def test_data_current():
dp = load_datapackage(ZipFS(dirpath / "a-first.zip")).filter_by_attribute(
"group", "x-third"
)
expected = np.array([101, -111, 112])
mm = MappedMatrix(packages=[dp], matrix="matrix-b")
for group in mm.groups:
assert np.allclose(group.data_current, expected)
dp = load_datapackage(ZipFS(dirpath / "a-first.zip")).filter_by_attribute(
"group", "x-third"
)
expected = np.array([101, -111])
mm = MappedMatrix(
packages=[dp], matrix="matrix-b", custom_filter=lambda x: x["row"] < 3
)
for group in mm.groups:
assert np.allclose(group.data_current, expected)
dp = load_datapackage(ZipFS(dirpath / "a-first.zip")).filter_by_attribute(
"group", "x-third"
)
expected = np.array([-111, 112])
am = ArrayMapper(array=np.array([2, 3]))
mm = MappedMatrix(packages=[dp], matrix="matrix-b", row_mapper=am)
for group in mm.groups:
assert np.allclose(group.data_current, expected)
dp = load_datapackage(ZipFS(dirpath / "a-first.zip")).filter_by_attribute(
"group", "x-third"
)
expected = np.array([-111])
am = ArrayMapper(array=np.array([2, 3]))
mm = MappedMatrix(
packages=[dp],
matrix="matrix-b",
row_mapper=am,
custom_filter=lambda x: x["row"] < 3,
)
for group in mm.groups:
assert np.allclose(group.data_current, expected)
def test_flip_masked():
dp = load_datapackage(ZipFS(dirpath / "a-first.zip")).filter_by_attribute(
"group", "x-third"
)
expected = np.array([True])
am = ArrayMapper(array=np.array([2, 3]))
mm = MappedMatrix(
packages=[dp],
matrix="matrix-b",
row_mapper=am,
custom_filter=lambda x: x["row"] < 3,
)
for group in mm.groups:
assert np.allclose(group.flip, expected)
def test_indices_transposed():
dp = load_datapackage(ZipFS(dirpath / "a-first.zip")).filter_by_attribute(
"group", "x-third"
)
cexpected = np.array([-1, 0, 1])
rexpected = np.array([0, 1, -1])
am = ArrayMapper(array=np.array([2, 3]))
mm = MappedMatrix(
packages=[dp],
matrix="matrix-b",
col_mapper=am,
custom_filter=lambda x: x["col"] < 3,
transpose=True,
)
for group in mm.groups:
assert np.allclose(group.row_mapped, rexpected)
assert np.allclose(group.col_mapped, cexpected)
def test_all_empty_after_mapping():
s = bwp.create_datapackage()
s.add_persistent_vector(
matrix="foo",
data_array=np.arange(2),
indices_array=np.array([(0, 0), (1, 0)], dtype=bwp.INDICES_DTYPE),
)
am = ArrayMapper(array=np.array([2, 3]))
mm = MappedMatrix(
packages=[s],
matrix="foo",
row_mapper=am,
)
# doesn't error out because matrix has shape from array mapper
assert not mm.matrix.sum()
assert mm.matrix.shape == (2, 1)
assert not mm.matrix.tocoo().data.sum()
def test_indices_matrix_with_aggregation():
dp = create_datapackage(sum_intra_duplicates=True)
data_array = np.array([101, 111, 112, 113])
indices_array = np.array([(1, 4), (2, 5), (3, 6), (3, 6)], dtype=INDICES_DTYPE)
dp.add_persistent_vector(
matrix="matrix-b",
data_array=data_array,
name="x-third",
indices_array=indices_array,
)
expected = np.array([0, 1, 2])
mm = MappedMatrix(packages=[dp], matrix="matrix-b")
for group in mm.groups:
assert np.allclose(group.row_matrix, expected)
assert np.allclose(group.col_matrix, expected)
expected = np.array([0, 1])
mm = MappedMatrix(
packages=[dp], matrix="matrix-b", custom_filter=lambda x: x["row"] < 3
)
for group in mm.groups:
assert np.allclose(group.row_matrix, expected)
assert np.allclose(group.col_matrix, expected)
rexpected = np.array([0, 1])
cexpected = np.array([1, 2])
am = ArrayMapper(array=np.array([2, 3]))
mm = MappedMatrix(packages=[dp], matrix="matrix-b", row_mapper=am)
for group in mm.groups:
assert np.allclose(group.row_matrix, rexpected)
assert np.allclose(group.col_matrix, cexpected)
rexpected = np.array([0])
cexpected = np.array([1])
mm = MappedMatrix(
packages=[dp],
matrix="matrix-b",
row_mapper=am,
custom_filter=lambda x: x["row"] < 3,
)
for group in mm.groups:
assert np.allclose(group.row_matrix, rexpected)
assert np.allclose(group.col_matrix, cexpected)
def test_one_empty_after_mapping():
s = bwp.create_datapackage()
s.add_persistent_vector(
matrix="foo",
data_array=np.arange(2),
indices_array=np.array([(0, 0), (1, 0)], dtype=bwp.INDICES_DTYPE),
)
s.add_persistent_vector(
matrix="foo",
data_array=np.arange(10, 12),
indices_array=np.array([(10, 1), (11, 1)], dtype=bwp.INDICES_DTYPE),
)
am = ArrayMapper(array=np.array([10, 11]))
mm = MappedMatrix(
packages=[s],
matrix="foo",
row_mapper=am,
)
assert mm.matrix.sum() == 21