Creating a live plot of CSV data with Matplotlib

Question

I am trying to use Matplotlib to visualize some measurements. The measurements typically last about 24hrs and will include ~30k lines of data in the csv. I\'ve been struggling m

Answer 1

You have two issues. One is because you're effectively drawing things twice, the second is purely human psychology (the plot appears to slow over time because you're adding one point to 10000 vs adding one point to 10 or 100).

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Let's discuss the double-draw first:

The FuncAnimation will draw things for you, but by telling it to use blitting, it's only updating the inside of the axes and not the ticks, etc. Therefore, you need to manually call draw, but the animation will be calling draw_artist, as well.

You should be able to get at least a 2x speedup by removing blit=True and plt.draw()

Furthermore, by setting interval=0, you're forcing it to draw constantly, which will effectively force things to lock up. Set the interval to something more reasonable, e.g. 25 (the interval is in milliseconds. "25" is 40 fps.).

For example, this is very smooth for me:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation

y = np.random.normal(0, 1, 10000).cumsum(axis=0)
x = np.arange(y.size)

fig, ax = plt.subplots()
line, = ax.plot([], [], '.')
ax.margins(0.05)

def init():
    line.set_data(x[:2],y[:2])
    return line,

def animate(i):
    i = min(i, x.size)
    xdata = x[:i]
    ydata = y[:i]
    line.set_data(xdata, ydata)
    ax.relim()
    ax.autoscale()
    return line,

anim = animation.FuncAnimation(fig, animate, init_func=init, interval=25)

plt.show()

I've also added ax.margins(0.05) to avoid the axes limits snapping to the next nearest "even" number and giving a "jerky" appearance.

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However, because you're progressively plotting more and more data, the rate of change will appear to slow, simply because less of the data appears to be changing over time. Adding one point at the end of 10000 is hardly noticeable, but adding one point at the end of 10 is very noticable.

Therefore, the plot looks much more "exciting" at the beginning than at the end even though it's updating at the same rate.

This has nothing whatsoever to do with matplotlib, and is a consequence of the way you're choosing to animate your data.

To get around that, you might consider moving a "sliding window" through your data and plotting a constant number of points at a time. As an example:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation

y = np.random.normal(0, 1, 1000000).cumsum(axis=0)
x = np.arange(y.size) + 1

fig, ax = plt.subplots()
line, = ax.plot([], [], 'k-')
ax.margins(0.05)

def init():
    line.set_data(x[:2],y[:2])
    return line,

def animate(i):
    win = 300
    imin = min(max(0, i - win), x.size - win)
    xdata = x[imin:i]
    ydata = y[imin:i]
    line.set_data(xdata, ydata)
    ax.relim()
    ax.autoscale()
    return line,

anim = animation.FuncAnimation(fig, animate, init_func=init, interval=25)

plt.show()

Answer 2

I have below columns as an operation of pandas filter column :

    Object      A_START_TIME   B_START_TIME  B_STOP_TIME       A_STOP_TIME
0     17          27158762       27158803      27178575          27259032 

1     18          27259557       27259605      27263225          27323232

2     19          27323266       27323299      27332959          27341878

I am looking at plotting something like explained below :

for 17 : A_START_TIME----[B_START_TIME========B_STOP_TIME]-------A_STOP_TIME

For 18 : A_START_TIME----[B_START_TIME========B_STOP_TIME]-------A_STOP_TIME