加速C ++实践编程实例中的概念今天仍然存在吗？(Do the concepts in Accelerated C++ Practical Programming by Example still hold up today? [closed])

 我经常在Fortran中编写数据并用Python读取它。 我将在这里解释一个多3D阵列的情况，因为它更通用，很容易适应一维阵列。 
 
 从Fortran，我用以下循环编写数据： 
 
file = 'my_fortran_data.dat'
open(99, file=file, status = 'replace', action = 'write', form = 'unformatted', access='stream')
    do k = 1,L
        do j = 1,M
            do i = 1,N
                write(99) u(i,j,k), v(i,j,k), w(i,j,k)
            end do
        end do
    end do
close(99)
 
 请注意，我在stream访问中编写数据。 因此，我不记录每条记录的开头和结尾（使文件大小变小）。 
 
 从Python，我使用以下函数来读取数据： 
 
def read_data(file, dtype, stream):
    """
    Return the components of a 3D vector field stored in binary format.
    The data field is supposed to have been written as: (for k; for j; for i;) where the last dimension
    is the quickest varying index. Each record should have been written as: u, v, w.
    The returned components are always converted in np.double precision type.

    Args:
        dim: number of dimensions
        dtype: numpy dtype object. Single or double precision expected.
        stream: type of access of the binary output. If true, the file can only contain data. 
    If false, there is a 4-byte header and footer around each "record"
            in the binary file (can happen in some Fortran compilers if access != 'stream').
    """
    if stream:
        shape = (L, M, N, 3)
        f = open(file, 'rb')
        data = np.fromfile(file=f, dtype=dtype).reshape(shape)
        f.close()
        u = data[:, :, :, 0].transpose(2, 1, 0)
        v = data[:, :, :, 1].transpose(2, 1, 0)
        w = data[:, :, :, 2].transpose(2, 1, 0)
        del data

    else:
        shape = (L, M, N, 5)
        f = open(file, 'rb')
        data = np.fromfile(file=f, dtype=dtype).reshape(shape)
        f.close()
        u = data[:, :, :, 1].transpose(2, 1, 0)
        v = data[:, :, :, 2].transpose(2, 1, 0)
        w = data[:, :, :, 3].transpose(2, 1, 0)
        del data

    u = u.astype(np.float64, copy=False)
    v = v.astype(np.float64, copy=False)
    w = w.astype(np.float64, copy=False)
    return(u, v, w)
 
 请注意，我总是将数据转换为双精度，但如果不需要，可以省略最后一步。 
 
 对于您的情况，使用shape=(10,2)进行stream访问，否则使用shape=(10,4) 。 

I often write data in Fortran and read it in Python. I will explain it here for a multiple 3D arrays case since it is more general and easily adaptable to a 1D array.
 
From Fortran, I write the data with the following loop:
 
file = 'my_fortran_data.dat'
open(99, file=file, status = 'replace', action = 'write', form = 'unformatted', access='stream')
    do k = 1,L
        do j = 1,M
            do i = 1,N
                write(99) u(i,j,k), v(i,j,k), w(i,j,k)
            end do
        end do
    end do
close(99)
 
Note that I write my data in stream access. Hence I do not record the begining and end of each record (making the file size smaller).
 
From Python, I use the following function to read the data in:
 
def read_data(file, dtype, stream):
    """
    Return the components of a 3D vector field stored in binary format.
    The data field is supposed to have been written as: (for k; for j; for i;) where the last dimension
    is the quickest varying index. Each record should have been written as: u, v, w.
    The returned components are always converted in np.double precision type.

    Args:
        dim: number of dimensions
        dtype: numpy dtype object. Single or double precision expected.
        stream: type of access of the binary output. If true, the file can only contain data. 
    If false, there is a 4-byte header and footer around each "record"
            in the binary file (can happen in some Fortran compilers if access != 'stream').
    """
    if stream:
        shape = (L, M, N, 3)
        f = open(file, 'rb')
        data = np.fromfile(file=f, dtype=dtype).reshape(shape)
        f.close()
        u = data[:, :, :, 0].transpose(2, 1, 0)
        v = data[:, :, :, 1].transpose(2, 1, 0)
        w = data[:, :, :, 2].transpose(2, 1, 0)
        del data

    else:
        shape = (L, M, N, 5)
        f = open(file, 'rb')
        data = np.fromfile(file=f, dtype=dtype).reshape(shape)
        f.close()
        u = data[:, :, :, 1].transpose(2, 1, 0)
        v = data[:, :, :, 2].transpose(2, 1, 0)
        w = data[:, :, :, 3].transpose(2, 1, 0)
        del data

    u = u.astype(np.float64, copy=False)
    v = v.astype(np.float64, copy=False)
    w = w.astype(np.float64, copy=False)
    return(u, v, w)
 
Note that I always convert the data into double precision but you can omit this last step if not required.
 
For your case use shape=(10,2) for the stream access or shape=(10,4) otherwise.