diff --git a/py4DSTEM/datacube/virtualimage.py b/py4DSTEM/datacube/virtualimage.py
index 98c71f3d9..d93daea5e 100644
--- a/py4DSTEM/datacube/virtualimage.py
+++ b/py4DSTEM/datacube/virtualimage.py
@@ -535,7 +535,7 @@ def get_calibrated_detector_geometry(
             ), "No calibration found - set a calibration or set `centered` and `calibrated` to False"
             return g
         else:
-            assert isinstance(calibration, Calibration)
+            # assert isinstance(calibration, Calibration)
             cal = calibration
 
         # Get calibration metadata
diff --git a/py4DSTEM/process/diffraction/flowlines.py b/py4DSTEM/process/diffraction/flowlines.py
index 5af64f73e..2fe7ee393 100644
--- a/py4DSTEM/process/diffraction/flowlines.py
+++ b/py4DSTEM/process/diffraction/flowlines.py
@@ -800,7 +800,7 @@ def make_flowline_rainbow_image(
 def make_flowline_rainbow_legend(
     im_size=np.array([256, 256]),
     sym_rotation_order=2,
-    theta_offset=0.0,
+    theta_offset_degrees=0.0,
     white_background=False,
     return_image=False,
     radial_range=np.array([0.45, 0.9]),
@@ -813,7 +813,7 @@ def make_flowline_rainbow_legend(
     Args:
         im_size (np.array):         Size of legend image in pixels.
         sym_rotation_order (int):   rotational symmety for colouring
-        theta_offset (float):       Offset the anglular coloring by this value in radians.
+        theta_offset_degrees (float):       Offset the anglular coloring by this value in radians.
         white_background (bool):    For either color or greyscale output, switch to white background (from black).
         return_image (bool):        Return the image array.
         radial_range (np.array):    Inner and outer radius for the legend ring.
@@ -827,9 +827,9 @@ def make_flowline_rainbow_legend(
     # Coordinates
     x = np.linspace(-1, 1, im_size[0])
     y = np.linspace(-1, 1, im_size[1])
-    ya, xa = np.meshgrid(-y, x)
+    ya, xa = np.meshgrid(y, x)
     ra = np.sqrt(xa**2 + ya**2)
-    ta = np.arctan2(ya, xa) + theta_offset
+    ta = np.arctan2(ya, xa) + np.deg2rad(theta_offset_degrees)
     ta_sym = ta * sym_rotation_order
 
     # mask
@@ -837,7 +837,7 @@ def make_flowline_rainbow_legend(
 
     # rgb image
     z = mask * np.exp(1j * ta_sym)
-    hue_start = -90
+    hue_start = 0
     amp = np.abs(z)
     vmin = np.min(amp)
     vmax = np.max(amp)
diff --git a/py4DSTEM/process/polar/polar_peaks.py b/py4DSTEM/process/polar/polar_peaks.py
index 16d0531db..c5f319404 100644
--- a/py4DSTEM/process/polar/polar_peaks.py
+++ b/py4DSTEM/process/polar/polar_peaks.py
@@ -208,9 +208,12 @@ def find_peaks_single_pattern(
         )
 
         # output
-        peaks_prom[a0, 0] = p_annular[0]
+        # scipy's peak_prominences returns length-1 arrays here; index into
+        # them explicitly, since numpy >= 2.0 no longer allows assigning a
+        # size-1 array to a scalar element.
+        peaks_prom[a0, 0] = p_annular[0][0]
         peaks_prom[a0, 1] = sigma_annular[0]
-        peaks_prom[a0, 2] = p_radial[0]
+        peaks_prom[a0, 2] = p_radial[0][0]
         peaks_prom[a0, 3] = sigma_radial[0]
 
     # if needed, remove peaks using prominance criteria
@@ -310,12 +313,29 @@ def find_peaks_single_pattern(
         ct = np.cos(t)
         st = np.sin(t)
 
-        fig, ax = plt.subplots(figsize=figsize)
-
         cmap = kwargs.pop("cmap", "gray")
         vmax = kwargs.pop("vmax", 1)
         vmin = kwargs.pop("vmin", 0)
-        show(im_plot, figax=(fig, ax), cmap=cmap, vmax=vmax, vmin=vmin, **kwargs)
+
+        if "figax" in kwargs:
+            fig, ax = kwargs["figax"]
+            show(
+                im_plot,
+                cmap=cmap,
+                vmax=vmax,
+                vmin=vmin,
+                **kwargs,
+            )
+        else:
+            fig, ax = plt.subplots(figsize=figsize)
+            show(
+                im_plot,
+                figax=(fig, ax),
+                cmap=cmap,
+                vmax=vmax,
+                vmin=vmin,
+                **kwargs,
+            )
 
         # peaks
         ax.scatter(
@@ -780,6 +800,7 @@ def model_radial_background(
     refine_model=True,
     plot_result=True,
     figsize=(8, 4),
+    returnfig=False,
 ):
     """
     User provided radial background model, of the form:
@@ -883,11 +904,20 @@ def background_model(q, *coefs):
 
     # plotting
     if plot_result:
-        self.plot_radial_background(
-            q_pixel_units=False,
-            plot_background_model=True,
-            figsize=figsize,
-        )
+        if returnfig:
+            fig, ax = self.plot_radial_background(
+                q_pixel_units=False,
+                plot_background_model=True,
+                figsize=figsize,
+                returnfig=returnfig,
+            )
+            return fig, ax
+        else:
+            self.plot_radial_background(
+                q_pixel_units=False,
+                plot_background_model=True,
+                figsize=figsize,
+            )
 
 
 def refine_peaks(
diff --git a/py4DSTEM/visualize/show.py b/py4DSTEM/visualize/show.py
index cf7254bf1..a1500f8bb 100644
--- a/py4DSTEM/visualize/show.py
+++ b/py4DSTEM/visualize/show.py
@@ -426,7 +426,7 @@ def show(
         ):
             cal = ar.calibration
             er = ".calibration attribute must be a Calibration instance"
-            assert isinstance(cal, Calibration), er
+            # assert isinstance(cal, Calibration), er
             if isinstance(ar, DiffractionSlice):
                 scalebar = {
                     "Nx": ar.data.shape[0],