def test_fixedlength(self):
"""correctly returns data with specified length"""
aln = make_aligned_seqs(data=[("a",
"GCAAGCGTTTAT"), ("b", "GCTTTTGTCAAT")])
fl = sample.fixed_length(4)
got = fl(aln)
self.assertEqual(len(got), 4)
fl = sample.fixed_length(9, moltype="dna")
got = fl(aln)
self.assertEqual(len(got), 9)
self.assertEqual(list(got.moltype), list(DNA))
alns = [
make_aligned_seqs(data=[("a", "GCAAGCGTTTAT"),
("b", "GCTTTTGTCAAT")],
moltype=DNA),
make_aligned_seqs(data=[("a", "GGAAGCGT"), ("b", "GCTTT-GT")],
moltype=DNA),
]
fl = sample.fixed_length(9)
got = [a for a in map(fl, alns) if a]
self.assertEqual(len(got[0]), 9)
expected = dict((("a", "GCAAGCGTT"), ("b", "GCTTTTGTC")))
self.assertEqual(got[0].to_dict(), expected)
fl = sample.fixed_length(600)
got = [a for a in map(fl, alns) if a]
expected = []
self.assertEqual(got, expected)
# returns NotCompletedResult if nothing satisifies
got = fl(alns[0])
self.assertTrue(type(got) == sample.NotCompleted)
fl = sample.fixed_length(9, random=True)
got = fl(aln)
self.assertEqual(len(got), 9)
self.assertEqual(set(aln.names), set("ab"))
# these will be just a subset as sampling one triplet
fl = sample.fixed_length(3, random=True, motif_length=3)
d = make_aligned_seqs(data=[("a", "GCAAGCGTGTAT"), ("b",
"GCTACTGTCAAT")])
expect = d.to_dict()
got = fl(d)
self.assertEqual(len(got), 3)
for name, seq in got.to_dict().items():
self.assertIn(seq, expect[name])
fl = sample.fixed_length(9, start=2)
got = fl(aln)
self.assertEqual(len(got), 9)
self.assertEqual(got.to_dict(), aln[2:11].to_dict())
fl = sample.fixed_length(4, start="random")
expect = aln.to_dict()
got = fl(aln)
self.assertEqual(len(got), 4)
for name, seq in got.to_dict().items():
self.assertIn(seq, expect[name])
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"),
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.trim_stop_codons(gc=1),
translate.select_translatable(),
tree.quick_tree(),
tree.scale_branches(),
tree.uniformize_tree(),
]
return applications
def _get_all_composable_apps():
applications = [
align.align_to_ref(),
align.progressive_align(model="GY94"),
sample.fixed_length(100),
sample.min_length(100),
io.write_seqs(os.getcwd()),
sample.omit_bad_seqs(),
sample.omit_degenerates(),
sample.take_codon_positions(1),
sample.take_named_seqs(),
sample.trim_stop_codons(gc=1),
]
return applications
def test_composite_pickleable(self):
"""composable functions should be pickleable"""
from pickle import dumps
from cogent3.app import io, sample, evo, tree, translate, align
read = io.load_aligned(moltype="dna")
dumps(read)
trans = translate.select_translatable()
dumps(trans)
aln = align.progressive_align("nucleotide")
dumps(aln)
just_nucs = sample.omit_degenerates(moltype="dna")
dumps(just_nucs)
limit = sample.fixed_length(1000, random=True)
dumps(limit)
mod = evo.model("HKY85")
dumps(mod)
qt = tree.quick_tree()
dumps(qt)
proc = read + trans + aln + just_nucs + limit + mod
dumps(proc)
