Python realtime plotting

Question

I acquire some data in two arrays: one for the time, and one for the value. When I reach 1000 points, I trigger a signal and plot these points (x=time, y=value).

I need to keep on the same figure the previous plots, but only a reasonable number to avoid slowing down the process. For example, I would like to keep 10,000 points on my graph. The matplotlib interactive plot works fine, but I don't know how to erase the first points and it slows my computer very quickly. I looked into matplotlib.animation, but it only seems to repeat the same plot, and not really actualise it.

I'm really looking for a light solution, to avoid any slowing.

As I acquire for a very large amount of time, I erase the input data on every loop (the 1001st point is stored in the 1st row and so on).

Here is what I have for now, but it keeps all the points on the graph:

import matplotlib.pyplot as plt

def init_plot():
  plt.ion()
  plt.figure()
  plt.title("Test d\'acqusition", fontsize=20)
  plt.xlabel("Temps(s)", fontsize=20)
  plt.ylabel("Tension (V)", fontsize=20)
  plt.grid(True)

def continuous_plot(x, fx, x2, fx2):
  plt.plot(x, fx, 'bo', markersize=1)
  plt.plot(x2, fx2, 'ro', markersize=1)
  plt.draw()

I call the init function once, and the continous_plot is in a process, called every time I have 1000 points in my array.

Answer 1

The lightest solution you may have is to replace the X and Y values of an existing plot. (Or the Y value only, if your X data does not change. A simple example:

import matplotlib.pyplot as plt
import numpy as np
import time

fig = plt.figure()
ax = fig.add_subplot(111)

# some X and Y data
x = np.arange(10000)
y = np.random.randn(10000)

li, = ax.plot(x, y)

# draw and show it
ax.relim() 
ax.autoscale_view(True,True,True)
fig.canvas.draw()
plt.show(block=False)

# loop to update the data
while True:
    try:
        y[:-10] = y[10:]
        y[-10:] = np.random.randn(10)

        # set the new data
        li.set_ydata(y)

        fig.canvas.draw()

        time.sleep(0.01)
    except KeyboardInterrupt:
        break

This solution is quite fast, as well. The maximum speed of the above code is 100 redraws per second (limited by the time.sleep), I get around 70-80, which means that one redraw takes around 4 ms. But YMMV depending on the backend, etc.

Answer 2

To be totally interactive, you could use Bokeh for this. Concretely you could use an update function that is called every X ms and stream the new data.

Here there is a fragment I use:

def update():
     candle_data.stream(new_data, 300)   

plot = figure(x_axis_type='datetime',x_range=(start_day, final_day), width=1500, height=900, title='Live Chart', sizing_mode='scale_both')
plot.segment(x0='time', y0='highest', x1='time', y1='lowest', color='black', source=candle_data)
plot.vbar(x='time', width = 0.5*60*60*50 ,bottom='open', top='close',fill_color='color', line_color='black', source = candle_data) 
doc.add_root(column([plot]))
doc.add_periodic_callback(update, 20000)
doc.title = "Candle Data Live Rates"

Answer 3

I know I'm late to answer this question, bt for your issue you could look into the "joystick" package. It is based on the line.set_data() and canvas.draw() methods, with optional axes re-scaling. It also allows for interactive text logging or image plotting (in addition to graph plotting). No need to do your own loops in a separate thread, the package takes care of it, just give the update frequency you wish. Plus the console remains available for additional monitoring commands. See http://www.github.com/ceyzeriat/joystick/ or https://pypi.python.org/pypi/joystick (use pip install joystick to install)

try:

import joystick as jk
import numpy as np
import time

class test(jk.Joystick):
    # initialize the infinite loop decorator
    _infinite_loop = jk.deco_infinite_loop()

    def _init(self, *args, **kwargs):
        """
        Function called at initialization, see the doc
        """
        self._t0 = time.time()  # initialize time
        self.xdata = np.array([self._t0])  # time x-axis
        self.ydata = np.array([0.0])  # fake data y-axis
        # create a graph frame
        self.mygraph = self.add_frame(jk.Graph(name="test", size=(500, 500), pos=(50, 50), fmt="go-", xnpts=10000, xnptsmax=10000, xylim=(None, None, 0, 1)))

    @_infinite_loop(wait_time=0.2)
    def _generate_data(self):  # function looped every 0.2 second to read or produce data
        """
        Loop starting with the simulation start, getting data and
    pushing it to the graph every 0.2 seconds
        """
        # concatenate data on the time x-axis
        self.xdata = jk.core.add_datapoint(self.xdata, time.time(), xnptsmax=self.mygraph.xnptsmax)
        # concatenate data on the fake data y-axis
        self.ydata = jk.core.add_datapoint(self.ydata, np.random.random(), xnptsmax=self.mygraph.xnptsmax)
        self.mygraph.set_xydata(t, self.ydata)

t = test()
t.start()
t.stop()

Answer 4

Use a fixed size array and plot that using matplot.

 import collections
 array = collections.deque([None] * 1000, maxlen=1000)

Whenver you append to the array it will remove the first element.