misc

src/scgenerator/plotting.py

@@ -437,6 +437,7 @@ def transform_2D_propagation(
     log: Union[int, float, bool, str] = "1D",
     skip: int = 1,
     params: Parameters = None,
+    conserved_quantity: bool = True,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     """
     transforms raws values into plottable values
@@ -447,16 +448,20 @@ def transform_2D_propagation(
         values to transform
     plt_range : Union[PlotRange, RangeType]
         range
-    x_axis : np.ndarray
+    x_axis : np.ndarray, shape (nt,)
         corresponding x values in SI units
-    y_axis : np.ndarray
+    y_axis : np.ndarray, shape (n,)
         corresponding y values in SI units
-    params : Parameters, optional
-        parameters of the simulation
     log : Union[int, float, bool, str], optional
         see apply_log, by default "1D"
+    params : Parameters, optional
+        parameters of the simulation, used to automatically fill in x and y axes
     skip : int, optional
-        take one every skip values, by default 1
+        take one every skip values, by default 1 (take all values)
+    conserved_quantity : bool, optional
+        if the target axis is wavelength, the transformation is not linear has to be corrected.
+        This is necessary when values is interpreted as averaged over a bin (e.g. amplitude),
+        but shouldn't be used when it's not the case (e.g. coherence). by default True
 
     Returns
     -------
@@ -484,8 +489,8 @@ def transform_2D_propagation(
     # if params.full_field and plt_range.unit.type == "TIME":
     #     values = envelope_2d(x_axis, values)
 
-    x_axis, values = uniform_axis(x_axis, values, plt_range)
-    y_axis, values.T[:] = uniform_axis(y_axis, values.T, None)
+    x_axis, values = uniform_axis(x_axis, values, plt_range, conserved_quantity)
+    y_axis, values.T[:] = uniform_axis(y_axis, values.T, None, conserved_quantity)
     values = apply_log(values, log)
     return x_axis[::skip], y_axis, values[:, ::skip]
 
@@ -599,8 +604,7 @@ def transform_mean_values(
         all the values
     """
     if values.ndim != 2:
-        print(f"Shape was {values.shape}. Can only plot 2D arrays")
-        return
+        raise ValueError(f"Shape was {values.shape}. Can only plot 2D arrays")
     is_complex, plt_range = prep_plot_axis(values, plt_range, params)
     if is_complex:
         values = abs2(values)
@@ -957,6 +961,7 @@ def uniform_axis(
     axis: np.ndarray,
     values: np.ndarray,
     new_axis_spec: Union[PlotRange, RangeType, str],
+    conserved_quantity: bool = True,
 ) -> tuple[np.ndarray, np.ndarray]:
     """
     given some values(axis), creates a new uniformly spaced axis and interpolates
@@ -1001,7 +1006,7 @@ def uniform_axis(
         new_axis = tmp_axis
         values = values[:, ind]
     else:
-        if plt_range.unit.type == "WL" and plt_range.conserved_quantity:
+        if plt_range.unit.type == "WL" and conserved_quantity:
             values[:, ind] = np.apply_along_axis(units.to_WL, 1, values[:, ind], tmp_axis)
         new_axis = np.linspace(tmp_axis.min(), tmp_axis.max(), len(tmp_axis))
         values = linear_interp_2d(tmp_axis, values[:, ind], new_axis)

src/scgenerator/spectra.py

@@ -182,11 +182,6 @@ class Propagation:
         return self._load_slice(slice(None))
 
 
-def load_all(path: os.PathLike) -> Spectrum:
-    io = ZipFileIOHandler(path)
-    return Propagation(io).load_all()
-
-
 def propagation(
     file_or_params: os.PathLike | Parameters,
     params: Parameters | None = None,