def __init__(self,filter_function):
self._filter_function = filter_function
@property
def ff(self):
return self._filter_function
def __getitem__(self,item):
"""Calls method of other class, filters the return value.
Make sure you also inherit from MemoryMappedBinaryDataFile!"""
return self.ff(MemoryMappedBinaryDataFile.__getitem__(self,item))
if __name__=="__main__":
print "Test speed for moving_windows with and without multithreading"
import time
eeg = eegpy.F32("/media/story/SchlafGed/iEEG/data/canseven_bp.f32")
num_windows = eeg.moving_windows_num(size=4096,overlap=1000)
start1 = time.time()
for i,(w,start) in enumerate(eeg.moving_windows(size=4096,overlap=1000)):
print "%i / %i" %(i,num_windows)
time.sleep(0.01)
t_normal = time.time()-start1
start2 = time.time()
for i,(w,start) in enumerate(eeg.moving_windows(size=4096,overlap=1000,threads=True)):
print "%i / %i" %(i,num_windows)
time.sleep(0.01)
t_threads = time.time()-start2
print t_normal, t_threads
def getChannelNames(self,fn):
eeg = eegpy.F32(os.path.join(self._dirname,fn))
rv = eeg.channel_names
eeg.close()
return rv
def freqfilt_eeg(fn_in,
fn_out,
btype="lp",
fl=None,
fh=None,
border=3,
windowed=False):
eeg = eegpy.open_eeg(fn_in)
out = eegpy.F32(fn_out,
"w+",
shape=eeg.shape,
cNames=eeg.channel_names,
Fs=eeg.Fs)
if btype == "lp":
if not windowed:
out[:, :] = filtfilt_low(fl, eeg[:, :], Fs=eeg.Fs, border=border)
else:
for i in range(0, out.num_datapoints, 100000):
out[i:min(i + 100000, out.num_datapoints), :] = filtfilt_low(
fl,
eeg[i:min(i + 100000, out.num_datapoints), :],
Fs=eeg.Fs,
border=border)
elif btype == "hp":
#for i_c in range(eeg.num_channels):
if not windowed:
out[:, :] = filtfilt_high(fh, eeg[:, :], Fs=eeg.Fs, border=border)
else:
for i in range(0, out.num_datapoints, 100000):
out[i:min(i + 100000, out.num_datapoints), :] = filtfilt_high(
fh,
eeg[i:min(i + 100000, out.num_datapoints), :],
Fs=eeg.Fs,
border=border)
elif btype == "bp":
if not windowed:
out[:, :] = filtfilt_band(fl,
fh,
eeg[:, :],
Fs=eeg.Fs,
border=border)
else:
for i in range(0, out.num_datapoints, 100000):
out[i:min(i + 100000, out.num_datapoints), :] = filtfilt_band(
fl,
fh,
eeg[i:min(i + 100000, out.num_datapoints), :],
Fs=eeg.Fs,
border=border)
elif btype == "bs":
if not windowed:
out[:, :] = filtfilt_bandstop(fl,
fh,
eeg[:, :],
Fs=eeg.Fs,
border=border)
else:
for i in range(0, out.num_datapoints, 100000):
out[i:min(i +
100000, out.num_datapoints), :] = filtfilt_bandstop(
fl,
fh,
eeg[i:min(i + 100000, out.num_datapoints), :],
Fs=eeg.Fs,
border=border)