def get_surface_channel_gamma(path,
start=2.,
end=10.,
sampling_rate=2500,
plot=False):
mm = np.memmap(path, dtype=np.int16, mode='r')
num_channels = get_channel_count(os.path.dirname(path))
chunk = get_chunk(mm, start, end, num_channels, sampling_rate)
gm = []
good_channels = []
for ch in range(np.shape(chunk)[0]):
if ch not in skip_channels:
f, pXX = welch_power(chunk[ch, :], start=2, window=8)
gm.extend([pXX[np.where(f > 40.)[0][0]]])
good_channels.extend([ch])
threshold = np.max(
gm[::-1][:5]
) #assumes the last 5 are out of the brain; uses the max gamma on these channels as the threshold
surface_channel = good_channels[mlab.cross_from_above(
gaussian_filter1d(gm, 0), threshold)[0]]
if plot:
plt.plot(good_channels, gaussian_filter1d(gm, 2))
plt.gca().axhline(threshold, color='r')
del mm
return surface_channel
def get_surface_channel_freq(path,frequency_range=[1,5],start=2.,end=10.,sampling_rate=2500,filter_size=2,sigma=2.,plot=False,filter=False,probemap=None):
mm = np.memmap(path, dtype=np.int16, mode='r')
num_channels = get_channel_count(os.path.dirname(path),from_channel_map=False)
chunk = get_chunk(mm,start,end,num_channels,sampling_rate)
if probemap is not None:
chunk = chunk[probemap,:]
gm = []
good_channels = []
for ch in range(np.shape(chunk)[0]):
if ch not in skip_channels:
if filter:
data = filtr(chunk[ch,:],0.1,300,sampling_rate,3)
else:
data = chunk[ch,:]
f,pXX = welch_power(chunk[ch,:],start=2,window=8)
gm.extend([np.mean(pXX[np.where((f>frequency_range[0])&(f<frequency_range[1]))[0]])])
good_channels.extend([ch])
#threshold = np.mean(gm[::-1][:5]) #assumes the last 5 are out of the brain; uses the max gamma on these channels as the threshold
threshold = np.mean(gaussian_filter1d(gm,filter_size)[::-1][:5])+np.std(gaussian_filter1d(gm,filter_size)[::-1][:5])*sigma
if plot:
plt.plot(good_channels,gaussian_filter1d(gm,filter_size))
plt.gca().axhline(threshold,color='r')
plt.xlabel('channel number')
plt.ylabel('power in '+str(frequency_range[0])+' to '+str(frequency_range[1])+' band')
try:
surface_channel = good_channels[mlab.cross_from_above(gaussian_filter1d(gm,filter_size),threshold)[-1]]
return surface_channel
except:
return None
del mm
return surface_channel
def get_surface_channel_spikeband(path,
start=2.,
end=10.,
sampling_rate=30000,
plot=False,
filter_size=2,
sigma=1.,
filter=False,
probemap=None):
mm = np.memmap(path, dtype=np.int16, mode='r')
print(os.path.dirname(path))
num_channels = get_channel_count(os.path.dirname(path),
from_channel_map=False)
print(num_channels)
chunk = get_chunk(mm, start, end, num_channels, sampling_rate)
if probemap is not None:
chunk = chunk[probemap, :]
plt.imshow(chunk[:, :30000])
plt.gca().set_aspect(100)
plt.figure()
rms = []
good_channels = []
for ch in range(np.shape(chunk)[0]):
if ch not in skip_channels:
if filter:
data = filtr(chunk[ch, :], 300, 6000, sampling_rate, 3)
else:
data = chunk[ch, :]
rms.extend([RMS(data)])
good_channels.extend([ch])
threshold = np.mean(gaussian_filter1d(rms, filter_size)[::-1][:5]) + np.std(
gaussian_filter1d(rms, filter_size)[::-1][:5]
) * sigma #assumes the last 5 are out of the brain; uses the mean + sd of these 5 as the threshold for pial surface
# print(np.where(np.array(rms)<8.))
# print(good_channels[np.where(np.array(rms)<8.)[0].astype(int)])
if plot:
plt.plot(good_channels, gaussian_filter1d(rms, filter_size))
plt.gca().axhline(threshold, color='r')
plt.xlabel('channel number')
plt.ylabel('spike band RMS')
#print(np.where(np.array(rms)<6.))
del mm
try:
surface_channel = good_channels[mlab.cross_from_above(
gaussian_filter1d(rms, filter_size), threshold)[0]]
return surface_channel
except:
return None