[Numpy-discussion] Fastest way to parsing a specific binay file

Gökhan Sever gokhansever@gmail....
Wed Sep 2 23:59:54 CDT 2009

On Wed, Sep 2, 2009 at 1:58 PM, Robert Kern <robert.kern@gmail.com> wrote:

> On Wed, Sep 2, 2009 at 13:28, Gökhan Sever<gokhansever@gmail.com> wrote:
> > Put the reference manual in:
> >
> > http://drop.io/1plh5rt
> >
> > First few pages describe the data format they use.
> Ah. The fields are *not* delimited by a fixed value. Regexes are no
> help to you for pulling out the information you need, except perhaps
> later to parse the text fields. I think you are also getting spurious
> results because your regex matches things inside data fields.
> Instead, you have a header containing the length of the data field
> followed by the data field. Create a structured dtype that corresponds
> to the DataDir struct on page 15. Note that "unsigned int" there is
> actually a numpy.uint16, not a uint32.
>  dt = np.dtype([('tagNumber', np.uint16), ('dataOffset', np.uint16),
> ('numberBytes', np.uint16), ('samples', np.uint16), ('bytesPerSample',
> np.uint16), ('type', np.uint8), ('param1', np.uint8), ('param2',
> np.uint8), ('param3', np.uint8), ('address', np.uint16)])
> Now read dt.itemsize bytes from the file and use
>  header = fromstring(f.read(dt.itemsize), dt)[0]
> to get a record object that corresponds to the header. Use the
> dataOffset and numberBytes fields to extract the actual data bytes
> from the file.
> For example, if we go to the second header field:
> In [28]: f.seek(dt.itemsize,0)
> In [29]: header = np.fromstring(f.read(dt.itemsize), dt)[0]
> In [30]: header
> Out[30]: (65530, 100, 8, 1, 8, 255, 0, 0, 0, 43605)
> In [31]: f.seek(header['dataOffset'], 0)
> In [32]: f.read(header['numberBytes'])
> Out[32]: 'prj.300\x00'
> There are still some semantic issues you need to work out, still.
> There are multiple "buffers" per file, and the dataOffsets are
> relative to the start of the buffer, not the file.
> --
> Robert Kern
> "I have come to believe that the whole world is an enigma, a harmless
> enigma that is made terrible by our own mad attempt to interpret it as
> though it had an underlying truth."
>  -- Umberto Eco
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You must have thrown a couple RTFM's while replying my emails :) I usually
take trial-error approaches initially, and don't give up unless I hit a
hurdle so fast, which in this case resulted with the unsuccessful regex
approach. However from the good point I have learnt the basics of regular
expressions and realized how powerful could they be during a text parsing

Enough prattle, below is what I am working on:

So far I was successfully able to extract the file names and the data
associated with those names (with the exception of multiple buffer per file

However not reading time increments correctly, I should be seeing 1 sec
incremental time ticks from the time segment reading, but all it does is to
return the same first time information.

Furthermore, I still couldn't figure out how to wrap the main looping suite
(range(500) is just a dummy number which will let me process whole binary
data) I don't know yet how to make the range input generic which will work
any size of similar binary file.

import numpy as np
import struct

f = open('test.sea', 'rb')

dt = np.dtype([('tagNumber', np.uint16), ('dataOffset', np.uint16),
('numberBytes', np.uint16), ('samples', np.uint16), ('bytesPerSample',
np.uint16), ('type', np.uint8), ('param1', np.uint8), ('param2',
np.uint8), ('param3', np.uint8), ('address', np.uint16)])

start = 0
ct = 0

for i in range(500):

    header = np.fromstring(f.read(dt.itemsize), dt)[0]

    if header['tagNumber'] == 65530:
        loc = f.tell()
        f.seek(start + header['dataOffset'])
    elif header['tagNumber'] == 65531:
        loc = f.tell()
        f.seek(start + header['dataOffset'])
        start = f.tell()
    elif header['tagNumber'] == 0:
        loc = f.tell()
        f.seek(start + header['dataOffset'])
        print f.tell()
        k = f.read(header['numberBytes']
        print struct.unpack('9h', k[:18])
        ct += 1

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