reformatted merge_bam_coverage.py and calc_percent_cov.py with python…

… black

bin/calc_percent_cov.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 
-#version = '1.0.0'
+# version = '1.0.0'
 
 # Modified from script https://github.com/CDPHE-bioinformatics/CDPHE-influenza/blob/main/scripts/calc_percent_cov.py
 
@@ -15,13 +15,48 @@
 import subprocess
 
 ### Segment length dictionary
-ref_len_dict = {'A_MP': 982,'A_NP': 1497,'A_NS': 863,'A_PA': 2151,'A_PB1': 2274,'A_PB2': 2280,
-    'A_HA_H1': 1704,'A_HA_H10': 1686,'A_HA_H11': 1698,'A_HA_H12': 1695,'A_HA_H13': 1701,'A_HA_H14': 1707,
-    'A_HA_H15': 1713,'A_HA_H16': 1698,'A_HA_H2': 1689,'A_HA_H3': 1704,'A_HA_H4': 1695,'A_HA_H5': 1707,
-    'A_HA_H6': 1704,'A_HA_H7': 1713,'A_HA_H8': 1701,'A_HA_H9': 1683,'A_NA_N1': 1413,'A_NA_N2': 1410,
-    'A_NA_N3': 1410,'A_NA_N4': 1413,'A_NA_N5': 1422,'A_NA_N6': 1413,'A_NA_N7': 1416,
-    'A_NA_N8': 1413,'A_NA_N9': 1413,'B_HA': 1758,'B_MP': 1139,'B_NA': 1408,'B_NP': 1683,
-    'B_NS': 1034,'B_PA': 2181,'B_PB1': 2263,'B_PB2': 2313}
+ref_len_dict = {
+    "A_MP": 982,
+    "A_NP": 1497,
+    "A_NS": 863,
+    "A_PA": 2151,
+    "A_PB1": 2274,
+    "A_PB2": 2280,
+    "A_HA_H1": 1704,
+    "A_HA_H10": 1686,
+    "A_HA_H11": 1698,
+    "A_HA_H12": 1695,
+    "A_HA_H13": 1701,
+    "A_HA_H14": 1707,
+    "A_HA_H15": 1713,
+    "A_HA_H16": 1698,
+    "A_HA_H2": 1689,
+    "A_HA_H3": 1704,
+    "A_HA_H4": 1695,
+    "A_HA_H5": 1707,
+    "A_HA_H6": 1704,
+    "A_HA_H7": 1713,
+    "A_HA_H8": 1701,
+    "A_HA_H9": 1683,
+    "A_NA_N1": 1413,
+    "A_NA_N2": 1410,
+    "A_NA_N3": 1410,
+    "A_NA_N4": 1413,
+    "A_NA_N5": 1422,
+    "A_NA_N6": 1413,
+    "A_NA_N7": 1416,
+    "A_NA_N8": 1413,
+    "A_NA_N9": 1413,
+    "B_HA": 1758,
+    "B_MP": 1139,
+    "B_NA": 1408,
+    "B_NP": 1683,
+    "B_NS": 1034,
+    "B_PA": 2181,
+    "B_PB1": 2263,
+    "B_PB2": 2313,
+}
+
 
 #### FUNCTIONS #####
 def getOptions():
@@ -33,44 +68,49 @@ def getOptions():
 
 
 def get_fasta_file_basename(fasta_file_path):
-    basename = fasta_file_path.split('/')[-1] # strip directories
+    basename = fasta_file_path.split("/")[-1]  # strip directories
     return basename
 
+
 def get_segment_name(fasta_file_path):
-    basename = fasta_file_path.split('/')[-1] # strip directories
-    segment_name = basename.split('.')[0] # remove file extension
+    basename = fasta_file_path.split("/")[-1]  # strip directories
+    segment_name = basename.split(".")[0]  # remove file extension
     return segment_name
 
+
 def get_gene_name(fasta_file_path):
-    basename = fasta_file_path.split('/')[-1] # strip directories
-    segment_name = basename.split('.')[0] # remove file extension
-    gene_name = segment_name.split('_')[1] # extract gene name
+    basename = fasta_file_path.split("/")[-1]  # strip directories
+    segment_name = basename.split(".")[0]  # remove file extension
+    gene_name = segment_name.split("_")[1]  # extract gene name
     return gene_name
 
+
 def get_seq_length(fasta_file_path):
     # read in fasta file
-    record = SeqIO.read(fasta_file_path, 'fasta')
+    record = SeqIO.read(fasta_file_path, "fasta")
 
     # get length of non ambigous bases
     seq = record.seq
-    seq_length = (seq.count('A') + seq.count('C') + seq.count('G') + seq.count('T'))
+    seq_length = seq.count("A") + seq.count("C") + seq.count("G") + seq.count("T")
 
     return seq_length
 
+
 def calc_percent_cov(seq_length, ref_len_dict, segment_name):
     # calcuate per cov based on expected ref length
     expected_length = ref_len_dict[segment_name]
-    percent_coverage = round(((seq_length/expected_length)*100), 2)
+    percent_coverage = round(((seq_length / expected_length) * 100), 2)
 
     return percent_coverage
 
+
 def create_output(meta_id, segment_name, seq_length, percent_coverage, reference_length):
     df = pd.DataFrame()
-    df['Sample'] = [meta_id]
-    df['segment_name'] = [segment_name]
-    df['reference_length'] = [reference_length]
-    df['seq_length'] = [seq_length]
-    df['percent_coverage'] = [percent_coverage]
+    df["Sample"] = [meta_id]
+    df["segment_name"] = [segment_name]
+    df["reference_length"] = [reference_length]
+    df["seq_length"] = [seq_length]
+    df["percent_coverage"] = [percent_coverage]
 
     # Construct the output filename header
     output_header = "Sample\tsegment_name\treference_length\tseq_length\tpercent_coverage"
@@ -79,41 +119,31 @@ def create_output(meta_id, segment_name, seq_length, percent_coverage, reference
     output_filename = f"{meta_id}.{segment_name}.perc_cov_results.tsv"
 
     # Write the dataframe to the output file
-    with open(output_filename, 'w') as f:
-        f.write(output_header + '\n')
-        df.to_csv(f, sep='\t', index=False, header=False)
+    with open(output_filename, "w") as f:
+        f.write(output_header + "\n")
+        df.to_csv(f, sep="\t", index=False, header=False)
 
-#### MAIN ####
-if __name__ == '__main__':
 
+#### MAIN ####
+if __name__ == "__main__":
     options = getOptions()
     fasta_file_path = options.fasta_files
     meta_id = options.meta_id
 
-    basename = get_fasta_file_basename(fasta_file_path = fasta_file_path)
+    basename = get_fasta_file_basename(fasta_file_path=fasta_file_path)
 
     segment_name = get_segment_name(fasta_file_path=fasta_file_path)
     gene_name = get_gene_name(fasta_file_path=fasta_file_path)
 
     seq_length = get_seq_length(fasta_file_path=fasta_file_path)
-    percent_coverage = calc_percent_cov(seq_length=seq_length, 
-                                ref_len_dict=ref_len_dict, 
-                                segment_name=segment_name)
+    percent_coverage = calc_percent_cov(seq_length=seq_length, ref_len_dict=ref_len_dict, segment_name=segment_name)
 
     reference_length = ref_len_dict[segment_name]
 
-    create_output(meta_id=meta_id, 
-                    segment_name=segment_name, 
-                    seq_length=seq_length, 
-                    percent_coverage=percent_coverage,
-                    reference_length=reference_length)
-
-
-
-
-
-
-
-
-
-
+    create_output(
+        meta_id=meta_id,
+        segment_name=segment_name,
+        seq_length=seq_length,
+        percent_coverage=percent_coverage,
+        reference_length=reference_length,
+    )

bin/merge_bam_coverage.py

@@ -1,11 +1,12 @@
 import pandas as pd
 import sys
 
+
 def main():
     if len(sys.argv) != 4:
         print("Usage: python script.py <bam_results> <coverage_results> <output_file>")
         sys.exit(1)
-    
+
     bam_file_path = sys.argv[1]
     coverage_file_path = sys.argv[2]
     output_file_path = sys.argv[3]
@@ -15,8 +16,8 @@ def main():
     coverage_df = pd.read_csv(coverage_file_path, sep="\t")
 
     # Make sure 'Sample' is the first column in both DataFrames
-    bam_df = bam_df[['Sample'] + [col for col in bam_df.columns if col != 'Sample']]
-    coverage_df = coverage_df[['Sample'] + [col for col in coverage_df.columns if col != 'Sample']]
+    bam_df = bam_df[["Sample"] + [col for col in bam_df.columns if col != "Sample"]]
+    coverage_df = coverage_df[["Sample"] + [col for col in coverage_df.columns if col != "Sample"]]
 
     # Merge the DataFrames on the 'Sample' column
     merged_df = pd.merge(bam_df, coverage_df, on="Sample", how="outer")
@@ -25,10 +26,11 @@ def main():
     merged_df = merged_df.round(2)
 
     # Make sure 'Sample' is the first column in the merged DataFrame
-    merged_df = merged_df[['Sample'] + [col for col in merged_df.columns if col != 'Sample']]
+    merged_df = merged_df[["Sample"] + [col for col in merged_df.columns if col != "Sample"]]
 
     # Write the merged DataFrame to a TSV file
     merged_df.to_csv(output_file_path, sep="\t", index=False)
 
+
 if __name__ == "__main__":
     main()