Arrays and plots · Learning

Topics covered

Misinterpreting array axes or time intervals can give a physically incorrect meaning even when the calculation runs.

Overall workflow

Sensor and request → format and unit checks → storage and state changes → query and response checks

Assign meaning to array axes

If rows represent measurement times and columns represent sensor channels, axis=0 aggregation summarizes each channel. A correct Shape does not ensure the intended calculation. Choose elementwise or matrix multiplication according to its physical meaning.

Time and noise

To interpret differences as a rate of change, divide by the measurement time interval. A moving average reduces noise but blurs abrupt changes, and the sample count may differ at boundaries. Compare before and after filtering over the same time interval.

Worked example

$A=\begin{bmatrix}1&2&3\\4&5&6\end{bmatrix}$, $B=\begin{bmatrix}2&0\\1&3\\0&1\end{bmatrix}$.

import numpy as np
A=np.array([[1,2,3],[4,5,6]])
B=np.array([[2,0],[1,3],[0,1]])
print(A@B)

[[ 4  9]
 [13 21]]

Reference example

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Reference materials