def analyse_ramp(directory):
if directory[-1] == '/':
directory = directory[:-1]
h5_files = list_h5_files(directory, True)
if len(h5_files) == 0:
print('No H5 files in %s.' % directory)
return None
rheobase = np.zeros(len(h5_files))
try:
Ke = np.loadtxt(directory + 'kernel.dat')
except:
Ke = None
for i, file in enumerate(h5_files):
entities, info = lcg.loadH5Trace(file)
for ntt in entities:
if ntt['name'] == 'Waveform':
I = ntt['data']
elif ntt['name'] == 'AnalogInput':
if Ke is None:
V = ntt['data']
else:
V = aec.compensate(ntt['data'], I, Ke)
idx = extract_spikes(V)
rheobase[i] = I[idx[0]]
return (np.mean(rheobase), np.std(rheobase))
def analyse_ramp(directory):
if directory[-1] == '/':
directory = directory[:-1]
h5_files = list_h5_files(directory,True)
if len(h5_files) == 0:
print('No H5 files in %s.' % directory)
return None
rheobase = np.zeros(len(h5_files))
try:
Ke = np.loadtxt(directory + 'kernel.dat')
except:
Ke = None
for i,file in enumerate(h5_files):
entities,info = lcg.loadH5Trace(file)
for ntt in entities:
if ntt['name'] == 'Waveform':
I = ntt['data']
elif ntt['name'] == 'AnalogInput':
if Ke is None:
V = ntt['data']
else:
V = aec.compensate(ntt['data'],I,Ke)
idx = extract_spikes(V)
rheobase[i] = I[idx[0]]
return (np.mean(rheobase),np.std(rheobase))
def compensateWithKernelOffline(ent,kernelFiles=[]):
#TODO: Add check for compensation online!!!
# names = np.array([e['name'] for e in ent])
units = np.array([e['units'] for e in ent])
findEntityByName = lambda entityName:np.where(
names==entityName)[0]
findEntityByUnit = lambda entityUnit:np.where(
units==entityUnit)[0]
if len(kernelFiles) > 0:
# Active Electrode Compensation offline
Iidx = findEntityByUnit('pA')
Vidx = findEntityByUnit('mV')
for ii,kfile in enumerate(kernelFiles):
Ke = np.loadtxt(kfile)
if ii >= len(Vidx):
print(
'''Specified too many kernel files,
contact developers please.''')
break
if len(Vidx) == 1:
#Then there is only one channel, sum the currents
I = np.sum([ent[jj]['data'] for jj in Iidx], axis=0)
else:
I = Iidx[Iidx[ii]]['data']
V = ent[Vidx[ii]]['data']
if not 'metadata' in ent[Vidx[ii]].keys():
ent[Vidx[ii]]['data'] = aec.compensate(V, I, Ke*1e-9)
def compensateVoltage(ent, Ke, entNames=["AnalogInput", "Waveform"]):
for e in ent:
if e["name"] in [entNames[0]]:
V = e["data"]
if e["name"] in [entNames[1]]:
I = e["data"]
metadata = e["metadata"]
V = aec.compensate(V, I, Ke)
return V, I, metadata
def compensateVoltage(ent, Ke, entNames=['AnalogInput', 'Waveform']):
for e in ent:
if e['name'] in [entNames[0]]:
V = e['data']
if e['name'] in [entNames[1]]:
I = e['data']
metadata = e['metadata']
V = aec.compensate(V, I, Ke)
return V, I, metadata
def analyse_last_file():
'''
Extracts the spiketrain statistics from the last file.
'''
files = glob.glob('*.h5')
files.sort()
data_file = files[-1]
ent, info = lcg.loadH5Trace(data_file)
V = ent[1]['data']
I = ent[0]['data']
kernel_file = glob.glob('*.dat')
kernel_file.sort()
if len(kernel_file):
kernel_file = kernel_file[-1]
Ke = np.loadtxt(kernel_file)
V = aec.compensate(V, I, Ke / 1.0e9)
t = np.arange(0, len(V) - 1) * info['dt']
spks = lcg.findSpikes(t, V, thresh=-10)
isi = np.diff(spks)
return np.mean(isi), np.std(isi) / np.mean(isi), np.mean(V), np.std(V)
def analyse_last_file():
'''
Extracts the spiketrain statistics from the last file.
'''
files = glob.glob('*.h5')
files.sort()
data_file = files[-1]
ent,info = lcg.loadH5Trace(data_file)
V = ent[1]['data']
I = ent[0]['data']
kernel_file = glob.glob('*.dat')
kernel_file.sort()
if len(kernel_file):
kernel_file = kernel_file[-1]
Ke = np.loadtxt(kernel_file)
V = aec.compensate(V,I,Ke/1.0e9)
t = np.arange(0,len(V)-1)*info['dt']
spks = lcg.findSpikes(t, V, thresh=-10)
isi = np.diff(spks)
return np.mean(isi), np.std(isi)/np.mean(isi),np.mean(V),np.std(V)
def computeElectrodeKernel(filename, Kdur=5e-3, interval=[], saveFile=True, fullOutput=False):
import matplotlib.pyplot as p
import aec
p.ion()
entities,info = loadH5Trace(filename)
Ksize = int(Kdur/info['dt'])
Kt = np.arange(0,Kdur,info['dt'])[:Ksize]
for ntt in entities:
if 'metadata' in ntt and ntt['units'] == 'pA':
I = ntt['data']
stimtimes = np.cumsum(ntt['metadata'][:,0])
pulse = np.nonzero(ntt['metadata'][:,0] == 0.01)[0][0]
if len(interval) != 2:
# Find the gaussian noise
idx = np.where(ntt['metadata'][:,1] == 11)[0][0]
interval = stimtimes[idx-1:idx+1]
elif (ntt['name'] == 'AnalogInput' or ntt['name'] == 'InputChannel') and ntt['units'] == 'mV':
V = ntt['data']
t = np.arange(len(V)) * info['dt']
idx = np.intersect1d(np.nonzero(t >= interval[0])[0], np.nonzero(t <= interval[1])[0])
Vn = V[idx]
V[-Ksize:] = 0
In = I[idx]
K,V0 = aec.full_kernel(Vn,In,Ksize,True)
# Kernels plot
fig = p.figure(1,figsize=[5,5],facecolor='w',edgecolor=None)
p.plot(Kt*1e3,K*1e3,'k.-',label='Full')
p.xlabel('t (ms)')
p.ylabel('R (MOhm)')
while True:
try:
startTail = int(raw_input('Enter index of tail start (1 ms = %d samples): ' % int(1e-3/info['dt'])))
if startTail >= len(K):
print('The index must be smaller than %d.' % len(K))
continue
except:
continue
Ke,Km = aec.electrode_kernel(K,startTail,True)
print('R = %g MOhm.\nV0 = %g mV.' % (np.sum(Ke*1e3),V0))
# Print kernels and compensated traces
p.close()
fig = p.figure(1,figsize=[10,5],facecolor='w',edgecolor=None)
plt = []
plt.append(fig.add_axes([0.1,0.1,0.3,0.8]))
plt[0].plot(Kt*1e3,K*1e3,'k.-',label='Full')
plt[0].set_xlabel('t (ms)')
plt[0].set_ylabel('R (MOhm)')
plt.append(fig.add_axes([0.5,0.1,0.4,0.8]))
plt[0].plot([Kt[0]*1e3,Kt[-1]*1e3],[0,0],'g')
plt[0].plot(Kt*1e3,Km*1e3,'b',label='Membrane')
plt[0].plot(Kt[:len(Ke)]*1e3,Ke*1e3,'r',label='Electrode')
plt[0].set_xlabel('t (ms)')
plt[0].set_ylabel('R (MOhm)')
plt[0].legend(loc='best')
# AEC offline
ndx = np.intersect1d(np.nonzero(t > max(stimtimes[pulse]-20e-3,0))[0],
np.nonzero(t < min(stimtimes[pulse]+80e-3,t[-1]))[0])
Vc = aec.compensate(V[ndx],I[ndx],Ke)
plt[1].plot(t[ndx]-t[ndx[0]], V[ndx], 'k', label='Recorded')
plt[1].plot(t[ndx]-t[ndx[0]], Vc, 'r', label='Compensated')
plt[1].set_xlabel('t (ms)')
plt[1].set_ylabel('V (mV)')
plt[1].legend(loc='best')
fig.show()
# Ask user what to do
ok = raw_input('Ke[-1] / max(Ke) = %.2f %%. Ok? [Y/n] ' % (Ke[-1]/np.max(Ke)*100))
if len(ok) == 0 or ok.lower() == 'y' or ok.lower() == 'yes':
break
p.close()
if saveFile:
np.savetxt(filename[:-3] + '_kernel.dat', Ke*1e9, '%.10e')
if fullOutput:
return Ke*1e3,V0,data.values()[0][idx],Vm
else:
return Ke*1e3
def computeElectrodeKernel(filename,
Kdur=5e-3,
interval=[],
saveFile=True,
fullOutput=False):
import matplotlib.pyplot as p
import aec
p.ion()
entities, info = loadH5Trace(filename)
Ksize = int(Kdur / info['dt'])
Kt = np.arange(0, Kdur, info['dt'])[:Ksize]
for ntt in entities:
if 'metadata' in ntt and ntt['units'] == 'pA':
I = ntt['data']
stimtimes = np.cumsum(ntt['metadata'][:, 0])
pulse = np.nonzero(ntt['metadata'][:, 0] == 0.01)[0][0]
if len(interval) != 2:
# Find the gaussian noise
idx = np.where(ntt['metadata'][:, 1] == 11)[0][0]
interval = stimtimes[idx - 1:idx + 1]
elif (ntt['name'] == 'AnalogInput'
or ntt['name'] == 'InputChannel') and ntt['units'] == 'mV':
V = ntt['data']
t = np.arange(len(V)) * info['dt']
idx = np.intersect1d(
np.nonzero(t >= interval[0])[0],
np.nonzero(t <= interval[1])[0])
Vn = V[idx]
V[-Ksize:] = 0
In = I[idx]
K, V0 = aec.full_kernel(Vn, In, Ksize, True)
# Kernels plot
fig = p.figure(1, figsize=[5, 5], facecolor='w', edgecolor=None)
p.plot(Kt * 1e3, K * 1e3, 'k.-', label='Full')
p.xlabel('t (ms)')
p.ylabel('R (MOhm)')
while True:
try:
startTail = int(
raw_input('Enter index of tail start (1 ms = %d samples): ' %
int(1e-3 / info['dt'])))
if startTail >= len(K):
print('The index must be smaller than %d.' % len(K))
continue
except:
continue
Ke, Km = aec.electrode_kernel(K, startTail, True)
print('R = %g MOhm.\nV0 = %g mV.' % (np.sum(Ke * 1e3), V0))
# Print kernels and compensated traces
p.close()
fig = p.figure(1, figsize=[10, 5], facecolor='w', edgecolor=None)
plt = []
plt.append(fig.add_axes([0.1, 0.1, 0.3, 0.8]))
plt[0].plot(Kt * 1e3, K * 1e3, 'k.-', label='Full')
plt[0].set_xlabel('t (ms)')
plt[0].set_ylabel('R (MOhm)')
plt.append(fig.add_axes([0.5, 0.1, 0.4, 0.8]))
plt[0].plot([Kt[0] * 1e3, Kt[-1] * 1e3], [0, 0], 'g')
plt[0].plot(Kt * 1e3, Km * 1e3, 'b', label='Membrane')
plt[0].plot(Kt[:len(Ke)] * 1e3, Ke * 1e3, 'r', label='Electrode')
plt[0].set_xlabel('t (ms)')
plt[0].set_ylabel('R (MOhm)')
plt[0].legend(loc='best')
# AEC offline
ndx = np.intersect1d(
np.nonzero(t > max(stimtimes[pulse] - 20e-3, 0))[0],
np.nonzero(t < min(stimtimes[pulse] + 80e-3, t[-1]))[0])
Vc = aec.compensate(V[ndx], I[ndx], Ke)
plt[1].plot(t[ndx] - t[ndx[0]], V[ndx], 'k', label='Recorded')
plt[1].plot(t[ndx] - t[ndx[0]], Vc, 'r', label='Compensated')
plt[1].set_xlabel('t (ms)')
plt[1].set_ylabel('V (mV)')
plt[1].legend(loc='best')
fig.show()
# Ask user what to do
ok = raw_input('Ke[-1] / max(Ke) = %.2f %%. Ok? [Y/n] ' %
(Ke[-1] / np.max(Ke) * 100))
if len(ok) == 0 or ok.lower() == 'y' or ok.lower() == 'yes':
break
p.close()
if saveFile:
np.savetxt(filename[:-3] + '_kernel.dat', Ke * 1e9, '%.10e')
if fullOutput:
return Ke * 1e3, V0, data.values()[0][idx], Vm
else:
return Ke * 1e3
