Merge pull request #2250 from mikedh/np2/overflow

Release: NP2 Overflow Hotfix

Dockerfile

@@ -72,10 +72,8 @@ USER root
 RUN trimesh-setup --install=test,gmsh,gltf_validator,llvmpipe,binvox
 USER user
 
-# install things like pytest
-# install prerelease for tests and make sure we're on Numpy 2.X
+# install things like pytest and make sure we're on Numpy 2.X
 RUN pip install .[all] && \
-    pip install --pre --upgrade --force-reinstall numpy && \
     python -c "import numpy as n; assert(n.__version__.startswith('2'))"
 
 # check for lint problems

pyproject.toml

@@ -5,7 +5,7 @@ requires = ["setuptools >= 61.0", "wheel"]
 [project]
 name = "trimesh"
 requires-python = ">=3.8"
-version = "4.4.2"
+version = "4.4.3"
 authors = [{name = "Michael Dawson-Haggerty", email = "[email protected]"}]
 license = {file = "LICENSE.md"}
 description = "Import, export, process, analyze and view triangular meshes."

trimesh/grouping.py

@@ -198,15 +198,15 @@ def hashable_rows(
         d_min, d_max = as_int.min(), as_int.max()
         # since we are quantizing the data down we need every value
         # to fit in a partial integer so we have to check against extrema
-        threshold = 2 ** (precision - 1)
+        threshold = (2 ** (precision - 1)) - 1
 
         # if the data is within the range of our precision threshold
         if d_max < threshold and d_min > -threshold:
             # the resulting package
             hashable = np.zeros(len(as_int), dtype=np.uint64)
             # offset to the middle of the unsigned integer range
             # this array should contain only positive values
-            bitbang = as_int.astype(np.uint64).T + threshold
+            bitbang = (as_int.T + (threshold + 1)).astype(np.uint64)
             # loop through each column and bitwise xor to combine
             # make sure as_int is int64 otherwise bit offset won't work
             for offset, column in enumerate(bitbang):
@@ -223,7 +223,7 @@ def hashable_rows(
     return result
 
 
-def float_to_int(data, digits: Optional[Integer] = None):
+def float_to_int(data, digits: Optional[Integer] = None) -> NDArray[np.int64]:
     """
     Given a numpy array of float/bool/int, return as integers.
 
@@ -244,8 +244,10 @@ def float_to_int(data, digits: Optional[Integer] = None):
 
     # we can early-exit if we've been passed data that is already
     # an integer, unsigned integer, boolean, or empty
-    if data.dtype.kind in "iub" or data.size == 0:
+    if data.dtype == np.int64:
         return data
+    elif data.dtype.kind in "iub" or data.size == 0:
+        return data.astype(np.int64)
     elif data.dtype.kind != "f":
         # if it's not a floating point try to make it one
         data = data.astype(np.float64)
@@ -256,16 +258,10 @@ def float_to_int(data, digits: Optional[Integer] = None):
     elif not isinstance(digits, (int, np.integer)):
         raise TypeError("Digits must be `None` or `int`, not `{type(digits)}`")
 
-    # see if we can use a smaller integer
-    d_extrema = max(abs(data.min()), abs(data.max())) * 10**digits
-
-    # compare against `np.iinfo(np.int32).max`
-    dtype = [np.int32, np.int64][int(d_extrema > 2147483646)]
-
     # multiply by requested power of ten
     # then subtract small epsilon to avoid "go either way" rounding
     # then do the rounding and convert to integer
-    return np.round((data * 10**digits) - 1e-6).astype(dtype)
+    return np.round((data * 10**digits) - 1e-6).astype(np.int64)
 
 
 def unique_ordered(data, return_index=False, return_inverse=False):