* starting Lua port of python implmention. Syncing commit
* Bulk of Lua module port from Python done. Not tested, only static analysis. Need to work on binary strings. Started work on flatc lua code generation
* Fixed all the basic errors to produced a binary output from the builder, don't know if it is generated correctly, but it contains data, so that must be good
* fixed binary set command that was extending the array improperly
* continued improvement
* Moved lua submodules down a directory so their names don't clash with potential other modules. Added compat module to provide Lua versioning logic
* Successful sample port from Python
* working on testing Lua code with formal tests
* continued to work on tests and fixes to code to make tests pass
* Added reading buffer test
* Changed binaryarray implmentation to use a temporary table for storing data, and then serialize it to a string when requested. This double the rate of building flatbuffers compared to the string approach.
* Didn't need encode module as it just added another layer of indirection that isn't need
* profiled reading buffers, optimizations to increase read performance of monster data to ~7 monster / millisecond
* Writing profiler improvments. Get about
~2 monsters/millisecond building rate
* removed Numpy generation from Lua (came from the Python port)
* math.pow is deprecated in Lua 5.3, so changed to ^ notation. Also added .bat script for starting Lua tests
* adding results of generate_code.bat
* simple edits for code review in PR.
* There was a buffer overflow in inserting the keywords into the unorder set for both the Lua and Python code gens. Changed insertion to use iterators.
* fixed spacing issue
* basic documenation/tutorial updates. Updated sample_binary.lua to reflect the tutorial better
* removed windows-specific build step in Lua tests
* Python: Generated enum member names are now escaped if they correspond to a Python keyword.
* Keyword list in Python generator is now a const char* instead of an std::string array.
* Moved static functions and keyword list of Python generator into the PythonGenerator class.
* Python generator escapes keyword identifiers in all definitions.
* Fix issue #4389, if field is ommitted the return type is the same for python generator
* Fix issue #4389, bytes is returned when field is omitted
* Fix issue #4389, added generated python code after generator modification
A Namespace object is now guaranteed unique.
This cleaned up some old workarounds and latent bugs.
Change-Id: Ic3f12d89947871b03b2c449ba51b3186f953adde
Tested: on Linux.
Bug: 21336857
* Add numpy accessor to python flatbuffers scalar vectors
* Update python tests to test numpy vector accessor
* Update appveyor CI to run Python tests, save generated code as artifact
* Update example generated python code
* Add numpy info to python usage docs
* Update test schema and python tests w/ multi-byte vector
* did not mean to push profiling code
* adding float64 numpy tests
* Added <autogenerated> in cs generator file header comment
#4287
* CS: Added xml-correct file header comment including "<auto-generated>...</auto-generated>"
code_generators.cpp: FlatBuffersGeneratedWarning() function replaced by compile time constant "GeneratedWarning"
also removed extra newlines at end of GeneratedWarning to be able to generate a xml well formed file comment for cs files
#4291
* code_generators: Changed static string GeneratedWarning back to function FlatBuffersGeneratedWarning()
#4291
* Added modified Unit-Test files
#4291
* idl_gen_general: Add autogenerated only in C# code
#4291
Also fixed most codegenerators using the wrong namespace when multiple
namespace were used in a file, with some files not being generated.
Change-Id: Ib42969221239d7244e431cbd667ef69200fc415f
Tested: on Linux.
Bug: 29338474
Implement code generation and self-contained runtime library for Python.
The test suite verifies:
- Correctness of generated Python code by comparing output to that of
the other language ports.
- The exact bytes in the Builder buffer during many scenarios.
- Vtable deduplication correctness.
- Edge cases for table construction, via a fuzzer derived from the Go
implementation.
- All code is simultaneously valid in Python 2.6, 2.7, and 3.4.
The test suite includes benchmarks for:
- Building 'gold' data.
- Parsing 'gold' data.
- Deduplicating vtables.
All tests pass on this author's system for the following Python
implementations:
- CPython 2.6.7
- CPython 2.7.8
- CPython 3.4.2
- PyPy 2.5.0 (CPython 2.7.8 compatible)
