Matplotlib.pyplot.gcf() en Python

Matplotlib es una increíble biblioteca de visualización en Python para gráficos 2D de arrays. Matplotlib es una biblioteca de visualización de datos multiplataforma basada en arrays NumPy y diseñada para funcionar con la pila SciPy más amplia.
 

matplotlib.pyplot.gcf()

matplotlib.pyplot.gcf() se usa principalmente para obtener la cifra actual. Si no hay una cifra actual disponible, se crea una con la ayuda de la función figure().
Sintaxis:
 

matplotlib.pyplot.gcf()

Ejemplo 1:

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()

Producción: 
 

Ejemplo 2: 
 

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()

Producción: