Appending varying values to loop results in list of last value only-CodePudding

I am simulating a position moving randomly and want to create a list of the position. I create a start position randomly and then add steps of random size to this start position:

import numpy as np

pos_range=1E-6
position=pos_range*np.random.rand(2)

steps=10
step_size=20E-9;

position_list=[]

for i in range(0,steps):
    step=step_size*np.random.rand(2)
    position =step
    print(position)
    position_list.append(position)

The result of the print is

[6.47220682e-07 2.84186976e-07]
[6.48019947e-07 2.89315337e-07]
[6.50324286e-07 2.96818080e-07]
[6.64316483e-07 3.10052685e-07]
[6.79662022e-07 3.20408865e-07]
[6.85052985e-07 3.31529075e-07]
[6.97773479e-07 3.45764518e-07]
[7.11152822e-07 3.46809336e-07]
[7.14484731e-07 3.54165996e-07]
[7.20412104e-07 3.58339358e-07]

which is my desired result. But position_list only contains the same data of the last position value in all rows:

    position_list
array([[7.20412104e-07, 3.58339358e-07],
       [7.20412104e-07, 3.58339358e-07],
       [7.20412104e-07, 3.58339358e-07],
       [7.20412104e-07, 3.58339358e-07],
       [7.20412104e-07, 3.58339358e-07],
       [7.20412104e-07, 3.58339358e-07],
       [7.20412104e-07, 3.58339358e-07],
       [7.20412104e-07, 3.58339358e-07],
       [7.20412104e-07, 3.58339358e-07],
       [7.20412104e-07, 3.58339358e-07]])

I suspect it has something to with how the numpy array is stored in memory or the mixture of lists and numpy array. All the workarounds that I tried are tedious and I am interested why this code does not work as intended.

CodePudding user response：

Change

position_list.append(position)

position_list.append(list(position))

CodePudding user response：

position = step modifies position in place. You are appending the same object to position_list over and over again.

We can confirm this by looking at the set of unique object ids in position_list.

>>> set(map(id, position_list))
{1637236197296}

There is an easy fix. Change position = step to position = position step. This avoids calling numpy.ndarray.__iadd__.

Also see Is i = i n truly the same as i = n?