I've developed an gui with python pyqt. There I have a matplotlib figure with x,y-Data and vlines that needs to change dynamically with a QSlider.
Right now I change the data just with deleting everything and plot again but this is not effective
This is how I do it:
def update_verticalLines(self, Data, xData, valueSlider1, valueSlider2, PlotNr, width_wg):
if PlotNr == 2:
self.axes.cla()
self.axes.plot(xData, Data, color='b', linewidth=2)
self.axes.vlines(valueSlider1,min(Data),max(Data),color='r',linewidth=1.5, zorder = 4)
self.axes.vlines(valueSlider2,min(Data),max(Data),color='r',linewidth=1.5, zorder = 4)
self.axes.text(1,0.8*max(Data),str(np.round(width_wg,2))+u"µm", fontsize=16, bbox=dict(facecolor='m', alpha=0.5))
self.axes.text(1,0.6*max(Data),"Pos1: "+str(round(valueSlider1,2))+u"µm", fontsize=16, bbox=dict(facecolor='m', alpha=0.5))
self.axes.text(1,0.4*max(Data),"Pos2: "+str(round(valueSlider2,2))+u"µm", fontsize=16, bbox=dict(facecolor='m', alpha=0.5))
self.axes.grid(True)
self.draw()
"vlines" are LineCollections in matplotlib. I searched in the documentation but could not find any hint to a function like 'set_xdata' How can I change the x value of vertical lines when they are already drawn and embedded into FigureCanvas?
I have the same problem with changing the x and y data. When trying the known functions of matplotlib like 'set_data', I get an error that AxisSubPlot does not have this attribute.
In the following is my code for the FigureCanvas Class. The def update_verticalLines should only contain commands for changing the x coord of the vlines and not complete redraw.
Edit: solution
Thanks #Craigular Joe
This was not exactly how it worked for me. I needed to change something:
def update_verticalLines(self, Data, xData, valueSlider1, valueSlider2, PlotNr, width_wg):
self.vLine1.remove()
self.vLine1 = self.axes.vlines(valueSlider1,min(Data), max(Data), color='g', linewidth=1.5, zorder = 4)
self.vLine2.remove()
self.vLine2 = self.axes.vlines(valueSlider2,min(Data), max(Data), color='g', linewidth=1.5, zorder = 4)
self.axes.draw_artist(self.vLine1)
self.axes.draw_artist(self.vLine2)
#self.update()
#self.flush_events()
self.draw()
update() did not work without draw(). (The old vlines stayed)
flush_events() did some crazy stuff. I have two instances of FigureCanvas. flush_events() caused that within the second instance call the vlines moved with the slider but moved then back to the start position.
When you create the vlines, save a reference to them, e.g.
self.my_vlines = self.axes.vlines(...)
so that when you want to change them, you can just remove and replace them, e.g.
self.my_vlines.remove()
self.my_vlines = self.axes.vlines(...)
# Redraw vline
self.axes.draw_artist(self.my_vlines)
# Add newly-rendered lines to drawing backend
self.update()
# Flush GUI events for figure
self.flush_events()
By the way, in the future you should try your best to pare down your code sample to just the essential parts. Having a lot of unnecessary sample code makes it hard to understand your question. :)
Related
I have a function "makeGrid(tensor)" that takes as an argument a pytorch tensor of shape (B,3,H,W), and uses ImageGrid to make a figure that displays the batch of figures in a grid.
Now, the model that outputs "tensor", depends on one parameter, "alpha". I would like to include a slider on the figure such that I can modify alpha "live". I am using the "Slider" widget from matplotlib roughly as such :
result = model(tensor)
images,grid = makeGrid(result)
ifig = plt.figure()# Figure with slider
axalpha = ifig.add_axes([0.25, 0.1, 0.65, 0.03])
# How to add the "images" to ifig ???
alpha_slider = Slider(
ax=axsmol,
valmin=-2,
valmax=2,
valinit=1,
)
def update(val):
model.alpha = alpha_slider.val
result= model(img_batch)
images,grid = makeGrid(result)
# Same problem, need to update ifig with new images
alpha_slider.on_changed(update)
plt.show()
So, my main problem is I have no idea how to use the already created figure (images) and/or grid (which is an ImageGrid object, roughly a list of axes afaik) as a subplot of "ifig", the interactive figure which contains slider and images.
Very sorry as this seems to be a basic question, but searching for "how to add already created figure as subplot of figure" or other things didn't yield solutions to my problem (or at least, in my limited point of view).
I'm a novice using matplotlib as an embedded control in my PyQt4 application to display image data. I'd like to be able to allow the user to interactively draw a line on the image by clicking and dragging. I have it working but it is so slow as to be unusable, leading me to believe I'm not going about it the correct way. The only way I can get the line to appear is by forcing the canvas to redraw each time the mouse moves (I suspect this is the cause of the slowdown).
For example, on the mouse down event I store the current coordinates and add a Line2D object to the plot as follows:
def onMouseMove(self, event):
if self.drawingLine:
self.lineStartX = event.xdata
self.lineStopX = event.xdata
self.lineStartY = event.ydata
self.lineStopY = event.ydata
self.line = Line2D([self.lineStartX, self.lineStopX], [self.lineStartY, self.lineStopY], linewidth = 1.5, color = 'r')
self.axes.add_line(self.line)
Then, in my mouse move event I redraw the line as follows:
def onMouseMove(self, event):
if self.drawingLine:
self.lineStopX = event.xdata
self.lineStopY = event.ydata
# Adjust the line to the new endpoint:
self.line.set_data([self.lineStartX, self.lineStopX], [self.lineStartY, self.lineStopY])
# Force a redraw otherwise you don't see any changes:
self.fig.canvas.draw()
As I've stated this approach is unusably slow and hence probably wrong. Can somebody please clue me in to what the proper approach is here? Thank you all in advance.
First off, you will already gain a little by using
self.fig.canvas.draw_idle()
instead of draw(). This redraws the canvas only when it's not currently beeing repainted, saving you a lot of draws.
If this is not enough, you would need to use the technique of blitting. Now since you don't have a minimal example, I will not provide any complete solution for this here, but e.g. the answer to this question, why is plotting with Matplotlib so slow?, has an example of that.
The idea is to store the background, and only redraw the part that changes (here the line).
background = fig.canvas.copy_from_bbox(ax.bbox)
# then during mouse move
fig.canvas.restore_region(background)
line.set_data(...)
ax.draw_artist(line)
fig.canvas.blit(ax.bbox)
# only after mouse has stopped moving
fig.canvas.draw_idle()
This technique is also used internally by some matplotlib widgets, e.g. matplotlib.widgets.Cursor to let the lines follow the cursor quickly.
This brings me to the last point, which is: You don't need to reinvent the wheel. There is a matplotlib.widgets.RectangleSelector, which by defaut draws a rectangle for selection. But you may use its drawtype='line' argument, to change the selection to a line, together with the argument blit=True this should already give you what you need - you will just need to add the code to finally draw a line once the selection is finished.
Note that in the newest matplotlib version, there is even a matplotlib.widgets.PolygonSelector, which may directly be what you need.
matplotlib is built to be flexible and to work with multiple different backends. It is very slow at real-time plotting. The problem is that your mouse move events are very rapid. Anything trying to keep up with the mouse movement will probably be slow. You need to call the plot less often. You can do this by checking the time in your mouse move function and trying to limit the plotting calls to whatever works.
import time
def onMouseMove(self, event):
if self.drawingLine and time.time() - last_time > 0.03: # Change the 0.03 to change how often you plot.
last_time = time.time()
...
I highly suggest pyqtgraph. pyqtgraph has built in rate limiting signals that you can work with to do this.
Below is a basic example of how you can do this.
# Change the style to look like matplotlib
pyqtgraph.setConfigOption("background", QtGui.QColor.fromRgbF(230/255, 230/255, 234/255, 255/255))
pyqtgraph.setConfigOption("background", 'w')
pyqtgraph.setConfigOption("foreground", 'k')
pyqtgraph.setConfigOption("antialias", True)
# Create the widgets and plot items
glw = pyqtgraph.GraphicsLayoutWidget()
pg = glw.addPlot(0, 0)
class MyClass(object):
...
...
def onMouseMove(self, event):
if self.drawingLine:
scene_pos = event[0]
data_pos = pg.getViewBox().mapSceneToView(scene_pos)
x, y = data_pos.x(), data_pos.y()
self.lineStopX = x
self.lineStopY = y
# Adjust the line to the new endpoint:
if not self.line:
self.line = pg.plot(x=[], y=[])
self.line.setData(x=[self.lineStartX, self.lineStopX],
y=[self.lineStartY, self.lineStopY])
mouse_move_sig = pyqtgraph.SignalProxy(pg.scene().sigMouseMoved,
rateLimit=60, slot=onMouseMove)
EDIT: ImportanceOfBeingErnest provided the answer, however I am still inviting you all to explain, why is savefig logic different from animation logic.
I want to make a video in matplotlib. I went through manuals and examples and I just don't get it. (regarding matplotlib, I always copy examples, because after five years of python and two years of mathplotlib I still understand 0.0% of matplotlib syntax)
After half a dozen hours here is what I came up to. Well, I get empty video. No idea why.
import os
import math
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot as plt
import matplotlib.animation as animation
# Set up formatting for the movie files
Writer = animation.writers['ffmpeg']
writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)
numb=100
temp=[0.0]*numb
cont=[0.0]*numb
for i in range(int(4*numb/10),int(6*numb/10)):
temp[i]=2
cont[i]=2
fig = plt.figure()
plts=fig.add_subplot(1,1,1)
plts.set_ylim([0,2.1])
plts.get_xaxis().set_visible(False)
plts.get_yaxis().set_visible(False)
ims = []
for i in range(1,10):
line1, = plts.plot(range(0,numb),temp, linewidth=1, color='black')
line2, = plts.plot(range(0,numb),cont, linewidth=1, color='red')
# savefig is here for testing, works perfectly!
# fig.savefig('test'+str(i)+'.png', bbox_inches='tight', dpi=300)
ims.append([line1,line2])
plts.lines.remove(line1)
plts.lines.remove(line2)
for j in range(1,10):
tempa=0
for k in range (1,numb-1):
tempb=temp[k]+0.51*(temp[k-1]-2*temp[k]+temp[k+1])
temp[k-1]=tempa
tempa=tempb
temp[numb-1]=0
for j in range(1,20):
conta=0
for k in range (1,numb-1):
contb=cont[k]+0.255*(cont[k-1]-2*cont[k]+cont[k+1])
cont[k-1]=conta
conta=contb
cont[numb-1]=0
im_ani = animation.ArtistAnimation(fig, ims, interval=50, repeat_delay=3000,blit=True)
im_ani.save('im.mp4', writer=writer)
Can someone help me with this?
If you want to have a plot which is not empty, the main idea would be not to remove the lines from the plot.
That is, delete the two lines
plts.lines.remove(line1)
plts.lines.remove(line2)
If you delete these two lines the output will look something like this
[Link to orginial size animation]
Now one might ask, why do I not need to remove the artist in each iteration step, as otherwise all of the lines would populate the canvas at once?
The answer is that the ArtistAnimation takes care of this. It will only show those artists in the supplied list that correspond to the given time step. So while at the end of the for loop you end up with all the lines drawn to the canvas, once the animation starts they will all be removed and only one set of artists is shown at a time.
In such a case it is of course not a good idea to use the loop for saving the individual images as the final image would contain all of the drawn line at once,
The solution is then either to make two runs of the script, one for the animation, and one where the lines are removes in each timestep. Or, maybe better, use the animation istself to create the images.
im_ani.save('im.png', writer="imagemagick")
will create the images as im-<nr>.png in the current folder. It will require to have imagemagick installed.
I'm trying here to answer the two questions from the comments:
1. I have appended line1 and line2 before deleting them. Still they disappeared in the final result. How come?
You have appended the lines to a list. After that you removed the lines from the axes. Now the lines are in the list but not part of the axes. When doing the animation, matplotlib finds the lines in the list and makes them visible. But they are not in the axes (because they have been removed) so the visibility of some Line2D object, which does not live in any axes but only somewhere in memory, is changed. But that isn't reflected in the plot because the plot doesn't know this line any more.
2. If I understand right, when you issue line1, = plts.plot... command then the line1 plot object is added to the plts graph object. However, if you change the line1 plot object by issuing line1, = plts.plot... command again, matplotlib does change line1 object but before that saves the old line1 to the plts graph object permanently. Is this what caused my problem?
No. The first step is correct, line1, = plts.plot(..) adds a Line2D object to the axes. However, in a later loop step line1, = plts.plot() creates another Line2D object and puts it to the canvas. The initial Line2D object is not changed and it doesn't know that there is now some other line next to it in the plot. Therefore, if you don't remove the lines they will all be visible in the static plot at the end.
I'm trying to put two scatterplots side-by-side in the same figure. I'm also using prettyplotlib to make the graphs look a little nicer. Here is the code
fig, ax = ppl.subplots(ncols=2,nrows=1,figsize=(14,6))
for each in ['skimmer','dos','webapp','losstheft','espionage','crimeware','misuse','pos']:
ypos = df[df['pattern']==each]['ypos_m']
xpos = df[df['pattern']==each]['xpos_m']
ax[0] = ppl.scatter(ypos,xpos,label=each)
plt.title("Multi-dimensional Scaling: Manhattan")
for each in ['skimmer','dos','webapp','losstheft','espionage','crimeware','misuse','pos']:
ypos = df[df['pattern']==each]['ypos_e']
xpos = df[df['pattern']==each]['xpos_e']
ax[1] = ppl.scatter(ypos,xpos,label=each)
plt.title("Multi-dimensional Scaling: Euclidean")
plt.show()
I don't get any error when the code runs, but what I end up with is one row with two graphs. One graph is completely empty and not styled by prettyplotlib at all. The right side graphic seems to have both of my scatterplots in it.
I know that ppl.subplots is returning a matplotlib.figure.Figure and a numpy array consisting of two matplotlib.axes.AxesSubplot. But I also admit that I don't quite get how axes and subplotting works. Hopefully it's just a simple mistake somewhere.
I think ax[0] = ppl.scatter(ypos,xpos,label=each) should be ax[0].scatter(ypos,xpos,label=each) and ax[1] = ppl.scatter(ypos,xpos,label=each) should be ax[1].scatter(ypos,xpos,label=each), change those and see if your problem get solved.
I am quite sure that the issue is: you are calling ppl.scatter(...), which will try to draw on the current axis, which is the 1st axes of 2 axes you generated (and it is the left one)
Also you may find that in the end, the ax list contains two matplotlib.collections.PathCollections, bot two axis as you may expect.
Since the solution above removes the prettiness of prettyplot, we shall use an alternative solution, which is to change the current working axis, by adding:
plt.sca(ax[0_or_1])
Before ppl.scatter(), inside each loop.
I am making log-log plots for different data sets and need to include the best fit line equation. I know where in the plot I should place the equation, but since the data sets have very different values, I'd like to use relative coordinates in the annotation. (Otherwise, the annotation would move for every data set.)
I am aware of the annotate() function of matplotlib, and I know that I can use textcoords='axes fraction' to enable relative coordinates. When I plot my data on the regular scale, it works. But then I change at least one of the scales to log and the annotation disappears. I get no error message.
Here's my code:
plt.clf()
samplevalues = [100,1000,5000,10^4]
ax = plt.subplot(111)
ax.plot(samplevalues,samplevalues,'o',color='black')
ax.annotate('hi',(0.5,0.5), textcoords='axes fraction')
ax.set_xscale('log')
ax.set_yscale('log')
plt.show()
If I comment out ax.set_xcale('log') and ax.set_ycale('log'), the annotation appears right in the middle of the plot (where it should be). Otherwise, it doesn't appear.
Thanks in advance for your help!
It may really be a bug as pointed out by #tcaswell in the comment but a workaround is to use text() in axis coords:
plt.clf()
samplevalues = [100,1000,5000,10^4]
ax = plt.subplot(111)
ax.loglog(samplevalues,samplevalues,'o',color='black')
ax.text(0.5, 0.5,'hi',transform=ax.transAxes)
plt.show()
Another approach is to use figtext() but that is more cumbersome to use if there are already several plots (panels).
By the way, in the code above, I plotted the data using log-log scale directly. That is, instead of:
ax.plot(samplevalues,samplevalues,'o',color='black')
ax.set_xscale('log')
ax.set_yscale('log')
I did:
ax.loglog(samplevalues,samplevalues,'o',color='black')