def write_anndata_x_matrix_to_cxg(self, output_cxg_directory, ctx,
sparse_threshold):
matrix_container = f"{output_cxg_directory}/X"
x_matrix_data = self.anndata.X
is_sparse = is_matrix_sparse(x_matrix_data, sparse_threshold)
if not is_sparse:
col_shift = get_column_shift_encode_for_matrix(
x_matrix_data, sparse_threshold)
is_sparse = col_shift is not None
else:
col_shift = None
if col_shift is not None:
logging.info(
"Converting matrix X as sparse matrix with column shift encoding"
)
x_col_shift_name = f"{output_cxg_directory}/X_col_shift"
convert_ndarray_to_cxg_dense_array(x_col_shift_name, col_shift,
ctx)
convert_matrix_to_cxg_array(matrix_container, x_matrix_data, is_sparse,
ctx, col_shift)
tiledb.consolidate(matrix_container, ctx=ctx)
if hasattr(tiledb, "vacuum"):
tiledb.vacuum(matrix_container)
def test__is_matrix_sparse__partially_populated_dense_matrix_returns_false(self):
matrix = np.zeros([2, 2])
matrix[0][0] = 1.0
matrix[0][1] = 2.2
matrix[1][1] = 3.7
self.assertFalse(is_matrix_sparse(matrix, 50))
def test__is_matrix_sparse_with_column_shift_encoding__giant_matrix_returns_false_early(self):
matrix = np.random.rand(20000, 20)
with self.assertLogs(level="INFO") as logger:
self.assertFalse(is_matrix_sparse(matrix, 1))
# Because the function returns early a log will output the _estimate_ instead of the _exact_ percentage of
# non-zero elements in the matrix.
self.assertIn("Percentage of non-zero elements (estimate)", logger.output[0])
def test__is_matrix_sparse__zero_and_one_hundred_percent_threshold(self):
matrix = np.array([1, 2, 3])
self.assertFalse(is_matrix_sparse(matrix, 0))
self.assertTrue(is_matrix_sparse(matrix, 100))
def test__is_matrix_sparse__partially_populated_sparse_matrix_returns_true(self):
matrix = np.zeros([3, 4])
matrix[2][3] = 1.0
matrix[1][1] = 2.2
self.assertTrue(is_matrix_sparse(matrix, 50))
def main():
parser = argparse.ArgumentParser()
parser.add_argument("input", help="input cxg directory")
parser.add_argument("output", help="output cxg directory")
parser.add_argument("--overwrite",
action="store_true",
help="replace output cxg directory")
parser.add_argument("--verbose",
"-v",
action="count",
default=0,
help="verbose output")
parser.add_argument(
"--sparse-threshold",
"-s",
type=float,
default=5.0, # default is 5% non-zero values
help=
"The X array will be sparse if the percent of non-zeros falls below this value",
)
args = parser.parse_args()
if os.path.exists(args.output):
print("output dir exists:", args.output)
if args.overwrite:
print("output dir removed:", args.output)
shutil.rmtree(args.output)
else:
print("use the overwrite option to remove the output directory")
sys.exit(1)
if not os.path.isdir(args.input):
print("input is not a directory", args.input)
sys.exit(1)
shutil.copytree(args.input,
args.output,
ignore=shutil.ignore_patterns("X", "X_col_shift"))
ctx = tiledb.Ctx({
"sm.num_reader_threads": 32,
"sm.num_writer_threads": 32,
"sm.consolidation.buffer_size": 1 * 1024 * 1024 * 1024,
})
with tiledb.DenseArray(os.path.join(args.input, "X"), mode="r",
ctx=ctx) as X_in:
x_matrix_data = X_in[:, :]
matrix_container = args.output
is_sparse = is_matrix_sparse(x_matrix_data, args.sparse_threshold)
if not is_sparse:
col_shift = get_column_shift_encode_for_matrix(
x_matrix_data, args.sparse_threshold)
is_sparse = col_shift is not None
else:
col_shift = None
if col_shift is not None:
x_col_shift_name = f"{args.output}/X_col_shift"
convert_ndarray_to_cxg_dense_array(x_col_shift_name, col_shift,
ctx)
tiledb.consolidate(matrix_container, ctx=ctx)
if is_sparse:
convert_matrix_to_cxg_array(matrix_container, x_matrix_data,
is_sparse, ctx, col_shift)
tiledb.consolidate(matrix_container, ctx=ctx)
if not is_sparse:
print("The array is not sparse, cleaning up, abort.")
shutil.rmtree(args.output)
sys.exit(1)
