I have a 2d space of (x,y) coordinates that I want to model in python and want to know a way to define the 2d space in python where I can assign multiple values to a point (x,y). Later values at coordinates will be changed based on some coordinate dependent calculations.
I thought about using numpy array to create the 2d array based on the size entered by the user. I started by creating a 2d n*m numpy array of zeros and later parts of the code calculations are done on points. But in this way each point (x,y) only has one value.
import numpy as np
x_coor=135
y_coor=120
grid=np.zeros((x_coor,y_coor)
Is there a way to make it grid[x,y]=(value1,value2), and is there a better way to define the grid other than a numpy array?
You could indeed use numpy for this. One way would be to define a 3d array as np.zeros((x_coor, y_coor, 2)) and save each of the coordinates along the last axis.
Another way to obtain the desired structure using numpy could be to define an ndarray of tuples, and uptade each point in the mentioned fashion, i.e. grid[x,y] = (value1,value2). Here's how you could do it:
x_coor=135
y_coor=120
grid = np.zeros((5,3), dtype='i,i')
grid[0,0] = (1,2)
grid[2,2] = (5,1)
grid[1,0] = (3,5)
print(grid)
array([[(1, 2), (0, 0), (0, 0)],
[(3, 5), (0, 0), (0, 0)],
[(0, 0), (0, 0), (5, 1)],
[(0, 0), (0, 0), (0, 0)],
[(0, 0), (0, 0), (0, 0)]], dtype=[('f0', '<i4'), ('f1', '<i4')])
If you want to update several values at once using multiple coordinates you could do:
grid = np.zeros((5,3), dtype='i,i')
coordinates = np.array([(1,2),(2,2), (0,0)], dtype='i,i')
new_vals = np.array([(12,2),(4,1), (0,9)], dtype='i,i')
grid[tuple(zip(*coordinates))] = new_vals
print(grid)
array([[( 0, 9), ( 0, 0), ( 0, 0)],
[( 0, 0), ( 0, 0), (12, 2)],
[( 0, 0), ( 0, 0), ( 4, 1)],
[( 0, 0), ( 0, 0), ( 0, 0)],
[( 0, 0), ( 0, 0), ( 0, 0)]], dtype=[('f0', '<i4'), ('f1', '<i4')])
Do note though that tuples are inmutable, so if you're planning to perform operations with these coordinates you should go with the first approach.