subagent transcript

I need to set up the environment and explore the existing toolchain to build numpy for the static no-pthread CPython 3.14 wasm target, registering its C extensions through inittab instead of using dlopen.

I need to investigate what env.sh is supposed to do and make sure it's being sourced correctly to set up the necessary tools.

The issue is that each bash invocation creates a new shell, so the PATH changes from sourcing env.sh don't persist. I need to source env.sh within each command rather than separately, then verify that emcc is actually available.

Now I'm examining the sysconfig data from the emscripten Python build and checking pyconfig.h to understand the platform assumptions. I should also look at how FreeCAD handles Python module linking, and see what numpy version would work with Python 3.14, similar to how Pyodide approaches it.

I'm getting a clearer picture of the WebAssembly Python build configuration—it's using CPython 3.14.5 as a static build for Emscripten, with the extension suffix and ABI tags reflecting the wasm32-emscripten target. The long double type is notably 16 bytes, which is worth keeping in mind for compatibility considerations.

For building numpy on this platform, the main hurdles are getting the build system to work with Emscripten's constraints. Numpy 2.x uses meson, which requires a cross-compilation file for Emscripten. The critical dependencies are BLAS/LAPACK—numpy 2.x technically requires them, but I can use the bundled netlib-lite fallback with the `-Dallow-noblas=true` flag, which is what  …

I've got numpy 2.4.6 available on the host with Python 3.14 support, so I can use that as a reference. My plan is to grab the numpy source, set up an emscripten meson cross file for cross-compilation, and attempt the build. First, I need to examine the host numpy structure and figure out how to configure meson properly for emscripten.

I'm examining the numpy source structure, specifically what gets imported when you do `import numpy`. Looking at the __init__.py file, I can see the core imports like _core and _multiarray_umath, plus the submodules like linalg, fft, and random that need to be loaded. I should check the meson build configuration to understand the full dependency chain.

I'm diving into the _multiarray_umath extension module and how numpy's multi-target CPU dispatch system works with SIMD features like SSE/AVX/NEON. This is a significant challenge for WebAssembly since numpy expects CPU feature detection, but Pyodide typically disables SIMD dispatch or falls back to a baseline wasm implementation.

I also need to check the top-level meson.build configuration for options that control optimization, CPU baseline, CPU dispatch, and BLAS support — these settings are crucial for understanding how to configure numpy for wasm compatibility.

Looking at the build configuration options for WebAssembly compilation, I need to understand which flags are critical for the wasm target. The threading flag is essential since we're targeting an environment without pthread support, and I should verify whether Highway's SIMD capabilities work with wasm or need to be disabled as well.

Now I'm setting up the meson cross file for emscripten, which is the key piece that tells meson to use the emscripten compiler toolchain and describes the wasm32 target architecture. Meson has some built-in emscripten awareness, but I still need to configure the cross file with the right binaries, host machine settings, and properties to handle the cross-compil  …

NumPy has built-in WebAssembly support through its CPU detection header, which is helpful. The issue is that the sysconfigdata module points to a build-time include directory that no longer exists, so I'll need to override the include path either through the Meson cross-compilation file or by passing compiler flags directly.

The cleanest approach would be to create a patched copy of sysconfigdata with corrected paths pointing to the actual installation directory at /opt/toolchains/python-wasm, updating the prefix, include directories, and library paths accordingly. Before committing to that though, let me verify whether the original build-time paths still exist—if they do, the headers might  …

The build-tree path doesn't actually exist—only the base Python directory is there, while the real headers are located in the wasm toolchain directory. Since there's no crossenv or meson-python available, I need to figure out whether to install meson-python first.

Actually, I can skip meson-python and invoke meson directly instead, since numpy's build works fine with plain `meson setup` as long as the meson.build file is self-contained. The real challenge is that the sysconfigdata module is pointing to the wrong paths, so I'll need to create a patched version that remaps everything from the non-existent cross-build directory to the actual `/opt/toolchains/python-wasm` location.

My strategy  …

I need to track down the correct path for the pyodide cross file and fetch the numpy recipe from the right location.

I'm going to stop digging through the Pyodide source and just write my own Meson cross file for Emscripten based on what I know about the format, then refine it as needed.

Writing Meson cross file...

I need to verify that the sysconfig override works correctly with the host Python, then generate the meson cross file to ensure the environment variables properly configure sysconfig for the target system.

The issue is that `sysconfig.get_path('include')` relies on the installation scheme rather than directly using INCLUDEPY from sysconfigdata, so it's computing the path based on the host interpreter's base prefix instead of the target's. I need to explicitly extract INCLUDEPY from the target sysconfigdata instead of relying on get_path.

Looking at how meson's python dependency module works in cross-compilation mode, it runs an introspection script that pulls both install_paths and variables from sysconfig, but there's ambiguity in which one it prioritizes for the include directory. The safest approach is to add the include path directly to the cross file's c_args so the compiler can find Pyt  …

There are two run checks blocking the build: one in the numpy core meson file that detects long double binary representation, which is a known issue, and another in the highway meson file that we can work around by disabling highway. There's also something about `cc.sizeof()` checks in meson that got cut off, but it seems related to these compilation blockers.

Actually, meson has a clever compile-time fallback for cross-compilation using static asserts to determine sizes without running code, so `sizeof()` shouldn't be the problem. The real blocker is that `longdouble_format` run check at line 444. I can either provide an exe_wrapper so meson can run the compiled binaries under node, or pat  …

So the Emscripten-compiled code works fine in Node, and I'm seeing that long double values are stored as 128-bit IEEE quad precision floats—the test value 1.0L shows up in memory as those specific bytes in little-endian format, which matches the expected bit pattern for that representation.

For the Meson cross-compilation setup, when Emscripten compiles a test executable without an extension, it produces both a JavaScript wrapper file and a `.wasm` binary. The key insight is that setting `exe_wrapper = node` tells Meson to run test executables through Node, so `node output` will work because the JS file automatically loads its paired `.wasm` sibling. This is the standard approach used in Em  …

I can use `meson.get_external_property('longdouble_format', 'UNKNOWN')` to read the format from the cross file's `[properties]` section, which numpy explicitly supports for cross-compilation. Setting `longdouble_format = 'IEEE_QUAD_LE'` in the cross file eliminates the need for any patches.

For the meson options, I'll disable Highway, optimization, threading, and SVML to keep things simple for WebAssembly — just baseline scalar code without runtime CPU dispatch. I'll use `--buildtype=release` and configure the cross file with appropriate compiler settings, Python includes, and emscripten-specific handling.

The tricky part is the Python dependency introspection. Meson's `find_installation()  …

I need to check how python.py handles INCLUDEPY versus the paths dictionary around line 557 to understand whether my patched sysconfigdata will properly set the include path that meson expects.

Meson is collecting include paths with the target's Python headers first via INCLUDEPY from my patched sysconfigdata, followed by the host system paths, so the target headers take precedence during compilation.

When meson runs `python_info.py` through the build Python with my environment overrides, it gets the target configuration variables including the correct INCLUDEPY, while the extension suffix (EXT_SUFFIX) also comes from the target configuration to ensure the compiled module has the right naming convention for a cross build.

For the executable wrapper, meson's emscripten support doesn't automatically run tests through Node, so I need to either set an explicit `exe_wrapper` pointing   …

I'm noticing some key configuration patterns here. The meson emscripten setup uses a `.js` suffix for test binaries and relies on Node as an exe_wrapper to run them. There's also a conditional for the highway library based on a disable flag, and the threading option maps to an NPY_NO_SMP environment variable that disables numpy's SMP functionality.

Looking at the disable-optimization flag, when it's true, numpy skips the multi-target dispatch build and compiles everything at baseline CPU features—which is actually ideal for WebAssembly since there's no runtime dispatch overhead. The dispatch-able loops still get compiled once at baseline, so all the necessary functionality remains intact. I  …

I'm running the meson setup command and logging the output.

The cross-compilation setup is working with emcc/em++ recognized as host compilers, but now I need to handle the cython dependency. The issue is that the top-level meson.build requires cython, though _multiarray_umath itself is pure C and doesn't need it. I'm checking whether I can build just _multiarray_umath without cython, or if the top-level configuration forces the requirement.

I need to install cython via pip on the host machine to satisfy the build requirements.

I need to work around Arch's PEP 668 restrictions, so I'll create a virtual environment to isolate the build dependencies like cython, meson, and ninja rather than using --break-system-packages. Since meson and ninja are already system-installed, I mainly need to get cython into the venv, then configure the build to use the venv's Python interpreter when running meson.

Cython 3.2.8 is installed in the venv, but meson isn't importable there—the system has a standalone meson binary at /bin/meson instead. I'll use that system meson while ensuring the venv's bin directory (which has cython) is prepended to PATH so meson can find the right python and cython executables for introspection.

The venv python is 3.14 with system-site-packages enabled, so it can access system dependencies if needed by code generators. With the sysconfigdata override active, the venv python's sysconfig will still report the target paths correctly. When meson's `find_installation()` searches for python3 on PATH, it'll find the venv version first, and that python's INCLUDEPY will be pat  …

Good news on the build front—Cython is available, the host and build architectures are properly detected, Python 3.14 is found, and the Python.h header is accessible thanks to my INCLUDEPY patch. But now I'm hitting a different issue: the numpy CPU features meson module isn't being found when trying to load the x86 build configuration.

The real problem is becoming clear—numpy uses a vendored fork of meson that includes a custom `features` module for CPU SIMD dispatch, but the system meson doesn't have it. This means I can't just use whatever meson is installed; I need to use numpy's vendored meson specifically, which should be located in their source tree.

I see that numpy has its own vendored meson at `vendored-meson/meson/meson.py` with the features module I need, so I should use that instead of the system meson by invoking it as a Python script. The vendored meson appears to be a full fork based on an older version, so I need to check what version it actually is before proceeding.

Let me try running the setup with the vendored meson using the venv Python (3.14), though there might be compatibility issues if the vendored meson is quite old.

The meson setup completed successfully and configured NumPy 2.4.6 with baseline targets only, which is exactly what I need for WebAssembly. Now I'm moving on to the ninja build step—I should check what targets are available and decide whether to build just the core `_multiarray_umath` extension module or go for a full build, keeping in mind that emcc will handle the .so generation for the extension modules.

I'm targeting the NumPy WASM module for compilation, which requires linking together several object files and static libraries including libnpymath.a and libunique_hash.a. Let me run the build for this specific target and see how the linking proceeds with undefined symbols allowed.

The build succeeded with all 109 objects compiled and linked successfully. The emcc warning is revealing something crucial about how meson handles the extension module with the `-shared` flag—it's emulating a static object file rather than creating a true runtime shared library, which explains the behavior we're seeing.

So the `.so` file that was produced is actually a relocatable WASM object containing all the linked code plus the PyInit symbol. I could potentially use it directly as an object in the final link, but the cleaner approach would be to archive all the `.o` files into a `.a` library. That way, when FreeCAD does the final link with the inittab reference to `PyInit__multiarray_um  …

The `.so` file is a WebAssembly binary that's been statically linked together, and `PyInit__multiarray_umath` is a global symbol defined in the multiarray module object file that's now present in the combined binary. I'm trying to figure out whether this `.so` is a proper relocatable object that can be re-linked into the final module, or if Emscripten treats it as a special shared WebAssembly object.

Since the `-shared` flag produces a static relocatable object file, I can either link the `.so` directly into FreeCAD's final link or create a `.a` archive from it. Given that the integrator uses `FREECAD_WASM_EXTRA_LINK_LIBS` as a group of archives, creating an archive makes more sense. The co  …

I'm looking at the undefined symbols that need resolving: there are 317 Python symbols from CPython that get resolved through libpython3.14.a during FreeCAD's linking, plus a comprehensive set of math library functions including the long double and complex variants that come from emscripten's libc and libm, along with a few core libc functions like abort, calloc, and backtrace.

The real question is whether emscripten's libm actually provides proper implementations of the long double math functions (acosl, atanl, cosl, etc.) and their complex variants (cabsl, cargl, ccosl, cpowl, csinl) on WebAssembly, since long double is 128-bit quad precision there rather than the typical 64-bit double. I  …

I'm realizing my test approach was wrong — I declared these functions as `extern void` and tried to reference their addresses, but wasm-ld is reporting them as undefined even though they should be in libc and libm. The real issue is becoming clearer as I think through why these standard library symbols aren't being found. Maybe the issue is that emscripten's libc symbols are weak or the archive isn't being searched properly with `-lc-debug`. Let me test this more directly by actually calling a few functions like `acos` and `acosl` instead of just taking their addresses, to see if the long double variant is specifically the problem or if my test setup is broken.

The linker exited with an error code but didn't produce the typical "undefined symbol" messages, so I need to look at the complete error output to figure out what went wrong.

I see the issue — I was compiling with em++ which treats `complex` as a keyword, but `cabs()` returns a real double, not a complex number. Let me switch to emcc (C compiler) and write valid C code that properly handles the complex number functions and long double variants.

The long-double and complex math functions are linking correctly and resolving properly—the NaN values appearing in the output are expected given the test inputs I'm using, since some functions like acoshl have domain restrictions that my arbitrary values violate. The complex number calculation itself computed fine, so this confirms emscripten's math library is working as intended.

Now I need to verify that the full set of non-Python undefined symbols actually resolves, not just the math functions. My earlier address-array approach failed, but I'm realizing the issue might be that emscripten implements some libc functions in JavaScript rather than in the wasm archive itself—functions like `  …

Looking at numpy's import structure, I see that `_core`, `lib`, and `matrixlib` are imported directly at the top level of `__init__.py`, but I need to verify whether `linalg`, `fft`, and `random` are also exposed through the main namespace—they might be imported later in the file or handled through lazy loading.

The critical dependency is `_multiarray_umath` (the C extension), which `_core` requires. Since `lib` and `matrixlib` are pure Python, a minimal working import of numpy fundamentally needs just that C extension. However, if `linalg`, `fft`, and `random` are eagerly imported in `__init__.py`, they'd need their own C extensions built as well.

Rather than trace through the entire impo  …

Numpy 2.4.6 compiled completely for WebAssembly with all 19 C extension modules building successfully—zero failures, zero errors. The build includes the core multiarray and math modules, FFT support with the pocketfft library, linear algebra with bundled lapack, and all the random number generation Cython modules.

Now I need to extract the PyInit symbols from each compiled module, map them to their full dotted module names for the inittab registration, then run an authoritative test by linking everything with libpython and actually importing numpy to verify it works end-to-end. After that I'll package the numpy Python tree for the filesystem and finalize the spec.

The key question is wheth  …

I've identified all 19 modules with their PyInit symbols, and for a complete numpy import I need to load the core extension modules like the multiarray and FFT components, along with the linear algebra libraries.

The real challenge now is handling duplicate symbols when combining these `.so` files—each one was independently linked and contains its own copies of static library code like npymath, so merging them directly would create conflicts. I need to either build archives from the raw object files with deduplication, or find an empirical way to resolve the overlapping symbols when combining the relocatable objects.

The cleaner approach is to use only the module-specific objects from the   …

I'm mapping out the shared library dependencies across the NumPy extension modules—libnpymath and libnpyrandom are used by multiple modules, while some libraries like the multiarray umath targets and unique hash are only needed by specific modules, plus pocketfft as a separate dependency.

Now I'm working through the assembly strategy for creating the final archive: I'll combine all the object files from the 13 non-test modules with the shared static libraries, but I need to verify whether the baseline loop libraries are already embedded in the multiarray umath targets library or if they need to be linked separately—I'll test the archive linking to see if there are any undefined symbols that  …

The glob issue with the `mods` variable containing spaces was breaking the loop, but I've identified the real issue: `lib_multiarray_umath_mtargets.a` actually contains all the baseline dispatch libraries as embedded members, so it's self-contained. The duplicate check failed because of that shell glob problem. Let me redo the verification with a better approach.

Writing bash script...

I'm debugging a shell script that's using `set -e` to exit on errors, and I'm noticing the weak symbol handling might be causing issues — weak duplicates should be allowed by the linker, so I need to separate those out. The script produced no output, which suggests it either failed early on the glob expansion when a directory has no object files, or something errored silently and got cut off by the tail command.

The issue is that `setup-env.sh` sources `env.sh` with output redirected to `/dev/null`, but if `env.sh` exits with a nonzero status, the `set -e` flag causes the entire script to abort immediately before any output is printed, leaving an empty log.

I'll remove the `set -e` flag and restructure the script to source the environment variables first, then proceed with the rest of the logic.

I'm discovering that the shared libraries are thin archives, which means I can't extract them the usual way—instead I need to reference the object files directly from disk using the archive table of contents. More critically, I'm finding a massive issue: there are 668 duplicate strong symbols, and they're all LAPACK and BLAS routines that are conflicting.

The root cause is that both `lapack_lite.so` and `_umath_linalg.so` independently compiled and bundled their own copy of the netlib LAPACK-lite source code, so when I try to combine them, I get these symbol collisions. The solution is to use `wasm-ld -r` to relocate and localize all non-PyInit symbols within each module object, making rout  …

I'm realizing that llvm-objcopy won't work for wasm symbol localization since it only supports section operations on wasm objects, not symbol manipulation. I need to look at wasm-ld's relocatable mode instead, which might offer the functionality I need.

For the LAPACK duplication issue, I'm considering whether I can deduplicate at the object level by including the lapack-lite objects just once and letting both modules reference them. The key question is whether the f2c LAPACK sources are compiled into separate object files within each module's build directory, and whether those objects are identical across both builds. If they are, I could include lapack_lite's objects and skip the duplicat  …

So dropping lapack_lite eliminates 557 duplicate symbols that it shares with _umath_linalg, but that still leaves 66 duplicates—all the random distribution functions like random_beta, random_binomial, and random_normal that come from the npyrandom module.

The puzzle is why these are duplicated: mtrand defines 96 symbols including random_* functions, but according to the dependency list, mtrand only links libnpymath.a, not libnpyrandom.a. So either mtrand is compiling its own copy of the distributions code, or I'm misunderstanding how these symbols are being counted.

If mtrand does bundle its own distributions.c, then both mtrand.p and libnpyrandom.a would define the same random_* symbols,   …

Looking at the indentation and context, these numpy submodule imports at lines 709-718 are nested inside a conditional block—likely within a lazy loading mechanism like `__getattr__` or a try/except structure that handles dynamic imports.

The numpy.random module has its own initialization that pulls in multiple dependencies including the bit generators and mtrand, so if random is imported eagerly during the main numpy import, all those submodules become mandatory. But the key insight is that _umath_linalg.p contains duplicate lapack object files that are already in lapack_lite.p—I can deduplicate by keeping the full lapack_lite.p and extracting only the umath_linalg.cpp.o object from _umath  …

I'm realizing that several major NumPy submodules like `linalg`, `fft`, `random`, `dtypes`, `polynomial`, and `ma` use lazy loading through `module.__getattr__` rather than being imported upfront. This means a basic `import numpy` only brings in the core `_multiarray_umath` module and the pure-Python package structure—the specialized submodules don't load until you actually access them like `numpy.linalg` or `numpy.random`.

The strategy is to deliver this in tiers: first verify that Tier 1 (core NumPy with `_multiarray_umath`) works by building an Emscripten executable that links the Python runtime, the core extension module, and the NumPy package tree, then runs basic operations like array  …

In CPython 3.14, `BuiltinImporter.find_spec` no longer has that path check—it just calls `_imp.is_builtin()`, which looks for an exact name match in the inittab. So registering "numpy._core._multiarray_umath" there would make `is_builtin()` return true for that specific module name.

This means dotted inittab names work in 3.14, confirming the FreeCAD PySide6.QtCore pattern. The fake name returned 0 because it's not in the host Python's inittab, but once the wasm build registers it via `PyImport_AppendInittab`, it'll return 1.

Now I need to build a runtime harness to validate this end-to-end. I'll copy the host numpy 2.4.6 into the filesystem but strip out all .so files so PathFinder doesn'  …

The meson install succeeded and I've got the numpy package staged with 407 Python files and 19 WebAssembly object files. Now I'm preparing the harness by copying this tree, stripping out the .so files since those extensions will come from the inittab, and preloading it. I'm about to set up the filesystem for the harness.

I need to verify how numpy's _core/__init__.py imports _multiarray_umath to ensure the inittab name matches, and I'm using emscripten's --preload-file to bundle the numpy directory to /np/numpy in the virtual filesystem. I'll set STACK_SIZE and memory generously since numpy imports are memory-intensive, and I'll handle sys.path setup in the harness to make sure Python can f  …

I'm checking how the `_multiarray_umath` module is imported in numpy's core initialization, and I can see from the error handler that it's referenced by its full dotted name. Now I need to set up the filesystem tree to continue investigating.

I'm running into some linker errors related to the `backtrace` symbol from numpy. It's provided by emscripten but might need a specific flag or library link — I could try adding `-sDEMANGLE_SUPPORT`, using emscripten's `emscripten_get_callstack` instead, or providing a stub implementation.

For the compression library symbols (`crc32`, `adler32`, `deflate`, `BZ2_*`), I need to link against zlib and bzip2. Emscripten has ports for these, so I'll add `-sUSE_ZLIB=1 -sUSE_BZIP2=1` and `-sUSE_SQLITE3` to handle the sqlite3 module as well.

The `PyInit__multiarray_umath()` error is a name mangling issue — my main.c is being compiled as C++ by em++, which is causing the symbol mismatch with the C d  …

I realize ERROR_LIMIT isn't an emscripten flag, so I need to remove that and instead pass the linker option directly to wasm-ld, or just drop it entirely to see what errors come up. Let me strip out that flag and try again.

Looking at the build output, the link succeeded but something unexpected happened — the Node.js file content is appearing in the output, which suggests either a syntax error caused Node to print the offending line, or the JavaScript file is being concatenated directly instead of executed.

The output is quite large at 64KB, so Node likely printed an error stack trace that includes the minified source. I need to search through the saved output file to find the actual probe results like NUMPY_VERSION or NP_CORE_OK, or identify what error occurred.

The grep is picking up the minified JavaScript instead of the actual error message, and Node is likely dumping the entire module source to stdout when it hits an exception. I need to run Node directly and isolate just the error output without getting caught up in the JS file content.

The issue is that CPython 3.14 compiled to WebAssembly requires `__PyRuntime` to be exported for the trampoline mechanism to work, but it's not currently being included in the exported functions list.

I need to update the harness build flags to export the full set of required symbols: `_main`, `_Py_Version`, `__PyRuntime`, `_PyGILState_GetThisThreadState`, `__Py_DumpTraceback`, and `__PyEM_EMSCRIPTEN_TRAMPOLINE_OFFSET`. This is a known CPython-wasm requirement for the trampoline to function properly.

Python's failing because it can't locate the standard library modules. I need to mount the stdlib from `/opt/toolchains/python-wasm/lib/python3.14/` onto the filesystem and configure PYTHONHOME and PYTHONPATH to point to it so Python can find the core modules like `encodings`. Setting environment variables in main.c before Python initializes seems cleanest — I'll use setenv to configure PYTHONHOME and PYTHONPATH pointing to the preloaded stdlib and numpy directories. The concern is whether the encodings module will be found early enough, but if the stdlib is properly preloaded at /pyroot/lib/python3.14, it should work. Alternatively, I could use the python3.14.zip archive instead of preloadi  …

I'm hitting a WebAssembly table growth issue—the function table can't expand dynamically, which CPython needs for its trampolines and dynamic function registration. The fix is to enable table growth in emscripten with the right compiler flag, since numpy and CPython both register function pointers at runtime through mechanisms like ctypes and the trampoline system.

I'm hitting a reference error because explicitly setting `EXPORTED_FUNCTIONS` overrides the defaults, which means `_malloc` and `_free` aren't being exported anymore—but the file packager's preload code needs them to load files into memory. I need to include `_malloc`, `_free`, and possibly `_memcpy` in my exported functions list.

Numpy is actually running now—the C extension loaded successfully from the inittab and got through the import chain, but it's hitting an `OSError` about dynamic linking not being enabled when trying to load `_internal`.

py. The issue is likely that `ctypes` or something in that module is trying to call `dlopen`, which emscripten blocks unless the harness is built as a MAIN_MODULE. Let me check what's actually happening at line 19 of `_internal.py` to see if ctypes is being imported or used there.

The issue is that `import ctypes` is wrapped in a try/except that only catches ImportError, but when ctypes tries to load dynamic libraries on import, it raises an OSError instead—specifically from emscripten's dlopen stub complaining about dynamic linking not being enabled. So the exception slips through uncaught. I'm checking what ctypes/__init__.py actually does on import to see where this OSError originates. ctypes of PyDLL(None) at module import time, which tries to dlopen the main program — something that fails on emscripten without MAIN_MODULE enabled. So the real issue is that `import ctypes` itself breaks on this wasm python build, not just numpy's usage of it.

Now I need to figure  …

I'm tracing the root cause: ctypes tries to dynamically load the Python API at module import time, which fails on Emscripten when dynamic linking is disabled, and since numpy imports ctypes early in its initialization, the whole numpy import chain breaks. This is a broader ctypes-on-Emscripten compatibility issue, not specific to numpy.

The FreeCAD build could have dynamic linking enabled (with MAIN_MODULE set), in which case ctypes and numpy would work fine, or it could be statically linked, which would cause the same failure. I need to check the actual FreeCAD build configuration to see which scenario applies. For my test harness, I could enable MAIN_MODULE to prove numpy works in that co  …

I need to dig deeper into the build.ninja file to find where the main executable's link flags are actually defined—they might be buried in a linker response file or hidden in a different rule. Let me search for the FreeCADMainPy link command and trace through its flags to see where the Python exports are being configured.

The FreeCAD wasm build is a static build without MAIN_MODULE, which means dlopen isn't available—so importing ctypes would fail there just like in my test harness. This means numpy's immediate ctypes import in `_internal.py` is a blocker for getting numpy working in the FreeCAD static wasm environment.

I have a few paths forward: I could patch numpy to catch OSError alongside ImportError when importing ctypes, which is a minimal one-line change that lets numpy load without ctypes (sacrificing only the ndarray.ctypes attribute and ctypeslib, which are rarely used anyway). Or I could fix ctypes itself so PyDLL(None) doesn't crash, which would help all ctypes users in this environment. Let me   …

I'm trying to figure out the most efficient way to test my changes — I could patch the stage tree directly since that's what the harness copies from, but I need to make sure the stage gets regenerated properly with my changes to _internal.py.

Numpy's making real headway now—the ctypes patch got us past the internal module, and we're progressing through the core initialization. We're currently deep in the import chain where matrixlib is trying to load matrix_power from linalg, which is pulling in the compiled _umath_linalg module.

The issue is that _umath_linalg isn't registered in my embedded Python harness—I only registered _multiarray_umath so far. Since matrixlib eagerly imports from linalg during the main numpy initialization, _umath_linalg becomes a hard requirement, not something I can defer. I need to add it to the inittab alongside _multiarray_umath, but there's a potential complication: both modules bundle their own cop  …

Looking at the import chain for numpy, I can see that `numpy.linalg` only pulls in `_umath_linalg` through its imports, not `lapack_lite` (which is just mentioned in comments). Meanwhile, `fft` and `random` aren't part of the eager loading chain since the matrix library doesn't import them. This means the minimal set needed for a basic `import numpy` is just `_multiarray_umath` and `_umath_linalg`.

Now I'm planning the full archive assembly. Since `_umath_linalg` already includes lapack as its canonical implementation, I can drop the standalone `lapack_lite` entirely—it's redundant and saves space. For the complete numpy experience with fft and random available on demand, I'll include all t  …

The archive built successfully with no duplicate symbols at 10.8MB containing 153 members and all 12 required PyInit symbols, but I'm getting warnings about missing members in the thin archives—the member names from `emar t` came back without paths, so my resolution logic can't locate them in the expected directories.

The real problem is that npymath and some npyrandom objects weren't actually included in the archive, which means critical symbols like npy_log would be undefined at final link. For thin archives, the member objects should live in directories like `libnpymath.a.p/`, but the bare basenames returned by `emar t` aren't resolving to their actual locations. I need to search those `  …

The member object files are located at `<lib>.a.p/<member>`, so I need to update the archive resolution logic to check `${lib}.p/${mem}` first. I'm modifying make-archive.sh to handle this path structure correctly. I also notice that libnpyrandom.a includes the same distributions object file that was removed from mtrand, confirming that distributions.c originates from libnpyr.

The archive is now complete with 27 shared-lib members totaling 162 members and all 12 PyInit symbols with no duplicate definitions, bringing libnumpy_wasm.a to 11MB. Next I need to update the harness to link against the full archive instead of the single module, register all the inittab entries, and run a comprehensive test importing numpy.

linalg, fft, and random modules.

I'm verifying that all needed objects are reachable from the 12 PyInit symbols — since main.c references all of them via AppendInittab, the linker will pull each module object and its transitive dependencies from the archive without needing `--whole-archive`. Now I'll update main.c to declare and register all 12 PyInit   …

I'm testing with `--whole-archive` first to ensure all archive members are included and the functionality works, then I'll validate that reference-based pulling also works correctly, which is how the integrator will actually link the group. Switching to plain archive linking without whole-archive to match the realistic integrator setup.

The probe completed successfully with NumPy 2.4.6, and all the numerical operations are working correctly—array operations, matrix multiplication, universal functions, and linear algebra routines including eigenvalue decomposition and singular value decomposition all returned the expected results.

Now I'm verifying that the entire NumPy stack (core, linear algebra, FFT, and random number generation) works in WebAssembly under Node.js, which confirms this is a complete, production-ready build. The static linking via inittab worked perfectly with no undefined symbols, and I have all the pieces needed for the deliverable: the archive with 162 object members and 12 PyInit entry points, the init  …

I see the issue with my emnm parsing—it's not correctly handling the format where undefined symbols have a blank address field followed by `U` and the symbol name. Let me rewrite the grep pattern to properly extract those undefined symbols.

The issue is that running `emnm` on an archive file reports undefined symbols for each member independently, without resolving references between members. So symbols defined in one object file but used in another appear as undefined in the per-member analysis, even though they're actually resolved within the archive. The true set of external undefined symbols needs to exclude these intra-archive references.

Since the harness already linked successfully with error checking enabled, I know the archive is self-contained except for the expected external dependencies (Python symbols, emscripten libc/libm, and backtrace stubs). Now I need to compute the actual external undefined set by subtractin  …

I'm looking at the breakdown of undefined symbols: 677 total, with 391 coming from Python and the rest split between libm functions (trigonometric, exponential, logarithmic, and their long double and complex variants all provided by emscripten's libc), plus compiler-rt and libgcc soft-float implementations for 128-bit long double operations.

The remaining symbols come from emscripten's libc (memory allocation, string operations, I/O, sorting, locale functions), C++ runtime support from libc++ and libc++abi (operator new/delete, exception handling, type information), and emscripten's exception handling infrastructure for invoke and throw operations.

Since FreeCAD is already a large C++ emsc  …

The self-contained archive with the bundled backtrace stub compiles and links successfully, and the full probe test passes—this confirms the deliverable is solid. The main.c file now only handles the inittab registration and probe setup, mirroring exactly what the integrator's FreeCAD Main.cpp will do. Now I'm wrapping up the final deliverables: the static library is complete at 11MB and fully self-contained, and I need to package the numpy Python tree for integration.

I'm preparing a clean copy of the patched numpy directory (without compiled extensions, since those are bundled in the inittab) at the deliverable location and documenting where it should mount on the Python path—likely /pyth  …

The deploy mounts Python packages at `/python` with `PYTHONPATH` set accordingly, so numpy would be accessible at `/python/numpy`. Following the pattern used for supplementary data files like freecad-pyside.data, I'd package numpy as a `freecad-numpy.data` archive and mount it at the same location, then load it in index.html.

I'm setting up a clean deliverable numpy tree at `/home/magik6k/lcad-wasm/pyside-port/build/numpy-wasm/numpy-py-tree/numpy` by stripping out compiled extensions, cache directories, and test files to reduce the overall size. The integrator can then run the file packager on this tree to generate the data archive and JavaScript loader.

I've packaged the numpy tree down to 172 Python files without tests, totaling 6.1MB compressed to 11MB in the archive. Now I need to verify the trimmed version still works properly—specifically that `import numpy` functions correctly without the test files, since the harness currently pulls from the full staged version.

I'll update the build script to use the trimmed tree instead and rebuild the preload file, then run a quick test to confirm everything imports and works as expected.

The deliverable tree is working as expected across all checks. Now I'm verifying the exception handling model to make sure numpy was compiled with the same JS-exception approach that FreeCAD uses, and confirming the archive doesn't have any conflicting wasm-EH markers that would cause issues. The build harness was set up with the appropriate Emscripten C++ exception flags, so I'm just double-checking everything aligns properly for the integrator. I need to figure out whether FreeCAD has table growth enabled, since my test harness required `-sALLOW_TABLE_GROWTH=1` to handle dynamic function pointer registration, but FreeCAD's dumped flags don't show it explicitly. The key insight is that Free  …

FreeCAD's exception handling configuration with `-fexceptions` and `-sDISABLE_EXCEPTION_CATCHING=0` matches numpy's setup exactly, so they're compatible. The asyncify flag FreeCAD uses will instrument numpy's code at the final link stage even though numpy wasn't compiled with asyncify, and since numpy doesn't make async calls, this works fine.

For table growth, FreeCAD's working CPython build already handles the dynamic function-pointer registration that the trampoline mechanism requires, so numpy uses the same infrastructure without introducing new constraints—I don't need to flag this as a separate requirement since it's already proven to work.

Now I'm working out the precise module regi  …

I've verified the format is correct—the registration name gets split so that "numpy._core._multiarray_umath=_multiarray_umath" produces the extern declaration for PyInit__multiarray_umath and the corresponding import table entry with the full module path.

Now I need to check how FREECAD_WASM_EXTRA_LINK_LIBS handles the archive, specifically whether it's wrapped in a link group with start-group/end-group directives so I can just append it to that cache variable.

I'm checking how the FREECAD_WASM_EXTRA_LINK_LIBS gets integrated into the target link libraries, specifically whether the linker group markers are already included in the EXTRA set or if they're applied separately through target_link_options around line 170.

The mission is clear: EXTRA_LINK_LIBS is itself a `--start-group;...;--end-group` list, so I just need to append my numpy archive to that existing group where the PySide and Python support libraries already live. Since it's wrapped in a linker group, symbol resolution order doesn't matter and the inittab PyInit references will pull in the numpy objects correctly.

I've verified the key points: the archive is self-contained with backtra  …

I've got all the build scripts in place already—setup-env.sh, the emscripten configuration, the archive script, and the harness directory—so rather than creating a separate report, I should consolidate everything into a single reproducible build recipe shell script that captures the complete workflow.

Now I'm mapping out the StructuredOutput fields: the archive path for the compiled library, the Python module initialization entries, the primary init symbol, and the external symbols that need resolving at link time (Python runtime symbols, math library functions, and soft-float operations).

I'm doing a final check on the directory structure and the archive creation process to make sure the stub file isn't being duplicated—the make-archive.sh script rebuilds from scratch with a clean slate, so the stub gets re-added properly by BUILD.sh's conditional logic. Everything checks out, and I've already confirmed the full harness works end-to-end.

Now I'm mapping out the StructuredOutput fields: the archive lives at the requested location under /home/magik6k/lcad-wasm/pyside-port/build/numpy-wasm/, the inittab entries are the 12 module initializers joined together, and I need to document the external symbols resolved at final link—CPython C-API symbols from libpython3.14.a, libm funct  …

The numpy build succeeded completely—it's fully functional in the wasm target with static linking through inittab and verified working under node/V8. Now I'm preparing the structured output documenting the successful integration with all the key details.