landau.calculate

Calculates phase diagrams from sets of Phases.

landau.calculate.calc_phase_diagram(phases, Ts, mu, refine=True, keep_unstable=False)

Calculate phase diagram at given sampling points.

Parameters:

• phases (Iterable[Phase])

• Ts (Union[Iterable[float], float]) – sampling points in temperature

• mu (Union[Iterable[float], float, int]) – sampling points in chemical potential; if int guess sampling points with guess_mu_range at max(Ts)

• refine (bool) – add additional sampling points at exact phase transitions

• keep_unstable (bool) – only keep entries of stable phases, otherwise keep entries of all phases at all sampling points

Returns:

dataframe of phase points

landau.calculate.cluster_T_c(dd, *, distance_threshold)

Cluster points by (T, c) coordinates; used by cluster_phase.

Parameters:

• dd – DataFrame with columns ‘T’ and ‘c’.

• distance_threshold – Agglomerative clustering cut-off in normalised (T, c) space. Smaller values split more aggressively; larger values merge more. 0.5 was hand-tuned for 2D T-c diagrams — lower it (e.g. 0.2) when disconnected stable segments within one phase are incorrectly merged.

Return type:

Series

landau.calculate.cluster_T_c_mu(dd, *, distance_threshold)

Cluster points by (T, c, mu); rows with mu=±inf get their own distinct labels.

Used by get_transitions. The mu=±inf rows are border edges from _border_edges and must not be fed to AgglomerativeClustering.

Parameters:

• dd – DataFrame with columns ‘T’, ‘c’, and ‘mu’.

• distance_threshold – Agglomerative clustering cut-off in normalised (T, c, mu) space. See cluster_T_c() for tuning guidance.

Return type:

Series

landau.calculate.get_transitions(df)

Identify “continuous” two-phase transition lines in mu/T space, i.e. transitions between the same two phases and along which mu/T are continuous.

Useful for plotting below, but potentially also to augment the existing refining routines and acquire additional Free energies from calphy/etc. to improve the diagram.