Use Python 3.9.2 read the beginning of TB size binary file (piece of it) as below:

file=open(filename,'rb')
bytes=file.read(8)
print(bytes)
b'\x14\x00\x80?\xb5\x0c\xf81'

I tried np.fromfile np.fromfile(np.complex64) ways to read the file filename.

float_data1 = np.fromfile(filename,np.float32)
float_data2 = np.fromfile(filename,np.complex64)

As the binary file always bigger than 500GB,even TB size,how to read complex data from TB size binary file, fast and keep the most acuuracy?

This is related to your ham post.

samples = np.fromfile(filename, np.complex128)

and

Those codes equal to -1.9726906072368233e-31,+3.6405886029665884e-23.

No, they don't equal that. That's just your interpretation of bytes as float64. That interpretation is incorrect!

You assume these are 64-bit floating point numbers. They are not; you really need to stop assuming that; it's wrong, and we can't help you if you still act as if it were 64-bit floats forming a 128 bit complex value.

Besides documents,I compare the byte content in the answer,that is more than reading docs.

As I already pointed out, that is wrong. Your computer can read anything as any type, just as you tell them, even if it's not the original type it's been stored in. You stored complex64, but read complex128. That's why your values are so inplausible.

It's 32-bit floats, forming a 64 bit complex value. The official block documentation for the file sink also points that out, and even explains the numpy dtype you need to use!

Anyways, you can use numpy's memmap functionality to map the file contents without reading them all to RAM. That works. Again, you need to use the right dtype, which is, to repeat this the 10th time, not complex128.

It's really easy:

data = numpy.memmap(filename, dtype=numpy.complex64)

done.