rliterman@0: #!/usr/bin/env python3
rliterman@0: 
rliterman@0: import sys
rliterman@0: import os
rliterman@0: import pandas as pd
rliterman@0: import datetime
rliterman@0: from pybedtools import BedTool,helpers
rliterman@0: import concurrent.futures
rliterman@0: import time
rliterman@0: from Bio.Seq import Seq
rliterman@0: from Bio.SeqRecord import SeqRecord
rliterman@0: from Bio.Align import MultipleSeqAlignment
rliterman@0: from Bio import AlignIO
rliterman@0: from itertools import combinations
rliterman@0: import numpy as np
rliterman@0: import uuid
rliterman@0: import traceback
rliterman@0: import shutil
rliterman@0: import argparse
rliterman@0: 
rliterman@0: def fetchHeaders(snpdiffs_file):
rliterman@0:     
rliterman@0:     with open(snpdiffs_file, 'r') as file:
rliterman@0:         top_line = file.readline().strip().split('\t')[1:]
rliterman@0: 
rliterman@0:     header_cols = [item.split(':')[0] for item in top_line]
rliterman@0:     header_vals = [item.split(':')[1] for item in top_line]
rliterman@0:     
rliterman@0:     header_data = pd.DataFrame(columns = header_cols)
rliterman@0:     header_data.loc[0] = header_vals
rliterman@0:     header_data.loc[:, 'File_Path'] = snpdiffs_file
rliterman@0:       
rliterman@0:     return header_data
rliterman@0: 
rliterman@0: def processBED(bed_rows,snpdiffs_orientation):
rliterman@0:     
rliterman@0:     bed_columns = ['Ref_Contig','Ref_Start','Ref_End','Ref_Length','Ref_Aligned',
rliterman@0:                    'Query_Contig','Query_Start','Query_End','Query_Length','Query_Aligned',
rliterman@0:                    'Perc_Iden']
rliterman@0:         
rliterman@0:     reverse_columns = ['Query_Contig','Query_Start','Query_End','Query_Length','Query_Aligned',
rliterman@0:                      'Ref_Contig','Ref_Start','Ref_End','Ref_Length','Ref_Aligned',
rliterman@0:                      'Perc_Iden']
rliterman@0:     
rliterman@0:     int_columns = ['Ref_Start', 'Ref_End', 'Ref_Length', 'Ref_Aligned',
rliterman@0:                 'Query_Start', 'Query_End', 'Query_Length', 'Query_Aligned']
rliterman@0: 
rliterman@0:     float_columns = ['Perc_Iden']
rliterman@0:     
rliterman@0:     if len(bed_rows) > 0:
rliterman@0:         
rliterman@0:         bed_df = pd.DataFrame(bed_rows, columns=bed_columns)
rliterman@0:         
rliterman@0:         # Swap columns if reversed
rliterman@0:         if snpdiffs_orientation == -1:
rliterman@0:             bed_df = bed_df[reverse_columns].copy()
rliterman@0:             bed_df.columns = bed_columns
rliterman@0: 
rliterman@0:         # Gather covered loci
rliterman@0:         covered_bed_df = bed_df[(bed_df['Ref_Start'] != ".") & (bed_df['Query_Start'] != ".")].copy()
rliterman@0:         if covered_bed_df.shape[0] > 0:
rliterman@0:             for col in int_columns:
rliterman@0:                 covered_bed_df.loc[:, col] = covered_bed_df.loc[:, col].astype(float).astype(int)
rliterman@0:             for col in float_columns:
rliterman@0:                 covered_bed_df.loc[:, col] = covered_bed_df.loc[:, col].astype(float)
rliterman@0:         
rliterman@0:         return covered_bed_df
rliterman@0:     else:
rliterman@0:         return pd.DataFrame(columns=bed_columns)
rliterman@0: 
rliterman@0: def processSNPs(snp_rows,snpdiffs_orientation):
rliterman@0:     
rliterman@0:     snp_columns = ['Ref_Contig','Start_Ref','Ref_Pos',
rliterman@0:                 'Query_Contig','Start_Query','Query_Pos',
rliterman@0:                 'Ref_Loc','Query_Loc',
rliterman@0:                 'Ref_Start','Ref_End',
rliterman@0:                 'Query_Start','Query_End',
rliterman@0:                 'Ref_Base','Query_Base',
rliterman@0:                 'Dist_to_Ref_End','Dist_to_Query_End',
rliterman@0:                 'Ref_Aligned','Query_Aligned',
rliterman@0:                 'Query_Direction','Perc_Iden','Cat']
rliterman@0:     
rliterman@0:     return_columns = ['Ref_Contig','Start_Ref','Ref_Pos',
rliterman@0:             'Query_Contig','Start_Query','Query_Pos',
rliterman@0:             'Ref_Loc','Query_Loc',
rliterman@0:             'Ref_Start','Ref_End',
rliterman@0:             'Query_Start','Query_End',
rliterman@0:             'Ref_Base','Query_Base',
rliterman@0:             'Dist_to_Ref_End','Dist_to_Query_End',
rliterman@0:             'Ref_Aligned','Query_Aligned',
rliterman@0:             'Perc_Iden','Cat']
rliterman@0:     
rliterman@0:     reverse_columns = ['Query_Contig','Start_Query','Query_Pos',
rliterman@0:                     'Ref_Contig','Start_Ref','Ref_Pos',
rliterman@0:                     'Query_Loc','Ref_Loc',
rliterman@0:                     'Query_Start','Query_End',
rliterman@0:                     'Ref_Start','Ref_End',
rliterman@0:                     'Query_Base','Ref_Base',
rliterman@0:                     'Dist_to_Query_End','Dist_to_Ref_End',
rliterman@0:                     'Query_Aligned','Ref_Aligned',
rliterman@0:                     'Query_Direction','Perc_Iden','Cat']
rliterman@0:     
rliterman@0:     reverse_complement = {'A':'T','T':'A','G':'C','C':'G',
rliterman@0:                           'a':'T','t':'A','c':'G','g':'C'}
rliterman@0:     
rliterman@0:     # Columns to convert to integer
rliterman@0:     int_columns = ['Start_Ref', 'Ref_Pos', 'Start_Query', 'Query_Pos',
rliterman@0:                 'Dist_to_Ref_End', 'Dist_to_Query_End', 'Ref_Aligned', 'Query_Aligned']
rliterman@0: 
rliterman@0:     # Columns to convert to float
rliterman@0:     float_columns = ['Perc_Iden']
rliterman@0:     
rliterman@0:     if len(snp_rows) > 0:
rliterman@0:         snp_df = pd.DataFrame(snp_rows, columns= snp_columns).copy()
rliterman@0:         
rliterman@0:         if snpdiffs_orientation == -1:
rliterman@0:             snp_df = snp_df[reverse_columns].copy()
rliterman@0:             snp_df.columns = snp_columns
rliterman@0:             
rliterman@0:             # 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']
rliterman@0:             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)
rliterman@0:             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)
rliterman@0:         
rliterman@0:         for col in int_columns:
rliterman@0:             snp_df.loc[:, col] = snp_df.loc[:, col].astype(float).astype(int)
rliterman@0:         for col in float_columns:
rliterman@0:             snp_df.loc[:, col] = snp_df.loc[:, col].astype(float)
rliterman@0:         
rliterman@0:     else:
rliterman@0:         snp_df = pd.DataFrame(columns = return_columns)
rliterman@0: 
rliterman@0:     return snp_df[return_columns]
rliterman@0: 
rliterman@0: def swapHeader(header_data):
rliterman@0:             
rliterman@0:     raw_header_cols = [x for x in header_data.columns]
rliterman@0:     reverse_header_cols = [item.replace('Reference', 'temp').replace('Query', 'Reference').replace('temp', 'Query') for item in raw_header_cols]
rliterman@0:     reversed_header_data = header_data[reverse_header_cols].copy()
rliterman@0:     reversed_header_data.columns = raw_header_cols
rliterman@0: 
rliterman@0:     return reversed_header_data
rliterman@0:     
rliterman@0: def parseSNPDiffs(snpdiffs_file,snpdiffs_orientation):
rliterman@0: 
rliterman@0:     bed_rows = []
rliterman@0:     snp_rows = []
rliterman@0:     
rliterman@0:     with open(snpdiffs_file, 'r') as file:
rliterman@0:         lines = file.readlines()
rliterman@0: 
rliterman@0:     for line in lines:
rliterman@0:         if line[0:2] == "#\t":
rliterman@0:             pass
rliterman@0:         elif line[0:3] == "##\t":
rliterman@0:             bed_rows.append(line.strip().split("\t")[1:])
rliterman@0:         else:
rliterman@0:             snp_rows.append(line.strip().split("\t"))
rliterman@0: 
rliterman@0:     bed_df = processBED(bed_rows,snpdiffs_orientation)
rliterman@0:     snp_df = processSNPs(snp_rows,snpdiffs_orientation)
rliterman@0:     return (bed_df,snp_df)
rliterman@0: 
rliterman@0: def calculate_total_length(bedtool):
rliterman@0:     return sum(len(interval) for interval in bedtool)
rliterman@0: 
rliterman@0: def filterSNPs(raw_snp_df,bed_df,log_file, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps):
rliterman@0: 
rliterman@0:     if temp_dir != "":
rliterman@0:         helpers.set_tempdir(temp_dir)
rliterman@0:     
rliterman@0:     # Grab raw data    
rliterman@0:     total_snp_count = raw_snp_df.shape[0]
rliterman@0:     
rliterman@0:     # Get unique SNPs relative to the reference genome
rliterman@0:     unique_ref_snps = raw_snp_df['Ref_Loc'].unique()
rliterman@0:     unique_snp_count = len(unique_ref_snps)
rliterman@0:     
rliterman@0:     snp_tally_df = pd.DataFrame()
rliterman@0:     
rliterman@0:     with open(log_file,"a+") as log:
rliterman@0:         log.write(f"\n\t- Raw SNP + indel count: {total_snp_count}\n")
rliterman@0:         log.write(f"\n\t- Unique SNP positions in reference genome: {unique_snp_count}\n")
rliterman@0:     
rliterman@0:     # Set all sites to SNP
rliterman@0:     raw_snp_df['Filter_Cat'] = "SNP"
rliterman@0:     
rliterman@0:     # Filter out SNPs based on --min_len and --min_iden
rliterman@0:     reject_length = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] < min_len) & (raw_snp_df['Perc_Iden'] >= min_iden)].copy()
rliterman@0:     if reject_length.shape[0] > 0:
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t\t- Purged (Alignment Length): {reject_length.shape[0]}\n")
rliterman@0:         reject_length['Filter_Cat'] = "Purged_Length"
rliterman@0:         snp_tally_df = pd.concat([snp_tally_df,reject_length]).reset_index(drop=True)
rliterman@0:         
rliterman@0:     reject_iden = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] >= min_len) & (raw_snp_df['Perc_Iden'] < min_iden)].copy()
rliterman@0:     if reject_iden.shape[0] > 0:
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t\t- Purged (Alignment Identity): {reject_iden.shape[0]}\n")
rliterman@0:         reject_iden['Filter_Cat'] = "Purged_Identity"
rliterman@0:         snp_tally_df = pd.concat([snp_tally_df,reject_iden]).reset_index(drop=True)
rliterman@0: 
rliterman@0:     reject_lenIden = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] < min_len) & (raw_snp_df['Perc_Iden'] < min_iden)].copy()
rliterman@0:     if reject_lenIden.shape[0] > 0:
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t\t- Purged (Alignment Length + Identity): {reject_lenIden.shape[0]}\n")
rliterman@0:         reject_lenIden['Filter_Cat'] = "Purged_LengthIdentity"
rliterman@0:         snp_tally_df = pd.concat([snp_tally_df,reject_lenIden]).reset_index(drop=True)
rliterman@0:     
rliterman@0:     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)
rliterman@0:     
rliterman@0:     # Invalid processing
rliterman@0:     reject_invalid = pass_filter[pass_filter['Cat'] == "Invalid"].copy()
rliterman@0:     if reject_invalid.shape[0] > 0:
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t\t- Purged (Invalid Base): {reject_invalid.shape[0]}\n")
rliterman@0:         reject_invalid['Filter_Cat'] = "Purged_Invalid"
rliterman@0:         snp_tally_df = pd.concat([snp_tally_df,reject_invalid]).reset_index(drop=True)
rliterman@0:     pass_filter = pass_filter.loc[pass_filter['Cat'] != "Invalid"].copy()
rliterman@0:     
rliterman@0:     # Indel processing
rliterman@0:     reject_indel = pass_filter[pass_filter['Cat'] == "Indel"].copy()
rliterman@0:     if reject_indel.shape[0] > 0:
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t\t- Purged (Indel): {reject_indel.shape[0]}\n")
rliterman@0:         reject_indel['Filter_Cat'] = "Purged_Indel"
rliterman@0:         snp_tally_df = pd.concat([snp_tally_df,reject_indel]).reset_index(drop=True)
rliterman@0:     pass_filter = pass_filter.loc[pass_filter['Cat'] != "Indel"].copy()
rliterman@0:     
rliterman@0:     # Check for heterozygous SNPs
rliterman@0:     check_heterozygous = pass_filter.groupby('Ref_Loc').filter(lambda x: x['Query_Base'].nunique() > 1)
rliterman@0:     if check_heterozygous.shape[0] > 0:      
rliterman@0:         reject_heterozygous = pass_filter.loc[pass_filter['Ref_Loc'].isin(check_heterozygous['Ref_Loc'])].copy()
rliterman@0:         reject_heterozygous['Filter_Cat'] = "Purged_Heterozygous"
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t\t- Purged (Heterozygotes): {reject_heterozygous.shape[0]}\n")  
rliterman@0:         snp_tally_df = pd.concat([snp_tally_df,reject_heterozygous]).reset_index(drop=True)
rliterman@0:         pass_filter = pass_filter.loc[~pass_filter['Ref_Loc'].isin(check_heterozygous['Ref_Loc'])].copy()
rliterman@0:    
rliterman@0:     # Check for duplicate SNPs and take the longest, best hit
rliterman@0:     check_duplicates = pass_filter.groupby('Ref_Loc').filter(lambda x: x.shape[0] > 1)
rliterman@0:     if check_duplicates.shape[0] > 0:
rliterman@0:         reject_duplicate = pass_filter.loc[pass_filter['Ref_Loc'].isin(check_duplicates['Ref_Loc'])].copy()
rliterman@0:         pass_filter = pass_filter.loc[~pass_filter['Ref_Loc'].isin(check_duplicates['Ref_Loc'])].copy()
rliterman@0:                 
rliterman@0:         best_snp = reject_duplicate.groupby('Ref_Loc').apply(lambda x: x.sort_values(by=['Ref_Aligned', 'Perc_Iden'], ascending=[False, False]).head(1))
rliterman@0:         pass_filter = pd.concat([pass_filter,best_snp]).reset_index(drop=True)
rliterman@0: 
rliterman@0:         dup_snps = reject_duplicate[~reject_duplicate.apply(lambda x: x in best_snp, axis=1)]        
rliterman@0:         dup_snps['Filter_Cat'] = "Purged_Duplicate"
rliterman@0:         
rliterman@0:         snp_tally_df = pd.concat([snp_tally_df,dup_snps]).reset_index(drop=True)
rliterman@0:                         
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t\t- Purged (Duplicates): {dup_snps.shape[0]}\n")    
rliterman@0:     
rliterman@0:     # Assert that Ref_Loc and Query_Loc are unique in pass_filter
rliterman@0:     helpers.cleanup(verbose=False,remove_all = False)
rliterman@0:     assert pass_filter['Ref_Loc'].nunique() == pass_filter.shape[0]
rliterman@0:     assert pass_filter['Query_Loc'].nunique() == pass_filter.shape[0]
rliterman@0:     
rliterman@0:     # Density filtering
rliterman@0:     density_locs = []
rliterman@0:     ref_locs = pass_filter['Ref_Loc'].tolist()
rliterman@0:         
rliterman@0:     if len(density_windows) == 0:
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write("\n\t- Density filtering disabled...\n")
rliterman@0:     elif len(ref_locs) > 0:
rliterman@0:         density_df = pd.DataFrame([item.split('/') for item in ref_locs], columns=['Ref_Contig','Ref_End'])
rliterman@0:         density_df['Ref_Start'] = density_df['Ref_End'].astype(float).astype(int) - 1
rliterman@0:         density_bed = BedTool.from_dataframe(density_df[['Ref_Contig','Ref_Start','Ref_End']])
rliterman@0: 
rliterman@0:         # For each density window, remove all SNPs that fall in a window with > max_snps
rliterman@0:         for i in range(0,len(density_windows)):
rliterman@0:             window_df = density_bed.window(density_bed,c=True, w=density_windows[i]).to_dataframe()
rliterman@0:             problematic_windows = window_df[window_df['name'] > max_snps[i]].copy()
rliterman@0:             if not problematic_windows.empty:
rliterman@0:                 temp_locs = []            
rliterman@0:                 for _, row in problematic_windows.iterrows():
rliterman@0:                         purge_window_df = window_df[window_df['chrom'] == row['chrom']].copy()
rliterman@0:                         purge_window_df['Dist'] = abs(purge_window_df['end'] - row['end'])
rliterman@0:                         window_snps = purge_window_df.sort_values(by=['Dist'],ascending=True).head(row['name'])
rliterman@0:                         temp_locs = temp_locs + ["/".join([str(x[0]),str(x[1])]) for x in list(zip(window_snps.chrom, window_snps.end))]
rliterman@0:                 density_locs.extend(list(set(temp_locs)))
rliterman@0:     
rliterman@0:     density_locs = list(set(density_locs))
rliterman@0:     reject_density = pass_filter[pass_filter['Ref_Loc'].isin(density_locs)].copy() 
rliterman@0:        
rliterman@0:     if reject_density.shape[0] > 0:
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t\t- Purged (Density): {reject_density.shape[0]}\n")
rliterman@0:         reject_density['Filter_Cat'] = "Purged_Density"
rliterman@0:         snp_tally_df = pd.concat([snp_tally_df,reject_density]).reset_index(drop=True)
rliterman@0:         pass_filter = pass_filter[~pass_filter['Ref_Loc'].isin(density_locs)].copy()
rliterman@0: 
rliterman@0:     reject_query_edge = pass_filter[(pass_filter['Dist_to_Query_End'] < query_edge) & (pass_filter['Dist_to_Ref_End'] >= ref_edge)].copy()
rliterman@0:     reject_ref_edge = pass_filter[(pass_filter['Dist_to_Ref_End'] < ref_edge) & (pass_filter['Dist_to_Query_End'] >= query_edge)].copy()
rliterman@0:     reject_both_edge = pass_filter[(pass_filter['Dist_to_Query_End'] < query_edge) & (pass_filter['Dist_to_Ref_End'] < ref_edge)].copy()
rliterman@0:     
rliterman@0:     if reject_query_edge.shape[0] > 0:
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t\t- Purged (Query Edge): {reject_query_edge.shape[0]}\n")
rliterman@0:         reject_query_edge['Filter_Cat'] = "Filtered_Query_Edge"
rliterman@0:         snp_tally_df = pd.concat([snp_tally_df,reject_query_edge]).reset_index(drop=True)
rliterman@0:             
rliterman@0:     if reject_ref_edge.shape[0] > 0:
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t\t- Purged (Ref Edge): {reject_ref_edge.shape[0]}\n")
rliterman@0:         reject_ref_edge['Filter_Cat'] = "Filtered_Ref_Edge"
rliterman@0:         snp_tally_df = pd.concat([snp_tally_df,reject_ref_edge]).reset_index(drop=True)
rliterman@0:             
rliterman@0:     if reject_both_edge.shape[0] > 0:
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t\t- Purged (Both Edge): {reject_both_edge.shape[0]}\n")
rliterman@0:         reject_both_edge['Filter_Cat'] = "Filtered_Both_Edge"
rliterman@0:         snp_tally_df = pd.concat([snp_tally_df,reject_both_edge]).reset_index(drop=True)
rliterman@0:         
rliterman@0:     pass_filter = pass_filter[(pass_filter['Dist_to_Query_End'] >= query_edge) & (pass_filter['Dist_to_Ref_End'] >= ref_edge)].copy()
rliterman@0:     
rliterman@0:     helpers.cleanup(verbose=False,remove_all = False)
rliterman@0: 
rliterman@0:     assert snp_tally_df.shape[0] + pass_filter.shape[0] == total_snp_count
rliterman@0:     return_df = pd.concat([pass_filter,snp_tally_df]).reset_index(drop=True).sort_values(by=['Ref_Loc'])
rliterman@0:         
rliterman@0:     return return_df.drop(columns=['Cat']).rename({'Filter_Cat':'Cat'}, axis=1)
rliterman@0:     
rliterman@0: def screenSNPDiffs(snpdiffs_file,trim_name, min_cov, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps,reference_id,log_directory):
rliterman@0:     
rliterman@0:     screen_start_time = time.time()
rliterman@0:     
rliterman@0:     if temp_dir != "":
rliterman@0:         helpers.set_tempdir(temp_dir)
rliterman@0: 
rliterman@0:     # Set CSP2 variables to NA
rliterman@0:     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"
rliterman@0:     filtered_snp_df = pd.DataFrame()
rliterman@0:     good_reference_bed_df = pd.DataFrame()
rliterman@0:     
rliterman@0:     # Ensure snpdiffs file exists
rliterman@0:     if not os.path.exists(snpdiffs_file) or not snpdiffs_file.endswith('.snpdiffs'):
rliterman@0:         run_failed = True       
rliterman@0:         sys.exit(f"Invalid snpdiffs file provided: {snpdiffs_file}")
rliterman@0:         
rliterman@0:     # Ensure header can be read in
rliterman@0:     try:
rliterman@0:         header_data = fetchHeaders(snpdiffs_file)
rliterman@0:         header_query = header_data['Query_ID'][0].replace(trim_name,'')
rliterman@0:         header_ref = header_data['Reference_ID'][0].replace(trim_name,'')
rliterman@0:     except:
rliterman@0:         run_failed = True       
rliterman@0:         sys.exit(f"Error reading headers from snpdiffs file: {snpdiffs_file}")
rliterman@0:         
rliterman@0:     # Check snpdiffs orientation
rliterman@0:     if (header_ref == reference_id):
rliterman@0:         snpdiffs_orientation = 1
rliterman@0:         query_id = header_query
rliterman@0:     elif (header_query == reference_id):
rliterman@0:         snpdiffs_orientation = -1
rliterman@0:         query_id = header_ref
rliterman@0:         header_data = swapHeader(header_data)
rliterman@0:     else:
rliterman@0:         run_failed = True       
rliterman@0:         sys.exit(f"Error: Reference ID not found in header of {snpdiffs_file}...")      
rliterman@0: 
rliterman@0:     # Establish log file
rliterman@0:     log_file = f"{log_directory}/{query_id}__vs__{reference_id}.log"
rliterman@0:     with open(log_file,"w+") as log:
rliterman@0:         log.write("Reference Screening for SNP Pipeline Analysis\n")
rliterman@0:         log.write(f"Query Isolate: {query_id}\n")
rliterman@0:         log.write(f"Reference Isolate: {reference_id}\n")
rliterman@0:         log.write(str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))+"\n")
rliterman@0:         log.write("-------------------------------------------------------\n\n")
rliterman@0:         if snpdiffs_orientation == 1:
rliterman@0:             log.write("\t- SNPDiffs file is in the forward orientation\n")
rliterman@0:             log.write("-------------------------------------------------------\n\n")
rliterman@0:         else:
rliterman@0:             log.write("\t- SNPDiffs file is in the reverse orientation\n")
rliterman@0:             log.write("-------------------------------------------------------\n\n")
rliterman@0: 
rliterman@0:     
rliterman@0:     # Set variables from header data
rliterman@0:     raw_snps = int(header_data['SNPs'][0])
rliterman@0:     raw_indels = int(header_data['Indels'][0])
rliterman@0:     raw_invalid = int(header_data['Invalid'][0])
rliterman@0:     
rliterman@0:     kmer_similarity = float(header_data['Kmer_Similarity'][0])
rliterman@0:     shared_kmers = int(header_data['Shared_Kmers'][0])
rliterman@0:     query_unique_kmers = int(header_data['Query_Unique_Kmers'][0])
rliterman@0:     reference_unique_kmers = int(header_data['Reference_Unique_Kmers'][0])
rliterman@0:     mummer_gsnps = int(header_data['gSNPs'][0])
rliterman@0:     mummer_gindels = int(header_data['gIndels'][0])
rliterman@0:     
rliterman@0:     query_bases = int(header_data['Query_Assembly_Bases'][0])
rliterman@0:     reference_bases = int(header_data['Reference_Assembly_Bases'][0])
rliterman@0:     
rliterman@0:     query_contigs = int(header_data['Query_Contig_Count'][0])
rliterman@0:     reference_contigs = int(header_data['Reference_Contig_Count'][0])
rliterman@0:     
rliterman@0:     raw_query_percent_aligned = float(header_data['Query_Percent_Aligned'][0])
rliterman@0:     raw_ref_percent_aligned = float(header_data['Reference_Percent_Aligned'][0])
rliterman@0:         
rliterman@0:     # If the reference is not covered by at least min_cov, STOP
rliterman@0:     if raw_ref_percent_aligned < min_cov:
rliterman@0:         query_percent_aligned = raw_query_percent_aligned
rliterman@0:         reference_percent_aligned = raw_ref_percent_aligned
rliterman@0:         screen_category = "Low_Coverage"
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t- Reference genome coverage: {raw_ref_percent_aligned}% \n")
rliterman@0:             log.write(f"\t- Query covers less than --min_cov ({min_cov}%)...Screen halted...\n")
rliterman@0:             log.write("-------------------------------------------------------\n\n")
rliterman@0: 
rliterman@0:     elif raw_snps + raw_indels + raw_invalid > 10000:
rliterman@0:         query_percent_aligned = raw_query_percent_aligned
rliterman@0:         reference_percent_aligned = raw_ref_percent_aligned
rliterman@0:         screen_category = "SNP_Cutoff"
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write(f"\t- {raw_snps} detected...\n")
rliterman@0:             log.write("\t- > 10,000 SNPs, indels, or invalid sites detected by MUMmer...Screen halted...\n")
rliterman@0:             log.write("-------------------------------------------------------\n\n")
rliterman@0:    
rliterman@0:     else:
rliterman@0:     
rliterman@0:         ##### 02: Read in BED/SNP data #####
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:                 log.write("Step 1: Reading in snpdiffs BED/SNP data...")
rliterman@0:         try:
rliterman@0:             bed_df,snp_df = parseSNPDiffs(snpdiffs_file,snpdiffs_orientation)
rliterman@0:             
rliterman@0:             with open(log_file,"a+") as log:
rliterman@0:                 log.write("Done!\n")
rliterman@0:                 log.write("-------------------------------------------------------\n\n")
rliterman@0:                 
rliterman@0:         except Exception as e:
rliterman@0:             run_failed = True       
rliterman@0:             with open(log_file,"a+") as log:
rliterman@0:                 log.write(f"\nError reading BED/SNP data from file: {snpdiffs_file}\n{str(e)}")
rliterman@0:             sys.exit(f"Error reading BED/SNP data from file: {snpdiffs_file}\n{str(e)}")
rliterman@0:             
rliterman@0:         ##### 03: Filter genome overlaps #####
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write("Step 2: Filtering for short overlaps and low percent identity...")
rliterman@0:         
rliterman@0:         good_bed_df = bed_df[(bed_df['Ref_Aligned'] >= min_len) & (bed_df['Perc_Iden'] >= min_iden)].copy()
rliterman@0:         
rliterman@0:         if good_bed_df.shape[0] == 0:
rliterman@0:             screen_category = "Low_Quality_Coverage"
rliterman@0:             with open(log_file,"a+") as log:
rliterman@0:                 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")
rliterman@0:                 log.write("-------------------------------------------------------\n\n")
rliterman@0: 
rliterman@0:                 
rliterman@0:         else:
rliterman@0:             # Create a BED file for alignments that pass basic QC
rliterman@0:             good_query_bed_df = good_bed_df[['Query_Contig','Query_Start','Query_End']].copy()
rliterman@0:             good_reference_bed_df = good_bed_df[['Ref_Contig','Ref_Start','Ref_End']].copy()
rliterman@0:             good_reference_bed_df.loc[:, 'Query_ID'] = query_id
rliterman@0: 
rliterman@0:             good_query_aligned = calculate_total_length(BedTool.from_dataframe(good_query_bed_df).sort().merge())
rliterman@0:             good_reference_aligned = calculate_total_length(BedTool.from_dataframe(good_reference_bed_df[['Ref_Contig','Ref_Start','Ref_End']]).sort().merge())
rliterman@0:             
rliterman@0:             query_percent_aligned = (good_query_aligned / query_bases) * 100
rliterman@0:             reference_percent_aligned = (good_reference_aligned / reference_bases) * 100
rliterman@0:             
rliterman@0:             if reference_percent_aligned < min_cov:
rliterman@0:                 screen_category = "Low_Quality_Coverage"
rliterman@0:                 with open(log_file,"a+") as log:
rliterman@0:                     log.write(f"\n\t- Raw reference genome coverage was {raw_ref_percent_aligned}% \n")
rliterman@0:                     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")
rliterman@0:                     log.write(f"\t- Query covers less than --min_cov ({min_cov}%) of reference after filtering...Screen halted...\n")
rliterman@0:                     log.write("-------------------------------------------------------\n\n")
rliterman@0: 
rliterman@0:             else:
rliterman@0:                 screen_category = "Pass"
rliterman@0:                 with open(log_file,"a+") as log:
rliterman@0:                     log.write("Done!\n")
rliterman@0:                     log.write(f"\t- Raw reference genome coverage was {raw_ref_percent_aligned}% \n")
rliterman@0:                     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")
rliterman@0:                     log.write("-------------------------------------------------------\n\n")
rliterman@0: 
rliterman@0:             
rliterman@0:                 # Filter SNPs
rliterman@0:                 with open(log_file,"a+") as log:
rliterman@0:                     log.write("Step 3: Filtering SNPs to get final SNP distances...")
rliterman@0:                 
rliterman@0:                 if raw_snps == 0:
rliterman@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
rliterman@0:                     with open(log_file,"a+") as log:
rliterman@0:                         log.write("Done!\n")
rliterman@0:                         log.write("\t- No SNPs detected in MUMmer output, no filtering required\n")
rliterman@0:                         log.write("-------------------------------------------------------\n\n")
rliterman@0: 
rliterman@0:                 else:
rliterman@0:                     filtered_snp_df = filterSNPs(snp_df,bed_df,log_file, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps)
rliterman@0:                     
rliterman@0:                     csp2_screen_snps = filtered_snp_df[filtered_snp_df.Cat == "SNP"].shape[0]
rliterman@0:                     purged_length = filtered_snp_df[filtered_snp_df.Cat == "Purged_Length"].shape[0]
rliterman@0:                     purged_identity = filtered_snp_df[filtered_snp_df.Cat == "Purged_Identity"].shape[0]
rliterman@0:                     purged_lengthIdentity = filtered_snp_df[filtered_snp_df.Cat == "Purged_LengthIdentity"].shape[0]
rliterman@0:                     purged_invalid = filtered_snp_df[filtered_snp_df.Cat == "Purged_Invalid"].shape[0]
rliterman@0:                     purged_indel = filtered_snp_df[filtered_snp_df.Cat == "Purged_Indel"].shape[0]
rliterman@0:                     purged_het = filtered_snp_df[filtered_snp_df.Cat == "Purged_Heterozygous"].shape[0]                    
rliterman@0:                     purged_duplicate = filtered_snp_df[filtered_snp_df.Cat == "Purged_Duplicate"].shape[0]
rliterman@0:                     purged_density = filtered_snp_df[filtered_snp_df.Cat == "Purged_Density"].shape[0]
rliterman@0:                     filtered_query_edge = filtered_snp_df[filtered_snp_df.Cat == "Filtered_Query_Edge"].shape[0]
rliterman@0:                     filtered_ref_edge = filtered_snp_df[filtered_snp_df.Cat == "Filtered_Ref_Edge"].shape[0]
rliterman@0:                     filtered_both_edge = filtered_snp_df[filtered_snp_df.Cat == "Filtered_Both_Edge"].shape[0]
rliterman@0:                                              
rliterman@0:                     # Write filtered SNP data to file
rliterman@0:                     snp_file = log_file.replace(".log","_SNPs.tsv")
rliterman@0:                     filtered_snp_df.to_csv(snp_file, sep="\t", index=False)
rliterman@0:                     
rliterman@0:                     filtered_snp_df.loc[:, 'Query_ID'] = query_id
rliterman@0:                     
rliterman@0:                     with open(log_file,"a+") as log:
rliterman@0:                         log.write("Done!\n")
rliterman@0:                         log.write(f"\t- {csp2_screen_snps} SNPs detected between {query_id} and {reference_id} after filtering\n")
rliterman@0:                         log.write(f"\t- Full SNP data saved to {snp_file}\n")
rliterman@0:                         log.write("-------------------------------------------------------\n\n")
rliterman@0:     
rliterman@0:     screen_end_time = time.time()
rliterman@0:     with open(log_file,"a+") as log:
rliterman@0:         log.write(f"Screening Time: {screen_end_time - screen_start_time:.2f} seconds\n")
rliterman@0:     
rliterman@0:     # Clean up pybedtools temp
rliterman@0:     helpers.cleanup(verbose=False, remove_all=False)
rliterman@0:     
rliterman@0:     return ([str(item) for item in [query_id,reference_id,screen_category,csp2_screen_snps,
rliterman@0:             f"{query_percent_aligned:.2f}",f"{reference_percent_aligned:.2f}",
rliterman@0:             query_contigs,query_bases,reference_contigs,reference_bases,
rliterman@0:             raw_snps,purged_length,purged_identity,purged_lengthIdentity,purged_invalid,purged_indel,purged_duplicate,purged_het,purged_density,
rliterman@0:             filtered_query_edge,filtered_ref_edge,filtered_both_edge,
rliterman@0:             kmer_similarity,shared_kmers,query_unique_kmers,reference_unique_kmers,
rliterman@0:             mummer_gsnps,mummer_gindels]],good_reference_bed_df,filtered_snp_df)
rliterman@0: 
rliterman@0: def assessCoverage(query_id,site_list):
rliterman@0:     
rliterman@0:     if temp_dir != "":
rliterman@0:         helpers.set_tempdir(temp_dir)
rliterman@0:         
rliterman@0:     if len(site_list) == 0:
rliterman@0:         return pd.DataFrame(columns=['Ref_Loc','Query_ID','Cat'])
rliterman@0:     else:
rliterman@0:         coverage_df = pass_filter_coverage_df[pass_filter_coverage_df['Query_ID'] == query_id].copy()
rliterman@0:         
rliterman@0:         if coverage_df.shape[0] == 0:
rliterman@0:             uncovered_loc_df = pd.DataFrame({
rliterman@0:                 'Ref_Loc': site_list,
rliterman@0:                 'Query_ID': [query_id] * len(site_list),
rliterman@0:                 'Cat': ["Uncovered"] * len(site_list)
rliterman@0:             })
rliterman@0:             return uncovered_loc_df
rliterman@0:         else:
rliterman@0:             coverage_bed = BedTool.from_dataframe(coverage_df[['Ref_Contig','Ref_Start','Ref_End']]).sort()
rliterman@0:             snp_bed_df = pd.DataFrame([item.split('/') for item in site_list], columns=['Ref_Contig','Ref_End'])
rliterman@0:             snp_bed_df['Ref_Start'] = snp_bed_df['Ref_End'].astype(float).astype(int) - 1
rliterman@0:             snp_bed_df['Ref_Loc'] = site_list
rliterman@0:             snp_bed = BedTool.from_dataframe(snp_bed_df[['Ref_Contig','Ref_Start','Ref_End','Ref_Loc']]).sort()
rliterman@0:             
rliterman@0:             # Ref_Locs from snp_bed that intersect with coverage_bed go into covered_locs, the rest go into uncovered_locs
rliterman@0:             covered_locs = snp_bed.intersect(coverage_bed, wa=True)
rliterman@0:             uncovered_locs = snp_bed.intersect(coverage_bed, v=True, wa=True)
rliterman@0: 
rliterman@0:             covered_loc_df = pd.DataFrame({
rliterman@0:                 'Ref_Loc': [snp.fields[3] for snp in covered_locs],
rliterman@0:                 'Query_ID': [query_id] * covered_locs.count(),
rliterman@0:                 'Cat': ["Ref_Base"] * covered_locs.count()
rliterman@0:             }) if covered_locs.count() > 0 else pd.DataFrame(columns=['Ref_Loc','Query_ID','Cat'])
rliterman@0: 
rliterman@0:             uncovered_loc_df = pd.DataFrame({
rliterman@0:                 'Ref_Loc': [snp.fields[3] for snp in uncovered_locs],
rliterman@0:                 'Query_ID': [query_id] * uncovered_locs.count(),
rliterman@0:                 'Cat': ["Uncovered"] * uncovered_locs.count()
rliterman@0:             }) if uncovered_locs.count() > 0 else pd.DataFrame(columns=['Ref_Loc','Query_ID','Cat'])
rliterman@0:             
rliterman@0:             # Clean up pybedtools temp
rliterman@0:             helpers.cleanup(verbose=False, remove_all=False)
rliterman@0:             
rliterman@0:             return pd.concat([covered_loc_df.drop_duplicates(['Ref_Loc']),uncovered_loc_df])
rliterman@0: 
rliterman@0: def getPairwise(chunk, sequences, ids):
rliterman@0:     results = []
rliterman@0:     
rliterman@0:     for i, j in chunk:
rliterman@0:         seq1, seq2 = sequences[i], sequences[j]
rliterman@0:         actg_mask1 = np.isin(seq1, list('ACTGactg'))
rliterman@0:         actg_mask2 = np.isin(seq2, list('ACTGactg'))
rliterman@0:         cocalled_mask = actg_mask1 & actg_mask2
rliterman@0:         
rliterman@0:         snps_cocalled = np.sum(cocalled_mask)
rliterman@0:         snp_distance = np.sum((seq1 != seq2) & cocalled_mask)
rliterman@0:         
rliterman@0:         results.append([ids[i], ids[j], snp_distance, snps_cocalled])
rliterman@0:     
rliterman@0:     return results
rliterman@0: 
rliterman@0: def parallelAlignment(alignment, chunk_size=5000):
rliterman@0:     sequences = [np.array(list(record.seq)) for record in alignment]
rliterman@0:     ids = [record.id for record in alignment]
rliterman@0:     pairwise_combinations = list(combinations(range(len(sequences)), 2))
rliterman@0: 
rliterman@0:     # Create chunks of pairwise combinations
rliterman@0:     chunks = [pairwise_combinations[i:i + chunk_size] for i in range(0, len(pairwise_combinations), chunk_size)]
rliterman@0: 
rliterman@0:     results = []
rliterman@0:     with concurrent.futures.ProcessPoolExecutor() as executor:
rliterman@0:         future_to_chunk = {executor.submit(getPairwise, chunk, sequences, ids): chunk for chunk in chunks}
rliterman@0:         for future in concurrent.futures.as_completed(future_to_chunk):
rliterman@0:             chunk_results = future.result()
rliterman@0:             results.extend(chunk_results)
rliterman@0:     return results
rliterman@0: 
rliterman@0: def getFinalPurge(df):
rliterman@0:     # Returns the 'farthest along' category for a given Ref_Loc
rliterman@0:     if "Purged_Density" in df['Cat'].values:
rliterman@0:         return "Purged_Density"
rliterman@0:     elif "Purged_Heterozygous" in df['Cat'].values:
rliterman@0:         return "Purged_Heterozygous"
rliterman@0:     elif "Purged_Indel" in df['Cat'].values:
rliterman@0:         return "Purged_Indel"
rliterman@0:     elif "Purged_Invalid" in df['Cat'].values:
rliterman@0:         return "Purged_Invalid"
rliterman@0:     elif "Purged_Length" in df['Cat'].values:
rliterman@0:         return "Purged_Length"
rliterman@0:     else:
rliterman@0:         return "Purged_Identity"
rliterman@0: 
rliterman@0:     
rliterman@0: # Read in arguments
rliterman@0: global run_failed
rliterman@0: run_failed = False
rliterman@0: 
rliterman@0: start_time = time.time()
rliterman@0: 
rliterman@0: parser = argparse.ArgumentParser(description='CSP2 SNP Pipeline Analysis')
rliterman@0: parser.add_argument('--reference_id', type=str, help='Reference Isolate')
rliterman@0: parser.add_argument('--output_directory', type=str, help='Output Directory')
rliterman@0: parser.add_argument('--log_directory', type=str, help='Log Directory')
rliterman@0: parser.add_argument('--snpdiffs_file', type=str, help='Path to SNPdiffs file')
rliterman@0: parser.add_argument('--min_cov', type=float, help='Minimum coverage')
rliterman@0: parser.add_argument('--min_len', type=int, help='Minimum length')
rliterman@0: parser.add_argument('--min_iden', type=float, help='Minimum identity')
rliterman@0: parser.add_argument('--ref_edge', type=int, help='Reference edge')
rliterman@0: parser.add_argument('--query_edge', type=int, help='Query edge')
rliterman@0: parser.add_argument('--density_windows', type=str, help='Density windows')
rliterman@0: parser.add_argument('--max_snps', type=str, help='Maximum SNPs')
rliterman@0: parser.add_argument('--trim_name', type=str, help='Trim name')
rliterman@0: parser.add_argument('--max_missing', type=float, help='Maximum missing')
rliterman@0: parser.add_argument('--tmp_dir', type=str, help='Temporary directory')
rliterman@0: parser.add_argument('--rescue', type=str, help='Rescue edge SNPs (rescue/norescue)')
rliterman@0: args = parser.parse_args()
rliterman@0: 
rliterman@0: reference_id = args.reference_id
rliterman@0: output_directory = os.path.abspath(args.output_directory)
rliterman@0: log_directory = os.path.abspath(args.log_directory)
rliterman@0: log_file = f"{output_directory}/CSP2_SNP_Pipeline.log"
rliterman@0: snpdiffs_file = args.snpdiffs_file
rliterman@0: min_cov = args.min_cov
rliterman@0: min_len = args.min_len
rliterman@0: min_iden = args.min_iden
rliterman@0: ref_edge = args.ref_edge
rliterman@0: query_edge = args.query_edge
rliterman@0: density_windows = [int(x) for x in args.density_windows.split(",")]
rliterman@0: max_snps = [int(x) for x in args.max_snps.split(",")]
rliterman@0: trim_name = args.trim_name
rliterman@0: max_missing = args.max_missing
rliterman@0: 
rliterman@0: # Establish log file
rliterman@0: with open(log_file,"w+") as log:
rliterman@0:     log.write("CSP2 SNP Pipeline Analysis\n")
rliterman@0:     log.write(f"Reference Isolate: {reference_id}\n")
rliterman@0:     log.write(str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))+"\n")
rliterman@0:     log.write("-------------------------------------------------------\n\n")
rliterman@0:     log.write("Reading in SNPDiffs files...")
rliterman@0: 
rliterman@0: 
rliterman@0: # Read in all lines and ensure each file exists
rliterman@0: snpdiffs_list = [line.strip() for line in open(snpdiffs_file, 'r')]
rliterman@0: snpdiffs_list = [line for line in snpdiffs_list if line]
rliterman@0: for snpdiffs_file in snpdiffs_list:
rliterman@0:     if not os.path.exists(snpdiffs_file):
rliterman@0:         run_failed = True
rliterman@0:         sys.exit("Error: File does not exist: " + snpdiffs_file)
rliterman@0: 
rliterman@0: snpdiffs_list = list(set(snpdiffs_list))
rliterman@0: 
rliterman@0: if len(snpdiffs_list) == 0:
rliterman@0:     run_failed = True
rliterman@0:     sys.exit("No SNPdiffs files provided...")
rliterman@0: 
rliterman@0: with open(log_file, "a+") as log:
rliterman@0:     log.write("Done!\n")
rliterman@0:     log.write(f"\t- Read in {len(snpdiffs_list)} SNPdiffs files\n")
rliterman@0:     log.write("-------------------------------------------------------\n\n")
rliterman@0: 
rliterman@0: global temp_dir
rliterman@0: if args.tmp_dir != "":
rliterman@0:     random_temp_id = str(uuid.uuid4())
rliterman@0:     temp_dir = f"{os.path.normpath(os.path.abspath(args.tmp_dir))}/{random_temp_id}"
rliterman@0:     try:
rliterman@0:         os.mkdir(temp_dir)
rliterman@0:         helpers.set_tempdir(temp_dir)
rliterman@0:     except OSError as e:
rliterman@0:         run_failed = True       
rliterman@0:         print(f"Error: Failed to create directory '{temp_dir}': {e}")
rliterman@0: else:
rliterman@0:     temp_dir = ""
rliterman@0: 
rliterman@0: rescue_edge = str(args.rescue)
rliterman@0: if rescue_edge not in ["rescue","norescue"]:
rliterman@0:     with open(log_file,"a+") as log:
rliterman@0:         log.write(f"\t- Unexpected rescue_edge variable ('{rescue_edge}'). Not performing SNP edge rescuing (any SNPs found within {query_edge}bp of a query contig edge will be purged)...\n")
rliterman@0:         log.write("-------------------------------------------------------\n\n")
rliterman@0:     rescue_edge = "norescue"
rliterman@0: elif rescue_edge == "rescue":
rliterman@0:     with open(log_file,"a+") as log:
rliterman@0:         log.write(f"\t- Rescuing edge SNPs within {query_edge}bp of query contig edges if found more centrally in another query...\n")
rliterman@0:         log.write("-------------------------------------------------------\n\n")
rliterman@0: else:
rliterman@0:     with open(log_file,"a+") as log:
rliterman@0:         log.write(f"\t- Not performing SNP edge rescuing (any SNPs found within {query_edge}bp of a query contig edge will be purged)...\n")
rliterman@0:         log.write("-------------------------------------------------------\n\n")
rliterman@0: 
rliterman@0: try:
rliterman@0:     # Establish output files
rliterman@0:     reference_screening_file = f"{output_directory}/Reference_Screening.tsv"
rliterman@0:     locus_category_file = f"{output_directory}/Locus_Categories.tsv"
rliterman@0:     query_coverage_file = f"{output_directory}/Query_Coverage.tsv"
rliterman@0:     raw_loclist = f"{output_directory}/snplist.txt"
rliterman@0:     raw_alignment = f"{output_directory}/snpma.fasta"
rliterman@0:     preserved_loclist = f"{output_directory}/snplist_preserved.txt"
rliterman@0:     preserved_alignment_file = f"{output_directory}/snpma_preserved.fasta"
rliterman@0:     raw_pairwise = f"{output_directory}/snp_distance_pairwise.tsv"
rliterman@0:     raw_matrix = f"{output_directory}/snp_distance_matrix.tsv"
rliterman@0:     preserved_pairwise = f"{output_directory}/snp_distance_pairwise_preserved.tsv"
rliterman@0:     preserved_matrix = f"{output_directory}/snp_distance_matrix_preserved.tsv"
rliterman@0: 
rliterman@0:     with open(log_file,"a+") as log:
rliterman@0:         log.write("Screening all queries against reference...")
rliterman@0:     with concurrent.futures.ProcessPoolExecutor() as executor:
rliterman@0:         results = [executor.submit(screenSNPDiffs,snp_diff_file,trim_name, min_cov, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps,reference_id,log_directory) for snp_diff_file in snpdiffs_list]
rliterman@0: 
rliterman@0:     # Combine results into a dataframe
rliterman@0:     output_columns = ['Query_ID','Reference_ID','Screen_Category','CSP2_Screen_SNPs',
rliterman@0:                 'Query_Percent_Aligned','Reference_Percent_Aligned',
rliterman@0:                 'Query_Contigs','Query_Bases','Reference_Contigs','Reference_Bases',
rliterman@0:                 'Raw_SNPs','Purged_Length','Purged_Identity','Purged_LengthIdentity','Purged_Invalid','Purged_Indel','Purged_Duplicate','Purged_Het','Purged_Density',
rliterman@0:                 'Filtered_Query_Edge','Filtered_Ref_Edge','Filtered_Both_Edge',
rliterman@0:                 'Kmer_Similarity','Shared_Kmers','Query_Unique_Kmers','Reference_Unique_Kmers',
rliterman@0:                 'MUMmer_gSNPs','MUMmer_gIndels']
rliterman@0: 
rliterman@0:     # Save reference screening
rliterman@0:     results_df = pd.DataFrame([item.result()[0] for item in results], columns = output_columns)
rliterman@0:     results_df.to_csv(reference_screening_file, sep="\t", index=False)
rliterman@0: 
rliterman@0:     # Get reference bed dfs
rliterman@0:     covered_df = pd.concat([item.result()[1] for item in results])
rliterman@0: 
rliterman@0:     # Get snp dfs
rliterman@0:     filtered_snp_df = pd.concat([item.result()[2] for item in results])
rliterman@0: 
rliterman@0:     # Separate isolates that pass QC
rliterman@0:     pass_qc_isolates = list(set(results_df[results_df['Screen_Category'] == "Pass"]['Query_ID']))
rliterman@0:     fail_qc_isolates = list(set(results_df[results_df['Screen_Category'] != "Pass"]['Query_ID']))
rliterman@0: 
rliterman@0:     if len(pass_qc_isolates) == 0:
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write("Done!\n")
rliterman@0:             log.write(f"\t- Reference screening data saved to {reference_screening_file}\n")
rliterman@0:             log.write(f"\t- Of {len(snpdiffs_list)} comparisons, no isolates passed QC. Pipeline cannot continue.\n")
rliterman@0:             log.write(f"\t- {len(fail_qc_isolates)} comparisons failed QC\n")
rliterman@0:             for isolate in fail_qc_isolates:
rliterman@0:                 isolate_category = results_df[results_df['Query_ID'] == isolate]['Screen_Category'].values[0]
rliterman@0:                 log.write(f"\t\t- {isolate}: {isolate_category}\n")
rliterman@0:             log.write("-------------------------------------------------------\n\n")
rliterman@0:         sys.exit(0)
rliterman@0:     else:    
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write("Done!\n")
rliterman@0:             log.write(f"\t- Reference screening data saved to {reference_screening_file}\n")
rliterman@0:             log.write(f"\t- Of {len(snpdiffs_list)} comparisons, {len(pass_qc_isolates)} covered at least {min_cov}% of the reference genome after removing poor alignments\n")
rliterman@0:             if len(fail_qc_isolates) > 0:
rliterman@0:                 log.write(f"\t- {len(fail_qc_isolates)} comparisons failed QC\n")
rliterman@0:                 for isolate in fail_qc_isolates:
rliterman@0:                     isolate_category = results_df[results_df['Query_ID'] == isolate]['Screen_Category'].values[0]
rliterman@0:                     log.write(f"\t\t- {isolate}: {isolate_category}\n")
rliterman@0:             log.write("-------------------------------------------------------\n\n")
rliterman@0: 
rliterman@0:     with open(log_file,"a+") as log:
rliterman@0:         log.write(f"Compiling SNPs across {len(pass_qc_isolates)} samples...\n")
rliterman@0: 
rliterman@0:     if filtered_snp_df.shape[0] == 0:
rliterman@0:         snp_count = 0
rliterman@0:         with open(log_file,"a+") as log:
rliterman@0:             log.write("\t- No SNPs detected across samples...Skipping to output...\n")     
rliterman@0:     else:
rliterman@0: 
rliterman@0:         # Remove samples that failed QC
rliterman@0:         pass_filter_snps = filtered_snp_df[filtered_snp_df['Query_ID'].isin(pass_qc_isolates)].copy()
rliterman@0:         pass_filter_snp_list = list(set(pass_filter_snps['Ref_Loc']))
rliterman@0:         pass_filter_snp_count = len(pass_filter_snp_list)
rliterman@0:     
rliterman@0:         global pass_filter_coverage_df
rliterman@0:         pass_filter_coverage_df = covered_df[covered_df['Query_ID'].isin(pass_qc_isolates)].copy()
rliterman@0: 
rliterman@0:         # Get SNP counts
rliterman@0:         snp_df = pass_filter_snps[pass_filter_snps['Cat'] == "SNP"].copy()
rliterman@0:         
rliterman@0:         if snp_df.shape[0] == 0:
rliterman@0:             snp_count = 0
rliterman@0:             with open(log_file,"a+") as log:
rliterman@0:                 log.write(f"\t- {pass_filter_snp_count} total SNPs detected across all samples...\n")     
rliterman@0:                 log.write("\t- No SNPs passed QC filtering in any sample...Skipping to output...\n")
rliterman@0:         
rliterman@0:         else:
rliterman@0:             snp_list = list(set(snp_df['Ref_Loc']))
rliterman@0:             snp_count = len(snp_list)
rliterman@0:             
rliterman@0:             with open(log_file,"a+") as log:
rliterman@0:                 log.write(f"\t- {pass_filter_snp_count} total SNPs detected across all samples...\n")     
rliterman@0:                 log.write(f"\t- {snp_count} unique SNPs passed QC filtering in at least one sample...\n")
rliterman@0:         
rliterman@0:             # Note SNPs lost irrevocably to reference edge trimming
rliterman@0:             ref_edge_df = pass_filter_snps[pass_filter_snps['Cat'].isin(["Filtered_Ref_Edge",'Filtered_Both_Edge'])].copy()    
rliterman@0:             if ref_edge_df.shape[0] > 0:        
rliterman@0:                 with open(log_file,"a+") as log:
rliterman@0:                     log.write(f"\t- {ref_edge_df['Ref_Loc'].nunique()} unique SNPs were within {ref_edge}bp of a reference contig end and were not considered in any query...\n")
rliterman@0:         
rliterman@0:             # Create Ref_Base df
rliterman@0:             ref_base_df = snp_df[['Ref_Loc', 'Ref_Base']].copy().drop_duplicates().rename(columns = {'Ref_Base':'Query_Base'})
rliterman@0:             ref_base_df.loc[:,'Query_ID'] = reference_id
rliterman@0:             ref_base_df.loc[:,'Cat'] = "Reference_Isolate"
rliterman@0:             ref_base_df = ref_base_df.loc[:,['Ref_Loc','Query_ID','Query_Base','Cat']]
rliterman@0:             
rliterman@0:             # Rescue SNPs that are near the edge if they are valid SNPs in other samples
rliterman@0:             rescued_edge_df = pass_filter_snps[(pass_filter_snps['Cat'] == "Filtered_Query_Edge") & (pass_filter_snps['Ref_Loc'].isin(snp_list))].copy()
rliterman@0:             
rliterman@0:             if rescue_edge == "norescue":
rliterman@0:                 with open(log_file,"a+") as log:
rliterman@0:                     log.write("\t- Skipping edge resucing...\n")
rliterman@0:             
rliterman@0:             elif rescued_edge_df.shape[0] > 0:
rliterman@0:                 
rliterman@0:                 # Remove rescued sites from pass_filter_snps
rliterman@0:                 rescue_merge = pass_filter_snps.merge(rescued_edge_df, indicator=True, how='outer')
rliterman@0:                 pass_filter_snps = rescue_merge[rescue_merge['_merge'] == 'left_only'].drop(columns=['_merge']).copy()            
rliterman@0:                 
rliterman@0:                 # Add rescued SNPs to snp_df
rliterman@0:                 rescued_edge_df.loc[:,'Cat'] = "Rescued_SNP"    
rliterman@0:                 snp_df = pd.concat([snp_df,rescued_edge_df]).reset_index(drop=True)
rliterman@0:                 with open(log_file,"a+") as log:
rliterman@0:                     log.write(f"\t- Rescued {rescued_edge_df.shape[0]} query SNPs that fell within {query_edge}bp of the query contig edge...\n")
rliterman@0:             
rliterman@0:             else:
rliterman@0:                 with open(log_file,"a+") as log:
rliterman@0:                     log.write(f"\t- No query SNPs that fell within {query_edge}bp of the query contig edge were rescued...\n")
rliterman@0:                                 
rliterman@0:             # Gather base data for all valid SNPs
rliterman@0:             snp_base_df = snp_df[['Ref_Loc','Query_ID','Query_Base','Cat']].copy()
rliterman@0:             
rliterman@0:             # Process purged sites
rliterman@0:             purged_snp_df = pd.DataFrame(columns=['Ref_Loc','Query_ID','Query_Base','Cat'])
rliterman@0:             purged_df = pass_filter_snps[pass_filter_snps['Cat'] != "SNP"].copy()
rliterman@0:                     
rliterman@0:             if purged_df.shape[0] > 0:
rliterman@0: 
rliterman@0:                 # Remove rows from purged_df if the Ref_Loc/Query_ID pair is already in snp_base_df
rliterman@0:                 purged_df = purged_df[~purged_df[['Ref_Loc','Query_ID']].apply(tuple,1).isin(snp_base_df[['Ref_Loc','Query_ID']].apply(tuple,1))].copy()
rliterman@0:                 
rliterman@0:                 if purged_df.shape[0] > 0:
rliterman@0: 
rliterman@0:                     # Get purged SNPs where no query has a valid SNP
rliterman@0:                     non_snp_df = purged_df[~purged_df['Ref_Loc'].isin(snp_list)].copy()
rliterman@0:                     if non_snp_df.shape[0] > 0:
rliterman@0:                         non_snp_merge = purged_df.merge(non_snp_df, indicator=True, how='outer')
rliterman@0:                         purged_df = non_snp_merge[non_snp_merge['_merge'] == 'left_only'].drop(columns=['_merge']).copy()
rliterman@0: 
rliterman@0:                         with open(log_file,"a+") as log:
rliterman@0:                             log.write(f"\t- {len(list(set(non_snp_df['Ref_Loc'])))} unique SNPs were purged in all queries they were found in, and were not considered in the final dataset...\n")
rliterman@0:             
rliterman@0:             if purged_df.shape[0] > 0:
rliterman@0: 
rliterman@0:                 purged_snp_df = purged_df[['Ref_Loc','Query_ID']].copy().drop_duplicates()
rliterman@0:                 purged_snp_df.loc[:, 'Query_Base'] = "N"
rliterman@0:                 final_purge_df = purged_df.groupby(['Ref_Loc','Query_ID']).apply(getFinalPurge).reset_index().rename(columns={0:'Cat'})
rliterman@0:                 purged_snp_df = purged_snp_df.merge(final_purge_df, on=['Ref_Loc','Query_ID'], how='inner')
rliterman@0: 
rliterman@0:             # Genomic positions that do not occur- in the SNP data are either uncovered or match the reference base
rliterman@0:             missing_df = pd.DataFrame(columns=['Ref_Loc','Query_ID','Query_Base','Cat'])
rliterman@0: 
rliterman@0:             covered_snps = pd.concat([snp_base_df,purged_snp_df]).copy()
rliterman@0:             ref_loc_sets = covered_snps.groupby('Query_ID')['Ref_Loc'].agg(set).to_dict()
rliterman@0:             isolates_with_missing = [isolate for isolate in pass_qc_isolates if len(set(snp_list) - ref_loc_sets.get(isolate, set())) > 0]
rliterman@0:             
rliterman@0:             uncovered_df = pd.DataFrame()
rliterman@0:             
rliterman@0:             if isolates_with_missing:
rliterman@0:                 isolate_data = [(isolate, list(set(snp_list) - ref_loc_sets.get(isolate, set()))) for isolate in isolates_with_missing]        
rliterman@0:                 
rliterman@0:                 with concurrent.futures.ProcessPoolExecutor() as executor:
rliterman@0:                     results = [executor.submit(assessCoverage, query, sites) for query, sites in isolate_data]
rliterman@0:                     coverage_dfs = [result.result() for result in concurrent.futures.as_completed(results)]
rliterman@0: 
rliterman@0:                 coverage_df = pd.concat(coverage_dfs)
rliterman@0:                 covered_df = coverage_df[coverage_df['Cat'] == 'Ref_Base']
rliterman@0:                 uncovered_df = coverage_df[coverage_df['Cat'] == 'Uncovered']
rliterman@0:                 
rliterman@0:                 if not uncovered_df.empty:
rliterman@0:                     uncovered_df.loc[:, 'Query_Base'] = "?"
rliterman@0:                     missing_df = pd.concat([missing_df, uncovered_df[['Ref_Loc', 'Query_ID', 'Query_Base', 'Cat']]])
rliterman@0:         
rliterman@0:                 if not covered_df.empty:
rliterman@0:                     ref_base_snp_df = covered_df.merge(ref_base_df[['Ref_Loc', 'Query_Base']], on='Ref_Loc', how='left')
rliterman@0:                     missing_df = pd.concat([missing_df, ref_base_snp_df[['Ref_Loc', 'Query_ID', 'Query_Base', 'Cat']]])        
rliterman@0:             
rliterman@0:             with open(log_file,"a+") as log:
rliterman@0:                 log.write("\t- Processed coverage information...\n")
rliterman@0: 
rliterman@0:             final_snp_df = pd.concat([snp_base_df,purged_snp_df,missing_df,ref_base_df]).sort_values(by=['Ref_Loc','Query_ID']).reset_index(drop=True)
rliterman@0:             snp_counts = final_snp_df.groupby('Query_ID')['Ref_Loc'].count().reset_index().rename(columns={'Ref_Loc':'SNP_Count'})
rliterman@0: 
rliterman@0:             # Assert that all snp_counts == snp_count
rliterman@0:             assert snp_counts['SNP_Count'].nunique() == 1
rliterman@0:             assert snp_counts['SNP_Count'].values[0] == snp_count
rliterman@0: 
rliterman@0:             # Get locus coverage stats        
rliterman@0:             snp_coverage_df = final_snp_df[final_snp_df['Cat'].isin(['SNP','Rescued_SNP'])].groupby('Ref_Loc')['Query_ID'].count().reset_index().rename(columns={'Query_ID':'SNP_Count'})
rliterman@0:             ref_base_coverage_df = final_snp_df[final_snp_df['Cat'].isin(["Ref_Base","Reference_Isolate"])].groupby('Ref_Loc')['Query_ID'].count().reset_index().rename(columns={'Query_ID':'Ref_Base_Count'}) 
rliterman@0:             
rliterman@0:             if uncovered_df.shape[0] > 0:
rliterman@0:                 uncovered_count_df = final_snp_df[final_snp_df['Cat'] == "Uncovered"].groupby('Ref_Loc')['Query_ID'].count().reset_index().rename(columns={'Query_ID':'Uncovered_Count'}).copy()
rliterman@0:             else:
rliterman@0:                 uncovered_count_df = pd.DataFrame(columns=['Ref_Loc','Uncovered_Count'])
rliterman@0: 
rliterman@0:             possible_purged_cols = ['Purged_Length','Purged_Identity','Purged_Invalid','Purged_Indel','Purged_Heterozygous','Purged_Density']
rliterman@0:             if purged_snp_df.shape[0] > 0:
rliterman@0:                 purged_count_df = final_snp_df[final_snp_df['Cat'].isin(possible_purged_cols)].groupby('Ref_Loc')['Query_ID'].count().reset_index().rename(columns={'Query_ID':'Purged_Count'}).copy()
rliterman@0:             else:
rliterman@0:                 purged_count_df = pd.DataFrame(columns=['Ref_Loc','Purged_Count'])
rliterman@0: 
rliterman@0:             locus_coverage_df = snp_coverage_df.merge(ref_base_coverage_df, how='outer', on='Ref_Loc').merge(uncovered_count_df, how='outer', on='Ref_Loc').merge(purged_count_df, how='outer', on='Ref_Loc').fillna(0)
rliterman@0:             locus_coverage_df.loc[:, ['SNP_Count','Ref_Base_Count','Uncovered_Count','Purged_Count']] = locus_coverage_df.loc[:, ['SNP_Count','Ref_Base_Count','Uncovered_Count','Purged_Count']].astype(int)
rliterman@0:             locus_coverage_df['Missing_Ratio'] = ((locus_coverage_df['Uncovered_Count'] + locus_coverage_df['Purged_Count']) / (1+len(pass_qc_isolates))) * 100
rliterman@0:             locus_coverage_df.to_csv(locus_category_file, sep="\t", index=False)
rliterman@0:             
rliterman@0:             # Get isolate coverage stats
rliterman@0:             min_isolate_cols = ['Query_ID','SNP','Ref_Base','Percent_Missing','Purged','Uncovered','Rescued_SNP','Purged_Ref_Edge']
rliterman@0:             isolate_coverage_df = final_snp_df.groupby('Query_ID')['Cat'].value_counts().unstack().fillna(0).astype(float).astype(int).reset_index().drop(columns=['Reference_Isolate'])
rliterman@0:             isolate_coverage_df.loc[isolate_coverage_df['Query_ID'] == reference_id, 'Ref_Base'] = snp_count
rliterman@0:             
rliterman@0:             if "Rescued_SNP" not in isolate_coverage_df.columns.tolist():
rliterman@0:                 isolate_coverage_df.loc[:,'Rescued_SNP'] = 0
rliterman@0:             isolate_coverage_df['SNP'] = isolate_coverage_df['SNP'] + isolate_coverage_df['Rescued_SNP']
rliterman@0:             
rliterman@0:             for col in ['Uncovered'] + possible_purged_cols:
rliterman@0:                 if col not in isolate_coverage_df.columns.tolist():
rliterman@0:                     isolate_coverage_df.loc[:,col] = 0            
rliterman@0:                 
rliterman@0:             isolate_coverage_df['Purged'] = isolate_coverage_df[possible_purged_cols].sum(axis=1)
rliterman@0: 
rliterman@0:             isolate_coverage_df['Percent_Missing'] = (isolate_coverage_df['Uncovered'] + isolate_coverage_df['Purged'])/(isolate_coverage_df['Uncovered'] + isolate_coverage_df['Purged'] + isolate_coverage_df['Ref_Base'] + isolate_coverage_df['SNP']) * 100
rliterman@0:             
rliterman@0:             isolate_coverage_df.loc[:,'Purged_Ref_Edge'] = 0
rliterman@0:             if ref_edge_df.shape[0] > 0:
rliterman@0:                 isolate_coverage_df.loc[isolate_coverage_df['Query_ID'] == reference_id, 'Purged_Ref_Edge'] = ref_edge_df['Ref_Loc'].nunique()
rliterman@0:             
rliterman@0:             isolate_coverage_df = isolate_coverage_df[min_isolate_cols + possible_purged_cols].sort_values(by = 'Percent_Missing',ascending = False).reset_index(drop=True)
rliterman@0:             isolate_coverage_df.to_csv(query_coverage_file, sep="\t", index=False)
rliterman@0:             
rliterman@0:             with open(log_file,"a+") as log:
rliterman@0:                 log.write(f"\t- SNP coverage information: {locus_category_file}\n")
rliterman@0:                 log.write(f"\t- Query coverage information: {query_coverage_file}\n")
rliterman@0:                 log.write("-------------------------------------------------------\n\n")
rliterman@0:             
rliterman@0:             with open(log_file,"a+") as log:
rliterman@0:                 log.write("Processing alignment data...")
rliterman@0: 
rliterman@0:             alignment_df = final_snp_df[['Query_ID','Ref_Loc','Query_Base']].copy().rename(columns={'Query_Base':'Base'}).pivot(index='Query_ID', columns='Ref_Loc', values='Base')
rliterman@0:             csp2_ordered = alignment_df.columns
rliterman@0: 
rliterman@0:             with open(raw_loclist,"w+") as loclist:
rliterman@0:                 loclist.write("\n".join(csp2_ordered)+"\n")
rliterman@0:             
rliterman@0:             seq_records = [SeqRecord(Seq(''.join(row)), id=query,description='') for query,row in alignment_df.iterrows()]
rliterman@0:             
rliterman@0:             alignment = MultipleSeqAlignment(seq_records)
rliterman@0:             AlignIO.write(alignment,raw_alignment,"fasta")
rliterman@0: 
rliterman@0:             with open(log_file,"a+") as log:
rliterman@0:                 log.write("Done!\n")
rliterman@0:                 log.write(f"\t- Saved alignment of {snp_count} SNPs to {raw_alignment}\n")
rliterman@0:                 log.write(f"\t- Saved ordered loc list to {raw_loclist}\n")
rliterman@0:             
rliterman@0:             if max_missing == float(100):
rliterman@0:                 locs_pass_missing = csp2_ordered
rliterman@0:                 preserved_alignment = alignment
rliterman@0:                 
rliterman@0:                 AlignIO.write(preserved_alignment,preserved_alignment_file,"fasta")
rliterman@0:                 with open(preserved_loclist,"w+") as loclist:
rliterman@0:                     loclist.write("\n".join(csp2_ordered)+"\n")
rliterman@0:                 
rliterman@0:                 with open(log_file,"a+") as log:
rliterman@0:                     log.write("Skipping SNP preservation step...\n")
rliterman@0:                     log.write(f"\t- Saved duplicate alignment to {preserved_alignment_file}\n")
rliterman@0:                     log.write(f"\t- Saved duplicate ordered loc list to {preserved_loclist}\n")
rliterman@0:             else:
rliterman@0:                 with open(log_file,"a+") as log:
rliterman@0:                     log.write(f"\t- Preserving SNPs with at most {max_missing}% missing data...\n")
rliterman@0:                 
rliterman@0:                 # Parse missing data
rliterman@0:                 locs_pass_missing = list(set(locus_coverage_df[locus_coverage_df['Missing_Ratio'] <= max_missing]['Ref_Loc']))
rliterman@0:                 
rliterman@0:                 if len(locs_pass_missing) == 0:
rliterman@0:                     with open(log_file,"a+") as log:
rliterman@0:                         log.write(f"\t- Of {snp_count} SNPs, no SNPs pass the {max_missing}% missing data threshold...\n")
rliterman@0:                         log.write("-------------------------------------------------------\n\n")
rliterman@0:                 else:
rliterman@0:                     preserved_alignment_df = alignment_df[locs_pass_missing].copy()
rliterman@0:                     
rliterman@0:                     preserved_ordered = preserved_alignment_df.columns
rliterman@0:                     with open(preserved_loclist,"w+") as loclist:
rliterman@0:                         loclist.write("\n".join(preserved_ordered)+"\n")
rliterman@0:                     
rliterman@0:                     seq_records = [SeqRecord(Seq(''.join(row)), id=query,description='') for query,row in preserved_alignment_df.iterrows()]
rliterman@0:                     preserved_alignment = MultipleSeqAlignment(seq_records)
rliterman@0:                     AlignIO.write(preserved_alignment,preserved_alignment_file,"fasta")
rliterman@0:                     with open(log_file,"a+") as log:
rliterman@0:                         log.write(f"\t- Of {snp_count} SNPs, {len(locs_pass_missing)} SNPs pass the {max_missing}% missing data threshold...\n")
rliterman@0:                         log.write(f"\t- Saved preserved alignment to {preserved_alignment_file}\n")
rliterman@0:                         log.write(f"\t- Saved preserved ordered loc list to {preserved_loclist}\n")
rliterman@0:                         log.write("-------------------------------------------------------\n\n")
rliterman@0: 
rliterman@0:     with open(log_file,"a+") as log:
rliterman@0:         log.write("Processing pairwise comparisons files...")
rliterman@0: 
rliterman@0:     # Get pairwise comparisons between all pass_qc_isolates and reference_id
rliterman@0:     pairwise_combinations = [sorted(x) for x in list(combinations([reference_id] + pass_qc_isolates, 2))]
rliterman@0: 
rliterman@0:     if snp_count == 0:
rliterman@0:         pairwise_df = pd.DataFrame([(pairwise[0], pairwise[1], 0,np.nan) for pairwise in pairwise_combinations],columns = ['Query_1','Query_2','SNP_Distance','SNPs_Cocalled'])
rliterman@0:         preserved_pairwise_df = pairwise_df.copy()
rliterman@0:         
rliterman@0:         pairwise_df.to_csv(raw_pairwise, sep="\t", index=False)
rliterman@0:         preserved_pairwise_df.to_csv(preserved_pairwise, sep="\t", index=False)
rliterman@0:         
rliterman@0:         # Create matrix
rliterman@0:         idx = sorted(set(pairwise_df['Query_1']).union(pairwise_df['Query_2']))
rliterman@0:         mirrored_distance_df = pairwise_df.pivot(index='Query_1', columns='Query_2', values='SNP_Distance').reindex(index=idx, columns=idx).fillna(0, downcast='infer').pipe(lambda x: x+x.values.T).applymap(lambda x: format(x, '.0f'))
rliterman@0:         mirrored_distance_df.index.name = ''
rliterman@0:         mirrored_distance_df.to_csv(raw_matrix,sep="\t")
rliterman@0:         mirrored_distance_df.to_csv(preserved_matrix,sep="\t")
rliterman@0: 
rliterman@0:     else:
rliterman@0:         raw_distance_results = parallelAlignment(alignment)
rliterman@0:         raw_pairwise_df = pd.DataFrame(raw_distance_results, columns=['Query_1', 'Query_2', 'SNP_Distance', 'SNPs_Cocalled'])
rliterman@0:         raw_pairwise_df.to_csv(raw_pairwise, sep="\t", index=False)
rliterman@0: 
rliterman@0:         if len(locs_pass_missing) == snp_count:
rliterman@0:             preserved_pairwise_df = raw_pairwise_df.copy()
rliterman@0:             preserved_pairwise_df.to_csv(preserved_pairwise, sep="\t", index=False)
rliterman@0:         elif len(locs_pass_missing) == 0:
rliterman@0:             preserved_pairwise_df = pd.DataFrame([(pairwise[0], pairwise[1], 0,np.nan) for pairwise in pairwise_combinations],columns = ['Query_1','Query_2','SNP_Distance','SNPs_Cocalled'])
rliterman@0:             preserved_pairwise_df.to_csv(preserved_pairwise, sep="\t", index=False)
rliterman@0:         else:
rliterman@0:             preserved_distance_results = parallelAlignment(preserved_alignment)
rliterman@0:             preserved_pairwise_df = pd.DataFrame(preserved_distance_results, columns=['Query_1', 'Query_2', 'SNP_Distance', 'SNPs_Cocalled'])
rliterman@0:             preserved_pairwise_df.to_csv(preserved_pairwise, sep="\t", index=False)
rliterman@0:     
rliterman@0:         # Create matrix
rliterman@0:         idx = sorted(set(raw_pairwise_df['Query_1']).union(raw_pairwise_df['Query_2']))
rliterman@0:         mirrored_distance_df = raw_pairwise_df.pivot(index='Query_1', columns='Query_2', values='SNP_Distance').reindex(index=idx, columns=idx).fillna(0, downcast='infer').pipe(lambda x: x+x.values.T).applymap(lambda x: format(x, '.0f'))
rliterman@0:         mirrored_distance_df.index.name = ''
rliterman@0:         mirrored_distance_df.to_csv(raw_matrix,sep="\t")
rliterman@0:         
rliterman@0:         idx = sorted(set(preserved_pairwise_df['Query_1']).union(preserved_pairwise_df['Query_2']))
rliterman@0:         mirrored_distance_df = preserved_pairwise_df.pivot(index='Query_1', columns='Query_2', values='SNP_Distance').reindex(index=idx, columns=idx).fillna(0, downcast='infer').pipe(lambda x: x+x.values.T).applymap(lambda x: format(x, '.0f'))
rliterman@0:         mirrored_distance_df.index.name = ''
rliterman@0:         mirrored_distance_df.to_csv(preserved_matrix,sep="\t")
rliterman@0: 
rliterman@0:     # Clean up pybedtools temp
rliterman@0:     helpers.cleanup(verbose=False,remove_all = False)
rliterman@0: 
rliterman@0:     end_time = time.time()
rliterman@0:     with open(log_file,"a+") as log:
rliterman@0:         log.write("Done!\n")
rliterman@0:         if snp_count == 0:
rliterman@0:             log.write(f"\t- No SNPs detected, zeroed pairwise distance files saved to {raw_pairwise}/{preserved_pairwise}/{raw_matrix}/{preserved_matrix}\n")
rliterman@0:         else:
rliterman@0:             log.write(f"\t- Saved raw pairwise distances to {raw_pairwise}\n")
rliterman@0:             log.write(f"\t- Saved raw pairwise matrix to {raw_matrix}\n")
rliterman@0: 
rliterman@0:             if max_missing == float(100):
rliterman@0:                 log.write("Skipped SNP preservation step...\n")
rliterman@0:                 log.write(f"\t- Saved duplicated preserved pairwise distances to {preserved_pairwise}\n")
rliterman@0:                 log.write(f"\t- Saved duplicated preserved pairwise matrix to {preserved_matrix}\n")
rliterman@0:             elif len(locs_pass_missing) == 0:
rliterman@0:                 log.write(f"\t- No SNPs passed the {max_missing}% missing data threshold, zeroed pairwise distance files saved to {preserved_pairwise}/{preserved_matrix}\n")
rliterman@0:             else:
rliterman@0:                 log.write(f"\t- Saved preserved pairwise distances to {preserved_pairwise}\n")
rliterman@0:                 log.write(f"\t- Saved preserved pairwise matrix to {preserved_matrix}\n")
rliterman@0:         log.write(f"Total Time: {end_time - start_time:.2f} seconds\n")
rliterman@0:         log.write("-------------------------------------------------------\n\n")
rliterman@0: 
rliterman@0: except:
rliterman@0:     run_failed = True
rliterman@0:     print("Exception occurred:\n", traceback.format_exc())
rliterman@0: finally:
rliterman@0:     helpers.cleanup(verbose=False, remove_all=False)
rliterman@0:     if temp_dir != "":
rliterman@0:         shutil.rmtree(temp_dir)
rliterman@0:     if run_failed:
rliterman@0:         sys.exit(1)