def main(period):
taskHandle = daqmx.TaskHandle()
daqmx.CreateTask("", taskHandle)
physicalChannel = "Dev1/ao0"
t = np.linspace(0, 2 * pi / period, 1000)
data = 2 * np.sin(t)
daqmx.CreateAOVoltageChan(taskHandle, physicalChannel, "", -10.0, 10.0,
daqmx.Val_Volts, None)
daqmx.CfgSampClkTiming(taskHandle, "", 1000.0, daqmx.Val_Rising,
daqmx.Val_ContSamps, 1000)
daqmx.WriteAnalogF64(taskHandle, 1000, 0, 10.0, daqmx.Val_GroupByChannel,
data)
daqmx.StartTask(taskHandle)
for n in range(100):
time.sleep(0.3)
daqmx.StopTask(taskHandle)
daqmx.ClearTask(taskHandle)
def write_daqmx(self,ai):
daqmx.WriteAnalogF64(self.AOtask,self.o_buffer,1,self.timeout,daqmx.Val_GroupByChannel,self.pa[ai,:]*3)
def write_daqmx_zero(self):
daqmx.WriteAnalogF64(self.AOtask,self.o_buffer,1,self.timeout,daqmx.Val_GroupByChannel,np.zeros(self.o_buffer))
t = np.linspace(0, 100.0, 1000)
data = 2 * np.sin(t)
daqmx.CreateAOVoltageChan(AOtaskHandle, "Dev1/ao0", "", -10.0, 10.0,
daqmx.Val_Volts, None)
daqmx.CfgSampClkTiming(AOtaskHandle, "", sr, daqmx.Val_Rising,
daqmx.Val_ContSamps, 1000)
daqmx.CreateAIVoltageChan(AItaskHandle, "Dev1/ai1", "", daqmx.Val_Diff, -10.0,
10.0, daqmx.Val_Volts, None)
daqmx.CfgSampClkTiming(AItaskHandle, "", sr, daqmx.Val_Rising,
daqmx.Val_ContSamps, 1000)
written = daqmx.WriteAnalogF64(AOtaskHandle, 1000, 0, 10.0,
daqmx.Val_GroupByChannel, data)
dataread, nread = daqmx.ReadAnalogF64(AItaskHandle, 1000, 10.0,
daqmx.Val_GroupByChannel, 1000, 1)
daqmx.StartTask(AOtaskHandle)
daqmx.StartTask(AItaskHandle)
print written, "samples written"
print nread, "samples read"
plt.plot(dataread)
daqmx.StopTask(AItaskHandle)
daqmx.ClearTask(AItaskHandle)
daqmx.StopTask(AOtaskHandle)
def run(self, Nepisode, targetQN, train=True):
self.setup_DAQmx()
# Parameters for plotting
# initiate
seq = np.zeros((self.num,self.NICH))
read = np.zeros((0,self.NCH))
state_t = np.zeros((self.SPC,self.NICH))
b = np.ones(self.num_mave)/self.num_mave
tilen = int(self.intispn / 0.01)
# start read analog
daqmx.StartTask(self.AItask, fatalerror=False)
# first loop
# you read
# you do not act
# this loop is for caliburation of reward
# you need to satrt writeanalog in first loop
# 'cause the function takes time to start
for n in range(self.nloops):
read_t, _ = daqmx.ReadAnalogF64(self.AItask,self.SPC,self.TO,self.MODE,self.ASS,self.NCH)
read_t = read_t/self.sensor_coff
# moving average filter
for ich in range(self.NICH):
state_t[self.num_mave-1:,ich] = np.convolve(read_t[:,self.IAIs[ich]], b, mode='vaild')
state_ave = ( np.average(np.split(state_t,int(self.num/tilen),0),axis=1) + self.dynamic_pressre )/self.dynamic_pressre
seq[self.num-int(self.num/tilen):self.num,:] = np.round(state_ave,2)
# action
# adopt output timing and action zero
if n!=10 or n!=20 or n!=40 or n!=50 or n!=60 or n!=80 or n!=90:
daqmx.WriteAnalogF64(self.AOtask,self.OB,1,self.TO,daqmx.Val_GroupByChannel,np.zeros(self.OB))
# reward
reward_t = np.average(read_t[:,self.CH100])
self.memory.add_local(seq,0,reward_t,0)
read = np.append(read,read_t,axis=0)
seq = shift(seq,[-self.num/tilen,0],cval=0)
# calibulate
self.memory.calc_calibulation()
# second loop
# you read
# you act
for n in range(self.nloops):
read_t, _ = daqmx.ReadAnalogF64(self.AItask,self.SPC,self.TO,self.MODE,self.ASS,self.NCH)
read_t = read_t/self.sensor_coff
# moving average filter
for ich in range(self.NICH):
state_t[self.num_mave-1:,ich] = np.convolve(read_t[:,self.IAIs[ich]], b, mode='vaild')
state_ave = ( np.average(np.split(state_t,int(self.num/tilen),0),axis=1) + self.dynamic_pressre )/self.dynamic_pressre
seq[self.num-int(self.num/tilen):self.num,:] = np.round(state_ave,2)
# action
ai = self.actor.get_action(seq.reshape(-1,self.num,self.NICH), self.QN,train)
daqmx.WriteAnalogF64(self.AOtask,self.OB,1,self.TO,daqmx.Val_GroupByChannel,self.PA[ai,:]*3)
# reward
reward_t = np.average(read_t[:,self.CH100])
self.memory.add_local(seq,ai,reward_t,0)
read = np.append(read,read_t,axis=0)
seq = shift(seq,[-self.num/tilen,0],cval=0)
# third loop
# you read
# you do not act
# make sure PA turn off
for n in range(self.nloops):
read_t, _ = daqmx.ReadAnalogF64(self.AItask,self.SPC,self.TO,self.MODE,self.ASS,self.NCH)
read_t = read_t/self.sensor_coff
# moving average filter
for ich in range(self.NICH):
state_t[self.num_mave-1:,ich] = np.convolve(read_t[:,self.IAIs[ich]], b, mode='vaild')
state_ave = ( np.average(np.split(state_t,int(self.num/tilen),0),axis=1) + self.dynamic_pressre )/self.dynamic_pressre
seq[self.num-int(self.num/tilen):self.num,:] = np.round(state_ave,2)
# action
# action zero
daqmx.WriteAnalogF64(self.AOtask,self.OB,1,self.TO,daqmx.Val_GroupByChannel,np.zeros(self.OB))
# reward
reward_t = np.average(read_t[:,self.CH100])
self.memory.add_local(seq,0,reward_t,0)
read = np.append(read,read_t,axis=0)
seq = shift(seq,[-self.num/tilen,0],cval=0)
# stop DAQmx
self.stop_DAQmx()
# edit experience in buffer
self.memory.edit_experience_local()
self.save(read,Nepisode)
total_reward = self.memory.totalreward()
# move current experience to global buffer
self.memory.add_global(total_reward)
if (self.memory.len() > self.batch_num) and train:
self.loss=self.QN.replay(self.memory,self.batch_num,self.gamma,targetQN)
self.actor.reduce_epsilon()
return total_reward, self.loss
EveryNCallback = daqmx.EveryNSamplesEventCallbackPtr(EveryNCallback_py)
daqmx.RegisterEveryNSamplesEvent(AItaskHandle, daqmx.Val_Acquired_Into_Buffer,
buffsize, 0, EveryNCallback, id_data)
def DoneCallback_py(taskHandle, status, callbackData_ptr):
print "Status", status.value
return 0
DoneCallback = daqmx.DoneEventCallbackPtr(DoneCallback_py)
daqmx.RegisterDoneEvent(AItaskHandle, 0, DoneCallback, None)
written = daqmx.WriteAnalogF64(AOtaskHandle, buffsize, False, 10.0,
daqmx.Val_GroupByChannel, dataw)
daqmx.StartTask(AOtaskHandle)
daqmx.StartTask(AItaskHandle)
t0 = time.time()
elapsed = 0.0
while elapsed < period * numperiods:
elapsed = time.time() - t0
daqmx.StopTask(AItaskHandle)
daqmx.ClearTask(AItaskHandle)
daqmx.StopTask(AOtaskHandle)
daqmx.ClearTask(AOtaskHandle)
def run(self):
self.rampeddown = False
self.cleared = False
self.making = False
# Compute the voltage time series associated with the wave
self.wave.gen_ts_volts()
# Get parameters from the wave object
self.period = self.wave.period
self.height = self.wave.height
self.buffsize = self.wave.sbuffsize
self.sr = self.wave.sr
# Get data to write from the wave object
self.ts_plot = self.wave.ts_elev
self.dataw = self.wave.ts_volts
# If random waves, divide up time series into 120 256 sample parts
if self.wavetype != "Regular":
tsparts = np.reshape(self.dataw, (120, 256))
self.dataw = tsparts[0, :]
# Compute spectrum for plot
if self.wavetype == "Regular":
self.outf, self.outspec = self.wave.comp_spec()
else:
self.outf, self.outspec = self.wave.f, self.wave.spec
# Ramp time series
rampup_ts = ramp_ts(self.dataw, "up")
# Set making variable true
self.making = True
self.AOtaskHandle = daqmx.TaskHandle()
daqmx.CreateTask("", self.AOtaskHandle)
daqmx.CreateAOVoltageChan(self.AOtaskHandle, "Dev1/ao0", "", -10.0,
10.0, daqmx.Val_Volts, None)
daqmx.CfgSampClkTiming(self.AOtaskHandle, "", self.sr,
daqmx.Val_Rising, daqmx.Val_ContSamps,
self.buffsize)
daqmx.SetWriteRegenMode(self.AOtaskHandle, daqmx.Val_DoNotAllowRegen)
# Setup a callback function to run once the DAQmx driver finishes
def DoneCallback_py(taskHandle, status, callbackData_ptr):
self.rampeddown = True
return 0
DoneCallback = daqmx.DoneEventCallbackPtr(DoneCallback_py)
daqmx.RegisterDoneEvent(self.AOtaskHandle, 0, DoneCallback, None)
# Output the rampup time series
daqmx.WriteAnalogF64(self.AOtaskHandle, self.buffsize, False, 10.0,
daqmx.Val_GroupByChannel, rampup_ts)
daqmx.StartTask(self.AOtaskHandle)
# Wait a second to allow the DAQmx buffer to empty
if self.wavetype == "Regular":
time.sleep(self.period * 0.99) # was self.period*0.4
else:
time.sleep(0.99)
# Set iteration variable to keep track of how many chunks of data
# have been written
i = 1
# Main running loop that writes data to DAQmx buffer
while self.enable:
writeSpaceAvail = daqmx.GetWriteSpaceAvail(self.AOtaskHandle)
if writeSpaceAvail >= self.buffsize:
if self.wavetype != "Regular":
if i >= 120:
self.dataw = tsparts[i % 120, :]
else:
self.dataw = tsparts[i, :]
daqmx.WriteAnalogF64(self.AOtaskHandle, self.buffsize, False,
10.0, daqmx.Val_GroupByChannel,
self.dataw)
i += 1
if self.wavetype == "Regular":
time.sleep(0.99 * self.period)
else:
time.sleep(0.99) # Was self.period
# After disabled, initiate rampdown time series
if self.wavetype != "Regular":
if i >= 120:
self.rampdown_ts = ramp_ts(tsparts[i % 120, :], "down")
else:
self.rampdown_ts = ramp_ts(tsparts[i, :], "down")
else:
self.rampdown_ts = ramp_ts(self.dataw, "down")
# Write rampdown time series
daqmx.WriteAnalogF64(self.AOtaskHandle, self.buffsize, False, 10.0,
daqmx.Val_GroupByChannel, self.rampdown_ts)
# Keep running, part of PyDAQmx callback syntax
while True:
pass