Mercurial > repos > ecology > ecoregion_cluster_estimate
changeset 0:0f6542d0986e draft
planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/Ecoregionalization_workflow commit 2a2ae892fa2dbc1eff9c6a59c3ad8f3c27c1c78d
| author | ecology |
|---|---|
| date | Wed, 18 Oct 2023 09:59:06 +0000 |
| parents | |
| children | e94a25eed489 |
| files | Nb_cluster.xml brt.R cluster_ceamarc.R crea_carte_G.R nb_clust_G.R recup_liste_taxon.R test-data/1_brts_pred_ceamarc.txt 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/Summary_of_taxa_model.csv test-data/ceamarc_env.csv test-data/cnidaria_filtered.csv test-data/ecoregions.png test-data/points_clus.txt |
| diffstat | 17 files changed, 156442 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Nb_cluster.xml Wed Oct 18 09:59:06 2023 +0000 @@ -0,0 +1,103 @@ +<tool id="ecoregion_cluster_estimate" name="ClusterEstimate" version="0.1.0+galaxy0" profile="22.05"> + <description>Find an optimal number of cluster with SIH index</description> + <requirements> + <requirement type="package" version="4.2.3">r-base</requirement> + <requirement type="package" version="2.1.4">r-cluster</requirement> + <requirement type="package" version="1.1.1">r-dplyr</requirement> + <requirement type="package" version="2.0.0">r-tidyverse</requirement> + </requirements> + <command detect_errors="exit_code"><![CDATA[ + Rscript + '$__tool_directory__/nb_clust_G.R' + '$envfile' + '$taxafile' + '$predictionfile' + '$max_k' + '$metric' + '$sample' + '$output1' + '$output2' + '$output3' + ]]> + </command> + <inputs> + <param name="envfile" type="data" format="txt,csv,tabular" label="Environment file"/> + <param name="taxafile" type="data" format="txt" label="Taxa selected file (List of taxa from TaxaSeeker tool)"/> + <param name="predictionfile" type="data" format="txt" multiple="true" label="Prediction files"/> + <param name="max_k" type="integer" value="2" min="1" label="Number of Cluster to test"/> + <param name="metric" type="select" label="What metric to use to calculate dissimilarities between observations ?"> + <option value="manhattan">manhattan</option> + <option value="euclidean">euclidean</option> + <option value="jaccard">jaccard</option> + </param> + <param name="sample" type="integer" label="The number of samples to be drawn from the dataset" min="5" value="10"/> + </inputs> + <outputs> + <data name="output1" from_work_dir="Indices_SIH.png" format="png" label="SIH index plot"/> + <data name="output2" from_work_dir="data_to_clus.tsv" format="tsv" label="Data to cluster"/> + <data name="output3" from_work_dir="data_bio.tsv" format="tsv" label="Data.bio table "/> + </outputs> + <tests> + <test> + <param name="envfile" value="ceamarc_env.csv"/> + <param name="taxafile" value="List_of_taxa.txt"/> + <param name="predictionfile" value="1_brts_pred_ceamarc.txt"/> + <param name='max_k' value="2"/> + <param name='metric' value="manhattan"/> + <param name='sample' value="10"/> + <output name='output1' value="SIH_index_plot.png"/> + <output name='output2' value="Data_to_cluster.tsv"/> + <output name='output3' value="Data.bio_table.tsv"/> + </test> + </tests> + <help><![CDATA[ +================== +**What it does ?** +================== + +The tool enables the determination of the optimal number of clusters for partition-based clustering, along with generating files used in the subsequent ecoregionalization workflow. + +=================== +**How to use it ?** +=================== + +The tool takes three inputs files: a file containing the environmental parameter values for each environment layer pixel (latitude-longitude), a file containing the list of selected taxa from previous step of the workflow and the file containing the BRT predictions. See example below. + +Then there are few parameters : + +- the maximum number of clusters to be tested with a minimum of two clusters + +- the metric used to calculate the dissimilarities between the observations: Manhattan, Euclidean and Jaccard + +- the sample size that will be used to perform clustering. Indeed, the clara function is used to clustering large data using a representative sample rather than the entire data set. This will speed up the clustering process and make the calculation more efficient. A fairly high value representative of the data is recommended. It is important to note that using too small a sample may result in loss of information compared to using the entire data set. + +The tool will produce three outputs. The first two are files that will be used in the rest of the workflow: a file containing four pieces of information, latitude, longitude, presence prediction and corresponding taxon, and a file containing the data to be partitioned. The third output corresponds to the main information of the tool, a graph presenting the value of the HIS index according to the number of clusters. The silhouette index provides a measure of the separation between clusters and the compactness within each cluster. The silhouette index ranges from -1 to 1. Values close to 1 indicate that objects are well grouped and separated from other clusters, while values close to -1 indicate that objects are poorly grouped and may be closer to other clusters. A value close to 0 indicates a situation where objects are located at the border between two neighboring clusters. + +**Example of the environemental file :** + ++------+------+---------+------+--------------+-----+ +| long | lat | Carbo | Grav | Maxbearing | ... | ++------+------+---------+------+--------------+-----+ +|139.22|-65.57| 0.88 |28.59 | 3.67 | ... | ++------+------+---------+------+--------------+-----+ +|139.22|-65.57| 0.88 |28.61 | 3.64 | ... | ++------+------+---------+------+--------------+-----+ +| ... | ... | ... | ... | ... | ... | ++------+------+---------+------+--------------+-----+ + +**Example of the Brt prediction file :** + ++-----------+----------+-----------------------+-------------+ +| lat | long | Prediction.index | spe | ++-----------+----------+-----------------------+-------------+ +| -65.57 | 139.22 | 0.122438487221909 | Acarnidae | ++-----------+----------+-----------------------+-------------+ +| -65.57 | 139.32 | 0.119154535627801 | Acarnidae | ++-----------+----------+-----------------------+-------------+ +| ... | ... | ... | ... | ++-----------+----------+-----------------------+-------------+ + + ]]> + </help> +</tool> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/brt.R Wed Oct 18 09:59:06 2023 +0000 @@ -0,0 +1,109 @@ +#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))) + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cluster_ceamarc.R Wed Oct 18 09:59:06 2023 +0000 @@ -0,0 +1,52 @@ +##13/04/2023 +##Seguineau Pauline +### Clustering with Clara algorithm + +#load library +library(cluster) +library(dplyr) +library(tidyverse) + +#load arguments +args = commandArgs(trailingOnly=TRUE) +if (length(args)==0) +{ + stop("This tool needs at least one argument") +}else{ + data <- args[1] + enviro <- args[2] + data.bio <- args[3] + k <- as.numeric(args[4]) + metric <- args[5] + sample <- as.numeric(args[6]) +} + +#load data +env.data <- read.table(enviro, header=TRUE, sep=" ",dec = ".", na.strings = "-9999.00") +data.bio <- read.table(data.bio, header=TRUE, sep="\t") +test3 <- read.table(data, header = TRUE, sep="\t") + +###################################################################################################### +#Make clustering + +k <- k #number of clusters +test5 <- clara(test3, k, metric = metric, samples = sample, sampsize = min(nrow(test3), (nrow(data.bio)/nrow(test3))+2*k)) + +####################################################################################################### +#save results + +png("sih.png") +plot(silhouette(test5)) +dev.off() + +clus <- cbind(data.bio[1:nrow(test3), 1:2],test5$clustering) +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) + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/crea_carte_G.R Wed Oct 18 09:59:06 2023 +0000 @@ -0,0 +1,38 @@ +#Author : Seguineau Pauline + + +#Create a map from cluster + +library(sf) +library(tmap) +library(dplyr) + +args = commandArgs(trailingOnly=TRUE) +if (length(args)==0) +{ + stop("This tool needs at least one argument") +}else{ + data <- args[1] +} + +clus <- read.table(data, header=TRUE, na.strings = "na") + +#tmap method + +sf_data <- st_as_sf(clus, coords = c("long", "lat"), crs =4326) + +grouped_data <- sf_data %>% + group_by(cluster) %>% + summarize() + +map <- tm_shape(grouped_data) + + tm_dots(col = "cluster", palette = "Accent", size = 0.1, title = "écorégions")+ + tm_scale_bar(position = c("right","top"))+ + tm_compass(position = c("right","top"))+ + tm_layout(frame = FALSE,legend.position = c("left","bottom"))+ + tm_xlab("Longitude")+ + tm_ylab("Latitude")+ + tm_grid(alpha = 0.2) + +#Save the map +tmap_save(map, "ecoregions.png")
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/nb_clust_G.R Wed Oct 18 09:59:06 2023 +0000 @@ -0,0 +1,73 @@ +# Script to determine the optimal number of clusters thanks to the optimization of the SIH index and to produce the files needed in the next step of clustering + +#load packages +library(cluster) +library(dplyr) +library(tidyverse) + +#load arguments +args = commandArgs(trailingOnly=TRUE) +if (length(args)==0) +{ + stop("This tool needs at least one argument") +}else{ + enviro <- args[1] + taxa_list <- args[2] + preds <- args[3] + max_k <- as.numeric(args[4]) + metric <- args[5] + sample <- as.numeric(args[6]) +} + +#load data + +env.data <- read.table(enviro, header = TRUE, dec = ".", na.strings = "-9999.00") + +##List of modelled taxa used for clustering +tv <- read.table(taxa_list, dec=".", sep=" ", header=F, na.strings = "NA") +names(tv) <- c("a") + +################Grouping of taxa if multiple prediction files entered ################ + +data_split = str_split(preds,",") +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.bio <- rbind(data.bio,data.bio1) +remove(data.bio1) +} + +names(data.bio) <- c("lat", "long", "pred", "taxon") + +#keep selected taxa +data.bio <- data.bio[which(data.bio$taxon %in% tv$a),] + +write.table(data.bio,file="data_bio.tsv",sep="\t",quote=F,row.names=F) + +#format data + +test3 <- matrix(data.bio$pred , nrow = nrow(env.data), ncol = nrow(data.bio)/nrow(env.data)) +test3 <- data.frame(test3) +names(test3) <- unique(data.bio$taxon) + +write.table(test3, file="data_to_clus.tsv", sep="\t",quote=F,row.names=F) + +#Max number of clusters to test +max_k <- max_k + +# Initialization of vectors to store SIH indices +sih_values <- rep(0, max_k) + +# Calculation of the SIH index for each number of clusters +for (k in 2:max_k) { + # Clara execution + clara_res <- clara(test3, k, metric =metric, samples = sample, sampsize = min(nrow(test3), (nrow(data.bio)/nrow(test3))+2*k)) + # Calculation of the SIH index + sih_values[k] <- clara_res$silinfo$avg.width +} + +# Plot SIH Index Chart by Number of Clusters +png("Indices_SIH.png") +plot(2:max_k, sih_values[2:max_k], type = "b", xlab = "Nombre de clusters", ylab = "Indice SIH") +dev.off()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/recup_liste_taxon.R Wed Oct 18 09:59:06 2023 +0000 @@ -0,0 +1,93 @@ +#This script allows us to create a file telling us for each taxon if we obtained a BRT model. As well as the list of taxa. + +#load packages +library(dplyr, warn.conflicts = FALSE) +library(taxonomyCleanr, warn.conflicts = FALSE) +library(stringr, warn.conflicts = FALSE) + +#load arguments +args = commandArgs(trailingOnly=TRUE) + +if (length(args)==0){ + stop("This tool needs at least one argument") +}else{ + data <- args[1] + preds <- args[2] + enviro <- args[3] +} + +env = read.table(enviro, header=T, na.strings = "na") +occurrence_files = strsplit(data,",") +preds_files = strsplit(preds,",") + +#########functions########## + +`%!in%` <- Negate(`%in%`) + +have_model = data.frame() +pres = 0 + +have.model <- function(taxon_phylum,noms_sp,comptage_sp,brt_phylum){ + for (tax in taxon_phylum) { + if (tax %in% names(noms_sp)){ + pres = sum(comptage_sp[tax]) + } + if (tax %in% brt_phylum$spe ) { + brt = c(tax,"Yes", pres) + have_model = rbind(have_model,brt, make.row.names = F)} + else { + brt = c(tax,"No", pres) + have_model = rbind(have_model,brt, make.row.names = F)} + } + colnames(have_model) = c("Taxa","Model","Occurences") + return(have_model)} + +##########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")) +} + +for (i in 1:length(occurrence_files[[1]])) { + occurrence <- NULL + cmpt <- NULL + taxon <- list() + + occurrence <- read.table(occurrence_files[[1]][i], dec = ",", sep = ";", header = TRUE, na.strings = "na") + + taxon_names <- names(occurrence) + new_taxon <- taxon_names[!(taxon_names %in% names(env)) & taxon_names != "station"] + taxon <- c(taxon, new_taxon) + + cmpt <- occurrence[, new_taxon] + cmpt <- as.data.frame(cmpt) + + have_model <- have.model(taxon, occurrence, cmpt, brt) +} + +#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") + +#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) + +have_model2$Taxa <- as.character(trim_taxa(have_model2$Taxa)) + +#Second clean-up (elimination of all taxa ending in sp1./sp2 etc which represents a duplicate) + +have_model2 <- have_model2 %>% filter(!str_ends(Taxa, "sp.1|sp[0-9]")) +have_model3 <- have_model3 %>% filter(!str_ends(Taxa, "sp.1|sp[0-9]")) +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) + +#getting list of taxa for next if not using worms +list_taxon = have_model3$Taxa +write.table(list_taxon, file= "list_taxa.txt", quote = F, row.names = F, col.names = F) + +#getting the final list to submit to worms +liste_taxon = have_model2$Taxa +write.table(liste_taxon,file = "list_taxa_clean.txt", quote = F, row.names = F, col.names = F) + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/1_brts_pred_ceamarc.txt Wed Oct 18 09:59:06 2023 +0000 @@ -0,0 +1,16 @@ +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
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Data.bio_table.tsv Wed Oct 18 09:59:06 2023 +0000 @@ -0,0 +1,16 @@ +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
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Data_to_cluster.tsv Wed Oct 18 09:59:06 2023 +0000 @@ -0,0 +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
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/List_of_taxa.txt Wed Oct 18 09:59:06 2023 +0000 @@ -0,0 +1,7 @@ +Acanthogorgiidae +Actiniaria +Ainigmaptilon_edisto +Alcyonacea +Anthozoa +Thouarella_variabilis +Thouarella_vulpicauda
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/List_of_taxa_clean.txt Wed Oct 18 09:59:06 2023 +0000 @@ -0,0 +1,7 @@ +Acanthogorgiidae +Actiniaria +Ainigmaptilon edisto +Alcyonacea +Anthozoa +Thouarella variabilis +Thouarella vulpicauda
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Summary_of_taxa_model.csv Wed Oct 18 09:59:06 2023 +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,No,24
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ceamarc_env.csv Wed Oct 18 09:59:06 2023 +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 +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
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cnidaria_filtered.csv Wed Oct 18 09:59:06 2023 +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 +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 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