Input/Output

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Input/Output#

MatX file IO helpers read and write common array file formats through the optional MATX_ENABLE_FILEIO support. Include matx.h and use the matx::io namespace functions shown below.

Read functions can write into an already-sized tensor, or into a default-constructed tensor with the desired rank and value type. When the tensor has no storage yet, MatX allocates it after discovering the shape from the file.

CSV#

CSV files support rank-1 and rank-2 tensors. The delimiter is passed explicitly, and read_csv skips the first row by default.

tensor_t<float, 2> samples;
io::read_csv(samples, "samples.csv", ",");

io::write_csv(samples, "samples_out.csv", ",");

To read a file without skipping the first row, pass false for the final argument.

io::read_csv(samples, "samples_out.csv", ",", false);

MAT#

MAT files can contain multiple named variables. read_mat and write_mat therefore take a variable name in addition to the file name.

tensor_t<float, 2> A;
io::read_mat(A, "arrays.mat", "A");

auto B = io::read_mat<tensor_t<float, 2>>("arrays.mat", "B");

io::write_mat(A, "arrays_out.mat", "A");

MATLAB v7.3 MAT files are HDF5-based, but the MatX MAT helpers are variable oriented and use SciPy’s MAT-file routines. Treat them as MAT-file readers and writers rather than as a general HDF5 interface.

NPY#

NPY files store a single NumPy array per file.

tensor_t<float, 2> x;
io::read_npy(x, "x.npy");

io::write_npy(x, "x_out.npy");
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