def test_sequences3():
s1 = "GAAAAAAAT"
s2 = "GAATA"
value, matrix = alignment.needleman_wunsch(s1, s2)
algn, s1a1, s2a1 = alignment.best_alignment(matrix, s1, s2, gap='-')
algn_sol1 = [list('GAA-AAAAAT'), list('GAATA-----')]
assert s1a1 == algn_sol1[0]
assert s2a1 == algn_sol1[1]
def test_sequences3():
s1 = "GAAAAAAAT"
s2 = "GAATA"
value, scores, paths = alignment.needleman_wunsch(s1, s2)
algn, s1a1, s2a1 = alignment.best_alignment(paths, s1, s2, gap='-')
algn_sol1 = [list('GAAAAAAAT'), list('G---AATA-')]
assert value == -1
assert s1a1 == algn_sol1[0]
assert s2a1 == algn_sol1[1]
def route_mismatch_factor(map_con,
path_pred,
path_grnd,
window=None,
dist_fn=None,
keep_mismatches=False):
# type: (BaseMap, List[int], List[int], Optional[int], Optional[Callable], bool) -> Tuple[float, float, float, float, List[Tuple[int, int]], float, float]
"""Evaluation method from Newson and Krumm (2009).
:math:`f = \frac{d_{-} + d_{+}}{d_0}`
With :math:`d_{-}` the length that is erroneously subtracted,
:math:`d_{+}` the length that is erroneously added, and :math:`d_0` the
distance of the correct route.
This function only supports connected states (thus not switching between states
that are not connected (e.g. parallel roads).
Also computes the Accuracy by Number (AN) and Accuracy by Length (AL) metrics from
Zheng et al. (2009).
"""
if dist_fn is None:
dist_fn = dist_latlon.distance
_, matrix = needleman_wunsch(path_pred, path_grnd, window=window)
print(matrix[:10, :10])
algn, _, _ = best_alignment(matrix)
print(algn[:10])
d_plus = 0 # length erroneously added
d_min = 0 # length erroneously subtracted
d_zero = 0 # length of correct route
cnt_matches = 0 # number of perfect matches
cnt_mismatches = 0
mismatches = [] if keep_mismatches else None
prev_grnd_pi = None
for pred_pi, grnd_pi in algn:
pred_p = path_pred[pred_pi]
grnd_p = path_grnd[grnd_pi]
grnd_d = map_con.path_dist(grnd_p)
d_zero += grnd_d
if pred_p == grnd_p:
cnt_matches += 1
else:
# print(f"Mismatch: {pred_p} != {grnd_p}")
cnt_mismatches += 1
pred_d = map_con.path_dist(pred_p)
d_plus += pred_d
d_min += grnd_d
if keep_mismatches:
mismatches.append((pred_p, grnd_p))
prev_grnd_pi = grnd_pi
factor = (d_min + d_plus) / d_zero
an = cnt_matches / len(path_grnd)
al = (d_zero - d_min) / d_zero
return factor, cnt_matches, cnt_mismatches, d_zero, mismatches, an, al
def test_sequences_blosum():
scores = read_substitution_matrix(
Path(__file__).parent / "rsrc" / "substitution.txt")
substitution = alignment.make_substitution_fn(scores)
s1 = "AGACTAGTTAC"
s2 = "CGAGACGT"
value, scores, paths = alignment.needleman_wunsch(
s1, s2, substitution=substitution)
algn, s1a1, s2a1 = alignment.best_alignment(paths, s1, s2, gap='-')
algn_sol1 = [list('--AGACTAGTTAC'), list('CGAGAC--G-T--')]
assert s1a1 == algn_sol1[0]
assert s2a1 == algn_sol1[1]
def test_sequences4():
s1 = "AGACTAGTTACC"
s2 = "CGAGACGTC"
value, matrix = alignment.needleman_wunsch(s1, s2)
algn, s1a1, s2a1 = alignment.best_alignment(matrix, s1, s2, gap='-')
# print(matrix)
# print(algn)
# print(s1a1)
# print(s2a1)
algn_sol1 = [list("--AGACTAGTTACC"), list("CGAGAC--GT--C-")]
assert s1a1 == algn_sol1[0]
assert s2a1 == algn_sol1[1]
def test_sequences_custom():
scores = read_substitution_matrix(
Path(__file__).parent / "rsrc" / "substitution.txt")
substitution = alignment.make_substitution_fn(scores)
s1 = "CCAGG"
s2 = "CCGA"
value, scores, paths = alignment.needleman_wunsch(s1, s2)
algn, s1a1, s2a1 = alignment.best_alignment(paths, s1, s2, gap='-')
algn_sol1 = [list("CCAGG"), list("CC-GA")]
assert value == 1
assert s1a1 == algn_sol1[0]
assert s2a1 == algn_sol1[1]
value, scores, paths = alignment.needleman_wunsch(
s1, s2, substitution=substitution)
algn, s1a2, s2a2 = alignment.best_alignment(paths, s1, s2, gap='-')
algn_sol2 = [list("CC-AGG"), list("CCGA--")]
assert s1a2 == algn_sol2[0]
assert s2a2 == algn_sol2[1]
def test_sequences5():
s1 = "ATGAGT"
s2 = "ATGGCGT"
value, scores, paths = alignment.needleman_wunsch(s1, s2)
algn, s1a1, s2a1 = alignment.best_alignment(paths, s1, s2, gap='-')
# print(scores)
# print(algn)
# print(s1a1)
# print(s2a1)
algn_sol1 = [list("AT-GAGT"), list("ATGGCGT")]
assert value == 3
assert s1a1 == algn_sol1[0]
assert s2a1 == algn_sol1[1]
def test_sequences4():
s1 = "AGACTAGTTACC"
s2 = "CGAGACGTC"
value, scores, paths = alignment.needleman_wunsch(s1, s2)
algn, s1a1, s2a1 = alignment.best_alignment(paths, s1, s2, gap='-')
# print(scores)
# print(algn)
# print(s1a1)
# print(s2a1)
algn_sol1 = [list("--AGACTAGTTACC"), list("CGAGAC--G-T--C")]
assert value == 0
assert s1a1 == algn_sol1[0]
assert s2a1 == algn_sol1[1]
def test_sequences1():
"""Example from https://en.wikipedia.org/wiki/Needleman–Wunsch_algorithm . """
s1 = "GATTACA"
s2 = "GCATGCU"
value, matrix = alignment.needleman_wunsch(s1, s2)
algn, s1a1, s2a1 = alignment.best_alignment(matrix, s1, s2, gap='-')
matrix_sol = [[-0., -1., -2., -3., -4., -5., -6., -7.],
[-1., 1., -0., -1., -2., -3., -4., -5.],
[-2., -0., -0., 1., -0., -1., -2., -3.],
[-3., -1., -1., -0., 2., 1., -0., -1.],
[-4., -2., -2., -1., 1., 1., -0., -1.],
[-5., -3., -3., -1., -0., -0., -0., -1.],
[-6., -4., -2., -2., -1., -1., 1., -0.],
[-7., -5., -3., -1., -2., -2., -0., -0.]]
algn_sol1 = [['G', '-', 'A', 'T', 'T', 'A', 'C', 'A'],
['G', 'C', 'A', 'T', '-', 'G', 'C', 'U']]
assert value == 0.0
assert np.array_equal(matrix, matrix_sol)
assert s1a1 == algn_sol1[0]
assert s2a1 == algn_sol1[1]
