💬 Update __doc__ for fit models

src/arpes/fits/fit_models/decay.py

@@ -79,7 +79,10 @@ def guess(
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = (
+        "A model for fitting an exponential decay with a constant background."
+        + lf.models.COMMON_INIT_DOC
+    )
     guess.__doc__ = lf.models.COMMON_GUESS_DOC
 
 
@@ -136,5 +139,7 @@ def guess(
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = (
+        "Like `exponential_decay_c`, except with two timescales." + lf.models.COMMON_INIT_DOC
+    )
     guess.__doc__ = lf.models.COMMON_GUESS_DOC

src/arpes/fits/fit_models/dirac.py

@@ -70,5 +70,7 @@ def guess(self, **kwargs: float) -> lf.Parameters:
         pars = self.make_params()
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = (
+        "Model for dirac_dispersion symmetric about the dirac point." + lf.models.COMMON_INIT_DOC
+    )
     guess.__doc__ = lf.models.COMMON_GUESS_DOC

src/arpes/fits/fit_models/fermi_edge.py

@@ -109,7 +109,9 @@ def guess(self, data: XrTypes, **kwargs: float) -> lf.Parameters:
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = (
+        "affine density of states broadened by Fermi-Dirac " + lf.models.COMMON_INIT_DOC
+    )
     guess.__doc__ = lf.models.COMMON_GUESS_DOC
 
 
@@ -175,7 +177,9 @@ def guess(
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = (
+        "Lorentzian multiplied by a gstepb background model" + lf.models.COMMON_INIT_DOC
+    )
     guess.__doc__ = lf.models.COMMON_GUESS_DOC
 
 
@@ -201,7 +205,7 @@ def guess(self, data: DataType, **kwargs: Incomplete) -> lf.Parameters:
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = "Fermi-Dirc distribution model" + lf.models.COMMON_INIT_DOC
     guess.__doc__ = lf.models.COMMON_GUESS_DOC
 
 
@@ -247,7 +251,9 @@ def guess(
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = (
+        """Fermi functions with a linear background model""" + lf.models.COMMON_INIT_DOC
+    )
     guess.__doc__ = lf.models.COMMON_GUESS_DOC
 
 
@@ -389,10 +395,6 @@ def __init__(self, **kwargs: Unpack[ModelArgs]) -> None:
         self.set_param_hint("offset", min=-10)
         self.set_param_hint("center", vary=False)
 
-        """
-
-        """
-
     def guess(
         self,
         data: XrTypes,
@@ -493,7 +495,10 @@ def guess(
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = (
+        "Fermi Dirac function with affine background multiplied, then all convolved with Gaussian"
+        + lf.models.COMMON_INIT_DOC
+    )
     guess.__doc__ = lf.models.COMMON_GUESS_DOC
 
 
@@ -553,7 +558,10 @@ def guess(
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = (
+        "Fermi-Dirac distribution function with a linear background model"
+        + lf.models.COMMON_INIT_DOC
+    )
     guess.__doc__ = lf.models.COMMON_GUESS_DOC
 
 
@@ -611,7 +619,10 @@ def guess(
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = (
+        """A model for fitting Fermi functions with a linear background."""
+        + lf.models.COMMON_INIT_DOC
+    )
     guess.__doc__ = lf.models.COMMON_GUESS_DOC
 
 
@@ -682,5 +693,8 @@ def guess(
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = (
+        "A model for (two lorentzians with an affine background) multiplied by a gstepb."
+        + lf.models.COMMON_INIT_DOC
+    )
     guess.__doc__ = lf.models.COMMON_GUESS_DOC

src/arpes/fits/fit_models/misc.py

@@ -57,7 +57,7 @@ def guess(
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = "Quadratic model" + lf.models.COMMON_INIT_DOC
     guess.__doc__ = lf.models.COMMON_GUESS_DOC
 
 
@@ -100,7 +100,7 @@ def guess(self, **kwargs: float) -> lf.Parameters:
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = "Fermi velocity renormalization model" + lf.models.COMMON_INIT_DOC
     guess.__doc__ = lf.models.COMMON_GUESS_DOC
 
 
@@ -155,5 +155,5 @@ def guess(self, **kwargs: float) -> lf.Parameters:
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = "Log renormalization model" + lf.models.COMMON_INIT_DOC
     guess.__doc__ = lf.models.COMMON_GUESS_DOC

src/arpes/fits/fit_models/peaks.py

@@ -78,7 +78,7 @@ def guess(
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = "Two gaussian model" + lf.models.COMMON_INIT_DOC
     guess.__doc__ = lf.models.COMMON_GUESS_DOC
 
 
@@ -121,5 +121,5 @@ def guess(
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = "Two lorenzian model" + lf.models.COMMON_INIT_DOC
     guess.__doc__ = lf.models.COMMON_GUESS_DOC

src/arpes/fits/fit_models/two_dimensional.py

@@ -28,7 +28,7 @@ class Gaussian2DModel(XModelMixin):
     """2D Gaussian fitting."""
 
     n_dims = 2
-    dimension_order: ClassVar = [any_dim_sentinel, any_dim_sentinel]
+    dimension_order: ClassVar[list[str | None]] = [any_dim_sentinel, any_dim_sentinel]
 
     @staticmethod
     def gaussian_2d_bkg(  # noqa: PLR0913
@@ -169,5 +169,5 @@ def guess(
 
         return update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lf.models.COMMON_INIT_DOC
+    __init__.__doc__ = lf.models.COMMON_INIT_DOC
     guess.__doc__ = lf.models.COMMON_GUESS_DOC

src/arpes/fits/fit_models/x_model_mixin.py

@@ -5,7 +5,7 @@
 import operator
 import warnings
 from logging import DEBUG, INFO, WARNING, Formatter, StreamHandler, getLogger
-from typing import TYPE_CHECKING, Required, TypedDict
+from typing import TYPE_CHECKING, ClassVar, Required, TypedDict
 
 import lmfit as lf
 import numpy as np
@@ -109,7 +109,7 @@ class XModelMixin(lf.Model):
     """
 
     n_dims = 1
-    dimension_order = None
+    dimension_order: ClassVar[list[str | None]] = [None]
 
     def guess_fit(  # noqa: PLR0913
         self,
@@ -282,7 +282,7 @@ def _real_data_etc_from_xarray(
         real_data, flat_data = data.values, data.values
         assert len(real_data.shape) == self.n_dims
         coord_values = {}
-        new_dim_order = None
+        new_dim_order: list[str]
         if self.n_dims == 1:
             coord_values["x"] = data.coords[next(iter(data.indexes))].values
         else:
@@ -297,14 +297,16 @@ def find_appropriate_dimension(dim_or_dim_list: str | list[str]) -> str:
                 return next(iter(intersect))
 
             # resolve multidimensional parameters
-            if self.dimension_order is None or all(d is None for d in self.dimension_order):
-                new_dim_order = data.dims
+            if all(d is None for d in self.dimension_order):
+                new_dim_order = [str(dim) for dim in data.dims]
             else:
                 new_dim_order = [
-                    find_appropriate_dimension(dim_options) for dim_options in self.dimension_order
+                    find_appropriate_dimension(dim_options)
+                    for dim_options in self.dimension_order
+                    if dim_options is not None
                 ]
 
-            if list(new_dim_order) != list(data.dims):
+            if new_dim_order != list(data.dims):
                 warnings.warn("Transposing data for multidimensional fit.", stacklevel=2)
                 data = data.transpose(*new_dim_order)