Description
Description-Separate-1
This Python script utilizes the NIDAQmx library to continuously read analog voltage data from channel "Dev1/ai0" of a DAQ device. The script configures the DAQ settings, sets up a circular buffer, and dynamically updates a real-time plot of the acquired analog input signal.
The script reads 50 samples per iteration, updates the circular buffer, and refreshes the plot to display the most recent 5 seconds of data. Exception handling ensures proper cleanup when the user interrupts the program.
"""
Example: DAQmx-Python Analog Input Acquisition and Plot With History (NI 2023)
Author: Davit Danielyan
DISCLAIMER: The attached Code is provided As Is. It has not been tested or validated as a product, for use in a
deployed application or system, or for use in hazardous environments. You assume all risks for use of the Code and
use of the Code is subject to the Sample Code License Terms which can be found at: http://ni.com/samplecodelicense
"""
import numpy as np
import matplotlib.pyplot as plt
import nidaqmx
from nidaqmx.constants import AcquisitionType, TerminalConfiguration
from collections import deque
import time
# Configure NI DAQmx settings
task = nidaqmx.Task()
task.ai_channels.add_ai_voltage_chan("Dev1/ai0", terminal_config=TerminalConfiguration.RSE)
task.timing.cfg_samp_clk_timing(rate=1000, sample_mode=AcquisitionType.CONTINUOUS)
# Initialize variables for data storage and plotting
history_length = 5 # seconds
num_samples = int(task.timing.samp_clk_rate * history_length)
time_values = np.linspace(-history_length, 0, num_samples)
data_buffer = deque(maxlen=num_samples)
time_buffer = deque(maxlen=num_samples)
# Create the plot
plt.ion() # Enable interactive mode for dynamic updating
fig, ax = plt.subplots()
line, = ax.plot(time_values, np.zeros(num_samples))
ax.set_xlabel('Time (s)')
ax.set_ylabel('Voltage (V)')
ax.set_title('Analog Input from Dev1/ai0')
task.start()
while True:
try:
new_data = task.read(number_of_samples_per_channel=50) # Read 50 samples
timestamp = time.time()
data_buffer.extend(new_data)
time_buffer.extend([timestamp] * len(new_data))
time_diff = np.array(time_buffer) - time_buffer[-1]
mask = time_diff > -history_length
line.set_xdata(-time_diff[mask])
line.set_ydata(np.array(data_buffer)[mask])
ax.relim()
ax.autoscale_view()
plt.pause(0.01) # Pause to allow the plot to update
except (KeyboardInterrupt,SystemExit):
task.stop()
task.close()
plt.ioff() # Turn off interactive mode
break
