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Python aliasing - aliasing - “Binding two or more names to the same object. Aliasing happens naturally all the time in any language where variables and function parameters hold references to objects. To avoid confusion, just forget the idea that variables are boxes that store objects (an object can’t be in two boxes at the same time). It’s better to think of them as labels attached to objects (an object can have more than one label). See alias (Python alias).” (FlntPy 2022)

“Aliasing occurs when two or more handles (variables) reference the same memory location. Call by reference, for example, sets up an alias between the formal and the actual parameters. Aliasing may be used for efficiency since it allows data to be shared, and thus avoids copying. However, aliases must be tracked carefully for data integrity.”

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Snippet from Wikipedia: Aliasing

In signal processing and related disciplines, aliasing is the overlapping of frequency components resulting from a sample rate below the Nyquist rate. This overlap results in distortion or artifacts when the signal is reconstructed from samples which causes the reconstructed signal to differ from the original continuous signal. Aliasing that occurs in signals sampled in time, for instance in digital audio or the stroboscopic effect, is referred to as temporal aliasing. Aliasing in spatially sampled signals (e.g., moiré patterns in digital images) is referred to as spatial aliasing.

Aliasing is generally avoided by applying low-pass filters or anti-aliasing filters (AAF) to the input signal before sampling and when converting a signal from a higher to a lower sampling rate. Suitable reconstruction filtering should then be used when restoring the sampled signal to the continuous domain or converting a signal from a lower to a higher sampling rate. For spatial anti-aliasing, the types of anti-aliasing include fast approximate anti-aliasing (FXAA), multisample anti-aliasing, and supersampling.

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