metric module in rdt

docs/source/api/modules.rst

@@ -12,6 +12,16 @@ galpynostatic modules
    :member-order: bysource
 
 
+``galpynostatic.metric`` module
+-------------------------------
+
+.. automodule:: galpynostatic.metric
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :member-order: bysource
+
+
 .. _galpynostatic.model:
 
 ``galpynostatic.model`` module

galpynostatic/datasets/__init__.py

@@ -14,14 +14,14 @@
 r"""The ``galpynostatic.dataset`` module loads the data needed for the fits.
 
 These datasets were obtained using a continuous computational physics model for
-different geometries [2]_. They come from a cutoff to multiple galvanostatic
+different geometries [3]_. They come from a cutoff to multiple galvanostatic
 profiles at a given cell potential, with respect to equilibrium, with different
 combinations of the internal parameters :math:`\Xi` and :math:`\ell` covering a
 wide range of possible values of the experimental variables involved.
 
 References
 ----------
-.. [2] E. M. Gavilán-Arriazu, D. E. Barraco, D., Y. Ein-Eli and E. P. M. Leiva.
+.. [3] E. M. Gavilán-Arriazu, D. E. Barraco, D., Y. Ein-Eli and E. P. M. Leiva.
    "Galvanostatic Fast Charging of Alkali‐Ion Battery Materials at the
    Single‐Particle Level: A Map‐Driven Diagnosis.i" `ChemPhysChem` 24, no. 6
    (2023): e202200665.

galpynostatic/model.py

@@ -40,14 +40,14 @@
 class GalvanostaticRegressor(BaseEstimator, RegressorMixin):
     r"""A heuristic regressor for SOC versus C-rates galvanostatic data.
 
-    This physics-based heuristic model [1]_ uses the maps in
+    This physics-based heuristic model [2]_ uses the maps in
     :ref:`galpynostatic.datasets` to perform a grid search by taking different
     combinations of the diffusion coefficient, :math:`D`, and the
     kinetic-rate constant, :math:`k^0`, to fit experimental data of the
     State-of-Charge (SOC) of the electrode material as a function of the
     C-rates. This is done considering invariant all the other experimental
     values involved in the parameters :math:`\Xi` and :math:`\ell` of the
-    maps of the continuous galvanostatic model [1]_, such as the
+    maps of the continuous galvanostatic model [2]_, such as the
     characteristic diffusion length, :math:`d`, and the geometrical factor,
     :math:`z` (see :ref:`galpynostatic.utils`).
 
@@ -110,7 +110,7 @@ class GalvanostaticRegressor(BaseEstimator, RegressorMixin):
 
     References
     ----------
-    .. [1] F. Fernandez, E. M. Gavilán-Arriazu, D. E. Barraco, A. Visintin,
+    .. [2] F. Fernandez, E. M. Gavilán-Arriazu, D. E. Barraco, A. Visintin,
        Y. Ein-Eli and E. P. M. Leiva. "Towards a fast-charging of LIBs
        electrode materials: a heuristic model based on galvanostatic
        simulations." `Electrochimica Acta 464` (2023): 142951.

galpynostatic/utils.py

@@ -49,7 +49,7 @@ def logell(c_rate, d, z, dcoeff):
 
     Returns
     -------
-    logell : float
+    logell : float or array-like
         The log 10 value of :math:`\ell` internal parameter.
     """
     return np.log10((c_rate * d**2) / (3600 * z * dcoeff))
@@ -74,7 +74,7 @@ def logxi(c_rate, dcoeff, k0):
 
     Returns
     -------
-    logxi : float
+    logxi : float or array-like
         The log 10 value of :math:`\Xi` internal parameter.
     """
     return np.log10(k0 * np.sqrt(3600 / (c_rate * dcoeff)))