Matplotlib.pyplot.gcf() in Python (original) (raw)

Last Updated : 28 Apr, 2025

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.

matplotlib.pyplot.gcf()

matplotlib.pyplot.gcf() is primarily used to get the current figure. If no current figure is available then one is created with the help of the figure() function.
Syntax:

matplotlib.pyplot.gcf()

Example 1:

Python3 `

import numpy as np from matplotlib.backends.backend_agg import FigureCanvasAgg import matplotlib.pyplot as plot

plot.plot([2, 3, 4])

implementation of the

matplotlib.pyplot.gcf()

function

figure = plot.gcf().canvas

ag = figure.switch_backends(FigureCanvasAgg) ag.draw() A = np.asarray(ag.buffer_rgba())

Pass off to PIL.

from PIL import Image img = Image.fromarray(A)

show image

img.show()

`

Output:

Example 2:

Python3 `

import matplotlib.pyplot as plt from matplotlib.tri import Triangulation from matplotlib.patches import Polygon import numpy as np

helper function to update

the polygon

def polygon_updater(tr): if tr == -1: points = [0, 0, 0] else: points = tri.triangles[tr] x_axis = tri.x[points] y_axis = tri.y[points] polygon.set_xy(np.column_stack([x_axis, y_axis]))

helper function to set the motion

of polygon

def motion_handler(e): if e.inaxes is None: tr = -1 else: tr = trifinder(e.xdata, e.ydata) polygon_updater(tr) e.canvas.draw()

Making the Triangulation.

all_angles = 16 all_radii = 5 minimum_radii = 0.25 radii = np.linspace(minimum_radii, 0.95, all_radii) triangulation_angles = np.linspace(0, 2 * np.pi, all_angles, endpoint = False)

triangulation_angles = np.repeat(triangulation_angles[..., np.newaxis], all_radii, axis = 1)

triangulation_angles[:, 1::2] += np.pi / all_angles a = (radii * np.cos(triangulation_angles)).flatten() b = (radii * np.sin(triangulation_angles)).flatten() tri = Triangulation(a, b) tri.set_mask(np.hypot(a[tri.triangles].mean(axis = 1), b[tri.triangles].mean(axis = 1)) < minimum_radii)

Using TriFinder object from

Triangulation

trifinder = tri.get_trifinder()

Setting up the plot and the callbacks.

plt.subplot(111, aspect ='equal') plt.triplot(tri, 'g-')

dummy data for (x-axis, y-axis)

polygon = Polygon([[0, 0], [0, 0]], facecolor ='b') polygon_updater(-1) plt.gca().add_patch(polygon)

implementation of the matplotlib.pyplot.gcf() function

plt.gcf().canvas.mpl_connect('motion_notification', motion_handler) plt.show()

`

Output: