I need to open a .bi5 file and read the contents to cut a long story short. The problem: I have tens of thousands of .bi5 files containing time-series data that I need to decompress and process (read, dump into pandas).
I ended up installing Python 3 (I use 2.7 normally) specifically for the lzma library, as I ran into compiling nightmares using the lzma back-ports for Python 2.7, so I conceded and ran with Python 3, but with no success. The problems are too numerous to divulge, no one reads long questions!
I have included one of the .bi5 files, if someone could manage to get it into a Pandas Dataframe and show me how they did it, that would be ideal.
ps the fie is only a few kb, it will download in a second. Thanks very much in advance.
(The file) http://www.filedropper.com/13hticks
The code below should do the trick. First, it opens a file and decodes it in lzma and then uses struct to unpack the binary data.
import lzma
import struct
import pandas as pd
def bi5_to_df(filename, fmt):
chunk_size = struct.calcsize(fmt)
data = []
with lzma.open(filename) as f:
while True:
chunk = f.read(chunk_size)
if chunk:
data.append(struct.unpack(fmt, chunk))
else:
break
df = pd.DataFrame(data)
return df
The most important thing is to know the right format. I googled around and tried to guess and '>3i2f' (or >3I2f) works quite good. (It's big endian 3 ints 2 floats. What you suggest: 'i4f' doesn't produce sensible floats - regardless whether big or little endian.) For struct and format syntax see the docs.
df = bi5_to_df('13h_ticks.bi5', '>3i2f')
df.head()
Out[177]:
0 1 2 3 4
0 210 110218 110216 1.87 1.12
1 362 110219 110216 1.00 5.85
2 875 110220 110217 1.00 1.12
3 1408 110220 110218 1.50 1.00
4 1884 110221 110219 3.94 1.00
Update
To compare the output of bi5_to_df with https://github.com/ninety47/dukascopy,
I compiled and run test_read_bi5 from there. The first lines of the output are:
time, bid, bid_vol, ask, ask_vol
2012-Dec-03 01:00:03.581000, 131.945, 1.5, 131.966, 1.5
2012-Dec-03 01:00:05.142000, 131.943, 1.5, 131.964, 1.5
2012-Dec-03 01:00:05.202000, 131.943, 1.5, 131.964, 2.25
2012-Dec-03 01:00:05.321000, 131.944, 1.5, 131.964, 1.5
2012-Dec-03 01:00:05.441000, 131.944, 1.5, 131.964, 1.5
And bi5_to_df on the same input file gives:
bi5_to_df('01h_ticks.bi5', '>3I2f').head()
Out[295]:
0 1 2 3 4
0 3581 131966 131945 1.50 1.5
1 5142 131964 131943 1.50 1.5
2 5202 131964 131943 2.25 1.5
3 5321 131964 131944 1.50 1.5
4 5441 131964 131944 1.50 1.5
So everything seems to be fine (ninety47's code reorders columns).
Also, it's probably more accurate to use '>3I2f' instead of '>3i2f' (i.e. unsigned int instead of int).