update for new ruff rules

trimesh/__init__.py

@@ -82,11 +82,11 @@
 
 
 __all__ = [
-    "PointCloud",
     "Geometry",
-    "Trimesh",
+    "PointCloud",
     "Scene",
     "Trimesh",
+    "Trimesh",
     "__version__",
     "available_formats",
     "boolean",
@@ -103,8 +103,8 @@
     "graph",
     "grouping",
     "inertia",
-    "iteration",
     "intersections",
+    "iteration",
     "load",
     "load_mesh",
     "load_path",

trimesh/exchange/binvox.py

@@ -201,7 +201,9 @@ def voxel_from_binvox(rle_data, shape, translate=None, scale=1.0, axis_order="xz
     elif axis_order is None or axis_order == "xyz":
         encoding = encoding.reshape(shape)
     else:
-        raise ValueError("Invalid axis_order '%s': must be None, 'xyz' or 'xzy'")
+        raise ValueError(
+            "Invalid axis_order '%s': must be None, 'xyz' or 'xzy'", axis_order
+        )
 
     assert encoding.shape == shape
 
@@ -423,7 +425,7 @@ def __init__(
             )
 
         if dimension > 1024 and not exact:
-            raise ValueError("Maximum dimension using exact is 1024, got %d" % dimension)
+            raise ValueError("Maximum dimension using exact is 1024, got %d", dimension)
         if file_type not in Binvoxer.SUPPORTED_OUTPUT_TYPES:
             raise ValueError(
                 f"file_type {file_type} not in set of supported output types {Binvoxer.SUPPORTED_OUTPUT_TYPES!s}"
@@ -471,7 +473,7 @@ def __init__(
             times = np.log2(downsample_factor)
             if int(times) != times:
                 raise ValueError(
-                    "downsample_factor must be a power of 2, got %d" % downsample_factor
+                    "downsample_factor must be a power of 2, got %d", downsample_factor
                 )
             args.extend(("-down",) * int(times))
         if downsample_threshold is not None:

trimesh/exchange/gltf.py

@@ -1029,14 +1029,14 @@ def _build_accessor(array):
         if vec_length > 4:
             raise ValueError("The GLTF spec does not support vectors larger than 4")
         if vec_length > 1:
-            data_type = "VEC%d" % vec_length
+            data_type = f"VEC{int(vec_length)}"
         else:
             data_type = "SCALAR"
 
     if len(shape) == 3:
         if shape[2] not in [2, 3, 4]:
             raise ValueError("Matrix types must have 4, 9 or 16 components")
-        data_type = "MAT%d" % shape[2]
+        data_type = f"MAT{int(shape[2])}"
 
     # get the array data type as a str stripping off endian
     lookup = array.dtype.str.lstrip("<>")

trimesh/interfaces/__init__.py

@@ -1,4 +1,4 @@
 from . import blender, gmsh
 
 # add to __all__ as per pep8
-__all__ = ["gmsh", "blender"]
+__all__ = ["blender", "gmsh"]

trimesh/typed.py

@@ -62,21 +62,21 @@
     "Any",
     "ArrayLike",
     "BinaryIO",
+    "Callable",
     "Dict",
+    "Hashable",
     "Integer",
     "Iterable",
     "List",
     "Loadable",
+    "Mapping",
     "NDArray",
     "Number",
     "Optional",
     "Sequence",
+    "Set",
     "Stream",
     "Tuple",
     "float64",
     "int64",
-    "Mapping",
-    "Callable",
-    "Hashable",
-    "Set",
 ]

trimesh/voxel/encoding.py

@@ -768,17 +768,20 @@ def __init__(self, encoding, shape):
             size = np.abs(size)
             if self._data.size % size != 0:
                 raise ValueError(
-                    "cannot reshape encoding of size %d into shape %s"
-                    % (self._data.size, str(self._shape))
+                    "cannot reshape encoding of size %d into shape %s",
+                    self._data.size,
+                    str(self._shape),
                 )
+
             rem = self._data.size // size
             self._shape = tuple(rem if s == -1 else s for s in self._shape)
         elif nn > 2:
             raise ValueError("shape cannot have more than one -1 value")
         elif np.prod(self._shape) != self._data.size:
             raise ValueError(
-                "cannot reshape encoding of size %d into shape %s"
-                % (self._data.size, str(self._shape))
+                "cannot reshape encoding of size %d into shape %s",
+                self._data.size,
+                str(self._shape),
             )
 
     def _from_base_indices(self, base_indices):
@@ -818,9 +821,11 @@ def __init__(self, base_encoding, perm):
             raise ValueError(f"base_encoding must be an Encoding, got {base_encoding!s}")
         if len(base_encoding.shape) != len(perm):
             raise ValueError(
-                "base_encoding has %d ndims - cannot transpose with perm %s"
-                % (base_encoding.ndims, str(perm))
+                "base_encoding has %d ndims - cannot transpose with perm %s",
+                base_encoding.ndims,
+                str(perm),
             )
+
         super().__init__(base_encoding)
         perm = np.array(perm, dtype=np.int64)
         if not all(i in perm for i in range(base_encoding.ndims)):
@@ -898,7 +903,7 @@ def __init__(self, encoding, axes):
         super().__init__(encoding)
         if not all(0 <= a < self._data.ndims for a in axes):
             raise ValueError(
-                "Invalid axes %s for %d-d encoding" % (str(axes), self._data.ndims)
+                "Invalid axes %s for %d-d encoding", str(axes), self._data.ndims
             )
 
     def _to_base_indices(self, indices):

trimesh/voxel/morphology.py

@@ -42,7 +42,7 @@ def _sparse_indices(encoding, rank=None):
 
 def _assert_rank(value, rank):
     if len(value.shape) != rank:
-        raise ValueError("Expected rank %d, got shape %s" % (rank, str(value.shape)))
+        raise ValueError("Expected rank %d, got shape %s", rank, str(value.shape))
 
 
 def _assert_sparse_rank(value, rank=None):
@@ -51,7 +51,7 @@ def _assert_sparse_rank(value, rank=None):
     if rank is not None:
         if value.shape[-1] != rank:
             raise ValueError(
-                "sparse_indices.shape[1] must be %d, got %d" % (rank, value.shape[-1])
+                "sparse_indices.shape[1] must be %d, got %d", rank, value.shape[-1]
             )
 
 

trimesh/voxel/runlength.py

@@ -390,8 +390,9 @@ def sorted_rle_gather_1d(rle_data, ordered_indices):
                 start += next(data_iter)
             except StopIteration:
                 raise IndexError(
-                    "Index %d out of range of raw_values length %d" % (index, start)
+                    "Index %d out of range of raw_values length %d", index, start
                 )
+
         try:
             while index < start:
                 yield value
@@ -533,8 +534,9 @@ def sorted_brle_gather_1d(brle_data, ordered_indices):
                 start += next(data_iter)
             except StopIteration:
                 raise IndexError(
-                    "Index %d out of range of raw_values length %d" % (index, start)
+                    "Index %d out of range of raw_values length %d", index, start
                 )
+
         try:
             while index < start:
                 yield value