added easy uniform_2d method

This new function is made to lighten syntax when

src/scgenerator/plotting.py

@@ -9,7 +9,7 @@ from matplotlib.axes import Axes
 from matplotlib.colors import ListedColormap
 from matplotlib.figure import Figure
 from matplotlib.transforms import offset_copy
-from scipy.interpolate import UnivariateSpline
+from scipy.interpolate import UnivariateSpline, interp1d
 
 from scgenerator import math
 from scgenerator.const import PARAM_SEPARATOR
@@ -63,9 +63,7 @@ def plot_setup(
 
     # ensure no overwrite
     ind = 0
-    while (
-        full_path := (out_dir / (plot_name + f"{PARAM_SEPARATOR}{ind}." + file_type))
-    ).exists():
+    while (full_path := (out_dir / (plot_name + f"{PARAM_SEPARATOR}{ind}." + file_type))).exists():
         ind += 1
 
     if mode == "default":
@@ -215,9 +213,7 @@ def create_zoom_axis(
     return inset
 
 
-def corner_annotation(
-    text, ax, position="tl", rel_x_offset=0.05, rel_y_offset=0.05, **text_kwargs
-):
+def corner_annotation(text, ax, position="tl", rel_x_offset=0.05, rel_y_offset=0.05, **text_kwargs):
     """puts an annotatin in a corner of an ax
     Parameters
     ----------
@@ -487,6 +483,29 @@ def transform_2D_propagation(
     return x_axis[::skip], y_axis, values[:, ::skip]
 
 
+def uniform_2d(
+    old_x: np.ndarray, old_y: np.ndarray, new_x: np.ndarray, new_y: np.ndarray, values: np.ndarray
+) -> np.ndarray:
+    """
+    interpolates a 2d array according to the provides old and new axis
+
+    Parameters
+    ----------
+    old_x : np.ndarray, shape (n,)
+    old_y : np.ndarray, shape (m,)
+    new_x : np.ndarray, shape (N,)
+    new_y : np.ndarray, shape (M,)
+    values : np.ndarray, shape (m, n)
+
+    Returns
+    -------
+    np.ndarray, shape (M, N)
+    """
+    values = interp1d(old_x, values, fill_value=0, bounds_error=False, axis=1)(new_x)
+    values = interp1d(old_y, values, fill_value=0, bounds_error=False, axis=0)(new_y)
+    return values
+
+
 def get_x_axis(plt_range, x_axis, params) -> np.ndarray:
     if x_axis is None and params is not None:
         if plt_range.unit.type in {"WL", "FREQ", "AFREQ"}:
@@ -514,9 +533,7 @@ def mean_values_plot(
     mean_style: dict[str, Any] = None,
     individual_style: dict[str, Any] = None,
 ) -> tuple[plt.Line2D, list[plt.Line2D]]:
-    x_axis, mean_values, values = transform_mean_values(
-        values, plt_range, params, log, spacing
-    )
+    x_axis, mean_values, values = transform_mean_values(values, plt_range, params, log, spacing)
     if renormalize and log is False:
         maxi = mean_values.max()
         mean_values = mean_values / maxi
@@ -585,9 +602,7 @@ def transform_mean_values(
 
     if isinstance(spacing, (float, np.floating)):
         tmp_axis = np.linspace(*span(new_axis), int(len(new_axis) / spacing))
-        values = np.array(
-            [UnivariateSpline(new_axis, v, k=4, s=0)(tmp_axis) for v in values]
-        )
+        values = np.array([UnivariateSpline(new_axis, v, k=4, s=0)(tmp_axis) for v in values])
         new_axis = tmp_axis
     elif isinstance(spacing, (int, np.integer)) and spacing > 1:
         values = values[:, ::spacing]
@@ -644,9 +659,7 @@ def plot_mean(
     transpose : bool, optional
         rotate the plot 90° counterclockwise, by default False
     """
-    individual_style = (
-        defaults["muted_style"] if individual_style is None else individual_style
-    )
+    individual_style = defaults["muted_style"] if individual_style is None else individual_style
     mean_style = defaults["highlighted_style"] if mean_style is None else mean_style
     labels = defaults["avg_line_labels"] if line_labels is None else line_labels
     lines = []
@@ -686,9 +699,7 @@ def single_position_plot(
     y_label: str = None,
     **line_kwargs,
 ) -> tuple[Figure, Axes, plt.Line2D, np.ndarray, np.ndarray]:
-    x_axis, values = transform_1D_values(
-        values, plt_range, x_axis, params, log, spacing
-    )
+    x_axis, values = transform_1D_values(values, plt_range, x_axis, params, log, spacing)
     if renormalize:
         values = values / values.max()
 
@@ -977,9 +988,7 @@ def uniform_axis(
         values = values[:, ind]
     else:
         if plt_range.unit.type == "WL" and plt_range.conserved_quantity:
-            values[:, ind] = np.apply_along_axis(
-                units.to_WL, 1, values[:, ind], tmp_axis
-            )
+            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)
     return new_axis, values.squeeze()
@@ -1036,18 +1045,14 @@ def prep_plot_axis(
     return is_spectrum, plt_range
 
 
-def white_bottom_cmap(
-    name, start=0, end=1, new_name="white_background", c_back=(1, 1, 1, 1)
-):
+def white_bottom_cmap(name, start=0, end=1, new_name="white_background", c_back=(1, 1, 1, 1)):
     """returns a new colormap based on "name" but that has a solid bacground (default=white)"""
     top = plt.get_cmap(name, 1024)
     n_bottom = 80
     bottom = np.ones((n_bottom, 4))
     for i in range(4):
         bottom[:, i] = np.linspace(c_back[i], top(start)[i], n_bottom)
-    return ListedColormap(
-        np.vstack((bottom, top(np.linspace(start, end, 1024)))), name=new_name
-    )
+    return ListedColormap(np.vstack((bottom, top(np.linspace(start, end, 1024)))), name=new_name)
 
 
 def default_marker_style(k):
@@ -1074,9 +1079,7 @@ def default_marker_style(k):
 
 def arrowstyle(direction=1, color="white"):
     return dict(
-        arrowprops=dict(
-            arrowstyle="->", connectionstyle=f"arc3,rad={direction*0.3}", color=color
-        ),
+        arrowprops=dict(arrowstyle="->", connectionstyle=f"arc3,rad={direction*0.3}", color=color),
         color=color,
         backgroundcolor=(0.5, 0.5, 0.5, 0.5),
     )
@@ -1121,9 +1124,7 @@ def measure_and_annotate_fwhm(
     _, (left, right), *_ = pulse.find_lobe_limits(unit.inv(t), field)
     arrow_label = f"{right - left:.1f} {unit.name}"
 
-    annotate_fwhm(
-        ax, left, right, arrow_label, field.max(), side, arrow_length_pts, arrow_props
-    )
+    annotate_fwhm(ax, left, right, arrow_label, field.max(), side, arrow_length_pts, arrow_props)
     return right - left
 
 
@@ -1143,9 +1144,7 @@ def annotate_fwhm(
     if color:
         arrow_dict |= dict(color=color)
         annotate_kwargs |= dict(color=color)
-    text_kwargs = (
-        dict(ha="right" if side == "left" else "left", va="center") | annotate_kwargs
-    )
+    text_kwargs = dict(ha="right" if side == "left" else "left", va="center") | annotate_kwargs
     if arrow_props is not None:
         arrow_dict |= arrow_props
     txt = {}