--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/CSP2/bin/screenSNPDiffs.py	Mon Dec 02 10:40:55 2024 -0500
@@ -0,0 +1,647 @@
+#!/usr/bin/env python3
+
+import sys
+import os
+import pandas as pd
+import datetime
+from pybedtools import BedTool,helpers
+import concurrent.futures
+import time
+import uuid
+import traceback
+import shutil
+import argparse
+
+
+def fetchHeaders(snpdiffs_file):
+    
+    with open(snpdiffs_file, 'r') as file:
+        top_line = file.readline().strip().split('\t')[1:]
+
+    header_cols = [item.split(':')[0] for item in top_line]
+    header_vals = [item.split(':')[1] for item in top_line]
+    
+    header_data = pd.DataFrame(columns = header_cols)
+    header_data.loc[0] = header_vals
+    header_data.loc[:, 'File_Path'] = snpdiffs_file
+      
+    return header_data
+
+def processBED(bed_rows,snpdiffs_orientation):
+    
+    bed_columns = ['Ref_Contig','Ref_Start','Ref_End','Ref_Length','Ref_Aligned',
+                   'Query_Contig','Query_Start','Query_End','Query_Length','Query_Aligned',
+                   'Perc_Iden']
+    
+    reverse_columns = ['Query_Contig','Query_Start','Query_End','Query_Length','Query_Aligned',
+                     'Ref_Contig','Ref_Start','Ref_End','Ref_Length','Ref_Aligned',
+                     'Perc_Iden']
+    
+    int_columns = ['Ref_Start', 'Ref_End', 'Ref_Length', 'Ref_Aligned',
+                'Query_Start', 'Query_End', 'Query_Length', 'Query_Aligned']
+
+    float_columns = ['Perc_Iden']
+    
+    if len(bed_rows) > 0:
+        
+        bed_df = pd.DataFrame(bed_rows, columns=bed_columns)
+        
+        # Swap columns if reversed
+        if snpdiffs_orientation == -1:
+            bed_df = bed_df[reverse_columns].copy()
+            bed_df.columns = bed_columns
+            
+        # Remove any rows where Query_Contig or Ref_Contig == "." (Unaligned)
+        covered_bed_df = bed_df[(bed_df['Ref_Start'] != ".") & (bed_df['Query_Start'] != ".")].copy()
+        
+        if covered_bed_df.shape[0] > 0:
+            for col in int_columns:
+                covered_bed_df.loc[:, col] = covered_bed_df.loc[:, col].astype(float).astype(int)
+            for col in float_columns:
+                covered_bed_df.loc[:, col] = covered_bed_df.loc[:, col].astype(float)
+            return covered_bed_df        
+        else:
+            return pd.DataFrame(columns=bed_columns)           
+    else:
+        return pd.DataFrame(columns=bed_columns)
+
+def processSNPs(snp_rows,snpdiffs_orientation):
+    
+    snp_columns = ['Ref_Contig','Start_Ref','Ref_Pos',
+                'Query_Contig','Start_Query','Query_Pos',
+                'Ref_Loc','Query_Loc',
+                'Ref_Start','Ref_End',
+                'Query_Start','Query_End',
+                'Ref_Base','Query_Base',
+                'Dist_to_Ref_End','Dist_to_Query_End',
+                'Ref_Aligned','Query_Aligned',
+                'Query_Direction','Perc_Iden','Cat']
+        
+    reverse_columns = ['Query_Contig','Start_Query','Query_Pos',
+                    'Ref_Contig','Start_Ref','Ref_Pos',
+                    'Query_Loc','Ref_Loc',
+                    'Query_Start','Query_End',
+                    'Ref_Start','Ref_End',
+                    'Query_Base','Ref_Base',
+                    'Dist_to_Query_End','Dist_to_Ref_End',
+                    'Query_Aligned','Ref_Aligned',
+                    'Query_Direction','Perc_Iden','Cat']
+    
+    return_columns = ['Ref_Contig','Start_Ref','Ref_Pos',
+            'Query_Contig','Start_Query','Query_Pos',
+            'Ref_Loc','Query_Loc',
+            'Ref_Start','Ref_End',
+            'Query_Start','Query_End',
+            'Ref_Base','Query_Base',
+            'Dist_to_Ref_End','Dist_to_Query_End',
+            'Ref_Aligned','Query_Aligned',
+            'Perc_Iden','Cat']
+    
+    reverse_complement = {'A':'T','T':'A','G':'C','C':'G',
+                          'a':'T','t':'A','c':'G','g':'C'}
+    
+    # Columns to convert to integer
+    int_columns = ['Start_Ref', 'Ref_Pos', 'Start_Query', 'Query_Pos',
+                'Dist_to_Ref_End', 'Dist_to_Query_End', 'Ref_Aligned', 'Query_Aligned']
+
+    # Columns to convert to float
+    float_columns = ['Perc_Iden']
+    
+    if len(snp_rows) > 0:
+        snp_df = pd.DataFrame(snp_rows, columns= snp_columns).copy()
+        
+        if snpdiffs_orientation == -1:
+            snp_df = snp_df[reverse_columns].copy()
+            snp_df.columns = snp_columns
+            
+            # Replace Query_Base and Reference_Base with reverse complement if Query_Direction is -1 and base is in ['A','T','G','C','a','c','t','g']
+            snp_df.loc[snp_df['Query_Direction'] == '-1','Query_Base'] = snp_df.loc[snp_df['Query_Direction'] == '-1','Query_Base'].apply(lambda x: reverse_complement[x] if x in reverse_complement else x)
+            snp_df.loc[snp_df['Query_Direction'] == '-1','Ref_Base'] = snp_df.loc[snp_df['Query_Direction'] == '-1','Ref_Base'].apply(lambda x: reverse_complement[x] if x in reverse_complement else x)
+
+            
+        for col in int_columns:
+            snp_df.loc[:, col] = snp_df.loc[:, col].astype(float).astype(int)
+        for col in float_columns:
+            snp_df.loc[:, col] = snp_df.loc[:, col].astype(float)
+        
+    else:
+        snp_df = pd.DataFrame(columns = return_columns)
+
+    return snp_df[return_columns]
+
+def swapHeader(header_data):
+            
+    raw_header_cols = [x for x in header_data.columns]
+    reverse_header_cols = [item.replace('Reference', 'temp').replace('Query', 'Reference').replace('temp', 'Query') for item in raw_header_cols]
+    reversed_header_data = header_data[reverse_header_cols].copy()
+    reversed_header_data.columns = raw_header_cols
+
+    return reversed_header_data
+    
+def parseSNPDiffs(snpdiffs_file,snpdiffs_orientation):
+
+    bed_rows = []
+    snp_rows = []
+    
+    with open(snpdiffs_file, 'r') as file:
+        lines = file.readlines()
+
+    for line in lines:
+        if line[0:2] == "#\t":
+            pass
+        elif line[0:3] == "##\t":
+            bed_rows.append(line.strip().split("\t")[1:])
+        else:
+            snp_rows.append(line.strip().split("\t"))
+
+    bed_df = processBED(bed_rows,snpdiffs_orientation)
+    snp_df = processSNPs(snp_rows,snpdiffs_orientation)
+    return (bed_df,snp_df)
+
+def calculate_total_length(bedtool):
+    return sum(len(interval) for interval in bedtool)
+
+def filterSNPs(raw_snp_df,bed_df,log_file, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps):
+    
+    if temp_dir != "":
+        helpers.set_tempdir(temp_dir)
+    
+    # Grab raw data    
+    total_snp_count = raw_snp_df.shape[0]
+    
+    # Get unique SNPs relative to the reference genome
+    unique_ref_snps = raw_snp_df['Ref_Loc'].unique()
+    unique_snp_count = len(unique_ref_snps)
+    
+    snp_tally_df = pd.DataFrame()
+    
+    with open(log_file,"a+") as log:
+        log.write(f"\n\t- Raw SNP + indel count: {total_snp_count}\n")
+        log.write(f"\n\t- Unique SNP positions in reference genome: {unique_snp_count}\n")
+    
+    # Set all sites to SNP
+    raw_snp_df['Filter_Cat'] = "SNP"
+    
+    # Filter out SNPs based on --min_len and --min_iden
+    reject_length = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] < min_len) & (raw_snp_df['Perc_Iden'] >= min_iden)].copy()
+    if reject_length.shape[0] > 0:
+        with open(log_file,"a+") as log:
+            log.write(f"\t\t- Purged (Alignment Length): {reject_length.shape[0]}\n")
+        reject_length['Filter_Cat'] = "Purged_Length"
+        snp_tally_df = pd.concat([snp_tally_df,reject_length]).reset_index(drop=True)
+        
+    reject_iden = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] >= min_len) & (raw_snp_df['Perc_Iden'] < min_iden)].copy()
+    if reject_iden.shape[0] > 0:
+        with open(log_file,"a+") as log:
+            log.write(f"\t\t- Purged (Alignment Identity): {reject_iden.shape[0]}\n")
+        reject_iden['Filter_Cat'] = "Purged_Identity"
+        snp_tally_df = pd.concat([snp_tally_df,reject_iden]).reset_index(drop=True)
+
+    reject_lenIden = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] < min_len) & (raw_snp_df['Perc_Iden'] < min_iden)].copy()
+    if reject_lenIden.shape[0] > 0:
+        with open(log_file,"a+") as log:
+            log.write(f"\t\t- Purged (Alignment Length + Identity): {reject_lenIden.shape[0]}\n")
+        reject_lenIden['Filter_Cat'] = "Purged_LengthIdentity"
+        snp_tally_df = pd.concat([snp_tally_df,reject_lenIden]).reset_index(drop=True)
+    
+    pass_filter = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] >= min_len) & (raw_snp_df['Perc_Iden'] >= min_iden)].copy().reset_index(drop=True)
+    
+    # Invalid processing
+    reject_invalid = pass_filter[pass_filter['Cat'] == "Invalid"].copy()
+    if reject_invalid.shape[0] > 0:
+        with open(log_file,"a+") as log:
+            log.write(f"\t\t- Purged (Invalid Base): {reject_invalid.shape[0]}\n")
+        reject_invalid['Filter_Cat'] = "Purged_Invalid"
+        snp_tally_df = pd.concat([snp_tally_df,reject_invalid]).reset_index(drop=True)
+    pass_filter = pass_filter.loc[pass_filter['Cat'] != "Invalid"].copy()
+    
+    # Indel processing
+    reject_indel = pass_filter[pass_filter['Cat'] == "Indel"].copy()
+    if reject_indel.shape[0] > 0:
+        with open(log_file,"a+") as log:
+            log.write(f"\t\t- Purged (Indel): {reject_indel.shape[0]}\n")
+        reject_indel['Filter_Cat'] = "Purged_Indel"
+        snp_tally_df = pd.concat([snp_tally_df,reject_indel]).reset_index(drop=True)
+    pass_filter = pass_filter.loc[pass_filter['Cat'] != "Indel"].copy()
+    
+    # Check for heterozygous SNPs
+    check_heterozygous = pass_filter.groupby('Ref_Loc').filter(lambda x: x['Query_Base'].nunique() > 1)
+    if check_heterozygous.shape[0] > 0:      
+        reject_heterozygous = pass_filter.loc[pass_filter['Ref_Loc'].isin(check_heterozygous['Ref_Loc'])].copy()
+        reject_heterozygous['Filter_Cat'] = "Purged_Heterozygous"
+        with open(log_file,"a+") as log:
+            log.write(f"\t\t- Purged (Heterozygotes): {reject_heterozygous.shape[0]}\n")  
+        snp_tally_df = pd.concat([snp_tally_df,reject_heterozygous]).reset_index(drop=True)
+        pass_filter = pass_filter.loc[~pass_filter['Ref_Loc'].isin(check_heterozygous['Ref_Loc'])].copy()
+   
+    # Check for duplicate SNPs and take the longest, best hit
+    check_duplicates = pass_filter.groupby('Ref_Loc').filter(lambda x: x.shape[0] > 1)
+    if check_duplicates.shape[0] > 0:
+        reject_duplicate = pass_filter.loc[pass_filter['Ref_Loc'].isin(check_duplicates['Ref_Loc'])].copy()
+        pass_filter = pass_filter.loc[~pass_filter['Ref_Loc'].isin(check_duplicates['Ref_Loc'])].copy()
+                
+        best_snp = reject_duplicate.groupby('Ref_Loc').apply(lambda x: x.sort_values(by=['Ref_Aligned', 'Perc_Iden'], ascending=[False, False]).head(1))
+        pass_filter = pd.concat([pass_filter,best_snp]).reset_index(drop=True)
+
+        dup_snps = reject_duplicate[~reject_duplicate.apply(lambda x: x in best_snp, axis=1)]        
+        dup_snps['Filter_Cat'] = "Purged_Duplicate"
+        
+        snp_tally_df = pd.concat([snp_tally_df,dup_snps]).reset_index(drop=True)
+                        
+        with open(log_file,"a+") as log:
+            log.write(f"\t\t- Purged (Duplicates): {dup_snps.shape[0]}\n")    
+    
+    # Assert that Ref_Loc and Query_Loc are unique in pass_filter
+    helpers.cleanup(verbose=False,remove_all = False)
+    assert pass_filter['Ref_Loc'].nunique() == pass_filter.shape[0]
+    assert pass_filter['Query_Loc'].nunique() == pass_filter.shape[0]
+    
+    # Density filtering
+    density_locs = []
+    ref_locs = pass_filter['Ref_Loc'].tolist()
+        
+    if len(density_windows) == 0:
+        with open(log_file,"a+") as log:
+            log.write("\n\t- Density filtering disabled...\n")
+    elif len(ref_locs) > 0:
+        density_df = pd.DataFrame([item.split('/') for item in ref_locs], columns=['Ref_Contig','Ref_End'])
+        density_df['Ref_Start'] = density_df['Ref_End'].astype(float).astype(int) - 1
+        density_bed = BedTool.from_dataframe(density_df[['Ref_Contig','Ref_Start','Ref_End']])
+
+        # For each density window, remove all SNPs that fall in a window with > max_snps
+        for i in range(0,len(density_windows)):
+            window_df = density_bed.window(density_bed,c=True, w=density_windows[i]).to_dataframe()
+            problematic_windows = window_df[window_df['name'] > max_snps[i]].copy()
+            if not problematic_windows.empty:
+                temp_locs = []            
+                for _, row in problematic_windows.iterrows():
+                        purge_window_df = window_df[window_df['chrom'] == row['chrom']].copy()
+                        purge_window_df['Dist'] = abs(purge_window_df['end'] - row['end'])
+                        window_snps = purge_window_df.sort_values(by=['Dist'],ascending=True).head(row['name'])
+                        temp_locs = temp_locs + ["/".join([str(x[0]),str(x[1])]) for x in list(zip(window_snps.chrom, window_snps.end))]
+                density_locs.extend(list(set(temp_locs)))
+    
+    density_locs = list(set(density_locs))
+    reject_density = pass_filter[pass_filter['Ref_Loc'].isin(density_locs)].copy() 
+           
+    if reject_density.shape[0] > 0:
+        with open(log_file,"a+") as log:
+            log.write(f"\t\t- Purged (Density): {reject_density.shape[0]}\n")
+        reject_density['Filter_Cat'] = "Purged_Density"
+        snp_tally_df = pd.concat([snp_tally_df,reject_density]).reset_index(drop=True)
+        pass_filter = pass_filter[~pass_filter['Ref_Loc'].isin(density_locs)].copy()
+
+    reject_query_edge = pass_filter[(pass_filter['Dist_to_Query_End'] < query_edge) & (pass_filter['Dist_to_Ref_End'] >= ref_edge)].copy()
+    reject_ref_edge = pass_filter[(pass_filter['Dist_to_Ref_End'] < ref_edge) & (pass_filter['Dist_to_Query_End'] >= query_edge)].copy()
+    reject_both_edge = pass_filter[(pass_filter['Dist_to_Query_End'] < query_edge) & (pass_filter['Dist_to_Ref_End'] < ref_edge)].copy()
+    
+    if reject_query_edge.shape[0] > 0:
+        with open(log_file,"a+") as log:
+            log.write(f"\t\t- Purged (Query Edge): {reject_query_edge.shape[0]}\n")
+        reject_query_edge['Filter_Cat'] = "Filtered_Query_Edge"
+        snp_tally_df = pd.concat([snp_tally_df,reject_query_edge]).reset_index(drop=True)
+            
+    if reject_ref_edge.shape[0] > 0:
+        with open(log_file,"a+") as log:
+            log.write(f"\t\t- Purged (Ref Edge): {reject_ref_edge.shape[0]}\n")
+        reject_ref_edge['Filter_Cat'] = "Filtered_Ref_Edge"
+        snp_tally_df = pd.concat([snp_tally_df,reject_ref_edge]).reset_index(drop=True)
+            
+    if reject_both_edge.shape[0] > 0:
+        with open(log_file,"a+") as log:
+            log.write(f"\t\t- Purged (Both Edge): {reject_both_edge.shape[0]}\n")
+        reject_both_edge['Filter_Cat'] = "Filtered_Both_Edge"
+        snp_tally_df = pd.concat([snp_tally_df,reject_both_edge]).reset_index(drop=True)
+        
+    pass_filter = pass_filter[(pass_filter['Dist_to_Query_End'] >= query_edge) & (pass_filter['Dist_to_Ref_End'] >= ref_edge)].copy()
+    
+    helpers.cleanup(verbose=False,remove_all = False)
+
+    assert snp_tally_df.shape[0] + pass_filter.shape[0] == total_snp_count
+    return_df = pd.concat([pass_filter,snp_tally_df]).reset_index(drop=True).sort_values(by=['Ref_Loc'])
+        
+    return return_df.drop(columns=['Cat']).rename({'Filter_Cat':'Cat'}, axis=1)
+ 
+
+def screenSNPDiffs(snpdiffs_file,trim_name, min_cov, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps,ref_ids):
+    
+    if temp_dir != "":
+        helpers.set_tempdir(temp_dir)
+
+    screen_start_time = time.time()
+
+    # Set CSP2 variables to NA
+    csp2_screen_snps = purged_length = purged_identity = purged_invalid = purged_indel = purged_lengthIdentity = purged_duplicate = purged_het = purged_density = filtered_ref_edge = filtered_query_edge = filtered_both_edge = "NA"
+
+    # Ensure snpdiffs file exists
+    if not os.path.exists(snpdiffs_file) or not snpdiffs_file.endswith('.snpdiffs'):
+        run_failed = True
+        sys.exit(f"Invalid snpdiffs file provided: {snpdiffs_file}")
+        
+    # Ensure header can be read in
+    try:
+        header_data = fetchHeaders(snpdiffs_file)
+        header_query = header_data['Query_ID'][0].replace(trim_name,'')
+        header_ref = header_data['Reference_ID'][0].replace(trim_name,'')
+    except:
+        run_failed = True       
+        sys.exit(f"Error reading headers from snpdiffs file: {snpdiffs_file}")
+        
+    # Check snpdiffs orientation
+    if ref_ids == []:
+        snpdiffs_orientation = 1
+        query_id = header_query
+        reference_id = header_ref
+    elif (header_query not in ref_ids) and (header_ref in ref_ids):
+        snpdiffs_orientation = 1
+        query_id = header_query
+        reference_id = header_ref
+    elif (header_query in ref_ids) and (header_ref not in ref_ids):
+        snpdiffs_orientation = -1
+        query_id = header_ref
+        reference_id = header_query
+        header_data = swapHeader(header_data)
+    else:
+        snpdiffs_orientation = 2
+        query_id = header_query
+        reference_id = header_ref        
+
+    # Establish log file
+    log_file = f"{log_dir}/{query_id}__vs__{reference_id}.log"
+    with open(log_file,"w+") as log:
+        log.write("Screening Analysis\n")
+        log.write(f"Query Isolate: {query_id}\n")
+        log.write(f"Reference Isolate: {reference_id}\n")
+        log.write(str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))+"\n")
+        log.write("-------------------------------------------------------\n\n")
+        if ref_ids == []:
+            log.write("\t- No explicit references set, processing in forward orientation\n")
+            log.write("-------------------------------------------------------\n\n")    
+        elif snpdiffs_orientation == 1:
+            log.write("\t- SNPDiffs file is in the forward orientation\n")
+            log.write("-------------------------------------------------------\n\n")
+        elif snpdiffs_orientation == -1:
+            log.write("\t- SNPDiffs file is in the reverse orientation\n")
+            log.write("-------------------------------------------------------\n\n")
+        else:
+            snpdiffs_orientation = 1
+            log.write("\t- SNPDiffs file not contain a reference and non-reference sample, processing in forward orientation\n")
+            log.write("-------------------------------------------------------\n\n")
+
+    
+    # Set variables from header data
+    raw_snps = int(header_data['SNPs'][0])
+    raw_indels = int(header_data['Indels'][0])
+    raw_invalid = int(header_data['Invalid'][0])
+    
+    kmer_similarity = float(header_data['Kmer_Similarity'][0])
+    shared_kmers = int(header_data['Shared_Kmers'][0])
+    query_unique_kmers = int(header_data['Query_Unique_Kmers'][0])
+    reference_unique_kmers = int(header_data['Reference_Unique_Kmers'][0])
+    mummer_gsnps = int(header_data['gSNPs'][0])
+    mummer_gindels = int(header_data['gIndels'][0])
+    
+    query_bases = int(header_data['Query_Assembly_Bases'][0])
+    reference_bases = int(header_data['Reference_Assembly_Bases'][0])
+    
+    query_contigs = int(header_data['Query_Contig_Count'][0])
+    reference_contigs = int(header_data['Reference_Contig_Count'][0])
+    
+    raw_query_percent_aligned = float(header_data['Query_Percent_Aligned'][0])
+    raw_ref_percent_aligned = float(header_data['Reference_Percent_Aligned'][0])
+        
+    # If the reference is not covered by at least min_cov, STOP
+    if raw_ref_percent_aligned < min_cov:
+        query_percent_aligned = raw_query_percent_aligned
+        reference_percent_aligned = raw_ref_percent_aligned
+        screen_category = "Low_Coverage"
+        with open(log_file,"a+") as log:
+            log.write(f"\t- Reference genome coverage: {raw_ref_percent_aligned}% \n")
+            log.write(f"\t- Query covers less than --min_cov ({min_cov}%)...Screen halted...\n")
+            log.write("-------------------------------------------------------\n\n")
+
+    elif raw_snps + raw_indels + raw_invalid > 10000:
+        query_percent_aligned = raw_query_percent_aligned
+        reference_percent_aligned = raw_ref_percent_aligned
+        screen_category = "SNP_Cutoff"
+        with open(log_file,"a+") as log:
+            log.write(f"\t- {raw_snps} detected...\n")
+            log.write("\t- > 10,000 SNPs, indels, or invalid sites detected by MUMmer...Screen halted...\n")
+            log.write("-------------------------------------------------------\n\n")
+   
+    else:
+    
+        ##### 02: Read in BED/SNP data #####
+        with open(log_file,"a+") as log:
+                log.write("Step 1: Reading in snpdiffs BED/SNP data...")
+        try:
+            bed_df,snp_df = parseSNPDiffs(snpdiffs_file,snpdiffs_orientation)
+            with open(log_file,"a+") as log:
+                log.write("Done!\n")
+                log.write("-------------------------------------------------------\n\n")
+        except:
+            with open(log_file,"a+") as log:
+                log.write(f"Error reading BED/SNP data from file: {snpdiffs_file}")
+            run_failed = True
+            sys.exit(f"Error reading BED/SNP data from file: {snpdiffs_file}")
+            
+        ##### 03: Filter genome overlaps #####
+        with open(log_file,"a+") as log:
+            log.write("Step 2: Filtering for short overlaps and low percent identity...")
+        
+        good_bed_df = bed_df[(bed_df['Ref_Aligned'] >= min_len) & (bed_df['Perc_Iden'] >= min_iden)].copy()
+        
+        if good_bed_df.shape[0] == 0:
+            screen_category = "Low_Quality_Coverage"
+            with open(log_file,"a+") as log:
+                log.write(f"\n\t- After filtering based on --min_len ({min_len}) and --min_iden ({min_iden}) , no valid alignments remain...Screen halted...\n")
+                log.write("-------------------------------------------------------\n\n")
+
+                
+        else:
+            # Create a BED file for alignments that pass basic QC
+            good_query_bed_df = good_bed_df[['Query_Contig','Query_Start','Query_End']].copy()
+            good_reference_bed_df = good_bed_df[['Ref_Contig','Ref_Start','Ref_End']].copy()
+            
+            good_query_aligned = calculate_total_length(BedTool.from_dataframe(good_query_bed_df).sort().merge())
+            good_reference_aligned = calculate_total_length(BedTool.from_dataframe(good_reference_bed_df).sort().merge())
+            
+            query_percent_aligned = (good_query_aligned / query_bases) * 100
+            reference_percent_aligned = (good_reference_aligned / reference_bases) * 100
+            
+            if reference_percent_aligned < min_cov:
+                screen_category = "Low_Quality_Coverage"
+                with open(log_file,"a+") as log:
+                    log.write(f"\n\t- Raw reference genome coverage was {raw_ref_percent_aligned}% \n")
+                    log.write(f"\t- After filtering based on --min_len ({min_len}) and --min_iden ({min_iden}), reference genome coverage was {reference_percent_aligned:.2f}% \n")
+                    log.write(f"\t- Query covers less than --min_cov ({min_cov}%) of reference after filtering...Screen halted...\n")
+                    log.write("-------------------------------------------------------\n\n")
+
+            else:
+                screen_category = "Pass"
+                with open(log_file,"a+") as log:
+                    log.write("Done!\n")
+                    log.write(f"\t- Raw reference genome coverage was {raw_ref_percent_aligned}% \n")
+                    log.write(f"\t- After filtering based on --min_len ({min_len}) and --min_iden ({min_iden}), reference genome coverage was {reference_percent_aligned:.2f}% \n")
+                    log.write("-------------------------------------------------------\n\n")
+
+            
+                # Filter SNPs
+                with open(log_file,"a+") as log:
+                    log.write("Step 3: Filtering SNPs to get final SNP distances...")
+                
+                if raw_snps == 0:
+                    csp2_screen_snps = purged_length = purged_identity = purged_lengthIdentity = purged_indel = purged_invalid = purged_duplicate = purged_het = purged_density = filtered_ref_edge = filtered_query_edge = filtered_both_edge = 0
+                    with open(log_file,"a+") as log:
+                        log.write("Done!\n")
+                        log.write("\t- No SNPs detected in MUMmer output, no filtering required\n")
+                        log.write("-------------------------------------------------------\n\n")
+
+                else:
+                    filtered_snp_df = filterSNPs(snp_df,bed_df,log_file, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps)
+                    
+                    # Write filtered SNP data to file
+                    snp_file = log_file.replace(".log","_SNPs.tsv")
+                    filtered_snp_df.to_csv(snp_file, sep="\t", index=False)
+                    
+                    csp2_screen_snps = filtered_snp_df[filtered_snp_df.Cat == "SNP"].shape[0]
+                    
+                    purged_length = filtered_snp_df[filtered_snp_df.Cat == "Purged_Length"].shape[0]
+                    purged_identity = filtered_snp_df[filtered_snp_df.Cat == "Purged_Identity"].shape[0]
+                    purged_lengthIdentity = filtered_snp_df[filtered_snp_df.Cat == "Purged_LengthIdentity"].shape[0]
+                    purged_invalid = filtered_snp_df[filtered_snp_df.Cat == "Purged_Invalid"].shape[0]
+                    purged_indel = filtered_snp_df[filtered_snp_df.Cat == "Purged_Indel"].shape[0]
+                    purged_het = filtered_snp_df[filtered_snp_df.Cat == "Purged_Heterozygous"].shape[0]                    
+                    purged_duplicate = filtered_snp_df[filtered_snp_df.Cat == "Purged_Duplicate"].shape[0]
+                    purged_density = filtered_snp_df[filtered_snp_df.Cat == "Purged_Density"].shape[0]
+                    filtered_query_edge = filtered_snp_df[filtered_snp_df.Cat == "Filtered_Query_Edge"].shape[0]
+                    filtered_ref_edge = filtered_snp_df[filtered_snp_df.Cat == "Filtered_Ref_Edge"].shape[0]
+                    filtered_both_edge = filtered_snp_df[filtered_snp_df.Cat == "Filtered_Both_Edge"].shape[0]
+                   
+                    with open(log_file,"a+") as log:
+                        log.write("Done!\n")
+                        log.write(f"\t- {csp2_screen_snps} SNPs detected between {query_id} and {reference_id} after filtering\n")
+                        log.write(f"\t- SNP data saved to {snp_file}\n")
+                        log.write("-------------------------------------------------------\n\n")
+    
+    screen_end_time = time.time()
+    helpers.cleanup(verbose=False, remove_all=False)
+
+    with open(log_file,"a+") as log:
+        log.write(f"Screening Time: {screen_end_time - screen_start_time:.2f} seconds\n")
+    
+    # Clean up pybedtools temp
+    helpers.cleanup(verbose=False, remove_all=False)
+    
+    return [str(item) for item in [query_id,reference_id,screen_category,csp2_screen_snps,
+            f"{query_percent_aligned:.2f}",f"{reference_percent_aligned:.2f}",
+            query_contigs,query_bases,reference_contigs,reference_bases,
+            raw_snps,purged_length,purged_identity,purged_lengthIdentity,purged_invalid,purged_indel,purged_duplicate,purged_het,purged_density,
+            filtered_query_edge,filtered_ref_edge,filtered_both_edge,
+            kmer_similarity,shared_kmers,query_unique_kmers,reference_unique_kmers,
+            mummer_gsnps,mummer_gindels]]
+
+# Read in arguments
+global run_failed
+run_failed = False
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--snpdiffs_file", help="Path to the file containing SNP diffs")
+parser.add_argument("--log_dir", help="Path to the log directory")
+parser.add_argument("--min_cov", type=float, help="Minimum coverage")
+parser.add_argument("--min_len", type=int, help="Minimum length")
+parser.add_argument("--min_iden", type=float, help="Minimum identity")
+parser.add_argument("--ref_edge", type=int, help="Reference edge")
+parser.add_argument("--query_edge", type=int, help="Query edge")
+parser.add_argument("--density_windows", help="Density windows (comma-separated)")
+parser.add_argument("--max_snps", help="Maximum SNPs (comma-separated)")
+parser.add_argument("--trim_name", help="Trim name")
+parser.add_argument("--output_file", help="Output file")
+parser.add_argument("--ref_id", help="Reference IDs file")
+parser.add_argument("--tmp_dir", help="TMP dir")
+
+args = parser.parse_args()
+
+snpdiffs_list = [line.strip() for line in open(args.snpdiffs_file, 'r')]
+snpdiffs_list = [line for line in snpdiffs_list if line]
+for snpdiffs_file in snpdiffs_list:
+    if not os.path.exists(snpdiffs_file):
+        run_failed = True
+        sys.exit("Error: File does not exist: " + snpdiffs_file)
+
+snpdiffs_list = list(set(snpdiffs_list))
+
+log_dir = os.path.normpath(os.path.abspath(args.log_dir))
+
+min_cov = args.min_cov
+min_len = args.min_len
+min_iden = args.min_iden
+
+ref_edge = args.ref_edge
+query_edge = args.query_edge
+
+input_density = args.density_windows
+input_maxsnps = args.max_snps
+
+if input_density == "0":
+    density_windows = []
+    max_snps = []
+else:
+    density_windows = [int(x) for x in args.density_windows.split(",")]
+    max_snps = [int(x) for x in args.max_snps.split(",")]
+assert len(density_windows) == len(max_snps)
+
+trim_name = args.trim_name
+
+output_file = os.path.abspath(args.output_file)
+
+if os.stat(args.ref_id).st_size == 0:
+    ref_ids = []
+else:
+    ref_ids = [line.strip() for line in open(args.ref_id, 'r')]
+
+global temp_dir
+if args.tmp_dir != "":
+    random_temp_id = str(uuid.uuid4())
+    temp_dir = f"{os.path.normpath(os.path.abspath(args.tmp_dir))}/{random_temp_id}"
+    try:
+        os.mkdir(temp_dir)
+        helpers.set_tempdir(temp_dir)
+    except OSError as e:
+        run_failed = True
+        print(f"Error: Failed to create directory '{temp_dir}': {e}")
+else:
+    temp_dir = ""
+
+try:    
+    with concurrent.futures.ProcessPoolExecutor() as executor:
+        results = [executor.submit(screenSNPDiffs,snp_diff_file,trim_name, min_cov, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps,ref_ids) for snp_diff_file in snpdiffs_list]
+
+    # Clean up pybedtools temp
+    helpers.cleanup(verbose=False,remove_all = False)
+
+    # Combine results into a dataframe
+    output_columns = ['Query_ID','Reference_ID','Screen_Category','CSP2_Screen_SNPs',
+                'Query_Percent_Aligned','Reference_Percent_Aligned',
+                'Query_Contigs','Query_Bases','Reference_Contigs','Reference_Bases',
+                'Raw_SNPs','Purged_Length','Purged_Identity','Purged_LengthIdentity','Purged_Invalid','Purged_Indel','Purged_Duplicate','Purged_Het','Purged_Density',
+                'Filtered_Query_Edge','Filtered_Ref_Edge','Filtered_Both_Edge',
+                'Kmer_Similarity','Shared_Kmers','Query_Unique_Kmers','Reference_Unique_Kmers',
+                'MUMmer_gSNPs','MUMmer_gIndels']
+
+    results_df = pd.DataFrame([item.result() for item in results], columns = output_columns)
+    results_df.to_csv(output_file, sep="\t", index=False)
+except:
+    run_failed = True
+    print("Exception occurred:\n", traceback.format_exc())
+finally:
+    helpers.cleanup(verbose=False, remove_all=False)
+    if temp_dir != "":
+        shutil.rmtree(temp_dir)
+    if run_failed:
+        sys.exit(1)
+
+
+
+
+