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 npfrom matplotlib.backends.backend_agg import FigureCanvasAggimport matplotlib.pyplot as plotplot.plot([2, 3, 4])# implementation of the # matplotlib.pyplot.gcf()# functionfigure = plot.gcf().canvasag = figure.switch_backends(FigureCanvasAgg)ag.draw()A = np.asarray(ag.buffer_rgba())# Pass off to PIL.from PIL import Imageimg = Image.fromarray(A)# show imageimg.show() |
Output:
Example 2:
Python3
import matplotlib.pyplot as pltfrom matplotlib.tri import Triangulationfrom matplotlib.patches import Polygonimport numpy as np# helper function to update # the polygondef 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 polygondef 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 = 16all_radii = 5minimum_radii = 0.25radii = 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_anglesa = (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 # Triangulationtrifinder = 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() functionplt.gcf().canvas.mpl_connect('motion_notification', motion_handler)plt.show() |
Output:
