I am given a two-dimensional array. Each element represents the x,y coordinates of a trapezium. I do not want negative values so I need to adjust the minimum x value to zero and I want to adjust the minimum y value to zero. I can do this (see below), but in a very long winded way. Is there a more elegant way to normalise the arrays?
import numpy as np
# This line is given
pa = np.array([[ 213.00002 , 213.00002 ],[ -213.00002 , 213.00002 ],[ 213.00002 , -213.00002 ],[ -213.00002 , -213.00002 ]])
# This line is given
#1 get values
pa_x_values = np.array([(pa[0][0]),(pa[1][0]),(pa[2][0]),(pa[3][0])])
pa_y_values = np.array([(pa[0][1]),(pa[1][1]),(pa[2][1]),(pa[3][1])])
#2 get minimum value
pa_min_x = min(pa_x_values)
pa_min_y = min(pa_y_values)
#3 calculate difference from zero
p1_dx = 0 - pa_min_x
p1_dy = 0 - pa_min_y
#4 make new array
p1 = np.array([[0,0],[0,0],[0,0],[0,0]],dtype=np.float32)
#5 normalise
for i in range(4):
p1[i][0] = (pa[i][0]) + p1_dx
p1[i][1] = (pa[i][1]) + p1_dy
# RESULT
# p1
# [[426.00003, 426.00003], [ 0, 426.00003], [426.00003, 0 ], [ 0, 0 ]]
You can use .min(0) to get the minimum for each of x and y individually and then subtract that from the entire array.
pa -= pa.min(0)