[med-svn] [microbegps] 08/13: use 2to3 to port to Python3 - seems to be successful
Andreas Tille
tille at debian.org
Mon Sep 21 18:37:13 UTC 2015
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository microbegps.
commit 911129f1ebd26dd5ab806dd4d27f4ee1877b436c
Author: Andreas Tille <tille at debian.org>
Date: Thu May 7 08:08:36 2015 +0000
use 2to3 to port to Python3 - seems to be successful
---
debian/control | 12 +-
debian/patches/2to3.patch | 558 ++++++++++++++++++++++++++++++++++++++++++++++
debian/patches/series | 1 +
debian/rules | 2 +-
4 files changed, 566 insertions(+), 7 deletions(-)
diff --git a/debian/control b/debian/control
index 20c7f14..d389447 100644
--- a/debian/control
+++ b/debian/control
@@ -5,9 +5,9 @@ Maintainer: Debian Med Packaging Team <debian-med-packaging at lists.alioth.debian.
Uploaders: Andreas Tille <tille at debian.org>
Build-Depends: debhelper (>= 9),
dh-python,
- python,
- python-setuptools,
- python-qt4
+ python3,
+ python3-setuptools,
+ python3-pyqt4
Standards-Version: 3.9.6
Vcs-Browser: http://anonscm.debian.org/viewvc/debian-med/trunk/packages/microbegps/trunk/
Vcs-Svn: svn://anonscm.debian.org/debian-med/trunk/packages/microbegps/trunk/
@@ -16,9 +16,9 @@ Homepage: http://sourceforge.net/projects/microbegps/
Package: microbegps
Architecture: any
Depends: ${misc:Depends},
- ${python:Depends},
- python-matplotlib-venn,
- python-qt4
+ ${python3:Depends},
+ python3-matplotlib-venn,
+ python3-pyqt4
Description: Explorative Taxonomic Profiling Tool for Metagenomic Data
MicrobeGPS is a bioinformatics tool for the analysis of metagenomic
sequencing data. The goal is to profile the composition of metagenomic
diff --git a/debian/patches/2to3.patch b/debian/patches/2to3.patch
new file mode 100644
index 0000000..2b3e8b1
--- /dev/null
+++ b/debian/patches/2to3.patch
@@ -0,0 +1,558 @@
+Author: Andreas Tille <tille at debian.org>
+Last-Update: Wed, 06 May 2015 15:53:45 +0200
+Description: Patch created by 2to3 to run with Python3
+
+--- a/microbegps/gps.py
++++ b/microbegps/gps.py
+@@ -5,11 +5,11 @@ Analysis tools used by MicrobeGPS
+
+ import glob
+ import numpy as np
+-import valcov
++from . import valcov
+ import re
+ import os
+ import gzip
+-import taxonomy
++from . import taxonomy
+
+ class MappedRead:
+ def __init__(self):
+@@ -203,7 +203,7 @@ def filter_raw(target_table,read_table,m
+ if lrt > max_matches:
+ del_reads.append(mp)
+ if lrt == 1:
+- target_table[iter(read_table[mp].matches).next()].unique += 1
++ target_table[next(iter(read_table[mp].matches))].unique += 1
+ unique_reads += 1
+ for d in del_reads:
+ for gn in read_table[d].matches:
+@@ -291,15 +291,15 @@ def get_reference_table(target_table, re
+
+ # calculate simple metrics over references
+ for r in ref_table:
+- lengths = [x.length for x in ref_table[r].targets.itervalues()]
+- homog = [x.cov_homog for x in ref_table[r].targets.itervalues()]
+- qual = [x.map_qual for x in ref_table[r].targets.itervalues()]
+- unique = sum([x.unique for x in ref_table[r].targets.itervalues()])
++ lengths = [x.length for x in ref_table[r].targets.values()]
++ homog = [x.cov_homog for x in ref_table[r].targets.values()]
++ qual = [x.map_qual for x in ref_table[r].targets.values()]
++ unique = sum([x.unique for x in ref_table[r].targets.values()])
+ t_len = float(sum(lengths))
+
+ readnames = set()
+- for t in ref_table[r].targets.itervalues():
+- readnames.update(t.reads.iterkeys())
++ for t in ref_table[r].targets.values():
++ readnames.update(iter(t.reads.keys()))
+ reads = len(readnames)
+
+ t_homog = 0.
+@@ -353,15 +353,15 @@ def get_reference_table_NCBI(target_tabl
+
+ # calculate simple metrics over references
+ for r in ref_table:
+- lengths = [x.length for x in ref_table[r].targets.itervalues()]
+- homog = [x.cov_homog for x in ref_table[r].targets.itervalues()]
+- qual = [x.map_qual for x in ref_table[r].targets.itervalues()]
+- unique = sum([x.unique for x in ref_table[r].targets.itervalues()])
++ lengths = [x.length for x in ref_table[r].targets.values()]
++ homog = [x.cov_homog for x in ref_table[r].targets.values()]
++ qual = [x.map_qual for x in ref_table[r].targets.values()]
++ unique = sum([x.unique for x in ref_table[r].targets.values()])
+ t_len = float(sum(lengths))
+
+ readnames = set()
+- for t in ref_table[r].targets.itervalues():
+- readnames.update(t.reads.iterkeys())
++ for t in ref_table[r].targets.values():
++ readnames.update(iter(t.reads.keys()))
+ reads = len(readnames)
+
+ t_homog = 0.
+@@ -402,9 +402,9 @@ def filter_ref_table(ref_table,read_tabl
+ pr('--- Updating Unique Reads information.')
+ for rf in ref_table:
+ ref_table[rf].unique = 0
+- for rd in read_table.itervalues():
++ for rd in read_table.values():
+ if len(rd.matches) == 1:
+- ref_table[iter(rd.matches).next()].unique += 1
++ ref_table[next(iter(rd.matches))].unique += 1
+
+
+
+@@ -491,7 +491,7 @@ def create_groups(usrmat, thr=0.1, statu
+ shared = {gr:np.sum(np.logical_and(groups[gr].usrreads,usrmat[:,i])) for gr in groups}
+ sig = {gr:shared[gr] for gr in shared if shared[gr]>mapped*thr}
+ N = len(sig)
+- print "Current reference:",i," Number of groups:",N
++ print("Current reference:",i," Number of groups:",N)
+ if N == 0:
+ # no significant similarity to existing group --> create new group
+ gr = Group()
+@@ -501,16 +501,16 @@ def create_groups(usrmat, thr=0.1, statu
+ groups[grID] = gr
+ elif N == 1:
+ # significant similarity with exactly 1 group --> assign to that group
+- grID = sig.keys()[0]
++ grID = list(sig.keys())[0]
+ groups[grID].members.append(i)
+ groups[grID].usrreads = np.logical_or(groups[grID].usrreads,usrmat[:,i])
+ else:
+ # significant similarity with > 1 groups: assign to first group
+- grID = sig.keys()[0]
++ grID = list(sig.keys())[0]
+ groups[grID].members.append(i)
+ groups[grID].usrreads = np.logical_or(groups[grID].usrreads,usrmat[:,i])
+ # ... and add other groups
+- for otherID in sig.keys()[1:]:
++ for otherID in list(sig.keys())[1:]:
+ for m in groups[otherID].members:
+ groups[grID].members.append(m)
+ groups[grID].usrreads = np.logical_or(groups[grID].usrreads,groups[otherID].usrreads)
+@@ -530,7 +530,7 @@ def create_groups_2(usrmat, allmat, thr=
+
+ for i in range(rfs):
+ if status: status('--- Grouping reference %i of %i'%(i+1,rfs))
+- print "Current reference:",i," Number of groups:",len(groups)
++ print("Current reference:",i," Number of groups:",len(groups))
+ sig = dict()
+
+ usr_reads = np.sum(usrmat[:,i])
+@@ -558,13 +558,13 @@ def create_groups_2(usrmat, allmat, thr=
+ groups[grID] = gr
+ elif N == 1:
+ # significant similarity with exactly 1 group --> assign to that group
+- grID = sig.keys()[0]
++ grID = list(sig.keys())[0]
+ groups[grID].members.append(i)
+ groups[grID].usrreads = np.logical_or(groups[grID].usrreads,usrmat[:,i])
+ groups[grID].allreads = np.logical_or(groups[grID].allreads,allmat[:,i])
+ else:
+ # significant similarity with > 1 groups: assign to best group
+- grID = sig.keys()[np.argmax(sig.values())]
++ grID = list(sig.keys())[np.argmax(list(sig.values()))]
+ groups[grID].members.append(i)
+ groups[grID].usrreads = np.logical_or(groups[grID].usrreads,usrmat[:,i])
+ groups[grID].allreads = np.logical_or(groups[grID].allreads,allmat[:,i])
+@@ -588,7 +588,7 @@ def create_groups_dc(usrmat, allmat, n2i
+ item = level1.setdefault(name[0:3], list())
+ item.append(n2i[name])
+ level2 = dict()
+- for b,bucket in enumerate(level1.itervalues()):
++ for b,bucket in enumerate(level1.values()):
+ l1_groups = dict()
+ for i in bucket:
+ sig = dict()
+@@ -618,7 +618,7 @@ def create_groups_dc(usrmat, allmat, n2i
+ l1_groups[l1ID] = gr
+ else:
+ # significant similarity with > 1 groups: assign to best group
+- l1ID = sig.keys()[np.argmax(sig.values())]
++ l1ID = list(sig.keys())[np.argmax(list(sig.values()))]
+ l1_groups[l1ID].members.append(i)
+ l1_groups[l1ID].usrreads = np.logical_or(l1_groups[l1ID].usrreads,usrmat[:,i])
+ l1_groups[l1ID].allreads = np.logical_or(l1_groups[l1ID].allreads,allmat[:,i])
+@@ -650,7 +650,7 @@ def create_groups_dc(usrmat, allmat, n2i
+ groups[grID] = level2[l1ID]
+ else:
+ # significant similarity with > 1 groups: assign to best group
+- grID = sig.keys()[np.argmax(sig.values())]
++ grID = list(sig.keys())[np.argmax(list(sig.values()))]
+ groups[grID].members.extend(level2[l1ID].members)
+ groups[grID].usrreads = np.logical_or(groups[grID].usrreads,level2[l1ID].usrreads)
+ groups[grID].allreads = np.logical_or(groups[grID].allreads,level2[l1ID].allreads)
+@@ -671,19 +671,19 @@ def enrich_groups(groups,references,read
+ for m in groups[g]:
+ egroups[g].members[i2n[m]] = references[i2n[m]]
+ for t in references[i2n[m]].targets:
+- readnames.update(references[i2n[m]].targets[t].reads.iterkeys())
++ readnames.update(iter(references[i2n[m]].targets[t].reads.keys()))
+ t_len += float(references[i2n[m]].length)
+
+ egroups[g].reads = len(readnames)
+- egroups[g].max_val = max([m.validity for m in egroups[g].members.itervalues()])
+- egroups[g].cov = sum([m.coverage*m.length/t_len for m in egroups[g].members.itervalues()])
+- egroups[g].cov_homog = sum([m.cov_homog*m.length/t_len for m in egroups[g].members.itervalues()])
+- egroups[g].map_qual = sum([m.map_qual*m.length/t_len for m in egroups[g].members.itervalues()])
++ egroups[g].max_val = max([m.validity for m in egroups[g].members.values()])
++ egroups[g].cov = sum([m.coverage*m.length/t_len for m in egroups[g].members.values()])
++ egroups[g].cov_homog = sum([m.cov_homog*m.length/t_len for m in egroups[g].members.values()])
++ egroups[g].map_qual = sum([m.map_qual*m.length/t_len for m in egroups[g].members.values()])
+
+ # count the group-unique reads, i.e. reads mapping only to that group
+ group_unique = set()
+- for mbm in egroups[g].members.itervalues():
+- for trg in mbm.targets.itervalues():
++ for mbm in egroups[g].members.values():
++ for trg in mbm.targets.values():
+ for rd in trg.reads:
+ if rd in group_unique:
+ continue
+--- a/microbegps/gui.py
++++ b/microbegps/gui.py
+@@ -7,7 +7,7 @@ import sys, math
+ import pkgutil
+ import pkg_resources
+ import threading
+-import cPickle
++import pickle
+
+ from PyQt4 import QtGui, QtCore
+
+@@ -19,11 +19,11 @@ import matplotlib_venn as venn
+ try:
+ import microbegps.gps as gps
+ except ImportError:
+- import gps
+-import taxonomy
+-import modules
++ from . import gps
++from . import taxonomy
++from . import modules
+
+-from __version__ import __version__,__url__
++from .__version__ import __version__,__url__
+
+
+ #from gui.help_texts import HelpTexts as HelpTexts
+@@ -531,7 +531,7 @@ class Pipeline(QtGui.QWidget):
+
+ from multiprocessing.pool import ThreadPool
+ pool = ThreadPool(5)
+- pool.map(gps.calculate_valcov_one,p.ref_table.itervalues())
++ pool.map(gps.calculate_valcov_one,iter(p.ref_table.values()))
+ pool.close()
+
+
+@@ -714,7 +714,7 @@ class Pipeline(QtGui.QWidget):
+ try:
+ from multiprocessing.pool import ThreadPool
+ pool = ThreadPool(5)
+- pool.map(gps.calculate_valcov_one,ref_table.itervalues())
++ pool.map(gps.calculate_valcov_one,iter(ref_table.values()))
+ pool.close()
+ #GpsTools.calculate_valcov(p.ref_table,printer)
+ def custom_filt2(ref):
+@@ -892,7 +892,7 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ self.pr('<b><font color="DarkRed">Failed loading module %s</font></b><br>Message: <i>%s</i>'%(mod_name,e.message))
+ self.modules = modules_d
+ if len(self.modules) > 0:
+- self.pr('Loaded modules: <i>%s</i>'%(', '.join(self.modules.iterkeys())))
++ self.pr('Loaded modules: <i>%s</i>'%(', '.join(iter(self.modules.keys()))))
+
+ def initUI(self):
+ self.pipelineGui = Pipeline(self)
+@@ -1123,7 +1123,7 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ # Create entry for the group
+ n_mem = len(grp.members)
+ # find a suitable name. Try to use LCA of all supporting references.
+- taxids = [ref.name for ref in grp.members.itervalues()]
++ taxids = [ref.name for ref in grp.members.values()]
+ cand_name = taxonomy.find_lowest_common_ancestor_name(taxids, self.taxonomy_nodes, self.taxonomy_names)
+ if not cand_name:
+ # find member with most unique reads -> use as representative
+@@ -1229,7 +1229,7 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ self.pr("Collecting all reads for "+ref_name,False)
+ # collect all read names
+ reads = dict()
+- for trg in self.references[ref_name].targets.itervalues():
++ for trg in self.references[ref_name].targets.values():
+ for rd in trg.reads:
+ reads[rd] = trg.reads[rd][2]
+ for rd in reads:
+@@ -1244,7 +1244,7 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ self.pr("Collecting all unique reads for "+ref_name,False)
+ # collect all read names
+ reads = dict()
+- for trg in self.references[ref_name].targets.itervalues():
++ for trg in self.references[ref_name].targets.values():
+ for rd in trg.reads:
+ if len(self.reads[str(rd)].matches) == 1:
+ reads[rd] = trg.reads[rd][2]
+@@ -1259,7 +1259,7 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ self.pr("Collecting all matches of read "+rd_name,False)
+ for mtc in self.reads[rd_name].matches:
+ map_qual = 1
+- for trg in self.references[mtc].targets.itervalues():
++ for trg in self.references[mtc].targets.values():
+ if rd_name in trg.reads:
+ map_qual = trg.reads[rd_name][2]
+ break
+@@ -1284,7 +1284,7 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ self.pr("Collecting all genomes sharing reads with "+ref_name,False)
+ group_id = self.ref2group[ref_name]
+ related = dict()
+- for trg in self.references[ref_name].targets.itervalues():
++ for trg in self.references[ref_name].targets.values():
+ for rd in trg.reads:
+ for mtc in self.reads[rd].matches:
+ if self.ref2group[mtc] != group_id:
+@@ -1333,7 +1333,7 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ quals = []
+ uquals = []
+ name = str(item.text(0))
+- for trg in self.references[name].targets.itervalues():
++ for trg in self.references[name].targets.values():
+ for rd in trg.reads:
+ if len(self.reads[rd].matches) == 1:
+ uquals.append(trg.reads[rd][2])
+@@ -1356,7 +1356,7 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ quals = []
+ uquals = []
+ name = str(item.parent().text(0))
+- for trg in self.references[name].targets.itervalues():
++ for trg in self.references[name].targets.values():
+ for rd in trg.reads:
+ if len(self.reads[rd].matches) == 1:
+ uquals.append(trg.reads[rd][2])
+@@ -1461,7 +1461,7 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ name = str(item.text(0))
+ taxid = self.references[name].name
+ reads[taxid] = set()
+- for trg in self.references[name].targets.itervalues():
++ for trg in self.references[name].targets.values():
+ reads[taxid].update(trg.reads) # add all read names to the set
+ elif item.i_type == "candidate":
+ for ref in range(item.childCount()):
+@@ -1470,10 +1470,10 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ taxid = self.references[name].name
+ if not taxid in reads:
+ reads[taxid] = set()
+- for trg in self.references[name].targets.itervalues():
++ for trg in self.references[name].targets.values():
+ reads[taxid].update(trg.reads) # add all read names to the set
+ # build the lineage of every taxid
+- taxids = reads.keys()
++ taxids = list(reads.keys())
+ for taxid in taxids:
+ lineage = [taxid]
+ while True:
+@@ -1490,7 +1490,7 @@ class GPSAnalyzer(QtGui.QMainWindow):
+
+ if self.enablePlottingBox.isChecked():
+ # now color the tree
+- for taxid,item in self.taxid_to_item.iteritems():
++ for taxid,item in self.taxid_to_item.items():
+ if not taxid == 1:
+ item.setBackground(0,QtGui.QBrush(QtGui.QColor(255,255,255)))
+ item.setText(1,'')
+@@ -1515,11 +1515,11 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ nm1 = str(sel_items[0].text(0))
+ nm2 = str(sel_items[1].text(0))
+ reads1 = set()
+- for t in self.references[nm1].targets.itervalues():
+- reads1.update(t.reads.keys())
++ for t in self.references[nm1].targets.values():
++ reads1.update(list(t.reads.keys()))
+ reads2 = set()
+- for t in self.references[nm2].targets.itervalues():
+- reads2.update(t.reads.keys())
++ for t in self.references[nm2].targets.values():
++ reads2.update(list(t.reads.keys()))
+ nreads1 = len(reads1)
+ nreads2 = len(reads2)
+ shared = len(reads1.intersection(reads2))
+@@ -1536,14 +1536,14 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ nm2 = str(sel_items[1].text(0))
+ nm3 = str(sel_items[2].text(0))
+ reads1 = set()
+- for t in self.references[nm1].targets.itervalues():
+- reads1.update(t.reads.keys())
++ for t in self.references[nm1].targets.values():
++ reads1.update(list(t.reads.keys()))
+ reads2 = set()
+- for t in self.references[nm2].targets.itervalues():
+- reads2.update(t.reads.keys())
++ for t in self.references[nm2].targets.values():
++ reads2.update(list(t.reads.keys()))
+ reads3 = set()
+- for t in self.references[nm3].targets.itervalues():
+- reads3.update(t.reads.keys())
++ for t in self.references[nm3].targets.values():
++ reads3.update(list(t.reads.keys()))
+ reg100 = len(reads1.difference(reads2).difference(reads3))
+ reg010 = len(reads2.difference(reads1).difference(reads3))
+ reg110 = len(reads1.intersection(reads2).difference(reads3))
+@@ -1629,7 +1629,7 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ 'reads':self.reads,
+ 'settings_pipeline':self.settings_pipeline,
+ 'settings_analyzer':self.settings_analyzer}
+- cPickle.dump(s_data,open(fname,'wb'),protocol=-1)
++ pickle.dump(s_data,open(fname,'wb'),protocol=-1)
+ except Exception as e:
+ self.pr('Error while saving file: '+e.message)
+ raise
+@@ -1648,7 +1648,7 @@ class GPSAnalyzer(QtGui.QMainWindow):
+ self.pr("Attention: this may take a while!")
+ def loadthisfile():
+ try:
+- settings = cPickle.load(open(fname,'rb'))
++ settings = pickle.load(open(fname,'rb'))
+ assert type(settings) == dict
+ try:
+ sgroups = settings['candidates']
+--- a/microbegps/__init__.py
++++ b/microbegps/__init__.py
+@@ -1,3 +1,3 @@
+-from __version__ import __version__,__url__
++from .__version__ import __version__,__url__
+ #__version__='1.0.0'
+ #__url__='https://sourceforge.net/p/microbegps'
+\ No newline at end of file
+--- a/microbegps/modules/composition_analysis.py
++++ b/microbegps/modules/composition_analysis.py
+@@ -79,15 +79,15 @@ class GPSModule:
+
+ # collect all read names for each reference
+ self.reads = dict()
+- for ref in self.GPS.references.itervalues():
++ for ref in self.GPS.references.values():
+ taxid = ref.name
+ self.reads[taxid] = set()
+- for trg in ref.targets.itervalues():
++ for trg in ref.targets.values():
+ self.reads[taxid].update(trg.reads) # add all read names to the set
+
+ self.rank_tids = dict() # set of TaxIDs for a specific rank
+ # calculate the read sets at the higher taxonomic ranks
+- for taxid in self.reads.keys():
++ for taxid in list(self.reads.keys()):
+ # trace the lineage of each taxid and propagate the reads to the higher ranks
+ lineage = [taxid]
+ while True:
+@@ -104,7 +104,7 @@ class GPSModule:
+
+ # we are done with the analysis. Fill the Combo Box widgets accordingly
+ self.taxRankBox.clear()
+- self.taxRankBox.addItems(self.rank_tids.keys())
++ self.taxRankBox.addItems(list(self.rank_tids.keys()))
+ self.taxRankBox.setEnabled(True)
+ self.taxGroupBox.setEnabled(True)
+ self.getFromTree.setEnabled(True)
+@@ -128,7 +128,7 @@ class GPSModule:
+ if currentItem < 0:
+ return
+ taxon = str(self.taxGroupBox.currentText())
+- children = [tid for tid in self.reads.keys() if self.GPS.taxonomy_names.get(self.GPS.taxonomy_nodes.get(tid))==taxon]
++ children = [tid for tid in list(self.reads.keys()) if self.GPS.taxonomy_names.get(self.GPS.taxonomy_nodes.get(tid))==taxon]
+ self.taxSelectedText.setText('Found %i taxa below %s'%(len(children),taxon))
+ self.selectedTaxa = children
+ self.selectedParent = taxon
+@@ -138,7 +138,7 @@ class GPSModule:
+ return
+ self.GPS.graphicsTab.setCurrentIndex(1)
+ taxon = self.GPS.phyloTreeWidget.currentItem().text(0)
+- children = [tid for tid in self.reads.keys() if self.GPS.taxonomy_names.get(self.GPS.taxonomy_nodes.get(tid))==taxon]
++ children = [tid for tid in list(self.reads.keys()) if self.GPS.taxonomy_names.get(self.GPS.taxonomy_nodes.get(tid))==taxon]
+ self.taxSelectedText.setText('Found %i taxa below %s'%(len(children),taxon))
+ self.selectedTaxa = children
+ self.selectedParent = taxon
+@@ -148,7 +148,7 @@ class GPSModule:
+ return
+ tax_names = [self.GPS.taxonomy_names.get(tid,tid) for tid in self.selectedTaxa]
+ tax_abundances = [len(self.reads[tid]) for tid in self.selectedTaxa]
+- sort_order = sorted(range(len(tax_abundances)), key=tax_abundances.__getitem__)[::-1]
++ sort_order = sorted(list(range(len(tax_abundances))), key=tax_abundances.__getitem__)[::-1]
+ tax_names = [tax_names[i] for i in sort_order]
+ for i in range(7,len(tax_names)):
+ tax_names[i] = ''
+--- a/microbegps/modules/export_table.py
++++ b/microbegps/modules/export_table.py
+@@ -98,7 +98,7 @@ class GPSModule:
+ ofile.write(str(i+1))
+
+ # Candidate name: Try to use LCA of all supporting references.
+- taxids = [ref.name for ref in grp.members.itervalues()]
++ taxids = [ref.name for ref in grp.members.values()]
+ cand_name = taxonomy.find_lowest_common_ancestor_name(taxids, self.GPS.taxonomy_nodes, self.GPS.taxonomy_names)
+ if not cand_name:
+ # find member with most unique reads -> use as representative
+--- a/microbegps/taxonomy.py
++++ b/microbegps/taxonomy.py
+@@ -14,7 +14,7 @@ def parse_nodes_dmp(node_file):
+ for line in node_file:
+ fields = line.rstrip('\t|\n').split('\t|\t')
+ if len(fields) < 4:
+- print 'skipped line',line
++ print('skipped line',line)
+ continue
+ taxid = int(fields[0])
+ parent_id = int(fields[1])
+@@ -30,7 +30,7 @@ def parse_names_dmp(names_file):
+ for line in names_file:
+ fields = line.rstrip('\t|\n').split('\t|\t')
+ if len(fields) != 4:
+- print 'skipped line',line
++ print('skipped line',line)
+ continue
+ taxid = int(fields[0])
+ name = fields[1]
+@@ -123,7 +123,7 @@ def candidates_to_LCA_tree(groups, nodes
+ num_reads = []
+ for grp in groups:
+ # collect all taxids
+- taxids = [m.name for m in grp.members.itervalues()]
++ taxids = [m.name for m in grp.members.values()]
+ lca = find_lowest_common_ancestor(taxids, nodes)
+ if lca:
+ lca_list.append(lca)
+--- a/microbegps/valcov.py
++++ b/microbegps/valcov.py
+@@ -123,9 +123,9 @@ class NBinom(Distribution):
+ maxiter=10000)
+ self._p2 = (self._p1)/(self._p1+mean)
+ except:
+- print "Warning: MLE for negative binomial failed. Using MOM."
++ print("Warning: MLE for negative binomial failed. Using MOM.")
+ if var < mean:
+- print "Warning: var < mean"
++ print("Warning: var < mean")
+ var = 1.01*mean
+ self._p1 = mean**2 / (var - mean)
+ self._p2 = mean / var
+@@ -185,7 +185,7 @@ class NbTail(TailDistribution):
+ if isinstance(nbinom, NBinom):
+ self._parent = nbinom
+ else:
+- raise(Exception("NbTail must be connected to a NBinom object"))
++ raise Exception
+
+ def pmf(self, x):
+ if np.isscalar(x) and x == 0:
+@@ -222,7 +222,7 @@ class PoissonTail(TailDistribution):
+ if isinstance(poisson, Poisson):
+ self._parent = poisson
+ else:
+- raise(Exception("PoissonTail must be connected to a Poisson object"))
++ raise Exception
+
+ def pmf(self, x):
+ if self._p1 < 2.:
+@@ -322,9 +322,9 @@ def init_gamma(mixture_model, dataset):
+ class DS_cov:
+ def __init__(self,ref):
+ """ fill dataset with data provided in Reference ref """
+- t_lns = [t.length for t in ref.targets.itervalues()]
++ t_lns = [t.length for t in ref.targets.values()]
+ # r_info contains read position and length for each read
+- r_info = [[[r[0],r[1]] for r in t.reads.itervalues()] for t in ref.targets.itervalues()]
++ r_info = [[[r[0],r[1]] for r in t.reads.values()] for t in ref.targets.values()]
+
+ # pile up coverage for each target and summarize in one array
+ cov = [np.zeros((l,),dtype=np.int16) for l in t_lns]
+@@ -346,9 +346,9 @@ class DS_cov:
+ class DS_dst:
+ def __init__(self,ref):
+ """ fill dataset with data provided in Reference ref """
+- t_lns = [t.length for t in ref.targets.itervalues()]
+- r_pos = [[r[0] for r in t.reads.itervalues()] for t in ref.targets.itervalues()]
+- r_len = [[r[1] for r in t.reads.itervalues()] for t in ref.targets.itervalues()]
++ t_lns = [t.length for t in ref.targets.values()]
++ r_pos = [[r[0] for r in t.reads.values()] for t in ref.targets.values()]
++ r_len = [[r[1] for r in t.reads.values()] for t in ref.targets.values()]
+ r_len = np.concatenate([np.array(ls) for ls in r_len])
+ positions = [np.sort(np.array(ps)) for ps in r_pos]
+ distances = np.concatenate([p[1:] - p[:-1] for p in positions])
diff --git a/debian/patches/series b/debian/patches/series
new file mode 100644
index 0000000..998d10f
--- /dev/null
+++ b/debian/patches/series
@@ -0,0 +1 @@
+2to3.patch
diff --git a/debian/rules b/debian/rules
index f76d975..12c5036 100755
--- a/debian/rules
+++ b/debian/rules
@@ -4,5 +4,5 @@ export DH_VERBOSE := 1
export PYBUILD_NAME=microbegps
%:
- dh $@ --with python2 --buildsystem=pybuild
+ dh $@ --with python3 --buildsystem=pybuild
--
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