| Previous changeset 0:32849c52aa54 (2023-10-18) Next changeset 2:24a81631a8b5 (2024-03-21) |
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Commit message:
planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/Ecoregionalization_workflow commit 459ba1277acd7d8d4a02f90dbd7ff444bf8eac92 |
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modified:
brt.R cluster.xml cluster_ceamarc.R crea_carte_G.R nb_clust_G.R recup_liste_taxon.R test-data/Data.bio_table.tsv test-data/Data_to_cluster.tsv test-data/List_of_taxa.txt test-data/List_of_taxa_clean.txt test-data/SIH_index_plot.png test-data/ecoregions.png |
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added:
test-data/1_brts_pred_ceamarc.tsv test-data/Summary_of_taxa_model.tsv test-data/ceamarc_env.tsv test-data/cnidaria_filtered.tsv test-data/points_clus.tsv |
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removed:
test-data/1_brts_pred_ceamarc.txt test-data/Summary_of_taxa_model.csv test-data/ceamarc_env.csv test-data/cnidaria_filtered.csv test-data/points_clus.txt |
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| diff -r 32849c52aa54 -r edb8d19735a6 brt.R --- a/brt.R Wed Oct 18 09:59:19 2023 +0000 +++ b/brt.R Wed Jan 24 15:53:07 2024 +0000 |
| [ |
| b'@@ -1,109 +1,121 @@\n-#16/02/2023\r\n-## Analyse BRT data Ceamarc\r\n-\r\n-### Clean environment \r\n-rm(list = ls(all.names = TRUE))\r\n-options(warn=-1)\r\n-\r\n-### load packages\r\n-\r\n-library(dismo, warn.conflicts = FALSE)\r\n-library(gbm, warn.conflicts = FALSE)\r\n-library(ggplot2, warn.conflicts = FALSE)\r\n-\r\n-\r\n-#load arguments\r\n-args = commandArgs(trailingOnly=TRUE) \r\n-if (length(args)==0)\r\n-{\r\n- stop("This tool needs at least one argument")\r\n-}else{\r\n- enviro <- args[1]\r\n- species_files <- args[2]\r\n- abio_para <- args[3]\r\n-}\r\n-\r\n-### load data\r\n-\r\n-env = read.table(enviro, header = TRUE, dec = ".", na.strings = "-9999")\r\n-pred.vars = strsplit(abio_para, ",")[[1]] \r\n-data_files = strsplit(species_files,",")\r\n-\r\n-#environemental parameters\r\n-#Carbo,Grav,Maxbearing,Maxmagnit,Meancurmag,Meansal,Meantheta,Mud,Prof,Rugosity,Sand,Seaice_prod,Sili,Slope,Standcurmag,Standsal,Standtheta\r\n-\r\n-#Load functions\r\n-\r\n-make.brt <- function(spe,data,pred.vars,env,nb_file){\r\n- 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)\r\n- #plot\r\n- if (is.null(brt_step)==FALSE){\r\n- pdf(file = paste("BRT-",spe,".pdf"))\r\n- gbm.plot(brt_step, write.title = T,show.contrib = T, y.label = "fitted function",plot.layout = c(3,3))\r\n- dev.off()\r\n- #total deviance explained as (Leathwick et al., 2006)\r\n- total_deviance <- brt_step$self.statistics$mean.null\r\n- cross_validated_residual_deviance <- brt_step$cv.statistics$deviance.mean\r\n- total_deviance_explained <- (total_deviance - cross_validated_residual_deviance)/total_deviance\r\n- #Validation file\r\n- valid = cbind(spe,brt_step$cv.statistics$discrimination.mean,brt_step$gbm.call$tree.complexity,total_deviance_explained)\r\n- write.table(valid, paste(nb_file,"_brts_validation_ceamarc.tsv",sep=""), quote=FALSE, dec=".",sep="\\t" ,row.names=F, col.names=F,append = T)}\r\n- \r\n- return(brt_step)\r\n- }\r\n-\r\n-make.prediction.brt <- function(brt_step){\r\n- #predictions\r\n- preds <- predict.gbm(brt_step,env,n.trees=brt_step$gbm.call$best.trees, type="response")\r\n- preds <- as.data.frame(cbind(env$lat,env$long,preds))\r\n- colnames(preds) <- c("lat","long","Prediction.index")\r\n- #carto\r\n- ggplot()+\r\n- geom_raster(data = preds , aes(x = long, y = lat, fill = Prediction.index))+\r\n- geom_raster(data = preds , aes(x = long, y = lat, alpha = Prediction.index))+\r\n- scale_alpha(range = c(0,1), guide = "none")+\r\n- scale_fill_viridis_c(\r\n- alpha = 1,\r\n- begin = 0,\r\n- end = 1,\r\n- direction = -1,\r\n- option = "D",\r\n- values = NULL,\r\n- space = "Lab",\r\n- na.value = "grey50",\r\n- guide = "colourbar",\r\n- aesthetics = "fill")+\r\n- xlab("Longitude") + ylab("Latitude")+ ggtitle(paste(spe,"Plot of BRT predictions"))+\r\n- theme(plot.title = element_text(size = 10))+\r\n- theme(axis.title.y = element_text(size = 10))+\r\n- theme(axis.title.x = element_text(size = 10))+\r\n- theme(axis.text.y = element_text(size = 10))+\r\n- theme(axis.text.x = element_text(size = 10))+\r\n- theme(legend.text = element_text(size = 10))+\r\n- theme(legend.title = element_text(size = 10))+ \r\n- coord_quickmap()\r\n- output_directory <- ggsave(paste("BRT-",spe,"_pred_plot.png"))\r\n- \r\n- #Write prediction in a file\r\n- preds <- cbind(preds,spe)\r\n- write.table(preds, paste(nb_file,"_brts_pred_ceamarc.txt",sep=""), quote=FALSE, dec=".", row.names=F, col.names=T,append = T)\r\n-}\r\n-\r\n-#### RUN BRT ####\r\n-nb_file = 0\r\n-\r\n-for (file in data_files[[1]]) {\r\n- species_data <- read.table(file, dec = ",", sep = ";", header = TRUE, na.strings = "na", colClasses = "numeric")\r\n- nb_file = nb_file + 1\r\n- `%!in%` <- Negate(`%in%`)\r\n- sp = list()\r\n- for (n in names(species_data)) {\r\n- if (n %!in% names(env) && n != \'station\'){\r\n- sp = cbind(sp,n)\r\n- }\r\n- }\r\n- \r\n- for (spe in sp){\r\n- try(make.prediction.brt(make.brt(spe,species_data,p'..b's = commandArgs(trailingOnly=TRUE) \n+if (length(args)==0)\n+{\n+ stop("This tool needs at least one argument")\n+}else{\n+ enviro <- args[1]\n+ species_files <- args[2]\n+ abio_para <- args[3]\n+ dec_env <- args[8]\n+ dec_species <- args[9]\n+}\n+\n+### load data\n+\n+env = read.table(enviro, dec = dec_env, header = TRUE, sep="\\t", na.strings = "-9999")\n+pred_vars = strsplit(abio_para, ",")[[1]] \n+data_files = strsplit(species_files,",")\n+\n+pred.vars <- character(length(pred_vars))\n+\n+for (i in seq_along(pred_vars)) {\n+ pred_var_col <- as.numeric(pred_vars[i])\n+ pred.vars[i] <- names(env)[pred_var_col]}\n+ \n+#environemental parameters\n+#Carbo,Grav,Maxbearing,Maxmagnit,Meancurmag,Meansal,Meantheta,Mud,Prof,Rugosity,Sand,Seaice_prod,Sili,Slope,Standcurmag,Standsal,Standtheta\n+\n+#Load functions\n+\n+make.brt <- function(spe,data,pred.vars,env,nb_file){\n+ 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)\n+ #plot\n+ if (is.null(brt_step)==FALSE){\n+ pdf(file = paste("BRT-",spe,".pdf"))\n+ gbm.plot(brt_step, write.title = T,show.contrib = T, y.label = "fitted function",plot.layout = c(3,3))\n+ dev.off()\n+ #total deviance explained as (Leathwick et al., 2006)\n+ total_deviance <- brt_step$self.statistics$mean.null\n+ cross_validated_residual_deviance <- brt_step$cv.statistics$deviance.mean\n+ total_deviance_explained <- (total_deviance - cross_validated_residual_deviance)/total_deviance\n+ #Validation file\n+ valid = cbind(spe,brt_step$cv.statistics$discrimination.mean,brt_step$gbm.call$tree.complexity,total_deviance_explained)\n+ write.table(valid, paste(nb_file,"_brts_validation_ceamarc.tsv",sep=""), quote=FALSE, dec=".",sep="\\t" ,row.names=F, col.names=F,append = T)}\n+ \n+ return(brt_step)\n+ }\n+\n+make.prediction.brt <- function(brt_step){\n+ #predictions\n+ preds <- predict.gbm(brt_step,env,n.trees=brt_step$gbm.call$best.trees, type="response")\n+ preds <- as.data.frame(cbind(env$lat,env$long,preds))\n+ colnames(preds) <- c("lat","long","Prediction.index")\n+ #carto\n+ ggplot()+\n+ geom_raster(data = preds , aes(x = long, y = lat, fill = Prediction.index))+\n+ geom_raster(data = preds , aes(x = long, y = lat, alpha = Prediction.index))+\n+ scale_alpha(range = c(0,1), guide = "none")+\n+ scale_fill_viridis_c(\n+ alpha = 1,\n+ begin = 0,\n+ end = 1,\n+ direction = -1,\n+ option = "D",\n+ values = NULL,\n+ space = "Lab",\n+ na.value = "grey50",\n+ guide = "colourbar",\n+ aesthetics = "fill")+\n+ xlab("Longitude") + ylab("Latitude")+ ggtitle(paste(spe,"Plot of BRT predictions"))+\n+ theme(plot.title = element_text(size = 10))+\n+ theme(axis.title.y = element_text(size = 10))+\n+ theme(axis.title.x = element_text(size = 10))+\n+ theme(axis.text.y = element_text(size = 10))+\n+ theme(axis.text.x = element_text(size = 10))+\n+ theme(legend.text = element_text(size = 10))+\n+ theme(legend.title = element_text(size = 10))+ \n+ coord_quickmap()\n+ output_directory <- ggsave(paste("BRT-",spe,"_pred_plot.png"))\n+ \n+ #Write prediction in a file\n+ preds <- cbind(preds,spe)\n+ write.table(preds, paste(nb_file,"_brts_pred_ceamarc.tsv",sep=""), quote=FALSE, dec=".", row.names=F, col.names=T,append = T,sep="\\t")\n+}\n+\n+#### RUN BRT ####\n+nb_file = 0\n+\n+for (file in data_files[[1]]) {\n+ species_data <- read.table(file, dec = dec_species, sep = "\\t", header = TRUE, na.strings = "NA", colClasses = "numeric")\n+ nb_file = nb_file + 1\n+ `%!in%` <- Negate(`%in%`)\n+ sp = list()\n+ for (n in names(species_data)) {\n+ if (n %!in% names(env) && n != \'station\'){\n+ sp = cbind(sp,n)\n+ }\n+ } \n+ for (spe in sp){\n+ try(make.prediction.brt(make.brt(spe,species_data,pred.vars,env,nb_file)))\n+ }\n+}\n+\n+cat("Here is the list of your abiotic parameters:\\n")\n+cat(paste(pred.vars, collapse = ", "), "\\n")\n+\n+\n' |
| b |
| diff -r 32849c52aa54 -r edb8d19735a6 cluster.xml --- a/cluster.xml Wed Oct 18 09:59:19 2023 +0000 +++ b/cluster.xml Wed Jan 24 15:53:07 2024 +0000 |
| b |
| @@ -20,9 +20,9 @@ '$output3' ]]></command> <inputs> - <param name="predictionmatrix" type="data" format="tabular" label="Prediction matrix (data to cluster from Cluster Estimate tool) "/> - <param name="envfile" type="data" format="txt,csv,tabular" label="Environmental file"/> - <param name="predictionfile" type="data" format="tabular" label="Prediction file (data.bio table from Cluster Estimate tool)"/> + <param name="envfile" type="data" format="tabular" label="Environmental file (tabular only)"/> + <param name="predictionmatrix" type="data" format="tabular" label="Prediction matrix (file 'data to cluster' from Cluster Estimate tool)"/> + <param name="predictionfile" type="data" format="tabular" label="Prediction file (file 'data.bio table' from Cluster Estimate tool)"/> <param name="k" type="integer" label="Number of Cluster wanted" min= "1" value="2"/> <param name="metric" type="select" label="What metric to use to calculate dissimilarities between observations ?"> <option value = "manhattan">manhattan</option> @@ -33,30 +33,31 @@ </inputs> <outputs> <data name="output1" from_work_dir="sih.png" format="png" label="SIH plot"/> - <data name="output2" from_work_dir="points_clus.txt" format="txt" label="Cluster points"/> - <data name="output3" from_work_dir="clus.txt" format="txt" label="Cluster info"/> + <data name="output2" from_work_dir="points_clus.tsv" format="tabular" label="Cluster points"/> + <data name="output3" from_work_dir="clus.tsv" format="tabular" label="Cluster info"/> </outputs> <tests> <test> <param name="predictionmatrix" value="Data_to_cluster.tsv"/> - <param name="envfile" value="ceamarc_env.csv"/> + <param name="envfile" value="ceamarc_env.tsv"/> <param name="predictionfile" value="Data.bio_table.tsv"/> <param name='k' value="2"/> <param name='metric' value="manhattan"/> <param name='sample' value="10"/> <output name='output1'> <assert_contents> - <has_size value="8128" delta="500"/> + <has_size value="7369" delta="1000"/> </assert_contents> </output> <output name='output2'> <assert_contents> - <has_size value="255" delta="1000" /> + <has_n_columns n="3"/> + <has_text text="lat"/> </assert_contents> </output> <output name='output3' > <assert_contents> - <has_size value="2008" delta="1000000" /> + <has_size value="2298" delta="50" /> </assert_contents> </output> </test> |
| b |
| diff -r 32849c52aa54 -r edb8d19735a6 cluster_ceamarc.R --- a/cluster_ceamarc.R Wed Oct 18 09:59:19 2023 +0000 +++ b/cluster_ceamarc.R Wed Jan 24 15:53:07 2024 +0000 |
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| @@ -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) |
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| diff -r 32849c52aa54 -r edb8d19735a6 crea_carte_G.R --- a/crea_carte_G.R Wed Oct 18 09:59:19 2023 +0000 +++ b/crea_carte_G.R Wed Jan 24 15:53:07 2024 +0000 |
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| @@ -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"))+ |
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| diff -r 32849c52aa54 -r edb8d19735a6 nb_clust_G.R --- a/nb_clust_G.R Wed Oct 18 09:59:19 2023 +0000 +++ b/nb_clust_G.R Wed Jan 24 15:53:07 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() |
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| diff -r 32849c52aa54 -r edb8d19735a6 recup_liste_taxon.R --- a/recup_liste_taxon.R Wed Oct 18 09:59:19 2023 +0000 +++ b/recup_liste_taxon.R Wed Jan 24 15:53:07 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 |
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| diff -r 32849c52aa54 -r edb8d19735a6 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 15:53:07 2024 +0000 |
| b |
| @@ -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 |
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| diff -r 32849c52aa54 -r edb8d19735a6 test-data/1_brts_pred_ceamarc.txt --- a/test-data/1_brts_pred_ceamarc.txt Wed Oct 18 09:59:19 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| @@ -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 |
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| diff -r 32849c52aa54 -r edb8d19735a6 test-data/Data.bio_table.tsv --- a/test-data/Data.bio_table.tsv Wed Oct 18 09:59:19 2023 +0000 +++ b/test-data/Data.bio_table.tsv Wed Jan 24 15:53:07 2024 +0000 |
| b |
| @@ -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 |
| b |
| diff -r 32849c52aa54 -r edb8d19735a6 test-data/Data_to_cluster.tsv --- a/test-data/Data_to_cluster.tsv Wed Oct 18 09:59:19 2023 +0000 +++ b/test-data/Data_to_cluster.tsv Wed Jan 24 15:53:07 2024 +0000 |
| b |
| @@ -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 |
| b |
| diff -r 32849c52aa54 -r edb8d19735a6 test-data/List_of_taxa.txt --- a/test-data/List_of_taxa.txt Wed Oct 18 09:59:19 2023 +0000 +++ b/test-data/List_of_taxa.txt Wed Jan 24 15:53:07 2024 +0000 |
| b |
| @@ -1,7 +1,2 @@ -Acanthogorgiidae Actiniaria -Ainigmaptilon_edisto -Alcyonacea -Anthozoa -Thouarella_variabilis Thouarella_vulpicauda |
| b |
| diff -r 32849c52aa54 -r edb8d19735a6 test-data/List_of_taxa_clean.txt --- a/test-data/List_of_taxa_clean.txt Wed Oct 18 09:59:19 2023 +0000 +++ b/test-data/List_of_taxa_clean.txt Wed Jan 24 15:53:07 2024 +0000 |
| b |
| @@ -1,7 +1,2 @@ -Acanthogorgiidae Actiniaria -Ainigmaptilon edisto -Alcyonacea -Anthozoa -Thouarella variabilis Thouarella vulpicauda |
| b |
| diff -r 32849c52aa54 -r edb8d19735a6 test-data/SIH_index_plot.png |
| b |
| Binary file test-data/SIH_index_plot.png has changed |
| b |
| diff -r 32849c52aa54 -r edb8d19735a6 test-data/Summary_of_taxa_model.csv --- a/test-data/Summary_of_taxa_model.csv Wed Oct 18 09:59:19 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -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 |
| b |
| diff -r 32849c52aa54 -r edb8d19735a6 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 15:53:07 2024 +0000 |
| b |
| @@ -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 |
| b |
| diff -r 32849c52aa54 -r edb8d19735a6 test-data/ceamarc_env.csv --- a/test-data/ceamarc_env.csv Wed Oct 18 09:59:19 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -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 |
| b |
| diff -r 32849c52aa54 -r edb8d19735a6 test-data/ceamarc_env.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ceamarc_env.tsv Wed Jan 24 15:53:07 2024 +0000 |
| b |
| @@ -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 |
| b |
| diff -r 32849c52aa54 -r edb8d19735a6 test-data/cnidaria_filtered.csv --- a/test-data/cnidaria_filtered.csv Wed Oct 18 09:59:19 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -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 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| b |
| diff -r 32849c52aa54 -r edb8d19735a6 test-data/cnidaria_filtered.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cnidaria_filtered.tsv Wed Jan 24 15:53:07 2024 +0000 |
| b |
| @@ -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 +1 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 +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 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| diff -r 32849c52aa54 -r edb8d19735a6 test-data/points_clus.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/points_clus.tsv Wed Jan 24 15:53:07 2024 +0000 |
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|
| b |
| diff -r 32849c52aa54 -r edb8d19735a6 test-data/points_clus.txt --- a/test-data/points_clus.txt Wed Oct 18 09:59:19 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,155824 +0,0 @@\n-lat long cluster\n--65.57 139.22 1\n--65.57 139.22 1\n--65.57 139.23 1\n--65.57 139.24 1\n--65.57 139.24 1\n--65.57 139.25 1\n--65.57 139.26 1\n--65.57 139.26 1\n--65.57 139.27 1\n--65.57 139.28 1\n--65.57 139.28 1\n--65.57 139.29 1\n--65.57 139.3 1\n--65.57 139.3 1\n--65.57 139.31 1\n--65.57 139.32 1\n--65.57 139.32 1\n--65.57 139.33 1\n--65.57 139.34 1\n--65.57 139.34 1\n--65.57 139.35 1\n--65.57 139.36 1\n--65.57 139.36 1\n--65.57 139.37 1\n--65.57 139.38 1\n--65.57 139.38 1\n--65.57 139.39 1\n--65.57 139.4 1\n--65.57 139.4 1\n--65.57 139.41 1\n--65.57 139.42 1\n--65.57 139.42 2\n--65.57 139.43 2\n--65.57 139.44 2\n--65.57 139.44 1\n--65.57 139.45 1\n--65.57 139.46 1\n--65.57 139.46 1\n--65.57 139.47 1\n--65.57 139.48 1\n--65.57 139.48 1\n--65.57 139.49 1\n--65.57 139.5 1\n--65.57 139.5 1\n--65.57 139.51 1\n--65.57 139.52 1\n--65.57 139.52 1\n--65.57 139.53 1\n--65.57 139.54 3\n--65.57 139.54 3\n--65.57 139.55 3\n--65.57 139.56 3\n--65.57 139.56 3\n--65.57 139.57 3\n--65.57 139.58 3\n--65.57 139.58 3\n--65.57 139.59 3\n--65.57 139.6 3\n--65.57 139.6 3\n--65.57 139.61 3\n--65.57 139.62 3\n--65.57 139.62 3\n--65.57 139.63 3\n--65.57 139.64 3\n--65.57 139.64 3\n--65.57 139.65 3\n--65.57 139.66 3\n--65.57 139.66 3\n--65.57 139.67 3\n--65.57 139.68 3\n--65.57 139.68 3\n--65.57 139.69 3\n--65.57 139.7 3\n--65.57 139.7 3\n--65.57 139.71 3\n--65.57 139.72 3\n--65.57 139.72 3\n--65.57 139.73 3\n--65.57 139.74 3\n--65.57 139.74 3\n--65.57 139.75 3\n--65.57 139.76 3\n--65.57 139.76 3\n--65.57 139.77 3\n--65.57 139.78 3\n--65.57 139.78 3\n--65.57 139.79 3\n--65.57 139.8 3\n--65.57 139.8 3\n--65.57 139.81 3\n--65.57 139.82 3\n--65.57 139.82 3\n--65.57 139.83 3\n--65.57 139.84 3\n--65.57 139.84 3\n--65.57 139.85 3\n--65.57 139.85 3\n--65.57 139.86 3\n--65.57 139.87 3\n--65.57 139.87 3\n--65.57 139.88 3\n--65.57 139.89 3\n--65.57 139.89 3\n--65.57 139.9 3\n--65.57 139.91 3\n--65.57 139.91 3\n--65.57 139.92 3\n--65.57 139.93 3\n--65.57 139.93 3\n--65.57 139.94 3\n--65.57 139.95 3\n--65.57 139.95 3\n--65.57 139.96 3\n--65.57 139.97 3\n--65.57 139.97 3\n--65.57 139.98 3\n--65.57 139.99 3\n--65.57 139.99 3\n--65.57 140 3\n--65.57 140.01 3\n--65.57 140.01 3\n--65.57 140.02 3\n--65.57 140.03 3\n--65.57 140.03 3\n--65.57 140.04 3\n--65.57 140.05 3\n--65.57 140.05 3\n--65.57 140.06 3\n--65.57 140.07 3\n--65.57 140.07 3\n--65.57 140.08 3\n--65.57 140.09 3\n--65.57 140.09 3\n--65.57 140.1 3\n--65.57 140.11 3\n--65.57 140.11 3\n--65.57 140.12 3\n--65.57 140.13 3\n--65.57 140.13 3\n--65.57 140.14 3\n--65.57 140.15 3\n--65.57 140.15 3\n--65.57 140.44 3\n--65.57 140.45 3\n--65.57 140.45 3\n--65.57 140.46 3\n--65.57 140.47 3\n--65.57 140.47 3\n--65.57 140.48 3\n--65.57 140.49 3\n--65.57 140.49 3\n--65.57 140.5 3\n--65.57 140.51 3\n--65.57 140.51 3\n--65.57 140.52 3\n--65.57 140.53 3\n--65.57 140.53 3\n--65.57 140.54 3\n--65.57 140.55 3\n--65.57 140.55 3\n--65.57 140.56 3\n--65.57 140.57 3\n--65.57 140.57 3\n--65.57 140.58 3\n--65.57 140.59 3\n--65.57 140.59 3\n--65.57 140.6 3\n--65.57 140.61 3\n--65.57 140.61 3\n--65.57 140.62 3\n--65.57 140.63 3\n--65.57 140.63 3\n--65.57 140.64 3\n--65.57 140.65 3\n--65.57 140.65 3\n--65.57 140.66 3\n--65.57 140.67 3\n--65.57 140.67 3\n--65.57 140.68 3\n--65.57 140.69 3\n--65.57 140.69 3\n--65.57 140.7 3\n--65.57 140.71 3\n--65.57 140.71 3\n--65.57 140.72 3\n--65.57 140.73 3\n--65.57 140.73 3\n--65.57 140.74 3\n--65.57 140.75 3\n--65.57 140.75 3\n--65.57 140.76 3\n--65.57 140.76 3\n--65.57 140.77 3\n--65.57 140.78 3\n--65.57 140.78 3\n--65.57 140.79 3\n--65.57 140.8 3\n--65.57 140.8 3\n--65.57 140.81 3\n--65.57 140.82 3\n--65.57 140.82 3\n--65.57 140.83 3\n--65.57 140.84 3\n--65.57 140.84 3\n--65.57 140.85 3\n--65.57 140.86 3\n--65.57 140.86 3\n--65.57 140.92 3\n--65.57 140.93 3\n--65.57 140.94 3\n--65.57 140.94 3\n--65.57 140.95 3\n--65.57 140.96 3\n--65.57 140.96 3\n--65.57 140.97 3\n--65.57 140.98 3\n--65.57 140.98 3\n--65.57 140.99 3\n--65.57 141 3\n--65.57 141.4 3\n--65.57 141.4 3\n--65.57 141.41 3\n--65.57 141.42 3\n--65.57 141.42 3\n--65.57 141.43 3\n--65.57 141.44 3\n--65.57 141.44 3\n--65.57 141.45 3\n--65.57 141.46 3\n--65.57 141.46 3\n--65.57 141.47 3\n--65.57 141.48 3\n--65.57 141.48 3\n--65.57 141.49 3\n--65.57 1'..b' 2\n--67.01 144.82 1\n--67.01 144.83 1\n--67.01 144.84 1\n--67.01 144.84 1\n--67.01 144.85 1\n--67.01 144.86 1\n--67.01 144.86 1\n--67.01 144.87 1\n--67.01 144.88 1\n--67.01 144.88 1\n--67.01 144.89 1\n--67.01 144.9 5\n--67.01 144.9 5\n--67.01 144.91 5\n--67.01 144.92 5\n--67.01 144.92 5\n--67.01 144.93 5\n--67.01 144.94 5\n--67.01 144.94 5\n--67.01 144.95 5\n--67.01 144.96 5\n--67.01 144.96 5\n--67.01 144.97 5\n--67.01 144.98 5\n--67.01 144.98 5\n--67.01 144.99 5\n--67.01 145 5\n--67.01 145 5\n--67.01 145.01 5\n--67.01 145.02 5\n--67.01 145.02 5\n--67.01 145.03 5\n--67.01 145.04 5\n--67.01 145.04 5\n--67.01 145.05 5\n--67.01 145.06 5\n--67.01 145.06 5\n--67.01 145.07 5\n--67.01 145.08 5\n--67.01 145.08 5\n--67.01 145.09 5\n--67.01 145.1 5\n--67.01 145.1 5\n--67.01 145.11 5\n--67.01 145.12 5\n--67.01 145.18 5\n--67.01 145.18 5\n--67.01 145.19 5\n--67.01 145.2 5\n--67.01 145.2 5\n--67.01 145.21 5\n--67.01 145.22 5\n--67.01 145.22 5\n--67.01 145.23 5\n--67.01 145.24 5\n--67.01 145.24 5\n--67.01 145.25 5\n--67.01 145.25 5\n--67.01 145.26 5\n--67.01 145.27 5\n--67.01 145.27 5\n--67.01 145.28 5\n--67.01 145.29 5\n--67.01 145.29 5\n--67.01 145.3 5\n--67.01 145.31 5\n--67.01 145.31 5\n--67.01 145.32 5\n--67.01 145.33 5\n--67.01 145.33 5\n--67.01 145.34 5\n--67.01 145.35 5\n--67.01 145.35 5\n--67.01 145.36 5\n--67.01 145.37 5\n--67.01 145.37 5\n--67.01 145.38 5\n--67.01 145.39 5\n--67.01 145.39 5\n--67.01 145.4 5\n--67.01 145.41 5\n--67.01 145.41 5\n--67.01 145.42 5\n--67.01 145.43 5\n--67.01 145.43 5\n--67.01 145.44 5\n--67.01 145.45 5\n--67.01 145.45 5\n--67.01 145.46 5\n--67.01 145.47 5\n--67.01 145.47 5\n--67.01 145.48 5\n--67.01 145.49 5\n--67.01 145.49 5\n--67.01 145.5 5\n--67.01 145.51 5\n--67.01 145.51 5\n--67.01 145.52 5\n--67.01 145.53 5\n--67.01 144.01 2\n--67.01 144.02 1\n--67.01 144.03 1\n--67.01 144.03 1\n--67.01 144.04 1\n--67.01 144.05 1\n--67.01 144.05 1\n--67.01 144.06 1\n--67.01 144.07 1\n--67.01 144.07 1\n--67.01 144.08 1\n--67.01 144.09 1\n--67.01 144.09 2\n--67.01 144.1 2\n--67.01 144.11 2\n--67.01 144.11 2\n--67.01 144.12 2\n--67.01 144.13 2\n--67.01 144.13 2\n--67.01 144.14 2\n--67.01 144.15 2\n--67.01 144.15 2\n--67.01 144.16 2\n--67.01 144.64 2\n--67.01 144.65 2\n--67.01 144.66 2\n--67.01 144.66 2\n--67.01 144.68 2\n--67.01 144.69 2\n--67.01 144.7 2\n--67.01 144.7 2\n--67.01 144.71 2\n--67.01 144.72 2\n--67.01 144.72 2\n--67.01 144.73 2\n--67.01 144.74 2\n--67.01 144.74 2\n--67.01 144.75 2\n--67.01 144.76 2\n--67.01 144.76 2\n--67.01 144.77 2\n--67.01 144.78 2\n--67.01 144.78 2\n--67.01 144.79 2\n--67.01 144.8 2\n--67.01 144.8 2\n--67.01 144.81 2\n--67.01 144.82 2\n--67.01 144.82 2\n--67.01 144.83 1\n--67.01 144.84 1\n--67.01 144.84 1\n--67.01 144.85 1\n--67.01 144.86 1\n--67.01 144.86 1\n--67.01 144.87 1\n--67.01 144.88 1\n--67.01 144.88 1\n--67.01 144.89 1\n--67.01 144.9 5\n--67.01 144.9 5\n--67.01 144.91 5\n--67.01 144.92 5\n--67.01 144.92 5\n--67.01 144.93 5\n--67.01 144.94 5\n--67.01 144.94 5\n--67.01 144.95 5\n--67.01 144.96 5\n--67.01 144.96 5\n--67.01 144.97 5\n--67.01 144.98 5\n--67.01 144.98 5\n--67.01 144.99 5\n--67.01 145 5\n--67.01 145 5\n--67.01 145.01 5\n--67.01 145.02 5\n--67.01 145.02 5\n--67.01 145.03 5\n--67.01 145.04 5\n--67.01 145.06 5\n--67.01 145.06 5\n--67.01 145.07 5\n--67.01 145.08 5\n--67.01 145.08 5\n--67.01 145.19 5\n--67.01 145.2 5\n--67.01 145.2 5\n--67.01 145.21 5\n--67.01 145.22 5\n--67.01 145.22 5\n--67.01 145.23 5\n--67.01 145.24 5\n--67.01 145.24 5\n--67.01 145.25 5\n--67.01 145.25 5\n--67.01 145.26 5\n--67.01 145.27 5\n--67.01 145.27 5\n--67.01 145.28 5\n--67.01 145.29 5\n--67.01 145.29 5\n--67.01 145.3 5\n--67.01 145.31 5\n--67.01 145.31 5\n--67.01 145.32 5\n--67.01 145.33 5\n--67.01 145.33 5\n--67.01 145.34 5\n--67.01 145.35 5\n--67.01 145.35 5\n--67.01 145.36 5\n--67.01 145.37 5\n--67.01 145.37 5\n--67.01 145.38 5\n--67.01 145.39 5\n--67.01 145.39 5\n--67.01 145.4 5\n--67.01 145.41 5\n--67.01 145.41 5\n--67.01 145.42 5\n--67.01 145.43 5\n--67.01 145.43 5\n--67.01 145.44 5\n--67.01 145.45 5\n--67.01 145.45 5\n--67.01 145.46 5\n--67.01 145.47 5\n--67.01 145.47 5\n--67.01 145.48 5\n--67.01 145.49 5\n--67.01 145.49 5\n--67.01 145.5 5\n--67.01 145.51 5\n--67.01 145.51 1\n--67.01 145.52 1\n--67.01 145.53 1\n' |
