plotnine and pyqt5

Hello,

plotnine is a wonderful package. I am trying to figure out how to incorporate plots into a pyqt5 application. For matplotlib, there are a number of examples, but with plotnine, I don’t know where to start. Any guidance is greatly appreciated.

Thank you.

[breisfeld]

The solution I am using may not be at all useful to others because of the way my application is structured. In any case, here is a little about what I am doing at the moment.

My present application is a PyQT5 wrapper that creates inputs for, runs, and analyzes outputs from a computational engine written in C. I am using plotnine for the analysis phase.

As of now, I am pickling figures from matplotlib (and can now do the same with plotnine) during the analysis phase. These then get stored in an sqlite database. The GUI accesses the database, unpickles the figures, and displays them.

The relevant code for the display (which I have as a QDialog) is something like

from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT
from matplotlib.figure import Figure

class PlotsDialog(QDialog):
    def __init__(self, title, simplot, parent=None, *args):
        super().__init__(parent, *args)
        self._title = title
        self._plot = simplot
        self.setup_ui()

    def setup_ui(self):

        self.figure = Figure()
        self.canvas = FigureCanvas(self._plot)
        self.toolbar = NavigationToolbar2QT(self.canvas, self)

        # set the layout
        layout = QVBoxLayout()
        layout.addWidget(self.toolbar)
        layout.addWidget(self.canvas)
        self.setLayout(layout)
        self.setWindowTitle(f"Simulation results: {self._title}")

        buttons = QDialogButtonBox(QDialogButtonBox.Ok)
        buttons.accepted.connect(self.accept)
        buttons.rejected.connect(self.reject)

        self.show()

Here, the simplot argument is the unpickled figure object.

This approach allows panning and zooming of static plots, which is adequate for now. If matplotlib figure pickles contain the underlying plotted data, something much more interactive could be implemented.

[has2k1]

Added to the tutorial.