style: fix issues reported

Signed-off-by: Henry Schreiner <[email protected]>

.pre-commit-config.yaml

@@ -39,7 +39,7 @@ repos:
   - id: mypy
     files: ^src
     args: []
-    additional_dependencies: ["numpy~=1.26.0", "matplotlib>=3.4", "boost-histogram~=1.4.0", "uhi~=0.3.1", "pandas-stubs>=2.0.1.230501"]
+    additional_dependencies: ["numpy~=2.2.0", "matplotlib>=3.4", "boost-histogram~=1.5.0", "uhi~=0.3.1", "pandas-stubs>=2.0.1.230501"]
 
 - repo: https://github.com/codespell-project/codespell
   rev: v2.3.0

src/hist/basehist.py

@@ -196,11 +196,11 @@ def from_columns(
         for ax in axes:
             if isinstance(ax, str):
                 assert ax in data, f"{ax} must be present in data={list(data)}"
-                cats = set(data[ax])  # type: ignore[arg-type]
+                cats = set(data[ax])
                 if all(isinstance(a, str) for a in cats):
-                    axes_list.append(hist.axis.StrCategory(sorted(cats), name=ax))  # type: ignore[arg-type]
+                    axes_list.append(hist.axis.StrCategory(sorted(cats), name=ax))
                 elif all(isinstance(a, int) for a in cats):
-                    axes_list.append(hist.axis.IntCategory(sorted(cats), name=ax))  # type: ignore[arg-type]
+                    axes_list.append(hist.axis.IntCategory(sorted(cats), name=ax))
                 else:
                     raise TypeError(
                         f"{ax} must be all int or strings if axis not given"
@@ -214,7 +214,7 @@ def from_columns(
 
         self = cls(*axes_list, storage=storage)
         data_list = {x.name: data[x.name] for x in axes_list}
-        self.fill(**data_list, weight=weight_arr)  # type: ignore[arg-type]
+        self.fill(**data_list, weight=weight_arr)
         return self
 
     def project(self, *args: int | str) -> Self | float | bh.accumulators.Accumulator:

src/hist/interop.py

@@ -99,7 +99,7 @@ def broadcast_and_flatten(
             try:
                 arrays.append(np.asarray(arg))
             except (TypeError, ValueError):
-                return NotImplemented
+                return NotImplemented  # type: ignore[no-any-return]
 
         return tuple(np.ravel(x) for x in np.broadcast_arrays(*arrays))
 
@@ -126,6 +126,6 @@ def broadcast_and_flatten(
                 try:
                     arrays.append(np.asarray(arg))
                 except (TypeError, ValueError):
-                    return NotImplemented
+                    return NotImplemented  # type: ignore[no-any-return]
 
             return tuple(np.ravel(x) for x in np.broadcast_arrays(*arrays))

src/hist/intervals.py

@@ -107,7 +107,7 @@ def clopper_pearson_interval(
     interval = np.stack((interval_min, interval_max))
     interval[0, num == 0.0] = 0.0
     interval[1, num == denom] = 1.0
-    return interval  # type: ignore[no-any-return]
+    return interval
 
 
 def ratio_uncertainty(

src/hist/plot.py

@@ -460,7 +460,7 @@ def plot_ratio_array(
                 valid_ratios + ratio_uncert[1][valid_ratios_idx],
             ]
         )
-        max_delta = np.amax(np.abs(extrema - central_value))
+        max_delta: float = np.amax(np.abs(extrema - central_value))
         ratio_extrema = np.abs(max_delta + central_value)
 
         _alpha = 2.0

src/hist/svgplots.py

@@ -70,7 +70,7 @@ def svg_hist_1d(h: hist.BaseHist) -> svg:
     (edges,) = h.axes.edges
     norm_edges = (edges - edges[0]) / (edges[-1] - edges[0])
     density = h.density()
-    max_dens = np.amax(density) or 1
+    max_dens: float = np.amax(density)
     norm_vals: np.typing.NDArray[Any] = density / max_dens
 
     arr: np.typing.NDArray[np.float64] = np.empty(
@@ -121,7 +121,7 @@ def svg_hist_1d_c(h: hist.BaseHist) -> svg:
     (edges,) = h.axes.edges
     norm_edges = (edges - edges[0]) / (edges[-1] - edges[0]) * np.pi * 2
     density = h.density()
-    max_dens = np.amax(density) or 1
+    max_dens: float = np.amax(density)
     norm_vals: np.typing.NDArray[Any] = density / max_dens
 
     arr: np.typing.NDArray[np.float64] = np.empty((2, len(norm_vals) * 2), dtype=float)
@@ -155,7 +155,7 @@ def svg_hist_2d(h: hist.BaseHist) -> svg:
     ey = -(e1 - e1[0]) / (e1[-1] - e1[0]) * height
 
     density = h.density()
-    max_dens = np.amax(density) or 1
+    max_dens: float = np.amax(density)
     norm_vals: np.typing.NDArray[Any] = density / max_dens
 
     boxes = []