def test_tabulate_wrapped_FASTA():
"""Test that a the proper exception is raised if a wrapped FASTA is
provided. Input FASTA must be one line for label, one line for sequence"""
seqf = open("./tests/data/sequences_wrapped.fasta")
metadata_mapping = read_metadata("./tests/data/metadata_mapping.txt",
"time_points", "time_mask")
with pytest.raises(AssertionError):
seqcount = tabulate(seqf, metadata_mapping, False)
seqf.close()
def test_tabulate_skipped_sample_warning():
"""Test that a warning is provided if sequences in the FASTA file are not
used because there is no corresponding sample in the metadata mapping"""
seqf = open("./tests/data/sequences_extra_sample.fasta")
metadata_mapping = read_metadata("./tests/data/metadata_mapping.txt",
"time_points", "time_mask")
with pytest.warns(UserWarning):
seqcount = tabulate(seqf, metadata_mapping, False)
seqf.close()
def test_write_csr(temp_dir):
"""Test of the write_csr function, and verify it produces a proper FASTA
file output, and Ananke TimeSeriesData object.
Validate the CSR data in the TimeSeriesData object."""
seqf = open("./tests/data/sequences_no_size.fasta")
outseqf = open(temp_dir + "/seq.unique.fasta", 'w')
timeseriesdb = TimeSeriesData(temp_dir + "/ananke.h5")
metadata_mapping = read_metadata("./tests/data/metadata_mapping.txt",
"time_points", "time_mask")
seqcount = tabulate(seqf, metadata_mapping, False)
sample_name_array = np.array(metadata_mapping["#SampleID"])
ngenes = len(seqcount)
nsamples = len(sample_name_array)
nobs = 0
for sequence, abundance_dict in seqcount.items():
nobs += len(abundance_dict)
timeseriesdb.resize_data(ngenes, nsamples, nobs)
timeseriesdb.insert_array_by_chunks("samples/names", sample_name_array)
timeseriesdb.insert_array_by_chunks("samples/time",
metadata_mapping["time_points"],
transform_func = float)
timeseriesdb.insert_array_by_chunks("samples/mask",
metadata_mapping["time_mask"])
unique_indices = write_csr(timeseriesdb, seqcount,
outseqf, sample_name_array)
seqf.close()
outseqf.close()
# Check the table in the TimeSeriesData object
true_data = [[2.0, 1.0, 3.0, 4.0],
[1.0, 1.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 0.0],
[0.0, 2.0, 0.0, 0.0]]
generated_data = timeseriesdb.get_sparse_matrix().todense()
# Note: the inner lists have a specific ordering (temporal)
# but the order of those lists (i.e., the order of the sequences
# is not important
for data in true_data:
assert data in generated_data
del timeseriesdb
# Check the output FASTA file
assert compare_fasta_unordered(temp_dir + "/seq.unique.fasta",
"tests/data/seq.unique.fasta")
def test_tabulate_size_labels():
"""Test that a FASTA file with size labels is properly tabulated"""
seqf = open("./tests/data/sequences_size.fasta")
metadata_mapping = read_metadata("./tests/data/metadata_mapping.txt",
"time_points", "time_mask")
seqcount = tabulate(seqf, metadata_mapping, True)
true_data = {'ATGCTATCGATCGATGCATCGATCGATGCTATCGTACGTGGCA':
{'15':4, '72c':2, 'qpa':3, '29':1},
'TTGCTATCGATCGATGCATCGATCGATGCTATCGTACGTGGCA':
{'15':1, '72c':1, 'qpa':1, '29':1},
'TTGCTATCGATCGATGCATCGATCGATGCTATCGTACGTGGCT':
{'qpa':1},
'GATGTCGTAGCTGTAGCTAGCTAGCTAGCTGCTG':
{'29':2}}
seqf.close()
assert seqcount == true_data
def test_tabulate_unordered():
"""Test that a FASTA file with sequences presented in a random order
(i.e., sequences next to one another don't necessarily belong to the same
sample/time point) is properly tabulated"""
seqf = open("./tests/data/sequences_unordered.fasta")
metadata_mapping = read_metadata("./tests/data/metadata_mapping.txt",
"time_points", "time_mask")
seqcount = tabulate(seqf, metadata_mapping, False)
true_data = {'ATGCTATCGATCGATGCATCGATCGATGCTATCGTACGTGGCA':
{'15':4, '72c':2, 'qpa':3, '29':1},
'TTGCTATCGATCGATGCATCGATCGATGCTATCGTACGTGGCA':
{'15':1, '72c':1, 'qpa':1, '29':1},
'TTGCTATCGATCGATGCATCGATCGATGCTATCGTACGTGGCT':
{'qpa':1},
'GATGTCGTAGCTGTAGCTAGCTAGCTAGCTGCTG':
{'29':2}}
seqf.close()
assert seqcount == true_data