diff --git a/arpes/analysis/deconvolution.py b/arpes/analysis/deconvolution.py
index 5574f664..7af67de3 100644
--- a/arpes/analysis/deconvolution.py
+++ b/arpes/analysis/deconvolution.py
@@ -2,19 +2,22 @@
 from __future__ import annotations
 
 import contextlib
-from typing import TYPE_CHECKING
+import warnings
+from typing import TYPE_CHECKING, Literal
 
 import numpy as np
 import scipy
 import scipy.ndimage
 import xarray as xr
-from tqdm import tqdm_notebook
+from tqdm.notebook import tqdm
 
 from arpes.fits.fit_models.functional_forms import gaussian
 from arpes.provenance import update_provenance
 from arpes.utilities import normalize_to_spectrum
 
 if TYPE_CHECKING:
+    from collections.abc import Iterable
+
     from _typeshed import Incomplete
 
     from arpes._typing import DataType
@@ -27,7 +30,12 @@
 
 
 @update_provenance("Approximate Iterative Deconvolution")
-def deconvolve_ice(data: DataType, psf, n_iterations: int = 5, deg: int | None = None) -> DataType:
+def deconvolve_ice(
+    data: DataType,
+    psf: xr.DataArray,
+    n_iterations: int = 5,
+    deg: int | None = None,
+) -> DataType:
     """Deconvolves data by a given point spread function.
 
     The iterative convolution extrapolation method is used.
@@ -74,20 +82,21 @@ def deconvolve_ice(data: DataType, psf, n_iterations: int = 5, deg: int | None =
 @update_provenance("Lucy Richardson Deconvolution")
 def deconvolve_rl(
     data: DataType,
-    psf=None,
-    n_iterations=10,
-    axis=None,
-    sigma=None,
-    mode="reflect",
+    psf: xr.DataArray | None = None,
+    n_iterations: int = 10,
+    axis: str = "",
+    sigma: float = 0,
+    mode: Literal["reflect", "constant", "nearest", "mirror", "wrap"] = "reflect",
+    *,
     progress: bool = True,
-) -> DataType:
+) -> xr.DataArray:
     """Deconvolves data by a given point spread function using the Richardson-Lucy method.
 
     Args:
         data
         axis
         sigma
-        mode
+        mode: pass to ndimage.convolve
         progress
         psf: for 1d, if not specified, must specify axis and sigma
         n_iterations: the number of convolutions to use for the fit
@@ -97,21 +106,37 @@ def deconvolve_rl(
     """
     arr = normalize_to_spectrum(data)
 
-    if psf is None and axis is not None and sigma is not None:
+    if psf is None and axis != "" and sigma != 0:
         # if no psf is provided and we have the information to make a 1d one
         # note: this assumes gaussian psf
         psf = make_psf1d(data=arr, dim=axis, sigma=sigma)
 
     if len(data.dims) > 1:
-        if axis is not None:
+        if not axis:
             # perform one-dimensional deconvolution of multidimensional data
 
             # support for progress bars
-            def wrap_progress(x, *args: Incomplete, **kwargs: Incomplete):
+            def wrap_progress(
+                x: Iterable[int],
+                *args: Incomplete,
+                **kwargs: Incomplete,
+            ) -> Iterable[int]:
+                if args:
+                    for arg in args:
+                        warnings.warn(
+                            f"unused args is set in deconvolution.py/wrap_progress: {arg}",
+                            stacklevel=2,
+                        )
+                if kwargs:
+                    for k, v in kwargs.items():
+                        warnings.warn(
+                            f"unused args is set in deconvolution.py/wrap_progress: {k}: {v}",
+                            stacklevel=2,
+                        )
                 return x
 
             if progress:
-                wrap_progress = tqdm_notebook
+                wrap_progress = tqdm
 
             # dimensions over which to iterate
             other_dim = list(data.dims)
@@ -142,7 +167,7 @@ def wrap_progress(x, *args: Incomplete, **kwargs: Incomplete):
                         data=iteration,
                         psf=psf,
                         n_iterations=n_iterations,
-                        axis=None,
+                        axis="",
                         mode=mode,
                     )
                     # build results out of these pieces
@@ -173,7 +198,7 @@ def wrap_progress(x, *args: Incomplete, **kwargs: Incomplete):
                             data=iteration1,
                             psf=psf,
                             n_iterations=n_iterations,
-                            axis=None,
+                            axis="",
                             mode=mode,
                         )
                         # build results out of these pieces
@@ -184,7 +209,7 @@ def wrap_progress(x, *args: Incomplete, **kwargs: Incomplete):
                 # separate code
                 msg = "high-dimensional data not yet supported"
                 raise NotImplementedError(msg)
-        elif axis is None:
+        elif not axis:
             # crude attempt to perform multidimensional deconvolution.
             # not clear if this is currently working
             # TODO: may be able to do this as a sequence of one-dimensional deconvolutions, assuming
@@ -224,13 +249,13 @@ def wrap_progress(x, *args: Incomplete, **kwargs: Incomplete):
 
 
 @update_provenance("Make 1D-Point Spread Function")
-def make_psf1d(data: DataType, dim, sigma):
+def make_psf1d(data: DataType, dim: str, sigma: float) -> xr.DataArray:
     """Produces a 1-dimensional gaussian point spread function for use in deconvolve_rl.
 
     Args:
-        data
-        dim
-        sigma
+        data (DataType): xarray object
+        dim (str): dimension name
+        sigma (float): sigma value
 
     Returns:
         A one dimensional point spread array.
@@ -245,15 +270,14 @@ def make_psf1d(data: DataType, dim, sigma):
 
 
 @update_provenance("Make Point Spread Function")
-def make_psf(data: DataType, sigmas):
+def make_psf(data: DataType, sigmas: dict[str, float]) -> xr.DataArray:
     """Produces an n-dimensional gaussian point spread function for use in deconvolve_rl.
 
     Not yet operational.
 
     Args:
-        data
-        dim
-        sigma
+        data (DataType):
+        sigmas (dict[str, float]): sigma values for each dimension.
 
     Returns:
         The PSF to use.
diff --git a/arpes/bootstrap.py b/arpes/bootstrap.py
index e381020d..851d3ea4 100644
--- a/arpes/bootstrap.py
+++ b/arpes/bootstrap.py
@@ -22,7 +22,7 @@
 import numpy as np
 import scipy.stats
 import xarray as xr
-from tqdm import tqdm_notebook
+from tqdm.notebook import tqdm
 
 from arpes.analysis.sarpes import to_intensity_polarization
 from arpes.provenance import update_provenance
@@ -176,7 +176,7 @@ def bootstrap_counts(data: DataType, N: int = 1000, name: str | None = None) ->
     desc_fragment = f" {name}"
 
     resampled_sets = []
-    for _ in tqdm_notebook(range(N), desc=f"Resampling{desc_fragment}..."):
+    for _ in tqdm(range(N), desc=f"Resampling{desc_fragment}..."):
         resampled_sets.append(resample_true_counts(data))
 
     resampled_arr = np.stack([s.values for s in resampled_sets], axis=0)
@@ -349,7 +349,7 @@ def get_label(i):
             "Fair warning 2: Ensure that the data to resample is in a DataArray and not a Dataset",
         )
 
-        for _ in tqdm_notebook(range(N), desc="Resampling..."):
+        for _ in tqdm(range(N), desc="Resampling..."):
             new_args = list(args)
             new_kwargs = copy.copy(kwargs)
             for i in resample_indices:
diff --git a/arpes/endstations/plugin/SSRF_NSRL.py b/arpes/endstations/plugin/SSRF_NSRL.py
index 316c5cfd..28318af7 100644
--- a/arpes/endstations/plugin/SSRF_NSRL.py
+++ b/arpes/endstations/plugin/SSRF_NSRL.py
@@ -74,7 +74,7 @@ class DA30_L(SingleFileEndstation):
         "chi",  # convert kspcae need them
     ]
 
-    RENAME_KEYS: ClassVar[dict[str, float | int | str]] = {
+    RENAME_KEYS: ClassVar[dict[str, float | str]] = {
         "sample": "sample_name",
         "spectrum_name": "spectrum_type",
         "low_energy": "sweep_low_energy",
diff --git a/arpes/fits/utilities.py b/arpes/fits/utilities.py
index 5c0c456c..06c69d86 100644
--- a/arpes/fits/utilities.py
+++ b/arpes/fits/utilities.py
@@ -19,7 +19,7 @@
 import numpy as np
 import xarray as xr
 from packaging import version
-from tqdm import tqdm_notebook
+from tqdm.notebook import tqdm
 
 import arpes.fits.fit_models
 from arpes.provenance import update_provenance
@@ -29,7 +29,7 @@
 from . import mp_fits
 
 if TYPE_CHECKING:
-    from collections.abc import Callable
+    from collections.abc import Callable, Iterable
 
     import lmfit
 
@@ -186,10 +186,10 @@ def broadcast_model(
     # parse_model just reterns model_cls as is.
 
     if progress:
-        wrap_progress = tqdm_notebook
+        wrap_progress = tqdm
     else:
 
-        def wrap_progress(x, *_, **__):
+        def wrap_progress(x: Iterable[int], *_, **__) -> Iterable[int]:
             return x
 
     serialize = parallelize
diff --git a/arpes/plotting/dispersion.py b/arpes/plotting/dispersion.py
index 49e786f9..0bc228a0 100644
--- a/arpes/plotting/dispersion.py
+++ b/arpes/plotting/dispersion.py
@@ -23,8 +23,8 @@
 
     import xarray as xr
     from _typeshed import Incomplete
-    from matplotlib.figure import Figure, FigureBase
     from matplotlib.colors import Normalize
+    from matplotlib.figure import Figure, FigureBase
 
     from arpes._typing import DataType
 
diff --git a/arpes/plotting/fermi_surface.py b/arpes/plotting/fermi_surface.py
index a9e139c4..07dc76b3 100644
--- a/arpes/plotting/fermi_surface.py
+++ b/arpes/plotting/fermi_surface.py
@@ -61,7 +61,7 @@ def fermi_surface_slices(
     if out:
         renderer = hv.renderer("matplotlib").instance(fig="svg", holomap="gif")
         filename = path_for_plot(out)
-        renderer.save(layout, path_for_holoviews(filename))
+        renderer.save(layout, path_for_holoviews(str(filename)))
         return filename
     return layout
 
@@ -72,6 +72,7 @@ def magnify_circular_regions_plot(
     magnified_points: NDArray[np.float_] | list[float],
     mag: float = 10,
     radius: float = 0.05,
+    # below this can be treated as kwargs?
     cmap: Colormap | ColorType = "viridis",
     color: ColorType | None = None,
     edgecolor: ColorType = "red",
diff --git a/arpes/plotting/spin.py b/arpes/plotting/spin.py
index 3c2a46c9..513b0f3b 100644
--- a/arpes/plotting/spin.py
+++ b/arpes/plotting/spin.py
@@ -60,7 +60,7 @@ def spin_colored_spectrum(
     if len(intensity.dims) == 1:
         inset_ax = inset_axes(ax, width="30%", height="5%", loc=1)
         coord = intensity.coords[intensity.dims[0]]
-        points = np.array([coord.values, intensity.values]).G.reshape(-1, 1, 2)
+        points = np.array([coord.values, intensity.values]).reshape(-1, 1, 2)
         pol.values[np.isnan(pol.values)] = 0
         pol.values[pol.values > 1] = 1
         pol.values[pol.values < -1] = -1
@@ -83,7 +83,7 @@ def spin_colored_spectrum(
         polarization_colorbar(inset_ax)
 
     if out:
-        savefig(out, dpi=400)
+        savefig(str(out), dpi=400)
         plt.clf()
         return path_for_plot(out)
     plt.show()
@@ -113,7 +113,7 @@ def spin_difference_spectrum(
     if len(intensity.dims) == 1:
         inset_ax = inset_axes(ax, width="30%", height="5%", loc=1)
         coord = intensity.coords[intensity.dims[0]]
-        points = np.array([coord.values, intensity.values]).G.reshape(-1, 1, 2)
+        points = np.array([coord.values, intensity.values]).reshape(-1, 1, 2)
         pol.values[np.isnan(pol.values)] = 0
         pol.values[pol.values > 1] = 1
         pol.values[pol.values < -1] = -1
@@ -136,7 +136,7 @@ def spin_difference_spectrum(
         polarization_colorbar(inset_ax)
 
     if out:
-        savefig(out, dpi=400)
+        savefig(str(out), dpi=400)
         plt.clf()
         return path_for_plot(out)
     plt.show()
@@ -228,7 +228,7 @@ def spin_polarized_spectrum(
     plt.tight_layout()
 
     if out:
-        savefig(out, dpi=400)
+        savefig(str(out), dpi=400)
         plt.clf()
         return path_for_plot(out)
 
diff --git a/arpes/plotting/utils.py b/arpes/plotting/utils.py
index 7c87b5cc..cfb5159d 100644
--- a/arpes/plotting/utils.py
+++ b/arpes/plotting/utils.py
@@ -1,7 +1,6 @@
 """Contains many common utility functions for managing matplotlib."""
 from __future__ import annotations
 
-import collections
 import contextlib
 import datetime
 import errno
@@ -12,7 +11,7 @@
 import re
 import warnings
 from collections import Counter
-from collections.abc import Sequence
+from collections.abc import Generator, Iterable, Sequence
 from datetime import UTC
 from logging import DEBUG, Formatter, StreamHandler, getLogger
 from pathlib import Path
@@ -310,7 +309,7 @@ def simple_ax_grid(
 
 
 @contextlib.contextmanager
-def dark_background(overrides: dict[str, Incomplete]):
+def dark_background(overrides: dict[str, Incomplete]) -> Generator:
     """Context manager for plotting "dark mode"."""
     defaults = {
         "axes.edgecolor": "white",
@@ -784,8 +783,8 @@ def dos_axes(
 def inset_cut_locator(
     data: DataType,
     reference_data: DataType,
-    ax: Axes | None = None,
-    location: dict[str, Incomplete] | None = None,
+    ax: Axes,
+    location: dict[str, Incomplete],
     color: RGBColorType = "red",
     **kwargs: Incomplete,
 ) -> None:
@@ -818,10 +817,9 @@ def inset_cut_locator(
         "beta": lambda: reference_data.S.beta,
         "phi": lambda: reference_data.S.phi,
     }
-
-    missing_dims = [d for d in data.dims if d not in location]
-    missing_values = {d: missing_dim_resolvers[d]() for d in missing_dims}
-    ordered_selector = [location.get(d, missing_values.get(d)) for d in data.dims]
+    missing_dims = [dim for dim in data.dims if dim not in location]
+    missing_values = {dim: missing_dim_resolvers[dim]() for dim in missing_dims}
+    ordered_selector = [location.get(dim, missing_values.get(dim)) for dim in data.dims]
 
     n = 200
 
@@ -839,7 +837,7 @@ def resolve(name: str, value: slice | int) -> NDArray[np.float_]:
 
         return np.ones((n,)) * value
 
-    n_cut_dims = len([d for d in ordered_selector if isinstance(d, collections.Iterable | slice)])
+    n_cut_dims = len([d for d in ordered_selector if isinstance(d, Iterable | slice)])
     ordered_selector = [resolve(d, v) for d, v in zip(data.dims, ordered_selector)]
 
     if missing_dims:
diff --git a/arpes/utilities/conversion/base.py b/arpes/utilities/conversion/base.py
index fdf1bb3a..1720cd74 100644
--- a/arpes/utilities/conversion/base.py
+++ b/arpes/utilities/conversion/base.py
@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import warnings
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -46,8 +47,6 @@ def __init__(
         arr: xr.DataArray,
         dim_order: list[str] | None = None,
         calibration: DetectorCalibration | None = None,
-        *args: Incomplete,
-        **kwargs: Incomplete,
     ) -> None:
         """Intern the volume so that we can check on things during computation."""
         self.arr = arr
@@ -94,13 +93,18 @@ def kspace_to_BE(  # noqa: N802
         self,
         binding_energy: NDArray[np.float_],
         *args: NDArray[np.float_],
-        **kwargs: Incomplete,
     ) -> NDArray[np.float_]:
         """The energy conservation equation for ARPES.
 
         This does not depend on any details of the angular conversion (it's the identity) so we can
         put the conversion code here in the base class.
         """
+        if args:
+            for arg in args:
+                warnings.warn(
+                    f"unused args is set in base.py/kspace_to_BE: {arg}",
+                    stacklevel=2,
+                )
         return binding_energy
 
     def conversion_for(self, dim: str):
diff --git a/arpes/utilities/conversion/kx_ky_conversion.py b/arpes/utilities/conversion/kx_ky_conversion.py
index 93468e9e..e364346a 100644
--- a/arpes/utilities/conversion/kx_ky_conversion.py
+++ b/arpes/utilities/conversion/kx_ky_conversion.py
@@ -189,8 +189,6 @@ def kspace_to_phi(
         self,
         binding_energy: NDArray[np.float_],
         kp: NDArray[np.float_],
-        *args: Incomplete,
-        **kwargs: Incomplete,
     ) -> NDArray[np.float_]:
         """Converts from momentum back to the analyzer angular axis."""
         if self.phi is not None:
@@ -391,8 +389,6 @@ def kspace_to_phi(
         binding_energy: NDArray[np.float_],
         kx: NDArray[np.float_],
         ky: NDArray[np.float_],
-        *args: Incomplete,
-        **kwargs: Incomplete,
     ) -> NDArray[np.float_]:
         """Converts from momentum back to the analyzer angular axis."""
         if self.phi is not None:
@@ -435,8 +431,6 @@ def kspace_to_perp_angle(
         binding_energy: NDArray[np.float_],
         kx: NDArray[np.float_],
         ky: NDArray[np.float_],
-        *args: Incomplete,
-        **kwargs: Incomplete,
     ) -> NDArray[np.float_]:
         """Converts from momentum back to the scan angle perpendicular to the analyzer."""
         if self.perp_angle is not None:
diff --git a/arpes/utilities/conversion/kz_conversion.py b/arpes/utilities/conversion/kz_conversion.py
index 96e425a0..549572a2 100644
--- a/arpes/utilities/conversion/kz_conversion.py
+++ b/arpes/utilities/conversion/kz_conversion.py
@@ -127,8 +127,6 @@ def kspace_to_hv(
         binding_energy: NDArray[np.float_],
         kp: NDArray[np.float_],
         kz: NDArray[np.float_],
-        *args: Incomplete,
-        **kwargs: Incomplete,
     ) -> NDArray[np.float_]:
         """Converts from momentum back to the raw photon energy."""
         if self.hv is None:
@@ -156,14 +154,12 @@ def kspace_to_phi(
         binding_energy: NDArray[np.float_],
         kp: NDArray[np.float_],
         kz: NDArray[np.float_],
-        *args: Incomplete,
-        **kwargs: Incomplete,
     ) -> NDArray[np.float_]:
         """Converts from momentum back to the hemisphere angle axis."""
         if self.phi is not None:
             return self.phi
         if self.hv is None:
-            self.kspace_to_hv(binding_energy, kp, kz, *args, **kwargs)
+            self.kspace_to_hv(binding_energy, kp, kz)
         assert self.hv is not None
         if self.arr.S.energy_notation == "Binding":
             kinetic_energy = binding_energy + self.hv - self.arr.S.analyzer_work_function
diff --git a/arpes/utilities/conversion/trapezoid.py b/arpes/utilities/conversion/trapezoid.py
index 14b086c0..196ff0bb 100644
--- a/arpes/utilities/conversion/trapezoid.py
+++ b/arpes/utilities/conversion/trapezoid.py
@@ -129,8 +129,6 @@ def phi_to_phi(
         self,
         binding_energy: NDArray[np.float_],
         phi: NDArray[np.float_],
-        *args: Incomplete,
-        **kwargs: Incomplete,
     ):
         """[TODO:summary].
 
@@ -152,8 +150,6 @@ def phi_to_phi_forward(
         self,
         binding_energy: NDArray[np.float_],
         phi: NDArray[np.float_],
-        *args: Incomplete,
-        **kwargs: Incomplete,
     ):
         """[TODO:summary].
 
diff --git a/arpes/utilities/jupyter.py b/arpes/utilities/jupyter.py
index a9167f5d..130c6e40 100644
--- a/arpes/utilities/jupyter.py
+++ b/arpes/utilities/jupyter.py
@@ -9,11 +9,12 @@
 from pathlib import Path
 from typing import TYPE_CHECKING
 
-from tqdm import tqdm_notebook
+from tqdm.notebook import tqdm
 
 if TYPE_CHECKING:
-    from _typeshed import Incomplete
+    from collections.abc import Iterable
 
+    from _typeshed import Incomplete
 __all__ = (
     "get_full_notebook_information",
     "get_notebook_name",
@@ -24,12 +25,17 @@
 )
 
 
-def wrap_tqdm(x, interactive=True, *args: Incomplete, **kwargs: Incomplete):
-    """Wraps with tqdm_notebook but supports disabling with a flag."""
+def wrap_tqdm(
+    x: Iterable[int],
+    interactive: bool = True,
+    *args: Incomplete,
+    **kwargs: Incomplete,
+) -> Iterable[int]:
+    """Wraps with tqdm but supports disabling with a flag."""
     if not interactive:
         return x
 
-    return tqdm_notebook(x, *args, **kwargs)
+    return tqdm(x, *args, **kwargs)
 
 
 def get_full_notebook_information() -> dict | None: