[Numpy-discussion] fast numpy.fromfile skipping data chunks
Wed Mar 13 08:45:23 CDT 2013
Hi everybody, I hope this has not been discussed before, I couldn't find a solution elsewhere.
I need to read some binary data, and I am using numpy.fromfile to do this. Since the files are huge, and would make me run out of memory, I need to read data skipping some records (I am reading data recorded at high frequency, so basically I want to read subsampling).
At the moment, I came up with the code below, which is then compiled using cython. Despite the significant performance increase from the pure python version, the function is still much slower than numpy.fromfile, and only reads one kind of data (in this case uint32), otherwise I do not know how to define the array type in advance. I have basically no experience with cython nor c, so I am a bit stuck. How can I try to make this more efficient and possibly more generic?
import numpy as np
cimport numpy as np
from libc.stdint cimport uint32_t
def cffskip32(fid, int count=1, int skip=0):
cdef int k=0
cdef np.ndarray[uint32_t, ndim=1] data = np.zeros(count, dtype=np.uint32)
data[k] = np.fromfile(fid, count=1, dtype=np.uint32)
data = data[:k]
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