def on_checkBox_clicked(self, checked):
"""
Slot documentation goes here.
"""
if (checked):
print "waveform sine_bic fed"
# pipeInData = spike_train(firing_rate = 10)
#pipeInData = gen_sin(F = 0.5, AMP = 5000.0, BIAS = 5001.0, T = 2.0)
# pipeInData = gen_tri(T = 2.0)
# pipeInData_bic = gen_sin(F = 0.5, AMP = 50000.0, BIAS = 0.0, T = 2.0) # was 150000 for CN_general
pipeInData_bic = gen_ramp(T = [0.0, 0.10, 0.11, 0.39, 0.40, 2.0], L = [0.0, 0.0, 150000.0, 150000.0, 0.0, 0.0], FILT = False)
pipeInDataBic=[]
for i in xrange(0, 2048):
pipeInDataBic.append(max(0.0, pipeInData_bic[i]))
# elif choice == "middleBoard_sine_Tri":
print "waveform sine_tri fed"
# pipeIndata_tri = -gen_sin(F = 0.5, AMP = 50000.0, BIAS = 0.0, T = 2.0)
pipeIndata_tri = gen_ramp(T = [0.0, 1.10, 1.11, 1.39, 1.40, 2.0], L = [0.0, 0.0, 150000.0, 150000.0, 0.0, 0.0], FILT = False)
pipeInDataTri=[]
for i in xrange(0, 2048):
pipeInDataTri.append(max(0.0, pipeIndata_tri[i]))
self.xemList[0].SendPipe(pipeInDataTri)
self.xemList[1].SendPipe(pipeInDataBic)
def on_comboBox_activated(self, p0):
"""
Slot documentation goes here.
"""
choice = p0
if choice == "waveform 1":
# pipeInData = gen_ramp(T = [0.0, 0.1, 0.3, 1.0, 1.2, 2.0], L = [0.0, 0.0, 120000.0, 120000.0, 0.0, 0.0], FILT = False)
# pipeInData = gen_ramp(T = [0.0, 0.1, 0.3, 1.0, 1.2, 2.0], L = [0.0, 0.0, 1.4, 1.4, 0.0, 0.0], FILT = False)
pipeInData = gen_ramp(T = [0.0, 0.1, 0.2, 0.25, 0.3, 1.1, 1.2, 1.25, 1.3, 2.0], L = [0.8, 0.8, 1.4, 1.4, 0.8, 0.8, 1.4, 1.4, 0.8, 0.8], FILT = False)
print "waveform 1 fed"
# pipeInData = gen_sin(F = 1.0, AMP = 100.0, T = 2.0)
elif choice == "waveform 2":
print "waveform fed"
# pipeInData = spike_train(firing_rate = 10)
# pipeInData = gen_sin(F = 0.5, AMP = 5000.0, BIAS = 5001.0, T = 2.0)
# pipeInData = gen_tri(T = 2.0)
#pipeInData = gen_ramp(T = [0.0, 0.1, 0.9, 1.4, 1.9, 2.0], L = [0.5, 0.5, 1.5, 1.5, 0.5, 0.5], FILT = False)
#pipeInData = gen_ramp(T = [0.0, 1.8, 2.0], L = [0.0, 30000.0, 0.0], FILT = False)
#pipeInData = abs(gen_sin(F = 0.5, AMP = 17000.0, BIAS = 0.0, T = 2.0)) #big sine wave for training stdp
#pipeInData[:] = [1 - x for x in pipeInData] #( 1 - pipeIndata)
pipeInData = gen_rand(T=16.0, BIAS = 1500.0, AMP = 4400.0) # 16 seconds
pipeInData2 = gen_rand(T=16.0, BIAS = 1500.0, AMP = 4400.0) # 16 seconds
#pipeInData = gen_rand(BIAS = 3500.0, AMP = 400.0) # also works. input current bordered around threshold
#pipeInData2 = gen_rand(BIAS = 3500.0, AMP = 400.0)
elif choice == "waveform 3":
# pipeInData = gen_tri()
# pipeInData = spike_train(firing_rate = 1)
print "waveform 3 fed"
# pipeInData = gen_sin(F = 0.5, AMP = 0.15, BIAS = 1.15, T = 2.0)
pipeInData = abs(gen_sin(F = 0.5, AMP = 17000.0, BIAS = 0.0, T = 2.0)) #big sine wave for training stdp
#pipeInData = gen_ramp(T = [0.0, 0.1, 0.2, 0.8, 0.9, 2.0], L = [1.0, 1.0, 1.3, 1.3, 1.0, 1.0], FILT = False)
# pipeInData = gen_ramp(T = [0.0, 0.4, 1.5, 1.55, 1.6, 2.0], L = [0, 0, 15000, 15000, 0, 0], FILT = False)
# pipeInData = gen_ramp(T = [0.0, 0.2, 0.25, 1.75, 1.8, 2.0], L = [1.0, 1.0, 5000.0, 5000.0, 1.0, 1.0], FILT = False) # abrupt rise / fall
# pipeInData = spike_train(firing_rate = 1000)
self.nerfModel.SendPipe(pipeInData)
self.nerfModel.SendPipe2(pipeInData2)
def point2pointForce(self, checked):
if (checked) :
print 'checked'
# pipeInData_bic = gen_ramp(T = [0.0, 0.01, 0.02, 0.22, 0.23, 2.0], L = [0.0, 0.0, 480000.0, 480000.0, 0.0, 0.0], FILT = False)
# pipeInData_bic = gen_sin(F = 1.0, AMP = 480000.0, BIAS = 0.0, T = 2.0) # was 150000 for CN_general
b=0.77
# pipeInData_bic = gen_ramp(T = [0.0, b+0.01, b+0.02, b+0.23, b+0.24, b+0.43, b+0.44, b+0.54, b+0.55, 2.0], L = [0.0, 0.0, 460000.0, 460000.0, 50000.0, 50000.0, 80000.0, 80000.0, 0.0, 0.0], FILT = False) # dont change: healthy, b=0.77
pipeInData_bic = gen_ramp(T = [0.0, b+0.01, b+0.02, b+0.23, b+0.24, b+0.43, b+0.44, b+0.64, b+0.65, 2.0], L = [0.0, 0.0, 520000.0, 520000.0, 70000.0, 70000.0, 140000.0, 140000.0, 0.0, 0.0], FILT = False)
pipeInDataBic=[]
for i in xrange(0, 2048):
pipeInDataBic.append(max(0.0, pipeInData_bic[i]))
# pipeIndata_tri = gen_ramp(T = [0.0, 0.21, 0.22, 0.52, 0.53, 2.0], L = [0.0, 0.0, 480000.0, 480000.0, 0.0, 0.0], FILT = False)
# pipeIndata_tri = -gen_sin(F = 1.0, AMP = 480000.0, BIAS = 0.0, T = 2.0)
# pipeIndata_tri = gen_ramp(T = [0.0, 0.21, 0.22, 0.52, 0.53, 2.0], L = [0.0, 0.0, 480000.0, 480000.0, 0.0, 0.0], FILT = False)
a=0.01
# pipeIndata_tri = gen_ramp(T = [0.0, a+0.18, a+0.19, a+0.38, a+0.39, a+0.50, a+0.51, a+0.74, a+0.75, 2.0], L = [0.0, 0.0, 360000.0, 360000.0, 50000.0, 50000.0, 30000.0, 30000.0, 0.0, 0.0], FILT = False) # don't change: healthy: a=0.01
pipeIndata_tri = gen_ramp(T = [0.0, a+0.18, a+0.19, a+0.38, a+0.39, a+0.50, a+0.51, a+1.75, a+1.76, 2.0], L = [0.0, 0.0, 470000.0, 470000.0, 60000.0, 60000.0, 50000.0, 50000.0, 0.0, 0.0], FILT = False) # don't change: healthy: a=0.01
pipeInDataTri=[]
for i in xrange(0, 2048):
pipeInDataTri.append(max(0.0, pipeIndata_tri[i]))
xem_cortical_bic.SendPipe(pipeInDataBic)
xem_cortical_tri.SendPipe(pipeInDataTri)
##
xem_cortical_tri.SendButton(True, BUTTON_RESET_SIM) #
xem_cortical_bic.SendButton(True, BUTTON_RESET_SIM) #
xem_cortical_tri.SendButton(False, BUTTON_RESET_SIM) #
xem_cortical_bic.SendButton(False, BUTTON_RESET_SIM) #
def on_comboBox_activated(self, p0):
"""
Slot documentation goes here.
"""
choice = p0
if choice == "Spike Train 1Hz":
# pipeInData = spike_train(firing_rate = 1)
pipeInData = gen_sin(F = 1.0, AMP = 100.0, T = 2.0)
elif choice == "Spike Train 10Hz":
# pipeInData = spike_train(firing_rate = 10)
# pipeInData = gen_sin(F = 4.0, AMP = 0.3)
pipeInData = gen_tri(T = 2.0)
elif choice == "Spike Train 20Hz":
# pipeInData = gen_tri()
# pipeInData = gen_ramp(T = [0.0, 0.1, 0.2, 0.8, 0.9, 2.0], L = [1.0, 1.0, 1.1, 1.1, 1.0, 1.0], FILT = False)
pipeInData = gen_ramp(T = [0.0, 0.1, 1.8, 1.85, 1.9, 2.0], L = [0, 0, 15000, 15000, 0, 0], FILT = False)
# pipeInData = spike_train(firing_rate = 100)
self.nerfModel.SendPipe(pipeInData)
def on_comboBox_activated(self, p0):
"""
Slot documentation goes here.
"""
choice = p0
if choice == "Spike Train 1Hz":
# pipeInData = spike_train(firing_rate = 1)
pipeInData = gen_sin(F = 1.0, AMP = 100.0, T = 2.0)
elif choice == "Spike Train 10Hz":
# pipeInData = spike_train(firing_rate = 10)
# pipeInData = gen_sin(F = 4.0, AMP = 0.3)
pipeInData = gen_tri(T = 2.0)
elif choice == "Spike Train 20Hz":
# pipeInData = gen_tri()
pipeInData = gen_ramp(T = [0.0, 0.1, 0.2, 0.8, 0.9, 2.0], L = [1.0, 1.0, 1.3, 1.3, 1.0, 1.0], FILT = False)
# pipeInData = gen_ramp(T = [0.0, 0.4, 1.5, 1.55, 1.6, 2.0], L = [0, 0, 15000, 15000, 0, 0], FILT = False)
# pipeInData = gen_ramp(T = [0.0, 0.2, 0.25, 1.75, 1.8, 2.0], L = [1.0, 1.0, 5000.0, 5000.0, 1.0, 1.0], FILT = False) # abrupt rise / fall
# pipeInData = spike_train(firing_rate = 1000)
self.xemList[0].SendPipe(pipeInData)
def on_comboBox_activated(self, p0):
"""
Slot documentation goes here.
"""
choice = p0
if choice == "Spike Train 1Hz":
# pipeInData = spike_train(firing_rate = 1)
pipeInData = gen_sin(F=1.0, AMP=0.6, T=2.0)
elif choice == "Spike Train 10Hz":
# pipeInData = spike_train(firing_rate = 10)
# pipeInData = gen_sin(F = 4.0, AMP = 0.3)
pipeInData = gen_tri(T=2.0)
elif choice == "Spike Train 20Hz":
# pipeInData = gen_tri()
pipeInData = gen_ramp(T=[0.0, 0.1, 0.2, 0.8, 0.9, 2.0],
L=[1.0, 1.0, 1.1, 1.1, 1.0, 1.0],
FILT=False)
# pipeInData = spike_train(firing_rate = 100)
self.nerfModel.SendPipe(pipeInData)
def on_comboBox_activated(self, p0):
"""
Slot documentation goes here.
"""
choice = p0
if choice == "waveform 1":
# pipeInData = gen_ramp(T = [0.0, 0.1, 0.11, 0.65, 0.66, 16.0], L = [0.0, 0.0, 1.4, 1.4, 0.0, 0.0], FILT = False)
# pipeInData = gen_ramp(T = [0.0, 0.1, 0.3, 1.0, 1.2, 2.0], L = [0.0, 0.0, 120000.0, 120000.0, 0.0, 0.0], FILT = False)
# pipeInData = gen_ramp(T = [0.0, 0.1, 0.3, 1.0, 1.2, 2.0], L = [0.0, 0.0, 1.4, 1.4, 0.0, 0.0], FILT = False)
# pipeInData = gen_ramp(T = [0.0, 0.1, 0.2, 0.3, 1.1, 1.2,1.3, 2.0], L = [0.8, 0.8, 1.4, 0.8, 0.8, 1.4, 0.8, 0.8], FILT = False) # 100ms rise
# pipeInData = gen_ramp(T = [0.0, 0.1, 0.11, 0.12, 1.1, 1.11,1.12, 2.0], L = [0.8, 0.8, 1.4, 0.8, 0.8, 1.4, 0.8, 0.8], FILT = False) # 10ms rise
# pipeInData = gen_ramp(T = [0.0, 0.1, 0.2, 0.3, 1.1, 1.2, 1.25, 1.3, 2.0], L = [0.8, 0.8, 1.4, 0.8, 0.8, 1.4, 1.4, 0.8, 0.8], FILT = False)
pipeInData, self.gamma_dyn, self.gamma_sta = self.gen_from_file()
self.individualWireIn('gamma_sta', float(self.gamma_sta))
self.individualWireIn('gamma_dyn', float(self.gamma_dyn))
"""
up_pulse, dummy = gen_jerk(xi=1.0, xf = 1.5, T = 0.05)
down_pulse, dummy = gen_jerk(xi=1.5, xf=1.0, T=0.05)
flat_tail = np.array([1.0]*np.floor((1.0-0.1)*1024 + 1))
pipeInData = np.hstack((up_pulse, down_pulse, flat_tail, up_pulse, down_pulse, flat_tail))
print len(pipeInData)
"""
# pipeInData = np.append(p1, flat_tail)
print pipeInData
print "waveform 1 fed"
# pipeInData = gen_sin(F = 1.0, AMP = 100.0, T = 2.0)
elif choice == "waveform 2":
print "waveform fed"
# pipeInData = spike_train(firing_rate = 10)
# pipeInData = gen_sin(F = 0.5, AMP = 5000.0, BIAS = 5001.0, T = 2.0)
# pipeInData = gen_tri(T = 2.0)
# pipeInData = gen_sin(F = 1.0, AMP = 0.15, BIAS = 1.15, T = 2.0)
pipeInData = gen_ramp(T = [0.0, 0.1, 0.3, 0.8, 0.9, 2.0], L = [1.0, 1.0, 1.30, 1.30, 1.0, 1.0], FILT = False)
# pipeInData = gen_ramp(T = [0.0, 0.1, 0.11, 0.51, 0.52, 1.0, 1.1, 1.11, 1.51, 1.52, 2.0], L = [0.7, 0.7, 1.5, 1.5, 0.7, 0.7, 0.7, 1.5, 1.5, 0.7, 0.7], FILT = False) # one second repeat
# pipeInData = gen_ramp(T = [0.0, 0.1, 0.11, 0.51, 0.52, 1.0, 2.0], L = [0.7, 0.7, 1.5, 1.5, 0.7, 0.7, 0.7], FILT = False) # two second repeat
# pipeInData = gen_ramp(T = [0.0, 0.1, 0.101, 0.121, 0.122, 1.0, 1.1, 1.101, 1.121, 1.122, 2.0], L = [0.8, 0.8, 1.5, 1.5, 0.8, 0.8, 0.8, 1.5, 1.5, 0.8, 0.8], FILT = False) # 20ms pulse for LLSR
elif choice == "waveform 3":
# pipeInData = gen_tri()
# pipeInData = spike_train(firing_rate = 1)
print "waveform 3 fed"
#pipeInData = gen_sin(F = 0.5, AMP = 0.4, BIAS = 1.0, T = 2.0)
self.j1List=[]
self.j2List=[]
self.j3List=[]
self.j4List=[]
self.j5List=[]
self.j6List=[]
# for line in open('/home/eric/Dropbox/MATLAB/WonJoon_code/matlab_wjsohn/posAllData.txt', "r").readlines():
# j1 , j2, j3, j4, j5, j6= line.split('\t')
# j1 = float(j1)
# j2 = float(j2)
# print type(j1)
# print j1
# self.j1List.append(j1) #
# self.j2List.append(j2) #
# self.j3List.append(j3) #
# self.j4List.append(j4) #
# self.j5List.append(j5) #
# self.j6List.append(j6) #
#
for line in open('/home/eric/nerf_verilog_eric/source/py/1125_resampled/expt_rampnhold.gd_160.gs_160.rep_5.dev_fpga_resampled.txt', "r").readlines():
j1, j2 = line.split('\n')
# j1 = line.splitlines()
j1 = float(j1)
print type(j1)
print j1
self.j1List.append(j1) #
# print self.j1List
pipeInData_bf = gen_wave(L=self.j1List, FILT = False)
pipeInData = [x for x in pipeInData_bf]
#pipeInData = gen_ramp(T = [0.0, 0.1, 0.2, 0.8, 0.9, 2.0], L = [1.0, 1.0, 1.3, 1.3, 1.0, 1.0], FILT = False)
# pipeInData = gen_ramp(T = [0.0, 0.4, 1.5, 1.55, 1.6, 2.0], L = [0, 0, 15000, 15000, 0, 0], FILT = False)
# pipeInData = gen_ramp(T = [0.0, 0.2, 0.25, 1.75, 1.8, 2.0], L = [1.0, 1.0, 5000.0, 5000.0, 1.0, 1.0], FILT = False) # abrupt rise / fall
# pipeInData = spike_train(firing_rate = 1000)
self.nerfModel.SendPipe(pipeInData)
