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?
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Answer
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.