Approximation

gaussian_fit(x, y, mu=1, sigma=1, amp=1)

Approximates given points with a normal function

Parameters:

• x: list (n,)
• y: list (n,)
• mu: float
• sigma: float
• amp: float

Returns: mus, sigmas, amps

gaussian_fit_bimodal(x, y, mu1=100, mu2=240, sigma1=30, sigma2=30, amp1=1, amp2=1)

Approximates given points with a bimodal normal function

Parameters:

• x: list (n,)
• y: list (n,)
• mu1: float
• mu2: float
• sigma1: float
• sigma2: float
• amp1: float
• amp2: float

Returns: mus, sigmas, amps

gaussian_fit_termodal(x, y, mu1=10, mu2=100, mu3=240, sigma1=10, sigma2=30, sigma3=30, amp1=1, amp2=1, amp3=1)

Approximates given points with a trimodal normal function

Parameters:

• x: list (n,)
• y: list (n,)
• mu1: float
• mu2: float
• mu3: float
• sigma1: float
• sigma2: float
• sigma3: float
• amp1: float
• amp2: float
• amp3: float

Returns: mus, sigmas, amps

lin_regr_approx(x, y)

Approximates distribution with a linear function and creates a plot from distribution parameters

Parameters:

• x: list (n,)
• y: list (n,)

Returns: (x_pred, y_pred), k, b, angle, score

bimodal_gauss_approx(x, y)

Approximates distribution with a bimodal Gaussian

Parameters:

• x: list (n,)
• y: list (n,)

Returns: (x_gauss, y_gauss), mus, sigmas, amps