--- a/vsnp_add_zero_coverage.py	Wed Dec 02 09:11:24 2020 +0000
+++ b/vsnp_add_zero_coverage.py	Thu Dec 10 15:25:22 2020 +0000
@@ -1,9 +1,7 @@
 #!/usr/bin/env python
 
 import argparse
-import multiprocessing
 import os
-import queue
 import re
 import shutil
 
@@ -11,179 +9,124 @@
 import pysam
 from Bio import SeqIO
 
-INPUT_BAM_DIR = 'input_bam_dir'
-INPUT_VCF_DIR = 'input_vcf_dir'
-OUTPUT_VCF_DIR = 'output_vcf_dir'
-OUTPUT_METRICS_DIR = 'output_metrics_dir'
 
-
-def get_base_file_name(file_path):
+def get_sample_name(file_path):
     base_file_name = os.path.basename(file_path)
     if base_file_name.find(".") > 0:
         # Eliminate the extension.
         return os.path.splitext(base_file_name)[0]
-    elif base_file_name.endswith("_vcf"):
-        # The "." character has likely
-        # changed to an "_" character.
-        return base_file_name.rstrip("_vcf")
     return base_file_name
 
 
-def get_coverage_and_snp_count(task_queue, reference, output_metrics, output_vcf, timeout):
-    while True:
-        try:
-            tup = task_queue.get(block=True, timeout=timeout)
-        except queue.Empty:
-            break
-        bam_file, vcf_file = tup
-        # Create a coverage dictionary.
-        coverage_dict = {}
-        coverage_list = pysam.depth(bam_file, split_lines=True)
-        for line in coverage_list:
-            chrom, position, depth = line.split('\t')
-            coverage_dict["%s-%s" % (chrom, position)] = depth
-        # Convert it to a data frame.
-        coverage_df = pandas.DataFrame.from_dict(coverage_dict, orient='index', columns=["depth"])
-        # Create a zero coverage dictionary.
-        zero_dict = {}
-        for record in SeqIO.parse(reference, "fasta"):
-            chrom = record.id
-            total_len = len(record.seq)
-            for pos in list(range(1, total_len + 1)):
-                zero_dict["%s-%s" % (str(chrom), str(pos))] = 0
-        # Convert it to a data frame with depth_x
-        # and depth_y columns - index is NaN.
-        zero_df = pandas.DataFrame.from_dict(zero_dict, orient='index', columns=["depth"])
-        coverage_df = zero_df.merge(coverage_df, left_index=True, right_index=True, how='outer')
-        # depth_x "0" column no longer needed.
-        coverage_df = coverage_df.drop(columns=['depth_x'])
-        coverage_df = coverage_df.rename(columns={'depth_y': 'depth'})
-        # Covert the NaN to 0 coverage and get some metrics.
-        coverage_df = coverage_df.fillna(0)
-        coverage_df['depth'] = coverage_df['depth'].apply(int)
-        total_length = len(coverage_df)
-        average_coverage = coverage_df['depth'].mean()
-        zero_df = coverage_df[coverage_df['depth'] == 0]
-        total_zero_coverage = len(zero_df)
-        total_coverage = total_length - total_zero_coverage
-        genome_coverage = "{:.2%}".format(total_coverage / total_length)
-        # Process the associated VCF input.
-        column_names = ["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO", "FORMAT", "Sample"]
-        vcf_df = pandas.read_csv(vcf_file, sep='\t', header=None, names=column_names, comment='#')
-        good_snp_count = len(vcf_df[(vcf_df['ALT'].str.len() == 1) & (vcf_df['REF'].str.len() == 1) & (vcf_df['QUAL'] > 150)])
-        base_file_name = get_base_file_name(vcf_file)
-        if total_zero_coverage > 0:
-            header_file = "%s_header.csv" % base_file_name
-            with open(header_file, 'w') as outfile:
-                with open(vcf_file) as infile:
-                    for line in infile:
-                        if re.search('^#', line):
-                            outfile.write("%s" % line)
-            vcf_df_snp = vcf_df[vcf_df['REF'].str.len() == 1]
-            vcf_df_snp = vcf_df_snp[vcf_df_snp['ALT'].str.len() == 1]
-            vcf_df_snp['ABS_VALUE'] = vcf_df_snp['CHROM'].map(str) + "-" + vcf_df_snp['POS'].map(str)
-            vcf_df_snp = vcf_df_snp.set_index('ABS_VALUE')
-            cat_df = pandas.concat([vcf_df_snp, zero_df], axis=1, sort=False)
-            cat_df = cat_df.drop(columns=['CHROM', 'POS', 'depth'])
-            cat_df[['ID', 'ALT', 'QUAL', 'FILTER', 'INFO']] = cat_df[['ID', 'ALT', 'QUAL', 'FILTER', 'INFO']].fillna('.')
-            cat_df['REF'] = cat_df['REF'].fillna('N')
-            cat_df['FORMAT'] = cat_df['FORMAT'].fillna('GT')
-            cat_df['Sample'] = cat_df['Sample'].fillna('./.')
-            cat_df['temp'] = cat_df.index.str.rsplit('-', n=1)
-            cat_df[['CHROM', 'POS']] = pandas.DataFrame(cat_df.temp.values.tolist(), index=cat_df.index)
-            cat_df = cat_df[['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'Sample']]
-            cat_df['POS'] = cat_df['POS'].astype(int)
-            cat_df = cat_df.sort_values(['CHROM', 'POS'])
-            body_file = "%s_body.csv" % base_file_name
-            cat_df.to_csv(body_file, sep='\t', header=False, index=False)
-            if output_vcf is None:
-                output_vcf_file = os.path.join(OUTPUT_VCF_DIR, "%s.vcf" % base_file_name)
-            else:
-                output_vcf_file = output_vcf
-            with open(output_vcf_file, "w") as outfile:
-                for cf in [header_file, body_file]:
-                    with open(cf, "r") as infile:
-                        for line in infile:
-                            outfile.write("%s" % line)
-        else:
-            if output_vcf is None:
-                output_vcf_file = os.path.join(OUTPUT_VCF_DIR, "%s.vcf" % base_file_name)
-            else:
-                output_vcf_file = output_vcf
-            shutil.copyfile(vcf_file, output_vcf_file)
-        bam_metrics = [base_file_name, "", "%4f" % average_coverage, genome_coverage]
-        vcf_metrics = [base_file_name, str(good_snp_count), "", ""]
-        if output_metrics is None:
-            output_metrics_file = os.path.join(OUTPUT_METRICS_DIR, "%s.tabular" % base_file_name)
-        else:
-            output_metrics_file = output_metrics
-        metrics_columns = ["File", "Number of Good SNPs", "Average Coverage", "Genome Coverage"]
-        with open(output_metrics_file, "w") as fh:
-            fh.write("# %s\n" % "\t".join(metrics_columns))
-            fh.write("%s\n" % "\t".join(bam_metrics))
-            fh.write("%s\n" % "\t".join(vcf_metrics))
-        task_queue.task_done()
+def get_coverage_df(bam_file):
+    # Create a coverage dictionary.
+    coverage_dict = {}
+    coverage_list = pysam.depth(bam_file, split_lines=True)
+    for line in coverage_list:
+        chrom, position, depth = line.split('\t')
+        coverage_dict["%s-%s" % (chrom, position)] = depth
+    # Convert it to a data frame.
+    coverage_df = pandas.DataFrame.from_dict(coverage_dict, orient='index', columns=["depth"])
+    return coverage_df
+
+
+def get_zero_df(reference):
+    # Create a zero coverage dictionary.
+    zero_dict = {}
+    for record in SeqIO.parse(reference, "fasta"):
+        chrom = record.id
+        total_len = len(record.seq)
+        for pos in list(range(1, total_len + 1)):
+            zero_dict["%s-%s" % (str(chrom), str(pos))] = 0
+    # Convert it to a data frame with depth_x
+    # and depth_y columns - index is NaN.
+    zero_df = pandas.DataFrame.from_dict(zero_dict, orient='index', columns=["depth"])
+    return zero_df
 
 
-def set_num_cpus(num_files, processes):
-    num_cpus = int(multiprocessing.cpu_count())
-    if num_files < num_cpus and num_files < processes:
-        return num_files
-    if num_cpus < processes:
-        half_cpus = int(num_cpus / 2)
-        if num_files < half_cpus:
-            return num_files
-        return half_cpus
-    return processes
+def output_zc_vcf_file(base_file_name, vcf_file, zero_df, total_zero_coverage, output_vcf):
+    column_names = ["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO", "FORMAT", "Sample"]
+    vcf_df = pandas.read_csv(vcf_file, sep='\t', header=None, names=column_names, comment='#')
+    good_snp_count = len(vcf_df[(vcf_df['ALT'].str.len() == 1) & (vcf_df['REF'].str.len() == 1) & (vcf_df['QUAL'] > 150)])
+    if total_zero_coverage > 0:
+        header_file = "%s_header.csv" % base_file_name
+        with open(header_file, 'w') as outfile:
+            with open(vcf_file) as infile:
+                for line in infile:
+                    if re.search('^#', line):
+                        outfile.write("%s" % line)
+        vcf_df_snp = vcf_df[vcf_df['REF'].str.len() == 1]
+        vcf_df_snp = vcf_df_snp[vcf_df_snp['ALT'].str.len() == 1]
+        vcf_df_snp['ABS_VALUE'] = vcf_df_snp['CHROM'].map(str) + "-" + vcf_df_snp['POS'].map(str)
+        vcf_df_snp = vcf_df_snp.set_index('ABS_VALUE')
+        cat_df = pandas.concat([vcf_df_snp, zero_df], axis=1, sort=False)
+        cat_df = cat_df.drop(columns=['CHROM', 'POS', 'depth'])
+        cat_df[['ID', 'ALT', 'QUAL', 'FILTER', 'INFO']] = cat_df[['ID', 'ALT', 'QUAL', 'FILTER', 'INFO']].fillna('.')
+        cat_df['REF'] = cat_df['REF'].fillna('N')
+        cat_df['FORMAT'] = cat_df['FORMAT'].fillna('GT')
+        cat_df['Sample'] = cat_df['Sample'].fillna('./.')
+        cat_df['temp'] = cat_df.index.str.rsplit('-', n=1)
+        cat_df[['CHROM', 'POS']] = pandas.DataFrame(cat_df.temp.values.tolist(), index=cat_df.index)
+        cat_df = cat_df[['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'Sample']]
+        cat_df['POS'] = cat_df['POS'].astype(int)
+        cat_df = cat_df.sort_values(['CHROM', 'POS'])
+        body_file = "%s_body.csv" % base_file_name
+        cat_df.to_csv(body_file, sep='\t', header=False, index=False)
+        with open(output_vcf, "w") as outfile:
+            for cf in [header_file, body_file]:
+                with open(cf, "r") as infile:
+                    for line in infile:
+                        outfile.write("%s" % line)
+    else:
+        shutil.move(vcf_file, output_vcf)
+    return good_snp_count
+
+
+def output_metrics_file(base_file_name, average_coverage, genome_coverage, good_snp_count, output_metrics):
+    bam_metrics = [base_file_name, "", "%4f" % average_coverage, genome_coverage]
+    vcf_metrics = [base_file_name, str(good_snp_count), "", ""]
+    metrics_columns = ["File", "Number of Good SNPs", "Average Coverage", "Genome Coverage"]
+    with open(output_metrics, "w") as fh:
+        fh.write("# %s\n" % "\t".join(metrics_columns))
+        fh.write("%s\n" % "\t".join(bam_metrics))
+        fh.write("%s\n" % "\t".join(vcf_metrics))
+
+
+def output_files(vcf_file, total_zero_coverage, zero_df, output_vcf, average_coverage, genome_coverage, output_metrics):
+    base_file_name = get_sample_name(vcf_file)
+    good_snp_count = output_zc_vcf_file(base_file_name, vcf_file, zero_df, total_zero_coverage, output_vcf)
+    output_metrics_file(base_file_name, average_coverage, genome_coverage, good_snp_count, output_metrics)
+
+
+def get_coverage_and_snp_count(bam_file, vcf_file, reference, output_metrics, output_vcf):
+    coverage_df = get_coverage_df(bam_file)
+    zero_df = get_zero_df(reference)
+    coverage_df = zero_df.merge(coverage_df, left_index=True, right_index=True, how='outer')
+    # depth_x "0" column no longer needed.
+    coverage_df = coverage_df.drop(columns=['depth_x'])
+    coverage_df = coverage_df.rename(columns={'depth_y': 'depth'})
+    # Covert the NaN to 0 coverage and get some metrics.
+    coverage_df = coverage_df.fillna(0)
+    coverage_df['depth'] = coverage_df['depth'].apply(int)
+    total_length = len(coverage_df)
+    average_coverage = coverage_df['depth'].mean()
+    zero_df = coverage_df[coverage_df['depth'] == 0]
+    total_zero_coverage = len(zero_df)
+    total_coverage = total_length - total_zero_coverage
+    genome_coverage = "{:.2%}".format(total_coverage / total_length)
+    # Output a zero-coverage vcf fil and the metrics file.
+    output_files(vcf_file, total_zero_coverage, zero_df, output_vcf, average_coverage, genome_coverage, output_metrics)
 
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
 
+    parser.add_argument('--bam_input', action='store', dest='bam_input', help='bam input file')
     parser.add_argument('--output_metrics', action='store', dest='output_metrics', required=False, default=None, help='Output metrics text file')
     parser.add_argument('--output_vcf', action='store', dest='output_vcf', required=False, default=None, help='Output VCF file')
     parser.add_argument('--reference', action='store', dest='reference', help='Reference dataset')
-    parser.add_argument('--processes', action='store', dest='processes', type=int, help='User-selected number of processes to use for job splitting')
+    parser.add_argument('--vcf_input', action='store', dest='vcf_input', help='vcf input file')
 
     args = parser.parse_args()
 
-    # The assumption here is that the list of files
-    # in both INPUT_BAM_DIR and INPUT_VCF_DIR are
-    # equal in number and named such that they are
-    # properly matched if the directories contain
-    # more than 1 file (i.e., hopefully the bam file
-    # names and vcf file names will be something like
-    # Mbovis-01D6_* so they can be # sorted and properly
-    # associated with each other).
-    bam_files = []
-    for file_name in sorted(os.listdir(INPUT_BAM_DIR)):
-        file_path = os.path.abspath(os.path.join(INPUT_BAM_DIR, file_name))
-        bam_files.append(file_path)
-    vcf_files = []
-    for file_name in sorted(os.listdir(INPUT_VCF_DIR)):
-        file_path = os.path.abspath(os.path.join(INPUT_VCF_DIR, file_name))
-        vcf_files.append(file_path)
-
-    multiprocessing.set_start_method('spawn')
-    queue1 = multiprocessing.JoinableQueue()
-    num_files = len(bam_files)
-    cpus = set_num_cpus(num_files, args.processes)
-    # Set a timeout for get()s in the queue.
-    timeout = 0.05
-
-    # Add each associated bam and vcf file pair to the queue.
-    for i, bam_file in enumerate(bam_files):
-        vcf_file = vcf_files[i]
-        queue1.put((bam_file, vcf_file))
-
-    # Complete the get_coverage_and_snp_count task.
-    processes = [multiprocessing.Process(target=get_coverage_and_snp_count, args=(queue1, args.reference, args.output_metrics, args.output_vcf, timeout, )) for _ in range(cpus)]
-    for p in processes:
-        p.start()
-    for p in processes:
-        p.join()
-    queue1.join()
-
-    if queue1.empty():
-        queue1.close()
-        queue1.join_thread()
+    get_coverage_and_snp_count(args.bam_input, args.vcf_input, args.reference, args.output_metrics, args.output_vcf)