def test_composable_apps(self):
"""checks the ability of these two apps(fast_slow_dist and quick_tree) to communicate"""
path = os.path.join(data_path, "brca1_5.paml")
aln1 = load_aligned_seqs(path, moltype=DNA)
fast_slow_dist = dist.fast_slow_dist(fast_calc="hamming", moltype="dna")
quick = tree_app.quick_tree(drop_invalid=False)
proc = fast_slow_dist + quick
self.assertEqual(
str(proc),
"fast_slow_dist(type='distance', distance=None, moltype='dna',\n"
"fast_calc='hamming', slow_calc=None) + quick_tree(type='tree',\n"
"drop_invalid=False)",
)
self.assertIsInstance(proc, tree_app.quick_tree)
self.assertEqual(proc._type, "tree")
self.assertIsInstance(proc.input, dist.fast_slow_dist)
self.assertIs(proc.output, None)
self.assertIsInstance(proc._input_types, frozenset)
self.assertIsInstance(proc._output_types, frozenset)
self.assertIsInstance(proc._in, dist.fast_slow_dist)
self.assertIs(proc._out, None)
tree1 = proc(aln1)
self.assertIsInstance(tree1, PhyloNode)
self.assertIsNotNone(tree1.children)
self.assertEqual(set(tree1.get_tip_names()), set(aln1.names))
# tests when distances contain None
data = dict(
seq1="AGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
seq2="TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
)
aln2 = make_aligned_seqs(data=data, moltype=DNA)
tree2 = proc(aln2)
self.assertIsInstance(tree2, NotCompleted)
def _get_all_composables(tmp_dir_name):
test_model1 = evo.model("HKY85")
test_model2 = evo.model("GN")
test_hyp = evo.hypothesis(test_model1, test_model2)
test_num_reps = 100
applications = [
align.align_to_ref(),
align.progressive_align(model="GY94"),
dist.fast_slow_dist(moltype="dna", fast_calc="hamming"),
evo.ancestral_states(),
evo.bootstrap(hyp=test_hyp, num_reps=test_num_reps),
evo.hypothesis(test_model1, test_model2),
evo.model("GN"),
evo.tabulate_stats(),
sample.fixed_length(100),
sample.min_length(100),
io.write_db(tmp_dir_name, create=True),
io.write_json(tmp_dir_name, create=True),
io.write_seqs(tmp_dir_name, create=True),
sample.omit_bad_seqs(),
sample.omit_degenerates(),
sample.omit_duplicated(),
sample.take_codon_positions(1),
sample.take_named_seqs(),
sample.take_n_seqs(2),
sample.trim_stop_codons(gc=1),
translate.select_translatable(),
tree.quick_tree(),
tree.scale_branches(),
tree.uniformize_tree(),
]
return applications
def test_composes_with_write_tabular(self):
"""correctly links to tabular"""
with TemporaryDirectory(dir=".") as dirname:
writer = io.write_tabular(dirname)
dist_calc = dist_app.fast_slow_dist(distance="hamming",
moltype="protein")
_ = dist_calc + writer
def test_init(self):
"""tests if fast_slow_dist can be initialised correctly"""
fast_slow_dist = dist_app.fast_slow_dist(fast_calc="hamming",
moltype="dna")
self.assertIsInstance(fast_slow_dist.fast_calc, HammingPair)
self.assertIsNone(fast_slow_dist._sm)
fast_slow_dist = dist_app.fast_slow_dist(distance="TN93")
self.assertIsInstance(fast_slow_dist.fast_calc, TN93Pair)
self.assertEqual(fast_slow_dist._sm.name, "TN93")
fast_slow_dist = dist_app.fast_slow_dist(distance="GTR")
self.assertEqual(fast_slow_dist._sm.name, "GTR")
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
self.assertEqual(fast_slow_dist._sm.name, "TN93")
self.assertIsNone(fast_slow_dist.fast_calc)
with self.assertRaises(ValueError):
fast_slow_dist = dist_app.fast_slow_dist(distance="TN93",
fast_calc="TN93",
slow_calc="TN93")
with self.assertRaises(ValueError):
fast_slow_dist = dist_app.fast_slow_dist(fast_calc="GTR")
with self.assertRaises(ValueError):
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="hamming")
def test_quick_tree(self):
"""correctly calc a nj tree"""
path = os.path.join(data_path, "brca1_5.paml")
aln = load_aligned_seqs(path, moltype=DNA)
fast_slow_dist = dist.fast_slow_dist(fast_calc="hamming", moltype="dna")
dist_matrix = fast_slow_dist(aln)
quick1 = tree_app.quick_tree()
tree1 = quick1.quick_tree(dist_matrix)
self.assertEqual(set(tree1.get_tip_names()), set(aln.names))
def test_functions_as_composable(self):
"""works as a composable app"""
from pathlib import Path
loader = io.load_aligned(moltype="dna", format="paml")
dist = dist_app.fast_slow_dist("hamming", moltype="dna")
with TemporaryDirectory(dir=".") as dirname:
dirname = Path(dirname)
writer = io.write_tabular(dirname)
proc = loader + dist + writer
_ = proc("data/brca1_5.250.paml")
output = dirname / "brca1_5.250.tsv"
self.assertTrue(output.exists())
def test_composable_apps(self):
"""tests two composable apps"""
composable_apps = _get_all_composable_apps()
fast_slow_dist = dist_app.fast_slow_dist(fast_calc="hamming",
moltype="dna")
for app in composable_apps:
# Compose two composable applications, there should not be exceptions.
got = app + fast_slow_dist
self.assertIsInstance(got, dist_app.fast_slow_dist)
self.assertEqual(got._type, "distance")
self.assertIs(got.input, app)
self.assertIs(got.output, None)
self.assertIsInstance(got._input_types, frozenset)
self.assertIsInstance(got._output_types, frozenset)
self.assertIs(got._in, app)
self.assertIs(got._out, None)
app.disconnect()
fast_slow_dist.disconnect()
def test_compatible_parameters(self):
"""tests if the input parameters are compatible with fast_slow_dist initialisation"""
fast_slow_dist = dist_app.fast_slow_dist(fast_calc="hamming",
moltype="dna")
fast_slow_dist = dist_app.fast_slow_dist(fast_calc="TN93")
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
fast_slow_dist = dist_app.fast_slow_dist(fast_calc="TN93")
# fails for paralinear or hamming if no moltype
with self.assertRaises(ValueError):
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="hamming")
with self.assertRaises(ValueError):
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="paralinear")
# fails for hamming as slow_calc
with self.assertRaises(ValueError):
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="hamming",
moltype="dna")
with self.assertRaises(ValueError):
fast_slow_dist = dist_app.fast_slow_dist(fast_calc="GTR")
def __init__(
self,
model,
gc=None,
param_vals=None,
guide_tree=None,
unique_guides=False,
indel_length=1e-1,
indel_rate=1e-10,
distance="percent",
):
"""
Parameters
----------
model
substitution model instance or name. If 'codon'
(uses MG94HKY), 'nucleotide' (uses HKY85), 'protein'
(uses WG01). These choices provide also provide default
settings for param_vals.
gc : int or string
the genetic code for a codon alignment, defaults to the standard
genetic code
param_vals : dict
param name, values for parameters in model. Overrides
default choices.
guide_tree
newick string, tree instance (must have branch lengths), or a
callable that will build a tree from unaligned collection. If not
provided, estimated ONCE via constructing a crude alignment. In the
case of callable, or not provided, the computed guide tree is stored
in the returned alignment.info['guide_tree'].
unique_guides : bool
whether each alignment requires a new guide tree
indel_rate : float
probability of gap insertion
indel_length : float
probability of gap extension
distance : string
the distance measure for building a guide tree. Default is 'percent',
the proportion of differences. This is applicable for any moltype,
and sequences with very high percent identity. For more diverged
sequences we recommend 'paralinear'.
"""
super(progressive_align, self).__init__(
input_types=self._input_types,
output_types=self._output_types,
data_types=self._data_types,
)
self._param_vals = {
"codon": dict(omega=0.4, kappa=3),
"nucleotide": dict(kappa=3),
}.get(model, param_vals)
sm = {"codon": "MG94HKY", "nucleotide": "HKY85", "protein": "JTT92"}.get(
model, model
)
self._formatted_params()
kwargs = {} if gc is None else dict(gc=gc)
sm = get_model(sm, **kwargs)
moltype = sm.alphabet.moltype
self._model = sm
self._scalar = sm.word_length
self._indel_length = indel_length
self._indel_rate = indel_rate
self._moltype = moltype
self._unique_guides = unique_guides
self._distance = distance
if callable(guide_tree):
self._make_tree = guide_tree
guide_tree = None # callback takes precedence
else:
al_to_ref = align_to_ref(moltype=self._moltype)
dist_calc = dist.fast_slow_dist(
distance=self._distance, moltype=self._moltype
)
est_tree = quick_tree()
self._make_tree = al_to_ref + dist_calc + est_tree
if guide_tree is not None:
if type(guide_tree) == str:
guide_tree = make_tree(treestring=guide_tree, underscore_unmunge=True)
if guide_tree.children[0].length is None:
raise ValueError("Guide tree must have branch lengths")
# make sure no zero lengths
guide_tree = scale_branches()(guide_tree)
self._guide_tree = guide_tree
self._kwargs = dict(
indel_length=self._indel_length,
indel_rate=self._indel_rate,
tree=self._guide_tree,
param_vals=self._param_vals,
show_progress=False,
)
self.func = self.multiple_align
def test_est_dist_pair_slow(self):
"""tests the distance between seq pairs in aln"""
aligner = align.align_to_ref()
aln3 = aligner(self.seqs3)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
got = fast_slow_dist(aln3).to_dict()
assert_allclose(got[("Human", "Mouse")], got[("Mouse", "Human")])
self.assertTrue(0 <= got[("Mouse", "Human")])
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
got = fast_slow_dist(aln3).to_dict()
assert_allclose(got[("Human", "Mouse")], got[("Mouse", "Human")])
self.assertTrue(0 <= got[("Mouse", "Human")])
aligner = align.align_to_ref(ref_seq="Human")
aln3 = aligner(self.seqs3)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
got = fast_slow_dist(aln3).to_dict()
assert_allclose(got[("Human", "Mouse")], got[("Mouse", "Human")])
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
got = fast_slow_dist(aln3).to_dict()
assert_allclose(got[("Human", "Mouse")], got[("Mouse", "Human")])
self.assertTrue(0 <= got[("Mouse", "Human")])
aligner = align.align_to_ref(ref_seq="Mouse")
aln3 = aligner(self.seqs3)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(0 <= got[("Mouse", "Human")])
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(0 <= got[("Mouse", "Human")])
aligner = align.align_to_ref()
aln3 = aligner(self.seqs4)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(0 <= got[("Human", "Opossum")])
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(0 <= got[("Human", "Opossum")])
aligner = align.align_to_ref(ref_seq="Human")
aln3 = aligner(self.seqs4)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(0 <= got[("Human", "Opossum")])
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(0 <= got[("Human", "Opossum")])
aligner = align.align_to_ref(ref_seq="Opossum")
aln3 = aligner(self.seqs4)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(0 <= got[("Human", "Opossum")])
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(0 <= got[("Human", "Opossum")])
treestring = "(Human:0.2,Bandicoot:0.2)"
aligner = align.progressive_align(model="WG01", guide_tree=treestring)
_ = aligner(self.seqs5)
def test_est_dist_pair_slow(self):
"""tests the distance between seq pairs in aln"""
aligner = align.align_to_ref()
aln3 = aligner(self.seqs3)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
got = fast_slow_dist(aln3).to_dict()
assert_allclose(got[("Human", "Mouse")], got[("Mouse", "Human")])
self.assertTrue(got[("Mouse", "Human")] >= 0)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
got = fast_slow_dist(aln3).to_dict()
assert_allclose(got[("Human", "Mouse")], got[("Mouse", "Human")])
self.assertTrue(got[("Mouse", "Human")] >= 0)
aligner = align.align_to_ref(ref_seq="Human")
aln3 = aligner(self.seqs3)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
got = fast_slow_dist(aln3).to_dict()
assert_allclose(got[("Human", "Mouse")], got[("Mouse", "Human")])
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
got = fast_slow_dist(aln3).to_dict()
assert_allclose(got[("Human", "Mouse")], got[("Mouse", "Human")])
self.assertTrue(got[("Mouse", "Human")] >= 0)
aligner = align.align_to_ref(ref_seq="Mouse")
aln3 = aligner(self.seqs3)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(got[("Mouse", "Human")] >= 0)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(got[("Mouse", "Human")] >= 0)
aligner = align.align_to_ref()
aln3 = aligner(self.seqs4)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(got[("Human", "Opossum")] >= 0)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(got[("Human", "Opossum")] >= 0)
aligner = align.align_to_ref(ref_seq="Human")
aln3 = aligner(self.seqs4)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(got[("Human", "Opossum")] >= 0)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(got[("Human", "Opossum")] >= 0)
aligner = align.align_to_ref(ref_seq="Opossum")
aln3 = aligner(self.seqs4)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="GTR")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(got[("Human", "Opossum")] >= 0)
fast_slow_dist = dist_app.fast_slow_dist(slow_calc="TN93")
got = fast_slow_dist(aln3).to_dict()
self.assertTrue(got[("Human", "Opossum")] >= 0)
# now as a process
proc = align.align_to_ref() + dist_app.fast_slow_dist(
fast_calc="hamming", moltype="dna")
got = proc(self.seqs1)
self.assertEqual(got[("Human", "Rhesus")], 1)
treestring = "(Human:0.2,Bandicoot:0.2)"
aligner = align.progressive_align(model="WG01", guide_tree=treestring)
_ = aligner(self.seqs5)
