🔥 Preparation a deep refactoring of band_analyais.py

remove weird codes

src/arpes/analysis/band_analysis.py

@@ -321,8 +321,8 @@ def fit_patterned_bands(  # noqa: PLR0913
 
     1. Fit directions, these are coordinates along the 1D (or maybe later 2D) marginals
     2. Broadcast directions, these are directions used to interpolate against the patterned
-       directions
     3. Free directions, these are broadcasted but they are not used to extract initial values of the
+       directions
        fit parameters
 
     For instance, if you laid out band patterns in a E, k_p, delay spectrum at delta_t=0, then if
@@ -478,21 +478,21 @@ def _instantiate_band(partial_band: dict[str, Any]) -> lf.Model:
 
 def fit_bands(
     arr: xr.DataArray,
-    band_description: list[BandDescription] | BandDescription,
+    band_descriptions: list[BandDescription],
     direction: Literal["edc", "mdc", "EDC", "MDC"] = "mdc",
-    step: Literal["initial", None] = None,
 ) -> tuple[xr.DataArray | None, None, lf.ModelResult | None]:
     """Fits bands and determines dispersion in some region of a spectrum.
 
     Args:
         arr(xr.DataArray): ARPES data for fit.
-        band_description: A description of the bands to fit in the region
+        band_descriptions: List of the description of the bands to fit in the region
         direction: fit direction (along the enegy or momentum),
             default is "mdc" (Momentum Distribution Curve).
-        step: if "Initial" is set, ....
 
     Returns:
         Fitted bands.
+
+    ToDo: Deep refactoring. The current version may not work.
     """
     assert direction in {"edc", "mdc", "EDC", "MDC"}
 
@@ -509,20 +509,13 @@ def fit_bands(
 
     # Let the first band be given by fitting the raw data to this band
     # Find subsequent peaks by fitting models to the residuals
-    raw_bands = [band.get("band") if isinstance(band, dict) else band for band in band_description]
+    raw_bands = [band_description.get("band") for band_description in band_descriptions]
     initial_fits = None
     all_fit_parameters = {}
 
-    if step == "initial":
-        residual.plot()
-
-    for band in band_description:
-        if isinstance(band, dict):
-            band_inst = band.get("band")
-            params = band.get("params", {})
-        else:
-            band_inst = band
-            params = None
+    for band_description in band_descriptions:
+        band_inst = band_description.get("band")
+        params = band_description.get("params", {})
         fit_model = band_inst.fit_cls(prefix=band_inst.label)
         initial_fit = fit_model.guess_fit(residual, params=params)
         if initial_fits is None:
@@ -538,13 +531,6 @@ def fit_bands(
             # alright for now
             pass
 
-        if step == "initial":
-            residual.plot()
-            (residual - residual + initial_fit.best_fit).plot()
-
-    if step == "initial":
-        return None, None, residual
-
     template = arr.sum(broadcast_direction)
     band_results = xr.DataArray(
         np.ndarray(shape=template.values.shape, dtype=object),
@@ -563,7 +549,7 @@ def fit_bands(
         for c, v in all_fit_parameters.items():
             delta = np.array(c) - frozen_coordinate
             current_distance = delta.dot(delta)
-            if current_distance < dist and direction == "mdc":  # TODO: remove me
+            if current_distance < dist and direction in {"mdc", "MDC"}:  # TODO: remove me
                 closest_model_params = v
 
         # TODO: mix in any params to the model params