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I am using pyqtgraph and trying to use a histogramLUTable inside of a viewbox to try and plot some data and I have this black box with a little A that is obstructing the graph origin/axis in the lower left hand corner and I can't seem to find what it is named and/or how to get rid of it. I attached a picture with a little red box around what I am talking about.
The image in question with little red box circling what I am referring to:
There is also a little black box on the lower right hadn side obfuscating the x axis but doesn't go into the level adjustment on the right hand side.
image showing issue with first code written :
For reference, my code so far:
import PyQt5
from pyqtgraph.Qt import QtGui, QtCore
import numpy as np
import pandas as pd
import pyqtgraph as pg
up = np.linspace(0,1,100)
down = np.flip(up)
nparray = np.hstack([up, down])
data = np.vstack([[nparray], [nparray],[nparray]])
data = np.transpose(data)
app = QtGui.QApplication([])
win = pg.GraphicsLayoutWidget(show=True, title="pyqtGraph attempt", size=[800,600])
view = pg.PlotItem()
win.addItem(view)
img = pg.ImageItem(data, border='w')
histogram = pg.HistogramLUTItem()
histogram.setImageItem(img)
win.addItem(histogram)
if __name__ == '__main__':
pg.mkQApp().exec_()
EDIT (again):
I took a different approach to try and mitigate the little black box by using a plotItem and Viewbox rather than the HistogramLUTable but when I go this route I get the same issue. Originally I said this gives me another black box in the lower right hand corner but if you look at my original screen shot the black box is there originally as well.
import PyQt5
from pyqtgraph.Qt import QtGui, QtCore
import numpy as np
import pandas as pd
import pyqtgraph as pg
up = np.linspace(0,1,100)
down = np.flip(up)
nparray = np.hstack([up, down])
data = np.vstack([[nparray], [nparray],[nparray]])
app = QtGui.QApplication([])
win = pg.GraphicsLayoutWidget(show=True, title="pyqtGraph attempt", size=[800,600])
view = pg.PlotItem()
view.hideButtons()
win.addItem(view)
img = pg.ImageItem()
view.addItem(img)
img.setImage(data)
if __name__ == '__main__':
pg.mkQApp().exec_()
image showing issue with second code written:
This element is a button that allows the plot to auto-scale, therefore it appears when you zoom. One possible solution is to hide it using hideButtons() method:
view = pg.PlotItem()
view.hideButtons()
win.addItem(view)
So after a lot of playing with the code the issue appeared to be using:
app = QtGui.QApplication([])
in conjunction with:
pg.mkQApp().exec_()
It causes issues, I assume from interfering with each other, because your trying to call two application execution methods but this is just an assumption.
Preferred solution if using pyqtgraph is to stick with removing
app = QtGui.QApplication([])
altogether and only including
if __name__ == '__main__': pg.mkQapp().exec_()
But an alternate also includes using
app = QtGui.QApplication([])
with
if __name__ == '__main__': app.exec_()
But it depends on how you are using pyqtgraph, if you are using it for it's graphing methods then you will probably need to use app.addItem(plotItem) and therefore you need to call app.exec_() however if you allow pyqtgraph to take care of generating the GUI you can let it take care of executing the application via pg.mkQapp().exec_()
I was following this tutorial on how to write a chess program in Python.
It uses the python-chess engine. The functions from that engine apparently return SVG data, that could be used to display a chessboard.
Code from the tutorial:
import chess
import chess.svg
from IPython.display import SVG
board = chess.Board()
SVG(chess.svg.board(board=board,size=400))
but when I run that code, all I see is a line in the terminal and no image.
<IPython.core.display.SVG object>
The tutorial makes a passing reference to Jupyter Notebooks and how they can be used to display SVG images. I have no experience with Jupyter Notebooks and even though I installed the package from pip and I dabbled a little into how to use it, I couldn't make much progress with regards to my original chessboard problem. But what I do have, is, experience with Qt development using C++ and since Qt has Python bindings, I decided to use those bindings.
Here is what I wrote:
import sys
import chess
import chess.svg
from PyQt5 import QtGui, QtSvg
from PyQt5.QtWidgets import QApplication
from IPython.display import SVG, display
app = QApplication(sys.argv);
board = chess.Board();
svgWidget = QtSvg.QSvgWidget(chess.svg.board(board=board, size=400));
#svgWidget.setGeometry(50,50,759,668)
svgWidget.show()
sys.exit(app.exec_())
A Qt window opens and shows nothing and in the terminal I see a lot of text - (apparently the SVG data is ending up in the console and not in the Qt window that is opening?).
I figured I have to install some SVG library under python so I installed drawSvg from pip. But it seems that library generates SVG images. And was of no use for me.
What is even more strange is, after seeing this SO question, I tried the following:
import sys
import chess
import chess.svg
from PyQt5 import QtGui, QtSvg
from PyQt5.QtWidgets import QApplication
from IPython.display import SVG, display
app = QApplication(sys.argv);
board = chess.Board();
svgWidget = QtSvg.QSvgWidget('d:\projects\python_chess\Zeichen_123.svg');
#svgWidget.setGeometry(50,50,759,668)
svgWidget.show()
sys.exit(app.exec_())
And it showed an image - an SVG image! What is the difference then between my case and this case?
Question: So my question is, what I am doing wrong in the case of the chessboard SVG data? Is the SVG data generated by the python-chess library not compatible with QtSvg?
I think you are getting confused by the scripting nature of Python. You say, you have experience with Qt development under C++. Wouldn't you create a main window widget there first and add to it your SVG widget within which you would call or load SVG data?
I would rewrite your code something like this.
import chess
import chess.svg
from PyQt5.QtSvg import QSvgWidget
from PyQt5.QtWidgets import QApplication, QWidget
class MainWindow(QWidget):
def __init__(self):
super().__init__()
self.setGeometry(100, 100, 1100, 1100)
self.widgetSvg = QSvgWidget(parent=self)
self.widgetSvg.setGeometry(10, 10, 1080, 1080)
self.chessboard = chess.Board()
self.chessboardSvg = chess.svg.board(self.chessboard).encode("UTF-8")
self.widgetSvg.load(self.chessboardSvg)
if __name__ == "__main__":
app = QApplication([])
window = MainWindow()
window.show()
app.exec()
EDIT
It would be even better if you would add a paint function to the MainWindow class. Because for sure in future, you would want to repaint your board image many times, whenever you would move a piece. So I would do something like this.
def paintEvent(self, event):
self.chessboardSvg = chess.svg.board(self.chessboard).encode("UTF-8")
self.widgetSvg.load(self.chessboardSvg)
this is how i display SVG image with python, no need third party to do that.
First i save SVG image on disk
second i just call it like os.Startfile("exemple.svg")
Exemple:
boardsvg = svg.board(board(fen), size=600, coordinates=True)
with open('temp.svg', 'w') as outputfile:
outputfile.write(boardsvg)
time.sleep(0.1)
os.startfile('temp.svg')
that's it !
Consider the following python3 PyQt code to display an interactive matplotlib graph with toolbar
import sys, sip
import numpy as np
from PyQt5 import QtGui, QtWidgets
from PyQt5.Qt import *
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
import matplotlib.pyplot as plt
app = QApplication(sys.argv)
top = QWidget()
fig = plt.figure()
ax = fig.gca()
x = np.linspace(0,5,100)
ax.plot(x,np.sin(x))
canvas = FigureCanvas(fig)
toolbar = NavigationToolbar(canvas, top)
def pick(event):
if (event.xdata is None) or (event.ydata is None): return
ax.plot([0,event.xdata],[0,event.ydata])
canvas.draw()
canvas.mpl_connect('button_press_event', pick)
layout = QtWidgets.QVBoxLayout()
layout.addWidget(toolbar)
layout.addWidget(canvas)
top.setLayout(layout)
top.show()
app.exec_()
Now I'd like to achieve the same by using PyQt with QML instead. I have some experience with creating QML GUIs in C++ and I really like the fact that the layout code is nicely separated from the core logic of the code.
I have found several examples on how to show plots in PyQt and on how to use Python with QML, but nothing that combines the two.
To start off, my python and QML snippets look as follows:
Python:
import sys, sip
import numpy as np
from PyQt5 import QtGui, QtWidgets
from PyQt5.Qt import *
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
import matplotlib.pyplot as plt
app = QApplication(sys.argv)
engine = QQmlApplicationEngine()
engine.load(QUrl('layout.qml'))
root = engine.rootObjects()[0]
root.show()
sys.exit(app.exec_())
Layout:
import QtQuick 2.7
import QtQuick.Controls 1.4
ApplicationWindow {
visible: true
width: 400
height: 400
Canvas {
// canvas may not be the right choice here
id: mycanvas
anchors.fill: parent
}
}
But I am quite lost on how to continue.
More concretely, the question would be: Is there a way to display an interactive matplotlib plot in QML (by interactive I mean not just a figure that has been saved as an image, ideally with the standard toolbar for zoom etc.)
Can anyone help? Or is the combination of QML and plots just simply discouraged (this question suggests python and QML should work together quite well)?
I don't have a full solution, but if you're OK with just displaying charts and the fact that you'll have to provide any interactive controls by yourself, then there's a reasonably simple way to do that.
First of all, you will need to convert your matplotlib chart into a QImage. Fortunately doing so is surprisingly easy. The canonical backend (renderer) for matplotlib is *Agg`, and it allows you to render your Figure into a memory. Just make a suitable Canvas object for you Figure, then call .draw(). The QImage constructor will take generated data directly as inputs.
canvas = FigureCanvasAgg(figure)
canvas.draw()
img = QtGui.QImage(canvas.buffer_rgba(), *canvas.get_width_height(), QtGui.QImage.Format_RGBA8888).copy()
The Qt way to provide that image into QML is to use QQuickImageProvider. It will get "image name" as input from QML and should provide a suitable image as output. This allows you to serve all matplotlib charts in your app with just one Image provider. When I was working on a small visualization app for internal use, I ended up with a code like this:
import PyQt5.QtCore as QtCore
import PyQt5.QtGui as QtGui
import PyQt5.QtQuick as QtQuick
import PyQt5.QtQml as QtQml
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
class MatplotlibImageProvider(QtQuick.QQuickImageProvider):
figures = dict()
def __init__(self):
QtQuick.QQuickImageProvider.__init__(self, QtQml.QQmlImageProviderBase.Image)
def addFigure(self, name, **kwargs):
figure = Figure(**kwargs)
self.figures[name] = figure
return figure
def getFigure(self, name):
return self.figures.get(name, None)
def requestImage(self, p_str, size):
figure = self.getFigure(p_str)
if figure is None:
return QtQuick.QQuickImageProvider.requestImage(self, p_str, size)
canvas = FigureCanvasAgg(figure)
canvas.draw()
w, h = canvas.get_width_height()
img = QtGui.QImage(canvas.buffer_rgba(), w, h, QtGui.QImage.Format_RGBA8888).copy()
return img, img.size()
Whenever I need to draw a plot in python code, I just create Figure using this addFigure to give it a name and let the Qt to know about it. Once you got Figure, rest of matplotlib drawing happens exactly as usual. Make axes and plot.
self.imageProvider = MatplotlibImageProvider()
figure = self.imageProvider.addFigure("eventStatisticsPlot", figsize=(10,10))
ax = figure.add_subplot(111)
ax.plot(x,y)
Then in QML code I can simply refer matplotlib image by name ("eventStatisticsPlot")
Image {
source: "image://perflog/eventStatisticsPlot"
}
Note that URL is prefixed by "image://" to tell QML that we need to get image from QQuickImageProvider and includes name ("perflog") of a particular provider to use. For this stuff to work we need to register our provider during QML initialization with a call to addImageProvider. For example,
engine = QtQml.QQmlApplicationEngine()
engine.addImageProvider('perflog', qt_model.imageProvider)
engine.load(QtCore.QUrl("PerflogViewer.qml"))
At this point you should be able to see static graphs shown, but they will not be updated properly because Image component in QML assumes that image that we provide does not change. I found no good solution for it, but an ugly workaround is fairly simple. I added a signal called eventStatisticsPlotChanged to my helper class that exposes Python app data to QML and .emit() it whenever the relevant plot is changed. E.g. here's a chunk of code where I get data from QML on a time interval selected by user.
#QtCore.pyqtSlot(float, float)
def selectTimeRange(self, min_time, max_time):
self.selectedTimeRange = (min_time, max_time)
_, ax, _ = self.eventStatisticsPlotElements
ax.set_xlim(*self.selectedTimeRange)
self.eventStatisticsPlotChanged.emit()
See that .emit() in the end? In QML this event forces image to reload URL like this:
Image {
source: "image://perflog/eventStatisticsPlot"
cache: false
function reload() { var t = source; source = ""; source = t; }
}
Connections {
target: myDataSourceObjectExposedFromPython
onEventStatisticsPlotChanged: eventStatisticsPlot.reload()
}
So whenever user moves a control, following happens:
QML sends updated time interval to my data source via selectTimeRange() call
My code calls .set_xlim on appopriate matplotlib object and emit() a signal to notify QML that chart changed
QML queries my imageProvider for updated chart image
My code renders matplotlib chart into new QImage with Agg and passes it to Qt
QML shows that image to user
It might sound a bit complicated, but its actually easy to design and use.
Here's an example of how all this looks in our small visualization app. That's pure Python + QML, with pandas used to organize data and matplotlib to show it. Scroll-like element on bottom of the screen essentially redraws chart on every event and it happens so fast that it feels real-time.
I also tried to use SVG as a way to feed vector image into QML. It's also possible and it also works. Matplotlib offers SVG backend (matplotlib.backends.backend_svg) and Image QML component support inline SVG data as a Source. The SVG data is text so it can be easily passed around between python and QML. You can update (source) field with new data and image will redraw itself automatically, you can rely on data binding. It could've worked quite well, but sadly SVG support in Qt 4 and 5 is poor. Clipping is not supported (charts will go out of the axes); resizing Image does not re-render SVG but resizes pixel image of it; changing SVG causes image to blink; performance is poor. Maybe this will change one day later, but for now stick to agg backend.
I really love design of both matlpotlib and Qt. It's smart and it meshes well without too much effort or boilerplate code.
It's in a fairly basic state, but https://github.com/jmitrevs/matplotlib_backend_qtquick provides a workable model to start from.
To quickly summarize the gist of the example provided with it:
The library provides the types FigureCanvasQtQuickAgg and NavigationToolbar2QtQuick.
The FigureCanvasQtQuickAgg type is registered with QML:
QtQml.qmlRegisterType(FigureCanvasQtQuickAgg, "Backend", 1, 0, "FigureCanvas")
This allows you to use it from within QML:
FigureCanvas {
id: mplView
objectName : "figure"
dpi_ratio: Screen.devicePixelRatio
anchors.fill: parent
}
The objectName property allows the canvas instance to be found from within the Python code.
The toolbar is made out of QML buttons,
In the demo, they provide a DisplayBridge Python class that is linked to the canvas and is responsible for the actual plotting and for forwarding events from the various toolbar buttons
Internally, the FigureCanvasQtQuickAgg backend is a QQuickPaintedItem. In its paint function it copies data from the renderer attribute of the matplotlib FigureCanvasAgg base class into a QImage and the QImage is then painted. This is a fairly similar design to how the QWidget version of matplotlib works.
I have a python project that outputs several Matplotlib figures; each figure contains several charts. The problem that project launches about 15 figures (windows) every run, which I can not reduce.
Is it possible to concatenate all these figures (windows) to a single tabbed window so that each tab represents one figure?
Any help is much appreciated.
Thanks in advance
Workaround
Thanks to #mobiusklein comments below he suggested a workaround, to export the figures as myltipage pdf file as shown here.
Important note about the multipage pdf example mentioned above.
I tried it, but I got an error regarding the LaTeX use in matplotlib. Because fixing this error is beyond the scope of this question, so I suggest if it occurs to anyone, to set plt.rc('text', usetex=False) instead of usetex=True
I still hope if someone have other solution or workaround to post it for the benefit of others.
I wrote a simple wrapper for matplotlib that does something like you're describing. You need pyqt5 for it to work though.
Here is the code, you build a plotWindow object and feed it figure handles. It'll create a new tab for each figure.
import matplotlib
# prevent NoneType error for versions of matplotlib 3.1.0rc1+ by calling matplotlib.use()
# For more on why it's nececessary, see
# https://stackoverflow.com/questions/59656632/using-qt5agg-backend-with-matplotlib-3-1-2-get-backend-changes-behavior
matplotlib.use('qt5agg')
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
from PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QTabWidget, QVBoxLayout
import matplotlib.pyplot as plt
import sys
class plotWindow():
def __init__(self, parent=None):
self.app = QApplication(sys.argv)
self.MainWindow = QMainWindow()
self.MainWindow.__init__()
self.MainWindow.setWindowTitle("plot window")
self.canvases = []
self.figure_handles = []
self.toolbar_handles = []
self.tab_handles = []
self.current_window = -1
self.tabs = QTabWidget()
self.MainWindow.setCentralWidget(self.tabs)
self.MainWindow.resize(1280, 900)
self.MainWindow.show()
def addPlot(self, title, figure):
new_tab = QWidget()
layout = QVBoxLayout()
new_tab.setLayout(layout)
figure.subplots_adjust(left=0.05, right=0.99, bottom=0.05, top=0.91, wspace=0.2, hspace=0.2)
new_canvas = FigureCanvas(figure)
new_toolbar = NavigationToolbar(new_canvas, new_tab)
layout.addWidget(new_canvas)
layout.addWidget(new_toolbar)
self.tabs.addTab(new_tab, title)
self.toolbar_handles.append(new_toolbar)
self.canvases.append(new_canvas)
self.figure_handles.append(figure)
self.tab_handles.append(new_tab)
def show(self):
self.app.exec_()
if __name__ == '__main__':
import numpy as np
pw = plotWindow()
x = np.arange(0, 10, 0.001)
f = plt.figure()
ysin = np.sin(x)
plt.plot(x, ysin, '--')
pw.addPlot("sin", f)
f = plt.figure()
ycos = np.cos(x)
plt.plot(x, ycos, '--')
pw.addPlot("cos", f)
pw.show()
This is also posted at: https://github.com/superjax/plotWindow
Hopefully this could be a good starting point for your application.
The backend you choose to use for matplotlib controls how each figure is displayed. Some backends just render figures to file, while others like the tk, qt, or gtk backends render figures in graphical windows. Those backends determine what functionality those GUI windows have.
The existing backends don't support the type of tabbed navigation you're looking for. Someone else here implemented this using Qt4.
You might also try writing your own report files with PDF or HTML which would let you more easily write more complex image arrangements with simpler libraries.
Something that is functionally similar might be implemented using the widgets. For example, provide a row of buttons, one button for each "tab", and repaint the graphical portion of the window in response to each button.
The buttons example as a workaround is creative. As another stated you can use PyQt to create a tabbed window.
It is for this reason I use PyQtGraph. PyQtGraph only uses PyQt as a backend and therefor "natively" supports both tabbed windows and "docks". Docks allow for movable tabs and splits as well as breaking off a tab or split to a new floating window.
In general, PyQtGraph's docks provide a method for organizing your graphs/plots/images that I haven't been able to get with other libraries.
Bokeh offers tabbed windows through their Panels and Tabs widgets.
I know it is not always feasible to move away from matplotlib but I felt like there was a lack of representation of libraries which have considered and implemented tools specifically for your use case.
I shared my code that allows docking and tabbing with drag-n-drop (qt docking system). It acts as a matplotlib backend, so it's easy to integrate.
https://github.com/peper0/mpldock
I recently released a python package which contains a class called DockablePlotWindow which provides a solution similar to superjax's answer but which provides a little more flexibility to organize how the plots are initially displayed.
I'm interested to continue improving this package so feel free to open pull requests or issues on github. You can find information about it here: https://github.com/nanthony21/mpl_qt_viz
and here:
https://nanthony21.github.io/mpl_qt_viz/
I'm taking a screenshot using this line
screen = QPixmap.grabWindow(QApplication.desktop().winId())
but apparently that doesn't grab the full desktop if the user has several monitors.
Is there a way to grab the desktop of all monitors into a single image?
According to this blog, just add the x, y, width, and height to grab the full desktop.
Just need to loop over QApplication.screens() and grab them one at a time like so...
Note: using Python 3.8 and PyQt5
import os
from PyQt5.QtWidgets import QApplication
app = QApplication(sys.argv)
for screen in QApplication.screens():
screen_path = os.path.expanduser(f"~/Desktop/{screen.name()}.jpg")
screen.grabWindow(0).save(screen_path, 'jpg')
# grabWindow(0) means full screen
# for area use following format; x=0, y=0, w=-1, h=-1