install

Author: alecjacobson

CMakeLists.txt

@@ -135,22 +135,27 @@ function(pyigl_include prefix name)
     set(output_dir "${PYIGL_OUTPUT_DIRECTORY}/${subpath}")
     file(MAKE_DIRECTORY ${output_dir})
     file(WRITE "${output_dir}/__init__.py" "from .${target_name} import *\n")
+    install(FILES "${output_dir}/__init__.py" DESTINATION "igl/${subpath}")
     if("${name}" STREQUAL "core" AND "${prefix}" STREQUAL "")
+      install(FILES "${CMAKE_CURRENT_SOURCE_DIR}/igl/_version.py" DESTINATION "igl/${subpath}")
       file(APPEND "${output_dir}/__init__.py" "from ._version import __version__\n")
     endif()
+
     set_target_properties(${target_name} PROPERTIES 
         LIBRARY_OUTPUT_DIRECTORY         "${output_dir}"
         RUNTIME_OUTPUT_DIRECTORY         "${output_dir}"
         LIBRARY_OUTPUT_DIRECTORY_RELEASE "${output_dir}"
         RUNTIME_OUTPUT_DIRECTORY_RELEASE "${output_dir}"
     )
+    set(PYI_OUTPUT "${output_dir}/${target_name}.pyi")
     nanobind_add_stub(
       ${target_name}_stub
       MODULE ${target_name}
-      OUTPUT "${output_dir}/${target_name}.pyi"
+      OUTPUT ${PYI_OUTPUT}
       PYTHON_PATH $<TARGET_FILE_DIR:${target_name}>
       DEPENDS ${target_name}
     )
+    install(FILES "${PYI_OUTPUT}" DESTINATION "igl/${subpath}")
 
     # just to add dependency?
     if("${name}" STREQUAL "core")

README.md

@@ -35,3 +35,10 @@ According to the [scikit-build-core documentation](https://scikit-build-core.rea
 ```
 python -m pip install --no-build-isolation --config-settings=editable.rebuild=true -Cbuild-dir=build -ve.
 ```
+
+## Testing cibuildwheel locally
+
+Install whichever version of Python from the [official website](https://www.python.org/downloads/) and then run:
+
+    /Library/Frameworks/Python.framework/Versions/[version]/bin/python -m pip install cibuildwheel
+    CIBW_BUILD="cp311-*" python -m cibuildwheel --output-dir wheelhouse --platform macos

igl/helpers.py

@@ -16,62 +16,62 @@ def test_version():
     pass
 
 
-##def rand_sparse(n,density):
-##    n_features = n
-##    n_samples = n
-##    rng1 = np.random.RandomState(42)
-##    rng2 = np.random.RandomState(43)
-##
-##    nnz = int(n_samples*n_features*density)
-##
-##    row = rng1.randint(n_samples, size=nnz)
-##    cols = rng2.randint(n_features, size=nnz)
-##    data = rng1.rand(nnz)
-##
-##    S = scipy.sparse.coo_matrix((data, (row, cols)), shape=(n_samples, n_features))
-##    return S.tocsc()
-##
-##def time_noop():
-##    def helper(N,I,SN,SI):
-##        igl.noop(SN=SN)
-##        # start timer
-##        runs = 100
-##        start = time.time()
-##        for i in range(runs):
-##            igl.noop(SN=SN)
-##        # end timer
-##        end = time.time()
-##        return (end - start)/runs
-##    n = 10000
-##    m = 10
-##    N64_f = np.asfortranarray(np.random.randn(n,m).astype(np.float64))
-##    I64_f = np.asfortranarray(np.random.randn(n,m).astype(np.int64))
-##    # random sparse matrix
-##    SN64 = rand_sparse(n,1.0/(n))
-##    # print number of nonzeros
-##    SI64 = (rand_sparse(n,1.0/(n))*1000).astype(np.int64)
-##    print(f"noop<{n},{m}>: {helper(N64_f,I64_f,SN64,SI64)} secs")
-##
-##time_noop()
-#
-## print(igl.matlab_format(V,"V"))
-## print(igl.matlab_format_index(F,"F"))
-## print(igl.matlab_format(dV,"dV"))
-## print(igl.matlab_format_index(dF,"dF"))
+#def rand_sparse(n,density):
+#    n_features = n
+#    n_samples = n
+#    rng1 = np.random.RandomState(42)
+#    rng2 = np.random.RandomState(43)
+#
+#    nnz = int(n_samples*n_features*density)
+#
+#    row = rng1.randint(n_samples, size=nnz)
+#    cols = rng2.randint(n_features, size=nnz)
+#    data = rng1.rand(nnz)
+#
+#    S = scipy.sparse.coo_matrix((data, (row, cols)), shape=(n_samples, n_features))
+#    return S.tocsc()
+#
+#def time_noop():
+#    def helper(N,I,SN,SI):
+#        igl.noop(SN=SN)
+#        # start timer
+#        runs = 100
+#        start = time.time()
+#        for i in range(runs):
+#            igl.noop(SN=SN)
+#        # end timer
+#        end = time.time()
+#        return (end - start)/runs
+#    n = 10000
+#    m = 10
+#    N64_f = np.asfortranarray(np.random.randn(n,m).astype(np.float64))
+#    I64_f = np.asfortranarray(np.random.randn(n,m).astype(np.int64))
+#    # random sparse matrix
+#    SN64 = rand_sparse(n,1.0/(n))
+#    # print number of nonzeros
+#    SI64 = (rand_sparse(n,1.0/(n))*1000).astype(np.int64)
+#    print(f"noop<{n},{m}>: {helper(N64_f,I64_f,SN64,SI64)} secs")
+#
+#time_noop()
 
+# print(igl.matlab_format(V,"V"))
+# print(igl.matlab_format_index(F,"F"))
+# print(igl.matlab_format(dV,"dV"))
+# print(igl.matlab_format_index(dF,"dF"))
 
-# seed numpy's random number generator
-#
-#def triangulated_square():
-#    V = np.array([[0,0,0],[1,0,0],[1,1,0],[0,1,0]],dtype=np.float64)
-#    F = np.array([[0,1,2],[0,2,3]],dtype=np.int64)
-#    return V,F
-#
-#def single_tet():
-#    V = np.array([[0,0,0],[1,0,0],[0,1,0],[0,0,1]],dtype=np.float64)
-#    F = np.array([[2,1,0],[1,3,0],[3,2,0],[2,3,1]],dtype=np.int64)
-#    T = np.array([[0,1,2,3]],dtype=np.int64)
-#    return V,F,T
+
+#seed numpy's random number generator
+
+def triangulated_square():
+    V = np.array([[0,0,0],[1,0,0],[1,1,0],[0,1,0]],dtype=np.float64)
+    F = np.array([[0,1,2],[0,2,3]],dtype=np.int64)
+    return V,F
+
+def single_tet():
+    V = np.array([[0,0,0],[1,0,0],[0,1,0],[0,0,1]],dtype=np.float64)
+    F = np.array([[2,1,0],[1,3,0],[3,2,0],[2,3,1]],dtype=np.int64)
+    T = np.array([[0,1,2,3]],dtype=np.int64)
+    return V,F,T
 
 #def test_edges():
 #    F = np.array([[0,1,2],[0,2,3]],dtype=np.int64)
@@ -170,10 +170,10 @@ def test_version():
 #    igl.writeMSH("out.msh",V,F,T)
 #    V,F,T,_,_,_,_,_,_,_ = igl.readMSH("out.msh")
 #
-#def test_bvh():
-#    V,F,T = single_tet()
-#    tree = igl.AABB()
-#    tree.init(V,T)
+def test_bvh():
+    V,F,T = single_tet()
+    tree = igl.AABB()
+    tree.init(V,T)
 
 #    P = np.array([[0.5,0.5,0.0],[0.5,0.5,0.5]],dtype=np.float64)
 #    # first row of P