The FFT is based on the assumption that the signal is periodic. The basic period is the length of the input: a sinusoid that fits exactly once into this length is the lowest possible frequency. Let's call this frequency f. The FFT is a formula that shows how the signal (in this case your pulse) can be written as a weighted sum of (complex-valued) sinusoidal terms that are integer multiples of this f. So what you are doing when you apply the FFT to something like a pulse waveform is you are pretending that the pulse is one of an infinite stream of identical pulses. The spectrum you get from the FFT is the spectrum of this infinitely-extended waveform. Here's a post showing many of the details of this procedure in the case where the signal is derived from a .wav file.

How to approximate a given WAV file with trigonometric series?

The FFT is based on the assumption that the signal is periodic. The basic period is the length of the input: a sinusoid that fits exactly once into this length is the lowest possible frequency. Let's call this frequency f. The FFT is a formula that shows how the signal (in this case your pulse) can be written as a weighted sum of (complex-valued) sinusoidal terms that are integer multiples of this f. So what you are doing when you apply the FFT to something like a pulse waveform is you are pretending that the pulse is one of an infinite stream of identical pulses. The spectrum you get from the FFT is the spectrum of this infinitely-extended waveform. Here's a post showing many of the details of this procedure in the case where the signal is derived from a .wav file.

How to approximate a given WAV file with trigonometric series?

The FFT is based on the assumption that the signal is periodic. The basic period is the length of the input: a sinusoid that fits exactly once into this length is the lowest possible frequency. Let's call this frequency f. The FFT is a formula that shows how the signal (in this case your pulse) can be written as a weighted sum of (complex-valued) sinusoidal terms that are integer multiples of this f. So what you are doing when you apply the FFT to something like a pulse waveform is you are pretending that the pulse is one of an infinite stream of pulses. The spectrum you get from the FFT is the spectrum of this infinitely-extended waveform. Here's a post showing many of the details of this procedure in the case where the signal is derived from a .wav file.

How to approximate a given WAV file with trigonometric series?

The FFT is based on the assumption that the signal is periodic. The basic period is the length of the input: a sinusoid that fits exactly once into this length is the lowest possible frequency. Let's call this frequency f. The FFT is a formula that shows how the signal (in this case your pulse) can be written as a weighted sum of (complex-valued) sinusoidal terms that are integer multiples of this f. So what you are doing when you apply the FFT to something like a pulse waveform is you are pretending that the pulse is one of an infinite stream of identical pulses. The spectrum you get from the FFT is the spectrum of this infinitely-extended waveform. Here's a post showing many of the details of this procedure in the case where the signal is derived from a .wav file.

How to approximate a given WAV file with trigonometric series?