🎨 Sort the methods in G class.

In the future they are split into three

src/arpes/analysis/band_analysis.py

@@ -168,15 +168,15 @@ def unpack_bands_from_fit(
         label = identified_band_results.loc[first_coordinate].values.item()[i]
 
         def dataarray_for_value(param_name: str, i: int = i, *, is_value: bool) -> xr.DataArray:
-            """[TODO:summary].
+            """Return DataArray representing the fit results.
 
             Args:
                 param_name (str): [TODO:description]
                 i (int): [TODO:description]
                 is_value (bool): [TODO:description]
             """
-            values: NDArray[np.float_] = np.ndarray(
-                shape=identified_band_results.values.shape,
+            values: NDArray[np.float_] = np.zeros_like(
+                identified_band_results.values,
                 dtype=float,
             )
             it = np.nditer(values, flags=["multi_index"], op_flags=[["writeonly"]])
@@ -185,7 +185,6 @@ def dataarray_for_value(param_name: str, i: int = i, *, is_value: bool) -> xr.Da
                 param = band_results.values[it.multi_index].params[prefix + param_name]
                 it[0] = param.value if is_value else param.stderr
                 it.iternext()
-
             return xr.DataArray(
                 values,
                 identified_band_results.coords,
@@ -219,8 +218,8 @@ def _identified_band_results_etc(
             return of broadcast_model().results
         weights (tuple[float, float, float]): weight values for sigma, amplitude, center
 
-    Returns:
-        [TODO:description]
+    Returns: tuple[xr.DataArray, dict[Hashable, float], list[str]]
+        identified_band_results, first_coordinate, prefixes
     """
     band_results = band_results if isinstance(band_results, xr.DataArray) else band_results.results
     prefixes = [component.prefix for component in band_results.values[0].model.components]

src/arpes/models/band.py

@@ -42,7 +42,7 @@ class Band:
 
     Attribute:
         label (str): label of the band.
-        _data (xr.Dataset): consists of several DataArrays representing the fitting results.
+        _data (xr.Dataset): Dataset consists of several DataArrays representing the fitting results.
             `data_vars` are "center", "center_stderr", "amplitude", "amplitude_stdrr",
             "sigma", and "sigma_stderr"
     """
@@ -65,16 +65,7 @@ def velocity(self) -> xr.DataArray:
         """
         spacing = float(self.coords[self.dims[0]][1] - self.coords[self.dims[0]][0])
 
-        def embed_nan(values: NDArray[np.float_], padding: int) -> NDArray[np.float_]:
-            embedded: NDArray[np.float_] = np.full(
-                shape=(values.shape[0] + 2 * padding,),
-                fill_value=np.nan,
-                dtype=np.float_,
-            )
-            embedded[padding:-padding] = values
-            return embedded
-
-        raw_values = embed_nan(self.center.values, 50)
+        raw_values = self.embed_nan(self.center.values, 50)
 
         masked = np.copy(raw_values)
         masked[raw_values != raw_values] = 0
@@ -178,6 +169,25 @@ def dims(self) -> tuple[str, ...]:
         assert isinstance(self._data, xr.Dataset)
         return self._data.center.dims
 
+    @staticmethod
+    def embed_nan(values: NDArray[np.float_], padding: int) -> NDArray[np.float_]:
+        """Return np.ndarray padding before and after the original NDArray with nan.
+
+        Args:
+            values: [TODO:description]
+            padding: the length of the padding
+
+        Returns: NDArray[np.float_]
+            [TODO:description]
+        """
+        embedded: NDArray[np.float_] = np.full(
+            shape=(values.shape[0] + 2 * padding,),
+            fill_value=np.nan,
+            dtype=np.float_,
+        )
+        embedded[padding:-padding] = values
+        return embedded
+
 
 class MultifitBand(Band):
     """Convenience class that reimplements reading data out of a composite fit result."""