# User Guide¶

## Usage¶

The fssa package expects finite-size data in the following setting.

$A_L(\varrho) = L^{\zeta/\nu} \tilde{f}\left(L^{1/\nu} (\varrho - \varrho_c)\right), \qquad (L \to \infty, \varrho \to \varrho_c),$

l is like a 1-D numpy array which contains the finite system sizes $$L$$. rho is like a 1-D numpy array which contains the parameter values $$\varrho$$. a is like a 2-D numpy array which contains the observations (the data) $$A_L(\varrho)$$, where a[i, j] is the data at the i-th system size and the j-th parameter value. da is like a 2-D numpy array which contains the standard errors in the observations.

The fssa.autoscale function attempts to determine the critical parameter and exponents which entail an optimal data collapse. The initial guesses for $$\varrho_c, \nu, \zeta$$ are rho_c0, nu0, and zeta0.

>>> import fssa
>>> fssa.autoscale(l, rho, a, da, rho_c0, nu0, zeta0)