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Matplotlib.pyplot.gcf() in Python
  • Last Updated : 19 Apr, 2020

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() 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.



Example 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)
A = np.asarray(ag.buffer_rgba())
# Pass off to PIL.
from PIL import Image
img = Image.fromarray(A)
# show image


Example 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]
        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
        tr = trifinder(e.xdata, e.ydata)
# 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,
                                   endpoint = False)
triangulation_angles = np.repeat(triangulation_angles[...,
                                 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)
# Useing 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'
# implementation of the matplotlib.pyplot.gcf() function


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