def dcoutDelayed(taskName,
devNum,
channelName,
voltage,
bTrig=0,
trigChan="PFI0"):
try:
daq = initDAQ(devNum)
except IndexError:
print("No NI DAQ device connected")
raise SystemExit
except Exception:
print("Unknown Error")
raise SystemExit
with nidaqmx.Task() as task:
channelAddr = "Dev%d/%s" % (devNum + 1, channelName)
fileName = fileName + str(devNum + 1) + channelName + ".pkl"
task.ao_channels.add_ao_voltage_chan(channelAddr)
task.timing.cfg_samp_clk_timing(rate=2,
sample_mode=AcquisitionType.FINITE,
samps_per_chan=2)
if bTrig:
task.triggers.start_trigger.cfg_dig_edge_start_trig(
trigChan, Edge.RISING)
writer = AnalogSingleChannelWriter(task.out_stream, auto_start=False)
samples = np.ones(2) * voltage
writer.write_many_sample(samples)
task.start()
task.save(save_as=taskName, overwrite_existing_task=True)
def run(self):
"""
Starts writing a waveform continuously to the patchclamp. While reading
the buffer periodically
"""
self.patchVoltOutChan = self.configs["VpPatch"]
self.patchCurOutChan = self.configs["Ip"]
self.patchVoltInChan = self.configs["patchAO"]
# DAQ
with nidaqmx.Task() as writeTask, nidaqmx.Task() as readTask:
writeTask.ao_channels.add_ao_voltage_chan(self.patchVoltInChan)
readTask.ai_channels.add_ai_voltage_chan(self.patchVoltOutChan)
readTask.ai_channels.add_ai_voltage_chan(self.patchCurOutChan)
self.setTiming(writeTask, readTask)
reader = AnalogMultiChannelReader(readTask.in_stream)
writer = AnalogSingleChannelWriter(writeTask.out_stream)
writer.write_many_sample(self.wave)
"""Reading data from the buffer in a loop.
The idea is to let the task read more than could be loaded in the buffer for each iteration.
This way the task will have to wait slightly longer for incoming samples. And leaves the buffer
entirely clean. This way we always know the correct numpy size and are always left with an empty
buffer (and the buffer will not slowly fill up)."""
# output = np.zeros([2, self.readNumber])
writeTask.start() # Will wait for the readtask to start so it can use its clock
readTask.start()
def test_many_sample(self, x_series_device, seed):
# Reset the pseudorandom number generator with seed.
random.seed(seed)
number_of_samples = random.randint(20, 100)
sample_rate = random.uniform(1000, 5000)
# Select a random loopback channel pair on the device.
loopback_channel_pairs = self._get_analog_loopback_channels(
x_series_device)
loopback_channel_pair = random.choice(loopback_channel_pairs)
with nidaqmx.Task() as write_task, nidaqmx.Task() as read_task, \
nidaqmx.Task() as sample_clk_task:
# Use a counter output pulse train task as the sample clock source
# for both the AI and AO tasks.
sample_clk_task.co_channels.add_co_pulse_chan_freq(
'{0}/ctr0'.format(x_series_device.name), freq=sample_rate)
sample_clk_task.timing.cfg_implicit_timing(
samps_per_chan=number_of_samples)
samp_clk_terminal = '/{0}/Ctr0InternalOutput'.format(
x_series_device.name)
write_task.ao_channels.add_ao_voltage_chan(
loopback_channel_pair.output_channel, max_val=10, min_val=-10)
write_task.timing.cfg_samp_clk_timing(
sample_rate, source=samp_clk_terminal, active_edge=Edge.RISING,
samps_per_chan=number_of_samples)
read_task.ai_channels.add_ai_voltage_chan(
loopback_channel_pair.input_channel, max_val=10, min_val=-10)
read_task.timing.cfg_samp_clk_timing(
sample_rate, source=samp_clk_terminal,
active_edge=Edge.FALLING, samps_per_chan=number_of_samples)
writer = AnalogSingleChannelWriter(write_task.out_stream)
reader = AnalogSingleChannelReader(read_task.in_stream)
# Generate random values to test.
values_to_test = numpy.array(
[random.uniform(-10, 10) for _ in range(number_of_samples)],
dtype=numpy.float64)
writer.write_many_sample(values_to_test)
# Start the read and write tasks before starting the sample clock
# source task.
read_task.start()
write_task.start()
sample_clk_task.start()
values_read = numpy.zeros(number_of_samples, dtype=numpy.float64)
reader.read_many_sample(
values_read, number_of_samples_per_channel=number_of_samples,
timeout=2)
numpy.testing.assert_allclose(
values_read, values_to_test, rtol=0.05, atol=0.005)
def measure(self):
"""
Starts writing a waveform continuously to the patchclamp. While reading
the buffer periodically.
"""
self.patchVoltOutChan = self.configs["Vp"]
self.patchCurOutChan = self.configs["Ip"]
self.patchVoltInChan = self.configs["patchAO"]
# DAQ
with nidaqmx.Task() as writeTask, nidaqmx.Task() as readTask:
writeTask.ao_channels.add_ao_voltage_chan(self.patchVoltInChan)
readTask.ai_channels.add_ai_voltage_chan(self.patchVoltOutChan)
readTask.ai_channels.add_ai_voltage_chan(self.patchCurOutChan)
self.setTiming(writeTask, readTask)
reader = AnalogMultiChannelReader(readTask.in_stream)
writer = AnalogSingleChannelWriter(writeTask.out_stream)
writer.write_many_sample(self.wave)
"""Reading data from the buffer in a loop.
The idea is to let the task read more than could be loaded in the buffer for each iteration.
This way the task will have to wait slightly longer for incoming samples. And leaves the buffer
entirely clean. This way we always know the correct numpy size and are always left with an empty
buffer (and the buffer will not slowly fill up)."""
if self.mode == 'voltageclamp':
output = np.zeros([2, self.readNumber])
writeTask.start() # Will wait for the readtask to start so it can use its clock
readTask.start()
self.isRunning = True
while self.isRunning:
reader.read_many_sample(data=output, number_of_samples_per_channel=self.readNumber)
# Emiting the data just received as a signal
self.measurement.emit(output[0,:], output[1,:])
elif self.mode == 'zap':
writeTask.start() # Will wait for the readtask to start so it can use its clock
readTask.start()
else:
pass
print('sealtest thread stopped')
def dcoutNow(devNum,
channelName,
voltage,
bTrig=0,
trigChan="PFI0",
fileName="MostRecentOutputVoltageOn"):
bTrig = int(bTrig)
devNum = int(devNum)
try:
daq = initDAQ(devNum)
except IndexError:
print("No NI DAQ device connected")
raise SystemExit
except Exception:
print("Unknown Error")
raise SystemExit
with nidaqmx.Task() as task:
channelAddr = "Dev%d/%s" % (devNum + 1, channelName)
fileName = fileName + str(devNum + 1) + channelName + ".pkl"
task.ao_channels.add_ao_voltage_chan(channelAddr)
task.timing.cfg_samp_clk_timing(rate=2,
sample_mode=AcquisitionType.FINITE,
samps_per_chan=2)
if bTrig:
task.triggers.start_trigger.cfg_dig_edge_start_trig(
trigChan, Edge.RISING)
writer = AnalogSingleChannelWriter(task.out_stream, auto_start=False)
samples = np.ones(2) * voltage
writer.write_many_sample(samples)
task.start()
if bTrig:
task.wait_until_done()
saveVariable(voltage, fileName,
"Most Recent Output Voltage on " + channelAddr)
return 0
def arbitraryoutDelayed(taskName,
devNum,
channelName,
voltagelist,
outputRate,
outputTime,
bTrig=0,
trigChan="PFI0"):
devNum = int(devNum)
outputRate = int(outputRate)
bTrig = int(bTrig)
assert len(voltagelist) == int(outputRate * outputTime)
try:
daq = initDAQ(devNum)
except IndexError:
print("No NI DAQ device connected")
raise SystemExit
except Exception:
print("Unknown Error")
raise SystemExit
with nidaqmx.Task() as task:
channelAddr = "Dev%d/%s" % (devNum + 1, channelName)
task.ao_channels.add_ao_voltage_chan(channelAddr)
nsamples = int(outputRate * outputTime)
task.timing.cfg_samp_clk_timing(rate=outputRate,
sample_mode=AcquisitionType.FINITE,
samps_per_chan=nsamples)
if bTrig:
task.triggers.start_trigger.cfg_dig_edge_start_trig(
trigChan, Edge.RISING)
writer = AnalogSingleChannelWriter(task.out_stream, auto_start=False)
writer.write_many_sample(voltagelist)
task.start()
task.save(save_as=taskName, overwrite_existing_save=True)
def run(self):
"""
Starts writing a waveform continuously to the patchclamp. While reading
the buffer periodically
"""
self.patchVoltOutChan = self.configs.patchVoltOutChannel
self.patchCurOutChan = self.configs.patchCurOutChannel
self.patchVoltInChan = self.configs.patchVoltInChannel
#DAQ
with nidaqmx.Task() as writeTask, nidaqmx.Task() as readTask:
writeTask.ao_channels.add_ao_voltage_chan(self.patchVoltInChan)
readTask.ai_channels.add_ai_voltage_chan(self.patchVoltOutChan)
readTask.ai_channels.add_ai_voltage_chan(self.patchCurOutChan)
self.setTiming(writeTask, readTask)
reader = AnalogMultiChannelReader(readTask.in_stream)
writer = AnalogSingleChannelWriter(writeTask.out_stream)
writer.write_many_sample(self.wave)
"""Reading data from the buffer in a loop.
The idea is to let the task read more than could be loaded in the buffer for each iteration.
This way the task will have to wait slightly longer for incoming samples. And leaves the buffer
entirely clean. This way we always know the correct numpy size and are always left with an empty
buffer (and the buffer will not slowly fill up)."""
output = np.zeros([2, self.readNumber])
writeTask.start(
) #Will wait for the readtask to start so it can use its clock
readTask.start()
while not self.isInterruptionRequested():
reader.read_many_sample(
data=output, number_of_samples_per_channel=self.readNumber)
#Emiting the data just received as a signal
#output = np.around(output, 7) #Round all values
self.measurement.emit(output[0, :], output[1, :])
def calibrate(sRate, aiChan, aoChan, xValues):
writingRate = sRate
readingRate = writingRate
outputData = xValues
inputData = np.zeros(outputData.size + 1)
with nidaqmx.Task() as writeTask, nidaqmx.Task() as readTask:
writeTask.ao_channels.add_ao_voltage_chan(aoChan)
readTask.ai_channels.add_ai_voltage_chan(aiChan)
#In the USB6001 DAQ there seems to be no SampleClock, therefore we cannot sync the signals without an external trigger/clock
#sample_clock = '/Dev2/ai/SampleClock'
writeTask.timing.cfg_samp_clk_timing(
rate=writingRate,
sample_mode=nidaqmx.constants.AcquisitionType.CONTINUOUS,
samps_per_chan=outputData.size)
readTask.timing.cfg_samp_clk_timing(
rate=readingRate,
sample_mode=nidaqmx.constants.AcquisitionType.FINITE,
samps_per_chan=inputData.size)
#writeTask.triggers.start_trigger.cfg_dig_edge_start_trig(trigger_source = '/Dev2/ai/StartTrigger') #Setting the trigger on the analog input
#readTask.triggers.start_trigger.cfg_dig_edge_start_trig(trigger_source = '/Dev2/ao/StartTrigger')
reader = AnalogSingleChannelReader(readTask.in_stream)
writer = AnalogSingleChannelWriter(writeTask.out_stream)
writer.write_many_sample(outputData)
writeTask.start()
reader.read_many_sample(inputData,
timeout=outputData.size / readingRate + 4)
#The ai/StartTrigger 'takes' one sample from writing so the actual writing starts one sample later, therefore the inputData starts one sample later
inputData = inputData[1::]
return outputData, inputData
class ExpFlowSeparation():
def __init__(self, simlator_args):
self.get_setting(simlator_args)
def get_setting(self, arg):
# analog input
self.sample_rate = arg["sample_rate"]
self.number_of_samples = arg["number_of_samples"]
self.dynamic_pressre = arg["dynamic_pressure"]
self.reward_indicator = arg["reward_indicator"]
self.input_channel = arg["input_channels"]
sens_coff = np.array(
arg["sens_cofficients"]) / arg["unit_change"] * arg["gain"]
self.sens_coff = sens_coff.reshape(sens_coff.shape[0], 1)
self.num_i = len(self.sens_coff)
self.nb_actions = arg["nb_actions"]
# state channels for nueral network
self.state_channel = arg["state_channels"]
self.num_s = len(self.state_channel)
self.loc_100 = arg["reward_channel"]
# analog output
self.output_channel = arg["output_channel"]
# another parameters
self.timeout = arg["timeout"]
self.dt = 1 / self.sample_rate
self.total_time = arg["total_time"]
self.n_loop = int(self.sample_rate * self.total_time /
self.number_of_samples)
# plama actuator
self.burst_wave = self.get_burst_wave(arg["plasma_actuator_csv"])
def get_burst_wave(self, filename):
PA = np.zeros((self.nb_actions + 1, self.number_of_samples))
print(filename)
with open(filename, "r") as f:
reader = csv.reader(f)
for i, row in enumerate(reader):
PA[i, :] = row
return PA * 5
def load_args(self, filename):
with open(filename, "r") as f:
args = json.load(f)
return args
def setup_DAQmx(self):
self.read_task = nidaqmx.Task()
self.write_task = nidaqmx.Task()
self.sample_clk_task = nidaqmx.Task()
# Use a counter output pulse train task as the sample clock source
# for both the AI and AO tasks.
self.sample_clk_task.co_channels.add_co_pulse_chan_freq(
'Dev1/ctr0', freq=self.sample_rate, idle_state=Level.LOW)
self.sample_clk_task.timing.cfg_implicit_timing(
sample_mode=AcquisitionType.CONTINUOUS,
samps_per_chan=self.number_of_samples)
self.sample_clk_task.control(TaskMode.TASK_COMMIT)
samp_clk_terminal = '/Dev1/Ctr0InternalOutput'
self.read_task.ai_channels.add_ai_voltage_chan(self.input_channel,
max_val=10,
min_val=-10)
self.read_task.timing.cfg_samp_clk_timing(
self.sample_rate,
source=samp_clk_terminal,
active_edge=Edge.FALLING,
sample_mode=AcquisitionType.CONTINUOUS,
samps_per_chan=self.number_of_samples)
self.write_task.ao_channels.add_ao_voltage_chan(self.output_channel,
max_val=10,
min_val=-10)
self.write_task.timing.cfg_samp_clk_timing(
self.sample_rate,
source=samp_clk_terminal,
active_edge=Edge.FALLING,
sample_mode=AcquisitionType.CONTINUOUS,
samps_per_chan=self.number_of_samples)
self.write_task.out_stream.regen_mode = RegenerationMode.DONT_ALLOW_REGENERATION
self.write_task.out_stream.auto_start = False
self.writer = AnalogSingleChannelWriter(self.write_task.out_stream)
self.reader = AnalogMultiChannelReader(self.read_task.in_stream)
def reset(self):
self.env_memory = np.zeros((0, self.num_i))
self.buffer_memory = np.zeros((0, 4 + self.nb_actions))
self.step_count = 0
self.setup_DAQmx()
# start read analog
self._start_reading()
self._start_writing()
return self._reading()
def step(self, action):
self._writing(action)
observation = self._reading()
reward = np.average(observation[:, self.loc_100])
if action != self.nb_actions:
self.step_count += 1
if self.step_count < self.n_loop:
return observation, reward, False
else:
return observation, reward, True
def _start_reading(self):
# start read analog
self.read_task.start()
self.sample_clk_task.start()
def _start_writing(self):
self._writing(0)
self.write_task.start()
for _ in range(3):
self._writing(0)
def stop_DAQmx(self):
self.read_task.close()
self.write_task.close()
self.sample_clk_task.close()
def _reading(self):
values_read = np.zeros((self.num_i, self.number_of_samples),
dtype=np.float64)
self.reader.read_many_sample(
values_read,
number_of_samples_per_channel=self.number_of_samples,
timeout=2)
return (((values_read / self.sens_coff) + self.dynamic_pressre) /
self.dynamic_pressre).T
def _writing(self, action):
print(self.burst_wave[action].shape)
print(self.burst_wave[action])
self.writer.write_many_sample(self.burst_wave[action])
class Simulator():
def __init__(self, args, Property):
self.property = Property
self.dynamic_pressre = 57
self.reward_indicator = -0.55
self.sample_rate, self.number_of_samples = self.property.rate()
self.input_channel, self.sens_coff = self.property.i_channels()
self.output_channel = self.property.output()
self.state_channel, self.loc_100 = self.property.s_channels()
self.total_time, self.n_loop = self.property.control()
self.burst_wave_file_name = self.property.get_burst_wave_file()
self.num_i = len(self.sens_coff)
self.num_s = len(self.state_channel)
self.frame_width = args.frame_width
self.frame_height = args.frame_height
self.state_length = args.state_length
self.num_actions = args.n_actions
# 10 moving averege
self.num_mave = 10
self.b = np.ones(self.num_mave) / self.num_mave
self.burst_wave = self.get_burst_wave()
self.before_reward = 0
self.attachment_count = 0
def get_burst_wave(self):
PA = np.zeros((self.num_actions, self.number_of_samples))
with open(self.burst_wave_file_name, 'r') as f:
reader = csv.reader(f)
for i, row in enumerate(reader):
PA[i, :] = row
return PA
def setup_DAQmx(self):
self.read_task = nidaqmx.Task()
self.write_task = nidaqmx.Task()
self.sample_clk_task = nidaqmx.Task()
# Use a counter output pulse train task as the sample clock source
# for both the AI and AO tasks.
self.sample_clk_task.co_channels.add_co_pulse_chan_freq(
'Dev1/ctr0', freq=self.sample_rate, idle_state=Level.LOW)
self.sample_clk_task.timing.cfg_implicit_timing(
sample_mode=AcquisitionType.CONTINUOUS,
samps_per_chan=self.number_of_samples)
self.sample_clk_task.control(TaskMode.TASK_COMMIT)
samp_clk_terminal = '/Dev1/Ctr0InternalOutput'
self.read_task.ai_channels.add_ai_voltage_chan(self.input_channel,
max_val=10,
min_val=-10)
self.read_task.timing.cfg_samp_clk_timing(
self.sample_rate,
source=samp_clk_terminal,
active_edge=Edge.FALLING,
sample_mode=AcquisitionType.CONTINUOUS,
samps_per_chan=self.number_of_samples)
self.write_task.ao_channels.add_ao_voltage_chan(self.output_channel,
max_val=10,
min_val=-10)
self.write_task.timing.cfg_samp_clk_timing(
self.sample_rate,
source=samp_clk_terminal,
active_edge=Edge.FALLING,
sample_mode=AcquisitionType.CONTINUOUS,
samps_per_chan=self.number_of_samples)
self.write_task.out_stream.regen_mode = RegenerationMode.DONT_ALLOW_REGENERATION
self.write_task.out_stream.auto_start = False
self.writer = AnalogSingleChannelWriter(self.write_task.out_stream)
self.reader = AnalogMultiChannelReader(self.read_task.in_stream)
def start_reading(self):
# start read analog
self.read_task.start()
time.sleep(0.1)
self.sample_clk_task.start()
def stop_DAQmx(self):
self.read_task.close()
self.write_task.close()
self.sample_clk_task.close()
def get_observation(self):
values_read = np.zeros((self.num_i, self.number_of_samples),
dtype=np.float64)
self.reader.read_many_sample(
values_read,
number_of_samples_per_channel=self.number_of_samples,
timeout=2)
return (((values_read / self.sens_coff) + self.dynamic_pressre) /
self.dynamic_pressre).T
def get_initial_state(self):
return np.zeros(
(self.state_length * self.frame_height, self.frame_width))
def preprocess(self, observation):
state_t = np.array([
np.convolve(observation[:, self.state_channel[i]],
self.b,
mode='same') for i in range(self.num_s)
]).T
return np.round(
(np.average(np.split(state_t, self.frame_height, 0), axis=1)),
2).reshape(self.frame_height, self.frame_width)
def write_daqmx_zero(self):
values_zero = np.zeros(self.number_of_samples)
self.writer.write_many_sample(values_zero)
def write_daqmx(self, action):
self.writer.write_many_sample(self.burst_wave[action] * 5)
def get_reward(self, reward_ori):
reward = self.reward_indicator < reward_ori
return reward.astype(np.int)
def get_reward_with_punish(self, reward_ori, action):
reward = self.reward_indicator < reward_ori
if reward:
return reward.astype(np.int) * 0.7 - (action - 1) * 0.3
else:
return reward.astype(np.int) * 0.7
def get_reward_with_keep_attachment(self, reward_ori, action):
reward = self.reward_indicator < reward_ori
if (reward.astype(np.int) - self.before_reward) >= 0 and reward:
self.attachment_count += 0.01
else:
self.attachment_count = 0.0
if reward:
return self.attachment_count - (action - 1) * 0.1
else:
return self.attachment_count
class Simulator():
def __init__(self, statenum, num_actions, Actor, QN, memory, gamma,
batch_num, sample_rate, number_of_samples, input_channel,
state_channel, loc_100, sensor_coff, output_channel, nloops,
PA, current_id):
self.actor = Actor
self.num_actions = num_actions
self.QN = QN
self.memory = memory
self.gamma = gamma
self.batch_num = batch_num
self.sample_rate = sample_rate
self.number_of_samples = number_of_samples
self.input_channel = input_channel # input cahnnels
self.state_channel = state_channel
self.output_channel = output_channel
self.number_of_channels = sensor_coff.shape[0]
self.state_length = statenum
self.state_width = len(state_channel) # number of input cahnnels
self.CH100 = loc_100
self.nloops = nloops
self.burst_wave = PA
self.loss = 0
self.dynamic_pressre = 60
self.sensor_coff = sensor_coff
self.intispn = 0.1 # input time span
self.tilen = int(self.intispn / 0.01)
self.state_height = int(self.state_length / self.tilen)
self.id = current_id
# 10 moving averege
self.num_mave = 10
self.b = np.ones(self.num_mave) / self.num_mave
# inital action of DNN
self.init_model_action()
self.total_reward = 0
self.total_q_max = 0
self.reward_indicator = -0.55
self.a_buffer = [0] * self.num_actions
def init_model_action(self):
# first call of keras needs 0.22s
seq = np.zeros(self.state_length * self.state_width).reshape(
-1, self.state_length, self.state_width)
#seq = np.zeros(self.state_length*self.state_width).reshape(-1,self.state_length)
# keras needs to drive in 0.001s
for _ in range(5):
start = time.time()
self.actor.get_action(seq, self.QN, train=False)
end = time.time()
print(start - end)
def save_csv_3Dto2D(self, filename, data3d):
#get absolute path
name = os.path.dirname(os.path.abspath(__name__))
#conbine absolute and relative path
joined_path = os.path.join(name, '../data/', self.id, filename)
# change to absolute path
data_path = os.path.normpath(joined_path)
data2d = np.zeros(
(self.nloops * 3, self.state_length * self.state_width))
for i in range(len(data3d)):
data2d[i] = data3d[i].T.reshape(self.state_length *
self.state_width)
df = pd.DataFrame(data2d)
df.to_csv(data_path)
def save_csv(self, filename, data):
#get absolute path
name = os.path.dirname(os.path.abspath(__name__))
#conbine absolute and relative path
joined_path = os.path.join(name, '../data/', self.id, filename)
# change to absolute path
data_path = os.path.normpath(joined_path)
df = pd.DataFrame(data)
df.to_csv(data_path)
def save(self, read, i):
#save data to csv file
localdata = "runs/state/s%d.csv" % (i)
self.save_csv_3Dto2D(
localdata, self.memory.episode_local[:, 0:self.state_length, :])
localdata = "runs/state_/s_%d.csv" % (i)
self.save_csv_3Dto2D(
localdata,
self.memory.episode_local[:, self.state_length +
2:self.state_length * 2 + 2, :])
localdata = "runs/action/a%d.csv" % (i)
self.save_csv(localdata,
self.memory.episode_local[:, self.state_length, 0])
localdata = "runs/reward/r%d.csv" % (i)
self.save_csv(localdata,
self.memory.episode_local[:, self.state_length + 1, 0])
localdata = "runs/cpte/cp%d.csv" % (i)
self.save_csv(
localdata, self.memory.episode_local[:, self.state_length * 2 + 2,
0])
localdata = "runs/punish/p%d.csv" % (i)
self.save_csv(
localdata, self.memory.episode_local[:, self.state_length * 2 + 3,
0])
localdata = "runs/result/run%d.csv" % (i)
self.save_csv(localdata, self.buffer_memory)
readdata = "origin/data%d.csv" % (i)
self.save_csv(readdata, read)
def setup_DAQmx(self):
self.read_task = nidaqmx.Task()
self.write_task = nidaqmx.Task()
self.sample_clk_task = nidaqmx.Task()
# Use a counter output pulse train task as the sample clock source
# for both the AI and AO tasks.
self.sample_clk_task.co_channels.add_co_pulse_chan_freq(
'Dev1/ctr0', freq=self.sample_rate, idle_state=Level.LOW)
self.sample_clk_task.timing.cfg_implicit_timing(
sample_mode=AcquisitionType.CONTINUOUS,
samps_per_chan=self.number_of_samples)
self.sample_clk_task.control(TaskMode.TASK_COMMIT)
samp_clk_terminal = '/Dev1/Ctr0InternalOutput'
self.read_task.ai_channels.add_ai_voltage_chan(self.input_channel,
max_val=10,
min_val=-10)
self.read_task.timing.cfg_samp_clk_timing(
self.sample_rate,
source=samp_clk_terminal,
active_edge=Edge.FALLING,
sample_mode=AcquisitionType.CONTINUOUS,
samps_per_chan=self.number_of_samples)
self.write_task.ao_channels.add_ao_voltage_chan(self.output_channel,
max_val=10,
min_val=-10)
self.write_task.timing.cfg_samp_clk_timing(
self.sample_rate,
source=samp_clk_terminal,
active_edge=Edge.FALLING,
sample_mode=AcquisitionType.CONTINUOUS,
samps_per_chan=self.number_of_samples)
self.write_task.out_stream.regen_mode = RegenerationMode.DONT_ALLOW_REGENERATION
self.write_task.out_stream.auto_start = True
self.writer = AnalogSingleChannelWriter(self.write_task.out_stream)
self.reader = AnalogMultiChannelReader(self.read_task.in_stream)
def start_reading(self):
# start read analog
self.read_task.start()
time.sleep(0.1)
self.sample_clk_task.start()
def read_daqmx(self):
values_read = np.zeros(
(self.number_of_channels, self.number_of_samples),
dtype=np.float64)
self.reader.read_many_sample(
values_read,
number_of_samples_per_channel=self.number_of_samples,
timeout=2)
return (((values_read.T / self.sensor_coff) + self.dynamic_pressre) /
self.dynamic_pressre)
def write_daqmx_zero(self):
values_zero = np.zeros(self.number_of_samples)
self.writer.write_many_sample(values_zero)
def write_daqmx(self, action):
self.writer.write_many_sample(self.burst_wave[action] * 5)
def stop_DAQmx(self):
self.read_task.close()
self.write_task.close()
self.sample_clk_task.close()
def preprocess(self, observation):
# moving average filter
state_t = np.zeros((self.number_of_samples, self.state_width))
for ich in range(self.state_width):
state_t[:, ich] = np.convolve(observation[:,
self.state_channel[ich]],
self.b,
mode='same')
processed_observation = np.round(
(np.average(np.split(state_t, self.state_height, 0), axis=1)), 2)
return processed_observation.reshape(self.state_height,
self.state_width)
def get_initial_state(self):
return np.zeros((self.state_length, self.state_width))
def get_reward(self, reward_ori):
reward = self.reward_indicator < reward_ori
return reward.astype(np.int)
def run(self, Nepisode, targetQN, train=True):
self.setup_DAQmx()
# Parameters for plotting
# initiate
read = np.zeros((0, self.number_of_channels))
state = self.get_initial_state()
self.buffer_memory = np.zeros((0, 4 + self.num_actions))
self.total_q_max = 0
# start read analog
self.start_reading()
# 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
self.write_daqmx_zero()
self.write_task.start()
self.write_daqmx_zero()
self.write_daqmx_zero()
for n in range(int(self.nloops)):
# adopt output timing and action zero
self.write_daqmx_zero()
observation = self.read_daqmx()
processed_observation = self.preprocess(observation)
next_state = np.append(state[self.state_height:, :],
processed_observation,
axis=0)
# reward
cpte = np.average(observation[:, self.CH100])
reward = self.get_reward(cpte)
read = np.append(read, observation, axis=0)
memory = [0, cpte, 0, 0]
memory.extend(self.a_buffer)
self.memory.add_local(state, 0, next_state, cpte, 0, 0)
self.buffer_memory = np.append(self.buffer_memory, [memory],
axis=0)
state = next_state
# calibulatkoe
self.memory.calc_calibulation()
# second loop
# you read
# you act
for n in range(self.nloops):
# action
action, q, q_max = self.actor.get_action(
state.reshape(-1, self.state_length, self.state_width),
self.QN, train)
#action, q, q_max = self.actor.get_action(state.reshape(-1,self.state_length), self.QN,train)
# adopt output timing and action zero
self.write_daqmx(action)
observation = self.read_daqmx()
processed_observation = self.preprocess(observation)
next_state = np.append(state[self.state_height:, :],
processed_observation,
axis=0)
# reward
cpte = np.average(observation[:, self.CH100])
reward = self.get_reward(cpte)
read = np.append(read, observation, axis=0)
memory = [action, cpte, reward, np.argmax(q)]
memory.extend(q)
self.memory.add_local(state, action, next_state, cpte, reward, 0)
self.buffer_memory = np.append(self.buffer_memory, [memory],
axis=0)
self.total_q_max += q_max
state = next_state
# third loop
# you read
# you do not act
# make sure PA turn off
for n in range(int(self.nloops)):
# adopt output timing and action zero
self.write_daqmx_zero()
self.write_daqmx_zero()
observation = self.read_daqmx()
processed_observation = self.preprocess(observation)
next_state = np.append(state[self.state_height:, :],
processed_observation,
axis=0)
# reward
cpte = np.average(observation[:, self.CH100])
reward = self.get_reward(cpte)
read = np.append(read, observation, axis=0)
memory = [0, cpte, reward, 0]
memory.extend(self.a_buffer)
self.memory.add_local(state, 0, next_state, cpte, 0, 0)
self.buffer_memory = np.append(self.buffer_memory, [memory],
axis=0)
state = next_state
# stop DAQmx
self.stop_DAQmx()
# edit experience in buffer
self.memory.edit_experience_local()
self.save(read, Nepisode)
self.total_reward = self.memory.totalreward()
# move current experience to global buffer
self.memory.add_global(self.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 self.total_reward, self.loss, self.total_q_max / self.nloops
import numpy as np
import nidaqmx
from nidaqmx.constants import *
from nidaqmx.stream_writers import AnalogSingleChannelWriter
ttime = 10 # Total time (s)
sigRate = 1 # Frequency (Hz)
nsamples = 100 # Number of samples per cycle
writingRate = nsamples * sigRate # Signal generation frequency
Ax = 10 # Amplitude (V)
nv = int(ttime * sigRate)
output = np.tile(np.linspace(-Ax, Ax, int(nsamples)), nv)
with nidaqmx.Task() as writeTask:
writeTask.ao_channels.add_ao_voltage_chan("Dev1/ao0")
writeTask.timing.cfg_samp_clk_timing(
rate=writingRate,
sample_mode=nidaqmx.constants.AcquisitionType.FINITE,
samps_per_chan=int(nsamples * nv))
writer = AnalogSingleChannelWriter(writeTask.out_stream)
writer.write_many_sample(output, timeout=ttime + 5)
writeTask.start()
print("Start writing")
writeTask.wait_until_done(timeout=ttime + 5)
print("Done with data")
class ExpFlowSeparation():
def __init__(self, simlator_args):
self.get_setting(simlator_args)
def get_setting(self, arg):
# analog input
self.sample_rate = arg["sample_rate"]
self.number_of_samples = arg["number_of_samples"]
self.dynamic_pressre = arg["dynamic_pressure"]
self.reward_indicator = arg["reward_indicator"]
self.input_channel = arg["input_channels"]
sens_coff = np.array(arg["sens_cofficients"])/arg["unit_change"]*arg["gain"]
self.sens_coff = sens_coff.reshape(sens_coff.shape[0],1)
self.num_i = len(self.sens_coff)
# state channels for nueral network
self.state_channel = arg["state_channels"]
self.num_s = len(self.state_channel)
self.loc_100 = arg["reward_channel"]
# analog output
self.output_channel = arg["output_channel"]
# another parameters
self.timeout = arg["timeout"]
self.dt = 1/self.sample_rate*self.number_of_samples
self.total_time = arg["total_time"]
self.n_loop = int(self.sample_rate*self.total_time/self.number_of_samples)
# plama actuator
self.burst_wave = self.get_burst_wave(arg)
self.nb_actions = self.burst_wave.shape[0] - 1
def get_burst_wave(self,arg):
if arg["mode"]=="gate":
wave = self.get_gate_wave(arg["gate_mode"]["plasma_actuator_csv"])
elif arg["mode"]=="sin":
wave = self.create_burst_wave(**arg["sin_mode"])
print("MODE: " + arg["mode"])
zero_wave = np.zeros((1,self.number_of_samples))
wave = np.append(zero_wave,wave,axis=0)
return wave
def get_gate_wave(self, filename):
df_wave = pd.read_csv(filename, header=None).values
np_wave = np.array([ i_wave for i_wave in df_wave ])
return np_wave
def create_burst_wave(self,base_frequency, burst_frequency, burst_ratio, voltage):
### example -> burst_freq=600[Hz], burst_ratio=0.1[-], voltage=3[kV]
time = np.linspace(0.0, self.dt, self.number_of_samples)
tmp_sin = np.sin(2*np.pi*int(base_frequency)*time)
tmp_sq = [(signal.square(2 * np.pi * bf_i * time, duty=br_i)+1)/2 for br_i in burst_ratio for bf_i in burst_frequency]
wave = [(tmp_sin * tmp_sq_i) * vi for tmp_sq_i in tmp_sq for vi in voltage]
zero_wave = np.zeros((1,self.number_of_samples))
wave = np.append(wave,zero_wave,axis=0)# plus off at last array
return wave
def load_args(self, filename):
with open(filename,"r") as f:
args = json.load(f)
return args
def setup_DAQmx(self):
self.read_task = nidaqmx.Task()
self.write_task = nidaqmx.Task()
self.sample_clk_task = nidaqmx.Task()
# Use a counter output pulse train task as the sample clock source
# for both the AI and AO tasks.
self.sample_clk_task.co_channels.add_co_pulse_chan_freq('Dev1/ctr0', freq=self.sample_rate, idle_state=Level.LOW)
self.sample_clk_task.timing.cfg_implicit_timing(sample_mode=AcquisitionType.CONTINUOUS,samps_per_chan=self.number_of_samples)
self.sample_clk_task.control(TaskMode.TASK_COMMIT)
samp_clk_terminal = '/Dev1/Ctr0InternalOutput'
self.read_task.ai_channels.add_ai_voltage_chan(self.input_channel, max_val=10, min_val=-10)
self.read_task.timing.cfg_samp_clk_timing(self.sample_rate, source=samp_clk_terminal, active_edge=Edge.FALLING,sample_mode=AcquisitionType.CONTINUOUS, samps_per_chan=self.number_of_samples)
self.write_task.ao_channels.add_ao_voltage_chan(self.output_channel, max_val=10, min_val=-10)
self.write_task.timing.cfg_samp_clk_timing(self.sample_rate, source=samp_clk_terminal, active_edge=Edge.FALLING,sample_mode=AcquisitionType.CONTINUOUS, samps_per_chan=self.number_of_samples)
self.write_task.out_stream.regen_mode = RegenerationMode.DONT_ALLOW_REGENERATION
self.write_task.out_stream.auto_start = False
self.writer = AnalogSingleChannelWriter(self.write_task.out_stream)
self.reader = AnalogMultiChannelReader(self.read_task.in_stream)
def reset(self):
self.env_memory = np.zeros((0,self.num_i))
self.buffer_memory = np.zeros((0,4+self.nb_actions))
self.step_count = 0
self.setup_DAQmx()
# start read analog
self._start_reading()
self._start_writing()
return self._reading()
def step(self, action):
self._writing(action)
observation = self._reading()
reward = np.average(observation[:,self.loc_100])
if action != self.nb_actions :
self.step_count += 1
if self.step_count < self.n_loop:
return observation, reward, False
else:
return observation, reward, True
def _start_reading(self):
# start read analog
self.read_task.start()
self.sample_clk_task.start()
def _start_writing(self):
self._writing(0)
self.write_task.start()
for _ in range(3):
self._writing(0)
def stop_DAQmx(self):
self.read_task.close()
self.write_task.close()
self.sample_clk_task.close()
def _reading(self):
values_read = np.zeros((self.num_i,self.number_of_samples), dtype=np.float64)
self.reader.read_many_sample(values_read, number_of_samples_per_channel=self.number_of_samples,timeout=2)
return (((values_read / self.sens_coff) + self.dynamic_pressre ) / self.dynamic_pressre).T
def _writing(self,action):
self.writer.write_many_sample(self.burst_wave[action])