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

Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. Pyplot is a state-based interface to a Matplotlib module which provides a MATLAB-like interface. There are various plots which can be used in Pyplot are Line Plot, Contour, Histogram, Scatter, 3D Plot, etc.

matplotlib.pyplot.close() Function

The close() function in pyplot module of matplotlib library is used to close a figure window.

Syntax: matplotlib.pyplot.close(fig=None)

Parameters: This method accept only one parameters.
fig : This parameter accepts the following values:

  1. None: This value will close the current figure
  2. Figure: This value will close the given Figure instance
  3. int: This value will close a figure number
  4. str: This value will close a figure name
  5. ‘all’:This value will close all figures

Returns: This method does not returns any values.



Below examples illustrate the matplotlib.pyplot.close() function in matplotlib.pyplot:

Example 1:




# Implementation of matplotlib function
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LogNorm
  
  
dx, dy = 0.015, 0.05
x = np.arange(-4.0, 4.0, dx)
y = np.arange(-4.0, 4.0, dy)
X, Y = np.meshgrid(x, y)
   
extent = np.min(x), np.max(x), np.min(y), np.max(y)
   
Z1 = np.add.outer(range(8), range(8)) % 2
plt.imshow(Z1, cmap ="binary_r"
           interpolation ='nearest',
           extent = extent, 
           alpha = 1)
   
def geeks(x, y):
    return (1 - x / 2 + x**5 + y**6) * np.exp(-(x**2 + y**2))
   
Z2 = geeks(X, Y)
   
x = plt.imshow(Z2, cmap ="Greens"
               alpha = 0.7
               interpolation ='bilinear',
               extent = extent)
plt.close()
plt.title('matplotlib.pyplot.close Example')
plt.show()

Output:

Example 2:




# Implementation of matplotlib function
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LogNorm
import matplotlib.tri as tri
      
dx, dy = 0.015, 0.05
x = np.arange(-4.0, 4.0, dx)
y = np.arange(-4.0, 4.0, dy)
X, Y = np.meshgrid(x, y)
   
extent = np.min(x), np.max(x), np.min(y), np.max(y)
   
Z1 = np.add.outer(range(8), range(8)) % 2
plt.imshow(Z1, cmap ="binary_r"
           interpolation ='nearest',
           extent = extent,
           alpha = 1)
   
def geeks(x, y):
    return (1 - x / 2 + x**5 + y**6) * np.exp(-(x**2 + y**2))
   
Z2 = geeks(X, Y)
   
x = plt.imshow(Z2, cmap ="Greens",
               alpha = 0.7
               interpolation ='bilinear',
               extent = extent)
plt.close(1)
ang = 40
rad = 10
radm = 0.35
radii = np.linspace(radm, 0.95, rad)
     
angles = np.linspace(0, 0.5 * np.pi, ang)
angles = np.repeat(angles[...,
                          np.newaxis], 
                   rad, axis = 1)
  
angles[:, 1::2] += np.pi / ang
     
x = (radii * np.cos(angles)).flatten()
y = (radii * np.sin(angles)).flatten()
z = (np.sin(4 * radii) * np.cos(4 * angles)).flatten()
     
triang = tri.Triangulation(x, y)
triang.set_mask(np.hypot(x[triang.triangles].mean(axis = 1),
                         y[triang.triangles].mean(axis = 1))
                < radm)
     
tpc = plt.tripcolor(triang, z,
                    shading ='flat')
  
plt.colorbar(tpc)
plt.plasma()
  
plt.title('matplotlib.pyplot.close() Example')
plt.show()

Output:

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