src/main/python/systemds/utils/converters.py

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import numpy as np
from py4j.java_gateway import JavaClass, JavaObject, JVMView

def numpy_to_matrix_block(sds: 'SystemDSContext', np_arr: np.array):
    """Converts a given numpy array, to internal matrix block representation.

    :param sds: The current systemds context.
    :param np_arr: the numpy array to convert to matrixblock.
    """
    assert (np_arr.ndim <= 2), "np_arr invalid, because it has more than 2 dimensions"
    rows = np_arr.shape[0]
    cols = np_arr.shape[1] if np_arr.ndim == 2 else 1
    # If not numpy array then convert to numpy array
    if not isinstance(np_arr, np.ndarray):
        np_arr = np.asarray(np_arr, dtype=np.float64)

    jvm: JVMView = sds.java_gateway.jvm

    # flatten and prepare byte buffer.
    if np_arr.dtype is np.dtype(np.uint8):
        arr = np_arr.ravel()
        value_type = jvm.org.apache.sysds.common.Types.ValueType.UINT8
    elif np_arr.dtype is np.dtype(np.int32):
        arr = np_arr.ravel()
        value_type = jvm.org.apache.sysds.common.Types.ValueType.INT32
    elif np_arr.dtype is np.dtype(np.float32):
        arr = np_arr.ravel()
        value_type = jvm.org.apache.sysds.common.Types.ValueType.FP32
    else:
        arr = np_arr.ravel().astype(np.float64)
        value_type = jvm.org.apache.sysds.common.Types.ValueType.FP64
    buf = bytearray(arr.tobytes())

    # Send data to java.
    try:
        j_class: JavaClass = jvm.org.apache.sysds.runtime.util.Py4jConverterUtils
        return j_class.convertPy4JArrayToMB(buf, rows, cols, value_type)
    except Exception as e:
        sds.exception_and_close(e)


def matrix_block_to_numpy(jvm: JVMView, mb: JavaObject):
    """Converts a MatrixBlock object in the JVM to a numpy array.

    :param jvm: The current JVM instance running systemds.
    :param mb: A pointer to the JVM's MatrixBlock object.
    """
    num_ros = mb.getNumRows()
    num_cols = mb.getNumColumns()
    buf = jvm.org.apache.sysds.runtime.util.Py4jConverterUtils.convertMBtoPy4JDenseArr(
        mb)
    return np.frombuffer(buf, count=num_ros * num_cols, dtype=np.float64).reshape((num_ros, num_cols))