Fast plotting data in python

Question

I'm trying to plot data from mpu6050 imu with arduino. MPU6050 sends data faster than plot. Arduino code gives 6 data which are yaw, pitch, roll, ax,ay and az from serial port. I need suggestions for fast plot .

Python Code:

import serial
import matplotlib.pyplot as plt #import matplotlib library
from drawnow import * 

ser = serial.Serial('COM9', 115200)
yaw = 0.0
pitch =0.0
roll =0.0
ax =0.0
ay =0.0
az =0.0
o_yaw= [0]
o_pitch= [0]
o_roll= [0]
o_ax= [0]
o_ay= [0]
o_az= [0]
plt.ion()
cnt=0
def makeFig(): 
    plt.ylim(-1000,1000)                                 
    plt.grid(True)
    plt.ylabel('Magnitude')  
    plt.plot(olculen_ax, 'ro-', label='ax') 
    plt.plot(olculen_ay, 'bo-', label='ay')  
    plt.plot(olculen_az, 'go-', label='az')                               
    plt.legend()                    
while True:
    incoming=ser.readline()
    if ("hand" in incoming):
        incoming=incoming.split(":")
        if len(incoming)==8:
            yaw = float(incoming[1])
            pitch = float(incoming[2])
            roll = float(incoming[3])
            ax = float(incoming[4])
            ay = float(incoming[5])
            az = float(incoming[6])
            print "Split works"
    else:
        print incoming
    o_ax.append(ax)                    
    o_ay.append(ay)    
    o_az.append(az)
    o_yaw.append(yaw)
    o_pitch.append(pitch)
    o_roll.append(roll)              

    drawnow(makeFig)                       
    plt.pause(.00001)                     
    cnt=cnt+1
    if(cnt>50):                            
        o_ax.pop(0)
        o_ay.pop(0)                     
        o_az.pop(0)

Arduino Code (I just add loop. code derived from this):

void loop() {
    if (!dmpReady) return;
    while (!mpuInterrupt && fifoCount < packetSize) {
    }

    mpuInterrupt = false;
    mpuIntStatus = mpu.getIntStatus();

    fifoCount = mpu.getFIFOCount();

     if ((mpuIntStatus & 0x10) || fifoCount == 1024) {
    mpu.resetFIFO();
    //Serial.println(F("FIFO overflow!"));
} else if (mpuIntStatus & 0x02) {
    while (fifoCount < packetSize) fifoCount = mpu.getFIFOCount();
    mpu.getFIFOBytes(fifoBuffer, packetSize);
    fifoCount -= packetSize;
    mpu.dmpGetQuaternion(&q, fifoBuffer);
    mpu.dmpGetAccel(&aa, fifoBuffer);
    mpu.dmpGetGravity(&gravity, &q);
    mpu.dmpGetLinearAccel(&aaReal, &aa, &gravity);
    mpu.dmpGetLinearAccelInWorld(&aaWorld, &aaReal, &q);
    mpu.dmpGetYawPitchRoll(ypr, &q, &gravity);
    Serial.print("hand:");
    Serial.print(ypr[0] * 180/M_PI);
    Serial.print(":");
    Serial.print(ypr[1] * 180/M_PI);
    Serial.print(":");
    Serial.print(ypr[2] * 180/M_PI);
    Serial.print(":");
    Serial.print(aaWorld.x);
    Serial.print(":");
    Serial.print(aaWorld.y);
    Serial.print(":");
    Serial.print(aaWorld.z);
    Serial.println(":");
}

}

acs · Accepted Answer · 2015-06-23 22:39:21Z

7

The pyqtgraph module is a great solution. It is very fast and easy.

Here is new code:

from pyqtgraph.Qt import QtGui, QtCore
import numpy as np
import pyqtgraph as pg
from pyqtgraph.ptime import time
import serial

app = QtGui.QApplication([])

p = pg.plot()
p.setWindowTitle('live plot from serial')
curve = p.plot()

data = [0]
raw=serial.Serial('COM9', 115200)


def update():
    global curve, data
    line = raw.readline()
    if ("hand" in line):
       line=line.split(":")
       if len(line)==8:
            data.append(float(line[4]))
            xdata = np.array(data, dtype='float64')
            curve.setData(xdata)
            app.processEvents()

timer = QtCore.QTimer()
timer.timeout.connect(update)
timer.start(0)

if __name__ == '__main__':
    import sys
    if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
        QtGui.QApplication.instance().exec_()

answered Jun 23, 2015 at 22:39

acs

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2 Comments

Landon Over a year ago

This is not fast at all.

acs Over a year ago

@Landon That's the fastest I could find at that time. Share if you know faster.

jcoppens · Accepted Answer · 2015-06-23 13:27:12Z

Plotting real-time in Matplotlib? There's price to pay for all the power you are buying. Autoscaling, autoaxis, etc... It all costs time!

A much faster solution is using a toolkit such as Gtk, and doing the plotting yourself, with a canvas such as GooCanvas.

Still, there are a few things that you can do to speed up your plotting.

Why the Pause statement?
Split the communication to another thread and make the plot take the following sample when it's ready (skipping values from the input queue)
You are appending, which makes the plot rescale frequently. Remove the first sample after N samples have been acquired.
I believe it is possible to plot in the background with MatPlotLib (not sure) and then copy to the screen less frequently.

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