In this article, we will discuss the numpy mgrid() function in python provided by the Numpy library. The mgrid () function helps to get a dense multi-dimensional ‘meshgrid’. An instance of numpy.lib.index_tricks.nd_grid returns a fleshed out mesh-grid when indexed. Each argument returned has the same shape. Moreover, the number and dimension of the output arrays are equal to the number of indexing dimensions.
Numpy Mgrid is a special type of numpy array that creates a 2d array with similar values. This method calls the meshgrid method to initialize dense multidimensional arrays. Moreover, mgrid also accepts complex numbers as parameter.
Syntax of Numpy mgrid() function:
Mgrid function accepts indexes and dimension as the initializing parameters.
numpy.mgrid = <numpy.lib.index_tricks.nd_grid object>
Return value:
Mesh grid of multi dimensional arrays all of same size and dimension.
Examples for different step size
Example 1: For natural non complex number –
import numpy as np
a = np.mgrid[0:4, 0:4]
print(a)
Output:
array([[[0, 0, 0, 0],
[1, 1, 1, 1],
[2, 2, 2, 2],
[3, 3, 3, 3]],
[[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3]]])
Example 2: For complex number –
import numpy as np
a = np.mgrid[-2:2:4j]
print(a)
Output:
array([-2.0, -0.66666667, 0.66666667, 2.0])
Explanation:
With the above examples, we see how the Numpy mgrid() function is used to obtain ndarrays. The ‘ndarrays’ are of the same dimension. The stop value is not inclusive. If the step length is not a complex number. However, if the step length is a complex number (e.g., 4j). Then the integer part of its magnitude is interpreted. And the number of points to create between the start and stop. In this case, the stop value is inclusive.
Numpy mgrid() v/s meshgrid() function in Python
Numpy mgrid() fleshes out the indexes given by broadcasting them to form dense grids. Simultaneously, the meshgrid() function is useful for creating coordinate arrays. To vectorize function evaluations over a grid. Meshgrid() function is inspired by MATLAB. Moreover, the meshgrid() creates a rectangular grid out of two given one-dimensional arrays representing the Cartesian indexes.
Example of Numpy meshgrid() function:
import numpy as np
x = np.linspace(-4, 4, 9)
# numpy.linspace creates an array of 9 linearly placed elements between -4 and 4, both inclusive
y = np.linspace(-5, 5, 11)
x_1, y_1 = np.meshgrid(x, y)
print("x_1 = ")
print(x_1)
print("y_1 = ")
print(y_1)
Output:
x_1 =
[[-4. -3. -2. -1. 0. 1. 2. 3. 4.]
[-4. -3. -2. -1. 0. 1. 2. 3. 4.]
[-4. -3. -2. -1. 0. 1. 2. 3. 4.]
[-4. -3. -2. -1. 0. 1. 2. 3. 4.]
[-4. -3. -2. -1. 0. 1. 2. 3. 4.]
[-4. -3. -2. -1. 0. 1. 2. 3. 4.]
[-4. -3. -2. -1. 0. 1. 2. 3. 4.]
[-4. -3. -2. -1. 0. 1. 2. 3. 4.]
[-4. -3. -2. -1. 0. 1. 2. 3. 4.]
[-4. -3. -2. -1. 0. 1. 2. 3. 4.]
[-4. -3. -2. -1. 0. 1. 2. 3. 4.]]
y_1 =
[[-5. -5. -5. -5. -5. -5. -5. -5. -5.]
[-4. -4. -4. -4. -4. -4. -4. -4. -4.]
[-3. -3. -3. -3. -3. -3. -3. -3. -3.]
[-2. -2. -2. -2. -2. -2. -2. -2. -2.]
[-1. -1. -1. -1. -1. -1. -1. -1. -1.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[ 2. 2. 2. 2. 2. 2. 2. 2. 2.]
[ 3. 3. 3. 3. 3. 3. 3. 3. 3.]
[ 4. 4. 4. 4. 4. 4. 4. 4. 4.]
[ 5. 5. 5. 5. 5. 5. 5. 5. 5.]]
Numpy mgrid() v/s ogrid() function in Python
Unlike mgrid() function, which converts indexes into dense mesh grids of the same sizes, the Ogrid stands for “open grid.”It basically provides a way to act on an image’s specific pixels based on their row and column index.
Basic example of Numpy ogrid():
from numpy import ogrid
x,y = np.ogrid[0:11, 0:6]
print(x)
print(y)
Output:
array([[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
[9],
[10]])
array([[0, 1, 2, 3, 4, 5]])
Numpy mgrid() using vstack() function in Python
Firstly, the vstack() function can be used to implement mgrid() function in Python. The vstack() function stacks the sequence of input arrays vertically to make a single array. Also, the stacked array is similar to the dense grids formed in the mgrid() function. The only point of difference between mgrid() and vstack is in their input values. The mgrid() returns dense grids based on the input index values. Whereas vstack just stacks the input arrays into a single array.
Example of Numpy vstack():
import numpy as np
in_a1 = np.array([[ 1, 2, 3], [ -1, -2, -3]] )
print ("1st Input array : \n", in_a1)
in_a2 = np.array([[ 4, 5, 6], [ -4, -5, -6]] )
print ("2nd Input array : \n", in_a2)
# Stacking the two arrays vertically
out_arr = np.vstack((in_arr1, in_arr2))
print ("Output stacked array :\n ", out_arr)
Output:
1st Input array :
[[ 1 2 3]
[-1 -2 -3]]
2nd Input array :
[[ 4 5 6]
[-4 -5 -6]]
Output stacked array :
[[ 1 2 3]
[-1 -2 -3]
[ 4 5 6]
[-4 -5 -6]]
Numpy linspace() function to implement mgrid() function
The numpy.linspace() function returns number spaces evenly concerning the interval. Also, we can implement mgrid() function using linspace() as it returns an output array just like that given by mgrid() function. However, it takes the intervals to inform sample numbers, unlike step size as in the case of mgrid().
Example of numpy.linspace():
import numpy as np
# restep set to True
print("A\n", np.linspace(2.0, 3.0, num=5, retstep=True), "\n")
Output:
[ 0. 0.22039774 0.42995636 0.6183698 0.77637192 0.8961922
0.9719379 0.99988386 0.9786557 0.90929743]
What’s Next?
NumPy is mighty and incredibly essential for information science in Python. That being true if you are interested in data science in Python. As a result, you really ought to find out more about Python.
You might like our following tutorials on numpy.
- Numpy Mean: Implementation and Importance
- Using Numpy Random Function to Create Random Data
- NumPy Reshape: Reshaping Arrays With Ease
- In-depth Explanation of np.power() With Examples
Conclusion
We have discussed using the numpy mgrid() function. And also various ways of implementing it. There are very subtle differences between Numpy mgrid(), mesh grid(), and ogrid() functions, which are also highlighted in this article. The linspace() function of is used to implement the mgrid() function in python up to some extent. It has also been discussed with examples. I hope you find this article useful while implementing the Numpy mgrid() function in your python programs.
If you still have any questions regarding the NumPy multiply function. Leave your question in the comments below.
Happy Pythonning!