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Python – numpy.meshgrid
numpy.meshgrid() is used to return coordinate matrices from coordinate vectors. Its syntax is as follows −
numpy.meshgrid(*xi, **kwargs)
Parameters
Meshgrid can accept the following parameters −
x1, x2, …, xn − It represents the coordinates of a grid.
indexing − It is an optional parameter which defines the Cartesian 'xy' by default and matrix 'ij' index of output.
sparse − It is an optional parameter. If we like to use sparse grid for conserving memory, then we have to set this parameter to True. By default, it is False.
copy − It returns a copy of the original array for conversing memory when the parameter is True. By default, it is False.
Example 1
Let us consider the following example −
# Import numpy
import numpy as np
# input array
x = np.array([1,2,3,4,5])
y = np.array([11, 12, 13, 14, 15])
print("Input x :\n", x)
print("Input y :\n", y)
# meshgrid() function
xx, yy = np.meshgrid(x, y, sparse=True)
print("Meshgrid of X:", xx)
print("Meshgrid of Y:\n", yy)Output
It will generate the following output −
Input x : [1 2 3 4 5] Input y : [11 12 13 14 15] Meshgrid of X: [[1 2 3 4 5]] Meshgrid of Y: [[11] [12] [13] [14] [15]]
Example 2
Let us take another example. It highlights the difference between linspace and meshgrid.
# Import numpy
import numpy as np
# linspace function
a = np.linspace(3, 4, 4)
b = np.linspace(4, 5, 6)
print("linspace of a :", a)
print("linspace of b :", b)
# meshgrid function
xa, xb = np.meshgrid(a, b)
print("Meshgrid of xa :\n", xa)
print("Meshgrid of xb :\n", xb)Output
The above program will generate the following output −
linspace of a : [3. 3.33333333 3.66666667 4. ] linspace of b : [4. 4.2 4.4 4.6 4.8 5. ] Meshgrid of xa : [[3. 3.33333333 3.66666667 4. ] [3. 3.33333333 3.66666667 4. ] [3. 3.33333333 3.66666667 4. ] [3. 3.33333333 3.66666667 4. ] [3. 3.33333333 3.66666667 4. ] [3. 3.33333333 3.66666667 4. ]] Meshgrid of xb : [[4. 4. 4. 4. ] [4.2 4.2 4.2 4.2] [4.4 4.4 4.4 4.4] [4.6 4.6 4.6 4.6] [4.8 4.8 4.8 4.8] [5. 5. 5. 5. ]]
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