I have an external service that, every now and then, triggers the function callback(points) — check the code below — in order to update a Polygon patch being plotted with matplotlib.
fig, ax = plt.subplots()
polygon = Polygon([[0, 0]])
patches = []
patches.append(polygon)
collection = PatchCollection(patches, animated=True, alpha=0.4)
ax.add_collection(collection)
ax.autoscale_view(True)
plt.show()
def callback(points):
polygon.set_xy(points)
fig.canvas.draw()
fig.canvas.flush_events()
However, this is not working... The plot is not being updated. Are canvas.draw and canvas.flush_events not the right things to call?
I have seen other examples on the web which use ArtistAnimation but I cannot use this since I cannot simulate my inputs; rather, they are coming from the external service that calls callback().
How can I make this work? Thanks!
OK, I modified this example slightly (which makes use of an UpdatablePatchCollection) and it works now.
Here is a working minimal example:
%matplotlib notebook # Use this on Jupyter Notebooks
import matplotlib.collections as mcollections
import matplotlib.pyplot as plt
import matplotlib as mpl
import time
class UpdatablePatchCollection(mcollections.PatchCollection):
def __init__(self, patches, *args, **kwargs):
self.patches = patches
mcollections.PatchCollection.__init__(self, patches, *args, **kwargs)
def get_paths(self):
self.set_paths(self.patches)
return self._paths
plt.ion()
rect = mpl.patches.Rectangle((0,0),1,1)
collection = UpdatablePatchCollection([rect])
fig, ax = plt.subplots()
ax.set_xlim(0,5)
ax.set_ylim(0,3)
ax.add_artist(collection)
def update_plot(i):
rect.set_xy((i,1))
fig.canvas.draw()
fig.canvas.flush_events()
for i in range(5):
time.sleep(0.5)
update_plot(i)
This part is important to "render" each frame.
fig.canvas.draw()
fig.canvas.flush_events()
And notice the important plt.ion() call. Without it, the plot will not interactively animate the changes to the patch collection.
Related
I am making an animation in Matplotlib where new artists (specifically patches) are added every few frames, but when I run it, every frame in which a new artist is added is completely blank. I know there is some issue with the blitting since it works when I turn that off, but I need it on. I return every shape that is created or modified in each frame, just like the documentation requires. I am using the MacOSX backend.
My code looks similar to this:
from random import random
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
fig = plt.figure()
axe = fig.add_axes([0, 0, 1, 1], frameon=False)
circles = []
def update(i):
if not i % 10:
new_circle = plt.Circle((random(), random()), 0.05, color='black')
axe.add_patch(new_circle)
circles.append(new_circle)
for circle in circles:
circle.center = (random(), random())
return circles
animation = FuncAnimation(fig, update, frames=60, interval=1000/30, repeat=False, blit=True)
plt.show()
This appears to be a bug with matplotlib in the MacOSX backend, so the solution is just to work around it by using a different backend or not blitting if possible.
I have tried to animate two different artists plt.quiver() and plt.hist() in matplotlib recently and both times I ran into the same problem. Apparently those classes (I hope my OOP literacy is holding up) both don't have a set_data like method. Well, technically plt.quiver() does have set_UVC, but that doesn't work with Line3D instances, only with Line2D. Also, there is an example for animating a histogram, but it seemed like some serious jerry-rigging to me. I tried to simply define my artist with new values in the update() function and then just return the new artist instead of defining the artist outside the update() and then updating the data of the artist using a set_data() method. But this only results in an animation in which all frames are kept in the plot and overlap. Below are the animations for both the Histogram and the Quiver plot.
Histogram:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
"""
evolution of mean values produced by 1000 dice rolls with
more and more dices, which lead to a narrowing variance
with a growing number of dices.
"""
fig, ax = plt.subplots()
def update(i):
k = [np.mean(np.random.randint(0,7,i)) for j in range(1000)]
lol = ax.hist(k,bins=20)
return lol
ani = FuncAnimation(fig, update, frames=(1,2,10,100,1000))
plt.show()
Quiver:
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.animation import FuncAnimation
def rot_z(angle):
o = 2*np.pi*(angle/360)
mat = np.array(((np.cos(o),-np.sin(o),0),
(np.sin(o), np.cos(o),0),
( 0 , 0 ,0)))
return mat
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_xlim(-1.5,1.5)
ax.set_ylim(-1.5,1.5)
ax.set_zlim(-1.5,1.5)
def update(frame):
x,y,z = rot_z(frame).dot(np.array((1,1,1)))
quiv = ax.quiver(0,
0,
0,
x,
y,
z,
length=1)
return quiv
ani = FuncAnimation(fig, update, frames=np.linspace(0,360,100))
plt.show()
If you run them, you can see the issue. So I wanted to know: Isn't there an easier, abstractable way of animating artists, or am I at the mercy of potentially non-existent setters? I have checked both dir(plt.quiver), dir(plt.hist) to see if I was simply overlooking those methods in the docs, but the example of the animated histogram seemed to confirm my fears.
You could try to clear the image at every update with ax.clear(). Maybe the histogram animation would be more smooth if you would extend an array of throws instead of restarting from scratch at each frame?
Edit: the code below includes a test to reuse the same samples
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
fig, ax = plt.subplots()
randnums = [np.random.randint(0,7,1000) for j in range(1000)]
def update(i):
k = [np.mean(randnums[j][:i]) for j in range(1000)]
ax.clear()
lol = ax.hist(k,bins=20)
return lol
ani = FuncAnimation(fig, update, frames=[2**t for t in range(11)])
plt.show()
I'm just starting using Matplotlib the "right" way. I'm writing various programs that will each give me back a time series, and I'm looking to superimpose the graphs of the various time series, like this:
I think what I want is a single Axes instance defined in the main function, then I call each of my little functions, and they all return a Line2D instance, and then I'll put them all on the Axes object I created.
But I'm having trouble taking an existing Line2D object and adding it to an existing Axes object (like I'd want to do with the output of my function.) I thought of taking a Line2D called a and say ax.add_line(a).
import matplotlib.pyplot as plt
a, = plt.plot([1,2,3], [3,4,5], label = 'a')
fig, ax = plt.subplots()
ax.add_line(a)
Gives me a RuntimeError: "Can not put single artist in more than one figure."
I'm guessing that over time Matplotlib has stopped wanting users to be able to add a given line to any Axes they want. A similar thing is discussed in the comments of this answer, except there they're talking about an Axes object in two different Figure objects.
What's the best way to accomplish what I want? I'd rather keep my main script tidy, and not say ax.plot(some_data) over and over when I want to superimpose these lines.
Indeed, you cannot add the same artist to more than one axes or figure.
But for what I understand from your question, that isn't really necessary.
So let's just do as you propose;
"I thought of taking a Line2D called a and say ax.add_line(a)."
import numpy as np
import matplotlib.pyplot as plt
def get_line(label="a"):
return plt.Line2D(np.linspace(0,1,10), np.random.rand(10), label = label)
fig, ax = plt.subplots()
ax.add_line(get_line(label="a"))
ax.add_line(get_line(label="b"))
ax.add_line(get_line(label="z"))
ax.legend()
plt.show()
The way matplotlib would recommend is to create functions that take an axes as input and plot to that axes.
import numpy as np
import matplotlib.pyplot as plt
def plot_line(ax=None, label="a"):
ax = ax or plt.gca()
line, = ax.plot(np.linspace(0,1,10), np.random.rand(10), label = label)
return line
fig, ax = plt.subplots()
plot_line(ax, label="a")
plot_line(ax, label="b")
plot_line(ax, label="z")
ax.legend()
plt.show()
A possible work around for your problem:
import matplotlib.pyplot as plt
x = np.array([1,2,3])
y = np.array([3,4,5])
label = '1'
def plot(x,y,label):
a, = plt.plot(x,y, label = label)
return a
fig, ax = plt.subplots()
plot(x,y,label)
plot(x,1.5*y,label)
You can put your plot command now in a loop with changing labels. You can still use the ax handle to modify/define the plot parameters.
Some seaborn methods like JointPlot create new figures on each call. This makes it impossible to create a simple animation like in matplotlib where iterative calls to plt.cla() or plt.clf() allow to update the contents of a figure without closing/opening the window each time.
The only solution I currently see is:
for t in range(iterations):
# .. update your data ..
if 'jp' in locals():
plt.close(jp.fig)
jp = sns.jointplot(x=data[0], y=data[1])
plt.pause(0.01)
This works because we close the previous window right before creating a new one. But of course, this is far from ideal.
Is there a better way? Can the plot somehow be done directly on a previously generated Figure object? Or is there a way to prevent these methods to generate new figures on each call?
sns.jointplot creates a figure by itself. In order to animate the jointplot, one might therefore reuse this created figure instead of recreating a new one in each iteration.
jointplot internally creates a JointGrid, so it makes sense to directly use this and plot the joint axes and the marginals individually. In each step of the animation one would then update the data, clear the axes and set them up just as during creation of the grid. Unfortunately, this last step involves a lot of code lines.
The final code may then look like:
import matplotlib.pyplot as plt
import matplotlib.animation
import seaborn as sns
import numpy as np
def get_data(i=0):
x,y = np.random.normal(loc=i,scale=3,size=(2, 260))
return x,y
x,y = get_data()
g = sns.JointGrid(x=x, y=y, size=4)
lim = (-10,10)
def prep_axes(g, xlim, ylim):
g.ax_joint.clear()
g.ax_joint.set_xlim(xlim)
g.ax_joint.set_ylim(ylim)
g.ax_marg_x.clear()
g.ax_marg_x.set_xlim(xlim)
g.ax_marg_y.clear()
g.ax_marg_y.set_ylim(ylim)
plt.setp(g.ax_marg_x.get_xticklabels(), visible=False)
plt.setp(g.ax_marg_y.get_yticklabels(), visible=False)
plt.setp(g.ax_marg_x.yaxis.get_majorticklines(), visible=False)
plt.setp(g.ax_marg_x.yaxis.get_minorticklines(), visible=False)
plt.setp(g.ax_marg_y.xaxis.get_majorticklines(), visible=False)
plt.setp(g.ax_marg_y.xaxis.get_minorticklines(), visible=False)
plt.setp(g.ax_marg_x.get_yticklabels(), visible=False)
plt.setp(g.ax_marg_y.get_xticklabels(), visible=False)
def animate(i):
g.x, g.y = get_data(i)
prep_axes(g, lim, lim)
g.plot_joint(sns.kdeplot, cmap="Purples_d")
g.plot_marginals(sns.kdeplot, color="m", shade=True)
frames=np.sin(np.linspace(0,2*np.pi,17))*5
ani = matplotlib.animation.FuncAnimation(g.fig, animate, frames=frames, repeat=True)
plt.show()
using the celluloid package (https://github.com/jwkvam/celluloid) I was able to animate seaborn plots without much hassle:
import numpy as np
from celluloid import Camera
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure()
camera = Camera(fig)
# animation draws one data point at a time
for i in range(0, data.shape[0]):
plot = sns.scatterplot(x=data.x[:i], y=data.y[:i])
camera.snap()
anim = camera.animate(blit=False)
anim.save('animation.mp4')
I'm sure similar code could be written for jointplots
Goal
Hi,
I am trying to animate a complex figure with several subplots and have started testing with the artist animation and the function animation methods.
For now, my goal is to have the subplot on the left show a moving colored line (not the problem) and the subplot on the right show an updated representation of a brain scan (the problem). Static, this looks something like this.
# Imports
import nilearn as nil
from nilearn import plotting as nlp
from matplotlib import pyplot as plt
window = np.arange(0,200-50)
fig = plt.figure(figsize=(7,4))
ax = fig.add_subplot(121)
ax.set_xlim([0, 200])
a = ax.axvspan(window[0], window[0]+50, color='blue', alpha=0.5)
ay = fig.add_subplot(122)
b = nlp.plot_stat_map(nil.image.index_img(s_img, 0), axes=ay, colorbar=False, display_mode='x', cut_coords=(0,))
Problem
As you can see, I am using nilearn for plotting the brain image. For some reason, the nilearn object from plot_stat_map does not have an attribute set_visible unlike the matplotlib object from axvspan.
So when I attempt a simple animation like so:
fig = plt.figure(figsize=(7,4))
ax = fig.add_subplot(121)
ax.set_xlim([0, 200])
ay = fig.add_subplot(122)
iml = list()
for i in np.arange(50):
a = ax.axvspan(window[i], window[i]+50, color='blue', alpha=0.5)
b = nlp.plot_stat_map(nil.image.index_img(s_img, i), axes=ay)
iml.append((a,b))
ani = animation.ArtistAniTruemation(fig, iml, interval=50, blit=False,
repeat_delay=1000)
it crashes with the following error:
/home/surchs/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/animation.pyc in _init_draw(self)
974 for f in self.new_frame_seq():
975 for artist in f:
--> 976 artist.set_visible(False)
977 # Assemble a list of unique axes that need flushing
978 if artist.axes not in axes:
AttributeError: 'OrthoSlicer' object has no attribute 'set_visible'
Makes sense, nilearn does maybe not conform to matplotlibs expectations. So I try the function animation method like so:
def show_things(i, window, ax, ay):
ax.axvspan(window[i], window[i]+50, color='blue', alpha=0.5)
nlp.plot_stat_map(nil.image.index_img(s_img, i), axes=ay, colorbar=False, display_mode='x', cut_coords=(0,))
fig = plt.figure(figsize=(7,4))
ax = fig.add_subplot(121)
ax.set_xlim([0, 200])
ay = fig.add_subplot(122)
ani = animation.FuncAnimation(fig, show_things, interval=10, blit=False, fargs=(window, ax, ay))
Although I am not sure if I am using things correctly, this gives me an animated brain plot on the right. However, the plot on the left is now not updated but just drawn over. So instead of a sliding bar, I get an expanding color surface. Something like this:
Question
How do I
get the plot on the left to update (as opposed to overwrite) on each iteration when using the function animation method? I already tried the ax.cla() function in matplotlib but since this also clears all axis attributes (like xlim) this is not a solution for me. Are there altneratives?
get the plot on the right to work with the artist animation method even though the custom plotting class is obviously missing a crucial attribute.
Also, I am not sure if I am doing the whole implementation part right, so any advice on that front is also very appreciated.
I suspect you may need to clear the axvspan axis between plots with ax.cla() to get the correct left plot (N.B. probably should clear the right plot too). To get round the problem of missing attributes, I'd suggest extracting the data from the returned handle from nlp.plot_stat_map and plotting with matplotlib pcolormesh (or imshow). Another possibility is creating a child class and adding this method yourself. It may also be worth submitting a bug/feature request to nilearn if this should be present.
By the way, if you're only after a quick and easy plot, you can do a poor man's version of animation using interactive plots, as a minimal example,
import matplotlib.pyplot as plt
import numpy as np
import time
#Interactive plot
plt.ion()
#Setup figures
fig = plt.figure(figsize=(7,4))
ax = fig.add_subplot(121)
ay = fig.add_subplot(122)
plt.show()
x = np.linspace(0,2*np.pi)
for i in range(10000):
print(i)
#Clear axes
ax.cla(); ay.cla()
#Update data
yx = np.sin(x+i*0.1)
yy = np.sin(2.*(x+i*0.1))
#Replot
ax.plot(x,yx)
ay.plot(x,yy)
#Pause to allow redraw
plt.draw()
plt.pause(0.01)