Derived Parameters

Derived Parameters#

derived_param lets you define SME parameters as functions of other parameters during fitting, without adding them to the free-parameter list.

What It Does#

In solve(...), PySME:

  1. sets the current free parameters,

  2. evaluates each function in derived_param,

  3. writes the returned value back to sme,

  4. runs synthesis and computes residuals.

So derived parameters are updated at every iteration.

API#

from pysme.solve import solve

sme = solve(
    sme,
    param_names=[...],             # free parameters
    derived_param={...},           # derived parameters
)

  • derived_param is a dict[str, callable].

  • Key: SME parameter name (for example "vmic" or "abund Mg").

  • Value: function f(sme) -> float.

Rules and Notes#

  • A parameter cannot be both free and derived in the same run.

  • dynamic_param is still accepted as a legacy alias, but it is deprecated.

  • For abundance keys (for example "abund Mg"), return the final abundance you want in the usual abundance scale. PySME applies the internal monh conversion for you when writing into sme.abund.

Example 1: Tie vmic to teff and logg#

import numpy as np
from pysme.solve import solve

derived = {
    "vmic": lambda s: np.clip(1.1 + 1e-4 * (s.teff - 5500.0) - 0.3 * (s.logg - 4.0), 0.2, 5.0)
}

fit = ["teff", "logg", "monh", "vsini"]
sme = solve(sme, fit, derived_param=derived)

Here vmic is never fitted directly; it is recomputed from the current model state at each iteration.

Example 2: Enforce fixed [Mg/Fe]#

from pysme.abund import Abund
from pysme.solve import solve

solar_mg = Abund.solar()["Mg"]

# Target relation: [Mg/Fe] = +0.20  ->  A(Mg) = A_sun(Mg) + [M/H] + 0.20
derived = {
    "abund Mg": lambda s: solar_mg + s.monh + 0.20
}

sme = solve(sme, ["teff", "logg", "monh"], derived_param=derived)
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