I am new to python and trying to do what have been doing in MATLAB for so long. My current challenge is to dynamically update a plot without drawing a new figure in a for or while loop. I am aware there are similar questions and answers but most of them are too complicated and I believe it should be easier.
I got the example from here
https://pythonspot.com/matplotlib-update-plot/
But I can't see the figure, no error, no nothing. I added two lines just to see if I can see the static plot and I can.
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10*np.pi, 100)
y = np.sin(x)
# This is just a test just to see if I can see the plot window
plt.plot(x, y)
plt.show()
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111)
line1, = ax.plot(x, y, 'b-')
for phase in np.linspace(0, 10*np.pi, 100):
line1.set_ydata(np.sin(0.5 * x + phase))
fig.canvas.draw()
Any idea why I can't see the dynamic plot?
Thank you
Erdem
try to add plt.pause(0.0001) inside the loop after plt.show(block=False), and a final plt.show() outside the loop. This should work fine with plt.ion(); ref to some older answers Plot one figure at a time without closing old figure (matplotlib)
Here is the code of plotting the figures. But why are there always two empty figures before the third expected figure, it seems I created two blank fig.
And I cannot save the figure in my local computer fig.savefig('Sens.png'). There is an error The C++ part of the object has been deleted, attribute access no longer allowed(actually successfully saved only for one time).
fig = plt.figure(figsize=(10,10))
m = 1
for s in dataList:
plt.subplot(2,2,m)
f = interp1d(FXSpotList, s, 'cubic')
xnew = np.linspace(FXSpotList[0], FXSpotList[-1], 40, True)
plt.plot(xnew, f(xnew), '-')
plt.xlabel('Spot')
plt.ylabel(titleList[m-1])
plt.axvline(x=tradeTest.Pair().Spot(), linestyle='--')
plt.axhline(y=0, linestyle='--')
m = m + 1
plt.figtext(0.5, 0.01, 'Type='+str(tradeTest.Types()[0]), ha='center')
plt.tight_layout()
plt.show()
plt.close()
fig.savefig('Sens.png')
Although you did not provide a Minimal, Complete, and Verifiable example, it is obvious that there are things wrong with your loop construction. You show, close, then save the plot in every loop, which is probably not, what you are intending to do. A minimal example of your loop would be
import numpy as np
from matplotlib import pyplot as plt
#sample list to iterate over
dataList = ["fig1", "fig2", "fig3"]
plt.figure(figsize=(10,10))
#loop over the list, retrieve data entries and index
for i, s in enumerate(dataList):
#define position of the plot in a 2 x 2 grid
plt.subplot(2, 2, i + 1)
#random plot, insert your calculations here
plt.plot(range(3), np.random.randint(0, 10, 3))
#utilize list data
plt.title(s)
#save figure
plt.savefig('test.png')
#show figure
plt.show()
I want to use MatPlotLib to plot a graph, where the plot changes over time. At every time step, an additional data point will be added to the plot. However, there should only be one graph displayed, whose appearance evolves over time.
In my test example, the plot is a simple linear plot (y = x). Here is what I have tried:
for i in range(100):
x = range(i)
y = range(i)
plt.plot(x, y)
plt.ion()
plt.show()
time.sleep(1)
However, what happens here is that multiple windows are created, so that by the end of the loop I have 100 windows. Also, I have noticed that for the most recent window, it is just a white window, and the plot only appears on the next step.
So, my two questions are:
1) How can I change my code so that only a single window is displayed, whose contents changes over time?
2) How can I change my code so that for the most recent timestep, the plot is actually displayed on the window, rather than it only displaying a white window?
Thanks!
(1)
You can set plt.ion() at the beginning and plot all graphs to the same window. Within the loop use plt.draw() to show the graph and plt.pause(t) to make a pause. Note that t can be very small, but the command needs to be there for the animation to work on most backends.
You might want to clear the axes before plotting new content using plt.gca().cla().
import matplotlib.pyplot as plt
plt.ion()
for i in range(100):
x = range(i)
y = range(i)
# plt.gca().cla() # optionally clear axes
plt.plot(x, y)
plt.title(str(i))
plt.draw()
plt.pause(0.1)
plt.show(block=True) # block=True lets the window stay open at the end of the animation.
Alternatively to this very simple approach, use any of the examples for animations provided in http://matplotlib.org/examples/animation/index.html
(2)
In order to get each plot in a new window, use plt.figure() and remove plt.ion(). Also only show the windows at the end:
import matplotlib.pyplot as plt
for i in range(100):
x = range(i)
y = range(i)
plt.figure()
plt.plot(x, y)
plt.title(str(i))
plt.show()
Note that you might find that in both cases the first plot is empty simply because for i=0, range(i) == [] is an empty list without any points. Even for i=1 there is only one point being plotted, but of course no line can connect a single point with itself.
I think the best way is to create one line plot and then update data in it. Then you will have single window and single graph that will continuously update.
import matplotlib.pyplot as plt
plt.ion()
fig = plt.figure(figsize=(16,8))
axes = fig.add_subplot(111)
data_plot=plt.plot(0,0)
line, = axes.plot([],[])
for i in range(100):
x = range(i)
y = range(i)
line.set_ydata(y)
line.set_xdata(x)
if len(y)>0:
axes.set_ylim(min(y),max(y)+1) # +1 to avoid singular transformation warning
axes.set_xlim(min(x),max(x)+1)
plt.title(str(i))
plt.draw()
plt.pause(0.1)
plt.show(block=True)
I am plotting iteratively using matplotlib in python. I am setting the axis of the plot, so as to display e.g. only 50 lines at a time. A pseudo code is given below as an example:
x = 0
y = 1
line_plot = 50
axis.set_ylim(0 , line_plot)
while True:
plot(x,y)
y = y+1
if y > line_plot :
axis.set_ylim(y , y+line_plot)
This code will run indefinitely, and eventually the memory required for the plot will get quite large, even if only 50 lines are present on the graph (since all data points are kept in memory). I would like to know if there is a command in python to delete all data that is out of axis limits, freeing some memory space.
Thank you,
Gaelle
This will depend a little bit on how exactly your script looks like. You need some method to determine the y-coordinates of every line, and based on some criteria remove them or not. But if you do something like:
x = np.arange(1)
y = np.ones(1)
pl.figure()
l1 = pl.plot(x,y)[0]
y[:] += 1
l2 = pl.plot(x,y)[0]
and call get_ydata() on both lines, they will have the same y-values, so get_ydata() seems to return the original array, not necessarily the values drawn in the plot (which apparently is a bug, see: this matplotlib issue). If, instead of y[:] += 1 you make an actual copy of the array (y = y.copy()+1), you can use get_ydata(). If this is the case in your real-world problem, such a solution might work:
import matplotlib
import matplotlib.pylab as pl
import numpy as np
pl.close('all')
x = np.arange(100000)
y = np.ones(x.size)
pl.figure()
ax = pl.gca()
line_plot = 50
ax.set_ylim(0, line_plot)
for i in range(200):
pl.plot(x, y)
y = y.copy() + 1
if y[0] > line_plot:
ax.set_ylim(y[0]-line_plot, y[0])
for l in ax.get_lines():
yval = l.get_ydata()[0]
if(yval < ax.get_ylim()[0]):
l.remove()
If I remove the for l in ax.get_lines part, the memory usage scales with i, with this part included the memory usage stays constant, even for very large values of i
You want look at the animation examples
# make a figure and axes object
fig, ax = plt.subplots()
# make a Line2D artist
ln, = ax.plot([], [], linestyle='', marker='o')
# local version of the data
xdata, ydata = [], []
for j in range(200):
# update your copy of the data
xdata.append(j)
ydata.append(j*j)
xdata = xdata[-50:]
ydata = ydata[-50:]
# update the Line2D objects copy of the data
ln.set_data(xdata, ydata)
# autoscale limits to new data
ax.relim()
ax.autoscale()
# needed in non-interactive mode and/or mpl < 1.5
# fig.canvas.draw_idle()
# sleep, but run the GUI event loop
plt.pause(.1)
I'm currently evaluating different python plotting libraries. Right now I'm trying matplotlib and I'm quite disappointed with the performance. The following example is modified from SciPy examples and gives me only ~ 8 frames per second!
Any ways of speeding this up or should I pick a different plotting library?
from pylab import *
import time
ion()
fig = figure()
ax1 = fig.add_subplot(611)
ax2 = fig.add_subplot(612)
ax3 = fig.add_subplot(613)
ax4 = fig.add_subplot(614)
ax5 = fig.add_subplot(615)
ax6 = fig.add_subplot(616)
x = arange(0,2*pi,0.01)
y = sin(x)
line1, = ax1.plot(x, y, 'r-')
line2, = ax2.plot(x, y, 'g-')
line3, = ax3.plot(x, y, 'y-')
line4, = ax4.plot(x, y, 'm-')
line5, = ax5.plot(x, y, 'k-')
line6, = ax6.plot(x, y, 'p-')
# turn off interactive plotting - speeds things up by 1 Frame / second
plt.ioff()
tstart = time.time() # for profiling
for i in arange(1, 200):
line1.set_ydata(sin(x+i/10.0)) # update the data
line2.set_ydata(sin(2*x+i/10.0))
line3.set_ydata(sin(3*x+i/10.0))
line4.set_ydata(sin(4*x+i/10.0))
line5.set_ydata(sin(5*x+i/10.0))
line6.set_ydata(sin(6*x+i/10.0))
draw() # redraw the canvas
print 'FPS:' , 200/(time.time()-tstart)
First off, (though this won't change the performance at all) consider cleaning up your code, similar to this:
import matplotlib.pyplot as plt
import numpy as np
import time
x = np.arange(0, 2*np.pi, 0.01)
y = np.sin(x)
fig, axes = plt.subplots(nrows=6)
styles = ['r-', 'g-', 'y-', 'm-', 'k-', 'c-']
lines = [ax.plot(x, y, style)[0] for ax, style in zip(axes, styles)]
fig.show()
tstart = time.time()
for i in xrange(1, 20):
for j, line in enumerate(lines, start=1):
line.set_ydata(np.sin(j*x + i/10.0))
fig.canvas.draw()
print 'FPS:' , 20/(time.time()-tstart)
With the above example, I get around 10fps.
Just a quick note, depending on your exact use case, matplotlib may not be a great choice. It's oriented towards publication-quality figures, not real-time display.
However, there are a lot of things you can do to speed this example up.
There are two main reasons why this is as slow as it is.
1) Calling fig.canvas.draw() redraws everything. It's your bottleneck. In your case, you don't need to re-draw things like the axes boundaries, tick labels, etc.
2) In your case, there are a lot of subplots with a lot of tick labels. These take a long time to draw.
Both these can be fixed by using blitting.
To do blitting efficiently, you'll have to use backend-specific code. In practice, if you're really worried about smooth animations, you're usually embedding matplotlib plots in some sort of gui toolkit, anyway, so this isn't much of an issue.
However, without knowing a bit more about what you're doing, I can't help you there.
Nonetheless, there is a gui-neutral way of doing it that is still reasonably fast.
import matplotlib.pyplot as plt
import numpy as np
import time
x = np.arange(0, 2*np.pi, 0.1)
y = np.sin(x)
fig, axes = plt.subplots(nrows=6)
fig.show()
# We need to draw the canvas before we start animating...
fig.canvas.draw()
styles = ['r-', 'g-', 'y-', 'm-', 'k-', 'c-']
def plot(ax, style):
return ax.plot(x, y, style, animated=True)[0]
lines = [plot(ax, style) for ax, style in zip(axes, styles)]
# Let's capture the background of the figure
backgrounds = [fig.canvas.copy_from_bbox(ax.bbox) for ax in axes]
tstart = time.time()
for i in xrange(1, 2000):
items = enumerate(zip(lines, axes, backgrounds), start=1)
for j, (line, ax, background) in items:
fig.canvas.restore_region(background)
line.set_ydata(np.sin(j*x + i/10.0))
ax.draw_artist(line)
fig.canvas.blit(ax.bbox)
print 'FPS:' , 2000/(time.time()-tstart)
This gives me ~200fps.
To make this a bit more convenient, there's an animations module in recent versions of matplotlib.
As an example:
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import numpy as np
x = np.arange(0, 2*np.pi, 0.1)
y = np.sin(x)
fig, axes = plt.subplots(nrows=6)
styles = ['r-', 'g-', 'y-', 'm-', 'k-', 'c-']
def plot(ax, style):
return ax.plot(x, y, style, animated=True)[0]
lines = [plot(ax, style) for ax, style in zip(axes, styles)]
def animate(i):
for j, line in enumerate(lines, start=1):
line.set_ydata(np.sin(j*x + i/10.0))
return lines
# We'd normally specify a reasonable "interval" here...
ani = animation.FuncAnimation(fig, animate, xrange(1, 200),
interval=0, blit=True)
plt.show()
Matplotlib makes great publication-quality graphics, but is not very well optimized for speed.
There are a variety of python plotting packages that are designed with speed in mind:
http://vispy.org
http://pyqtgraph.org/
http://docs.enthought.com/chaco/
http://pyqwt.sourceforge.net/
[ edit: pyqwt is no longer maintained; the previous maintainer is recommending pyqtgraph ]
http://code.google.com/p/guiqwt/
To start, Joe Kington's answer provides very good advice using a gui-neutral approach, and you should definitely take his advice (especially about Blitting) and put it into practice. More info on this approach, read the Matplotlib Cookbook
However, the non-GUI-neutral (GUI-biased?) approach is key to speeding up the plotting. In other words, the backend is extremely important to plot speed.
Put these two lines before you import anything else from matplotlib:
import matplotlib
matplotlib.use('GTKAgg')
Of course, there are various options to use instead of GTKAgg, but according to the cookbook mentioned before, this was the fastest. See the link about backends for more options.
For the first solution proposed by Joe Kington ( .copy_from_bbox & .draw_artist & canvas.blit), I had to capture the backgrounds after the fig.canvas.draw() line, otherwise the background had no effect and I got the same result as you mentioned. If you put it after the fig.show() it still does not work as proposed by Michael Browne.
So just put the background line after the canvas.draw():
[...]
fig.show()
# We need to draw the canvas before we start animating...
fig.canvas.draw()
# Let's capture the background of the figure
backgrounds = [fig.canvas.copy_from_bbox(ax.bbox) for ax in axes]
This may not apply to many of you, but I'm usually operating my computers under Linux, so by default I save my matplotlib plots as PNG and SVG. This works fine under Linux but is unbearably slow on my Windows 7 installations [MiKTeX under Python(x,y) or Anaconda], so I've taken to adding this code, and things work fine over there again:
import platform # Don't save as SVG if running under Windows.
#
# Plot code goes here.
#
fig.savefig('figure_name.png', dpi = 200)
if platform.system() != 'Windows':
# In my installations of Windows 7, it takes an inordinate amount of time to save
# graphs as .svg files, so on that platform I've disabled the call that does so.
# The first run of a script is still a little slow while everything is loaded in,
# but execution times of subsequent runs are improved immensely.
fig.savefig('figure_name.svg')