如何使用 ADC 在 Raspbery Pi 中获得最高采样率?

仅使用 SPI ADC，您不会在 Pi 上达到 150ksps.不是即使只有一个频道.我想我听说过的最好的是 50ksps，而且频率会有一定的抖动采样.2 通道 * 150ksps = 300ksps 有开销，假设每个样本大约 32 位，您正在查看9.6mbps 的原始数据只有一个 Pi 和 ADC 是不可能的.您需要一个外部微控制器/adc 将数据发送到 Pi通过 USB 或以太网和这里、基本问题是:Raspberry Pi 并非为高速数据收集而设计MCP 系列 ADC 在 5V 时达到 ~2.7Mhz SPI 时钟使用 RPi 的 SPI 延迟Pi 上的 SPI 接口根本无法准确地以精确的间隔从 ADC 读取 100,000 个样本.I am working in a project using Raspberry Pi 3 B where I get data from a IR sensor(Sharp GP2Y0A21YK0F) through a ADC MPC3008 and display it in real-time using PyQtgraph library.The datasheet of the ADC says that at 5.0V, the sampling rate is 200khz. However I am only getting about 30 samples per second.Is it possible to achieve 200khz using Raspberry pi?If yes, what should I study or implement in order to obtain it?If not, what should I do to obtain the highest sample rate possible and how can I find out what is the highest sample rate?Here is my code:# -*- coding: utf-8 -*-import timeimport Adafruit_GPIO.SPI as SPIimport Adafruit_MCP3008from collections import dequeimport serialimport pyqtgraph as pgfrom pyqtgraph.Qt import QtCore, QtGuiimport numpy as npSPI_PORT = 0SPI_DEVICE = 0mcp = Adafruit_MCP3008.MCP3008(spi=SPI.SpiDev(SPI_PORT, SPI_DEVICE))win = pg.GraphicsWindow()win.setWindowTitle('pyqtgraph example: Scrolling Plots')nsamples=600 #tamanho das matrizes para os dadostx_aq = 0 #velocidade da aquisiçãointervalo_sp = 0.5 #intervalo para secao de poincare# 1) Simplest approach -- update data in the array such that plot appears to scroll# In these examples, the array size is fixed.p1 = win.addPlot()p1.setRange(yRange=[0,35])p2 = win.addPlot()p2.setRange(yRange=[-100,100])p3 = win.addPlot()p3.setRange(yRange=[-100,100])p3.setRange(xRange=[-0,35])#p3.plot(np.random.normal(size=100), pen=(200,200,200), symbolBrush=(255,0,0), symbolPen='w')'''p3.setDownsampling(mode='peak')p3.setClipToView(True)p3.setRange(xRange=[-100, 0])p3.setLimits(xMax=0)'''data1= np.zeros((nsamples,2),float) #ARMAZENAR POSICAOvec_0=deque()vec_1=deque()vec_2=deque()ptr1 = 0data2= np.zeros((nsamples,2),float) #ARMAZENAR VELOCIDADEdiff=np.zeros((2,2),float)diff_v=deque()data3= np.zeros((nsamples,2),float)data3_sp=np.zeros((1,2),float)ptr3=0curve1 = p1.plot(data1)curve2 = p2.plot(data2)curve3 = p3.plot(data3)#Coeficientes da calibração do IRc1=-7.246c2=44.17c3=-95.88c4=85.28tlast=time.clock()tlast_sp=time.clock()#print tlastdef getdata(): global vec_0, vec_1, vec_2, tlast timenow=time.clock() if timenow-tlast>=tx_aq: #name=input("HUGO") tlast=timenow t0=float(time.clock()) str_0 =mcp.read_adc(0) t1=float(time.clock()) str_1 =mcp.read_adc(0) t2=float(time.clock()) str_2 =mcp.read_adc(0) d0x=(float(str_0))*(3.3/1023) d0= c1*d0x**3+c2*d0x**2+c3*d0x+c4 vec_0=(t0, d0) d1x=(float(str_1))*(3.3/1023) d1= c1*d1x**3+c2*d1x**2+c3*d1x+c4 vec_1=(t1, d1) d2x=(float(str_2))*(3.3/1023) d2= c1*d2x**3+c2*d2x**2+c3*d2x+c4 vec_2=(t2, d2) functions()def diferenciar(): global data2 diff=(data1[-1,1]-data1[-3,1])/(data1[-1,0]-data1[-3,0]) data2[:-1] = data2[1:] data2[-1,1] = diff data2[-1,0] = data1[-2,0]def organizar(): global data1, data3 data1[:-1] = data1[1:] vec_x1=np.array(vec_1) data1[-1]=vec_x1def EF(): #ESPACO DE FASE global data3, ptr3 data3[:-1] = data3[1:] data3[-1,0]=data1[-1,1] data3[-1,1]=data2[-1,1]def SP(): global timenow_sp, tlast_sp timenow_sp=time.clock() if timenow_sp-tlast_sp>=intervalo_sp: tlast_sp=timenow_sp data3_sp[0,0]=data3[-2,0] data3_sp[0,1]=data3[-2,1] p3.plot(data3_sp, pen=None, symbol='o', symbolPen=None, symbolSize=4, symbolBrush=('r')) #print data3_spdef plotar(): global ptr1 curve1.setData(data1) ptr1 += 1 curve2.setData(data2) #curve2.setPos(ptr1, 0) #p3.plot(data3)def functions(): diferenciar() organizar() EF() SP() plotar()def update1(): global data1, curve1, ptr1 getdata()# update all plotsdef update(): update1()timer = pg.QtCore.QTimer()timer.timeout.connect(update)timer.start(50)## Start Qt event loop unless running in interactive mode or using pyside.if __name__ == '__main__': import sys if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'): QtGui.QApplication.instance().exec_()I am trying to find a way to solve it, but I have failed so far.Would you guys help me with that or at least point me out where I can find information about this? 解决方案 This kind of Sampling rate is not achievable with a general-purpose computer like Raspberry Pi, especially with MCP3008. The reason being the MCP series of ADC's tops out at ~2.7Mhz SPI clock at 5V.In order to read at 200KHz rate, you would need a dedicated board.However, you can try PCM1803A which could evidently achieve sampling rate of up to 96 kHz,This is also discussed here, as follows,and here, 这篇关于如何使用 ADC 在 Raspbery Pi 中获得最高采样率?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！