I have the following code which I found here. I reduced the code a bit to make it more suitable to my question.
import sys
import matplotlib
matplotlib.use("Qt5Agg")
from PyQt5 import QtCore
from PyQt5.QtCore import pyqtSlot, pyqtSignal, QObject
from PyQt5.QtWidgets import *
from numpy import arange, sin, pi
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
class MyMplCanvas(FigureCanvas):
"""Ultimately, this is a QWidget (as well as a FigureCanvasAgg, etc.)."""
def __init__(self, parent=None, width = 5, height = 3, dpi=100):
fig = Figure(figsize=(width, height), dpi=dpi)
self.axes = fig.add_subplot(111)
self.compute_initial_figure()
FigureCanvas.__init__(self, fig)
self.setParent(parent)
FigureCanvas.updateGeometry(self)
class MyStaticMplCanvas(MyMplCanvas):
"""Simple canvas with a sine plot."""
def compute_initial_figure(self):
t = arange(0.0, 3.0, 0.01)
s = sin(2*pi*t)
self.axes.plot(t, s)
self.axes.set_ylabel('label2')
self.axes.set_xlabel('label1')
self.axes.grid(True)
class ApplicationWindow(QMainWindow):
def __init__(self):
QMainWindow.__init__(self)
self.setMinimumWidth(800)
self.setMinimumHeight(300)
self.setMaximumWidth(800)
self.setMaximumHeight(300)
self.main_widget = QWidget(self)
self.sc = MyStaticMplCanvas(self.main_widget, width=5, height=4, dpi=100)
l = QVBoxLayout(self.main_widget)
l.addWidget(self.sc)
self.setCentralWidget(self.main_widget)
if __name__ == '__main__':
app = QApplication(sys.argv)
aw = ApplicationWindow()
aw.setWindowTitle("PyQt5 Matplotlib Example")
aw.show()
app.exec_()
The problem is that the plot is shown in principle correctly but the x-label is missing (fell out of the frame which is displayed). So, how can I adjust the size of the axes object to make PyQt also display the x-label?
I know that the figure size is adjustable through the 'figsize' argument. But so far I could not find a similar command for a diagram inside of the figure.
Also, I heard of the gridspec package of matplotlib but I think it is not suitable here since I only have one plot to display.