👌 Remove unused args and kwags in kspace_to_*

    * Excepting kspace_to_BE(*args)

⬆️  follow the tqdm 5.0
💬  update type hints

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.

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:

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",

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

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
 

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",

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)
 

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: