def test_get_coverages_from_bed():
names = list(core.read_bed(cm.BEDFILE)["name"].unique())
sample_count = 3
bamlist = cm.create_fake_bamlist(sample_count)
df = core.get_coverages_from_bed(bamlist,cm.BEDFILE, show_progress= False)
assert set(df.columns) == set([f"fake_{i}" for i in range(sample_count)])
def test_compute_coverage():
names = list(core.read_bed(cm.BEDFILE)["name"].unique())
sample_count = 10
bamlist = cm.create_fake_bamlist(sample_count)
df = core.compute_coverage(bamlist, cm.BEDFILE, show_progress=False)
assert list(df.columns) == [f"fake_{i}" for i in range(sample_count)]
assert list(df.index.names) == ["name", "chrom", "pos"]
def test_from_hdf5():
model_file = tempfile.gettempdir() + "/test_iscard_model.h5"
model = Model()
bamlist = cm.create_fake_bamlist(2)
model.learn(bamlist, cm.BEDFILE, show_progress=False)
model.to_hdf5(model_file)
second_model = Model(model_file)
assert second_model.bamlist == model.bamlist
def test_to_hdf5():
model_file = tempfile.gettempdir() + "/test_iscard_model.h5"
bamlist = cm.create_fake_bamlist(2)
model.learn(bamlist, cm.BEDFILE, show_progress=False)
model.to_hdf5(model_file)
hdf = pd.HDFStore(model_file)
hdf_keys = [key[1:] for key in hdf.keys()]
assert "bamlist" in hdf_keys
assert "raw" in hdf_keys
assert "metadata" in hdf_keys
assert "inter_model" in hdf_keys
assert "intra_model" in hdf_keys
hdf.close()
def test_learn():
bamlist = cm.create_fake_bamlist(2)
model.learn(bamlist, cm.BEDFILE, show_progress=False)
assert model.intra_model is not None
assert model.inter_model is not None