def opto_get_all_lfp_analytic_quick(opto_dataset,fa,fb):
Fs = 1000.0
order = 4
data = metaloadmat(opto_dataset_compact)['lfp']
data = data.transpose((0,2,1))
data = bandfilter(hilbert(data),fa,fb,Fs,order)
return data
def opto_get_lfp_filtered(opto_dataset,channel,fa,fb,order=4):
'''
Retrieves channel or channels from opto LFP dataset
Channels are 1-indexed
Parameters
----------
opto_dataset : string
path or string identifier for a dataset
channel:
1-indexed channel ID or None to return a NTimes x NChannel array
of all LFP data
fa:
low frequency of band-pass, or 'None' to use a low-pass filter.
if fb is 'None' then this is the cutoff for a high-pass filter.
fb:
high-frequency of band-pass, or 'None to use a high-pass filter.
if fa is 'None' then this is the cutoff for a low-pass filter
'''
Fs = opto_get_Fs(opto_dataset)
if channel is None:
data = metaloadmat(opto_dataset)['LFP'].T
#return array([bandfilter(x,fa,fb,Fs,order) for x in data])
problems = [(i,x,fa,fb,Fs,order) for i,x in enumerate(data)]
data = np.array(parmap(__opto_get_lfp_filtered_helper__,problems))
return squeeze(data)
else:
assert channel>=1
data = metaloadmat(opto_dataset)['LFP'][:,channel-1]
return bandfilter(data,fa,fb,Fs,order)
def __opto_get_lfp_filtered_helper__((i,data,fa,fb,Fs,order)):
return i,bandfilter(data,fa,fb,Fs,order)
def __opto_get_lfp_analytic_helper__((i,data,fa,fb,Fs,order)):
'''
Parallel function wrapper for opto_get_lfp_analytic
'''
print 5,i
return i,hilbert(bandfilter(data,fa,fb,Fs,order))