I'm working on a program that displays drawings, with the option of animating the drawing to show which order the lines should be drawn in. When I used plt.show() to display the drawing as a still image, everything works as expected: the code pauses, and then resumes again as soon as the popup window is closed. However, when I use the same function to display an animated drawing, the code remains frozen even after I close the popup window. The only way to get it unstuck is to fully restart the python shell - it doesn't respond to KeyboardInterrupt.
UPDATE:
When I set repeat to False in the FuncAnimation call, it behaves slightly differently. If I close the popup window while the animation is running, the glitch happens, locking up my program. However, if I close the popup after the animation has finished, the program continues as intended. It seems like the glitch here has something to do with closing the window before the animation is done.
UPDATE 2:
For some reason, replacing all of the plt.plot() calls in the animate_pattern function with ax.plot() fixes the issue. I have no idea why this works, because as far as I know the two functions do the same thing. However, the problem is solved.
Below is the code for the module that handles the animation. Some notes:
Normally, I create the animations by calling plot_animated() from a different module. However, the bug happens whether or not I create the animation that way or do it through the code in this module's if name == main statement.
convert_to_points() is a function from the main module that turns the data it's given into a list of x-values and a list of y-values to be plotted.
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation,PillowWriter
from functools import partial
from os.path import isfile
import json
end_marker = [None]
smooth = 40
def animate_pattern(f,anim_data):
x_anim,y_anim,scale = anim_data
global end_marker
# starting point
if x_anim[f] is None:
end_marker = plt.plot(x_anim[1],y_anim[1],marker='o',ms=1.8*scale,mew=0.4*scale,mec="black",c="#ff6bff")
# segment going into a point
if f%smooth == 1:
plt.plot(x_anim[f-1:f+1],y_anim[f-1:f+1],c="#ff6bff",lw=scale)
plt.plot(x_anim[f],y_anim[f],marker='o',ms=1.8*scale,mew=0.4*scale,mec="black",c="#ff6bff")
# segment coming out of a point
elif f%smooth in (2,4):
plt.plot(x_anim[f-1:f+1],y_anim[f-1:f+1],c="#ff6bff",lw=scale)
plt.plot(x_anim[f-f%smooth+1],y_anim[f-f%smooth+1],marker='o',ms=1.8*scale,mew=0.4*scale,mec="black",c="#ff6bff")
# all other segments
else:
plt.plot(x_anim[f-1:f+1],y_anim[f-1:f+1],c="#ff6bff",lw=scale)
# marker for current endpoint of animated line
if x_anim[f]:
end_marker[0].remove()
end_marker = plt.plot(x_anim[f],y_anim[f],marker='h',ms=2.4*scale,mew=0.5*scale,mec="#547dd6",c="#6bc9e8")
def init_pattern(plot_data,settings):
x_vals,y_vals,scale = plot_data[:3]
# clear the canvas
plt.cla()
plt.gca().axis("off")
# draw the full pattern in the background
for i in range(len(x_vals)-1):
plt.plot(x_vals[i:i+2],y_vals[i:i+2],color=settings["monochrome_color"],lw=scale)
plt.plot(x_vals[i],y_vals[i],'ko',ms=2*scale)
plt.plot(x_vals[-1],y_vals[-1],'ko',ms=2*scale)
def anim_interpolate(plot_data):
x_vals,y_vals,scale = plot_data[:3]
x_anim,y_anim = [None],[None]
# create five interpolated points after each point
for i in range(len(x_vals)-1):
x_dist = x_vals[i+1] - x_vals[i]
y_dist = y_vals[i+1] - y_vals[i]
x_anim += [x_vals[i]+x_dist*(1/smooth)*j for j in range(smooth)]
y_anim += [y_vals[i]+y_dist*(1/smooth)*j for j in range(smooth)]
# add the last point
x_anim.append(x_vals[-1])
y_anim.append(y_vals[-1])
return x_anim,y_anim,scale
def plot_animated(plot_data,settings,):
# convert basic pointlist into special version for animating
anim_data = anim_interpolate(plot_data)
# create animation object by repeatedly invoking animate_pattern()
ani = FuncAnimation(plt.gcf(),
func=animate_pattern,
fargs=[anim_data],
frames=len(anim_data[0]),
init_func=partial(init_pattern,plot_data,settings),
interval=1000/smooth,
repeat=True)
return ani
if __name__ == "__main__":
with open("settings.json",mode="r") as file:
settings = json.load(file)
from hex_draw import convert_to_points
print("Displaying test animation...")
plot_data = convert_to_points("qeewdweddw","northeast",settings)
ax = plt.figure(figsize=(4,4)).add_axes([0,0,1,1])
ax.set_aspect("equal")
ani = plot_animated(plot_data,settings)
plt.show()
Related
I wish to plot some data from an array with multiple columns, and would like each column to be a different line on the same scrolling graph. As there are many columns, I think it would make sense to plot them within a loop. I'd also like to plot a second scrolling graph with a single line.
I can get the single line graph to scroll correctly, but the graph containing the multiple lines over-plots from the updated array without clearing the previous lines.
How do I get the lines to clear within the for loop. I thought that setData, might do the clearing. Do I have to have a pg.QtGui.QApplication.processEvents() or something similar within the loop? I tried to add that call but had it no effect.
My code:
#Based on example from PyQtGraph documentation
import numpy as np
import pyqtgraph as pg
win = pg.GraphicsLayoutWidget(show=True)
win.setWindowTitle('pyqtgraph example: Scrolling Plots')
timer = pg.QtCore.QTimer()
plot_1 = win.addPlot()
plot_2 = win.addPlot()
data1 = np.random.normal(size=(300))
curve1 = plot_1.plot(data1)
data_2d = np.random.normal(size=(3,300))
def update_plot():
global data1, data_2d
data1[:-1] = data1[1:]
data1[-1] = np.random.normal()
curve1.setData(data1)
for idx, n in enumerate(data_2d):
n[:-1] = n[1:]
n[-1] = np.random.normal()
curve2 = plot_2.plot(n,pen=(idx))
curve2.setData(n)
#pg.QtGui.QApplication.processEvents() #Does nothing
timer = pg.QtCore.QTimer()
timer.timeout.connect(update_plot)
timer.start(50)
if __name__ == '__main__':
pg.exec()
You could clear the plot of all curves each time with .clear(), but that wouldn't be very performant. A better solution would be to keep all the curve objects around and call setData on them each time, like you're doing with the single-curve plot. E.g.
curves_2d = [plot_2.plot(pen=idx) for idx, n in enumerate(data_2d)]
# ... in update_plot
curves_2d[idx].setData(n)
I am trying to animate a plot using visvis.
This is the example code they have:
import visvis as vv
# read image
ims = [vv.imread('astronaut.png')]
# make list of images: decrease red channel in subsequent images
for i in range(9):
im = ims[i].copy()
im[:,:,0] = im[:,:,0]*0.9
ims.append(im)
# create figure, axes, and data container object
a = vv.gca()
m = vv.MotionDataContainer(a)
# create textures, loading them into opengl memory, and insert into container.
for im in ims:
t = vv.imshow(im)
t.parent = m
and I added:
app = vv.use()
app.Run()
This worked. But I needed to animate a plot, not an image, so I tried doing this:
import visvis as vv
from visvis.functions import getframe
# create figure, axes, and data container object
a = vv.gca()
m = vv.MotionDataContainer(a, interval=100)
for i in range(3):
vv.plot([0, 2+i*10], [0, 2+i*10])
f = getframe(a)
t = vv.imshow(f)
t.parent = m
a.SetLimits(rangeX=[-2, 25], rangeY=[-2, 25])
app = vv.use()
app.Run()
The axes are being initialized very big, that is why I am using set limits, and the output is not animated. I am getting only the last frame so a line from (0,0) to (22, 22).
Does anyone know a way of doing this with visvis?
It turns out adding the frame as a child of MotionDataContainer was not the way to go. The function vv.plot returns an instance of the class Line, and one should add the line as a child. If anyone is having the same problem, I could write a more detailed answer.
EDIT Adding a more detailed answer as requested:
To animate a plot made of lines, one must simply add the lines as children of MotionDataContainer. Taking my example in the question above, one would write:
import visvis as vv
# create figure, axes, and data container object
a = vv.gca()
m = vv.MotionDataContainer(a, interval=100)
for i in range(3):
line = vv.plot([0, 2+i*10], [0, 2+i*10])
line.parent = m
app = vv.use()
app.Run()
In my special case, I even needed to animate multiple lines being drawn at the same time.
To do this, I ended up defining a new class that, like MotionDataContainer, also inherits from MotionMixin, and change the class attribute delta which specifies how many objects should be made visible at the same time. For that, one has to also rewrite the function _SetMotionIndex.
(See visvis official source code: https://github.com/almarklein/visvis/blob/master/wobjects/motion.py)
Disclaimer: Concerning the animation of multiple objects, I have no idea if this is the intended use or if this is the easiest solution, but this is what worked for me.
I'm trying to use ginput to register clicks on a map, and wanted to add action buttons using matplotlib widgets. In the following code, I can pass back the value of action to the main code by declaring it a global. If I click on the map, action=0, if I click on the button, action=1, as desired.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Button
class Index:
def test(self, event):
global action
action=1
# fake data
x=np.arange(30)
y=x**2
fig,ax=plt.subplots()
ax.plot(x,y)
callback = Index()
buttonname=['test']
colors=['white']
idx=[0.2]
bax,buttons={},{}
# set up list of buttons.
for i,col,button in zip(idx,colors,buttonname):
bax[button] = plt.axes([0.92, i, 0.07, 0.07])
buttons[button] = Button(bax[button],button,color=col,hovercolor='green')
buttons[button].on_clicked(getattr(callback,button))
# register click on plot
while True:
pts=plt.ginput(1)
plt.pause(0.5)
print("action is ",action)
action=0 # reset
But my confusion is, if I take the exact same code and place it in a def block, the value of action is no longer passed back, action is always zero.
def subtest():
class Index:
def test(self, event):
global action
action=1
# fake data
action=0
x=np.arange(30)
y=x**2
fig,ax=plt.subplots()
ax.plot(x,y)
callback = Index()
buttonname=['test']
colors=['white']
idx=[0.2]
bax,buttons={},{}
# set up list of buttons.
for i,col,button in zip(idx,colors,buttonname):
bax[button] = plt.axes([0.92, i, 0.07, 0.07])
buttons[button] = Button(bax[button],button,color=col,hovercolor='green')
buttons[button].on_clicked(getattr(callback,button))
# register click on plot
while True:
pts=plt.ginput(1)
plt.pause(0.5)
print("action is ",action)
action=0 # reset
res=subtest()
I'm very confused as to why this happens. I tried moving the class definition out into the main code but that didn't help. I'm happy for any kind of solution (e.g. passing action through an argument, which I have not understood how to do with widgets), as I think that the use of global is often frowned apon. But also a global -based solution is fine.
action inside subtest is local to subtest, while action inside Index.test is global. Either declare action global in subtest, or use nonlocal in Index.test.
(I suspect there may be better solutions without globals, but since I'm not familiar with the GUI toolkit I'll leave that to someone else.)
you want to check out this article on closures and this other article on closures. I think you need to declare action outside of both your class and functions and then reference it with global in the class.
I don't think you need to use a class - you can get away with passing a variable, boolean, dictionary around functions and achieve the same thing.
You can use the "on_click" event to do what you want see this widget tutorial widget tutorial and this on matplotlib event handling
Here is an example piece of code using "global" to effect state changes. Better to pass around a variable or use the event to trigger whatever you want the state to effect.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Button
state = False
def change_state(btn_action):
#do something
btn_action= not(btn_action)
return btn_action
def grid(val):
global state
#do something
state =change_state(state)
print(state)
ax.grid()
fig.canvas.draw()
#reset value
state =change_state(state)
print(f'resetting state to {state}')
# fake data
x=np.arange(30)
y=x**2
#create fig, ax
fig =plt.figure()
ax= fig.subplots()
p, = ax.plot(x,y)
# set up list of buttons.
buttonname=['test']
colors=['white']
idx=[0.2]
bax,buttons={},{}
for i,col,button in zip(idx,colors,buttonname):
bax[button] = plt.axes([0.92, i, 0.07, 0.07])
buttons[button] = Button(bax[button],button,color=col,hovercolor='green')
buttons[button].on_clicked(grid)
#show plot
fig.canvas.draw()
plt.show()
I wrote a code to display live feed of analog data. The code uses pyfirmata to define pins and pull readings. I've set the funcanimation to pull all 12 channels when the port is open. Currently, matplotlib checkbutton is used to show/hide live feed of the channels.
I'd like to manipulate the matplotlib checkbutton so that only the channels that are checked are actually read instead of just being hidden.
The matplotlib widget module is a little too sophisticated for me to break down to a level where I can modify it. What I'd like to do is write a true/false status on each index depending on its visibility then put a nested if statements in the funcanimation to read only the visible lines. I'd appreciate if anyone could share me a sample code to allow me to do that.
Here is a segment of my code:
##check buttons
lines = [ln0, ln1, ln2, ln3, ln4, ln5, ln6, ln7, ln8, ln9, ln10, ln11]
labels = [str(ln0.get_label()) for ln0 in lines]
visibility = [ln0.get_visible() for ln0 in lines]
check = CheckButtons(ax1, labels, visibility)
for i, c in enumerate(colour):
check.labels[i].set_color(c)
def func(label):
index = labels.index(label)
lines[index].set_visible(not lines[index].get_visible())
check.on_clicked(func)
## define pins
a0 = due.get_pin('a:0:i')
a1 = due.get_pin('a:1:i')
a2 = due.get_pin('a:2:i')
a3 = ...
##funcanimation
def rt(i):
t.append(datetime.now())
if due.is_open == True:
T0.append(round(a0.read()*3.3/0.005, 1))
T1.append(round(a1.read()*3.3/0.005, 1))
...
Here is the graph and checkbuttons when run:
click here
Thanks,
I figured it out. There is a get_status function embedded in the matplotlib widget which returns a tuple of trues and falses to indicate the status of check buttons. I used this to write a nested if statements in the funcanimation so that only checked ones are read.
I know that there are plenty of similar topics in StackOverflow but none of the answers solved my specific problem.
First of all, I am trying plot 2-dimensional data points from different classes with scatter command to the figure. The program uses matplotlib events like button_press_event and motion_notify_event and I assume that right after event commands plt.show() should be used. The main problem is that once some tasks are done with events (i.e. labeling), I want to update the whole figure but plt.show() is blocking the option to continue the program. Actually plt.show() should block the program until the user of program decides to go forward. Is there any solution to control this blocking attribute?
I have tried plt.ion(), plt.ioff(), plt.show(block=False), plt.draw(), plt.pause(0.001), global variables with while loop etc. without success. The only way the program works somehow correctly is when all the figures are closed inside of button_press_event when so called forward condition is met but it is not very user-friendly solution if all figures are closed every time data points are updated.
Here is the the glimpse of the code:
def draw_original_figure(random_sample, random_sample_label, random_sample_image, X_train_chosen, y_train_chosen, images_train_chosen, classes, accuracy, embedding, model, h=0.05):
global points1, im1, s_im1, xybox, x1, y1, fig1, classes1, points_to_plot, y_train_chosen1, accuracy1, random_sample1, embedding1, y_train_chosen1, h1, random_sample_label1, result1
fig1 = plt.gcf()
.
.
.
original_figure_plot()
fig1.canvas.mpl_connect('motion_notify_event', hover)
fig1.canvas.mpl_connect('button_press_event', click)
plt.show()
def hover(event):
# if the mouse is over the scatter points
if points1.contains(event)[0]:
# find out the index within the array from the event
inds = points1.contains(event)[1]["ind"]
ind = inds[0]
# get the figure size
w,h = fig1.get_size_inches()*fig1.dpi
ws = (event.x > w/2.)*-1 + (event.x <= w/2.)
hs = (event.y > h/2.)*-1 + (event.y <= h/2.)
# if event occurs in the top or right quadrant of the figure,
# change the annotation box position relative to mouse.
ab1.xybox = (xybox[0]*ws, xybox[1]*hs)
# make annotation box visible
ab1.set_visible(True)
# place it at the position of the hovered scatter point
ab1.xy =(x1[ind], y1[ind])
# set the image corresponding to that point
im1.set_data(s_im1[ind,:,:])
else:
#if the mouse is not over a scatter point
ab1.set_visible(False)
fig1.canvas.draw_idle()
def click(event):
# if the mouse is over the scatter points
if points1.contains(event)[0]:
# find out the index within the array from the event
inds = points1.contains(event)[1]["ind"]
ind = inds[0]
# if one specific point is chosen
if ind == len(x1)-1:
plt.scatter(x1[ind], y1[ind], s=25, marker='x', c='#556B2F')
q = question(True, ind)
# do nothing
if q == "":
original_figure_plot()
# quit the program
elif q == "q":
exit()
# continue the program without updating labels
elif q == "n":
result1 = copy.deepcopy(y_train_chosen1)
plt.close("all")
# continue the program after labels are updated
else:
result1 = copy.deepcopy(y_train_chosen1)
result1 = np.append(result1, [int(q)], axis=0)
plt.close("all")
else:
# if anyone else point is chosen
plt.scatter(x1[ind], y1[ind], s=8, c='k')
q = question(False, ind)
# do nothing
if q == "":
original_figure_plot()
# quit the program
elif q == "q":
exit()
# update labels
else:
y_train_chosen1[ind] = int(q)
original_figure_plot()
fig1.canvas.draw_idle()
Probably it is better to use for example other libraries like plotly or dash but is it really true that you cannot update figure without closing it if you are using matplotlib events?? I can provide all the project files but I think so that if there is a solution, it should be done inside of these functions.
It took the whole day to find the answer but here it is!
I use now plt.show() in interactive-mode with command plt.ion() and do blocking manually with commands fig.canvas.start_event_loop() and fig.canvas.stop_event_loop(). To be honest, it was surprisingly difficult to find the solution to this problem but the lesson is learned.
matplotlib figure does not continue program flow after close event triggered inside tk app