def test__get_neighbor_directions():
direction_dict = {
(0, 0): 0,
(1, 1): 0,
(2, 2): 0,
(3, 3): 0,
(0, 1): 1,
(1, 0): -1,
(0, 2): 1,
(2, 0): -1,
(1, 3): 1,
(3, 1): -1,
(2, 3): 1,
(3, 2): -1
}
gpm = GenotypePhenotypeMap(["00", "01", "10", "11"])
gpm.get_neighbors()
for i in range(len(gpm.neighbors)):
edge = gpm.neighbors.edge[i]
dir = gpm.neighbors.direction[i]
assert direction_dict[edge] == dir
# Flip map, should flip directions except 0
gpm.wildtype = "11"
for i in range(len(gpm.neighbors)):
edge = gpm.neighbors.edge[i]
dir = gpm.neighbors.direction[i]
assert direction_dict[edge] == -dir
def test_getters(test_data):
"""
Test sundry getters.
"""
for d in test_data:
gpm = GenotypePhenotypeMap(wildtype=d["wildtype"],
genotype=d["genotype"],
phenotype=d["phenotype"],
uncertainty=d["uncertainty"])
assert gpm.length == d["length"]
assert gpm.num_genotypes == d["num_genotypes"]
assert gpm.wildtype == d["wildtype"]
assert gpm.mutant == d["mutant"]
# Make sure that the .data is the object, *not* a copy of ._data
assert gpm.data is gpm._data
assert gpm.encoding_table is gpm._encoding_table
for i in range(len(gpm.mutations)):
assert np.array_equal(gpm.mutations[i], d["mutations"][i])
assert np.array_equal(gpm.genotype, d["genotype"])
assert np.array_equal(gpm.binary, d["binary"])
assert np.array_equal(gpm.phenotype, d["phenotype"])
assert np.array_equal(gpm.uncertainty, d["uncertainty"])
# Make sure initial values of neighors are set properly
assert gpm.neighbors is None
assert gpm.neighbor_function is None
assert gpm.neighbor_cutoff is None
# Run get_neighbors call, which will now populate neighbors,
# neighbor_function, and neighbor_cutoff
gpm.get_neighbors()
assert isinstance(gpm.neighbors, pd.DataFrame)
assert gpm._neighbors is gpm.neighbors
assert gpm.neighbor_function == "hamming"
assert gpm.neighbor_cutoff == 1
gpm.get_neighbors(cutoff=17)
assert isinstance(gpm.neighbors, pd.DataFrame)
assert gpm._neighbors is gpm.neighbors
assert gpm.neighbor_function == "hamming"
assert gpm.neighbor_cutoff == 17
def test__trim_neighbors(test_data):
for d in test_data:
# Build map again.
gpm = GenotypePhenotypeMap(genotype=d["genotype"])
# Get the neighbors
gpm.get_neighbors()
num_neighbors_initial = len(gpm.neighbors)
# Delete a node
gpm._data = gpm._data.iloc[np.arange(len(gpm._data) - 1, dtype=int)]
# Since we removed a node, we should have rebuilt neighbors. The length
# of the neighbors will thus be shorter
assert len(gpm.neighbors) < num_neighbors_initial
def test_get_neighbors():
for use_cython in [False, True]:
print("Use cython?", use_cython)
# Check hamming = 1
gpm = GenotypePhenotypeMap(genotype=["SG", "PF", "SF", "PG"])
gpm.get_neighbors("hamming", cutoff=1, use_cython=use_cython)
new_edges = list(gpm._neighbors.loc[:, "edge"])
new_edges.sort()
hamming_edges = [(0, 0), (0, 2), (2, 0), (0, 3), (3, 0), (1, 1),
(1, 2), (2, 1), (1, 3), (3, 1), (2, 2), (3, 3)]
hamming_edges.sort()
for i in range(len(new_edges)):
assert np.array_equal(hamming_edges[i], new_edges[i])
# Check hamming = 2
gpm = GenotypePhenotypeMap(genotype=["SG", "PF", "SF", "PG"])
gpm.get_neighbors("hamming", cutoff=2, use_cython=use_cython)
new_edges = list(gpm._neighbors.loc[:, "edge"])
new_edges.sort()
hamming_edges = [(0, 0), (0, 1), (1, 0), (0, 2), (2, 0), (0, 3),
(3, 0), (1, 1), (1, 2), (2, 1), (1, 3), (3, 1),
(2, 2), (2, 3), (3, 2), (3, 3)]
hamming_edges.sort()
for i in range(len(new_edges)):
assert np.array_equal(hamming_edges[i], new_edges[i])
# Check amino acid codon = 1
gpm = GenotypePhenotypeMap(genotype=["SG", "PF", "SF", "PG"])
gpm.get_neighbors("codon", cutoff=1, use_cython=use_cython)
new_edges = list(gpm._neighbors.loc[:, "edge"])
new_edges.sort()
codon_edges = [(0, 0), (0, 3), (3, 0), (1, 1), (1, 2), (2, 1), (2, 2),
(3, 3)]
codon_edges.sort()
for i in range(len(new_edges)):
assert np.array_equal(codon_edges[i], new_edges[i])
