Linting

Author: raymondEhlers

pyproject.toml

@@ -165,6 +165,7 @@ ignore = [
   "ISC001",   # Conflicts with formatter
   "G004",     # Logging format string. This isn't best practices, but I use it too much to change for now
   "PLC0414",  # Messes with the re-export of imports needed from mypy
+  "PLC0415",  # Wants all imports at the top of file, but it's often very convenient to delay them
 ]
 unfixable = [
   "T20",      # Removes print statements

src/pachyderm/binned_data.py

@@ -175,6 +175,9 @@ def __eq__(self, other: Any) -> bool:
             return np.allclose(self.bin_edges, other.bin_edges)
         return False
 
+    # Hashing is not suitable here since numpy arrays are not hashable.
+    __hash__ = None  # type: ignore[assignment]
+
     def __getitem__(self, selection: int | slice) -> Axis:
         """Select a subset of the axis.
 
@@ -352,6 +355,9 @@ def __eq__(self, other: Any) -> bool:
             return all(a == b for a, b in itertools.zip_longest(self, other))
         return False
 
+    # Hashing is not suitable here since numpy arrays are not hashable.
+    __hash__ = None  # type: ignore[assignment]
+
     @classmethod
     def to_yaml(
         cls: type[AxesTuple], representer: ruamel.yaml.representer.BaseRepresenter, obj: AxesTuple
@@ -792,6 +798,9 @@ def __eq__(self, other: Any) -> bool:
         # All arrays and the metadata must agree.
         return all(agreement) and axes_agree and metadata_agree
 
+    # Hashing is not suitable here since numpy arrays are not hashable.
+    __hash__ = None  # type: ignore[assignment]
+
     @classmethod
     def from_hepdata(cls: type[BinnedData], hist: Mapping[str, Any]) -> list[BinnedData]:  # pylint: disable=unused-argument
         """Convert (a set) of HEPdata histogram(s) to BinnedData objects.

src/pachyderm/fit/base.py

@@ -86,7 +86,7 @@ def correlation_matrix(self) -> dict[tuple[str, str], float]:
         """
         try:
             # We attempt to cache the covariance matrix, so first try to return that.
-            return self._correlation_matrix
+            return self._correlation_matrix  # type: ignore[no-any-return,has-type]
         except AttributeError:
 
             def corr(i_name: str, j_name: str) -> float:
@@ -267,6 +267,9 @@ def __eq__(self, other: Any) -> bool:
             return keys_agree and all(values_agree)
         return NotImplemented
 
+    # Hashing is not suitable here since numpy arrays are not hashable.
+    __hash__ = None  # type: ignore[assignment]
+
 
 def extract_function_values(func: Callable[..., float], fit_result: BaseFitResult) -> tuple[dict[str, Any], list[str]]:
     """Extract the parameters relevant to the given function from a fit result.

src/pachyderm/generic_class.py

@@ -10,7 +10,7 @@
 _T = TypeVar("_T", bound="EqualityMixin")
 
 
-class EqualityMixin:
+class EqualityMixin:  # noqa: PLW1641
     """Mixin generic comparison operations using `__dict__`.
 
     Can then be mixed into any other class using multiple inheritance.

src/pachyderm/generic_config.py

@@ -100,22 +100,22 @@ def override_options(
                 if isinstance(config[k], list):
                     # Clear out the existing list entries
                     del config[k][:]
-                    if isinstance(override_dict[k], str | int | float | bool):
+                    if isinstance(v, str | int | float | bool):
                         # We have to treat str carefully because it is an iterable, but it will be expanded as
                         # individual characters if it's treated the same as a list, which is not the desired
                         # behavior! If we wrap it in [], then it will be treated as the only entry in the list
                         # NOTE: We also treat the basic types this way because they will be passed this way if
                         #       overriding indirectly with anchors (since the basic scalar types don't yet support
                         #       reassignment while maintaining their anchors).
-                        config[k].append(override_dict[k])
+                        config[k].append(v)
                     else:
                         # Here we just assign all entries of the list to all entries of override_dict[k]
-                        config[k].extend(override_dict[k])
+                        config[k].extend(v)
                 elif isinstance(config[k], dict):
                     # Clear out the existing entries because we are trying to replace everything
                     # Then we can simply update the dict with our new values
                     config[k].clear()
-                    config[k].update(override_dict[k])
+                    config[k].update(v)
                 elif isinstance(config[k], int | float | bool):
                     # This isn't really very good (since we lose information), but there's nothing that can be done
                     # about it at the moment (Dec 2018)

src/pachyderm/histogram.py

@@ -544,7 +544,7 @@ 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.
@@ -752,6 +752,9 @@ def __eq__(self, other: Any) -> bool:
         # All arrays and the metadata must agree.
         return all(agreement) and metadata_agree
 
+    # Hashing is not suitable here since numpy arrays are not hashable.
+    __hash__ = None  # type: ignore[assignment]
+
     @staticmethod
     def _from_uproot(hist: Any) -> tuple[npt.NDArray[Any], npt.NDArray[Any], npt.NDArray[Any], dict[str, Any]]:
         """Convert a uproot histogram to a set of array for creating a Histogram.