import argparse import alignment import alignment_sol import alignment_util import random import sys labels = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" D1 = alignment_util.readScoringMatrix("DNA1.txt") D2 = alignment_util.readScoringMatrix("DNA2.txt") D3 = alignment_util.genScoringMatrix(10, -5) D4 = alignment_util.genScoringMatrix(5,0) B = alignment_util.readScoringMatrix("Blosum62.txt") ############################# def test_SW(seq1, seq2, S, g): s, a1, a2 = alignment.SmithWaterman(seq1, seq2, S, g) s_sol, a1_sol, a2_sol = alignment_sol.SmithWaterman(seq1, seq2, S, g) score = 0 # First: test that the function has returned has an optimal alignment if alignment_util.scoreAlignment(a1, a2, S, g) == s_sol: score += 70 # Second: test that the function has returned an optimal score if s == s_sol: score += 20 # Third: Test that the function has returned the correct score for the alignment
def setUp(self): self.S1 = alignment_util.readScoringMatrix("DNA1.txt") self.S2 = alignment_util.readScoringMatrix("DNA2.txt") self.S3 = alignment_util.genScoringMatrix(10, -10) self.S4 = alignment_util.readScoringMatrix("Blosum62.txt")
def setUp(self): self.S1 = alignment_util.readScoringMatrix("DNA1.txt") self.S2 = alignment_util.readScoringMatrix("DNA2.txt") self.S3 = alignment_util.genScoringMatrix(10,-10) self.S4 = alignment_util.readScoringMatrix("Blosum62.txt")
import argparse import alignment import alignment_sol import alignment_util import random import sys labels = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" D1 = alignment_util.readScoringMatrix("DNA1.txt") D2 = alignment_util.readScoringMatrix("DNA2.txt") D3 = alignment_util.genScoringMatrix(10, -5) D4 = alignment_util.genScoringMatrix(5, 0) B = alignment_util.readScoringMatrix("Blosum62.txt") ############################# def test_SW(seq1, seq2, S, g): s, a1, a2 = alignment.SmithWaterman(seq1, seq2, S, g) s_sol, a1_sol, a2_sol = alignment_sol.SmithWaterman(seq1, seq2, S, g) score = 0 # First: test that the function has returned has an optimal alignment if alignment_util.scoreAlignment(a1, a2, S, g) == s_sol: score += 70 # Second: test that the function has returned an optimal score if s == s_sol: score += 20 # Third: Test that the function has returned the correct score for the alignment