Skipping bytes while reading a binary file?

[Lionel]

On Feb 5, 2:48 pm, MRAB <goo... at mrabarnett.plus.com> wrote:
> Lionel wrote:
>
>  > Hello,
>  > I have data stored in binary files. Some of these files are
>  > huge...upwards of 2 gigs or more. They consist of 32-bit float complex
>  > numbers where the first 32 bits of the file is the real component, the
>  > second 32bits is the imaginary, the 3rd 32-bits is the real component
>  > of the second number, etc.
>  >
>  > I'd like to be able to read in just the real components, load them
>  > into a numpy.ndarray, then load the imaginary coponents and load them
>  > into a numpy.ndarray.  I need the real and imaginary components stored
>  > in seperate arrays, they cannot be in a single array of complex
>  > numbers except for temporarily. I'm trying to avoid temporary storage,
>  > though, because of the size of the files.
>  >
>  > I'm currently reading the file scanline-by-scanline to extract rows of
>  > complex numbers which I then loop over and load into the real/
>  > imaginary arrays as follows:
>  >
>  >
>  >         self._realData         = numpy.empty((Rows, Columns), dtype =
>  > numpy.float32)
>  >         self._imaginaryData = numpy.empty((Rows, Columns), dtype =
>  > numpy.float32)
>  >
>  >         floatData = array.array('f')
>  >
>  >         for CurrentRow in range(Rows):
>  >
>  >             floatData.fromfile(DataFH, (Columns*2))
>  >
>  >             position = 0
>  >             for CurrentColumn in range(Columns):
>  >
>  >                  self._realData[CurrentRow, CurrentColumn]          =
>  > floatData[position]
>  >                 self._imaginaryData[CurrentRow, CurrentColumn]  =
>  > floatData[position+1]
>  >                 position = position + 2
>  >
>  >
>  > The above code works but is much too slow. If I comment out the body
>  > of the "for CurrentColumn in range(Columns)" loop, the performance is
>  > perfectly adequate i.e. function call overhead associated with the
>  > "fromfile(...)" call is not very bad at all. What seems to be most
>  > time-consuming are the simple assignment statements in the
>  > "CurrentColumn" for-loop.
>  >
> [snip]
> Try array slicing. floatData[0::2] will return the real parts and
> floatData[1::2] will return the imaginary parts. You'll have to read up
> how to assign to a slice of the numpy array (it might be
> "self._realData[CurrentRow] = real_parts" or "self._realData[CurrentRow,
> :] = real_parts").
>
> BTW, it's not the function call overhead of fromfile() which takes the
> time, but actually reading data from the file.

Very nice! I like that! I'll post the improvement (if any).

L