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I wonder if an efficient method exist to compute how much a signal is periodic, it should be ~1.0 when the signal is totally periodic (like a sinusoïdal signal) and ~0.0 when totally random, like a white noise.
Edit : This question came with a big misunderstanding, it has been modified to fit the answer.
I suggest using Spectral Flatness, aka Wiener Entropy. It is defined as a ratio of geometric and arithmetic mean of the magnitude spectra $X(k)$:
$$\Xi=\dfrac{\sqrt[K]{\prod_{k=0}^{K} X(k)}}{\frac{1}{K}\sum_{k=0}^{K}X(k)} $$
For signals which have flat spectra, its value tends towards $1$, whereas for tonal signals it is close to $0$. In your particular application, you might want to consider $1-\Xi$ as a measure of "tonalness" in your signal.
Equivalently, in order to avoid underflows, one can replace the geometric mean implementation with $$GM=\exp\left( \frac{1}{K} \sum_{k=0}^{K}\ln X(k) \right) $$
The unbiased, normalized autocorrelation function (ACF) $r_{xx}[\tau]$ directly computes the desired "periodicity coefficient". It is defined as
$$ r_{xx}[\tau] = \frac{N}{\left\|x\right\|_2^2\cdot(N-\tau)}\sum_{i=0}^{N-\tau-1}x[i]x[i+\tau]\quad\text{ for }0\leq \tau \leq (N-1), $$
with $\left\|x\right\|_2^2$ the squared (Frobenius) norm (e.g., the energy) and $N$ the length of $x$.
You just have to determine the height of the first maximum with a time lag $\tau$ different from zero, $\tau_\text{max} = \text{arg max}\left\{r_{xx}[\tau]\right\}\text{ for }\tau \neq 0$.
Values $-1 \leq r_{xx}[\tau_\text{max}] \leq 1$ are a measure for to which degree the signal is periodic.
The periodicity measure you asked for, with a range of $0\ldots 1$, can, e.g., be implemented by discarding all values $r_{xx}[\tau_\text{max}] < 0$, resulting in:
$$ p = \text{max}\left\{0, r_{xx}[\tau_\text{max}]\right\}. $$
