def filter_1way(b,a,in_memmap,out_memmap):
X_nc = in_memmap
n_s, n_ch = X_nc.shape
Y_mc = out_memmap
assert Y_mc.shape == (n_s,n_ch)
n_out_chunk = 10000
n_in_chunk = n_out_chunk
t_warmup = max(a.size,b.size)*3
pbar = ProgressBar(maxval = n_s).start()
z = np.zeros((max(a.size,b.size)-1,n_ch))
for i_ch in xrange(n_ch):
_,z[:,i_ch] = lfilter(b,a,X_nc[t_warmup-1::-1,i_ch],zi=lfilter_zi(b,a))
for i_in,i_out in zip(xrange(0,n_s,n_in_chunk),xrange(0,n_s,n_in_chunk)):
for i_ch in xrange(n_ch):
Y_mc[i_out:i_out+n_out_chunk,i_ch],z[:,i_ch] = lfilter(b,a,X_nc[i_in:i_in+n_in_chunk,i_ch],zi=z[:,i_ch])
pbar.update(i_in)
pbar.finish()
def downsample_1way(in_memmap, out_memmap, ratio=16):
b, a = butter(BUTTER_ORD, 1 / (2 * ratio), 'low')
X_nc = in_memmap
n_s, n_ch = X_nc.shape
n_s2 = n_s // ratio
Y_mc = out_memmap
assert Y_mc.shape == (n_s2, n_ch)
n_out_chunk = 10000
n_in_chunk = n_out_chunk * ratio
t_warmup = max(a.size, b.size) * 3
pbar = ProgressBar(maxval=n_s).start()
z = np.zeros((BUTTER_ORD, n_ch))
for i_ch in xrange(n_ch):
_, z[:, i_ch] = lfilter(b,
a,
X_nc[t_warmup - 1::-1, i_ch],
zi=lfilter_zi(b, a))
for (i_in, i_out) in zip(xrange(0, n_s, n_in_chunk),
xrange(0, n_s2, n_out_chunk)):
for i_ch in xrange(n_ch):
Low_nc, z[:, i_ch] = lfilter(b,
a,
X_nc[i_in:i_in + n_in_chunk, i_ch],
zi=z[:, i_ch])
Y_mc[i_out:i_out + n_out_chunk, i_ch] = Low_nc[::ratio]
pbar.update(i_in)
pbar.finish()
def filter_1way(b, a, in_memmap, out_memmap):
X_nc = in_memmap
n_s, n_ch = X_nc.shape
Y_mc = out_memmap
assert Y_mc.shape == (n_s, n_ch)
n_out_chunk = 10000
n_in_chunk = n_out_chunk
t_warmup = max(a.size, b.size) * 3
pbar = ProgressBar(maxval=n_s).start()
z = np.zeros((max(a.size, b.size) - 1, n_ch))
for i_ch in xrange(n_ch):
_, z[:, i_ch] = lfilter(b,
a,
X_nc[t_warmup - 1::-1, i_ch],
zi=lfilter_zi(b, a))
for i_in, i_out in zip(xrange(0, n_s, n_in_chunk),
xrange(0, n_s, n_in_chunk)):
for i_ch in xrange(n_ch):
Y_mc[i_out:i_out + n_out_chunk,
i_ch], z[:, i_ch] = lfilter(b,
a,
X_nc[i_in:i_in + n_in_chunk,
i_ch],
zi=z[:, i_ch])
pbar.update(i_in)
pbar.finish()
def downsample_1way(in_memmap,out_memmap,ratio=16):
b,a = butter(BUTTER_ORD,1/(2*ratio),'low')
X_nc = in_memmap
n_s, n_ch = X_nc.shape
n_s2 = n_s//ratio
Y_mc = out_memmap
assert Y_mc.shape == (n_s2,n_ch)
n_out_chunk = 10000
n_in_chunk = n_out_chunk*ratio
t_warmup = max(a.size,b.size)*3
pbar = ProgressBar(maxval = n_s).start()
z = np.zeros((BUTTER_ORD,n_ch))
for i_ch in xrange(n_ch):
_,z[:,i_ch] = lfilter(b,a,X_nc[t_warmup-1::-1,i_ch],zi=lfilter_zi(b,a))
for (i_in,i_out) in zip(xrange(0,n_s,n_in_chunk),xrange(0,n_s2,n_out_chunk)):
for i_ch in xrange(n_ch):
Low_nc,z[:,i_ch] = lfilter(b,a,X_nc[i_in:i_in+n_in_chunk,i_ch],zi=z[:,i_ch])
Y_mc[i_out:i_out+n_out_chunk,i_ch] = Low_nc[::ratio]
pbar.update(i_in)
pbar.finish()
