# HG changeset patch
# User ecology
# Date 1706111563 0
# Node ID 9dc992f80c257b0bce6e19211639205350f14010
# Parent  e3cd588fd14a37908e34d3aeef300ceb7ffe63d7
planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/Ecoregionalization_workflow commit 459ba1277acd7d8d4a02f90dbd7ff444bf8eac92

diff -r e3cd588fd14a -r 9dc992f80c25 brt.R
--- a/brt.R	Wed Oct 18 09:58:17 2023 +0000
+++ b/brt.R	Wed Jan 24 15:52:43 2024 +0000
@@ -1,109 +1,121 @@
-#16/02/2023
-## Analyse BRT data Ceamarc
-
-### Clean environment 
-rm(list = ls(all.names = TRUE))
-options(warn=-1)
-
-### load packages
-
-library(dismo, warn.conflicts = FALSE)
-library(gbm, warn.conflicts = FALSE)
-library(ggplot2, warn.conflicts = FALSE)
-
-
-#load arguments
-args = commandArgs(trailingOnly=TRUE) 
-if (length(args)==0)
-{
-    stop("This tool needs at least one argument")
-}else{
-    enviro <- args[1]
-    species_files <- args[2]
-    abio_para <- args[3]
-}
-
-### load data
-
-env = read.table(enviro, header = TRUE, dec = ".", na.strings = "-9999")
-pred.vars = strsplit(abio_para, ",")[[1]] 
-data_files = strsplit(species_files,",")
-
-#environemental parameters
-#Carbo,Grav,Maxbearing,Maxmagnit,Meancurmag,Meansal,Meantheta,Mud,Prof,Rugosity,Sand,Seaice_prod,Sili,Slope,Standcurmag,Standsal,Standtheta
-
-#Load functions
-
-make.brt <- function(spe,data,pred.vars,env,nb_file){
-   brt_step <- gbm.step(data= data, gbm.x = pred.vars, gbm.y = spe, family = "bernoulli", tree.complexity = 2, learning.rate = 0.0001,max.trees = 10000,plot.main = F)
-   #plot
-   if (is.null(brt_step)==FALSE){
-     pdf(file = paste("BRT-",spe,".pdf"))
-     gbm.plot(brt_step, write.title = T,show.contrib = T, y.label = "fitted function",plot.layout = c(3,3))
-     dev.off()
-     #total deviance explained as (Leathwick et al., 2006)
-     total_deviance <- brt_step$self.statistics$mean.null
-     cross_validated_residual_deviance <- brt_step$cv.statistics$deviance.mean
-     total_deviance_explained <- (total_deviance - cross_validated_residual_deviance)/total_deviance
-     #Validation file
-     valid = cbind(spe,brt_step$cv.statistics$discrimination.mean,brt_step$gbm.call$tree.complexity,total_deviance_explained)
-     write.table(valid, paste(nb_file,"_brts_validation_ceamarc.tsv",sep=""), quote=FALSE, dec=".",sep="\t" ,row.names=F, col.names=F,append = T)}
-   
-   return(brt_step)
-   }
-
-make.prediction.brt <- function(brt_step){
-  #predictions
-  preds <- predict.gbm(brt_step,env,n.trees=brt_step$gbm.call$best.trees, type="response")
-  preds <- as.data.frame(cbind(env$lat,env$long,preds))
-  colnames(preds) <- c("lat","long","Prediction.index")
-  #carto
-  ggplot()+
-    geom_raster(data = preds , aes(x = long, y = lat, fill = Prediction.index))+
-    geom_raster(data = preds , aes(x = long, y = lat, alpha = Prediction.index))+
-    scale_alpha(range = c(0,1), guide = "none")+
-    scale_fill_viridis_c(
-      alpha = 1,
-      begin = 0,
-      end = 1,
-      direction = -1,
-      option = "D",
-      values = NULL,
-      space = "Lab",
-      na.value = "grey50",
-      guide = "colourbar",
-      aesthetics = "fill")+
-    xlab("Longitude") + ylab("Latitude")+ ggtitle(paste(spe,"Plot of BRT predictions"))+
-    theme(plot.title = element_text(size = 10))+
-    theme(axis.title.y = element_text(size = 10))+
-    theme(axis.title.x = element_text(size = 10))+
-    theme(axis.text.y = element_text(size = 10))+
-    theme(axis.text.x = element_text(size = 10))+
-    theme(legend.text = element_text(size = 10))+
-    theme(legend.title = element_text(size = 10))+ 
-    coord_quickmap()
-  output_directory <- ggsave(paste("BRT-",spe,"_pred_plot.png"))
-  
-  #Write prediction in a file
-  preds <- cbind(preds,spe)
-  write.table(preds, paste(nb_file,"_brts_pred_ceamarc.txt",sep=""), quote=FALSE, dec=".", row.names=F, col.names=T,append = T)
-}
-
-#### RUN BRT ####
-nb_file = 0
-
-for (file in data_files[[1]]) {
-  species_data <- read.table(file, dec = ",", sep = ";", header = TRUE, na.strings = "na", colClasses = "numeric")
-  nb_file = nb_file + 1
-  `%!in%` <- Negate(`%in%`)
-  sp = list()
-  for (n in names(species_data)) {
-    if (n %!in% names(env) && n != 'station'){
-       sp = cbind(sp,n)
-    }
-  }
-  
-  for (spe in sp){
-   try(make.prediction.brt(make.brt(spe,species_data,pred.vars,env,nb_file)))
-   }
-}
+#16/02/2023
+## Analyse BRT data
+
+### Clean environment 
+rm(list = ls(all.names = TRUE))
+options(warn=-1)
+
+### load packages
+
+library(dismo, warn.conflicts = FALSE)
+library(gbm, warn.conflicts = FALSE)
+library(ggplot2, warn.conflicts = FALSE)
+
+
+#load arguments
+args = commandArgs(trailingOnly=TRUE) 
+if (length(args)==0)
+{
+    stop("This tool needs at least one argument")
+}else{
+    enviro <- args[1]
+    species_files <- args[2]
+    abio_para <- args[3]
+    dec_env <- args[8]
+    dec_species <- args[9]
+}
+
+### load data
+
+env = read.table(enviro, dec = dec_env, header = TRUE, sep="\t", na.strings = "-9999")
+pred_vars = strsplit(abio_para, ",")[[1]] 
+data_files = strsplit(species_files,",")
+
+pred.vars <- character(length(pred_vars))
+
+for (i in seq_along(pred_vars)) {
+       pred_var_col <- as.numeric(pred_vars[i])
+       pred.vars[i] <- names(env)[pred_var_col]}
+       
+#environemental parameters
+#Carbo,Grav,Maxbearing,Maxmagnit,Meancurmag,Meansal,Meantheta,Mud,Prof,Rugosity,Sand,Seaice_prod,Sili,Slope,Standcurmag,Standsal,Standtheta
+
+#Load functions
+
+make.brt <- function(spe,data,pred.vars,env,nb_file){
+   brt_step <- gbm.step(data= data, gbm.x = pred.vars, gbm.y = spe, family = "bernoulli", tree.complexity = 2, learning.rate = 0.0001,max.trees = 10000,plot.main = F)
+   #plot
+   if (is.null(brt_step)==FALSE){
+     pdf(file = paste("BRT-",spe,".pdf"))
+     gbm.plot(brt_step, write.title = T,show.contrib = T, y.label = "fitted function",plot.layout = c(3,3))
+     dev.off()
+     #total deviance explained as (Leathwick et al., 2006)
+     total_deviance <- brt_step$self.statistics$mean.null
+     cross_validated_residual_deviance <- brt_step$cv.statistics$deviance.mean
+     total_deviance_explained <- (total_deviance - cross_validated_residual_deviance)/total_deviance
+     #Validation file
+     valid = cbind(spe,brt_step$cv.statistics$discrimination.mean,brt_step$gbm.call$tree.complexity,total_deviance_explained)
+     write.table(valid, paste(nb_file,"_brts_validation_ceamarc.tsv",sep=""), quote=FALSE, dec=".",sep="\t" ,row.names=F, col.names=F,append = T)}
+   
+   return(brt_step)
+   }
+
+make.prediction.brt <- function(brt_step){
+  #predictions
+  preds <- predict.gbm(brt_step,env,n.trees=brt_step$gbm.call$best.trees, type="response")
+  preds <- as.data.frame(cbind(env$lat,env$long,preds))
+  colnames(preds) <- c("lat","long","Prediction.index")
+  #carto
+  ggplot()+
+    geom_raster(data = preds , aes(x = long, y = lat, fill = Prediction.index))+
+    geom_raster(data = preds , aes(x = long, y = lat, alpha = Prediction.index))+
+    scale_alpha(range = c(0,1), guide = "none")+
+    scale_fill_viridis_c(
+      alpha = 1,
+      begin = 0,
+      end = 1,
+      direction = -1,
+      option = "D",
+      values = NULL,
+      space = "Lab",
+      na.value = "grey50",
+      guide = "colourbar",
+      aesthetics = "fill")+
+    xlab("Longitude") + ylab("Latitude")+ ggtitle(paste(spe,"Plot of BRT predictions"))+
+    theme(plot.title = element_text(size = 10))+
+    theme(axis.title.y = element_text(size = 10))+
+    theme(axis.title.x = element_text(size = 10))+
+    theme(axis.text.y = element_text(size = 10))+
+    theme(axis.text.x = element_text(size = 10))+
+    theme(legend.text = element_text(size = 10))+
+    theme(legend.title = element_text(size = 10))+ 
+    coord_quickmap()
+  output_directory <- ggsave(paste("BRT-",spe,"_pred_plot.png"))
+  
+  #Write prediction in a file
+  preds <- cbind(preds,spe)
+  write.table(preds, paste(nb_file,"_brts_pred_ceamarc.tsv",sep=""), quote=FALSE, dec=".", row.names=F, col.names=T,append = T,sep="\t")
+}
+
+#### RUN BRT ####
+nb_file = 0
+
+for (file in data_files[[1]]) {
+  species_data <- read.table(file, dec = dec_species, sep = "\t", header = TRUE, na.strings = "NA", colClasses = "numeric")
+  nb_file = nb_file + 1
+  `%!in%` <- Negate(`%in%`)
+  sp = list()
+  for (n in names(species_data)) {
+    if (n %!in% names(env) && n != 'station'){
+       sp = cbind(sp,n)
+    }
+  }     
+  for (spe in sp){
+   try(make.prediction.brt(make.brt(spe,species_data,pred.vars,env,nb_file)))
+   }
+}
+
+cat("Here is the list of your abiotic parameters:\n")
+cat(paste(pred.vars, collapse = ", "), "\n")
+
+
diff -r e3cd588fd14a -r 9dc992f80c25 cluster_ceamarc.R
--- a/cluster_ceamarc.R	Wed Oct 18 09:58:17 2023 +0000
+++ b/cluster_ceamarc.R	Wed Jan 24 15:52:43 2024 +0000
@@ -22,7 +22,7 @@
 }
 
 #load data 
-env.data <- read.table(enviro, header=TRUE, sep=" ",dec = ".", na.strings = "-9999.00")
+env.data <- read.table(enviro, header=TRUE, sep="\t",dec = ".", na.strings = "-9999")
 data.bio <- read.table(data.bio, header=TRUE, sep="\t")
 test3 <- read.table(data, header = TRUE, sep="\t") 
 
@@ -43,8 +43,8 @@
 names(clus) <- c("lat", "long", "cluster")
 clus <- cbind(clus,test3,env.data[,3:19])
 
-write.table(clus[1:3], file = "points_clus.txt",quote = FALSE, row.names = FALSE)
-write.table(clus, file = "clus.txt",quote = FALSE, row.names = FALSE)
+write.table(clus[1:3], file = "points_clus.tsv", quote = FALSE, sep="\t", row.names = FALSE)
+write.table(clus, file = "clus.tsv", quote = FALSE, sep="\t", row.names = FALSE)
 
 
 
diff -r e3cd588fd14a -r 9dc992f80c25 crea_carte_G.R
--- a/crea_carte_G.R	Wed Oct 18 09:58:17 2023 +0000
+++ b/crea_carte_G.R	Wed Jan 24 15:52:43 2024 +0000
@@ -15,7 +15,7 @@
     data <- args[1]
 }
 
-clus <- read.table(data, header=TRUE, na.strings = "na")
+clus <- read.table(data, sep="\t" ,header=TRUE, na.strings = "na")
 
 #tmap method
 
@@ -26,7 +26,7 @@
   summarize()
 
 map <- tm_shape(grouped_data) + 
-  tm_dots(col = "cluster", palette = "Accent", size = 0.1, title = "écorégions")+
+  tm_dots(col = "cluster", palette = "Accent", n=256, size = 0.1, title = "ecoregions")+
   tm_scale_bar(position = c("right","top"))+
   tm_compass(position = c("right","top"))+
   tm_layout(frame = FALSE,legend.position = c("left","bottom"))+
diff -r e3cd588fd14a -r 9dc992f80c25 nb_clust_G.R
--- a/nb_clust_G.R	Wed Oct 18 09:58:17 2023 +0000
+++ b/nb_clust_G.R	Wed Jan 24 15:52:43 2024 +0000
@@ -21,7 +21,7 @@
 
 #load data 
 
-env.data <- read.table(enviro, header = TRUE, dec = ".", na.strings = "-9999.00") 
+env.data <- read.table(enviro, sep="\t", header = TRUE, dec = ".", na.strings = "-9999") 
 
 ##List of modelled taxa used for clustering
 tv <- read.table(taxa_list, dec=".", sep=" ", header=F, na.strings = "NA") 
@@ -33,7 +33,7 @@
 data.bio = NULL
 
 for (i in 1:length(data_split[[1]])) {
-data.bio1 <- read.table(data_split[[1]][i], dec=".", sep=" ", header=T, na.strings = "NA")
+data.bio1 <- read.table(data_split[[1]][i], dec=".", sep="\t", header=T, na.strings = "NA")
 data.bio <- rbind(data.bio,data.bio1)
 remove(data.bio1)
 }
@@ -69,5 +69,5 @@
 
 # Plot SIH Index Chart by Number of Clusters
 png("Indices_SIH.png")
-plot(2:max_k, sih_values[2:max_k], type = "b", xlab = "Nombre de clusters", ylab = "Indice SIH")
+plot(2:max_k, sih_values[2:max_k], type = "b", xlab = "Number of clusters", ylab = "SIH index")
 dev.off()
diff -r e3cd588fd14a -r 9dc992f80c25 recup_list.xml
--- a/recup_list.xml	Wed Oct 18 09:58:17 2023 +0000
+++ b/recup_list.xml	Wed Jan 24 15:52:43 2024 +0000
@@ -16,21 +16,21 @@
         '$output3' 
     ]]></command>
     <inputs>
-         <param name="occurencefile" type="data" format="txt" multiple="True" label="Occurences file"/>
-         <param name="predictionfile" type="data" format="txt" multiple="True" label="Predictions file"/>
-         <param name="envfile" type="data" format="txt,csv,tabular" label="Environement file"/>
+         <param name="envfile" type="data" format="tabular" label="Environement file (tabular format only)" help="See example below"/>
+         <param name="occurencefile" type="data" format="tabular" multiple="True" label="Occurences file(s) (tabular format only)" help="See example below"/>
+         <param name="predictionfile" type="data" format="tabular" multiple="True" label="Predictions file(s)" help="See example below"/>
     </inputs>
     <outputs>
-     <data name="output1" from_work_dir="have_model.csv" format="csv" label="Summary of taxa model "/>
+     <data name="output1" from_work_dir="have_model.tsv" format="tabular" label="Summary of taxa model "/>
      <data name="output2" from_work_dir="list_taxa.txt" format="txt" label="List of taxa"/>
      <data name="output3" from_work_dir="list_taxa_clean.txt" format="txt" label="List of taxa clean"/>
     </outputs>
     <tests>
         <test>
-            <param name="occurencefile" value="cnidaria_filtered.csv"/>
-            <param name="predictionfile" value="1_brts_pred_ceamarc.txt"/>
-            <param name="envfile" value="ceamarc_env.csv"/>
-            <output name='output1' value="Summary_of_taxa_model.csv"/>
+            <param name="occurencefile" value="cnidaria_filtered.tsv"/>
+            <param name="predictionfile" value="1_brts_pred_ceamarc.tsv"/>
+            <param name="envfile" value="ceamarc_env.tsv"/>
+            <output name='output1' value="Summary_of_taxa_model.tsv"/>
             <output name='output2' value="List_of_taxa.txt"/>
             <output name='output3' value="List_of_taxa_clean.txt"/>
         </test>
@@ -53,7 +53,7 @@
 **How to use it ?**
 ===================
 
-This tool takes three inputs : Occurrences file, predictions file and Environement file. See example below. 
+This tool takes three inputs : Occurrences file(s), predictions file(s) and environement file. See example below. 
 
 
 **Example of environmentfile :** 
@@ -71,15 +71,15 @@
 
 **Example of occurences file :** 
 
-+---------+----------+-----------+------------------------+-----------+-----+
-| station |   lat    |   long    |Acanthorhabdus_fragilis | Acarnidae | ... |
-+---------+----------+-----------+------------------------+-----------+-----+
-|    1    |-65,999946|142,3360535|           0            |     1     | ... |
-+---------+----------+-----------+------------------------+-----------+-----+
-|   10    |-66,335407| 141,3028  |           0            |     1     | ... |
-+---------+----------+-----------+------------------------+-----------+-----+
-|   ...   |   ...    |   ...     |          ...           |    ...    | ... |
-+---------+----------+-----------+------------------------+-----------+-----+
++---------+----------+-----------+------------------------+-----------+-----+------+--------------+-----+
+| station |   lat    |   long    |Acanthorhabdus_fragilis | Acarnidae | ... | Grav |  Maxbearing  | ... |
++---------+----------+-----------+------------------------+-----------+-----+------+--------------+-----+
+|    1    |-65,999946|142,3360535|           0            |     1     | ... |28.59 |     3.67     | ... |
++---------+----------+-----------+------------------------+-----------+-----+------+--------------+-----+
+|   10    |-66,335407| 141,3028  |           0            |     1     | ... |28.61 |     3.64     | ... |
++---------+----------+-----------+------------------------+-----------+-----+------+--------------+-----+
+|   ...   |   ...    |   ...     |          ...           |    ...    | ... | ...  |     ...      | ... |
++---------+----------+-----------+------------------------+-----------+-----+------+--------------+-----+
 
 *Example of the predictions file :**
 
diff -r e3cd588fd14a -r 9dc992f80c25 recup_liste_taxon.R
--- a/recup_liste_taxon.R	Wed Oct 18 09:58:17 2023 +0000
+++ b/recup_liste_taxon.R	Wed Jan 24 15:52:43 2024 +0000
@@ -16,7 +16,7 @@
     enviro <- args[3]
 }
 
-env = read.table(enviro, header=T, na.strings = "na")
+env = read.table(enviro, sep="\t", dec=".", header=T, na.strings = "-9999")
 occurrence_files = strsplit(data,",")
 preds_files = strsplit(preds,",")
 
@@ -45,7 +45,7 @@
 ##########Execution########
 brt = NULL
 for (j in 1:length(preds_files[[1]])){
-    brt <- rbind(brt,read.table(preds_files[[1]][j], header = TRUE, na.strings = "na"))
+    brt <- rbind(brt,read.table(preds_files[[1]][j], sep="\t", header = TRUE, na.strings = "na"))
 }
 
 for (i in 1:length(occurrence_files[[1]])) {
@@ -53,7 +53,7 @@
   cmpt <- NULL
   taxon <- list()
   
-  occurrence <- read.table(occurrence_files[[1]][i], dec = ",", sep = ";", header = TRUE, na.strings = "na")
+  occurrence <- read.table(occurrence_files[[1]][i], sep = "\t", header = TRUE, na.strings = "na")
   
   taxon_names <- names(occurrence)
   new_taxon <- taxon_names[!(taxon_names %in% names(env)) & taxon_names != "station"]
@@ -66,8 +66,8 @@
 }
 
 #Taxa for which a model was obtained
-have_model2 = subset(have_model, have_model$`Model` != "N")
-have_model3 = subset(have_model, have_model$`Model` != "N")
+have_model2 = subset(have_model, have_model$`Model` != "No")
+have_model3 = subset(have_model, have_model$`Model` != "No")
 
 #Obtain a list of taxa (cleaned) that have obtained a BRT model (file that can be submitted to the match taxa tool of the WoRMS database to obtain their classification and be able to sort duplicates between taxonomic ranks)
 
@@ -80,7 +80,7 @@
 have_model <- have_model %>% filter(!str_ends(Taxa, "sp.1|sp[0-9]"))
 
 #extraction of the have_model object
-write.csv(have_model,file = "have_model.csv", quote = F, row.names = F)
+write.table(have_model,file = "have_model.tsv", sep="\t", quote = F, row.names = F)
 
 #getting list of taxa for next if not using worms
 list_taxon = have_model3$Taxa
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/1_brts_pred_ceamarc.tsv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/1_brts_pred_ceamarc.tsv	Wed Jan 24 15:52:43 2024 +0000
@@ -0,0 +1,32 @@
+lat	long	Prediction.index	spe
+-65.57	139.22	0.841140843659849	Actiniaria
+-65.57	139.22	0.84803516905332	Actiniaria
+-65.57	139.23	0.845987230372577	Actiniaria
+-65.57	139.24	0.845807234659773	Actiniaria
+-65.57	139.24	0.845815355113451	Actiniaria
+-65.57	139.25	0.838300654503413	Actiniaria
+-65.57	139.26	0.836294936412005	Actiniaria
+-65.57	139.26	0.836091676730536	Actiniaria
+-65.57	139.27	0.835788235641246	Actiniaria
+-65.57	139.28	0.83554848351174	Actiniaria
+-65.57	139.28	0.842919257873871	Actiniaria
+-65.57	139.29	0.843113627396164	Actiniaria
+-65.57	139.3	0.837674310999705	Actiniaria
+-65.57	139.3	0.78950055854148	Actiniaria
+-65.57	139.31	0.789217507689643	Actiniaria
+lat	long	Prediction.index	spe
+-65.57	139.22	0.247351772498557	Thouarella_vulpicauda
+-65.57	139.22	0.247098939858693	Thouarella_vulpicauda
+-65.57	139.23	0.246144373571671	Thouarella_vulpicauda
+-65.57	139.24	0.245908115738853	Thouarella_vulpicauda
+-65.57	139.24	0.245908115738853	Thouarella_vulpicauda
+-65.57	139.25	0.245908115738853	Thouarella_vulpicauda
+-65.57	139.26	0.292387985632338	Thouarella_vulpicauda
+-65.57	139.26	0.24323878088903	Thouarella_vulpicauda
+-65.57	139.27	0.233523508895553	Thouarella_vulpicauda
+-65.57	139.28	0.222381660561301	Thouarella_vulpicauda
+-65.57	139.28	0.22225741167332	Thouarella_vulpicauda
+-65.57	139.29	0.215965799041526	Thouarella_vulpicauda
+-65.57	139.3	0.224167139202414	Thouarella_vulpicauda
+-65.57	139.3	0.251901045968841	Thouarella_vulpicauda
+-65.57	139.31	0.251653126333346	Thouarella_vulpicauda
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/1_brts_pred_ceamarc.txt
--- a/test-data/1_brts_pred_ceamarc.txt	Wed Oct 18 09:58:17 2023 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,16 +0,0 @@
-lat long Prediction.index spe
--65.57 139.22 0.841029593071458 Actiniaria
--65.57 139.22 0.84809523959125 Actiniaria
--65.57 139.23 0.84593837029871 Actiniaria
--65.57 139.24 0.845795319302531 Actiniaria
--65.57 139.24 0.845803531789338 Actiniaria
--65.57 139.25 0.838195173926856 Actiniaria
--65.57 139.26 0.836419710602451 Actiniaria
--65.57 139.26 0.836029330691246 Actiniaria
--65.57 139.27 0.835738500636347 Actiniaria
--65.57 139.28 0.835442888556642 Actiniaria
--65.57 139.28 0.84289272242705 Actiniaria
--65.57 139.29 0.843110664350552 Actiniaria
--65.57 139.3 0.837595245997102 Actiniaria
--65.57 139.3 0.791165108348526 Actiniaria
--65.57 139.31 0.790943656953059 Actiniaria
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/Data.bio_table.tsv
--- a/test-data/Data.bio_table.tsv	Wed Oct 18 09:58:17 2023 +0000
+++ b/test-data/Data.bio_table.tsv	Wed Jan 24 15:52:43 2024 +0000
@@ -1,16 +1,31 @@
 lat	long	pred	taxon
--65.57	139.22	0.841029593071458	Actiniaria
--65.57	139.22	0.84809523959125	Actiniaria
--65.57	139.23	0.84593837029871	Actiniaria
--65.57	139.24	0.845795319302531	Actiniaria
--65.57	139.24	0.845803531789338	Actiniaria
--65.57	139.25	0.838195173926856	Actiniaria
--65.57	139.26	0.836419710602451	Actiniaria
--65.57	139.26	0.836029330691246	Actiniaria
--65.57	139.27	0.835738500636347	Actiniaria
--65.57	139.28	0.835442888556642	Actiniaria
--65.57	139.28	0.84289272242705	Actiniaria
--65.57	139.29	0.843110664350552	Actiniaria
--65.57	139.3	0.837595245997102	Actiniaria
--65.57	139.3	0.791165108348526	Actiniaria
--65.57	139.31	0.790943656953059	Actiniaria
+-65.57	139.22	0.841140843659849	Actiniaria
+-65.57	139.22	0.84803516905332	Actiniaria
+-65.57	139.23	0.845987230372577	Actiniaria
+-65.57	139.24	0.845807234659773	Actiniaria
+-65.57	139.24	0.845815355113451	Actiniaria
+-65.57	139.25	0.838300654503413	Actiniaria
+-65.57	139.26	0.836294936412005	Actiniaria
+-65.57	139.26	0.836091676730536	Actiniaria
+-65.57	139.27	0.835788235641246	Actiniaria
+-65.57	139.28	0.83554848351174	Actiniaria
+-65.57	139.28	0.842919257873871	Actiniaria
+-65.57	139.29	0.843113627396164	Actiniaria
+-65.57	139.3	0.837674310999705	Actiniaria
+-65.57	139.3	0.78950055854148	Actiniaria
+-65.57	139.31	0.789217507689643	Actiniaria
+-65.57	139.22	0.247351772498557	Thouarella_vulpicauda
+-65.57	139.22	0.247098939858693	Thouarella_vulpicauda
+-65.57	139.23	0.246144373571671	Thouarella_vulpicauda
+-65.57	139.24	0.245908115738853	Thouarella_vulpicauda
+-65.57	139.24	0.245908115738853	Thouarella_vulpicauda
+-65.57	139.25	0.245908115738853	Thouarella_vulpicauda
+-65.57	139.26	0.292387985632338	Thouarella_vulpicauda
+-65.57	139.26	0.24323878088903	Thouarella_vulpicauda
+-65.57	139.27	0.233523508895553	Thouarella_vulpicauda
+-65.57	139.28	0.222381660561301	Thouarella_vulpicauda
+-65.57	139.28	0.22225741167332	Thouarella_vulpicauda
+-65.57	139.29	0.215965799041526	Thouarella_vulpicauda
+-65.57	139.3	0.224167139202414	Thouarella_vulpicauda
+-65.57	139.3	0.251901045968841	Thouarella_vulpicauda
+-65.57	139.31	0.251653126333346	Thouarella_vulpicauda
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/Data_to_cluster.tsv
--- a/test-data/Data_to_cluster.tsv	Wed Oct 18 09:58:17 2023 +0000
+++ b/test-data/Data_to_cluster.tsv	Wed Jan 24 15:52:43 2024 +0000
@@ -1,16 +1,16 @@
-Actiniaria
-0.841029593071458
-0.84809523959125
-0.84593837029871
-0.845795319302531
-0.845803531789338
-0.838195173926856
-0.836419710602451
-0.836029330691246
-0.835738500636347
-0.835442888556642
-0.84289272242705
-0.843110664350552
-0.837595245997102
-0.791165108348526
-0.790943656953059
+Actiniaria	Thouarella_vulpicauda
+0.841140843659849	0.247351772498557
+0.84803516905332	0.247098939858693
+0.845987230372577	0.246144373571671
+0.845807234659773	0.245908115738853
+0.845815355113451	0.245908115738853
+0.838300654503413	0.245908115738853
+0.836294936412005	0.292387985632338
+0.836091676730536	0.24323878088903
+0.835788235641246	0.233523508895553
+0.83554848351174	0.222381660561301
+0.842919257873871	0.22225741167332
+0.843113627396164	0.215965799041526
+0.837674310999705	0.224167139202414
+0.78950055854148	0.251901045968841
+0.789217507689643	0.251653126333346
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/List_of_taxa.txt
--- a/test-data/List_of_taxa.txt	Wed Oct 18 09:58:17 2023 +0000
+++ b/test-data/List_of_taxa.txt	Wed Jan 24 15:52:43 2024 +0000
@@ -1,7 +1,2 @@
-Acanthogorgiidae
 Actiniaria
-Ainigmaptilon_edisto
-Alcyonacea
-Anthozoa
-Thouarella_variabilis
 Thouarella_vulpicauda
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/List_of_taxa_clean.txt
--- a/test-data/List_of_taxa_clean.txt	Wed Oct 18 09:58:17 2023 +0000
+++ b/test-data/List_of_taxa_clean.txt	Wed Jan 24 15:52:43 2024 +0000
@@ -1,7 +1,2 @@
-Acanthogorgiidae
 Actiniaria
-Ainigmaptilon edisto
-Alcyonacea
-Anthozoa
-Thouarella variabilis
 Thouarella vulpicauda
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/SIH_index_plot.png
Binary file test-data/SIH_index_plot.png has changed
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/Summary_of_taxa_model.csv
--- a/test-data/Summary_of_taxa_model.csv	Wed Oct 18 09:58:17 2023 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,8 +0,0 @@
-Taxa,Model,Occurences
-Acanthogorgiidae,No,0
-Actiniaria,Yes,50
-Ainigmaptilon_edisto,No,2
-Alcyonacea,No,43
-Anthozoa,No,62
-Thouarella_variabilis,No,3
-Thouarella_vulpicauda,No,24
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/Summary_of_taxa_model.tsv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Summary_of_taxa_model.tsv	Wed Jan 24 15:52:43 2024 +0000
@@ -0,0 +1,8 @@
+Taxa	Model	Occurences
+Acanthogorgiidae	No	0
+Actiniaria	Yes	50
+Ainigmaptilon_edisto	No	2
+Alcyonacea	No	43
+Anthozoa	No	62
+Thouarella_variabilis	No	3
+Thouarella_vulpicauda	Yes	24
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/ceamarc_env.csv
--- a/test-data/ceamarc_env.csv	Wed Oct 18 09:58:17 2023 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,16 +0,0 @@
-long lat Carbo Grav Maxbearing Maxmagnit Meancurmag Meansal Meantheta Mud Prof Rugosity Sand Seaice_prod Sili Slope Standcurmag Standsal Standtheta long_round lat_round
-139.22 -65.57 0.88 28.59 3.67 0.03 0.03 34.62 -0.13 22.72 -441.00 -9999.00 55.76 0.24 3.27 0.28 0.01 0.01 0.18 139,22 -65,57
-139.22 -65.57 0.88 28.61 3.64 0.02 0.03 34.62 -0.13 22.48 -439.00 -9999.00 55.74 0.24 3.29 0.27 0.01 0.01 0.18 139,22 -65,57
-139.23 -65.57 0.92 28.62 3.59 0.02 0.03 34.62 -0.14 22.25 -438.00 -9999.00 56.28 0.25 3.32 0.22 0.01 0.01 0.19 139,23 -65,57
-139.24 -65.57 0.92 28.63 3.51 0.01 0.03 34.62 -0.14 21.95 -436.00 -9999.00 56.57 0.26 3.30 0.08 0.01 0.01 0.19 139,24 -65,57
-139.24 -65.57 0.92 28.64 3.35 0.01 0.03 34.62 -0.14 21.70 -437.00 -9999.00 56.58 0.26 3.28 0.05 0.01 0.01 0.19 139,24 -65,57
-139.25 -65.57 0.93 28.65 3.00 9.6293305978179e-03 0.03 34.62 -0.15 21.44 -436.00 -9999.00 56.63 0.26 3.26 0.29 0.01 0.01 0.19 139,25 -65,57
-139.26 -65.57 0.93 28.63 2.49 8.71255807578564e-03 0.03 34.62 -0.15 21.11 -432.00 -9999.00 56.67 0.26 3.23 0.43 0.01 0.01 0.19 139,26 -65,57
-139.26 -65.57 0.93 28.64 2.01 0.01 0.03 34.62 -0.16 20.83 -429.00 -9999.00 56.71 0.26 3.21 0.37 0.01 0.01 0.19 139,26 -65,57
-139.27 -65.57 0.94 28.65 1.71 0.01 0.03 34.62 -0.16 20.55 -427.00 -9999.00 56.75 0.26 3.19 0.32 0.01 0.01 0.20 139,27 -65,57
-139.28 -65.57 0.94 28.66 1.54 0.01 0.03 34.62 -0.16 20.21 -424.00 -9999.00 56.80 0.26 3.17 0.28 0.01 0.01 0.20 139,28 -65,57
-139.28 -65.57 0.94 28.67 1.44 0.02 0.03 34.62 -0.17 19.74 -422.00 -9999.00 56.84 0.26 3.14 0.26 0.01 0.01 0.20 139,28 -65,57
-139.29 -65.57 0.94 28.68 1.74 0.01 0.03 34.62 -0.17 20.86 -421.00 -9999.00 56.87 0.26 3.13 0.22 0.01 0.01 0.20 139,29 -65,57
-139.30 -65.57 0.95 28.70 3.46 0.01 0.03 34.62 -0.17 21.30 -420.00 -9999.00 56.91 0.26 3.11 0.32 0.01 0.01 0.20 139,30 -65,57
-139.30 -65.57 0.95 28.71 3.91 0.03 0.03 34.62 -0.18 21.01 -414.00 -9999.00 57.18 0.26 3.09 0.59 0.01 0.01 0.21 139,30 -65,57
-139.31 -65.57 0.96 28.72 4.03 0.05 0.03 34.62 -0.18 20.76 -406.00 -9999.00 57.54 0.26 3.07 0.53 0.01 0.01 0.21 139,31 -65,57
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/ceamarc_env.tsv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/ceamarc_env.tsv	Wed Jan 24 15:52:43 2024 +0000
@@ -0,0 +1,16 @@
+long	lat	Carbo	Grav	Maxbearing	Maxmagnit	Meancurmag	Meansal	Meantheta	Mud	Prof	Rugosity	Sand	Seaice_prod	Sili	Slope	Standcurmag	Standsal	Standtheta	long_round	lat_round
+1	139.22	-65.57	0.88	28.59	3.67	0.03	0.03	34.62	-0.13	22.72	-441	-9999	55.76	0.24	3.27	0.28	0.01	0.01	0.18	139,22	-65,57
+2	139.22	-65.57	0.88	28.61	3.64	0.02	0.03	34.62	-0.13	22.48	-439	-9999	55.74	0.24	3.29	0.27	0.01	0.01	0.18	139,22	-65,57
+3	139.23	-65.57	0.92	28.62	3.59	0.02	0.03	34.62	-0.14	22.25	-438	-9999	56.28	0.25	3.32	0.22	0.01	0.01	0.19	139,23	-65,57
+4	139.24	-65.57	0.92	28.63	3.51	0.01	0.03	34.62	-0.14	21.95	-436	-9999	56.57	0.26	3.3	0.08	0.01	0.01	0.19	139,24	-65,57
+5	139.24	-65.57	0.92	28.64	3.35	0.01	0.03	34.62	-0.14	21.7	-437	-9999	56.58	0.26	3.28	0.05	0.01	0.01	0.19	139,24	-65,57
+6	139.25	-65.57	0.93	28.65	3	0.0096293305978179	0.03	34.62	-0.15	21.44	-436	-9999	56.63	0.26	3.26	0.29	0.01	0.01	0.19	139,25	-65,57
+7	139.26	-65.57	0.93	28.63	2.49	0.00871255807578564	0.03	34.62	-0.15	21.11	-432	-9999	56.67	0.26	3.23	0.43	0.01	0.01	0.19	139,26	-65,57
+8	139.26	-65.57	0.93	28.64	2.01	0.01	0.03	34.62	-0.16	20.83	-429	-9999	56.71	0.26	3.21	0.37	0.01	0.01	0.19	139,26	-65,57
+9	139.27	-65.57	0.94	28.65	1.71	0.01	0.03	34.62	-0.16	20.55	-427	-9999	56.75	0.26	3.19	0.32	0.01	0.01	0.2	139,27	-65,57
+10	139.28	-65.57	0.94	28.66	1.54	0.01	0.03	34.62	-0.16	20.21	-424	-9999	56.8	0.26	3.17	0.28	0.01	0.01	0.2	139,28	-65,57
+11	139.28	-65.57	0.94	28.67	1.44	0.02	0.03	34.62	-0.17	19.74	-422	-9999	56.84	0.26	3.14	0.26	0.01	0.01	0.2	139,28	-65,57
+12	139.29	-65.57	0.94	28.68	1.74	0.01	0.03	34.62	-0.17	20.86	-421	-9999	56.87	0.26	3.13	0.22	0.01	0.01	0.2	139,29	-65,57
+13	139.3	-65.57	0.95	28.7	3.46	0.01	0.03	34.62	-0.17	21.3	-420	-9999	56.91	0.26	3.11	0.32	0.01	0.01	0.2	139,30	-65,57
+14	139.3	-65.57	0.95	28.71	3.91	0.03	0.03	34.62	-0.18	21.01	-414	-9999	57.18	0.26	3.09	0.59	0.01	0.01	0.21	139,30	-65,57
+15	139.31	-65.57	0.96	28.72	4.03	0.05	0.03	34.62	-0.18	20.76	-406	-9999	57.54	0.26	3.07	0.53	0.01	0.01	0.21	139,31	-65,57
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/cnidaria_filtered.csv
--- a/test-data/cnidaria_filtered.csv	Wed Oct 18 09:58:17 2023 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,64 +0,0 @@
-station;lat;long;Carbo;Grav;Maxbearing;Maxmagnit;Meancurmag;Meansal;Meantheta;Mud;Prof;Rugosity;Sand;Seaice_prod;Sili;Slope;Standcurmag;Standsal;Standtheta;Acanthogorgiidae;Actiniaria;Ainigmaptilon_edisto;Alcyonacea;Anthozoa;Thouarella_variabilis;Thouarella_vulpicauda
-1;-65,99;142,33;5,51;12,59;2,03;0,16;0,07;34,6;-1,47;29,8;-233;1;56,16;0,11;3,94;0,05;0,03;0,03;0,24;0;1;0;0;1;0;0
-10;-66,33;141,3;3,17;5,31;5,2;0,18;0,08;34,61;-1,35;21,5;-215;1;67,39;0,17;5,8;0,24;0,03;0,02;0,28;0;1;0;1;1;0;0
-11;-66,56;141,29;3,1;3,28;4,93;0,2;0,08;34,65;-1,73;34,89;-192;1;73,13;0,87;4,48;0,71;0,03;0,04;0,24;0;0;0;1;1;0;0
-12;-66,55;140,82;2,78;3,08;3,74;0,34;0,1;34,59;-1,19;25,83;-217;1;68,37;2,62;4,05;2,57;0,06;0,02;0,24;0;1;0;1;1;1;0
-13;-66,15;140,65;2,06;7,62;5,36;0,21;0,09;34,56;-1,04;13,18;-217;1;71,68;0,07;2,89;0,37;0,04;0,02;0,26;0;1;0;0;1;0;0
-14;-66,33;140,67;2,96;3,12;2,42;0,16;0,08;34,54;-1,02;12,15;-171;1;73,16;0,78;1,17;0,08;0,03;0,01;0,22;0;1;0;0;1;0;0
-15;-66,38;139,8;1,06;1,65;1,87;0,14;0,05;34,64;-1,53;69,08;-855;1;48,44;1,43;13,55;2,94;0,03;0,01;0,06;0;1;0;1;1;0;0
-16;-66,34;139,99;1,34;2,78;1,61;0,15;0,05;34,6;-1,12;41,06;-538;1;54,72;1,23;5,97;3,7;0,03;0,00839;0,08;0;1;0;0;1;0;0
-17;-66,17;139,96;1,71;8,13;4,39;0,31;0,12;34,54;-0,96;17,16;-151;1;61,42;0,54;1,99;0,25;0,05;0,02;0,3;0;1;0;1;1;1;0
-18;-66,16;139,65;1,49;7,75;2,24;0,15;0,06;34,59;-1,01;32,71;-432;1;51,4;0,56;3,35;0,42;0,03;0,01;0,26;0;1;0;1;1;0;1
-19;-66,15;139,31;1,18;8,58;5,7;0,12;0,04;34,61;-1,36;47,4;-674;1;46,41;0,34;9,92;0,31;0,02;0,00722;0,09;0;1;0;0;1;0;0
-2;-65,99;141,32;3,3;14,21;5,01;0,16;0,07;34,6;-1,26;14,05;-235;1;67,07;-0,01;2,09;0,13;0,03;0,03;0,32;0;1;1;1;1;0;1
-20;-66;139,99;1,95;20,59;4,98;0,19;0,09;34,56;-1,05;13,25;-192;1;63,46;0,27;1,92;0,21;0,03;0,03;0,26;0;1;0;0;1;0;0
-21;-66;139,64;2,26;18,21;2,85;0,16;0,07;34,58;-1,04;44,81;-277;1;57,14;0,76;5,88;1,66;0,03;0,01;0,26;0;1;0;0;1;0;0
-22;-66;139,31;1,39;19,2;4,87;0,1;0,04;34,6;-1,05;37,84;-476;1;49,4;0,65;7,09;0,25;0,02;0,00884;0,15;0;1;0;1;1;0;0
-26;-66,52;140,02;1,51;1,47;4,4;0,12;0,05;34,6;-1,11;49,11;-247;1;49,52;2,38;10,29;3,16;0,02;0,00984;0,16;0;1;0;0;1;0;0
-27;-66,02;142,74;2,61;11,58;5,42;0,13;0,05;34,64;-1,73;25,07;-440;1;46,97;0,24;2,19;0,23;0,02;0,03;0,17;0;1;0;1;1;0;1
-28;-65,99;143,02;2,15;11,5;5,37;0,14;0,05;34,64;-1,77;39,29;-467;1;38,66;0,24;2,07;0,32;0,02;0,03;0,12;0;1;0;1;1;0;1
-29;-66,02;143,29;1,77;9,98;5,41;0,14;0,05;34,64;-1,78;62,2;-470;1;38,73;0,33;4,81;0,19;0,02;0,03;0,1;0;1;0;1;1;0;1
-3;-65,99;141,98;5,27;14,32;5,31;0,01;0,06;34,6;-1,4;22,84;-245;1;61,04;-0,00886;3,93;0,15;0,03;0,03;0,25;0;1;0;0;1;0;0
-30;-66;143,68;2,06;9,47;5,42;0,15;0,06;34,61;-1,71;54,73;-429;1;41,27;0,39;1,03;0,22;0,03;0,03;0,19;0;1;0;0;1;0;0
-31;-66,55;144,99;1,05;19,55;5,28;0,12;0,03;34,62;-1,73;35,57;-437;1;39,47;5,14;1,77;0,45;0,02;0,03;0,16;0;0;0;1;1;0;1
-34;-66,33;144,34;1,26;6,31;4,92;0,13;0,04;34,63;-1,77;44,96;-455;1;40,49;1,87;2,96;0,39;0,02;0,03;0,12;0;1;0;1;1;0;1
-35;-66,33;144,01;1,2;4,9;2,11;0,12;0,04;34,64;-1,78;49,09;-511;1;39,41;1,54;4,15;0,32;0,02;0,02;0,1;0;1;0;1;1;0;0
-36;-66,32;143,65;1,12;4,29;2,16;0,12;0,04;34,64;-1,79;53,85;-565;1;38,83;1,26;8,68;0,38;0,02;0,02;0,13;0;1;0;1;1;0;1
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-63;-65,85;142,98;3,71;20,11;5,24;0,27;0,11;34,61;-1,22;24,73;-423;1;41,04;0,19;1,03;0,56;0,05;0,02;0,44;0;0;0;1;1;0;1
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-66;-65,75;143,04;NA;NA;4,48;NA;NA;NA;NA;NA;NA;NA;NA;NA;NA;NA;NA;NA;NA;0;0;0;1;1;0;0
-7;-66,55;142,64;2,12;4,31;1,77;0,18;0,07;34,66;-1,75;15,34;-255;1;65,49;1,46;1,38;6,15;0,03;0,03;0,18;0;0;0;0;0;0;0
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-71;-66,39;140,48;2,22;2,24;5,27;0,06;0,02;34,62;-1,36;19,67;-768;1;67,65;1,36;3,1;7,04;0,01;0,01;0,04;0;1;0;1;1;0;1
-72;-66,34;140,48;2,18;2,6;2,34;0,09;0,04;34,58;-1,14;17,49;-407;1;67,61;1,01;2,1;2,74;0,01;0,01;0,11;0;1;0;1;1;0;0
-79;-65,7;140,56;1,83;25,43;4,77;0,34;0,16;34,6;-0,94;3,7;-506;1;66,84;0,09;1,91;5,63;0,06;0,02;0,38;0;1;0;1;1;0;1
-8;-66,56;142,33;3,36;3,2;1,96;0,16;0,06;34,68;-1,85;67,67;-374;1;60,65;1,04;11,68;1,03;0,03;0,03;0,1;0;1;0;1;1;0;1
-81;-65,65;140,44;1,42;28,49;4,93;0,3;0,15;34,62;-0,81;3,75;-1193;1;66,73;0,13;1,29;5,59;0,05;0,02;0,32;0;0;0;1;1;0;0
-84;-65,45;139,37;0,98;28,81;1,16;0,06;0,03;34,62;-0,21;17,56;-395;NA;58,04;0,28;2,92;0,29;0,01;0,01;0,22;0;0;0;1;1;0;0
-86;-65,47;139,35;0,96;28,78;2,13;0,00947;0,03;34,62;-0,2;18,67;-398;NA;57,91;0,27;2,96;0,26;0,01;0,01;0,22;0;1;0;1;1;0;0
-87;-65,49;139,33;0,95;28,75;4,01;0,05;0,03;34,62;-0,19;20,03;-400;NA;57,77;0,27;3,02;0,42;0,01;0,01;0,21;0;1;0;0;1;0;0
-9;-66,55;141,99;3,19;3,31;5,38;0,17;0,05;34,68;-1,85;40,51;-357;1;65,13;0,81;6,85;3,19;0,03;0,03;0,1;0;0;0;1;1;0;1
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/cnidaria_filtered.tsv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cnidaria_filtered.tsv	Wed Jan 24 15:52:43 2024 +0000
@@ -0,0 +1,64 @@
+station	lat	long	Carbo	Grav	Maxbearing	Maxmagnit	Meancurmag	Meansal	Meantheta	Mud	Prof	Rugosity	Sand	Seaice_prod	Sili	Slope	Standcurmag	Standsal	Standtheta	Acanthogorgiidae	Actiniaria	Ainigmaptilon_edisto	Alcyonacea	Anthozoa	Thouarella_variabilis	Thouarella_vulpicauda
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+2	10	-66,33	141,3	3,17	5,31	5,2	0,18	0,08	34,61	-1,35	21,5	-215	1	67,39	0,17	5,8	0,24	0,03	0,02	0,28	0	1	0	1	1	0	0
+3	11	-66,56	141,29	3,1	3,28	4,93	0,2	0,08	34,65	-1,73	34,89	-192	1	73,13	0,87	4,48	0,71	0,03	0,04	0,24	0	0	0	1	1	0	0
+4	12	-66,55	140,82	2,78	3,08	3,74	0,34	0,1	34,59	-1,19	25,83	-217	1	68,37	2,62	4,05	2,57	0,06	0,02	0,24	0	1	0	1	1	1	0
+5	13	-66,15	140,65	2,06	7,62	5,36	0,21	0,09	34,56	-1,04	13,18	-217	1	71,68	0,07	2,89	0,37	0,04	0,02	0,26	0	1	0	0	1	0	0
+6	14	-66,33	140,67	2,96	3,12	2,42	0,16	0,08	34,54	-1,02	12,15	-171	1	73,16	0,78	1,17	0,08	0,03	0,01	0,22	0	1	0	0	1	0	0
+7	15	-66,38	139,8	1,06	1,65	1,87	0,14	0,05	34,64	-1,53	69,08	-855	1	48,44	1,43	13,55	2,94	0,03	0,01	0,06	0	1	0	1	1	0	0
+8	16	-66,34	139,99	1,34	2,78	1,61	0,15	0,05	34,6	-1,12	41,06	-538	1	54,72	1,23	5,97	3,7	0,03	0,00839	0,08	0	1	0	0	1	0	0
+9	17	-66,17	139,96	1,71	8,13	4,39	0,31	0,12	34,54	-0,96	17,16	-151	1	61,42	0,54	1,99	0,25	0,05	0,02	0,3	0	1	0	1	1	1	0
+10	18	-66,16	139,65	1,49	7,75	2,24	0,15	0,06	34,59	-1,01	32,71	-432	1	51,4	0,56	3,35	0,42	0,03	0,01	0,26	0	1	0	1	1	0	1
+11	19	-66,15	139,31	1,18	8,58	5,7	0,12	0,04	34,61	-1,36	47,4	-674	1	46,41	0,34	9,92	0,31	0,02	0,00722	0,09	0	1	0	0	1	0	0
+12	2	-65,99	141,32	3,3	14,21	5,01	0,16	0,07	34,6	-1,26	14,05	-235	1	67,07	-0,01	2,09	0,13	0,03	0,03	0,32	0	1	1	1	1	0	1
+13	20	-66	139,99	1,95	20,59	4,98	0,19	0,09	34,56	-1,05	13,25	-192	1	63,46	0,27	1,92	0,21	0,03	0,03	0,26	0	1	0	0	1	0	0
+14	21	-66	139,64	2,26	18,21	2,85	0,16	0,07	34,58	-1,04	44,81	-277	1	57,14	0,76	5,88	1,66	0,03	0,01	0,26	0	1	0	0	1	0	0
+15	22	-66	139,31	1,39	19,2	4,87	0,1	0,04	34,6	-1,05	37,84	-476	1	49,4	0,65	7,09	0,25	0,02	0,00884	0,15	0	1	0	1	1	0	0
+16	26	-66,52	140,02	1,51	1,47	4,4	0,12	0,05	34,6	-1,11	49,11	-247	1	49,52	2,38	10,29	3,16	0,02	0,00984	0,16	0	1	0	0	1	0	0
+17	27	-66,02	142,74	2,61	11,58	5,42	0,13	0,05	34,64	-1,73	25,07	-440	1	46,97	0,24	2,19	0,23	0,02	0,03	0,17	0	1	0	1	1	0	1
+18	28	-65,99	143,02	2,15	11,5	5,37	0,14	0,05	34,64	-1,77	39,29	-467	1	38,66	0,24	2,07	0,32	0,02	0,03	0,12	0	1	0	1	1	0	1
+19	29	-66,02	143,29	1,77	9,98	5,41	0,14	0,05	34,64	-1,78	62,2	-470	1	38,73	0,33	4,81	0,19	0,02	0,03	0,1	0	1	0	1	1	0	1
+20	3	-65,99	141,98	5,27	14,32	5,31	0,01	0,06	34,6	-1,4	22,84	-245	1	61,04	-0,00886	3,93	0,15	0,03	0,03	0,25	0	1	0	0	1	0	0
+21	30	-66	143,68	2,06	9,47	5,42	0,15	0,06	34,61	-1,71	54,73	-429	1	41,27	0,39	1,03	0,22	0,03	0,03	0,19	0	1	0	0	1	0	0
+22	31	-66,55	144,99	1,05	19,55	5,28	0,12	0,03	34,62	-1,73	35,57	-437	1	39,47	5,14	1,77	0,45	0,02	0,03	0,16	0	0	0	1	1	0	1
+23	34	-66,33	144,34	1,26	6,31	4,92	0,13	0,04	34,63	-1,77	44,96	-455	1	40,49	1,87	2,96	0,39	0,02	0,03	0,12	0	1	0	1	1	0	1
+24	35	-66,33	144,01	1,2	4,9	2,11	0,12	0,04	34,64	-1,78	49,09	-511	1	39,41	1,54	4,15	0,32	0,02	0,02	0,1	0	1	0	1	1	0	0
+25	36	-66,32	143,65	1,12	4,29	2,16	0,12	0,04	34,64	-1,79	53,85	-565	1	38,83	1,26	8,68	0,38	0,02	0,02	0,13	0	1	0	1	1	0	1
+26	37	-66,55	143,31	1,34	4,32	2,02	0,12	0,04	34,66	-1,82	78,15	-820	1	47,92	2,76	13,57	0,26	0,02	0,03	0,11	0	1	0	0	1	0	0
+27	38	-66,33	143,31	0,82	3,84	2,2	0,1	0,04	34,65	-1,8	84,13	-703	1	41,01	0,98	16,47	0,25	0,02	0,03	0,12	0	1	0	1	1	1	0
+28	39	-66,56	143,02	1,67	4,11	1,5	0,13	0,05	34,65	-1,8	47,63	-862	1	56,71	2,22	7,3	0,18	0,02	0,02	0,1	0	1	0	0	1	0	0
+29	40	-66,65	142,98	1,94	3,93	5,42	0,13	0,04	34,63	-1,75	41,4	-598	1	58,72	3,57	5,71	1,57	0,02	0,03	0,12	0	0	0	1	1	0	1
+30	41	-66,76	142,65	2,67	2,67	0,14	0,15	0,04	34,65	-1,76	38,32	-598	1	62,33	4,43	6,01	4,29	0,02	0,04	0,16	0	0	0	1	1	0	1
+31	42	-66,87	142,66	2,92	1,71	5,49	0,13	0,04	34,63	-1,72	47,93	-391	1	62,77	8,27	6,89	1,19	0,02	0,05	0,2	0	0	0	1	1	0	1
+32	43	-66,74	143,33	2	3,28	4,67	0,19	0,06	34,61	-1,74	62,82	-332	1	54,92	7,42	9,35	3,78	0,03	0,05	0,16	0	1	0	1	1	0	0
+33	45	-66,75	144	2,19	3,12	5,5	0,16	0,04	34,61	-1,8	42,36	-661	1	43,64	7,49	5,95	4,51	0,03	0,02	0,07	0	1	0	1	1	0	1
+34	46	-66,87	144,1	2,59	1,31	5,89	0,17	0,05	34,59	-1,78	38,87	-561	1	43,42	10,54	4,09	1,16	0,03	0,04	0,14	0	0	0	1	1	0	1
+35	48	-66,93	144,65	1,59	0,58	5,75	0,21	0,06	34,6	-1,83	73,19	-407	1	40,23	11,89	10,52	5,23	0,03	0,03	0,09	0	1	0	1	1	0	0
+36	5	-66,32	142,29	3,03	4,93	5,05	0,2	0,1	34,63	-1,61	39,53	-216	1	61,67	0,27	2,63	0,11	0,04	0,02	0,17	0	0	0	1	1	0	1
+37	50	-66,75	145,27	1,02	5,5	5,49	0,13	0,04	34,64	-1,77	52,4	-596	1	39,8	9,53	4,66	0,23	0,02	0,03	0,1	0	1	0	1	1	0	1
+38	51	-66,74	145,48	0,95	6,47	5,5	0,2	0,05	34,62	-1,78	49,32	-535	1	40,91	10,66	3,37	0,55	0,03	0,04	0,11	0	1	0	0	1	0	0
+39	52	-66,55	145,31	1,04	19,26	5,26	0,09	0,03	34,59	-1,67	36,48	-409	1	42,62	6,4	1,12	0,09	0,01	0,03	0,22	0	1	0	0	1	0	0
+40	53	-66,33	144,66	1,24	6,73	5,36	0,13	0,04	34,61	-1,71	62,77	-420	1	42,45	2,27	3,99	0,17	0,02	0,04	0,18	0	1	0	0	1	0	0
+41	54	-65,91	144,02	3,17	12,05	5,16	0,15	0,06	34,57	-0,92	31,38	-375	1	46,27	0,41	2,1	2,62	0,02	0,02	0,4	0	1	1	1	1	0	1
+42	55	-66,33	145,01	1,12	8,85	5,25	0,12	0,04	34,56	-1,68	40,75	-387	1	44,3	3,06	2,2	0,58	0,02	0,03	0,26	0	1	0	1	1	0	1
+43	56	-66,56	144,67	1,3	16,82	2,34	0,13	0,04	34,65	-1,79	45,94	-582	1	39,22	4,26	3,93	0,38	0,02	0,03	0,1	0	1	0	0	1	0	0
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diff -r e3cd588fd14a -r 9dc992f80c25 test-data/ecoregions.png
Binary file test-data/ecoregions.png has changed
diff -r e3cd588fd14a -r 9dc992f80c25 test-data/points_clus.tsv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/points_clus.tsv	Wed Jan 24 15:52:43 2024 +0000
@@ -0,0 +1,155824 @@
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diff -r e3cd588fd14a -r 9dc992f80c25 test-data/points_clus.txt
--- a/test-data/points_clus.txt	Wed Oct 18 09:58:17 2023 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,155824 +0,0 @@
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