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:
Matplotlib.pyplot.gcf() in Python
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.