使用nidaqmx python包从国家仪器DAQ连续读取模拟量

受问题答案的启发，我尝试了以下代码：

import nidaqmx
from nidaqmx import stream_readers
from nidaqmx import constants
import time

sfreq = 1000
bufsize = 100

data = np.zeros((1, 1), dtype = np.float32)  # initializes total data file

with nidaqmx.Task() as task:
    task.ai_channels.add_ai_voltage_chan("cDAQ2Mod1/ai1")
    task.timing.cfg_samp_clk_timing(rate = sfreq, sample_mode = constants.AcquisitionType.CONTINUOUS,
                                    samps_per_chan = bufsize)  # unclear samps_per_chan is needed or why it would be different than bufsize
    stream = stream_readers.AnalogMultiChannelReader(task.in_stream)

    def reading_task_callback(task_id, event_type, num_samples, callback_data=None):  # num_samples is set to bufsize
        buffer = np.zeros((1, num_samples), dtype = np.float32)  # probably better to define it here inside the callback
        stream.read_many_sample(buffer, num_samples, timeout = constants.WAIT_INFINITELY)
        data = np.append(data, buffer, axis = 1)  # hopping to retrieve this data after the read is stopped

    task.register_every_n_samples_acquired_into_buffer_event(bufsize, reading_task_callback)

预期行为：它从通道连续读取。我甚至不想让它做一些特定的事情（比如实时绘图），但我希望python控制台一直运行到停止为止，因为它的目标是持续阅读

观察到的行为：运行此代码将快速进行，并返回控制台提示

问题：在我看来，这根本不是持续阅读。此外，

数据

变量没有像我希望的那样追加（我知道检索一定数量的数据样本不需要使用nidaqmx进行如此复杂的编码；这只是我认为可以尝试的一种方法，看看这是否符合我的要求，即连续读取并不断地将缓冲样本值附加到

数据

，这样我就可以查看总的

数据

采集教育部）

任何帮助都将不胜感激。我基本上确信实现这一点的方法是通过使用这些作为nidaqmx一部分的回调，但不知何故，我似乎没有很好地管理它们。注意，我已经能够通过使用

read\u many\u sample

从模拟输入通道读取预定义和有限数量的数据样本

详细信息：NI cDAQ 9178，插入NI 9205模块，安装在运行Windows Home 10、python 3.7和用于python的nidaqmx软件包的联想笔记本电脑上

编辑：对于任何感兴趣的人，我现在可以通过以下方式进行操作，使用matplotlib进行实时视觉反馈，并且-还不是100%确定-即使目标是长时间采集（>10分钟），也似乎没有缓冲区问题。下面是代码（未清理，抱歉）：

---

当然，欢迎您发表评论。这可能还不够理想。

我不太了解nidaqmx，但看看代码，我感觉您是在创建任务而没有运行它。我希望使用另一个命令，如task.exec（）或task.wait（）.Aouch，你当然是对的。对不起，我没有太多使用像这样的高级Python包的经验（我发现它也是一个文档非常贫乏的包）。谢谢，这有助于我获得不同的错误，这就是进步！你解决了你的问题@Gustavo Lucena Gómez吗？我也遇到了同样的问题…当然，至少在第一个近似值中，问题是这样的（愚蠢的我）我甚至没有开始这项任务。请看上面@Marco的回复！现在我收到了大量的缓冲区错误，我正在尝试让它连续、顺利地读取，没有错误或警告，并不时保存数据。事实上，这将在下周的整个周内完成。

"""
Analog data acquisition for QuSpin's OPMs via National Instruments' cDAQ unit
The following assumes:
"""

# Imports
import matplotlib.pyplot as plt
import numpy as np

import nidaqmx
from nidaqmx.stream_readers import AnalogMultiChannelReader
from nidaqmx import constants
# from nidaqmx import stream_readers  # not needed in this script
# from nidaqmx import stream_writers  # not needed in this script

import threading
import pickle
from datetime import datetime
import scipy.io


# Parameters
sampling_freq_in = 1000  # in Hz
buffer_in_size = 100
bufsize_callback = buffer_in_size
buffer_in_size_cfg = round(buffer_in_size * 1)  # clock configuration
chans_in = 3  # set to number of active OPMs (x2 if By and Bz are used, but that is not recommended)
refresh_rate_plot = 10  # in Hz
crop = 10  # number of seconds to drop at acquisition start before saving
my_filename = 'test_3_opms'  # with full path if target folder different from current folder (do not leave trailing /)



# Initialize data placeholders
buffer_in = np.zeros((chans_in, buffer_in_size))
data = np.zeros((chans_in, 1))  # will contain a first column with zeros but that's fine


# Definitions of basic functions
def ask_user():
    global running
    input("Press ENTER/RETURN to stop acquisition and coil drivers.")
    running = False


def cfg_read_task(acquisition):  # uses above parameters
    acquisition.ai_channels.add_ai_voltage_chan("cDAQ2Mod1/ai1:3")  # has to match with chans_in
    acquisition.timing.cfg_samp_clk_timing(rate=sampling_freq_in, sample_mode=constants.AcquisitionType.CONTINUOUS,
                                           samps_per_chan=buffer_in_size_cfg)


def reading_task_callback(task_idx, event_type, num_samples, callback_data):  # bufsize_callback is passed to num_samples
    global data
    global buffer_in

    if running:
        # It may be wiser to read slightly more than num_samples here, to make sure one does not miss any sample,
        # see: https://documentation.help/NI-DAQmx-Key-Concepts/contCAcqGen.html
        buffer_in = np.zeros((chans_in, num_samples))  # double definition ???
        stream_in.read_many_sample(buffer_in, num_samples, timeout=constants.WAIT_INFINITELY)

        data = np.append(data, buffer_in, axis=1)  # appends buffered data to total variable data

    return 0  # Absolutely needed for this callback to be well defined (see nidaqmx doc).


# Configure and setup the tasks
task_in = nidaqmx.Task()
cfg_read_task(task_in)
stream_in = AnalogMultiChannelReader(task_in.in_stream)
task_in.register_every_n_samples_acquired_into_buffer_event(bufsize_callback, reading_task_callback)


# Start threading to prompt user to stop
thread_user = threading.Thread(target=ask_user)
thread_user.start()


# Main loop
running = True
time_start = datetime.now()
task_in.start()


# Plot a visual feedback for the user's mental health
f, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex='all', sharey='none')
while running:  # make this adapt to number of channels automatically
    ax1.clear()
    ax2.clear()
    ax3.clear()
    ax1.plot(data[0, -sampling_freq_in * 5:].T)  # 5 seconds rolling window
    ax2.plot(data[1, -sampling_freq_in * 5:].T)
    ax3.plot(data[2, -sampling_freq_in * 5:].T)
    # Label and axis formatting
    ax3.set_xlabel('time [s]')
    ax1.set_ylabel('voltage [V]')
    ax2.set_ylabel('voltage [V]')
    ax3.set_ylabel('voltage [V]')
    xticks = np.arange(0, data[0, -sampling_freq_in * 5:].size, sampling_freq_in)
    xticklabels = np.arange(0, xticks.size, 1)
    ax3.set_xticks(xticks)
    ax3.set_xticklabels(xticklabels)

    plt.pause(1/refresh_rate_plot)  # required for dynamic plot to work (if too low, nulling performance bad)


# Close task to clear connection once done
task_in.close()
duration = datetime.now() - time_start


# Final save data and metadata ... first in python reloadable format:
filename = my_filename
with open(filename, 'wb') as f:
    pickle.dump(data, f)
'''
Load this variable back with:
with open(name, 'rb') as f:
    data_reloaded = pickle.load(f)
'''
# Human-readable text file:
extension = '.txt'
np.set_printoptions(threshold=np.inf, linewidth=np.inf)  # turn off summarization, line-wrapping
with open(filename + extension, 'w') as f:
    f.write(np.array2string(data.T, separator=', '))  # improve precision here!
# Now in matlab:
extension = '.mat'
scipy.io.savemat(filename + extension, {'data':data})


# Some messages at the end
num_samples_acquired = data[0,:].size
print("\n")
print("OPM acquisition ended.\n")
print("Acquisition duration: {}.".format(duration))
print("Acquired samples: {}.".format(num_samples_acquired - 1))


# Final plot of whole time course the acquisition
plt.close('all')
f_tot, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex='all', sharey='none')
ax1.plot(data[0, 10:].T)  # note the exclusion of the first 10 iterations (automatically zoomed in plot)
ax2.plot(data[1, 10:].T)
ax3.plot(data[2, 10:].T)
# Label formatting ...
ax3.set_xlabel('time [s]')
ax1.set_ylabel('voltage [V]')
ax2.set_ylabel('voltage [V]')
ax3.set_ylabel('voltage [V]')
xticks = np.arange(0, data[0, :].size, sampling_freq_in)
xticklabels = np.arange(0, xticks.size, 1)
ax3.set_xticks(xticks)
ax3.set_xticklabels(xticklabels)
plt.show()