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Vector Projection using Python

  • Last Updated : 29 Nov, 2019

A vector is a geometric object which has both magnitude (i.e. length) and direction. A vector is generally represented by a line segment with a certain direction connecting the initial point A and the terminal point B as shown in the figure below and is denoted by $\overrightarrow{AB}$

Projection of a Vector on another vector

The projection of a vector $\overrightarrow{u}$ onto another vector $\overrightarrow{v}$ is given as
  $proj_{\vec{v}}({\vec{u}}) = \frac{\vec{u}.\vec{v}}{||\vec{v}||^{2}} \vec{v}$

Computing vector projection onto another vector in Python:




# import numpy to perform operations on vector
import numpy as np
  
u = np.array([1, 2, 3])   # vector u
v = np.array([5, 6, 2])   # vector v:
  
# Task: Project vector u on vector v
  
# finding norm of the vector v
v_norm = np.sqrt(sum(v**2))    
  
# Apply the formula as mentioned above
# for projecting a vector onto another vector
# find dot product using np.dot()
proj_of_u_on_v = (np.dot(u, v)/v_norm**2)*v
  
print("Projection of Vector u on Vector v is: ", proj_of_u_on_v)

Output:

Projection of Vector u on Vector v is:  [1.76923077 2.12307692 0.70769231]

One liner code for projecting a vector onto another vector:






(np.dot(u, v)/np.dot(v, v))*v

Projection of a Vector onto a Plane

The projection of a vector $\overrightarrow{u}$ onto a plane is calculated by subtracting the component of $\overrightarrow{u}$ which is orthogonal to the plane from $\overrightarrow{u}$.
  $proj_{Plane}({\vec{u}}) ={\vec{u}} - proj_{\vec{n}}({\vec{u}}) = {\vec{u}} - \frac{\vec{u}.\vec{n}}{||\vec{n}||^{2}} \vec{n}$
where, $\overrightarrow{n}$ is the plane normal vector.

Computing vector projection onto a Plane in Python:




# import numpy to perform operations on vector
import numpy as np
  
# vector u 
u = np.array([2, 5, 8])       
  
# vector n: n is orthogonal vector to Plane P
n = np.array([1, 1, 7])       
   
# Task: Project vector u on Plane P
  
# finding norm of the vector n 
n_norm = np.sqrt(sum(n**2))    
   
# Apply the formula as mentioned above
# for projecting a vector onto the orthogonal vector n
# find dot product using np.dot()
proj_of_u_on_n = (np.dot(u, n)/n_norm**2)*n
  
# subtract proj_of_u_on_n from u: 
# this is the projection of u on Plane P
print("Projection of Vector u on Plane P is: ", u - proj_of_u_on_n)

Output:

Projection of Vector u on Plane P is:  [ 0.76470588  3.76470588 -0.64705882]

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