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How to generate 2-D Gaussian array using NumPy?
  • Last Updated : 01 Oct, 2020

In this article, Let’s discuss how to generate a 2-D Gaussian array using NumPy. To create a 2 D Gaussian array using Numpy python module

Functions used:

  • numpy.meshgrid()It is used to create a rectangular grid out of two given one-dimensional arrays representing the Cartesian indexing or Matrix indexing. 

Syntax:

numpy.meshgrid(*xi, copy=True, sparse=False, indexing=’xy’)
 

Syntax:

numpy.linspace(start, stop, num = 50, endpoint = True, retstep = False, dtype = None) 
 



  • numpy.exp()this mathematical function helps the user to calculate the exponential of all the elements in the input array.

Syntax:

numpy.exp(array, out = None, where = True, casting = ‘same_kind’, order = ‘K’, dtype = None)
 

Example 1:

Python3

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# Importing Numpy package
import numpy as np
  
# Initializing value of x-axis and y-axis
# in the range -1 to 1
x, y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))
dst = np.sqrt(x*x+y*y)
  
# Intializing sigma and muu
sigma = 1
muu = 0.000
  
# Calculating Gaussian array
gauss = np.exp(-( (dst-muu)**2 / ( 2.0 * sigma**2 ) ) )
  
print("2D Gaussian array :\n")
print(gauss)

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Output:

2D Gaussian array:
[[0.36787944 0.44822088 0.51979489 0.57375342 0.60279818 0.60279818
0.57375342 0.51979489 0.44822088 0.36787944]
[0.44822088 0.54610814 0.63331324 0.69905581 0.73444367 0.73444367
0.69905581 0.63331324 0.54610814 0.44822088]
[0.51979489 0.63331324 0.73444367 0.81068432 0.85172308 0.85172308
0.81068432 0.73444367 0.63331324 0.51979489]
[0.57375342 0.69905581 0.81068432 0.89483932 0.9401382  0.9401382
0.89483932 0.81068432 0.69905581 0.57375342]
[0.60279818 0.73444367 0.85172308 0.9401382  0.98773022 0.98773022
0.9401382  0.85172308 0.73444367 0.60279818]
[0.60279818 0.73444367 0.85172308 0.9401382  0.98773022 0.98773022
0.9401382  0.85172308 0.73444367 0.60279818]
[0.57375342 0.69905581 0.81068432 0.89483932 0.9401382  0.9401382
0.89483932 0.81068432 0.69905581 0.57375342]
[0.51979489 0.63331324 0.73444367 0.81068432 0.85172308 0.85172308
0.81068432 0.73444367 0.63331324 0.51979489]
[0.44822088 0.54610814 0.63331324 0.69905581 0.73444367 0.73444367
0.69905581 0.63331324 0.54610814 0.44822088]
[0.36787944 0.44822088 0.51979489 0.57375342 0.60279818 0.60279818
0.57375342 0.51979489 0.44822088 0.36787944]]
 

Example 2:

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# Importing Numpy package
import numpy as np
  
# Initializing value of x-axis and y-axis
# in the range -2 to +2
x, y = np.meshgrid(np.linspace(-2,2,15), np.linspace(-2,2,15))
dst = np.sqrt(x*x+y*y)
  
# Intializing sigma and muu
sigma = 1
muu = 0.000
  
# Calculating Gaussian array
gauss = np.exp(-( (dst-muu)**2 / ( 2.0 * sigma**2 ) ) )
  
print("2D Gaussian array :\n")
print(gauss)

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Output:

2D Gaussian array:
[[0.01831564 0.03113609 0.0487813  0.07043526 0.09372907 0.11494916
 0.12992261 0.13533528 0.12992261 0.11494916 0.09372907 0.07043526
 0.0487813  0.03113609 0.01831564]
[0.03113609 0.0529305  0.08292689 0.11973803 0.15933686 0.19541045
 0.2208649  0.2300663  0.2208649  0.19541045 0.15933686 0.11973803
 0.08292689 0.0529305  0.03113609]
[0.0487813  0.08292689 0.12992261 0.1875951  0.24963508 0.30615203
 0.34603184 0.36044779 0.34603184 0.30615203 0.24963508 0.1875951
 0.12992261 0.08292689 0.0487813 ]
[0.07043526 0.11973803 0.1875951  0.27086833 0.36044779 0.44205254
 0.49963495 0.52045012 0.49963495 0.44205254 0.36044779 0.27086833
 0.1875951  0.11973803 0.07043526]
[0.09372907 0.15933686 0.24963508 0.36044779 0.47965227 0.58824471
 0.66487032 0.69256932 0.66487032 0.58824471 0.47965227 0.36044779
 0.24963508 0.15933686 0.09372907]
[0.11494916 0.19541045 0.30615203 0.44205254 0.58824471 0.72142229
 0.81539581 0.84936582 0.81539581 0.72142229 0.58824471 0.44205254
 0.30615203 0.19541045 0.11494916]
[0.12992261 0.2208649  0.34603184 0.49963495 0.66487032 0.81539581
 0.92161045 0.96000544 0.92161045 0.81539581 0.66487032 0.49963495
 0.34603184 0.2208649  0.12992261]
[0.13533528 0.2300663  0.36044779 0.52045012 0.69256932 0.84936582
 0.96000544 1.         0.96000544 0.84936582 0.69256932 0.52045012
 0.36044779 0.2300663  0.13533528]
[0.12992261 0.2208649  0.34603184 0.49963495 0.66487032 0.81539581
 0.92161045 0.96000544 0.92161045 0.81539581 0.66487032 0.49963495
 0.34603184 0.2208649  0.12992261]
[0.11494916 0.19541045 0.30615203 0.44205254 0.58824471 0.72142229
 0.81539581 0.84936582 0.81539581 0.72142229 0.58824471 0.44205254
 0.30615203 0.19541045 0.11494916]
[0.09372907 0.15933686 0.24963508 0.36044779 0.47965227 0.58824471
 0.66487032 0.69256932 0.66487032 0.58824471 0.47965227 0.36044779
 0.24963508 0.15933686 0.09372907]
[0.07043526 0.11973803 0.1875951  0.27086833 0.36044779 0.44205254
 0.49963495 0.52045012 0.49963495 0.44205254 0.36044779 0.27086833
 0.1875951  0.11973803 0.07043526]
[0.0487813  0.08292689 0.12992261 0.1875951  0.24963508 0.30615203
 0.34603184 0.36044779 0.34603184 0.30615203 0.24963508 0.1875951
 0.12992261 0.08292689 0.0487813 ]
[0.03113609 0.0529305  0.08292689 0.11973803 0.15933686 0.19541045
 0.2208649  0.2300663  0.2208649  0.19541045 0.15933686 0.11973803
 0.08292689 0.0529305  0.03113609]
[0.01831564 0.03113609 0.0487813  0.07043526 0.09372907 0.11494916
 0.12992261 0.13533528 0.12992261 0.11494916 0.09372907 0.07043526
 0.0487813  0.03113609 0.01831564]]

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