I am building a GUI in Python 3.12.4 to stream data from a BNC-2090A in real time via NI PCIe-6363. I want to sample at 1000 Hz from 16 channels, and it works for 1 channel. The problem: when I add more than one aivoltage channel, the stream lags, intersperses gaps in the data, and can no longer keep up. With each additional channel, the problem gets worse [Case A: SampleRate = 1000]. I can get around this problem by reducing the sampling rate by a factor equal to the number of channels [Case B: SampleRate = 1000/n_chan]. But the hardware should be able to keep up with 16 channels at 1000 Hz without issue, so I think this is an operator, coding error. Open to suggestions and any advice, please!
I am using the main thread to update a matplotlib canvas with tkinter and listen to a toggle button callback (to start or stop acquisition). I created a background thread to run a DAQ function that uses AnalogMultiChannelReader functionality to record at least 1 channel at 1 kHz sampling rate. In the comparison below, both cases use n_chan=3.
Case A: SampleRate = 1000
Size of data array being plotted ("ai_data"): (3,3000)
Mean iteration duration at 20 seconds: 212.7 ms
Size of buffer queue ("buff_data"): (4, 30)
Case B: SampleRate = 1000/n_chan
Size of data array being plotted: (3,3000)
Mean iteration duration at 20 seconds: 5.2 ms
Size of buffer queue ("buff_data"): (4, 30)
In writing this, I realized (perhaps incorrectly and fortunate coincidence?) the size of the buffer queue should not be the same between Case A and B if the sampling rates are different. So, I changed the number of samples per channel in the task cfg_samp_clk_timing (DAQ func, parameter is "Nsamples") to make the acquisition duration consistent (i.e. grabbing samples 3x faster needs to acquire 3x the number of samples to allow the task the same amount of time). Changing the definition from "Nsamples = 30" to "Nsamples = 30*n_chan" helped speed up the n_chan=3 lag, but the solution does not scale to 7 or 16 channels.
Snippet below, full code attached.
with open(os.path.join('meta_data','config.yml'), 'r') as file:
yaml_config = yaml.safe_load(file)
for each_var in yaml_config:
for k,v in each_var.items(): locals()[k] = v
def now():
return Quantity(time.time_ns(),'ns',scale='ms')
last_update = now()
def DAQ(q,n_chan,chan_names,stop_event):
global last_update
with nidaqmx.Task() as task:
# task.ai_channels.add_ai_voltage_chan(
# physical_channel = "Dev3/ai0",
# name_to_assign_to_channel = "Dummy channel"
# )
for nc,cn in zip(range(n_chan), chan_names):
task.ai_channels.add_ai_voltage_chan(
physical_channel = "Dev3/ai{0}".format(nc),
name_to_assign_to_channel = cn
)
task.timing.cfg_samp_clk_timing(SampleRate, source="", sample_mode=AcquisitionType.CONTINUOUS, samps_per_chan=Nsamples)
reader = AnalogMultiChannelReader(task.in_stream)
d_out = np.zeros([n_chan, Nsamples])
task.start()
total_read=0
itercount=0
old_time = Quantity(time.time_ns(),'ns',scale='ms')
while not stop_event.is_set():
# try:
reader.read_many_sample(data=d_out, number_of_samples_per_channel=Nsamples)# read from DAQ
now_time = Quantity(time.time_ns(),'ns',scale='ms')
t_out = np.linspace(0,(now_time-old_time).real,np.shape(d_out)[1]).reshape(1,np.shape(d_out)[1])
out = np.around(np.append(t_out,d_out.astype(np.uint8),axis=0), 2) #Round all values to 6 decimals to avoid overflow
q.put_nowait(out)
old_time = Quantity(time.time_ns(),'ns',scale='ms')
itercount+=1
total_read+=np.shape(d_out)[1]
# except:
# task.stop()
# print('Task crashed after {0} samples.'.format(total_read))
# var_sizes(list(locals().items()))
# task.start()
last_update = now()
