Linting

Author: raymondEhlers

src/pachyderm/binned_data.py

@@ -1141,8 +1141,8 @@ def to_boost_histogram(self) -> Any:
         # Need to shape the array properly so that it will actually be able to assign to the boost histogram.
         arr = np.zeros(shape=h.view().shape, dtype=h.view().dtype)
         # NOTE: We're relying on the h.view() supporting structured arrays. Surprisingly, this seems to work.
-        arr["value"] = self.values  # type: ignore[call-overload]
-        arr["variance"] = self.variances  # type: ignore[call-overload]
+        arr["value"] = self.values
+        arr["variance"] = self.variances
         h[...] = arr
 
         return h

src/pachyderm/histogram.py

@@ -546,7 +546,7 @@ def _integral(
         # Provide the opportunity to scale by bin width
         widths = np.ones(len(self.y))
         if multiply_by_bin_width:
-            widths = self.bin_widths  # type: ignore[assignment]
+            widths = self.bin_widths
 
         # Integrate by summing up all of the bins and the errors.
         # Perform the integral.