Develop Reference

How to show sliding windows of a numpy array with matplotlib FuncAnimation

I am developing a simple algorithm for the detection of peaks in a signal. To troubleshoot my algorithm (and to showcase it), I would like to observe the signal and the detected peaks all along the signal duration (i.e. 20 minutes at 100Hz = 20000 time-points).
I thought that the best way to do it would be to create an animated plot with matplotlib.animation.FuncAnimation that would continuously show the signal sliding by 1 time-points and its superimposed peaks within a time windows of 5 seconds (i.e. 500 time-points). The signal is stored in a 1D numpy.ndarray while the peaks information are stored in a 2D numpy.ndarray containing the x and y coordinates of the peaks.
This is a "still frame" of how the plot would look like.
Now the problem is that I cannot wrap my head around the way of doing this with FuncAnimation.
If my understanding is correct I need three main pieces: the init_func parameter, a function that create the empty frame upon which the plot is drawn, the func parameter, that is the function that actually create the plot for each frame, and the parameter frames which is defined in the help as Source of data to pass func and each frame of the animation.
Looking at examples of plots with FuncAnimation, I can only find use-cases in which the data to plot are create on the go, like here, or here, where the data to plot are created on the basis of the frame.
What I do not understand is how to implement this with data that are already there, but that are sliced on the basis of the frame. I would thus need the frame to work as a sort of sliding window, in which the first window goes from 0 to 499, the second from 1to 500 and so on until the end of the time-points in the ndarray, and an associated func that will select the points to plot on the basis of those frames. I do not know how to implement this.
I add the code to create a realistic signal, to simply detect the peaks and to plot the 'static' version of the plot I would like to animate:
import neurokit2 as nk
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from scipy.signal import find_peaks
#create realistic data
data = nk.ecg_simulate(duration = 50, sampling_rate = 100, noise = 0.05,\
random_state = 1)
#scale data
scaler = MinMaxScaler()
scaled_arr = scaler.fit_transform(data.reshape(-1,1))
#find peaks
peak = find_peaks(scaled_arr.squeeze(), height = .66,\
distance = 60, prominence = .5)
#plot
plt.plot(scaled_arr[0:500])
plt.scatter(peak[0][peak[0] < 500],\
peak[1]['peak_heights'][peak[0] < 500],\
color = 'red')

I've created an animation using the data you presented; I've extracted the data in 500 increments for 5000 data and updated the graph. To make it easy to extract the data, I have created an index of 500 rows, where id[0] is the start row, id1 is the end row, and the number of frames is 10. This code works, but the initial settings and dataset did not work in the scatter plot, so I have drawn the scatter plot directly in the loop process.
import neurokit2 as nk
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from scipy.signal import find_peaks
import numpy as np
#create realistic data
data = nk.ecg_simulate(duration = 50, sampling_rate = 100, noise = 0.05, random_state = 1)
#scale data
scaler = MinMaxScaler()
scaled_arr = scaler.fit_transform(data.reshape(-1,1))
#find peaks
peak = find_peaks(scaled_arr.squeeze(), height = .66, distance = 60, prominence = .5)
ymin, ymax = min(scaled_arr), max(scaled_arr)
fig = plt.figure()
ax = fig.add_subplot(111)
line, = ax.plot([],[], lw=2)
scat = ax.scatter([], [], s=20, facecolor='red')
idx = [(s,e) for s,e in zip(np.arange(0,len(scaled_arr), 1), np.arange(499,len(scaled_arr)+1, 1))]
def init():
line.set_data([], [])
return line,
def animate(i):
id = idx[i]
#print(id[0], id[1])
line.set_data(np.arange(id[0], id[1]), scaled_arr[id[0]:id[1]])
x = peak[0][(peak[0] > id[0]) & (peak[0] < id[1])]
y = peak[1]['peak_heights'][(peak[0] > id[0]) & (peak[0] < id[1])]
#scat.set_offsets(x, y)
ax.scatter(x, y, s=20, c='red')
ax.set_xlim(id[0], id[1])
ax.set_ylim(ymin, ymax)
return line,scat
anim = FuncAnimation(fig, animate, init_func=init, frames=50, interval=50, blit=True)
plt.show()

Probably not exactly what you want, but hope it can help,
import neurokit2 as nk
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from sklearn.preprocessing import MinMaxScaler
import numpy as np
from matplotlib.animation import FuncAnimation
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# This function is called periodically from FuncAnimation
def animate(i, xs, ys):
xs = xs[i]
ys = ys[i]
# Draw x and y lists
ax.clear()
ax.plot(xs, ys)
if __name__=="__main__":
data = nk.ecg_simulate(duration = 50, sampling_rate = 100, noise = 0.05, random_state = 1)
scaler = MinMaxScaler()
scaled_arr = scaler.fit_transform(data.reshape(-1,1))
ys = scaled_arr.flatten()
ys = [ys[0:50*i] for i in range(1, int(len(ys)/50)+1)]
xs = [np.arange(0, len(ii)) for ii in ys ]
ani = animation.FuncAnimation(fig, animate, fargs=(xs, ys), interval=500)
ani.save('test.gif')

Animating a line plot over time in Python

Time series data is data over time. I am trying to animate a line plot of time series data in python. In my code below this translates to plotting xtraj as they and trange as the x. The plot does not seem to be working though.
I have found similar questions on Stack overflow but none of the solutions provided here seem to work. Some similar questions are matplotlib animated line plot stays empty, Matplotlib FuncAnimation not animating line plot and a tutorial referencing the help file Animations with Matplotlib.
I begin by creating the data with the first part and simulating it with the second. I tried renaming the data that would be used as y-values and x-values in order to make it easier to read.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation
dt = 0.01
tfinal = 5.0
x0 = 0
sqrtdt = np.sqrt(dt)
n = int(tfinal/dt)
xtraj = np.zeros(n+1, float)
trange = np.linspace(start=0,stop=tfinal ,num=n+1)
xtraj[0] = x0
for i in range(n):
xtraj[i+1] = xtraj[i] + np.random.normal()
x = trange
y = xtraj
# animation line plot example
fig = plt.figure(4)
ax = plt.axes(xlim=(-5, 5), ylim=(0, 5))
line, = ax.plot([], [], lw=2)
def init():
line.set_data([], [])
return line,
def animate(i):
line.set_data(x[:i], y[:i])
return line,
anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(x)+1,interval=200, blit=False)
plt.show()
Any help would be highly appreciated. I am new to working in Python and particularly trying to animate plots. So I must apologize if this question is trivial.
Summary
So to summarize my question how does one animate time series in Python, iterating over the time steps (x-values).

Check this code:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation
dt = 0.01
tfinal = 1
x0 = 0
sqrtdt = np.sqrt(dt)
n = int(tfinal/dt)
xtraj = np.zeros(n+1, float)
trange = np.linspace(start=0,stop=tfinal ,num=n+1)
xtraj[0] = x0
for i in range(n):
xtraj[i+1] = xtraj[i] + np.random.normal()
x = trange
y = xtraj
# animation line plot example
fig, ax = plt.subplots(1, 1, figsize = (6, 6))
def animate(i):
ax.cla() # clear the previous image
ax.plot(x[:i], y[:i]) # plot the line
ax.set_xlim([x0, tfinal]) # fix the x axis
ax.set_ylim([1.1*np.min(y), 1.1*np.max(y)]) # fix the y axis
anim = animation.FuncAnimation(fig, animate, frames = len(x) + 1, interval = 1, blit = False)
plt.show()
The code above reproduces this animation:

ArtistAnimation of subplots with different framerates

Consider the following code which implements ArtistAnimation to animate two different subplots within the same figure object.
import numpy as np
import itertools
import matplotlib.pyplot as plt
import matplotlib.mlab as ml
import matplotlib.animation as animation
def f(x,y,a):
return ((x/a)**2+y**2)
avals = np.linspace(0.1,1,10)
xaxis = np.linspace(-2,2,9)
yaxis = np.linspace(-2,2,9)
xy = itertools.product(xaxis,yaxis)
xy = list(map(list,xy))
xy = np.array(xy)
x = xy[:,0]
y = xy[:,1]
fig, [ax1,ax2] = plt.subplots(2)
ims = []
for a in avals:
xi = np.linspace(min(x), max(x), len(x))
yi = np.linspace(min(y), max(y), len(y))
zi = ml.griddata(x, y, f(x, y, a), xi, yi, interp='linear') # turn it into grid data, this is what imshow takes
title = plt.text(35,-4,str(a), horizontalalignment = 'center')
im1 = ax1.imshow(zi, animated = True, vmin = 0, vmax = 400)
im2 = ax2.imshow(zi, animated=True, vmin=0, vmax=400)
ims.append([im1,im2, title])
ani = animation.ArtistAnimation(fig, ims, interval = 1000, blit = False)
plt.show()
In this case the number of items in im1 and im2 are the same, and the frame rate for each subplot is identical.
Now, imagine I have 2 lists with different numbers of items, and that I wish ArtistAnimate to go through the frames in the same total time. Initially I thought of manipulating the interval keyword in the ArtistAnimation call but this implies that you can set different intervals for different artists, which I don't think is possible.
Anyway, I think the basic idea is pretty clear len(im1) is not equal to len(im2), but the animation needs to go through them all in the same amount of time.
Is there any way to do this please? Thanks
EDIT
While I try out the answer provided below, I should add that I would rather use ArtistAnimation due to the structure of my data. If there are no alternatives I will revert to the solution below.

Yes that is possible, kinda, using Funcanimation and encapsulating your data inside func.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
arr1 = np.random.rand(300,3,4)
arr2 = np.random.rand(200,5,6)
fig, (ax1, ax2) = plt.subplots(1,2)
img1 = ax1.imshow(arr1[0])
img2 = ax2.imshow(arr2[0])
# set relative display rates
r1 = 2
r2 = 3
def animate(ii):
if ii % r1:
img1.set_data(arr1[ii/r1])
if ii % r2:
img2.set_data(arr2[ii/r2])
return img1, img2
ani = animation.FuncAnimation(fig, func=animate, frames=np.arange(0, 600))
plt.show()

Updating a matplotlib bar graph?

I have a bar graph which retrieves its y values from a dict. Instead of showing several graphs with all the different values and me having to close every single one, I need it to update values on the same graph. Is there a solution for this?

Here is an example of how you can animate a bar plot.
You call plt.bar only once, save the return value rects, and then call rect.set_height to modify the bar plot.
Calling fig.canvas.draw() updates the figure.
import matplotlib
matplotlib.use('TKAgg')
import matplotlib.pyplot as plt
import numpy as np
def animated_barplot():
# http://www.scipy.org/Cookbook/Matplotlib/Animations
mu, sigma = 100, 15
N = 4
x = mu + sigma*np.random.randn(N)
rects = plt.bar(range(N), x, align = 'center')
for i in range(50):
x = mu + sigma*np.random.randn(N)
for rect, h in zip(rects, x):
rect.set_height(h)
fig.canvas.draw()
fig = plt.figure()
win = fig.canvas.manager.window
win.after(100, animated_barplot)
plt.show()

I've simplified the above excellent solution to its essentials, with more details at my blogpost:
import numpy as np
import matplotlib.pyplot as plt
numBins = 100
numEvents = 100000
file = 'datafile_100bins_100000events.histogram'
histogramSeries = np.loadtext(file)
fig, ax = plt.subplots()
rects = ax.bar(range(numBins), np.ones(numBins)*40) # 40 is upper bound of y-axis
for i in range(numEvents):
for rect,h in zip(rects,histogramSeries[i,:]):
rect.set_height(h)
fig.canvas.draw()
plt.pause(0.001)

How to update a plot in matplotlib

I'm having issues with redrawing the figure here. I allow the user to specify the units in the time scale (x-axis) and then I recalculate and call this function plots(). I want the plot to simply update, not append another plot to the figure.
def plots():
global vlgaBuffSorted
cntr()
result = collections.defaultdict(list)
for d in vlgaBuffSorted:
result[d['event']].append(d)
result_list = result.values()
f = Figure()
graph1 = f.add_subplot(211)
graph2 = f.add_subplot(212,sharex=graph1)
for item in result_list:
tL = []
vgsL = []
vdsL = []
isubL = []
for dict in item:
tL.append(dict['time'])
vgsL.append(dict['vgs'])
vdsL.append(dict['vds'])
isubL.append(dict['isub'])
graph1.plot(tL,vdsL,'bo',label='a')
graph1.plot(tL,vgsL,'rp',label='b')
graph2.plot(tL,isubL,'b-',label='c')
plotCanvas = FigureCanvasTkAgg(f, pltFrame)
toolbar = NavigationToolbar2TkAgg(plotCanvas, pltFrame)
toolbar.pack(side=BOTTOM)
plotCanvas.get_tk_widget().pack(side=TOP)

You essentially have two options:
Do exactly what you're currently doing, but call graph1.clear() and graph2.clear() before replotting the data. This is the slowest, but most simplest and most robust option.
Instead of replotting, you can just update the data of the plot objects. You'll need to make some changes in your code, but this should be much, much faster than replotting things every time. However, the shape of the data that you're plotting can't change, and if the range of your data is changing, you'll need to manually reset the x and y axis limits.
To give an example of the second option:
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 6*np.pi, 100)
y = np.sin(x)
# You probably won't need this if you're embedding things in a tkinter plot...
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111)
line1, = ax.plot(x, y, 'r-') # Returns a tuple of line objects, thus the comma
for phase in np.linspace(0, 10*np.pi, 500):
line1.set_ydata(np.sin(x + phase))
fig.canvas.draw()
fig.canvas.flush_events()

You can also do like the following:
This will draw a 10x1 random matrix data on the plot for 50 cycles of the for loop.
import matplotlib.pyplot as plt
import numpy as np
plt.ion()
for i in range(50):
y = np.random.random([10,1])
plt.plot(y)
plt.draw()
plt.pause(0.0001)
plt.clf()

This worked for me. Repeatedly calls a function updating the graph every time.
import matplotlib.pyplot as plt
import matplotlib.animation as anim
def plot_cont(fun, xmax):
y = []
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
def update(i):
yi = fun()
y.append(yi)
x = range(len(y))
ax.clear()
ax.plot(x, y)
print i, ': ', yi
a = anim.FuncAnimation(fig, update, frames=xmax, repeat=False)
plt.show()
"fun" is a function that returns an integer.
FuncAnimation will repeatedly call "update", it will do that "xmax" times.

This worked for me:
from matplotlib import pyplot as plt
from IPython.display import clear_output
import numpy as np
for i in range(50):
clear_output(wait=True)
y = np.random.random([10,1])
plt.plot(y)
plt.show()

I have released a package called python-drawnow that provides functionality to let a figure update, typically called within a for loop, similar to Matlab's drawnow.
An example usage:
from pylab import figure, plot, ion, linspace, arange, sin, pi
def draw_fig():
# can be arbitrarily complex; just to draw a figure
#figure() # don't call!
plot(t, x)
#show() # don't call!
N = 1e3
figure() # call here instead!
ion() # enable interactivity
t = linspace(0, 2*pi, num=N)
for i in arange(100):
x = sin(2 * pi * i**2 * t / 100.0)
drawnow(draw_fig)
This package works with any matplotlib figure and provides options to wait after each figure update or drop into the debugger.

In case anyone comes across this article looking for what I was looking for, I found examples at
How to visualize scalar 2D data with Matplotlib?
and
http://mri.brechmos.org/2009/07/automatically-update-a-figure-in-a-loop
(on web.archive.org)
then modified them to use imshow with an input stack of frames, instead of generating and using contours on the fly.
Starting with a 3D array of images of shape (nBins, nBins, nBins), called frames.
def animate_frames(frames):
nBins = frames.shape[0]
frame = frames[0]
tempCS1 = plt.imshow(frame, cmap=plt.cm.gray)
for k in range(nBins):
frame = frames[k]
tempCS1 = plt.imshow(frame, cmap=plt.cm.gray)
del tempCS1
fig.canvas.draw()
#time.sleep(1e-2) #unnecessary, but useful
fig.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
win = fig.canvas.manager.window
fig.canvas.manager.window.after(100, animate_frames, frames)
I also found a much simpler way to go about this whole process, albeit less robust:
fig = plt.figure()
for k in range(nBins):
plt.clf()
plt.imshow(frames[k],cmap=plt.cm.gray)
fig.canvas.draw()
time.sleep(1e-6) #unnecessary, but useful
Note that both of these only seem to work with ipython --pylab=tk, a.k.a.backend = TkAgg
Thank you for the help with everything.

All of the above might be true, however for me "online-updating" of figures only works with some backends, specifically wx. You just might try to change to this, e.g. by starting ipython/pylab by ipython --pylab=wx! Good luck!

Based on the other answers, I wrapped the figure's update in a python decorator to separate the plot's update mechanism from the actual plot. This way, it is much easier to update any plot.
def plotlive(func):
plt.ion()
#functools.wraps(func)
def new_func(*args, **kwargs):
# Clear all axes in the current figure.
axes = plt.gcf().get_axes()
for axis in axes:
axis.cla()
# Call func to plot something
result = func(*args, **kwargs)
# Draw the plot
plt.draw()
plt.pause(0.01)
return result
return new_func
Usage example
And then you can use it like any other decorator.
#plotlive
def plot_something_live(ax, x, y):
ax.plot(x, y)
ax.set_ylim([0, 100])
The only constraint is that you have to create the figure before the loop:
fig, ax = plt.subplots()
for i in range(100):
x = np.arange(100)
y = np.full([100], fill_value=i)
plot_something_live(ax, x, y)