Migrate away from travis-ci

.flake8

@@ -1,2 +1,3 @@
 [flake8]
 ignore = E211,E221,E226,E501
+exclude = .git, env, .tox

.github/workflows/python-package.yml

@@ -0,0 +1,40 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python package
+
+on:
+  push:
+    branches: [ "master" ]
+  pull_request:
+    branches: [ "master" ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install tox pytest flake8
+        pip install -r requirements.txt
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=12 --max-line-length=127 --statistics
+    - name: Test with tox
+      run: |
+        python -m pytest tests

.gitignore

@@ -1,3 +1,5 @@
 *.pyc
 *~
 *.html
+env
+.tox

README.md

@@ -1,11 +1,11 @@
 # A Simple Partial Order Alignment implementation
 
-[![Build Status](https://travis-ci.org/ljdursi/poapy.svg?branch=master)](https://travis-ci.org/ljdursi/poapy)
+![Build Status](https://github.com/ljdursi/poapy/actions/workflows/python-package.yml/badge.svg)
 
 This is a simple python implementation of a Partial Order Alignment for MSA,
 based on
 
-[Multiple sequence alignment using partial order graphs](http://bioinformatics.oxfordjournals.org/content/18/3/452.short) (2002) by Lee, Grasso, and Sharlow 
+[Multiple sequence alignment using partial order graphs](http://bioinformatics.oxfordjournals.org/content/18/3/452.short) (2002) by Lee, Grasso, and Sharlow
  and
 [Generating consensus sequences from partial order ...](http://bioinformatics.oxfordjournals.org/content/19/8/999.short) (2003) by Lee
 

poagraph.py

@@ -75,7 +75,7 @@ def __init__(self, inNodeID=-1, outNodeID=-1, label=None):
         self.outNodeID = outNodeID
         if label is None:
             self.labels = []
-        elif type(label) == list:
+        elif type(label) is list:
             self.labels = label
         else:
             self.labels = [label]
@@ -180,8 +180,8 @@ def nEdges(self):
         return self._nedges
 
     def _simplified_graph_rep(self):
-        ## TODO: The need for this suggests that the way the graph is currently represented
-        ## isn't really right and needs some rethinking.
+        # TODO: The need for this suggests that the way the graph is currently represented
+        # isn't really right and needs some rethinking.
 
         node_to_pn = {}
         pn_to_nodes = {}
@@ -217,18 +217,18 @@ def toposort(self):
         sortedlist = []
         completed = set([])
 
-        ##
-        ## The topological sort of this graph is complicated by the alignedTo edges;
-        ## we want to nodes connected by such edges to remain near each other in the
-        ## topological sort.
-        ##
-        ## Here we'll create a simple version of the graph that merges nodes that
-        ## are alignedTo each other, performs the sort, and then decomposes the
-        ## 'pseudonodes'.
-        ##
-        ## The need for this suggests that the way the graph is currently represented
-        ## isn't quite right and needs some rethinking.
-        ##
+        #
+        # The topological sort of this graph is complicated by the alignedTo edges;
+        # we want to nodes connected by such edges to remain near each other in the
+        # topological sort.
+        #
+        # Here we'll create a simple version of the graph that merges nodes that
+        # are alignedTo each other, performs the sort, and then decomposes the
+        # 'pseudonodes'.
+        #
+        # The need for this suggests that the way the graph is currently represented
+        # isn't quite right and needs some rethinking.
+        #
 
         pseudonodes = self._simplified_graph_rep()
 
@@ -383,7 +383,7 @@ def consensus(self, excludeLabels=None):
 
             for neighbourID in self.nodedict[nodeID].outEdges:
                 e = self.nodedict[nodeID].outEdges[neighbourID]
-                weight = len([l for l in e.labels if l not in excludeLabels])
+                weight = len([label for label in e.labels if label not in excludeLabels])
                 weightScoreEdge = (weight, scores[neighbourID], neighbourID)
 
                 if weightScoreEdge > bestWeightScoreEdge:
@@ -423,8 +423,8 @@ def allConsenses(self, maxfraction=0.5):
 
                 labelcounts = collections.defaultdict(int)
                 for ll in labellists:
-                    for l in ll:
-                        labelcounts[l] += 1
+                    for label in ll:
+                        labelcounts[label] += 1
 
                 for label, seq in zip(self.__labels, self.__seqs):
                     if label in labelcounts and labelcounts[

seqgraphalignment.py

@@ -50,7 +50,7 @@ def matchscoreVec(self, c, v):
     def alignStringToGraphSimple(self):
         """Align string to graph, following same approach as smith waterman
         example"""
-        if type(self.sequence) != str:
+        if type(self.sequence) is not str:
             raise TypeError("Invalid Type")
 
         nodeIDtoIndex, nodeIndexToID, scores, backStrIdx, backGrphIdx = self.initializeDynamicProgrammingData()
@@ -79,14 +79,14 @@ def alignStringToGraphSimple(self):
     def alignStringToGraphFast(self):
         """Align string to graph - using numpy to vectorize across the string
         at each iteration."""
-        if not type(self.sequence) == str:
+        if type(self.sequence) is not str:
             raise TypeError("Invalid Type")
 
         l2 = len(self.sequence)
         seqvec = numpy.array(list(self.sequence))
 
         nodeIDtoIndex, nodeIndexToID, scores, backStrIdx, backGrphIdx = self.initializeDynamicProgrammingData()
-        inserted = numpy.zeros((l2), dtype=numpy.bool)
+        inserted = numpy.zeros((l2), dtype=bool)
 
         # having the inner loop as a function improves performance
         # can use Cython, etc on this for significant further improvements
@@ -111,11 +111,11 @@ def insertions(i, l2, scores, inserted):
 
             # First calculate for the first predecessor, over all string posns:
             deletescore = scores[predecessors[0]+1, 1:] + self._gap
-            bestdelete = numpy.zeros((l2), dtype=numpy.int)+predecessors[0]+1
+            bestdelete = numpy.zeros((l2), dtype=numpy.int32)+predecessors[0]+1
 
             matchpoints = self.matchscoreVec(gbase, seqvec)
             matchscore = scores[predecessors[0]+1, 0:-1] + matchpoints
-            bestmatch = numpy.zeros((l2), dtype=numpy.int)+predecessors[0]+1
+            bestmatch = numpy.zeros((l2), dtype=numpy.int32)+predecessors[0]+1
 
             # then, the remaining
             for predecessor in predecessors[1:]:
@@ -176,7 +176,7 @@ def initializeDynamicProgrammingData(self):
 
         # Dynamic Programming data structures; scores matrix and backtracking
         # matrix
-        scores = numpy.zeros((l1+1, l2+1), dtype=numpy.int)
+        scores = numpy.zeros((l1+1, l2+1), dtype=numpy.int32)
 
         # initialize insertion score
         # if global align, penalty for starting at head != 0
@@ -192,8 +192,8 @@ def initializeDynamicProgrammingData(self):
                 scores[index+1, 0] = best + self._gap
 
         # backtracking matrices
-        backStrIdx = numpy.zeros((l1+1, l2+1), dtype=numpy.int)
-        backGrphIdx = numpy.zeros((l1+1, l2+1), dtype=numpy.int)
+        backStrIdx = numpy.zeros((l1+1, l2+1), dtype=numpy.int32)
+        backGrphIdx = numpy.zeros((l1+1, l2+1), dtype=numpy.int32)
 
         return nodeIDtoIndex, nodeIndexToID, scores, backStrIdx, backGrphIdx
 

simplefasta.py

@@ -14,7 +14,7 @@ def readfasta(infile):
     cursequence = ""
 
     def updatelists():
-        if len(cursequence) is not 0:
+        if len(cursequence) != 0:
             sequences.append(cursequence)
             if curlabel is not None:
                 labels.append(curlabel)

tests/test_alignment_column_order.py

@@ -1,7 +1,6 @@
 """
 Tests for Issue #6, provided by @rlorigro
 """
-import pytest
 import poagraph
 import seqgraphalignment
 

tox.ini

@@ -2,13 +2,6 @@
 envlist = py27, py34, py35, py36, flake8
 skipsdist=True
 
-[travis]
-python =
-    3.6: py36
-    3.5: py35
-    3.4: py34
-    2.7: py27
-
 [testenv:flake8]
 basepython = python
 deps = flake8