So I'm using FuncAnimation from matplotlib so dynamically plot some data as it arrives from a serial port (in my project is the vehicle class from dronekit, which is displayed with the green dot), what I have basically is the animation called which every loop is receiving a new vehicle class with data changed so it can be plotted, but for some reason it plots but after a couple of seconds later after the thread of the mission(which allows the "refresh" of the vehicle data it pops up and kills python (Wheel of death), here's what I get:
I've put some tracking prints inside the function that is called when the FuncAnimation starts running, looks like this:
def droneAnimation(i, vehicle, droneScatter):
time.sleep(1)
lat = [vehicle.location.global_relative_frame.lat]
lon = [vehicle.location.global_relative_frame.lon]
alt = [vehicle.location.global_relative_frame.alt]
print("Alt received: " + str(alt))
droneScatter._offsets3d = (lat,lon,alt)
print("Changed pos")
As you can see those prints are triggered the first few seconds but still crashes after a few iterations.
The FuncAnimation is called like this:
fig,droneScatter = plotLiveSimpleFacade(vehicle,w,2)
ani = FuncAnimation(fig,droneAnimation, fargs = (vehicle,droneScatter))
plt.draw()
plt.pause(0.1)
m = threading.Thread(target=MissionStart(vehicle,hmax) , name = "MISSION")
m.start()
For reference: fig is a plt.figure(),droneScatter is just a scatter point, vehicle is the vehicle class containing the data that dynamically updates and the MissionStart thread is just a thread to make the vehicle class change overtime.
I would like to mention as well that the fig is on interactive mode on, and the axes limits are set well (I saw that when you dynamically change data but don't scale the axes may have problems) also, trying different combinations of plt.draw() and plt.plot(block = False) leads me to not plotting at all or just a blank plot.
Since I have no idea of what is causing this I'll put the dronekit tag on this and the threading to see if anyone has any idea!
I've looked onto threading with matplotlib and looks like threading with this said library it's not the best as it's not thread safe, the best bet is to look at multiprocessing with python or approaching the problem in a different manner.
You can find more information at this post
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).
1)This is the code snippet that I am trying to run.The main idea is that, I want the ipywidgets to be interactive, while the data is constantly being fetched from the data source. And the data is being updated at certain interval using the while loop.The objective is to plot the principal components interactively by changing the number of principal components(PC) to be considered, using the #interact function.
Also it runs perfectly fine without while loop, that is, when we are not considering the auto-update for the dataset, with the while loop. But when I include the while loop, it doesn't handle the interactive-ness of the widgets(that is, the interaction of the number of PC).
My feeling is that "while True" loop does not let the interaction of ipywidget to happen, due to some execution issue.
2) Also I looked into threading but i am unsure of how to use the fucntion which is in functools(select_data), be called using threading.Thread.
Any sort of help would be appreciated. Thanks
def data_import_date(start_date,end_date):
end_date1=end_date.strftime('%Y-%m-%dT%H:%M:%S')
start_date=pd.Timestamp(start_date)
end_date=pd.Timestamp(end_date1)
button=widgets.Button(description='Pull Data')
button.on_click(functools.partial(select_data,rs_=[start_date,end_date]))
vbox=widgets.VBox([button])
display(vbox,out)
def select_data(b,rs_):
# clear_output()
start_date=rs_[0]
end_date1=rs_[1]
print("Data pulling started")
with out:
clear_output()
seeq_login()
[item1,item2]=query_seeq_for_data()
i=0
while True:
end_date=end_date1.strftime('%Y-%m-%dT%H:%M:%S')
print("Start & End date: ",start_date,end_date)
if i==0:
[X_data,Y_data]=pull_data(item1,item2,start_date,end_date)
else:
[X_data_live,Y_data_live]=pull_data(item1,item2,start_date,end_date)
X_data=X_data.append(X_data_live)
Y_data=Y_data.append(Y_data_live)
print("Data pulling completed.\nNow you're ready for your analysis")
clear_output()
[X_train,X_test,Y_train,Y_test]=train_test(X_data,Y_data)
[Xp_train,components,explained_variance_ratio,_,_]=apply_PCA(X_train,X_test)
plot_PC_variance(X_data,explained_variance_ratio)
plot_PC(X_data,components)
time.sleep(20)
start_date=end_date
end_date1=end_date1+datetime.timedelta(days=1)
i+=1
a=interact(data_import_date,
start_date=widgets.DatePicker(value=pd.to_datetime(start_date)),
end_date=widgets.DatePicker(value=pd.to_datetime(end_date)))
def plot_PC(X_data,components):
Np_comp=(1,len(X_data.columns),1)
#interact
def principal_components(PC1=Np_comp,PC2=Np_comp):
fig,ax=plt.subplots(1,1,figsize=(10,10))
plt.figure(5)
print(PC1, PC2)
ax.set_xlabel("Principal Component {}".format(PC1), fontsize=14)
ax.set_ylabel("Principal Component {}".format(PC2), fontsize=14)
ax.set_title("Principal components {0} & {1}".format(PC1,PC2), fontsize=(20))
ax.scatter(components[:,PC1],components[:,PC2])
A fairly rudimentary example that shows how you can run a loop whilst polling for widget changes, but it's not really my area of expertise.
Drag the slider whilst the values are printing and you should see the printed value change to reflect current slider position.
import threading
from IPython.display import display
import ipywidgets as widgets
import time
float_val = widgets.FloatSlider()
display(float_val)
def work(progress):
total = 10
for i in range(total):
time.sleep(1)
print(float_val.value)
thread = threading.Thread(target=work, args=(progress,))
thread.start()
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)
Plots is simple and powerful but sometimes I would like to have a little bit more control over individual elements of the plot to fine-tune its appearance.
Is it possible to update the plot object of the backend directly?
E.g., for the default pyplot backend, I tried
using Plots
p = plot(sin)
p.o[:axes][1][:xaxis][:set_ticks_position]("top")
but the plot does not change. Calling p.o[:show]() afterwards does not help, either.
In other words: Is there a way to use the PyPlot interface for a plot that was initially created with Plots?
Edit:
The changes to the PyPlot object become visible (also in the gui) when saving the figure:
using Plots
using PyPlot
p = Plots.plot(sin, top_margin=1cm)
gui() # not needed when using the REPL
gca()[:xaxis][:set_ticks_position]("top")
PyPlot.savefig("test.png")
Here, I used p.o[:axes][1] == gca(). One has to set top_margin=1cm because the plot area is not adjusted automatically (for my actual fine-tuning, this doesn't matter).
This also works for subsequent updates as long as only the PyPlot interface is used. E.g., after the following commands, the plot will have a red right border in addition to labels at the top:
gca()[:spines]["right"][:set_color]("red")
PyPlot.savefig("test.png")
However, when a Plots command like plot!(xlabel="foo") is used, all previous changes made with PyPlot are overwritten (which is not suprising).
The remaining question is how to update the gui interactively without having to call PyPlot.savefig explicitly.
No - the plot is a Plots object, not a PyPlot object. In your specific example you can do plot(sin, xmirror = true).
I'm trying to do the same but didn't find a solution to update an existing plot. But here is a partial answer: you can query information from the PyPlot axes object
julia> Plots.plot(sin, 1:4)
julia> Plots.PyPlot.plt[:xlim]()
(1.0,4.0)
julia> Plots.plot(sin, 20:24)
julia> ax = Plots.PyPlot.plt[:xlim]()
(20.0,24.0)
and it gets updated.
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. :)