def voltageBiPhasicPulse(offset, ampBits_1, ampBits_2, samples_1=4, samples_2=4):
c = mea1k.Config()
c.add( mea1k.cmdDelaySamples(400) ) # waits 20ms
c.add( mea1k.cmdStatusOut( 1 ) ) #..#
# Turn on stimulation buffer
c.add( mea1k.cmdDAC( 0, offset-ampBits_1, 512 ) )#pulse one towards up
c.add( mea1k.cmdDelaySamples( samples_1 ) ) #=4 ~ 200us ?? doesn't add up
c.add( mea1k.cmdDAC( 0, offset+ampBits_2, 512 ) )#pulse two towards down
c.add( mea1k.cmdDelaySamples( samples_2 ) )#=4 ~ 200us ?? doesn't add up
c.add( mea1k.cmdDAC( 0, offset, 512 ) )#pulse three towards ~0
return c
def sineVari(t_period, n_samples, amp=5, periods=1):
lc.stopLoop()
lc.clear()
lc.reset()
lc.setStart() #Start the period'
for i in range(0,n_samples):
v = int(-amp*math.sin( periods*2*math.pi /n_samples * i)) #Amplitude of the sin-wave in bits
s = int(20e3 * t_period/n_samples) #Time between the samples in bits. 1 bit = 50us = sampling freq.
#print i,v,s
lc.toLoop( mea1k.cmdVRefVari( 2048+v ) ) #Program v around 2048 bits. DAC has 12 bits
#(=max. 4098 bits)
lc.toLoop( mea1k.cmdDelaySamples( s ))
lc.setStop()
lc.download() #Download the loop to the FPGA
def stimulation(self, circle_path, outputDir):
#Paramters of Stimulation
number_of_repetitions = 10
interTrainDelay = 20000 # 10000 = 0.5s
bin_length = 500 #Number of datapoints to crop after stimulus
amp1 = 10
amp2 = 10
phase1 = 4
phase2 = 4
#Set gain back to normal operation conditions
mea1kusr.init.board()
mea1kusr.init.chip()
save = stst.save('localhost')
save.mkDir(outputDir)
save.reset()
print 'make chip'
chip = libarray.Chip()
print 'done'
#mea1k.go(mea1k.cmdVRefMosR(1100)) #gives stable traces but many out of bounds after stim
# Turn on power for the stimulation buffers
mea1k.go(
mea1k.cmdCore(
onChipBias=1,
stimPowerDown=0,
outputEn=1,
spi=0, # 0 == DataxDO off
tx=0, # 0 == DAC0
rstMode=0, # 0 == auto
offsetCyc=7,
resetCyc=7,
wlCyc=7))
mea1k.go(
mea1k.cmdReadout(
s1Bypass=0,
s1Gain=1, # 1 == x16 0 == x7
s1RstMode=0, # 0 == disconnect
s2Bypass=0,
s2Gain=5, # 5 == x16
s3Bypass=0,
s3Gain=0)) # 0 ==x2
#Import the information about the stimulation electrodes
el_list = h5py.File(circle_path + '/segmentation_logfile.h5', 'r')
routed_el = [
values['stim_el/gotten_el'][:]
for key, values in el_list.iteritems()
]
rec_el = [
values['rec_el/gotten_el'][:]
for key, values in el_list.iteritems()
]
el_list.close()
logfile = h5py.File(outputDir + '/stimlist_stim_config_.hdf5', 'w')
lengths = [i.size for i in routed_el]
#Start iterating over each electrode
print '{0:02} iterations to be done.'.format(max(lengths))
for i in range(max(lengths)):
configFile = circle_path + '/config_structure/circles.hex.nrk'
chip.loadHEX(configFile)
print 'configFile loaded...'
print 'Offset MEA around 512 bits...'
Api = mea1kusr.api.Api()
Api.binary_offset(512)
#Offset the electrodes around 512 bits.
print 'Offset done'
stimlist = []
for j in routed_el:
if i < j.size:
stimlist.append(j[i])
# Save the stimulation electrodes as attributes
stim_group = logfile.create_group('stim_config_' + str(i) +
'/stimlist')
for k, p in enumerate(stimlist):
stim_group.attrs.create('channel_' + str(k), p)
rec_group = logfile.create_group('stim_config_' + str(i) +
'/recording_electrodes')
for k, p in enumerate(rec_el):
rec_group.attrs.create('channel_' + str(k), p)
stimChannels = []
for k in stimlist:
chip.electrodeToStim(k)
stimChannels.append(chip.queryStimAtElectrode(k))
chip.download()
time.sleep(2)
print 'El. \tbuffer'
for p, k in enumerate(stimChannels):
print stimlist[p], '\t', k
while -1 in stimChannels:
stimChannels.remove(-1)
print 'Offset MEA around 512 bits...'
Api = mea1kusr.api.Api()
Api.binary_offset(512)
#Offset the electrodes around 512 bits.
print 'Offset done'
save.openDir(outputDir)
save.mapping(chip.get_mapping())
c = mea1k.Config()
c.add(self.switchOffAllChannels())
c.add(self.switchOnChannels(stimChannels, 1))
c.add(mea1k.cmdDelaySamples(100))
for l in range(number_of_repetitions):
c.add(
mea1k.cmdDelaySamples(interTrainDelay - (phase1 + phase2))
) #-8 to account for the stimulus length
c.add(
self.voltageBiPhasicPulse(512, amp1, amp2, phase1, phase2))
train_time = (number_of_repetitions * interTrainDelay) / 20000.
save.start('raw_stim_config_' + str(i))
c.send()
time.sleep(train_time + 2)
save.stop()
time.sleep(3)
raw_file = h5py.File(
outputDir + '/raw_stim_config_' + str(i) + '.raw.h5', 'r')
DAC = raw_file['sig'][1024, :]
over_threshold_islets = np.where(DAC > 512 + (amp1 / 2))[
0] #Sometimes the stimulation buffer doesn't send a DAC value
stim_edges = over_threshold_islets[np.hstack(
(0,
np.asarray([
1 + el
for el in np.where(np.diff(over_threshold_islets) != 1)[0]
])))] #Extract the timing of the stimuli
index = np.asarray(
[np.arange(0, bin_length) + c for c in stim_edges])
seconds = index / 20000.
el_indices = []
missing = {}
for keys, values in logfile[
'stim_config_' + str(i) +
'/recording_electrodes'].attrs.iteritems():
missing[keys] = []
for it in values[:]:
try:
el_indices.append(
np.where(
raw_file['mapping']['electrode'] == it)[0][0])
except:
missing[keys].append(it)
print 'miss.el. keys \t values'
for keys, values in missing.iteritems():
if values:
print '\t', keys, values
true_rec_el = list(
logfile['stim_config_' + str(i) +
'/recording_electrodes'].attrs[keys])
del logfile['stim_config_' + str(i) +
'/recording_electrodes'].attrs[keys]
for val in values:
true_rec_el.remove(val)
logfile['stim_config_' + str(i) +
'/recording_electrodes'].attrs.modify(
keys, true_rec_el)
trace = np.empty(
(1028, bin_length * stim_edges.size)
) #number_of_repetitions instead of stim_edges.size doesn't always work because sometimes the buffer missses some commands
raw_file['sig'].read_direct(trace,
source_sel=np.s_[:, index.flatten()])
f = h5py.File(outputDir + '/stim_config_' + str(i) + '.h5', 'w')
cropped_trace = trace[sorted(el_indices)]
if not stim_edges.size == number_of_repetitions:
print 'DAC channel transmitted {0:03} out of {1:01} stimuli.'.format(
stim_edges.size, number_of_repetitions)
print 'Raw_trace is padded with zeros to get desired size.'
corrected_traces = 512 * np.ones(
(len(el_indices), bin_length * number_of_repetitions))
corrected_traces[:,
bin_length * stim_edges.size] = cropped_trace
f.create_dataset('sig', data=corrected_traces)
else:
f.create_dataset('sig', data=cropped_trace)
f['mapping'] = raw_file['mapping'][sorted(el_indices)]
f['proc0/spikeTimes'] = raw_file['proc0/spikeTimes'][:]
f['time'] = seconds
f['settings'] = raw_file['settings/gain'][:]
f['version'] = raw_file['version'][:]
raw_file.close()
os.remove(outputDir + '/raw_stim_config_' + str(i) + '.raw.h5')
f.close()
print 'Iteration and postprocessing no. {0:03}/{1:01} done.'.format(
i, max(lengths))
logfile.close()
chip.download()
time.sleep(2)
print stimChannels
print 'Offset MEA around 512 bits...'
import mea1kusr.api
Api = mea1kusr.api.Api()
Api.binary_offset(512)
#Offset the electrodes around 512 bits.
print 'Offset done'
save.openDir(outputDir)
save.mapping(chip.get_mapping())
c = mea1k.Config()
c.add(switchOffAllChannels())
c.add(switchOnChannels(stimChannels, 1))
c.add(mea1k.cmdDelaySamples(100))
for u in range(number_of_repetitions):
c.add(voltageBiPhasicPulse(512, amp1, amp2, phase1, phase2))
c.add(mea1k.cmdDelaySamples(interTrainDelay))
train_time = (number_of_repetitions * interTrainDelay) / 10000. + 1
save.start('configstructure_{0:03}'.format(t + 1))
c.send()
time.sleep(train_time)
save.stop()
print t
time.sleep(2)