I am trying to embed a matplotlib figure in my QT application, using which I want to show different plots upon different user actions.
I have created a custom figure canvas, in which I initialize a figure and a subplot. The code is as followed:
from matplotlib.backends.backend_qt5 import FigureCanvasQT, FigureManagerQT
from PyQt5 import QtWidgets
import matplotlib.pyplot as plt
import numpy as np
class CustomFigureCanvas(FigureCanvasQT):
def __init__(self, parent=None, cmap_name="coolwarm"):
fig = plt.Figure()
self.color_map = plt.get_cmap(cmap_name)
self.axes = fig.add_subplot(111)
super().__init__(fig)
self.setParent(parent)
self.setBaseSize(300, 300)
self.setMaximumSize(400, 400)
self.setMinimumSize(250, 250)
self.setSizePolicy(
QtWidgets.QSizePolicy.MinimumExpanding,
QtWidgets.QSizePolicy.MinimumExpanding,
)
def set_clf_2d(self, clf_2d):
self.clf = clf_2d
def plot_new_datapoints(self, x2D):
self.add_datapoint(x2D)
@staticmethod
def _make_meshgrid(x, y, h=0.02):
x_min, x_max = x.min() - 1, x.max() + 1
y_min, y_max = y.min() - 1, y.max() + 1
XX, YY = np.meshgrid(
np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)
)
return XX, YY
def _plot_contours(self, xx, yy, **params):
"""Plot the decision boundaries for a classifier.
Parameters
----------
ax: matplotlib axes object
clf: a classifier
xx: meshgrid ndarray
yy: meshgrid ndarray
params: dictionary of params to pass to contourf, optional
"""
Z = self.clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
self.axes.contourf(xx, yy, Z, **params)
def plot_data(self, x2D, y):
"""plots the given array and the decision function bounday.
Arguments:
x2D {np.array} -- [2d array]
y {np.array} -- [1d array]
"""
x0, x1 = x2D[:, 0], x2D[:, 1]
xx, yy = CustomFigureCanvas._make_meshgrid(x0, x1)
labels = ["Cognitive", "Not Cognitive"]
colors = ["r", "b"]
self.axes.clear()
self._plot_contours(xx, yy, cmap=self.color_map, alpha=0.8)
target_ids = [0, 1]
for i, c, label in zip(target_ids, colors, labels):
print(i, label)
self.axes.scatter(
x0[y == i, 0],
x1[y == i, 1],
color=c,
label=label,
marker="o",
s=(15, 15),
)
self.axes.set_xlim(xx.min(), xx.max())
self.axes.set_ylim(yy.min(), yy.max())
self.axes.set_title("2D Representation using PCA")
self.axes.legend(fontsize=8)
self.axes.plot()
def add_datapoint(self, x2d):
"""Adds a new datapoint to the plot
Arguments:
x2d {a 2d single point, [x,y]} -- [np.array with shape (1,2)]
axes {plt.axes} -- [description]
"""
print(x2d, type(x2d))
self.axes.scatter(
x2d[:, 0],
x2d[:, 1],
color="k",
label="Current Text",
marker="o",
s=(15, 15),
)
self.axes.legend(fontsize=8)
My problem is that whatever I do, my FigureCanvas will always be filled with black, and won't be changed on any circumstances.
By using the CustomFigureCanvas you can reproduce my problem.
