36,44,32,27,41,15,14,23,20,14,15,15,25,9,9,1,7,5,3,13,6,9,1,7,10,9,3,8,7,7,7,5,3,0,5,6,11,2,6,1,3,6,5,1,1,8,6,6,2,1,4,1,1,6,2,1,0,1,2,2,3,2,2,4,2,1,0,3,1,0,1,3,2,7,1,1,0,5,10,1,1,0,1,0,3,1,0,1,1,0,1,0,0,0,3,0,2,0,6,0,0,0,1,1,0,0,1,0,0,0,0,0,0,2,0,0,0,1,1,0,0,0,1,2,1,0,3,1,0,1,0,0,3,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,2,0,0,0,0,3,0,0,2,73,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Percev_33000-36000,ChrTr,NA,158,210,163,99,115,75,57,69,47,54,52,37,38,18,31,28,16,9,33,10,24,14,20,26,11,12,0,13,7,15,9,8,8,3,4,10,12,3,9,12,4,9,7,7,0,5,3,11,5,1,7,4,5,7,8,0,7,6,5,3,1,4,3,3,1,0,1,1,0,1,0,2,0,3,4,2,0,1,2,1,1,1,0,4,3,0,1,0,0,2,1,1,3,3,2,2,1,0,1,1,2,2,0,1,1,0,0,3,1,5,0,1,1,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,1,0,1,2,0,0,1,0,3,0,0,0,1,1,0,0,2,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,1,3,0,0,0,0,0,0,0,0,1,0,1,1,63,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,6,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Percev_36000-39000,ChrTr,NA,172,138,137,101,107,110,64,69,48,56,34,27,35,32,18,28,15,21,11,30,27,21,11,27,14,13,0,14,11,28,10,3,11,3,12,10,8,6,6,8,2,6,2,12,4,3,1,4,0,2,3,4,6,2,6,1,7,11,7,5,1,4,2,3,6,2,2,5,1,2,1,5,3,0,3,3,5,2,2,4,2,1,2,4,4,2,0,0,0,1,1,1,2,2,1,2,0,0,1,1,4,1,0,0,1,1,0,0,1,4,0,1,2,2,1,0,1,0,1,0,1,0,0,1,3,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,2,82,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0
+ChrTr_Percev_39000-42000,ChrTr,NA,148,158,136,56,126,100,43,75,35,104,41,27,33,21,22,21,11,15,14,26,32,13,18,24,8,9,2,11,7,70,30,5,8,0,8,13,3,1,7,18,1,6,6,4,0,4,1,4,9,2,2,5,5,3,7,1,8,7,8,11,1,4,6,2,6,0,0,2,0,1,0,3,4,3,4,3,1,1,1,4,1,0,1,1,3,0,1,0,1,2,0,0,0,2,0,1,1,0,0,2,1,3,0,1,2,3,0,1,3,1,0,0,2,4,1,2,1,1,0,1,1,0,4,0,5,1,2,0,1,0,0,0,3,0,0,3,1,1,0,1,1,2,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,1,0,1,1,0,0,2,0,0,0,2,2,0,0,0,1,0,1,0,0,0,0,0,60,0,1,1,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0
+ChrTr_Percev_42000-45000,ChrTr,NA,156,154,143,92,111,104,58,69,56,73,45,24,59,19,12,24,26,15,13,18,23,26,17,38,7,9,6,11,7,20,9,7,8,0,11,17,13,6,10,17,4,10,6,3,0,2,2,8,14,7,2,6,3,5,6,0,8,7,4,6,1,7,6,2,2,0,1,6,0,1,0,2,4,4,4,2,2,0,2,2,1,0,1,2,1,3,0,0,4,1,0,1,0,0,0,2,1,1,0,1,2,1,2,1,0,0,0,1,2,2,0,2,2,2,0,1,0,0,0,1,0,0,0,0,2,1,1,1,1,2,0,0,0,0,1,2,0,0,0,0,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,82,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Percev_45000-48000,ChrTr,NA,208,131,146,109,111,101,45,65,44,58,35,21,60,24,35,24,22,22,16,19,12,30,20,25,16,12,3,8,9,0,9,13,16,6,15,7,6,5,5,17,2,5,5,8,2,2,3,9,6,4,1,3,7,5,7,1,3,1,8,0,0,6,5,2,3,1,5,0,3,1,1,3,0,1,3,1,2,1,2,3,1,1,3,1,3,1,2,1,2,5,2,2,0,4,3,1,2,3,0,0,1,2,1,5,1,0,0,0,3,0,0,3,2,3,0,0,2,0,0,0,1,0,3,0,2,0,1,3,0,7,0,0,1,0,1,0,0,1,0,0,0,2,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,3,0,1,0,92,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0
+ChrTr_Percev_48000-51000,ChrTr,NA,171,164,127,81,113,89,52,68,54,92,33,32,65,15,16,25,15,8,17,21,30,20,19,27,12,5,3,15,6,19,17,8,8,5,10,16,9,5,2,12,7,3,7,4,1,8,1,10,4,4,5,7,5,2,12,1,6,5,2,4,0,6,2,4,2,0,2,2,2,0,4,3,5,1,3,5,2,1,1,9,0,4,1,0,2,3,1,0,2,0,1,2,1,3,3,2,1,0,0,1,3,2,1,0,0,1,0,2,4,4,0,1,3,3,1,4,0,0,0,1,1,1,0,0,4,0,0,1,1,0,0,1,1,0,1,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,2,0,0,1,0,1,1,0,0,0,0,1,0,0,1,1,0,0,1,0,0,1,0,1,0,4,0,1,0,90,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Percev_51000-54000,ChrTr,NA,157,174,150,77,117,80,63,60,52,122,36,40,39,15,24,13,14,16,18,24,30,20,18,40,12,13,2,9,5,40,22,6,14,2,13,7,8,0,7,10,2,3,5,3,2,2,3,5,2,2,3,3,4,2,10,1,6,4,2,4,1,9,3,2,2,0,2,4,0,2,2,6,0,3,1,1,2,0,4,5,1,3,2,2,3,0,0,0,2,1,0,0,0,0,2,2,0,0,0,1,0,6,0,0,0,1,0,0,0,1,0,1,2,2,0,10,3,0,1,4,0,1,1,0,3,0,1,0,1,0,1,0,0,0,0,1,1,2,0,1,0,0,0,0,0,1,1,1,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,81,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+ChrTr_Yvain_0-3000,ChrTr,NA,148,105,132,109,122,118,59,72,44,104,41,16,21,24,27,28,36,11,18,17,26,22,25,35,10,14,11,16,8,21,16,8,23,15,13,11,7,2,5,10,1,12,5,4,0,3,4,3,8,4,4,1,2,3,11,0,5,3,5,1,0,6,5,2,1,2,4,3,1,2,2,3,1,3,3,2,0,1,1,4,1,2,0,3,2,3,1,0,0,4,1,2,5,1,3,3,0,1,0,7,2,2,0,2,0,1,0,0,0,1,0,1,0,0,0,0,0,0,1,1,1,0,2,1,2,1,0,0,0,0,0,1,1,0,1,1,2,1,0,0,0,0,0,0,1,0,0,2,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,95,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0
+ChrTr_Yvain_3000-6000,ChrTr,NA,183,150,142,102,121,106,54,60,59,61,35,28,30,14,34,40,16,15,13,6,25,15,21,24,9,15,6,19,8,5,20,4,13,4,13,11,5,7,8,5,2,4,3,4,1,2,2,8,9,11,2,6,2,7,11,0,9,9,0,0,2,7,1,0,0,1,3,2,6,0,5,7,1,3,3,0,0,1,2,2,1,4,1,1,3,3,1,1,3,6,1,3,2,0,2,3,1,3,0,3,3,3,0,3,0,3,0,2,0,0,0,0,0,0,0,0,3,3,1,0,0,0,1,0,1,0,0,0,0,1,0,0,0,1,2,0,1,3,1,0,3,1,0,1,0,0,1,0,0,0,2,1,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,4,0,0,0,70,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0
+ChrTr_Yvain_6000-9000,ChrTr,NA,135,180,138,73,117,123,48,64,52,51,46,32,35,29,29,45,6,24,20,21,20,18,28,23,22,13,5,5,13,10,14,3,21,5,13,21,8,2,7,14,5,8,6,9,0,5,6,7,2,3,2,3,4,0,6,2,6,5,7,1,0,9,1,6,4,1,6,3,2,4,3,0,3,4,5,2,3,2,2,2,0,0,0,2,0,2,0,1,1,0,1,2,3,1,3,4,0,1,0,2,0,2,0,5,0,4,1,2,0,0,0,3,2,1,0,0,0,0,1,3,1,0,0,0,6,0,0,1,0,7,0,0,3,1,1,2,0,0,0,1,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,4,1,0,0,0,0,0,0,0,95,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Yvain_9000-12000,ChrTr,NA,94,206,92,76,125,120,61,76,68,78,41,38,73,31,21,29,11,20,17,30,30,21,34,22,11,12,4,14,6,11,11,6,9,3,15,23,5,1,3,15,5,8,7,6,0,4,4,14,3,4,5,1,6,4,12,0,6,11,2,0,3,10,4,0,9,1,4,3,1,1,5,6,5,3,3,0,8,1,1,1,0,0,1,2,5,5,0,1,0,1,1,0,0,1,1,2,0,0,0,1,0,3,0,3,1,0,1,3,0,0,0,2,1,1,0,0,4,0,0,0,0,0,0,0,5,0,1,1,0,3,0,0,0,0,0,1,0,0,1,1,0,0,1,5,2,1,0,0,0,0,0,0,0,0,2,0,0,1,0,2,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,1,0,79,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Yvain_12000-15000,ChrTr,NA,167,168,151,94,113,96,63,64,35,48,56,37,48,33,40,38,15,9,15,19,21,27,13,25,10,11,3,15,4,0,16,6,14,2,6,6,8,3,5,9,5,10,4,6,1,7,2,11,4,8,4,4,7,3,6,0,9,3,3,0,2,8,4,1,0,1,2,4,1,5,5,2,0,2,3,1,1,0,3,1,4,5,2,7,0,3,1,1,0,3,0,2,4,0,2,3,2,0,0,0,2,1,0,1,0,1,3,3,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,0,2,1,0,0,0,0,2,1,0,0,1,0,0,3,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,74,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0
+ChrTr_Yvain_15000-18000,ChrTr,NA,174,198,141,97,124,101,67,71,48,31,48,41,29,13,33,43,18,13,15,12,18,21,25,17,11,8,5,23,8,11,14,7,14,1,14,5,6,8,7,8,4,9,5,14,1,5,3,4,2,7,3,7,5,1,5,1,7,4,1,0,2,8,3,2,0,1,6,2,2,3,5,6,1,6,8,1,1,1,4,5,1,0,2,4,2,2,0,0,1,1,2,1,3,0,2,1,0,0,0,0,3,2,0,0,1,1,1,2,1,1,0,0,0,1,0,0,3,1,4,1,1,1,1,0,2,0,1,0,0,1,0,0,1,0,0,0,0,2,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,70,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
+ChrTr_Yvain_18000-21000,ChrTr,NA,188,247,168,83,119,75,57,92,59,17,29,49,11,18,27,56,15,19,25,14,20,20,25,24,13,29,4,21,4,0,1,4,16,4,5,3,6,3,10,9,8,2,5,12,2,4,3,3,4,1,7,4,3,4,9,1,7,7,2,0,1,5,5,1,2,1,4,8,3,4,5,6,4,5,3,1,2,0,2,1,0,6,2,7,1,1,2,2,0,1,2,1,2,0,2,1,1,0,0,4,0,0,0,3,1,1,0,3,0,0,1,0,2,3,0,0,2,2,1,3,0,0,3,0,2,0,1,1,0,0,0,2,0,1,1,0,0,1,0,1,2,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,2,0,2,2,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,55,0,0,0,0,0,0,1,1,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0
+ChrTr_Yvain_21000-24000,ChrTr,NA,146,178,123,78,123,101,72,82,43,95,46,31,57,26,20,37,25,13,16,41,19,12,18,21,13,13,3,23,8,1,23,4,19,8,11,17,3,2,3,9,5,8,7,4,1,2,3,8,3,6,0,5,3,2,4,0,4,4,2,1,1,10,4,3,4,0,5,2,0,3,2,5,0,1,2,0,5,0,2,5,0,0,1,1,3,2,0,0,2,3,0,1,4,0,1,1,4,1,0,5,1,5,0,1,0,3,0,0,1,0,0,0,1,2,1,0,0,1,1,1,1,0,0,0,4,1,3,3,0,2,0,0,1,0,0,0,0,0,0,0,1,0,0,6,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,0,2,0,0,0,1,0,0,0,0,2,0,0,0,79,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0
+ChrTr_Yvain_24000-27000,ChrTr,NA,181,221,133,84,130,94,63,76,46,42,42,45,19,17,24,38,19,16,21,21,21,29,19,19,12,17,5,16,9,4,8,7,11,14,9,7,3,6,6,8,4,10,7,3,1,3,6,4,3,1,4,3,4,2,7,0,9,4,4,1,3,8,3,4,0,0,7,4,1,1,1,4,6,3,2,2,0,0,1,4,0,2,0,4,3,1,0,0,2,3,1,4,0,0,1,3,1,0,0,0,2,1,0,1,2,1,0,2,2,0,0,1,1,4,0,1,1,0,1,0,2,0,0,0,4,0,4,0,1,3,0,0,0,0,4,1,0,1,0,0,0,0,0,5,0,0,0,2,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,0,59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0
+ChrTr_Yvain_27000-30000,ChrTr,NA,157,226,131,73,133,106,62,82,50,48,40,67,24,22,27,39,13,16,26,21,29,25,21,19,8,9,1,16,1,10,11,17,8,2,13,12,9,5,11,5,3,6,6,2,0,2,3,8,3,7,0,4,3,5,6,0,3,8,3,3,2,4,1,2,3,0,1,9,2,3,1,2,2,2,2,3,4,3,0,1,0,3,2,7,0,2,2,0,3,3,3,2,1,3,1,0,2,0,0,1,4,1,0,0,0,0,3,5,3,1,0,1,1,0,0,0,1,0,1,4,2,0,0,0,5,1,1,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,64,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,1,0,0,0,0,0,0,0
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diff --git a/scripts/functions.R b/scripts/functions.R
new file mode 100755
index 0000000..1519371
--- /dev/null
+++ b/scripts/functions.R
@@ -0,0 +1,605 @@
+# Load and clean a text
+
+
+
+# Functions for stylometric analysis
+
+## Distance measures
+
+### Cosine distance
+
+cosineDist <- function(x){
+  as.dist(1 - x%*%t(x)/(sqrt(rowSums(x^2) %*% t(rowSums(x^2))))) 
+}
+
+### MinMax metric
+
+MinMax =
+  function(x){
+    myDist = matrix(nrow = ncol(x),ncol = ncol(x), dimnames = list(colnames(x),colnames(x)))
+    for(i in 1:ncol(x)){
+      for(j in 1:ncol(x)){
+        min = sum(apply(cbind(x[,i],x[,j]), 1, min))
+        max = sum(apply(cbind(x[,i],x[,j]), 1, max))
+        resultat = 1 - (min / max)
+        myDist[i,j] = resultat
+      }
+    }
+    return(myDist)
+  }
+
+
+### Normalisations
+
+relativeFreqs = function(x){
+  # Relative frequencies
+  for(i in 1:ncol(x)){
+    x[,i] = x[,i]/sum(x[,i])
+  }
+  return(x)
+}
+# Z-scores
+ZTransf = function(x){
+  for(i in 1:nrow(x)){
+    x[i,] = ( x[i,] - mean(x[i,]) )  / sd(x[i,])
+  }
+  return(x)
+}
+
+normalisations = function(x){
+  # Z-transformation  
+  x = ZTransf(x)
+  
+  # Vector length normalisation
+  for(i in 1:ncol(x)){
+    x[,i] = x[,i] / sqrt(sum(x[,i]^2))
+  }
+  return(x)
+}
+
+### Feature selection (Moisl 2011)
+
+selection = function(x, z = 1.96){
+  
+  # Conversion to probabilities
+  probs = relativeFreqs(x)
+  
+  # Prepare output
+  results = matrix(nrow = nrow(probs), ncol = 4, dimnames = list(rownames(probs), c('freq', 'mean prob', 'sample size necessary', 'passes')))
+  results = as.data.frame(results)
+  results[,1] = rowSums(x)
+  results[,2] = rowMeans(probs)
+  
+  for (i in 1:nrow(probs)){
+    var = probs[i,]
+    # hist(probs[i,])
+    # Calculates mirror-image to compensate for non normality
+    mirror = ( max(var) + min(var) ) - var
+    var = c(var,mirror)
+    # e as function of sigma
+    e = 2 * sd(var)
+    results[i,3] = mean(var) * (1 - mean(var)) * (z / e )^2 
+  }
+  
+  # And now, mark as false all the rows that would necessit bigger sample than available
+  results[,4] = results[,3] <= min(colSums(x))
+  
+  return(results)
+}
+
+### SOM
+
+library("kohonen")
+
+somCluster = function(x, gridx = 10, gridy = 10){
+  maSOM = som(x, grid=somgrid(gridx, gridy,"hexagonal"))
+  #plot(maSOM, type="mapping", labels=rownames(maSOM$data))
+  results = cluster::agnes(t(as.data.frame(maSOM$codes)), metric  = "manhattan", method = "ward")
+  return(results)
+}
+
+
+### Robustness checks
+
+# Gives the cluster purity with reference to alledged authors, and an adjusted Rand index in comparison with the analysis showed in fig. 1
+robustnessChecks = function(data, refCAH, k = "10"){
+  # Get classes from the reference CAH
+  refCAHClasses = cutree(refCAH, k = k)
+  # Prepare results
+  results = matrix(ncol = 3, nrow = 0, dimnames = list(NULL, c("N", "CPAuteurs", "CPReference")))
+  for (i in list(
+    seq(0, 1, 0.001),
+    seq(0, 1, 0.01),
+    seq(0, 1, 0.1),
+    seq(0, 1, 0.25),
+    seq(0, 1, 0.5),
+    seq(0, 0.5, 0.25),
+    seq(0, 1, 1)) ) {
+    # First, get the cutoffs: first 1000-quantile, first 100-quantile, first decile, all
+    selec = quantile(rowSums(data), probs = i)
+    selec = selec[length(selec) - 1]
+    
+    myData = data[rowSums(data) >= selec, , drop = FALSE]
+    myData = normalisations(myData)
+    myCAH = cluster::agnes(t(myData), metric = "manhattan", method="ward")
+    
+    # Classes as per alledged author
+    expected = sub("_.*", "", rownames(myCAH$data))
+    # Cluster purity
+    classes = cutree(myCAH, k = k)
+    
+    N = nrow(myData)
+    purity = NMF::purity(as.factor(classes), expected)
+    #NMF::entropy(as.factor(classes), expected)
+    purityRef = NMF::purity(as.factor(classes), as.factor(refCAHClasses))
+    #Rand = mclust::adjustedRandIndex(classes, refCAHClasses)
+    
+    MF = paste(round(100 - as.numeric(sub("%", "", names(selec))), digits = 2), "%", sep = "")
+    
+    #localRes = matrix(data = c(N, purity, purityRef, Rand), nrow = 1, ncol = 4, dimnames = list(MF, NULL))
+    localRes = matrix(data = c(N, purity, purityRef), nrow = 1, ncol = 3, dimnames = list(MF, NULL))
+    results = rbind(results, localRes)
+  }
+  return(results)
+}
+
+# Gives the cluster purity with reference to alledged authors, and an adjusted Rand index in comparison with the analysis showed in fig. 1
+compareHC = function(cahList, k = "10"){
+  cahList = lapply(cahList, as.hclust)
+  # Prepare results
+  results = matrix(ncol = length(cahList), nrow = length(cahList), dimnames = list(labels(cahList), labels(cahList)))
+  for (i in 1:length(cahList)){
+    for (j in 1:length(cahList)){
+      classes1 = cutree(cahList[[i]], k = k)
+      classes2 = cutree(cahList[[j]], k = k)
+      results[i, j] = purityRef = NMF::purity(as.factor(classes1), as.factor(classes2))
+      #Rand = mclust::adjustedRandIndex(classes, refCAHClasses)
+    }
+  }
+  
+  return(results)
+}
+
+### Gives, for each HC, three indicators
+# - Purity with regard to Meyer Leg. A/B/C and Wauchier
+# - Quality of clustering with HC
+# - Stability: ARI with regard to a reference HC
+benchmarkHC = function(refCAH, cahList, k = 10){
+  # Prepare results
+  results = matrix(ncol = 4, nrow = length(cahList), 
+                   dimnames = list(labels(cahList), c("N", "AC", "CPMeyer", "CPREF")))
+  for (i in 1:length(cahList)){
+    N = ncol(cahList[[i]]$data)
+    # 1. Purity with regard to Wauchier/non-Wauchier
+    classes1 = cutree(as.hclust(cahList[[i]]), k = k)
+    expected = classes1
+    # Now we set 1 for Wauchier, 2 for NOT Wauchier
+    
+    expected[grep("Leg.A", rownames(cahList[[i]]$data))] = "Leg-A"
+    expected[grep("Leg.B", rownames(cahList[[i]]$data))] = "Leg-B"
+    expected[grep("Leg.C", rownames(cahList[[i]]$data))] = "Leg-C"
+    expected[grep("_Wau_", rownames(cahList[[i]]$data))] = "Wauchier"
+    CPMeyer = NMF::purity(as.factor(classes1), as.factor(expected))
+    
+    # 2. Quality of clustering with HC
+    AC = cahList[[i]]$ac
+    
+    # 3. Stability: CP with regard to a reference HC
+    
+    classes2 = cutree(as.hclust(refCAH), k = k)
+    CPRef = NMF::purity(as.factor(classes1), as.factor(classes2))
+    
+    results[i, ] = c(N, AC, CPMeyer, CPRef)
+    
+  }
+  return(results)
+}
+
+volatility = function(cahList, k = 9){
+  textsLabels = cahList[[1]]$order.lab
+  results = matrix(nrow = length(textsLabels), ncol = 1, 
+                   dimnames = list(textsLabels, "V_i"))
+  
+  classes = lapply(cahList, as.hclust)
+  classes = lapply(classes, cutree, k = k)
+  nclasses = length(classes)
+  for (i in 1:length(textsLabels)){
+    # Find all X with their freqs. Start with as much row as labels, will remove
+    # unused ones later
+    X = matrix(ncol = 1, nrow = length(textsLabels), dimnames = list(textsLabels, 'Freq'), data = 0)
+    for (j in 1:length(classes)){
+      myMembers = labels(classes[[j]][ classes[[j]] ==  classes[[j]][textsLabels[i]]])
+      X[myMembers, ] = X[myMembers, ] + 1
+    }
+    # Remove 0s
+    X = X[X > 0, ]
+    # Compute index
+    V_i = sum( (X - (nclasses - X) )/ nclasses ) / length(X)
+    results[i, ] = V_i
+  }
+  return(results)
+}
+
+###########################################
+### Compare results with other datasets ###
+###########################################
+
+#### First, a function to replicate all other analyses on an alternate dataSet
+
+## subfunction to perform analysis
+
+readData = function(toKeep, path){
+  # toKeep: texts to keep for analysis
+  # path: path to data
+  # return: data…
+  data = read.csv(path, header = TRUE, row.names = 1)
+  data = t(data)
+  data = data[, toKeep]
+  data = data[rowSums(data) > 0, ]
+  return(data)
+}
+
+readAnnotData = function(toKeep, path){
+  # toKeep: texts to keep for analysis
+  # path: path to data
+  # return: data…
+  data = read.csv(path, header = TRUE, row.names = 1, sep = ";")
+  data = data[, -1]
+  colnames(data) = gsub("^X", "", colnames(data))
+  colnames(data) = gsub(".decolumnized", "", colnames(data))
+  colnames(data) = gsub("Leg.", "Leg-", colnames(data))
+  data = data[, toKeep]
+  data = data[rowSums(data) > 0, ]
+  data = as.matrix(data)
+  return(data)
+}
+
+performAnalysis = function(data){
+  # data: well, … data.
+  # returns: a HC (agnes object)
+  # Selection based on Moisl 2011
+  select = selection(data, z = 1.645)
+  select = select[,4]
+  # Normalisations
+  data = relativeFreqs(data)
+  data = data[select,]
+  data = normalisations(data)
+  myCAH = cluster::agnes(t(data), metric = "manhattan", method="ward")
+  return(myCAH)
+}
+
+## and the one to replicate
+
+replicateAnalysis = function(toKeep, path_raw_char3grams, 
+                             path_expanded_words, path_pos3_gr, path_lemmas, 
+                             functionWords, functionLemmas){
+  # toKeep: list of texts to keep for analysis
+  # path_raw_char3grams: path to raw_char3grams file;
+  # path_expanded_words, path_pos3_gr, path_lemmas: you get the idea…
+  # functionWords: list of functionWords
+  # functionLemmas: well, in fact, a list of functionLemmas (really)
+  
+  data3grams = readData(toKeep, path_raw_char3grams)
+  CAHRaw3gr = performAnalysis(data3grams)
+  dataWords = readData(toKeep, path_expanded_words)
+  CAHForms  = performAnalysis(dataWords)
+  dataAffs = countAffixes(dataWords)
+  CAHAffs = performAnalysis(dataAffs)
+  
+  # Now, function words, a bit more annoying (should put it in readData function, yeah, right)
+  dataFW = relativeFreqs(dataWords)
+  dataFW = dataFW[functionWords,]
+  dataFWsave = dataFW
+  dataFW = normalisations(dataFW)
+  CAHFW = cluster::agnes(t(dataFW), metric = "manhattan", method="ward")
+  
+  # back to normal. Or not.
+  dataPOS = readAnnotData(toKeep, path_pos3_gr)
+  CAHPOS3gr = performAnalysis(dataPOS)
+  dataLemmas = readAnnotData(toKeep, path_lemmas)
+  CAHLemmas = performAnalysis(dataLemmas)
+  
+  # And now back to functionLemmas
+  dataFL = relativeFreqs(dataLemmas)
+  dataFL = dataFL[functionLemmas,]
+  dataFL = normalisations(dataFL)
+  CAHFL = cluster::agnes(t(dataFL), metric = "manhattan", method="ward")
+  
+  # And NOOOOOW: Affixes + POS 3-gr + Function words (unnorm)
+  
+  # Select relevant data
+  dataList = list(dataAffs, dataPOS)
+  results = matrix(ncol = ncol(dataAffs), nrow = 0, dimnames = list(NULL, colnames(dataAffs)))
+  
+  for (i in 1:length(dataList)){
+    select = selection(dataList[[i]], z = 1.645)
+    select = select[,4]
+    # Normalisations
+    dataList[[i]] = relativeFreqs(dataList[[i]])
+    results = rbind(results, dataList[[i]][select,])
+  }
+  results = rbind(results, dataFWsave)
+  rm(dataList)
+  dataGlob = normalisations(results)
+  CAHGlob2 = cluster::agnes(t(dataGlob), metric = "manhattan", method="ward")
+  
+  # AND NOW: lemmas and words
+  # Select relevant data
+  dataList = list(dataLemmas, dataWords)
+  results = matrix(ncol = ncol(dataLemmas), nrow = 0, dimnames = list(NULL, colnames(dataLemmas)))
+  for (i in 1:length(dataList)){
+    select = selection(dataList[[i]], z = 1.645)
+    select = select[,4]
+    # Normalisations
+    dataList[[i]] = relativeFreqs(dataList[[i]])
+    results = rbind(results, dataList[[i]][select,])
+  }
+  rm(dataList)
+  dataWordsLemmas = normalisations(results)
+  CAHWordsLemmas = cluster::agnes(t(dataWordsLemmas), metric = "manhattan", method="ward")
+  
+  cahList = list(raw3grams = CAHRaw3gr, Affs = CAHAffs, FunctWords = CAHFW, FunctLemm = CAHFL, POS3gr = CAHPOS3gr, FWPOSandAffs = CAHGlob2, Forms = CAHForms,  Lemmas = CAHLemmas, WordsLemmas = CAHWordsLemmas)
+  return(cahList)
+}
+
+### And now for something entirely different
+### Compare one to one the analyses
+
+compareReplications = function(refCahList, replicatedCahList, k = 5){
+  results = matrix(nrow = length(names(refCahList)), ncol = 1,  
+                   dimnames = list(names(refCahList), NULL))
+  for(i in 1:nrow(results)){
+    classes1 = cutree(as.hclust(refCahList[[i]]), k = k)
+    classes2 = cutree(as.hclust(replicatedCahList[[i]]), k = k)
+    results[i, 1] = NMF::purity(as.factor(classes1), as.factor(classes2))
+  }
+  return(results)
+}
+
+
+### Plots and layout
+
+#### ACP
+
+library(ggfortify)
+pcaPlot = function(x, main ="Plot"){
+  dat = x
+  auts = vector(length=ncol(dat))
+  auts[startsWith(colnames(dat), "CORNEILLEP_")] = 'CORNEILLEP'
+  auts[startsWith(colnames(dat), "CORNEILLET_")] = 'CORNEILLET'
+  auts[startsWith(colnames(dat), "MOLIERE_")] = 'MOLIERE'
+  auts[startsWith(colnames(dat), "ROTROU_")] = 'ROTROU'
+  auts[startsWith(colnames(dat), "SCARRON_")] = 'SCARRON'
+  auts[startsWith(colnames(dat), "OUVILLE_")] = 'OUVILLE'
+  # rename texts
+  colnames(dat) = sub('^[^_]+_', '', colnames(dat))
+  colnames(dat) = substring(colnames(dat), 1, 10)
+  dat = dat[rowSums(dat) != 0,]
+  
+  ggplot2::autoplot(prcomp(t(dat), scale. = TRUE, center = TRUE), label = TRUE, data = cbind.data.frame(t(dat), auts),  colour='auts', main = main, label.show.legend=FALSE)
+}
+
+#### HC
+
+cahPlot = function(x, main="Plot", xlab = paste(ncol(x$data), "features"), k = 6){
+  x$order.lab = sub("CORNEILLEP","CP", x$order.lab)
+  x$order.lab = sub("CORNEILLET","CT", x$order.lab)
+  x$order.lab = sub("MOLIERE","M", x$order.lab)
+  x$order.lab = sub("OUVILLE","O", x$order.lab)
+  x$order.lab = sub("ROTROU","R", x$order.lab)
+  x$order.lab = sub("SCARRON","S", x$order.lab)
+  x$order.lab = sub("BOISSY","B", x$order.lab)
+  x$order.lab = sub("DANCOURT","DA", x$order.lab)
+  x$order.lab = sub("DUFRESNY","DU", x$order.lab)
+  x$order.lab = sub("NIVELLE","N", x$order.lab)
+  x$order.lab = sub("REGNARD","R", x$order.lab)
+  x$order.lab = sub("VOLTAIRE","V", x$order.lab)
+  x$order.lab = sub("BOURSAULT","B", x$order.lab)
+  x$order.lab = sub("CHEVALIER","C", x$order.lab)
+  x$order.lab = sub("DONNEAUDEVISE","DDV", x$order.lab)
+  x$order.lab = sub("DORIMOND","DOR", x$order.lab)
+  x$order.lab = sub("GILLET","G", x$order.lab)
+  x$order.lab = sub("LAFONTAINE","LF", x$order.lab)
+  x$order.lab = sub("QUINAULT","Q", x$order.lab)
+  #TODO: modif temporaire pour lisibilité
+  x$order.lab = substring(x$order.lab, 1, 10)
+  plot(x, main=main, xlab=xlab, which.plots = 2)
+  myCAH2 = as.hclust(x)
+  # Cut in 6 groups
+  rect.hclust(myCAH2, k = k, border = 2:5)
+}
+
+
+cahPlotCol = function(x, main="Plot", xlab = paste(ncol(x$data), "features"), k = 3, lth = 7, lrect = -12, cex = 0.6, ylab = "height"){
+  # Redefining labels and  Coloring them
+  # Redefining labels
+  x$order.lab = substring(x$order.lab, 1, 25)
+  # Coloring them
+  labels = vector(length = length(x$order.lab))
+  labels[grep("GerbM_", x$order.lab)] = "darkgreen"
+  labels[grep("ChrTr_", x$order.lab)] = "red"
+  #labels[grep("D+M_", x$order.lab)] = "pink"
+  labels[grep("JnRen_", x$order.lab)] = "orange"
+  labels[grep("ANO_", x$order.lab)] = "grey"
+  labels[grep("ChrX_", x$order.lab)] = "lightblue"
+  xlab = paste(xlab, "|| Agglomerative coefficient = ", round(x$ac, digits = 2))
+  
+  factoextra::fviz_dend(x, k = k, 
+                        k_colors = rep("black", k), 
+                        color_labels_by_k = FALSE, 
+                        rect = TRUE, 
+                        labels_track_height = lth, 
+                        label_cols = labels, 
+                        cex = cex,
+                        lower_rect = lrect,
+                        main = main, xlab = xlab, ylab = ylab) + theme(plot.margin = margin(5,15,5,5))
+}
+
+
+#### Boxplots and descriptive statistics
+
+myDescPlot = function(x, type = "boxplot",  main = "", ylab = "freq", xlab = "", withOuville = FALSE){
+  names = c('CP', 'CT', 'M','R', 'S')
+  if(withOuville){
+    names = c(names, 'O')
+  }
+  CORNEILLEP = x[,grepl('CORNEILLEP', colnames(x))]
+  CORNEILLET = x[,grepl('CORNEILLET', colnames(x))]
+  MOLIERE = x[,grepl('MOLIERE', colnames(x))]
+  ROTROU = x[,grepl('ROTROU', colnames(x))]
+  SCARRON = x[,grepl('SCARRON', colnames(x))]
+  if(withOuville){
+    OUVILLE = x[,grepl('OUVILLE', colnames(x))]
+  }
+  if('counts' %in% type){
+    return(list(CORNEILLEP, CORNEILLET, MOLIERE, ROTROU, SCARRON, OUVILLE))
+  }
+  if('boxplot' %in% type){ 
+    #boxplot
+    if(withOuville){
+      boxplot(list(CORNEILLEP, CORNEILLET, MOLIERE, ROTROU, SCARRON, OUVILLE), names=names, main=main,ylab=ylab) 
+    }
+    else{
+      boxplot(list(CORNEILLEP, CORNEILLET, MOLIERE, ROTROU, SCARRON), names=names, main=main,ylab=ylab) 
+    }
+  }
+  if('violinplot' %in% type){ 
+    #violinplot
+    data = cbind(as.data.frame(t(x)), sub("_.*$", "", colnames(x)))
+    colnames(data)[2] = "author"
+    
+    levels(data[,2]) = sub("CORNEILLEP","CP", levels(data[,2]))
+    levels(data[,2]) = sub("CORNEILLET","CT", levels(data[,2]))
+    levels(data[,2]) = sub("MOLIERE","M", levels(data[,2]))
+    levels(data[,2]) = sub("OUVILLE","O", levels(data[,2]))
+    levels(data[,2]) = sub("ROTROU","R", levels(data[,2]))
+    levels(data[,2]) = sub("SCARRON","S", levels(data[,2]))
+    levels(data[,2]) = sub("BOISSY","B", levels(data[,2]))
+    levels(data[,2]) = sub("DANCOURT","DA", levels(data[,2]))
+    levels(data[,2]) = sub("DUFRESNY","DU", levels(data[,2]))
+    levels(data[,2]) = sub("NIVELLE","N", levels(data[,2]))
+    levels(data[,2]) = sub("REGNARD","R", levels(data[,2]))
+    levels(data[,2]) = sub("VOLTAIRE","V", levels(data[,2]))
+    levels(data[,2]) = sub("BOURSAULT","B", levels(data[,2]))
+    levels(data[,2]) = sub("CHEVALIER","C", levels(data[,2]))
+    levels(data[,2]) = sub("DONNEAUDEVISE","DDV", levels(data[,2]))
+    levels(data[,2]) = sub("DORIMOND","DOR", levels(data[,2]))
+    levels(data[,2]) = sub("GILLET","G", levels(data[,2]))
+    levels(data[,2]) = sub("LAFONTAINE","LF", levels(data[,2]))
+    levels(data[,2]) = sub("QUINAULT","Q", levels(data[,2]))
+    
+    violinplot <- ggplot(data, aes_(x = quote(author), y = as.name(colnames(data)[1]))) +
+      ggtitle(main) +
+      ylab(ylab) +
+      xlab(xlab) +
+      geom_violin() + 
+      geom_boxplot(width=0.1) +
+      theme(axis.text.x = element_text(size = rel(0.7)))
+    
+    return(violinplot)
+    
+  }
+  if('barplot' %in% type){ 
+    data = cbind(as.data.frame(t(x)), sub("_.*$", "", colnames(x)))
+    colnames(data)[2] = "author"
+    barplot = ggplot(data, aes_(x = quote(author), y = as.name(colnames(data)[1]))) +
+      ggtitle(main) +
+      ylab(ylab) +
+      xlab("") +
+      geom_col() # equivalent to geom_bar(stat=identity)
+    
+    return(barplot)
+  }
+}
+
+classesDesc = function(x, y, k = "10"){
+  # Classes description
+  classes = cutree(x, k = k)
+  #Add classes to data frame
+  dataClassif = t(y)
+  dataClassif = cbind(as.data.frame(dataClassif), as.factor(classes))
+  colnames(dataClassif[length(dataClassif)])[] = "Classes"
+  dataClassif[length(dataClassif)]
+  # Desc
+  classDesc = FactoMineR::catdes(dataClassif, num.var = length(dataClassif))
+  return(classDesc)
+}
+
+specifPlot = function(freq_abs, myCAH, k = 5, nfeats = 5){
+  # freq_abs: absolute frequencies original data
+  # myCAH : the CAH to cut
+  # k : the number of classes
+  # nfeats: number of positive and negative feats to 
+  # TODO: Mieux, utiliser seuil de banalité
+  classes = cutree(myCAH, k = k)
+  freq_abs_class = matrix(nrow = nrow(freq_abs), ncol = length(unique(classes)), dimnames = list(rownames(freq_abs), unique(classes)))
+  for(i in 1:length(unique(classes))){
+    # sum the values for each member of the class
+    freq_abs_class[, i] = rowSums(freq_abs[, classes == i])
+  }
+  specs = textometry::specificities(freq_abs_class)
+  
+  plots = list()
+  for(i in 1:ncol(specs)){
+    # et maintenant, on peut regarder les spécificités de la classe 1 
+    # positives ou négatives
+    values = c(head(sort(specs[, i], decreasing = TRUE), n = nfeats), head(sort(specs[, i]), n = nfeats))
+    colors = vector(mode="logical", length = length(values))
+    colors[values>0] = TRUE
+    colors[values<=0] = FALSE
+    df = cbind(labels(values), as.data.frame(values), as.data.frame(colors))
+    colnames(df)[1] = "labels"
+    plots[[i]] = 
+      ggplot(df, aes(x = reorder(labels, values), y = values, fill = colors)) +
+      geom_col(position = "identity", colour = "black", size = 0.25) +
+      scale_fill_manual(values = c("#CCEEFF", "#FFDDDD"), guide = FALSE) +
+      ggtitle(paste("Specificities for class ", i)) +
+      ylab("Specif.") +
+      xlab("feats")
+    
+  }
+  myfun <- get("grid.arrange", asNamespace("gridExtra"))
+  do.call("myfun", c(plots, ncol=2))
+} 
+
+
+### Data manipulation
+
+aggrAndClean = function(x){
+  # aggregate
+  aggr = aggregate(x~rownames(x),FUN = sum)
+  # cleaning
+  x = as.matrix(aggr[,-1])
+  rownames(x) = aggr[,1]
+  return(x)
+}
+
+countAffixes = function(x){
+  # Prefixes
+  prefs = x
+  # Remove words shorter than 3+1 chars
+  prefs = prefs[stringr::str_length(rownames(prefs)) > 3,]
+  # Extract the first three chars as new rownames
+  rownames(prefs) = paste("$", substr(rownames(prefs), 1, 3), sep = "")
+  prefs = aggrAndClean(prefs)
+  
+  # Space prefixes
+  spPrefs = x 
+  rownames(spPrefs) = paste("_", substr(rownames(spPrefs), 1, 2), sep = "")
+  spPrefs = aggrAndClean(spPrefs)
+  
+  # Suffixes
+  sufs = x
+  sufs = sufs[stringr::str_length(rownames(sufs)) > 3,]
+  rownames(sufs) = paste(stringr::str_sub(rownames(sufs), -3), "^", sep = "")
+  sufs = aggrAndClean(sufs)
+  
+  # Space suffixes
+  spSufs = x
+  rownames(spSufs) = paste(stringr::str_sub(rownames(spSufs), -2), "_", sep = "")
+  spSufs = aggrAndClean(spSufs)
+  
+  results = rbind(prefs, spPrefs, sufs, spSufs)
+  
+  return(results)
+}