update for python 3

svtyper/classic.py

@@ -13,6 +13,10 @@
 from svtyper.utils import *
 from svtyper.statistics import bayes_gt
 
+try:
+    xrange
+except NameError:
+    xrange = range
 # --------------------------------------
 # define functions
 
@@ -126,7 +130,7 @@ def sv_genotype(bam_string,
             bam_list.append(pysam.AlignmentFile(b, mode='rb'))
         elif b.endswith('.cram'):
             bam_list.append(pysam.AlignmentFile(b,
-                mode='rc',reference_filename=ref_fasta,format_options=["required_fields=7167"]))
+                mode='rc',reference_filename=ref_fasta,format_options=[b"required_fields=7167"]))
         else:
             sys.stderr.write('Error: %s is not a valid alignment file (*.bam or *.cram)\n' % b)
             exit(1)
@@ -413,12 +417,12 @@ def sv_genotype(bam_string,
                         out_bam_written_reads = write_alignment(read, out_bam, out_bam_written_reads)
 
             if debug:
-                print '--------------------------'
-                print 'ref_span:', ref_span
-                print 'alt_span:', alt_span
-                print 'ref_seq:', ref_seq
-                print 'alt_seq:', alt_seq
-                print 'alt_clip:', alt_clip
+                print('--------------------------')
+                print('ref_span:', ref_span)
+                print('alt_span:', alt_span)
+                print('ref_seq:', ref_seq)
+                print('alt_seq:', alt_seq)
+                print('alt_clip:', alt_clip)
 
             # in the absence of evidence for a particular type, ignore the reference
             # support for that type as well
@@ -443,12 +447,12 @@ def sv_genotype(bam_string,
                 QR = int(split_weight * ref_seq) + int(disc_weight * ref_span)
                 QA = int(split_weight * alt_splitters) + int(disc_weight * alt_span)
                 gt_lplist = bayes_gt(QR, QA, is_dup)
-                best, second_best = sorted([ (i, e) for i, e in enumerate(gt_lplist) ], key=lambda(x): x[1], reverse=True)[0:2]
+                best, second_best = sorted([ (i, e) for i, e in enumerate(gt_lplist) ], key=lambda x: x[1], reverse=True)[0:2]
                 gt_idx = best[0]
 
                 # print log probabilities of homref, het, homalt
                 if debug:
-                    print gt_lplist
+                    print(gt_lplist)
 
                 # set the overall variant QUAL score and sample specific fields
                 var.genotype(sample.name).set_format('GL', ','.join(['%.0f' % x for x in gt_lplist]))
@@ -573,7 +577,7 @@ def main():
 def cli():
     try:
         sys.exit(main())
-    except IOError, e:
+    except IOError as e:
         if e.errno != 32:  # ignore SIGPIPE
             raise
 

svtyper/parsers.py

@@ -3,15 +3,16 @@
 import time, re, json, sys
 from collections import Counter
 
-from svtyper.statistics import mean, stdev, median, upper_mad
+from svtyper.statistics import mean, stdev, median, upper_mad, xrange
+
 
 # ==================================================
 # VCF parsing tools
 # ==================================================
 def confidence_interval(var, tag, alt_tag, max_ci_dist):
-    ci = map(int, var.info[tag].split(','))
+    ci = list(map(int, var.info[tag].split(',')))
     if ci[1] - ci[0] > max_ci_dist:
-        return map(int, var.info[alt_tag].split(','))
+        return list(map(int, var.info[alt_tag].split(',')))
     return ci
 
 
@@ -480,7 +481,7 @@ def from_bam(cls,
         for r in bam.header['RG']:
             try:
                 in_lib = r['LB'] == lib_name
-            except KeyError, e:
+            except KeyError as e:
                 in_lib = lib_name == ''
 
             if in_lib:
@@ -520,8 +521,10 @@ def calc_read_length(self):
         for read in self.bam.fetch():
             if read.get_tag('RG') not in self.readgroups:
                 continue
-            if read.infer_query_length() > max_rl:
-                max_rl = read.infer_query_length()
+            rl = read.infer_query_length()
+            if rl is None: continue
+            if rl > max_rl:
+                max_rl = rl
             if counter == num_samp:
                 break
             counter += 1
@@ -669,7 +672,7 @@ def from_bam(cls,
         for r in bam.header['RG']:
             try:
                 lib_name=r['LB']
-            except KeyError, e:
+            except KeyError as e:
                 lib_name=''
 
             # add the new library
@@ -877,7 +880,7 @@ def p_concordant(self, var_length=None):
         try:
             p = float(self.lib.dens[ospan_length]) * conc_prior / (conc_prior * self.lib.dens[ospan_length] + disc_prior * (self.lib.dens[ospan_length - var_length]))
         except ZeroDivisionError:
-            p = None
+            return False
 
         return p > 0.5
 

svtyper/singlesample.py

@@ -47,17 +47,33 @@ def get_args():
     return args
 
 def ensure_valid_alignment_file(afile):
-    if not (afile.endswith('.bam') or afile.endswith('.cram')):
-        die('Error: %s is not a valid alignment file (*.bam or *.cram)\n' % afile)
+    print(afile.__class__.__name__, file=sys.stderr)
+    if not isinstance(afile, str):
+        afile = afile.decode()
+
+    try:
+        if not (afile.endswith('.bam') or afile.endswith('.cram')):
+            die('Error: %s is not a valid alignment file (*.bam or *.cram)\n' % afile)
+    except TypeError:
+        if not (afile.endswith(b'.bam') or afile.endswith(b'.cram')):
+            die('Error: %s is not a valid alignment file (*.bam or *.cram)\n' % afile)
 
 def open_alignment_file(afile, reference_fasta):
     fd = None
-    if afile.endswith('.bam'):
-        fd = pysam.AlignmentFile(afile, mode='rb')
-    elif afile.endswith('.cram'):
-        fd = pysam.AlignmentFile(afile, mode='rc', reference_filename=reference_fasta)
-    else:
-        die('Error: %s is not a valid alignment file (*.bam or *.cram)\n' % afile)
+    try:
+        if afile.endswith('.bam'):
+            fd = pysam.AlignmentFile(afile, mode='rb')
+        elif afile.endswith('.cram'):
+            fd = pysam.AlignmentFile(afile, mode='rc', reference_filename=reference_fasta)
+        else:
+            die('Error: %s is not a valid alignment file (*.bam or *.cram)\n' % afile)
+    except TypeError:
+        if afile.endswith(b'.bam'):
+            fd = pysam.AlignmentFile(afile, mode='rb')
+        elif afile.endswith(b'.cram'):
+            fd = pysam.AlignmentFile(afile, mode='rc', reference_filename=reference_fasta)
+        else:
+            die('Error: %s is not a valid alignment file (*.bam or *.cram)\n' % afile)
 
     return fd
 
@@ -417,7 +433,7 @@ def bayesian_genotype(breakpoint, counts, split_weight, disc_weight, debug):
 
     # the actual bayesian calculation and decision
     gt_lplist = bayes_gt(QR, QA, is_dup)
-    best, second_best = sorted([ (i, e) for i, e in enumerate(gt_lplist) ], key=lambda(x): x[1], reverse=True)[0:2]
+    best, second_best = sorted([ (i, e) for i, e in enumerate(gt_lplist) ], key=lambda x: x[1], reverse=True)[0:2]
     gt_idx = best[0]
 
     # print log probabilities of homref, het, homalt
@@ -846,7 +862,7 @@ def main():
 def cli():
     try:
         sys.exit(main())
-    except IOError, e:
+    except IOError as e:
         if e.errno != 32:  # ignore SIGPIPE
             raise
 

svtyper/statistics.py

@@ -1,6 +1,11 @@
 import math
 from collections import Counter
 
+try:
+    xrange
+except NameError:
+    xrange = range
+
 # ==================================================
 # Statistical tools
 # ==================================================

svtyper/utils.py

@@ -47,6 +47,10 @@ def write_sample_json(sample_list, lib_info_file):
         lib_info[sample.name] = s
 
     # write the json file
+    for k, sample in lib_info.items():
+        if isinstance(sample["bam"], bytes):
+            lib_info[k]["bam"] = sample["bam"].decode()
+
     json.dump(lib_info, lib_info_file, indent=4)
     lib_info_file.close()