def filtfiltlong(finname, foutname, fmt, b, a, buffer_len=100000, overlap_len=100, max_len=-1):
"""Use memmap and chunking to filter continuous data.
Inputs:
finname -
foutname -
fmt - data format eg 'i'
b,a - filter coefficients
buffer_len - how much data to process at a time
overlap_len - how much data do we add to the end of each chunk to smooth out filter transients
max_len - how many samples to process. If set to -1, processes the whole file
Outputs:
y - The memmapped array pointing to the written file
Notes on algorithm:
1. The arrays are memmapped, so we let pylab (numpy) take care of handling large arrays
2. The filtering is done in chunks:
Chunking details:
|<------- b1 ------->||<------- b2 ------->|
-----[------*--------------{-----*------]--------------*------}----------
|<-------------- c1 -------------->|
|<-------------- c2 -------------->|
From the array of data we cut out contiguous buffers (b1,b2,...) and to each buffer we add some extra overlap to
make chunks (c1,c2). The overlap helps to remove the transients from the filtering which would otherwise appear at
each buffer boundary.
"""
x = pylab.memmap(finname, dtype=fmt, mode='r')
if max_len == -1:
max_len = x.size
y = pylab.memmap(foutname, dtype=fmt, mode='w+', shape=max_len)
for buff_st_idx in xrange(0, max_len, buffer_len):
chk_st_idx = max(0, buff_st_idx - overlap_len)
buff_nd_idx = min(max_len, buff_st_idx + buffer_len)
chk_nd_idx = min(x.size, buff_nd_idx + overlap_len)
rel_st_idx = buff_st_idx - chk_st_idx
rel_nd_idx = buff_nd_idx - chk_st_idx
this_y_chk = filtfilt(b, a, x[chk_st_idx:chk_nd_idx])
y[buff_st_idx:buff_nd_idx] = this_y_chk[rel_st_idx:rel_nd_idx]
return y
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