How to generate 2-D Gaussian array using NumPy?
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:
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning - Basic Level Course
numpy.meshgrid(*xi, copy=True, sparse=False, indexing=’xy’)
- numpy.linespace()– returns number spaces evenly w.r.t interval.
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
# Importing Numpy packageimport numpy as np # Initializing value of x-axis and y-axis# in the range -1 to 1x, y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))dst = np.sqrt(x*x+y*y) # Intializing sigma and muusigma = 1muu = 0.000 # Calculating Gaussian arraygauss = np.exp(-( (dst-muu)**2 / ( 2.0 * sigma**2 ) ) ) print("2D Gaussian array :\n")print(gauss) |
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:
Python3
# Importing Numpy packageimport numpy as np # Initializing value of x-axis and y-axis# in the range -2 to +2x, y = np.meshgrid(np.linspace(-2,2,15), np.linspace(-2,2,15))dst = np.sqrt(x*x+y*y) # Intializing sigma and muusigma = 1muu = 0.000 # Calculating Gaussian arraygauss = np.exp(-( (dst-muu)**2 / ( 2.0 * sigma**2 ) ) ) print("2D Gaussian array :\n")print(gauss) |
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]]
