Plotting live data

Besides plotting data from files, we can use matplotlib to plot sensor data as it is sampled. To achieve this, we can use the plot-animation feature, which automatically calls a function to collect new data and update our plot.

Create the following script, called live_graph.py:

#!/usr/bin/python3 
#live_graph.py 
import numpy as np 
import matplotlib.pyplot as plt 
import matplotlib.animation as animation 
import data_local as dataDevice 
 
PADDING=5 
myData = dataDevice.device() 
dispdata = [] 
timeplot=0 
fig, ax = plt.subplots() 
line, = ax.plot(dispdata) 
 
def update(data): 
  global dispdata,timeplot 
  timeplot+=1 
  dispdata.append(data) 
  ax.set_xlim(0, timeplot) 
  ymin = min(dispdata)-PADDING 
  ymax = max(dispdata)+PADDING 
  ax.set_ylim(ymin, ymax) 
  line.set_data(range(timeplot),dispdata) 
  return line 
 
def data_gen(): 
  while True: 
    yield myData.getNew()[1]/1000 
 
ani = animation.FuncAnimation(fig, update,  
                              data_gen, interval=1000) 
plt.show() 
#End 

We start by defining our dataDevice object and creating an empty array, dispdata[], which will hold all the data which has been collected. Next, we define our subplot and the line we are going to plot.

The FuncAnimation() function allows us to update a figure (fig) by defining an update function and a generator function. The generator function (data_gen()) will be called every interval (1,000 ms) and will produce a data value.

This example uses the core temperature reading that, when divided by 1,000, gives the actual temperature in degC:

To use the ADC data instead, change the import for dataDevice to data_adc and adjust the following line to use a channel other than [1] and apply a scaling that is different from 1,000:

 

yield myData.getNew()[1]/1000

Raspberry Pi plotting in real time

The data value is passed to the update() function, which allows us to add it to our dispdata[] array that will contain all the data values to be displayed in the plot. We adjust the x axis range to be near the min and max values of the data. We also adjust the y axis to grow as we continue to sample more data.

The FuncAnimation() function requires the data_gen() object to be a special type of function called a generator. A generator function produces a continuous series of values each time it is called, and can even use its previous state to calculate the next value if required. This is used to perform continuous calculations for plotting; this is why it is used here. In our case, we just want to run the same sampling function (new_data()) continuously so that each time it is called, it will yield a new sample.

Finally, we update the x and y axes data with our dispdata[] array (using the set_data() function), which will plot our samples against the number of seconds we are sampling. To use other data, or to plot data from the ADC, adjust the import for dataDevice and select the required channel (and scaling) in the data_gen() function.