Save data to 3D numpy array

Question

I have accelerometer data (x,y,z) which is being updated every 50ms. I need to store 80 values of the data into a 3D numpy array (1, 80, 3). For example:

[[[x,y,z] (at 0ms)
  [x,y,z] (at 50ms)
  ...
  [x,y,z]]] (at 4000ms)

After getting the first 80 values, I need to update the array with upcoming values, for example:

[[[x,y,z] (at 50ms)
  [x,y,z] (at 100ms)
  ...
  [x,y,z]]] (at 4050ms)

I'm sure there is a way to update the array without needing to manually write 80 variables to store the data into, but I can't think of it. Would really appreciate some help here.

Hello, Airidas I would like to know what is the type of data in the input. Is it a np.array, a list or what else? — Mazziotti Raffaele
– Mazziotti Raffaele, Commented May 8, 2020 at 1:26
I send the values over wifi as a char array and on the receiving end I convert them to float variables: ax = gauti_duomenys[0], same for y and z values. — Airidas Jeznas
– Airidas Jeznas, Commented May 8, 2020 at 1:29

Paddy Harrison · Accepted Answer · 2020-05-08 14:33:39Z

2

It sounds like you want your array to always be 80 long, so what I would suggest is roll the array and then update the last value.

import numpy as np

data = np.arange(80*3).reshape(80, 3)
data
>>> array([[  0,   1,   2],
           [  3,   4,   5],
           [  6,   7,   8],
           ...,
           [231, 232, 233],
           [234, 235, 236],
           [237, 238, 239]])

data = np.roll(data, -1, axis=0)
data
>>> array([[  3,   4,   5], # this is second row (index 1) in above array
           [  6,   7,   8], # third row
           [  9,  10,  11], # etc.
           ...,
           [234, 235, 236],
           [237, 238, 239],
           [  0,   1,   2]]) # the first row has been rolled to the last position

# now update last position with new data
data[-1] = [x, y, z] # new xyz data
data
>>> data
>>> array([[  3,   4,   5], 
           [  6,   7,   8],
           [  9,  10,  11],
           ...,
           [234, 235, 236],
           [237, 238, 239],
           [ 76,  76,  76]]) # new data updates in correct position in array

edited May 8, 2020 at 14:33

answered May 8, 2020 at 10:25

Paddy Harrison

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Airidas Jeznas Over a year ago

This is exactly what I was looking for. Just changed .reshape(80 , 3) to .reshape(-1, 80, 3) , data[-1] = [x, y, z] to data[0][-1] = [x, y, z] and data = np.roll(data, -1, axis=0) to data = np.roll(data, -1, axis=1) and it's working as expected. Thank you very much!

Mazziotti Raffaele · Accepted Answer · 2020-05-08 10:23:47Z

0

You can use vstack (initializing the array for the first iteration):

data=[x,y,x]  # first iteration
data=np.vstack([data,[x,y,z]]) # for the rest

print(data) # you would have a Nx3 array

For the update every N seconds it is easier if you use a FIFO or a ring buffer: https://pypi.org/project/numpy_ringbuffer/

edited May 8, 2020 at 10:23

answered May 8, 2020 at 2:17

Mazziotti Raffaele

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