diff --git a/scripts/partitioning.sh b/scripts/partitioning.sh
new file mode 100755
index 0000000..837206b
--- /dev/null
+++ b/scripts/partitioning.sh
@@ -0,0 +1,79 @@
+#!/bin/bash
+
+# Exit on error, undefined variables, and pipe failures
+set -euo pipefail
+#set -x # for debugging
+
+# Check if all required arguments are provided
+if [ $# -ne 4 ]; then
+    echo "Usage: $0 <paf_file> <fasta_index> <window_size> <sample_name>"
+    exit 1
+fi
+
+# Input parameters
+PAF=$1
+FASTA_FAI=$2
+AVG_WINDOW_SIZE=$3
+SAMPLE=$4
+
+# Prepare initial windows
+WINDOWS_BED=windows.bed
+grep $SAMPLE "$FASTA_FAI" -w | awk -v OFS='\t' '{print($1,"0",$2)}' > "$SAMPLE.bed"
+bedtools makewindows -b "$SAMPLE.bed" -w "$AVG_WINDOW_SIZE" > "$WINDOWS_BED"
+
+# Prepare mask file
+MASK_BED=mask.bed
+cat /dev/null > "$MASK_BED"
+
+# Create initial missing regions file
+MISSING_BED=missing.bed
+cut -f 1,2 "$PAF" | sort | uniq | awk -v OFS='\t' '{print($1,"0",$2,".",".","+")}' > "$MISSING_BED"
+
+# Initialize partition counter
+num=0
+
+# Process windows until no missing regions remain
+while [ -s "$WINDOWS_BED" ]; do
+    echo "Processing new window set"
+
+    while IFS=$'\t' read -r chrom start end; do
+        # Get partition, sort, and merge to avoid duplicates
+        REGION_FORMATTED="${chrom}:${start}-${end}"
+
+        echo "-- Querying region $REGION_FORMATTED"
+        impg -p "$PAF" -r "$REGION_FORMATTED" -x | bedtools sort | bedtools merge -s -c 4,5,6 -o distinct -d 10000 > "partition$num.tmp.bed"
+
+        # Apply mask
+        bedtools subtract -a "partition$num.tmp.bed" -b "$MASK_BED" -s > "partition$num.bed"
+
+        # Check if the partition is not empty
+        if [ -s "partition$num.bed" ]; then
+            echo "-- Processing partition $num"
+
+            # Update masked regions
+            cat "partition$num.bed" "$MASK_BED" | bedtools sort > "$num.mask.bed"
+            bedtools merge -i "$num.mask.bed" -s -c 4,5,6 -o distinct > "$MASK_BED"
+
+            # Update missing regions
+            bedtools subtract -a "$MISSING_BED" -b "partition$num.bed" -s > "$num.missing.bed"
+            cp "$num.missing.bed" "$MISSING_BED"
+
+            num=$((num + 1))
+        fi
+    done < $WINDOWS_BED
+
+    # Check if there are any missing regions not covered by mask
+    bedtools subtract -a "$MISSING_BED" -b "$MASK_BED" -s > "$num.remaining.bed"
+    if [ ! -s "$num.remaining.bed" ]; then
+        # If no remaining regions, we're done
+        break
+    fi
+
+    # Create new windows from longest remaining region
+    awk -v OFS='\t' 'BEGIN{max_len=0} {len=$3-$2; if(len>max_len){max_len=len; line=$0}} END{print line}' "$num.remaining.bed" > "$num.new.bed"
+
+    bedtools makewindows -b "$num.new.bed" -w "$AVG_WINDOW_SIZE" > "$WINDOWS_BED"
+done
+
+# Cleanup
+rm -f "$SAMPLE.bed" "$WINDOWS_BED" "$MASK_BED" "$MISSING_BED" partition[0-9]*.tmp.bed [0-9]*.{mask,missing,remaining,new}.bed
diff --git a/scripts/plot_partitioning_stats.R b/scripts/plot_partitioning_stats.R
new file mode 100644
index 0000000..f500ad5
--- /dev/null
+++ b/scripts/plot_partitioning_stats.R
@@ -0,0 +1,235 @@
+library(tidyverse)
+library(ggplot2)
+library(stringr)
+library(patchwork)  # For combining plots
+options(scipen=10000)
+
+# Function to read and process a single BED file
+process_bed_file <- function(file_path) {
+  # Extract partition number from filename
+  partition_num <- as.numeric(str_extract(basename(file_path), "\\d+"))
+  
+  # Read BED file
+  bed_data <- read_tsv(file_path, 
+                       col_names = c("name", "start", "end", "score", "strand", "orientation"),
+                       col_types = "ciiccc")
+  
+  # Calculate interval lengths
+  bed_data <- bed_data %>%
+    mutate(length = end - start)
+  
+  # Extract sample and haplotype information
+  bed_data <- bed_data %>%
+    mutate(
+      sample = str_extract(name, "^[^#]+"),
+      haplotype = str_extract(name, "^[^#]+#([^#]+)#", group = 1)
+    ) %>%
+    mutate(
+      haplotype = paste0(sample, "#", haplotype)
+    )
+  
+  # Add partition information
+  bed_data$partition <- partition_num
+  
+  return(bed_data)
+}
+
+# Modified function to create separate sample and haplotype count plots
+create_sample_plot <- function(data) {
+  sample_counts <- data %>%
+    group_by(partition) %>%
+    summarize(count = n_distinct(sample))
+  
+  # Calculate y-axis breaks with smaller intervals
+  max_count <- max(sample_counts$count)
+  y_breaks <- seq(0, ceiling(max_count), by = max(1, ceiling(max_count/20)))
+  
+  ggplot(sample_counts, aes(x = factor(partition), y = count)) +
+    geom_bar(stat = "identity", fill = "#2E86C1", width = 0.8) +
+    scale_y_continuous(
+      breaks = y_breaks,
+      limits = c(0, NA),
+      expand = expansion(mult = c(0, 0.05))
+    ) +
+    #theme_minimal() +
+    labs(
+      title = "Sample Counts by Partition",
+      x = "Partition Number",
+      y = "Number of Samples"
+    ) +
+    theme(
+      plot.title = element_text(hjust = 0.5),
+      axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 5),
+      panel.grid.minor = element_line(color = "gray90")
+    )
+}
+
+create_haplotype_plot <- function(data) {
+  haplotype_counts <- data %>%
+    group_by(partition) %>%
+    summarize(count = n_distinct(haplotype))
+  
+  # Calculate y-axis breaks with smaller intervals
+  max_count <- max(haplotype_counts$count)
+  y_breaks <- seq(0, ceiling(max_count), by = max(1, ceiling(max_count/20)))
+  
+  ggplot(haplotype_counts, aes(x = factor(partition), y = count)) +
+    geom_bar(stat = "identity", fill = "#E74C3C", width = 0.8) +
+    scale_y_continuous(
+      breaks = y_breaks,
+      limits = c(0, NA),
+      expand = expansion(mult = c(0, 0.05))
+    ) +
+    #theme_minimal() +
+    labs(
+      title = "Haplotype Counts by Partition",
+      x = "Partition Number",
+      y = "Number of Haplotypes"
+    ) +
+    theme(
+      plot.title = element_text(hjust = 0.5),
+      axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 5),
+      panel.grid.minor = element_line(color = "gray90")
+    )
+}
+
+create_length_plot <- function(data) {
+  lengths <- data %>%
+    group_by(partition) %>%
+    summarize(total_length = sum(length) / 1e6)  # Convert to megabases
+  
+  # Calculate y-axis breaks with smaller intervals
+  max_length <- max(lengths$total_length)
+  y_breaks <- seq(0, ceiling(max_length), by = max(1, ceiling(max_length/20)))
+  
+  ggplot(lengths, aes(x = factor(partition), y = total_length)) +
+    geom_bar(stat = "identity", fill = "#27AE60", width = 0.8) +
+    scale_y_continuous(
+      breaks = y_breaks,
+      labels = function(x) format(x, big.mark = ",", scientific = FALSE),
+      limits = c(0, NA),
+      expand = expansion(mult = c(0, 0.05))
+    ) +
+    #theme_minimal() +
+    labs(
+      title = "Total Sequence Length by Partition",
+      x = "Partition Number",
+      y = "Total Length (Mb)"
+    ) +
+    theme(
+      plot.title = element_text(hjust = 0.5),
+      axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 5),
+      panel.grid.minor = element_line(color = "gray90")
+    )
+}
+
+directory <- '/home/guarracino/Desktop/Garrison/impg/'
+
+# List all partition BED files
+bed_files <- list.files(directory, pattern = "partition\\d+\\.bed$", full.names = TRUE)
+
+# Process each file and combine results
+all_data <- map_df(bed_files, process_bed_file)
+
+# Create individual plots
+sample_plot <- create_sample_plot(all_data)
+haplotype_plot <- create_haplotype_plot(all_data)
+length_plot <- create_length_plot(all_data)
+
+# Combine plots using patchwork
+combined_plot <- sample_plot / haplotype_plot / length_plot +
+  plot_layout(heights = c(1, 1, 1)) +
+  plot_annotation(
+    title = "Pangenome Partitions",
+    theme = theme(plot.title = element_text(hjust = 0.5, size = 16))
+  )
+
+# Save combined plot
+ggsave("partition_analysis.pdf", combined_plot, width = 26, height = 12)
+
+
+# Read chromosome data
+chr_data <- read_tsv("/home/guarracino/Desktop/Garrison/impg/scerevisiae8.chromosomes.tsv",
+                     col_names = c("name", "chromosome"),
+                     col_types = "cc")
+
+# Join with chromosome data
+all_data_with_chr <- all_data %>%
+  left_join(chr_data, by = "name")
+
+create_chromosome_composition_plot <- function(data) {
+  # Calculate the total length per chromosome per partition
+  chr_composition <- data %>%
+    group_by(partition, chromosome) %>%
+    summarize(total_length = sum(length) / 1e6, .groups = 'drop') %>%
+    group_by(partition) %>%
+    mutate(
+      percentage = total_length / sum(total_length) * 100,
+      partition = factor(partition)
+    )
+  
+  # Create a stacked bar plot
+  ggplot(chr_composition, aes(x = partition, y = percentage, fill = chromosome)) +
+    geom_bar(stat = "identity", width = 0.8) +
+    scale_y_continuous(
+      breaks = seq(0, 100, 10),
+      limits = c(0, 101),
+      expand = expansion(mult = c(0, 0.05))
+    ) +
+    scale_fill_viridis_d(option = "turbo") +  # Use viridis color palette
+    #theme_minimal() +
+    labs(
+      title = "Chromosome Composition by Partition",
+      x = "Partition Number",
+      y = "Percentage of Sequence Length",
+      fill = "Chromosome"
+    ) +
+    theme(
+      plot.title = element_text(hjust = 0.5),
+      axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 5),
+      panel.grid.minor = element_line(color = "gray90"),
+      legend.position = "bottom"
+    ) +
+    guides(fill = guide_legend(nrow = 2))
+}
+
+# Create chromosome composition plot
+chr_plot <- create_chromosome_composition_plot(all_data_with_chr)
+
+ggsave("partition_analysis.chr-composition.pdf", chr_plot, width = 26, height = 4.5)
+
+
+create_faceted_chromosome_plot <- function(data) {
+  # Calculate the total length per chromosome per partition
+  chr_composition <- data %>%
+    group_by(partition, chromosome) %>%
+    summarize(total_length = sum(length) / 1e6, .groups = 'drop') %>%
+    ungroup()
+  
+  # Create a faceted bar plot
+  ggplot(chr_composition, aes(x = chromosome, y = total_length, fill = chromosome)) +
+    geom_bar(stat = "identity", width = 0.8) +
+    scale_fill_viridis_d(option = "turbo") +  # Use viridis color palette
+    facet_wrap(~partition, scales = "free_y", ncol = 4) +
+    theme_minimal() +
+    labs(
+      title = "Chromosome Distribution by Partition",
+      x = "Chromosome",
+      y = "Total Length (Mb)",
+      fill = "Chromosome"
+    ) +
+    theme(
+      plot.title = element_text(hjust = 0.5),
+      axis.text.x = element_text(angle = 90, hjust = 1, size = 16),
+      panel.grid.minor = element_line(color = "gray90"),
+      legend.position = "none",  # Remove legend since x-axis shows the same information
+      strip.text = element_text(size = 18),
+      axis.text = element_text(size = 16)
+    )
+}
+
+# Create faceted chromosome plot
+faceted_chr_plot <- create_faceted_chromosome_plot(all_data_with_chr)
+
+# Save the new plot
+ggsave("partition_analysis.chr-faceted.pdf", faceted_chr_plot, width = 30, height = ceiling(n_distinct(all_data_with_chr$partition)/20) * 16, limitsize = FALSE)